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Estimating precipitation is of critical importance to climate systems and decision-making processes. This paper presents Espresso, a deep learning model designed for estimating precipitation from satellite observations on a global scale. Conventional methods, like ground-based radars, are limited in terms of spatial coverage. Satellite observations, on the other hand, allow global coverage. Combined with deep learning methods, these observations offer the opportunity to address the challenge of estimating precipitation on a global scale. This research paper presents the development of a deep learning model using geostationary satellite data as input and generating instantaneous rainfall rates, calibrated using data from the Global Precipitation Measurement Core Observatory (GPMCO). The performance impact of various input data configurations on Espresso was investigated. These configurations include a sequence of four images from geostationary satellites and the optimal selection of channels. Additional descriptive features were explored to enhance the model's robustness for global applications. When evaluated against the GPMCO test set, Espresso demonstrated highly accurate precipitation estimation, especially within equatorial regions. A comparison against six other operational products using multiple metrics indicated its competitive performance. The model's superior storm localization and intensity estimation were further confirmed through visual comparisons in case studies. Espresso has been incorporated as an operational product at Météo-France, delivering high-quality, real-time global precipitation estimates every 30 min. [ABSTRACT FROM AUTHOR]

The effects of precipitation of varying intensity on the operation of a ship's radar are considered. It is shown that during the propagation of the electromagnetic wave emitted by the antenna of a ship's radar, its energy is absorbed by precipitation. To determine the power loss of an electromagnetic wave due to its absorption by particles of precipitation, the effective absorption area, specific absorption coefficient, and specific effective total scattering area are used. The effective areas of absorption and total scattering depend on the shape of the precipitation particles, their size, state of aggregation, and the length of the electromagnetic wave emitted by the antenna of the ship's radar. The dependence of the effective absorption area on the particle size of precipitation is obtained at two wavelengths of 3 cm and 10 cm, on which the ship's radars operate. A decrease in the tracking range of an object tracked by a ship's radar in the case of rain falling is established compared with the tracking range of an object in clear weather. It is shown that a decrease in the tracking range of an object occurs only with moderate, strong, and very heavy rain. The attenuation of electromagnetic energy in rain with an intensity of 12.5 mm/h at a wavelength of 3 cm is obtained by calculation, which amounted to 24 dB with a length of the falling rain zone of 50 km, as well as with rain with an intensity of 100 mm/h with the same length of the precipitation zone. [ABSTRACT FROM AUTHOR]

Ice shelves surrounding the Antarctic perimeter moderate ice discharge towards the ocean through buttressing. Ice-shelf evolution and integrity depend on the local surface accumulation, basal melting and on the spatially variable ice-shelf viscosity. These components of ice-shelf mass balance are often poorly constrained by observations and introduce uncertainties in ice-sheet projections. Isochronal radar stratigraphy is an observational archive for the atmospheric, oceanographic and ice-flow history of ice shelves. Here, we predict the stratigraphy of locally accumulated ice on ice shelves with a kinematic forward model for a given atmospheric and oceanographic scenario. This delineates the boundary between local meteoric ice (LMI) and continental meteoric ice (CMI). A large LMI to CMI ratio hereby marks ice shelves whose buttressing strength is more sensitive to changes in atmospheric precipitation patterns. A mismatch between the steady-state predictions of the kinematic forward model and observations from radar can highlight inconsistencies in the atmospheric and oceanographic input data or be an indicator for a transient ice-shelf history not accounted for in the model. We discuss pitfalls in numerical diffusion when calculating the age field and validate the kinematic model with the full Stokes ice-flow model Elmer/Ice. The Roi Baudouin Ice Shelf (East Antarctica) serves as a test case for this approach. There, we find a significant east–west gradient in the LMI / CMI ratio. The steady-state predictions concur with observations on larger spatial scales (>10 km), but deviations on smaller scales are significant, e.g., because local surface accumulation patterns near the grounding zone are underestimated in Antarctic-wide estimates. Future studies can use these mismatches to optimize the input data or to pinpoint transient signatures in the ice-shelf history using the ever growing archive of radar observations of internal ice stratigraphy. [ABSTRACT FROM AUTHOR]

This study of the Quantitative Estimation Precipitation (QEP) of rainfall, detected by two Meteorology Radars over Chi Basin, North-east Thailand, used data from the Thai Meteorological Department (TMD). The rainfall data from 129 rain gauge stations in the Chi Basin area, covering a period of two years, was also used. The study methodology consists of: firstly, deriving the QPE between radar and rainfall based on meteorological observations using the Marshall Palmer Stratiform, the Summer Deep Convection, and Regression Model and calibrating with rain gauge station data; secondly, Bias Correction using statistical method; thirdly, determining spatial variation using three methods, namely Kriging, Inverse Distance Weight (IDW), and the Minimum Curvature Method. The results of the study demonstrated the accuracy of estimating precipitation using meteorological radar. Estimated precipitation compared against an equivalent of 2 years of rain station measurement had a probability of detection (POD) of 0.927, where a value of 1 indicated perfect agreement, demonstrating the effectiveness of the method used to calibrate the radar data. The bias correction method gave high accuracy compared with measured rainfall. Furthermore, of the spatial estimation of rainfall methods, the Kriging methodology showed the best fit between estimation of rainfall distribution and measured rainfall distribution. Therefore, the results of this study showed that the rainfall estimation, using data from a meteorology radar, has good accuracy and can be useful, especially in areas where it is not possible to install and operate rainfall measurement stations, such as in heavily forested areas and/or in steep terrain. Additionally, good accuracy rainfall data derived from radar data can be integrated with other data used for water management and natural disasters for applications to reduce economic losses, as well as losses of life and property. [ABSTRACT FROM AUTHOR]

Clouds and precipitation have vital roles in the global hydrological cycle and the radiation budget of the atmosphere–Earth system and are closely related to both the regional and the global climate. Changes in the status of the atmosphere inside clouds and precipitation systems are also important, but the use of multi-source datasets is hampered by their different spatial and temporal resolutions. We merged the precipitation parameters measured by the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) with the multi-channel cloud-top radiance measured by the visible and infrared scanner (VIRS) and atmospheric parameters in the ERA5 reanalysis dataset. The merging of pixels between the precipitation parameters and multi-channel cloud-top radiance was shown to be reasonable. The 1B01-2A25 dataset of pixel-merged data (1B01-2A25-PMD) contains cloud parameters for each PR pixel. The 1B01-2A25 gridded dataset (1B01-2A25-GD) was merged spatially with the ERA5 reanalysis data. The statistical results indicate that gridding has no unacceptable influence on the parameters in 1B01-2A25-PMD. In one orbit, the difference in the mean value of the near-surface rain rate and the signals measured by the VIRS was no more than 0.87 and the standard deviation was no more than 2.38. The 1B01-2A25-GD and ERA5 datasets were spatiotemporally collocated to establish the merged 1B01-2A25 gridded dataset (M-1B01-2A25-GD). Three case studies of typical cloud and precipitation events were analyzed to illustrate the practical use of M-1B01-2A25-GD. This new merged gridded dataset can be used to study clouds and precipitation systems and provides a perfect opportunity for multi-source data analysis and model simulations. The data which were used in this paper are freely available at 10.5281/zenodo.4458868 (Sun and Fu, 2021). [ABSTRACT FROM AUTHOR]

Whether tall storms produce heavy precipitation is currently a controversial topic. Here, we used seven years of observations from the dual-frequency precipitation radar and found that there is a rare but unique vertical precipitation structure over the Tibetan Plateau. The radar echo peaks above the freezing height, which we refer to as a "ghost echo". The existence of a ghost echo increases the echo-top height but suppresses the increase in droplet size below it, and therefore weakens the near-surface precipitation. Compared with normal echoes, ghost echoes appear more often in the afternoon. The potentially unstable environment produced by thermal forcing is the main cause of ghost echoes, rather than the dynamic factor of wind shear. The ghost echo, which is essentially a mechanism of ice-phase precipitation enhancement, represents a type of tall but weak precipitation. Its existence adds to our current perception of the nature of precipitation. [ABSTRACT FROM AUTHOR]

