Cloud Horizontal Inhomogeneity Research Articles

Performance of Cloud 3D Solvers in Ice Cloud Shortwave Radiation Closure Over the Equatorial Western Pacific Ocean

AbstractFor retrieving cloud optical properties from satellite images or computing these properties from climate model output, computationally efficient treatments of cloud horizontal inhomogeneity include the Monte Carlo Independent Column Approximation (McICA) and the Tripleclouds method. Computationally efficient treatment of cloud horizontal radiation exchanges includes the SPeedy Algorithm for Radiative TrAnsfer through CloUd Sides (SPARTACUS). As a test to derive properties from satellite images, we collocate Moderate Resolution Imaging Spectroradiometer (MODIS) cloud retrievals with near‐nadir Cloud and the Earth's Radiant Energy System (CERES) footprints in July 2008 over an equatorial western Pacific Ocean region to compare the performance of the McICA, Tripleclouds, and SPARTACUS solvers to the conventional plane‐parallel homogeneous (PPH) treatment. PPH overestimates cloud albedo, and the three solvers effectively reduce overestimation with root mean square error of shortwave upwelling irradiance decreasing between 15.72 and 18.53 W m−2, or about 22%–25%. Although cloud top variability does not get fed into the simulations, all three solvers also reduce the effect of cloud top variability on cloud albedo. Entrapment (energy reflected downward from clouds) and horizontal radiation transfer have opposite effects on the SPARTACUS cloud albedo simulation. The net effect depends on the cloud vertical extent, the unawareness of which limits the performance of the SPARTACUS solver.

Towards an improved treatment of cloud–radiation interaction in weather and climate models: exploring the potential of the Tripleclouds method for various cloud types using libRadtran 2.0.4

Abstract. The representation of unresolved clouds in radiation schemes of coarse-resolution weather and climate models has progressed noticeably over the past years. Nevertheless, a lot of room remains for improvement, as the current picture is by no means complete. The main objective of the present study is to advance the cloud–radiation interaction parameterization, focusing on the issues related to model misrepresentation of cloud horizontal inhomogeneity. This subject is addressed with the Tripleclouds radiative solver, the fundamental feature of which is the inclusion of the optically thicker and thinner cloud fraction. The research challenge is to optimally set the pair of cloud condensates characterizing the two cloudy regions and the corresponding geometrical split of layer cloudiness. A diverse cloud field data set was collected for the analysis, comprising case studies of stratocumulus, cirrus and cumulonimbus. The primary goal is to assess the validity of the global cloud variability estimate along with various condensate distribution assumptions. More sophisticated parameterizations are subsequently explored, optimizing the treatment of overcast as well as extremely heterogeneous cloudiness. The radiative diagnostics including atmospheric heating rate and net surface flux are consistently studied using the Tripleclouds method, evaluated against a three-dimensional radiation computation. The performance of Tripleclouds mostly significantly surpasses the calculation on horizontally homogeneous cloudiness. The effect of horizontal photon transport is further quantified. The overall conclusions are intrinsically different for each particular cloud type, encouraging endeavors to enhance the use of cloud-regime-dependent methodologies in next-generation atmospheric models. This study, highlighting the Tripleclouds potential for three essential cloud types, signifies the need for more research examining a broader spectrum of cloud morphologies.

An Efficient and Accurate Algorithm for Computing Grid-Averaged Solar Fluxes for Horizontally Inhomogeneous Clouds

Abstract A computationally efficient method is presented to account for the horizontal cloud inhomogeneity by using a radiatively equivalent plane-parallel homogeneous (PPH) cloud. The algorithm can accurately match the calculations of the reference (rPPH) independent column approximation (ICA) results but uses only the same computational time required for a single plane-parallel computation. The effective optical depth of this synthetic sPPH cloud is derived by exactly matching the direct transmission to that of the inhomogeneous ICA cloud. The effective scattering asymmetry factor is found from a precalculated albedo inverse lookup table that is allowed to vary over the range from −1.0 to 1.0. In the special cases of conservative scattering and total absorption, the synthetic method is exactly equivalent to the ICA, with only a small bias (about 0.2% in flux) relative to ICA resulting from imperfect interpolation in using the lookup tables. In principle, the ICA albedo can be approximated accurately regardless of cloud inhomogeneity. For a more complete comparison, the broadband shortwave albedo and transmission calculated from the synthetic sPPH cloud and averaged over all incident directions have RMS biases of 0.26% and 0.76%, respectively, for inhomogeneous clouds over a wide variation of particle size. The advantages of the synthetic PPH method are that 1) it is not required that all the cloud subcolumns have uniform microphysical characteristic, 2) it is applicable to any 1D radiative transfer scheme, and 3) it can handle arbitrary cloud optical depth distributions and an arbitrary number of cloud subcolumns with uniform computational efficiency.

