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Abstract
The effects of multiple mesoscale convective systems (MCSs) on the formation of Typhoon Ketsana (2003) are analyzed in this study. Numerical simulations using the Weather Research and Forecasting (WRF) model with assimilation of Quick Scatterometer (QuikSCAT) and Special Sensor Microwave Imager (SSM/I) oceanic winds and total precipitable water are performed. The WRF model simulates well the large-scale features, the convective episodes associated with the MCSs and their periods of development, and the formation time and location of Ketsana. With the successive occurrence of MCSs, midlevel average relative vorticity is strengthened through generation of mesoscale convective vortices (MCVs) mainly via the vertical stretching mechanism. Scale separation shows that the activity of the vortical hot tower (VHT)-type meso-γ-scale vortices correlated well with the development of the MCSs. These VHTs have large values of positive relative vorticity induced by intense low-level convergence, and thus play an important role in the low-level vortex enhancement with aggregation of VHTs as one of the possible mechanisms.
Four sensitivity experiments are performed to analyze the possible different roles of the MCSs during the formation of Ketsana by modifying the vertical relative humidity profile in each MCS and consequently the strength of convection within. The results show that the development of an MCS depends substantially on that of the prior ones through remoistening of the midtroposphere, and thus leading to different scenarios of system intensification during the tropical cyclone (TC) formation. The earlier MCSs are responsible for the first stage vortex enhancement, and depending on the location can affect quite largely the simulated formation location. The extreme convection within the last MCS before formation largely determines the formation time.
Abstract
The effects of multiple mesoscale convective systems (MCSs) on the formation of Typhoon Ketsana (2003) are analyzed in this study. Numerical simulations using the Weather Research and Forecasting (WRF) model with assimilation of Quick Scatterometer (QuikSCAT) and Special Sensor Microwave Imager (SSM/I) oceanic winds and total precipitable water are performed. The WRF model simulates well the large-scale features, the convective episodes associated with the MCSs and their periods of development, and the formation time and location of Ketsana. With the successive occurrence of MCSs, midlevel average relative vorticity is strengthened through generation of mesoscale convective vortices (MCVs) mainly via the vertical stretching mechanism. Scale separation shows that the activity of the vortical hot tower (VHT)-type meso-γ-scale vortices correlated well with the development of the MCSs. These VHTs have large values of positive relative vorticity induced by intense low-level convergence, and thus play an important role in the low-level vortex enhancement with aggregation of VHTs as one of the possible mechanisms.
Four sensitivity experiments are performed to analyze the possible different roles of the MCSs during the formation of Ketsana by modifying the vertical relative humidity profile in each MCS and consequently the strength of convection within. The results show that the development of an MCS depends substantially on that of the prior ones through remoistening of the midtroposphere, and thus leading to different scenarios of system intensification during the tropical cyclone (TC) formation. The earlier MCSs are responsible for the first stage vortex enhancement, and depending on the location can affect quite largely the simulated formation location. The extreme convection within the last MCS before formation largely determines the formation time.
Abstract
This study extends an earlier dynamical initialization (DI) scheme for tropical cyclones (TCs) to situations under the influence of terrain. When any terrain lower than 1 km exists between 150 and 450 km from the TC center, topographic variables are defined and a filtering algorithm is used to remove noise due to the presence of terrain before the vortex separation is conducted. When any terrain higher than 1 km exists between 150 and 300 km from the TC center, or the TC center is within 150 km of land, a semi-idealized integration without the terrain is conducted to spin up an axisymmetric TC vortex before the inclusion of the terrain and the merging of the TC vortex with the large-scale analysis field. In addition, a procedure for the vortex size/intensity adjustment is introduced to reduce the initial errors before the forecast run. Two sets of hindcasts, one without (CTRL run) and one with the new DI scheme (DI run), are conducted for nine TCs affected by terrain over the western North Pacific in 2015. Results show that the new DI scheme largely reduces the initial position and intensity errors. The 72-h position errors and the intensity errors up to the 36-h forecasts are smaller in DI runs than in CTRL runs and smaller than those from the HWRF forecasts for the same TCs as well. The new DI scheme is also shown to produce the TC inner-core structure and rainbands more consistent with satellite and radar observations.
