Assessment of Micro Rain Radar (MRR) observing network data and sensor density
Brian J. Etherton, University of North Carolina, Chapel Hill, NC; and J. L. Losego
The state of North Carolina is one of a small number of states that has had over 21 billion-dollar climate and weather related disaster events since 1980. Due to the high incidence of costly disasters, the Renaissance Computing Institute (RENCI) is working with other groups across the state to use technology to solve disaster and emergency management related problems. Once such problem is winter weather events, specifically ice storms, which can severely impact the state. A lack of upper air data and surface observations make short-term forecasting of these events difficult. To explore this problem, RENCI acquired two Micro Rain Radars for testing in North Carolina.
The Micro Rain Radar (MRR) is a compact vertically pointed radar that is designed to measure the vertical profiles of drop size distributions. From the drop size distributions, several parameters can be derived, including the characteristic fall velocity, the liquid water content, the rain rate, and reflectivity. The MRR detects very small amounts of precipitation that are below the threshold of conventional rain gauges and detects the bright band, or height of the freezing level above ground, during winter storms.
During the winters of 2007-2009, data was collected and used in real-time for short-term winter weather forecasting by the local National Weather Service office. The data has proven to be valuable, but since a MRR only provides data for one point in space many more radars are needed to gain full utility of the data. By creating a network of radars, a three-dimensional view of the bright band structure across a region would be available during times of precipitation.
To demonstrate what data a network of MRRs would provide, we have conducted an Observing System Simulation Experiments (OSSE), for the 2-day period beginning at 0000 UTC 27 January 2009 and ending at 0000 UTC 29 January 2009, for Kentucky. An ice storm on these days caused Kentucky's largest power outage on record, with 609,000 homes and businesses without power across the state. Property damage was widespread, with the damage due to falling trees, large tree limbs and power lines weighed down by ice.
For our OSSE, the Weather Research and Forecasting (WRF) model is used for our nature run and forecast run, the nature run being the inner nest of a one-way nested run. The nature run (inner nest) is at 2km resolution; the outer nest (forecast) is at 6km resolution. Results from our OSSE demonstrate what type of data a network of MRRs in North Carolina would provide to forecasters if a similar storm affected the state. Results also provide guidance as to the optimal density of MRRs needed to sufficiently observe changes in the freezing level throughout the storm.
Extended Abstract (1020K)
Supplementary URL: http://mmrr.renci.org/
Session 9B, Experiments involving observations, real or hypothetical: data impact tests and observing system simulation experiments (OSSEs) III
Wednesday, 20 January 2010, 1:30 PM-2:30 PM, B306
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