My aim is to downscale the SWE from ERA-5 data set with the use of the statistical downscaling approach and also with machine learning techniques.
The attached paper combines the AMSR-E SWE data ( 25 km spatial resolution) and the enhanced MODIS SCA dataset to derive a SWE product at a sub-pixel spatial resolution of 500 m. You can develop similar kind of scheme to downscale ERA5 SWE.Article Towards an enhanced method to map snow cover areas and deriv...
Dear Susen
As you know SD (Statistical Downscaling) uses equations to associate the variables simulated well by GCMs (predictors) and surface climate variables based on observed records (predictands).
The three most commonly used approaches for statistical downscaling are (1) transfer functions (Imbert and Benestad, 2005), (2) weather typing (Huth et al., 2008), and (3) stochastic weather generator (Buishand et al., 2003). Several variations on a fourth approach Bias Correction (BC), have been developed to downscale climate variables from climate models (Maraun, 2016).
However, I suggest you to visit the following link and review the available help file to use an applicable tool for applying SD methods, such as Delta, QM, and EQM.
https://agrimetsoft.com/SD-GCM.aspx
In this regard, for calibration and validation processes you should have in-situ data or gridded-datasets (such as MODIS SCA dataset) beside the outputs of CMIP5 models. If you study the mentioned link you can get a better clarifications of applying SD methods.
If you have further question let me know by sending an email to me:
Kindest Regards,
Nasrin
Amrit Thapa Nasrin Salehnia Thank you very much both of you for your response. I will go through the mentioned literature and communicate in the future.
- Badges
- Science topic