Fire Weather Products for Public and Emergency Use: Extending Professional Resources to the Public

NASA Astrophysics Data System (ADS)

Rogers, M. A.; Schranz, S.; Kriederman, L.

2012-12-01

Large wildfires require significant resources to combat, including dedicated meteorological support to provide accurate and timely forecasts to assist incident commanders in making decisions for logistical and tactical firefighting operations. Smaller fires often require the same capabilities for understanding fire and the fire weather environment, but access to needed resources and tools is often limited due to technical, training, or education limitations. Providing fire weather information and training to incident commanders for smaller wildfires should prove to enhance firefighting capabilities and improve safety for both firefighters and for the public as well. One of the premier tools used to support fire weather forecasting for the largest wildfires is the FX-Net product, a thin-client version of the Advanced Weather Interactive Processing System used by NWS incident meteorologists (IMETs) deployed to large wildfires. We present results from an ongoing project to extend the sophisticated products available from FX-Net to more accessible and mobile software platforms, such as Google Earth. The project involves input from IMETs and fire commanders to identify the key parameters used in fighting wildfires, and involves a large training component for fire responders to utilize simplified products to improve understanding of fire weather in the context of firefighting operations.

Model Analyses and Guidance

Science.gov Websites

Fire Weather Sun/Moon Long Range Forecasts Climate Prediction Past Weather Past Weather Heating/Cooling Space Weather Sun (Ultraviolet Radiation) Safety Campaigns Wind Drought Winter Weather Information

Forecasting distributions of large federal-lands fires utilizing satellite and gridded weather information

Treesearch

H.K. Preisler; R.E. Burgan; J.C. Eidenshink; J.M. Klaver; R.W. Klaver

2009-01-01

The current study presents a statistical model for assessing the skill of fire danger indices and for forecasting the distribution of the expected numbers of large fires over a given region and for the upcoming week. The procedure permits development of daily maps that forecast, for the forthcoming week and within federal lands, percentiles of the distributions of (i)...

Seasonal Forecasts of Extreme Conditions for Wildland Fire Management in Alaska using NMME

NASA Astrophysics Data System (ADS)

Bhatt, U. S.; Bieniek, P.; Thoman, R.; York, A.; Ziel, R.

2016-12-01

The summer of 2015 was the second largest Alaska fire season since 1950 where approximately the land area of Massachusetts burned. The record fire year of 2004 resulted in 6.5 million acres burned and was costly from property loss (> 35M) and emergency personnel (> 17M). In addition to requiring significant resources, wildfire smoke impacts air quality in Alaska and downstream into North America. Fires in Alaska result from lightning strikes coupled with persistent (extreme) dry warm conditions in remote areas with limited fire management and the seasonal climate/weather determine the extent of the fire season in Alaska. Fire managers rely on weather/climate outlooks for allocating staff and resources from days to a season in advance. Though currently few tested products are available at the seasonal scale. Probabilistic forecasts of the expected seasonal climate/weather would aid tremendously in the planning process. Advanced knowledge of both lightning and fuel conditions would assist managers in planning resource allocation for the upcoming season. For fuel conditions, the Canadian Forest Fire Weather Index System (CFFWIS) has been used since 1992 because it better suits the Alaska fire regime than the standard US National Fire Danger Rating System (NFDRS). This CFFWIS is based on early afternoon values of 2-m air temperature, relative humidity, and 10-m winds and daily total precipitation. Extremes of these indices and the variables are used to calculate these indices will be defined in reference to fire weather for the boreal forest. The CFFWIS will be applied and evaluated for the NMME hindcasts. This study will evaluate the quality of the forecasts comparing the hindcast NMME CFFWIS to acres burned in Alaska. Spatial synoptic patterns in the NMME related to fire weather extremes will be constructed using self-organized maps and probabilities of occurrence will be evaluated against acres burned.

Fire danger assessment using ECMWF weather prediction system

NASA Astrophysics Data System (ADS)

Di Giuseppe, Francesca; Pappemberger, Florian; Wetterhall, Fredrik

2015-04-01

Weather plays a major role in the birth, growth and death of a wildfire wherever there is availability of combustible vegetation and suitable terrain topography. Prolonged dry periods creates favourable conditions for ignitions, wind can then increase the fire spread, while higher relative humidity, and precipitation (rain or snow) may decrease or extinguish it altogether. The European Forest Fire Information System (EFFIS), started in 2011 under the lead of the European Joint Research Centre (JRC) to monitor and forecast fire danger and fire behaviour in Europe. In 2012 a collaboration with the European Centre for Medium range Weather Forecast (ECMWF) was established to explore the potential of using state of the art weather forecast systems as driving forcing for the calculations of fire risk indices. From this collaboration in 2013 the EC-fire system was born. It implements the three most commonly used fire danger rating systems (NFDRS, FWI and MARK-5) and it is both initialised and forced by gridded atmospheric fields provided either by ECMWF re-analysis or ECMWF ensemble prediction systems. For consistency invariant fields (i.e fuel maps, vegetation cover, topogarphy) and real-time weather information are all provided on the same grid. Similarly global climatological vegetation stage conditions for each day of the year are provided by remote satellite observations. These climatological static maps substitute the traditional man judgement in an effort to create an automated procedure that can work in places where local observations are not available. The system has been in operation for the last year providing an ensemble of daily forecasts for fire indices with lead-times up to 10 days over Europe and Globally. An important part of the system is provided by its (re)-analysis dataset obtained by using the (re)-analysis forcings as drivers to calculate the fire risk indices. This is a crucial part of the whole chain since these fields are used to establish the initial conditions from which the forecast is subsequently run. The reanalysis dataset goes back to year 1980 (the starting year of ERA-Interim integrations) and is updated in quasi real time. In addition of providing the staring point for the operational forecasts it is a very useful dataset for the scope of calibration and verification of the system. Assuming reanalysis fields are good proxies for observations then, by comparison with fire events which really occurred, this dataset can be used to assess the potential predictability of fire risk indices. In this work we will introduce the EC-fire system. Then the reanalysis dataset will be used to identify regions of high fire risk predictability and where the system might be in need of further refinement.

Storm Prediction Center Day 3-8 Fire Weather Forecast Issued on May 27,

Science.gov Websites

National RADAR Product Archive NOAA Weather Radio Research Non-op. Products Forecast Tools Svr. Tstm information in MS-Word or PDF. Note: Through September 29, 2015 the SPC will issue Experimental Probabilistic

Forecasting distributions of large federal-lands fires utilizing satellite and gridded weather information

USGS Publications Warehouse

Preisler, H.K.; Burgan, R.E.; Eidenshink, J.C.; Klaver, Jacqueline M.; Klaver, R.W.

2009-01-01

The current study presents a statistical model for assessing the skill of fire danger indices and for forecasting the distribution of the expected numbers of large fires over a given region and for the upcoming week. The procedure permits development of daily maps that forecast, for the forthcoming week and within federal lands, percentiles of the distributions of (i) number of ignitions; (ii) number of fires above a given size; (iii) conditional probabilities of fires greater than a specified size, given ignition. As an illustration, we used the methods to study the skill of the Fire Potential Index an index that incorporates satellite and surface observations to map fire potential at a national scale in forecasting distributions of large fires. ?? 2009 IAWF.

Assessing the value of increased model resolution in forecasting fire danger

Treesearch

Jeanne Hoadley; Miriam Rorig; Ken Westrick; Larry Bradshaw; Sue Ferguson; Scott Goodrick; Paul Werth

2003-01-01

The fire season of 2000 was used as a case study to assess the value of increasing mesoscale model resolution for fire weather and fire danger forecasting. With a domain centered on Western Montana and Northern Idaho, MM5 simulations were run at 36, 12, and 4-km resolutions for a 30 day period at the height of the fire season. Verification analyses for meteorological...

Development of a Statistical Validation Methodology for Fire Weather Indices

Treesearch

Brian E. Potter; Scott Goodrick; Tim Brown

2003-01-01

Fire managers and forecasters must have tools, such as fire indices, to summarize large amounts of complex information. These tools allow them to identify and plan for periods of elevated risk and/or wildfire potential. This need was once met using simple measures like relative humidity or maximum daily temperature (e.g., Gisborne, 1936) to describe fire weather, and...

Forecasting wildland fire behavior using high-resolution large-eddy simulations

NASA Astrophysics Data System (ADS)

Munoz-Esparza, D.; Kosovic, B.; Jimenez, P. A.; Anderson, A.; DeCastro, A.; Brown, B.

2016-12-01

Wildland fires are responsible for large socio-economic impacts. Fires affect the environment, damage structures, threaten lives, cause health issues, and involve large suppression costs. These impacts can be mitigated via accurate fire spread forecast to inform the incident management team. To this end, the state of Colorado is funding the development of the Colorado Fire Prediction System (CO-FPS). The system is based on the Weather Research and Forecasting (WRF) model enhanced with a fire behavior module (WRF-Fire). Realistic representation of wildland fire behavior requires explicit representation of small scale weather phenomena to properly account for coupled atmosphere-wildfire interactions. Moreover, transport and dispersion of biomass burning emissions from wildfires is controlled by turbulent processes in the atmospheric boundary layer, which are difficult to parameterize and typically lead to large errors when simplified source estimation and injection height methods are used. Therefore, we utilize turbulence-resolving large-eddy simulations at a resolution of 111 m to forecast fire spread and smoke distribution using a coupled atmosphere-wildfire model. This presentation will describe our improvements to the level-set based fire-spread algorithm in WRF-Fire and an evaluation of the operational system using 12 wildfire events that occurred in Colorado in 2016, as well as other historical fires. In addition, the benefits of explicit representation of turbulence for smoke transport and dispersion will be demonstrated.

Forecasting wildland fire behavior using high-resolution large-eddy simulations

NASA Astrophysics Data System (ADS)

Munoz-Esparza, D.; Kosovic, B.; Jimenez, P. A.; Anderson, A.; DeCastro, A.; Brown, B.

2017-12-01

Wildland fires are responsible for large socio-economic impacts. Fires affect the environment, damage structures, threaten lives, cause health issues, and involve large suppression costs. These impacts can be mitigated via accurate fire spread forecast to inform the incident management team. To this end, the state of Colorado is funding the development of the Colorado Fire Prediction System (CO-FPS). The system is based on the Weather Research and Forecasting (WRF) model enhanced with a fire behavior module (WRF-Fire). Realistic representation of wildland fire behavior requires explicit representation of small scale weather phenomena to properly account for coupled atmosphere-wildfire interactions. Moreover, transport and dispersion of biomass burning emissions from wildfires is controlled by turbulent processes in the atmospheric boundary layer, which are difficult to parameterize and typically lead to large errors when simplified source estimation and injection height methods are used. Therefore, we utilize turbulence-resolving large-eddy simulations at a resolution of 111 m to forecast fire spread and smoke distribution using a coupled atmosphere-wildfire model. This presentation will describe our improvements to the level-set based fire-spread algorithm in WRF-Fire and an evaluation of the operational system using 12 wildfire events that occurred in Colorado in 2016, as well as other historical fires. In addition, the benefits of explicit representation of turbulence for smoke transport and dispersion will be demonstrated.

National Weather Service

MedlinePlus

... Data SAFETY Floods Tsunami Beach Hazards Wildfire Cold Tornadoes Fog Air Quality Heat Hurricanes Lightning Safe Boating ... Winter Weather Forecasts River Flooding Latest Warnings Thunderstorm/Tornado Outlook Hurricanes Fire Weather Outlooks UV Alerts Drought ...

Application of the Haines Index in the fire warning system

NASA Astrophysics Data System (ADS)

Kalin, Lovro; Marija, Mokoric; Tomislav, Kozaric

2016-04-01

Croatia, as all Mediterranean countries, is strongly affected by large wildfires, particularly in the coastal region. In the last two decades the number and intensity of fires has been significantly increased, which is unanimously associated with climate change, e.g. global warming. More extreme fires are observed, and the fire-fighting season has been expanded to June and September. The meteorological support for fire protection and planning is therefore even more important. At the Meteorological and Hydrological Service of Croatia a comprehensive monitoring and warning system has been established. It includes standard components, such as short term forecast of Fire Weather Index (FWI), but long range forecast as well. However, due to more frequent hot and dry seasons, FWI index often does not provide additional information of extremely high fire danger, since it regularly takes the highest values for long periods. Therefore the additional tools have been investigated. One of widely used meteorological products is the Haines index (HI). It provides information of potential fire growth, taking into account only the vertical instability of the atmosphere, and not the state of the fuel. Several analyses and studies carried out at the Service confirmed the correlation of high HI values with large and extreme fires. The Haines index forecast has been used at the Service for several years, employing European Centre for Medium Range Weather Forecast (ECMWF) global prediction model, as well as the limited-area Aladin model. The verification results show that these forecast are reliable, when compared to radiosonde measurements. All these results provided the introduction of the additional fire warnings, that are issued by the Service's Forecast Department.

Weather monitoring and forecasting over eastern Attica (Greece) in the frame of FLIRE project

NASA Astrophysics Data System (ADS)

Kotroni, Vassiliki; Lagouvardos, Konstantinos; Chrysoulakis, Nektarios; Makropoulos, Christtos; Mimikou, Maria; Papathanasiou, Chrysoula; Poursanidis, Dimitris

2015-04-01

In the frame of FLIRE project a Decision Support System has been built with the aim to support decision making of Civil Protection Agencies and local stakeholders in the area of east Attica (Greece), in the cases of forest fires and floods. In this presentation we focus on a specific action that focuses on the provision of high resolution short-term weather forecasting data as well as of dense meteorological observations over the study area. Both weather forecasts and observations serve as an input in the Weather Information Management Tool (WIMT) of the Decision Support System. We focus on: (a) the description of the adopted strategy for setting-up the operational weather forecasting chain that provides the weather forecasts for the FLIRE project needs, (b) the presentation of the surface network station that provides real-time weather monitoring of the study area and (c) the strategy adopted for issuing smart alerts for thunderstorm forecasting based of real-time lightning observations as well as satellite observations.

Predicting Near-surface Winds with WindNinja for Wind Energy Applications

NASA Astrophysics Data System (ADS)

Wagenbrenner, N. S.; Forthofer, J.; Shannon, K.; Butler, B.

2016-12-01

WindNinja is a high-resolution diagnostic wind model widely used by operational wildland fire managers to predict how near-surface winds may influence fire behavior. Many of the features which have made WindNinja successful for wildland fire are also important for wind energy applications. Some of these features include flexible runtime options which allow the user to initialize the model with coarser scale weather model forecasts, sparse weather station observations, or a simple domain-average wind for what-if scenarios; built-in data fetchers for required model inputs, including gridded terrain and vegetation data and operational weather model forecasts; relatively fast runtimes on simple hardware; an extremely user-friendly interface; and a number of output format options, including KMZ files for viewing in Google Earth and GeoPDFs which can be viewed in a GIS. The recent addition of a conservation of mass and momentum solver based on OpenFOAM libraries further increases the utility of WindNinja to modelers in the wind energy sector interested not just in mean wind predictions, but also in turbulence metrics. Here we provide an evaluation of WindNinja forecasts based on (1) operational weather model forecasts and (2) weather station observations provided by the MesoWest API. We also compare the high-resolution WindNinja forecasts to the coarser operational weather model forecasts. For this work we will use the High Resolution Rapid Refresh (HRRR) model and the North American Mesoscale (NAM) model. Forecasts will be evaluated with data collected in the Birch Creek valley of eastern Idaho, USA between June-October 2013. Near-surface wind, turbulence data, and vertical wind and temperature profiles were collected at very high spatial resolution during this field campaign specifically for use in evaluating high-resolution wind models like WindNinja. This work demonstrates the ability of WindNinja to generate very high-resolution wind forecasts for wind energy applications and evaluates the forecasts produced by two different initialization methods with data collected in a broad valley surrounded by complex terrain.

Modeling fire behavior on tropical islands with high-resolution weather data

Treesearch

John W. Benoit; Francis M. Fujioka; David R. Weise

2009-01-01

In this study, we consider fire behavior simulation in tropical island scenarios such as Hawaii and Puerto Rico. The development of a system to provide real-time fire behavior prediction in Hawaii is discussed. This involves obtaining fuels and topography information at a fine scale, as well as supplying daily high-resolution weather forecast data for the area of...

Fire danger rating over Mediterranean Europe based on fire radiative power derived from Meteosat

NASA Astrophysics Data System (ADS)

Pinto, Miguel M.; DaCamara, Carlos C.; Trigo, Isabel F.; Trigo, Ricardo M.; Feridun Turkman, K.

2018-02-01

We present a procedure that allows the operational generation of daily forecasts of fire danger over Mediterranean Europe. The procedure combines historical information about radiative energy released by fire events with daily meteorological forecasts, as provided by the Satellite Application Facility for Land Surface Analysis (LSA SAF) and the European Centre for Medium-Range Weather Forecasts (ECMWF). Fire danger is estimated based on daily probabilities of exceedance of daily energy released by fires occurring at the pixel level. Daily probability considers meteorological factors by means of the Canadian Fire Weather Index (FWI) and is estimated using a daily model based on a generalized Pareto distribution. Five classes of fire danger are then associated with daily probability estimated by the daily model. The model is calibrated using 13 years of data (2004-2016) and validated against the period of January-September 2017. Results obtained show that about 72 % of events releasing daily energy above 10 000 GJ belong to the extreme class of fire danger, a considerably high fraction that is more than 1.5 times the values obtained when using the currently operational Fire Danger Forecast module of the European Forest Fire Information System (EFFIS) or the Fire Risk Map (FRM) product disseminated by the LSA SAF. Besides assisting in wildfire management, the procedure is expected to help in decision making on prescribed burning within the framework of agricultural and forest management practices.

Ecohydrology: When will the jungle burn?

NASA Astrophysics Data System (ADS)

Bowman, David

2017-06-01

Fire weather indices are unsuited to forecast fire in tropical rainforests. Now research shows the area burnt across Borneo is related to drought-depleted water tables, presenting the opportunity to predict fire danger in these environments.

Meteorological Controls on Biomass Burning During Santa Ana Events in Southern California

NASA Technical Reports Server (NTRS)

Veraverbeke, Sander; Capps, Scott; Hook, Simon J.; Randerson, James T.; Jin, Yufang; Hall, Alex

2013-01-01

Fires occurring during Santa Ana (SA) events in southern California are driven by extreme fire weather characterized by high temperatures, low humidities, and high wind speeds. We studied the controls on burned area and carbon emissions during two intensive SA burning periods in 2003 and 2007. We therefore used remote sensing data in parallel with fire weather simulations of the Weather and Regional Forecast model. Total carbon emissions were approximately 1800 gigagrams in 2003 and 900 gigagrams in 2007, based on a daily burned area and a fire emission model that accounted for spatial variability in fuel loads and combustion completeness. On a regional scale, relatively strong positive correlations were found between the daily Fosberg fire weather index and burned area/emissions (probability is less than 0.01). Our analysis provides a quantitative assessment of relationships between fire activity and weather during severe SA fires in southern California.

Adjustment of relative humidity and temperature for differences in elevation.

Treesearch

Owen P. Cramer

1961-01-01

The variation of fire-weather elements in mountainous terrain is complex at any one time, and the patterns vary considerably with time. During periods of serious fire weather, this variation becomes important. Much information is obtainable by local interpretation of available forecasts and observations. Optimum use of available information requires some understanding...

Wildland Fire Forecasting: Predicting Wildfire Behavior, Growth, and Feedbacks on Weather

NASA Astrophysics Data System (ADS)

Coen, J. L.

2005-12-01

Recent developments in wildland fire research models have represented more complex of fire behavior. The cost has been to increase the computational requirements. When operational constraints are included, such as the need to produce such forecasts faster than real time, the challenge becomes a balance of how much complexity (with corresponding gains in realism) and accuracy can be achieved in producing the quantities of interest while meeting the specified operational constraints. Current field tools are calculator or Palm-Pilot based algorithms such as BEHAVE and BEHAVE Plus that produce timely estimates of instantaneous fire spread rates, flame length, and fire intensity at a point using readily estimated inputs of fuel model, terrain slope, and atmospheric wind speed at a point. At the cost of requiring a PC and slower calculation, FARSITE represents two-dimensional fire spread and adds capabilities including a parameterized representation of crown fire ignition, This work describes how a coupled atmosphere-fire model previously used as a research tool has been adapted for production of real-time forecasts of fire growth and its interactions with weather over a domain focusing on Colorado during summer 2004. The coupled atmosphere-wildland fire-environment (CAWFE) model composed of a 3-dimensional atmospheric prediction model that has been two-way coupled with an empirical fire spread model. The models are connected in that atmospheric conditions (and fuel conditions influenced by the atmosphere) affect the rate and direction of fire propagation, which releases sensible and latent heat (i.e. thermal and water vapor fluxes) to the atmosphere that in turn alter the winds and atmospheric structure around the fire. Thus, it can represent time and spatially-varying weather and the fire feedbacks on the atmospheric which are at the heart of sudden changes in fire behavior and examples of extreme fire behavior such as blow ups, which are now not predictable with current tools. Thus, although this work shows that is it possible to perform more detailed simulations in real time, fire behavior forecasting remains a challenging problem. This is due to challenges in weather prediction, particularly at fine spatial and temporal scales considered "nowcasting" (0-6 hrs), uncertainties in fire behavior even with known meteorological conditions, limitations in quantitative datasets on fuel properties such as fuel loading, and verification. This work describes efforts to advance these capabilities with input from remote sensing data on fuel characteristics and dynamic steering and object-based verification with remotely sensed fire perimeters.

Using Haines Index coupled with fire weather model predicted from high resolution LAM forecasts to asses wildfire extreme behaviour in Southern Europe.

NASA Astrophysics Data System (ADS)

Gaetani, Francesco; Baptiste Filippi, Jean; Simeoni, Albert; D'Andrea, Mirko

2010-05-01

Haines Index (HI) was developed by USDA Forest Service to measure the atmosphere's contribution to the growth potential of a wildfire. The Haines Index combines two atmospheric factors that are known to have an effect on wildfires: Stability and Dryness. As operational tools, HI proved its ability to predict plume dominated high intensity wildfires. However, since HI does not take into account the fuel continuity, composition and moisture conditions and the effects of wind and topography on fire behaviour, its use as forecasting tool should be carefully considered. In this work we propose the use of HI, predicted from HR Limited Area Model forecasts, coupled with a Fire Weather model (i.e., RISICO system) fully operational in Italy since 2003. RISICO is based on dynamic models able to represent in space and in time the effects that environment and vegetal physiology have on fuels and, in turn, on the potential behaviour of wildfires. The system automatically acquires from remote databases a thorough data-set of input information both of in situ and spatial nature. Meteorological observations, radar data, Limited Area Model weather forecasts, EO data, and fuel data are managed by a Unified Interface able to process a wide set of different data. Specific semi-physical models are used in the system to simulate the dynamics of the fuels (load and moisture contents of dead and live fuel) and the potential fire behaviour (rate of spread and linear intensity). A preliminary validation of this approach will be provided with reference to Sardinia and Corsica Islands, two major islands of the Mediterranean See frequently affected by extreme plume dominated wildfires. A time series of about 3000 wildfires burnt in Sardinia and Corsica in 2007 and 2008 will be used to evaluate the capability of HI coupled with the outputs of the Fire Weather model to forecast the actual risk in time and in space.

NCEP-ECPC monthly to seasonal US fire danger forecasts

Treesearch

J. Roads; P. Tripp; H. Juang; J. Wang; F. Fujioka; S. Chen

2010-01-01

Five National Fire Danger Rating System indices (including the Ignition Component, Energy Release Component, Burning Index, Spread Component, and the Keetch–Byram Drought Index) and the Fosberg Fire Weather Index are used to characterise US fire danger. These fire danger indices and input meteorological variables, including temperature, relative humidity, precipitation...

Fire Consortia for Advanced Modeling of Meteorology and Smoke-FCAMMS: a National Paradigm for Wildland Fire and Smoke Management

Treesearch

A. R. Riebau; D. G. Fox

2003-01-01

Fires can be catastrophic, but only when the weather permits. Predicting the weather more than a few hours into the future with accuracy, precision and reliability is an on-going challenge to researchers. Accurate and precise forecasting for more than a few hours into the future has been virtually unrealizable until the latter half of the 20th Century. In the modern...

Land cover, more than monthly fire weather, drives fire-size distribution in Southern Québec forests: Implications for fire risk management.

