Satellite Precipitation Estimates Research Articles

Performance assessment of satellite rainfall estimates in rain detection capabilities at different thresholds over Nigeria

ABSTRACT In Nigeria, where rainfall plays a pivotal role in agriculture and disaster management, assessing the accuracy of satellite rainfall estimates at various thresholds is imperative. This study assesses the areal averages of nine satellite precipitation estimates (SPEs) against 48 ground-based raingauge observations in Nigeria. Employing categorical statistical metrics and compromise programming, the research assesses the performance of SPEs at seven rainfall thresholds. Results reveal that 82–94% of rainfall estimates align with ground truth, while 5–14% of rainy days are incorrectly detected by raingauges. Notably, SPEs tend to overestimate low rain between 1 mm and 5 mm d−1 and underestimate low heavy rain rates (10 mm ≤ rain < 20 mm), impacting flood monitoring and disaster preparedness. Global Precipitation Climatology Centre (CPCC), Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT) African Rainfall Climatology And Time series (TARCAT), and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) emerge as more reliable SPEs, particularly in highland regions. This research underscores the importance of choosing appropriate satellite precipitation estimates for enhanced disaster preparedness and resource management across different Nigerian regions.

Quantile mapping technique for enhancing satellite-derived precipitation data in hydrological modelling: a case study of the Lam River Basin, Vietnam

ABSTRACT Accurate precipitation is crucial for hydrological modelling in sparse gauge regions like the Lam River Basin (LRB) in Vietnam. Gridded precipitation data from satellite and numerical models offer significant advantages in such areas. However, satellite precipitation estimates (SPEs) are subject to uncertainties, especially in high variable of topography and precipitation. This study focuses on enhancing the accuracy of Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), Climate Prediction Center morphing technique (CMORPH) using the Quantile Mapping (QM) technique, aligning the cumulative distribution functions of the observed precipitation data with those of the SPEs, and assessing the impact on hydrological predictions. The study highlights that the post-correction IMERG precipitation using QM performs better than other data sets, enhancing the hydrological model's performance for the LRB at different temporal scales. Nash–Sutcliffe efficiency values increased from 0.60 to 0.77, surpassing the original IMERG's 0.52 to 0.74, and correlation coefficients improved from 0.79 to 0.89 (compared with the previous 0.75–0.86) for hydrological modelling. Additionally, Percent Bias (PBIAS) decreased from approximately −1.66 to −2.21% (contrasting with the initial −20.22 and 4.6%) with corrected SPEs. These findings have implications for water resource management and disaster risk reduction initiatives in Vietnam and other countries.

A study of rainfall thresholds for landslides in Badung Regency using satellite-derived rainfall grid datasets

Integrating field rainfall data with satellite data improves data accuracy and overcomes rainfall data limitations for rain thresholds. Integration can involve field rainfall data, satellite rainfall data, or a different satellite dataset. Merging these rainfall data sources provides more spatial coverage of satellite data. To determine how well rainfall thresholds predict rainfall-triggered landslides, the threshold model must be validated. This study will evaluate satellite rainfall data before and after integration in developing a rainfall threshold model for landslide prediction in Badung Regency. To do so, the study used a cumulative rainfall threshold over 3, 7, 15, and 30 days and two rainfall satellite products (integrated merged multi-satellite retrievals (IMERG) and precipitation estimation from remotely sensed information using artificial neural networks (PERSIANN)). Median, first, and third quartiles were used to set thresholds. The area under the curve (AUC) was calculated to validate rainfall threshold outcomes using receiver operating characteristic (ROC) curves. Analysis showed that integrating satellite rainfall data into the rainfall threshold model for landslide prediction yields better results than other methods. An AUC value of 0.903 (90.3%) for the 30-day cumulative rainfall thresholds supports this claim. This model could be a good input for a landslide early warning system in Badung Regency.

