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Some of the deadliest wildfires in the U.S., such as California’s 2018 wildfires, have been ignited by power systems. In an effort to prevent and minimize the ignition of wildfires, or control them if ignited, energy companies have developed wildfire mitigation plans. This paper provides energy companies and power system operators, engineers, researchers, and suppliers an overview of the state-of-the-art studies that address key topics in these wildfire mitigation plans and compares the wildfire mitigation plans of several energy companies. The key topics include grid design and system hardening, asset management and inspection, situational awareness and forecasting, operational response, vegetation management, public safety power shutoff, and risk-spend efficiency. This paper also presents a comparison of several energy companies’ decision-making criteria for initiating a public safety power shutoff. Finally, we discuss opportunities for future research studies that could help energy companies prevent wildfire ignitions. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between daily carbon emissions for California's savanna and forest wildfires and regional meteorology over the past 18 years. For each fuel type, the associated weather [daily maximum wind, daily vapor pressure deficit (VPD), and 30-day-prior VPD] is determined for all fire days, the first day of each fire, and the day of maximum emissions of each fire at each fire location. Carbon emissions, used as a marker of wildfire existence and growth, for both savanna and forest wildfires are found to vary greatly with regional meteorology, with the relationship between emissions and meteorology varying with the amount of emissions, fire location, and fuel type. Weak emissions are associated with climatologically typical dryness and wind. For moderate emissions, increasing emissions are associated with higher VPD from increased warming and only display a weak relationship with wind speed. High emissions, which encompass ∼85% of the total emissions but only ∼4% of the fire days, are associated with strong winds and large VPDs. Using spatial meteorological composites for California subregions, we find that weak-to-moderate emissions are associated with modestly warmer-than-normal temperatures and light winds across the domain. In contrast, high emissions are associated with strong winds and substantial temperature anomalies, with colder-than-normal temperatures east of the Sierra Nevada and warmer-than-normal conditions over the coastal zone and the interior of California. Significance Statement: The purpose of this work is to better understand the influence of spatially and temporally variable meteorology and spatially variable surface fuels on California's fires. This is important because much research has focused on large climatic scales that may dilute the true influence of weather (here, high winds and dryness) on fire growth. We use a satellite-recorded fire emissions dataset to quantify daily wildfire existence and growth and to determine the relationship between regional meteorology and wildfires across varying emissions in varying fuels. The result is a novel view of the relationship between California wildfires and rapidly variable, regional meteorology. [ABSTRACT FROM AUTHOR]

This paper proposes a modeling approach for characterizing the probability of wildfire ignition caused by faults on power distribution systems. The proposed model serves as a starting point in research literature to illustrate, from an analytical perspective, the many factors that influence wildfire ignitions in power distribution systems. This paper presents the series of events that leads to power-system-related wildfire ignitions, and characterizes the wildfire ignition probability as a combination of the probability that a fault occurs along a power distribution line segment, and the probability that the fault results in the sustained ignition of a vegetation fuel bed surrounding the line. The proposed model integrates a variety of data including environmental conditions, power system protection settings, and power system line flows. A case study is performed on a test 33-bus distribution system using observed historical weather data from a high-threat fire district in California. The California case study is utilized to investigate the effects of three primary factors (wind speed, line congestion, and protection settings) on wildfire ignition probability. [ABSTRACT FROM AUTHOR]

This paper presents a methodological framework to evaluate the resilience, with the primary focus on performance degradation, of levee-protected electric power networks to flooding in a changing climate. To this end, a multi-disciplinary framework is established by integrating climate science, hydrology, and electric power network analysis. The framework quantifies the effect of climate change on flood hazard levels in a levee-protected area and the subsequent changes in the resilience of the electric power network. In the first step, the changes in the exposure of levee-protected regions to flooding hazard in a warming climate are calculated. Then, the probabilities of failure of power network components due to flooding under current and projected future climate are determined. Finally, the power system resilience index is used to assess the system resiliency for pre-flooding (baseline), historic flooding, and two projected future flooding scenarios. For demonstration, the proposed framework is applied to a levee-protected area in Northern California. The IEEE 118-bus standard test system is employed to represent the power network in the study area. Results reveal that climate change can considerably decrease the system resilience of the levee-protected electric power network. The findings of this study can contribute towards more resilient power network systems under a changing climate. [ABSTRACT FROM AUTHOR]