Storms are convective cells responsible for the major fraction of convective precipitation. Here, the pre-monsoon and monsoon season characteristics of storms are reported at Lucknow, Patna, Bhopal, and Nagpur in India using equivalent radar reflectivity factor ( Z e ) given at these radar locations. It is observed that the lifetime, speed of propagation, area, volume, echo top height and thickness lie in ranges 0.3–3 h, 5–60 km h - 1 , 4–184 km 2 , 8–1600 km 3 , 2–14 km, and 0.5–16 km respectively. For both seasons, the relationships between radar estimated rain volume (RERV; range 10 4 – 10 7 m 3 ) and area-time integral (ATI; range 1–100 km 2 h) are established which are considered as a representative of total precipitation resulted from an individual storm during its life cycle. The results from statistical analysis of RERV-ATI pairs suggest that storms at Lucknow have similar seasonal characteristics at 87% confidence interval while other locations exhibit dissimilarities. In addition, the vertical profiles of radar reflectivity (VPRRs) of storms are constructed at their life phases, namely cumulus, mature and dissipation. It is concluded that the vertical Z e gradient in mixed-phase region (5–8 km) is lower (2–2.9 dBZ km - 1 ) at cumulus and dissipation phases than at mature phase (3.6–4.4 dBZ km - 1 ) in monsoon season. For pre-monsoon season, this gradient lies between 3.3–5.2 dBZ km - 1 at mature phase. Our results are of great importance for advancing knowledge about storm-scale, which has implications in short-range weather forecasting as well as developing new convective parametrization schemes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Based on unprecedented 22‐year records from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and the Global Precipitation Measurement (GPM) dual‐frequency precipitation radar (DPR), this study aims to investigate the global distribution and linkage between both convections and precipitation extremes quasi‐globally. Overall, the spatial distributions of extreme events show significant regional variability and land‐ocean contrast. Extreme precipitation is dominant over tropical ocean while extreme convections are exclusive over land from tropics to higher latitude. Besides, there are strong positive relationships between extreme convections and precipitation based on multiple threshold‐based definitions of extremes, which provides more consistent physical properties of extreme events globally. The correlation is significant over land but relatively weak over oceans. Seasonal occurrences of extreme events are also examined. Furthermore, extreme events exhibit notable diurnal characteristics over global land with most extreme convections occurring in the afternoon, while diurnal cycles are more homogeneous over ocean. Plain Language Summary: Extreme rainfall and convection events are of significant social interest due to their close linkage with a variety of natural hazards, while the two types of events are used too interchangeably in literature. To document the spatial variability and linkage between extreme convections and extreme precipitation, we use 22‐year records from the precipitation radar onboard Tropical Rainfall Measuring Mission satellite and the dual‐frequency precipitation radar onboard Global Precipitation Measurement satellite. Threshold‐based definitions of extremes are used. Results show that the correlation between extreme convections and precipitation is strong over land, while ocean region shows a little weaker association from tropics to higher latitudes. The occurrences of extreme events show notable seasonal variations and exhibit a significant peak in the afternoon. Key Points: The spatial distributions of extreme events show significantly regional variability and land‐ocean contrastExtreme precipitation events have highly positive correlation with extreme convections particularly in land regionsExtreme convections exhibit substantial afternoon peaks over land, while extreme precipitation exhibit more homogenous diurnal cycles [ABSTRACT FROM AUTHOR]

Variations in raindrop size distribution (DSD) during the southwest monsoon (SWM) season over different climatic regions in the Indian subcontinent and adjoining seas are studied in this paper using five years (2014 - 2018) of global precipitation measurement dual-frequency precipitation radar derived DSDs. The rain rate (R) stratified DSD measurements show clearly that land, sea, and orography differ in their mass-weighted mean diameter (Dm) values. Irrespective of R, Dm values of deep rain were found to be larger in continental rain than in maritime and orographic rain. However, for shallow storms, the Dm values were smaller for continental rain than for orographic and maritime rain. Based on the Dm values and their variations with R of the deep systems, the regions could be categorized into four groups, within which the Dm values were nearly equal: (1) the northwest India (NWI) and the southeast peninsular India (SEPI); (2) the foothills of the Himalayas (FHH) and the central India (CI); (3) the northeast India (NEI) and the Bay of Bengal (BOB); and (4) the Arabian Sea (AS), the Western Ghats (WG), and the Myanmar coast (MC). Compared to other geographical regions of the Indian subcontinent, the Dm values of the deep systems were the largest over NWI and SEPI and the smallest over the WG, MC, and AS; while for shallow systems, the Dm values were the largest over the BOB and AS and the smallest over the SEPI and NWI regions. Though the cloud drops were smaller over the continental regions, the raindrops were larger than in the maritime and orographic rain regions. The microphysical and dynamical processes that occur during precipitation play a vital role in altering the DSDs of continental rain. [ABSTRACT FROM AUTHOR]

Improving our ability to predict future weather and climate conditions is strongly linked to achieving significant advancements in our understanding of cloud and precipitation processes. Observations are critical to making these advancements because they both improve our understanding of these processes and provide constraints on numerical models. Historically, instruments for observing cloud properties have limited cloud–aerosol investigations to a small subset of cloud-process interactions. To address these challenges, the last decade has seen the U.S. DOE ARM facility significantly upgrade and expand its surveillance radar capabilities toward providing holistic and multiscale observations of clouds and precipitation. These upgrades include radars that operate at four frequency bands covering a wide range of scattering regimes, improving upon the information contained in earlier ARM observations. The traditional ARM emphasis on the vertical column is maintained, providing more comprehensive, calibrated, and multiparametric measurements of clouds and precipitation. In addition, the ARM radar network now features multiple scanning dual-polarization Doppler radars to exploit polarimetric and multi-Doppler capabilities that provide a wealth of information on storm microphysics and dynamics under a wide range of conditions. Although the diversity in wavelengths and detection capabilities are unprecedented, there is still considerable work ahead before the full potential of these radar advancements is realized. This includes synergy with other observations, improved forward and inverse modeling methods, and well-designed data–model integration methods. The overarching goal is to provide a comprehensive characterization of a complete volume of the cloudy atmosphere and to act as a natural laboratory for the study of cloud processes. [ABSTRACT FROM AUTHOR]

The first X-band dual polarized phased array weather radar (DP-PAWR), which simultaneously transmits pulses of horizontal and vertical polarized radiation, was developed and installed at Saitama University, Japan, in December 2017. The DP-PAWR uses mechanical and electronic scanning at azimuth and elevation angles, respectively. It provides polarimetric precipitation measurements via 3-D volume scanning with an update rate between 10 and 60 s, for a range of up to 80 km. Here, we describe the initial DP-PAWR observation results. To evaluate the DP-PAWR observation accuracy, we compared the observational data with radar variables derived from Parsivel disdrometer data. In comparison with the disdrometer, the relative observation accuracy for the DP-PAWR radar reflectively factor had a standard deviation of 1.1 dB and mean value of 0.4 dB. We also conducted detailed observations of a developing thunderstorm using a specific differential phase (Kdp) column, focusing on the Kdp core during the storm. The Kdp core movements provided useful information about the convection flow during the storm. [ABSTRACT FROM AUTHOR]

Accurate precipitation estimation is significant since it matters to everyone on social and economic activities and is of great importance to monitor and forecast disasters. The traditional method utilizes an exponential relation between radar reflectivity factors and precipitation called Z-R relationship which has a low accuracy in precipitation estimation. With the rapid development of computing power in cloud computing, recent researches show that artificial intelligence is a promising approach, especially deep learning approaches in learning accurate patterns and appear well suited for the task of precipitation estimation, given an ample account of radar data. In this study, we introduce these approaches to the precipitation estimation, proposing two models based on the back propagation neural networks (BPNN) and convolutional neural networks (CNN) respectively, to compare with the traditional method in meteorological service systems. The results of the three approaches show that deep learning algorithms outperform the traditional method with 75.84% and 82.30% lower mean square errors respectively. Meanwhile, the proposed method with CNN achieves a better performance than that with BPNN for its ability to preserve the spatial information by maintaining the interconnection between pixels, which improves 26.75% compared to that with BPNN. [ABSTRACT FROM AUTHOR]

