Engineering topics

Vasilkov, Alexander

Publications and source records attributed to Vasilkov, Alexander.

12 records

NASA NTRS2018

A Cloud Algorithm Based on the O2-O2 477 nm Absorption Band Featuring an Advanced Spectral Fitting Method and the Use of Surface Geometry-Dependent Lambertian-Equivalent Reflectivity

We discuss a new cloud algorithm that retrieves an effective cloud pressure, also known as cloud optical centroid pressure (OCP), from oxygen dimer (O2-O2) absorption at 477nm after determining an effective cloud fraction (ECF) at 466nm, a wavelength not significantly affected by trace-gas absorption and rotational Raman scattering. The retrieved cloud products are intended for use as inputs to the operational nitrogen dioxide (NO2) retrieval algorithm for the Ozone Monitoring Instrument (OMI) flying on the Aura satellite. The cloud algorithm uses temperature-dependent O2-O2 cross sections and incorporates flexible spectral fitting techniques that account for specifics of the surface reflectivity. The fitting procedure derives O2-O2 slant column densities (SCDs) from radiances after O3, NO2, and H2O absorption features have been removed based on estimates of the amounts of these species from independent OMI algorithms. The cloud algorithm is based on the frequently used mixed Lambertian-equivalent reflectivity (MLER) concept. A geometry-dependent Lambertian-equivalent reflectivity (GLER), which is a proxy of surface bidirectional reflectance, is used for the ground reflectivity in our implementation of the MLER approach. The OCP is derived from a match of the measured O2-O2 SCD to that calculated with the MLER method. Temperature profiles needed for computation of vertical column densities are taken from the Global Modeling Initiative (GMI) model. We investigate the effect of using GLER instead of climatological LER on the retrieved ECF and OCP. For evaluation purposes, the retrieved ECFs and OCPs are compared with those from the operational OMI cloud product, which is also based on the same O2-O2 absorption band. Impacts of the application of the newly developed cloud algorithm to the OMI NO2 retrieval are discussed.

cloud fraction and pressure↗

NASA NTRS2017

Using Neural Networks to Improve the Performance of Radiative Transfer Modeling Used for Geometry Dependent Surface Lambertian-Equivalent Reflectivity Calculations

Surface Lambertian-equivalent reflectivity (LER) is important for trace gas retrievals in the direct calculation of cloud fractions and indirect calculation of the air mass factor. Current trace gas retrievals use climatological surface LER's. Surface properties that impact the bidirectional reflectance distribution function (BRDF) as well as varying satellite viewing geometry can be important for retrieval of trace gases. Geometry Dependent LER (GLER) captures these effects with its calculation of sun normalized radiances (I/F) and can be used in current LER algorithms (Vasilkov et al. 2016). Pixel by pixel radiative transfer calculations are computationally expensive for large datasets. Modern satellite missions such as the Tropospheric Monitoring Instrument (TROPOMI) produce very large datasets as they take measurements at much higher spatial and spectral resolutions. Look up table (LUT) interpolation improves the speed of radiative transfer calculations but complexity increases for non-linear functions. Neural networks perform fast calculations and can accurately predict both non-linear and linear functions with little effort.

Geometry Dependent LER (GLER) capture↗

NASA NTRS2017

Effects of Surface BRDF on the OMI Cloud and NO2 Retrievals: A New Approach Based on Geometry-Dependent Lambertian Equivalent Reflectivity (GLER) Derived from MODIS

The Ozone Monitoring Instrument (OMI) cloud and NO2 algorithms use a monthly gridded surface reflectivity climatology that does 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 (GLER). Implementation within the existing OMI cloud and NO2 retrieval infrastructure requires changes only to the input surface reflectivity database. GLER is calculated using a vector radiative transfer model with high spatial resolution BRDF information from MODIS over land and the Cox Munk slope distribution over ocean with a contribution from water-leaving radiance. We compare GLER and climatological LER at 466 nm, which is used in the OMI O2-O2cloud algorithm to derive effective cloud fractions. A detailed comparison of the cloud fractions and pressures derived with climatological and GLERs is carried out. GLER and corresponding retrieved cloud products are then used as input to the OMI NO2 algorithm. We find that replacing the climatological OMI-based LERs with GLERs 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.

