Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “retrievals”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

High Spectral Resolution Lidar and MPLNET Micro Pulse Lidar Aerosol Optical Property Retrieval Intercomparison During the 2012 7-SEAS Field Campaign at Singapore

From August 2012 to February 2013 a High Resolution Spectral Lidar (HSRL; 532 nm) was deployed at that National University of Singapore near a NASA Micro Pulse Lidar NETwork (MPLNET; 527 nm) site. A primary objective of the MPLNET lidar project is the production and dissemination of reliable Level 1 measurements and Level 2 retrieval products. This paper characterizes and quantifies error in Level 2 aerosol optical property retrievals conducted through inversion techniques that derive backscattering and extinction coefficients from MPLNET elastic single-wavelength datasets. MPLNET Level 2 retrievals for aerosol optical depth and extinction/backscatter coefficient profiles are compared with corresponding HSRL datasets, for which the instrument collects direct measurements of each using a unique optical configuration that segregates aerosol and cloud backscattered signal from molecular signal. The intercomparison is performed, and error matrices reported, for lower (0-5km) and the upper (>5km) troposphere, respectively, to distinguish uncertainties observed within and above the MPLNET instrument optical overlap regime.

MPL↗
NASA NTRS2016

Performance of the Falling Snow Retrieval Algorithms for the Global Precipitation Measurement (GPM) Mission

Retrievals of falling snow from space represent an important data set for understanding the Earth's atmospheric, hydrological, and energy cycles, especially during climate change. Estimates of falling snow must be captured to obtain the true global precipitation water cycle, snowfall accumulations are required for hydrological studies, and without knowledge of the frozen particles in clouds one cannot adequately understand the energy and radiation budgets. While satellite-based remote sensing provides global coverage of falling snow events, the science is relatively new and retrievals are still undergoing development with challenges remaining). This work reports on the development and testing of retrieval algorithms for the Global Precipitation Measurement (GPM) mission Core Satellite, launched February 2014.

Microwave satellite↗
NASA NTRS2015

Evaluating the Utility of Satellite Soil Moisture Retrievals over Irrigated Areas and the Ability of Land Data Assimilation Methods to Correct for Unmodeled Processes

Earth's land surface is characterized by tremendous natural heterogeneity and human-engineered modifications, both of which are challenging to represent in land surface models. Satellite remote sensing is often the most practical and effective method to observe the land surface over large geographical areas. Agricultural irrigation is an important human-induced modification to natural land surface processes, as it is pervasive across the world and because of its significant influence on the regional and global water budgets. In this article, irrigation is used as an example of a human-engineered, often unmodeled land surface process, and the utility of satellite soil moisture retrievals over irrigated areas in the continental US is examined. Such retrievals are based on passive or active microwave observations from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), the Advanced Microwave Scanning Radiometer 2 (AMSR2), the Soil Moisture Ocean Salinity (SMOS) mission, WindSat and the Advanced Scatterometer (ASCAT). The analysis suggests that the skill of these retrievals for representing irrigation effects is mixed, with ASCAT-based products somewhat more skillful than SMOS and AMSR2 products. The article then examines the suitability of typical bias correction strategies in current land data assimilation systems when unmodeled processes dominate the bias between the model and the observations. Using a suite of synthetic experiments that includes bias correction strategies such as quantile mapping and trained forward modeling, it is demonstrated that the bias correction practices lead to the exclusion of the signals from unmodeled processes, if these processes are the major source of the biases. It is further shown that new methods are needed to preserve the observational information about unmodeled processes during data assimilation.

