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Carlos Domenech, Ernesto Lopez-Baeza, David P. Donovan, and Tobias Wehr

Abstract

The instantaneous top-of-atmosphere (TOA) radiance-to-flux conversion for the broadband radiometer (BBR) on board the Earth Clouds, Aerosols, and Radiation Explorer (EarthCARE) was assessed in Part I of this paper, by developing theoretical angular distribution models (ADMs) specifically designed for the instrument viewing configuration. This paper validates the BBR ADMs by comparing derived flux estimates with flux retrievals obtained from the Clouds and the Earth’s Radiant Energy System (CERES) Terra models. A CERES BBR-like database is employed in the assessment, which is an optimum dataset to validate the BBR algorithms and to determine the benefits of the multiangular conversion procedures in the BBR instrument. The validation of theoretical results with empirical data is essential to prepare the conversion algorithms prior to the launch of EarthCARE. This paper demonstrates that the application of a linear combination method is not recommended when outgoing radiances do not follow the response modeled in the radiative transfer calculations. An effective radiance averaged model outperforms all other developed models, in terms of the coefficient of variation of the root-mean-square error, in the validation study of the shortwave (SW) regime (clear sky 1.9%; cloudy 7.1%) while an effective radiance along-track model obtains the best comparisons for the longwave (LW) regime (clear sky 1.4%; cloudy 1.5%). The evaluation of the multiangular models with scenes with high anisotropy shows that multiview flux conversion algorithms can statistically improve CERES ADM results when CERES flux discrepancies of a target are higher than 4 W m−2 in the LW domain and SW clear-sky scenes and higher than 20 W m−2 in scenes with cloudy conditions.

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Carlos Domenech, Ernesto Lopez-Baeza, David P. Donovan, and Tobias Wehr

Abstract

The forthcoming broadband radiometer (BBR) on board the Earth Clouds, Aerosols, and Radiation Explorer (EarthCARE) will provide quasi-instantaneous top-of-atmosphere radiance measurements for three different viewing angles. The role of BBR data will be to constrain the vertical radiative flux divergence profiles derived from EarthCARE measurements. Thus, the development of an instantaneous radiance-to-flux conversion procedure is of paramount importance. This paper studies the scientific basis for determining fluxes from radiances measured by the BBR instrument. This is an attempt to evaluate a possible solution and assess its potential advantages and drawbacks. The approach considered has been to construct theoretical angular distribution models (ADMs) based on the multiangular pointing feature of this instrument. This configuration provides extra information on the anisotropy of the observed radiance field, which can be employed to construct accurate inversion schemes. The proposal relies on radiative transfer calculations performed with a Monte Carlo algorithm. Considering the intrinsic difficulty associated with addressing the range of atmospheric conditions needed to determine reliable ADMs, a synthetic database has been thoroughly constructed that considers a diverse range of surface, atmospheric, and cloud conditions that are conditioned to the EarthCARE orbit and physical constraints. Three inversion methodologies have been specifically designed for the BBR flux retrieval algorithm. In particular, an optimized classical inversion procedure in which the definition of an effective radiance leads to derive fluxes with averaged errors up to 1.2 and 5.2 W m−2 for shortwave clear and cloudy sky and 1.5 W m−2 for longwave radiation scenes and a linear combination of the three instantaneous radiances from which averaged errors up to 0.4 and 2.7 W m−2 for shortwave clear and cloudy sky and 0.5 W m−2 for longwave scenes can be obtained.

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Andrew J. Heymsfield, Gerd-Jan van Zadelhoff, David P. Donovan, Frederic Fabry, Robin J. Hogan, and Anthony J. Illingworth

Abstract

This two-part study addresses the development of reliable estimates of the mass and fall speed of single ice particles and ensembles. Part I of the study reports temperature-dependent coefficients for the mass-dimensional relationship, m = aDb, where D is particle maximum dimension. The fall velocity relationship, Vt = ADB, is developed from observations in synoptic and low-latitude, convectively generated, ice cloud layers, sampled over a wide range of temperatures using an assumed range for the exponent b. Values for a, A, and B were found that were consistent with the measured particle size distributions (PSD) and the ice water content (IWC).

To refine the estimates of coefficients a and b to fit both lower and higher moments of the PSD and the associated values for A and B, Part II uses the PSD from Part I plus coincident, vertically pointing Doppler radar returns. The observations and derived coefficients are used to evaluate earlier, single-moment, bulk ice microphysical parameterization schemes as well as to develop improved, statistically based, microphysical relationships. They may be used in cloud and climate models, and to retrieve cloud properties from ground-based Doppler radar and spaceborne, conventional radar returns.

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Robin J. Hogan, Malcolm E. Brooks, Anthony J. Illingworth, David P. Donovan, Claire Tinel, Dominique Bouniol, and J. Pedro V. Poiares Baptista

