SVD Iteration Model and Its Use in Prediction of Summer Precipitation

A new short-term climatic prediction model based on the singular value decomposition (SVD) iteration was designed with solid mathematics and strict logical reasoning. Taking predictors into prediction model, using iteration computation, and substituting the last results into the next computation, we can acquire better results with improved precision.Precipitation prediction experiments were separately done for 16 stations in North China and 30 stations in the mid-lower catchment of the Yangtze River during 1991-2000. Their average mean square errors are 0.352 and 0.312, and the results are very stable. Mean square errors of 9 yr are less than 0.5 while only that of 1 yr is more than 0.5. The mean sign correlation coe cients between forecast and observed summer precipitation during 1991-2000 are 0.575 in North China and 0.623 in the mid-lower catchment of the Yangtze River. Librations of them in North China during the 10 years are small. Only in 1996 the sign correlation coefficient is below 0.5; the others are all over 0.5. But sign correlation coe cients in the mid-lower catchment of the Yangtze River vary obviously. The lowest is only 0.3 in 1992, and the highest is 0.9 in 1998, As the distribution of the forecast precipitation anomaly field in the summer 1998 of is examined,it is known that the model captured the positive and negative anomalyies of precipitation,and also well forecasted the anomaly distributions. But the errors are obvious in quantities between the forecast and the observed precipitation anomalies.Climate characteristics of large scale meteorological elements, such as summer precipitation have obvious differences in spatial distribution. We can forecast better if we divide a big region into many subregions according to the discrepancy of climatic characteristics in the region, and predict in each subregion. The research shows that the model of SVD iteration is a very effective forecast model and has a strongly applicable value.