4D‐Var assimilation of MERIS total column water‐vapour retrievals over land

Experiments with the active assimilation of total column water‐vapour retrievals from Envisat MERIS observations have been performed at the European Centre for Medium‐Range Weather Forecasts (ECMWF), focusing on the summer 2006 African Monsoon Multidisciplinary Analysis (AMMA) field campaign period....

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Veröffentlicht in:Quarterly journal of the Royal Meteorological Society Jg. 135; H. 644; S. 1852 - 1862
1. Verfasser: Bauer, Peter
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Chichester, UK John Wiley & Sons, Ltd 01.10.2009
Wiley
Schlagworte:
Bias
Climatology
data assimilation
Density
Dynamics
Earth, ocean, space
Exact sciences and technology
External geophysics
Meteorology
Moisture
Physics of the high neutral atmosphere
Radiosondes
Retrieval
satellite
Weather
Africa
Weather forecast
quality controls
Bias
Regional scope
clouds
Summer monsoon
Medium range forecast
Four dimensional models
Daylight
corrections
Short range forecast
water vapor
Numerical forecast
satellite
Data assimilation
ISSN:0035-9009, 1477-870X, 1477-870X
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Zusammenfassung:Experiments with the active assimilation of total column water‐vapour retrievals from Envisat MERIS observations have been performed at the European Centre for Medium‐Range Weather Forecasts (ECMWF), focusing on the summer 2006 African Monsoon Multidisciplinary Analysis (AMMA) field campaign period. A mechanism for data quality control, observation error definition and variational bias correction has been developed so that the data can be safely treated within 4D‐Var, like other observations that are currently assimilated in the operational system. While data density is limited due to the restriction to daylight and cloud‐free conditions, a systematic impact on mean moisture analysis was found, with distinct regional and seasonal features. The impact can last 1‐‐2 days into the forecast but has little effect on forecast accuracy in terms of both moisture and dynamics. This is mainly explained by the weak dynamic activity in the areas of largest data impact. Analysis and short‐range forecast evaluation with radiosonde observations revealed a strong dependence on radiosonde type. Compared with Vaisala RS92 observations, the addition of MERIS total column water‐vapour observations produced neutral to positive impact, while contradictory results were obtained when all radiosonde types were used in generating the statistics. This highlights the issue of radiosonde moisture biases and the importance of sonde humidity bias correction in numerical weather prediction (NWP). Copyright © 2009 Royal Meteorological Society
Bibliographie:ObjectType-Article-1
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ISSN:0035-9009
1477-870X
1477-870X
DOI:10.1002/qj.509