Abstract

The combination of radar and lidar in space offers the unique potential to retrieve vertical profiles of ice water content and particle size globally, and two algorithms developed recently claim to have overcome the principal difficulty with this approach—that of correcting the lidar signal for extinction. In this paper “blind tests” of these algorithms are carried out, using realistic 94-GHz radar and 355-nm lidar backscatter profiles simulated from aircraft-measured size spectra, and including the effects of molecular scattering, multiple scattering, and instrument noise. Radiation calculations are performed on the true and retrieved microphysical profiles to estimate the accuracy with which radiative flux profiles could be inferred remotely. It is found that the visible extinction profile can be retrieved independent of assumptions on the nature of the size distribution, the habit of the particles, the mean extinction-to-backscatter ratio, or errors in instrument calibration. Local errors in retrieved extinction can occur in proportion to local fluctuations in the extinction-to-backscatter ratio, but down to 400 m above the height of the lowest lidar return, optical depth is typically retrieved to better than 0.2. Retrieval uncertainties are greater at the far end of the profile, and errors in total optical depth can exceed 1, which changes the shortwave radiative effect of the cloud by around 20%. Longwave fluxes are much less sensitive to errors in total optical depth, and may generally be calculated to better than 2 W m−2 throughout the profile. It is important for retrieval algorithms to account for the effects of lidar multiple scattering, because if this is neglected, then optical depth is underestimated by approximately 35%, resulting in cloud radiative effects being underestimated by around 30% in the shortwave and 15% in the longwave. Unlike the extinction coefficient, the inferred ice water content and particle size can vary by 30%, depending on the assumed mass–size relationship (a problem common to all remote retrieval algorithms). However, radiative fluxes are almost completely determined by the extinction profile, and if this is correct, then errors in these other parameters have only a small effect in the shortwave (around 6%, compared to that of clear sky) and a negligible effect in the longwave.

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Andrew J. Heymsfield, Alain Protat, Dominique Bouniol, Richard T. Austin, Robin J. Hogan, Julien Delanoë, Hajime Okamoto, Kaori Sato, Gerd-Jan van Zadelhoff, David P. Donovan, and Zhien Wang

Abstract

Vertical profiles of ice water content (IWC) can now be derived globally from spaceborne cloud satellite radar (CloudSat) data. Integrating these data with Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data may further increase accuracy. Evaluations of the accuracy of IWC retrieved from radar alone and together with other measurements are now essential. A forward model employing aircraft Lagrangian spiral descents through mid- and low-latitude ice clouds is used to estimate profiles of what a lidar and conventional and Doppler radar would sense. Radar reflectivity Ze and Doppler fall speed at multiple wavelengths and extinction in visible wavelengths were derived from particle size distributions and shape data, constrained by IWC that were measured directly in most instances. These data were provided to eight teams that together cover 10 retrieval methods. Almost 3400 vertically distributed points from 19 clouds were used. Approximate cloud optical depths ranged from below 1 to more than 50. The teams returned retrieval IWC profiles that were evaluated in seven different ways to identify the amount and sources of errors. The mean (median) ratio of the retrieved-to-measured IWC was 1.15 (1.03) ± 0.66 for all teams, 1.08 (1.00) ± 0.60 for those employing a lidar–radar approach, and 1.27 (1.12) ± 0.78 for the standard CloudSat radar–visible optical depth algorithm for Ze > −28 dBZe. The ratios for the groups employing the lidar–radar approach and the radar–visible optical depth algorithm may be lower by as much as 25% because of uncertainties in the extinction in small ice particles provided to the groups. Retrievals from future spaceborne radar using reflectivity–Doppler fall speeds show considerable promise. A lidar–radar approach, as applied to measurements from CALIPSO and CloudSat, is useful only in a narrow range of ice water paths (IWP) (40 < IWP < 100 g m−2). Because of the use of the Rayleigh approximation at high reflectivities in some of the algorithms and differences in the way nonspherical particles and Mie effects are considered, IWC retrievals in regions of radar reflectivity at 94 GHz exceeding about 5 dBZe are subject to uncertainties of ±50%.

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Dominique Bouniol, Alain Protat, Julien Delanoë, Jacques Pelon, Jean-Marcel Piriou, François Bouyssel, Adrian M. Tompkins, Damian R. Wilson, Yohann Morille, Martial Haeffelin, Ewan J. O’Connor, Robin J. Hogan, Anthony J. Illingworth, David P. Donovan, and Henk-Klein Baltink

Abstract

The ability of four operational weather forecast models [ECMWF, Action de Recherche Petite Echelle Grande Echelle model (ARPEGE), Regional Atmospheric Climate Model (RACMO), and Met Office] to generate a cloud at the right location and time (the cloud frequency of occurrence) is assessed in the present paper using a two-year time series of observations collected by profiling ground-based active remote sensors (cloud radar and lidar) located at three different sites in western Europe (Cabauw, Netherlands; Chilbolton, United Kingdom; and Palaiseau, France). Particular attention is given to potential biases that may arise from instrumentation differences (especially sensitivity) from one site to another and intermittent sampling. In a second step the statistical properties of the cloud variables involved in most advanced cloud schemes of numerical weather forecast models (ice water content and cloud fraction) are characterized and compared with their counterparts in the models. The two years of observations are first considered as a whole in order to evaluate the accuracy of the statistical representation of the cloud variables in each model. It is shown that all models tend to produce too many high-level clouds, with too-high cloud fraction and ice water content. The midlevel and low-level cloud occurrence is also generally overestimated, with too-low cloud fraction but a correct ice water content. The dataset is then divided into seasons to evaluate the potential of the models to generate different cloud situations in response to different large-scale forcings. Strong variations in cloud occurrence are found in the observations from one season to the same season the following year as well as in the seasonal cycle. Overall, the model biases observed using the whole dataset are still found at seasonal scale, but the models generally manage to well reproduce the observed seasonal variations in cloud occurrence. Overall, models do not generate the same cloud fraction distributions and these distributions do not agree with the observations. Another general conclusion is that the use of continuous ground-based radar and lidar observations is definitely a powerful tool for evaluating model cloud schemes and for a responsive assessment of the benefit achieved by changing or tuning a model cloud parameterization.

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