Evaluation of Vertical Motion: Past, Present, and Future

Dunn, Lawrence B.

doi:10.1175/1520-0434(1991)006<0065:eovmpp>2.0.co;2

Cited by 21 publications

(11 citation statements)

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“…The deployment of the Weather Surveillance Radar-1988 Doppler (WSR-88D) nationwide has allowed forecasters to better detect banded precipitation events (Dankers 1994;Grumm and Nicosia 1997). In addition, gridded model output in NWS offices has vastly improved forecaster's abilities to diagnose vertical motion and obtain a better understanding of atmospheric processes (Dunn 1991;Wiesmueller and Brady 1993;Nicosia 1995). With gridded model output available at NWS offices, frontogenesis and CSI can be diagnosed operationally and incorporated into the forecast process (Wiesmueller and Zubrick 1998).…”

Section: Introductionmentioning

confidence: 99%

Mesoscale Band Formation in Three Major Northeastern United States Snowstorms

Nicosia¹,

Grumm²

1999

Wea. Forecasting

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The National Centers for Environmental Prediction's 29-km version Meso Eta Model and Weather Surveillance Radar-1988 Doppler base reflectivity data were used to diagnose intense mesoscale snowbands in three northeastern United States snowstorms. Snowfall rates within these snowbands were extreme and, in one case, were close to 15 cm (6 in.) per hour. The heaviest total snowfall with each snowstorm was largely associated with the positioning of these mesoscale snowbands. Each snowstorm exhibited strong midlevel frontogenesis in conjunction with a deep layer of negative equivalent potential vorticity (EPV). The frontogenesis and negative EPV were found in the deformation zone, north of the developing midlevel cyclone. Cross-sectional analyses (oriented perpendicular to the isotherms) indicated that the mesoscale snowbands formed in close correlation to the intense midlevel frontogenesis and deep layer of negative EPV. It was found that the EPV was significantly reduced on the warm side of the midlevel frontogenetic region as midlevel dry air, associated with a midlevel dry tongue jet, overlaid a low-level moisture-laden easterly jet, north of each low-level cyclone. The continual reduction of EPV on the warm side of the frontogenetic region is postulated to have created the deep layer of negative EPV in the warm advection zone of each cyclone. The negative EPV was mainly associated with conditional symmetric instability (CSI). Each case exhibited a much smaller region of conditional instability (CI) on the warm side of the frontogenesis maximum for a short period of time. The CSI and, to a lesser extent, CI are postulated to have been released as air parcels ascended the moist isentropes, north of the warm front, upon reaching saturation. This likely was a major factor in the mesoscale band formation and heavy snowfall with each snowstorm. The results indicate that model frontogenesis and EPV fields can be used to predict the potential development of mesoscale snowbands. When a deep layer of negative EPV and strong midlevel frontogenesis are forecast by the models, forecasters can anticipate the regions where mesoscale snowbands may form. Inspection of saturation equivalent potential temperature in conjunction with EPV is suggested to determine whether CI is present in a negative EPV region. If CI is present in addition to CSI, then upright convection may dominate over slantwise convection leading to heavier snowfall rates. The region where the frontogenesis and negative EPV are forecast to persist the longest (usually left of the 700-hPa low track) is where the heaviest storm total snowfall will occur. Once mesoscale bands are detected on radar, accurate short-term forecasts of areas that will receive heavy snowfall can be made.

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Section: Introductionmentioning

confidence: 99%

Mesoscale Band Formation in Three Major Northeastern United States Snowstorms

Nicosia¹,

Grumm²

1999

Wea. Forecasting

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“…The rainfall is related to upward motion, and its variation is associated with multi-scale upward motion. The Q Q Q vector is a powerful tool for estimating the vertical motion (Dunn, 1991). Recently, Yue (2009b) proposed an ageostrophic Q Q Q vector (Q Q Q D ) that can be calculated using one-layer data.…”

