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Abstract
In this study, the development of an extreme precipitation event along the southeastern margin of the Tibetan Plateau (TP) by the approach of Tropical Cyclone (TC) Rashmi (2008) from the Bay of Bengal is examined using a global reanalysis and all available observations. Results show the importance of an anomalous southerly flow, resulting from the merging of Rashmi into a meridionally deep trough at the western periphery of a subtropical high, in steering the storm and transporting tropical warm–moist air, thereby supplying necessary moisture for precipitation production over the TP. A mesoscale data analysis reveals that (i) the Rashmi vortex maintained its TC identity during its northward movement in the warm sector with weak-gradient flows; (ii) the extreme precipitation event occurred under potentially stable conditions; (iii) topographical uplifting of the southerly warm–moist air, enhanced by the approaching vortex with some degree of slantwise instability, led to the development of heavy to extreme precipitation along the southeastern margin of the TP; and (iv) the most influential uplifting of the intense vortex flows carrying ample moisture over steep topography favored the generation of the record-breaking daily snowfall of 98 mm (in water depth), and daily precipitation of 87 mm with rain–snow–rain changeovers at two high-elevation stations, respectively. The extreme precipitation and phase changeovers could be uncovered by an unusual upper-air sounding that shows a profound saturated layer from the surface to upper troposphere with a moist adiabatic upper 100-hPa layer and a bottom 100-hPa melting layer. The results appear to have important implications to the forecast of TC-related heavy precipitation over high mountains.
Significance Statement
This study attempts to gain insight into the multiscale dynamical processes leading to the development of an extreme precipitation event over the southeastern margin of the Tibet Plateau as a Bay of Bengal tropical cyclone (TC) approached. Results show (i) the importance of an anomalous southerly flow with a wide zonal span in steering the relatively large-sized TC and transporting necessary moisture into the region; and (ii) the subsequent uplifting of the warm and moist TC vortex by steep topography, producing the extreme precipitation event under potentially stable conditions, especially the record-breaking daily snowfall of 98 mm (in water depth). The results have important implications to the forecast of TC-related heavy precipitation over the Tibet Plateau and other high mountainous regions.
Abstract
In this study, the development of an extreme precipitation event along the southeastern margin of the Tibetan Plateau (TP) by the approach of Tropical Cyclone (TC) Rashmi (2008) from the Bay of Bengal is examined using a global reanalysis and all available observations. Results show the importance of an anomalous southerly flow, resulting from the merging of Rashmi into a meridionally deep trough at the western periphery of a subtropical high, in steering the storm and transporting tropical warm–moist air, thereby supplying necessary moisture for precipitation production over the TP. A mesoscale data analysis reveals that (i) the Rashmi vortex maintained its TC identity during its northward movement in the warm sector with weak-gradient flows; (ii) the extreme precipitation event occurred under potentially stable conditions; (iii) topographical uplifting of the southerly warm–moist air, enhanced by the approaching vortex with some degree of slantwise instability, led to the development of heavy to extreme precipitation along the southeastern margin of the TP; and (iv) the most influential uplifting of the intense vortex flows carrying ample moisture over steep topography favored the generation of the record-breaking daily snowfall of 98 mm (in water depth), and daily precipitation of 87 mm with rain–snow–rain changeovers at two high-elevation stations, respectively. The extreme precipitation and phase changeovers could be uncovered by an unusual upper-air sounding that shows a profound saturated layer from the surface to upper troposphere with a moist adiabatic upper 100-hPa layer and a bottom 100-hPa melting layer. The results appear to have important implications to the forecast of TC-related heavy precipitation over high mountains.
Significance Statement
This study attempts to gain insight into the multiscale dynamical processes leading to the development of an extreme precipitation event over the southeastern margin of the Tibet Plateau as a Bay of Bengal tropical cyclone (TC) approached. Results show (i) the importance of an anomalous southerly flow with a wide zonal span in steering the relatively large-sized TC and transporting necessary moisture into the region; and (ii) the subsequent uplifting of the warm and moist TC vortex by steep topography, producing the extreme precipitation event under potentially stable conditions, especially the record-breaking daily snowfall of 98 mm (in water depth). The results have important implications to the forecast of TC-related heavy precipitation over the Tibet Plateau and other high mountainous regions.
