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David P. Duda
and
Patrick Minnis

Abstract

Two cases of aircraft dissipation trails (distrails) with associated fall streak clouds were analyzed with multispectral geostationary satellite data. One dissipation trail was observed in a single cloud layer on 23 July 2000 over southeastern Virginia and the Chesapeake Bay. Another set of trails developed at the top of multilayer cloudiness off the coasts of Georgia and South Carolina on 6 January 2000. The distrails on both days formed in optically thin, midlevel stratified clouds with cloud-top heights between 7.6 and 9.1 km. The distrail features remained intact and easily visible from satellite images over a period of 1–2 h despite winds near 50 kt at cloud level. The width of the distrails became as large as 20 km within a period of 90 min or less. Differences between the optical properties of the fall streak particles inside the distrails and those of the clouds surrounding the trails allowed for the easy identification of the fall streak clouds in either the 3.9-μm brightness temperature imagery, or the 10.7-μm minus 12.0-μm brightness temperature difference imagery. Two independent remote sensing retrievals of both distrail cases showed that the fall streaks had larger particle sizes than the clouds outside of the trails, although the three-channel infrared retrieval was better at retrieving cloud properties in the multilayer cloud case.

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David P. Duda
and
Patrick Minnis

Abstract

Straightforward application of the Schmidt–Appleman contrail formation criteria to diagnose persistent contrail occurrence from numerical weather prediction data is hindered by significant bias errors in the upper-tropospheric humidity. Logistic models of contrail occurrence have been proposed to overcome this problem, but basic questions remain about how random measurement error may affect their accuracy. A set of 5000 synthetic contrail observations is created to study the effects of random error in these probabilistic models. The simulated observations are based on distributions of temperature, humidity, and vertical velocity derived from Advanced Regional Prediction System (ARPS) weather analyses. The logistic models created from the simulated observations were evaluated using two common statistical measures of model accuracy: the percent correct (PC) and the Hanssen–Kuipers discriminant (HKD). To convert the probabilistic results of the logistic models into a dichotomous yes/no choice suitable for the statistical measures, two critical probability thresholds are considered. The HKD scores are higher (i.e., the forecasts are more skillful) when the climatological frequency of contrail occurrence is used as the critical threshold, whereas the PC scores are higher (i.e., the forecasts are more accurate) when the critical probability threshold is 0.5. For both thresholds, typical random errors in temperature, relative humidity, and vertical velocity are found to be small enough to allow for accurate logistic models of contrail occurrence. The accuracy of the models developed from synthetic data is over 85% for the prediction of both contrail occurrence and nonoccurrence, although, in practice, larger errors would be anticipated.

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David P. Duda
and
Patrick Minnis

Abstract

A probabilistic forecast to accurately predict contrail formation over the conterminous United States (CONUS) is created by using meteorological data based on hourly meteorological analyses from the Advanced Regional Prediction System (ARPS) and the Rapid Update Cycle (RUC) combined with surface and satellite observations of contrails. Two groups of logistic models were created. The first group of models (SURFACE models) is based on surface-based contrail observations supplemented with satellite observations of contrail occurrence. The most common predictors selected for the SURFACE models tend to be related to temperature, relative humidity, and wind direction when the models are generated using RUC or ARPS analyses. The second group of models (OUTBREAK models) is derived from a selected subgroup of satellite-based observations of widespread persistent contrails. The most common predictors for the OUTBREAK models tend to be wind direction, atmospheric lapse rate, temperature, relative humidity, and the product of temperature and humidity.

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Y. Takano
,
K. N. Liou
, and
P. Minnis

Abstract

Using a model that combines single-scattering properties for spheroidal and hexagonal ice crystals, the thermal infrared radiative properties of cirrus clouds have been investigated. Infrared scattering and absorption properties for randomly oriented spheroids and hexagons are parameterized based on the anomalous diffraction theory and a geometric ray-tracing method, respectively. Using observed ice crystal size distributions, upwelling radiances at the top of cirrus cloudy atmospheres have been computed. Results show that the presence of small ice crystals can produce significant brightness temperature differences between two infrared wavelengths in the 10-μm window. Theoretical results have been compared with observed brightness temperature differences between 8.35 and 11.16 μm and between 11.16 and 12 μm. The observed values were obtained from the High-Spectral Resolution Interferometer Sounder. It is shown that the use of the present nonspherical model for ice crystals in radiative transfer calculations leads to a significantly better interpretation of the observed data than does the use of the spherical model.

