Aerosol Optical Thickness Retrieval Research Articles

Aerosol retrieval over snow using the RemoTAP algorithm

Abstract. In order to conduct accurate aerosol retrieval over snow, the Remote Sensing of Trace Gases and Aerosol Products (RemoTAP) algorithm developed by SRON Netherlands Institute for Space Research is extended with a bi-directional reflection distribution function (BRDF) for snow surfaces. The capability of the extended algorithm is validated with both synthetic measurements and real satellite measurements from the Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL), and a comparison has been made to retrievals with the baseline RemoTAP (without a snow kernel). For retrievals with real PARASOL observations, we use pixels over Aerosol Robotic Network (AERONET) stations for validation and we use the MODIS snow cover products to identify pixels over snow. We evaluate the retrieved aerosol optical thickness (AOT) at 550 nm (τ550), single-scattering albedo (SSA) at 550 nm (ω550) and Ångström exponent (AE) for 440–870 nm (AE440−870). The experiments with both synthetic and real data show that the extended RemoTAP maintains capability for snow-free pixels and has obvious advantages in accuracy and the fraction of successful retrievals for retrieval over snow, especially over surfaces with snow cover >75 %. According to the real-data experiment, we find that the retrieval algorithm has difficulty in fitting the PARASOL 1020 nm band, where snow reflectance is significantly lower than that for the visible bands. When we perform a four-band retrieval (490, 565, 670, 865 nm) with the extended RemoTAP, we obtain a good retrieval result for τ550, ω550 and AE440−870. Therefore, the four-band retrieval with the extended RemoTAP is recommended for aerosol retrieval over snow.

Aerosol Shortwave Radiative Heating and Cooling by the 2017 and 2023 Chilean Wildfire Smoke Plumes

AbstractThe aerosol shortwave, direct radiative effects of smoke plumes from Chilean wildfires in 2017 and 2023 were derived from satellite observations in both cloud‐free and cloud scenes. At the top of the atmosphere, the aerosol DRE changes sign when aerosol overly clouds or open ocean, confirmed by both measurements and a simulation study. The cloud‐free daily‐mean DRE, computed using an offline radiative transfer model (RTM), was 66 W m−2 in 2023 and 42 W m−2 in 2017, due to absorption by smoke. However, the total radiative effects were larger in 2017 due to a larger plume size compared to 2023. The method presented here provides a new conceptual model to quickly assess the radiative effects of wildfire smoke plumes using satellite measurements and pre‐computed RTM results. The presented estimates are strongly affected by the uncertainty of aerosol optical thickness retrievals from satellite, which can be large in the presence of clouds.

Bayesian atmospheric correction over land: Sentinel-2/MSI and Landsat 8/OLI

Abstract. Mitigating the impact of atmospheric effects on optical remote sensing data is critical for monitoring intrinsic land processes and developing Analysis Ready Data (ARD). This work develops an approach to this for the NERC NCEO medium resolution ARD Landsat 8 (L8) and Sentinel 2 (S2) products, called Sensor Invariant Atmospheric Correction (SIAC). The contribution of the work is to phrase and solve that problem within a probabilistic (Bayesian) framework for medium resolution multispectral sensors S2/MSI and L8/OLI and to provide per-pixel uncertainty estimates traceable from assumed top-of-atmosphere (TOA) measurement uncertainty, making progress towards an important aspect of CEOS ARD target requirements. A set of observational and a priori constraints are developed in SIAC to constrain an estimate of coarse resolution (500 m) aerosol optical thickness (AOT) and total column water vapour (TCWV), along with associated uncertainty. This is then used to estimate the medium resolution (10–60 m) surface reflectance and uncertainty, given an assumed uncertainty of 5 % in TOA reflectance. The coarse resolution a priori constraints used are the MODIS MCD43 BRDF/Albedo product, giving a constraint on 500 m surface reflectance, and the Copernicus Atmosphere Monitoring Service (CAMS) operational forecasts of AOT and TCWV, providing estimates of atmospheric state at core 40 km spatial resolution, with an associated 500 m resolution spatial correlation model. The mapping in spatial scale between medium resolution observations and the coarser resolution constraints is achieved using a calibrated effective point spread function for MCD43. Efficient approximations (emulators) to the outputs of the 6S atmospheric radiative transfer code are used to estimate the state parameters in the atmospheric correction stage. SIAC is demonstrated for a set of global S2 and L8 images covering AERONET and RadCalNet sites. AOT retrievals show a very high correlation to AERONET estimates (correlation coefficient around 0.86, RMSE of 0.07 for both sensors), although with a small bias in AOT. TCWV is accurately retrieved from both sensors (correlation coefficient over 0.96, RMSE <0.32 g cm−2). Comparisons with in situ surface reflectance measurements from the RadCalNet network show that SIAC provides accurate estimates of surface reflectance across the entire spectrum, with RMSE mismatches with the reference data between 0.01 and 0.02 in units of reflectance for both S2 and L8. For near-simultaneous S2 and L8 acquisitions, there is a very tight relationship (correlation coefficient over 0.95 for all common bands) between surface reflectance from both sensors, with negligible biases. Uncertainty estimates are assessed through discrepancy analysis and are found to provide viable estimates for AOT and TCWV. For surface reflectance, they give conservative estimates of uncertainty, suggesting that a lower estimate of TOA reflectance uncertainty might be appropriate.

