Publications by Adam Povey


Description and evaluation of aerosol in UKESM1 and HadGEM3-GC3.1 CMIP6 historical simulations

Geoscientific Model Development Copernicus Publications 13 (2020) 6383-6423

C Jones, A Povey, C Scott, A Sellar, S Turnock, M Woodhouse, N Abraham, M Andrews, N Bellouin, J Browse, K Carslaw, M Dalvi, M Glover, D Grosvenor, B Johnson, A Jones, Z Kipling, J Palmiéri, C Reddington, S Rumbold, M Richardson, N Schutgens, P Stier, M Stringer, Y Tang

We document and evaluate the aerosol schemes as implemented in the physical and Earth system models, the Global Coupled 3.1 configuration of the Hadley Centre Global Environment Model version 3 (HadGEM3-GC3.1) and the United Kingdom Earth System Model (UKESM1), which are contributing to the sixth Coupled Model Intercomparison Project (CMIP6). The simulation of aerosols in the present-day period of the historical ensemble of these models is evaluated against a range of observations. Updates to the aerosol microphysics scheme are documented as well as differences in the aerosol representation between the physical and Earth system configurations. The additional Earth system interactions included in UKESM1 lead to differences in the emissions of natural aerosol sources such as dimethyl sulfide, mineral dust and organic aerosol and subsequent evolution of these species in the model. UKESM1 also includes a stratospheric–tropospheric chemistry scheme which is fully coupled to the aerosol scheme, while GC3.1 employs a simplified aerosol chemistry mechanism driven by prescribed monthly climatologies of the relevant oxidants. Overall, the simulated speciated aerosol mass concentrations compare reasonably well with observations. Both models capture the negative trend in sulfate aerosol concentrations over Europe and the eastern United States of America (US) although the models tend to underestimate sulfate concentrations in both regions. Interactive emissions of biogenic volatile organic compounds in UKESM1 lead to an improved agreement of organic aerosol over the US. Simulated dust burdens are similar in both models despite a 2-fold difference in dust emissions. Aerosol optical depth is biased low in dust source and outflow regions but performs well in other regions compared to a number of satellite and ground-based retrievals of aerosol optical depth. Simulated aerosol number concentrations are generally within a factor of 2 of the observations, with both models tending to overestimate number concentrations over remote ocean regions, apart from at high latitudes, and underestimate over Northern Hemisphere continents. Finally, a new primary marine organic aerosol source is implemented in UKESM1 for the first time. The impact of this new aerosol source is evaluated. Over the pristine Southern Ocean, it is found to improve the seasonal cycle of organic aerosol mass and cloud droplet number concentrations relative to GC3.1 although underestimations in cloud droplet number concentrations remain. This paper provides a useful characterisation of the aerosol climatology in both models and will facilitate understanding in the numerous aerosol–climate interaction studies that will be conducted as part of CMIP6 and beyond.


Cloud_cci ATSR-2 and AATSR data set version 3: a 17-year climatology of global cloud and radiation properties

Earth System Science Data Copernicus Publications 12 (2020) 2121-2135

C Poulsen, G McGarragh, G Thomas, M Stengel, M Christensen, A Povey, S Proud, E Carboni, R Hollmann, R Grainger

