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Satellite infrared detectors cannot penetrate clouds, especially precipitating clouds. Improving precipitation estimation accuracy based on infrared brightness temperature has always been important but challenging. In this paper, based on the infrared brightness temperature of the Advanced Geosynchronous Radiation Imager (AGRI) onboard China's Feng‐Yun 4A satellite, we develop and evaluate a new precipitation estimation method. First, using static data, physical characteristics of clouds, cloud image texture features, temporal motion features, and AGRI infrared channel brightness temperature, we construct features for a machine learning model. Then, we develop precipitation estimation methods. Precipitation is estimated in two steps: classification and regression. We employ a random forest classification model to identify whether there is precipitation in a given field of view. If there is precipitation, a multi‐model ensemble regression learning method is used to estimate the areas with this precipitation. The ensemble learning method uses convex optimization to integrate prediction results based on the optimization of hyperparameters of five basic models (i.e., those of random forest, XGBoost, LightGBM, decision tree, and extra tree models). Furthermore, two regression stacking ensemble models—the Least Absolute Shrinkage and Selection Operator (herein referred to as Stacking1‐LASSO) and K‐nearest neighbor (herein referred to as Stacking2‐KNN)—are used to predict the results of the aforementioned basic models. The results of basic models are used as inputs of these two stacking models. Finally, based on the Integrated Multi‐satellitE Retrievals for GPM (IMERG) precipitation product and rain gauge precipitation data, we conduct precipitation estimation experiments and evaluate our methods. The results show that ensemble learning models have greater accuracy in estimating precipitation than the basic models. When using IMERG precipitation as the target precipitation, ensemble learning models can estimate the central area of heavy precipitation during typhoons Ampil and Maria. The ensemble learning estimation effect is better than that of Stacking2‐KNN. Moreover, when rain gauge data is used as the target precipitation, ensemble learning can also estimate the center of heavy precipitation and with good consistency with recorded satellite brightness temperature data. Key Points: A convex optimization weighted and stacked ensemble learning method for satellite data estimation of precipitation is developedNear real‐time precipitation estimates can be obtained throughout the day using only geographic information and Advanced Geosynchronous Radiation Imager infrared brightness temperature dataThe proposed ensemble learning method can estimate the central areas of typhoons and short‐term heavy rainfall [ABSTRACT FROM AUTHOR]

Historically, the long wavelength side (6–7.5 μm) of the mid‐infrared water vapor absorption band has been used for imaging from the geostationary perspective. This began with the 6.4 μm band on Europe's Meteosat‐1. While geostationary sounders for moisture profiling, including China's hyperspectral resolution infrared sounder and a planned sounder from Europe, are or will be measuring the short wavelength side of the water vapor band, this is not the case for geostationary imagers. Shorter wavelength (5–6 μm) spectral bands for imaging applications should be considered for observing moisture in the mid and lower troposphere because of several potential advantages offered by this spectral range. The short wavelength side of the water vapor band contains fewer additional absorbing gases that overlap the water vapor absorption lines. In addition, the shorter wavelengths would show less diffraction blurring which could enable finer spatial resolution for turbulence detection. This study considers some of the differences and potential advantages of the spectral information in the short wavelength side of the water vapor absorption band from the perspective of geostationary imaging. For dry conditions land heating could impact the qualitative use of observed brightness temperatures at 5.1 μm, while the solar reflection component over most clear‐sky scenes is small for a 5.1 μm band and essentially zero for a 5.6 μm band. Plain Language Summary: Long before images are sent back to Earth from satellites, the needed spectral bands have to be specified. The band specification includes attributes such as the wavelength centers, widths, and signal‐to‐noise ratio. This paper investigates the rationale for including data from a region of the electromagnetic spectrum that has not been used for imaging from geostationary orbit. If implemented, these short wavelength water vapor bands would provide unique information regarding low‐level moisture and mid‐level turbulence. This paper quantifies the expected reflected component from the sun and the influence of land heating on observations, both of which are relatively larger at shorter wavelengths. Key Points: Historically, the long wavelength side of the mid‐infrared water vapor absorption band has been used for imagingShort wavelength (near 5 μm) spectral bands should be considered, especially with finer spatial resolutionsOver most clear‐sky surfaces, the reflective component ranges from small to zero for 5.1 and 5.6 μm bands [ABSTRACT FROM AUTHOR]