California state is among the leading producers of strawberries in the world. The value of the California strawberry crop is approximately $2.6 billion, which makes it one of the most valuable fruit crops for the state and nation’s economy. California’s weather provides ideal conditions for strawberry production and changes in weather pattern could have a significant impact on strawberry fruit production. Evaluating relationships between meteorological parameters and strawberry yield can provide valuable information and early indications of yield forecasts that growers can utilize to their advantage. Objectives of this paper were to evaluate correlations of meteorological parameters on strawberry yield for Santa Maria region and to develop meteorology based empirical yield forecasting models for strawberries. Results showed significant correlation between meteorological parameters and strawberry yield and provided a basis for yield forecasting with lead time. Results from empirical models showed that cross-validated yields were closely associated with observed yield with lead time of 2 to 5 months. Overall, this study showed great potential in developing meteorology based yield forecast using principal components. This study only looked at meteorology based yield forecasts. Skills of these models can be further improved by adding physiological parameters of strawberry to existing models for strawberry. [ABSTRACT FROM AUTHOR]

This study investigates the impacts of climate change on meteorology and air quality conditions in California by dynamically downscaling Parallel Climate Model (PCM) data to high resolution (4 km) using the Weather Research and Forecast (WRF) model. This paper evaluates the present years'' (2000--06) downscaling results driven by either PCM or National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) reanalysis data. The analyses focused on the air quality--related meteorological variables, such as planetary boundary layer height (PBLH), surface temperature, and wind. The differences of the climatology from the two sets of downscaling simulations and the driving global datasets were compared, which illustrated that most of the biases of the downscaling results were inherited from the driving global climate model (GCM). The downscaling process added mesoscale features but also introduced extra biases into the driving global data. The main source of bias in the PCM data is an imprecise prediction of the location and strength of the Pacific subtropical high (PSH). The analysis implied that using simulation results driven by PCM data as the input for air quality models will likely underestimate air pollution problems in California. Regional-averaged statistics of the downscaling results were estimated for two highly polluted areas, the South Coast Air Basin (SoCAB) and the San Joaquin Valley (SJV), by comparing to observations. The simulations driven by GFS data overestimated surface temperature and wind speed for most of the year, indicating that WRF has systematic errors in these two regions. The simulation matched the observations better during summer than winter in terms of bias. WRF has difficulty reproducing weak surface wind, which normally happens during stagnation events in these two regions. The shallow summer PBLH in the Central Valley is caused by the dominance of high pressure systems over the valley and the strong valley wind during summer. The change of meteorology and air quality in California due to climate change will be explored in Part II of this study, which compares the future (2047--53) and present (2000--06) simulation results driven by PCM data and is presented in a separate paper. [ABSTRACT FROM AUTHOR]

Over the last decade, simple models of the convective boundary layer (CBL) have been suggested as an approach to inferring regionally averaged land-air exchanges of heat, water and trace gases, because the properties of the CBL respond to an average of the underlying small-scale heterogeneity. This paper explores the use of an integral CBL method to infer regionally averaged fluxes in a landscape that has at least three major sources of heterogeneity – irrigated and non-irrigated rural land use and a large urban area (Sacramento region, California). The first part of the paper assesses the validity of the simple slab model of the CBL – this is integrated forwards in time using local-scale measured heat and water vapour fluxes, to predict mixed-layer depth, temperature and humidity. Of the four different CBL growth schemes used, the Tennekes and Driedonks model is found to give the best performance. Evaluation of the model performance with different weightings of heat and water vapour fluxes based on the land use characteristics in the region suggest that the source area for the boundary-layer sonde measurements is larger than physically-based estimates would suggest. Finally, measured time series of potential temperature are used to infer regionally averaged sensible heat fluxes using an integral CBL (ICBL) method. These ICBL fluxes are compared with those measured at the local scale over the three land use types that comprise the region of interest. They are found to be closest to the heat fluxes calculated by appropriately weighting the measured heat fluxes in the source area calculated for the ICBL. We conclude that the integral CBL budget method provides adequate estimates of regionally-averaged surface heat fluxes in a landscape that is characterised by surface types with distinctly different surface energy budgets. [ABSTRACT FROM AUTHOR]

