### Author Topic: Statistical downscaling of GCMs  (Read 13701 times)

#### ASHWATHI V K

• Team river
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• Institute : Ashoka Trust for research in Ecology and Environment
• Programming language : Matlab
##### Re: Statistical downscaling of GCMs
« Reply #15 on: June 29, 2017, 10:02:15 AM »
Hai,

Thankyou Rohith.

Do you have any material on quantile mapping method of downscaling?

With regards
Ashwathi V K

• Team tributary
• Karma: +0/-0
• Programming language : Matlab, R.
##### Re: Statistical downscaling of GCMs
« Reply #16 on: July 08, 2017, 05:09:08 PM »
You will find many materials and papers if you just google.
you can also have look at old discussions in the group about quantile mapping.
Rohith A N
Hydraulics and Water Resources Engineering.

#### ASHWATHI V K

• Team river
• Karma: +1/-0
• Institute : Ashoka Trust for research in Ecology and Environment
• Programming language : Matlab
##### Re: Statistical downscaling of GCMs
« Reply #17 on: July 14, 2017, 11:28:08 AM »
Hai,

I have downloaded some predictor variables from GFDL climate model of CMIP5 project which is in netcdf format. It has four dimension (lat, lon, time, and pressure levels). I want extract data for one pressure level (500Hpa). Is there any method to extract the values? I have tried with R program. But i have failed. Is there any method. Please help me.

With regards
Ashwathi V K

• Team tributary
• Karma: +0/-0
• Programming language : Matlab, R.
##### Re: Statistical downscaling of GCMs
« Reply #18 on: July 14, 2017, 02:22:58 PM »
hey you can use Matlab functions (ncinfo or ncdisp to know the contents and units of the variable in nc file ) and ncread to extract the data.
Rohith A N
Hydraulics and Water Resources Engineering.

#### ASHWATHI V K

• Team river
• Karma: +1/-0
• Institute : Ashoka Trust for research in Ecology and Environment
• Programming language : Matlab
##### Re: Statistical downscaling of GCMs
« Reply #19 on: August 10, 2017, 11:11:46 AM »
Hai,

While doing statistical downscaling, after selecting predictor variables of all the surrounding grids of the study area, should i take the average of all the grid for each of these variables before doing Principal component Analysis?

With Regards
Ashwathi V K

#### Alok Pandey

• Team sea
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• Trying my best to achieve unbiasedness :)
• Institute : Indian Institute of Science, Bangalore
• Programming language : Matlab, R, ArcGIS, Grads
##### Re: Statistical downscaling of GCMs
« Reply #20 on: August 11, 2017, 10:00:29 AM »
Not needed. After standardization, you can perform PCA.
Alok Pandey,
PhD Scholar,
Water Resources and Environmental Engineering,
IISc Bangalore

#### ASHWATHI V K

• Team river
• Karma: +1/-0
• Institute : Ashoka Trust for research in Ecology and Environment
• Programming language : Matlab
##### Re: Statistical downscaling of GCMs
« Reply #21 on: August 30, 2017, 05:25:32 PM »
Hai,
I have a netcdf file( GCM data) with 2.5*2 degree resolution which is of 4 dimension (lat, lon, time and pressure level) and  has 1 variable (Example - uwind). I also have Era Interim climate data which is of 2.5*2.5 degree resolution. I want change resolution of GCM data to that of Era Interim data so that points of both the file overlap and then extract data from resampled GCM data for points in between Latitude 7.5 degree N and 15 degree N and Longitude 72.5 and 80 degree East for 500Hpa and 750Hpa pressure levels and convert it to csv file, so that i can use data directly for PCA. Likewise I have to perform these for about 100 files. Is there any way to do this for all the files together? I tried with R program. But I failed. Please help me.

With Regards
Ashwathi V K

#### Alok Pandey

• Team sea
• Karma: +93/-0
• Trying my best to achieve unbiasedness :)
• Institute : Indian Institute of Science, Bangalore
• Programming language : Matlab, R, ArcGIS, Grads
##### Re: Statistical downscaling of GCMs
« Reply #22 on: August 31, 2017, 04:17:57 PM »
You can use Grads to regrid and automate regriding for many variables/files.
Alok Pandey,
PhD Scholar,
Water Resources and Environmental Engineering,
IISc Bangalore

#### ASHWATHI V K

• Team river
• Karma: +1/-0
• Institute : Ashoka Trust for research in Ecology and Environment
• Programming language : Matlab
##### Re: Statistical downscaling of GCMs
« Reply #23 on: September 28, 2017, 05:34:06 PM »
Hai,

