Global Precipitation Measurement Mission Research Articles

Hydrometeor Identification Using GMI Passive Microwave Brightness Temperatures

Abstract Using passive microwave brightness temperatures (Tbs) from the Global Precipitation Measurement (GPM) mission Microwave Imager (GMI) and hydrometeor identification (HID) data from dual-polarization ground radars, empirical lookup tables are developed for a multifrequency estimation of the likelihood a precipitation column includes certain hydrometeor types, as a function of Tb. Eight years of co-located Tbs and HID data from the GPM Validation Network are used for development and testing of the GMI-based HID retrieval, with 2015-2020 used for training and 2021-2022 used for testing the GMI-based HID retrieval. The occurrence of profiles with hail and graupel are both slightly underpredicted by the lookup tables, but the percentage of profiles predicted is highly correlated with the percentage observed (0.98 correlation coefficient for hail, and 0.99 for graupel). By having snow appear before rain in the hierarchy, the sample size for rain, without ice aloft, is fairly small, and the percentage of rain profiles is less than snow for all Tbs.

UAV based comprehensive modelling approach for flood hazard assessment and mitigation planning

The assessment of flood hazard in semi-arid region under the scare data situation is a challenging task under the changing climatic situation. Therefore, the present research is carry out for Rel river catchment, Gujrat, India as case study and presented the comprehensive approach to identify the possible flood extend and flood mitigation measures for better flood assessment. The UAV based high resolution (3.6 cm × 3.6 cm) DEM is generated for most flood vulnerable area (i.e. Dhanera city), whereas scare precipitation data under ungauged condition is generated using GEE and National Aeronautics and Space Administration (NASA) Global Precipitation Measurement Mission (GPM) system. The HEC-HMS based hydrologic model is utilized for simulation of stream flow under the observed data, however, the 2D HEC-RAS based open-source model is used for flood assessment in 2D under extreme flood, dam break and levees condition. The flood decision making maps i.e water surface elevation, flood inundation, flood arrival time, and flood velocity are prepared for flood event 2017. The six flood hazard classes were selected from H1 (Low) to H6 (High) categories. The hazard maps of flood for different scenario i.e., 2D hydrodynamic analysis, 2D dam breach, 2D analysis with extended levees, and Dam break analysis with extended levees are derived. The model was validated with observed river gauge (water level) and discharge data at Dhanera City of flood 2017. The developed UAV based comprehensive modelling approach for flood hazard and mitigation planning provides the systematic approach for flood hazard assessment and would be adopted as a system framework for flood risk assessment for any similar case worldwide.

An integrated modeling approach for estimating monthly global rainfall erosivity

Modeling monthly rainfall erosivity is vital to the optimization of measures to control soil erosion. Rain gauge data combined with satellite observations can aid in enhancing rainfall erosivity estimations. Here, we presented a framework which utilized Geographically Weighted Regression approach to model global monthly rainfall erosivity. The framework integrates long-term (2001–2020) mean annual rainfall erosivity estimates from IMERG (Global Precipitation Measurement (GPM) mission’s Integrated Multi-satellitE Retrievals for GPM) with station data from GloREDa (Global Rainfall Erosivity Database, n = 3,286 stations). The merged mean annual rainfall erosivity was disaggregated into mean monthly values based on monthly rainfall erosivity fractions derived from the original IMERG data. Global mean monthly rainfall erosivity was distinctly seasonal; erosivity peaked at ~ 200 MJ mm ha−1 h−1 month−1 in June–August over the Northern Hemisphere and ~ 700 MJ mm ha−1 h−1 month−1 in December–February over the Southern Hemisphere, contributing to over 60% of the annual rainfall erosivity over large areas in each hemisphere. Rainfall erosivity was ~ 4 times higher during the most erosive months than the least erosive months (December–February and June–August in the Northern and Southern Hemisphere, respectively). The latitudinal distributions of monthly and seasonal rainfall erosivity were highly heterogeneous, with the tropics showing the greatest erosivity. The intra-annual variability of monthly rainfall erosivity was particularly high within 10–30° latitude in both hemispheres. The monthly rainfall erosivity maps can be used for improving spatiotemporal modeling of soil erosion and planning of soil conservation measures.

