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...

Full description

Bibliographic Details
Main Authors: E A Irvine, S L Gray, J Methven, I A Renfrew
Other Authors: The Pennsylvania State University CiteSeerX Archives
Format: Text
Language:English
Subjects:
Online Access:http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1050.8166
id ftciteseerx:oai:CiteSeerX.psu:10.1.1.1050.8166
record_format openpolar
spelling 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
institution Open Polar
collection Unknown
op_collection_id ftciteseerx
language English
description 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.
author2 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
op_rights Metadata may be used without restrictions as long as the oai identifier remains attached to it.
_version_ 1766014724691984384