Hello, scientific community hope you are doing well. I am modeling the prediction of meteorological drought using the climate data (RCP4.5 & 8.5). However, I got stuck in the bias correction of downloaded data. Could anyone please help me out with this? I have tried to do it in Cmhyd software earlier but did not get the desired results. If anyone has Python code to do it, please share it in DM. Happy modeling. Thanks in advance.
Dear Kalpesh,
There are a few Python packages that could be suitable for your work (depending on what variable you want to bias-correct and if you have a preference for a particular bias-correction method). I would suggest ISIMIP3 (https://github.com/xiaohuihuiwang/isimip3/tree/master, https://www.isimip.org/documents/413/ISIMIP3b_bias_adjustment_fact_sheet_Gnsz7CO.pdf). Just came across this as well https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1481/egusphere-2023-1481.pdf.
In addition, you can also find bias-adjusted climate variables for the different SSP-RCP scenarios. NASA-NEX-GDDP CMIP6 provides bias-adjusted GCMs' variables at 0.25 x 0.25 deg resolution for almost 35 GCMs, but the number of GCMs will defer by variables and scenarios.
Could you clarify what you mean by "I am modeling the prediction of meteorological drought using the climate data". Do you mean a statistical/machine learning model to project droughts in the future? (If you use climate model data from CMIP5 or CMIP6, these are projections, not predictions, unless you are planning to do something different that I didnt' understand).
Rgds,
Malcolm
Malcolm Mistry thank you so much sir for your clarification. We want predict drought using drought indices using climate model data from CMIP5 or CMIP6.
If your data are in netcdf files CDO IS very easy for solve your problem.