A weather radar signal simulator that produces an output consisting of a vector of I and Q values representing the radar return permits investigation of the performance of different estimators for the weather signal parameters and their sensitivity when varying radar parameters and precipitation models. Although several empirical statistical models are available to describe precipitation behavior, the creation of a physical model enables adaptation to actual data (e.g. rain rate, wind shears) thereby making it possible to apply and examine different scanning schemes, especially rapid scanning schemes. A physical model allows gradual improvements to realism to study the effects on the radar return for different phenomena. A Weather Radar Signal Simulator has been developed in MATLAB. Several different functionalities have been implemented allowing for stepped frequency, multiple PRFs, pulse compression using a chirp, and variation of both weather and radar input parameters. Post processing capabilities include autocorrelation and FFT (for single PRF only); estimation of weather parameters such as reflectivity factor, Z; average doppler, radial velocity, and velocity spread; pedagogical plots including a Phasor plot of phase change over time and a velocity histogram, instantaneous observed reflectivity and power for each pulse over time.

TRACT (maximum 200 words) The Northern Oscillation Index (NOI), an atmospheric climate index relating climate variations in the tropical Pacific and Northeast Pacific was used to selectively average output from the Parallel Ocean Climate Model (POCM 4C) for 1979-1998. Composites, or smart climatologies, were made representing El Nino (EN) and La Nina (LN) conditions, as well as a long term mean (LTM) average or traditional climatology, for November to March. Conditions in the California Current System (CCS) in the smart climatologies were consistent with large scale features noted in previously published studies of EN and LN. Overall, the patterns of anomalies (POCM 4C Smart Climatology minus POCM 4C Traditional Climatology) in salinity, temperature, and currents were opposite in sign and magnitude between the EN and LN composites. This was expected for opposite phases of the same climate variation, and many of the model's EN/LN differences were found to be statistically significant. Therefore, POCM 4C smart climatologies provide better estimates of ocean state and circulation patterns than traditional climatology. Such smart climatologies offer improved environmental information to Naval operational and strategic planners. They are also useful for studying climate variations, and in improving boundary and initial conditions for ocean and atmosphere models.

Extended Range Munitions (ERMs) are gun-launched rocket-boosted munitions having an effective range ocer 27 km. In accordance with Sea power 21 and the Marine Corps's requirements for sea-based fire support, three ERMs are being developed. The purpose of this work is to increase the range and lethality of these munitions by applying environmental effects when computing the projectiles' trajectory. A broad review of artillery and munitions literature reveals that historically 66% of ballistic error can be attributed to meteorological factors. The most important factors are wind (speed and direction), temperature, and pressure. It has also been shown that global atmospheric numerical weather presictions (NWP) data typically outperforms the traditional radiosonde data and is suitable for use in ballistic corrections. Forecasted NWP products provided by the Fleet Numerical Meteorology and Oceanographic Center (FNMOC) are integrated using the Joint Meteorology and Oceanographic (METOC) Broker Language (JMBL) into a Five Degree of Freedom (5DOF) aerodynamic model within the Autonomous Unmanned Vehicle (AUV) Workbench producing a ballistic correction (BALCOR) for the munition. This new capability can significantly enhance naval gunfire effectiveness since the BALCOR increase the munitions' range and the ability apply kinetic energy onto the target rather than using it to maneuver to the target.

Based on recent advances, skilled objectively-determined probabilistic forecasts of some weather phenomena may be provided to operational decision-makers. Objective probabilistic forecasts that are generated from ensemble prediction systems (EPS) are attractive as a forecast methodology for Department of Defense (DoD) applications for three reasons: first, atmospheric scientists understand that the atmosphere has a limit of predictability, which means that traditional deterministic forecasts lack important uncertainty information; second, it has been demonstrated that quantifying uncertainty may improve a weather forecast user's ability to make a better decision based on their own utility function, which translates to better operational risk management (ORM) for the DoD; and finally, progress points towards a future with machine-to-machine warfare. These assertions are examined by applying probabilistic forecasts from an ensemble-based aircraft-scale turbulence forecast system to several cases and scenarios. Results clearly demonstrate the advantage of using ensemble-based probabilistic forecasts versus deterministic forecasts. Additionally, application of ensemble-based probabilistic forecast information to DoD operations is shown to be possible through its ORM programs. Specifically, air refueling scenarios are identified that demonstrate the integration of probabilistic turbulence forecast guidance into the U.S. Air Force ORM process.

