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Milky seas are a rare form of marine bioluminescence where the nocturnal ocean surface produces a widespread, uniform and steady whitish glow. Mariners have compared their appearance to a daylit snowfield that extends to all horizons. Encountered most often in remote waters of the northwest Indian Ocean and the Maritime Continent, milky seas have eluded rigorous scientific inquiry, and thus little is known about their composition, formation mechanism, and role within the marine ecosystem. The Day/Night Band (DNB), a new-generation spaceborne low-light imager, holds potential to detect milky seas, but the capability has yet to be demonstrated. Here, we show initial examples of DNB-detected milky seas based on a multi-year (2012–2021) search. The massive bodies of glowing ocean, sometimes exceeding 100,000 km2 in size, persist for days to weeks, drift within doldrums amidst the prevailing sea surface currents, and align with narrow ranges of sea surface temperature and biomass in a way that suggests water mass isolation. These findings show how spaceborne assets can now help guide research vessels toward active milky seas to learn more about them.

A combined algorithm comprising multiple dust detection methods was developed using infrared (IR) channels onboard the GEOstationary Korea Multi-Purpose SATellite 2A equipped with the Advanced Meteorological Imager (GK2A/AMI). Six cloud tests using brightness temperature difference (BTD) were utilized to reduce errors caused by clouds. For detecting dust storms, three standard BTD tests (i.e., $${BT}_{12.3}-{BT}_{10.5}$$, $${BT}_{8.7}-{BT}_{10.5}$$, and $${BT}_{11.2}-{BT}_{10.5}$$) were combined with the polarized optical depth index (PODI). The combined algorithm normalizes the indices for cloud and dust detection, and adopts weighted combinations of dust tests depending on the observation time (day/night) and surface type (land/sea). The dust detection results were produced as quantitative confidence factors and displayed as false color imagery, applying a dynamic enhancement background reduction algorithm (DEBRA). The combined dust detection algorithm was qualitatively assessed by comparing it with dust RGB imageries and ground-based lidar data. The combined algorithm especially improved the discontinuity in weak dust advection to the sea and considerably reduced false alarms as compared to previous dust monitoring methods. For quantitative validation, we used aerosol optical thickness (AOT) and fine mode fraction (FMF) derived from low Earth orbit (LEO) satellites in daytime. For both severe and weakened dust cases, the probability of detection (POD) ranged from 0.667 to 0.850 and it indicated that the combined algorithm detects more potential dust pixels than other satellites. In particular, the combined algorithm was advantageous in detecting weak dust storms passing over the warm and humid Yellow Sea with low dust height and small AOT.

On 16–17 February 2020, dust within a Saharan Air Layer (SAL) from western Africa moved over the eastern Atlantic Ocean. Satellite imagery and products from ABI on GOES-16, VIIRS on NOAA-20, and CALIOP on CALIPSO along with retrieved values of layer and total precipitable water (TPW) from MiRS and NUCAPS, respectively, were used to identify dust within the SAL over the eastern Atlantic Ocean. Use of various satellite imagery and products were also used to characterize the distribution of water vapor within the SAL. There was a distinct pattern between dust detection and dust masking and values of precipitable water. Specifically, dust was detected when values of layer or TPW were approximately 14 mm; in addition, dust was masked when values of layer or TPW were approximately 28 mm. In other words, water vapor masked infrared dust detection if sufficient amounts of water vapor existed in a column. Results herein provide observational support to two recent numerical studies that concluded water vapor can mask infrared detection of airborne dust.

The objective of this research is to develop techniques for assimilating GOES-R series observations in precipitating scenes for the purpose of improving short-term convective-scale forecasts of high-impact weather hazards. Whereas one approach is radiance assimilation, the information content of GOES-R radiances from its Advanced Baseline Imager saturates in precipitating scenes, and radiance assimilation does not make use of lightning observations from the GOES Lightning Mapper. Here, a convolutional neural network (CNN) is developed to transform GOES-R radiances and lightning into synthetic radar reflectivity fields to make use of existing radar assimilation techniques. We find that the ability of CNNs to utilize spatial context is essential for this application and offers breakthrough improvement in skill compared to traditional pixel-by-pixel based approaches. To understand the improved performance, we use a novel analysis method that combines several techniques, each providing different insights into the network’s reasoning. Channel-withholding experiments and spatial information–withholding experiments are used to show that the CNN achieves skill at high reflectivity values from the information content in radiance gradients and the presence of lightning. The attribution method, layerwise relevance propagation, demonstrates that the CNN uses radiance and lightning information synergistically, where lightning helps the CNN focus on which neighboring locations are most important. Synthetic inputs are used to quantify the sensitivity to radiance gradients, showing that sharper gradients produce a stronger response in predicted reflectivity. Lightning observations are found to be uniquely valuable for their ability to pinpoint locations of strong radar echoes.

The CoVID-19 pandemic has infected almost 73 million people and is responsible for over 1.63 million fatalities worldwide since early December 2019, when it was first reported in Wuhan, China. In the early stages of the pandemic, social distancing measures, such as lockdown restrictions, were applied in a non-uniform way across the world to reduce the spread of the virus. While such restrictions contributed to flattening the curve in places like Italy, Germany, and South Korea, it plunged the economy in the United States to a level of recession not seen since WWII, while also improving air quality due to the reduced mobility. Using daily Earth observation data (Day/Night Band (DNB) from the National Oceanic and Atmospheric Administration Suomi-NPP and NO2 measurements from the TROPOspheric Monitoring Instrument TROPOMI) along with monthly averaged cell phone derived mobility data, we examined the economic and environmental impacts of lockdowns in Los Angeles, California; Chicago, Illinois; Washington DC from February to April 2020—encompassing the most profound shutdown measures taken in the U.S. The preliminary analysis revealed that the reduction in mobility involved two major observable impacts: (i) improved air quality (a reduction in NO2 and PM2.5 concentration), but (ii) reduced economic activity (a decrease in energy consumption as measured by the radiance from the DNB data) that impacted on gross domestic product, poverty levels, and the unemployment rate. With the continuing rise of CoVID-19 cases and declining economic conditions, such knowledge can be combined with unemployment and demographic data to develop policies and strategies for the safe reopening of the economy while preserving our environment and protecting vulnerable populations susceptible to CoVID-19 infection.

