Remote Automated Weather Stations Research Articles

Dead Fuel Moisture Content Reanalysis Dataset for California (2000–2020)

This study presents a novel reanalysis dataset of dead fuel moisture content (DFMC) across California from 2000 to 2020 at a 2 km resolution. Utilizing a data assimilation system that integrates a simplified time-lag fuel moisture model with 10-h fuel moisture observations from remote automated weather stations (RAWS) allowed predictions of 10-h fuel moisture content by our method with a mean absolute error of 0.03 g/g compared to the widely used Nelson model, with a mean absolute error prediction of 0.05 g/g. For context, the values of DFMC in California are commonly between 0.05 g/g and 0.30 g/g. The presented product provides gridded hourly moisture estimates for 1-h, 10-h, 100-h, and 1000-h fuels, essential for analyzing historical fire activity and understanding climatological trends. The methodology presented here demonstrates significant advancements in the accuracy and robustness of fuel moisture estimates, which are critical for fire forecasting and management.

Autumn Surface Wind Trends over California during 1979–2020

Surface winds over California can compound fire risk during autumn, yet their long-term trends in the face of decadal warming are less clear compared to other climate variables like temperature, drought, and snowmelt. To determine where and how surface winds are changing most, this article uses multiple reanalyses and Remote Automated Weather Stations (RAWS) to calculate autumn 10 m wind speed trends during 1979–2020. Reanalysis trends show statistically significant increases in autumn night-time easterlies on the western slopes of the Sierra Nevada. Although downslope windstorms are frequent to this region, trends instead appear to result from elevated gradients in warming between California and the interior continent. The result is a sharper horizontal temperature gradient over the Sierra crest and adjacent free atmosphere above the foothills, strengthening the climatological nocturnal katabatic wind. While RAWS records show broad agreement, their trend is likely influenced by year-to-year changes in the number of observations.

Fire-Environment Analysis: An Example of Army Garrison Camp Williams, Utah

The planning of fuel treatments for ecological or societal purposes requires an in-depth understanding of the conditions associated with the occurrence of free-burning fire behavior for the area of concern. Detailed fire-environment analysis for Army Garrison Camp Williams (AGCW) in north-central Utah was completed as a prerequisite for fuel treatment planning, using a procedure that could be generally applied. Vegetation and fuels data, topographic and terrain features, and weather and climate data, were assessed and integrated into predictive fuel models to aid planning. A fire behavior fuel model map was developed from biophysical variables, vegetation type, and plot survey data using random forests, and resulted in an overall classification rate of 72%. The predominate vegetation type-fuel complex was grass, followed by lesser amounts of Gambel oak, Wyoming big sagebrush and Utah juniper. The majority of AGCW is mountainous in nature, characterized by slopes less than 40% in steepness with slightly more northerly and easterly aspects than south and west, and elevations that ranged from 1650 to 1950 m above mean sea level. Local fire weather data compiled from the three nearest remote automated weather stations indicated that average temperature maxima (32 °C) and relative humidity minima (12%) usually occurred between 1400 to 1500 h daily, and from July to August, seasonally. The semi-arid climate at AGCW, coupled with the corresponding preponderance of flashy fuel types and sloping terrain, constitutes a formidable fire environment in which to plan for mitigating against adverse fire behavior.

Air-Quality Challenges of Prescribed Fire in the Complex Terrain and Wildland Urban Interface Surrounding Bend, Oregon

