Optical Depth Retrievals Research Articles

Optimizing cirrus optical depth retrievals over the ocean from collocated CALIPSO and AMSR-E observations.

Retrievals of particulate optical depths and extinction coefficients from the cloud-aerosol lidar with orthogonal polarization (CALIOP) instrument deployed on the CALIPSO satellite mainly rely on a single global mean extinction-to-backscatter ratio, also known as the lidar ratio. However, the lidar ratio depends on the microphysical properties of particulates. An alternative approach is adopted to infer single-layer semi-transparent cirrus optical depths (CODs) over the open ocean that does not rely on an assumed lidar ratio. Instead, the COD is inferred directly from backscatter measurements obtained from the CALIOP lidar in conjunction with collocated sea surface wind speed data obtained from AMSR-E. This method is based on a Gram-Charlier ocean surface reflectance model relating wind-driven wave slope variances to sea surface wind speeds. To properly apply this method, the impact of multiple scattering between the sea surface and ice clouds should be taken into account. We take advantage of the 532nm cross-polarization feature of CALIOP and introduce an empirical method based on the depolarization change at the sea surface to correct for potential bias in sea surface backscatter caused by whitecaps, bubbles, foam, and multiple scattering. After the correction, the COD can be derived for individual CALIOP retrievals in a single cloud layer over the ocean with this method. The global mean COD was found to be roughly 14% higher than the current values determined by the Version 4 CALIOP extinction retrieval algorithm. This study is relevant to future improvements of CALIOP operational products and is expected to lead to more accurate COD retrievals.

Improving the image fusion procedure for high-spatiotemporal aerosol optical depth retrieval: a case study of urban area in Taiwan

Numerous studies employed remote sensing techniques on regional air quality in terms of aerosols, in particular, the observations from polar-orbiting satellites offer more detail on spatial distribution. Since the air pollutants/aerosols dramatically vary in location with time, diurnal observations on a timescale are restricted by the temporal resolution of the polar-orbiting satellite. To address this issue, this research proposes a spatially and temporally adaptive reflectance fusion model for measuring atmospheric properties to synthesize high-spatial–temporal resolution images from polar and geostationary satellite imagery for air quality monitoring. The reflectivity from short-wave infrared is employed to preserve the atmospheric effect within the fused image in the green band for further aerosol optical depth (AOD) retrieval. Taking the Landsat-8 Operational Land Imager as the reference, the spatial resolution of the Himawari-8 Advanced Himawari Imager (in kilometers) can thus be resampled into 30 m every 10 min during the daytime, by considering the surface bidirectional reflectivity from the variation of the solar zenith angle. The AOD retrieved with fused images containing atmospheric effect could have a better performance after comparison with in situ measurements, and therefore, be suggested for high-spatial–temporal aerosol monitoring.

Aerosol retrieval algorithm based on adaptive land–atmospheric decoupling for polarized remote sensing over land surfaces

Land–atmospheric decoupling is important for the retrieval accuracy in atmospheric remote sensing. In this study, we developed an algorithm that adaptively decouples the land surface and atmosphere reflected solar radiation and retrieves the aerosol properties (adaptive land–atmospheric decoupling algorithm, ALAD). The ALAD algorithm allows precise global aerosol optical depth (AOD) retrievals. The four sub-procedures in ALAD comprise estimating a first guess for surface properties, aerosol property retrieval, atmospheric correction, and assessing the results. The ALAD algorithm was applied to PARASOL (Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar) and the retrieved AODs were compared with the AOD products from the Moderate resolution Imaging Spectroradiometer (MODIS) and AErosol RObotic NETwork (AERONET). ALAD and MODIS retrieved AOD values were with good consistency in terms of their spatial distribution, regardless of the region or the global distribution. The correlation coefficient between the global ALAD and AERONET AOD was 0.875, and about 60.7% of the AODs retrieved by ALAD were within the expected error range of ±(15% AOD + 0.05). The results obtained using ALAD agreed well with the AERONET values. We also investigated the aerosol and land surface properties that might introduce errors into the AOD values retrieved by ALAD. It was found that the value of AOD had the greatest effect on the accuracy of the retrieval results. The errors introduced by the Angstrom exponent and Normalized Difference Vegetation Index varied slightly but they made no significant contributions.

