Improvement of a combination of TMPA (or IMERG) and ground-based precipitation and application to a typical region of the East China Plain.

ABSTRACT

Hydrological model and water resource assessment performance are highly dependent on the quality of the precipitation input, which can be improved by means of the optimal interpolation method for the merged precipitation. However, the traditional first-guess field of satellite precipitation often increases the merging error on account of its inherent bias. Some authors have suggested the need of generating a more accurate first-guess field for the merged precipitation, but the research in this improvement is rarely reported. Therefore, an improved merging method is proposed in this paper in which the precipitation from rain gauges is added to the first-guess field when combining the precipitation estimates of Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42 with rain gauges in a typical region of the East China Plain, China. Furthermore, the influence of the gauge station densities on the merged accuracy of the precipitation is investigated based on the traditional and improved methods. The results show that the improved merging method has effectively reduced the influence of the uncertainty caused by the error of the first-guess field owing to the consideration of the spatial distribution of TMPA precipitation and the precision of the gauge precipitation. Compared with results of traditional interpolation methods using only gauge data, the precipitation-merging method in this study can obtain better performance results only when the observation density is lower than 6.0 × 103 km2 per gauge under average conditions of many years. The higher the observation density, the more notably the accuracy increases. In addition, the greater the precipitation, the more homogeneous the spatial and temporal distribution of the precipitation and the better the improved effect of the merging method. The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) data is also used to validate the conclusions here.