Prescribed fires in forest ecosystems can negatively impact human health and safety by transporting smoke downwind into nearby communities. Smoke transport to communities is known to occur around Bend, Oregon, United States of America (USA), where burning at the wildland–urban interface in the Deschutes National Forest resulted in smoke intrusions into populated areas. The number of suitable days for prescribed fires is limited due to the necessity for moderate weather conditions, as well as wind directions that do not carry smoke into Bend. To better understand the conditions leading to these intrusions and to assess predictions of smoke dispersion from prescribed fires, we collected data from an array of weather and particulate monitors over the autumn of 2014 and spring of 2015 and historical weather data from nearby remote automated weather stations (RAWS). We characterized the observed winds to compare with meteorological and smoke dispersion models using the BlueSky smoke modeling framework. The results from this study indicated that 1–6 days per month in the spring and 2–4 days per month in the fall met the general meteorological prescription parameters for conducting prescribed fires in the National Forest. Of those, 13% of days in the spring and 5% of days in the fall had “ideal” wind patterns, when north winds occurred during the day and south winds did not occur at night. The analysis of smoke intrusions demonstrated that dispersion modeling can be useful for anticipating the timing and location of smoke impacts, but substantial errors in wind speed and direction of the meteorological models can lead to mischaracterizations of intrusion events. Additionally, for the intrusion event modeled using a higher-resolution 1-km meteorological and dispersion model, we found improved predictions of both the timing and location of smoke delivery to Bend compared with the 4-km meteorological model. The 1-km-resolution model prediction fell within 1 h of the observed event, although with underpredicted concentrations, and demonstrated promise for high-resolution modeling in areas of complex terrain.

Wildfire Smoke Cools Summer River and Stream Water Temperatures

AbstractTo test the hypothesis that wildfire smoke can cool summer river and stream water temperatures by attenuating solar radiation and air temperature, we analyzed data on summer wildfire smoke, solar radiation, air temperatures, precipitation, river discharge, and water temperatures in the lower Klamath River Basin in Northern California. Previous studies have focused on the effect of combustion heat on water temperatures during fires and the effect of riparian vegetation losses on postfire water temperatures, but we know of no studies of the effects of wildfire smoke on river or stream water temperatures. Wildfire smoke is difficult to quantify, but we successfully used a newly available daily high‐resolution (1 km) data set of aerosol optical thickness (AOT) derived from satellite imagery to represent smoke density during 6 years with extensive wildfire activity (2006, 2008, and 2012–2015). Smoke reduced solar radiation by 121 W m−2 per 1.0 AOT relative to clear‐sky conditions. Linear mixed‐effects models showed that on average, smoke cooled daily maximum and mean air temperatures by 0.98 °C and 0.47 °C per 1.0 AOT, respectively, across 19 remote automated weather stations. Smoke had a cooling effect on water temperatures at all 12 river and stream locations analyzed. On average, smoke cooled daily maximum and mean water temperatures by 1.32 °C and 0.74 °C per 1.0 AOT, respectively. This smoke‐induced cooling has the potential to benefit cold‐water adapted species, particularly because wildfires are more likely to occur during the warmest and driest years and seasons.

A 22-Year Climatology of Cool Season Hourly Precipitation Thresholds Conducive to Shallow Landslides in California

Abstract California’s winter storms produce intense rainfall capable of triggering shallow landslides, threatening lives and infrastructure. This study explores where hourly rainfall in the state meets or exceeds published values thought to trigger landslides after crossing a seasonal antecedent precipitation threshold. We answer the following questions: 1) Where in California are overthreshold events most common? 2) How are events distributed within the cool season (October–May) and interannually? 3) Are these events related to atmospheric rivers? To do this, we compile and quality control hourly precipitation data over a 22-yr period for 147 Remote Automated Weather Stations (RAWS). Stations in the Transverse and Coast Ranges and portions of the northwestern Sierra Nevada have the greatest number of rainfall events exceeding thresholds. Atmospheric rivers coincide with 60%–90% of these events. Overthreshold events tend to occur in the climatological wettest month of the year, and they commonly occur multiple times within a storm. These statewide maps depict where to expect intense rainfalls that have historically triggered shallow landslides. They predict that some areas of California are less susceptible to storm-driven landslides solely because high-intensity rainfall is unlikely.

Data assimilation of dead fuel moisture observations from remote automated weather stations

Fuel moisture has a major influence on the behaviour of wildland fires and is an important underlying factor in fire risk assessment. We propose a method to assimilate dead fuel moisture content (FMC) observations from remote automated weather stations (RAWS) into a time lag fuel moisture model. RAWS are spatially sparse and a mechanism is needed to estimate fuel moisture content at locations potentially distant from observational stations. This is arranged using a trend surface model (TSM), which allows us to account for the effects of topography and atmospheric state on the spatial variability of FMC. At each location of interest, the TSM provides a pseudo-observation, which is assimilated via Kalman filtering. The method is tested with the time lag fuel moisture model in the coupled weather-fire code WRF–SFIRE on 10-h FMC observations from Colorado RAWS in 2013. Using leave-one-out testing we show that the TSM compares favourably with inverse squared distance interpolation as used in the Wildland Fire Assessment System. Finally, we demonstrate that the data assimilation method is able to improve on FMC estimates in unobserved fuel classes.

