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- Author or Editor: A. Garcia Diez x
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Abstract
In an earlier work, the authors introduced an objective forecast model for a 24-h prediction of the number of daily forest fires based on a 2-day lag autoregressive model. The meteorological inputs required for this model (temperature and geopotential height at 850 and 700 hPa and dewpoint at 850 hPa) may be predicted by a medium-range numerical weather forecast model such as that of the European Centre for Medium-Range Weather Forecasts. These predicted meteorological elements may be used to extend the range of daily forest fire forecasting. Since the forest fire forecast model is based on a categorization (type of day), an error in the meteorological predictions may not be an error in the predictive model. A meteorological error will only imply error for the model if it produces a change in the type of day (category).
The forecast range for the number of forest fires per day has been extended to five days with this new model. Moreover, assuming that the weather forecast is perfect, a validation of the prediction model for forest fires is carried out.
Abstract
In an earlier work, the authors introduced an objective forecast model for a 24-h prediction of the number of daily forest fires based on a 2-day lag autoregressive model. The meteorological inputs required for this model (temperature and geopotential height at 850 and 700 hPa and dewpoint at 850 hPa) may be predicted by a medium-range numerical weather forecast model such as that of the European Centre for Medium-Range Weather Forecasts. These predicted meteorological elements may be used to extend the range of daily forest fire forecasting. Since the forest fire forecast model is based on a categorization (type of day), an error in the meteorological predictions may not be an error in the predictive model. A meteorological error will only imply error for the model if it produces a change in the type of day (category).
The forecast range for the number of forest fires per day has been extended to five days with this new model. Moreover, assuming that the weather forecast is perfect, a validation of the prediction model for forest fires is carried out.
Abstract
Following the theoretical model proposed in previous papers in which four types of days and their associated fire risk (daily fire risk, DFR) were defined for each size of fire, the authors conclude that the meteorological conditions that favor the generation of fires must be similar to those that are favorable to their development. In a study of burned areas, comparative results with previous works are obtained, and the parameters DFR and NDFR (normalized DFR) are proven to be in agreement with their previously assigned physical meaning. The development rather than the ignition of forest fires is better described using the DFR and NDFR parameters.
Abstract
Following the theoretical model proposed in previous papers in which four types of days and their associated fire risk (daily fire risk, DFR) were defined for each size of fire, the authors conclude that the meteorological conditions that favor the generation of fires must be similar to those that are favorable to their development. In a study of burned areas, comparative results with previous works are obtained, and the parameters DFR and NDFR (normalized DFR) are proven to be in agreement with their previously assigned physical meaning. The development rather than the ignition of forest fires is better described using the DFR and NDFR parameters.
Abstract
Daily fire risk (DFR) is a forecasting index defined on the basis of two meteorological parameters. Such parameters are associated with the local atmospheric column: dry stability e in 850–700-hPa layer and saturation deficit D in 850-hPa level. In an earlier study, and from data collected over 10 years, a categorization of four type days based on DFR was established. In this way, from evaluation of e and D at 0000 UTC for each particular day, the associated type day was deduced. Consequently, it is possible to know whether that day had either very high, high, low, or very low fire activity. With this technique it is not possible to forecast a numerical value for the number of fires, however.
In this paper a model for estimating the outbreak of fires is presented. On the basis of an autoregressive process, AR(2), it is possible to obtain the predicted number of fires (PNF) during a day d as PNF(d) = F[TD(d), RNF(D − 1), RNF(d − 2)], where TD(d) is the type day according to the categorization established on the basis of e and D (deduced from rawinsoundings at 0000 UTC) and RNF(d − 1) and RNF(d − 2) are the numbers of fires registered over the area during two previous days.
In contrast to other papers in the literature, all fires are considered. No limitations are placed on the burned area or other measures of fire activity. Several statistical computations confirm the validity of this model.
Abstract
Daily fire risk (DFR) is a forecasting index defined on the basis of two meteorological parameters. Such parameters are associated with the local atmospheric column: dry stability e in 850–700-hPa layer and saturation deficit D in 850-hPa level. In an earlier study, and from data collected over 10 years, a categorization of four type days based on DFR was established. In this way, from evaluation of e and D at 0000 UTC for each particular day, the associated type day was deduced. Consequently, it is possible to know whether that day had either very high, high, low, or very low fire activity. With this technique it is not possible to forecast a numerical value for the number of fires, however.
In this paper a model for estimating the outbreak of fires is presented. On the basis of an autoregressive process, AR(2), it is possible to obtain the predicted number of fires (PNF) during a day d as PNF(d) = F[TD(d), RNF(D − 1), RNF(d − 2)], where TD(d) is the type day according to the categorization established on the basis of e and D (deduced from rawinsoundings at 0000 UTC) and RNF(d − 1) and RNF(d − 2) are the numbers of fires registered over the area during two previous days.
In contrast to other papers in the literature, all fires are considered. No limitations are placed on the burned area or other measures of fire activity. Several statistical computations confirm the validity of this model.
