Retrievals For Global Precipitation Measurement Research Articles

Preprocessing satellite rainfall products improves hydrological simulations with machine learning

A new preprocessing methodology for gridded satellite precipitation products (SPP) is developed to improve the performance of Machine Learning (ML) algorithms using this type of input data for runoff prediction. A preprocessing approach was applied to capture the rainfall patterns across a watershed, and to select relevant input data for runoff prediction. This approach was tested using the FeedForward Neural Network (FFNN) and the Extreme Learning Machine (ELM) given their flexibility and ability in hydrological modeling. The methodology has been tested in a semi-arid transboundary watershed located in North Africa (Algeria, Tunisia) with the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM-IMERG) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) satellite rainfall products. The results demonstrated the effectiveness of the proposed approach when using all employed SPPs. In terms of Nash criterion, the suggested pre-processing technique improved the prediction ability of FFNN by 13%, and the ELM model by 15%, which highlights how preprocessing techniques significantly enhance ML models with SPPs data.

Preprocessing satellite rainfall products improves hydrological simulations with machine learning

A new preprocessing methodology for gridded satellite precipitation products (SPP) is developed to improve the performance of Machine Learning (ML) algorithms using this type of input data for runoff prediction. A preprocessing approach was applied to capture the rainfall patterns across a watershed, and to select relevant input data for runoff prediction. This approach was tested using the FeedForward Neural Network (FFNN) and the Extreme Learning Machine (ELM) given their flexibility and ability in hydrological modeling. The methodology has been tested in a semi-arid transboundary watershed located in North Africa (Algeria, Tunisia) with the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM-IMERG) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) satellite rainfall products. The results demonstrated the effectiveness of the proposed approach when using all employed SPPs. In terms of Nash criterion, the suggested pre-processing technique improved the prediction ability of FFNN by 13%, and the ELM model by 15%, which highlights how preprocessing techniques significantly enhance ML models with SPPs data.

Evaluating Precipitation Events Using GPM IMERG 30-Minute Near-Real-Time Precipitation Estimates

Abstract Accurately detecting and estimating precipitation at near–real time (NRT) is of utmost importance for the early detection and monitoring of hydrometeorological hazards. The precipitation product, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), provides NRT 0.1° and 30-min precipitation estimates across the globe with only a 4-h latency. This study was an evaluation of the GPM IMERG version 6 level-3 early run 30-min precipitation product for precipitation events from 2014 through 2020. The purpose of this research was to identify when, where, and why GPM IMERG misidentified and failed to detect precipitation events in California, Nevada, Arizona, and Utah in the United States. Precipitation events were identified based on 15-min precipitation from gauges and 30-min precipitation from the IMERG multisatellite constellation. False-positive and false-negative precipitation events were identified and analyzed to determine their characteristics. Precipitation events identified by gauges had longer duration and had higher cumulative precipitation than those identified by GPM IMERG. GPM IMERG had many false event detections during the summer months, suggesting possible virga event detection, which is when precipitation falls from a cloud but evaporates before it reaches the ground. The frequency and timing of the merged passive microwave (PMW) product and forward propagation were responsible for IMERG overestimating cumulative precipitation during some precipitation events and underestimating others. This work can inform experts that are using the GPM IMERG NRT product to be mindful of situations where GPM IMERG–estimated precipitation events may not fully resolve the hydrometeorological conditions driving these hazards. Significance Statement Accurately estimating rainfall to detect and monitor a precipitation event at near–real time is of utmost importance for hydrometeorological hazards. We used a state-of-the-art rainfall estimation product called GPM IMERG that uses infrared and passive microwave measurements collected from a constellation of satellites to produce near-real-time rainfall estimates every 30 min worldwide. The purpose of our research was to identify when, where, and why GPM IMERG falsely detected and missed precipitation events. Our results suggest that the frequency and timing of passive microwave precipitation with forward propagation were responsible for IMERG missing events, overestimating total rainfall during some precipitation events, and underestimating total rainfall in other precipitation events. Our future work will further investigate precipitation events using the GPM IMERG version 7 near-real-time product.