AttUnet_R_SFT: A Novel Network to Explore the Application of Complex Terrain Information in Satellite Precipitation Estimating

AbstractAccurate rainfall measurement with a precise spatial and temporal resolution is essential for making informed decisions during disasters and conducting scientific studies, particularly in regions characterized by intricate terrain and limited coverage of automated weather stations. Retrieval of precipitation with satellite is currently the most effective means to obtain precipitation over large‐scale areas. The key to enhancing the accuracy of precipitation estimation and forecasting in regions with complex terrain lies in effectively integrating satellite data with topographic information. This paper introduces a deep learning approach called AttUnet_R_SFT that utilizes high temporal, spatial, and spectral resolution data obtained from the Fengyun 4A satellite, and incorporates the Deep Spatial Feature Transform (SFT) layer to incorporate geographical data for estimating half‐hourly precipitation in northeastern China. We assess it by compared to operational near‐real‐time satellite precipitation products demonstrated to be successful in estimating precipitation and baseline deep learning models. According to the experimental findings, the AttUnet_R_SFT model outperforms practical precipitation products and baseline deep learning models in both identifying and estimating precipitation. The main enhancement of the model performance is shown in the windward slope of the Greater Khingan Mountains as a result of the successful incorporation of geographical data. Hence, the suggested framework holds the capability to function as a superior and dependable satellite‐derived precipitation estimation solution in regions characterized by intricate terrain and infrequent rainfall. The findings of this study indicate that the utilization of deep learning algorithms for satellite precipitation estimation shows potential as a fruitful avenue for further research.

Multi-scale analysis of satellite, reanalysis and muti-source precipitation estimates over the Tibetan Plateau

Due to the important hydrological and meteorological significance, the performance of different kinds of precipitation products for the Tibetan Plateau (TP) has been evaluated in previous studies. However, the performance of the products on a high spatial-temporal scale has not been clearly shown. Based on the Chinese Meteorological Administration (CMA) ground rain gauge observations during 2017–2018 over the TP, this paper analyzes the multi-scale performance of four satellite-based datasets (IMERG_Uncal, IMERG_Cal, GSMaP_MVK, and GSMaP_Gauge), two reanalysis datasets (ERA5 and ERA5-L), and one multi-source merged dataset (CMFD) for different precipitation intensities with a special attention to the characterization of spatiotemporal variability of heavy precipitation. Firstly, this study examines how the quality of precipitation products varies with time scale and precipitation intensity. CMFD generally shows the best quality and has the most stable quality at all temporal scales. Satellite products perform better in summer while reanalysis products show better results in cold seasons. Another significant issue is the impact of gauge-based calibration. GSMaP_Gauge with daily-scale gauge calibration is often considered to outperform other products. However, our study finds that GSMaP_Gauge is problematic at smaller spatiotemporal scales, especially in capturing heavy precipitation. The long-term performance of multi spatiotemporal scale comparison of different precipitation products is the focus of this study, which can help with their application and improvement.

Which Error Components in TRMM-Based Multisatellite Precipitation Estimates Reduce over Chinese Mainland after Official Bias Adjustments: Systematic or Random?

Abstract Climatological calibration algorithm (CCA) and satellite–gauge combination (SG) are two official bias adjustments for satellite precipitation estimates (SPE) in the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA). The CCA is designed for the near-real-time SPEs, while the SG procedure is a final step to merge pure SPEs with gauge observations. This study explored the impacts of CCA and SG on the systematic and random errors of TMPA SPEs. The errors of TMPA version-7 near-real-time products before and after CCA (RT_UC, RT_C), and the research product TMPA 3B42 (V7), were decomposed into systematic and random components, benchmarked by the China Gauge-based Daily Precipitation Analysis (CGDPA). After being calibrated by CCA, RT_C reduced the systematic errors relative to RT_UC over the Chinese mainland, except in the Tibetan Plateau and Tianshan Mountains. However, CCA did not aid in reducing random errors; instead, it even exacerbated the random errors. On the other hand, the SG merging is more effective in reducing systematic errors of SPEs than CCA calibration because of the direct inclusion of simultaneous gauge data from the Global Precipitation Climatology Centre (GPCC). We also found that SG merging reduced the random errors of pure SPEs over regions with relatively higher elevations. Despite lower random errors in V7 over the complex terrain region, the SG unfavorably increased the random errors over southeastern China. The results reported here may offer valuable insights into the effects of CCA and SG techniques drawn from TMPA, with the potential to advance the development of bias-adjusting algorithms for SPEs in the future.

