13.2 Geographic and Seasonal Variability of Accuracy in Water Vapor Measurements from WVSS-II

Thursday, 26 January 2017: 1:45 PM
Conference Center: Skagit 2 (Washington State Convention Center )
Skylar S. Williams, University of Wisconsin, Madison, WI; and T. J. Wagner, R. A. Petersen, S. A. Ackerman, and W. F. Feltz
Manuscript (2.6 MB)

While National Weather Service (NWS) radiosondes remain a significant part of the upper-level observing network, the limited spatial and temporal coverage of these observations create large gaps in our characterization of the atmosphere.  One way to increase the number of observations and fill in data gaps is to use data collected by commercial aircraft during routine flights.  These data have been collected in real-time by the Aircraft Meteorological Data Reports (AMDAR) system and traditionally have contained observations of temperature, wind, and pressure.

In the past, water vapor observations have not been collected since separate instrumentation was required and these observations were not required in-flight for aircraft observations.  The development and deployment of the Water Vapor Sensing System II (WVSS-II) on select commercial aircraft have allowed water vapor measurements to be added to this data set.  This sensor was designed to be lightweight, require little maintenance, and be highly reliable. WVSS-II allows vertical profiles of moisture to be collected during takeoff and landing. The additional water vapor data allow for complete thermodynamic and kinematic profiles of the atmosphere to be observed at higher frequencies than by radiosondes alone.

Radiosonde-based validation of the WVSS-II has been limited to short-term field studies in climatologically similar regions, which limits understanding of its performance in different environments.  In the present study, the WVSS-II is compared to operational NWS radiosondes throughout the continental United States, enabling sensor validation in all seasons and multiple climate regimes.  Locational and seasonal biases are explored, and the performance of the sensor in both high and low water vapor environments is determined.  Characterizing potential biases in the WVSS-II dataset will improve data assimilation processes of this data into numerical weather prediction models and create confidence for both governmental and aviation forecasters regardless of location or time of year.

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