Forecast Impact of Targeted Observations: Sensitivity to Observation Error and Proximity to Steep Orography

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...

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Bibliographic Details
Published in:Monthly weather review 2011, Vol.139 (1), p.69-78
Main Authors: IRVINE, E. A, GRAY, S. L, METHVEN, J, RENFREW, I. A
Format: Article
Language:English
Subjects:
Atmospheric sciences
Boundary layers
Coastal environments
Data assimilation
Data collection
Degradation
Dropsonde
Dropsonde observations
Dropsondes
Earth, ocean, space
Errors
Exact sciences and technology
External geophysics
Forecast improvement
Mathematical models
Meteorology
Orography
Spreading
Weather forecasting
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Summary: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.
ISSN:0027-0644
1520-0493
DOI:10.1175/2010MWR3459.1