The Arctic is a region of exceptional sensitivity to climate change: the observed rate of temperature increase is twice that of the rest of the world, and there is strong evidence for thinning and retreat of Arctic ice pack ice, which reached a record minimum during 2007. Although models agree that the Arctic will continue to have a strong response to increasing greenhouse gas concentrations, they also show much greater variability between models here than elsewhere, and fail even to reproduce current conditions adequately. The response of the Arctic to continued climate change is thus extremely uncertain. Model performance in the Arctic is poor in large part because the parameterizations used are derived from observations in mid-latitudes or the tropics; there are very few measurements within the Arctic, with the result that our understanding of the processes controlling local conditions: cloud dynamics and microphysics, radiative forcing, surface exchange, etc, is poor. This PhD project will study the dynamical and microphysical processes in summertime Arctic stratus clouds using the Met Office Large Eddy Model and a single column version of the Unified Model. The aims are to understand the interactions between the cloud dynamics and microphysics, radiative forcing, and surface coupling, with the ultimate objective of improving the representation of Arctic boundary layer clouds in climate models. The project will assess 1) how the dynamics of Arctic stratus differ from typical marine stratocumulus, 2) what the role of entrainment is in maintaining the cloud in the unique humidity structure of the Arctic lower atmosphere, 3) how the cloud responds to different sources of aerosol: from the surface or entrained from above, since it is currently unknown where the CCN required to maintain Arctic stratus derive from. Such studies are of paramount importance since Arctic stratus is the dominant controlling factor for the surface energy budget, and differ substantially from their mid-latitude counterparts; failure to adequately represent these clouds in climate models makes it impossible to properly simulate Arctic climate. The project is directly linked to a major International Polar Year (IPY) field study taking place in the central Arctic in summer 2008: the Arctic Summer Cloud-Ocean Study (ASCOS). The ASCOS programme has been organised to address the issues above from an observational stance, and will provide an extensive data set for this project to draw upon. Measurements of direct relevance to this study include: aerosol physics and chemistry, boundary layer mean and turbulence structure from the surface through cloud top, surface energy budget, radiative fluxes, and cloud properties - both remotely sensed by cloud radar, lidar, microwave radiometers, etc, and in-situ measurements from a NASA P3 research aircraft. The ASCOS observations provide both the basis for establishing initial conditions for the modelling studies, and a means of assessing the fidelity of the simulations. The modelling study will in turn enhance the observational study by allowing controlled tests of the relative importance of the various factors controlling the cloud properties. The link to ASCOS and the IPY make the project particularly timely, provides access to a unique data set, and adds significant value to current NERC funded research. This link also provides exceptional training opportunities through interaction with the many international collaborators, involvement in ASCOS and IPY workshops and international meetings on polar science following the IPY. The primary supervisor (Brooks) is PI on the UK contribution to ASCOS, and a member of the programme steering group. The supervision team has extensive experience of analysing observational data, integrating it into models, and undertaking detailed process studies; Met Office collaboration results from their strong interest in resolving the issue of Arctic stratus in the UM.

