Reanalysis Precipitation Products Research Articles

Can Satellite or Reanalysis Precipitation Products Depict the Location and Intensity of Rainfall at Flash Flood Scale Over the Eastern Mountainous Area of the Tibetan Plateau?

AbstractThis study conducted evaluation and analysis on various precipitation products over the eastern Tibetan Plateau (ETP), including four sets of satellite precipitation data (i.e., IMERG_Uncal, IMERG_Cal, GSMaP_MVK, GSMaP_Gauge) and one set of model reanalysis data (i.e., ERA5‐land, hereafter ERA5‐L). We evaluated the spatial‐temporal distribution of their quality at an hourly temporal scale and 0.1° spatial scale, with a special focus on capturing different types of precipitation. The results show that: (a) GSMaP_Gauge exhibits the highest correlation with ground‐based gauges, while IMERG_Uncal and IMERG_Cal perform best in the estimation of the amount of precipitation. Satellite products generally perform better during summer while ERA5‐L sometimes outperforms satellite products in spring and autumn. (b) The evaluation results for different precipitation types reveal that all the QPE products face significant challenges in accurately describing convective precipitation. They tend to underestimate convective precipitation and fail to properly capture the intensity and location of heavy precipitation. (c) In heavy convective precipitation cases, the evaluated QPE products show various issues in accurately capturing the intensity and spatiotemporal variation of precipitation. Almost all QPE products underestimate maximum precipitation (both hourly precipitation and accumulated precipitation) and small‐scale (about 50 km or less) spatial variability of precipitation. IMERG_Uncal, IMERG_Cal, and GSMaP_MVK perform better than other products in heavy convective precipitation cases. This study provides new insights into the quality of QPE products in different types of precipitation. The analysis of the quality of these QPE products serves as a valuable indicator of their potential applications, particularly in flash flood simulations, while also underscoring the critical need for improving precipitation product quality.

Intercomparison of reanalysis and satellite precipitation products in endorheic and exorheic basins on the Tibetan Plateau

Study Region: Endorheic and exorheic basins of the Tibetan Plateau (TP). Study Focus: Reanalysis and satellite precipitation products provide alternatives for regions of sparse ground precipitation observation, but pose a tough task to select a suitable one for the TP. This study conducts a multi-scale evaluation of six reanalysis and satellite precipitation products in endorheic and exorheic basins using water balance and extended triple collocation (ETC) methods. The reanalysis precipitation products include ECMWF Re-Analysis version 5 (ERA5-Land), China Meteorological Forcing Dataset (CMFD), Global Land Data Assimilation Systems (GLDAS), and High-resolution Precipitation dataset for the Third Pole region (TPHiPr). The satellite precipitation data include Global Precipitation Measurements (GPM) and Tropical Rainfall Measuring Mission (TRMM) products. New Hydrological Insights for the Region: The precipitation products vary in accuracy from basin to basin, with better performance in exorheic than endorheic basins. Reanalysis-based ERA5-land, TPHiPr, and CMFD perform well in most basins at annual scale, among which TPHiPr performs best at daily scale. At regional scale, GPM performs well in endorheic region, and ERA5-land in exorheic region. While all the products increase significantly in accuracy from basin to regional scale in endorheic region, ERA5-land shows best performance at annual and multi-year scales in the entire region. Our findings provide valuable supports for precipitation product selection in the Tibetan endorheic and exorheic basins.

Spatiotemporal performance evaluation of high-resolution multiple satellite and reanalysis precipitation products over the semiarid region of India.