Abstract
The real-time multivariate Madden–Julian oscillation (MJO) (RMM) index has now been widely applied as a standard in operational subseasonal prediction and monitoring. Its calculation procedures involve the extraction of major intraseasonal variability (ISV) by subtracting the prior 120-day mean. However, this study uncovers that such a real-time strategy artificially creates unwanted low-frequency variability (LFVartificial) that might cause nonnegligible influences on the RMM amplitude and phase. Compared to the real LFV, the LFVartificial explains more (∼70% in boreal summer) of the residual LFV (LFVresidual) in the RMM index. It occupies 33% of all days that the LFVresidual explains more than one-half of total RMM amplitude, 19% that the LFV contribution exceeds ISV, and 10% that the LFVartificial-associated RMM amplitude surpasses 0.8. The RMM-defined “MJO” is obscured by the LFVresidual in such a way that the eastward-propagating mode is stronger and bigger with a slower phase speed, as compared with the “true” MJO derived from the 20–100-day filtered data. The interference effects of LFVresidual on the MJO might be particularly strong when the background state is changing rapidly with time. However, these issues can be well avoided when one chooses to remove the centered 120-day mean, as evidenced by the largely reduced three percentages (17%, 8%, and 1%) mentioned above in the so-derived index. These results give us a reminder that more attention should be paid to monitoring or predicting an MJO using the RMM index in a rapidly changing low-frequency background or in the boreal summer.
Significance Statement
The real-time multivariate MJO (RMM) index has been widely applied in the monitoring and prediction of the MJO, the major tropical intraseasonal variability influencing global weather and climate. Using observational analysis, we reveal that there exist such scenarios (∼16%) when large-amplitude RMM indices do not represent a strong MJO, mainly due to the obscuring effect of residual, while largely artificial, low-frequency variability introduced by the RMM calculation procedures. This finding is of great significance as it informs the research community that serious caution should be given when relating large RMM amplitude to the MJO, especially in a condition when the low-frequency background state is rapidly changing with time or in the boreal summer.
Abstract
The real-time multivariate Madden–Julian oscillation (MJO) (RMM) index has now been widely applied as a standard in operational subseasonal prediction and monitoring. Its calculation procedures involve the extraction of major intraseasonal variability (ISV) by subtracting the prior 120-day mean. However, this study uncovers that such a real-time strategy artificially creates unwanted low-frequency variability (LFVartificial) that might cause nonnegligible influences on the RMM amplitude and phase. Compared to the real LFV, the LFVartificial explains more (∼70% in boreal summer) of the residual LFV (LFVresidual) in the RMM index. It occupies 33% of all days that the LFVresidual explains more than one-half of total RMM amplitude, 19% that the LFV contribution exceeds ISV, and 10% that the LFVartificial-associated RMM amplitude surpasses 0.8. The RMM-defined “MJO” is obscured by the LFVresidual in such a way that the eastward-propagating mode is stronger and bigger with a slower phase speed, as compared with the “true” MJO derived from the 20–100-day filtered data. The interference effects of LFVresidual on the MJO might be particularly strong when the background state is changing rapidly with time. However, these issues can be well avoided when one chooses to remove the centered 120-day mean, as evidenced by the largely reduced three percentages (17%, 8%, and 1%) mentioned above in the so-derived index. These results give us a reminder that more attention should be paid to monitoring or predicting an MJO using the RMM index in a rapidly changing low-frequency background or in the boreal summer.
Significance Statement
The real-time multivariate MJO (RMM) index has been widely applied in the monitoring and prediction of the MJO, the major tropical intraseasonal variability influencing global weather and climate. Using observational analysis, we reveal that there exist such scenarios (∼16%) when large-amplitude RMM indices do not represent a strong MJO, mainly due to the obscuring effect of residual, while largely artificial, low-frequency variability introduced by the RMM calculation procedures. This finding is of great significance as it informs the research community that serious caution should be given when relating large RMM amplitude to the MJO, especially in a condition when the low-frequency background state is rapidly changing with time or in the boreal summer.
