scholarly journals Nitrogen dioxide observations from the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument: Retrieval algorithm and measurements during DISCOVER-AQ Texas 2013

2016 ◽  
Vol 9 (6) ◽  
pp. 2647-2668 ◽  
Author(s):  
Caroline R. Nowlan ◽  
Xiong Liu ◽  
James W. Leitch ◽  
Kelly Chance ◽  
Gonzalo González Abad ◽  
...  
Keyword(s):  
Air Quality ◽  
Near Infrared ◽  
Infrared Light ◽  
Trace Gas ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Test Bed ◽  
Mixing Ratios ◽  
Sensor Optimization

Abstract. The Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument is a test bed for upcoming air quality satellite instruments that will measure backscattered ultraviolet, visible and near-infrared light from geostationary orbit. GeoTASO flew on the NASA Falcon aircraft in its first intensive field measurement campaign during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) Earth Venture Mission over Houston, Texas, in September 2013. Measurements of backscattered solar radiation between 420 and 465 nm collected on 4 days during the campaign are used to determine slant column amounts of NO2 at 250 m  ×  250 m spatial resolution with a fitting precision of 2.2 × 1015 moleculescm−2. These slant columns are converted to tropospheric NO2 vertical columns using a radiative transfer model and trace gas profiles from the Community Multiscale Air Quality (CMAQ) model. Total column NO2 from GeoTASO is well correlated with ground-based Pandora observations (r = 0.90 on the most polluted and cloud-free day of measurements and r = 0.74 overall), with GeoTASO NO2 slightly higher for the most polluted observations. Surface NO2 mixing ratios inferred from GeoTASO using the CMAQ model show good correlation with NO2 measured in situ at the surface during the campaign (r = 0.85). NO2 slant columns from GeoTASO also agree well with preliminary retrievals from the GEO-CAPE Airborne Simulator (GCAS) which flew on the NASA King Air B200 (r = 0.81, slope = 0.91). Enhanced NO2 is resolvable over areas of traffic NOx emissions and near individual petrochemical facilities.

scholarly journals Nitrogen dioxide observations from the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument: retrieval algorithm and measurements during DISCOVER-AQ Texas 2013

2015 ◽  
Vol 8 (12) ◽  
pp. 13099-13155 ◽  
Author(s):  
C. R. Nowlan ◽  
X. Liu ◽  
J. W. Leitch ◽  
K. Chance ◽  
G. González Abad ◽  
...  
Keyword(s):  
Air Quality ◽  
Near Infrared ◽  
Infrared Light ◽  
Trace Gas ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Mixing Ratios ◽  
Sensor Optimization

Abstract. The Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument is a testbed for upcoming air quality satellite instruments that will measure backscattered ultraviolet, visible and near-infrared light from geostationary orbit. GeoTASO flew on the NASA Falcon aircraft in its first intensive field measurement campaign during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) Earth Venture Mission over Houston, Texas in September 2013. Measurements of backscattered solar radiation between 420–465 nm collected on four days during the campaign are used to determine slant column amounts of NO2 at 250 m × 250 m spatial resolution with a fitting precision of 2.2 × 1015 molecules cm−2. These slant columns are converted to tropospheric NO2 vertical columns using a radiative transfer model and trace gas profiles from the Community Multiscale Air Quality (CMAQ) model. Total column NO2 from GeoTASO is well correlated with ground-based Pandora observations (r = 0.90 on the most polluted and cloud-free day of measurements), with GeoTASO NO2 slightly higher for the most polluted observations. Surface NO2 mixing ratios inferred from GeoTASO using the CMAQ model show good correlation with NO2 measured in situ at the surface during the campaign (r = 0.91 for the most polluted day). NO2 slant columns from GeoTASO also agree well with preliminary retrievals from the GEO-CAPE Airborne Simulator (GCAS) which flew on the NASA King Air B200 (r = 0.84, slope = 0.94). Enhanced NO2 is resolvable over areas of traffic NOx emissions and near individual petrochemical facilities.

scholarly journals Level 2 processing for the imaging Fourier transform spectrometer GLORIA: derivation and validation of temperature and trace gas volume mixing ratios from calibrated dynamics mode spectra

2015 ◽  
Vol 8 (6) ◽  
pp. 2473-2489 ◽  
Author(s):  
J. Ungermann ◽  
J. Blank ◽  
M. Dick ◽  
A. Ebersoldt ◽  
F. Friedl-Vallon ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Horizontal Resolution ◽  
Trace Gas ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Ionization Mass ◽  
Fourier Transform Spectrometer ◽  
Upper Troposphere ◽  
Mixing Ratios