Value-added imagery is a useful means of communicating multispectral environmental satellite radiometer data to the human analyst. The most effective techniques strike a balance between science and art. The science side requires engineering physical algorithms capable of distilling the complex scene into a reduced set of key parameters. The artistic side involves design and construction of visually intuitive displays that maximize information content within the product image. The utility of such imagery to human analysts depends on the extent to which parameters or features of interest are conveyed unambiguously. Here, we detail and demonstrate a dynamic blended imagery technique, based on spatially variant transparency factors whose values are tied to algorithmically isolated parameters. The technique enables seamless display of multivariate information, and is applicable to any imaging system based on red–green–blue composites. We illustrate this technique in the context of GeoColor—an application of the Geostationary Operational Environmental Satellite R (GOES-R) series Advanced Baseline Imager (ABI) supporting operational forecasting and used widely in public communication of weather information.

Lofted mineral dust over data-sparse regions presents considerable challenges to satellite-based remote sensing methods and numerical weather prediction alike. The Southwest Asia domain is replete with such examples, with its diverse array of dust sources, dust mineralogy, and meteorologically-driven lofting mechanisms on multiple spatial and temporal scales. A microcosm of these challenges occurred over 3–4 August 2016 when two dust plumes, one lofted within an inland dry air mass and another embedded within a moist air mass, met over the Southern Arabian Peninsula. Whereas conventional infrared-based techniques readily detected the dry air mass dust plume, they experienced marked difficulties in detecting the moist air mass dust plume, which only became apparent when visible reflectance revealed it crossing over an adjacent dark water background. In combining information from numerical modelling, multi-satellite/multi-sensor observations of lofted dust and moisture profiles, and idealized radiative transfer simulations, we develop a better understanding of the environmental controls of this event, characterizing the sensitivity of infrared-based dust detection to column water vapor, dust vertical extent, and dust optical properties. Differences in assumptions of dust complex refractive index translate to variations in the sign and magnitude of the split-window brightness temperature difference commonly used for detecting mineral dust. A multi-sensor technique for mitigating the radiative masking effects of water vapor via modulation of the split-window dust-detection threshold, predicated on idealized simulations tied to these driving factors, is proposed and demonstrated. The new technique, indexed to independent-sensor description of the surface-to-500 mb atmospheric column moisture, reveals parts of the missing dust plume embedded in the moist air mass, with best performance over land surfaces.

This high-resolution numerical modeling study investigates the potential range of impact of surface-lofted dust aerosols on the mean radiative fluxes and temperature changes associated with a dust-lofting episode over the Arabian Peninsula (2–5 August 2016). Assessing the potential for lofted dust to impact the radiation budget and temperature response in regions of the world that are prone to intense dust storms is important due to the impact of such temperature perturbations on thermally driven mesoscale circulations such as sea breezes and convective outflows. As such, sensitivity simulations using various specifications of the dust-erodible fraction were performed using two high-resolution mesoscale models that use similar dust-lofting physics based on threshold friction wind velocity and soil characteristics. The dust-erodible fraction, which represents the fraction (0.0 to 1.0) of surface soil that could be mechanically lifted by the wind and controls the location and magnitude of surface dust flux, was varied for three experiments with each model. The “Idealized” experiments, which used an erodible fraction of 1.0 over all land grid cells, represent the upper limit on dust lofting within each modeling framework, the “Ginoux” experiments used a 1∘ resolution, spatially varying erodible fraction dataset based on topographic depressions, and the “Walker” experiments used satellite-identified, 1 km resolution data with known lofting locations given an erodible fraction of 1.0. These simulations were compared with a “No-Dust” experiment in which no dust aerosols were permitted. The use of erodible fraction databases in the Ginoux and Walker simulations produced similar dust loading which was more realistic than that produced in the Idealized lofting simulations. Idealized lofting in this case study generated unrealistically large amounts of dust compared with observations of aerosol optical depth (AOD) due to the lack of locational constraints. Generally, the simulations with enhanced dust mass via surface lofting experienced reductions in daytime insolation due to aerosol scattering effects as well as reductions in nighttime radiative cooling due to aerosol absorption effects. These radiative responses were magnified with increasing amounts of dust loading. In the Idealized simulation with extreme (AOD > 5) dust amounts, these radiative responses suppressed the diurnal temperature range. In the Ginoux and Walker simulations with moderate (AOD ∼1–3) amounts of lofted dust, the presence of dust still strongly impacted the radiative fluxes but only marginally modified the low-level temperature. The dust-induced near-surface temperature change was limited due to competing thermal responses to changes in the net radiative fluxes and the dust-layer radiative heating rates. Compared to the Ginoux simulation, the use of increased resolution in dust-erodible fraction inventories in the Walker simulations led to enhanced fine-scale horizontal variability in lofted dust and a modest increase in the mean dust concentration profile and radiative or thermal responses. This study discusses the utility of using high-resolution dust source databases for simulating lofted dust, the need for greater spatial coverage of in situ aerosol observations in dust-prone regions, the impacts of dust on the local radiation budget and surface thermal conditions, and the potential dust radiative impacts on thermally driven mesoscale features.

We present an algorithm for retrieving aerosol layer height (ALH) and aerosol optical depth (AOD) for smoke and dust over vegetated land and water surfaces from measurements of the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR). Our algorithm uses EPIC atmospheric window bands to determine AOD and then takes advantage of oxygen A and B bands to derive ALH. We applied this algorithm on several dust and smoke events. Validation shows our results are of high accuracy.

