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Abstract
Tropical cyclone (TC) activity over the southeast Indian Ocean has been studied far less than other TC basins, such as the North Atlantic and northwest Pacific. The authors examine the interannual TC variability of the northwest Australian (NWAUS) subbasin (0°–35°S, 105°–135°E), using an Australian TC dataset for the 39-yr period of 1970–2008. Thirteen TC metrics are assessed, with emphasis on annual TC frequencies and total TC days.
Major findings are that for the NWAUS subbasin, there are annual means of 5.6 TCs and 42.4 TC days, with corresponding small standard deviations of 2.3 storms and 20.0 days. For intense TCs (WMO category 3 and higher), the annual mean TC frequency is 3.0, with a standard deviation of 1.6, and the annual average intense TC days is 7.6 days, with a standard deviation of 4.5 days. There are no significant linear trends in either mean annual TC frequencies or TC days. Notably, all 13 variability metrics show no trends over the 39-yr period and are less dependent upon standard El Niño–Southern Oscillation (ENSO) variables than many other TC basins, including the rest of the Australian region basin. The largest correlations with TC frequency were geopotential heights for June–August at 925 hPa over the South Atlantic Ocean (r = −0.65) and for April–June at 700 hPa over North America (−0.64). For TC days the largest correlations are geopotential heights for July–September at 1000 hPa over the South Atlantic Ocean (−0.7) and for April–June at 850 hPa over North America (−0.58). Last, wavelet analyses of annual TC frequencies and TC days reveal periodicities at ENSO and decadal time scales. However, the TC dataset is too short for conclusive evidence of multidecadal periodicities.
Given the large correlations revealed by this study, developing and testing of a multivariate seasonal TC prediction scheme has commenced, with lead times up to 6 months.
Abstract
Tropical cyclone (TC) activity over the southeast Indian Ocean has been studied far less than other TC basins, such as the North Atlantic and northwest Pacific. The authors examine the interannual TC variability of the northwest Australian (NWAUS) subbasin (0°–35°S, 105°–135°E), using an Australian TC dataset for the 39-yr period of 1970–2008. Thirteen TC metrics are assessed, with emphasis on annual TC frequencies and total TC days.
Major findings are that for the NWAUS subbasin, there are annual means of 5.6 TCs and 42.4 TC days, with corresponding small standard deviations of 2.3 storms and 20.0 days. For intense TCs (WMO category 3 and higher), the annual mean TC frequency is 3.0, with a standard deviation of 1.6, and the annual average intense TC days is 7.6 days, with a standard deviation of 4.5 days. There are no significant linear trends in either mean annual TC frequencies or TC days. Notably, all 13 variability metrics show no trends over the 39-yr period and are less dependent upon standard El Niño–Southern Oscillation (ENSO) variables than many other TC basins, including the rest of the Australian region basin. The largest correlations with TC frequency were geopotential heights for June–August at 925 hPa over the South Atlantic Ocean (r = −0.65) and for April–June at 700 hPa over North America (−0.64). For TC days the largest correlations are geopotential heights for July–September at 1000 hPa over the South Atlantic Ocean (−0.7) and for April–June at 850 hPa over North America (−0.58). Last, wavelet analyses of annual TC frequencies and TC days reveal periodicities at ENSO and decadal time scales. However, the TC dataset is too short for conclusive evidence of multidecadal periodicities.
Given the large correlations revealed by this study, developing and testing of a multivariate seasonal TC prediction scheme has commenced, with lead times up to 6 months.
Abstract
In the first half of this research, this study examines the trend in tropical cyclone (TC) activity over the economically important northwest Western Australia (NWA) TC basin (equator–40°S, 80°–140°E) based on statistical analyses of the International Best Track Archive for Climate Stewardship (IBTrACS) and large-scale environmental variables, which are known to be closely linked to the formation and longevity of TCs, from NCEP–NCAR reanalyses. In the second half, changes in TC activity from climate model projections for 2000–60 are compared for (i) no scenario change (CNTRL) and (ii) the moderate IPCC Special Report on Emission Scenarios (SRES) A1B scenario (EGHG). The aims are to (i) determine differences in mean annual TC frequency and intensity trends, (ii) test for differences between genesis and decay positions of CNTRL and EGHG projections using a nonparametric permutation test, and (iii) use kernel density estimation (KDE) for a cluster analysis of CNTRL and EGHG genesis and decay positions and generate their probability distribution functions.
