Comparison of semivariogram models for kriging monthly rainfall in eastern China

An exploratory spatial data analysis method (ESDA) was designed Apr.28,2002 for kriging monthly rainfall. Samples were monthly rainfall observed at 61 weather stations in eastern China over the period 1961-1998. Comparison of five semivariogram models (Spherical, Exponential, Linear, Gaussian and Rational Quadratic) indicated that kriging fulfills the objective of finding better ways to estimate interpolation weights and can provide error information for monthly rainfall interpolation. ESDA yielded the three most common forms of experimental semivariogram for monthly rainfall in the area. All five models were appropriate for monthly rainfall interpolation but under different circumstances. Spherical, Exponential and Linear models perform as smoothing interpolator of the data, whereas Gaussian and Rational Quadratic models serve as an exact interpolator. Spherical, Exponential and Linear models tend to underestimate the values. On the contrary, Gaussian and Rational Quadratic models tend to overestimate the values. Since the suitable model for a specific month usually is not unique and each model does not show any bias toward one or more specific months, an ESDA is recommended for a better interpolation result.

Neuroid BP-type Model Applied to the Study of Monthly Rainfall Forecasting

A neuroid BP-type three-layer mapping model is used for monthly rainfall forecasting in terms of 1946-1985Naming monthly precipitation records as basic sequences and the model has the form i×j=8×3, K=1; by steadilymodifying the weighing coefficient, long-range monthly forecasts for January to December, 1986 are constructed and1986 month-to-month predictions are made based on, say, the January measurement for February rainfall and soon, with mean absolute error reaching 6,07 and 5,73 mm, respectively. Also, with a different monthly initial value forJune through September, 1994, neuroid forecasting is done,indicating the same result of the drought in Naming during the summer, an outcome that is in sharp agreement with the observation.

A Nonlinear Time-lag Differential Equation Mdel for Predicting Monthly Precipitation

This paper investigates the nonlinear prediction of monthly rainfall time series which consists of phase space continuation of one-dimensional sequence, followed by least-square determination of the coefficients for the terms ofthe time-lag differential equation model and then fitting of the prognostic expression is made to 1951-1980 monthlyrainfall datasets from Changsha station. Results show that the model is likely to describe the nonlinearity of the allnual cycle of precipitation on a monthly basis and to provide a basis for flood prevention and drought combating forthe wet season.

Study on the Rainfall Predication Equation Established by the Downscaling Technique

[Objective] The research aimed to study a kind of precipitation predication model which was established by using the multi-level mode circulation output field. [Method] The downscaling prediction method which was run in Anhui business was improved. The high related zone with the precipitation was found in the multi-level mode circulation field. Moreover, the optimal subset regression model was used to screen and assemble the forecast factors. The predication equation of monthly rainfall was formed. Finally, the actual and mode circulation fields during 2005-2009 were respectively set into the equation, and the prediction scores of two kinds of data schemes were contrasted. The monthly score was analyzed, and the feasibility of business operation was inspected. [Result] Compared with the traditional downscaling method, the data content of precipitation prediction model which was established by using the multi-level mode circulation output field was richer. Seen from the prediction effect, the average anomaly symbol consistence rate was 63%, and PS was 75 scores. It was not only higher than that of downscaling method of business operation, but also higher than the predicted score of business issue. In addition, the prediction effect of method on the typical flooding month was better. It showed that the method had the good prediction capability on the abnormal value. [Conclusion] The research provided the reference for enriching the downscaling technology scheme.

Predicting June Mean Rainfall in the Middle/Lower Yangtze River Basin

We demonstrate that there is significant skill in the GloSea5 operational seasonal forecasting system for predicting June mean rainfall in the middle/lower Yangtze River basin up to four months in advance.Much of the rainfall in this region during June is contributed by the mei-yu rain band.We find that similar skill exists for predicting the East Asian summer monsoon index(EASMI)on monthly time scales,and that the latter could be used as a proxy to predict the regional rainfall.However,there appears to be little to be gained from using the predicted EASMI as a proxy for regional rainfall on monthly time scales compared with predicting the rainfall directly.Although interannual variability of the June mean rainfall is affected by synoptic and intraseasonal variations,which may be inherently unpredictable on the seasonal forecasting time scale,the major influence of equatorial Pacific sea surface temperatures from the preceding winter on the June mean rainfall is captured by the model through their influence on the western North Pacific subtropical high.The ability to predict the June mean rainfall in the middle and lower Yangtze River basin at a lead time of up to 4 months suggests the potential for providing early information to contingency planners on the availability of water during the summer season.