High-Resolution Hourly Precipitation Grids Since 1961: A Novel Deterministic Approach

Climate Generator (CLIGEN) is a stochastic weather generator that has been widely used to generate daily precipitation and storm patterns for hydrological and erosion prediction models. Rainfall data with measurement intervals ≤ 30 min are required to compute two parameters for generating storm patterns, namely, the cumulative distribution of the time to peak rainfall intensity (TimePk) and the mean daily maximum 30-min rainfall intensity (MX.5P). High-resolution rainfall data, however, are not widely available around the world. One-minute precipitation data for 18 stations in eastern and central China were aggregated into hourly intervals to evaluate methods to optimally prepare TimePk and MX.5P for CLIGEN. Four sets of the two parameters were used to run CLIGEN for comparison:C0, using the original 1-min data;C1, replacing TimePk with those computed with hourly data;C2, replacing MX.5P with those computed with hourly data with an adjustment factor; andC3, replacing both parameters with those computed with hourly data, and the MX.5P was adjusted as forC2. Results showed that 1) MX.5P computed with hourly data was systematically lower than that computed with the 1-min data, and the bias could be corrected by multiplying by an adjustment factor of 1.40; 2) the difference in generated storm patterns betweenC0andC1was insignificant; and 3) results fromC2andC3agreed well with those generated fromC0. Hourly precipitation data can be used to prepare CLIGEN input parameter values for generating storm patterns for sites where only hourly data are available.

Punctuality monitoring and analysis aim to optimize resource allocation for enhancing rail traffic performance and quality. Adverse weather conditions, particularly heavy precipitation events, are recognized as significant drivers of delays and reduced punctuality of the rail system. This study addresses two key research questions using high-speed rail (HSR) as an example—what is the impact of rainfall on HSR’s delay and to what extent are HSR vulnerable to rainstorms. The data for the study were collected from the HSR on the major lines of eastern China in the rainy season of 2015–17 which lasted from May to October. High-resolution precipitation data are integrated with nonspatial HSR operational data using GIS to create composite grids covering buffer zones around HSR lines. These grids match the spatial scale of historical hourly precipitation data and enable regression analyses to assess how precipitation affects HSR operations. The results indicate that extreme rainfall significantly contributes to delays and reduced punctuality, with varying impacts observed across different HSR lines. Specifically, daily areal precipitation significantly delays services on the Hangzhou–Shenzhen and Nanjing–Hangzhou HSR lines. Rainfall intensity has a more pronounced impact on delay services of the Beijing–Shanghai HSR, while extreme precipitation most frequently affects the Shanghai–Nanjing and Jinhua–Wenzhou HSR lines. The case analysis enhances understanding of HSR vulnerability to heavy rainfall conditions and recommends regional adaptation strategies to manage climate-related uncertainties.

How to prevent the influence of precipitation’s localized and sudden characteristics is the most formidable challenge in the quality control (QC) of precipitation observations. However, with sufficiently high spatiotemporal resolution in observational data, nuanced information can aid us in accurately distinguishing between intense, localized precipitation events, and anomalies in precipitation data. China has deployed over 70,000 automatic weather stations (AWSs) that provide high spatiotemporal resolution surface meteorological observations. This study developed a new method for performing QC of precipitation data based on the high spatiotemporal resolution characteristics of observations from surface AWSs in China. The proposed QC algorithm uses the cumulative average method to standardize the probability distribution characteristics of precipitation data and further uses the empirical orthogonal function (EOF) decomposition method to effectively identify the small-scale spatial structure of precipitation data. Leveraging the spatial correlation characteristics of precipitation, partitioned EOF detection with a 0.5° spatial coverage effectively minimizes the influence of local precipitation on quality control. Analysis of precipitation probability distribution reveals that reconstruction based on the first three EOF modes can accurately capture the organized structural features of precipitation within the detection area. Thereby, based on the randomness characteristics of the residuals, when the residual of a certain observation is greater than 2.5 times the standard deviation calculated from all residuals in the region, it can be determined that the data are erroneous. Although the quality control is primarily aimed at accumulated precipitation, the randomness of erroneous data indicates that 84 continuous instances of error data in accumulated precipitation can effectively trace back to erroneous hourly precipitation observations. This ultimately enables the QC of hourly precipitation data from surface AWSs. Analysis of the QC of precipitation data from 2530 AWSs in Jiangxi Province (China) revealed that the new method can effectively identify incorrect precipitation data under the conditions of extreme weather and complex terrain, with an average rejection rate of about 5%. The EOF-based QC method can accurately detect strong precipitation events resulting from small-scale weather disturbances, thereby preventing local heavy rainfall from being incorrectly classified as erroneous data. Comparison with the quality control results in the Tianqing System, an operational QC system of the China Meteorological Administration, revealed that the proposed method has advantages in handling extreme and scattered outliers, and that the precipitation observation data, following quality control procedures, exhibits enhanced similarity with the CMAPS merged precipitation data. The novel quality control approach not only elevates the average spatial correlation coefficient between the two datasets by 0.01 but also diminishes the root mean square error by 1 mm.

