Modified analogue forecasting in the hidden Markov frameworkfor meteorological droughts
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摘要
An analogue method(AM) is a nonparametric approach that has been applied to predict the future states of a dynamic system by following the evolution of the analogues in the historical archive. In this study, we proposed a hidden Markov model(HMM)framework for a modified analogue forecasting(MAF) approach for meteorological droughts in Korea. The unobservable(hidden) state process in the framework aims to model the underlying drought state, while the observation process was formed from the time series of the standardized precipitation index(SPI) as a drought index. Within the framework, the likelihood estimator was used as the measure of similarity between past SPI analogues and current data. The MAF approach was conducted on the selected analogues to make forecasts at lead times of one and three months. The proposed model was applied to five selected stations in Korea using the SPI data from 1973 to 2016. The forecasting performance of the proposed model was tested during the validation period(2003–2016) using several statistical criteria and it was compared to a persistence-based benchmark model. The results showed significant improvement in the forecasting capacity, and satisfactory performance for numerical SPI forecasting and categorical drought forecasting. The results also suggested that the proposed model was able to provide useful information for determining future drought categories for early drought warning with a lead time of up to three months.
An analogue method(AM) is a nonparametric approach that has been applied to predict the future states of a dynamic system by following the evolution of the analogues in the historical archive. In this study, we proposed a hidden Markov model(HMM)framework for a modified analogue forecasting(MAF) approach for meteorological droughts in Korea. The unobservable(hidden) state process in the framework aims to model the underlying drought state, while the observation process was formed from the time series of the standardized precipitation index(SPI) as a drought index. Within the framework, the likelihood estimator was used as the measure of similarity between past SPI analogues and current data. The MAF approach was conducted on the selected analogues to make forecasts at lead times of one and three months. The proposed model was applied to five selected stations in Korea using the SPI data from 1973 to 2016. The forecasting performance of the proposed model was tested during the validation period(2003–2016) using several statistical criteria and it was compared to a persistence-based benchmark model. The results showed significant improvement in the forecasting capacity, and satisfactory performance for numerical SPI forecasting and categorical drought forecasting. The results also suggested that the proposed model was able to provide useful information for determining future drought categories for early drought warning with a lead time of up to three months.
An analogue method(AM) is a nonparametric approach that has been applied to predict the future states of a dynamic system by following the evolution of the analogues in the historical archive. In this study, we proposed a hidden Markov model(HMM)framework for a modified analogue forecasting(MAF) approach for meteorological droughts in Korea. The unobservable(hidden) state process in the framework aims to model the underlying drought state, while the observation process was formed from the time series of the standardized precipitation index(SPI) as a drought index. Within the framework, the likelihood estimator was used as the measure of similarity between past SPI analogues and current data. The MAF approach was conducted on the selected analogues to make forecasts at lead times of one and three months. The proposed model was applied to five selected stations in Korea using the SPI data from 1973 to 2016. The forecasting performance of the proposed model was tested during the validation period(2003–2016) using several statistical criteria and it was compared to a persistence-based benchmark model. The results showed significant improvement in the forecasting capacity, and satisfactory performance for numerical SPI forecasting and categorical drought forecasting. The results also suggested that the proposed model was able to provide useful information for determining future drought categories for early drought warning with a lead time of up to three months.