Recent studies indicate that strong cold fronts develop frequently downstream of the Sierra Nevada over the Intermountain West. To help ascertain why, this paper examines the influence of the Sierra Nevada on the rapidly developing Intermountain cold front of 25 March 2006. Comparison of a Weather Research and Forecasting (WRF) model control simulation with a simulation in which the height of the Sierra Nevada is restricted to 1500 m (roughly the elevation of the valleys and basins of the Intermountain West) shows that the interaction of southwesterly prefrontal flow with the formidable southern High Sierra produces a leeward orographic warm anomaly that enhances the cross-front temperature contrast. Several processes generate this orographic warm anomaly, including flow modification by the Sierra Nevada (i.e., windward blocking of low-level Pacific air, leeward subsidence, and increased southerly flow from the Mojave Desert and lower Colorado River basin into the Intermountain West), diabatic heating and water vapor loss associated with orographic precipitation, and increased sensible heating and reduced subcloud diabatic cooling in the downstream cloud and precipitation shadow. In contrast, the postfrontal air mass experiences comparatively little orographic modification as it moves across the relatively low northern Sierra Nevada. These results show that the Sierra Nevada can enhance frontal development, which may contribute to the high frequency of strong cold-frontal passages over the Intermountain West. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, the reduction of freight vehicle trips during peak hours has been a common policy goal. To this end, policies have been implemented to shift logistics operations to nighttime hours. The purpose of such policies has generally been to mitigate congestion and environmental impacts. However, the atmospheric boundary layer is generally more stable during the night than the day. Consequently, shifting logistics operations to the night may increase 24-h average concentrations of diesel exhaust pollutants in many locations. This paper presents realistic scenarios for two California cities, which provide diesel exhaust concentration and human intake estimates after temporal redistributions of daily logistics operations. Estimates are made for multiple redistribution patterns, including from 07:00–19:00 to 19:00–07:00, similar to daytime congestion charging polices, and from 03:00–18:00 to 18:00–03:00, corresponding to the PierPASS program at the ports of Los Angeles and Long Beach. Results for these two redistribution scenarios indicate that 24-h average exhaust concentrations would increase at most locations in California, and daily human intake is likely to worsen or be unimproved at best. These results are shown to be worse for inland than coastal settings, due to differences in meteorology. Traffic congestion effects are considered, using a new graphical method, which depicts how off-peak policies can be environmentally improving or damaging, depending on traffic speeds and meteorology. [ABSTRACT FROM AUTHOR]

This paper evaluates the application of dispersion models to estimate near-field pollutant concentrations in two case studies. The Industrial Source Complex Short-Term Model (ISCST3) was evaluated with hexavalent chromium measurements collected within 100 m of two facilities in Barrio Logan, San Diego, CA. ISCST3 provided reasonable estimates for higher pollutant concentrations but underestimated lower concentrations. To understand the observed distribution of concentrations in Barrio Logan, a recently conducted tracer experiment was analyzed. The tracer, sulfur hexafluoride, was released at ambient temperature from an urban facility at the University of California at Riverside, and concentrations were measured within 20mof the source. Modeling results indicated that Industrial Source Complex--Plume Rise Model Enhancement and American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model--Plume Rise Model Enhancement overestimated high concentrations and underestimated low concentrations. A diagnostic study with a simple Gaussian dispersion model that incorporated site-specific meteorology was used to evaluate model results. This study found that incorporating lateral meandering for nonbuoyant urban plumes in Gaussian dispersion models could improve concentration estimates even when downwash is not considered. Incorporating a meandering component in ISCST3 resulted in improvements in estimating hexavalent chromium concentrations in Barrio Logan. Credible near-source concentration estimates depend on accurate characterization of emissions, onsite micrometeorology, and a method to account for lateral meandering in the near field. [ABSTRACT FROM AUTHOR]

This paper identifies mechanisms that led to the observed rapid evolution of a landfalling weak cold front along the steep mountainous northern California coast on 1 December 1995. This event was simulated down to 3-km horizontal grid spacing using the Pennsylvania State University–NCAR Mesoscale Model version 5 (MM5). The MM5 simulation reproduced the basic features such as the timing, location, and orientation of the cold front and associated precipitation evolution, as well as the tendency for enhanced precipitation to extend ∼50–100 km upwind of the coastal barrier, with the heaviest amounts occurring over the windward slopes (0–20 km inland); locally, however, the model underestimated the magnitude of the prefrontal terrain-enhanced flow by as much as 30% since the simulated low-level static stability was weaker than observed. The MM5 simulations illustrate the complex thermal, wind, and precipitation structures in the coastal zone. Upstream flow blocking by the steep coastal terrain led to the development of a mesoscale pressure ridge and prefrontal terrain-enhanced winds exceeding 25 m s[sup -1] . Because of the irregular coastline and highly three-dimensional terrain, the low-level winds were not uniform along the coast. Rather, prefrontal southerly flow was significantly reduced downwind of the major capes (viz. Mendocino and Blanco), while there were localized downgradient accelerations adjacent to regions of higher topography along uninterrupted stretches of coastline. Terrain–front interactions resulted in a slowing of the front as the system made landfall, and blocking contributed to a “tipped forward” baroclinic structure below 800 mb. The MM5 was used to investigate some of the reasons for the rapid intensification of the frontal temperature gradient and banded precipitation in the coastal zone. During this event the large-scale vertical motions increased in an environment favorable for moist... [ABSTRACT FROM AUTHOR]