Linking the Weather Generator with Weather Forecasts for Use in Crop Yield Forecasting

One of the hot challenges in agrometeorology is seasonal crop yield forecasting, which is a critical aspect of food production planning. The seasonal crop yield forecasting may be optionally based on crop growth models. In this approach, the meteorological data fed into the crop models typically consist of observational weather data up to the forecast date, followed by weather data from weather forecasts (WFs), climate models, 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 conditionally on 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 of the stochastically generated series in a way resulting in a series that fits the weather forecast 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 forecast.

To demonstrate the two methodologies, we run crop models Daisy and Hermes for 1 experimental site in Czechia and five main crops – winter wheat, spring barley, winter rape, maize and sugar beet.

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).