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Andrew M. Sayer

Publications and source records attributed to Andrew M. Sayer.

14 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

PACE Technical Report Series, Volume 12: The PACE Level 1C data format

NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will make global ocean color and atmospheric measurements to provide extended data records of ocean ecology and global biogeochemistry, along with polarimetric measurements for advanced observations of aerosols, clouds and the ocean. PACE will contain three instruments: the primary Ocean Color Instrument (OCI), and two multi-angle polarimeters (MAPs). The latter instruments are contributed under a ‘Do-No-Harm’ (to the rest of the PACE mission) principle, and the PACE Science Data Processing System (SDPS) is only required to produce Level-1b (geolocated radiances with calibration applied) data without performance requirements. However, there is a strong desire to produce data in a format that merges the disparate spatial resolutions, viewing geometry and sampling nature of the three instruments. Our terminology for this format is Level 1c (L1C). This format will be an input to Level 2 algorithms produced from standalone MAP instrument observations, or from algorithms employing multi-sensor fusion. Creating the L1C format has several components. This includes choice of projection method, the means by which multi-angle views are properly incorporated into that projection (‘aggregation’) the means to represent wavelength and light polarization state, the selection of data to be included within the L1C file, and the handling of ancillary data either required for L1C file generation or needed in that format for L2 processing.

PACE↗
NASA NTRS2024

Spectral Correlation in MODIS Water-Leaving Reflectance Retrieval Uncertainty

Spectral remote sensing reflectance, Rrs(λ) (sr−1), is the fundamental quantity used to derive a host of bio-optical and biogeochemical properties of the water column from satellite ocean color measurements. Estimation of uncertainty in those derived geophysical products is therefore dependent on knowledge of the uncertainty in satellite-retrieved R rs . Furthermore, since the associated algorithms require R rs at multiple spectral bands, the spectral (i.e., band-to-band)error covariance in R rs is needed to accurately estimate the uncertainty in those derived properties. This study establishes a derivative-based approach for propagating instrument random noise, instrument systematic uncertainty, and forward model uncertainty into R rs as retrieved using NASA’s multiple-scattering epsilon (MSEPS) atmospheric correction algorithm, to generate pixel-level error covariance in R rs . The approach is applied to measurements from Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite and verified using Monte Carlo (MC) analysis. We also make use of this full spectral error covariance in R rs to calculate uncertainty in phytoplankton pigment chlorophyll-a concentration (chl a , mg/m 3 ) and diffuse attenuation coefficient of downwelling irradiance at 490 nm (K d (490), m -1 ). Accounting for the error covariance in R rs generally reduces the estimated relative uncertainty in chl a by ∼1-2% (absolute value) in waters with chl a < 0.25 mg/m 3 where the color index (CI) algorithm is used. The reduction is ∼5-10% in waters with chl a > 0.35 mg/m 3 where the blue-green ratio (OCX) algorithm is used. Such reduction can be higher than 30% in some regions. For K d (490), the reduction by error covariance is generally ∼2%, but can be higher than 20% in some regions. The error covariance in R rs is further verified through forward-calculating chl a from MODIS-retrieved and in situ R rs and comparing estimated uncertainty with observed differences. An 8-day global composite of propagated uncertainty shows that the goal of 35% uncertainty in chl a can be achieved over deep ocean waters (chl a ≤ 0.1 mg/m3). While the derivative-based approach generates reasonable error covariance in R rs some assumptions should be updated as our knowledge improves. These include the inter-band error correlation in top-of-atmosphere reflectance, and uncertainties in the calibration of MODIS 869 nm band, in ancillary data, and in the in situ data used for system vicarious calibration.

Ocean color↗
NASA NTRS2023

Calibration of the SNPP and NOAA 20 VIIRS Sensors for Continuity of the MODIS Climate Data Records

