A new framework for probabilistic seasonal forecasts based on circulation type classifications and driven by an ensemble global model
<p>In the last years coupled atmospheric ocean climate models have remarkably improved medium range seasonal forecasts, especially on middle latitude areas such as Europe and the Mediterranean basin. In this study a new framework for medium range seasonal forecasts is proposed. It is based...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2018-08-01
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| Series: | Advances in Science and Research |
| Online Access: | https://www.adv-sci-res.net/15/183/2018/asr-15-183-2018.pdf |
| _version_ | 1818549465135448064 |
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| author | G. Messeri G. Messeri R. Benedetti A. Crisci B. Gozzini M. Rossi M. Rossi R. Vallorani R. Vallorani G. Maracchi |
| author_facet | G. Messeri G. Messeri R. Benedetti A. Crisci B. Gozzini M. Rossi M. Rossi R. Vallorani R. Vallorani G. Maracchi |
| author_sort | G. Messeri |
| collection | DOAJ |
| description | <p>In the last years coupled atmospheric ocean climate
models have remarkably improved medium range seasonal forecasts, especially
on middle latitude areas such as Europe and the Mediterranean basin. In this
study a new framework for medium range seasonal forecasts is proposed. It is
based on circulation types extracted from long range global ensemble models
and it aims at two goals: (i) an easier use of the information contained in
the complex system of atmospheric circulations, through their reduction to a
limited number of circulation types and (ii) the computation of high spatial
resolution probabilistic forecasts for temperature and precipitation. The
proposed framework could be also useful to lead predictions of
weather-derived parameters, such as the risk of heavy rainfall, drought or
heat waves, with important impacts on agriculture, water management and
severe weather risk assessment. Operatively, starting from the ensemble
predictions of mean sea level pressure and geopotential height at 500 hPa of
the NCEP – CFSv2 long range forecasts, the third-quantiles probabilistic
maps of 2 m temperature and precipitation are computed through a Bayesian
approach by using E-OBS 0.25° gridded datasets. Two different
classification schemes with nine classes were used: (i) Principal Component
Transversal (PCT9), computed on mean sea level pressure and (ii) Simulated
Annealing Clustering (SAN9), computed on geopotential height at 500 hPa.
Both were chosen for their best fit concerning the ground-level
precipitation and temperature stratification for the Italian peninsula.
Following this approach an operative chain based on a very flexible and
exportable method was implemented, applicable wherever spatially and
temporally consistent datasets of weather observations are available.</p><p>In this paper the model operative chain, some output examples and a first
attempt of qualitative verification are shown. In particular three case
studies (June 2003, February 2012 and July 2014) were examined, assuming
that the ensemble seasonal model correctly predicts the circulation type
occurrences. At least on this base, the framework here proposed has shown
promising performance.</p> |
| first_indexed | 2024-12-12T08:33:41Z |
| format | Article |
| id | doaj.art-f97c2e2b2a3e4fb7b5a1693cabfb4118 |
| institution | Directory Open Access Journal |
| issn | 1992-0628 1992-0636 |
| language | English |
| last_indexed | 2024-12-12T08:33:41Z |
| publishDate | 2018-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Advances in Science and Research |
| spelling | doaj.art-f97c2e2b2a3e4fb7b5a1693cabfb41182022-12-22T00:31:02ZengCopernicus PublicationsAdvances in Science and Research1992-06281992-06362018-08-011518319010.5194/asr-15-183-2018A new framework for probabilistic seasonal forecasts based on circulation type classifications and driven by an ensemble global modelG. Messeri0G. Messeri1R. Benedetti2A. Crisci3B. Gozzini4M. Rossi5M. Rossi6R. Vallorani7R. Vallorani8G. Maracchi9Institute of Biometeorology of the National Research Council (IBIMET-CNR) – Florence, ItalyLaboratory of Monitoring and Environmental Modelling for the Sustainable Development (LaMMA consortium) – Sesto Fiorentino – Florence, ItalyLaboratory of Monitoring and Environmental Modelling for the Sustainable