Fram Strait is the passageway where most drifting sea ice exits the Arctic Ocean into the North Atlantic, and the sea‐ice velocity (SIV) is the most critical parameter for the variability of the sea ice area flux through Fram Strait. Sea ice flux estimates through the Fram Strait based on satellite remote sensing exist. However, they show discrepancies, which mainly result from the sparse valid satellite observations and the increased uncertainty of sea ice motion in the Fram Strait. In order to improve the sea ice flux estimates, we develop an improved sea‐ice velocity retrieval algorithm from 89 GHz brightness temperature fields of Advanced Microwave Scanning Radiometer for EOS and AMSR2. The improved retrieval algorithm employs an adjusted size of the template window and performs bilinear interpolation on candidate windows before the matching procedure. Instead of depending on external data sources, we construct the SIV uncertainty fields depending only on the maximum correlation coefficients of the image itself. Compared with two similar Passive Microwave (PM) sea ice velocity datasets, the new sea‐ice velocity has lower bias and root‐mean‐square error with respect to buoy observations. In addition, the data density of the new SIV is 14% and 36% higher than the two reference datasets, respectively. Monthly sea ice area flux estimates through the Fram Strait are acquired with the new daily sea‐ice velocity data and the existed sea‐ice concentration. And it indicates that the improved sea ice area flux estimated by PM sea ice velocity agrees well with the Synthetic‐aperture radar‐derived sea ice area flux. Plain Language Summary: The sea ice outflux through the Fram Strait is critical for the mass balance of Arctic sea ice cover. However, previous studies tend to underestimate ice flux based on satellite data sets due to the weaker southward ice velocity with respect to buoy measurements. In order to acquire the accurate ice velocity in the Fram Strait to improve the ice flux estimates, we improve a pattern‐tracking sea ice retrieval algorithm derived from 89 GHz brightness temperature in the Fram Strait. Besides, we acquire the uncertainties with spatiotemporal variability of ice velocity based on the pattern features rather than any external data sources. This improved sea ice velocity data set will be helpful to future studies on data assimilation and model improvements. Key Points: An optimal sea ice motion tracking method based on passive microwave image in the Fram Strait has been investigatedUncertainty of sea ice velocity has been estimated based on the maximum correlation coefficient of templateImproved sea ice area flux through Fram Strait during winter has been acquired based on improved sea ice velocity data sets [ABSTRACT FROM AUTHOR]

Solar irradiance received at the lunar surface is crucial for interpreting brightness temperatures detected by orbiters and for understanding the thermal, physical, and dielectric properties of the lunar regolith. We developed a real‐time effect solar irradiance (ESI) model that accounts for the influence of surface relief and terrain shading. This model was integrated with a standard thermal model to examine ESI fluctuations and their impacts on the diurnal physical temperature variations. To assess the effects of spatial resolution, we selected four locations with significant ESI disparities for simulation, then compared lunar surface temperatures at various spatial scales, ranging from 20 m to 25 km. Utilizing brightness temperature data obtained from the Chang'E‐2 (CE‐2) microwave radiometer (MRM), we integrated the shallow physical temperature profiles with the radiative transfer equation to simulate brightness temperatures and determine dielectric loss at different frequencies. In the Von Kármán crater, the received ESI exhibits a cyclical pattern of approximately 18 years and areas with rugged topography may exhibit larger ESI variations (∼7%). We found that the spatial resolution of ESI has a minimal effect on the physical and brightness temperatures at resolutions of 10 km or coarser. At the shallow layer, the average dielectric loss values are 0.0128–0.0170, 0.0083–0.0110, 0.0055–0.0073, and 0.0061–0.0081 for the CE‐2 frequencies of 3, 7.8, 19.35, and 37 GHz, respectively. The integration of real‐time ESI modeling, thermal dynamics, radiative transfer equations, and observational data enhances our comprehension of the physical temperature profile and thermal characteristics of shallow regolith. Plain Language Summary: The combination of solar irradiance and temperature data sets detected by orbiters can help to explain the temperature variation and thermophysical properties of shallow lunar surface. This study develops a real‐time effect solar irradiance (ESI) model that takes into account