Category Archives: data assimilation

Covid-19: Using tools from geophysics to assess, monitor and predict a pandemic

By: Alison Fowler, Alberto Carrassi, Javier Amezcua The emergence of a new coronavirus disease, known as Covid-19, that could be transmitted between people was identified in China in December 2019. By 3rd March 2020 it had spread to every continent … Continue reading

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Building a predictive framework for studying causality in complex systems

By: Nachiketa Chakraborty I’m Nachiketa Chakraborty, a postdoctoral researcher working on the ERC project CUNDA (Causality under Non-linear Data Assimilation) led by Peter Jan van Leeuwen. My central goal is to come up with a Bayesian framework for studying causal … Continue reading

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Don’t (always) blame the weather forecaster

By: Ross Bannister There are (I am sure) numerous metaphors that suggest that a small, almost immeasurable event, can have a catastrophic outcome – that adding the proverbial straw to the load of the camel will break its back. In 1972, … Continue reading

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Probing the atmosphere with sound waves

By: Javier Amezcua Summer is a quiet time for both the University of Reading and the town itself. The buzzing that fills campus during term time is gone, the population decreases and activities are reduced. Some people find it relaxing … Continue reading

Posted in Climate, data assimilation, Stratosphere, Wind | Leave a comment

Rescuing the Weather

By: Ed Hawkins Over the past 12 months, thousands of volunteer ‘citizen scientists’ have been helping climate scientists rescue millions of lost weather observations. Why? Figure 1: Data from Leighton Park School in Reading from February 1903. If we are to … Continue reading

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DARE to use datasets of opportunity

By Joanne Waller To accurately forecast the weather, we must first describe what is currently happening in the atmosphere. To determine the current atmospheric state, we could use: Previous forecasts (data from complex computational models of the atmosphere) which provide … Continue reading

Posted in data assimilation, earth observation, Flooding, University of Reading, Weather, Weather forecasting | Leave a comment

The “size” of the NWP/DA problem

By Javier Amezcua There is a professor in the University of Reading that likes to say that the Data Assimilation (DA) problem in Numerical Weather Prediction (NWP) is larger than the size of the universe (estimated to be around 1080 … Continue reading

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Improving estimates of soil moisture over Ghana

By Ewan Pinnington This work aims to improve estimates of soil moisture over Ghana as part of the ERADACS project. In regions where the population relies on subsistence farming it is soil moisture, rather than precipitation per se, that is … Continue reading

Posted in Africa, Climate, Climate modelling, data assimilation, Hydrology, land use, Numerical modelling | Tagged | Leave a comment

What’s in a number?

By Nancy Nichols Should you care about the numerical accuracy of your computer? After all, most machines now retain about 16 digits of accuracy, but usually only about 3-4 figures of accuracy are needed for most applications;  so what’s the … Continue reading

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Can observations of the ocean help predict the weather?

By Amos Lawless It has long been recognized that there are strong interactions between the atmosphere and the ocean. For example, the sea surface temperature affects what happens in the lower boundary of the atmosphere, while heat, momentum and moisture … Continue reading

Posted in Boundary layer, data assimilation, Numerical modelling, Oceans, Weather forecasting | Leave a comment