<p>Wind is used as an energy source all over the world. To be able to use this effectively, there is a need for as good forecasts and forecast models as possible. One of these models is Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS®) that is used to calculate short time forecasts. This model is used here to calculate wind speeds at two different areas in Västra Götaland, Bengtsfors and Vänersborg. There are also wind measurements with SODAR stations for these areas. The first part of this work is to investigate the difference between two model resolutions, 36 and 12 km, for the model results compared with the measurements. The comparison is done by calculating some different statistical values. The results of these parameters show that the difference between the two resolutions is fairly small and that the lower resolution gives a slightly better result.</p><p>The second and major part of this work is to use two different regression models to adjust the result of the forecast models to the result of the measurements. These regression models will then be possible to use even when there are no measurements to compare with. The idea of these regression models is to find a way to describe the difference between the result of the forecast model and the SODAR measurements. This difference is then subtracted from the result of the forecast model so that you get an adjustment and more accurate result. The first regression model calculates the difference according to time of the day, the other model calculates the difference according to the wind speed.</p><p>Furthermore, the measurements used are taken from 75 meters height above the ground. These are then compared to some different results from the forecast model, for example different model heights and different resolutions, and also the model results adjusted with the regression models. The comparison is done by calculating the same statistic values as before, both with and without an adjustment with the regression models, and also to look at histograms that show the distribution of the difference. It is shown that with the regression adjustment, there is a clear improvement of the statistical values compared to the original results of the forecasts. For example the value of the absolute mean difference is reduced with approximately 0.4-0.7 m/s with an adjustment of the regression model. The histograms clearly show that a more even distribution occurs after the adjustment with the regression models. From having a major part of the differences at 1-2 m/s to now having the major part at around 0 m/s and furthermore there is also generally a lower difference between the measurements and the results from the forecast model.</p>

abstract: Wind measurements are fundamental inputs for the evaluation of potential energy yield and performance of wind farms. Three-dimensional scanning coherent Doppler lidar (CDL) may provide a new basis for wind farm site selection, design, and control. In this research, CDL measurements obtained from multiple wind energy developments are analyzed and a novel wind farm control approach has been modeled. The possibility of using lidar measurements to more fully characterize the wind field is discussed, specifically, terrain effects, spatial variation of winds, power density, and the effect of shear at different layers within the rotor swept area. Various vector retrieval methods have been applied to the lidar data, and results are presented on an elevated terrain-following surface at hub height. The vector retrieval estimates are compared with tower measurements, after interpolation to the appropriate level. CDL data is used to estimate the spatial power density at hub height. Since CDL can measure winds at different vertical levels, an approach for estimating wind power density over the wind turbine rotor-swept area is explored. Sample optimized layouts of wind farm using lidar data and global optimization algorithms, accounting for wake interaction effects, have been explored. An approach to evaluate spatial wind speed and direction estimates from a standard nested Coupled Ocean and Atmosphere Mesoscale Prediction System (COAMPS) model and CDL is presented. The magnitude of spatial difference between observations and simulation for wind energy assessment is researched. Diurnal effects and ramp events as estimated by CDL and COAMPS were inter-compared. Novel wind farm control based on incoming winds and direction input from CDL's is developed. Both yaw and pitch control using scanning CDL for efficient wind farm control is analyzed. The wind farm control optimizes power production and reduces loads on wind turbines for various lidar wind speed and direction inputs, accounting for wind farm wake losses and wind speed evolution. Several wind farm control configurations were developed, for enhanced integrability into the electrical grid. Finally, the value proposition of CDL for a wind farm development, based on uncertainty reduction and return of investment is analyzed. / Dissertation/Thesis / Ph.D. Mechanical Engineering 2013

