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Gerald G. Mace
,
Yuying Zhang
,
Steven Platnick
,
Michael D. King
,
Patrick Minnis
, and
Ping Yang

Abstract

The Moderate Resolution Imaging Spectroradiometer (MODIS) on board the NASA Terra satellite has been collecting global data since March 2000 and the one on the Aqua satellite since June 2002. In this paper, cirrus cloud properties derived from ground-based remote sensing data are compared with similar cloud properties derived from MODIS data on Terra. To improve the space–time correlation between the satellite and ground-based observations, data from a wind profiler are used to define the cloud advective streamline along which the comparisons are made. In this paper, approximately two dozen cases of cirrus are examined and a statistical approach to the comparison that relaxes the requirement that clouds occur over the ground-based instruments during the overpass instant is explored. The statistical comparison includes 168 cloudy MODIS overpasses of the Southern Great Plains (SGP) region and approximately 300 h of ground-based cirrus observations. The physical and radiative properties of cloud layers are derived from MODIS data separately by the MODIS Atmospheres Team and the Clouds and the Earth’s Radiant Energy System (CERES) Science Team using multiwavelength reflected solar and emitted thermal radiation measurements. Using two ground-based cloud property retrieval algorithms and the two MODIS algorithms, a positive correlation in the effective particle size, the optical thickness, the ice water path, and the cloud-top pressure between the various methods is shown, although sometimes there are significant biases. Classifying the clouds by optical thickness, it is demonstrated that the regionally averaged cloud properties derived from MODIS are similar to those diagnosed from the ground. Because of a conservative approach toward identifying thin cirrus pixels over this region, the area-averaged cloud properties derived from the MODIS Atmospheres MOD06 product tend to be biased slightly toward the optically thicker pixels. This bias tendency has implications for model validation and parameterization development applied to thin cirrus retrieved over SGP-like land surfaces. A persistent bias is also found in the derived cloud tops of thin cirrus with both satellite algorithms reporting cloud top several hundred meters less than that reported by the cloud radar. Overall, however, it is concluded that the MODIS retrieval algorithms characterize with reasonable accuracy the properties of thin cirrus over this region.

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Yali Luo
,
Steven K. Krueger
,
Gerald G. Mace
, and
Kuan-Man Xu

Abstract

Cloud radar data collected at the Atmospheric Radiation Measurement (ARM) Program's Southern Great Plains site were used to evaluate the properties of cirrus clouds that occurred in a cloud-resolving model (CRM) simulation of the 29-day summer 1997 intensive observation period (IOP). The simulation was “forced” by the large-scale advective temperature and water vapor tendencies, horizontal wind velocity, and turbulent surface fluxes observed at the Southern Great Plains site. The large-scale advective condensate tendency was not observed. The correlation of CRM cirrus amount with Geostationary Operational Environmental Satellite (GOES) high cloud amount was 0.70 for the subperiods during which cirrus formation and decay occurred primarily locally, but only 0.30 for the entire IOP. This suggests that neglecting condensate advection has a detrimental impact on the ability of a model (CRM or single-column model) to properly simulate cirrus cloud occurrence.

The occurrence, vertical location, and thickness of cirrus cloud layers, as well as the bulk microphysical properties of thin cirrus cloud layers, were determined from the cloud radar measurements for June, July, and August 1997. The composite characteristics of cirrus clouds derived from this dataset are well suited for evaluating CRMs because of the close correspondence between the timescales and space scales resolved by the cloud radar measurements and by CRMs. The CRM results were sampled at eight grid columns spaced 64 km apart using the same definitions of cirrus and thin cirrus as the cloud radar dataset. The composite characteristics of cirrus clouds obtained from the CRM were then compared to those obtained from the cloud radar.

Compared with the cloud radar observations, the CRM cirrus clouds occur at lower heights and with larger physical thicknesses. The ice water paths in the CRM's thin cirrus clouds are similar to those observed. However, the corresponding cloud-layer-mean ice water contents are significantly less than observed due to the CRM's larger cloud-layer thicknesses. The strong dependence of cirrus microphysical properties on layer-mean temperature and layer thickness as revealed by the observations is reproduced by the CRM. In addition, both the CRM and the observations show that the thin cirrus ice water path during large-scale ascent is only slightly greater than during no ascent or descent.

