Linking the Weather Generator with Weather Forecasts for Use in Forecasting Weather-Dependent Processes

Weather Generator (WGs) are tools, which produce synthetic weather series which are statistically similar to the weather series used to calibrate the WG. Though the underlying models of the WGs (frequently based on Markov chains and autoregressive models) include a prognostic component, so that the WGs could be hypothetically used to make a weather forecast, the precision of such forecast quickly converge (with increasing lead time) to zero. In our contribution, we do not use our generator for weather forecasting, but we use it to produce an ensemble of synthetic weather series which fit an available weather forecast.  

One of the hot challenges in agrometeorology is a seasonal crop yield forecasting, which is a critical aspect of food production planning. The seasonal crop yield forecasting may be based on crop growth models run with daily time step. In this approach, the meteorological data fed into these models typically consist of observational weather data up to the forecast date, followed by weather forecast data (WF), mean climatic data, or weather generators (WGs).

In our contribution, we propose an improvement of the WG-based methodology. In contrast to approaches described in the literature, where WGs synthesize data independently of any WF, we are developing a methodology in which our single-site parametric M&Rfi WG (run with daily step) synthesizes multiple realisations of weather series which fit available WFs. Two approaches are proposed: (A) For use in operational crop yield forecasting, WG produces synthetic weather series starting with D₀ day (which comes after the last day with weather observations and for which WF is available), so that the synthetic series smoothly follows available observations. In our experiments, (a) WF is defined for the upcoming days/weeks/months either in terms of the absolute values of individual weather variables or deviations from their climatological normals, (b) WF may optionally include information on its accuracy (e.g. in terms of standard errors or min-max intervals), (c) Precipitation forecast is assumed to be given in terms of amount and probability of precipitation occurrence, (d) WF may be defined separately for a set of time intervals (e.g. for next three days, next week, next months, etc.). The procedure for linking the generation process with WF is based on a continuous adjusting the stochastically generated series in a way resulting in a series that fits the WF while the internal structure (e.g. relations between variables) of the series remains realistic. (B) the “Research” approach: Unlike A approach, the B approach aims to answer the question: How the use of WF of given accuracy may contribute to the accuracy of seasonal forecast of the crop yields? The process of adjusting the stochastically generated series is similar to A method, but now, we care only about the dispersion of individual realisations, so that the magnitude of the dispersion corresponds to the known accuracy of the weather forecast.

Acknowledgements: The experiments were made within the frame of projects PERUN (supported by TACR, no SS0203004000), OP JAK (supported by MSMT, no. CZ.02.01.01/00/22_008/0004605) and AdAgriF (supported by MSMT, no. CZ.02.01.01/00/22_008/0004635).