Recent advances and applications of WRF–SFIRE

Abstract. Coupled atmosphere–fire models can now generate forecasts in real time, owing to recent advances in computational capabilities. WRF–SFIRE consists of the Weather Research and Forecasting (WRF) model coupled with the fire-spread model SFIRE. This paper presents new developments, which were introduced as a response to the needs of the community interested in operational testing of WRF–SFIRE. These developments include a fuel-moisture model and a fuel-moisture-data-assimilation system based on the Remote Automated Weather Stations (RAWS) observations, allowing for fire simulations across landscapes and time scales of varying fuel-moisture conditions. The paper also describes the implementation of a coupling with the atmospheric chemistry and aerosol schemes in WRF–Chem, which allows for a simulation of smoke dispersion and effects of fires on air quality. There is also a data-assimilation method, which provides the capability of starting the fire simulations from an observed fire perimeter, instead of an ignition point. Finally, an example of operational deployment in Israel, utilizing some of the new visualization and data-management tools, is presented.

Modeling Regional-Scale Wildland Fire Emissions with the Wildland Fire Emissions Information System*

Abstract As carbon modeling tools become more comprehensive, spatial data are needed to improve quantitative maps of carbon emissions from fire. The Wildland Fire Emissions Information System (WFEIS) provides mapped estimates of carbon emissions from historical forest fires in the United States through a web browser. WFEIS improves access to data and provides a consistent approach to estimating emissions at landscape, regional, and continental scales. The system taps into data and tools developed by the U.S. Forest Service to describe fuels, fuel loadings, and fuel consumption and merges information from the U.S. Geological Survey (USGS) and National Aeronautics and Space Administration on fire location and timing. Currently, WFEIS provides web access to Moderate Resolution Imaging Spectroradiometer (MODIS) burned area for North America and U.S. fire-perimeter maps from the Monitoring Trends in Burn Severity products from the USGS, overlays them on 1-km fuel maps for the United States, and calculates fuel consumption and emissions with an open-source version of the Consume model. Mapped fuel moisture is derived from daily meteorological data from remote automated weather stations. In addition to tabular output results, WFEIS produces multiple vector and raster formats. This paper provides an overview of the WFEIS system, including the web-based system functionality and datasets used for emissions estimates. WFEIS operates on the web and is built using open-source software components that work with open international standards such as keyhole markup language (KML). Examples of emissions outputs from WFEIS are presented showing that the system provides results that vary widely across the many ecosystems of North America and are consistent with previous emissions modeling estimates and products.

Using a prescribed fire to test custom and standard fuel models for fire behaviour prediction in a non‐native, grass‐invaded tropical dry shrubland

AbstractQuestionsDo fuel models developed for North American fuel types accurately represent fuel beds found in non‐native, grass‐invaded tropical dry shrublands? Do standard or custom fuel models used in fire behaviour models with in situ or remote automated weather stations (RAWS) measured fuel moistures affect the accuracy of predicted fire behaviour in grass‐invaded tropical shrublands?LocationHawai'i Volcanoes National Park, Hawai'i, USA.MethodsPre‐fire fuel loads of coarse woody debris, live herbaceous and live woody fuel loads were quantified with Brown's transects and biomass sampling to create a custom fuel model for non‐native grass‐invaded tropical dry shrublands in Hawai'i. In situ fuel moistures were quantified using oven‐dried vegetation samples, and compared to RAWS estimated fuel moistures. Fire behaviour was recorded on a stationary video camera to quantify flame length (FL) and rate of spread (ROS). Observed fire behaviour was compared to BehavePlus predicted fire behaviour parameterized with both standard and customized fuel models, and in situ and RAWS‐based estimates of fuel moisture.ResultsThe custom fuel model and measured fuel moistures performed better than most standard models, but over‐predicted actual ROS and top decile FL by 29% and 26%, respectively. The best match between observed and modelled fire behaviour came from a standard fuel model for shrublands with a grassy matrix (23% under‐prediction for ROS and 9% under‐prediction for FL) using measured fuel moistures. Using fuel moistures and wind speeds estimated from the nearest RAWS station (5 km from the fire) substantially decreased prediction accuracy of the custom fuel model and increased its relative error to 71% over‐prediction of ROS and 45% over‐prediction of FL.ConclusionsFire behaviour in at least some tropical fuel beds can be accurately modelled using certain standard or custom fuel models. Standard fuel models should not be applied uncritically to systems outside of North America, as our comparison showed widely ranging accuracy across six standard models. In addition, the current reliance on RAWS data for meteorological inputs to predict fire behaviour in the tropics, especially in the US‐affiliated tropical Pacific, must be used with caution. Instead, field‐measured fuel moistures should be used when possible.

