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- Author or Editor: Rudolf O. Weber x
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Abstract
The standard deviation of horizontal wind direction is a central quantity in the description of atmospheric turbulence and of great practical use in dispersion models. As horizontal wind direction is a circular variable, its standard deviation cannot be directly estimated by on-line methods. For a mathematically strict determination of the angular standard deviation, it is necessary to store all observations and perform off-line calculations. A more practical approach is to calculate on-line moments of linear variables and to parameterize angular standard deviation in terms of these moments. A variety of such estimators is compared by means of a large dataset from an ultrasonic anemometer. The paper systematically investigates which types of linear variables lead to the best estimators and which parameterizations are best within each group of linear variables. Estimators based on moments of the sine and cosine of the wind direction turned out to be most robust. The parameterizations based on an isotropic Gaussian model of turbulence gave the estimators with smallest error within the different groups.
Abstract
The standard deviation of horizontal wind direction is a central quantity in the description of atmospheric turbulence and of great practical use in dispersion models. As horizontal wind direction is a circular variable, its standard deviation cannot be directly estimated by on-line methods. For a mathematically strict determination of the angular standard deviation, it is necessary to store all observations and perform off-line calculations. A more practical approach is to calculate on-line moments of linear variables and to parameterize angular standard deviation in terms of these moments. A variety of such estimators is compared by means of a large dataset from an ultrasonic anemometer. The paper systematically investigates which types of linear variables lead to the best estimators and which parameterizations are best within each group of linear variables. Estimators based on moments of the sine and cosine of the wind direction turned out to be most robust. The parameterizations based on an isotropic Gaussian model of turbulence gave the estimators with smallest error within the different groups.
Abstract
Ten years of global tropospheric data from the European Centre for Medium-Range Weather Forecasts analyses were used to obtain a climatology of quasi-stationary waves and transient normal-mode Rossby waves. The data were split up into a mean annual cycle, reflecting the forced fields, and a transient part, containing the traveling waves. Data were then projected onto Hough normal modes, yielding a mean annual behavior of the quasi-stationary fields and time series of expansion coefficients for the transient waves. The latter were analyzed by a space-time spectral method independently for each of the four seasons. The Hough normal modes with low zonal wavenumber and low meridional index show clear peaks in the power spectra at theoretically predicted frequencies. Some modes have a strong seasonality.
Abstract
Ten years of global tropospheric data from the European Centre for Medium-Range Weather Forecasts analyses were used to obtain a climatology of quasi-stationary waves and transient normal-mode Rossby waves. The data were split up into a mean annual cycle, reflecting the forced fields, and a transient part, containing the traveling waves. Data were then projected onto Hough normal modes, yielding a mean annual behavior of the quasi-stationary fields and time series of expansion coefficients for the transient waves. The latter were analyzed by a space-time spectral method independently for each of the four seasons. The Hough normal modes with low zonal wavenumber and low meridional index show clear peaks in the power spectra at theoretically predicted frequencies. Some modes have a strong seasonality.
Abstract
In earlier works of several authors, wind fields or flow patterns were grouped by means of trajectory calculations or by use of a principal component analysis. A new automated classification method is proposed that makes use only of wind observations and does not require predefined circulation patterns, a priori rules, or spatial or temporal interpolation. A distance measure is defined between pairs of wind fields, represented by a set of observations. Based on the resulting distance matrix, a hierarchical cluster analysis is performed that also provides an indication for the choice of an appropriate number of clusters. The classification method is applied to a monthly record of 1-h averages of wind measurements in the region of Basel, Switzerland. Different clustering schemes are tested, and the complete linkage method is chosen as most appropriate. For the test case a relatively small number of classes (12) provides a sufficient description of the different flow patterns in this complex topography. The strong channeling of flow in the valleys of the Basel region seems to result in only a small number of distinguishable flow patterns.
Abstract
In earlier works of several authors, wind fields or flow patterns were grouped by means of trajectory calculations or by use of a principal component analysis. A new automated classification method is proposed that makes use only of wind observations and does not require predefined circulation patterns, a priori rules, or spatial or temporal interpolation. A distance measure is defined between pairs of wind fields, represented by a set of observations. Based on the resulting distance matrix, a hierarchical cluster analysis is performed that also provides an indication for the choice of an appropriate number of clusters. The classification method is applied to a monthly record of 1-h averages of wind measurements in the region of Basel, Switzerland. Different clustering schemes are tested, and the complete linkage method is chosen as most appropriate. For the test case a relatively small number of classes (12) provides a sufficient description of the different flow patterns in this complex topography. The strong channeling of flow in the valleys of the Basel region seems to result in only a small number of distinguishable flow patterns.
