Reanalysis Precipitation Products Research Articles

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

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).

Evaluation of ERA5 and ERA5-Land reanalysis precipitation datasets over Spain (1951–2020)

Reanalysis precipitation estimates are widely used in the fields of meteorology and hydrology because they can provide physical, spatial, and temporal coherent long time series at a global scale. Nevertheless, as a pre-requisite for many applications their performance needs to be assessed. The objective of this study was to evaluate the European Centre for Medium-Range Weather Forecasts (ECMWF) latest fifth-generation reanalysis precipitation products, i.e., ERA5 and ERA5-Land, at country scale in Spain. For doing so, we compared it against a high-resolution precipitation product of the Spanish Meteorological Agency which spans approximately 70 years (1951–2020). A comprehensive assessment (continuous, categorical, probability distribution function (pdf), spatial pattern, and temporal trend) was performed in order to ascertain the quality of the reanalysis products. Results of the analysis revealed a general agreement between observations and ERA5-Land/ERA5 estimates: spearman correlation values between 0.5 and 0.9, Root Mean Square Error (RMSE) mostly between 2 and 8 mm/d and Kling Gupta Efficiency (KGE) values >0.4. Categorical assessment additionally indicated a good performance (Heiken Skill score (HSS) score, also known as kappa, between 0.4 and 0.8). Nevertheless, performance was found to be dependent on the climatic region, precipitation intensity and orography. Correlation revealed a north-west (higher values) south-east (lower values) spatial gradient while relative bias (RBIAS) and RMSE spatial patterns were positively correlated with slope (ρ = 0.41/0.35, 0.69/0.70, respectively). In addition, as indicated by the categorical analysis, along the Mediterranean coast a wet bias (i.e., overestimation of days with precipitation) was found. Reanalysis detection capacity (kappa) shown a negative correlation with the slope (ρ = −0.29/−0.34). Worst model performance is obtained during summer months, with a generalized overestimation. The pdf assessment revealed that the ERA5-Land/ERA5 tended to overestimate light (≥1 and < 5 mm/day), and moderate (≥5 and < 20 mm/day) precipitation categories while underestimating the heavy (≥20 and < 40 mm/day) and violent (≥40 mm/day) categories. Moderate precipitation provided the best detection capacity, as indicated by the precipitation-intensity analysis. ERA5-Land/ERA5 showed a good capacity to reproduce the spatial patterns and temporal trends of the observations. ERA5-Land and ERA5, with a different spatial resolution, performed very similar in all the analysis considered. Mediterranean and northern coast were highlighted as the most critical for reanalysis modelling purposes because of its performance.

Evaluation and Comparison of Six High-Resolution Daily Precipitation Products in Mainland China

Satellite-based and reanalysis precipitation products have experienced increasing popularity in agricultural, hydrological and meteorological applications, but their accuracy is still uncertain in different areas. In this study, six frequently used high-resolution daily precipitation products, including Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Global Satellite Mapping of Precipitation (GSMaP), Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) and European Center for Medium-Range Weather Forecasts Reanalysis V5-Land (ERA5-Land), were comprehensively evaluated and compared in nine regions of mainland China between 2015 and 2019. The results reveal that, in general, GSMaP is the best precipitation product in different agricultural regions, especially based on the Pearson correlation coefficient (CC) and critical success index (CSI). ERA5-Land and MSWEP tend to have the highest probability of detection (POD) values, and MSWEP tends to have the smallest relative root mean squared error (RRMSE) values. GSMaP performs better at almost all precipitation levels and in most agricultural regions in each season, while MSWEP has the best performance for capturing the time series of mean daily precipitation. In addition, all precipitation products perform better in summer and worse in winter, and they are more accurate in the eastern region. The findings of this study will contribute to understanding the uncertainties of precipitation products, improving product quality and guiding product selection.

Error Decomposition of CRA40-Land and ERA5-Land Reanalysis Precipitation Products over the Yongding River Basin in North China

Long-term and high-resolution reanalysis precipitation datasets provide important support for research on climate change, hydrological forecasting, etc. The comprehensive evaluation of the error performances of the newly released ERA5-Land and CRA40-Land reanalysis precipitation datasets over the Yongding River Basin in North China was based on the two error decomposition schemes, namely, decomposition of the total mean square error into systematic and random errors and decomposition of the total precipitation bias into hit bias, missed precipitation, and false precipitation. Then, the error features of the two datasets and precipitation intensity and terrain effects against error features were analyzed in this study. The results indicated the following: (1) Based on the decomposition approach of systematic and random errors, the total error of ERA5-Land is generally greater than that of CRA40-Land. Additionally, the proportion of random errors was higher in summer and over mountainous areas, specifically, the ERA5-Land accounts for more than 75%, while the other was less than 70%; (2) Considering the decomposition method of hit, missed, and false bias, the total precipitation bias of ERA5-Land and CRA40-Land was consistent with the hit bias. The magnitude of missed precipitation and false precipitation was less than the hit bias. (3) When the precipitation intensity is less than 38 mm/d, the random errors of ERA5-Land and CRA40-Land are larger than the systematic error. The relationship between precipitation intensity and hit, missed, and false precipitation is complicated, for the hit bias of ERA5-L is always smaller than that of CRA40-L, and the missed precipitation and false precipitation are larger than those ofCRA40-L when the precipitation is small. The error of ERA5-Land and CRA40-Land was significantly correlated with elevation. A comprehensive understanding of the error features of the two reanalysis precipitation datasets is valuable for error correction and the construction of a multi-source fusion model with gauge-based and satellite-based precipitation datasets.

