Postdoctoral Research Positions:
Research Associate – Atmospheric Modelling, Fire Emissions & Air Quality – King’s College London
Department : School Of Global Affairs School Office
King’s College London invites applications for the post of Postdoctoral Researcher working on landscape fire emissions and atmospheric modelling to support air quality impact assessments. The post will be located in the Department of Geography, King’s College London.
The postholder will provide an atmospheric modelling capability within the Wildfire Research Team led by Professor Martin Wooster at King’s College London. As part of their research portfolio, this group develop satellite remote sensing-based landscape fire emissions inventories, and wish to augment this with an atmospheric modelling capability in order to support the quantification of air quality impacts of the emitted species. It is anticipated that the post-holder will have experience with WRF-Chem and/or CMAQ. Initially the post will be primarily funded by an STFC Global Challenge Research Fund (GCRF) project, based at King’s College London and with collaborative partners in Leicester (UK) and at several sites in India which will be the target of the initial investigations.
The post will be Fixed Term Contract for 12 months and can be taken up full or part-time.
The selection process will include a presentation and a panel interview.
Grade and Salary : Grade 6, £39,484 annum inclusive of £3,223 London Weighting Allowance per annum
Job ID : 006066
Closing Date : 20-Nov-2018
Contact : Professor Martin Wooster, email: Martin.Wooster@kcl.ac.uk
More details including how to apply are here
PhD Studentship Opportunities:
NCEO PhD Studentships at the Scenario Doctoral Training Partnership
Full details including how to apply are here
Bias correction in Data Assimilation for Numerical Weather Prediction
Lead Supervisor: Alison M. Fowler, University of Reading, Department of Meteorology
Co-supervisors: Amos S. Lawless, NCEO University of Reading; John Eyre, Met Office
Data assimilation, the process of initializing a computer model forecast using the latest observational data, has proven fundamental to the accuracy of modern day weather forecasting. Every day of the order of 107 observations are assimilated at the Met Office. These observations come from a myriad of different instruments onboard various platforms; including weather balloons, aircrafts and ships. However, observations from instruments onboard satellites have been shown to have the greatest impact on producing accurate forecasts. This is due to their extensive coverage, high sampling resolution and the information they provide about key model variables: temperature, humidity and winds. The problem is that satellite data often exhibit systematic errors, for example due to poor calibration, adverse environmental effects, or errors in the radiative transfer equations that relate the observations to the model variables. Systematic errors in the data violate the theory that is central to data assimilation and so for satellite data to be useful they must first be bias corrected. The methods currently in use for performing the bias correction rely on the assumption that the computer model assimilating the observational data itself is unbiased. Unfortunately this is rarely true and is becoming a limiting factor in the use of satellite data. This project will develop new mathematical techniques for performing bias correction that are able to distinguish and correct for biases in both the observations and model.
Coupled carbon, water and heat fluxes over the global land surface
Lead Supervisor: Keith Haines, NCEO University of Reading, Department of Meteorology
Co-supervisors: Tristan Quaife, NCEO University of Reading; Debbie Hemming UK Met Office
Simulating changes to the Earth’s energy, water and carbon cycles is a key goal of climate and earth system models. However we need to know the regional fluxes and transports of these quantities much more accurately from observations to provide strong constraints for models such as those used at the Met Office for climate predictions, and to inform developments across the wider global modelling community. This is now recognized by IPCC who will have separate chapters on the energy, water and carbon cycles in the next Assessment Report.
We have developed an energy-water cycle coupled inverse method in the department which uses many independently observed satellite datasets and their errors to develop closed heat and water budgets on a global scale following an earlier NASA Energy and Water cycle Study (NEWS) L’Ecuyer et al (2015), Rodell et al (2015), see www.nasa-news.org. We have extended the NEWS study getting better results over the oceans by improving the error estimates used for the satellite derived fluxes, and by using additional ocean transport estimates based on ship measurements. Another project is now underway collaborating with the NASA team and the UK Met Office to extend the inverse study to produce more regional results and to solve for interannual variability based on the last 20 years of satellite data.
