A Probabilistic Multimodel Ensemble Approach to Seasonal Prediction

Min, Young-Mi; Kryjov, Vladimir N.; Park, Chung-Kyu

doi:10.1175/2008waf2222140.1

Cited by 42 publications

(43 citation statements)

References 36 publications

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“…The choice of the Gaussian distribution for the seasonal precipitation totals is based on [15] where it was shown that the use of gamma-, empirical, and the Gaussian distributions provides comparable results.…”

Section: Methods Of Estimationmentioning

confidence: 99%

“…To minimize the influence of random errors, the individual model forecasts were given weights inversely proportional to the maximum random error in forecast probability due to the error in the estimate of the mean m predicted by each model. This combination method was developed in APCC and described in [15]. According to the square root rule, the error of the mean e m (sampling error) is inversely proportional to the square root of the sample size (ensemble size) n:…”

Section: Methods Of Estimationmentioning

confidence: 99%

“…At present, the multimodel ensemble systems are developed at several centers: International Research Institute for Climate and Society (IRI), Meteorological Service of Canada (MSC), European Multimodel Seasonal to Interannual Prediction (EUROSIP) System, and APCC (Asia-Pacific Economic Cooperation (APEC) Climate Center) [15].…”

Section: Introductionmentioning

confidence: 99%

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Assessment of probability multimodel seasonal forecast based on the APCC model data

Bundel

Kryzhov

Min³

et al. 2011

Russ. Meteorol. Hydrol.

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The probability multimodel forecast system based on the Asia-Pacific Economic Cooperation Climate Center (APCC) model data is verified. The winter and summer seasonal mean fields T 850 and precipitation seasonal totals are estimated. To combine the models into a multimodel ensemble, the probability forecast is calculated for each of single models first, and then these forecasts are combined using the total probability formula. It is shown that the multimodel forecast is considerably more skilful than the single-model forecasts. The forecast quality is higher in the tropics compared to the mid-and high latitudes. The multimodel ensemble temperature forecasts outperform the random and climate forecasts for Northern Eurasia in the above-and below-normal categories. Precipitation forecast is less successful. For winter, the combination of single-model ensembles provides the precipitation forecast skill exceeding that of the random forecast for both Northern Eurasia and European Russia.

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Section: Methods Of Estimationmentioning

confidence: 99%

Section: Methods Of Estimationmentioning

confidence: 99%

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Assessment of probability multimodel seasonal forecast based on the APCC model data

Bundel

Kryzhov

Min³

et al. 2011

Russ. Meteorol. Hydrol.

Self Cite

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“…Seasonal climate forecasts are usually issued in tercile probabilities, which are probability of below-normal (BN), near-normal (NN), and above-normal (AN) [10]. Climate forecasting can facilitate risk assessment, preparation and adaptation for global decision-making and planning in various sectors such as energy, water and food security [11].…”

Section: Fundamental Principle Of Stochastic Weather Generatorsmentioning

confidence: 99%

Designing and Implementing Weather Generators as Web Services

Chinnachodteeranun

Hung²,

Honda

et al. 2016

Future Internet

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Climate and weather realizations are essential inputs for simulating crop growth and yields to analyze the risks associated with future conditions. To simplify the procedure of generating weather realizations and make them available over the Internet, we implemented novel mechanisms for providing weather generators as web services, as well as a mechanism for sharing identical weather realizations given a climatological information. A web service for preparing long-term climate data was implemented based on an international standard, Sensor Observation Service (SOS). The weather generator services, which are the core components of the framework, analyze climatological data, and can take seasonal climate forecasts as inputs for generating weather realizations. The generated weather realizations are encoded in a standard format, which are ready for use to crop modeling. All outputs are generated in SOS standard, which broadens the extent of data sharing and interoperability with other sectoral applications, e.g., water resources management. These services facilitate the development of other applications requiring input weather realizations, as these can be obtained easily by just calling the service. The workload of analysts related to data preparation and handling of legacy weather generator programs can be reduced. The architectural design and implementation presented here can be used as a prototype for constructing further services on top of an interoperable sensor network system.

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“…Multimodel ensemble (MME) techniques have been used to produce skillful seasonal forecasts by reducing uncertainties in the individual global climate model (e.g., Krishnamurti et al, 1999;Palmer and Shukla, 2000). The Asia-Pacific Economic Cooperation Climate Center (APCC) has produced 6-month lead predictions based on a fully operational MME seasonal forecast system since 2005 (Min et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Assessment of Multimodel Ensemble Seasonal Hindcasts for Satellite-Based Rice Yield Prediction

Chun

Kim

Lee

et al. 2016

J. Agric. Meteorol.

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Several pre-harvest rice yield estimation methods have often failed to accurately estimate rice yields due to weather variability. We attempted to assess the APEC Climate Center Multimodel Ensemble (APCC MME) seasonal hindcasts to a satellite-based rice yield prediction model to timely provide estimates of rice yields for efficacious intervention plans. The developed model by a multiple regression analysis is Yield = 5.635NDVI -0.0012P 9 + 0.91 (where yield is the white rice yield in t ha -1 and P 9 is the observed monthly precipitation in September in mm month -1 ). The goodness-of-fit measures were 0.66, -0.14%, 0.13 t ha -1 , and 2.25%, for adjusted R 2 (coefficient of determination), Percent bias (PBI-AS), Root Mean Square Error (RMSE), and Mean Absolute percentage Error (MAPE), respectively. A statistical downscaling method using Empirical Orthogonal Function Analysis (EOFA) and Singular Value Decomposition Analysis (SDVA) was used to predict monthly precipitation hindcasts in September required for the developed model. Even though the estimates of rice yield using the predicted monthly precipitation for whole study period were not as good as the estimates using the 9.15 sampling method, the estimates for the two years of 2008 and 2009, when the 9.15 sampling method largely underestimated, were better than those using the 9.15 sampling method. It is concluded that the proposed approach can be used to timely provide rice yield estimates that reflect the meteorological conditions for more effective intervention plans in the rice market.

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A Probabilistic Multimodel Ensemble Approach to Seasonal Prediction

Cited by 42 publications

References 36 publications

Assessment of probability multimodel seasonal forecast based on the APCC model data

Assessment of probability multimodel seasonal forecast based on the APCC model data

Designing and Implementing Weather Generators as Web Services

Assessment of Multimodel Ensemble Seasonal Hindcasts for Satellite-Based Rice Yield Prediction

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