PV‐based diagnostic quantities of heavy precipitation: Solenoidal vorticity and potential solenoidal vorticity. Issue 11 (14th June 2013)

• Record Type: Journal Article
• Authors: Ran, Lingkun; Li, Na; Gao, Shouting
• Is Part Of: Journal of geophysical research. Volume 118:Issue 11(2013:Nov.)
• Page Start: 5710
• Page End: 5723
• Publication Date: 2013-06-14
• Subjects: diagnostic quantities -- solenoidal vorticity -- potential solenoidal vorticity -- heavy precipitation; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/jgrd.50294
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract : [1] Two new diagnostic quantities called solenoidal vorticity (SV) and potential solenoidal vorticity (PSV) are defined in this paper for a moist atmosphere based on the generalized moist potential vorticity (GMPV). SV and PSV are introduced to incorporate vorticity information along the isentropic surfaces that GMPV (or PV) has lost. SV is the scalar product of vorticity and the moist solenoidal term ∇ p × ∇ α *, while PSV is the scalar product of vorticity and (∇ p × ∇ α *) × ∇ θ * (a new vector). Application of GMPV, SV, and PSV to a diagnostic study of a heavy rainfall case shows that SV reflects the coupling of vertical wind shear and the baroclinity of the moist atmosphere weighted by the vertical gradient of pressure, while PSV reflects the coupling of vertical wind shear and baroclinity, the static stability, and the vertical gradient of pressure. Although all three quantities exhibit strong signals over the observed precipitation region, SV and PSV are more capable of distinguishing between precipitation and non‐precipitation areas. The following three main factors are concluded to contribute to the very different distribution patterns of GMPV, SV, and PSV: static stability, the vertical gradient of pressure, and different ways of coupling vertical wind shear and baroclinity. These factors are responsible for the good performance of SV and PSV in tracing and predicting heavy rainfall, as demonstrated by the correlation coefficient between SV (PSV) and the rainfall amount obtained from long time series analyses. The results of this study suggest that SV and PSV are good detectors of heavy rainfall and may be used as precipitation prediction parameters. Key Points: Two quantities SV and PSV were derived Physical meanings of SV and PSV were discussed SV and PSV were used to dectect and predict heavy rainfall