Global Precipitation Research Articles

Increasing global precipitation whiplash due to anthropogenic greenhouse gas emissions

Precipitation whiplash, including abrupt shifts between wet and dry extremes, can cause large adverse impacts on human and natural systems. Here we quantify observed and projected changes in characteristics of sub-seasonal precipitation whiplash and investigate the role of individual anthropogenic influences on these changes. Results show that the occurrence frequency of global precipitation whiplash is projected to be 2.56 ± 0.16 times higher than in 1979–2019 by the end of the 21st Century, with increasingly rapid and intense transitions between two extremes. The most dramatic increases of whiplash show in the polar and monsoon regions. Changes in precipitation whiplash show a much higher percentage change than precipitation totals. In historical simulations, anthropogenic greenhouse gas (GHG) and aerosol emissions have increased and decreased precipitation whiplash occurrences, respectively. By 2079, anthropogenic GHGs are projected to increase 55 ± 4% of the occurrences risk of precipitation whiplash, which is driven by shifts in circulation patterns conducive to precipitation extremes.

Performance Evaluation of Multi-Typed Precipitation Products for Agricultural Research in the Amur River Basin over the Sino–Russian Border Region

Precipitation data are crucial for research on agricultural production, vegetation growth, and other topics related to environmental resources and ecology. With an increasing number of multi-typed gridded precipitation products (PPs), it is important to validate the applicability of PPs and improve their subsequent monitoring capabilities to ensure accurate precipitation-based research. This study evaluates the performance of four mainstream PPs—European Centre for Medium-Range Weather Forecasts Reanalysis V5 (ERA5), ERA5-Land, Multi-Source Weighted-Ensemble Precipitation (MSWEP), and integrated multi-satellite retrievals for the Global Precipitation Mission (GPM)—in capturing the characteristics of precipitation intensity and derived agricultural drought in the crop-enrichment area over the Sino–Russian border region. The results show that, overall, GPM has the most balanced capability among the different experimental scenarios, with well-identified seasonal precipitation intensities. ERA5-Land had strong abilities in depicting annual distribution from spatial/stationary outcomes and obtained advantages in daily multi-parameter consistency verification. When evaluating monthly data in different agroclimatic areas, MSWEP and GPM had outstanding performances in the regions of Russia and China, respectively. For evaluating precipitation intensities and agricultural drought based on daily and monthly precipitation, MSWEP and GPM demonstrated finer performances based on combined agricultural thematic areas (ATAs). However, seasonal effects and affiliated material features were found to be the main factors in exhibiting identification capabilities under different scenarios. Despite good handling of intensity recognition in the eastern Chinese area, ERA5′s capabilities need to be improved by extending sources for calibrating gauged data and information on dry–wet conditions. Overall, this study provides insight into the characterization of PP performances and supports optimal product selection for different applications.

Dominant modes of precipitation over Africa, and their associated atmospheric circulations from observations

AbstractThis study uses the empirical orthogonal function (EOF) analysis to identify and describe continental‐scale seasonal (MAM, JJA, SON and DJF) precipitation modes over Africa based on observation (Global Precipitation Climatology Centre; GPCC) and reanalysis (ERA5) data from 1982 to 2014. Using composite analysis, we attempt to identify the atmospheric circulations (wind and relative humidity) associated with each precipitation mode. Precipitation from ERA5 has a good agreement with that of GPCC, as observed in the good spatial congruence in the EOF analysis and precipitation composites. The EOF results for each season show that the loading patterns of Mode 1 (variance >20% for all seasons) match the long‐term mean precipitation distribution. Atmospheric conditions across the continent are primarily driven by the four main high‐pressure systems (Azores, St. Helena, Arabian and Mascarene High) that influence moisture distribution and subsequently modulate the seasonal rain belt distribution. Modes 2 (variance >11% for all seasons) and 3 (variance >10% for all seasons) are deviations of the leading mode associated with smaller‐scale atmospheric systems. However, large‐scale factors still dominate the overall precipitation pattern by modulating the seasonal energy transfer between the hemispheres. The results provide a comprehensive understanding of the continental‐wide African seasonal precipitation modes and the associated atmospheric circulation from observations. The findings can be used as a reference for future work using model data for historical and projection studies.

