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NASA NTRS2004

Implications of Version 8 TOMS and SBUV Data for Long-Term Trend Analysis

Total ozone data from the Total Ozone Mapping Spectrometer (TOMS) and profile/total ozone data from the Solar Backscatter Ultraviolet (SBUV; SBW/2) series of instruments have recently been reprocessed using new retrieval algorithms (referred to as Version 8 for both) and updated calibrations. In this paper, we incorporate the Version 8 data into a TOMS/SBW merged total ozone data set and an S B W merged profile ozone data set. The Total Merged Ozone Data (Total MOD) combines data from multiple TOMS and SBW instruments to form an internally consistent global data set with virtually complete time coverage from October 1978 through December 2003. Calibration differences between instruments are accounted for using external adjustments based on instrument intercomparisons during overlap periods. Previous results showed errors due to aerosol loading and sea glint are significantly reduced in the V8 TOMS retrievals. Using SBW as a transfer standard, calibration differences between V8 Nimbus 7 and Earth Probe TOMS data are approx. 1.3%, suggesting small errors in calibration remain. We will present updated total ozone long-term trends based on the Version 8 data. The Profile Merged Ozone Data (Profile MOD) data set is constructed using data from the SBUV series of instruments. In previous versions, SAGE data were used to establish the long-term external calibration of the combined data set. The SBW Version 8 we assess the V8 profile data through comparisons with SAGE and between SBW instruments in overlap periods. We then construct a consistently-calibrated long term time series. Updated zonal mean trends as a function of altitude and season from the new profile data set will be shown, and uncertainties in determining the best long-term calibration will be discussed.

Frith, Stacey M.↗
NASA NTRS1995

Calibration of the NOAA 11 solar backscatter ultraviolet (SBUV/2) ozone data set from 1989 to 1993 using in-flight calibration data and SSBUV

Total ozone and ozone profiles are currently being measured by solar backscatter ultraviolet (SBUV/2) instruments onboard NOAA polar orbiting spacecraft using the backscattered ultraviolet technique. The NOAA 11 SBUV/2 operational data set, reprocessed from January 1989 to May 1993 (version 6), includes an updated algorithm and revised prelaunch and postlaunch calibrations of the geometrical albedo observations used to derive ozone values. The postlaunch calibration revisions remove time dependent errors in the ozone amounts due to instrument drift, while the revised prelaunch calibration corrects the absolute value of retrieved ozone. The prelaunch corrections are a result of calibration checks from in-orbit comparisons of ultraviolet geometric albedos measured by shuttle SBUV (SSBUV) and the NOAA 11 SBUV/2. Geometric albedo comparison data are further corrected using a radiative transfer code to account for the small difference in observing conditions between the two spacecraft. The postlaunch corrections rely on in-flight calibration and solar irradiance data to account for time dependent changes in instrument gain, thermal response, and instrument diffuser degradation over time. Comparison of data from three SSBUV flights, which occurred about one year apart, with concurrent SBUV/2 data provided an independent check of the time dependent change derived from the in-flight calibration data. Time independent corrections result in an increase of about 1% for total ozone, 5% for ozone at 1 mbar, and near 0% at 15 mbar. The time dependent corrections amount to an increase of 2% for total ozone, 10% for ozone near 1 mbar, and 3% at 15 mbar at the end of the current record in May 1993. Recent laboratory studies indicate that the absolute radiance calibrations may still be in error by a few percent which results in ozone profile values that are too low. The SBUV/2 total and ozone profile data are compared to the Nimbus SBUV data during the period when the data overlapped. Total ozone values agree to about 1%, while ozone profile differences range from -4% to +6%, depending on latitude and altitude, relative to SBUV. These differences are not statistically significant given the uncertainties of the two data sets.

Hilsenrath, E.↗
NASA NTRS1985

Intercomparison of satellite ozone profile measurements

Ozone profile data are intercompared with those from the LIMS and SBUV flown on Nimbus 7, SAGE flown on Atmospheric Explorer Mission 2, and the Ultraviolet and Infrared Spectrometers flown on the SME. Ozone data were derived from the measurements with independently derived processing algorithms. The data cover different time periods and have different spatial and temporal resolutions. The similarities and differences between the individual data sets are determined with effort focused on directly comparing the spacecraft data sets in the form of individual profiles, zonal mean profiles and global analyses. Comparisons with ground data from balloons and Umkehr stations are made.

