🌐 Other SWG Packages

Many other SWG packages, Git repositories, and codebases exist — though detailed documentation is often lacking. Below is a far from exhaustive list^[1] that we (the authors and contributors) will aim to complete over time.

Note

If you know of a useful model with code not listed here, feel free to suggest it or contribute via Pull Request.

Logo	Name	Type	Description	Paper	Comments
	MSTWeatherGen	Package	Spatial, multivariate	Obakrim et al. (2024)	Documentation
—	GWEX	Package	Multisite Precipitation and Temperature	Evin et al. (2018)	—
—	GWGEN	Package	Globally applicable weather generator inspired by the original Richardson model (1981).	Sommer et al. (2017)	Sort of Documentation. Not sure what "globally applicable" means.
	WeaGETS	Package	Multivariate Markov/AR based SWG for `RR`, `TX` and `TN`	Feng et al. (2012)	—
	AWE-GEN-2d	Code	AWE-GEN-2d is an advanced stochastic weather generator that combines physical and stochastic approaches to simulate multiple variables like precipitation, temperature, radiation, and cloud cover at high spatial (2 km) and temporal (5 min) resolution. It has been developed to support complex hydrological simulations under changing climate scenarios.	Peleg et al. (2024)	Other models available from the author Nadav Peleg.

Two Languages Problem

Having two languages inside a software makes it difficult to read, maintain, and compose with other packages. Some of the packages listed here use a "friendly" but slower language for most of the program (e.g. Matlab, R, Python), along with a "less-friendly" but faster language (e.g. C++, Fortran) for the core functions. This is known (mostly in the Julia world) as the Two Languages Problem, which Julia is designed to solve. One advantage of this approach is that the source code is "easy" to read and write while still being very fast. Moreover, it can integrate with state-of-the-art DataFrame libraries, optimization solvers, and statistical packages. All of this makes writing a Stochastic Weather Generator in Julia highly relevant.

Chen, J.; Brissette, F. P. and Leconte, R. (2012). WeaGETS – a Matlab-based Daily Scale Weather Generator for Generating Precipitation and Temperature. Procedia Environmental Sciences 13, 2222–2235.
Obakrim, S.; Benoit, L. and Allard, D. (2024). A Multivariate and Space-Time Stochastic Weather Generator Using a Latent Gaussian Framework. HAL.
Peleg, N.; Fatichi, S.; Paschalis, A.; Molnar, P. and Burlando, P. (2017). An Advanced Stochastic Weather Generator for Simulating 2-D High-Resolution Climate Variables. Journal of Advances in Modeling Earth Systems 9, 1595–1627.
Richardson, C. W. (1981). Stochastic Simulation of Daily Precipitation, Temperature, and Solar Radiation. Water Resources Research 17, 182–190.
Sommer, P. S. and Kaplan, J. O. (2017). A Globally Calibrated Scheme for Generating Daily Meteorology from Monthly Statistics: Global-WGEN (GWGEN) v1.0. Geoscientific Model Development 10, 3771–3791.

1Focusing on open source code only.