4,480 results on '"RADAR"'
Search Results
202. A Noninterpolated Estimate of Horizontal Spatial Covariance from Nonorthogonally and Irregularly Sampled Scalar Velocities.
- Author
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Yoo, Jang Gon, Kim, Sung Yong, Cornuelle, Bruce D., Kosro, P. Michael, and Kurapov, Alexander L.
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SPATIAL analysis (Statistics) , *ORTHOGONAL functions , *WAVENUMBER , *ANALYSIS of covariance , *RADAR - Abstract
This paper presents a least squares method to estimate the horizontal (isotropic or anisotropic) spatial covariance of two-dimensional orthogonal vector components, without introducing an intervening mapping step and biases, from the spatial covariance of the nonorthogonally and irregularly sampled raw scalar velocities. The field is assumed to be locally homogeneous in space and sampled in an ensemble so the unknown spatial covariance is a function of spatial lag only. The transformation between the irregular grid on which nonorthogonal scalar projections of the vector are sampled and the regular orthogonal grid on which they will be mapped is created using the geometry of the problem. The spatial covariance of the orthogonal velocity components of the field is parameterized by either the energy (power) spectrum in the wavenumber domain or the lagged covariance in the spatial domain. The energy spectrum is constrained to be nonnegative definite as part of the solution of the inverse problem. This approach is applied to three example sets of data, using nonorthogonally and irregularly sampled radial velocity data obtained from 1) a simple spectral model, 2) a regional numerical model, and 3) an array of high-frequency radars. In tests where the true covariance is known, the proposed direct approaches fitting to parameterizations of the nonorthogonally and irregularly sampled raw data in the wavenumber domain and spatial domain outperform methods that map the data to a regular grid before estimating the covariance. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
203. An Evaluation of Radar-Based Tornado Track Estimation Products by Oklahoma Public Safety Officials.
- Author
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Kuster, Charles M., Heinselman, Pamela L., Snyder, Jeffrey C., Wilson, Katie A., Speheger, Douglas A., and Hocker, James E.
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TORNADOES , *NOWCASTING (Meteorology) , *RADAR , *EMERGENCY management , *PUBLIC safety - Abstract
Many public safety officials (e.g., emergency managers and first responders) use weather-radar data to inform many life-saving decisions, such as sounding outdoor warning sirens and directing storm spotters. Therefore, to include this important user group in ongoing radar applications research, a knowledge coproduction framework is used to interact with, learn from, and provide information to public safety officials. From these interactions, it became clear that radar-based products that estimate a tornado's location, intensity, or both could be valuable to public safety officials. Therefore, a survey was conducted and a focus group formed to 1) collect feedback on several of these products currently under development, 2) identify potential decisions that could be made with these products, and 3) examine the impact of radar update time on product usefulness. An analysis of the survey and focus group responses revealed that public safety officials preferred simple interactive products provided to them using multiple communication methods. Once received, any product that could clearly communicate where a tornado may have occurred would likely help public safety officials focus search and rescue efforts in the immediate aftermath of a tornado. Additionally, public safety officials preferred products created using rapid-update data (1-2-min volumetric updates) over conventional-update data (4-5-min volumetric updates) because it provided them with more complete information. [ABSTRACT FROM AUTHOR]
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- 2017
- Full Text
- View/download PDF
204. The Chilean Coastal Orographic Precipitation Experiment: Observing the Influence of Microphysical Rain Regimes on Coastal Orographic Precipitation.
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Massmann, Adam K., Minder, Justin R., Garreaud, René D., Kingsmill, David E., Valenzuela, Raul A., Montecinos, Aldo, Fults, Sara Lynn, and Snider, Jefferson R.
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METEOROLOGICAL precipitation , *RAINFALL , *MOUNTAIN ecology , *RAIN gauges , *RADAR - Abstract
The Chilean Coastal Orographic Precipitation Experiment (CCOPE) was conducted during the austral winter of 2015 (May-August) in the Nahuelbuta Mountains (peak elevation 1.3 km MSL) of southern Chile (38°S). CCOPE used soundings, two profiling Micro Rain Radars, a Parsivel disdrometer, and a rain gauge network to characterize warm and ice-initiated rain regimes and explore their consequences for orographic precipitation. Thirty-three percent of foothill rainfall fell during warm rain periods, while 50% of rainfall fell during ice-initiated periods. Warm rain drop size distributions were characterized by many more and relatively smaller drops than ice-initiated drop size distributions. Both the portion and properties of warm and ice-initiated rainfall compare favorably with observations of coastal mountain rainfall at a similar latitude in California. Orographic enhancement is consistently strong for rain of both types, suggesting that seeding from ice aloft is not a requisite for large orographic enhancement. While the data suggest that orographic enhancement may be greater during warm rain regimes, the difference in orographic enhancement between regimes is not significant. Sounding launches indicate that differences in orographic enhancement are not easily explainable by differences in low-level moisture flux or nondimensional mountain height between the regimes. [ABSTRACT FROM AUTHOR]
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- 2017
- Full Text
- View/download PDF
205. A Real-Time Algorithm to Identify Convective Precipitation Adjacent to or within the Bright Band in the Radar Scan Domain
- Author
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Meilin Yang, Ziwei Zhu, Nan Wang, Donghuan Li, Yin Yang, Qiyuan Hu, Zhe Zhang, and Youcun Qi
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Atmospheric Science ,Scan domain ,law ,Environmental science ,Real time algorithm ,Radar ,Convective precipitation ,law.invention ,Remote sensing - Abstract
Hydrological hazards usually occur after heavy precipitation, especially during strong convection. Therefore, accurately identifying convective precipitation is very helpful for hydrological warning and forecasting. However, separating the convective, bright band (BB), and stratiform precipitation is found to be challenging when the convection is adjacent to or within the BB region. A new convection/BB/stratiform precipitation segregation algorithm is proposed in this study to resolve this challenging issue. This algorithm is applicable for a single radar volume scan data in native (polar) coordinates and consists of four processes: 1) check the freezing (0°C) level to roughly assess whether convection is occurring or not; 2) identify the convective cores through analyzing composite reflectivity (maximum reflectivity for a given range gate among all the sweeps), vertically integrated liquid water (VIL), VIL horizontal gradient, and reflectivity at the levels of 0°, −10°, and above −10°C; 3) delineate the whole convective region through the seeded region growing method by taking account of the microphysical differences between the BB and convective regions; and 4) delineate BB features in the stratiform region. The proposed algorithm utilizes the physical characteristics of different precipitation types for precisely segregating the convective, BB, and stratiform precipitation. This algorithm has been tested with radar data of different precipitation events and evaluated with three months of rain gauge data. The results show that the proposed algorithm performs consistently well for challenging precipitation events with the convection adjacent to or within a strong BB. Furthermore, the proposed algorithm could be used to improve the vertical profile of reflectivity (VPR) correction and reduce the overestimation of rainfall in the BB precipitation region.
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- 2021
206. The Impact of Assimilating Satellite-Derived Layered Precipitable Water, Cloud Water Path, and Radar Data on Short-Range Thunderstorm Forecasts
- Author
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Xuguang Wang, Sijie Pan, Yunheng Wang, Jidong Gao, Jun Li, and Thomas A. Jones
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Atmospheric Science ,Meteorology ,Precipitable water ,Weather forecasting ,Cloud water ,computer.software_genre ,law.invention ,law ,Path (graph theory) ,Thunderstorm ,Range (statistics) ,Environmental science ,Satellite ,Radar ,computer - Abstract
With the launch of GOES-16 in November 2016, effective utilization of its data in convective-scale numerical weather prediction (NWP) has the potential to improve high-impact weather (HIWeather) forecasts. In this study, the impact of satellite-derived layered precipitable water (LPW) and cloud water path (CWP) in addition to NEXRAD observations on short-term convective-scale NWP forecasts are examined using three severe weather cases that occurred in May 2017. In each case, satellite-derived CWP and LPW products and radar observations are assimilated into the Advanced Research Weather Research and Forecasting (WRF-ARW) Model using the NSSL hybrid Warn-on-Forecast (WoF) analysis and forecast system. The system includes two components: the GSI-EnKF system and a deterministic 3DEnVAR system. This study examines deterministic 0–6-h forecasts launched from the hybrid 3DEnVAR analyses for the three severe weather events. Three types of experiments are conducted and compared: (i) the control experiment (CTRL) without assimilating any data, (ii) the radar experiment (RAD) with the assimilation of radar and surface observations, and (iii) the satellite experiment (RADSAT) with the assimilation of all observations including surface-, radar-, and satellite-derived CWP and LPW. The results show that assimilating additional GOES products improves short-range forecasts by providing more accurate initial conditions, especially for moisture and temperature variables.
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- 2021
207. Mobile Ka-Band Polarimetric Doppler Radar Observations of Wildfire Smoke Plumes
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T. Aydell and Craig B. Clements
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Atmospheric Science ,010504 meteorology & atmospheric sciences ,Microphysics ,0208 environmental biotechnology ,Doppler radar ,Polarimetry ,02 engineering and technology ,01 natural sciences ,020801 environmental engineering ,Plume ,law.invention ,Radial velocity ,symbols.namesake ,law ,symbols ,Environmental science ,Precipitation ,Radar ,Doppler effect ,0105 earth and related environmental sciences ,Remote sensing - Abstract
Remote sensing techniques have been used to study and track wildfire smoke plume structure and evolution; however, knowledge gaps remain because of the limited availability of observational datasets aimed at understanding fine-scale fire–atmosphere interactions and plume microphysics. Meteorological radars have been used to investigate the evolution of plume rise in time and space, but highly resolved plume observations are limited. In this study, we present a new mobile millimeter-wave (Ka band) Doppler radar system acquired to sample the fine-scale kinematics and microphysical properties of active wildfire smoke plumes from both wildfires and large prescribed fires. Four field deployments were conducted in autumn of 2019 during two wildfires in California and one prescribed burn in Utah. Radar parameters investigated in this study include reflectivity, radial velocity, Doppler spectrum width, differential reflectivity ZDR, and copolarized correlation coefficient ρHV. Observed radar reflectivity ranged between −15 and 20 dBZ in plume, and radial velocity ranged from 0 to 16 m s−1. Dual-polarimetric observations revealed that scattering sources within wildfire plumes are primarily nonspherical and oblate-shaped targets as indicated by ZDR values measuring above 0 and ρHV values below 0.8 within the plume. Doppler spectrum width maxima were located near the updraft core region and were associated with radar reflectivity maxima.
