Assessing the Impact of Mobile, Multi-Source, Observations on Forecast Accuracy
NOAA UAS Program SBIR Partner Briefs Testing Results at the American Meteorological Society (AMS) Annual Meeting
Data from mobile, particularly airborne, sensors have been shown to be extremely impactful relative to increasing the fidelity of NWP model output (Barwell et al. 1985, Kruus 1986, Baeda et al. 1987, Benjamin et al. 1991, Smith and Benjamin 1994, Graham et al. 2000, Schwartz et al. 2000, Zapotocny et al. 2000, Zhang et al. 2016). However, most aircraft sensing platforms do not have a mechanism for recording moisture; sensing platforms that do have a means of measuring moisture often are not assimilated into numerical weather prediction models (Moniger et al. 2003, Hoover et al. 2017). An experimental study was designed to investigate the impact of assimilating in-situ measures of pressure, temperature and humidity into the Weather Research and Forecast (WRF) model using data collected during the PEMDAS Air-Land-Sea (ALS) field campaign. The PEMDAS designed Airborne Sensing and Prediction System (ASAPS) provided high-frequency (1Hz), in-situ, data-linked measures of pressure, temperature, and humidity across each of the three sources. Observations were assimilated into a regional NWP model with performance assessed relative to representative control runs using NCAR’s Model Evaluation Tools verification package. Notable improvements for many parameters, including temperature and composite reflectivity are attributed to the inclusion of these high-frequency in-situ observations.
• Mobile observations of temperature, moisture, and pressure were collected at 1Hz sampling rate at various locations across New York and assimilated into regional model
• Modeled surface temperature significantly improved (when compared to NLDAS analysis) with the Post-Assimilation run compared to the Control run
• Post-Assimilation modeled composite reflectivity showed significant reduction in convective activity to the west and north of Lake Ontario, more closely resembling
• Root Mean Square Error (RMSE) reductions realized throughout atmospheric column for temperature, U-wind component, relative humidity, and geopotential height (V-wind component relatively unchanged)
• ASAPS observations were shown to provide improvement to regional model performance
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