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Abstract
Explosive cyclogenesis occurs on average once a year over the coast of New South Wales (NSW), Australia. Known locally as east coast lows, these storms are characterized by very strong winds and heavy rain. Intensity, size, proximity to the coast, and speed of movement of the cyclone are important in their impact on coastal NSW, especially Sydney. Predicting the location of the system, the maximum sustained wind speeds, and the rainfall totals all are operational forecasting challenges. Warnings are issued when predictions exceed threshold values. For example, land gale forecasts are issued if sustained wind speeds are expected to reach or exceed 34 kt (about 17 m s−1). The east coast low of 30–31 August 1996 featured land gales over the greater Sydney area. No warnings were issued as the forecasters estimated that the wind strength would fall below gale force. In this study, uncertainty in the predictions is estimated and reduced by providing, in addition to the routine single operational numerical weather prediction, a Monte Carlo–based short-range ensemble (SREF) approach. The intention is to improve the forecasts and also to provide valuable statistical information such as sea level pressure probability ellipses and estimates of the variances in the wind and rainfall predictions. For this event, both the unperturbed and ensemble forecasts predicted sustained maximum wind speeds in excess of 40 kt (20 m s−1) at the official Sydney observation station. However, the SREF provided vital additional information, namely, that over 70% of the forecasts were within one standard deviation (plus or minus 5 kt) of the mean. The SREF guidance therefore strongly supported the prediction of land gales. Moreover, although the ensemble forecast mean slightly underpredicted the rainfall total at Sydney, the forecast spread encompassed the observed 24-h total of 127 mm.
Abstract
Explosive cyclogenesis occurs on average once a year over the coast of New South Wales (NSW), Australia. Known locally as east coast lows, these storms are characterized by very strong winds and heavy rain. Intensity, size, proximity to the coast, and speed of movement of the cyclone are important in their impact on coastal NSW, especially Sydney. Predicting the location of the system, the maximum sustained wind speeds, and the rainfall totals all are operational forecasting challenges. Warnings are issued when predictions exceed threshold values. For example, land gale forecasts are issued if sustained wind speeds are expected to reach or exceed 34 kt (about 17 m s−1). The east coast low of 30–31 August 1996 featured land gales over the greater Sydney area. No warnings were issued as the forecasters estimated that the wind strength would fall below gale force. In this study, uncertainty in the predictions is estimated and reduced by providing, in addition to the routine single operational numerical weather prediction, a Monte Carlo–based short-range ensemble (SREF) approach. The intention is to improve the forecasts and also to provide valuable statistical information such as sea level pressure probability ellipses and estimates of the variances in the wind and rainfall predictions. For this event, both the unperturbed and ensemble forecasts predicted sustained maximum wind speeds in excess of 40 kt (20 m s−1) at the official Sydney observation station. However, the SREF provided vital additional information, namely, that over 70% of the forecasts were within one standard deviation (plus or minus 5 kt) of the mean. The SREF guidance therefore strongly supported the prediction of land gales. Moreover, although the ensemble forecast mean slightly underpredicted the rainfall total at Sydney, the forecast spread encompassed the observed 24-h total of 127 mm.
Abstract
A simple barotropic model is employed to investigate relative impacts on tropical cyclone motion forecasts in the Australian region when wind analyses from different tropospheric levels or layers are used as the input to the model. The model is initialized with selected horizontal wind analyses from individual pressure levels, and vertical averages of several pressure levels (layer-means).
The 48-h mean forecast errors (MFE) from this model are analyzed for 300 tropical cyclone cases that cover a wide range of intensities. A significant reduction in the track forecast errors results when the depth of the vertically-averaged initial wind analysis depends upon the initial storm intensity. Mean forecast errors show that the traditionally-utilized 1000-100-hPa deep layer-mean (DLM) analysis is a good approximation of future motion only in cases of very intense tropical cyclones. Shallower, lower-tropospheric layer-means consistently outperform single-level analyses, and are best correlated with future motion in weak and moderate intensity cases.
These results suggest that barotropic track forecasting in the Australian region can be significantly improved if the depth of the vertically-averaged initial wind analysis is based upon the tropical cyclone intensity.