the influence of lunar topography and integrates it with a thermal model to simulate physical temperature variations within the Von Kármán crater. We compared lunar surface physical temperature profiles at spatial resolutions of 20, 240, 500 m, 1, 5, 10, 15, and 25 km, each exhibiting distinct ESI characteristics, to assess the impact of spatial resolution. We then simulated the temperature data in the four frequencies consistent with Chang'E‐2 (CE‐2) microwave radiometer and obtained the average ranges of dielectric loss. At 3, 7.8, 19.35, and 37 GHz, the average dielectric loss ranges were determined to be 0.0128–0.0170, 0.0083–0.0110, 0.0055–0.0073, and 0.0061–0.0081, respectively. These findings provide valuable insights for analyzing lunar surface temperature and observed brightness temperature, as well as for inverting physical and dielectric properties of the shallow regolith (e.g., dielectric loss). Key Points: Analyzing lunar shallow temperature variation with real‐time effect solar irradiance (ESI) model considering terrain influenceAssessing the real‐time ESI impact on physical and brightness temperature curves at diverse spatial resolutionsEstimating the dielectric loss of shallow regolith by coupling simulated brightness temperature data with observations [ABSTRACT FROM AUTHOR]

Microwave observations of brightness temperature (TB) from multiple polar‐orbiting operational environmental satellite imagers and infrared observations of geostationary operational environmental satellite Himawari‐8 imager are used to investigate characteristic features of secondary eyewall evolution of Typhoon Lekima (2019). It is found that microwave observations showed a longer duration time of concentric double eyewalls than Advanced Himawari Imager (AHI) infrared observations, due to a stronger cloud penetration capability of microwave than infrared observations. The AHI infrared observations with high temporal and horizontal resolutions captured several actively propagating inner spiral rainbands before the secondary eyewall formation (SEF) of Lekima (2019). They are characterized by a wavenumber‐1 asymmetry propagating radially outward and wavenumbers‐2 and ‐3 asymmetries propagating anticlockwise. The wavenumber‐2 asymmetry approximates an azimuthal phase speed of wavenumber‐2 vortex Rossby wave. Later, an outer spiral rainband experienced an obvious axisymmetrization, then merged with multiple inner spiral rainbands propagating radially outward from the primary eyewall. The stagnation radius of these inner spiral rainbands coincides with the location of the highly symmetric outer spiral rainband, which accelerated the SEF. It is suggested that the SEF of Typhoon Lekima (2019) results from a concerted activity of inner and outer spiral rainbands. Plain Language Summary: Typhoon Lekima (2019) is taken as an example to show the features associated with the secondary eyewall formation (SEF) revealed directly by satellite microwave and infrared observations of brightness temperature (TB). Microwave observations see the SEF earlier than infrared TB observations when the lower‐level secondary eyewall formed below upper‐level clouds. The secondary eyewall of Lekima evolved from an axisymmetrization of an outer spiral rainband. Several inner spiral rainbands propagated radially outward from the primary eyewall and had their stagnation radius at the location of the outer spiral rainband. The merge of these inner spiral rainbands with the outer rainband accelerates the SEF. The findings in this study highlight the merit of satellite observations for studying structural changes of tropical cyclones. Key Points: The characteristic features of secondary eyewall of Typhoon Lekima are revealed by satellite microwave and infrared observationsThe inner‐core rainbands of Lekima in Advanced Himawari Imager infrared observations have propagating characteristics like vortex Rossby wavesThe secondary eyewall formation of Lekima is suggested to result from a concerted activity of inner and outer spiral rainbands [ABSTRACT FROM AUTHOR]

This study evaluates simulated radiance forecasts from a series of controlled experiments consisting of FV3‐LAM forecasts with different configurations of model physics and vertical resolution. The forecasts were produced during the 2020 Hazardous Weather Testbed Spring Forecasting Experiments on the same forecast cases. The evaluation includes grid‐point, neighborhood‐based and object‐based verification. The experiments include forecasts that were identical except for the physics (EMC‐LAM vs. EMC‐LAMx), vertical resolution (EMC‐LAMx vs. NSSL‐LAM), or combined initial conditions, physics and vertical resolution (GSL‐LAM). It is found that the EMC‐LAM generally provided better simulated radiance forecasts than the other three configurations at most forecast lead times, due to its unique physics configuration. All configurations generally over‐forecasted high level clouds. EMC‐LAM reduced the over‐forecasting of high clouds, but also under‐forecasted the coverage of mid‐level clouds. In contrast, at early lead times the EMC‐LAM had relatively poor