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NASA NTRS1999

Cloud Spatial Structure and 3D Radiative Transfer

Cloud radiative properties are sensitive to drop size and other parameters of cloud micro-structure, but also to cloud shape,spacing, and other parameters of cloud macro-structure, including internal fractal structure. New information on cloud structure is being derived from a variety of cloud radars, and ongoing field programs such as DoE/ARM. These programs are improving the measurement and modelling of physical and radiative properties of clouds. A parallel effort is underway to improve cloud remote sensing, especially from the new suite of EOS-AM1 instruments which will provide higher spectral, spatial resolution, and/or angular resolution. Key parameters for improving pixel-scale retrievals are cloud thickness and photon mean-free-path, which together determine the scale of "radiative smoothing" of cloud fluxes and radiances. This scale has been observed as a change in the spatial spectrum of Landsat cloud radiances, and was also recently found with the Goddard micropulse lidar, by searching for returns from directions nonparallel to the incident beam. "Offbeam" Lidar returns are now being used to estimate the cloud "radiative Green's function", G,which depends on cloud thickness and may be used to retrieve that important quantity. G is also being applied to improving simple IPA estimates of cloud radiative properties. This and other measurements of 3D transfer in clouds, coupled with Monte Carlo and other 3D transfer methods, are beginning to provide a better understanding of the dependence of radiation on cloud inhomogeneity, and to suggest new retrieval and parameterization algorithms which take account of cloud inhomogeneity.

Cahalan, Robert F.↗
NASA NTRS2001

Cloud Overlapping Detection Algorithm Using Solar and IR Wavelengths With GOSE Data Over ARM/SGP Site

One of the most perplexing problems in satellite cloud remote sensing is the overlapping of cloud layers. Although most techniques assume a 1-layer cloud system in a given retrieval of cloud properties, many observations are affected by radiation from more than one cloud layer. As such, cloud overlap can cause errors in the retrieval of many properties including cloud height, optical depth, phase, and particle size. A variety of methods have been developed to identify overlapped clouds in a given satellite imager pixel. Baum el al. (1995) used CO2 slicing and a spatial coherence method to demonstrate a possible analysis method for nighttime detection of multilayered clouds. Jin and Rossow (1997) also used a multispectral CO2 slicing technique for a global analysis of overlapped cloud amount. Lin et al. (1999) used a combination infrared, visible, and microwave data to detect overlapped clouds over water. Recently, Baum and Spinhirne (2000) proposed 1.6 and 11 microns. bispectral threshold method. While all of these methods have made progress in solving this stubborn problem, none have yet proven satisfactory for continuous and consistent monitoring of multilayer cloud systems. It is clear that detection of overlapping clouds from passive instruments such as satellite radiometers is in an immature stage of development and requires additional research. Overlapped cloud systems also affect the retrievals of cloud properties over the ARM domains (e.g., Minnis et al 1998) and hence should identified as accurately as possible. To reach this goal, it is necessary to determine which information can be exploited for detecting multilayered clouds from operational meteorological satellite data used by ARM. This paper examines the potential information available in spectral data available on the Geostationary Operational Environmental Satellite (GOES) imager and the NOAA Advanced Very High Resolution Radiometer (AVHRR) used over the ARM SGP and NSA sites to study the capability of detecting overlapping clouds

Kawamoto, Kazuaki↗
NASA NTRS2001

Cloud Overlapping Detection Algorithm Using Solar and IR Wavelengths with GOES Data Over ARM/SGP Site

One of the most perplexing problems in satellite cloud remote sensing is the overlapping of cloud layers. Although most techniques assume a one layer cloud system in a given retrieval of cloud properties, many observations are affected by radiation from more than one cloud layer. As such, cloud overlap can cause errors in the retrieval of many properties including cloud height, optical depth, phase, and particle size. A variety of methods have been developed to identify overlapped clouds in a given satellite imager pixel. Baum et al used CO2 slicing and a spatial coherence method to demonstrate a possible analysis method for nighttime detection of multilayered clouds. Jin and Rossow also used a multispectral CO2 slicing technique for a global analysis of overlapped cloud amount. Lin et al. used a combination infrared (IR), visible (VIS), and microwave data to detect overlapped clouds over water. Recently, Baum and Spinhirne proposed a 1.6 and 11 micron bispectral threshold method. While all of these methods have made progress in solving this stubborn problem none have yet proven satisfactory for continuous and consistent monitoring of multilayer cloud systems. It is clear that detection of overlapping clouds from passive instruments such as satellite radiometers is in an immature stage of development and requires additional research. Overlapped cloud systems also affect the retrievals of cloud properties over the Atmospheric Radiation Measurement (ARM) domains and hence should be identified as accurately as possible. To reach this goal, it is necessary to determine which information can be exploited for detecting multilayered clouds from operational meteorological satellite data used by ARM. This paper examines the potential information available in spectral data available on the Geostationary Operational Environmental Satellite (GOES) imager and the National Oceanic Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) used over the ARM Program's Southern Great Plains (SGP), and North Slope of Alaska (NSA) sites to study the capability of detecting overlapping clouds.

