Academic literature on the topic 'Synoptic models'

This study investigates the control of atmospheric circulation patterns on rainfall incidence in Spain. The main objective of the research is to evaluate a range of statistical synoptic approaches with the aim of identifying the scheme that best models circulation to association. Spatial patterns of rainfall in Spain are first investigated using Principal Components Analysis and Cluster Analysis. Distinct precipitation affinity groups emerge that display covariant rainfall behaviour and reflect differences in latitude, the influence of topography and distance from the synoptic feature responsible for rainfall. The method allows seasonal redefinition of boundaries and the investigation of the effect of climate change. In total 24 synoptic models are investigated. The best performing models (a daily weather type model and a monthly airflow index model) use standardized data and the 500hPa contour surface. Some of the problems associated with non-stationarity are attempted by modifying models using kinematic information. Adjustments to the models (inclusion of frontal information and stochastic modelling) can improve results on a sub-regional scale. Effective models are then used to empirically downscale from General Circulation Model (GCM) scenarios obtained from the Canadian Centre for Climate Modelling and Analysis. The downscaling procedure is of limited use due to errors in GCM output but results suggest strongly increasing anticyclonicity in the Iberian area and a decrease in rainfall in many areas. There are uncertainties associated with regional scale climate change estimation using current empirical methods, nevertheless as GCM output inevitably becomes more accurate the scope for detailed regional assessment will improve

The tracks of westward-propagating synoptic disturbances across the Intra-Americas Sea (IAS) and far-eastern Pacific, known as easterly waves or tropical depression (TD) waves, are an important feature of the region's climate. They are associated with heavy rainfall events, seed the majority of tropical cyclones, and contribute to the mean rainfall across the region. This study examines the ability of current climate models (CMIP5) to simulate TD-wave activity and associated environmental factors across the IAS and far-eastern Pacific as compared to reanalysis. Model projections for the future are then compared with the historical model experiment to investigate the southward shift in CMIP5 track density and the environmental factors that may contribute to it. While historical biases in TD-wave track-density patterns are well correlated with model biases in sea surface temperature and midlevel moisture, the projected southward shift of the TD track density by the end of the twenty-first century in CMIP5 models is best correlated with changes in deep wind shear and midlevel moisture. In addition, the genesis potential index is found to be a good indicator of both present and future regions of high TD-wave track density for the models in this region. This last result may be useful for understanding the more complex relationship between tropical cyclones and this index in models found in other studies.

Les modèles climatiques peinent à représenter précisément les structures de circulation atmosphérique liées aux événements extrêmes, et notamment leurs variations régionales.Cette thèse explore comment l'Allocation Latente de Dirichlet (LDA), une méthode d'apprentissage statistique issue du traitement du langage naturel, peut être utilisée pour évaluer la représentation par modèles climatiques de données telles que la pression au niveau de la mer (SLP).La LDA identifie un jeu de structures locales (ou motifs) à l'échelle synoptique, interprétables physiquement comme des cyclones et des anticyclones.La même base de motifs peut servir à décrire les données issues des modèles et des réanalyses, permettant de représenter toute carte SLP par une combinaison parcimonieuse de ces motifs.Les coefficients, ou poids, de ces combinaisons fournissent une information locale sur la configuration synoptique de la circulation.Les analyser permet de caractériser la structure de la circulation dans les réanalyses et les modèles, et ainsi d'identifier localement des biais globaux ou spécifiques aux événements extrêmes.Une erreur dynamique globale peut être définie à partir des différences de poids des données modèles avec les réanalyses.Cette méthodologie a été appliquée à quatre modèles de CMIP6.Bien que les modèles représentent correctement en général la circulation à grande échelle, leurs erreurs sont plus élevées pour les vagues de froid et de chaleur.Une source d'erreur dans tous les modèles est liée aux motifs méditerranéens.Des critères d'évaluation supplémentaires ont été proposés.L'un s'appuie sur la fréquence d'apparition des motifs dans la représentation des cartes de pression.L'autre consiste à combiner l'erreur dynamique globale avec l'erreur de température, ce qui permet de différentier entre les modèles.Ces résultats démontrent le potentiel de la LDA pour l'évaluation et la préselection des modèles<br>Climate models face challenges in accurately representing atmospheric circulation patterns related to extreme weather events, especially regarding regional variability.This thesis explores how Latent Dirichlet Allocation (LDA), a statistical learning method originating from natural language processing, can be adapted to evaluate the ability of climate models to represent data such as Sea-Level Pressure (SLP).LDA identifies a set of local synoptic-scale structures, physically interpretable as cyclones and anticyclones, referred to as motifs.A common basis of motifs can be used to describe reanalysis and model data so that any SLP map can be represented as a sparse combination of these motifs.The motif weights provide local information on the synoptic configuration of circulation.By analyzing the weights, we can characterize circulation patterns in both reanalysis data and models, allowing us to identify local biases, both in general data and during extreme events.A global dynamic error can be defined for each model run based on the differences between the average weights of the run and reanalysis data.This methodology was applied to four CMIP6 models.While large-scale circulation is well predicted by all models on average, higher errors are found for heatwaves and cold spells.In general, a major source of error is found to be associated with Mediterranean motifs, for all models.Additional evaluation criteria were considered: one was based on the frequency of motifs in the sparse map representation.Another one involved combining the global dynamic error with the temperature error, thus making it possible to discriminate between models.These results show the potential of LDA for model evaluation and preselection

