Podcasts about numerical weather prediction

Climate modeler Aditi Sheshadri says that while weather forecasting and climate projection are based on similar science, they are very different disciplines. Forecasting is about looking at next week, while projection is about looking at the next century. Sheshadri tells host Russ Altman how new data and techniques, like low-cost high-altitude balloons and AI, are reshaping the future of climate projection on this episode of Stanford Engineering's The Future of Everything podcast.Have a question for Russ? Send it our way in writing or via voice memo, and it might be featured on an upcoming episode. Please introduce yourself, let us know where you're listening from, and share your quest. You can send questions to thefutureofeverything@stanford.edu.Episode Reference Links:Stanford Profile: Aditi SheshadriConnect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads or Twitter/XConnect with School of Engineering >>> Twitter/XChapters:(00:00:00) IntroductionRuss Altman introduces guest Aditi Sheshadri, a professor of Earth systems science at Stanford University.(00:02:58) Climate Projection vs. Weather ForecastingThe differences between climate projection and weather forecasting.(00:04:58) The Window of ChaosThe concept of the "window of chaos" in climate modeling.(00:06:11) Scale of Climate ModelsThe limitations and scale of climate model boxes.(00:08:19) Computational ConstraintsComputational limitations on grid size and time steps in climate modeling.(00:10:56) Parameters in Climate ModelingEssential parameters measured, such as density, temperature, and water vapor.(00:12:18) Oceans in Climate ModelsThe role of oceans in climate modeling and their integration into projections.(00:14:35) Atmospheric Gravity WavesAtmospheric gravity waves and their impact on weather patterns.(00:18:51) Polar Vortex and CyclonesResearch on the polar vortex and on tropical cyclone frequency.(00:21:53) Climate Research and Public AwarenessCommunicating climate model findings to relevant audiences.(00:23:33) New Data SourcesHow unexpected data from a Google project aids climate research,(00:25:09) Geoengineering ConsiderationsGeoengineering and the need for thorough modeling before intervention.(00:28:19) Conclusion Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads or Twitter/XConnect with School of Engineering >>> Twitter/X

Andrea Montani Laurea in fisica (Università di Genova, I), Master e dottorato in Meteorologia (Università di Reading, UK). Dopo aver lavorato presso Arpa Emilia-Romagna Servizio IdroMeteoClima occupandosi di Modellistica Numerica Previsionale, lavora dal 2019 presso ECMWF, Computing Department, dove segue l'implementazione e il supporto dei sistemi produttivi operativi e time-critical. Andrea Montani - Degree in Physics (University of Genoa, I), Master and PhD in Meteorology (University of Reading, UK). After working at Arpa Emilia-Romagna Servizio IdroMeteoClima dealing with Numerical Weather Prediction, he has been working since 2019 at ECMWF, Computing Department, where he follows the implementation and the support of time-critical operational production systems. Siti, app, libri e link utili Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique European Centre for Medium-Range Weather Forecasts ECMWF linkedin  ECMWF what we do  R2B BOLOGNA RESEARCH TO BUSINESS ART-ER Attrattività Ricerca Territorio è la Società Consortile dell'Emilia-Romagna nata per favorire la crescita sostenibile della regione  Tecnopolo di Bologna  I libri da scegliere  Weather Forecast Analyst o Analista previsionale nel settore metereologico Un analista previsionale è responsabile della previsione della produzione futura e delle condizioni finanziarie, di un prodotto, di un servizio, di una previsione per un ente o per un'azienda, analizza le statistiche dei dati correnti. Può applicare la sua expertise in diversi settori compreso quello metereologico. Un Forecast Analyst presso ECMWF e cioè European Centre for Medium-Range Weather Forecasts o Centro europeo per le previsioni meteorologiche a medio termine in particolare è chiamato a supportare i sistemi di produzione operativi critici di ECMWF, con focus su acquisizione e preelaborazione dei dati osservativi, gestione della catena di produzione, generazione, diffusione e archiviazione dei prodotti meteorologici. Garantire un'adeguata qualità del servizio per i sistemi di produzione operativi critici, compreso il supporto per lo sviluppo e la pianificazione dell'evoluzione, la transizione verso le operazioni, la definizione dei processi di supporto operativo, il monitoraggio e la documentazione. Eseguire e fornire consulenza sulla garanzia della qualità per nuovi contributi e modifiche ai sistemi operativi critici. Fornire consulenza nella fase iniziale relativamente a nuovi sviluppi mirati all'implementazione operativa, garantendo compatibilità e livelli adeguati di ottimizzazione e affidabilità. Partecipare a un servizio di chiamata 24 ore su 24 per i servizi che sono fondamentali per il funzionamento di Servizio di consegna delle previsioni ECMWF.

