The effects of small ice crystals on the infrared radiative properties of cirrus clouds

To be successful in the development of satellite retrieval methodologies for the determination of cirrus cloud properties, we must have fundamental scattering and absorption data on nonspherical ice crystals that are found in cirrus clouds. Recent aircraft observations (Platt et al. 1989) reveal that there is a large amount of small ice particles, on the order of 10 micron, in cirrus clouds. Thus it is important to explore the potential differences in the scattering and absorption properties of ice crystals with respect to their sizes and shapes. In this study the effects of nonspherical small ice crystals on the infrared radiative properties of cirrus clouds are investigated using light scattering properties of spheroidal particles. In Section 2, using the anomalous diffraction theory for spheres and results from the exact spheroid scattering program, efficient parameterization equations are developed for calculations of the scattering and absorption properties for small ice crystals. Parameterization formulas are also developed for large ice crystals using results computed from the geometric ray-tracing technique and the Fraunhofer diffraction theory for spheroids and hexagonal crystals. This is presented in Section 3. Finally, applications to the satellite remote sensing are described in Section 4

Basic Diagnosis and Prediction of Persistent Contrail Occurrence using High-resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part I: Effects of Random Error

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 when the climatological frequency of contrail occurrence is used as the critical threshold, while the PC scores are higher 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 percent for both the prediction of contrail occurrence and non-occurrence, although in practice, larger errors would be anticipated

Basic Diagnosis and Prediction of Persistent Contrail Occurrence using High-resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part II: Evaluation of Sample Models

Previous studies have shown that probabilistic forecasting may be a useful method for predicting persistent contrail formation. A probabilistic forecast to accurately predict contrail formation over the contiguous United States (CONUS) is created by using meteorological data based on hourly meteorological analyses from the Advanced Regional Prediction System (ARPS) and from the Rapid Update Cycle (RUC) as well as GOES water vapor channel measurements, 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 second group of models (OUTBREAK models) is derived from a selected subgroup of satellite-based observations of widespread persistent contrails. The mean accuracies for both the SURFACE and OUTBREAK models typically exceeded 75 percent when based on the RUC or ARPS analysis data, but decreased when the logistic models were derived from ARPS forecast data

The impact of horizontal heterogeneities, cloud fraction, and liquid water path on warm cloud effective radii from CERES-like Aqua MODIS retrievals

The impact of horizontal heterogeneities, liquid water path (LWP from AMSR-E), and cloud fraction (CF) on MODIS cloud effective radius (<i>r</i>_e), retrieved from the 2.1 μm (<i>r</i>_e2.1) and 3.8 μm (<i>r</i>_e3.8) channels, is investigated for warm clouds over the southeast Pacific. Values of <i>r</i>_e retrieved using the CERES algorithms are averaged at the CERES footprint resolution (&sim;20 km), while heterogeneities (<i>H</i>_&sigma;) are calculated as the ratio between the standard deviation and mean 0.64 μm reflectance. The value of <i>r</i>_e2.1 strongly depends on CF, with magnitudes up to 5 μm larger than those for overcast scenes, whereas <i>r</i>_e3.8 remains insensitive to CF. For cloudy scenes, both <i>r</i>_e2.1 and <i>r</i>_e3.8 increase with <i>H</i>_&sigma; for any given AMSR-E LWP, but <i>r</i>_e2.1 changes more than for <i>r</i>_e3.8. Additionally, <i>r</i>_e3.8–<i>r</i>_e2.1 differences are positive (<1 μm) for homogeneous scenes (<i>H</i>_&sigma; < 0.2) and LWP > 45 gm⁻², and negative (up to −4 μm) for larger <i>H</i>_&sigma;. While <i>r</i>_e3.8–<i>r</i>_e2.1 differences in homogeneous scenes are qualitatively consistent with in situ microphysical observations over the region of study, negative differences – particularly evinced in mean regional maps – are more likely to reflect the dominant bias associated with cloud heterogeneities rather than information about the cloud vertical structure. The consequences for MODIS LWP are also discussed

