EGU2020-677, updated on 12 Jun 2020
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Probabilistic forecasts of the onset of the rainy season using global seasonal forecasts

Manuel Rauch1, Jan Bliefernicht1, Patrick Laux2, Seyni Salack3, Moussa Waongo4, and Harald Kunstmann1,2
Manuel Rauch et al.
  • 1Institute of Geography, University of Augsburg, 86159 Augsburg, Germany
  • 2Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Campus Alpin, 82467 Garmisch-Partenkirchen, Germany
  • 3Competence Center, West African Science Service Centre on Climate Change and Adapted Land Use, Ouagadougou 9507, Burkina Faso
  • 4Training and Research Department, AGRHYMET Regional Centre, Niamey PB 11011, Niger

Seasonal forecasts for monsoonal rainfall characteristics like the onset of the rainy season (ORS) are crucial in semi-arid regions to better support decision-making in water resources management, rain-fed agriculture and other socio-economic sectors. However, forecasts for these variables are rarely produced by weather services in a quantitative way. To overcome this problem, we developed an approach for seasonal forecasting of the ORS using global seasonal forecasts. The approach is not computationally intensive and is therefore operational applicable for forecasting centers in developing countries. It consists of a quantile-quantile-transformation for eliminating systematic differences between ensemble forecasts and observations, a fuzzy-rule based method for estimating the ORS date and a graphical method for an improved visualization of probabilistic ORS forecasts, called the onset of the rainy season index (ORSI). The performance of the approach is evaluated from 2000 to 2010 for several climate zones (Sahel, Sudan and Guinean zone) in West Africa, using hindcasts from the Seasonal Forecasting System 4 of ECMWF. Our studies show that seasonal ORS forecasts can be skillful for individual years and specific regions like the Guinean coasts, but also associated with large uncertainties, in particular for longer lead times. The spatial verification of the ORS fields emphasizes the importance of selecting appropriate performance measures to avoid an overestimation of the forecast skill. The ORSI delivers crucial information about an early, mean and late onset of the rainy season and it is much easier to interpret for users compared to the common categorical formats used in seasonal forecasting. Moreover, the new index can be transferred to other seasonal forecast variables, providing an important alternative to the common forecast formats used in seasonal forecasting. In this presentation we show (i) the operational practice of seasonal forecasting of ORS and other monsoonal precipitation characteristics, (ii) the methodology and results of the new ORS approach published in Rauch et al. (2019) and (iii) first results of an advanced statistical algorithm using ECMW-SYS5 hindcasts over a period of 30 years (1981-2010) in combination with an improved observational database.

Rauch, M., Bliefernicht, J., Laux, P., Salack, S., Waongo, M., & Kunstmann, H. (2019). Seasonal forecasting of the onset of the rainy season in West Africa. Atmosphere, 10(9), 528.

How to cite: Rauch, M., Bliefernicht, J., Laux, P., Salack, S., Waongo, M., and Kunstmann, H.: Probabilistic forecasts of the onset of the rainy season using global seasonal forecasts, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-677,, 2019

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Display material version 1 – uploaded on 03 May 2020
  • CC1: Comment on EGU2020-677, Talardia Gbangou, 07 May 2020

    Thank you for your presentation. Interesting approach for defining onset and verifying the quality of onset dates categories with the System4 seasonal climate forecasts. Can you or to what extent the approach can be used to explore the performance of onset dates for different lead-months ? Considering that this might give further insights for both the quality of the seasonal forecasts and its application (with regards to end-users lead-time needs)

    • AC1: Reply to CC1, Manuel Rauch, 08 May 2020

      Thank you very much for your comment. This is of course an important point. In the analysis shown, 15 ensemble members were used, which were initialized on February 1 with a 7-month simulation. I think longer lead times would increase e.g. preparation time for farmers. On the other hand, increasing the lead time would result in reduced forecast periods for the rainy season. Maybe it also better to use a smaller domain and choose the lead month individually for each region. In general, in this approach the lead-month can be individually chosen with the condition that the time series covers at least the potential onset of the rainy season. 

      • CC2: Reply to AC1, Talardia Gbangou, 09 May 2020

        Thank you very much for your reply. Indeed, I agree with you, for small areas with short rainy seasons different leadmonths verification could be possible.