Tropical cyclones (TCs) are the most destructive weather phenomena to impact on tropical regions, and reliable predicttion of TC seasonal activity is important for preparedness of coastal communities in the tropics. I...Tropical cyclones (TCs) are the most destructive weather phenomena to impact on tropical regions, and reliable predicttion of TC seasonal activity is important for preparedness of coastal communities in the tropics. In investigating prospects for improving the skill of TC seasonal prediction in the South Indian and South Pacific Oceans, including the Australian Region, we used linear regression to model the relationship between the annual number of cyclones and three indices (SOI, NI?O3.4 and 5VAR) describing the strength of the El Ni?o-Southern Oscillation (ENSO). The correlation between the number of Australian Region (90?E - 160?E) TCs and the indices was strong (3-month 5VAR ?0.65, NI?O3.4 ?0.62 and SOI +0.64), and a cross-validation assessment demonstrated that the models which used July-August-September indices and the temporal trend as the predictors performed well. The predicted number of TCs in the Australian Region for 2010/2011 and 2011/2012 seasons was 14 (11 recorded) and 12, respectively. We also found that the correlation between the numbers of TCs in the western South Indian region (30?E to 90?E) and the eastern South Pacific region (east of 170?E) and the indices was weak, and it is therefore not sensible to build linear regression forecast models for these regions. We conclude that for the Australian Region, the new statistical model provides prospects for improvement in forecasting skill compared to the statistical model currently employed at the National Climate Centre, Australian Bureau of Meteorology. The next step towards improving the skill of TC seasonal prediction in the various regions of the Southern Hemisphere will be undertaken through analysis of outputs from the dynamical climate model POAMA (Predictive Ocean-Atmosphere Model for Australia).展开更多
A new seasonal prediction model for annual tropical storm numbers （ATSNs） over the western North Pacific was developed using the preceding January-February （JF） and April-May （AM） grid-point data at a resolution...A new seasonal prediction model for annual tropical storm numbers （ATSNs） over the western North Pacific was developed using the preceding January-February （JF） and April-May （AM） grid-point data at a resolution of 2.5° × 2.5°. The JF and AM mean precipitation and the AM mean 500-hPa geopotential height in the Northern Hemisphere, together with the JF mean 500-hPa geopotential height in the Southern Hemisphere, were employed to compose the ATSN forecast model via the stepwise multiple linear regression technique. All JF and AM mean data were confined to the Eastern ttemisphere. We established two empirical prediction models for ATSN using the ERA40 reanalysis and NCEP reanalysis datasets, respectively, together with the observed precipitation. The performance of the models was verified by cross-validation. Anomaly correlation coefficients （ACC） at 0.78 and 0.74 were obtained via comparison of the retrospective predictions of the two models and the observed ATSNs from 1979 to 2002. The multi-year mean absolute prediction errors were 3.0 and 3.2 for the two models respectively, or roughly 10% of the average ATSN. In practice, the final prediction was made by averaging the ATSN predictions of the two models. This resulted in a higher score, with ACC being further increased to 0.88, and the mean absolute error reduced to 1.92, or 6.13% of the average ATSN.展开更多
A new empirical approach for the seasonal prediction of annual Atlantic tropical storm number (ATSN) was developed using precipitation and 500 hPa geopotential height data from the preceding January February and April...A new empirical approach for the seasonal prediction of annual Atlantic tropical storm number (ATSN) was developed using precipitation and 500 hPa geopotential height data from the preceding January February and April May.The 2.5°×2.5° resolution reanalysis data from both the US National Center for Environmental Prediction/the National Center for Atmospheric Research (NCEP/NCAR) and the European Center for Medium-Range Weather Forecasting (ECMWF) were applied.The model was cross-validated using data from 1979 2002.The ATSN predictions from the two reanalysis models were correlated with the observations with the anomaly correlation coefficients (ACC) of 0.79 (NCEP/NCAR) and 0.78 (ECMWF) and the multi-year mean absolute prediction errors (MAE) of 1.85 and 1.76,respectively.When the predictions of the two models were averaged,the ACC increased to 0.90 and the MAE decreased to 1.18,an exceptionally high score.Therefore,this new empirical approach has the potential to improve the operational prediction of the annual tropical Atlantic storm frequency.展开更多