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- 2021
208. Polarimetric Radar Relations for Estimation of Visibility in Aggregated Snow
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Jacob T. Carlin, Alexander V. Ryzhkov, and Petar Bukovcic
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Estimation ,Atmospheric Science ,law ,Visibility (geometry) ,Polarimetry ,Ocean Engineering ,Radar ,Snow ,Geology ,Remote sensing ,law.invention - Abstract
The intrinsic uncertainty of radar-based retrievals in snow originates from a large diversity of snow growth habits, densities, and particle size distributions, all of which can make interpreting radar measurements of snow very challenging. The application of polarimetric radar for snow measurements can mitigate some of these issues. In this study, a novel polarimetric method for quantification of the extinction coefficient and visibility in snow, based on the joint use of radar reflectivity at horizontal polarization Z and specific differential phase KDP, is introduced. A large 2D-video-disdrometer snow dataset from central Oklahoma is used to derive a polarimetric bivariate power-law relation for the extinction coefficient, . The relation is derived for particle aspect ratios ranging from 0.5 to 0.8 and the width of the canting angle distribution ranging from 0° to 40°, values typical of aggregated snow, and validated via theoretical and analytical derivations/simulations. The multiplier of the relation is sensitive to variations in particles’ densities, the width of the canting angle distribution, and particles’ aspect ratios, whereas the relation’s exponents are practically invariant to changes in the latter two parameters. This novel approach is applied to polarimetric S-band WSR-88D data and verified against previous studies and in situ measurements of the extinction coefficient for four snow events in the eastern United States. The polarimetric radar estimates of the extinction coefficient exhibit smaller biases in comparison to previous studies concerning the ground measurements. The results indicate that there is good potential for reliable radar estimates of visibility from polarimetric weather radars, a parameter inversely proportional to the extinction coefficient.
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- 2021
209. Use of Power Transform Mixing Ratios as Hydrometeor Control Variables for Direct Assimilation of Radar Reflectivity in GSI En3DVar and Tests with Five Convective Storm Cases
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Rong Kong, Gang Zhao, Lianglyu Chen, Chengsi Liu, Ming Xue, and Youngsun Jung
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Atmospheric Science ,Data assimilation ,Meteorology ,law ,Convective storm detection ,Control variable ,Environmental science ,Assimilation (biology) ,Power transform ,Radar ,Radar reflectivity ,Mixing (physics) ,law.invention - Abstract
When directly assimilating radar data within a variational framework using hydrometeor mixing ratios (q) as control variables (CVq), the gradient of the cost function becomes extremely large when background mixing ratio is close to zero. This significantly slows down minimization convergence and makes the assimilation of radial velocity and other observations ineffective because of the dominance of the reflectivity observation term in the cost function gradient. Using logarithmic hydrometeor mixing ratios as control variables (CV logq) can alleviate the problem but the high nonlinearity of logarithmic transformation can introduce spurious analysis increments into mixing ratios. In this study, power transform of hydrometeors is proposed to form new control variables (CVpq) where the nonlinearity of transformation can be adjusted by a tuning exponent or power parameter p. The performance of assimilating radar data using CVpq is compared with those using CVq and CV logq for the analyses and forecasts of five convective storm cases from the spring of 2017. Results show that CVpq with p = 0.4 (CVpq0.4) gives the best reflectivity forecasts in terms of root-mean-square error and equitable threat score. Furthermore, CVpq0.4 has faster convergence of cost function minimization than CVq and produces less spurious analysis increment than CV logq. Compared to CVq and CV logq, CVpq0.4 has better skills of 0–3-h composite reflectivity forecasts, and the updraft helicity tracks for the 16 May 2017 Texas and Oklahoma tornado outbreak case are more consistent with observations when using CVpq0.4.
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- 2021
210. The Historic Rainfalls of Hurricanes Harvey and Florence: A Perspective from the Multi-Radar Multi-Sensor System
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Micheal J. Simpson, Lin Tang, Steven M. Martinaitis, Jian Zhang, Andrew P. Osborne, Stephen B. Cocks, and Kenneth W. Howard
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Atmospheric Science ,Meteorology ,law ,Perspective (graphical) ,Environmental science ,Radar ,Multi sensor ,law.invention - Abstract
Hurricane Harvey in 2017 generated one of the most catastrophic rainfall events in United States history. Numerous gauge observations in Texas exceeded 1200 mm, and the record accumulations resulted in 65 direct fatalities from rainfall-induced flooding. This was followed by Hurricane Florence in 2018, where multiple regions in North Carolina received over 750 mm of rainfall. The Multi-Radar Multi-Sensor (MRMS) system provides the unique perspective of applying fully automated seamless radar mosaics and locally gauge-corrected products for these two historical tropical cyclone rainfall events. This study investigates the performance of various MRMS quantitative precipitation estimation (QPE) products as it pertains to rare extreme tropical cyclone rainfall events. Various biases were identified in the radar-only approaches, which were mitigated in a new dual-polarimetric synthetic radar QPE approach. A local gauge correction of radar-derived QPE provided statistical improvements over the radar-only products but introduced consistent underestimation biases attributed to undercatch from tropical cyclone winds. This study then introduces a conceptual methodology to bulk correct for gauge wind undercatch across the numerous gauge networks ingested by the MRMS system. Adjusting the hourly gauge observations for wind undercatch resulted in increased storm-total accumulations for both tropical cyclones that better matched independent gauge observations, yet its application across large network collections highlighted the challenges of applying a singular wind undercatch correction scheme for significant wind events (e.g., tropical cyclones) while recognizing the need for increased metadata on gauge characteristics.
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- 2021
211. A Dual-Frequency Radar Retrieval of Two Parameters of the Snowfall Particle Size Distribution Using a Neural Network
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Stephen W. Nesbitt, Greg M. McFarquhar, and Randy J. Chase
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Atmospheric Science ,Artificial neural network ,Remote sensing (archaeology) ,law ,Particle-size distribution ,Environmental science ,Dual frequency radar ,Radar ,Snow ,law.invention ,Remote sensing - Abstract
With the launch of the Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM-DPR) in 2014, renewed interest in retrievals of snowfall in the atmospheric column has occurred. The current operational GPM-DPR retrieval largely underestimates surface snowfall accumulation. Here, a neural network (NN) trained on data that are synthetically derived from state-of-the-art ice particle scattering models and measured in situ particle size distributions (PSDs) is used to retrieve two parameters of the PSD: liquid equivalent mass-weighted mean diameter and the liquid equivalent normalized intercept parameter . Evaluations against a test dataset showed statistically significantly improved ice water content (IWC) retrievals relative to a standard power-law approach and an estimate of the current GPM-DPR algorithm. Furthermore, estimated median percent errors (MPE) on the test dataset were −0.7%, +2.6%, and +1% for , , and IWC, respectively. An evaluation on three case studies with collocated radar observations and in situ microphysical data shows that the NN retrieval has MPE of −13%, +120%, and +10% for , , and IWC, respectively. The NN retrieval applied directly to GPM-DPR data provides improved snowfall retrievals relative to the default algorithm, removing the default algorithm’s ray-to-ray instabilities and recreating the high-resolution radar retrieval results to within 15% MPE. Future work should aim to improve the retrieval by including PSD data collected in more diverse conditions and rimed particles. Furthermore, different desired outputs such as the PSD shape parameter and snowfall rate could be included in future iterations.
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- 2021
212. A Variational Method for Analyzing Vortex Flows in Radar-Scanned Tornadic Mesocyclones. Part II: Tests with Analytically Formulated Vortices
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Li Wei and Qin Xu
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Atmospheric Science ,Variational method ,law ,Mechanics ,Radar ,Mesocyclone ,Geology ,law.invention ,Vortex - Abstract
The variational method formulated in Part I for analyzing vortex flow (VF), called VF-Var, is tested with simulated radar radial-velocity observations from idealized and pseudo-operational Doppler scans of analytically formulated benchmark vortices with spiral-band structures to resemble VFs in observed tornadic mesocyclones. The idealized Doppler scans are unidirectional in parallel along horizontal grid lines of a coarse-resolution grid, so they measure only the horizontal components of three-dimensional velocities in the analysis domain. The pseudo-operational Doppler scans mimic a scan mode used by operational WSR-88Ds for severe storms. Paired numerical experiments are designed and performed to test the two-step analysis versus single-step analysis formulated in VF-Var. Both analyses perform very well with dual-Doppler scans and reasonably well with single-Doppler scans. Errors in the analyzed velocities from single-Doppler scans are mainly in the unobserved velocity components and only in fractions of the benchmark velocities. When the vortex is upright or slanted in the direction perpendicular to idealized single-Doppler scans, the two-step analysis slightly outperforms the single-step analysis for idealized Doppler scans and pseudo-operational dual-Doppler scans. When the vortex becomes slanted in the direction largely along or against Doppler scans, both analyses become less (more) accurate in analyzing the horizontal (slantwise vertical) velocity, and the single-step analysis outperforms the two-step analysis especially for single-Doppler scans. By considering the projections of analyzed velocity on radar beams in the original Cartesian coordinates, useful insights are gained for understanding why and how the analysis accuracies are affected by vortex slanting.
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- 2021
213. A Variational Method for Analyzing Vortex Flows in Radar-Scanned Tornadic Mesocyclones. Part I: Formulations and Theoretical Considerations
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Qin Xu
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Atmospheric Science ,Variational method ,law ,Mechanics ,Radar ,Mesocyclone ,Geology ,law.invention ,Vortex - Abstract
A variational method is formulated with theoretical considerations for analyzing vortex flows in Doppler radar–scanned tornadic mesocyclones. The method has the following features. (i) The vortex center axis (estimated as a continuous function of time and height in the four-dimensional space) is used as the vertical coordinate, so the coordinate system used for the analysis is slantwise curvilinear and nonorthogonal in general. (ii) The vortex flow (VF), defined by the three-dimensional vector wind minus the horizontal moving velocity of vortex center axis, is expressed in terms of the covariant basis vectors (tangent to the coordinate curves), so its axisymmetric part can be properly defined in that slantwise-curvilinear coordinate system. (iii) To satisfy the mass continuity automatically, the axisymmetric part is expressed by the scalar fields of azimuthally averaged tangential velocity and cylindrical streamfunction and the remaining asymmetric part is expressed by the scalar fields of streamfunction and vertically integrated velocity potential. (iv) VF-dependent covariance functions are formulated for these scalar variables and then deconvoluted to construct the square root of background error covariance matrix analytically with the latter used to transform the control vector to precondition the cost function. (v) The deconvoluted covariance functions and their transformed control variables satisfy two required boundary conditions (i.e., zero vertical velocity at the lower rigid boundary and zero cross-axis velocity along the vortex center axis), so the analyzed VF satisfies not only the mass continuity but also the two boundary conditions automatically.
