Satellite-based Precipitation Estimates Research Articles

An unsupervised adaptive fusion framework for satellite-based precipitation estimation without gauge observations

An unsupervised adaptive fusion framework for satellite-based precipitation estimation without gauge observations

Performance Assessment of Satellite-Based Precipitation Products in the 2023 Summer Extreme Precipitation Events over North China

In the summer of 2023, North China experienced a rare extreme precipitation storm due to Typhoons Doksuri and Khanun, leading to significant secondary disasters and highlighting the urgent need for accurate rainfall forecasting. Satellite-based quantitative precipitation estimation (QPE) products like Integrated Multi-Satellite Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) from the Global Precipitation Measurement (GPM) Mission have great potential for enhancing forecasts, necessitating a quantitative evaluation before deployment. This study uses a dense rain gauge as a benchmark to assess the accuracy and capability of the latest version 7B IMERG and version 8 GSMaP satellite-based QPE products for the 2023 summer extreme precipitation in North China. These satellite-based QPE products include four satellite-only products, namely IMERG early run (IMERG_ER) and IMERG late run (IMERG_LR), GSMaP near-real-time (GSMaP_NRT), and GSMaP microwave-infrared reanalyzed (GSMaP_MVK), along with two gauge-corrected products, namely IMERG final run (IMERG_FR) and GSMaP gauge adjusted (GSMaP_Gauge). The results show that (1) GSMaP_MVK, IMERG_LR, and IMERG_FR effectively capture the space distribution of the extreme rainfall, with relatively high correlation coefficients (CCs) of approximately 0.77, 0.75, and 0.79. The IMERG_ER, GSMaP_NRT, and GSMaP_Gauge products exhibit a less accurate spatial pattern capture (CCs about 0.66, 0.73, and 0.67, respectively). Each of the six QPE products tends to underestimate rainfall (RBs < 0%). (2) The IMERG products surpass the corresponding GSMaP products in serial rainfall measurement. IMERG_LR demonstrates superior performance with the lowest root-mean-square error (RMSE) (about 0.38 mm), the highest CC (0.97), and less underestimation (RB about −6.37%). (3) The IMERG products at rainfall rates ≥ 30 mm/h, GSMaP_NRT and GSMaP_MVK products at rainfall rates ≥ 55 mm/h, and GSMaP_Gauge products at ≥ 40 mm/h showed marked limitations in event detection, with a near-zero probability of detection (POD) and a nearly 100% false alarm ratio (FAR). In this extreme precipitation event, caution is needed when using the IMERG and GSMaP products.

NPreciSe - An Automated Satellite Precipitation Product Assessment Tool.

Satellite-based Quantitative Precipitation Estimates (QPE) are indirect estimates of precipitation rates and as such are often prone to errors, warranting a need for characterizing the associated uncertainties before being used in application-specific studies. Moreover, multiple satellite-based QPE products are offered through different agencies, each with their own specifications, formats and requirements, posing a challenge to understanding the products uncertainties. This manuscript presents a standardized validation system named NPreciSe - NOAA Satellite-based Precipitation Validation System, which assesses the performance of satellite-based precipitation products in near real-time over the continental United States. NPreciSe is coupled with a user-interactive web platform and built using an open-source software, Python. It is structured to help (1) the end-users determine the best satellite QPE for their specific application, and (2) the algorithm developers identify systematic biases in QPE retrievals. This manuscript presents the capabilities of the NPreciSe, discusses the methodology adopted in developing the standardized validation system, and introduces the web portal.

Has IMERG_V07 Improved the Precision of Precipitation Retrieval in Mainland China Compared to IMERG_V06?

Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) (IMERG) is the primary high spatiotemporal resolution precipitation product of the GPM era. To assess the applicability of the latest released IMERG_V07 in mainland China, this study systematically evaluates the error characteristics of IMERG_V07 from the perspective of different seasons, precipitation intensity, topography, and climate regions on an hourly scale. Ground-based meteorological observations are used as the reference, and the performance improvement of IMERG_V07 relative to IMERG_V06 is verified. Error evaluation is conducted in terms of precipitation amount and precipitation frequency, and an improved error component procedure is utilized to trace the error sources. The results indicate that IMERG_V07 exhibits a smaller RMSE in mainland China, especially with significant improvements in the southeastern region. IMERG_V07 shows better consistency with ground station data. IMERG_V07 shows an overall improvement of approximately 4% in capturing regional average precipitation events compared to IMERG_V06, with the northwest region showing particularly notable enhancement. The error components of IMERG_V06 and IMERG_V07 exhibit similar spatial distributions. IMERG_V07 outperforms V06 in terms of lower Missed bias but slightly underperforms in Hit bias and False bias compared to IMERG_V06. IMERG_V07 shows improved ability in capturing precipitation frequency for different intensities, but challenges remain in capturing heavy precipitation events, missing light precipitation, and winter precipitation events. Both IMERG_V06 and IMERG_V07 exhibit notable topography dependency in terms of Total bias and error components. False bias is the primary error source for both versions, except in winter, where high-altitude regions (DEM > 1200 m) primarily contribute to Missed bias. IMERG_V07 has enhanced the accuracy of precipitation retrieval in high-altitude areas, but there are still limitations in capturing precipitation events. Compared to IMERG_V06, IMERG_V07 demonstrates more concentrated error component values in the four climatic regions, with reduced data dispersion and significant improvement in Missed bias. The algorithm improvements in IMERG_V07 have the most significant impact in arid regions. False bias serves as the primary error source for both satellite-based precipitation estimations in the four climatic regions, with a secondary contribution from Hit bias. The evaluation results of this study offer scientific references for enhancing the algorithm of IMERG products and enhancing users’ understanding of error characteristics and sources in IMERG.

U-HRNet+: a real-time precipitation estimation method based on the fusion of temporal and spatial domain features

ABSTRACT The precipitation estimation with high spatial and temporal resolution over Tibet is of great significance for monitoring the stability of the plateau ecosystem and early warning of natural disasters. In response to the problem of the unsatisfactory accuracy in traditional real-time precipitation inversion methods and quantitative accuracy estimation products in this region. This paper adopts deep-learning methods to explore the corresponding relationship between precipitation and multi-band observation data from geostationary satellite imagery. The U-High Resolution Network (U-HRNet) as backbone is fully exploited to generate multi-scale features and fused using attention gates to extract high-resolution spatial representations representing precipitation clouds, thereby achieving precipitation estimation of target clouds at different scales. In addition, in response to the characteristics of high precipitation intensity and drastic cloud cluster changes in summer convective weather within a short period of time, a multi-head attention mechanism is introduced to extract temporal information from time series inputs and fuse it with spatial features to improve the accuracy of real-time precipitation estimation. Compared with the operational precipitation products and baseline deep-learning models, the classification metrics such as Probability of detection (POD), False Alarm Ratio (FAR) and Critical success index (CSI) show that the proposed model (U-HRNet+) has better performance in test set and has high generalization ability for estimating short-term heavy rainfall over Tibet and its surrounding areas in summer. Therefore, the proposed model has the potential to serve as a more accurate and reliable satellite-based precipitation estimation product.

Investigating the utility of satellite-based precipitation products for simulating extreme discharge events: an exhaustive model-driven approach for a tropical river basin in India.

Satellite-based precipitation estimates are a critical source of information for understanding and predicting hydrological processes at regional or global scales. Given the potential variability in the accuracy and reliability of these estimates, comprehensive performance assessments are essential before their application in specific hydrological contexts. In this study, six satellite-based precipitation products (SPPs), namely, CHIRPS, CMORPH, GSMaP, IMERG, MSWEP, and PERSIANN, were evaluated for their utility in hydrological modeling, specifically in simulating streamflow using the Variable Infiltration Capacity (VIC) model. The performance of the VIC model under varying flow conditions and timescales was assessed using statistical indicators, viz., R2, KGE, PBias, RMSE, and RSR. The findings of the study demonstrate the effectiveness of VIC model in simulating hydrological components and its applicability in evaluating the accuracy and reliability of SPPs. The SPPs were shown to be valuable for streamflow simulation at monthly and daily timescales, as confirmed by various performance measures. Moreover, the performance of SPPs for simulating extreme flow events (streamflow above 75%, 90%, and 95%) using the VIC model was assessed and a significant decrease in the performance was observed for high-flow events. Comparative analysis revealed the superiority of IMERG and CMORPH for streamflow simulation at daily timescale and high-flow conditions. In contrast, the performances of CHIRPS and PERSIANN were found to be poor. This study highlights the importance of thoroughly assessing the SPPs in modeling diverse flow conditions.