The present investigation evaluates three satellite precipitation products (SPPs), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Global Precipitation Climatology Centre (GPCC), Climate Hazard Infrared Precipitation with Station Data (CHIRPS), and two reanalysis datasets, namely, the ERA5 atmosphere reanalysis dataset (ERA5) and Indian Monsoon Data Assimilation and Analysis (IMDAA), against the good quality gridded reference dataset (1991-2022) developed by the India Meteorological Department (IMD). The evaluation was carried out in terms of the rainfall detection ability and estimation accuracy of the products using metrics such as the false alarm ratio (FAR), probability of detection (POD), misses, root mean square error (RMSE), and percent bias (PBIAS). Among all the rainfall products, ERA5 had the best ability to capture rainfall events with a higher POD, followed by MSWEP. Both MSWEP and ERA5 had PODs of 70-100% in more than 90% of the grids and less than 35% of missing rainfall events in the entire Tamil Nadu. In the case of the rainfall estimation accuracy evaluation, the MSWEP exhibited superior performance, with lower RMSEs and biases ranging from - 25 to 25% at the annual and seasonal scales. In northeast monsoon (NEM), CHIRPS demonstrated a comparable performance to that of MSWEP in terms of the RMSE and PBIAS. These findings will help product users select the best reliable rainfall dataset for improved research, diversified applications in various sectors, and policy-making decisions.

Blending gauge, multi-satellite and atmospheric reanalysis precipitation products to facilitate drought monitoring

Long-term, continuous, and highly accurate precipitation estimation is crucial for reliable drought monitoring. Precipitation estimation based on gauge measurements, satellite retrieval, and reanalysis datasets exhibits heterogeneous uncertainties across different regions of mainland China. This study introduces two modifiable weighting schemes (Cheng-Kling-Gupta Efficiency (CKGE) weighted-ensemble model (CWEM) and Bayesian Model Averaging (BMA)), utilizing posterior probabilities generated by BMA and the performance of CKGE, to merge 7 monthly precipitation datasets into new weighted precipitation (BMA Ensemble Precipitation (BMAEP) and CKGE Weighted-Ensemble Precipitation (CWEP)) using various quantity combination schemes. Subsequently, the precision and drought monitoring utility of the weighted precipitation are evaluated and compared with the representative fused precipitation product Multi-Source Weighted-Ensemble Precipitation (MSWEP) in mainland China using gauged data. The amalgamated results demonstrate that, compared to individual datasets, certain weighted precipitation schemes exhibit superiority over MSWEP. In particular, BMAEP-2P demonstrates superior performance in the composite index CKGE, achieving a value of 0.828. CWEP-4P exhibits a higher correlation coefficient (CC), reaching 0.905. CWEP-2P excels in terms of relative bias (BIAS) and root mean square error (RMSE), with values of 0.579 % and 20.755 mm, respectively. Furthermore, BMAEP or CWEP performs optimally in drought monitoring applications across all sub-regions of mainland China at different time scales (1, 3, 6, 12 and 24 months), with the average of the highest values of CC and probability of detection (POD) reached 0.919 and 0.844, respectively. Further contribution analysis reveals CPC as the dominant factor contributing to the greatest improvement in the performance (excluding CKGE, CC_SPEI1, CC_SPEI24 and POD_SPEI24) of the fusion models, among which it boosted MERRA2′s performance on POD_SPEI6 by 9.41 %. In conclusion, the merging schemes based on CWEM and BMA methods effectively generate a new precipitation dataset, integrating information from multiple products into drought monitoring applications.

Hydrological Evaluation of CRA40 and ERA5 Reanalysis Precipitation Products over Ganjiang River Basin in Humid Southeastern China

This study evaluates two reanalysis precipitation products (CRA40 and ERA5) over the Ganjiang River Basin with precipitation data from 37 ground rainfall gauges and surface-observed stream flow data from January 1998 to December 2008. Direct comparison with rain gauge observations shows that both CRA40 and ERA5 can capture the spatial and temporal characteristics of precipitation at the basin scale of the Ganjiang River and reflect most of the precipitation events, but there are pronounced differences in the quality of precipitation between them. ERA5 performs better on the daily scale, capturing precipitation changes more accurately over short periods of time, while CRA40 performs better on the monthly scale, providing more stable and long-term precipitation trends. The results of stream flow simulations using two reanalysis precipitation products driving the VIC hydrological model show that (1) CRA40 outperforms ERA5 with a better Nash–Sutcliffe Efficiency (NSE, 0.65 and 0.6) and higher CC (0.96 and 0.91) in daily and monthly scale stream flow simulations, and ERA5 has a good CC (0.86 and 0.93, respectively), but its NSE is poor (0.29 and 0.30, respectively); (2) both CRA40 and ERA5 generally overestimate basin stream flows, especially during the flood season (April–September), with ERA5’s overestimation being more pronounced. This study is expected to provide a basis for the selection of reliable reanalysis products for Ganjiang River Basin precipitation and hydrological simulation.

