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NASA NTRS2000

Light Scattering By Nonspherical Particles: Current Status and Challenging Issues

Quantitative analyses of remote sensing measurements of aerosols, clouds, precipitation, and particulate surfaces as well as computations of the Earth's radiation balance require detailed understanding of the interaction of small particles with light and other electromagnetic radiation. The convenient availability of the Lorenz-Mie theory has led to a widespread practice of treating all particles as if they were spheres. However, many natural and anthropogenic particles have nonspherical shapes, and the accumulated knowledge suggests that their scattering and radiative properties can be dramatically different from those of equivalent spheres. This presentation will summarize the recent significant progress achieved in the area of electromagnetic scattering by nonspherical particles and outline major problems that still await solution. The talk will cover the following specific topics: (1) comparison of most widely used exact and approximate theoretical techniques; (2) outline of laboratory and field measurement techniques; (3) compare theory and experiment; (4) need for a statistical approach in dealing with natural particles; (5) remote sensing and radiative transfer applications; and (6) major unsolved problems.

Mishchenko, Michael↗
NASA NTRS2002

Software for Simulation of Hyperspectral Images

A package of software generates simulated hyperspectral images for use in validating algorithms that generate estimates of Earth-surface spectral reflectance from hyperspectral images acquired by airborne and spaceborne instruments. This software is based on a direct simulation Monte Carlo approach for modeling three-dimensional atmospheric radiative transport as well as surfaces characterized by spatially inhomogeneous bidirectional reflectance distribution functions. In this approach, 'ground truth' is accurately known through input specification of surface and atmospheric properties, and it is practical to consider wide variations of these properties. The software can treat both land and ocean surfaces and the effects of finite clouds with surface shadowing. The spectral/spatial data cubes computed by use of this software can serve both as a substitute for and a supplement to field validation data.

Richtsmeier, Steven C.↗
NASA NTRS2004

The Goddard Profiling Algorithm (GPROF): Description and Current Applications

Atmospheric scientists use different methods for interpreting satellite data. In the early days of satellite meteorology, the analysis of cloud pictures from satellites was primarily subjective. As computer technology improved, satellite pictures could be processed digitally, and mathematical algorithms were developed and applied to the digital images in different wavelength bands to extract information about the atmosphere in an objective way. The kind of mathematical algorithm one applies to satellite data may depend on the complexity of the physical processes that lead to the observed image, and how much information is contained in the satellite images both spatially and at different wavelengths. Imagery from satellite-borne passive microwave radiometers has limited horizontal resolution, and the observed microwave radiances are the result of complex physical processes that are not easily modeled. For this reason, a type of algorithm called a Bayesian estimation method is utilized to interpret passive microwave imagery in an objective, yet computationally efficient manner.

Olson, William S.↗
NASA NTRS2006

Estimating Random Errors Due to Shot Noise in Backscatter Lidar Observations

In this paper, we discuss the estimation of random errors due to shot noise in backscatter lidar observations that use either photomultiplier tube (PMT) or avalanche photodiode (APD) detectors. The statistical characteristics of photodetection are reviewed, and photon count distributions of solar background signals and laser backscatter signals are examined using airborne lidar observations at 532 nm using a photon-counting mode APD. Both distributions appear to be Poisson, indicating that the arrival at the photodetector of photons for these signals is a Poisson stochastic process. For Poisson-distributed signals, a proportional, one-to-one relationship is known to exist between the mean of a distribution and its variance. Although the multiplied photocurrent no longer follows a strict Poisson distribution in analog-mode APD and PMT detectors, the proportionality still exists between the mean and the variance of the multiplied photocurrent. We make use of this relationship by introducing the noise scale factor (NSF), which quantifies the constant of proportionality that exists between the root-mean-square of the random noise in a measurement and the square root of the mean signal. Using the NSF to estimate random errors in lidar measurements due to shot noise provides a significant advantage over the conventional error estimation techniques, in that with the NSF uncertainties can be reliably calculated from/for a single data sample. Methods for evaluating the NSF are presented. Algorithms to compute the NSF are developed for the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar and tested using data from the Lidar In-space Technology Experiment (LITE). OCIS Codes:

Liu, Zhaoyan↗
NASA NTRS2004

Simulation of Hyperspectral Images

A software package generates simulated hyperspectral imagery for use in validating algorithms that generate estimates of Earth-surface spectral reflectance from hyperspectral images acquired by airborne and spaceborne instruments. This software is based on a direct simulation Monte Carlo approach for modeling three-dimensional atmospheric radiative transport, as well as reflections from surfaces characterized by spatially inhomogeneous bidirectional reflectance distribution functions. In this approach, "ground truth" is accurately known through input specification of surface and atmospheric properties, and it is practical to consider wide variations of these properties. The software can treat both land and ocean surfaces, as well as the effects of finite clouds with surface shadowing. The spectral/spatial data cubes computed by use of this software can serve both as a substitute for, and a supplement to, field validation data.

