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NASA NTRS1989

Fractal statistics of cloud fields

Landsat Multispectral Scanner (MSS) and Thematic Mapper (TM) data, with 80 and 30 m spatial resolution, respectively, have been employed to study the spatial structure of boundary-layer and intertropical convergence zone (ITCZ) clouds. The probability distributions of cloud areas and cloud perimeters are found to approximately follow a power-law, with a different power (i.e., fractal dimension) for each cloud type. They are better approximated by a double power-law behavior, indicating a change in the fractal dimension at a characteristic size which depends upon cloud type. The fractal dimension also changes with threshold. The more intense cloud areas are found to have a higher perimeter fractal dimension, perhaps indicative of the increased turbulence at cloud top. A detailed picture of the inhomogeneous spatial structure of various cloud types will contribute to a better understanding of basic cloud processes, and also has implications for the remote sensing of clouds, for their effects on remote sensing of other parameters, and for the parameterization of clouds in general circulation models, all of which rely upon plane-parallel radiative transfer algorithms.

Cahalan, Robert F.↗
NASA NTRS2008

Evaluation of Long-Term Cloud-Resolving Model Simulations Using Satellite Radiance Observations and Multi-Frequency Satellite Simulators

This paper proposes a methodology known as the Tropical Rainfall Measuring Mission (TRMM) Triple-Sensor Three-step Evaluation Framework (T3EF) for the systematic evaluation of precipitating cloud types and microphysics in a cloud-resolving model (CRM). T3EF utilizes multi-frequency satellite simulators and novel statistics of multi-frequency radiance and backscattering signals observed from the TRMM satellite. Specifically, T3EF compares CRM and satellite observations in the form of combined probability distributions of precipitation radar (PR) reflectivity, polarization-corrected microwave brightness temperature (Tb), and infrared Tb to evaluate the candidate CRM. T3EF is used to evaluate the Goddard Cumulus Ensemble (GCE) model for cases involving the South China Sea Monsoon Experiment (SCSMEX) and Kwajalein Experiment (KWAJEX). This evaluation reveals that the GCE properly captures the satellite-measured frequencies of different precipitating cloud types in the SCSMEX case but underestimates the frequencies of deep convective and deep stratiform types in the KWAJEX case. Moreover, the GCE tends to simulate excessively large and abundant frozen condensates in deep convective clouds as inferred from the overestimated GCE-simulated radar reflectivities and microwave Tb depressions. Unveiling the detailed errors in the GCE s performance provides the best direction for model improvements.

Matsui, Toshihisa↗
NASA NTRS1988

Ozone on Mars - The effects of clouds and airborne dust

Photochemistry in the winter polar atmosphere of Mars is examined for several latitudes, cloud types, and dust abundances. Variations in cloud opacities and cloud types change O3 abundances by only a few percent. However, typical dust abundances induce 10-50 percent increases in O3 abundances, primarily because photodissociation rates are drastically reduced by dust absorption. Furthermore, annual, latitudinal, and seasonal cycles in dust opacity cause variations of 50 percent or greater in the corresponding cycles in O3 abundances. The reflectance spectroscopy technique that has been used to measure the O3 abundance may have difficulty detecting these variations.

Lindner, Bernhard Lee↗
NASA NTRS1991

Diurnal variations of outgoing longwave radiation and albedo from ERBE scanner data

The scanning instruments of the Earth Radiation Budget Experiment provide measurements of instantaneous broadband albedo and outgoing longwave radiation (OLR) with a spatial resolution of about 50 km. Data from the Earth Radiation Budget Satellite (ERBS), which is in an orbit that precesses through local time at the rate of one hour every three days, can be used to describe the mean, hourly diurnal variations in the distribution of OLR and albedo on this scale. Much of this variation is caused by cloud type and amount changes. Two-dimensional histograms show the coevolution of OLR and albedo with the diurnal cycle, and the distribution of albedo-OLR pairings associated with the cloud distribution in a particular region and season. The albedo-OLR pairing characterizes a cloud type and determines its net effect on the energy balance at the top of the atmosphere. Diurnal variations in cloud type and amount in many regions are sufficient to cause substantial errors in radiation budget quantities and cloud properties estimated from observations taken from a single sun-synchronous orbit. Errors in estimated net radiation can be as large as 50 W/sq m for oceanic stratus regions and for land regions during summer.