- Published
- 2021
214. Testing Passive Microwave-Based Hail Retrievals Using GPM DPR Ku-Band Radar
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Daniel J. Cecil and Sarah D. Bang
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Atmospheric Science ,law ,Environmental science ,Radar ,Ku band ,Microwave ,law.invention ,Remote sensing - Abstract
Several studies in the literature have developed approaches to diagnose hail storms from satellite-borne passive microwave imagery and build nearly global climatologies of hail. This paper uses spaceborne Ku-band radar measurements from the Global Precipitation Measurement (GPM) mission Dual-Frequency Precipitation Radar (DPR) to validate several passive microwave approaches. We assess the retrievals on the basis of how tightly they constrain the radar reflectivity at −20°C and how this measured radar reflectivity aloft varies geographically. The algorithm that combines minimum 19-GHz polarization corrected temperature (PCT) with a 37-GHz PCT depression normalized by tropopause height constrains the radar reflectivity most tightly and gives the least appearance of regional biases. A retrieval that is based on a 19-GHz PCT threshold of 261K also produces tightly clustered profiles of radar reflectivity, with little regional bias. An approach using regionally adjusted minimum 37-GHz PCT performs relatively well, but our results indicate it may overestimate hail in some subtropical and midlatitude regions. A threshold applied to the minimum 37-GHz PCT (≤230 K), without any scaling by region or probability of hail, overestimates hail in the tropics and underestimates beyond the tropics. For all retrieval approaches, storms identified as having hail tended to have radar reflectivity profiles that are consistent with general expectations for hailstorms (reflectivity > 50 dBZ below the 0°C level, and > 40 dBZ extending far above 0°C). Profiles from oceanic regions tended to have more rapidly decreasing reflectivity with height than profiles from other regions. Subtropical, high-latitude, and high-terrain land profiles had the slowest decreases of reflectivity with height.
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- 2021
215. Evaluation of GPM Dual-Frequency Precipitation Radar (DPR) Rainfall Products Using the Rain Gauge Network over China
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Yang Gao, Tongwen Wu, Shihao Tang, and Jun Wang
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Atmospheric Science ,Rain gauge ,Meteorology ,law ,Environmental science ,Dual frequency ,Precipitation ,Radar ,China ,law.invention - Abstract
The Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) mission core satellite provides the new-generation global observation of rain since 2014. The main objective of this paper is to evaluate the suitability and limitation of GPM-DPR level-2 products over China. The DPR rain rate products are compared with rain gauge data during the summers of 5 years (2014–18). The ground observation network is composed of more than 50 000 rain gauges. The DPR precipitation products for all scans (DPR_NS, DPR_MS, and DPR_HS) generally underestimate rain rates. However, DPR_MS agrees better with gauge estimates than DPR_NS and DPR_HS, yielding the lowest mean error, systematic deviation, and highest Pearson correlation coefficient. In addition, all three swath types show obvious overestimation over gauge estimates between 0.5 and 1 mm h−1 and underestimation when gauge estimates are larger than 1 mm h−1. The DPR_HS and DPR_MS agree better with gauge estimates below and above 2.5 mm h−1, respectively. A deeper investigation was carried out to analyze the variation of DPR_MS’s performance with respect to terrains over China. An obvious underestimation, relative to gauge estimates, occurs in Tibetan Plateau while a slight overestimation occurs in the North China Plain. Furthermore, our comprehensive analysis suggests that in Sichuan Basin, the DPR_MS exhibit the best agreement with gauge estimates.
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- 2021
216. An Operational Multi-Radar Multi-Sensor QPE System in Taiwan
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Kenneth W. Howard, Pin-Fang Lin, Yu-Shuang Tang, Chia-Rong Chen, Jian Zhang, Lin Tang, Carrie Langston, Brian Kaney, and Pao-Liang Chang
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Atmospheric Science ,Quantitative precipitation estimation ,law ,Environmental science ,Radar ,Remote sensing ,law.invention ,Multi sensor - Abstract
Over the last two decades, the Central Weather Bureau of Taiwan and the U.S. National Severe Storms Laboratory have been involved in a research and development collaboration to improve the monitoring and prediction of river flooding, flash floods, debris flows, and severe storms for Taiwan. The collaboration resulted in the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) system. The QPESUMS system integrates observations from multiple mixed-band weather radars, rain gauges, and numerical weather prediction model fields to produce high-resolution (1 km) and rapid-update (10 min) rainfall and severe storm monitoring and prediction products. The rainfall products are widely used by government agencies and emergency managers in Taiwan for flood and mudslide warnings as well as for water resource management. The 3D reflectivity mosaic and QPE products are also used in high-resolution radar data assimilation and for the verification of numerical weather prediction model forecasts. The system facilitated collaborations with academic communities for research and development of radar applications, including quantitative precipitation estimation and nowcasting. This paper provides an overview of the operational QPE capabilities in the Taiwan QPESUMS system.
- Published
- 2021
217. Radar Applications in Northern Scotland (RAiNS)
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David Dufton, Ryan R. Neely, Chris G. Collier, Lindsay Bennett, and Louise Parry
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Atmospheric Science ,Meteorology ,law ,Environmental science ,Radar ,law.invention - Abstract
The Radar Applications in Northern Scotland (RAiNS) experiment took place from February to August 2016 near Inverness, Scotland. The campaign was motivated by the need to provide enhanced weather radar observations for hydrological applications for the Inverness region. Here we describe the campaign in detail and observations over the summer period of the campaign that show the improvements that high-resolution polarimetric radar observations may have on quantitative precipitation estimates in this region compared to concurrently generated operational radar quantitative precipitation estimates (QPEs). We further provide suggestions of methods for generating QPE using dual-polarization X-band radars in similar regions.
- Published
- 2021
218. Radar-Based Bayesian Estimation of Ice Crystal Growth Parameters within a Microphysical Model
- Author
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Marcus van Lier-Walqui, Anders A. Jensen, Yao Sheng Chen, Jerry Y. Harrington, Matthew R. Kumjian, and Robert S. Schrom
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Atmospheric Science ,Bayes estimator ,Ice crystals ,law ,Radar ,Physics::Atmospheric and Oceanic Physics ,Geology ,law.invention ,Remote sensing - Abstract
The potential for polarimetric Doppler radar measurements to improve predictions of ice microphysical processes within an idealized model–observational framework is examined. In an effort to more rigorously constrain ice growth processes (e.g., vapor deposition) with observations of natural clouds, a novel framework is developed to compare simulated and observed radar measurements, coupling a bulk adaptive-habit model of vapor growth to a polarimetric radar forward model. Bayesian inference on key microphysical model parameters is then used, via a Markov chain Monte Carlo sampler, to estimate the probability distribution of the model parameters. The statistical formalism of this method allows for robust estimates of the optimal parameter values, along with (non-Gaussian) estimates of their uncertainty. To demonstrate this framework, observations from Department of Energy radars in the Arctic during a case of pristine ice precipitation are used to constrain vapor deposition parameters in the adaptive habit model. The resulting parameter probability distributions provide physically plausible changes in ice particle density and aspect ratio during growth. A lack of direct constraint on the number concentration produces a range of possible mean particle sizes, with the mean size inversely correlated to number concentration. Consistency is found between the estimated inherent growth ratio and independent laboratory measurements, increasing confidence in the parameter PDFs and demonstrating the effectiveness of the radar measurements in constraining the parameters. The combined Doppler and polarimetric observations produce the highest-confidence estimates of the parameter PDFs, with the Doppler measurements providing a stronger constraint for this case.
- Published
- 2021
219. Applications of Dynamic Land Surface Information for Passive Microwave Precipitation Retrieval
- Author
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Sarah Ringerud, Christa D. Peters-Lidard, Yalei You, and Joe Munchak
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Atmospheric Science ,Radiometer ,010504 meteorology & atmospheric sciences ,Optimal estimation ,010505 oceanography ,Ocean Engineering ,01 natural sciences ,Article ,law.invention ,law ,Emissivity ,Environmental science ,Gprof ,Satellite ,Radar ,Global Precipitation Measurement ,Microwave ,0105 earth and related environmental sciences ,Remote sensing - Abstract
Accurate, physically based precipitation retrieval over global land surfaces is an important goal of the NASA/JAXA Global Precipitation Measurement Mission (GPM). This is a difficult problem for the passive microwave constellation, as the signal over radiometrically warm land surfaces in the microwave frequencies means that the measurements used are indirect and typically require inferring some type of relationship between an observed scattering signal and precipitation at the surface. GPM, with collocated radiometer and dual-frequency radar, is an excellent tool for tackling this problem and improving global retrievals. In the years following the launch of the GPM Core Observatory satellite, physically based passive microwave retrieval of precipitation over land continues to be challenging. Validation efforts suggest that the operational GPM passive microwave algorithm, the Goddard profiling algorithm (GPROF), tends to overestimate precipitation at the low (−1) end of the distribution over land. In this work, retrieval sensitivities to dynamic surface conditions are explored through enhancement of the algorithm with dynamic, retrieved information from a GPM-derived optimal estimation scheme. The retrieved parameters describing surface and background characteristics replace current static or ancillary GPROF information including emissivity, water vapor, and snow cover. Results show that adding this information decreases probability of false detection by 50% and, most importantly, the enhancements with retrieved parameters move the retrieval away from dependence on ancillary datasets and lead to improved physical consistency.
