The new‐generation polar‐orbiting operational environmental sensor, the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar‐orbiting Partnership (S‐NPP) satellite, provides critical daily global aerosol observations. As older satellite sensors age out, the VIIRS aerosol product will become the primary observational source for global assessments of aerosol emission and transport, aerosol meteorological and climatic effects, air quality monitoring, and public health. To prove their validity and to assess their maturity level, the VIIRS aerosol products were compared to the spatiotemporally matched Aerosol Robotic Network (AERONET) measurements. Over land, the VIIRS aerosol optical thickness (AOT) environmental data record (EDR) exhibits an overall global bias against AERONET of −0.0008 with root‐mean‐square error (RMSE) of the biases as 0.12. Over ocean, the mean bias of VIIRS AOT EDR is 0.02 with RMSE of the biases as 0.06. The mean bias of VIIRS Ocean Ångström Exponent (AE) EDR is 0.12 with RMSE of the biases as 0.57. The matchups between each product and its AERONET counterpart allow estimates of expected error in each case. Increased uncertainty in the VIIRS AOT and AE products is linked to specific regions, seasons, surface characteristics, and aerosol types, suggesting opportunity for future modifications as understanding of algorithm assumptions improves. Based on the assessment, the VIIRS AOT EDR over land reached Validated maturity beginning 23 January 2013; the AOT EDR and AE EDR over ocean reached Validated maturity beginning 2 May 2012, excluding the processing error period 15 October to 27 November 2012. These findings demonstrate the integrity and usefulness of the VIIRS aerosol products that will transition from S‐NPP to future polar‐orbiting environmental satellites in the decades to come and become the standard global aerosol data set as the previous generations' missions come to an end.

[1] The GOES Aerosol/Smoke Product (GASP) is a retrieval of the aerosol optical depth (AOD) using visible imagery. The product currently runs operationally at NOAA/NESDIS in near-real time at 30 min intervals. This high temporal resolution is not possible with polar orbiting instruments which produce one daily image. This work evaluates the GASP AOD from the GOES-12 Imager over North America at various temporal and spatial scales based on comparisons with AOD from the Aerosol Robotic Network (AERONET) and the Moderate Resolution Imaging Spectroradiometer (MODIS). We find a mean GASP/AERONET correlation of 0.79, rms difference of 0.13 and slope of 0.8, based on a statistical analysis at 10 northeastern U.S. and Canadian sites. The GASP AOD has a slight negative bias when the AOD is greater than 0.35 and a positive bias elsewhere. The absolute bias is less than 30% when the AOD is greater than 0.1. When the AOD is less than 0.15 we find poor correlation and biases greater than 30%. The GASP/ AERONET statistics also indicate that GASP can be used to examine the seasonal and diurnal variability in the AOD over the eastern United States between 1215 and 2115 UTC. GASP/AERONET AOD correlations were generally less than 0.5 elsewhere in the continental United States. Comparisons between the MODIS and GASP AOD over the eastern United States in the summer of 2004 showed agreement within 20% and correlations greater than 0.7 under elevated AOD conditions. Simultaneous comparisons between GASP, MODIS, and AERONET AODs showed good agreement over the northeastern United States and Canada, with higher correlation and lower rms differences in the MODIS/AERONET comparisons than in the GASP/AERONET comparisons.

A dust aerosol index (DAI) algorithm based on measurements in deep blue (412 nm), blue (440 nm), and shortwave IR (2130 nm) wavelengths using Moderate Resolution Imaging Spectroradiometer (MODIS) observations has been developed. Contrary to some dust detection algorithms that use measurements at thermal IR bands, this algorithm takes advantage of the spectral dependence of Rayleigh scattering, surface reflectance, and dust absorption to detect airborne dust. The DAI images generated by this algorithm agree qualitatively with the location and extent of dust observed in MODIS true color images. Quantitatively, the dust index generated for hundreds of dust outbreaks observed between 2006 and 2013 were compared to Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) Vertical Feature Mask (VFM) product and the detections are found to be accurate at 70% over land and 82% over ocean. The Probability of Correct Detection (POCD) is 80% over land and 76% over ocean. The dust detections with DAI‐based dust identification algorithm were also compared to 5 years of Aerosol Robotic Network (AERONET) observations for 13 stations with a wide range of geographical coverage. The average detection accuracy is ~70%, whereas the POCD is ~67%. The performance of DAI‐based dust detection against AERONET is slightly weaker than that against CALIOP VFM because of the limited number of matchups for some stations. For stations close to source region or coastal and island stations, the accuracy and POCD can be as high as ~85% and ~89%, respectively.