Abstract
This study extends an earlier dynamical initialization (DI) scheme for tropical cyclones (TCs) to situations under the influence of terrain. When any terrain lower than 1 km exists between 150 and 450 km from the TC center, topographic variables are defined and a filtering algorithm is used to remove noise due to the presence of terrain before the vortex separation is conducted. When any terrain higher than 1 km exists between 150 and 300 km from the TC center, or the TC center is within 150 km of land, a semi-idealized integration without the terrain is conducted to spin up an axisymmetric TC vortex before the inclusion of the terrain and the merging of the TC vortex with the large-scale analysis field. In addition, a procedure for the vortex size/intensity adjustment is introduced to reduce the initial errors before the forecast run. Two sets of hindcasts, one without (CTRL run) and one with the new DI scheme (DI run), are conducted for nine TCs affected by terrain over the western North Pacific in 2015. Results show that the new DI scheme largely reduces the initial position and intensity errors. The 72-h position errors and the intensity errors up to the 36-h forecasts are smaller in DI runs than in CTRL runs and smaller than those from the HWRF forecasts for the same TCs as well. The new DI scheme is also shown to produce the TC inner-core structure and rainbands more consistent with satellite and radar observations.
Abstract
Combining dynamical model output and statistical information in historical observations is an innovative approach to predicting severe or extreme weather. In this study, in order to examine a dynamical–statistical method for precipitation forecasting of landfalling tropical cyclones (TC), an objective TC track similarity area index (TSAI) is developed. TSAI represents an area of the enclosed scope surrounded by two TC tracks and two line segments connecting the initiating and ending points of the two tracks. The smaller the TSAI value, the greater the similarity of the two TC tracks, where a value of 0 indicates that the two tracks overlap completely. The TSAI is then preliminarily applied to a precipitation forecast test of landfalling TCs over South China. Given the considerable progress made in TC track forecasting over past few decades, TC track forecast products are also used. Through this test, a track-similarity-based landfalling TC precipitation dynamical–statistical ensemble forecast (LTP_DSEF) model is established, which consists of four steps: adopting the predicted TC track, determining the TC track similarity, checking the seasonal similarity, and making an ensemble prediction. Its application to the precipitation forecasts of landfalling TCs over South China reveals that the LTP_DSEF model is superior to three numerical weather prediction models (i.e., ECMWF, GFS, and T639/China), especially for intense precipitation at large thresholds (i.e., 100 or 250 mm) in both the training (2012–14) and independent (2015–16) samples.
Abstract
Combining dynamical model output and statistical information in historical observations is an innovative approach to predicting severe or extreme weather. In this study, in order to examine a dynamical–statistical method for precipitation forecasting of landfalling tropical cyclones (TC), an objective TC track similarity area index (TSAI) is developed. TSAI represents an area of the enclosed scope surrounded by two TC tracks and two line segments connecting the initiating and ending points of the two tracks. The smaller the TSAI value, the greater the similarity of the two TC tracks, where a value of 0 indicates that the two tracks overlap completely. The TSAI is then preliminarily applied to a precipitation forecast test of landfalling TCs over South China. Given the considerable progress made in TC track forecasting over past few decades, TC track forecast products are also used. Through this test, a track-similarity-based landfalling TC precipitation dynamical–statistical ensemble forecast (LTP_DSEF) model is established, which consists of four steps: adopting the predicted TC track, determining the TC track similarity, checking the seasonal similarity, and making an ensemble prediction. Its application to the precipitation forecasts of landfalling TCs over South China reveals that the LTP_DSEF model is superior to three numerical weather prediction models (i.e., ECMWF, GFS, and T639/China), especially for intense precipitation at large thresholds (i.e., 100 or 250 mm) in both the training (2012–14) and independent (2015–16) samples.
Abstract
The characteristics of 24-h official forecast errors (OFEs) of tropical cyclone (TC) intensity are analyzed over the North Atlantic, east Pacific, and western North Pacific. The OFE is demonstrated to be strongly anticorrelated with TC intensity change with correlation coefficients of −0.77, −0.77, and −0.68 for the three basins, respectively. The 24-h intensity change in the official forecast closely follows a Gaussian distribution with a standard deviation only ⅔ of that in nature, suggesting the current official forecasts estimate fewer cases of large intensity change. The intensifying systems tend to produce negative errors (underforecast), while weakening systems have consistent positive errors (overforecast). This asymmetrical bias is larger for extreme intensity change, including rapid intensification (RI) and rapid weakening (RW). To understand this behavior, the errors are analyzed in a simple objective model, the trend-persistence model (TPM). The TPM exhibits the same error-intensity change correlation. In the TPM, the error can be understood as it is exactly inversely proportional to the finite difference form of the concavity or second derivative of the intensity–time curve. The occurrence of large negative (positive) errors indicates the intensity–time curve is concave upward (downward) in nature during the TC’s rapid intensification (weakening) process. Thus, the fundamental feature of the OFE distribution is related to the shape of the intensity–time curve, governed by TC dynamics. All forecast systems have difficulty forecasting an accelerating rate of change, or a large second derivative of the intensity–time curve. TPM may also be useful as a baseline in evaluating the skill of official forecasts. According to this baseline, official forecasts are more skillful in RW than in RI.