Kawamoto, K.↗
NASA NTRS2005

Scanning Backscatter Lidar Observations for Characterizing 4-D Cloud and Aerosol Fields to Improve Radiative Transfer Parameterizations

Clouds have a powerful influence on atmospheric radiative transfer and hence are crucial to understanding and interpreting the exchange of radiation between the Earth's surface, the atmosphere, and space. Because clouds are highly variable in space, time and physical makeup, it is important to be able to observe them in three dimensions (3-D) with sufficient resolution that the data can be used to generate and validate parameterizations of cloud fields at the resolution scale of global climate models (GCMs). Simulation of photon transport in three dimensionally inhomogeneous cloud fields show that spatial inhomogeneities tend to decrease cloud reflection and absorption and increase direct and diffuse transmission, Therefore it is an important task to characterize cloud spatial structures in three dimensions on the scale of GCM grid elements. In order to validate cloud parameterizations that represent the ensemble, or mean and variance of cloud properties within a GCM grid element, measurements of the parameters must be obtained on a much finer scale so that the statistics on those measurements are truly representative. High spatial sampling resolution is required, on the order of 1 km or less. Since the radiation fields respond almost instantaneously to changes in the cloud field, and clouds changes occur on scales of seconds and less when viewed on scales of approximately 100m, the temporal resolution of cloud properties should be measured and characterized on second time scales. GCM time steps are typically on the order of an hour, but in order to obtain sufficient statistical representations of cloud properties in the parameterizations that are used as model inputs, averaged values of cloud properties should be calculated on time scales on the order of 10-100 s. The Holographic Airborne Rotating Lidar Instrument Experiment (HARLIE) provides exceptional temporal (100 ms) and spatial (30 m) resolution measurements of aerosol and cloud backscatter in three dimensions. HARLIE was used in a ground-based configuration in several recent field campaigns. Principal data products include aerosol backscatter profiles, boundary layer heights, entrainment zone thickness, cloud fraction as a function of altitude and horizontal wind vector profiles based on correlating the motions of clouds and aerosol structures across portions of the scan. Comparisons will be made between various cloud detecting instruments to develop a baseline performance metric.

Schwemmer, Geary K.↗
NASA NTRS2007

Comparison of Cloud Properties from CALIPSO-CloudSat and Geostationary Satellite Data

Cloud properties are being derived in near-real time from geostationary satellite imager data for a variety of weather and climate applications and research. Assessment of the uncertainties in each of the derived cloud parameters is essential for confident use of the products. Determination of cloud amount, cloud top height, and cloud layering is especially important for using these real -time products for applications such as aircraft icing condition diagnosis and numerical weather prediction model assimilation. Furthermore, the distribution of clouds as a function of altitude has become a central component of efforts to evaluate climate model cloud simulations. Validation of those parameters has been difficult except over limited areas where ground-based active sensors, such as cloud radars or lidars, have been available on a regular basis. Retrievals of cloud properties are sensitive to the surface background, time of day, and the clouds themselves. Thus, it is essential to assess the geostationary satellite retrievals over a variety of locations. The availability of cloud radar data from CloudSat and lidar data from CALIPSO make it possible to perform those assessments over each geostationary domain at 0130 and 1330 LT. In this paper, CloudSat and CALIPSO data are matched with contemporaneous Geostationary Operational Environmental Satellite (GOES), Multi-functional Transport Satellite (MTSAT), and Meteosat-8 data. Unlike comparisons with cloud products derived from A-Train imagers, this study considers comparisons of nadir active sensor data with off-nadir retrievals. These matched data are used to determine the uncertainties in cloud-top heights and cloud amounts derived from the geostationary satellite data using the Clouds and the Earth s Radiant Energy System (CERES) cloud retrieval algorithms. The CERES multi-layer cloud detection method is also evaluated to determine its accuracy and limitations in the off-nadir mode. The results will be useful for constraining the use of the passive retrieval data in models and for improving the accuracy of the retrievals.

