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- Author or Editor: Jinwon Kim x
- Weather and Forecasting x
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Abstract
Despite tremendous advancements in dynamical models for weather forecasting, statistical models continue to offer various possibilities for tropical cyclone (TC) track forecasting. Herein, a track-pattern-based approach was developed to predict a TC track for a lead time of 6–8 days over the western North Pacific (WNP), utilizing historical tracks in conjunction with dynamical forecasts. It is composed of four main steps: 1) clustering historical tracks similar to that of an operational 5-day forecast in their early phase into track patterns, and calculating the daily mean environmental fields (500-hPa geopotential height and steering flow) associated with each track; 2) deriving the two environmental variables forecasted by dynamical models; 3) evaluating pattern correlation coefficients between the two environmental fields from step 1 and those from dynamical model for a lead times of 6–8 days; and 4) producing the final track forecast based on relative frequency maps obtained from the historical tracks in step 1 and the pattern correlation coefficients obtained from step 3. TCs that formed in the WNP and lasted for at least 7 days, during the 9-yr period 2011–19 were selected to verify the resulting track-pattern-based forecasts. In addition to the performance comparable to dynamical models under certain conditions, the track-pattern-based model is inexpensive, and can consistently produce forecasts over large latitudinal or longitudinal ranges. Machine learning techniques can be implemented to incorporate nonlinearity in the present model for improving medium-range track forecasts.
Abstract
Despite tremendous advancements in dynamical models for weather forecasting, statistical models continue to offer various possibilities for tropical cyclone (TC) track forecasting. Herein, a track-pattern-based approach was developed to predict a TC track for a lead time of 6–8 days over the western North Pacific (WNP), utilizing historical tracks in conjunction with dynamical forecasts. It is composed of four main steps: 1) clustering historical tracks similar to that of an operational 5-day forecast in their early phase into track patterns, and calculating the daily mean environmental fields (500-hPa geopotential height and steering flow) associated with each track; 2) deriving the two environmental variables forecasted by dynamical models; 3) evaluating pattern correlation coefficients between the two environmental fields from step 1 and those from dynamical model for a lead times of 6–8 days; and 4) producing the final track forecast based on relative frequency maps obtained from the historical tracks in step 1 and the pattern correlation coefficients obtained from step 3. TCs that formed in the WNP and lasted for at least 7 days, during the 9-yr period 2011–19 were selected to verify the resulting track-pattern-based forecasts. In addition to the performance comparable to dynamical models under certain conditions, the track-pattern-based model is inexpensive, and can consistently produce forecasts over large latitudinal or longitudinal ranges. Machine learning techniques can be implemented to incorporate nonlinearity in the present model for improving medium-range track forecasts.
Abstract
This study evaluates the Korea Meteorological Administration (KMA) Asian Dust Aerosol Model 2 (ADAM2) for Asian dust events over the dust source regions in northern China during the first half of 2017. Using the observed hourly particulate matter (PM) concentration from the China Ministry of Environmental Protection (MEP) and station weather reports, we find that a threshold value of PM10–PM2.5 = 400 μg m−3 works well in defining an Asian dust event for both the MEP-observed and the ADAM2-simulated data. In northwestern China, ADAM2 underestimates the observed dust days mainly due to underestimation of dust emissions; ADAM2 overestimates the observed Asian dust days over Manchuria due to overestimation of dust emissions. Performance of ADAM2 in estimating Asian dust emissions varies quite systematically according to dominant soil types within each region. The current formulation works well for the Gobi and sand soil types, but substantially overestimates dust emissions for the loess-type soils. This suggests that the ADAM2 model errors are likely to originate from the soil-type-dependent dust emissions formulation and that the formulation for the mixed and loess-type soils needs to be recalibrated. In addition, inability to account for the concentration of fine PMs from anthropogenic sources results in large false-alarm rates over heavily industrialized regions. Direct calculation of PM2.5 in the upcoming ADAM3 model is expected to alleviate the problems related to anthropogenic PMs in identifying Asian dust events.
Abstract
This study evaluates the Korea Meteorological Administration (KMA) Asian Dust Aerosol Model 2 (ADAM2) for Asian dust events over the dust source regions in northern China during the first half of 2017. Using the observed hourly particulate matter (PM) concentration from the China Ministry of Environmental Protection (MEP) and station weather reports, we find that a threshold value of PM10–PM2.5 = 400 μg m−3 works well in defining an Asian dust event for both the MEP-observed and the ADAM2-simulated data. In northwestern China, ADAM2 underestimates the observed dust days mainly due to underestimation of dust emissions; ADAM2 overestimates the observed Asian dust days over Manchuria due to overestimation of dust emissions. Performance of ADAM2 in estimating Asian dust emissions varies quite systematically according to dominant soil types within each region. The current formulation works well for the Gobi and sand soil types, but substantially overestimates dust emissions for the loess-type soils. This suggests that the ADAM2 model errors are likely to originate from the soil-type-dependent dust emissions formulation and that the formulation for the mixed and loess-type soils needs to be recalibrated. In addition, inability to account for the concentration of fine PMs from anthropogenic sources results in large false-alarm rates over heavily industrialized regions. Direct calculation of PM2.5 in the upcoming ADAM3 model is expected to alleviate the problems related to anthropogenic PMs in identifying Asian dust events.