Satellite Aerosol Retrieval Using Scene Simulation and Deep Belief Network

Aerosol satellite remote sensing retrieval is of great importance in the study of climatic and environmental effects. However, due to varied factors affecting the signals reaching satellite sensors, accurate aerosol retrieval remains challenging. Focusing on limitations in the availability of aerosol data from real scenes, we simulated real scenes to obtain sample data to achieve aerosol retrieval using a deep belief network (DBN). The 6S atmospheric radiative transfer model was used to simulate various possible parameters of earth-atmosphere, sun, and sensor in real scenes. A large amount of simulated data was generated and used as sample datasets of DBN training to obtain the aerosol inversion model. Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to perform aerosol optical thickness (AOT) retrieval experiments. Global-scale aerosol retrieval experiments were conducted based on the following three representative regions: 1) Beijing-Tianjin-Hebei region in China; 2) Midwestern and Southern United States; and 3) Central and Western Europe. The retrieval results were verified using Aerosol Robotic Network (AERONET) datasets in comparison with MCD19A2 aerosol products. Five indicators, including mean absolute error (MAE) and within expected error (f $_{=EE}$ ), were used for the evaluation. The evaluation indicators of the proposed method, in which MAEs were 0.0626, 0.0366, and 0.0487, and f $_{=EE}$ 's were 86.28%, 85.21%, and 80.71%, performed better than MCD19A2 in three typical regions. The most significant advantage of the proposed method is that high-precision retrieval of spatially continuous AOT can be achieved using single-temporal satellite imagery data, which is not possible realizing in current aerosol retrieval methods.

Sensor calibration impacts on dust detection based on MODIS and VIIRS thermal emissive bands

Dust detection using remotely sensed measurements has been one of the challenging problems encountered by atmospheric scientists. MODerate Resolution Imaging Spectroradiometer (MODIS) on the Terra (T) and Aqua (A) platforms have been a versatile sensor for well over 21 and 18 years respectively, and have been extremely useful in the retrieval of aerosol information over the entire globe. The MODIS radiances from the Level 1B in general are expected to be within 5% accuracy in the reflective wavelengths and within 1% in the thermal emissive wavelengths. In this paper, we evaluate the sensitivity of previously developed dust detection technique based on thermal emissive wavelengths, which correspond to MODIS bands 20, 29, 31, and 32 respectively. The Thermal Emissive Dust Index (TEDI) performed very comparably to the traditional Aerosol Optical Thickness (AOT) retrievals by MODIS reflective channels. Since the MODIS Thermal Emissive Bands (TEB) are well calibrated on-orbit using a BlackBody (BB) source, the calibration of these long wave infrared bands is quite robust. As A-MODIS continues to perform well beyond its designed lifetime of 6 years, the instrument has undergone various levels of degradation during its mission time. As a consequence, it is imperative to check the impacts of calibration on the higher-level retrievals. In this paper, we rigorously analyze the sensitivity of TEDI due to the impact of calibration by the afore-mentioned TEB. The perturbation of the dominant (linear) calibration term demonstrated the following: first, there was a correlation in the sensitivity of the TEDI due to the uncertainty in the linear calibration term. Based on a perturbation in the linear calibration term for all aforementioned bands over a range of ±5% yielded the TEDI sensitivity to vary from approximately −3.2% to about −3.6%. When considering the uncertainty in each individual band significant changes were observed. The least change was observed for the perturbation in the calibration of band 20 with the TEDI sensitivity and the largest sensitivity in TEDI was observed in the perturbation of band 31 calibration. Thus, in the case of TEDI, noticeable sensitivity due to calibration uncertainty was observed in bands 29, 31, and 32, reiterating the importance of the TEB calibration in these bands. Also, the dust detection scheme based on A-MODIS was successfully transferred to the follow-on sensors such as Suomi (SNPP) and NOAA 20 (N20) VIIRS. The results presented in this paper would be extremely helpful in understanding impacts of calibration on the higher-level products for both current and future missions based on the MODIS heritage. Finally, the work also identifies the importance of radiometric fidelity in maintaining the accuracy of the dust detection. Results presented will show drastic improvement of the Saharan dust detection after the reduction of the electronic crosstalk in the 8.5 µm channel of T-MODIS.