ABSTRACT
This study focuses on different evolutions of the low-level atmospheric circulations between eastern Pacific (EP) El Niño and central Pacific-II (CP-II) El Niño. The western North Pacific anomalous anticyclone (WNPAC) originates from the northern South China Sea for EP El Niño, and moves to the western North Pacific (WNP) afterward. Compared with EP El Niño, the origin of the WNPAC is farther west during CP-II El Niño, with the center over the Indochina Peninsula. Moreover, the WNPAC shows a weaker eastward shift. Such discrepancies are attributed to different evolutions of the cyclonic response over the WNP, which can suppress the convection in the western flank of the anomalous cyclone. The eastward retreat of the anomalous cyclone is significant for EP El Niño, but less evident for CP-II El Niño. These discrepancies are related to zonal evolutions of the increased precipitation over the equatorial Pacific. Following the southward migration of the intertropical convergence zone (ITCZ), the deep-convection region extends eastward along the equator, reinforcing the atmospheric response to the eastern Pacific warming in EP El Niño. For CP-II El Niño, the atmospheric response is insignificant over the eastern Pacific without warming. Moreover, the meridional migration of the ITCZ can modulate zonal variations of the easterly trade wind and specific humidity as well. Due to the combined effects of the climatological background and atmospheric anomalies, the specific humidity–induced and wind-induced moist enthalpy advection contribute to different shifts of the precipitation center.
ABSTRACT
This study focuses on different evolutions of the low-level atmospheric circulations between eastern Pacific (EP) El Niño and central Pacific-II (CP-II) El Niño. The western North Pacific anomalous anticyclone (WNPAC) originates from the northern South China Sea for EP El Niño, and moves to the western North Pacific (WNP) afterward. Compared with EP El Niño, the origin of the WNPAC is farther west during CP-II El Niño, with the center over the Indochina Peninsula. Moreover, the WNPAC shows a weaker eastward shift. Such discrepancies are attributed to different evolutions of the cyclonic response over the WNP, which can suppress the convection in the western flank of the anomalous cyclone. The eastward retreat of the anomalous cyclone is significant for EP El Niño, but less evident for CP-II El Niño. These discrepancies are related to zonal evolutions of the increased precipitation over the equatorial Pacific. Following the southward migration of the intertropical convergence zone (ITCZ), the deep-convection region extends eastward along the equator, reinforcing the atmospheric response to the eastern Pacific warming in EP El Niño. For CP-II El Niño, the atmospheric response is insignificant over the eastern Pacific without warming. Moreover, the meridional migration of the ITCZ can modulate zonal variations of the easterly trade wind and specific humidity as well. Due to the combined effects of the climatological background and atmospheric anomalies, the specific humidity–induced and wind-induced moist enthalpy advection contribute to different shifts of the precipitation center.
Abstract
Accurate spatiotemporal predictions of surface soil moisture (SM) are important for many critical applications. Machine learning models provide a powerful method for building an accurate and reliable predictive model of SM. However, the models used in recent studies have some limitations, including lack of spatial autocorrelation (SAC), vague representation of important features, and primarily focused on the one-step forecast. Thus, we proposed an attention-based convolutional long short-term memory model (AttConvLSTM) for multistep forecasting. The model includes three layers, spatial compression, axial attention, and encoder–decoder prediction, which are used for compressing spatial information, feature extraction, and multistep prediction, respectively. The model was trained using surface SM from the Soil Moisture Active Passive L4 product at 18-km spatial resolution over the United States. The results show that AttConvLSTM predicts 24 h ahead SM with mean R 2 and RMSE is equal to 0.82 and 0.02, respectively. Compared with LSTM, AttConvLSTM improves the model performance over 73.6% of regions, with an improvement of 8.4% and 17.4% in R2 and RMSE, respectively. The performance of the model is mainly influenced by temporal autocorrelation (TAC). Moreover, we also highlight the importance of SAC on model performance, especially over regions with high SAC and low TAC. Our model is also competent for SM predictions from several hours to several days, which could be a useful tool for predicting all meteorological variables and forecasting extremes.
