介绍计算标准降水指数SPI的NCL模块

hillside

注：本文只是简单地直接转载，详情需要有心的网友自行分析了。

http://www.ncl.ucar.edu/Document/Functions/Built-in/dim_spi_n.shtml

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dim_spi_n

Calculates the standardized precipitation index (SPI) by fitting a gamma or a Pearson Type III distribution to monthly precipitation values.

                               
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Available in version 6.1.0 and later.（注：根据本行说明，计算标准降水指数SPI的NCL模块已经内置于NCL6.1.0版及更高版本。至于模块细节，看来要查询NCL的源代码了，是否公开我也不太清楚）

Prototype        function dim_spi_n (                x        : numeric,  ; float, double                nrun [1] : integer,                  opt      : logical,                  dims : integer           )        return_val  :  float or double

Argumentsx

Monthly precipitation of type 'float' or 'double' and any dimensionality. The size of the specified dims must be divisible by 12. Since a distribution is being fit, there should be a 'reasonably' large sample size. At least 30 years of monthly data (360=12*30) is recommended.

nrun

A scalar that specifies the number of months over which the standardized precipitation index is to be calculated. Common values are 3, 6, 12, 24,36.

opt

Options parameter.

As of NCL version 6.3.0, if opt=True, you can set opt@spi_type = 3 to have this function calculate the standardized precipitation index (SPI) using the Pearson type III distribution.

dims

The dimension(s) of x to be used to estimate the SPI. Usually, this is the record ('time') dimension.

Return value

The returned SPI will be the same shape, size and type as x.

Description

This function calculates the Standardized Precipitation Index (SPI), a probability index that gives a better representation of abnormal wetness and dryness than the Palmer Severe Drought Index (PSDI).

The World Meteorological Organization (WMO) recommends, that all national meteorological and hydrological services should use the SPI for monitoring of dry spells. Some advantages of the SPI:

• It requires only monthly precipitation.
• It can be compared across regions with markedly different climates.
• The standardization of the SPI allows the index to determine the rarity of a current drought.
• It can be created for differing periods of 1-to-36 months.
  

A shortcoming of the SPI, as noted by Trenberth et al (2014):

    "the SPI are based on precipitation alone and provide a measure only for water supply.      They are very useful as a measure of precipitation deficits or meteorological drought      but are limited because they do not deal with the ET [evapotranspiration] side of the issue."

As of NCL version 6.3.0, this function will calculate the SPI using two possible methods:

• Applying a 2-parameter gamma distribution fit (default).
• Applying a Pearson type III distribution (opt@spi_type = 3).
  

The default implementation of dim_spi_n uses a 2-parameter gamma distribution fit (dim_gamfit_n) where the shape and scale parameters are maximum likelihood estimates as described in

        A Note on the Gamma Distribution        Thom (1958): Monthly Weather Review, pp 117-122.                     specifically: eqn 22 for gamma; just above eqn 21

However, there is some variation in the methods used to derive the SPI. Guttman (1998, 1999) recommends that the Pearson III distribution be used, which is available in NCL V6.3.0 by setting

opt@spi_type

=3. This option uses code made available at the National Climate Data Center (NCDC). See  spi.f（注：作为原型的FORTRAN版可以下载）

Note: In 2015, the NCDC was merged with the National Geophysical Data Center (NGDC) and the National Oceanic Data Center (NODC) into the National Centers for Environmental Information (NCEI).

Generally, the Pearson III distribution is likely to give essentially equivalent results to the 2-parameter gamma distribution fit. In some instances, where monthly and seasonal precipitation of zero is common, it will give slightly better results.

Generally, monthly precipitation is not normally distributed so a transformation is performed such that the derived SPI values follow a normal distribution. The SPI is the number of standard deviations that the observed value would deviate from the long-term mean, for a normally distributed random variable. One interpretation of the resultant values is:

        [+,-]2.00 and above/below: exceptionally [wet,dry]      [+,-]1.60 to 1.99: extremely [wet,dry]        [+,-]1.30 to 1.59: severely [wet,dry]          [+,-]0.80 to 1.29: moderately [wet,dry]          [+,-]0.51 to 0.79: abnormally [wet,dry]          [+,-]0.50:  near normal

An explanation of the SPI at different lengths

and  sample spatial pattterns over the USA

at different run times are available.

More information can be obtained at the ClimateDataGuide.

未维

{:eb513:}看来不是一下子就可以接受~~工程浩大

ayzqs

谢谢啦，很不错哦！

lqhlqhlqh

很好啊，程序很实用谢谢

474410945

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段短

正好需要~

oprim

学习学习{:lxm_23:}{:lxm_23:}

glc20022003

感谢分享，支持一下！

sisi20042004

这个好像不太容易理解啊楼主！

fjtzf

{:eb502:}{:eb502:}