Nonlinear response of hail precipitation rate to environmental moisture content: A real case modeling study of an episodic midlatitude severe convective event

Abstract

The dependence of hail production on initial moisture content in a simulated midlatitude episodic convective event occurred in northeast China on 10–11 June 2005 was investigated using the Weather Research and Forecasting (WRF) model with a double‐moment microphysics scheme where both graupel and hail are considered. Three sensitivity experiments were performed by modifying the initial water vapor mixing ratio profile to 90% (“Q−10%”), 105% (“Q+5%”), and 110% (“Q+10%”) of the initial conditions used for the control simulation. It was found that increasing the initial water vapor content caused the hail and total precipitation rates to increase during the first 5 h. The precipitation response to increasing water vapor content was monotonic for this first episode; however, for the event's second episode, the hail precipitation rate responds to the initial water vapor profile nonlinearly, while the total precipitation rate responds mostly monotonically. In particular, simulation Q+5% achieves the largest hail production rate while simulation Q+10% has the largest total precipitation rate. In contrast, during the second episode simulation Q−10% has the strongest vertical motion, produces the most cloud ice and snow, but has the lowest hail production. Analysis shows that increasing the initial moisture content directly increases the precipitation during the first episode, which subsequently induces a stronger, longer‐lasting cold pool that limits the development of deep convection during the second episode.

Sensitivity of Extreme Rainfall to Atmospheric Moisture Content in the Arid/Semiarid Southwestern United States: Implications for Probable Maximum Precipitation Estimates

“…Response of rainfall to atmospheric moisture content is markedly different between the first and second storm episodes, with rainfall rate monotonically related to moisture content for the second storm episode as opposed to that for the first storm episode. A recent study by Li et al (2017) found contrasting responses Figure 16. Time series of domain-averaged rain rate (blue line, mm h −1 ) and convergence of water vapor (red line, mm h −1 ) from the RH80 simulation.…”

“…Response of rainfall to atmospheric moisture content is markedly different between the first and second storm episodes, with rainfall rate monotonically related to moisture content for the second storm episode as opposed to that for the first storm episode. A recent study by Li et al () found contrasting responses of both hail precipitation and total precipitation rate to initial moisture profile for different storm episodes within a hailstorm event and attributed the contrasts to the impacts of storm structure and microphysical processes. In this study, we find that the nonmonotonic relationship is particularly evident for extreme rainfall at small spatial scales, indicating a nonlinear dependence of extreme rainfall on precipitable water where small‐scale convection plays a dominant role in determining spatial and temporal rainfall variability.…”

Contrasting Responses of Hailstorms to Anthropogenic Climate Change in Different Synoptic Weather Systems

“…For the squall line system, convection initiation is sensitive to the intensity of the cold pool (i.e., Chen et al., 2015; M. Li et al., 2017). We investigated the cold pool changes for the three squall line cases: Cases 1 and 2 from the frontal group and Case 5 from the GPLLJ group.…”

“…The increased PPG in the frontal systems from the current to the future climate probably results from the stronger horizontal pressure gradient on the trough, whereas the small change in the horizontal pressure gradient on the ridge of the high-pressure system in the GPLLJs may be the reason for the small increase in PPG. For the squall line system, convection initiation is sensitive to the intensity of the cold pool (i.e., Chen et al, 2015;M. Li et al, 2017).…”

Sensitivity of Hail Precipitation to Ensembles of Uncertainties of Representative Initial Environmental Conditions From ECMWF

“…The members in the EC_All set were separated into Top10 (i.e., those with the top 10 largest hail precipitation rates), Bottom10 (those with the 10 smallest hail precipitation rates), and Middle30 groups (those with the 30 middle hail precipitation rates; Figure a, red, blue, and gray lines, respectively). We focus on hail precipitation rate because it proved meaningful for comparing numerical simulations (M. Li et al, ). Each group represents a quintile of the ensemble; quintiles are used here to exemplify the range of uncertainties and to determine the underlying factors that drive the divergence of the two extreme quintiles (e.g., Munsell & Zhang, ).…”