We present version 3 (V3) of the Cloud_cci Along-Track Scanning Radiometer (ATSR) and Advanced ATSR (AATSR) data set. The data set was created for the European Space Agency (ESA) Cloud_cci (Climate Change Initiative) programme. The cloud properties were retrieved from the second ATSR (ATSR-2) on board the second European Remote Sensing Satellite (ERS-2) spanning 1995–2003 and the AATSR on board Envisat, which spanned 2002–2012. The data are comprised of a comprehensive set of cloud properties: cloud top height, temperature, pressure, spectral albedo, cloud effective emissivity, effective radius, and optical thickness, alongside derived liquid and ice water path. Each retrieval is provided with its associated uncertainty. The cloud property retrievals are accompanied by high-resolution top- and bottom-of-atmosphere shortwave and longwave fluxes that have been derived from the retrieved cloud properties using a radiative transfer model. The fluxes were generated for all-sky and clear-sky conditions. V3 differs from the previous version 2 (V2) through development of the retrieval algorithm and attention to the consistency between the ATSR-2 and AATSR instruments. The cloud properties show improved accuracy in validation and better consistency between the two instruments, as demonstrated by a comparison of cloud mask and cloud height with co-located CALIPSO data. The cloud masking has improved significantly, particularly in its ability to detect clear pixels. The Kuiper Skill score has increased from 0.49 to 0.66. The cloud top height accuracy is relatively unchanged. The AATSR liquid water path was compared with the Multisensor Advanced Climatology of Liquid Water Path (MAC-LWP) in regions of stratocumulus cloud and shown to have very good agreement and improved consistency between ATSR-2 and AATSR instruments. The correlation with MAC-LWP increased from 0.4 to over 0.8 for these cloud regions. The flux products are compared with NASA Clouds and the Earth's Radiant Energy System (CERES) data, showing good agreement within the uncertainty. The new data set is well suited to a wide range of climate applications, such as comparison with climate models, investigation of trends in cloud properties, understanding aerosol–cloud interactions, and providing contextual information for co-located ATSR-2/AATSR surface temperature and aerosol products.


The Evaluation of the North Atlantic Climate System in UKESM1 Historical Simulations for CMIP6

JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS 12 (2020) ARTN e2020MS002126

J Robson, Y Aksenov, TJ Bracegirdle, O Dimdore-Miles, PT Griffiths, DP Grosvenor, DLR Hodson, J Keeble, C MacIntosh, A Megann, S Osprey, AC Povey, D Schroder, M Yang, AT Archibald, KS Carslaw, L Gray, C Jones, B Kerridge, D Knappett, T Kuhlbrodt, M Russo, A Sellar, R Siddans, B Sinha, R Sutton, J Walton, LJ Wilcox


A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing

Atmospheric Measurement Techniques European Geosciences Union 13 (2020) 373-404

AM Sayer, A Povey, Y Govaerts, P Kolmonen, A Lipponen, M Luffarelli, T Mielonen, F Patadia, T Popp, K Stebel, ML Witek

Recent years have seen the increasing inclusion of per-retrieval prognostic (predictive) uncertainty estimates within satellite aerosol optical depth (AOD) data sets, providing users with quantitative tools to assist in the optimal use of these data. Prognostic estimates contrast with diagnostic (i.e. relative to some external truth) ones, which are typically obtained using sensitivity and/or validation analyses. Up to now, however, the quality of these uncertainty estimates has not been routinely assessed. This study presents a review of existing prognostic and diagnostic approaches for quantifying uncertainty in satellite AOD retrievals, and it presents a general framework to evaluate them based on the expected statistical properties of ensembles of estimated uncertainties and actual retrieval errors. It is hoped that this framework will be adopted as a complement to existing AOD validation exercises; it is not restricted to AOD and can in principle be applied to other quantities for which a reference validation data set is available. This framework is then applied to assess the uncertainties provided by several satellite data sets (seven over land, five over water), which draw on methods from the empirical to sensitivity analyses to formal error propagation, at 12 Aerosol Robotic Network (AERONET) sites. The AERONET sites are divided into those for which it is expected that the techniques will perform well and those for which some complexity about the site may provide a more severe test. Overall, all techniques show some skill in that larger estimated uncertainties are generally associated with larger observed errors, although they are sometimes poorly calibrated (i.e. too small or too large in magnitude). No technique uniformly performs best. For powerful formal uncertainty propagation approaches such as optimal estimation, the results illustrate some of the difficulties in appropriate population of the covariance matrices required by the technique. When the data sets are confronted by a situation strongly counter to the retrieval forward model (e.g. potentially mixed land–water surfaces or aerosol optical properties outside the family of assumptions), some algorithms fail to provide a retrieval, while others do but with a quantitatively unreliable uncertainty estimate. The discussion suggests paths forward for the refinement of these techniques.