Analyses of groundwater level in a data-scarce region based on assessed precipitation products and machine learning

Groundwater, a vital natural resource globally, faces challenges related to data scarcity, particularly in data-limited regions. To address this issue in the region of Bahira in Morocco, we developed an innovative approach for estimating groundwater levels by utilizing precipitation products and employed a random forest (RF) machine learning (ML) model to fill data gaps. Our study involved a comprehensive assessment of ten precipitation products, comprising six Reanalysis Precipitation Products (RPPs) and four Satellite-based Precipitation Products (SPPs). We evaluated their performance across various time scales, including daily, monthly, and seasonal, across different topographical classes. The outcomes highlighted the consistency of ERA5-based datasets, boasting daily correlation to values higher than 0.6, whereas monthly and seasonal correlations exceed 0.8, except during summer. GPM-IMERG, MERRA2, and CPC-UPP also demonstrated commendable accuracy, particularly in plain and mountainous areas. Nonetheless, CFSR, CHIRPS, PERSIANN CDR, and TRMM datasets exhibited limitations, particularly in high mountain areas. To address data gaps, we initially explored correlations between RPPs, SPPs, and groundwater data. However, these correlations failed to meet the accuracy standards required for precise predictions. Notably, the strongest correlations were observed in monitoring stations located in mountainous regions, indicating significant aquifer recharge activities in these areas. In the subsequent phase, the Multiple Imputation by Chained Equations (MICE) machine learning-based imputation method served as a valuable tool for estimating groundwater levels in regions where ground observations were insufficient. Our trend analysis yielded significant insights, with approximately 95% of groundwater points displaying negative trends, with a maximum rate of −0.91 m. In contrast, 69% of precipitation stations exhibited negative trends, with a maximum rate of −0.06 mm. Our approach offers a promising potential to address the challenges associated with the scarcity of groundwater and precipitation data, making it a valuable tool for the assessment, monitoring, and management of groundwater resources.

Bimodality in simulated precipitation frequency distributions and its relationship with convective parameterizations

Bimodality in precipitation frequency distributions is often evident in atmospheric models, but rarely in observations. This study i) proposes a metric to objectively quantify the bimodality in precipitation distributions, ii) evaluates model simulations contributed to the Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5), phase 6 (CMIP6), and the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) project by comparing them to satellite-based and reanalysis precipitation products, and iii) investigates possible origins of bimodal precipitation distributions. Our results reveal that about 83% (20 out of 24) of CMIP5 and 70% (21 out of 30) of CMIP6 models used in this study exhibit bimodal distributions. The few DYAMOND models that use a deep convective parameterization also show bimodal distributions, while most DYAMOND models do not. Predictably, the bimodality originates from the separation of precipitation process between resolved grid-scale and parameterized subgrid-scale. However, in a larger number of models bimodality arises from the parameterized subgrid-scale convective precipitation alone.

A hybrid ensemble learning merging approach for enhancing the super drought computation over Lake Victoria Basin

This study introduces a novel Hybrid Ensemble Machine-Learning (HEML) algorithm to merge long-term satellite-based reanalysis precipitation products (SRPPs), enabling the estimation of super drought events in the Lake Victoria Basin (LVB) during the period of 1984 to 2019. This study considers three widely used Machine learning (ML) models, including RF (Random Forest), GBM (Gradient Boosting Machine), and KNN (k-nearest Neighbors), for the emerging HEML approach. The three SRPPs, including CHIRPS (Climate Hazards Group Infra-Red Precipitation with Station), ERA5-Land, and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record), were used to merge for developing new precipitation estimates from HEML model. Additionally, classification and regression models were employed as base learners in developing this algorithm. The newly developed HEML datasets were compared with other ML and SRPP products for super-drought monitoring. The Standardized precipitation evapotranspiration index (SPEI) was used to estimate super drought characteristics, including Drought frequency (DF), Drought Duration (DD), and Drought Intensity (DI) from machine learning and SRPPs products in LVB and compared with RG observation. The results revealed that the HEML algorithm shows excellent performance (CC = 0.93) compared to the single ML merging method and SRPPs against observation. Furthermore, the HEML merging product adeptly captures the spatiotemporal patterns of super drought characteristics during both training (1984–2009) and testing (2010–2019) periods. This research offers crucial insights for near-real-time drought monitoring, water resource management, and informed policy decisions.