- Published
- 2021
220. Development and Interpretation of a Neural-Network-Based Synthetic Radar Reflectivity Estimator Using GOES-R Satellite Observations
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Kyle Hilburn, Imme Ebert-Uphoff, and Steven D. Miller
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Atmospheric Science ,010504 meteorology & atmospheric sciences ,Computer science ,0207 environmental engineering ,02 engineering and technology ,01 natural sciences ,Lightning ,Convolutional neural network ,law.invention ,Data assimilation ,law ,Geostationary orbit ,Radiance ,Satellite ,Radar ,020701 environmental engineering ,Spatial analysis ,0105 earth and related environmental sciences ,Remote sensing - Abstract
The objective of this research is to develop techniques for assimilating GOES-R series observations in precipitating scenes for the purpose of improving short-term convective-scale forecasts of high-impact weather hazards. Whereas one approach is radiance assimilation, the information content of GOES-R radiances from its Advanced Baseline Imager saturates in precipitating scenes, and radiance assimilation does not make use of lightning observations from the GOES Lightning Mapper. Here, a convolutional neural network (CNN) is developed to transform GOES-R radiances and lightning into synthetic radar reflectivity fields to make use of existing radar assimilation techniques. We find that the ability of CNNs to utilize spatial context is essential for this application and offers breakthrough improvement in skill compared to traditional pixel-by-pixel based approaches. To understand the improved performance, we use a novel analysis method that combines several techniques, each providing different insights into the network’s reasoning. Channel-withholding experiments and spatial information–withholding experiments are used to show that the CNN achieves skill at high reflectivity values from the information content in radiance gradients and the presence of lightning. The attribution method, layerwise relevance propagation, demonstrates that the CNN uses radiance and lightning information synergistically, where lightning helps the CNN focus on which neighboring locations are most important. Synthetic inputs are used to quantify the sensitivity to radiance gradients, showing that sharper gradients produce a stronger response in predicted reflectivity. Lightning observations are found to be uniquely valuable for their ability to pinpoint locations of strong radar echoes.
- Published
- 2021
221. Comparisons of Hybrid En3DVar with 3DVar and EnKF for Radar Data Assimilation: Tests with the 10 May 2010 Oklahoma Tornado Outbreak
- Author
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Rong Kong, Ming Xue, Chengsi Liu, and Youngsun Jung
- Subjects
Atmospheric Science ,Data assimilation ,Meteorology ,law ,Tornado outbreak ,Environmental science ,Radar ,law.invention - Abstract
In this study, a hybrid En3DVar data assimilation (DA) scheme is compared with 3DVar, EnKF, and pure En3DVar for the assimilation of radar data in a real tornadic storm case. Results using hydrometeor mixing ratios (CVq) or logarithmic mixing ratios (CVlogq) as the control variables are compared in the variational DA framework. To address the lack of radial velocity impact issues when using CVq, a procedure that assimilates reflectivity and radial velocity data in two separate analysis passes is adopted. Comparisons are made in terms of the root-mean-square innovations (RMSIs) as well as the intensity and structure of the analyzed and forecast storms. For pure En3DVar that uses 100% ensemble covariance, CVlogq and CVq have similar RMSIs in the velocity analyses, but errors grow faster during forecasts when using CVlogq. Introducing static background error covariance at 5% in hybrid En3DVar (with CVlogq) significantly reduces the forecast error growth. Pure En3DVar produces more intense reflectivity analyses than EnKF that more closely match the observations. Hybrid En3DVar with 50% outperforms other weights in terms of the RMSIs and forecasts of updraft helicity and is thus used in the final comparison with 3DVar and EnKF. The hybrid En3DVar is found to outperform EnKF in better capturing the intensity and structure of the analyzed and forecast storms and outperform 3DVAR in better capturing the intensity and evolution of the rotating updraft.
- Published
- 2021
222. Combining Radar Attenuation and Partial Beam Blockage Corrections for Improved Quantitative Application
- Author
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Haonan Chen and Yabin Gou
- Subjects
Atmospheric Science ,Remote sensing (archaeology) ,law ,Attenuation ,Environmental science ,Precipitation ,Radar ,Beam (structure) ,Remote sensing ,law.invention - Abstract
Partial beam blockage (PBB) correction is an indispensable step in weather radar data quality control and subsequent quantitative applications, such as precipitation estimation, especially in urban and/or complex terrain environments. This paper developed a novel PBB correction procedure based on the improved ZPHI method for attenuation correction and regional specific differential propagation phase (KDP)–reflectivity (ZH) relationship derived from in situ raindrop size distribution (DSD) measurements. The practical performance of this PBB correction technique was evaluated through comparing the spatial continuity of reflectivity measurements, the consistency between radar-measured and DSD-derived KDP and ZH relationships, as well as rainfall estimates based on R(ZH) and R(KDP). The results showed that through incorporating attenuation and PBB corrections (i) the spatial continuity of ZH measurements can effectively be enhanced; (ii) the distribution of radar-measured KDP versus ZH is more consistent with the DSD-derived KDP versus ZH; (iii) the measured ZH from a C-band radar in the PBB-affected area becomes more consistent with collocated S-band measurements, particularly in the rainstorm center area where ZH is larger than 30 dBZ; and (iv) rainfall estimates based on R(ZH) in the PBB-affected area are incrementally improved with better spatial continuity and the performance tends to be more comparable with R(KDP).
- Published
- 2021
223. Polarimetric Radar Variables in Snowfall at Ka- and W-Band Frequency Bands: A Comparative Analysis
- Author
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Sergey Y. Matrosov
- Subjects
Atmospheric Science ,W band ,law ,Polarimetry ,Ocean Engineering ,Radar ,Snow ,Geology ,Radio spectrum ,law.invention ,Remote sensing - Abstract
Dual-frequency millimeter-wavelength radar observations in snowfall are analyzed in order to evaluate differences in conventional polarimetric radar variables such as differential reflectivity (ZDR) specific differential phase shift (KDP) and linear depolarization ratio (LDR) at traditional cloud radar frequencies at Ka and W bands (~35 and ~94 GHz, correspondingly). Low radar beam elevation (~5°) measurements were performed at Oliktok Point, Alaska, with a scanning fully polarimetric radar operating in the horizontal–vertical polarization basis. This radar has the same gate spacing and very close beam widths at both frequencies, which largely alleviates uncertainties associated with spatial and temporal data matching. It is shown that observed Ka- and W-band ZDR differences are, on average, less than about 0.5 dB and do not have a pronounced trend as a function of snowfall reflectivity. The observed ZDR differences agree well with modeling results obtained using integration over nonspherical ice particle size distributions. For higher signal-to-noise ratios, KDP data derived from differential phase measurements are approximately scaled as reciprocals of corresponding radar frequencies indicating that the influence of non-Rayleigh scattering effects on this variable is rather limited. This result is also in satisfactory agreement with data obtained by modeling using realistic particle size distributions. Observed Ka- and W-band LDR differences are strongly affected by the radar hardware system polarization “leak” and are generally less than 4 dB. Smaller differences are observed for higher depolarizations, where the polarization “leak” is less pronounced. Realistic assumptions about particle canting and the system polarization isolation lead to modeling results that satisfactorily agree with observational dual-frequency LDR data.
- Published
- 2021
224. NWP and Radar Extrapolation: Comparisons and Explanation of Errors
- Author
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James O. Pinto, Dan Megenhardt, and James W. Wilson
- Subjects
Atmospheric Science ,010504 meteorology & atmospheric sciences ,law ,0207 environmental engineering ,Extrapolation ,Environmental science ,02 engineering and technology ,Radar ,020701 environmental engineering ,01 natural sciences ,0105 earth and related environmental sciences ,Remote sensing ,law.invention - Abstract
This paper examines nowcasts of precipitation from the High-Resolution Rapid Refresh (HRRRv2) model from the summer of 2017 along the Colorado Front Range. It was found that model nowcasts (2 h or less) of precipitation amount were less skillful than extrapolation of the KFTG WSR-88-D data at a spatial scale of 120 km. It was also found that local-scale (mesoscale) influences on rainfall intensity and amount have a much greater impact on rainfall intensity than large-scale (synoptic) influences. Thus, large-scale trends are not useful for modifying extrapolation nowcasts on the local scale. Errors in the HRRR nowcasts are attributed to an inability of the model and data assimilation to resolve convergence along outflow boundaries and other terrain-influenced mesogamma-scale flows that contribute to storm formation and evolution. While the HRRRv2 1-h nowcasts were strongly correlated with observed precipitation events, the nowcast precipitation amounts were in error by more than a factor of 2 about 50% of the time, with half of the cases being overestimates and half being underestimates. A large fraction of the HRRRv2 overestimates were associated with stratiform rain events. It is speculated that this was a result of misinterpretation of the radar bright band as more intense precipitation aloft by the data assimilation scheme. A large fraction of the HRRRv2 underestimates occurred when the data assimilation and model were unable to fully resolve the low-level convergence along small-scale, narrow boundaries that led to new storm initiation and/or storm growth.
- Published
- 2020
225. Evaluation, Tuning, and Interpretation of Neural Networks for Working with Images in Meteorological Applications
- Author
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Kyle Hilburn and Imme Ebert-Uphoff
- Subjects
Atmospheric Science ,Data assimilation ,Artificial neural network ,law ,Computer science ,Data mining ,Radar ,computer.software_genre ,computer ,law.invention ,Interpretation (model theory) - Abstract
The method of neural networks (aka deep learning) has opened up many new opportunities to utilize remotely sensed images in meteorology. Common applications include image classification, e.g., to determine whether an image contains a tropical cyclone, and image-to-image translation, e.g., to emulate radar imagery for satellites that only have passive channels. However, there are yet many open questions regarding the use of neural networks for working with meteorological images, such as best practices for evaluation, tuning, and interpretation. This article highlights several strategies and practical considerations for neural network development that have not yet received much attention in the meteorological community, such as the concept of receptive fields, underutilized meteorological performance measures, and methods for neural network interpretation, such as synthetic experiments and layer-wise relevance propagation. We also consider the process of neural network interpretation as a whole, recognizing it as an iterative meteorologist-driven discovery process that builds on experimental design and hypothesis generation and testing. Finally, while most work on neural network interpretation in meteorology has so far focused on networks for image classification tasks, we expand the focus to also include networks for image-to-image translation.