Conselho Nacional de Desenvolvimento Científico e Tecnológico<br>O objetivo principal deste trabalho baseou-se na elaboração e estudo das freqüências de distribuições espaciais das Correntes de Jato próximo do Nordeste Brasileiro (CJNEB) e sistemas sinóticos associados. Esta pesquisa foi realizada devido à necessidade de um maior conhecimento sobre a influência desse sistema na região e a pouca quantidade de artigos relacionados a este tipo de pesquisa. O período escolhido contou com 16 anos de análises entre os anos de 1994 e 2009. Foram utilizados os dados de reanálise do National Centers for Environmental Prediction (NCEP) no nível de 200 hPa e imagens de satélite no canal infravermelho do banco de dados do Space Science and Engineering Center (SSEC) da University of Wisconsin. A partir daí foram encontrados 1.100 casos desta corrente, representando um total de 19% dos dias analisados e 4.740 (81%) casos de ventos com velocidades entre 20 e 30 m/s. Apesar de serem registrados ventos acima de 30 m/s em todas as estações do ano, os meses que contaram com as maiores freqüências e ocorrências deste vento foram os de outono e inverno. O vento máximo registrado para esta corrente de ar em nível superior ocorreu no mês de inverno, alcançando 64 m/s (230 km/h). Em todo período foram notadas variações anuais com ciclos de 4 ou 5 anos de maiores ou menores ocorrências. Os sistemas associados à CJNEB foram os vórtices ciclônicos de altos níveis (VCAN s), cavados (CAV), alta da Bolívia (AB), ciclones do hemisfério norte (CHN) e anticiclones no atlântico sul (AAS) próximo ao equador. As distribuições espaciais encontradas das CJNEB foram referentes às direções de NW-SE, SW-NE, SE-NW, W-E, S-N e N-S. As direções de NW-SE e SW-NE foram os casos mais observadas durante todo o estudo. Foram elaborados modelos conceituais dos três tipos de CJNEB associados com seguintes sistemas sinóticos: I) AB junto com VCAN do tipo clássico perto do NEB no Atlântico e ou cavado; II) Anticiclone do Atlântico junto com VCAN no NEB tipo clássico e ou cavado; III) cavado perto do NEB junto com ciclone do hemisfério norte. Estes tipos de CJNEB tinham seguintes direções: tipo 1 de sul, sudeste e sudoeste; tipo 2 de norte e noroeste; e tipo 3 de leste e sudoeste.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences, 1987.<br>Microfiche copy available in Archives and Science.<br>Bibliography: leaves 197-204.<br>by Wesley Nobuo Ebisuzaki.<br>Ph.D.

Extratropical cyclones are responsible for a substantial portion of midlatitude climate variability and contribute to widespread impacts. The characteristics of extratropical cyclones, such as their spatial distribution and intensity, are thought to be dependent on the large scale circulation. The relationship between cyclone characteristics and modes of large-scale climate variability has been investigated in previous studies, but interactions between modes of climate variability have largely been ignored. Since extratropical cyclone characteristics may be related to interactions between phases, quantifying these relationships is an important step in improving the climatology of extratropical cyclones. The goal of this study is to quantify relationships between modes of climate variability and characteristics of strong cyclones in the contiguous United States. Using historical sea-level pressure data, cyclone intensity, frequency, and spatial distribution are investigated using a cyclone definition that combines the requirement for low pressure (1000 hPa or lower) and positive (cyclonic) vorticity. The large scale modes of climate variability considered include El Niño Southern Oscillation (ENSO), the Pacific North American (PNA) mode, and the Arctic Oscillation (AO). The analysis is divided into three phases focusing on (1) establishing a background cyclone climatology within the study area, (2) quantifying differences in cyclone characteristics between the positive and negative phases of the individual modes of climate variability, and (3) examining the interactions between the modes of climate variability as they relate to extratropical cyclone characteristics. The results are expected to provide an improved baseline for evaluation of coupled climate models and also have the potential to improve seasonal climate predictability.

One important component for successful air quality modelling is the utilisation of a reliable meteorological simulator. Evaluating the model with respect to its overall performance in predicting natural processes is no easy task. The problem is twofold, firstly there is the availability and suitability of field data with which to compare a model with and secondly there is the method of evaluation. The Air Pollution Model (TAPM), developed by the CSIRO was used to simulate the winds in Southeast Queensland (SEQ). The complex nature of the airshed makes it difficult to compare modelled data with observational data as the observational data may be influenced by local phenomena. Evaluation of the model through the use of standard statistics and monthly and seasonal statistics illustrated that overall the model predicted the annual average wind speeds and temperatures well. Through the use of synoptic clustering, more detail on model performance was gained and it was found that TAPM predicted sea breezes that occurred on high pollution days. The sensitivity of the model to the selection of input parameters such as soil type, land use, vegetation, and rain processes was also investigated.