Abstract. The Gimballed Limb Observer for Radiance Imaging of the Atmosphere (GLORIA) is an airborne infrared limb imager combining a two-dimensional infrared detector with a Fourier transform spectrometer. It was operated aboard the new German Gulfstream G550 High Altitude LOng Range (HALO) research aircraft during the Transport And Composition in the upper Troposphere/lowermost Stratosphere (TACTS) and Earth System Model Validation (ESMVAL) campaigns in summer 2012. This paper describes the retrieval of temperature and trace gas (H2O, O3, HNO3) volume mixing ratios from GLORIA dynamics mode spectra that are spectrally sampled every 0.625 cm−1. A total of 26 integrated spectral windows are employed in a joint fit to retrieve seven targets using consecutively a fast and an accurate tabulated radiative transfer model. Typical diagnostic quantities are provided including effects of uncertainties in the calibration and horizontal resolution along the line of sight. Simultaneous in situ observations by the Basic Halo Measurement and Sensor System (BAHAMAS), the Fast In-situ Stratospheric Hygrometer (FISH), an ozone detector named Fairo, and the Atmospheric chemical Ionization Mass Spectrometer (AIMS) allow a validation of retrieved values for three flights in the upper troposphere/lowermost stratosphere region spanning polar and sub-tropical latitudes. A high correlation is achieved between the remote sensing and the in situ trace gas data, and discrepancies can to a large extent be attributed to differences in the probed air masses caused by different sampling characteristics of the instruments. This 1-D processing of GLORIA dynamics mode spectra provides the basis for future tomographic inversions from circular and linear flight paths to better understand selected dynamical processes of the upper troposphere and lowermost stratosphere.

scholarly journals Differences in ozone retrieval in MIPAS channels A and AB: a spectroscopic issue

2018 ◽  
Vol 11 (8) ◽  
pp. 4707-4723 ◽  
Author(s):  
Norbert Glatthor ◽  
Thomas von Clarmann ◽  
Gabriele P. Stiller ◽  
Michael Kiefer ◽  
Alexandra Laeng ◽  
...  
Keyword(s):  
Spectroscopic Data ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Forward Modelling ◽  
Hitran Database ◽  
Processing Scheme ◽  
Mixing Ratios ◽  
Level 1 ◽  
Standing Problem

Abstract. Discrepancies in ozone retrievals in MIPAS channels A (685–970 cm−1) and AB (1020–1170 cm−1) have been a long-standing problem in MIPAS data analysis, amounting to an interchannel bias (AB–A) of up to 8 % between ozone volume mixing ratios in the altitude range 30–40 km. We discuss various candidate explanations, among them forward model and retrieval algorithm errors, interchannel calibration inconsistencies and spectroscopic data inconsistencies. We show that forward-modelling errors as well as errors in the retrieval algorithm can be ruled out as an explanation because the bias can be reproduced with an entirely independent retrieval algorithm (GEOFIT), relying on a different forward radiative transfer model. Instrumental and calibration issues can also be refuted as an explanation because ozone retrievals based on balloon-borne measurements with a different instrument (MIPAS-B) and an independent level-1 data processing scheme produce a rather similar interchannel bias. Thus, spectroscopic inconsistencies in the MIPAS database used for ozone retrieval are practically the only reason left. To further investigate this issue, we performed retrievals using additional spectroscopic databases. Various versions of the HITRAN database generally produced rather similar channel AB–A differences. Use of a different database, namely GEISA-2015, led to similar results in channel AB, but to even higher ozone volume mixing ratios for channel A retrievals, i.e. to a reversal of the bias. We show that the differences in MIPAS channel A retrievals result from about 13 % lower air-broadening coefficients of the strongest lines in the GEISA-2015 database. Since the errors in line intensity of the major lines used in MIPAS channels A and AB are reported to be considerably lower than the observed bias, we posit that a major part of the channel AB–A differences can be attributed to inconsistent air-broadening coefficients as well. To corroborate this assumption we show some clearly inconsistent air-broadening coefficients in the HITRAN-2008 database. The interchannel bias in retrieved ozone amounts can be reduced by increasing the air-broadening coefficients of the lines in MIPAS channel AB in the HITRAN-2008 database by 6 %–8 %.

scholarly journals Impacts of updated spectroscopy on thermal infrared retrievals of methane evaluated with HIPPO data