Observation of absorbing aerosols above clouds over the south-east Atlantic Ocean from the geostationary satellite SEVIRI – Part 2: Comparison with MODIS and aircraft measurements from the CLARIFY-2017 field campaign

Abstract. To evaluate the SEVIRI retrieval for aerosols above clouds presented in Part 1 of the companion paper, the algorithm is applied over the south-east Atlantic Ocean during the CLARIFY-2017 field campaign period. The first step of our analysis compares the retrieved aerosol and cloud properties against equivalent products from the MODIS MOD06ACAERO retrieval (Meyer et al., 2015). While the correlation between the two satellite retrievals of the above-cloud aerosol optical thickness (AOT) is good (R = 0.78), the AOT retrieved by SEVIRI is 20.3 % smaller than that obtained from the MODIS retrieval. This difference in AOT is attributed mainly to the more absorbing aerosol model assumed for the SEVIRI retrieval compared to MODIS. The underlying cloud optical thickness (COT) derived from the two satellites is in good agreement (R = 0.90). The cloud droplet effective radius (CER) retrieved by SEVIRI is consistently smaller than MODIS by 2.2 µm, which is mainly caused by the use of different spectral bands of the satellite instruments. In the second part of our analysis, we compare the forecast water vapour profiles used for the SEVIRI atmospheric correction as well as the aforementioned aerosol and cloud products with in situ measurements made from the Facility for Airborne Atmospheric Measurements (FAAM) aircraft platform during the CLARIFY-2017 campaign. Around Ascension Island, the column water vapour used to correct the SEVIRI signal is overestimated by 3.1 mm in the forecast compared to that measured by dropsondes. However, the evidence suggests that the accuracy of the atmospheric correction improves closer to the African coast. Consistency is observed between the SEVIRI above-cloud AOT and in situ measurements (from cavity ring-down spectroscopy instruments) when the measured single-scattering albedo is close to that assumed in the retrieval algorithm. On the other hand, the satellite retrieval overestimates the AOT when the assumed aerosol model is not absorbing enough. Consistency is also found between the cloud properties retrieved by SEVIRI and the CER measured by a cloud droplet probe and the liquid water path derived from a microwave radiometer. Despite the instrumental limitations of the geostationary satellite, the consistency obtained between SEVIRI, MODIS and the aircraft measurements demonstrates the ability of the retrieval in providing additional information on the temporal evolution of the aerosol properties above clouds.

ICOR Atmospheric Correction on Sentinel-3/OLCI over Land: Intercomparison with AERONET, RadCalNet, and SYN Level-2

To validate the iCOR atmospheric correction algorithm applied to the Sentinel-3 Ocean and Land Color Instrument (OLCI), Top-of-Atmosphere (TOA) observations over land, globally retrieved Aerosol Optical Thickness (AOT), Top-of-Canopy (TOC) reflectance, and Vegetation Indices (VIs) were intercompared with (i) AERONET AOT and AERONET-based TOC reflectance simulations, (ii) RadCalNet surface reflectance observations, and (iii) SYN Level 2 (L2) AOT, TOC reflectance, and VIs. The results reveal that, overall, iCOR’s statistical and temporal consistency is high. iCOR AOT retrievals overestimate relative to AERONET, but less than SYN L2. iCOR and SYN L2 TOC reflectances exhibit a negative bias of ~−0.01 and −0.02, respectively, in the Blue bands compared to the simulations. This diminishes for RED and NIR, except for a +0.02 bias for SYN L2 in the NIR. The intercomparison with RadCalNet shows relative differences < ±6%, except for bands Oa02 (Blue) and Oa21 (NIR), which is likely related to the reported OLCI “excess of brightness”. The intercomparison between iCOR and SYN L2 showed R2 = 0.80–0.93 and R2 = 0.92–0.96 for TOC reflectance and VIs, respectively. iCOR’s higher temporal smoothness compared to SYN L2 does not propagate into a significantly higher smoothness for TOC reflectance and VIs. Altogether, we conclude that iCOR is well suitable to retrieve statistically and temporally consistent AOT, TOC reflectance, and VIs over land surfaces from Sentinel-3/OLCI observations.

