Clear Scenes Research Articles

Cape by Kevin Johnson

Reviewed by: Cape by Kevin Johnson Kate Quealy-Gainer, Editor Johnson, Kevin Cape; illus. by Kitt Thomas. Roaring Brook, 2023 [40p] Trade ed. ISBN 9781250840509 $18.99 Reviewed from digital galleys R* 4-8 yrs Our young Black protagonist knows the day ahead will be hard, so after getting dressed in his new suit, he makes sure to also put on his bright red cape. He’s hoping [End Page 328] the cape will hold off any unwelcome memories as he and his auntie “ride in a slow parade of cars,” eventually coming to a cemetery where they bury his father. After, family and friends fill the house, telling stories and sharing pictures, but the boy holds fast to his cape, wanting nothing to do with other people, and instead runs from his memories: “Memories make my swallow hurt. I block them with my cape. I fly them to outer space. I hide them in a sunken ship on the ocean floor.” Of course, even his best efforts can’t stop the grief, and as he finally lets himself remember his father, he also remembers the love they shared. The boy’s narration is simple, weighted with an aching loss that turns each delicate phrase into a profound moment of grief (“There’s no rain, but my cape gets wet,” he says as they stand graveside). Thomas’ digital illustrations echo that pain with a keen effectiveness, especially in the boy’s body language, as he alternates between a protective slouch covered by the cape, a swirl of anger as he tries to hide the memories away, and finally, an open armed hug for his auntie. Mourners are cast in shadow as the boy walks stoically with his cape flying behind him, but slowly, yellow orbs fill the pages, at first hinting at shapes and then shifting into clear scenes of the boy’s father as the boy opens up to his grief. Paired with Howden’s The Tunnel (BCCB 02/22), this offers comfort and sympathy to mourning readers. Copyright © 2023 The Board of Trustees of the University of Illinois

A Neural Network-based Cloud Mask for PREFIRE and Evaluation with Simulated Observations

Abstract The Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) will fill a gap in our understanding of polar processes and the polar climate by offering widespread, spectrally-resolved measurements through the Far InfraRed (FIR) with two identical CubeSat spacecraft. While the polar regions are typically difficult for skillful cloud identification due to cold surface temperatures, the reflection by bright surfaces, and frequent temperature inversions, the inclusion of the FIR may offer increased spectral sensitivity, allowing for the detection of even thin ice clouds. This study assesses the potential skill, as well as limitations, of a neural network-based cloud mask using simulated spectra mimicking what the PREFIRE mission will capture. Analysis focuses on the polar regions. Clouds are found to be detected approximately 90% of time using the derived neural network. The NN’s assigned confidence for whether a scene is ‘clear’ or ‘cloudy’ proves to be a skillful way in which quality flags can be attached to predictions. Clouds with higher cloud top heights are typically more easily detected. Low-altitude clouds over polar surfaces, which are the most difficult for the NN to detect, are still detected over 80% of the time. The FIR portion of the spectrum is found to increase the detection of clear scenes and increase mid-to-high altitude cloud detection. Cloud detection skill improves through the use of the overlapping fields of view produced by the PREFIRE instrument’s sampling strategy. Overlapping fields of view increase accuracy relative to the baseline NN while simultaneously predicting on a sub-FOV scale.

Work from Home – A Growing Trend in IT Companies Benefits, Drawbacks, Opportunities, and Challenges

Objective: The objective of this study was to describe the threaten situation in the COVID-19 scenario and to know the benefits, drawbacks, opportunities, and challenges of trend work from home. It will be a golden opportunity for people who work from home in an organized way to enjoy with family and earn money together. Background: In past decades, lots of changes of working methods have occurred. These revolutionary changes are very useful and it is a need of time. Methods: This is a narrative review of literature compiling the findings of literature from searches of computerized databases, textbooks, and discussion with community. Discussion: The collected data from the literature review are presented into themes to get clear scenes and ideas. Some recommendations are discussed to use it in a proper way. Conclusion: This study identifies elements that affect workers' physical, mental, and emotional health and well-being while working from home, and it lays the groundwork for thinking about how to best support a pleasant work from home experience. As per ideas establish a routine, be organized, have a sufficient workspace, boost your productive work, be responsible, avoid lot of multitasking, enhance communication and network, be balanced, study remote research possibilities, and learn from issues.

