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- Author or Editor: Daniel Sempere Torres x
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Abstract
A general phenomenological formulation for drop size distribution (DSD), written down as a scaling law, is proposed. It accounts for all previous fitted DSDs. As a main implication of the expression proposed, the integral rainfall variables are related by power functions and agree with experimental evidence. Additional consequences are also analyzed. From this formulation there follows a general methodology for scaling all data in a unique plot, leading to more robust fits of the DSD. An illustrative example on real data is provided.
Abstract
A general phenomenological formulation for drop size distribution (DSD), written down as a scaling law, is proposed. It accounts for all previous fitted DSDs. As a main implication of the expression proposed, the integral rainfall variables are related by power functions and agree with experimental evidence. Additional consequences are also analyzed. From this formulation there follows a general methodology for scaling all data in a unique plot, leading to more robust fits of the DSD. An illustrative example on real data is provided.
Abstract
The optical spectropluviometer is a shadowgraph instrument able to measure independently the equivalent diameter and the fall speed of raindrops at ground level. Hardware and software modifications are proposed and tested. A modern digital signal processing system allows for the simultaneous sampling and analyzing of the signal delivered by the sensor. The IR light transmission is pulsed to avoid interference with natural radiation and the protection of the optics is improved. The validation procedure consists of comparing the rain rates derived from the measured drop size distributions with rain rates delivered by nearby rain gauges. The results obtained during 65 storm events show that the proposed improvements reduce the bias of the rain-rate estimation from 34% to 16%. Suggestions are given to further improve the performance of this instrument.
Abstract
The optical spectropluviometer is a shadowgraph instrument able to measure independently the equivalent diameter and the fall speed of raindrops at ground level. Hardware and software modifications are proposed and tested. A modern digital signal processing system allows for the simultaneous sampling and analyzing of the signal delivered by the sensor. The IR light transmission is pulsed to avoid interference with natural radiation and the protection of the optics is improved. The validation procedure consists of comparing the rain rates derived from the measured drop size distributions with rain rates delivered by nearby rain gauges. The results obtained during 65 storm events show that the proposed improvements reduce the bias of the rain-rate estimation from 34% to 16%. Suggestions are given to further improve the performance of this instrument.
Abstract
Nowcasting precipitation is a key element in the anticipation of floods in warning systems. In this framework, weather radars are very useful because of the high resolution of their measurements both in time and space. The aim of this study is to assess the performance of a recently proposed nowcasting technique (S-PROG) from a hydrological point of view in a Mediterranean environment. S-PROG is based on the advection of weather radar fields according to the motion field derived with an algorithm based on tracking radar echoes by correlation (TREC), and it has the ability of filtering out the most unpredictable scales of these fields as the forecasting time increases. Validation of this nowcasting technique was done from two different perspectives: (i) comparing forecasted precipitation fields against radar measurements, and (ii) by means of a distributed rainfall runoff model, comparing hydrographs simulated with a hydrological model using rainfall fields forecasted by S-PROG against hydrographs generated with the model using the entire series of radar measurements. In both cases, results obtained by a simpler nowcasting technique are used as a reference to evaluate improvements. Validation showed that precipitation fields forecasted with S-PROG seem to be better than fields forecasted using simpler techniques. Additionally, hydrological validation led the authors to point out that the use of radar-based nowcasting techniques allows the anticipation window in which flow estimates are forecasted with enough quality to be sensibly extended.
Abstract
Nowcasting precipitation is a key element in the anticipation of floods in warning systems. In this framework, weather radars are very useful because of the high resolution of their measurements both in time and space. The aim of this study is to assess the performance of a recently proposed nowcasting technique (S-PROG) from a hydrological point of view in a Mediterranean environment. S-PROG is based on the advection of weather radar fields according to the motion field derived with an algorithm based on tracking radar echoes by correlation (TREC), and it has the ability of filtering out the most unpredictable scales of these fields as the forecasting time increases. Validation of this nowcasting technique was done from two different perspectives: (i) comparing forecasted precipitation fields against radar measurements, and (ii) by means of a distributed rainfall runoff model, comparing hydrographs simulated with a hydrological model using rainfall fields forecasted by S-PROG against hydrographs generated with the model using the entire series of radar measurements. In both cases, results obtained by a simpler nowcasting technique are used as a reference to evaluate improvements. Validation showed that precipitation fields forecasted with S-PROG seem to be better than fields forecasted using simpler techniques. Additionally, hydrological validation led the authors to point out that the use of radar-based nowcasting techniques allows the anticipation window in which flow estimates are forecasted with enough quality to be sensibly extended.
Abstract
Because echoes caused by nonmeteorological targets significantly affect radar scans, contaminated bins must be identified and eliminated before precipitation can be quantitatively estimated from radar measurements.
Under mean propagation conditions, clutter echoes (mainly caused by targets such as mountains or large buildings) can be found in almost fixed locations. However, in anomalous propagation conditions, new clutter echoes may appear (sometimes over the sea), and they may be difficult to distinguish from precipitation returns. Therefore, an automatic algorithm is needed to identify clutter on radar scans, especially for operational uses of radar information (such as real-time hydrology).
In this study, a new algorithm is presented based on fuzzy logic, using volumetric data. It uses some statistics to highlight clutter characteristics (namely, shallow vertical extent, high spatial variability, and low radial velocities) to output a value that quantifies the possibility of each bin being affected by clutter (in order to remove those in which this factor exceeds a certain threshold).
The performance of this algorithm was compared against that of simply removing mean clutter echoes. Satisfactory results were obtained from an exhaustive evaluation of this algorithm, especially in those cases in which anomalous propagation played an important role.