Impact of satellite precipitation estimation methods on the hydrological response: case study Wadi Nu’man basin, Saudi Arabia

Impact of satellite precipitation estimation methods on the hydrological response: case study Wadi Nu’man basin, Saudi Arabia

Disentangling Satellite Precipitation Estimate Errors of Heavy Rainfall at the Daily and Sub-Daily Scales in the Western Mediterranean

In the last decade, substantial improvements have been achieved in quantitative satellite precipitation estimates, which are essential for a wide range of applications. In this study, we evaluated the performance of Integrated Multi-satellitE Retrievals for GPM (IMERG V06B) at the sub-daily and daily scales. Ten years of half-hourly precipitation records aggregated at different sub-daily periods were evaluated over a region in the Western Mediterranean. The analysis at the half-hourly scale examined the contribution of passive microwave (PMW) and infrared (IR) sources in IMERG estimates, as well as the relationship between various microphysical cloud properties using Cloud Microphysics (CMIC–NWC SAF) data. The results show the following: (1) a marked tendency to underestimate precipitation compared to rain gauges which increases with rainfall intensity and temporal resolution, (2) a weaker negative bias for retrievals with PMW data, (3) an increased bias when filling PMW gaps by including IR information, and (4) an improved performance in the presence of precipitating ice clouds compared to warm and mixed-phase clouds. This work contributes to the understanding of the factors affecting satellite estimates of extreme precipitation. Their relationship with the microphysical characteristics of clouds generates added value for further downstream applications and users’ decision making.

Capability of satellite data to estimate observed precipitation in southeastern South America

AbstractPrecipitation is a fundamental component of the water cycle. Satellite‐derived precipitation estimates with high spatial resolution and daily to subdaily temporal resolution become very important in regions with a limited ground‐based measurement network, such as southeastern South America (SESA). This study evaluates the performance of four state‐of‐the‐art satellite products, including IMERG V.06 Final Run, PERSIANN, PERSIANN CCS‐CDR and PDIR‐NOW in representing observed precipitation over SESA during the 2001–2020 period. The ability of each product to represent observed annual and seasonal precipitation patterns was assessed. Statistical and categorical evaluation metrics were used to evaluate the performance of satellite precipitation estimates at monthly and daily timescales. Our results report that IMERG and CCS‐CDR achieve the best performance in estimating observed precipitation patterns at annual and seasonal timescales. While all satellite products effectively identify autumn and spring precipitation patterns, they struggle to represent winter and summer patterns. Notably, all satellite precipitation products have a better agreement with observed precipitation in wetter regions compared to drier regions, as indicated by the spatial distribution of continuous validation metrics. IMERG stands out as the most accurate product, reaching the highest correlation coefficients (0.75 &lt; CC &lt; 0.95) and Kling–Gupta efficiencies (0.65 &lt; KGE &lt; 0.85, rate as good to very good performance). Regarding categorical statistical metrics, IMERG correctly estimates the fraction of observed rainy days (POD &gt; 0.7, CSI &gt; 0.6) and shows the lowest fraction of estimated precipitation events that did not occur. PERSIANN, CCS‐CDR and PDIR‐NOW exhibit lower performances, mainly in drier areas. Moreover, PERSIANN and PDIR‐NOW tend to overestimate observed precipitation in almost the entire SESA region. We expect this validation study will provide greater reliability to satellite precipitation estimates, in order to provide an alternative that complement the scarce observed information available for decision‐making in water management and agricultural planning.

Estimation of Rainfall via IMERG-FR and Its Relationship with the Records of a Rain Gauge Network with Spatio-Temporal Variation, Case of Study: Mexican Semi-Arid Region

