Since the atmosphere is a chaotic system, small errors in the initial condition of any numerical weather prediction (NWP) model amplify as the forecast evolves. To estimate and possibly reduce the uncertainty of NWP associated with initial-condition uncertainty (ICU), ensemble forecasting has been proposed which is a method of, differently from the traditional deterministic forecasting, running several model forecasts starting from slightly different initial states. In this dissertation, the impact of ICU and short-range ensemble forecasting (SREF) on quantitative precipitation forecasts (QPFs), as well as on sea-level cyclone position and central pressure, is examined for a case of explosive cyclogenesis that occurred over the contiguous United States. A limited-area model (the PSU/NCAR MM4) is run at 80-km horizontal resolution and 15 layers to produce a 25-member, 36-h forecast ensemble. Lateral boundary conditions for the MM4 model are provided by ensemble forecasts from a global spectral model (the NCAR CCM1). The initial perturbations of the ensemble members possess a magnitude and spatial decomposition which closely match estimates of global analysis error, but they were not dynamically-conditioned. Results for 80-km ensemble forecast are compared to forecasts from the then operational Nested Grid Model (NGM), a single 40-km MM4 forecast, and a second 25-member MM4 ensemble based on a different cumulus parameterization and slightly different initial conditions. Acute sensitivity to ICU marks ensemble QPF and the forecasts of cyclone position and central pressure. Ensemble averaging always reduces the rms error for QPF. Nearly 90% of the improvement is obtainable using ensemble sizes as small as 8-10. However, ensemble averaging can adversely affect the forecasts related to precipitation areal coverage because of its smoothing nature. Probabilistic forecasts for five mutually exclusive, completely exhaustive categories are found to be skillful relative to a climatological forecast. Ensemble sizes of --, 10 can account for 90% of improvement in probability density function. Our results indicate that SREF techniques can now provide useful QPF guidance and increase the accuracy of precipitation, cyclone position, and cyclone's central pressure forecasts. With current analysis/forecast systems, the benefit from simple ensemble averaging is comparable to or exceed that obtainable from improvement in the analysis/forecast system.

published_or_final_version / Geography and Geology / Master / Master of Philosophy

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (p. 173-185).

Thesis (M.S. in Meteorology)--Naval Postgraduate School, June 2009. / Thesis Advisor(s): Murphree, Tom ; Meyer, David W. "June 2009." Description based on title screen as viewed on 13 July 2009. Author(s) subject terms: Tropical cyclones, tropical cyclogenesis, North Atlantic, intraseasonal forecasting, smart climatology, tropical genesis parameters, large scale environmental factors, NCEP Climate Forecast System. Includes bibliographical references (p. 67-70). Also available in print.

Thesis (M.S.)--University of Wisconsin--Madison, 1965. / eContent provider-neutral record in process. Description based on print version record. Bibliography: l. 20.

Thesis (M.S. in Meteorology)--Naval Postgraduate School, March 1982. / Bibliography: l. 146-148. Also available online.

Thesis (M.S. in Meteorology)--Naval Postgraduate School, March 2005. / Thesis Advisor(s): C.-P. Chang. Includes bibliographical references (p. 49-51). Also available online.

Thesis (M.S.)--Florida State University, 2005. / Advisor: Dr. Xiaolei Zou, Florida State University, College of Arts and Sciences, Dept. of Meteorology. Title and description from dissertation home page (viewed Sept. 29, 2005). Document formatted into pages; contains xvi, 119 pages. Includes bibliographical references.

The components of the northern hemisphere cryosphere and their relationship to Atlantic tropical cyclone activity are investigated in this study. Multiple ordinary least-squares regression with a stepwise selection procedure is used to develop a new statistical forecasting scheme for 13 seasonal tropical cyclone parameters at four lead times for the period 1980-2010. Sea ice area and sea ice extent in 10 geographic regions, snow cover extent in three geographic regions and five indices reflecting major modes of climate variability were analyzed as possible predictors. Three model groups, based on predictors, were constructed and evaluated: 1) only climate mode predictors, 2) only cryosphere predictors, and 3) both cryosphere and climate mode predictors. Models using only climate mode predictors showed poor predictability of the tropical cyclone parameters across all four lead times while the models using only cryosphere predictors and those using both sets of predictors showed improved predictability. Baffin Bay and Hudson Bay sea ice area were found to be the most significant predictors, exhibiting an inverse relationship with overall tropical cyclone activity. The developed models were also compared to current operational statistical models of tropical cyclone activity. While the operational models were generally more skillful, June hindcasts of major hurricanes outperformed the operational models by as much as 20%.

Extratropical cyclones are responsible for a substantial portion of midlatitude climate variability and contribute to widespread impacts. The characteristics of extratropical cyclones, such as their spatial distribution and intensity, are thought to be dependent on the large scale circulation. The relationship between cyclone characteristics and modes of large-scale climate variability has been investigated in previous studies, but interactions between modes of climate variability have largely been ignored. Since extratropical cyclone characteristics may be related to interactions between phases, quantifying these relationships is an important step in improving the climatology of extratropical cyclones. The goal of this study is to quantify relationships between modes of climate variability and characteristics of strong cyclones in the contiguous United States. Using historical sea-level pressure data, cyclone intensity, frequency, and spatial distribution are investigated using a cyclone definition that combines the requirement for low pressure (1000 hPa or lower) and positive (cyclonic) vorticity. The large scale modes of climate variability considered include El Niño Southern Oscillation (ENSO), the Pacific North American (PNA) mode, and the Arctic Oscillation (AO). The analysis is divided into three phases focusing on (1) establishing a background cyclone climatology within the study area, (2) quantifying differences in cyclone characteristics between the positive and negative phases of the individual modes of climate variability, and (3) examining the interactions between the modes of climate variability as they relate to extratropical cyclone characteristics. The results are expected to provide an improved baseline for evaluation of coupled climate models and also have the potential to improve seasonal climate predictability.