What is the key technology behind weather prediction? How can computer "models" predict the weather days in advance? You will learn about this in my podcast today. And I will provide the Memorial Day weekend forecast, which portends lots of sun and warming temperatures. Look forward to highs reaching 80F in western Washington early next week, and over 100F in portions of eastern Washington.

On this week’s Tech Nation, Moira speaks with the people who predict the weather - Todd Hutchinson, the Director of Numerical Weather Prediction at The Weather Company, and Cameron Clayton, the General Manager of Watson Media & Weather. And another question – what’s in the food we eat? And where did it come from? Moira speaks with Brigid McDermott, Vice President of IBM Food Trust. From one field to one processor to one supermarket, technology can keep tiny problems tiny.

On this week’s Tech Nation, Moira speaks with the people who predict the weather - Todd Hutchinson, the Director of Numerical Weather Prediction at The Weather Company, and Cameron Clayton, the General Manager of Watson Media & Weather. And another question – what’s in the food we eat? And where did it come from? Moira speaks with Brigid McDermott, Vice President of IBM Food Trust. From one field to one processor to one supermarket, technology can keep tiny problems tiny.

On this week’s Tech Nation, Moira speaks with the people who predict the weather - Todd Hutchinson, the Director of Numerical Weather Prediction at The Weather Company, and Cameron Clayton, the General Manager of Watson Media & Weather. And another question – what’s in the food we eat? And where did it come from? Moira speaks with Brigid McDermott, Vice President of IBM Food Trust. From one field to one processor to one supermarket, technology can keep tiny problems tiny.

On this week’s Tech Nation, Moira speaks with the people who predict the weather - Todd Hutchinson, the Director of Numerical Weather Prediction at The Weather Company, and Cameron Clayton, the General Manager of Watson Media & Weather. And another question – what’s in the food we eat? And where did it come from? Moira speaks with Brigid McDermott, Vice President of IBM Food Trust. From one field to one processor to one supermarket, technology can keep tiny problems tiny.

On this week’s Tech Nation, Moira speaks with the people who predict the weather - Todd Hutchinson, the Director of Numerical Weather Prediction at The Weather Company, and Cameron Clayton, the General Manager of Watson Media & Weather. And another question – what’s in the food we eat? And where did it come from? Moira speaks with Brigid McDermott, Vice President of IBM Food Trust. From one field to one processor to one supermarket, technology can keep tiny problems tiny.

Met Office scientists take a lively look at the fascinating history and science of weather and climate research. The panel discuss LF Richardson; the father of Numerical Weather Prediction and an inspiration to Mandelbrot's fractal geometry. The Met Office is the United Kingdom's national weather service. The Met Office website carries the latest UK and global weather forecasts, detailed information on weather types and climate science and UK weather records for previous months, seasons and years. https://www.metoffice.gov.uk/