Factors controlling contrail cirrus optical depth

Aircraft contrails develop into contrail cirrus by depositional growth and sedimentation of ice particles and horizontal spreading due to wind shear. Factors controlling this development include temperature, ice supersaturation, thickness of ice-supersaturated layers, and vertical gradients in the horizontal wind field. An analytical microphysical cloud model is presented and validated that captures these processes. Many individual contrail cirrus are simulated that develop differently owing to the variability in the controlling factors, resulting in large samples of cloud properties that are statistically analyzed. Contrail cirrus development is studied over the first four hours past formation, similar to the ages of line-shaped contrails that were tracked in satellite imagery on regional scales. On these time scales, contrail cirrus optical depth and microphysical variables exhibit a marked variability, expressed in terms of broad and skewed probability distribution functions. Simulated mean optical depths at a wavelength of 0.55 &lt;i&gt;&amp;mu;&lt;/i&gt;m range from 0.05-0.5 and a substantial fraction 20-50% of contrail cirrus stay subvisible (optical depth &lt;0.02), depending on meteorological conditions. &lt;br&gt;&lt;br&gt; A detailed analysis based on an observational case study over the continental USA suggests that previous satellite measurements of line-shaped persistent contrails have missed about 89%, 50%, and 11% of contrails with optical depths 0-0.05, 0.05-0.1, and 0.1-0.2, respectively, amounting to 65% of contrail coverage of all optical depths. When comparing observations with simulations and when estimating the contrail cirrus climate impact, not only mean values but also the variability in optical depth and microphysical properties need to be considered

A 10-Year Climatology of Cloud Cover and Vertical Distribution Derived from Both Surface and GOES Observations Over the DOE ARM SGP Site

Analysis of a decade of ARM radar-lidar and GOES observations at the SGP site reveal that 0.5 and 4-hr averages of the surface cloud fraction correspond closely to 0.5deg and 2.5deg averages of GOES cloudiness, respectively. The long-term averaged surface and GOES cloud fractions agree to within 0.5%. Cloud frequency increases and cloud amount decreases as the temporal and spatial averaging scales increase. Clouds occurred most often during winter and spring. Single-layered clouds account for 61.5% of the total cloud frequency. There are distinct bimodal vertical distributions of clouds with a lower peak around 1 km and an upper one that varies from 7.5 to 10.8 km between winter and summer, respectively. The frequency of occurrence for nighttime GOES high-cloud tops agree well with the surface observations, but are underestimated during the day

Surface-Based Observations of Contrail Occurrence Over the US, Apr. 1993 to Apr. 1994

Surface observers stationed at 19 U.S. Air Force Bases and Army Air Stations recorded the daytime occurrence of contrails and cloud fraction on an hourly basis for the period April 1993 through April 1994. Each observation uses one of four main categories to report contrails as unobserved, non-persistent, persistent, and indeterminate. Additional classification includes the co-occurrence of cirrus with each report. The data cover much of the continental U.S. including locations near major commercial air routes. The mean annual frequency of occurrence in unobstructed viewing conditions is 13 percent for these sites. Contrail occurrence varied substantially with location and season. Most contrails occurred during the winter months and least during the summer with a pronounced minimum during July. Although nocturnal observations are not available, it appears that the contrails have a diurnal variation that peaks during mid morning over most areas. Contrails were most often observed in areas near major commercial air corridors and least often over areas far removed from the heaviest air traffic. A significant correlation exists between mean contrail frequency and aircraft fuel usage above 7 km suggesting predictive potential for assessing future contrail effects on climate

Nitric Acid Particles in Cold Thick Ice Clouds Observed at Global Scale: Link with Lightning, Temperature, and Upper Tropospheric Water Vapor

Signatures of nitric acid particles (NAP) in cold thick ice clouds have been derived from satellite observations. Most NAP are detected in the Tropics (9 to 20% of clouds with T less than 202.5 K). Higher occurrences were found in the rare mid-latitudes very cold clouds. NAP occurrence increases as cloud temperature decreases and NAP are more numerous in January than July. Comparisons of NAP and lightning distributions show that lightning is the main source of the NOx, which forms NAP in cold clouds. Qualitative comparisons of NAP with upper tropospheric humidity distributions suggest that NAP play a role in the dehydration of the upper troposphere when the tropopause is colder than 195K