This study developed a parameterization method to investigate the impacts of inhomogeneous land surfaces on mesoscale model simulations using a high-resolution 1-d PBL model. Then, the 1-d PBL model was used to investigate the inhomogeneity-caused model errors in applying the GOES satellite skin temperature assimilation technique into the MM5 over the Houston metropolitan area (HOU). In order to investigate the surface inhomogeneity impacts on the surface fluxes and PBL variables over HOU, homo- and inhomogeneous 1-d PBL model simulations were performed over HOU and compared to each other. The 1-d PBL model was constructed so that the surface inhomogeneities were able to be represented within model grid elements using a methodology similar to Avissar and Pielke (1989). The surface inhomogeneities over HOU were defined using 30-m resolution land cover data produced by Global Environment Management (GEM), Inc. The inhomogeneity parameterization method developed in the 1-d model was applied to a standard MM5 simulation to test the applicability of the parameterization to 3-d mesoscale model simulations. From the 1-d simulations it was inferred that the surface inhomogeneities would enhance the sensible heat flux by about 36 % and reduce the latent heat flux by about 25 %, thereby inducing the warmer (0.7 %) and drier (-1.0 %) PBL and the colder and moister PBL top induced by greater turbulent diffusivities. The 3-d application of the inhomogeneity parameterization indicated consistent results with the 1-d in general, with additional effects of advection and differential local circulation. The original GOES simulation was warmer compared to observations over HOU than over surrounding areas. The satellite data assimilation itself would lead to a warm bias due to erroneous estimation of gridpoint-mean skin temperature by the satellite, but 1-d simulations indicate that the impact of this error should be much weaker than what was observed. It seems that, unless the already existing warm and dry bias of the MM5 is corrected, the inhomogeneity parameterization in the MM5 would adversely affect the MM5 performance. Therefore, consideration of the surface inhomogeneities in the urban area needs to be confined to the GOES skin temperature retrieval errors at the moment.

This study developed a parameterization method to investigate the impacts of inhomogeneous land surfaces on mesoscale model simulations using a high-resolution 1-d PBL model. Then, the 1-d PBL model was used to investigate the inhomogeneity-caused model errors in applying the GOES satellite skin temperature assimilation technique into the MM5 over the Houston metropolitan area (HOU). In order to investigate the surface inhomogeneity impacts on the surface fluxes and PBL variables over HOU, homo- and inhomogeneous 1-d PBL model simulations were performed over HOU and compared to each other. The 1-d PBL model was constructed so that the surface inhomogeneities were able to be represented within model grid elements using a methodology similar to Avissar and Pielke (1989). The surface inhomogeneities over HOU were defined using 30-m resolution land cover data produced by Global Environment Management (GEM), Inc. The inhomogeneity parameterization method developed in the 1-d model was applied to a standard MM5 simulation to test the applicability of the parameterization to 3-d mesoscale model simulations. From the 1-d simulations it was inferred that the surface inhomogeneities would enhance the sensible heat flux by about 36 % and reduce the latent heat flux by about 25 %, thereby inducing the warmer (0.7 %) and drier (-1.0 %) PBL and the colder and moister PBL top induced by greater turbulent diffusivities. The 3-d application of the inhomogeneity parameterization indicated consistent results with the 1-d in general, with additional effects of advection and differential local circulation. The original GOES simulation was warmer compared to observations over HOU than over surrounding areas. The satellite data assimilation itself would lead to a warm bias due to erroneous estimation of gridpoint-mean skin temperature by the satellite, but 1-d simulations indicate that the impact of this error should be much weaker than what was observed. It seems that, unless the already existing warm and dry bias of the MM5 is corrected, the inhomogeneity parameterization in the MM5 would adversely affect the MM5 performance. Therefore, consideration of the surface inhomogeneities in the urban area needs to be confined to the GOES skin temperature retrieval errors at the moment.