Hydrologic applicability of satellite-based precipitation estimates for irrigation water management in the data-scarce region

Reliable precipitation estimates are crucial for planning and managing water resources, monitoring hydrologic extremes, and fulfilling irrigation water requirements. Accurate precipitation estimates are particularly challenging in complex mountain terrains, where monitoring gauges are often sparsely distributed due to their remote locations, and high installation and long-term operation costs. Recent advances in satellite-based precipitation estimates offer promising opportunities to improve our understanding of hydrologic processes and their applications for irrigation water management. Several datasets are available varying considerably in terms of their data sources, quality control methods, estimation procedure, and spatiotemporal resolutions. Choosing the most suitable dataset for a particular application is a complex task. In this study, we (1) evaluate the performance of six satellite-based precipitation estimates (SPEs): i) CHIRPS v2.0, ii) CMORPH v1.0, iii) ERA5, iv) IMERG v6, v) MSWEP v2.8, and vi) PERSIANN-CDR against the gauge precipitation using continuous statistical and categorical indices, (2) integrate SPEs with a calibrated semi-distributed hydrologic model to predict streamflow, and (3) demonstrate practical implications of improved streamflow prediction for irrigation water management in the central Himalayan region, Nepal. Our results illustrate that satellite-based precipitation estimates have competitive performance in capturing a wide range of rainfall characteristics, with demonstrated variability across river basins and time scales. There are no significant discrepancies observed in satellite-based precipitation estimates for estimating irrigation water requirements for the three major crops (maize, wheat, and paddy) during the cropping period across the selected river basins, showing a greater promise for irrigation water management planning and decision making.

Evaluation of ERA5 and CHIRPS rainfall estimates against observations across Ethiopia

Satellite-based precipitation estimates and global reanalysis products bear the promise of supporting the development of accurate and timely climate information for end users in sub-Sharan Africa. The accuracy of these global models, however, may be reduced in data-scarce regions and should be carefully evaluated. This study evaluates the performance of ERA5 reanalysis data and CHIRPS precipitation data against ground-based measurements from 167 rain gauges in Ethiopia, a region with complex topography and diverse climates. Focusing over a 38-year period (1981–2018), our study utilizes a point-to-pixel analysis to compare daily, monthly, seasonal, and annual precipitation data, conducting an evaluation based on continuous and categorical metrics. Our findings indicate that over Ethiopia CHIRPS generally outperforms ERA5, particularly in high-altitude areas, demonstrating a better capability in detecting high-intensity rainfall events. Both datasets, however, exhibit lower performance in Ethiopia's lowland regions, possibly the influence of sparse rain gauge networks informing gridded datasets. Notably, both CHIRPS and ERA5 were found to underestimate rainfall variability, with CHIRPS displaying a slight advantage in representing the erratic nature of Ethiopian rainfall. The study’s results highlight considerable performance differences between CHIRPS and ERA5 across varying Ethiopian landscapes and climatic conditions. CHIRPS’ effectiveness in high-altitude regions, especially for daily rainfall estimation, emphasizes its suitability in similar geographic contexts. Conversely, the lesser performance of ERA5 in these areas suggests a need for refined calibration and validation processes, particularly for complex terrains. These insights are essential for the application of satellite-based and reanalysis of rainfall data in meteorological, agricultural, and hydrological contexts, particularly in topographically and climatically diverse regions.

