2011: Forecast Impact of Targeted Observations: Sensitivity to Observation Error and Proximity to Steep Orography
ABSTRACT For a targeted observations case, the dependence of the size of the forecast impact on the targeted dropsonde observation error in the data assimilation is assessed. The targeted observations were made in the lee of Greenland; the dependence of the impact on the proximity of the observation...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.1050.8166 2023-05-15T16:25:53+02:00 2011: Forecast Impact of Targeted Observations: Sensitivity to Observation Error and Proximity to Steep Orography E A Irvine S L Gray J Methven I A Renfrew The Pennsylvania State University CiteSeerX Archives application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1050.8166 en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1050.8166 Metadata may be used without restrictions as long as the oai identifier remains attached to it. https://archive.uea.ac.uk/%7Ee046/reprints/irvine_etal_covariances_orography_MWR_2011.pdf text ftciteseerx 2020-04-05T00:28:55Z ABSTRACT For a targeted observations case, the dependence of the size of the forecast impact on the targeted dropsonde observation error in the data assimilation is assessed. The targeted observations were made in the lee of Greenland; the dependence of the impact on the proximity of the observations to the Greenland coast is also investigated. Experiments were conducted using the Met Office Unified Model (MetUM), over a limited-area domain at 24-km grid spacing, with a four-dimensional variational data assimilation (4D-Var) scheme. Reducing the operational dropsonde observation errors by one-half increases the maximum forecast improvement from 5% to 7%-10%, measured in terms of total energy. However, the largest impact is seen by replacing two dropsondes on the Greenland coast with two farther from the steep orography; this increases the maximum forecast improvement from 5% to 18% for an 18-h forecast (using operational observation errors). Forecast degradation caused by two dropsonde observations on the Greenland coast is shown to arise from spreading of data by the background errors up the steep slope of Greenland. Removing boundary layer data from these dropsondes reduces the forecast degradation, but it is only a partial solution to this problem. Although only from one case study, these results suggest that observations positioned within a correlation length scale of steep orography may degrade the forecast through the anomalous upslope spreading of analysis increments along terrain-following model levels. Text Greenland Unknown Greenland |
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ABSTRACT For a targeted observations case, the dependence of the size of the forecast impact on the targeted dropsonde observation error in the data assimilation is assessed. The targeted observations were made in the lee of Greenland; the dependence of the impact on the proximity of the observations to the Greenland coast is also investigated. Experiments were conducted using the Met Office Unified Model (MetUM), over a limited-area domain at 24-km grid spacing, with a four-dimensional variational data assimilation (4D-Var) scheme. Reducing the operational dropsonde observation errors by one-half increases the maximum forecast improvement from 5% to 7%-10%, measured in terms of total energy. However, the largest impact is seen by replacing two dropsondes on the Greenland coast with two farther from the steep orography; this increases the maximum forecast improvement from 5% to 18% for an 18-h forecast (using operational observation errors). Forecast degradation caused by two dropsonde observations on the Greenland coast is shown to arise from spreading of data by the background errors up the steep slope of Greenland. Removing boundary layer data from these dropsondes reduces the forecast degradation, but it is only a partial solution to this problem. Although only from one case study, these results suggest that observations positioned within a correlation length scale of steep orography may degrade the forecast through the anomalous upslope spreading of analysis increments along terrain-following model levels. |
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The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
E A Irvine S L Gray J Methven I A Renfrew |
spellingShingle |
E A Irvine S L Gray J Methven I A Renfrew 2011: Forecast Impact of Targeted Observations: Sensitivity to Observation Error and Proximity to Steep Orography |
author_facet |
E A Irvine S L Gray J Methven I A Renfrew |
author_sort |
E A Irvine |
title |
2011: Forecast Impact of Targeted Observations: Sensitivity to Observation Error and Proximity to Steep Orography |
title_short |
2011: Forecast Impact of Targeted Observations: Sensitivity to Observation Error and Proximity to Steep Orography |
title_full |
2011: Forecast Impact of Targeted Observations: Sensitivity to Observation Error and Proximity to Steep Orography |
title_fullStr |
2011: Forecast Impact of Targeted Observations: Sensitivity to Observation Error and Proximity to Steep Orography |
title_full_unstemmed |
2011: Forecast Impact of Targeted Observations: Sensitivity to Observation Error and Proximity to Steep Orography |
title_sort |
2011: forecast impact of targeted observations: sensitivity to observation error and proximity to steep orography |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1050.8166 |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland |
genre_facet |
Greenland |
op_source |
https://archive.uea.ac.uk/%7Ee046/reprints/irvine_etal_covariances_orography_MWR_2011.pdf |
op_relation |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1050.8166 |
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Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
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1766014724691984384 |