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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...
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... retrieval algorithms did not process pixels in the coastline, as the surface albedo values could be incorrect in these regions. (Table 4). The majority of the pixels where the neural network algorithm differed from the line-by-line counterpart by more than 200 meters were for absorbing aerosol index values less than 2.0 ( Figure 9c). ...Similar publications
An inverse P-Cygni profile of H13CO+ (1-0) in G31.41+0.31 was recently observed, which indicates the presence of an infalling gas envelope. Also, an outflow tracer, SiO, was observed. Here, exclusive radiative transfer modelings have been implemented to generate synthetic spectra of some key species (H13 CO+, HCN, SiO, NH3, CH3 CN, CH3OH, CH3SH, an...
We present optical observations and Monte Carlo radiative transfer modeling of the Type Ia supernova (SN Ia) SN 2011aa. With a Δ m 15 ( B ) of 0.59 ± 0.07 mag and a peak magnitude M B of −19.30 ± 0.27 mag, SN 2011aa has the slowest decline rate among SNe Ia. The secondary maximum in the I band is absent or as equally bright as the primary maximum....
We present optical observations and Monte Carlo radiative transfer modeling of the Type Ia supernova SN 2011aa. With a $\Delta m_{15} (B)$ of $0.59 \pm 0.07$ mag and a peak magnitude $M_{\rm B}$ of $-19.30 \pm 0.27$ mag, SN 2011aa has the slowest decline rate among SNe Ia. The secondary maximum in the $I$-band is absent or equally bright as the pri...
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 the aerosol layer height...
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... For instance, the OMI aerosol retrieval algorithm fits narrow-band radiance in several atmospheric window channels rather than a fine spectral structure (Torres et al., 2007). Last but not least, fitting the fine spectral structure of observations consumes far more time, resulting in unacceptable computational burden for an operational algorithm producing near-real-time products; this high computational demand is one of the motivations to use the neural network (NN)-based forward model in the current TRO-POMI ALH operational algorithm (Nanda et al., 2019). In contrast, fitting narrow-band measurements through look-up tables, as done in several aerosol retrieval algorithms, is computationally efficient and fast. ...
Constraint of the vertical distribution of aerosol particles is crucial for the study of aerosol plume structure, aerosol radiative effects, and ultimately monitoring surface air pollution. We developed an algorithm to retrieve the aerosol optical central height (AOCH) of absorbing aerosols by using, for the first time, the oxygen (O2) A and B absorption band measurements from the TROPOspheric Monitoring Instrument (TROPOMI) over dark targets. For the retrieval, narrow band radiance at seven channels ranging from ultraviolet (UV) to shortwave infrared (SWIR) are convolved from TROPOMI hyperspectral measurements. Subsequently, cloudy pixels are screened out by using the slope of spectral reflectance, while aerosol types (dust and smoke) are classified by the wavelength dependence of aerosol path radiance in conjunction with UV aerosol index. Surface reflectance over land is derived from the MODIS surface bi-directional reflectance climatology, and over water from the GOME-2 surface Lambert-equivalent reflectivity (LER) database. The aerosol optical depth (AOD) and AOCH are retrieved through an approach of look-up-table accounting for AERONET-based dust and smoke optical properties. For multiple smoke and dust plume events around the world, our retrieved AOCH values agree with space-borne lidar CALIOP counterparts, with a mean bias of <0.15 km and a correlation coefficient of 0.85–0.87. Due in part to adding the O2 B band, our retrieval represents an aerosol extinction peak height better than the TROPOMI operational Level 2 aerosol layer height retrieved from only the O2 A band. The latter shows 0.5–2 km low bias, especially over land. Finally, the high potential of AOCH for improving surface PM2.5 estimates is also illustrated with a case study in which the high bias of surface PM2.5 in MERRA-2 data is corrected after being scaled by the retrieved AOCH.
... This is important because UVAI is sensitive to the aerosol layer vertical location [18], [24], [28], [25]. Many efforts have been made on measuring aerosol vertical structures [29], including ground-based lidar systems [91], [92], space-borne lidar missions [93], [94], multi-angle measurements [95], polarimetry [96], oxygen absorption at A-band [97], [98], [99], [100], [101], [102], [103], [56], oxygen absorption in the visible band [104] and thermal infrared [105], [106]. However, currently an aerosol vertical distribution product based on observations that has a daily global coverage as that of UVAI is still missing. ...