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Seiji Kato
,
Gerald G. Mace
,
Eugene E. Clothiaux
,
James C. Liljegren
, and
Richard T. Austin

Abstract

A cloud particle size retrieval algorithm that uses radar reflectivity factor and Doppler velocity obtained by a 35-GHz Doppler radar and liquid water path estimated from microwave radiometer radiance measurements is developed to infer the size distribution of stratus cloud particles. Assuming a constant, but unknown, number concentration with height, the algorithm retrieves the number concentration and vertical profiles of liquid water content and particle effective radius. A novel aspect of the retrieval is that it depends upon an estimated particle median radius vertical profile that is derived from a statistical model that relates size to variations in particle vertical velocity; the model posits that the median particle radius is proportional to the fourth root of the particle velocity variance if the radii of particles in a parcel of zero vertical velocity is neglected. The performance of the retrieval is evaluated using data from two stratus case study days 1.5 and 8.0 h in temporal extent. Aircraft in situ microphysical measurements were available on one of the two days and the retrieved number concentrations and effective radii are consistent with them. The retrieved liquid water content and effective radius increase with height for both stratus cases, which agree with earlier studies. Error analyses suggest that the error in the liquid water content vanishes and the magnitudes of the fractional error in the effective radius and shortwave extinction coefficient computed from retrieved cloud particle size distributions are half of the magnitudes of the fractional error in the estimated cloud particle median radius if the fractional error in the median radius is constant with height.

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Min Deng
,
Gerald G. Mace
,
Zhien Wang
,
J.-L. F. Li
, and
Yali Luo

Abstract

Retrieved bulk microphysics from remote sensing observations is a composite of ice, snow, and graupel in the three-species ice-phase bulk microphysics parameterization. In this study, density thresholds are used to partition the retrieved ice particle size distribution (PSD) into small, median, and large particle size modes from millimeter cloud radar (MMCR) observations in the tropics and global CloudSat and CALIPSO ice cloud property product (2C-ICE) observations. It shows that the small mode can contribute to more than 60% of the total ice water content (IWC) above 12 km (colder than 220 K). Below that, dominant small mode transitions to dominant median mode. The large mode contributes to less than 10%–20% at all height levels. The PSD assumption in retrieval may cause about 10% error in the IWC partition ratio. The lidar-only region in 2C-ICE is dominated by the small mode, while the median mode dominates the radar-only region.

For the three-species ice-phase bulk microphysics parameterizations, the cloud ice mass mainly consists of the small mode. But snow and graupel in the models are not equivalent to the median and large modes in the observations, respectively. Therefore, they need to be repartitioned with rebuilt PSDs from the model assumptions using the same partition technique as the observations. The repartitioned IWCs in each mode from different ice species need to be added together and then compared with the corresponding mode from observations.

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Betty Carlin
,
Qiang Fu
,
Ulrike Lohmann
,
Gerald G. Mace
,
Kenneth Sassen
, and
Jennifer M. Comstock

Abstract

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 W m−2 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.

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Gerald G. Mace
,
Stephanie Houser
,
Sally Benson
,
Stephen A. Klein
, and
Qilong Min

Abstract

Given the known shortcomings in representing clouds in global climate models (GCMs), comparisons with observations are critical. The International Satellite Cloud Climatology Project (ISCCP) diagnostic products provide global descriptions of cloud-top pressure and column optical depth that extend over multiple decades. Given the characteristics of the ISCCP product, the model output must be converted into what the ISCCP algorithm would diagnose from an atmospheric column with similar physical characteristics. This study evaluates one component of this so-called ISCCP simulator by comparing ISCCP results with simulated ISCCP diagnostics that are derived from data collected at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) Climate Research Facility. It is shown that if a model were to simulate the cloud radiative profile with the same accuracy as can be derived from the ARM data, the likelihood of that occurrence being classified with similar cloud-top pressure and optical depth as ISCCP would range from 30% to 70% depending on optical depth. The ISCCP simulator improved the agreement of cloud-top pressure between ground-based remote sensors and satellite observations, and we find only minor discrepancies because of the parameterization of cloud-top pressure in the ISCCP simulator. The differences seem to be primarily due to discrepancies between satellite and ground-based sensors in the visible optical depth. The source of the optical depth bias appears to be due to subpixel cloud field variability in the retrieval of optical depths from satellite sensors. These comparisons suggest that caution should be applied to comparisons between models and ISCCP observations until the differences in visible optical depths are fully understood. The simultaneous use of ground-based and satellite retrievals in the evaluation of model clouds is encouraged.

