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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Linear Diagnostics to Assess the Performance of an Ensemble Forecast System

Satterfield, Elizabeth A. 2010 August 1900 (has links)
The performance of an ensemble prediction system is inherently flow dependent. This dissertation investigates the flow dependence of the ensemble performance with the help of linear diagnostics applied to the ensemble perturbations in a small local neighborhood of each model grid point location ℓ. A local error covariance matrix Pℓ is defined for each local region and the diagnostics are applied to the linear space Sℓ defined by the range of the ensemble based estimate of Pℓ. The particular diagnostics are chosen to help investigate the ability of Sℓ to efficiently capture the space of true forecast or analysis uncertainties, accurately predict the magnitude of forecast or analysis uncertainties, and to distinguish between the importance of different state space directions. Additionally, we aim to better understand the roots of the underestimation of the magnitude of uncertainty by the ensemble at longer forecast lead times. Numerical experiments are carried out with an implementation of the Local Ensemble Transform Kalman Filter (LETKF) data assimilation system on a reduced (T62L28) resolution version of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). Both simulated observations under the perfect model scenario and observations of the real atmosphere are used in these experiments. It is found that (i) paradoxically, the linear space Sℓ provides an increasingly better estimate of the space of forecast uncertainties as the time evolution of the ensemble perturbations becomes more nonlinear with increasing forecast time, (ii) Sℓ provides a more reliable linear representation of the space of forecast uncertainties for cases of more rapid error growth, (iii) the E-dimension is a reliable predictor of the performance of Sℓ in predicting the space of forecast uncertainties, (iv) the ensemble grossly underestimates the forecast error variance in Sℓ, (v) when realistic observation coverage is used, the ensemble typically overestimates the uncertainty in the leading eigen-directions of ˆP ℓ and underestimates the uncertainty in the trailing directions at analysis time and underestimates the uncertainty in all directions by the 120-hr forecast lead time, and (vi) at analysis time, with a constant covariance inflation factor, the ensemble typically underestimates uncertainty in densely observed regions and overestimates the uncertainty in sparsely observed regions.
2

ADVANCING SEQUENTIAL DATA ASSIMILATION METHODS FOR ENHANCED HYDROLOGIC FORECASTING IN SEMI-URBAN WATERSHEDS

Leach, James January 2019 (has links)
Accurate hydrologic forecasting is vital for proper water resource management. Practices that are impacted by these forecasts include power generation, reservoir management, agricultural water use, and flood early warning systems. Despite these needs, the models largely used are simplifications of the real world and are therefore imperfect. The forecasters face other challenges in addition to the model uncertainty, which includes imperfect observations used for model calibration and validation, imperfect meteorological forecasts, and the ability to effectively communicate forecast results to decision-makers. Bayesian methods are commonly used to address some of these issues, and this thesis will be focused on improving methods related to recursive Bayesian estimation, more commonly known as data assimilation. Data assimilation is a means to optimally account for the uncertainties in observations, models, and forcing data. In the literature, data assimilation for urban hydrologic and flood forecasting is rare; therefore the main areas of study in this thesis are urban and semi-urban watersheds. By providing improvements to data assimilation methods, both hydrologic and flood forecasting can be enhanced in these areas. This work explored the use of alternative data products as a type of observation that can be assimilated to improve hydrologic forecasting in an urban watershed. The impact of impervious surfaces in urban and semi-urban watersheds was also evaluated in regards to its impact on remotely sensed soil moisture assimilation. Lack of observations is another issue when it comes to data assimilation, particularly in semi- or fully-distributed models; because of this, an improved method for updating locations which do not have observations was developed which utilizes information theory’s mutual information. Finally, we explored extending data assimilation into the short-term forecast by using prior knowledge of how a model will respond to forecasted forcing data. Results from this work found that using alternative data products such as those from the Snow Data Assimilation System or the Soil Moisture and Ocean Salinity mission, can be effective at improving hydrologic forecasting in urban watersheds. They also were effective at identifying a limiting imperviousness threshold for soil moisture assimilation into urban and semi-urban watersheds. Additionally, the inclusion of mutual information between gauged and ungauged locations in a semi-distributed hydrologic model was able to provide better state updates in models. Finally, by extending data assimilation into the short-term forecast, the reliability of the forecasts could be improved substantially. / Dissertation / Doctor of Philosophy (PhD) / The ability to accurately model hydrological systems is essential, as that allows for better planning and decision making in water resources management. The better we can forecast the hydrologic response to rain and snowmelt events, the better we can plan and manage our water resources. This includes better planning and usage of water for agricultural purposes, better planning and management of reservoirs for power generation, and better preparing for flood events. Unfortunately, hydrologic models primarily used are simplifications of the real world and are therefore imperfect. Additionally, our measurements of the physical system responses to atmospheric forcing can be prone to both systematic and random errors that need to be accounted for. To address these limitations, data assimilation can be used to improve hydrologic forecasts by optimally accounting for both model and observation uncertainties. The work in this thesis helps to further advance and improve data assimilation, with a focus on enhancing hydrologic forecasting in urban and semi-urban watersheds. The research presented herein can be used to provide better forecasts, which allow for better planning and decision making.
3

Méthodes de prévision d’ensemble pour l’étude de la prévisibilité à l’échelle convective des épisodes de pluies intenses en Méditerranée / Convective scale predictability of highly precipitating events in the south-east of France : a study using ensemble prediction systems.

