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At least 289 records · Page 16

Next generation Arctic vegetation maps: Aboveground plant biomass and woody dominance mapped at 30 m resolution across the tundra biome

The Arctic is warming faster than anywhere else on Earth, placing tundra ecosystems at the forefront of global climate change. Plant biomass is a fundamental ecosystem attribute that is sensitive to changes in climate, closely tied to ecological function, and crucial for constraining ecosystem carbon dynamics. However, the amount, functional composition, and distribution of plant biomass are only coarsely quantified across the Arctic. Therefore, we developed the first moderate resolution (30 m) maps of live aboveground plant biomass (g m −2 ) and woody plant dominance (%) for the Arctic tundra biome, including the mountainous Oro Arctic. We modeled biomass for the year 2020 using a new synthesis dataset of field biomass harvest measurements, Landsat satellite seasonal synthetic composites, ancillary geospatial data, and machine learning models. Additionally, we quantified pixel-wise uncertainty in biomass predictions using Monte Carlo simulations and validated the models using a robust, spatially blocked and nested cross-validation procedure. Observed plant and woody plant biomass values ranged from 0 to ∼6000 g m −2 (mean ≈ 350 g m −2 ), while predicted values ranged from 0 to ∼4000 g m −2 (mean ≈ 275 g m −2 ), resulting in model validation root-mean-squared-error (RMSE) ≈ 400 g m −2 and R 2 ≈ 0.6. Our maps not only capture large-scale patterns of plant biomass and woody plant dominance across the Arctic that are linked to climatic variation (e.g., thawing degree days), but also illustrate how fine-scale patterns are shaped by local surface hydrology, topography, and past disturbance. By providing data on plant biomass across Arctic tundra ecosystems at the highest resolution to date, our maps can significantly advance research and inform decision-making on topics ranging from Arctic vegetation monitoring and wildlife conservation to carbon accounting and land surface modeling.

Climate change↗

Chemical Environment and Structural Variations in High Entropy Oxide Thin Film Probed with Electron Microscopy

For this work, we employ analytical transmission electron microscopy (TEM) to correlate the structural and chemical environment variations within a stacked epitaxial thin film of the high entropy oxide (HEO) Mg 0.2 Co 0.2 Ni 0.2 Cu 0.2 Zn 0.2 O (J14), with two layers grown at different substrate temperatures (500 and 200 °C) using pulsed laser deposition (PLD). Electron diffraction and atomically resolved STEM imaging reveal the difference in out-of-plane lattice parameters in the stacked thin film, which is further quantified on a larger scale using four-dimensional STEM (4D-STEM). In the layer deposited at a lower temperature, electron energy loss spectroscopy (EELS) mapping indicates drastic changes in the oxidation states and bonding environment for Co ions, and energy-dispersive X-ray spectroscopy (EDX) mapping detects more significant cation deficiency. Ab initio density functional theory (DFT) calculations validate that vacancies on the cation sublattice of J14 result in significant electronic and structural changes. The experimental and computational analyses indicate that low temperatures during film growth result in cation deficiency, an altered chemical environment, and reduced lattice parameters while maintaining a single phase. Our results demonstrate that the complex correlation of configurational entropy, kinetics, and thermodynamics can be utilized for accessing a range of metastable configurations in HEO materials without altering cation proportions, enabling further engineering of functional properties of HEO materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Krylov Subspace Methods for Quantum Dynamics with Time-Dependent Generators

Krylov subspace methods in quantum dynamics identify the minimal subspace in which a process unfolds. To date, their use is restricted to time evolutions governed by time-independent generators. Here, we introduce a generalization valid for driven quantum systems governed by a time-dependent Hamiltonian that maps the evolution to a diffusion problem in a one-dimensional lattice with nearest-neighbor hopping probabilities that are inhomogeneous and time dependent. This representation is used to establish a novel class of fundamental limits to the quantum speed of evolution and operator growth. We also discuss generalizations of the algorithm, adapted to discretized time evolutions and periodic Hamiltonians, with applications to many-body systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

United States crustal thickness

The thickness of the crust, the thickness of the basal (intermediate or lower) crustal layer, and the average velocity at the top of the mantle have been mapped using all available deep-penetrating seismic-refraction profiles in the conterminous United States and surrounding border areas. These profiles are indexed to their literature data sources. The more significant long wavelength anomalies on the three maps are briefly discussed and analyzed. An attempt to use Bouguer gravity to validate mantle structure was inconclusive.

