Daily rainfall projections from general circulation models with a downscaling nonhomogeneous hidden Markov model (NHMM) for south-eastern Australia

2012 ◽  
Vol 27 (25) ◽  
pp. 3663-3673 ◽  
Author(s):  
Guobin Fu ◽  
Stephen P. Charles ◽  
Sergey Kirshner
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
General Circulation ◽  
Hidden Markov ◽  
Daily Rainfall ◽  
General Circulation Models ◽  
South Eastern ◽  
Eastern Australia ◽  
South Eastern Australia

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.

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.

Studies in fleece-rot of sheep: some ecological aspects.

1955 ◽  
Vol 6 (4) ◽  
pp. 466 ◽  
Author(s):  
RH Hayman
Keyword(s):  
Climatic Factors ◽  
Daily Rainfall ◽  
Frequent Occurrence ◽  
Field Station ◽  
Broad Definition ◽  
South Eastern ◽  
Ecological Aspects ◽  
Eastern Australia ◽  
Occurrence Records ◽  
South Eastern Australia

Previous work on this subject has indicated the importance of climatic factors, particularly rainfall, in the development of fleece-rot. Over an 8-year period 19 outbreaks were recorded on the F. D. McMaster Field Station, C.S.I.R.O. Of these, 16 occurred during calendar months which had eight or more wet days and 4 or more in. of rain. Using these data as criteria, rainfall records for the period 1911-1940 were examined in order to give broad definition to zones in which fleece-rot might be expected as a rare, or frequent, occurrence. Records were taken from 18 selected towns in south-eastern Australia. In the zone in which occurrence of the condition was expected to be high, there were differences between localities as to the season of the year at which expectation was highest, in the frequency of wet days per month, and in the intensity of daily rainfall. In some localities shearing during certain months of the year would assist sheep to escape fleece-rot, as they would be in short wool when it was most likely to occur.

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.

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing
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