Our People
Dr Adam Povey
Lecturer in Earth Observation
EO Data-Model Evaluation
Research interests
I observe the interactions between clouds, aerosols, and their environment in order to understand how humans affect the climate, precipitation, and air quality. That involves processing petabytes of satellite images with the Optimal Retrieval of Aerosol and Cloud, integrated with ground-based observations, and applying statistical analyses able to handle such large datasets, with a particular focus on the uncertainty in the data.
Recent publications
Geostationary aerosol retrievals of extreme biomass burning plumes during the 2019–2020 Australian bushfires. 2024-05-29
DOI: https://doi.org/10.5194/amt-17-3279-2024
Characterization of dust aerosols from ALADIN and CALIOP measurements. 2024-04-26
DOI: https://doi.org/10.5194/amt-17-2521-2024
Uncertainty in aerosol–cloud radiative forcing is driven by clean conditions. 2023-04-05
DOI: http://dx.doi.org/10.5194/acp-23-4115-2023
Is Anthropogenic Global Warming Accelerating?. 2022-12-15
DOI: http://dx.doi.org/10.1175/jcli-d-22-0081.1
Uncertainty-bounded estimates of ash cloud properties using the ORAC algorithm: application to the 2019 Raikoke eruption. 2022-10-20
DOI: http://dx.doi.org/10.5194/amt-15-5985-2022
Opportunistic experiments to constrain aerosol effective radiative forcing. 2022-01-17
DOI: http://dx.doi.org/10.5194/acp-22-641-2022
Cloud_cci ATSR-2 and AATSR data set version 3: a 17-year climatology of global cloud and radiation properties. 2020-09-09
DOI: http://dx.doi.org/10.5194/essd-12-2121-2020
The Evaluation of the North Atlantic Climate System in UKESM1 Historical Simulations for CMIP6. 2020-09
DOI: http://dx.doi.org/10.1029/2020ms002126
Description and evaluation of aerosol in UKESM1 and HadGEM3-GC3.1 CMIP6 historical simulations. 2020-03-25
DOI: http://dx.doi.org/10.5194/gmd-2019-357
A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing. 2020-02-03
DOI: http://dx.doi.org/10.5194/amt-13-373-2020
Description and evaluation of aerosol in UKESM1 and HadGEM3-GC3.1 CMIP6 historical simulations. 2020
DOI: http://www.scopus.com/inward/record.url?eid=2-s2.0-85091786847&partnerID=MN8TOARS
Toward More Representative Gridded Satellite Products. 2019-05
DOI: https://doi.org/10.1109/LGRS.2018.2881762
The Community Cloud retrieval for CLimate (CC4CL) – Part 2: The optimal estimation approach. 2018-06-13
DOI: http://dx.doi.org/10.5194/amt-11-3397-2018
The Community Cloud retrieval for CLimate (CC4CL) – Part 1: A framework applied to multiple satellite imaging sensors. 2018-06-13
DOI: http://dx.doi.org/10.5194/amt-11-3373-2018
Finding Ocean States That Are Consistent with Observations from a Perturbed Physics Parameter Ensemble. 2018-06
DOI: http://dx.doi.org/10.1175/jcli-d-17-0514.1
Cloud property datasets retrieved from AVHRR, MODIS, AATSR and MERIS in the framework of the Cloud_cci project. 2017
Uncertainty information in climate data records from Earth observation. 2017
Unveiling aerosol-cloud interactions – Part 1: Cloud contamination in satellite products enhances the aerosol indirect forcing estimate. 2017
Retrieval of aerosol backscatter, extinction, and lidar ratio from Raman lidar with optimal estimation. 2014-03-13
DOI: https://doi.org/10.5194/amt-7-757-2014
Estimation of a lidar’s overlap function and its calibration by nonlinear regression. 2012
The Radiation Tolerance of Specific Optical Fibers for the LHC Upgrades. 2012
DOI: http://www.scopus.com/inward/record.url?eid=2-s2.0-84890886006&partnerID=MN8TOARS
The radiation tolerance of specific optical fibres exposed to 650 kGy(Si) of ionizing radiation. 2009