Abstract
Accurate spatiotemporal predictions of surface soil moisture (SM) are important for many critical applications. Machine learning models provide a powerful method for building an accurate and reliable predictive model of SM. However, the models used in recent studies have some limitations, including lack of spatial autocorrelation (SAC), vague representation of important features, and primarily focused on the one-step forecast. Thus, we proposed an attention-based convolutional long short-term memory model (AttConvLSTM) for multistep forecasting. The model includes three layers, spatial compression, axial attention, and encoder–decoder prediction, which are used for compressing spatial information, feature extraction, and multistep prediction, respectively. The model was trained using surface SM from the Soil Moisture Active Passive L4 product at 18-km spatial resolution over the United States. The results show that AttConvLSTM predicts 24 h ahead SM with mean R 2 and RMSE is equal to 0.82 and 0.02, respectively. Compared with LSTM, AttConvLSTM improves the model performance over 73.6% of regions, with an improvement of 8.4% and 17.4% in R2 and RMSE, respectively. The performance of the model is mainly influenced by temporal autocorrelation (TAC). Moreover, we also highlight the importance of SAC on model performance, especially over regions with high SAC and low TAC. Our model is also competent for SM predictions from several hours to several days, which could be a useful tool for predicting all meteorological variables and forecasting extremes.
Abstract
Subseasonal to seasonal (S2S) weather forecasting has made significant advances and several products have been made available. However, to date few studies utilize these products to extend the hydrological forecast time range. This study evaluates S2S precipitation from eight model ensembles in the hydrological simulation of extreme events at the catchment scale. A superior bias correction method is used to correct the bias of S2S precipitation for hydrological forecasts, and the results are compared with direct bias correction of hydrological forecasts using raw precipitation forecasts as input. The study shows that the S2S models can skillfully forecast daily precipitation within a lead time of 11 days. The S2S precipitation data from the European Centre for Medium-Range Weather Forecasts (ECMWF), Korea Meteorological Administration (KMA), and United Kingdom’s Met Office (UKMO) models present lower mean error than that of other models and have higher correlation coefficients with observations. Precipitation data from the ECMWF, KMA, and UKMO models also perform better than that of other models in simulating multiple-day precipitation processes. The bias correction method effectively reduces the mean error of daily S2S precipitation for all models while also improving the correlation with observations. Moreover, this study found that the bias correction procedure can apply to either precipitation or streamflow simulations for improving the hydrological forecasts, even though the degree of improvement is dependent on the hydrological variables. Overall, S2S precipitation has a potential to be applied for hydrological forecasts, and a superior bias correction method can increase the forecasts’ reliability, although further studies are still needed to confirm its effect.
Abstract
Subseasonal to seasonal (S2S) weather forecasting has made significant advances and several products have been made available. However, to date few studies utilize these products to extend the hydrological forecast time range. This study evaluates S2S precipitation from eight model ensembles in the hydrological simulation of extreme events at the catchment scale. A superior bias correction method is used to correct the bias of S2S precipitation for hydrological forecasts, and the results are compared with direct bias correction of hydrological forecasts using raw precipitation forecasts as input. The study shows that the S2S models can skillfully forecast daily precipitation within a lead time of 11 days. The S2S precipitation data from the European Centre for Medium-Range Weather Forecasts (ECMWF), Korea Meteorological Administration (KMA), and United Kingdom’s Met Office (UKMO) models present lower mean error than that of other models and have higher correlation coefficients with observations. Precipitation data from the ECMWF, KMA, and UKMO models also perform better than that of other models in simulating multiple-day precipitation processes. The bias correction method effectively reduces the mean error of daily S2S precipitation for all models while also improving the correlation with observations. Moreover, this study found that the bias correction procedure can apply to either precipitation or streamflow simulations for improving the hydrological forecasts, even though the degree of improvement is dependent on the hydrological variables. Overall, S2S precipitation has a potential to be applied for hydrological forecasts, and a superior bias correction method can increase the forecasts’ reliability, although further studies are still needed to confirm its effect.
