Rain Gauge Observations Research Articles

Locally defined seasonal rainfall characteristics within the Horn of Africa drylands from rain gauge observations

Abstract Seasonal rainfall is critical to lives and livelihoods within the Horn of Africa drylands (HAD), but it is highly variable in space and time. The HAD rainfall seasons are typically defined as March-April-May (MAM) and October-November-December (OND). However, these three-month periods are only generalised definitions of seasonality across the HAD, and local experience of rainfall may depart from these substantially. Here, we use daily rain gauge data with a duration of at least 10 years from 69 stations across Kenya, Somalia, and Ethiopia to locally delineate key rainfall seasons. By calculating local seasonal rainfall timings, totals, and extremes we obtain more accurate estimates of the spatial variability in rainfall delivery across the HAD, as well as climatological patterns. Results show high spatial variability in season onset, cessation, and length across the region, indicating that a homogenous classification of rainfall seasons across the HAD (e.g. MAM and OND) is inadequate for representing local rainfall characteristics. Our results show that the ‘long rains’ season is not significantly longer than ‘short rains’ season over the period of study. This could be related to the previously documented decline of the ‘long rains’ rainfall totals over recent decades. Several rainfall metrics also vary spatially between seasons, and the rainfall on the most extreme days can accumulate to double the mean seasonal total. The locally defined rainfall seasons better capture the bulk of the rainfall during the season, giving improved characterisation of rainfall metrics, consistent with the aim of a better understanding of rainfall impacts on local communities.

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

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

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.

Northeast China Cold Vortex Amplifies Extreme Precipitation Events in the Middle and Lower Reaches Yangtze River Basin

The middle and lower reaches of the Yangtze River (MLYR) frequently experience extreme precipitation events (EPEs) during June and July, the so-called Meiyu season. This study investigated EPEs in the MLYR during Meiyu seasons over 1961–2022, using rain gauge observations and ERA5 reanalysis data. EPEs associated with the Northeast China cold vortex featured more undulating westerlies with a distinct wave train pattern from Europe to Northeast Asia. Due to robust Rossby wave energy, the trough deepened from Northeast China towards the MLYR and was confronted with a westward extension of the western Pacific subtropical high. Such a configuration enhanced the warm and moist monsoon conveyor belt and convergence of water vapor flux from southwestern China to the MLYR. The warm and moist air favored upward motion. The increased rainfall prevailed from southwestern China to the MLYR. In contrast, ordinary EPEs were characterized by zonal westerlies and weaker Rossby wave propagation. The Meiyu trough was comparatively shallow and confined to the MLYR with less westward expansion of the subtropical high. In response, the warm and moist monsoon conveyor belt was more localized, resulting in weaker EPEs in the MLYR.

Regional and seasonal assessment of biases on high-resolution satellite precipitation estimations in peninsular Malaysia: 2011–2020

ABSTRACT Satellite precipitation estimations (SPEs) have become important to estimate rainfall in remote and inaccessible areas. The study evaluates two high-resolution SPEs (IMERG and CHIRPS) in Peninsular Malaysia from 2011 to 2020. In situ rain gauge observation data were used as reference data, and a series of statistic indices were used to evaluate the performance of SPEs. In order to identify the source of error in the SPEs, an error decomposition technique was proposed whereby the bias is segregated into four different independent components. The study found that IMERG outperformed CHIRPS, with both satellites performing well in the east coast region but poor in the central region. A superior correlation between the SPEs and rain gauge observations was found during the northeast monsoon. The false bias has shown the widest range compared to other error components, indicating that it is the main contributor to the total bias of both SPEs in Peninsular Malaysia.

