ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Volume 16,Issue 5,1990 Table of Contents
1 An analysis of a heavy rain process during the first Mesoscale Rainstorm Experiment in North China
1990, 16(5):3-10.
DOI: 10.7519/j.issn.1000-0526.1990.05.001
Abstract:
With the intensive observations from the Joint Defence' Operational Experiment of the Mesoscale Rainstorm of North China, a, cloudburst process from 30 to 31 July, 1988 was analysed comprehensively. We discussed the weather situation of the process which initiated the cloudburst and various parameter fields, as well as the characteristics of the meso-scale weather system. In this paper, some significant results have been given.
With the intensive observations from the Joint Defence' Operational Experiment of the Mesoscale Rainstorm of North China, a, cloudburst process from 30 to 31 July, 1988 was analysed comprehensively. We discussed the weather situation of the process which initiated the cloudburst and various parameter fields, as well as the characteristics of the meso-scale weather system. In this paper, some significant results have been given.
1990, 16(5):11-14.
DOI: 10.7519/j.issn.1000-0526.1990.05.002
Abstract:
In this paper, the five-point finite difference scheme is analysed. And by the uniformlly rotational velocity field along with the various initial conditions, a two-dimentional advectio'n equation is integrated numerically in a long period, an optimum five-point finite difference scheme is achieved.
In this paper, the five-point finite difference scheme is analysed. And by the uniformlly rotational velocity field along with the various initial conditions, a two-dimentional advectio'n equation is integrated numerically in a long period, an optimum five-point finite difference scheme is achieved.
1990, 16(5):15-19.
DOI: 10.7519/j.issn.1000-0526.1990.05.003
Abstract:
The multivariate regression model is often used in the statistical forecasts. But the forecast accuracy is influenced by both non-linearity of predictors and the non-normality of the date. The model of nonlinear robust regression is introduced in this paper, in order to improve the forecast accuracy greatly.
The multivariate regression model is often used in the statistical forecasts. But the forecast accuracy is influenced by both non-linearity of predictors and the non-normality of the date. The model of nonlinear robust regression is introduced in this paper, in order to improve the forecast accuracy greatly.
1990, 16(5):20-25.
DOI: 10.7519/j.issn.1000-0526.1990.05.004
Abstract:
Here the water abnormal index given by Palmer is used to calculate Palmer index in spring over North China, which is considered as a comprehensive index of the dry and humidity conditions in spring over the regions. The method of the empirical orthogonal function was used to analyse the character of temporal-spatial distributions of the dry and humidity condition in spring, and its stability was discussed, too. It is thought that the, spatial distribution characters of the first 4 items and their. temporal evolution laws are very steady.
Here the water abnormal index given by Palmer is used to calculate Palmer index in spring over North China, which is considered as a comprehensive index of the dry and humidity conditions in spring over the regions. The method of the empirical orthogonal function was used to analyse the character of temporal-spatial distributions of the dry and humidity condition in spring, and its stability was discussed, too. It is thought that the, spatial distribution characters of the first 4 items and their. temporal evolution laws are very steady.
1990, 16(5):26-29.
DOI: 10.7519/j.issn.1000-0526.1990.5.005
Abstract:
This paper gives a routine prediction model for cotton prediction of China. The main idea here is that the whole cotton production area of China would be divided into three sub-region, the Yellow River cotton production region, the Yangtze River cotton production region and the westen China cotton production region. The regional prediction model for each region and the state prediction model are established with the cotton yield difference and the meteorological elements difference between the nabouring years.
This paper gives a routine prediction model for cotton prediction of China. The main idea here is that the whole cotton production area of China would be divided into three sub-region, the Yellow River cotton production region, the Yangtze River cotton production region and the westen China cotton production region. The regional prediction model for each region and the state prediction model are established with the cotton yield difference and the meteorological elements difference between the nabouring years.
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ISSN:1000-0526 CN:11-2282/P