Vié, Benoît 29 November 2012 (has links)
L'évaluation de l'incertitude associée à la prévision numérique du temps à haute résolution, et en particulier l'estimation de la prévisibilité des événements de fortes précipitations en région méditerranéenne, sont les objectifs de ce travail de thèse. Nous avons procédé à l'étude de quatre sources d'incertitude contrôlant la prévisibilité de ces événements : la description des conditions d'échelle synoptique, la représentation des conditions atmosphériques à méso-échelle (notamment le flux de basses couches alimentant le système convectif), le rôle de processus physiques complexes tels que l'établissement d'une plage froide sous orage, et enfin la définition des conditions de surface. Pour quantifier l'impact de ces différentes sources d'incertitude, nous avons opté pour la méthode des prévisions d'ensemble avec le modèle AROME. Chaque source d'incertitude est étudiée individuellement à travers la génération de perturbations pertinentes, et les ensembles ainsi obtenus sont évalués dans un premier temps pour des cas de fortes précipitations. Nous avons aussi procédé à une évaluation statistique du comportement des prévisions d'ensemble réalisées sur des périodes de prévision longues de deux à quatre semaines. Cette évaluation, ainsi que celle de systèmes de prévision d'ensemble échantillonnant plusieurs sources d'incertitude simultanément, permettent d'établir une hiérarchisation de ces sources d'incertitude et enfin quelques recommandations en vue de la mise en place d'un système de prévision d'ensemble à échelle convective opérationnel à Météo-France / This PhD thesis aims at quantifying the uncertainty of convection-permitting numerical weather forecasts, with a particular interest in the predictability of Mediterranean heavy precipitating events. Four uncertainty sources, which impact the predictability of these events, were investigated : the description of the synoptic-scale circulation, the representation of meso-scale atmospheric conditions (especially the low-level jet feeding the convective systems with moist and unstable air), the impact of complex physical processes such as the setting up of a cold pool, and the definition of surface conditions. To quantify the impact of these four uncertainty sources, the ensemble forecasting technique was chosen, using the AROME model. Each uncertainty source is studied separately through the definition of dedicated perturbations, and the resulting ensembles are first evaluated over heavy precipitation case studies. We then proceed to a statistical evaluation of the ensembles for 2- and 4-week long forecast periods. This evaluation, completed with the design of ensembles sampling several uncertainty sources together, allows us to draw some practical tips for the design of an operational convective scale ensemble forecasting system at Météo-France
4

Previsão hidrometeorológica probabilística na Bacia do Alto Iguaçu-PR com os modelos WRF e TopModel / Probabilistic Hydrometeorological Forecast on Alto Iguaçu Basin with WRF and TopModel Models