Evaluation of the quality indicators in dehazed images: Color, contrast, naturalness, and visual pleasingness

Hazy images suffer from low quality due to blurring, veiling effects, and low contrast. To improve their visibility, dehazing methods attempt to restore them to their corresponding clear scenes, often by focusing more on obtaining an accurate estimate based on a known ground truth. The perceptual quality of dehazed images, which can be described by means of objective and subjective quality assessments, is often not considered. This paper provides a quality assessment of dehazed images, focusing on aspects, e.g., color, image structure, and naturalness. Four image dehazing methods are considered, i.e., Contrast Limited Adapted Histogram Equalization (CLAHE), Dark Channel Prior and Refinement (DCP-R), Perception Inspired Deep Dehazing Network with Refinement (PDR-Net) and Conditional Generative Adversarial Network (CGAN) Pix2pix. The dehazing results are then put through objective and subjective assessments, for a comprehensive evaluation on image quality. Overall, Pix2pix shows the best results objectively, excelling in the recovery of color and image structure. Although it is outperformed by DCP-R in terms of naturalness, our subjective assessment shows that Pix2pix is also most preferred by human observers.

Air Pollution Reduces Interpersonal Trust: The Roles of Emotion and Emotional Susceptibility.

Air pollution has been shown to have detrimental effects on physical and mental health, yet little is known about how air pollution affects psychosocial functioning in everyday life. We conducted three studies that utilized experimental methods and web crawler technology to examine the effect of hazy environmental conditions on perceived interpersonal trust, and to investigate the roles of emotion and emotional susceptibility in mediating or moderating the negative impact of air pollution. In Study 1, participants were presented with landscape photos that showed either hazy scenes or clear scenes. Those who viewed photos of hazy scenes reduced their levels of interpersonal trust. In Study 2, emotion data were collected from social media with web crawler technology, in connection with meteorological monitoring data during the same period. Hazy conditions were associated with reduced expressions of positive emotion on social media, whereas clearer conditions were associated with enhanced positive emotional expressions. In Study 3, we simulated Weibo communications in the laboratory. The findings showed that emotional susceptibility moderated the negative effect of hazy conditions on interpersonal trust, and negative emotion mediated the effect of hazy conditions on interpersonal trust. The findings advance the understanding of the impact of air pollution on interpersonal trust and social relations and the associated psychological mechanisms and boundary conditions. They have important real-life implications.

Evaluation of Cloud Mask and Cloud Top Height from Fengyun-4A with MODIS Cloud Retrievals over the Tibetan Plateau

The Tibetan Plateau (TP) has profound thermal and dynamic influences on the atmospheric circulation, energy, and water cycles of the climate system, which make the clouds over the TP the forefront of atmospheric and climate science. However, the highest altitude and most complex terrain of the TP make the retrieval of cloud properties challenging. In order to understand the performance and limitations of cloud retrievals over the TP derived from the state-of-the-art Advanced Geosynchronous Radiation Imager (AGRI) onboard the new generation of Chinese Geostationary (GEO) meteorological satellites Fengyun-4 (FY-4), a three-month comparison was conducted between FY-4A/AGRI and the Moderate Resolution Imaging Spectroradiometer (MODIS) for both cloud detection and cloud top height (CTH) pixel-level retrievals. For cloud detection, the AGRI and MODIS cloud mask retrievals showed a fractional agreement of 0.93 for cloudy conditions and 0.73 for clear scenes. AGRI tended to miss lower CTH clouds due to the lack of thermal contrast between the clouds and the surface of the TP. For cloud top height retrievals, the comparison showed that on average, AGRI underestimated the CTH relative to MODIS by 1.366 ± 2.235 km, and their differences presented a trend of increasing with height.