Characterization of the Real Part of Dry Aerosol Refractive Index Over North America From the Surface to 12 km

AbstractThis study reports a characterization of the real part of dry particle refractive index (n) at 532 nm based on airborne measurements over the United States, Canada, the Pacific Ocean, and the Gulf of Mexico from the 2012 Deep Convective Clouds and Chemistry (DC3) and 2013 Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) campaigns. Effective n values are reported, with the limitations and uncertainties discussed. Eight air mass types were identified based on criteria related to gas‐phase tracer concentrations, location, and altitude. Average values of n for these air types ranged from 1.50 to 1.53. Values of n for the organic aerosol (OA) fraction (nOA) were calculated using a linear mixing rule for each air mass type, with 1.52 shown to be a good approximation for all OA. Case studies detailing vertical structure revealed that n and nOA increased with altitude, simultaneous with enhancements in the mass fraction of OA. Values of nOA were positively (negatively) correlated with the O:C (H:C) ratio in the absence of biomass burning influence; in contrast, the cumulative data set revealed a slight decrease in nOA as a function of the O:C ratio. The performance of parametric (multiple linear regression) and nonparametric (Gaussian process regression) methods in predicting n based on aerosol composition data is discussed. It is shown that even small perturbations in n values significantly impact aerosol optical depth retrievals, radiative forcing, and optical sizing instruments, emphasizing the importance of further improving the understanding of this important aerosol property.

Is the Awareness of the Aerosol State Useful in Predicting Enhanced Lightning for Lightning-Producing Storms over Northern Alabama?

AbstractThe Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals from theTerraandAquasatellites currently provide the largest satellite aerosol dataset for investigating relationships to meteorological phenomena, such as aerosol impact on electrification in deep convection. The usefulness of polar-orbiting satellite aerosol retrievals in lightning inference is examined by correlating MODIS AOD retrievals with lightning observations of the thunderstorms in the summers during 2002–14 over northern Alabama. Lightning flashes during the 1400–1700 local standard time peak period show weak but positive correlations with the MODIS AOD retrievals 2–4 h earlier. The correlation becomes stronger in particular meteorological conditions, including weak vertical wind shear and prevailing northerly winds over northern Alabama. Results show that the MODIS AOD retrievals are less useful in predicting enhanced lightning flash rate for lightning-producing storms than the forecasts of other meteorological variables that are more closely linked to the intensification of convective storms. However, when relatively weaker convective available potential energy (CAPE) is forecast, the probability of enhanced lightning flash rate increases in a more polluted environment, making the knowledge of aerosols more useful in lightning inference in such CAPE regimes. The aerosol enhancement of lightning, if present, may be associated with enhanced convergence in the boundary layer and secondary convection.

Parameterizing cloud top effective radii from satellite retrieved values, accounting for vertical photon transport: quantification and correction of the resulting bias in droplet concentration and liquid water path retrievals