Development (LaMMA consortium) – Sesto Fiorentino – Florence, ItalyInstitute of Biometeorology of the National Research Council (IBIMET-CNR) – Florence, ItalyLaboratory of Monitoring and Environmental Modelling for the Sustainable Development (LaMMA consortium) – Sesto Fiorentino – Florence, ItalyInstitute of Biometeorology of the National Research Council (IBIMET-CNR) – Florence, ItalyLaboratory of Monitoring and Environmental Modelling for the Sustainable Development (LaMMA consortium) – Sesto Fiorentino – Florence, ItalyInstitute of Biometeorology of the National Research Council (IBIMET-CNR) – Florence, ItalyLaboratory of Monitoring and Environmental Modelling for the Sustainable Development (LaMMA consortium) – Sesto Fiorentino – Florence, ItalyAccademia dei Georgofili – Florence, Italy<p>In the last years coupled atmospheric ocean climate models have remarkably improved medium range seasonal forecasts, especially on middle latitude areas such as Europe and the Mediterranean basin. In this study a new framework for medium range seasonal forecasts is proposed. It is based on circulation types extracted from long range global ensemble models and it aims at two goals: (i) an easier use of the information contained in the complex system of atmospheric circulations, through their reduction to a limited number of circulation types and (ii) the computation of high spatial resolution probabilistic forecasts for temperature and precipitation. The proposed framework could be also useful to lead predictions of weather-derived parameters, such as the risk of heavy rainfall, drought or heat waves, with important impacts on agriculture, water management and severe weather risk assessment. Operatively, starting from the ensemble predictions of mean sea level pressure and geopotential height at 500 hPa of the NCEP – CFSv2 long range forecasts, the third-quantiles probabilistic maps of 2 m temperature and precipitation are computed through a Bayesian approach by using E-OBS 0.25° gridded datasets. Two different classification schemes with nine classes were used: (i) Principal Component Transversal (PCT9), computed on mean sea level pressure and (ii) Simulated Annealing Clustering (SAN9), computed on geopotential height at 500 hPa. Both were chosen for their best fit concerning the ground-level precipitation and temperature stratification for the Italian peninsula. Following this approach an operative chain based on a very flexible and exportable method was implemented, applicable wherever spatially and temporally consistent datasets of weather observations are available.</p><p>In this paper the model operative chain, some output examples and a first attempt of qualitative verification are shown. In particular three case studies (June 2003, February 2012 and July 2014) were examined, assuming that the ensemble seasonal model correctly predicts the circulation type occurrences. At least on this base, the framework here proposed has shown promising performance.</p>https://www.adv-sci-res.net/15/183/2018/asr-15-183-2018.pdf |
| spellingShingle | G. Messeri G. Messeri R. Benedetti A. Crisci B. Gozzini M. Rossi M. Rossi R. Vallorani R. Vallorani G. Maracchi A new framework for probabilistic seasonal forecasts based on circulation type classifications and driven by an ensemble global model Advances in Science and Research |
| title | A new framework for probabilistic seasonal forecasts based on circulation type classifications and driven by an ensemble global model |
| title_full | A new framework for probabilistic seasonal forecasts based on circulation type classifications and driven by an ensemble global model |
| title_fullStr | A new framework for probabilistic seasonal forecasts based on circulation type classifications and driven by an ensemble global model |
| title_full_unstemmed | A new framework for probabilistic seasonal forecasts based on circulation type classifications and driven by an ensemble global model |
| title_short | A new framework for probabilistic seasonal forecasts based on circulation type classifications and driven by an ensemble global model |
| title_sort | new framework for probabilistic seasonal forecasts based on circulation type classifications and driven by an ensemble global model |
| url | https://www.adv-sci-res.net/15/183/2018/asr-15-183-2018.pdf |
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