Hartmann, Dennis L.↗
NASA NTRSDate not supplied

Examining the Feasibility of Detailed Decomposition of Radiative Flux Anomalies to Cloud Changes

This presentation will explore the degree to which radiative flux anomalies in the last two decades seen by CERES can be interpreted as the result of changes in particular cloud types. A study of this kind is potentially enabled by the new CERES FlxByCldTyp (FBCT) product which provides combined Terra and Aqua daytime 1°-regional gridded daily and monthly top-of-the-atmosphere radiative fluxes and associated MODIS-derived cloud properties stratified by Cloud Top Pressure (CTP) and Cloud Optical Thickness (TAU), i.e., arrays of cloud properties and radiative fluxes resolved in CTP-TAU bins. It has been shown (Sun et al. 2022) that the FBCT time series of global flux anomalies tracks closely its SSF1deg and EBAF counterparts, indicating that these three products provide fluxes of consistent stability for interannual variability studies. We are thus in position to examine whether CERES regional or global radiative fluxes anomalies and trends are driven by changes in certain elements of FBCT flux arrays corresponding to particular cloud types. Anomalies in flux array elements will in turn be investigated in terms of cloud property variations within the CTP-TAU bins where the biggest flux anomalies are encountered. We will essentially seek to obtain the relative contributions to the anomalies of binned all-sky fluxes of changes in cloud fraction within cloud types and of changes in their overcast fluxes modulated by other cloud properties. Such an analysis holds great promise of unveiling the root causes of cloud-driven changes in the planetary radiation budget of the last two decades.

cloud↗
NASA NTRSDate not supplied

TPSAS-NF1676L-33601-DND

The Clouds and the Earth's Radiant Energy System (CERES) project now has over 18 years of an accurately observed top-of-the-atmosphere (TOA) flux record for climate monitoring and diagnostic studies. CERES provides the climate community the following parameters: coincident instantaneous 1? gridded CERES observed TOA fluxes, computed profile and surface fluxes, as well as MODIS cloud and aerosol retrievals. Clouds and radiation interaction one key factor that dominates climate feedbacks and is also the most difficult problem with large uncertainty. To further advance our understanding of the cloud-radiation interaction, the climate community need data with accurate fluxes and their associated cloud properties for both observational and modelling study. The new CERES FluxByCldTyp data product is produced for this purpose. The flux product combines for the first time CERES measured TOA fluxes along with the associated MODIS cloud properties, which links radiative flux directly to a specific cloud type. This was achieved by computing sub-footprint fluxes for the clear-sky and cloudy portions of the CERES footprint. The spatially distributed cloud properties within the CERES footprint were retrieved from 2-km MODIS pixels, that were stratified by cloud type. The sub-footprint fluxes were estimated from MODIS radiances based on empirically derived narrowband to broadband coefficients. The combined sub-footprint fluxes can then be validated with the observed footprint flux. This presentation will focus on the algorithm development of the flux-by-cloud-type product and its validation.