Classifying microwave radiometer observations over The Netherlands into dry, shallow-, and non-shallow precipitation using a random forest model

Abstract Spaceborne microwave radiometers represent an important component of the Global Precipitation Measurement (GPM) mission due to their frequent sampling of rain systems. Microwave radiometers measure microwave radiation (brightness temperatures, Tb), which can be converted into precipitation estimates with appropriate assumptions. However, detecting shallow precipitation systems using space-borne radiometers is challenging, especially over land, as their weak signals are hard to differentiate from those associated with dry conditions. This study uses a random forest model (RF) to classify microwave radiometer observations as dry, shallow, or non-shallow over the Netherlands - a region with varying surface conditions and frequent occurrence of shallow precipitation. The RF is trained on five years of data (2016-2020) and tested with two independent years (2015, 2021). The observations are classified using ground-based weather radar echo top heights. Various RF models are assessed, such as using only GPM’s Microwave Imager (GMI) Tb values as input features or including spatially aligned ERA-5 2-meter temperature and freezing level reanalysis and/or Dual Precipitation Radar (DPR) observations. Independent of the input features, the model performs best in summer and worst in winter. The model classifies observations from high-frequency channels (≥85 GHz) with lower Tb-values as non-shallow, higher values as dry, and those in between as shallow. Misclassified footprints exhibit radiometric characteristics corresponding to their assigned class. Case studies reveal dry observations misclassified as shallow are associated with lower Tb-values, likely resulting from the presence of ice particles in non-precipitating clouds. Shallow footprints misclassified as dry are likely related to the absence of ice particles.

Hydrologic Evaluation of the Global Precipitation Measurement Mission over the U.S.: Effect of Spatial and Temporal Scales

This research focuses on the effects of spatio-temporal resolutions of global satellite precipitation on simulated flood events characteristics. The analysis is carried out by spatially, temporally, and spatiotemporally upscaling fine-scale precipitation forcings from the ground-radar-based Multi-Radar Multi-Sensor system (MRMS) to the resolution of the Integrated Multi-satellitE Retrievals for GPM (IMERG) satellite precipitation product. These upscaled products were then run through the Ensemble Framework for Flash Flood Forecasting hydrologic modeling framework over the U.S. to assess how the different spatial and temporal resolutions impact the simulated outputs of flood event magnitude, duration, and timing. It was found that the quantitative uncertainties generated by the IMERG Early (IMERG-E) simulations overpowered those associated with resolution across all flood characteristics. When compared to native MRMS simulations, however, the upscaled precipitation estimates tended to underestimate peak discharges and event durations, associated with distinct negative water balance errors. It was also found that the upscaled precipitation simulations exhibited a scaling effect with regards to error contribution of peak discharge, with higher contributions at smaller basin sizes and decreasing contributions as basin sizes increased. Looking at how upscaling can affect the modeled output desired allows for improved understanding of which resolutions lead to the greatest changes in simulation accuracy, increasing the potential global use and utility of current or future satellite products, especially in regions where high-resolution ground radars are sparse.

Assessing precipitation event characteristics throughout North Carolina derived from GPM IMERG data products

The Global Precipitation Measurement (GPM) mission provides near-real time precipitation estimates that can be used for monitoring water supply infrastructure. To better understand the potential use of GPM data products for use in relevant applications, this study examines the performance of the Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation products throughout North Carolina to capture rainfall events. Event characteristics derived from IMERG precipitation products: early (4 h latency), late (14 h latency), and final (3.5 month latency) are compared to characteristics derived from gauges (N = 282) for the period 2000–2021. Results show that the IMERG data products identify events reasonably well (mean event errors of 6.0, 6.8, and 1.7 mm for early, late, and final products; probability of detection: 0.83, 0.83, 0.86; false alarm ratio: 0.20, 0.18, 0.18, respectively). While the final product performs best, the early and late products perform similarly. While additional research is needed to better understand factors leading to under- and over-estimates of event magnitudes, the findings from this study support the potential use of early/late IMERG data products in water supply monitoring or flood warning systems, where rapid estimates of event precipitation characteristics are needed.