Richsmeier, Steven C.↗
NASA NTRS2015

Role of Longwave Cloud-Radiation Feedback in the Simulation of the Madden-Julian Oscillation

The role of the cloud-radiation interaction in the simulation of the Madden-Julian oscillation (MJO) is investigated. A special focus is on the enhancement of column-integrated diabatic heating due to the greenhouse effects of clouds and moisture in the region of anomalous convection. The degree of this enhancement, the greenhouse enhancement factor (GEF), is measured at different precipitation anomaly regimes as the negative ratio of anomalous outgoing longwave radiation to anomalous precipitation. Observations show that the GEF varies significantly with precipitation anomaly and with the MJO cycle. The greenhouse enhancement is greater in weak precipitation anomaly regimes and its effectiveness decreases monotonically with increasing precipitation anomaly. The GEF also amplifies locally when convection is strengthened in association with the MJO, especially in the weak precipitation anomaly regime (less than 5 mm day(exp -1)). A robust statistical relationship is found among CMIP5 climate model simulations between the GEF and the MJO simulation fidelity. Models that simulate a stronger MJO also simulate a greater GEF, especially in the weak precipitation anomaly regime (less than 5 mm day(exp -1)). Models with a greater GEF in the strong precipitation anomaly regime (greater than 30 mm day(-1)) represent a slightly slower MJO propagation speed. Many models that lack the MJO underestimate the GEF in general and in particular in the weak precipitation anomaly regime. The results herein highlight that the cloud-radiation interaction is a crucial process for climate models to correctly represent the MJO.

Clouds↗
NASA NTRS2014

TPSAS-NF1676L-19256-DND

The NASA/GEWEX (Global Energy and Water Exchanges project) Surface Radiation Budget (SRB) project produces longwave and shortwave radiative fluxes for the surface and top-of-atmosphere (TOA) (see, Stackhouse et al., 2011, GEWEX News). The primary inputs of cloud and meteorology data have been undergoing improvements in quality and spatial and temporal resolution. In anticipation of the release of these data, the SRB longwave flux algorithm has been updated both to accommodate these data and to improve the algorithm used in deriving radiative fluxes. Changes in the inputs and algorithm are as follows: - Synchronized algorithm with version used by NASA CERES (Rose et al. 2006). - Atmospheric aerosols were added. - Beta version of ISCCP H cloud properties used. - Beta version of ISCCP nnHIRS temperature and humidity used. - Option of using a newly derived beta Princeton land surface temperature and diurnal Seaflux sea surface temperature as surface skin temperature added - Updated ozone profile with GISS ozone and MEaSUREs GOZCARDS. - Updated maps of surface topography, vegetation type, and snow/ice by ISCCP. The beta GLW v4 IP has enhanced the output fluxes by adding pristine-sky (no clouds or aerosols) fluxes. The new version also computes fluxes at selected atmospheric levels (including the tropopause) rather than just at the surface and TOA. The purpose of this study is to explore the effect of the above changes on the surface and top-of-atmosphere fluxes. These fluxes are compared to the previous version (release 3.1). We show preliminary results for the mid-seasonal months of 2007.

J Colleen Mikovitz↗
NASA NTRS2000

North Atlantic Aerosol Radiative Impacts Based on Satellite Measurements and Aerosol Intensive Properties from TARFOX and ACE-2

We estimate the impact of North Atlantic aerosols on the net shortwave flux at the tropopause by combining maps of satellite-derived aerosol optical depth (AOD) with model aerosol properties. We exclude African dust, primarily by restricting latitudes to 25-60 N. Aerosol properties were determined via column closure analyses in two recent experiments, TARFOX and ACE 2. The analyses use in situ measurements of aerosol composition and air- and ship-borne sunphotometer measurements of AOD spectra. The resulting aerosol model yields computed flux sensitivities (dFlux/dAOD) that agree with measurements by airborne flux radiometers in TARFOX. It has a midvisible single-scattering albedo of 0.9, which is in the range obtained from in situ measurements of aerosol scattering and absorption in both TARFOX and ACE 2. Combining seasonal maps of AVHRR-derived midvisible AOD with the aerosol model yields maps of 24-hour average net radiative flux changes at the tropopause. For cloud-free conditions, results range from -9 W/sq m near the eastern US coastline in the summer to -1 W/sq m in the mid-Atlantic during winter; the regional annual average is -3.5 W/sq m. Using a non- absorbing aerosol model increases these values by about 30%. We estimate the effect of clouds using ISCCP cloud-fraction maps. Because ISCCP midlatitude North Atlantic cloud fractions are relatively large, they greatly reduce the computed aerosol-induced flux changes. For example, the regional annual average decreases from -3.5 W/sq m to -0.8 W/sq m. We compare results to previous model calculations for a variety of aerosol types.

