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Messages  ASHWATHI V K
1
« on: March 26, 2018, 04:55:56 PM »
Dear All,
I am trying to do statistical downscale precipitation of CMIP5 GCM output using Bias correction(quantile mapping) Spatial disaggregation method (with reference to a paper(attached)). I have following doubt:
1. In this method, is it okay to use only one GCM output predictor variable (precipitation) instead of group of predictor variables such as geopotential height, uwind, vwind etc?
2. After selecting predictor variable, should we divide the data for each month and perform analysis?
3. In the paper, it was mentioned that for each day we have to find moving window of +/ 15 days and then find CDF. Can anybody guide me how to perform this and find cdf ?
I am little confused. Please guide me..
With Regards Ashwathi V K
2
« on: February 20, 2018, 04:30:08 PM »
Hai,
Can any one explain the procedure to do "change factor method of statistical downscaling"?
With Regards Ashwathi
3
« 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
4
« 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
5
« 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
6
« 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 MRICGCM3 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 0.650.8. 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 20202100. 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
7
« 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
8
« 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?
Please help me..
With Regards Ashwathi V K
9
« 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
10
« on: June 29, 2017, 02:36:34 PM »
I have simulated streamflow using QGis interface of SWAT model in a semiarid catchment that has forest and agricultural ecosystem for total 13years (3 years warmup, 8 years calibration, 2 years validation). Area of catchment is 1200 sq.km. My warmup period is 3 years. After simulation R^2 value and NS was found to be 0.78 and 0.21. I tried calibrating the model using SWATCUP , by following the paper http://www.sciencedirect.com/science/article/pii/S0022169415001985 for calibration period. In SWATCUP, initially I set the result of initial model, with period of simulation as calibration period.. Then my NS is found to be 1.16 and R^2 is found to be 0.54. Since overall flow overestimates the observed flow, in first two simulation i have decreased CN, increased sol_AWC, ESCO and in the third and fourth simulation i have increased GW_revap, GW_qmn, GW_Delay and decreased Revapmn, Alpha_BF. I have done 4 iteration of 150 simulation but there is no much improvement in the performance. I have attached my output flow from QSWAT model. I request you to suggest parameter that is to be needed to change during calibration, so that i get a good performance. With Regards Ashwathi V K
11
« on: June 29, 2017, 10:59:18 AM »
Hai,
I had same problem. Error is due to the difference in the path of input file in global parameter file from actual path.
With Regards Ashwathi
12
« 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
13
« on: May 29, 2017, 04:19:30 PM »
Hai
Is there GCMs of finer resolution (1 degree x 1 degree) under CMIP5 project with RCP 2.6 scenario available?
With Regards Ashwathi V K
14
« on: May 25, 2017, 12:04:08 PM »
Hai,
In downscaling, i have selected predictor variables such as air temperature at 2m, air temperature at 250Hpa, u wind speed at 250Hpa, V wind speed at 250Hpa, geopotential height at 500Hpa, mean sea level pressure, specific humidity at 2m and vertical wind velocity from NCEPNCAR reanalysis data. I have standardized the data and done Principal component analysis using XLSTAT. I got 5 principal components. Then from observed station data (predictand) i have deducted mean and then performed multiple regression using XLSTAT. After regression my correlation is found to be very poor approximately 0.2. Is this acceptable? How can i improve correlation? Is there any mistake in this? Please help me.
With regards Ashwathi V K
15
« on: May 09, 2017, 05:07:14 PM »
Hai,
In procedure to do statistical downscaling of precipitation, I have selected 8 variables from NCEPNCAR reanalysis data and have done principal component analysis using XLSTAT. I have got some results after PCA (attached). Based on what value of the result will we have to select important variables out of 8 variables? I am totally confused. Please help me.
With regards Ashwathi
