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Live fuel moisture content plays a significant and complex role in wildfire propagation. However, in situ historical and near real-time live fuel moisture measurements are temporally and spatially sparse within wildfire-prone regions. Routine bi-weekly sampling intervals are sometimes exceeded if the weather is unfavourable and/or field personnel are unavailable. To fill these spatial and temporal gaps, we have developed a daily gridded chamise (Adenostoma fasciculatum) live fuel moisture product that can be used, in conjunction with other predictors, to assess current and historical wildfire danger/behaviour. Chamise observations for 52 new- and 41 old-growth California sites from the National Fuel Moisture Database were statistically related to dynamically downscaled high-resolution weather predictors using a random forest machine learning model. This model captures reasonably well the temporal and spatial variability of chamise live fuel moisture content within California. Compared with observations, model-predicted live fuel moisture values have an overall R², root mean squared error (RMSE) and bias of 0.79, 15.34% and 0.26%, respectively, for new growth and 0.63, 8.81% and 0.11% for old growth. Given the success of the model, we have begun to use it to produce daily forecasts of chamise live fuel moisture content for California utilities. [ABSTRACT FROM AUTHOR]

Vitamin K antagonists are the only approved oral anticoagulants for long-term prophylaxis against valve thrombosis and thromboembolism in patients with a mechanical heart valve. Despite the proven efficacy and safety of anticoagulation with the oral direct factor Xa inhibitor apixaban compared with warfarin in high-risk populations including subjects with atrial fibrillation or with venous thromboembolism, it remains unknown whether patients with a mechanical heart valve can be safely managed with apixaban. The On-X Aortic Heart Valve and On-X Ascending Aortic Prosthesis with the Vascutek Gelweave Valsalva Graft may have lower rates of valve thrombosis and thromboembolism than conventional bileaflet and tilting disc valves due its unique pyrolytic carbon composition and flared inlet design. DESIGN: PROACT Xa is a randomized, multicenter, open-label, active-controlled trial comparing apixaban with warfarin in patients with an On-X Aortic Heart Valve or On-X Ascending Aortic Prosthesis with the Vascutek Gelweave Valsalva Graft. The study will randomize approximately 1,000 patients from approximately 60 sites in North America who underwent aortic valve replacement at least 3 months prior. Patients will be randomized 1:1 to receiving apixaban 5 mg twice daily or warfarin with a target international normalized ratio of 2.0-3.0. The last randomized participant will be followed for at least 2 years. The primary efficacy outcome is the composite of valve thrombosis and valve-related thromboembolism, and the primary safety outcome is major bleeding. Assuming the primary outcome occurs in warfarin-anticoagulated patients at a rate of 1.75%/patient-year, the study has more than 90% power to assess noninferiority of apixaban treatment with an absolute noninferiority margin of 1.75%/patient-year. A second co-primary analysis is to compare the hazard rate for the apixaban arm to twice the objective performance criterion for thromboembolism and valve thrombosis, that is, 3.4%/patient-year. SUMMARY: PROACT Xa will determine whether patients with an On-X Aortic Heart Valve can be anticoagulated with apixaban as an alternative to warfarin. [ABSTRACT FROM AUTHOR]

The criteria used to define Santa Ana winds (SAWs) are dependent upon both the impact of interest (e.g., catastrophic wildfires) and the location and/or time of day examined. We employ a comprehensive definition and methodology for constructing a climatological SAW time series from 1981 through 2016 for two Southern California regions, Los Angeles and San Diego. For both regions, we examine SAW climatology, distinguish SAW-associated synoptic-scale atmospheric patterns, and detect long-term, significant SAW trends. San Diego has 30% fewer SAW days compared to Los Angeles with 80% of SAW events starting in Los Angeles first. Further, 45% of San Diego SAW events are single-day events compared to 35% for Los Angeles. The longest duration event spanned 16 days for Los Angeles (27 November–12 December 1988) and 8 days for San Diego (9–16 January 2009). Although SAW-driven fires can be large and devastating, these types of fires occurred on only 6% and 5% of SAW days for the Los Angeles and San Diego regions, respectively. Finally, we find and investigate an extended period of elevated SAW day count occurring after 2005. This new climatology will allow us to produce month- and season-ahead forecasts of SAW days, which is useful for planning end-of-year staffing coverage by the local, state, and federal fire agencies. [ABSTRACT FROM AUTHOR]

