Integrated Multi-satellitE Retrievals For GPM Research Articles

Assessing the influence of El Niño on the California precipitation regime during the satellite precipitation era

AbstractA strong El Niño condition is likely to bring heavy precipitation over California. However, the 2015/2016 El‐Niño generally considered similar and comparable to another extreme event—the 1997/1998 El Niño—failed to bring above‐average precipitation over California. The muted influence of the 2015–2016 El‐Niño on California rainfall has renewed interest in the relationship between El Niño and California precipitation variability. This study assesses seasonal and regional aspects of the precipitation regimes in California for the past two decades using primarily satellite‐based precipitation products. Two satellite‐based precipitation datasets—Integrated Multi‐satellitE Retrievals for GPM (IMERG) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks‐Climate Data Record (PERSIANN‐CDR)—along with the ground‐based measurement—Global Precipitation Climatology Centre (GPCC) dataset as a reference—are analysed to understand the seasonal and regional influence of El‐Niño on precipitation regimes during the 2001–2019 period using Multivariate El Niño Southern Oscillation Index (MEI). The results show that El Niño's impact on precipitation rate varies in different seasons and regions. Specifically, we observe a statistically significant strong negative correlation (−0.72 to −0.66) between MEI and precipitation rate over southern California during the El Niño months. Both satellite precipitation products and ground‐based precipitation measurements indicate a similar El Niño–precipitation relationship over California during the study period. These results contradict the pre 2000 ‘classic’ story that El Niño was positively correlated with higher California precipitation.

Characterisation of the observed diurnal cycle of precipitation over the Maritime Continent

AbstractThis study investigates the temporal and spatial complexities of the mean diurnal cycle (DC) of precipitation over the Maritime Continent during the wet season using the Integrated Multi‐satellite Retrievals for GPM (IMERG) data product and highlights systematic inaccuracies of amplitude and phase representation using the first diurnal harmonic (FDH). The first‐order nature of the DC of precipitation is already well documented, typically featuring heavy precipitation over near‐coastal land areas in the late afternoon and evening followed by maximum precipitation overnight over the surrounding seas, with offshore propagation evident in places. The DC is often described concisely in terms of an amplitude and phase based on the FDH parameters, however the omission of higher‐order components of variability results in the FDH parameters being poor indicators of the magnitude and peak time of diurnal variability in many locations. This study improves the accuracy of the amplitude and phase parameters by characterising the DC using two novel waveforms—a skew‐permitting waveform and a spike‐permitting waveform—which are constructed to characterise single‐peak cycles with rapid transitions more accurately. Key characterisation improvements include correction of a phase lag (averaging approximately 1 h) over near‐coastal land areas and capture of the short‐lasting but extreme peak in precipitation rate over Java which increases the amplitude by the order of 20%. The new skew parameter shows that locations close to coastlines experience rapid intensification and gradual weakening of diurnal precipitation, while there is a tendency toward gradual intensification and rapid weakening far inland and offshore. The new spike parameter shows that near‐coastal land experiences a brief and precisely timed peak in precipitation, whereas diurnal activity over inland locations is longer‐lasting and less precisely timed, and waters surrounding Java experience a precisely timed suppression of precipitation. Other potential applications of the novel waveforms used in this study are discussed.

Global Precipitation Nowcasting of Integrated Multi-satellitE Retrievals for GPM: A U-Net Convolutional LSTM Architecture