In the last few years, Satellite Precipitation Estimates (SPE) have been increasingly used for rainfall estimation applications. Their validity and accuracy are influenced by several factors related to the location where the SPEs are applied. The objective of this study is to evaluate the performance of the Integrated Multisatellite Retrievals for Global Precipitation Measurement Version 06 Half-Hour Temporal Resolution (IMERG-FR V06 HH) for rainfall estimation, as well as to determine its relationships with the hourly and daily rain gauge network data in a semiarid region during 2019–2021. The methodology contemplates the temporality, elevation, rainfall intensity, and rain gauge density variables, carrying out a point-to-pixel analysis using continuous, (Bias, r, ME, and RMSE), categorical (POD, FAR, and CSI), and volumetric (VHI, VFAR, and VCSI) statistical metrics to understand the different behaviors between the rain gauge and IMERG-FR V06 HH data. IMERG-FR greatly underestimated the heavy rainfall events in values of −63.54 to −23.58 mm/day and −25.29 to −11.74 mm/30 min; however, it overestimates the frequency of moderate rain events (1 to 25 mm/day). At making the correlation (r) between the temporal scales, the monthly temporal resolution was the one that better relates the measured and estimated data, as well as reported r values of 0.83 and 0.85, where records at shorter durations in IMERG-FR do not detect them. The weakness of this system, according to the literature and confirmed by the research findings, in the case of hydrological phenomena, is that recording or estimating short durations is essential for the water project, and therefore, the placement of rain gauges. The 1902–2101 m.a.s.l. range elevation has the best behavior between the data with the lowest error and best detection ability, of which IMERG-FR tended to overestimate the rain at higher altitudes. Considering that the r for two automated rain gauges per IMERG-FR pixel density was 0.74, this indicates that the automated rain gauges versus IMERG-FR have a better data fit than the rain gauges versus IMERG-FR. The distance to centroid and climatic evaluations did not show distinctive differences in the performance of IMERG. These findings are useful to improve the IMERG-FR algorithms, guide users about its performance at semiarid plateau regions, and assist in the recording of data for hydrological projects.

Classification of tropical cyclone rain patterns using convolutional autoencoder

Heavy rainfall produced by tropical cyclones (TCs) frequently causes wide-spread damage. TCs have different patterns of rain depending on their development stage, geographical location, and surrounding environmental conditions. However, an objective system for classifying TC rain patterns has not yet been established. This study objectively classifies rain patterns of North Atlantic TCs using a Convolutional Autoencoder (CAE). The CAE is trained with 11,991 images of TC rain rates obtained from satellite precipitation estimates during 2000−2020. The CAE consists of an encoder which compresses the original TC rain image into low-dimensional features and a decoder which reconstructs an image from the compressed features. Then, TC rain images are classified by applying a k-means method to the compressed features from the CAE. We identified six TC rain patterns over the North Atlantic and confirmed that they exhibited unique characteristics in their spatial patterns (e.g., area, asymmetry, dispersion) and geographical locations. Furthermore, the characteristics of rain patterns in each cluster were closely related to storm intensity and surrounding environmental conditions of moisture supply, vertical wind shear, and land interaction. This classification of TC rain patterns and further investigations into their evolution and spatial variability can improve forecasts and help mitigate damage from these systems.

Evaluation of GSMaP Version 8 Precipitation Products on an Hourly Timescale over Mainland China

A thorough evaluation of the recently released Global Satellite Mapping of Precipitation (GSMaP) is critical for both end-users and algorithm developers. In this study, six products from three versions of GSMaP version 8, including real time (NOW-R and NOW-C), near real time (NRT-R and NRT-C), and post-real time (MVK-R and MVK-C), are systematically and quantitatively evaluated based on time-by-time observations from 2167 stations in mainland China. Among each version, both products with and without gauge correction are adopted to detect the gauge correction effect. Error quantification is carried out on an hourly timescale. Three common statistical indices (i.e., correlation coefficient (CC), relative bias (RB), and root mean square error (RMSE)) and three event detection capability indices (i.e., probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI)) were adopted to analyze the inversion errors in precipitation amount and precipitation event frequency across the various products. Additionally, in this study, we examine the dependence of GSMaP errors on rainfall intensity and elevation. The following main results can be concluded: (1) MVK-C exhibits the best ability to retrieve rainfall on the hourly timescale, with higher CC values (0.31 in XJ to 0.47 in SC), smaller RMSE values (0.14 mm/h in XJ to 0.99 mm/h in SC), and lower RB values (−4.78% in XJ to 16.03% in NC). (2) Among these three versions, the gauge correction procedure plays a crucial role in reducing errors, especially in the post-real-time version. After being corrected, MVK-C demonstrates an obvious CC value improvement (&gt;0.3 on the hourly timescale) in various sub-regions, increasing the percentage of sites with CC values above 0.5 from 0.03% (MVK-R) to 28.47% (MVK-C). (3) GSMaP products generally exhibit error dependencies on precipitation intensity and elevation, particularly in areas with drastic elevation changes (such as 1200–1500 m and 3000–3300 m), where the accuracy of satellite precipitation estimates is significantly affected. (4) CC values decreased with an increasing rainfall intensity; RB and RMSE values increased with an increasing rainfall intensity. The results of this study may be helpful for algorithm developers and end-users and provide a scientific reference for different hydrological applications and disaster risk reduction.

