4-9 September 2022, Bonn, Germany
EMS Annual Meeting Abstracts
Vol. 19, EMS2022-258, 2022
https://doi.org/10.5194/ems2022-258
EMS Annual Meeting 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Generating weather symbol data in IMPROVER

Stephen Moseley and Ben Ayliffe
Stephen Moseley and Ben Ayliffe
  • Met Office, Exeter, United Kingdom

IMPROVER (Integrated Model Post-Processing and Verification) has been developed by the Met Office as an open-source probability-based post-processing system to fully exploit our convection permitting, hourly cycling ensemble forecasts. Post-processed MOGREPS-UK model forecasts are blended with deterministic UKV model forecasts and data from the coarser resolution global ensemble, MOGREPS-G, to produce seamless probabilistic forecasts from now out to 7 days ahead. For precipitation, an extrapolation nowcast is also blended in at the start. Forecasts are converted to probabilities at the start, and all initial stages of post-processing are performed on gridded data, with site-specific forecasts extracted as a final step, helping to ensure consistency. Data are processed on a 10km global grid and on a 2km UK-centred grid. An overview of IMPROVER will be given in a separate talk.

 

Weather symbols provide the general public with a simple, pictorial view of the weather for a time of interest and include sun and cloud conditions, mist and fog, hail and lightning, and three phases of precipitation, both as showers or continuous, and light or heavy. This talk describes how a deterministic most-likely weather type code is generated using a decision tree approach from probabilistic multi model IMPROVER data for 1 hour, 3 hour and day time periods that are consistent with each other. Recent work to make these weather codes representative of a time-window, rather than an instant, will be discussed. We will present some verification, comparing IMPROVER weather symbols and the current operational Met Office symbols with SYNOP present weather reports.

How to cite: Moseley, S. and Ayliffe, B.: Generating weather symbol data in IMPROVER, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-258, https://doi.org/10.5194/ems2022-258, 2022.

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