Abstract
A simple barotropic model is employed to investigate relative impacts on tropical cyclone motion forecasts in the Australian region when wind analyses from different tropospheric levels or layers are used as the input to the model. The model is initialized with selected horizontal wind analyses from individual pressure levels, and vertical averages of several pressure levels (layer-means).
The 48-h mean forecast errors (MFE) from this model are analyzed for 300 tropical cyclone cases that cover a wide range of intensities. A significant reduction in the track forecast errors results when the depth of the vertically-averaged initial wind analysis depends upon the initial storm intensity. Mean forecast errors show that the traditionally-utilized 1000-100-hPa deep layer-mean (DLM) analysis is a good approximation of future motion only in cases of very intense tropical cyclones. Shallower, lower-tropospheric layer-means consistently outperform single-level analyses, and are best correlated with future motion in weak and moderate intensity cases.
These results suggest that barotropic track forecasting in the Australian region can be significantly improved if the depth of the vertically-averaged initial wind analysis is based upon the tropical cyclone intensity.
Abstract
It is shown that an optimal linear combination of independent forecasts of tropical cyclone tracks significantly reduces the mean forecast-position errors. In this study the independent forecasts are provided by a statistical scheme (CLIPER) and a numerical weather prediction (NWP) model operating over the Australian tropics.
A comparison is made between the optimal linear combination and four other forecast techniques, over the five Australian tropical cyclone seasons 1984/85–1987/88. The combination method gave a mean position error of 157 km at 24 h using independent “best track” data, an improvement of 15% over the next most accurate method. At 48 h, the mean position error of 312 km was 17% less than the next most accurate scheme.
The combination method was assessed further in a real-time trial on operational data during the 1988/89 Australian tropical cyclone season. The results of this trial confirmed the superiority of the combination technique over the other methods. It will be used operationally in the next Australian tropical cyclone season (1989/90) either in its present form or as part of an integrated “expert” system being developed specifically for tropical cyclone motion prediction.
Abstract
It is shown that an optimal linear combination of independent forecasts of tropical cyclone tracks significantly reduces the mean forecast-position errors. In this study the independent forecasts are provided by a statistical scheme (CLIPER) and a numerical weather prediction (NWP) model operating over the Australian tropics.
A comparison is made between the optimal linear combination and four other forecast techniques, over the five Australian tropical cyclone seasons 1984/85–1987/88. The combination method gave a mean position error of 157 km at 24 h using independent “best track” data, an improvement of 15% over the next most accurate method. At 48 h, the mean position error of 312 km was 17% less than the next most accurate scheme.
The combination method was assessed further in a real-time trial on operational data during the 1988/89 Australian tropical cyclone season. The results of this trial confirmed the superiority of the combination technique over the other methods. It will be used operationally in the next Australian tropical cyclone season (1989/90) either in its present form or as part of an integrated “expert” system being developed specifically for tropical cyclone motion prediction.
Abstract
During the spring and summer months, the southeast coast of Australia often experiences abrupt southerly wind changes, the leading edge being known locally as a “southerly buster.” The main characteristic of this phenomenon is the sudden shift in wind direction, usually from north or northwesterly to southerly. Associated with this wind surge is a significant temperature drop and sea level pressure rise. A severe southerly buster has wind speeds exceeding gale force (17 m s−1) and poses a threat to human safety.
Southerly busters have been the subject of a number of studies over several decades. These have focused on the development and propagation of the wind surge. The aims of this study are quite different, namely, to assess the ability of a real-time, high-resolution, numerical weather prediction (NWP) model to simulate some of the key features of the southerly buster, notably the time of passage and strength at various locations along the southeast coast and at two inland stations.
A large number (20) of case studies of southerly wind changes along the east coast of New South Wales has been selected to verify 40 simulations from the numerical model. The focus of the case studies was on quantifying the skill of the model in simulating the timing and speed of propagation of the southerly buster. The measure of skill adopted here was one based on a direct comparison of model predictions with observations. It was found that the performance of the model was good overall but was highly case dependent, particularly according to season and time of day, with some poor and some excellent simulations. This ability of the NWP model to provide predictions within an acceptable error has positive implications as a useful tool in real-time forecasting.
