Quality Evaluation of the 0.01° Multi-Source Fusion Precipitation Product and Its Application in Extreme Precipitation Event

High-resolution and high-quality precipitation data play an important role in Numerical Weather Prediction Model testing, mountain flood geological disaster monitoring, hydrological monitoring and prediction and have become an urgent need for the development of modern meteorological business. The 0.01° multi-source fusion precipitation product is the latest precipitation product developed by the National Meteorological Information Center to meet the above needs. Taking the hourly precipitation observation data of 2400 national automatic stations as the evaluation base, independent and non-independent test methods are used to evaluate the 0.01° multi-source fusion precipitation product in 2020. The product quality differences between the 0.01° precipitation product and the 0.05° precipitation product are compared, and their application in extreme precipitation events are analyzed. The results show that, in the independent test, the product quality of the 0.01° precipitation product and the 0.05° precipitation product are basically the same, which is better than that of each single input data source, and the product quality in winter and spring is slightly lower than that in summer, and both products have better quality in the east in China. The evaluation results of the 0.01° precipitation product in the non-independent test are far better than that of the 0.05° product. The root mean square error and the correlation coefficient of the 0.01° multi-source fusion precipitation product are 0.169 mm/h and 0.995, respectively. In the extreme precipitation case analysis, the 0.01° precipitation product, which is more consistent with the station observation values, effectively improves the problem that the extreme value of the 0.05° product is lower than that of station observation values and greatly improves the accuracy of the precipitation extreme value in the product. The 0.01° multi-source fusion precipitation product has better spatial continuity, a more detailed description of precipitation spatial distribution and a more accurate reflection of precipitation extreme values, which will better provide precipitation data support for refined meteorological services, major activity support, disaster prevention and reduction, etc.

A comprehensive five-year evaluation of IMERG Late Run precipitation estimates over the Netherlands

Applications like drought monitoring and forecasting can profit from the global and near real-time availability of satellite-based precipitation estimates once their related uncertainties and challenges are identified and treated. To this end, this study evaluates the IMERG V06B Late Run precipitation product from the Global Precipitation Measurement mission (GPM), a multi-satellite product that combines space-based radar, passive microwave (PMW), and infrared (IR) data into gridded precipitation estimates. The evaluation is performed on the spatiotemporal resolution of IMERG (0.1° × 0.1°, 30 min) over the Netherlands over a five-year period. A gauge-adjusted radar precipitation product from the Royal Netherlands Meteorological Institute (KNMI) is used as reference, against which IMERG shows a large positive bias. To find the origin of this systematic overestimation, the data is divided into seasons, rainfall intensity ranges, echo top height (ETH) ranges, and categories based on the relative contributions of IR, morphing, and PMW data to the IMERG estimates. Furthermore, the specific radiometer is identified for each PMW-based estimate. IMERG’s detection performance improves with higher ETH and rainfall intensity, but the associated error and relative bias increase as well. Severe overestimation occurs during low-intensity rainfall events and is especially linked to PMW observations. All individual PMW instruments show the same pattern: overestimation of low-intensity events and underestimation of high-intensity events. IMERG misses a large fraction of shallow rainfall events, which is amplified when IR data is included. Space-based retrieval of shallow and low-intensity precipitation events should improve before IMERG can be implemented over the middle and high-latitudes.

Evaluation of remotely sensed precipitation product in a hydrological model of the Bednja watershed

In this paper, a statistical and spatial analysis of precipitation for the period 2000-2018 for the Bednja basin was performed, were the measured data from meteorological and/or rainfall stations of Croatian Meteorological and Hydrological Service (DHMZ) were compared with the data in form of remotely sensed precipitation product - CHIRPS (Climate Hazards Group InfraRed Precipitation with Station). The results of the analysis in the form of the annual sum, monthly distribution within the year and the spatial distribution and input data ratio over the basin show a good correlation between the measured and remotely sensed precipitation. In order to further evaluate the quality of the remotely sensed product, a SWAT hydrological runoff model was created.

