Hi All,
I'm currently planning to run LSMs (e.g., Noah-MP) on 0.25-degree spatial resolution using different CMIP5-based data. However, the spatial resolution of GCM-based model forcing is too coarse.
I'm just wondering what's the most appropriate way of pre-processing the GCM meteorological forcing?
Thanks in advances and any suggestions are appreciated.
Jianzhi Dong
It depends on the purpose of your study, but I would like to roughly suggest three methods. The first method is to dynamically and physically downscale the GCM data using a mesocale model such as the WRF model. One of my recent studies is partly related with your question: https://www.jstage.jst.go.jp/article/sola/14/0/14_2018-004/_pdf/-char/en
Secondly, you may downscale it using a statistical algorithm.
Thirdly, you may use local observation data.
Thank you for your answer, Seung-Jae. I will read through your paper.
Jianzhi
To add on what Seung-Jae Lee said, you can also interpolate the data to your model grid. There are different interpolation techniques, nearest neighbour, linear etc.
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