Forecasts of the likelihood of occurrence of various amounts of precipitation are very important, since excessive precipitation amounts over relatively short time periods can have adverse effects on public safety and economic efficiency, As a result, forecasters at the National Weather Service Forecast Office in San Antonio, Texas were asked to formulate subjective probabilistic quantitative precipitation forecasts on an experimental basis beginning in February 1981. This study describes methods of evaluating probability forecasts of this ordinal variable and presents some results of the first year of the experiment. Scalar and vector evaluation procedures are described. In the case of scalar evaluation, the inclusion of a no-skill line and a no-correlation line on reliability diagrams is helpful in representing the skill, reliability, and resolution qeometrically in two-state situations. Geometrical interpretations of attributes of forecasts can also be accomplished in three-state situations based on vector evaluation procedures. A skill score for subsample forecasts is shown to be useful in identifying systematic errors made by forecasters or forecast systems. A beta model is developed to obtain a forecaster's predictive distributions (i.e., the distribution of use of probability values). The experimental results show that the skill of the subjective forecasts is generally higher than the skill of objective guidance forecasts for measurable precipitation (i.e., precipitation amounts exceeding a threshold of 0.01 inches), but that the opposite is true for threshold associated with larger precipitation amounts. This result is due primarily to the forecaster's tendency to over forecast for the events associated with higher precipitation thresholds. The tendency to over forecast is most pronounced in the nighttime forecasts and in the forecasts for drier stations. The MCS objective guidance forecasts, on the other hand, are quite reliable for both periods and all stations. The vector evaluation approach indicates that the degree of overforecasting is quite high for bimodal forecasts and that the skill contribution from bimodal forecasts is negative in many cases. / Graduation date: 1983

Thesis (M.S.)--University of Wisconsin--Madison, 1981. / Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 81-82).

The design, management and operation (as well as the associated costs) of major water resource projects are directly related to the assessment of the anticipated volumes of runoff to be handled by the project. In remote or sparsely gauged regions it is often very difficult to determine these volumes due to a lack of data. The meteorological satellites, and in particular, the Geostationary Operational Environmental Satellites (GOES) can provide good areal coverage of the Earth and its weather systems potentially every half hour day and night. Since the first meteorological satellite images were transmitted, many attempts have been made to estimate rainfall using the images to identify specific cloud characteristics and correlating these with expected rainfall. However, these methods have been limited to convective rainfall in the tropics or near tropics. A method is presented here for estimating half-hourly rainfall which relates the vertical updraft velocities and hence, the moisture flux into the cloud, to the rate of vertical and horizontal growth of the cloud top as revealed in the GOES infrared images. The method performs well in estimating rainfall from widespread frontal systems common over British Columbia. A number of computational difficulties which arose during the research were resolved. One was to ascertain cloud top temperature contours for those GOES infrared images which were not enhanced. This involved the use of a video camera-special effects generator-video monitor system. The second was one of bookkeeping to "keep track of" the individual cloud cells. This was taken care of through the use of computer routines which directed the input and output of data, accounted for the growth and movement of the storm cells over the region and interpolated for rainfall at those locations which fell between adjacent precipitation contours. The method was used to estimate rainfall for a number of test storms occurring over British Columbia. The results were remarkably successful although there were some local inadequacies. An updating -procedure was developed in which the satellite estimated values of rainfall were improved by taking into consideration the information provided by concurrent rainfall observations. Furthermore, the parameters of the updating model (determined for gauged -locations) can be used to update rainfall estimates for ungauged locations. In the light of present raingauge installation and operating costs and the limitations of radar in mountainous areas, the satellite rainfall estimation procedure provides an economical operational supplement to existing conventional precipitation data collection. / Applied Science, Faculty of / Civil Engineering, Department of / Graduate

Thesis (M.S.)--University of Utah, 1996. Thesis from the University of Utah's Department of Meteorology explores the sensitivity of the pecipitation-predicting model known as the Utah Limited Area Model (LAM) to the way that the lateral and upper boundary conditions are applied. The approach is different from most past studies of LAM boundary specification because it is founded upon a medium-range simulation using real data. Many other studies of boundary conditions have used idealized cases or short-term (a few days or less) predictions. / Title from web page (viewed Oct. 30, 2003). "96-084." "August 1996." Includes bibliographical references p. [110]-112. Also available in print version.

