Machine Learning seminar series

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Recordings and slides of past talks

Enhancing Western United States Sub-Seasonal Forecasts: Forecast Rodeo Prize Competition Series | 1 December | 16:00 GMT

Speaker: Kenneth Nowak

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Machine-learning-model-data-integration for a better understanding of the Earth System | 24 November | 16:00 GMT

Speaker: Markus Reichstein

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Presentation slides to follow

Causal Networks as a framework for climate science to improve process understanding | 27 October

Speaker: Marlene Kretschmer

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Probabilistic downscaling to detect regional present and future climate hazards | 28 April

Speaker: Sherman Lo (University of Warwick)

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Exploring Machine Learning for Data Assimilation | 7 May

Speaker: Alban Farchi (ECMWF)

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MetNet: A Neural Weather Model for Precipitation Forecasting | 12 May

Speaker: Nal Kalchbrenner (Google Research Amsterdam)

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AI, a change in science/technology ... or culture? | 14 July

Speaker: Alberto Arribas (Met Office)

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Spatiotemporal complexity and time-dependent networks in mid- to late Holocene simulations | 28 July

Speaker: Dr Annalisa Bracco (Georgia Institute of Technology)
 

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Building trustworthy AI for environmental science | 8 September

Speaker: Amy McGovern (University of Oklahoma)

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From research to applications – Examples of operational ensemble post-processing using machine learning |1 October

Speaker: Maxime Taillardat (Météo-France)

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Other Machine Learning Talks at ECMWF

18 June
at 10:30 BST

VIRTUAL

Link to join the seminar

Title: "ecPoint” - a Post-processing Tool that improves Forecasts and highlights Systematic Model Errors

Speaker: Timothy Hewson

Abstract

In April 2019 ECMWF introduced a new, experimental, “point rainfall” forecast product onto its ecCharts web display platform, based on the post-processing package “ecPoint”, to give site-specific forecasts for everywhere in the world up to day 10. Prior to this development forecasters had access to just raw ensemble output in ecCharts, which provides gridbox average totals. ecPoint aims to incorporate probabilistically the expected sub-grid variability, and simultaneously apply gridscale bias corrections. Both these adjustments depend critically on “gridbox-weather-type”.

This presentation will describe the meteorology-based calibration rationale that underpins ecPoint, how this is different to pre-existing post-processing methods, and how it can also be applied to other surface variables such as 2m temperature. Numerous benefits will be highlighted.

The conditional verification concepts underpinning the calibration allow one to identify weather-situation-dependant gridscale biases. Examples will illustrate the diagnostic power of this approach, showing where and when rainfall is typically under- and over-forecast, providing pointers for future model improvements. And using an open source GUI one can apply the calibration code to data from other models, and thereby intercompare performance in different weather situations.

The forecast improvements that then arise will be discussed, using both long term global verification up to day 10, and illustrative case studies, with a focus on how extreme localised rainfall, that might lead to flash floods, is better handled. It will be shown how the post-processing can usefully shift the emphasis for warning issue from one region to another, when one compares with raw ensemble output.

There will be brief reference, from collaborative work, to how ecPoint output seems to compare favourably with the post-processed output of convection-resolving limited area ensembles.

The talk will conclude by discussing, in the context of ongoing and potential projects, numerous future applications of ecPoint, such as bias-corrected inputs to hydrological models, point rainfall re-analyses and tests of theories such as city impact on rainfall. Avenues for improving the methodology will also be highlighted.

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