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By accounting for surface-based light source emissions and top-of-atmosphere (TOA) downward lunar fluxes, we adapted the spherical harmonics discrete ordinate method (SHDOM) 3-dimensional (3-D) radiative transfer model (RTM) to simulate nighttime 3-D TOA radiances as observed from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) on board the Suomi-NPP satellite platform. Used previously for daytime 3-D applications, these new SHDOM enhancements allow for the study of the impacts of various observing conditions and aerosol properties on simulated VIIRS-DNB TOA radiances. Observations over Dakar, Senegal, selected for its bright city lights and a large range of aerosol optical depth (AOD), were investigated for potential applications and opportunities for using observed radiances containing VIIRS-DNB “bright pixels” from artificial light sources to conduct aerosol retrievals. We found that using the standard deviation (SD) of such bright pixels provided a more stable quantity for nighttime AOD retrievals than direct retrievals from TOA radiances. Further, both the mean TOA radiance and SD of TOA radiances over artificial sources are significantly impacted by satellite viewing angles. Light domes, the enhanced radiances adjacent to artificial light sources, are strong functions of aerosol properties and especially aerosol vertical distribution, which may be further utilized for retrieving aerosol layer height in future studies. Through inter-comparison with both day- and nighttime Aerosol Robotic Network (AERONET) data, the feasibility of retrieving nighttime AODs using 3-D RTM SHDOM over artificial light sources was demonstrated. Our study shows strong potential for using artificial light sources for nighttime AOD retrievals, while also highlighting larger uncertainties in quantifying surface light source emissions. This study underscores the need for surface light emission source characterizations as a key boundary condition, which is a complex task that requires enhanced input data and further research. We demonstrate how quality-controlled nighttime light data from the NASA's Black Marble product suite could serve as a primary input into estimations of surface light source emissions for nighttime aerosol retrievals.

Background Inflammatory bowel disease (IBD) commonly leads to iron deficiency anemia (IDA). Rates of screening and treatment of IDA are often low. A clinical decision support system (CDSS) embedded in an electronic health record could improve adherence to evidence-based care. Rates of CDSS adoption are often low due to poor usability and fit with work processes. One solution is to use human-centered design (HCD), which designs CDSS based on identified user needs and context of use and evaluates prototypes for usefulness and usability. Objectives this study aimed to use HCD to design a CDSS tool called the IBD Anemia Diagnosis Tool, IADx. Methods Interviews with IBD practitioners informed creation of a process map of anemia care that was used by an interdisciplinary team that used HCD principles to create a prototype CDSS. The prototype was iteratively tested with “Think Aloud” usability evaluation with clinicians as well as semi-structured interviews, a survey, and observations. Feedback was coded and informed redesign. Results Process mapping showed that IADx should function at in-person encounters and asynchronous laboratory review. Clinicians desired full automation of clinical information acquisition such as laboratory trends and analysis such as calculation of iron deficit, less automation of clinical decision selection such as laboratory ordering, and no automation of action implementation such as signing medication orders. Providers preferred an interruptive alert over a noninterruptive reminder. Conclusion Providers preferred an interruptive alert, perhaps due to the low likelihood of noticing a noninterruptive advisory. High levels of desire for automation of information acquisition and analysis with less automation of decision selection and action may be generalizable to other CDSSs designed for chronic disease management. This underlines the ways in which CDSSs have the potential to augment rather than replace provider cognitive work.

