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- Author or Editor: Chung-Yu Wu x
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Abstract
Subseasonal variations in warm-season (May–August) precipitation over the central and eastern United States are shown to be strongly linked to variations in the moisture entering the continent from the Gulf of Mexico within a longitudinally confined “channel” (referred to here as the Texas corridor or TC). These variations reflect the development of low-level southerly wind maxima (or jets) on a number of different timescales in association with distinct subcontinental and larger-scale phenomena. On the diurnal timescale, the TC moisture flux variations are tied to the development of the Great Plains low-level jet. The composite nighttime anomalies are characterized by a strong southerly moisture flux covering northeast Mexico and the southern Great Plains, and enhanced boundary layer convergence and precipitation over much of the upper Great Plains. The strongest jets tend to be associated with an anomalous surface low over the Great Plains, reflecting a predilection for periods when midlatitude weather systems are positioned to produce enhanced southerly flow over this region. On subsynoptic (2–4 days) timescales the TC moisture flux variations are associated with the development and evolution of a warm-season lee cyclone. These systems, which are most prevalent during the early part of the warm season (May and June), form over the central Great Plains in association with an upper-level shortwave and enhanced upper-tropospheric cross-mountain westerly flow. A low-level southerly wind maximum or jet develops underneath and perpendicular to the advancing edge of enhanced midtropospheric westerlies. The clash of anomalous southerly moisture flux and a deep intrusion of anomalous northerly low-level winds results in enhanced precipitation eventually stretching from Texas to the Great Lakes. On synoptic (4–8 days) timescales the TC moisture flux variations are associated with the propagation and intensification of a warm-season midlatitude cyclone. This system, which also occurs preferentially during May and June, develops offshore and intensifies as it crosses the Rocky Mountains and taps moisture from the Gulf of Mexico. Low-level southerly wind anomalies develop parallel to the mid- and upper-level winds on the leading edge of the trough. Widespread precipitation anomalies move with the propagating system with reduced rainfall occurring over the anomalous surface high, and enhanced rainfall occurring over the anomalous surface low. On still longer timescales (8–16 days) the variations in the TC moisture transport are tied to slow eastward-moving systems. The evolution and structure of the mid- and low-level winds are similar to those of the synoptic-scale system with, however, a somewhat larger zonal scale and spatially more diffuse southerly moisture flux and precipitation anomalies.
Abstract
Subseasonal variations in warm-season (May–August) precipitation over the central and eastern United States are shown to be strongly linked to variations in the moisture entering the continent from the Gulf of Mexico within a longitudinally confined “channel” (referred to here as the Texas corridor or TC). These variations reflect the development of low-level southerly wind maxima (or jets) on a number of different timescales in association with distinct subcontinental and larger-scale phenomena. On the diurnal timescale, the TC moisture flux variations are tied to the development of the Great Plains low-level jet. The composite nighttime anomalies are characterized by a strong southerly moisture flux covering northeast Mexico and the southern Great Plains, and enhanced boundary layer convergence and precipitation over much of the upper Great Plains. The strongest jets tend to be associated with an anomalous surface low over the Great Plains, reflecting a predilection for periods when midlatitude weather systems are positioned to produce enhanced southerly flow over this region. On subsynoptic (2–4 days) timescales the TC moisture flux variations are associated with the development and evolution of a warm-season lee cyclone. These systems, which are most prevalent during the early part of the warm season (May and June), form over the central Great Plains in association with an upper-level shortwave and enhanced upper-tropospheric cross-mountain westerly flow. A low-level southerly wind maximum or jet develops underneath and perpendicular to the advancing edge of enhanced midtropospheric westerlies. The clash of anomalous southerly moisture flux and a deep intrusion of anomalous northerly low-level winds results in enhanced precipitation eventually stretching from Texas to the Great Lakes. On synoptic (4–8 days) timescales the TC moisture flux variations are associated with the propagation and intensification of a warm-season midlatitude cyclone. This system, which also occurs preferentially during May and June, develops offshore and intensifies as it crosses the Rocky Mountains and taps moisture from the Gulf of Mexico. Low-level southerly wind anomalies develop parallel to the mid- and upper-level winds on the leading edge of the trough. Widespread precipitation anomalies move with the propagating system with reduced rainfall occurring over the anomalous surface high, and enhanced rainfall occurring over the anomalous surface low. On still longer timescales (8–16 days) the variations in the TC moisture transport are tied to slow eastward-moving systems. The evolution and structure of the mid- and low-level winds are similar to those of the synoptic-scale system with, however, a somewhat larger zonal scale and spatially more diffuse southerly moisture flux and precipitation anomalies.
Abstract
Forecast skill from dynamical forecast models decreases quickly with projection time due to various errors. Therefore, postprocessing methods, from simple bias correction methods to more complicated multiple linear regression–based model output statistics, are used to improve raw model forecasts. Usually, these methods show clear forecast improvement over the raw model forecasts, especially for short-range weather forecasts. However, linear approaches have limitations because the relationship between predictands and predictors may be nonlinear. This is even truer for extended range forecasts, such as week-3–4 forecasts. In this study, neural network techniques are used to seek or model the relationships between a set of predictors and predictands, and eventually to improve week-3–4 precipitation and 2-m temperature forecasts made by the NOAA/NCEP Climate Forecast System. Benefitting from advances in machine learning techniques in recent years, more flexible and capable machine learning algorithms and availability of big datasets enable us not only to explore nonlinear features or relationships within a given large dataset, but also to extract more sophisticated pattern relationships and covariabilities hidden within the multidimensional predictors and predictands. Then these more sophisticated relationships and high-level statistical information are used to correct the model week-3–4 precipitation and 2-m temperature forecasts. The results show that to some extent neural network techniques can significantly improve the week-3–4 forecast accuracy and greatly increase the efficiency over the traditional multiple linear regression methods.
Abstract
Forecast skill from dynamical forecast models decreases quickly with projection time due to various errors. Therefore, postprocessing methods, from simple bias correction methods to more complicated multiple linear regression–based model output statistics, are used to improve raw model forecasts. Usually, these methods show clear forecast improvement over the raw model forecasts, especially for short-range weather forecasts. However, linear approaches have limitations because the relationship between predictands and predictors may be nonlinear. This is even truer for extended range forecasts, such as week-3–4 forecasts. In this study, neural network techniques are used to seek or model the relationships between a set of predictors and predictands, and eventually to improve week-3–4 precipitation and 2-m temperature forecasts made by the NOAA/NCEP Climate Forecast System. Benefitting from advances in machine learning techniques in recent years, more flexible and capable machine learning algorithms and availability of big datasets enable us not only to explore nonlinear features or relationships within a given large dataset, but also to extract more sophisticated pattern relationships and covariabilities hidden within the multidimensional predictors and predictands. Then these more sophisticated relationships and high-level statistical information are used to correct the model week-3–4 precipitation and 2-m temperature forecasts. The results show that to some extent neural network techniques can significantly improve the week-3–4 forecast accuracy and greatly increase the efficiency over the traditional multiple linear regression methods.