Fleig, A. J.↗
NASA NTRS2002

Upper-Stratospheric Ozone Trends 1979-1998

Extensive analyses of ozone observations between 1978 and 1998 measured by Dobson Umkehr, Stratospheric Aerosol and Gas Experiment (SAGE) I and II, and Solar Backscattered Ultraviolet (SBUV) and (SBUV)/2 indicate continued significant ozone decline throughout the extratropical upper stratosphere from 30-45 km altitude. The maximum annual linear decline of -0.8 +/- 0.2 %/yr(2sigma) occurs at 40 km and is well described in terms of a linear decline modulated by the 11-year solar variation. The minimum decline of -0.110.1% yr-1(2o) occurs at 25 km in midlatitudes, with remarkable symmetry between the Northern and Southern Hemispheres at 40 km altitude. Midlatitude upper-stratospheric zonal trends exhibit significant seasonal variation (+/- 30% in the Northern Hemisphere, +/- 40% in the Southern Hemisphere) with the most negative trends of -1.2%/yr occurring in the winter. Significant seasonal trends of -0.7 to -0.9%/yr occur at 40 km in the tropics between April and September. Subjecting the statistical models used to calculate the ozone trends to intercomparison tests on a variety of common data sets yields results that indicate the standard deviation between trends estimated by 10 different statistical models is less than 0.1%/yr in the annual-mean trend for SAGE data and less than 0.2%/yr in the most demanding conditions (seasons with irregular, sparse data) [World Meteorological Organization (WMO), 1998]. These consistent trend results between statistical models together with extensive consistency between the independent measurement-system trend observations by Dobson Umkehr, SAGE I and II, and SBUV and SBUV/2 provide a high degree of confidence in the accuracy of the declining ozone amounts reported here. Additional details of ozone trend results from 1978 to 1996 (2 years shorter than reported here) along with lower-stratospheric and tropospheric ozone trends, extensive intercomparisons to assess relative instrument drifts, and retrieval algorithm details are given by WMO [1998].

Newchurch, M. J.↗
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 NTRS2022

A New Machine Learning Based Analysis for Improving Satellite Retrieved Atmospheric Composition Data: OMI SO2 as an Example

Despite recent progress, satellite retrievals of anthropogenic SO2 still suffer from relatively low signal-tonoise ratios. In this study, we demonstrate a new machine learning data analysis method to improve the quality of satellite SO2 products. In the absence of large ground-truth datasets for SO2, we start from SO2 slant column densities (SCDs) retrieved from the Ozone Monitoring Instrument (OMI) using a data-driven, physically based algorithm and calculate the ratio between the SCD and the root mean square (rms) of the fitting residuals for each pixel. To build the training data, we select presumably clean pixels with small SCD / rms ratios (SRRs) and set their target SCDs to zero. For polluted pixels with relatively large SRRs, we set the target to the original retrieved SCDs. We then train neural networks (NNs) to reproduce the target SCDs using predictors including SRRs for individual pixels, solar zenith, viewing zenith and phase angles, scene reflectivity, and O3 column amounts, as well as the monthly mean SRRs. For data analysis, we employ two NNs: (1) one trained daily to produce analyzed SO2 SCDs for polluted pixels each day and (2) the other trained once every month to produce analyzed SCDs for less polluted pixels for the entire month. Test results for 2005 show that our method can significantly reduce noise and artifacts over background regions. Over polluted areas, the monthly mean NN-analyzed and original SCDs generally agree to within ±15 %, indicating that our method can retain SO2 signals in the original retrievals except for large volcanic eruptions. This is further confirmed by running both the NN-analyzed and original SCDs through a topdown emission algorithm to estimate the annual SO2 emissions for ∼ 500 anthropogenic sources, with the two datasets yielding similar results. We also explore two alternative approaches to the NN-based analysis method. In one, we employ a simple linear interpolation model to analyze the original SCD retrievals. In the other, we develop a PCA–NN algorithm that uses OMI measured radiances, transformed and dimension-reduced with a principal component analysis (PCA) technique, as inputs to NNs for SO2 SCD retrievals. While the linear model and the PCA–NN algorithm can reduce retrieval noise, they both underestimate SO2 over polluted areas. Overall, the results presented here demonstrate that our new data analysis method can significantly improve the quality of existing OMI SO2 retrievals. The method can potentially be adapted for other sensors and/or species and enhance the value of satellite data in air quality research and applications.