Abstract. Droplet concentration (Nd) and liquid water path (LWP) retrievals from passive satellite retrievals of cloud optical depth (τ) and effective radius (re) usually assume the model of an idealized cloud in which the liquid water content (LWC) increases linearly between cloud base and cloud top (i.e. at a fixed fraction of the adiabatic LWC). Generally it is assumed that the retrieved re value is that at the top of the cloud. In reality, barring re retrieval biases due to cloud heterogeneity, the retrieved re is representative of smaller values that occur lower down in the cloud due to the vertical penetration of photons at the shortwave-infrared wavelengths used to retrieve re. This inconsistency will cause an overestimate of Nd and an underestimate of LWP (referred to here as the “penetration depth bias”), which this paper quantifies via a parameterization of the cloud top re as a function of the retrieved re and τ. Here we estimate the relative re underestimate for a range of idealized modelled adiabatic clouds using bispectral retrievals and plane-parallel radiative transfer. We find a tight relationship between gre=recloud top/reretrieved and τ and that a 1-D relationship approximates the modelled data well. Using this relationship we find that gre values and hence Nd and LWP biases are higher for the 2.1 µm channel re retrieval (re2.1) compared to the 3.7 µm one (re3.7). The theoretical bias in the retrieved Nd is very large for optically thin clouds, but rapidly reduces as cloud thickness increases. However, it remains above 20 % for τ<19.8 and τ<7.7 for re2.1 and re3.7, respectively. We also provide a parameterization of penetration depth in terms of the optical depth below cloud top (dτ) for which the retrieved re is likely to be representative. The magnitude of the Nd and LWP biases for climatological data sets is estimated globally using 1 year of daily MODIS (MODerate Imaging Spectroradiometer) data. Screening criteria are applied that are consistent with those required to help ensure accurate Nd and LWP retrievals. The results show that the SE Atlantic, SE Pacific and Californian stratocumulus regions produce fairly large overestimates due to the penetration depth bias with mean biases of 32–35 % for re2.1 and 15–17 % for re3.7. For the other stratocumulus regions examined the errors are smaller (24–28 % for re2.1 and 10–12 % for re3.7). Significant time variability in the percentage errors is also found with regional mean standard deviations of 19–37 % of the regional mean percentage error for re2.1 and 32–56 % for re3.7. This shows that it is important to apply a daily correction to Nd for the penetration depth error rather than a time–mean correction when examining daily data. We also examine the seasonal variation of the bias and find that the biases in the SE Atlantic, SE Pacific and Californian stratocumulus regions exhibit the most seasonality, with the largest errors occurring in the December, January and February (DJF) season. LWP biases are smaller in magnitude than those for Nd (−8 to −11 % for re2.1 and −3.6 to −6.1 % for re3.7). In reality, and especially for more heterogeneous clouds, the vertical penetration error will be combined with a number of other errors that affect both the re and τ, which are potentially larger and may compensate or enhance the bias due to vertical penetration depth. Therefore caution is required when applying the bias corrections; we suggest that they are only used for more homogeneous clouds.

Aerosol Optical Depth Retrieval From Landsat 8 OLI Images Over Urban Areas Supported by MODIS BRDF/Albedo Data

This letter presents a new algorithm that allows the retrieval of the aerosol optical depth (AOD) at a high (500 m) spatial resolution from Landsat 8 Operational Land Imager (OLI) data over urban areas. Because of the complex structure over urban surfaces, the bidirectional reflectance characteristic is obvious; however, most of the current aerosol retrieval algorithms over land do not account for the anisotropic effect of the surface. This letter improves the quality of AOD retrieval by providing the surface reflectance based on the multiyear MODIS bidirectional reflectance distribution function (BRDF)/Albedo model parameters product (MCD43A1) and the RossThick-LiSparse reciprocal kernel-driven BRDF model. The ground-based Aerosol Robotic Network (AERONET) AOD measurements from five sites located in urban and suburban areas are used to validate the AOD retrievals, and the MODIS Terra Collection 6 (C6) dark target/deep blue AOD products (MOD04) at 10-km spatial resolution are obtained for comparison. The validation results show that the AOD retrievals from the OLI images are well correlated with the AERONET AOD measurements ( $R = 0.987$ ), with a low root-mean-square error of 0.07, a mean absolute error of 0.036, and a relative mean bias of 1.029; approximately 95.3% of the collocations fall within the expected error. The analysis indicates that the BRDF is essential in ensuring the accuracy of AOD retrieval. Compared with the MOD04 AOD retrievals, the OLI AOD retrievals have better spatial continuity and higher accuracy. The new algorithm can provide continuous and detailed spatial distributions of the AOD over complex urban surfaces.

Remote Sensing of Droplet Number Concentration in Warm Clouds: A Review of the Current State of Knowledge and Perspectives.