Investigating the comparative utility of ECMWF precipitation forecasts as an alternative to reanalysis data

ABSTRACT Many near-real-time applications require near-real-time precipitation estimates, and in the absence of reanalysis datasets due to their usually delayed release, precipitation forecasts could offer a potential alternative. This motivates the inter-comparison of forecast and reanalysis products conducted in this study investigating the statistical accuracy and hydrological utility of three precipitation datasets from European Centre for Medium-Range Weather Forecasts (ECMWF) [i.e. high-resolution (HRES) forecasts, Ensemble Mean (EM) forecasts, and ECMWF’s 5th generation reanalysis (ERA5)] over Türkiye and Germany for 2007–2018 using ground-based observed data as truth. ERA5 has higher bias than HRES and EM in both regions while HRES has the lowest daily correlations. ERA5 (EM) shows the highest hydrological utility in Germany (Türkiye). ERA5 showed improved monthly correlations compared to forecasts; the improvement over Germany (i.e. 0.02) is better than over Türkiye (~0.01). Wetness and topographical complexity of a region affect precipitation estimation uncertainty there.

Efficiency of global precipitation datasets in tropical and subtropical catchments revealed by large sampling hydrological modelling

Satellite-based and reanalysis precipitation products are widely adopted as complementary information to in situ measurements for estimating river discharge using hydrological modelling. However, there is still a notable research gap in the literature associated with assessing the accuracy of satellite-based or reanalysis products in different tropical and sub-tropical catchments at large-sampling hydrological modelling with sensitivity analysis. We investigated the accuracy of precipitation, hydrological model performance and parameter sensitivity related to seven precipitation data sets based on satellite and reanalysis products, i.e., CHIRPS, TRMM, GLDAS, IMERG, MERRA-2, PERSIANN-CDR, and ERA5 over 714 contrasting tropical and subtropical catchments located in Brazil. We used the Génie Rural Journalier 4 (GR4J) hydrological model to simulate the hydrological processes of the different catchments with two approaches for the calibration: using measured ground-based precipitation data (approach I) or using each individual satellite/reanalysis precipitation products (approach II) to calibrate the models. The results showed that the precipitation products tend to overestimate precipitation, with the exception of ERA5 and MERRA-2. CHIRPS is the only product that produces unbiased precipitation estimates for most catchments. The model calibration using each precipitation product individually improved the hydrological model performance. CHIRPS, IMERG, and MERRA-2 showed good accuracy in terms of both, precipitation estimation and hydrological simulation performance in the calibration period. In the validation period, the best products in terms of KGE were CHIRPS, IMERG and TRMM (KGE > 0.64). The errors in precipitation products are better compensated via hydrological modelling in wet regions. The model parameter sensitivity varies according to precipitation input, climate, and catchment aridity. Overall, all seven products exhibited their worst hydrological performance in arid regions. This study helps to improve our understanding of the catchment response in tropical and subtropical regions while also providing key insights into the reliability of satellite/reanalysis rainfall products for streamflow simulation. This study is valuable in hydrometeorological applications, climate change assessment, water resources and disaster management, especially in regions with a relatively sparse density of precipitation stations.

Integrating satellite and reanalysis precipitation products for SWAT hydrological simulation in the Jing River Basin, China.