Establishing rainfall intensity-duration-frequency curves for Baghdad, Iraq using satellite data

Abstract Rainfall intensity-duration-frequency (IDF) curves were generated for Baghdad by utilising three satellite precipitation datasets: Global Satellite Mapping of Precipitation Near Real-Time (GSMaP NRT), gauge-corrected (GSMaP GC) and Global Precipitation Measurement Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG). Maximum annual rainfall data was fitted using several probability distribution methods. The calculated coefficients from the best-fit distribution were used as fitting parameters to generate IDF curves for return periods of 2, 5, 10, 25, 50 and 100 years using the Sherman equation. To address discrepancies between the satellite-derived IDF curves and observed data, bias correction was performed based on the differences. The analysis revealed that the Generalized Extreme Value Distribution model accurately described the hourly rainfall distribution. GSMaP GC exhibited the highest correlation with the observed data, making it the preferred option for generating IDF curves. The study highlighted the importance of gauge correction for satellite rainfall data to minimise the underestimation or overestimation of rainfall. GSMaP GC demonstrated reasonable accuracy in estimating rainfall in Iraq’s mainly arid climate area. By assisting in the creation of efficient methods for dealing with rainstorm events, the created IDF curves are a major step towards advancing sustainable urban stormwater management in the country.

Can IMERG QPE product capture the heavy rain on urban flood scale?

Urban areas are increasingly vulnerable to sudden flooding disasters caused by intense rainfall and high imperviousness degree, resulting in great economic losses and human casualties. Interactions between rainfall data and urban catchment characteristics highlight the urgent need of accurate and effective precipitation data to apply in reliable hydrological simulations. However, it remains a challenge to obtain accurate rainfall datasets on such small scales in urban areas. As satellite remote sensing is the only method that can achieve global observation, it is important to evaluate satellite precipitation products in their ability to accurately capture intense precipitation on urban flood scales. This study evaluates the performance of the latest version 06B (V06B) Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) in North China Plain, with using the Radar-Gauge merged precipitation estimates as reference data. First, it could be concluded that IMERG fails to accurately estimate precipitation in the whole study area, having the problem of overestimating light precipitation and underestimating heavy precipitation. Second, results show that IMERG has poor ability to capture heavy precipitation on small scales, with the percentage of Hit nearly 0 and the percentage of Miss higher than 40 % for all the precipitation cases. Third, with the expansion of heavy precipitation centers' coverage, the problem of IMERG not to detect heavy precipitation gets mitigated, with the percentage of Miss decreasing by 14 % (19 %). However, the ability to capture both spatial location and precipitation intensity is still not good, the percentage of Hit ranging from 0.05 % to 7 %, without obvious improvement. When IMERG is able to capture the center of strong precipitation, it also tends to overestimate the weak precipitation around the center of strong precipitation. Results of this study provide an improved understanding of how well the V06B IMERG products capture the heavy precipitation center at small scales in urban areas, which will be useful for both developers and users of IMERG.

Impacts of spatiotemporal resolutions of precipitation on flood event simulation based on multimodel structures – a case study over the Xiang River basin in China