Accurate long-term sensor calibration and periodic re-processing to ensure consistency and continuity of atmospheric, land and ocean geophysical retrievals from space within the mission period and across different missions is a major requirement of climate data records. In this work, we applied the Multi-Angle Implementation of Atmospheric Correction (MAIAC)-based vicarious calibration technique over Libya-4 desert site to perform calibration analysis of Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20 satellites. For both VIIRS sensors we characterized residual linear calibration trends and cross-calibrated both sensors to MODerate resolution Imaging Spectroradiometer (MODIS) Aqua regarded as a calibration standard. The relative spectral response (RSR) differences were accounted for using the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS) hyperspectral surface reflectance data. Our results agree with independent vicarious calibration results of both the MODIS/VIIRS Characterization Support Team as well as the CERES Imager and Geostationary Calibration Group within estimated uncertainty of 1–2%. Analysis of MAIAC geophysical products with the new calibration shows a high level of agreement of MAIAC aerosol, surface reflectance and NDVI records between MODIS and VIIRS. Excluding high aerosol optical depth (AOD), all three sensors agree in AOD with mean difference (MD) less than 0.01 and residual mean squared difference rmsd ∼ 0.04. Spectral geometrically normalized surface reflectance agrees within rmsd of 0.003–0.005 in the visible and 0.01–0.012 at longer wavelengths. The residual surface reflectance differences are fully explained by differences in spectral filter functions. Finally, difference in NDVI is characterized by rmsd ∼ 0.02 and MD less than 0.003 for NDVI based on VIIRS imagery bands I1/I2 and less than 0.01 for NDVI based on VIIRS radiometric bands M5/M7. In practical sense, these numbers indicate consistency and continuity in MAIAC records ensuring the smooth transition from MODIS to VIIRS.

MAIAC↗
NASA NTRS2023

Assessment of the Impact of Discontinuity in Satellite Instruments and Retrievals on Global PM2.5

Exposure to fine particulate matter (PM2.5) is the leading environmental risk factor for mortality globally. Satellite-derived estimates of surface PM2.5 developed from a combination of satellites, simulations, and ground monitor data are relied upon for health impact studies. The ability to develop satellite-derived PM2.5 estimates requires the continued availability of aerosol optical depth (AOD) sources. This work examines the impact of the addition or loss of satellite AOD data sources on global PM2.5 estimation and the impact of continuing the longterm record with AOD from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi-National Polar orbiting Partnership (S-NPP) satellite after the loss of the MODIS (MODerate resolution Imaging Spectroradiometer) and MISR (Multi-angle Imaging Spectroradiometer) instruments on board the Terra and Aqua satellites. We find that the addition of VIIRS S-NPP AOD products to geophysical PM2.5 estimates from satellites and simulations causes regional differences that correspond to differences in the VIIRS and MODIS Deep Blue AOD algorithms and sampling. Changes in long-term trends and timeseries due to the addition or loss of AOD data sources are generally within their uncertainties. Statistical fusion with ground monitor data partially corrects for changes due to sampling differences when introducing the VIIRS AOD products, but uncertainty remains over desert regions where ground monitor coverage is sparse. This work provides promise for the sustained development of global satellite-derived PM2.5 estimates, despite discontinuities in instruments and retrieval methods.

Remote sensing↗
NASA NTRS2023

Reducing Aerosol Forcing Uncertainty by Combining Models With Satellite and Within-The-Atmosphere Observations: A Three-Way Street

Aerosol forcing uncertainty represents the largest climate forcing uncertainty overall. Its magnitude has remained virtually undiminished over the past 20 years despite considerable advances in understanding most of the key contributing elements. Recent work has produced modest increases only in the confidence of the uncertainty estimate itself. This review summarizes the contributions toward reducing the uncertainty in the aerosol forcing of climate made by satellite observations, measurements taken within the atmosphere, as well as modeling and data assimilation. We adopt a more measurement-oriented perspective than most reviews of the subject in assessing the strengths and limitations of each; gaps and possible ways to fill them are considered. Currently planned programs supporting advanced, global-scale satellite and surface-based aerosol, cloud, and precursor gas observations, climate modeling, and intensive field campaigns aimed at characterizing the underlying physical and chemical processes involved, are all essential. But in addition, new efforts are needed: (a) to obtain systematic aircraft in situ measurements capturing the multi-variate probability distribution functions of particle optical, microphysical, and chemical properties (and associated uncertainty estimates), as well as co-variability with meteorology, for the major aerosol airmass types; (b) to conceive, develop, and implement a suborbital (aircraft plus surface-based) program aimed at systematically quantifying the cloud-scale microphysics, cloud optical properties, and cloud-related vertical velocities associated with aerosol-cloud interactions; and (c) to focus much more research on integrating the unique contributions of satellite observations, suborbital measurements, and modeling, to reduce the persistent uncertainty in aerosol climate forcing.

aerosol climate forcing↗
NASA NTRS2023

Uncertainty in Aerosol–Cloud Radiative Forcing Is Driven By Clean Conditions

Atmospheric aerosols and their impact on cloud properties remain the largest uncertainty in the human forcing of the climate system. By increasing the concentration of cloud droplets (Nd), aerosols reduce droplet size and increase the reflectivity of clouds (a negative radiative forcing). Central to this climate impact is the susceptibility of cloud droplet number to aerosol (β), the diversity of which explains much of the variation in the radiative forcing from aerosol–cloud interactions (RFaci) in global climate models. This has made measuring β a key target for developing observational constraints of the aerosol forcing.