- Published
- 2020
226. Defender and Expositor of the Bergen Methods of Synoptic Analysis: Significance, History, and Translation of Bergeron’s (1928) 'Three-Dimensionally Combining Synoptic Analysis'
- Author
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Huw C. Davies, David M. Schultz, Bogdan Antonescu, and Hans Volkert
- Subjects
Atmospheric Science ,History ,010504 meteorology & atmospheric sciences ,Frontolysis ,Extratropical cyclones ,0207 environmental engineering ,02 engineering and technology ,Jet stream ,01 natural sciences ,German ,Clouds ,Cyclogenesis ,History of Meteorology heating ,020701 environmental engineering ,Mass analysis ,0105 earth and related environmental sciences ,Lidar ,Radar ,Verkehrsmeteorologie ,Aircraft observations ,Assertion ,language.human_language ,Epistemology ,Indirect aerology ,Norwegian cyclone model ,language - Abstract
Tor Bergeron was a key member of the Bergen School of Meteorology that developed some of the most influential contributions to synoptic analysis in the twentieth century: airmass analysis, polar-front theory, and the Norwegian cyclone model. However, the eventual success of these so-called Bergen methods of synoptic analysis was not guaranteed. Concerns and criticisms of the methods—in part from the lack of referencing to prior studies, overly simplified conceptual models, and lack of real data in papers by J. Bjerknes and Solberg—were inhibiting worldwide adoption. Bergeron’s research output in the 1920s was aimed at addressing these concerns. His doctoral thesis, written in German, was published as a journal article in Geofysiske Publikasjoner in 1928. Here, an accessible and annotated English translation is provided along with a succinct overview of this seminal study. Major interlaced themes of Bergeron’s study were the first comprehensive description of the Bergen methods: a vigorous defense of cyclogenesis as primarily a lower-tropospheric process as opposed to an upper-tropospheric–lower-stratospheric one; a nuanced explanation of the assertion that meteorology constituted a distinct and special scientific discipline; and, very understandably, a thorough account of Bergeron’s own contributions to the Bergen School. His contributions included identifying how deformation results in frontogenesis and frontolysis, classifying the influence of aerosols on visibility, and explaining the role of the ambient conditions in the onset of drizzle as opposed to rain showers—a distinction that led the formulation of the Wegener–Bergeron–Findeisen process.
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- 2020
227. Improving Afternoon Thunderstorm Prediction over Taiwan through 3DVar-Based Radar and Surface Data Assimilation
- Author
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Chin-Tzu Fong, Ya-Ting Tsai, I-Han Chen, and Jing-Shan Hong
- Subjects
Atmospheric Science ,Data assimilation ,Meteorology ,law ,Thunderstorm ,Environmental science ,Radar ,law.invention - Abstract
Recently, the Central Weather Bureau of Taiwan developed a WRF- and WRF data assimilation (WRFDA)-based convective-scale data assimilation system to increase model predictability toward high-impact weather. In this study, we focus on afternoon thunderstorm (AT) prediction and investigate the following questions: 1) Is the designation of a rapid update cycle strategy with a blending scheme effective? 2) Can surface data assimilation contribute positively to AT prediction under the complex geography of Taiwan island? 3) What is the relative importance between radar and surface observation to AT prediction? 4) Can we increase the AT forecast lead time in the morning through data assimilation? Consecutive ATs from 30 June to 8 July 2017 are investigated. Five experiments, each having 240 continuous cycles, are designed. Results show that employing continuous cycles with a blending scheme mitigates model spinup compared with downscaled forecasts. Although there are few radar echoes before AT initiation, assimilating radar observations is still crucial since it largely corrects model errors in cycles. However, assimilating surface observations is more important compared with radar in terms of extending forecast lead time in the morning. Either radar or surface observations contribute positively, and assimilating both has the highest QPF score. Assimilating surface observations systematically improves surface wind and temperature predictions based on 240 cases. A case study demonstrates that the model can capture the AT initiation and development by assimilating surface and radar observations. Its cold pool and outflow boundary prediction are also improved. In this case, the assimilation of surface wind and water vapor in the morning contributes more compared with temperature and pressure.
- Published
- 2020
228. Cloud-Resolving Model Applied to Nowcasting: An Evaluation of Radar Data Assimilation and Microphysics Parameterization
- Author
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Rute Costa Ferreira, Bruno Z. Ribeiro, Edmilson Dias de Freitas, Renato Galante Negri, Luiz A. T. Machado, and Éder Paulo Vendrasco
- Subjects
Atmospheric Science ,010504 meteorology & atmospheric sciences ,Microphysics ,Nowcasting ,Meteorology ,business.industry ,0207 environmental engineering ,Cloud computing ,02 engineering and technology ,01 natural sciences ,law.invention ,Data assimilation ,law ,Environmental science ,Radar ,020701 environmental engineering ,business ,0105 earth and related environmental sciences - Abstract
This research explores the benefits of radar data assimilation for short-range weather forecasts in southeastern Brazil using the Weather Research and Forecasting (WRF) Model’s three-dimensional variational data assimilation (3DVAR) system. Different data assimilation options are explored, including the cycling frequency, the number of outer loops, and the use of null-echo assimilation. Initially, four microphysics parameterizations are evaluated (Thompson, Morrison, WSM6, and WDM6). The Thompson parameterization produces the best results, while the other parameterizations generally overestimate the precipitation forecast, especially WDSM6. Additionally, the Thompson scheme tends to overestimate snow, while the Morrison scheme overestimates graupel. Regarding the data assimilation options, the results deteriorate and more spurious convection occurs when using a higher cycling frequency (i.e., 30 min instead of 60 min). The use of two outer loops produces worse precipitation forecasts than the use of one outer loop, and the null-echo assimilation is shown to be an effective way to suppress spurious convection. However, in some cases, the null-echo assimilation also removes convective clouds that are not observed by the radar and/or are still not producing rain, but have the potential to grow into an intense convective cloud with heavy rainfall. Finally, a cloud convective mask was implemented using ancillary satellite data to prevent null-echo assimilation from removing potential convective clouds. The mask was demonstrated to be beneficial in some circumstances, but it needs to be carefully evaluated in more cases to have a more robust conclusion regarding its use.
- Published
- 2020
229. Cloud Assumption of Precipitation Retrieval Algorithms for the Dual-Frequency Precipitation Radar
- Author
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Masaki Satoh, Riko Oki, John Kwiatkowski, Takuji Kubota, Toshio Iguchi, Takeshi Masaki, Shinta Seto, and Tomoe Nasuno
- Subjects
Atmospheric Science ,010504 meteorology & atmospheric sciences ,business.industry ,Ocean Engineering ,Cloud computing ,010502 geochemistry & geophysics ,01 natural sciences ,law.invention ,Satellite observations ,law ,Dual frequency ,Environmental science ,Precipitation ,Radars/Radar observations ,Radar ,business ,Retrieval algorithm ,Algorithms ,0105 earth and related environmental sciences ,Remote sensing - Abstract
形態: カラー図版あり, Physical characteristics: Original contains color illustrations, 資料番号: PA2110044000
- Published
- 2020
230. A Dual-Polarization Radar Synthetic QPE for Operations
- Author
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Stephen B. Cocks, Kenneth W. Howard, Carrie Langston, Pengfei Zhang, Brian Kaney, Alexander V. Ryzhkov, Lin Tang, and Jian Zhang
- Subjects
Atmospheric Science ,Quantitative precipitation estimation ,law ,Attenuation ,Product (mathematics) ,Environmental science ,Weather radar ,Radar ,Differential phase ,law.invention ,Remote sensing - Abstract
A new dual-polarization (DP) radar synthetic quantitative precipitation estimation (QPE) product was developed using a combination of specific attenuation A, specific differential phase KDP, and reflectivity Z to calculate the precipitation rate R. Specific attenuation has advantages of being insensitive to systematic biases in Z and differential reflectivity ZDR due to partial beam blockage, attenuation, and calibration while more linearly related to R than other radar variables. However, the R(A) relationship is not applicable in areas containing ice. Therefore, the new DP QPE applies R(A) in areas where radar is observing pure rain, R(KDP) in regions potentially containing hail, and R(Z) elsewhere. Further, an evaporation correction was applied to minimize false light precipitation related to virga. The new DP QPE was evaluated in real time over the conterminous United States and showed significant improvements over previous radar QPE techniques that were based solely on R(Z) relationships. The improvements included reduced dry biases in heavy to extreme precipitation during the warm season. The new DP QPE also reduced errors and spatial discontinuities in regions impacted by partial beam blockage. Further, the new DP QPE reduced wet bias for scattered light precipitation in the southwest and north central United States where there is significant boundary layer evaporation.
- Published
- 2020
231. A Technique for Estimating Liquid Droplet Diameter and Liquid Water Content in Stratocumulus Clouds Using Radar and Lidar Measurements
- Author
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Jørgen Jensen, Jothiram Vivekanandan, Scott Ellis, Virendra P. Ghate, and M. Christian Schwartz
- Subjects
Atmospheric Science ,Lidar ,010504 meteorology & atmospheric sciences ,law ,Liquid water content ,Environmental science ,Ocean Engineering ,010501 environmental sciences ,Radar ,01 natural sciences ,0105 earth and related environmental sciences ,law.invention ,Remote sensing - Abstract
This paper describes a technique for estimating the liquid water content (LWC) and a characteristic particle diameter in stratocumulus clouds using radar and lidar observations. The uncertainty in LWC estimate from radar and lidar measurements is significantly reduced once the characteristic particle diameter is known. The technique is independent of the drop size distribution. It is applicable for a broad range of W-band reflectivityZbetween −30 and 0 dBZand all values of lidar backscatterβobservations. No partitioning of cloud or drizzle is required on the basis of an arbitrary threshold ofZas in prior studies. A method for estimating droplet diameter and LWC was derived from the electromagnetic simulations of radar and lidar observations. In situ stratocumulus cloud and drizzle probe spectra were input to the electromagnetic simulation. The retrieved droplet diameter and LWC were validated using in situ measurements from the southeastern Pacific Ocean. The retrieval method was applied to radar and lidar measurements from the northeastern Pacific. Uncertainty in the retrieved droplet diameter and LWC that are due to the measurement errors in radar and lidar backscatter measurements are 7% and 14%, respectively. The retrieved LWC was validated using the concurrent G-band radiometer estimates of the liquid water path.