The accurate and timely forecasting of extreme rainfall events is of the utmost importance to both life and property. Satellite observations from multi-spectral, multi-sensor observations provide information that aid the forecasting of such events. To advance the accuracy of these forecasts at the necessary temporal and spatial resolutions, further research is necessary to address the optimal use of such information. Satellite observations exist at a range of temporal and spatial resolutions, and different wavelengths sense different depths within weather systems. These effects lead to different errors in modelling the observations arising from, for example, heterogeneous field of views, and representativeness leading both to correlated and uncorrelated components of error. It is not well understood how these errors propagate into the analysis using variational data assimilation. The UK Meteorological Office operates a number of numerical weather prediction models, of which the 1.5km convective scale model provides very high spatial and temporal resolution short-range forecasts of weather systems. However, the satellite observations assimilated into the high-resolution model have generally much coarser resolutions (both spatially and temporally). The impact of this scale mis-match will assessed quantitatively through the comparison with the results from the Meteorological Office's North Atlantic and European model (12km resolution). A number of UK weather events will be selected for this study covering both high-profile events (such as the Morpeth floods of September 2008) and more representative weather situations. The output of the NWP simulations will be used to simulate the expected satellite observations through radiative transfer models, which in turn will be compared with the actual satellite observations. The overall aim of this project is to investigate the scale-induced errors associated with assimilation of satellite radiances into numerical weather prediction models. The objectives are: 1. To characterise the errors in radiative transfer modelling arising from the computationally affordable treatment of fine scale structure in clouds and precipitation; 2. To improve our understanding of the information availability in different channels with varying resolutions; 3. To assess the different assumptions within the 1D analysis scheme for a range of meteorological conditions; and, 4. To evaluate the effects of beam-filling inherent in low-resolution satellite observations. The project will be based in the School of Geography, Earth and Environmental Sciences at the University of Birmingham, with supervision provided by Dr Chris Kidd and Dr Xiaoming Cai. The Satellite Radiance Assimilation Group of the UK Meteorological Office in Exeter will provide the necessary 'industry' support through the expertise of Stephen English. Both project partners will be supported by members of their research teams to provide the necessary research and training environment. Dr Chris Kidd will provide expertise in satellite observations and retievals supported by Dr Xiaoming Cai with expertise in atmospheric modelling. Dr Stephen English will provide expertise in microwave radiative transfer modelling, data assimilation and applications of infrared and microwave sounding data. Both organisations have complementary experience in satellite meteorology and data assimilation that can be exploited in the proposed studentship. The proposed research is important to both parties in that it addresses advancing the effective use of cloud and precipitation information from satellites observations in high resolution NWP. On-going work on the assimilation radar observations is essentially restricted to land-areas, consequently the proposed research will complement this by advancing our ability to assimilate available information over ocean areas.

Space weather has various effects on out technology. One important effect is on atmospheric density and thus space operations. Space weather driven atmospheric density variations in particular affect low Earth orbit (LEO) satellites, which represent a several hundred million EUR per year business. These LEO satellites (which include the International Space Station) are crucial for earth observation and communication, are affected by space weather effects during all phases of their operational lifetime. Likewise, all rocket launches and re-entry events and some space debris are affected. A better understanding of space weather processes and their impact on atmospheric density is thus critical for satellite operations. The ‘Space Weather Atmosphere Model and Indices’ (SWAMI) project aims to enhance this understanding by: • developing improved neutral atmosphere and thermosphere models, • make a major leap forward by combining these physics-based and empirical models, • exploiting new geomagnetic, and • improve the forecast of the activity indices. The project stands out by providing an integrated approach to the satellite neutral environment, in which the various space weather drivers are addressed together with model improvement. The outcomes of SWAMI will provide a pathway to improved space weather services as the project will not only address the science issues, but also the transition of models into operational services. Our overarching aim is to give Europe a strategic advantage in whole atmosphere modelling, geomagnetic and solar activity forecasting, and the associated LEO satellite operator services for orbit maintenance, re-entry estimations, as well as launch operations. The objectives of the project are to: Develop a unique new whole atmosphere model, by extending and blending the Unified Model (UM), and the Drag Temperature Model (DTM), which are leading models of their kind in the field. A user-focused operational tool for satellite applications sh

Space weather can have detrimental, and in some cases catastrophic, effects upon a multitude of technologies on which we depend as part our daily lives. Adverse space weather is now known to result from solar flares and coronal mass ejections released from the turbulent and highly complex magnetic fields of active regions. Understanding how active region magnetic fields evolve and produce these events is therefore of fundamental importance to developing accurate and reliable space-weather monitoring and forecasting capabilities. We therefore propose to develop an advanced flare prediction system (Flare Likelihood And Region Eruption Forecasting; FLARECAST) that is based on automatically extracted physical properties of active regions coupled with state-of-the-art flare prediction methods and validated using the most appropriate forecast verification measures. Active region properties, such as area, magnetic flux, shear, magnetic complexity, helicity and proxies for magnetic energy, will be extracted from solar magnetogram and white-light images in near-realtime using advanced image-processing techniques. Once active region properties have been extracted, they will be correlated with solar flare activity and used to optimize prediction algorithms based on statistical, unsupervised clustering and supervised learning methods. This will enable us to validate our image processing and flare prediction algorithms before launching a near-realtime flare forecasting service, the first of its kind in the world. FLARECAST will therefore form the basis of the first quantitative, physically motivated and autonomous active region monitoring and flare forecasting system, which will be of use to space-weather researchers and forecasters in Europe and around the globe.