Intersatellite radiance comparisons for the 19 infrared channels of the High-Resolution Infrared Radiation Sounders (HIRS) on board NOAA-15, -16, and -17 are performed with simultaneous nadir observations at the orbital intersections of the satellites in the polar regions, where each pair of the HIRS views the same earth target within a few seconds. Analysis of such datasets from 2000 to 2003 reveals unambiguous intersatellite radiance differences as well as calibration anomalies. The results show that in general, the intersatellite relative biases are less than 0.5 K for most HIRS channels. The large biases in different channels differ in both magnitude and sign, and are likely to be caused by the differences and measurement uncertainties in the HIRS spectral response functions. The seasonal bias variation in the stratosphere channels is found to be highly correlated with the lapse rate factor approximated by the channel radiance differences. The method presented in this study works particularly well for channels sensing the stratosphere because of the relative spatial uniformity and stability of the stratosphere, for which the intercalibration accuracy and precision are mostly limited by the instrument noise. This method is simple and robust, and the results are highly repeatable and unambiguous. Intersatellite radiance calibration with this method is very useful for the on-orbit verification and monitoring of instrument performance, and is potentially useful for constructing long-term time series for climate studies.

The Visible/Infrared Imager Radiometer Suite (VIIRS) on board the Suomi National Polar‐orbiting Partnership (S‐NPP) satellite has been retrieving aerosol optical thickness (AOT), operationally and globally, over ocean and land since shortly after S‐NPP launch in 2011. However, the current operational VIIRS AOT retrieval algorithm over land has two limitations in its assumptions for land surfaces: (1) it only retrieves AOT over the dark surfaces and (2) it assumes that the global surface reflectance ratios between VIIRS bands are constants. In this work, we develop a surface reflectance ratio database over land with a spatial resolution 0.1° × 0.1° using 2 years of VIIRS top of atmosphere reflectances. We enhance the current operational VIIRS AOT retrieval algorithm by applying the surface reflectance ratio database in the algorithm. The enhanced algorithm is able to retrieve AOT over both dark and bright surfaces. Over bright surfaces, the VIIRS AOT retrievals from the enhanced algorithm have a correlation of 0.79, mean bias of −0.008, and standard deviation (STD) of error of 0.139 when compared against the ground‐based observations at the global AERONET (Aerosol Robotic Network) sites. Over dark surfaces, the VIIRS AOT retrievals using the surface reflectance ratio database improve the root‐mean‐square error from 0.150 to 0.123. The use of the surface reflectance ratio database also increases the data coverage of more than 20% over dark surfaces. The AOT retrievals over bright surfaces are comparable to MODIS Deep Blue AOT retrievals.

Collocated Interagency Monitoring of Protected VisualEnvironments (IMPROVE) particulate matter (PM) less than 2.5 m in aerodynamic diameter (PM 2.5 ) chemically speciated data, mass of PM less than 10 m in aerodynamic diameter (PM 10 ), and Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) and size distribution at Bondville, IL, were compared with satellitederived AOD. This was done to evaluate the quality of the Geostationary Operational Environmental Satellite (GOES) and Moderate Resolution Imaging Spectroradiometer (MODIS) AOD data and their potential to predict surface PM 2.5 concentrations. MODIS AOD correlated better to AERONET AOD (r ϭ 0.835) than did GOES AOD (r ϭ 0.523). MODIS and GOES AOD compared better to AERONET AOD when the particle size distribution was dominated by fine mode. For all three AOD methods, correlation between AOD and PM 2.5 concentration was highest in autumn and lowest in winter. The AERONET AOD-PM 2.5 relationship was strongest with moderate relative humidity (RH). At low RH, AOD attributable to coarse mass degrades the relationship; at high RH, added AOD from water growth appears to mask the relationship. For locations such as many in the central and western United States with substantial coarse mass, coarse mass contributions to AOD may make predictions of PM 2.5 from AOD data problematic. Seasonal and diurnal variations in particle size distributions, RH, and seasonal changes in boundary layer height need to be accounted for to use satellite AOD to predict surface PM 2.5 .