Comparison of gridded precipitation estimates for regional hydrological modeling in West and Central Africa

Study regionData-scarce basins located in West Africa and northern Central Africa. Study focusMultiple studies have shown that global gridded precipitation datasets could provide an alternative to the lack of observed data in Sub-Saharan Africa. This work evaluated 15 precipitation datasets based on satellite rainfall (ARC v.2, CHIRP v.2, CHIRPS v.2, PERSIANN-CDR, MSWEP v2.2 and TAMSAT v3), reanalysis (ERA5, JRA-55 Adj, MERRA-2 PRECTOT, MERRA-2 PRECTOTCORR, WFDEI-CRU and WFDEI-GPCC) and ground measurements (CPC v.1, CRU TS v.4.00 and GPCC v.7), as well as a regional estimation method, based on spatial proximity, for the parameters of a simple monthly water balance model, GR2M. The regional simulations of the GR2M model were evaluated based on a Kling-Gupta Efficiency score in a split-sample spatiotemporal validation scheme. New hydrological insights for the regionThe results show that among all the precipitation products, CHIRPS is the most effective for hydrological modeling in West and Central Africa at a monthly timestep. Also, among the top five products are WFDEI-CRU, CRU, WFDEI-GPCC and GPCC. Overall, regional hydrological modeling is more effective for basins smaller than 80,000 km2. The method of regionalization by spatial proximity causes an overall drop in the ability of the various precipitation products to reproduce discharge, most notably with WFDEI-GPCC and GPCC. CHIRPS remains the best product in terms of KGE2 values in regionalization.

Atmospheric patterns and winter precipitation over north‐western South Asia

AbstractMid‐latitude storms act as crucial atmospheric bridges between global teleconnection patterns and regional precipitation distribution during boreal winter over South Asia. In this work, we study the spatio‐temporal variability of seasonal precipitation over South Asia over the last four decades, which reveals distinct regional precipitation patterns across seasons. For the winter region of South Asia (WRSA), the second Empirical orthogonal function (EOF2) of 500 hPa geopotential heights shows a significant positive correlation with precipitation. Furthermore, using regression analysis, we estimate that the first six EOFs explain most of the interannual variability in winter precipitation during the positive phase of AMO. The results show that six standard climate indices influence winter precipitation over WRSA. We observed a decadal variability in the relationship between ENSO and winter precipitation over WRSA that is modulated by Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO). The positive phase of AMO strengthens the positive relationship between ENSO and winter precipitation over WRSA. The model results also show that two other oscialltions, DMI and AAO, significantly explain the variability of winter precipitation over WRSA. The atmospheric patterns extracted in this study significantly improve winter precipitation forecasting skill of linear regression models compared to those based on standard climate indices, especially during positive AMO winters. The results from this study can be used to improve seasonal predictability of winter precipitation over WRSA.

Accuracy of satellite precipitation products in data-scarce Inner Tibetan Plateau comprehensively evaluated using a novel ground observation network

Study regionInner Tibetan Plateau (ITP) is the largest plateau in the world, with only four national basic stations covering an area of 5.76 × 105 km2. Study focusSatellite precipitation products (SPPs) have widely been evaluated on a regional scale. However, a comprehensive quantitative evaluation of SPPs using multiple indices has not been reported. Here, 16 SPPs were quantitatively and comprehensively evaluated for precipitation occurrence and time series using an improved rank score (RS) method. The new observation network, which had more than the number of national stations, was applied for evaluating SPPs to increase the reliability of results. New hydrological insights for the regionIntegrated Multi-satellite Retrievals for the Global Precipitation Measurement (IMERG)-Early and IMERG-Late runs showed the best overall performance, with an RS value of 0.91. The optimal SPPs varied according to the evaluation indices but mostly belonged to IMERG-Early, IMERG-Late, Multi-Source Weighted-Ensemble Precipitation (MSWEP), Global Satellite Mapping of Precipitation near-real-time (GSMaP-NRT), and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) Dynamic Infrared Rain Rate near-real-time (PDIR-Now). Overall, most SPPs detected precipitation; however, the SPPs poorly measured the precipitation time series but adequately detected precipitation in the range of 10–25 mm in terms of the three indices of precipitation occurrence. The proposed improved RS method can be used to comprehensively evaluate other products and models.