Evaluation and comparison of a machine learning cloud identification algorithm for the SLSTR in polar regions

A Feed Forward Neural Net (NN) approach to distinguish between clouds and the surface has been applied to the Sea and Land Surface Temperature Radiometer in polar regions. The masking algorithm covers the Arctic, Antarctic and regions typically classified as the cryosphere such as northern hemisphere permafrost. The mask has been trained using collocations with the CALIOP active lidar, which in narrow strips provide more accurate detection of cloud, and was subsequently evaluated as a function of cloud type and surface type. The mask was compared with the existing operational Bayesian and Empirical cloud masks by eye and also statistically using CALIOP data. It was found to perform exceptionally well in the polar regions. The Kuiper skill score improved from 0.28, for the operational Bayesian and 0.17 for the Empirical masks to 0.77 for the NN. The NN algorithm also has a much more homogeneous performance over all surface types. The key improvement came from better identification of clear scenes; for the NN mask, the same performance in terms of contamination of cloudy pixels in the sample of identified clear pixels can be achieved while retaining 40% of the clear pixels compared with 10% for the operational cloud identification. The algorithm performed with almost the same skill over sea and land. The best performance was achieved for opaque clouds while transparent and broken clouds showed slightly reduced accuracy.

A geometry-dependent surface Lambertian-equivalent reflectivity product for UV–Vis retrievals – Part 2: Evaluation over open ocean

Abstract. Satellite-based cloud, aerosol, and trace-gas retrievals from ultraviolet (UV) and visible (Vis) wavelengths depend on the accurate representation of surface reflectivity. Current UV and Vis retrieval algorithms typically use surface reflectivity climatologies that do not account for variation in satellite viewing geometry or surface roughness. The concept of geometry-dependent surface Lambertian-equivalent reflectivity (GLER) is implemented for water surfaces to account for surface anisotropy using a Case 1 water optical model and the Cox–Munk slope distribution for ocean surface roughness. GLER is compared with Lambertian-Equivalent reflectivity (LER) derived from the Ozone Monitoring Instrument (OMI) for clear scenes at 354, 388, 440, and 466 nm. We show that GLER compares well with the measured LER data over the open ocean and captures the directionality effects not accounted for in climatological LER databases. Small biases are seen when GLER and the OMI-derived LER are compared. GLER is biased low by up to 0.01–0.02 at Vis wavelengths and biased high by around 0.01 in the UV, particularly at 354 nm. Our evaluation shows that GLER is an improvement upon climatological LER databases as it compares well with OMI measurements and captures the directionality effects of surface reflectance.

ASTER Cloud Coverage Assessment and Mission Operations Analysis Using Terra/MODIS Cloud Mask Products

Since the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument cannot detect clouds accurately for snow-covered or nighttime images due to a lack of spectral bands, Terra/MODIS cloud mask (MOD35) products have been alternatively used in cloud assessment for all ASTER images. In this study, we evaluated ASTER cloud mask images generated from MOD35 products and used them to analyze the mission operations of ASTER. In the evaluation, ASTER cloud mask images from different MOD35 versions (Collections 5, 6, and 6.1) showed a large discrepancy in low- or high-latitude areas, and the rate of ASTER scenes with a high uncertain-pixel rate (≥30%) showed to be 2.2% in daytime and 12.0% in nighttime. In the visual evaluation with ASTER browse images, about 2% of cloud mask images showed some problems such as mislabeling and artifacts. In the mission operations analysis, the cloud avoidance function implemented in the ASTER observation scheduler showed a decrease in the mean cloud coverage (MCC) and an increase in the rate of clear scenes by 10% to 15% in each. Although 19-year-old time-series of MCC in five areas showed weather-related fluctuations such as the El Niño Southern Oscillation (ENSO), they indicated a small percent reduction in MCC by enhancement of the cloud avoidance function in April 2012. The global means of the number of clear ASTER scenes were 15.7 and 6.6 scenes in daytime and nighttime, respectively, and those of the success rate were 33.3% and 40.4% in daytime and nighttime, respectively. These results are expected to contribute not only to the ASTER Project but also to other optical sensor projects.