PubMed

Marchal, Jean; Cumming, Steve G; McIntire, Eliot J B

2017-01-01

Fire activity in North American forests is expected to increase substantially with climate change. This would represent a growing risk to human settlements and industrial infrastructure proximal to forests, and to the forest products industry. We modelled fire size distributions in southern Québec as functions of fire weather and land cover, thus explicitly integrating some of the biotic interactions and feedbacks in a forest-wildfire system. We found that, contrary to expectations, land-cover and not fire weather was the primary driver of fire size in our study region. Fires were highly selective on fuel-type under a wide range of fire weather conditions: specifically, deciduous forest, lakes and to a lesser extent recently burned areas decreased the expected fire size in their vicinity compared to conifer forest. This has large implications for fire risk management in that fuels management could reduce fire risk over the long term. Our results imply, for example, that if 30% of a conifer-dominated landscape were converted to hardwoods, the probability of a given fire, occurring in that landscape under mean fire weather conditions, exceeding 100,000 ha would be reduced by a factor of 21. A similarly marked but slightly smaller effect size would be expected under extreme fire weather conditions. We attribute the decrease in expected fire size that occurs in recently burned areas to fuel availability limitations on fires spread. Because regenerating burned conifer stands often pass through a deciduous stage, this would also act as a negative biotic feedback whereby the occurrence of fires limits the size of nearby future for some period of time. Our parameter estimates imply that changes in vegetation flammability or fuel availability after fires would tend to counteract shifts in the fire size distribution favoring larger fires that are expected under climate warming. Ecological forecasts from models neglecting these feedbacks may markedly overestimate the consequences of climate warming on fire activity, and could be misleading. Assessments of vulnerability to climate change, and subsequent adaptation strategies, are directly dependent on integrated ecological forecasts. Thus, we stress the need to explicitly incorporate land-cover's direct effects and feedbacks in simulation models of coupled climate-fire-fuels systems.

National Centers for Environmental Prediction (NCEP)

Science.gov Websites

Tropical Marine Fire Weather Forecast Maps Unified Surface Analysis Climate Climate Prediction Climate forecasts of hazardous flight conditions at all levels within domestic and international air space. Climate Prediction Center monitors and forecasts short-term climate fluctuations and provides information on the

Land cover, more than monthly fire weather, drives fire-size distribution in Southern Québec forests: Implications for fire risk management

PubMed Central

Marchal, Jean; Cumming, Steve G.; McIntire, Eliot J. B.

2017-01-01

Fire activity in North American forests is expected to increase substantially with climate change. This would represent a growing risk to human settlements and industrial infrastructure proximal to forests, and to the forest products industry. We modelled fire size distributions in southern Québec as functions of fire weather and land cover, thus explicitly integrating some of the biotic interactions and feedbacks in a forest-wildfire system. We found that, contrary to expectations, land-cover and not fire weather was the primary driver of fire size in our study region. Fires were highly selective on fuel-type under a wide range of fire weather conditions: specifically, deciduous forest, lakes and to a lesser extent recently burned areas decreased the expected fire size in their vicinity compared to conifer forest. This has large implications for fire risk management in that fuels management could reduce fire risk over the long term. Our results imply, for example, that if 30% of a conifer-dominated landscape were converted to hardwoods, the probability of a given fire, occurring in that landscape under mean fire weather conditions, exceeding 100,000 ha would be reduced by a factor of 21. A similarly marked but slightly smaller effect size would be expected under extreme fire weather conditions. We attribute the decrease in expected fire size that occurs in recently burned areas to fuel availability limitations on fires spread. Because regenerating burned conifer stands often pass through a deciduous stage, this would also act as a negative biotic feedback whereby the occurrence of fires limits the size of nearby future for some period of time. Our parameter estimates imply that changes in vegetation flammability or fuel availability after fires would tend to counteract shifts in the fire size distribution favoring larger fires that are expected under climate warming. Ecological forecasts from models neglecting these feedbacks may markedly overestimate the consequences of climate warming on fire activity, and could be misleading. Assessments of vulnerability to climate change, and subsequent adaptation strategies, are directly dependent on integrated ecological forecasts. Thus, we stress the need to explicitly incorporate land-cover’s direct effects and feedbacks in simulation models of coupled climate–fire–fuels systems. PMID:28609467

Developing the U.S. Wildland Fire Decision Support System

Treesearch

Erin Noonan-Wright; Tonja S. Opperman; Mark A. Finney; Tom Zimmerman; Robert C. Seli; Lisa M. Elenz; David E. Calkin; John R. Fiedler

2011-01-01

A new decision support tool, the Wildland Fire Decision Support System (WFDSS) has been developed to support risk-informed decision-making for individual fires in the United States. WFDSS accesses national weather data and forecasts, fire behavior prediction, economic assessment, smoke management assessment, and landscape databases to efficiently formulate and apply...

FireMap: A Web Tool for Dynamic Data-Driven Predictive Wildfire Modeling Powered by the WIFIRE Cyberinfrastructure

NASA Astrophysics Data System (ADS)

Block, J.; Crawl, D.; Artes, T.; Cowart, C.; de Callafon, R.; DeFanti, T.; Graham, J.; Smarr, L.; Srivas, T.; Altintas, I.

2016-12-01

The NSF-funded WIFIRE project has designed a web-based wildfire modeling simulation and visualization tool called FireMap. The tool executes FARSITE to model fire propagation using dynamic weather and fire data, configuration settings provided by the user, and static topography and fuel datasets already built-in. Using GIS capabilities combined with scalable big data integration and processing, FireMap enables simple execution of the model with options for running ensembles by taking the information uncertainty into account. The results are easily viewable, sharable, repeatable, and can be animated as a time series. From these capabilities, users can model real-time fire behavior, analyze what-if scenarios, and keep a history of model runs over time for sharing with collaborators. Firemap runs FARSITE with national and local sensor networks for real-time weather data ingestion and High-Resolution Rapid Refresh (HRRR) weather for forecasted weather. The HRRR is a NOAA/NCEP operational weather prediction system comprised of a numerical forecast model and an analysis/assimilation system to initialize the model. It is run with a horizontal resolution of 3 km, has 50 vertical levels, and has a temporal resolution of 15 minutes. The HRRR requires an Environmental Data Exchange (EDEX) server to receive the feed and generate secondary products out of it for the modeling. UCSD's EDEX server, funded by NSF, makes high-resolution weather data available to researchers worldwide and enables visualization of weather systems and weather events lasting months or even years. The high-speed server aggregates weather data from the University Consortium for Atmospheric Research by way of a subscription service from the Consortium called the Internet Data Distribution system. These features are part of WIFIRE's long term goals to build an end-to-end cyberinfrastructure for real-time and data-driven simulation, prediction and visualization of wildfire behavior. Although Firemap is a research product of WIFIRE, developed in collaboration with a number of fire departments, the tool is operational in pilot form for providing big data-driven predictive fire spread modeling. Most recently, FireMap was used for situational awareness in the July 2016 Sand Fire by LA City and LA County Fire Departments.

Estimating contribution of wildland fires to ambient ozone levels in National Parks in the Sierra Nevada, California

Treesearch

Haiganoush K. Preisler; Shiyuan (Sharon) Zhong; Annie Esperanza; Timothy J. Brown; Andrzej Bytnerowicz; Leland Tarnay

2010-01-01

Data from four continuous ozone and weather monitoring sites operated by the National Park Service in Sierra Nevada, California, are used to develop an ozone forecasting model and to estimate the contribution of wildland fires on ambient ozone levels. The analyses of weather and ozone data pointed to the transport of ozone precursors from the Central Valley as an...

Relation of weather forecasts to the prediction of dangerous forest fire conditions

Treesearch

R. H. Weidman

1923-01-01

The purpose of predicting dangerous forest-fire conditions, of course, is to reduce the great cost and damage caused by forest fires. In the region of Montana and northern Idaho alone the average cost to the United States Forest Service of fire protection and suppression is over $1,000,000 a year. Although the causes of forest fires will gradually be reduced by...

Meteorological conditions affecting the Freeman Lake (Idaho) fire

Treesearch

George M. Jemison

1932-01-01

Measurements of meteorological conditions prevailing during the rapid spread of forest fires are greatly needed so that when their recurrence seems probable, fire-weather forecasters may issue warnings of the danger. Such determinations also can be used by forest protective agencies which operate meteorological stations to guide their own action in the distribution of...

Modelling the meteorological forest fire niche in heterogeneous pyrologic conditions.

PubMed

De Angelis, Antonella; Ricotta, Carlo; Conedera, Marco; Pezzatti, Gianni Boris

2015-01-01

Fire regimes are strongly related to weather conditions that directly and indirectly influence fire ignition and propagation. Identifying the most important meteorological fire drivers is thus fundamental for daily fire risk forecasting. In this context, several fire weather indices have been developed focussing mainly on fire-related local weather conditions and fuel characteristics. The specificity of the conditions for which fire danger indices are developed makes its direct transfer and applicability problematic in different areas or with other fuel types. In this paper we used the low-to-intermediate fire-prone region of Canton Ticino as a case study to develop a new daily fire danger index by implementing a niche modelling approach (Maxent). In order to identify the most suitable weather conditions for fires, different combinations of input variables were tested (meteorological variables, existing fire danger indices or a combination of both). Our findings demonstrate that such combinations of input variables increase the predictive power of the resulting index and surprisingly even using meteorological variables only allows similar or better performances than using the complex Canadian Fire Weather Index (FWI). Furthermore, the niche modelling approach based on Maxent resulted in slightly improved model performance and in a reduced number of selected variables with respect to the classical logistic approach. Factors influencing final model robustness were the number of fire events considered and the specificity of the meteorological conditions leading to fire ignition.

Modelling the Meteorological Forest Fire Niche in Heterogeneous Pyrologic Conditions

PubMed Central

De Angelis, Antonella; Ricotta, Carlo; Conedera, Marco; Pezzatti, Gianni Boris

2015-01-01

Fire regimes are strongly related to weather conditions that directly and indirectly influence fire ignition and propagation. Identifying the most important meteorological fire drivers is thus fundamental for daily fire risk forecasting. In this context, several fire weather indices have been developed focussing mainly on fire-related local weather conditions and fuel characteristics. The specificity of the conditions for which fire danger indices are developed makes its direct transfer and applicability problematic in different areas or with other fuel types. In this paper we used the low-to-intermediate fire-prone region of Canton Ticino as a case study to develop a new daily fire danger index by implementing a niche modelling approach (Maxent). In order to identify the most suitable weather conditions for fires, different combinations of input variables were tested (meteorological variables, existing fire danger indices or a combination of both). Our findings demonstrate that such combinations of input variables increase the predictive power of the resulting index and surprisingly even using meteorological variables only allows similar or better performances than using the complex Canadian Fire Weather Index (FWI). Furthermore, the niche modelling approach based on Maxent resulted in slightly improved model performance and in a reduced number of selected variables with respect to the classical logistic approach. Factors influencing final model robustness were the number of fire events considered and the specificity of the meteorological conditions leading to fire ignition. PMID:25679957

Automated system for smoke dispersion prediction due to wild fires in Alaska

NASA Astrophysics Data System (ADS)

Kulchitsky, A.; Stuefer, M.; Higbie, L.; Newby, G.

2007-12-01

Community climate models have enabled development of specific environmental forecast systems. The University of Alaska (UAF) smoke group was created to adapt a smoke forecast system to the Alaska region. The US Forest Service (USFS) Missoula Fire Science Lab had developed a smoke forecast system based on the Weather Research and Forecasting (WRF) Model including chemistry (WRF/Chem). Following the successful experience of USFS, which runs their model operationally for the contiguous U.S., we develop a similar system for Alaska in collaboration with scientists from the USFS Missoula Fire Science Lab. Wildfires are a significant source of air pollution in Alaska because the climate and vegetation favor annual summer fires that burn huge areas. Extreme cases occurred in 2004, when an area larger than Maryland (more than 25000~km2) burned. Small smoke particles with a diameter less than 10~μm can penetrate deep into lungs causing health problems. Smoke also creates a severe restriction to air transport and has tremendous economical effect. The smoke dispersion and forecast system for Alaska was developed at the Geophysical Institute (GI) and the Arctic Region Supercomputing Center (ARSC), both at University of Alaska Fairbanks (UAF). They will help the public and plan activities a few days in advance to avoid dangerous smoke exposure. The availability of modern high performance supercomputers at ARSC allows us to create and run high-resolution, WRF-based smoke dispersion forecast for the entire State of Alaska. The core of the system is a Python program that manages the independent pieces. Our adapted Alaska system performs the following steps \\begin{itemize} Calculate the medium-resolution weather forecast using WRF/Met. Adapt the near real-time satellite-derived wildfire location and extent data that are received via direct broadcast from UAF's "Geographic Information Network of Alaska" (GINA) Calculate fuel moisture using WRF forecasts and National Fire Danger Rating System (NFDRS) fuel maps Calculate smoke emission components using a first order fire emission model Model the smoke plume rise yielding a vertically distribution that accounts for one-dimensional (vertical) concentrations of smoke constituents in the atmosphere above the fire Run WRF/Chem at high resolution for the forecast Use standard graphical tools to provide accessible smoke dispersion The system run twice each day at ARSC. The results will be freely available from a dedicated wildfire smoke web portal at ARSC.

Evaluation of fire weather forecasts using PM2.5 sensitivity analysis

NASA Astrophysics Data System (ADS)

Balachandran, Sivaraman; Baumann, Karsten; Pachon, Jorge E.; Mulholland, James A.; Russell, Armistead G.

2017-01-01

Fire weather forecasts are used by land and wildlife managers to determine when meteorological and fuel conditions are suitable to conduct prescribed burning. In this work, we investigate the sensitivity of ambient PM2.5 to various fire and meteorological variables in a spatial setting that is typical for the southeastern US, where prescribed fires are the single largest source of fine particulate matter. We use the method of principle components regression to estimate sensitivity of PM2.5, measured at a monitoring site in Jacksonville, NC (JVL), to fire data and observed and forecast meteorological variables. Fire data were gathered from prescribed fire activity used for ecological management at Marine Corps Base Camp Lejeune, extending 10-50 km south from the PM2.5 monitor. Principal components analysis (PCA) was run on 10 data sets that included acres of prescribed burning activity (PB) along with meteorological forecast data alone or in combination with observations. For each data set, observed PM2.5 (unitless) was regressed against PCA scores from the first seven principal components (explaining at least 80% of total variance). PM2.5 showed significant sensitivity to PB: 3.6 ± 2.2 μg m-3 per 1000 acres burned at the investigated distance scale of ∼10-50 km. Applying this sensitivity to the available activity data revealed a prescribed burning source contribution to measured PM2.5 of up to 25% on a given day. PM2.5 showed a positive sensitivity to relative humidity and temperature, and was also sensitive to wind direction, indicating the capture of more regional aerosol processing and transport effects. As expected, PM2.5 had a negative sensitivity to dispersive variables but only showed a statistically significant negative sensitivity to ventilation rate, highlighting the importance of this parameter to fire managers. A positive sensitivity to forecast precipitation was found, consistent with the practice of conducting prescribed burning on days when rain can naturally extinguish fires. Perhaps most importantly for land managers, our analysis suggests that instead of relying on the forecasts from a day before, prescribed burning decisions should be based on the forecasts released the morning of the burn when possible, since these data were more stable and yielded more statistically robust results.

Forecasting European Wildfires Today and in the Future

NASA Astrophysics Data System (ADS)

Navarro Abellan, Maria; Porras Alegre, Ignasi; María Sole, Josep; Gálvez, Pedro; Bielski, Conrad; Nurmi, Pertti

2017-04-01

Society as a whole is increasingly exposed and vulnerable to natural disasters due to extreme weather events exacerbated by climate change. The increased frequency of wildfires is not only a result of a changing climate, but wildfires themselves also produce a significant amount of greenhouse gases that, in-turn, further contribute to global warming. I-REACT (Improving Resilience to Emergencies through Advanced Cyber Technologies) is an innovation project funded by the European Commission , which aims to use social media, smartphones and wearables to improve natural disaster management by integrating existing services, both local and European, into a platform that supports the entire emergency management cycle. In order to assess the impact of climate change on wildfire hazards, METEOSIM designed two different System Processes (SP) that will be integrated into the I-REACT service that can provide information on a variety of time scales. SP1 - Climate Change Impact The climate change impact on climate variables related to fires is calculated by building an ensemble based on the Coupled Model Intercomparison Project Phase 5 (CMIP5) and CORDEX data. A validation and an Empirical-Statistical Downscaling (ESD) calibration are done to assess the changes in the past of the climatic variables related to wildfires (temperature, precipitation, wind, relative humidity and Fire Weather Index). Calculations in the trend and the frequency of extreme events of those variables are done for three time scales: near-term (2011-2040), mid-term (2041-2070) and long term (2071-2100). SP2 - Operational daily forecast of the Canadian Forest Fire Weather Index (FWI) Using ensemble data from the ECMWF and from the GLAMEPS (multi-model ensemble) models, both supplied by the Finnish Meteorological Institute (FMI), the Fire Weather Index (FWI) and its index components are produced for each ensemble member within a wide forecast time range, from a few hours up to 10 days resulting in a probabilistic output of the FWI for different regions in Europe. This work will improve the currently available information to various wildfire information users such as fire departments, the civil protection, local authorities, etc., where accurate and reliable information in extreme weather situations are vital for improving planning and risk management.

NFDRSPC: The National Fire-Danger Rating System on a Personal Computer

Treesearch

Bryan G. Donaldson; James T. Paul

1990-01-01

This user's guide is an introductory manual for using the 1988 version (Burgan 1988) of the National Fire-Danger Rating System on an IBM PC or compatible computer. NFDRSPC is a window-oriented, interactive computer program that processes observed and forecast weather with fuels data to produce NFDRS indices. Other program features include user-designed display...

Changing Weather Extremes Call for Early Warning of Potential for Catastrophic Fire

NASA Astrophysics Data System (ADS)

Boer, Matthias M.; Nolan, Rachael H.; Resco De Dios, Víctor; Clarke, Hamish; Price, Owen F.; Bradstock, Ross A.

2017-12-01

Changing frequencies of extreme weather events and shifting fire seasons call for enhanced capability to forecast where and when forested landscapes switch from a nonflammable (i.e., wet fuel) state to the highly flammable (i.e., dry fuel) state required for catastrophic forest fires. Current forest fire danger indices used in Europe, North America, and Australia rate potential fire behavior by combining numerical indices of fuel moisture content, potential rate of fire spread, and fire intensity. These numerical rating systems lack the physical basis required to reliably quantify forest flammability outside the environments of their development or under novel climate conditions. Here, we argue that exceedance of critical forest flammability thresholds is a prerequisite for major forest fires and therefore early warning systems should be based on a reliable prediction of fuel moisture content plus a regionally calibrated model of how forest fire activity responds to variation in fuel moisture content. We demonstrate the potential of this approach through a case study in Portugal. We use a physically based fuel moisture model with historical weather and fire records to identify critical fuel moisture thresholds for forest fire activity and then show that the catastrophic June 2017 forest fires in central Portugal erupted shortly after fuels in the region dried out to historically unprecedented levels.

A new website with real-time dissemination of information on fire activity and meteorological fire danger in Portugal

NASA Astrophysics Data System (ADS)

DaCamara, Carlos; Trigo, Ricardo; Nunes, Sílvia; Pinto, Miguel; Oliveira, Tiago; Almeida, Rui

2017-04-01

In Portugal, like in Mediterranean Europe, fire activity is a natural phenomenon linking climate, humans and vegetation and is therefore conditioned by natural and anthropogenic factors. Natural factors include topography, vegetation cover and prevailing weather conditions whereas anthropogenic factors encompass land management practices and fire prevention policies. Land management practices, in particular the inadequate use of fire, is a crucial anthropogenic factor that accounts for about 90% of fire ignitions. Fire prevention policies require adequate and timely information about wildfire potential assessment, which is usually based on fire danger rating systems that provide indices to be used on an operational and tactical basis in decision support systems. We present a new website designed to provide the user community with relevant real-time information on fire activity and meteorological fire danger that will allow adopting the adequate measures to mitigate fire damage. The fire danger product consists of forecasts of fire danger over Portugal based on a statistical procedure that combines information about fire history derived from the Fire Radiative Power product disseminated by the Land Surface Analysis Satellite Application Facility (LSA SAF) with daily meteorological forecasts provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). The aim of the website is fourfold; 1) to concentrate all information available (databases and maps) relevant to fire management in a unique platform so that access by end users becomes easier, faster and friendlier; 2) to supervise the access of users to the different products available; 3) to control and assist the access to the platform and obtain feedbacks from users for further improvements; 4) to outreach the operational community and foster the use of better information that increase efficiency in risk management. The website is sponsored by The Navigator Company, a leading force in the global pulp and paper market. Since the operational start of the website, the number of registered users has been steadily increasing up to a total of 300 users from a wide community that encompasses forest managers, firemen and civil protection officers, personnel from municipalities, academic researchers and private owners.

An Implementing Strategy for Improving Wildland Fire Environmental Literacy

NASA Astrophysics Data System (ADS)

McCalla, M. R.; Andrus, D.; Barnett, K.

2007-12-01

Wildland fire is any planned or unplanned fire which occurs in wildland ecosystems. Wildland fires affect millions of acres annually in the U.S. An average of 5.4 million acres a year were burned in the U.S. between 1995 and 2004, approximately 142 percent of the average burned area between 1984 and 1994. In 2005 alone, Federal agencies spent nearly $1 billion on fire suppression and state and local agencies contributed millions more. Many Americans prefer to live and vacation in relatively remote surroundings, (i.e., woods and rangelands). These choices offer many benefits, but they also present significant risks. Most of North America is fire-prone and every day developed areas and home sites are extending further into natural wildlands, which increases the chances of catastrophic fire. In addition, an abundance of accumulated biomass in forests and rangelands and persistent drought conditions are contributing to larger, costlier wildland fires. To effectively prevent, manage, suppress, respond to, and recover from wildland fires, fire managers, and other communities which are impacted by wildland fires (e.g., the business community; healthcare providers; federal, state, and local policymakers; the media; the public, etc.) need timely, accurate, and detailed wildland fire weather and climate information to support their decision-making activities. But what are the wildland fire weather and climate data, products, and information, as well as information dissemination technologies, needed to reach out and promote wildland fire environmental literacy in these communities? The Office of the Federal Coordinator for Meteorological Services and Supporting Research (OFCM) conducted a comprehensive review and assessment of weather and climate needs of providers and users in their wildland fire and fuels management activities. The assessment has nine focus areas, one of which is environmental literacy (e.g., education, training, outreach, partnering, and collaboration). The OFCM model for promoting wildland fire environmental literacy, the model's component parts, as well as an implementing strategy to execute the model will be presented. That is, the presentation will lay out the framework and methodology which the OFCM used to systematically define the wildland fire weather and climate education and outreach needs through interdepartmental collaboration within the OFCM coordinating infrastructure. A key element of the methodology is to improve the overall understanding and use of wildland fire forecast and warning climate and weather products and to exploit current and emerging technologies to improve the dissemination of customer-tailored forecast and warning information and products to stakeholders and users. Thus, the framework and methodology define the method used to determine the target public, private, and academic sector audiences. The methodology also identifies the means for determining the optimal channels, formats, and content for informing end users in time for effective action to be taken.

Using weather in forest management

Treesearch

Michael A. Fosberg

1986-01-01

The summer of 1933 in northwest Oregon had been exceptionally hot and dry. When in mid-August, hot, dry winds blew in from the east, all the fire crews were ready. But there were not enough of them. Scattered fires that started in the coast range merged into what became known as the Tillamook Bum. In 1986, the Forest Service is researching procedures to forecast...

Modeling Future Fire danger over North America in a Changing Climate

NASA Astrophysics Data System (ADS)

Jain, P.; Paimazumder, D.; Done, J.; Flannigan, M.

2016-12-01

Fire danger ratings are used to determine wildfire potential due to weather and climate factors. The Fire Weather Index (FWI), part of the Canadian Forest Fire Danger Rating System (CFFDRS), incorporates temperature, relative humidity, windspeed and precipitation to give a daily fire danger rating that is used by wildfire management agencies in an operational context. Studies using GCM output have shown that future wildfire danger will increase in a warming climate. However, these studies are somewhat limited by the coarse spatial resolution (typically 100-400km) and temporal resolution (typically 6-hourly to monthly) of the model output. Future wildfire potential over North America based on FWI is calculated using output from the Weather, Research and Forecasting (WRF) model, which is used to downscale future climate scenarios from the bias-corrected Community Climate System Model (CCSM) under RCP8.5 scenarios at a spatial resolution of 36km. We consider five eleven year time slices: 1990-2000, 2020-2030, 2030-2040, 2050-2060 and 2080-2090. The dynamically downscaled simulation improves determination of future extreme weather by improving both spatial and temporal resolution over most GCM models. To characterize extreme fire weather we calculate annual numbers of spread days (days for which FWI > 19) and annual 99th percentile of FWI. Additionally, an extreme value analysis based on the peaks-over-threshold method allows us to calculate the return values for extreme FWI values.