Abstract This paper presents a deep supervised learning architecture for 30 min global precipitation nowcasts with a 4-hour lead time. The architecture follows a U-Net structure with convolutional long short-term memory (ConvLSTM) cells empowered by ConvLSTM-based skip connections to reduce information loss due to the pooling operation. The training uses data from the Integrated MultisatellitE Retrievals for GPM (IMERG) and a few key drivers of precipitation from the Global Forecast System (GFS). The impacts of different training loss functions, including the mean-squared error (regression) and the focal-loss (classification), on the quality of precipitation nowcasts are studied. The results indicate that the regression network performs well in capturing light precipitation (<1.6 mm hr−1) while the classification network can outperform the regression counterpart for nowcasting of high-intensity precipitation (>8 mm hr−1), in terms of the critical success index (CSI). It is uncovered that including the forecast variables can improve precipitation nowcasting, especially at longer lead times in both networks. Taking IMERG as a relative reference, a multi-scale analysis, in terms of fractions skill score (FSS), shows that the nowcasting machine remains skillful for precipitation rate above 1 mm hr−1 at the resolution of 10 km compared to 50 km for GFS. For precipitation rates greater than 4 mm hr−1, only the classification network remains FSS-skillful on scales greater than 50 km within a 2-hour lead time.

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.

FY‐4A/AGRI Infrared Brightness Temperature Estimation of Precipitation Based on Multi‐Model Ensemble Learning

AbstractSatellite infrared detectors cannot penetrate clouds, especially precipitating clouds. Improving precipitation estimation accuracy based on infrared brightness temperature has always been important but challenging. In this paper, based on the infrared brightness temperature of the Advanced Geosynchronous Radiation Imager (AGRI) onboard China's Feng‐Yun 4A satellite, we develop and evaluate a new precipitation estimation method. First, using static data, physical characteristics of clouds, cloud image texture features, temporal motion features, and AGRI infrared channel brightness temperature, we construct features for a machine learning model. Then, we develop precipitation estimation methods. Precipitation is estimated in two steps: classification and regression. We employ a random forest classification model to identify whether there is precipitation in a given field of view. If there is precipitation, a multi‐model ensemble regression learning method is used to estimate the areas with this precipitation. The ensemble learning method uses convex optimization to integrate prediction results based on the optimization of hyperparameters of five basic models (i.e., those of random forest, XGBoost, LightGBM, decision tree, and extra tree models). Furthermore, two regression stacking ensemble models—the Least Absolute Shrinkage and Selection Operator (herein referred to as Stacking1‐LASSO) and K‐nearest neighbor (herein referred to as Stacking2‐KNN)—are used to predict the results of the aforementioned basic models. The results of basic models are used as inputs of these two stacking models. Finally, based on the Integrated Multi‐satellitE Retrievals for GPM (IMERG) precipitation product and rain gauge precipitation data, we conduct precipitation estimation experiments and evaluate our methods. The results show that ensemble learning models have greater accuracy in estimating precipitation than the basic models. When using IMERG precipitation as the target precipitation, ensemble learning models can estimate the central area of heavy precipitation during typhoons Ampil and Maria. The ensemble learning estimation effect is better than that of Stacking2‐KNN. Moreover, when rain gauge data is used as the target precipitation, ensemble learning can also estimate the center of heavy precipitation and with good consistency with recorded satellite brightness temperature data.

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.

Rainfall variability in Indonesia new capital associated with the Madden-Julian Oscillation and its contribution to flood events

Accurate and current rainfall analysis is crucial for planning and development in the new capital city of Indonesia (IKN). This study examines the effect of the Madden-Julian Oscillation (MJO) on rainfall variability in IKN using a 20-year dataset of Integrated Multi-Satellite Retrievals for GPM (IMERG) version 6 and automatic weather station (AWS) data. The study analyzes flood events in IKN by examining flood information provided by the National Agency for Disaster Countermeasures (BNPB) from 2008 to 2022 and the contribution of the MJO to these events. The findings indicate that the MJO significantly influences rainfall variability in IKN, with a more pronounced effect during the dry season (JJASO) than in the wet season (NDJFMAM). This is demonstrated by the higher occurrence of wet days and increased daily rainfall intensity, which is linked to extreme rainfall and longer-duration events, mainly between midnight and morning (0400–0600 LST). Notably, although floods are more common during the rainy season, the MJO can intensify flood events in IKN during the dry season. Therefore, when developing an effective flood disaster mitigation system for IKN, it is crucial to consider the amplitude and phase of the MJO.

