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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. DOI: 10.1175/2010JAMC2333.1

The differential phase ([[PHI].sub.DP]) measured by polarimetric radars is recognized to be a very good indicator of the path integrated by rain. Moreover, if a linear relationship is assumed between the specific differential phase ([K.sub.DP]) and the specific attenuation ([A.sub.H]) and specific differential attenuation ([A.sub.DP]), then attenuation can easily be corrected. The coefficients of proportionality, [[gamma].sub.H] and [[gamma].sub.DP], are, however, known to be dependent in rain upon drop temperature, drop shapes, drop size distribution, and the presence of large drops causing Mie scattering. In this paper, the authors extensively apply a physically based method, often referred to as the 'Smyth and Illingworth constraint,' which uses the constraint that the value of the differential reflectivity [Z.sub.DR] on the far side of the storm should be low to retrieve the [[gamma].sub.DP] coefficient. More than 30 convective episodes observed by the French operational C-band polarimetric Trappes radar during two summers (2005 and 2006) are used to document the variability of [[gamma].sub.DP] with respect to the intrinsic three-dimensional characteristics of the attenuating cells. The Smyth and Illingworth constraint could be applied to only 20% of all attenuated rays of the 2-yr dataset so it cannot be considered the unique solution for attenuation correction in an operational setting but is useful for characterizing the properties of the strongly attenuating cells. The range of variation of [[gamma].sub.DP] is shown to be extremely large, with minimal, maximal, and mean values being, respectively, equal to 0.01, 0.11, and 0.025 [dB[degrees].sup.-1]. Coefficient [[gamma].sub.DP] appears to be almost linearly correlated with the horizontal reflectivity ([Z.sub.H]), differential reflectivity ([Z.sub.DR]), and specific differential phase ([K.sub.DP]) and correlation coefficient ([[rho].sub.HV]) of the attenuating cells. The temperature effect is negligible with respect to that of the microphysical properties of the attenuating cells. Unusually large values of [[gamma].sub.DP], above 0.06 [dB[degrees].sup.-1], often referred to as 'hot spots,' are reported for 15%--a nonnegligible figure--of the rays presenting a significant total differential phase shift ([DELTA][phi].sub.DP] > 30[degrees]). The corresponding strongly attenuating cells are shown to have extremely high [Z.sub.DR] (above 4 dB) and [Z.sub.H] (above 55 dBZ), very low [[rho].sub.HV] (below 0.94), and high [K.sub.DP] (above 4[degrees] [km.sup.-1]). Analysis of 4 yr of observed raindrop spectra does not reproduce such low values of [[rho].sub.HV], suggesting that (wet) ice is likely to be present in the precipitation medium and responsible for the attenuation and high phase shifts. Furthermore, if melting ice is responsible for the high phase shifts, this suggests that [K.sub.DP] may not be uniquely related to rainfall rate but can result from the presence of wet ice. This hypothesis is supported by the analysis of the vertical profiles of horizontal reflectivity and the values of conventional probability of hail indexes. DOI: 10.1175/2009JAMC2039.1

A new method of accurately calculating the capacitance of realistic ice particles is described: such values are key to accurate estimates of deposition and evaporation (sublimation) rates in numerical weather models. The trajectories of diffusing water molecules are directly sampled, using random 'walkers.' By counting how many of these trajectories intersect the surface of the ice particle (which may be any shape) and how many escape outside a spherical boundary far from the particle, the capacitances of a number of model ice particle habits have been estimated, including hexagonal columns and plates, 'scalene' columns and plates, bullets, bullet rosettes, dendrites, and realistic aggregate snowflakes. For ice particles with sharp edges and corners this method is an efficient and straightforward way of solving Laplace's equation for the capacitance. Provided that a large enough number of random walkers are used to sample the particle geometry (~[10.sup.4]) the authors expect the calculated capacitances to be accurate to within -1%. The capacitance for the modeled aggregate snowflakes (C/[D.sub.max] = 0.25, normalized by the maximum dimension [D.sub.max]) is shown to be in close agreement with recent aircraft measurements of snowflake sublimation rates. This result shows that the capacitance of a sphere (C/[D.sub.max] = 0.5), which is commonly used in numerical models, overestimates the evaporation rate of snowflakes by a factor of 2. The effect of vapor 'screening' by crystals growing in the vicinity of one another has also been investigated. The results clearly show that neighboring crystals growing on a filament in cloud chamber experiments can strongly constrict the vapor supply to one another, and the resulting growth rate measurements may severely underestimate the rate for a single crystal in isolation (by a factor of 3 in this model setup).