Moguo Sun↗
NASA NTRSDate not supplied

TPSAS-NF1676L-33753-DND

The Clouds and the Earth's Radiant Energy System (CERES) project now has over 18 years of an accurately observed top-of-the-atmosphere (TOA) flux record for climate monitoring and diagnostic studies. CERES provides the climate community the following parameters: coincident instantaneous 1 gridded CERES observed TOA fluxes, computed profile and surface fluxes, as well as MODIS cloud and aerosol retrievals. Clouds and radiation interaction one key factor that dominates climate feedbacks and is also the most difficult problem with large uncertainty. To further advance our understanding of the cloud-radiation interaction, the climate community need data with accurate fluxes and their associated cloud properties for both observational and modelling study. The new CERES FluxByCldTyp data product is produced for this purpose. The flux product combines for the first time CERES measured TOA fluxes along with the associated MODIS cloud properties, which links radiative flux directly to a specific cloud type. This was achieved by computing sub-footprint fluxes for the clear-sky and cloudy portions of the CERES footprint. The spatially distributed cloud properties within the CERES footprint were retrieved from 2-km MODIS pixels, that were stratified by cloud type. The sub-footprint fluxes were estimated from MODIS radiances based on empirically derived narrowband to broadband coefficients. The combined sub-footprint fluxes can then be validated with the observed footprint flux. This presentation will focus on the algorithm development of the flux-by-cloud-type product and its validation. The paper will also discuss CERES FlxbyCldTyp simulator and its application.

Moguo Sun↗
NASA NTRS2019

Polarimetric Retrievals of Cloud Droplet Number Concentrations

Cloud droplet number concentration (Nd) is an important parameter of liquid clouds and is crucial to understanding aerosol-cloud interactions. It couples boundary layer aerosol composition, size and concentration with cloud reflectivity. It affects cloud evolution, precipitation, radiative forcing, global climate and, through observation, can be used to partially monitor the first indirect effect. With its unique combination of multi-wavelength, multi-angle, total and polarized reflectance measurements, the Research Scanning Polarimeter (RSP) retrieves Nd with relatively few assumptions. The approach involves measuring cloud optical thickness, mean droplet extinction cross-section and cloud physical thickness. Polarimetric observations are capable of measuring the effective variance, or width, of the droplet size distribution. Estimating cloud geometrical thickness is also an important component of the polarimetric Nd retrieval, which is accomplished using polarimetric measurements in a water vapor absorption band to retrieve the amount of in-cloud water vapor and relating this to physical thickness. We highlight the unique abilities and quantify uncertainties of the polarimetric approach. We validate the approach using observational data from the North Atlantic and Marine Ecosystems Study (NAAMES). NAAMES targets specific phases in the seasonal phytoplankton lifecycle and ocean-atmosphere linkages. This study provides an excellent opportunity for the RSP to evaluate its approach of sensing Nd over a range of concentrations and cloud types with in situ measurements from a Cloud Droplet Probe (CDP). The RSP and CDP, along with an array of other instruments, are flown on the NASA C-130 aircraft, which flies in situ and remote sensing legs in sequence. Cloud base heights retrieved by the RSP compare well with those derived in situ (R=0.83) and by a ceilometer aboard the R.V. Atlantis (R=0.79). Comparing geometric mean values from 12 science flights throughout the NAAMES-1 and NAAMES-2 campaigns, we find a strong correlation between Nd retrieved by the RSP and CDP (R=0.96). A linear least squares fit has a slope of 0.92 and an intercept of 0.3 cm−3. Uncertainty in this comparison can be attributed to cloud 3D effects, nonlinear liquid water profiles, multilayered clouds, measurement uncertainty, variation in spatial and temporal sampling, and assumptions used within the method. Radiometric uncertainties of the RSP measurements lead to biases on derived optical thickness and cloud physical thickness, but these biases largely cancel out when deriving Nd for most conditions and geometries. We find that a polarimetric approach to sensing Nd is viable and the RSP is capable of accurately retrieving Nd for a variety of cloud types and meteorological conditions.

Droplet concentration↗
NASA NTRS2021

Using Intelligent Targeting to increase the science return of a Smart Ice Storm Hunting Radar