Modeling run-off flow hydrographs using remote sensing data: an application to the Bashar basin, Iran

Abstract Precipitation is hard to access in countries like Iran, due to inadequate number of rain gauge stations. Remote sensing provides an alternative source of rainfall estimation. In this study, the effectiveness of the HEC-HMS model was evaluated using GPM (Global Precipitation Measurement Mission) satellite and rain gauge station data. The model was calibrated and validated using 5 flood events' data of a hydrometric station at the outlet of Bashar basin. Important flood parameters including peak discharge (QP), flood volume (V) and time of concentration (TC) were used to evaluate and compare the application of satellite and ground station data in the model, using various statistical indices. The accuracy of QP and V estimations using rain gauge data was higher than those obtained by satellite data. However, the difference between mean relative error (MRE) in QP estimation was about 1% (9.9% and 10.6% for rain gauge and satellite data, respectively). Conversely, higher accuracies were met for TC estimation using satellite (with MRE 9.1% and 10.2% for GPM and rain gauge data, respectively). Such results imply the sole utilization of satellite precipitation data is reliable for modeling hydrological key parameters, which can be helpful in areas with limited ground station coverage.

Enhancing consistency of microphysical properties of precipitation across the melting layer in dual-frequency precipitation radar data

Abstract. Stratiform rain and the overlying ice play crucial roles in Earth's climate system. From a microphysics standpoint, water mass flux primarily depends on two variables: particles' concentration and their mass. The Dual-frequency Precipitation Radar (DPR) on the Global Precipitation Measurement mission core satellite is a spaceborne instrument capable of estimating these two quantities through dual-wavelength measurements. In this study, we evaluate bulk statistics on the ice particle properties derived from dual-wavelength radar data in relation to the properties of rain underneath. Specifically, we focus on DPR observations over stratiform precipitation, characterized by columns exhibiting a prominent bright band, where the melting layer can be easily detected. Our analysis reveals a large increase in the retrieved mass flux as we transition from the ice to the rain phase in the official DPR product. This observation is in disagreement with our expectation that mass flux should remain relatively stable across the bright band in cold-rain conditions. To address these discrepancies, we propose an alternative retrieval algorithm that ensures a gradual transition of Dm (mean mass-weighted particle melted-equivalent diameter) and the precipitation rate across the melting zone. This approach also helps in estimating bulk ice density above the melting level. These findings demonstrate that DPR observations can not only quantify ice particle content and their size above stratiform rain regions but also estimate bulk density, provided uniform conditions that minimize uncertainties related to partial beam filling.

Isolated deep convections over the Tibetan Plateau in the rainy season during 2001–2020