Surface albedo and toc-r 300 m products from PROBA-V instrument in the framework of Copernicus Global Land Service

PROBA-V instrument launched in 2013 is offering a global daily coverage at pixel resolutions of 333 m and 1 km in three spectral bands (BLUE, RED, NIR) and 600 m for shortwave infrared (SWIR). The PROBA-V mission is the follow-on of the VEGETATION program started in 2000, which allowed generating long-term series at 1 km pixel resolution. The PROBA-V products belong to the Copernicus Global Land Service portfolio (http://land.copernicus.eu/global/). The sensor design of PROBA-V with oriented cameras offers a wide field of view (FOV) for sampling the BRDF (Bidirectional Reflectance Distribution Function). This paper details the methodology implemented at the premises of VITO (Flemish Institute for Technological Research) with the aim to disseminate routinely from PROBA-V daily observations for both surface albedo (SA) and top-of-canopy corrected reflectance (TOC-R) products. The method classically operates a selection of cloudless scenes, performs atmospheric corrections, and finally applies a correction of directional effects on a pixel per pixel basis. The synthesis period is the decade and the composite period is 20 days. Such choice is a pointwise sampling as being a trade-off between the availability of clear scenes and the timescale for phenology. Regarding the albedo catalogue, a narrow-band to broadband conversion is stipulated. A recurrent technique serves for gap-filling based on the spread of weighed a priori data. Additional information concerns the quality flag and the age of the product. Preliminary accuracy assessment is performed through a comparison with the Moderate Imaging Spectroradiometer (MODIS) Collection 6. Dependable spatial consistency is reached except for wintertime with deviations in terms of rmse (root mean square errors) about 0.03 for visible and shortwave domains, and 0.04 for near infrared. Besides, both PROBA-V and MODIS C6 exhibit close time profiles, marked by smoothness or rapid transitions. Results over 10 confidence sites reveals rmse values of 0.032 and bias of 0.01 over the 2014 full annual cycle.

Assessing reanalysis quality with early sounders Nimbus-4 IRIS (1970) and Nimbus-6 HIRS (1975)

This paper revisits the data collected by early sounders Nimbus-4 IRIS (1970) and Nimbus-6 HIRS (1975), after recovery of ageing tapes by NASA GES DISC. New quality controls are proposed to screen out erroneous or suspicious mission data, based on instrument health status data records and other inspection of the data. Radiative transfer coefficients are derived for the fast computation of clear-sky radiative transfer simulations. Atmospheric profiles from ERA-40 and ERA-20C reanalyses are used in input. These spatio-temporally complete datasets are interpolated to each sounding location, using the closest estimate in time. A modern cloud detection method derived for current hyperspectral sounders is applied to IRIS and yields maps of cloud cover that are in line with current knowledge of cloud climatology. For clear scenes, the standard deviation of brightness temperature differences between IRIS observations and simulations from ERA-20C is around 1 K for the lower-peaking temperature channels of the 15 μm CO2 band, and lower than 1 K for simulations from ERA-40. The IRIS and HIRS instrumental data records are projected in a common sub-space to alleviate issues with different field-of-view resolutions and spectral resolutions. A proxy cloud detection scheme screens out clouds in the same manner in both data records. Considering the month of August, common to both missions, a detailed analysis of the departures from observations suggests that ERA-40 suffers from spurious tropospheric warming, possibly caused by changes in the observation input during the 1970s including a known error in ERA-40 radiance assimilation bias correction. This result, confirmed by considering a climate model integration, demonstrates that it is possible to exploit early sounder data records to derive detailed insight from reanalyses, such as attempting to qualify separately random and systematic errors in reanalyses, even at times when few other independent observation data are available.

Determination of CERES TOA fluxes using Machine learning algorithms. Part I: Classification and retrieval of CERES cloudy and clear scenes.

