Avaliação do impacto da assimilação de dados de umidade do solo nas variáveis hidroclimatológicas sobre o semiárido brasileiro e sobre a Bacia do Prata

• Autor(a) principal: Ramos, Cláudia Adam
• Data de Publicação: 2013
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações do LNCC
• Texto Completo: https://tede.lncc.br/handle/tede/151
• topic: Kalman, Filtragem de; Kalman filtering; CNPQ::CIENCIAS EXATAS E DA TERRA::GEOCIENCIAS::METEOROLOGIA::CLIMATOLOGIA

This dissertation did a study on the impact of soil moisture data assimilation on the hydroclimatological variables for the Brazilian semiarid and the La Plata River Basin. The choice of this theme was a result of the understanding that the accuracy of estimating soil moisture is directly associated with the accuracy of the weather forecast and climate simulations. The regions, in turn, were chosen because, despite presenting extremely different geographic and socio-economic features with episodes of lack and/or excess water. In this context, it is understood that accurate, representative forecasts are of great importance in developing strategies for managing water use, so relevant to these areas. The study was conducted using NCEP's NOAH land surface model, responsible for describing the dynamics of soil water and generating the estimate of the variable studied as necessary for the assimilation scheme. In addition, we used, as observations for the amount of water in the soil, soil moisture records from the AMSR-E sensor, coupled to the AQUA satellite, and also from ground-based, automatic agrometeorological stations maintained by INPE (Brazilian National Institute for Space Research). These observations were used directly into the assimilation scheme. The Ensemble Kalman Filter was the method used to perform the data assimilation experiments. The last step was to investigate the temporal evolution of the terms, in the assimilation scheme, containing estimates of model and analysis errors. These terms are fundamental parts of the Kalman Filter formulation and have a great in uence in the calculation of the result and its quality.