The cloud droplet number concentration (N d) is of central interest to improve the understanding of cloud physics and for quantifying the effective radiative forcing by aerosol‐cloud interactions. Current standard satellite retrievals do not operationally provide N d, but it can be inferred from retrievals of cloud optical depth (τ c) cloud droplet effective radius (r e) and cloud top temperature. This review summarizes issues with this approach and quantifies uncertainties. A total relative uncertainty of 78% is inferred for pixel‐level retrievals for relatively homogeneous, optically thick and unobscured stratiform clouds with favorable viewing geometry. The uncertainty is even greater if these conditions are not met. For averages over 1° ×1° regions the uncertainty is reduced to 54% assuming random errors for instrument uncertainties. In contrast, the few evaluation studies against reference in situ observations suggest much better accuracy with little variability in the bias. More such studies are required for a better error characterization. N d uncertainty is dominated by errors in r e, and therefore, improvements in r e retrievals would greatly improve the quality of the N d retrievals. Recommendations are made for how this might be achieved. Some existing N d data sets are compared and discussed, and best practices for the use of N d data from current passive instruments (e.g., filtering criteria) are recommended. Emerging alternative N d estimates are also considered. First, new ideas to use additional information from existing and upcoming spaceborne instruments are discussed, and second, approaches using high‐quality ground‐based observations are examined.

Improved retrieval of aerosol optical depth from POLDER/PARASOL polarization data based on a self-defined aerosol model

The characterization of surface reflection and the selection of an appropriate aerosol model are key problems in aerosol retrieval over land. Due to the amount of polarized light reflected by ground targets is small, the selection of an aerosol model is the main problem in aerosol retrieval based on polarization data. In this study, a self-defined aerosol model is proposed to retrieve aerosol optical depth (AOD) from 2008 to 2009 in the Yangtze River Delta from Polarization and Directionality of the Earth’s Reflectance (POLDER) and Polarization and Anisotropy of Reflectance for Atmospheric Sciences coupled with Observation from a Lidar (PARASOL) data. The model was established using the volume percentages of aerosol components retrieved by Aerosol Robotic Network (AERONET) ground-based measurements. The results show that the volume percentages of aerosol components in the study area are 0.31, 0.55, 0.01 and 0.13 for dust, water-soluble, oceanic and soot particles, respectively, which are quite different from those of the continental model defined in the standard radiation atmosphere (SRA) aerosol models. The accuracy of AOD retrieved by the self-defined aerosol model (r = 0.75, RMSE = 0.20) is higher than that by the continental model (r = 0.41, RMSE = 0.39). Therefore, selecting a more suitable aerosol model can significantly improve the retrieval precision of AOD.

Retrieval of Aerosol Optical Depth From INSAT‐3D Imager Over Asian Landmass and Adjoining Ocean: Retrieval Uncertainty and Validation

AbstractAn algorithm for aerosol optical depth (τ) retrieval from imager data onboard geostationary Indian National Satellite (INSAT‐3D) is described. The basic principles of the algorithm are adopted from Geostationary Observational Environmental Satellite Aerosol and Smoke Algorithm, where darkest observations over a time period and constant background aerosol optical depth (τb) value are used to derive surface reflectance. However, in INSAT‐3D algorithm spatially and temporally dynamic τb derived from Moderate Resolution Imaging Spectroradiometer (MODIS)‐aqua is used. The theoretical simulations suggest retrieval uncertainty of around ±30–45% depending on the certainty of aerosol optical properties and other parameters used in radiative transfer calculations. Retrieval uncertainty is less over dark regions and lower scattering angles. INSAT‐3D τ at 0.55 μm is derived operationally for the first time over Asia and adjoining ocean at 30‐min temporal resolution. The retrieval algorithm is validated against in situ Sun‐sky radiometer measurements at 26 Aerosol Robotic Network sites and Sun‐photometer measurements during Sagar‐Sampada cruise in the northern Arabian Sea. The validation study encompassing 3,803 and 844 data points over land and ocean stations show correlations ranging from 0.70 to 0.91 and 0.79 to 0.91, respectively. The uncertainty of retrievals is within 45% over land and within 30% over ocean. The diurnal variability of INSAT‐3D and Aerosol Robotic Network τ in India and southeast Asia is also presented. The comparison of INSAT‐3D and MODIS‐aqua monthly average τ shows correlation ranging from 0.74 to 0.89 and 0.65 to 0.97 over Indian landmass and adjoining ocean, respectively. The validation suggests that INSAT‐3D can be used for aerosol monitoring and can be merged with MODIS‐aqua/terra aerosol product to generate combined aerosol product at high temporal resolution.

