Francis Fujioka

Seasonal fire danger forecasts for the USA

The Scripps Experimental Climate Prediction Center has been making experimental, near-real-time, weekly to seasonal fire danger forecasts for the past 5 years. US fire danger forecasts and validations are based on standard indices from the National Fire Danger Rating System (NFDRS), which include the ignition component (IC), energy release component (ER), burning index (BI), spread component (SC), and the Keetch–Byram drought index (KB). The Fosberg fire weather index, which is a simplified form of the BI, has been previously used not only for the USA but also for other global regions and is thus included for comparison. As will be shown, all of these indices can be predicted well at weekly times scales and there is even skill out to seasonal time scales over many US West locations. The most persistent indices (BI and ER) tend to have the greatest seasonal forecast skill. The NFDRS indices also have a weak relation to observed fire characteristics such as fire counts and acres burned, especially when the validation fire danger indices are used.

ECPC's Weekly to Seasonal Global Forecasts

Abstract The Scripps Experimental Climate Prediction Center (ECPC) has been making experimental, near–real–time seasonal global forecasts since 26 September 1997 with the NCEP global spectral model used for the reanalysis. Images of these forecasts, at daily to seasonal timescales, are provided on the World Wide Web and digital forecast products are provided on the ECPC anonymous FTP site to interested researchers. These forecasts are increasingly being used to drive regional models at the ECPC and elsewhere as well as various application models. The purpose of this paper is to describe the forecast and analysis system, various biases and errors in the forecasts, as well as the significant skill of the forecasts. Forecast near–surface meteorological parameters, including temperature, precipitation, soil moisture, relative humidity, wind speed, and a fire weather index (a nonlinear combination of temperature, wind speed, and relative humidity) are skillful at weekly to seasonal timescales over much of the ...

Wildland fire probabilities estimated from weather model-deduced monthly mean fire danger indices

The National Fire Danger Rating System indices deduced from a regional simulation weather model were used to estimate probabilities and numbers of large fire events on monthly and 1-degree grid scales. The weather model simulations and forecasts are ongoing experimental products from the Experimental Climate Prediction Center at the Scripps Institution of Oceanography. The monthly average Fosberg Fire Weather Index, deduced from the weather simulation, along with the monthly average Keetch–Byram Drought Index and Energy Release Component, were found to be more strongly associated with large fire events on a monthly scale than any of the other stand-alone fire weather or danger indices. These selected indices were used in the spatially explicit probability model to estimate the number of large fire events. Historic probabilities were also estimated using spatially smoothed historic frequencies of large fire events. It was shown that the probability model using four fire danger indices outperformed the historic model, an indication that these indices have some skill. Geographical maps of the estimated monthly wildland fire probabilities, developed using a combination of four indices, were produced for each year and were found to give reasonable matches to actual fire events. This method paves a feasible way to assess the skill of climate forecast outputs, from a dynamical meteorological model, in forecasting the probability of wildland fire severity with known precision.

Fire-climate relationships and long-lead seasonal wildfire prediction for Hawaii

We examined statistical relationships between the seasonal Southern Oscillation Index (SOI) and total acreages burned (TAB) and the number of fires in the Hawaiian Islands. A composite of TAB during four El Nino/Southern Oscillation (ENSO) events reveals that a large total of acres burned is likely to occur from spring to summer in the year following an ENSO event. The correlation is most significant between the TAB in summer and the SOI of the antecedent winter. This relationship provides a potential for long-lead (i.e. 2 seasons in advance) prediction of wildfire activity in the Hawaiian Islands. Logistic regression is applied to predict events of large acreages burned by wildfires. The goodness of predictions is measured by specificity, sensitivity, and correctness using a cross-validation method. A comparison of prediction skill for four major islands in Hawaii is made using the summer TAB as the response variable and the preceding winter SOI as the predictor variable. For predicting the probability of events (sensitivity), results indicate rather successful skills for the islands of Oahu and Kauai, but less so for Maui and Hawaii. It is more difficult to predict non-events (specificity), with the exception of Oahu. As a result, only Oahu has a high overall correctness rate among the four islands tested.

