Assimilation of OLCI total column water vapour in the Met Office global numerical weather prediction system
The representation of water vapour in numerical weather prediction models is still subject to significant uncertainties, which are partly due to the lack of observations of water vapour in the lower troposphere over land and sea‐ice areas. There are now several satellite datasets of total column wat...
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Published in | Meteorological applications Vol. 28; no. 5 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
Chichester, UK
John Wiley & Sons, Ltd
01.09.2021
John Wiley & Sons, Inc Wiley |
Subjects | |
Online Access | Get full text |
ISSN | 1350-4827 1469-8080 |
DOI | 10.1002/met.2029 |
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Abstract | The representation of water vapour in numerical weather prediction models is still subject to significant uncertainties, which are partly due to the lack of observations of water vapour in the lower troposphere over land and sea‐ice areas. There are now several satellite datasets of total column water vapour available, which make use of the reflected radiances from the surface in the near‐infrared spectral region where there are water vapour absorption bands. The ocean and land cover imager (OLCI) on the Sentinel‐3A and 3B satellites measures the top of atmosphere radiances in the near infrared, and a total column water vapour product is retrieved and made available in near real time. Comparisons of the total column water vapour from OLCI with NWP model 6‐h forecasts, collocated ground‐based GNSS measurements and radiosonde profiles have been undertaken to determine the accuracy of the product. Following the monitoring, some experiments were made to assimilate the OLCI total column water vapour over land using the Met Office 4D‐Var assimilation system. A 5‐month trial assimilating the OLCI data has shown consistently positive impacts on the forecast scores with some changes to the water vapour distribution in the model.
The difference between the OLCI measured total column water vapour and the corresponding background field of a 6‐h forecast from the Met Office unified model for 4 May 2020 over land areas in kg/m2. Assimilation of the OLCI data leads to improved analyses of specific humidity in the tropics and forecasts of geopotential and wind fields at all latitudes. |
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AbstractList | Abstract The representation of water vapour in numerical weather prediction models is still subject to significant uncertainties, which are partly due to the lack of observations of water vapour in the lower troposphere over land and sea‐ice areas. There are now several satellite datasets of total column water vapour available, which make use of the reflected radiances from the surface in the near‐infrared spectral region where there are water vapour absorption bands. The ocean and land cover imager (OLCI) on the Sentinel‐3A and 3B satellites measures the top of atmosphere radiances in the near infrared, and a total column water vapour product is retrieved and made available in near real time. Comparisons of the total column water vapour from OLCI with NWP model 6‐h forecasts, collocated ground‐based GNSS measurements and radiosonde profiles have been undertaken to determine the accuracy of the product. Following the monitoring, some experiments were made to assimilate the OLCI total column water vapour over land using the Met Office 4D‐Var assimilation system. A 5‐month trial assimilating the OLCI data has shown consistently positive impacts on the forecast scores with some changes to the water vapour distribution in the model. The representation of water vapour in numerical weather prediction models is still subject to significant uncertainties, which are partly due to the lack of observations of water vapour in the lower troposphere over land and sea‐ice areas. There are now several satellite datasets of total column water vapour available, which make use of the reflected radiances from the surface in the near‐infrared spectral region where there are water vapour absorption bands. The ocean and land cover imager (OLCI) on the Sentinel‐3A and 3B satellites measures the top of atmosphere radiances in the near infrared, and a total column water vapour product is retrieved and made available in near real time. Comparisons of the total column water vapour from OLCI with NWP model 6‐h forecasts, collocated ground‐based GNSS measurements and radiosonde profiles have been undertaken to determine the accuracy of the product. Following the monitoring, some experiments were made to assimilate the OLCI total column water vapour over land using the Met Office 4D‐Var assimilation system. A 5‐month trial assimilating the OLCI data has shown consistently positive impacts on the forecast scores with some changes to the water vapour distribution in the model. The representation of water vapour in numerical weather prediction models is still subject to significant uncertainties, which are partly due to the lack of observations of water vapour in the lower troposphere over land and sea‐ice areas. There are now several satellite datasets of total column water vapour available, which make use of the reflected radiances from the surface in the near‐infrared spectral region where there are water vapour absorption bands. The ocean and land cover imager (OLCI) on the Sentinel‐3A and 3B satellites measures the top of atmosphere radiances in the near infrared, and a total column water vapour product is retrieved and made available in near real time. Comparisons of the total column water vapour from OLCI with NWP model 6‐h forecasts, collocated ground‐based GNSS measurements and radiosonde profiles have been undertaken to determine the accuracy of the product. Following the monitoring, some experiments were made to assimilate the OLCI total column water vapour over land using the Met Office 4D‐Var assimilation system. A 5‐month trial assimilating the OLCI data has shown consistently positive impacts on the forecast scores with some changes to the water vapour distribution in the model. The difference between the OLCI measured total column water vapour and the corresponding background field of a 6‐h forecast from the Met Office unified model for 4 May 2020 over land areas in kg/m2. Assimilation of the OLCI data leads to improved analyses of specific humidity in the tropics and forecasts of geopotential and wind fields at all latitudes. |
Author | Saunders, Roger |
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Cites_doi | 10.1002/qj.2819 10.3390/rs13050932 10.3390/rs11030251 10.1029/2002JD003023 10.5194/amt-9-5385-2016 10.1002/qj.32 10.1109/TGRS.2020.3015257 10.1175/1520-0477(2000)081<0677:SARNGN>2.3.CO;2 10.5194/amtd-5-6423-2012 10.1175/BAMS-84-9-1249 10.1256/qj.05.108 10.1080/014311699212416 10.1002/qj.2054 10.1175/MWR-D-11-00156.1 10.5194/essd-12-647-2020 10.1063/1.4804812 10.5194/amt-6-765-2013 10.5194/amt-5-631-2012 10.1002/wea.3913 |
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Snippet | The representation of water vapour in numerical weather prediction models is still subject to significant uncertainties, which are partly due to the lack of... Abstract The representation of water vapour in numerical weather prediction models is still subject to significant uncertainties, which are partly due to the... |
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SubjectTerms | Absorption bands Absorption spectra Assimilation Atmospheric models Biological assimilation data assimilation Global navigation satellite system Global weather Land cover Lower troposphere Meteorological satellites Near infrared radiation Numerical prediction Numerical weather forecasting numerical weather prediction Prediction models Radiosondes satellite observation specific humidity total column water vapour Troposphere Water vapor Water vapour Weather forecasting |
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Title | Assimilation of OLCI total column water vapour in the Met Office global numerical weather prediction system |
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