Inaccurate mixed‐phase cloud parameterizations over the Southern Ocean remain one of the largest sources of disagreement among global models in determining shortwave cloud radiative feedbacks. Suitable global observations supporting model improvements are currently unavailable. The conventional satellite cloud phase retrieval from passive radiometers is strongly biased toward cloud top without further information on the subcloud phase. Mixed‐phase clouds with the liquid‐top mixed‐phase (LTMP) structures are often classified simply as supercooled liquid. This paper presents a daytime multispectral detection algorithm for LTMP clouds, based on differential absorption between liquid and ice in shortwave infrared bands (1.61 and 2.25 μm). The LTMP algorithm, previously developed for polar‐orbiting sensors, is applied to Himawari‐8 Advanced Himawari Imager (the first of the next‐generation geostationary satellites) to probe subcloud phase for mixed‐phase clouds over the Southern Ocean. The results are compared with spaceborne active sensor data from CloudSat and CALIPSO. Ship‐based field experiment measurements are examined for selected cases to provide a more direct assessment of algorithm performance. The results show that applying the LTMP algorithm to geostationary satellites has potential to provide advanced time‐resolved observations for mixed‐phase clouds globally with improved sublayer cloud phase information that can support enhancement and validation of global models.

Observations of top-of-atmosphere radiances from the Advanced Himawari Imager (AHI) blue, green, and red spectral bands can be used to produce high-temporal-resolution, true-color imagery at 1-km spatial resolution over the Asia–Pacific region. To enhance interpretability and aesthetic appearance of these images, the top-of-atmosphere radiance data are processed to remove the Rayleigh-scattered atmospheric component, corrected for limb effects, blended with brightness temperature data from a thermal infrared window band at night, and the resultant imagery adjusted to optimize contrast. The contribution of Rayleigh scattering to the AHI observations is calculated by interpolating radiative transfer parameters from a preconstructed set of lookup tables, which are specifically created for the Himawari-8 AHI instrument. A surface reflectance value for each pixel is calculated after the Rayleigh contribution is removed. The spectrally dependent reflectance values produced from the lookup table differ from the exact calculation by up to 18% at the planetary limb, over 100% at the solar terminator, and by less than 0.5% at low to moderate solar and sensor zenith angles. The subsequent corrections applied for limb effects mitigate the areas with high interpolation error, which slightly reduces the spatial coverage, but provides Rayleigh-corrected surface reflectance products that have interpolation errors at or below 0.5%. Resolution sharpening increases the nominal pixel size from 1000 to 500 m while still producing sharp images. The resultant images are colorful, visually intuitive, high contrast, and of sufficient spatial and temporal resolution to provide a unique and complementary observational tool for use by weather forecasters and the general public alike.

Sudden wind direction and speed shifts from outflow boundaries (OFBs) associated with deep convection significantly affect weather in the lower troposphere. Specific OFB impacts include rapid variation in wildfire spread rate and direction, the formation of convection, aviation hazards, and degradation of visibility and air quality due to mineral dust aerosol lofting. Despite their recognized importance to operational weather forecasters, OFB characterization (location, timing, intensity, etc.) in numerical models remains challenging. Thus, there remains a need for objective OFB identification algorithms to assist decision support services. With two operational next-generation geostationary satellites now providing coverage over North America, high-temporal- and high-spatial-resolution satellite imagery provides a unique resource for OFB identification. A system is conceptualized here designed around the new capabilities to objectively derive dense mesoscale motion flow fields in the Geostationary Operational Environmental Satellite 16 (GOES-16) imagery via optical flow. OFBs are identified here by isolating linear features in satellite imagery and backtracking them using optical flow to determine if they originated from a deep convection source. This “objective OFB identification” is tested with a case study of an OFB-triggered dust storm over southern Arizona. The results highlight the importance of motion discontinuity preservation, revealing that standard optical flow algorithms used with previous studies underestimate wind speeds when background pixels are included in the computation with cloud targets. The primary source of false alarms is the incorrect identification of line-like features in the initial satellite imagery. Future improvements to this process are described to ultimately provide a fully automated OFB identification algorithm.

Here we describe Pittosporum roimata, the only vascular plant endemic to the Poor Knights Islands, Northland, Aotearoa/New Zealand. This new species has previously been referred to as a distinct yellow-flowered variant of Pittosporum cornifolium (tāwhiri karo, wharewhareatua), a morphologically similar epiphytic shrub known from both main islands of New Zealand as well as other offshore islands. We have segregated this new species from P. cornifolium based on morphology, DNA sequence variation, as well as distribution. Pittosporum roimata is distinguished from P. cornifolium by flowers with yellow petals, larger inflorescences, and producing several terminal fruits per stem. Herbarium specimens of P. roimata statistically have larger leaves and shorter petioles than specimens of P. cornifolium. Pittosporum roimata is locally common on the islands of Tawhiti Rahi, Aorangi, and Aorangaia, growing on rocky substrates associated with Xeronema callistemon (raupo taranga) and Metrosideros excelsa (pōhutukawa). Using the current New Zealand Threat Classification System, we suggest a preliminary classification of this new species as ‘at risk/naturally uncommon’ qualified data poor [DP], one location [OL].

As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from the public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers. The project followed a value chain approach to determine key research and technology needs to reach desired results.Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynamic Integrated Forecast (DICast) System, which forms the basis of the system beyond about 6 h. For short-range (0–6 h) forecasts, Sun4Cast leverages several observation-based nowcasting technologies. These technologies are blended via the Nowcasting Expert System Integrator (NESI). The NESI and DICast systems are subsequently blended to produce short- to midterm irradiance forecasts for solar array locations. The irradiance forecasts are translated into power with uncertainties quantified using an analog ensemble approach and are provided to the industry partners for real-time decision-making. The Sun4Cast system ran operationally throughout 2015 and results were assessed.This paper analyzes the collaborative design process, discusses the project results, and provides recommendations for best-practice solar forecasting.