Allenby, R. J.↗

Near-Horizontal, Two-Phase Flow Patterns of Nitrogen and Hydrogen at Low Mass Heat and Flux (on CD-ROM)

One reason for NASA's interest in cryogenic two-phase flow with low mass and heat flux is the need to design spacecraft heat exchangers used for vaporizing cryogenic propellants. The CD-ROM provides digitized movies of particular flow patterns observed in experimental work. The movies have been provided in (QuickTime9Trademark) format, encoded at 320w x 240h pixels, 15 fps, using the Sorenson(Trademark) Video Codec for compression. Experiments were conducted to obtain data on the two-phase (liquid and vapor) flow behavior of cryogenic nitrogen and hydrogen under low mass and heat flux conditions. Tests were performed in normal gravity with a 1.5 degree up flow configuration. View ports in the apparatus permitted visual observation of the two-phase flow patterns. Computer codes to predict flow patterns were developed from theoretical/empirical models reported in the literature. Predictions from the computer codes were compared with experimental flow pattern observations. Results are presented employing the traditional two-dimensional flow pattern map format using the liquid and gas superficial velocities as coordinates. In general, the agreement between the experimental results and the analytical predictive methods is reasonably good. Small regions of the flow pattern maps are identified where the models are deficient as a result of neglecting phase change phenomena. Certain regions of the maps were beyond the range of the experiments and could not be completely validated. Areas that could benefit from further work include modeling of the transition from separated flow, collection of additional data in the bubble and annular flow regimes, and collection of experimental data at other inclination angles, tube diameters and high heat flux.

TWO PHASE FLOW↗

Monitoring Geothermal Features in Yellowstone National Park with ATLAS Multispectral Imagery

The National Park Service (NPS) must produce an Environmental Impact Statement for each proposed development in the vicinity of known geothermal resource areas (KGRAs) in Yellowstone National Park. In addition, the NPS monitors indicator KGRAs for environmental quality and is still in the process of mapping many geothermal areas. The NPS currently maps geothermal features with field survey techniques. High resolution aerial multispectral remote sensing in the visible, NIR, SWIR, and thermal spectral regions could enable YNP geothermal features to be mapped more quickly and in greater detail In response, Yellowstone Ecosystems Studies, in partnership with NASA's Commercial Remote Sensing Program, is conducting a study on the use of Airborne Terrestrial Applications Sensor (ATLAS) multispectral data for monitoring geothermal features in the Upper Geyser Basin. ATLAS data were acquired at 2.5 meter resolution on August 17, 2000. These data were processed into land cover classifications and relative temperature maps. For sufficiently large features, the ATLAS data can map geothermal areas in terms of geyser pools and hot springs, plus multiple categories of geothermal runoff that are apparently indicative of temperature gradients and microbial matting communities. In addition, the ATLAS maps clearly identify geyserite areas. The thermal bands contributed to classification success and to the computation of relative temperature. With masking techniques, one can assess the influence of geothermal features on the Firehole River. Preliminary results appear to confirm ATLAS data utility for mapping and monitoring geothermal features. Future work will include classification refinement and additional validation.