Comprehensive evaluation of Satellite-Based and reanalysis precipitation products over the Mediterranean region in Turkey

Precipitation is an important component of the hydrological and energy cycles, as well as a key input parameter for many applications in the fields of hydrology, climatology, meteorology, and weather forecasting research. As a result, estimating precipitation accurately is critical. The purpose of this research is to conduct a comprehensive and comparative evaluation of grid-based precipitation products over Turkey's Mediterranean region from 2017 to 2021 at monthly and grid scales, using data from 193 ground-based meteorological stations as a reference. PERCIANN CCS, PDIR-Now, GSMaP MVK, PERSIANN CDR, CHIRPS, IMERG v6, GSMaP Gauge, and ERA5 are the eight grid-based precipitation products. Several prospective were used to evaluate the products, including magnitude agreement with gauge stations for the entire region and the six hydrological sub-basins included in the region, performance in capturing various intensity categories, and elevation dependency. According to the evaluation results, PERCIANN CDR, CHIRPS, IMERG v6, GSMaP Gauge, and ERA5 performed well in all evaluation aspects, whereas PERCIANN CCS, PDIR-Now, and GSMaP MVK performed poorly in all metrics. The majority of the products underestimated heavy rainfall events, while all products performed better at low and moderate precipitation events. As a result, the products performed better in the summer and spring months (March to October) than in the winter months (December to February). Furthermore, the results showed that the performance of the majority of the products degraded for elevations greater than 1000 m. The evaluation suggests that PERSIANN CDR, CHIRPS, IMERG v6, GSMaP Gauge, and ERA5 can be used as good precipitation data sources and as a complement to ground-based meteorological stations in Turkey's Mediterranean region.

How well do satellite and reanalysis precipitation products capture North American monsoon season in Arizona and New Mexico?

Assessment of the precipitation products with ground-based data is essential to building confidence in these datasets. Precipitation products tend to have large errors in semi-arid regions such as the Southwest United States, where accurate precipitation quantification is critical for water resource management and flood mitigation measures. Therefore, this region, with its high density of ground-based data, is important for the evaluation of the products. The Southwest United States is also interesting due to its monsoonal precipitation pattern, in which changes in circulation patterns that bring tropical moisture to the region yield roughly 50% of the region's precipitation between the months of June–September. In the present study, the performance of precipitation products was evaluated over Arizona and New Mexico for the monsoon seasons of the 2002–2021 period, with an emphasis on the recent extreme years of 2020 and 2021. Results indicate that all satellite products tend to capture interannual variations of precipitation rate but struggled to capture high-intensity events. IMERG Final notably has better performance than other products, with the lowest root mean square error and highest correlation with Stage IV, which was 3–60 percent better than other products. IMERG Final had the best detection capacity for rainy days as well. ERA5-Land performed well in capturing the average monsoon precipitation rate; however, showed limited skill in the detection of trace, light, and extreme precipitation events. IMERG Late and PDIR-Now showed difficulty detecting light precipitation events and overestimated extreme events. This study shows the importance of gauge adjustment for satellite products (e.g., IMERG) as well as the need for improvement of reanalysis products over arid regions, and better representation of orographic precipitation.

Multiscale Ground Validation of Satellite and Reanalysis Precipitation Products over Diverse Climatic and Topographic Conditions

The validity of two reanalysis (ERA5 and MEERA2) and seven satellite-based (CHIRPS, IMERG, PERSIANN-CCS, PERSIANN-CDR, PERSIANN-PDIR, PERSIANN, and TRMM) precipitation products was assessed in relation to the observations of in situ weather stations installed in different topographical and climatic regions of Pakistan. From 2010 to 2018, all precipitation products were evaluated on daily, monthly, seasonal, and annual bases at a point-to-pixel scale and over the entire spatial domain. The accuracy of the products was evaluated using commonly used evaluation and categorical indices, including Root Mean Square Error (RMSE), Correlation Coefficient (CC), Bias, Relative Bias (rBias), Critical Success Index (CSI), Success Ratio (SR) Probability of Detection (POD), and False Alarm Ratio (FAR). The results show that: (1) Over the entire country, the spatio-temporal distribution of observed precipitation could be represented by IMERG and TRMM products. (2) All products (reanalysis and SPPs) demonstrated good agreement with the reference data at the monthly scale compared to the daily data (CC &gt; 0.7 at monthly scale). (3) All other products were outperformed by IMERG and TRMM in terms of their capacity to detect precipitation events throughout the year, regardless of the season (i.e., winter, spring, summer, and autumn). Furthermore, both products (IMERG and TRMM) consistently depicted the incidence of precipitation events across Pakistan’s various topography and climatic regimes. (4) Generally, CHIRPS and ERA5 products showed moderate performances in the plan areas. PERSIANN, PERSIANN-CCS, PDIR, PERSIANN-CDR, and MEERA2 products were uncertain to detect the occurrence and precipitation over the higher intensities and altitudes. Considering the finding of this assessment, we recommend the use of daily and monthly estimates of the IMERG product for hydro climatic studies in Pakistan.