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 = a[D.sup.b], where D is particle maximum dimension. The fall velocity relationship, [V.sub.1] = [AD.sub.B], 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.

Most current general circulation models (GCMs) calculate radiative fluxes through partially cloudy grid boxes by weighting clear and cloudy fluxes by the fractional area of cloud cover ([C.sub.a]), but most GCM cloud schemes calculate cloud fraction as the volume of the grid box that is filled with cloud ([C.sub.v]). In this paper, 1 yr of cloud radar and lidar observations from Chilbolton in southern England, are used to examine this discrepancy. With a vertical resolution of 300 m it is found that, on average, [C.sub.a] is 20% greater than [C.sub.v], and with a vertical resolution of 1 km, [C.sub.a] is greater than [C.sub.v] by a factor of 2. The difference is around a factor of 2 larger for liquid water clouds than for ice clouds, and also increases with wind shear. Using [C.sub.a] rather than [C.sub.v], calculated on an operational model grid, increases the mean total cloud cover from 53% to 63%. and so is of similar importance to the cloud overlap assumption. A simple parameterization, [C.sub.a] = [1 + [e.sup.(-f) ([[C.sup.-1.sub.v])].sup.-1], is proposed to correct for this underestimate based on the observation that the observed relationship between the mean [C.sub.a] and [C.sub.v] is symmetric about the line [C.sub.a] = 1 - [C.sub.v]. The parameter f is a simple function of the horizontal (H) and vertical (V) grid-box dimensions, where for ice clouds f = 0.0880 [V.sup.0.7696] [H.sup.-0.2254] and for liquid clouds f = 0.1635 [V.sup.0.6694] [H.sup.-0.1882]. Implementing this simple parameterization, which excludes the effect of wind shear, on an independent 6-month dataset of cloud radar and lidar observations, accounts for the mean underestimate of [C.sub.a] for all horizontal and vertical resolutions considered to within 3% of the observed [C.sub.a], and reduces the rms error for each individual box from typically 100% to approximately 30%. Small biases remain for both weakly and strongly sheared cases, but this is significantly reduced by incorporating a simple shear dependence in the calculation of the parameter f. which also slightly improves the overall performance of the parameterization for all of the resolutions considered.

Stratocumulus is one of the most common cloud types globally, with a profound effect on the earth's radiation budget, and the drizzle process is fundamental in understanding the evolution of these boundary layer clouds. In this paper a combination of 94-GHz Doppler radar and backscatter lidar is used to investigate the microphysical properties of drizzle falling below the base of stratocumulus clouds. The ratio of the radar to lidar backscatter power is proportional to the fourth power of mean size, and so potentially it can provide an accurate size estimate. Information about the shape of the drop size distribution is then inferred from the Doppler spectral width. The algorithm estimates vertical profiles of drizzle parameters such as liquid water content, liquid water flux, and vertical air velocity, assuming that the drizzle size spectrum may be represented by a gamma distribution. The depletion time scale of cloud liquid water through the drizzle process can be estimated when the liquid water path of the cloud is available from microwave radiometers, and observations suggest that this time scale varies from a few days in light drizzle to a few hours in strong drizzle events. Radar and lidar observations from Chilbolton (in southern England) and aircraft size spectra taken during the Atlantic Stratocumulus Transition Experiment have both been used to derive the following power-law relationship between liquid water flux (LWF) (g [m.sup.-2] [s.sup.-1]) and radar reflectivity (Z) ([mm.sup.6] [m.sup.-3]): LWF = 0.0093[Z.sup.0.69]. (This relation is valid for frequencies up to 94 GHz and therefore would allow a forthcoming spaceborne radar to measure liquid water flux around the globe to within a factor of 2 for values of Z above -20 dBZ.