Evaluating the Performance and Applicability of Satellite Precipitation Products over the Rio Grande–San Juan Basin in Northeast Mexico

Accurate observation of precipitation data is crucial for hydrometeorological applications, requiring temporal and spatial precision. Satellite precipitation products offer a promising solution for obtaining precipitation estimates, facilitating long-term observations from global to local scales. However, assessing their accuracy compared to rain gauge observations is essential. This study aims to assess the accuracy and applicability of precipitation data from CMORPH, IMERG, and PERSIANN CCS in the Rio Grande–San Juan Basin in northeast Mexico. The evaluation of estimated precipitation was assessed using the Pearson and Spearman correlations, RMSE, MAE, and BIAS for both monthly and yearly averages. CMORPH showed minimal errors and low underestimation, while IMERG exhibited high correlations with consistent underestimation. PERSIANN CCS had lower correlations, significant overestimation, and higher errors. The Mann–Kendall (MK) test was used to determinate the precipitation trends of observed and estimated data. The observed data showed a significant positive trend in monthly averages, which is not reflected in the annual trend. Furthermore, negative annual trends were found in at least 10 stations across the basin. The application of satellite precipitation data yielded mixed outcomes, with CMORPH showing the highest level of agreement with the trend analysis results from rain gauge data. This demonstrates its reliability for weather and climate studies and suggests the potential for CMORPH to be used as an input in hydrological modeling.

Statistical Downscaling of Remote Sensing Precipitation Estimates Using MODIS Cloud Properties Data over Northeastern Greece

The aim of this study is to spatially downscale the daily precipitation data from the Global Precipitation Measurement (GPM) mission, using the Integrated Multi-satellite Retrievals for GPM (IMERG), utilizing cloud properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. Cloud optical thickness (COT), cloud effective radius (CER), and cloud water path (CWP) are used to statistically downscale IMERG precipitation estimates from 0.1 to 0.01° spatial resolution, using the Multivariate Linear Regression (MLR) and residual correction methods. The downscaled precipitation estimates were subsequently validated using in situ rain gauge measurements. The residual corrected IMERG downscaled precipitation estimates were found to be more accurate than the downscaled predicted precipitation without the implementation of the residual correction algorithm (up to 37%), with a respective decrease of the Root Mean Square Error (RMSE) (up to 75%), Normalized Root Mean Square Error (NRMSE) (up to 79%), and the Percent Bias (PB) (up to 98%). In addition, the final downscaled product after the MLR method implementation with residual correction was better correlated with the rain gauge observations than the initial IMERG product (up to 20%). Thus, the implementation of the MLR method in conjunction with the residual correction algorithm is an efficient tool for downscaling remote sensing products with a coarse spatial resolution.

CON-SST-RAIN: Continuous Stochastic Space–Time Rainfall generation based on Markov chains and transposition of weather radar data

In this study, we present CON-SST-RAIN, a novel stochastic space–time rainfall generator specialized for model-based urban drainage design and planning. CON-SST-RAIN is based on Markov Chains for sequences of dry/rainy days and uses stochastic storm transposition (SST) to generate realistic rainfall fields from weather radar data. CON-SST-RAIN generates continuous areal rainfall time series at arbitrary lengths. We propose a method for updating the Markov Chains by each passing year to better incorporate low-frequency variation in inter-annual rainfall values. The performance of CON-SST-RAIN is tested against multi-year records from rain gauges at both point and catchment scales. We find that updating the Markov Chains has a significant impact on the inter-annual variation of rainfall, but has little effect on mean annual/seasonal precipitation and dry/wet spell lengths. CON-SST-RAIN shows good preservation of extreme rain rates (including sub-hourly values) compared to observed rain gauge data and the original SST framework.

Evaluation of Satellite-Based Rainfall Estimates against Rain Gauge Observations across Agro-Climatic Zones of Nigeria, West Africa

Satellite rainfall estimates (SREs) play a crucial role in weather monitoring, forecasting and modeling, particularly in regions where ground-based observations may be limited. This study presents a comprehensive evaluation of three commonly used SREs—African Rainfall Climatology version 2 (ARC2), Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) and Tropical Application of Meteorology using SATellite data and ground-based observation (TAMSAT)— with respect to their performance in detecting rainfall patterns in Nigeria at daily scales from 2002 to 2022. Observed data obtained from the Nigeria Meteorological Agency (NiMet) are used as reference data. Evaluation metrics such as correlation coefficient, root mean square error, mean error, bias, probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) are employed to assess the performance of the SREs. The results show that all the SREs exhibit low bias during the major rainfall season from May to October, and the products significantly overestimate observed rainfall during the dry period from November to March in the Sahel and Savannah Zones. Similarly, over the Guinea Zone, all the products indicate overestimation in the dry season. The underperformance of SREs in dry seasons could be attributed to the rainfall retrieval algorithms, intensity of rainfall occurrence and spatial-temporal resolution. These factors could potentially lead to the accuracy of the rainfall retrieval being reduced due to intense stratiform clouds. However, all the SREs indicated better detection capabilities and less false alarms during the wet season than in dry periods. CHIRPS and TAMSAT exhibited high POD and CSI values with the least FAR across agro-climatic zones during dry periods. Generally, CHIRPS turned out to be the best SRE and, as such, would provide a useful dataset for research and operational use in Nigeria.