Abstract
A two-stage classification scheme with outlier detection is proposed to find groups of wind fields. A hierarchical cluster analysis according to the complete linkage method is combined with a k-means procedure with detection and exclusion of outliers. The classification method is applied to a 1-yr dataset of 1-h mean wind observations from the MISTRAL field experiment. A small number of typical regional flow patterns is identified. An analysis of temperature observations shows that some of the 12 regional flow patterns have thermally forced wind systems. The main spatial forcing patterns are revealed by a principal component analysis of temperature observations. A comparison of the regional flow patterns and the synoptic-scale weather types of the Alpine region shows that only weak connections between the local flow and the synoptic-scale weather type exist.
Abstract
A two-stage classification scheme with outlier detection is proposed to find groups of wind fields. A hierarchical cluster analysis according to the complete linkage method is combined with a k-means procedure with detection and exclusion of outliers. The classification method is applied to a 1-yr dataset of 1-h mean wind observations from the MISTRAL field experiment. A small number of typical regional flow patterns is identified. An analysis of temperature observations shows that some of the 12 regional flow patterns have thermally forced wind systems. The main spatial forcing patterns are revealed by a principal component analysis of temperature observations. A comparison of the regional flow patterns and the synoptic-scale weather types of the Alpine region shows that only weak connections between the local flow and the synoptic-scale weather type exist.
Abstract
The relationship between the surface and synoptic wind direction is examined climatologically in a complex terrain region. Surface winds were observed over a 1-yr period during the MISTRAL project in the Basel, Switzerland, area. The measurement sites were located in various topographical settings, namely, in broad and narrow valleys, on slopes, at hilltops, on passes, and at mountaintops. The synoptic winds above the MISTRAL area are approximated by upper-level winds from routine rawinsonde observations. The relationship between this synoptic wind and the surface wind at each site was compared to a conceptual model. According to the conceptual model used, there are four mechanisms for the forcing of near-surface winds by synoptic winds. Three of the four forcing mechanisms leading to channeled flow are identified in the MISTRAL area. In this region with its complex terrain, different channeling mechanisms act at different locations as well as different mechanisms may occur at the same location. The study shows that the type of channeling depends on the topography surrounding the observation site. The combination of several channeling mechanisms with the multitude of valley orientations in a complex terrain setting explains the variety of observed flow patterns. One mechanism, the thermal forcing of valley winds, is examined in more detail. Two minima in the averaged diurnal cycle of the wind speed are found. Both minima occur at the time when the direction of the thermally forced wind reverses—one in the morning and one in the evening. The daytime upvalley flow is, on average, stronger than the nighttime downvalley flow. In the MISTRAL region, the frequency of days with thermally driven flow does not have a significant annual cycle.
Abstract
The relationship between the surface and synoptic wind direction is examined climatologically in a complex terrain region. Surface winds were observed over a 1-yr period during the MISTRAL project in the Basel, Switzerland, area. The measurement sites were located in various topographical settings, namely, in broad and narrow valleys, on slopes, at hilltops, on passes, and at mountaintops. The synoptic winds above the MISTRAL area are approximated by upper-level winds from routine rawinsonde observations. The relationship between this synoptic wind and the surface wind at each site was compared to a conceptual model. According to the conceptual model used, there are four mechanisms for the forcing of near-surface winds by synoptic winds. Three of the four forcing mechanisms leading to channeled flow are identified in the MISTRAL area. In this region with its complex terrain, different channeling mechanisms act at different locations as well as different mechanisms may occur at the same location. The study shows that the type of channeling depends on the topography surrounding the observation site. The combination of several channeling mechanisms with the multitude of valley orientations in a complex terrain setting explains the variety of observed flow patterns. One mechanism, the thermal forcing of valley winds, is examined in more detail. Two minima in the averaged diurnal cycle of the wind speed are found. Both minima occur at the time when the direction of the thermally forced wind reverses—one in the morning and one in the evening. The daytime upvalley flow is, on average, stronger than the nighttime downvalley flow. In the MISTRAL region, the frequency of days with thermally driven flow does not have a significant annual cycle.
Abstract
Analysis of vector quantities or directional data, such as the variables characterizing flow, is of significant interest to geophysical fluid dynamicists. For flows with strong channeling, a new simple correlation coefficient is defined. It is demonstrated by application to a model of channeled flow that the new correlation captures the flow features in the case of channeling better than other correlations taken from the literature. The new correlation coefficient is applied to wind data from a mesoscale network of anemometers in complex terrain. A cluster analysis based on the correlation matrix is used to group observation sites into classes with similar behavior of the channeled flow. Sites within the same class are not necessarily geographically close. A similar behavior of the wind directions indicated by these classes seems to be more closely related to the orographic features and to the altitude of the sites than to the horizontal distance between them.
Abstract
Analysis of vector quantities or directional data, such as the variables characterizing flow, is of significant interest to geophysical fluid dynamicists. For flows with strong channeling, a new simple correlation coefficient is defined. It is demonstrated by application to a model of channeled flow that the new correlation captures the flow features in the case of channeling better than other correlations taken from the literature. The new correlation coefficient is applied to wind data from a mesoscale network of anemometers in complex terrain. A cluster analysis based on the correlation matrix is used to group observation sites into classes with similar behavior of the channeled flow. Sites within the same class are not necessarily geographically close. A similar behavior of the wind directions indicated by these classes seems to be more closely related to the orographic features and to the altitude of the sites than to the horizontal distance between them.