This PhD project will focus on improving the land surface processes. On land, soil moisture and vegetation properties largely determine how much energy the surface can store, and hence the resultant land surface temperatures (LST), which are now well measured from satellite. Water, sunlight and temperature also determine photosynthesis and biomass growth, taking up CO2 from the Earth’s atmosphere. Biomass growth and CO2 uptake can also be monitored from satellite measurements and provide additional datasets that can be used with our inverse method, and in the process this will couple the land carbon sink to the energy and water cycles. The aim of the PhD will therefore be to use these new satellite observations as constraints to improve our global flux estimates. The inverse method will be extended to include carbon budgets alongside the water and energy budgets to produce a truly coupled Earth system cycling framework which could lead to many new applications.
The student will explore energy-water-carbon flux exchanges with the atmosphere, and storage over land using local observations from Fluxnet measurement towers around the globe, and then seek larger scale relationships using satellite data. Parameterizations and simulations with the JULES land surface model will be used to explore relationships and to help in developing uncertainty estimates. The ultimate aims will be (i) to allow EO land surface temperature measurements to constrain energy fluxes and water storage within the inverse method, and (ii) to extend the inverse method to include a carbon budget, where the land surface component is constrained by plant photosynthesis/growth measurements from NDVI and SIF. The student will explore the sensitivity of the inverse method to these additional constraints. Additional carbon budget observational data e.g. atmospheric measurements of CO2 from the NASA OCO satellite, may be brought in at a later stage.
NCEO PhD Studentships at the CENTA Doctoral Training Partnership, Leicester
Further details and how to apply are here
Identification of types of tropical forest disturbance using satellite data and artificial intelligence
PI: Prof. Heiko Balzter, National Centre for Earth Observation (NCEO), University of Leicester, firstname.lastname@example.org
Co-I: Prof. Ivan Tyukin, Dept. of Mathematics, University of Leicester, email@example.com
Co-I: Dr. Pedro Rodriguez-Veiga, National Centre for Earth Observation (NCEO), University of Leicester, firstname.lastname@example.org
Recent advances in computing technology, cloud computing and high-performance computing are paralleled with advanced artificial intelligence (AI) algorithms and significant investment in the European Copernicus Earth Observation programme and its Sentinel satellite missions. AI enables automatic detection of spatial patterns in environmental data such as satellite images based on training data. The paradigm of looking for spatial patterns instead of the historic focus on spectral information in satellite imagery allows the identification of the types of forest disturbances. AI can also be used to accurately estimate from space forest biophysical parameters that are difficult to measure in the ground such as aboveground biomass (Rodriguez-Veiga et al, 2017)
Machine learning / AI (Le Cun et al. 2015) have previously been applied to hyperspectral image classification (Hu et al. 2015), CORINE land cover mapping from Sentinel-1 SAR images (Balzter et al. 2015), forest biomass mapping using a combination or SAR and optical images (Rodriguez-Veiga et al, 2016), providing evidence of slavery from WorldView satellite images (Boyd et al. 2018).
This interdisciplinary studentship (National Centre for Earth Observation and Department of Mathematics) aims to explore the application of AI to operational automated forest monitoring, focusing on tropical forests (cases in Kenyan and Colombian forests). Time-series stacks of multispectral optical and SAR sensors will be input into the AI. The AI will be trained based on mesurements collected from in-situ forest inventories and visual interpretation of very high resolution images.
1. How accurately can an AI be trained to identify types of forest disturbances based on satellite time-series information?
2. Over and above the type of disturbance how accurately can the associated aboveground biomass loss be estimated?
Chasing convective storm evolution with swarms of space-borne Ka-band radars
PI: Alessandro Battaglia, NCEO &University of Leicester, email@example.com
Co-I: H. Boesch, NCEO &University of Leicester, firstname.lastname@example.org,
K. Mroz, NCEO, email@example.com
Convective storms are the heart of the Earth’s weather and climate system: they convey most of the transport of water and air from near the Earth’s surface to the upper troposphere, they affect the large-scale atmospheric circulation, they are linked to the Earth’s water budget by producing large amounts of rainfall and they influence the Earth’s radiation budget via formation of widespread high clouds. Though convective vertical transport plays a pivotal role, predictions of current weather and future climates remain limited because the parametrizations of such transport are crude; this represents a major roadblock towards the refinement of weather forecasting and climate models. Global observations of convective vertical mass flux are urgently needed for significant progress to occur.