Continuous monitoring of the Earth radiation budget (ERB) is critical to our understanding of the Earth's climate and its variability with time. The Clouds and the Earth's Radiant Energy System (CERES) instrument is able to provide a long record of ERB for such scientific studies. This manuscript, which is first of a two-part paper, describes the new CERES algorithm for improving the clear/cloudy scene classification without the use of coincident cloud imager data. This new CERES algorithm is based on a subset of modern artificial intelligence (AI) paradigm called Machine Learning (ML) algorithms. This paper describes development and application of the ML algorithm known as Random Forests (RF) which is used to classify CERES broadband footprint measurements into clear and cloudy scenes. Results from the RF analysis carried using the CERES Single Scanner Footprint (SSF) data for the months of January and July are presented in the manuscript. The daytime RF misclassification rate (MCR) shows relatively large values (>30%) for snow, sea ice and bright desert surface types while lower values of (<10%) for forest surface type. MCR values observed for the nighttime data in general show relatively larger values for most of the surface types compared to the daytime MCR values. The modified MCR values show lower values (< 4%) for most surface types after thin cloud data is excluded from the analysis. Sensitivity analysis shows that the number of input variables and decision trees used in the RF analysis has substantial influence in determining the classification error.

The potential of clear-sky carbon dioxide satellite retrievals

Abstract. Since the launch of the Greenhouse Gases Observing Satellite (GOSAT) in 2009, retrieval algorithms designed to infer the column-averaged dry-air mole fraction of carbon dioxide (XCO2) from hyperspectral near-infrared observations of reflected sunlight have been greatly improved. They now generally include the scattering effects of clouds and aerosols, as early work found that absorption-only retrievals, which neglected these effects, often incurred unacceptably large errors, even for scenes with optically thin cloud or aerosol layers. However, these “full-physics” retrievals tend to be computationally expensive and may incur biases from trying to deduce the properties of clouds and aerosols when there are none present. Additionally, algorithms are now available that can quickly and effectively identify and remove most scenes in which cloud or aerosol scattering plays a significant role. In this work, we test the hypothesis that non-scattering, or “clear-sky”, retrievals may perform as well as full-physics retrievals for sufficiently clear scenes. Clear-sky retrievals could potentially avoid errors and biases brought about by trying to infer properties of clouds and aerosols when none are present. Clear-sky retrievals are also desirable because they are orders of magnitude faster than full-physics retrievals. Here we use a simplified version of the Atmospheric Carbon Observations from Space (ACOS) XCO2 retrieval algorithm that does not include the scattering and absorption effects of clouds or aerosols. It was found that for simulated Orbiting Carbon Observatory-2 (OCO-2) measurements, the clear-sky retrieval had errors comparable to those of the full-physics retrieval. For real GOSAT data, the clear-sky retrieval had errors 0–20 % larger than the full-physics retrieval over land and errors roughly 20–35 % larger over ocean, depending on filtration level. In general, the clear-sky retrieval had XCO2 root-mean-square errors (RMSEs) of less than 2.0 ppm, relative to Total Carbon Column Observing Network (TCCON) measurements and a suite of CO2 models, when adequately filtered through the use of a custom genetic algorithm filtering system. These results imply that non-scattering XCO2 retrievals are potentially more useful than previous literature suggests, as the filtering methods we employ are able to remove measurements in which scattering can cause significant errors. Additionally, the computational benefits of non-scattering retrievals means they may be useful for certain applications that require large amounts of data but have less stringent error requirements.

Variational Bayes and the Principal Component Analysis Coupled With Bayesian Regulation Backpropagation Network to Retrieve Total Precipitable Water (TPW) From GCOM-W1/AMSR2

The Bayes Principal components Backpropagation Network (BPBN) is proposed to retrieve total precipitable water (TPW) from the AMSR2 instrument on-board recently launched GCOM-W1 satellite. The proposed algorithm is a physical inversion method, developed using a radiative transfer model to assure that the geophysical retrieval of the TPW is consistent with the radiative transfer theory. The algorithm is comprised of- a Bayes variational algorithm for bias correction, the principal components transformation of the bias-corrected radiometric brightness temperature, and finally, a Bayesian regulation backpropagation network to translate the principal components to TPW estimate in the geophysical space. The algorithm is applicable over ocean, and in clear and cloudy scenes. However, the rainy and sea ice scenes are excluded in the retrieval. A random forest classifier and NASA sea ice temperature retrieval algorithm are used to detect and suppress the rainy and sea ice scenes, respectively. On the whole, the BPBN is a “comprehensive” algorithm, from discarding the redundant scenes to transforming the radiometric information to TPW estimate, and doesn’t use any auxiliary data. This will make it very useful for assimilating into the numerical weather prediction models. The retrieval accuracy of the BPBN algorithm is around 2 $\hbox{kg/m}^2$ .