2014 ◽  
Vol 7 (9) ◽  
pp. 10059-10107
Author(s):  
M. J. Alvarado ◽  
V. H. Payne ◽  
K. E. Cady-Pereira ◽  
J. D. Hegarty ◽  
S. S. Kulawik ◽  
...  
Keyword(s):  
Nitrous Oxide ◽  
Trace Gases ◽  
Thermal Infrared ◽  
Trace Gas ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Spectroscopic Parameters ◽  
Mixing Ratios ◽  
Aircraft Observations ◽  
The Impact

Abstract. Errors in the spectroscopic parameters used in the forward radiative transfer model can introduce altitude-, spatially-, and temporally-dependent biases in trace gas retrievals. For well-mixed trace gases such as methane, where the variability of tropospheric mixing ratios is relatively small, reducing such biases is particularly important. We use aircraft observations from all five missions of the HIAPER Pole-to-Pole Observations (HIPPO) of the Carbon Cycle and Greenhouse Gases Study to evaluate the impact of updates to spectroscopic parameters for methane (CH4), water vapor (H2O), and nitrous oxide (N2O) on thermal infrared retrievals of methane from the NASA Aura Tropospheric Emission Spectrometer (TES). We find that updates to the spectroscopic parameters for CH4 result in a substantially smaller mean bias in the retrieved CH4 when compared with HIPPO observations. After an N2O-based correction, the bias in TES methane upper tropospheric representative values for measurements between 50° S and 50° N decreases from 56.9 to 25.7 ppbv, while the bias in the lower tropospheric representative value increases only slightly (from 27.3 to 28.4 ppbv). For retrievals with less than 1.6 DOFS, the bias is reduced from 26.8 to 4.8 ppbv. We also find that updates to the spectroscopic parameters for N2O reduce the errors in the retrieved N2O profile.

scholarly journals Millimeter-Wave Precipitation Retrievals and Observed-versus-Simulated Radiance Distributions: Sensitivity to Assumptions

2007 ◽  
Vol 64 (11) ◽  
pp. 3808-3826 ◽  
Author(s):  
Chinnawat Surussavadee ◽  
David H. Staelin
Keyword(s):  
Millimeter Wave ◽  
Mesoscale Model ◽  
Mie Scattering ◽  
Precipitation Rate ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Test Bed ◽  
Surface Precipitation ◽  
Mixing Ratios ◽  
Nwp Model

Abstract Brightness temperature histograms observed at 50–191 GHz by the Advanced Microwave Sounding Unit (AMSU) on operational NOAA satellites are shown to be consistent with predictions made using a mesoscale NWP model [the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5)] and a radiative transfer model [TBSCAT/F(λ)] for a global set of 122 storms coincident with the AMSU observations. Observable discrepancies between the observed and modeled histograms occurred when 1) snow and graupel mixing ratios were increased more than 15% and 25%, respectively, or their altitudes increased more than ∼25 mb; 2) the density, F(λ), of equivalent Mie-scattering ice spheres increased more than 0.03 g cm−3; and 3) the two-stream ice scattering increased more than ∼1%. Using the same MM5/TBSCAT/F(λ) model, neural networks were developed to retrieve the following from AMSU and geostationary microwave satellites: hydrometeor water paths, 15-min average surface-precipitation rates, and cell-top altitudes, all with 15-km resolution. Simulated AMSU rms precipitation-rate retrieval accuracies ranged from 0.4 to 21 mm h−1 when grouped by octaves of MM5 precipitation rate between 0.1 and 64 mm h−1, and were ∼3.8 mm h−1 for the octave 4–8 mm h−1. AMSU and geostationary microwave (GEM) precipitation-rate retrieval accuracies for random 50–50 mixtures of profiles simulated with either the baseline or a modified-physics model were largely insensitive to changes in model physics that would be clearly evident in AMSU observations if real. This insensitivity of retrieval accuracies to model assumptions implies that MM5/TBSCAT/F(λ) simulations offer a useful test bed for evaluating alternative millimeter-wave satellite designs and methods for retrieval and assimilation, to the extent that surface effects are limited.

scholarly journals Simulation of GOES-R ABI aerosol radiances using WRF-CMAQ: a case study approach

2013 ◽  
Vol 13 (7) ◽  
pp. 18713-18748
Author(s):  
S. A. Christopher
Keyword(s):  
Air Quality ◽  
Near Infrared ◽  
Sparse Matrix ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Eastern United States ◽  
Study Approach ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Primary Focus