An Accurate Fire-Spread Algorithm in the Weather Research and Forecasting Model Using the Level-Set Method

NASA Astrophysics Data System (ADS)

Muñoz-Esparza, Domingo; Kosović, Branko; Jiménez, Pedro A.; Coen, Janice L.

2018-04-01

The level-set method is typically used to track and propagate the fire perimeter in wildland fire models. Herein, a high-order level-set method using fifth-order WENO scheme for the discretization of spatial derivatives and third-order explicit Runge-Kutta temporal integration is implemented within the Weather Research and Forecasting model wildland fire physics package, WRF-Fire. The algorithm includes solution of an additional partial differential equation for level-set reinitialization. The accuracy of the fire-front shape and rate of spread in uncoupled simulations is systematically analyzed. It is demonstrated that the common implementation used by level-set-based wildfire models yields to rate-of-spread errors in the range 10-35% for typical grid sizes (Δ = 12.5-100 m) and considerably underestimates fire area. Moreover, the amplitude of fire-front gradients in the presence of explicitly resolved turbulence features is systematically underestimated. In contrast, the new WRF-Fire algorithm results in rate-of-spread errors that are lower than 1% and that become nearly grid independent. Also, the underestimation of fire area at the sharp transition between the fire front and the lateral flanks is found to be reduced by a factor of ≈7. A hybrid-order level-set method with locally reduced artificial viscosity is proposed, which substantially alleviates the computational cost associated with high-order discretizations while preserving accuracy. Simulations of the Last Chance wildfire demonstrate additional benefits of high-order accurate level-set algorithms when dealing with complex fuel heterogeneities, enabling propagation across narrow fuel gaps and more accurate fire backing over the lee side of no fuel clusters.

Using JPSS VIIRS Fire Radiative Power Data to Forecast Biomass Burning Emissions and Smoke Transport by the High Resolution Rapid Refresh Model

NASA Astrophysics Data System (ADS)

Ahmadov, R.; Grell, G. A.; James, E.; Alexander, C.; Stewart, J.; Benjamin, S.; McKeen, S. A.; Csiszar, I. A.; Tsidulko, M.; Pierce, R. B.; Pereira, G.; Freitas, S. R.; Goldberg, M.

2017-12-01

We present a new real-time smoke modeling system, the High Resolution Rapid Refresh coupled with smoke (HRRR-Smoke), to simulate biomass burning (BB) emissions, plume rise and smoke transport in real time. The HRRR is the NOAA Earth System Research Laboratory's 3km grid spacing version of the Weather Research and Forecasting (WRF) model used for weather forecasting. Here we make use of WRF-Chem (the WRF model coupled with chemistry) and simulate fine particulate matter (smoke) emissions emitted by BB. The HRRR-Smoke modeling system ingests fire radiative power (FRP) data from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (S-NPP) satellite to calculate BB emissions. The FRP product is based on processing 750m resolution "M" bands. The algorithms for fire detection and FRP retrieval are consistent with those used to generate the MODIS fire detection data. For the purpose of ingesting VIIRS fire data into the HRRR-Smoke model, text files are generated to provide the location and detection confidence of fire pixels, as well as FRP. The VIIRS FRP data from the text files are processed and remapped over the HRRR-Smoke model domains. We process the FRP data to calculate BB emissions (smoldering part) and fire size for the model input. In addition, HRRR-Smoke uses the FRP data to simulate the injection height for the flaming emissions using concurrently simulated meteorological fields by the model. Currently, there are two 3km resolution domains covering the contiguous US and Alaska which are used to simulate smoke in real time. In our presentation, we focus on the CONUS domain. HRRR-Smoke is initialized 4 times per day to forecast smoke concentrations for the next 36 hours. The VIIRS FRP data, as well as near-surface and vertically integrated smoke mass concentrations are visualized for every forecast hour. These plots are provided to the public via the HRRR-Smoke web-page: https://rapidrefresh.noaa.gov/HRRRsmoke/. Model evaluations for a case study are presented, where simulated smoke concentrations are compared with hourly PM2.5 measurements from EPA's Air Quality System network. These comparisons demonstrate the model's ability in simulating high aerosol loadings during major wildfire events in the western US.

[Change trends of summer fire danger in great Xing' an Mountains forest region of Heilongjiang Province, Northeast China under climate change].

PubMed

Yang, Guang; Shu, Li-Fu; Di, Xue-Ying

2012-11-01

By using Delta and WGEN downscaling methods and Canadian Forest Fire Weather Index, this paper analyzed the variation characteristics of summer fire in Great Xing' an Mountains forest region of Heilongjiang Province in 1966-2010, estimated the change trends of the summer fire danger in 2010-2099, compared the differences of the forest fire in summer, spring, and autumn, and proposed the prevention and control strategies of the summer fire based on the fire environment. Under the background of climate warming, the summer forest fire in the region in 2000-2010 showed a high incidence trend. In foreseeable future, the summer forest fire across the region in 2010-2099, as compared to that in the baseline period 1961-1990, would be increased by 34%, and the increment would be obviously greater than that of spring and autumn fire. Relative to that in 1961-1990, the summer fire in 2010-2099 under both SRES A2a and SRES B2a scenarios would have an increasing trend, and, with the lapse of time, the trend would be more evident, and the area with high summer fire would become wider and wider. Under the scenario of SRES A2a, the summer fire by the end of the 21st century would be doubled, as compared to that in 1961-1990, and the area with high summer fire would be across the region. In the characteristics of fire source, attributes of forest fuel, and fire weather conditions, the summer forest fire was different from the spring and autumn forest fire, and thus, the management of fire source and forest fuel load as well as the forest fire forecast (mid-long term forecast in particular) in the region should be strengthened to control the summer forest fire.

Using the Fire Weather Index (FWI) to improve the estimation of fire emissions from fire radiative power (FRP) observations

NASA Astrophysics Data System (ADS)

Di Giuseppe, Francesca; Rémy, Samuel; Pappenberger, Florian; Wetterhall, Fredrik

2018-04-01

The atmospheric composition analysis and forecast for the European Copernicus Atmosphere Monitoring Services (CAMS) relies on biomass-burning fire emission estimates from the Global Fire Assimilation System (GFAS). The GFAS is a global system and converts fire radiative power (FRP) observations from MODIS satellites into smoke constituents. Missing observations are filled in using persistence, whereby observed FRP values from the previous day are progressed in time until a new observation is recorded. One of the consequences of this assumption is an increase of fire duration, which in turn translates into an increase of emissions estimated from fires compared to what is available from observations. In this study persistence is replaced by modelled predictions using the Canadian Fire Weather Index (FWI), which describes how atmospheric conditions affect the vegetation moisture content and ultimately fire duration. The skill in predicting emissions from biomass burning is improved with the new technique, which indicates that using an FWI-based model to infer emissions from FRP is better than persistence when observations are not available.

WRF-based fire risk modelling and evaluation for years 2010 and 2012 in Poland

NASA Astrophysics Data System (ADS)

Stec, Magdalena; Szymanowski, Mariusz; Kryza, Maciej

2016-04-01

Wildfires are one of the main ecosystems' disturbances for forested, seminatural and agricultural areas. They generate significant economic loss, especially in forest management and agriculture. Forest fire risk modeling is therefore essential e.g. for forestry administration. In August 2015 a new method of forest fire risk forecasting entered into force in Poland. The method allows to predict a fire risk level in a 4-degree scale (0 - no risk, 3 - highest risk) and consists of a set of linearized regression equations. Meteorological information is used as predictors in regression equations, with air temperature, relative humidity, average wind speed, cloudiness and rainfall. The equations include also pine litter humidity as a measure of potential fuel characteristics. All these parameters are measured routinely in Poland at 42 basic and 94 auxiliary sites. The fire risk level is estimated for a current (basing on morning measurements) or next day (basing on midday measurements). Entire country is divided into 42 prognostic zones, and fire risk level for each zone is taken from the closest measuring site. The first goal of this work is to assess if the measurements needed for fire risk forecasting may be replaced by the data from mesoscale meteorological model. Additionally, the use of a meteorological model would allow to take into account much more realistic spatial differentiation of weather elements determining the fire risk level instead of discrete point-made measurements. Meteorological data have been calculated using the Weather Research and Forecasting model (WRF). For the purpose of this study the WRF model is run in the reanalysis mode allowing to estimate all required meteorological data in a 5-kilometers grid. The only parameter that cannot be directly calculated using WRF is the litter humidity, which has been estimated using empirical formula developed by Sakowska (2007). The experiments are carried out for two selected years: 2010 and 2012. The year 2010 was characterized by the smallest number of wildfires and burnt area whereas 2012 - by the biggest number of fires and the largest area of conflagration. The data about time, localization, scale and causes of individual wildfire occurrence in given years are taken from the National Forest Fire Information System (KSIPL), administered by Forest Fire Protection Department of Polish Forest Research Institute. The database is a part of European Forest Fire Information System (EFFIS). Basing on this data and on the WRF-based fire risk modelling we intend to achieve the second goal of the study, which is the evaluation of the forecasted fire risk with an occurrence of wildfires. Special attention is paid here to the number, time and the spatial distribution of wildfires occurred in cases of low-level predicted fire risk. Results obtained reveals the effectiveness of the new forecasting method. The outcome of our investigation allows to draw a conclusion that some adjustments are possible to improve the efficiency on the fire-risk estimation method.

Investigating Over Critical Thresholds of Forest Megafires Danger Conditions in Europe Utilising the ECMWF ERA-Interim Reanalysis

NASA Astrophysics Data System (ADS)

Petroliagkis, Thomas I.; Camia, Andrea; Liberta, Giorgio; Durrant, Tracy; Pappenberger, Florian; San-Miguel-Ayanz, Jesus

2014-05-01

The European Forest Fire Information System (EFFIS) has been established by the Joint Research Centre (JRC) and the Directorate General for Environment (DG ENV) of the European Commission (EC) to support the services in charge of the protection of forests against fires in the EU and neighbour countries, and also to provide the EC services and the European Parliament with information on forest fires in Europe. Within its applications, EFFIS provides current and forecast meteorological fire danger maps up to 6 days. Weather plays a key role in affecting wildfire occurrence and behaviour. Meteorological parameters can be used to derive meteorological fire weather indices that provide estimations of fire danger level at a given time over a specified area of interest. In this work, we investigate the suitability of critical thresholds of fire danger to provide an early warning for megafires (fires > 500 ha) over Europe. Past trends of fire danger are analysed computing daily fire danger from weather data taken from re-analysis fields for a period of 31 years (1980 to 2010). Re-analysis global data sets coming from the construction of high-quality climate records, which combine past observations collected from many different observing and measuring platforms, are capable of describing how Fire Danger Indices have evolved over time at a global scale. The latest and most updated ERA-Interim dataset of the European Centre for Medium-Range Weather Forecast (ECMWF) was used to extract meteorological variables needed to compute daily values of the Canadian Fire Weather Index (CFWI) over Europe, with a horizontal resolution of about 75x75 km. Daily time series of CFWI were constructed and analysed over a total of 1,071 European NUTS3 centroids, resulting in a set of percentiles and critical thresholds. Such percentiles could be used as thresholds to help fire services establish a measure of the significance of CFWI outputs as they relate to levels of fire potential, fuel conditions and fire danger. Median percentile values of fire days accumulated over the 31-year period were compared to median values of all days from that period. As expected, the CWFI time series exhibit different values on fire days than on all days. In addition, a percentile analysis was performed in order to determine the behaviour of index values corresponding to fire events falling into the megafire category. This analysis resulted in a set of critical thresholds based on percentiles. By utilising such thresholds, an initial framework of an early warning system has being established. By lowering the value of any of these thresholds, the number of hits could be increased until all extremes were captured (resulting in zero misses). However, in doing so, the number of false alarms tends to increase significantly. Consequently, an optimal trade-off between hits and false alarms has to be established when setting different (critical) CFWI thresholds.

Complex systems approach to fire dynamics and climate change impacts

NASA Astrophysics Data System (ADS)

Pueyo, S.

2012-04-01

I present some recent advances in complex systems theory as a contribution to understanding fire regimes and forecasting their response to a changing climate, qualitatively and quantitatively. In many regions of the world, fire sizes have been found to follow, approximately, a power-law frequency distribution. As noted by several authors, this distribution also arises in the "forest fire" model used by physicists to study mechanisms that give rise to scale invariance (the power law is a scale-invariant distribution). However, this model does not give and does not pretend to give a realistic description of fire dynamics. For example, it gives no role to weather and climate. Pueyo (2007) developed a variant of the "forest fire" model that is also simple but attempts to be more realistic. It also results into a power law, but the parameters of this distribution change through time as a function of weather and climate. Pueyo (2007) observed similar patterns of response to weather in data from boreal forest fires, and used the fitted response functions to forecast fire size distributions in a possible climate change scenario, including the upper extreme of the distribution. For some parameter values, the model in Pueyo (2007) displays a qualitatively different behavior, consisting of simple percolation. In this case, fire is virtually absent, but megafires sweep through the ecosystem a soon as environmental forcings exceed a critical threshold. Evidence gathered by Pueyo et al. (2010) suggests that this is realistic for tropical rainforests (specifically, well-conserved upland rainforests). Some climate models suggest that major tropical rainforest regions are going to become hotter and drier if climate change goes ahead unchecked, which could cause such abrupt shifts. Not all fire regimes are well described by this model. Using data from a tropical savanna region, Pueyo et al. (2010) found that the dynamics in this area do not match its assumptions, even though fire sizes are also well fitted by a power law. A possible interpretation is that the spatial structure of fire in savannas is strongly constrained by the spatial structure of their environment. Instead of resulting from ecosystem self-organization as in the model, in this case the scale invariance in fire events would be just a reflection of scale invariance in the environment in which the ecosystem lives. These results suggest at least three major types of fire dynamics: endogenous scaling, percolating, and exogenous scaling, in addition to intermediate options. The world's biomes can be classified based on the type of dynamics that is most likely to apply in each of them, and forecasts can be carried out with the tools developed for each of these types.

A robust scientific workflow for assessing fire danger levels using open-source software

NASA Astrophysics Data System (ADS)

Vitolo, Claudia; Di Giuseppe, Francesca; Smith, Paul

2017-04-01

Modelling forest fires is theoretically and computationally challenging because it involves the use of a wide variety of information, in large volumes and affected by high uncertainties. In-situ observations of wildfire, for instance, are highly sparse and need to be complemented by remotely sensed data measuring biomass burning to achieve homogeneous coverage at global scale. Fire models use weather reanalysis products to measure energy release and rate of spread but can only assess the potential predictability of fire danger as the actual ignition is due to human behaviour and, therefore, very unpredictable. Lastly, fire forecasting systems rely on weather forecasts to extend the advance warning but are currently calibrated using fire danger thresholds that are defined at global scale and do not take into account the spatial variability of fuel availability. As a consequence, uncertainties sharply increase cascading from the observational to the modelling stage and they might be further inflated by non-reproducible analyses. Although uncertainties in observations will only decrease with technological advances over the next decades, the other uncertainties (i.e. generated during modelling and post-processing) can already be addressed by developing transparent and reproducible analysis workflows, even more if implemented within open-source initiatives. This is because reproducible workflows aim to streamline the processing task as they present ready-made solutions to handle and manipulate complex and heterogeneous datasets. Also, opening the code to the scrutiny of other experts increases the chances to implement more robust solutions and avoids duplication of efforts. In this work we present our contribution to the forest fire modelling community: an open-source tool called "caliver" for the calibration and verification of forest fire model results. This tool is developed in the R programming language and publicly available under an open license. We will present the caliver R package, illustrate the main functionalities and show the results of our preliminary experiments calculating fire danger thresholds for various regions on Earth. We will compare these with the existing global thresholds and, lastly, demonstrate how these newly-calculated regional thresholds can lead to improved calibration of fire forecast models in an operational setting.

The Simulations of Wildland Fire Smoke PM25 in the NWS Air Quality Forecasting Systems

NASA Astrophysics Data System (ADS)

Huang, H. C.; Pan, L.; McQueen, J.; Lee, P.; ONeill, S. M.; Ruminski, M.; Shafran, P.; Huang, J.; Stajner, I.; Upadhayay, S.; Larkin, N. K.

2017-12-01

The increase of wildland fire intensity and frequency in the United States (U.S.) has led to property loss, human fatality, and poor air quality due to elevated particulate matters and surface ozone concentrations. The NOAA/National Weather Service (NWS) built the National Air Quality Forecast Capability (NAQFC) based on the U.S. Environmental Protection Agency (EPA) Community Multi-scale Air Quality (CMAQ) Modeling System driven by the NCEP North American Mesoscale Forecast System meteorology to provide ozone and fine particulate matter (PM2.5) forecast guidance publicly. State and local forecasters use the NWS air quality forecast guidance to issue air quality alerts in their area. The NAQFC PM2.5 predictions include emissions from anthropogenic and biogenic sources, as well as natural sources such as dust storms and wildland fires. The wildland fire emission inputs to the NAQFC is derived from the NOAA National Environmental Satellite, Data, and Information Service Hazard Mapping System fire and smoke detection product and the emission module of the U.S. Forest Service (USFS) BlueSky Smoke Modeling Framework. Wildland fires are unpredictable and can be ignited by natural causes such as lightning or be human-caused. It is extremely difficult to predict future occurrences and behavior of wildland fires, as is the available bio-fuel to be burned for real-time air quality predictions. Assumptions of future day's wildland fire behavior often have to be made from older observed wildland fire information. The comparisons between the NAQFC modeled PM2.5 and the EPA AirNow surface observation show that large errors in PM2.5 prediction can occur if fire smoke emissions are sometimes placed at the wrong location and/or time. A configuration of NAQFC CMAQ-system to re-run previous 24 hours, during which wildland fires were observed from satellites has been included recently. This study focuses on the effort performed to minimize the error in NAQFC PM2.5 predictions resulting from incorporating fire smoke emissions into the NAQFC from a recently updated newer version of USFS BlueSky system. This study will show how new approaches has improved the PM2.5 predictions at both nearby and downstream areas from fire sources. Furthermore, Environment and Climate Change Canada (ECCC) fire emissions data are being tested.

JPSS Products, Applications and Training

NASA Astrophysics Data System (ADS)

Torres, J. R.; Connell, B. H.; Miller, S. D.

2017-12-01

The Joint Polar Satellite System (JPSS) is a new generation polar-orbiting operational environmental satellite system that will monitor the weather and environment around the globe. JPSS will provide technological and scientific improvements in environmental monitoring via high resolution satellite imagery and derived products that stand to improve weather forecasting capabilities for National Weather Service (NWS) forecasters and complement operational Geostationary satellites. JPSS will consist of four satellites, JPSS-1 through JPSS-4, where JPSS-1 is due to launch in Fall 2017. A predecessor, prototype and operational risk-reduction for JPSS is the Suomi-National Polar-orbiting Partnership (S-NPP) satellite, launched on 28 October 2011. The following instruments on-board S-NPP will also be hosted on JPSS-1: Visible Infrared Imaging Radiometer Suite (VIIRS), Cross-track Infrared Sounder (CrIS), Advanced Technology Microwave Sounder (ATMS), Ozone Mapping and Profiler Suite (OMPS) and the Clouds and Earth's Radiant Energy System (CERES). JPSS-1 instruments will provide satellite imagery, products and applications to users. The applications include detecting water and ice clouds, snow, sea surface temperatures, fog, fire, severe weather, vegetation health, aerosols, and sensing reflected lunar and emitted visible-wavelength light during the nighttime via the Day/Night Band (DNB) sensor included on VIIRS. Currently, there are only a few polar products that are operational for forecasters, however, more products will become available in the near future via Advanced Weather Interactive Processing System-II (AWIPS-II)-a forecasting analysis software package that forecasters can use to analyze meteorological data. To complement the polar products an wealth of training materials are currently in development. Denoted as the Satellite Foundational Course for JPSS (SatFC-J), this training will benefit NWS forecasters to utilize satellite data in their forecasts and daily operations as they discover their operational value in the NWS forecast process. As JPSS-1 launch nears, training materials will be produced in the form of modules, videos, quick guides, fact sheets, and hands-on exercises.

Weather Research and Forecasting Model Sensitivity Comparisons for Warm Season Convective Initiation

NASA Technical Reports Server (NTRS)

Watson, Leela R.; Hoeth, Brian; Blottman, Peter F.

2007-01-01

Mesoscale weather conditions can significantly affect the space launch and landing operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). During the summer months, land-sea interactions that occur across KSC and CCAFS lead to the formation of a sea breeze, which can then spawn deep convection. These convective processes often last 60 minutes or less and pose a significant challenge to the forecasters at the National Weather Service (NWS) Spaceflight Meteorology Group (SMG). The main challenge is that a "GO" forecast for thunderstorms and precipitation at the Shuttle Landing Facility is required at the 90 minute deorbit decision for End Of Mission (EOM) and at the 30 minute Return To Launch Site (RTLS) decision. Convective initiation, timing, and mode also present a forecast challenge for the NWS in Melbourne, FL (MLB). The NWS MLB issues such tactical forecast information as Terminal Aerodrome Forecasts (TAF5), Spot Forecasts for fire weather and hazardous materials incident support, and severe/hazardous weather Watches, Warnings, and Advisories. Lastly, these forecasting challenges can also affect the 45th Weather Squadron (45 WS), which provides comprehensive weather forecasts for shuttle launch, as well as ground operations, at KSC and CCAFS. The need for accurate mesoscale model forecasts to aid in their decision making is crucial. This study specifically addresses the skill of different model configurations in forecasting warm season convective initiation. Numerous factors influence the development of convection over the Florida peninsula. These factors include sea breezes, river and lake breezes, the prevailing low-level flow, and convergent flow due to convex coastlines that enhance the sea breeze. The interaction of these processes produces the warm season convective patterns seen over the Florida peninsula. However, warm season convection remains one of the most poorly forecast meteorological parameters. To determine which configuration options are best to address this specific forecast concern, the Weather Research and Forecasting (WRF) model, which has two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM) was employed. In addition to the two dynamical cores, there are also two options for a "hot-start" initialization of the WRF model - the Local Analysis and Prediction System (LAPS; McGinley 1995) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS; Brewster 1996). Both LAPS and ADAS are 3- dimensional weather analysis systems that integrate multiple meteorological data sources into one consistent analysis over the user's domain of interest. This allows mesoscale models to benefit from the addition of highresolution data sources. Having a series of initialization options and WRF cores, as well as many options within each core, provides SMG and MLB with considerable flexibility as well as challenges. It is the goal of this study to assess the different configurations available and to determine which configuration will best predict warm season convective initiation.

A comparison of three approaches for simulating fine-scale surface winds in support of wildland fire management: Part I. Model formulation and comparison against measurements

Treesearch

Jason M. Forthofer; Bret W. Butler; Natalie S. Wagenbrenner

2014-01-01

For this study three types of wind models have been defined for simulating surface wind flow in support of wildland fire management: (1) a uniform wind field (typically acquired from coarse-resolution (,4 km) weather service forecast models); (2) a newly developed mass-conserving model and (3) a newly developed mass and momentumconserving model (referred to as the...

Configuring the HYSPLIT Model for National Weather Service Forecast Office and Spaceflight Meteorology Group Applications

NASA Technical Reports Server (NTRS)

Dreher, Joseph; Blottman, Peter F.; Sharp, David W.; Hoeth, Brian; Van Speybroeck, Kurt

2009-01-01

The National Weather Service Forecast Office in Melbourne, FL (NWS MLB) is responsible for providing meteorological support to state and county emergency management agencies across East Central Florida in the event of incidents involving the significant release of harmful chemicals, radiation, and smoke from fires and/or toxic plumes into the atmosphere. NWS MLB uses the National Oceanic and Atmospheric Administration Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to provide trajectory, concentration, and deposition guidance during such events. Accurate and timely guidance is critical for decision makers charged with protecting the health and well-being of populations at risk. Information that can describe the geographic extent of areas possibly affected by a hazardous release, as well as to indicate locations of primary concern, offer better opportunity for prompt and decisive action. In addition, forecasters at the NWS Spaceflight Meteorology Group (SMG) have expressed interest in using the HYSPLIT model to assist with Weather Flight Rules during Space Shuttle landing operations. In particular, SMG would provide low and mid-level HYSPLIT trajectory forecasts for cumulus clouds associated with smoke plumes, and high-level trajectory forecasts for thunderstorm anvils. Another potential benefit for both NWS MLB and SMG is using the HYSPLIT model concentration and deposition guidance in fog situations.