The incorporation of the Tripleclouds concept into the <i>δ</i>-Eddington two-stream radiation scheme: solver characterization and its application to shallow cumulus clouds

Abstract. The treatment of unresolved cloud–radiation interactions in weather and climate models has considerably improved over the recent years, compared to conventional plane-parallel radiation schemes, which previously persisted in these models for multiple decades. One such improvement is the state-of-the-art Tripleclouds radiative solver, which has one cloud-free and two cloudy regions in each vertical model layer and is thereby capable of representing cloud horizontal inhomogeneity. Inspired by the Tripleclouds concept, primarily introduced by Shonk and Hogan (2008), we incorporated a second cloudy region into the widely employed δ-Eddington two-stream method with the maximum-random overlap assumption for partial cloudiness. The inclusion of another cloudy region in the two-stream framework required an extension of vertical overlap rules. While retaining the maximum-random overlap for the entire layer cloudiness, we additionally assumed the maximum overlap of optically thicker cloudy regions in pairs of adjacent layers. This extended overlap formulation implicitly places the optically thicker region towards the interior of the cloud, which is in agreement with the core–shell model for convective clouds. The method was initially applied on a shallow cumulus cloud field, evaluated against a three-dimensional benchmark radiation computation. Different approaches were used to generate a pair of cloud condensates characterizing the two cloudy regions, testing various condensate distribution assumptions along with global cloud variability estimate. Regardless of the exact condensate setup, the radiative bias in the vast majority of Tripleclouds configurations was considerably reduced compared to the conventional plane-parallel calculation. Whereas previous studies employing the Tripleclouds concept focused on researching the top-of-the-atmosphere radiation budget, the present work applies Tripleclouds to atmospheric heating rate and net surface flux. The Tripleclouds scheme was implemented in the comprehensive libRadtran radiative transfer package and can be utilized to further address key scientific issues related to unresolved cloud–radiation interplay in coarse-resolution atmospheric models.

Impact of cloud horizontal inhomogeneity and directional sampling on the retrieval of cloud droplet size by the POLDER instrument

Abstract. The principles of cloud droplet size retrieval via Polarization and Directionality of the Earth's Reflectance (POLDER) requires that clouds be horizontally homogeneous. The retrieval is performed by combining all measurements from an area of 150 km × 150 km to compensate for POLDER's insufficient directional sampling. Using POLDER-like data simulated with the RT3 model, we investigate the impact of cloud horizontal inhomogeneity and directional sampling on the retrieval and analyze which spatial resolution is potentially accessible from the measurements. Case studies show that the sub-grid-scale variability in droplet effective radius (CDR) can significantly reduce valid retrievals and introduce small biases to the CDR (~ 1.5 μm) and effective variance (EV) estimates. Nevertheless, the sub-grid-scale variations in EV and cloud optical thickness (COT) only influence the EV retrievals and not the CDR estimate. In the directional sampling cases studied, the retrieval using limited observations is accurate and is largely free of random noise. Several improvements have been made to the original POLDER droplet size retrieval. For example, measurements in the primary rainbow region (137–145°) are used to ensure retrievals of large droplet (> 15 μm) and to reduce the uncertainties caused by cloud heterogeneity. We apply the improved method using the POLDER global L1B data from June 2008, and the new CDR results are compared with the operational CDRs. The comparison shows that the operational CDRs tend to be underestimated for large droplets because the cloudbow oscillations in the scattering angle region of 145–165° are weak for cloud fields with CDR > 15 μm. Finally, a sub-grid-scale retrieval case demonstrates that a higher resolution, e.g., 42 km × 42 km, can be used when inverting cloud droplet size distribution parameters from POLDER measurements.