Spruce, Joseph↗

Total Lightning Observations of Extreme Weather Events over the Contiguous United States

The overall objective is to investigate total lightning characteristics of extreme weather events over the contiguous United States (CONUS) using TRMM (Tropical Rainfall Measuring Mission) LIS (Lightning Image Sensor) and OTD (Optical Transient Detector) satellite observations. A large LIS (10+ years) and OTD (5 years) data base is available to study the instantaneous total or cloud-to-ground (CG) plus intracloud (IC) lightning characteristics of extreme weather events. More specifically, the LIS and OTD data are combined with National Lightning Detection Network (NLDN) observations to examine the total and CG lightning flash rate and density, the IC:CG ratio, and positive CG percentage. These instantaneous lightning characteristics can be used for basic science studies to better understand the physical and dynamical linkages between lightning and precipitation and their environmental controls. They can also provide a first-look of extreme weather events leading up to future satellite observations (e.g., NOAA GOES-R Geostationary Lightning Mapper [GLM]) for use in climate studies and the short-term prediction and warning process. Extreme weather events are defined by the NOAA Storm Data reports of tornadoes, large hail (at least 0.75 inch) and strong straight-line winds (at least 50 kts). Over CONUS, there are over 70,000 severe storm reports in the TRMM spatial domain (< 35 N) from 1998-2007 and over 100,000 storm reports in the OTD spatial domain (5/1995-4/2000). Temporal co-location is on the order of 1% (i.e., 1000 s of coincident overpasses), providing a statistically significant sample of instantaneous total lightning properties. This instantaneous behavior of lightning in extreme weather is then compared to that of typical thunderstorm events, or randomly sampled LIS/OTD events in which the extreme events have been eliminated from the population. Results describing the instantaneous behavior of total lightning within a large sample of extreme and typical thunderstorms over CONUS will be presented. When possible, coincident VHF lightning observations from the ground-based Northern Alabama Lightning Mapping Array (LMA) are compared to the LIS/OTD optical lightning signatures, providing a validation source for instantaneous space-based optical lightning properties and a means to extend lightning inferences over the life-cycle of extreme weather.

Carey, Lawrence D.↗

Performance Measurement of Advanced Stirling Convertors (ASC-E3)

NASA Glenn Research Center (GRC) has been supporting development of the Advanced Stirling Radioisotope Generator (ASRG) since 2006. A key element of the ASRG project is providing life, reliability, and performance testing data of the Advanced Stirling Convertor (ASC). The latest version of the ASC (ASC-E3, to represent the third cycle of engineering model test hardware) is of a design identical to the forthcoming flight convertors. For this generation of hardware, a joint Sunpower and GRC effort was initiated to improve and standardize the test support hardware. After this effort was completed, the first pair of ASC-E3 units was produced by Sunpower and then delivered to GRC in December 2012. GRC has begun operation of these units. This process included performance verification, which examined the data from various tests to validate the convertor performance to the product specification. Other tests included detailed performance mapping that encompassed the wide range of operating conditions that will exist during a mission. These convertors were then transferred to Lockheed Martin for controller checkout testing. The results of this latest convertor performance verification activity are summarized here.

Stirling Cycle↗

A Web-Based Search Service to Support Imaging Spectrometer Instrument Operations

Imaging spectrometers yield rich and informative data products, but interpreting them demands time and expertise. There is a continual need for new algorithms and methods for rapid first-draft analyses to assist analysts during instrument opera-tions. Intelligent data analyses can summarize scenes to draft geologic maps, searching images to direct op-erator attention to key features. This validates data quality while facilitating rapid tactical decision making to select followup targets. Ideally these algorithms would operate in seconds, never grow bored, and be free from observation bias about the kinds of mineral-ogy that will be found.

hyperspectral imagery↗

Assimilation of Global Radar Backscatter and Radiometer Brightness Temperature Observations to Improve Soil Moisture and Land Evaporation Estimates