Global monitoring of tropical cyclones (TC) is enhanced by the unique capabilities provided by the day–night band (DNB), a sensor included on the Visible Infrared Imaging Radiometer Suite (VIIRS) flying on board the Suomi National Polar-Orbiting Partnership (SNPP) satellite. The DNB, a low-light visible–near-infrared-band passive radiometer, can leverage unconventional (i.e., nonsolar) sources of visible light illumination such as moonlight to infer storm structure at night. The DNB provides an unprecedented capability to resolve moonlit clouds at high resolution, offering numerous potential benefits to both operational TC analysts and researchers developing new methods of monitoring TCs occurring within the largely data-void tropical oceanic basins. DNB digital data provide significant enhancements over older nighttime visible data from the Defense Meteorological Satellite Program’s (DMSP) Operational Linescan System (OLS) by leveraging accurate calibration, high sensitivity, and sub-kilometer-scale imagery that covers 2–3 times the moon’s lunar cycle than the OLS. By leveraging these attributes, DNB data can enable the use of automated objective applications instead of subjective image interpretation. Here, the authors detail ways in which critical information about TC structure, location, intensity changes, shear environment, lightning, and other characteristics can be extracted when the DNB data are used in isolation or in a multichannel approach with coincident infrared (IR) channels.

Monitoring rat populations by standard tracking tunnels assumes that rats do not avoid treading on the tracking ink. We observed the behaviour of 20 rats in four groups of five, spending 2 nights e...

Observation of atmospheric gravity waves provides critical insight to weather and climate researches. Some gravity waves survive middle-atmospheric filtering as they propagate upward to the mesopause region and disturb the nightglow emission layer near 90 km AMSL, making the waves visible to both ground and space sensors. Based on the high-resolution images obtained by Day/Night Band on NOAA/NASA Suomi National Polar-orbiting Partnership environmental satellite, four representative gravity wave events over China are analyzed. With the help of VIIRS thermal infrared brightness temperature and MERRA wind data, we surmise that these waves originated from orography, thunderstorm, typhoon and baroclinic, respectively. Nadir viewing satellite observations cover a wide area and unlike the surface-based perspective, do not suffer from cloud obscuration. These new observations over remote areas provide important guidance for future deployment of ground based camera systems in China.

Sea breeze fronts propagate inland from the coastline, driving convective initiation and aerosol redistribution. Forecasting sea breezes is challenging due to uncertainties in the initial conditions, as well as the covariance and interaction of various meteorological and surface parameters. Using the Regional Atmospheric Modeling System coupled to an interactive land-surface model, we conduct an ensemble of 130 idealized cloud-resolving simulations by simultaneously perturbing six atmospheric and four surface parameters describing the initial conditions. To identify the key parameters impacting the inland characteristics and the intensity of the sea breeze convection in a tropical rainforest, we apply statistical emulation and variance-based sensitivity analysis techniques. This study extends a previous study which explored the impacts of various parameters on sea breeze characteristics in arid environments devoid of moist convection. Wind speed is identified as the main contributor to the inland extent, similar to the arid environment study. However, the relative impacts of surface properties on the inland extent are less significant in the moist environment where land-surface heating can be suppressed via moist convective processes and vegetation-atmosphere interactions. Two sea breeze-initiated convection regimes are also identified: shallow and deep. Over the shallow regime, where convective available potential energy is limited, the inversion layer strength is the primary control of the convective intensity. Over the deep regime, boundary layer temperature exerts a robust control over the convective available potential energy and hence the convective intensity. The potential vertical redistribution of aerosols is closely related to the convective intensity.

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.

Atmospheric gravity waves (AGWs) are among the important energy and momentum transfer mechanisms from the troposphere to the middle and upper atmosphere. Despite their understood importance in governing the structure and dynamics of these regions, mesospheric AGWs remain poorly measured globally, and largely unconstrained in numerical models. Since late 2011, the Suomi National Polar-orbiting Partnership (NPP) Visible/Infrared Imaging Radiometer Suite (VIIRS) day&ndash, night band (DNB) has observed global AGWs near the mesopause by virtue of its sensitivity to weak emissions of the OH* Meinel bands. The wave features, detectable at 0.75 km spatial resolution across its 3000 km imagery swath, are often confused by the upwelling emission of city lights and clouds reflecting downwelling nightglow. The Ionosphere, Mesosphere, upper Atmosphere and Plasmasphere (IMAP)/ Visible and near-Infrared Spectral Imager (VISI) O2 band, an independent measure of the AGW structures in nightglow based on the International Space Station (ISS) during 2012&ndash, 2015, contains much less noise from the lower atmosphere. However, VISI offers much coarser resolution of 14&ndash, 16 km and a narrower swath width of 600 km. Here, we present preliminary results of comparisons between VIIRS/DNB and VISI observations of AGWs, focusing on several concentric AGW events excited by the thunderstorms over Eastern Asia in August 2013. The comparisons point toward suggested improvements for future spaceborne airglow sensor designs targeting AGWs.

Using nighttime observations from Visible Infrared Imager Radiometer Suite (VIIRS) Day/Night band (DNB), the characteristics of artificial light sources are evaluated as functions of observation conditions, and incremental improvements are documented on nighttime aerosol retrievals using VIIRS DNB data on a regional scale. We find that the standard deviation of instantaneous radiance for a given artificial light source is strongly dependent upon the satellite viewing angle but is weakly dependent on lunar fraction and lunar angle. Retrieval of nighttime aerosol optical thickness (AOT) based on the novel use of these artificial light sources is demonstrated for three selected regions (United States, Middle East and India) during 2015. Reasonable agreement is found between nighttime AOTs from the VIIRS DNB and temporally adjacent daytime AOTs from the AErosol RObotic NETwork (AERONET) as well as from coincident nighttime AOT retrievals from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), indicating the potential of this method to begin filling critical gaps in diurnal AOT information at both regional and global scales. Issues related to cloud, snow and ice contamination during the winter season, as well as data loss due to the misclassification of thick aerosol plumes as clouds, must be addressed to make the algorithm operationally robust.