Short-term precipitation forecast in local areas based on radar reflectance images has become a hot spot issue in the meteorological field, which has an important impact on daily life. Recently, deep learning techniques have been applied to this field, and the effect is promoted remarkably compared with traditional methods. However, existing deep learning-based methods have not considered the problem that different areas and channels exert different influence on precipitation. In this paper, we propose to incorporate the multihead attention into a dual-channel neural network to highlight the key areas for precipitation forecast. Furthermore, to solve the problem of excessive loss of global information caused by the attention mechanism, the residual connection is introduced into the proposed model. Quantitative and qualitative results demonstrate that the proposed method achieves the state-of-the-art precipitation forecast accuracy on the radar echo dataset. [ABSTRACT FROM AUTHOR]

Upright convection can impact extratropical cyclone (ETC) precipitation and dynamics differently than isentropic ascent, but how often it occurs and how much it contributes to total precipitation within an ETC have not been systematically documented in previous literature. Herein, convection in ETCs is analyzed using ground‐based radar observations over the Eastern United States. Convection occupies 1%–5% of the precipitating region and constitutes 1%–15% of total cyclone precipitation. Furthermore, the location of convective activity occurs preferentially in the warm sector early in a cyclone's life cycle, and then as the storm evolves, more convection occurs along and behind the cold front. The precipitation rates in regions with convection are more likely to be stronger than those in other precipitating regions of the cyclone. However, the cyclones with the largest area‐average precipitation rates include only a small contribution from convection. Plain Language Summary: This research is motivated by the potential influence of convection on extratropical cyclone circulation. We utilize a newly derived dataset of upright convection and a cyclone‐tracking framework to quantify convective activity in extratropical cyclones over the United States, east of the Rocky Mountains. We find that upright convection only occupies a small portion of the precipitating region, and the relative contribution to total precipitation has relatively small variability as the cyclone evolves. We find that the location of convective activity occurs preferentially in the warm sector early in a cyclone's life cycle, and as the storm evolves, more convection occurs along and behind the cold front. We find that precipitation rates in regions with convection are more likely to be stronger than those in other precipitating regions of the cyclone. However, the cyclones with the largest area‐average precipitation rates include only a small contribution from convection. Key Points: Convection occurs in 1%–5% of precipitating regions of extratropical cyclones over eastern United States and contributes 1%–15% of total precipitationThe majority of upright convection occurs ahead of the cold front and disperses into other regions as the cyclone agesWhile convection produces larger rain rates, cyclones with greater total precipitation do not have a large convective contribution [ABSTRACT FROM AUTHOR]

The paper is focused on the problem of weather radar precipitation compositing and aims to produce the data for the whole measurement domain in a way that ensures the quality of the composite is as high as possible. This is achieved by employing information about the data quality in the form of a quality index (QI) map. Different techniques that take the QI values as the compositing criterion are investigated; they apply the maximum or the weighted mean from particular radars. The proposed method employs a combination of the QI value and the distance from the radar site as a criterion in a weighted mean approach. Different versions of the quality‐based techniques are compared with standard techniques that are based on the maximum of estimated precipitation and distance from the nearest radar site. Verification of the techniques was performed employing statistical metrics by comparison against rain gauge observations. Moreover qualitative verification with assessment of seamlessness and the presence of contamination was carried out on long‐term accumulations of the composites generated by means of different methods. The proposed method based on QI value and distance from the radar site was found to be the most suitable for radar precipitation compositing. [ABSTRACT FROM AUTHOR]

A Bayesian Markov chain Monte Carlo (MCMC) algorithm is utilized to compare the skill of an A-Trainlike observing system with a cloud, convection, and precipitation (CCP) observing system like that contemplated for the 2020s by the 2017 National Academy of Sciences Decadal Survey. The main objective is to demonstrate a framework for observational trade space studies. This initial work focuses on weakly precipitating warm shallow cumulus constructed from in situ data. Radiative computations are based on Mie theory with spherical assumptions. Simulated measurements in the CCP configuration consist of W- and Kaband radar reflectivity and path-integrated attenuation, 31 and 94 GHz brightness temperatures (Tb), and visible and near-infrared reflectances. The collection of measurements in the CloudSat configuration is identical, but includes a single 94 GHz radar frequency, and the uncertainty in the 94 GHz microwave brightness temperature is increased to mimic the CloudSat Tb product. The experiments demonstrate that it remains a challenge to diagnose cloud properties in the presence of light rain because of the tendency of microwave remote sensing to respond to the higher moments of the hydrometeor populations. Rain properties are significantly better constrained than cloud properties, even in the optimal CCP configuration. The addition of Ka-band measurements places substantial constraints on the precipitation rain effective radius and rain rates. The Tb offers important information regarding the column-integrated condensate mass, the measurement accuracy of which appears more likely to affect the retrievals of clouds with low liquid water path. The constraints provided by reflectances are largely restricted to regions near the cloud top, particularly in the raining cases. [ABSTRACT FROM AUTHOR]

A precipitation separation approach using support vector machine method was developed and tested on a C-band polarimetric radar located in Taiwan (RCMK). Different from some existing separation methods that require a whole volume radar data, the proposed approach utilizes the polarimetric radar data from the lowest tilt to classify precipitation echoes into either stratiform or convective type. Through a support vector machine method, the inputs of radar reflectivity, differential reflectivity, and the separation index are utilized in the classification. The feature vector and weight vector in the support vector machine were optimized using well-classified training data. The proposed approach was tested with multiple precipitation events including two widespread mixture of stratiform and convective events, a tropical typhoon precipitation event, and a stratiform precipitation event. In the evaluation, the results from the multi-radar-multi-sensor (MRMS) precipitation classification approach were used as the ground truth, and the performances from proposed approach were further compared with the approach using separation index only with different thresholds. It was found that the proposed method can accurately identify the convective cells from stratiform storms with the radar data only from the lowest scanning tilt. It can produce better results than using the separation index only. [ABSTRACT FROM AUTHOR]

This study uses a dense rain gauge network, radar observations, and an upslope model to document the detailed aspects of precipitation over Da-Tun Mountain (DT) of northern Taiwan under prevailing northeasterly monsoonal flow. DT is one of the most concentrated areas of heavy rainfall in Taiwan, with a size of ~15 km and a terrain peak of ~1 km (MSL), and it constitutes a concave ridge with two windward ridge arms that encompass a funnel-shaped valley. Twenty-one rainfall events identified over DT from January 2011 to February 2015 are chosen for analysis. More than half of the studied cases exhibit two local maxima of rainfall over the ridge arms, and asymmetric characteristics between these two maxima are evident. The other frequent rainfall pattern, observed as upstream winds are more from the east-northeast, is characterized by a local maximum of precipitation inside the valley. Analyses from the upslope model confirm that the occurrence of the windward maxima of rainfall is primarily caused by upslope lifting, and their asymmetric characteristics are closely related to the difference in the azimuthal variations of slope steepness between the two ridge arms. The observed characteristics of rainfall intensities inside the valley, however, cannot be well described by the upslope model. It is found that the lateral flow confluence induced by the deflected flows over the ridge arms may play an essential role in intensifying upslope-forced precipitation within the valley. This effect emerges as upstream winds are roughly parallel to the central axis of funneling regions located between the ridge arms. [ABSTRACT FROM AUTHOR]

Machine learning algorithms should be tested for use in quantitative precipitation estimation models of rain radar data in South Korea because such an application can provide a more accurate estimate of rainfall than the conventional ZR relationship-based model. The applicability of random forest, stochastic gradient boosted model, and extreme learning machine methods to quantitative precipitation estimation models was investigated using case studies with polarization radar data from Gwangdeoksan radar station. Various combinations of input variable sets were tested, and results showed that machine learning algorithms can be applied to build the quantitative precipitation estimation model of the polarization radar data in South Korea. The machine learning-based quantitative precipitation estimation models led to better performances than ZR relationship-based models, particularly for heavy rainfall events. The extreme learning machine is considered the best of the algorithms used based on evaluation criteria. [ABSTRACT FROM AUTHOR]