“A comprehensive assessment of suitability of Global Precipitation Products for hydro-meteorological applications in a data-sparse Himalayan region”

Observation rainfall networks in developing countries such as Nepal face many challenges (like availability and quality). Global Precipitation Products (GPPs) could be an alternative to gauge-based observed rainfall (GOR) in hydro-meteorological studies. However, GPPs performance across the Himalayan regions is still unknown and is influenced by several factors such as spatial and temporal resolutions, primary data sources, etc. We have comprehensively assessed the suitability of the latest GPPs using categorical and continuous variable performance metrics for the Gandak River Basin in the Nepalese Himalayas. We then ranked GPPs for the first time using the Multicriteria Decision-Making technique. 11 out of 12 GPPs considered underestimated the annual rainfall in the basin. Performance of GPPs was also inconsistent for monthly/annual and daily timescales. At longer timescales, CHIRPS and IMERG_Final are better at representing the spatial and temporal pattern of the rainfall (spatial correlation of 0.75) and least percentage bias (PBIAS < 12%). At a daily timescale, IMERG_Final, ERA5, and PERSIANN_CDR stand out for probability of detection (POD) of rainfall, while all GPPs perform poorly in false alarm ratio (FAR). Although all GPPs have relatively high RMSE (6-14 mm/day), correlation (CC) with observed rainfall was high for IMERG_Final, ERA5, and MERRA_2 in most of the sub-basins. With elevation, the performance of all GPPs is reduced, as evidenced by higher PBIAS, and lower CC. Although there is plenty of room for improvements in rainfall estimation by GPPs, among the existing dataset, IMERG_Final scored best in the majority of the performance indicators and ranked first in five out of six sub-basins. It would be relatively the better choice in the data sparse Himalayan region when daily rainfall data is required. For applications that require monthly/annual rainfall, both CHIRPS and IMERG_Final are equally suitable. The method proposed in the study for assessing GPPs can be readily applied in other river basins and at sub-daily timescales.

Percentile-Based Relationship between Daily Precipitation and Surface Air Temperature over the Qinghai–Tibet Plateau

Abstract Climate changes significantly impact the hydrological cycle. Precipitation is one of the most important atmospheric inputs to the terrestrial hydrologic system, and its variability considerably influences environmental and socioeconomic development. Atmospheric warming intensifies the hydrological cycle, increasing both atmospheric water vapor concentration and global precipitation. The relationship between heavy precipitation and temperature has been extensively investigated in literature. However, the relationship in different percentile ranges has not been thoroughly analyzed. Moreover, a percentile-based regression provides a simple but effective framework for investigation into other factors (precipitation type) affecting this relationship. Herein, a comprehensive investigation is presented on the temperature dependence of daily precipitation in various percentile ranges over the Qinghai–Tibet Plateau. The results show that 1) most stations exhibit a peaklike scaling structure, while the northeast part and south margin of the plateau exhibit monotonic positive and negative scaling structures, respectively. The scaling structure is associated with the precipitation type, and 2) the positive and negative scaling rates exhibit similar spatial patterns, with stronger (weaker) sensitivity in the south (north) part of the plateau. The overall increase rate of daily precipitation with temperature is scaled by Clausius–Clapeyron relationship. 3) The higher percentile of daily precipitation shows a larger positive scaling rate than the lower percentile. 4) The peak-point temperature is closely related to the local temperature, and the regional peak-point temperature is roughly around 10°C. Significance Statement This study aims to better understand the relationship between precipitation and surface air temperature in various percentile ranges over the Qinghai–Tibet Plateau. This is important because percentile-based regression not only accurately describes the response of precipitation to warming temperature but also provides a simple but effective framework for investigating other factors (precipitation type) that may be affecting this relationship. Furthermore, the sensitivity and peak-point temperature are evaluated and compared among different regions and percentile ranges; this study also attempts to outline their influencing factors. To our knowledge, this study is the first integration of percentile-based analysis of the dependence of daily precipitation on surface air temperature.