Active radar backscatter (s◦) observations from the Advanced Scatterometer (ASCAT) and passive radiometer brightness temperature (TB) observations from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated either individually or jointly into the Global Land Evaporation Amsterdam Model (GLEAM) to improve its simulations of soil moisture and land evaporation. To enable s◦ and TB assimilation, GLEAM is coupled to the Water Cloud Model and the L-band Microwave Emission from the Biosphere (L-MEB) model. The innovations, i.e. differences between observations and simulations, are mapped onto the model soil moisture states through an Ensemble Kalman Filter. The validation of surface (0-10 cm) soil moisture simulations over the period 2010-2014 against in situ measurements from the International Soil Moisture Network (ISMN) shows that assimilating s◦ or TB alone improves the average correlation of seasonal anomalies (Ran) from 0.514 to 0.547 and 0.548, respectively. The joint assimilation further improves Ran to 0.559. Associated enhancements in daily evaporative flux simulations by GLEAM are validated based on measurements from 22 FLUXNET stations. Again, the singular assimilation improves Ran from 0.502 to 0.536 and 0.533, respectively for s◦ and TB, whereas the best performance is observed for the joint assimilation (Ran = 0.546). These results demonstrate the complementary value of assimilating radar backscatter observations together with brightness temperatures for improving estimates of hydrological variables, as their joint assimilation outperforms the assimilation of each observation type separately.

GLEAM↗

Satellite Validation: A Project to Create a Data-Logging System to Monitor Lake Tahoe

Flying aboard the satellite Terra, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is an imaging instrument used to acquire detailed maps of Earth's surface temperature, elevation, emissivity, and reflectance. An automated site consisting of four buoys was established 6 years ago at Lake Tahoe for the validation of ASTERS thermal infrared data. Using Campbell CR23X Dataloggers, a replacement system to be deployed on a buoy was designed and constructed for the measurement of the lake's temperature profile, surrounding air temperature, humidity, wind direction and speed, net radiation, and surface skin temperature. Each Campbell Datalogger has been programmed to control, power, and monitor 14 different temperature sensors, a JPL-built radiometer, and an RM Young 32500 meteorological station. The logger communicates with the radiometer and meteorological station through a Campbell SDM-SIO4 RS232 serial interface, sending polling commands, and receiving filtered data back from the sensors. This data is then cataloged and sent back across a cellular modem network every hour to JPL. Each instrument is wired via a panel constructed with 18 individual plugs that allow for simple installation and expansion. Data sent back from the system are analyzed at JPL, where they are used to calibrate ASTER data.

Roy, Rudy A.↗

Evaluation of Atmospheric Loading and Improved Troposphere Modelling

Forward modeling of non-tidal atmospheric loading displacements at geodetic tracking stations have not routinely been included in Doppler Orbitography and Radiopositionning Integrated by Satellite (DORIS) or Satellite Laser Ranging (SLR) station analyses for either POD applications or reference frame determination. The displacements which are computed from 6-hourly models such as the ECMWF and can amount to 3-10 mm in the east, north and up components depending on the tracking station locations. We evaluate the application of atmospheric loading in a number ways using the NASA GSFC GEODYN software: First we assess the impact on SLR & DORIS-determined orbits such as Jason-2, where we evaluate the impact on the tracking data RMS of fit and how the total orbits are changed with the application of this correction. Preliminary results show an RMS radial change of 0.5 mm for Jason-2 over 54 cycles and a total change in the Z-centering of the orbit of 3 mm peak-to-peak over one year. We also evaluate the effects on other DORIS-satellites such as Cryosat-2, Envisat and the SPOT satellites. In the second step, we produce two SINEX time series based on data from available DORIS satellites and assess the differences in WRMS, scale and Helmert translation parameters. Troposphere refraction is obviously an important correction for radiometric data types such as DORIS. We evaluate recent improvements in DORIS processing at GSFC including the application of the Vienna Mapping Function (VMF1) grids with a-priori hydrostatic (VZHDs) and wet (VZWDs) zenith delays. We reduce the gridded VZHD at the stations height using pressure and temperature derived from GPT (strategy 1) and Saastamoinen. We discuss the validation of the VMF1 implementation and its application to the Jason-2 POD processing, compared to corrections using the Niell mapping function and the GMF. Using one year of data, we also assess the impact of the new troposphere corrections on the DORIS-only solutions, most importantly on the scale of the weekly solutions.