Solar radiation is the ultimate source of energy for all atmospheric motions. The visible wavelength range of solar radiation represents a significant contribution to the Earth’s energy budget and visible light is a vital indicator for the composition and thermodynamic processes of the atmosphere from the smallest weather to the largest climate scales. The accurate and fast description of light propagation in the atmosphere and its lower boundary environment is therefore of critical importance for the simulation and prediction of weather and climate. Simulated Weather Imagery (SWIm) is a new, fast and physically based visible wavelength 3-dimensional radiative transfer model. Given the location and intensity of the sources of light (natural or artificial) and the composition (e.g., clear or turbid air with aerosols, liquid or ice clouds, and precipitating rain, snow, or ice hydrometeors) of the atmosphere, it describes the propagation of light and produces visually and physically realistic hemispheric or 360° spherical panoramic color images of the atmosphere and the underlying terrain from any specified vantage point either on or above the Earth's surface. Applications of SWIm include the visualization of atmospheric and land surface conditions simulated or forecast by numerical weather or climate analysis and prediction systems for either scientific or lay audiences. Simulated SWim imagery can also be generated for and compared with observed camera images to (i) assess the fidelity, (ii) and improve the performance of numerical atmospheric and land surface models, as well as (iii) through their inclusion into an observational data assimilation scheme, improve the estimate of the state of atmospheric and land surface initial conditions for situational awareness and NWP forecast initialization applications.

The detection and characterization of aerosols are inherently limited at night because the important information provided by visible spectrum observations is not available and infrared bands have limited sensitivity to aerosols. The VIIRS Day–Night Band (DNB) onboard the Suomi-NPP satellite is a first-of-its-kind calibrated sensor capable of collecting visible and near-infrared observations during both day and night. Multiple studies have suggested that anthropogenic light emissions such as those from cities and gas flares may be useable as light sources for the retrieval of atmospheric properties, including cloud and aerosol optical depth. However, their use in this capacity requires proper characterization of their intrinsic variation, which represents a source of retrieval uncertainty. In this study we use 18 months of cloud-cleared VIIRS data collected over five selected geographic domains to assess the stability of anthropogenic light emissions and their response to varied satellite and lunar geometries. Time series are developed for each location in each domain for DNB radiance, four infrared channels, and satellite and lunar geometric variables, and spatially resolved correlation coefficients are computed between DNB radiance and each of the other variables. This analysis finds that while many emissive light sources are too unstable to be used reliably for atmospheric retrievals, some sources exhibit a sufficient stability (relative standard deviation  %). Additionally, we find that while the radiance variability of surrounding surfaces (i.e., unpopulated land and ocean) is largely dependent on lunar geometry, the anthropogenic light sources are more strongly correlated with satellite viewing geometry. Understanding the spatially resolved relationships between DNB radiance and other parameters is a necessary first step towards characterizing anthropogenic light emissions and establishes a framework for a model to describe variability in a more general sense.

Satellite observations of nighttime lights (NTL) from Suomi-NPP and NOAA-20 VIIRS Day/Night Band data have been widely used to estimate human activities. Long-term changes such as urban development and abrupt short-term changes such as power outages have been monitored from temporal NTL acquired by satellites. While high temporal NTL variation has been found across NTL data of varying temporal scale (e.g., daily, monthly, and annual composites), the sources of measurement error and uncertainty are poorly understood. This paper quantifies the sources of VIIRS-derived NTL uncertainty due to view-illumination geometry, surface Bidirectional Reflectance Distribution Function (BRDF)/albedo, and the effects of snow cover, lunar irradiance, aerosol loading, cloud mask, vegetation, geometry, and ephemeral artifacts (e.g., the Aurora Borealis). Based on this current assessment of NASA Black Marble retrievals (VNP46, Collection V001), we found that angular and atmospheric effects dominate retrieval uncertainty. Errors introduced by upstream data inputs (e.g., a coarser nighttime snow cover flag and misclassification errors in the existing VIIRS nighttime cloud mask) were also found to impact retrieval quality. Despite these challenges, a consistent daily NTL time series record can be routinely generated from top-of-atmosphere VNP46 radiances. Key recommendations include: (1) the use of lunar-BRDF adjusted and atmospherically corrected NTL (i.e., as identified as high-quality retrievals in the VNP46 QA fields), (2) development and improvement to the VIIRS snow cover and cloud masks algorithms to accurately reflect NTL retrieval conditions, (3) characterizing seasonal variations in NTL due to vegetation and snow, (4) reducing geometric effects due to the spatial mismatch of gridded pixel and observation footprint, (5) employing angularly-consistent NTL observations from multiple VIIRS instruments (i.e., Suomi-NPP and NOAA-20) to reduce pixel-based uncertainties and address persistent data gaps, and (6) being mindful of surface-reflected radiance from aurora events at mid-to-high latitudes.

The operational VIIRS cloud-base height (CBH) product from the Suomi–National Polar-Orbiting Partnership (SNPP) satellite is compared against observations of CBH from the cloud profiling radar (CPR) on board CloudSat. Because of the orbits of SNPP and CloudSat, these instruments provide nearly simultaneous observations of the same locations on Earth for a ~4.5-h period every 2–3 days. The methodology by which VIIRS and CloudSat observations are spatially and temporally matched is outlined. Based on four 1-month evaluation periods representing each season from June 2014 to April 2015, statistics related to the VIIRS CBH retrieval performance have been collected. Results indicate that when compared against CloudSat, the VIIRS CBH retrieval does not meet the error specifications set by the Joint Polar Satellite System (JPSS) program, with a root-mean-square error (RMSE) of 3.7 km for all clouds globally. More than half of all matching VIIRS pixels and CloudSat profiles have CBH errors exceeding the 2-km error requirement. Underscoring the significance of these statistics, it is shown that a simple estimate based on a constant cloud geometric thickness of 2 km outperforms the current operational CBH algorithm. It was found that the performance of the CBH product is impacted by the accuracy of upstream retrievals [primarily cloud-top height (CTH)] and the a priori information used by the CBH retrieval algorithm. However, even when CTH errors were small, CBH errors still exceed the JPSS program error specifications with an RMSE of 2.3 km.