Gauging the ungauged: estimating rainfall in a West African urbanized river basin using ground-based and spaceborne sensors

ABSTRACT Accurate precipitation observations are crucial for hydrological forecasts, notably over rapidly responding urban areas. This study evaluated the accuracy of three gridded spaceborne rainfall products (Integrated Multi-satellitE Retrievals for GPM (IMERG), Meteosat Second Generation Visible (MSG-VIS), and MSG-Infrared (MSG-IR)) and the non-governmental Trans-African Hydro-Meteorological Observatory (TAHMO) gauges across the Odaw catchment (Accra, Ghana) from January 2020-July 2022. IMERG is hardly able to capture the strong spatial variability of rainfall required for flood forecasting, but agrees in annual sums with TAHMO and MSG-IR. MSG-IR has difficulties during the wet season. MSG-VIS, only available during daylight, shows limited accuracy and gives high estimates while other products do not detect rain. TAHMO gauges effectively record high-intensity events and their strong spatial variability, although some (daily) accumulations are doubtful and data gaps exist due to technical issues. These findings assist hydrological modelers in selecting appropriate datasets at suitable spatiotemporal resolutions for their research.

Sensitivity of summer precipitation simulation to the physical parameterizations in WRF over the Tibetan Plateau: A case study of 2018

The Tibetan Plateau (TP) holds a pivotal position in regional and global climate, whereas the atmospheric characteristics vary greatly in the simulation with different physical parameterization schemes at the gray-zone scale (around 9 km). In this study, 24 sets of experiments were set up with different cumulus parameterization schemes (CPSs), microphysics parameterization schemes (MPSs) and planetary boundary layer parameterization schemes (PBLSs) using the WRF model driven by the ERA5 reanalysis to explore the sensitivity and try to summarize a combination of parameterization schemes suitable for the simulation over TP. Based on comparisons against in-situ observations, most CPS experiments showed large wet bias and overestimated occurrence of precipitation events, and CTL without CPS and CU6 with Tiedtke CPS show better performance at gray-zone scale. The influence of MPS and PBLS is less than CPS on the simulation, and Thompson MPS and UW PBLS showed a better performance. Most of the CPS experiments reach an earlier and more intensive peak compared to the Integrated Multi-satellite Retrievals for GPM (IMERG) version 6 satellite precipitation product, except CTL, CU11, and CU6, which show closer but weaker peaks. CPS experiments simulate more precipitation associated with water vapor transporting into TP and anomalous cyclonic circulation against CTL. The less water vapor transport from the south edge of TP in the CTL against CU14, CU16, and CU2, leads to more local precipitation. Graupel particles and the warm-rain process play a crucial role in the precipitation simulation with different MPSs. Although with less water vapor in the low atmosphere, MP2 simulates more precipitation due to more graupel particles and warm-rain processes against CTL. PBLSs have a substantial impact on latent heat, then influence evaporation and precipitation. The lower PBL height usually leads to more latent heat and more precipitation.

Comparison of GPM IMERG Version 06 Final Run Products and Its Latest Version 07 Precipitation Products across Scales: Similarities, Differences and Improvements

