Over the last few months there has been extreme drought in Amazonia. This may be related to warming of the North Atlantic and Gulf of Mexico, the same feature that helped generate unusually violent hurricanes and contributed to 2005 breaking the record as the most active year for Atlantic tropical cyclones since records began. The Amazon drought may have been a similarly unusual event. In western Amazonia particularly this may have been the most intense drought since weather records began in this region in the mid 20th-century. By October, river stage levels along the middle and lower reaches of the Amazon river had reached the lowest marks for 35 to 60 years, which indicates that most of the vast Amazon basin (about 5 million km2) has seen exceptionally dry conditions for many months. The drought led to a state of emergency in parts of Brazil, where boats could no longer be used to supply towns and villages with essential supplies. Reports from Amazonian towns such as Iquitos (Peru), Leticia (Colombia), and Manaus (Brazil) suggest that temperatures approached, and perhaps exceeded, their all-time temperature records. The drought appears to be ending now. This project will attempt to assess the impacts of this unusual event on the Amazon forest / which harbours more carbon and more species than any other ecosystem on earth. Water is essential for plant growth, so the growth rates of trees may have been severely reduced, and also the rates of tree death may have increased. Changes in rates of tree growth and death impact on the amount of carbon stored. However, at the moment, the severity of these effects is not known. However strong these effects may (or may not) have been, the drought does represent a scientific opportunity that must be seized, because it may provide a window into the future. Human-driven climate change is expected to increase temperatures substantially in this region (by 2 to 5 Celsius within the century), and probably to diminish rainfall. Studying the effects of this drought in detail on the structure of forest canopies, the structure of leaves and branches, and how different species and types of tree respond, can provide the information to make predictions of how Amazonian forests might look in future. This research team is in a unique position to study the effects of this drought. A network of long term monitoring plots has been established over the last five years, building on plots established as long ago as 1970. With our South American colleagues these plots are regularly monitored, and many were remeasured during the last 12 months. In a few, select sites, we have also been looking frequently (as often as every fortnight) at short-term ecological processes such as leaf litter-fall, and measuring the weather that the plots are experiencing. In the proposed research we set out a strategy for measuring the effect that this remarkable drought has had. Not only will we return as soon as possible to make the long-term measurements such as tree growth, death, in as many plots as possible, but we will also make the high-intensity, short-term measurements (such as litterfall) for an additional year following the drought so that we can understand in more detail how Amazon forests recover from the drought. Together with this intensive fieldwork and subsequent laboratory analyses we will also synthesize existing weather data from across the Amazon to understand the precise magnitude, intensity, and distribution of the drought, and also satellite-based measurements of forest canopy properties to understand how the extreme conditions have affected the larger region, and to put our localised fieldwork results in context of the whole region. The overall outcome of the project will be to discover just how serious this event has been for plants in the region, and therefore to allow us to make much better predictions of what might happen in the future.

Ozone in the stratosphere protects life on Earth from harmful ultraviolet radiation. In the troposphere, however, it is a harmful pollutant, increasing the incidence of lung disease and decreasing the productivity of crops. Human activities do not emit much ozone directly. However, they do emit many molecules which participate in chemical reactions which form ozone. So, before we can control the levels of ozone, we need to understand where these other molecules come from and how they cause ozone to form. A major source of ozone precursor molecules is the burning of biomass: this also contributes to poor air quality in other ways. Pollution from biomass burning spreads around the globe, affecting areas at great distances from its sources. The chemistry of the troposphere is complex, requiring detailed computer models in order to simulate its behaviour. Because some of the ozone in the troposphere comes from the stratosphere, it is advantageous to use a single model that simulates both regions and the transport of air between them. The TOMCAT/SLIMCAT three-diemnsional model is a state-of-the-art model of this type. Satellites have made global measurements of trace chemicals in the stratosphere for several decades. To do the same for the troposphere is much more difficult. Aura is a satellite, launched in July 2004, which carries out this mission. Four instruments fly on Aura of which three make measurements of tropospheric chemistry. These three instruments operate in different ways and have very different strengths and weaknesses. The purpose of this proposal is to gain an improved understanding of the processes that produce tropospheric ozone. To achieve this, we will combine data from Aura with the TOMCAT/SLIMCAT model. It will first be necessary to assess the degree to which the model agrees with the measurements. In order for this comparison to be made, it is necessary to extract data from the model at the same times and places and in the same manner as the measurements are made. With this assessment done, we then intend to work backwards from the measurements, in order to estimate how much of various pollutant molecules are being emitted and hence how much biomass is being burned. We will also estimate how much of the ozone in the troposphere comes from the stratosphere.

