Jamie Dyer

Role Of Energy Budget Components On Snow Ablation From A Mid-Latitude Prairie Snowpack

This work considers what aspects of the surface energy budget were most important for the development of rapid snow melt during the spring of 1997 in the Red River Valley of North Dakota and Minnesota. The rapid snow melt of an exceptionally large snowpack that season led to catastrophic flooding. We use this event as a case study in order to better understand what meteorological forcings are most important with regard to rapid melting of a large winter snowpack in a mid-latitude prairie region. The SNTHERM model was used as a means to estimate snow melt flux and energy budget components. It was found that the net radiation balance was the dominant factor in snow melt throughout the winter months until late March, but ablation rates were small due to the extremely low temperatures. As warm air masses began to traverse the region in late March, the snow melt flux began to increase significantly due to the influences of sensible and latent heat fluxes on the surface of the snowpack. After the passing of a strong late-season blizzard, temperature and humidity increased rapidly with a corresponding increase in wind speed. The sensible heat flux then dominated as temperatures rose well above freezing, melting off the snowpack extremely quickly.

Evaluation of Surface and Radar-Estimated Precipitation Data Sources Over the Lower Mississippi River Alluvial Plain

The Mississippi River floodplain in northwestern Mississippi and eastern Arkansas, often referred to as the Mississippi Delta, is extremely important for regional economic stability and growth due to the widespread agriculture in the area. Precipitation is often the limiting factor in crop production, and thus knowledge of precipitation variability is essential. The region is unique in currently having three sources of precipitation observations: (1) multi-sensor precipitation estimates from the National Weather Service (NWS) NEXRAD network; (2) surface observations from NWS recording stations; and (3) surface observations from U.S. Department of Agriculture (USDA) Soil Climate Analysis Network (SCAN) recording stations. For meteorological and climatological precipitation research, quantitatively defining the biases associated with available precipitation data sources is critical in selecting which source to use for a given application. Results of this research indicate that the multi-sensor data are in best agreement with surface observations during the cool season (October-March) when stratiform precipitation dominates, whereas only moderate correlations exist during the warm season. Additionally, overall bias values among the data sources decrease after 2002, at which point the remotely sensed and surface-based estimates show improved agreement.

Basin-Scale Precipitation Analysis for Southeast U. S. Watersheds Using High-Resolution Radar Precipitation Estimates

The southeastern United States maintains a sensitivity to water resources for agriculture, hydroelectric power production, and basic environmental sustainability; therefore, it is important to understand the patterns of regional precipitation variability. Since high resolution precipitation data are necessary to estimate the local precipitation distribution over each basin, radar-derived precipitation estimates from 1996-2006 will be used for analysis. To quantify regional patterns of precipitation in the southeast United States, analysis will be conducted over three individual watersheds in the southeast: the Yazoo River watershed, the Savannah River watershed, and the Everglades/Lake Okeechobee drainage in southern Florida. Results of the study show a clear separation between maximum warm-season precipitation depth and frequency over southern Florida. In the Savannah River, precipitation maxima in the mountainous headwaters and coastal lowlands are revealed, along with a third peak in the southern Piedmont due to a localized affect resulting from spatial changes in soil characteristics. The Yazoo River watershed shows a similar effect during the summer months, leading to heightened precipitation in the eastern portion of the basin. Additionally, this study shows that radar-derived multisensor precipitation estimates are a viable data source for analyzing spatial and temporal precipitation patterns for the purposes of water resources research.

Analysis of a Warm-Season Surface-Influenced Mesoscale Convective Boundary in Northwest Mississippi

AbstractThe lower Mississippi River alluvial valley in southeastern Arkansas, northeastern Louisiana, and northwestern Mississippi is characterized by widespread agriculture with few urban areas. Land use is predominantly cultivated cropland with minimal topographic variation; the eastern edge of the alluvial valley is defined by a rapid, although small, change in elevation into a heavily forested landscape, however. This change in land use/land cover has been shown to potentially enhance precipitation through generation of a weak mesoscale convective boundary. This project defines the influence of the land surface on associated precipitation processes by simulating a convective rainfall event that was influenced by regional surface features. Analysis was conducted using a high-resolution simulated dataset generated by the Weather Research and Forecasting Model (WRF). Results show that the strongest uplift coincides with an abrupt low-level thermal boundary, developed primarily by a rapid change from sens...