land data↗
NASA NTRS2016

The Sensitivity of SeaWiFS Ocean Color Retrievals to Aerosol Amount and Type

As atmospheric reflectance dominates top-of-the-atmosphere radiance over ocean, atmospheric correction is a critical component of ocean color retrievals. This paper explores the operational Sea-viewing Wide Field-of-View Sensor (SeaWiFS) algorithm atmospheric correction with approximately 13 000 coincident surface-based aerosol measurements. Aerosol optical depth at 440 nm (AOD(sub 440)) is overestimated for AOD below approximately 0.1-0.15 and is increasingly underestimated at higher AOD; also, single-scattering albedo (SSA) appears overestimated when the actual value less than approximately 0.96.AOD(sub 440) and its spectral slope tend to be overestimated preferentially for coarse-mode particles. Sensitivity analysis shows that changes in these factors lead to systematic differences in derived ocean water-leaving reflectance (Rrs) at 440 nm. The standard SeaWiFS algorithm compensates for AOD anomalies in the presence of nonabsorbing, medium-size-dominated aerosols. However, at low AOD and with absorbing aerosols, in situ observations and previous case studies demonstrate that retrieved Rrs is sensitive to spectral AOD and possibly also SSA anomalies. Stratifying the dataset by aerosol-type proxies shows the dependence of the AOD anomaly and resulting Rrs patterns on aerosol type, though the correlation with the SSA anomaly is too subtle to be quantified with these data. Retrieved chlorophyll-a concentrations (Chl) are affected in a complex way by Rrs differences, and these effects occur preferentially at high and low Chl values. Absorbing aerosol effects are likely to be most important over biologically productive waters near coasts and along major aerosol transport pathways. These results suggest that future ocean color spacecraft missions aiming to cover the range of naturally occurring and anthropogenic aerosols, especially at wavelengths shorter than 440 nm, will require better aerosol amount and type constraints.

single scattering albedo↗
NASA NTRS2016

Biospecimen Retrieval from NASA's Rodent Research-1: Maximizing Science Return from Flight Missions

Rodent Research (RR)-1 was conducted to validate flight hardware, operations, and science capabilities that were developed to support long duration missions on the International Space Station. After 37 days in microgravity twenty mice were euthanized and frozen on orbit. Upon return to Earth the carcasses were dissected and yielded 32 different types of tissues from each mouse and over 3200 tissue aliquots. Many tissues were distributed to the Space Life and Physical Sciences (SLPS) Biospecimen Sharing Program (BSP) Principal Investigators (PIs) through the Ames Life Science Data Archive (ALSDA). A second round of dissections was performed to collect additional tissues from the remaining carcasses thawed for a second time for additional BSP PIs. Tissues retrieved included vaginal walls, aorta, pelvis, brown adipose tissue, tail, spine and forearms. Although the analyses are still in progress, some of the PIs have reported that the quality of the tissues was acceptable for their study. In a separate experiment we tested the RNA quality of the tissues that were dissected from frozen carcasses that were subjected to euthanasia, freezing, first and second thaw dissections. Timelines simulated the on-orbit RR-1 procedures to assess the quality of the tissues retrieved from the second thaw dissections. We analyzed the RIN values of select tissues including kidney, brain, white adipose tissue (WAT) and brown adipose tissue (BAT). Overall the RIN values from the second thaw were lower compared to those from the first by about a half unit; however, the tissues yielded RNA that are acceptable quality for some quantitative gene expression assays. Interestingly, RIN values of brain tissues were 8.4+/-0.6 and 7.9+/-0.7 from first and second round dissections, respectively (n=5). Kidney and WAT yielded RIN values less than 8 but they can still be used for qPCR. BAT yielded higher quality RNA (8.2+/-0.5) than WAT (5.22+/-0.9), possibly due to the high fat content. Together, these data show that select tissues can be utilized for gene expression studies even if they are retrieved from carcasses that were subjected to at least two freezing and thawing processes; this further expands science return from valuable and infrequent rodent experiments in space.

rodent↗
NASA NTRS2016

Biospecimen Retrieval from NASA's Rodent Research-1: Maximizing Science Return from Flight Missions