Seasonal prediction of East Asia(EA) summer rainfall, especially with a longer-lead time, is in great demand, but still very challenging. The present study aims to make long-lead prediction of EA subtropical frontal r...Seasonal prediction of East Asia(EA) summer rainfall, especially with a longer-lead time, is in great demand, but still very challenging. The present study aims to make long-lead prediction of EA subtropical frontal rainfall(SFR) during early summer(May-June mean, MJ) by considering Arctic sea ice(ASI) variability as a new potential predictor. A MJ SFR index(SFRI), the leading principle component of the empirical orthogonal function(EOF) analysis applied to the MJ precipitation anomaly over EA, is defined as the predictand. Analysis of 38-year observations(1979-2016) revealed three physically consequential predictors. A stronger SFRI is preceded by dipolar ASI anomaly in the previous autumn, a sea level pressure(SLP) dipole in the Eurasian continent, and a sea surface temperature anomaly tripole pattern in the tropical Pacific in the previous winter. These precursors foreshadow an enhanced Okhotsk High, lower local SLP over EA, and a strengthened western Pacific subtropical high. These factors are controlling circulation features for a positive SFRI. A physical-empirical model was established to predict SFRI by combining the three predictors. Hindcasting was performed for the 1979-2016 period, which showed a hindcast prediction skill that was, unexpectedly, substantially higher than that of a four-dynamical models’ ensemble prediction for the 1979-2010 period(0.72 versus 0.47). Note that ASI variation is a new predictor compared with signals originating from the tropics to mid-latitudes. The long-lead hindcast skill was notably lower without the ASI signals included, implying the high practical value of ASI variation in terms of long-lead seasonal prediction of MJ EA rainfall.展开更多
Analysis is done of monthly and seasonal variations as climatic features of the tracks from 1196 tropical cyclones originating in the western North Pacific over the period 1949 to 1980, followed by the investigation o...Analysis is done of monthly and seasonal variations as climatic features of the tracks from 1196 tropical cyclones originating in the western North Pacific over the period 1949 to 1980, followed by the investigation of 301 onland cyclone tracks over China mainland in terms of methodology for nonlinear system. Obtained by computing the accumulated distance distribution function of the tracks Cm (l) is the characteristic chaos quantity for the related dynamic systems and then the fractual dimensionality d = 4.86 and Kolmogorov entropy approximation K2 = 0.0164, thereby leading to the predictability time scale = 2.54 days. It is found that the reference path among the onland typhoon No.23 of 1971, or Bess in the international nomenclature. Our results could be of operational use as a kind of reference.展开更多
Here we use a Discriminant Genetic Algorithm Extended (DGAE) model to diagnose and predict seasonal sand and dust storm (SDS) activities occurring in Northeast Asia. The study employed the regular meteorological data,...Here we use a Discriminant Genetic Algorithm Extended (DGAE) model to diagnose and predict seasonal sand and dust storm (SDS) activities occurring in Northeast Asia. The study employed the regular meteorological data, including surface data, upper air data, and NCEP reanalysis data, collected from 1980–2006. The regional, seasonal, and annual differences of 3-D atmospheric circulation structures and SDS activities in the context of spatial and temporal distributions were given. Genetic algorithms were introduced with the further extension of promoting SDS seasonal predication from multi-level resolution. Genetic probability was used as a substitute for posterior probability of multi-level discriminants, to show the dual characteristics of crossover inheritance and mutation and to build a non-linear adaptability function in line with extended genetic algorithms. This has unveiled the spatial distribution of the maximum adaptability, allowing the forecast field to be defined by the population with the largest probability, and made discriminant genetic extension possible. In addition, the effort has led to the establishment of a regional model for predicting seasonal SDS activities in East Asia. The model was tested to predict the spring SDS activities occurring in North China from 2007 to 2009. The experimental forecast resulted in highly discriminant intensity ratings and regional distributions of SDS activities, which are a meaningful reference for seasonal SDS predictions in the future.展开更多
