Adjusting the Weather Generator for Use in Operational Forecasting Weather-Dependent Processes

Weather generators (WGs) produce synthetic weather series, which are statistically similar to the real world weather series. The generators are used in assessing responses of weather-dependent processes on climate change (CC) or variability. Individual types of generators may differ in various parameters: (a) they may be parametric or non-parametric, (b) single-site or multi-site, (c) they differ in number of weather variables being generated and (d) the time step. Choice of these parameters depends on the purpose of their use. For example, in agrometeorology, single site (4-6)-variate daily generators are used to assess CC impacts on crop yields, which may include assessment of the sensitivity of the yields to changes in various climate characteristics.

In this contribution, we present our approach to using the generator in crop yield forecasting. Specifically, the crop yields are simulated by crop models, while the input weather series consisting of observational data till day D0 (when the forecast is made) are seamlessly followed by the synthetic series produced by the parametric single-site daily weather generator M&Rfi. Two approaches were implemented in M&Rfi to produce such series: (1) In the first, “operational” mode, the synthetic series are “forced” to exactly fit the available weather forecast, which accounts for the possible uncertainties and spans for the rest of the growing season; to make a probabilistic crop yield forecast, large number of possible weather series realisations is produced. (2) In the second, “research” mode, we do not assume to have a specific weather forecast, but we rather assume to have a knowledge on the accuracy of the available weather forecasts, which may be expressed as a function of the weather forecast error on the lead time. Having this function, we may produce a large ensemble of possible weather forecasts and corresponding ensemble of synthetic weather series.

Our methodology of producing synthetic weather series, which fit available weather forecasts, may be applied also for other weather dependent processes, for example in hydrological applications.

Acknowledgements: The experiment was made within the frame of projects PERUN (supported by TACR, no. SS0203004000) and YiPeeO (supported by ESA, no. 4000141154/23/I-EF).