Evaluation of GSMap (Global Satellite Mapping of Precipitation) with the reference to rain gauge observations in Banjarbaru City, Indonesia

Climate change as one of the essential impacts of global warming in the last few decades, has resulted many disasters in different ways, i.e., floods and landslides, as the effect of extreme rainfall events. At the end of February 2023, a severe flood event occurred in Banjarbaru City, resulted an inundation in many areas. A flood control system is recommended to face this problem. In this research, the flood control presented is a non-structural method by analyzing rainfall data based on field data and GSMaP satellite-based rainfall data. This research aims to determine the type of correlation coefficient relationship between BMKG data and GSMaP satellite-based rainfall data. The data collection includes primary data in the form of field surveys to identify rain events and secondary data obtained from GSMap satellite-based rainfall data and field rainfall data obtained from BMKG online. The Melchior design flood calculation method is used for calculating a flood-designed discharge. The results show that GSMaP data have significant values that correlate to rain gauge observations from BMKG stations in Banjarbaru City. An enough to high correlations values indicate that this GSMaP can be used as an alternative source of rainfall data for hydrological analysis, in particular to ungauged basin.

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.

Quality control of hourly rain gauge data based on radar and satellite multi-source data

ABSTRACT Rain gauge networks provide direct precipitation measurements and have been widely used in hydrology, synoptic-scale meteorology, and climatology. However, rain gauge observations are subject to a variety of error sources, and quality control (QC) is required to ensure the reasonable use. In order to enhance the automatic detection ability of anomalies in data, the novel multi-source data quality control (NMQC) method is proposed for hourly rain gauge data. It employs a phased strategy to reduce the misjudgment risk caused by the uncertainty from radar and satellite remote-sensing measurements. NMQC is applied for the QC of hourly gauge data from more than 24,000 hydro-meteorological stations in the Yangtze River basin in 2020. The results show that its detection ratio of anomalous data is 1.73‰, only 1.73% of which are suspicious data needing to be confirmed by experts. Moreover, the distribution characteristics of anomaly data are consistent with the climatic characteristics of the study region as well as measurement and maintenance modes of rain gauges. Overall, NMQC has a strong ability to label anomaly data automatically, while identifying a lower proportion of suspicious data. It can greatly reduce manual intervention and shorten the impact time of anomaly data in the operational work.

Integrated Evaluation and error decomposition of four gridded precipitation products using dense rain gauge observations over the Yunnan-Kweichow Plateau, China

ABSTRACT Evaluating the precision and applicability of high-quality precipitation products in the distinctive terrain and intricate climate of the Yunnan-Kweichow Plateau (YKP) is pivotal for climate research. This study comprehensively assesses four gridded precipitation datasets (AERA5-Asia, AIMERG, ERA5-Land, and IMERG-Final) using the China Meteorological Administration’s surface precipitation data. It employs eight statistical indicators and error decomposition methods at various spatiotemporal scales. The main findings are as follows: (1) AERA5-Asia, AIMERG, and IMERG-Final show similar precipitation patterns, with ERA5-Land overestimating. While all display minor seasonal variations, AERA5-Asia underestimates summer rain. ERA5-Land tends to overstate, whereas AIMERG and IMERG-Final are generally accurate but slightly undervalued in southern YKP. (2) Hourly analysis reveals AERA5-Asia leads in performance metrics (CC: 0.23, MAE: 0.49 mm/hour, RMSE: 0.18 mm/hour, CSI: 0.27). In contrast, ERA5-Land lags, marked by the lowest BIAS (35.39%), FAR (0.74), and FBI (2.85). AIMERG and IMERG-Final display comparable results but underperform in CC (0.16, 0.13), POD (0.31, 0.30), and CSI (0.19, 0.18). (3) False bias significantly contributes to the total bias of precipitation products. AERA5-Asia and AIMERG mitigate total bias and enhance false precipitation situations through calibration algorithms, albeit introducing missed bias in the central region of YKP. The study findings offer valuable insights into YKP precipitation, informing the development of grid-based fusion algorithms in the region’s complex terrain.