Rodent Research (RR)-1 was conducted to validate flight hardware, operations, and science capabilities that were developed to support long duration missions on the International Space Station. After 37 days in microgravity twenty mice were euthanized and frozen on orbit. Upon return to Earth the carcasses were dissected and yielded 32 different types of tissues from each mouse and over 3200 tissue aliquots. Many tissues were distributed to the Space Life and Physical Sciences (SLPS) Biospecimen Sharing Program (BSP) Principal Investigators (PIs) through the Ames Life Science Data Archive (ALSDA). A second round of dissections was performed to collect additional tissues from the remaining carcasses thawed for a second time for additional BSP PIs. Tissues retrieved included vaginal walls, aorta, pelvis, brown adipose tissue, tail, spine and forearms. Although the analyses are still in progress, some of the PIs have reported that the quality of the tissues was acceptable for their study. In a separate experiment we tested the RNA quality of the tissues that were dissected from frozen carcasses that were subjected to euthanasia, freezing, first and second thaw dissections. Timelines simulated the on-orbit RR-1 procedures to assess the quality of the tissues retrieved from the second thaw dissections. We analyzed the RIN values of select tissues including kidney, brain, white adipose tissue (WAT) and brown adipose tissue (BAT). Overall the RIN values from the second thaw were lower compared to those from the first by about a half unit; however, the tissues yielded RNA that are acceptable quality for some quantitative gene expression assays. Interestingly, RIN values of brain tissues were 8.4+/-0.6 and 7.9+/-0.7 from first and second round dissections, respectively (n5). Kidney and WAT yielded RIN values less than 8 but they can still be used for qPCR. BAT yielded higher quality RNA (8.2+/-0.5) than WAT (5.2+/-20.9), possibly due to the high fat content. Together, these data show that select tissues can be utilized for gene expression studies even if they are retrieved from carcasses that were subjected to at least two freezing and thawing processes; this further expands science return from valuable and infrequent rodent experiments in space.

rodents↗
NASA NTRS2017

Trade Study: Storing NASA HDF5/netCDF-4 Data in the Amazon Cloud and Retrieving Data via Hyrax Server / THREDDS Data Server

As part of the overall effort to understand implications of migrating ESDIS data and services to the cloud we are testing several common OPeNDAP and HDF use cases against three architectures for general performance and cost characteristics. The architectures include retrieving entire files, retrieving datasets using HTTP range gets, and retrieving elements of datasets (chunks) with HTTP range gets. We will describe these architectures and discuss our approach to estimating cost.

HDF↗
NASA NTRS2016

Evaluating A Priori Ozone Profile Information Used in TEMPO Tropospheric Ozone Retrievals

Ozone (O3) is a greenhouse gas and toxic pollutant which plays a major role in air quality. Typically, monitoring of surface air quality and O3 mixing ratios is primarily conducted using in situ measurement networks. This is partially due to high-quality information related to air quality being limited from space-borne platforms due to coarse spatial resolution, limited temporal frequency, and minimal sensitivity to lower tropospheric and surface-level O3. The Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite is designed to address these limitations of current space-based platforms and to improve our ability to monitor North American air quality. TEMPO will provide hourly data of total column and vertical profiles of O3 with high spatial resolution to be used as a near-real-time air quality product. TEMPO O3 retrievals will apply the Smithsonian Astrophysical Observatory profile algorithm developed based on work from GOME, GOME-2, and OMI. This algorithm uses a priori O3 profile information from a climatological data-base developed from long-term ozone-sonde measurements (tropopause-based (TB) O3 climatology). It has been shown that satellite O3 retrievals are sensitive to a priori O3 profiles and covariance matrices. During this work we investigate the climatological data to be used in TEMPO algorithms (TB O3) and simulated data from the NASA GMAO Goddard Earth Observing System (GEOS-5) Forward Processing (FP) near-real-time (NRT) model products. These two data products will be evaluated with ground-based lidar data from the Tropospheric Ozone Lidar Network (TOLNet) at various locations of the US. This study evaluates the TB climatology, GEOS-5 climatology, and 3-hourly GEOS-5 data compared to lower tropospheric observations to demonstrate the accuracy of a priori information to potentially be used in TEMPO O3 algorithms. Here we present our initial analysis and the theoretical impact on TEMPO retrievals in the lower troposphere.