Smart Ice Cloud Sensing (SMICES) is a small-sat concept in which a radar intelligently targets ice storms based on information collected by a lookahead radiometer. Often space observations are performed by continuously collecting data from an instrument aimed at nadir (e.g. directly below the space platform). However, if the platform has the ability to assess science utility of features being overflown, an intelligent measurement scheme can improve science return. This can be achieved by controlling the on/off state of the instrument if it is not able to continuously operate (e.g. due to energy or thermal constraints), and by allowing the instrument to view off nadir if it has pointing capabilities.In the case of SMICES, power constraints and the rarity of storms means that with blind nadir targeting SMICES would collect a limited amount of ice storm radar data. The algorithms proposed acquire measurements to maximize acquired high interest storms while concurrently collecting a background sampling of all features. We use a cloud classification system to identify five different cloud types. Six algorithms ranging from “blind” to more selective are described and results from evaluation on a dataset of 13 ground swaths covering 72,399,600 km2 of data are presented. This data is from high quality science simulations that contain all five cloud types and multiple storms. When utilizing the radiometer’s lookahead and the full range of the radar the results show a 23.7x and 1.9x increase over the base algorithm in the most and second most important cloud types respectively.

Cooke, Caitlyn↗
NASA NTRS2021

Identifying Meteorological Influences on Marine Low Cloud Mesoscale Morphology Using Satellite Classifications

Marine low cloud mesoscale morphology in the southeastern Pacific Ocean is analyzed using a large dataset of machine-learning generated classifications spanning three years. Meteorological variables and cloud properties are composited 10by mesoscale cloud type, showing distinct meteorological regimes of marine low cloud organization from the tropics to the midlatitudes. The presentation of mesoscale cellular convection, with respect to geographic distribution, boundary layer structure, and large-scale environmental conditions, agrees with prior knowledge. Two tropical and subtropical cumuliform boundary layer regimes, suppressed cumulus and clustered cumulus, are studied in detail. The patterns in precipitation, circulation, column water vapor, and cloudiness are consistent with the representation of marine shallow mesoscale convective 15 self-aggregation by large eddy simulations of the boundary layer. Although they occur under similar large-scale conditions, the suppressed and clustered low cloud types are found to be well-separated by variables associated with low-level mesoscale circulation, with surface wind divergence being the clearest discriminator between them, whether reanalysis or satellite observations are used. Clustered regimes are associated with surface convergence and suppressed regimes are associated with surface divergence.

Johannes Mohrmann↗
NASA NTRS2022

Detailing Cloud Property Feedbacks with a Regime-Based Decomposition

Diagnosing the root causes of cloud feedback in climate models and reasons for inter-model disagreement is a necessary first step in understanding their wide variation in climate sensitivities. Here we bring together two analysis techniques that illuminate complementary aspects of cloud feedback. The first quantifies feedbacks from changes in cloud amount, altitude, and optical depth, while the second separates feedbacks due to cloud property changes within specific cloud regimes from those due to regime occurrence frequency changes. We find that in the global mean, shortwave cloud feedback averaged across ten models comes solely from a positive within-regime cloud amount feedback countered slightly by a negative within-regime optical depth feedback. These within-regime feedbacks are highly uniform: In nearly all regimes, locations, and models, cloud amount decreases and cloud albedo increases with warming. In contrast, global-mean across-regime components vary widely across models but are very small on average. This component, however, is dominant in setting the geographic structure of the shortwave cloud feedback: Thicker, more extensive cloud types increase at the expense of thinner, less extensive cloud types in the extratropics, and vice versa at low latitudes. The prominent negative extratropical optical depth feedback has contributions from both within- and across-regime components, suggesting that thermodynamic processes affecting cloud properties as well as dynamical processes that favor thicker cloud regimes are important. The feedback breakdown presented herein may provide additional targets for observational constraints by isolating cloud property feedbacks within specific regimes without the obfuscating effects of changing dynamics that may differ across timescales.

climate sensitivity↗
NASA NTRS1990

Evolution and detectability of comet clouds during post-main-sequence stellar evolution

The destruction of volatile-rich comet disks and Oort-type clouds around luminous post-main-sequence stars is modeled. The models are in agreement with several aspects of existing observations of water and complex molecules in the envelopes of giant and supergiant stars. If confirmed, these results would establish the common existence of Oort-type clouds around other stars and would constitute indirect evidence for sites of past planetary formation.