Calvetti, Leonardo 08 November 2011 (has links)
Previsões probabilísticas de precipitação foram obtidas a partir de um conjunto de simulações pelo modelo WRF e utilizadas como condição de contorno no modelo hidrológico TopModel para previsão hidrometeorológica na bacia do Rio Iguaçu, no estado do Paraná. Nas simulações de cheias, durante o período de elevação do volume de precipitação, o erro médio aritmético do conjunto de previsões foi menor que cada um dos membros utilizados nesse conjunto, indicando melhor destreza do conjunto médio em relação a qualquer previsão determinística. Na dissipação dos sistemas precipitantes, alguns membros obtiveram resultados melhores que o conjunto médio e, em geral, as previsões são confluentes. As melhores previsões de precipitação com o WRF foram obtidas com as combinações de microfísica Lin e convecção de Kain Fritsch, microfísica WSM 5 e convecção de Kain Fritsch e simulações defasadas em 6 horas. As simulações inicializadas em horários mais próximos da ocorrência do fenômeno não garantiram uma melhoria na distribuição de precipitação na bacia. A avaliação do sistema de previsão por conjuntos pelo índice de Brier (IB) e seus termos demonstrou níveis suficientes de confiabilidade e destreza para ser utilizada na maioria dos eventos de precipitação sobre a bacia do rio Iguaçu. Os valores do IB estiveram entre 0,15 e 0,3 com picos isolados. Os valores obtidos para o termo de incerteza estiveram entre 0,1 e 0,25 indicando bons resultados visto que o desejável é o mais próximo de zero. Nos eventos de chuva, o termo de confiabilidade apresentou valores próximos a 0,2 no período da manhã e valores entre 0,3 e 0,4 no período da tarde, com um acréscimo no final da integração. O índice de acerto foi de 60 % a 90 % durante o período de integração (48 horas) para o conjunto médio de previsões e entre 50 a 80% para a previsão determinística. Em todos os horários de simulação o erro de fase foi maior que o erro de amplitude, possivelmente devido aos atrasos da propagação dos sistemas precipitantes e aos efeitos de ajuste das condições físicas iniciais da atmosfera. Os erros de fase e amplitude foram menores na previsão probabilística em todo o período de integração. Assim como na previsão de precipitação, nas simulações de vazão o erro de fase foi maior que o erro de amplitude, indicando que o atraso nas previsões de variação da vazão ainda é o um desafio na previsão hidrometeorológica. Observou-se que o modelo hidrológico é bastante sensível a previsão de precipitação e, portanto, a melhoria das previsões de vazão é diretamente proporcional a diminuição dos erros nas previsões de precipitação. / Probabilistic forecast of precipitation from WRF model simulations was used as input in hydrological TopModel for streamlines forecast in Iguaçu Basin, Parana, southern Brazil. The arithmetic error of precipitation ensemble forecast was smaller than each individual member forecast error in the streamflow increase stage. It means the use of ensemble forecast was better than any deterministic forecast. But when the streamflow decreases, the results are confluent and some individual member forecast was better than ensemble. Simulations using Lin microphysical parameterization and Kain Fritsch, WSM 5 and Kain Fritsch and 6h lagged obtained the better results of precipitation over the basin. The use of runs with initial conditions near the precipitation time did not guarantee better results in the distribution of precipitation on the basin. The Brier Score (BS) of the ensemble system demonstrated that the system is very skillful with values between 0.15 and 0.3. Both uncertainty and reliability terms of BS, 0.1 0.25 and 0.2- 0.4, respectively, were encouraging for use hourly ensemble forecast of precipitation on the watershed. Ensemble forecast provide high values of hit scores (0.6 to 0.9) than deterministic forecast (0.5 to 0.8) at all period of integration. Due the delay in the forecasts of the precipitation systems, the phase error is predominant over amplitude during all time. Both errors were reduced using the ensemble forecasts. The phase errors in hydrological were greater than amplitude such as precipitation forecasts. Thus, for increase streamflow forecast it should reduced the errors in QPF forecasts.
5

ENABLING HYDROLOGICAL INTERPRETATION OF MONTHLY TO SEASONAL PRECIPITATION FORECASTS IN THE CORE NORTH AMERICAN MONSOON REGION

Maitaria, Kazungu January 2009 (has links)
The aim of the research undertaken in this dissertation was to use medium-range to seasonal precipitation forecasts for hydrologic applications for catchments in the core North American Monsoon (NAM) region. To this end, it was necessary to develop a better understanding of the physical and statistical relationships between runoff processes and the temporal statistics of rainfall. To achieve this goal, development of statistically downscaled estimates of warm season precipitation over the core region of the North American Monsoon Experiment (NAME) were developed. Currently, NAM precipitation is poorly predicted on local and regional scales by Global Circulation Models (GCMs). The downscaling technique used here, the K-Nearest Neighbor (KNN) model, combines information from retrospective GCM forecasts with simultaneous historical observations to infer statistical relationships between the low-resolution GCM fields and the locally-observed precipitation records. The stochastic nature of monsoon rainfall presents significant challenges for downscaling efforts and, therefore, necessitate a regionalization and an ensemble or probabilistic-based approach to quantitative precipitation forecasting. It was found that regionalization of the precipitation climatology prior to downscaling using KNN offered significant advantages in terms of improved skill scores.Selected output variables from retrospective ensemble runs of the National Centers for Environmental Predictions medium-range forecast (MRF) model were fed into the KNN downscaling model. The quality of the downscaled precipitation forecasts was evaluated in terms of a standard suite of ensemble verification metrics. This study represents the first time the KNN model has been successfully applied within a warm season convective climate regime and shown to produce skillful and reliable ensemble forecasts of daily precipitation out to a lead time of four to six days, depending on the forecast month.Knowledge of the behavior of the regional hydrologic systems in NAM was transferred into a modeling framework aimed at improving intra-seasonal hydrologic predictions. To this end, a robust lumped-parameter computational model of intermediate conceptual complexity was calibrated and applied to generate streamflow in three unregulated test basins in the core region of the NAM. The modeled response to different time-accumulated KNN-generated precipitation forcing was investigated. Although the model had some difficulty in accurately simulating hydrologic fluxes on the basis of Hortonian runoff principles only, the preliminary results achieved from this study are encouraging. The primary and most novel finding from this study is an improved predictability of the NAM system using state-of-the-art ensemble forecasting systems. Additionally, this research significantly enhanced the utility of the MRF ensemble forecasts and made them reliable for regional hydrologic applications. Finally, monthly streamflow simulations (from an ensemble-based approach) have been demonstrated. Estimated ensemble forecasts provide quantitative estimates of uncertainty associated with our model forecasts.
6

