Hidden Markov models for modeling daily rainfall occurrence over Brazil
Abstract A hidden Markov model (HMM) is used to describe daily rainfall occurrence at ten gauge stations in the state of Ceará in northeast Brazil during the February-April wet season 1975. The model assumes that rainfall occurrence is governed by a few discrete states, with Markovian daily transiti...
| Main Authors: | , , |
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| Format: | Text |
| Language: | English |
| Published: |
2003
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| Subjects: | |
| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1083.4661 http://www.datalab.uci.edu/papers/tr0327_bw.pdf |
| Summary: | Abstract A hidden Markov model (HMM) is used to describe daily rainfall occurrence at ten gauge stations in the state of Ceará in northeast Brazil during the February-April wet season 1975. 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 Niño-Southern Oscillation and the North Atlantic Oscillation. A trend toward greater rainfall occurrence in the north of Ceará compared to the south since the 1980s is identified with the second pair of states. A non-homogeneous HMM (NHMM) is then used to downscale daily precipitation occurrence at the ten stations, using general circulation model (GCM) simulations of seasonal-mean large-scale precipitation, obtained with historical sea surface temperatures prescribed globally. Interannual variability of the GCM's large-scale precipitation simulation is well correlated with seasonal-and spatial-averaged station rainfall-occurrence data. Simulations from the NHMM are found to be able to reproduce this relationship. The GCM-NHMM simulations are also able to capture quite well interannual changes in daily rainfall occurrence and 10-day dry spell frequencies at some individual stations. It is suggested that the NHMM provides a useful tool (a) to understand the statistics of daily rainfall occurrence at the station level in terms of large-scale atmospheric patterns, and (b) to produce station-scale daily rainfall sequence scenarios for input into crop models etc. |
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