CN107944466A - A kind of rainfall bias correction method based on segmentation thought - Google Patents
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Abstract
The invention discloses a kind of rainfall bias correction method based on segmentation thought, it is intended to improves a series of rainfall simulation precision of rainfall models such as climatic model, NO emissions reduction model and rainfall generation model.The described method includes：1) quantile based on actual measurement rainfall sequence, is divided into minimum, conventional value and maximum three parts by actual measurement rainfall sequence；2) quantile based on simulated rainfall sequence, is equally divided into minimum, conventional value and maximum three parts by simulated rainfall sequence；3) three segment datas of simulated rainfall sequence are carried out bias correction by three segment datas based on actual measurement rainfall sequence respectively；4) a kind of evaluation index of new proposition is used, rainfall bias correction effect is effectively assessed.The beneficial effects of the invention are as follows：Effectively eliminate rainfall model simulated rainfall and survey the error between rainfall, improve the rainfall simulation precision of rainfall model；Bias correction for rainfall model future anticipation rainfall establishes solid foundation, improves its forecasting reliability.
Description
Technical field
The present invention relates to the bias correction technical field of rainfall model simulated rainfall, is thought more particularly, to one kind based on segmentation
The rainfall bias correction method thought.
Background technology
Rainfall data are one of most important factors in hydrologic cycle, its accuracy simulated directly affects Runoff Simulation
And the precision of engineering design.Rainfall digital simulation is for influence of the research climate change to hydrographic water resource and solves non-avaible
, there is vital effect in the problems such as Data bottlenecks in area.Especially, in recent years, with the rise of Global Temperature, weather
The aggravation of variation phenomenon, the space-time distribution of rainfall are inevitably affected, how to carry out rainfall simulation and improve its simulation
Precision has been increasingly becoming the problem of scientific circles personage needs extensive concern.
At present, the instrument of simulated rainfall has the whole world or A Regional Climate Model (such as GCM or RCM), NO emissions reduction model and day
A series of models such as gas generator.Wherein, GCM and RCM has been increasingly becoming one of research most important instrument of climate change.But because
Usually there is very big deviation between measured data in spatial and temporal resolution coarse GCM and RCM, its meteorological data simulated, it is impossible to
It is directly used in the hydrological analysis of Watershed Scale.Moreover, mould of the model such as NO emissions reduction model and Stochastic Weather Generators to rainfall
Simulation may can also be had by, which intending, is not allowed or there are hydrological analysis the problems such as relatively large deviation, is directly used it for simulation may be caused to tie
Fruit precision is poor.Rainfall data therefore, it is necessary to be simulated to rainfall model carry out bias correction.The deviation for appealing to rainfall data is entangled
Positive technology, will significantly improve the simulation precision and utilizability of rainfall model rainfall.At present, common rainfall bias correction method
Linear analogy method, local strength's analogy method, index transformation approach, distribution map method and quantile reflection method etc., wherein, distribution is reflected
The bias correction effect for penetrating method method is preferable and the most commonly used.
Despite the presence of numerous bias correction methods, but these conventional methods are only to the spy of rainfall data some aspects
Property carry out bias correction, it is or poor to the bias correction effect of rainfall extreme value, cause rainfall model simulation drop after bias correction
Larger deviation is still suffered between rain and actual measurement rainfall.
The content of the invention
To solve the deficiencies in the prior art, it is an object of the invention to provide a kind of rainfall deviation based on segmentation thought to entangle
Correction method, this method will survey rainfall sequence and simulated rainfall sequence is segmented, and to the rainfall data point of different segmentations
Bias correction is not carried out, and bias correction can be not only carried out from the various aspects such as rainfall average, variance and quantile and distribution, and
And the phenomenon ineffective to rainfall extreme value bias correction can be improved at the same time, so as to improve the essence of rainfall model simulated rainfall
Degree.