Regional Precipitation Regimes and Evaluation of National Precipitation Datasets against Satellite-Based Precipitation Estimates, Republic of Georgia

Abstract Analyzing water resources in areas with few hydrometeorological stations, such as those in post-Soviet countries, is difficult due to station closures after 1989. In Caucasus, evaluations often rely on outdated data from nearby rivers. We evaluated one national-level precipitation dataset, the Water Balance of Georgia (WBG) with two satellite-based precipitation products from 1981 to 2021, including the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data and CHIRPS blended with a dense rain gauge network (geoCHIRPS). We modeled mean annual precipitation from geoCHIRPS as a function of coastal distance and elevation. CHIRPS underestimated precipitation in the cold and wet seasons (R2 = 0.74, r = 0.86) and overestimated dry season precipitation, while geoCHIRPS performed well in all seasons (R2 = 0.86, r = 0.92). Distance from the coast was a more important predictor of precipitation than elevation in western Georgia, while precipitation correlated positively with elevation in the east. At four hydroelectric plants, the underperformance as a percentage of capacity (∼37%) corresponds with the percentage difference between differences in precipitation products (∼38%), suggesting that plants designed based on WBG may be systematically overdesigned, but further work is needed to determine the reasons for the underperformance of the plants and frequency. We conclude that 1) the existing WBG does not accurately reflect elevation–precipitation relationships near the coast, and 2) for accurate analysis of spatiotemporal precipitation variability and its impacts on hydropower generation and environmental and sustainable water resource management, it is essential to calibrate satellite-based precipitation estimates with additional rain gauge data.

Investigating twelve mainstream global precipitation datasets: Which one performs better on the Tibetan Plateau?

Acquiring precipitation information is a critical yet challenging task for the Tibetan Plateau (TP) with sparse gauges and high altitudes. Presently, there is a multitude of operational precipitation products merged from different data sources. A major concern within the TP science community is which one of these available precipitation datasets performs best on the TP. To address this concern, we selected and compared twelve widely used global precipitation datasets, including six satellite-based precipitation estimates (GSMaP-Gauge, IMERG-Final, TMPA-v7, CMORPH-BLD, PERSIANN-CDR, CHIRPS), five reanalysis data (ERA5, JRA-55, MERRA2, GLDAS, NCEP2), and one multi-source fusion product (MSWEP). Benchmarked against both automatic weather stations and independent field observations, we comprehensively investigate the spatiotemporal accuracy and regional applicability of these global precipitation products on the TP. The evaluation results indicate that almost all the datasets have a consistent tendency to overestimate lower precipitation rates and underestimate higher ones. However, four out of the twelve studied datasets (i.e., GSMaP-Gauge, IMERG-Final, ERA5, and MSWEP) significantly outperform others. Specifically, the two GPM-based satellite precipitation products, GSMaP-Gauge and IMERG-Final, can better reflect the difference between different rain intensities and perform better at moderate rain rates. In contrast, the reanalysis-based ERA5 exhibits a more clustered data series, with relatively higher accuracy at low precipitation rates. Moreover, ERA5 demonstrates a stable superiority in the mountainous areas characterized by complex local terrains. Furthermore, we find that the applicability of different precipitation datasets has an obvious region-related feature. Thus, a regional distribution map was made to clearly outline the optimal choice of current mainstream global precipitation datasets over different climate zones within the TP. This study can offer valuable insights for TP researchers, especially aiding them to select the appropriate precipitation data for their specific studies.

Open gridded climate datasets can help investigating the relation between meteorological anomalies and geomorphic hazards in mountainous areas

The initiation of geomorphic hazards in mountainous environments is highly susceptible to temperature and precipitation forcing. To assess future changes in these hazards, statistical methods linking meteorological anomalies with the occurrence of past hazards can be employed. These methods are currently trained using long records from in-situ meteorological stations. This requires large efforts for data access, collection and homogenisation, which severely limit the scalability of these approaches. Here, we use a consolidated statistical method and a vast catalogue of geomorphic hazards occurred across the Alpine range to show that open-access datasets of gridded temperature and satellite-based precipitation estimates can be used as surrogates of in-situ observations. Further, we find that satellite data can capture precipitation anomalies leading to debris flows that are missed by in-situ observations.