The Tibetan Plateau (TP) is a prevalent region for convection systems due to its unique thermodynamic forcing. This study investigated isolated deep convections (IDCs), which have a smaller spatial and temporal size than mesoscale convective systems (MCSs), over the TP in the rainy season (June–September) during 2001–2020. The authors used satellite precipitation and brightness temperature observations from the Global Precipitation Measurement mission. Results show that IDCs mainly concentrate over the southern TP. The IDC number per rainy season decreases from around 140 over the southern TP to around 10 over the northern TP, with an average 54.2. The initiation time of IDCs exhibits an obvious diurnal cycle, with the peak at 1400–1500 LST and the valley at 0900–1000 LST. Most IDCs last less than five hours and more than half appear for only one hour. IDCs generally have a cold cloud area of 7422.9 km2, containing a precipitation area of approximately 65%. The larger the IDC, the larger the fraction of intense precipitation it contains. IDCs contribute approximately 20%–30% to total precipitation and approximately 30%–40% to extreme precipitation over the TP, with a larger percentage in July and August than in June and September. In terms of spatial distribution, IDCs contribute more to both total precipitation and extreme precipitation over the TP compared to the surrounding plain regions. IDCs over the TP account for a larger fraction than MCSs, indicating the important role of IDCs over the region.摘要本文利用卫星观测资料, 研究了2001–2020年雨季 (6–9月) 青藏高原上孤立深对流 (Isolated deep convections, IDCs) 的气候特征. IDCs定义为比中尺度对流系统 (Mesoscale convective systems, MCSs) 时空尺度小的对流. 结果显示, 每年雨季青藏高原上平均的IDC数量为54.2个, 主要分布在高原的南部. IDCs的初始时刻呈现明显的日循环, 在下午14–15时为峰值, 在上午9–10时为谷值. 大部分IDCs持续时间在5小时以内, 超过一半的IDCs仅持续1小时. IDCs的冷云平均面积约为7422.9km2, 其中包含65%的降水面积. IDC面积越大, 包含的强降水范围也越大. IDCs对青藏高原总降水的贡献约为20%–30%, 对极端降水贡献约为30%–40%, 在7月和8月的占比大于6月和9月. 在空间分布方面, 青藏高原上IDCs对总降水和极端降水的贡献大于周围平原地区. 青藏高原上IDCs对降水的贡献大于MCSs, 表明IDCs在该地区起着重要作用.

Enhanced Tropical Cyclone Precipitation Prediction in the Northwest Pacific Using Deep Learning Models and Ensemble Techniques

This study focuses on optimizing precipitation forecast induced by tropical cyclones (TCs) in the Northwest Pacific region, with lead times ranging from 6 to 72 h. The research employs deep learning models, such as U-Net, UNet3+, SE-Net, and SE-UNet3+, which utilize precipitation forecast data from the Global Forecast System (GFS) and real-time GFS environmental background data using a U-Net structure. To comprehensively make use of the precipitation forecasts from these models, we additionally use probabilistic matching (PM) and simple averaging (AVR) in rainfall prediction. The precipitation data from the Global Precipitation Measurement (GPM) Mission serves as the rainfall observation. The results demonstrate that the root mean squared errors (RMSEs) of U-Net, UNet3+, SE-UNet, SE-UNet3+, AVR, and PM are lowered by 8.7%, 10.1%, 9.7%, 10.0%, 11.4%, and 11.5%, respectively, when compared with the RMSE of the GFS TC precipitation forecasts, while the mean absolute errors are reduced by 9.6%, 11.3%, 9.0%, 12.0%, 12.8%, and 13.0%, respectively. Furthermore, the neural network model improves the precipitation threat scores (TSs). On average, the TSs of U-Net, UNet3+, SE-UNet, SE-UNet3+, AVR, and PM are raised by 12.8%, 21.3%, 19.3%, 20.7%, 22.5%, and 22.9%, respectively, compared with the GFS model. Notably, AVR and PM outperform all other individual models, with PM’s performance slightly better than AVR’s. The most important feature variables in optimizing TC precipitation forecast in the Northwest Pacific region based on the UNet-based neural network include GFS precipitation forecast data, land and sea masks, latitudinal winds at 500 hPa, and vertical winds at 500 hPa.