Design and evaluation of an inexpensive radiation shield for monitoring surface air temperatures

Inexpensive temperature sensors are widely used in agricultural and forestry research. This paper describes a low-cost (∼3USD) radiation shield (radshield) designed for monitoring surface air temperatures in harsh outdoor environments. We compared the performance of the radshield paired with low-cost temperature sensors at three sites in western Montana to several types of commercially available instruments. Comparisons included observations made under a tree canopy and in full sun with both passive and mechanically aspirated radiation shields. Beneath a forest canopy, temperature sensors housed within the radshield showed bias of less than 0.5°C for hourly temperatures when compared with the same sensors housed in an unaspirated Gill-style shield. Sensors and shields mounted on poles in full sun were slightly warmer under low-wind conditions, but overall were cooler than data from an adjacent Remote Automated Weather Station (RAWS). When compared with observations from a high-quality temperature sensor housed in a mechanically aspirated solar radiation shield used in the Automated Surface Observing Systems (ASOS), observations from inexpensive temperature sensors housed within radshields were biased with mean absolute error of 0.99°C, but performed as well as those housed within a more expensive, commercially available Gill-style radiation shield. Our initial evaluation suggests that the radshield, instrumented with a low-cost sensor is suitable for monitoring surface air temperatures across a range of outdoor environments.

Diagnosing Santa Ana Winds in Southern California with Synoptic-Scale Analysis

Abstract Santa Ana winds (SAW) are among the most notorious fire-weather conditions in the United States and are implicated in wildfire and wind hazards in Southern California. This study employs large-scale reanalysis data to diagnose SAW through synoptic-scale dynamic and thermodynamic factors using mean sea level pressure gradient and lower-tropospheric temperature advection, respectively. A two-parameter threshold model of these factors exhibits skill in identifying surface-based characteristics of SAW featuring strong offshore winds and extreme fire weather as viewed through the Fosberg fire weather index across Remote Automated Weather Stations in southwestern California. These results suggest that a strong northeastward gradient in mean sea level pressure aligned with strong cold-air advection in the lower troposphere provide a simple, yet effective, means of diagnosing SAW from synoptic-scale reanalysis. This objective method may be useful for medium- to extended-range forecasting when mesoscale model output may not be available, as well as being readily applied retrospectively to better understand connections between SAW and wildfires in Southern California.

Continental wind patterns associated with Colorado alpine dust deposition: An application of the BLM/USFS RAWS network

The winter and early spring of 2008-2009 brought an unusually high number of alpine dust deposition events to the Rocky Mountains of Colorado. The greatest dust accumulations were observed in the San Juan Mountains of southwestern Colorado. Significant dust accumulation was even observed along the Continental Divide in northern Colorado. The primary source for this dust has previously been identified as the Colorado Plateau. Analysis using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) atmospheric trajectory model along with satellite imagery showed that dust from the 2009 events also originated from the Colorado Plateau, especially from areas in and around northeastern Arizona that were experiencing abnormally dry conditions that spring. The study utilized data from the BLM/USFS Remote Automated Weather Station (RAWS) network in the southwestern U.S. to identify periods of high winds corresponding to documented Colorado dust events. The RAWS database, once considered to be brief and unsuitable as a climate resource, is quickly approaching 30 years of record and provides a valuable resource for application to various climate questions. Analysis of wind data from these RAWS sites during known dust events show that a minimum threshold wind velocity exists before dust storm generation occurs, and that this threshold velocity occurs from a southwesterly direction. Threshold velocity for the daily mean speed was found to be 15 mph and 44 mph for daily maximum gusts. Wind speeds for the study region were then evaluated for the period January 1 A joint research project through the Bureau of Land Management and the Colorado Climate Center

