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A novel Bayesian Monte Carlo integration (BMCI) technique was developed to retrieve geophysical variables from satellite microwave radiometer data in the presence of tropical cyclones. The BMCI technique includes three steps: generating a stochastic database, simulating satellite brightness temperatures using a radiative transfer model, and retrieving geophysical variables such as profiles of temperature, relative humidity, and cloud liquid and ice water content from real observations. The technique also provides uncertainty estimates for each retrieval and can output the error covariance matrix of selected parameters. The measurements from the Advanced Technology Microwave Sounder (ATMS) on board Suomi National Polar-Orbiting Partnership (Suomi NPP) and the Global Precipitation Measurement (GPM) Microwave Imager (GMI) were used as input. A new technique was developed to correct the ATMS and GMI observations for the beam-filling effect, which is due to small-scale variability of precipitation and clouds when compared with the instrument footprint and also the nonlinear relation between the brightness temperature and precipitation. In addition, the assimilation of the BMCI retrievals into the NASA GEOS model is discussed for Hurricane Maria. The results show that assimilating the BMCI retrievals can influence the dynamical features of the cyclone, including a stronger warm core, a symmetric eye, and vertically aligned wind columns. Two possible factors that may limit the impact of the BMCI retrievals include 1) the resolution of the model (about 25 km), which was too coarse to show the potential of the BMCI data in improving the representation of tropical storms in the model forecast, and 2) the data assimilation system not being able to consider vertically correlated observation errors.

Changes in the concentration of greenhouse gases within the atmosphere lead to changes in radiative fluxes within the atmosphere and at its boundaries. This paper describes an experiment within the...

This paper describes a forward radiative transfer model and retrieval system (FMRS) for the Tropospheric Water and cloud ICE (TWICE) CubeSat instrument. We use the FMRS to simulate radiances for the TWICE's 14 millimeter- and submillimeter-wavelength channels for a tropical atmospheric state produced by a Weather Research and Forecasting (WRF) model simulation. We also perform simultaneous retrievals of cloud ice particle size, ice water content (IWC), water vapor content (H2O), and temperature from the simulated TWICE radiances using the FMRS. We show that the TWICE instrument is capable of retrieving ice particle size in the range of ~50-1000 μm in mass mean effective diameter with approximately 50% uncertainty. The uncertainties of other retrievals from TWICE are about 1 K for temperature, 50% for IWC and 20% for H2O.

This paper describes a forward radiative transfer model and retrieval system (FMRS) for the Tropospheric Water and cloud ICE (TWICE) CubeSat instrument. We use the FMRS to simulate radiances for the TWICE's 14 millimeter‐ and submillimeter‐wavelength channels for a tropical atmospheric state produced by a Weather Research and Forecasting model simulation. We also perform simultaneous retrievals of cloud ice particle size, ice water content (IWC), water vapor content (H2O), and temperature from the simulated TWICE radiances using the FMRS. We show that the TWICE instrument is capable of retrieving ice particle size in the range of ~50–1000 μm in mass mean effective diameter with approximately 50% uncertainty. The uncertainties of other retrievals from TWICE are about 1 K for temperature, 50% for IWC, and 20% for H2O., Key Points Cloud ice particle size is an important parameter that determines cloud radiative effect, precipitation, and climate sensitivityA simulation experiment for a CubeSat instrument is conducted to determine the accuracy of ice particle size, humidity, and temperature retrievalsThe results show that the CubeSat instrument is capable of fulfilling the requirements of measuring ice particle size, humidity, and temperature

Results from a cloud-resolving model are systematically compared with a variety of observations, both ground based and satellite, in order to better understand the mean background errors and their correlations. This is a step in the direction of developing a background error covariance matrix for use in cloud data assimilation. Observation sources include the Geostationary Operational Environmental Satellite (GOES), the Atmospheric Emitted Radiance Interferometer (AERI), a microwave radiometer (MWR), radiosonde, and cloud radar. When exploring model biases in temperature, precipitable water vapor, and liquid water path, a warm and moist bias at night and a cool and dry bias during the day are observed. Values for the background decorrelation length of water variables are determined. In addition, a dynamic cloud mask is presented to give more control in the assimilation of cloudy satellite radiances, allowing different cloud types to be excluded from the assimilation as well as establishing values for the maximum residuals to be considered.