The present invention provides a kind of rainfall bias correction method based on segmentation thought, and this method comprises the following steps：
(1) simulated rainfall sequence of the rainfall model in meteorological site is obtained, while collects meteorological site actual measurement of the same period
Rainfall sequence, is consistent by the temporal resolution processing for surveying rainfall sequence and simulated rainfall sequence；
(2) according to the minimum quantile of actual measurement rainfall sequence and very big quantile, minimum, conventional value and pole are classified as
Big value three parts data；
(3) according to the minimum quantile of rainfall model simulated rainfall sequence and very big quantile, equally it is classified as minimum
Value, conventional value and maximum three parts data；
(4) the probability distribution accumulation shape based on meteorological site actual measurement rainfall data, using three Gamma distribution function pair moulds
The probability distribution accumulation shape for intending rainfall data carries out bias correction；
(5) based on quantile deviation accumulation thought, a kind of bias correction effective evaluation index is proposed, and to rainfall model mould
The bias correction effect for intending rainfall is assessed.
In above-mentioned technical proposal, rainfall model includes GCMs (Global Climate Models) and RCMs (Regional
Climate Models) etc. a series of models such as climatic model, NO emissions reduction model and Stochastic Weather Generators, be referred to as herein
Rainfall model.
Minimum quantile in the step (2) and (3) uses 25%, and very big quantile uses 75%.
Three Gamma distributions in the step (4), are the probability point to minimum, conventional value and maximum three parts data
Cloth accumulation shape is simulated using single Gamma distribution function, altogether referred to as three Gamma distributions.Three Gamma distributions it is inclined
Poor correcting method is as follows：
In formula, α1,o,β1,o、α2,o,β2,oAnd α3,o,β3,oRespectively survey rainfall data less than 25%, positioned at 25%~
Single Gamma distribution function parameter between 75% and higher than its 75% quantile, α1,m,β1,m、α2,m,β2,mAnd α3,m,β3,mRespectively
It is that rainfall model simulated rainfall data are less than 25%, single gal between 25%~75% and higher than its 75% quantile
Agate distribution function parameter,For the simulated rainfall after bias correction, xm(i) it is the original analog rainfall before bias correction, Fm
(xm(i)|α1,m,β1, m) specifically calculated such as the cumulative distribution function of single Gamma distribution, the probability density function of single Gamma distribution
Under：
In formula, α is form parameter, and β is dimensional parameters, and Γ () is Gamma distribution function.
The quantile deviation accumulation thought used in the step (5), it is intended to rainfall modeling under different quantiles
Rainfall represents rainfall model simulated rainfall ECDF (Empirical Cumulative with surveying the absolute deviation accumulated value of rainfall
Distribution Function) difference in areas between actual measurement rainfall ECDF, rainfall model mould is represented with both difference in areas
Intend rainfall and survey the overall deviation between rainfall.Therefore, a kind of bias correction effective evaluation proposed in the step (5) refers to
Mark, specific formula for calculation are：
In formula, Vo(i)、Vm(i) and Vm,cor(i) it is respectively actual measurement rainfall data, rainfall model original rainfall data and rainfall
The corresponding numerical value of i-th of quantile of rainfall data, the method use N number of quantile altogether after model bias is corrected.Accordingly | Vm
(i)-Vo(i) | and | Vm,cor(i)-Vo(i) | it is respectively rainfall model simulated rainfall and reality before bias correction under i-th of quantile
Survey the deviation still suffered from after existing deviation and bias correction between rainfall model simulated rainfall and measured data between rainfall.WithEntirety before expression bias correction between rainfall model rainfall and measured value respectively
Overall deviation AD after deviation AD and bias correction between the twocor.C represents overall bias correction effect, and C values are got over
Close to 1, represent that bias correction effect is better.
Quantile employed in bias correction effective evaluation index C totally 19, i.e. N=19, are respectively rainfall data
5%th, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%,
80%th, 85%, 90% and 95% quantile.