Dominant roles of subgrid‐scale cloud structures in model diversity of cloud radiative effects

Today, large model discrepancies exist in estimated cloud radiative effects (CREs) and irradiances across 1‐D radiative transfer schemes aimed for climate models. The primary purpose of this study is to understand physical causes of such model discrepancies, especially in CREs under partly cloudy sky. To achieve this goal, the unique Cloud‐Aerosol‐Radiation (CAR) ensemble modeling system was employed, offline driven by the ERA‐Interim global data for July 2004 with no feedback considered. For evaluating each individual contribution from the existing scheme diversity of cloud horizontal inhomogeneity, cloud optical properties, cloud vertical overlap, and gas absorptions, several sets of numerical experiments were conducted. It is the first time to explicitly demonstrate that after removing most of the disagreement in cloud fields, model spreads of CREs among the CAR's seven major radiation schemes, as well as those of radiative fluxes, dramatically diminish. Taking global mean CREs for example, their current model ranges can decrease to <4 W m−2 from about 10 W m−2 for shortwave and also to <4 W m−2 from 5–8 W m−2 for longwave. Dominant roles of subgrid‐scale cloud structures (including vertical overlap and horizontal variability) were proven in general, explaining about 40–75% of the total model spreads. We have also found that model spreads of CREs are very sensitive to cloud cover fractions. Such nonlinear sensitivity can be largely reduced after removing the model difference in the treatments of cloud vertical overlap.

Effects of cloud horizontal inhomogeneity and drizzle on remote sensing of cloud droplet effective radius: Case studies based on large‐eddy simulations

This study investigates effects of drizzle and cloud horizontal inhomogeneity on cloud effective radius (re) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS). In order to identify the relative importance of various factors, we developed a MODIS cloud property retrieval simulator based on the combination of large‐eddy simulations (LES) and radiative transfer computations. The case studies based on synthetic LES cloud fields indicate that at high spatial resolution (∼100 m) 3‐D radiative transfer effects, such as illumination and shadowing, can induce significant differences between retrievals ofre based on reflectance at 2.1 μm (re,2.1) and 3.7 μm (re,3.7). It is also found that 3‐D effects tend to have stronger impact onre,2.1 than re,3.7, leading to positive difference between the two (Δre,3.7−2.1) from illumination and negative Δre,3.7−2.1from shadowing. The cancellation of opposing 3‐D effects leads to overall reasonable agreement betweenre,2.1 and re,3.7 at high spatial resolution as far as domain averages are concerned. At resolutions similar to MODIS, however, re,2.1 is systematically larger than re,3.7when averaged over the LES domain, with the difference exhibiting a threshold‐like dependence on bothre,2.1and an index of the sub‐pixel variability in reflectance (Hσ), consistent with MODIS observations. In the LES cases studied, drizzle does not strongly impact reretrievals at either wavelength. It is also found that opposing 3‐D radiative transfer effects partly cancel each other when cloud reflectance is aggregated from high spatial resolution to MODIS resolution, resulting in a weaker net impact of 3‐D radiative effects onre retrievals. The large difference at MODIS resolution between re,3.7 and re,2.1 for highly inhomogeneous pixels with Hσ> 0.4 can be largely attributed to what we refer to as the “plane‐parallelrebias,” which is attributable to the impact of sub‐pixel level horizontal variability of cloud optical thickness onre retrievals and is greater for re,2.1 than re,3.7. These results suggest that there are substantial uncertainties attributable to 3‐D radiative effects and plane‐parallelre bias in the MODIS re,2.1retrievals for pixels with strong sub‐pixel scale variability, and theHσ index can be used to identify these uncertainties.

Impact of improved representation of horizontal and vertical cloud structure in a climate model

Many studies have investigated the effects that misrepresentation of sub-grid cloud structure can have on the radiation budget. In this study, we perform 20-year simulations of the current climate using an atmosphere-only version of the Met Office Unified Model to investigate the effects of cloud approximation on model climate. We apply the “Tripleclouds” scheme for representing horizontal cloud inhomogeneity and “exponential-random” overlap, both separately and in combination, in place of a traditional plane-parallel representation with maximum-random overlap, to the clouds within the radiation scheme. The resulting changes to both the radiation budget and other meteorological variables, averaged over the 20 years, are compared. The combined global effect of the parameterizations on top-of-atmosphere short-wave and long-wave radiation budget is less than 1 W m−2, but changes of up to 10 W m−2 are identified in marine stratocumulus regions. A cooling near the surface over the winter polar regions of up to 3°C is also identified when horizontal cloud inhomogeneity is represented, and a warming of similar magnitude is found when exponential-random overlap is implemented. Corresponding changes of the same sign are also found in zonally averaged temperature, with maximum changes in the upper tropical troposphere of up to 0.5°C. Changes in zonally averaged cloud fraction in this location were of opposite sign and up to 0.02. The individual effects on tropospheric temperature of improving the two components of cloud structure are of similar magnitudes to about 2% of the warming created by a quadrupling of carbon dioxide.

