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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]

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 new hybrid statistical-dynamical downscaling technique is described to project mid- and end-of-twenty-first-century local precipitation changes associated with 36 global climate models (GCMs) in phase 5 of the Coupled Model Intercomparison Project archive over the greater Los Angeles region. Land-averaged precipitation changes, ensemble-mean changes, and the spread of those changes for both time slices are presented. It is demonstrated that the results are similar to what would be produced if expensive dynamical downscaling techniques were instead applied to all GCMs. Changes in land-averaged ensemble-mean precipitation are near zero for both time slices, reflecting the region's typical position in the models at the node of oppositely signed large-scale precipitation changes. For both time slices, the intermodel spread of changes is only about 0.2-0.4 times as large as natural interannual variability in the baseline period. A caveat to these conclusions is that interannual variability in the tropical Pacific is generally regarded as a weakness of the GCMs. As a result, there is some chance the GCM responses in the tropical Pacific to a changing climate and associated impacts on Southern California precipitation are not credible. It is subjectively judged that this GCM weakness increases the uncertainty of regional precipitation change, perhaps by as much as 25%. Thus, it cannot be excluded that the possibility that significant regional adaptation challenges related to either a precipitation increase or decrease would arise. However, the most likely downscaled outcome is a small change in local mean precipitation compared to natural variability, with large uncertainty on the sign of the change. [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]

Changes in wintertime 10 m winds due to the El Niño-Southern Oscillation are examined using a 6 km resolution climate simulation of Southern California covering the period from 1959 through 2001. Wind speed statistics based on regional averages reveal a general signal of increased mean wind speeds and wind speed variability during El Niño across the region. An opposite and nearly as strong signal of decreased wind speed variability during La Niña is also found. These signals are generally more significant than the better-known signals in precipitation. In spite of these regional-scale generalizations, there are significant sub-regional mesoscale structures in the wind speed impacts. In some cases, impacts on mean winds and wind variability at the sub-regional scale are opposite to those of the region as a whole. All of these signals can be interpreted in terms of shifts in occurrences of the region's main wind regimes due to the El Niño phenomenon. The results of this study can be used to understand how interannual wind speed variations in regions of Southern California are influenced by the El Niño phenomenon. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]