[1] During the July 2002 Cirrus Regional Study of Tropical Anvils and Cirrus LayersFlorida Area Cirrus Experiment (CRYSTAL-FACE), flights of a WB-57F aircraft revealed mixing ratios of nitric oxide 10-50 times background over distances of 25-175 km in the anvils of thunderstorms and in clear air downwind of storm systems due to lightning activity and possible transport from the boundary layer. Estimates of the total mass of NO x injected into the middle and upper troposphere differed considerably for a moderately versus highly electrically active storm system as expected. However, assuming that the total mass is dominated by lightning production, rough estimates of the production per average lightning flash for a moderately and a highly active storm also yielded quite different ranges of (0.33-0.66) Â 10 26 and (1.7-2.3) Â 10 26 molecules NO/flash, respectively. If the common assumption is made that intracloud flashes have 1/10th the NO production efficiency of cloud-to-ground (CG) flashes, the ranges of production for the moderately and highly active storms were (0.88-1.8) Â 10 26 and (4.5-6.1) Â 10 26 molecules NO/CG flash, respectively. The observed CG flash accumulations and NO x mass production estimate for the month of July 2002 over the Florida area are compared with results from the MOZART-2 global chemistry-transport model that uses a common lightning flash parameterization. Reasonable agreement was found after a correction to the lightning parameterization was made. Finally, broad-scale median mixing ratios of NO within anvils over Florida were significantly larger than found in storms previously investigated over Colorado and New Mexico.

[1] In the near future, NASA plans to fly satellites carrying a two-wavelength polarization lidar and a 94-GHz cloud profiling radar in formation to provide complete global profiling of cloud and aerosol properties. The Cirrus Regional Study of Tropical Anvils and Cirrus Layers-Florida Area Cirrus Experiment (CRYSTAL-FACE) field campaign, conducted during July 2002, provided the first high-altitude collocated measurements from lidar and cloud profiling radar to simulate these spaceborne sensors. The lidar and radar provide complementary measurements with varying degrees of vertical measurement overlap within cloud layers. This paper presents initial results of the combined airborne lidar-radar measurements during CRSYTAL-FACE. A comparison of instrument sensitivity is presented within the context of particular CRYSTAL-FACE observations. It was determined that optically thin cirrus clouds are frequently missed by the radar but are easily profiled with the lidar. In contrast, optically thick clouds and convective cores quickly extinguish the lidar signal but are easily probed with the radar. Results are presented to quantify the portion of atmospheric features sensed independently by each instrument and the portion sensed simultaneously by the two instruments. To capture some element of varying atmospheric characteristics, two cases are analyzed, one with convective systems and one having synoptic cirrus and considerable clear air. The two cases show quite different results, primarily due to differences in cloud microphysics.

[1] In situ measurements of cirrus ice water content (IWC) by the Harvard water vapor and total water instruments during Cirrus Regional Study of Tropical Anvils and Cirrus Layers-Florida Area Cirrus Experiment are compared with remote sensing data made by the Cloud Radar System instrument in order to derive and validate an empirical IWC-radar relflectivity Z e relationship. The comparisons show that for measurements of in situ IWC and remotely measured radar reflectivity, collocated within 2 km of each other, a single IWC-Z e relationship can be found that fits the data with an uncertainty of ±20-30%. A cloud resolving model shows this level of uncertainty to be consistent with sampling errors associated with comparing two measurements that are not collocated. Uncertainties are quantified in the use of in situ data to validate the retrieval algorithms used to derive the IWC of clouds from remote sensing observations, such as radar reflectivity Z e . Uncertainties are classified into instrumental uncertainties, uncertainties related to sampling errors, and uncertainties in using a single IWC-Z e relationship to describe a cloud.

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