JCSDA seminar 20/10/2014 Satellite Data Assimilation at ECMWF. Stephen English with special thanks to: - PowerPoint PPT Presentation
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Satellite Data Assimilation at ECMWFStephen English
with special thanks to:Tony McNally, Niels Bormann, Alan Geer, Marco Matricardi, Sean Healy, Cristina Lupu, Marta Janisková, Michael Rennie, Massimo Bonavita, Lars Isaksen, Mike Fisher, Richard Engelen, Peter Bauer, David Richardson, Thomas Laiden and Erland Källén.
Skill relative to ERA-I at day 5. Verification against analysis for 500 hPa geopotential (Z500), 850 hPa temperature (T850), mean sea level pressure (MSLP) and 2 m temperature (T2M_AN), using RMSE as a metric; against SYNOP for 2 m temperature (T2M), 10 m wind speed (V10), and total cloud cover (TCC), using error standard deviation.
M Janiskova S Di MicheleNew technologies in space: Cloud radar and lidar
Radar Lidar
O
B
A
Observed (O), Background (B) and Analysis (A) for spaceborne radar (Cloudsat) and lidar (Calipso) data. ECMWF short range forecast
captures 2D cloud structures seen in the observations.
New technology can fill gaps in the Global Observing System. Many years of sustained effort are needed to develop the capability to fully understand and make use of highly innovative new observations in a data assimilation system.
Improved assimilation: “all-sky” microwave soundingModelling cloud effects extract more benefit (blue colours) from MHS
than trying to screen out cloud-affected data.
All sky assimilation of microwave imagers improves wind and humidity fields. SSMIS and MHS humidity sounders are being moved to all-sky, and infrared humidity observations may follow.
Fastem-1: English and Hewison 1998: Created for 20-90 AMSU-A.Fastem-2: Deblonde and English 2002: Extend to MW imagers.Fastem-3: English 2007: Extend to polarimetric imagers.Fastem-4: Liu et al. 2011: Extend to 1-20 GHz and 90-200 GHz.Fastem-5: Liu et al. 2012: Fixed some weaknesses identified in Fastem-4 by users.Fastem-6: Kazumori and English 2014: Fixed anisotropy model in Fastem-5
Fastem-6: more accurate ocean emissivity for RTTOV-11/CY40R3 + 2 QJ papersUse of the Ocean Surface Wind Direction Signal in Microwave Radiance Assimilation
Masahiro Kazumori and Stephen J. English
Asymmetric features of oceanic microwave brightness temperature in high surface wind speed condition
Masahiro Kazumori, Akira Shibata, and Stephen J. English
Summary and thoughtsForecast skill continues to improve rapidly.By 2016 90+ satellite instruments processed operationally – a peak?Most satellite DA is achieved through collaboration (e.g. NWPSAF,
CMA-ECMWF partnership for FY3, Horizon-2020, Copernicus…).
New instruments: Radar, lidar, L-band, Limb sounders all demonstrating impact. Who will pay for an operational service?
Handling cloud and rain affected satellite data will become mainstream – potential being realised, this is the way forward.
System is robust to losing ANY observation type, even the most important. But note in situ data remains important.
ECMWF still thinks 4D-var has a bright future, but we are evaluating other approaches.
24h weak vs 24h strong24h weak-constraint (red) significantly better than 24h strong-constraint in NH.Small improvement also in SH.Verification is against own analyses.
24h weak vs 12h strong24h weak-constraint (red) remains worse than 12h strong-constraint in NH.Similar scores in SH (although some degradation at short range).Verification is against own analyses.