scholarly journals Downscaling of Daily Rainfall Occurrence over Northeast Brazil Using a Hidden Markov Model

2004 ◽  
Vol 17 (22) ◽  
pp. 4407-4424 ◽  
Author(s):  
Andrew W. Robertson ◽  
Sergey Kirshner ◽  
Padhraic Smyth
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
General Circulation ◽  
Large Scale ◽  
Hidden Markov ◽  
Southern Oscillation ◽  
Daily Rainfall ◽  
Northeast Brazil ◽  
Rainfall Occurrence ◽  
Dry Spell

Abstract A hidden Markov model (HMM) is used to describe daily rainfall occurrence at 10 gauge stations in the state of Ceará 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-versus-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 nonhomogeneous HMM (NHMM) is then used to downscale daily precipitation occurrence at the 10 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.

scholarly journals Multisite Downscaling of Seasonal Predictions to Daily Rainfall Characteristics over Pacific–Andean River Basins in Ecuador and Peru Using a Nonhomogeneous Hidden Markov Model

2016 ◽  
Vol 17 (2) ◽  
pp. 481-498 ◽  
Author(s):  
Luis E. Pineda ◽  
Patrick Willems
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
General Circulation ◽  
Hidden Markov ◽  
Daily Rainfall ◽  
General Circulation Models ◽  
Rainfall Characteristics ◽  
The Pacific ◽  
Thermally Driven ◽  
Hydrological Prediction

Abstract The seasonal predictability of daily rainfall characteristics is examined over 21 hydrologic units in the Pacific–Andean region of Ecuador and Peru (PAEP) using a nonhomogeneous hidden Markov model (NHMM) and retrospective seasonal information from general circulation models (GCMs). First, a hidden Markov model is used to diagnose four states that play distinct roles in the December–May rainy season. The estimated daily states fall into two wet states, one dry state, and one transitional dry–wet state, and show a systematic seasonal evolution together with intraseasonal and interannual variability. The first wet state represents regionwide wet conditions, while the second one represents north–south gradients. The former could be associated with the annual moisture offshore of the PAEP, thermally driven by the climatological maximum of sea surface temperatures in the Niño-1.2 region. The latter corresponds with the dynamically noisy component of the PAEP rainfall signal, associated with the annual displacement of the intertropical convergence zone. Then, a four-state NHMM is coupled with GCM information to simulate daily sequences at each station. Simulations of the GCM–NHMM approach represent daily rainfall characteristics at station level well. The best skills were found in reproducing the interannual variation of seasonal rainfall amount and mean intensity at the regional-averaged level with correlations equal to 0.60 and 0.64, respectively. At catchment level, the best skills appear over catchments south of 4°S, where hydrologically relevant characteristics are well simulated. It is thus shown that the GCM–NHMM approach provides the potential to produce precipitation information relevant for hydrological prediction in this climate-sensitive region.

Non-Homogeneous Hidden Markov Model for Daily Rainfall Amount in Peninsular Malaysia

2013 ◽  
Vol 63 (2) ◽  
Author(s):  
Wei Lun Tan ◽  
Fadhilah Yusof ◽  
Zulkifli Yusop
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Large Scale ◽  
Hidden Markov ◽  
Monsoon Season ◽  
Daily Rainfall ◽  
Reanalysis Data ◽  
Peninsular Malaysia ◽  
Northeast Monsoon ◽  
Hidden States

The non-homogeneous hidden Markov model (NHMM) generates the rainfall observation depends on few weather states which serve as a link between the large scale atmospheric measures. The daily rainfall at 20 stations from Peninsular Malaysia for 33 years sequences is analyzed using NHMM during the northeast monsoon season. A NHMM with six hidden states are identified. The atmospheric variable was obtained from NCEP Reanalysis Data as predictor. The gridded atmospheric fields are summarized through the principle component analysis (PCA) technique. PCA is applied to sea level pressure (SLP) to identify their principal spatial patterns co-varying with rainfall. The NHMM can accurately simulate the observed daily mean rainfall, correlations between stations for daily rainfall amounts and the quantile-quantile plots. It can be concluded that the NHMM is a useful method to simulate the daily rainfall amounts that may be used to prepare strategies and planning for the unpredicted disaster such as flood and drought.

scholarly journals Detection of structural variations in densely-labelled optical DNA barcodes: A hidden Markov model approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259670
Author(s):  
Albertas Dvirnas ◽  
Callum Stewart ◽  
Vilhelm Müller ◽  
Santosh Kumar Bikkarolla ◽  
Karolin Frykholm ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Large Scale ◽  
Hidden Markov ◽  
Sequence Information ◽  
True Positive ◽  
Dna Barcodes ◽  
Structural Variations ◽  
Genomic Alterations ◽  
Data Set

