Area-weighted global temperature anomalies
Calculate standard and area-weighted global temperature average anomalies using NCEP/NCAR
Wavelet analysis with Python
This Jupyter notebook implements Dr. Toru Miyama's Python code for univariate Wavelet analysis.
The following is inspired from his IPython notebook available at:
https://github.com/tmiyama/WaveletAnalysis/blob/main/wavelet_test_ElNino3_Liu.ipynb
See also:
https://github.com/tmiyama/WaveletAnalysis/blob/main/wavelet_test_sine.ipynb
References:
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Liu, Y., X.S. Liang, and R.H. Weisberg, 2007: Rectification of the bias in the wavelet power spectrum. Journal of Atmospheric and Oceanic Technology, 24(12), 2093-2102. http://ocgweb.marine.usf.edu/~liu/wavelet.html
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Torrence, C., and G. P. Compo, 1998: A practical guide to wavelet analysis. Bull. Amer. Meteor. Soc., 79, 61–78. http://paos.colorado.edu/research/wavelets/
Auckland cycling and weather
This Jupyter notebook presents an analysis of cycling counts along a dedicated cycle lane popular with commuters and recreational cyclists alike (Tamaki Drive, in Auckland central) and examines how weather conditions (rainfall, temperature, wind, sunshine fraction) influence the number of cyclists on a day to day basis.
Building an ENSO tracker with Matplotlib
In this post, I will show how to play with matplotlib's patches to create a gauge or meter, the goal will be to get something like the Australian Bureau of Meteorology's ENSO tracker below
