GCM correction method of considering day-by-day data fluctuation features

A correction method and a technology of fluctuation characteristics, applied in the field of GCM correction, can solve the problems of lack of daily data accuracy analysis and judgment, and achieve accurate results

Active Publication Date: 2018-05-08
HOHAI UNIV
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Problems solved by technology

However, the traditional downscaling or bias correction methods only correct the climate model data...

Method used

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  • GCM correction method of considering day-by-day data fluctuation features
  • GCM correction method of considering day-by-day data fluctuation features
  • GCM correction method of considering day-by-day data fluctuation features

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Experimental program
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Effect test

Embodiment

[0066] Bias correction was performed on the radiation data of BCC-CSM1.1(m) in Kunshan area in 2030. Specific steps are as follows:

[0067] (1) Extract the daily measured and GCM radiation data in Kunshan area from 1961 to 2000, and the daily radiation data in 2030 to be bias-corrected. Based on this data, the monthly mean values ​​of the radiation data in the historical measurement period and the GCM historical period are calculated respectively, and the multi-year monthly mean value of the measured radiation data is calculated using the arithmetic mean method. The calculation method is as follows:

[0068]

[0069] Among them, M o,i is the multi-year average of the i-th month of historical observations, i=1,2,3...12; R o,i,k,j The measured radiation value on the j day of the i month, k year, and j day in the historical period, k=1,2,3...40, j=1,2,3...28 / 29 / 30 / 31.

[0070] The formula for calculating the multi-year monthly mean value of radiation data in the historical...

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Abstract

The invention discloses a GCM correction method of considering day-by-day data fluctuation features. Months are used as calculation units to extract day-by-day actually-measured data of a historical period of a certain meteorological factor, day-by-day data of GCM of the same historical period and to-be-corrected day-by-day data of the GCM of a future period; multi-year monthly average values of the historical actually-measured data and the historical GCM data are calculated; average-value correction factors are constructed; thus day-by-day average-value correction is carried out on the futuremeteorological data; variance correction factors are constructed; and variance correction is carried out on the future meteorological data after average-value correction, and variance after correction is used as constraint conditions to obtain a new future GCM meteorological-sequence. According to the method, consistency of distribution features of the future GCM data and distribution features ofthe actually-measured data is considered, deviation correction is carried out on the future GCM data, and thus results obtained by simulation of a model adopting the GCM data are enabled to be more accurate.

Description

technical field [0001] The invention relates to a GCM correction method considering daily data fluctuation characteristics, and belongs to the technical field of climate analysis and statistics. technical background [0002] The global climate model is an important data basis for assessing the impact of climate change on human society and natural systems. However, general circulation models (GCM) generally have low resolution, and there are regional systematic errors. Therefore, it is necessary to downscale the climate model data based on the measured data of meteorological stations to achieve the scale matching between the atmospheric circulation model data and the meteorological station data. However, the traditional downscaling or bias correction methods only correct the climate model data from the perspective of the monthly mean value, without analyzing and judging the accuracy of the daily data. In fact, in many research fields, such as agricultural growth water consu...

Claims

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Application Information

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IPC IPC(8): G06F19/00
CPCG16Z99/00Y02A90/10
Inventor 王卫光丁一民鲍金丽邢万秋董青傅健宇
Owner HOHAI UNIV
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