Precipitation is an essential element in earth system research, which greatly benefits from the emergence of Satellite Precipitation Products (SPPs). Therefore, assessment of the accuracy of the SPPs is necessary both scientifically and practically. The Integrated Multi-Satellite Retrievals for GPM (IMERG) is one of the most widely used SPPs in the scientific community. However, there is a lack of comprehensive evaluation for the performance of the newly released IMERG Version 07, which is essential for determining its effectiveness and reliability in precipitation estimation. In this study, we compare the IMERG V07 Final Run (V07_FR) with its predecessor IMERG V06_FR across scales from January 2016 to December 2020 over the globe (cross-compare their similarities and differences) and a focused study on mainland China (validate against 2481 rain gauges). The results show that: (1) Globally, the annual mean precipitation of V07_FR increases 2.2% compared to V06_FR over land but decreases 5.8% over the ocean. The two SPPs further exhibit great differences as indicated by the Critical Success Index (CSI = 0.64) and the Root Mean Squared Difference (RMSD = 3.42 mm/day) as compared to V06_FR to V07_FR. (2) Over mainland China, V06_FR and V07_FR detect comparable precipitation annually. However, the Probability of Detection (POD) improves by 5.0%, and the RMSD decreases by 3.7% when analyzed by grid cells. Further, the POD (+0%~+6.1%) and CSI (+0%~+8.8%) increase and the RMSD (−11.1%~0%) decreases regardless of the sub-regions. (3) Under extreme rainfall rates, V07_FR measures 4.5% lower extreme rainfall rates than V06_FR across mainland China. But V07_FR tends to detect more accurate extreme precipitation at both daily and event scales. These results can be of value for further SPP development, application in climatological and hydrological modeling, and risk analysis.

Spatiotemporal Variability of Rainfall and Surface Salinity in the Eastern Pacific Fresh Pool: A Joint In Situ and Satellite Analysis During the SPURS‐2 Field Campaign

AbstractWe perform a statistical characterization of the 2016–2017 SPURS‐2 field campaign in situ data and coincident satellite data spanning 8°–12°N, 120°–130°W to quantify the spatial and temporal scales of variability of rain and near‐surface salinity in the Eastern Pacific Fresh Pool. Observations of rain rate and near‐surface to surface salinity are obtained from ships, moorings, autonomous platforms, and satellite remote sensing: Integrated Multi‐satellitE Retrievals for GPM (IMERG); and Soil Moisture Active Passive (NASA SMAP L3 V5). The integral length and time scales of rain and near‐surface salinity vary seasonally. In the rainy season (August–October) when the Intertropical Convergence Zone (ITCZ) migrates over the SPURS‐2 study site, the integral time scales of rain were about 30–60 min and those of near‐surface salinity were closer to that of the rain, 1–2 days, indicating forcing by rain. Meanwhile, the zonal integral length scale of in situ near‐surface salinity was twice as large as the meridional scale (50 vs. 20 km), consistent with the ITCZ's zonally‐propagating and ‐organized rain features. The magnitude and seasonal variation of the sea surface salinity integral time scale were not captured by SMAP since the rainy ITCZ‐period scales were smaller than SMAP resolution (70 km, 8‐day running mean). In the dry season (February–May), the in situ rain integral time scale reduced to less than 30 min while that of the near‐surface salinity increased to 1–5 days, the ocean mesoscale. IMERG overestimated the rain integral time scale by a factor of two to ten in both seasons.

Roles of tropical cyclones with varying intensities in the re-distribution of aerosols

The study analyzes three tropical cyclones (TC) with varying intensities, namely BOB02, a deep depression in the Bay of Bengal; Tauktae, an extremely severe cyclone in the Arabian Sea (AS); and Amphan, a supercyclone in the Bay of Bengal, which developed on October 11, 2020, May 13, 2021, and May 16, 2020, respectively. The analysis focuses on studying the re-distribution of the aerosols over the Indian region using these distinct datasets: Modern-Era Retrospective Analysis for Research and Application, version 2 (MERRA2), Integrated Multi-Satellite Retrievals for GPM (IMERG), and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). Throughout the cyclonic period, which includes the period from the inception of the low-pressure area to its dissipation as well as the pre-cyclonic and post-cyclonic periods (one week prior to and after the cyclonic period, respectively), various parameters related to aerosols and background meteorological conditions were collected. The primary parameter of interest was the aerosol optical depth (AOD). The analysis revealed significant variations in meteorological parameters such as wind speed, temperature, relative humidity, and rainfall along the cyclone track during both the cyclonic and post-cyclonic conditions compared to the pre-cyclonic conditions of these TCs. The AOD surrounding the cyclone track decreases during cyclonic and post-cyclonic conditions compared to pre-cyclonic conditions. This is due to the scavenging of both anthropogenic and natural (dust) aerosols. Conversely, AOD increases along the cyclone track, mainly due to an increase in natural (sea salt) aerosols. During pre-cyclonic conditions, both natural (dust) and anthropogenic (black carbon, organic carbon, and sulfate) aerosols contribute significantly to the total AOD, whereas during pre-cyclonic conditions, the main contributors are natural (sea-salt) aerosols. The coarse-mode particles are more dominant during cyclonic and post-cyclonic conditions than pre-cyclonic ones. Furthermore, our observations indicate that the atmospheric boundary layer (ABL) height increases by ∼0.1–0.6 km along the cyclone track during cyclonic conditions compared to pre-cyclonic conditions. While AOD from the free troposphere (FT) contributes more to the total AOD, contributions from ABL increase, while those from the FT decrease during cyclonic and post-cyclonic conditions.