Approved for public release; distribution in unlimited. / As mesoscale models increase in resolution there is a greater need to understand predictability on smaller scales. The predictability of a model is related to forecast skill. It is possible that the uncertainty of one scale of motion can affect the other scales due to the nonlinearity of the atmosphere. Some suggest that topography is one factor that can lead to an increase of forecast skill and therefore predictability. This study examines the uncertainty of a mesoscale model and attempts to characterize the predictability of the wind field. The data collected is from the summer, when the synoptic forcing is relatively benign. Mesoscale Model 5 (MM5) lagged forecasts are used to create a three-member ensemble over a 12-hour forecast cycle. The differences in these forecasts are used to determine the spread of the wind field. Results show that some mesoscale features have high uncertainty and others have low uncertainty, shedding light on the potential predictability of these features with a mesoscale model. Results indicate that topography is a large source of uncertainty. This is seen in all data sets, contrary to other studies. The ability of the model to properly forecast the diurnal cycle also impacted substantially on the character and evolution of forecast spread. The persistent mesoscale features were represented reasonably well, however the detailed structure of these features had a fair amount of uncertainty. / Lieutenant Junior Grade, United States Navy

Divergence structures associated with the spectrum of precipitating systems in the subtropics and midlatitudes are not well documented. A mesoscale model (MM5) is employed to quantify the relative importance different baroclinic environments have on divergence profiles for common storm types in southeast Texas, a subtropical region. Divergence profiles averaged over a 100 x 100 nested grid with 3-km grid spacing are calculated from the model-derived wind fields for each storm. The divergence profiles simulated for selected storms are consistent with those calculated from an S-band radar using the velocity-azimuth display (VAD) technique. Divergence profiles from well-modeled storms vary in magnitude and structure across the spectrum of baroclinicities and storm types common in southeast Texas. Barotropic storms more characteristic of the Tropics generate the most elevated divergence (and thus diabatic heating) structures with the largest magnitudes. As the degree of baroclinicity increases, stratiform area fractions increase while the levels of non-divergence (LNDs) decrease. However, some weakly baroclinic storms contain stratiform area fractions and divergence profiles with magnitudes and LNDs that are similar to barotropic storms, despite having lower tropopause heights and less deep convection. Additional convection forms after the passage of some of the modeled barotropic and weakly baroclinic storms that contain elevated divergence signatures, circumstantially suggesting that heating at upper-levels may cause diabatic feedbacks that help drive regions of persistent convection in the subtropics. Applying a two-dimensional stratiform-convective separation algorithm to MM5 reflectivity data generates realistic stratiform and convective divergence signals. Stratiform regions in barotropic storms contain thicker, more elevated mid-level convergence structures with larger magnitudes than strongly baroclinic storms, while weakly baroclinic storms have LNDs that fall somewhere in between with magnitudes similar to barotropic storms. Divergence profiles within convective regions typically become more elevated as baroclinicity decreases, although variations in magnitude are less coherent. These simulations suggest that MM5 adequately captures mass field perturbations within convective and stratiform regions, the latter of which produces diabatic feedbacks capable of generating additional convection. As a result, future research determining the climatological dynamic response caused by divergence profiles in MM5 may be feasible.