performance relative to the other forecasts. Furthermore, EMC‐LAM was an outlier in terms of the vertical structure of clouds. It is also found that the NSSL‐LAM consistently improved upon the EMC‐LAMx, which had fewer vertical levels than NSSL‐LAM. Compared to EMC‐LAMx, NSSL‐LAM had less cloud over‐forecasting bias, especially with small cloud objects, and less overall error. The differences between EMC‐LAMx and GSL‐LAM were generally much smaller than the differences between EMC‐LAMx and EMC‐LAM/NSSL‐LAM. Finally, it is found that a non‐linear bias correction conditioned on symmetric brightness temperature reduced the overall root‐mean‐square error by about a factor of 2 while improving the unrealistic vertical structure of clouds in the EMC‐LAM. Plain Language Summary: While forecasts from different convection‐allowing model (CAM) configurations are often evaluated in terms of simulated reflectivity or precipitation, many users are also interested in simulated brightness temperature and associated cloud features. This study evaluates the forecast performance of several configurations of the FV3‐LAM CAM in terms of simulated infrared brightness temperature. The different configurations highlight the impacts of vertical resolution and physical parameterizations, in particular. It is found that the older physics parameterization, with Geophysical Fluid Dynamics Laboratory microphysics and Eddy‐Diffusivity Mass‐Flux boundary layer, performed generally better than a newer configuration, with Thompson microphysics and Mellor‐Yamada‐Nakanishi‐Niino boundary layer, that had previously been found to improve precipitation and simulated reflectivity forecasts. The difference is related to both the presence of an additional cumulus parameterization in the older physics, as well as the tendency of the newer Thompson microphysics scheme to produce large regions of high cloud near deep convection. However, at early lead times the older physics configuration took longer to spin up ongoing convection. It is also found that enhanced vertical resolution consistently improved the forecasts. A caveat to these conclusions is that the FV3‐LAM and its physics configuration are undergoing continuous development which may change these results in the newest model versions. Key Points: FV3‐LAM physics configuration strongly impacts the performance of simulated brightness temperature forecastsVertical resolution has a consistent impact, related to a reduction in over‐forecasting of small clouds with enhanced vertical resolutionBias correction greatly reduces the brightness temperature forecast errors as well as reducing errors in cloud vertical structure [ABSTRACT FROM AUTHOR]

Weather forecasting centers mainly assimilate infrared sounder data in clear‐conditions or in channels with their main sensitivity to the atmosphere well above the cloud tops. Sometimes channels with stronger cloud sensitivity are used in overcast conditions, but currently no cloud information is used from infrared sounders, and all‐sky assimilation approaches are still under development. However, cloudy radiances could already be used for validating the quality of clouds in forecasts. We illustrate this by comparing the brightness temperatures observed (obs) with AIRS (Atmospheric Infrared Sounder) to those calculated (cal) based on the clouds specified in the ECMWF (European Centre for Medium Range Weather Forecasting) Integrated Forecast System (IFS). Our analysis is based on a 12 hr ingest of AIRS data into the ECMWF assimilation system. We show that the standard deviation of (obs‐cal) using the 1,231 cm−1 atmospheric window channel is a metric of the fidelity of the clouds in the IFS. The global standard deviation of 5 K after accounting for likely space/time interpolation errors, appears to be dominated by clouds in the IFS which are not seen in the AIRS data, and vice versa. Our metric capitalizes on the unique sensitivity of infrared sounders to clouds for the routine monitoring of the fidelity of clouds in weather forecasts. Plain Language Summary: Key to a good weather forecast is clouds of the right variety in the right places at the right time. We evaluate this for 12 hr of European Centre for Medium Range Weather Forecasting (ECMWF) data by comparing the clouds created from the temperature and water vapor profiles and first principle physics with the clouds observed with the Atmospheric Infrared Sounder (AIRS). For the global mean, there is a good match, but locally the ECMWF finds deep convective clouds which are not seen in the AIRS data, and vice versa, in clusters on a scale of hundreds of kilometers. We propose a metric for the cloud fidelity, which allows monitoring and quantifying improvements as new algorithms or data are included in the Integrated Forecast System. Key Points: We compare the clouds observed from space to the clouds in a medium range weather forecast system, where clouds are created from first principle physics aloneWe propose a metric, which allows the numerical assessments of the cloud fidelity of the current forecast system to be compared with future upgradesOn average, the agreement between the forecast clouds and Atmospheric Infrared Sounder (AIRS) observations is very good, but, particularly in areas prone to deep convection, there are large cloudy areas which are not seen in the AIRS data, and vice versa [ABSTRACT FROM AUTHOR]

