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  • 1
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    Multimodel Ensemble Forecasts of Precipitation Based on an Object-Based Diagnostic Evaluation (2020)
    American Meteorological Society
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    Publication Date: 2020-06-01
    Description: We analyzed 24-h accumulated precipitation forecasts over the 4-month period from 1 May to 31 August 2013 over an area located in East Asia covering the region 15.05°–58.95°N, 70.15°–139.95°E generated with the ensemble prediction systems (EPS) from ECMWF, NCEP, UKMO, JMA, and CMA contained in the TIGGE dataset. The forecasts are first evaluated with the Method for Object-Based Diagnostic Evaluation (MODE). Then a multimodel ensemble (MME) forecast technique that is based on weights derived from object-based scores is investigated and compared with the equally weighted MME and the traditional gridpoint-based MME forecast using weights derived from the point-to-point metric, mean absolute error (MAE). The object-based evaluation revealed that attributes of objects derived from the ensemble members of the five individual EPS forecasts and the observations differ consistently. For instance, their predicted centroid location is more southwestward, their shape is more circular, and their orientation is more meridional than in the observations. The sensitivity of the number of objects and their attributes to methodological parameters is also investigated. An MME prediction technique that is based on weights computed from the object-based scores, median of maximum interest, and object-based threat score is explored and the results are compared with the ensemble forecasts of the individual EPS, the equally weighted MME forecast, and the traditional superensemble forecast. When using MODE statistics for the forecast evaluation, the object-based MME prediction outperforms all other predictions. This is mainly because of a better prediction of the objects’ centroid locations. When using the precipitation-based fractions skill score, which is not used in either of the weighted MME forecasts, the object-based MME forecasts are slightly better than the equally weighted MME forecasts but are inferior to the traditional superensemble forecast that is based on weights derived from the point-to-point metric MAE.
    Print ISSN: 0027-0644
    Electronic ISSN: 1520-0493
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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  • 2
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    Prediction Skill of Extended Range 2-m Maximum Air Temperature Probabilistic Forecasts Using Machine Learning Post-Processing Methods (2020)
    Molecular Diversity Preservation International
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    Publication Date: 2020-08-04
    Description: The extended range temperature prediction is of great importance for public health, energy and agriculture. The two machine learning methods, namely, the neural networks and natural gradient boosting (NGBoost), are applied to improve the prediction skills of the 2-m maximum air temperature with lead times of 1–35 days over East Asia based on the Environmental Modeling Center, Global Ensemble Forecast System (EMC-GEFS), under the Subseasonal Experiment (SubX) of the National Centers for Environmental Prediction (NCEP). The ensemble model output statistics (EMOS) method is conducted as the benchmark for comparison. The results show that all the post-processing methods can efficiently reduce the prediction biases and uncertainties, especially in the lead week 1–2. The two machine learning methods outperform EMOS by approximately 0.2 in terms of the continuous ranked probability score (CRPS) overall. The neural networks and NGBoost behave as the best models in more than 90% of the study area over the validation period. In our study, CRPS, which is not a common loss function in machine learning, is introduced to make probabilistic forecasting possible for traditional neural networks. Moreover, we extend the NGBoost model to atmospheric sciences of probabilistic temperature forecasting which obtains satisfying performances.
    Electronic ISSN: 2073-4433
    Topics: Geosciences
    Published by Molecular Diversity Preservation International
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  • 3
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    Risks of precipitation extremes over Southeast Asia: does 1.5 °C or 2 °C global warming make a difference? (2019)
    Institute of Physics
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    Publication Date: 2019-04-02
    Print ISSN: 1748-9318
    Electronic ISSN: 1748-9326
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Published by Institute of Physics
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  • 4
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    Probabilistic Precipitation Forecasting over East Asia Using Bayesian Model Averaging (2019)
    American Meteorological Society
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    Publication Date: 2019-04-01
    Description: Bayesian model averaging (BMA) was applied to improve the prediction skill of 1–15-day, 24-h accumulated precipitation over East Asia based on the ensemble prediction system (EPS) outputs of ECMWF, NCEP, and UKMO from the TIGGE datasets. Standard BMA deterministic forecasts were accurate for light-precipitation events but with limited ability for moderate- and heavy-precipitation events. The categorized BMA model based on precipitation categories was proposed to improve the BMA capacity for moderate and heavy precipitation in this study. Results showed that the categorized BMA deterministic forecasts were superior to the standard one, especially for moderate and heavy precipitation. The categorized BMA also provided a better calibrated probability of precipitation and a sharper prediction probability density function than the standard one and the raw ensembles. Moreover, BMA forecasts based on multimodel EPSs outperformed those based on a single-model EPS for all lead times. Comparisons between the two BMA models, logistic regression, and raw ensemble forecasts for probabilistic precipitation forecasts illustrated that the categorized BMA method performed best. For 10–15-day extended-range probabilistic forecasts, the initial BMA performances were inferior to the climatology forecasts, while they became much better after preprocessing the initial data with the running mean method. With increasing running steps, the BMA model generally had better performance for light to moderate precipitation but had limited ability for heavy precipitation. In general, the categorized BMA methodology combined with the running mean method improved the prediction skill of 1–15-day, 24-h accumulated precipitation over East Asia.
    Print ISSN: 0882-8156
    Electronic ISSN: 1520-0434
    Topics: Geography , Physics
    Published by American Meteorological Society
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  • 5
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    Probabilistic Forecasting of the 500 hPa Geopotential Height over the Northern Hemisphere Using TIGGE Multi-model Ensemble Forecasts (2021)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2021-02-15
    Description: Bayesian model averaging (BMA) and ensemble model output statistics (EMOS) were used to improve the prediction skill of the 500 hPa geopotential height field over the northern hemisphere with lead times of 1–7 days based on ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP), and UK Met Office (UKMO) ensemble prediction systems. The performance of BMA and EMOS were compared with each other and with the raw ensembles and climatological forecasts from the perspective of both deterministic and probabilistic forecasting. The results show that the deterministic forecasts of the 500 hPa geopotential height distribution obtained from BMA and EMOS are more similar to the observed distribution than the raw ensembles, especially for the prediction of the western Pacific subtropical high. BMA and EMOS provide a better calibrated and sharper probability density function than the raw ensembles. They are also superior to the raw ensembles and climatological forecasts according to the Brier score and the Brier skill score. Comparisons between BMA and EMOS show that EMOS performs slightly better for lead times of 1–4 days, whereas BMA performs better for longer lead times. In general, BMA and EMOS both improve the prediction skill of the 500 hPa geopotential height field.
    Electronic ISSN: 2073-4433
    Topics: Geosciences
    Published by Molecular Diversity Preservation International
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