FMI - Weather Forecast Production and NWP Postprocessing

Jussi Ylhäisi, Leila Hieta, Mikko Rauhala
Finnish Meteorological Institute
  10 May, 1:30 pm, in 2890

Finnish Meteorological Institute (FMI) employs ca. 700 people. FMI provides weather forecasts for the public audience and professional customers and is the sole authority in Finland providing official weather warnings and services for other authorities. A part of the applied research and development done in the institute is aimed at improving the data quality of our forecasts. A brief overview of these development projects and our production system is given in this presentation. An emphasis is given to the various postprocessing activities and projects that we currently develop.

The primary local area model (LAM) currently used at FMI is a 10-member HARMONIE-AROME-based MEPS with a 2.5km horizontal resolution. The ensemble consists a unique setup between Nordic countries, where the members jointly share supercomputing and development resources between each other. The LAM is run for the whole Scandinavian domain and different ensemble members are spread over several super computers. FMI has had an open data policy since 2016: Most of our observations and model forecasts are available for the general audience.

In recent years FMI has put substantially more effort in postprocessing of NWP forecasts. As a part of our operative forecasts, we incorporate both various statistical postprocessing frameworks and diagnostic postprocessing algorithms written by our experienced duty forecasters. Our main tool for diagnostic postprocessing is an open-sourced postprocessing package HIMAN, which is currently able to calculate more than 60 diagnostic variables from the output of any NWP model. For statistical postprocessing, we have since February 2017 operatively produced point-based and Kriging-gridded Model Output Statistics (MOS) forecasts over the European domain, based on ECMWF HRES model. As a part of our long-term strategy, we are currently building a grid-based framework for model blending, where we aim to generate a consensus forecast over Scandinavian area through blending our MOS forecasts with the direct model output (DMO) from other NWP models.

Despite having a heterogeneous training sample for our MOS forecasts, the operative verification clearly shows the improvement over the ECMWF DMO forecasts. Our preliminary results for the blended forecasts also show an improvement over the MOS forecasts, which again includes the potential to reduce the data sources the forecaster needs to take into account in shift work.