A stochastic cluster model for hourly precipitation data

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

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.

Based on the hourly precipitation data of 34 meteorological stations in Chongqing in the summers (June to August) from 1996 to 2015, the spatial distribution and daily variation of precipitation amount (PA), precipitation intensity (PI), precipitation frequency (PF), and precipitation extremes in Chongqing are analyzed. The results show that, from the perspective of spatial distribution, the precipitation amount (PA) in Chongqing presents a distribution pattern of more around and less in the middle; the area with high precipitation intensity (PI) is mainly located in the northeast of Chongqing; the large value centers of precipitation frequency (PF) are located in the south and west of Chongqing and near Chengkou. On the spatial distribution of hourly precipitation, the precipitation in most areas of Chongqing is mainly concentrated at night [0200–0900 BT (1800–0100 UTC)], and the rain belt spreads from west to east with the passage of time. On the whole, interannual evolution characteristics of summer precipitation amount, precipitation intensity, and precipitation frequency in Chongqing are basically the same, showing a fluctuation characteristic without obvious trend, but there are some peaks and valleys. From the perspective of diurnal cycle, a larger peak of PA in Chongqing appears near 0300 BT (1900 UTC), another lower peak around 1200 BT (0400 UTC), a larger peak of PI around 0300 BT (1900 UTC), another smaller peak around 1500 BT (0700 UTC), and only one peak of PF around 0700 BT (2300 UTC). The extreme precipitation of different duration in summer in Chongqing is closely related to the topographic characteristics and weather system, the extreme centers of each diachronic precipitation are mainly located near Shapingba, Kaizhou, Youyang, and Shizhu, and the time evolution characteristics of the extreme precipitation are not obvious, but the trend of the extreme precipitation accumulated in 1 h, 3 h, 6 h, or 12 h is basically the same.

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.

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.

The PannEx is a GEWEX-initiated, community driven research network in the Pannonian Basin. One of the main scientific issues to address in PannEx is the investigation of precipitation extremes. Meteorological Services in the PannEx area collected the hourly precipitation data and commonly used a computer program, which was developed in the INTENSE project, to produce a set of global hydro-climatic indices. Calculations are carried out on data aggregated 1-, 3- and 6-h intervals. Selected indices are analyzed in this paper to assess the general climatology of the short-term precipitation in the Pannonian basin. The following indices are illustrated on maps and graphs: the annual mean and maxima of 1-h, 3-h and 6-h sums, the count of 3-hr periods greater than 20 mm thresholds, the maximum length of wet hours, the timing of wettest hour and the 1-h precipitation intensity. The seasonal trends of the 1-h precipitation intensity were tested from 1998 to 2019. Analysis of sub-daily precipitation has been limited by the availability of data on a global or a regional scale. The international effort made in this work through collaboration in the PannEx initiative contributes to enlarging the data availability for regional and global analysis of sub-daily precipitation extremes.

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.

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.

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.

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. 

High-resolution meteorological datasets are urgently needed for understanding the hydrological cycle of the Tibetan Plateau (TP), where ground-based meteorological stations are sparse. Rapid advances in remote sensing create possibilities to represent spatiotemporal properties of precipitation at a high resolution. In this study, the hourly precipitation characteristics over the TP from two gridded precipitation products, one from global reanalysis (the fifth generation of the European Center for Medium-Range Weather Forecasts atmospheric reanalysis of the global climate; ERA5) and the other is simulated by Global-to-Regional Integrated forecast SysTem (GRIST) global nonhydrostatic model, are compared against satellite-gauge merged precipitation analysis (China Merged Precipitation Analysis; CMPA) from 27 July to 31 August 2014, and a satellite-retrieved precipitation estimate from the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) is also evolved. Two aspects are mainly focused on: the spatial distribution and the elevation dependence of hourly precipitation characteristics (including precipitation amount, frequency, intensity, diurnal variations, and frequency–intensity structure). Results indicate that: (1) The precipitation amount, frequency, and intensity of CMPA and IMERG decrease with altitude in the Yarlung Tsangpo river valley (YTRV), but increase at first and then decrease with altitude (except for intensity) in the eastern periphery of TP (EPTP). ERA5 performed well on the variation of precipitation amount with altitude (especially in EPTP), but poorly on the frequency and intensity. GRIST is the antithesis of ERA5, but they all overestimate (underestimate) the frequency (intensity) at all heights; (2) With increasing altitude, the diurnal phase of precipitation of CMPA and IMERG shifted from night to evening in the two sub-regions. IMERG’s diurnal phase is 1 to 3 h earlier than CMPA’s, and the discrepancy decreases (increases) as the altitude increases in YTRV (EPTP). The diurnal phase of precipitation amount and frequency in ERA5 and GRIST is significantly earlier than CMPA, and the frequency peaks around midday except in the basin. GRIST’s simulation of the diurnal variation in intensity at various altitudes is consistent with CMPA; (3) ERA5 and GRIST overestimate (underestimate) the frequency of weak (intense) precipitation, with ERA5’s deviance being the most severe. The deviations increased with altitude. These findings provide intensive metrics to evaluate precipitation in complex terrain and are helpful for deepening the understanding of simulated biases for further improving performance in high-resolution simulation.

Properly exposed, calibrated, and evaluated, the customary 8-inch-diameter weighing rain gage yields hourly precipitation values with standard errors of about .01 inch, so that reliability within .02 inch can be assumed. But two identical gages 10 feet apart on a windy ridge top can differ consistently in catch by 50 per cent of the smaller. Four pairs of identical gages, exposed side by side at four sites in Santa Barbara County, California, January–April 1959, were studied.

Effective precipitation duration for runoff peaks based on catchment modelling

This study developed a rain-gauge-based hourly precipitation dataset to analyze the heavy precipitation event of July 2018 in Japan (H2018). We modified the APHRODITE algorithm to treat hourly precipitation data, and we detected orographically induced heavy precipitation patterns in western Japan. We compared the heavy precipitation pattern along with moisture transport with that of another disastrous precipitation event in 2014 over Hiroshima (H2014). It is evident that heavy precipitation occurred over a much wider area in Chugoku district during H2018 than in H2014, and extreme precipitation which exceeds 10mm/hr appeared three times in H2018 while at one time in H2014.