Nguyen, L.↗
NASA NTRS2009

Sensitivity of Photolysis Frequencies and Key Tropospheric Oxidants in a Global Model to Cloud Vertical Distributions and Optical Properties

Clouds affect tropospheric photochemistry through modification of solar radiation that determines photolysis frequencies. As a follow-up study to our recent assessment of the radiative effects of clouds on tropospheric chemistry, this paper presents an analysis of the sensitivity of such effects to cloud vertical distributions and optical properties (cloud optical depths (CODs) and cloud single scattering albedo), in a global 3-D chemical transport model (GEOS-Chem). GEOS-Chem was driven with a series of meteorological archives (GEOS1- STRAT, GEOS-3 and GEOS-4) generated by the NASA Goddard Earth Observing System data assimilation system. Clouds in GEOS1-STRAT and GEOS-3 have more similar vertical distributions (with substantially smaller CODs in GEOS1-STRAT) while those in GEOS-4 are optically much thinner in the tropical upper troposphere. We find that the radiative impact of clouds on global photolysis frequencies and hydroxyl radical (OH) is more sensitive to the vertical distribution of clouds than to the magnitude of column CODs. With random vertical overlap for clouds, the model calculated changes in global mean OH (J(O1D), J(NO2)) due to the radiative effects of clouds in June are about 0.0% (0.4%, 0.9%), 0.8% (1.7%, 3.1%), and 7.3% (4.1%, 6.0%), for GEOS1-STRAT, GEOS-3 and GEOS-4, respectively; the geographic distributions of these quantities show much larger changes, with maximum decrease in OH concentrations of approx.15-35% near the midlatitude surface. The much larger global impact of clouds in GEOS-4 reflects the fact that more solar radiation is able to penetrate through the optically thin upper-tropospheric clouds, increasing backscattering from low-level clouds. Model simulations with each of the three cloud distributions all show that the change in the global burden of ozone due to clouds is less than 5%. Model perturbation experiments with GEOS-3, where the magnitude of 3-D CODs are progressively varied from -100% to 100%, predict only modest changes (<5%) in global mean OH concentrations. J(O1D), J(NO2) and OH3 concentrations show the strongest sensitivity for small CODs and become insensitive at large CODs due to saturation effects. Caution should be exercised not to use in photochemical models a value for cloud single scattering albedo lower than about 0.999 in order to be consistent with the current knowledge of cloud absorption at the ultraviolet wavelengths.

Liu, Hongyu↗
NASA NTRS2010

The Invigoration of Deep Convective Clouds Over the Atlantic: Aerosol Effect, Meteorology or Retrieval Artifact?

Associations between cloud properties and aerosol loading are frequently observed in products derived from satellite measurements. These observed trends between clouds and aerosol optical depth suggest aerosol modification of cloud dynamics, yet there are uncertainties involved in satellite retrievals that have the potential to lead to incorrect conclusions. Two of the most challenging problems are addressed here: the potential for retrieved aerosol optical depth to be cloud-contaminated, and as a result, artificially correlated with cloud parameters; and the potential for correlations between aerosol and cloud parameters to be erroneously considered to be causal. Here these issues are tackled directly by studying the effects of the aerosol on convective clouds in the tropical Atlantic Ocean using satellite remote sensing, a chemical transport model, and a reanalysis of meteorological fields. Results show that there is a robust positive correlation between cloud fraction or cloud top height and the aerosol optical depth, regardless of whether a stringent filtering of aerosol measurements in the vicinity of clouds is applied, or not. These same positive correlations emerge when replacing the observed aerosol field with that derived from a chemical transport model. Model-reanalysis data is used to address the causality question by providing meteorological context for the satellite observations. A correlation exercise between the full suite of meteorological fields derived from model reanalysis and satellite-derived cloud fields shows that observed cloud top height and cloud fraction correlate best with model pressure updraft velocity and relative humidity. Observed aerosol optical depth does correlate with meteorological parameters but usually different parameters from those that correlate with observed cloud fields. The result is a near-orthogonal influence of aerosol and meteorological fields on cloud top height and cloud fraction. The results strengthen the case that the aerosol does play a role in invigorating convective clouds.