The launch of the first ever cloud 8-mm wavelength radar on a Cubesat (RainCube) in July 2018 has paved the way towards a new era for space-borne observations of convective systems. The small size (10 cm×20 cm×20 cm), moderate mass (21 kg) and low power (10 W peak power) requirement of the instrument enable constellation missions, which can augment our ability to observe weather systems and their dynamics and thermodynamics down to temporal resolutions of few minutes, as required for observing developing convection (see Figure 1). This offers a cheaper but unprecedented solution for capturing the storm dynamics. When a constellation of micro-satellites is flying in formation 60-90 seconds apart time-sequenced profiles of radar reflectivity (Z) separated seconds apart (ΔZ) can be acquired. Together Z and ΔZ/ Δt can be used to provide: (i) the mass fluxes of condensed water mass and dry air and (ii) the rates at which the upper regions of convective storms are moistened. The profiles of Z additionally provide profiles of condensed water M in the column and the precipitation falling from convective storms. The radar has demonstrated the maturity of the technology and NASA is planning to launch a constellation of such Raincube to better understand convective processes which remain one of the major roadblocks in the improvement of numerical weather prediction. The challenge now is to use the radar data to produce scientific relevant results.
Satellite Observations of CO2 in support of the Paris Agreement for Emission Reduction
PI: Prof. Hartmut Boesch, National Centre for Earth Observation NCEO, University of Leicester, email: firstname.lastname@example.org
Dr. Robert Parker, National Centre for Earth Observation NCEO, University of Leicester, email: email@example.com
Dr. Joshua Vande Hey, University of Leicester, email: firstname.lastname@example.org
Mitigating or slowing down global warming is one of the primary challenges humankind faces in the 21st century. Globally we are already approaching a warming mark of 1°C and we are seeing effects, such as droughts, reduced snowpack, and increased forest fires. This calls for decisive action. In the 2015 United Nations Climate Change Conference held in Paris, participants agreed to reduce greenhouse gas emissions to prevent an increase of more than 1.5°C in global surface temperature. European countries have positioned themselves at the forefront of climate change mitigation through a number of ambitious policies and programs. However, the development of climate change mitigation policies is hampered by critical knowledge gaps in our understanding the global carbon cycle, its sources and sinks including anthropogenic emissions and the interplay of the carbon cycle processes with climate change.
More and better observations are needed if we want to advance our quantitative understanding of the carbon exchange between the surface and the atmosphere. Recent advances in space-based remote sensing methods provide us now with opportunities to augment the coarse spatial and temporal resolution and coverage of ground-based networks. Pioneering space-based missions, such as the Japanese GOSAT and NASA’s OCO-2 mission, have impressively demonstrated the power of satellites to provide a “top-down” view on carbon sources and sinks. More advanced CO2 satellite missions which focus on natural as well as of anthropogenic carbon fluxes are now developed by the international space agencies. One of the most ambitious programs is the European Copernicus Evolution for anthropogenic CO2 emission monitoring developed to support the Paris agreement.
The success of these future programs will critically depend if we are able to extend satellite CO2 measurements towards major emissions hotspots such as megacities and to key regions such as the Tropics and Subtropics which host many emerging economies. This will only be possible if we can bring together remote sensing methods used for CO2 with those for atmospheric aerosols and clouds. This will represent a major advancement in CO2 remote sensing leading to more reliable information on carbon surface flux including major emission hotspots.
Towards improved weather forecasting: the retrieval of temperature and cloud properties from IASI using new carbon dioxide spectroscopic line parameters
PI: Dr Jeremy Harrison (National Centre for Earth Observation and University of Leicester, email: email@example.com)
Co-I: Dr Stephan Havemann (UK Met Office, Exeter, email: firstname.lastname@example.org); Dr David Moore (National Centre for Earth Observation and University of Leicester, email: email@example.com)
The Infrared Atmospheric Sounding Interferometer (IASI) instruments, on board the MetOp-A and MetOp-B satellites (a third on MetOp-C should be launched in November 2018), are primarily meteorological instruments designed to provide accurate atmospheric temperature and humidity profiles with which to improve numerical weather prediction (NWP). These instruments are able to detect trace gases in the atmosphere using their distinctive spectral infrared fingerprints, thereby providing information on atmospheric chemistry, climate, and pollution.
