Search Results
You are looking at 1 - 3 of 3 items for :
- Author or Editor: Florian Pappenberger x
- Journal of Applied Meteorology and Climatology x
- Refine by Access: All Content x
Abstract
Heat waves represent a threat to human health and excess mortality is one of the associated negative effects. A health-based definition for heat waves is therefore relevant, especially for early warning purposes, and it is here investigated via the universal thermal climate index (UTCI). The UTCI is a bioclimate index elaborated via an advanced model of human thermoregulation that estimates the thermal stress induced by air temperature, wind speed, moisture, and radiation on the human physiology. Using France as a test bed, the UTCI was computed from meteorological reanalysis data to assess the thermal stress conditions associated with heat-attributable excess mortality in five cities. UTCI values at different climatological percentiles were defined and evaluated in their ability to identify periods of excess mortality (PEMs) over 24 years. Using verification metrics such as the probability of detection (POD), the false alarm ratio (FAR), and the frequency bias (FB), daily minimum and maximum heat stress levels equal to or above corresponding UTCI 95th percentiles (15° ± 2°C and 34.5° ± 1.5°C, respectively) for 3 consecutive days are demonstrated to correlate to PEMs with the highest sensitivity and specificity (0.69 ≤ POD ≤ 1, 0.19 ≤ FAR ≤ 0.46, 1 ≤ FB ≤ 1.48) than minimum, maximum, and mean heat stress level singularly and other bioclimatological percentiles. This finding confirms the detrimental effect of prolonged, unusually high heat stress at day- and nighttime and suggests the UTCI 95th percentile as a health-meaningful threshold for a potential heat-health watch warning system.
Abstract
Heat waves represent a threat to human health and excess mortality is one of the associated negative effects. A health-based definition for heat waves is therefore relevant, especially for early warning purposes, and it is here investigated via the universal thermal climate index (UTCI). The UTCI is a bioclimate index elaborated via an advanced model of human thermoregulation that estimates the thermal stress induced by air temperature, wind speed, moisture, and radiation on the human physiology. Using France as a test bed, the UTCI was computed from meteorological reanalysis data to assess the thermal stress conditions associated with heat-attributable excess mortality in five cities. UTCI values at different climatological percentiles were defined and evaluated in their ability to identify periods of excess mortality (PEMs) over 24 years. Using verification metrics such as the probability of detection (POD), the false alarm ratio (FAR), and the frequency bias (FB), daily minimum and maximum heat stress levels equal to or above corresponding UTCI 95th percentiles (15° ± 2°C and 34.5° ± 1.5°C, respectively) for 3 consecutive days are demonstrated to correlate to PEMs with the highest sensitivity and specificity (0.69 ≤ POD ≤ 1, 0.19 ≤ FAR ≤ 0.46, 1 ≤ FB ≤ 1.48) than minimum, maximum, and mean heat stress level singularly and other bioclimatological percentiles. This finding confirms the detrimental effect of prolonged, unusually high heat stress at day- and nighttime and suggests the UTCI 95th percentile as a health-meaningful threshold for a potential heat-health watch warning system.
Abstract
In the absence of a dynamical fire model that could link the emissions to the weather dynamics and the availability of fuel, atmospheric composition models, such as the European Copernicus Atmosphere Monitoring Services (CAMS), often assume persistence, meaning that constituents produced by the biomass burning process during the first day are assumed constant for the whole length of the forecast integration (5 days for CAMS). While this assumption is simple and practical, it can produce unrealistic predictions of aerosol concentration due to an excessive contribution from biomass burning. This paper introduces a time-dependent factor 
Abstract
In the absence of a dynamical fire model that could link the emissions to the weather dynamics and the availability of fuel, atmospheric composition models, such as the European Copernicus Atmosphere Monitoring Services (CAMS), often assume persistence, meaning that constituents produced by the biomass burning process during the first day are assumed constant for the whole length of the forecast integration (5 days for CAMS). While this assumption is simple and practical, it can produce unrealistic predictions of aerosol concentration due to an excessive contribution from biomass burning. This paper introduces a time-dependent factor
Abstract
A global fire danger rating system driven by atmospheric model forcing has been developed with the aim of providing early warning information to civil protection authorities. The daily predictions of fire danger conditions are based on the U.S. Forest Service National Fire-Danger Rating System (NFDRS), the Canadian Forest Service Fire Weather Index Rating System (FWI), and the Australian McArthur (Mark 5) rating systems. Weather forcings are provided in real time by the European Centre for Medium-Range Weather Forecasts forecasting system at 25-km resolution. The global system’s potential predictability is assessed using reanalysis fields as weather forcings. The Global Fire Emissions Database (GFED4) provides 11 yr of observed burned areas from satellite measurements and is used as a validation dataset. The fire indices implemented are good predictors to highlight dangerous conditions. High values are correlated with observed fire, and low values correspond to nonobserved events. A more quantitative skill evaluation was performed using the extremal dependency index, which is a skill score specifically designed for rare events. It revealed that the three indices were more skillful than the random forecast to detect large fires on a global scale. The performance peaks in the boreal forests, the Mediterranean region, the Amazon rain forests, and Southeast Asia. The skill scores were then aggregated at the country level to reveal which nations could potentially benefit from the system information to aid decision-making and fire control support. Overall it was found that fire danger modeling based on weather forecasts can provide reasonable predictability over large parts of the global landmass.
Abstract
A global fire danger rating system driven by atmospheric model forcing has been developed with the aim of providing early warning information to civil protection authorities. The daily predictions of fire danger conditions are based on the U.S. Forest Service National Fire-Danger Rating System (NFDRS), the Canadian Forest Service Fire Weather Index Rating System (FWI), and the Australian McArthur (Mark 5) rating systems. Weather forcings are provided in real time by the European Centre for Medium-Range Weather Forecasts forecasting system at 25-km resolution. The global system’s potential predictability is assessed using reanalysis fields as weather forcings. The Global Fire Emissions Database (GFED4) provides 11 yr of observed burned areas from satellite measurements and is used as a validation dataset. The fire indices implemented are good predictors to highlight dangerous conditions. High values are correlated with observed fire, and low values correspond to nonobserved events. A more quantitative skill evaluation was performed using the extremal dependency index, which is a skill score specifically designed for rare events. It revealed that the three indices were more skillful than the random forecast to detect large fires on a global scale. The performance peaks in the boreal forests, the Mediterranean region, the Amazon rain forests, and Southeast Asia. The skill scores were then aggregated at the country level to reveal which nations could potentially benefit from the system information to aid decision-making and fire control support. Overall it was found that fire danger modeling based on weather forecasts can provide reasonable predictability over large parts of the global landmass.
