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- Author or Editor: A. A. Tsonis x
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Abstract
In this paper the evaluation of very simple approaches to delineate the rain area from satellite imagery is assessed in terms of single thresholding. Results and comparison with other more complicated techniques indicate that single thresholding may be quite adequate in delineating instantaneous rainfall areas from a single visible or a single infrared image. The implication of these findings for large scale (space/time) rainfall retrieval from satellites is also discussed.
Abstract
In this paper the evaluation of very simple approaches to delineate the rain area from satellite imagery is assessed in terms of single thresholding. Results and comparison with other more complicated techniques indicate that single thresholding may be quite adequate in delineating instantaneous rainfall areas from a single visible or a single infrared image. The implication of these findings for large scale (space/time) rainfall retrieval from satellites is also discussed.
Abstract
The spatial and spectral characteristics of the GOES visible and infrared images are examined. From that analysis, a scheme is developed which identifies and separates the following classes: clear skies/no snow cover, clear skies/snow cover and clouds. Clouds are then classified as high or low broken clouds and overcast. The scheme is tested for various weather situations. Comparison of the classification results with reports from ground synoptic stations and maps reflect an average accuracy of approximately 72%, and a higher accuracy (∼87%) when high or low broken clouds and overcast are considered as one class (i.e., clouds). The differentiation between clouds and snow, or no snow-covered ground has been found to be very satisfactory, even in cases of temperature inversions.
Abstract
The spatial and spectral characteristics of the GOES visible and infrared images are examined. From that analysis, a scheme is developed which identifies and separates the following classes: clear skies/no snow cover, clear skies/snow cover and clouds. Clouds are then classified as high or low broken clouds and overcast. The scheme is tested for various weather situations. Comparison of the classification results with reports from ground synoptic stations and maps reflect an average accuracy of approximately 72%, and a higher accuracy (∼87%) when high or low broken clouds and overcast are considered as one class (i.e., clouds). The differentiation between clouds and snow, or no snow-covered ground has been found to be very satisfactory, even in cases of temperature inversions.
Abstract
This study employs principal component analysis to correct tropical precipitation estimates in the University of Wisconsin—Milwaukee (UWM)/Comprehensive Ocean–Atmosphere Dataset (COADS) data. The idea was to use a matrix made up of the other variables in the set, to reduce the dimensionality of the matrix by considering a small number of principal components, and then to regress precipitation to these principal components. The results indicate that, although some information on precipitation could be restored by this method, overall the resulting precipitation estimates are not reliable. This result is traced to the intrinsic complexity of precipitation and possibly to a newly discovered bias in the UWM/COADS data.
Abstract
This study employs principal component analysis to correct tropical precipitation estimates in the University of Wisconsin—Milwaukee (UWM)/Comprehensive Ocean–Atmosphere Dataset (COADS) data. The idea was to use a matrix made up of the other variables in the set, to reduce the dimensionality of the matrix by considering a small number of principal components, and then to regress precipitation to these principal components. The results indicate that, although some information on precipitation could be restored by this method, overall the resulting precipitation estimates are not reliable. This result is traced to the intrinsic complexity of precipitation and possibly to a newly discovered bias in the UWM/COADS data.
Abstract
Using satellite and weather radar data, a simple clustering analysis has been used in order to differentiate between raining and nonraining clouds. Based on these results, a scheme is proposed for instantaneous rain area delineation in the midlatitudes. Delineation of the rain areas will not require coextensive radar data which are only used to develop and evaluate the method. Warm season data during daylight hours were used to test the scheme. Results indicate that the proposed scheme has very good skills in delineating rain areas in the midlatitudes, resulting in an average probability of detection of about 66% and an average false alarm ratio of about 37%.
Abstract
Using satellite and weather radar data, a simple clustering analysis has been used in order to differentiate between raining and nonraining clouds. Based on these results, a scheme is proposed for instantaneous rain area delineation in the midlatitudes. Delineation of the rain areas will not require coextensive radar data which are only used to develop and evaluate the method. Warm season data during daylight hours were used to test the scheme. Results indicate that the proposed scheme has very good skills in delineating rain areas in the midlatitudes, resulting in an average probability of detection of about 66% and an average false alarm ratio of about 37%.
Abstract
The aircraft measurements from the HIPLEX-1 weather modification experiment have been examined to determine if the nature of the change of liquid water content (LWC) in the supercooled portion of the clouds can be simply described, Three different data sets were created from the −8 and −5°C aircraft data base. Neither a simple linear nor a simple polynomial fit to the data are suitable for reasons discussed in the text. Two different forms of an exponential model were fit to two of the data sets. When a model for the decay of the maximum 1-km liquid water content (χ) of the form χ=χ0 ebt was fit to data set number two, this yielded a cloud liquid water decay constant (τ) of 560 s (9.5 min), with a correlation coefficient r=0.47 and r 2=0.22. This reduce the mean first pass χ value of 1.05 g m−3 to e−1 or 0.39 g m−3 in 9.5 min and to (2e)−1 or 0.14 g m−3 in 19 min. The best fit to the observations, however, comes from a calculation of an average rate of change of LWC at a constant altitude (−8°C) in the clouds. This is of the form χ=χ0+bt and gives a lifetime of 15 min for the maximum 1-km average LWC in the 20 HIPLEX-1 clouds. That is, the highest LWC regions in the upper part of the clouds would be expected to completely disappear in about 15 Min. Regions of lower LWC would disappear more quickly. This is a major limitation on both natural and artificial rain forming processes.
