基于MATLAB的栅格数据温度极端值计算

栅格数据温度极端值计算"/>

基于MATLAB的栅格数据温度极端值计算

基于MATLAB的栅格数据温度极端值计算

For each station year, we summed the number of frost days (Tn <= 0℃) and warm days (Tm > 15℃) andexamined the temporal trends over the study period… Therefore, for interstation comparisons of the variability in the extreme (relatively rare) cold and warm events, we calculated the time-varying percentiles of the daily Tn (for cold events) and daily Tm (for warm events), respectively. For each month, the daily Tn and Tm of a given station were ranked, then the temperature values at various percentile breaks were selected. For a hypothetical example, in January of 1990 the ninetieth percentile of daily Tm (or the tenth percentile of daily Tn) of station A as 5℃ (or -10℃) would mean that 10% of the 31 observations of that month were higher (or lower) than this specific value. We then examined the temporal trends in the temperature percentiles for each month of the year after averaging the monthly percentiles for the entire study region. For example, temperatures of relatively rare warm extreme events can be defined as the temperature of those days that exceed the ninetieth percentile of Tm, while temperatures of the cold extreme events can be defined as the temperature of those days that are below the tenth percentile of Tn.

关于温度极端值，找到一篇文献，Temporal trends and variability of daily maximum and minimum,extreme temperature events, and growing season length over the eastern and central Tibetan Plateau during 1961–2003。提到：
“计算每个站点Tn<=0霜冻/冷日，Tm>15温暖日，的天数…因此，对于极端(相对罕见)冷事件和暖事件的站点间差异比较，我们分别计算了每日Tn(冷事件)和Tm(暖事件)的时变百分位数。对每个月给定站的日Tn和Tm进行排序，然后选择不同百分位间断点的温度值。举个假设的例子，在1990年1月，每天Tm的第90百分位(或每天Tn的第10百分位)的a站as 5℃(或-10℃)表示该月31次观测值中有10%高于(或低于)该特定值。然后，在对整个研究区域的月百分位数进行平均后，我们检查了一年中每个月的温度百分位数的时间趋势。例如,温度相对罕见的温暖的极端事件可以被定义为那些日子的温度超过第九十百分位的Tm,气温寒冷的极端事件可以被定义为那些日子的温度低于第十百分位的Tn。”

数据

1961-2018年三维日Tmax、Tmin。

方法

利用百分位函数 prctile

Tm温度极端值

yTmax=SW_Tmax;
[m,n]=size(yTmax(:,:,1));
yTmax=reshape(yTmax,m*n