Towards more representative gridded satellite products

IEEE Geoscience and Remote Sensing Letters IEEE 16 (2018) 672-676

A Povey, R Grainger

The most widely used satellite products are averages of data onto a regular spatiotemporal grid, known as Level 3 data. Some atmospheric variables can vary rapidly in response to changing conditions. Over the scales of Level 3 averaging, the combination of observations across different conditions may result in data that is not normally distributed, such that a simple mean is not representative. The problem is illustrated by the distribution of aerosol optical depth from different sensors and algorithms. A simple statistical technique is proposed to better convey the diversity of satellite observations to users whereby a multimodal log-normal distribution is fit to the distribution of data observed within each grid cell. Allowing multiple modes within each cell is shown to improve the agreement between satellite products by highlighting regions of significant variability and isolating systematic differences between instruments.


The Community Cloud retrieval for Climate (CC4CL). Part I: A framework applied to multiple satellite imaging sensors

Atmospheric Measurement Techniques Copernicus Publications (2018)

O Sus, M Stengel, S Stapelberg, GR McGarragh, C Poulsen, AC Povey, C Schlundt, G Thomas, M Christensen, S Proud, M Jerg, R Grainger, R Hollmann

<p>We present here the key features of the Community Cloud retrieval for CLimate (CC4CL) processing algorithm. We focus on the novel features of the framework: the optimal estimation approach in general, explicit uncertainty quantification through rigorous propagation of all known error sources into the final product, and the consistency of our long-term, multi-platform time series provided at various resolutions, from 0.5 to 0.02°.</p> <br/> <p>By describing all key input data and processing steps, we aim to inform the user about important features of this new retrieval framework and its potential applicability to climate studies. We provide an overview of the retrieved and derived output variables. These are analysed for four, partly very challenging, scenes collocated with CALIOP (Cloud- Aerosol lidar with Orthogonal Polarization) observations in the high latitudes and over the Gulf of Guinea–West Africa.</p> <br/> <p>The results show that CC4CL provides very realistic estimates of cloud top height and cover for optically thick clouds but, where optically thin clouds overlap, returns a height between the two layers. CC4CL is a unique, coherent, multiinstrument cloud property retrieval framework applicable to passive sensor data of several EO missions. Through its flexibility, CC4CL offers the opportunity for combining a variety of historic and current EO missions into one dataset, which, compared to single sensor retrievals, is improved in terms of accuracy and temporal sampling.</p>


The Community Cloud retrieval for CLimate (CC4CL). Part II: The optimal estimation approach

Atmospheric Measurement Techniques Copernicus Publications 11 (2018) 3397-3431

G McGarragh, CA Poulsen, GE Thomas, AC Povey, O Sus, S Stapelberg, C Schlundt, SR Proud, MW Christensen, M Stengel, R Hollmann, RG Grainger

The Community Cloud retrieval for Climate (CC4CL) is a cloud property retrieval system for satellite-based multispectral imagers and is an important component of the Cloud Climate Change Initiative (Cloud_cci) project. In this paper we discuss the optimal estimation retrieval of cloud optical thickness, effective radius and cloud top pressure based on the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm. Key to this method is the forward model, which includes the clear-sky model, the liquid water and ice cloud models, the surface model including a bidirectional reflectance distribution function (BRDF), and the "fast" radiative transfer solution (which includes a multiple scattering treatment). All of these components and their assumptions and limitations will be discussed in detail. The forward model provides the accuracy appropriate for our retrieval method. The errors are comparable to the instrument noise for cloud optical thicknesses greater than 10. At optical thicknesses less than 10 modeling errors become more significant. The retrieval method is then presented describing optimal estimation in general, the nonlinear inversion method employed, measurement and a priori inputs, the propagation of input uncertainties and the calculation of subsidiary quantities that are derived from the retrieval results. An evaluation of the retrieval was performed using measurements simulated with noise levels appropriate for the MODIS instrument. Results show errors less than 10 % for cloud optical thicknesses greater than 10. Results for clouds of optical thicknesses less than 10 have errors up to 20 %.