Spectral bands of carbon dioxide (CO2) are used to retrieve atmospheric temperature profiles from IASI observations. The channels primarily used are those with minimal sensitivity to other absorbing species such as water. Most commonly the absorption bands of CO2 used are ~667 cm-1 (15 μm) and ~2350 cm-1 (4.3 μm), although the latter band in IASI spectra has higher radiometric noise. The 15 μm band is also used to determine cloud top pressure and cloud fraction, using for example the CO2-slicing method. The detection of clouds is a large source of uncertainty in infrared satellite data assimilation in NWP. Unlike optically thick clouds, optically thin clouds such as cirrus (which cover up to 25 % of the globe) are more difficult to detect. If cloud-contaminated radiances are treated as clear-sky measurements and then assimilated into NWP models, the forecasts can be significantly degraded.
The atmospheric radiative transfer models used to analyse the IASI radiances use spectroscopic line parameters from atmospheric spectroscopic databases such as HITRAN to model the absorption of trace gases, including CO2. The Voigt lineshape, the default lineshape in HITRAN, is inadequate in accurately respresenting real atmospheric spectra. New lineshape models have been proposed, for example the Hartmann–Tran profile, which accounts for effects such as Dicke narrowing and speed-dependence; the effects of collisional interferences between lines (i.e. line-mixing) can be accounted for using the Rosenkranz first-order approximation.
New spectroscopic measurements of CO2 have recently been made, from which new non-Voigt line parameters will be derived as part of this project. These will then be used in radiative transfer calculations and retrievals of temperature and cloud properties, and improvements in these retrieved quantities will be investigated.
Developing a long-term merged all-sky surface temperature record for evaluation and application in climate models
PI: Prof John Remedios, NCEO Leicester, firstname.lastname@example.org
Co-I: Dr Darren Ghent, NCEO Leicester, email@example.com
Land surface temperature (LST) is a fundamental, spatial quantity which is a fascinating, emerging observable for environmental science. LST is a key boundary condition state variable in land surface models, which determine the surface -to- atmosphere fluxes of heat, water and carbon compounds and represents the boundary condition in climate models. It also influences cloud cover, precipitation and atmospheric chemistry predictions within these models. Model deficiencies in representing LST often provide an indication of problems in surface energy fluxes and soil moisture that can affect the actual performance of Earth System Models at various temporal scales. There is an increasing focus on the opportunity to exploit satellite LST data to confront the challenges of climate science; and our research group is leading the international effort on LST science.
LST can be determined from thermal emission in either infrared (IR) or microwave (MW) atmospheric windows. Infrared skin temperature is defined as the temperature measured by an IR radiometer in cloud-free conditions, typically operating at wavelengths 3.7-12 µm. It is the temperature of the top few micrometres of the surface (whether bare ground or canopy leaves), Microwave skin temperature represents the surface temperature at depths up to a few millimetres, depending on wavelength, view angle and surface conditions.
Retrievals in the IR are generally more accurate than MW retrievals due to smaller variation of surface emissivities and stronger dependence of the radiance on temperature. Nevertheless, microwave measurements have been shown to complement those in the IR due to their lower sensitivity to clouds, thus increasing sampling in cloudy conditions; although their spatial resolutions are significantly lower than their IR counterparts.
Use of LST for climate studies has been hindered because longer-term datasets are based on IR observations, which are limited to clear-sky. This presents a problem for many applications as resulting trends may be clear-sky biased, and climate models include both cloudy and clear-sky simulations. This project will progress our ability to overcome this by better understanding the physical differences between the observations and how robust relationships can be developed to enable observations to be merged into a consistent record.
To meet the most significant LST requirements for climate science, integrated products can take advantage of the strengths of each data stream (IR and MW; polar orbiting and geostationary; and where available, in situ). The ultimate aim is to provide sub-daily, near-global coverage to better understand the diurnal (24hr) variability in LST. The first steps are to perform some initial comparisons of the IR and MW datasets and then to develop a draft process for merging the data. Much of the detailed research will develop from these key factors and will establish robust relationships between temperature measured from the two different techniques as a function of other parameters such as cloud thickness and wind speed. The results will improve IR and MW retrievals, build a definitive LST dataset derived from the individual data sets and utilise the product to evaluate climate models and other surface temperature datasets.