The main findings are there is little difference in the mean TC number over the period, but there is a difference in mean intensity; CNTRL and EGHG projections differ in mean genesis and decay positions in both latitude and longitude; and the KDE reveals just one cluster in both CNTRL and EGHG mean genesis and decay positions. The EGHG KDE is possibly disjoint, with a wider longitudinal spread. The results can be explained in terms of physical, meteorological, and sea surface temperature (SST) conditions, which provide natural limits to the spread of the genesis and decay points.
Abstract
In the first half of this research, this study examines the trend in tropical cyclone (TC) activity over the economically important northwest Western Australia (NWA) TC basin (equator–40°S, 80°–140°E) based on statistical analyses of the International Best Track Archive for Climate Stewardship (IBTrACS) and large-scale environmental variables, which are known to be closely linked to the formation and longevity of TCs, from NCEP–NCAR reanalyses. In the second half, changes in TC activity from climate model projections for 2000–60 are compared for (i) no scenario change (CNTRL) and (ii) the moderate IPCC Special Report on Emission Scenarios (SRES) A1B scenario (EGHG). The aims are to (i) determine differences in mean annual TC frequency and intensity trends, (ii) test for differences between genesis and decay positions of CNTRL and EGHG projections using a nonparametric permutation test, and (iii) use kernel density estimation (KDE) for a cluster analysis of CNTRL and EGHG genesis and decay positions and generate their probability distribution functions.
The main findings are there is little difference in the mean TC number over the period, but there is a difference in mean intensity; CNTRL and EGHG projections differ in mean genesis and decay positions in both latitude and longitude; and the KDE reveals just one cluster in both CNTRL and EGHG mean genesis and decay positions. The EGHG KDE is possibly disjoint, with a wider longitudinal spread. The results can be explained in terms of physical, meteorological, and sea surface temperature (SST) conditions, which provide natural limits to the spread of the genesis and decay points.
Abstract
As a conveyor belt transferring inland ice to ocean, ice shelves shed mass through large, systematic tabular calving, which also plays a major role in the fluctuation of the buttressing forces. Tabular iceberg calving involves two stages: first is systematic cracking, which develops after the forward-slanting front reaches a limiting extension length determined by gravity–buoyancy imbalance; second is fatigue separation. The latter has greater variability, producing calving irregularity. Whereas ice flow vertical shear determines the timing of the systematic cracking, wave actions are decisive for ensuing viscoplastic fatigue. Because the frontal section has its own resonance frequency, it reverberates only to waves of similar frequency. With a flow-dependent, nonlocal attrition scheme, the present ice model [Scalable Extensible Geoflow Model for Environmental Research-Ice flow submodel (SEGMENT-Ice)] describes an entire ice-shelf life cycle. It is found that most East Antarctic ice shelves have higher resonance frequencies, and the fatigue of viscoplastic ice is significantly enhanced by shoaling waves from both storm surges and infragravity waves (~5 × 10−3 Hz). The two largest embayed ice shelves have resonance frequencies within the range of tsunami waves. When approaching critical extension lengths, perturbations from about four consecutive tsunami events can cause complete separation of tabular icebergs from shelves. For shelves with resonance frequencies matching storm surge waves, future reduction of sea ice may impose much larger deflections from shoaling, storm-generated ocean waves. Although the Ross Ice Shelf (RIS) total mass varies little in the twenty-first century, the mass turnover quickens and the ice conveyor belt is ~40% more efficient by the late twenty-first century, reaching 70 km3 yr−1. The mass distribution shifts oceanward, favoring future tabular calving.