Bias Correction of IMERG Data in the Mountainous Areas of Sumatra Based on A Single Gauge Observation

The performance of surface precipitation data from satellite precipitation products (SPPs) in mountainous areas has greater error and bias than in plain areas. In this study, linear scaling (LS), local intensity (LOCI), power transformation (PT), and cumulative distribution function (CDF) methods are used to correct the bias of Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) data in the mountainous region of Sumatra based on long-term and high-resolution optical rain gauge (ORG) observations. The ORG is installed at Equatorial Atmospheric Observatory (EAO) in Kototabang, West Sumatra, Indonesia (100.32 °E, 0.20 °S, 865 m above sea level (ASL) with an observation period from 2002 to 2016. The impact of the bias correction method is tested based on accuracy and capability detection tests. The bias correction method is more effective at the daily resolution than the hourly resolution of the IMERG data in the mountainous region of Sumatra. The LS method exhibited the best improvement in accuracy with reduced root-mean-square error (RMSE) and relative bias (RB), although there was no significant increase in coefficient correlation (CC) values. However, the accuracy improvement was not observed in the bias correction for hourly data. The lack of improvement in the accuracy of the hourly IMERG data is due to the high local variability of rainfall in the mountainous area of Sumatra. The high data variability causes large differences in the mean and variance of the IMERG calibration and evaluation data periods. On the other hand, the LOCI, PT, and CDF methods were successfully improved the rain detection capability of IMERG, as indicated by the better critical succession index (CSI) values compared to the original hourly and daily IMERG data. It increased the CSI value by reducing false alarms for rain with intensity below 2 mm/h. Furthermore, the CDF method can improve the analysis of extreme rainfall in the mountainous region of Sumatra by improving the estimation of the extreme rainfall index. Therefore, these methods can be applied to improve the accuracy and detectability of IMERG data in the mountainous region of Sumatra. However, the scale factor and transfer function constructed in this study need to be further evaluated on other rain gauge observation data in Sumatra’s mountainous region to improve performance. HIGHLIGHTS The LS method shows the best improvement in the accuracy of IMERG data in the mountainous area of Sumatera compared to the LOCI, PT, and CDF methods, as indicated by the largest decrease in RMSE and RB values CSI values prove that LOCI, PT, and CDF methods successfully improve the detection capability of IMERG hourly and daily data in the mountainous region of Sumatra The CDF method shows the best quality in improving extreme rainfall observations in the mountainous region of Sumatra GRAPHICAL ABSTRACT

Comparative analysis of gridded rainfall datasets over the Bagmati river basin, India

Abstract In this study, three high-resolution gridded rainfall datasets, viz., Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Modern-Era Retrospective Analysis for Research and Applications 2 (MERRA-2), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) have been collected, analyzed, and compared against the ground-based observed rain gauge datasets of Indian Meteorological Department (IMD) of the Bagmati river basin from 2001 to 2014 in the Bihar State of India. Comparison analyses were performed at daily, monthly, seasonal, and annual time scales. Various statistical parameters, contingency tests, trend analysis, and rainfall anomaly index were used for comparison of datasets. Though MERRA-2 had the highest probability of detection (POD) and lowest false alarm ratio (FAR), analysis showed that IMERG data were closely matching with the observed data, whereas MERRA-2 and PERSIANN underestimated the extreme values. For the monthly scale, again IMERG had the most optimal Coefficient of Determination (R2) and Nash-Sutcliffe Efficiency (NSE) values. IMERG also performed well in detecting rainfall trends and identifying wet and dry years. Overall, IMERG was the most suitable dataset at all time scales for future studies in the basin.