An adaptive atmospheric prediction algorithm to improve density forecasting for aerocapture guidance processes

Wagner, John Joseph 12 January 2015 (has links)
Many modern entry guidance systems depend on predictions of atmospheric parameters, notably atmospheric density, in order to guide the entry vehicle to some desired final state. However, in highly dynamic atmospheric environments such as the Martian atmosphere, the density may vary by as much as 200% from predicted pre-entry trends. This high level of atmospheric density uncertainty can cause significant complications for entry guidance processes and may in extreme scenarios cause complete failure of the entry. In the face of this uncertainty, mission designers are compelled to apply large trajectory and design safety margins which typically drive the system design towards less efficient solutions with smaller delivered payloads. The margins necessary to combat the high levels of atmospheric uncertainty may even preclude scientifically interesting destinations or architecturally useful mission modes such as aerocapture. Aerocapture is a method for inserting a spacecraft into an orbit about a planetary body with an atmosphere without the need for significant propulsive maneuvers. This can reduce the required propellant and propulsion hardware for a given mission which lowers mission costs and increases the available payload fraction. However, large density dispersions have a particularly acute effect on aerocapture trajectories due to the interaction of the high required speeds and relatively low densities encountered at aerocapture altitudes. Therefore, while the potential system level benefits of aerocapture are great, so too are the risks associated with this mission mode in highly uncertain atmospheric environments such as Mars. Contemporary entry guidance systems utilize static atmospheric density models for trajectory prediction and control. These static models are unable to alter the fundamental nature of the underlying state equations which are used to predict atmospheric density. This limits both the fidelity and adaptive freedom of these models and forces the guidance system to retroactively correct for the density prediction errors after those errors have already impacted the trajectory. A new class of dynamic density estimator called a Plastic Ensemble Neural System (PENS) is introduced which is able to generate high fidelity, adaptable density forecast models by altering the underlying atmospheric state equations to better agree with observed atmospheric trends. A new construct called an ensemble echo is also introduced which creates an associative learning architecture, permitting PENS to evolve with increasing atmospheric exposure. The PENS estimator is applied to a numerical guidance system and the performance of the composite system is investigated with over 144,000 guided trajectory simulations. The results demonstrate that the PENS algorithm achieves significant reductions in both the required post-aerocapture performance, and the aerocapture failure rates relative to historical density estimators.
7

Previsão por conjunto de vazões afluentes a reservatórios em grandes bacias hidrográficas brasileiras

