Thomas J. Kopp

First-Light Imagery from Suomi NPP VIIRS

The Suomi National Polar-Orbiting Partnership (NPP) satellite was launched on 28 October 2011, heralding the next generation of operational U.S. polar-orbiting satellites. It carries the Visible– Infrared Imaging Radiometer Suite (VIIRS), a 22-band visible/infrared sensor that combines many of the best aspects of the NOAA Advanced Very High Resolution Radiometer (AVHRR), the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. VIIRS has nearly all the capabilities of MODIS, but offers a wider swath width (3,000 versus 2,330 km) and much higher spatial resolution at swath edge. VIIRS also has a day/night band (DNB) that is sensitive to very low levels of visible light at night such as those produced by moonlight reflecting off low clouds, fog, dust, ash plumes, and snow cover. In addition, VIIRS detects light emissions from cities, ships, oil flares, and ...

Automated cloud detection and classification of data collected by the Visible Infrared Imager Radiometer Suite (VIIRS)

The Visible Infrared Imager Radiometer Suite (VIIRS) is a high‐resolution Earth imager of the United States National Polar‐orbiting Operational Environmental Satellite System (NPOESS). VIIRS has its heritage in three sensors currently collecting imagery of the Earth—the Advanced Very High Resolution Radiometer, the Moderate Resolution Imaging Spectroradiometer, and the Operational Linescan Sensor. The first launch of the VIIRS sensor is on NASAs NPOESS Preparatory Project (NPP). Data collected by VIIRS will provide products to a variety of users, supporting applications from real‐time to long‐term climate change timescales. VIIRS has been uniquely designed to satisfy this full range of requirements. Cloud masks derived from the automated analyses of VIIRS data are critical data products for the NPOESS program. In this paper, the VIIRS cloud mask (VCM) performance requirements are highlighted, along with the algorithm developed to satisfy these requirements. The expected performance of the VCM algorithm is established using global synthetic cloud simulations and manual cloud analyses of VIIRS proxy imagery. These results show the VCM analyses will satisfy the performance expectations of products created from it, including land and ocean surface products, cloud microphysical products, and automated cloud forecast products. Finally, minor deficiencies that remain in the VCM algorithm logic are identified along with a mitigation plan to resolve each prior to NPP launch or shortly thereafter.

The VIIRS Cloud Mask: Progress in the first year of S-NPP toward a common cloud detection scheme

The Visible Infrared Imager Radiometer Suite (VIIRS) Cloud Mask (VCM) determines, on a pixel-by-pixel basis, whether or not a given location contains cloud. The VCM serves as an intermediate product (IP) between the production of VIIRS sensor data records and 22 downstream Environmental Data Records that each depends upon the VCM output. As such, the validation of the VCM IP is critical to the success of the Suomi National Polar-orbiting Partnership (S-NPP) product suite. The methods used to validate the VCM and the current results are presented in this paper. Detailed analyses of golden granules along with tools providing deep insights into granule performance, and specific cloud detection tests reveal the details behind a given granules performance. Matchup results with CALIPSO, in turn, indicate the large-scale performance of the VCM and whether or not it is meeting its specifications. Comparisons with other cloud masks indicate comparable performance for the determination of clear pixels. As of September 2013 the VCM is either meeting or within 2% of all of its documented requirements.

A Geometry-Based Approach to Identifying Cloud Shadows in the VIIRS Cloud Mask Algorithm for NPOESS

Abstract A geometry-based approach is presented to identify cloud shadows using an automated cloud classification algorithm developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program. These new procedures exploit both the cloud confidence and cloud phase intermediate products generated by the Visible/Infrared Imager/Radiometer Suite (VIIRS) cloud mask (VCM) algorithm. The procedures have been tested and found to accurately detect cloud shadows in global datasets collected by NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and are applied over both land and ocean background conditions. These new procedures represent a marked departure from those used in the heritage MODIS cloud mask algorithm, which utilizes spectral signatures in an attempt to identify cloud shadows. However, they more closely follow those developed to identify cloud shadows in the MODIS Surface Reflectance (MOD09) data product. Significant differences were necessary in the im...

Distinguishing Aerosols from Clouds in Global, Multispectral Satellite Data with Automated Cloud Classification Algorithms

Abstract A new approach is presented to distinguish between clouds and heavy aerosols with automated cloud classification algorithms developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program. These new procedures exploit differences in both spectral and textural signatures between clouds and aerosols to isolate pixels originally classified as cloudy by the Visible/Infrared Imager/Radiometer Suite (VIIRS) cloud mask algorithm that in reality contains heavy aerosols. The procedures have been tested and found to accurately distinguish clouds from dust, smoke, volcanic ash, and industrial pollution over both land and ocean backgrounds in global datasets collected by NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. This new methodology relies strongly upon data collected in the 0.412-μm bandpass, where smoke has a maximum reflectance in the VIIRS bands while dust simultaneously has a minimum reflectance. The procedures benefit from the VIIRS desig...