Finding ocean states that are consistent with observations from a perturbed physics parameter ensemble

Journal of Climate American Meterological Society 31 (2018) 4639-4656

S Sparrow, R Millar, K Yamazaki, N Massey, AC Povey, A Bowery, RG Grainger, DCH Wallom, M Allen

A very large ensemble is used to identify subgrid-scale parameter settings for the HadCM3 model that are capable of best simulating the ocean state over the recent past (1980–2010). A simple particle filtering technique based upon the agreement of basin mean sea surface temperature (SST) and upper 700-m ocean heat content with EN3 observations is applied to an existing perturbed physics ensemble with initial conditions perturbations. A single set of subgrid-scale parameter values was identified from the wide range of initial parameter sets that gave the best agreement with ocean observations for the period studied. The parameter set, different from the standard model parameters, has a transient climate response of 1.68 K. The selected parameter set shows an improved agreement with EN3 decadal-mean SST patterns and the Atlantic meridional overturning circulation (AMOC) at 26°N as measured by the Rapid Climate Change (RAPID) array. Particle filtering techniques as demonstrated here could have a useful role in improving the starting point for traditional model-tuning exercises in coupled climate models.


Cloud property datasets retrieved from AVHRR, MODIS, AATSR and MERIS in the framework of the Cloud_cci project

EARTH SYSTEM SCIENCE DATA 9 (2017) 881-904

M Stengel, S Stapelberg, O Sus, C Schlundt, C Poulsen, G Thomas, M Christensen, CC Henken, R Preusker, J Fischer, A Devasthale, U Willen, K-G Karlsson, GR McGarragh, S Proud, AC Povey, RG Grainger, JF Meirink, A Feofilov, R Bennartz, JS Bojanowski, R Hollmann


Unveiling aerosol–cloud interactions – Part 1: Cloud contamination in satellite products enhances the aerosol indirect forcing estimate

Atmospheric Chemistry and Physics European Geosciences Union 17 (2017) 13151-13164

M Christensen, D Neubauer, CA Poulsen, GE Thomas, GR McGarragh, AC Povey, SR Proud, RG Grainger

Increased concentrations of aerosol can enhance the albedo of warm low-level cloud. Accurately quantifying this relationship from space is challenging due in part to contamination of aerosol statistics near clouds. Aerosol retrievals near clouds can be influenced by stray cloud particles in areas assumed to be cloud-free, particle swelling by humidification, shadows and enhanced scattering into the aerosol field from (3-D radiative transfer) clouds. To screen for this contamination we have developed a new cloud–aerosol pairing algorithm (CAPA) to link cloud observations to the nearest aerosol retrieval within the satellite image. The distance between each aerosol retrieval and nearest cloud is also computed in CAPA. <p> Results from two independent satellite imagers, the Advanced Along-Track Scanning Radiometer (AATSR) and Moderate Resolution Imaging Spectroradiometer (MODIS), show a marked reduction in the strength of the intrinsic aerosol indirect radiative forcing when selecting aerosol pairs that are located farther away from the clouds (−0.28±0.26 W m−2) compared to those including pairs that are within 15 km of the nearest cloud (−0.49±0.18 W m−2). The larger aerosol optical depths in closer proximity to cloud artificially enhance the relationship between aerosol-loading, cloud albedo, and cloud fraction. These results suggest that previous satellite-based radiative forcing estimates represented in key climate reports may be exaggerated due to the inclusion of retrieval artefacts in the aerosol located near clouds.</p>


The Community Cloud retrieval for CLimate (CC4CL). Part II: The optimal estimation approach

Atmospheric Measurement Techniques Discussions European Geosciences Union (2017)

G McGarragh, CA Poulsen, GE Thomas, AC Povey, O Sus, S Stapelberg, C Schlundt, SR Proud, MW Christensen, M Stengel, R Hollmann, R Grainger