Abstract
This study assesses the ability of the Coupled Model Intercomparison Project phase 5 (CMIP5) simulations in capturing the interdecadal precipitation enhancement over the Yangtze River valley (YRV) and investigates the contributions of Arctic temperature and mid- to high-latitude warming to the interdecadal variability of the East Asian summer monsoon rainfall. Six CMIP5 historical simulations including models from the Canadian Centre for Climate Modeling and Analysis (CCCma), the Beijing Climate Center, the Max Planck Institute for Meteorology, the Meteorological Research Institute, the Met Office Hadley Centre, and NCAR are used. The NCEP–NCAR reanalysis and observed precipitation are also used for comparison. Among the six CMIP5 simulations, only CCCma can approximately simulate the enhancement of interdecadal summer precipitation over the YRV in 1990–2005 relative to 1960–75; the various relationships between the summer precipitation and surface temperature (Ts ), 850-hPa winds, and 500-hPa height field (H500); and the relationships between Ts and H500 determined using regression, correlation, and singular value decomposition (SVD) analyses. It is found that CCCma can reasonably simulate the interdecadal surface warming over the boreal mid- to high latitudes in winter, spring, and summer. The summer Baikal blocking anomaly is postulated to be the bridge that links the winter and spring surface warming over the mid- to high latitude and Arctic with the enhancement of summer precipitation over the YRV. Models that missed some or all of these relationships found in CCCma and the reanalysis failed to simulate the interdecadal enhancement of precipitation over the YRV. This points to the importance of Arctic and mid- to high-latitude processes on the interdecadal variability of the East Asian summer monsoon and the challenge for global climate models to correctly simulate the linkages.
Abstract
This study assesses the ability of the Coupled Model Intercomparison Project phase 5 (CMIP5) simulations in capturing the interdecadal precipitation enhancement over the Yangtze River valley (YRV) and investigates the contributions of Arctic temperature and mid- to high-latitude warming to the interdecadal variability of the East Asian summer monsoon rainfall. Six CMIP5 historical simulations including models from the Canadian Centre for Climate Modeling and Analysis (CCCma), the Beijing Climate Center, the Max Planck Institute for Meteorology, the Meteorological Research Institute, the Met Office Hadley Centre, and NCAR are used. The NCEP–NCAR reanalysis and observed precipitation are also used for comparison. Among the six CMIP5 simulations, only CCCma can approximately simulate the enhancement of interdecadal summer precipitation over the YRV in 1990–2005 relative to 1960–75; the various relationships between the summer precipitation and surface temperature (Ts ), 850-hPa winds, and 500-hPa height field (H500); and the relationships between Ts and H500 determined using regression, correlation, and singular value decomposition (SVD) analyses. It is found that CCCma can reasonably simulate the interdecadal surface warming over the boreal mid- to high latitudes in winter, spring, and summer. The summer Baikal blocking anomaly is postulated to be the bridge that links the winter and spring surface warming over the mid- to high latitude and Arctic with the enhancement of summer precipitation over the YRV. Models that missed some or all of these relationships found in CCCma and the reanalysis failed to simulate the interdecadal enhancement of precipitation over the YRV. This points to the importance of Arctic and mid- to high-latitude processes on the interdecadal variability of the East Asian summer monsoon and the challenge for global climate models to correctly simulate the linkages.
Evaluation of a Support Vector Machine–Based Single-Doppler Wind Retrieval AlgorithmAbstract
Wind retrieval algorithms are required for Doppler weather radars. In this article, a new wind retrieval algorithm of single-Doppler radar with a support vector machine (SVM) is analyzed and compared with the original algorithm with the least squares technique. Through an analysis of coefficient matrices of equations corresponding to the optimization problems for the two algorithms, the new algorithm, which contains a proper penalization parameter, is found to effectively reduce the condition numbers of the matrices and thus has the ability to acquire accurate results, and the smaller the analysis volume is, the smaller the condition number of the matrix. This characteristic makes the new algorithm suitable to retrieve mesoscale and small-scale and high-resolution wind fields. Afterward, the two algorithms are applied to retrieval experiments to implement a comparison and a discussion. The results show that the penalization parameter cannot be too small, otherwise it may cause a large condition number; it cannot be too large either, otherwise it may change the properties of equations, leading to retrieved wind direction along the radial direction. Compared with the original algorithm, the new algorithm has definite superiority with the appropriate penalization parameters for small analysis volumes. When the suggested small analysis volume dimensions and penalization parameter values are adopted, the retrieval accuracy can be improved by 10 times more than the traditional method. As a result, the new algorithm has the capability to analyze the dynamical structures of severe weather, which needs high-resolution retrieval, and the potential for quantitative applications such as the assimilation in numerical models, but the retrieval accuracy needs to be further improved in the future.