Abstract
During the spring and summer months, the southeast coast of Australia often experiences abrupt southerly wind changes, the leading edge being known locally as a “southerly buster.” The main characteristic of this phenomenon is the sudden shift in wind direction, usually from north or northwesterly to southerly. Associated with this wind surge is a significant temperature drop and sea level pressure rise. A severe southerly buster has wind speeds exceeding gale force (17 m s−1) and poses a threat to human safety.
Southerly busters have been the subject of a number of studies over several decades. These have focused on the development and propagation of the wind surge. The aims of this study are quite different, namely, to assess the ability of a real-time, high-resolution, numerical weather prediction (NWP) model to simulate some of the key features of the southerly buster, notably the time of passage and strength at various locations along the southeast coast and at two inland stations.
A large number (20) of case studies of southerly wind changes along the east coast of New South Wales has been selected to verify 40 simulations from the numerical model. The focus of the case studies was on quantifying the skill of the model in simulating the timing and speed of propagation of the southerly buster. The measure of skill adopted here was one based on a direct comparison of model predictions with observations. It was found that the performance of the model was good overall but was highly case dependent, particularly according to season and time of day, with some poor and some excellent simulations. This ability of the NWP model to provide predictions within an acceptable error has positive implications as a useful tool in real-time forecasting.
Abstract
Error characteristics of model output statistics (MOS) temperature forecasts are calculated for over 200 locations around the continental United States. The forecasts are verified on a station-by-station basis for the year 2001. Error measures used include mean algebraic error (bias), mean absolute error (MAE), relative frequency of occurrence of bias and MAE values, and the daily forecast errors themselves. A case study examining the spatial and temporal evolution of MOS errors is also presented.
The error characteristics presented here, together with the case study, provide a more detailed evaluation of MOS performance than may be obtained from regionally averaged error statistics. Knowledge concerning locations where MOS forecasts have large errors or biases and why those errors or biases exist is of great value to operational forecasters. Not only does such knowledge help improve their forecasts, but forecaster performance is often compared to MOS predictions. Examples of biases in MOS forecast errors are illustrated by examining two stations in detail. Significant warm and cold biases are found in maximum temperature forecasts for Los Angeles, California (LAX), and minimum temperature forecasts for Las Vegas, Nevada (LAS), respectively. MAE values for MOS temperature predictions calculated in this study suggest that coastal stations tend to have lower MAE values and lower variability in their errors, while forecasts with high MAE and error variability are more frequent in the interior of the United States. Therefore, MAE values from samples of MOS forecasts are directly proportional to the variance in the observations. Additionally, it is found that daily maximum temperature forecast errors exhibit less variability during the summer months than they do over the rest of the year, and that forecasts for any one station rarely follow a consistent temporal pattern for more than two or three consecutive days. These inconsistent error patterns indicate that forecasting temperatures based on recent trends in MOS forecast errors at an individual station is usually not a good strategy. As shown in earlier studies by other authors and demonstrated again here, MOS temperature forecasts are often inaccurate in the vicinity of strong temperature gradients, for locations affected by shallow cold air masses, or for stations in regions of anomalously warm or cold temperatures.
Finally, a case study is presented examining the spatial and temporal distributions of MOS temperature forecast errors across the United States from 13 to 15 February 2001. During this period, two surges of cold arctic air moved south into the United States. In contrast to error trends at individual stations, nationwide spatial and temporal patterns of MOS forecast errors could prove to be a powerful forecasting tool. Nationwide plots of errors in MOS forecasts would be useful if made available in real time to operational forecasters.
Abstract
Error characteristics of model output statistics (MOS) temperature forecasts are calculated for over 200 locations around the continental United States. The forecasts are verified on a station-by-station basis for the year 2001. Error measures used include mean algebraic error (bias), mean absolute error (MAE), relative frequency of occurrence of bias and MAE values, and the daily forecast errors themselves. A case study examining the spatial and temporal evolution of MOS errors is also presented.
