Toolkit for Extreme Climate Analysis (TECA)
TECA is a collection of climate analysis algorithms geared toward rapidly analyzing extreme events in petabyte-scale datasets using either C++ or Python.
Loring, Burlen; O’Brien, Travis A; Elbashandy, Abdelrahman E; Johnson, Jeffrey N; Krishnan, Harinarayan; Keen, Noel; Prabhat; the CASCADE SFA (2016): Toolkit for Extreme Climate Analysis. Lawrence Berkeley National Lab. Software. https://doi.org/10.20358/C8C651
The BayesNSGP package for R
Enables off-the-shelf functionality for fully Bayesian, nonstationary Gaussian process modeling using a personal laptop. We furthermore implement approximate Gaussian process inference to account for moderately large spatial data sets. Bayesian inference and posterior prediction is carried out using Markov chain Monte Carlo methods.
Risser, M. D., & Turek, D. (2020). Bayesian inference for high-dimensional nonstationary Gaussian processes. arXiv preprint arXiv:1910.14101.
ENSO Longitude Index (ELI)
The ENSO Longitude Index (ELI) is a simple index that tracks the average longitude of tropical Pacific deep convection and, for the first time, characterizes the diversity of ENSO in a single index. ELI accounts for the nonlinear response of deep convection to sea-surface temperature, and provides a continuous time series for analyses of ENSO dynamics.
Williams, I. N., & Patricola, C. M. (2018) Diversity of ENSO events unified by convective threshold sea surface temperature: A nonlinear ENSO index. Geophysical Research Letters.
Link: NERSC ELI
Simulated tropical cyclone data
The convoSPAT package for R
Fits convolution-based nonstationary Gaussian process models to point-referenced spatial data via local likelihood techniques. Also provided are functions to fit stationary spatial models for comparison, calculate the kriging predictor and standard errors, and create various plots to visualize nonstationarity.
Risser, M.D., Calder, C.A. (2017). Local Likelihood Estimation for Covariance Functions with Spatially-Varying Parameters: The convoSPAT Package for R. Journal of Statistical Software, 81(14), 1-32. DOI: 10.18637/jss.v081.i14
The climextRemes package for R and Python
climextRemes is an R and Python package for extreme value analysis of climate data, including generalized extreme value and peaks-over-threshold (using the point process approach) models, as well as tools for estimating risk ratios with uncertainty.
Paciorek C, Stone D, Wehner M (2018). “Quantifying statistical uncertainty in the attribution of human influence on severe weather.” Weather and Climate Extremes, 20:69-80. doi: 10.1016/j.wace.2018.01.002
Link: Bitbucket climextRemes
A probabilistic gridded product for daily precipitation extremes over the United States
Given that gridded daily precipitation data products dampen local extremes, we create a new “probabilistic” gridded product specifically designed to characterize the climatological properties of extreme precipitation by applying spatial statistical analyses to daily measurements of precipitation from the GHCN over CONUS.
Risser, M.D., Paciorek, C.J., O’Brien, T.A., Wehner, M.F., Collins, W.D. (2019). A probabilistic gridded product for daily precipitation extremes over the United States. Climate Dynamics, 53(5):2517-2538. DOI: 10.1007/s00382-019-04636-0
fastKDE is Python code for doing fast and robust kernel density estimation.
O’Brien, T. A., Kashinath, K., Cavanaugh, N. R., Collins, W. D. & O’Brien, J. P. A fast and objective multidimensional kernel density estimation method: fastKDE. Comput. Stat. Data Anal. 101, 148–160 (2016). http://dx.doi.org/10.1016/j.csda.2016.02.014