Wind is used as an energy source all over the world. To be able to use this effectively, there is a need for as good forecasts and forecast models as possible. One of these models is Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS®) that is used to calculate short time forecasts. This model is used here to calculate wind speeds at two different areas in Västra Götaland, Bengtsfors and Vänersborg. There are also wind measurements with SODAR stations for these areas. The first part of this work is to investigate the difference between two model resolutions, 36 and 12 km, for the model results compared with the measurements. The comparison is done by calculating some different statistical values. The results of these parameters show that the difference between the two resolutions is fairly small and that the lower resolution gives a slightly better result. The second and major part of this work is to use two different regression models to adjust the result of the forecast models to the result of the measurements. These regression models will then be possible to use even when there are no measurements to compare with. The idea of these regression models is to find a way to describe the difference between the result of the forecast model and the SODAR measurements. This difference is then subtracted from the result of the forecast model so that you get an adjustment and more accurate result. The first regression model calculates the difference according to time of the day, the other model calculates the difference according to the wind speed. Furthermore, the measurements used are taken from 75 meters height above the ground. These are then compared to some different results from the forecast model, for example different model heights and different resolutions, and also the model results adjusted with the regression models. The comparison is done by calculating the same statistic values as before, both with and without an adjustment with the regression models, and also to look at histograms that show the distribution of the difference. It is shown that with the regression adjustment, there is a clear improvement of the statistical values compared to the original results of the forecasts. For example the value of the absolute mean difference is reduced with approximately 0.4-0.7 m/s with an adjustment of the regression model. The histograms clearly show that a more even distribution occurs after the adjustment with the regression models. From having a major part of the differences at 1-2 m/s to now having the major part at around 0 m/s and furthermore there is also generally a lower difference between the measurements and the results from the forecast model.

Approved for public release, distribution is unlimited / Performance of high resolution mesoscale models has been in a continuous state of refinement since their inception. Mesoscale models have become quite skillful in forecasting synoptic scale events such as mid-latitude cyclones. However, atmospheric forcing becomes a much more complicated process when faced with the challenge of forecasting near topography along the coastline. Phenomena such as gap flows, blocked flow winds and low level stratification become important to predictability at these scales. The problem is further complicated by the dynamics of a frontal passage event. The skill of mesoscale models in predicting these winds is not as well developed. This study examines several forecasts by the Coupled Ocean Atmospheric Mesoscale Prediction System (COAMPS) during frontal passage events for the Winter of 2003-2004. An attempt is made to characterize the predictability of the wind speed and direction both before and after frontal passage along the California coast. Synoptic forcing during this time is strong due to the effects of the mid-latitude cyclones propagate across the Pacific. The study's results indicate that the wind field predictability is subject to several consistent errors associated with the passage of fronts over topography. These errors arise due to difficulty in the model capturing weak thermal advection events and topographic wind funneling. The deficiencies in model representation of topography contributes to these errors. / Lieutenant, United States Navy

Approved for public release, distribution is unlimited / A study of the performance of the Coupled Ocean Atmosphere Mesoscale Prediction System (COAMPS) was performed based on collected METOC properties affecting radar propagation during the Roughness and Evaporation Duct (RED) experiment conducted off the windward coast of Oahu, HI. The measured refractivity influencing parameters (SST, air temperature, humidity, and wind speed) were compared to COAMPS predicted values. Using the NPS bulk evaporation duct model, profiles of the modified refractivity were computed from the buoy data and compared to profiles computed from the COAMPS data. The profiles were obtained concurrently with S-Band propagation measurements along a 26-km path. The radar propagation predictions created by APM from the modified refractivity profiles, derived from the measured METOC values and COAMPS modeled values, were compared to the in situ measured propagation losses. The mean RMS error of the prop loss predictions derived from the COAMPS forecasted METOC values was <4 dB compared to a mean RMS error of <3 dB from the in situ measurement derived prop loss predictions. Significantly larger errors occurred at the COAMPS analysis times. Overall, the results are very promising for this trade wind region, where the air is cooler than the relatively warm sea surface. / Lieutenant, United States Navy