Can Li↗
NASA NTRSDate not supplied

Principal Component and Machine Learning Approach to Gap Fill Hyperspectral Ocean Color Satellite Retrievals

Retrievals of ocean color properties from space are important for monitoring the health of the ocean ecosystem but such retrievals can be limited spatially due to conditions such as clouds, aerosols, and sun glint. Gap filling of ocean color retrievals is typically performed by combining retrievals from multiple satellites or temporally averaging multiple days of retrievals. Despite these techniques large gaps still exist posing challenges for near real time monitoring of events like harmful algae blooms. To address these limitations, we developed a spatial gap filling approach applying machine learning approach to hyperspectral instruments to learn how to perform an atmospheric correction under challenging retrieval conditions. In this approach a principal component analysis is used to decompose the hyperspectral measurements into spectral components that describe the scattering and absorption of the atmosphere mixed with the surface spectral signatures. The coefficients of the principal components are used to train a neural network to predict ocean color properties derived from a standard MODIS ocean color algorithm. We apply the approach to two hyperspectral UV/VIS sensors, the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) to show that it can be used to estimate ocean color properties such as chlorophyll, remote sensing reflectance, and fluorescence line height. This method could be used as a gap-filling technique for the future Ocean Color Instrument (OCI) onboard upcoming NASA's Plankton, Aerosol Cloud, ocean Ecosystem (PACE) satellite to provide additional information for monitoring the health of our global oceans. Additionally, it could be applied to the first NASA and Smithsonian geostationary Tropospheric Emissions: Monitoring of Pollution (TEMPO) spectrometer to better understand diurnal variability in inland and coastal ocean ecology.

Zachary Fasnacht↗
NASA NTRS2003

Lightning and Other Influences On Tropical Tropospheric Ozone: Empirical Studies of Covariation

Tropical and subtropical tropospheric ozone are important radiatively active species, with particularly large effects in the upper third of the troposphere. Temporal variability of O3 has proved difficult to simulate day by day in process models. Thus, individual roles of lightning, biomass burning, and other pollution in providing precursor NO(x), radicals, and chain carriers (CO, hydrocarbons) remain unquantified by simulation, and it is theoretically reasonable that individual roles are magnified by a joint synergy. We use wavelet analysis and Burg-algorithm maximum entropy spectral analyses to describe time-scales and correlation of ozone with proxies for processes controlling its concentration. Our empirical studies link time variations apparent in several datasets: the SHADOZ (Southern Hemisphere Additional Ozonesondes) network stations (Nairobi, Fiji), and auxiliary series with power to explain ozone-determining processes, with some interpretation based on the TTO (Tropical Tropospheric Ozone) product derived from TOMS (the Total Ozone Mapping Spectrometer). The auxiliary series are The OTD/LIS(Optical Transient Detector/Lightning Imaging Sensor) measurements of the lightning NO(x) source, the OLR (Outgoing Longwave Radiation)measurement of high-topped clouds, and standard meteorological variables from the United States NCEP (National Centers for Environmental Prediction) and Data Assimilation Office analyses. Concentrating on equatorial ozone, we compare the statistical evidence on the variability of tropospheric ozone. Important variations occur on approximately two-week, two-month (Madden-Julian Oscillation) and annual scales, and relations with OLR suggest controls associated with continental clouds. Hence we are now using the Lightning Imaging Sensor data set to indicate NO(x) sources. We report initial results defining relative roles of the process mentioned affecting O3 using their covariance properties.