Abstract. The primary focus of this paper is to simulate visible and near-infrared reflectances of the GOES-R Advanced Baseline Imager (ABI) for cases of high aerosol loading containing regional haze and smoke over the eastern United States. The simulations are performed using the Weather Research and Forecasting (WRF), Sparse Matrix Operator Kernel Emissions (SMOKE), and Community Multiscale Air Quality (CMAQ) models. Geostationary satellite-derived biomass burning emissions are also included as an input to CMAQ. Using the CMAQ aerosol concentrations and Mie calculations, radiance is computed from the discrete ordinate atmospheric radiative transfer model. We present detailed methods for deriving aerosol extinction from WRF and CMAQ outputs. Our results show that the model simulations create a realistic set of reflectance in various aerosol scenarios. The simulated reflectance provides distinct spectral features of aerosols which is then compared to data from the Moderate Resolution Imaging Spectroradiometer (MODIS). We also present a simple technique to synthesize green band reflectance (which will not be available on the ABI), using the model-simulated blue and red band reflectance. This study is an example of the use of air quality modeling in improving products and techniques for Earth observing missions.

scholarly journals Evaluation of the MODIS LAI/FPAR Algorithm Based on 3D-RTM Simulations: A Case Study of Grassland

2020 ◽  
Vol 12 (20) ◽  
pp. 3391
Author(s):  
Jiabin Pu ◽  
Kai Yan ◽  
Guohuan Zhou ◽  
Yongqiao Lei ◽  
Yingxin Zhu ◽  
...  
Keyword(s):  
Model Uncertainty ◽  
Large Scale ◽  
Near Infrared ◽  
Evaluation Method ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Scale Dependency ◽  
Area Index ◽  
Input Uncertainty

Uncertainty assessment of the moderate resolution imaging spectroradiometer (MODIS) leaf area index (LAI) and the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) retrieval algorithm can provide a scientific basis for the usage and improvement of this widely-used product. Previous evaluations generally depended on the intercomparison with other datasets as well as direct validation using ground measurements, which mix the uncertainties from the model, inputs, and assessment method. In this study, we adopted the evaluation method based on three-dimensional radiative transfer model (3D RTM) simulations, which helps to separate model uncertainty and other factors. We used the well-validated 3D RTM LESS (large-scale remote sensing data and image simulation framework) for a grassland scene simulation and calculated bidirectional reflectance factors (BRFs) as inputs for the LAI/FPAR retrieval. The dependency between LAI/FPAR truth and model estimation serves as the algorithm uncertainty indicator. This paper analyzed the LAI/FPAR uncertainty caused by inherent model uncertainty, input uncertainty (BRF and biome classification), clumping effect, and scale dependency. We found that the uncertainties of different algorithm paths vary greatly (−6.61% and +84.85% bias for main and backup algorithm, respectively) and the “hotspot” geometry results in greatest retrieval uncertainty. For the input uncertainty, the BRF of the near-infrared (NIR) band has greater impacts than that of the red band, and the biome misclassification also leads to nonnegligible LAI/FPAR bias. Moreover, the clumping effect leads to a significant LAI underestimation (−0.846 and −0.525 LAI difference for two clumping types), but the scale dependency (pixel size ranges from 100 m to 1000 m) has little impact on LAI/FPAR uncertainty. Overall, this study provides a new perspective on the evaluation of LAI/FPAR retrieval algorithms.

Impact of 3D cloud structures on tropospheric NO2 column measurements from UV-VIS sounders

2021 ◽  
Author(s):  
Huan Yu ◽  
Arve Kylling ◽  
Claudia Emde ◽  
Bernhard Mayer ◽  
Michel Van Roozendael ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Synthetic Data ◽  
Sensitivity Study ◽  
Trace Gas ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Boundary Layer Clouds ◽  
Cloud Parameters ◽  
The Impact

<p>Operational retrievals of tropospheric trace gases from space-borne spectrometers are made using 1D radiative transfer models. To minimize cloud effects generally only partially cloudy pixels are analysed using simplified cloud contamination treatments based on radiometric cloud fraction estimates and photon path length corrections based on oxygen collision pair (O2-O2) or O2A-absorption band measurements. In reality, however, the impact of clouds can be much more complex, involving unresolved sub-pixel clouds, scattering of clouds in neighbouring pixels, and cloud shadow effects, such that 3D radiation scattering from unresolved boundary layer clouds may give significant biases in the trace gas retrievals. In order to quantify this impact, we use the MYSTIC 3D radiative transfer model to generate synthetic data. The realistic 3D cloud fields, needed for MYSTIC input, are generated by the ICOsahedral Non-hydrostatic (ICON) atmosphere model for a region including Germany, the Netherlands and parts of other surrounding countries. The retrieval algorithm is applied to the synthetic data and comparison to the known input trace gas concentrations yields the retrieval error due to 3D cloud effects. <br>In this study, we study NO2, which is a key tropospheric trace gas measured by TROPOMI and the future atmospheric Sentinels (S4 and S5). The work starts with a sensitivity study for the simulations with a simple 2D box cloud. The influence of cloud parameters (e.g., cloud top height, cloud optical thickness), observation geometry, and spatial resolution are studied, and the most significant dependences of retrieval biases are identified and investigated. Several approaches to correct the NO2 retrieval in the cloud shadow are explored and ultimately applied to both synthetic data with realistic 3D clouds and real observations.</p>