Exposure Estimation Technology for Large Environmental Chemical Releases: Feasibility of an Atmospheric Plume Dispersion Model Without Microenvironmental Weather Data

O-30B7-2 Background/Aims: The collection of air monitoring data during the emergency response phase of a toxic chemical release is nearly impossible. Plume dispersion modeling is a technological solution which can provide data for use in health studies. We examined the feasibility of using a sophisticated plume dispersion model to estimate personal exposure for quasi-experimental human dose-response study following a large chlorine gas release within a community. The purpose of this study was to test if the absence of micro-environmental weather data significantly reduced model performance. Methods: A HPAC plume dispersion model was generated for the 60 ton Chlorine spill in Graniteville, South Carolina without any microenvironmental weather data. Estimates of mean chlorine concentration (kg-sec/m3) at each location at 1.5 meters above ground level for each minute during the time period were output from the HPAC model. Archived meteorological surface observations recorded at 12 Remote Automated Weather Stations (RAWS) were collected for the 24-hour period beginning 0:00 hours on the day of the spill. RAWS data were, also, collected from the same 12 stations for 5 nights from 7 January through 12 January (10 January was excluded due to missing data) to compare with readings from the portable microenvironmental meteorological station set up at the incident site late in the morning of the release. Concentration and surface dosage were calculated at 1024 points across a 3 × 3 mile grid with identical source terms using RAWS and microenvironmental weather data separately. Results: Data from the HPAC plume dispersion model without microenvironmental weather data were highly significantly correlated with the model that included microenvironmental data (r2 = 0.98). Conclusion: In the United States, the HPAC plume dispersion model can provide valid Chlorine gas exposure estimates of human exposures following a large release in a community even without localized microenvironmental weather data.

An Evaluation of the Distribution of Remote Automated Weather Stations (RAWS)

Abstract This study estimates whether surface observations of temperature, moisture, and wind at some stations in the continental United States are less critical than others for specifying weather conditions in the vicinity of those stations. Two-dimensional variational analyses of temperature, relative humidity, and wind were created for selected midday hours during summer 2008. This set of 8925 control analyses was derived from 5-km-resolution background fields and Remote Automated Weather Station (RAWS) and National Weather Service (NWS) observations within roughly 4° × 4° latitude–longitude domains. Over 570 000 cross-validation experiments were completed to assess the impact of removing each RAWS and NWS station. The presence of observational assets within relatively close proximity to one another is relatively common. The sensitivity to removing temperature, relative humidity, or wind observations varies regionally and depends on the complexity of the surrounding terrain and the representativeness of the observations. Cost savings for the national RAWS program by removing a few stations may be possible. However, nearly all regions of the country remain undersampled, especially mountainous regions of the western United States frequently affected by wildfires.

Estimation of Fire Danger in Hawai'i Using Limited Weather Data and Simulation

The presence of fire in Hawai'i has increased with introduction of nonnative grasses. Fire danger estimation using the National Fire Danger Rating System (NFDRS) typically requires 5 to 10 yr of data to determine percentile weather values and fire activity. The U.S. Army Pōhakuloa Training Area in Hawai'i is located in the interface zone between windward and leeward weather conditions and needed to develop fire danger values but did not have sufficient weather or fire occurrence data. Use of simulation to estimate fire danger (expressed as fire risk) for areas with limited weather data was investigated. Influence of spatial resolution of weather information on fire risk was examined by comparing fire risk calculated using one or three weather stations and gridded weather predictions from the Mesoscale Spectral Model. Predicted gridded temperature was positively correlated with observed temperature; predicted and observed relative humidity were not significantly correlated. Simulated fire risk differed between weather data percentiles and between weather data resolutions. Predicted risk estimated from gridded weather data agreed more closely with observed risk estimated from weather data observed at all three remote automated weather stations. Correlation between simulated fire risk and the NFDRS Ignition Component was statistically significant for the single weather station simulations. Correlations between risk and the Ignition Component were not statistically significant for the three station and gridded weather data scenarios, which illustrates the difference between fire danger determined at broad spatial scales and fire risk resolved at finer spatial scales. Fire spread simulation modeling to estimate fire risk in areas with limited historical weather and fire occurrence data can provide finer-scale information than the NFDRS, which is better suited to larger, homogeneous areas with more complete fire and weather data. Values for the NFDRS Burning Index were determined and incorporated into the wildland fire management plan for Pōhakuloa Training Area.