In hydrological studies, satellite and reanalysis precipitation products are increasingly being used to supplement gauge observation data. This study designed the composite simulation index (COSI), considering two factors: F1 (data accuracy assessment) and F2 (hydrological simulation performance), to compare the performance of the latest satellite-based and reanalysis-based precipitation products (IMERG, ERA5, ERA5-Land), the prior precipitation products (TRMM, CMADS), and the multi-source weighted-ensemble precipitation (MSWEP). The Soil and Water Assessment Tool (SWAT) model was then applied to compare and analyze the hydrological simulation performance of four preferred products using three data fusion methods including simple model averaging, variance-based weighted averaging, and the latest quantile-based Bayesian model averaging (QBMA). The results can be summarized as follows: (1) Reanalysis products are superior to satellite-based products in terms of F1. However, the satellite-based precipitation products exhibit less BIAS and relatively higher F2, while the MSWEP has relatively high performance on both F1 and F2. (2) Among reanalysis-based precipitation products, CMADS has the best COSI value of 0.53. Although ERA5-Land shows good performance for individual parameters, the comprehensive assessment reveals that ERA5 outperforms ERA5-Land in terms of both F1 and F2. (3) IMERG and TRMM exhibit similar spatiotemporal patterns and similar F1, but IMERG is superior in F2. (4) QBMA outperformed traditional methods in F2, improving the NS coefficient of SWAT model from 0.74 to 0.85. These findings provide a useful reference for analyzing the strengths and limitations of satellite-based and reanalysis precipitation products, and also provide valuable ideas for the combined application of multi-source precipitation products in hydrological studies.

Comparison of satellite-based and reanalysis precipitation products for hydrological modeling over a data-scarce region

Comparison of satellite-based and reanalysis precipitation products for hydrological modeling over a data-scarce region

Assessing NASA-POWER Precipitation Product in Predicting Surface Flow: Lower Jhelum River Case Study

The Lower Jhelum River watershed has variable topography (i.e. northern mountains and southern plains) and a subtropical climate with hot summers and cool winters. This emphasized the extensive use of state-of-the-art POWER reanalysis precipitation product rather than gauge estimates for hydrological investigations (modeling rainfall runoff, floods, and droughts etc.). To understand the consistency and potential of the precipitation values from both sources, the primary objective of this study was to establish a relationship between POWER data and rain gauge data by utilizing 13 rain gauge stations present in the catchment. While analyzing the rainfall data, a relatively weaker linear association ( R), ranging from .6, .68 to .78, was found between the ground-based observatories and the POWER product especially for the arid and semi-arid regions. For the mountainous part, covering a wide glacial range, POWER product showed a better match with ground-based observatories that is, R value ranging from .78, .88 to .91. The simulated stream flow results by integrated HEC-HMS model showed that the POWER product can become a good asset in developing the weather data for those areas where the rain gauges are either absent or inaccessible.

An Evaluation of CRA40 and ERA5 Precipitation Products over China

Precipitation datasets derived from reanalysis products play a crucial role in weather forecasting and hydrological applications. This paper aims to evaluate the performance of two distinct reanalysis precipitation products, i.e., the first-generation Chinese global land-surface reanalysis precipitation product (CRA40) and the fifth-generation European reanalysis precipitation product (ERA5), over mainland China. The evaluation is based on continuous and categorical statistical indicators with daily-scale gridded-point rain gauge data obtained from Chinese surface meteorological stations. The findings of this study can be summarized as follows: (1) CRA40 demonstrates a clear superiority over ERA5 in terms of the 13-year daily mean precipitation and seasonal daily precipitation. CRA40 exhibits better correlation coefficients (0.97), relative biases (5.25%), root mean square errors (0.34 mm), and fractional standard errors (0.05). (2) Both reanalyzed precipitation products generally exhibit an overestimation of precipitation in mainland China. The degree of overestimation is particularly pronounced in dry climatic regions (e.g., QZ, XJ), while wet regions (e.g., CJ, HN) demonstrate relatively less overestimation. (3) ERA5 shows better performance in the detection of daily precipitation. Neither CRA40 nor ERA5 can capture heavy precipitation events well. These findings are expected to advance our understanding of the strengths and limitations of the reanalysis precipitation products, CRA40 and ERA5, over China.