Abstract
The characteristics of 24-h official forecast errors (OFEs) of tropical cyclone (TC) intensity are analyzed over the North Atlantic, east Pacific, and western North Pacific. The OFE is demonstrated to be strongly anticorrelated with TC intensity change with correlation coefficients of −0.77, −0.77, and −0.68 for the three basins, respectively. The 24-h intensity change in the official forecast closely follows a Gaussian distribution with a standard deviation only ⅔ of that in nature, suggesting the current official forecasts estimate fewer cases of large intensity change. The intensifying systems tend to produce negative errors (underforecast), while weakening systems have consistent positive errors (overforecast). This asymmetrical bias is larger for extreme intensity change, including rapid intensification (RI) and rapid weakening (RW). To understand this behavior, the errors are analyzed in a simple objective model, the trend-persistence model (TPM). The TPM exhibits the same error-intensity change correlation. In the TPM, the error can be understood as it is exactly inversely proportional to the finite difference form of the concavity or second derivative of the intensity–time curve. The occurrence of large negative (positive) errors indicates the intensity–time curve is concave upward (downward) in nature during the TC’s rapid intensification (weakening) process. Thus, the fundamental feature of the OFE distribution is related to the shape of the intensity–time curve, governed by TC dynamics. All forecast systems have difficulty forecasting an accelerating rate of change, or a large second derivative of the intensity–time curve. TPM may also be useful as a baseline in evaluating the skill of official forecasts. According to this baseline, official forecasts are more skillful in RW than in RI.
Abstract
This study analyzes the microphysics of convective cells in an outer rainband of Typhoon Nida (2016) using data collected by a newly upgraded operational polarimetric radar in China. The life cycle of these convective cells is divided into three stages: developing, mature, and decaying according to the intensity of the corresponding updraft. Composite analysis shows that deep columns of Z DR and K DP collocate well with the enhanced updraft as the cells develop to their mature stage. A layered microphysical structure is observed in the ice region with riming near the −5°C level within the updraft, aggregation around the −15°C level, and deposition anywhere above the 0°C level. These ice-phase microphysical processes are important pathways of particle growth in the outer rainbands. In particular, riming contributes significantly to surface heavy rainfall. These contrast to previously documented inner rainbands, where warm-rain processes are the predominant pathway of particle growth.
Abstract
This study analyzes the microphysics of convective cells in an outer rainband of Typhoon Nida (2016) using data collected by a newly upgraded operational polarimetric radar in China. The life cycle of these convective cells is divided into three stages: developing, mature, and decaying according to the intensity of the corresponding updraft. Composite analysis shows that deep columns of Z DR and K DP collocate well with the enhanced updraft as the cells develop to their mature stage. A layered microphysical structure is observed in the ice region with riming near the −5°C level within the updraft, aggregation around the −15°C level, and deposition anywhere above the 0°C level. These ice-phase microphysical processes are important pathways of particle growth in the outer rainbands. In particular, riming contributes significantly to surface heavy rainfall. These contrast to previously documented inner rainbands, where warm-rain processes are the predominant pathway of particle growth.
The Beijing 2008 Olympics Research and Development Project (B08RDP), initiated in 2004 under the World Meteorological Organization (WMO) World Weather Research Programme (WWRP), undertook the research and development of mesoscale ensemble prediction systems (MEPSs) and their application to weather forecast support during the Beijing Olympic Games. Six MEPSs from six countries, representing the state-of-the-art regional EPSs with near-real-time capabilities and emphasizing on the 6–36-h forecast lead times, participated in the project.