Photopolarimetric Sensitivity to Black Carbon Content of Wildfire Smoke: Results From the 2016 ImPACT‐PM Field Campaign

AbstractDetailed characterization of the aerosol content of wildfire smoke plumes is typically performed through in situ aircraft observations, which have limited temporal and spatial coverage. Extending such observations to regional or global scales requires new remote sensing approaches, such as retrievals that make use of spectropolarimetric, multiangle imaging. In this work measurements made during the Imaging Polarimetric Assessment and Characterization of Tropospheric Particulate Matter (ImPACT‐PM) field campaign in a smoke plume near the town of Lebec in Southern California by the Navy Center for Interdisciplinary Remotely Piloted Aircraft Studies Twin Otter aircraft on 8 July 2016 are used in conjunction with near‐coincident measurements from the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI) on the National Aeronautics and Space Administration ER‐2 high‐altitude research aircraft to assess the sensitivity of spectropolarimetric measurements to the black carbon content of the plume. Tracking visible features in the smoke through the sequence of AirMSPI observations allowed the height of the plume to be estimated through geometric techniques. Then, by constraining the fractional amounts of the aerosol constituents with the in situ data, radiative closure was obtained through simulations performed with a polarimetric radiative transfer code, demonstrating the ability to constrain the black carbon mass fraction to approximately 5%, given the uncertainties in the AirMSPI measurements and the assumption of external mixing of aerosol components. The AirMSPI retrieval, made using a limited set of observations from the 470 nm polarimetric spectral band alone, was also generally consistent with operational retrievals of aerosol optical depth and surface reflectance made by the Multi‐Angle Implementation of Atmospheric Correction algorithm at 1 km resolution.

Preliminary Investigation of a New AHI Aerosol Optical Depth (AOD) Retrieval Algorithm and Evaluation with Multiple Source AOD Measurements in China

The Himawari-8 geostationary weather satellite, which is an Earth observing satellite launched in October 2014, has been applied in climate, environment, and air quality studies. Using hourly observation data from the Advanced Himawari Imager (AHI) on board Himawari-8, a new dark target algorithm was proposed to retrieve the aerosol optical depth (AOD) at 1 km and 5 km resolutions over mainland China. Because of the short satellite operation time and lack of AErosol RObotic NETwork (AERONET) sites across China, we cannot derive robust and representative surface reflectance relationships for the visible to near-infrared channels by atmospheric correction. Therefore, we inherited the empirical reflectance relationship from the Moderate Resolution Imaging Spectroradiometer (MODIS) and we used the AHI and MODIS spectral response functions to make the relationship more suitable for AHI. Ultimately, our AOD products can better reflect the regional characteristics with the AHI sensor. Seasonal averages showed that our product is more similar to MODIS Collection 6 (C6) Dark Target (DT) AOD than the Japan Aerospace Exploration Agency (JAXA) AHI AOD, but the difference is largest in winter. In addition, we evaluated several satellite retrieval products (our AHI AOD, JAXA AHI AOD, the National Oceanic and Atmospheric Administration (NOAA) VIIRS AOD, MODIS DT AOD, and MODIS DB AOD) against AERONET AOD from July 2016 to June 2017. The results showed that our AHI measurements demonstrate good agreement with, but exhibit a little overestimation, as compared to ground-based AERONET measurements with a correlation coefficient of 0.83 and an root-mean-square error (RMSE) of 0.112. The hourly validation also showed stable statistical results. A time series comparison with ground-based observations from two AERONET sites (Beijing-CAMS and XiangHe) showed that our AHI AOD products have trends as those in MODIS DB AOD, but that the bias in Beijing-CAMS is positive and higher than that in XiangHe. This error and the slight overestimation may be caused by the single continental aerosol model assumption and not considering the Normalized Difference Vegetation Index (NDVI).

