Reliability of hourly precipitation data

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 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.

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.

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.

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.

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.

A stochastic cluster model for hourly precipitation data

Rice yield have been affected by the increased extreme precipitation events in recent decades. Yet, the spatio-temporal patterns of extreme precipitation by rice type and phenology remain elusive. Here, we investigate the characteristics of four extreme precipitation indices across China’s rice paddy and their potential association with crop yields, by using hourly precipitation data from 1,215 stations and rice phenology observations from 45 sub-regions. The data indicate that hourly extreme precipitation have significantly increased in 1961–2012 for single rice and early rice in China but not for late rice. Rice were mainly exposed to extreme precipitation from transplantation to flowering stages. The frequency and proportion of extreme precipitation were significantly increased by 2.0–4.7% and 2.3–2.9% per decade, respectively, mainly in south China and Yangtze River Basin. The precipitation intensity and maximum hourly precipitation were increased by 0.7–1.1% and 0.9–2.8% per decade, respectively, mainly in central China and southeast coastal area. These extreme precipitation indices played a role as important as accumulated precipitation and mean temperature on the interannual variability of rice yields, regardless of rice types. Our results also highlight the urgencies to uncover the underlying mechanisms of extreme precipitation on rice growth, which in turn strengthens the predictability of crop models.

Impact of urbanization on hourly precipitation in Beijing, China: Spatiotemporal patterns and causes

Spatiotemporal pattern networks of heavy rain among automatic weather stations, which reflect the mobility of heavy rain, were constructed and analyzed based on the hourly precipitation data over the last ten years (from 2003 to 2012) in South Korea. Moreover, a new algorithm applying the constructed heavy-rain pattern networks to very-short-term heavy-rain prediction was developed, and significant prediction results could be obtained.

Recently, flood damage has become a social problem owing to unexperienced weather conditions arising from climate change. An immediate response to heavy rain is important for the mitigation of economic losses and also for rapid recovery. Spatiotemporal precipitation forecasts may enhance the accuracy of dam inflow prediction, more than 6 h forward for flood damage mitigation. However, the ordinary ConvLSTM has the limitation of predictable range more than 3-timesteps in real-world precipitation forecasting owing to the irreducible bias between target prediction and ground-truth value. This paper proposes a rain-code approach for spatiotemporal precipitation code-to-code forecasting. We propose a novel rainy feature that represents a temporal rainy process using multi-frame fusion for the timestep reduction. We perform rain-code studies with various term ranges based on the standard ConvLSTM. We applied to a dam region within the Japanese rainy term hourly precipitation data, under 2006 to 2019 approximately 127 thousands hours, every year from May to October. We apply the radar analysis hourly data on the central broader region with an area of 136 × 148 km2. Finally we have provided sensitivity studies between the rain-code size and hourly accuracy within the several forecasting range.

Long-term trend analysis on total and extreme precipitation over Shasta Dam watershed

Urban flooding has become an increasingly pressing issue under the combined influeces of climate change and urbanization. This study focues on Urban flood in Zhengzhou, a city as major transportation hub in China, where railway culverts represent critical infrastructure prone to waterlogging during extreme storm events. Utilizing ten years(2015-2024) of hourly precipitation data obtained from NASA, this study conducts a comprehensive time-series analysis to explore seasonal and interannual trends in rainfall intensity and frequency. A disaster model was developed to simulate flood formation processes in culvert areas, integrateing precipitation, land use, drainage, and terrain data. The model identifies a consistent temporal pattern: in most flood events, waterlogging occurred when 30–50% of the total rainfall duration had elapsed. The well-known July 20, 2021 (“7·20”) event was found to be an outlier, but overall, the frequency of extreme precipitation has shown signs of increase in recent years. These findings offer a new perspective for characterizing extreme rainfall and provide reference values for early warning systems and urban flood resilience planning.

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.

The Idukki district of Kerala experienced its worst disaster since 1924 in 2018 due to the excessive rain during the monsoon season from June to August. There were devastating landslides throughout the district accounting for the highest among the state which included debris flow, soil slides and rockfalls,  ever since making the area more susceptible to landslides. Thus, forging the impending need of an early warning system is crucial. Here, the study attempts to create a rainfall threshold to predict landslides through a satellite based precipitation product - GPM (Global Precipitation Mission Integrated Multi-satellitE Retrievals). The global coverage and the improved resolution of the data makes it more effective in mitigating landslide disaster risk.  Hourly precipitation data was obtained from GPM for the 23 grids within the district where there was an occurrence of debris flow. As the largest number of landslides happened during a second spell of intense rainfall that lasted from July 11 to August 19, 2018, the rainfall during this time was analyzed. A total of 4 days in these two months were identified with high amounts of rainfall with August 15th showing continuous rainfall for 24 hours. Using the rainfall and the landslide data for 2018 the overall regional threshold over the district was estimated to be I= 5.42D-0.16, where I is the rainfall intensity in mm/hr and D is the event duration. A more precise threshold could be obtained by considering grid-wise rainfall thresholds in a more detailed analysis.