opportunità di lavoro
ECMWF: Posizione di dottorato presso il centro Europeo

Posizione di dottorato presso il centro Europeo (ECMWF)

Segnaliamo una posizione PhD full time disponibile al centro Europeo ECMWF (European Centre for Medium-Range Weather Forecasts) nel dipartimento di Matematica e Statistica presso l’University of Reading dal titolo: “Mathematics: Randomized parallel algorithms for data assimilation in numerical weather prediction“, con scandenza il 20 Gennaio 2023.

Supervisors:  Jennifer Scott (UoR), Amos Lawless (UoR), Massimo Bonavita (ECMWF), Nicolas Bousserez (ECMWF).

Maggiori informazioni sulla domanda di ammissione sono disponibili al seguente link.

seminari corsi meteorologia
Masterclass RMetS e Università di Reading

MASTERCLASS Series: “Advances in weather and climate forecasting”

Continua la serie di lezioni online frutto della partnership tra la Royal Meteorological Society e l’Università di Reading; la prossima, che si terrà in modalità remota il 23 marzo dalle 15 alle 16:30 UTC, sarà tenuta dal prof. Peter Clarke:

How do we use the “Weather” in “Numerical Weather Prediction”?

Abstract: We have been forecasting using computer models for well over 50 years. However, we soon became used to the idea that so-called ‘NWP’ models predict the synoptic-scale meteorology, such as the position and strength of low- or high-pressure regions. They lacked the resolution or sufficiently sophisticated representation of physical processes to actually forecast the ‘weather’ such as rain, cloud, fog without additional help from some kind of post-processing or interpretation by meteorologists. 
Vast increases in computer power have led, in part at least, to increases in model resolution and sophistication, pioneered in regional models to the extent that they now do represent much of the ‘weather’ directly. Model horizontal grid lengths of 1-2 km are now common, and some centres are investigating resolutions 10 times higher, such that some of the motions we would label as ‘turbulence’ are explicitly simulated! 
This revolution in resolution has contributed to huge improvements in forecasting the ‘meteorology’, but, paradoxically, smaller scales are less predictable than larger scales, partly because of higher sensitivity to physical processes like cloud microphysics and turbulence. We are faced with a real dilemma over what we can believe in models, how we represent our uncertain knowledge through stochastic parametrizations and how we extract the best information from what we have. This talk and discussion will highlight these issues and discuss some of the work going on to help us make best use of these advances. 

La descrizione dettagliata di questa e delle altre lezioni della serie, le biografie degli oratori e le informazioni per partecipare si possono trovare a questo link.