Abstract
Optimal averaging is a method to estimate some area mean of datasets with imperfect spatial sampling. The accuracy of the method is tested by application to time series of January temperature fields simulated by the NCAR Community Climate Model. Some restrictions to the application of optimal averaging are given. It is demonstrated that the proper choice of a spatial correlation model is crucial. It is shown that the optimal averaging procedures provide a better approximation to the true mean of a region than simple area-weight averaging does. The inclusion of measurement errors of realistic size at each observation location hardly changes the value of the optimal average nor does it substantially alter the sampling error. of the optimal average.
Abstract
Optimal averaging is a method to estimate some area mean of datasets with imperfect spatial sampling. The accuracy of the method is tested by application to time series of January temperature fields simulated by the NCAR Community Climate Model. Some restrictions to the application of optimal averaging are given. It is demonstrated that the proper choice of a spatial correlation model is crucial. It is shown that the optimal averaging procedures provide a better approximation to the true mean of a region than simple area-weight averaging does. The inclusion of measurement errors of realistic size at each observation location hardly changes the value of the optimal average nor does it substantially alter the sampling error. of the optimal average.
Abstract
The method of optimal interpolation, which is widely used in meteorological data assimilation, relies very much on good approximations of spatial correlation functions. Therefore, many models for such functions have been developed. These models should fulfill certain mathematical constraints; particularly, they should be positive-definite functions. For the classes of homogeneous and isotropic processes, the positivity property and its consequences are reviewed. A special class of correlation models based on so-called spatial autoregressive processes is critically examined. It is shown that models of this type are not positive definite on the meteorological relevant spaces. Some other models taken from the literature are shown to lack this property also. Three strategies to obtain models that have the appropriate mathematical properties are outlined.
Abstract
The method of optimal interpolation, which is widely used in meteorological data assimilation, relies very much on good approximations of spatial correlation functions. Therefore, many models for such functions have been developed. These models should fulfill certain mathematical constraints; particularly, they should be positive-definite functions. For the classes of homogeneous and isotropic processes, the positivity property and its consequences are reviewed. A special class of correlation models based on so-called spatial autoregressive processes is critically examined. It is shown that models of this type are not positive definite on the meteorological relevant spaces. Some other models taken from the literature are shown to lack this property also. Three strategies to obtain models that have the appropriate mathematical properties are outlined.
Abstract
Measurements of the horizontal and vertical wind component by a crosswind scintillometer during foehn, the chinooklike downslope windstorm in the Alps, are presented. Because of the sparsity of vertical velocity measurements in the immediate vicinity, the scintillometer calibration is checked mainly with horizontal wind measurements. Then it is assumed that the calibration is the same for both components. The concept was tested during the Mesoscale Alpine Programme field campaign in the autumn of 1999, during which two scintillometers were deployed. Strong, long-lasting, quasi-stationary downward motions on the order of 5 m s−1 and horizontal wind speeds of over 30 m s−1 were detected during strong foehn phases within the valley. Aircraft measurements of various transects near the light paths are compared with two crosswind evaluation techniques. One of them, the slope method, tends to overestimate the actual wind speed by about 20%, whereas the peak technique gives values that are about 10% too low for high wind speeds. The peak method also fails to measure meaningful vertical crosswind speeds. The scintillometer data of one particular foehn storm are compared with nearby Doppler lidar data. The agreement of the horizontal measurements is reasonable. Discrepancies are attributed to topographic and dynamic effects that cause significant spatial inhomogeneities in the wind field. The applicability of continuous scintillometer vertical crosswind measurements in mountainous terrain is demonstrated.
Abstract
Measurements of the horizontal and vertical wind component by a crosswind scintillometer during foehn, the chinooklike downslope windstorm in the Alps, are presented. Because of the sparsity of vertical velocity measurements in the immediate vicinity, the scintillometer calibration is checked mainly with horizontal wind measurements. Then it is assumed that the calibration is the same for both components. The concept was tested during the Mesoscale Alpine Programme field campaign in the autumn of 1999, during which two scintillometers were deployed. Strong, long-lasting, quasi-stationary downward motions on the order of 5 m s−1 and horizontal wind speeds of over 30 m s−1 were detected during strong foehn phases within the valley. Aircraft measurements of various transects near the light paths are compared with two crosswind evaluation techniques. One of them, the slope method, tends to overestimate the actual wind speed by about 20%, whereas the peak technique gives values that are about 10% too low for high wind speeds. The peak method also fails to measure meaningful vertical crosswind speeds. The scintillometer data of one particular foehn storm are compared with nearby Doppler lidar data. The agreement of the horizontal measurements is reasonable. Discrepancies are attributed to topographic and dynamic effects that cause significant spatial inhomogeneities in the wind field. The applicability of continuous scintillometer vertical crosswind measurements in mountainous terrain is demonstrated.