Gridded precipitation data with a high spatiotemporal resolution are of great importance for studies in hydrology, meteorology, and agronomy. Observational data from meteorological stations cannot accurately reflect the spatiotemporal distribution and variations of precipitation over a large area. Meanwhile, radar-derived precipitation data are restricted by low accuracy in areas of complex terrain and satellite-based precipitation data by low spatial resolution. Therefore, hourly precipitation models were employed to merge data from meteorological stations, Radar, and satellites; the models used five machine learning algorithms (XGBoost, gradient boosting decision tree, random forests (RF), LightGBM, and multiple linear regression (MLR)), as well as the CoKriging method. In the north of Guangdong Province, data of four heavy rainfall events in 2018 were processed with geographic data to obtain merged hourly precipitation data. The CoKriging method secured the best prediction of spatial distribution of accumulated precipitation, followed by the tree-based machine learning (ML) algorithms, and significantly, the prediction of MLR deviated from the actual pattern. All machine learning methods showed poor performances for timepoints with little precipitation during the heavy rainfall events. The tree-based ML method showed poor performance at some timepoints when precipitation was over-related to latitude, longitude, and distance from the coast.

The assessment of hydrometeorological variables using climate scenario data derived from global climate models (GCMs) is essential for adapting and mitigating climate‐induced changes. However, GCM outputs are too coarse to apply at the basin scale. Therefore, spatial downscaling of GCM outputs has been widely used to assess hydrological changes on the basin scale. Temporal downscaling is also required to obtain hourly precipitation data for small or medium basin analysis because only several hours of data are required to determine peak flows after rainfall. This study presents the spatiotemporal distribution of downscaled hourly precipitation over South Korea for two representative concentration pathway scenarios (RCP4.5 and RCP8.5), as well as its implications over hydrological responses. Under the RCP8.5 scenario, the mean hourly precipitation significantly increases over the southern part of South Korea, especially during the morning, with a smaller increase at later times of the day. However, this increase is not propagated to the mainland because of mountainous areas in the southern part of the country. Furthermore, the hydrological responses determined by a distributed rainfall‐runoff model reveal a significant increase in the peak flow under the RCP8.5 scenario but a slight decrease under the RCP4.5 scenario. In addition, the observed relation between extreme precipitation and temperature is compared with that under RCP8.5. The results indicate that the downscaled hourly precipitation data adequately reproduce the observed relationship. We conclude that the temporal downscaling method used in this study is suitable for obtaining hourly precipitation data from daily GCM scenarios. In addition, rainfall‐runoff simulations using downscaled hourly precipitation data are useful for investigating hydrological response variability related to future climate scenarios.

The Climate Prediction Center at NCEP compiled a multi-year gridded hourly precipitation data set over the United States. One of the main goals of the project was to produce a manageable research-quality data set suitable for studies of precipitation variability on timescales ranging from diurnal to interannual. This data set was derived from the hourly observations of approximately 2500 U.S. stations. The observations were gridded, using a modified Cressman scheme, into 2-degree latitude by 2.5-degree longitude boxes.