Narrow regions of intense, banded snowfall present hazardous travel conditions due to rapid onset, high precipitation rates, and lowered visibility. Despite their importance, there are few verification studies of snowbands in operational forecast models. The objective of this study is to evaluate the ability of the High-Resolution Rapid Refresh (HRRR) model to predict snowbands in the United States east of the Rocky Mountains. An automated band-detection algorithm was applied to a 3-yr period of simulated and observed radar reflectivity to compare snowband climatologies. This algorithm uses the distributions of reflectivities in contiguous precipitation regions to determine a band intensity threshold. The predictability of snowbands on a case-by-case basis was also evaluated using an object-oriented approach. The distribution of HRRR forecast banding resembles that of the observations, but with a significant positive frequency bias. This may partially be due to underrepresentation of observed bands in our verification dataset due to limited radar coverage in portions of the central United States. On a case-by-case basis, traditional skill metrics indicate limited predictability, but allowing for small timing discrepancies dramatically improves scores. Object-oriented verification yields mixed results, with 30% of forecasts receiving a score indicative of a well-predicted event. However, 69% of cases have at least one forecast lead demonstrating skill, suggesting the HRRR is successful in depicting environments conducive to band formation. These results suggest adopting a probabilistic, ensemble approach, and indicate that the deterministic HRRR is best suited for the identification of regions of elevated snowband risk and not precise timing or location information. [ABSTRACT FROM AUTHOR]

Machine learning algorithms are trained on a 10-yr archive of composite weather radar images in the Swiss Alps to nowcast precipitation growth and decay in the next few hours in moving coordinates (Lagrangian frame). The hypothesis of this study is that growth and decay is more predictable in mountainous regions, which represent a potential source of practical predictability by machine learning methods. In this paper, artificial neural networks (ANN) are employed to learn the complex nonlinear dependence relating the growth and decay to the input predictors, which are geographical location, mesoscale motion vectors, freezing level height, and time of the day. The average long-term growth and decay patterns are effectively reproduced by the ANN, which allows exploring their climatology for any combination of predictors. Due to the low intrinsic predictability of growth and decay, its prediction in real time is more challenging, but is substantially improved when adding persistence information to the predictors, more precisely the growth and decay and precipitation intensity in the immediate past. The improvement is considerable in mountainous regions, where, depending on flow direction, the root-mean-square error of ANN predictions can be 20%–30% lower compared with persistence. Because large uncertainty is associated with precipitation forecasting, deterministic machine learning predictions should be coupled with a model for the predictive uncertainty. Therefore, we consider a probabilistic perspective by estimating prediction intervals based on a combination of quantile decision trees and ANNs. The probabilistic framework is an attempt to address the problem of conditional bias, which often characterizes deterministic machine learning predictions obtained by error minimization. [ABSTRACT FROM AUTHOR]

Storm is a convective cell much smaller than a mesoscale convective system (MCS) but typically larger than a cumulonimbus cloud and heavy precipitation and lightning are often associated with it. Storms contribute major fraction of the convective precipitation in MCSs. Storm characteristics and precipitation estimates around New Delhi (28.6°N, 77.2°E; an Indian land location) during June–September period of the year 2013 are reported here using data of C-band polarimetric Doppler weather radar. Storms, defined based on radar reflectivity thresholds (30 dBZ for simple storms and 40 dBZ for intense storms), are tracked and their properties are extracted. Our results show that about 80% of storms exist for 1 h or less. The areas of 90% of simple storms are less than 100 km2 and the largest area averaged over storm lifespan does not exceed 400 km2. The majority of storms (> 80%) move with speeds less than 30 km h−1. About 60–65% of simple/intense storms have echo top heights between 6 and 10 km, while only few of them exceed 17 km. The values of average thickness of simple and intense storms lie between ~ 2–10 and ~ 1–7 km, respectively. It is not the vertical extent of a storm but its area-time integral that correlates better with the total precipitation amount. Around the New Delhi area, daily accumulated precipitation derived from relations incorporating polarimetric variables is in good agreement with the rain gauge measurements while that obtained from relations based on radar reflectivity factor (Zh) alone highly underestimates precipitation. This suggests that polarimetric capability is needed in Doppler weather radars to get the realistic precipitation estimates. The mean precipitation water content derived from Zh (~ 0.96 g m−3) is about 30–40% less compared to that derived from polarimetric relations. Our findings on storm properties have implications for cloud parameterizations and in short-term weather forecasting. [ABSTRACT FROM AUTHOR]

Attenuation correction of measured reflectivity (Z H) and differential reflectivity (Z DR) is critical in short-wavelength radar applications, especially during extreme precipitation events such as heavy rain. This paper develops an improved self-consistent approach (improved ZPHI method) for attenuation correction in practical environment. In particular, a non-negative constraint is imposed on the specific attenuation, which is inferred from the monotonic increasing characteristic of differential propagation phase (Φ DP). The copolar correlation coefficient (ρ HV) is used to partition the Φ DP profiles into independent range segments, which are featured by different hydrometeor phases such as liquid rain or mixed-phase precipitation. Additional minimization constraint is imposed on the cost function of the difference between preprocessed Φ DP and reconstructed Φ DP to ensure its appropriate convergence. In addition, an exponential Z DR − Z H relation derived from local raindrop size distribution (DSD) data is applied in the procedure of Z DR correction. Polarimetric measurements from a C-band radar (CPOL) in Hangzhou of China during two extreme precipitation events are used to demonstrate the improved attenuation correction method. DSD observations from disdrometers and radar data from a nearby system operating at non-attenuated frequency (i.e., S-band) at Huangshan Mountain are used to evaluate the attenuation correction performance. This paper also studies the impact of attenuation correction on the radar-derived quantitative precipitation estimation (QPE) product, through cross-comparison with rain gauge observations. Results show that the polarimetric observations from CPOL radar are effectively enhanced and more consistent with collocated S-band measurements and the simulated radar moments based on DSD data. Hourly rainfall products derived from R (Z H) and R (Z H , Z DR) are significantly improved, which are comparable to R (K DP) after attenuation correction. • An improved attenuation correction method is proposed for C-band polarimetric radar applications. • The routinely operating C-band radar in Hangzhou, China is used to demonstrate the proposed attenuation correction scheme. • Cross-comparison with disdrometer data and collocated S-band observations shows great performance of this correction method. • The QPE products derived from R (Z H) and R (Z H , Z DR) are significantly improved after attenuation correction. [ABSTRACT FROM AUTHOR]

Remote sensing of precipitation is critical for regional, continental, and global water and climate research. This study develops a deep learning mechanism to link between point‐wise rain gauge measurements, ground‐based, and spaceborne radar reflectivity observations. Two neural network models are designed to construct a hybrid rainfall system, where the ground radar is used to bridge the scale gaps between rain gauge and satellite. The first model is trained for ground radar using rain gauge data as target labels, whereas the second model is for spaceborne Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) using ground radar estimates as training labels. Data from 1 year of observations in Florida during 2009 are utilized to illustrate the application of this hybrid rainfall system. Validation using independent data in 2009, as well as 2‐year comparison against the standard PR products, demonstrates the promising performance and generality of this innovative rainfall algorithm. Plain Language Summary: The Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) was the first spaceborne active sensor for observing precipitation over the tropics and subtropics. During its 17 years (1997–2014) in orbit and beyond, PR has been an important tool to characterize tropical precipitation microphysics and quantify rainfall rate over the globe. Ground validation is a critical component in the development of TRMM products. However, the ground‐based sensors have different characteristics from PR in terms of resolution, viewing angle, and uncertainties in the sensing environments, which are not taken into account in the operational parametric rainfall relations applied to PR measurements. This study develops a nonparametric machine learning technique for PR rainfall estimation. In the regions where substantial gauge and ground radar data are available, this approach can produce better rainfall estimates compared to the standard PR algorithm. In areas such as ocean and remote regions where no gauge or radar available, the proposed rainfall algorithm is easy to implement, and it can still produce reasonable estimates. With more and more gauges and radars being deployed and many of them become operational, this algorithm can be trained at different locations represented by different atmosphere properties to further improve the performance and generality. Key Points: Conventional parametric relationships between radar reflectivity Z and rain rate R are not sufficient to capture precipitation variabilitiesA hybrid deep neural network system is designed for improved space radar rainfall estimation [ABSTRACT FROM AUTHOR]

