Nowcasting Rainfall from Meteosat Data for Africa
Abstract
There is an urgent need for improved predictions of high-impact weather in Africa, particularly of the heavy rain and floods that can result from intense, large or slow-moving systems of deep moist convection. Numerical weather prediction is inherently challenging in the tropics, and in Africa this is compounded by a lack of in-situ observations for model initialisation, and as a result skill of NWP for convective rainfall is very often very low, even at short lead times. Nowcasting provides a complementary approach to NWP, with the often large and long-lived nature of the high-impact convective events making forward extrapolation of observed systems valuable. Outside of South Africa there is minimal coverage by meteorological radars, but excellent spatial and temporal coverage from geostationary satellite. Here we apply optical flow algorithms to rain rates retrieved from Meteosat data, showing that there can be skill for many hours, and investigating skill as a function of the meteorology. This work is taking place within the GCRF Africa SWIFT project, and is running alongside developing new AI techniques for probabilistic nowcasts. We will highlight strengths and weaknesses of each approach in this context.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2020
- Bibcode:
- 2020AGUFMH141.0019B
- Keywords:
-
- 3305 Climate change and variability;
- ATMOSPHERIC PROCESSES;
- 1817 Extreme events;
- HYDROLOGY;
- 1854 Precipitation;
- HYDROLOGY;
- 4318 Statistical analysis;
- NATURAL HAZARDS