Abstract
Wind retrieval algorithms are required for Doppler weather radars. In this article, a new wind retrieval algorithm of single-Doppler radar with a support vector machine (SVM) is analyzed and compared with the original algorithm with the least squares technique. Through an analysis of coefficient matrices of equations corresponding to the optimization problems for the two algorithms, the new algorithm, which contains a proper penalization parameter, is found to effectively reduce the condition numbers of the matrices and thus has the ability to acquire accurate results, and the smaller the analysis volume is, the smaller the condition number of the matrix. This characteristic makes the new algorithm suitable to retrieve mesoscale and small-scale and high-resolution wind fields. Afterward, the two algorithms are applied to retrieval experiments to implement a comparison and a discussion. The results show that the penalization parameter cannot be too small, otherwise it may cause a large condition number; it cannot be too large either, otherwise it may change the properties of equations, leading to retrieved wind direction along the radial direction. Compared with the original algorithm, the new algorithm has definite superiority with the appropriate penalization parameters for small analysis volumes. When the suggested small analysis volume dimensions and penalization parameter values are adopted, the retrieval accuracy can be improved by 10 times more than the traditional method. As a result, the new algorithm has the capability to analyze the dynamical structures of severe weather, which needs high-resolution retrieval, and the potential for quantitative applications such as the assimilation in numerical models, but the retrieval accuracy needs to be further improved in the future.
Abstract
Ningaloo Niño—the interannually occurring warming episode in the southeast Indian Ocean (SEIO)—has strong signatures in ocean temperature and circulation and exerts profound impacts on regional climate and marine biosystems. Analysis of observational data and eddy-resolving regional ocean model simulations reveals that the Ningaloo Niño/Niña can also induce pronounced variability in ocean salinity, causing large-scale sea surface salinity (SSS) freshening of 0.15–0.20 psu in the SEIO during its warm phase. Model experiments are performed to understand the underlying processes. This SSS freshening is mutually caused by the increased local precipitation (~68%) and enhanced freshwater transport of the Indonesian Throughflow (ITF; ~28%) during Ningaloo Niño events. The effects of other processes, such as local winds and evaporation, are secondary (~18%). The ITF enhances the southward freshwater advection near the eastern boundary, which is critical in causing the strong freshening (>0.20 psu) near the Western Australian coast. Owing to the strong modulation effect of the ITF, SSS near the coast bears a higher correlation with El Niño–Southern Oscillation (0.57, 0.77, and 0.70 with the Niño-3, Niño-4, and Niño-3.4 indices, respectively) than sea surface temperature (−0.27, −0.42, and −0.35) during 1993–2016. Yet, an idealized model experiment with artificial damping for salinity anomaly indicates that ocean salinity has limited impact on ocean near-surface stratification and thus minimal feedback effect on the warming of Ningaloo Niño.
Abstract
Ningaloo Niño—the interannually occurring warming episode in the southeast Indian Ocean (SEIO)—has strong signatures in ocean temperature and circulation and exerts profound impacts on regional climate and marine biosystems. Analysis of observational data and eddy-resolving regional ocean model simulations reveals that the Ningaloo Niño/Niña can also induce pronounced variability in ocean salinity, causing large-scale sea surface salinity (SSS) freshening of 0.15–0.20 psu in the SEIO during its warm phase. Model experiments are performed to understand the underlying processes. This SSS freshening is mutually caused by the increased local precipitation (~68%) and enhanced freshwater transport of the Indonesian Throughflow (ITF; ~28%) during Ningaloo Niño events. The effects of other processes, such as local winds and evaporation, are secondary (~18%). The ITF enhances the southward freshwater advection near the eastern boundary, which is critical in causing the strong freshening (>0.20 psu) near the Western Australian coast. Owing to the strong modulation effect of the ITF, SSS near the coast bears a higher correlation with El Niño–Southern Oscillation (0.57, 0.77, and 0.70 with the Niño-3, Niño-4, and Niño-3.4 indices, respectively) than sea surface temperature (−0.27, −0.42, and −0.35) during 1993–2016. Yet, an idealized model experiment with artificial damping for salinity anomaly indicates that ocean salinity has limited impact on ocean near-surface stratification and thus minimal feedback effect on the warming of Ningaloo Niño.