We describe and validate a method of calculating convective cloud top altitudes and potential temperatures up to 50° latitude at high spatial (0.25°) and temporal (3 hr) resolution. The approach uses the statistics of the CloudSat cloud radar deep convective cloud classification product coupled with nighttime CALIOP lidar measurements to effectively "calibrate" the high frequency, high resolution global rainfall and brightness temperature data that is used to derive convective cloud top altitudes. Thus, our product agrees well with the statistics of the CloudSat/CALIOP convective cloud tops, especially in the tropics and over oceanic regions. Agreement is reasonable, but not as good, for land‐based convection. The estimated uncertainty in cloud top altitudes in our product is 0.5–1.0 km over land areas, with smaller uncertainties over the oceans. The diurnal cycle of the new convection data set is in good agreement with precipitation radar convective climatology. Plain Language Summary: Convection modifies the water vapor concentration and injects trace gases into the upper troposphere and the lower stratosphere. To quantify the impact of convection, we have developed a data set of convective cloud top altitudes. Our algorithm uses lidar and cloud radar data to calibrate a combination of rainfall and geostationary satellite brightness temperature data to produce a database of convective cloud top altitudes. The statistics of our high‐resolution convective cloud top altitudes agree well with the statistics of other more limited convective cloud top data sets, especially over the oceans and in the tropics. Key Points: A high resolution data set of convective cloud top altitudes is calculated based on rainfall and brightness temperature observationsThe frequency statistics and diurnal cycle of this data set show good agreement with other observationsThe uncertainty in the data set of convective cloud tops is 0.5–1.0 km, or 5–15 K potential temperature [ABSTRACT FROM AUTHOR]

The newly launched early morning satellite Fengyun‐3E (FY‐3E) helps to form a three‐orbit constellation for better observing the first Typhoon Malakas in 2022. Together with MetOp‐B and NOAA‐20, global observations are made available 6 times daily from three temperature sounders of MWTS‐3, AMSU‐A, and ATMS onboard FY‐3E, MetOp‐B, and NOAA‐20, respectively. Having channel frequencies much less than 200 GHz, brightness temperatures (TBs) at different sounding channels are linearly related to temperatures at different altitudes. This allows Malakas's warm cores to be retrieved from MWTS‐3, AMST‐A, and ATMS TB observations. The warm‐core maxima of Malakas at 250 hPa has a single‐peaked diurnal cycle, with its maximum and minimum peaking around midnight and noon, respectively. FY‐3E MWTS‐3 observations allowed the intensity and phase of the diurnal cycle better captured. The diurnal variations of warm core retrieved from all‐sky TB simulations of the ERA5 reanalysis and National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) analysis compared well with the three‐orbit constellation retrieval. All‐sky simulations of TB from the NCEP GFS analysis compared more favorably with FY‐4A AGRI TB observations than those from the ERA5 reanalysis. Plain Language Summary: As the world's first early morning‐orbit meteorological satellite for civil use, Fengyun‐3E (FY‐3E) was launched successfully on 5 July 2021. The FY‐3E satellite fills the vacancy of the global early morning‐orbit satellite observations. This study examines an added value of FY‐3E MWTS‐3 for observing the first typhoon in 2022 when working together with the mid‐morning‐orbit MetOp‐B and the afternoon‐orbit NOAA‐20 satellites. A temperature retrieval algorithm was applied to TB observations from four microwave temperature sounders MWTS‐3, ATMS, AMSU‐A and MWTS‐2 onboard FY‐3E, NOAA‐20, MetOp‐B and FY‐3D, respectively. We showed that FY‐3E MWTS‐3 observations made a non‐negligible impact on describing a single‐peaked diurnal cycle of Malakas's warm cores, especially the phase of the diurnal cycle. The diurnal variations and the vertical extents of warm core retrieved from satellite TB observations compared well with those from all‐sky TB simulations of the ERA5 reanalysis and National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) analysis. Besides, All‐sky simulations of TB from the NCEP GFS analysis compared more favorably with FY‐4A AGRI TB observations than those from the ERA5 reanalysis. Key Points: The first typhoon in 2022 was well observed by a newly formed three‐orbit constellation of polar‐orbiting operational satellitesThe early morning satellite FY‐3E MWTS‐3 observations improved the retrieval accuracy of the diurnal cycle of typhoon warm‐core intensityAll‐sky simulations from ERA5 reanalysis and National Centers for Environmental Prediction Global Forecast System analysis compared well with satellite retrieval of typhoon temperature anomaly [ABSTRACT FROM AUTHOR]