Russell, Philip B.↗
NASA NTRS2000

North Atlantic Aerosol Radiative Effects Based on Satellite Measurements and Aerosol Intensive Properties from TARFOX and ACE-2

We estimate the impact of North Atlantic aerosols on the net shortwave flux at the tropopause by combining maps of satellite-derived aerosol optical depth (AOD) with model aerosol properties. We exclude African dust, primarily by restricting latitudes to 25-60 N. Aerosol properties were determined via column closure analyses in two recent experiments, TARFOX and ACE 2. The analyses use in situ measurements of aerosol composition and air- and ship-borne sunphotometer measurements of AOD spectra. The resulting aerosol model yields computed flux sensitivities (dFlux/dAOD) that agree with measurements by airborne flux radiometers in TARFOX. It has a midvisible single-scattering albedo of 0.9, which is in the range obtained from in situ measurements of aerosol scattering and absorption in both TARFOX and ACE 2. Combining seasonal maps of AVHRR-derived midvisible AOD with the aerosol model yields maps of 24-hour average net radiative flux changes at the tropopause. For cloud-free conditions, results range from -9 W/sq m near the eastern US coastline in the summer to -1 W/sq m in the mid-Atlantic during winter; the regional annual average is -3.5 W/sq m. Using a non- absorbing aerosol model increases these values by about 30%. We estimate the effect of clouds using ISCCP cloud-fraction maps. Because ISCCP midlatitude North Atlantic cloud fractions are relatively large, they greatly reduce the computed aerosol-induced flux changes. For example, the regional annual average decreases from -3.5 W/sq m to -0.8 W/sq m. We compare results to previous model calculations for a variety of aerosol types.

Bergstrom, Robert W.↗
NASA NTRS2018

Surface Irradiances of Edition 4.0 Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Data Product

The algorithm to produce the Clouds and the Earth’s Radiant Energy System (CERES) Edition 4.0 (Ed4) Energy Balanced and Filled (EBAF)-surface data product is explained. The algorithm forces computed topof- atmosphere (TOA) irradiances to match with Ed4 EBAF-TOA irradiances by adjusting surface, cloud, and atmospheric properties. Surface irradiances are subsequently adjusted using radiative kernels. The adjustment process is composed of two parts: bias correction and Lagrange multiplier. The bias in temperature and specific humidity between 200 and 500 hPa used for the irradiance computation is corrected based on observations by Atmospheric Infrared Sounder (AIRS). Similarly, the bias in the cloud fraction is corrected based on observations by Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat. Remaining errors in surface, cloud, and atmospheric properties are corrected in the Lagrange multiplier process. Ed4 global annual mean (January 2005 through December 2014) surface net shortwave (SW) and longwave (LW) irradiances increase by 1.3Wm22 and decrease by 0.2Wm22, respectively, compared to EBAF Edition 2.8 (Ed2.8) counterparts (the previous version), resulting in an increase in net SW 1LWsurface irradiance of 1.1Wm22. The uncertainty in surface irradiances over ocean, land, and polar regions at various spatial scales are estimated. The uncertainties in all-sky global annual mean upward and downward shortwave irradiance are 3 and 4Wm22, respectively, and the uncertainties in upward and downward longwave irradiance are 3 and 6Wm22, respectively. With an assumption of all errors being independent, the uncertainty in the global annual mean surface LW 1 SW net irradiance is 8Wm22.

Kato, Seiji↗
NASA NTRS1998

Monte Carlo Calculations of Polarized Microwave Radiation Emerging from Cloud Structures

The last decade has seen tremendous growth in cloud dynamical and microphysical models that are able to simulate storms and storm systems with very high spatial resolution, typically of the order of a few kilometers. The fairly realistic distributions of cloud and hydrometeor properties that these models generate has in turn led to a renewed interest in the three-dimensional microwave radiative transfer modeling needed to understand the effect of cloud and rainfall inhomogeneities upon microwave observations. Monte Carlo methods, and particularly backwards Monte Carlo methods have shown themselves to be very desirable due to the quick convergence of the solutions. Unfortunately, backwards Monte Carlo methods are not well suited to treat polarized radiation. This study reviews the existing Monte Carlo methods and presents a new polarized Monte Carlo radiative transfer code. The code is based on a forward scheme but uses aliasing techniques to keep the computational requirements equivalent to the backwards solution. Radiative transfer computations have been performed using a microphysical-dynamical cloud model and the results are presented together with the algorithm description.

