Sensitivity of short-range weather forecasts to local potential vorticity modifications

• Published in: Monthly weather review Vol. 127; no. 5; pp. 922 - 939
• Main Authors: DEMIRTAS, M, THORPE, A. J
• Format: Journal Article
• Language: English
• Published: Boston, MA American Meteorological Society 01.05.1999
• Subjects: Earth, ocean, space; Exact sciences and technology; External geophysics; Meteorology; Weather analysis and prediction; Weather forecasting; Atlantic Ocean; North Atlantic; Short term; Water; Vapor; Image analysis; Weather forecast; Forecast skill; Potential vorticity; Satellite observation; Numerical forecast; Cyclone depression
• ISSN: 0027-0644; 1520-0493
• DOI: 10.1175/1520-0493(1999)127<0922:SOSRWF>2.0.CO;2

A new method is described to interpret satellite water vapor (WV) imagery in dynamical terms using potential vorticity (PV) concepts. The method involves the identification of mismatches between the WV imagery and a numerical weather prediction model description of the upper-level PV distribution at the analysis time. These mismatches are usually associated with horizontal positioning errors in the tropopause location in the oceanic storm-track region in midlatitudes. The PV distribution is locally modified to minimize this mismatch, and PV inversion is carried out to provide dynamically consistent additional initial data with which to reinitialize the numerical forecast. One of the advantages of using this method is that it is possible to generate wind and temperature data suitable for inclusion as initial data for numerical weather forecasts. By using PV additional data can be inferred that cannot otherwise be simply derived from the WV data. In this way dynamical concepts add considerable value to the WV imagery, which by themselves would probably not have as significant a forecast impact. Several examples of the use of this method are given here including cases of otherwise poorly forecast North Atlantic cyclones. In cases where the analysis errors occur at upper levels of the troposphere, the method leads to a significant improvement in the short-range forecast skill. In general, it is useful in highlighting where forecast problems are arising.