Abstract. Accurate flood event simulation and prediction, enabled by effective models and reliable data, are critical for mitigating the potential risk of flood disaster. This study aims to investigate the impacts of spatiotemporal resolutions of precipitation on flood event simulation in a large-scale catchment of China. We use high-spatiotemporal-resolution Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) products and a gauge-based product as precipitation forcing for hydrologic simulation. Three hydrological models (HBV, SWAT and DHSVM) and a data-driven model (long short-term memory (LSTM) network) are utilized for flood event simulation. Two calibration strategies are carried out, one of which targets matching of the flood events, with peak discharge exceeding 8600 m3 s−1 between January 2015 and December 2017, and the other one is the conventional strategy for matching the entire streamflow time series. The results indicate that the event-based calibration strategy improves the performance of flood event simulation compared with a conventional calibration strategy, except for DHSVM. Both hydrological models and LSTM yield better flood event simulation at a finer temporal resolution, especially in flood peak simulation. Furthermore, SWAT and DHSVM are less sensitive to the spatial resolutions of IMERG, while the performance of LSTM obtains improvement when degrading the spatial resolution of IMERG-L. Generally, LSTM outperforms the hydrological models in most flood events, which implies the usefulness of the deep learning algorithms for flood event simulation.

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.

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.

Interannual Variation of Half-Hourly Extreme Precipitation in Africa: The Assessment of the Warmest Decade (2011–2020) Since the Beginning of Observations

AbstractAfrica is experiencing widespread extreme precipitation. However, the continent remains understudied regarding extreme precipitation variation, particularly at high temporal resolution. This study assessed the changes in spatial distribution, frequency, and intensity of half-hourly violent precipitation (≥ 50 mm/h) in Africa during 2011–2020. The data were sourced from the Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) program. The results reflect that the 50–60 mm/h precipitation category was the most densely spatially distributed with less-discernable interannual changes. There was a strong interannual variation in the spatial distribution of the 60–70 mm/h and ≥ 70 mm/h precipitation categories. There was a general decrease in the frequency of occurrence of violent precipitation in all regions, except in North Africa (NA). Among the study regions, NA had the broadest precipitation intensity distribution extended to high values and consequently, on average, the most intense violent precipitation, followed by Southern Africa (SA) and Southwest Indian Ocean (SWIO). The regions in the tropics, i.e., West Africa (WA), Central Africa (CA), and East Africa (EA), had a relatively narrow intense violent precipitation distribution. The results further illustrate that the strength of climate variability modes has a strong influence in the distribution, frequency, and intensity of violent precipitation over the continent particularly the ≥ 70 mm/h precipitation category. This study underscores a need for a strengthened capacity in forecasting climate variability modes impacting on Africa, augmentation of continental precipitation observation network, and improved extreme precipitation adaptation infrastructure.

A D-vine copula-based quantile regression towards merging satellite precipitation products over rugged topography: a case study in the upper Tekeze–Atbara Basin

Abstract. Precipitation is a vital key element in various studies of hydrology, flood prediction, drought monitoring, and water resource management. The main challenge in conducting studies over remote regions with rugged topography is that weather stations are usually scarce and unevenly distributed. However, open-source satellite-based precipitation products (SPPs) with a suitable resolution provide alternative options in these data-scarce regions, which are typically associated with high uncertainty. To reduce the uncertainty of individual satellite products, we have proposed a D-vine copula-based quantile regression (DVQR) model to merge multiple SPPs with rain gauges (RGs). The DVQR model was employed during the 2001–2017 summer monsoon seasons and compared with two other quantile regression methods based on the multivariate linear (MLQR) and the Bayesian model averaging (BMAQ) techniques, respectively, and with two traditional merging methods – the simple modeling average (SMA) and the one-outlier-removed average (OORA) – using descriptive and categorical statistics. Four SPPs have been considered in this study, namely, Tropical Applications of Meteorology using SATellite (TAMSAT v3.1), the Climate Prediction Center MORPHing Product Climate Data Record (CMORPH-CDR), Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG v06), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN-CDR). The bilinear (BIL) interpolation technique was applied to downscale SPPs from a coarse to a fine spatial resolution (1 km). The rugged-topography region of the upper Tekeze–Atbara Basin (UTAB) in Ethiopia was selected as the study area. The results indicate that the precipitation data estimates with the DVQR, MLQR, and BMAQ models and with traditional merging methods outperform the downscaled SPPs. Monthly evaluations reveal that all products perform better in July and September than in June and August due to precipitation variability. The DVQR, MLQR, and BMAQ models exhibit higher accuracy than the traditional merging methods over the UTAB. The DVQR model substantially improved all of the statistical metrics (CC = 0.80, NSE = 0.615, KGE = 0.785, MAE = 1.97 mm d−1, RMSE = 2.86 mm d−1, and PBIAS = 0.96 %) considered compared with the BMAQ and MLQR models. However, the DVQR model did not outperform the BMAQ and MLQR models with respect to the probability of detection (POD) and false-alarm ratio (FAR), although it had the best frequency bias index (FBI) and critical success index (CSI) among all of the employed models. Overall, the newly proposed merging approach improves the quality of SPPs and demonstrates the value of the proposed DVQR model in merging multiple SPPs over regions with rugged topography such as the UTAB.