Stern, S. Alan↗
NASA NTRS1992

Pattern recognition analysis of polar clouds during summer and winter

A pattern recognition algorithm is demonstrated which classifies eighteen surface and cloud types in high-latitude AVHRR imagery based on several spectral and textural features, then estimates the cloud properties (fractional coverage, albedo, and brightness temperature) using a hybrid histogram and spatial coherence technique. The summertime version of the algorithm uses both visible and infrared data (AVHRR channels 1-4), while the wintertime version uses only infrared data (AVHRR channels 3-5). Three days of low-resolution AVHRR imagery from the Arctic and Antarctic during January and July 1984 were analyzed for cloud type and fractional coverage. The analysis showed significant amounts of high cloudiness in the Arctic during one day in winter. The Antarctic summer scene was characterized by heavy cloud cover in the southern ocean and relatively clear conditions in the continental interior. A large region of extremely low brightness temperatures in East Antarctica during winter suggests the presence of polar stratospheric cloud.

Ebert, Elizabeth E.↗
NASA NTRSDate not supplied

Improvement of Radiative Fluxes for the CERES FluxByCldTyp Data Product Based on Machine Learning Technique

The NASA Clouds and the Earth's Radiant Energy System (CERES) product provides over 20 years of accurately observed top-of-the-atmosphere and surface flux data for climate studies. The interaction between clouds and radiation interaction is a key factor that dominate climate feedbacks but is not well understood. To further advance our understanding of the cloud-radiation interaction, a new CERES FluxByCldTyp (FBCT) product has been developed that contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models. The FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) narrow-band (NB) imager channel radiances partitioned by cloud-type within a CERES footprint to estimate their broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. This study takes advantage of recent progress in machine learning (ML) field by applying deep neural network algorithm to improve fluxes based on MODIS NB radiances. The preliminary study shows ML produce are an improvement over the current FBCT Edition 4 NB2BB algorithm. Furthermore, unlike Ed4 NB2BB, the new ML method convert NB radiances directly to broadband fluxes. For future Ed5, new NB radiances are proposed and used by ML to improve fluxes calculation. Preliminary results show significant LW improvement.

Sun, Moguo↗
NASA NTRS1994

The sensitivity of tropospheric chemistry to cloud interactions

Clouds, although only occupying a relatively small fraction of the troposphere volume, can have a substantial impact on the chemistry of the troposphere. In newly formed clouds, or in clouds with air rapidly flowing through, the chemistry is expected to be far more active than in aged clouds with stagnant air. Thus, frequent cycling of air through shortlived clouds, i.e. cumulus clouds, is likely to be a much more efficient media for altering the composition of the atmosphere than an extensive cloud cover i.e. frontal cloud systems. The impact of clouds is tested out in a 2-D channel model encircling the globe in a latitudinal belt from 30 to 60 deg N. The model contains a detailed gas phase chemistry. In addition physiochemical interactions between the gas and aqueous phases are included. For species as H2O2, CH2O, O3, and SO2, Henry's law equilibria are assumed, whereas HNO3 and H2SO4 are regarded as completed dissolved in the aqueous phase. Absorption of HO2 and OH is assumed to be mass-transport limited. The chemistry of the aqueous phase is characterized by rapid cycling of odd hydrogen, (H2O2, HO2, and OH). O2(-) (produced through dissociation of HO2) reacting with dissolved O3 is a major source of OH in the aqueous phase. This reaction can be a significant sink for O3 in the troposphere. In the interstitial cloud air, odd hydrogen is depleted, whereas NO(x) remains in the gas phase, thus reducing ozone production due to the reaction between NO and HO2. Our calculations give markedly lower ozone levels when cloud interactions are included. This may in part explain the overpredictions of ozone levels often experienced in models neglecting cloud chemical interactions. In the present study, the existence of clouds, cloud types, and their lifetimes are modeled as pseudo random variables. Such pseudo random sequences are in reality deterministic and may, given the same starting values, be reproduced. The effects of cloud interactions on the overall chemistry of the troposphere are discussed. In particular, tests are performed to determine the sensitivity of cloud frequencies and cloud types.

