Aerosol optical depth (AOD) is a crucial data record to understand aerosols and their direct and indirect effects on air quality and climate forcing. The Dark Target aerosol retrieval product includes AOD and other properties derived from multispectral satellite imagers, available for MODIS on Terra (from 2000), MODIS on Aqua (from 2002), and VIIRS on Suomi-NPP (from 2012). Although Terra now has over 25 years of observations, the record must continue onto VIIRS beyond the end of the MODIS mission to meet requirements as a Global Climate Observing System (GCOS) climate data record. We present the recent update to version 2.0 of the VIIRS product, which now includes NOAA-20 VIIRS (from 2017) and algorithm improvements. The combined MODIS-VIIRS dataset is examined for consistency and to ascertain aerosol trends. Overall, the VIIRS products show consistency with the MODIS products. To assess regional trends, two time intervals are studied: a 22-year record that compares Terra and Aqua, and a more recent 12-year record (the VIIRS era) that compares three sensors. According to linear regressions of monthly average AOD for each global 1°×1° grid cell, AOD has decreased by between 0.003 and 0.01 per year over parts of China, the United States, Brazil, and much of Europe, while increasing on the same scale over India and parts of Canada, while more modestly but significantly increasing over the southern oceans. For seven regions with significant AOD trends, this study examines the seasonal dependence, relationship to aerosol size parameters, and whether the sign or magnitude of these trends have changed. With high agreement among sensors, we are confident that the Dark Target AOD record can extend into the 2030s and beyond.

For reflected sunlight observed from space at visible and near-infrared wavelengths, particles suspended in Earth's atmosphere provide contrast with vegetation or dark water at the surface. This is the physical motivation for the Dark Target (DT) aerosol retrieval algorithm developed for the Moderate Resolution Imaging Spectrometer (MODIS). To extend the data record of aerosol optical depth (AOD) beyond the expected 20-year lifespan of the MODIS sensors, DT must be adapted for other sensors. A version of the DT AOD retrieval for the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi-National Polar-Orbiting Partnership (SNPP) is now mature enough to be released as a standard data product, and includes some upgraded features from the MODIS version. Differences between MODIS Aqua and VIIRS SNPP lead to some inevitable disagreement between their respective AOD measurements, but the offset between the VIIRS SNPP and MODIS Aqua records is smaller than the offset between those of MODIS Aqua and MODIS Terra. The VIIRS SNPP retrieval shows good agreement with ground-based measurements. For most purposes, DT for VIIRS SNPP is consistent enough and in close enough agreement with MODIS to continue the record of satellite AOD. The reasons for the offset from MODIS Aqua, and its spatial and temporal variability, are investigated in this study.

Space Science and Engineering Center (SSEC) and its Cooperative Institute for Meteorological Satellite Studies (CIMSS) have supported the international Direct Broadcast/Readout (DB/DR) user community since 1985 through the distribution of the International TOVS and ATOVS Processing Packages (ITPP, IAPP) for NOAA Polar Orbiting Environmental Satellite (POES), and since 2000 via the International MODIS/AIRS Processing Package (IMAPP) for NASA Terra and Aqua. Since 2007, SSEC/CIMSS has also participated in the development of regional versions of software for generating Cross-Track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) Sensor Data Records (SDRs), and for Visible Infrared Imaging Radiometer Suite (VIIRS) atmosphere and cloud Environmental Data Records (EDRs). Currently SSEC/CIMSS is supported by the NOAA JPSS program scientist and NASA to continue facilitating the use of polar orbiter satellite data through the initial development of a newly conceived Community Satellite Processing Package (CSPP) that will support the Suomi-NPP/JPSS and, subsequently, build up over time to support GOES-R with CSPP Geosynchronous Earth Orbit (GEO) component, as well as other international polar orbiting and geostationary meteorological and environmental satellites and their regional user communities.