This paper examines the tradeoffs between computational cost and accuracy for two new state-of-the-art codes for computing three-dimensional radiative transfer: a community Monte Carlo model and a parallel implementation of the Spherical Harmonics Discrete Ordinate Method (SHDOM). Both codes are described and algorithmic choices are elaborated. Two prototype problems are considered: a domain filled with stratocumulus clouds and another containing scattered shallow cumulus, absorbing aerosols, and molecular scatterers. Calculations are performed for a range of resolutions and the relationships between accuracy and computational cost, measured by memory use and time to solution, are compared. Monte Carlo accuracy depends primarily on the number of trajectories used in the integration. Monte Carlo estimates of intensity are computationally expensive and may be subject to large sampling noise from highly peaked phase functions. This noise can be decreased using a range of variance reduction techniques, but these techniques can compromise the excellent agreement between the true error and estimates obtained from unbiased calculations. SHDOM accuracy is controlled by both spatial and angular resolution; different output fields are sensitive to different aspects of this resolution, so the optimum accuracy parameters depend on which quantities are desired as well as on the characteristics of the problem being solved. The accuracy of SHDOM must be assessed through convergence tests and all results from unconverged solutions may be biased. SHDOM is more efficient (i.e., has lower error for a given computational cost) than Monte Carlo when computing pixel-by-pixel upwelling fluxes in the cumulus scene, whereas Monte Carlo is more efficient in computing flux divergence and downwelling flux in the stratocumulus scene, especially at higher accuracies. The two models are comparable for downwelling flux and flux divergence in cumulus and upwelling flux in stratocumulus. SHDOM is substantially more efficient when computing pixel-by-pixel intensity in multiple directions; the models are comparable when computing domain-average intensities. In some cases memory use, rather than computation time, may limit the resolution of SHDOM calculations.

The importance of multidimensional (MD) longwave radiative effects on cloud dynamics is evaluated in an eddy-resolving model (ERM)—the two-dimensional analog to large-eddy simulation (LES)—framework employing multidimensional radiative transfer [Spherical Harmonics Discrete Ordinate Method (SHDOM)]. Simulations are performed for a case of unbroken, marine boundary layer stratocumulus and a broken field of trade cumulus. “Snapshot” calculations of MD and independent pixel approximation (IPA; 1D) radiative transfer applied to simulated cloud fields show that the total radiative forcing changes only slightly, although the MD effects significantly modify the spatial structure of the radiative forcing. Simulations of each cloud type employing MD and IPA radiative transfer, however, differ little. For the solid cloud case, relative to using IPA, the MD simulation exhibits a slight reduction in entrainment rate and boundary layer total kinetic energy (TKE) relative to the IPA simulation. This reduction is consistent with both the slight decrease in net radiative forcing and a negative correlation between local vertical velocity and radiative forcing, which implies a damping of boundary layer eddies. Snapshot calculations of the broken cloud case suggest a slight increase in radiative cooling, although few systematic differences are noted in the interactive simulations. This result is attributed to the fact that radiative cooling is a relatively minor contribution to the total energetics. For the cloud systems in this study, the use of IPA longwave radiative transfer is sufficiently accurate to capture the dynamical behavior of boundary layer clouds. Further investigations are required to generalize this conclusion for other cloud types and longer time integrations.