Abstract
The aircraft measurements from the HIPLEX-1 weather modification experiment have been examined to determine if the nature of the change of liquid water content (LWC) in the supercooled portion of the clouds can be simply described, Three different data sets were created from the −8 and −5°C aircraft data base. Neither a simple linear nor a simple polynomial fit to the data are suitable for reasons discussed in the text. Two different forms of an exponential model were fit to two of the data sets. When a model for the decay of the maximum 1-km liquid water content (χ) of the form χ=χ0 ebt was fit to data set number two, this yielded a cloud liquid water decay constant (τ) of 560 s (9.5 min), with a correlation coefficient r=0.47 and r 2=0.22. This reduce the mean first pass χ value of 1.05 g m−3 to e−1 or 0.39 g m−3 in 9.5 min and to (2e)−1 or 0.14 g m−3 in 19 min. The best fit to the observations, however, comes from a calculation of an average rate of change of LWC at a constant altitude (−8°C) in the clouds. This is of the form χ=χ0+bt and gives a lifetime of 15 min for the maximum 1-km average LWC in the 20 HIPLEX-1 clouds. That is, the highest LWC regions in the upper part of the clouds would be expected to completely disappear in about 15 Min. Regions of lower LWC would disappear more quickly. This is a major limitation on both natural and artificial rain forming processes.
Abstract
Recent studies have shown how concepts from information theory can be applied to climate models to better understand the problem of climate prediction. This paper describes how information theory, specifically the concept of entropy, can be used in the analysis of short-term precipitation records. The ideas are illustrated through analysis and comparisons of two long, hourly precipitation records. From the results it is concluded that the records are not periodic and are definitely more complex than records of random origin. This complexity, however, arises from underlying deterministic rules indicating the potential for predictability.
Abstract
Recent studies have shown how concepts from information theory can be applied to climate models to better understand the problem of climate prediction. This paper describes how information theory, specifically the concept of entropy, can be used in the analysis of short-term precipitation records. The ideas are illustrated through analysis and comparisons of two long, hourly precipitation records. From the results it is concluded that the records are not periodic and are definitely more complex than records of random origin. This complexity, however, arises from underlying deterministic rules indicating the potential for predictability.
Abstract
Ensemble prediction has become an indispensable tool in weather forecasting. One of the issues in ensemble prediction is that, regardless of the method, the prediction error does not map well to the underlying physics (i.e., error estimates do not project strongly onto physical structures). This paper is driven by the hypothesis that prediction error includes a deterministic component, which can be isolated and then removed, and that removing the error would enable researchers and forecasters to better map the error to the physics and improve prediction of atmospheric transitions. Here, preliminary experimental evidence is provided that supports this hypothesis. This evidence is provided from results obtained from two low-order but highly chaotic systems, one of which incorporates atmospheric flow transitions. Using neural networks to probe the deterministic component of forecast error, it is shown that the error recovery relates to the underlying type of flow and that it can be used to better forecast transitions in the atmospheric flow using ensemble data. A discussion of methods to extend these ideas to more realistic forecast settings is provided.
Abstract
Ensemble prediction has become an indispensable tool in weather forecasting. One of the issues in ensemble prediction is that, regardless of the method, the prediction error does not map well to the underlying physics (i.e., error estimates do not project strongly onto physical structures). This paper is driven by the hypothesis that prediction error includes a deterministic component, which can be isolated and then removed, and that removing the error would enable researchers and forecasters to better map the error to the physics and improve prediction of atmospheric transitions. Here, preliminary experimental evidence is provided that supports this hypothesis. This evidence is provided from results obtained from two low-order but highly chaotic systems, one of which incorporates atmospheric flow transitions. Using neural networks to probe the deterministic component of forecast error, it is shown that the error recovery relates to the underlying type of flow and that it can be used to better forecast transitions in the atmospheric flow using ensemble data. A discussion of methods to extend these ideas to more realistic forecast settings is provided.
We present a brief overview of some new methodologies for making predictions on time-series data. These ideas stem from two rapidly growing fields: nonlinear dynamics (chaos) theory and parallel distributed processing. Examples are presented that show the usefulness of such methods in making short-term predictions. It is suggested that such methodologies are capable of distinguishing between chaos and noise. Implications of these ideas and methods in the study of weather and climate are discussed.
We present a brief overview of some new methodologies for making predictions on time-series data. These ideas stem from two rapidly growing fields: nonlinear dynamics (chaos) theory and parallel distributed processing. Examples are presented that show the usefulness of such methods in making short-term predictions. It is suggested that such methodologies are capable of distinguishing between chaos and noise. Implications of these ideas and methods in the study of weather and climate are discussed.
Some of the basic principles of the theory of dynamical systems are presented, introducing the reader to the concepts of chaos theory and strange attractors and their implications in meteorology. New numerical techniques to analyze weather data according to the above theory are also presented.
Some of the basic principles of the theory of dynamical systems are presented, introducing the reader to the concepts of chaos theory and strange attractors and their implications in meteorology. New numerical techniques to analyze weather data according to the above theory are also presented.