Edward Gryspeerdt↗
NASA NTRS2023

The CHROMA Cloud-Top Pressure Retrieval Algorithm for the Plankton, Aerosol, Cloud, Ocean Ecosystem (PACE) Satellite Mission

This paper provides the theoretical basis and simulated retrievals for the Cloud Height Retrieval from O2 Molecular Absorption (CHROMA) algorithm. Simulations are performed for the Ocean Color Instrument (OCI), which is the primary payload on the forthcoming NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, and the Ocean Land Colour Instrument (OLCI) currently flying on the Sentinel 3 satellites. CHROMA is a Bayesian approach which simultaneously retrieves cloud optical thickness (COT), cloud-top pressure and height (CTP and CTH respectively), and (with a significant prior constraint) surface albedo. Simulated retrievals suggest that the sensor and algorithm should be able to meet the PACE mission goal for CTP error, which is ±60 mb for 65 % of opaque (COT ≥3) single-layer clouds on global average. CHROMA will provide pixel-level uncertainty estimates, which are demonstrated to have skill at telling low-error situations from high-error ones. CTP uncertainty estimates are well-calibrated in magnitude, although COT uncertainty is overestimated relative to observed errors. OLCI performance is found to be slightly better than OCI overall, demonstrating that it is a suitable proxy for the latter in advance of PACE's launch. CTP error is only weakly sensitive to correct cloud phase identification or assumed ice crystal habit/roughness. As with other similar algorithms, for simulated retrievals of multi-layer systems consisting of optically thin cirrus clouds above liquid clouds, retrieved height tends to be underestimated because the satellite signal is dominated by the optically thicker lower layer. Total (liquid plus ice) COT also becomes underestimated in these situations. However, retrieved CTP becomes closer to that of the upper ice layer for ice COT ≈3 or higher.

PACE↗
NASA NTRS2022

Effective Uncertainty Quantification for Multi-Angle Polarimetric Aerosol Remote Sensing Over Ocean

Multi-angle polarimetric (MAP) measurements can enable detailed characterization of aerosol microphysical and optical properties and improve atmospheric correction in ocean color remote sensing. Advanced retrieval algorithms have been developed to obtain multiple geophysical parameters in the atmosphere–ocean system. Theoretical pixel-wise retrieval uncertainties based on error propagation have been used to quantify retrieval performance and determine the quality of data products. However, standard error propagation techniques in high-dimensional retrievals may not always represent true retrieval errors well due to issues such as local minima and the nonlinear dependence of the forward model on the retrieved parameters near the solution. In this work, we analyze these theoretical uncertainty estimates and validate them using a flexible Monte Carlo approach. The Fast Multi-Angular Polarimetric Ocean coLor (FastMAPOL) retrieval algorithm, based on efficient neural network forward models, is used to conduct the retrievals and uncertainty quantification on both synthetic HARP2 (Hyper-Angular Rainbow Polarimeter 2) and AirHARP (airborne version of HARP2) datasets. In addition, for practical application of the uncertainty evaluation technique in operational data processing, we use the automatic differentiation method to calculate derivatives analytically based on the neural network models. Both the speed and accuracy associated with uncertainty quantification for MAP retrievals are addressed in this study. Pixel-wise retrieval uncertainties are further evaluated for the real AirHARP field campaign data. The uncertainty quantification methods and results can be used to evaluate the quality of data products, as well as guide MAP algorithm development for current and future satellite systems such as NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission.