The spherical harmonics discrete ordinate method for plane-parallel data assimilation (SHDOMPPDA) model is an unpolarized plane-parallel radiative transfer forward model, with corresponding tangent linear and adjoint models, suitable for use in assimilating cloudy sky visible and infrared radiances. It is derived from the spherical harmonics discrete ordinate method plane-parallel (SHDOMPP, also described in this article) version of the spherical harmonics discrete ordinate method (SHDOM) model for three-dimensional atmospheric radiative transfer. The inputs to the SHDOMPPDA forward model are profiles of pressure, temperature, water vapor, and mass mixing ratio and number concentration for a number of hydrometeor species. Hydrometeor optical properties, including detailed phase functions, are determined from lookup tables as a function of mass mean radius. The SHDOMPP and SHDOMPPDA algorithms and construction of the tangent-linear and adjoint models are described. The SHDOMPPDA forward model is validated against the Discrete Ordinate Radiative Transfer Model (DISORT) by comparing upwelling radiances in multiple directions from 100 cloud model columns at visible and midinfrared wavelengths. For this test in optically thick clouds the computational time for SHDOMPPDA is comparable to DISORT for visible reflection, and roughly 5 times faster for thermal emission. The tangent linear and adjoint models are validated by comparison to finite differencing of the forward model.

Cumulus clouds can become tilted or elongated in the presence of wind shear. Nevertheless, most studies of the interaction of cumulus clouds and radiation have assumed these clouds to be isotropic. This paper describes an investigation of the effect of fair-weather cumulus cloud field anisotropy on domain-averaged solar fluxes and atmospheric heating rate profiles. A stochastic field generation algorithm was used to produce 20 three-dimensional liquid water content fields based on the statistical properties of cloud scenes from a large eddy simulation. Progressively greater degrees of x–z plane tilting and horizontal stretching were imposed on each of these scenes, so that an ensemble of scenes was produced for each level of distortion. The resulting scenes were used as input to a three-dimensional Monte Carlo radiative transfer model. Domain-averaged transmission, reflection, and absorption of broadband solar radiation were computed for each scene along with the average heating rate profile. Both tilt and horizontal stretching were found to significantly affect calculated fluxes, with the amount and sign of flux differences depending strongly on sun position relative to cloud distortion geometry. The mechanisms by which anisotropy interacts with solar fluxes were investigated by comparisons to independent pixel approximation and tilted independent pixel approximation computations for the same scenes. Cumulus anisotropy was found to most strongly impact solar radiative transfer by changing the effective cloud fraction (i.e., the cloud fraction with respect to the solar beam direction).

The in situ cloud lidar is designed to measure cloud volumes of millions of cubic meters to overcome the sampling limitations of traditional cloud probes in inhomogeneous clouds. This technique sends laser pulses horizontally from an aircraft inside an optically thick cloud and measures the time series of the multiply scattered light with wide field-of-view detectors viewing upward and downward. The extinction in liquid clouds averaged over tens to hundreds of meters and the distance to cloud boundaries can be retrieved from the signal measured by a single-wavelength in situ lidar. This paper describes the design and operation of an in situ cloud lidar. A laser in the aircraft cabin outputs 532-nm wavelength pulses at 10 Hz, which are sent through beam-expanding optics for eye safety. The upward- and downward-viewing detectors use photomultiplier tubes and operate with either daytime (3° half angle; 0.37-nm solar-blocking filter) or nighttime (30°) optics. Example daytime lidar signals in dense cloud have a dynamic range of 1000 after solar background subtraction. Results from a nighttime flight in marine stratus are analyzed in detail. The variations in the lidar signals with aircraft travel are much smoother for the longer photon travel times, indicating that the later times sample volumes hundreds of meters in size. Extinction retrievals for 25-m-radius volumes have high correlation (R2 = 0.84) with Forward Scattering Spectrometer Probe (FSSP)-derived extinction, while the correlation is relatively low (R2 = 0.40) for 200-m volumes due to cloud inhomogeneity. Lidar retrievals of cloud-base and -top height from inside the cloud are consistent with cloud boundaries obtained from aircraft penetrations on ascents and descents.

