Scientific Objectives, Measurement Needs, and Challenges Motivating the PARAGON Aerosol Initiative

Aerosols are involved in a complex set of processes that operate across many spatial and temporal scales. Understanding these processes, and ensuring their accurate representation in models of transport, radiation transfer, and climate, requires knowledge of aerosol physical, chemical, and optical properties and the distributions of these properties in space and time. To derive aerosol climate forcing, aerosol optical and microphysical properties and their spatial and temporal distributions, and aerosol interactions with clouds, need to be understood. Such data are also required in conjunction with size-resolved chemical composition in order to evaluate chemical transport models and to distinguish natural and anthropogenic forcing. Other basic parameters needed for modeling the radiative influences of aerosols are surface reflectivity and three-dimensional cloud fields. This large suite of parameters mandates an integrated observing and modeling system of commensurate scope. The Progressive Aerosol Retrieval and Assimilation Global Observing Network (PARAGON) concept, designed to meet this requirement, is motivated by the need to understand climate system sensitivity to changes in atmospheric constituents, to reduce climate model uncertainties, and to analyze diverse collections of data pertaining to aerosols. This paper highlights several challenges resulting from the complexity of the problem. Approaches for dealing with them are offered in the set of companion papers

A New Data Processing System for Generating Sea Ice Surface Roughness and Cloud Mask Data Products from the Multi-Angle Imaging SpectroRadiometer (MISR)

This study describes two novel data products derived from Multi-angle Imaging SpectroRadiometer (MISR) imagery: Arctic-wide maps of sea ice roughness and a binary cloud detection algorithm. The sea ice roughness maps were generated using a data processing system that matched MISR pixels with co-located and concurrent lidar-derived roughness measurements from Airborne Topographic Mapper (ATM), calibrated the multi- angle data to values of surface roughness using a K-Nearest Neighbor (KNN) algorithm, and then applied the algorithm to Arctic-wide MISR data for two 16-day periods in April and July 2016. The resulting maps show good agreement with independent ATM roughness data and enable characterization of the roughness of different ice types. The binary cloud detection algorithm was developed using a neural network approach and a training dataset constructed from Top-of-Atmosphere red band values from all MISR’s nine different viewing cameras for the same two months in various regions of the Arctic. The algorithm showed good performance in classifying pixels into cloudy and clear categories in MISR images, with better performance for clear pixels in April 2016 and better performance for cloudy pixels in July 2016. The algorithm also provides a significant advantage over existing MISR cloud mask products SDCM and ASCM in terms of accuracy and spatial resolution, with a resolution of 275 meters. The data products presented here can be used to gain insights into the seasonal and interannual changes in sea ice roughness and cloud cover over the Arctic and to develop and improve more accurate classification algorithms in the field of remote sensing

Critical Evaluations Of Modis And Misr Satellite Aerosol Products For Aerosol Modeling Applications

The study of uncertainties in satellite aerosol products is essential to aerosol data assimilation and modeling efforts. In this study, with the assistance of ground- based observations, uncertainties in Moderate Resolution Imaging Spectroradiometer (MODIS) collection 5 Deep Blue (DB), Multi-Angle Imaging Spectroradiometer (MISR) version 22 aerosol products, and the newly released collection 6 Dark Target over-ocean and DB products were evaluated. For each product, systematic biases were analyzed against observing conditions. Empirical correction procedures and data filtering steps were generated to develop noise and bias reduced DA-quality aerosol products for modeling related applications. Special attention was also directed at the potential low bias in satellite aerosol optical depth (AOD) climatology due to misclassification of aerosols as clouds over Asia. A heavy aerosol identifying system (HAIS) was developed through the combined use of the Ozone Monitoring Instrument (OMI) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) products for detecting heavy smoke aerosol plumes. Upon extensive evaluation, HAIS was applied to one year of collocated OMI, CALIOP, and MODIS data to study the misclassifications related low bias. This study suggests that the misclassification of heavy smoke aerosol plumes by MODIS is rather infrequent and thus introduces an insignificant low bias to its AOD climatology. Still, this study confirms that misclassification happens in both active- and passive- based satellite aerosol products and needs to be studied for forecasting these events

Application of Stereo-Photogrammetric Methods to the Advanced Along Track Scanning Radiometer for the Atmospheric Sciences

This thesis studies photogrammetric techniques applied to the ATSR instruments for the extraction of atmospheric parameters with the objective of generating new scientific datasets. The atmospheric parameters under observation are cloud top height, smoke plume injection height, and tropospheric wind components. All have important applications in various tasks, including the initialisation and validation of climate models. To generate accurate stereo measurements from the ATSR imagery the forward and nadir views need to be accurately co-registered. Currently this is not the case, with differences of up to 2 pixels in both axes recorded. In this thesis an automated image tie-pointing and image warping algorithm that improves ATSR co-registration to ≤1 pixel is presented. This thesis also identifies the census stereo matching algorithm for application to the ATSR instruments. When compared against a collocated DEM, census outperforms the previous stereo matching algorithm applied to the ATSR instrument, known as M4, significantly: RMSE ~700m vs. ~1200m; bias ~60m vs ~600m; R2 ~0.9 vs ~0.7. Furthermore, this thesis reviews the M6 algorithm developed for application within the ESA ALANIS Smoke Plume project. Using census a climatological cloud fraction by altitude dataset over Greenland is generated and demonstrated to agree well with current observational datasets from MISR, MODIS and AATSR. The 11μm channel stereo output provides insights into high cloud characteristics over Greenland and appears to be, in comparison with CALIOP, practically unbiased. The ALANIS Smoke plume project is introduced and the inter-comparison of the M6 algorithm against MISR and CALIOP is presented. M6 demonstrates some ability for determining smoke plumes injection heights above 1km in elevation. However, the smoke plume masking approach currently employed is demonstrated to be lacking in quality. Finally, this thesis presents the determination of cloud tracked tropospheric winds from the ATSR2-AATSR tandem operation using the Farneback optical flow algorithm. This algorithm offers accuracy on the order of 0.5 ms-1 at full image resolution, which is unprecedented in comparison to similarly derived datasets