Abstract
Because echoes caused by nonmeteorological targets significantly affect radar scans, contaminated bins must be identified and eliminated before precipitation can be quantitatively estimated from radar measurements.
Under mean propagation conditions, clutter echoes (mainly caused by targets such as mountains or large buildings) can be found in almost fixed locations. However, in anomalous propagation conditions, new clutter echoes may appear (sometimes over the sea), and they may be difficult to distinguish from precipitation returns. Therefore, an automatic algorithm is needed to identify clutter on radar scans, especially for operational uses of radar information (such as real-time hydrology).
In this study, a new algorithm is presented based on fuzzy logic, using volumetric data. It uses some statistics to highlight clutter characteristics (namely, shallow vertical extent, high spatial variability, and low radial velocities) to output a value that quantifies the possibility of each bin being affected by clutter (in order to remove those in which this factor exceeds a certain threshold).
The performance of this algorithm was compared against that of simply removing mean clutter echoes. Satisfactory results were obtained from an exhaustive evaluation of this algorithm, especially in those cases in which anomalous propagation played an important role.
Abstract
Normalization of drop size distributions (DSDs) is reexamined here. First, an extension of the scaling normalization that uses one moment of the DSD as a scaling parameter to a more general scaling normalization that uses two moments as scaling parameters of the normalization is presented. In addition, the proposed formulation includes all two-parameter normalizations recently introduced in the literature. Thus, a unified vision of the question of DSD normalization and a good model representation of DSDs are given. Data analysis of some convective and stratiform DSDs shows that, from the point of view of the compact representation of DSDs, the double-moment normalization is preferred. However, in terms of physical interpretation, the scaling exponent of the single-moment normalization clearly indicates two different rain regimes, whereas in the double-moment normalization the two populations are not readily separated. It is also shown that DSD analytical models (exponential, gamma, and generalized gamma DSD) have the same scaling properties, indicating that the scaling formalism of DSDs is a very general way of describing DSDs.
Abstract
Normalization of drop size distributions (DSDs) is reexamined here. First, an extension of the scaling normalization that uses one moment of the DSD as a scaling parameter to a more general scaling normalization that uses two moments as scaling parameters of the normalization is presented. In addition, the proposed formulation includes all two-parameter normalizations recently introduced in the literature. Thus, a unified vision of the question of DSD normalization and a good model representation of DSDs are given. Data analysis of some convective and stratiform DSDs shows that, from the point of view of the compact representation of DSDs, the double-moment normalization is preferred. However, in terms of physical interpretation, the scaling exponent of the single-moment normalization clearly indicates two different rain regimes, whereas in the double-moment normalization the two populations are not readily separated. It is also shown that DSD analytical models (exponential, gamma, and generalized gamma DSD) have the same scaling properties, indicating that the scaling formalism of DSDs is a very general way of describing DSDs.
Abstract
During the second week of September 2013, a seasonally uncharacteristic weather pattern stalled over the Rocky Mountain Front Range region of northern Colorado bringing with it copious amounts of moisture from the Gulf of Mexico, Caribbean Sea, and the tropical eastern Pacific Ocean. This feed of moisture was funneled toward the east-facing mountain slopes through a series of mesoscale circulation features, resulting in several days of unusually widespread heavy rainfall over steep mountainous terrain. Catastrophic flooding ensued within several Front Range river systems that washed away highways, destroyed towns, isolated communities, necessitated days of airborne evacuations, and resulted in eight fatalities. The impacts from heavy rainfall and flooding were felt over a broad region of northern Colorado leading to 18 counties being designated as federal disaster areas and resulting in damages exceeding $2 billion (U.S. dollars). This study explores the meteorological and hydrological ingredients that led to this extreme event. After providing a basic timeline of events, synoptic and mesoscale circulation features of the event are discussed. Particular focus is placed on documenting how circulation features, embedded within the larger synoptic flow, served to funnel moist inflow into the mountain front driving several days of sustained orographic precipitation. Operational and research networks of polarimetric radar and surface instrumentation were used to evaluate the cloud structures and dominant hydrometeor characteristics. The performance of several quantitative precipitation estimates, quantitative precipitation forecasts, and hydrological forecast products are also analyzed with the intention of identifying what monitoring and prediction tools worked and where further improvements are needed.
Abstract
During the second week of September 2013, a seasonally uncharacteristic weather pattern stalled over the Rocky Mountain Front Range region of northern Colorado bringing with it copious amounts of moisture from the Gulf of Mexico, Caribbean Sea, and the tropical eastern Pacific Ocean. This feed of moisture was funneled toward the east-facing mountain slopes through a series of mesoscale circulation features, resulting in several days of unusually widespread heavy rainfall over steep mountainous terrain. Catastrophic flooding ensued within several Front Range river systems that washed away highways, destroyed towns, isolated communities, necessitated days of airborne evacuations, and resulted in eight fatalities. The impacts from heavy rainfall and flooding were felt over a broad region of northern Colorado leading to 18 counties being designated as federal disaster areas and resulting in damages exceeding $2 billion (U.S. dollars). This study explores the meteorological and hydrological ingredients that led to this extreme event. After providing a basic timeline of events, synoptic and mesoscale circulation features of the event are discussed. Particular focus is placed on documenting how circulation features, embedded within the larger synoptic flow, served to funnel moist inflow into the mountain front driving several days of sustained orographic precipitation. Operational and research networks of polarimetric radar and surface instrumentation were used to evaluate the cloud structures and dominant hydrometeor characteristics. The performance of several quantitative precipitation estimates, quantitative precipitation forecasts, and hydrological forecast products are also analyzed with the intention of identifying what monitoring and prediction tools worked and where further improvements are needed.