Shallow oceanic precipitation variability is documented using three second-generation radar systems located at the Atmospheric Radiation Measurement (ARM) Eastern North Atlantic observatory: ARM zenith radar (KAZR2), the Ka-band scanning ARM cloud radar (KaSACR2) and the X-band scanning ARM precipitation radar (XSAPR2). First, the radar systems and measurement post-processing techniques, including sea-clutter removal and calibration against colocated disdrometer and Global Precipitation Mission (GPM) observations are described. Then, we present how a combination of profiling radar and lidar observations can be used to estimate adaptive (in both time and height) parameters that relate radar reflectivity (Z) to precipitation rate (R) in the form Z=αRβ , which we use to estimate precipitation rate over the domain observed by XSAPR2. Furthermore, constant altitude plan position indicator (CAPPI) gridded XSAPR2 precipitation rate maps are also constructed. Hourly precipitation rate statistics estimated from the three radar systems differ because KAZR2 is more sensitive to shallow virga and XSAPR2 suffers from less attenuation than KaSACR2 and as such is best suited for characterizing intermittent and mesoscale-organized precipitation. Further analysis reveals that precipitation rate statistics obtained by averaging 12 h of KAZR2 observations can be used to approximate that of a 40 km radius domain averaged over similar time periods. However, it was determined that KAZR2 is unsuitable for characterizing domain-averaged precipitation rate over shorter periods. But even more fundamentally, these results suggest that these observations cannot produce an objective domain precipitation estimate and that the simultaneous use of forward simulators is desirable to guide model evaluation studies. [ABSTRACT FROM AUTHOR]

The orbit of the tropical rainfall measuring mission (TRMM) satellite was boosted from 350 to 402.5 km in August 2001 to extend its lifetime by conserving fuel. Since the timing between transmission and reception by the precipitation radar (PR) onboard the TRMM satellite was a fixed constant for measurement from an original altitude of 350 km, the PR encountered a mismatch between the transmitting and receiving beams after the TRMM orbit boost. Although the PR algorithm in TRMM Version 7 employs a correction for the beam mismatch, its error remains as an underestimation of the precipitation estimates near surface in the second half of the swath. This paper aims to mitigate the beam-mismatch correction error in Version 7. The beam-mismatch correction needs to estimate the radar echo that would be measured in the virtual intermediate beam between the beam in question and the previous adjacent beam. The beam-mismatch correction developed in this paper assumes that the surface and precipitation echoes change linearly in the horizontal direction in parallel to the surface between the two beams. The new correction is tested with observational data, indicating that the method improves the accuracy of the correction at off-nadir angles. Statistics of the surface normalized radar cross sections and the bright band peak intensities at off-nadir angles are improved using the method. The asymmetric bias of the precipitation estimates with respect to the scan angle in Version 7 is mitigated by 95.9% over ocean and 72.5% over land with the new correction. [ABSTRACT FROM AUTHOR]

This study presents a dynamic forecast combination approach adapted to incorporate multiple sources of precipitation. Dynamic combination serves to utilise the varying merit each data source exhibits with time. The dynamic model combination framework presented merges a nonparametric k-nearest neighbour (k-nn) estimation of radar precipitation with Thin Plate Spline (TPS) interpolated gauge precipitation. Since air temperature is an essential variable to discriminate the phase of the precipitation in cold climates, this study uses radar precipitation and air temperature as the two variables in the dynamic combination algorithm. The merging of k-nn and TPS estimates is shown to reduce the RMSE by 25% compared to the original radar precipitation rates. The usefulness of air temperature is found not to be as significant in the combination as it is in the formulation of the nonparametric radar precipitation fields for cold incident temperatures. • A framework to merge radar and gauge precipitation in cold climates is developed. • Radar data are first adjusted using gauge information and then merged to gauge data. • Proposed method results in a 25% reduction in error in original radar precipitation. • Almost all gauge locations show a clear improvement in precipitation estimation. • Temperature contributes to adjustment of radar data, but marginally to merging. [ABSTRACT FROM AUTHOR]

Motor vehicle crashes remain a leading cause of accidental death in the United States, and weather is frequently cited as a contributing factor in fatal crashes. Previous studies have investigated the link between these crashes and precipitation typically using station-based observations that, while providing a good estimate of the prevailing conditions on a given day or hour, often fail to capture the conditions present at the actual time and location of a crash. Using a multiyear, high-resolution radar reanalysis and information on 125,012 fatal crashes spanning the entire continental United States over a 6-yr period, we find that the overall risk of a fatal crash increases by approximately 34% during active precipitation. The risk is significant in all regions of the continental United States, and it is highest during the morning rush hour and during the winter months. [ABSTRACT FROM AUTHOR]

In this work, long-term (10 years) raindrop size distribution (RSD) measurements from the Joss-Waldvogel Disdrometer (JWD) installed at the National Central University (NCU) (24°58'6N, 121°11'27E), Taiwan, and the vertical profile of radar reflectivity were used to analyze the variations in the gamma parameters of six seasons (winter, spring, mei-yu, summer, typhoon, and autumn) and types of precipitation. The normalized gamma distribution of RSD revealed that the highest mean Dm (mass-weighted average diameter) values occurred in the summer, whereas the highest mean log10 Nw (normalized intercept parameter) values were found in the winter. Furthermore, most of the rain falling at a rate of less than 20 mm h-1 occurs in Northern Taiwan. In this study, we used radar reflectivity to differentiate between convective and stratiform systems. It was revealed that the mean Dm values are higher in convective systems, whereas the mean log10 Nw values are higher in stratiform systems. The structure of RSD in stratiform systems remains constant in all seasons; however, convection is similar to maritime type. The microphysical characteristics that are responsible for the different RSD features in different seasons and types of precipitation are illustrated with the help of contoured frequency by altitude diagrams of radar reflectivity. [ABSTRACT FROM AUTHOR]

Tropical Rainfall Measuring Mission (TRMM) precipitation radar measurements are used to examine the variation in vertical structure of precipitation with sea surface temperature (SST) over the Arabian Sea (AS) and Bay of Bengal (BOB). The variation in reflectivity and precipitation echo top with SST is remarkable over the AS but small over the BOB. The reflectivity increases with SST (from 26 to 31 ∘ C) by ∼1 and 4 dBZ above and below 6 km, respectively, over the AS, while its variation is <0.5 dBZ over the BOB. The transition from shallow storms at lower SSTs (≤27 ∘ C) to deeper storms at higher SSTs is strongly associated with the decrease in stability and mid-tropospheric wind shear over the AS. In contrary, the storms are deeper at all SSTs over the BOB due to weaker stability and mid-tropospheric wind shear. At lower SSTs, the observed high aerosol optical depth (AOD) and low total column water (TCW) over AS results in the small cloud effective radius (CER) and weaker reflectivity. As SST increases, AOD decreases and TCW increases, leading to a large CER and high reflectivity. The changes in these parameters with SST are marginal over the BOB and hence the CER and reflectivity. The predominance of collision–coalescence process below the bright band is responsible for the observed negative slopes in the reflectivity over both the seas. The observed variations in reflectivity originate at the cloud formation stage over both the seas, and these variations are magnified during the descent of hydrometeors to the ground. [ABSTRACT FROM AUTHOR]

Cloud Feedback Model Intercomparison Project Observational Simulator Package (COSP) has been widely used to diagnose model performance and physical processes via an apple-to-apple comparison to satellite measurements. Although the COSP provides useful information about clouds and their climatic impact, outputs that have a subcolumn dimension require large amounts of data. This can cause a bottleneck when conducting sets of sensitivity experiments or multiple model intercomparisons. Here, we incorporate two diagnostics for warm-rain microphysical processes into COSP2, the latest version of the simulator. The approach used here employs existing diagnostic methodologies that probe how the warm-rain processes occur using statistics constructed from simulators of multiple satellite instruments along with their subcolumn information. The new diagnostics are designed to produce statistics online during the COSP execution, eliminating the need to output subcolumn variables. Users can also readily conduct regional analysis tailored to their particular research interest (e.g., land-ocean differences), using an auxiliary post-process package after the COSP calculation. This inline tool also generates global maps of the occurrence frequency of warm-rain regimes (i.e., non-precipitating, drizzling, and precipitating) classified according to CloudSat radar reflectivity, putting the warm-rain process diagnostics into the context of geographical distributions of precipitation. The inline diagnostics are applied to the MIROC6 GCM to demonstrate how known biases common among multiple GCMs relative to satellite observations are revealed. The inline multisensor diagnostics are intended to serve as a tool that facilitates process-oriented model evaluations in a manner that reduces the burden on modelers for their diagnostics effort. [ABSTRACT FROM AUTHOR]