Cloud variability on scales smaller than the gridbox size of numerical forecast and climate models is believed to be important in determining the radiative effects of clouds, and increasingly attempts are being made to parameterize these fluctuations in the radiation schemes of current models. In order to calculate the radiative effects of an inhomogeneous cloud, a model needs to know not only the degree of variability within a gridbox but also the degree to which the inhomogeneities in vertically adjacent levels are overlapped. In this paper these two parameters are derived for ice clouds from an 18-month midlatitude 94-GHz cloud radar dataset and parameterized in terms of horizontal gridbox size (d), the vertical shear of the horizontal wind (s), and the vertical position in the cloud. The vertical decorrelation length [DELTA][Z.sub.0] (i.e., the depth over which the correlation coefficient of either ice water content or optical extinction coefficient in separate vertical levels falls to [e.sup.-1]) is found to be well represented in the mean by [log.sub.10][DELTA][Z.sub.0] = 0.3 [log.sub.10]d - 0.031s - 0.315, where [DELTA][Z.sub.0] and d are in kilometers and s is in meters per second per kilometer. As expected, higher shear results in more rapid decorrelation, although the rms deviation from this expression is around a factor of 2.5. It is found that the probability distribution of ice water content within a gridbox is usually well represented by a lognormal or gamma distribution. The fractional variance in ice water content ([f.sub.IWC]) may be expressed to within a factor of 2 by [log.sub.10] [f.sub.IWC] = 0.3 [log.sub.10]d - 0.04s - 0.93, valid for d < 60 km, above which [f.sub.IWC] is constant with increasing d. The expression for the fractional variance of visible extinction coefficient is the same except with the -0.93 term replaced by -0.96. The s dependence indicates a tendency for increased shear to result in decreased cloud variability. This can be explained by the presence of ice fallstreaks in a sheared flow: a parcel of air in the middle of a cloud is alternately fed from above by ice-rich and ice-poor air, resulting in a homogenization of the layer at a rate dependent on the shear. A more complicated formula is derived to express the dependence of [f.sub.IWC] on the vertical position within the cloud; it is found that fractional variance tends to be largest at cloud top and decreases into the interior before increasing again in the lowest third of the cloud. Thicker clouds tend to have lower fractional variance. No significant dependence on temperature or absolute altitude was found for either [f.sub.IWC] or [DELTA][Z.sub.0].

CloudSat is a satellite experiment designed to measure the vertical structure of clouds from space. The expected launch of CloudSat is planned for 2004, and once launched, CloudSat will orbit in formation as part of a constellation of satellites (the A-Train) that includes NASA's Aqua and Aura satellites, a NASA-CNES lidar satellite (CALIPSO), and a CNES satellite carrying a polarimeter (PARASOL). A unique feature that CloudSat brings to this constellation is the ability to fly a precise orbit enabling the fields of view of the CloudSat radar to be overlapped with the CALIPSO lidar footprint and the other measurements of the constellation. The precision and near simultaneity of this overlap creates a unique multisatellite observing system for studying the atmospheric processes essential to the hydrological cycle. The vertical profiles of cloud properties provided by CloudSat on the global scale fill a critical gap in the investigation of feedback mechanisms linking clouds to climate. Measuring these profiles requires a combination of active and passive instruments, and this will be achieved by combining the radar data of CloudSat with data from other active and passive sensors of the constellation. This paper describes the underpinning science and general overview of the mission, provides some idea of the expected products and anticipated application of these products, and the potential capability of the A-Train for cloud observations. Notably, the CloudSat mission is expected to stimulate new areas of research on clouds. The mission also provides an important opportunity to demonstrate active sensor technology for future scientific and tactical applications. The CloudSat mission is a partnership between NASA's JPL, the Canadian Space Agency, Colorado State University, the U.S. Air Force, and the U.S. Department of Energy. (Page 1771)

