Analysis of the MODIS above-cloud aerosol retrieval algorithm using MCARS

The Multi-sensor Cloud and Aerosol Retrieval Simulator (MCARS) presently produces synthetic radiance data from Goddard Earth Observing System version 5 (GEOS-5) model output as if the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of atmospheric column inclusive of clouds, aerosols, and a variety of gases and land–ocean surface at a specific location. In this paper we use MCARS to study the MODIS Above-Cloud AEROsol retrieval algorithm (MOD06ACAERO). MOD06ACAERO is presently a regional research algorithm able to retrieve aerosol optical thickness over clouds, in particular absorbing biomass-burning aerosols overlying marine boundary layer clouds in the southeastern Atlantic Ocean. The algorithm's ability to provide aerosol information in cloudy conditions makes it a valuable source of information for modeling and climate studies in an area where current clear-sky-only operational MODIS aerosol retrievals effectively have a data gap between the months of June and October. We use MCARS for a verification and closure study of the MOD06ACAERO algorithm. The purpose of this study is to develop a set of constraints a model developer might use during assimilation of MOD06ACAERO data. Our simulations indicate that the MOD06ACAERO algorithm performs well for marine boundary layer clouds in the SE Atlantic provided some specific screening rules are observed. For the present study, a combination of five simulated MODIS data granules were used for a dataset of 13.5 million samples with known input conditions. When pixel retrieval uncertainty was less than 30 %, optical thickness of the underlying cloud layer was greater than 4, and scattering angle range within the cloud bow was excluded, MOD06ACAERO retrievals agreed with the underlying ground truth (GEOS-5 cloud and aerosol profiles used to generate the synthetic radiances) with a slope of 0.913, offset of 0.06, and RMSE=0.107. When only near-nadir pixels were considered (view zenith angle within ±20∘) the agreement with source data further improved (0.977, 0.051, and 0.096 respectively). Algorithm closure was examined using a single case out of the five used for verification. For closure, the MOD06ACAERO code was modified to use GEOS-5 temperature and moisture profiles as an ancillary. Agreement of MOD06ACAERO retrievals with source data for the closure study had a slope of 0.996 with an offset of −0.007 and RMSE of 0.097 at a pixel uncertainty level of less than 40 %, illustrating the benefits of high-quality ancillary atmospheric data for such retrievals.

The Impact of COVID-19 Lockdowns on Satellite-Observed Aerosol Optical Thickness over the Surrounding Coastal Oceanic Areas of Megacities in the Coastal Zone

Nearly 40 years of aerosol optical thickness (AOT) climate data record (CDR) derived from NOAA operational satellite Advanced Very High Resolution Radiometer (AVHRR) observation over the global oceans is used to study the AOT changes due to the COVID-19 lockdown over the surrounding coastal oceanic areas of 18 megacities in the coast zone (MCCZ). The AOT difference between the annual mean AOT values of 2020 with COVID-19 lockdown and 2019 without the lockdown along with the 2020 AOT annual anomaly are used to effectively identify the AOT changes that are a result of the lockdown. We found that for most of the 18 MCCZ, the COVID-19 lockdowns implemented to contain the spread of the coronavirus resulted in a decrease between 1% and 30% in AOT due to reduced anthropogenic emissions associated with the lockdowns. However, the AOT long-term trend and other aerosol interannual variations due to favorable or unfavorable meteorological conditions may mask AOT changes due to the lockdown effect in some MCCZ. Different seasonal variations of aerosol amount in 2020 relative to 2019 due to other natural aerosol emission sources not influenced by the lockdown, such as dust storms and natural biomass burning and smoke, may also conceal a limited reduction in the annual mean AOT due to the lockdown in MCCZ with relatively loose lockdown. This study indicates that the use of long-term satellite observation is helpful for studying and monitoring the aerosol changes due to the emission reduction associated with the COVID-19 lockdown in the surrounding coastal oceanic areas of MCCZ, which will benefit the future development of the mitigation strategy for air pollution and emissions in megacities.

Variation of aerosol optical thickness with atmospheric water vapour : A case study over a continental station Mysore, India

Atmospheric measurements in a continental, low latitude station Mysore (12.3° N) has been carried out, for the period December 2003 to June 2006. Measurements were made using a sunphotometer with five bands in the visible and near-infrared range of the solar spectrum. To bring out the wavelength dependence of Aerosol Optical Thickness (AOT) on atmospheric water vapour, typically two wavelength channels are being used, one at 500 nm and the other at 1020 nm. A linear dependence between AOT and water vapour on meteorologically calm days is the important observation made. Growth rate of AOT is found to be larger at shorter wavelength (500 nm) than that of the longer wavelength (1020 nm). A mass-plot representation is followed on monthly basis, which is nothing but the graphical plot of spectral AOT versus water vapour of the scans for all the clear sky days of a particular month. Further investigations reveal that some months exhibit a single trend of growth of AOT with water vapour whereas double trend is the scenario for other months. These results provide insight into the changes in the atmospheric aerosol characteristics with precipitable water vapour, which is the subject matter of this paper.