Knowledge of cloud-base height (CBH) is important to describe cloud radiative feedbacks in numerical models and is of practical relevance to the aviation community. Whereas satellite remote sensing with passive radiometers traditionally has provided a ready means for estimating cloud-top height (CTH) and cloud water path (CWP), assignment of CBH requires heavy assumptions on the distribution of CWP within the cloud profile. An attempt to retrieve CBH has been included as part of the VIIRS environmental data records, produced operationally as part of the Suomi–National Polar-Orbiting Partnership (SNPP) and the forthcoming Joint Polar Satellite System. Through formal validation studies tied to the program, it was found that the operational CBH algorithm failed to meet performance specifications in many cases. This paper presents a new methodology for retrieving CBH of the uppermost cloud layer, developed through statistical analyses relating cloud geometric thickness (CGT) to CTH and CWP. The semiempirical approach, which relates these parameters via piecewise fitting, enlists A-Train satellite data [CloudSat cloud profiling radar (CPR), CALIPSO/CALIOP, and Aqua MODIS]. CBH is provided as the residual difference between CTH and CGT. By eliminating cloud type–dependent assumptions on CWP distribution, artifacts common to the operational algorithm (which contribute to high errors) are reduced. Special accommodations are made for handling optically thin cirrus and deep convection. An application to SNPP VIIRS is demonstrated, and the results are compared against global CloudSat observations. From the VIIRS–CloudSat daytime matchups (September–October 2013 and January–May 2015), the new algorithm outperforms the operational SNPP VIIRS algorithm, particularly when the retrieved CTH is accurate. Best performance is expected for single-layer liquid-phase clouds.

The Sun4Cast solar power forecasting system, designed to predict solar irradiance and power generation at solar farms, is composed of several component models operating on both the nowcasting (0–6 h) and day-ahead forecast horizons. The different nowcasting models include a statistical forecasting model (StatCast), two satellite-based forecasting models [the Cooperative Institute for Research in the Atmosphere Nowcast (CIRACast) and the Multisensor Advection-Diffusion Nowcast (MADCast)], and a numerical weather prediction model (WRF-Solar). It is important to better understand and assess the strengths and weaknesses of these short-range models to facilitate further improvements. To that end, each of these models, including four WRF-Solar configurations, was evaluated for four case days in April 2014. For each model, the 15-min average predicted global horizontal irradiance (GHI) was compared with GHI observations from a network of seven pyranometers operated by the Sacramento Municipal Utility District (SMUD) in California. Each case day represents a canonical sky-cover regime for the SMUD region and thus represents different modeling challenges. The analysis found that each of the nowcasting models perform better or worse for particular lead times and weather situations. StatCast performs best in clear skies and for 0–1-h forecasts; CIRACast and MADCast perform reasonably well when cloud fields are not rapidly growing or dissipating; and WRF-Solar, when configured with a high-spatial-resolution aerosol climatology and a shallow cumulus parameterization, generally performs well in all situations. Further research is needed to develop an optimal dynamic blending technique that provides a single best forecast to energy utility operators.

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.

Runoff from mountain snowpack is an important freshwater supply for many parts of the world. The deposition of aeolian dust on snow decreases snow albedo and increases the absorption of solar irradiance. This absorption accelerates melting, impacting the regional hydrological cycle in terms of timing and magnitude of runoff. The Moderate Resolution Imaging Spectroradiometer (MODIS) Dust Radiative Forcing in Snow (MODDRFS) satellite product allows estimation of the instantaneous (at time of satellite overpass) surface radiative forcing caused by dust. While such snapshots are useful, energy balance modeling requires temporally resolved radiative forcing to represent energy fluxes to the snowpack, as modulated primarily by varying cloud cover. Here, the instantaneous MODDRFS estimate is used as a tie point to calculate temporally resolved surface radiative forcing. Dust radiative forcing scenarios were considered for 1) clear-sky conditions and 2) all-sky conditions using satellite-based cloud observations. Comparisons against in situ stations in the Rocky Mountains show that accounting for the temporally resolved all-sky solar irradiance via satellite retrievals yields a more representative time series of dust radiative effects compared to the clear-sky assumption. The modeled impact of dust on enhanced snowmelt was found to be significant, accounting for nearly 50% of the total melt at the more contaminated station sites. The algorithm is applicable to regional basins worldwide, bearing relevance to both climate process research and the operational management of water resources.

The launch of the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Sumo-NPP satellite in 2011 ushered in a new era of using visible light and shortwave radiation at night to characterize aerosol and fire distributions from space. In order to exploit the full range of unprecedented observational capabilities of VIIRS, we have developed a nighttime shortwave radiative transfer model capability in the UNified and Linearized Radiative Transfer Model (UNL-VRTM). This capability is based on the use of additional source functions to treat illumination from the Moon, from fires, and from artificial lights. We have applied this model to address fundamental questions associated with the VIIRS sensing of aerosol and fire at night. Detailed description of model developments and validation (either directly with surface measurements of lunar spectra or indirectly through cross validation) are presented. Our analysis reveals that: (a) when convolution with the broad-range (500–900 nm) relative spectral response (RSR) function of the VIIRS Day-Night Band (DNB) is omitted, AOD retrieval from the DNB have uncertainties up to a factor of two in conditions with low or moderate AOD (

Strongly coupled data assimilation frameworks provide a mechanism for including additional information about aerosols through the coupling between aerosol and atmospheric variables, effectively utilizing atmospheric observations to change the aerosol analysis. Here, we investigate the impact of these observations on aerosol using the Maximum Likelihood Ensemble Filter (MLEF) algorithm with Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) which includes the Godard Chemistry Aerosol Radiation and Transport (GOCART) module. We apply this methodology to a dust storm event over the Arabian Peninsula and examine in detail the error covariance and in particular the impact of atmospheric observations on improving the aerosol initial conditions. The assimilated observations include conventional atmospheric observations and Aerosol Optical Depth (AOD) retrievals. Results indicate a positive impact of using strongly coupled data assimilation and atmospheric observations on the aerosol initial conditions, quantified using Degrees of Freedom for Signal.

The use of dust erodible fraction geographical databases in case-study numerical simulations provides more realistic dust loading compared to idealized, non-spatially-constrained methods. Idealized lofting in case study scenarios tends to generate unrealistically large amounts of dust compared to observations, due to the lack of locational constraints. Generally, simulations of enhanced dust mass via surface lofting lead to reductions in daytime insolation due to aerosol scattering effects, as well as reductions in nighttime radiative cooling due to aerosol absorption effects. In simulations with extreme dust concentrations, these effects noticeably suppress the model environment’s diurnal temperature range. In simulations with lesser amounts of lofted dust, the presence of dust is shown to still strongly impact the radiative fluxes while only marginally modifying the low-level temperature. Increased resolution in dust erodible fraction inventories are shown to enhance the details of dust lofting locations and potential, thereby, mitigate over-lofting and associated radiative response biases over regions that are unlikely source locations.