Validation of Satellite-Based Precipitation Products from TRMM to GPM

The global precipitation measurement mission (GPM) has been in operation for seven years and continues to provide a vast quantity of global precipitation data at finer temporospatial resolutions with improved accuracy and coverage. GPM’s signature algorithm, the integrated multisatellite retrievals for GPM (IMERG) is a next-generation of precipitation product expected for wide variety of research and operational applications. This study evaluates the latest version (V06B) of IMERG and its predecessor, the tropical rainfall measuring mission (TRMM) multisatellite precipitation (TMPA) 3B42 (V7) using ground-based and gauge-corrected multiradar multisensor system (MRMS) precipitation products over the conterminous United States (CONUS). The spatial distributions of all products are analyzed. The error characteristics are further examined for 3B42 and IMERG in winter and summer by an error decomposition approach, which partitions total bias into hit bias, biases due to missed precipitation and false precipitation. The volumetric and categorical statistical metrics are used to quantitatively evaluate the performance of the two satellite-based products. All products show a similar precipitation climatology with some regional differences. The two satellite-based products perform better in the eastern CONUS than in the mountainous Western CONUS. The evaluation demonstrates the clear improvement in IMERG precipitation product in comparison with its predecessor 3B42, especially in reducing missed precipitation in winter and summer, and hit bias in winter, resulting in better performance in capturing lighter and heavier precipitation.

Evaluation of the ERA5 reanalysis as a reference dataset for fine-scale hydrological modelling over alpine basins

<p>The European Center for Medium-Range Weather Forecasts (ECMWF) has recently released its most advanced reanalysis product, the ERA5 dataset. It was designed and generated with methods giving it multiple advantages over the previous release, the ERA-Interim reanalysis product. Notably, it has a finer spatial resolution, is archived at the hourly time step, uses a more advanced assimilation system, and includes more sources of data. This paper aims to evaluate the ERA5 reanalysis as a potential reference dataset for hydrological modelling by considering the ERA5 precipitation and temperatures as proxies for observations in the hydrological modelling process. This is obtained by using a semi-distributed hydrological model over basins ranging from 40km<sup>2</sup> to 6900 km<sup>2</sup> over the Upper Adige river basin in the Eastern Italian Alps. This study shows that ERA5-based precipitation product is affected by a significant bias which translates to biased runoff at all spatial scales considered in the study. We observed that ERA5 precipitation product generally overestimate low-intensity rainfall and underestimate high rainfall intensity in the region. We analysed how this affects simulation of annual max floods over the study area. The results show that flood simulations are in general surprisingly good, as they result from the combination of two cascading errors: i) overestimation of the soil moisture conditions at the start of the event and ii) the underestimation of the event forcing rainfall. Differences between ERA5 and observation datasets are mostly linked to precipitation, as temperature only marginally influences the hydrological simulation outcomes.</p>

Climatologia da Chuva em Maceió: Aspectos Climáticos e Ambientais

The objectives of the study are: i) to evaluate the climatology of rain in Maceió based on observed data, with emphasis on climatic and environmental aspects and ii) to validate the precipitation product for the municipality. Data from 1979 to 2013 of the precipitation product CHELSA (Climatologies at High Resolution for the Earth's Land Surface Areas) were validated by rainfall data from the National Water Agency (NWA) from 1960 to 2016. Statistical indicators showed a high coefficient of determination and linear correlation between CHELSA and observed data (R2 = 0.80; r = 0.89) and the smallest errors (SEE = 6.58 mm and RMSE = 18.76 mm), therefore the CHELSA product can be applied in the region. The time series presented a period 1 (P1) - (1960 to 1989) with rainfall above the historical average and a period 2 (P2) - (1990 to 2016) with a significant reduction in rainfall. Observed data versus climatological normals showed a significant decrease in normal 1 (1961-1990) in the rainy season, while in relation to normal 2 (1981-2010) there was an increase in the months of February, March and April (between 10 to 20%) and October and December (between 5 to 15%). The spatial distribution of monthly rainfall via the CHELSA product showed the formation of a pluviometric gradient between the coast and the upper part of Maceió. The topography influences the rainfall regime in neighborhoods belonging to the administrative regions (AR) - (R4, R5 and R6) with the highest rainfall records. The ENOS phases are directly responsible for the variability of interannual rain, while the decadal variability corresponded to the PDO phase change and changes in land use and occupation in Maceió.