I am not that much familiar with the climate models work. I am civil engineering grad student working on a project to assist the impact of climate change on water resources in a small area in North California. Two downscaling methods have been adopted for the study, Bias correction and statistical disaggregation (BCSD) and Constructed Analogue (CA).
My question here is what is the best/easiest available softwares to generate the different scenarios from the GCM to RCM applying the above two mentioned statistical methods (BCSD & CA) ?
Thanks in advance!
I would just add that there is a very wide range of ESD methods and which method you use can have very large consequences. Some methods like BCSD are fairly border line in that they draw signal directly from the GCM grid scale precipitation which is widely accepted to be one of the least skillful outputs of a GCM. Other methods use circulation based predictors which are likely to have far higher skill. Sorry to punt a paper I'm co-authored on by it does cover these issues quite well. Attached.
If you use the R statistical programming language there is a nice R package to support various ESD methods: ftp://ftp.met.no/users/rasmusb/esd_0.4-1.tar.gz
ftp://ftp.met.no/users/rasmusb/esd_0.4-1.tar.gz
Article Interrogating empirical-statistical downscaling
You can just directly download the climate data that downscaled using the BCSD approach at http://www.engr.scu.edu/~emaurer/global_data/.
I hope it helps.. All the best in your project..
You can try using the Statistical Downscaling Model (SDSM): www.sdsm.org.uk/. You just have to register and then you can freely download the model. There is a paper of Wilby and Dawson (2013) that assess the evolution of the SDSM. You may take a look: http://onlinelibrary.wiley.com/doi/10.1002/joc.3544/abstract
Have a nice day!
http://onlinelibrary.wiley.com/doi/10.1002/joc.3544/abstract
www.sdsm.org.uk/
Article The Statistical DownScaling Model: insights from one decade ...
Hi,
There are several softwares available online that you can use for this purpose.
I suggest developing your own code if this is a part of you project.
As you are working with climate models and you want to adjust bias, there are many issues you want to deal with.
Just be careful while downscaling you should remove noise from the product.
Well I will go with Juan Rivera, if you are a newbie in downscaling .
SDSM is free and simple, even you don't have to go for bias correction, SDSM handles everything for you.
I prefer using Support Vector Machine (SVM) or Stochastic approach (distribution function).
Multi Linear Regression (MLR) also gives good results.
Be sure to choose your predictor wisely.
Best Wishes
http://www.esat.kuleuven.be/sista/lssvmlab/
Article Stochastic downscaling of climate model precipitation output...
Article Stochastic rainfall interpolation and downscaling
Use simply liner regression method : OBS = MODEL*SLOPE+INTERCEPT
for details see this paper: Improved Seasonal Precipitation Forecasts for the Asian Monsoon Using 16 Atmosphere-Ocean Coupled Models. Part I: Climatology
I would just add that there is a very wide range of ESD methods and which method you use can have very large consequences. Some methods like BCSD are fairly border line in that they draw signal directly from the GCM grid scale precipitation which is widely accepted to be one of the least skillful outputs of a GCM. Other methods use circulation based predictors which are likely to have far higher skill. Sorry to punt a paper I'm co-authored on by it does cover these issues quite well. Attached.
If you use the R statistical programming language there is a nice R package to support various ESD methods: ftp://ftp.met.no/users/rasmusb/esd_0.4-1.tar.gz
ftp://ftp.met.no/users/rasmusb/esd_0.4-1.tar.gz
Article Interrogating empirical-statistical downscaling
You may also try to use Long Ashton Research Station Weather Generator (LARS-WG) stochastic weather generator to downscale weather data for a single site for the current and future climate forcing. url: http://www.rothamsted.ac.uk/mas-models/larswg
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