Chatfield, Robert B.↗
NASA NTRS2001

Retrieval of Polar Stratospheric Cloud Microphysical Properties from Lidar Measurements: Dependence on Particle Shape Assumptions

A retrieval algorithm has been developed for the microphysical analysis of polar stratospheric cloud (PSC) optical data obtained using lidar instrumentation. The parameterization scheme of the PSC microphysical properties allows for coexistence of up to three different particle types with size-dependent shapes. The finite difference time domain (FDTD) method has been used to calculate optical properties of particles with maximum dimensions equal to or less than 2 mu m and with shapes that can be considered more representative of PSCs on the scale of individual crystals than the commonly assumed spheroids. Specifically. these are irregular and hexagonal crystals. Selection of the optical parameters that are input to the inversion algorithm is based on a potential data set such as that gathered by two of the lidars on board the NASA DC-8 during the Stratospheric Aerosol and Gas Experiment 0 p (SAGE) Ozone Loss Validation experiment (SOLVE) campaign in winter 1999/2000: the Airborne Raman Ozone and Temperature Lidar (AROTEL) and the NASA Langley Differential Absorption Lidar (DIAL). The 0 microphysical retrieval algorithm has been applied to study how particle shape assumptions affect the inversion of lidar data measured in leewave PSCs. The model simulations show that under the assumption of spheroidal particle shapes, PSC surface and volume density are systematically smaller than the FDTD-based values by, respectively, approximately 10-30% and approximately 5-23%.

Reichardt, J.↗
NASA NTRS2022

First Observations of Mesospheric OH Emission Profiles from OMPS/LP

The hydroxyl radical (OH) is one of the most reactive trace species and plays several important roles in the photochemical equilibrium and energy balance in the mesosphere. Global observations of OH from satellite instruments have a role to play in the study of OH and water vapor variations. This study describes an advanced algorithm to detect mesospheric OH emission profiles from the Suomi NPP satellite Ozone Mapping and Profiler Suite Limb Profiler (OMPS/LP). A triplet technique has been adapted to the OMPS/LP radiance measurements for determining OH emission signatures and OH index (OHI) from the OH A2Σ+-X2Π 0-0 band near the 308.8-nm wavelength. The derived mesospheric profiles provide an overall picture of the vertical distribution of OHI between 55 and 84 km and seasonal and latitudinal variability of the strength and height of the OHI. The observed annual cycle is correlated with the water vapor cycle and anticorrelated with the mesospheric temperature cycle. The data show that the relationships persist during the period of April 2012–December 2020. The seasonal behavior of OHI may be associated with variations in solar illumination or mesospheric water vapor abundance. The influence of solar illumination is dominant in the midlatitudes, while the OHI pattern is dominated by water vapor photolysis and other influences in the tropics.

Observations of Mesospheric OH Emission↗
NASA NTRS1994

An asymptotic method for estimating the vertical ozone distribution in the Earth's atmosphere from satellite measurements of backscattered solar UV-radiation

An asymptotic approach to solution of the inverse problems of remote sensing is presented. It consists in changing integral operators characteristic of outgoing radiation into their asymptotic analogues. Such approach does not add new principal uncertainties into the problem and significantly reduces computation time that allows to develop the real (or about) time algorithms for interpretation of satellite measurements. The asymptotic approach has been realized for estimating vertical ozone distribution from satellite measurements of backscatter solar UV radiation in the Earth's atmosphere.

Ishov, Alexander G.↗
NASA NTRS2010

Using MERRA Gridded Innovations for Quantifying Uncertainties in Analysis Fields and Diagnosing Observing System Inhomogeneities

MERRA is a NASA reanalysis for the satellite era using a major new version of the Goddard Earth Observing System Data Assimilation System Version 5 (GEOS-5). The project focuses on historical analyses of the hydrological cycle on a broad range of weather and climate time scales and places the NASA EOS suite of observations in a climate context. The characterization of uncertainty in reanalysis fields is a commonly requested feature by users of such data. While intercomparison with reference data sets is common practice for ascertaining the realism of the datasets, such studies typically are restricted to long term climatological statistics and seldom provide state dependent measures of the uncertainties involved. In principle, variational data assimilation algorithms have the ability of producing error estimates for the analysis variables (typically surface pressure, winds, temperature, moisture and ozone) consistent with the assumed background and observation error statistics. However, these "perceived error estimates" are expensive to obtain and are limited by the somewhat simplistic errors assumed in the algorithm. The observation minus forecast residuals (innovations) by-product of any assimilation system constitutes a powerful tool for estimating the systematic and random errors in the analysis fields. Unfortunately, such data is usually not readily available with reanalysis products, often requiring the tedious decoding of large datasets and not so-user friendly file formats. With MERRA we have introduced a gridded version of the observations/innovations used in the assimilation process, using the same grid and data formats as the regular datasets. Such dataset empowers the user with the ability of conveniently performing observing system related analysis and error estimates. The scope of this dataset will be briefly described. We will present a systematic analysis of MERRA innovation time series for the conventional observing system, including maximum-likelihood estimates of background and observation errors, as well as global bias estimates. Starting with the joint PDF of innovations and analysis increments at observation locations we propose a technique for diagnosing bias among the observing systems, and document how these contextual biases have evolved during the satellite era covered by MERRA.

