Category Archives: Numerical modelling

Clouds, climate and the Roaring 40s

By Richard Allan In our new research we have traced large and long-standing biases in computer simulations of climate, affecting the tempestuous Southern Ocean, to errors in cloud that emerge rapidly within the atmospheric models. Biases evolve over time through … Continue reading

Posted in Climate, Climate change, Climate modelling, Clouds, earth observation, Energy budget, Numerical modelling, Oceans, Solar radiation | Leave a comment

Why was the sky Orange?

By William Davies I was sitting in my house one morning in October 2017, engrossed in what I was doing. Gradually I noticed that an eerie darkness was smothering the natural light in the room. I stopped and looked outside. … Continue reading

Posted in Aerosols, Atmospheric chemistry, Atmospheric optics, Climate, Climate modelling, earth observation, Environmental hazards, Numerical modelling, Remote sensing, University of Reading | Leave a comment

Characteristics of cumulus population and microphysical properties observed over Southeast Atlantic

By Yann Blanchard Figure 1. Cumulus in the vicinity of Ascension Island, in a 100 x 100km image (which is close to global climate model spatial resolution) from MODIS onboard AQUA (22 July 2016) Shallow cumulus cover large areas in … Continue reading

Posted in Aerosols, Atlantic, Atmospheric chemistry, Climate modelling, earth observation, Numerical modelling, Oceans, Remote sensing, Solar radiation, University of Reading | Leave a comment

Sting jets in winter storms : how do the winds get so strong?

By Ambrogio Volonté Figure 1: Windstorm Tini (12 Feb 2014) passes over the British Isles bringing extreme winds. A sting jet has been identified in the storm. Image courtesy of NASA Earth Observatory The arrival of a winter storm battering … Continue reading

Posted in Climate, Climate change, extratropical cyclones, Monsoons, Numerical modelling, sting jet, University of Reading, Weather, Weather forecasting | Leave a comment

Exploring the impact of Gulf Stream temperature biases on the global atmospheric circulation

By Robert Lee The climate state in numerical models often have differences when compared to a climatology from observations. These differences are often termed ‘biases’ and can be considered as a kind of error or deficiency in the model. These … Continue reading

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Tibetan Plateau Vortices

By Julia Curio Tibetan Plateau Vortices (TPVs) are meso-scale cyclones that originate over the Tibetan Plateau and move eastwards steered by the subtropical westerly jet above. These storms can also move off the Tibetan Plateau (TP) and travel as far … Continue reading

Posted in China, earth observation, extratropical cyclones, Flooding, Monsoons, Numerical modelling, University of Reading, 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

Chaotic Convection

By Todd Jones In the traditional global climate model (GCM) configuration, models simulate atmospheric motions explicitly on spatial grids with spacings on the order of 100 km. Motions on finer scales are not directly simulated. Instead, we use parameterizations, some … Continue reading

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Exploring the impact of the Atlantic Multidecadal Variability (AMV)

By Dan Hodson After 140 years of observations, we now know that the temperature of the surface of the Atlantic ocean slowly varied over time, cooling and warming over periods of decades (Figure 1). These slow variations in temperature sit … Continue reading

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