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- Author or Editor: Wei Yu x
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Abstract
An incremental analysis update (IAU) scheme is successfully implemented into a WRF/WRFDA-based hourly cycling data assimilation system with the goal to reduce the imbalance introduced by the high-frequency intermittent data assimilation, especially when radar data are included. With the application of IAU, the analysis increment is smoothly introduced into the model integration over a time window centered at the analysis time. As in digital filter initialization (DFI), the IAU scheme is able to limit large shocks in the early part of a model forecast. Compared to DFI, IAU does better in hydrometeor spinup and produces more continuous precipitation forecasts from cycle to cycle. The run with IAU is shown to improve the precipitation forecast skills (10+% for CSI scores) compared to the regular cycling forecasts without IAU. The data assimilation system with IAU is also able to accept more observations due to balanced first-guess fields. Comparable results are obtained in IAU tests when the time-varying weights are used versus constant weights. Because of its better property, the IAU with the time-varying weights is implemented in the operational system.
Abstract
An incremental analysis update (IAU) scheme is successfully implemented into a WRF/WRFDA-based hourly cycling data assimilation system with the goal to reduce the imbalance introduced by the high-frequency intermittent data assimilation, especially when radar data are included. With the application of IAU, the analysis increment is smoothly introduced into the model integration over a time window centered at the analysis time. As in digital filter initialization (DFI), the IAU scheme is able to limit large shocks in the early part of a model forecast. Compared to DFI, IAU does better in hydrometeor spinup and produces more continuous precipitation forecasts from cycle to cycle. The run with IAU is shown to improve the precipitation forecast skills (10+% for CSI scores) compared to the regular cycling forecasts without IAU. The data assimilation system with IAU is also able to accept more observations due to balanced first-guess fields. Comparable results are obtained in IAU tests when the time-varying weights are used versus constant weights. Because of its better property, the IAU with the time-varying weights is implemented in the operational system.
Abstract
Based on daily rainfall observations and Japanese 25-year Reanalysis Project data during ~1981–2010, a three-dimensional circulation structure that formed before heavy summer rainfall in central north China (CNC) is revealed in this study. Composite analyses of circulation in advance of 225 heavy rain days show that the circulation structure is characterized by a remarkable upper-tropospheric warm anomaly (UTWA), which covers most of northern China with a center at ~300 hPa. Under hydrostatic and geostrophic equilibriums, the UTWA contributes to the generation of an anticyclonic (cyclonic) anomaly above (below). The anticyclonic anomaly strengthens (weakens) westerly winds to the north (south) of the warm center and pushes the high-level westerly jet to the north. The cyclonic anomaly deepens the trough upstream of CNC and intensifies lower southwesterly winds to the mideast of the warm center. As a result, the northerly stretched high-level jet produces upper divergence in its right-front side and the intensified southwesterly winds induce lower moisture convergence in its left-front side, causing heavy rainfall in CNC. Correlation analyses further confirm the close connections between UTWA and circulation in the upper and lower troposphere. The correlation coefficients between UTWA and the upper geopotential height, upper westerly jet, and lower southerly flow reach 0.95, 0.70, and 0.39, implying that the two critical factors leading to intense rainfall in CNC, the high-level jet and the low-level southerly flow, are closely connected with the UTWA. Consequently, in the future analyses and forecasts of heavy rainfall over northern China, more attention should be paid to the temperature in the upper troposphere.
Abstract
Based on daily rainfall observations and Japanese 25-year Reanalysis Project data during ~1981–2010, a three-dimensional circulation structure that formed before heavy summer rainfall in central north China (CNC) is revealed in this study. Composite analyses of circulation in advance of 225 heavy rain days show that the circulation structure is characterized by a remarkable upper-tropospheric warm anomaly (UTWA), which covers most of northern China with a center at ~300 hPa. Under hydrostatic and geostrophic equilibriums, the UTWA contributes to the generation of an anticyclonic (cyclonic) anomaly above (below). The anticyclonic anomaly strengthens (weakens) westerly winds to the north (south) of the warm center and pushes the high-level westerly jet to the north. The cyclonic anomaly deepens the trough upstream of CNC and intensifies lower southwesterly winds to the mideast of the warm center. As a result, the northerly stretched high-level jet produces upper divergence in its right-front side and the intensified southwesterly winds induce lower moisture convergence in its left-front side, causing heavy rainfall in CNC. Correlation analyses further confirm the close connections between UTWA and circulation in the upper and lower troposphere. The correlation coefficients between UTWA and the upper geopotential height, upper westerly jet, and lower southerly flow reach 0.95, 0.70, and 0.39, implying that the two critical factors leading to intense rainfall in CNC, the high-level jet and the low-level southerly flow, are closely connected with the UTWA. Consequently, in the future analyses and forecasts of heavy rainfall over northern China, more attention should be paid to the temperature in the upper troposphere.