The performance of IMERG near-real-time estimations during the record-breaking Meiyu season in 2020

The extreme Meiyu season, characterized by continuous heavy precipitation, is often leads to numerous flood-related disasters. Accurate and timely monitoring of extreme Meiyu precipitation is crucial for the effective prevention and mitigation of these disasters. The near-real-time (NRT) products from Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), specifically IMERG-E and IMERG-L products, have high potential utility to acquire timely precipitation data. Therefore, it is important to quantify their errors and uncertainties during the extreme Meiyu season. In this study, we assess and compare the performance of IMERG NRT products with Global Satellite Mapping of Precipitation Motion Vector Kalman product (GSMaP-MVK) using hourly gauge observations during the recor-breaking Meiyu event in 2020. The main conclusions are as follows: all three satellite products generally capture the spatial–temporal evolution of Meiyu precipitation, with IMERG-L showing the closest agreement with gauge observations, particularly for the precipitation center. In terms of the statistical and volumetric categorical metrics, IMERG-L performs best among the three products, while IMERG-E does not exhibit superior performance compared to GSMaP-MVK. Regarding the diurnal cycle, IMERG-L generally reproduces the diurnal cycle of observed precipitation amount with a peak time occurring two hours earlier. In contrast, both IMERG-E and GSMaP-MVK exhibit notably poorer performance. When it comes to the diurnal cycle of precipitation frequency and intensity, both IMERG products display bad performance in reproducing their diurnal variations. Furthermore, all three satellite products show a wet biases for light precipitation and a dry biases for heavy precipitation, which is consistent with previous evaluation studies. However, a novel phenomenon is noticed that theses biases tend to worsen significantly in the afternoon compared to the morning. Although this study is limited to a case study, the findings can serve as a valuable reference for the NRT applications of IMERG products during the extreme Meiyu seasons and provide insights for the improvement of IMERG algorithm.

Evaluation and Comparison of Reanalysis Data for Runoff Simulation in the Data-Scarce Watersheds of Alpine Regions

Reanalysis datasets provide a reliable reanalysis of climate input data for hydrological models in regions characterized by limited weather station coverage. In this paper, the accuracy of precipitation, the maximum and minimum temperatures of four reanalysis datasets, the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS), time-expanded climate forecast system reanalysis (CFSR+), the European Centre for Medium-Range Weather Forecast Reanalysis (ERA). and the China Meteorological Forcing Dataset (CMFD), were evaluated by using data from 28 ground-based observations (OBs) in the Source of the Yangtze and Yellow Rivers (SYYR) region and were used as input data for the SWAT model for runoff simulation and performance evaluation, respectively. And, finally, the CMADS was optimized using Integrated Calibrated Multi-Satellite Retrievals for Global Precipitation Measurement (AIMERG) data. The results show that CMFD is the most representative reanalysis data for precipitation characteristics in the SYYR region among the four reanalysis datasets evaluated in this paper, followed by ERA5 and CFSR, while CMADS performs satisfactorily for temperature simulations in this region, but underestimates precipitation. And we contend that the accuracy of runoff simulations is notably contingent upon the precision of daily precipitation within the reanalysis dataset. The runoff simulations in this region do not effectively capture the extreme runoff characteristics of the Yellow River and Yangtze River sources. The refinement of CMADS through the integration of AIMERG satellite precipitation data emerges as a potent strategy for enhancing the precision of runoff simulations. This research can provide a reference for selecting meteorological data products and optimization methods for hydrological process simulation in areas with few meteorological stations.