Using nighttime observations from Visible/Infrared Imager/Radiometer Suite (VIIRS) Day/Night band (DNB), the characteristics of artificial light sources are evaluated as functions of observation conditions and incremental improvements are documented on nighttime aerosol retrievals using VIIRS DNB data on a regional scale. We find that the standard deviation of instantaneous radiance for a given artificial light source is strongly dependent upon the satellite viewing angle, but is weakly dependent on lunar fraction and lunar angle. Retrieval of nighttime aerosol optical thickness (AOT) based on the novel use of these artificial light sources is demonstrated for three selected regions (United States, Middle East, and India) during 2015. Reasonable agreements are found between nighttime AOTs from VIIRS DNB and temporally adjacent daytime AOTs from AErosol RObotic NETwork (AERONET) as well as from coincident nighttime AOT retrievals from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), indicating the potential of this method to begin filling critical gaps in diurnal AOT information at both regional and global scales. Issues related to cloud, snow, and ice contamination during the winter season, as well as data loss due to the misclassification of thick aerosol plumes as clouds, must be addressed to make the algorithm operationally robust.

This paper reports mesospheric bore events observed by Day/Night Band (DNB) of the Visible/Infrared Imaging Radiometer Suite (VIIRS) on the National Oceanic and Atmospheric Administration/National Aeronautics and Space Administration (NOAA/NASA) Suomi National Polar-orbiting Partnership (NPP) environmental satellite over five years (2013&ndash, 2017). Two types of special mesospheric bore events were observed, enabled by the wide field of view of VIIRS: extremely wide bores (&gt, 2000 km extension perpendicular to the bore propagation direction), and those exhibiting more than 15 trailing crests and troughs. A mesospheric bore event observed simultaneously from space and ground was investigated in detail. DNB enables the preliminary global observation of mesospheric bores for the first time. DNB mesospheric bores occurred more frequently in March, April and May. Their typical lengths are between 300 km and 1200 km. The occurrence rate of bores at low latitudes is higher than that at middle latitudes. Among the 61 bore events, 39 events occurred in the tropical region (20&deg, S&ndash, 20&deg, N). The high occurrence rate of mesospheric bores during the spring months in the tropical region coincides with the reported seasonal and latitudinal variations of mesospheric inversion layers.

The ship rat invasion of Big South Cape Island/Taukihepa in the 1960s was an ecological catastrophe that marked a turning point for the management of rodents on offshore islands of New Zealand. Despite the importance of this event in the conservation history of New Zealand, and subsequent major advances in rodent eradication and biosecurity, the source and pathway of the rat invasion of Big South Cape Island has never been identified. Using modern molecular methods on contemporary and historical tissue samples, we identify the mitochondrial DNA (mtDNA) haplotype of ship rats (Rattus rattus) on Big South Cape Island and compare it to that of ship rats in the neighbouring regions of Stewart Island/Rakiura and southern New Zealand, all hypothesised as possible source sites for the invasion. We identify two haplotype clusters, each comprising three closely related haplotypes; one cluster unique to Stewart Island, and the other found in southern New Zealand and elsewhere. By a process of elimination we rule that the ship rat invasion of Big South Cape Island was neither by swimming nor boat transport from Stewart Island, and is unlikely to have come from the south coast ports of New Zealand. However, because the ship rat haplotype found on Big South Cape Island is cosmopolitan to New Zealand's South Island and elsewhere, we can only confirm that the invasion likely originated from some distance, but are not able to identify the invasion source more precisely. An unexpected consequence of our study is the discovery of five new mtDNA haplotypes for R. rattus that have not been previously reported.

NASA's Black Marble nighttime lights product suite (VNP46) is available at 500 m resolution since January 2012 with data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP). The retrieval algorithm, developed and implemented for routine global processing at NASA's Land Science Investigator-led Processing System (SIPS), utilizes all high-quality, cloud-free, atmospheric-, terrain-, vegetation-, snow-, lunar-, and stray light-corrected radiances to estimate daily nighttime lights (NTL) and other intrinsic surface optical properties. Key algorithm enhancements include: (1) lunar irradiance modeling to resolve non-linear changes in phase and libration; (2) vector radiative transfer and lunar bidirectional surface anisotropic reflectance modeling to correct for atmospheric and BRDF effects; (3) geometric-optical and canopy radiative transfer modeling to account for seasonal variations in NTL; and (4) temporal gap-filling to reduce persistent data gaps. Extensive benchmark tests at representative spatial and temporal scales were conducted on the VNP46 time series record to characterize the uncertainties stemming from upstream data sources. Initial validation results are presented together with example case studies illustrating the scientific utility of the products. This includes an evaluation of temporal patterns of NTL dynamics associated with urbanization, socioeconomic variability, cultural characteristics, and displaced populations affected by conflict. Current and planned activities under the Group on Earth Observations (GEO) Human Planet Initiative are aimed at evaluating the products at different geographic locations and time periods representing the full range of retrieval conditions.

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.

The prediction of tropical cyclone rapid intensification is one of the most pressing unsolved problems in hurricane forecasting. The signatures of gravity waves launched by strong convective updrafts are often clearly seen in airglow and carbon dioxide thermal emission spectra under favorable atmospheric conditions. By continuously monitoring the Atlantic hurricane belt from the main development region to the vulnerable sections of the continental United States at high cadence, it will be possible to investigate the utility of storm-induced gravity wave observations for the diagnosis of impending storm intensification. Such a capability would also enable significant improvements in our ability to characterize the 3D transient behavior of upper-atmospheric gravity waves and point the way to future observing strategies that could mitigate the risk to human life caused by severe storms. This paper describes a new mission concept involving a midinfrared imager hosted aboard a geostationary satellite positioned at approximately 80°W longitude. The sensor’s 3-km pixel size ensures that the gravity wave horizontal structure is adequately resolved, while a 30-s refresh rate enables improved definition of the dynamic intensification process. In this way the transient development of gravity wave perturbations caused by both convective and cyclonic storms may be discerned in near–real time.