Fan, Fernando Mainardi January 2015 (has links)
A previsão com antecedência de curto e médio prazo da vazão em diferentes locais de bacias hidrográficas geralmente é benéfica ao permitir uma resposta antecipada a eventos hidrológicos como cheias, e a operação mais eficiente de obras hidráulicas como barragens. Atualmente, cada vez mais se tem reconhecida a importância da inclusão das incertezas na geração de previsões hidrológicas, feita através de previsões por conjunto (ou ensemble). Neste tipo de previsão são feitas inferências sobre cenários possíveis futuros através da consideração de, por exemplo, múltiplas trajetórias possíveis dos estados da atmosfera, que ao serem aplicadas em um modelo hidrológico resultam em distribuições de trajetórias de vazões. Várias aplicações recentes tem sugerido a possibilidade da tomada de melhores decisões para o futuro quando fundamentadas neste conhecimento das incertezas. No Brasil, um uso predominante de previsões hidrológicas é na operação de reservatórios de usinas hidroelétricas, que constituem a maior fonte de energia do País. As previsões nestes casos são utilizadas tanto para a operação normal do sistema nacional, feita de forma centralizada, como para a operação local das usinas em casos de cheia, onde é necessário velar pela segurança da barragem e pela atenuação de impactos a jusante e/ou a montante dos barramentos. Contudo, a forma como as previsões de vazão são geradas e usadas no cenário nacional não são baseadas em técnicas de previsão por conjunto, onde a própria pesquisa local sobre os potenciais benefícios destas formas de geração de previsões pode ser classificada como incipiente. Assim, o objetivo principal deste estudo é investigar benefícios em termos de qualidade e persistência do uso de previsões de afluência por conjunto para reservatórios em grandes bacias hidrográficas brasileiras em curto e médio prazo. Para cumprir com estes objetivos foram propostos ensaios de previsão de vazão por conjunto para três bacias hidrográficas brasileiras: Alto São Francisco, Doce, e Tocantins. O modelo hidrológico MGBIPH foi aplicado para a execução de previsões retroativas (hindcastings) alimentado por dados de chuva provindos de três diferentes sistemas de previsão meteorológica por conjunto (ECMWF-pf, GEFS, e CPTEC-pf) e mais uma previsão determinística de referência (ECMWF-fc), todos disponíveis na base de dados denominada TIGGE. De uma forma geral, as previsões por conjunto, principalmente dos modelos ECMWF-pf e GEFS, se mostraram superiores em termos de qualidade e persistência na comparação com a previsão determinística. E o uso do Super Ensemble, formado pela combinação dos três modelos mostrou-se uma alternativa entre as melhores testadas, principalmente por ser também uma estratégia robusta. Para uma estratégia de defesa contra cheias, as análises indicam benefícios para a consideração dos membros superiores dos conjuntos, e já para uma estratégia de operação de reservatórios essa visão pode ser mais focada em vazões médias, as quais podem conter algum viés. Já a comparação entre as bacias mostrou que resultados não podem ser transportados de um local para outro, apesar de estarem no mesmo clima. Em relação às incertezas, notou-se que a modelagem hidrológica amplifica as incertezas na previsão na medida em que os estados do modelo da grande bacia evoluem. De qualquer forma, acreditase que os resultados mostram que mais investimentos em técnicas de previsão por conjunto e suas aplicações são um caminho a ser seguido para ampliar os benefícios do uso de previsões hidrológicas. / Short to medium-term streamflow forecasts at different locations in a watershed are generally beneficial to allow an early response to hydrological events such as floods, and more efficient operation of hydraulic structures such as dams. Currently, an increasingly recognition has been given to the including of uncertainties in the generation of hydrological forecasts, what is usually made producting the so called Ensemble Forecast. In this kind of forecast inferences about possible future scenarios are made by considering, for example, multiple possible trajectories of the atmospheric states, which when applied to a hydrological model results in streamflow trajectories distributions. Several recent applications suggested the possibility of better decisions making based on this uncertainties knowledge. In Brazil, a predominant use of hydrological forecasts is for hydropower reservoirs operation, which are the largest source of energy for the country. Future inflows estimates in these cases are used either for normal operation of the national system, done centrally, as for local operation of the dams in cases of floods, where it is necessary to ensure the dam safety and the mitigation of impacts downstream and/or upstream. However, the currently technique used to generate the operational forecasts is not based on ensembles, and the Brazilian local research on the potential benefits of these forms of forecasts production can be classified as incipient. Thus, the aim of this Thesis was to investigate benefits in terms of quality and persistence of using short to medium-term ensemble inflow forecast for reservoirs located on large Brazilian river basins. To fulfill these objectives streamflow forecast tests have been proposed for three Brazilian river basins: Alto San Francisco, Doce, and Tocantins. The hydrological model MGB-IPH was applied to perform retroactive forecasts (hindcastings) within a period of tests forced by rainfall data from three different ensemble weather forecasting systems (ECMWFpf, GEFs, and CPTEC-pf) and a deterministic prediction reference (ECMWF-fc), all available in the TIGGE archive. In general, the ensemble predictions, especially from ECMWF-pf and GEFs models, were superior in quality and persistence in comparison to the deterministic reference. And the use of the Super ensemble composed by the combination of the three ensemble models was shown to be among the results, and also a robust strategy. For a flood protection strategy, the analyzes indicate benefits in the consideration of the upper bounds of the ensembles, and for a reservoir operation strategy that vision could be more focused on average flow rates, which may contain some verified bias. The comparison between the basins results showed that one can not transport results and considerations from one location to another, despite being in the same climate region. Regarding uncertainties, it was noted that hydrological modeling amplifies the uncertainty in the forecasts, in some extent due to the large basin evolution of state variables. Anyway, it is believed that more investments in ensemble forecasting techniques and its applications shown to be a good way to make better use of forecasts.
8

Previsão por conjunto de vazões afluentes a reservatórios em grandes bacias hidrográficas brasileiras