Hourly precipitation data were collected from 143 first-order US weather stations during the period from 1980 to 2009 to assess the internal distribution of precipitation events lasting at least three hours. A total of 46,595 individual precipitation events were identified and evaluated using the mean, standard deviation, skewness, kurtosis, and the number of peaks occurring within an event. Mean event duration is longest along the West and Northwest coasts, the Mid-South, the Mid-Atlantic, and the Northeast; while shorter-duration events are more frequent in the Rocky Mountains, the Southwest, and the Great Plains. Mean event precipitation and standard deviation are greatest along the Gulf Coast and decrease inland. Precipitation events are positively skewed, indicating that more precipitation tends to occur earlier in the event. The most positively-skewed events are also located in regions flanking the Gulf of Mexico, while less-skewed events are common in the Northwest and Rocky Mountain regions. Event kurtosis is negative throughout the entire USA, with the highest negative values generally west of the Front Range, where cyclonic development and transition produce more evenly distributed precipitation within storms. Intra-event precipitation maxima were also evaluated, with western Florida and the desert Southwest having the greatest number per event.

ABSTRACTPrecipitation, a crucial component of the Earth system processes, regulates the spatiotemporal cyclicity of water, energy, and carbon fluxes. Accurate precipitation datasets leverage the understanding of precipitation dynamics and are vital for hydro‐climatological studies. South Asian monsoon is a complex, multi‐scale interacting, synoptic, and ocean–land–atmosphere coupled system, contributing to significant spatial and temporal variability in summer monsoonal rainfall across India. This study evaluates four types of gridded (observational, satellite, reanalysis, and hybrid) precipitation products in their ability to replicate Indian Summer Monsoonal Rainfall (ISMR) characteristics using the India Meteorological Department (IMD) 0.25° gridded data as the baseline. A comparative assessment is performed in this study that uses several continuous and interval‐based performance measures to evaluate the overall rainfall magnitude detectability and time‐matched capturing of rainfall events. A new metric, rank score, is developed by aggregating multiple measures to find the best product. The analyses based on several performance measures indicate that MSWEP is the best dataset (rank one) that closely approximates the occurrence and magnitude of IMD‐based rainfall events, while APHRODITE, CHIRPS, and IMDAA are ranked as the next best set of products. PGF is ranked the lowest among all products evaluated and is not recommended for applications. Nonetheless, APHRODITE suffers from strong negative biases, while the reanalysis (IMDAA, ERA5‐Land, PGF) datasets show significant positive biases. Among the products evaluated, APHRODITE, ERA5‐Land, and IMDAA have shown a limited ability to detect excess, normal, and deficit monsoon years, respectively. In general, the performance of satellite‐based data products is superior to that of reanalysis datasets in accurately characterising the monsoon years. ERA5‐Land is noted to be the best‐performing dataset among the reanalysis products. The comprehensive comparative assessment carried out in this study benefits the selection and use of appropriate gridded precipitation products for hydroclimatic modelling, climate variability, and change studies.

The spatial and temporal distribution of precipitation significantly impacts human lives. While reanalysis datasets provide consistent long-term global precipitation information that allows investigations of rainfall-driven hazards like larger-scale flooding, they lack the resolution to capture the high spatio-temporal variability of precipitation and miss intense local rainfall events. Here, we introduce spateGAN-ERA5, the first deep learning-based spatio-temporal downscaling of precipitation data on a global scale. SpateGAN-ERA5 enhances ERA5 precipitation data from 24 km and 1 h to 2 km and 10 min, delivering high-resolution rainfall fields with realistic spatio-temporal patterns and accurate rain rate distribution, including extremes. Its computational efficiency enables the generation of a large ensemble of solutions, addressing uncertainties inherent to downscaling challenges and supports practical applicability for generating high-resolution precipitation data for arbitrary ERA5 time periods and regions on demand. Trained solely on data from Germany and validated in the US and Australia, considering diverse climates, including tropical rainfall regimes, spateGAN-ERA5 demonstrates strong generalization, indicating robust global applicability. It fulfills critical needs for high-resolution precipitation data in hydrological and meteorological research.

