Multi-Sensor Retrieval of Aerosol Optical Properties for Near-Real-Time Applications Using the Metop Series of Satellites: Concept, Detailed Description, and First Validation

The Polar Multi-Sensor Aerosol product (PMAp) is based on the synergistic use of three instruments from the Metop platform, GOME-2, AVHRR, and IASI. The retrieval algorithm includes three major steps: a pre-identification of the aerosol class, a selection of the aerosol model, and a calculation of the Aerosol Optical Depth (AOD). This paper provides a detailed description of the PMAp retrieval, which combines information provided by the three instruments. The retrieved AOD is qualitatively evaluated, and a good temporal as well as spatial performance is observed, including for the transition between ocean and land. More quantitatively, the performance is evaluated by comparison to AERONET in situ measurements. Very good consistency is also observed when compared to other space-based data such as MODIS or VIIRS. The paper demonstrates the ability of this first generation of synergistic products to derive reliable AOD, opening the door for the development of synergistic products from the instruments to be embarked on the coming Metop Second Generation platform. PMAp has been operationally distributed in near-real-time since 2014 over ocean, and 2016 over land.

Quantifying the structural uncertainty of the aerosol mixing state representation in a modal model

Aerosol mixing state is an important emergent property that affects aerosol radiative forcing and aerosol–cloud interactions, but it has not been easy to constrain this property globally. This study aims to verify the global distribution of aerosol mixing state represented by modal models. To quantify the aerosol mixing state, we used the aerosol mixing state indices for submicron aerosol based on the mixing of optically absorbing and non-absorbing species (χo), the mixing of primary carbonaceous and non-primary carbonaceous species (χc), and the mixing of hygroscopic and non-hygroscopic species (χh). To achieve a spatiotemporal comparison, we calculated the mixing state indices using output from the Community Earth System Model with the four-mode version of the Modal Aerosol Module (MAM4) and compared the results with the mixing state indices from a benchmark machine-learned model trained on high-detail particle-resolved simulations from the particle-resolved stochastic aerosol model PartMC-MOSAIC. The two methods yielded very different spatial patterns of the mixing state indices. In some regions, the yearly averaged χ value computed by the MAM4 model differed by up to 70 percentage points from the benchmark values. These errors tended to be zonally structured, with the MAM4 model predicting a more internally mixed aerosol at low latitudes and a more externally mixed aerosol at high latitudes compared to the benchmark. Our study quantifies potential model bias in simulating mixing state in different regions and provides insights into potential improvements to model process representation for a more realistic simulation of aerosols towards better quantification of radiative forcing and aerosol–cloud interactions.

A Decade of Poland-AOD Aerosol Research Network Observations

The Poland-AOD aerosol research network was established in 2011 to improve aerosol–climate interaction knowledge and provide a real-time and historical, comprehensive, and quantitative database for the aerosol optical properties distribution over Poland. The network consists of research institutions and private owners operating 10 measurement stations and an organization responsible for aerosol model transport simulations. Poland-AOD collaboration provides observations of spectral aerosol optical depth (AOD), Ångstrom Exponent (AE), incoming shortwave (SW) and longwave (LW) radiation fluxes, vertical profiles of aerosol optical properties and surface aerosol scattering and absorption coefficient, as well as microphysical particle properties. Based on the radiative transfer model (RTM), the aerosol radiative forcing (ARF) and the heating rate are simulated. In addition, results from GEM-AQ and WRF-Chem models (e.g., aerosol mass mixing ratio and optical properties for several particle chemical components), and HYSPLIT back-trajectories are used to interpret the results of observation and to describe the 3D aerosol optical properties distribution. Results of Poland-AOD research indicate progressive improvement of air quality and atmospheric turbidity during the last decade. The AOD was reduced by about 0.02/10 yr (at 550 nm), which corresponds to positive trends in ARF. The estimated clear-sky ARF trend is 0.34 W/m2/10yr and 0.68 W/m2/10yr, respectively, at TOA and at Earth’s surface. Therefore, reduction in aerosol load observed in Poland can significantly contribute to climate warming.

