﻿ 西藏地区积雪深度模型分析与研究 Analysis and Research on the Model of Snow Depth in Tibet Area

Climate Change Research Letters
Vol. 08  No. 02 ( 2019 ), Article ID: 29347 , 13 pages
10.12677/CCRL.2019.82027

Analysis and Research on the Model of Snow Depth in Tibet Area

Deyuan Chen, Chao Wang, Lei Wang

Chengdu University of Information Technology, Chengdu Sichuan

Received: Mar. 3rd, 2019; accepted: Mar. 14th, 2019; published: Mar. 21st, 2019

ABSTRACT

In this paper, by using 35-year daily observation data of ground, it establishes model for snow depth in Tibet. By the use of impact factor such as daily precipitation, average temperature, the lowest temperature, the highest temperature, snow depth, numerical forecast precipitation of the next day, and average temperature, it establishes multiple regression equations that predict snow depth of the next day. Due to the lack of numerical forecasting data, it uses the observed data of the next day instead of numerical forecasting data. It selects five sites in which daily average amount of snow is largest to draw scatter diagram, and obtain correlation coefficient (R) and standard deviation (SEE) to test. Then it establishes the seasonal model of snow depth by improving method and analyses. Comparing imitative effect before and after improvement, it shows that required correlation coefficient (R) of equal has increased and standard deviation (SEE) has decreased after improving. All of the above descriptions’ mean analog value after improving method is much closer to actual value.

Keywords:Snow Depth, Tibet, Model Building, Model Improvement

1. 引言

2. 资料与方法

2.1. 数据介绍

Figure 1. Distribution of stations in the study area

2.2. 研究方法

Table 1. Basic information of the station in the study area

3. 结果与讨论

3.1. 积雪模型建立

$y^={b}_{0}+{b}_{1}{x}_{1}+{b}_{2}{x}_{2}+{b}_{3}{x}_{3}+{b}_{4}{x}_{4}+{b}_{5}{x}_{5}+{b}_{6}{x}_{6}+{b}_{7}{x}_{7}$ (6)

Table 2. Coefficient table of snow model

Continued

3.2. 积雪模型检验

Table 3. Coefficient table of snow model at five stations in which daily average amount of snow is largest

(a) 聂拉木 (b) 帕里 (c) 普兰 (d) 错那 (e) 嘉黎

Figure 2. Test chart of scattered points at five stations in which daily average amount of snow is largest

(a) 春 (a) 秋 (a) 冬 (b) 春 (b) 秋 (b) 冬 (c) 春 (c) 秋 (c) 冬 (d) 春 (d) 秋 (d) 冬 (e) 春 (e) 秋 (e) 冬

Figure 3. Seasonal test chart of scattered points at five stations in which daily average amount of snow is largest ((a) Nielamu; (b) Pali; (c) Pulan; (d) Cuona; (e) Jiali)

Table 4. Basic test parameters at five stations in which daily average amount of snow is largest

Table 5. Seasonal test of correlation coefficient (R) at five stations in which daily average amount of snow is largest

3.3. 积雪模型改进

Table 6. Seasonal coefficient table of snow depth model at five stations in which daily average amount of snow is largest

Continued

(a) 春 (a) 秋 (a) 冬 (b) 春 (b) 秋 (b) 冬 (c) 春 (c) 秋 (c) 冬 (d) 春 (d) 秋 (d) 冬 (e) 春 (e) 秋 (e) 冬

Figure 4. Improved seasonal test chart of scattered points at five stations in which daily average amount of snow is largest ((a) Nielamu; (b) Pali; (c) Pulan; (d) Cuona; (e) Jiali)

Figure 5. Comparison of correlation coefficients (R) before/after improvement

Table 7. Comparison of correlation coefficients (R) before/after improvement

Table 8. Comparison of standard deviation (SEE) before/after improvement

Figure 6. Comparison of standard deviation (SEE) before/after improvement

4. 结论

1) 对积雪量不同的台站分别建立模型，可以发现积雪量较大的台站相关系数明显高于积雪量较小的台站，并且积雪量较大的台站拟合效果好于积雪量较小的台站。例如日平均积雪深度最大的聂拉木站的散点图拟合效果最好，而日平均积雪最小的嘉黎站的拟合效果最差。

2) 温度和降水分季节对积雪深度的影响不同，分季节检验后发现拟合值和观测值有较大偏差，说明用同一个方程来分析不同季节的拟合效果并不科学。所以在建立积雪深度模型时不能采用一个观测站建一个模型的方法，要分季节建立模型进行讨论才比较具有可靠性。

Analysis and Research on the Model of Snow Depth in Tibet Area[J]. 气候变化研究快报, 2019, 08(02): 234-246. https://doi.org/10.12677/CCRL.2019.82027

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