The impact of earth fissures on flood hazard extent for the Northern part of the Qaa’Jahran Basin, Dhamar, Yemen

This study addresses a crucial gap in our understanding of flooding in the predominantly agricultural Qaa’Jahran watershed in Yemen, where urbanization, though currently limited in extent, is rapidly expanding. We aim to discern the effects of earth fissures on flood dynamics, particularly focusing on their present significance. It is noteworthy that, in the long run, these fissures are anticipated to accumulate soil and sediment, potentially heightening the risk of future flooding. To achieve this, we developed a comprehensive floodplain delineation model, incorporating field observations and rainfall data from the Integrated Multi-satellitE Retrievals for GPM (IMERG) algorithm. Additionally, advanced hydrologic and hydraulic models were integrated to accurately simulate runoff hydrographs and create flood inundation maps. Our findings underscore the considerable influence of earth fissures, effectively mitigating runoff volume, peak discharge, flood depths and inundated areas during storm events. It is imperative to emphasize that specific areas remain persistently vulnerable, with water levels surpassing one meter during historical floods. This reinforces the ongoing risk of flooding in targeted zones and emphasizes the necessity of implementing tailored flood management strategies. In conclusion, this study makes a significant contribution to the field, providing a comprehensive analysis of the intricate factors influencing flooding in the Qaa’Jahran watershed. The integration of flood inundation mapping, advanced hydrologic modelling and meticulous consideration of earth fissures offers a distinctive perspective on flood dynamics in a rapidly evolving and urbanizing environment. Furthermore, it highlights the critical implications for effective flood risk management and urban planning, not only in the Qaa’Jahran watershed but also in similar urbanizing regions worldwide.

An Evaluation of IMERG and ERA5 Quantitative Precipitation Estimates over the Southern Ocean Using Shipborne Observations

Abstract Recent voyages of the Australian R/V Investigator across the remote Southern Ocean have provided unprecedented observations of precipitation made with both an Ocean Rainfall and Ice-Phase Precipitation Measurement Network (OceanRAIN) maritime disdrometer and a dual-polarization C-band weather radar (OceanPOL). This present study employs these observations to evaluate the Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) and the fifth major global reanalysis produced by ECMWF (ERA5) precipitation products. Working at a resolution of 60 min and 0.25° (∼25 km), light rain and drizzle are most frequently observed across the region. The IMERG product overestimated precipitation intensity when evaluated against the OceanRAIN but captured the frequency of occurrence well. Looking at the synoptic/process scale, IMERG was found to be the least accurate (overestimated intensity) under warm-frontal and high-latitude cyclone conditions, where multilayer clouds were commonly present. Under postfrontal conditions, IMERG underestimated the precipitation frequency. In comparison, ERA5’s skill was more consistent across various synoptic conditions, except for high pressure conditions where the precipitation frequency (intensity) was highly overestimated (underestimated). Using the OceanPOL radar, an area-to-area analysis (fractional skill score) finds that ERA5 has greater skill than IMERG. There is little agreement in the phase classification between the OceanRAIN disdrometer, IMERG, and ERA5. The comparisons are complicated by the various assumptions for phase classification in the different datasets. Significance Statement Our best quantitative estimates of precipitation over the remote, pristine Southern Ocean (SO) continue to suffer from a high degree of uncertainty, with large differences present among satellite-based and reanalysis products. New instrumentation on the R/V Investigator, specifically a dual-polarization C-band weather radar (OceanPOL) and a maritime disdrometer (OceanRAIN), provide unprecedented high-quality observations of precipitation across the SO that will aid in improving precipitation estimates in this region. We use these observations to evaluate the IMERG and ERA5 precipitation products. We find that, in general, IMERG overestimated precipitation intensity, but captured the frequency of occurrence well. In comparison, ERA5 was found to overestimate the frequency of precipitation. Using the OceanPOL radar, an area-to-area analysis finds that ERA5 has greater skill than IMERG.