This paper presents the cloud-parameter data records derived from High Resolution Infrared Radiation Sounder (HIRS) measurements from 1980 through 2015 on the NOAA and MetOp polar-orbiting platforms. Over this time period, the HIRS sensor has been flown on 16 satellites from TIROS-N through NOAA-19 and MetOp-A and MetOp-B, forming a 35-yr cloud data record. Intercalibration of the Infrared Advanced Sounding Interferometer (IASI) and HIRS on MetOp-A has created confidence in the onboard calibration of this HIRS as a reference for others. A recent effort to improve the understanding of IR-channel response functions of earlier HIRS sensor radiance measurements using simultaneous nadir overpasses has produced a more consistent sensor-to-sensor calibration record. Incorporation of a cloud mask from the higher-spatial resolution Advanced Very High Resolution Radiometer (AVHRR) improves the subpixel cloud detection within the HIRS measurements. Cloud-top pressure and effective emissivity (epsilon f, or cloud emissivity multiplied by cloud fraction) are derived using the 15-mu m spectral bands in the carbon dioxide (CO2) absorption band and implementing the CO2-slicing technique; the approach is robust for high semitransparent clouds but weak for low clouds with little thermal contrast from clear-sky radiances. This paper documents the effort to incorporate the recalibration of the HIRS sensors, notes the improvements to the cloud algorithm, and presents the HIRS cloud data record from 1980 to 2015. The reprocessed HIRS cloud data record reports clouds in 76.5% of the observations, and 36.1% of the observations find high clouds.

An approach was developed to retrieve the ocean slope bispectrum, which describes the nonlinearity of the slope surface, from ocean sunglint data. The departure from Gaussianity of the ocean slope was described using an N-dimensional slope joint probability density function, which was derived using a perturbative approach. The resulting Edgeworth series had various slope cumulants and cumulant functions as series coefficients, and multidimensional Hermite polynomials as the series basis functions. The slope probability density was used to specify a series of relationships between the slope and glint cumulants and cumulant functions up to third order. These relationships were inverted to retrieve the slope third cumulant function, from which we obtained the slope bispectrum via Fourier transformation. The retrieval method was validated using synthetic 1-D ocean wave slope datasets with controlled phase correlations imposed on a subset of the wave spectrum components.

The retrieval of ocean wave spectra from sunglint patterns holds considerable promise as a remote sensing technology which could potentially be implemented on a variety of platforms using cheap, compact, commercial off-the-shelf equipment. Following the development of an inversion technique which assumed linear hydrodynamics of the sea surface, a new, generalised approach has been formulated to incorporate nonlinear effects which are pervasive in the real ocean environment. In each method, the starting point for the retrieval is the calculation of the relevant cumulant functions of the binary glint pattern, as defined by means of a brightness thresholding operation on the raw sunglint image. Here we consider the effects of the choice of threshold value, and whether an objective set of criteria exist for its selection.

The activities of the Atmosphere PEATE are discussed with respect to the evaluation of MODIS and VIIRS cloud and aerosol products within the LEOCAT development framework. The relevant properties of the MODIS and VIIRS sensors are compared.

Presented in this paper is a method of retrieving higher order statistical functions of the ocean wave surface from sunglint, or solar optical radiation specularly reflected from the surface. An expression was derived for the modelled slope probability density, which contains as parameters the desired cumulants and cumulant functions which we wish to retrieve. We then modelled the higher order statistical functions of the sunglint by integrating the slope density over a clipped domain representing the finite angular extent the solar disk subtends at the wave surface. This relationship was then inverted in order to retrieve the slope cumulant and cumulant functions from the corresponding functions of simulated sunglint data.

The activities of the Atmosphere PEATE are discussed with respect to the evaluation of MODIS and VIIRS cloud products within the LEOCAT development framework. The relevant properties of the MODIS and VIIRS sensors are compared.

A new expression for the mean value of ocean surface sunglint, modeled as a binary-valued random process, is calculated. Multiple sunglint realizations are generated by applying the specular condition for particular viewing geometries to ocean surface elevations with Gaussian roughness spectra. The sunglint mean value is used to determine the relationship between the second-order statistics of the surface slope and sunglint random processes, and this relationship is successfully inverted to retrieve the slope autocorrelation and power spectrum from the simulated sunglint data. The inversion model is then applied to an image recorded coincident with the NASA AIRSAR PACRIM2 field campaign, to retrieve the 1-D elevation power spectrum.

A nonlinear retrieval model was used to invert wave image data containing sunglint to obtain the elevation power spectrum. The sunglint images were thresholded to obtain binary glint images, from which the glint autocorrelation was calculated in one direction. A relationship was determined connecting the glint and slope random variables, which was then inverted to obtain the slope autocorrelation, from which the elevation power spectrum was obtained by integration and Fourier transform.