The detection of multilayer clouds in the atmosphere can be particularly challenging from passive visible and infrared imaging radiometers since cloud boundary information is limited primarily to the topmost cloud layer. Yet detection of low clouds in the atmosphere is important for a number of applications, including aviation nowcasting and general weather forecasting. In this work, we develop pixel-based machine learning–based methods of detecting low clouds, with a focus on improving detection in multilayer cloud situations and specific attention given to improving the Cloud Cover Layers (CCL) product, which assigns cloudiness in a scene into vertical bins. The random forest (RF) and neural network (NN) implementations use inputs from a variety of sources, including GOES Advanced Baseline Imager (ABI) visible radiances, infrared brightness temperatures, auxiliary information about the underlying surface, and relative humidity (which holds some utility as a cloud proxy). Training and independent validation enlists near-global, actively sensed cloud boundaries from the radar and lidar systems on board the CloudSat and CALIPSO satellites. We find that the RF and NN models have similar performances. The probability of detection (PoD) of low cloud increases from 0.685 to 0.815 when using the RF technique instead of the CCL methodology, while the false alarm ratio decreases. The improved PoD of low cloud is particularly notable for scenes that appear to be cirrus from an ABI perspective, increasing from 0.183 to 0.686. Various extensions of the model are discussed, including a nighttime-only algorithm and expansion to other satellite sensors. Significance Statement Using satellites to detect the heights of clouds in the atmosphere is important for a variety of weather applications, including aviation weather forecasting. However, detecting low clouds can be challenging if there are other clouds above them. To address this, we have developed machine learning–based models that can be used with passive satellite instruments. These models use satellite observations at visible and infrared wavelengths, an estimate of relative humidity in the atmosphere, and geographic and surface-type information to predict whether low clouds are present. Our results show that these models have significant skill at predicting low clouds, even in the presence of higher cloud layers.

To assess the accuracy of a real-time benefit tool (RTBT) that is compliant with the standards of the National Council for Prescription Drug Programs (NCPDP) in a large academic medical center.Observational study of electronic health records and pharmacy records from July 14, 2019, through January 14, 2020, across all ambulatory clinics and outpatient pharmacies in the health system.Main assessments included (1) demographic characteristics of patients in whom the RTBT was used and those in whom it was not used, (2) types of changes most frequently made to medication orders upon reviewing the RTBT, and (3) comparison of the out-of-pocket costs for prescriptions vs the RTBT-generated estimates.The most common modifications made to prescriptions due to RTBT use were changes in days' supply (44%) and the quantity of medication (69%). In more than 98% of prescription orders, patients' out-of-pocket costs were either equivalent to or lower than the estimates generated by the RTBT.Current standards established by NCPDP yield accurate patient out-of-pocket estimates and could serve as a national standard for all Part D sponsors.

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.

OBJECTIVE: Use of telemedicine in pediatric gastroenterology has increased dramatically in response to the coronavirus disease 2019 (COVID-19) pandemic. The goal of this study was to systematically assess the usability of telemedicine in the field of pediatric gastroenterology. METHODS: The previously validated Telehealth Usability Questionnaire was distributed to physician pediatric gastroenterologist members of North American Society for Pediatric Gastroenterology Hepatology and Nutrition. Physician demographic and practice characteristics were collected. Data were analyzed using descriptive, linear mixed-effect, and ordinary least squares regression methods. RESULTS: One hundred sixty pediatric gastroenterologists completed the survey. The majority were from academic practice (77%) with experience ranging from trainee (11%) to over 20 years of clinical practice (34%). Most (82%) had no experience with telemedicine before the pandemic. The average usability score (scale 1-5) was 3.87 (σ = 0.67) with the highest domain in the usefulness of telemedicine (µâ€Š= 4.29, σ = 0.69) and physician satisfaction (µâ€Š= 4.13, σ = 0.79) and the lowest domain in reliability (µâ€Š= 3.02, σ = 0.87). When comparing trainees to attending physicians, trainees' responses were almost one point lower on satisfaction with telemedicine (trainee effect = -0.97, Bonferroni adjusted 95% confidence interval = -1.71 to -0.23). CONCLUSION: Pediatric gastroenterologists who responded to the survey reported that the technology for telemedicine was usable, but trainees indicated lower levels of satisfaction when compared to attending physicians. Future study is needed to better understand user needs and the impacts of telemedicine on providers with different levels are experience to inform efforts to promote implementation and use of telemedicine beyond the pandemic.

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.

