Search Results (8)

Heavy rainfall and flood disasters are frequent in mountainous watersheds in southwest China, and forecasting runoff floods in some mountainous watersheds is difficult. In this study, a typical watershed in the southwest mountainous region, the Qingyi River (13,000 km²), was selected for the lack of precipitation observation data in the watershed, and the BTOPMC (block-wise use of the topographic-based hydrologic model (TOPMODEL)) was used, using CMPA-Hourly (China Hourly Merged Precipitation Analysis combining observations from automatic weather stations, meteorological satellite, and weather radar at 0.05° × 0.05° grid) to improve the accuracy of flood forecasting. The results show that the Nash–Sutcliffe efficiency (NSE) of the flood forecast for the verification period in the Jiajiang section of the Qingyi River using CMPA-Hourly improved from 0.66 to 0.78, the flood error reduced from 18% to 9%, and the overall accuracy reached grade B or above. The results indicate that CMPA-Hourly, which integrates ground observation–radar–satellite precipitation, effectively combined the advantages of different sources of data to improve the resolution and accuracy of precipitation data, and then CMPA-Hourly can be used to improve the accuracy of runoff and flood forecasting. Full article

The High Resolution Model Intercomparison Project (HighResMIP) experiment within the Coupled Model Intercomparison Project Phase 6 (CMIP6) has enabled the evaluation of the performance of climate models over complex terrain for the first time. The study aims to evaluate summer (June to August) precipitation characteristics over the Tibetan Plateau (TP). Precipitation derived from HighResMIP models and ERA5 are compared against the China Merged Precipitation Analysis (CMPA). The nineteen models that participated in HighResMIP are classified into three categories based on their horizontal resolution: high resolution (HR), middle resolution (MR), and low resolution (LR). The multimodel ensemble means (MMEs) of the three categories of models are evaluated. The spatial distribution and elevation dependency of the hourly precipitation characteristics, which include the diurnal peak hour, diurnal variation amplitude, and frequency–intensity structure, are our main focus. The MME-HR and ERA5 both show comparable ability in simulating precipitation in the TP. The MME-HR has a smaller deviation in the precipitation amount and diurnal variation at various altitudes. The ERA5 can better simulate the elevation dependence of the frequency–intensity structure, but its elevation dependence of diurnal variation shows a trend opposite to the observations. Although the MME-HR produces the best simulation results among the three MMEs, the simulation effects of HighResMIP’s precipitation in the TP do not necessarily improve with increasing the horizontal resolution from LR to MR. The finer model resolution has a small impact on the simulation effect of precipitation intensity, but the coarser model resolution will limit the generation of heavy precipitation. These findings give intensive measures for evaluating precipitation in complex terrain and can help us in comprehending rainfall biases in global climate model simulation. Full article

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. Full article

The recently developed gauge-radar-satellite merged hourly precipitation dataset (CMPAS-NRT) offers broad applications in scientific research and operations, such as intelligent grid forecasting, meteorological disaster monitoring and warning, and numerical model testing and evaluation. In this paper, we take a super-long Meiyu precipitation process experienced in the Yangtze River basin in the summer of 2020 as the research object, and evaluate the monitoring capability of the CMPAS-NRT for the process from multiple perspectives, such as error indicators, precipitation characteristics, and daily variability in different rainfall areas, using dense surface rain-gauge observation data as a reference. The results show that the error indicators for CMPAS-NRT are in good agreement with the gauge observations. The CMPAS-NRT can accurately reflect the evolution of precipitation during the whole rainy season, and can accurately capture the spatial distribution of rainbands, but there is an underestimation of extreme precipitation. At the same time, the CMPAS-NRT product features the phenomenon of overestimation of precipitation at the level of light rain. In terms of daily variation of precipitation, the precipitation amount, frequency, and intensity are basically consistent with the observations, except that there is a lag in the peak frequency of precipitation, and the frequency of precipitation at night is less than observed, and the intensity of precipitation is higher than observed. Overall, the CMPAS-NRT product can successfully reflect the precipitation characteristics of this super-heavy Meiyu precipitation event, and has a high potential hydrological utilization value. However, further improvement of the precipitation algorithm is needed to solve the problems of overestimation of light rainfall and underestimation of extreme precipitation in order to provide more accurate hourly precipitation monitoring dataset. Full article