Seasonal Storm Characteristics Govern Urban Flash Floods: Insights from the Arid Las Vegas Wash Watershed

Abstract In the arid and semiarid southwestern United States, both cool- and warm-season storms result in flash flooding, although the former storms have been much less studied. Here, we investigate a catalog of 52 flash-flood-producing storms over the 1996–2021 period for the arid Las Vegas Wash watershed using rain gauge observations, reanalysis fields, radar reflectivities, cloud-to-ground lightning flashes, and streamflow records. Our analyses focus on the hydroclimatology, convective intensity, and evolution of these storms. At the synoptic scale, cool-season storms are associated with open wave and cutoff low weather patterns, whereas warm-season storms are linked to classic and troughing North American monsoon (NAM) patterns. At the storm scale, cool-season events are southwesterly and southeasterly under open wave and cutoff low conditions, respectively, with long duration and low to moderate rainfall intensity. Warm-season storms, however, are characterized by short-duration, high-intensity rainfall, with either no apparent direction or southwesterly under classic and troughing NAM patterns, respectively. Atmospheric rivers and deep convection are the principal agents for the extreme rainfall and upper-tail flash floods in cool and warm seasons, respectively. Additionally, intense rainfall over the developed low valley is imperative for urban flash flooding. The evolution properties of seasonal storms and the resulting streamflows show that peak flows of comparable magnitude are “intensity driven” in the warm season but “volume driven” in the cool season. Furthermore, the distinctive impacts of complex terrain and climate change on rainfall properties are discussed with respect to storm seasonality.

The New Version 3.2 Global Precipitation Climatology Project (GPCP) Monthly and Daily Precipitation Products

Abstract The Global Precipitation Climatology Project (GPCP) Version 3.2 Precipitation Analysis provides globally complete analyses of surface precipitation on a 0.5° × 0.5° latitude–longitude grid at both monthly and daily time scales, covering from 1983 to the present and from June 2000 to the present, respectively. These merged products continue the GPCP heritage of incorporating precipitation estimates from low-orbit satellite microwave data, geosynchronous-orbit satellite infrared data, sounder-based estimates, and surface rain gauge observations emphasizing the strengths of various inputs and striving for time and space homogeneity. Furthermore, these analyses incorporate modern algorithms, refined intercalibrations among sensors, climatologies of recent high-quality satellite precipitation data, and fine-scale multisatellite estimates. New data fields have been introduced to better characterize the precipitation, including the fraction of the precipitation that is liquid (rain) in both the monthly and daily products, and a quality index for the monthly product. Compared to the operational GPCP Version 2.3 Monthly, the Version 3.2 Monthly product provides a more reasonable climatology in the Southern Ocean and increases the estimated global average precipitation by about 4.5%, which is similar to estimates from recent global water budget assessments. Global and regional trends for 1983–2020 with this new Monthly dataset are very similar to those computed from Version 2.3. Compared to the operational One-Degree Daily (Version 1.3) product, the new Version 3.2 Daily is designed to better represent the histogram of precipitation rates, particularly at high values and shifts the start of less-certain high-latitude estimates from 40° to 58° latitude in each hemisphere. Significance Statement Studies of Earth’s climate require long-term global datasets based on observations to show how the climate functions and to validate numerical climate models. This study describes an important upgrade to the monthly and daily precipitation (rain and snow) products computed by the Global Precipitation Climatology Project. We use modern analysis schemes, add new sources of data, and deliver results on a finer-scale 0.5° × 0.5° latitude–longitude grid [roughly 55 km (34 mi) on a side at the equator]. The new data show improved agreement with other studies and depict more reasonable behavior in the Southern Ocean. The daily product shows improved estimates of how often different intensities of precipitation occur around the world, particularly the high amounts that drive floods and landslides.

