84th AMS Annual Meeting

Tuesday, 13 January 2004
Using PV concepts to diagnose a poorly-predicted heavy snow band in New England (67 January 2003)
Room 4AB
Ron McTaggart-Cowan, McGill University, Montreal, QC, Canada; and J. Gyakum and P. Sisson
The failure of operational modelling systems to predict the formation of a heavy snowfall band in New England on 6-7 January 2002 resulted in the most noteworthy forecast bust of the winter in the region. During the 24 hour period leading up to 1200 UTC 7 January, snow accumulations of between one and two feet were observed in a wide swath through central Pennsylvania, southeastern New York, southern Vermont and central New Hampshire. Neither deterministic forecasts nor ensemble products showed skill in predicting the intensity, or even the existence, of the snow band. Instead, a surface cyclone tracking northeastward along the coast was expected to be the major player in precipitation patterns over the period. Although this oceanic system produced over an inch of precipitation along the New Jersey coast, if fell almost entirely as rain.

The case is analyzed using a traditional synoptic/mesoscale framework in order to place the subsequent potential vorticity (PV) based diagnosis in context. Dynamic tropopause features and their associated coupling index patterns are used to show that numerical models are able to hint at the correct solution despite the lack skill in QPF. Moist component PV, a moisture-sensitive PV variable previously employed in the study of tropical systems, is used to highlight the source region of the moisture released over New England on 6-7 January. Modifications to the initial moisture fields of a numerical model, well within the bounds of observational uncertainty in the radiosonde network, show strong sensitivity of the snow band to moisture over Tennessee at 0000 UTC 6 January.

The results of this study indicate that the application of state-of-the-art diagnostics can add value to operational forecasts since precipitation indicators and forcings are often better predicted in models than is QPF itself. As well, the application of moist component PV in a winter storm context shows that this quantity may be useful in tracking moisture anomalies in real time.

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