WRF simulation of downslope wind events in coastal Santa Barbara County

NASA Astrophysics Data System (ADS)

Cannon, Forest; Carvalho, Leila M. V.; Jones, Charles; Hall, Todd; Gomberg, David; Dumas, John; Jackson, Mark

2017-07-01

The National Weather Service (NWS) considers frequent gusty downslope winds, accompanied by rapid warming and decreased relative humidity, among the most significant weather events affecting southern California coastal areas in the vicinity of Santa Barbara (SB). These extreme conditions, commonly known as "sundowners", have affected the evolution of all major wildfires that impacted SB in recent years. Sundowners greatly increase fire, aviation and maritime navigation hazards and are thus a priority for regional forecasting. Currently, the NWS employs the Weather Research Forecasting (WRF) model at 2 km resolution to complement forecasts at regional-to-local scales. However, no systematic study has been performed to evaluate the skill of WRF in simulating sundowners. This research presents a case study of an 11-day period in spring 2004 during which sundowner events were observed on multiple nights. We perform sensitivity experiments for WRF using available observations for validation and demonstrate that WRF is skillful in representing the general mesoscale structure of these events, though important shortcomings exist. Furthermore, we discuss the generation and evolution of sundowners during the case study using the best performing configuration, and compare these results to hindcasts for two major SB fires. Unique, but similar, profiles of wind and stability are observed over SB between case studies despite considerable differences in large-scale circulation, indicating that common conditions may exist across all events. These findings aid in understanding the evolution of sundowner events and are potentially valuable for event prediction.

AEGIS: a wildfire prevention and management information system

NASA Astrophysics Data System (ADS)

Kalabokidis, K.; Ager, A.; Finney, M.; Athanasis, N.; Palaiologou, P.; Vasilakos, C.

2015-10-01

A Web-GIS wildfire prevention and management platform (AEGIS) was developed as an integrated and easy-to-use decision support tool (http://aegis.aegean.gr). The AEGIS platform assists with early fire warning, fire planning, fire control and coordination of firefighting forces by providing access to information that is essential for wildfire management. Databases were created with spatial and non-spatial data to support key system functionalities. Updated land use/land cover maps were produced by combining field inventory data with high resolution multispectral satellite images (RapidEye) to be used as inputs in fire propagation modeling with the Minimum Travel Time algorithm. End users provide a minimum number of inputs such as fire duration, ignition point and weather information to conduct a fire simulation. AEGIS offers three types of simulations; i.e. single-fire propagations, conditional burn probabilities and at the landscape-level, similar to the FlamMap fire behavior modeling software. Artificial neural networks (ANN) were utilized for wildfire ignition risk assessment based on various parameters, training methods, activation functions, pre-processing methods and network structures. The combination of ANNs and expected burned area maps produced an integrated output map for fire danger prediction. The system also incorporates weather measurements from remote automatic weather stations and weather forecast maps. The structure of the algorithms relies on parallel processing techniques (i.e. High Performance Computing and Cloud Computing) that ensure computational power and speed. All AEGIS functionalities are accessible to authorized end users through a web-based graphical user interface. An innovative mobile application, AEGIS App, acts as a complementary tool to the web-based version of the system.

Climate change and wildland firefighter health and safety.

PubMed

Withen, Patrick

2015-02-01

The author examines how climate change is impacting wildland firefighters. Climate change has made wildland fires more frequent and more intense. The increase in frequency and intensity of fires has pushed the number of fatalities and injuries higher in recent decades. The most common hazards on fires follow the trend of fire in general in that these hazards become more frequent and intense. Burnovers, heat exhaustion, tree hazards, and many other common fire hazards are more likely. The fire suppression agencies are making every effort to improve health and safety on fires by improving communication, weather forecasting, mapping, fire shelters, decision making and more. Despite these efforts, wildfires are becoming ever more hazardous because of climate change and the increasing frequency and intensity of wildfires. © 2015 SAGE Publications.

AEGIS: a wildfire prevention and management information system

NASA Astrophysics Data System (ADS)

Kalabokidis, Kostas; Ager, Alan; Finney, Mark; Athanasis, Nikos; Palaiologou, Palaiologos; Vasilakos, Christos

2016-03-01

We describe a Web-GIS wildfire prevention and management platform (AEGIS) developed as an integrated and easy-to-use decision support tool to manage wildland fire hazards in Greece (http://aegis.aegean.gr). The AEGIS platform assists with early fire warning, fire planning, fire control and coordination of firefighting forces by providing online access to information that is essential for wildfire management. The system uses a number of spatial and non-spatial data sources to support key system functionalities. Land use/land cover maps were produced by combining field inventory data with high-resolution multispectral satellite images (RapidEye). These data support wildfire simulation tools that allow the users to examine potential fire behavior and hazard with the Minimum Travel Time fire spread algorithm. End-users provide a minimum number of inputs such as fire duration, ignition point and weather information to conduct a fire simulation. AEGIS offers three types of simulations, i.e., single-fire propagation, point-scale calculation of potential fire behavior, and burn probability analysis, similar to the FlamMap fire behavior modeling software. Artificial neural networks (ANNs) were utilized for wildfire ignition risk assessment based on various parameters, training methods, activation functions, pre-processing methods and network structures. The combination of ANNs and expected burned area maps are used to generate integrated output map of fire hazard prediction. The system also incorporates weather information obtained from remote automatic weather stations and weather forecast maps. The system and associated computation algorithms leverage parallel processing techniques (i.e., High Performance Computing and Cloud Computing) that ensure computational power required for real-time application. All AEGIS functionalities are accessible to authorized end-users through a web-based graphical user interface. An innovative smartphone application, AEGIS App, also provides mobile access to the web-based version of the system.

Two global data sets of daily fire emission injection heights since 2003

NASA Astrophysics Data System (ADS)

Rémy, Samuel; Veira, Andreas; Paugam, Ronan; Sofiev, Mikhail; Kaiser, Johannes W.; Marenco, Franco; Burton, Sharon P.; Benedetti, Angela; Engelen, Richard J.; Ferrare, Richard; Hair, Jonathan W.

2017-02-01

The Global Fire Assimilation System (GFAS) assimilates fire radiative power (FRP) observations from satellite-based sensors to produce daily estimates of biomass burning emissions. It has been extended to include information about injection heights derived from fire observations and meteorological information from the operational weather forecasts of ECMWF. Injection heights are provided by two distinct methods: the Integrated Monitoring and Modelling System for wildland fires (IS4FIRES) parameterisation and the one-dimensional plume rise model (PRM). A global database of daily biomass burning emissions and injection heights at 0.1° resolution has been produced for 2003-2015 and is continuously extended in near-real time with the operational GFAS service of the Copernicus Atmospheric Monitoring Service (CAMS). In this study, the two injection height data sets were compared with the new MPHP2 (MISR Plume Height Project 2) satellite-based plume height retrievals. The IS4FIRES parameterisation showed a better overall agreement than the observations, while the PRM was better at capturing the variability of injection heights. The performance of both parameterisations is also dependent on the type of vegetation. Furthermore, the use of biomass burning emission heights from GFAS in atmospheric composition forecasts was assessed in two case studies: the South AMerican Biomass Burning Analysis (SAMBBA) campaign which took place in September 2012 in Brazil, and a series of large fire events in the western USA in August 2013. For these case studies, forecasts of biomass burning aerosol species by the Composition Integrated Forecasting System (C-IFS) of CAMS were found to better reproduce the observed vertical distribution when using PRM injection heights from GFAS compared to aerosols emissions being prescribed at the surface. The globally available GFAS injection heights introduced and evaluated in this study provide a comprehensive data set for future fire and atmospheric composition modelling studies.

Colorado Lightning Mapping Array Collaborations through the GOES-R Visiting Scientist Program

NASA Technical Reports Server (NTRS)

Stano, Geoffrey T.; Szoke, Edward; Rydell, Nezette; Cox, Robert; Mazur, Rebecca

2014-01-01

For the past two years, the GOES-R Proving Ground has solicited proposals for its Visiting Scientist Program. NASA's Short-term Prediction Research and Transition (SPoRT) Center has used this opportunity to support the GOES-R Proving Ground by expanding SPoRT's total lightning collaborations. In 2012, this expanded the evaluation of SPoRT's pseudo-geostationary lightning mapper product to the Aviation Weather Center and Storm Prediction Center. This year, SPoRT has collaborated with the Colorado Lightning Mapping Array (COLMA) and potential end users. In particular, SPoRT is collaborating with the Cooperative Institute for Research in the Atmosphere (CIRA) and Colorado State University (CSU) to obtain these data in real-time. From there, SPoRT is supporting the transition of these data to the local forecast offices in Boulder, Colorado and Cheyenne, Wyoming as well as to Proving Ground projects (e.g., the Hazardous Weather Testbed's Spring Program and Aviation Weather Center's Summer Experiment). This presentation will focus on the results of this particular Visiting Scientist Program trip. In particular, the COLMA data are being provided to both forecast offices for initial familiarization. Additionally, several forecast issues have been highlighted as important uses for COLMA data in the operational environment. These include the utility of these data for fire weather situations, situational awareness for both severe weather and lightning safety, and formal evaluations to take place in the spring of 2014.

An Examination of Extreme Fire Behavior and its Impact on Smoke Injection Altitude using Remote Sensing and Meteorological Data

NASA Astrophysics Data System (ADS)

Peterson, D. A.; Hyer, E. J.; Campbell, J. R.; Fromm, M. D.; Hair, J. W.; Butler, C. F.; Fenn, M. A.

2014-12-01

A variety of regional smoke forecasting applications are currently available to identify air quality, visibility, and societal impacts during large fire events. However, these systems typically assume persistent fire activity, and therefore can have large errors before, during, and after short-term periods of extreme fire behavior. This study employs a wide variety of ground, airborne, and satellite observations, including data collected during a major NASA airborne and field campaign, to examine the conditions required for both extreme spread and pyrocumulonimbus (pyroCb) development. Results highlight the importance of upper-level and nocturnal meteorology, as well as the limitations of traditional fire weather indices. Increasing values of fire radiative power (FRP) at the pixel and sub-pixel level are shown to systematically correspond to higher altitude smoke plumes, and an increased probability of injection above the boundary layer. Lidar data collected during the 2013 Rim Fire, one of the most severe fire events in California's history, show that high FRP observed during extreme spread can facilitate long-distance smoke transport, but fails to loft smoke to the altitude of a large pyroCb. The most extreme fire spread was also observed on days without pyroCb activity or significant regional convection. By incorporating additional fire events across North America, conflicting hypotheses surrounding the primary source of moisture during pyroCb development are examined. The majority of large pyroCbs, and therefore the highest direct injection altitude of smoke particles, is shown to occur with conditions very similar to those that produce dry thunderstorms. The current suite of automated forecasting applications predict only general trends in fire behavior, and specifically do not predict (1) extreme fire spread events and (2) injection of smoke to high altitudes. While (1) and (2) are related, results show that they are not predicted by the same set of conditions and variables. The combination of meteorology from numerical forecast models and satellite observations exhibits great potential for improving regional forecasts of fire behavior and smoke production in automated systems, especially in remote areas where detailed observations are unavailable

Fire activity as a function of fire-weather seasonal severity and antecedent climate across spatial scales in southern Europe and Pacific western USA

NASA Astrophysics Data System (ADS)

Urbieta, Itziar R.; Zavala, Gonzalo; Bedia, Joaquín; Gutiérrez, José M.; San Miguel-Ayanz, Jesús; Camia, Andrea; Keeley, Jon E.; Moreno, José M.

2015-11-01

Climate has a strong influence on fire activity, varying across time and space. We analyzed the relationships between fire-weather conditions during the main fire season and antecedent water-balance conditions and fires in two Mediterranean-type regions with contrasted management histories: five southern countries of the European Union (EUMED)(all fires); the Pacific western coast of the USA (California and Oregon, PWUSA)(national forest fires). Total number of fires (≥1 ha), number of large fires (≥100 ha) and area burned were related to mean seasonal fire weather index (FWI), number of days over the 90th percentile of the FWI, and to the standardized precipitation-evapotranspiration index (SPEI) from the preceding 3 (spring) or 8 (autumn through spring) months. Calculations were made at three spatial aggregations in each area, and models related first-difference (year-to-year change) of fires and FWI/climate variables to minimize autocorrelation. An increase in mean seasonal FWI resulted in increases in the three fire variables across spatial scales in both regions. SPEI contributed little to explain fires, with few exceptions. Negative water-balance (dry) conditions from autumn through spring (SPEI8) were generally more important than positive conditions (moist) in spring (SPEI3), both of which contributed positively to fires. The R2 of the models generally improved with increasing area of aggregation. For total number of fires and area burned, the R2 of the models tended to decrease with increasing mean seasonal FWI. Thus, fires were more susceptible to change with climate variability in areas with less amenable conditions for fires (lower FWI) than in areas with higher mean FWI values. The relationships were similar in both regions, albeit weaker in PWUSA, probably due to the wider latitudinal gradient covered in PWUSA than in EUMED. The large variance explained by some of the models indicates that large-scale seasonal forecast could help anticipating fire activity in the investigated areas.

Fire activity as a function of fire–weather seasonal severity and antecedent climate across spatial scales in southern Europe and Pacific western USA

USGS Publications Warehouse

Urbieta, Itziar R.; Zavala, Gonzalo; Bedia, Joaquin; Gutierrez, Jose M.; San Miguel-Ayanz, Jesus; Camia, Andrea; Keeley, Jon E.; Moreno, Jose M.

2015-01-01

Climate has a strong influence on fire activity, varying across time and space. We analyzed the relationships between fire–weather conditions during the main fire season and antecedent water-balance conditions and fires in two Mediterranean-type regions with contrasted management histories: five southern countries of the European Union (EUMED)(all fires); the Pacific western coast of the USA (California and Oregon, PWUSA)(national forest fires). Total number of fires (≥1 ha), number of large fires (≥100 ha) and area burned were related to mean seasonal fire weather index (FWI), number of days over the 90th percentile of the FWI, and to the standardized precipitation-evapotranspiration index (SPEI) from the preceding 3 (spring) or 8 (autumn through spring) months. Calculations were made at three spatial aggregations in each area, and models related first-difference (year-to-year change) of fires and FWI/climate variables to minimize autocorrelation. An increase in mean seasonal FWI resulted in increases in the three fire variables across spatial scales in both regions. SPEI contributed little to explain fires, with few exceptions. Negative water-balance (dry) conditions from autumn through spring (SPEI8) were generally more important than positive conditions (moist) in spring (SPEI3), both of which contributed positively to fires. The R2 of the models generally improved with increasing area of aggregation. For total number of fires and area burned, the R2 of the models tended to decrease with increasing mean seasonal FWI. Thus, fires were more susceptible to change with climate variability in areas with less amenable conditions for fires (lower FWI) than in areas with higher mean FWI values. The relationships were similar in both regions, albeit weaker in PWUSA, probably due to the wider latitudinal gradient covered in PWUSA than in EUMED. The large variance explained by some of the models indicates that large-scale seasonal forecast could help anticipating fire activity in the investigated areas.

Data Assimilation of SMAP Observations and the Impact on Weather Forecasts and Heat Stress

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley; Case, Jonathan; Blankenship, Clay; Crosson, William; White, Khristopher

2014-01-01

SPoRT produces real-time LIS soil moisture products for situational awareness and local numerical weather prediction over CONUS, Mesoamerica, and East Africa ?Currently interact/collaborate with operational partners on evaluation of soil moisture products ?Drought/fire ?Extreme heat ?Convective initiation ?Flood and water borne diseases ?Initial efforts to assimilate L2 soil moisture observations from SMOS (as a precursor for SMAP) have been successful ?Active/passive blended product from SMAP will be assimilated similarly and higher spatial resolution should improve on local-scale processes

Sensitivity analysis of numerical weather prediction radiative schemes to forecast direct solar radiation over Australia

NASA Astrophysics Data System (ADS)

Mukkavilli, S. K.; Kay, M. J.; Taylor, R.; Prasad, A. A.; Troccoli, A.

2014-12-01

The Australian Solar Energy Forecasting System (ASEFS) project requires forecasting timeframes which range from nowcasting to long-term forecasts (minutes to two years). As concentrating solar power (CSP) plant operators are one of the key stakeholders in the national energy market, research and development enhancements for direct normal irradiance (DNI) forecasts is a major subtask. This project involves comparing different radiative scheme codes to improve day ahead DNI forecasts on the national supercomputing infrastructure running mesoscale simulations on NOAA's Weather Research & Forecast (WRF) model. ASEFS also requires aerosol data fusion for improving accurate representation of spatio-temporally variable atmospheric aerosols to reduce DNI bias error in clear sky conditions over southern Queensland & New South Wales where solar power is vulnerable to uncertainities from frequent aerosol radiative events such as bush fires and desert dust. Initial results from thirteen years of Bureau of Meteorology's (BOM) deseasonalised DNI and MODIS NASA-Terra aerosol optical depth (AOD) anomalies demonstrated strong negative correlations in north and southeast Australia along with strong variability in AOD (~0.03-0.05). Radiative transfer schemes, DNI and AOD anomaly correlations will be discussed for the population and transmission grid centric regions where current and planned CSP plants dispatch electricity to capture peak prices in the market. Aerosol and solar irradiance datasets include satellite and ground based assimilations from the national BOM, regional aerosol researchers and agencies. The presentation will provide an overview of this ASEFS project task on WRF and results to date. The overall goal of this ASEFS subtask is to develop a hybrid numerical weather prediction (NWP) and statistical/machine learning multi-model ensemble strategy that meets future operational requirements of CSP plant operators.

Assessing the predictability of fire occurrence and area burned across phytoclimatic regions in Spain

NASA Astrophysics Data System (ADS)

Bedia, J.; Herrera, S.; Gutiérrez, J. M.

2014-01-01

Most fire protection agencies throughout the world have developed forest fire risk forecast systems, usually building upon existing fire danger indices and meteorological forecast data. In this context, the daily predictability of wildfires is of utmost importance in order to allow the fire protection agencies to issue timely fire hazard alerts. In this study, we address the predictability of daily fire occurrence using the components of the Canadian Fire Weather Index (FWI) System and related variables calculated from the latest ECMWF (European Centre for Medium Range Weather Forecasts) reanalysis, ERA-Interim. We develop daily fire occurrence models in peninsular Spain for the period 1990-2008 and, considering different minimum burned area thresholds for fire definition, assess their ability to reproduce the inter-annual fire frequency variability. We based the analysis on a phytoclimatic classification aiming the stratification of the territory into homogeneous units in terms of climatic and fuel type characteristics, allowing to test model performance under different climate/fuel conditions. We then extend the analysis in order to assess the predictability of monthly burned areas. The sensitivity of the models to the level of spatial aggregation of the data is also evaluated. Additionally, we investigate the gain in model performance with the inclusion of socioeconomic and land use/land cover (LULC) covariates in model formulation. Fire occurrence models have attained good performance in most of the phytoclimatic zones considered, being able to faithfully reproduce the inter-annual variability of fire frequency. Total area burned has exhibited some dependence on the meteorological drivers, although model performance was poor in most cases. We identified temperature and some FWI system components as the most important explanatory variables, highlighting the adequacy of the FWI system for fire occurrence prediction in the study area. The results were improved when using aggregated data across regions compared to when data were sampled at the grid-box level. The inclusion of socioeconomic and LULC covariates contributed marginally to the improvement of the models, and in most cases attained no relevant contribution to total explained variance - excepting northern Spain, where anthropogenic factors are known to be the major driver of fires. Models of monthly fire counts performed better in the case of fires larger than 0.1 ha, and for the rest of the thresholds (1, 10 and 100 ha) the daily occurrence models improved the predicted inter-annual variability, indicating the added value of daily models. Fire frequency predictions may provide a preferable basis for past fire history reconstruction, long-term monitoring and the assessment of future climate impacts on fire regimes across regions, posing several advantages over burned area as a response variable. Our results leave the door open to the development a more complex modelling framework based on daily data from numerical climate model outputs based on the FWI system.

Online coupled regional meteorology-chemistry models in Europe: current status and prospects

NASA Astrophysics Data System (ADS)

Baklanov, A.; Schluenzen, K. H.; Suppan, P.; Baldasano, J.; Brunner, D.; Aksoyoglu, S.; Carmichael, G.; Douros, J.; Flemming, J.; Forkel, R.; Galmarini, S.; Gauss, M.; Grell, G.; Hirtl, M.; Joffre, S.; Jorba, O.; Kaas, E.; Kaasik, M.; Kallos, G.; Kong, X.; Korsholm, U.; Kurganskiy, A.; Kushta, J.; Lohmann, U.; Mahura, A.; Manders-Groot, A.; Maurizi, A.; Moussiopoulos, N.; Rao, S. T.; Savage, N.; Seigneur, C.; Sokhi, R.; Solazzo, E.; Solomos, S.; Sørensen, B.; Tsegas, G.; Vignati, E.; Vogel, B.; Zhang, Y.

2013-05-01

The simulation of the coupled evolution of atmospheric dynamics, pollutant transport, chemical reactions and atmospheric composition is one of the most challenging tasks in environmental modelling, climate change studies, and weather forecasting for the next decades as they all involve strongly integrated processes. Weather strongly influences air quality (AQ) and atmospheric transport of hazardous materials, while atmospheric composition can influence both weather and climate by directly modifying the atmospheric radiation budget or indirectly affecting cloud formation. Until recently, however, due to the scientific complexities and lack of computational power, atmospheric chemistry and weather forecasting have developed as separate disciplines, leading to the development of separate modelling systems that are only loosely coupled. The continuous increase in computer power has now reached a stage that enables us to perform online coupling of regional meteorological models with atmospheric chemical transport models. The focus on integrated systems is timely, since recent research has shown that meteorology and chemistry feedbacks are important in the context of many research areas and applications, including numerical weather prediction (NWP), AQ forecasting as well as climate and Earth system modelling. However, the relative importance of online integration and its priorities, requirements and levels of detail necessary for representing different processes and feedbacks can greatly vary for these related communities: (i) NWP, (ii) AQ forecasting and assessments, (iii) climate and earth system modelling. Additional applications are likely to benefit from online modelling, e.g.: simulation of volcanic ash or forest fire plumes, pollen warnings, dust storms, oil/gas fires, geo-engineering tests involving changes in the radiation balance. The COST Action ES1004 - European framework for online integrated air quality and meteorology modelling (EuMetChem) - aims at paving the way towards a new generation of online integrated atmospheric chemical transport and meteorology modelling with two-way interactions between different atmospheric processes including dynamics, chemistry, clouds, radiation, boundary layer and emissions. As its first task, we summarise the current status of European modelling practices and experience with online coupled modelling of meteorology with atmospheric chemistry including feedback mechanisms and attempt reviewing the various issues connected to the different modules of such online coupled models but also providing recommendations for coping with them for the benefit of the modelling community at large.

Linking Satellite-Derived Fire Counts to Satellite-Derived Weather Data in Fire Prediction Models to Forecast Extreme Fires in Siberia

NASA Astrophysics Data System (ADS)

Westberg, David; Soja, Amber; Stackhouse, Paul, Jr.

2010-05-01

Fire is the dominant disturbance that precipitates ecosystem change in boreal regions, and fire is largely under the control of weather and climate. Boreal systems contain the largest pool of terrestrial carbon, and Russia holds 2/3 of the global boreal forests. Fire frequency, fire severity, area burned and fire season length are predicted to increase in boreal regions under climate change scenarios. Meteorological parameters influence fire danger and fire is a catalyst for ecosystem change. Therefore to predict fire weather and ecosystem change, we must understand the factors that influence fire regimes and at what scale these are viable. Our data consists of NASA Langley Research Center (LaRC)-derived fire weather indices (FWI) and National Climatic Data Center (NCDC) surface station-derived FWI on a domain from 50°N-80°N latitude and 70°E-170°W longitude and the fire season from April through October for the years of 1999, 2002, and 2004. Both of these are calculated using the Canadian Forest Service (CFS) FWI, which is based on local noon surface-level air temperature, relative humidity, wind speed, and daily (noon-noon) rainfall. The large-scale (1°) LaRC product uses NASA Goddard Earth Observing System version 4 (GEOS-4) reanalysis and NASA Global Precipitation Climatology Project (GEOS-4/GPCP) data to calculate FWI. CFS Natural Resources Canada uses Geographic Information Systems (GIS) to interpolate NCDC station data and calculate FWI. We compare the LaRC GEOS- 4/GPCP FWI and CFS NCDC FWI based on their fraction of 1° grid boxes that contain satellite-derived fire counts and area burned to the domain total number of 1° grid boxes with a common FWI category (very low to extreme). These are separated by International Geosphere-Biosphere Programme (IGBP) 1°x1° resolution vegetation types to determine and compare fire regimes in each FWI/ecosystem class and to estimate the fraction of each of the 18 IGBP ecosystems burned, which are dependent on the FWI. On days with fire counts, the domain total of 1°x1° grid boxes with and without daily fire counts and area burned are totaled. The fraction of 1° grid boxes with fire counts and area burned to the total number of 1° grid boxes having common FWI category and vegetation type are accumulated, and a daily mean for the burning season is calculated. The mean fire counts and mean area burned plots appear to be well related. The ultimate goal of this research is to assess the viability of large-scale (1°) data to be used to assess fire weather danger and fire regimes, so these data can be confidently used to predict future fire regimes using large-scale fire weather data. Specifically, we related large-scale fire weather, area burned, and the amount of fire-induced ecosystem change. Both the LaRC and CFS FWI showed gradual linear increase in fraction of grid boxes with fire counts and area burned with increasing FWI category, with an exponential increase in the higher FWI categories in some cases, for the majority of the vegetation types. Our analysis shows a direct correlation between increased fire activity and increased FWI, independent of time or the severity of the fire season. During normal and extreme fire seasons, we noticed the fraction of fire counts and area burned per 1° grid box increased with increasing FWI rating. Given this analysis, we are confident large-scale weather and climate data, in this case from the GEOS-4 reanalysis and the GPCP data sets, can be used to accurately assess future fire potential. This increases confidence in the ability of large-scale IPCC weather and climate scenarios to predict future fire regimes in boreal regions.