The Hydrometeorological Automated Data System (HADS) is a real-time data acquisition, processing, and distribution system operated by the Office of Hydrologic Development (OHD) of NOAA’s National Weather Service (NWS). The initial reprocessing of HADS data from its original format since its inception in July 1996 has been completed at NOAA’s National Climatic Data Center (NCDC). The quality of the reprocessed HADS hourly precipitation data from rain gauges is assessed by two objective metrics: the average fraction of missing values and the percentage of top-of-the-hour observations for a 3-yr period (2003–05). Pairwise comparisons between the reprocessed product and the real-time product are made using representative samples (about 13%) from the 48 contiguous United States. The monthly average of missing values varies from 0.5% to 2% in the reprocessed product and from 1.7% to 10.1% in the real-time product. Except for January 2003, the reprocessed product consistently reduced missing values, by as much as 9.4% in October 2004. The availability of top-of-the-hour observations is about 85% in the reprocessed product, while the real-time product has top-of-the-hour observations only about 50% of the time. This paper discusses real-time product quality issues, additional quality assurance algorithms used in the reprocessing environment, and the design of system-wide performance comparisons. Thus, the benefits to users of reprocessing the HADS data are the correction of 4-h observation time errors during 1 July–11 August 2005 and the demonstration of diurnals pattern of precipitation frequencies in regional domains. A Web-based interactive quality assessment tool for reprocessed HADS hourly precipitation data and access to the data are also presented.

Flood intensification due to climate variability and urbanization necessitates advanced forecasting tools, particularly in regions undergoing rapid transformation where drainage infrastructure data is often scarce. This study presents a methodological framework for flood forecasting in the Ribeirão Anhumas watershed (Campinas, Brazil), specifically applied to the International Hub for Sustainable Development (HIDS–Unicamp). As an innovation district currently under implementation, HIDS represents a unique opportunity to integrate predictive modeling into early-stage urban planning.The methodology addresses data scarcity by integrating physical modeling with machine learning. We have established a simulation environment using PCSWMM to replicate hydrological behavior under distinct infrastructure scenarios. These simulations, driven by high-resolution precipitation (10-min) and geospatial data (1 m DTM), generate the necessary synthetic training data for regions where sensor networks are yet to be deployed. The proposed architecture is designed to perform a binary classification of flood occurrence (Flood/No Flood), utilizing a multi-model approach: Recurrent Neural Networks (RNNs) for temporal dynamics, Convolutional Neural Networks (CNNs) for spatial patterns, and Graph Neural Networks (GNNs) to explicitly model the hydrological connectivity of the watershed.In this contribution, we present the complete data processing pipeline and the defined model architecture. The study focuses on evaluating the comparative performance of these architectures using classification metrics (accuracy, precision, recall, F1-score, and ROC curve). Furthermore, to ensure the model is transparent for decision-makers, we outline the application of Explainable AI (XAI) techniques, specifically SHAP and LIME. These are intended to identify the contribution of input variables to flood predictions, bridging the gap between "black-box" deep learning and interpretable hydrological processes. The final results aim to demonstrate how hybrid modeling can support the strengthening of early warning systems and resilience strategies in developing urban territories.

The best available high-resolution precipitation, GDP, available freshwater and withdrawal data sets are used in a combined global analysis of physical and economic water scarcity at 50 km resolution. We find that approximately 40.7 million people are living in areas with concurrent severe economic and water-scarcity constraints. These areas are mostly in semi-arid parts of Sub-Saharan Africa, the Middle East and Central Asia.

Assessment of the hydrological impact of an hourly precipitation distribution correction method in the SASER modeling system

This paper investigates the hourly precipitation estimation capacities of ANN using raw data and reconstructed data using proposed Precipitation Sliding Window Period (PSWP) method. The precipitation data from 11 Automatic Weather Station (AWS) of Delhi has been obtained from Jan 2015 to Feb 2016. The proposed PSWP method uses both time and space dimension to fill the missing precipitation values. Hourly precipitation follows patterns in particular period along with its neighbor stations. Based on these patterns of precipitation, Local Cluster Sliding Window Period (LCSWP) and Global Cluster Sliding Window Period (GCSWP) are defined for single AWS and all AWSs respectively. Further, GCSWP period is classified into four different categories to fill the missing precipitation data based on patterns followed in it. The experimental results indicate that ANN trained with reconstructed data has better estimation results than the ANN trained with raw data. The average RMSE for ANN trained with raw data is 0.44 and while that for neural network trained with reconstructed data is 0.34.

A stochastic cluster model for hourly precipitation data