Three-dimensional radiative transfer calculations are accurate, though computationally expensive, if the spatial distribution of cloud properties is known. The difference between these calculations and those using the much less expensive independent column approximation is called the 3D radiative transfer effect. Assessing the magnitude of this effect in the real atmosphere requires that many realistic cloud fields be obtained, and profiling instruments such as ground-based radars may provide the best long-term observations of cloud structure. Cloud morphology can be inferred from a time series of vertical profiles obtained from profilers by converting time to horizontal distance with an advection velocity, although this restricts variability to two dimensions. This paper assesses the accuracy of estimates of the 3D effect in shallow cumulus clouds when cloud structure is inferred in this way. Large-eddy simulations provide full three-dimensional, time-evolving cloud fields, which are sampled every 10 s to provide a “radar’s eye view” of the same cloud fields. The 3D effect for shortwave surface fluxes is computed for both sets of fields using a broadband Monte Carlo radiative transfer model, and intermediate calculations are made to identify reasons why estimates of the 3D effect differ in these fields. The magnitude of the 3D effect is systematically underestimated in the two-dimensional cloud fields because there are fewer cloud edges that cause the effect, while the random error in hourly estimates is driven by the limited sample observed by the profiling instrument.

The datasets currently being collected by the Atmospheric Radiation Measurement (ARM) program on the islands of Nauru and Manus represent the longest time series of ground-based cloud measurements in the tropical western Pacific region. In this series of papers, a shortwave flux closure study is presented using observations collected at the Nauru site between June 1999 and May 2000. The first paper presented frequency of occurrence of nonprecipitating clouds detected by the millimeter-wavelength cloud radar (MMCR) at Nauru and statistics of their retrieved microphysical properties. This paper presents estimates of the cloud radiative effect over the study period and results from a closure study in which retrieved cloud properties are input to a radiative transfer model and the modeled surface fluxes are compared to observations. The average surface shortwave cloud radiative forcing is 48.2 W m−2, which is significantly smaller than the cloud radiative forcing estimates found during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) field project. The difference in the estimates during the two periods is due to the variability in cloud amount over Nauru during different phases of the El Niño–Southern Oscillation (ENSO). In the closure study, modeled and observed surface fluxes show large differences at short time scales, due to the temporal and spatial variability of the clouds observed at Nauru. Averaging over 60 min reduces the average root-mean-square difference in total flux to 10% of the observed flux. Modeled total downwelling fluxes are unbiased with respect to the observed fluxes while direct fluxes are underestimated and diffuse fluxes are overestimated. Examination of the differences indicates that cloud amount derived from the ground-based measurements is an overestimate of the radiatively important cloud amount due to the anisotropy of the cloud field at Nauru, interpolation of the radar data, uncertainty in the microwave brightness temperature measurements for thin clouds, and the uncertainty in relating the sixth moment of the droplet size distribution observed by the radar to the more radiatively important moments.

An algorithm is described for generating stochastic three-dimensional (3D) cloud fields from time–height fields derived from vertically pointing radar. This model is designed to generate cloud fields that match the statistics of the input fields as closely as possible. The major assumptions of the algorithm are that the statistics of the fields are translationally invariant in the horizontal and independent of horizontal direction; however, the statistics do depend on height. The algorithm outputs 2D or 3D stochastic fields of liquid water content (LWC) and (optionally) effective radius. The algorithm is a generalization of the Fourier filtering methods often used for stochastic cloud models. The Fourier filtering procedure generates Gaussian stochastic fields from a “Gaussian” cross-correlation matrix, which is a function of a pair of heights and the horizontal distance (or “lag”). The Gaussian fields are nonlinearly transformed to give the correct LWC histogram for each height. The “Gaussian” cross-correlation matrix is specially chosen so that, after the nonlinear transformation, the cross-correlation matrix of the cloud mask fields approximately matches that derived from the input LWC fields. The cloud mask correlation function is chosen because the clear/cloud boundaries are thought to be important for 3D radiative transfer effects in cumulus. The stochastic cloud generation algorithm is tested with 3 months of boundary layer cumulus cloud data from an 8.6-mm wavelength radar on the island of Nauru. Winds from a 915-MHz wind profiler are used to convert the radar fields from time to horizontal distance. Tests are performed comparing the statistics of 744 radar-derived input fields to the statistics of 100 2D and 3D stochastic output fields. The single-point statistics as a function of height agree nearly perfectly. The input and stochastic cloud mask cross-correlation matrices agree fairly well. The cloud fractions agree to within 0.005 (the total cloud fraction is 18%). The cumulative distributions of optical depth, cloud thickness, cloud width, and intercloud gap length agree reasonably well. In the future, this stochastic cloud field generation algorithm will be used to study domain-averaged 3D radiative transfer effects in cumulus clouds.