da Silva, Arlindo↗
NASA NTRS1992

A contribution toward understanding the biospherical significance of Antarctic ozone depletion

The paper presents and compares measurements of biologically active UV radiation made by the NSF scanning spectroradiometer (UV-monitor) at Palmer Station, Antarctica, during the Austral springs of 1988, 1989, and 1990. Column ozone abundance above Palmer Station is computed from these measurements using a multiple wavelength algorithm. Two contrasting action spectra are employed to estimate the biologically relevant dose from the spectral measurements: a standard weighting function for damage to DNA, and a new action spectrum representing the potential for photosynthesis inhibition in Antarctic phytoplankton. The former weights only UV-B wavelengths (280-320 nm) and gives the most weight to wavelengths shorter than 300 nm, while the latter includes large contributions out to 355 nm. Ozone abundances and dose-weighted irradiances provided by the NSF UV-monitor are used to derive the radiation amplification factors for both DNA- and phytoplankton-effective irradiance.

Lubin, Dan↗
NASA NTRS2000

SeaWiFS Postlaunch Calibration and Validation Analyses

The effort to resolve data quality issues and improve on the initial data evaluation methodologies of the SeaWiFS Project was an extensive one. These evaluations have resulted, to date, in three major reprocessings of the entire data set where each reprocessing addressed the data quality issues that could be identified up to the time of the reprocessing. Three volumes of the SeaWiFS Postlaunch Technical Report Series (Volumes 9, 10, and 11) are needed to document the improvements implemented since launch. Volume 10 continues the sequential presentation of postlaunch data analysis and algorithm descriptions begun in Volume 9. Chapter 1 of Volume 10 describes an absorbing aerosol index, similar to that produced by the Total Ozone Mapping Spectrometer (TOMS) Project, which is used to flag pixels contaminated by absorbing aerosols, such as, dust and smoke. Chapter 2 discusses the algorithm being used to remove SeaWiFS out-of-band radiance from the water-leaving radiances. Chapter 3 provides an itemization of all significant changes in the processing algorithms for each of the first three reprocessings. Chapter 4 shows the time series of global clear water and deep-water (depths greater than 1,000m) bio-optical and atmospheric properties (normalized water-leaving radiances, chlorophyll, atmospheric optical depth, etc.) based on the eight-day composites as a check on the sensor calibration stability. Chapter 5 examines the variation in the derived products with scan angle using high resolution data around Hawaii to test for residual scan modulation effects and atmospheric correction biases. Chapter 6 provides a methodology for evaluating the atmospheric correction algorithm and atmospheric derived products using ground-based observations. Similarly, Chapter 7 presents match-up comparisons of coincident satellite and in situ data to determine the accuracy of the water-leaving radiances, chlorophyll a, and K(490) products.

Hooker, Stanford B.↗
NASA NTRS2026

Methods for Validation of Random Uncertainty Estimates and Their Applications to Ozone Profiles From Limb-Viewing Satellite Instruments

For satellite measurements of atmospheric composition, the random uncertainty estimates provided by retrieval algorithms might be imperfect due to various approximations used in the retrievals or the presence of unknown error sources. This paper presents an overview of the methods used for the validation of random uncertainty estimates. All methods discussed in this study are categorized, and assumptions and limitations of each method are discussed. This overview evaluates these methods in application to ozone profile measurements from limb and occultation satellite instruments and provides practical illustrations of random uncertainty validation.