Abstract
As Typhoon Goni (2015) passed over Ishigaki Island, a maximum gust speed of 71 m s−1 was observed by a surface weather station. During Typhoon Goni’s passage, mountaintop radar recorded antenna elevation angle oscillations, with a maximum amplitude of ~0.2° at an elevation angle of 0.2°. This oscillation phenomenon was reflected in the reflectivity and Doppler velocity fields as Typhoon Goni’s eyewall encompassed Ishigaki Island. The main antenna oscillation period was approximately 0.21–0.38 s under an antenna rotational speed of ~4 rpm. The estimated fundamental vibration period of the radar tower is approximately 0.25–0.44 s, which is comparable to the predominant antenna oscillation period and agrees with the expected wind-induced vibrations of buildings. The reflectivity field at the 0.2° elevation angle exhibited a phase shift signature and a negative correlation of −0.5 with the antenna oscillation, associated with the negative vertical gradient of reflectivity. FFT analysis revealed two antenna oscillation periods at 0955–1205 and 1335–1445 UTC 23 August 2015. The oscillation phenomenon ceased between these two periods because Typhoon Goni’s eye moved over the radar site. The VAD analysis-estimated wind speeds at a range of 1 km for these two antenna oscillation periods exceeded 45 m s−1, with a maximum value of approximately 70 m s−1. A bandpass filter QC procedure is proposed to filter out the predominant wavenumbers (between 40 and 70) for the reflectivity and Doppler velocity fields. The proposed QC procedure is indicated to be capable of mitigating the major signals resulting from antenna oscillations.
Abstract
As Typhoon Goni (2015) passed over Ishigaki Island, a maximum gust speed of 71 m s−1 was observed by a surface weather station. During Typhoon Goni’s passage, mountaintop radar recorded antenna elevation angle oscillations, with a maximum amplitude of ~0.2° at an elevation angle of 0.2°. This oscillation phenomenon was reflected in the reflectivity and Doppler velocity fields as Typhoon Goni’s eyewall encompassed Ishigaki Island. The main antenna oscillation period was approximately 0.21–0.38 s under an antenna rotational speed of ~4 rpm. The estimated fundamental vibration period of the radar tower is approximately 0.25–0.44 s, which is comparable to the predominant antenna oscillation period and agrees with the expected wind-induced vibrations of buildings. The reflectivity field at the 0.2° elevation angle exhibited a phase shift signature and a negative correlation of −0.5 with the antenna oscillation, associated with the negative vertical gradient of reflectivity. FFT analysis revealed two antenna oscillation periods at 0955–1205 and 1335–1445 UTC 23 August 2015. The oscillation phenomenon ceased between these two periods because Typhoon Goni’s eye moved over the radar site. The VAD analysis-estimated wind speeds at a range of 1 km for these two antenna oscillation periods exceeded 45 m s−1, with a maximum value of approximately 70 m s−1. A bandpass filter QC procedure is proposed to filter out the predominant wavenumbers (between 40 and 70) for the reflectivity and Doppler velocity fields. The proposed QC procedure is indicated to be capable of mitigating the major signals resulting from antenna oscillations.
Abstract
Tropical cyclone (TC) genesis is a problem of great significance in climate and weather research. Although various environmental conditions necessary for TC genesis have been recognized for a long time, prediction of TC genesis remains a challenge due to complex and stochastic processes involved during TC genesis. Different from traditional statistical and dynamical modeling of TC genesis, in this study, a machine learning framework is developed to determine whether a mesoscale convective system (MCS) would evolve into a tropical cyclone. The machine learning models 1) are built upon a number of essential environmental predictors associated with MCSs/TCs, 2) predict whether MCSs can become TCs at different lead times, and 3) provide information about the relative importance of each predictor, which can be conducive to discovering new aspects of TC genesis. The results indicate that the machine learning classifier, AdaBoost, is able to achieve a 97.2% F1-score accuracy in predicting TC genesis over the entire tropics at a 6-h lead time using a comprehensive set of environmental predictors. A robust performance can still be attained when the lead time is extended to 12, 24, and 48 h, and when this machine learning classifier is separately applied to the North Atlantic Ocean and the western North Pacific Ocean. In contrast, the conventional approach based on the genesis potential index can have no more than an 80% F1-score accuracy. Furthermore, the machine learning classifier suggests that the low-level vorticity and genesis potential index are the most important predictors to TC genesis, which is consistent with previous discoveries.
Abstract
Tropical cyclone (TC) genesis is a problem of great significance in climate and weather research. Although various environmental conditions necessary for TC genesis have been recognized for a long time, prediction of TC genesis remains a challenge due to complex and stochastic processes involved during TC genesis. Different from traditional statistical and dynamical modeling of TC genesis, in this study, a machine learning framework is developed to determine whether a mesoscale convective system (MCS) would evolve into a tropical cyclone. The machine learning models 1) are built upon a number of essential environmental predictors associated with MCSs/TCs, 2) predict whether MCSs can become TCs at different lead times, and 3) provide information about the relative importance of each predictor, which can be conducive to discovering new aspects of TC genesis. The results indicate that the machine learning classifier, AdaBoost, is able to achieve a 97.2% F1-score accuracy in predicting TC genesis over the entire tropics at a 6-h lead time using a comprehensive set of environmental predictors. A robust performance can still be attained when the lead time is extended to 12, 24, and 48 h, and when this machine learning classifier is separately applied to the North Atlantic Ocean and the western North Pacific Ocean. In contrast, the conventional approach based on the genesis potential index can have no more than an 80% F1-score accuracy. Furthermore, the machine learning classifier suggests that the low-level vorticity and genesis potential index are the most important predictors to TC genesis, which is consistent with previous discoveries.