Comparison and Synthesis of Precipitation Data from CloudSat CPR and GPM KaPR

Employing different bands of radar to detect precipitation information in identical regions enables the acquisition of a more comprehensive precipitation cloud structure, thereby refining the continuity and completeness of precipitation measurements. This study first compared the coincident data from CloudSat W-band cloud profiling radar (CPR) and Global Precipitation Measurement Mission (GPM) Ka-band precipitation radar (KaPR) from 2014 to 2017, and then a synthesis of the radar reflectivity from CPR and KaPR was attempted to obtain a complete cloud and precipitation structure. The findings of the reflectivity comparisons indicated that the echo-top height identified by CPR is on average 3.6 to 4.2 km higher than that from KaPR, due to the higher sensitivity. Because of strong attenuation of CPR by liquid-phase particles, the reflectivity below the height of the melting layer usually shows an opposite gradient to KaPR with decreasing altitude. The difference in the near-surface rain rates of CPR and KaPR was found to be related to reflectivity gradients in the vertical direction, and the larger the reflectivity gradients, the greater the differences in near-surface rain rates. For better representing the complete vertical structure of precipitation clouds and improving the consistency of the reflectivity and precipitation rate, the radar reflectivity was weighted, synthesized from CPR and KaPR based on the gradient difference of the reflectivity from the two radars. We presented the synthesis results for a stratiform cloud and a deep convective case, and Spearman’s rank correlation coefficient (rs) between the GPM combined radiometer precipitation rate and the radar reflectivity was utilized to analyze the performance of the synthesis. The consistency between synthesized reflectivity and precipitation rate in the non-liquid phase was improved compared with KaPR, and the rs of the ice and mixed phases was increased by about 12% and 10%, respectively.

GPROF V7 and beyond: assessment of current and potential future versions of the GPROF passive microwave precipitation retrievals against ground radar measurements over the continental US and the Pacific Ocean

Abstract. The Goddard Profiling Algorithm (GPROF) is used operationally for the retrieval of surface precipitation and hydrometeor profiles from the passive microwave (PMW) observations of the Global Precipitation Measurement (GPM) mission. Recent updates have led to GPROF V7, which has entered operational use in May 2022. In parallel, development is underway to improve the retrieval by transitioning to a neural-network-based algorithm called GPROF-NN. This study validates retrievals of liquid precipitation over snow-free and non-mountainous surfaces from GPROF V7 and multiple configurations of GPROF-NN against ground-based radar measurements over the conterminous United States (CONUS) and the tropical Pacific. GPROF retrievals from the GPM Microwave Imager (GMI) are validated over several years, and their ability to reproduce regional precipitation characteristics and effective resolution is assessed. Moreover, the retrieval accuracy for several other sensors of the constellation is evaluated. The validation of GPROF V7 indicates that the retrieval produces reliable estimates of liquid precipitation over the CONUS. During all four assessed years, annual mean precipitation is within 8 % of gauge-corrected radar measurements. Although biases of up to 25 % are observed over sub-regions of the CONUS and the tropical Pacific, the retrieval reliably reproduces each region's diurnal and seasonal precipitation characteristics. The effective resolution of GPROF V7 is found to be 51 km over the CONUS and 18 km over the tropical Pacific. GPROF V7 also produces robust precipitation estimates for the other sensors of the GPM constellation. The evaluation further shows that the GPROF-NN retrievals have the potential to significantly improve the GPM PMW precipitation retrievals. GPROF-NN 1D, the most basic neural network implementation of GPROF, improves the mean-squared error, mean absolute error, correlation and symmetric mean absolute percentage error of instantaneous precipitation estimates by about 20 % for GPROF GMI while the effective resolution is improved to 31 km over land and 15 km over oceans. The two GPROF-NN retrievals that are based on convolutional neural networks can further improve the accuracy up to the level of the combined radar–radiometer retrievals from the GPM core observatory. However, these retrievals are found to overfit on the viewing geometry at the center of the swath, reducing their overall accuracy to that of GPROF-NN 1D. For the other sensors of the constellation, the GPROF-NN retrievals produce larger biases than GPROF V7 and only GPROF-NN 3D achieves consistent improvements compared to GPROF V7 in terms of the other assessed error metrics. This points to shortcomings in the hydrometeor profiles or radiative transfer simulations used to generate the training data for the other sensors of the GPM constellation as a critical limitation for improving GPM PMW retrievals.

