Sensitivity of Northern Great Plains Convection Forecasts to Upstream and Downstream Forecast Errors

Abstract

The role of earlier forecast errors on subsequent convection forecasts is evaluated for a northern Great Plains severe convective event on 11–12 June 2013 during the Mesoscale Predictability Experiment (MPEX) by applying the ensemble-based sensitivity technique to Weather Research and Forecasting (WRF) Model ensemble forecasts with explicit convection. This case was characterized by two distinct modes of convection located 150 km apart in western Nebraska and South Dakota, which formed on either side of an axis of high, lower-tropospheric equivalent potential temperature . Convection forecasts over both regions are found to be sensitive to the position of this axis. The convection in Nebraska is sensitive to the position of the western edge of the axis near an upstream dryline, which modulates the preconvective prior to the diurnal maximum. In contrast, the convection in South Dakota is sensitive to the position of the eastern edge of the axis near a cold front, which also modulates the preconvective in that location. The position of the axis is modulated by the positions of both upstream and downstream mid- to upper-tropospheric potential vorticity anomalies, and can be traced backward in time to the initial conditions. Dropsondes sampling the region prior to convective initiation indicate that ensemble members with better representations of upstream conditions in sensitive regions are associated with better convective forecasts over Nebraska.

Sensitivity of Dryline Convection Forecasts to Upstream Forecast Errors for Two Weakly Forced MPEX Cases

“…Forecast sensitivities are calculated from the same ensemble analysis and forecast system that was used in Torn and Romine (2015) and Berman et al (2017), which were generated in real time during the MPEX field phase. A short summary of these forecasts is given here; the interested reader is directed to Schwartz et al (2015) for a complete description of the system outlined below.…”

“…As a consequence, this study employs the maximum vertical kinetic energy [( 1 /2)w 2 max , where w max is the maximum vertical motion in a column) forecast metric averaged over a geographic area and time period (hereafter J VKE ). 1 This metric has been used in previous studies (e.g., Berman et al 2017) and unlike precipitation or reflectivity-based metrics, which have been used in other sensitivity studies (e.g., Bednarczyk and Ancell 2015;Torn and Romine 2015;Hill et al 2016), it is relatively straightforward to separate convection from extensive stratiform precipitation. Moreover, the time period of the greatest variability in this metric occurs between 22 and 25 h (2200-0100 UTC); therefore, this time period is used in the metric calculation.…”

“…There are numerous methods to estimate forecast sensitivities, including adjoint (e.g., Errico et al 2003) and ensemble-based methods (e.g., Martin and Xue 2006;Melhauser and Zhang 2012;Bednarczyk and Ancell 2015;Torn and Romine 2015;Berman et al 2017;Hill et al 2016). Ensemble-based methods may be better posed for convective applications because they employ a set of nonlinear forecast trajectories to estimate sensitivities, rather than assuming linear forecast error growth (i.e., adjoints).…”

“…Here, the ensemble-based sensitivity method applied to convection-permitting ensemble forecasts will be used to quantitatively compare how uncertainty in various upstream features and preconvective environments can impact subsequent convection forecasts along the Texas dryline. The sensitivities for these cases with weak forcing will be compared to the forecast sensitivities for strongly forced cases studied in Torn and Romine (2015) and Berman et al (2017).…”

“…Histograms of the 21-h (d) CIN and (e) column-maximum upward vertical motion averaged over the forecast metric region for the 10 members with the largest J VKE (red) and the 10 members with the smallest J VKE (blue). Torn and Romine 2015;Berman et al 2017;Hill et al 2016), one hypothesis is that J VKE is sensitive to the dryline position. At 1800 UTC (3 h prior to CI), the sensitivity of J VKE to 750-850-hPa water vapor exhibits positive values along the west Texas dryline (up to 8.0 m 2 s 22 per standard deviation) and negative sensitivity values farther south along the dryline along the Texas-Mexico border and into Mexico (up to 27.0 m 2 s 22 per standard deviation; Fig.…”