The High-Resolution Infrared Radiation Sounder (HIRS) has been carried on NOAA satellites for more than two decades, and the HIRS data have been widely used for geophysical retrievals, climate studies, and radiance assimilation for numerical weather prediction models. However, given the legacy of the filterwheel radiometer originally designed in the 1970s, the HIRS measurement accuracy is neither well documented nor well understood, despite the importance of this information for data users, instrument manufacturers, and calibration scientists. The advent of hyperspectral sounders, such as the Atmospheric Infrared Sounder (AIRS), and intersatellite calibration techniques makes it possible to independently assess the accuracy of the HIRS radiances. This study independently assesses the data quality and calibration accuracy of HIRS by comparing the radiances between HIRS on NOAA-16 and AIRS on Aqua with simultaneous nadir overpass (SNO) observations for the year 2004. The results suggest that the HIRS radiometric bias relative to the AIRS-convolved HIRS radiance is on the order of ϳ0.5 K, except channel 16, which has a bias of 0.8 K. For all eight spectrally overlapped channels, the observations by HIRS are warmer than the corresponding AIRS-convolved HIRS channel. Other than channel 16, the biases are temperature dependent. The root causes of the bias can be traced to a combination of the HIRS blackbody emissivity, nonlinearity, and spectral uncertainties. This study further demonstrates the utility of high-spectralresolution radiance measurements for high-accuracy assessments of broadband radiometer calibration with the SNO observations.

Abstract. Aerosol optical depth (AOD) retrievals from geostationary satellites have high temporal resolution compared to the polar orbiting satellites and thus enable us to monitor aerosol motion. However, current Geostationary Operational Environmental Satellites (GOES) have only one visible channel for retrieving aerosols and hence the retrieval accuracy is lower than those from the multichannel polar-orbiting satellite instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS). The operational GOES AOD retrieval algorithm (GOES Aerosol/Smoke Product, GASP) uses 28-day composite images from the visible channel to derive surface reflectance, which can produce large uncertainties. In this work, we develop a new AOD retrieval algorithm for the GOES imager by applying a modified MultiAngle Implementation of Atmospheric Correction (MAIAC) algorithm. The algorithm assumes the surface Bidirectional Reflectance Distribution Function (BRDF) in the channel 1 of GOES is proportional to seasonal average MODIS BRDF in the 2.1 µm channel. The ratios between them are derived through time series analysis of the GOES visible channel images. The results of AOD and surface reflectance retrievals are evaluated through comparisons against those from Aerosol Robotic Network (AERONET), GASP, and MODIS. The AOD retrievals from the new algorithm demonstrate good agreement with AERONET retrievals at several sites across the US with correlation coefficients ranges from 0.71 to 0.85 at five out of six sites. At the two western sites Railroad Valley and UCSB, the MAIAC AOD retrievals have Correspondence to: H. Zhang (hazhang@umbc.edu) correlations of 0.8 and 0.85 with AERONET AOD, and are more accurate than GASP retrievals, which have correlations of 0.7 and 0.74 with AERONET AOD. At the three eastern sites, the correlations with AERONET AOD are from 0.71 to 0.81, comparable to the GASP retrievals. In the western US where surface reflectance is higher than 0.15, the new algorithm also produces larger AOD retrieval coverage than both GASP and MODIS.