Development and Evaluation of the Aerosol Forecast Member in NCEP’s Global Ensemble Forecast System (GEFS-Aerosols v1)

NOAA’s National Weather Service (NWS) is on its way to deploy various operational prediction applications using the Unified Forecast System (https://ufscommunity.org/), a community-based coupled, comprehensive Earth modeling system. An aerosol model component developed in a collaboration between the Global Systems Laboratory, Chemical Science Laboratory, the Air Resources Laboratory, and Environmental Modeling Center (GSL, CSL, ARL, EMC) was coupled online with the FV3 Global Forecast System (FV3GFS) using the National Unified Operational Prediction Capability (NUOPC)-based NOAA Environmental Modeling System (NEMS) software framework. This aerosol prediction system replaced the NEMS GFS Aerosol Component (NGAC) system in the National Center for Environment Prediction (NCEP) production suite in September 2020 as one of the ensemble members of the Global Ensemble Forecast System (GEFS), dubbed GEFS-Aerosols v1. The aerosol component of atmospheric composition in GEFS is based on the Weather Research and Forecasting model (WRF-Chem) that was previously included into FIM-Chem (Zhang et al, 2021). GEFS-Aerosols includes bulk modules from the Goddard Chemistry Aerosol Radiation and Transport model (GOCART). Additionally, the biomass burning plume rise module from High-Resolution Rapid Refresh (HRRR)-Smoke was implemented; the GOCART dust scheme was replaced by the FENGSHA dust scheme (developed by ARL); the Blended Global Biomass Burning Emissions Product (GBBEPx V3) provides biomass burning emission and Fire Radiative Power (FRP) data; and the global anthropogenic emission inventories are derived from the Community Emissions Data System (CEDS). All sub-grid scale transport and deposition is handled inside the atmospheric physics routines, which required consistent implementation of positive definite tracer transport and wet scavenging in the physics parameterizations used by NCEP’s operational Global Forecast System based on FV3 (FV3GFS). This paper describes the details of GEFS-Aerosols model development and evaluation of real-time and retrospective runs using different observations from in situ measurement, satellite and aircraft data. GEFS-Aerosols predictions demonstrate substantial improvements for both composition and variability of aerosol distributions over those from the former operational NGAC system.

Aerosol models from the AERONET data base. Application to surface reflectance validation

Aerosols play a critical role in radiative transfer within the atmosphere, and they have a significant impact on climate change. As part of the validation of atmospheric correction of remote sensing data affected by the atmosphere, it is critical to utilize appropriate aerosol models as aerosols are a main source of error. In this paper, we propose and demonstrate a framework for building and identifying an aerosol model. For this purpose, we define the aerosol model by recalculating the aerosol microphysical properties (Cvf, Cvc, %Cvf, %Cvc, rvf, rvc, σr, σc, nr440, nr650, nr850, nr1020, ni440, ni650, ni850, ni1020, %Sph) based on the optical thickness at 440 nm τ440 and the Ångström coefficient α440–870 obtained from numerous AERosol RObotic NETwork (AERONET) sites. Using aerosol microphysical properties provided by the AERONET dataset, we were able to evaluate our own retrieved microphysical properties. The associated uncertainties are up to 23 %, except for the challenging, imaginary part of the refractive index ni (about 38 %). Uncertainties of the retrieved aerosol microphysical properties were incorporated in the framework for validating surface reflectance derived from space-borne Earth observation sensors. Results indicate that the impact of aerosol microphysical properties varies 3.5 × 10−5 to 10−3 in reflectance units. Finally, the uncertainties of the microphysical properties yielded an overall uncertainty of approximately of 1 to 3 % of the retrieved surface reflectance in the MODIS red spectral band (620–670 nm), which corresponds to the specification used for atmospheric correction.

Estimating cloud condensation nuclei concentrations from CALIPSO lidar measurements

We present a novel methodology to estimate cloud condensation nuclei (CCN) concentrations from spaceborne CALIPSO lidar measurements. The algorithm utilizes (i) the CALIPSO-derived backscatter and extinction coefficient, depolarization ratio, and aerosol subtype information, (ii) the normalized volume size distributions and refractive indices from the CALIPSO aerosol model, and (iii) the MOPSMAP optical modelling package. For each CALIPSO height bin, we first select the aerosol-type specific size distribution and then adjust it to reproduce the extinction coefficient derived from the CALIPSO retrieval. The scaled size distribution is integrated to estimate the aerosol number concentration which is then used in the CCN parameterizations to calculate CCN concentrations at different supersaturations. To account for the hygroscopicity of continental and marine aerosols, we use the kappa parameterization and correct the size distributions before the scaling step. We have studied the sensitivity of the thus derived CCN concentration to the effect of variations of the initial size distributions. It is found that the uncertainty associated with the algorithm can range between a factor of 2 and 3. We have also compared our results with the POLIPHON and found comparable results for extinction coefficients larger than 0.05 km−1. An initial application to a case with coincident airborne in-situ measurements for independent validation shows promising results and illustrates the potential of CALIPSO for constructing a global height-resolved CCN climatology.

