I have 3-hourly precipitation of TRMM 3B42 for 17 years with HDF format . and I must compare this data with ground gauge station data . is there anybody know which software can I use for this goal?
Hi,
this reference may help you, although it is at daily time scale:
Nerini D. et al. (2015) A Comparative Analysis of TRMM–Rain Gauge Data Merging Techniques at the Daily Time Scale for Distributed Rainfall–Runoff Modeling Applications. Journal of Hydrometeorology.
There are easy to use libraries to read/write HDF files in most programming languages (python, r-project, C++, etc).
Good luck
hi Sertel
the GrADS is only for Mac and Linux and unfortunately, I have windows 10. I'm searching for a software that when combined with special code automatically done for the whole year of daily data.
Hi,
Not sure it can help or not. But if your problem is about installing GrADS, you can refer this link:
http://opengrads.org/pdf/windows_grads2.pdf
Don't just reach for the nearest software package. You should really think about what you are comparing and how different the measurements are. Differences can arise for several reasons (e.g., . Jameson, A.R., 2016: Spatial and temporal network sampling effects on the correlation and variance structures of rain observations, . J. Hydromet., DOI: 10.1175/JHM-D-16-0129.1; Jameson, A.R., 2016: Quantifying drop size distribution variability over areas: Some implications for ground validation experiments. J. Hydromet., 17, 2689-2698, DOI:10.1175/JHM-D-16-0094.1.)
Hello,
I would suggest you to use GrADs for this purpose with less time you can get much result,while you can also work with matlab .....As I have done some thing while I was having my summer internship.
Good Luck,
Spatial Efficiency metric (SPAEF) is proven to be robust when comparing two raster maps. Python and Matlab codes are available at: http://space.geus.dk/tools_products/index.html
Two papers on the metric:
1) https://www.hydrol-earth-syst-sci.net/22/1299/2018/hess-22-1299-2018-assets.html
2) https://www.geosci-model-dev-discuss.net/gmd-2017-238/
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