Koren, Ilan↗
NASA NTRS2012

Using ISCCP Weather States to Decompose Cloud Radiative Effects

The presentation will examine the shortwave (SW) and longwave (LW) cloud radiative effect CRE (aka "cloud radiative forcing") at the top-of-the-atmosphere and surface of ISCCP weather states (aka "cloud regimes") in three distinct geographical zones, one tropical and two mid-latitude. Our goal is to understand and quantify the contribution of the different cloud regimes to the planetary radiation budget. In the tropics we find that the three most convectively active states are the ones with largest SW, LW and net TOA CRE contributions to the overall daytime tropical CRE budget. They account for 59%, 71% and 55% of the total CRE, respectively. The boundary layer-dominated weather states account for only 34% of the total SW CRE and 41% of the total net CRE, so to focus only on them in cloud feedback studies may be imprudent. We also find that in both the northern and southern midlatitude zones only two weather states, the first and third most convectively active with large amounts of nimbostratus-type clouds, contribute ",40% to both the SW and net TOA CRE budgets, highlighting the fact that cloud regimes associated with frontal systems are not only important for weather (precipitation) but also for climate (radiation budget). While all cloud regimes in all geographical zones have a slightly larger SFC than TOA SW CRE, implying cooling of the surface and slight warming of the atmosphere, their LW radiative effects are more subtle: in the tropics the weather states with plentiful high clouds warm the atmosphere while those with copious amounts of low clouds cool the atmosphere. In both midlatitude zones only the weather states with peak cloud fractions at levels above 440 mbar warm the atmosphere while all the rest cool it. These results make the connection of the contrasting CRE effects to the atmospheric dynamics more explicit - "storms" tend to warm the atmosphere whereas fair weather clouds cool it, suggesting a positive feedback of clouds on weather systems. The breakdown of CRE by cloud regime are however not entirely similar between the two midlatitude zones. Despite the existence of an additional state in the nort!lern midlatitudes, only four weather states have net daytime CREs with absolute values above 100 Watts per square meter compared to six in the south. This reminds us that the environment where clouds occur also has a crucial role in determining their radiative effects. All the above make evident that reproducing grand averages of current CRE by climate models in only part of the challenge. If existing cloud regimes and shifts in their distributions and frequency of occurrence in a changed climate are not properly simulated, the radiative role of clouds will not be adequately predicted.

Oreopoulos, L.↗
NASA NTRS2012

On the Nature and Extent of Optically Thin Marine low Clouds

Macrophysical properties of optically thin marine low clouds over the nonpolar oceans (60 deg S-60 deg N) are measured using 2 years of full-resolution nighttime data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). Optically thin clouds, defined as the subset of marine low clouds that do not fully attenuate the lidar signal, comprise almost half of the low clouds over the marine domain. Regionally, the fraction of low clouds that are optically thin (f(sub thin,cld)) exhibits a strong inverse relationship with the low-cloud cover, with maxima in the tropical trades (f(sub thin,cld) greater than 0.8) and minima in regions of persistent marine stratocumulus and in midlatitudes (f(sub thin,cld) less than 0.3). Domain-wide, a power law fit describes the cloud length distribution, with exponent beta = 2.03 +/- 0.06 (+/-95% confidence interval). On average, the fraction of a cloud that is optically thin decreases from approximately 1 for clouds smaller than 2 km to less than 0.3 for clouds larger than 30 km. This relationship is found to be independent of region, so that geographical variations in the cloud length distribution explain three quarters of the variance in f(sub thin,cld). Comparing collocated trade cumulus observations from CALIOP and the airborne High Spectral Resolution Lidar reveals that clouds with lengths smaller than are resolvable with CALIOP contribute approximately half of the low clouds in the region sampled. A bounded cascade model is constructed to match the observations from the trades. The model shows that the observed optically thin cloud behavior is consistent with a power law scaling of cloud optical depth and suggests that most optically thin clouds only partially fill the CALIOP footprint.

Leahy, L. V.↗
NASA NTRS2013

Vertical Structure of Ice Cloud Layers From CloudSat and CALIPSO Measurements and Comparison to NICAM Simulations