Abstract
As a conveyor belt transferring inland ice to ocean, ice shelves shed mass through large, systematic tabular calving, which also plays a major role in the fluctuation of the buttressing forces. Tabular iceberg calving involves two stages: first is systematic cracking, which develops after the forward-slanting front reaches a limiting extension length determined by gravity–buoyancy imbalance; second is fatigue separation. The latter has greater variability, producing calving irregularity. Whereas ice flow vertical shear determines the timing of the systematic cracking, wave actions are decisive for ensuing viscoplastic fatigue. Because the frontal section has its own resonance frequency, it reverberates only to waves of similar frequency. With a flow-dependent, nonlocal attrition scheme, the present ice model [Scalable Extensible Geoflow Model for Environmental Research-Ice flow submodel (SEGMENT-Ice)] describes an entire ice-shelf life cycle. It is found that most East Antarctic ice shelves have higher resonance frequencies, and the fatigue of viscoplastic ice is significantly enhanced by shoaling waves from both storm surges and infragravity waves (~5 × 10−3 Hz). The two largest embayed ice shelves have resonance frequencies within the range of tsunami waves. When approaching critical extension lengths, perturbations from about four consecutive tsunami events can cause complete separation of tabular icebergs from shelves. For shelves with resonance frequencies matching storm surge waves, future reduction of sea ice may impose much larger deflections from shoaling, storm-generated ocean waves. Although the Ross Ice Shelf (RIS) total mass varies little in the twenty-first century, the mass turnover quickens and the ice conveyor belt is ~40% more efficient by the late twenty-first century, reaching 70 km3 yr−1. The mass distribution shifts oceanward, favoring future tabular calving.
Abstract
The potential for predicting the skill of 36-h forecasts from the Australian region limited area model is investigated using three predictors of model forecast error (MFE) for mean sea level pressure. Two of the predictors utilize single forecasts: one is based on statistical regression of the MFE against the initial analysis and the forecast; and the other uses a measure of the degree of persistence in the forecast. The third predictor utilizes the divergence, or spread, of an ensemble of forecasts from other NWP centers.
Based on a 5-month period of daily 36-h forecasts, correlations were found between the above predictors and the MFE of 0.58, 0.18, and 0.40, respectively. Combining the three predictors in an optimal linear manner increased the correlation to 0.71. Further testing of the combined predictors on a 2-month independent dataset produced a correlation of 0.67. Thus, application of the technique to both dependent and independent datasets explained approximately 50% of the variance in the MFE. This demonstrates that the technique has operational utility for differentiating overall poor and good model forecasts. Using case studies concentrating on southeastern Australia, it is further demonstrated that the predictors can provide excellent differentiation of forecast skill across the forecast domain.
Abstract
The potential for predicting the skill of 36-h forecasts from the Australian region limited area model is investigated using three predictors of model forecast error (MFE) for mean sea level pressure. Two of the predictors utilize single forecasts: one is based on statistical regression of the MFE against the initial analysis and the forecast; and the other uses a measure of the degree of persistence in the forecast. The third predictor utilizes the divergence, or spread, of an ensemble of forecasts from other NWP centers.
Based on a 5-month period of daily 36-h forecasts, correlations were found between the above predictors and the MFE of 0.58, 0.18, and 0.40, respectively. Combining the three predictors in an optimal linear manner increased the correlation to 0.71. Further testing of the combined predictors on a 2-month independent dataset produced a correlation of 0.67. Thus, application of the technique to both dependent and independent datasets explained approximately 50% of the variance in the MFE. This demonstrates that the technique has operational utility for differentiating overall poor and good model forecasts. Using case studies concentrating on southeastern Australia, it is further demonstrated that the predictors can provide excellent differentiation of forecast skill across the forecast domain.
Abstract
The Helmholtz-type equation arises in many areas of fluid dynamics, and, in recent years, there has been a rapid increase in the numerical procedures available for solving the equation. In this note, the various methods currently available are discussed, and representatives from the main categories are compared.
We suggest that for certain problems, the most important of which is Poisson's equation on a rectangle, direct methods are now available that are far superior to widely used iterative methods. For problems involving irregular domains, mixed boundary conditions, and variable Helmholtz coefficients, however, existing direct methods often cannot be used with the same flexibility as iterative methods; there is a continuing need to extend direct methods to these more general cases.
Abstract
The Helmholtz-type equation arises in many areas of fluid dynamics, and, in recent years, there has been a rapid increase in the numerical procedures available for solving the equation. In this note, the various methods currently available are discussed, and representatives from the main categories are compared.
We suggest that for certain problems, the most important of which is Poisson's equation on a rectangle, direct methods are now available that are far superior to widely used iterative methods. For problems involving irregular domains, mixed boundary conditions, and variable Helmholtz coefficients, however, existing direct methods often cannot be used with the same flexibility as iterative methods; there is a continuing need to extend direct methods to these more general cases.
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.
