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**Models / Statistical downscaling of GCMs with bias correction**

« **on:**December 18, 2017, 04:20:15 PM »

Hai,

Please find the following procedure that i have used for downscaling:

Downscaling Method:

GCM- GFDL- CM3, MRI CGCM 3 from CMIP5 Project for RCP2.6, 4.5, 8.5

Predictor variables - Sea level pressure, Surface air temperature, Zonal wind speed at 500Hpa, Zonal wind speed at 850Hpa, Meridonal wind speed at 500Hpa, Meridonal wind speed at 850Hpa, Zonal wind speed at surface, Meridonal wind speed at surface, Geopotential height at 500Hpa, Geopotential height at 850Hpa.

Calibration data - ERA INTERIM Daily data of all the predictor variables.

Procedure-

- Downloaded daily data of above mentioned predictor variables from ERA INTERIM website for a period from 1979

-2012 (for GFDL - 2.5 degree resolution data and for CGCM3 1.125 degree resolution data). Data include all the

surrounding grids of study area.

- Extracted data from netcdf files to excel files using R program.Removed Feb 29 data of leap year..

- Standardised all the data of predicted variable for all the grids.

- Done Principal Component Analysis using SPSS software and selected 50 Principal components which give 97% variance.

- Divided data into Calibration and validation datasets.

- Done Multiple linear regression between 50 Principal Components in calibration data set and observed station

temperature using SPSS statistical software. And found correlation coeficients.

- Using the coefficients from regression during calibration, station temperature was found from 50 Principal Components

in Validation Period and found Correlation coefficients.

- Calibration and Validation results are attached (PFA).

- Downloaded daily data of above mentioned predictor variables of GFDL- CM3 and CGCM3 model from CMIP5 project

for three different scenarios for a period from 2020 to 2100.

- Extracted and resampled the data of all the predictor variables of GCM using R program so that final selected grid are

same as that of ERA INTERIM datasets and converted to excel format. This was done for each scenario for each GCM

separately.

- Done Standardisation of all the variables

- Done Principal component Analysis of all standardised variables and selected 50 Principal Components which give 97%

variance

- Using the coefficients obtained during Multiple linear regression during Calibration, temperature for each station was

found.

After following above procedure if am not getting a proper downscaled value. I wanted to check how my result changes when i include bias correction.

How can i include bias correction in the above procedure? can i apply bias correction directly to the downscaled result??

Please help me.

With Regards

Ashwathi V K

Please find the following procedure that i have used for downscaling:

Downscaling Method:

GCM- GFDL- CM3, MRI CGCM 3 from CMIP5 Project for RCP2.6, 4.5, 8.5

Predictor variables - Sea level pressure, Surface air temperature, Zonal wind speed at 500Hpa, Zonal wind speed at 850Hpa, Meridonal wind speed at 500Hpa, Meridonal wind speed at 850Hpa, Zonal wind speed at surface, Meridonal wind speed at surface, Geopotential height at 500Hpa, Geopotential height at 850Hpa.

Calibration data - ERA INTERIM Daily data of all the predictor variables.

Procedure-

- Downloaded daily data of above mentioned predictor variables from ERA INTERIM website for a period from 1979

-2012 (for GFDL - 2.5 degree resolution data and for CGCM3 1.125 degree resolution data). Data include all the

surrounding grids of study area.

- Extracted data from netcdf files to excel files using R program.Removed Feb 29 data of leap year..

- Standardised all the data of predicted variable for all the grids.

- Done Principal Component Analysis using SPSS software and selected 50 Principal components which give 97% variance.

- Divided data into Calibration and validation datasets.

- Done Multiple linear regression between 50 Principal Components in calibration data set and observed station

temperature using SPSS statistical software. And found correlation coeficients.

- Using the coefficients from regression during calibration, station temperature was found from 50 Principal Components

in Validation Period and found Correlation coefficients.

- Calibration and Validation results are attached (PFA).

- Downloaded daily data of above mentioned predictor variables of GFDL- CM3 and CGCM3 model from CMIP5 project

for three different scenarios for a period from 2020 to 2100.

- Extracted and resampled the data of all the predictor variables of GCM using R program so that final selected grid are

same as that of ERA INTERIM datasets and converted to excel format. This was done for each scenario for each GCM

separately.

- Done Standardisation of all the variables

- Done Principal component Analysis of all standardised variables and selected 50 Principal Components which give 97%

variance

- Using the coefficients obtained during Multiple linear regression during Calibration, temperature for each station was

found.

After following above procedure if am not getting a proper downscaled value. I wanted to check how my result changes when i include bias correction.

How can i include bias correction in the above procedure? can i apply bias correction directly to the downscaled result??

Please help me.

With Regards

Ashwathi V K