Thursday 19th
Parametrizing cloud processes in weather and climate models
The dynamics of clouds are determined by a complex ‘web’ of interconnected processes. Computational fluid dynamics models (eg, Large Eddy Simulation; LES) of clouds are valuable tools for understanding the physical implications of this complexity, but these models are computationally too expensive to use for making operational predictions of the real atmosphere on the scales required for weather forecasts and climate projections. Hence, methods are needed to simplify the representations of cloud process to the point where they can be included (“parametrized”) in weather and climate models. In this talk I will describe some approaches which have proved helpful for achieving these simplifications, including the use of LES, observations and theory. I will also show how many of the biases and uncertainties in weather and climate predictions are highly sensitive to details and assumptions made in parametrizations.
Dr Kalli Furtado, UK Met Office
Kalli joined the Met Office in 2010. Between 2008-2010 Kalli was a research scientist at the Institute for Energy Technology in Norway. In 2007 Kalli obtained a doctorate in Theoretical Physics from the Rudolf Peierls Centre for Theoretical Physics at the University of Oxford. Kalli works in the cloud scale modelling group that is part of Atmospheric Processes and Parametrizations. The cloud scale modelling group focuses on process studies of cloud microphysics. This work is used to improve the physical representation of cloud microphysical parametrizations. Currently, Kalli is working on the parametrization of ice and mixed-phase (liquid, ice, vapour) clouds, the evaluation of models against satellite and aircraft observations and the role of cloud-microphysics in high-resolution (convection-permitting) simulations of the East Asian Monsoon. Much of this research is motivated by understanding and reducing model biases. Kalli is working on the role of high clouds in tropical tropopause temperature biases and investigating approaches to mixed-phase microphysics that reduce Southern Ocean cloud and radiation biases.
Data assimilation for weather and climate forecasting
Data assimilation (DA) is a powerful mathematical technique that allows information in models to be combined with observations. The output is a more accurate knowledge of the variables of interest. In numerical weather prediction this is often a forecast model’s initial state, but could also be improved values of model parameters, or a probability density function. In this way DA allows models of the atmosphere to stay in line with reality as measured by a vast variety of instruments.
Within this series of lectures, we will show how many of the most commonly used DA algorithms can be derived from Bayes’ theorem, posing the problem as that of finding the posterior probability density function. We will examine how assumptions of linearity and Gaussianity allow for efficient data assimilation algorithms that can be applied to the large-scale problem of initialising weather forecasts. Roadmaps for implementing DA for a variety of geophysical problems will be provided. The methods introduced will include variational and ensemble Kalman filter techniques.
Dr Alison Fowler, University of Reading
Dr Alison Fowler is a core Research Fellow at the National Centre for Earth Observation based at the University of Reading. Her work focuses on understanding uncertainty associated with Earth observation data and developing methods to optimise observation strategies and networks. In close collaboration with the UK Met Office, Dr. Fowler has worked on the development of data assimilation algorithms for the treatment of positional errors in the boundary layer capping inversion, and theory to advance the development of coupled atmosphere-ocean data assimilation.
Dr Ross Banister, University of Reading
Ross Bannister is a data assimilation scientist at the National Centre for Earth Observation, Department of Meteorology, University of Reading. His work concerns data assimilation for numerical weather prediction, especially at convective scales, and for global trace gas source estimation. Most of his work has a focus on developing efficient ‘models’ of error covariances needed to solve these problems. His background is in physics, and completed a PhD in physics of correlated electron systems from the University of Warwick in 1998.