Applying FP_ILM to the retrieval of geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) daily maps from UVN satellite measurements
<p>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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-03-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://www.atmos-meas-tech.net/13/985/2020/amt-13-985-2020.pdf |
| Summary: | <p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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.</p> |
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| ISSN: | 1867-1381 1867-8548 |