The background, objectives, and implementation of B08RDP, as well as the six MEPSs, are reviewed. The accomplishments are summarized, which include 1) providing value-added service to the Olympic Games, 2) advancing MEPS-related research, 3) accelerating the transition from research to operations, and 4) training forecasters in utilizing forecast uncertainty products. The B08RDP has fulfilled its research (MEPS development) and demonstration (value-added service) purposes. The research conducted covers the areas of verification, examining the value of MEPS relative to other numerical weather prediction (NWP) systems, combining multimodel or multicenter ensembles, bias correction, ensemble perturbations [initial condition (IC), lateral boundary condition (LBC), land surface IC, and model physics], downscaling, forecast applications, data assimilation, and storm-scale ensemble modeling. Seven scientific issues important to MEPS have been identified. It is recognized that the daily use of forecast uncertainty information by forecasters remains a challenge. Development of forecaster-friendly products and training activities should be a long-term effort and needs to be continuously enhanced.
The B08RDP dataset is also a valuable asset to the research community. The experience gained in international collaboration, organization, and implementation of a multination regional EPS for a common goal and to address common scientific issues can be shared by the ongoing projects The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble—Limited Area Models (TIGGE-LAM) and North American Ensemble Forecast System—Limited Area Models (NAEFS-LAM).
The Beijing 2008 Olympics Research and Development Project (B08RDP), initiated in 2004 under the World Meteorological Organization (WMO) World Weather Research Programme (WWRP), undertook the research and development of mesoscale ensemble prediction systems (MEPSs) and their application to weather forecast support during the Beijing Olympic Games. Six MEPSs from six countries, representing the state-of-the-art regional EPSs with near-real-time capabilities and emphasizing on the 6–36-h forecast lead times, participated in the project.
The background, objectives, and implementation of B08RDP, as well as the six MEPSs, are reviewed. The accomplishments are summarized, which include 1) providing value-added service to the Olympic Games, 2) advancing MEPS-related research, 3) accelerating the transition from research to operations, and 4) training forecasters in utilizing forecast uncertainty products. The B08RDP has fulfilled its research (MEPS development) and demonstration (value-added service) purposes. The research conducted covers the areas of verification, examining the value of MEPS relative to other numerical weather prediction (NWP) systems, combining multimodel or multicenter ensembles, bias correction, ensemble perturbations [initial condition (IC), lateral boundary condition (LBC), land surface IC, and model physics], downscaling, forecast applications, data assimilation, and storm-scale ensemble modeling. Seven scientific issues important to MEPS have been identified. It is recognized that the daily use of forecast uncertainty information by forecasters remains a challenge. Development of forecaster-friendly products and training activities should be a long-term effort and needs to be continuously enhanced.
The B08RDP dataset is also a valuable asset to the research community. The experience gained in international collaboration, organization, and implementation of a multination regional EPS for a common goal and to address common scientific issues can be shared by the ongoing projects The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble—Limited Area Models (TIGGE-LAM) and North American Ensemble Forecast System—Limited Area Models (NAEFS-LAM).
Abstract
Landfalling tropical cyclones (TCs) often experience drastic changes in their motion, intensity, and structure due to complex multiscale interactions among atmospheric processes and among the coastal ocean, land, and atmosphere. Because of the lack of comprehensive data and low capability of numerical models, understanding of and ability to predict landfalling TCs are still limited. A 10-yr key research project on landfalling TCs was initiated and launched in 2009 in China. The project has been jointly supported by the China Ministry of Science and Technology, China Meteorological Administration (CMA), Ministry of Education, and Chinese Academy of Sciences. Its mission is to enhance understanding of landfalling TC processes and improve forecasting skills on track, intensity, and distributions of strong winds and precipitation in landfalling TCs. This article provides an overview of the project, together with highlights of some new findings and new technical developments, as well as planned future efforts.
Abstract
Landfalling tropical cyclones (TCs) often experience drastic changes in their motion, intensity, and structure due to complex multiscale interactions among atmospheric processes and among the coastal ocean, land, and atmosphere. Because of the lack of comprehensive data and low capability of numerical models, understanding of and ability to predict landfalling TCs are still limited. A 10-yr key research project on landfalling TCs was initiated and launched in 2009 in China. The project has been jointly supported by the China Ministry of Science and Technology, China Meteorological Administration (CMA), Ministry of Education, and Chinese Academy of Sciences. Its mission is to enhance understanding of landfalling TC processes and improve forecasting skills on track, intensity, and distributions of strong winds and precipitation in landfalling TCs. This article provides an overview of the project, together with highlights of some new findings and new technical developments, as well as planned future efforts.