- Published
- 2020
232. An Analysis of an Ostensible Anticyclonic Tornado from 9 May 2016 Using High-Resolution, Rapid-Scan Radar Data
- Author
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Dylan W. Reif, Charles M. Kuster, Howard B. Bluestein, Zachary B. Wienhoff, and Jeffrey C. Snyder
- Subjects
Atmospheric Science ,Rapid scan ,010504 meteorology & atmospheric sciences ,Nowcasting ,Meteorology ,Anticyclonic tornado ,0207 environmental engineering ,High resolution ,02 engineering and technology ,01 natural sciences ,law.invention ,Radar observations ,law ,Convective storm detection ,Radar ,020701 environmental engineering ,Geology ,0105 earth and related environmental sciences - Abstract
Tornadic supercells moved across parts of Oklahoma on the afternoon and evening of 9 May 2016. One such supercell, while producing a long-lived tornado, was observed by nearby WSR-88D radars to contain a strong anticyclonic velocity couplet on the lowest elevation angle. This couplet was located in a very atypical position relative to the ongoing cyclonic tornado and to the supercell’s updraft. A storm survey team identified damage near where this couplet occurred, and, in the absence of evidence refuting otherwise, the damage was thought to have been produced by an anticyclonic tornado. However, such a tornado was not seen in near-ground, high-resolution radar data from a much closer, rapid-scan, mobile radar. Rather, an elongated velocity couplet was observed only at higher elevation angles at altitudes similar to those at which the WSR-88D radars observed the strong couplet. This paper examines observations from two WSR-88D radars and a mobile radar from which it is argued that the anticyclonic couplet (and a similar one ~10 min later) were actually quasi-horizontal vortices centered ~1–1.5 km AGL. The benefits of having data from a radar much closer to the convective storm being sampled (e.g., better spatial resolution and near-ground data coverage) and providing more rapid volume updates are readily apparent. An analysis of these additional radar data provides strong, but not irrefutable, evidence that the anticyclonic tornado that may be inferred from WSR-88D data did not exist; consequently, upon discussions with the National Weather Service, it was not included in Storm Data.
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- 2020
233. Implementing Quality Control of High-Frequency Radar Estimates and Application to Gulf Stream Surface Currents.
- Author
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Haines, Sara, Seim, Harvey, and Muglia, Mike
- Subjects
- *
QUALITY control , *RADAR , *GULF Stream , *REGRESSION analysis , *OCEAN currents - Abstract
Quality control procedures based on nonvelocity parameters for use with a short-range radar system are applied with slight modification to long-range radar data collected offshore of North Carolina. The radar footprint covers shelf and slope environments and includes a segment of the Gulf Stream (GS). Standard processed and quality controlled (QCD) radar data are compared with 4 months of acoustic Doppler current profiler (ADCP) time series collected at three different sites within the radar footprint. Two of the ADCP records are from the shelf and the third is on the upper slope and is frequently within the GS. Linear regression and Bland-Altman diagrams are used to quantify the comparison. QCD data at all sites have reduced scatter and improved correlation with ADCP observations relative to standard processed data. Uncertainty is reduced by approximately 20%, and linear regression slopes and correlation coefficients increase by about 0.1. At the upper slope site, the QCD data also produced a significant increase in the mean speed. Additionally, a significant increase, averaging roughly 20%, in mean speed in the GS is apparent when comparing standard processed data and QCD data, concentrated at large range and at the azimuthal extremes of radial site coverage. Shifts in the distributions of the standard processed and QCD velocity estimates are consistent with the removal of zero-mean noise from the observations, which has minimal impact where the radial site range is <70 km and a large impact at greater range in the GS where mean currents exceed 1 m s−1. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
234. A Comparison between the GPM Dual-Frequency Precipitation Radar and Ground-Based Radar Precipitation Rate Estimates in the Swiss Alps and Plateau.
- Author
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Speirs, Peter, Gabella, Marco, and Berne, Alexis
- Subjects
- *
METEOROLOGICAL precipitation , *CLIMATE change , *RADAR , *RAINFALL probabilities , *RAINFALL - Abstract
The Global Precipitation Measurement (GPM) mission Dual-Frequency Precipitation Radar (DPR) provides a unique set of three-dimensional radar precipitation estimates across much of the globe. Both terrain and climatic conditions can have a strong influence on the reliability of these estimates. Switzerland provides an ideal testbed to evaluate the performance of the DPR in complex terrain: it consists of a mixture of very complex terrain (the Alps) and the far flatter Swiss Plateau. It is also well instrumented, covered with a dense gauge network as well as a network of four dual-polarization C-band weather radars, with the same instrument network used in both the Plateau and the Alps. Here an evaluation of the GPM DPR rainfall rate products against the MeteoSwiss radar rainfall rate product for the first two years of the GPM DPR's operation is presented. Errors in both detection and estimation are considered, broken down by terrain complexity, season, precipitation phase, precipitation type, and precipitation rate. Errors are considered both integrated across the entire domain and spatially, and consistent underestimation of precipitation by GPM is found. This rises to −51% in complex terrain in the winter, primarily due to the predominance of DPR measurements wholly in the solid phase, where problems are caused by lower reflectivities. The smaller vertical extent of precipitation in winter is also likely a cause. Both detection and estimation performance are found to be significantly better in summer than in winter, in liquid than in solid precipitation, and in flatter terrain than in complex terrain. [ABSTRACT FROM AUTHOR]
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- 2017
- Full Text
- View/download PDF
235. Polarimetric Signatures of Midlatitude Warm-Rain Precipitation Events.
- Author
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CARR, N., KIRSTETTER, P. E., GOURLEY, J. J., and HONG, Y.
- Subjects
- *
POLARIMETRIC remote sensing , *RAINFALL , *METEOROLOGICAL precipitation , *METEOROLOGICAL precipitation analysis , *RADAR , *NATURAL satellites - Abstract
Precipitation events in which rainfall is generated primarily below the freezing level via warm-rain processes have traditionally presented a significant challenge for radar and satellite quantitative precipitation estimation (QPE) algorithms. It is possible to improve QPE in warm-rain events if they are correctly identified/classified as warm rain prior to precipitation estimation. Additionally, it is anticipated that classification schemes incorporating polarimetric radar data will be able to leverage precipitation microphysical information to better identify warm-rain precipitation events. This study lays the groundwork for the development of a polarimetric warm-rain classification algorithm by documenting the typical three-dimensional polarimetric characteristics associated with midlatitude warm-rain precipitation events. These characteristics are then compared with those observed in non-warm-rain events. Nearly all warm-rain precipitation events were characterized by lower median values of Z, ZDR, and KDP relative to the non-warm-rain convective cases. Furthermore, droplet coalescence was determined to be the dominant microphysical process in the majority of warm-rain events, while in non-warm-rain stratiform events, evaporation and breakup appeared to be the dominant (warm) microphysical processes. Most warm-rain events were also associated with sharp decreases in reflectivity, with height above the freezing level coincident with low echo-top heights and freezing-level ZDR values near 0, indicating limited ice- and mixed-phase precipitation growth processes. These results support the feasibility of a future polarimetric warm-rain identification algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
236. Understanding the Sources of Satellite Passive Microwave Rainfall Retrieval Systematic Errors Over Land.
- Author
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PETKOVIĆ, VELJKO and KUMMEROW, CHRISTIAN D.
- Subjects
- *
RAINFALL , *MEASUREMENT errors , *RADAR , *METEOROLOGICAL precipitation , *BOUNDARY layer (Aerodynamics) , *ATMOSPHERE - Abstract
Analyses of the Tropical Rainfall Measuring Mission (TRMM) satellite rainfall estimates reveal a substantial disagreement between its active [Precipitation Radar (PR)] and passive [TRMM Microwave Imager (TMI)] sensors over certain regions. This study focuses on understanding the role of the synoptic state of atmosphere in these discrepancies over land regions where passive microwave (PMW) retrievals are limited to scattering signals. As such the variability in the relationship between the ice-induced scattering signal and the surface rainfall is examined. Using the Amazon River and central Africa regions as a test bed, it is found that the systematic difference seen between PR and TMI rainfall estimates is well correlated with both the precipitating system structure and the level of its organization. Relying on a clustering technique to group raining scenes into three broad but distinct organizational categories, it is found that, relative to the PR, deep-organized systems are typically overestimated by TMI while the shallower ones are underestimated. Results suggest that the storm organization level can explain up to 50% of the regional systematic difference between the two sensors. Because of its potential for retrieval improvement, the ability to forecast the level of systems organization is tested. The state of the atmosphere is found to favor certain storm types when constrained by CAPE, wind shear, dewpoint depression, and vertical humidity distribution. Among other findings, the observations reveal that the ratio between boundary layer and midtropospheric moisture correlates well with the organization level of convection. If adjusted by the observed PR-to-TMI ratio under a given environment, the differences between PMW and PR rainfall estimates are diminished, at maximum, by 30% in RMSE and by 40% in the mean. [ABSTRACT FROM AUTHOR]
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- 2017
- Full Text
- View/download PDF
237. Interpolation of Operational Radar Data to a Regular Cartesian Grid Exemplified by Munich's Airport Radar Configuration.
- Author
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AUGST, AYLA and HAGEN, MARTIN
- Subjects
- *
INTERPOLATION , *RADAR , *WIND measurement , *DOPPLER velocimetry , *AIRPORTS - Abstract
Two methods for avoiding errors in the interpolation of operational radar data to a regular grid are presented. The issue is the interpolation of radial velocity and the subsequent estimation of horizontal wind components. It is shown how a vertical gradient of the horizontal wind in combination with gaps of data between scans with different elevation angles affect the interpolation. Simulated radar data for the radar configuration covering the Munich airport in southern Germany are used for illustration. The origin of the abovementioned errors is explained using simplified wind fields. With wind fields generated by the German nonhydrostatic atmospheric prediction model COSMO-DE, the effectiveness of the methods is presented. Both methods contribute to a reduction in interpolation error-by 44% and 35%, respectively-compared to a standard interpolation scheme used for many operational radar configurations. [ABSTRACT FROM AUTHOR]
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- 2017
- Full Text
- View/download PDF
238. Parameters of Cloud Ice Particles Retrieved from Radar Data.
- Author
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MELNIKOV, VALERY
- Subjects
- *
PARTICLE analysis , *CLOUDS , *RADAR , *FLUCTUATIONS (Physics) , *PERTURBATION theory - Abstract
The mean axis ratio (length/width) and the degree of orientation of cloud ice particles are retrieved from radar differential reflectivity (ZDR) and the copolar correlation coefficient (ρhv) measured with the S-band WSR-88D radar. Hardware differential phases and amplifications in the polarimetric channels affect measured ZDR and ρhv and are taken into consideration in the retrieval procedure. The retrieval is performed for particles in shapes of hexagonal prisms, which are closer to shapes of real cloud particles than frequently used spheroids. The median retrieved axis ratio for prisms is larger than that for spheroids. The statistical 1s retrieval errors caused by fluctuations of radar returns are about 40% in areas of signal-to-noise ratios stronger than 10 dB. The values of the degree of orientation lie in an interval from 28 to 238, which points to significant perturbations in the orientations of ice particles most likely caused by the wind field. [ABSTRACT FROM AUTHOR]
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- 2017
- Full Text
- View/download PDF
239. Cloud-Base Height Estimation from VIIRS. Part I: Operational Algorithm Validation against CloudSat.
- Author
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SEAMAN, CURTIS J., YOO-JEONG NOH, MILLER, STEVEN D., HEIDINGER, ANDREW K., and LINDSEY, DANIEL T.
- Subjects
- *
SURFACE tension measurement , *DEW point , *ARTIFICIAL satellites , *LIDAR , *RADAR - Abstract
The operational VIIRS cloud-base height (CBH) product from the Suomi-National Polar-Orbiting Partnership (SNPP) satellite is compared against observations of CBH from the cloud profiling radar (CPR) on board CloudSat. Because of the orbits of SNPP and CloudSat, these instruments provide nearly simultaneous observations of the same locations on Earth for a ;4.5-h period every 2-3 days. The methodology by which VIIRS and CloudSat observations are spatially and temporally matched is outlined. Based on four 1-month evaluation periods representing each season from June 2014 to April 2015, statistics related to the VIIRS CBH retrieval performance have been collected. Results indicate that when compared against CloudSat, the VIIRS CBH retrieval does not meet the error specifications set by the Joint Polar Satellite System (JPSS) program, with a root-mean-square error (RMSE) of 3.7 km for all clouds globally. More than half of all matching VIIRS pixels and CloudSat profiles have CBH errors exceeding the 2-km error requirement. Underscoring the significance of these statistics, it is shown that a simple estimate based on a constant cloud geometric thickness of 2 km outperforms the current operational CBH algorithm. It was found that the performance of the CBH product is impacted by the accuracy of upstream retrievals [primarily cloud-top height (CTH)] and the a priori information used by the CBH retrieval algorithm. However, even when CTH errors were small, CBH errors still exceed the JPSS program error specifications with an RMSE of 2.3 km. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
240. Characteristics of Stratospheric Winds over Jiuquan (41.1°N, 100.2°E) Using Rocketsonde Data in 1967-2004.
- Author
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SHENG, Z., LI, J. W., JIANG, Y., ZHOU, S. D., and SHI, W. L.
- Subjects
- *
STRATOSPHERIC winds , *WIND speed measurement , *LOGNORMAL distribution , *RADAR , *INTERFEROMETERS - Abstract
Stratospheric winds play a significant role in middle atmosphere dynamics, model research, and carrier rocket experiments. For the first time, 65 sets of rocket sounding experiments conducted at Jiuquan (41.1°N, 100.2°E), China, from 1967 to 2004 are presented to study horizontal wind fields in the stratosphere.At a fixed height, wind speed obeys the lognormal distribution. Seasonal mean winds are westerly in winter and easterly in summer. In spring and autumn, zonal wind directions change from the upper to the lower stratosphere. The monthly zonal mean winds have an annual cycle period with large amplitudes at high altitudes. The correlation coefficients for zonal winds between observations and the Horizontal Wind Model (HWM) with all datasets are 0.7. The MERRA reanalysis is in good agreement with rocketsonde data according to the zonal winds comparison with a coefficient of 0.98. The sudden stratospheric warming is an important contribution to biases in the HWM, because it changes the zonal wind direction in the midlatitudes. Both the model and the reanalysis show dramatic meridional wind differences with the observation data. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
241. MRMS QPE Performance East of the Rockies during the 2014 Warm Season.
- Author
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COCKS, STEPHEN B., JIAN ZHANG, MARTINAITIS, STEVEN M., YOUCUN QI, KANEY, BRIAN, and HOWARD, KENNETH
- Subjects
- *
METEOROLOGICAL precipitation , *RADAR , *WEATHER forecasting , *CLIMATOLOGY - Abstract
Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation (QPE) radar only (Q3RAD), Q3RAD local gauge corrected (Q3gc), dual polarization (Dual Pol), legacy Precipitation Processing System (PPS), and National Centers for Environmental Prediction (NCEP) stage IV product performance were evaluated for data collected east of the Rockies during the 2014 warm season. For over 22 000 radar QPE-gauge data pairs, Q3RAD had a higher correlation coefficient (0.85) and a lower mean absolute error (9.4 mm) than the Dual Pol (0.83 and 10.5 mm, respectively) and PPS (0.79 and 10.8 mm, respectively). Q3RAD performed best when the radar beam sampled precipitation within or above the melting layer because of its use of a reflectivity mosaic corrected for brightband contamination. Both NCEP stage IV and Q3gc showed improvement over the radar-only QPEs; while stage IV exhibited the lower errors, the performance of Q3gc was remarkable considering the estimates were automatically generated in near-real time. Regional analysis indicated Q3RAD outperformed Dual Pol and PPS over the southern plains, Southeast/mid-Atlantic, and Northeast. Over the northern United States, Q3RAD had a higher wet bias below the melting layer than both Dual Pol and PPS; within and above the melting layer, Q3RAD exhibited the lowest errors. The Q3RAD wet bias was likely due to MRMS's overestimation of tropical rain areas in continental regions and applying a high yield reflectivity-rain-rate relationship. An adjustment based on precipitation climatology reduced the wet bias errors by ~22% and will be implemented in the operational MRMS in the fall of 2016. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
242. Understanding Overland Multisensor Satellite Precipitation Error in TMPA-RT Products.
- Author
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Gebregiorgis, Abebe Sine, Kirstetter, Pierre-Emmanuel, Hong, Yang E., Carr, Nicholas J., Gourley, Jonathan J., Petersen, Walt, and Zheng, Yaoyao
- Subjects
- *
PRECIPITATION forecasting , *REMOTE sensing , *ALGORITHMS , *RADAR , *RAINFALL - Abstract
The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) has provided the global community a widely used multisatellite (and multisensor type) estimate of quasi-global precipitation. One of the TMPA level-3 products, 3B42RT/TMPA-RT (where RT indicates real time), is a merged product of microwave (MW) and infrared (IR) precipitation estimates, which attempts to exploit the most desirable aspects of both types of sensors, namely, quality rainfall estimation and spatiotemporal resolution. This study extensively and systematically evaluates multisatellite precipitation errors by tracking the sensor-specific error sources and quantifying the biases originating from multiple sensors. High-resolution, ground-based radar precipitation estimates from the Multi-Radar Multi-Sensor (MRMS) system, developed by the National Severe Storms Laboratory (NSSL), are utilized as reference data. The analysis procedure involves segregating the grid precipitation estimate as a function of sensor source, decomposing the bias, and then quantifying the error contribution per grid. The results of this study reveal that while all three aspects of detection (i.e., hit, missed-rain, and false-rain biases) contribute to the total bias associated with IR precipitation estimates, overestimation bias (positive hit bias) and missed precipitation are the dominant error sources for MW precipitation estimates. Considering only MW sensors, the TRMM Microwave Imager (TMI) shows the largest missed-rain and overestimation biases (nearly double that of the other MW estimates) per grid box during the summer and winter seasons. The Special Sensor Microwave Imagers/Sounders (SSMIS on board F17 and F16) also show major error during winter and spring, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
243. A Scheme for Rain Gauge Network Design Based on Remotely Sensed Rainfall Measurements.
- Author
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Dai, Qiang, Bray, Michaela, Zhuo, Lu, Islam, Tanvir, and Han, Dawei
- Subjects
- *
RAIN gauges , *RAINFALL , *REMOTE sensing , *PRINCIPAL components analysis , *RADAR - Abstract
A remarkable decline in the number of rain gauges is being faced in many areas of the world, as a compromise to the expensive cost of operating and maintaining rain gauges. The question of how to effectively deploy new or remove current rain gauges in order to create optimal rainfall information is becoming more and more important. On the other hand, larger-scaled, remotely sensed rainfall measurements, although poorer quality compared with traditional rain gauge rainfall measurements, provide an insight into the local storm characteristics, which are sought by traditional methods for designing a rain gauge network. Based on these facts, this study proposes a new methodology for rain gauge network design using remotely sensed rainfall datasets that aims to explore how many gauges are essential and where they should be placed. Principal component analysis (PCA) is used to analyze the redundancy of the radar grid network and to determine the number of rain gauges while the potential locations are determined by cluster analysis (CA) selection. The proposed methodology has been performed on 373 different storm events measured by a weather radar grid network and compared against an existing dense rain gauge network in southwestern England. Because of the simple structure, the proposed scheme could be easily implemented in other study areas. This study provides a new insight into rain gauge network design that is also a preliminary attempt to use remotely sensed data to solve the traditional rain gauge problems. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
244. A Simple Method for Attenuation Correction in Local X-Band Radar Measurements Using C-Band Radar Data.
- Author
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Lengfeld, Katharina, Clemens, Marco, Merker, Claire, Münster, Hans, and Ament, Felix
- Subjects
- *
WEATHER radar networks , *RADAR , *METEOROLOGICAL precipitation , *ATMOSPHERIC physics , *ALGORITHMS - Abstract
This paper presents a novel, simple method to correct reflectivity measurements of weather radars that operate in attenuation-influenced frequency bands using observations from less attenuated radar systems. In recent years radar systems operating in the X-band frequency range have been developed to provide precipitation fields for areas of special interest in high temporal (≤1 min) and spatial (≤250 m) resolution in complement to nationwide radar networks. However, X-band radars are highly influenced by attenuation. C- and S-band radars typically have coarser resolution (250 m-1 km and 5 min) but are less affected by attenuation. Correcting for attenuation effects in simple (non-Doppler) single-polarized X-band radars remains challenging and is often dependent on restriction parameters, for example, those derived from mountain returns. Therefore, these algorithms are applicable only in limited areas. The method proposed here uses measurements from C-band radars and hence can be applied in all regions covered by nationwide C- (or S-) band radar networks. First, a single scan of X-band radar measurements is used exemplary to identify advantages and disadvantages of the novel algorithm compared to a standard single radar algorithm. The performance of the correction algorithms in different types of precipitation is examined in nine case studies. The proposed method provides very promising results for each type of precipitation. Additionally, it is evaluated in a 5-month comparison with Micro Rain Radar (MRR) observations. The bias between uncorrected X-band radar and MRR data is nearly eliminated by the attenuation correction algorithm, and the RMSE is reduced by 20% while the correlation of ~0.9 between both systems remains nearly constant. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
245. Sampling Issues in Estimating Radar Variables from Disdrometer Data.
- Author
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Smith, Paul L.
- Subjects
- *
RAINDROPS , *RAINDROP size , *POLARIMETRIC remote sensing , *RAINFALL , *RADAR , *HYDROMETEOROLOGY - Abstract
Simulation of sampling from gamma-distributed raindrop populations demonstrates that significant biases and substantial errors can occur in estimates of polarimetric radar variables based on samples of raindrop populations obtained with disdrometers. Biases and RMS errors of 0.5 dB or more in estimates of differential reflectivity Zdr can occur with samples of even a few hundred drops; significant biases and errors also occur in estimates of reflectivity ZH or specific differential phase Kdp. The results indicate that very large samples would be required to obtain adequate representation of the population characteristics for many radar applications. They also suggest that greater attention is needed to the sample sizes in the disdrometer data used in developing polarimetric rainfall-rate estimators or hydrometeor classification algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
246. A Finescale Radar Examination of the Tornadic Debris Signature and Weak-Echo Reflectivity Band Associated with a Large, Violent Tornado.
- Author
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Houser, Jana Lesak, Bluestein, Howard B., and Snyder, Jeffrey C.
- Subjects
- *
RADAR , *REFLECTANCE , *POLARIMETRIC remote sensing , *ARTIFICIAL satellites , *POLARIMETRY , *REMOTE sensing - Abstract
High-resolution data of the tornadic debris signature (TDS) and weak-echo reflectivity band (WRB) associated with a large, violent tornado on 24 May 2011 in central Oklahoma are examined using a rapid-scan, X-band, polarimetric, mobile Doppler radar. Various characteristics of these features and their evolution are examined over time intervals of 20 s or less. The formation of the TDS, debris fallout, and inhomogeneities in the TDS structure, are analyzed from volumetric and single-elevation observations. Constant-radius vertical cross sections of Doppler velocity, reflectivity, and copolar cross-correlation coefficient are compared at various times during the tornado's life cycle; from them it is found that the weak echo column (WEC) is considerably narrower than the TDS and the WEC is confined to the strong gradient of Doppler velocities in the tornado's core. The TDS of the mature tornado extends radially outward, bound approximately by the 40 m s−1 radial isodop. Rapid-scan, near-surface data were collected for a period of 6 min, during which 2-s single-elevation PPI updates at 1° were available at heights below 100 m above radar level. During this period, a WRB associated with a visually observed horizontal vortex developed east of the tornado, along the leading edge of the secondary rear-flank gust front, as the tornado was rapidly intensifying. A relationship was noted between reduced radar-observed reflectivity and increased radar-observed radial convergence/divergence in the vicinity of the horizontal vortex as it strengthened. This feature is qualitatively analyzed and hypotheses explaining its generation and structure are discussed. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
247. Understanding Heavy Lake-Effect Snowfall: The Vertical Structure of Radar Reflectivity in a Deep Snowband over and downwind of Lake Ontario.
- Author
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Welsh, Dan, Geerts, Bart, Jing, Xiaoqin, Bergmaier, Philip T., Minder, Justin R., Steenburgh, W. James, and Campbell, Leah S.
- Subjects
- *
SNOW , *RADAR , *LAKE effect precipitation , *ATMOSPHERIC boundary layer , *CONVECTIVE clouds - Abstract
The distribution of radar-estimated precipitation from lake-effect snowbands over and downwind of Lake Ontario shows more snowfall in downwind areas than over the lake itself. Here, two nonexclusive processes contributing to this are examined: the collapse of convection that lofts hydrometeors over the lake and allows them to settle downwind; and stratiform ascent over land, due to the development of a stable boundary layer, frictional convergence, and terrain, leading to widespread precipitation there. The main data sources for this study are vertical profiles of radar reflectivity and hydrometeor vertical velocity in a well-defined, deep long-lake-axis-parallel band, observed on 11 December 2013 during the Ontario Winter Lake-effect Systems (OWLeS) project. The profiles are derived from an airborne W-band Doppler radar, as well as an array of four K-band radars, an X-band profiling radar, a scanning X-band radar, and a scanning S-band radar. The presence of convection offshore is evident from deep, strong (up to 10 m s−1) updrafts producing bounded weak-echo regions and locally heavily rimed snow particles. The decrease of the standard deviation, skewness, and peak values of Doppler vertical velocity during the downwind shore crossing is consistent with the convection collapse hypothesis. Consistent with the stratiform ascent hypothesis are (i) an increase in mean vertical velocity over land; and (ii) an increasing abundance of large snowflakes at low levels and over land, due to depositional growth and aggregation, evident from flight-level and surface particle size distribution data, and from differences in reflectivity values from S-, X-, K-, and W-band radars at nearly the same time and location. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
248. Interaction of an Upper-Tropospheric Jet with a Squall Line Originating along a Cold Frontal Boundary.
- Author
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Stechman, Daniel M., Rauber, Robert M., McFarquhar, Greg M., Jewett, Brian F., and Jorgensen, David P.
- Subjects
- *
TROPOSPHERIC chemistry , *TROPOSPHERIC ozone , *FRONTS (Meteorology) , *DOPPLER effect , *RADAR - Abstract
On 8 June 2003, an expansive squall line along a surface cold frontal boundary was sampled during the Bow Echo and Mesoscale Convective Vortex Experiment. The Naval Research Laboratory P-3 aircraft and the National Oceanic and Atmospheric Administration P-3 aircraft simultaneously sampled the leading and trailing edge of this squall line, respectively, with X-band Doppler radars. Data from these two airborne radar systems have been synthesized to produce a pseudo-quad-Doppler analysis of the squall line, yielding a detailed three-dimensional kinematic analysis of its structure. A simulation of the squall line was carried out using the Weather Research and Forecasting Model to complement the pseudo-quad-Doppler analysis. The simulation employed a 3-km, convection-allowing, nested domain centered over the pseudo-quad-Doppler domain, along with a 9-km parent domain to capture the larger synoptic-scale cyclone. The pseudo-quad-Doppler analysis reveals that the convective line was embedded within the upper-tropospheric jet stream, causing local decelerations and deviations in the jet-level flow. The vertical transport of low momentum air from the boundary layer via convective updrafts is shown to significantly decelerate jet-level flow. Pressure perturbations associated with the intrusion of low momentum air into the jet stream-level flow led to deviation of the jet stream flow around the squall line that resulted in counter-rotating ribbons of vertical vorticity parallel to the squall line. Model results indicate that disturbances in the jet stream structure persisted downwind of the squall line for several hours. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
249. Kinematic Structure of Mesovortices in the Eyewall of Hurricane Ike (2008) Derived from Ground-Based Dual-Doppler Analysis.
- Author
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Wingo, Stephanie M. and Knupp, Kevin R.
- Subjects
- *
TROPICAL cyclones , *HURRICANES , *TYPHOONS , *RADAR , *DETECTORS - Abstract
Previous work has shown that vorticity mixing in the tropical cyclone (TC) inner core can promote mesovortex (MV) formation and impact storm intensity. Observations of MVs have largely been serendipitous but are necessary to improve understanding of these features and their role in TC dynamics. This study presents nearly 10 h of ground-based dual-Doppler analysis of MVs in the eyewall of Hurricane Ike (2008) near and during landfall. Derived 3D winds, vertical vorticity, horizontal divergence, and perturbation pressures are analyzed. Results indicate persistent kinematic field arrangements and evolving vertical structures. Perturbation pressure retrievals suggest local pressure minima associated with the MVs. Preferential updraft locations appear to transition cyclonically about the local vorticity maximum as the MVs progress around the eye. Based on published observational datasets, the dual-Doppler updraft magnitudes in Ike's MVs are within the top 5%-10% of TC vertical velocities. The MVs are marked by peak vorticity in the lowest 2 km and contain vertically coherent vorticity structures extending to 8 km AGL. After prolonged land interaction, the MV structures deteriorate. First, the vertical extent of localized vorticity diminishes, followed by a deterioration in the prelandfall characteristic kinematic arrangements. This supports the notion that the replenishment of a high vorticity annulus contributes to MV production and maintenance, and when the elevated vorticity aloft is not maintained, MV kinematic patterns become less consistent. It is unclear whether the decay of the vertically coherent vorticity structures occurs in response to land interaction, TC inner core processes, or some combination of both. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
250. Columnar Vertical Profile (CVP) Methodology for Validating Polarimetric Radar Retrievals in Ice Using In Situ Aircraft Measurements
- Author
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Alexander V. Ryzhkov, Pengfei Zhang, and Amanda M. Murphy
- Subjects
In situ ,Atmospheric Science ,010504 meteorology & atmospheric sciences ,0207 environmental engineering ,Polarimetry ,Ocean Engineering ,02 engineering and technology ,01 natural sciences ,law.invention ,Radar observations ,Atmosphere ,law ,Remote sensing (archaeology) ,Environmental science ,Radar ,020701 environmental engineering ,0105 earth and related environmental sciences ,Remote sensing - Abstract
A novel way to process polarimetric radar data collected via plan position indicator (PPI) scans and display those data in a time–height format is introduced. The columnar vertical profile (CVP) methodology uses radar data collected via multiple elevation scans, limited to data within a set region in range and azimuth relative to the radar, to create vertical profiles of polarimetric radar data representative of that limited region in space. This technique is compared to others existing in the literature, and various applications are discussed. Polarimetric ice microphysical retrievals are performed on CVPs created within the stratiform rain region of two mesoscale convective systems sampled during two field campaigns, where CVPs follow the track of research aircraft. Aircraft in situ data are collocated to microphysical retrieval data, and the accuracy of these retrievals is tested against other retrieval techniques in the literature.
- Published
- 2020
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