The Community Cloud retrieval for Climate (CC4CL) is a cloud property retrieval system for satellite-based multispectral imagers and is an important component of the Cloud Climate Change Initiative (Cloud_cci) project. In this paper we discuss the optimal estimation retrieval of cloud optical thickness, effective radius and cloud top pressure based on the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm. Key to this method is the forward model which, includes the 5 clear-sky model, the liquid water and ice cloud models, the surface model including a bidirectional reflectance distribution function (BRDF), the “fast” radiative transfer solution (which includes a multiple scattering treatment) All of these components and their assumptions and limitations will be discussed in detail. The forward model provides the accuracy appropriate for our retrieval method. The errors are comparable to the instrument noise for cloud optical thicknesses greater than 10. At optical thicknesses less than 10 modelling errors become more significant. The retrieval method is then presented describing 10 optimal estimation in general, the non-linear inversion method employed, measurement and a priori inputs, the propagation of input uncertainties and the calculation of subsidiary quantities that are derived from the retrieval results. An evaluation of the retrieval was performed using measurements simulated with noise levels appropriate for the MODIS instrument. Results show errors less than 10% for cloud optical thicknesses greater than 10. Results for clouds of optical thicknesses less than 10 have errors ranging up to 20%.


Uncertainty information in climate data records from Earth observation

Earth System Science Data 9 (2017) 511-527

CJ Merchant, F Paul, T Popp, M Ablain, S Bontemps, P Defourny, R Hollmann, T Lavergne, A Laeng, G de Leeuw, J Mittaz, C Poulsen, AC Povey, M Reuter, S Sathyendranath, S Sandven, VF Sofieva, W Wagner


Development, Production and Evaluation of Aerosol Climate Data Records from European Satellite Observations (Aerosol_cci)

Remote Sensing MDPI 8 (2016) 421-421

T Popp, G de Leeuw, C Bingen, C Brühl, V Capelle, A Chedin, L Clarisse, O Dubovik, R Grainger, J Griesfeller, A Heckel, L Lelli, P Litvinov, L Mei, A Povey, C Robert, M Schulz, L Sogacheva, K Stebel, D Stein Zweers, L Tilstra, S Vandenbussche, P Veefkind, M Vountas, Y Xue

Producing a global and comprehensive description of atmospheric aerosols requires integration of ground-based, airborne, satellite and model datasets. Due to its complexity, aerosol monitoring requires the use of several data records with complementary information content. This paper describes the lessons learned while developing and qualifying algorithms to generate aerosol Climate Data Records (CDR) within the European Space Agency (ESA) Aerosol_cci project. An iterative algorithm development and evaluation cycle involving core users is applied. It begins with the application-specific refinement of user requirements, leading to algorithm development, dataset processing and independent validation followed by user evaluation. This cycle is demonstrated for a CDR of total Aerosol Optical Depth (AOD) from two subsequent dual-view radiometers. Specific aspects of its applicability to other aerosol algorithms are illustrated with four complementary aerosol datasets. An important element in the development of aerosol CDRs is the inclusion of several algorithms evaluating the same data to benefit from various solutions to the ill-determined retrieval problem. The iterative approach has produced a 17-year AOD CDR, a 10-year stratospheric extinction profile CDR and a 35-year Absorbing Aerosol Index record. Further evolution cycles have been initiated for complementary datasets to provide insight into aerosol properties (i.e., dust aerosol, aerosol absorption).


Known and unknown unknowns: Uncertainty estimation in satellite remote sensing

Atmospheric Measurement Techniques European Geosciences Union 8 (2015) 4699-4718

A Povey, RG Grainger

This paper discusses a best-practice representation of uncertainty in satellite remote sensing data. An estimate of uncertainty is necessary to make appropriate use of the information conveyed by a measurement. Traditional error propagation quantifies the uncertainty in a measurement due to well-understood perturbations in a measurement and in auxiliary data - known, quantified "unknowns". The under-constrained nature of most satellite remote sensing observations requires the use of various approximations and assumptions that produce non-linear systematic errors that are not readily assessed - known, unquantifiable "unknowns". Additional errors result from the inability to resolve all scales of variation in the measured quantity - unknown "unknowns". The latter two categories of error are dominant in under-constrained remote sensing retrievals, and the difficulty of their quantification limits the utility of existing uncertainty estimates, degrading confidence in such data. This paper proposes the use of ensemble techniques to present multiple self-consistent realisations of a data set as a means of depicting unquantified uncertainties. These are generated using various systems (different algorithms or forward models) believed to be appropriate to the conditions observed. Benefiting from the experience of the climate modelling community, an ensemble provides a user with a more complete representation of the uncertainty as understood by the data producer and greater freedom to consider different realisations of the data.