Section: Q Vector Partitioningmentioning

confidence: 99%

A diagnostic study of the asymmetric distribution of rainfall during the landfall of typhoon Haitang (2005)

et al. 2015

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The precipitation during landfall of typhoon Haitang (2005) showed asymmetric structures (left side/right side of the track). Analysis of Weather Research and Forecasting model simulation data showed that rainfall on the right side was more than 15 times stronger than on the left side. The causes were analyzed by focusing on comparing the water vapor flux, stability and upward motion between the two sides. The major results were as follows: (1) Relative humidity on both sides was over 80%, whereas the convergence of water vapor flux in the lower troposphere was about 10 times larger on the right side than on the left side.(2) Both sides featured conditional symmetric instability [MPV (moist potential vorticity) <0], but the right side was more unstable than the left side. (3) Strong (weak) upward motion occurred throughout the troposphere on the right (left) side. The Q Q Q vector diagnosis suggested that large-scale and mesoscale forcing accounted for the difference in vertical velocity. Orographic lift and surface friction forced the development of the asymmetric precipitation pattern. On the right side, strong upward motion from the forcing of different scale weather systems and topography caused a substantial release of unstable energy and the transportation of water vapor from the lower to the upper troposphere, which produced torrential rainfall. However, the above conditions on the left side were all much weaker, which led to weaker rainfall. This may have been the cause of the asymmetric distribution of rainfall during the landfall of typhoon Haitang.Citation: Yue, C. J., S. T. Gao, L. Liu, and X. F. Li, 2015: A diagnostic study of the asymmetric distribution of rainfall during the landfall of typhoon Haitang (2005).

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“…Hoskins, Dagbici, and Darics (1978) introduced the quasi-geostrophic Q-vector and derived the quasi-geostrophic ω equation, which uses the divergence of the quasi-geostrophic Q-vector as the only forcing term. Since then, the Q-vector has been used extensively in vertical motion calculations and the diagnosis of strong convection weather (Dunn 1991;Keyser, Schmidit, and Duffy 1992;Xu 1992;Yue 1999). Limited by the quasigeostrophic approximation, the Q-vector derived by Hoskins, Dagbici, and Darics (1978) does not perform well at low latitudes and in some sub-synoptic-scale weather systems.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of deformation-derived ascending areas in a rainband

Ran

Gao

2018

Atmospheric and Oceanic Science Letters

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This paper demonstrates that, for a moist baroclinic frontal system, the large-value deformation belt in the low-level atmosphere overlaps with precipitation. To precisely describe the relationship between deformation and heavy precipitation, deformation is introduced into the nongeostrophic Q #-vector. Q # is then decomposed into three parts: the divergence-related term, the vorticity-related term, and the deformation-related term. By calculating the divergence of Q # and its components, it is found that in strong ascending areas within precipitation regions the nongeostrophic Q #-vector divergence shows strong negative values. Its deformational component can contribute about 68% to these negative values. This verifies that strong deformation in a precipitating atmosphere is favorable for the development of convection and precipitation. In addition, by calculating the correlation coefficients between the Q #-vector (including its components) divergence and vertical motions, it is also found that the Q #-vector divergence shows higher correlation with vertical motion within the precipitation belt and lower correlation in the non-precipitation areas, which indicates a larger contribution of Q # to vertical motion when precipitation occurs and implies an effect of Q # to the precipitation distribution or spatial variability. Among the three components of the Q #-vector, the correlation coefficients between the deformational component and vertical motion are the most similar in pattern to that of the correlation coefficients between the Q #-vector and vertical motion, which further reflects the important contribution of deformation to the large spatial variability of precipitation.

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Evaluation of Vertical Motion: Past, Present, and Future

Cited by 21 publications

References 0 publications

Mesoscale Band Formation in Three Major Northeastern United States Snowstorms

Mesoscale Band Formation in Three Major Northeastern United States Snowstorms

A diagnostic study of the asymmetric distribution of rainfall during the landfall of typhoon Haitang (2005)

Diagnosis of deformation-derived ascending areas in a rainband

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