The shape of the vertical profile of ice cloud layers is examined using 4 months of CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) global measurements taken on January, April, July, and October 2007. Ice clouds are selected using temperature profiles when the cloud base is located above the 253K temperature level. The obtained ice water content (IWC), effective radius, or extinction coefficient profiles are normalized by their layer mean values and are expressed in the normalized vertical coordinate, which is defined as 0 and 1 at the cloud base and top heights, respectively. Both CloudSat and CALIPSO observations show that the maximum in the IWC and extinction profiles shifts toward the cloud bottom, as the cloud depth increases. In addition, clouds with a base reaching the surface in a high-latitude region show that the maximum peak of the IWC and extinction profiles occurs near the surface, which is presumably due to snow precipitation. CloudSat measurements show that the seasonal difference in normalized cloud vertical profiles is not significant, whereas the normalized cloud vertical profile significantly varies depending on the cloud type and the presence of precipitation. It is further examined if the 7 day Nonhydrostatic Icosahedral Atmospheric Model (NICAM) simulation results from 25 December 2006 to 1 January 2007 generate similar cloud profile shapes. NICAM IWC profiles also show maximum peaks near the cloud bottom for thick cloud layers and maximum peaks at the cloud bottom for low-level clouds near the surface. It is inferred that oversized snow particles in the NICAM cloud scheme produce a more vertically inhomogeneous IWC profile than observations due to quick sedimentation.

Ham, Seung-Hee↗
NASA NTRS2017

The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6.

The primary objective of CFMIP is to inform future assessments of cloud feedbacks through improved understanding of cloud-climate feedback mechanisms and better evaluation of cloud processes and cloud feedbacks in climate models. However, the CFMIP approach is also increasingly being used to understand other aspects of climate change, and so a second objective has now been introduced, to improve understanding of circulation, regional-scale precipitation, and non-linear changes. CFMIP is supporting ongoing model inter-comparison activities by coordinating a hierarchy of targeted experiments for CMIP6, along with a set of cloud-related output diagnostics. CFMIP contributes primarily to addressing the CMIP6 questions 'How does the Earth system respond to forcing?' and 'What are the origins and consequences of systematic model biases?' and supports the activities of the WCRP Grand Challenge on Clouds, Circulation and Climate Sensitivity. A compact set of Tier 1 experiments is proposed for CMIP6 to address this question: (1) what are the physical mechanisms underlying the range of cloud feedbacks and cloud adjustments predicted by climate models, and which models have the most credible cloud feedbacks? Additional Tier 2 experiments are proposed to address the following questions. (2) Are cloud feedbacks consistent for climate cooling and warming, and if not, why? (3) How do cloud-radiative effects impact the structure, the strength and the variability of the general atmospheric circulation in present and future climates? (4) How do responses in the climate system due to changes in solar forcing differ from changes due to CO2, and is the response sensitive to the sign of the forcing? (5) To what extent is regional climate change per CO2 doubling state-dependent (non-linear), and why? (6) Are climate feedbacks during the 20th century different to those acting on long-term climate change and climate sensitivity? (7) How do regional climate responses (e.g. in precipitation) and their uncertainties in coupled models arise from the combination of different aspects of CO2 forcing and sea surface warming? CFMIP also proposes a number of additional model outputs in the CMIP DECK, CMIP6 Historical and CMIP6 CFMIP experiments, including COSP simulator outputs and process diagnostics to address the following questions. 1. How well do clouds and other relevant variables simulated by models agree with observations? 2. What physical processes and mechanisms are important for a credible simulation of clouds, cloud feedbacks and cloud adjustments in climate models? 3. Which models have the most credible representations of processes relevant to the simulation of clouds? 4. How do clouds and their changes interact with other elements of the climate system?

carbon dioxide↗
NASA NTRS2017

HST WFC3 Observations of Uranus' 2014 Storm Clouds and Comparison with VLT/SINFONI and IRTF/Spex Observations