Wild Fire Emissions for the NOAA Operational HYSPLIT Smoke Model

NASA Astrophysics Data System (ADS)

Huang, H. C.; ONeill, S. M.; Ruminski, M.; Shafran, P.; McQueen, J.; DiMego, G.; Kondragunta, S.; Gorline, J.; Huang, J. P.; Stunder, B.; Stein, A. F.; Stajner, I.; Upadhayay, S.; Larkin, N. K.

2015-12-01

Particulate Matter (PM) generated from forest fires often lead to degraded visibility and unhealthy air quality in nearby and downstream areas. To provide near-real time PM information to the state and local agencies, the NOAA/National Weather Service (NWS) operational HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory Model) smoke modeling system (NWS/HYSPLIT smoke) provides the forecast of smoke concentration resulting from fire emissions driven by the NWS North American Model 12 km weather predictions. The NWS/HYSPLIT smoke incorporates the U.S. Forest Service BlueSky Smoke Modeling Framework (BlueSky) to provide smoke fire emissions along with the input fire locations from the NOAA National Environmental Satellite, Data, and Information Service (NESDIS)'s Hazard Mapping System fire and smoke detection system. Experienced analysts inspect satellite imagery from multiple sensors onboard geostationary and orbital satellites to identify the location, size and duration of smoke emissions for the model. NWS/HYSPLIT smoke is being updated to use a newer version of USFS BlueSky. The updated BlueSky incorporates the Fuel Characteristic Classification System version 2 (FCCS2) over the continental U.S. and Alaska. FCCS2 includes a more detailed description of fuel loadings with additional plant type categories. The updated BlueSky also utilizes an improved fuel consumption model and fire emission production system. For the period of August 2014 and June 2015, NWS/HYSPLIT smoke simulations show that fire smoke emissions with updated BlueSky are stronger than the current operational BlueSky in the Northwest U.S. For the same comparisons, weaker fire smoke emissions from the updated BlueSky were observed over the middle and eastern part of the U.S. A statistical evaluation of NWS/HYSPLIT smoke predicted total column concentration compared to NOAA NESDIS GOES EAST Aerosol Smoke Product retrievals is underway. Preliminary results show that using the newer version of BlueSky leads to improved performance of NWS/HYSPLIT-smoke for June 2015. These results are partially due to the default fuel loading selected for Canadian fires that lead to stronger fire emissions there. The use of more realistic Canadian fuel loading may improve NWS/HYSPLIT smoke forecast.

Effects of Lightning and Other Meteorological Factors on Fire Activity in the North American Boreal Forest: Implications for Fire Weather Forecasting

NASA Technical Reports Server (NTRS)

Peterson, D.; Wang, J.; Ichoku, C.; Remer, L. A.

2010-01-01

The effects of lightning and other meteorological factors on wildfire activity in the North American boreal forest are statistically analyzed during the fire seasons of 2000-2006 through an integration of the following data sets: the MODerate Resolution Imaging Spectroradiometer (MODIS) level 2 fire products, the 3-hourly 32-kin gridded meteorological data from North American Regional Reanalysis (NARR), and the lightning data collected by the Canadian Lightning Detection Network (CLDN) and the Alaska Lightning Detection Network (ALDN). Positive anomalies of the 500 hPa geopotential height field, convective available potential energy (CAPE), number of cloud-to-ground lightning strikes, and the number of consecutive dry days are found to be statistically important to the seasonal variation of MODIS fire counts in a large portion of Canada and the entirety of Alaska. Analysis of fire occurrence patterns in the eastern and western boreal forest regions shows that dry (in the absence of precipitation) lightning strikes account for only 20% of the total lightning strikes, but are associated with (and likely cause) 40% of the MODIS observed fire counts in these regions. The chance for ignition increases when a threshold of at least 10 dry strikes per NARR grid box and at least 10 consecutive dry days is reached. Due to the orientation of the large-scale pattern, complex differences in fire and lightning occurrence and variability were also found between the eastern and western sub-regions. Locations with a high percentage of dry strikes commonly experience an increased number of fire counts, but the mean number of fire counts per dry strike is more than 50% higher in western boreal forest sub-region, suggesting a geographic and possible topographic influence. While wet lightning events are found to occur with a large range of CAPE values, a high probability for dry lightning occurs only when 500 hPa geopotential heights are above 5700m and CAPE values are near the maximum observed level, underscoring the importance of low-level instability to boreal fire weather forecasts-

Predicting moisture content: fuel moisture indicator sticks in the Pacific Northwest.

Treesearch

Owen P. Cramer

1961-01-01

Successful day-to-day planning of presuppression activities requires accurate prediction of burning index. In the Pacific Northwest, forecasts of burning index are prepared by the fire-control man and are based on predictions of windspeed and fuel moisture. Although fuel moisture is affected by a number of weather elements and is consequently difficult to predict, the...

Use of spatially refined satellite remote sensing fire detection data to initialize and evaluate coupled weather-wildfire growth model simulations

NASA Astrophysics Data System (ADS)

Schroeder, W.; Coen, J.; Oliva, P.

2013-12-01

Availability of spatially refined satellite active fire detection data is gradually increasing. For example, the new 375 m Visible Infrared Imaging Radiometer Suite (VIIRS) data show improved active fire detection performance for both small and large size fires. The VIIRS data have proved superior to MODIS for mapping of wildfires events spanning several days to weeks of either continued or intermittent activity, delivering 12-h active fire data of improved spatial fidelity. The VIIRS active fire data are complemented by other satellite active fire data sets of similar or higher spatial resolution, including the new 30 m Landsat-8. Additional assets should include the upcoming 20 m Sentinel-2 Landsat-class satellite program by the European Space Agency to be launched in 2014-15. These improved active fire data sets are fostering new applications that rely on higher resolution input fire data. In this study, we describe the characteristics of the new VIIRS and Landsat-8 data and demonstrate one such new application of satellite active fire data in support of fire behavior modeling. We present results for a wildfire observed in June 2012 in New Mexico using an innovative approach to improving the simulation of large, long-duration wildfires, either for retrospective studies or forecasting in a number of geophysical applications. The approach uses (1) the Coupled Atmosphere-Wildland Fire Environment (CAWFE) Model, a numerical weather prediction model two-way coupled with a module representing the rate of spread of a wildfire's flaming front, its rate of consumption of different wildland fuels, and the feedback of this heat release upon the atmosphere - i.e. 'how a fire creates its own weather', combined with (2) spatially refined 375 m VIIRS active fire data, which is used for initialization of a wildfire already in progress in the model and evaluation of its simulated progression at the time of the next pass. Results show that initializing a fire that is 'in progress' with VIIRS data and a weather simulation based on more recent atmospheric analyses can overcome several issues and improve the simulation of late-developing fires and of later periods (particularly those with growth periods separated by lulls) in a long-lived fire.

Assessing fire risk in Portugal during the summer fire season

NASA Astrophysics Data System (ADS)

Dacamara, C. C.; Pereira, M. G.; Trigo, R. M.

2009-04-01

Since 1998, Instituto de Meteorologia, the Portuguese Weather Service has relied on the Canadian Fire Weather Index (FWI) System (van Wagner, 1987) to produce daily forecasts of fire risk. The FWI System consists of six components that account for the effects of fuel moisture and wind on fire behavior. The first three components, i.e. the Fine Fuel Moisture Code (FFMC), the Duff Moisture Code (DMC) and the Drought Code (DC) respectively rate the average moisture content of surface litter, decomposing litter, and organic (humus) layers of the soil. Wind effects are then added to FFMC leading to the Initial Spread Index (ISI) that rates fire spread. The remaining two fuel moisture codes (DMC and DC) are in turn combined to produce the Buildup Index (BUI) that is a rating of the total amount of fuel available for combustion. BUI is finally combined with ISI to produce the Fire Weather Index (FWI) that represents the rate of fire intensity. Classes of fire danger and levels of preparedness are commonly defined on an empirical way for a given region by calibrating the FWI System against wildfire activity as defined by the recorded number of events and by the observed burned area over a given period of time (Bovio and Camia, 1998). It is also a well established fact that distributions of burned areas are heavily skewed to the right and tend to follow distributions of the exponential-type (Cumming, 2001). Based on the described context, a new procedure is presented for calibrating the FWI System during the summer fire season in Portugal. Two datasets were used covering a 28-year period (1980-2007); i) the official Portuguese wildfire database which contains detailed information on fire events occurred in the 18 districts of Continental Portugal and ii) daily values of the six components of the FWI System as derived from reanalyses (Uppala et al., 2005) of the European Centre for Medium-Range Weather Forecasts (ECMWF). Calibration of the FWI System is then performed in two steps; 1) a truncated Weibull distribution is fitted to the sample of burned areas and 2) the quality of the fitted statistical model is improved by incorporating components of the FWI System as covariates. Obtained model allows estimating on a daily basis the probability of occurrence of fires larger than a given threshold as well as producing maps of fire risk. Results as obtained from a prototype currently being developed will be presented and discussed. In particular, it will be shown that results provide additional evidence of the known fact that the extent of burned area in Portugal is controlled by two main atmospheric factors (Pereira et al. 2005): i) a long-term control related to the regime of temperature and precipitation in spring and ii) a short-term control exerted by the occurrence of very intense dry spells in days of extreme synoptic situations. Bovio, G., and A. Camia. 1998. An analysis of large forest fire danger conditions in Europe. In Proc. 3rd Int. Conf. on Forest Fire Research & 14th Conf. on Fire and Forest Meteorology, Viegas, D.X. (Ed.), Luso, 16-20 Nov., ADAI, 975-994. Cumming, S.G., 2001. Parametric models of the fire size distribution. Can J. For. Res., 31, 1297-1303. Pereira, M.G., Trigo, R.M., DaCamara, C.C., Pereira, J.M.C. and Leite, S.M., 2005. Synoptic patterns associated with large summer forest fires in Portugal. Agr. and For. Meteorol., 129 (1-2), 11-25. Uppala, S.M. et al., 2005: The ERA-40 re-analysis. Quart. J. R. Meteorol. Soc., 131, 2961-3012. Van Wagner, C.E., 1987. Development and structure of the Canadian forest fire weather index system. Canadian Forestry Service, Forest Technical Report 35, Ottawa, 37 pp.

Weather forecasting expert system study

NASA Technical Reports Server (NTRS)

1985-01-01

Weather forecasting is critical to both the Space Transportation System (STS) ground operations and the launch/landing activities at NASA Kennedy Space Center (KSC). The current launch frequency places significant demands on the USAF weather forecasters at the Cape Canaveral Forecasting Facility (CCFF), who currently provide the weather forecasting for all STS operations. As launch frequency increases, KSC's weather forecasting problems will be great magnified. The single most important problem is the shortage of highly skilled forecasting personnel. The development of forecasting expertise is difficult and requires several years of experience. Frequent personnel changes within the forecasting staff jeopardize the accumulation and retention of experience-based weather forecasting expertise. The primary purpose of this project was to assess the feasibility of using Artificial Intelligence (AI) techniques to ameliorate this shortage of experts by capturing aria incorporating the forecasting knowledge of current expert forecasters into a Weather Forecasting Expert System (WFES) which would then be made available to less experienced duty forecasters.

Using High Resolution Model Data to Improve Lightning Forecasts across Southern California

NASA Astrophysics Data System (ADS)

Capps, S. B.; Rolinski, T.

2014-12-01

Dry lightning often results in a significant amount of fire starts in areas where the vegetation is dry and continuous. Meteorologists from the USDA Forest Service Predictive Services' program in Riverside, California are tasked to provide southern and central California's fire agencies with fire potential outlooks. Logistic regression equations were developed by these meteorologists several years ago, which forecast probabilities of lightning as well as lightning amounts, out to seven days across southern California. These regression equations were developed using ten years of historical gridded data from the Global Forecast System (GFS) model on a coarse scale (0.5 degree resolution), correlated with historical lightning strike data. These equations do a reasonably good job of capturing a lightning episode (3-5 consecutive days or greater of lightning), but perform poorly regarding more detailed information such as exact location and amounts. It is postulated that the inadequacies in resolving the finer details of episodic lightning events is due to the coarse resolution of the GFS data, along with limited predictors. Stability parameters, such as the Lifted Index (LI), the Total Totals index (TT), Convective Available Potential Energy (CAPE), along with Precipitable Water (PW) are the only parameters being considered as predictors. It is hypothesized that the statistical forecasts will benefit from higher resolution data both in training and implementing the statistical model. We have dynamically downscaled NCEP FNL (Final) reanalysis data using the Weather Research and Forecasting model (WRF) to 3km spatial and hourly temporal resolution across a decade. This dataset will be used to evaluate the contribution to the success of the statistical model of additional predictors in higher vertical, spatial and temporal resolution. If successful, we will implement an operational dynamically downscaled GFS forecast product to generate predictors for the resulting statistical lightning model. This data will help fire agencies be better prepared to pre-deploy resources in advance of these events. Specific information regarding duration, amount, and location will be especially valuable.

Towards the development of full-fledged forest fire information systems

NASA Astrophysics Data System (ADS)

Baetens, J.; De Baets, B.

2012-12-01

Throughout the last decades much efforts have been spent in obtaining an increased understanding of wildfire dynamics and the way it is influenced by prevailing environmental conditions and settings, such as temperature, humidity, topography, vegetation abundance, and so on, since such a profound apprehension is a prerequisite for achieving enhanced wildfire prevention measures, as well as for optimizing fire fighting and disaster management. Amongst other things, this pursuit has culminated in the deployment of wildfire information systems, such as the Canadian Wildfire Information System (CWFIS), the European Forest Fire Information System (EFFIS) and the United States Active Fire Mapping Program and Landscape Fire and Resource Management Planning Tools (LANDFIRE), which inform any interested stakeholder, be it a citizen or a government official, about the current fire risk, the extent and location of current fires, the inflammability of the vegetation, and so on. Taking into account the coverage of these systems, it should be clear that they strongly rely upon satellite imagery that is obtained from dedicated sensors, such as the Moderate-Resolution Imaging Spectroradiometer (MODIS) on board of NASA's Terra and Aqua satellites and the Advanced Very High Resolution Radiometer (AVHRR) that is carried by NOAA satellites, or more general-purpose instruments on board of spacecrafts such as Landsat or SPOT. Yet, to this day the aforementioned information systems have not yet embraced the power of mathematical modeling in order to enable trustworthy forecasts of the spatio-temporal propagation of wildfires given their current extent, which would nonetheless be extremely useful for optimizing fire fighting and disaster management, taking appropriate preventive measures, and so on. The deployment of such full-fledged wildfire information systems requires a high-level integration of (real-time) satellite imagery, weather reports and forecasts, geographic information, and finally mathematical models that constitute a mathematization of the underlying environmental processes, and which are indispensable for attaining sound and trustworthy wildfire forecasts, just as their meteorological counterparts are exploited to yield meaningful weather forecasts. As a very first step towards the development of a full-fledged wildfire information system, we demonstrate how MODIS imagery, Anderson fuel maps and geographic information can be combined to achieve meaningful wildfire forecasts given the current extent of the considered wildfire. Such a high-level integration is illustrated for a wildfire that swept through a natural area in Arizona, United States, near the border with New Mexico, between days 148 and 166 of the year 2011. Taking into account the spatial discreteness of the exploited information, which follows from its storage in geographical information systems, we rely upon a spatially discrete mathematical model, i.e. a coupled-map lattice, for mimicking the spatio-temporal wildfire propagation that can be extended in a next stage. Since setting up a full-fledged wildfire information system requires a highly multidisciplinary approach in which foresters, mathematicians, computer scientists, physicists, ecologists and others need to be involved, we hope to stimulate the joint efforts in accomplishing this task by means of our contribution.

Analysis of meteorological conditions for the Yakima Smoke Intrusion Case Study, 28 September 2009

Treesearch

Miriam Rorig; Robert Solomon; Candace Krull; Janice Peterson; Julia Ruthford; Brian Potter

2013-01-01

On 28 September 2009, the Naches Ranger District on the Okanogan-Wenatchee National Forest in south-central Washington state ignited an 800-ha prescribed fire. Later that afternoon, elevated PM2.5 concentrations and visible smoke were reported in Yakima, Washington, about 40 km east of the burn unit. The U.S. National Weather Service forecast for the day had predicted...

Weather Research and Forecasting Model Sensitivity Comparisons for Warm Season Convective Initiation

NASA Technical Reports Server (NTRS)

Watson, Leela R.; Hoeth, Brian; Blottman, Peter F.

2007-01-01

Mesoscale weather conditions can significantly affect the space launch and landing operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). During the summer months, land-sea interactions that occur across KSC and CCAFS lead to the formation of a sea breeze, which can then spawn deep convection. These convective processes often last 60 minutes or less and pose a significant challenge to the forecasters at the National Weather Service (NWS) Spaceflight Meteorology Group (SMG). The main challenge is that a "GO" forecast for thunderstorms and precipitation is required at the 90 minute deorbit decision for End Of Mission (EOM) and at the 30 minute Return To Launch Site (RTLS) decision at the Shuttle Landing Facility. Convective initiation, timing, and mode also present a forecast challenge for the NWS in Melbourne, FL (MLB). The NWS MLB issues such tactical forecast information as Terminal Aerodrome Forecasts (TAFs), Spot Forecasts for fire weather and hazardous materials incident support, and severe/hazardous weather Watches, Warnings, and Advisories. Lastly, these forecasting challenges can also affect the 45th Weather Squadron (45 WS), which provides comprehensive weather forecasts for shuttle launch, as well as ground operations, at KSC and CCAFS. The need for accurate mesoscale model forecasts to aid in their decision making is crucial. Both the SMG and the MLB are currently implementing the Weather Research and Forecasting Environmental Modeling System (WRF EMS) software into their operations. The WRF EMS software allows users to employ both dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). There are also data assimilation analysis packages available for the initialization of the WRF model- the Local Analysis and Prediction System (LAPS) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS). Having a series of initialization options and WRF cores, as well as many options within each core, provides SMG and NWS MLB with a lot of flexibility. It also creates challenges, such as determining which configuration options are best to address specific forecast concerns. The goal of this project is to assess the different configurations available and to determine which configuration will best predict warm season convective initiation in East-Central Florida. Four different combinations of WRF initializations will be run (ADAS-ARW, ADAS-NMM, LAPS-ARW, and LAPS-NMM) at a 4-km resolution over the Florida peninsula and adjacent coastal waters. Five candidate convective initiation days using three different flow regimes over East-Central Florida will be examined, as well as two null cases (non-convection days). Each model run will be integrated 12 hours with three runs per day, at 0900, 1200, and 1500 UTe. ADAS analyses will be generated every 30 minutes using Level II Weather Surveillance Radar-1988 Doppler (WSR-88D) data from all Florida radars to verify the convection forecast. These analyses will be run on the same domain as the four model configurations. To quantify model performance, model output will be subjectively compared to the ADAS analyses of convection to determine forecast accuracy. In addition, a subjective comparison of the performance of the ARW using a high-resolution local grid with 2-way nesting, I-way nesting, and no nesting will be made for select convective initiation cases. The inner grid will cover the East-Central Florida region at a resolution of 1.33 km. The authors will summarize the relative skill of the various WRF configurations and how each configuration behaves relative to the others, as well as determine the best model configuration for predicting warm season convective initiation over East-Central Florida.

Convective Weather Forecast Accuracy Analysis at Center and Sector Levels

NASA Technical Reports Server (NTRS)

Wang, Yao; Sridhar, Banavar

2010-01-01

This paper presents a detailed convective forecast accuracy analysis at center and sector levels. The study is aimed to provide more meaningful forecast verification measures to aviation community, as well as to obtain useful information leading to the improvements in the weather translation capacity models. In general, the vast majority of forecast verification efforts over past decades have been on the calculation of traditional standard verification measure scores over forecast and observation data analyses onto grids. These verification measures based on the binary classification have been applied in quality assurance of weather forecast products at the national level for many years. Our research focuses on the forecast at the center and sector levels. We calculate the standard forecast verification measure scores for en-route air traffic centers and sectors first, followed by conducting the forecast validation analysis and related verification measures for weather intensities and locations at centers and sectors levels. An approach to improve the prediction of sector weather coverage by multiple sector forecasts is then developed. The weather severe intensity assessment was carried out by using the correlations between forecast and actual weather observation airspace coverage. The weather forecast accuracy on horizontal location was assessed by examining the forecast errors. The improvement in prediction of weather coverage was determined by the correlation between actual sector weather coverage and prediction. observed and forecasted Convective Weather Avoidance Model (CWAM) data collected from June to September in 2007. CWAM zero-minute forecast data with aircraft avoidance probability of 60% and 80% are used as the actual weather observation. All forecast measurements are based on 30-minute, 60- minute, 90-minute, and 120-minute forecasts with the same avoidance probabilities. The forecast accuracy analysis for times under one-hour showed that the errors in intensity and location for center forecast are relatively low. For example, 1-hour forecast intensity and horizontal location errors for ZDC center were about 0.12 and 0.13. However, the correlation between sector 1-hour forecast and actual weather coverage was weak, for sector ZDC32, about 32% of the total variation of observation weather intensity was unexplained by forecast; the sector horizontal location error was about 0.10. The paper also introduces an approach to estimate the sector three-dimensional actual weather coverage by using multiple sector forecasts, which turned out to produce better predictions. Using Multiple Linear Regression (MLR) model for this approach, the correlations between actual observation and the multiple sector forecast model prediction improved by several percents at 95% confidence level in comparison with single sector forecast.

Non-supervised method for early forest fire detection and rapid mapping

NASA Astrophysics Data System (ADS)

Artés, Tomás; Boca, Roberto; Liberta, Giorgio; San-Miguel, Jesús

2017-09-01

Natural hazards are a challenge for the society. Scientific community efforts have been severely increased assessing tasks about prevention and damage mitigation. The most important points to minimize natural hazard damages are monitoring and prevention. This work focuses particularly on forest fires. This phenomenon depends on small-scale factors and fire behavior is strongly related to the local weather. Forest fire spread forecast is a complex task because of the scale of the phenomena, the input data uncertainty and time constraints in forest fire monitoring. Forest fire simulators have been improved, including some calibration techniques avoiding data uncertainty and taking into account complex factors as the atmosphere. Such techniques increase dramatically the computational cost in a context where the available time to provide a forecast is a hard constraint. Furthermore, an early mapping of the fire becomes crucial to assess it. In this work, a non-supervised method for forest fire early detection and mapping is proposed. As main sources, the method uses daily thermal anomalies from MODIS and VIIRS combined with land cover map to identify and monitor forest fires with very few resources. This method relies on a clustering technique (DBSCAN algorithm) and on filtering thermal anomalies to detect the forest fires. In addition, a concave hull (alpha shape algorithm) is applied to obtain rapid mapping of the fire area (very coarse accuracy mapping). Therefore, the method leads to a potential use for high-resolution forest fire rapid mapping based on satellite imagery using the extent of each early fire detection. It shows the way to an automatic rapid mapping of the fire at high resolution processing as few data as possible.

Web service tools in the era of forest fire management and elimination

NASA Astrophysics Data System (ADS)

Poursanidis, Dimitris; Kochilakis, Giorgos; Chrysoulakis, Nektarios; Varella, Vasiliki; Kotroni, Vassiliki; Eftychidis, Giorgos; Lagouvardos, Kostas

2014-10-01

Wildfires in forests and forested areas in South Europe, North America, Central Asia and Australia are a diachronic threat with crucial ecological, economic and social impacts. Last decade the frequency, the magnitude and the intensity of fires have increased even more because of the climate change. An efficient response to such disasters requires an effective planning, with an early detection system of the ignition area and an accurate prediction of fire propagation to support the rapid response mechanisms. For this reason, information systems able to predict and visualize the behavior of fires, are valuable tools for fire fighting. Such systems, able also to perform simulations that evaluate the fire development scenarios, based on weather conditions, become valuable Decision Support Tools for fire mitigation planning. A Web-based Information System (WIS) developed in the framework of the FLIRE (Floods and fire risk assessment and management) project, a LIFE+ co-funded by the European Commission research, is presented in this study. The FLIRE WIS use forest fuel maps which have been developed by using generalized fuel maps, satellite data and in-situ observations. Furthermore, it leverages data from meteorological stations and weather forecast from numerical models to feed the fire propagation model with the necessary for the simulations inputs and to visualize the model's results for user defined time periods and steps. The user has real-time access to FLIRE WIS via any web browser from any platform (PC, Laptop, Tablet, Smartphone).

