{"id":23650,"date":"2022-03-02T15:37:21","date_gmt":"2022-03-02T15:37:21","guid":{"rendered":"https:\/\/research.reading.ac.uk\/research-blog\/?post_type=tribe_events&p=23650"},"modified":"2022-03-02T15:37:21","modified_gmt":"2022-03-02T15:37:21","slug":"royal-meteorological-society-masterclass-data-assimilation-and-crowdsourced-observations-in-numerical-weather","status":"publish","type":"tribe_events","link":"https:\/\/research.reading.ac.uk\/research-blog\/event\/royal-meteorological-society-masterclass-data-assimilation-and-crowdsourced-observations-in-numerical-weather\/","title":{"rendered":"Royal Meteorological Society Masterclass: Data Assimilation and Crowdsourced Observations in Numerical Weather"},"content":{"rendered":"

Speaker: Professor Sarah Dance, University of Reading<\/strong><\/p>\n

Weather forecasts are obtained by combining weather observations with computational predictions using a data assimilation process. Forecast accuracy relies on accurate estimates of the uncertainty in these weather observations. Professor Dance will introduce data assimilation, the observations used in numerical weather prediction and how observation uncertainty is dealt with in the data assimilation process.<\/p>\n

New, inexpensive crowdsourced observations are being investigated for numerical weather prediction to fill gaps in existing scientific observing networks. However, the uncertainty comparisons between crowdsourced observations and numerical model predictions are not well understood. For example, the measurements will be affected by their local environment (e.g. a temperature measurement in a sheltered street will give a different reading to one made on the top of a skyscraper). Therefore, data assimilation algorithms must take account of the discrepancy in space and time scales represented by the model and those observed in the actual process.<\/p>\n

Examples will be given from recent research, including temperature and wind observations from air traffic control reports and temperature observations from private cars. This talk will also discuss how modern deep learning techniques could be used alongside these datasets to improve numerical weather predictions in the future.<\/p>\n

About RMetS and University of Reading Meteorological Masterclasses<\/strong><\/p>\n

Continuing our online Meteorological Masterclasses in partnership with the University of Reading, we are pleased to announce a new Masterclass series in \u201cAdvances in weather and climate forecasting\u201d.<\/p>\n

During this series, three leading experts from the University of Reading will discuss the latest scientific advances for understanding and predicting weather, climate and its impacts. Topics to be covered include data assimilation and machine learning; identification of causal pathways in atmospheric teleconnections; and modelling advances in resolution and parameterization for weather forecasting.<\/p>\n

These masterclasses are intended to provide support for professionals working in Meteorology and Climate Science, and its operational applications who wish to remain up to date on recent scientific developments in the field.<\/p>\n

Masterclasses will run weekly on Wednesday\u2019s 16th, 23rd and 30th March 2022 from 3 pm to 4.30 pm (UTC), consisting of a presentation followed by the opportunity for questions and discussion with the speaker. Whilst the webinars are part of a series, attendance at all three events is not compulsory.<\/p>\n","protected":false},"excerpt":{"rendered":"

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