The effects of sudden oak death on foliar moisture content and crown fire potential in tanoak

The introduction of non-native pathogens can have profound effects on forest ecosystems resulting in loss of species, changes in species composition, and altered fuel structure. The introduction of Phytophthora ramorum, the pathogen recognized as causing Sudden Oak Death (SOD), leads to rapid decline and mortality of tanoak ( Lithocarpus densiflorus) in forests of coastal California, USA. We tracked foliar moisture content (FMC) of uninfected tanoaks, SOD-infected tanoaks, SOD-killed (dead) tanoaks, and surface litter for 12 months. We found that FMC values differed significantly among the three categories of infection. FMC of uninfected tanoaks averaged 82.3% for the year whereas FMC of infected tanoaks had a lower average of 77.8% (ANOVA, P = 0.04). Dead trees had a significantly lower FMC, averaging 12.3% (ANOVA, P < 0.01) for the year. During fire season (June–September), dead tanoak FMC reached a low of 5.8%, with no significant difference between dead canopy fuels and surface litter (ANOVA, P = 0.44). Application of low FMC values to a crown ignition model results in extremely high canopy base height values to escape crown ignition. Remote estimation of dead FMC using 10-h timelag fuel moisture shows a strong correlation between remote automated weather station (RAWS) 10-h timelag fuel moisture data and the FMC of dead leaves ( R 2 = 0.78, P < 0.01). Results from this study will help refine the decision support tools for fire managers in SOD-affected areas as well as conditions in other forests where diseases and insect epidemics have altered forest canopy fuels.

Sensitivity of Surface Analyses over the Western United States to RAWS Observations

Abstract Federal, state, and other wildland resource management agencies contribute to the collection of weather observations from over 1000 Remote Automated Weather Stations (RAWS) in the western United States. The impact of RAWS observations on surface objective analyses during the 2003/04 winter season was assessed using the Advanced Regional Prediction System (ARPS) Data Assimilation System (ADAS). A set of control analyses was created each day at 0000 and 1200 UTC using the Rapid Update Cycle (RUC) analyses as the background fields and assimilating approximately 3000 surface observations from MesoWest. Another set of analyses was generated by withholding all of the RAWS observations available at each time while 10 additional sets of analyses were created by randomly withholding comparable numbers of observations obtained from all sources. Random withholding of observations from the analyses provides a baseline estimate of the analysis quality. Relative to this baseline, removing the RAWS observations degrades temperature (wind speed) analyses by an additional 0.5°C (0.9 m s−1) when evaluated in terms of rmse over the entire season. RAWS temperature observations adjust the RUC background the most during the early morning hours and during winter season cold pool events in the western United States while wind speed observations have a greater impact during active weather periods. The average analysis area influenced by at least 1.0°C (2.5°C) by withholding each RAWS observation is on the order of 600 km2 (100 km2). The spatial influence of randomly withheld observations is much less.

Evaluation of the Experimental Climate Prediction Center's fire danger forecasts with remote automated weather station observations

The Scripps Experimental Climate Prediction Center has been routinely making regional forecasts of atmospheric elements and fire danger indices since 27 September 1997. This study evaluates these forecasts using selected remote automated weather station observations over the western USA. Bias and anomaly correlations are computed for daily 2-m maximum, minimum, average temperature, 2-m maximum, minimum and average relative humidity, precipitation and afternoon 10-m wind speed, and four National Fire Danger Rating System indices—ignition component, spread component, burning index and energy release component. Of the atmospheric elements, temperature generally correlates well, but relative humidity, precipitation and wind speed are less correlated. Fire danger indices have much lower correlations, but do show useful spatial structure in some areas such as Southern California, Arizona and Nevada.