A First Step towards Meteosat Third Generation Day-2 Precipitation Rate Product: Deep Learning for Precipitation Rate Retrieval from Geostationary Infrared Measurements

The estimate of precipitation from satellite measurements is an indirect estimate if compared to rain gauges or disdrometer measurements, but it has the advantage of complete coverage over oceans, mountainous regions, and sparsely populated areas where other sources of precipitation data (e.g., weather radar) are unavailable or unreliable. Among the satellite-based precipitation estimates, geostationary (GEO) data ensure the highest spatial and temporal resolution. At the same time, the IR/VIS channels deployed on GEO satellites have lower capabilities than microwave (MW) channels in characterizing the cloud structure. Machine learning (ML) techniques can be considered a powerful tool to overcome the limitations related to the physical relationship between IR/VIS channels and precipitation estimation. This study describes the development of a convolutional neural network (U-Net) to retrieve the precipitation rate using IR measurements only from the Meteosat Second Generation (MSG) satellite. Its performances are evaluated through a comparison with H SAF and NASA operational products (e.g., H60B or H03B and IMERG-E, respectively), of which the algorithms are based on different principles. The results highlight a lower error in precipitation rate estimates for the U-Net with respect to the other products but also some issues in correctly estimating the more intense precipitation (>5 mmh−1). On the other hand, the precipitation detection capabilities of the U-Net outperform the H SAF products for lower precipitation rate, while IMERG-E shows the best performance regardless of the precipitation regime. Furthermore, the U-Net is able to account for and correct the parallax displacement that affects the measurement as the satellite viewing angle increases.

Paired Satellite and NWP Precipitation for Global Flood Forecasting

Abstract Precipitation data are known to be the key driver of hydrological simulations. Hence, reliable quantitative precipitation estimates and forecasts are vital for accurate hydrological forecasting. Satellite-based precipitation estimates from Integrated Multi-satellitE Retrievals for GPM Early Run (IMERG-E) and forecasted precipitation from NASA’s Goddard Earth Observing System Forward Processing (GEOS-FP) have shown values in global flood nowcasting and forecasting. However, few studies have comprehensively evaluated their hydrological performance let alone explored the potential value of combining them. Therefore, this study undertakes a quasi-global evaluation of their utility in real-time hydrological monitoring and 1–5-day forecasting with the Dominant River Tracing-Routing Integrated with Variable Infiltration Capacity (VIC) Environment (DRIVE) model. The gauge-corrected IMERG Final Run precipitation estimates and corresponding hydrological simulation are used as the references. Results showed that the hit bias is the dominant error source of IMERG-E, while the false precipitation is more noticeable in GEOS-FP. In terms of hydrological performance, the GEOS-FP-driven model (DRIVE-FP) performance is close to the IMERG-E-driven model (DRIVE-E) performance on day 1, indicating that GEOS-FP could nicely fill the gap of nowcasting caused by the IMERG-E time latency. For longer lead-time forecasts, the bias tends to diminish in most regions, likely because the under- or overestimation in IMERG-E is generally offset by the distinct types of misestimation in GEOS-FP. The skillful initial hydrological conditions present outperformed forecasts in most regions, except for tropical areas where the accuracy of GEOS-FP prevails. Overall, this study provides a valuable view of the combined use of IMERG-E and GEOS-FP precipitation in the context of hydrological nowcasts and forecasts.