Improving Societal Benefit Areas from Applications Enhanced by the Joint Polar Satellite System

NASA Astrophysics Data System (ADS)

Goldberg, M.

2016-12-01

Applications of satellite data are paramount to transform science and technology to product and services which are used in critical decision making for societal benefits. For the satellite community, good representations of technology are the satellite sensors, while science provides the instrument calibration and derived geophysical parameters. Weather forecasting is an application of the science and technology provided by remote sensing satellites. The Joint Polar Satellite System, which includes the Suomi National Polar-orbiting Partnership (S-NPP) provides formidable science and technology to support many applications and includes support to 1) weather forecasting - data from the JPSS Cross-track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS) are used to forecast weather events out to 7 days - nearly 85% of all data used in weather forecasting are from polar orbiting satellites; 2) environmental monitoring -data from the JPSS Visible Infrared Imager Radiometer Suite (VIIRS) are used to monitor the environment including the health of coastal ecosystems, drought conditions, fire, smoke, dust, snow and ice, and the state of oceans, including sea surface temperature and ocean color; and 3) climate monitoring - data from JPSS instruments, including OMPS and CERES will provide continuity to climate data records established using NOAA POES and NASA Earth Observing System (EOS) satellite observations. To bridge the gap between products and applications, the JPSS Program has established a proving ground program to optimize the use of JPSS data with other data sources to improve key products and services. A number of operational and research applications will be presented along with how the data and applications support a large number of societal benefit areas of the Global Earth Observation Systems of Systems (GEOSS).

JPSS Operational and Research Applications: The Pathway from Observations to Applications to Information.

NASA Astrophysics Data System (ADS)

Goldberg, M.; Sjoberg, W.; Layns, A. L.

2017-12-01

Applications of satellite data are paramount to transform science and technology to product and services which are used in critical decision making. For the satellite community, good representations of technology are the satellite sensors, while science provides the instrument calibration and derived geophysical parameters. Weather forecasting is an application of the science and technology provided by remote sensing satellites. The Joint Polar Satellite System, which includes the Suomi National Polar-orbiting Partnership (S-NPP) provides formidable science and technology to support many applications and includes support to 1) weather forecasting - data from the JPSS Cross-track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS) are used to forecast weather events out to 7 days - nearly 85% of all data used in weather forecasting are from polar orbiting satellites; 2) environmental monitoring -data from the JPSS Visible Infrared Imager Radiometer Suite (VIIRS) are used to monitor the environment including the health of coastal ecosystems, drought conditions, fire, smoke, dust, snow and ice, and the state of oceans, including sea surface temperature and ocean color; and 3) climate monitoring - data from JPSS instruments, including OMPS and CERES will provide continuity to climate data records established using NOAA POES and NASA Earth Observing System (EOS) satellite observations. To bridge the gap between products and applications, the JPSS Program has established the Proving Ground and Risk Reduction (PGRR) Program to identify opportunities to maximize the operational application of current JPSS capabilities. The PGRR Program also helps identify and evaluate the use of JPSS capabilities for new operational missions. New PGRR initiatives focus on hydrological, Arctic, data assimilation, atmospheric chemistry, ocean ecosystem applications. At the conference, the benefits of JPSS data on societal benefits will be presented along with results from the PGRR initiatives.

Emergency assessment of post-fire debris-flow hazards for the 2013 Powerhouse fire, southern California

USGS Publications Warehouse

Staley, Dennis M.; Smoczyk, Gregory M.; Reeves, Ryan R.

2013-01-01

Wildfire dramatically alters the hydrologic response of a watershed such that even modest rainstorms can produce dangerous flash floods and debris flows. Existing empirical models were used to predict the probability and magnitude of debris-flow occurrence in response to a 10-year recurrence interval rainstorm for the 2013 Powerhouse fire near Lancaster, California. Overall, the models predict a relatively low probability for debris-flow occurrence in response to the design storm. However, volumetric predictions suggest that debris flows that occur may entrain a significant volume of material, with 44 of the 73 basins identified as having potential debris-flow volumes between 10,000 and 100,000 cubic meters. These results suggest that even though the likelihood of debris flow is relatively low, the consequences of post-fire debris-flow initiation within the burn area may be significant for downstream populations, infrastructure, and wildlife and water resources. Given these findings, we recommend that residents, emergency managers, and public works departments pay close attention to weather forecasts and National-Weather-Service-issued Debris Flow and Flash Flood Outlooks, Watches, and Warnings and that residents adhere to any evacuation orders.

14 CFR 135.213 - Weather reports and forecasts.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 14 Aeronautics and Space 3 2011-01-01 2011-01-01 false Weather reports and forecasts. 135.213... Operating Limitations and Weather Requirements § 135.213 Weather reports and forecasts. (a) Whenever a person operating an aircraft under this part is required to use a weather report or forecast, that person...

14 CFR 135.213 - Weather reports and forecasts.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 14 Aeronautics and Space 3 2014-01-01 2014-01-01 false Weather reports and forecasts. 135.213... Operating Limitations and Weather Requirements § 135.213 Weather reports and forecasts. (a) Whenever a person operating an aircraft under this part is required to use a weather report or forecast, that person...

14 CFR 135.213 - Weather reports and forecasts.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 14 Aeronautics and Space 3 2013-01-01 2013-01-01 false Weather reports and forecasts. 135.213... Operating Limitations and Weather Requirements § 135.213 Weather reports and forecasts. (a) Whenever a person operating an aircraft under this part is required to use a weather report or forecast, that person...

14 CFR 135.213 - Weather reports and forecasts.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 14 Aeronautics and Space 3 2010-01-01 2010-01-01 false Weather reports and forecasts. 135.213... Operating Limitations and Weather Requirements § 135.213 Weather reports and forecasts. (a) Whenever a person operating an aircraft under this part is required to use a weather report or forecast, that person...

14 CFR 135.213 - Weather reports and forecasts.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 14 Aeronautics and Space 3 2012-01-01 2012-01-01 false Weather reports and forecasts. 135.213... Operating Limitations and Weather Requirements § 135.213 Weather reports and forecasts. (a) Whenever a person operating an aircraft under this part is required to use a weather report or forecast, that person...

Regional variation in fire weather controls the reported occurrence of Scottish wildfires

PubMed Central

Legg, Colin J.

2016-01-01

Fire is widely used as a traditional habitat management tool in Scotland, but wildfires pose a significant and growing threat. The financial costs of fighting wildfires are significant and severe wildfires can have substantial environmental impacts. Due to the intermittent occurrence of severe fire seasons, Scotland, and the UK as a whole, remain somewhat unprepared. Scotland currently lacks any form of Fire Danger Rating system that could inform managers and the Fire and Rescue Services (FRS) of periods when there is a risk of increased of fire activity. We aimed evaluate the potential to use outputs from the Canadian Fire Weather Index system (FWI system) to forecast periods of increased fire risk and the potential for ignitions to turn into large wildfires. We collated four and a half years of wildfire data from the Scottish FRS and examined patterns in wildfire occurrence within different regions, seasons, between urban and rural locations and according to FWI system outputs. We used a variety of techniques, including Mahalanobis distances, percentile analysis and Thiel-Sen regression, to scope the best performing FWI system codes and indices. Logistic regression showed significant differences in fire activity between regions, seasons and between urban and rural locations. The Fine Fuel Moisture Code and the Initial Spread Index did a tolerable job of modelling the probability of fire occurrence but further research on fuel moisture dynamics may provide substantial improvements. Overall our results suggest it would be prudent to ready resources and avoid managed burning when FFMC > 75 and/or ISI > 2. PMID:27833814

Regional variation in fire weather controls the reported occurrence of Scottish wildfires.

PubMed

Davies, G Matt; Legg, Colin J

2016-01-01

Fire is widely used as a traditional habitat management tool in Scotland, but wildfires pose a significant and growing threat. The financial costs of fighting wildfires are significant and severe wildfires can have substantial environmental impacts. Due to the intermittent occurrence of severe fire seasons, Scotland, and the UK as a whole, remain somewhat unprepared. Scotland currently lacks any form of Fire Danger Rating system that could inform managers and the Fire and Rescue Services (FRS) of periods when there is a risk of increased of fire activity. We aimed evaluate the potential to use outputs from the Canadian Fire Weather Index system (FWI system) to forecast periods of increased fire risk and the potential for ignitions to turn into large wildfires. We collated four and a half years of wildfire data from the Scottish FRS and examined patterns in wildfire occurrence within different regions, seasons, between urban and rural locations and according to FWI system outputs. We used a variety of techniques, including Mahalanobis distances, percentile analysis and Thiel-Sen regression, to scope the best performing FWI system codes and indices. Logistic regression showed significant differences in fire activity between regions, seasons and between urban and rural locations. The Fine Fuel Moisture Code and the Initial Spread Index did a tolerable job of modelling the probability of fire occurrence but further research on fuel moisture dynamics may provide substantial improvements. Overall our results suggest it would be prudent to ready resources and avoid managed burning when FFMC > 75 and/or ISI > 2.

Resolution of Probabilistic Weather Forecasts with Application in Disease Management.

PubMed

Hughes, G; McRoberts, N; Burnett, F J

2017-02-01

Predictive systems in disease management often incorporate weather data among the disease risk factors, and sometimes this comes in the form of forecast weather data rather than observed weather data. In such cases, it is useful to have an evaluation of the operational weather forecast, in addition to the evaluation of the disease forecasts provided by the predictive system. Typically, weather forecasts and disease forecasts are evaluated using different methodologies. However, the information theoretic quantity expected mutual information provides a basis for evaluating both kinds of forecast. Expected mutual information is an appropriate metric for the average performance of a predictive system over a set of forecasts. Both relative entropy (a divergence, measuring information gain) and specific information (an entropy difference, measuring change in uncertainty) provide a basis for the assessment of individual forecasts.

Economic Value of Weather and Climate Forecasts

NASA Astrophysics Data System (ADS)

Katz, Richard W.; Murphy, Allan H.

1997-06-01

Weather and climate extremes can significantly impact the economics of a region. This book examines how weather and climate forecasts can be used to mitigate the impact of the weather on the economy. Interdisciplinary in scope, it explores the meteorological, economic, psychological, and statistical aspects of weather prediction. Chapters by area specialists provide a comprehensive view of this timely topic. They encompass forecasts over a wide range of temporal scales, from weather over the next few hours to the climate months or seasons ahead, and address the impact of these forecasts on human behavior. Economic Value of Weather and Climate Forecasts seeks to determine the economic benefits of existing weather forecasting systems and the incremental benefits of improving these systems, and will be an interesting and essential text for economists, statisticians, and meteorologists.

NASA Products to Enhance Energy Utility Load Forecasting

NASA Technical Reports Server (NTRS)

Lough, G.; Zell, E.; Engel-Cox, J.; Fungard, Y.; Jedlovec, G.; Stackhouse, P.; Homer, R.; Biley, S.

2012-01-01

Existing energy load forecasting tools rely upon historical load and forecasted weather to predict load within energy company service areas. The shortcomings of load forecasts are often the result of weather forecasts that are not at a fine enough spatial or temporal resolution to capture local-scale weather events. This project aims to improve the performance of load forecasting tools through the integration of high-resolution, weather-related NASA Earth Science Data, such as temperature, relative humidity, and wind speed. Three companies are participating in operational testing one natural gas company, and two electric providers. Operational results comparing load forecasts with and without NASA weather forecasts have been generated since March 2010. We have worked with end users at the three companies to refine selection of weather forecast information and optimize load forecast model performance. The project will conclude in 2012 with transitioning documented improvements from the inclusion of NASA forecasts for sustained use by energy utilities nationwide in a variety of load forecasting tools. In addition, Battelle has consulted with energy companies nationwide to document their information needs for long-term planning, in light of climate change and regulatory impacts.

Impact of Lidar Wind Sounding on Mesoscale Forecast

NASA Technical Reports Server (NTRS)

Miller, Timothy L.; Chou, Shih-Hung; Goodman, H. Michael (Technical Monitor)

2001-01-01

An Observing System Simulation Experiment (OSSE) was conducted to study the impact of airborne lidar wind sounding on mesoscale weather forecast. A wind retrieval scheme, which interpolates wind data from a grid data system, simulates the retrieval of wind profile from a satellite lidar system. A mesoscale forecast system based on the PSU/NCAR MM5 model is developed and incorporated the assimilation of the retrieved line-of-sight wind. To avoid the "identical twin" problem, the NCEP reanalysis data is used as our reference "nature" atmosphere. The simulated space-based lidar wind observations were retrieved by interpolating the NCEP values to the observation locations. A modified dataset obtained by smoothing the NCEP dataset was used as the initial state whose forecast was sought to be improved by assimilating the retrieved lidar observations. Forecasts using wind profiles with various lidar instrument parameters has been conducted. The results show that to significantly improve the mesoscale forecast the satellite should fly near the storm center with large scanning radius. Increasing lidar firing rate also improves the forecast. Cloud cover and lack of aerosol degrade the quality of the lidar wind data and, subsequently, the forecast.

Climate drives inter-annual variability in probability of high severity fire occurrence in the western United States

NASA Astrophysics Data System (ADS)

Keyser, Alisa; Westerling, Anthony LeRoy

2017-05-01

A long history of fire suppression in the western United States has significantly changed forest structure and ecological function, leading to increasingly uncharacteristic fires in terms of size and severity. Prior analyses of fire severity in California forests showed that time since last fire and fire weather conditions predicted fire severity very well, while a larger regional analysis showed that topography and climate were important predictors of high severity fire. There has not yet been a large-scale study that incorporates topography, vegetation and fire-year climate to determine regional scale high severity fire occurrence. We developed models to predict the probability of high severity fire occurrence for the western US. We predict high severity fire occurrence with some accuracy, and identify the relative importance of predictor classes in determining the probability of high severity fire. The inclusion of both vegetation and fire-year climate predictors was critical for model skill in identifying fires with high fractional fire severity. The inclusion of fire-year climate variables allows this model to forecast inter-annual variability in areas at future risk of high severity fire, beyond what slower-changing fuel conditions alone can accomplish. This allows for more targeted land management, including resource allocation for fuels reduction treatments to decrease the risk of high severity fire.

Modeled Forecasts of Dengue Fever in San Juan, Puerto Rico Using NASA Satellite Enhanced Weather Forecasts

NASA Astrophysics Data System (ADS)

Morin, C.; Quattrochi, D. A.; Zavodsky, B.; Case, J.

2015-12-01

Dengue fever (DF) is an important mosquito transmitted disease that is strongly influenced by meteorological and environmental conditions. Recent research has focused on forecasting DF case numbers based on meteorological data. However, these forecasting tools have generally relied on empirical models that require long DF time series to train. Additionally, their accuracy has been tested retrospectively, using past meteorological data. Consequently, the operational utility of the forecasts are still in question because the error associated with weather and climate forecasts are not reflected in the results. Using up-to-date weekly dengue case numbers for model parameterization and weather forecast data as meteorological input, we produced weekly forecasts of DF cases in San Juan, Puerto Rico. Each week, the past weeks' case counts were used to re-parameterize a process-based DF model driven with updated weather forecast data to generate forecasts of DF case numbers. Real-time weather forecast data was produced using the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) system enhanced using additional high-resolution NASA satellite data. This methodology was conducted in a weekly iterative process with each DF forecast being evaluated using county-level DF cases reported by the Puerto Rico Department of Health. The one week DF forecasts were accurate especially considering the two sources of model error. First, weather forecasts were sometimes inaccurate and generally produced lower than observed temperatures. Second, the DF model was often overly influenced by the previous weeks DF case numbers, though this phenomenon could be lessened by increasing the number of simulations included in the forecast. Although these results are promising, we would like to develop a methodology to produce longer range forecasts so that public health workers can better prepare for dengue epidemics.

National Weather Service Forecast Office - Honolulu, Hawai`i

Science.gov Websites

Locations - Coastal Forecast Kauai Northwest Waters Kauai Windward Waters Kauai Leeward Waters Kauai Channel Oahu Forecast Oahu Surf Forecast Coastal Wind Observations Buoy Reports, and current weather conditions for selected locations tides, sunrise and sunset information Coastal Waters Forecast general weather

Improving High-resolution Weather Forecasts using the Weather Research and Forecasting (WRF) Model with Upgraded Kain-Fritsch Cumulus Scheme

EPA Science Inventory

High-resolution weather forecasting is affected by many aspects, i.e. model initial conditions, subgrid-scale cumulus convection and cloud microphysics schemes. Recent 12km grid studies using the Weather Research and Forecasting (WRF) model have identified the importance of inco...

Ensemble flare forecasting: using numerical weather prediction techniques to improve space weather operations

NASA Astrophysics Data System (ADS)

Murray, S.; Guerra, J. A.

2017-12-01

One essential component of operational space weather forecasting is the prediction of solar flares. Early flare forecasting work focused on statistical methods based on historical flaring rates, but more complex machine learning methods have been developed in recent years. A multitude of flare forecasting methods are now available, however it is still unclear which of these methods performs best, and none are substantially better than climatological forecasts. Current operational space weather centres cannot rely on automated methods, and generally use statistical forecasts with a little human intervention. Space weather researchers are increasingly looking towards methods used in terrestrial weather to improve current forecasting techniques. Ensemble forecasting has been used in numerical weather prediction for many years as a way to combine different predictions in order to obtain a more accurate result. It has proved useful in areas such as magnetospheric modelling and coronal mass ejection arrival analysis, however has not yet been implemented in operational flare forecasting. Here we construct ensemble forecasts for major solar flares by linearly combining the full-disk probabilistic forecasts from a group of operational forecasting methods (ASSA, ASAP, MAG4, MOSWOC, NOAA, and Solar Monitor). Forecasts from each method are weighted by a factor that accounts for the method's ability to predict previous events, and several performance metrics (both probabilistic and categorical) are considered. The results provide space weather forecasters with a set of parameters (combination weights, thresholds) that allow them to select the most appropriate values for constructing the 'best' ensemble forecast probability value, according to the performance metric of their choice. In this way different forecasts can be made to fit different end-user needs.

LINKS to NATIONAL WEATHER SERVICE MARINE FORECAST OFFICES

Science.gov Websites

Coastal Flooding Tsunamis 406 EPIRB's National Weather Service Marine Forecasts LINKS to NATIONAL WEATHER Marine Forecasts in text form ) Coastal NWS Forecast Offices have regionally focused marine webpages which are overflowing with information such as coastal forecasts, predicted tides, and buoy observations

Flare forecasting at the Met Office Space Weather Operations Centre

NASA Astrophysics Data System (ADS)

Murray, S. A.; Bingham, S.; Sharpe, M.; Jackson, D. R.

2017-04-01

The Met Office Space Weather Operations Centre produces 24/7/365 space weather guidance, alerts, and forecasts to a wide range of government and commercial end-users across the United Kingdom. Solar flare forecasts are one of its products, which are issued multiple times a day in two forms: forecasts for each active region on the solar disk over the next 24 h and full-disk forecasts for the next 4 days. Here the forecasting process is described in detail, as well as first verification of archived forecasts using methods commonly used in operational weather prediction. Real-time verification available for operational flare forecasting use is also described. The influence of human forecasters is highlighted, with human-edited forecasts outperforming original model results and forecasting skill decreasing over longer forecast lead times.

Flight Departure Delay and Rerouting Under Uncertainty in En Route Convective Weather

NASA Technical Reports Server (NTRS)

Mukherjee, Avijit; Grabbe, Shon; Sridhar, Banavar

2011-01-01

Delays caused by uncertainty in weather forecasts can be reduced by improving traffic flow management decisions. This paper presents a methodology for traffic flow management under uncertainty in convective weather forecasts. An algorithm for assigning departure delays and reroutes to aircraft is presented. Departure delay and route assignment are executed at multiple stages, during which, updated weather forecasts and flight schedules are used. At each stage, weather forecasts up to a certain look-ahead time are treated as deterministic and flight scheduling is done to mitigate the impact of weather on four-dimensional flight trajectories. Uncertainty in weather forecasts during departure scheduling results in tactical airborne holding of flights. The amount of airborne holding depends on the accuracy of forecasts as well as the look-ahead time included in the departure scheduling. The weather forecast look-ahead time is varied systematically within the experiments performed in this paper to analyze its effect on flight delays. Based on the results, longer look-ahead times cause higher departure delays and additional flying time due to reroutes. However, the amount of airborne holding necessary to prevent weather incursions reduces when the forecast look-ahead times are higher. For the chosen day of traffic and weather, setting the look-ahead time to 90 minutes yields the lowest total delay cost.

The Weather Forecast Using Data Mining Research Based on Cloud Computing.

NASA Astrophysics Data System (ADS)

Wang, ZhanJie; Mazharul Mujib, A. B. M.

2017-10-01

Weather forecasting has been an important application in meteorology and one of the most scientifically and technologically challenging problem around the world. In my study, we have analyzed the use of data mining techniques in forecasting weather. This paper proposes a modern method to develop a service oriented architecture for the weather information systems which forecast weather using these data mining techniques. This can be carried out by using Artificial Neural Network and Decision tree Algorithms and meteorological data collected in Specific time. Algorithm has presented the best results to generate classification rules for the mean weather variables. The results showed that these data mining techniques can be enough for weather forecasting.

Evaluation and economic value of winter weather forecasts

NASA Astrophysics Data System (ADS)

Snyder, Derrick W.

State and local highway agencies spend millions of dollars each year to deploy winter operation teams to plow snow and de-ice roadways. Accurate and timely weather forecast information is critical for effective decision making. Students from Purdue University partnered with the Indiana Department of Transportation to create an experimental winter weather forecast service for the 2012-2013 winter season in Indiana to assist in achieving these goals. One forecast product, an hourly timeline of winter weather hazards produced daily, was evaluated for quality and economic value. Verification of the forecasts was performed with data from the Rapid Refresh numerical weather model. Two objective verification criteria were developed to evaluate the performance of the timeline forecasts. Using both criteria, the timeline forecasts had issues with reliability and discrimination, systematically over-forecasting the amount of winter weather that was observed while also missing significant winter weather events. Despite these quality issues, the forecasts still showed significant, but varied, economic value compared to climatology. Economic value of the forecasts was estimated to be 29.5 million or 4.1 million, depending on the verification criteria used. Limitations of this valuation system are discussed and a framework is developed for more thorough studies in the future.

United States Geological Survey fire science: fire danger monitoring and forecasting

USGS Publications Warehouse

Eidenshink, Jeff C.; Howard, Stephen M.

2012-01-01

Each day, the U.S. Geological Survey produces 7-day forecasts for all Federal lands of the distributions of number of ignitions, number of fires above a given size, and conditional probabilities of fires growing larger than a specified size. The large fire probability map is an estimate of the likelihood that ignitions will become large fires. The large fire forecast map is a probability estimate of the number of fires on federal lands exceeding 100 acres in the forthcoming week. The ignition forecast map is a probability estimate of the number of fires on Federal land greater than 1 acre in the forthcoming week. The extreme event forecast is the probability estimate of the number of fires on Federal land that may exceed 5,000 acres in the forthcoming week.

Dynamic Domains in Data Production Planning

NASA Technical Reports Server (NTRS)

Golden, Keith; Pang, Wanlin

2005-01-01

This paper discusses a planner-based approach to automating data production tasks, such as producing fire forecasts from satellite imagery and weather station data. Since the set of available data products is large, dynamic and mostly unknown, planning techniques developed for closed worlds are unsuitable. We discuss a number of techniques we have developed to cope with data production domains, including a novel constraint propagation algorithm based on planning graphs and a constraint-based approach to interleaved planning, sensing and execution.

Cloud Statistics for NASA Climate Change Studies

NASA Technical Reports Server (NTRS)

Wylie, Donald P.