Evaluation of IMERG and GSMaP for Tropical Cyclone Applications

AbstractThis study compares rainfall from NASA Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement (IMERG V06) and JAXA Global Satellite Mapping of Precipitation (GSMaP V4) for tropical cyclone (TC) applications against satellite microwave‐derived Goddard Profiling Algorithm (GPROF V05) precipitation data retrieved from 2000 to 2012. From a global data set of storms, all three products show consistent patterns in 1‐dimensional azimuthal averages and in 2‐dimensional rainfall distributions (where spatial correlation values are near 1.0). However, both IMERG and GSMaP overestimate precipitation amounts against GPROF, and IMERG overestimations are much higher than GSMaP within 125 km of the storm center. Based on this analysis, IMERG and GSMaP rainfall could be used to analyze TC precipitation patterns at high spatiotemporal resolutions. However, caution is required if high accuracy TC precipitation amplitude is required, particularly for IMERG. This study highlights opportunities to improve future versions of IMERG and GSMaP retrieval processing to reduce the discrepancies with GPROF.

Bias Correction of IMERG Data in the Mountainous Areas of Sumatra Based on A Single Gauge Observation

The performance of surface precipitation data from satellite precipitation products (SPPs) in mountainous areas has greater error and bias than in plain areas. In this study, linear scaling (LS), local intensity (LOCI), power transformation (PT), and cumulative distribution function (CDF) methods are used to correct the bias of Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) data in the mountainous region of Sumatra based on long-term and high-resolution optical rain gauge (ORG) observations. The ORG is installed at Equatorial Atmospheric Observatory (EAO) in Kototabang, West Sumatra, Indonesia (100.32 °E, 0.20 °S, 865 m above sea level (ASL) with an observation period from 2002 to 2016. The impact of the bias correction method is tested based on accuracy and capability detection tests. The bias correction method is more effective at the daily resolution than the hourly resolution of the IMERG data in the mountainous region of Sumatra. The LS method exhibited the best improvement in accuracy with reduced root-mean-square error (RMSE) and relative bias (RB), although there was no significant increase in coefficient correlation (CC) values. However, the accuracy improvement was not observed in the bias correction for hourly data. The lack of improvement in the accuracy of the hourly IMERG data is due to the high local variability of rainfall in the mountainous area of Sumatra. The high data variability causes large differences in the mean and variance of the IMERG calibration and evaluation data periods. On the other hand, the LOCI, PT, and CDF methods were successfully improved the rain detection capability of IMERG, as indicated by the better critical succession index (CSI) values compared to the original hourly and daily IMERG data. It increased the CSI value by reducing false alarms for rain with intensity below 2 mm/h. Furthermore, the CDF method can improve the analysis of extreme rainfall in the mountainous region of Sumatra by improving the estimation of the extreme rainfall index. Therefore, these methods can be applied to improve the accuracy and detectability of IMERG data in the mountainous region of Sumatra. However, the scale factor and transfer function constructed in this study need to be further evaluated on other rain gauge observation data in Sumatra’s mountainous region to improve performance. HIGHLIGHTS The LS method shows the best improvement in the accuracy of IMERG data in the mountainous area of Sumatera compared to the LOCI, PT, and CDF methods, as indicated by the largest decrease in RMSE and RB values CSI values prove that LOCI, PT, and CDF methods successfully improve the detection capability of IMERG hourly and daily data in the mountainous region of Sumatra The CDF method shows the best quality in improving extreme rainfall observations in the mountainous region of Sumatra GRAPHICAL ABSTRACT