Fan, Fernando Mainardi January 2015 (has links)
A previsão com antecedência de curto e médio prazo da vazão em diferentes locais de bacias hidrográficas geralmente é benéfica ao permitir uma resposta antecipada a eventos hidrológicos como cheias, e a operação mais eficiente de obras hidráulicas como barragens. Atualmente, cada vez mais se tem reconhecida a importância da inclusão das incertezas na geração de previsões hidrológicas, feita através de previsões por conjunto (ou ensemble). Neste tipo de previsão são feitas inferências sobre cenários possíveis futuros através da consideração de, por exemplo, múltiplas trajetórias possíveis dos estados da atmosfera, que ao serem aplicadas em um modelo hidrológico resultam em distribuições de trajetórias de vazões. Várias aplicações recentes tem sugerido a possibilidade da tomada de melhores decisões para o futuro quando fundamentadas neste conhecimento das incertezas. No Brasil, um uso predominante de previsões hidrológicas é na operação de reservatórios de usinas hidroelétricas, que constituem a maior fonte de energia do País. As previsões nestes casos são utilizadas tanto para a operação normal do sistema nacional, feita de forma centralizada, como para a operação local das usinas em casos de cheia, onde é necessário velar pela segurança da barragem e pela atenuação de impactos a jusante e/ou a montante dos barramentos. Contudo, a forma como as previsões de vazão são geradas e usadas no cenário nacional não são baseadas em técnicas de previsão por conjunto, onde a própria pesquisa local sobre os potenciais benefícios destas formas de geração de previsões pode ser classificada como incipiente. Assim, o objetivo principal deste estudo é investigar benefícios em termos de qualidade e persistência do uso de previsões de afluência por conjunto para reservatórios em grandes bacias hidrográficas brasileiras em curto e médio prazo. Para cumprir com estes objetivos foram propostos ensaios de previsão de vazão por conjunto para três bacias hidrográficas brasileiras: Alto São Francisco, Doce, e Tocantins. O modelo hidrológico MGBIPH foi aplicado para a execução de previsões retroativas (hindcastings) alimentado por dados de chuva provindos de três diferentes sistemas de previsão meteorológica por conjunto (ECMWF-pf, GEFS, e CPTEC-pf) e mais uma previsão determinística de referência (ECMWF-fc), todos disponíveis na base de dados denominada TIGGE. De uma forma geral, as previsões por conjunto, principalmente dos modelos ECMWF-pf e GEFS, se mostraram superiores em termos de qualidade e persistência na comparação com a previsão determinística. E o uso do Super Ensemble, formado pela combinação dos três modelos mostrou-se uma alternativa entre as melhores testadas, principalmente por ser também uma estratégia robusta. Para uma estratégia de defesa contra cheias, as análises indicam benefícios para a consideração dos membros superiores dos conjuntos, e já para uma estratégia de operação de reservatórios essa visão pode ser mais focada em vazões médias, as quais podem conter algum viés. Já a comparação entre as bacias mostrou que resultados não podem ser transportados de um local para outro, apesar de estarem no mesmo clima. Em relação às incertezas, notou-se que a modelagem hidrológica amplifica as incertezas na previsão na medida em que os estados do modelo da grande bacia evoluem. De qualquer forma, acreditase que os resultados mostram que mais investimentos em técnicas de previsão por conjunto e suas aplicações são um caminho a ser seguido para ampliar os benefícios do uso de previsões hidrológicas. / Short to medium-term streamflow forecasts at different locations in a watershed are generally beneficial to allow an early response to hydrological events such as floods, and more efficient operation of hydraulic structures such as dams. Currently, an increasingly recognition has been given to the including of uncertainties in the generation of hydrological forecasts, what is usually made producting the so called Ensemble Forecast. In this kind of forecast inferences about possible future scenarios are made by considering, for example, multiple possible trajectories of the atmospheric states, which when applied to a hydrological model results in streamflow trajectories distributions. Several recent applications suggested the possibility of better decisions making based on this uncertainties knowledge. In Brazil, a predominant use of hydrological forecasts is for hydropower reservoirs operation, which are the largest source of energy for the country. Future inflows estimates in these cases are used either for normal operation of the national system, done centrally, as for local operation of the dams in cases of floods, where it is necessary to ensure the dam safety and the mitigation of impacts downstream and/or upstream. However, the currently technique used to generate the operational forecasts is not based on ensembles, and the Brazilian local research on the potential benefits of these forms of forecasts production can be classified as incipient. Thus, the aim of this Thesis was to investigate benefits in terms of quality and persistence of using short to medium-term ensemble inflow forecast for reservoirs located on large Brazilian river basins. To fulfill these objectives streamflow forecast tests have been proposed for three Brazilian river basins: Alto San Francisco, Doce, and Tocantins. The hydrological model MGB-IPH was applied to perform retroactive forecasts (hindcastings) within a period of tests forced by rainfall data from three different ensemble weather forecasting systems (ECMWFpf, GEFs, and CPTEC-pf) and a deterministic prediction reference (ECMWF-fc), all available in the TIGGE archive. In general, the ensemble predictions, especially from ECMWF-pf and GEFs models, were superior in quality and persistence in comparison to the deterministic reference. And the use of the Super ensemble composed by the combination of the three ensemble models was shown to be among the results, and also a robust strategy. For a flood protection strategy, the analyzes indicate benefits in the consideration of the upper bounds of the ensembles, and for a reservoir operation strategy that vision could be more focused on average flow rates, which may contain some verified bias. The comparison between the basins results showed that one can not transport results and considerations from one location to another, despite being in the same climate region. Regarding uncertainties, it was noted that hydrological modeling amplifies the uncertainty in the forecasts, in some extent due to the large basin evolution of state variables. Anyway, it is believed that more investments in ensemble forecasting techniques and its applications shown to be a good way to make better use of forecasts.
9

Previsão por conjunto de vazões afluentes a reservatórios em grandes bacias hidrográficas brasileiras