What Does Global Land Climate Look Like at 2°C Warming?

AbstractConstraining an increase in global mean temperature below 2°C compared to pre‐industrial levels is critical to limiting dangerous and cascading impacts of anthropogenic climate change. Understanding future climatic changes and their spatial heterogeneity at 2°C warming is thus important for policy makers to prepare actionable adaptation and mitigation plans by identifying where and to what extent lives and livelihoods will be impacted. This study uses the recently released NASA Earth eXchange Global Daily Downscaled Projections (NEX‐GDDP) CMIP6 data to provide a broad overview of projected changes in six key climate variables and two climate impact indicators at a time when warming exceeds 2°C. Analysis of global mean temperature changes indicates the 2040s as the decade when most CMIP6 models reach 2°C warming with respect to a pre‐industrial period (1850–1900). During the 2040s, we find that global mean temperature, precipitation, relative humidity, downwelling shortwave and longwave radiation, and wind speed over land under the high emission scenario are projected to change by +2.8°C, +22.4 mm/year, −0.73%, −2.23 , +15.9 W/m2, and −0.04 m/s, respectively. Many of the future changes are expected to exacerbate climate impacts including heat stress and fire danger. Our analysis shows geographic patterns of policy‐relevant climatic changes, as parts of the globe will experience significant climate impacts even if the goal to keep warming below 2°C goal is achieved. Our results highlight the urgent need for further studies focused on identifying key hotspots and advancing region‐specific actionable adaptation and mitigation plans.

Grid Satellite Rainfall Products Potential Application for Developing I-D and E-D Thresholds for Landslide Early Alert System over Bali Island

Bali has been one of the most popular tourist destinations in Indonesia. However, on the other hand, Bali has a high risk of natural disaster vulnerability. The number of landslides in Bali took the first position compared to other natural disasters. Currently, remote sensing platforms can present Grid Satellite Rainfall Products (GSRPs), which provide rainfall information that can identify rainfall conditions for landslide events. This study aims to analyze the potential GSRPs application of Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN), the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG), and Global Satellite Mapping of Precipitation (GSMaP) in determining the mean rainfall intensities and duration (I-D); accumulated rainfall and duration (E-D) thresholds for landslide occurrences over Bali Island. The method used to develop I-D and E-D thresholds is the power-law equation and frequentist sampling method in various probability levels (5%, 10%, 20%, 30%, 40%, and 50%). The result shows that I-D and E-D thresholds established by GSRPs are generally lower than the threshold defined by rain gauge observations. Among the three GSRPs, IMERG is performing the best in establishing the I-D and E-D thresholds for landslide phenomena. The level of potential that IMERG can use in developing the I-D and E-D thresholds is 59.16% and 84.06%, respectively. The E-D threshold derived from the IMERG product can be used to establish the landslide early alert system over Bali Island because it has a high spatial-temporal resolution, word-wide coverage, and near-real-time observation.