An atmosphere-only model system for making seasonal prediction and projecting future intensities of landfalling tropical cyclones(TCs)along the South China coast is upgraded by including ocean and wave models.A total ...An atmosphere-only model system for making seasonal prediction and projecting future intensities of landfalling tropical cyclones(TCs)along the South China coast is upgraded by including ocean and wave models.A total of 642 TCs have been re-simulated using the new system to produce a climatology of TC intensity in the South China Sea.Detailed comparisons of the simulations from the atmosphere-only and the fully coupled systems reveal that the inclusion of the additional ocean and wave models enable differential sea surface temperature responses to various TC characteristics such as translational speed and size.In particular,interaction with the ocean does not necessarily imply a weakening of the TC,with the coastal bathymetry possibly playing a role in causing a near-shore intensification of the TC.These results suggest that to simulate the evolution of TC structure more accurately,it is essential to use an air-sea coupled model instead of an atmosphere-only model.展开更多
This study summarized the procedure for the seasonal predictions of tropical cyclones(TCs)over the western North Pacific(WNP),which is currently operating at the Korea Meteorological Administration(KMA),Republic of Ko...This study summarized the procedure for the seasonal predictions of tropical cyclones(TCs)over the western North Pacific(WNP),which is currently operating at the Korea Meteorological Administration(KMA),Republic of Korea.The methodology was briefly described,and its prediction accuracy was verified.Seasonal predictions were produced by synthesizing spatiotemporal evolutions of various climate factors such as El Ni no–Southern Oscillation(ENSO),monsoon activity,and Madden–Julian Oscillation(MJO),using four models:a statistical,a dynamical,and two statistical–dynamical models.The KMA forecaster predicted the number of TCs over the WNP based on the results of the four models and season to season climate variations.The seasonal prediction of TCs is announced through the press twice a year,for the summer on May and fall on August.The present results showed low accuracy during the period 2014–2020.To advance forecast skill,a set of recommendations are suggested.展开更多
Tropical cyclone(TC)track predictions of the 10-km resolution WRF(provisionally named"AAMC-WRF")of the Hong Kong Observatory(HKO),spanning(20°S-60°N,45°E-160°E)is studied for a 1-year per...Tropical cyclone(TC)track predictions of the 10-km resolution WRF(provisionally named"AAMC-WRF")of the Hong Kong Observatory(HKO),spanning(20°S-60°N,45°E-160°E)is studied for a 1-year period from April 2018 to Mar 2019.Real-time predictions,up to 4 times a day and T+48 h ahead,are verified against operational analysis positions of HKO for storms over the South China Sea(SCS)and Western North Pacific(WNP);and of the New Delhi Regional Specialised Meteorological Centre(RSMC)for storms over the North Indian Ocean basin(NIO;including the Bay of Bengal).Out of 21 named TCs over SCS and WNP,mean positional errors of the AAMC-WRF are 33 km(T+0),63 km(T+24),and 107 km(T+48)based on 209,178 and 142 forecasts.The AAMC-WRF outperformed Meso-NHM,also run in real-time at HKO,with mean error reduction up to 34 km or 24%.Mean positional errors for 13 NIO storms are 38 km(T+0),69 km(T+24)and 107 km(T+48)based on 183,131 and 85 forecasts.This is the first study in which TC predictions of a regional model are simultaneously examined over the SCS,WNP and NIO basins through real-time experiments.展开更多
文摘Tropical cyclones (TCs) are the most destructive weather phenomena to impact on tropical regions, and reliable predicttion of TC seasonal activity is important for preparedness of coastal communities in the tropics. In investigating prospects for improving the skill of TC seasonal prediction in the South Indian and South Pacific Oceans, including the Australian Region, we used linear regression to model the relationship between the annual number of cyclones and three indices (SOI, NI?O3.4 and 5VAR) describing the strength of the El Ni?o-Southern Oscillation (ENSO). The correlation between the number of Australian Region (90?E - 160?E) TCs and the indices was strong (3-month 5VAR ?0.65, NI?O3.4 ?0.62 and SOI +0.64), and a cross-validation assessment demonstrated that the models which used July-August-September indices and the temporal trend as the predictors performed well. The predicted number of TCs in the Australian Region for 2010/2011 and 2011/2012 seasons was 14 (11 recorded) and 12, respectively. We also found that the correlation between the numbers of TCs in the western South Indian region (30?E to 90?E) and the eastern South Pacific region (east of 170?E) and the indices was weak, and it is therefore not sensible to build linear regression forecast models for these regions. We conclude that for the Australian Region, the new statistical model provides prospects for improvement in forecasting skill compared to the statistical model currently employed at the National Climate Centre, Australian Bureau of Meteorology. The next step towards improving the skill of TC seasonal prediction in the various regions of the Southern Hemisphere will be undertaken through analysis of outputs from the dynamical climate model POAMA (Predictive Ocean-Atmosphere Model for Australia).