Kummerow, Christian↗
NASA NTRS1992

Modelling of IR emission of interstellar clouds. I - Emission of isolated clouds and dust abundance variations

A model for the IR emission and extinction properties of small dust particles is used here to compute the IR emissions in the IRAS photometric bands for a set of isolated, spherical, and nonhomogeneous clouds heated by the local interstellar radiation field. It is predicted that the IR limb brightening (LB) in the IRAS photometric bands caused by selective absorption of UV photons of different energies in the cloud generally happens for central extinctions over 4 mag at 12 microns and greater than 10 mag at 100 microns and intermediate extinctions for the other IRAS bands. The surface brightness in the four IRAS bands is limited to about 0.4, 0.6, 3, and 10 MJy/sr at 12, 25, 60, and 100 microns respectively when no strong density discontinuity is present at the cloud edge.

Bernard, J. P.↗
NASA NTRS2005

Scattering Properties and Brightness Temperatures Associated with Solid Precipitation

In the past few years, early solid precipitation detection and retrieval algorithms have been developed and shown to be applicable for snowing clouds and blizzards. NOAA has an operational snow versus rain classifier based on AMSU-B observations. Solid precipitation retrieval algorithms reported in the literature over the past two years include those that rely on neural nets, statistics, or physical relationships. All of the algorithms require the use of millimeter-wave radiometer observations. The millimeter-wave frequencies are especially sensitive to the scattering and emission properties of frozen particles due to the ice particle refractive index. Passive radiometric channels respond to both the integrated particle mass throughout the volume and field of view, and to the amount, location, and size distribution of the frozen (and liquid) particles with the sensitivity varying for different frequencies and hydrometeor types. This investigation probes the sensitivity of scattering and absorption coefficients, and hence computed brightness temperatures, resulting from variations in solid precipitation cloud profiles. The first study compares the single scattering, absorption, and asymmetry parameters associated with snow particles in clouds. Several methodologies are used to convert the physical characteristics (e.g., shape, size distributions, ice-air-water ratios) of ice particles to electromagnetic properties (e.g., absorption, scattering, and asymmetry factors). These methodologies include: conversion to solid ice particles, homogeneous dielectric mixing, or discrete dipole approximation. Changes in the conversion methodology can produce computed brightness temperature differences greater than 50 Kelvin.

Skofronick-Jackson, Gail M.↗
NASA NTRS2011

Clouds and Earth Radiant Energy System (CERES), a Review: Past, Present and Future

The Clouds and Earth Radiant Energy System (CERES) project s objectives are to measure the reflected solar radiance (shortwave) and Earth-emitted (longwave) radiances and from these measurements to compute the shortwave and longwave radiation fluxes at the top of the atmosphere (TOA) and the surface and radiation divergence within the atmosphere. The fluxes at TOA are to be retrieved to an accuracy of 2%. Improved bidirectional reflectance distribution functions (BRDFs) have been developed to compute the fluxes at TOA from the measured radiances with errors reduced from ERBE by a factor of two or more. Instruments aboard the Terra and Aqua spacecraft provide sampling at four local times. In order to further reduce temporal sampling errors, data are used from the geostationary meteorological satellites to account for changes of scenes between observations by the CERES radiometers. A validation protocol including in-flight calibrations and comparisons of measurements has reduced the instrument errors to less than 1%. The data are processed through three editions. The first edition provides a timely flow of data to investigators and the third edition provides data products as accurate as possible with resources available. A suite of cloud properties retrieved from the MODerate-resolution Imaging Spectroradiometer (MODIS) by the CERES team is used to identify the cloud properties for each pixel in order to select the BRDF for each pixel so as to compute radiation fluxes from radiances. Also, the cloud information is used to compute radiation at the surface and through the atmosphere and to facilitate study of the relationship between clouds and the radiation budget. The data products from CERES include, in addition to the reflected solar radiation and Earth emitted radiation fluxes at TOA, the upward and downward shortwave and longwave radiation fluxes at the surface and at various levels in the atmosphere. Also at the surface the photosynthetically active radiation and ultraviolet radiation (total, UVA and UVB) are computed. The CERES instruments aboard the Terra and Aqua spacecraft have served well past their design life times. A CERES instrument has been integrated onto the NPP platform and is ready for launch in 2011. Another CERES instrument is being built for launch in 2014, and plans are being made for a series of follow-on missions.

