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降水相态分离单临界气温模型建立和检验
引用本文:刘玉莲,任国玉,孙秀宝.降水相态分离单临界气温模型建立和检验[J].应用气象学报,2018,29(4):449-459.
作者姓名:刘玉莲  任国玉  孙秀宝
作者单位:1.黑龙江省气候中心, 哈尔滨 150030
基金项目:国家自然科学基金项目(41771067),中国气象局气候变化专项(CCSF201842),中央引导地方科技发展专项(ZY18C12),公益性行业(气象)科研专项(GYHY201206012,GYHY201106013)
摘    要:使用雪日直接界定法,建立了中国大陆长江以北地区(30°N以北)降水相态分离单临界气温统计模型,分东部季风区、西北干燥区和青藏高原区3个不同气候区独立样本建模,检验模型外推使用的可能性,并对单临界气温分离的雨夹雪偏差进行分析。结果表明:所有地区独立样本建立模型估算的单临界气温与根据天气现象记录确定的单临界气温相关性均达到0.05显著性水平,3个气候区独立建模能够估算出降水相态单临界气温的范围及区域特性;以东部季风区和青藏高原区为样本独立建模的估算结果好于西北干燥区;3个独立模型估算的单临界气温偏差绝对值不大于1℃的气象站都多于74%,估算的标准差偏差在-0.5~0.5℃之间的气象站数量占比77%,在-1.0~1.0℃之间的气象站数量占比90%;日平均气温低于单临界气温的雨夹雪日数和降水量与实际降雪日和降雪量的比率北部略小、南部较大,东部季风区的南部雨夹雪界定的雪日和雪量比率均超过100%;使用统计模型确定不同区域雨夹雪中界定的雪日和雪量比率分布也具有可行性。

关 键 词:降水    相态    分离    单临界气温    统计模型
收稿时间:2017/12/20 0:00:00
修稿时间:2018/5/11 0:00:00

Establishment and Verification of Single Threshold Temperature Model for Partition Precipitation Phase Separation
Liu Yulian,Ren Guoyu and Sun Xiubao.Establishment and Verification of Single Threshold Temperature Model for Partition Precipitation Phase Separation[J].Quarterly Journal of Applied Meteorology,2018,29(4):449-459.
Authors:Liu Yulian  Ren Guoyu and Sun Xiubao
Affiliation:1.Heilongjiang Provincial Climate Center, Harbin 1500302.Department of Atmospheric Science, School of Environmental Science, China University of Geosciences, Wuhan 4300743.Laboratory for Climate Studies, National Climate Center, CMA, Beijing 100081
Abstract:Precipitation is an important parameter to characterize climate characteristics and climate change in a region. It is a key link in global water cycle and energy cycle. Under the same rainfall conditions, effects of different-phase precipitation in the earth's surface system are significantly different. The social and economic impacts produced by them also have significant differences. It is of great theoretical and practical significance to distinguish and recognize snowfall characteristics, their anomalies and causes. In addition, when monitoring and studying the global climate change background and long-term changes of the extreme precipitation events above the sub-continental scale, it is also necessary to distinguish rainfall and snowfall information from historical precipitation data.A statistical model with single temperature thresholds is established for separating cold season rainfall and snowfall with independent samples of different climate zones to the north of the Yangtze River in mainland China (north of 30°N). The applicability of the model is tested, and the deviation of the model on sleet recognizing is analyzed.Results show that correlations between estimated temperature thresholds and actual thresholds pass the significance level test for three areas, i.e., Eastern Monsoon Region, Northwest Arid Region and Qinghai-Tibet Plateau. Models for three areas can well simulate the temperature thresholds, the range and regional characteristics. Simulations in East Monsoon Region and Qinghai-Tibet Plateau are better than those in Northwest Arid Region. Among all of the stations in three regions, stations with absolute bias of the simulated threshold temperature less than 1℃ account for more than 74%, and stations with standard deviation biases between -0.5-0.5℃ account for 77%, and those between -1.0-1.0℃ account for 90%. When the daily mean temperature gets lower than thresholds, ratios of sleet days and precipitation compared to snow days and actual precipitation are slightly smaller in the north and larger in the south. In the south of Eastern Monsoon Region, ratios are more than 1. Statistical models can also be used to determine the distribution of the ratio in different areas.Climate types to north of the Yangtze River include temperate monsoon climate, temperate continental climate, and high-cold climate. Results of the independent sample test in three climatic zones indicate that applying this model to estimate the threshold temperature of precipitation phase in sub-continental snow-rich areas, although still limited, is basically feasible.
Keywords:precipitation  phase  separation  single threshold temperature  statistical mode
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