Dissertação(mestrado) - Universidade Federal do Rio Grande, Programa de Pós-Graduação em Engenharia Oceânica, Escola de Engenharia, 2005. / Submitted by Lilian M. Silva (lilianmadeirasilva@hotmail.com) on 2013-04-20T21:03:34Z No. of bitstreams: 1 Estudo para o desenvolvimento de um previsor descargas elétricas atmosféricas aplicado à região costeira do estado do Rio de Janeiro.pdf: 5929611 bytes, checksum: 6d12186a0ae17fe81eca3382c1b88f42 (MD5) / Approved for entry into archive by Bruna Vieira(bruninha_vieira@ibest.com.br) on 2013-06-04T16:31:08Z (GMT) No. of bitstreams: 1 Estudo para o desenvolvimento de um previsor descargas elétricas atmosféricas aplicado à região costeira do estado do Rio de Janeiro.pdf: 5929611 bytes, checksum: 6d12186a0ae17fe81eca3382c1b88f42 (MD5) / Made available in DSpace on 2013-06-04T16:31:08Z (GMT). No. of bitstreams: 1 Estudo para o desenvolvimento de um previsor descargas elétricas atmosféricas aplicado à região costeira do estado do Rio de Janeiro.pdf: 5929611 bytes, checksum: 6d12186a0ae17fe81eca3382c1b88f42 (MD5) Previous issue date: 2005 / The atmospheric dynamics evidently is very complex. There are many macro and micron scales processes and meteorological variables involved in the atmospheric physical phenomena. The storms with electrical dischargesare distinguished, among these phenomena, by the damage consequences to the human beings, directly or indirectly. Many researchers have pursued the possibility of forecasting the occurrence of a storm with electrical discharges, principally in the last three decades. However, there are not improvements in forecast performance, mainly due to phenomenon complexity. The main objective of the present dissertation was to accomplish a study to determine the viability or not of constructing a forecast system of atmospheric electrical discharges from artificial intelligence techniques, specifically artificial neural networks (NN). The base of the system was constituted of numerical simulations results of the atmospheric dynamics obtained from the mesoscale model MM5. It was identified meteorological variables (outputs of MM5) that would have some correlation with the electrical discharges. These variables act as input in the NN, which generate the forecast, consisting in the number of electrical discharges that will reach the ground some posterior time to the relative time of the simulated atmospheric fields. The region chosen for this study includes the state of Rio de Janeiro, the south of Espírito Santo, the southeast of Minas Gerais and the AtlanticOcean. Besides possessing a detection system of electrical discharges, this region contains the most important concentration of oil platforms of Brazil, being strategic to better know the atmospheric behavior in this place. Before developing the forecast system based on NN, some comparative tests were made using the MM5 simulation results and GOES infrared imagery, in order to survey the model prognostic capability. The forecast system showed reasonable results, indicating that the NN application may be a promising way to the electrical discharge forecast. However, it is necessary a better investigation, mainly with relation to the accomplishment of others tests with a bigger set of electrical discharges real data. / A dinâmica da atmosfera é evidentemente bastante complexa. Muitos são os processos físicos de macro e micro escalas e as variáveis meteorológicas envolvidos nos fenômenos atmosféricos. As tempestades com descargas elétricas destacam-se, dentre estes fenômenos, pelas conseqüências danosas causadas aos seres humanos, direta ou indiretamente. Diversos pesquisadores têm procurado investigar a possibilidade de prever a ocorrência de tempestades com descargas elétricas, principalmente nas úl timas três décadas, entretanto, progressos na performance da previsão ainda não foram alcançados devido à complexidade do fenômeno. O principal objetivo da presente dissertação foi realizar um estudo para determinar a viabilidade ou não de construir um sistema de previsão de descargas elétricas atmosféricas a partir de técnicas de inteligência artificial, mais precisamente redes neurais artificiais (RNA). A base do sistema constituiu-se de resultados de simulações numéricas da dinâmica atmosférica obtidos com o modelo de mesoescala MM5. Variáveis meteorológicas (saídas do MM5), que teriam alguma correlação com as descargas elétricas, foram identificadas e selecionadas como entradas na RNA, a qual gera a previsão, isto é, o número de descargas elétricas que atingirá o solo algum tempo posterior ao tempo relativo dos campos atmosféricos simulados. A região escolhida para este estudo, abrangendo o Estado do Rio de Janeiro, o sul do Estado do Espírito Santo, o sudeste do Estado de Minas Gerais e o Oceano Atlântico, além de possuir um sistema de detecção e monitoramento de descargas elétricas, contém a mais importante concentração de plataformas de petróleo do Brasil, sendo, portanto, estratégico conhecer melhor o seu comportamento atmosférico. Antes de desenvolver o sistema de previsão baseado em RNA, alguns testes comparativ os foram realizados usando resultados de simulação do MM5 e imagens infravermelhas de satélite geoestacionário, a fim de aferir a capacidade preditiva do modelo. O sistema de previsão apresentou resultados razoáveis, indicando que a aplicação da RNA é um cami nho promissor na previsão de descargas elétricas. Contudo, faz-se necessária uma mel hor investigação, principalmente quanto à realização de outros testes com um conjunto maior de dados reais de descargas elétricas.