The datasets currently being collected at the Atmospheric Radiation Measurement (ARM) program's sites on the islands of Nauru and Manus represent the longest time series of ground-based cloud measurements available in the tropical western Pacific region. In this and a companion paper, a shortwave flux closure study is presented using observations collected at the Nauru site between June 1999 and May 2000. This paper presents frequency of occurrence of nonprecipitating liquid and ice clouds detected by the millimeter wavelength cloud radar (MMCR) and statistics of retrieved microphysical properties. The companion paper presents results from a closure study in which the retrieved cloud properties are input to a radiative transfer model, and the modeled surface fluxes are compared to observations. The liquid cloud properties are retrieved from MMCR and microwave radiometer (MWR) measurements using a Bayesian retrieval technique. Properties of ice phase clouds are retrieved from MMCR measurements usi...

A retrieval method has been developed to directly retrieve statistics of cloud parameters from Geostationary Operational Environmental Satellite (GOES) visible and infrared imager data. This method retrieves cloud parameter probability density functions (PDFs) directly from distributions of multichannel satellite-observed radiances. For example, the joint probability distribution of ice water content and effective radius can be retrieved from a four-dimensional histogram of GOES radiances. A forward radiative transfer model creates a mapping from cloud parameter space to satellite radiance space. The cloud parameter space is described by vertically inhomogeneous liquid and ice cloud layers with variable liquid water path, effective radius, height, and thickness. A Monte Carlo procedure uses the mapping to transform probability density from the observed satellite radiance histogram, or radiance PDF, to a two-dimensional cloud property PDF. An estimate of the uncertainty in the retrieved PDF is cal...

Since three-dimensional (3-D) radiative transfer in cloudy atmospheres is too expensive for large-scale atmospheric models, approximate radiative transfer methods are used. The accuracy of these approximations for a large sample of realistic cloud fields has not been determined. This study examines 150 fields of small marine tropical cumulus to assess the magnitude of 3-D effects on domain average solar fluxes and to evaluate the accuracy of these approximations in actual cumulus fields. The cloud fields are derived from Moderate-Resolution Imaging Spectroradiometer Airborne Simulator (MAS) visible and thermal infrared imagery. Domain average broadband solar fluxes in these fields are simulated with a Monte Carlo radiative transfer model using four radiative transfer methods: full three-dimensional, the independent pixel approximation (IPA), the tilted IPA, and the plane-parallel approximation. The average 3-D radiative effects of these cumulus cloud fields are small, with mean reflected flux errors up to 3 W/m2 for overhead Sun and less than 1 W/m2 for the daytime average. The mean errors for column-absorbed fluxes are less than 0.3 W/m2 for all Sun angles. The small absolute flux errors are a result of the average field having small cloud fraction (10%), low cloud optical depth (4.4), and shallow clouds. Some individual fields have large 3-D absolute reflected flux effects, and the normalized reflected flux errors are significant. The errors correlate well with the cloud fraction; so it may be possible to make corrections to the approximations.