Fan, Fernando Mainardi January 2015 (has links)
A previsão com antecedência de curto e médio prazo da vazão em diferentes locais de bacias hidrográficas geralmente é benéfica ao permitir uma resposta antecipada a eventos hidrológicos como cheias, e a operação mais eficiente de obras hidráulicas como barragens. Atualmente, cada vez mais se tem reconhecida a importância da inclusão das incertezas na geração de previsões hidrológicas, feita através de previsões por conjunto (ou ensemble). Neste tipo de previsão são feitas inferências sobre cenários possíveis futuros através da consideração de, por exemplo, múltiplas trajetórias possíveis dos estados da atmosfera, que ao serem aplicadas em um modelo hidrológico resultam em distribuições de trajetórias de vazões. Várias aplicações recentes tem sugerido a possibilidade da tomada de melhores decisões para o futuro quando fundamentadas neste conhecimento das incertezas. No Brasil, um uso predominante de previsões hidrológicas é na operação de reservatórios de usinas hidroelétricas, que constituem a maior fonte de energia do País. As previsões nestes casos são utilizadas tanto para a operação normal do sistema nacional, feita de forma centralizada, como para a operação local das usinas em casos de cheia, onde é necessário velar pela segurança da barragem e pela atenuação de impactos a jusante e/ou a montante dos barramentos. Contudo, a forma como as previsões de vazão são geradas e usadas no cenário nacional não são baseadas em técnicas de previsão por conjunto, onde a própria pesquisa local sobre os potenciais benefícios destas formas de geração de previsões pode ser classificada como incipiente. Assim, o objetivo principal deste estudo é investigar benefícios em termos de qualidade e persistência do uso de previsões de afluência por conjunto para reservatórios em grandes bacias hidrográficas brasileiras em curto e médio prazo. Para cumprir com estes objetivos foram propostos ensaios de previsão de vazão por conjunto para três bacias hidrográficas brasileiras: Alto São Francisco, Doce, e Tocantins. O modelo hidrológico MGBIPH foi aplicado para a execução de previsões retroativas (hindcastings) alimentado por dados de chuva provindos de três diferentes sistemas de previsão meteorológica por conjunto (ECMWF-pf, GEFS, e CPTEC-pf) e mais uma previsão determinística de referência (ECMWF-fc), todos disponíveis na base de dados denominada TIGGE. De uma forma geral, as previsões por conjunto, principalmente dos modelos ECMWF-pf e GEFS, se mostraram superiores em termos de qualidade e persistência na comparação com a previsão determinística. E o uso do Super Ensemble, formado pela combinação dos três modelos mostrou-se uma alternativa entre as melhores testadas, principalmente por ser também uma estratégia robusta. Para uma estratégia de defesa contra cheias, as análises indicam benefícios para a consideração dos membros superiores dos conjuntos, e já para uma estratégia de operação de reservatórios essa visão pode ser mais focada em vazões médias, as quais podem conter algum viés. Já a comparação entre as bacias mostrou que resultados não podem ser transportados de um local para outro, apesar de estarem no mesmo clima. Em relação às incertezas, notou-se que a modelagem hidrológica amplifica as incertezas na previsão na medida em que os estados do modelo da grande bacia evoluem. De qualquer forma, acreditase que os resultados mostram que mais investimentos em técnicas de previsão por conjunto e suas aplicações são um caminho a ser seguido para ampliar os benefícios do uso de previsões hidrológicas. / Short to medium-term streamflow forecasts at different locations in a watershed are generally beneficial to allow an early response to hydrological events such as floods, and more efficient operation of hydraulic structures such as dams. Currently, an increasingly recognition has been given to the including of uncertainties in the generation of hydrological forecasts, what is usually made producting the so called Ensemble Forecast. In this kind of forecast inferences about possible future scenarios are made by considering, for example, multiple possible trajectories of the atmospheric states, which when applied to a hydrological model results in streamflow trajectories distributions. Several recent applications suggested the possibility of better decisions making based on this uncertainties knowledge. In Brazil, a predominant use of hydrological forecasts is for hydropower reservoirs operation, which are the largest source of energy for the country. Future inflows estimates in these cases are used either for normal operation of the national system, done centrally, as for local operation of the dams in cases of floods, where it is necessary to ensure the dam safety and the mitigation of impacts downstream and/or upstream. However, the currently technique used to generate the operational forecasts is not based on ensembles, and the Brazilian local research on the potential benefits of these forms of forecasts production can be classified as incipient. Thus, the aim of this Thesis was to investigate benefits in terms of quality and persistence of using short to medium-term ensemble inflow forecast for reservoirs located on large Brazilian river basins. To fulfill these objectives streamflow forecast tests have been proposed for three Brazilian river basins: Alto San Francisco, Doce, and Tocantins. The hydrological model MGB-IPH was applied to perform retroactive forecasts (hindcastings) within a period of tests forced by rainfall data from three different ensemble weather forecasting systems (ECMWFpf, GEFs, and CPTEC-pf) and a deterministic prediction reference (ECMWF-fc), all available in the TIGGE archive. In general, the ensemble predictions, especially from ECMWF-pf and GEFs models, were superior in quality and persistence in comparison to the deterministic reference. And the use of the Super ensemble composed by the combination of the three ensemble models was shown to be among the results, and also a robust strategy. For a flood protection strategy, the analyzes indicate benefits in the consideration of the upper bounds of the ensembles, and for a reservoir operation strategy that vision could be more focused on average flow rates, which may contain some verified bias. The comparison between the basins results showed that one can not transport results and considerations from one location to another, despite being in the same climate region. Regarding uncertainties, it was noted that hydrological modeling amplifies the uncertainty in the forecasts, in some extent due to the large basin evolution of state variables. Anyway, it is believed that more investments in ensemble forecasting techniques and its applications shown to be a good way to make better use of forecasts.
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Previsão hidrometeorológica probabilística na Bacia do Alto Iguaçu-PR com os modelos WRF e TopModel / Probabilistic Hydrometeorological Forecast on Alto Iguaçu Basin with WRF and TopModel Models