MODIS BRDF OMI clouds NO2 GLER↗

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

Top-of-the-Atmosphere Shortwave Flux Estimation from Satellite Observations: An Empirical Neural Network Approach Applied with Data from the A-Train Constellation

Estimates of top-of-the-atmosphere (TOA) radiative flux are essential for the understanding of Earth's energy budget and climate system. Clouds, aerosols, water vapor, and ozone (O3) are among the most important atmospheric agents impacting the Earth's shortwave (SW) radiation budget. There are several sensors in orbit that provide independent information related to these parameters. Having coincident information from these sensors is important for understanding their potential contributions. The A-train constellation of satellites provides a unique opportunity to analyze data from several of these sensors. In this paper, retrievals of cloud/aerosol parameters and total column ozone (TCO) from the Aura Ozone Monitoring Instrument (OMI) have been collocated with the Aqua Clouds and Earth's Radiant Energy System (CERES) estimates of total reflected TOA outgoing SW flux (SWF). We use these data to develop a variety of neural networks that estimate TOA SWF globally over ocean and land using only OMI data and other ancillary information as inputs and CERES TOA SWF as the output for training purposes. OMI-estimated TOA SWF from the trained neural networks reproduces independent CERES data with high fidelity. The global mean daily TOA SWF calculated from OMI is consistently within 1% of CERES throughout the year 2007. Application of our neural network method to other sensors that provide similar retrieved parameters, both past and future, can produce similar estimates TOA SWF. For example, the well-calibrated Total Ozone Mapping Spectrometer (TOMS) series could provide estimates of TOA SWF dating back to late 1978.

CERES data with high fidelity↗

NASA NTRS2014

OMMYDCLD: a New A-train Cloud Product that Co-locates OMI and MODIS Cloud and Radiance Parameters onto the OMI Footprint

Clouds cover approximately 60% of the earth's surface. When obscuring the satellite's field of view (FOV), clouds complicate the retrieval of ozone, trace gases and aerosols from data collected by earth observing satellites. Cloud properties associated with optical thickness, cloud pressure, water phase, drop size distribution (DSD), cloud fraction, vertical and areal extent can also change significantly over short spatio-temporal scales. The radiative transfer models used to retrieve column estimates of atmospheric constituents typically do not account for all these properties and their variations. The OMI science team is preparing to release a new data product, OMMYDCLD, which combines the cloud information from sensors on board two earth observing satellites in the NASA A-Train: Aura/OMI and Aqua/MODIS. OMMYDCLD co-locates high resolution cloud and radiance information from MODIS onto the much larger OMI pixel and combines it with parameters derived from the two other OMI cloud products: OMCLDRR and OMCLDO2. The product includes histograms for MODIS scientific data sets (SDS) provided at 1 km resolution. The statistics of key data fields - such as effective particle radius, cloud optical thickness and cloud water path - are further separated into liquid and ice categories using the optical and IR phase information. OMMYDCLD offers users of OMI data cloud information that will be useful for carrying out OMI calibration work, multi-year studies of cloud vertical structure and in the identification and classification of multi-layer clouds.

OMMYDCLD↗

NASA NTRS2013

GEO-CAPE Aerosol Working Group Report

GEO-CAPE will measure a suite of short-lived species that are relevant to both air quality and climate. The document was presented at the 2013 AEROCENTER Annual Meeting held at the GSFC Visitors Center, May 31, 2013. The Organizers of the meeting are posting the talks to the public Aerocentr website, after the meeting.

