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For How Long Should What Data Be Assimilated for the Mesoscale Forecasting of Convection and Why? Part II: On the Observation Signal from Different Sensors

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  • 1 McGill University, Montreal, Canada
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Abstract

The ability of data assimilation to correct for initial conditions depends on the presence of a usable signal in the variables observed as well as on the capability of instruments to detect that signal. In , the nature, properties, and limits in the usability of signals in model variables were investigated. Here, the focus is on studying the skill of measurements to pull out a useful signal for data assimilation systems to use. Using model runs of the evolution of convective storms in the Great Plains over an active 6-day period, simulated measurements from a variety of instruments are evaluated in terms of their ability to detect various initial condition errors and to provide a signal above and beyond measurement errors. The usability of the signal for data assimilation is also investigated. Imaging remote sensing systems targeting cloud and precipitation properties such as radars and thermal IR imagers provided both the strongest signals and the hardest ones to assimilate to recover fields other than clouds and precipitation because of the nonlinear behavior of the sensors combined with the limited predictability of the signal observed. The performance of other sensors was also evaluated, leading to several unexpected results. If used with caution, these findings can help determine assimilation priorities for improving mesoscale forecasting.

Corresponding author address: Frédéric Fabry, Department of Atmospheric and Oceanic Sciences, McGill University, 805 Sherbrooke St. West, Montreal, QC H3A 2K6, Canada. Email: frederic.fabry@mcgill.ca

Abstract

The ability of data assimilation to correct for initial conditions depends on the presence of a usable signal in the variables observed as well as on the capability of instruments to detect that signal. In , the nature, properties, and limits in the usability of signals in model variables were investigated. Here, the focus is on studying the skill of measurements to pull out a useful signal for data assimilation systems to use. Using model runs of the evolution of convective storms in the Great Plains over an active 6-day period, simulated measurements from a variety of instruments are evaluated in terms of their ability to detect various initial condition errors and to provide a signal above and beyond measurement errors. The usability of the signal for data assimilation is also investigated. Imaging remote sensing systems targeting cloud and precipitation properties such as radars and thermal IR imagers provided both the strongest signals and the hardest ones to assimilate to recover fields other than clouds and precipitation because of the nonlinear behavior of the sensors combined with the limited predictability of the signal observed. The performance of other sensors was also evaluated, leading to several unexpected results. If used with caution, these findings can help determine assimilation priorities for improving mesoscale forecasting.

Corresponding author address: Frédéric Fabry, Department of Atmospheric and Oceanic Sciences, McGill University, 805 Sherbrooke St. West, Montreal, QC H3A 2K6, Canada. Email: frederic.fabry@mcgill.ca

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