UNL-VRTM, A Testbed for Aerosol Remote Sensing: Model Developments and Applications

Xu, Xiaoguang; Wang, Jun

doi:10.1007/978-3-030-20587-4_1

Cited by 11 publications

(6 citation statements)

References 89 publications

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“…RT Simulation. We used the UNified Linearized Vector Radiative Transfer Model (UNL-VRTM) [65,66] to generate a synthetic dataset of radiances observed by a satellite instrument (i.e., TROPOMI) and the corresponding input variables (box 1 of Figure 1). UNL-VRTM facilitates a userfriendly interface for modifying surface and atmospheric optical parameters, which were fed to the Vector Linearized Discrete Ordinate Radiative Transfer (VLIDORT) [67] RT code to calculate the top of atmosphere (TOA) radiances.…”

Section: Neural Network-based No 2 Columnmentioning

confidence: 99%

Direct Retrieval of NO ₂ Vertical Columns from UV-Vis (390-495 nm) Spectral Radiances Using a Neural Network

Liu

et al. 2022

J Remote Sens

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Satellite retrievals of columnar nitrogen dioxide (NO2) are essential for the characterization of nitrogen oxides (NOx) processes and impacts. The requirements of modeled a priori profiles present an outstanding bottleneck in operational satellite NO2 retrievals. In this work, we instead use neural network (NN) models trained from over 360,000 radiative transfer (RT) simulations to translate satellite radiances across 390-495 nm to total NO2 vertical column (NO2C). Despite the wide variability of the many input parameters in the RT simulations, only a small number of key variables were found essential to the accurate prediction of NO2C, including observing angles, surface reflectivity and altitude, and several key principal component scores of the radiances. In addition to the NO2C, the NN training and cross-validation experiments show that the wider retrieval window allows some information about the vertical distribution to be retrieved (e.g., extending the rightmost wavelength from 465 to 495 nm decreases the root-mean-square-error by 0.75%) under high-NO2C conditions. Applying to four months of TROPOMI data, the trained NN model shows strong ability to reproduce the NO2C observed by the ground-based Pandonia Global Network. The coefficient of determination (R2, 0.75) and normalized mean bias (NMB, -33%) are competitive with the level 2 operational TROPOMI product (R2=0.77, NMB=−29%) over clear (geometric cloud fraction<0.2) and polluted (NO2C≥7.5×1015 molecules/cm2) regions. The NN retrieval approach is ~12 times faster than predictions using high spatial resolution (~3 km) a priori profiles from chemical transport modeling, which is especially attractive to the handling of large volume satellite data.

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Section: Neural Network-based No 2 Columnmentioning

confidence: 99%

Direct Retrieval of NO ₂ Vertical Columns from UV-Vis (390-495 nm) Spectral Radiances Using a Neural Network

Liu

et al. 2022

J Remote Sens

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“…In this study, we used full disk imagery reflectances at the 640 nm wavelength (ABI Band 02; 500 m resolution at nadir) and retrieved AOD over ocean pixels. To retrieve AOD from ABI reflectances, we used a look‐up table (LUT) approach, based on calculations from the Unified Linearized Vector Radiative Transfer Model (UNL‐VRTM, Xu & Wang, 2019) with VLIDORT (Spurr, 2006) as the core radiative transfer code. The UNL‐VRTM has the capability for line‐by‐line gas absorption calculations from the HITRAN 2012 database (Rothman et al., 2013), including its ancillary UV‐visible cross‐sections for water vapor continuum absorption and Chappuis ozone absorption (Wang et al., 2014).…”

Section: Methodsmentioning

confidence: 99%

“…The UNL‐VRTM has the capability for line‐by‐line gas absorption calculations from the HITRAN 2012 database (Rothman et al., 2013), including its ancillary UV‐visible cross‐sections for water vapor continuum absorption and Chappuis ozone absorption (Wang et al., 2014). The model has been validated in and used by several remote sensing theory studies (Ding et al., 2016; Xu & Wang, 2015) and aerosol retrieval algorithms for surface (Xu et al., 2015), airborne (Hou et al., 2020), and space‐borne instruments (Xu & Wang, 2019; Xu et al., 2017).…”

Section: Methodsmentioning

confidence: 99%

Constraining Aerosol Phase Function Using Dual‐View Geostationary Satellites

et al. 2021

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Passive satellite observations play an important role in monitoring global aerosol properties and helping quantify aerosol radiative forcing in the climate system. The quality of aerosol retrievals from the satellite platform relies on well‐calibrated radiance measurements from multiple spectral bands, and the availability of appropriate particle optical models. Inaccurate scattering phase function assumptions can introduce large retrieval errors. The high‐spatial resolution, dual‐view observations from the advanced baseline imagers onboard the two most recent geostationary operational environmental satellites (GOES), East and West, provide a unique opportunity to better constrain the aerosol phase function. Using dual GOES reflectance measurements for a dust event in the Gulf of Mexico in 2019, we demonstrate how a first‐guess phase function can be reconstructed by considering the variations in observed scattering angles throughout the day. Using the reconstructed phase function, aerosol optical depth retrievals from the two satellites are self‐consistent and agree well with surface‐based optical depth estimates. We evaluate our methodology and reconstructed phase function against independent retrievals made from low‐Earth‐orbit multi‐angle observations for a different dust event in 2020. Our new aerosol optical depth retrievals have a root‐mean‐square‐difference of 0.019–0.047. Furthermore, the retrievals between the two geostationary satellites for this case agree within about 0.059 ± 0.072, as compared to larger discrepancies between the operational GOES products at times, which do not employ the dual‐view technique.