Spatial and Temporal Variations of Aerosol Optical Thickness over the China Seas from Himawari-8

Six years of hourly aerosol optical thickness (AOT) data retrieved from Himawari-8 were used to investigate the spatial and temporal variations, especially diurnal variations, of aerosols over the China Seas. First, the Himawari-8 AOT data were consistent with the AERONET measurements over most of the China Seas, except for some coastal regions. The spatial feature showed that AOT over high latitude seas was generally larger than over low latitude seas, and it is distributed in strips along the coastline and decreases gradually with increasing distance from the coastline. AOT undergoes diurnal variation as it decreases from 9:00 a.m. local time, reaching a minimum at noon, and then begins to increase in the afternoon. The percentage daily departure of AOT over the East China Seas generally ranged ±20%, increasing sharply in the afternoon; however, over the northern part of the South China Sea, daily departure reached a maximum of >40% at 4:00 p.m. The monthly variation in AOT showed a pronounced annual cycle. Seasonal variations of the spatial pattern showed that the largest AOT was usually observed in spring and varies in other seasons for different seas.

Cancer-Cell Deep-Learning Classification by Integrating Quantitative-Phase Spatial and Temporal Fluctuations

We present a new classification approach for live cells, integrating together the spatial and temporal fluctuation maps and the quantitative optical thickness map of the cell, as acquired by common-path quantitative-phase dynamic imaging and processed with a deep-learning framework. We demonstrate this approach by classifying between two types of cancer cell lines of different metastatic potential originating from the same patient. It is based on the fact that both the cancer-cell morphology and its mechanical properties, as indicated by the cell temporal and spatial fluctuations, change over the disease progression. We tested different fusion methods for inputting both the morphological optical thickness maps and the coinciding spatio-temporal fluctuation maps of the cells to the classifying network framework. We show that the proposed integrated triple-path deep-learning architecture improves over deep-learning classification that is based only on the cell morphological evaluation via its quantitative optical thickness map, demonstrating the benefit in the acquisition of the cells over time and in extracting their spatio-temporal fluctuation maps, to be used as an input to the classifying deep neural network.

THE VARIABILITY OF AEROSOL OPTICAL THICKNESS IN MOUNTAINOUS AND FOOTHILL AREAS OF THE NORTH OSSETIA ACCORDING TO SATELLITE MEASUREMENTS

Atmospheric aerosol is one of the indicators of air quality that affects the environmental situation. The aerosol optical thickness (AOT) of the atmosphere is studied in the mountainous, foothill, and plain regions of Republic of North Ossetia-Alania (Russian Federation) based on satellite data EOS Terra, Aqua. Several geographical locations at different heights were selected for the analysis. Daily series of AOT, air temperature and precipitation data were obtained on period over 20 years for each point. The results of statistical analysis of long-term values of AOT, temperature and precipitation are shown. There is a statistically significant relationship between the averaged values, and there is also a dependence on the height of the location and the proximity of mountains. Long-term average dependencies provide a basis for predicting the AOT value based on the measured temperature at height of 2 m for territories located in a complex landscape at different altitudes.

Retrieval of Aerosol Optical Thickness with Custom Aerosol Model Using SKYNET Data over the Chiba Area

The retrieval of the aerosol optical thickness (AOT) from remotely-sensed data relies on the adopted aerosol model. However, the method of this technique has been rather limited because of the high variability of the surface albedo, in addition to the spatial variability in the aerosol properties over the land surfaces. To overcome unsolved problems, we proposed a method for the visibility-derived AOT estimation from SKYNET-based measurement and daytime satellite images with a custom aerosol model over the Chiba area (35.62° N, 140.10° E), which is located in the greater Tokyo metropolitan area in Japan. Different from conventionally-used aerosol models for the boundary layer, we created a custom aerosol model by using sky-radiometer observation data of aerosol volume size distribution and refractive indices, coupled with spectral response functions (SPFs) of satellite visible bands to alleviate the wide range of path-scattered radiance. We utilized the radiative transfer code 6S to implement the radiative transfer calculation based on the created custom aerosol model. The concurrent data from ground-based measurement are used in the radiative analysis, namely the temporal variation of AOT from SKYNET. The radiative estimation conducted under clear-sky conditions with minimum aerosol loading is used for the determination of the surface albedo, so that the 6S simulation yields a well-defined relation between total radiance and surface albedo. We made look-up tables (LUTs) pixel-by-pixel over the Chiba area for the custom aerosol model to retrieve the satellite AOT distribution based on the surface albedo. Therefore, such a reference of surface albedo generated from clear-sky conditions, in turn, can be employed to retrieve the spatial distribution of AOT on both clear and relatively turbid days. The value for the AOTs retrieved using the custom aerosol model is found to be stable than conventionally-used typical aerosol models, indicating that our method yields substantially better performance.