Simulated reflectance above snow constrained by airborne measurements of solar radiation: implications for the snow grain morphology in the Arctic

Abstract. Accurate knowledge of the reflectance from snow/ice-covered surfaces is of fundamental importance for the retrieval of snow parameters and atmospheric constituents from space-based and airborne observations. In this paper, we simulate the reflectance in a snow–atmosphere system, using the phenomenological radiative transfer model SCIATRAN, and compare the results with that of airborne measurements. To minimize the differences between measurements and simulation, we determine and employ the key atmospheric and surface parameters, such as snow grain morphologies (or habits). First, we report on a sensitivity study. This addresses the requirement for adequate a priori knowledge about snow models and ancillary information about the atmosphere. For this aim, we use the well-validated phenomenological radiative transfer model, SCIATRAN. Second, we present and apply a two-stage snow grain morphology (i.e., size and shape of ice crystals in the snow) retrieval algorithm. We then describe the use of this new retrieval for estimating the most representative snow model, using different types of snow morphologies, for the airborne observation conditions performed by NASA's Cloud Absorption Radiometer (CAR). Third, we present a comprehensive comparison of the simulated reflectance (using retrieved snow grain size and shape and independent atmospheric data) with that from airborne CAR measurements in the visible (0.670 µm) and near infrared (NIR; 0.870 and 1.6 µm) wavelength range. The results of this comparison are used to assess the quality and accuracy of the radiative transfer model in the simulation of the reflectance in a coupled snow–atmosphere system. Assuming that the snow layer consists of ice crystals with aggregates of eight column ice habit and having an effective radius of ∼99 µm, we find that, for a surface covered by old snow, the Pearson correlation coefficient, R, between measurements and simulations is 0.98 (R2∼0.96). For freshly fallen snow, assuming that the snow layer consists of the aggregate of five plates ice habit with an effective radius of ∼83 µm and having surface inhomogeneity, the correlation is ∼0.97 (R2∼0.94) in the infrared and 0.88 (R2∼0.77) in the visible wavelengths. The largest differences between simulated and measured values are observed in the glint area (i.e., in the angular regions of specular and near-specular reflection), with relative azimuth angles <±40∘ in the forward-scattering direction. The absolute difference between the modeled results and measurements in off-glint regions, with a viewing zenith angle of less than 50∘, is generally small ∼±0.025 and does not exceed ±0.05. These results will help to improve the calculation of snow surface reflectance and relevant assumptions in the snow–atmosphere system algorithms (e.g., aerosol optical thickness retrieval algorithms in the polar regions).

Retrospective assessment of pregnancy exposure to particulate matter from desert dust on a Caribbean island: could satellite-based aerosol optical thickness be used as an alternative to ground PM10 concentration?

Desert dust transported from the Saharan-Sahel region to the Caribbean Sea is responsible for peak exposures of particulate matter (PM). This study explored the potential added value of satellite aerosol optical thickness (AOT) measurements, compared to the PM concentration at ground level, to retrospectively assess exposure during pregnancy. MAIAC MODIS AOT retrievals in blue band (AOT470) were extracted for the French Guadeloupe archipelago. AOT470 values and PM10 concentrations were averaged over pregnancy for 906 women (2005-2008). Regression modeling was used to examine the AOT470-PM10 relationship during pregnancy and test the association between dust exposure estimates and preterm birth. Moderate agreement was shown between mean AOT470 retrievals and PM10 ground-based measurements during pregnancy (R2 = 0.289). The magnitude of the association between desert dust exposure and preterm birth tended to be lower using the satellite method compared to the monitor method. The latter remains an acceptable trade-off between epidemiological relevance and exposure misclassification, in areas with few monitoring stations and complex topographical/meteorological conditions, such as tropical islands.