The error characteristics presented here, together with the case study, provide a more detailed evaluation of MOS performance than may be obtained from regionally averaged error statistics. Knowledge concerning locations where MOS forecasts have large errors or biases and why those errors or biases exist is of great value to operational forecasters. Not only does such knowledge help improve their forecasts, but forecaster performance is often compared to MOS predictions. Examples of biases in MOS forecast errors are illustrated by examining two stations in detail. Significant warm and cold biases are found in maximum temperature forecasts for Los Angeles, California (LAX), and minimum temperature forecasts for Las Vegas, Nevada (LAS), respectively. MAE values for MOS temperature predictions calculated in this study suggest that coastal stations tend to have lower MAE values and lower variability in their errors, while forecasts with high MAE and error variability are more frequent in the interior of the United States. Therefore, MAE values from samples of MOS forecasts are directly proportional to the variance in the observations. Additionally, it is found that daily maximum temperature forecast errors exhibit less variability during the summer months than they do over the rest of the year, and that forecasts for any one station rarely follow a consistent temporal pattern for more than two or three consecutive days. These inconsistent error patterns indicate that forecasting temperatures based on recent trends in MOS forecast errors at an individual station is usually not a good strategy. As shown in earlier studies by other authors and demonstrated again here, MOS temperature forecasts are often inaccurate in the vicinity of strong temperature gradients, for locations affected by shallow cold air masses, or for stations in regions of anomalously warm or cold temperatures.
Finally, a case study is presented examining the spatial and temporal distributions of MOS temperature forecast errors across the United States from 13 to 15 February 2001. During this period, two surges of cold arctic air moved south into the United States. In contrast to error trends at individual stations, nationwide spatial and temporal patterns of MOS forecast errors could prove to be a powerful forecasting tool. Nationwide plots of errors in MOS forecasts would be useful if made available in real time to operational forecasters.
Abstract
The real-time prediction of the location, strength, and structure of the summertime heat trough is a major forecasting problem over Western Australia. Maximum temperatures, wind strength and direction along the west coast, low-level coastal cloud, and thunderstorm activity are vulnerable to forecast errors in the heat trough.
This study has three main parts. First, prediction errors of the operational Australian region numerical weather prediction (NWP) model were quantified over the period December 1991 to February 1992. Second, a newly developed regional NWP model, which will be the next operational regional model, was compared with the current operational model. The new model has more efficient numerics than the present operational model, allowing higher-resolution forecasts and a more sophisticated representation of physical processes. The third part was a set of sensitivity experiments to assess the relative importance of the differences.
The dominant errors in the current operational model are a large westward bias in the trough location, a wide spread of errors in the intensity of the low in the northern section of the heat trough, a sizable range of coastal pressure gradient errors, and a northward bias in the latitude of the subtropical ridge axis between longitudes 110° and 120°E. It was demonstrated that these errors are reduced significantly in the new model, especially the subtropical ridge error, which has been virtually eliminated. The sensitivity studies revealed the importance of each of the differences between the models, and that the relative impact varies from case to case.
Abstract
The real-time prediction of the location, strength, and structure of the summertime heat trough is a major forecasting problem over Western Australia. Maximum temperatures, wind strength and direction along the west coast, low-level coastal cloud, and thunderstorm activity are vulnerable to forecast errors in the heat trough.
This study has three main parts. First, prediction errors of the operational Australian region numerical weather prediction (NWP) model were quantified over the period December 1991 to February 1992. Second, a newly developed regional NWP model, which will be the next operational regional model, was compared with the current operational model. The new model has more efficient numerics than the present operational model, allowing higher-resolution forecasts and a more sophisticated representation of physical processes. The third part was a set of sensitivity experiments to assess the relative importance of the differences.
The dominant errors in the current operational model are a large westward bias in the trough location, a wide spread of errors in the intensity of the low in the northern section of the heat trough, a sizable range of coastal pressure gradient errors, and a northward bias in the latitude of the subtropical ridge axis between longitudes 110° and 120°E. It was demonstrated that these errors are reduced significantly in the new model, especially the subtropical ridge error, which has been virtually eliminated. The sensitivity studies revealed the importance of each of the differences between the models, and that the relative impact varies from case to case.
