Improvement to Mesoscale Analyses and Forecasts by Assimilating Dense Pressure Observations

Surface pressure observations have been shown to provide valuable information to synoptic-scale forecasts and analyses, but their ability to describe mesoscale phenomena, many of which exhibit distinct pressure signatures, remains underexplored.  To capture these features, a very dense network of observations was sought by exploring novel, yet extant observation platforms.  Pressure observations from citizen observers and backyard meteorologists are readily obtainable and were found to increase the density of observations by an order of magnitude over the ASOS network in the Pacific Northwest.  Quality control and bias correction methods for these pressure observations, including the option of using pressure tendency observations, were developed.  A month-long series of experiments using the University of Washington Real-Time Ensemble Kalman Filter system examined the impact of assimilating these additional pressure observations on mesoscale analyses and short-term forecasts. The assimilation of these additional pressure observations made localized adjustments to the surface pressure, wind and temperature fields surrounding various mesoscale phenomena. Short-term forecasts following analyses produced using the dense pressure observations had statistically significant reductions in errors throughout the lower troposphere.  In addition, assimilating these observations also yielded improved forecasts of frontal passage timing and convective development.