A prototype quantitative precipitation estimate (QPE) algorithm that utilizes specific attenuation A and specific differential phase KDP was developed for inclusion into the Multi-Radar Multi-Sensor (MRMS) system and the Weather Surveillance Radar-1988 Doppler (WSR-88D) network. Special attention is given to the optimization of the factor α used for computation of a path-integrated attenuation from a total span of differential phase along the propagation path in rain. It is suggested to estimate α from a slope of the ZDR dependence on Z in rain. The use of real-time adjusted α allows us to capture the variations of the drop size distributions, and therefore improve the QPE accuracy. It is demonstrated that the factor α is generally higher for tropical rain type compared to continental rain. Since the R(A) approach is only valid for pure rainfall, the R(KDP) relation is suggested as a complement in areas of hail contamination. The paper contains a description of the basic version of the R(A) and R(KDP) algorithm and recommendations for its further optimization. [ABSTRACT FROM AUTHOR]

The quantitative precipitation estimate (QPE) algorithm developed and described in Part I was validated using data collected from 33 Weather Surveillance Radar 1988-Doppler (WSR-88D) radars on 37 calendar days east of the Rocky Mountains. A key physical parameter to the algorithm is the parameter alpha α, defined as the ratio of specific attenuation A to specific differential phase KDP. Examination of a significant sample of tropical and continental precipitation events indicated that α was sensitive to changes in drop size distribution and exhibited lower (higher) values when there were lower (higher) concentrations of larger (smaller) rain drops. As part of the performance assessment, the prototype algorithm generated QPEs utilizing a real-time estimated and a fixed α were created and evaluated. The results clearly indicated ~26% lower errors and a 26% better bias ratio with the QPE utilizing a real-time estimated α as opposed to using a fixed value as was done in previous studies. Comparisons between the QPE utilizing a real-time estimated α and the operational dual-polarization (dual-pol) QPE used on the WSR-88D radar network showed the former exhibited ~22% lower errors, 7% less bias, and 5% higher correlation coefficient when compared to quality controlled gauge totals. The new QPE also provided much better estimates for moderate to heavy precipitation events and performed better in regions of partial beam blockage than the operational dual-pol QPE. [ABSTRACT FROM AUTHOR]

Wind warnings are the second-most-frequent advisory issued by the U.S. Air Force's 45th Weather Squadron (45WS) at Cape Canaveral, Florida. Given the challenges associated with nowcasting convection in Florida during the warm season, improvements in 45WS warnings for convective wind events are desired. This study aims to explore the physical bases of dual-polarization radar signatures within wet downbursts around Cape Canaveral and identify signatures that may assist the 45WS during real-time convective wind nowcasting. Data from the 45WS's C-band dual-polarization radar were subjectively analyzed within an environmental context, with quantitative wind measurements recorded by weather tower sensors for 32 threshold-level downbursts with near-surface winds ≥ 35 kt (1 kt ≈ 0.51 m s−1) and 32 null downbursts. Five radar signatures were identified in threshold-level downburst-producing storms: peak height of 1-dB differential reflectivity ZDR column, peak height of precipitation ice signature, peak reflectivity, height below 0°C level where ZDR increases to 3 dB within a descending reflectivity core (DRC), and vertical ZDR gradient within DRC. Examining these signatures directly in updraft–downdraft cycles that produced threshold-level winds yielded mean lead times of 20.0–28.2 min for cumulus and mature stage signatures and 12.8–14.9 min for dissipating stage signatures, with higher signature test values generally yielding higher skill scores. A conceptual test of utilizing signatures within earlier cells in multicell storms to indirectly predict the potential for intense downbursts in later cells was performed, which offered increased lead times and skill scores for an Eulerian forecast region downstream from the storm initiation location. [ABSTRACT FROM AUTHOR]

Multiplatform measurements of the active and passive instruments from A‐Train were employed to observationally characterize particle structures over a spectrum of precipitating clouds from shallow cumulus to deep convection. Radar reflectivity profiles were composited as a function of temperature, with particle type superimposed to depict how storm regimes exert different particle habit structures. The deep convective system was found to have a relatively simple structure, which is dominated by randomly oriented ice, followed by snow and rain at the bottom, whereas the shallow cold system consisting high cloud tops with precipitation far below contains various hydrometeors, such as ice plates and drizzles. The deep convective system was further analyzed to demonstrate how the vertical microphysical structure tends to systematically transition from nonprecipitating to precipitating characteristics with differing cloud top buoyancies indicative of the "life stage" of convective development. The analysis offers a link between dynamical characteristics of convective systems and their inner hydrometeors structures. Plain Language Summary: Clouds are composed of liquid water droplets and condensed ice crystals or a mixture of both. Compared to liquid droplets, ice crystals are generally larger in size and, even with the same mass, allow more sunlight to reach the Earth's surface. Ice crystals also grow faster and end up falling as snow or melted rain, shortening the lifetime of a cloud. The phase states of these liquid and ice particles do not only persist over the lifetime of the cloud, but thermodynamic transitions between them often occur. Although these cloud‐precipitation and thermodynamic phase processes occur in the vertical direction of the atmosphere, the vertical internal phase compositions of cloud‐precipitation systems are still an open question in the atmospheric sciences, resulting in uncertainties in the surface temperature estimations in warmed atmospheres. In our study, we integrated independent measurements from space, analyzed various precipitating clouds from shallow to towering convective systems, and revealed variabilities in the vertical particle constituents among the precipitating cloud regimes. Studying the "static" measurement in a more "dynamical" context, we also show the observational insight of how the vertical structure of deep convective systems changes from "cloud mode" to "precipitation mode" during the course of their development. Key Points: Characterized vertical structures of global precipitating clouds over the spectrum of storm regimes from shallow cumulus to deep convectionIdentified clear differences in hydrometeor constituents, including particle phase and shapes, depending on the precipitating cloud systemsDeep convective system exerted transition of its microphysical structure from nonprecipitating to precipitating mode with changing buoyancy [ABSTRACT FROM AUTHOR]

The bright band, a layer of enhanced radar reflectivity associated with melting ice particles, is a major source of significant overestimation in quantitative precipitation estimation (QPE) based on the Z-R (reflectivity factor-rain rate) relationship. The effects of the bright band on radar-based QPE can be eliminated by vertical profile of reflectivity (VPR) correction. In this study, we applied bright-band correction algorithms to evaluate three different bands (S-, C- and X-band) of dual-polarized radars and to reduce overestimation errors in Z-R relationship-based QPEs. After the reflectivity was corrected by the algorithms using average VPR (AVPR) alone and a combination of average VPR and the vertical profile of the copolar correlation coefficient (AVPR+CC), the QPEs were derived. The bright-band correction and resulting QPEs were evaluated in eight precipitation events by comparing to the uncorrected reflectivity and rain-gauge observations, separately. The overestimation of Z-R relationship-based QPEs associated with the bright band was reduced after correction by the two schemes for which hourly rainfall was less than 5 mm. For the verification metrics of RMSE (root-mean-square error), RMAE (relative mean absolute error) and RMB (relative mean bias) of QPEs, averaged over all eight cases, the AVPR method improved from 2.28, 0.94 and 0.78 to 1.55, 0.60 and 0.40, respectively, while the AVPR+CC method improved to 1.44, 0.55 and 0.30, respectively. The QPEs after AVPR+CC correction had less overestimation than those after AVPR correction, and similar conclusions were drawn for all three different bands of dual-polarized radars. [ABSTRACT FROM AUTHOR]