Polarization radar techniques essentially rely on detecting the oblateness of raindrops to provide a measure of mean raindrop size and then using this information to give a better estimate of rainfall rate R than is available from radar reflectivity Z alone. To derive rainfall rates from these new parameters such as differential reflectivity [Z.sub.DR] and specific differential phase shift [K.sub.DP] and to gauge their performance, it is necessary to know the range of naturally occurring raindrop size spectra. A three parameter gamma function is in widespread use, with the three variables [N.sub.o] [D.sub.o], and [mu] providing a measure of drop concentration, mean size, and spectral shape, respectively. It has become standard practice to derive the range of these three variables in rain by comparing the 69 published values of the constants a and b in the empirical relationships Z = a[R.sup.b] with the values of a and b obtained when R and Z are derived by integrating the appropriately weighted gamma function. The relationships in common use both for inferring R from Z, [Z.sub.DR], and [K.sub.DP], and for developing attenuation correction routines have been derived from a best fit through the values obtained by cycling over these predicted ranges of [N.sub.o], [D.sub.o], and [mu]. It is pointed out that this derivation of the predicted range of [N.sub.o] [D.sub.o], and [mu] arises using a flawed logic for a particular nonnormalized form of the gamma function, and it is shown that the predicted ranges give rise to some very unrealistic drop spectra, including many with high rainfall and very small drop sizes. It is suggested that attenuation correction routines relying on differential phase may be suspect and the commonly used relationships between rainfall rate and Z, [Z.sub.DR], and [K.sub.DP] need to be reexamined. When more realistic drop shapes are also used, it may be that published relationships for deriving R from Z and [Z.sub.DR] are in error by over a factor of 2; a new equation is proposed that, in the absence of hail and attenuation, should yield values of R accurate to 25%, provided that [Z.sub.DR] can be estimated to 0.2 dB and Z is calibrated to 1 dB. Relationships of the form R = a[K.sup.b.sub.DP], with b = 1.15, are in widespread use, but more realistic drop spectra and drop shapes yield a value of b closer to 1.4, similar to the exponent in Z-R relationships. In accord, although [K.sub.DP] has the advantage of insensitivity to hail, it may have the same sensitivity to variations in drop spectra as Z does. In addition, the higher value of the exponent b implies that the proposed use of the total phase shift to give the path-integrated total rainfall is also questionable. However, the consistency of Z, [Z.sub.DR], and [K.sub.DP] in rain can be used to provide absolute calibration of Z to 0.5 dB (12%), and when it fails it indicates that hail is present, in which case a relationship of the form [K.sub.DP] = a[R.sup.1.4] should be used. The technique should work at S, C, and X band, but, in all cases, paths should be chosen so that the total phase shift is not large enough to introduce significant attenuation of Z and [Z.sub.DR].

The radiative characteristics of stratocumulus clouds are dependent upon their microphysical properties, primarily the liquid water content and effective radius of the drop population. Aircraft observations of droplet spectra in warm stratocumulus over the North Atlantic and around the British Isles by the Hercules C-130 aircraft of the U.K. Meteorological Office Meteorological Research Flight have been used to calculate the radar reflectivity, liquid water content, and effective radius. Empirically derived relationships, found from more than 4000 km of flight data on 11 separate days, that link reflectivity with either liquid water content or effective radius have been derived. These empirical relationships are significantly different from those predicted if the cloud droplet spectrum is modeled as a gamma function. Occasional drizzle-sized drops are frequently present within the cloud, and even though their concentration is very low, they dominate the reflectivity and these empirical relationships fail. However, although the drizzle drops increase the reflectivity, they have a negligible effect on the liquid water content and effective radius of the cloud. As these drops have a significant fall velocity in comparison to the cloud droplets, it is suggested that a ground-based Doppler radar could separate the components of the reflectivity due to bimodal drop spectra and the vertical structure of the cloud properties that determine radiative transfer could be retrieved.