Development of non-linear models predicting daily fine particle concentrations using aerosol optical depth retrievals and ground-based measurements at a municipality in the Brazilian Amazon region

Epidemiological studies generally use particulate matter measurements with diameter less 2.5 μm (PM2.5) from monitoring networks. Satellite aerosol optical depth (AOD) data has considerable potential in predicting PM2.5 concentrations, and thus provides an alternative method for producing knowledge regarding the level of pollution and its health impact in areas where no ground PM2.5 measurements are available. This is the case in the Brazilian Amazon rainforest region where forest fires are frequent sources of high pollution. In this study, we applied a non-linear model for predicting PM2.5 concentration from AOD retrievals using interaction terms between average temperature, relative humidity, sine, cosine of date in a period of 365,25 days and the square of the lagged relative residual. Regression performance statistics were tested comparing the goodness of fit and R2 based on results from linear regression and non-linear regression for six different models. The regression results for non-linear prediction showed the best performance, explaining on average 82% of the daily PM2.5 concentrations when considering the whole period studied. In the context of Amazonia, it was the first study predicting PM2.5 concentrations using the latest high-resolution AOD products also in combination with the testing of a non-linear model performance. Our results permitted a reliable prediction considering the AOD-PM2.5 relationship and set the basis for further investigations on air pollution impacts in the complex context of Brazilian Amazon Region.

Improving MISR AOD Retrievals With Low-Light-Level Corrections for Veiling Light

Operational retrievals of aerosol optical depth (AOD) from Multi-angle Imaging SpectroRadiometer (MISR) data have been shown to have a high bias in pristine oceanic areas. One line of evidence involves comparison with Maritime Aerosol Network (MAN) observations, including the areas of low aerosol loading close to Antarctica. In this paper, a principal reason for the AOD overestimation is identified, which is stray light measured by the MISR cameras in dark regions of high-contrast scenes. A small fraction of the light from surrounding bright areas, such as clouds or sea ice, is redistributed to dark areas, artificially increasing their brightness. Internal reflections and light scattering from optical elements in MISR’s pushbroom cameras contribute to this veiling light effect. A simple correction model is developed that relies on the average scene brightness and an empirically determined set of veiling light coefficients for each MISR camera and wavelength. Several independent methods are employed to determine these coefficients. Three sets of coefficients are further implemented and tested in prototype MISR 4.4-km AOD retrievals. The results show dramatic improvements in retrieved AODs compared against MAN observations and the currently operational V22 MISR retrievals. For the best performing set of coefficients, the bias is reduced by 51%, from 0.039 to 0.019, the RMSE is lowered by 19%, from 0.062 to 0.050, and 84% of retrievals fall within the uncertainty envelope compared with 66% of retrievals in V22. The best performing set will be implemented operationally in the next V23 MISR AOD product release.

Satellite Ocean Aerosol Retrieval (SOAR) algorithm extension to S-NPP VIIRS as part of the 'Deep Blue' aerosol project.

The Suomi National Polar-Orbiting Partnership (S-NPP) satellite, launched in late 2011, carries the Visible Infrared Imaging Radiometer Suite (VIIRS) and several other instruments. VIIRS has similar characteristics to prior satellite sensors used for aerosol optical depth (AOD) retrieval, allowing the continuation of space-based aerosol data records. The Deep Blue algorithm has previously been applied to retrieve AOD from Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectro-radiometer (MODIS) measurements over land. The SeaWiFS Deep Blue data set also included a SeaWiFS Ocean Aerosol Retrieval (SOAR) algorithm to cover water surfaces. As part of NASA's VIIRS data processing, Deep Blue is being applied to VIIRS data over land, and SOAR has been adapted from SeaWiFS to VIIRS for use over water surfaces. This study describes SOAR as applied in version 1 of NASA's S-NPP VIIRS Deep Blue data product suite. Several advances have been made since the SeaWiFS application, as well as changes to make use of the broader spectral range of VIIRS. A preliminary validation against Maritime Aerosol Network (MAN) measurements suggests a typical uncertainty on retrieved 550nm AOD of order ±(0.03+10%), comparable to existing SeaWiFS/MODIS aerosol data products. Retrieved Ångström exponent and fine mode AOD fraction are also well-correlated with MAN data, with small biases and uncertainty similar to or better than SeaWiFS/MODIS products.