引文
1 IPCC.Climate Change 2013:The Physical Science Basis.Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change,Summary for Policymakers.Cambridge:Cambridge University Press,2013
2 Thavorntam W,Tantemsapya N,Armstrong L.A combination of meteorological and satellite-based drought indices in a better drought assessment and forecasting in Northeast Thailand.Nat Hazards,2015,77:1453-1474
3 Zhao J,Xu J,Xie X,et al.Drought monitoring based on TIGGE and distributed hydrological model in Huaihe River Basin,China.Sci Total Environ,2016,553:358-365
4 Li F,Zhang J,Jiang W,et al.Spatial health risk assessment and hierarchical risk management for mercury in soils from a typical contaminated site,China.Environ Geochem Health,2017,39:923-934
5 Deo R C,Byun H R,Adamowski J F,et al.Application of effective drought index for quantification of meteorological drought events:Acase study in Australia.Theor Appl Climatol,2017,128:359-379
6 Mishra A K,Singh V P.Drought modeling-A review.J Hydrol,2011,403:157-175
7 Lyon B,Bell M A,Tippett M K,et al.Baseline probabilities for the seasonal prediction of meteorological drought.J Appl Meteor Climatol,2012,51:1222-1237
8 Bergen R E,Harnack R P.Long-range temperature prediction using a simple analog approach.Mon Wea Rev,1982,110:1083-1099
9 Mullan A B,Thompson C S.Analogue forecasting of New Zealand climate anomalies.Int J Climatol,2006,26:485-504
10 Gibergans-Báguena J,Llasat M C.Improvement of the analog forecasting method by using local thermodynamic data.Application to autumn precipitation in Catalonia.Atmos Res,2007,86:173-193
11 Svensson C.Seasonal river flow forecasts for the United Kingdom using persistence and historical analogues.Hydrol Sci J,2016,61:19-35
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13 Yu H,Huang J,Chou J.Improvement of medium-range forecasts using the analog-dynamical method.Mon Wea Rev,2014,142:1570-1587
14 Ben Daoud A,Sauquet E,Bontron G,et al.Daily quantitative precipitation forecasts based on the analogue method:Improvements and application to a French large river basin.Atmos Res,2016,169:147-159
15 Hao Z,Hao F,Xia Y,et al.A statistical method for categorical drought prediction based on NLDAS-2.J Appl Meteor Climatol,2016,55:1049-1061
16 Madadgar S,AghaKouchak A,Shukla S,et al.A hybrid statisticaldynamical framework for meteorological drought prediction:Application to the southwestern United States.Water Resour Res,2016,52:5095-5110
17 McKee T B,Doesken N J,Kleist J.The relationship of drought frequency and duration to time scales.In:Proceedings of the 8th Conference on applied climatology,American Meteorological Society.Boston,1993
18 Hayes M,Svoboda M,Wall N,et al.The Lincoln declaration on drought indices:Universal meteorological drought index recommended.Bull Amer Meteor Soc,2011,92:485-488
19 McDermott P L,Wikle C K.A model-based approach for analog spatio-temporal dynamic forecasting.Environmetrics,2016,27:70-82
20 Mallya G,Tripathi S,Kirshner S,et al.Probabilistic assessment of drought characteristics using hidden Markov model.J Hydrol Eng,2013,18:834-845
21 Ramadas M,Govindaraju R S.Choice of hydrologic variables for probabilistic drought classification.J Irrig Drain Eng,2015,142:05015013
22 Jiang P,Liu X,Zhang J,et al.A framework based on hidden Markov model with adaptive weighting for microcystin forecasting and earlywarning.Decision Support Syst,2016,84:89-103
23 Bonaccorso B,Bordi I,Cancelliere A,et al.Spatial variability of drought:An analysis of SPI in Sicily.Water Resour Manage,2003,17:273-296
24 Green P J.Reversible jump Markov chain Monte Carlo computation and Bayesian model determination.Biometrika,1995,82:711-732
25 Chen S,Shin J Y,Kim T W.Probabilistic forecasting of drought:Ahidden Markov model aggregated with the RCP 8.5 precipitation projection.Stoch Environ Res Risk Assess,2016,31:1061-1076
26 Olivier C,Eric M,Tobias R.Inference in Hidden Markov Models(Springer Series in Statistics).Lund:Springe,2005
27 Fraedrich K,Rückert B.Metric adaption for analog forecasting.Physica A,1998,253:379-393