Jonson, Jan E.↗
NASA NTRS2005

Nighttime Cirrus Detection using Atmospheric Infrared Sounder Window Channels and Total Column Water Vapor

A method of cirrus detection at nighttime is presented that utilizes 3.8 and 10.4 (micro)m infrared (IR) window brightness temperature differences (dBT) and total column precipitable water (PW) measurements. This technique is applied to the Atmospheric Infrared Sounder (AIRS) and Advanced Microwave Sounding Unit A (AMSU-A) instrument suite on board EOS-Aqua, where dBT is determined from sets of carefully selected AIRS window channels, while PW is derived from the synergistic AIRS and AMSU-A water vapor retrievals. Simulated and observed dBT for a particular value of PW are not constant; several physical factors impact dBT, including the variability in temperature and relative humidity profiles, surface emissivity, instrument noise, and skin/ near-surface air temperature differences. We simulate clear-sky dBT over a realistic range of PWs using 8350 radiosondes that have varying temperature and relative humidity profiles. Thresholds between cloudy and uncertain sky conditions are derived once the scatter in the clear-sky dBT is determined. Simulations of optically thin cirrus indicate that this technique is most sensitive to cirrus optical depth in the 10 (micro)m window of 0.1-0.15 or greater over the tropical and subtropical oceans, where surface emissivity and skin/near-surface air temperature impacts on the IR radiances are minimal. The method at present is generally valid over oceanic regions only, specifically, the tropics and subtropics. The detection of thin cirrus, and other cloud types, is validated using observations at the Atmospheric Radiation Measurement (ARM) program site located at Manus Island in the tropical western Pacific for 89 coincident EOS-Aqua overpasses. Even though the emphasis of this work is on the detection of thin cirrus at nighttime, this technique is sensitive to a broad cloud morphology. The cloud detection technique agrees with ARM-detected clouds 82-84% of the time, which include thin cirrus, as well as other cloud types. Most of the disagreements are well explained by AIRS footprint-scale heterogeneity compared to ARM point measurements, cirrus overlying lower-layer water clouds, possible mixed phase microphysics in midlevel clouds, and significant IR channel noise for cold BT scenes over deep convective towers.

Atmospheric Radiation Measurement (RAM)↗
NASA NTRS1971

Experiment S007: Cloud top spectrometry

During the Gemini 5 mission, 26 spectrographic observations on various cloud types were obtained using the oxygen A band (7600 A). An example of the types of spectrum and photograph involved represents a cloud in the intertropical convergence zone. Densitometer traces of the spectra of three types of clouds (high, medium, and low) are shown. It was apparent qualitatively that radiation transmission in the oxygen band for a high cloud was much greater than that for a low cloud. The results proved the feasibility of cloud altitude measurements from a spacecraft by this method.

Saiedy, F.↗
NASA NTRS2009

Comparison of GOES Cloud Classification Algorithms Employing Explicit and Implicit Physics

Cloud-type classification based on multispectral satellite imagery data has been widely researched and demonstrated to be useful for distinguishing a variety of classes using a wide range of methods. The research described here is a comparison of the classifier output from two very different algorithms applied to Geostationary Operational Environmental Satellite (GOES) data over the course of one year. The first algorithm employs spectral channel thresholding and additional physically based tests. The second algorithm was developed through a supervised learning method with characteristic features of expertly labeled image samples used as training data for a 1-nearest-neighbor classification. The latter's ability to identify classes is also based in physics, but those relationships are embedded implicitly within the algorithm. A pixel-to-pixel comparison analysis was done for hourly daytime scenes within a region in the northeastern Pacific Ocean. Considerable agreement was found in this analysis, with many of the mismatches or disagreements providing insight to the strengths and limitations of each classifier. Depending upon user needs, a rule-based or other postprocessing system that combines the output from the two algorithms could provide the most reliable cloud-type classification.

EXPLICIT PHYSICS ALGORITHMS↗