In November 2014 Uranus was observed with the Wide Field Camera 3 (WFC3) instrument of the Hubble Space Telescope as part of the Hubble 2020: Outer Planet Atmospheres Legacy program, OPAL. OPAL annually maps Jupiter, Uranus and Neptune (and will also map Saturn from 2018) in several visible near- infrared wavelength filters. The Uranus 2014 OPAL observations were made on the 89th November at a time when a huge cloud complex, first observed by de Pater et al. (2015) and subsequently tracked by professional and amateur astronomers (Sayanagi et al., 2016), was present at 30-40deg N. We imaged the entire visible atmosphere, including the storm system, in seven filters spanning 467924 nm, capturing variations in the coloration of Uranus clouds and also vertical distribution due to wavelength dependent changes in Rayleigh scattering and methane absorption optical depth. Here we analyse these new HST observations with the NEMESIS radiative-transfer and retrieval code in multiple-scattering mode to determine the vertical cloud structure in and around the storm cloud system. The same storm system was also observed in the H-band (1.4-1.8 micrometers) with the SINFONI Integral Field Unit Spectrometer on the Very Large Telescope (VLT) on 31st October and 11th November, reported by Irwin et al. (2016, 10.1016j.icarus.2015.09.010). To constrain better the cloud particle sizes and scattering properties over a wide wavelength range we also conducted a limb-darkening analysis of the background cloud structure in the 30-40deg N latitude band by simultaneously fitting: a) these HSTOPAL observations at a range of zenith angles; b) the VLTSINFONI observations at a range of zenith angles; and c) IRTFSpeX observations of this latitude band made in 2009 at a single zenith angle of 23deg, spanning the wavelength range 0.8-1.8 micrometers (Irwin et al., 2015, 10.1016j.icarus.2014.12.020). We find that the HST observations, and the combined HSTVLTIRTF observations at all locations are well modelled with a three-component cloud comprised of: 1) a vertically thin, but optically thick deep tropospheric cloud at a pressure of approximately 2 bars; 2) a methane-ice cloud based at the methane-condensation level of approximately 1.23 bar, with variable vertical extent; and 3) a vertically extended tropospheric haze, also based at the methane-condensation level of 1.23 bar. We find that modelling both haze and tropospheric cloud with particles having an effective radius of approximately 0.1 micron provides a good fit the observations, although for the tropospheric cloud, particles with an effective radius as large as 1.0 micron provide a similarly good fit. We find that the particles in both the tropospheric cloud and haze are more scattering at short wave- lengths, giving them a blue color, but are more absorbing at longer wavelengths, especially for the tropospheric haze. We find that the spectra of the storm clouds are well modelled by localized thickening and vertical extension of the methane-ice cloud. For the particles in the storm clouds, which we assume to be composed of methane ice particles, we find that their mean radii must lie somewhere in the range 0. 1 1. 0 m. We find that the high clouds have low integrated opacity, and that streamers reminiscent of convective thunderstorm anvils are confined to levels deeper than 1 bar. These results argue against vigorous moist convective origins for the cloud features.

atmospheres↗
NASA NTRS2016

Cloud Motion in the GOCI COMS Ocean Colour Data

The Geostationary Ocean Colour Imager (GOCI) instrument, on Koreas Communications, Oceans, and Meteorological Satellite (COMS), can produce a spectral artefact arising from the motion of clouds the cloud is spatially shifted and the amount of shift varies by spectral band. The length of time it takes to acquire all eight GOCI bands for a given slot (portion of a scene) is sucient to require that cloud motion be taken into account to fully mask or correct the eects of clouds in all bands. Inter-band correlations can be used to measure the amount of cloud shift, which can then be used to adjust the cloud mask so that the union of all shifted masks can act as a mask for all bands. This approach reduces the amount of masking required versus a simple expansion of the mask in all directions away from clouds. Cloud motion can also aect regions with unidentied clouds thin or fractional clouds that evade the cloud identication process yielding degraded quality in retrieved ocean colour parameters. Areas with moving and unidentied clouds require more elaborate masking algo-rithms to remove these degraded retrievals. Correction for the eects of moving fractional clouds may also be possible. The cloud shift information can be used to determine cloud motion and thus wind at the cloud levels on sub-minute timescales. The benecial and negative eects of moving clouds should be con-sidered for any ocean colour instrument design and associated data processing plans.

Robinson, Wayne D.↗
NASA NTRSDate not supplied

Understanding Relationships Between Satellite, Model, and Ground-Based Surface Temperature Characterizations From Overcast to Clear Conditions in Support of Satellite Remote Sensing of Clouds and Radiation

Accurate and consistent global estimates of cloud coverage and their properties are fundamental to long-term Earth radiation budget (ERB) monitoring efforts like the Clouds and the Earth’s Radiant Energy System (CERES) project. Cloud detection algorithms often apply thresholding approaches to identify where clouds occur by comparing satellite-measured radiances with those that are expected under cloud-free conditions. In addition, once a cloud is detected, the derivation of cloud optical and microphysical properties also requires knowledge of the background radiances below the cloud. In the infrared, knowledge of the surface emissivity and the expected skin temperature under both cloudy and cloud-free conditions is needed. These traits are generally well known over the oceans. Over land, however, comparisons between satellite-derived land surface temperature (LST) with that characterized in numerical weather analyses reveal large differences in many parts of the world, often exceeding 5 K, which can lead to significant satellite cloud detection and cloud property retrieval errors. Furthermore, clouds have a dramatic influence on the LST, and therefore characterization of that model parameter also depends on the capability of the model to accurately resolve clouds. Thus, the LST characterized in models is, at times, a poor approximation for what would otherwise be observed, thereby impeding accurate satellite cloud retrievals. As a result, we seek to develop a more robust method for estimating the LST required for satellite cloud characterizations. This effort is accomplished through a combination of surface emission/air temperature relationship studies in all-sky conditions using ground measurement stations, along with deep neural network (DNN) estimates of expected LST under overcast and cloud-free conditions. We demonstrate that substituting DNN-predicted LST for that generated by numerical models can mitigate model-inherent diurnal dependencies and reduce overall bias and uncertainty relative to satellite/ground observations by 0.5–4 K and 0.5–2 K, respectively. It is expected that this work will lead to improved satellite cloud retrievals that enhance ERB monitoring efforts.

