4.6 Article

Use and impact of Arctic observations in the ECMWF Numerical Weather Prediction system

Journal

QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
Volume 145, Issue 725, Pages 3432-3454

Publisher

WILEY
DOI: 10.1002/qj.3628

Keywords

atmosphere; data assimilation; numerical methods and NWP; observations; polar regions; Year of Polar Prediction

Funding

  1. European Union [727862 APPLICATE]

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This paper presents an assessment of the usage of Arctic atmospheric observations in the Numerical Weather Prediction (NWP) system of the European Centre for Medium-Range Weather Forecasts, and of their impact on the quality of short- to medium-range forecasts. The Arctic has low coverage of conventional data north of 70 degrees N but one of the highest levels of coverage of satellite sounding data on Earth. The impact of Arctic observations on forecast skill was assessed by performing Observing System Experiments, in which different observation types were removed from the full observing system. This assessment was complemented by an analysis of Forecast Sensitivity to Observation Impact diagnostics. To our knowledge it is the first time that comprehensive numerical experimentation has been carried out to explore the role of different Arctic observations in a state-of-the-art global operational NWP system. All Arctic observations were found to have a positive impact on forecast skill in the Arctic region, with the greatest tropospheric impacts on both short- and medium-range forecasts due to microwave, conventional and infrared sounding observations. Results indicate the great importance of microwave sounding data and conventional data, which are found to be the key observing systems in the summer and winter seasons, respectively. These observations were found to have positive and statistically significant impacts on forecasts not only in the Arctic but also in the midlatitude regions at longer lead times. Differences between the seasons are most likely due to problems assimilating microwave sounding observations over snow and sea ice, leading to a reduced impact in winter. There is also the suggestion of increased importance of conventional data in winter, and other factors may also play a role.

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