ERACE: The environmental response to aerosols observed in CCI ECVs

Sixth ESA CCI collocation meeting European Space Agency (2015)

A Povey, M Christensen, GR McGarragh, C Poulsen, GE Thomas, RG Grainger


The application of optimal estimation to lidar

RSPSoc - NCEO - CEOI-ST Joint Conference Centre for Instrumentation (2015)

A Povey, RG Grainger, DM Peters

Lidars are ideally placed to investigate the effects of aerosol and cloud on the climate system due to their unprecedented vertical and temporal resolution. Dozens of techniques have been developed in recent decades to retrieve the extinction and backscatter of atmospheric particulates in a variety of conditions. These methods, though often very successful, are fairly ad hoc in their construction, utilising a wide variety of approximations and assumptions that makes comparing the resulting data products with independent measurements difficult and their implementation in climate modelling virtually impossible. As with its application to satellite retrievals at the turn of the century, the methods of non-linear regression can improve this situation by providing a mathematical framework in which the various approximations, estimates of experimental error, and any additional knowledge of the atmosphere can be clearly defined and included in a mathematically `optimal' retrieval method, providing rigorously derived error estimates. In addition to making it easier for scientists outside of the lidar field to understand and utilise lidar data, it also simplifies the process of moving beyond extinction and backscatter coefficients and retrieving microphysical properties of aerosols and cloud particles. A technique to estimate the lidar's overlap function using an analytic model of the optical system and a simple extinction profile has been developed. This is used to calibrate the system such that the profile of extinction and backscatter coefficients can be retrieved using the elastic and nitrogen Raman backscatter signals. These methods have been used to extract value from compromised data collected with a prototype Raman lidar system. Selected events will be presented, with the hope that others may be inspired to apply the techniques to a more robust system.


Known and unknown unknowns: Uncertainty estimation in satellite remote sensing data

RSPSoc - NCEO - CEOI-ST Joint Conference Centre for Instrumentation (2015)

A Povey, RG Grainger

An estimate of uncertainty is necessary to make appropriate use of the information conveyed by a measurement. Traditional error propagation quantifies the uncertainty in a measurement due to well-understood perturbations in a measurement and auxiliary data – known, quantified `unknowns'. The underconstrained nature of most satellite remote sensing observations requires the use of approximations and assumptions that produce non-linear systematic errors that are not readily assessed – known, unquantifiable `unknowns'. Additional errors result from the inability of a measurement to resolve all scales and aspects of variation in a system – unknown `unknowns'. The latter two categories of error are dominant in satellite remote sensing and the difficulty of their quantification limits the utility of existing uncertainty estimates, degrading confidence in such data. Ensemble techniques present multiple self-consistent realisations of a data set as a means of depicting unquantified uncertainties, generated using various algorithms or forward models believed to be appropriate to the conditions observed. Benefiting from the experience of the climate modelling community, an ensemble provides a user with a more accurate representation of the uncertainty as understood by the data producer and greater freedom to exploit the advantages and disadvantages of different manners of describing a physical system. The technique will be demonstrated with retrievals of aerosol, cloud, and surface properties, for which many sources of error cannot currently be quantified (such as the assumed aerosol microphysical properties). The Optimal Retrieval of Aerosol and Cloud (ORAC) can produce an ensemble by evaluating data with a succession of microphysical models (e.g. liquid cloud, urban aerosol, etc.). A further ensemble can be formed from products produced by various European institutions. These will be used to demonstrate uncertainties in such observations that are poorly characterised in current products.