Oceanic Validation of IMERG-GMI Version 6 Precipitation Using the GPM Validation Network

Abstract NASA’s multisatellite precipitation product from the Global Precipitation Measurement (GPM) mission, the Integrated Multi-satellitE Retrievals for GPM (IMERG) product, is validated over tropical and high-latitude oceans from June 2014 to August 2021. This oceanic study uses the GPM Validation Network’s island-based radars to assess IMERG when the GPM Core Observatory’s Microwave Imager (GMI) observes precipitation at these sites (i.e., IMERG-GMI). Error tracing from the Level 3 (gridded) IMERG V06B product back through to the input Level 2 (satellite footprint) Goddard Profiling Algorithm GMI V05 climate (GPROF-CLIM) product quantifies the errors separately associated with each step in the gridding and calibration of the estimates from GPROF-CLIM to IMERG-GMI. Mean relative bias results indicate that IMERG-GMI V06B overestimates Alaskan high-latitude oceanic precipitation by +147% and tropical oceanic precipitation by +12% with respect to surface radars. GPROF-CLIM V05 overestimates Alaskan oceanic precipitation by +15%, showing that the IMERG algorithm’s calibration adjustments to the input GPROF-CLIM precipitation estimates increase the mean relative bias in this region. In contrast, IMERG adjustments are minimal over tropical waters with GPROF-CLIM overestimating oceanic precipitation by +14%. This study discovered that the IMERG V06B gridding process incorrectly geolocated GPROF-CLIM V05 precipitation estimates by 0.1° eastward in the latitude band 75°N–75°S, which has been rectified in the IMERG V07 algorithm. Correcting for the geolocation error in IMERG-GMI V06B improved oceanic statistics, with improvements greater in tropical waters than Alaskan waters. This error tracing approach enables a high-precision diagnosis of how different IMERG algorithm steps contribute to and mitigate errors, demonstrating the importance of collaboration between evaluation studies and algorithm developers. Significance Statement Evaluation of IMERG’s oceanic performance is very limited to date. This study uses the GPM Validation Network to conduct the first extensive assessment of IMERG V06B at its native resolution over both high-latitude and tropical oceans, and traces errors in IMERG-GMI back through to the input GPROF-CLIM GMI product. IMERG-GMI overestimates tropical oceanic precipitation (+12%) and strongly overestimates Alaskan oceanic precipitation (+147%) with respect to the island-based radars studied. IMERG’s GMI estimates are assessed as these should be the optimal estimates within the multisatellite product due to the GMI’s status as calibrator of the GPM passive microwave constellation.

Evaluation of GPM DPR Rain Parameters with North Taiwan Disdrometers

Abstract Global precipitation demonstrates a substantial role in the hydrological cycle and offers tremendous implications in hydrometeorological studies. Advanced remote sensing instrumentations, such as the NASA Global Precipitation Measurement (GPM) mission Dual-Frequency Precipitation Radar (DPR), can estimate precipitation and cloud properties and have a unique capability to estimate the raindrop size information globally at snapshots in time. The present study validates the Level-2 data products of the GPM DPR with the long-term measurements of seven north Taiwan Joss–Waldvogel disdrometers from 2014 to 2022. The precipitation and drop size distribution parameters like rainfall rate (R; mm h−1), radar reflectivity factor (dBZ), mass-weighted mean drop diameter (Dm; mm), and normalized intercept parameter (Nw; m−3 mm−1) of the GPM DPR are compared with the disdrometers. Four different comparison approaches (point match, 5-km average, 10-km average, and optimal method) are used for the validation; among these four, the optimal strategy provided reasonable agreement between the GPM DPR and the disdrometers. The GPM DPR revealed superior performance in estimating the rain parameters in stratiform precipitation than the convective precipitation. Irrespective of algorithm type (dual- or single-frequency algorithm), sensitivity analysis revealed superior agreement for the mass-weighted mean diameter and inferior agreement for the normalized intercept parameter.