scholarly journals A neural network radiative transfer model approach applied to TROPOMI’s aerosol height algorithm

2019 ◽  
Author(s):  
Swadhin Nanda ◽  
Martin de Graaf ◽  
J. Pepijn Veefkind ◽  
Mark ter Linden ◽  
Maarten Sneep ◽  
...  
Keyword(s):  
Neural Network ◽  
Radiative Transfer ◽  
Near Infrared ◽  
Forward Model ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Aerosol Layer ◽  
Neural Network Models ◽  
Layer Height

Abstract. To retrieve aerosol properties from satellite measurements of the oxygen A-band in the near infrared, a line-by-line radiative transfer model implementation requires a large number of calculations. These calculations severely restrict a retrieval algorithm's operational capability as it can take several minutes to retrieve aerosol layer height for a single ground pixel. This paper proposes a forward modeling approach using artificial neural networks to speed up the retrieval algorithm. The forward model outputs are trained into a set of neural network models to completely replace line-by-line calculations in the operational processor. Results of comparing the forward model to the neural network alternative show encouraging results with good agreements between the two when applied to retrieval scenarios using both synthetic and real measured spectra from TROPOMI (TROPOspheric Monitoring Instrument) on board the ESA Sentinel-5 Precursor mission. With an enhancement of the computational speed by three orders of magnitude, TROPOMI's operational aerosol layer height processor is now able to retrieve aerosol layer heights well within operational capacity.

scholarly journals Applying FP_ILM to the retrieval of geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) daily maps from UVN satellite measurements

2020 ◽  
Vol 13 (2) ◽  
pp. 985-999 ◽  
Author(s):  
Diego G. Loyola ◽  
Jian Xu ◽  
Klaus-Peter Heue ◽  
Walter Zimmer
Keyword(s):  
Near Infrared ◽  
Machine Learning Techniques ◽  
Trace Gas ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Satellite Measurements ◽  
Actual Surface ◽  
Satellite Missions ◽  
Learning Machine ◽  
Sky Conditions

Abstract. The retrieval of trace gas, cloud, and aerosol measurements from ultraviolet, visible, and near-infrared (UVN) sensors requires precise information on surface properties that are traditionally obtained from Lambertian equivalent reflectivity (LER) climatologies. The main drawbacks of using LER climatologies for new satellite missions are that (a) climatologies are typically based on previous missions with significantly lower spatial resolutions, (b) they usually do not account fully for satellite-viewing geometry dependencies characterized by bidirectional reflectance distribution function (BRDF) effects, and (c) climatologies may differ considerably from the actual surface conditions especially with snow/ice scenarios. In this paper we present a novel algorithm for the retrieval of geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) from UVN sensors; the algorithm is based on the full-physics inverse learning machine (FP_ILM) retrieval. Radiances are simulated using a radiative transfer model that takes into account the satellite-viewing geometry, and the inverse problem is solved using machine learning techniques to obtain the GE_LER from satellite measurements. The GE_LER retrieval is optimized not only for trace gas retrievals employing the DOAS algorithm, but also for the large amount of data from existing and future atmospheric Sentinel satellite missions. The GE_LER can either be deployed directly for the computation of air mass factors (AMFs) using the effective scene approximation or it can be used to create a global gapless geometry-dependent LER (G3_LER) daily map from the GE_LER under clear-sky conditions for the computation of AMFs using the independent pixel approximation. The GE_LER algorithm is applied to measurements of TROPOMI launched in October 2017 on board the EU/ESA Sentinel-5 Precursor (S5P) mission. The TROPOMI GE_LER/G3_LER results are compared with climatological OMI and GOME-2 LER datasets and the advantages of using GE_LER/G3_LER are demonstrated for the retrieval of total ozone from TROPOMI.

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