Hyperspectral infrared sounding contains information about clouds, which plays an important role in modulating Earth's climate. However, there is a great deal of uncertainty in modeling the radiative effect of clouds due to its complex dependence on various parameters. Therefore, cloudy scenarios are often neglected in retrievals of infrared spectral measurements and in data assimilation. One‐dimensional radiative transfer (RT) models have a limited capability to represent the cloud three‐dimensional multilayer structure. This issue is typically resolved by using a multiple independent column approach, which is computationally demanding. Therefore, it is necessary to find a balance between computational speed and accuracy for infrared RT all‐sky radiance simulations. In this study, we utilize the Community Radiative Transfer Model with four different cloud overlap schemes and compare against observations made by the Atmospheric Infrared Sounder (AIRS) using a statistical metric called the first Wasserstein distance. Our results show that the average cloud overlap scheme performs the best and successfully predicts the overall probability distribution of brightness temperature over nonfrozen oceans for a wide range of wavelengths. The mean absolute differences are less than 0.7 K for 846 selected AIRS channels between 790 cm−1 and 1231 cm−1. Plain Language Summary: Clouds have a major impact on Earth's climate. However, modeling the three‐dimensional effects of clouds is complicated and computationally intensive, necessitating approximations with respect to their vertical structure. We utilize a commonly used RT model and evaluate a variety of cloud overlap schemes against observations made by the Atmospheric Infrared Sounder. This work introduces two novelties to the evaluation. First, we perform comparisons for hundreds of wavelengths to exploit the hyperspectral nature of the measurements. Second, we utilize the first Wasserstein distance metric rather than the traditionally used Pearson correlation. We demonstrate that the former is a much better approach to compare probability distributions. The best performing overlap scheme obtains a very good match with measurements. Key Points: Hyperspectral simulations, for more than 80,000 scenarios, using different cloud overlap schemes are compared with Atmospheric Infrared Sounder (AIRS) observationsAverage overlap scheme provides best results compared with AIRS observationsFirst Wasserstein distance is a more suitable metric than the commonly used Pearson correlation for comparing probability distributions [ABSTRACT FROM AUTHOR]

Clouds play a significant role in the Earth's energy balance and hydrologic cycle through their effects on radiation and precipitation, and therefore are crucial for life on Earth. Earth's NexT‐generation ICE mission (ENTICE) is proposed to measure diurnally resolved global vertical profiles of cloud ice particle size, ice water content, and in‐cloud humidity and temperature combining a 94 GHz cloud radar and multi‐frequency sub‐millimeter (sub‐mm) microwave radiometers from space. The scientific objective of ENTICE is to identify the important processes by which anvil clouds evolve and interact with ambient thermodynamic conditions to advance our fundamental understanding of clouds and reduce uncertainties in cloud climate feedback. Whether such a science objective could be achieved depends on the orbital sampling characteristics of the mission. In this study, ENTICE sampling statistics are simulated with more than 1 billion (109) profiles and using five different scanning methods in a 400 km altitude precession orbit with an inclination of 65°: nadir, forward pointing, side scanning, and conical scanning for the radiometers, and nadir pointing for the radar. These statistics can then be used to help with future mission planning efforts. Using the GEOS‐5 model atmosphere (produced by nature‐run at 7‐km and 30‐min resolution), the simulator calculates sampling statistics related to cloud types and overpass times with enhancement from radar. The wide swath of ENTICE radiometers by conical and side scanning methods ensures ample high cloud samples (1.3 × 109 samples) gathered by ENTICE over its 2‐year mission for different types of clouds with sufficient sampling over the diurnal cycles. Sampling differences between radar and radiometers at nadir shows the combination of radar and radiometers will allow for measurements of cloud vertical profiles. Therefore, our results show that the designed orbit sampling of ENTICE is sufficient to fulfill the mission science goals. Plain Language Summary: We present a satellite orbit sampling simulator for cloud measurements from space based on the novel design of a new satellite instrument called Earth's NexT‐generation ICE mission (ENTICE), which is a combined platform of multi‐frequency passive microwave radiometers and radar detector. In this study, the simulator is flying through a modeled atmosphere at a 400 km