I have a doubt on results of PCA regression statistical downscaling of temperature. I have downscaled GFDL- CM3 and MRI-CGCM3 of CMIP5 project for Scenarios RCP2.6, 4.5, 8.5 and found temperature for a period of 2020 to 2100 for different stations.  I have used ERA INTERIM daily data for Calibration. Correlation coefficient of  Calibration and Validation results for different stations ranges between 0.65-0.8.
But in the result it was found that  50% of downscaled temperature data has lesser value for higher RCP. i.e, temperature of RCP 8.5 is lower than that of RCP2.6 for certain days in the period between 2020-2100. I have checked the procedure that i have done. I am not able solve the above mentioned problem.

Can you please suggest any solution for the above mention problem?

With Regards
Ashwathi V K

• Team tributary
• Karma: +0/-0
• Programming language : Matlab, R.
##### Re: Statistical downscaling of GCMs
« Reply #24 on: October 04, 2017, 10:09:24 AM »
hi

I think it can be possible in the near future (maybe till 2050s), because rcp2.6 is a "peak and decline" scenario, which reaches the peak around 2050 and declines towards the end of century. While rcp8.5 is raising scenario towards end of the century.

You can refer this
Quote
DOI 10.1007/s10584-011-0148-z

I have not worked much with temperature data, but this is what i feel.

Rohith A N
Rohith A N
Hydraulics and Water Resources Engineering.

#### Alok Pandey

• Team sea
• Karma: +93/-0
• Trying my best to achieve unbiasedness :)
• Institute : Indian Institute of Science, Bangalore
• Programming language : Matlab, R, ArcGIS, Grads
##### Re: Statistical downscaling of GCMs
« Reply #25 on: October 05, 2017, 05:38:50 PM »
Quote
But in the result it was found that  50% of downscaled temperature data has lesser value for higher RCP. i.e, temperature of RCP 8.5 is lower than that of RCP2.6 for certain days in the period between 2020-2100. I have checked the procedure that i have done. I am not able solve the above mentioned problem.

Can you please suggest any solution for the above mention problem?

My view :
50% of downscaled temperature data has lesser value for higher RCP is "an observation based on analysis performed and not a problem" . Also it doesn't say anything about trends (which may be present and should be look into) in the results obtained. Check for the frequency of events (above certain threshold) obtained in results.
Alok Pandey,
PhD Scholar,
Water Resources and Environmental Engineering,
IISc Bangalore

#### ASHWATHI V K

• Team river
• Karma: +1/-0
• Institute : Ashoka Trust for research in Ecology and Environment
• Programming language : Matlab
##### Re: Statistical downscaling of GCMs
« Reply #26 on: October 06, 2017, 11:14:04 AM »
Hai Alok and Rohith,

But my doubt is that , for RCP 8.5, most of the temperature  should be greater than that of RCP 2.6 because of higher emission in RCP8.5? And average of daily downscaled temperature for RCP2.6,4.5,8.5 over a period from 2020 to 2100 is excatly same.

With Regards
Ashwathi V K

#### Alok Pandey

• Team sea
• Karma: +93/-0
• Trying my best to achieve unbiasedness :)
• Institute : Indian Institute of Science, Bangalore
• Programming language : Matlab, R, ArcGIS, Grads
##### Re: Statistical downscaling of GCMs
« Reply #27 on: October 18, 2017, 05:08:49 PM »
Hi,

The problem you are facing needs thorough look into data used, model created and the results obtained. It will be very difficult for anyone to give satisfactory answer to this problem without checking all the steps followed. I would suggest plotting the raw data of variables of all grid points, bias corrected data, and the downscaled data and then comparing the all the plots for better understanding.
Alok Pandey,
PhD Scholar,
Water Resources and Environmental Engineering,
IISc Bangalore

#### ASHWATHI V K

• Team river
• Karma: +1/-0
• Institute : Ashoka Trust for research in Ecology and Environment
• Programming language : Matlab
##### Re: Statistical downscaling of GCMs
« Reply #28 on: October 25, 2017, 03:02:17 PM »
Hai,

Can anybody tell the function in r program to get values of optimum parameters after performing support vector regression?

With regards
Ashwathi V K

#### ASHWATHI V K

• Team river
• Karma: +1/-0
• Institute : Ashoka Trust for research in Ecology and Environment
• Programming language : Matlab
##### Statistical downscaling of GCMs with bias correction
« Reply #29 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??