Validation of new satellite aerosol optical depth retrieval algorithm using Raman lidar observations at radiative transfer laboratory in Warsaw

During an exceptionally warm September of 2016, the unique, stable weather conditions over Poland allowed for an extensive testing of the new algorithm developed to improve the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) aerosol optical depth (AOD) retrieval. The development was conducted in the frame of the ESA-ESRIN SAMIRA project. The new AOD algorithm aims at providing the aerosol optical depth maps over the territory of Poland with a high temporal resolution of 15 minutes. It was tested on the data set obtained between 11-16 September 2016, during which a day of relatively clean atmospheric background related to an Arctic airmass inflow was surrounded by a few days with well increased aerosol load of different origin. On the clean reference day, for estimating surface reflectance the AOD forecast available on-line via the Copernicus Atmosphere Monitoring Service (CAMS) was used. The obtained AOD maps were validated against AODs available within the Poland-AOD and AERONET networks, and with AOD values obtained from the PollyXT-UW lidar. of the University of Warsaw (UW).

Impact of Ice Cloud Microphysics on Satellite Cloud Retrievals and Broadband Flux Radiative Transfer Model Calculations

Ice cloud particles exhibit a range of shapes and sizes affecting a cloud’s single-scattering properties. Because they cannot be inferred from passive visible/infrared imager measurements, assumptions about the bulk single-scattering properties of ice clouds are fundamental to satellite cloud retrievals and broadband radiative flux calculations. To examine the sensitivity to ice particle model assumptions, three sets of models are used in satellite imager retrievals of ice cloud fraction, thermodynamic phase, optical depth, effective height, and particle size, and in top-of-atmosphere (TOA) and surface broadband radiative flux calculations. The three ice particle models include smooth hexagonal ice columns (SMOOTH), roughened hexagonal ice columns, and a two-habit model (THM) comprising an ensemble of hexagonal columns and 20-element aggregates. While the choice of ice particle model has a negligible impact on daytime cloud fraction and thermodynamic phase, the global mean ice cloud optical depth retrieved from THM is smaller than from SMOOTH by 2.3 (28%), and the regional root-mean-square difference (RMSD) is 2.8 (32%). Effective radii derived from THM are 3.9 μm (16%) smaller than SMOOTH values and the RMSD is 5.2 μm (21%). In contrast, the regional RMSD in TOA and surface flux between THM and SMOOTH is only 1% in the shortwave and 0.3% in the longwave when a consistent ice particle model is assumed in the cloud property retrievals and forward radiative transfer model calculations. Consequently, radiative fluxes derived using a consistent ice particle model assumption throughout provide a more robust reference for climate model evaluation compared to ice cloud property retrievals.

The effects of different footprint sizes and cloud algorithms on the top-of-atmosphere radiative flux calculation from the Clouds and Earth's Radiant Energy System (CERES) instrument on Suomi National Polar-orbiting Partnership (NPP)

Abstract. Only one Clouds and Earth's Radiant Energy System (CERES) instrument is onboard the Suomi National Polar-orbiting Partnership (NPP) and it has been placed in cross-track mode since launch; it is thus not possible to construct a set of angular distribution models (ADMs) specific for CERES on NPP. Edition 4 Aqua ADMs are used for flux inversions for NPP CERES measurements. However, the footprint size of NPP CERES is greater than that of Aqua CERES, as the altitude of the NPP orbit is higher than that of the Aqua orbit. Furthermore, cloud retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS), which are the imagers sharing the spacecraft with NPP CERES and Aqua CERES, are also different. To quantify the flux uncertainties due to the footprint size difference between Aqua CERES and NPP CERES, and due to both the footprint size difference and cloud property difference, a simulation is designed using the MODIS pixel-level data, which are convolved with the Aqua CERES and NPP CERES point spread functions (PSFs) into their respective footprints. The simulation is designed to isolate the effects of footprint size and cloud property differences on flux uncertainty from calibration and orbital differences between NPP CERES and Aqua CERES. The footprint size difference between Aqua CERES and NPP CERES introduces instantaneous flux uncertainties in monthly gridded NPP CERES measurements of less than 4.0 W m−2 for SW (shortwave) and less than 1.0 W m−2 for both daytime and nighttime LW (longwave). The global monthly mean instantaneous SW flux from simulated NPP CERES has a low bias of 0.4 W m−2 when compared to simulated Aqua CERES, and the root-mean-square (RMS) error is 2.2 W m−2 between them; the biases of daytime and nighttime LW flux are close to zero with RMS errors of 0.8 and 0.2 W m−2. These uncertainties are within the uncertainties of CERES ADMs. When both footprint size and cloud property (cloud fraction and optical depth) differences are considered, the uncertainties of monthly gridded NPP CERES SW flux can be up to 20 W m−2 in the Arctic regions where cloud optical depth retrievals from VIIRS differ significantly from MODIS. The global monthly mean instantaneous SW flux from simulated NPP CERES has a high bias of 1.1 W m−2 and the RMS error increases to 5.2 W m−2. LW flux shows less sensitivity to cloud property differences than SW flux, with uncertainties of about 2 W m−2 in the monthly gridded LW flux, and the RMS errors of global monthly mean daytime and nighttime fluxes increase only slightly. These results highlight the importance of consistent cloud retrieval algorithms to maintain the accuracy and stability of the CERES climate data record.