Leonardo Calvetti 08 November 2011 (has links)
Previsões probabilísticas de precipitação foram obtidas a partir de um conjunto de simulações pelo modelo WRF e utilizadas como condição de contorno no modelo hidrológico TopModel para previsão hidrometeorológica na bacia do Rio Iguaçu, no estado do Paraná. Nas simulações de cheias, durante o período de elevação do volume de precipitação, o erro médio aritmético do conjunto de previsões foi menor que cada um dos membros utilizados nesse conjunto, indicando melhor destreza do conjunto médio em relação a qualquer previsão determinística. Na dissipação dos sistemas precipitantes, alguns membros obtiveram resultados melhores que o conjunto médio e, em geral, as previsões são confluentes. As melhores previsões de precipitação com o WRF foram obtidas com as combinações de microfísica Lin e convecção de Kain Fritsch, microfísica WSM 5 e convecção de Kain Fritsch e simulações defasadas em 6 horas. As simulações inicializadas em horários mais próximos da ocorrência do fenômeno não garantiram uma melhoria na distribuição de precipitação na bacia. A avaliação do sistema de previsão por conjuntos pelo índice de Brier (IB) e seus termos demonstrou níveis suficientes de confiabilidade e destreza para ser utilizada na maioria dos eventos de precipitação sobre a bacia do rio Iguaçu. Os valores do IB estiveram entre 0,15 e 0,3 com picos isolados. Os valores obtidos para o termo de incerteza estiveram entre 0,1 e 0,25 indicando bons resultados visto que o desejável é o mais próximo de zero. Nos eventos de chuva, o termo de confiabilidade apresentou valores próximos a 0,2 no período da manhã e valores entre 0,3 e 0,4 no período da tarde, com um acréscimo no final da integração. O índice de acerto foi de 60 % a 90 % durante o período de integração (48 horas) para o conjunto médio de previsões e entre 50 a 80% para a previsão determinística. Em todos os horários de simulação o erro de fase foi maior que o erro de amplitude, possivelmente devido aos atrasos da propagação dos sistemas precipitantes e aos efeitos de ajuste das condições físicas iniciais da atmosfera. Os erros de fase e amplitude foram menores na previsão probabilística em todo o período de integração. Assim como na previsão de precipitação, nas simulações de vazão o erro de fase foi maior que o erro de amplitude, indicando que o atraso nas previsões de variação da vazão ainda é o um desafio na previsão hidrometeorológica. Observou-se que o modelo hidrológico é bastante sensível a previsão de precipitação e, portanto, a melhoria das previsões de vazão é diretamente proporcional a diminuição dos erros nas previsões de precipitação. / Probabilistic forecast of precipitation from WRF model simulations was used as input in hydrological TopModel for streamlines forecast in Iguaçu Basin, Parana, southern Brazil. The arithmetic error of precipitation ensemble forecast was smaller than each individual member forecast error in the streamflow increase stage. It means the use of ensemble forecast was better than any deterministic forecast. But when the streamflow decreases, the results are confluent and some individual member forecast was better than ensemble. Simulations using Lin microphysical parameterization and Kain Fritsch, WSM 5 and Kain Fritsch and 6h lagged obtained the better results of precipitation over the basin. The use of runs with initial conditions near the precipitation time did not guarantee better results in the distribution of precipitation on the basin. The Brier Score (BS) of the ensemble system demonstrated that the system is very skillful with values between 0.15 and 0.3. Both uncertainty and reliability terms of BS, 0.1 0.25 and 0.2- 0.4, respectively, were encouraging for use hourly ensemble forecast of precipitation on the watershed. Ensemble forecast provide high values of hit scores (0.6 to 0.9) than deterministic forecast (0.5 to 0.8) at all period of integration. Due the delay in the forecasts of the precipitation systems, the phase error is predominant over amplitude during all time. Both errors were reduced using the ensemble forecasts. The phase errors in hydrological were greater than amplitude such as precipitation forecasts. Thus, for increase streamflow forecast it should reduced the errors in QPF forecasts.

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