Comprehensive Evaluation of Global Precipitation Products and Their Accuracy in Drought Detection in Mainland China

Abstract Thorough evaluations of satellite precipitation products are necessary for accurately detecting meteorological drought. A comprehensive assessment of 15 state-of-the-art precipitation products (i.e., IMERG_cal, IMERG_uncal, GSMaP-G, CPC-Global, TRMM3B42, CMORPH-CRT, PERSIANN-CDR, PERSIANN, PERSIANN-CCS, SM2RAIN, CHIRPS, ERA5, ERA-Interim, MERRA-2, and GLDAS) is herein conducted for the period 2010–19 giving special attention to their performance in detecting meteorological drought over mainland China at 0.25° spatial resolution. The cited precipitation products are compared against China’s gridded gauge-based Daily Precipitation Analysis (CGDPA) product, derived from 2400 meteorological stations, and their quality is assessed at daily, seasonal, and annual precipitation time scales. Meteorological droughts in the datasets are determined by calculating the standardized precipitation evapotranspiration index (SPEI). The performance of the precipitation products for drought detection with respect to the SPEI is assessed at three time scales (1, 3, and 12 months). The results show that the GSMaP-G outperforms other satellite-based datasets in drought detection and precipitation estimation. The MERRA-2 and the ERA5 are on average closer to the CGDPA reference data than other reanalysis products for precipitation estimation and drought detection. These products capture well the spatial and temporal pattern of the SPEI in southern and eastern China having a probability of detection (POD) above 0.6 and a correlation coefficient (CC) above 0.65. CPC-Global, IMERG, and the ERA5 reanalysis product are ideal candidates for application in western China, especially in the Qinghai–Tibetan Plateau and the Xinjiang Province. Generally, the accuracy of precipitation products for drought detection is improved with longer time scales of the SPEI (i.e., SPEI-12). This study contributes to drought-hazard detection and hydrometeorological applications of satellite precipitation products. Significance Statement The purpose of this study is to comprehensively evaluate the quality of 15 global satellite-based, gauge-based, and reanalysis precipitation products for meteorological drought detection at 1-, 3-, and 12-month time scales. This work systematically evaluates these products’ capacity to capture precipitation occurrence and intensity in different seasons. This is followed by a comparison of the precipitation products’ performance in drought detection. This work’s findings and evaluation results will improve the ability of those who develop precipitation products in identifying error sources and further improving retrieval algorithms. This paper’s results will serve as a valuable reference for end users seeking to better understand the application of precipitation products to drought detection.

Performance assessment of multi-source, satellite-based and reanalysis precipitation products over variable climate of Turkey

Performance assessment of multi-source, satellite-based and reanalysis precipitation products over variable climate of Turkey

Assessing the skill of gridded satellite and reanalysis precipitation products over in East and Southern Africa

Validation of gridded precipitation products (GPP) increases the users’ confidence and highlights possible improvements in the algorithms to handle complex rain-forming processes. We evaluated the skill of three GGPs (CHIRPS-v2, CHELSA, and TerraClimate) in estimating the rain gauge observations and compared the precipitation trends derived from these products across the East and Southern Africa (ESA) region. We used Taylor diagrams and Kling-Gupta Efficiency (KGE) to assess the accuracy. A modified Mann-Kendal test and a Sen’s slope estimator were utilized to determine the trends’ significance and magnitude, respectively. The three GPPs had varied performance over temporal and altitudinal ranges. The skill of the three GPPs, at a monthly scale, was generally high but showed lower performance at elevations over 1500 masl, especially during the October-November-December (OND) season. The three GPPs performed equally well between the 1001 – 1500 masl elevation range. CHELSA-v2.1 was most accurate at 0-500 masl but had the lowest skill in both 501 – 1000 and above 1500 masl elevations, which caused over-estimation of the annual and seasonal precipitation trends over mountainous terrain and large inland water bodies. The quantified precipitation trends revealed high spatial-temporal variability. Generally, the skill and precipitation trends derived from CHIRPS-v2 and TC data showed substantial convergence except in Tanzania. Our results emphasize the importance of validating climate datasets to avoid error propagation in different models and applications. Moreover, we demonstrate that new or higher-resolution precipitation data are not always accurate since an algorithm update can introduce artifacts or biases.