AERONET-based nonspherical dust optical models and effects on the VIIRS Deep Blue/SOAR over-water aerosol product.

Aerosol Robotic Network (AERONET)-based nonspherical dust optical models are developed and applied to the Satellite Ocean Aerosol Retrieval (SOAR) algorithm as part of the Version 1 Visible Infrared Imaging Radiometer Suite (VIIRS) NASA 'Deep Blue' aerosol data product suite. The optical models are created using Version 2 AERONET inversion data at six distinct sites influenced frequently by dust aerosols from different source regions. The same spheroid shape distribution as used in the AERONET inversion algorithm is assumed to account for the nonspherical characteristics of mineral dust, which ensures the consistency between the bulk scattering properties of the developed optical models with the AERONET-retrieved microphysical and optical properties. For the Version 1 SOAR aerosol product, the dust optical models representative for Capo Verde site are used, considering the strong influence of Saharan dust over the global ocean in terms of amount and spatial coverage. Comparisons of the VIIRS-retrieved aerosol optical properties against AERONET direct-Sun observations at three island/coastal sites suggest that the use of nonspherical dust optical models significantly improves the retrievals of aerosol optical depth (AOD) and Ångström exponent by mitigating the well-known artifact of scattering angle dependence of the variables observed when incorrectly assuming spherical dust. The resulting removal of these artifacts results in a more natural spatial pattern of AOD along the transport path of Saharan dust to the Atlantic Ocean; i.e., AOD decreases with increasing distance transported, whereas the spherical assumption leads to a strong wave pattern due to the spurious scattering angle dependence of AOD.

Evaluation of MODIS Deep Blue Aerosol Algorithm in Desert Region of East Asia: Ground Validation and Intercomparison

AbstractThe abundant dust particles from widespread deserts in East Asia play a significant role in regional climate and air quality. In this study, we provide a comprehensive evaluation of the widely used Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) aerosol retrievals in desert regions of East Asia using ground‐based observations over eight sites of the China Aerosol Remote Sensing Network (CARSNET). Different from their well‐characterized performance in urban and cropland areas around the globe, DB aerosol optical depth (AOD) retrievals exhibit underestimation across the deserts in East Asia. We found that 38%–96% of satellite values fall out of an expected‐error envelope of ±(0.05 + 20%AODCARSNET), with the worst performance in Taklimakan Desert. In particular, DB retrievals erroneously give a nearly constant low values of 0.05 in Taklimakan Desert when AOD is below 0.5, which does not match with variation of moderate dust plumes. Comparison with Multi‐angle Imaging SpectroRadiometer AOD shows that a similar underestimation is prevalent over the extensive deserts. Inversion of sky light measurements show that single scattering albedos of the yellow dust in East Asia are mostly below 0.9 at 440 nm, much lower than the “whiter” and “redder” dust models applied in the DB algorithm. On the other hand, overestimation of surface reflectance dominantly contributes to the significant low constant AOD values in MODIS DB retrievals in Taklimakan Desert. These large biases, however, can be substantially reduced by considering unique characteristics of aerosols and surface over the arid regions in East Asia.