AbstractsBiology & Animal Science

Contributions of Dense Pressure Observations to Mesoscale Analyses and Forecasts

by Luke Edward Madaus

Institution: University of Washington
Year: 2013
Keywords: air pressure; data assimilation; forecast; mesoscale; weather modeling; Meteorology
Record ID: 2017477
Full text PDF: http://hdl.handle.net/1773/23477


In an effort to improve the analysis and subsequent short-term forecast of mesoscale phenomena, the assimilation of dense surface pressure observations is examined using an ensemble Kalman filter. Over the Pacific Northwest, an order of magnitude more regularly-reporting pressure observations than the standard METAR network observations were obtained. A bias correction procedure was developed to improve the usability of these observations. This procedure is shown to be effective at reducing errors in the analysis and subsequent forecasts after assimilating bias-corrected observations. Comparisons of assimilating different densities of pressure observations show that using additional pressure observations beyond the METAR network is able to reduce the domain-averaged surface pressure analysis errors by a statistically significant amount. The adjustments made by the additional pressure observations are localized to known mesoscale phenomena, and persist for several hours into subsequent forecasts from the new analyses. These adjusted analyses after assimilating dense pressure observations are shown to produce better forecasts of the timing of frontal passages and a localized convective band. Three-hour ensemble cycling experiments over a month-long period show that assimilating more dense pressure observations reduced domain-averaged three-hour forecast errors in surface pressure, 2m temperature, 10m V-wind component, and upper-level wind and temperature fields by statistically significant amounts. Furthermore, the assimilation of three-hour pressure tendency observations is also seen to yield three-hour forecast errors of surface fields that are competitive with errors when assimilating dense pressure observations, suggesting that pressure tendency can be a viable alternative to assimilating raw pressure without the need for bias correction.