AOD relative quality↗

NASA NTRS2009

Evaluation of the OMI Cloud Pressures Derived from Rotational Raman Scattering by Comparisons with other Satellite Data and Radiative Transfer Simulations

In this paper we examine differences between cloud pressures retrieved from the Ozone Monitoring Instrument (OMI) using the ultraviolet rotational Raman scattering (RRS) algorithm and those from the thermal infrared (IR) Aqua/MODIS. Several cloud data sets are currently being used in OMI trace gas retrieval algorithms including climatologies based on IR measurements and simultaneous cloud parameters derived from OMI. From a validation perspective, it is important to understand the OMI retrieved cloud parameters and how they differ with those derived from the IR. To this end, we perform radiative transfer calculations to simulate the effects of different geophysical conditions on the OMI RRS cloud pressure retrievals. We also quantify errors related to the use of the Mixed Lambert-Equivalent Reflectivity (MLER) concept as currently implemented of the OMI algorithms. Using properties from the Cloudsat radar and MODIS, we show that radiative transfer calculations support the following: (1) The MLER model is adequate for single-layer optically thick, geometrically thin clouds, but can produce significant errors in estimated cloud pressure for optically thin clouds. (2) In a two-layer cloud, the RRS algorithm may retrieve a cloud pressure that is either between the two cloud decks or even beneath the top of the lower cloud deck because of scattering between the cloud layers; the retrieved pressure depends upon the viewing geometry and the optical depth of the upper cloud deck. (3) Absorbing aerosol in and above a cloud can produce significant errors in the retrieved cloud pressure. (4) The retrieved RRS effective pressure for a deep convective cloud will be significantly higher than the physical cloud top pressure derived with thermal IR.

Vasilkov, Alexander↗

NASA NTRS2008

A New Technique for Retrieval of Tropospheric and Stratospheric Ozone Profiles using Sky Radiance Measurements at Multiple View Angles: Application to a Brewer Spectrometer

This paper describes and applies a new technique for retrieving diurnal variability in tropospheric ozone vertical distribution using ground-based measurements of ultraviolet sky radiances. The measured radiances are obtained by a polarization-insensitive modified Brewer double spectrometer located at Goddard Space Flight Center, in Greenbelt, Maryland, USA. Results demonstrate that the Brewer angular (0-72deg viewing zenith angle) and spectral (303-320 nm) measurements of sky radiance in the solar principal plane provide sufficient information to derive tropospheric ozone diurnal variability. In addition, the Brewer measurements provide stratospheric ozone vertical distributions at least twice per day near sunrise and sunset. Frequent measurements of total column ozone amounts from direct-sun observations are used as constraints in the retrieval. The vertical ozone profile resolution is shown in terms of averaging kernels to yield at least four points in the troposphere-low stratosphere, including good information in Umkehr layer 0 (0-5 km). The focus of this paper is on the derivation of stratospheric and tropospheric ozone profiles using both simulated and measured radiances. We briefly discuss the necessary modifications of the Brewer spectrometer that were used to eliminate instrumental polarization sensitivity so that accurate sky radiances can be obtained in the presence of strong Rayleigh scattering and aerosols. The results demonstrate that including a site-specific and time-dependent aerosol correction, based on Brewer direct-sun observations of aerosol optical thickness, is critical to minimize the sky radiance residuals as a function of observing angle in the optimal estimation inversion algorithm and improve the accuracy of the retrieved ozone profile.