Spatiotemporal scales of precipitation in the Central Tibetan Plateau identified by in-situ soil moisture observations

The spatiotemporal scale of precipitation is crucial for understanding the local hydrometeorological processes. It also plays a significant role in determining the layout of rain gauges. Convectional rainfalls are typical in the Tibetan Plateau (TP), but the spatiotemporal scales of precipitation events in the TP, particularly at scales less than 10 km, remain unclear, which is mainly due to limited in-situ precipitation observations and the relative coarse spatial resolution of satellite precipitation products. Building upon the strong response of soil moisture changes to precipitation, this study attempts to reveal the spatiotemporal scales of precipitation in the central TP during summertime (June–September) by investigating the inter-station correlation of accumulated positive soil moisture increments obtained from in-situ soil moisture measurements in a mesoscale region of the central TP. The soil moisture-implied spatiotemporal scales are further verified against precipitation from the Integrated Multi-satellitE Retrievals for GPM (IMERG) at spatial scales larger than 10 km. Results show that: 1) the variations of surface soil moisture increase and precipitation are fairly consistent, making positive increments in surface soil moisture an effective surrogate for reflecting the spatiotemporal scales of precipitation in central TP; 2) the spatial extents of precipitation as indicated by the precipitation-induced positive soil moisture increments are positively correlated with the temporal scales; 3) precipitation events with relatively short durations (0–6 h) and spatial extents of less than 16 km (and in particular, 0–3 h for scales less than 10 km) dominate the central TP. This study provides a novel approach for characterizing precipitation scales in regions with scarce in-situ precipitation observations, and is expected to inspire new way of assessing large scale soil moisture and precipitation products.

Ensemble Learning for Blending Gridded Satellite and Gauge-Measured Precipitation Data

Regression algorithms are regularly used for improving the accuracy of satellite precipitation products. In this context, satellite precipitation and topography data are the predictor variables, and gauged-measured precipitation data are the dependent variables. Alongside this, it is increasingly recognised in many fields that combinations of algorithms through ensemble learning can lead to substantial predictive performance improvements. Still, a sufficient number of ensemble learners for improving the accuracy of satellite precipitation products and their large-scale comparison are currently missing from the literature. In this study, we work towards filling in this specific gap by proposing 11 new ensemble learners in the field and by extensively comparing them. We apply the ensemble learners to monthly data from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) and IMERG (Integrated Multi-satellitE Retrievals for GPM) gridded datasets that span over a 15-year period and over the entire contiguous United States (CONUS). We also use gauge-measured precipitation data from the Global Historical Climatology Network monthly database, version 2 (GHCNm). The ensemble learners combine the predictions of six machine learning regression algorithms (base learners), namely the multivariate adaptive regression splines (MARS), multivariate adaptive polynomial splines (poly-MARS), random forests (RF), gradient boosting machines (GBM), extreme gradient boosting (XGBoost) and Bayesian regularized neural networks (BRNN), and each of them is based on a different combiner. The combiners include the equal-weight combiner, the median combiner, two best learners and seven variants of a sophisticated stacking method. The latter stacks a regression algorithm on top of the base learners to combine their independent predictions. Its seven variants are defined by seven different regression algorithms, specifically the linear regression (LR) algorithm and the six algorithms also used as base learners. The results suggest that sophisticated stacking performs significantly better than the base learners, especially when applied using the LR algorithm. It also beats the simpler combination methods.