Precipitation data with fine quality plays vital roles in hydrological-related applications. In this study, we choose the high-quality China Merged Precipitation Analysis data (CMPA) as the benchmark for evaluating four satellite-based precipitation products (PERSIANN-CCS, FY4A QPE, GSMap_Gauge, IMERG-Final) and one model-based precipitation product (ERA5-Land), respectively, at 0.1°, hourly scales over the Zhejiang province, China, in summer, from June to August 2019. The main conclusions were as follows—(1) all other products demonstrate similar patterns with CMPA (~325.60 mm/h, std ~0.07 mm/h), except FY4A QPE (~281.79 mm/h, std ~0.18 mm/h), while, overall, the PERSIANN-CCS underestimates the precipitation against CMPA with a mean value around 236.29 mm/h (std ~0.06 mm/h), and the ERA5-Land, GSMap_Guage, and IMERG-Final generally overestimate the precipitation with a mean value around 370.00 mm/h (std ~0.06 mm/h). (2) The GSMap_Gauge outperforms IMERG-Final against CMPA with CC ~0.50 and RMSE ~1.51 mm/h, and CC ~0.48 and RMSE ~1.64 mm/h, respectively. (3) The PERSIANN-CCS significantly underestimates the precipitation (CC ~0.26, bias ~−35.03%, RMSE ~1.81 mm/h, probability of detection, POD, ~0.33, false alarm ratio, FAR, ~0.47), potentially due to its weak abilities to capture precipitation events and estimate the precipitation. (4) Though ERA5-Land has the best ability to capture precipitation events (POD ~0.78), the largest misjudgments (FAR ~0.54) result in its great uncertainties with CC ~ 0.39, which performs worse than those of GSMap_Gauge and IMERG-Final. (5) The ranking of precipitation products, in terms of the general evaluation metrics, over Zhejiang province is GSMap_Gauge, IMERG-Final, ERA5-Land, PERSIANN-CCS, and FY4A QPE, which provides valuable recommendations for applying these products in various related application fields. Full article

Satellite-based precipitation estimates with high quality and spatial-temporal resolutions play a vital role in forcing global or regional meteorological, hydrological, and agricultural models, which are especially useful over large poorly gauged regions. In this study, we apply various statistical indicators to comprehensively analyze the quality and compare the performance of five newly released satellite and reanalysis precipitation products against China Merged Precipitation Analysis (CMPA) rain gauge data, respectively, with 0.1° × 0.1° spatial resolution and two temporal scales (daily and hourly) over southern China from June to August in 2019. These include Precipitation Estimates from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS), European Center for Medium-Range Weather Forecasts Reanalysis v5 (ERA5-Land), Fengyun-4 (FY-4A), Global Satellite Mapping of Precipitation (GSMaP), and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG). Results indicate that: (1) all five products overestimate the accumulated rainfall in the summer, with FY-4A being the most severe; additionally, FY-4A cannot capture the spatial and temporal distribution characteristics of precipitation over southern China. (2) IMERG and GSMaP perform better than the other three datasets at both daily and hourly scales; IMERG correlates slightly better than GSMaP against CMPA data, while it performs worse than GSMaP in terms of probability of detection (POD). (3) ERA5-Land performs better than PERSIANN-CCS and FY-4A at daily scale but shows the worst correlation coefficient (CC), false alarm ratio (FAR), and equitable threat score (ETS) of all precipitation products at hourly scale. (4) The rankings of overall performance on precipitation estimations for this region are IMERG, GSMaP, ERA5-Land, PERSIANN-CCS, and FY-4A at daily scale; and IMERG, GSMaP, PERSIANN-CCS, FY-4A, and ERA5-Land at hourly scale. These findings will provide valuable feedback for improving the current satellite-based precipitation retrieval algorithms and also provide preliminary references for flood forecasting and natural disaster early warning. Full article