Development of an Algorithm to Retrieve Aerosol Optical Properties Over Water Using an Artificial Neural Network Radiative Transfer Scheme: First Result From GOSAT-2/CAI-2

In this study, we developed a fast yet flexible remote sensing algorithm to estimate the aerosol optical properties over water for the Cloud and Aerosol Imager-2 (CAI-2) onboard the Greenhouse gases Observing SATellite-2 (GOSAT-2) launched in October 2018. The CAI-2 is the successor of GOSAT/CAI by providing more spectral and finer spatial data. The algorithm uses the optimal estimation approach to simultaneously retrieve aerosol and water substances (SIRAW), combined with an artificial neural network (ANN) solver to perform the radiative transfer (RT) calculation. The ANN was well constructed based on an improved learning scheme and educated from a coupled atmosphere-ocean vector RT model over both open and coastal water. To investigate the availability of SIRAW, the retrieval was conducted using the real CAI-2 data and preliminarily validated via the ground-based observation of aerosol robotic network and maritime aerosol network over different ocean regions from March to November in 2019. Results demonstrated that the retrieved aerosol optical thickness (AOT) at 550 nm from CAI-2 had a good consistency to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurement, of which about 70.37% of CAI-2 AOT fell within a ±(0.05+10%) envelope. The algorithm developed by this study performed generally well for the AOTs and oceanic suspended particles over the global ocean through the intercomparison to those of MODIS products. Moreover, the ultraviolet channel of CAI-2, which produces the first application with 500-m spatial resolution, shows a promising skill in the monitoring of the smoke plume.

Multi-information collaborative cloud identification algorithm in Gaofen-5 Directional Polarimetric Camera imagery

The Directional Polarimetric Camera (DPC) onboard China's Gaofen-5 (GF-5) satellite has provided multi-angle (3-polarization channel), large-scale (1850 km swath width and 3.3 km spatial resolution), and high-frequency (2-day revisit period) Earth observation since May 2018. These features make DPC imagery application scenarios extensively. However, like other optical imagery, the presence of clouds is also a pervasive and unavoidable issue in DPC imagery. To leverage both radiation and polarization properties of DPC, we proposed a multi-information collaborative (MIC) method to identify clouds in the DPC imagery. Instead of a fixed single threshold, the MIC method adopts dynamic thresholds obtained by simulation in different atmosphere models, at different times, and under different underlying surfaces. Specifically, we included surface albedo and ice/snow cover distribution libraries to the MIC method, as they compensate for fewer spectral bands in the DPC imagery, thereby improving the accuracy of cloud detection results, especially in special bright surface scenarios (e.g., desert, bare soil and ice/snow). We also added an ice/snow detection algorithm to further eliminate the issue of misidentifying ice/snow pixels as clouds. Finally, after obtaining the DPC cloud mask results based on the MIC method, we calculated four cloud confidence levels for different application requirements by cloud quality evaluation criteria.We evaluated the MIC algorithm by comparing it with two other independent satellite cloud observation products, namely Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Moderate Resolution Imaging Spectroradiometer (MODIS). We found that the MIC cloud mask is in good agreement with the other two cloud products, with agreement probabilities of 93.06% (CALIPSO) and 85% (MODIS), respectively. Furthermore, the detected high confidence cloud and clear sky results agree with the CALIPSO cloud confidence products by more than 97.35% and 96.13%, respectively. We therefore suggest that the MIC method can provide the basis for subsequent studies of atmospheric parameters, such as accurate retrieval of aerosol optical thickness (AOT), cloud optical thickness (COT), cloud droplet effective radius (CDR) and land surface reflectance.

Model Selection in Atmospheric Remote Sensing with an Application to Aerosol Retrieval from DSCOVR/EPIC, Part 1: Theory

The retrieval of aerosol and cloud properties such as their optical thickness and/or layer/top height requires the selection of a model that describes their microphysical properties. We demonstrate that, if there is not enough information for an appropriate microphysical model selection, the solution’s accuracy can be improved if the model uncertainty is taken into account and appropriately quantified. For this purpose, we design a retrieval algorithm accounting for the uncertainty in model selection. The algorithm is based on (i) the computation of each model solution using the iteratively regularized Gauss–Newton method, (ii) the linearization of the forward model around the solution, and (iii) the maximum marginal likelihood estimation and the generalized cross-validation to estimate the optimal model. The algorithm is applied to the retrieval of aerosol optical thickness and aerosol layer height from synthetic measurements corresponding to the Earth Polychromatic Imaging Camera (EPIC) instrument onboard the Deep Space Climate Observatory (DSCOVR) satellite. Our numerical simulations show that the heuristic approach based on the thesolution minimizing the residual, which is frequently used in literature, is completely unrealistic when both the aerosol model and surface albedo are unknown.