Tzortziou, Maria↗

NASA NTRS2008

A Sample of What We Have Learned from A-Train Cloud Measurements

The A-train active sensors CloudSat and CALIPSO provide detailed information about cloud vertical structure. Coarse vertical information can also be obtained from a combination of passive sensors (e.g. cloud liquid water content from AMSR-E, cloud ice properties from MLS and HIRDLS, cloud-top pressure from MODIS and AIRS, and UVNISINear IR absorption and scattering from OMI, MODIS, and POLDER). In addition, the wide swaths of instruments such as MODIS, AIRS, OMI, POLDER, and AMSR-E can be exploited to create estimates of the three-dimensional cloud extent. We will show how data fusion from A-train sensors can be used, e.g., to detect and map the presence of multiple layer/phase clouds. Ultimately, combined cloud information from Atrain instruments will allow for estimates of heating and radiative flux at the surface as well as UV/VIS/Near IR trace-gas absorption at the overpass time on a near-global daily basis. CloudSat has also dramatically improved our interpretation of visible and UV passive measurements in complex cloudy situations such as deep convection and multiple cloud layers. This has led to new approaches for unique and accurate constituent retrievals from A-train instruments. For example, ozone mixing ratios inside tropical deep convective clouds have recently been estimated using the Aura Ozone Monitoring Instrument (OMI). Field campaign data from TC4 provide additional information about the spatial variability and origin of trace-gases inside convective clouds. We will highlight some of the new applications of remote sensing in cloudy conditions that have been enabled by the synergy between the A-train active and passive sensors.

Joiner, Joanna↗

NASA NTRS2008

Absorption in Extended Inhomogeneous Clouds

The launch of several different sensors, including CloudSat, into the A-train constellation of satellites allows us for the first time to compute absorption that can occur in realistic vertically inhomogeneous clouds including multiple cloud decks. CloudSat data show that these situations are common. Therefore, understanding vertically inhomogeneous clouds is important from both climate and satellite atmospheric composition remote sensing perspectives. Satellite passive sensors that operate from the near IR to the UV often rely on radiative cloud pressures derived from absorption in oxygen bands (A, B, gamma, or O2-O2 bands) or from rotational-Raman scattering in order to retrieve information about atmospheric trace gases. The radiative cloud pressure is distinct from the physical cloud top derived from thermal infrared measurements. Therefore, the combination of information from different passive sensors yields some information about the cloud vertical profile. When either or both the clouds or atmospheric absorbers (trace gases and aerosols) are vertically inhomogeneous, the use of an effective cloud pressure derived from these approaches may lead to errors. Here, we focus on several scenarios (deep convective clouds and distinct two layer clouds) based on realistic cloud optical depth vertical profiles derived from the CloudSatfMODIS combination. We focus on implications for trace-gas column amount retrievals (specifically ozone and NO2) and derived surface UV irradiance from the Ozone Monitoring Instrument (OMI) on the Atrain Aura platform.

Joiner, Joanna↗

NASA NTRS1999

Global Mapping of Underwater UV Irradiances and DNA-Weighted Exposures using TOMS and SeaWiFS Data Products

The global stratospheric ozone-layer depletion results In an increase in biologically harmful ultraviolet (UV) radiation reaching the surface and penetrating to ecologically significant depths in natural waters. Such an increase can be estimated on a global scale by combining satellite estimates of UV irradiance at the ocean surface from the Total Ozone Mapping Spectrometer (TOMS) satellite instrument with the SeaWIFS satellite ocean-color measurements in the visible spectral region. In this paper we propose a model of seawater optical properties in the UV spectral region based on the Case I water model in the visible range. The inputs to the model are standard monthly SeaWiFS products: chlorophyll concentration and the diffuse attenuation coefficient at 490nm. Penetration of solar UV radiation to different depths in open ocean waters is calculated using the RT (radiative transfer) quasi-single scattering approximation (QSSA). The accuracy of the QSSA approximation in the water is tested using more accurate codes. The sensitivity study of the underwater UV irradiance to atmospheric and oceanic optical properties have shown that the main environmental parameters controlling the absolute levels of the UVB (280-320nm) and DNA-weighted irradiance underwater are: solar-zenith angle, cloud transmittance, water optical properties, and total ozone. Weekly maps of underwater UV irradiance and DNA-weighted exposure are calculated using monthly-mean SeaWiFS chlorophyll and diffuse attenuation coefficient products, daily SeaWiFS cloud fraction data, and the TOMS-derived surface UV irradiance daily maps. The final products include global maps of weekly-average UVB irradiance and DNA-weighted daily exposures at 3m and 10m, and depths where the UVB irradiance and DNA-weighted dose rate at local noon are equal to 10% of their surface values.

Vasilkov, Alexander↗