Thesis (M.S.)--University of Delaware, 2007. / Principal faculty advisor: David R. Legates, Dept. of Geography. Includes bibliographical references.

Thesis (Ph. D.)--University of Oklahoma, 1993. / Includes bibliographical references (leaves 189-195).

Over the past decade, statistical procedures have been employed to downscale the outputs from global climate models (GCM) to assess the potential impacts of climate change and variability on the hydrological regime. These procedures are based on the empirical relationships between large-scale atmospheric predictor variables and local surface parameters such as precipitation and temperature. This research is motivated by the recognized lack of a comprehensive yet physically and statistically significant downscaling methodology for daily precipitation at a local site. The primary objectives are to move beyond the 'black box' approaches currently employed within the downscaling community, and develop improved statistical downscaling models that could outperform both raw GCM output and the current standard: the SDSM method. In addition, the downscaling methods could provide a more robust physical interpretation of the relationships between large-scale predictor climate variables and the daily precipitation characteristics at a local site. / The first component of this thesis consists of developing linear regression based downscaling models to predict both the occurrence and intensity of daily precipitation at a local site using stepwise, weighted least squares, and robust regression methods. The performance of these models was assessed using daily precipitation and NCEP re-analysis climate data available at Dorval Airport in Quebec for the 1961-1990 period. It was found that the proposed models could describe more accurately the statistical and physical properties of the local daily precipitation process as compared to the CGCM1 model. Further, the stepwise model outperforms the SDSM model for seven months of the year and produces markedly fewer outliers than the latter, particularly for the winter and spring months. These results highlight the necessity of downscaling precipitation for a local site because of the unreliability of the large-scale raw CGCM1 output, and demonstrate the comparative performance of the proposed stepwise model as compared with the SDSM model in reproducing both the statistical and physical properties of the observed daily rainfall series at Dorval. / In the second part of the thesis, a new downscaling methodology based on the principal component regression is developed to predict both the occurrence and amounts of the daily precipitation series at a local site. The principal component analysis created statistically and physically meaningful groupings of the NCEP predictor variables which explained 90% of the total variance. All models formulated outperformed the SDSM model in the description of the statistical properties of the precipitation series, as well as reproduced 4 out of 6 physical indices more accurately than the SDSM model, except for the summer season. Most importantly, this analysis yields a single, parismonious model; a non-redundant model, not stratified by month or season, with a single set of parameters that can predict both precipitation occurrence and intensity for any season of the year. / The third component of the research uses covariance structural modeling to ascertain the best predictors within the principal components that were developed previously. Best fit models with significant paths are generated for the winter and summer seasons via an iterative process. The direct and indirect effects of the variables left in the final models indicate that for either season, three main predictors exhibit direct effects on the daily precipitation amounts: the meridional velocity at the 850 HPa level, the vorticity at the 500 HPa level, and the specific humidity at the 500 HPa level. Each of these variables is heavily loaded onto the first three principal components respectively. Further, a key fact emerges: From season to season, the same seven significant large-scale NCEP predictors exhibit a similar model structure when the daily precipitation amounts at Dorval Airport were used as a dependent variable. This fact indicated that the covariance structural model was physically more consistent than the stepwise regression one since different model structures with different sets of significant variables could be identified when a stepwise procedure is employed.

Thesis (M.S. in Applied Science)--Naval Postgraduate School, Sept. 1992. / Thesis Advisors: Wash, Carlyle H. ; Pastore, Michael J. "September, 1992." Description based on title screen as viewed on April 16, 2009. Includes bibliographical references (p. 90-92). Also available in print.

Thesis (M.Sc.)--McGill University (Canada), 2007. / Includes bibliographical references.

Thesis (Ph. D.)--Florida State University, 2004. / Advisor: Dr. I.M. Navon, Florida State University, College of Arts and Sciences, Dept. of Meteorology. Title and description from dissertation home page (viewed Sept. 23, 2004). Includes bibliographical references.