Parallel retrieval of aerosol and cloud

ATMOS 2015 European Space Agency (2015)

A Povey, C Poulsen, GR McGarragh, GE Thomas, S Oliver, C Schlundt, S Stapelberg, S Martin, RG Grainger

Due to similarities in their radiometric signature, it is not possible to retrieve aerosol and cloud properties simultaneously from satellite imagery. A plethora of filtering techniques have been developed to ensure aerosol and cloud are analysed separately, but this neglects the scientifically interesting regions of interaction between the two. It also limits the spatial coverage of such products, with up to 20% of the planet neglected because it is considered too cloudy to be suitable for an aerosol retrieval but insufficiently so for a cloud retrieval. The Optimal Retrieval of Aerosol and Cloud (ORAC) is a single algorithm that can retrieve the aerosol or cloud properties consistent with a single measurement. By performing radiative transfer calculations via look-up tables, various types of particle can be considered in parallel — such as liquid-phase cloud, different models of ice nuclei, and various clean and polluted aerosols — by simply running the program repeatedly using tables assuming different microphysical properties and vertical distributions. Bayesian statistics can determine the probability that the scene contains a specific species, classifying it as aerosol, cloud, or uncertain. The important but infrequently discussed `uncertain' region can then be used to investigate the impact of contamination and data coverage on existing products by, for example, observing how retrieved aerosol optical thickness varies as a function of the distance from the nearest cloud. It also provides a potential window for the study of aerosol-cloud interactions.


Retrieval of aerosol backscatter, extinction, and lidar ratio from Raman lidar with optimal estimation

Atmospheric Measurement Techniques European Geosciences Union 7 (2014) 757-776

A Povey, R Grainger, D Peters, JL Agnew

Optimal estimation retrieval is a form of nonlinear regression which determines the most probable circumstances that produced a given observation, weighted against any prior knowledge of the system. This paper applies the technique to the estimation of aerosol backscatter and extinction (or lidar ratio) from two-channel Raman lidar observations. It produces results from simulated and real data consistent with existing Raman lidar analyses and additionally returns a more rigorous estimate of its uncertainties while automatically selecting an appropriate resolution without the imposition of artificial constraints. Backscatter is retrieved at the instrument’s native resolution with an uncertainty between 2 and 20 %. Extinction is less well constrained, retrieved at a resolution of 0.1–1km depending on the quality of the data. The uncertainty in extinction is &gt;15 %, in part due to the consideration of short 1 min integrations, but is comparable to fair estimates of the error when using the standard Raman lidar technique. The retrieval is then applied to several hours of observation on 19 April 2010 of ash from the Eyjafjallajökull eruption. A depolarising ash layer is found with a lidar ratio of 20– 30 sr, much lower values than observed by previous studies. This potentially indicates a growth of the particles after 12– 24 h within the planetary boundary layer. A lower concentration of ash within a residual layer exhibited a backscatter of 10Mm−1 sr−1 and lidar ratio of 40 sr.


The application of optimal estimation retrieval to lidar observations

(2013)

A Povey

Optimal estimation retrieval is a nonlinear regression scheme to determine the conditions statistically most-likely to produce a given measurement, weighted against any a priori knowledge. The technique is applied to three problems within the field of lidar data analysis. A retrieval of the aerosol backscatter and either the extinction or lidar ratio from two-channel Raman lidar data is developed using the lidar equations as a forward model. It produces profiles consistent with existing techniques at a resolution of 10-1000 m and uncertainty of 5-20%, dependent on the quality of data. It is effective even when applied to noisy, daytime data but performs poorly in the presence of cloud. Two of the most significant sources of uncertainty in that retrieval are the nonlinearity of the detectors and the instrument's calibration (known as the dead time and overlap function). Attempts to retrieve a nonlinear correction from a pair of lidar profiles, one attenuated by a neutral density filter, are not successful as uncertainties in the forward model eliminate any information content in the measurements. The technique of Whiteman et al. [1992] is found to be the most accurate. More successful is a retrieval of the overlap function of a Raman channel using a forward model combining an idealised extinction profile and an adaptation of the equations presented in Halldórsson and Langerholc [1978]. After refinement, the retrieval is shown to be at least as accurate, and often superior to, existing methods of calibration from routine measurements, presenting uncertainties of 5-15%. These techniques are then applied to observations of ash over southern England from the Eyjafjallajökull eruption of April 2010. Lidar ratios of 50-60 sr were observed when the plume first appeared, which reduced to 20-30 sr after several days within the planetary boundary layer, indicating an alteration of the particles over time.

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