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Compendium on methods and tools to evaluate impacts of, and vulnerability and adaptation to, climate change

Weather Generators

Weather generators are not, strictly speaking, downscaling techniques, but are often used in conjunction with the techniques outlined in this section.

A weather generator is a statistical model used to generate realistic daily sequences of weather variables — precipitation, maximum and minimum temperature, humidity, etc. Such data are often referred to as synthetic data. Usually precipitation sequences are generated first, and other data sequences are derived using statistical relationships between these data and precipitation, with different relationships used for wet and dry days.

Precipitation is divided into an occurrence process (i.e., whether the day is wet or dry) modeled as a Markov chain, and an amount process (the amount of precipitation on a wet day) sampled randomly from an appropriate distribution, such as a Gamma distribution. By using different random seeds, a large number of sequences can be generated, all of which have the same statistical properties as the original data used to calibrate the statistical model — akin to realizations from a set of parallel universes. This is a crucial factor in assessing uncertainties associated with the chaotic nature of daily weather variability. The SDSM software has a weather generator component.

Appropriate Use Weather generators are used whenever impacts models require small-scale data on a daily time scale, provided suitable observed data are available to derive the statistical relationships.
Scope All locations
Key Output Station-level information on future precipitation, maximum and minimum temperatures, humidity, etc.
Key Input Appropriate observed data to calibrate and validate the statistical model(s). GCM data for future climate to drive the model(s).
Ease of Use There are a number of weather generator software packages requiring different levels of expertise for their use (see References below). The user-friendly software in SDSM’s weather generator component is largely self explanatory and comes with comprehensive instructions for use.
Training Required Requires little training for those familiar with basic climate science
Training Available There are currently no plans for future courses
Computer Requirements Personal computer. Specific requirements will depend on the selected weather generator.
Documentation Numerous publications in the scientific literature. The earliest papers date from the 1960s.

Widely applied in many regions and over a range of climate impact sectors. See References below.

Contacts for Framework, Documentation, Technical Assistance

New users of SDSM can register for free at

Cost Depends on the weather generator. SDSM, for example, is free.

Nicks, A.D., L.J. Lane, and G.A. Gander. 1985. Weather generator. In USDA-Water Erosion Prediction Project: Hillslope Profile and Watershed Model Documentation, D.C. Flanagan and M.A. Nearing (eds.). USDA-ARS National Soil Erosion Research Lab. Report No. 10, West Lafayette, IN.

Richardson, C.W. 1981. Stochastic Simulation of daily precipitation, temperature and solar radiation. Water Resources Research 17:182-190.

Wilby, R.L., Hay, L.E. and G.H. Leavesley. 1999. A comparison of downscaled and raw GCM output: implications for climate change scenarios in the San Juan river basin, Colorado. Journal of Hydrology 225:67-91.

Wilks, D.S. and R.L. Wilby. 1999. The weather generation game: A review of stochastic weather models. Progress in Physical Geography 23:329-357.

(See also SDSM).