Model Selection in Atmospheric Remote Sensing with Application to Aerosol Retrieval from DSCOVR/EPIC. Part 2: Numerical Analysis

An algorithm for retrieving aerosol parameters by taking into account the uncertainty in aerosol model selection is applied to the retrieval of aerosol optical thickness and aerosol layer height from synthetic measurements from the EPIC sensor onboard the Deep Space Climate Observatory. The synthetic measurements are generated using aerosol models derived from AERONET measurements at different sites, while other commonly used aerosol models, such as OPAC, GOCART, OMI, and MODIS databases are used in the retrieval. The numerical analysis is focused on the estimation of retrieval errors when the true aerosol model is unknown. We found that the best aerosol model is the one with a value of the asymmetry parameter and an angular variation of the phase function around the viewing direction that is close to the values corresponding to the reference aerosol model.

Season-dependent uncertainties in Baseline Surface Radiation Network (BSRN) solar radiation data

A novel radiation calibration method (Qiu et al., 2014) is improved using two new calibration selection criteria for quality control and is then used to analyze the uncertainties in global, direct and scattering solar irradiance (GSI, DSI and SSI, respectively) measurements at Izana, a Baseline Surface Radiation Network (BSRN) site. A total of 476 days of accepted calibration results from 2011 to 2014 are employed. The results reveal season-dependent uncertainties in the GSI, DSI and SSI measurements. The GSI, DSI and SSI measurements are probably lower by 3% ~ 6% in the summer and higher by 1% ~ 4% in the winter. The temperature dependency of solar radiometers is the most likely cause for this result. The calibration results are validated through two types of comparisons. The first is a comparison between aerosol optical thickness (AOT) retrievals from the vertical and horizontal DSI and AOT retrievals from the Aerosol Robotic Network (AERONET). The second is a comparison between in situ radiation measurements and model simulations. Higher GSI and DSI measurements in the summer result in systematically larger AOT retrievals, and they are also responsible for evidently lower measurements relative to model simulations. The winter case shows evidence to the contrary. Differences between in situ radiation measurements and model simulations also indicate the seasonal dependence of the measurement uncertainties. These results are highly significant for understanding the uncertainties in historical solar radiation data.

Retrieval of aerosol optical thickness and surface parameters based on multi-spectral and multi-viewing space-borne measurements

We present a novel approach to derive Aerosol Optical Thickness (AOT) at 0.5 µm and the surface reflectance for five spectral channels at native spatial resolution from the measurements of the Polarization and Directionality of Earth’s Reflectances-3 (POLDER) instrument aboard the Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) satellite. POLDER had multi-spectral and multi-viewing capabilities. This publication describes the first step in the design of a high-quality AOT and surface property retrieval algorithm, enabling the global evaluation of future missions providing multi-spectral and multi-viewing space-borne measurements. The developed retrieval approach is based on the radiative transfer and retrieval model SCIATRAN using an analytical linearized retrieval mode. The surface is parametrized according to the Ross-Li model and the aerosol typing was using prescribed types based on the approach by Levy et al. [26]. The minimization using SCIATRAN has been done by solving the quadratic programming problem which minimizes the considered system of equations (typically) based on linear constraints. In this study we constrained the retrieval so far only by assuming that the retrieved AOT at 0.5 µm is larger than 0.01 and smaller than 1.5. Until now, the retrieval is based only upon unpolarized POLDER data and shows promisingly weak dependence on a priori information.As a first validation, the retrieved AOT values have been compared with ground based measurements over Atmospheric Radiation Measurement Climate Research Facilities (ARM) in the Southern Great Plains (SGP). The SCIATRAN retrievals are mostly homogeneous and consistent in the region around the US ARM/SGP measurement stations. The retrieval results agree well with the ARM ground based measurements of AOT. The correlation coefficient was R=0.84and part of the remaining differences between ARM measurements and SCIATRAN retrievals can be attributed to unmasked clouds. Histogram based analysis of the AOT values showed reasonable distributions using SCIATRAN. Most distributions based on SCIATRAN AOT retrievals within a radius of 25 km around the stations are significantly broader than those derived from ARM measurements during 30 minutes but are well centered around the ARM AOT distributions.