Dynamic rainfall erosivity estimates derived from IMERG data

Abstract. Soil degradation is a critical threat to agriculture and food security around the world. Understanding the processes that drive soil erosion is necessary to support sustainable management practices and to reduce eutrophication of water systems from fertilizer runoff. The erosivity of precipitation is a primary control on the rate of soil erosion, but to calculate erosivity high-frequency precipitation data are required. Prior global-scale analysis has almost exclusively used ground-based rainfall gauges to calculate erosivity, but the advent of high-frequency satellite rainfall data provides an opportunity to estimate erosivity using globally consistent gridded satellite rainfall. In this study, I have tested the use of IMERG (Integrated Multi-satellitE Retrievals for GPM, Global Precipitation Mission) rainfall data to calculate global rainfall erosivity. I have tested three different approaches to assess whether simplification of IMERG data allows for robust calculation of erosivity, finding that the highest-frequency 30 min data are needed to best replicate gauge-based estimates. I also find that in areas where ground-based gauges are sparse, there is more disparity between the IMERG-derived estimates and the ground-based results, suggesting that IMERG may allow for improved erosivity estimates in data-poor areas. The global extent and accessibility of IMERG data allow for regular calculation of erosivity in a month-to-month time frame, permitting improved dynamic characterization of rainfall erosivity across the world in near-real time. These results demonstrate the value of satellite data to assess the impact of rainfall on soil erosion and may benefit practitioners of sustainable land management planning.

Comprehensive analysis of droughts over the Middle East using IMERG data over the past two decades (2001–2020)

The Middle East is home to approximately 475 million people, most of whom face food insecurity due to severe and extreme droughts that have affected most regions over the past two decades. In this study, we analyzed the spatio-temporal characteristics of different drought intensities during the period 2001–2020 using the Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation and ERA5 temperature datasets. As a first step, we compared the consistency of the IMERG-based 1-month Standardized Precipitation Evapotranspiration Index (SPEI1) with the station-based SPEI1. The results showed good agreement between them with R2 ranging from 64% to 84%. The principal component analysis method is used to show the different modes of the monthly 3-month SPEI (SPEI3) and the associated circulation types in this region. Spatially, SPEI3 shows a homogeneous region in the first five leading modes and for SPEI3 below −0.84 there was high spatial variability and inhomogeneity within and between countries. The most significant SPEI3 trend is observed in June–August with up to −0.1 SPEI3/year in some countries. Nine circulation types (CTs) were identified for the eight most severe and extreme drought episodes. The first circulation type in the positive and negative phases could explain the drought variability up to 30.1% and are closely associated with the Arabian thermal low pressure and the Siberian high pressure systems, respectively. In a final step, the moisture transport in each episode is analyzed using the vertical integral moisture flux (VIMF) and its divergence.

Effect of Land Cover Change and Deforestation Around Maninjau Lake on Rainfall and Surface Temperature

Fish deaths in Maninjau Lake have been frequently reported. Apart from fishery activities, this can also be caused by changes in atmospheric conditions around Lake Maninjau due to changes in land cover around Lake Maninjau. Therefore, this study analyzes land cover changes and deforestation in 2001 and 2020 and their relationships with surface temperature and rainfall. Rainfall data and extreme temperatures were obtained from the Integrated Multi-satellite Retrievals for GPM (IMERG) and ECMWF v5 (ERA5), respectively. Land cover data based on the Landsat satellite is used to see land changes. The results showed a decrease in the amount of vegetated land every year and an increase in residential and building land and agricultural land, which correlates with an increase in the surface temperature in the Maninjau area. Furthermore, the results also show a coherent relationship between rainfall trends and areas of deforestation.