Retrieval of Normalized Gamma Size Distribution Parameters Using Precipitation Imaging Package (PIP) Snowfall Observations during ICE-POP 2018

Abstract Parameters of the normalized gamma particle size distribution (PSD) have been retrieved from the Precipitation Image Package (PIP) snowfall observations collected during the International Collaborative Experiment–PyeongChang Olympic and Paralympic winter games (ICE-POP 2018). Two of the gamma PSD parameters, the mass-weighted particle diameter Dmass and the normalized intercept parameter NW, have median values of 1.15–1.31 mm and 2.84–3.04 log(mm−1 m−3), respectively. This range arises from the choice of the relationship between the maximum versus equivalent diameter, Dmx–Deq, and the relationship between the Reynolds and Best numbers, Re–X. Normalization of snow water equivalent rate (SWER) and ice water content W by NW reduces the range in NW, resulting in well-fitted power-law relationships between SWER/NW and Dmass and between W/NW and Dmass. The bulk descriptors of snowfall are calculated from PIP observations and from the gamma PSD with values of the shape parameter μ ranging from −2 to 10. NASA’s Global Precipitation Measurement (GPM) mission, which adopted the normalized gamma PSD, assumes μ = 2 and 3 in its two separate algorithms. The mean fractional bias (MFB) of the snowfall parameters changes with μ, where the functional dependence on μ depends on the specific snowfall parameter of interest. The MFB of the total concentration was underestimated by 0.23–0.34 when μ = 2 and by 0.29–0.40 when μ = 3, whereas the MFB of SWER had a much narrower range (from −0.03 to 0.04) for the same μ values.

Annual flow trend analyses in the Tuntang watershed caused by Kedungsepur national strategic area development

Tuntang watershed is one of the national priority watersheds in the Jratunseluna river basin that has a big potential for water availability. The development of a national strategic area in the Tuntang watershed causes changes in land cover in the Tuntang watershed which can result in reduced water catchment areas. The purpose of this study was to simulate the effect of land change on annual discharge trends in the Tuntang watershed and analyze the water balance projection based on land change. This study uses land cover data for the Tuntang watershed in 2019 and land cover for the Kedungsepur National Strategic Area (KSN) development plan. Daily rainfall data for 11 years from 2010 to 2020 was obtained through the Global Precipitation Measurement (GPM) satellite and validated using data from 7 rain stations located in the Tuntang watershed. Meanwhile, evapotranspiration data was obtained through the Global Land Data Assimilation System. In addition, Digital Elevation Model (DEM), flow accumulation, and direction flow data obtained through HydroSHEDS are used to provide information on the surface shape of the Tuntang watershed. Then the data that has been obtained is used as input for the simulation of the spatial hydrological model to determine the annual discharge in the Tuntang watershed using the Rainfall-runoff-inundation (RRI) model. The result of this simulation is an annual discharge for 11 years. Furthermore, a comparison is made between the flow rate of land cover map input in 2019 with land cover input for Kedungsepur KSN development. Based on the result, it is known that there has been a change in the area of the water absorption region in the development of the Kedungsepur KSN, this change has resulted in an increased extreme discharge trend and reduced annual water flow in the Tuntang watershed region.

Positive correlation between wet-day frequency and intensity linked to universal precipitation drivers

Understanding precipitation is essential for quantifying weather and climate-related risks. Changes in precipitation climatology are typically based on independent analysis of precipitation frequency and intensity. Here we show that where it rains more often, it also rains harder. When grouping global precipitation reanalysis data and observations from the past 40 years into regions of similar wet-day frequency, regardless of geographical separation, there is a strong correlation with wet-day intensity distributions. These wet-day-frequency regions are also more physically coherent than regions based on geographical location. We find the coherent relationship between wet-day frequency and intensity distributions is partially explained by wet-day-frequency regions having similar vertical velocity and convective available potential energy distributions, once polar regions are excluded. These represent dynamic and thermodynamic processes that indicate how conducive wet-day-frequency regions are to large-scale and convective precipitation. This suggests that the main drivers of precipitation are universal. We also show that extreme-precipitation metrics are dependent on wet-day frequency within our framework. Our results imply that wet-day frequency could be used to derive estimates of extreme-precipitation climate indices and corresponding uncertainties, these uncertainties being related to local processes.