Abstract
While fixed covariance localization can greatly increase the reliability of the background error covariance in filtering by suppressing the long-distance spurious correlations evaluated by a finite ensemble, it may degrade the assimilation quality in an ensemble Kalman filter (EnKF) as a result of restricted longwave information. Tuning an optimal cutoff distance is usually very expensive and time consuming, especially for a general circulation model (GCM). Here the authors present an approach to compensate the demerit in fixed localization. At each analysis step, after the standard EnKF is done, a multiple-scale analysis technique is used to extract longwave information from the observational residual (referred to the EnKF ensemble mean). Within a biased twin-experiment framework consisting of a global barotropical spectral model and an idealized observing system, the performance of the new method is examined. Compared to a standard EnKF, the hybrid method is superior when an overly small/large cutoff distance is used, and it has less dependence on cutoff distance. The new scheme is also able to improve short-term weather forecasts, especially when an overly large cutoff distance is used. Sensitivity studies show that caution should be taken when the new scheme is applied to a dense observing system with an overly small cutoff distance in filtering. In addition, the new scheme has a nearly equivalent computational cost to the standard EnKF; thus, it is particularly suitable for GCM applications.
Abstract
While fixed covariance localization can greatly increase the reliability of the background error covariance in filtering by suppressing the long-distance spurious correlations evaluated by a finite ensemble, it may degrade the assimilation quality in an ensemble Kalman filter (EnKF) as a result of restricted longwave information. Tuning an optimal cutoff distance is usually very expensive and time consuming, especially for a general circulation model (GCM). Here the authors present an approach to compensate the demerit in fixed localization. At each analysis step, after the standard EnKF is done, a multiple-scale analysis technique is used to extract longwave information from the observational residual (referred to the EnKF ensemble mean). Within a biased twin-experiment framework consisting of a global barotropical spectral model and an idealized observing system, the performance of the new method is examined. Compared to a standard EnKF, the hybrid method is superior when an overly small/large cutoff distance is used, and it has less dependence on cutoff distance. The new scheme is also able to improve short-term weather forecasts, especially when an overly large cutoff distance is used. Sensitivity studies show that caution should be taken when the new scheme is applied to a dense observing system with an overly small cutoff distance in filtering. In addition, the new scheme has a nearly equivalent computational cost to the standard EnKF; thus, it is particularly suitable for GCM applications.
Abstract
This study investigates the classification of developing and nondeveloping tropical disturbances in the western North Pacific (WNP) through the C4.5 algorithm. A decision tree is built based on this algorithm and can be used as a tool to predict future tropical cyclone (TC) genesis events. The results show that the maximum 800-hPa relative vorticity, SST, precipitation rate, divergence averaged between 1000- and 500-hPa levels, and 300-hPa air temperature anomaly are the five most important variables for separating the developing and nondeveloping tropical disturbances. This algorithm also unravels the thresholds of the five variables (i.e., 4.2 × 10−5 s−1 for maximum 800-hPa relative vorticity, 28.2°C for SST, 0.1 mm h−1 for precipitation rate, −0.7 × 10−6 s−1 for vertically averaged convergence, and 0.5°C for 300-hPa air temperature anomaly). Six rules are derived from the decision tree. The classification accuracy of this decision tree is 81.7% for the 2004–10 cases. The hindcast accuracy for the 2011–13 dataset is 84.6%.
Abstract
This study investigates the classification of developing and nondeveloping tropical disturbances in the western North Pacific (WNP) through the C4.5 algorithm. A decision tree is built based on this algorithm and can be used as a tool to predict future tropical cyclone (TC) genesis events. The results show that the maximum 800-hPa relative vorticity, SST, precipitation rate, divergence averaged between 1000- and 500-hPa levels, and 300-hPa air temperature anomaly are the five most important variables for separating the developing and nondeveloping tropical disturbances. This algorithm also unravels the thresholds of the five variables (i.e., 4.2 × 10−5 s−1 for maximum 800-hPa relative vorticity, 28.2°C for SST, 0.1 mm h−1 for precipitation rate, −0.7 × 10−6 s−1 for vertically averaged convergence, and 0.5°C for 300-hPa air temperature anomaly). Six rules are derived from the decision tree. The classification accuracy of this decision tree is 81.7% for the 2004–10 cases. The hindcast accuracy for the 2011–13 dataset is 84.6%.