La pollution de l'air est un sujet de préoccupation majeur au XXIème siècle affectant la santé et notre environnement. Deux types de pollution atmosphérique retiennent plus particulièrement l'attention des physicochimistes à l'heure actuelle : la pollution à l'ozone dite photochimique et la pollution particulaire (PM10, PM2.5). La région Nord-Pas-De-Calais (NPDC) est une des régions françaises ayant la plus forte densité de population (324 hab./km2 en 2006). Elle est également le lieu de passage d'un trafic routier transfrontalier particulièrement intense. Enfin, elle présente une forte activité industrielle (pétrochimie, sidérurgie, métallurgie) au niveau de la zone côtière de Dunkerque. Les émissions issues des secteurs du transport mais aussi industriel et tertiaire sont une source importante de composés primaires (NOx, COV et particules) précurseurs de la pollution à l'ozone et particulaire.Au cours du travail de thèse les chaînes de modélisation MM5-CHIMERE et MM5-FLEXPART ont été installées et utilisées sur plusieurs périodes d'études à l'échelle de la région NPDC. Le système MM5-CHIMERE a été utilisé sur la période juin-juillet 2006 propice au développement d'épisodes de pollution photochimique. Diverses applications ont été réalisées : étude de la relation entre concentration en polluant et conditions météorologiques, impact de l'intégration du cadastre d'émission régional, impact de la résolution de la grille et des données dynamiques, origine locale/régionale des niveaux de pollution observés en région NPDC. Puis, le système MM5-FLEXPART a été utilisé afin de déterminer l'origine de deux évènements intenses de pollution particulaire observés en mars et décembre 2007 au niveau de Dunkerque. Enfin, les performances dynamiques et chimiques du système ont été évaluées au niveau de la zone spécifique industrielle de Dunkerque sur deux périodes en avril et mai 2006. Au cours de ces périodes, les données dynamiques ont été collectées lors d'une campagne de mesures réalisée sur Dunkerque par le Laboratoire de Physico-Chimie de l'Atmosphère (LPCA) de l'Université du Littoral - Côte d'Opale. Les résultats concernant la performance et les applications des systèmes ainsi que les perspectives à court terme seront présentés et discutés.

In this study, Yuvacik Basin, which is located in southeastern part of Marmara Region of T&uuml / rkiye, is selected as the application basin and hydrologic modeling studies are performed for the basin. Basin is divided into three subbasins such as: Kirazdere, Kazandere, and Serindere and each subbasin is modeled with its own parameters. In subbasin and stream network delineation HEC-GeoHMS software is used and for the hydrologic modeling studies the new version of HEC-HMS hydrologic modeling software released in April 2006 is used. Modeling studies consist of four items: event-based hourly simulations, snow period daily simulations, daily runoff forecast using numerical weather prediction data, and runoff scenarios using intensity-duration-frequency curves. As a result of modeling studies, infiltration loss and baseflow parameters of each subbasin are calibrated with both hourly and daily simulations. Hourly parameters are used in spring, summer and fall seasons / daily parameters are used in late fall, winter and early spring (snowfall and snowmelt period) to predict runoff. Observed runoffs are compared with the forecasted runoffs that are obtained using MM5 grid data (precipitation and temperature) in the model. Goodness-of-fit between forecasted and observed runoffs is promising. Hence, the model can be used in real time runoff forecast studies. At last, runoffs that correspond to different return periods and probable maximum precipitation are predicted using intensity-duration-frequency data as input and frequency storm method of HEC-HMS. These runoffs can be used for flood control and flood damage estimation studies.

No description available.

No description available.

Esta tesis explora la precisión de dos métodos de estimación de precipitación, Auto-Estimator y CRR (Convective Rainfall Rate), generados a partir de imágenes infrarrojas y visibles del Meteosat. Ambos métodos junto con una serie de correcciones de la intensidad de lluvia estimada se aplican y se verifican en dos casos de inundaciones acaecidas en zonas mediterráneas. El primer caso ocurrió en Albania del 21 al 23 de septiembre de 2002 y el segundo, conocido como caso Montserrat, ocurrió en Cataluña la noche del 9 al 10 se junio de 2000. Por otro lado se investiga la posibilidad de realizar calibraciones de ambos métodos directamente con datos de estaciones pluviométricas cuando lo común es calibrar con datos de radares meteorológicos. También se propone cambios en algunas de las correcciones ya que parecen mejorar los resultados y se propone una nueva corrección muy eficiente que utiliza las descargas eléctricas para determinar la zonas más convectivas y de mayor precipitación de los sistemas nubosos. / This Thesis work explores the precision of two methods to estimate rainfall called Auto-Estimator and CRR (Convective Rainfall Rate). They are obtained by using infrared and visible images from Meteosat. Both Algorithms within a set of correction factors are applied and verified in two severe flood cases that took place in Mediterranean regions. The first case has occurred in Albania from 21 to 23 September 2002 and the second, known as the Montserrat case, has occurred in Catalonia the night from the 9 to 10 of June 2000. On the other hand it is explored new methods to perform calibrations to both satellite algorithms using direct rain rates from rain gauges. These kinds of adjustments are usually done using rain rates from meteorological radars. In addition it is proposed changes on some correction factors that seem to improve the results on estimations and it is defined an efficient correction factor that employ electrical discharges to detect the most convective and rainy areas in cloud systems.