Effect of improving representation of horizontal and vertical cloud structure on the Earth's global radiation budget. Part II: The global effects

AbstractReliably representing both horizontal cloud inhomogeneity and vertical cloud overlap is fundamentally important for the radiation budget of a general circulation model. Here, we build on the work of Part I of this two‐part paper by applying a pair of parametrizations that account for horizontal inhomogeneity and vertical overlap to global re‐analysis data. These are applied both together and separately in an attempt to quantify the effects of poor representation of the two components on radiation budget.Horizontal inhomogeneity is accounted for using the ‘Tripleclouds’ scheme, which uses two regions of cloud in each layer of a gridbox as opposed to one; vertical overlap is accounted for using ‘exponential‐random’ overlap, which aligns vertically continuous cloud according to a decorrelation height. These are applied to a sample of scenes from a year of ERA‐40 data. The largest radiative effect of horizontal inhomogeneity is found to be in areas of marine stratocumulus; the effect of vertical overlap is found to be fairly uniform, but with larger individual short‐wave and long‐wave effects in areas of deep, tropical convection. The combined effect of the two parametrizations is found to reduce the magnitude of the net top‐of‐atmosphere (TOA) cloud radiative forcing by 2.25 W m−2, with shifts of up to 10 W m−2 in areas of marine stratocumulus.The effects on radiation budget of the uncertainty in our parametrizations is also investigated. It is found that the uncertainty in the impact of horizontal inhomogeneity is of order ±60%, while the uncertainty in the impact of vertical overlap is much smaller. This suggests an insensitivity of the radiation budget to the exact nature of the global decorrelation height distribution derived in Part I. Copyright © 2010 Royal Meteorological Society

Evaluation of ground-based remotely sensed liquid water cloud properties using shortwave radiation measurements

Water cloud optical and microphysical properties are required for a better understanding of the impact of aerosols on the solar radiation feedback. The introduced retrieval technique provides droplet concentration, effective radius and optical thickness on a basis of ground-based remote sensing observations and a vertical cloud model. Mainly cloud radar, microwave radiometer and ceilometer observations are used in this approach. The model assumptions are related to a sub-adiabatic approach, in which cloud mixing processes are predefined to be homogeneous. A gamma droplet size distribution with a fixed shape parameter is considered to relate the observations with the retrieval products. The technique is applied on a water cloud case and the uncertainties in relation to the model assumptions and errors in the measurements are determined. The greatest uncertainty in the retrieval of droplet concentration is related to the fixed shape parameter, which requires in-situ data for validation processes to quantify the results. The optical parameters are less sensitive to the model assumptions and they are evaluated with a radiative closure experiment. They are used as input for radiative transfer calculations in order to compare the simulations with radiation measurements at the ground. There is a relatively good agreement between the simulated and measured radiation considering the horizontal cloud inhomogeneity, although a bias of around 5% still exists. Therefore the technique might be a suitable approach of retrieving quantitative cloud optical properties independently from radiation observations.

Tripleclouds: An Efficient Method for Representing Horizontal Cloud Inhomogeneity in 1D Radiation Schemes by Using Three Regions at Each Height

Abstract Radiation schemes in general circulation models currently make a number of simplifications when accounting for clouds, one of the most important being the removal of horizontal inhomogeneity. A new scheme is presented that attempts to account for the neglected inhomogeneity by using two regions of cloud in each vertical level of the model as opposed to one. One of these regions is used to represent the optically thinner cloud in the level, and the other represents the optically thicker cloud. So, along with the clear-sky region, the scheme has three regions in each model level and is referred to as “Tripleclouds.” In addition, the scheme has the capability to represent arbitrary vertical overlap between the three regions in pairs of adjacent levels. This scheme is implemented in the Edwards–Slingo radiation code and tested on 250 h of data from 12 different days. The data are derived from cloud retrievals using radar, lidar, and a microwave radiometer at Chilbolton, southern United Kingdom. When the data are grouped into periods equivalent in size to general circulation model grid boxes, the shortwave plane-parallel albedo bias is found to be 8%, while the corresponding bias is found to be less than 1% using Tripleclouds. Similar results are found for the longwave biases. Tripleclouds is then compared to a more conventional method of accounting for inhomogeneity that multiplies optical depths by a constant scaling factor, and Tripleclouds is seen to improve on this method both in terms of top-of-atmosphere radiative flux biases and internal heating rates.