NCEP–ECPC monthly to seasonal US fire danger forecasts

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, cloud cover and wind speed, can be skilfully predicted at weekly to seasonal time scales by a global to regional dynamical prediction system modified from the National Centers for Environmental Prediction’s Coupled Forecast System. The System generates global and regional spectral model ensemble forecasts, which in turn provide required input meteorological variables for fire danger. Seven-month US regional forecasts were generated every month from 1982 to 2007. This study shows that coarse-scale global predictions were more skilful than persistence, and fine-scale regional model predictions were more skilful than global predictions. The fire indices were better related to fire counts and area burned than meteorological variables, although relative humidity and temperature were useful predictors of fire characteristics.

Natural variability of the Keetch–Byram Drought Index in the Hawaiian Islands

The Hawaiian Islands experience damaging wildfires on a yearly basis. Soil moisture or lack thereof influences the amount and flammability of vegetation. Incorporating daily maximum temperatures and daily rainfall amounts, the Keetch–Byram Drought Index (KBDI) estimates the amount of soil moisture by tracking daily maximum temperatures and rainfall. A previous study found a strong link between the KBDI and total area burned on the four main Hawaiian Islands. The present paper further examines the natural variability of the KBDI. The times of year at which the KBDI is highest, representing the highest fire danger, are found at each of the 27 stations on the island chain. Spectral analysis is applied to investigate the variability of the KBDI on longer time scales. Windward and leeward stations are shown to have different sensitivities to large-scale climatic fluctuations. An El Nino signal displays a strong relationship with leeward stations, when examined with a band-pass filter and with a composite of standardized anomalies. Departure patterns of atmospheric circulations and sea surface temperatures over the North Pacific are investigated for composites of extremely high KBDI values when fire risk is high. The winter, spring, and fall show anomalous surface anticyclonic circulations, surface divergence, and subsidence over the islands for the upper quartile of KBDI. The winter, spring, and fall composites of equatorial sea surface temperatures for the upper quartile of KBDI are investigated for possible links to atmospheric circulations. These analyses are an effort to allow fire managers some lead time in predicting future fire risks.

ECPC’s Global to Regional Fire Weather Forecast System

Experimental global to regional and daily to seasonal atmospheric forecasts of fire weather variables are being developed at the Scripps Experimental Climate Prediction Center (ECPC). At the largest space and time scales, ECPC’s forecasting system uses the National Centers for Environmental Prediction’s (NCEP’s) global spectral model (GSM). The GSM is initialized from NCEP’s operational global analysis. A higher-resolution regional spectral model (RSM) is nested within this global model. An even higher resolution mesoscale spectral model (MSM) can be nested within the RSM or the GSM itself. Global to regional and daily to seasonal forecasts of a fire weather index (FWI), 2 m temperature, 2 m relative humidity, and 10 m wind as well as precipitation and soil moisture are currently displayed on the ECPC web site http://ecpc.ucsd.edu/. Skill evaluations of these forecasts are just beginning.

Weather and Climate Forecasts and Analyses at MHPCC

In Hawaii, where weather and climate variations are strongly affected by the steep island topography, there is a clear and acknowledged need for improved weather and climate forecasts at increased spatial resolution. The Hawaii Weather, Climate,Modeling Ohana (HWCMO) was therefore formed at the Maui High Performance Computing Center in 1997 to establish an experimental weather/climate mesoscale modeling effort for near real-time support to the local National Weather service (NWS), decision makers in federal and state agencies, and local researchers. This operational mesoscale forecasting effort is currently providing on an almost regular schedule, daily forecast products {http://www.mhpcc.edu/ wswx/), using 5 nodesof the MHPCC multi-node IBM SP2 cluster.