PACE↗
NASA NTRS2022

Estimating pixel-level uncertainty in ocean color retrievals from MODIS

The spectral distribution of marine remote sensing reflectance, R(rs), is the fundamental measurement of ocean color science, from which a host of bio-optical and biogeochemical properties of the water column can be derived. Estimation of uncertainty in these derived properties is thus dependent on knowledge of the uncertainty in satellite-retrieved R(rs) (u(c)(R(rs))) at each pixel. Uncertainty in R(rs), in turn, is dependent on the propagation of various uncertainty sources through the R(rs) retrieval process, namely the atmospheric correction (AC). A derivative-based method for uncertainty propagation is established here to calculate the pixel-level uncertainty in R(rs), as retrieved using NASA’s multiple-scattering epsilon (MSEPS) AC algorithm and verified using Monte Carlo (MC) analysis. The approach is then applied to measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite, with uncertainty sources including instrument random noise, instrument systematic uncertainty, and forward model uncertainty. The uc(Rrs) is verified by comparison with statistical analysis of coincident retrievals from MODIS and in situ Rrs measurements, and our approach performs well in most cases. Based on analysis of an example 8-day global products, we also show that relative uncertainty in R(rs) at blue bands has a similar spatial pattern to the derived concentration of the phytoplankton pigment chlorophyll-a (chl-a), and around 7.3%, 17.0%, and 35.2% of all clear water pixels (chl-a ≤ 0.1 mg/cu.m) with valid u(c)(R(rs)) have a relative uncertainty ≤ 5% at bands 412 nm, 443 nm, and 488 nm respectively, which is a common goal of ocean color retrievals for clear waters. While the analysis shows that u(c)(R(rs)) calculated from our derivative-based method is reasonable, some issues need further investigation, including improved knowledge of forward model uncertainty and systematic uncertainty in instrument calibration.

Pixel-level uncertainty↗
NASA NTRS2022

Optimal Estimation Framework for Ocean Color Atmospheric Correction and Pixel-level Uncertainty Quantification

Ocean color remote sensing requires compensation for atmospheric scattering and absorption (aerosol, Rayleigh, and trace gases), referred to as atmospheric correction (AC). AC allows inference of parameters such as spectrally resolved remote sensing reflectance ( R rs )(λ) ; sr 1 ) at the ocean surface from the top-of-atmosphere reflectance. Often, the uncertainty of this process is not fully explored. Bayesian inference techniques provide a simultaneous AC and uncertainty assessment via a full posterior distribution of the relevant variables, given the prior distribution of those variables and the radiative transfer (RT) likelihood function. Given uncertainties in the algorithm inputs, the Bayesian framework enables better constraints on the AC process by using the complete spectral information compared to traditional approaches that use only a subset of bands for AC. This paper investigates a Bayesian inference research method (Optimal Estimation, OE) for ocean color AC by simultaneously retrieving atmospheric and ocean properties using all visible and near-infrared spectral bands. The OE algorithm analytically approximates the posterior distribution of parameters based on normality assumptions and provides a potentially viable operational algorithm with a reduced computational expense. We developed a Neural Network (NN) RT forward model look-up-table-based emulator to increase algorithm efficiency further and thus speed up the likelihood computations. We then applied the OE algorithm to synthetic data and observations from the MODerate resolution Imaging Spectroradiometer (MODIS) on NASA’s Aqua spacecraft. We compared the R rs )(λ) retrieval and its uncertainty estimates from the OE method with in-situ validation data from the SeaWiFS Bio-optical Archive and Storage System (SeaBASS) and Aerosol Robotic Network Ocean Color (AERONET-OC) datasets. The OE algorithm improved R rs )(λ) estimates relative to the NASA standard operational algorithm by improving all statistical metrics at 443, 555, and 667 nm. Unphysical negative R rs )(λ) , which often appear in complex water conditions, was reduced by a factor of 3. The OE-derived pixel-level R rs )(λ) uncertainty estimates were also assessed relative to in-situ data and were shown to have skill.

Atmospheric correction↗
NASA NTRS2021

Evaluation of Novel NASA MODIS and VIIRS Aerosol Products and Assessment of Smoke Height Boundary Layer Ratio During Extreme Smoke Events in the Western U.S.