A novel microwave technique for simultaneously retrieving cirrus ice water path (IWP) and characteristic ice particle size is described. The retrieval algorithm exploits radiance measurements made at 150 and 220 GHz by the airborne Millimeter-Wave Imaging Radiometer (MIR). Other MIR channels additionally are used to test for the presence of liquid clouds and precipitation, which otherwise would have a contaminating effect on the retrievals. Forward radiative transfer modeling was used to generate a two-dimensional retrieval table in which brightness-temperature depressions (relative to clear-sky values) for both microwave channels were recorded as functions of IWP and characteristic particle size for gamma distributions of ice particles. Retrieval errors due to particle shape, size distribution, clear-sky water vapor variability, cirrus-cloud altitude variability, and instrument noise were estimated using Monte Carlo analysis. Particle shape uncertainty is believed to be the dominant source of retrieval error. The technique is demonstrated using MIR data recorded on the National Aeronautics and Space Administration ER-2 aircraft during the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment experiment in the tropical western Pacific Ocean in 1993. The retrieval technique with MIR data is suited only to high-IWP clouds with large ice particles, such as thick frontal cirrus and convective anvils. The general methodology, however, is applicable to higher frequencies that have greatly increased sensitivity to thinner cirrus.

The two-dimensional radiative transfer behavior of nine marine stratocumulus clouds observed by cloud radar during the Atlantic Stratocumulus Transition Experiment is examined. The cloud radar resolves the vertical structure to 37.5 m. The method of [Frisch et al., 1995] is used to convert radar reflectivities to extinction fields. Three constructions of the same cloud field help elucidate underlying causes of variability: one is fully two-dimensional, while the other two have vertically uniform extinction fields but possess either a flat cloud top or the original cloud top topography. Two-dimensional solar radiative transfer results are compared with the independent pixel approximation (IPA) result. At the scale of the domain (≈ 7km) the IPA albedo bias is small, even after including vertical structure. Locally, in contrast, the direct solar beam interaction with cloud top geometry competes with radiative smoothing as the dominant radiative process. Power spectral analysis of nadir reflectances is dominated by radiative smoothing for overhead Sun, and side illumination/shadowing of cloud top bumps for low Sun. A method that incorporates direct beam interactions with the cloud geometry, in addition to radiative smoothing, significantly improves correlations of a smoothed IPA radiance field with the 2-D reflectances. In a remote sensing application, optical depth and albedo retrieval biases from plane-parallel theory depend on the spatial scale chosen to emulate a satellite pixel size. For scales less than a few kilometers and with low Sun, cloud top topography can cause large positive optical depth biases even when averaged over the entire domain. A larger spatial scales the negative IPA bias always dominates. Domain-averaged monochromatic albedo retrieval errors remain below 0.005, a relative error of less then 1%.

Vertical profiles of drop size distribution (DSD) parameters are produced from data collected with the National Oceanographic and Atmospheric Administration 915- and 50-MHz Doppler radars at Darwin, Australia, for the 1993–94 monsoon season. An existing algorithm is used to retrieve gamma size distribution parameters from the VHF and UHF Doppler radar spectra. The clear-air mean velocities and spectral widths obtained from the VHF radar are used to fit DSDs accurately to UHF spectra. Uncertainties in retrieved precipitation parameters are estimated from errors in both VHF and UHF spectra. The statistics of the retrieved profiles of DSD parameters are summarized and compared with surface disdrometer data from a site near Darwin. Retrieved vertical profiles of gamma DSDs are input to a microwave radiative transfer model to determine realistic variations in upwelling 10- and 19-GHz brightness temperatures due to uncertainties in drop size distribution. These brightness temperature variations are the...