Predictability of Rainfall over Equatorial East Africa in the ECMWF Ensemble Reforecasts on short to medium-range time scales

Abstract Despite the enormous potential of precipitation forecasts to save lives and property in Africa, low skill has limited their uptake. To assess the skill and improve the performance of the forecast, validation and postprocessing should continuously be carried out. Here, we evaluate the quality of reforecasts from the European Centre for Medium-Range Weather Forecasts over Equatorial East Africa (EEA) against satellite and rain gauge observations for the period 2001–2018. 24-hour rainfall accumulations are analysed from short to medium-range time scales. Additionally, 48- and 120-hour rainfall accumulations were also assessed. The skill was assessed using an extended probabilistic climatology (EPC) derived from the observations. Results show that the reforecasts overestimate rainfall, especially during the rain seasons and over high-altitude areas. However, there is potential of skill in the raw forecasts up to 14-day lead-time. There is an improvement of up to 30% in Brier score/continuous rank probability score relative to EPC in most areas, especially the higher-altitude regions, decreasing with lead-time. Aggregating the reforecasts enhances the skill further, likely due to a reduction in timing mismatches. However, for some regions of the study domain, the predictive performance is worse than EPC, mainly due to biases. Postprocessing the reforecasts using isotonic distributional regression considerably improves skill, increasing the number of grid-points with positive Brier skill score (continuous rank probability score) by an average of 81% (91%) for lead-times 1–14 days ahead. Overall, the study highlights the potential of the reforecasts, the spatio-temporal variation in skill and benefit of postprocessing in EEA.

A Downscaling–Merging Scheme for Monthly Precipitation Estimation with High Resolution Based on CBAM-ConvLSTM

Satellite products have mediocre performance in precipitation estimation, while rain gauges are incapable of describing continuous spatial precipitation distributions. To obtain spatially continuous and accurate precipitation data, this paper proposes a two-step scheme incorporating environmental variables, satellite precipitation estimations, and rain gauge observations for the calibration of satellite precipitation data. First, the GPM data are downscaled from 0.1° to 0.01° based on the seasonal RF models to minimize the spatial differences between the satellite estimations and the rain gauge observations. Secondly, the fusion model combining ConvLSTM and CBAM explores the spatiotemporal correlation of downscaled satellite precipitation data with environmental co-variables and ground-based observations to correct GPM precipitation. The integrated scheme (CBAM-ConvLSTM) is applied to acquire monthly precipitation at a spatial resolution of 0.01° over Hanjiang River Basin from 2014 to 2018. Comparative analyses of model-based satellite products with in situ observations show that model-based precipitation products have a high-resolution spatial distribution along with high accuracy, which combines the advantages of in situ observations and satellite products. Compared to the original GPM product, the evaluation metric values of the merged precipitation products all improved: the RMSE decreased by 31% while the CC increased from 0.55 to 0.69, the bias decreased from about 25% to less than 1.8%, and the MAE decreased by 27.8% while the KGE increased from 0.28 to 0.52. This two-step scheme provides an effective way to derive a high-resolution and accurate monthly precipitation product for humid regions.

Hydrological modeling in an agricultural basin in the Brazilian Cerrado using satellite precipitation data

Precipitation data are one of the main inputs of a hydrological model and, therefore, the low density of rain gauges and large amounts of missing data in certain regions are obstacles to the development of hydrological research. In this sense, the quality of satellite precipitation products (SPP) was evaluated by comparing them with conventional data obtained from rain gauges (OP); subsequently the Soil and Water Assessment Tool (SWAT) model was calibrated and validated for three distinct periods according to the change in land use. Periods 1 and 2 were calibrated considering only the observed precipitation data, while period 3 included the addition of precipitation data from the SPP - Tropical Rainfall Measuring Mission - satellite due to the many years with information missing. Finally, the generated flows were examined, discussing the feasibility of using alternative precipitation data for hydrological simulation in a basin with a scarcity of data. The SPP showed a strong correlation with the OP for the hydrographic basin under study according to the statistical metrics analyzed, despite overestimation of figures for wet periods. The OPs satisfactorily simulated the flow rates of the basin under study for the two periods of analysis, as well as period 3, when the SPP was added. However, periods 1 and 2, which only contained information from rain gauge observations, presented better statistics than period 3. The values of the statistical coefficients attested to the potential of using the satellite data, in addition to observed data, to enable the calibration and validation processes of the SWAT model in regions with low station density or missing data.