Precipitation Microphysics in Tropical Cyclones: A Global Perspective Using the NASA Global Precipitation Measurement Mission Dual‐Frequency Precipitation Radar

AbstractPrecipitation microphysics in tropical cyclones (TCs) are often poorly represented in numerical simulations, which ultimately affects TC structure, evolution, and prediction. This provides a large incentive to better observe and understand the underlying microphysical processes in TCs in order to improve precipitation forecasts and warning operations. 112 TCs from 2014 to 2020 were matched up with overpasses from the NASA Global Precipitation Measurement (GPM) mission Dual‐Frequency Precipitation Radar (DPR) on a global scale to identify cloud properties and associated precipitation processes by quantifying vertical slopes of reflectivity in the liquid and ice phase. Further, vertical profiles of reflectivity were partitioned into different 850–200 hPa shear‐relative quadrants of each storm, different annuli from the storm center, and 5 TC ocean basins globally. Preliminary results showed the highest echo top heights occurred in the Northwest Pacific and Indian Ocean basins, with all basins and quadrants exhibiting median negative slopes of KuPR in the liquid phase. Additionally, all shear‐relative quadrants revealed negative slopes of reflectivity in the ice phase implying ice hydrometeor growth. These findings can potentially be used to improve the representation of cloud properties and the accuracy of the DPR particle size distribution algorithm in TCs.

Temporal Gap‐Filling of 12‐Hourly SMAP Soil Moisture Over the CONUS Using Water Balance Budgeting

AbstractTemporal gaps in satellite‐based soil moisture (SM) products are a persistent issue. This study presents an entirely observation‐based method to derive volumetric SM content for filling gaps in Soil Moisture Active Passive (SMAP) retrievals. Using a water balance equation, 12‐hr topsoil water amount variations are determined based on observed precipitation from the Global Precipitation Measurement Mission (inflow) and a hydrologic loss function (outflow) built on SMAP dry‐downs. A temporally seamless SM product, composed of SMAP dry‐downs and precipitation‐driven moisture approximations, was generated as a secondary outcome in determining optimal water balance parameters. This data set maintains the original SMAP SM dynamics with a median Pearson correlation (R) of 0.69 and an unbiased root‐mean‐square error (ubRMSE) of 0.05 m3/m3. Using these parameters and available SMAP observations, a 12‐hourly SM product was produced over the conterminous United States. Validated against in situ measurements, this 12‐hourly SM product exhibits good performance with a median R of 0.63 and captures most SM peaks induced by heavy rainfall. A time series examination revealed the produced 12‐hourly SM product closely corresponds to in situ SM variations and outperforms two other SMAP‐based 12‐hourly SM products gap‐filled using temporal linear interpolation and a three‐dimensional smoothing approach, especially during sparse SMAP data periods. The proposed scheme's validity is further verified by the comparable performance of the exclusive filled‐on SM estimates. Utilizing the 12‐hourly SM data set and its paired hydrologic losses could enhance the quantification connections among the hydrologic components and benefit the understanding of land‐surface hydrology.

Vertical structure of North Indian Ocean tropical cyclones: A composite analysis using TRMM and GPM

The present study deals with the vertical structure of Tropical Cyclones (TC) from 2010 to 2020 over the North Indian Ocean (NIO) using the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement Mission (GPM). We analyzed 31 TC which were characterized as severe cyclonic storms, very severe cyclonic storms, and super cyclones out of which 18 occurred over the Bay of Bengal and 13 over the Arabian Sea. The normalized intensity difference method of brightness temperature is used to distinguish the regions of the TC into eyewall, inner rainband, outer rainband, and outer region after identifying the eye. The eye radius (Re) of the cyclone is used to define the annular regions. The vertical structure is explored using the reflectivity in each region of the cyclone and also with respect to convective precipitation, stratiform precipitation, and sea surface temperature. The intensity of the convection in the eyewall and the rainbands is strong and reaches up to an altitude of 14 km. The outer rainbands were more of stratiform nature and a distinct bimodal distribution is observed with a peak below and above the bright band. The outermost region exhibited the characteristics of both the convective and stratiform, however, the intensity is observed to be less. The convection is observed to be strong in the eyewall for high SST compared to the marginal, however, the vertical extent is large in the marginal SST. The vertical structure of the TC over the NIO contained a wider distribution of reflectivity and vertically extended compared to that of the cyclones in the other oceanic basins.

