China’s 1 km Merged Gauge, Radar and Satellite Experimental Precipitation Dataset

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.

Which Precipitation Product Works Best in the Qinghai-Tibet Plateau, Multi-Source Blended Data, Global/Regional Reanalysis Data, or Satellite Retrieved Precipitation Data?

“…Thus, the problems related to downscaling and uncertainty can be solved. In China, the China Meteorological Administration blends data from radar-retrieved precipitation, a dense network of ground-based rain gauge observations, and SRP [75], which had a horizontal resolution of 1 km and performed well. In addition, a previous study using WRF assimilated SRP by 4D-Var Data Assimilation significantly improved WRF precipitation estimation over the Huaihe River Basin, China [76].…”

Reliability of Gridded Precipitation Products in the Yellow River Basin, China

“…where a is the number of hits, b is the number of false alarms, and c is the number of misses. Perfect values were FBI = 1, POD = 1, TS = 1, and FAR = 0 [48][49][50][51].…”

Evaluation of the Radar QPE and Rain Gauge Data Merging Methods in Northern China

“…The radar is operated by the China Meteorological Administration (CMA) and has an optimal detection range of 230 km. The radar obtains per base reflectivity data on every six-minute volume scan and completely covers the two study areas [48]. A radar QPE Group System (QPEGS) was developed by the CMA, providing hourly QPE data at a high spatial resolution.…”

“…The power law Marshall-Palmer relationship converted the radar reflectivity (Z) to rain rata (R), and a Z-R relationship calculated the accumulated rainfall for convective and stratiform rainfall types [50]. In the QPEGS, the common definition of "a" and "b" is shown in Equations (25) and (26) [48]. To ensure that the spatial resolution of radar data can reflect the precipitation, a higher radar precipitation, rather than a low threshold of 0.1 mm, was considered for the estimation of a 1 km × 1 km radar grid.…”