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Meteorologists and others using the imagery from multispectral instruments for operational applications are often limited in time, making it impractical to carefully sift through every single available piece of information to understand the particular scene of interest. In addition, sometimes certain features are not readily apparent from single band imagery, but instead require taking the difference between two bands and scaling the output appropriately. These higher-order data manipulations can be onerous for a forecaster working under tight deadlines. The development of red-green-blue (RGB) imagery products involves an appropriately scaled single channel, channel difference, or other preprocessed combination of imagery assigned to each of the red, green, and blue color “components.” Combining these components results in a single image based on multispectral information. There are many RGBs currently being used operationally from the Advanced Baseline Imager (ABI); this chapter delves into the inner-workings of RGBs and their utility to the operational forecaster.

Remote sensing of night light emissions in the visible band offers a unique opportunity to directly observe human activity from space. This has allowed a host of applications including mapping urban areas, estimating population and GDP, monitoring disasters and conflicts. More recently, remotely sensed night lights data have found use in understanding the environmental impacts of light emissions (light pollution), including their impacts on human health. In this review, we outline the historical development of night-time optical sensors up to the current state of the art sensors, highlight various applications of night light data, discuss the special challenges associated with remote sensing of night lights with a focus on the limitations of current sensors, and provide an outlook for the future of remote sensing of night lights. While the paper mainly focuses on space borne remote sensing, ground based sensing of night-time brightness for studies on astronomical and ecological light pollution, as well as for calibration and validation of space borne data, are also discussed. Although the development of night light sensors lags behind day-time sensors, we demonstrate that the field is in a stage of rapid development. The worldwide transition to LED lights poses a particular challenge for remote sensing of night lights, and strongly highlights the need for a new generation of space borne night lights instruments. This work shows that future sensors are needed to monitor temporal changes during the night (for example from a geostationary platform or constellation of satellites), and to better understand the angular patterns of light emission (roughly analogous to the BRDF in daylight sensing). Perhaps most importantly, we make the case that higher spatial resolution and multispectral sensors covering the range from blue to NIR are needed to more effectively identify lighting technologies, map urban functions, and monitor energy use. © 2019 Elsevier Inc., CCMK acknowledges funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 689443 via project GEOEssential, and funding from the Helmholtz Association Initiative and Networking Fund under grant ERC-RA-0031. Some aspects of this manuscript were based upon work from COST Action ES1204 LoNNe (Loss of the Night Network), supported by COST (European Cooperation in Science and Technology). ASdM acknowledges funding from the EMISS@N project (NERC grant NE/P01156X/1) and the Cities at Night project. QLZ acknowledges funding from the One Hundred Talents Program of the Chinese Academy of Science ([2015], No. 70). AJ is supported by the Leibniz Association, Germany within the ILES (SAW-2015-IGB-1) and CONNECT (SAW-K45/2017) projects and by the IGB Leibniz Institute through the Frontiers in Freshwater Science project (IGB Frontiers 2017). Black Marble product suite data created at NASA's Goddard Space Flight Center with support from the NASA's Earth Observing System Data and Information System, Terrestrial Ecology, and Group on Earth Observations programs under grants NNH16ZDA001N-GEO-0055 and NNH17ZDA001N-TASNPP-0007. We thank the anonymous reviewers for their constructive comments on previous versions of this manuscript. Finally, we would like to pay our respects to Abraham Haim, who passed away this January. Abraham was a leader in the field of ecological and health impacts of artificial light, and was involved in a number of studies relating remotely sensed night lights data to human health impacts.

Imagers flown on the global constellation of meteorological satellites are providing more and more quantitative information that is being used to assess the state of the atmosphere and forecast its evolution. This article highlights current progress in deriving quantitative cloud products from the visible, near-infrared, and infrared observations on imagers. The physics of cloud vertical structure retrievals using the IR channels are demonstrated on current imager data. The vertical structure includes both the cloud top extent and extent of the cloud base. One of the biggest applications of cloud vertical structure information is found in the aviation industry and examples of current cloud products are shown in this context. Another major area of quantitative cloud remote sensing is the generation of cloud optical and microphysical products. An overview of the bispectral techniques used to derive optical and effective particle size is given. The application of these products into the estimation cloud water path and precipitation is also given.

Multispectral band measurements from satellite radiometers have been widely used to detect and characterize the optical and microphysical properties of global clouds. These passive radiometer observations also provide information about cloud top phase. In situ observations have shown that clouds often contain a mixture of both liquid and ice at temperatures below 0°C. These mixed-phase clouds remain an important source of uncertainty in numerical models and satellite retrieval algorithms. Cloud phase information can be inferred from conventional satellite radiometers using thermal-infrared bands, but the sensitivity is biased strongly toward cloud top due to strong absorption in these bands. In situ observations of many mixed-phase clouds reveal a profile of supercooled liquid water at or near cloud top and a predominantly ice phase residing below, contrary to typical assumptions used in many retrieval algorithms. Based on cloud top sensitivity bias, conventional thermal infrared-based satellite algorithms would identify these clouds simply as supercooled liquid without any further information regarding the presence of a sub-cloud top phase. Here, we introduce a daytime detection method of liquid-top mixed-phase clouds from passive radiometer observations which utilizes reflected sunlight in narrow bands at 1.6 and 2.25 μm to “optically probe” within liquid-topped mixed-phase clouds in an attempt to detect sub-cloud top phase. The basis of the algorithm is established on differential absorption properties between liquid and ice particles, accounting for varying sun/sensor geometry and cloud optical properties. The algorithm has been applied experimentally to the S-NPP VIIRS and Himawari-8 AHI, and evaluated based on active sensor measurements from CloudSat and CALIPSO. The results showed that the algorithm has promising potential for distinction between supercooled liquid-top phase clouds with and without an underlying mixed phase component.

Ephemeral clouds and atmospheric aerosols pose the greatest challenges in exploiting sunlight as a viable (both stable and reliable) source of energy. The passage of cloud shadows across a solar array results in significant fluctuations, or ramps, in available energy, while scattering aerosols redistribute direct and diffuse components of solar irradiance in a subtle but pervasive and more sustained way. The timescales of these fluctuations are highly diverse, varying from seconds, in the case of fair-weather cumulus clouds, to hours, in the case of a prefrontal cirrus shield, and to days or more in association with aerosol loading within a synoptic-scale air mass. The spectrum of spatial scale for aerosol and cloud parameters is broad, and monitoring from terrestrially based systems is an inherently ill-posed problem from the standpoints of cost and coverage. Here, satellite-based observations, particularly those from geostationary platforms capable of monitoring the temporal evolution of clouds, provide unique and indispensable capabilities with regard to solar-energy forecasting and resource assessment. In this chapter, we provide a high-level cross-section of environmental satellite observing systems and considerations for their application to quantitative, physically based estimates of solar irradiance at the surface for use in solar forecasting.