1999-01-01

The Principal Investigator participated in two field experiments and developed a global data set on cirrus cloud frequency and optical depth to aid the development of numerical models of climate. Four papers were published under this grant. The accomplishments are summarized: (1) In SUCCESS (SUbsonic aircraft: Contrail & Cloud Effects Special Study) the Principal Investigator aided weather forecasters in the start of the field program. A paper also was published on the clouds studied in SUCCESS and the use of the satellite stereographic technique to distinguish cloud forms and heights of clouds. (2) In SHEBA (Surface Heat Budget in the Arctic) FIRE/ACE (Arctic Cloud Experiment) the Principal Investigator provided daily weather and cloud forecasts for four research aircraft crews, NASA's ER-2, UCAR's C-130, University of Washington's Convert 580, and the Canadian Atmospheric Environment Service's Convert 580. Approximately 105 forecasts were written. The Principal Investigator also made daily weather summaries with calculations of air trajectories for 54 flight days in the experiment. The trajectories show where the air sampled during the flights came from and will be used in future publications to discuss the origin and history of the air and clouds sampled by the aircraft. A paper discussing how well the FIRE/ACE data represent normal climatic conditions in the arctic is being prepared. (3) The Principal Investigator's web page became the source of information for weather forecasting by the scientists on the SHEBA ship. (4) Global Cirrus frequency and optical depth is a continuing analysis of global cloud cover and frequency distribution are being made from the NOAA polar orbiting weather satellites. This analysis is sensitive to cirrus clouds because of the radiative channels used. During this grant three papers were published which describe cloud frequencies, their optical properties and compare the Wisconsin FM Cloud Analysis to other global cloud data such as the International Satellite Cloud Climatology Program (ISCCP) and the Stratospheric Aerosol and Gas Experiment (SAGE). A summary of eight years of HIRS data will be published in late 1998. Important information from this study are: 1) cirrus clouds cover most of the earth, 2) they are found about 40% of the time globally, 3) in the tropics cirrus cloud frequencies are even higher, from 80-100%, 4) there is slight evidence that cirnis cloud cover is increasing in the northern hemisphere at about 0.5% per year, and 5) cirrus clouds have an average infrared transmittance of about 40% of the terrestrial radiation. (5) Global Cloud Frequency Statistics published on the Principal Investigator's web page have been used in the planning of the future CRYSTAL experiment and have been used for refinements of a global numerical model operated at the Colorado State University.

Weather Forecaster Understanding of Climate Models

NASA Astrophysics Data System (ADS)

Bol, A.; Kiehl, J. T.; Abshire, W. E.

2013-12-01

Weather forecasters, particularly those in broadcasting, are the primary conduit to the public for information on climate and climate change. However, many weather forecasters remain skeptical of model-based climate projections. To address this issue, The COMET Program developed an hour-long online lesson of how climate models work, targeting an audience of weather forecasters. The module draws on forecasters' pre-existing knowledge of weather, climate, and numerical weather prediction (NWP) models. In order to measure learning outcomes, quizzes were given before and after the lesson. Preliminary results show large learning gains. For all people that took both pre and post-tests (n=238), scores improved from 48% to 80%. Similar pre/post improvement occurred for National Weather Service employees (51% to 87%, n=22 ) and college faculty (50% to 90%, n=7). We believe these results indicate a fundamental misunderstanding among many weather forecasters of (1) the difference between weather and climate models, (2) how researchers use climate models, and (3) how they interpret model results. The quiz results indicate that efforts to educate the public about climate change need to include weather forecasters, a vital link between the research community and the general public.

SEASAT economic assessment. Volume 9: Ports and harbors case study and generalization. [economic benefits of SEASAT satellites to harbors and shipping industries through improved weather forecasting

NASA Technical Reports Server (NTRS)

1975-01-01

This case study and generalization quantify benefits made possible through improved weather forecasting resulting from the integration of SEASAT data into local weather forecasts. The major source of avoidable economic losses to shipping from inadequate weather forecasting data is shown to be dependent on local precipitation forecasting. The ports of Philadelphia and Boston were selected for study.

Integrating Satellite Measurements from Polar-orbiting instruments into Smoke Disperson Forecasts

NASA Astrophysics Data System (ADS)

Smith, N.; Pierce, R. B.; Barnet, C.; Gambacorta, A.; Davies, J. E.; Strabala, K.

2015-12-01

The IDEA-I (Infusion of Satellite Data into Environmental Applications-International) is a real-time system that currently generates trajectory-based forecasts of aerosol dispersion and stratospheric intrusions. Here we demonstrate new capabilities that use satellite measurements from the Joint Polar Satellite System (JPSS) Suomi-NPP (S-NPP) instruments (operational since 2012) in the generation of trajectory-based predictions of smoke dispersion from North American wildfires. Two such data products are used, namely the Visible Infrared Imaging Radiometer Suite (VIIRS) Aerosol Optical Depth (AOD) and the combined Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) NOAA-Unique CrIS-ATMS Processing System (NUCAPS) carbon monoxide (CO) retrievals. The latter is a new data product made possible by the release of full spectral-resolution CrIS measurements since December 2014. Once NUCAPS CO becomes operationally available it will be used in real-time applications such as IDEA-I along with VIIRS AOD and meteorological forecast fields to support National Weather Service (NWS) Incident Meteorologist (IMET) and air quality management decision making. By combining different measurements, the information content of the IDEA-I transport and dispersion forecast is improved within the complex terrain features that dominate the Western US and Alaska. The primary user community of smoke forecasts is the Western regions of the National Weather Service (NWS) and US Environmental Protection Agency (EPA) due to the significant impacts of wildfires in these regions. With this we demonstrate the quality of the smoke dispersion forecasts that can be achieved by integrating polar-orbiting satellite measurements with forecast models to enable on-site decision support services for fire incident management teams and other real-time air quality agencies.

Engaging Earth- and Environmental-Science Undergraduates Through Weather Discussions and an eLearning Weather Forecasting Contest

NASA Astrophysics Data System (ADS)

Schultz, David M.; Anderson, Stuart; Seo-Zindy, Ryo

2013-06-01

For students who major in meteorology, engaging in weather forecasting can motivate learning, develop critical-thinking skills, improve their written communication, and yield better forecasts. Whether such advances apply to students who are not meteorology majors has been less demonstrated. To test this idea, a weather discussion and an eLearning weather forecasting contest were devised for a meteorology course taken by third-year undergraduate earth- and environmental-science students. The discussion consisted of using the recent, present, and future weather to amplify the topics of the week's lectures. Then, students forecasted the next day's high temperature and the probability of precipitation for Woodford, the closest official observing site to Manchester, UK. The contest ran for 10 weeks, and the students received credit for participation. The top students at the end of the contest received bonus points on their final grade. A Web-based forecast contest application was developed to register the students, receive their forecasts, and calculate weekly standings. Students who were successful in the forecast contest were not necessarily those who achieved the highest scores on the tests, demonstrating that the contest was possibly testing different skills than traditional learning. Student evaluations indicate that the weather discussion and contest were reasonably successful in engaging students to learn about the weather outside of the classroom, synthesize their knowledge from the lectures, and improve their practical understanding of the weather. Therefore, students taking a meteorology class, but not majoring in meteorology, can derive academic benefits from weather discussions and forecast contests. Nevertheless, student evaluations also indicate that better integration of the lectures, weather discussions, and the forecasting contests is necessary.

Research Opportunities from Emerging Atmospheric Observing and Modeling Capabilities.

NASA Astrophysics Data System (ADS)

Dabberdt, Walter F.; Schlatter, Thomas W.

1996-02-01

The Second Prospectus Development Team (PDT-2) of the U.S. Weather Research Program was charged with identifying research opportunities that are best matched to emerging operational and experimental measurement and modeling methods. The overarching recommendation of PDT-2 is that inputs for weather forecast models can best be obtained through the use of composite observing systems together with adaptive (or targeted) observing strategies employing both in situ and remote sensing. Optimal observing systems and strategies are best determined through a three-part process: observing system simulation experiments, pilot field measurement programs, and model-assisted data sensitivity experiments. Furthermore, the mesoscale research community needs easy and timely access to the new operational and research datasets in a form that can readily be reformatted into existing software packages for analysis and display. The value of these data is diminished to the extent that they remain inaccessible.The composite observing system of the future must combine synoptic observations, routine mobile observations, and targeted observations, as the current or forecast situation dictates. High costs demand fuller exploitation of commercial aircraft, meteorological and navigation [Global Positioning System (GPS)] satellites, and Doppler radar. Single observing systems must be assessed in the context of a composite system that provides complementary information. Maintenance of the current North American rawinsonde network is critical for progress in both research-oriented and operational weather forecasting.Adaptive sampling strategies are designed to improve large-scale and regional weather prediction but they will also improve diagnosis and prediction of flash flooding, air pollution, forest fire management, and other environmental emergencies. Adaptive measurements can be made by piloted or unpiloted aircraft. Rawinsondes can be launched and satellites can be programmed to make adaptive observations at special times or in specific regions. PDT-2 specifically recommends the following forms of data gathering: a pilot field and modeling study should be designed and executed to assess the benefit of adaptive observations over the eastern Pacific for mesoscale forecasts over the contiguous United

Connecting smoke plumes to sources using Hazard Mapping System (HMS) smoke and fire location data over North America

NASA Astrophysics Data System (ADS)

Brey, Steven J.; Ruminski, Mark; Atwood, Samuel A.; Fischer, Emily V.

2018-02-01

Fires represent an air quality challenge because they are large, dynamic and transient sources of particulate matter and ozone precursors. Transported smoke can deteriorate air quality over large regions. Fire severity and frequency are likely to increase in the future, exacerbating an existing problem. Using the National Environmental Satellite, Data, and Information Service (NESDIS) Hazard Mapping System (HMS) smoke data for North America for the period 2007 to 2014, we examine a subset of fires that are confirmed to have produced sufficient smoke to warrant the initiation of a U.S. National Weather Service smoke forecast. We find that gridded HMS-analyzed fires are well correlated (r = 0.84) with emissions from the Global Fire Emissions Inventory Database 4s (GFED4s). We define a new metric, smoke hours, by linking observed smoke plumes to active fires using ensembles of forward trajectories. This work shows that the Southwest, Northwest, and Northwest Territories initiate the most air quality forecasts and produce more smoke than any other North American region by measure of the number of HYSPLIT points analyzed, the duration of those HYSPLIT points, and the total number of smoke hours produced. The average number of days with smoke plumes overhead is largest over the north-central United States. Only Alaska, the Northwest, the Southwest, and Southeast United States regions produce the majority of smoke plumes observed over their own borders. This work moves a new dataset from a daily operational setting to a research context, and it demonstrates how changes to the frequency or intensity of fires in the western United States could impact other regions.

Weather Forecasting Systems and Methods

NASA Technical Reports Server (NTRS)

Mecikalski, John (Inventor); MacKenzie, Wayne M., Jr. (Inventor); Walker, John Robert (Inventor)

2014-01-01

A weather forecasting system has weather forecasting logic that receives raw image data from a satellite. The raw image data has values indicative of light and radiance data from the Earth as measured by the satellite, and the weather forecasting logic processes such data to identify cumulus clouds within the satellite images. For each identified cumulus cloud, the weather forecasting logic applies interest field tests to determine a score indicating the likelihood of the cumulus cloud forming precipitation and/or lightning in the future within a certain time period. Based on such scores, the weather forecasting logic predicts in which geographic regions the identified cumulus clouds will produce precipitation and/or lighting within during the time period. Such predictions may then be used to provide a weather map thereby providing users with a graphical illustration of the areas predicted to be affected by precipitation within the time period.

Weather assessment and forecasting

NASA Technical Reports Server (NTRS)

1977-01-01

Data management program activities centered around the analyses of selected far-term Office of Applications (OA) objectives, with the intent of determining if significant data-related problems would be encountered and if so what alternative solutions would be possible. Three far-term (1985 and beyond) OA objectives selected for analyses as having potential significant data problems were large-scale weather forecasting, local weather and severe storms forecasting, and global marine weather forecasting. An overview of general weather forecasting activities and their implications upon the ground based data system is provided. Selected topics were specifically oriented to the use of satellites.

Emergency assessment of post-fire debris-flow hazards for the 2013 Mountain fire, southern California

USGS Publications Warehouse

Staley, Dennis M.; Gartner, Joseph E.; Smoczyk, Greg M.; Reeves, Ryan R.

2013-01-01

Wildfire dramatically alters the hydrologic response of a watershed such that even modest rainstorms can produce dangerous flash floods and debris flows. We use empirical models to predict the probability and magnitude of debris flow occurrence in response to a 10-year rainstorm for the 2013 Mountain fire near Palm Springs, California. Overall, the models predict a relatively high probability (60–100 percent) of debris flow for six of the drainage basins in the burn area in response to a 10-year recurrence interval design storm. Volumetric predictions suggest that debris flows that occur may entrain a significant volume of material, with 8 of the 14 basins identified as having potential debris-flow volumes greater than 100,000 cubic meters. These results suggest there is a high likelihood of significant debris-flow hazard within and downstream of the burn area for nearby populations, infrastructure, and wildlife and water resources. Given these findings, we recommend that residents, emergency managers, and public works departments pay close attention to weather forecasts and National Weather Service–issued Debris Flow and Flash Flood Outlooks, Watches and Warnings and that residents adhere to any evacuation orders.

Types of Forecast and Weather-Related Information Used among Tourism Businesses in Coastal North Carolina

NASA Astrophysics Data System (ADS)

Ayscue, Emily P.

This study profiles the coastal tourism sector, a large and diverse consumer of climate and weather information. It is crucial to provide reliable, accurate and relevant resources for the climate and weather-sensitive portions of this stakeholder group in order to guide them in capitalizing on current climate and weather conditions and to prepare them for potential changes. An online survey of tourism business owners, managers and support specialists was conducted within the eight North Carolina oceanfront counties asking respondents about forecasts they use and for what purposes as well as why certain forecasts are not used. Respondents were also asked about their perceived dependency of their business on climate and weather as well as how valuable different forecasts are to their decision-making. Business types represented include: Agriculture, Outdoor Recreation, Accommodations, Food Services, Parks and Heritage, and Other. Weekly forecasts were the most popular forecasts with Monthly and Seasonal being the least used. MANOVA and ANOVA analyses revealed outdoor-oriented businesses (Agriculture and Outdoor Recreation) as perceiving themselves significantly more dependent on climate and weather than indoor-oriented ones (Food Services and Accommodations). Outdoor businesses also valued short-range forecasts significantly more than indoor businesses. This suggests a positive relationship between perceived climate and weather dependency and forecast value. The low perceived dependency and value of short-range forecasts of indoor businesses presents an opportunity to create climate and weather information resources directed at how they can capitalize on positive climate and weather forecasts and how to counter negative effects with forecasted adverse conditions. The low use of long-range forecasts among all business types can be related to the low value placed on these forecasts. However, these forecasts are still important in that they are used to make more financially risky decisions such as investment decisions.

Near-term probabilistic forecast of significant wildfire events for the Western United States

Treesearch

Haiganoush K. Preisler; Karin L. Riley; Crystal S. Stonesifer; Dave E. Calkin; Matt Jolly

2016-01-01

Fire danger and potential for large fires in the United States (US) is currently indicated via several forecasted qualitative indices. However, landscape-level quantitative forecasts of the probability of a large fire are currently lacking. In this study, we present a framework for forecasting large fire occurrence - an extreme value event - and evaluating...

Road weather forecast quality analysis : project summary

DOT National Transportation Integrated Search

2006-03-01

The purpose of this research is to enhance the use of KDOTs Roadway Weather : Information System by improving the weather forecasts themselves and raising the level of : confidence in these forecasts.

Development of a High Resolution Weather Forecast Model for Mesoamerica Using the NASA Ames Code I Private Cloud Computing Environment

NASA Technical Reports Server (NTRS)

Molthan, Andrew; Case, Jonathan; Venner, Jason; Moreno-Madrinan, Max J.; Delgado, Francisco

2012-01-01

Two projects at NASA Marshall Space Flight Center have collaborated to develop a high resolution weather forecast model for Mesoamerica: The NASA Short-term Prediction Research and Transition (SPoRT) Center, which integrates unique NASA satellite and weather forecast modeling capabilities into the operational weather forecasting community. NASA's SERVIR Program, which integrates satellite observations, ground-based data, and forecast models to improve disaster response in Central America, the Caribbean, Africa, and the Himalayas.

Optimal Day-Ahead Scheduling of a Hybrid Electric Grid Using Weather Forecasts

DTIC Science & Technology

2013-12-01

ahead scheduling, Weather forecast , Wind power , Photovoltaic Power 15. NUMBER OF PAGES 107 16. PRICE CODE 17. SECURITY CLASSIFICATION OF...cost can be reached by accurately anticipating the future renewable power productions. This thesis suggests the use of weather forecasts to establish...reached by accurately anticipating the future renewable power productions. This thesis suggests the use of weather forecasts to establish day-ahead

Satellite Sounder Data Assimilation for Improving Alaska Region Weather Forecast

NASA Technical Reports Server (NTRS)

Zhu, Jiang; Stevens, E.; Zhang, X.; Zavodsky, B. T.; Heinrichs, T.; Broderson, D.

2014-01-01

A case study and monthly statistical analysis using sounder data assimilation to improve the Alaska regional weather forecast model are presented. Weather forecast in Alaska faces challenges as well as opportunities. Alaska has a large land with multiple types of topography and coastal area. Weather forecast models must be finely tuned in order to accurately predict weather in Alaska. Being in the high-latitudes provides Alaska greater coverage of polar orbiting satellites for integration into forecasting models than the lower 48. Forecasting marine low stratus clouds is critical to the Alaska aviation and oil industry and is the current focus of the case study. NASA AIRS/CrIS sounder profiles data are used to do data assimilation for the Alaska regional weather forecast model to improve Arctic marine stratus clouds forecast. Choosing physical options for the WRF model is discussed. Preprocess of AIRS/CrIS sounder data for data assimilation is described. Local observation data, satellite data, and global data assimilation data are used to verify and/or evaluate the forecast results by the MET tools Model Evaluation Tools (MET).

NOAA's weather forecasts go hyper-local with next-generation weather

Science.gov Websites

model NOAA HOME WEATHER OCEANS FISHERIES CHARTING SATELLITES CLIMATE RESEARCH COASTS CAREERS with next-generation weather model New model will help forecasters predict a storm's path, timing and intensity better than ever September 30, 2014 This is a comparison of two weather forecast models looking

Forecasting and visualization of wildfires in a 3D geographical information system

NASA Astrophysics Data System (ADS)

Castrillón, M.; Jorge, P. A.; López, I. J.; Macías, A.; Martín, D.; Nebot, R. J.; Sabbagh, I.; Quintana, F. M.; Sánchez, J.; Sánchez, A. J.; Suárez, J. P.; Trujillo, A.

2011-03-01

This paper describes a wildfire forecasting application based on a 3D virtual environment and a fire simulation engine. A novel open-source framework is presented for the development of 3D graphics applications over large geographic areas, offering high performance 3D visualization and powerful interaction tools for the Geographic Information Systems (GIS) community. The application includes a remote module that allows simultaneous connections of several users for monitoring a real wildfire event. The system is able to make a realistic composition of what is really happening in the area of the wildfire with dynamic 3D objects and location of human and material resources in real time, providing a new perspective to analyze the wildfire information. The user is enabled to simulate and visualize the propagation of a fire on the terrain integrating at the same time spatial information on topography and vegetation types with weather and wind data. The application communicates with a remote web service that is in charge of the simulation task. The user may specify several parameters through a friendly interface before the application sends the information to the remote server responsible of carrying out the wildfire forecasting using the FARSITE simulation model. During the process, the server connects to different external resources to obtain up-to-date meteorological data. The client application implements a realistic 3D visualization of the fire evolution on the landscape. A Level Of Detail (LOD) strategy contributes to improve the performance of the visualization system.

Examining Atmospheric and Ecological Drivers of Wildfires, Modeling Wildfire Occurrence in the Southwest United States, and Using Atmospheric Sounding Observations to Verify National Weather Service Spot Forecasts

NASA Astrophysics Data System (ADS)

Nauslar, Nicholas J.

This dissertation is comprised of three different papers that all pertain to wildland fire applications. The first paper performs a verification analysis on mixing height, transport winds, and Haines Index from National Weather Service spot forecasts across the United States. The final two papers, which are closely related, examine atmospheric and ecological drivers of wildfire for the Southwest Area (SWA) (Arizona, New Mexico, west Texas, and Oklahoma panhandle) to better equip operational fire meteorologists and managers to make informed decisions on wildfire potential in this region. The verification analysis here utilizes NWS spot forecasts of mixing height, transport winds and Haines Index from 2009-2013 issued for a location within 50 km of an upper sounding location and valid for the day of the fire event. Mixing height was calculated from the 0000 UTC sounding via the Stull, Holzworth, and Richardson methods. Transport wind speeds were determined by averaging the wind speed through the boundary layer as determined by the three mixing height methods from the 0000 UTC sounding. Haines Index was calculated at low, mid, and high elevation based on the elevation of the sounding and spot forecast locations. Mixing height forecasts exhibited large mean absolute errors and biased towards over forecasting. Forecasts of transport wind speeds and Haines Index outperformed mixing height forecasts with smaller errors relative to their respective means. The rainfall and lightning associated with the North American Monsoon (NAM) can vary greatly intra- and inter-annually and has a large impact on wildfire activity across the SWA by igniting or suppressing wildfires. NAM onset thresholds and subsequent dates are determined for the SWA and each Predictive Service Area (PSA), which are sub-regions used by operational fire meteorologists to predict wildfire potential within the SWA, April through September from 1995-2013. Various wildfire activity thresholds using the number of wildfires and large wildfires identified days or time periods with increased wildfire activity for each PSA and the SWA. Self-organizing maps utilizing 500 and 700 hPa geopotential heights and precipitable water were implemented to identify atmospheric patterns contributing to the NAM onset and busy days/periods for each PSA and the SWA. Resulting SOM map types also showed the transition to, during, and from the NAM. Northward and eastward displacements of the subtropical ridge (i.e., four-corners high) over the SWA were associated with NAM onset, and a suppressed subtropical ridge and breakdown of the subtropical ridge map types over the SWA were associated with increased wildfire activity. We implemented boosted regression trees (BRT) to model wildfire occurrence for all and large wildfires for different wildfire types (i.e., lightning, human) across the SWA by PSA. BRT models for all wildfires demonstrated relatively small mean and mean absolute errors and showed better predictability on days with wildfires. Cross-validated accuracy assessments for large wildfires demonstrated the ability to discriminate between large wildfire and non-large wildfire days across all wildfire types. Measurements describing fuel conditions (i.e., 100 and 1000-hour dead fuel moisture, energy release component) were the most important predictors when considering all wildfire types and sizes. However, a combination of fuels and atmospheric predictors (i.e., lightning, temperature) proved most predictive for large wildfire occurrence, and the number of relevant predictors increases for large wildfires indicating more conditions need to align to support large wildfires.

The Wind Forecast Improvement Project (WFIP). A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations -- the Northern Study Area

DOE Office of Scientific and Technical Information (OSTI.GOV)

Finley, Cathy

2014-04-30

This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements inmore » wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the individual wind plant and at the system-wide aggregate level over the one year study period showed that the research weather model-based power forecasts (all types) had lower overall error rates than the current operational weather model-based power forecasts, both at the individual wind plant level and at the system aggregate level. The bulk error statistics of the various model-based power forecasts were also calculated by season and model runtime/forecast hour as power system operations are more sensitive to wind energy forecast errors during certain times of year and certain times of day. The results showed that there were significant differences in seasonal forecast errors between the various model-based power forecasts. The results from the analysis of the various wind power forecast errors by model runtime and forecast hour showed that the forecast errors were largest during the times of day that have increased significance to power system operators (the overnight hours and the morning/evening boundary layer transition periods), but the research weather model-based power forecasts showed improvement over the operational weather model-based power forecasts at these times.« less

KSC-06pd1276

NASA Image and Video Library

2006-06-28

KENNEDY SPACE CENTER, FLA. - At the Cape Canaveral weather station in Florida, a member of the weather team looks over the weather balloons inside. The release of a Rawinsonde weather balloon was planned as part of a media tour prior to the launch of Space Shuttle Discovery on mission STS-121 July 1. At the facility, which is operated by the U.S. Air Force 45th Weather Squadron, media saw the tools used by the weather team to create the forecast for launch day. They received a briefing on how the launch weather forecast is developed by Shuttle Weather Officer Kathy Winters and met the forecasters for the space shuttle and the expendable launch vehicles. Also participating were members of the Applied Meteorology Unit who provide special expertise to the forecasters by analyzing and interpreting unusual or inconsistent weather data. The media were able to see the release of the Rawinsonde weather balloon carrying instruments aloft to be used as part of developing the forecast. Photo credit: NASA/George Shelton

Verification of Space Weather Forecasts using Terrestrial Weather Approaches

NASA Astrophysics Data System (ADS)

Henley, E.; Murray, S.; Pope, E.; Stephenson, D.; Sharpe, M.; Bingham, S.; Jackson, D.

