Abstract:

The spatial distribution of precipitation estimation is the basis of the meteorological service. Inadequate of the rain gauge observation site do influence the precision of the precipitation distribution estimation. In order to minimize the impact, it is necessary to introduce the precipitation factors for auxiliary operation. But due to reasons such as confidentiality, a lot of impact factors are difficult to obtain for ordinary researchers. In this paper, the problem was solved by using the Temporal Sequence coKriging method, which could mine temporal character from the historical precipitation data. This paper chose the precipitation data from 2006 to 2010 in Hubei Province as the research object. Through the analysis of the rainfall data, the value of Variation Function for every rain gauge as the factor that influenced rainfall intensity was introduced to the precipitation distribution estimation of multiple geographic space in statistics, and the Temporal Sequence coKriging method was used to construct a mathematical relationship model between rainfall intensity and the historical precipitations character. First of all, we calculated the Variation Function of each site in time dimension, and analyzed the relationship between the variation function values under different time scales and the distribution of rainfall. Then we estimated the spatial distribution of precipitation by choosing variation function values under different time scales as collaborative variables. At last, we evaluated the accuracy of the Temporal Sequence coKriging model and the Ordinary Kriging model by both the cross validation and the Error comparison of every Rain gauge station. In addition, according to the obvious seasonal trends of precipitation data, this paper studied the change of the Temporal Sequence coKriging models accuracy before and after the temporal decomposition, and then summarized the characteristics of the models time scale. The research results showed that the precision of the precipitation distribution estimation improved obviously by using the Temporal Sequence coKriging method than the Ordinary Kriging method both in the overall accuracy and in the single point accuracy. 〖JP2〗Data after timeseries decomposing could steadily improve the estimation precision than before. It was feasible to use the Temporal Sequence coKriging method for the precipitation distribution estimation under the condition which is full of historical precipitation data and short of other precipitation information. Meanwhile, the calculation of the Variation Function in time dimension was very easy, and could be adjusted dynamically according to the rain gauge observation site realtime data. Because the Temporal Sequence coKriging method had simple, strong timeliness with the characteristics of high precision, it was an effective method for the precipitation distribution estimation based on the rain gauge observation data