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- Author or Editor: H. Zhang x
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Abstract
This paper compares a number of probabilistic weather forecasting verification approaches. Forecasting skill scores from linear error in probability space and relative operating characteristics are compared with results from an alternative approach that first transforms probabilistic forecasts to yes/no form and then assesses the model forecasting skill. This approach requires a certain departure between the categorical probability from forecast models and its random expectation. The classical contingency table is revised to reflect the “nonapplicable” forecasts in the skill assessment.
The authors present a verification of an Australian seasonal rainfall forecast model hindcasts for the winter and summer seasons over the period from 1900 to 1995. Overall skill scores from different approaches demonstrate similar features. However there are advantages and disadvantages in each of those approaches. Using more than one skill assessment scheme is necessary and is also of practical value in the evaluation of the model forecasts and their applications.
Abstract
This paper compares a number of probabilistic weather forecasting verification approaches. Forecasting skill scores from linear error in probability space and relative operating characteristics are compared with results from an alternative approach that first transforms probabilistic forecasts to yes/no form and then assesses the model forecasting skill. This approach requires a certain departure between the categorical probability from forecast models and its random expectation. The classical contingency table is revised to reflect the “nonapplicable” forecasts in the skill assessment.
The authors present a verification of an Australian seasonal rainfall forecast model hindcasts for the winter and summer seasons over the period from 1900 to 1995. Overall skill scores from different approaches demonstrate similar features. However there are advantages and disadvantages in each of those approaches. Using more than one skill assessment scheme is necessary and is also of practical value in the evaluation of the model forecasts and their applications.
Abstract
An ensemble Kalman filter (EnKF) has been adopted and implemented at the Naval Research Laboratory (NRL) for mesoscale and storm-scale data assimilation to study the impact of ensemble assimilation of high-resolution observations, including those from Doppler radars, on storm prediction. The system has been improved during its implementation at NRL to further enhance its capability of assimilating various types of meteorological data. A parallel algorithm was also developed to increase the system’s computational efficiency on multiprocessor computers. The EnKF has been integrated into the NRL mesoscale data assimilation system and extensively tested to ensure that the system works appropriately with new observational data stream and forecast systems. An innovative procedure was developed to evaluate the impact of assimilated observations on ensemble analyses with no need to exclude any observations for independent validation (as required by the conventional evaluation based on data-denying experiments). The procedure was employed in this study to examine the impacts of ensemble size and localization on data assimilation and the results reveal a very interesting relationship between the ensemble size and the localization length scale. All the tests conducted in this study demonstrate the capabilities of the EnKF as a research tool for mesoscale and storm-scale data assimilation with potential operational applications.
Abstract
An ensemble Kalman filter (EnKF) has been adopted and implemented at the Naval Research Laboratory (NRL) for mesoscale and storm-scale data assimilation to study the impact of ensemble assimilation of high-resolution observations, including those from Doppler radars, on storm prediction. The system has been improved during its implementation at NRL to further enhance its capability of assimilating various types of meteorological data. A parallel algorithm was also developed to increase the system’s computational efficiency on multiprocessor computers. The EnKF has been integrated into the NRL mesoscale data assimilation system and extensively tested to ensure that the system works appropriately with new observational data stream and forecast systems. An innovative procedure was developed to evaluate the impact of assimilated observations on ensemble analyses with no need to exclude any observations for independent validation (as required by the conventional evaluation based on data-denying experiments). The procedure was employed in this study to examine the impacts of ensemble size and localization on data assimilation and the results reveal a very interesting relationship between the ensemble size and the localization length scale. All the tests conducted in this study demonstrate the capabilities of the EnKF as a research tool for mesoscale and storm-scale data assimilation with potential operational applications.
