Hidden Markov models for modeling daily rainfall occurrence over Brazil
A hidden Markov model (HMM) is used to describe daily rainfall occurrence at ten gauge stations in the state of Ceara in northeast Brazil during the February--April wet season 1975--2002. The model assumes that rainfall occurrence is governed by a few discrete states, with Markovian daily transition...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Text |
| Language: | English |
| Published: |
2003
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| Subjects: | |
| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.10.8491 http://www.datalab.uci.edu/papers/tr0327_color.pdf |
| Summary: | A hidden Markov model (HMM) is used to describe daily rainfall occurrence at ten gauge stations in the state of Ceara in northeast Brazil during the February--April wet season 1975--2002. The model assumes that rainfall occurrence is governed by a few discrete states, with Markovian daily transitions between them. Four "hidden" rainfall states are identified. One pair of the states represents wet vs. dry conditions at all stations, while a second pair of states represents north-south gradients in rainfall occurrence. The estimated daily state-sequence is characterized by a systematic seasonal evolution, together with considerable variability on intraseasonal, interannual and longer time scales. The first pair of states are shown to be associated with large-scale displacements of the tropical convergence zones, and with teleconnections typical of the El Nino-Southern Oscillation and the North Atlantic Oscillation. A trend toward greater rainfall occurrence in the north of Ceara compared to the south since the 1980s is identified with the second pair of states. |
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