基金Supported by the National Basic Research Program of China(2009CB421406)National Natural Science Foundation of China(40875048 and 40775049)
文摘A new seasonal prediction model for annual tropical storm numbers （ATSNs） over the western North Pacific was developed using the preceding January-February （JF） and April-May （AM） grid-point data at a resolution of 2.5° × 2.5°. The JF and AM mean precipitation and the AM mean 500-hPa geopotential height in the Northern Hemisphere, together with the JF mean 500-hPa geopotential height in the Southern Hemisphere, were employed to compose the ATSN forecast model via the stepwise multiple linear regression technique. All JF and AM mean data were confined to the Eastern ttemisphere. We established two empirical prediction models for ATSN using the ERA40 reanalysis and NCEP reanalysis datasets, respectively, together with the observed precipitation. The performance of the models was verified by cross-validation. Anomaly correlation coefficients （ACC） at 0.78 and 0.74 were obtained via comparison of the retrospective predictions of the two models and the observed ATSNs from 1979 to 2002. The multi-year mean absolute prediction errors were 3.0 and 3.2 for the two models respectively, or roughly 10% of the average ATSN. In practice, the final prediction was made by averaging the ATSN predictions of the two models. This resulted in a higher score, with ACC being further increased to 0.88, and the mean absolute error reduced to 1.92, or 6.13% of the average ATSN.
基金supported by the Major State Basic Research Development Program of China (Grant No.2009CB421406)the National Natural Science Foundation of China (Grant Nos. 40631005 and 40875048)
文摘A new empirical approach for the seasonal prediction of annual Atlantic tropical storm number (ATSN) was developed using precipitation and 500 hPa geopotential height data from the preceding January February and April May.The 2.5°×2.5° resolution reanalysis data from both the US National Center for Environmental Prediction/the National Center for Atmospheric Research (NCEP/NCAR) and the European Center for Medium-Range Weather Forecasting (ECMWF) were applied.The model was cross-validated using data from 1979 2002.The ATSN predictions from the two reanalysis models were correlated with the observations with the anomaly correlation coefficients (ACC) of 0.79 (NCEP/NCAR) and 0.78 (ECMWF) and the multi-year mean absolute prediction errors (MAE) of 1.85 and 1.76,respectively.When the predictions of the two models were averaged,the ACC increased to 0.90 and the MAE decreased to 1.18,an exceptionally high score.Therefore,this new empirical approach has the potential to improve the operational prediction of the annual tropical Atlantic storm frequency.
基金supported by the Global Change Research Program of China (No. 2015CB953904)the Nationa Natural Science Foundation of China (No. 41575067)
文摘Seasonal prediction of East Asia(EA) summer rainfall, especially with a longer-lead time, is in great demand, but still very challenging. The present study aims to make long-lead prediction of EA subtropical frontal rainfall(SFR) during early summer(May-June mean, MJ) by considering Arctic sea ice(ASI) variability as a new potential predictor. A MJ SFR index(SFRI), the leading principle component of the empirical orthogonal function(EOF) analysis applied to the MJ precipitation anomaly over EA, is defined as the predictand. Analysis of 38-year observations(1979-2016) revealed three physically consequential predictors. A stronger SFRI is preceded by dipolar ASI anomaly in the previous autumn, a sea level pressure(SLP) dipole in the Eurasian continent, and a sea surface temperature anomaly tripole pattern in the tropical Pacific in the previous winter. These precursors foreshadow an enhanced Okhotsk High, lower local SLP over EA, and a strengthened western Pacific subtropical high. These factors are controlling circulation features for a positive SFRI. A physical-empirical model was established to predict SFRI by combining the three predictors. Hindcasting was performed for the 1979-2016 period, which showed a hindcast prediction skill that was, unexpectedly, substantially higher than that of a four-dynamical models’ ensemble prediction for the 1979-2010 period(0.72 versus 0.47). Note that ASI variation is a new predictor compared with signals originating from the tropics to mid-latitudes. The long-lead hindcast skill was notably lower without the ASI signals included, implying the high practical value of ASI variation in terms of long-lead seasonal prediction of MJ EA rainfall.
基金This work is funded by the National Natural Science Foundation of China.
文摘Analysis is done of monthly and seasonal variations as climatic features of the tracks from 1196 tropical cyclones originating in the western North Pacific over the period 1949 to 1980, followed by the investigation of 301 onland cyclone tracks over China mainland in terms of methodology for nonlinear system. Obtained by computing the accumulated distance distribution function of the tracks Cm (l) is the characteristic chaos quantity for the related dynamic systems and then the fractual dimensionality d = 4.86 and Kolmogorov entropy approximation K2 = 0.0164, thereby leading to the predictability time scale = 2.54 days. It is found that the reference path among the onland typhoon No.23 of 1971, or Bess in the international nomenclature. Our results could be of operational use as a kind of reference.