B Scarino↗
NASA NTRSDate not supplied

NASA's Implementation of Cloud Services for Human Space Flight

Cloud is a tried-and-true technology used throughout United States government agencies, including the National Aeronautics and Space Administration (NASA). With reliable results and infrequent downtimes, cloud allows for secure remote access, customizability, and streamlined monitoring options, creating an environment for better data integrity and availability. As NASA increasingly migrates functions to the cloud, the Space Communications and Navigation Program (SCaN) program has been investigating how this capability can be leveraged to provide communication services to its users and customers. Currently, missions such as NASA-ISRO Synthetic Aperture Radar (NISAR), Plankton, Aerosol, Cloud, ocean Ecosystem (PACE), and Roman Space Telescope (RST) are planned to incorporate cloud into their data delivery architecture. However, SCaN is looking to expand further. This conversion to using cloud services allows for greater availability of mission data for both robotic and human space flight (HSF)missions. The SCaN program and the Near Space Network (NSN) are working to consolidate resources and create a cloud environment suitable for the entirety of the SCaN program network architecture. SCaN is in the process of finalizing its cloud architecture and soon will be implementing cloud services. The new services used will adhere to federal regulations including Federal Risk and Authorization Management Program (FedRAMP), which is built upon National Institute of Standards and Technology (NIST)documentation. While keeping in mind these security requirements, an auxiliary objective of the cloud integration is to ensure the most cost-efficient solution; providing a scalable, robust and resilient system. Using cloud services, NASA will gain access to better centralized monitoring and management features, along with customizable services on a pay-per-use plan. With the ever-growing NASA mission data volume needs, maintaining ample storage space is another major constraint. Processing and storing such large amounts of data, on the order of terabytes a day, requires dynamic processing capability which is inherently a strength of cloud computing. By routing this data from ground stations through the cloud, there will be greater ease of access for both SCaN and the user community. Artificial intelligence and other built-in cloud functions can also enhance efficiency, improving data processing time. Thereby also allowing for better data availability. As we look to the future of cloud services, NASA will continue to leverage capabilities that will benefit NASA’s ability to provide cost-effective communication services. This paper further outlines the evolution of cloud use by SCaN in the context of Human Space Flight.

cloud storage↗
NASA NTRSDate not supplied

Automated Tracking of Shallow Maritime Clouds on Geostationary Imagery to Extract Lifecycle Characteristics

Shallow moist convection is ubiquitous throughout the tropics and represents a key player in boundary layer processes. Satellites have provided valuable insight on shallow clouds, such as size, structure, and geographical coverage, from static views of recurring cloud fields. But determining why certain cloud features appear and persist for different periods requires a time-evolving view of their behaviors. Geostationary satellites provide a unique opportunity to follow the time evolution of individual convective features, given their enhanced spatial and temporal sampling. A cloud-tracking tool was developed to identify properties of cloud lifecycle from the NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2EX) field campaign of 2019. The mission conducted intensive sampling of shallow cumulus in the West Pacific Ocean, in tandem with Rapid Scan imagery from the Advanced Himawari Imager (AHI) on the Japan Meteorological Agency’s (JMA) Himawari-8 satellite. Shallow cumulus were segmented according to thresholds in 0.5-km visible reflectance and with blurring techniques. Despite being limited to daytime hours, the segmentations yielded the best resolution available for capturing cloud initiation, growth, and decay. The tracking procedure is based on a computer vision package that includes Kalman filters for motion prediction, object overlap search, and the Hungarian (or Kuhn-Munkres) matching algorithm for track designation. AHI radiances available within the tracked cloud boundaries are assembled to form individual spectral histories. The resulting catalog provides thousands of cloud histories for domains measuring only a few degrees in latitude and longitude. We present an overview of the cloud-tracking tool and its results for cloud fields sampled throughout CAMP2EX by the airborne P-3. Cloud tracks were selected from about 10 flights to form ensembles, specific groups of tracks occurring in a region with airborne sampling. Cloud lifecycle properties, including duration and maximum area, are calculated for all ensemble members, and analyzed for cloud behavior and P-3 coincidences. By following this strategy, we quantitatively assess the degree of airborne sampling for specific cloud classes defined by the lifecycle calculations. We can summarize which cloud classes had more sampling, the stage of development during sampling, and general differences in character (e.g., isolated congestus vs. cold-pool producer).