A new algorithm for modeling radiative transfer in inhomogeneous three-dimensional media is described. The spherical harmonics discrete ordinate method uses a spherical harmonic angular representation to reduce memory use and time computing the source function. The radiative transfer equation is integrated along discrete ordinates through a spatial grid to model the streaming of radiation. An adaptive grid approach, which places additional points where they are most needed to improve accuracy, is implemented. The solution method is a type of successive order of scattering approach or Picard iteration. The model computes accurate radiances or fluxes in either the shortwave or longwave regions, even for highly peaked phase functions. Broadband radiative transfer is computed efficiently with a k distribution. The results of validation tests and examples illustrating the efficiency and accuracy of the algorithm are shown for simple geometries and realistic simulated clouds.

Autocorrelation analysis was applied to data acquired by NASA’s Millimeter-wave Imaging Radiometer during the 1992–93 Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment to quantify the spatial variability of tropical mid- and upper-tropospheric water vapor. Scenes including thin cirrus (as confirmed by lidar measurements) are associated with greater spatial variability of relative humidity (RH) than clear-sky scenes. Typical clear-sky RH spatial variabilities range between 2% and 3%, whereas values for some cirrus scenes are well above 5%. For both cirrus and clear-sky scenes, most of the RH spatial variability is on spatial scales of less than 10 km. Both the amplitude and spatial scale of observed brightness temperature variabilities are in fair quantitative agreement with a model based on the effects of adiabatic vertical displacements generated by boundary layer–forced gravity waves.

This paper presents the results of a detailed study of the microwave single scattering properties of ice crystals expected in cirrus clouds. The discrete dipole approximation is used to compute scattering quantities of particles ranging in size from 30 to 2000 µm at 85.5, 157, 220, and 340 0Hz. Five shapes were simulated: solid and hollow columns, hexagonal plates, planar bullet rosettes, and equivalent-volume spheres. The scattering properties were computed for 18 Gamma size distributions with a range of characteristic particle size and distribution width. The results indicate that particle shape has a significant effect; for example, there is a range of about 3 in extinction over the five shapes for the same size distribution and ice water content. Crystal shape is the dominant effect on le polarization of the scattering, with the thinner shapes having the more polarizing effect. The characteristic particle size has the greatest impact on the extinction and single-scattering albedo, while the d...

This paper presents the results of polarized microwave radiative transfer modeling of cirrus clouds containing five different particle shoes and 18 Gamma size distributions. Upwelling brightness temperatures for tropical and midlatitude winter atmospheres are simulated at 85.5, 157, 220, and 340 GHz using scattering properties computed with the discrete dipole approximation (described in Part I). The key parameter for the results is the sensitivity (ΔTb/IWP), which relates the modeled brightness temperature depression to the ice water path. It is shown that for the higher frequencies or distributions of larger particles (i.e., in the scattering regime) the sensitivity is nearly independent of cloud temperature and details of the underlying atmosphere. As expected from the single-scattering results, the characteristic particle size has a large effect on the sensitivity, while the distribution width has only a minor effect. The range in sensitivity over the five particle shapes is typically a facto...

The three-dimensional variability of clouds has an important impact on the transfer of radiation in the Earth's atmosphere, and thus the current practice in climate modeling of assuming clouds are horizontally homogeneous entails significant errors. A new method, based on the backward Monte Carlo technique, has been developed for computing the mean outgoing radiation field from any type of stochastic ensemble of cloud structure. The accuracy of the technique has been verified by comparison with many realizations of deterministic radiative transfer. The method illustrates how spatial variability should be defined, and should lead to improved representations of the radiative effects of clouds in climate models.

Causes of anisotropy in fair-weather cumulus cloud fields were investigated using quantitative measures of anisotropy and a large-eddy simulation (LES) model. Case six of the Global Energy and Water Cycle Experiment (GEWEX) Cloud System Study Working Group 1 was used as the standard model scenario. This case represents radiatively forced development of cumulus clouds over the southern Great Plains. Cloud formation under a variety of environmental conditions was simulated and the degree of anisotropy in the output fields was calculated as a function of spatial scale. Wind shear was found to be the single greatest factor in the development of both vertically tilted and horizontally stretched cloud structures. Other factors included mean wind speed, initial water vapor mixing ratio, and the magnitude of the surface forcing.