基金supported by National S & T Support Program (Grant No. 2008BAC40B02)National Basic Research Program of China (Grant Nos. 2006CB403703 and 2006CB403701)Basic Research Fund under Chinese Academy of Meteorological Sciences (Grant Nos. 2009Y002, 2009Y001)
文摘Here we use a Discriminant Genetic Algorithm Extended (DGAE) model to diagnose and predict seasonal sand and dust storm (SDS) activities occurring in Northeast Asia. The study employed the regular meteorological data, including surface data, upper air data, and NCEP reanalysis data, collected from 1980–2006. The regional, seasonal, and annual differences of 3-D atmospheric circulation structures and SDS activities in the context of spatial and temporal distributions were given. Genetic algorithms were introduced with the further extension of promoting SDS seasonal predication from multi-level resolution. Genetic probability was used as a substitute for posterior probability of multi-level discriminants, to show the dual characteristics of crossover inheritance and mutation and to build a non-linear adaptability function in line with extended genetic algorithms. This has unveiled the spatial distribution of the maximum adaptability, allowing the forecast field to be defined by the population with the largest probability, and made discriminant genetic extension possible. In addition, the effort has led to the establishment of a regional model for predicting seasonal SDS activities in East Asia. The model was tested to predict the spring SDS activities occurring in North China from 2007 to 2009. The experimental forecast resulted in highly discriminant intensity ratings and regional distributions of SDS activities, which are a meaningful reference for seasonal SDS predictions in the future.
基金supported by Hong Kong Research Grants Council Grant CityU E-CityU101/16supported by the Natural Environment Research Council/UKRI(Grant No.NE/V017756/1).
文摘An atmosphere-only model system for making seasonal prediction and projecting future intensities of landfalling tropical cyclones(TCs)along the South China coast is upgraded by including ocean and wave models.A total of 642 TCs have been re-simulated using the new system to produce a climatology of TC intensity in the South China Sea.Detailed comparisons of the simulations from the atmosphere-only and the fully coupled systems reveal that the inclusion of the additional ocean and wave models enable differential sea surface temperature responses to various TC characteristics such as translational speed and size.In particular,interaction with the ocean does not necessarily imply a weakening of the TC,with the coastal bathymetry possibly playing a role in causing a near-shore intensification of the TC.These results suggest that to simulate the evolution of TC structure more accurately,it is essential to use an air-sea coupled model instead of an atmosphere-only model.
基金funded by the Korea Meteorological Administration Research and Development Programs, “Advancing Severe Weather Analysis and Forecast Technology” under Grant (KMA2018-00121) and “Development of typhoon analysis and forecast technology” under Grant (KMA2018-00722)。
文摘This study summarized the procedure for the seasonal predictions of tropical cyclones(TCs)over the western North Pacific(WNP),which is currently operating at the Korea Meteorological Administration(KMA),Republic of Korea.The methodology was briefly described,and its prediction accuracy was verified.Seasonal predictions were produced by synthesizing spatiotemporal evolutions of various climate factors such as El Ni no–Southern Oscillation(ENSO),monsoon activity,and Madden–Julian Oscillation(MJO),using four models:a statistical,a dynamical,and two statistical–dynamical models.The KMA forecaster predicted the number of TCs over the WNP based on the results of the four models and season to season climate variations.The seasonal prediction of TCs is announced through the press twice a year,for the summer on May and fall on August.The present results showed low accuracy during the period 2014–2020.To advance forecast skill,a set of recommendations are suggested.
文摘Tropical cyclone(TC)track predictions of the 10-km resolution WRF(provisionally named"AAMC-WRF")of the Hong Kong Observatory(HKO),spanning(20°S-60°N,45°E-160°E)is studied for a 1-year period from April 2018 to Mar 2019.Real-time predictions,up to 4 times a day and T+48 h ahead,are verified against operational analysis positions of HKO for storms over the South China Sea(SCS)and Western North Pacific(WNP);and of the New Delhi Regional Specialised Meteorological Centre(RSMC)for storms over the North Indian Ocean basin(NIO;including the Bay of Bengal).Out of 21 named TCs over SCS and WNP,mean positional errors of the AAMC-WRF are 33 km(T+0),63 km(T+24),and 107 km(T+48)based on 209,178 and 142 forecasts.The AAMC-WRF outperformed Meso-NHM,also run in real-time at HKO,with mean error reduction up to 34 km or 24%.Mean positional errors for 13 NIO storms are 38 km(T+0),69 km(T+24)and 107 km(T+48)based on 183,131 and 85 forecasts.This is the first study in which TC predictions of a regional model are simultaneously examined over the SCS,WNP and NIO basins through real-time experiments.