ozone↗
NASA NTRS2017

Accounting for the Effects of Surface BRDF on Satellite Cloud and Trace-Gas Retrievals: A New Approach Based on Geometry-Dependent Lambertian-Equivalent Reflectivity Applied to OMI Algorithms

Most satellite nadir ultraviolet and visible cloud, aerosol, and trace-gas algorithms make use of climatological surface reflectivity databases. For example, cloud and NO2 retrievals for the Ozone Monitoring Instrument (OMI) use monthly gridded surface reflectivity climatologies that do not depend upon the observation geometry. In reality, reflection of incoming direct and diffuse solar light from land or ocean surfaces is sensitive to the sun-sensor geometry. This dependence is described by the bidirectional reflectance distribution function (BRDF). To account for the BRDF, we propose to use a new concept of geometry-dependent Lambertian equivalent reflectivity (LER). Implementation within the existing OMI cloud and NO2 retrieval infrastructure requires changes only to the input surface reflectivity database. The geometry-dependent LER is calculated using a vector radiative transfer model with high spatial resolution BRDF information from the Moderate Resolution Imaging Spectroradiometer (MODIS) over land and the Cox-Munk slope distribution over ocean with a contribution from water-leaving radiance. We compare the geometry-dependent and climatological LERs for two wavelengths, 354 and 466 nm, that are used in OMI cloud algorithms to derive cloud fractions. A detailed comparison of the cloud fractions and pressures derived with climatological and geometry-dependent LERs is carried out. Geometry-dependent LER and corresponding retrieved cloud products are then used as inputs to our OMI NO2 algorithm. We find that replacing the climatological OMI-based LERs with geometry-dependent LERs can increase NO2 vertical columns by up to 50% in highly polluted areas; the differences include both BRDF effects and biases between the MODIS and OMI-based surface reflectance data sets. Only minor changes to NO2 columns (within 5 %) are found over unpolluted and overcast areas.

Lambert-equivalent reflectivity↗
NASA NTRS2016

Cirrus Heterogeneity Effects on Cloud Optical Properties Retrieved with an Optimal Estimation Method from MODIS VIS to TIR Channels.

This study presents preliminary results on the effect of cirrus heterogeneities on top-of-atmosphere (TOA) simulated radiances or reflectances for MODIS channels centered at 0.86, 2.21, 8.56, 11.01 and 12.03 micrometers , and on cloud optical properties retrieved with a research-level optimal estimation method (OEM). Synthetic cirrus cloud fields are generated using a 3D cloud generator (3DCLOUD) and radiances/reflectances are simulated using a 3D radiative transfer code (3DMCPOL). We find significant differences between the heterogeneity effects on either visible and near-infrared (VNIR) or thermal infrared (TIR) radiances. However, when both wavelength ranges are combined, heterogeneity effects are dominated by the VNIR horizontal radiative transport effect. As a result, small optical thicknesses are overestimated and large ones are underestimated. Retrieved effective diameter are found to be slightly affected, contrarily to retrievals using TIR channels only.

MODIS↗
NASA NTRS2017

Analysis of CrIs/ATMS Using AIRS Version-7 Retrieval and QC Methodology

The objective of this research is to develop and implement an algorithm to analyze a long term data record of CrIS/ATMS observations so as to produce monthly mean gridded Level-3 products which are consistent with, and will serve as a seamless follow on to, those of AIRS Version-7. We feel the best way to achieve this result is to analyze CrIS/ATMS data using retrieval and Quality Control (QC) methodologies which are scientifically equivalent to those used in AIRS Version-7. We developed and implemented a single retrieval program that uses as input either AIRS/AMSU or CrIS/ATMS radiance observations, and has appropriate switches that take into account the spectral and radiometric differences between CrIS and AIRS. Our methodology is call CHART (Climate Heritage AIRS Retrieval Technique).