By using above-mentioned technological means, beneficial effects of the present invention are：
(1) present invention is divided using the theoretical probability distribution shape for surveying rainfall come the theoretical probability of bias correction simulated rainfall
Cloth shape, can effectively eliminate rainfall model simulated rainfall and reality from various aspects such as the averages, variance and quantile of data
The deviation between rainfall is surveyed, improves the precision and reliability of rainfall model simulated rainfall；
(2) actual measurement rainfall and simulated rainfall are divided into minimum, conventional value and maximum three by the present invention based on segmentation thought
Part, and bias correction is carried out respectively to three segment datas using three Gamma distributions, not only the conventional rainfall big to the frequency of occurrences into
Bias correction is gone, while has taken into account minimum and maximum, the simulation precision of rainfall model rainfall is further lifted.
(3) present invention proposes a kind of index of evaluation error error-correcting effect, the index can before and after bias correction mould
Intend being compared and quantifying in the deviation of the ECDF and measured data ECDF of data, it is more easy to be directly perceived.
Brief description of the drawings
Fig. 1 is the flow diagram of the present invention
Fig. 2 is bias correction effective evaluation index C schematic diagrames
It is overall inclined between 9, certain basin weather station simulated rainfall and actual measurement rainfall that Fig. 3 is 10 GCMs before bias correction
Difference AD
It is overall inclined between 9, certain basin weather station simulated rainfall and actual measurement rainfall that Fig. 4 is 10 GCMs after bias correction
Difference AD
Fig. 5 is BCC models before and after bias correction in 1 simulated rainfall ECDF of meteorological site and actual measurement rainfall ECDF distribution maps
Fig. 6 is BCC models before and after bias correction in 2 simulated rainfall ECDF of meteorological site and actual measurement rainfall ECDF distribution maps
Embodiment
Below by example, and with reference to attached drawing, technical scheme is described in further details.In order to protrude this
The advantage of invention, carries out specific real using the simulated rainfall of the GCMs of one of current research the most frequently used instrument of climate change as case
Apply.
As shown in Figure 1, a kind of rainfall bias correction method based on segmentation thought of the present invention, comprises the following steps；
(1) data acquisition：The actual measurement daily rain amount data of 9, certain basin meteorological site 1971-2000 are collected, choose 10
Global climate model GCMs (such as BCC-CSM1-1, BNU-ESM, CanESM2, CNRM-CM5 etc.), and 10 GCMs are extracted at this
The moon rainfall data of 9, basin meteorological site 1971-2000.
(2) data prediction：First, the moon rainfall data of 30 years actual measurement daily rain amounts of each meteorological site are counted, and are averagely arrived
The day of each moon is on scale；Then, 10 GCMs are averagely arrived to the day scale of each moon in 9 weather stations moon rainfall data of 30 years
On, it is ensured that the time scale of actual measurement rainfall and simulated rainfall is consistent.
(3) data are split：According to step (2), obtain each weather station actual measurement day scale rainfall data and 10 GCMs 9
The day scale simulated rainfall data of a meteorological site.The actual measurement rainfall of each weather station is classified as by its 25% and 75% quantile
Three sections of minimum, conventional value and maximum；Similarly, simulated rainfall data of 10 GCMs in each weather station are pressed respective 25%
It is divided into minimum, conventional value and maximum three parts with 75% quantile.
(4) bias correction of simulated rainfall data：The minimum value part of rainfall is surveyed according to each weather station, using single gamma
Distribution removes 10 GCMs of bias correction in the minimum value part of each weather station, similarly, the routine of rainfall is surveyed according to each weather station
Value and very big value part, are respectively adopted conventional value and maximum that single Gamma distribution removes 10 GCMs of bias correction in each weather station
Part.Single gamma bias correction of three parts data, collectively " three Gamma distribution bias correction methods ".