Evaluating drought monitoring utility of the top-down and bottom-up satellite precipitation products over mainland China from a three-dimensional perspective

Satellite-based quantitative precipitation estimates (QPEs) have shown great potential in the precipitation-related extremes estimation. However, little is known about the capability of different QPEs to monitor the dynamic evolutions of drought on various spatio-temporal scales. Here, the drought utility of two top-down QPEs (i.e., Climate Hazards Group InfraRed Precipitation with Stations, CHIRPS; Integrated Multi-SatellitE Retrievals for Global precipitation measurement-Final run, IMERG-F) and one bottom-up QPE (i.e., Soil Moisture to Rain-Advanced SCATterometer, SM2RAIN-ASCAT), is evaluated comprehensively in mainland China, based on three commonly used drought indices (Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, Composite index of meteorological drought). The results indicate that the top-down QPEs generally show better performance in detecting drought occurrence over the humid and low-altitude regions in eastern China than the arid and high-altitude regions in western China. IMERG-F performs best in detecting drought occurrence in the entire mainland China, followed by CHIRPS, and the least satisfactory by SM2RAIN-ASCAT. The trajectory of drought centroids and the temporal variability of drought characteristics are highly dependent on both the drought indices selected and QPEs used. IMERG-F and CHIRPS can better reproduce the trajectories of large-scale drought events (e.g., the locations of drought origin and termination) than SM2RAIN-ASCAT. The ability in tracking the drought centroid trajectory of all three QPEs is higher at the smaller spatial scales, also it improves from the semi-arid to humid regions. Our results highlight the superiority of the top-down QPEs in drought monitoring compared with the bottom-up QPE in mainland China, particularly over the humid regions. The application of SM2RAIN-ASCAT in drought monitoring to the large-scale arid areas should be cautious.

Ground validation of satellite-based precipitation estimates over poorly gauged catchment: the case of the Drâa basin in Central-East Morocco

Ground validation of satellite-based precipitation estimates over poorly gauged catchment: the case of the Drâa basin in Central-East Morocco

Assessment and Data Fusion of Satellite-Based Precipitation Estimation Products over Ungauged Areas Based on Triple Collocation without In Situ Observations

Reliable assessment of satellite-based precipitation estimation (SPE) and production of more accurate precipitation data by data fusion is typically challenging in sparsely gauged and ungauged areas. Triple collocation (TC) is a novel assessment approach that does not require gauge observations; it provides a feasible solution for this problem. This study comprehensively validates the TC performance for assessing SPEs and performs data fusion of multiple SPEs using the TC-based merging (TCM) approach. The study area is the Tibetan Plateau (TP), a typical area lacking gauge observations. Three widely used SPEs are used: the integrated multi-satellite retrievals for global precipitation measurement (IMERG) “early run” product (IMERG-E), the precipitation estimation from remotely sensed information using artificial neural networks (PERSIANN) dynamic infrared (PDIR), and the Climate Prediction Center (CPC) morphing technique (CMORPH). Validation of the TC assessment approach shows that TC can effectively assess the SPEs’ accuracy, derive the spatial accuracy pattern of the SPEs, and reveal the accuracy ranking of the SPEs. TC can also detect the SPEs’ accuracy patterns, which are difficult to obtain from a traditional approach. The data fusion results of the SPEs show that TCM incorporates the regional advantages of the individual SPEs, providing more accurate precipitation data than the original SPEs, revealing that data fusion is reasonable and reliable in ungauged areas. In general, the TC approach performs well for the assessment and data fusion of SPEs, showing reasonable applicability in the TP and other areas lacking gauge data than other methods because it does not rely on gauge observations.

Downscaling Daily Satellite-Based Precipitation Estimates Using MODIS Cloud Optical and Microphysical Properties in Machine-Learning Models

This study proposes a method for downscaling the spatial resolution of daily satellite-based precipitation estimates (SPEs) from 10 km to 1 km. The method deliberates a set of variables that have close relationships with daily precipitation events in a Random Forest (RF) regression model. The considered variables include cloud optical thickness (COT), cloud effective radius (CER) an cloud water path (CWP), derived from MODIS, along with maximum and minimum temperature (Tx, Tn), derived from CHIRTS. Additionally, topographic features derived from ALOS-DEM are also investigated to improve the downscaling procedure. The approach consists of two main steps: firstly, the RF model training at the native 10 km spatial resolution of the studied SPEs (i.e., IMERG) using rain gauge observations as targets; secondly, the application of the trained RF model at a 1 km spatial resolution to downscale IMERG from 10 km to 1 km over a one-year period. To assess the reliability of the method, the RF model outcomes were compared with the rain gauge records not considered in the RF model training. Before the downscaling process, the CC, MAE and RMSE metrics were 0.32, 1.16 mm and 6.60 mm, respectively, and improved to 0.48, 0.99 mm and 4.68 mm after the downscaling process. This corresponds to improvements of 50%, 15% and 29%, respectively. Therefore, the method not only improves the spatial resolution of IMERG, but also its accuracy.