The use of ground-based precipitation measurements in radar precipitation estimation is well known in radar hydrology. However, the approach of using gauged precipitation and near-surface air temperature observations to improve radar precipitation estimates in cold climates is much less common. In cold climates, precipitation is in the form of snow, rain or a mixture of the two phases. Air temperature is intrinsic to the phase of the precipitation and could therefore be a possible covariate in the models used to ascertain radar precipitation estimates. In the present study, we investigate the use of air temperature within a non-parametric predictive framework to improve radar precipitation estimation for cold climates. A non-parametric predictive model is constructed with radar precipitation rate and air temperature as predictor variables and gauge precipitation as an observed response using a k nearest neighbour (k -nn) regression estimator. The relative importance of the two predictors is ascertained using an information theory-based weighting. Four years (2011–2015) of hourly radar precipitation rates from the Norwegian national radar network over the Oslo region, hourly gauged precipitation from 68 gauges and gridded observational air temperatures were used to formulate the predictive model, hence making our investigation possible. Gauged precipitation data were corrected for wind-induced under-catch before using them as true observed response. The predictive model with air temperature as an added covariate reduces root-mean-square error (RMSE) by up to 15 % compared to the model that uses radar precipitation rate as the sole predictor. More than 80 % of gauge locations in the study area showed improvement with the new method. Further, the associated impact of air temperature became insignificant at more than 85 % of gauge locations when the near-surface air temperature was warmer than 10 ∘ C, which indicates that the partial dependence of precipitation on air temperature is most useful for colder temperatures. [ABSTRACT FROM AUTHOR]

Current precipitation nowcasting systems primarily use the extrapolation of observed radar reflectivity. The study focuses on both the extrapolation and the limits of predictability of precipitation using the concept of decorrelation time (DCT). The DCT analysis is based on reflectivity measurements from two radars covering the Czech Republic during the warm season (May—September) of four years (2009—2012). The analysis shows that that the mean DCT for the extrapolation forecast is 45.4 min, whereas the DCT for the persistent forecast is 13.4 min shorter. However, the DCT may increase or decrease by >40% depending on current meteorological conditions. The investigation of the evolution of DCT in time during two storm events in the Czech Republic suggests that the DCT may significantly vary in time as a consequence of changing character of the precipitation systems. [ABSTRACT FROM AUTHOR]

Abstract. The core satellite of the Global Precipitation Measurement (GPM) mission provides precipitation observations measured with the Dual frequency Precipitation Radar (DPR). The precipitation can only be estimated from the radar data, and therefore, independent validations using direct precipitation observation on the ground as a true reference need to be performed. Moreover, the quality and the accuracy of the measurements depend on various influencing factors. In this way, a validation may help to minimise those uncertainties. The DPR provides three different radar rain rate estimates for the GPM core satellite: Ku-band-only rain rates, Ka-band-only rain rates and a product combining the two frequencies. This study presents an evaluation of the three GPM-DPR surface precipitation estimates based on the gridded precipitation data of the WegenerNet, a local scale terrestrial network of 153 meteorological stations in southeast Austria. The validation is based on a graphical and a statistical approach using only data where both Ku- and Ka-band measurements are available. The data delivered from the WegenerNet are gauge-based gridded rainfall observations; the meteorological winter is excluded due to technical reasons. The focus lies on the resemblance of the variability within the whole network and the over- and underestimation of the precipitation through the GPM-DPR. During the last four years 22 rainfall events were observed by the GPM-DPR over the WegenerNet and the analysis rests upon these rainfall events. The WegenerNet provides a large number of gauges within each GPM-DPR footprint. Its biases are well studied and corrected, thus, it can be taken as a robust ground reference. This work also includes considerations on the limits of such comparisons between small terrestrial networks with a high density of stations and precipitation observations from a satellite. Our results show that the GPM-DPR estimates basically match with the WegenerNet measurements, but absolute quantities are biased. The three types of radar estimates deliver similar results, where Ku-band and dual frequency estimates are very close to each other. On a general level, Ka-band precipitation estimates deliver the best results due to the high number of light rainfall events. [ABSTRACT FROM AUTHOR]

Abstract. Spaceborne precipitation radars, such as the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) Core Observatory, have been important platforms to provide a direct measurement of three-dimensional precipitation structure globally. Building upon the success of TRMM and GPM Core Observatory, the Japan Aerospace Exploration Agency (JAXA) is currently surveying the feasibility of a potential satellite mission equipped with a precipitation radar on a geostationary orbit. The quasi-continuous observation realized by the geostationary satellite radar would offer a new insight into meteorology and would advance numerical weather prediction (NWP) through their effective use by data assimilation. Although the radar would be beneficial, the radar on the geostationary orbit measures precipitation obliquely at off-nadir points. Besides, the observing resolution will be several times larger than those onboard TRMM and GPM Core Observatory due to the limited antenna size that we could deliver. The tilted sampling volume and the coarse resolution would result in more contaminations from the surface clutters. To investigate the impact of these limitations and to explore the potential usefulness of the geostationary satellite radar, this study simulates the observation data for a typhoon case using an NWP model and a radar simulator. The results demonstrate that it would be possible to obtain three-dimensional precipitation data. However, the quality of the observation depends on the beam width, the beam sampling span, and the position of precipitation systems. With a wide beam width and a coarse beam span, the radar cannot observe weak precipitation at low altitudes due to the surface clutters. The limitation can be mitigated by oversampling (i.e., a wide beam width and a fine sampling span). With a narrow beam width and a fine beam sampling span, the surface clutter interference is confined to the surface level. When the precipitation system is located far from the nadir, the precipitation signal is obtained only for strong precipitation. [ABSTRACT FROM AUTHOR]

The paper is motivated by the enormous variability of short-term rainfall time structures which need to be discretized into several typical variants and explained from the viewpoint of rainfall types producing them. We present six variants of the time structure of 6-h rainfalls in Czechia, distinguished by a novel methodology for designing synthetic hyetographs. Reference 6-h rainfall episodes were extracted from radar-derived precipitation time series with a time resolution of 10 min, adjusted by daily data from rain gauges. The variants were distinguished by three indexes that quantify the precipitation concentration within time steps from one to six hours. The episodes with steady precipitation intensity during the entire episode are mainly stratiform or possibly mixed and frequently take much longer than six hours because of circulation patterns producing them; central and northeastern cyclonic types are most represented. All other variants of episodes frequently occur when a trough is situated above Central Europe. Episodes with steady intensity lasting about three hours are still mainly stratiform or mixed while two variants represented by “two-humped” hyetographs are usually mixed or convective. Two variants of most concentrated episodes are mainly convective or possibly mixed; they are characterized by enhanced frequency of southwestern and eastern cyclonic types. Future research on cluster frequencies among maximum precipitation episodes in various regions will enable the improvement of design hydrographs of small streams where runoff is basically influenced by the rainfall time structure. [ABSTRACT FROM AUTHOR]

Instantaneous surface rain rate estimates from the Global Precipitation Measurement (GPM) mission's Dual-Frequency Precipitation Radar (DPR) and combined DPR and multifrequency microwave imager (CMB) version-5 products are compared to those from the Met Office Radarnet 4 system's Great Britain and Ireland (GBI) radar composite product. The spaceborne and ground-based rainfall products are collocated spatially and temporally and compared at 5- and 25-km resolutions over GBI during a 3-yr period (from May 2014 to April 2017). The comparison results are evaluated as a function of both the intensity and variability of precipitation within the DPR field of view and are stratified spatially and seasonally. CMB and DPR products underestimate rain rates with respect to the Radarnet product by 21% and 31%, respectively, when considering 25-km resolution data taken within 75 km of a ground-based radar. Large variability in the discrepancies between spaceborne and ground-based rain rate estimates is the result of limitations of both systems and random errors in the collocation of their measurements. The Radarnet retrieval is affected by issues with measuring the vertical extent of precipitation at far ranges, while the GPM system struggles in properly quantifying orographic precipitation. Part of the underestimation by the GPM products appears to be a consequence of an erroneous DPR clutter identification in the presence of low freezing levels. Both products are susceptible to seasonal variations in performance and decreases in precision with increased levels of heterogeneity within the instruments' field of view. [ABSTRACT FROM AUTHOR]