orbit above the surface. The cloud sampling is simulated using five different observation methods. We present results over a proposed 2‐year ENTICE mission for different types of clouds at different diurnal varying local times. Our results show that the designed orbit sampling of ENTICE is sufficient to fulfill the mission science goals. Key Points: A satellite orbital simulator for cloud sampling using passive radiometer and radar from space is developed for (Earth Next Generation Ice Mission) ENTICEThe simulator simulates four different sampling methods while flying in a model atmosphere at 400 km altitude precession orbit with an inclination of 65°The results demonstrate the designed orbit sampling of ENTICE generate sufficient data to fulfill the mission science goals [ABSTRACT FROM AUTHOR]

Marine boundary layer (MBL) clouds, ubiquitous to the world's oceans, help govern the radiative balance of Earth's climate system. Satellite remote sensing provides the most practical means to monitor cloud worldwide. Whereas visible‐based detection of MBL clouds from environmental satellites is relatively straightforward during the daytime, the night presents challenges. In certain conditions, the conventional infrared (IR) methods used for nocturnal cloud detection, such as the commonly used 11–3.9 μm brightness temperature difference, offer poor thermal and spectral contrast between clouds and the clear‐sky background—resulting in missed clouds. A less explored question is to what extent these IR techniques overstate the cloud field. The Day/Night Band (DNB), a novel low‐light sensor carried on the Joint Polar Satellite System constellation, is helping to address this question. By way of its daytime‐analog moonlight reflectance cloud detection, the DNB reveals situations where IR‐based techniques yield false‐alarm low clouds. These problems occur in conditions of cool surface and a warm/moist lower atmosphere, prevalent in areas of coastal upwelling, tidal mixing, river estuaries, and along oceanic fronts and cyclonic eddies. We show how differential sensitivity to MBL moisture can trigger algorithmic thresholds for cloud detection. Presented here are examples illustrating the problem and an evaluation of our hypothesis against idealized radiative transfer simulations. We consider the implications of such artifacts, including climate data records of sea surface temperature which rely upon IR‐based nocturnal cloud masks. The results provide a framework for designing algorithmic improvements to nighttime MBL cloud detection. Plain Language Summary: Marine boundary layer (MBL) clouds play a governing role in Earth's radiation balance. Satellite remote sensing provides the most practical means to collecting information about the frequency, distribution, and properties of MBL clouds, including their evolution across the day and night. Recent comparisons of operational cloud masks to novel nighttime moonlight reflectance observations from the Day/Night Band low‐light sensor has revealed instances where too many MBL clouds are being analyzed (i.e., false alarms) in the operational algorithms. This study explores the underlying physical basis for these issues, and shows how a special combination of atmosphere/surface conditions can produce a clear‐sky infrared (IR) spectral signature that is used in these algorithms as a test for the presence of low clouds. The false alarm mechanism is confirmed via radiative transfer simulations in both idealized and realistic environment scenarios. The implications of these findings are that localized to regional‐scale biases may exist in nocturnal MBL cloud descriptions, translating to possible biases in downstream environmental parameters reliant on clear‐sky scenes, such as sea surface temperature climate records. Increasing the frequency of nighttime visible observations and refining IR‐based algorithms in areas prone to false alarm could help to mitigate these issues. Key Points: The Day/Night Band, a low‐light satellite sensor, has revealed false alarms in infrared (IR)‐based nighttime low‐cloud detection algorithmsThe false alarms occur in situations of warm/moist air above a cool surface, due to sensitivity of IR observations to water vaporAreas of tidal mixing, coastal upwelling, and river discharge, may harbor biases in sea surface temperature records, which filter clouds [ABSTRACT FROM AUTHOR]