The Multiplatform Precipitation Feature (MPF) Database: A Storm‐Centric Synthesis of Space‐ and Ground‐Based Precipitation and Lightning Data Sets for Convective Studies

AbstractThis paper outlines the construction of a new Multiplatform Precipitation Feature (MPF) framework designed to facilitate storm‐based analyses of data sets from multiple independent observing platforms for convective studies. The MPF approach is applied to ground‐ and space‐based precipitation observations and retrievals from the Global Precipitation Measurement mission Validation Network with space‐based lightning measurements from the Lightning Imaging Sensor on board the International Space Station to create a prototype MPF database. The data are synthesized in a thunderstorm‐like, feature‐based framework that enables broad statistical analyses of the storm‐based relationships between the microphysics, kinematics, and electrical properties of convection that can contribute insight into their morphological, regional, and seasonal variations. This manuscript details how MPFs are defined from precipitation data and how observations from multiple additional platforms are combined within the context of an MPF thunderstorm boundary and used to characterize the feature. A demonstration of the prototype database as a proof‐of‐concept showcases its flexibility to conduct both large‐scale storm‐based statistical studies as well as MPF‐facilitated thunderstorm case studies.

Uncertainty Calibration of Passive Microwave Brightness Temperatures Predicted by Bayesian Deep Learning Models

Abstract Visible and infrared radiance products of geostationary orbiting platforms provide virtually continuous observations of Earth. In contrast, low-Earth orbiters observe passive microwave (PMW) radiances at any location much less frequently. Prior literature demonstrates the ability of a machine learning (ML) approach to build a link between these two complementary radiance spectra by predicting PMW observations using infrared and visible products collected from geostationary instruments, which could potentially deliver a highly desirable synthetic PMW product with nearly continuous spatiotemporal coverage. However, current ML models lack the ability to provide a measure of uncertainty of such a product, significantly limiting its applications. In this work, Bayesian deep learning is employed to generate synthetic Global Precipitation Measurement (GPM) Microwave Imager (GMI) data from Advanced Baseline Imager (ABI) observations with attached uncertainties over the ocean. The study first uses deterministic residual networks (ResNets) to generate synthetic GMI brightness temperatures with as little mean absolute error as 1.72 K at the ABI spatiotemporal resolution. Then, for the same task, we use three Bayesian ResNet models to produce a comparable amount of error while providing previously unavailable predictive variance (i.e., uncertainty) for each synthetic data point. We find that the Flipout configuration provides the most robust calibration between uncertainty and error across GMI frequencies, and then demonstrate how this additional information is useful for discarding high-error synthetic data points prior to use by downstream applications.

Hydrologic Evaluation of the Global Precipitation Measurement Mission over the U.S.: Error Budget Analysis

This study investigates the hydrologic utility of satellite precipitation estimates from the Global Precipitation Measurement mission by comparing flood signals produced across the Continental United States by a ten-year span of in-situ, ground-based radar and satellite-based precipitation data. The flood characteristics generated with radar and satellite precipitation through a distributed hydrologic model are contrasted against reference stream gauge data as a method of integrated validation to assess and quantify error budgets between precipitation products by highlighting precipitation products’ accuracy, hydrologic scaling effects, and the impact of the hydrologic model. It is found that systematic and random errors associated with flood characteristics behave similarly to trends previously seen in precipitation rate errors between precipitation products, establishing a clear link through propagation of errors into the water cycle. Additionally, behaviors associated with both water balance and routing schemes within the hydrologic model were shown to affect outputs. Errors generated by water balance tend to cause overestimation of peak discharge values, while errors associated with routing tend to cause underestimation of flood durations and push flood timings earlier than the stream gauge reference.