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B. P. Briegleb
,
P. Minnis
,
V. Ramanathan
, and
E. Harrison

Abstract

We have taken an important first step in validating climate models by comparing model and satellite inferred clear sky TOA (top-of-atmosphere) albedos. Model albodos were computed on a 1° × 1° latitude-longitude grid, allowing for variations in surface vegetation type, solar zenith angle, orography, spectral absorption/scattering at surface and within the atmosphere. Observed albedos were inferred from GOES-2 minimum narrowband (0.55–0.75 μm) brightness for November 1978 over South America and most of North America and adjacent ocean regions. Comparisons of TOA albedos over ocean agree within ±1% (the unit for albedo is in percent and the differences in percent denote absolute differences), and thus lie within both theoretical uncertainties (due to water vapor and aerosol concentrations, and ocean surface spectral reflectivity), as well as observational uncertainties. The ocean comparisons also show significant latitudinal variations in both model and observations. Albedos over land mostly agree within ±2% for the entire range of significant geographical variation of albedo from 13% over the Amazon Basin to 24% over mountains of western North America. These agreements lie within both theoretical uncertainties (due to surface type and spectral/zenith angle dependencies), as well as observational uncertainties (due to spectral and angular conversions of observed brightness to broadband albedos).

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H. Wang
,
R. T. Pinker
,
P. Minnis
, and
M. M. Khaiyer

Abstract

Solar radiation reaching the earth’s surface provides the primary forcing of the climate system, and thus, information on this parameter is needed at a global scale. Several satellite-based estimates of surface radiative fluxes are available, but they differ from each other in many aspects. The focus of this study is to highlight one aspect of such differences, namely, the way satellite-observed radiances are used to derive information on cloud optical properties and the impact this has on derived parameters such as surface radiative fluxes. Frequently, satellite visible radiance in a single channel is used to infer cloud transmission; at times, several spectral channels are utilized to derive cloud optical properties and use these to infer cloud transmission. In this study, an evaluation of these two approaches will be performed in terms of impact on the accuracy in surface radiative fluxes. The University of Maryland Satellite Radiation Budget (UMD/SRB) model is used as a tool to perform such an evaluation over the central United States. The estimated shortwave fluxes are evaluated against ground observations at the Atmospheric Radiation Measurement Program (ARM) Central Facility and at four ARM extended sites. It is shown that the largest differences between these two approaches occur during the winter season when snow is on the ground.

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David P. Duda
,
Patrick Minnis
,
Louis Nguyen
, and
Rabindra Palikonda

Abstract

Widespread persistent contrails over the western Great Lakes during 9 October 2000 were examined using commercial flight data, coincident meteorological data, and satellite remote sensing data from several platforms. The data were analyzed to determine the atmospheric conditions under which the contrails formed and to measure several physical properties of the contrails, including areal coverage, spreading rates, fall speeds, and optical properties. Most of the contrails were located between 10.6 and 11.8 km in atmospheric conditions consistent with a modified form of the Appleman contrail formation theory. However, the Rapid Update Cycle-2 analyses have a dry bias in the upper-tropospheric relative humidity with respect to ice (RHI), as indicated by persistent contrail generation during the outbreak where RHI ≥ 85%. The model analyses show that synoptic-scale vertical velocities affect the formation of persistent contrails. Areal coverage by linear contrails peaked at 30 000 km2, but the maximum contrail-generated cirrus coverage was over twice as large. Contrail spreading rates averaged around 2.7 km h−1, and the contrails were visible in the 4-km Geostationary Operational Environmental Satellite (GOES) imagery approximately 1 h after formation. Contrail fall speed estimates were between 0.00 and 0.045 m s−1 based on observed contrail advection rates. Optical depth measurements ranged from 0.1 to 0.6, with consistent differences between remote sensing methods. Contrail formation density was roughly correlated with air traffic density after the effects of competing cloud coverage, humidity, and vertical velocity were considered. Improved tropospheric humidity measurements are needed for realistic simulations of contrail and cirrus development.