Over 11 years of data acquired by the Diviner Lunar Radiometer Experiment instrument aboard Lunar Reconnaissance Orbiter have been compiled into a comprehensive data set of surface temperatures in the Lacus Mortis region which includes the landing ellipse of the Astrobotic Mission One lander mission. These data provide diurnal brightness temperatures at 128 pixels per degree (ppd) spatial resolution and 0.1 hr of local time resolution. From this data set, we highlight several features that display variations in radiative and thermophysical properties in the Lacus Mortis region and characterize the temperatures of the Astrobotic Mission One landing ellipse. We identify distinctly contrasting properties of materials in the walls of Bürg crater, hummocks of materials on the southeast margin of the mare basalts, and materials exposed or excavated by impacts. Additionally, we describe an exceptionally rocky fault scarp that predates the formation of Bürg crater suggesting the observed boulders are replenished on a timescale <1 Ga. Within the Astrobotic landing ellipse, temperatures are observed to range from ∼88 to ∼359 K with sunrise and sunset local times constrained to 5.8–6.3 hr and 17.8 and 18.1 hr respectively. Key Points: Over 11 years of Diviner data are compiled into gridded temperatures of the Lacus Mortis regionVariations in radiative, thermophysical, and compositional properties are mapped and contrasting properties of materials are identifiedTemperatures within the Astrobotic Mission One landing ellipse suggest the regolith properties are similar to the global average [ABSTRACT FROM AUTHOR]

Infrared sounding instruments use large arrays of detectors to make simultaneous observations in multiple field‐of‐view (FOV). For Numerical Weather Prediction (NWP) models to assimilate multiple detector observations, FOV responsivities should be well matched so that forecasts are not degraded due to dis‐similar FOV properties. In this pseudo‐observing system simulation experiment (OSSE) study, hyperspectral infrared observations are assimilated to assess the impact of FOV heterogeneity on analyses and forecasts. The Cross‐track Infrared Sounder (CrIS) was selected as the representative instrument because it is an operational instrument with the lowest noise. The use of CrIS to this study is only limited to its detector configuration, orbital parameters, magnitudes of the instrument noise, and detector‐to‐detector bias. Perturbations in radiance to simulate detector heterogeneity are modeled to be wavelength and detector dependent. Results show that detector heterogeneity can be detrimental to analyses and forecasts. The data thinning routine relies on the brightness temperature of a surface sensitive channel to determine which profiles are used. If the FOV heterogeneity results in an increase in the brightness temperature, the FOV will be preferred, and this extraneous information will be used. The variational bias correction scheme does not account for biases associated with different FOVs. Unremoved biases are assimilated as "information" resulting in a biased analysis. Biases propagate and grow with time as the global data assimilation system cycles. The bias correction coefficients of all satellite observations are solved simultaneously with the atmospheric state analysis. In this way, the detector biases can influence other satellite observations. Key Points: IR hyperspectral detector heterogeneity influences selection of clear profiles as NCEP's current thinning algorithm prefers warmer pixelsBias correction schemes poorly characterize biases introduced by detector heterogeneityUnremoved biases are assimilated as "information" resulting in a biased analyses and degraded forecasts [ABSTRACT FROM AUTHOR]

Selected Earth targets are commonly used for satellite sensor calibration assessments (e.g., sensor stability and inter‐comparisons). Moreover, typical scenes used for the calibration assessment of the thermal emissive bands (TEBs) include Dome Concordia (Dome‐C), ocean, desert, and deep convective clouds (DCC). Reference data used for these calibration assessments can come from another band, another instrument, or ground measurements. The Dome‐C site, covered with uniformly distributed permanent snow, is normally used for the assessment of the TEBs at cold temperatures. Furthermore, ocean and desert measurements prove useful for scenes with higher temperatures. The DCC, one of the most consistent and coldest targets, can be used for the TEBs calibration and product stability assessments. Moderate Resolution Imaging Spectroradiometer (MODIS) band 31 (∼11 μm) can be used as a reference for these scenes. However, measurements over these scenes have seasonal variations, and the DCC brightness temperatures (BTs) have asymmetrical distributions. These features can introduce additional uncertainty to the stability assessments. A normalization method is applied by using an empirical model to derive reference‐dependent BTs. Using the developed empirical model, measurements can be normalized to a reference BT in order to enhance the calibration assessment's accuracy. This method is evaluated using all four scene types (i.e., ocean, desert, snow (Dome‐C), and DCC) and applied to all the Terra and Aqua MODIS TEBs. Stability assessments over the instruments' entire data records are presented and discussed. The technique can be applied in future efforts to support MODIS TEBs calibration assessments. Key Points: A normalization method is developed to enhance the accuracy of stability assessments using Earth measurementsThe normalization technique is applied to measurements over Dome‐C, deep convective clouds, the ocean, and the desertModerate Resolution Imaging Spectroradiometer thermal emissive bands long‐term stability assessments are presented and discussed [ABSTRACT FROM AUTHOR]