Real‐Time Orbital Image Analysis Using Decision Forests, with a Deployment Onboard the IPEX Spacecraft

Automatic cloud recognition promises significant improvements in Earth science remote sensing. At any time, more than half of Earth's surface is covered by clouds, obscuring images and atmospheric measurements. This is particularly problematic for CubeSats, a new generation of small, low‐orbiting spacecraft with very limited communications bandwidth. Such spacecraft can use image analysis to autonomously select clear scenes for prioritized downlink. More agile spacecraft can also benefit from cloud screening by retargeting observations to cloud‐free areas. This could significantly improve the science yield of instruments such as the Orbiting Carbon Observatory 3 mission. However, most existing cloud detection algorithms are not suitable for these applications, because they require calibrated and georectified spectral data, which is not typically available onboard. Here, we describe a statistical machine‐learning method for real‐time autonomous scene interpretation using a visible camera with no radiometric calibration. A random forest classifies cloud and clear pixels based on local patterns of image texture. We report on experimental evaluation of images from the International Space Station (ISS) and present results from a deployment onboard the IPEX spacecraft. This demonstrates actual execution in flight and provides some preliminary lessons learned about operational use. It is a rare example of a machine‐learning system deployed to an autonomous spacecraft. To our knowledge, it is also the first instance of significant artificial intelligence deployed on board a CubeSat and the first ever deployment of visible image‐based cloud screening onboard any operational spacecraft.

Reduced major axis approach for correcting GPM/GMI radiometric biases to coincide with radiative transfer simulation

Correcting radiometric biases is crucial prior to the use of satellite observations in a physically based retrieval or data assimilation system. This study proposes an algorithm – RARMA (Radiometric Adjustment using Reduced Major Axis) for correcting the radiometric biases so that the observed radiances coincide with the simulation of a radiative transfer model. The RARMA algorithm is a static bias correction algorithm, which is developed using the reduced major axis (RMA) regression approach. NOAA's Community Radiative Transfer Model (CRTM) has been used as the basis of radiative transfer simulation for adjusting the observed radiometric biases. The algorithm is experimented and applied to the recently launched Global Precipitation Measurement (GPM) mission's GPM Microwave Imager (GMI). Experimental results demonstrate that radiometric biases are apparent in the GMI instrument. The RARMA algorithm has been able to correct such radiometric biases and a significant reduction of observation residuals is revealed while assessing the performance of the algorithm. The experiment is currently tested on clear scenes and over the ocean surface, where, surface emissivity is relatively easier to model, with the help of a microwave emissivity model (FASTEM-5).

Vision for action: saccadic and manual responses to clear threat and ambiguous negative scenes

Vision for action: saccadic and manual responses to clear threat and ambiguous negative scenes

Unveiling PM 2.5 Pollution Layer for Viewing Clear Scenes

Atmospheric pollution by PM2.5 is a serious problem now at Beijing, China and its neighboring countries. The de-hazing or de-fogging methods for degraded images have been a long-pending question at NASA Langley Research Center. Recently their basic Retinex model advanced into Visual Servo system. While the current main stream for unveiling the atmospheric pollution layer is based on scattering physics. Above all, a single image de-hazing model based on Dark Channel Prior hypothesis is most notable in practice. The keys to unveiling the pollution layer lie in the two points: [1] how to extract the skylight and [2] how to estimate the scene transmittance. This paper proposes a simple but effective de-hazing algorithm with banding-free and low computation costs referring to the Dark Channel Prior hypothesis. The simulation shows how the proposed model works to look the scene through heavy air pollution.