Research progress on the water vapor channel within the Yarlung Zsangbo Grand Canyon, China

The Second Tibetan Plateau Scientific Expedition and Research Program tasked a research team with the “Investigation of the water vapor channel of the Yarlung Zsangbo Grand Canyon (INVC)” in the southeastern Tibetan Plateau (TP). This paper summarizes the scientific achievements obtained from the data collected by the INVC observation network and highlights the progress in investigating the development of heavy rainfall events associated with water vapor changes. The rain gauge network of the INVC can represent the impacts of the Yarlung Zsangbo Grand Canyon (YGC) topography on precipitation at the hourly scale. The microphysical characteristics of the precipitation in the YGC are different than those in the lowland area. The GPM-IMERG (Integrated Multi-satellitE Retrievals for Global Precipitation Measurement) satellite precipitation data for the YGC region should be calibrated before they are used. The meridional water vapor flux through the YGC is more important than the zonal flux for the precipitation over the southeastern TP. The decreased precipitation around the YGC region is partly due to the decreased meridional water vapor flux passing through the YGC. High-resolution numerical models can benefit precipitation forecasting in this region by using a combination of specific schemes that capture the valley wind and water vapor flux along the valley floor.摘要第二次青藏高原科学考察研究在青藏高原东南部组建了雅鲁藏布大峡谷水汽通道科学考察分队, 本文主要总结了该科考分队近几年开展的观测研究以及利用该分队建立的观测网收集的观测数据所取得的科学成果, 重点介绍了与大峡谷水汽输送相关的强降雨过程的研究进展; 研究主要发现科考分队在大峡谷建立的雨量筒观测网可以代表该地区地形对小时降水量的空间影响; 藏东南降水的微物理特征与低海拔地区有明显差异; GPM卫星降水数据在大峡谷地区存在干偏差的问题, 使用前需进行校准; 穿越大峡谷的经向水汽输送对青藏高原东南部的降水有重要影响, 大峡谷周边区域降水量的减少可能是由于穿越大峡谷经向水汽通量的减少造成; 使用特定云降水方案的高分辨率数值模型可以较好的捕捉大峡谷内的风场和水汽输送时, 该模型能对该地区夜间强降水做出准确预报.

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 present-day extreme precipitation over the United States: an inter-comparison of convection and dynamic permitting configurations of E3SMv1

Accurate simulation of the present-day characteristics of mean and extreme precipitation at regional scales remains a challenge for Earth system models, which is due in part to deficiencies in model physics such as convective parameterization (CP), and coarse resolution. High horizontal resolution (HR, ∼25 km) and multiscale modeling framework (MMF, i.e. replacing conventional CP with embedded km-scale cloud-resolving models) are two promising directions that could help improve the interaction between subgrid-scale physical processes and large-scale climate. Here, we evaluate simulated extreme precipitation over the United States (US) across three configurations (i.e. low-resolution [LR], HR, and MMF) of the Energy Exascale Earth System Model (E3SMv1) and intercompare them against two gridded observation datasets (climate prediction center daily US precipitation and integrated multi-satellite retrievals for global precipitation measurement). We assess the model’s ability to simulate very heavy seasonal precipitation (illustrated by the difference between the 99th and 90th percentile values) as well as the spatial distributions of several extreme precipitation indices defined by the expert team on climate change detection and indices. Our results show that both the dry (i.e. consecutive dry days (CDD)) and wet (i.e. consecutive wet days, maximum 5 day precipitation, and very wet days) extremes evaluated herein show some improvement as well as degradation with MMF and HR relative to LR. These results vary across seasons and US subregions. For instance, only the very heavy precipitation of winter is improved with MMF and HR. Both configurations alleviate the well-known drizzling bias evident in LR across both winter and summer in many parts of the US, largely due to the overall improvement in intensity and frequency of precipitation. Additionally, our results suggest that while E3SMv1-MMF has higher intensity rates when it does rain, it has too many CDD during the summer, contributing to a low mean precipitation bias.