Zelensky, Nikita P.↗

(abstract) Monitoring Seasonal State and Mapping Species in Alaskan Taiga Using Imaging Radar as Input to CO(sub 2) Flux Models

Changes in the seasonal CO(sub 2) flux of the boreal forests may result from increased atmospheric CO(sub 2) concentrations and associated atmospheric warming. To monitor this potential change, a combination of remote sensing information and ecophysiological models are required. In this paper we address the use of synthetic aperture radar (SAR) data to provide some of the input to the ecophysiological models: forest type, freeze/thaw state which limits the growing season for conifers, and leaf on/off state which limits the growing season for deciduous species. AIRSAR data collected in March 1988 during an early thaw event and May 1991 during spring breakup are used to generate species maps and to determine the sensitivity of SAR to canopy freeze/thaw transitions. These data are also used to validate a microwave scattering model which is then used to determine the sensitivity of SAR to leaf on/off and soil freeze/thaw transitions. Finally, a CO(sub 2) flux algorithm which utilizes SAR data and an ecophysiological model to estimate CO(sub 2) flux is presented. CO(sub 2) flux maps are generated from which areal estimates of CO(sub 2) flux are derived.

leaf cover boreal forests radar backscatter↗

Nominal 30-M Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, is a starting point to develop high-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based Geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015-2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January-June 2016 and period 2: July-December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-cropland training samples (sample size 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of RF happened due to the noise of input training data, Support Vector Machine (SVM) was applied to compensate for such defects in specific areas. Third, the Recursive Hierarchical Segmentation (RHSeg) algorithm was employed to generate an object-oriented segmentation layer based on spectral and spatial properties from the same input data. This layer was merged with the pixel-based classification to improve segmentation accuracy. Accuracies of the merged 30-m crop extent product were computed using an error matrix approach in which 1754 independent validation samples were used. In addition, a comparison was performed with other available cropland maps as well as with LULC maps to show spatial similarity. Finally, the cropland area results derived from the map were compared with UN FAO statistics. The independent accuracy assessment showed a weighted overall accuracy of 94, with a producers accuracy of 85.9 (or omission error of 14.1), and users accuracy of 68.5 (commission error of 31.5) for the cropland class. The total net cropland area (TNCA) of Africa was estimated as 313 Mha for the nominal year 2015.

Cropland mapping; cropland areas; 30-m; Landsat-8;↗

CMB-PAInT: An inpainting tool for the cosmic microwave background

Abstract The presence of astrophysical emissions in microwave observations forces us to perform component separation to extract the Cosmic Microwave Background (CMB) signal. However, even in the most optimistic cases, there are still strongly contaminated regions, such as the Galactic plane or those with emission from extragalactic point sources, which require the use of a mask. Since many CMB analyses, especially the ones working in harmonic space, need the whole sky map, it is crucial to develop a reliable inpainting algorithm that replaces the values of the excluded pixels by others statistically compatible with the rest of the sky. This is especially important when working withQandUsky maps in order to obtainE- andB-mode maps which are free fromE-to-Bleakage. In this work we study a method based on Gaussian Constrained Realizations (GCR), that can deal with both intensity and polarization. Several tests have been performed to asses the validation of the method, including the study of the one-dimensional probability distribution function (1-PDF),E- andB-mode map reconstruction, and power spectra estimation. We have considered two scenarios for the input simulation: one case with only CMB signal and a second one including also Planck PR4 semi-realistic noise. Even if we are limited to low resolution maps, N side = 64 ifT,QandUare considered, we believe that this is a useful approach to be applied to future missions such as LiteBIRD, where the target are the largest scales.

