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 lightning flashes. NPP crosses the equator at about 0130 and 1330 local time, with VIIRS covering the entire Earth twice daily. Future members of the Joint Polar Satellite System (JPSS) constellation will also carry VIIRS. This paper presents dramatic early examples of multispectral VIIRS imagery capabilities and demonstrates basic applications of that imagery for a wide range of operational users, such as for fire detection, monitoring ice break up in rivers, and visualizing dust plumes over bright surfaces. VIIRS imagery, both single and multiband, as well as the day/night band, is shown to exceed both requirements and expectations.

The main purpose of the present study is to assess the value of synthetic satellite imagery as a tool for model evaluation performance in addition to more traditional approaches. For this purpose, synthetic GOES-10 imagery at 10.7 μm was produced using output from the Advanced Research Weather Research and Forecasting (ARW-WRF) numerical model. Use of synthetic imagery is a unique method to indirectly evaluate the performance of various microphysical schemes available within the ARW-WRF. In the present study, a simulation of an atmospheric river event that occurred on 30 December 2005 was used. The simulations were performed using the ARW-WRF numerical model with five different microphysical schemes [Lin, WRF single-moment 6 class (WSM6), Thompson, Schultz, and double-moment Morrison]. Synthetic imagery was created and scenes from the simulations were statistically compared with observations from the 10.7-μm band of the GOES-10 imager using a histogram-based technique. The results suggest that synthetic satellite imagery is useful in model performance evaluations as a complementary metric to those used traditionally. For example, accumulated precipitation analyses and other commonly used fields in model evaluations suggested a good agreement among solutions from various microphysical schemes, while the synthetic imagery analysis pointed toward notable differences in simulations of clouds among the microphysical schemes.

In 1967, at the dawn of the satellite era, the Applications Technology Satellite 3 (ATS-3) provided the first full-disk “true color” images of Earth. With its depiction of blue oceans, golden deserts, and green forestlands beneath white clouds, the imagery captured the iconic Blue Marble in a way that resonates strongly with human perception. After ATS-3, the standard fare of geostationary satellites entailed a single visible band with additional infrared spectral channels. While single-band visible satisfied the basic user requirements of daytime imagery, the loss of true-color capability and its inherent capability to distinguish myriad atmospheric and surface features via coloration left a notable void. Nearly half a century later, with the launch of Japan’s Himawari-8 in October 2014, there is once again a geostationary sensor—the Advanced Himawari Imager (AHI)—containing the multispectral visible bands required notionally for true color. However, it soon became apparent that AHI’s “green” band, centered at 0.51 μm, was not aligned with the chlorophyll reflectance signature near 0.55 μm. As a result, vegetation appears browner and deserts appear redder than legacy true-color imagery. Here, we describe a technique that attempts to mitigate these issues by blending information from a ref lective-infrared band at 0.86 μm to form a “hybrid” green band. When combining this method with Rayleigh corrections, AHI’s true-color performance is found to be consistent with that of the optimal 0.55-μm band, offering a stopgap solution adaptable to future satellites of similar design.

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 Earth's 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.

‘Natural’ (or ‘true’) colour imagery, so-called for its qualitative likeness to colour photography, is one of the most visually intuitive and readily communicable forms of satellite information. It is constructed by combining solar reflectance measurements from three narrow spectral bands defining the red, green and blue wavelengths of visible light. Natural colour facilitates the interpretation of multiple components in the complex earth/atmosphere scene and, therefore, it is widely used by experts and non-experts alike to visualize many forms of geophysical phenomena. Although sensors on board low-Earth-orbiting (LEO) satellites have long-demonstrated the superior quality of natural colour imagery over various other ‘false colour’ renditions, similar capabilities currently do not exist on sensors operating in geostationary orbits that offer distinct advantages over LEO in terms of high temporal refresh. The Advanced Baseline Imager (ABI) of the next-generation Geostationary Operational Environmental Satellite (GOES)-R series will include the blue and red bands, but is missing the 0.55 μm green band necessary for producing natural colour. The emphases of this article are twofold. First, we consider the merits of natural colour imagery from the standpoints of both science and operational users, and the philosophical roadblocks of a system definition process that seems inherently ill-equipped to consider qualitative user requirements. Second, we present a mitigation strategy for GOES-R ABI that entails synthesizing the missing ABI green band information via its correlation with spectrally adjacent available bands, with a first-order account for surface type dependencies. The technique is developed, demonstrated and evaluated here using Moderate-resolution Imaging Spectroradiometer (MODIS) data.