Assessment of cloud microphysics and cumulus convection schemes to model extreme rainfall events over the Paraiba do Sul River Basin

Highly urbanized areas with complex terrains and multiple land uses pose challenges to extreme precipitation forecasts. The present study investigates the performance of several cloud microphysics and cumulus convection parameterizations on a severe rainfall event over the Paraiba do Sul River Basin, an area periodically affected by heavy precipitation. The Weather Research and Forecasting (WRF) model was applied to perform high resolution simulations, and the results were compared against ground observations, weather radar data, and satellite-based precipitation estimates. The simulations tend to underestimate precipitation by an average bias of 55%; however, some experiments showed good fittings in terms of time correlations (over 90%). Overall, the radar vertical profile of reflectivity agrees with observations, where the height of the warm and mixed layers was accurately simulated, despite overpredictions of convective depth (by up to 3 km in regions of intense ascending currents). Typically, WRF was more sensitive to distinct microphysics schemes rather than cloud convection ones. Also, urban areas showed greater precipitation and hydrometeor content in comparison with non-urban ones, even under strong synoptic-scale forcing. The framework was able to reproduce convectively active environments, supporting its application on operational runs over urbanized regions for warning issuing and decision-making.

Multi-criteria evaluation of satellite-based precipitation estimates over agro-climatic zones of India

Satellite-based precipitation estimates (SPEs) available at a high spatio-temporal resolution with near-global coverage are substitutes for ground-based datasets. In this study, the quality of popular SPEs was evaluated at daily time scale over agro-climatic (ACL) zones of India. The SPEs were analyzed in two different groups, i.e., (i) satellite-only precipitation estimates (SOPEs) including 5 products namely, CMORPH, PERSIANN, SM2RAIN-ASCAT, SM2RAIN-CCI, and SM2RAIN-GPM, and (ii) gauge-corrected satellite precipitation estimates (GCSPEs) including 4 products namely, CHIRPS, GSMaP, IMERG and MSWEP. The analysis was carried out by quantifying the qualitative and quantitative performance metrics as well as by decomposing the errors into random and systematic components. In general, SPEs underestimated the high rainfall events and overestimated the low rainfall events. The performance of SPEs was observed to be poor over the western Himalayan region. The efficiency and agreement of Global Precipitation Measurement (GPM) based products (including SM2RAIN-GPM, GSMaP and IMERG) with observed precipitation were better. The superiority of SM2RAIN-GPM, GSMap and IMERG products has also been supported by the lower magnitude of errors and biases. The error decomposition reveals high random errors in most of the SPEs; however, SM2RAIN-based products have low random error. It was also observed that the total bias is highly underestimated due to opposite-natured bias components. Further, the association of vegetation and topographic complexity with the performance of precipitation products was investigated, and a negative/positive association of performance metrics of SPEs with topographic/vegetation characteristics was observed. A multi-criteria decision-making (MCDM) approach was then employed to rank the datasets based on multiple performance indicators. MCDM approach reveals the superiority of SM2RAIN-GPM product amongst SOPEs and GSMaP (followed by IMERG) amongst GCSPEs over a majority of the regions of India.

Editorial for Special Issue “Remote Sensing of Precipitation: Part III”

This Special Issue of Remote Sensing, which is the third in a series entitled “Remote Sensing of Precipitation”, comprises a collection of ten papers devoted to remote sensing applications for measuring precipitation; these include new satellite technologies for the remote sensing of precipitation, the validation of satellite-based precipitation estimates using rain gauge measurements and surface radar estimates, and comparisons between gridded precipitation data [...]