Impact of three‐dimensional radiative effects on satellite retrievals of cloud droplet sizes

There are several dozen papers that study the effects of cloud horizontal inhomogeneity on the retrievals of cloud optical thickness, but only a few of them deal with cloud droplet sizes. This paper is one of the first comprehensive attempts to fill this gap: It takes a close theoretical look at the radiative effects of cloud 3‐D structure in retrievals of droplet effective radii. Under some general assumptions, it was found that ignoring subpixel (unresolved) variability produces a negative bias in the retrieved effective radius, while ignoring cloud inhomogeneity at scales larger than a pixel scale (resolved variability), on the contrary, leads to overestimation of the domain average droplet size. The theoretical results are illustrated with examples from Large Eddy Simulations (LES) of cumulus (Cu) and stratocumulus (Sc) cloud fields. The analysis of cloud drop size distributions retrieved from both LES fields confirms that ignoring shadowing in 1‐D retrievals results in substantial overestimation of effective radii which is more pronounced for broken Cu than for Sc clouds. Collocated measurements of broken Cu clouds by Moderate Resolution Imaging Spectrometer (MODIS) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) are used to check simulations and theory with observations. The analysis of ASTER and MODIS data and associated derived products recommends against blindly using retrieved effective radii for broken cloud fields, especially if one wants to relate aerosol amounts to cloud droplet sizes.

Radiative Effects of Cloud Horizontal Inhomogeneity and Vertical Overlap Identified from a Monthlong Cloud-Resolving Model Simulation

Abstract The representation of subgrid horizontal and vertical variability of clouds in radiation schemes remains a major challenge for general circulation models (GCMs) due to the lack of cloud-scale observations and incomplete physical understanding. The development of cloud-resolving models (CRMs) in the last decade provides a unique opportunity to make progress in this area of research. This paper extends the study of Wu and Moncrieff to quantify separately the impacts of cloud horizontal inhomogeneity (optical property) and vertical overlap (geometry) on the domain-averaged shortwave and longwave radiative fluxes at the top of the atmosphere and the surface, and the radiative heating profiles. The diagnostic radiation calculations using the monthlong CRM-simulated tropical cloud optical properties and cloud fraction show that both horizontal inhomogeneity and vertical overlap of clouds are equally important for obtaining accurate radiative fluxes and heating rates. This study illustrates an objective approach to use long-term CRM simulations to separate cloud overlap and inhomogeneity effects, based on which GCM representation (such as mosaic treatment) of subgrid cloud–radiation interactions can be evaluated and improved.

Cloud Properties Simulated by a Single-Column Model. Part I: Comparison to Cloud Radar Observations of Cirrus Clouds

Abstract This study describes and demonstrates a new method for identifying deficiencies in how cloud processes are represented in large-scale models. Kilometer-scale-resolving cloud radar observations and cloud-resolving model (CRM) simulations were used to evaluate the representation of cirrus clouds in the single-column model (SCM) version of the National Centers for Environmental Prediction Global Forecast System model for a 29-day period during June and July 1997 at the Atmospheric Radiation Measurement Program site in Oklahoma. To produce kilometer-scale cirrus statistics from the SCM results, synthetic subgrid-scale (SGS) cloud fields were generated using the SCM’s cloud fraction and hydrometeor content profiles, and the SCM’s cloud overlap and horizontal inhomogeneity assumptions. Three sets of SCM synthetic SGS cloud fields were analyzed. Two NOSNOW sets were produced in which clouds did not include snow; one set used random overlap, the other, maximum/random. In the SNOW set, clouds included snow and random overlap was used. The three sets were sampled in the same way as the cloud-radar-detected cloud fields and the CRM-simulated cloud fields. The mean cirrus cloud occurrence frequency for the SCM NOSNOW cloud fields agrees with the observed value as well as the CRM’s does, while that for SCM SNOW cloud fields is only half that observed. In most aspects, the SCM’s cirrus properties differ significantly from the cloud radar’s and the CRM’s, which generally agree. In comparison, there are too many physically thin SCM NOSNOW cirrus layers (most occupy only a single model layer) and too many physically thick SCM SNOW cirrus layers (most are thicker than 4 km). For the optically thin subset of cirrus layers, 1) the mean, mode, and median ice water path, and layer-mean ice water content (IWC) values for the SCM are significantly larger than the observed and CRM values; 2) the SCM layer-mean IWCs decrease with cloud physical thickness, opposite to the observations and CRM results; and 3) the range of layer-mean effective radii in the SCM thin cirrus is too narrow.