Abstract
The synoptic pattern over northeastern Australia is dominated in the warmer months by a ridge–trough system. Accurate prediction of the location of the system is a significant forecasting problem for regional and global operational models. The regional model that was operational at the time of this study exhibited two significant weaknesses characteristic of many current operational global models, a westward bias in the location of the east coast ridge and errors in the location and strength of the inland trough. The present investigation had three aims:to compute model location errors of the ridge–trough system from a large (6 month, twice daily) dataset of operational forecasts, to explain these errors by evaluating a new regional model, and to confirm the diagnosis using a series of case studies and sensitivity studies. The operational model had a marked mean westward bias of about 2° longitude in the location of both the trough and the ridge. There was a noticeable latitudinal distribution in trough errors with the greatest errors in the north. Ridge location errors were much larger in the south. Overall, almost 60% of errors were 2° longitude or greater. The new model was far more skillful in forecasting the ridge–trough system with predicted locations of both ridges and troughs being superior at greater than the 99% confidence level. In the new model a mean westward error remained in the location of the ridges and troughs but was less than 1°. The percentage of errors greater than 2° longitude dropped to about 20% for ridges and 35% for troughs. The decreased location errors in the new model are attributed to improved representation of the steep coastal orography and of the simulations of both the heat low and inland trough to the west of the coastal ranges. This was confirmed in three case studies at very high resolution (15 km) using the new model but with operational data and also in two sensitivity studies with the new model using the operational model forecast surface temperatures. The forecasts showed similar trough location problems to the operational model.
Abstract
The synoptic pattern over northeastern Australia is dominated in the warmer months by a ridge–trough system. Accurate prediction of the location of the system is a significant forecasting problem for regional and global operational models. The regional model that was operational at the time of this study exhibited two significant weaknesses characteristic of many current operational global models, a westward bias in the location of the east coast ridge and errors in the location and strength of the inland trough. The present investigation had three aims:to compute model location errors of the ridge–trough system from a large (6 month, twice daily) dataset of operational forecasts, to explain these errors by evaluating a new regional model, and to confirm the diagnosis using a series of case studies and sensitivity studies. The operational model had a marked mean westward bias of about 2° longitude in the location of both the trough and the ridge. There was a noticeable latitudinal distribution in trough errors with the greatest errors in the north. Ridge location errors were much larger in the south. Overall, almost 60% of errors were 2° longitude or greater. The new model was far more skillful in forecasting the ridge–trough system with predicted locations of both ridges and troughs being superior at greater than the 99% confidence level. In the new model a mean westward error remained in the location of the ridges and troughs but was less than 1°. The percentage of errors greater than 2° longitude dropped to about 20% for ridges and 35% for troughs. The decreased location errors in the new model are attributed to improved representation of the steep coastal orography and of the simulations of both the heat low and inland trough to the west of the coastal ranges. This was confirmed in three case studies at very high resolution (15 km) using the new model but with operational data and also in two sensitivity studies with the new model using the operational model forecast surface temperatures. The forecasts showed similar trough location problems to the operational model.
Abstract
Winter storms in the southern United States can significantly impact infrastructure and the economy. In this study, National Centers for Environmental Information Storm Event Database and local climate summaries, are used to develop a spatial climatology of freezing precipitation (freezing rain and ice pellets) and snow over the southern Great Plains, 1993–2011. Principal component analysis is performed on the 500-hPa height field, at the approximate onset time of precipitation, for 33 freezing precipitation and 42 snow case studies, to differentiate common synoptic flow fields associated with precipitation type. The five leading patterns for each precipitation type are retained. Composites of temperature, moisture, pressure, and wind fields are constructed and extended 24 h before and after precipitation initiation to track the storm system evolution. Many 500-hPa flow fields are similar for both precipitation types. However, snow-dominant events have stronger and/or more frequent surface cyclone development. Freezing precipitation is associated with the southward propagation of an Arctic anticyclone well ahead of precipitation, weak or absent surface cyclone formation, and a more western trough axis. High-impact ice storms in the region often have slow-moving upper-level flow, persistent isentropic ascent over a surface quasi-stationary front with strongly positive moisture anomalies, and warm layer airmass trajectories originating over the Gulf of Mexico. The results here are based on a relatively small sample size. However, this work is intended to be useful for forecasters, in particular as a pattern recognition aid in predicting the evolution of precipitation within southern Great Plains winter storms.