Viktoria F Sofieva↗
NASA NTRS2021

Global Impact of COVID-19 Restrictions on the Atmospheric Concentrations of Nitrogen Dioxide and Ozone

Social-distancing to combat the COVID-19 pandemic has led to widespread reductions in air pollutant emissions. Quantifying these changes requires a business-as-usual counterfactual that accounts for the synoptic and seasonal variability of air pollutants. We use a machine learning algorithm driven by information from the NASA GEOS-CF model to assess changes in nitrogen dioxide (NO2) and ozone (O3) at 5,756 observation sites in countries from January through June 2020. Reductions in NO2 coincide with timing and intensity of COVID-19 restrictions, ranging from 60% in severely affected cities (e.g., Wuhan, Milan) to little change (e.g., Rio de Janeiro, Taipei). On average, NO2 concentrations were (13-23) % lower than business as usual from February 2020 onward. China experienced the earliest and steepest decline, but concentrations since April have mostly recovered and remained within 5% to the business-as-usual estimate. NO2 reductions in Europe and the US have been more gradual with a halting recovery starting in late March. We estimate that the global NOx (NO+NO2) emission reduction during the first 6 months of 2020 amounted to 3.1 (2.6-3.6) TgN, equivalent to 5.5(4.7-6.4) % of the annual anthropogenic total. The response of surface O3 is complicated by competing influences of non-linear atmospheric chemistry. While surface O3 increased by up to 50% in some locations, we find the overall net impact on daily average O3 between February -June 2020 to be small. However, our analysis indicates a flattening of the O3 diurnal cycle with an increase in nighttime ozone due to reduced titration and a decrease in daytime ozone, reflecting a reduction in photochemical production.

Christoph A Keller↗
NASA NTRS2015

Aerosol Correction for Improving OMPS/LP Ozone Retrieval

The Ozone Mapping and Profiler Suite Limb Profiler (OMPS-LP) on board the Suomi National Polar-orbiting Partnership (SNPP) satellite was launched on Oct. 28, 2011. Limb profilers measures the radiance scattered from the Earth's atmospheric in limb viewing mode from 290 to 1000 nm and infer ozone profiles from tropopause to 60 km. The recently released OMPS-LP Version 2 data product contains the first publicly released ozone profiles retrievals, and these are now available for the entire OMPS mission, which extends from April, 2012. The Version 2 data product retrievals incorporate several important improvements to the algorithm. One of the primary changes is to turn off the aerosol retrieval module. The aerosol profiles retrieved inside the ozone code was not helping the ozone retrieval and was adding noise and other artifacts. Aerosols including polar stratospheric cloud (PSC) and polar mesospheric clouds (PMC) have a detectable effect on OMPS-LP data. Our results show that ignoring the aerosol contribution would produce an ozone density bias of up to 10 percent in the region of maximum aerosol extinction. Therefore, aerosol correction is needed to improve the quality of the retrieved ozone concentration profile. We provide Aerosol Scattering Index (ASI) for detecting aerosols-PMC-PSC, defined as ln(Im-Ic) normalized at 45km, where Im is the measured radiance and Ic is the calculated radiance assuming no aerosols. Since ASI varies with wavelengths, latitude and altitude, we can start by assuming no aerosol profiles in calculating the ASIs and then use the aerosol profile to see if it significantly reduces the residuals. We also discuss the effect of aerosol size distribution on the ozone profile retrieval process. Finally, we present an aerosol-PMC-PSC correction scheme.

OMPS/LP↗
NASA NTRSDate not supplied

Comparisons of ozone profile retrievals from the OMPS Limb Profiler and ACE-FTS

The OMPS Limb Profiler (OMPS LP) was launched in October 2011, and has more than 10 years of overlap with ACE-FTS. Therefore, ACE-FTS offers an excellent opportunity to obtain independent ozone profile data with which to validate OMPS LP ozone retrievals. In this study we utilize ACE-FTS ozone retrievals to evaluate 10 years of OMPS LP profile measurements processed with the new NASA GSFC version 2.6 retrieval algorithm. The OMPS LP is a solar scattering instrument that makes observations in the sunlit portion of the Earth, whilst ACE-FTS is a solar occultation instrument that measures ozone only at sunrise and sunset. To account for these differences in measurement times we use the Goddard Diurnal Ozone Climatology (GDOC). Diurnal adjustments using this climatology have been shown to help reduce biases above 30 km. Our analysis shows that biases between OMPS LP v2.6 and ACE-FTS v5.2 are <10% between 18 and 45 km, and increase above and below these altitudes. The vertical patterns of the differences are similar across different latitudes and seasons, with larger seasonal variations observed in the Northern Hemisphere. We also analyze ozone time series from the two instruments.

Atmospheric Science, Stratospheric Ozone↗