There is a pressing need to quantify the exchange of mass between the world's oceans and polar ice caps, and this can only be achieved by measuring how their volumes are changing. Currently circa 50% of the observed sea level rise of 1.8 mm yr-1 cannot be explained. The required measurements can only be made effectively from space using satellites, and several missions are either in space now, or are about to be deployed to attack this problem. In simple terms the sea and ice topography, and how it changes, can be inferred by measuring ranges from the satellites to the surface, and then subtracting the ranges from the position of the satellites in a geocentric reference frame. The satellite position is calculated by the process of orbit determination, which requires mathematical modelling of the forces acting on the satellites. Errors in the satellite orbit map directly into errors in the inferred topography. Both the orbit determination process and the modelling of the time evolution of the sea and ice changes rely upon a 'reference frame' - put simply this is a list of coordinates and velocities of the tracking stations used to observe how the satellites move in space. Velocities are needed because the tracking stations are sited on tectonic plates, all of which are in continuous motion. As these kind of analyses model geophysical effects that last decades this motion of the tracking stations must be known accurately. In turn, the methods used to calculate the station positions (coordinates) and velocities are linked to the orbit determination process - so once again, errors in the orbit estimates create problems. Orbital accuracy in the satellite radial direction of around 1 cm is required to reduce the uncertainty in the target geophysical parameters. We believe this can be achieved by accurate modelling of the satellite forces. The principal problems here are satellite surface forces caused by solar radiation pressure, thermal effects and forces caused by radiation reflected and emitted by the Earth (termed albedo effects), as well as atmospheric drag effects. These forces, particularly the earth radiation effects, have very strong seasonal and latitudinal characteristics which, if not modelled appropriately, appear as seasonal and latitudinal variations in the inferred sea and ice topography. The PI and his group have developed a suite of software utilities to attack these force modelling problems that are recognised as the leading techniques in the world for dealing with complex, realistic models of the spacecraft response to its environment. The group has been invited to participate in several international experiments that involve modelling complexity that has never been attempted before, and this proposal seeks to extend the group's techniques and apply them to current missions to achieve the 1 cm goal. Failure to address this problem of systematic biases in the satellite orbits would seriously undermine any attempt to constrain climate change models on the basis of the estimated mass exchanges.

Robotics is changing the landscape of innovation. But traditional design approaches are not suited to novel or unknown habitats and contexts, for instance: robot colonies for ore mining, exploring or developing other planets or asteroids, or robot swarms for monitoring extreme environments on Earth. New design methodologies are needed that support optimising robot behaviour under different conditions for different purposes. It is accepted that behaviour is determined by a combination of the body (morphology, hardware) and the mind (controller, software). Embodied AI and morphological computing have made major progress in engineering artificial agents (i.e., robots) by focusing on the links between morphology and intelligence of natural agents (i.e., animals). While such a holistic body-mind approach has been hailed for its merits, we still lack an actual pathway to achieve this. While this goal is ambitious, it is achievable by introducing a unique methodology: a hybridisation of the physical evolutionary system with a virtual one. On the one hand, it is appreciated that an effective design methodology requires the use and testing of physical robots. This is because simulations are prone to hidden biases, errors and simplifications in the underlying models. Simulating populations of robots (rather than just simulating specific parts) leads to accumulated errors and a lack of physical plausibility: the evolved designs will not work in the real system. This is the notorious reality gap of evolutionary robotics. On the other hand, evolving everything in hardware is time and resource consuming. One of our major innovations is to run simulated evolution concurrently with the physical and hybridise them by cross-breeding, where a physical and a virtual robot can parent a child that may be born in the real world, in the virtual world or in both. The advantages of such a hybrid system are significant. Physical evolution is accelerated by the virtual component that can run faster to find good robot features with less time and resources; simulated evolution benefits from the influx of genes that are tested favourably in the real world. Furthermore, monitoring of and feedback from the physical system can improve the simulator, reducing the reality gap.