Santa Ana winds, common to Southern California from the fall through early spring, are a type of downslope windstorm originating from a direction generally ranging from 360°/0° to 100° and are usually accompanied by very low humidity. Since fuel conditions tend to be driest from late September through the middle of November, Santa Ana winds occurring during this time have the greatest potential to produce large, devastating fires upon ignition. Such catastrophic fires occurred in 1993, 2003, 2007, and 2008. Because of the destructive nature of such fires, there has been a growing desire to categorize Santa Ana wind events in much the same way that tropical cyclones have been categorized. The Santa Ana wildfire threat index (SAWTI) is a tool for categorizing Santa Ana wind events with respect to anticipated fire potential. The latest version of the index has been a result of a three-and-a-half-year collaboration effort between the USDA Forest Service, the San Diego Gas and Electric utility (SDG&E), and the University of California, Los Angeles (UCLA). The SAWTI uses several meteorological and fuel moisture variables at 3-km resolution as input to the Weather Research and Forecasting (WRF) Model to generate the index out to 6 days. In addition to the index, a 30-yr climatology of weather, fuels, and the SAWTI has been developed to help put current and future events into perspective. This paper outlines the methodology for developing the SAWTI, including a discussion on the various datasets employed and its operational implementation. [ABSTRACT FROM AUTHOR]

In this study (Part I), the mid-twenty-first-century surface air temperature increase in the entire CMIP5 ensemble is downscaled to very high resolution (2 km) over the Los Angeles region, using a new hybrid dynamical-statistical technique. This technique combines the ability of dynamical downscaling to capture finescale dynamics with the computational savings of a statistical model to downscale multiple GCMs. First, dynamical downscaling is applied to five GCMs. Guided by an understanding of the underlying local dynamics, a simple statistical model is built relating the GCM input and the dynamically downscaled output. This statistical model is used to approximate the warming patterns of the remaining GCMs, as if they had been dynamically downscaled. The full 32-member ensemble allows for robust estimates of the most likely warming and uncertainty resulting from intermodel differences. The warming averaged over the region has an ensemble mean of 2.3°C, with a 95% confidence interval ranging from 1.0° to 3.6°C. Inland and high elevation areas warm more than coastal areas year round, and by as much as 60% in the summer months. A comparison to other common statistical downscaling techniques shows that the hybrid method produces similar regional-mean warming outcomes but demonstrates considerable improvement in capturing the spatial details. Additionally, this hybrid technique incorporates an understanding of the physical mechanisms shaping the region's warming patterns, enhancing the credibility of the final results. [ABSTRACT FROM AUTHOR]

A physically-based statistical modeling approach to downscale coarse resolution reanalysis near-surface winds over a region of complex terrain is developed and tested in this study. Our approach is guided by physical variables and meteorological relationships that are important for determining near-surface wind flow. Preliminary fine scale winds are estimated by correcting the course-to-fine grid resolution mismatch in roughness length. Guided by the physics shaping near-surface winds, we then formulate a multivariable linear regression model which uses near-surface micrometeorological variables and the preliminary estimates as predictors to calculate the final wind products. The coarse-to-fine grid resolution ratio is approximately 10-1 for our study region of southern California. A validated 3-km resolution dynamically-downscaled wind dataset is used to train and validate our method. Winds from our statistical modeling approach accurately reproduce the dynamically-downscaled near-surface wind field with wind speed magnitude and wind direction errors of <1.5 ms and 30°, respectively. This approach can greatly accelerate the production of near-surface wind fields that are much more accurate than reanalysis data, while limiting the amount of computational and time intensive dynamical downscaling. Future studies will evaluate the ability of this approach to downscale other reanalysis data and climate model outputs with varying coarse-to-fine grid resolutions and domains of interest. [ABSTRACT FROM AUTHOR]

ABSTRACT Using output from a high-resolution meteorological simulation, we evaluate the sensitivity of southern California wind energy generation to variations in key characteristics of current wind turbines. These characteristics include hub height, rotor diameter and rated power, and depend on turbine make and model. They shape the turbine's power curve and thus have large implications for the energy generation capacity of wind farms. For each characteristic, we find complex and substantial geographical variations in the sensitivity of energy generation. However, the sensitivity associated with each characteristic can be predicted by a single corresponding climate statistic, greatly simplifying understanding of the relationship between climate and turbine optimization for energy production. In the case of the sensitivity to rotor diameter, the change in energy output per unit change in rotor diameter at any location is directly proportional to the weighted average wind speed between the cut-in speed and the rated speed. The sensitivity to rated power variations is likewise captured by the percent of the wind speed distribution between the turbines rated and cut-out speeds. Finally, the sensitivity to hub height is proportional to lower atmospheric wind shear. Using a wind turbine component cost model, we also evaluate energy output increase per dollar investment in each turbine characteristic. We find that rotor diameter increases typically provide a much larger wind energy boost per dollar invested, although there are some zones where investment in the other two characteristics is competitive. Our study underscores the need for joint analysis of regional climate, turbine engineering and economic modeling to optimize wind energy production. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]