Subseasonal to Seasonal Prediction

Nyhet

The Gap Between Weather and Climate Forecasting

Häftad, Engelska, 2025

Av Andrew Robertson, Frederic Vitart, USA) Robertson, Andrew (Senior Research Scientist, International Research Institute for Climate and Society, Earth Institute, Columbia University, NY, UK) Vitart, Frederic (Senior Research Scientist, European Centre for Medium-range Weather Forecasts (ECMWF)

1 679 kr

Beställningsvara. Skickas inom 11-20 vardagar

Fri frakt för medlemmar vid köp för minst 249 kr.

Sub-seasonal to Seasonal Prediction provides the latest thinking from experts in the fields of sub-seasonal to seasonal (S2S) predictability science, numerical modeling, operational forecasting, and developing application sectors. This fully updated second edition comprehensively covers the sources of S2S predictability, S2S modeling, and forecasting using dynamical or machine learning methods, and S2S applications. There are brand new chapters on the role of the ocean in sub-seasonal predictability, machine learning in S2S prediction, co-produced S2S climate services in Africa, S2S for energy, and marine weather prediction on S2S timescales. This valuable resource offers atmospheric and climate scientists the very latest developments in this rapidly evolving field.

Contributed chapters from experts in S2S science and forecasting updated for use in an emerging and interdisciplinary field
Synthesis of the state of S2S science, through the use of concrete examples that enable potential users of S2S forecasts to quickly grasp the potential for use in their own decision setting
Broad set of interdisciplinary linked topics, illustrated with graphic examples to powerfully illustrate the interdisciplinary linkages

Produktinformation

Utgivningsdatum2025-11-10
Mått191 x 235 x undefined mm
Vikt450 g
FormatHäftad
SpråkEngelska
Antal sidor896
Upplaga2
FörlagElsevier Science
ISBN9780443315381

Tillhör följande kategorier

Meteorologi och klimatologi inom Naturvetenskap och teknik

Dr Andrew Robertson is a Senior Research Scientist at the International Research Institute for Climate and Society, part of the Earth Institute at Columbia University. He heads the IRI Climate Group and teaches as an adjunct professor at Columbia. Graduating with a PhD in atmospheric dynamics, he has over 30 years of experience in topics ranging from midlatitude meteorology, coupled ocean-atmosphere climate dynamics, sub-seasonal and seasonal forecasting, downscaling, and tailoring of climate information for use in conjunction with sectoral models for climate adaptation and risk management. He has taught in capacity building training courses around the world. Frédéric Vitart is a Senior Research Scientist at the European Centre for Medium-range Weather Forecasts (ECMWF). After graduating with a PhD in atmospheric and oceanic sciences from Princeton University, he joined ECMWF in 1998, where he leads the research on ensemble sub-seasonal forecasts. He has over 20 years of experience in sub-seasonal and seasonal prediction, couple ocean-atmosphere modeling, tropical and mid-latitude meteorology, tropical cyclone prediction. He is the author of over 100 publications in the peer-review literature and has taught in several training courses around the world.

Part I: Setting the scene1. Introduction: Why S2S?2. Weather forecasting: What sets the forecast horizon?3. Weather within Climate: Sub-seasonal predictability of tropical daily rainfall characteristicsPart II: Sources of S2S Predictability4. The Madden-Julian Oscillation5. Extratropical sub-seasonal–to–seasonal oscillations and multiple regimes: The dynamical systems view6. Tropical-Extratropical Interactions and Teleconnections7. Land surface processes relevant to S2S prediction8. Role of ocean in sub-seasonal predictability9. The role of sea ice in sub-seasonal predictability10. Sub-seasonal Predictability and the StratospherePart III: S2S Modeling and Forecasting11. Forecast system design, configuration, complexity12. Ensemble generation: the TIGGE and S2S ensembles13. Forecast recalibration and multi-model combination14. Forecast verification for S2S time scales15. Machine learning S2S predictionPart IV: S2S Applications16. Sub-seasonal to Seasonal Prediction of Weather Extremes17. Communication and dissemination of forecasts and engaging user communities18. Seamless prediction of monsoon onset and active/break phases19. Predicting climate impacts on health at sub-seasonal to seasonal timescales20. Co-produced S2S Climate Services in Africa21. S2S for Energy22. Marine weather prediction on S2S timescales23. Epilogue