Cloud Tracking↗
NASA NTRS2016

Aircraft-Measured Indirect Cloud Effects from Biomass Burning Smoke in the Arctic and Subarctic

The incidence of wildfires in the Arctic and subarctic is increasing; in boreal North America, for example, the burned area is expected to increase by 200-300 over the next 50-100 years, which previous studies suggest could have a large effect on cloud microphysics, lifetime, albedo, and precipitation. However, the interactions between smoke particles and clouds remain poorly quantified due to confounding meteorological influences and remote sensing limitations. Here, we use data from several aircraft campaigns in the Arctic and subarctic to explore cloud microphysics in liquid-phase clouds influenced by biomass burning. Median cloud droplet radii in smoky clouds were 50 smaller than in background clouds. Based on the relationship between cloud droplet number (N(liq))/ and various biomass burning tracers (BBt/ across the multi-campaign dataset, we calculated the magnitude of subarctic and Arctic smoke aerosol-cloud interactions (ACI, where ACI = (1/3) x dln(N(liq))/dln(BBt)) to be 0.12 out of a maximum possible value of 0.33 that would be obtained if all aerosols were to nucleate cloud droplets. Interestingly, in a separate subarctic case study with low liquid water content (0.02 gm/ cu m) and very high aerosol concentrations (2000-3000 cu m) in the most polluted clouds, the estimated ACI value was only 0.06. In this case, competition for water vapor by the high concentration of CCN strongly limited the formation of droplets and reduced the cloud albedo effect, which highlights the importance of cloud feedbacks across scales. Using our calculated ACI values, we estimate that the smoke-driven cloud albedo effect may decrease shortwave radiative flux by 2 and 4 W/sq or more under some low and homogeneous cloud cover conditions in the subarctic, although the changes should be smaller in high surface albedo regions of the Arctic. We lastly show evidence to suggest that numerous northern latitude background Aitken particles can interact with combustion particles, perhaps impacting their properties as cloud condensation and ice nuclei. However, the influence of background particles on smoke-driven indirect effects is currently unclear.

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NASA NTRSDate not supplied

Reconstructing Subgrid Cloud Variability Guided by CloudSat/CALIPSO Observations

Predicting realistic cloud subgrid variability remains a challenge for Global Climate Models (GCMs) even though it can play an important role for proper representation of processes pertaining to cloud microphysics, precipitation, and radiation, but also for comparisons with satellite observations which are of much higher resolution than model grids. The diagnostic approach of subgrid cloud variability is commonly handled by subcolumn cloud generators. For a specific GCM, one ideally wants the subgrid variability used for comparisons with satellite observations to be created by the same generator and with the same rules as the one used for model integration. With this in mind, we have embarked in an effort to test and improve cloud subcolumn generators appropriate for GCMs. For this purpose, we use cloud (hydrometeor) products from active observations by the CloudSat radar (CPR) and the CALIPSO lidar (CALIOP). Cloud products from active sensors while suffering significant sampling and coverage drawbacks have the advantage of resolving both horizontal and vertical variability. The main question is: given a profile of cloud condensate mean and variance, can we create a subgrid cloud field that is statistically similar to the observed subgrid cloud field? By “statistically”, we suggest that we do not aspire to reproduce “well” each individual subgrid cloud field of a GCM-scale region, but that our generator performs well for a large ensemble of cases. We simulate radar, passive imager, and radiation flux fields from the observed 2D cloud fields and create one-point statistics; we use the profiles of cloud condensate mean and variance as input to the generator to create subgrid cloud fields; we compare the statistics; we adjust the rules of the generator to create the best possible agreement between the radar, imager and radiation field statistics. In this process, the active observations have actually a dual role: they provide the actual subgrid cloud field which can be reduced to a profile of mean and variance used by the subcolumn generator, but they also provide the rules needed by the generator, such as measures (e.g, decorrelation length) of the vertical overlap of cloud fraction and of the condensate horizontal variability. This presentation will show our progress using Cloudsat and CALIPSO products in this dual fashion.

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