Numerical weather prediction (NWP) and climate models have shown to have a challenge to correctly simulate stable boundary layers and diurnal cycles. This aim of this study is to evaluate, describe and give suggestions for improvements of the descriptions of stable boundary layers in operational NWP models. Two papers are included. Paper I focuses on the description of the surface and the interactions between the surface and the boundary layer in COAMPSR, a regional NWP model. The soil parametrization showed to be of great importance to the structure of the boundary layer. Moreover, it showed also that a low frequency of radiation calculations caused a bias in received solar energy at the surface. In paper II, the focus is on the formulation of the turbulent transport in stable boundary layers. There, an implementation of a diffusion parametrization based on the amount of turbulent kinetic energy (TKE) is tested in a single column model (SCM) version of the global NWP model Integrated Forecast System (IFS). The TKE parametrization turned out to behave similarly as the currently operational diffusion parametrization in convective regimes and neutral regimes, but showed to be less diffusive in weakly stable and stable conditions. The formulations of diffusion also turned out to be very dependent on the length scale formulation. If the turbulence and the gradients of wind temperature and wind are weak, the magnitude of turbulence can enter an oscillating mode. This oscillation can be avoided with the use of a lower limit of the length scale. / Det har visat sig att det är en stor utmaning för numeriska väderprognosmodeller (NWP-modeller) att simulera stabilt skiktade atmosfäriska gränsskikt och gränsskiktets dygnscykel på ett korrekt sätt. Syftet med denna studien är att utvärdera, beskriva och ge förslag på förbättringar av beskrivningen av gränsskiktet i NWP-modeller. Studien innehåller två artiklar. Den första fokuserar på beskrivningen av markytan och interaktionen mellan marken och gränsskiktet i den regionala NWP-modellen COAMPS R . Det visade sig att beskrivningen av markytan har en signifikant inverkan på gränsskiktets struktur. Det framkom också att strålningsberäkningarna endast görs en gång i timmen vilket bland annat orsakar en bias i inkommande solinstrålning vid markytan. Den andra artikeln fokuserar på beskrivningen av den turbulenta transporten i stabila skiktade gränsskikt. En implemenering av en diffusionsparametrisering som bygger på turbulent kinetisk energy (TKE) testas i en endimensionell version av NWP-modellen Integrated Forecast System (IFS), utvecklat vid European Center for Medium Range Weather Forecasts (ECMWF). Den TKE-baserade diffussionsparametriseringen är likvärdigt med den nuvaran de operationella parametriseringen i neutrala och konvektiva gränsskikt, menär mindre diffusivt i stabila gränsskikt. Diffusionens intensitet är beroende påden turbulenta längdskalan. Vidare kan turbulensen i TKE-formuleringen hamna i ett oscillerande läge om turbulensen är svag samtidigt som temperatur- och vindgradienten är kraftig. Denna oscillation kan förhindras om längdskalans minsta tillåtna värde begränsas.

Approved for public release, distribution is unlimited / The objective of this thesis is to investigate the forecastability of optical turbulence using the U.S. Navy's Coupled Ocean Atmosphere Mesoscale Prediction System (COAMPS). First, a detailed synoptic study was performed over the Eastern Pacific region for observation periods in October 2001 and March 2002 to focus on mesoscale features affecting Vandenberg AFB. Second, a modified version of COAMPS version 2.0.16 model output was evaluated to ensure reasonable modeling of the mesoscale. Next, temperature and dewpoint temperature vertical profiles of COAMPS, modified with the Turbulent Kinetic Energy (TKE) Method, were compared with balloon-launched rawinsondes, initially, then with higher resolution thermosondes. Optical turbulence parameters were then calculated from the data and a comparison between synthetic profiles and thermosonde-derived profiles were qualitatively and quantitatively studied. Then the vertical resolution of the model was increased for selected forecasts to determine the potential for forecast improvement. / Lieutenant Commander, United States Navy

Approved for public release; distribution in unlimited. / On day five of combat operations during Operation IRAQI FREEDOM, advances by coalition forces were nearly halted by a dust storm, initiated by the passage of a synoptically driven cold front. This storm impacted ground and air operations across the entire Area of Responsibility, and delayed an impending ground attack on the Iraqi capital. Military meteorologists were able to assist military planners in mitigating at least some of the effects of this storm. This thesis examines the synoptic conditions leading to the severe dust storm, evaluates the numerical weather prediction model performance in predicting the event, and reviews metrics pertaining to the overall impacts on the Operation IRAQI FREEDOM combined air campaign. In general, the numerical model guidance correctly predicted the location and onset of the dust storms on 25 March, 2003. As a result of this forecast guidance, mission planners were able to front load Air Tasking Orders with extra sorties prior to the onset of the dust storm, and were able to make changes to planned weapons loads, favoring GPS-guided munitions. / Captain, United States Air Force