The dominant hydrometeor types associated with Brazilian tropical precipitation systems are identified via research X-band dual-polarization radar deployed in the vicinity of the Manaus region (Amazonas) during both the GoAmazon2014/5 and ACRIDICON-CHUVA field experiments. The present study is based on an Agglomerative Hierarchical Clustering (AHC) approach that makes use of dual polarimetric radar observables (reflectivity at horizontal polarization ZH, differential reflectivity ZDR, specific differential phase KDP, and correlation coefficient ρHV) and temperature data inferred from sounding balloons. The sensitivity of the agglomerative clustering scheme for measuring the inter-cluster dissimilarities (linkage criterion) is evaluated through the wet season dataset. Both the weighted and Ward linkages exhibit better abilities to retrieve cloud microphysical species, whereas clustering outputs associated with the centroid linkage are poorly defined. The AHC method is then applied to investigate the microphysical structure of both the wet and dry seasons. The stratiform regions are composed of five hydrometeor classes: drizzle, rain, wet snow, aggregates, and ice crystals, whereas convective echoes are generally associated with light rain, moderate rain, heavy rain, graupels, aggregates and ice crystals. The main discrepancy between the wet and dry seasons is the presence of both low- and high-density graupels within convective regions, whereas the rainy period exhibits only one type of graupel. Finally, aggregate and ice crystal hydrometeors in the tropics are found to exhibit higher polarimetric values compared to those at mid-latitudes. [ABSTRACT FROM AUTHOR]

The article offers information on a study which involves use of radar imagery from the Sentinel-1a/b satellites for illuminating secular and seasonal trends modulated by changes in withdrawal rates and the magnitude of winter precipitation in Central California. It mentions information on complex spatial and temporal characteristics of ground deformation observed during and after the drought require consideration of processes related to both natural and anthropogenic influences.

This research is Part II of a two-part study that evaluates the ability of image-processing and select machine-learning algorithms to detect, classify, and track midlatitude mesoscale convective systems (MCSs) in radar-reflectivity images for the conterminous United States. This paper focuses on the tracking portion of this framework. Tracking is completed through a two-step process using slice (snapshots of instantaneous MCS intensity) data generated in Part I. The first step is to perform spatiotemporal matching, which associates slices through temporally adjacent radar-reflectivity images to generate swaths, or storm tracks. When multiple slices are found to be matches, a difference-minimization procedure is used to associate the most similar slice with the existing swath. Once this step is completed, a second step combines swaths that are spatiotemporally close. Tracking performance is assessed by calculating select metrics for all available swath-building perturbations to determine the optimal approach in tracking. Frequency maps and time series generated from the swaths suggest that the spatiotemporal occurrence of these swaths is reasonable as determined from previous work. Further, these events exhibit a diurnal cycle that is distinct from that of overall convection for the conterminous United States. Last, machine-learning predictions are found to limit areas of high MCS frequency to the central and eastern Great Plains. [ABSTRACT FROM AUTHOR]

This research evaluates the ability of image-processing and select machine-learning algorithms to identify midlatitude mesoscale convective systems (MCSs) in radar-reflectivity images for the conterminous United States. The process used in this study is composed of two parts: segmentation and classification. Segmentation is performed by identifying contiguous or semicontiguous regions of deep, moist convection that are organized on a horizontal scale of at least 100 km. The second part, classification, is performed by first compiling a database of thousands of precipitation clusters and then subjectively assigning each sample one of the following labels: 1) midlatitude MCS, 2) unorganized convective cluster, 3) tropical system, 4) synoptic system, or 5) ground clutter and/or noise. The attributes of each sample, along with their assigned label, are used to train three machine-learning algorithms: random forest, gradient boosting, and ''XGBoost.'' Results using a testing dataset suggest that the algorithms can distinguish between MCS and non-MCS samples with a high probability of detection and low probability of false detection. Further, the trained algorithm predictions are well calibrated, allowing reliable probabilistic classification. The utility of this two-step procedure is illustrated by generating spatial frequency maps of automatically identified precipitation clusters that are stratified by using various reflectivity and probabilistic prediction thresholds. These results suggest that machine learning can add value by limiting the amount of false-positive (non-MCS) samples that are not removed by segmentation alone. [ABSTRACT FROM AUTHOR]

This study used the first detailed radar measurements of the vertical structure of precipitation obtained in the central Andes of southern Peru and Bolivia to investigate the diurnal cycle and vertical structure of precipitation and melting-layer heights in the tropical Andes. Vertically pointing 24.1-GHz Micro Rain Radars in Cusco, Peru (3350m MSL, August 2014-February 2015), and La Paz, Bolivia (3440m MSL, October 2015-February 2017), provided continuous 1-min profiles of reflectivity and Doppler velocity. The time-height data enabled the determination of precipitation timing, melting-layer heights, and the identification of convective and stratiform precipitation features. Rawinsonde data, hourly observations of meteorological variables, and satellite and reanalysis data provided additional insight into the characteristics of these precipitation events. The radar data revealed a diurnal cycle with frequent precipitation and higher rain rates in the afternoon and overnight. Short periods with strong convective cells occurred in several storms. Longer-duration events with stratiform precipitation structures were more common at night than in the afternoon. Backward air trajectories confirmed previous work indicating an Amazon basin origin of storm moisture. For the entire dataset, median melting-layer heights were above the altitude of nearby glacier termini approximately 17%of the time in Cusco and 30%of the time in La Paz, indicating that some precipitation was falling as rain rather than snow on nearby glacier surfaces. During the 2015-16 El Niño, almost half of storms in La Paz had melting layers above 5000m MSL. [ABSTRACT FROM AUTHOR]

The factors affecting the inland and orographic enhancement of lake-effect precipitation are poorly understood, yet critical for operational forecasting. Here we use nine cool seasons (16 November-15 April) of radar data from the Montague/Ft. Drum, New York (KTYX), WSR-88D, the North American Regional Reanalysis (NARR), and observations from the Ontario Winter Lake-effect Systems (OWLeS) field campaign to examine variations in lake-effect precipitation enhancement east of Lake Ontario and over the Tug Hill Plateau (hereafter Tug Hill). Key factors affecting the inland and orographic enhancement in this region include the strength of the incident boundary layer flow, the intensity of the lake-induced convective available potential energy (LCAPE), and the mode of the lake-effect system. Stronger flow favors higher precipitation rates, a precipitation maximumdisplaced farther downwind, and greater inland and orographic enhancement. The effects of LCAPE depend upon the strength of the flow. During periods of weak flow, higher LCAPE favors lower precipitation rates, a maximum closer to the shoreline, and lesser inland and orographic enhancement. During periods of strong flow, higher LCAPE favors higher precipitation rates, a maximum displaced farther downwind, and greater inland and orographic enhancement. Banded (nonbanded) modes favor higher (lower) precipitation rates, lesser (greater) inland and orographic enhancement, and a maximum closer to the shoreline (over Tug Hill). These results, for both manually measured and radar-estimated precipitation, are robust when many lake-effect events are considered, but substantial variability exists during individual events. [ABSTRACT FROM AUTHOR]

A phased-array weather radar (PAWR) with fast scanning and wide elevation coverage capabilities has been developed. The PAWR transmits elevationally broad beams by feeding power to a limited number of its antenna elements, and receives signals reflected by precipitation media using all 128 antenna elements, each of which is connected to an analog-to-digital converter (ADC). After ADC sampling, digital processing of beamforming is applied to the received signals to accomplish receptions simultaneously over multiple angles. The PAWR can, thereby, make observations at 100-m range increments out to 60 km at 1° azimuth angle intervals over a range of 360° and at about 1° elevation-angle intervals from 0°–90° within the short time of 30 s. Reflectivity factors measured by the PAWR were compared with those from a collocated C-band radar, and were found to have a bias of 0.53 dB and a standard deviation of 3.68 dB, which indicates sufficient accuracy for observing precipitation. Furthermore, in a sample observation of convective rain, the PAWR detected a precipitation core 9 min before it reached the ground by its 30-s fast scanning and wide elevation coverage. It, thereby, became evident that the fast scanning and wide elevation coverage capabilities of the PAWR give earlier detection and a higher detection probability, which will enable earlier and more accurate warnings of severe weather phenomena and a better potential of mitigating the damage. [ABSTRACT FROM AUTHOR]