Cirrus cloud forecasting is of particular importance to various Department of Defense programs. This thesis takes a case study approach to study Air Force Weather Agency Mesoscale Model 5 (AFWA MM5) skill in forecasting cirrus clouds, which are not represented explicitly by the model (ice water mixing ratio is used as a surrogate.) Two cases are selected for study. For each case, an initial forecast time of interest is determined which serves as the beginning point for the case study. GOES data and 3-hourly MM5 data are then obtained at 3- hourly intervals to coincide with model forecast time steps between the initial time through the 30-hour forecast. A standard analysis is performed on all data to determine general atmospheric structure for each case at each 3- hourly point. Following this, the model's relative humidity with respect to ice, explicit ice water content, vertical velocity, and other fields are considered to determine if the model possesses the proper dynamical factors for cirrus formation. Finally, model coverage of ice cloud is compared to the ABL cloud mask results to determine how well the model s ice cloud forecasts verify against each 3-hourly observed ice water field taken from the GOES data. Results indicate that the MM5 underforecasts cirrus coverage, and that the 90% relative humidity field with respect to ice may be a better approximation of observed cirrus coverage than the ice water field.

model-derived wind gusts determined by each method are then compared to wind reports from the Storm Prediction Centerâ s severe storm reports archive and reports from observing stations. Model-derived wind gusts are then compared to the observed wind gusts for varying times of day and observed wind gust ranges. Wind gust frequency plots are examined for each wind method to determine accuracy and to characterize any patterns. The T1 method was the most accurate overall for this study, but was shown to be less sensitive to varying atmospheric conditions. The T2 method was the least accurate of the three methods during all situations. The WINDEX method performed well in most situations and was nearly as accurate as the T1 method, while WINDEX also proved to be the most sensitive of the three to varying mesoscale conditions.

This study investigates the impact of the synoptic-scale flow on the propagation and intensity of the sea breeze front at Eglin Air Force Base. The 925 mb wind direction and speed from the 12 UTC Tallahassee sounding was used to categorize 509 summer days as having an offshore, onshore, or coast parallel synoptic-scale flow regimes. Days with similar synoptic-scale flows were then composited together to create hourly surface analyses for each regime. Sea breeze frontogenesis, location and intensity were analyzed on hourly plots of temperature, winds and frontogenesis. Results indicate that the most intense sea breeze fronts formed under 3-5 1 ms- offshore, 7-9 1 ms- coast parallel easterly, and 3-5 1 ms- coast parallel westerly synoptic-scale flow while the weakest fronts formed under 0-3 1 ms- onshore and coast parallel westerly flow. The inland penetration of the sea breeze front was restricted under offshore flow but propagated through the Eglin Range Complex under onshore flow. The intensity of the sea breeze front was found to be a balance between convergence (frontogenetic) and turbulent mixing (frontolytic). Under onshore flow the sea breeze front formed late in the afternoon when convergence at the front was maximized and turbulent mixing decreased. Under offshore flow, the strongest sea breeze fronts formed early in the afternoon due to strong convergence between offshore and onshore winds and weak turbulent mixing.

The Air Force Weather Agency (AFWA) version of the Advanced Research Weather Research and Forecasting (ARW) model with a moving 4-km nested grid is examined for 10 track and intensity predictions of six western North Pacific tropical cyclones during 2005. In three of the 10 integrations, the ARW vortex tracker algorithm based on the 500-mb height minimum failed to appropriately move the nest and thus lost track of the storm vortex. For the other seven cases, the ARW track forecasts are more skillful than the AFWA MM5 forecasts and (except at 12 h) the CLIPER-type forecasts. The ARW intensity forecasts were less skillful than the MM5 and CLIPER-type forecasts at all forecast intervals, and were severely degraded by a large negative bias at the initial time. The deficiency in these intensity forecasts is shown to be related to model spin-up (lasting 12-54 h) problems caused by the lack of a bogus vortex and a cold start initialization from the interpolation of the NCEP Global Forecast System (GFS) analysis to the 12- km and 4-km grids. Thus, a more appropriate initial vortex representation will be required to improve intensity forecasts.