Protective Potential of an Autogenous Vaccine in an Aerogenous Model of Escherichia coli Infection in Broiler Breeders

In poultry, Escherichia coli is a common cause of high-cost infections. Consequently, autogenous vaccines are often used despite limited and conflicting evidence on their effectiveness have been presented. The present study aimed to investigate the efficacy of a commonly used autogenous vaccine, previously deemed ineffective, in an aerosol model of colibacillosis. Methods: Broiler breeders (n = 47) were randomly allocated to one of four groups (vaccinated and unvaccinated birds receiving an autogenous vaccine or sterile saline intramuscularly) and challenged with either aerosolised E. coli or vehicle at 29 weeks of age. Two days following inoculation, the birds were euthanised, thoroughly necropsied, and samples for bacteriology and histopathology were collected. Results: Vaccinated birds had a significantly lower bacteriology score compared to the unvaccinated group challenged with E. coli (p < 0.01) and a lower overall air sac lesion score (p < 0.05). Overall lung and spleen lesion scores only differed significantly between the unvaccinated E. coli challenged group compared to the vehicle inoculated groups. The overall gross pathology score was 2.8 and 1.95 in the unvaccinated and vaccinated E. coli challenge groups, respectively, whereas the vaccinated vehicle group had a score of 0.9 and the unvaccinated vehicle group a score of 1. Conclusions: A protective effect of an autogenous vaccine was found utilising an aerogenous model of colibacillosis through multiple methods of evaluation. The findings encourage the continued use of autogenous vaccines and underlines the necessity of discriminative experimental models with high predictive validity when evaluating vaccine interventions.

Bayesian uncertainty quantification in aerosol optical depth retrieval applied to TROPOMI measurements

We present here an aerosol model selection based statistical method in Bayesian framework for retrieving atmospheric aerosol optical depth (AOD) and pixel-level uncertainty. Especially, we focus on to provide more realistic uncertainty estimate by taking into account a model selection problem when searching for the solution by fitting look-up table (LUT) models to a satellite measured top-of-atmosphere reflectance. By means of Bayesian model averaging over the best-fitting aerosol models we take into account an aerosol model selection uncertainty and get also a shared inference about AOD. Moreover, we acknowledge model discrepancy, i.e. forward model error, arising from approximations and assumptions done in forward model simulations. We have estimated the model discrepancy empirically by a statistical approach utilizing residuals of model fits. We use the measurements from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor in ultraviolet and visible bands, and in one wavelength band 675 nm in near-infrared, in order to study the functioning of the retrieval in a broad wavelength range. We exploit a fundamental classification of the aerosol models as weakly absorbing, biomass burning and desert dust aerosols. For experimental purpose we have included some dust type of aerosols having non-spherical particle shapes. For this study we have created the aerosol model LUTs with radiative transfer simulations using the libRadtran software package. It is reasonably straightforward to experiment with different aerosol types and evaluate the most probable aerosol type by the model selection method. We demonstrate the method in wildfire and dust events in a pixel level. In addition, we have evaluated in detail the results against ground-based remote sensing data from the AErosol RObotic NETwork (AERONET). Based on the case studies the method has ability to identify the appropriate aerosol types, but in some wildfire cases the AOD is overestimated compared to the AERONET result. The resulting uncertainty when accounting for the model selection problem and the imperfect forward modelling is higher compared to uncertainty when only measurement error is included in an observation model, as can be expected.

Possibility of new shielding device for upper gastrointestinal endoscopy

Background and study aims Infection control is essential when performing endoscopic procedures, especially during the COVID-19 pandemic. Therefore, we have developed a new shielding device called STEP for infection control in upper gastrointestinal endoscopy. Patients and methods STEP consists of a mask worn by the patient and a drape that is connected to the mask and covers the endoscope. A suction tube attached to the mask prevents aerosols from spreading. The endoscopist operates the endoscope through the drape. Three endoscopists performed a total of 18 examinations using an upper endoscopy training model with and without STEP. Endoscopic images were evaluated by three other endoscopists, using a visual analog scale. We also simulated contact, droplet, and aerosol infection and evaluated the utility of STEP. Results All examinations were conducted without a problem. Mean procedure time was 126.3 ± 11.6 seconds with STEP and 122.3 ± 10.0 seconds without STEP. The mean visual analog score was 90.7 ± 10.1 with STEP and 90.4 ± 10.0 without STEP. In the contact model, adherence of simulated contaminants was 4.9 ± 1.4 % without STEP and 0 % with STEP. In the droplet model, the number of simulated contaminants attached to the paper was 338 273 ± 90 735 pixels without STEP and 0 with STEP. In the aerosol model, the total number of particles was 346 837 ± 9485 without STEP and was significantly reduced to 222 ± 174 with STEP. Conclusions No effect on examination time or endoscopic image quality was observed when using STEP in upper gastrointestinal endoscopy. Using STEP reduced the diffusion of simulated contaminants in all three infection models.