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- