Search

Comparisons of precipitation between general circulation models (GCMs) and observations are often confounded by a mismatch between model output and instrument measurements, including variable type and temporal and spatial resolutions. To mitigate these differences, the radar-simulator Quickbeam within the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) simulates reflectivity from model variables at the subgrid scale. This work adapts Quickbeam to the dual-frequency precipitation radar (DPR) on board the Global Precipitation Measurement (GPM) satellite. The longer wavelength of the DPR is used to evaluate moderate to heavy precipitation in GCMs, which is missed when Quickbeam is used as a cloud radar simulator. Latitudinal and land–ocean comparisons are made between COSP output from the Community Atmospheric Model version 5 (CAM5) and DPR data. Additionally, this work improves the COSP subgrid algorithm by applying a more realistic, nondeterministic approach to assigning GCM gridbox convective cloud cover when convective cloud is not provided as a model output. Instead of assuming a static 5% convective cloud coverage, DPR convective precipitation coverage is used as a proxy for convective cloud coverage. For example, DPR observations show that convective rain typically only covers about 1% of a 2° grid box, but that the median convective rain area increases to over 10% in heavy rain cases. In our CAM5 tests, the updated subgrid algorithm improved the comparison between reflectivity distributions when the convective cloud cover is provided versus the default 5% convective cloud-cover assumption.

This article summarizes research and risk reduction that will inform acquisition decisions regarding NOAA’s future national operational weather radar network. A key alternative being evaluated is polarimetric phased-array radar (PAR). Research indicates PAR can plausibly achieve fast, adaptive volumetric scanning, with associated benefits for severe-weather warning performance. We assess these benefits using storm observations and analyses, observing system simulation experiments, and real radar-data assimilation studies. Changes in the number and/or locations of radars in the future network could improve coverage at low altitude. Analysis of benefits that might be so realized indicates the possibility for additional improvement in severe-weather and flash-flood warning performance, with associated reduction in casualties. Simulations are used to evaluate techniques for rapid volumetric scanning and assess data quality characteristics of PAR. Finally, we describe progress in developing methods to compensate for polarimetric variable estimate biases introduced by electronic beam-steering. A research-to-operations (R2O) strategy for the PAR alternative for the WSR-88D replacement network is presented.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, 資料番号: PA2110044000

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.

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.

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.

The Multi-Radar-Multi-Sensor (MRMS) system was transitioned into operations at the National Centers for Environmental Prediction in the fall of 2014. It provides high-quality and high-resolution severe weather and precipitation products for meteorology, hydrology, and aviation applications. Among processing modules, the radar data quality control (QC) plays a critical role in effectively identifying and removing various nonhydrometeor radar echoes for accurate quantitative precipitation estimation (QPE). Since its initial implementation in 2014, the radar QC has undergone continuous refinements and enhancements to ensure its robust performance across seasons and all regions in the continental United States and southern Canada. These updates include 1) improved melting-layer delineation, 2) clearance of wind farm contamination, 3) mitigation of corrupt data impacts due to hardware issues, 4) mitigation of sun spikes, and 5) mitigation of residual ground/lake/sea clutter due to sidelobe effects and anomalous propagation. This paper provides an overview of the MRMS radar data QC enhancements since 2014.

Sampling error stems from the use of ensemble-based data assimilation (EDA) with a limited ensemble size and can result in spurious background error covariances, leading to false analysis corrections. The WRF-LETKF radar assimilation system (WLRAS) is performed separately with 256 and 40 members to investigate the characteristics of convective-scale sampling errors in the EDA and its impact on precipitation prediction based on a heavy rainfall event on 16 June 2008. The results suggest that the sampling errors for this event are sensitive to the relationships between the simulated observations and model variables, the intensity of reflectivity, and how the prevailing wind projects to the radial wind in the areas that the radar cannot resolve U or V wind. The sampling errors lead to an underprediction of heavy rainfall when the horizontal localization radius is inadequately large, but this can be mitigated when a more accurate moisture condition is provided. In addition, being able to use a larger vertical localization plays an important role in providing necessary adjustments for representing the vertical thermodynamical structure of convection, which further improves precipitation prediction. A strategy mitigating the impact of sampling errors associated with the limitation of radial wind measurement by inflating the observation error over sensitive areas can bring benefits to precipitation prediction.

Weather radar provides real-time, spatially distributed precipitation estimates, whereas traditional gauge data are restricted in space. The use of radar quantitative precipitation estimates (QPEs) as an input of hydrological models for hydrometeorological applications has increased with the development of weather radar worldwide. New dual-polarization technology and algorithms are showing improvements to radar QPEs. This study evaluates radar QPEs from C-band radar at King City, Canada (WKR), and NEXRAD S-band radar at Buffalo, New York (KBUF), to verify the reliability and accuracy for operational use in the Humber River (semiurban) and Don River (urban) watersheds in the Greater Toronto Area (GTA), Canada. Twenty rainfall events that occurred from 2011 to 2017 were determined from hourly gauge measurements and compared with nine radar QPEs. Rain rates were estimated with different algorithms using three dual-polarized reflectivity values: horizontal reflectivity Z, differential reflectivity ZDR, and specific differential phase KDP. The correlation coefficient, bias, detection, and root-mean-square error were calculated and averaged over all events for each gauge station to show the spatial distribution and in a similar pattern to represent the variation by the event. The quality of the results in terms of accuracy and reliability indicates that the radar QPEs from KBUF S-band and WKR C-band multiparameter rain rate estimators can be effectively used as precipitation forcing of hydrological models for hydrometeorological applications. The high spatiotemporal resolution, long-term data archive, and good percent detection of radar QPEs can facilitate hydrometeorological applications by providing a continuous time series for hydrological models.

Weather radars and gauge observations are the primary observations to determine the coverage and magnitude of precipitation; however, radar and gauge networks have significant coverage gaps, which can underrepresent or even miss the occurrence of precipitation. This is especially noticeable in mountainous regions and in shallow precipitation regimes. The following study presents a methodology to improve spatial representations of precipitation by seamlessly blending multiple precipitation sources within the Multi-Radar Multi-Sensor (MRMS) system. A high spatiotemporal resolution multisensor merged quantitative precipitation estimation (QPE) product (MSQPE) is generated by using gauge-corrected radar QPE as a primary precipitation source with a combination of hourly gauge observations, monthly precipitation climatologies, numerical weather prediction short-term precipitation forecasts, and satellite observations to use in areas of insufficient radar coverage. The merging of the precipitation sources is dependent upon radar coverage based on an updated MRMS radar quality index, surface and atmospheric conditions, topography, gauge locations, and precipitation values. Evaluations of the MSQPE product over the western United States resulted in improved statistical measures over its individual input precipitation sources, particularly the locally gauge-corrected radar QPE. The MSQPE scheme demonstrated its ability to sufficiently fill in areas where radar alone failed to detect precipitation due to significant beam blockage or poor coverage while minimizing the generation of false precipitation and underestimation biases that resulted from radar overshooting precipitation.

The attenuation-based rainfall estimator is less sensitive to the variability of raindrop size distributions (DSDs) than conventional radar rainfall estimators. For the attenuation-based quantitative precipitation estimation (QPE), the key is to accurately estimate the horizontal specific attenuation AH, which requires a good estimate of the ray-averaged ratio between AH and specific differential phase KDP, also known as the coefficient α. In this study, a variational approach is proposed to optimize the coefficient α for better estimates of AH and rainfall. The performance of the variational approach is illustrated using observations from an S-band operational weather radar with rigorous quality control in south China, by comparing against the α optimization approach using a slope of differential reflectivity ZDR dependence on horizontal reflectivity factor ZH. Similar to the ZDR-slope approach, the variational approach can obtain the optimized α consistent with the DSD properties of precipitation on a sweep-to-sweep basis. The attenuation-based hourly rainfall estimates using the sweep-averaged α values from these two approaches show comparable accuracy when verified against the gauge measurements. One advantage of the variational approach is its feasibility to optimize α for each radar ray, which mitigates the impact of the azimuthal DSD variabilities on rainfall estimation. It is found that, based on the optimized α for radar rays, the hourly rainfall amounts derived from the variational approach are consistent with gauge measurements, showing lower bias (1.0%), higher correlation coefficient (0.92), and lower root-mean-square error (2.35 mm) than the results based on the sweep-averaged α.

Although radar is our most useful tool for monitoring severe weather, the benefits of assimilating its data are often short lived. To understand why, we documented the assimilation requirements, the data characteristics, and the common practices that could hinder optimum data assimilation by traditional approaches. Within storms, radars provide dense measurements of a few highly variable storm outcomes (precipitation and wind) in atmospherically unstable conditions. However, statistical relationships between errors of observed and unobserved quantities often become nonlinear because the errors in these areas tend to become large rapidly. Beyond precipitating areas lie large regions for which radars provide limited new information, yet whose properties will soon shape the outcome of future storms. For those areas, any innovation must consequently be projected from sometimes distant precipitating areas. Thus, radar data assimilation must contend with a double need at odds with many traditional assimilation implementations: correcting in-storm properties with complex errors while projecting information at unusually far distances outside precipitating areas. To further complicate the issue, other data properties and practices, such as assimilating reflectivity in logarithmic units, are not optimal to correct all state variables. Therefore, many characteristics of radar measurements and common practices of their assimilation are incompatible with necessary conditions for successful data assimilation. Facing these dataset-specific challenges may force us to consider new approaches that use the available information differently.

Differential absorption radar (DAR) offers an active remote sensing solution to the problem of measuring humidity profiles with high vertical and horizontal resolution in hydrometeor layers. The Vapor In-Cloud Profiling Radar (VIPR) is a frequency-modulated continuous-wave (FMCW) G-band DAR tunable from 167 to 174.8 GHz being developed at the Jet Propulsion Laboratory (JPL). Here we describe ground-based measurements from VIPR performed at the Department of Energy’s Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site for humidity product validation. Two distinct measurement capabilities are investigated: 1) humidity profiles inside of cloudy volumes with 180 m vertical resolution, and 2) integrated water vapor (IWV) between the surface and cloud base. High radar sensitivity permits detection of upper-tropospheric clouds and retrieval of humidity profiles above 10 km in height. We develop an improved humidity retrieval algorithm based on a regularized least squares method that includes detailed accounting of measurement covariances and systematic error sources. This regularization mitigates high-spatial-frequency humidity biases that arise from frequency-dependent hydrometeor scattering, which is an important limitation for DAR systems. Through comparisons with over 20 coincident radiosondes, we find close agreement between in situ and remotely sensed humidity profiles, with a correlation coefficient of r = 0.96, root-mean-square error (RMSE) of 0.8 g m−3, and median retrieval precision of 0.5 g m−3. Using a merged radiosonde and Raman lidar product for surface-to-cloud-base IWV, we demonstrate precise column sounding capabilities with r = 1.00, RMSE of 1.2 mm, and median retrieval precision of 0.25 mm.

Severe thunderstorms and their associated tornadoes pose significant threats to life and property, and using radar data to accurately measure the rotational velocity of circulations in thunderstorms is essential for appropriate, timely warnings. One key factor in accurately measuring circulation velocity is the azimuthal spacing between radar data points, which is referred to as the azimuthal sampling interval. Previous studies have shown that reducing the azimuthal sampling interval can aid in measuring circulation velocity; however, this comes at the price of increased computational complexity. Thus, choosing the best compromise requires knowledge of the relationship between the radar azimuthal sampling interval and the accuracy of the circulation strength as measured from the radar data. In this work, we use simulations to quantify the impact of azimuthal sampling on the strength of radar-observed circulations and show that the improvements get progressively smaller as the azimuthal sampling interval decreases. Thus, improved characterization of circulations can be achieved without using the finest possible sampling grid. We use real data to validate the results of the simulations, which can be used to inform the selection of an appropriate azimuthal sampling interval that balances the accuracy of the radar-observed circulations and computational complexity.

This study demonstrates an implementation of the prototype quantitative precipitation R estimation algorithm using specific attenuation A for S-band polarimetric radar. The performance of R(A) algorithm is assessed, compared to the conventional algorithm using radar reflectivity Z, at multiple temporal scales. Because the factor α, defined as the net ratio of A to specific differential phase, is a key parameter of the algorithm characterized by drop size distributions (e.g., differential reflectivity Zdr dependence on Z), the estimation equations of α and a proper number of Zdr–Z samples required for a reliable α estimation are examined. Based on the dynamic estimation of α, the event-based evaluation using hourly rain gauge observations reveals that the performance of R(A) is superior to that of R(Z), with better agreement and lower variability. Despite its superiority, the study finds that R(A) leads to quite consistent overestimations of about 10%–30%. It is demonstrated that the application of uniform α over the entire radar domain yields the observed uncertainty because of the heterogeneity of precipitation in the domain. A climatological range-dependent feature of R(A) and R(Z) is inspected in the multiyear evaluation at yearly scale using rain totals for April–October. While R(Z) exposes a systematic shift and overestimation, each of which arise from the radar miscalibration and bright band effects, R(A) combining with multiple R(Z) values for solid/mixed precipitation shows relatively robust performance without those effects. The immunity of R(A) to partial beam blockage (PBB) based on both qualitative and quantitative analyses is also verified. However, the capability of R(A) regarding PBB is limited by the presence of the melting layer and its application requirement for the total span of differential phase (e.g., 3°), which is another challenge for light rain.

Diurnal cycles of precipitation and lightning are investigated by analyzing rain rates of the TRMM3G68 dataset, consisting of Precipitation Radar and Microwave Imager data only; rain rates of Global Satellite Mapping of Precipitation (GSMaP), for which infrared (IR) data are also used; lightning flash rates observed by TRMM Lightning Imaging Sensor (LIS); and lightning stroke rates of World Wide Lightning Location Network (WWLLN) over the tropics. Diurnal amplitudes relative to averages are generally larger for lightning than for precipitation. Over ocean, relative amplitudes are stronger in the stratocumulus deck region in the southeast Pacific than those over typical ocean regions. The phase of GSMaP is substantially delayed to TRMM3G68 due to the phase-delay problem of IR-based estimation. The diurnal peaks tend to occur between 1400 and 1800 LST over the continent after spatial averaging with a phase leading order of TRMM3G68, LIS, and WWLLN, and between 0000 and 0700 LST over oceanic regions where diurnal cycles are prominent in all datasets. Off-equatorward phase propagations are found in the precipitation in the Pacific and Indian Oceans. Over selected coastal regions, all data exhibit consistent oceanward phase propagation with the longest, medium, and shortest phase propagation distances for TRMM3G68 precipitation, WWLLN lightning, and LIS lightning, respectively, with a phase leading order of LIS, WWLLN, and TRMM3G68. The summertime diurnal cycle over the Gulf Stream also exhibits oceanward phase propagation, but with strong amplitude enhancement over the Gulf Stream. Diurnal cycle amplitude is also enhanced over the Kuroshio in the East China Sea in the baiu–mei-yu rainy season.

Radar and surface rainfall observations are two sources of operational data crucial for heavy rainfall prediction. Their individual values on improving convective forecasting through data assimilation have been examined in the past using convection-permitting numerical models. However, the benefit of their simultaneous assimilations has not yet been evaluated. The objective of this study is to demonstrate that, using a 4D-Var data assimilation system with a microphysical scheme, these two data sources can be assimilated simultaneously and the combined assimilation of radar data and estimated rainfall data from radar reflectivity and surface network can lead to improved short-term heavy rainfall prediction. In our study, a combined data assimilation experiment is compared with a rainfall-only and a radar-only (with or without reflectivity) experiments for a heavy rainfall event occurring in Taiwan during the passage of a mei-yu system. These experiments are conducted by applying the Weather Research and Forecasting (WRF) 4D-Var data assimilation system with a 20-min time window aiming to improve 6-h convective heavy rainfall prediction. Our results indicate that the rainfall data assimilation contributes significantly to the analyses of humidity and temperature whereas the radar data assimilation plays a crucial role in wind analysis, and further, combining the two data sources results in reasonable analyses of all three fields by eliminating large, unphysical analysis increments from the experiments of assimilating individual data only. The results also show that the combined assimilation improves forecasts of heavy rainfall location and intensity of 6-h accumulated rainfall for the case studied.

In a 2018 paper by Bukovčić et al., polarimetric bivariate power-law relations for estimating snowfall rate S and ice water content (IWC), and , were developed utilizing 2D video disdrometer snow measurements in Oklahoma. Herein, these disdrometer-based relations are generalized for the range of particle aspect ratios from 0.5 to 0.8 and the width of the canting angle distribution from 0° to 40° and are validated via analytical/theoretical derivations and simulations. In addition, a novel S(KDP, Zdr) polarimetric relation utilizing the ratio between specific differential phase KDP and differential reflectivity Zdr, , is derived. Both KDP and are proportionally affected by the ice particles’ aspect ratio and width of the canting angle distribution; therefore, the variables’ ratio tends to be almost invariant to the changes in these parameters. The S(KDP, Z) and S(KDP, Zdr) relations are applied to the polarimetric S-band WSR-88D data obtained from three geographical locations in Virginia, Oklahoma, and Colorado, and their performance is compared with estimations from the standard S(Z) relations and ground snow measurements. The polarimetric estimates of snow accumulations from the three cases exhibit smaller bias in comparison with the S(Z), indicating good potential for more reliable radar snow measurements.

A detailed damage survey is combined with high-resolution mobile, rapid-scanning X-band polarimetric radar data collected on the Shawnee, Oklahoma, tornado of 19 May 2013. The focus of this study is the radar data collected during a period when the tornado was producing damage rated EF3. Vertical profiles of mobile radar data, centered on the tornado, revealed that the radar reflectivity was approximately uniform with height and increased in magnitude as more debris was lofted. There was a large decrease in both the cross-correlation coefficient (ρhv) and differential radar reflectivity (ZDR) immediately after the tornado exited the damaged area rated EF3. Low ρhv and ZDR occurred near the surface where debris loading was the greatest. The 10th percentile of ρhv decreased markedly after large amounts of debris were lofted after the tornado leveled a number of structures. Subsequently, ρhv quickly recovered to higher values. This recovery suggests that the largest debris had been centrifuged or fallen out whereas light debris remained or continued to be lofted. Range–height profiles of the dual-Doppler analyses that were azimuthally averaged around the tornado revealed a zone of maximum radial convergence at a smaller radius relative to the leading edge of lofted debris. Low-level inflow into the tornado encountering a positive bias in the tornado-relative radial velocities could explain the existence of the zone. The vertical structure of the convergence zone was shown for the first time.

Typhoon Bolaven caused significant damage with severe rainfall all over South Korea, including Cheju Island, which received more than 250 mm in 2 days in August 2012. It was regarded as the most powerful storm to strike the Korean Peninsula in nearly a decade. The rainfall-rate datasets were obtained from S-band radar operated by the Korea Meteorological Administration to be analyzed and compared with the mesoscale Cloud Resolving Storm Simulator (CReSS) model simulation. Multifractal analysis was conducted to understand the structure of the rainfall rate with height in the typhoon system. The radar rainfall data presented with strong intermittency across scales at lower altitudes (1 and 2 km) and a more homogeneous rainfall field at high altitude (5 km) with two parameters (fractal codimension and multifractality index). The statistical scaling moment function and maximal singularities show clear significant differences between radar and the CReSS model.

A monetized flash flood casualty reduction benefit model is constructed for application to meteorological radar networks. Geospatial regression analyses show that better radar coverage of the causative rainfall improves flash flood warning performance. Enhanced flash flood warning performance is shown to decrease casualty rates. Consequently, these two effects in combination allow a model to be formed that links radar coverage to flash flood casualty rates. When this model is applied to the present-day contiguous U.S. weather radar network, results yield a flash flood–based benefit of $316 million (M) yr−1. The remaining benefit pools are more modest ($13 M yr−1 for coverage improvement and $69 M yr−1 maximum for all areas of radar quantitative precipitation estimation improvements), indicative of the existing weather radar network’s effectiveness in supporting the flash flood warning decision process. ©2020 Keywords: radars/radar observations; regression analysis; economic value; flood events; geographic information systems (gis); societal impacts

High-sensitivity weather radars easily detect nonmeteorological phenomena characterized by weak radar returns. Fireworks are the example presented here. To understand radar observations, an experiment was conducted in which the National Severe Storms Laboratory (NSSL)’s research (3-cm wavelength) dual-polarization radar and a video camera were located at 1 km from fireworks in Norman, Oklahoma. The fireworks from the 4 July 2017 celebration were recorded by both instruments. The experiment is described. Few bursts recorded by the camera are analyzed to obtain the height of the explosion, its maximum diameter, number of stars, and the duration of the visible image. Radar volume scans are examined to characterize the height of the observation, the maximum reflectivity, and its distribution with height. The fireworks location is close to the Terminal Doppler Weather Radar (TDWR) that operates in single polarization at a 5-cm wavelength and monitors hazardous weather over the Oklahoma City airport. A third radar with data from the event is the Weather Surveillance Radar-1988 Doppler (WSR-88D) located in Norman. It has a wavelength of 10 cm and supports technical developments at the Radar Operation Center. Reflectivity factors measured by the three radars are compared to infer the size of dominant scatterers. The polarimetric characteristics of fireworks returns are analyzed. Although these differ from those of precipitation, they are indistinguishable from insect returns. Radar observation of larger fireworks in Fort Worth, Texas, with a WSR-88D is included and compared with the observations of the smaller fireworks in Norman. We expect the detectability of explosions would be similar as of fireworks. Pinpointing locations would be useful to first responders, or air quality forecasters. A benefit of fireworks recognition in weather radar data is that it can prevent contamination of precipitation accumulations.

This study assesses the level-2 precipitation estimates from 10 radiometers relative to Global Precipitation Measurement (GPM) Ku-band precipitation radar (KuPR) in two parts. First, nine sensors—four imagers [Advanced Microwave Scanning Radiometer 2 (AMSR2) and three Special Sensor Microwave Imager/Sounders (SSMISs)] and five sounders [Advanced Technology Microwave Sounder (ATMS) and four Microwave Humidity Sounders (MHSs)]—are evaluated over the 65°S–65°N region. Over ocean, imagers outperform sounders, primarily due to the usage of low-frequency channels. Furthermore, AMSR2 is clearly superior to SSMISs, likely due to the finer footprint size. Over land all sensors perform similarly except the noticeably worse performance from ATMS and SSMIS-F17. Second, we include the Sondeur Atmospherique du Profil d’Humidite Intertropicale par Radiometrie (SAPHIR) into the evaluation process, contrasting it against other sensors in the SAPHIR latitudes (30°S–30°N). SAPHIR has a slightly worse detection capability than other sounders over ocean but comparable detection performance to MHSs over land. The intensity estimates from SAPHIR show a larger normalized root-mean-square-error over both land and ocean, likely because only 183.3-GHz channels are available. Currently, imagers are preferred to sounders when level-2 estimates are incorporated into level-3 products. Our results suggest a sensor-specific priority order. Over ocean, this study indicates a priority order of AMSR2, SSMISs, MHSs and ATMS, and SAPHIR. Over land, SSMIS-F17, ATMS and SAPHIR should be given a lower priority than the other sensors.

A well-designed deployment of well-maintained surface instruments as well as abundant rainfall provided an excellent dataset with which to evaluate the Micro Rain Radar (MRR) performance for estimating raindrop size distribution (DSD) and its integral rainfall parameters with respect to the consolidated devices during the Iowa Flood Studies (IFloodS) field campaign. The MRR was collocated with two-dimensional video disdrometer (2DVD) and Autonomous Parsivel2 Unit (APU) at three different sites located at 5–70-km distances from the National Aeronautics and Space Administration’s S-band dual-polarization Doppler radar (NPOL). A comparative study between MRR, 2DVD, APU, and NPOL was conducted including all rainy minutes as well as minutes of stratiform rain and convective rain. Considering 2DVD as a primary reference, a good agreement was evident for reflectivity between MRR’s lowest reliable height and 2DVD with an absolute bias of less than 2 dB even in convective rain except for one site. For rainfall rate, the percent absolute bias between MRR and 2DVD ranged between 25% and 35% in stratiform rain and about 10% higher in convective rain. Agreement for mean mass-weighted raindrop diameter was good (bias less than 0.1 mm), whereas MRR overestimated the normalized intercept parameter of the gamma DSD [mean bias among the three sites was −0.13 log(mm−1 m−3)]. The agreement between MRR and APU was slightly worse than the one between MRR and 2DVD. When the horizontal and differential reflectivities of NPOL were compared with the ones derived from the MRR DSD resampled within the radar volume, we found an absolute bias of approximately 3 and 0.4 dB, respectively.

The ground-based-radar-derived raindrop size distribution (DSD) parameters—mass-weighted drop diameter Dmass and normalized intercept parameter NW—are the sole resource for direct validation of the National Aeronautics and Space Administration (NASA) Global Precipitation Measurement (GPM) mission Core Observatory satellite-based retrieved DSD. Both Dmass and NW are obtained from radar-measured reflectivity ZH and differential reflectivity ZDR through empirical relationships. This study uses existing relationships that were determined for the GPM ground validation (GV) program and directly compares the NASA S-band polarimetric radar (NPOL) observables of ZH and ZDR and derived Dmass and NW with those calculated by two-dimensional video disdrometer (2DVD). The joint NPOL and 2DVD datasets were acquired during three GPM GV field campaigns conducted in eastern Iowa, southern Appalachia, and western Washington State. The comparative study quantifies the level of agreement for ZH, ZDR, Dmass, and log(NW) at an optimum distance (15–40 km) from the radar as well as at distances greater than 60 km from radar and over mountainous terrain. Interestingly, roughly 10%–15% of the NPOL ZH–ZDR pairs were well outside the envelope of 2DVD-estimated ZH–ZDR pairs. The exclusion of these pairs improved the comparisons noticeably.

This study contrasts midlatitude continental and tropical maritime deep convective cores using polarimetric radar observables and retrievals from selected deep convection episodes during the MC3E and TWPICE field campaigns. The continental convective cores produce stronger radar reflectivities throughout the profiles, while maritime convective cores produce more positive differential reflectivityZdrand larger specific differential phaseKdpabove the melting level. Hydrometeor identification retrievals revealed the presence of large fractions of rimed ice particles (snow aggregates) in the continental (maritime) convective cores, consistent with theZdrandKdpobservations. The regional cloud-resolving model simulations with bulk and size-resolved bin microphysics are conducted for the selected cases, and the simulation outputs are converted into polarimetric radar signals and retrievals identical to the observational composites. Both the bulk and the bin microphysics reproduce realistic land and ocean (L-O) contrasts in reflectivity, polarimetric variables of rain drops, and hydrometeor profiles, but there are still large uncertainties in describingZdrandKdpof ice crystals associated with the ice particle shapes/orientation assumptions. Sensitivity experiments are conducted by swapping background aerosols between the continental and maritime environments, revealing that background aerosols play a role in shaping the distinct L-O contrasts in radar reflectivity associated with raindrop sizes, in addition to the dominant role of background thermodynamics.

The performance of a numerical weather prediction model using convective-scale ensemble data assimilation with ground-based global navigation satellite systems-zenith total delay (ZTD) and radar data is investigated on a heavy rainfall event that occurred in Taiwan on 10 June 2012. The assimilation of ZTD and/or radar data is performed using the framework of the WRF local ensemble transform Kalman filter with a model grid spacing of 2 km. Assimilating radar data is beneficial for predicting the rainfall intensity of this local event but produces overprediction in southern Taiwan and underprediction in central Taiwan during the first 3 h. Both errors are largely overcome by assimilating ZTD data to improve mesoconvective-scale moisture analyses. Consequently, assimilating both the ZTD and radar data show advantages in terms of the location and intensity of the heavy rainfall. Sensitivity experiments involving this event indicate that the impact of ZTD data is improved by using a broader horizontal localization scale than the convective scale used for radar data assimilation. This optimization is necessary in order to consider more fully the network density of the ZTD observations and the horizontal scale of the moisture transport by the southwesterly flow in this case.

In this study, the mesoscale convective systems (MCSs) are tracked using high-resolution radar and satellite observations over the U.S. Great Plains during April–August from 2010 to 2012. The spatiotemporal variability of MCS precipitation is then characterized using the Stage IV product. We found that the spatial variability and nocturnal peaks of MCS precipitation are primarily driven by the MCS occurrence rather than the precipitation intensity. The tracked MCSs are further classified into convective core (CC), stratiform rain (SR), and anvil clouds regions. The spatial variability and diurnal cycle of precipitation in the SR regions of MCSs are not as significant as those of MCS precipitation. In the SR regions, the high-resolution, long-term ice cloud microphysical properties [ice water content (IWC) and ice water paths (IWPs)] are provided. The IWCs generally decrease with height. Spatially, the IWC, IWP, and precipitation are all higher over the southern Great Plains than over the northern Great Plains. Seasonally, those ice and precipitation properties are all higher in summer than in spring. Comparing the peak timings of MCS precipitation and IWPs from the diurnal cycles and their composite evolutions, it is found that when using the peak timing of IWPSR as a reference, the heaviest precipitation in the MCS convective core occurs earlier, while the strongest SR precipitation occurs later. The shift of peak timings could be explained by the stratiform precipitation formation process. The IWP and precipitation relationships are different at MCS genesis, mature, and decay stages. The relationships and the transition processes from ice particles to precipitation also depend on the low-level humidity.

This study targeted improving Collaborative Adaptive Sensing of the Atmosphere’s (CASA) 6-h lead time predictive ability by blending the radar-based nowcast with the NWP model over the Dallas–Fort Worth (DFW) urban radar network. This study also depicts the recent updates in CASA’s real-time reflectivity nowcast system by assessing nine precipitation cases over the DFW urban region. CASA’s nowcast framework displayed better primer outcomes than the WRF Model forecast for the lead time of 1 h and 30 min. After that time, the predictive ability of the nowcast framework began decreasing compared to the WRF Model. To broaden CASA’s predictive system lead time to 6 h, the WRF Model forecasts were blended with Dynamic and Adaptive Radar Tracking of Storms (DARTS) nowcast. The HRRR model analysis was used as initial and boundary conditions in the WRF Model. The high-resolution dual-pol radar observations were assimilated into the WRF Model through the 3DVAR data assimilation technique. Three kinds of blending strategies were used and the results were compared: 1) hyperbolic tangent curve (HTW), 2) critical success index (CSIW), and 3) salient cross dissolve (Sal CD). The sensitivity studies were conducted to decide desirable parameters in the blending techniques. The outcomes proved that blending enhanced the prediction skills. Also, the overall performance of blending relies on the accuracy of the WRF forecast. Even though blending results are mixed, the HTW-based technique performed better than the other two techniques.

Data assimilation (DA) approaches currently used for operational numerical weather prediction (NWP) generally assume that errors in the background state are Gaussian. At the same time, approaches that make no assumptions regarding the background state probability distribution are gaining attention in research. Most such approaches, including the particle filter, are ensemble DA methods that produce an ensemble of analysis states consistent with the background and observation distributions. The present study instead proposes a non-Gaussian deterministic (NGD) DA method for producing a single deterministic analysis state. Consequently, the usual challenge of maintaining an ensemble with sufficient spread and diversity is avoided. The NGD approach uses background ensembles generated by a standard ensemble Kalman filter. A series of noncycled DA experiments is conducted to evaluate the NGD approach for assimilating precipitation derived from North American weather radars to initialize limited-area deterministic forecasts. The resulting forecasts are compared with those produced using either a local ensemble transform Kalman filter (LETKF) deterministic analysis or latent heat nudging (LHN). The experimental results indicate that, for forecast lead times beyond 1.5 h, the NGD approach improves precipitation forecasts relative to LHN. The NGD approach also leads to better temperature and zonal wind forecasts at lead times up to 12 h when compared to those obtained with either LHN or the LETKF. For precipitation, the NGD and LETKF approaches produce forecasts that are of comparable quality. Finally, simple strategies are demonstrated that combine the NGD approach for assimilating radar-derived precipitation accumulations with the ensemble–variational approach for assimilating all other observations.

The National Aeronautics and Space Administration Global Precipitation Measurement (GPM) mission ground validation program uses dual-polarization radar moments to estimate raindrop size distribution (DSD) parameters, the mass-weighted mean drop diameter Dmass, and normalized intercept parameter NW, to validate the GPM Core Observatory–derived DSD parameters. The disdrometer-based Dmass and NW are derived through empirical relationships between Dmass and differential reflectivity ZDR, and between NW, reflectivity ZH, and Dmass. This study employs large datasets collected from two-dimensional video disdrometers (2DVD) during six different field studies to derive the requisite empirical relationships. The uncertainty of the derived Dmass(ZDR) relationship is evaluated through comparisons of 2DVD-calculated and ZDR-estimated Dmass, where ZDR is calculated directly from 2DVD observations. Similarly, the uncertainty of the NW(ZH, Dmass) relationship is evaluated through 2DVD-calculated and Dmass and ZH-estimated NW, where Dmass and ZH are directly calculated from 2DVD observations. This study also presents the sensitivity of Dmass(ZDR) relationships to climate regime and to disdrometer type after developing three additional Dmass(ZDR) relationships from second-generation Particle Size Velocity (PARSIVEL2) disdrometer (P2) observations collected in the Pacific Northwest, in Iowa, and at Kwajalein Atoll in the tropical Pacific Ocean. The application of P2-derived Dmass(ZDR) relationship based on precipitation in the northwestern United States to P2 observations collected over the tropical ocean resulted in the highest error among comparisons of the three datasets.

We investigate the practical predictability limits of deep convection in a state-of-the-art, high-resolution, limited-area ensemble prediction system. A combination of sophisticated predictability measures, namely, believable and decorrelation scale, are applied to determine the predictable scales of short-term forecasts in a hierarchy of model configurations. First, we consider an idealized perfect model setup that includes both small-scale and synoptic-scale perturbations. We find increased predictability in the presence of orography and a strongly beneficial impact of radar data assimilation, which extends the forecast horizon by up to 6 h. Second, we examine realistic COSMO-KENDA simulations, including assimilation of radar and conventional data and a representation of model errors, for a convectively active two-week summer period over Germany. The results confirm increased predictability in orographic regions. We find that both latent heat nudging and ensemble Kalman filter assimilation of radar data lead to increased forecast skill, but the impact is smaller than in the idealized experiments. This highlights the need to assimilate spatially and temporally dense data, but also indicates room for further improvement. Finally, the examination of operational COSMO-DE-EPS ensemble forecasts for three summer periods confirms the beneficial impact of orography in a statistical sense and also reveals increased predictability in weather regimes controlled by synoptic forcing, as defined by the convective adjustment time scale.

This study develops a flexible Bayesian technique to quantify uncertainties associated with the operational Weather Surveillance Radar-1988 Doppler (WSR-88D) quantitative precipitation estimation (QPE) products over complex terrain. Radar-only rainfall estimates and rain gauge observations over the Russian River watershed in Northern California are utilized to demonstrate this new bias correction approach. Conventional mean field bias (MFB) and local bias (LB) correction methods are also implemented for comparison purposes. Results show that the proposed Bayesian technique outperforms the conventional MFB and LB correction approaches. The radar QPE performance is dramatically improved after the Bayesian-based bias correction: the root-mean-square error is reduced from 4.2 to 1.71 mm, the normalized mean absolute error is reduced from 64.5% to 24.2%, and the correlation with gauge measurements increases from 0.11 to 0.74. In addition, the terrain impact on radar QPE bias correction performance is investigated. After incorporating the terrain elevation information in the Bayesian framework, the QPE performance is further enhanced. Overall, the QPE performance scores after including the terrain information are improved about 10% relative to those only based on rainfall intensity values.

This observational study of westward-directed gap flows through the Columbia River Gorge uses three radar wind profilers during two winter seasons between October 2015 and April 2017, with a focus on the gap-exit region at Troutdale, Oregon. Of the 92 gap-flow events identified at Troutdale, the mean duration was 38.5 h, the mean gap-jet speed was 12 m s−1, and the mean gap-flow depth was 570 m MSL. The mean gap-jet height and gap-flow depth were situated below the top of the inner gorge, while a maximum depth of 1087 m MSL was contained within the gorge’s outer-wall rim. The mean gap-flow depth was deepest in the cold-air source region east of the gorge and decreased westward to the coast. Strong gap-flow events were longer lived, deeper, and capped by stronger vertical shear than their weak counterparts, and strong (weak) events were forced primarily by a cold-interior anticyclone (offshore cyclone). Deep gap-flow events were longer lived, stronger, and had weaker capping vertical shear than shallow events, and represented a combination of gap-flow and synoptic forcing. Composite temporal analysis shows that gap-flow strength (depth) was maximized midevent (early event), freezing rain was most prevalent during the second half of the event, and accumulated precipitation was greatest late-event. Gap-flow events tended to begin (end) during the evening (morning) hours and were most persistent in January. Surface wind gusts and snow occurrences around Portland, Oregon, were associated primarily with the deepest gap flows, whereas freezing rain occurred predominantly during shallow gap flows.