Large-scale genomic alterations play an important role in disease, gene expression, and chromosome evolution. Optical DNA mapping (ODM), commonly categorized into sparsely-labelled ODM and densely-labelled ODM, provides sequence-specific continuous intensity profiles (DNA barcodes) along single DNA molecules and is a technique well-suited for detecting such alterations. For sparsely-labelled barcodes, the possibility to detect large genomic alterations has been investigated extensively, while densely-labelled barcodes have not received as much attention. In this work, we introduce HMMSV, a hidden Markov model (HMM) based algorithm for detecting structural variations (SVs) directly in densely-labelled barcodes without access to sequence information. We evaluate our approach using simulated data-sets with 5 different types of SVs, and combinations thereof, and demonstrate that the method reaches a true positive rate greater than 80% for randomly generated barcodes with single variations of size 25 kilobases (kb). Increasing the length of the SV further leads to larger true positive rates. For a real data-set with experimental barcodes on bacterial plasmids, we successfully detect matching barcode pairs and SVs without any particular assumption of the types of SVs present. Instead, our method effectively goes through all possible combinations of SVs. Since ODM works on length scales typically not reachable with other techniques, our methodology is a promising tool for identifying arbitrary combinations of genomic alterations.

Investigating teleconnection patterns associated with the rainy season of the northern northeast Brazil using a hidden Markov model

2020 ◽  
Vol 55 (7-8) ◽  
pp. 2075-2088
Author(s):  
Isamara de Mendonça Silva ◽  
Deusdedit Monteiro Medeiros ◽  
Michel d. S. Mesquita
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Rainy Season ◽  
Hidden Markov ◽  
Northeast Brazil ◽  
Teleconnection Patterns

scholarly journals Increasing the Efficiency of Genome-wide Association Mapping via Hidden Markov Models

2016 ◽  
Author(s):  
Hong Gao ◽  
Hua Tang ◽  
Carlos Bustamante
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Large Scale ◽  
Hidden Markov ◽  
Association Studies ◽  
Genome Wide Association ◽  
Trend Test ◽  
Genome Wide Association Studies ◽  
Data Set ◽  
Genome Wide

With the rapid production of high dimensional genetic data, one major challenge in genome-wide association studies is to develop effective and efficient statistical tools to resolve the low power problem of detecting causal SNPs with low to moderate susceptibility, whose effects are often obscured by substantial background noises. Here we present a novel method that serves as an optimal technique for reducing background noises and improving detection power in genome-wide association studies. The approach uses hidden Markov model and its derivate Markov hidden Markov model to estimate the posterior probabilities of a markers being in an associated state. We conducted extensive simulations based on the human whole genome genotype data from the GlaxoSmithKline-POPRES project to calibrate the sensitivity and specificity of our method and compared with many popular approaches for detecting positive signals including the χ^2 test for association and the Cochran-Armitage trend test. Our simulation results suggested that at very low false positive rates (<10^-6), our method reaches the power of 0.9, and is more powerful than any other approaches, when the allelic effect of the causal variant is non-additive or unknown. Application of our method to the data set generated by Welcome Trust Case Control Consortium using 14,000 cases and 3,000 controls confirmed its powerfulness and efficiency under the context of the large-scale genome-wide association studies.

scholarly journals A Self-Adaptive Hidden Markov Model for Emotion Classification in Chinese Microblogs

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Li Liu ◽  
Dashi Luo ◽  
Ming Liu ◽  
Jun Zhong ◽  
Ye Wei ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Large Scale ◽  
Hidden Markov ◽  
Training Dataset ◽  
Support Vector ◽  
Emotion Classification ◽  
Online Social Media ◽  
Hidden Knowledge ◽  
Self Adaptive

Microblogging is increasingly becoming one of the most popular online social media for people to express ideas and emotions. The amount of socially generated content from this medium is enormous. Text mining techniques have been intensively applied to discover the hidden knowledge and emotions from this huge dataset. In this paper, we propose a modified version of hidden Markov model (HMM) classifier, called self-adaptive HMM, whose parameters are optimized by Particle Swarm Optimization algorithms. Since manually labeling large-scale dataset is difficult, we also employ the entropy to decide whether a new unlabeled tweet shall be contained in the training dataset after being assigned an emotion using our HMM-based approach. In the experiment, we collected about 200,000 Chinese tweets from Sina Weibo. The results show that theF-score of our approach gets 76% on happiness and fear and 65% on anger, surprise, and sadness. In addition, the self-adaptive HMM classifier outperforms Naive Bayes and Support Vector Machine on recognition of happiness, anger, and sadness.

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