Smith, G. L.↗
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 many statistics 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 was 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 possible for capturing cloud initiation 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, strategies to identify development stages from cloud tracks, and preliminary results that document cumulus lifecycle properties from satellite. The application of AHI 0.5-km reflectance has both strengths and limitations when attempting to track lifecycles of the smallest resolvable clouds. We show that by aggregating cloud tracks from a few case studies of CAMP2EX, we can discern differences in cloud lifetime and development according to ensembles selected from areas of interest. The results demonstrate an ability to quantify lifetimes and assess rates of change in cloud characteristics that are likely controlled by the surrounding environment and meteorology.

Cloud Tracking↗
NASA NTRS2024

3D Cloud Masking Across A Broad Swath Using Multi-Angle Polarimetry and Deep Learning

Understanding the 3-dimensional structure of clouds is of crucial importance to modeling our changing climate. Active sensors, such as radar and lidar, provide accurate vertical cloud profiles, but are mostly restricted to along-track sampling. Passive sensors can capture a wide swath, but struggle to see beneath cloud tops. In essence, both types of products are restricted to two dimensions: as a cross-section in the active case, and an image in the passive case. However, multi-angle sensor configurations contain implicit information about 3D structure, due to parallax and atmospheric path differences. Extracting that implicit information can be challenging, requiring computationally expensive radiative transfer techniques. Machine learning, as an alternative, may be able to capture some of the complexity of a full 3D radiative transfer solution with significantly less computational expense. In this work, we make three contributions towards understanding 3D cloud structure from multi-angle polarimetry. First, we introduce a large-scale, publicly available dataset that fuses existing cloud products into a format more amenable to machine learning. This dataset treats multi-angle polarimetry as an input, and radar-based vertical cloud profiles as an output. Second, we describe and evaluate strong baseline machine learning models based that predict these profiles from the passive imagery. Notably, these models are trained only on center-swath labels, but can predict cloud profiles over the entire passive imagery swath. Third, we leverage the information-theoretic nature of machine learning to draw conclusions about the relative utility of various sensor configurations, including spectral channels, viewing angles, and polarimetry. These findings have implications for Earth-observing missions such as NASA’s Plankton, Aerosol, Cloud-ocean Ecosystem (PACE) and Atmosphere Observing System (AOS) missions, as well as in informing future applications of computer vision to atmospheric remote sensing.

clouds↗
NASA NTRS1980

Criteria for fragmentation in a collapsing rotating cloud

The isothermal collapse of a rotating protostar with pressure and self-gravity is calculated by a hydrodynamic computer code that treats three space dimensions. A number of initial conditions are tested to determine the conditions under which a cloud is unstable to fragmentation. It is shown that fragmentation does not proceed to a significant extent during the first free-fall time, but that in most cases fragmentation has occurred by 1.5 to 2 times the initial free-fall time. The extent to which the density in the fragments is enhanced over that in their surroundings on this time scale depends on the initial ratio of thermal to gravitational energy, but is only weakly dependent on the type and amplitude of the perturbation and on the initial rotational energy.

Bodenheimer, P.↗
NASA NTRS2010

A Principal Component-Based Radiative Transfer Forward Model (PCRTM) for Vertically in Homogeneous Cloud

A principal-component based radiative transfer model (PCRTM) is developed for simulating the infrared spectral radiance at the top of the atmosphere (TOA). The PCRTM approach removes the redundancy in radiative transfer calculation in high resolution infrared spectra, and saves significant amount of computational time with great accuracy. In PCRTM, both ice and water clouds are treated as effective transmissivity and reflectivity stored in a pre-calculated lookup tables. These quantities are calculated using cloud single scattering properties such as cloud optical depth, cloud particle size, cloud phase, etc. The cloud can be inserted into any pressure layer in the PCRTM model (up to 100 layers). The effective temperature of each cloud layer is treated as a function of its optical depth. To test the accuracy of this approximation, the results are compared with the more rigorous DISORT model, which treats cloud as a plane parallel layer. The root-mean-square error of PCRTM, with respect to DISORT results, is generally less than 0.5 K in brightness temperature. However, the CPU time required by PCRTM was approximately two orders of magnitude less than that required by DISORT.

Li, Hui↗