The interaction of clouds with solar and terrestrial radiation is one of the most important topics of climate research. In recent years it has been recognized that only a full three-dimensional (3D) treatment of this interaction can provide answers to many climate and remote sensing problems, leading to the worldwide development of numerous 3D radiative transfer (RT) codes. The international Intercomparison of 3D Radiation Codes (I3RC), described in this paper, sprung from the natural need to compare the performance of these 3D RT codes used in a variety of current scientific work in the atmospheric sciences. I3RC supports intercomparison and development of both exact and approximate 3D methods in its effort to 1) understand and document the errors/limits of 3D algorithms and their sources; 2) provide “baseline” cases for future code development for 3D radiation; 3) promote sharing and production of 3D radiative tools; 4) derive guidelines for 3D radiative tool selection; and 5) improve atmospheric science education in 3D RT. Results from the two completed phases of I3RC have been presented in two workshops and are expected to guide improvements in both remote sensing and radiative energy budget calculations in cloudy atmospheres.

[1] We present a new algorithm for retrieving optical depth and liquid water content and effective radius profiles of nonprecipitating liquid water clouds using millimeter wavelength radar reflectivity and dual-channel microwave brightness temperatures. The algorithm is based on Bayes’ theorem of conditional probability and combines prior information on cloud microphysics with the remote sensing observations. Prior probability distribution functions for liquid water clouds were derived from the second, third, and sixth moments of droplet size distributions measured by in situ aircraft probes in shallow tropical cumuli. The algorithm also calculates error bars for each retrieved parameter. To assess the algorithm, we perform retrieval simulations using radar reflectivity and brightness temperatures simulated from tropical cumulus fields calculated by a large eddy simulation model with explicit microphysics. These retrieval simulations show that the Bayesian algorithm has similar magnitude errors to current retrieval methods for liquid water content and liquid water path retrievals but has much smaller errors for effective radius and optical depth. We also perform retrievals on three months of data from the Atmospheric Radiation Measurement (ARM) Program’s site on Nauru in the tropical west Pacific. For nonprecipitating liquid water clouds over Nauru during June–August 1999 we retrieve a mean optical depth of 9.2, mean liquid water content of 0.112 g/m 3 , and mean effective radius of 7.8 mm. The Bayesian method is a flexible approach to cloud profile remote sensing and could be expanded to other sites or cloud types. INDEXTERMS: 3360 Meteorology and Atmospheric Dynamics: Remote sensing; 0320 Atmospheric Composition and Structure: Cloud physics and chemistry; 3394 Meteorology and Atmospheric Dynamics: Instruments and techniques; 3399 Meteorology and Atmospheric Dynamics: General or miscellaneous; KEYWORDS: remote sensing, clouds, radar, microwave radiometer, Bayes

Improved techniques for remote sensing of cirrus are needed to obtain global data for assessing the effect of cirrus in climate change models. Model calculations show that the far infrared/sub-millimeter spectral region is well suited for retrieving cirrus Ice Water Path and particle size parameters. Especially useful cirrus information is obtained at frequencies below 60 cm-1 where single particle scattering dominates over thermal emission for ice particles larger than about 50 micrometer. Earth radiance spectra have been obtained for a range of cloud conditions using an aircraft-based Fourier transform spectrometer. The Far InfraRed Sensor for Cirrus (FIRSC) is a Martin-Puplett interferometer which incorporates a polarizer for the beamsplitter and can be operated in either intensity or linear polarization measurement mode. Two detector channels span 10 to 140 cm-1 with a spectral resolution of 0.1 cm-1; achieving a Noise Equivalent Temperature of approximately 1K at 30 cm-1 in a 4 sec scan. Examples are shown of measured and modeled Earth radiance for a range of cloud conditions from 1998 and 1999 flights.