Downslope Windstorms of San Diego County. Part II: Physics Ensemble Analyses and Gust Forecasting

Abstract

The “Santa Ana” winds of Southern California represent a high-impact weather event because their dry, fast winds can significantly elevate the wildfire threat. This high-resolution numerical study of six events of moderate or greater strength employs physics parameterization and stochastic perturbation ensembles to determine the optimal model configuration for predicting winds in San Diego County, with verification performed against observations from the San Diego Gas and Electric (SDG&E) mesonet. Results demonstrate model physics can have a material effect on the strength, location, and timing of the winds, with the land surface model playing an outsized role via its specification of surface roughness lengths. Even when bias in the network-averaged sustained wind forecasts is minimized, systematic biases remain in that many stations are consistently over- or underforecasted. The argument is made that this is an “unavoidable” error that represents localized anemometer exposure issues revealed through the station gust factor. A very simple gust parameterization is proposed for the mesonet based on the discovery that the network-averaged gust factor is independent of weather conditions and results in unbiased forecasts of gusts at individual stations and the mesonet as a whole. Combined with atmospheric humidity and fuel moisture information, gust forecasts can help in the assessment of wildfire risks.

A Surface Observation Based Climatology of Diablo-Like Winds in California’s Wine Country and Western Sierra Nevada

“…However, minimum and maximum daily temperatures are not elevated relative to climatology (Figure 5b), indicating that Diablo-like wind events are not anomalously warm. Daily maximum wind speeds and wind speed gusts are elevated relative to climatological averages (Figure 5c), and both the wind speed and wind speed gust are highly correlated to each other (Figure 5d), which is in agreement with previous work on Santa Ana winds ( [3], cf. Figure 13a).…”

“…The wind speeds and wind speed gusts are both elevated during Diablo-like events, although the gust factor is not, indicating no support for Diablo-like winds being gustier than other high wind events that affect these stations under other atmospheric conditions. The identified relationship of gust factor versus sustained wind speed appears to be similar to that of Santa Ana's ( [3], cf. their Figure 11c).…”

“…Despite the massive destruction wrought by the Diablo winds, associated with the Northern California firestorm of October 2017 and the Oakland Hills firestorm of 1991, very little is known quantitatively about them, especially relative to the Southern California downslope winds that favor wildfires, such as Santa Ana [1][2][3][4][5][6][7][8][9][10] and Sundowner [11][12][13][14][15] winds. A prescient preliminary analysis of 'Santa Ana-like' winds in the Oakland Hills was performed in 1973 [16], some 18 years prior to the Oakland Hills fire of 1991, and analyses [17][18][19] subsequent to that fire confirmed their localization at the eastern San Francisco Bay Area.…”

The 2018 Camp Fire: Meteorological Analysis Using In Situ Observations and Numerical Simulations

“…Table 2 lists WRF model physics parameterizations used in this simulation. Consistent with [32][33][34], we tested a number of different physics configurations, including a HRRR-like setup, but found that the combination of the Pleim-Xiu land surface model and the Asymmetric Convection Model version 2 (ACM2) PBL scheme performed best overall (not shown) [35,36]. Additionally, this analysis compared WRF 10 m wind speed and direction directly to 6.1 m RAWS wind observations.…”

“…Additionally, this analysis compared WRF 10 m wind speed and direction directly to 6.1 m RAWS wind observations. We decided to not adjust the wind measurements from 10 m to 6.1 m due to the fact that operational weather models typically only report 10 m winds and recent studies [31,33] found that adjusting measurements lower had little effect on results. Outer domain D1 has a grid spacing of 6 km, with inner domains D2 and D3 grid spacing of 2 km and 0.666 km, respectively.…”