By accounting for surface-based light source emissions and Top-of-Atmosphere (TOA) downward lunar fluxes, we adapted the Spherical Harmonic Discrete Ordinate Method (SHDOM) 3-dimentional (RTM) radiative transfer model (RTM) to simulate nighttime 3-D TOA radiances as observed from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night-Band (DNB) onboard the Suomi-NPP satellite platform. Used previously for daytime 3D applications, new SHDOM enhancements allow for the study of the impacts of various observing conditions and aerosol properties on simulated VIIRS-DNB TOA radiances. Observations over Dakar, Senegal, were investigated for potential applications and opportunities for using observed radiances containing VIIRS-DNB ‘bright pixels’ from artificial light sources to conduct aerosol retrievals. We found that using the standard deviation (STD) of such bright-pixels provided a more stable quantity for nighttime aerosol optical depth (AOD) retrievals than direct retrievals from TOA radiances. Further, both the mean TOA radiance and STD of TOA radiances over artificial sources are significantly impacted by satellite viewing angles. Light domes, the enhanced radiances adjacent to artificial light sources, are strong functions of aerosol properties and especially aerosol vertical distribution, which may be further utilized for retrieving aerosol layer height in future studies. Through inter-comparison with both day- and night-time Aerosol Robotic Network (AERONET) data, the feasibility of retrieving nighttime AODs using 3-D RTM SHDOM over artificial light sources was demonstrated. Our study shows strong potential for using artificial light sources for nighttime AOD retrievals, while also highlighting larger uncertainties in quantifying surface light source emissions. This study underscores the need for surface light emission source characterizations as a key boundary condition, which is a complex task that requires enhanced input data and further research. We demonstrate how quality-controlled nighttime light data from the NASA’s Black Marble product suite could serve as a primary input into estimations of surface light source emissions for nighttime aerosol retrievals.

“Milky seas” are massive swaths of uniformly and steadily glowing ocean seen at night. The phenomenon is thought to be caused by luminous bacteria, but details of milky sea composition, structure, cause, and implications in nature remain largely uncertain. Between late July and early September 2019, specialized low-light satellite sensors detected a possible bioluminescent milky sea south of Java, Indonesia, spanning >100,000 km 2 . Upon learning of these findings, crew members of the yacht Ganesha reached out to confirm and share details of their personal encounter with this same event. Here, we document Ganesha ’s experience as recalled by the crew, compare their course to satellite data, and assess their photography of this milky sea.

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.

Passive satellite observations play an important role in monitoring global aerosol properties and helping quantify aerosol radiative forcing in the climate system. The quality of aerosol retrievals from the satellite platform relies on well-calibrated radiance measurements from multiple spectral bands, and the availability of appropriate particle optical models. Inaccurate scattering phase function assumptions can introduce large retrieval errors. The high-spatial resolution, dual-view observations from the advanced baseline imagers onboard the two most recent geostationary operational environmental satellites (GOES), East and West, provide a unique opportunity to better constrain the aerosol phase function. Using dual GOES reflectance measurements for a dust event in the Gulf of Mexico in 2019, we demonstrate how a first-guess phase function can be reconstructed by considering the variations in observed scattering angles throughout the day. Using the reconstructed phase function, aerosol optical depth retrievals from the two satellites are self-consistent and agree well with surface-based optical depth estimates. We evaluate our methodology and reconstructed phase function against independent retrievals made from low-Earth-orbit multi-angle observations for a different dust event in 2020. Our new aerosol optical depth retrievals have a root-mean-square-difference of 0.019–0.047. Furthermore, the retrievals between the two geostationary satellites for this case agree within about 0.059 ± 0.072, as compared to larger discrepancies between the operational GOES products at times, which do not employ the dual-view technique.

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.