Evaluation of CMIP6 Models and Multi-Model Ensemble for Extreme Precipitation over Arid Central Asia

Simulated historical extreme precipitation is evaluated for Coupled Model Intercomparison Project Phase 6 (CMIP6) models using precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The indices of 33 Global Circulation Models (GCMs) are evaluated against corresponding indices with observations from the Global Climate Center Precipitation Dataset (GPCC V2020) over five sub-regions across Arid Central Asia (ACA), using the Taylor diagram, interannual variability skill score (IVS) and comprehensive rating index (MR). Moreover, we compare four multi-model ensemble approaches: arithmetic average multi-model ensemble (AMME), median multi-model ensemble (MME), pattern performance-based multi-model ensemble (MM-PERF) and independence weighted mean (IWM). The results show that CMIP6 models have a certain ability to simulate the spatial distribution of extreme precipitation in ACA and the best ability to simulate simple daily intensity (SDII), but it is difficult to capture the spatial bias of consecutive wet days (CWD). Almost all models represent different degrees of wet bias in the southern Xinjiang (SX). Most GCMs are generally able to capture extreme precipitation trends, but to reproduce the performance of interannual variability for heavy precipitation days (R10mm), SDII and CWD need to be improved. The four multi-model ensemble methods can reduce the internal system bias and variability within individual models and outperform individual models in capturing the spatial and temporal variability of extreme precipitation. However, significant uncertainties remain in the simulation of extreme precipitation indices in SX and Tianshan Mountain (TM). Comparatively, IWM simulations of extreme precipitation in the ACA and its sub-regions are more reliable. The results of this study can provide a reference for the application of GCMs in ACA and sub-regions and can also reduce the uncertainty and increase the reliability of future climate change projections through the optimal multi-model ensemble method.

The Evaluation of GPM IMERG v.06 Rainfall Product over the Lau Simeme Watershed in Indonesia

In Indonesia, rainfall is still significant spatially and temporally. In order to gain optimal results from utilising water resources, we have to ensure that the precipitation data is provided in good quality and quantity. Several spatial rainfall measurement sources have become available in recent years, such as GPM data (Global Precipitation Measure). In this study, the GPM IMERG V.06 product was evaluated using rain gauge measurements in the Lau Simeme watershed in North Sumatra Province, Indonesia. The relevance of the GPM IMERG was tested by direct comparison with observations at different time scales (daily, monthly, annual and seasonal) between 2005 and 2019. Results show that the satellite product provides poor rainfall estimations at the daily and annual time scales. However, the accuracy of GPM IMERG Final datasets is improved when temporally average to monthly timescale (R2 of 0.728, RMSE of 68.318 mm and NSE of 0.725), wet seasonal time scale (R2 of 0.673, RMSE of 79.287 mm and NSE of 0.658) and dry seasonal time scale (R2 of 0.947, RMSE of 20.356 mm and NSE of 0.924).

Assessment of GPM Satellite Precipitation Performance after Bias Correction, for Hydrological Modeling in a Semi-Arid Watershed (High Atlas Mountain, Morocco)

Due to its important spatiotemporal variability, accurate rainfall monitoring is one of the most difficult issues in semi-arid mountainous environments. Moreover, due to the inconsistent distribution of gauge measurement, the availability of precipitation data is not always secured and totally reliable at the instantaneous time step. As a result, earth observation of precipitation estimations could be an alternative for overcoming this restriction. The current study presents a framework for either the hydro-statistical evaluation and bias correction of the Global Precipitation Measurement (GPM) Integrated Multi-SatellitE Retrievals version 06 Early (IMERG-E), Late (IMERG-L), and Final (IMERG-F) products. On a sub-daily duration, from the Taferiat rain gauge-based station, which was used as a benchmark from 1 September 2014 to 31 August 2018. Statistical analysis was performed to examine each precipitation product’s performance. The results showed that all Post_Real_Time and Real_Time IMERG had a high level of awareness accuracy. The IMERG-L results were statistically similar to the gauge data, succeeded by the IMERG-F and IMERG-E. The Cumulative Distribution Function (CDF) has been employed to adjust the precipitation values of the three IMERG products in order to decrease bias estimation. The three products were then integrated into the “HEC-HMS” hydrological model to assess their dependability in flow modeling. Six flood occurrences were calibrated and validated for each product at 30-minute time steps. With a mean Nash-Sutcliffe coefficient of NSE 0.82, the calibration findings demonstrate that IMERG-F provides satisfactory hydrological performance. With an NSE = 0.80, IMERG-L displayed good hydrological utility, slightly better than IMERG-E with an NSE = 0.77. However, when the flood events were validated using the initial soil conditions, IMERG F and IMERG E overestimated the discharge by 13% and 10%, respectively. While IMERG L passed the validation phase with an average score of NSE = 0.69.

