All‐sky satellite data assimilation at operational weather forecasting centres. (10th July 2018)
- Record Type:
- Journal Article
- Title:
- All‐sky satellite data assimilation at operational weather forecasting centres. (10th July 2018)
- Main Title:
- All‐sky satellite data assimilation at operational weather forecasting centres
- Authors:
- Geer, Alan J.
Lonitz, Katrin
Weston, Peter
Kazumori, Masahiro
Okamoto, Kozo
Zhu, Yanqiu
Liu, Emily Huichun
Collard, Andrew
Bell, William
Migliorini, Stefano
Chambon, Philippe
Fourrié, Nadia
Kim, Min‐Jeong
Köpken‐Watts, Christina
Schraff, Christoph - Abstract:
- Abstract : This article reviews developments towards assimilating cloud‐ and precipitation‐ affected satellite radiances at operational forecasting centres. Satellite data assimilation is moving beyond the "clear‐sky" approach that discards any observations affected by cloud. Some centres already assimilate cloud‐ and precipitation‐affected radiances operationally and the most popular approach is known as "all‐sky, " which assimilates all observations directly as radiances, whether they are clear, cloudy or precipitating, using models (for both radiative transfer and forecasting) that are capable of simulating cloud and precipitation with sufficient accuracy. Other frameworks are being tried, including the assimilation of humidity retrieved from cloudy observations using Bayesian techniques. Although the all‐sky technique is now proven for assimilation of microwave radiances, it has yet to be demonstrated operationally for infrared radiances, though several centres are getting close. Assimilating frequently available all‐sky infrared observations from geostationary satellites could give particular benefit for short‐range forecasting. More generally, assimilating cloud‐ and precipitation‐affected satellite observations improves forecasts in the medium range globally and can also improve the analysis and shorter‐range forecasting of otherwise poorly observed weather phenomena as diverse as tropical cyclones and wintertime low cloud. Abstract : This article reviews developmentsAbstract : This article reviews developments towards assimilating cloud‐ and precipitation‐ affected satellite radiances at operational forecasting centres. Satellite data assimilation is moving beyond the "clear‐sky" approach that discards any observations affected by cloud. Some centres already assimilate cloud‐ and precipitation‐affected radiances operationally and the most popular approach is known as "all‐sky, " which assimilates all observations directly as radiances, whether they are clear, cloudy or precipitating, using models (for both radiative transfer and forecasting) that are capable of simulating cloud and precipitation with sufficient accuracy. Other frameworks are being tried, including the assimilation of humidity retrieved from cloudy observations using Bayesian techniques. Although the all‐sky technique is now proven for assimilation of microwave radiances, it has yet to be demonstrated operationally for infrared radiances, though several centres are getting close. Assimilating frequently available all‐sky infrared observations from geostationary satellites could give particular benefit for short‐range forecasting. More generally, assimilating cloud‐ and precipitation‐affected satellite observations improves forecasts in the medium range globally and can also improve the analysis and shorter‐range forecasting of otherwise poorly observed weather phenomena as diverse as tropical cyclones and wintertime low cloud. Abstract : This article reviews developments towards assimilating cloud‐ and precipitation‐affected satellite radiances at operational forecasting centres. Satellite data assimilation is moving beyond "clear‐sky" towards assimilating all observations directly as radiances, whether they are clear, cloudy or precipitating. This is known as the "all‐sky" approach and it improves global forecasts and can improve the analysis and shorter‐range forecasts of otherwise poorly‐observed weather phenomena as diverse as tropical cyclones and wintertime low cloud. … (more)
- Is Part Of:
- Quarterly journal of the Royal Meteorological Society. Volume 144:Number 713(2018)
- Journal:
- Quarterly journal of the Royal Meteorological Society
- Issue:
- Volume 144:Number 713(2018)
- Issue Display:
- Volume 144, Issue 713 (2018)
- Year:
- 2018
- Volume:
- 144
- Issue:
- 713
- Issue Sort Value:
- 2018-0144-0713-0000
- Page Start:
- 1191
- Page End:
- 1217
- Publication Date:
- 2018-07-10
- Subjects:
- all‐sky -- cloud and precipitation -- data assimilation -- infrared -- microwave -- NWP -- satellite
Meteorology -- Periodicals
551.5 - Journal URLs:
- http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1477-870X/issues ↗
http://onlinelibrary.wiley.com/ ↗
http://www.ingentaselect.com/rpsv/cw/rms/00359009/contp1.htm ↗ - DOI:
- 10.1002/qj.3202 ↗
- Languages:
- English
- ISSNs:
- 0035-9009
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 7186.000000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 17131.xml