Impact of environmental pollution on the retrieval of hourly aerosol products from Advanced Himawari Imager (AHI) over Beijing

Advanced Himawari Imager (AHI) aboard Himawari-8 provides hourly Aerosol Optical Thickness (AOT) products, widely used to assimilation models and ground-level particulate matter (PM) concentration retrievals. However, the performance of AHI AOT products remains unclear under different air quality conditions. In this study, we evaluate the performance of the AHI hourly AOT products with ground-based AOT observations from four Aerosol Robotic NETwork (AERONET) sites for 2018 over Beijing under different pollution levels and aerosol types. Near-surface PM concentrations are used to categorize air quality as clean, moderate, and heavy pollution, and the aerosol types (biomass burning and urban/industrial (BMA_UIA), maritime (MA), dust (DA), mixed-type (MIXA)) are classified based on the threshold limits of AERONET aerosol properties. Overall, the AHI hourly AOT achieves good consistency with AERONET measurements in Beijing (correlation coefficient (R) = 0.78 and a root-mean-square-error (RMSE) = 0.29). Under clean conditions, the AOT successful retrieval rate is more than 80%, while it is deficient for moderate and heavy pollution. AOT for clean and moderate pollution performs better than that for heavy pollution. Moreover, AHI AOT retrievals for fine mode aerosol (BMA_UIA) are in good agreement with AERONET observations, and the performance is better than that under coarse mode aerosol (MA and DA) conditions. AHI AOT may capture the spatiotemporal distribution of fine particles pollution over Beijing. These findings will provide useful information for the improvement of the aerosol model of the retrieval algorithm and the monitoring of urban fine particles pollution.

An aerosol climatology for global models based on the tropospheric aerosol scheme in the Integrated Forecasting System of ECMWF

Abstract. An aerosol climatology to represent aerosols in the radiation schemes of global atmospheric models was recently developed. We derived the climatology from a reanalysis of atmospheric composition produced by the Copernicus Atmosphere Monitoring Service (CAMS). As an example of an application in a global atmospheric model, we discuss the technical aspects of the implementation in the European Centre for Medium Range Weather Forecasts Integrated Forecasting System (ECMWF-IFS) and the impact of the new climatology on the medium-range weather forecasts and 1-year simulations. The new aerosol climatology was derived by combining a set of model simulations with constrained meteorological conditions and an atmospheric composition reanalysis for the period 2003–2013 produced by the IFS. The aerosol fields of the reanalysis are constrained by assimilating the aerosol optical thickness (AOT) retrievals product by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments. In a further step, we used modelled aerosol fields to correct the aerosol speciation and the vertical profiles of the aerosol reanalysis fields. The new climatology provides the monthly-mean mass mixing ratio of five aerosol species constrained by assimilated MODIS AOT. Using the new climatology in the ECMWF-IFS leads to changes in the direct aerosol radiative effect compared to the climatology previously implemented, which have a small but non-impact on the forecast skill of large-scale weather patterns in the medium-range. However, details of the regional distribution of aerosol radiative forcing can have a large local impact. This is the case for the area of the Arabian Peninsula and the northern Indian Ocean. Here changes in the radiative forcing of the mineral dust significantly improve the summer monsoon circulation.

Retrieval of Aerosol Optical Thickness in the Arctic Snow-Covered Regions Using Passive Remote Sensing: Impact of Aerosol Typing and Surface Reflection Model