(5) evaluation error error-correcting effect：(schematic diagram is shown in figure to the bias correction effective evaluation index C proposed according to the present invention
2) overall deviations (AD) of 10 GCMs between the simulated rainfall of 9 weather stations and actual measurement rainfall before bias correction, is calculated,
The result is shown in overall deviations of 10 GCMs after Fig. 3, and bias correction between the simulated rainfall of 9 weather stations and actual measurement rainfall
It is worth (ADcor), the result is shown in Fig. 4, the bias correction effect C values of each weather station are finally assessed, statistical result is shown in Table 1.Fig. 3 and Fig. 4
In, abscissa represents site number, and ordinate represents GCM numberings.
In formula, Vo(i)、Vm(i) and Vm,cor(i) it is respectively actual measurement rainfall data, each GCM originals rainfall data and GCM deviations
The corresponding numerical value of i-th of quantile of rainfall data after correction, the method use N number of quantile altogether.Accordingly | Vm(i)-Vo(i)
| and | Vm,cor(i)-Vo(i) | it is respectively under i-th of quantile, exists before bias correction between GCM simulated rainfalls and actual measurement rainfall
Deviation and bias correction after the deviation that is still suffered between GCM simulated rainfalls and measured data.WithOverall deviation AD and bias correction before expression bias correction between GCM rainfalls and measured value respectively
Overall deviation AD between the two afterwardscor.C represents overall bias correction effect, and C values represent bias correction effect closer to 1
Fruit is better.
Bias correction effect statistics of 1 10 GCMs of table in 9 meteorological sites
BCC rainfall models are in website 1 and 2 simulated rainfall ECDF of website and reality before and after Fig. 5 and Fig. 6 only lists bias correction
The comparison diagram of rainfall ECDF is surveyed, other GCMs are similar in the error-correcting effect figure of other websites.From Fig. 3-Fig. 6 and table 1, Ke Yiming
It is aobvious to find out, bias correction effect of three gamma rainfall bias correction methods of the present invention based on segmentation thought to GCMs simulated rainfalls
Very significantly, 94.4% bias correction effect more than 0.85, or even 61.1% error-correcting effect more than 0.95.
It is described above that only the example of the present invention is implemented, it is not intended to limit the invention, to rainfall number in the present invention
According to the quantile being segmented, also can specifically be formulated according to different survey regions.Every claim in the present invention limits
In the range of, any modification, equivalent substitution, improvement and etc. done should all be within protection scope of the present invention.
Claims (8)
- A kind of 1. rainfall bias correction method based on segmentation thought, it is characterised in that comprise the following steps：1) simulated rainfall sequence of the rainfall model in meteorological site is obtained, while collects meteorological site actual measurement rainfall sequence of the same period Row, are consistent by the temporal resolution processing for surveying rainfall sequence and simulated rainfall sequence；2) according to the minimum quantile of actual measurement rainfall sequence and very big quantile, minimum, conventional value and maximum are classified as Three parts data；3) according to the minimum quantile of rainfall model simulated rainfall sequence and very big quantile, equally it is classified as minimum, often Rule are worth and maximum three parts data；4) the probability distribution accumulation shape based on meteorological site actual measurement rainfall data, using three Gamma distribution function pair simulated rainfalls The probability distribution accumulation shape of data carries out bias correction；5) based on quantile deviation accumulation thought, a kind of bias correction effective evaluation index is proposed, to rainfall model simulated rainfall Bias correction effect assessed.
- 2. the rainfall bias correction method according to claim 1 based on segmentation thought, it is characterised in that the rainfall Model includes climatic model, NO emissions reduction model and Stochastic Weather Generators, and the climatic model includes GCMs and RCMs.
- 3. the rainfall bias correction method according to claim 1 based on segmentation thought, it is characterised in that step 2) and 3) Employed in minimum quantile use 25%, the very big quantile use 75%.