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“…The at-sensor radiances from clear scenes are simulated using a vector radiative transfer code, the Unified Linearized Vector Radiative Transfer Model, UNL-VRTM, which integrates the linearized vector radiative transfer (VLIDORT) into a broader framework (Xu and Wang, 2019). The code can generate Stokes vectors from any scene defined by its view and solar geometry, surface reflectance, wavelength range, and atmospheric composition.…”

Section: Radiative Transfer Modelingmentioning

confidence: 99%

Polarization performance simulation for the GeoXO atmospheric composition instrument: NO₂ retrieval impacts

et al. 2022

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Abstract. NOAA's Geostationary Extended Observations (GeoXO) constellation will continue and expand on the capabilities of the current generation of geostationary satellite systems to support US weather, ocean, atmosphere, and climate operations. It is planned to consist of a dedicated atmospheric composition instrument (ACX) to support air quality forecasting and monitoring by providing capabilities similar to missions such as TEMPO (Tropospheric Emission: Monitoring Pollution), currently planned to launch in 2023, as well as OMI (Ozone Monitoring Instrument), TROPOMI (TROPOspheric Monitoring Instrument), and GEMS (Geostationary Environment Monitoring Spectrometer) currently in operation. As the early phases of ACX development are progressing, design trade-offs are being considered to understand the relationship between instrument design choices and trace gas retrieval impacts. Some of these choices will affect the instrument polarization sensitivity (PS), which can have radiometric impacts on environmental satellite observations. We conducted a study to investigate how such radiometric impacts can affect NO2 retrievals by exploring their sensitivities to time of day, location, and scene type with an ACX instrument model that incorporates PS. The study addresses the basic steps of operational NO2 retrievals: the spectral fitting step and the conversion of slant column to vertical column via the air mass factor (AMF). The spectral fitting step was performed by generating at-sensor radiance from a clear-sky scene with a known NO2 amount, the application of an instrument model including both instrument PS and noise, and a physical retrieval. The spectral fitting step was found to mitigate the impacts of instrument PS. The AMF-related step was considered for clear-sky and partially cloudy scenes, for which instrument PS can lead to errors in interpreting the cloud content, propagating to AMF errors and finally to NO2 retrieval errors. For this step, the NO2 retrieval impacts were small but non-negligible for high NO2 amounts; we estimated that a typical high NO2 amount can cause a maximum retrieval error of 0.25×1015 molec. cm−2 for a PS of 5 %. These simulation capabilities were designed to aid in the development of a GeoXO atmospheric composition instrument that will improve our ability to monitor and understand the Earth's atmosphere.

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UNL-VRTM, A Testbed for Aerosol Remote Sensing: Model Developments and Applications

Cited by 11 publications

References 89 publications

Direct Retrieval of NO ₂ Vertical Columns from UV-Vis (390-495 nm) Spectral Radiances Using a Neural Network

Direct Retrieval of NO ₂ Vertical Columns from UV-Vis (390-495 nm) Spectral Radiances Using a Neural Network

Constraining Aerosol Phase Function Using Dual‐View Geostationary Satellites

Polarization performance simulation for the GeoXO atmospheric composition instrument: NO₂ retrieval impacts

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UNL-VRTM, A Testbed for Aerosol Remote Sensing: Model Developments and Applications

Cited by 11 publications

References 89 publications

Direct Retrieval of NO 2 Vertical Columns from UV-Vis (390-495 nm) Spectral Radiances Using a Neural Network

Direct Retrieval of NO 2 Vertical Columns from UV-Vis (390-495 nm) Spectral Radiances Using a Neural Network

Constraining Aerosol Phase Function Using Dual‐View Geostationary Satellites

Polarization performance simulation for the GeoXO atmospheric composition instrument: NO2 retrieval impacts

Contact Info

Product

Resources

About

Direct Retrieval of NO ₂ Vertical Columns from UV-Vis (390-495 nm) Spectral Radiances Using a Neural Network

Direct Retrieval of NO ₂ Vertical Columns from UV-Vis (390-495 nm) Spectral Radiances Using a Neural Network

Polarization performance simulation for the GeoXO atmospheric composition instrument: NO₂ retrieval impacts