Assessment of Spatial Rainfall Variability over the Lower Mississippi River Alluvial Valley

AbstractA large portion of the lower Mississippi River alluvial valley (LMRAV) relies on irrigation from the regional alluvial aquifer for crop sustainability, which is expensive both in terms of water resources and farmer expenditures because of the large volume of water necessary to maintain crop production. As a result, knowledge of the seasonal frequency and distribution of precipitation over the LMRAV is critical for water resources management, the development of irrigation strategies, and economic planning. This project addresses the need for a detailed assessment of regional precipitation patterns through the use of rotated principal component analysis (RPCA) of high-resolution gridded radar-derived rainfall data, which provides quantification of the spatial and temporal characteristics of rainfall over the LMRAV from 1996 to 2011. Results of the project show that precipitation depths over the LMRAV are generally lower and more variable than adjacent eastern areas throughout the year, although ther...

Warm-season Thermodynamically-driven Rainfall Prediction with Support Vector Machines☆

Abstract Dynamic numerical weather prediction models have been designed to deal with large-scale, highly predictable midlatitude atmospheric patterns. However, the capability of these models to simulate thermodynamically driven warm-season rainfall events, such as afternoon airmass thunderstorm formation in subtropical summers, is highly limited. Current methods of addressing this issue have included ensemble numerical weather prediction simulations, where an ensemble mean of multiple simulations with varied model physics is used as an improved prediction over any individual ensemble member. These approaches still yield only modest skill primarily due to inherent biases in each ensemble member. As such, the current research will utilize machine learning to combine logically ensemble members into a single prediction of warm-season rainfall. In particular, a support vector machine classification scheme that employs members of a 30 member ensemble as predictors and observed rainfall patterns as a predictand will be formulated on multiple warm-season rainfall days in an effort to develop an improved prognosis of warm-season rainfall that can be implemented in operational meteorology forecasts. The primary goal of the work is to obtain a statistically significant improvement of predictive skill over currently utilized ensemble member approaches.

Defining the Influence of Horizontal Grid Spacing on Ensemble Uncertainty within a Regional Modeling Framework

AbstractNumerical weather prediction (NWP) models are limited with respect to initial and boundary condition data and possess an incomplete description of underlying physical processes. To account for this, modelers have adopted the method of ensemble prediction to quantify the uncertainty within a model framework; however, the generation of ensemble members requires considerably more computational time and/or resources than a single deterministic simulation, especially at convection-allowing horizontal grid spacings. One approach to solving this issue is the development of both a large and small horizontal grid spacing model framework over the same domain for ensemble and deterministic simulations, respectively. This approach assumes that model grid spacing has no influence on model uncertainty; therefore, the objective of this paper is to quantify the influence of horizontal grid spacing on the statistical spread of NWP model ensembles over a regional domain. A series of 24-h simulations using the Weath...

User Experience of Hurricane Visualization in an Immersive 3D Environment

Numerical models such as the Mesoscale Model 5 (MM5) or the Weather Research and Forecasting Model (WRF) are used by meteorologists in the prediction and the study of hurricanes. The outputs from such models vary greatly depending on the model, the initialization conditions, the simulation resolution and the computational resources available. The overwhelming amount of data that is generated can become very difficult to comprehend using traditional 2D visualization techniques. We studied the presentation of such data as well as methods to compare multiple model run outputs using 3D visualization techniques in an immersive virtual environment. We also relate the experiences and opinions of two meteorologists using our system. The datasets used in our study are outputs from two separate MM5 simulation runs of Hurricane Lili (2002) and a WRF simulation run of Hurricane Isabel (2003).

Investigating the link between \(\hbox {PM}_{2.5}\) and atmospheric profile variables via penalized functional quantile regression

Fine particulate matter (