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  • Author or Editor: P. Minnis x
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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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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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D. F. Young
,
P. Minnis
,
D. R. Doelling
,
G. G. Gibson
, and
T. Wong

Abstract

The Clouds and the Earth’s Radiant Energy System (CERES) is a NASA multisatellite measurement program for monitoring the radiation environment of the earth–atmosphere system. The CERES instrument was flown on the Tropical Rainfall Measuring Mission satellite in late 1997, and will be flown on the Earth Observing System morning satellite in 1998 and afternoon satellite in 2000. To minimize temporal sampling errors associated with satellite measurements, two methods have been developed for temporally interpolating the CERES earth radiation budget measurements to compute averages of top-of-the-atmosphere shortwave and longwave flux. The first method is based on techniques developed from the Earth Radiation Budget Experiment (ERBE) and provides radiation data that are consistent with the ERBE processing. The second method is a newly developed technique for use in the CERES data processing. This technique incorporates high temporal resolution data from geostationary satellites to improve modeling of diurnal variations of radiation due to changing cloud conditions during the day. The performance of these two temporal interpolation methods is evaluated using a simulated dataset. Simulation studies show that the introduction of geostationary data into the temporal interpolation process significantly improves the accuracy of hourly and daily radiative products. Interpolation errors for instantaneous flux estimates are reduced by up to 68% for longwave flux and 80% for shortwave flux.

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W. B. Rossow
,
F. Mosher
,
E. Kinsella
,
A. Arking
,
M. Desbois
,
E. Harrison
,
P. Minnis
,
E. Ruprecht
,
G. Seze
,
C. Simmer
, and
E. Smith

Abstract

The International Satellite Cloud Climatology Project (ISCCP) will provide a uniform global climatology of satellite-measured radiances and derive an experimental climatology of cloud radiative properties from these radiances. A pilot study to intercompare cloud analysis algorithms was initiated in 1981 to define a state-of-the-art algorithm for ISCCP. This study compared the results of applying six different algorithms to the same satellite radiance data. The results show that the performance of all current algorithms depends on how accurately the clear sky radiances are specified; much improvement in results is possible with better methods for obtaining these clear-sky radiances. A major difference between the algorithms is caused by their sensitivity to changes in the cloud size distribution and optical properties: all methods, which work well for some cloud types or climate regions, do poorly for other situations. Therefore, the ISCCP algorithm is composed of a series of steps, each of which is designed to detect some of the clouds present in the scene. This progressive analysis is used to retrieve an estimate of the clear sky radiances corresponding to each satellite image. Application of a bispectral threshold is then used as the last step to determine the cloud fraction. Cloudy radiances are interpreted in terms of a simplified model of cloud radiative effects to provide some measure of cloud radiative properties. Application of this experimental algorithm to produce a cloud climatology and field observation programs to validate the results will stimulate further research on cloud analysis techniques as part of ISCCP.

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M. Chiriaco
,
H. Chepfer
,
P. Minnis
,
M. Haeffelin
,
S. Platnick
,
D. Baumgardner
,
P. Dubuisson
,
M. McGill
,
V. Noël
,
J. Pelon
,
D. Spangenberg
,
S. Sun-Mack
, and
G. Wind

Abstract

This study compares cirrus-cloud properties and, in particular, particle effective radius retrieved by a Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO)-like method with two similar methods using Moderate-Resolution Imaging Spectroradiometer (MODIS), MODIS Airborne Simulator (MAS), and Geostationary Operational Environmental Satellite imagery. The CALIPSO-like method uses lidar measurements coupled with the split-window technique that uses the infrared spectral information contained at the 8.65-, 11.15-, and 12.05-μm bands to infer the microphysical properties of cirrus clouds. The two other methods, using passive remote sensing at visible and infrared wavelengths, are the operational MODIS cloud products (using 20 spectral bands from visible to infrared, referred to by its archival product identifier MOD06 for MODIS Terra) and MODIS retrievals performed by the Clouds and the Earth’s Radiant Energy System (CERES) team at Langley Research Center (LaRC) in support of CERES algorithms (using 0.65-, 3.75-, 10.8-, and 12.05-μm bands); the two algorithms will be referred to as the MOD06 and LaRC methods, respectively. The three techniques are compared at two different latitudes. The midlatitude ice-clouds study uses 16 days of observations at the Palaiseau ground-based site in France [Site Instrumental de Recherche par Télédétection Atmosphérique (SIRTA)], including a ground-based 532-nm lidar and the MODIS overpasses on the Terra platform. The tropical ice-clouds study uses 14 different flight legs of observations collected in Florida during the intensive field experiment known as the Cirrus Regional Study of Tropical Anvils and Cirrus Layers–Florida Area Cirrus Experiment (CRYSTAL-FACE), including the airborne cloud-physics lidar and the MAS. The comparison of the three methods gives consistent results for the particle effective radius and the optical thickness but discrepancies in cloud detection and altitudes. The study confirms the value of an active remote sensing method (CALIPSO like) for the study of subvisible ice clouds, in both the midlatitudes and Tropics. Nevertheless, this method is not reliable in optically very thick tropical ice clouds, because of their particular microphysical properties.

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