Extreme precipitation return levels for multiple durations on a global scale

Quantifying the magnitude and frequency of extreme precipitation events is key in translating climate observations to planning and engineering design. Past efforts have mostly focused on the estimation of daily extremes using gauge observations. Recent development of high-resolution global precipitation products, now allow estimation of global extremes. This research aims to quantitatively characterize the spatiotemporal behavior of precipitation extremes, by calculating extreme precipitation return levels for multiple durations on the global domain using the Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset. Both classical and novel extreme value distributions are used to provide insight into the spatial patterns of precipitation extremes. Our results show that the traditional Generalized Extreme Value (GEV) distribution and Peak-Over-Threshold (POT) methods, which only use the largest events to estimate precipitation extremes, are not spatially coherent. The recently developed Metastatistical Extreme Value (MEV) distribution, that includes all precipitation events, leads to smoother spatial patterns of local extremes. For durations of 5 and 10 days, however, there are less events per year to fit the distribution (37 and 22 on average, respectively), leading to larger inter-annual variability and possible overestimation of the extremes. While the GEV and POT methods predict a consistent shift from heavy to thin tails with increasing duration, the MEV method predicts a relatively constant heaviness of the tail for any precipitation duration, opening up an important research question on what is the ‘correct’ tail behavior of extreme precipitation for different durations. The generated extreme precipitation return levels and corresponding parameters are provided as the Global Precipitation EXtremes (GPEX) dataset. These data can be useful for studying the underlying physical processes causing the spatiotemporal variations of the heaviness of extreme precipitation distributions.

Improving satellite-based global rainfall erosivity estimates through merging with gauge data

Rainfall erosivity is a key factor that influences soil erosion by water. Rain-gauge measurements are commonly used to estimate rainfall erosivity. However, long-term gauge records with sub-hourly resolutions are lacking in large parts of the world. Satellite observations provide spatially continuous estimates of rainfall, but they are subject to biases that affect estimates of rainfall erosivity. We employed a novel approach to map global rainfall erosivity based on a high-temporal-resolution (30-min), long-term (2001–2020) satellite-based precipitation product—the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM-IMERG)—and mean annual rainfall erosivity from the Global Rainfall Erosivity Database (GloREDa) stations (n = 3286). We used a residual-based merging scheme to integrate GPM-IMERG-based rainfall erosivity with GloREDa using Geographically Weighted Regression (GWR). The accuracy of the GWR-based merging scheme was evaluated with a 10-fold cross-validation against GloREDa stations. Based on GPM-IMERG-only, the global mean annual rainfall erosivity was estimated to be 1173 MJ mm ha−1 h−1 yr−1 with a standard deviation of 1736 MJ mm ha−1 h−1 yr−1. The mean value estimated via GPM-IMERG merged with GloREDa was 2020 MJ mm ha−1 h−1 yr−1 with a standard deviation of 3415 MJ mm ha−1 h−1 yr−1. Overall, GPM-IMERG-only estimates underestimated rainfall erosivity. The underestimations were greatest in areas of high rainfall erosivity. The accuracy of rainfall erosivity estimates from GPM-IMERG merged with GloREDa substantially improved (Nash-Sutcliffe efficiency = 0.83, percent bias = −2.4%, and root mean square error = 1122 MJ mm ha−1 h−1 yr−1) compared to estimates by GPM-IMERG-only (Nash-Sutcliffe efficiency = 0.51, percent bias = 27.8%, and root mean square error = 1730 MJ mm ha−1 h−1 yr−1). Improving satellite-based global rainfall erosivity estimates through integrating with gauge data is relevant as it can contribute to enhancing soil erosion modeling and, in turn, support land degradation neutrality programs.