This project addresses the problem of "characterisation" of Extreme Environments (EE), by deploying and combining information from a variety of different Remote Sensing modalities. Our principle application area is nuclear decommissioning, however our research outputs will be relevant to other EE. Before nuclear decommissioning interventions can happen, the facility/plant being decommissioned must be "characterised", to understand: physical layout and 3D geometry; structural integrity; contents including particular objects of interest (e.g. fuel rod debris). 3D plant models must further be annotated with additional sensed data: thermal information; types/levels/locations of contamination (radiological, chemical etc.). Characterisation may be needed before, during or after POCO (Post Operation Clean Out). "Quiescent buildings" may be over half a century old, with uncertain internal layout and contents. Characterisation is needed in dry environments (e.g. contaminated concrete "caves") and wet environments (e.g. legacy storage ponds). Caves may be unlit, causing difficult vision problems (shadows, contrast, saturation) with robot-mounted spotlights. Underwater environments cause significant visibility degradation for RGB cameras, and render most depth/range sensors unusable. New technologies, e.g. acoustic cameras, engender interesting new challenges in developing algorithms to process these new kinds of image data. In many cases, robots are needed to deploy Remote Sensors into Extreme Environments and move them to desired locations and viewing poses. In some cases, robots must also assist characterisation by retrieving samples of contaminated materials. In many case real-time Remote Sensing data must also be applied to inform and control the actions of robots, while performing remote intervention tasks in EE. This project brings together a unique, cross-disciplinary and international team of researchers and institutes, spanning three continents, to address these challenges. End-users NNL and JAEA will advise on scenarios and challenges for Remote Sensing in nuclear environments. Active facilities at JPL will be used to measure degradation of sensors, chips and software under a variety of radiation types and doses. JPL and Essex researchers will use this data to develop new models for predicting such degradation. Essex researchers will then develop new methods for software and embedded hardware design, which overcome radiation damage by incorporating new approaches to fault detection, tolerance and recovery. The scenarios provided by the partners, and the degradation data measured by JPL, will be used to develop new benchmark data-sets comprising data from multiple sensing modalities (RGB cameras, depth/range cameras, IR thermal imaging, underwater acoustic imaging), featuring a vairiety of nuclear scenes and objects. UoB and Essex researchers will develop new algorithms for real-time 3D characterisation of scenes, with intelligent and adaptive fusion of multiple sensing modalities. First, new multi-sensor fusion methods will be developed for 3D modelling, semantic/meta-data labelling, recognition and understanding of scenes and objects. Second, these methods will be extended to incorporate new algorithms for overcoming extreme noise and other kinds of degradation in images and sensor data. Third, we will develop the robots and robot control methods needed to: i) deploy remote sensors into extreme environments; ii) exploit remote sensor data to guide robotic interventions and actions in these environments. Finally, we will carry out experimental deployments of these new technologies. Robust hardware and software solutions, developed by Essex, will be tested in active radiation environments at JPL. We will also carry out experimental robotic deployments of sensor payloads into inactive but plant-representative nuclear environments at NNL Workington and the Naraha Fukushima mock-up testing facilities in Japan.