Beam-filling effect correction with subpixel cloud fraction using a neural network

In this study, the effects of cloud inhomogeneity on microwave rain rate retrievals are investigated. A single-channel (85 GHz) empirically based algorithm using a neural network approach is presented. The objective is to correct the beam-filling error (BFE), that might occur because of the inherent variability within coarse microwave pixels, with subpixel information. To this aim, we used the Tropical Rainfall Measuring Mission passive microwave, thermal infrared and radar data. The integration of spatial information into the retrieval algorithm enables us to partially overcome the BFE. We use two parameters which characterize the horizontal cloud inhomogeneity within the microwave radiometer field of view, and we add them to simulated brightness temperatures as inputs of the neural network algorithm. The first one is the cloud fraction derived from infrared measurement, and the second corresponds to the fraction of the rainy area derived from radar measurements. The output rain rates were validated using the Precipitation Radar data. It was found that adding cloud fraction of microwave pixels, can lead to more accurate retrievals. Instantaneous precipitation estimates demonstrated correlations of /spl sim/0.6-0.7 and /spl sim/0.7-0.8 with radar-derived rain rates, for ocean and land retrievals respectively. In spite of the problem inherent in deriving the cloud (or rain) fraction, the initial validation results presented in this study are reasonably encouraging and show the advantage of utilizing the information from different sensors in order to optimize the retrieval of rainfall.

Cirrus cloud horizontal and vertical inhomogeneity effects in a GCM

A set of the inhomogeneity factor for high-level clouds derived from the ISCCP D1 dataset averaged over a five-year period has been incorporated in the UCLA atmospheric GCM to investigate the effect of cirrus cloud inhomogeneity on climate simulation. The inclusion of this inhomogeneous factor improves the global mean planetary albedo by about 4% simulated from the model. It also produces changes in solar fluxes and OLRs associated with changes in cloud fields, revealing that the cloud inhomogeneity not only affects cloud albedo directly, but also modifies cloud and radiation fields. The corresponding difference in the geographic distribution of precipitation is as large as 7 mm day−1. Using the climatology cloud inhomogeneity factor also produces a warmer troposphere related to changes in the cloudiness and the corresponding radiative heating, which, to some extent, corrects the cold bias in the UCLA AGCM. The region around 14 km, however, is cooler associated with increase in the reflected solar flux that leads to a warmer region above. An interactive parameterization for mean effective ice crystal size based on ice water content and temperature has also been developed and incorporated in the UCLA AGCM. The inclusion of the new parameterization produces substantial differences in the zonal mean temperature and the geographic distribution of precipitation, radiative fluxes, and cloud cover with respect to the control run. The vertical distribution of ice crystal size appears to be an important factor controlling the radiative heating rate and the consequence of circulation patterns, and hence must be included in the cloud-radiation parameterization in climate models to account for realistic cloud processes in the atmosphere.

Subpixel fractional cloud cover and inhomogeneity effects on microwave beam-filling error

Using synthetic geometrical clouds and radiative microwave model, we examine the possibility to correct the estimations of liquid water path (LWP) or rain rate with cloud cover measurement. This information may be gotten by co-localized measurements of microwave and infrared/visible measurements on new satellites (TRMM, ADEOS 2, …). In a first step, the effects of fractional cloud cover on microwave brightness temperatures (TB) are investigated in three typical cases of nonprecipitating and precipitating (stratiform and convective) clouds. The beam-filling error (BFE) on brightness temperatures may be analyzed with the known spatial variability using 1D or 3D radiative transfer model. Relationships between BFE and subpixel cloud fraction (CF) are discussed according to the cloud type. We tested several parameters that characterize the horizontal cloud inhomogeneity within a radiometer field of view. BFE was found very sensitive to cloud type and inhomogeneity and is maximum for raining cloud with open spatial structure. In order to account for the uncertainty introduced by the spatial distribution, dependence of BFE on textural-based parameters is also discussed using homogeneity, entropy and an indicator of CF horizontal gradient.