- 4. the rainfall bias correction method according to claim 1 based on segmentation thought, it is characterised in that institute in step 4) Three Gamma distributions stated, i.e., use list to the probability distribution accumulation shape of minimum, conventional value and maximum three parts data Gamma distribution function is simulated, altogether referred to as three Gamma distributions.The bias correction method of three Gamma distributions is as follows：<mrow> <msub> <mover> <mi>x</mi> <mo>~</mo> </mover> <mrow> <mi>m</mi> <mo>,</mo> <mi>c</mi> <mi>o</mi> <mi>r</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mi>F</mi> <mi>o</mi> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msubsup> <mrow> <mo>(</mo> <msub> <mi>F</mi> <mi>m</mi> </msub> <mo>(</mo> <mrow> <msub> <mi>x</mi> <mi>m</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>|</mo> <msub> <mi>&alpha;</mi> <mrow> <mn>1</mn> <mo>,</mo> <mi>m</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>&beta;</mi> <mrow> <mn>1</mn> <mo>,</mo> <mi>m</mi> </mrow> </msub> </mrow> <mo>)</mo> <mo>|</mo> <msub> <mi>&alpha;</mi> <mrow> <mn>1</mn> <mo>,</mo> <mi>o</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>&beta;</mi> <mrow> <mn>1</mn> <mo>,</mo> <mi>o</mi> </mrow> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mi>i</mi> <mi>f</mi> <mi> </mi> <msub> <mi>x</mi> <mi>m</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo><</mo> <msub> <mi>x</mi> <mrow> <mi>m</mi> <mo>,</mo> <mn>25</mn> <mi>t</mi> <mi>h</mi> </mrow> </msub> </mrow><mrow> <msub> <mover> <mi>x</mi> <mo>~</mo> </mover> <mrow> <mi>m</mi> <mo>,</mo> <mi>c</mi> <mi>o</mi> <mi>r</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mi>F</mi> <mi>o</mi> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msubsup> <mrow> <mo>(</mo> <msub> <mi>F</mi> <mi>m</mi> </msub> <mo>(</mo> <mrow> <msub> <mi>x</mi> <mi>m</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>|</mo> <msub> <mi>&alpha;</mi> <mrow> <mn>2</mn> <mo>,</mo> <mi>m</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>&beta;</mi> <mrow> <mn>2</mn> <mo>,</mo> <mi>m</mi> </mrow> </msub> </mrow> <mo>)</mo> <mo>|</mo> <msub> <mi>&alpha;</mi> <mrow> <mn>2</mn> <mo>,</mo> <mi>o</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>&beta;</mi> <mrow> <mn>2</mn> <mo>,</mo> <mi>o</mi> </mrow> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mi>i</mi> <mi>f</mi> <mi> </mi> <msub> <mi>x</mi> <mrow> <mi>m</mi> <mo>,</mo> <mn>25</mn> <mi>t</mi> <mi>h</mi> </mrow> </msub> <mo>&le;</mo> <msub> <mi>x</mi> <mi>m</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo><</mo> <msub> <mi>x</mi> <mrow> <mi>m</mi> <mo>,</mo> <mn>75</mn> <mi>t</mi> <mi>h</mi> </mrow> </msub> </mrow><mrow> <msub> <mover> <mi>x</mi> <mo>~</mo> </mover> <mrow> <mi>m</mi> <mo>,</mo> <mi>c</mi> <mi>o</mi> <mi>r</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mi>F</mi> <mi>o</mi> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msubsup> <mrow> <mo>(</mo> <msub> <mi>F</mi> <mi>m</mi> </msub> <mo>(</mo> <mrow> <msub> <mi>x</mi> <mi>m</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>|</mo> <msub> <mi>&alpha;</mi> <mrow> <mn>3</mn> <mo>,</mo> <mi>m</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>&beta;</mi> <mrow> <mn>3</mn> <mo>,</mo> <mi>m</mi> </mrow> </msub> </mrow> <mo>)</mo> <mo>|</mo> <msub> <mi>&alpha;</mi> <mrow> <mn>3</mn> <mo>,</mo> <mi>o</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>&beta;</mi> <mrow> <mn>3</mn> <mo>,</mo> <mi>o</mi> </mrow> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mi>i</mi> <mi>f</mi> <mi> </mi> <msub> <mi>x</mi> <mi>m</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>&GreaterEqual;</mo> <msub> <mi>x</mi> <mrow> <mi>m</mi> <mo>,</mo> <mn>75</mn> <mi>t</mi> <mi>h</mi> </mrow> </msub> </mrow>In formula, α1,o,β1,o、α2,o,β2,oAnd α3,o,β3,oRainfall data are respectively surveyed to be less than 25%, positioned at 25%~75% Between and single Gamma distribution function parameter higher than its 75% quantile, α1,m,β1,m、α2,m,β2,mAnd α3,m,β3,mRespectively drop Rain modeling rainfall data are less than 25%, single gamma point between 25%~75% and higher than its 75% quantile Cloth function parameter,For the simulated rainfall after bias correction, xm(i) it is the original analog rainfall before bias correction, Fm(xm (i)|α1,m,β1, m) for the cumulative distribution function of single Gamma distribution.