Susskind, Joel↗
NASA NTRS2017

High-Resolution NO2 Observations from the Airborne Compact Atmospheric Mapper: Retrieval and Validation

Nitrogen dioxide (NO2) is a short-lived atmospheric pollutant that serves as an air quality indicator and is itself a health concern. The Airborne Compact Atmospheric Mapper (ACAM) was flown on board the NASA UC-12 aircraft during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality Maryland field campaign in July 2011. The instrument collected hyperspectral remote sensing measurements in the 304-910 nm range, allowing daytime observations of several tropospheric pollutants, including nitrogen dioxide (NO2), at an unprecedented spatial resolution of 1.5 × 1.1 sq. km. Retrievals of slant column abundance are based on the differential optical absorption spectroscopy method. For the air mass factor computations needed to convert these retrievals to vertical column abundance, we include high-resolution information for the surface reflectivity by using bidirectional reflectance distribution function data from the Moderate Resolution Imaging Spectroradiometer. We use high-resolution simulated vertical distributions of NO2 from the Community Multiscale Air Quality and Global Modeling Initiative models to account for the temporal variation in atmospheric NO2 to retrieve middle and lower tropospheric NO2 columns (NO2 below the aircraft). We compare NO2 derived from ACAM measurements with in situ observations from NASA's P-3B research aircraft, total column observations from the ground-based Pandora spectrometers, and tropospheric column observations from the space-based Ozone Monitoring Instrument. The high-resolution ACAM measurements not only give new insights into our understanding of atmospheric composition and chemistry through observation of subsampling variability in typical satellite and model resolutions, but they also provide opportunities for testing algorithm improvements for forthcoming geostationary air quality missions.

Lamsal, L. N.↗
NASA NTRS2016

Retrieval of Daily Maps of PM2.5 Aerosol in the Problematic California Valleys: Bright, Speckled Reflectances, Thin AOT, but High Pollution

The San Joaquin Valley suffers from severe episodes of respirable aerosol (PM2.5) in wintertime.We provide maps of aerosol episodes using daily snapshots of PM2.5 and its changing features despite numerous difficulties inherent to sampling the region. Linear relationships relating aerosol optical thickness (AOT) to PM2.5 give an explained variance of approximately 3.The GEO-CAPE mission has as a goal the provision of relevant measures of respirable aerosol to the community,but has not formulated a science goal beyond the limited goal of retrieval of AOT, bringing the usefulness of GEO-CAPE into doubt.Our special focus was on the DISCOVER-AQ period, Jan-Feb 2013, which had many supporting measurements.Both high pollution and retrieval difficulties tend to occur in many Mediterranean agricultural regions like the San Joauin. One difficulty is the relatively bright surfaces with considerable exposed soil. NASAs MAIAC and MODIS Deep Blue retrieval techniques are shown to have considerable skill even at low aerosol optical thickness (AOT) values, as evaluated by concurrent AERONET sunphotometer measurements.More significantly, these AOT values can correspond to high daytime PM2.5 since aerosol mixed layer depth is thin and variable, 200m 600 m. The thin layers derive from typical subsidence of dry air between more stormy periods. This situation provides an advantage: water vapor column is also almost completely limited to a similar mixed layer depth, and can thus serve as a measure of aerosol dilution.Using the MAIAC Water Vapor Column:In order to make the maps below, we used the MAIAC data but subtracted partial water-vapor columns estimated from MERRA Reanalysis Data availabe from the GSFC GMAO using kriging. We did not use the mixed-layer estimates from MERRA, since such analyses were found problematic during our forecasting exercises for DISCOVER-AQ. Observations from the aircraft soundings suggested that this overlying moisture was mostly due to larger scale flows, not ML venting.However, the specific humidity at the surface and a nearly well-mixed ML was analyzed by kriging from the surface network (MesoWest, University of Utah). These were thought to be truer, uninfluenced by physical process modeling that combines with data observations. (TBD: How different are they?) Procedure: Subtract overlying partial water columns from MAIAC column water and divide this by a surface value of water vapor. (MAIAC column is expressed in cm of water, i.e., water vapor at surface conditions.This method appears to bring out useful details in the distribution of submicron particles in the very problematic Wintertime San Joaquin Valley, and allow analysis of pollution episodes throughout the valley, rather than long-term averages.

