• 气候与水文 •

### 1998-2012年青藏高原TRMM 3B43降水数据的校准

1. 南京信息工程大学地理与遥感学院, 江苏南京 210044
• 收稿日期:2014-12-28 修回日期:2015-03-09 出版日期:2015-09-25
• 通讯作者: 宋蕾(1989-),女,硕士研究生,主要从事定量遥感以及降水降尺度的研究.Email:pinot_nuist@163.com E-mail:pinot_nuist@163.com
• 作者简介:石玉立(1973-),男,博士,副教授,主要从事森林生物量估算以及降水降尺度的研究.Email:ylshi.nuist@gmail.com
• 基金资助:

### Calibration of TRMM 3B43 over Tibetan Plateau during 1998-2012

SHI Yu-li, SONG Lei

1. School of Geography & Remote Sensing, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China
• Received:2014-12-28 Revised:2015-03-09 Online:2015-09-25

Abstract: Precipitation is a vital element of the water cycle in the Earth System, which is closely related to the ecological, hydrological, and meteorological processes. Because of the special topographical and climatic conditions, the Tibetan Plateau has great influence in regional and atmospheric circulation patterns. And the hydrology cycle in the Plateau is an important aspect of study for thermal forcing effect on regional climate. Furthermore, various surface features on the Plateau(e.g. vegetation)have a close relationship with precipitation. So it is meaningful to study the precipitation of the Tibetan Plateau. Owing to the lack of ground observations, it is difficult to study the spatial and temporal patterns of precipitation over the plateau. The satellite remote sensing technology can be utilized to fill in the gaps where station data are not available. There are already a series of regional and global remote sensing precipitation products. The Tropical Rainfall Measuring Mission(TRMM)is one of the highest resolution(0.25°×0.25°)products among all the current satellite precipitation datasets. Unfortunately, because of the influence of the terrain and atmospheric condition, the accuracy of it is not very ideal in northwest mountainous areas of China. So it is critical to find a method to calibrate it for further use. Comparing to rain-gauges observed precipitation dataset collected from China Meteorological Data Sharing Service System, the TRMM 3B43 precipitation data over the Tibetan Plateau have some deviation. This paper analyzed the deviation and found it closely related to altitude, latitude, and longitude and precipitation distribution. Supposing that the rain-gauges observed precipitation data are accurate, point-based deviation between original TRMM precipitation and rain-gauge observed precipitation can be calculated. Then surface-based deviation can be obtained using some interpolation algorithm such as Kriging. What is noted above is called addictive correction method. In this paper, originally TRMM 3B43 precipitation data are calibrated using the additive correction model and random forest algorithm. The Random Forest (RF) is constructed based on the classification and regression trees (CART)algorithm. For regression in CART, there is response vector Y which represents the response values for each observation in variable matrix X. The matrix X and vector Y can be randomly split into different subsets to regress trees. In each terminal nodes of the tree, a simple and accurate model can be constructed to explain the relationship of X and Y in this node. Amongst many non-parametric regression approaches, the RF is receiving considerable attention of ecological and other applications. Finally, monthly, seasonal and annual calibrated TRMM 3B43 precipitation data over the Tibetan Plateau during 1998-2012 were obtained. The results demonstrate that the accuracy of calibrated TRMM 3B43 precipitation data increased significantly. The maximum values of R Square(R2) of monthly average TRMM 3B43 precipitation data are in March and October, and reach to 0.9. Minimum R Square is 0.5 in December and January, which may be due to that the low precipitation may be more sensitive to the error. All of coefficient of efficiency(E)of monthly average calibrated TRMM 3B43 precipitation data is positive. The maximum value of it is 90. It shows that the calibrated precipitation can be used in research already. The minimum R square of seasonal and annual average calibrated TRMM 3B43 precipitation data is 0.58. Monthly and seasonal results relating to the first quarter, January, February and December, are not as good as others. The method should be improved to calibrate these data more efficiently in future.

• P426.61