- 5. the rainfall bias correction method according to claim 4 based on segmentation thought, it is characterised in that single Gamma distribution Probability density function it is as follows：X ＞ 0 and α, β ＞ 0In formula, α is form parameter, and β is dimensional parameters, and Γ () is Gamma distribution function.
- 6. the rainfall bias correction method according to claim 1 based on segmentation thought, it is characterised in that institute in step 5) The quantile deviation accumulation thought of use, it is intended to rainfall modeling rainfall under different quantiles with surveying the absolute inclined of rainfall Poor accumulated value represents rainfall model simulated rainfall ECDF (Empirical Cumulative Distribution Function) the difference in areas between actual measurement rainfall ECDF, rainfall model simulated rainfall and actual measurement are represented with both difference in areas Overall deviation between rainfall.
- 7. the rainfall bias correction method according to claim 1 based on segmentation thought, it is characterised in that carried in step 5) A kind of bias correction effective evaluation index gone out, specific formula for calculation are：<mrow> <mi>C</mi> <mo>=</mo> <mn>1</mn> <mo>-</mo> <mfrac> <mrow> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mo>|</mo> <msub> <mi>V</mi> <mrow> <mi>m</mi> <mo>,</mo> <mi>c</mi> <mi>o</mi> <mi>r</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>V</mi> <mi>o</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>|</mo> </mrow> <mrow> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mo>|</mo> <msub> <mi>V</mi> <mi>m</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>V</mi> <mi>o</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>|</mo> </mrow> </mfrac> <mo>=</mo> <mn>1</mn> <mo>-</mo> <mfrac> <mrow> <msub> <mi>AD</mi> <mrow> <mi>c</mi> <mi>o</mi> <mi>r</mi> </mrow> </msub> </mrow> <mrow> <mi>A</mi> <mi>D</mi> </mrow> </mfrac> </mrow>In formula, Vo(i)、Vm(i) and Vm,cor(i) it is respectively actual measurement rainfall data, rainfall model original simulated rainfall data and rainfall mould The corresponding numerical value of i-th of quantile of simulated rainfall data after type bias correction, the method use N number of quantile altogether；Accordingly | Vm(i)-Vo(i) | and | Vm,cor(i)-Vo(i) | be respectively i-th of quantile under, before bias correction rainfall model simulated rainfall with The deviation still suffered between actual measurement rainfall after existing deviation and bias correction between rainfall model simulated rainfall and measured data；WithRespectively before expression bias correction between rainfall model simulated rainfall and measured value Overall deviation AD after overall deviation AD and bias correction between the twocor；C represents overall bias correction effect, C Value represents that bias correction effect is better closer to 1.
- 8. the rainfall bias correction method according to claim 7 based on segmentation thought, it is characterised in that bias correction has Imitate quantile totally 19, i.e. N=19 employed in evaluation index, respectively rainfall data 5%, 10%, 15%, 20%, 25%th, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% and 95% point Site.
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