A REFINING METHOD OF NON-LINEAR REGIONAL TM MODEL BASED ON RANDOM FOREST
Keywords: random forest, nonlinear Tm model, applicability
Abstract. Weighted mean temperature (Tm) is a critical parameters in GNSS technology to retrieve precipitable water vaper (PWV). By obtaining high-precision Tm, it can provide an important reference data source for regional strong convective weather and large-scale climate anomalies. The high-precision Tm of most areas can be obtained by using the BEVIS model and the surface temperature (Ts). The eastern coastal areas of China are affected by the monsoon climate, which makes the applicability of the method in this area to be improved. The research shows that the Tm which calculated by Fourier series analysis (FTm model) has better applicability in the region than the BEVIS model. However, the method has a single modeling factor, and the precision improvement effect in some area is not obvious. By using the observation data of 13 radiosonde stations in the eastern coastal areas of China from 2010 to 2015. Tm which calculated by numerical integration is used as the reference of the true value. Four of the observation data are selected by the method of random forest (RF). The eigenvalues include the pressure、surface temperature、water vapor pressure and specific humidity are used as input factors. The prediction corrections are added to the deviation of FTm model, and a new Tm is applied to the eastern coast of China which called RFF Tm. Taking the observation data from 2010 to 2014 as the training database, the research area is divided into three areas from south to north according to the latitude. The prediction results of different time scales are studied by the clamping criterion, and then the prediction of random forest is discussed. The correction effect is adaptable in the eastern coast areas of China. The results show that: (1) The RFF Tm model refinement method based on random forest has better adaptability in eastern coastal areas of China, and the applicability of first area is more stable with the prediction time scale than the FTm model. (2) On the time scale with a forecast period of one year, MAE and RMS are 4.7 and 4.6 in third area, 3.2 and 3.8 in second area, and 2.6 and 2.5 in first area. (3) The improvement effect of random forests in the eastern coastal areas of China gradually increases with the prediction period becoming shorter. The predicted deviation values of the eastern coast areas of China reach a steady state when the period is one month. The correction deviations is within 1.5K. The correction range of the third area is better than the second area and first area, which makes up for the shortcomings of the FTm model with low precision in the region. It can be used as a new multi-factor prediction and correction Tm model for GNSS remote sensing water vapor in the eastern coastal areas of China.