The use and application of decision support systems (DDS) that consider variation in climate and soil conditions has expanded in recent years. Most of these DSS are based on crop simulation models that require daily weather data, so access to weather data, at single sites as well as large amount of sites that may cover a region, becomes a critical issue. In many agricultural regions, especially in developing countries, the density of meteorological stations is low, and reliable long-term continuous data are scarce. Researchers can use interpolated surfaces of weekly or monthly climate variables and generate daily weather from these. Various software tools, called ‘weather generators’, are available to automate this data generation process. The main objective of this study was to compare the performance of three weather generators, MARKSIM, SIMMETEO and WGEN, with observed daily weather data for one of the major maize growing regions in northwest Mexico. A second objective was to evaluate the impact of using different generators for creating daily weather data for the simulation of maize and bean growth at nine locations. No single generator was clearly superior. However, considering data requirements, the weather generator SIMMETEO is robust and can be recommended for (crop) modeling applications at single point locations as well as for applications that use interpolated summary weather data as input. The weather generator MARKSIM created a high inter-annual variability and long chains of wet days that are not found in observed data, but the generator has use for areas of poor distribution of weather stations or where monthly means are unavailable. The results from this study can be considered valid for the subtropical region from which the test locations were selected. For climates in different regions of the world, we suggest repeating the evaluation process following procedures similar to those used in this paper.
Hartkamp, A. D., White, J. W., & Hoogenboom, G. (2003). Comparison of three weather generators for crop modeling: a case study for subtropical environments. Agricultural Systems, 76(2), 539-560. https://doi.org/10.1016/S0308-521X(01)00108-1