Abstract
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.
Highlights
Urban floods, flash floods and landslides due to heavy rainfall are a few of the world’s most severe natural disasters
With the development of the regional, high spatiotemporal, numerical weather prediction (NWP) model and the distributed hydrological model, a high-resolution precipitation product is urgently needed to verify the performance of the NWP model and to force the hydrological model
While weather radar is widely used in China for quantitative precipitation estimation (QPE) due to its unique advantages of high spatial (1 km) and temporal (5–6-min) resolution, radar precipitation products may contain large error due to the mixed calibration standard for different weather radar types, uncertainties in the Z-R relationships [3], as well as complicated types of precipitation in China
Summary
Flash floods and landslides due to heavy rainfall are a few of the world’s most severe natural disasters. Precipitation measurements at small spatial and short temporal scales are still a great challenge because of the high spatiotemporal variation and nonlinear nature of precipitation [2]. Surface precipitation is predominantly measured using rain gauges, weather radars and satellites. The uneven spatial distribution of gauges, especially over the Tibetan Plateau and vast ocean areas, limits their use. The coverage of the weather radar is spatially limited, rendering its application nearly useless over the complex terrain of Northwestern China, especially in the Tibetan Plateau, where satellite-based precipitation products are the only reliable way to increase precipitation coverage. Satellite-based precipitation products are generated by merging satellite infrared (IR) and passive microwave (PMW) measurements. Their spatial resolution can reach up to 10 km. The National Oceanic and Atmospheric Administration (NOAA) Climate Precipitation Center (CPC) Morphing Technique (CMORPH) has been producing global precipitation analyses on an 8-km grid and 30-min interval since 1998 [4,5]
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