Ensemble precipitation estimates based on an assessment of 21 gridded precipitation datasets to improve precipitation estimations across Madagascar

Study regionthis study focuses on Madagascar. This island is characterized by a great diversity of climate, due to trade winds and the varying topography. This country is also undergoing extreme rainfall events such as droughts and cyclones. Study focusthe rain gauge network of Madagascar is limited (about 30 stations). Consequently, we consider relevant satellite-based precipitation datasets to fill gaps in ground-based datasets. We assessed the reliability of 21 satellite-based and reanalysis precipitation products (P-datasets) through a direct comparison with 24 rain gauge station measurements at the monthly time step, using four statistical indicators: Kling-Gupta Efficiency (KGE), Correlation Coefficient (CC), Root Mean Square Error (RMSE), and Bias. Based on this first analysis, we produced a merged dataset based on a weighted average of the 21 products. New hydrological insights for the regionbased on the KGE and the CC scores, WFDEI (WATCH Forcing Data methodology applied to ERA-Interim), CMORPH-BLD (Climate Prediction Center MORPHing satellite-gauge merged) and MSWEP (Multi-Source Weighted Ensemble Precipitation) are the most accurate for estimating rainfall at the national scale. Additionally, the results reveal a high discrepancy between bio-climatic regions. The merged dataset reveals higher performance than the other products in all situations. These results demonstrate the usefulness of a merging approach in an area with a deficit of rainfall data and a climatic and topographic diversity.

Diverging identifications of extreme precipitation events from satellite observations and reanalysis products: A global perspective based on an object-tracking method

Extreme precipitation can trigger various natural hazards, causing catastrophic losses worldwide. As global warming accelerates, it is widely acknowledged that extreme precipitation will become more frequent and intense, calling for more accurate historical and projected precipitation estimation. Identifying extreme precipitation events is understudied particularly on the global scale, given that most studies only focus on pixel-by-pixel characteristics while ignoring the space-time continuity and evolution of extreme events. This study utilizes an object-based tracking method to track the precipitation system objects in time, extracting spatiotemporal attributes of precipitation events and investigates variable definitions of extremes for a better understanding of the performance of existing precipitation datasets. Five satellite and two reanalysis precipitation products (IMERG, GSMaP, PERSIANN-CCS, TRMM 3B42, CMORPH, ERA5, and ERA-Interim) are involved to investigate (1) the difference and similarity of those datasets in depicting the global pattern of extreme precipitation, (2) the impact of various definitions of extreme events, and (3) the trend of global extreme precipitation events. Results show that the object-based method can capture the motion of precipitation events. Different extreme definitions have a large impact on the distribution, trend, and features of extreme precipitation. The inter-comparison of extreme precipitation events from different products shows low correlation, indicating that accurately capturing the evolution of extreme events is still challenging. Reanalysis product-based analysis shows variable trends (magnitudes and signs) based on different attributes of precipitation objects and notable scale effect. Satellite precipitation products exhibit distinct inter-annual variability which could affect their hydrometeorological applications. In summary, this study leverages the object-based tracking method for comprehensive extreme precipitation analysis from the multi-dataset, multi-scale, and multi-definition perspectives, which can advance the understanding of the status and trend of global extreme precipitation.

Application of Machine Learning and Remote Sensing for Gap-filling Daily Precipitation Data of a Sparsely Gauged Basin in East Africa

Access to spatiotemporal distribution of precipitation is needed in many hydrological applications. However, gauges often have spatiotemporal gaps. To mitigate this, we considered three main approaches: (i) using remotely sensing and reanalysis precipitation products; (ii) machine learning-based approaches; and (iii) a gap-filling software explicitly developed for filling the gaps of daily precipitation records. This study evaluated all approaches over a sparsely gauged basin in East Africa. Among the examined precipitation products, PERSIANN-CDR outperformed other satellite products in terms of root mean squared error (7.3 mm), and correlation coefficient (0.46) while having a large bias (50%) compared to the available in situ precipitation records. PERSIANN-CDR also demonstrates the highest skill in distinguishing rainy and non-rainy days. On the other hand, Random Forest outperformed all other approaches (including PERSIANN-CDR) with the least relative bias (-2%), root mean squared error (6.9 mm), and highest correlation coefficient (0.53).