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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.

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.

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%.

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.

[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