How should we aggregate data? Methods accounting for the numerical distributions, with an assessment of aerosol  optical depth

Sayer, A. M.; Knobelspiesse, Kirk

doi:10.5194/acp-19-15023-2019

Cited by 43 publications

(52 citation statements)

References 104 publications

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“…These model parameters are factors which affect the TOA signal but typically insignificantly enough to be retrieved. For example, many AOD retrieval algorithms ingest meteorological reanalysis to correct for the impact of absorbing trace gases (such as H 2 O) on the satellite signal at TOA (Patadia et al, 2018) and to provide wind speed to calculate glint and whitecap contributions to sea surface reflectances (Sayer et al, 2010a). Sometimes these are represented in J instead by a "model parameter error" matrix denoted S b and similar squared deviations, although mathematically since the terms in Eq.…”

Section: Formal Error Propagationmentioning

confidence: 99%

“…These can be tested, to an extent, by examining the distributions of residuals (on measurements and a priori) and the cost function and comparing to theoretical expectations (e.g. Sayer et al, 2010aSayer et al, , 2012c.…”

Section: Formal Error Propagationmentioning

confidence: 99%

“…ORAC provides AOD retrievals over both land and ocean surfaces; the retrieval approaches are the same except for the surface reflectance models, which also inform the a priori and covariance matrices. Over water, surface reflectance is modelled according to Sayer et al (2010a) with fairly strong a priori constraints. Over land, two approaches have been implemented in ORAC; the one used here is a model developed initially for the SU (A)ATSR retrieval algorithm (North et al, 1999), which assumes that the ratio between forward and nadir surface reflectance is spectrally invariant and has very weak a priori constraints.…”

Section: Atsr Data Setsmentioning

confidence: 99%

“…ORAC tends to more strongly underestimate the retrieval uncertainty. The water surface reflectance model (Sayer et al, 2010a) is based on low-turbidity Case I water (Morel, 1988), so it is likely providing a low-biased a priori for the retrieval with too strong a constraint, leading to a high bias in AOD retrievals with overly high confidence in the solution, which becomes large when expressed in normalized terms.…”

Section: Water Sitesmentioning

confidence: 99%

“…the product of FMF and AOD) and coarse-mode AOD are used. While AOD is also positive definite, numerical issues associated with AOD near 0 can be removed if retrievals are performed in log space, reflecting the closerto-lognormal distributions of AOD found in nature (O'Neill et al, 2000;Sayer and Knobelspiesse, 2019); ORAC, for example, retrieves AOD in log space.…”

Section: The Points Inmentioning

confidence: 99%

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

et al. 2020

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Abstract. 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.

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Section: Formal Error Propagationmentioning

confidence: 99%

Section: Formal Error Propagationmentioning

confidence: 99%

Section: Atsr Data Setsmentioning

confidence: 99%

Section: Water Sitesmentioning

confidence: 99%

Section: The Points Inmentioning

confidence: 99%

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

et al. 2020

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Data assimilation for Numerical Smoke Prediction

Hyer

Camacho

Peterson

et al. 2022

Preprint

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Skillful forecasts of weather phenomena in numerical models begin with the most accurate set of initial conditions achievable from observational datasets. The process of combining observations with numerical model predictions is called data assimilation. This chapter describes the types of observations available for data assimilation in models that predict the transport, fate, and impacts of smoke pollution. Observation properties needed for effective data assimilation are identified based on experiences with a variety of observation types in data assimilation experiments, compiled from the published literature. The second half of the chapter surveys the data assimilation methodologies that have been applied to smoke aerosols, and describes specific problems associated with the smoke observations that require innovative techniques in data assimilation. The chapter concludes by providing an outlook for future research and development in data assimilation for smoke prediction models. Data assimilation for prediction of smoke is an emerging area of development that promises to greatly improve forecast skill as new datasets and techniques are applied.

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Contrasting nature of aerosols over South Asian cities and its surrounding environment

Pandey,

Aditi,

Baranwal

et al. 2024

Environmental Pollution

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How should we aggregate data? Methods accounting for the numerical distributions, with an assessment of aerosol optical depth

Abstract: Published by Copernicus Publications on behalf of the European Geosciences Union. 15024A. M. Sayer and K. D. Knobelspiesse: AOD frequency distributions and evaluation of data sets. The methodology is not restricted to AOD and can be applied to other quantities.

Cited by 43 publications

References 104 publications

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

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

Data assimilation for Numerical Smoke Prediction

Contrasting nature of aerosols over South Asian cities and its surrounding environment

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