Currently, no aerosol optical thickness (AOT) data set over the Arctic snow/ice-covered regions derived from space-borne passive remote sensing is available. The challenge is to develop an accurate and robust technique to derive AOT above highly variable and bright snow/ice surfaces. To extend data coverage of the eXtensible Bremen Aerosol/cloud and surfacE Retrieval (XBAER) AOT data product in the future, we propose a new algorithm for the retrieval of AOT and surface properties over snow/ice simultaneously. The algorithm utilizes the linear perturbation theory and does not use any simplified atmospheric correction techniques. Key issues like the selection of a proper aerosol type and optimal surface parameterization method for the retrieval of AOT over the Arctic have been investigated. The aerosol type is investigated using the aerosol climatology microphysical properties derived from four Aerosol Robotic Network (AERONET) sites (Barrow, Hornsund, Kangerlussuaq, and Tiksi). The three-parametric Ross-Li linear kernel model is used to describe the snow bidirectional reflectance distribution function (BRDF). The a priori knowledge of wavelength-dependent features of the coefficients in the Ross-Li linear kernel model is derived from Polarization and Directionality of the Earth's Reflectances (POLDER) measurements over the Arctic and utilized as constraints in the retrieval. The studies show that the combination of Ross-Li surface model and weakly absorbing aerosol parameterization provides an optimal way to derive AOT over the Arctic snow/ice-covered regions from passive remote sensing observations. The retrieved AOTs using POLDER show good agreement with AERONET observations.

Evaluation of Himawari-8 version 2.0 aerosol products against AERONET ground-based measurements over central and northern China

Himawari-8, a new generation geostationary satellite, carries the Advanced Himawari Imager (AHI), which can provide high temporal resolution observations (10 min) in monitoring the distribution and variation of aerosol over large areas. Recently, the AHI aerosol algorithm was refined, and the updated AHI version 2.0 Level 2 aerosol products were released. However, these products have not been comprehensively evaluated. This study aims to assess the accuracy of AHI version 2.0 Level 2 aerosol products over central and northern China. We compared nine hourly (Beijing time: 08:00 to 16:00) AHI Aerosol Optical Thickness (AOT) retrievals from 2016 to 2017 with AErosol RObotic NETwork (AERONET) measurements over four merged sites: Beijing-urban, Xianghe, Xuzhou and Baotou. The results show that the AHI AOT product provides accurate retrievals at 09:00, 10:00 and 11:00, but these values overestimated AOT at high aerosol loadings. In contrast, AHI AOT retrievals at 13:00, 14:00 and 15:00 exhibit significant overestimation. More than 67% of these retrievals fell above the Expected Error (EE: ± 0.15 × AOTAERONET ± 0.05). The AHI Quality Assurance (QA_flag) performs poorly in constraining overestimation of the AHI AOT product. The “no confidence” Ångström exponent (AE), additional cloud and surface reflectance factors in the QA_flag have negative impacts on AOT retrieval accuracy. For different aerosol models, the AHI AE and Optical Depth Ratio fine (RF) products exhibit large differences between the forenoon (09:00, 10:00 and 11:00) and afternoon (13:00, 14:00 and 15:00). However, the AHI Single-scattering albedo (SSA) values mainly distribute in the range from 0.93 to 0.94, exhibiting minor differences in different times, sites and aerosol models. These results indicated that these products have errors induced by the AHI algorithm.

The Uncharacteristic Occurrence of the June 2013 Biomass-Burning Haze Event in Southeast Asia: Effects of the Madden-Julian Oscillation and Tropical Cyclone Activity

One of the worst haze events to ever hit Peninsular Malaysia occurred in June 2013 due to smoke from Riau, Central Sumatra. While biomass-burning in the region is common, the early occurrence of a haze episode of this magnitude was uncharacteristic of the seasonality of extreme fire events, which usually occur between August and October in the Maritime Continent (MC). This study aims to investigate the phenomenology of the June 2013 haze event and its underlying meteorological forcing agents. The aerosol and meteorological environment during the event is examined using the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire hotspot detections and aerosol optical thickness retrievals, satellite-based precipitation retrievals, and meteorological indices. These datasets are then supported by a WRF-Chem simulation to provide a comprehensive picture of the event’s meteorology and aerosol transport phenomenology. While extreme fire events are more characteristic of El Nino years, the MODIS fire count over the MC in June for the years 2001–2015 was highest in 2013 when neutral El Nino/Southern Oscillation (ENSO) conditions prevailed. Although, the mean daily precipitation for June 2013 was below average for June 2003–2015. An early active tropical cyclone (TC) season occurred in 2013, and results show that the combined induced subsidence and flow enhancement due to TC Bebinca and the dry phases of a strong Madden–Julian Oscillation (MJO) event contributed to the event intensification. Results also show that Bebinca induced a decrease in surface relative humidity of at least 10% over Riau, where fire hotspots were concentrated.