Cirrus cloud genesis is a multiscale problem. This makes the parameterization in numerical weather prediction models a challenging task. In order to improve the prediction of cirrus clouds and ice supersaturation formation in the German Weather Service (DWD) model chain, the controlling physical processes are investigated and parameterised in a new cloud ice microphysics scheme. Scale dependencies of the ice microphysical scheme were assessed by conducting simulations with an idealised and realistic regional Consortium for Small-Scale Modeling (COSMO) model setup and a global model (GME). The developed two-moment two-mode cloud ice scheme includes state-of-the-art parameterisations for the two main ice creating processes, homogeneous and heterogeneous nucleation. Homogeneous freezing of supercooled liquid aerosols is triggered in regions with high atmospheric ice supersaturations (145-160 %) and high cooling rates. Heterogeneous nucleation depends mostly on the existence of sufficient ice nuclei in the atmosphere and occurs at lower ice supersaturations. The larger heterogeneously nucleated ice crystals can deplete ice supersaturation and inhibit subsequent homogenenous freezing. In order to avoid an overestimation of heterogeneous nucleation, cloud ice sedimentation and a prognostic budget variable for activated ice nuclei are introduced. A consistent treatment of the depositional growth of the two ice particle modes and the larger snowflakes using a relaxation timescale method was applied which ensures a physical representation for depleting ice supersaturation. Comparisons between the operational and the new cloud ice microphysics scheme in the GME revealed that the location of cirrus clouds is dominated by the model dynamics whereas the cirrus cloud structures strongly differed for the different schemes. Especially a reduction in the ice water content between 9 and 11 km was observed when using the new cloud ice scheme. This change is an improvement as demonstrated by a comparison with the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) ice water content product. Further comparisons of the GME with the Integrated Forecast System (IFS) model of the European Centre for Medium-Range Weather Forecasts (ECMWF) show a clear improvement of the ice supersaturation distribution with the new two-moment cloud ice scheme. In-cloud ice supersaturation is correctly captured, which is compliant with in-situ measurements. This is a more physical description then in the IFS model, where in-cloud ice saturation is assumed.

Gneiting, T (Heidelberg) Tuesday 21 December 2010, 10:00-11:00

This dissertation describes various aspects of improvements made in the representation of clouds in the global forecast model of the European Centre of Medium-Range Weather Fore- casts (ECMWF). Cloud parametrization has long been identified as one of the most crucial and uncertain aspects in General Circulation Models (GCMs) of the atmosphere, which are used for both Numerical Weather Prediction and the simulation of climate. It is therefore important to constantly monitor and improve the performance of cloud parametrizations in those models. The first part of the work describes the implementation of an existing cloud parametrization into ECMWF's forecasting system with special attention to a new treatment of the prognos- tic cloud variables in data assimilation. This is followed by an analysis of the performance of the parametrization during a 15-year long data assimilation experiment carried out in the context of the ECMWF reanalysis project. It is shown that despite an overall good perfor- mance, several weaknesses in the simulation of clouds exist. Subtropical stratocumulus and extratropical cloudiness are underestimated, while the cloud fraction in the trade cumulus areas and in the Intertropical Convergence Zone is overestimated. In the second part of the study detailed revisions of the parametrization of cloud generation by convective and non-convective processes are described. A consistent new description of cloud generation by convection is derived using the mass- ux approach. Furthermore an improved description of the generation of clouds by non-convective processes is introduced. The superiority of the new formulation compared to the existing one is demonstrated and links to other approaches to cloud parametrization are established. The third part of the work studies the role of vertically varying cloud fraction for the descrip- tion of microphysical processes. It is shown that the commonly used approach of representing precipitation in GCMs by means of grid-averaged quantities leads to serious errors in the parametrization of various physical processes such as the evaporation of precipitation, with severe consequences for the model's hydrological cycle. A new parametrization of the eects of vertically-varying cloud fraction based on a separation of cloudy and clear-sky precipita- tion uxes is developed and its performance assessed. It is shown that this parametrization alleviates most of the identied problems and thereby more realistically describes the pre- cipitation physics in the presence of cloud fraction variations. The final part of the dissertation takes a critical look at the way the results of cloud parametrizations are evaluated today. A number of studies using a variety of data sources and modelling approaches are described and the need for a coordinated use of the various existing validation techniques is highlighted. A strategy to achieve such coordination is proposed. This work provides contributions to virtually all facets of the development of cloud parame- trizations. It combines theoretical aspects with the use of a variety of modelling approaches and data sources for the assessment of the performance of the parametrization. All model improvements described here are now part of the operational version of the ECMWF forecast model.