san joaquin valley↗
NASA NTRS2019

Retrieval of Liquid Water Cloud Properties from POLDER-3 Measurements Using a Neural Network Ensemble Approach

This paper describes a neural network algorithm for the estimation of liquid water cloud optical properties from the Polarization and Directionality of Earth's Reflectances-3 (POLDER-3) instrument aboard the Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) satellite. The algorithm has been trained on synthetic multi-angle, multi-wavelength measurements of reflectance and polarization and has been applied to the processing of 1 year of POLDER-3 data. Comparisons of the retrieved cloud properties with Moderate Resolution Imaging Spectroradiometer (MODIS) products show that the neural network algorithm has a low bias of around 2 in cloud optical thickness (COT) and between 1 and 2µm in the cloud effective radius. Comparisons with existing POLDER-3 datasets suggest that the proposed scheme may have enhanced capabilities for cloud effective radius retrieval, at least over land. An additional feature of the presented algorithm is that it provides COT and effective radius retrievals at the native POLDER-3 Level 1B pixel level.

Di Noia, Antonio↗
NASA NTRS2019

Performance of Landsat-8 and Sentinel-2 Surface Reflectance Products for River Remote Sensing Retrievals of Chlorophyll-A and Turbidity

Rivers and other freshwater systems play a crucial role in ecosystems, industry, transportation and agriculture. Despite the more than 40 years of inland water observations made possible by optical remote sensing, a standardized reflectance product for inland waters is yet forthcoming. The aim of this work is to compare the standard USGS land surface reflectance product to two Landsat-8 and Sentinel-2 aquatic remote sensing reflectance products over the Amazon, Columbia and Mississippi rivers. Landsat-8 reflectance products from all three routines are then evaluated for their comparative performance in retrieving chlorophyll-a and turbidity in reference to shipborne, underway in situ validation measurements. The land surface product shows the best agreement (4 percent Mean Absolute Percent Difference) with field measurements of radiometry collected on the Amazon River and generates 36 percent higher reflectance values in the visible bands compared to aquatic methods (ACOLITE (Atmospheric Correction for OLI (Operational Land Imager) 'lite') and SeaDAS (Sea-viewing Wide Field-of-View Sensor (SeaWiFS) Data Analysis System)) with larger differences between land and aquatic products observed in Sentinel-2 (0.01 per steraradian) compared to Landsat-8 (0.001 per steraradian). Choice of atmospheric correction routine can bias Landsat-8 retrievals of chlorophyll-a and turbidity by as much as 59 percent and 35 percent respectively. Using a more restrictive time window for matching in situ and satellite imagery can reduce differences by 5–31 percent depending on correction technique. This work highlights the challenges of satellite retrievals over rivers and underscores the need for future optical and biogeochemical research aimed at improving our understanding of the absorbing and scattering properties of river water and their relationships to remote sensing reflectance.

Landsat-8↗
NASA NTRS2018

Understanding the Water Cycle Above the North Polar Cap on Mars Using MRO CRISM Retrievals of Water Vapor

