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Published March 2, 2021 | Version LARS-WG 6.0
Software Open

LARS-WG stochastic weather generator

Creators

  • 1. Rothamsted Research, UK

Description

LARS-WG 6.0, a stochastic weather generator, is a computationally inexpensive downscaling tool to generate local scale climate scenarios based on global or regional climate models for impact assessments of climate change. LARS-WG has been used in more than 75 countries for research and education. The current version of LARS-WG incorporates climate projections from the CMIP5 ensemble used in the IPCC Fifth Assessment Report. LARS-WG has been well validated in diverse climates around the world.

Files

LARSWG6.ZIP

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Additional details

Related works

Cites
Journal article: 10.1023/A:1005342632279 (DOI)
Journal article: 10.3354/cr00865 (DOI)
Journal article: 10.3354/cr00836 (DOI)
Journal article: 10.3354/cr01297 (DOI)

References

  • Racsko P, Szeidl L & Semenov MA (1991) A serial approach to local stochastic weather models. Ecological Modelling, 57:27-41, https://doi.org/10.1016/0304-3800(91)90053-4 2. Semenov MA & Barrow EM (1997) Use of a stochastic weather generator in the development of climate change scenarios Climatic Change, 35:397-414 https://doi.org/10.1023/A:1005342632279
  • Semenov MA & Barrow EM (1997) Use of a stochastic weather generator in the development of climate change scenarios Climatic Change, 35:397-414 https://doi.org/10.1023/A:1005342632279
  • Semenov MA & Brooks RJ (1999) Spatial interpolation of the LARS-WG stochastic weather generator in Great Britain. Climate Research 11:137-148 https://doi.org/10.3354/cr011137
  • Semenov MA (2008) Simulation of weather extreme events by a stochastic weather generator, Climate Research 35:203-212 https://doi.org/10.3354/cr00731
  • Semenov MA, Donatelli M, Stratonovitch P, Chatzidaki E, and Baruth B. (2010) ELPIS: a dataset of local-scale daily climate scenarios for Europe. Climate Research 44:3-15 https://doi.org/10.3354/cr00865
  • Semenov MA & Stratonovitch P (2010) The use of multi-model ensembles from global climate models for impact assessments of climate change. Climate Research 41:1-14 https://doi.org/10.3354/cr00836
  • Semenov MA, Stratonovitch P (2015) Adapting wheat ideotypes for climate change: accounting for uncertainties in CMIP5 climate projections. Clim Res 65: 123–139 https://doi.org/10.3354/cr01297