The Precipitation Characteristics of Mesoscale Convective Systems Over Europe

AbstractMesoscale convective systems (MCSs) are common over Europe and can produce severe weather, including extreme precipitation that leads to flash floods. The few studies analyzing the climatological characteristics of MCSs over Europe are either focusing on only few years of data or on limited subareas. Using the recent Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) satellite precipitation climatology, we identify and track MCSs for 16 years over Europe. The tracking algorithm relies on the overlap of precipitation features between consecutive time steps and, unlike previous studies, uses lightning data to distinguish convective from stratiform rain patches, which can reduce potential identification errors. We analyze this new European MCS climatology to characterize MCS precipitation properties and conclude the following results: MCSs overall occur most frequently over the Mediterranean and Atlantic during fall and winter, whereas during summer, they concentrate over the continent. Typically, more than a third of seasonal precipitation can be attributed to MCSs, and their contribution to extreme precipitation is even greater, often exceeding 60%. MCSs over the continent display a clear diurnal cycle peak during the afternoon, and some continental areas also show a second, nocturnal peak. The MCS diurnal cycle for coastal and oceanic regions is more variable. We find that the spatiotemporal distribution of MCS precipitation can be attributed to specific environmental variables, namely (sea) surface temperature, fronts occurrence and convective instability. While inland MCS precipitation is mostly constrained by thermodynamics, for the coastal MCSs the atmospheric dynamics plays an important role as well.

Comparative analysis of gridded rainfall datasets over the Bagmati river basin, India

Abstract In this study, three high-resolution gridded rainfall datasets, viz., Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Modern-Era Retrospective Analysis for Research and Applications 2 (MERRA-2), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) have been collected, analyzed, and compared against the ground-based observed rain gauge datasets of Indian Meteorological Department (IMD) of the Bagmati river basin from 2001 to 2014 in the Bihar State of India. Comparison analyses were performed at daily, monthly, seasonal, and annual time scales. Various statistical parameters, contingency tests, trend analysis, and rainfall anomaly index were used for comparison of datasets. Though MERRA-2 had the highest probability of detection (POD) and lowest false alarm ratio (FAR), analysis showed that IMERG data were closely matching with the observed data, whereas MERRA-2 and PERSIANN underestimated the extreme values. For the monthly scale, again IMERG had the most optimal Coefficient of Determination (R2) and Nash-Sutcliffe Efficiency (NSE) values. IMERG also performed well in detecting rainfall trends and identifying wet and dry years. Overall, IMERG was the most suitable dataset at all time scales for future studies in the basin.

Towards the selection of a longer record of quantitative precipitation forecast dataset from TIGGE ensembles for India during the monsoon season

ABSTRACT A reliable forecast of summer monsoon precipitation is crucial in India. To select a longer record of forecast data from the THORPEX Interactive Grand Global Ensemble (TIGGE) archives, we applied two criteria: the length of the dataset should be at least 10 years of continuous record, and the forecast product should have at least 20 ensemble members for a lead time of minimum 5 days. We evaluate the ensemble quantitative precipitation forecasts (QPFs) obtained from the four agencies throughout the monsoon season (June to September) from 2011 to 2020 in 22 river basins of India in forecasting normal and extreme precipitation events. The Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) is used as observation data. The proficiency of QPFs is evaluated using six criteria from deterministic, dichotomous, and probabilistic error statistics. The error values are classified into three categories – low, moderate, and high. A Forecast Reliability Index is formulated using the given four QPFs, and three categories for each of the six error statistics to answer (a) which QPF shows better performance in which river basin and (b) whether any conclusion can be made on the overall performance of a QPF for all the River Basins of India.