Principal Components of Multifrequency Microwave Land Surface Emissivities. Part I: Estimation under Clear and Precipitating Conditions

Abstract The upcoming Global Precipitation Measurement mission will provide considerably more overland observations over complex terrain, high-elevation river basins, and cold surfaces, necessitating an improved assessment of the microwave land surface emissivity. Current passive microwave overland rainfall algorithms developed for the Tropical Rainfall Measuring Mission (TRMM) rely upon hydrometeor scattering-induced signatures at high-frequency (85 GHz) brightness temperatures (TBs) and are empirical in nature. A multiyear global database of microwave surface emissivities encompassing a wide range of surface conditions was retrieved from Advanced Microwave Scanning Radiometer for Earth Observing System (EOS; AMSR-E) radiometric clear scenes using companion A-Train [CloudSat, Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and Atmospheric Infrared Sounder (AIRS)] data. To account for the correlated emissivity structure, the procedure first derives the TRMM Microwave Imager–like nine-channel emissivity principal component (PC) structure. Relations are derived to estimate the emissivity PCs directly from the instantaneous TBs, which allows subsequent TB observations to estimate the PC structure and reconstruct the emissivity vector without need for ancillary data regarding the surface or atmospheric conditions. Radiative transfer simulations matched the AMSR-E TBs within 5–7-K RMS difference in the absence of precipitation. Since the relations are derived specifically for clear-scene conditions, discriminant analysis was performed to find the PC discriminant that best separates clear and precipitation scenes. When this technique is applied independently to two years of TRMM data, the PC-based discriminant demonstrated superior relative operating characteristics relative to the established 85-GHz scattering index, most notably during cold seasons.

Comparison of measured and modeled outgoing longwave radiation for clear‐sky ocean and land scenes using coincident CERES and AIRS observations

Clear‐sky outgoing longwave radiation (OLR) is computed using the Atmospheric and Environmental Research (AER), Inc., Rapid Radiative Transfer Model (RRTM) for comparison with the observations of the Clouds and the Earth's Radiant Energy System (CERES) for both ocean and land scenes. CERES clear‐sky OLR is in agreement with RRTM model calculations to 0.2% accuracy using best estimate radiosondes (BE) launched coincident with NASA Aqua overpasses at the Atmospheric Radiation Measurement Southern Great Plains (SGP) site and 0.8% using retrieved profiles of temperature, water vapor, ozone, and surface parameters from the Atmospheric Infrared Sounder (AIRS) on the Aqua platform. A partial flux analysis using AIRS radiances implies an accuracy for the RRTM model in the far infrared of 0.4% (about 0.5 W/m2) for wave numbers less than 650 cm−1 (wavelengths greater than 15.4 μm). CERES minus model biases over clear‐sky ocean are similar to previously published results. Ordering the results according to the magnitude of the measured minus model mean bias for nighttime, tropical, ocean gives: +0.57 ± 1.9 W/m2 (Dessler/Fu‐Liou), +0.83 ± 1.5 W/m2 (Huang/MODTRAN5), +1.6 ± 1.6 W/m2 (Moy/RRTM), +3.7 ± 2.1 W/m2 (Dessler/Chou). Comparison of observed minus modeled OLR over land are included in this study. Excluding nonfrozen ocean, a mean difference over land of +2.0 W/m2 for nighttime cases and +1.0 W/m2 for daytime cases is found where the land classes are weighted inversely by their standard error. The nighttime bias is quite consistent across all the land classes. The daytime bias shows less consistency with a tendency toward larger CERES minus AIRS RRTM OLR bias for the land classes with smaller vegetation fraction. Comparison of clear‐sky CERES and AIRS RRTM OLR over cold snow‐/ice‐covered surfaces (mainly in the polar regions) is complicated by the use of the MODIS cloud mask in the identification of the clear CERES footprints used in the comparison. Clear scenes over cold surfaces can be identified more reliably in the daytime, for which the comparison between CERES and AIRS RRTM is better than 1.2 W/m2 indicating good agreement.