The Global Precipitation Measurement (GPM) mission has generated global precipitation products of improved accuracy and coverage that are promising for advanced hydrological and meteorological studies. This study evaluates three Integrated Multi-satellitE Retrievals for GPM (IMERG) Hourly products, including the Early-, Late-, and Final-run products (IMERG-HE, IMERG-HL, and IMERG-HF, respectively), over Sichuan Basin of China. This highly complex terrain of the steep mountainous region offers further scrutiny on the quality and applicability of the data. The China Meteorological Precipitation Analysis (CMPA) data from January 2016 to December 2018 are used as the reference for the evaluation. Results show that: (1) At grid scale, IMERG-HL and IMERG-HF outperform IMERG-HE in terms of correlation coefficient (CC) and root-mean-square error (RMSE), but IMERG-HL has smaller relative bias (RB) than that of the IMERG-HF (by 21.16%). IMERG-HF presents the highest probability of detection (POD = 0.52) and critical success index (CSI = 0.32), except for high false alarm ratio (FAR) for light precipitation. (2) At regional scale, IMERG-HF outperforms IMERG-HE and IMERG-HL in annual evaluation in all the metrics except for the serious overestimation as shown in RB (20.18%, 3.84%, and 4.97%, respectively). Its accumulative precipitation deviation mainly comes from moderate precipitation events (1–10 mm/h), while better detection capability is seen in light precipitation (<1 mm/h). Seasonally, IMERG-HF performs the best in winter, while IMERG-HL performs the best in the other seasons. (3) IMERG-HF captures the peak precipitation more accurately in all seasons. In reproducing the diurnal cycle, IMERG-HF performs better in winter, while IMERG-HL performs better in summer and autumn, and IMERG-HE in spring. However, all three products overestimate the early morning precipitation (01:00–08:00 local standard time) of the diurnal cycle in spring, summer, and autumn. Full article

Based on high-density gauge precipitation observations, high-resolution weather radar quantitative precipitation estimation (QPE) and seamless satellite-based precipitation estimates, a 1-km experimental gauge-radar-satellite merged precipitation dataset has been developed using the proposed local gauge correction (LGC) and optimal interpolation (OI) merging strategies. First, hourly precipitation analyses from approximately 40,000 automatic weather stations at 0.01° resolution were used to correct bias in the radar QPE Group System (QPEGS), developed by the China Meteorological Administration (CMA) and the Climate Prediction Center Morphing (CMORPH) precipitation products. As precipitation events tend to have a more localized distribution at the hourly and 0.01° resolutions, three core parameters were improved using the OI method. (a) The spatial dependence of the error variance for radar QPE was accounted for over six sub-regions in China and is shown as a non-linear function of the gauge precipitation analysis. (b) The spatial dependence of error correlation for the radar QPE decreased exponentially with distance. (c) The error of the hourly gauge-based precipitation analysis was quantified as a function of the precipitation amount and the gauge network density, using the Monte Carlo method to randomly sample the gauge observations over the dense gauge network. The performance of the 1-km experimental gauge-radar-satellite merged precipitation dataset (named as China Merged Precipitation Analysis: CMPA_1km) was assessed at 6 h-temporal resolutions and 0.03° × 0.03° spatial resolution using precipitation observations from 208 independent hydrological stations as a reference. Compared with radar QPE and CMORPH, the CMPA-1km showed obviously better accuracy in all sub-regions and during all seasons. In contrast, gauge analysis and CMPA-1km shared similar accuracy, but the latter could estimate heavy precipitation more accurately than the former, as well as the latter has the advantage of seamless spatial coverage. However, the CMPA-1km exhibits larger uncertainty during the cold season compared to the warm season, which will need further improvement in future work. The downscaled bias-corrected 0.01° resolution CMORPH was employed to fill the gaps in regions, mainly in Western China and the Tibetan Plateau, where gauge and radar measurements are limited. Full article