Assessment of multiple scattering and horizontal inhomogeneity in IR radiative transfer calculations of observed thin cirrus clouds

Outgoing longwave (LW) radiation (OLR) and infrared heating rate errors due to the neglect of multiple scattering by clouds and horizontal cloud inhomogeneity in infrared radiative transfer parameterizations are assessed with a modified version of the Fu and Liou [1992] radiative transfer model and field measurements of optically thin cirrus that were observed during the fall 1997 and spring 2000 intensive observation periods conducted at the Atmospheric Radiation Measurement Southern Great Plains site in Oklahoma. A rigorous method is first introduced for incorporating atmospheric and cloud optical and geometric properties derived from measurements in radiative transfer calculations and for ensuring the consistency of these calculations with observations. Part of that method is the use of optical depth derived from measurements of a Multi Filter Rotating Shadowband Radiometer (MFRSR) at 415 nm to determine the cloud ice water content for the longwave and shortwave radiative transfer calculations. The optical depth is corrected to account for forward scattering by cirrus crystals, which is dominant under the thin cirrus conditions observed. Surface downwelling fluxes from the radiative transfer calculations are compared against observations from the Baseline Surface Radiation Network for consistency, with primary emphasis placed on achieving good agreement between the observed and calculated direct normal components. For all cases considered, there is very good agreement in the solar direct irradiance and reasonably good agreement in the solar diffuse irradiance, but without the forward scattering correction the agreement for both would degrade; the diffuse agreement, in particular, would do so by 12 W m−2 (>30%). Very good agreement is achieved in the LW with an absolute mean difference of 0.02 W m−2. For the observed cases considered, neglecting multiple scattering of LW by thin cirrus overestimates OLR by 6–8 W m−2 or less (depending on cloud optical depth and particle size) because of exclusion of reflection of upwelling LW at the cloud base and results in heating rate errors of as much as 0.2 K d−1. The effect of horizontally homogeneous clouds is approximated by employing the half hourly mean of the derived optical and geometric properties in the calculation. The impact of the combined effect on OLR and LW heating rates is shown from these calculations to be as high as 30 to 35 W m−2, and heating rate errors are on the order of 0.5 to over 1 K d−1, which are very significant errors.

Solar radiative transfer for stratiform clouds with horizontal variations in liquid‐water path and droplet effective radius

AbstractEffects of horizontal variations in liquid‐water path (LWP) and droplet effective radius (re) on solar radiative‐transfer properties of stratiform clouds are addressed. To this end, a broadband Monte Carlo solar radiation code and in situ aircraft observations of cloud properties collected in the Radiation, Aerosol and Cloud Experiment are used. The experiments confirm that the radiative impact of cloud horizontal inhomogeneity is dominated by variations in LWP, which make cloud short‐wave albedo smaller than that for a corresponding plane‐parallel horizontally homogeneous cloud. When horizontal variations in re are accounted for, however, the impact due to horizontal variations in LWP is usually reduced. When re and LWP are highly correlated, as is typically the case, the impact on albedo due to LWP variations alone can be reduced easily by 30% when variations in re are included. Uncertainty factors involved in the calculations are also discussed. Copyright © 2003 Royal Meteorological Society.

High-Cloud Horizontal Inhomogeneity and Solar Albedo Bias

High ice cloud horizontal inhomogeneity is examined using optical depth retrievals from four midlatitude datasets. Three datasets include ice cloud microphysical profiles derived from millimeter cloud radar at the Southern Great Plains Atmospheric Radiation Measurement site in Oklahoma. A fourth dataset combines lidar and midinfrared radiometry (LIRAD), and is from the Facility for Atmospheric Remote Sensing at the University of Utah, Salt Lake City, Utah. Plane-parallel homogeneous (PPH) calculations of domain-averaged solar albedo for these four datasets are compared to independent column approximation (ICA) results. A solar albedo bias up to 25% is found over a low reflective surface at a high solar zenith angle. A spherical solar albedo bias as high as 11% is shown. The gamma-weighted radiative transfer (GWRT) scheme is shown to be an effective correction for the solar albedo bias suitable for GCM applications. The GWRT result was, in all cases, within 1‐2 Wm 22 of the ICA outgoing solar flux. The GWRT requires a parameterization of the standard deviation of cloud optical depth. It is suggested that the domain-averaged cloud optical depth and ice water path together can be used in a parameterization to account for 80% of the standard deviation in optical depth.