The north polar cap (NPC) on Mars is the major reservoir of atmospheric water (H2O) currently on Mars. The retrieval and monitoring of atmospheric water vapor abundance are crucial for tracking the cycle of water above the NPC. The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) aboard the Mars Reconnaissance Orbiter (MRO) has provided a wealth of data that extend over 5 + Martian years, covering the time period between 2006 and 2016. CRISM is ideally suited for spring and summer observations of the north polar region (latitudes poleward of 60 °N). The retrievals of water vapor column abundances over this extended period of time were performed over both ice-free and water ice covered surfaces, extending the coverage of the water vapor maps to include the permanent cap, where a maximum value of 90 precipitable micrometers (pr–µm) is retrieved, as compared to 60 pr–µm over ice-free regions in the North Polar Region. Away from summertime maximum, modest interannual variability in the water vapor abundance is observed. Zonal averages over all the observed Martian years combined show a developing water front that shifts northward towards summer, before dissipating over the permanent cap during mid-summer. A prominent feature at latitudes around 75 °N shows large abundances of water vapor, indicating a water vapor annulus encircling the retreating edge of the seasonal polar cap during late spring. Meridional transport of water modeled here show that the annulus may be a result of the convergence of water vapor from both south and north along the retreating edge of the NPC.

Khayat, Alain S. J.↗
NASA NTRS2019

Multi-Band Atmospheric Correction Algorithm for Ocean Color Retrievals

NASA's current Atmospheric Correction (AC) algorithm for ocean color utilizes two bands and their ratio in the Near Infrared (NIR) to estimate aerosol reflectance and aerosol type. The algorithm then extrapolates the spectral dependence of aerosol reflectance to the visible wavelengths based on modeled spectral dependence of the identified aerosol type. Future advanced ocean color sensors, such as the Ocean Color Instrument (OCI) that will be carried on the Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) satellite, will be capable of measuring the hyperspectral radiance from 340 to 890 nm at 5-nm spectral resolution and at 7 discrete Short-wave Infrared (SWIR) channels: 940, 1038,1250, 1378, 1615, 2130, and 2260 nm. To optimally employ this unprecedented instrument capability, we propose an improved AC algorithm that utilizes all atmospheric-window channels in the NIR to SWIR spectral range to reduce the uncertainty in the AC process. A theoretical uncertainty analysis of this, namely Multi-Band AC (MBAC), indicates that the algorithm can reduce the uncertainty in remote sensing reflectance (Rrs) retrievals of the ocean caused by sensor random noise. Furthermore, in optically complex waters, where the NIR signal is affected by contributions from highly-reflective turbid waters, the MBAC algorithm can be adaptively weighted to the strongly-absorbing SWIR channels to enable improved ocean color retrievals in coastal waters. We provide here a description of the algorithm and demonstrate the improved performance in ocean color retrievals, relative to the current NASA standard AC algorithm, through comparison with field measurements and assessment of propagated uncertainties in applying the MBAC algorithm to MODIS and simulated PACE OCI data.

PACE↗
NASA NTRS2019

SO2 Emission Estimates Using OMI SO2 Retrievals for 2005-2017

SO2 column densities from OMI provide important information on emission trends and missing sources, but there are discrepancies between different retrieval products. We employ three OMI SO2 retrieval products (NASA standard (SP), NASA prototype, and BIRA) to study the magnitude and trend of SO2 emissions. SO2 column densities from these retrievals are most consistent when viewing angles and solar zenith angles are small, suggesting more robust emission estimates in summer and at low latitudes. We then apply a hybrid 4DVar/mass balance emission inversion to derive monthly SO2 emissions from the NASA SP and BIRA products. Compared to HTAPv2 emissions in 2010, both posterior emission estimates are lower in US, India and Southeast China, but show different changes of emissions in North China Plain. The discrepancies between monthly NASA and BIRA posterior emissions in 2010 are less than or equal to 17% in China and 34% in India. SO2 emissions increase from 2005 to 2016 by 35% (NASA) 48% (BIRA) in India, but decrease in China by 23% (NASA) 33% (BIRA) since 2008. Compared to insitu measurements, the posterior GEOSChem surface SO2 concentrations have reduced NMB in China, the US, and India but not in South Korea in 2010. BIRA posteriors have better consistency with the annual growth rate of surface SO2 measurement in China and spatial variability of SO2 concentration in China, South Korea and India, whereas NASA SP posteriors have better seasonality. These evaluations demonstrate the capability to recover SO2 emissions using OMI observations.

Data assimilation↗