A workshop on weather radar polarimetry for research and operational applications was held on 22 and 23 February 1994 at the National Center for Atmospheric Research. Polarization radar can provide estimates of the shapes, sizes, phase, and fall orientations of hydrometeors. This information can be used to remove some of the ambiguities present when only the reflectivity is measured. The morning of 22 February was devoted to 16 short presentations highlighting recent advances in polarimetric radar. The afternoon was dedicated to four discussion sessions that further developed important topics. On the following day, plans for the upgrading and development of the existing NCAR polarization radar capability were presented and debated. The workshop resulted in four recommendations: three of which proposed specific field measurement programs to quantify the potential improvements provided by the polarization techniques.

Of great importance for the simulation of climate using general circulation models is their ability to represent accurately the vertical distribution of fractional cloud amount. In this paper, a technique to derive cloud fraction as a function of height using ground-based radar and lidar is described. The relatively unattenuated radar detects clouds and precipitation throughout the whole depth of the troposphere, whereas the lidar is able to locate cloud base accurately in the presence of rain or drizzle. From a direct comparison of 3 months of cloud fraction observed at Chilbolton, England, with the values held at the nearest grid box of the European Centre for Medium-Range Forecasts (ECMWF) model it is found that, on average, the model tends to underpredict cloud fraction below 7 km and considerably overpredict it above. The difference below 7 km can in large part be explained by the fact that the model treats snow and ice cloud separately, with snow not contributing to cloud fraction. Modifying the model cloud fraction to include the contribution from snow (already present in the form of fluxes between levels) results in much better agreement in mean cloud fraction, frequency of occurrence, and amount when present between 1 and 7 km. This, together with the fact that both the lidar and the radar echoes tend to be stronger in the regions of ice clouds that the model regards as snow, indicates that snow should not be treated as radiatively inert by the model radiation scheme. Above 7 km, the difference between the model and the observations is partly due to some of the high clouds in the model being associated with very low values of ice water content that one would not expect the radar to detect. However, removal of these from the model still leaves an apparent overestimate of cloud fraction by up to a factor of 2. A tendency in the lowest kilometer for the model to simulate cloud features up to 3 h before they are observed is also found. Overall, this study demonstrates the considerable potential of active instruments for validating the representation of clouds in models.

There has been considerable discussion concerning the accuracy of values of ice water content (IWC) in ice clouds derived from measurements of radar reflectivity (Z). In this paper, the various published relationships that are based on ice particle size spectra recorded from aircraft are analyzed, and it is shown that a relationship between ice water content and reflectivity can be derived (IWC = 0.137 [Z.sup.0.64] at 94 GHz and IWC = 0.097 [Z.sup.0-59] at 35 GHz), which only varies by 20%-30% for different climatological areas, providing the same ice density as a function of particle size is assumed. Uncertainty as to the true variation of density of ice particles with size may reduce the average IWC for a given Z by up to 30% for an IWC of [approximately equals] 0.1 g [m.sup.-3] and 20% for an IWC of [approximately equals]0.01 g m-3. Individual values of IWC derived from a single measurement of Z are likely to have an error of about + 100% and -50%, but if some characteristic size estimate is available, this is reduced to about +50% and -30%. The remaining errors are due to deviations of the size spectra from exponentiality, so there is no advantage in measuring the characteristic size more precisely than this limit. Remote sensing of ice particle size is not trivial, and it is shown that if instead of size, an estimate of the temperature of the ice cloud to within 6 K is available, then, rather surprisingly, the reduction in the error of IWC is almost as good as that achieved using size. Essentially this result is reflecting the well-known correlation of crystal size with temperature. When the mean values of IWC for a given Z and T are compared for a tropical and midlatitude dataset using a common ice density variation with size, then the difference is usually less than 25%. A spaceborne instrument may need to integrate over horizontal distances of 10 km to achieve sufficient sensitivity; this necessity may introduce a bias into the retrieved IWC because the relationship between IWC and Z is not linear, but analysis shows that any bias should be less than 10%.

Examines the accuracy of values of ice water content (IWC) in ice clouds based on the measurements of radar reflectivity (Z). Relationship between IWC and Z based on aircraft measurement of ice particle spectra; Effect of ice density on IWC inferred from Z; Impact of spatial integration on retrieved IWC.