Can Satellite and Atmospheric Reanalysis Products Capture Compound Moist Heat Stress-Floods?

Satellite-retrieved and model-based reanalysis precipitation products with high resolution have received increasing attention in recent decades. Their hydrological performance has been widely evaluated. However, whether they can be applied in characterizing the novel category of extreme events, such as compound moist heat-flood (CMHF) events, has not been fully investigated to date. The CMHF refers to the rapid transition from moist heat stress to devastating floods and has occurred increasingly frequently under the current warming climate. This study focuses on the applicability of the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) and the fifth generation of European Reanalysis (ERA5-Land) in simulating CMHF events over 120 catchments in China. Firstly, the precipitation accuracy of IMERG and ERA5-Land products is appraised for each catchment, using the gridded in situ meteorological dataset (CN05.1) as a baseline. Then, the ability of IMERG and ERA5-Land datasets in simulating the fraction, magnitude, and decade change of floods and CMHFs is comprehensively evaluated by forcing the XAJ and GR4J hydrological models. The results show that: (a) the IMERG and ERA5-Land perform similarly in terms of precipitation occurrences and intensity; (b) the IMERG yields discernably better performance than the ERA5-Land in streamflow simulation, with 71.7% and 50.8% of catchments showing the Kling–Gupta efficiency (KGE) higher than 0.5, respectively; (c) both datasets can roughly capture the frequency, magnitude, and their changes of floods and CMHFs in recent decades, with the IMERG exhibiting more satisfactory accuracy. Our results indicate that satellite remote sensing and atmospheric reanalysis precipitation can not only simulate individual hydrological extremes in most regions, but monitor compound events such as CMHF episodes, and especially, the IMERG satellite can yield better performance than the ERA5-Land reanalysis.

Retrospective sub-seasonal forecasts of extreme precipitation events in the Arabian Peninsula using convective-permitting modeling

This work demonstrates the potential of extreme cool-season precipitation forecasts in the Arabian Peninsula (AP) at sub-seasonal time scales, identifies regions and periods of forecast opportunity, and investigates the predictability of synoptic-scale forcing at sub-seasonal time scales. To this end, we simulate 18 extreme precipitation events using the convective-permitting Weather Research and Forecasting (CP-WRF) model with lateral boundary forcing from the European Centre of Medium-range Weather Forecasts sub-seasonal to seasonal reforecasts (ECMWF S2S reforecasts). The simulations are initiated at one-, two-, and three-week lead times. At all lead times, the CP-WRF improves the mean accumulated precipitation in the extratropical synoptic regimes over the west coastal and central AP and the central Red Sea compared to ECMWF S2S reforecasts as evaluated against the Global Precipitation Measurement Final (GPMF) and King Abdullah University of Science and Technology reanalysis (KAUST-RA) precipitation products. Based on categorical statistics with a threshold of 20 mm accumulated precipitation over 7 days, the CP-WRF skillfully forecasts the precipitation over Jeddah, the west coast of AP, and the central Red Sea up to three-week lead time. The relative operating characteristic curve reconfirmed the high forecast skill of the CP-WRF, with an area under the curve above 0.5 in most of the events at all lead times. Finally, the correlation coefficients between the ECMWF S2S reforecast and ECMWF reanalysis interim 500 hPa geopotential heights are higher in the events associated with an extratropical synoptic regime than in those associated with a tropical synoptic regime, regardless of lead time. Therefore, the convective-permitting model can potentially improve the accuracy of extreme winter precipitation forecasts at two-and three-week lead times over Jeddah, the west coast of AP, and the central Red Sea in the extratropical synoptic regime.

Evaluation of satellite precipitation products over Mexico using Google Earth Engine

Abstract Satellite-based precipitation products and reanalysis precipitation products have the potential to overcome the lack of information in regions where there are no or insufficient rain gauges to achieve any hydrological study. The Google Earth Engine (GEE) data analysis platform has products in its repository with global coverage that offers different geospatial information capable of measuring the amount of precipitation. However, it is necessary to evaluate the reliability of the products. There are precipitation information biases in Mexico due to the scarce presence of gauging stations, failed operations, access difficulty, and data capture errors. This study evaluates the reliability of satellite and reanalysis precipitation products hosted in the GEE repository against rain gauge observation from 2001 to 2017 using data from 4,658 stations over Mexico. The evaluation was carried out using statistical indicators comparing the behavior across different topographic, climatic, and temporal conditions. The results exhibit that the performance of the products hosted in GEE seems to depend on elevation conditions for other climatic regions in Mexico. The results show that all products can capture the general precipitation patterns at annual, seasonal, and monthly scales; however, the accuracy of the product is clearly lower at a daily scale. All products are highly biased on low precipitation events.