28 Wilks D S.Statistical Methods in the Atmospheric Sciences.2nd ed.London:Academic Press,2006
2 Thavorntam W,Tantemsapya N,Armstrong L.A combination of meteorological and satellite-based drought indices in a better drought assessment and forecasting in Northeast Thailand.Nat Hazards,2015,77:1453-1474
3 Zhao J,Xu J,Xie X,et al.Drought monitoring based on TIGGE and distributed hydrological model in Huaihe River Basin,China.Sci Total Environ,2016,553:358-365
4 Li F,Zhang J,Jiang W,et al.Spatial health risk assessment and hierarchical risk management for mercury in soils from a typical contaminated site,China.Environ Geochem Health,2017,39:923-934
5 Deo R C,Byun H R,Adamowski J F,et al.Application of effective drought index for quantification of meteorological drought events:Acase study in Australia.Theor Appl Climatol,2017,128:359-379
6 Mishra A K,Singh V P.Drought modeling-A review.J Hydrol,2011,403:157-175
7 Lyon B,Bell M A,Tippett M K,et al.Baseline probabilities for the seasonal prediction of meteorological drought.J Appl Meteor Climatol,2012,51:1222-1237
8 Bergen R E,Harnack R P.Long-range temperature prediction using a simple analog approach.Mon Wea Rev,1982,110:1083-1099
9 Mullan A B,Thompson C S.Analogue forecasting of New Zealand climate anomalies.Int J Climatol,2006,26:485-504
10 Gibergans-Báguena J,Llasat M C.Improvement of the analog forecasting method by using local thermodynamic data.Application to autumn precipitation in Catalonia.Atmos Res,2007,86:173-193
11 Svensson C.Seasonal river flow forecasts for the United Kingdom using persistence and historical analogues.Hydrol Sci J,2016,61:19-35
12 Lorenz E N.Atmospheric predictability as revealed by naturally occurring analogues.J Atmos Sci,1969,26:636-646
13 Yu H,Huang J,Chou J.Improvement of medium-range forecasts using the analog-dynamical method.Mon Wea Rev,2014,142:1570-1587
14 Ben Daoud A,Sauquet E,Bontron G,et al.Daily quantitative precipitation forecasts based on the analogue method:Improvements and application to a French large river basin.Atmos Res,2016,169:147-159
15 Hao Z,Hao F,Xia Y,et al.A statistical method for categorical drought prediction based on NLDAS-2.J Appl Meteor Climatol,2016,55:1049-1061
16 Madadgar S,AghaKouchak A,Shukla S,et al.A hybrid statisticaldynamical framework for meteorological drought prediction:Application to the southwestern United States.Water Resour Res,2016,52:5095-5110
17 McKee T B,Doesken N J,Kleist J.The relationship of drought frequency and duration to time scales.In:Proceedings of the 8th Conference on applied climatology,American Meteorological Society.Boston,1993
18 Hayes M,Svoboda M,Wall N,et al.The Lincoln declaration on drought indices:Universal meteorological drought index recommended.Bull Amer Meteor Soc,2011,92:485-488
19 McDermott P L,Wikle C K.A model-based approach for analog spatio-temporal dynamic forecasting.Environmetrics,2016,27:70-82
20 Mallya G,Tripathi S,Kirshner S,et al.Probabilistic assessment of drought characteristics using hidden Markov model.J Hydrol Eng,2013,18:834-845
21 Ramadas M,Govindaraju R S.Choice of hydrologic variables for probabilistic drought classification.J Irrig Drain Eng,2015,142:05015013
22 Jiang P,Liu X,Zhang J,et al.A framework based on hidden Markov model with adaptive weighting for microcystin forecasting and earlywarning.Decision Support Syst,2016,84:89-103
23 Bonaccorso B,Bordi I,Cancelliere A,et al.Spatial variability of drought:An analysis of SPI in Sicily.Water Resour Manage,2003,17:273-296
24 Green P J.Reversible jump Markov chain Monte Carlo computation and Bayesian model determination.Biometrika,1995,82:711-732
25 Chen S,Shin J Y,Kim T W.Probabilistic forecasting of drought:Ahidden Markov model aggregated with the RCP 8.5 precipitation projection.Stoch Environ Res Risk Assess,2016,31:1061-1076
26 Olivier C,Eric M,Tobias R.Inference in Hidden Markov Models(Springer Series in Statistics).Lund:Springe,2005
27 Fraedrich K,Rückert B.Metric adaption for analog forecasting.Physica A,1998,253:379-393
28 Wilks D S.Statistical Methods in the Atmospheric Sciences.2nd ed.London:Academic Press,2006