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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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Patrick Minnis
,
Donald P. Garber
,
David F. Young
,
Robert F. Arduini
, and
Yoshihide Takano

Abstract

The interpretation of satellite-observed radiances to derive cloud optical depth and effective particle size requires radiative transfer calculations relating these parameters to the reflectance, transmittance, and emittance of the cloud. Such computations can be extremely time consuming when used in an operational mode to analyze routine satellite data. Adding–doubling (AD) radiative transfer models are used here to compute reflectance and effective emittance at wavelengths commonly used by operational meteorological satellite imagers for droplet effective radii ranging from 2 to 32 μm and for distributions of randomly oriented hexagonal ice crystals with effective diameters varying from 6 to 135 μm. Cloud reflectance lookup tables were generated at the typical visible-channel wavelength of 0.65 μm and the solar–infrared (SI) at wavelengths of 3.75 and 3.90 μm. A combination of four-point Lagrangian and linear interpolation between the model nodal points is the most accurate and economical method for estimating reflectance as a function of particle size for any set of solar zenith, viewing zenith, and relative azimuth angles. Compared to exact AD calculations, the four-point method retrieves the reflectance to within ±3%–9% for water droplets and ice crystals, respectively. Most of the error is confined to scattering angles near distinct features in the phase functions. The errors are reduced to ∼±2% for ice when the assessment is constrained to only those angles that are actually useful in satellite retrievals. Effective emittance, which includes absorption and scattering effects, was computed at SI, infrared (IR; 10.7 and 10.8 μm), and split-window (WS; 11.9 and 12.0 μm) wavelengths for a wide range of surface and cloud temperatures using the same ice crystal and water droplet distributions. The results were parameterized with a 32-term polynomial model that depends on the clear-cloud radiating temperature difference, the clear-sky temperature, and viewing zenith angle. A four-point Lagrangian method is used to interpolate between optical depth nodes. The model reproduces the adding–doubling results with an overall accuracy better than ±2%, 0.4%, and 0.3%, respectively, for the SI, IR, and WS emittances, a substantial reduction in the error compared to earlier parameterizations. Temperatures simulated with the emittance models are within 0.6 and 1 K for water droplets and ice crystals, respectively, in the SI channels. The IR temperatures are accurate to better than ±0.05 K. During the daytime, the simulations of combined reflectance and emittance for the SI channels are as accurate as the emittance models alone except at particular scattering angles. The magnitudes of the errors depend on the angle, particle size, and solar zenith angle. Examples are given showing the parameterizations applied to satellite data. Computational time exceeds that of previous models but the accuracy gain should yield emittances that are more reliable for retrieval of global cloud microphysical properties.

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G. L. Potter
,
R. D. Cess
,
P. Minnis
,
E. F. Harrison
, and
V. Ramanathan

Abstract

This study addresses two aspects of the planetary albedo's diurnal cycle, the first of which refers to directional models of the planetary albodo. It is found that even for clear regions there appear to be deficiencies in our knowledge of how to model this quantity. Over land surfaces, for example, Nimbus-7 data for the directional planetary albedo compare best with model calculations for which a Lambertian surface is assumed, despite ample evidence that the albedo of land surfaces is dependent upon solar zenith angle. Similarly, over ocean surfaces both GOES and Nimbus-7 data produce a weaker dependence of the planetary albedo upon solar zenith angle than would be suggested by model calculations.

The second aspect of the study concerns a comparison of the diurnal amplitude factor, defined as the ratio of the diurnally averaged planetary albedo to that at noon, between two general circulation models (GCMs) and measurements made from a geostationary satellite (GOES). While these comparisons indicate reasonable consistency between the GCMs and the satellite measurements, this is due in part to compensating differences, such as an underestimate in cloud amount by a GCM being compensated for by a corresponding underestimate of the diurnal amplitude factor for overcast regions. The comparisons further underscore difficulties associated with converting local-time albedo measurements, as made from sun-synchronous satellites, to diurnally averaged albedos.

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