Search for: All records

Abstract Coastal Santa Barbara is among the most exposed communities to wildfire hazards in Southern California. Downslope, dry, and gusty windstorms are frequently observed on the south-facing slopes of the Santa Ynez Mountains that separate the Pacific Ocean from the Santa Ynez valley. These winds, known as “Sundowners,” peak after sunset and are strong throughout the night and early morning. The Sundowner Winds Experiment (SWEX) was a field campaign funded by the National Science Foundation that took place in Santa Barbara, California, between 1 April and 15 May 2022. It was a collaborative effort of 10 institutions to advance understanding and predictability of Sundowners, while providing rich datasets for developing new theories of downslope windstorms in coastal environments with similar geographic and climatic characteristics. Sundowner spatiotemporal characteristics are controlled by complex interactions among atmospheric processes occurring upstream (Santa Ynez valley), and downstream due to the influence of a cool and stable marine boundary layer. SWEX was designed to enhance spatial measurements to resolve local circulations and vertical structure from the surface to the midtroposphere and from the Santa Barbara Channel to the Santa Ynez valley. This article discusses how SWEX brought cutting-edge science and the strengths of multiple ground-based and mobile instrument platforms to bear on this important problem. Among them are flux towers, mobile and stationary lidars, wind profilers, ceilometers, radiosondes, and an aircraft equipped with three lidars and a dropsonde system. The unique features observed during SWEX using this network of sophisticated instruments are discussed here. 

This study investigates the predictability of downslope windstorms located in Santa Barbara County, California, locally referred to as Sundowner winds, from both observed relationships and a high-resolution, operational numerical weather prediction model. We focus on April 2022, during which the Sundowner Winds Experiment (SWEX) was conducted. We further refine our study area to the Montecito region owing to some of the highest wind measurements occurring at or near surface station MTIC1, situated on the coast-facing slope overlooking the area. Fires are not uncommon in this area, and the difficulty of egress makes the population particularly vulnerable. Area forecasters often use the sea-level pressure difference (ΔSLP) between Santa Barbara Airport (KSBA) and locations to the north such as Bakersfield (KBFL) to predict Sundowner windstorm occurrence. Our analysis indicates that ΔSLP by itself is prone to high false alarm rates and offers little information regarding downslope wind onset, duration, or magnitude. Additionally, our analysis shows that the high-resolution rapid refresh (HRRR) model has limited predictive skill overall for forecasting winds in the Montecito area. The HRRR, however, skillfully predicts KSBA-KBFL ΔSLP, as does GraphCast, a machine learning weather prediction model. Using a logistic regression model we were able to predict the occurrence of winds exceeding 9 m s−1 with a high probability of detection while minimizing false alarm rates compared to other methods analyzed. This provides a refined and easily computed algorithm for operational applications. 

Abstract We utilized high temporal resolution, near-surface observations of sustained winds and gusts from two networks, the primarily airport-based Automated Surface Observing System (ASOS) and the New York State Mesonet (NYSM), to evaluate forecasts from the operational High-Resolution Rapid Refresh (HRRR) model, versions 3 and 4. Consistent with past studies, we showed the model has a high degree of skill in reproducing the diurnal variation of network-averaged wind speed of ASOS stations, but also revealed several areas where improvements could be made. Forecasts were found to be underdispersive, deficient in both temporal and spatial variability, with significant errors occurring during local nighttime hours in all regions and in forested environments for all hours of the day. This explained why the model overpredicted the network-averaged wind in the NYSM because much of that network’s stations are in forested areas. A simple gust parameterization was shown not only to have skill in predicting gusts in both networks but also to mitigate systemic biases found in the sustained wind forecasts. Significance Statement Many users depend on forecasts from operational models and need to know their strengths, weaknesses, and limitations. We examined generally high-quality near-surface observations of sustained winds and gusts from the nationwide Automated Surface Observing System (ASOS) and the New York State Mesonet (NYSM) and used them to evaluate forecasts from the previous (version 3) and current (version 4) operational High-Resolution Rapid Refresh (HRRR) model for a selected month. Evidence indicated that the wind forecasts are excellent yet imperfect and areas for further improvement remain. In particular, we showed there is a high degree of skill in representing the diurnal variation of sustained wind at ASOS stations but insufficient spatial and temporal forecast variability and overprediction at night everywhere, in forested areas at all times of day, and at NYSM sites in particular, which are more likely to be sited in the forest. Gusts are subgrid even at the fine grid spacing of the HRRR (3 km) and thus must be parameterized. Our simple gust algorithm corrected for some of these systemic biases, resulting in very good predictions of the maximum hourly gust. 

We analyzed meteorological conditions that occurred during the December 2021 Boulder, Colorado, downslope windstorm. This event is of particular interest due to the ignition and spread of the Marshall Fire, which quickly became the most destructive wildfire in Colorado history. Observations indicated a rapid onset of fast winds with gusts as high as 51 m/s that generally remained confined to the east-facing slopes and foothills of the Rockies, similar to previous Boulder windstorms. After about 12 h, the windstorm shifted into a second, less intense phase. Midtropospheric winds above northwestern Colorado weakened prior to the onset of strong surface winds and the event strength started waning as stronger winds moved back into the area. Forecasts from NOAA high-resolution operational models initialized more than a few hours prior to windstorm onset did not simulate the start time, development rate and/or maximum strength of the windstorm correctly, and day-ahead runs even failed to develop strong downslope windstorms at all. Idealized modeling confirmed that predictability was limited by errors on the synoptic scale affecting the midtropospheric wind conditions representing the Boulder windstorm’s inflow environment. Gust forecasts for this event were critically evaluated. 

Abstract Subgrid-scale turbulence in numerical weather prediction models is typically handled by a PBL parameterization. These schemes attempt to represent turbulent mixing processes occurring below the resolvable scale of the model grid in the vertical direction, and they act upon temperature, moisture, and momentum within the boundary layer. This study varies the PBL mixing strength within 4-km WRF simulations of a 26–29 January 2015 snowstorm to assess the sensitivity of baroclinic cyclones to eddy diffusivity intensity. The bulk critical Richardson number for unstable regimes is varied between 0.0 and 0.25 within the YSU PBL scheme as a way of directly altering the depth and magnitude of subgrid-scale turbulent mixing. Results suggest that varying the bulk critical Richardson number is similar to selecting a different PBL parameterization. Differences in boundary layer moisture availability, arising from reduced entrainment of dry, free tropospheric air, lead to variations in the magnitude of latent heat release above the warm frontal region, producing stronger upper-tropospheric downstream ridging in simulations with less PBL mixing. The more amplified flow pattern impedes the northeastward propagation of the surface cyclone and results in a westward shift of precipitation. In addition, trajectory analysis indicates that ascending parcels in the less-mixing simulations condense more water vapor and terminate at a higher potential temperature level than do ascending parcels in the more-mixing simulations, suggesting stronger latent heat release when PBL mixing is reduced. These results suggest that spread within ensemble forecast systems may be improved by perturbing PBL mixing parameters that are not well constrained.