Objective The Leadership in Analytics and Data Science (LEADS) course was evaluated for effectiveness. LEADS was a 6-month program for working biomedical and health informatics (BMHI) professionals designed to improve analytics skills, knowledge of enterprise applications, data stewardship, and to foster an analytics community of practice through lectures, hands-on skill building workshops, networking events, and small group projects. Methods The effectiveness of the LEADS course was evaluated using the Kirkpatrick Model by assessing pre- and postcourse knowledge, analytics capabilities, goals, practice, class lecture reaction, and change in the size of participant professional networks. Differences in pre- and postcourse responses were analyzed with a Wilcoxon signed rank test to determine significance, and effect sizes were computed using a z-statistic. Results Twenty-nine students completed the course with 96% of respondents reporting that they were “very” or “extremely” likely to recommend the course. Participants reported improvement in several analytics capabilities including Epic data warehousing (p = 0.017), institutional review board policy (p = 0.005), and data stewardship (p = 0.007). Changes in practice patterns mirrored those in self-reported capability. On average, the participant professional network doubled. Conclusion LEADS was the first course targeted to working BMHI professional at a large academic medical center to have a formal effectiveness evaluation be published in the literature. The course achieved the goals of expansion of BMHI knowledge, skills, and professional networks. The LEADS course provides a template for continuing education of working BMHI professionals.

During 30 September to 9 October 2016, Hurricane Matthew traversed the Caribbean Sea to the east coast of the United States. During its period of greatest intensity, in the central Caribbean, Matthew excited a large number of concentric gravity waves (GWs, or CGWs). In this paper, we report on hurricane‐generated CGWs observed in both the stratosphere and mesosphere from space‐borne satellites and in the ionosphere by ground Global Positioning System (GPS) receivers. We found CGWs with horizontal wavelengths of ~200–300 km in the stratosphere (height of ~30–40 km) and in the airglow layer of the mesopause (height of ~85–90 km), and we found concentric traveling ionospheric disturbances (TIDs, or CTIDs) with horizontal wavelengths of ~250–350 km in the ionosphere (height of ~100–400 km). The observed TIDs lasted for more than several hours on 1, 2 and 7 October 2016. We also briefly discuss the vertical and horizontal propagation of the Hurricane Matthew‐induced GWs and TIDs. This study shows that Hurricane Matthew induced significant dynamical coupling between the troposphere and the entire middle and upper atmosphere via GWs. It is the first comprehensive satellite analysis of gravity wave propagation generated by hurricane event from the troposphere through the stratosphere and mesosphere into the ionosphere.

We describe a quantitative evaluation of maritime transparent cirrus cloud detection, which is based on Geostationary Operational Environmental Satellite – 16 (GOES-16) and developed with collocated Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) profiling. The detection algorithm is developed using one month of collocated GOES-16 Advanced Baseline Imager (ABI) Channel 4 (1.378 μm) radiance and CALIOP 0.532 μm column-integrated cloud optical depth (COD). First, the relationships between the clear-sky 1.378 μm radiance, viewing/solar geometry, and precipitable water vapor (PWV) are characterized. Using machine learning techniques, it is shown that the total atmospheric pathlength, proxied by airmass factor (AMF), is a suitable replacement for viewing zenith and solar zenith angles alone, and that PWV is not a significant problem over ocean. Detection thresholds are computed using the Ch. 4 radiance as a function of AMF. The algorithm detects nearly 50% of sub-visual cirrus (COD < 0.03), 80% of transparent cirrus (0.03 < COD < 0.3), and 90% of opaque cirrus (COD > 0.3). Using a conservative radiance threshold results in 84% of cloudy pixels being correctly identified and 4% of clear-sky pixels being misidentified as cirrus. A semi-quantitative COD retrieval is developed for GOES ABI based on the observed relationship between CALIOP COD and 1.378 μm radiance. This study lays the groundwork for a more complex, operational GOES transparent cirrus detection algorithm. Future expansion includes an over-land algorithm, a more robust COD retrieval that is suitable for assimilation purposes, and downstream GOES products such as cirrus cloud microphysical property retrieval based on ABI infrared channels.

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.