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Observations of upper tropospheric relative humidity obtained from Raman lidar and Cross-chain Loran Atmospheric Sounding System (CLASS) sonde instruments obtained during the First ISCCP Regional Experiment (FIRE) Cirrus-II field program are compared with satellite measurements from the GOES 6.7-micron channel. The 6.7-micron channel is sensitive to water vapor integrated over a broad layer in the upper troposphere (roughly 500-200 mbar). Instantaneous measurements of the upper tropospheric relative humidity from GOES are shown to agree to within roughly 6% of the nearest lidar observations and 9% of the nearest CLASS observations. The CLASS data exhibit a slight yet systematic dry bias in upper tropospheric humidity, a result which is consistent with previous radiosonde intercomparisons. Temporal stratification of the CLASS data indicates that the magnitude of the bias is dependent upon the time of day, suggesting a solar heating effect in the radiosonde sensor. Using CLASS profiles, the impact of vertical variability in relative humidity upon the GOES upper tropospheric humidity measurements is also examined. The upper tropospheric humidity inferred from the GOES 6.7-micron channel is demonstrated to agree to within roughly 5% of the relative humidity vertically averaged over the depth of atmosphere to which the 6.7-micron channel is sensitive. The results of this study encourage the use of satellite measurements in the 6.7-micron channel to quantitatively describe the distribution and temporal evolution of the upper tropospheric humidity field.

The conventional calculation of heat fluxes from a vegetated surface involving the coefficient of turbulent heat transfer which increases logarithmically with surface roughness, is inappropriate such highly structured surfaces as desert scrub or open forest. An approach is developed here for computing sensible heat flux from sparsely vegetated surfaces, where the absorption of insolation and the transfer of absorbed heat to the atmosphere are calculated separately for the plants and for the soil. This approach is applied to a desert-scrub surface in the northern Sinai, for which the turbulent transfer coefficient of sensible heat flux from the plants is much larger than that from the soil below, as shown by an analysis of plant, soil, and air temperatures. The plant density is expressed as the sum of products (plant-height) x (plant-diameter) of plants per unit horizontal surface area. The solar heat absorbed by the plants is assumed to be transferred immediately to the airflow. The effective turbulent transfer coefficient k(g-eff) for sensible heat from the desert-scrub/soil surface computed under this assumption increases sharply with increasing solar zenith angle, as the plants absorb a greater fraction of the incoming irradiation. The surface absorptivity (the coalbedo) also increases sharply with increasing solar zenith angle, and thus the sensible heat flux from such complex surfaces is a much broader function of time of day than when computed under constant k(g-eff) and constant albedo assumptions.

A time-dependent, two-dimensional Eulerian model is presented whose purpose is to obtain more realistic parameterizations of extended high level cloudiness, and the results of a numerical experiment using the model are reported. The model is anelastic and the Bousinesque assumption is invoked. Unresolved subgrid scale processes are parameterized as eddy diffusion processes. Two phases of water are incorporated and equilibrium between them is assumed. The effects of infrared radiative processes are parametrically represented. Two simulations were conducted with identical initial conditions; in one of them, the radiation term was never turned on. The mean values of perturbation potential temperature at each level in the domain are plotted versus height after 15, 30, and 60 minutes of simulated time. The influence of the radiative term is seen to impose a cooling trend, leading to an increased generation of ice water and an increased generation of turbulent kinetic energy in the cloud layer.