Abstract
Winter storms in the southern United States can significantly impact infrastructure and the economy. In this study, National Centers for Environmental Information Storm Event Database and local climate summaries, are used to develop a spatial climatology of freezing precipitation (freezing rain and ice pellets) and snow over the southern Great Plains, 1993–2011. Principal component analysis is performed on the 500-hPa height field, at the approximate onset time of precipitation, for 33 freezing precipitation and 42 snow case studies, to differentiate common synoptic flow fields associated with precipitation type. The five leading patterns for each precipitation type are retained. Composites of temperature, moisture, pressure, and wind fields are constructed and extended 24 h before and after precipitation initiation to track the storm system evolution. Many 500-hPa flow fields are similar for both precipitation types. However, snow-dominant events have stronger and/or more frequent surface cyclone development. Freezing precipitation is associated with the southward propagation of an Arctic anticyclone well ahead of precipitation, weak or absent surface cyclone formation, and a more western trough axis. High-impact ice storms in the region often have slow-moving upper-level flow, persistent isentropic ascent over a surface quasi-stationary front with strongly positive moisture anomalies, and warm layer airmass trajectories originating over the Gulf of Mexico. The results here are based on a relatively small sample size. However, this work is intended to be useful for forecasters, in particular as a pattern recognition aid in predicting the evolution of precipitation within southern Great Plains winter storms.
Abstract
The aim of this paper is to assess the ability of a numerical weather prediction model to simulate cold fronts over southeastern Australia. A total of nine summertime fronts is studied with the research version of the Australian Bureau of Meteorology's operational numerical weather prediction model. In each case it is shown that the simulations produce a well-defined frontal trough at the current operational resolution of 150 km, though in all cases the simulated movement lagged that in the atmosphere. Model statistics such as skill scores and rms errors have a large degree of spatial organization and tend to be associated with errors in frontal speed more than with poor representation of frontal structure. Increasing model resolution to 50 km produces an improved frontal structure but does not significantly alter the simulation of frontal position. Various diagnostics including vertical cross sections, isentropic relative flow fields and near-surface fields of ζ, |∇θ|, vertical velocity, horizontal convergence, Q vectors, and the frontogenesis function are presented for the simulated fronts. Consistent structural relationships are shown to exist between these fields. The front is seen as part of a larger-scale trough extending through the depth of the troposphere, and its location and movement occur in association with significant quasigeostrophic forcing. The line of maximum cyclonic ζ corresponds most closely to the surface wind shift line, and this feature represents the most unambiguous means of defining the front from the model fields. In situations where the manual analyses gave the front a double structure including a prefrontal trough, the numerical analysis-prognosis system combined these into one sharp trough. Cross sections normal to the frontal surface reveal much deeper cold air and a stronger and deeper warm-air jet than the equivalent east-west sections. Isentropic relative flow diagnostics reveal close agreement with the equivalent diagnostics in the Australian Cold Fronts Research Programme.
Abstract
The aim of this paper is to assess the ability of a numerical weather prediction model to simulate cold fronts over southeastern Australia. A total of nine summertime fronts is studied with the research version of the Australian Bureau of Meteorology's operational numerical weather prediction model. In each case it is shown that the simulations produce a well-defined frontal trough at the current operational resolution of 150 km, though in all cases the simulated movement lagged that in the atmosphere. Model statistics such as skill scores and rms errors have a large degree of spatial organization and tend to be associated with errors in frontal speed more than with poor representation of frontal structure. Increasing model resolution to 50 km produces an improved frontal structure but does not significantly alter the simulation of frontal position. Various diagnostics including vertical cross sections, isentropic relative flow fields and near-surface fields of ζ, |∇θ|, vertical velocity, horizontal convergence, Q vectors, and the frontogenesis function are presented for the simulated fronts. Consistent structural relationships are shown to exist between these fields. The front is seen as part of a larger-scale trough extending through the depth of the troposphere, and its location and movement occur in association with significant quasigeostrophic forcing. The line of maximum cyclonic ζ corresponds most closely to the surface wind shift line, and this feature represents the most unambiguous means of defining the front from the model fields. In situations where the manual analyses gave the front a double structure including a prefrontal trough, the numerical analysis-prognosis system combined these into one sharp trough. Cross sections normal to the frontal surface reveal much deeper cold air and a stronger and deeper warm-air jet than the equivalent east-west sections. Isentropic relative flow diagnostics reveal close agreement with the equivalent diagnostics in the Australian Cold Fronts Research Programme.
