84th AMS Annual Meeting

Thursday, 15 January 2004
Multiscale Analyses of Moisture Transport by the Central Plains Low-Level Jet during IHOP
Room 4AB
Edward I. Tollerud, NOAA/ERL/FSL, Boulder, CO; and B. D. Jamison, F. Caracena, S. E. Koch, D. L. Bartels, R. M. Hardesty, B. J. McCarty, C. Kiemle, and G. Ehret
Poster PDF (206.3 kB)
During the International H2O (IHOP) Project in the Southern U.S. Central Plains in May and June 2001, aircraft missions on June 3 and June 9 made detailed lidar and dropsonde observations of intense phases of the Low-Level Jet (LLJ) . Combined with standard and enhanced operational observations (primarily radiosondes and profilers) and other ground-based research observations, data from these missions represent an excellent opportunity to describe moisture transport in the LLJ at scales ranging from the synoptic scales resolved by the radiosonde network to sub-mesoscale features in the moisture and wind fields observed by airborne lidar instruments.

Two questions immediately present themselves: (1) Do focused observations at exceptionally high resolution provide details critical to our physical understanding of the LLJ; and (2) would inclusion of these details in model initialization fields significantly alter model-generated forecasts of LLJ evolution, transport, and subsequent precipitation generation? A practical way of stating (1) is, do small-scale correlations between moisture and wind fluctuations within the LLJ significantly alter larger-scale estimates of LLJ moisture transport? To illustrate this possibility, we present vertical sections across the LLJ of wind, moisture, and resulting moisture transport from multiple observation sets including radiosonde only, dropsondes, and simultaneous lidar measurements of moisture and wind. Preliminary results reveal that radiosonde-only analyses underestimate maximum LLJ magnitudes present in dropsonde sections by about 20%. Cumulative transport through the sections will also be presented to estimate possible scale effects. We directly address (2) by comparing numerical predictions from a control run of the Weather Research and Forecasting Model (WRF) made with operational datasets with an otherwise parallel run that has the advantage of input from research dropsonde observations and other research data. Lidar measurements are withheld from these runs in order to serve as independent verification for the simulations.

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