Code, functions and notebooks used for my Masters Project/Thesis.
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http://xarray.pydata.org/en/stable/user-guide/reshaping.html?highlight=stack#stack-and-unstack [READ THIS for refactoring]
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https://groups.google.com/g/xarray/c/fz7HHgpgwk0/m/h0umDBIHAAAJ [related to the link above]
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https://stackoverflow.com/questions/56485160/xarray-equivalent-of-pandas-qcut-function
- The theory behind the pseudo R-squared is here - https://stats.stackexchange.com/questions/129200/r-squared-in-quantile-regression
- https://scitools.org.uk/cartopy/docs/latest/gallery/lines_and_polygons/features.html?highlight=cfeatures#sphx-glr-download-gallery-lines-and-polygons-features-py (Cartopy features and coastlines)
- https://stats.stackexchange.com/questions/129200/r-squared-in-quantile-regression (Local measure of goodness for quantreg)
- https://towardsdatascience.com/calculating-confidence-interval-with-bootstrapping-872c657c058d (Bootstrap confidence interval calculation)
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Implementation of Dynamic and Thermodynamic Effects
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Inter-annual variability
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Precip-temp varying plot in regions of dipole behavior
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Complete winter season binning
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Repeat the regridding using the
conservative
method which is recommended for upscaling usingxesmf
- Theconservative
method is not working so sticking to thebilinear
method. The difference of the output is quite low.
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Implement quantile regression as an alternative regressor and also the ZM method from Ali 2018 paper and compare the time and results with the binning method. Link to the technique - click here and here or using SKlearn. (NOT DOING)
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Figure out the use of finding Block maxima and Climate change Indices - Resources -' https://search.brave.com/search?q=calculate+block+maxima+python&source=desktop https://pypi.org/project/evt/ https://kikocorreoso.github.io/scikit-extremes/index.html http://etccdi.pacificclimate.org/list_27_indices.shtml https://search.brave.com/search?q=calculation+of+climate+change+indices&source=desktop