Performance of the PERSIANN Family of Products over the Mekong River Basin and Their Application for the Analysis of Trends in Extreme Precipitation Indices

Near-real-time satellite precipitation estimation is indispensable in areas where ground-based measurements are not available. In this study, an evaluation of two near-real-time products from the Center for Hydrometeorology and Remote Sensing at the University of California, Irvine—PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks—Cloud Classification System) and PDIR-Now (PERSIANN-Dynamic Infrared Rain Rate near-real-time)—were compared to each other and evaluated against IMERG Final (Integrated Multi-satellite Retrievals for Global Precipitation Measurement—Final Run) from 2015 to 2020 over the Mekong River Basin and Delta (MRB) using a spatial resolution of 0.1∘ by 0.1∘ and at a daily scale. PERSIANN-CDR (PERSIANN-Climate Data Record) was also included in the evaluation but was not compared against the real-time products. In this evaluation, PDIR-Now exhibited a superior performance to that of PERSIANN-CCS, and the performance of PERSIANN-CDR was deemed satisfactory. The second part of the study entailed performing a Mann–Kendall trend test of extreme precipitation indices using 38 years of PERSIANN-CDR data over the MRB. This annual trend analysis showed that extreme precipitation over the 95th and 99th percentiles has decreased over the Upper Mekong River Basin, and the consecutive number of wet days has increased over the Lower Mekong River Basin.

Diurnal variability of atmospheric water vapour, precipitation and cloud top temperature across the global tropics derived from satellite observations and GNSS technique

The diurnal cycle of convection plays an important role in clouds and water vapour distribution across the global tropics. In this study, we utilize integrated moisture derived from the global navigation satellite system (GNSS), satellite precipitation estimates from TRMM and merged infrared dataset to investigate links between variability in tropospheric moisture, clouds development and precipitation at a diurnal time scale. Over 16 years of observations were analysed to estimate the diurnal cycles for 42 sites that span across the global tropics and assess the GNSS precipitable water vapour suitability in tropical moisture variability studies. Results show that the GNSS technique can be successfully used for studies related to the high-resolution temporal variability of integrated atmospheric moisture and, thus, in the analysis of multi-scale interactions and clouds development. For most of the analysed cases, GNSS integrated humidity shows one daily maximum (regardless of the season), which is related to the afternoon precipitation and cloud top temperature daily minimum. Although diurnal integrated moisture variations typically do not exceed 2 mm (usually below 5% of the daily mean value), in favourable large-scale conditions such relatively small changes can lead to the development of convective clouds and can affect precipitation distribution. Thus, GNSS technology allows reliable monitoring of tropospheric moisture variability at a diurnal time scale, which can be leveraged for improved monitoring and prediction of high-impact weather.

Evaluation of Multiple Satellite, Reanalysis, and Merged Precipitation Products for Hydrological Modeling in the Data-Scarce Tributaries of the Pearl River Basin, China

Satellite and reanalysis precipitation estimates of high quality are widely used for hydrological modeling, especially in ungauged or data-scarce regions. To improve flood simulations by merging different precipitation inputs or directly merging streamflow outputs, this study comprehensively evaluates the accuracy and hydrological utility of nine corrected and uncorrected precipitation products (TMPA-3B42V7, TMPA-3B42RT, IMERG-cal, IMERG-uncal, ERA5, ERA-Interim, GSMaP, GSMaP-RNL, and PERSIANN-CCS) from 2006 to 2018 on a daily timescale using the Coupled Routing and Excess Storage (CREST) hydrological model in two flood-prone tributaries, the Beijiang and Dongjiang Rivers, of the Pearl River Basin, China. The results indicate that (1) all the corrected precipitation products had better performance (higher CC, CSI, KGE’, and NSCE values) than the uncorrected ones, particularly in the Beijiang River, which has a larger drainage area; (2) after re-calibration under Scenario II, the two daily merged precipitation products (NSCE values: 0.73–0.87 and 0.69–0.82 over the Beijiang and Dongjiang Rivers, respectively) outperformed their original members for hydrological modeling in terms of BIAS and RMSE values; (3) in Scenario III, four evaluation metrics illustrated that merging multi-source streamflow simulations achieved better performance in streamflow simulation than merging multi-source precipitation products; and (4) under increasing flood levels, almost all the performances of streamflow simulations were reduced, and the two merging schemes had a similar performance. These findings will provide valuable information for improving flood simulations and will also be useful for further hydrometeorological applications of remote sensing data.