2015-12-01

The Met Office Space Weather Operations Centre (MOSWOC) provides a range of 24/7 operational space weather forecasts, alerts, and warnings, which provide valuable information on space weather that can degrade electricity grids, radio communications, and satellite electronics. Forecasts issued include arrival times of coronal mass ejections (CMEs), and probabilistic forecasts for flares, geomagnetic storm indices, and energetic particle fluxes and fluences. These forecasts are produced twice daily using a combination of output from models such as Enlil, near-real-time observations, and forecaster experience. Verification of forecasts is crucial for users, researchers, and forecasters to understand the strengths and limitations of forecasters, and to assess forecaster added value. To this end, the Met Office (in collaboration with Exeter University) has been adapting verification techniques from terrestrial weather, and has been working closely with the International Space Environment Service (ISES) to standardise verification procedures. We will present the results of part of this work, analysing forecast and observed CME arrival times, assessing skill using 2x2 contingency tables. These MOSWOC forecasts can be objectively compared to those produced by the NASA Community Coordinated Modelling Center - a useful benchmark. This approach cannot be taken for the other forecasts, as they are probabilistic and categorical (e.g., geomagnetic storm forecasts give probabilities of exceeding levels from minor to extreme). We will present appropriate verification techniques being developed to address these forecasts, such as rank probability skill score, and comparing forecasts against climatology and persistence benchmarks. As part of this, we will outline the use of discrete time Markov chains to assess and improve the performance of our geomagnetic storm forecasts. We will also discuss work to adapt a terrestrial verification visualisation system to space weather, to help MOSWOC forecasters view verification results in near real-time; plans to objectively assess flare forecasts under the EU Horizon 2020 FLARECAST project; and summarise ISES efforts to achieve consensus on verification.

Changes in fire weather distributions: effects on predicted fire behavior

Treesearch

Lucy A. Salazar; Larry S. Bradshaw

1984-01-01

Data that represent average worst fire weather for a particular area are used to index daily fire danger; however, they do not account for different locations or diurnal weather changes that significantly affect fire behavior potential. To study the effects that selected changes in weather databases have on computed fire behavior parameters, weather data for the...

Flight Deck Weather Avoidance Decision Support: Implementation and Evaluation

NASA Technical Reports Server (NTRS)

Wu, Shu-Chieh; Luna, Rocio; Johnson, Walter W.

2013-01-01

Weather related disruptions account for seventy percent of the delays in the National Airspace System (NAS). A key component in the weather plan of the Next Generation of Air Transportation System (NextGen) is to assimilate observed weather information and probabilistic forecasts into the decision process of flight crews and air traffic controllers. In this research we explore supporting flight crew weather decision making through the development of a flight deck predicted weather display system that utilizes weather predictions generated by ground-based radar. This system integrates and presents this weather information, together with in-flight trajectory modification tools, within a cockpit display of traffic information (CDTI) prototype. that the CDTI features 2D and perspective 3D visualization models of weather. The weather forecast products that we implemented were the Corridor Integrated Weather System (CIWS) and the Convective Weather Avoidance Model (CWAM), both developed by MIT Lincoln Lab. We evaluated the use of CIWS and CWAM for flight deck weather avoidance in two part-task experiments. Experiment 1 compared pilots' en route weather avoidance performance in four weather information conditions that differed in the type and amount of predicted forecast (CIWS current weather only, CIWS current and historical weather, CIWS current and forecast weather, CIWS current and forecast weather and CWAM predictions). Experiment 2 compared the use of perspective 3D and 21/2D presentations of weather for flight deck weather avoidance. Results showed that pilots could take advantage of longer range predicted weather forecasts in performing en route weather avoidance but more research will be needed to determine what combinations of information are optimal and how best to present them.

Capabilities of current wildfire models when simulating topographical flow

NASA Astrophysics Data System (ADS)

Kochanski, A.; Jenkins, M.; Krueger, S. K.; McDermott, R.; Mell, W.

2009-12-01

Accurate predictions of the growth, spread and suppression of wild fires rely heavily on the correct prediction of the local wind conditions and the interactions between the fire and the local ambient airflow. Resolving local flows, often strongly affected by topographical features like hills, canyons and ridges, is a prerequisite for accurate simulation and prediction of fire behaviors. In this study, we present the results of high-resolution numerical simulations of the flow over a smooth hill, performed using (1) the NIST WFDS (WUI or Wildland-Urban-Interface version of the FDS or Fire Dynamic Simulator), and (2) the LES version of the NCAR Weather Research and Forecasting (WRF-LES) model. The WFDS model is in the initial stages of development for application to wind flow and fire spread over complex terrain. The focus of the talk is to assess how well simple topographical flow is represented by WRF-LES and the current version of WFDS. If sufficient progress has been made prior to the meeting then the importance of the discrepancies between the predicted and measured winds, in terms of simulated fire behavior, will be examined.

On the effect of model parameters on forecast objects

NASA Astrophysics Data System (ADS)

Marzban, Caren; Jones, Corinne; Li, Ning; Sandgathe, Scott

2018-04-01

Many physics-based numerical models produce a gridded, spatial field of forecasts, e.g., a temperature map. The field for some quantities generally consists of spatially coherent and disconnected objects. Such objects arise in many problems, including precipitation forecasts in atmospheric models, eddy currents in ocean models, and models of forest fires. Certain features of these objects (e.g., location, size, intensity, and shape) are generally of interest. Here, a methodology is developed for assessing the impact of model parameters on the features of forecast objects. The main ingredients of the methodology include the use of (1) Latin hypercube sampling for varying the values of the model parameters, (2) statistical clustering algorithms for identifying objects, (3) multivariate multiple regression for assessing the impact of multiple model parameters on the distribution (across the forecast domain) of object features, and (4) methods for reducing the number of hypothesis tests and controlling the resulting errors. The final output of the methodology is a series of box plots and confidence intervals that visually display the sensitivities. The methodology is demonstrated on precipitation forecasts from a mesoscale numerical weather prediction model.

Clarus : a clear solution for road weather information.

DOT National Transportation Integrated Search

2011-01-01

Weather forecast for tonight: Dark. Continued dark overnight, with widely scattered light by morning. These words by comedian George Carlin, while not a real weather forecast, demonstrate how insuffi cient forecasting can be. While the accuracy...

Predicting Airspace Capacity Impacts Using the Consolidated Storm Prediction for Aviation

NASA Technical Reports Server (NTRS)

Russell, Carl

2010-01-01

Convective weather is currently the largest contributor to air traffic delays in the United States. In order to make effective traffic flow management decisions to mitigate these delays, weather forecasts must be made as early and as accurately as possible. A forecast product that could be used to mitigate convective weather impacts is the Consolidated Storm Prediction for Aviation. This product provides forecasts of cloud water content and convective top heights at 0- to 8-hour look-ahead times. The objective of this study was to examine a method of predicting the impact of convective weather on air traffic sector capacities using these forecasts. Polygons representing forecast convective weather were overlaid at multiple flight levels on a sector map to calculate the fraction of each sector covered by weather. The fractional volume coverage was used as the primary metric to determine convection s impact on sectors. Results reveal that the forecasts can be used to predict the probability and magnitude of weather impacts on sector capacity up to eight hours in advance.

Forecasting, Forecasting

Treesearch

Michael A. Fosberg

1987-01-01

Future improvements in the meteorological forecasts used in fire management will come from improvements in three areas: observational systems, forecast techniques, and postprocessing of forecasts and better integration of this information into the fire management process.

KSC-06pd1285

NASA Image and Video Library

2006-06-28

KENNEDY SPACE CENTER, FLA. - At the Cape Canaveral forecast facility in Florida, media were able to meet members of the weather team who review data used for forecasts as part of a tour of the facility. The team will play a role in the July 1 launch of Space Shuttle Discovery on mission STS-121. At the facility, which is operated by the U.S. Air Force 45th Weather Squadron, received a briefing on how the launch weather forecast is developed by Shuttle Weather Officer Kathy Winters and met the forecasters for the space shuttle and the expendable launch vehicles. Also participating were members of the Applied Meteorology Unit who provide special expertise to the forecasters by analyzing and interpreting unusual or inconsistent weather data. The media were able to see the release of the Rawinsonde weather balloon carrying instruments aloft to be used as part of developing the forecast. Photo credit: NASA/George Shelton

KSC-06pd1275

NASA Image and Video Library

2006-06-28

KENNEDY SPACE CENTER, FLA. - At the Cape Canaveral weather station in Florida, a member of the weather team prepares a Rawinsonde weather balloon for release. The release was planned as part of a media tour prior to the launch of Space Shuttle Discovery on mission STS-121 July 1. At the facility, which is operated by the U.S. Air Force 45th Weather Squadron, media saw the tools used by the weather team to create the forecast for launch day. They received a briefing on how the launch weather forecast is developed by Shuttle Weather Officer Kathy Winters and met the forecasters for the space shuttle and the expendable launch vehicles. Also participating were members of the Applied Meteorology Unit who provide special expertise to the forecasters by analyzing and interpreting unusual or inconsistent weather data. The media were able to see the release of the Rawinsonde weather balloon carrying instruments aloft to be used as part of developing the forecast. Photo credit: NASA/George Shelton

Long-term ensemble forecast of snowmelt inflow into the Cheboksary Reservoir under two different weather scenarios

NASA Astrophysics Data System (ADS)

Gelfan, Alexander; Moreydo, Vsevolod; Motovilov, Yury; Solomatine, Dimitri P.

2018-04-01

A long-term forecasting ensemble methodology, applied to water inflows into the Cheboksary Reservoir (Russia), is presented. The methodology is based on a version of the semi-distributed hydrological model ECOMAG (ECOlogical Model for Applied Geophysics) that allows for the calculation of an ensemble of inflow hydrographs using two different sets of weather ensembles for the lead time period: observed weather data, constructed on the basis of the Ensemble Streamflow Prediction methodology (ESP-based forecast), and synthetic weather data, simulated by a multi-site weather generator (WG-based forecast). We have studied the following: (1) whether there is any advantage of the developed ensemble forecasts in comparison with the currently issued operational forecasts of water inflow into the Cheboksary Reservoir, and (2) whether there is any noticeable improvement in probabilistic forecasts when using the WG-simulated ensemble compared to the ESP-based ensemble. We have found that for a 35-year period beginning from the reservoir filling in 1982, both continuous and binary model-based ensemble forecasts (issued in the deterministic form) outperform the operational forecasts of the April-June inflow volume actually used and, additionally, provide acceptable forecasts of additional water regime characteristics besides the inflow volume. We have also demonstrated that the model performance measures (in the verification period) obtained from the WG-based probabilistic forecasts, which are based on a large number of possible weather scenarios, appeared to be more statistically reliable than the corresponding measures calculated from the ESP-based forecasts based on the observed weather scenarios.

Time Relevance of Convective Weather Forecast for Air Traffic Automation

NASA Technical Reports Server (NTRS)

Chan, William N.

2006-01-01

The Federal Aviation Administration (FAA) is handling nearly 120,000 flights a day through its Air Traffic Management (ATM) system and air traffic congestion is expected to increse substantially over the next 20 years. Weather-induced impacts to throughput and efficiency are the leading cause of flight delays accounting for 70% of all delays with convective weather accounting for 60% of all weather related delays. To support the Next Generation Air Traffic System goal of operating at 3X current capacity in the NAS, ATC decision support tools are being developed to create advisories to assist controllers in all weather constraints. Initial development of these decision support tools did not integrate information regarding weather constraints such as thunderstorms and relied on an additional system to provide that information. Future Decision Support Tools should move towards an integrated system where weather constraints are factored into the advisory of a Decision Support Tool (DST). Several groups such at NASA-Ames, Lincoln Laboratories, and MITRE are integrating convective weather data with DSTs. A survey of current convective weather forecast and observation data show they span a wide range of temporal and spatial resolutions. Short range convective observations can be obtained every 5 mins with longer range forecasts out to several days updated every 6 hrs. Today, the short range forecasts of less than 2 hours have a temporal resolution of 5 mins. Beyond 2 hours, forecasts have much lower temporal. resolution of typically 1 hour. Spatial resolutions vary from 1km for short range to 40km for longer range forecasts. Improving the accuracy of long range convective forecasts is a major challenge. A report published by the National Research Council states improvements for convective forecasts for the 2 to 6 hour time frame will only be achieved for a limited set of convective phenomena in the next 5 to 10 years. Improved longer range forecasts will be probabilistic as opposed to the deterministic shorter range forecasts. Despite the known low level of confidence with respect to long range convective forecasts, these data are still useful to a DST routing algorithm. It is better to develop an aircraft route using the best information available than no information. The temporally coarse long range forecast data needs to be interpolated to be useful to a DST. A DST uses aircraft trajectory predictions that need to be evaluated for impacts by convective storms. Each time-step of a trajectory prediction n&s to be checked against weather data. For the case of coarse temporal data, there needs to be a method fill in weather data where there is none. Simply using the coarse weather data without any interpolation can result in DST routes that are impacted by regions of strong convection. Increasing the temporal resolution of these data can be achieved but result in a large dataset that may prove to be an operational challenge in transmission and loading by a DST. Currently, it takes about 7mins retrieve a 7mb RUC2 forecast file from NOAA at NASA-Ames Research Center. A prototype NCWF6 1 hour forecast is about 3mb in size. A Six hour NCWFG forecast with a 1hr forecast time-step will be about l8mb (6 x 3mb). A 6 hour NCWF6 forecast with a l5min forecast time-step will be about 7mb (24 x 3mb). Based on the time it takes to retrieve a 7mb RUC2 forecast, it will take approximately 70mins to retrieve a 6 hour NCWF forecast with 15min time steps. Until those issues are addressed, there is a need to develop an algorithm that interpolates between these temporally coarse long range forecasts. This paper describes a method of how to use low temporal resolution probabilistic weather forecasts in a DST. The beginning of this paper is a description of some convective weather forecast and observation products followed by an example of how weather data are used by a DST. The subsequent sections will describe probabilistic forecasts followed by a descrtion of a method to use low temporal resolution probabilistic weather forecasts by providing a relevance value to these data outside of their valid times.

Application of a Mesoscale Atmospheric Coupled Fire Model BRAMS-FIRE to Alentejo Woodland Fire and Comparison of Performance with the Fire Model WRF-Sfire.

NASA Astrophysics Data System (ADS)

Freitas, S. R.; Menezes, I. C.; Stockler, R.; Mello, R.; Ribeiro, N. A.; Corte-Real, J. A. M.; Surový, P.

2014-12-01

Models of fuel with the identification of vegetation patterns of Montado ecosystem in Portugal was incorporated in the mesoscale Brazilian Atmospheric Modeling System (BRAMS) and coupled with a spread woodland fire model. The BRAMS-FIRE is a new system developed by the "Centro de Previsão de Tempo e Estudos Climáticos" (CPTEC/INPE, Brazil) and the "Instituto de Ciências Agrárias e Ambientais Mediterrâneas" (ICAAM, Portugal). The fire model used in this effort was originally, developed by Mandel et al. (2013) and further incorporated in the Weather Research and Forecast model (WRF). Two grids of high spatial resolution were configured with surface input data and fuel models integrated for simulations using both models BRAMS-FIRE and WRF-SFIRE. One grid was placed in the plain land near Beja and the other one in the hills of Ossa to evaluate different types of fire propagation and calibrate BRAMS-FIRE. The objective is simulating the effects of atmospheric circulation in local scale, namely the movements of the heat front and energy release associated to it, obtained by this two models in an episode of woodland fire which took place in Alentejo area in the last decade, for application to planning and evaluations of agro woodland fire risks. We aim to model the behavior of forest fires through a set of equations whose solutions provide quantitative values of one or more variables related to the propagation of fire, described by semi-empirical expressions that are complemented by experimental data allow to obtain the main variables related advancing the perimeter of the fire, as the propagation speed, the intensity of the fire front and fuel consumption and its interaction with atmospheric dynamic system. References Mandel, J., J. D. Beezley, G. Kelman, A. K. Kochanski, V. Y. Kondratenko, B. H. Lynn, and M. Vejmelka, 2013. New features in WRF-SFIRE and the wildfire forecasting and danger system in Israel. Natural Hazards and Earth System Sciences, submitted, Numerical Wildfires, Cargèse, France, May 13-18, 2013.

Predicting wildfire ignitions, escapes, and large fire activity using Predictive Service’s 7-Day Fire Potential Outlook in the western USA

Treesearch

Karin L. Riley; Crystal Stonesifer; Haiganoush Preisler; Dave Calkin

2014-01-01

Can fire potential forecasts assist with pre-positioning of fire suppression resources, which could result in a cost savings to the United States government? Here, we present a preliminary assessment of the 7-Day Fire Potential Outlook forecasts made by the Predictive Services program. We utilized historical fire occurrence data and archived forecasts to assess how...

General-aviation's view of progress in the aviation weather system

NASA Technical Reports Server (NTRS)

Lundgren, Douglas J.

1988-01-01

For all its activity statistics, general-aviation is the most vulnerable to hazardous weather. Of concern to the general aviation industry are: (1) the slow pace of getting units of the Automated Weather Observation System (AWOS) to the field; (2) the efforts of the National Weather Service to withdraw from both the observation and dissemination roles of the aviation weather system; (3) the need for more observation points to improve the accuracy of terminal and area forecasts; (4) the need for improvements in all area forecasts, terminal forecasts, and winds aloft forecasts; (5) slow progress in cockpit weather displays; (6) the erosion of transcribed weather broadcasts (TWEB) and other deficiencies in weather information dissemination; (7) the need to push to make the Direct User Access Terminal (DUAT) a reality; and (7) the need to improve severe weather (thunderstorm) warning systems.

The pioneers of weather forecasting

NASA Astrophysics Data System (ADS)

Ballard, Susan

2016-01-01

In The Weather Experiment author Peter Moore takes us on a compelling journey through the early history of weather forecasting, bringing to life the personalities, lives and achievements of the men who put in place the building blocks required for forecasts to be possible.

Engaging Earth- and Environmental-Science Undergraduates through Weather Discussions and an eLearning Weather Forecasting Contest

ERIC Educational Resources Information Center

Schultz, David M.; Anderson, Stuart; Seo-Zindy, Ryo

2013-01-01

For students who major in meteorology, engaging in weather forecasting can motivate learning, develop critical-thinking skills, improve their written communication, and yield better forecasts. Whether such advances apply to students who are not meteorology majors has been less demonstrated. To test this idea, a weather discussion and an eLearning…

Inclusion of biomass burning in WRF-Chem: Impact of wildfires on weather forecasts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Grell, G. A.; Freitas, Saulo; Stuefer, Martin

2011-06-06

A plume rise algorithm for wildfires was included in WRF-Chem, and applied to look at the impact of intense wildfires during the 2004 Alaska wildfire season on weather forecasts using model resolutions of 10km and 2km. Biomass burning emissions were estimated using a biomass burning emissions model. In addition, a 1-D, time-dependent cloud model was used online in WRF-Chem to estimate injection heights as well as the final emission rates. It was shown that with the inclusion of the intense wildfires of the 2004 fire season in the model simulations, the interaction of the aerosols with the atmospheric radiation ledmore » to significant modifications of vertical profiles of temperature and moisture in cloud-free areas. On the other hand, when clouds were present, the high concentrations of fine aerosol (PM2.5) and the resulting large numbers of Cloud Condensation Nuclei (CCN) had a strong impact on clouds and microphysics, with decreased precipitation coverage and precipitation amounts during the first 12 hours of the integration, but significantly stronger storms during the afternoon hours.« less

KSC-06pd1284

NASA Image and Video Library

2006-06-28

KENNEDY SPACE CENTER, FLA. - At the Cape Canaveral forecast facility in Florida, Shuttle Weather Officer Kathy Winters briefs the media on how the launch weather forecast is developed. Attendees also were able to meet the forecasters for the space shuttle and the expendable launch vehicles. Also participating were members of the Applied Meteorology Unit who provide special expertise to the forecasters by analyzing and interpreting unusual or inconsistent weather data. The media were able to see the release of the Rawinsonde weather balloon carrying instruments aloft to be used as part of developing the forecast. Photo credit: NASA/George Shelton

Neural network based short-term load forecasting using weather compensation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chow, T.W.S.; Leung, C.T.

This paper presents a novel technique for electric load forecasting based on neural weather compensation. The proposed method is a nonlinear generalization of Box and Jenkins approach for nonstationary time-series prediction. A weather compensation neural network is implemented for one-day ahead electric load forecasting. The weather compensation neural network can accurately predict the change of actual electric load consumption from the previous day. The results, based on Hong Kong Island historical load demand, indicate that this methodology is capable of providing a more accurate load forecast with a 0.9% reduction in forecast error.

About the National Forecast Chart

Science.gov Websites

General Weather WPC Quantitative Precipitation Forecasts for coverage, and weather type from the NWS NDFD Weather Prediction Center 5830 University Research Court College Park, Maryland 20740 Weather Prediction

KSC-06pd1278

NASA Image and Video Library

2006-06-28

KENNEDY SPACE CENTER, FLA. - At the Cape Canaveral weather station in Florida, workers release an upper-level weather balloon while several newscasters watch. The release of the balloon was part of a media tour prior to the launch of Space Shuttle Discovery on mission STS-121 July 1. The radar-tracked balloon detects wind shears that can affect a shuttle launch. At the facility, which is operated by the U.S. Air Force 45th Weather Squadron, media saw the tools used by the weather team to create the forecast for launch day. They received a briefing on how the launch weather forecast is developed by Shuttle Weather Officer Kathy Winters and met the forecasters for the space shuttle and the expendable launch vehicles. Also participating were members of the Applied Meteorology Unit who provide special expertise to the forecasters by analyzing and interpreting unusual or inconsistent weather data. The media were able to see the release of the Rawinsonde weather balloon carrying instruments aloft to be used as part of developing the forecast. Photo credit: NASA/George Shelton

KSC-06pd1277

NASA Image and Video Library

2006-06-28

KENNEDY SPACE CENTER, FLA. - At the Cape Canaveral weather station in Florida, workers carry an upper-level weather balloon outside for release. The release was part of a media tour prior to the launch of Space Shuttle Discovery on mission STS-121 July 1. The radar-tracked balloon detects wind shears that can affect a shuttle launch. At the facility, which is operated by the U.S. Air Force 45th Weather Squadron, media saw the tools used by the weather team to create the forecast for launch day. They received a briefing on how the launch weather forecast is developed by Shuttle Weather Officer Kathy Winters and met the forecasters for the space shuttle and the expendable launch vehicles. Also participating were members of the Applied Meteorology Unit who provide special expertise to the forecasters by analyzing and interpreting unusual or inconsistent weather data. The media were able to see the release of the Rawinsonde weather balloon carrying instruments aloft to be used as part of developing the forecast. Photo credit: NASA/George Shelton

KSC-06pd1280

NASA Image and Video Library

2006-06-28

KENNEDY SPACE CENTER, FLA. - An upper-level weather balloon sails into the sky after release from the Cape Canaveral weather station in Florida. The release was planned as part of a media tour prior to the launch of Space Shuttle Discovery on mission STS-121 July 1. The radar-tracked balloon detects wind shears that can affect a shuttle launch. At the facility, which is operated by the U.S. Air Force 45th Weather Squadron, media saw the tools used by the weather team to create the forecast for launch day. They received a briefing on how the launch weather forecast is developed by Shuttle Weather Officer Kathy Winters and met the forecasters for the space shuttle and the expendable launch vehicles. Also participating were members of the Applied Meteorology Unit who provide special expertise to the forecasters by analyzing and interpreting unusual or inconsistent weather data. The media were able to see the release of the Rawinsonde weather balloon carrying instruments aloft to be used as part of developing the forecast. Photo credit: NASA/George Shelton

Ensemble superparameterization versus stochastic parameterization: A comparison of model uncertainty representation in tropical weather prediction

NASA Astrophysics Data System (ADS)

Subramanian, Aneesh C.; Palmer, Tim N.

2017-06-01

Stochastic schemes to represent model uncertainty in the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system has helped improve its probabilistic forecast skill over the past decade by both improving its reliability and reducing the ensemble mean error. The largest uncertainties in the model arise from the model physics parameterizations. In the tropics, the parameterization of moist convection presents a major challenge for the accurate prediction of weather and climate. Superparameterization is a promising alternative strategy for including the effects of moist convection through explicit turbulent fluxes calculated from a cloud-resolving model (CRM) embedded within a global climate model (GCM). In this paper, we compare the impact of initial random perturbations in embedded CRMs, within the ECMWF ensemble prediction system, with stochastically perturbed physical tendency (SPPT) scheme as a way to represent model uncertainty in medium-range tropical weather forecasts. We especially focus on forecasts of tropical convection and dynamics during MJO events in October-November 2011. These are well-studied events for MJO dynamics as they were also heavily observed during the DYNAMO field campaign. We show that a multiscale ensemble modeling approach helps improve forecasts of certain aspects of tropical convection during the MJO events, while it also tends to deteriorate certain large-scale dynamic fields with respect to stochastically perturbed physical tendencies approach that is used operationally at ECMWF.