Although gas turbine engines are designed to use dry air as the working fluid, the great demand over the last decades for air travel at several altitudes and speeds has increased aircraft’s exposure to inclement weather conditions. Although, they are required to perform safely under the effect of various meteorological phenomena, in which air entering the engine contains water, several incidents have been reported to the aviation authorities about power loss during flight at inclement weather. It was understood that the rain ingestion into a gas turbine engine influences the performance of the engine and particular the compressor and the combustor. The effects of water ingestion on gas turbine engines are aerodynamic, thermodynamic and mechanical. These effects occur simultaneously and affect each other. Considering the above effects and the fact that they are timedependent, there are few gas turbine performance simulation tools, which take into account the water ingestion phenomenon. This study is a new research of investigating theoretically the water ingestion effects on a gas turbine performance. It focuses on the aerodynamic and mechanical effects of the phenomenon on the compressor and the combustor. The application of Computational Fluid Dynamics (CFD) is the basic methodology to examine the details of the flow in an axial compressor and how it is affected by the presence of water. The calculations of water film thickness, which is formed on the rotor blade, its motion (direction and speed) and the extra torque demand, are provided by a code created by the author using FORTRAN programming language. Considering the change in blade’s profile and the wavy characteristics of the liquid film, the compressor’s performance deterioration is calculated. The compressor and combustor’s deterioration data are imported to a gas turbine simulation code, which is upgraded to calculate overall engine’s performance deterioration. The results show a considerable alteration in engine’s performance parameters and arrive at the same conclusions with the relevant experimental observations.

Master of Science / Department of Agronomy / Xiaomao Lin / The high-quality climate data and high-resolution soil property data in Kansas and adjacent states were used to develop drought datasets for the monthly Palmer Drought Severity Index (PDSI), Standardized Precipitation Index (SPI), and the Standardized Precipitation-Evapotranspiration Index (SPEI) over 1900 to 2014. The statistical analysis of these multiple drought indices were conducted to assess drought occurrence, duration, severity, intensity, and return period. Results indicated that the PDSI exhibited a higher frequency for every category of drought in central and western Kansas than the SPEI by up to 10%. Severe and extreme drought frequency was the highest in southwest Kansas around the Arkansas River lowlands and lowest in the southeast. The mean total drought frequency for eastern, central, and western Kansas was 36%, 39%, and 44%, respectively. The regional mean correlations between the SPI and SPEI were greater than or equal to 0.95 for all regions, but due to statistically significant increases in potential evaporation in western Kansas, the PDSI and SPEI are recommended over the SPI for meteorological and hydrological drought analysis. Drought variability of the last 115 years was analyzed through the Empirical Orthogonal Functions (EOFs) techniques and their Varimax rotations from 1900 to 2014 in Kansas. Large-scale synoptic patterns primarily dominated the Kansas spatial drought structures, especially during long-duration events. The EOFs indicated that the first principal components of drought explained approximately 70% of the drought variability across the state and demonstrated a statistically significant wetting trend over the last 115 years, oscillating at a period of about 14 years for all drought indices. The 99° W meridian line acted as the dominant transitional line demarcating the areas of Kansas’ climate and vegetation relationship as spatial drought presented. The Multivariate El Nino Index (MEI) signal , which modulates global and regional climate variabilities, provided a low-frequency indicator to couple with Kansas drought’s leading modes by varying leads of 3 to 7 months depending on the use of drought index and time steps selected. Large-scale predictors of surface temperature and precipitation are evaluated from the monthly forecasts in Climate Forecast System version 2.0 (CFSv2) from North Dakota down through central Texas (32.6 - 47.7°N and 92.8 - 104.1°W). By using singular value decomposition (SVD), the CFSv2 monthly forecasts of precipitation and 2-m temperature were statistically downscaled using ensemble mean predictions of reforecasts from 1982-2010. Precipitation skill was considerably less than temperature, and the highest skill occurred during the wintertime for 1-month lead time. Only the central and northern plains had statistically significant correlations between observed and modeled precipitation for 1-month lead time. Beyond a 1-month lead time, prediction skill was regionally and seasonally dependent. For the 3-month lead time, only the central plains demonstrated statistically significant mean anomaly correlation. After three-month lead times, the ensemble means of forecasts have shown limited reliable predictions which could make the forecast skill too low to be useful in practice for precipitation. However, temperature forecasts at lead times greater than five months showed some skill in predicting wintertime temperatures.