Abstract
Accurately predicting the formation, development and dissipation of fog and low stratus (LS) still poses a challenge for numerical weather prediction (NWP) models. Errors in the low cloud cover NWP forecasts directly impact the quality of photovoltaic (PV) power prediction. On days with LS, day-ahead forecast errors of Germany-wide PV power frequently lie within the magnitude of the balance energy and thus pose a challenge for maintaining grid stability. An indication in advance about the possible occurrence of a critical weather situation such as LS would represent a helpful tool for transmission system operators (TSOs) in their day-to-day business. In the following, a detection algorithm for low stratus risk (LSR) is developed and applied as post-processing to the NWP model forecasts of the regional non-hydrostatic model COSMO-DE, operational at the German Weather Service. The aim of the LSR product is to supply day-ahead warnings and to support the decision making process of the TSOs. The quality of the LSR is assessed by comparing the computed regions of LSR occurrence with a satellite based cloud classification product from the Nowcasting Satellite Facility (NWCSAF). The results show that the LSR provides additional information that should in particular be useful for risk adverse users.
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