This paper describes a Dynamic Enhancement Background Reduction Algorithm (DEBRA) applicable to multi-spectral satellite imaging radiometers. DEBRA uses ancillary information about the clear-sky background to reduce false detections of atmospheric parameters in complex scenes. Applied here to the detection of lofted dust, DEBRA enlists a surface emissivity database coupled with a climatological database of surface temperature to approximate the clear-sky equivalent signal for selected infrared-based multispectral dust detection tests. This background allows for suppression of false alarms caused by land surface features while retaining some ability to detect dust above those problematic surfaces. The algorithm is applicable to both day and nighttime observations and enables weighted combinations of dust detection tests. The results are provided quantitatively, as a detection confidence factor [0,1], but are also readily visualized as enhanced imagery. Utilizing the DEBRA confidence factor as a scaling factor in false color Red/Green/Blue (R/G/B) imagery enables depiction of the targeted parameter in the context of the local meteorology and topography. In this way, the method holds utility to both automated clients and human analysts alike. Examples of DEBRA performance from notable dust storms and comparisons against other detection methods and independent observations are presented.

Insects are important but overlooked components of forest ecosystems in New Zealand. For many insect species, information on foraging patterns and trophic relationships is lacking. We examined diet composition and selectivity in a large‐bodied insect, the Auckland tree wētā Hemideina thoracica, in three habitat zones in a lowland New Zealand forest. We asked whether H. thoracica selectively forage from available plant food sources, and whether these choices were lipid‐rich compared to nonpreferred available plants. We also identified the proportion of invertebrates in their frass as a proxy for omnivory. From reconnaissance plot sampling, together with fecal fragment analysis, we report that more than 93% of individual tree wētā had eaten invertebrates before capture. Additionally, wētā in the highest elevation hillslope habitat zone consumed significantly fewer species of plants on average than wētā on the low‐elevation terrace habitat. Upper hillslope wētā also had the highest average number of invertebrate fragments in their frass, significantly more than wētā in the low‐elevation terrace habitat zone. Wētā showed high variability in the consumption of fruit and seeds across all habitat zones. Generally, we did not observe diet differences between the sexes (although it appears that male wētā in the mid‐hillslope habitat ate fruits and seeds more voraciously than females), suggesting that the sexes have similar niche breadths and display similar degrees of omnivorous behavior. Extraction of leaf lipids demonstrated a range of lipid content values in available plants, and Ivlev's Electivity Index indicated that plant species which demonstrated high electivity tended to have higher concentrations of lipids in their leaves. Our findings indicate that H. thoracica forage omnivorously and selectively, and hence play multiple roles in native ecosystems and food webs.

Reduced competition is a frequent explanation for the success of many introduced species. In benthic marine biofouling communities, space limitation leads to high rates of overgrowth competition. Some species can utilise other living organisms as substrate (epibiosis), proffering a competitive advantage for the epibiont. Additionally, some species can prevent or reduce epibiotic settlement on their surfaces and avoid being basibionts. To test whether epibiotic pressure differs between native and introduced species, we undertook ex situ experiments comparing bryozoan larval settlement to determine if introduced species demonstrate a greater propensity to settle as epibionts, and a reduced propensity to be basibionts, than native species. Here we report that introduced species opportunistically settle on any space (bare, native, or introduced), whereas native species exhibit a strong tendency to settle on and near other natives, but avoid settling on or near introduced basibionts. In addition, larvae of native species experience greater larval wastage (mortality) than introduced species, both in the presence and absence of living substrates. Introduced species’ ability to settle on natives as epibionts, and in turn avoid epibiosis as basibionts, combined with significantly enhanced native larval wastage, provides a comprehensive suite of competitive advantages contributing to the invasion success of these biofouling species.

Cloud base height (CBH) is an important cloud macro parameter that plays a key role in global radiation balance and aviation flight. Building on a previous algorithm, CBH is estimated by combining measurements from CloudSat/CALIPSO and MODIS based on the International Satellite Cloud Climatology Project (ISCCP) cloud-type classification and a weighted distance algorithm. Additional constraints on cloud water path (CWP) and cloud top height (CTH) are introduced. The combined algorithm takes advantage of active and passive remote sensing to effectively estimate CBH in a wide-swath imagery where the cloud vertical structure details are known only along the curtain slice of the nonscanning active sensors. Comparisons between the estimated and observed CBHs show high correlation. The coefficient of association (R2) is 0.8602 with separation distance between donor and recipient points in the range of 0 to 100 km and falls off to 0.5856 when the separation distance increases to the range of 401 to 600 km. Also, differences are mainly within 1 km when separation distance ranges from 0 km to 600 km. The CBH estimation method was applied to the 3D cloud structure of Tropical CycloneBill, and the method is further assessed by comparing CTH estimated by the algorithm with the MODIS CTH product.

The Suomi National Polar-orbiting (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) measures visible and near-infrared light extending over seven orders of magnitude of dynamic range. This makes radiometric calibration difficult. We have observed that DNB imagery has striping, banding and other nonuniformities—day or night. We identified the causes as stray light, nonlinearity, detector crosstalk, hysteresis and mirror-side variation. We found that these affect both Earth-view and calibration signals. These present an obstacle to interpretation by users of DNB products. Because of the nonlinearity we chose the histogram matching destriping technique which we found is successful for daytime, twilight and nighttime scenes. Because of the very large dynamic range of the DNB, we needed to add special processes to the histogram matching to destripe all scenes, especially imagery in the twilight regions where scene illumination changes rapidly over short distances. We show that destriping aids image analysts, and makes it possible for advanced automated cloud typing algorithms. Manual or automatic identification of other features, including polar ice and gravity waves in the upper atmosphere are also discussed. In consideration of the large volume of data produced 24 h a day by the VIIRS DNB, we present methods for reducing processing time.