Suomi NPP VIIRS Imagery evaluation

The Visible Infrared Imaging Radiometer Suite (VIIRS) combines the best aspects of both civilian and military heritage instrumentation. VIIRS has improved capabilities over its predecessors: a wider swath width and much higher spatial resolution at swath edge. The VIIRS day-night band (DNB) is sensitive to very low levels of visible light and is capable of detecting low clouds, land surface features, and sea ice at night, in addition to light emissions from both man-made and natural sources. Imagery from the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite has been in the checkout process since its launch on 28 October 2011. The ongoing evaluation of VIIRS Imagery helped resolve several imagery-related issues, including missing radiance measurements. In particular, near-constant contrast imagery, derived from the DNB, had a large number of issues to overcome, including numerous missing or blank-fill images and a stray light leakage problem that was only recently resolved via software fixes. In spite of various sensor issues, the VIIRS DNB has added tremendous operational and research value to Suomi NPP. Remarkably, it has been discovered to be sensitive enough to identify clouds even in very low light new moon conditions, using reflected light from the Earths airglow layer. Impressive examples of the multispectral imaging capabilities are shown to demonstrate its applications for a wide range of operational users. Future members of the Joint Polar Satellite System constellation will also carry and extend the use of VIIRS. Imagery evaluation will continue with these satellites to ensure the quality of imagery for end users.

Comparisons between VIIRS cloud mask performance results from manually generated cloud masks of VIIRS imagery and CALIOP-VIIRS match-ups

Binary cloud masks generated from the manual interpretation of imagery serve as one source of truth data in the evaluation of the Visible Infrared Imaging Radiometer Suite (VIIRS) cloud mask (VCM) algorithm in the Suomi National Polar-orbiting Partnership (S-NPP) Calibration/Validation (CalVal) Program of the Joint Polar Satellite System (JPSS). The other and primary source of truth data used to establish global VCM performance comes from match-up datasets of VIIRS and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) data that are collected by the NASA CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) system. While manually generated cloud masks have long been used to quantitatively evaluate the performance of individual cloud-screening detection tests, complete cloud-screening algorithms, and even the results of cloud forecast models, there has not previously been the opportunity to compare results obtained with these cloud masks to an independent, highly accurate cloud dataset, as collected by CALIOP. Such an opportunity was afforded the S-NPP VCM CalVal Team as it prepared to assess the VCM algorithm’s performance to meet the JPSS Validation Phase-1 performance criteria milestone. This article provides an in-depth discussion on the use of data generated to evaluate VCM performance and the results obtained from comparisons to both manually generated cloud masks and CALIOP-VIIRS match-up datasets. Overall, the performance of the VCM algorithm performance is found to be consistent with each source of truth data, and while the evaluation of the VCM algorithm is still in progress, it is already satisfying the JPSS Level-1 System Requirements. However, the similarities in VCM performance using these two sets of performance results were surprising and that becomes the focus of this article. As an example, the probability of correct typing (PCT) with the VCM algorithm during daytime conditions over ocean, land, and desert backgrounds, was 96.5%, 94.4%, and 95.7% respectively, based upon the manually generated cloud masks. Similar results obtained using CALIOP-VIIRS match-up datasets were 95.0%, 93.9%, and 96.0% respectively. It is concluded that manually generated cloud masks, created from VIIRS imagery, provide unique insights into the VCM’s performance, which results in a robust CalVal program when augmented with results obtained from CALIOP-VIIRS match-up data.

Development of level 2 calibration and validation plans for GOES-R; What is a RIMP?

Calibration and Validation (Cal/Val) plans for Geostationary Operational Environmental Satellite version R (GOES-R) Level 2 (L2) products were documented via Resource, Implementation, and Management Plans (RIMPs) for all of the official L2 products required from the GOES-R Advanced Baseline Imager (ABI). In 2015 the GOES-R program decided to replace the typical Cal/Val plans with RIMPs that covered, for a given L2 product, what was required from that product, how it would be validated, and what tools would be used to do so. Similar to Level 1b products, the intent was to cover the full spectrum of planning required for the Cal/Val of L2 ABI products. Instead of focusing on step-by-step procedures, the RIMPs concentrated on the criteria for each stage of the validation process (Beta, Provisional, and Full Validation) and the many elements required to prove when each stage was reached.

Tackling the hydra, validation of the imagery environmental data record (EDR) and Cloud Mask

Both the imagery and VCM validation efforts required extensive coordination with users and other validation teams, along with the validation effort itself. Therefore the validation efforts from the very beginning included both the work necessary to validate the products as well as efficient mechanisms to communicate with external dependent communities.