Merging precipitation scheme design for improving the accuracy of regional precipitation products by machine learning and geographical deviation correction

Global satellite precipitation products (SPPs) effectively obtain spatial precipitation information but frequently fail to meet application requirements in small-scale areas due to low accuracy. This study aims to design a merging precipitation scheme based on remote sensing and surface parameters to address the inaccuracy of regional precipitation. The scheme data are based on daily IMERG-FR, CHIRPS, and PDIR-Now satellite precipitation products from 2011 to 2018, combined with WorldClim model climate data and 39 rain gauge observations. The scheme generates merged precipitation with high-accuracy and high-resolution (0.04°) through the streamlined operation of multiple methods in the Songhua River basin in northeast China. First, the stacking algorithm was employed to perform preliminary merging of SPPs and decrease data noise errors (mean absolute error and standard deviation were reduced by 13.80% and 14.44%, respectively). Second, the correlation of merged and observed precipitation was improved by 1.18%–4.46% in different seasons after geographically weighted regression. Finally, the merged data were subjected to local intensity scaling, which reduced the precipitation error by an average of 3.14%. When compared to the original SPPs, the final merged precipitation (FMP) improved the correlation (increased by 0.19) and reduced the errors (root mean square error and relative error decreased by 1.71 mm and 0.08 mm, respectively). FMP performed well under precipitation event cases, with an average difference of 0.24 mm from observed precipitation. The study realized the merging analysis of remote sensing precipitation data with varying precision and spatial resolution. Furthermore, a systematic merging precipitation scheme coupled with multiple algorithms of machine learning, geographic regression, and mathematical statistics was formed. This study provides a reference for merging precipitation at a regional scale, which can be applied to other study areas as a process-oriented scheme.

Inspection of IMERG precipitation estimates during Typhoon Cempaka using a new methodology for quantifying and evaluating bias

Bias analysis is a significant component in the performance evaluation of satellite precipitation products (SPPs), and the relative bias (RB) is usually adopted as a key metric in all relative studies. However, when calculating the RB, severe overestimation (positive RB) and underestimation (negative RB) with the opposite sign can be offset through summation when a small temporal scale changes to a large temporal scale, and the final result will ignore the actual situation, which weakens the validity and reliability of bias evaluation. To solve the above problem, the typhoon presenting capability (TPC), which consists of two metrics, the presenting capability indicator (PCI) and the bias type discriminant (BTD), was developed as a new methodology for error evaluation during tropical cyclone events. In this study, by applying the TPC, two quasi-real-time Integrated multi-satellite retrievals for global precipitation measurement (IMERG) products (i.e., the early run and late run) acquired during Typhoon Cempaka in July 2021 were evaluated. The main conclusions are as follows: (1) underestimation of the precipitation by the two IMERG products tended to occur at higher hourly rainfall intensities (>2 mm at gauge scale and 100 mm at regional scale), while overestimation occurred at lower intensities. (2) The false-alarm rate (FAR) and RB values were significantly higher in the Pearl River Delta (PRD) than in other regions of the study area. (3) Through calculation of the TPC, a positive and negative offset phenomenon was identified over the region around Zhanjiang City in western Guangdong. By introducing the TPC for performance evaluation of IMERG products during Typhoon Cempaka, it was concluded that TPC is an advanced replacement of the RB for disclosing bias situations when adopting IMERG to monitor and reconstruct typhoon processes.