Dependency of errors for four global reanalysis and satellite precipitation estimates on four crucial factors

Errors in the reanalysis and satellite precipitation products are closely related to four impacting factors, i.e., seasonality, topography, rainfall intensity, and climate type. Therefore, revealing the dependency of the errors of precipitation products on seasonality, topography, rainfall intensity, and climate type is important for data users and developers to improve the quality of precipitation products and enhance their applications in hydrometeorology. In this study, the links between errors and four impacting factors were used to investigate the dependency of errors in the ERA5 reanalysis and three satellite-only precipitation products (i.e., IMERG-Late, GSMaP-MVK, and PERSIANN-CCS) on the four impacting factors. Results from mainland China show that the errors of the four products depend on the four factors, whereas the dependency of different error indicators on the four factors differs significantly. In all seasons, ERA5 outperformed the three satellite-only precipitation products in most cases. In terms of topographic dependency, the probability of detection (POD) of ERA5 increases with topographic complexity but the opposite is true for the satellite-only precipitation products. Compared with purely infrared (IR) data, the fusion of passive microwave and IR data enhances the quality of precipitation estimates and reduces the topographical dependency of the errors. Regarding intensity-based performance, the performance of the four products in terms of the POD and normalized root-mean-squared error improves as the rain rate increases, whereas that of the three bias metrics (i.e., total bias, hit bias, and miss bias) deteriorates. In terms of the climatic dependency of the errors, the errors of the four products increase as the aridity increases in most cases. False bias contributes primarily to the total bias for the four products. More importantly, we found that the relationship between the false alarm ratio (FAR) and climate type cannot accurately characterize the dependency of false alarms based on climate type because of the interference of hit precipitation events. Based on this finding, the mean number of false alarms is recommended to replace the FAR to objectively investigate the false precipitation of various precipitation products.

AI-based estimation of precipitation in the Tibetan Plateau using multi-frame FY-4A satellite data

ABSTRACT The weather system over the Tibetan Plateau can cause disasters around the plateau and its downstream areas. Since there are few surface meteorological observation stations over the Tibetan Plateau, the precipitation here cannot be comprehensively detected. Therefore, satellite precipitation estimation products with high timeliness, accuracy, and full coverage are relied upon. This paper presents an AI-based precipitation estimation method using multi-frame FY-4A meteorological satellite data (FY-4A PRE-M). Firstly, multiple-derived features are constructed according to the brightness temperature of the satellite different channels, and the regional precipitation estimation model is then trained using the light gradient boosting machine (LGBM) algorithm. Based on the research results from precipitation retrieval using the infrared channel radiation features, this model analyzes the possible contribution of the radiation difference between channels at the same time and the radiation change in the same channel at different periods (15, 30, 45 and 60 min) to the precipitation estimation. The algorithm continuously updates the model with the history of available precipitation data. From 1 September 2021 to 31 August 2022, the precipitation estimations using the multi-frame FY-4A satellite data were evaluated through the ground rainfall gauge data based on the spatial – temporal matching. The results show that the multi-frame FY-4A satellite data have advantages in precipitation estimation. The heavy rainfall caused by a Tibetan Plateau shear line is evaluated using this algorithm. The results show that the precipitation estimations of FY-4A PRE-M and IMERG-L are close to the values of rain gauges. The FY-4A PRE-M can well describe the diurnal variations and the distribution of 24 h cumulative precipitation over the Tibetan Plateau, and the heavy rainfall belts in the central and northern Tibetan Plateau are also well monitored.

