EMS Annual Meeting Abstracts
Vol. 18, EMS2021-264, 2021
EMS Annual Meeting 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Seamless rainfall prediction skill comparison between GLOSEA5 and NEPS-G ensemble prediction systems

Seshagirirao Kolusu1, Marion Mittermaier1, Joanne Robbins1, Caroline Jones1, Raghavendra Ashrit2, and Ashis K Mitra2
Seshagirirao Kolusu et al.
  • 1Met Office, Weather Science, United Kingdom of Great Britain – England, Scotland, Wales (seshagirirao.kolusu@metoffice.gov.uk)
  • 2National Centre for Medium-Range Weather Forecasting (NCMRWF), Noida, India

The southwest monsoon rains in 2019 were the heaviest over India in a quarter of a century. The 2019 seasonal JJAS precipitation over the whole country was 110 % of its long period average (LPA) of 880mm. Precipitation is a cumulative field driven by many atmospheric processes both within nature and numerical prediction.  It’s a weather variable that impacts everyone’s life and hence is used routinely to assess the skill of modelling systems. In this study, we have analyzed the 2019 JJAS seasonal precipitation forecast skill of two global ensemble models: (1) the UK Met Office GloSea5 and (2) the National Center for Medium Range Weather Forecasting (NCMRWF) Global Ensemble Prediction System (NEPS-G). The Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG-GPM) rainfall and ERA5 winds with high spatial resolution and temporal data are used for verification of the model forecasts across a seamless range of time scales.  In order to compare a seamless range of time scales, we have summed forecast fields over time windows of forecast lead time from 1 day to 2 weeks. We also computed the actual skill and potential skill of the model ensemble forecasts at different lead windows. Our results for both models show large precipitation biases and reduced precipitation skills with forecast lead windows. We also found that the models’ actual and potential skill are sensitive to the number of ensemble members and type of ensemble generation. Moreover, the GloSea5 model actual skill is higher than the NEPS-G model over Indian homogeneous regions. To use the GloSea5 NWP forecast model ensemble members for more quantitative applications in downstream hazard and/or impact-based modelling and applications the between-ensemble-member bias introduced by the lagging needs to be addressed.

How to cite: Kolusu, S., Mittermaier, M., Robbins, J., Jones, C., Ashrit, R., and Mitra, A. K.: Seamless rainfall prediction skill comparison between GLOSEA5 and NEPS-G ensemble prediction systems, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-264, https://doi.org/10.5194/ems2021-264, 2021.


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