Technical note: Lowermost-stratospheric moist bias in ECMWF IFS model diagnosed from airborne GLORIA observations during winter/spring 2016
Numerical weather forecast systems like the ECMWF IFS (European Centre for Medium-Range Weather Forecasts – Integrated Forecasting System) are known to be affected by a moist bias in the extratropical lowermost stratosphere (LMS) which results in a systematic cold bias there. We use high spatial res...
| Main Authors: | , , , , , , , |
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| Format: | Text |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://doi.org/10.5194/acp-2020-367 https://www.atmos-chem-phys-discuss.net/acp-2020-367/ |
| Summary: | Numerical weather forecast systems like the ECMWF IFS (European Centre for Medium-Range Weather Forecasts – Integrated Forecasting System) are known to be affected by a moist bias in the extratropical lowermost stratosphere (LMS) which results in a systematic cold bias there. We use high spatial resolution water vapour measurements by the airborne 15 infrared limb-imager GLORIA (Gimballed Limb Observer for Radiance Imaging of the Atmosphere) during the PGS (POLSTRACC/GW-LCYLCE-II/SALSA) campaign to study the LMS moist bias in ECMWF analyses and 12 h forecasts in the season from January to March 2016. Thereby, we exploit the 2-dimensional observational capabilities of GLORIA, when compared to in situ observations, and the higher vertical and horizontal resolution, when compared to satellite observations. Using GLORIA observations taken during five flights in the polar sub-vortex region around Scandinavia and Greenland, we 20 diagnose a systematic moist bias in the LMS peaking at +50 % at potential vorticity levels of 6 to 10 PVU. In the diagnosed time period, the moist bias reduces at the highest and driest air masses observed, but clearly persists at lower levels until mid-March. Sensitivity experiments with more frequent temporal output, lower horizontal resolution, and higher/lower vertical resolution, show the short-term forecasts to be practically insensitive to these parameters on time scales of < 12 hours. Our results confirm that the diagnosed moist bias is present already in the initial conditions (i.e., the analysis) and thus supports 25 the hypothesis that the cold bias develops as a result of forecast initialisation. The moist bias in the analysis might be explained by a model bias and/or the lack of water vapour observations suitable for assimilation by the model above the tropopause. |
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