Abstract
The mass concentration of fine particulate matter (PM2.5; diameters less than 2.5 μm) estimated from geostationary satellite aerosol optical depth (AOD) data can supplement the network of ground monitors with high temporal (hourly) resolution. Estimates of PM2.5 over the United States were derived from NOAA’s operational geostationary satellites’ Advanced Baseline Imager (ABI) AOD data using a geographically weighted regression with hourly and daily temporal resolution. Validation versus ground observations shows a mean bias of −21.4% and −15.3% for hourly and daily PM2.5 estimates, respectively, for concentrations ranging from 0 to 1000 μg m−3. Because satellites only observe AOD in the daytime, the relation between observed daytime PM2.5 and daily mean PM2.5 was evaluated using ground measurements; PM2.5 estimated from ABI AODs were also examined to study this relationship. The ground measurements show that daytime mean PM2.5 has good correlation (r > 0.8) with daily mean PM2.5 in most areas of the United States, but with pronounced differences in the western United States due to temporal variations caused by wildfire smoke; the relation between the daytime and daily PM2.5 estimated from the ABI AODs has a similar pattern. While daily or daytime estimated PM2.5 provides exposure information in the context of the PM2.5 standard (>35 μg m−3), the hourly estimates of PM2.5 used in nowcasting show promise for alerts and warnings of harmful air quality. The geostationary satellite based PM2.5 estimates inform the public of harmful air quality 10 times more than standard ground observations (1.8 versus 0.17 million people per hour).
Significance Statement
Fine particulate matter (PM2.5; diameters less than 2.5 μm) are generated from smoke, dust, and emissions from industrial, transportation, and other sectors. They are harmful to human health and even lead to premature mortality. Data from geostationary satellites can help estimate surface PM2.5 exposure by filling in gaps that are not covered by ground monitors. With this information, people can plan their outdoor activities accordingly. This study shows that availability of hourly PM2.5 observations covering the entire continental United States is more informative to the public about harmful exposure to pollution. On average, 1.8 million people per hour can be informed using satellite data compared to 0.17 million people per hour based on ground observations alone.
Abstract
The mass concentration of fine particulate matter (PM2.5; diameters less than 2.5 μm) estimated from geostationary satellite aerosol optical depth (AOD) data can supplement the network of ground monitors with high temporal (hourly) resolution. Estimates of PM2.5 over the United States were derived from NOAA’s operational geostationary satellites’ Advanced Baseline Imager (ABI) AOD data using a geographically weighted regression with hourly and daily temporal resolution. Validation versus ground observations shows a mean bias of −21.4% and −15.3% for hourly and daily PM2.5 estimates, respectively, for concentrations ranging from 0 to 1000 μg m−3. Because satellites only observe AOD in the daytime, the relation between observed daytime PM2.5 and daily mean PM2.5 was evaluated using ground measurements; PM2.5 estimated from ABI AODs were also examined to study this relationship. The ground measurements show that daytime mean PM2.5 has good correlation (r > 0.8) with daily mean PM2.5 in most areas of the United States, but with pronounced differences in the western United States due to temporal variations caused by wildfire smoke; the relation between the daytime and daily PM2.5 estimated from the ABI AODs has a similar pattern. While daily or daytime estimated PM2.5 provides exposure information in the context of the PM2.5 standard (>35 μg m−3), the hourly estimates of PM2.5 used in nowcasting show promise for alerts and warnings of harmful air quality. The geostationary satellite based PM2.5 estimates inform the public of harmful air quality 10 times more than standard ground observations (1.8 versus 0.17 million people per hour).
Significance Statement
Fine particulate matter (PM2.5; diameters less than 2.5 μm) are generated from smoke, dust, and emissions from industrial, transportation, and other sectors. They are harmful to human health and even lead to premature mortality. Data from geostationary satellites can help estimate surface PM2.5 exposure by filling in gaps that are not covered by ground monitors. With this information, people can plan their outdoor activities accordingly. This study shows that availability of hourly PM2.5 observations covering the entire continental United States is more informative to the public about harmful exposure to pollution. On average, 1.8 million people per hour can be informed using satellite data compared to 0.17 million people per hour based on ground observations alone.