Applicability Assessment of GPM IMERG Satellite Heavy-Rainfall-Informed Reservoir Short-Term Inflow Forecast and Optimal Operation: A Case Study of Wan’an Reservoir in China

Satellite precipitation estimate (SPE) dedicated to reservoir inflow forecasting is very attractive as it can provide near-real-time information for reservoir monitoring. However, the potential of SPE retrievals with fine temporal resolution in supporting the high-quality pluvial flood inflow forecast and robust short-term operation of a reservoir remains unclear. In this study, the hydrological applicability of half-hourly Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM IMERG) heavy rainfall data was explored using a synthetic experiment of flood inflow forecast at sub-daily to daily lead times and resultant reservoir short-term operation. The event-based flood forecast was implemented via the rainfall–runoff model GR4H driven by the forecasted IMERG. Then, inflow forecast-informed reservoir multi-objective optimal operation was conducted via a numerical reservoir system and assessed by the risk-based robustness indices encompassing reliability, resilience, vulnerability for water supply, and flood risk ratio for flood prevention. Selecting the Wan’an reservoir located in eastern China as the test case, the results show that the flood forecast forced with IMERG exhibits slightly lower accuracy than that driven by the gauge rainfall across varying lead times. For a specific robustness index, its trends between IMERG and gauge rainfall inputs are comparable, while its magnitude depends on varying lead times and scale ratios (i.e., the reservoir scale). The pattern that the forecast errors in IMERG increase with the lead time is changed in the resultant inflow forecast series and dynamics in the robustness indices for the optimal operation decision. This indicates that the flood forecast model coupled with reservoir operation system could partly compensate the original SPE errors. Our study highlights the acceptable hydrological applicability of IMERG rainfall towards reservoir inflow forecast for robust operation, despite the intrinsic error in SPE.

Hydrologic Evaluation of the Global Precipitation Measurement Mission over the U.S.: Error Budget Analysis

This study investigates the hydrologic utility of satellite precipitation estimates from the Global Precipitation Measurement mission by comparing flood signals produced across the Continental United States by a ten-year span of in-situ, ground-based radar and satellite-based precipitation data. The flood characteristics generated with radar and satellite precipitation through a distributed hydrologic model are contrasted against reference stream gauge data as a method of integrated validation to assess and quantify error budgets between precipitation products by highlighting precipitation products’ accuracy, hydrologic scaling effects, and the impact of the hydrologic model. It is found that systematic and random errors associated with flood characteristics behave similarly to trends previously seen in precipitation rate errors between precipitation products, establishing a clear link through propagation of errors into the water cycle. Additionally, behaviors associated with both water balance and routing schemes within the hydrologic model were shown to affect outputs. Errors generated by water balance tend to cause overestimation of peak discharge values, while errors associated with routing tend to cause underestimation of flood durations and push flood timings earlier than the stream gauge reference.

Verification of satellite and model products against a dense rain gauge network for a severe flooding event in Kumasi, Ghana

AbstractFloods as a result of severe storms cause significant impacts on lives and properties. Therefore, timely and accurate forecasts of the storms will reduce the associated risks. In this study, we look at the characteristics of a storm on 28 June, 2018 in Kumasi from a rain gauge network and satellite data, and reanalysis data. The storm claimed at least 8 lives and displaced 293 people in Kumasi, Ghana. The ability of satellite and reanalysis data to capture the temporal variations of the storm was assessed using a high temporal resolution (accumulation per minute) rain gauge data. We employed the observation data from the Dynamics–Aerosol–Chemistry–Cloud Interactions in West Africa (DACCIWA) rain gauges to assess the storm's onset, duration, and cessation. Subsequently, the performance of the ERA5 reanalysis and Global Precipitation Measurement (GPM) satellite precipitation estimates in capturing the rainfall is assessed. Both GPM and the ERA5 had difficulty reproducing the hourly pattern of the rain. However, the GPM produced variability that is similar to the observed. Generally, the region of maximum rainfall was located in the southern parts of the study domain in ERA5, while GPM placed it in the northern parts. The study contributes a verification measure to improve weather forecasting in Ghana as part of the objectives of the GCRF African Science for Weather Information and Forecasting Techniques (SWIFT) project.