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Abstract 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.

Wildland fires in Southern California can be divided into two categories: fall fires, which are typically driven by strong offshore Santa Ana winds, and summer fires, which occur with comparatively weak onshore winds and hot and dry weather. Both types of fire contribute significantly to annual burned area and economic loss. An improved understanding of the relationship between Southern California's meteorology and fire is needed to improve predictions of how fire will change in the future and to anticipate management needs. We used output from a regional climate model constrained by reanalysis observations to identify Santa Ana events and partition fires into those occurring during periods with and without Santa Ana conditions during 1959–2009. We then developed separate empirical regression models for Santa Ana and non-Santa Ana fires to quantify the effects of meteorology on fire number and size. These models explained approximately 58% of the seasonal and interannual variation in the number of Santa Ana fires and 36% of the variation in non-Santa Ana fires. The number of Santa Ana fires increased during years when relative humidity during Santa Ana events and fall precipitation were below average, indicating that fuel moisture is a key controller of ignition. Relative humidity strongly affected Santa Ana fire size. Cumulative precipitation during the previous three winters was significantly correlated with the number of non-Santa Ana fires, presumably through increased fine fuel density and connectivity between infrastructure and nearby vegetation. Both relative humidity and the preceding wet season precipitation influenced non-Santa Ana fire size. Regression models driven by meteorology explained 57% of the temporal variation in Santa Ana burned area and 22% of the variation in non-Santa Ana burned area. The area burned by non-Santa Ana fires has increased steadily by 1.7% year−1 since 1959 (p

Using a combination of dynamical and statistical downscaling techniques, we projected mid-21stcentury warming in the Los Angeles region at 2-km (1.2-mile) resolution. To account for uncertainty associated with the trajectory of future greenhouse gas emissions and other factors affecting the planet's energy balance, we examined projections for both "business-as-usual" (RCP8.5) and "mitigation" (RCP2.6) emissions scenarios of the Fifth Coupled Model Intercomparison Project. To account for the considerable uncertainty associated with choice of global climate model, we downscaled results for all available global models. For the business-as-usual scenario, we find that by the mid-21stcentury, the most likely warming is roughly 4.6°F averaged over the region's land areas, with a 95% confidence that the warming lies between 1.7 and 7.5°F. The high resolution of the projections reveals a pronounced spatial pattern in the warming: High elevations and inland areas separated from the coast by at least one mountain complex warm 20 to 50% more than the areas near the coast or within the Los Angeles basin. This warming pattern is especially apparent in summertime. The summertime warming contrast between the inland and coastal zones has a large effect on the most likely expected number of extremely hot days per year. Coastal locations and areas within the Los Angeles basin see roughly two to three times the number of extremely hot days, while high elevations and inland areas typically experience approximately three to five times the number of extremely hot days. Under the mitigation emissions scenario, the most likely warming and increase in heat extremes are somewhat smaller. However, the majority of the warming seen in the business-as-usual scenario still occurs at all locations in the most likely case under the mitigation scenario, and heat extremes still increase significantly. Therefore adaptation to a changing climate over the next few decades is likely to be inevitable in the Los Angeles region.

For the first time, global ocean usable wind power is evaluated for modern offshore turbine characteristics including hub height, usable portion of the wind speed distribution, and siting depth. Mean wind power increases by 30%, 69%, and 73% within the tropics and Northern and Southern Hemisphere extratropics, respectively, between hub heights of 10 m and 100 m. A turbine with a cut-out speed of 25 m s−1 (30 m s−1) within the Northern Hemisphere storm track harvests between 55% (82%) and 85% (>98%) of available power. Within this region, a 2–3 m s−1 change in cut-out speed can result in a 5–7% change in usable power. Eighty meter wind power accumulates at a rate of 20–45 MW km2 m−2 per meter depth increase from the shore to the shelf break. Beyond the shelf break, wind power accumulates at a slower rate (

Global ocean wind power has recently been assessed (W. T. Liu et al., 2008) using scatterometry-based 10 m winds. We characterize, for the first time, wind power at 80 m (typical wind turbine hub height) above the global ocean surface, and account for the effects of surface layer stability. Accounting for realistic turbine height and atmospheric stability increases mean global ocean wind power by +58% and −4%, respectively. Our best estimate of mean global ocean wind power is 731 W m−2, about 50% greater than the 487 W m−2 based on previous methods. 80 m wind power is 1.2–1.5 times 10 m power equatorward of 30° latitude, between 1.4 and 1.7 times 10 m power in wintertime storm track regions and >6 times 10 m power in stable regimes east of continents. These results are relatively insensitive to methodology as wind power calculated using a fitted Weibull probability density function is within 10% of power calculated from discrete wind speed measurements over most of the global oceans.

Climatological surface wind speed probability density functions (PDFs) estimated from observations are characterized and used to evaluate, for the first time, contemporaneous wind PDFs predicted by a GCM. The observations include NASA’s global Quick Scatterometer (QuikSCAT) dataset, the NCEP/Department of Energy Global Reanalysis 2 (NCEP-2) 6-hourly reanalysis, and the Tropical Atmosphere Ocean (TAO)/Triangle Trans-Ocean Buoy Network (TRITON) moored buoy data, all from 2000 to 2005. Wind speed mean, 90th percentile, standard deviation, and Weibull shape parameter climatologies are constructed from these data. New features that emerge from the analysis include the identification of a stationary pattern to the wind speed variance in the equatorial Pacific. Interestingly, a distinct wind speed shape anomaly migrates with the ITCZ across this stationary background.The GCM despite its coarser spatial and temporal resolution predicts wind speed PDFs in general agreement with observations. Relative to QuikSCAT, the NCAR Community Atmosphere Model, version 3 (CAM3) GCM has a globally averaged positive mean wind speed bias of about 0.2 m s−1 originating primarily within the trades and Southern Hemisphere storm track. Global standard deviation biases are largest in the winter hemisphere storm tracks. The largest shape biases occur along the equatorial peripheries of the Northern Hemisphere and southern Indian Ocean anticyclones. Year-round negative shape and mean wind speed biases persist along the ITCZ. The GCM’s overactive tropical convection and slight subtropical anticyclone displacement contribute to positive mean speed, standard deviation, and shape trade biases.Surface heat and energy fluxes depend nonlinearly on wind speed magnitude, are sensitive to the tails of the wind distribution, and hence vary significantly on spatiotemporal scales not resolved by GCMs. Limited computing resources force the use of coarse-resolution GCMs, which do not resolve finer-scale wind speed fluctuations. Rather, surface fluxes are determined from the mean wind speed computed by averaging spatially and temporally over subgrid-scale features. Some surface flux routines account for gustiness during low mean winds resulting from thermally driven convection. The authors hypothesize that GCMs systematically underestimate surface momentum flux nonlinearities and that this biases surface wind predictions most in regions of strong winds with high variability. To test this, climate simulations that account for surface fluxes due to subgrid-scale GCM winds are performed. This significantly improves climatological surface wind speed statistics, particularly in the Southern Hemisphere storm track, consistent with the hypothesis. These wind speed improvements can be attributed to a reduction in GCM sea level pressure biases throughout the globe.

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]

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]