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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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Stanley G. Benjamin
,
Eric P. James
,
Ming Hu
,
Curtis R. Alexander
,
Therese T. Ladwig
,
John M. Brown
,
Stephen S. Weygandt
,
David D. Turner
,
Patrick Minnis
,
William L. Smith Jr.
, and
Andrew K. Heidinger

Abstract

Accurate cloud and precipitation forecasts are a fundamental component of short-range data assimilation/model prediction systems such as the NOAA 3-km High-Resolution Rapid Refresh (HRRR) or the 13-km Rapid Refresh (RAP). To reduce cloud and precipitation spinup problems, a nonvariational assimilation technique for stratiform clouds was developed within the Gridpoint Statistical Interpolation (GSI) data assimilation system. One goal of this technique is retention of observed stratiform cloudy and clear 3D volumes into the subsequent model forecast. The cloud observations used include cloud-top data from satellite brightness temperatures, surface-based ceilometer data, and surface visibility. Quality control, expansion into spatial information content, and forward operators are described for each observation type. The projection of data from these observation types into an observation-based cloud-information 3D gridded field is accomplished via identification of cloudy, clear, and cloud-unknown 3D volumes. Updating of forecast background fields is accomplished through clearing and building of cloud water and cloud ice with associated modifications to water vapor and temperature. Impact of the cloud assimilation on short-range forecasts is assessed with a set of retrospective experiments in warm and cold seasons using the RAPv5 model. Short-range (1–9 h) forecast skill is improved in both seasons for cloud ceiling and visibility and for 2-m temperature in daytime and with mixed results for other measures. Two modifications were introduced and tested with success: use of prognostic subgrid-scale cloud fraction to condition cloud building (in response to a high bias) and removal of a WRF-based rebalancing.

Open access