We analyze new aerosol products from NASA satellite retrievals over the western U.S. during August 2013, with special attention to locally generated wildfire smoke and downwind plume structures. Aerosol optical depth (AOD) at 550 nm from MODIS (Terra and Aqua Collections 6 and 6.1) and VIIRS Deep Blue (DB) and MODIS (Terra and Aqua) Multi‐Angle Implementation of Atmospheric Correction (MAIAC) retrievals are evaluated against ground‐based AErosol RObotic NETwork (AERONET) observations. We find a significant improvement in correlation with AERONET and other metrics in the latest DB AOD (MODIS C6.1 r^2=0.75, VIIRS r^2=0.79) compared to MODIS C6 (r2=0.62). In general, MAIAC (r^2=0.84) and DB (MODIS C6.1 and VIIRS) present similar statistical evaluation metrics for the western U.S. and are useful tools to characterize aerosol loading associated with wildfire smoke. We also evaluate three novel NASA MODIS plume injection height (PIH) products, one from MAIAC and two from the Aerosol Single scattering albedo and layer Height Estimation (ASHE) (MODIS and VIIRS) algorithm. Both Terra and Aqua MAIAC PIHs statistically agree with ground‐based and satellite lidar observations near the fire source, as do ASHE, although the latter is sensitive to assumptions about aerosol absorption properties. We introduce a first‐order approximation Smoke Height Boundary Layer Ratio (SHBLR) to qualitatively distinguish between aerosol pollution within the planetary boundary layer and the free troposphere. We summarize the scope, limitations, and suggestions for scientific applications of surface level aerosol concentrations specific to wildfire emissions and smoke plumes using these novel NASA MODIS and VIIRS aerosol products.

Plume injection height↗
NASA NTRS2020

An AeroCom–AeroSat study: intercomparison of satellite AOD datasets for aerosol model evaluation

To better understand and characterize current uncertainties in the important observational constraint of climate models of aerosol optical depth (AOD), we evaluate and intercompare 14 satellite products, representing nine different retrieval algorithm families using observations from five different sensors on six different platforms. The satellite products (super-observations consisting of 1°×1° daily aggregated retrievals drawn from the years 2006, 2008 and 2010) are evaluated with AErosol RObotic NETwork (AERONET) and Maritime Aerosol Network (MAN) data. Results show that different products exhibit different regionally varying biases (both under- and overestimates) that may reach ±50 %, although a typical bias would be 15 %–25 % (depending on the product). In addition to these biases, the products exhibit random errors that can be 1.6 to 3 times as large. Most products show similar performance, although there are a few exceptions with either larger biases or larger random errors. The intercomparison of satellite products extends this analysis and provides spatial context to it. In particular, we show that aggregated satellite AOD agrees much better than the spatial coverage (often driven by cloud masks) within the 1°×1° grid cells. Up to ∼50 % of the difference between satellite AOD is attributed to cloud contamination. The diversity in AOD products shows clear spatial patterns and varies from 10 % (parts of the ocean) to 100 % (central Asia and Australia). More importantly, we show that the diversity may be used as an indication of AOD uncertainty, at least for the better performing products. This provides modellers with a global map of expected AOD uncertainty in satellite products, allows assessment of products away from AERONET sites, can provide guidance for future AERONET locations and offers suggestions for product improvements. We account for statistical and sampling noise in our analyses. Sampling noise, variations due to the evaluation of different subsets of the data, causes important changes in error metrics. The consequences of this noise term for product evaluation are discussed.

Aerosol↗
NASA NTRS2020

How Long Is Too Long? Variogram Analysis of AERONET Data to Aid Aerosol Validation and Intercomparison Studies

Geophysical data sets derived from satellite sensors, ground/airborne instrumentation, and computational models are often compared against each other. A common example is the validation of satellite aerosol optical depth (AOD) retrievals against measurements from Aerosol Robotic Network (AERONET) Sun photometers. Spatiotemporal mismatch between data set sampling means that uncaptured variation in the underlying geophysical field introduces apparent disagreement into such comparisons, known as representation or collocation matchup uncertainty. This study uses variogram analysis of AERONET data to estimate temporal mismatch uncertainties and decorrelation time scales for the global AERONET record. As well as total AOD, the fine‐ and coarse‐mode AODs, Ångström Exponent (AE), and fine‐mode fraction (FMF) of AOD are analyzed. Globally, a time difference of 30 min typically induces from 0.011–0.035 variation in AOD. For total, fine, and coarse AODs the typical time to decorrelation is around 2–10 days. For AE and FMF it is 3–33 days; that is, aerosol systems often persist significantly longer than individual events in them. Biomass burning regions tend to show the largest and fastest subdaily AOD variability and also longest times to decorrelation. Some sites show significant season‐to‐season variations in behavior. These results can be used to inform site‐specific time collocation thresholds for aerosol validation analyses and account for temporal variation when estimating data set uncertainty. They also have implications for comparisons between different satellite products or models, data aggregation, and time series analyses. Results are provided on a site‐by‐site basis to facilitate use by other researchers.

satellite↗