Quantitative measurements of aerosol absorptive properties, e.g. the absorbing aerosol optical depth (AAOD) and the single scattering albedo (SSA), are important to reduce uncertainties of the aerosol climate radiative forcing assessments. Currently, global retrievals of AAOD and SSA are mainly provided by the ground-based Aerosol RObotic NETwork (AERONET), whereas it is still challenging to retrieve them from space. However, we found the AERONET AAOD has a relatively strong correlation with the satellite Ultra-Violet Aerosol Index (UVAI). Based on this, a numerical relation is built by a Deep Neural Network (DNN) to predict global AAOD and SSA over land from the long-term UVAI record (2006 to 2019) provided by the Ozone Monitoring Instrument (OMI) onboard Aura. The DNN predicted aerosol absorption is satisfying for samples with AOD at 550 nm larger than 0.1 and the model performance is better for smaller absorbing aerosols (e.g. smoke) than larger ones (e.g. mineral dust). The validation of the DNN predictions with AERONET shows a high correlation coefficient of 0.90 and a root mean square of 0.005 for the AAOD, and over 80% of samples are within the expected uncertainty of AERONET SSA (0.03).
... attention from the remote sensing community [47][48][49]. An additional feature that makes MLP NNs useful for emulating radiative transfer models for remote sensing applications is that the derivative of a MLP model with respect to its inputs can be computed analytically [50][51][52]. ...
For aerosol retrieval from multi-angle polarimetric (MAP) measurements over the ocean it is important to accurately account for the contribution of the ocean-body to the top-of-atmosphere signal, especially for wavelengths <500 nm. Performing online radiative transfer calculations in the coupled atmosphere ocean system is too time consuming for operational retrieval algorithms. Therefore, mostly lookup-tables of the ocean body reflection matrix are used to represent the lower boundary in an atmospheric radiative transfer model. For hyperspectral measurements such as those from Spectro-Polarimeter for Planetary Exploration (SPEXone) on the NASA Plankton, Aerosol, Cloud and ocean Ecosystem (PACE) mission, also the use of look-up tables is unfeasible because they will become too big. In this paper, we propose a new method for aerosol retrieval over ocean from MAP measurements using a neural network (NN) to model the ocean body reflection matrix. We apply the NN approach to synthetic SPEXone measurements and also to real data collected by SPEX airborne during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign. We conclude that the NN approach is well capable for aerosol retrievals over ocean, introducing no significant error on the retrieved aerosol properties
Abstract. Before the launch of TROPOMI, only two other satellite instruments were able to observe aerosol plume heights globally, MISR and CALIOP. The TROPOMI aerosol layer height is a potential game changer, since it has daily global coverage and the aerosol layer height retrieval is available in near-real time. The aerosol layer height can be useful for aviation and air quality alerts, as well as for improving air quality forecasting related to wildfires. Here, TROPOMI's aerosol layer height product is evaluated with MISR and CALIOP observations for wildfire plumes in North America for the 2018 fire season (June to August). Further, observing system simulation experiments were performed to interpret the fundamental differences between the different products. The results show that MISR and TROPOMI are, in theory, very close for aerosol profiles with single plumes. For more complex profiles with multiple plumes, however, different plume heights are retrieved: the MISR plume height represents the top layer, and the plume height retrieved with TROPOMI tends to be an average altitude of several plume layers.
The comparison between TROPOMI and MISR plume heights shows, that on average, the TROPOMI aerosol layer heights are lower, by approximately 600 m, compared to MISR which is likely due to the different measurement techniques. From the comparison to CALIOP, our results show that the TROPOMI aerosol layer height is more accurate for thicker plumes and plumes below approximately 4.5 km.
MISR and TROPOMI are further used to evaluate the plume height of Environment and Climate Change Canada's operational forecasting system FireWork with fire plume injection height estimates from the Canadian Forest Fire Emissions Prediction System (CFFEPS). The modelled plume heights are similar compared to the satellite observations, but tend to be slightly higher with average differences of 270–580 m and 60–320 m compared to TROPOMI and MISR, respectively.
The Tropospheric Monitoring Instrument's (TROPOMI) level-2 aerosol layer height (ALH) product has now been released to the general public. This product is retrieved using TROPOMI's measurements of the oxygen A-band, radiative transfer model (RTM) calculations augmented by neural networks and an iterative optimal estimation technique. The TROPOMI ALH product will deliver aerosol layer height estimates over cloud-free scenes over the ocean and land that contain aerosols above a certain threshold of the measured UV absorbing index (UVAI) in the ultraviolet region. This paper provides background for the ALH product and explores its quality by comparing ALH estimates to similar quantities derived from spaceborne lidars observing the same scene. The spaceborne lidar chosen for this study is the Cloud-Aerosol Lidar with Orthogonal Polarisation (CALIOP) on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission, which flies in formation with NASA's A-train constellation since 2006 and is a proven source of data for studying aerosol layer heights. The influence of the surface and clouds are discussed and the aspects of the TROPOMI ALH algorithm that will require future development efforts are highlighted.