Astronomy & Astrophysics↗

A moving-window parameter adaptive control system for the F8-DFBW aircraft

This paper describes the moving-window parameter adaptive control system developed for the NASA F8-DFBW aircraft. The control system employs a parameter identification process that, iteratively, adjusts parameters of a model of the aircraft motions in a batch processing manner so that responses generated from the model fit the outputs of sensors stored in a finite record referred to as the moving window. Tests are made on the validity of the parameter estimates before using the parameters in an on-line design process. The on-line design process is an algebraic mapping of the parameters of the model into primary control system feedback and feedforward gains. The mapping was selected to satisfy specific flying quality characteristics over the range of parameter variations expected. Results are presented from simulation studies on the identification algorithm, tests for parameter validity, and the on-line design process.

Montgomery, R. C.↗

Integrating very-high-resolution imagery, Sentinel-2 time-series data, and machine learning to map shrub fractional abundance across arid and semi-arid ecosystems in China

Shrub fractional abundance (SFA), the proportion of shrub cover per unit area, serves as a critical indicator of environmental aridity and ecosystem health in arid and semi-arid regions, particularly across the Mongolian steppe. However, large-scale SFA mapping in Mongolian steppe ecosystems remains challenging due to the small crown size of shrubs, their sparse distribution, and spectral overlap with coexisting low vegetation (e.g., grasses and herbs), which hinders accurate detection using coarser-resolution satellite data or traditional field surveys. To address these challenges, we developed a two-step approach that integrates very-high-resolution (VHR) imagery, time-series Sentinel-2 data, and deep learning techniques. First, we generated high-accuracy benchmark maps of individual shrub crowns from 0.5 m VHR imagery by combining manual segmentation with a hybrid deep learning framework (Dino V2 and convolutional neural networks). Second, we used these shrub crown maps as training data to build an XGBoost model for predicting SFA from 20 m Sentinel-2 time-series data, leveraging phenological information to improve estimation. We validated our approach across 70 sites (1km 2 each) in the Inner Mongolia Autonomous Region, which is representative of Mongolian steppe ecosystems. From VHR imagery, we mapped 1.31 million shrub crowns with an accuracy of R 2 = 0.92. Scaling up with Sentinel-2 data yielded regional SFA maps with an R 2 = 0.60. Further SHAP (SHapley Additive exPlanations) analysis on the developed XGBoost model revealed that phenological metrics (particularly observations in early-May, mid-July, and late-September), which distinguish shrub phenology from that of other land cover types (e.g., grasses and bare soil), were the most influential predictors of SFA. Finally, our regional SFA maps uncovered unimodal relationships between shrub distribution and climate variables, peaking at mean annual minimum temperatures near 0 °C and annual precipitation around 200 mm. Collectively, these findings demonstrate how the integration of multi-source remote sensing and machine learning can overcome historical limitations in SFA mapping, enabling accurate, spatially continuous assessments across vast Inner-Mongolian steppe ecosystems. Our framework has the potential to be applied to other steppe ecosystems and dryland ecosystems across the Mongolian steppe and beyond, offering a foundation for improved monitoring and ecological impact assessments in the face of global climate changes.

Arid and semi-arid landscapes↗

Structure and Variability of Water Vapor in the Upper Troposphere and Lower Stratosphere

An algorithm has been developed to synoptically-map asynoptic measurements, while rejecting small-scale under-sampled variance that is intrinsic to satellite measurements of water vapor, cloud, and other convective properties. The algorithm has been validated against high-resolution Global Cloud Imagery (GCI) , constructed from 6 satellites simultaneously viewing the global convective pattern. It has been applied to synoptically map Upper-tropospheric humidity (UTH) from UARS/MLS. Mapped distributions of UTH have been used, jointly with the GCI, to study how the upper troposphere is humidified. The time-mean distribution of UTH is spatially correlated to the time-mean distribution of cold cloud fraction eta(sub c) (T less than 230 K). Regions of large UTH coincide with regions of large eta(sub c), which mark deep convection. They also coincide with regions of reduced vertical stability, in which the vertical gradient of Theta is weakened by convective mixing. Coldest cloud cover is attended convective overshoots above the local tropopause, which is simultaneously coldest and highest. Together, these features reflect the upper-troposphere being ventilated by convection, which mixes in air from lower levels.

Salby, Murry L.↗