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Richard M. Schulte
,
Christian D. Kummerow
,
Christian Klepp
, and
Gerald G. Mace

Abstract

A significant part of the uncertainty in satellite-based precipitation products stems from differing assumptions about drop size distributions (DSDs). Satellite radar-based retrieval algorithms rely on DSD assumptions that may be overly simplistic, whereas radiometers further struggle to distinguish cloud water from rain. We utilize the Ocean Rainfall and Ice-phase Precipitation Measurement Network (OceanRAIN), version 1.0, dataset to examine the impact of DSD variability on the ability of satellite measurements to accurately estimate rates of warm rainfall. We use the binned disdrometer counts and a simple model of the atmosphere to simulate observations for three satellite architectures. Two are similar to existing instrument combinations on the GPM Core Observatory and CloudSat, and the third is a theoretical triple-frequency radar–radiometer architecture. Using an optimal estimation framework, we find that the assumed DSD shape can have a large impact on retrieved rain rate. A three-parameter normalized gamma DSD model is sufficient for describing and retrieving the DSDs observed in the OceanRAIN dataset. Assuming simpler single-moment DSD models can lead to significant biases in retrieved rain rate, on the order of 100%. Differing DSD assumptions could thus plausibly explain a large portion of the disagreement in satellite-based precipitation estimates.

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Richard M. Schulte
,
Christian D. Kummerow
,
Stephen M. Saleeby
, and
Gerald G. Mace

Abstract

There are many sources of uncertainty in satellite precipitation retrievals of warm rain. In this paper, the second of a two-part study, we focus on uncertainties related to spatial heterogeneity and surface clutter. A cloud-resolving model simulation of warm, shallow clouds is used to simulate satellite observations from three theoretical satellite architectures—one similar to the Global Precipitation Measurement Core Observatory, one similar to CloudSat, and one similar to the planned Atmosphere Observing System (AOS). Rain rates are then retrieved using a common optimal estimation framework. For this case, retrieval biases due to nonuniform beamfilling are very large, with retrieved rain rates negatively (low) biased by as much as 40%–50% (depending on satellite architecture) at 5 km horizontal resolution. Surface clutter also acts to negatively bias retrieved rain rates. Combining all sources of uncertainty, the theoretical AOS satellite is found to outperform CloudSat in terms of retrieved surface rain rate, with a bias of −19% as compared with −28%, a reduced spread of retrieval errors, and an additional 17.5% of cases falling within desired uncertainty limits. The results speak to the need for additional high-resolution modeling simulations of warm rain so as to better characterize the uncertainties in satellite precipitation retrievals.

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Bryan A. Baum
,
Richard A. Frey
,
Gerald G. Mace
,
Monica K. Harkey
, and
Ping Yang

Abstract

This study reports on recent progress toward the discrimination between pixels containing multilayered clouds, specifically optically thin cirrus overlying lower-level water clouds, and those containing single-layered clouds in nighttime Moderate Resolution Imaging Spectroradiometer (MODIS) data. Cloud heights are determined from analysis of the 15-μm CO2 band data (i.e., the CO2-slicing method). Cloud phase is inferred from the MODIS operational bispectral technique using the 8.5- and 11-μm IR bands. Clear-sky pixels are identified from application of the MODIS operational cloud-clearing algorithm. The primary assumption invoked is that over a relatively small spatial area, it is likely that two cloud layers exist with some areas that overlap in height. The multilayered cloud pixels are identified through a process of elimination, where pixels from single-layered upper and lower cloud layers are eliminated from the data samples. For two case studies (22 April 2001 and 28 March 2001), ground-based lidar and radar observations are provided by the Atmospheric Radiation Measurement (ARM) Program's Southern Great Plains (SGP) Clouds and Radiation Test Bed (CART) site in Oklahoma. The surface-based cloud observations provide independent information regarding the cloud layering and cloud height statistics in the time period surrounding the MODIS overpass.

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Gerald G. Mace
,
David O'C. Starr
,
Thomas P. Ackerman
, and
Patrick Minnis

Abstract

The evolution of synoptic-scale dynamics associated with a middle and upper tropospheric cloud event that occurred on 26 November 1991 is examined. The case under consideration occurred during the FIRE Cirrus-II Intensive Field Observing Period held in Coffeyville, Kansas, during November–December 1991. Using data from the wind profiler demonstration network and a temporarlly and spatially augmented radiosonde array, emphasis is given to explaining the evolution of the kinematically derived ageostrophic vertical circulations and correlating the circulation with the forcing of an extensively sampled cloud field. This is facilitated by decomposing the horizontal divergence into its component parts through a natural coordinate representation of the flow. Ageostrophic vertical circulations are inferred and compared to the circulation forcing arising from geostrophic confluence and shearing deformation derived from the Sawyer–Eliassen equation. It is found that a thermodynamically indirect vertical circulation existed in association with a jet streak exit region. The circulation was displaced to the cyclonic side of the jet axis due to the orientation of the jet exit between a deepening diffluent trough and a building ridge. The cloud line formed in the ascending branch of the vertical circulation, with the most concentrated cloud development occurring in conjunction with the maximum large-scale vertical motion. The relationship between the large-scale dynamics and the parameterization of middle and upper tropospheric clouds in large-scale models is discussed, and an example of ice water contents derived from a parameterization forced by the diagnosed vertical motions and observed water vapor contents is presented.

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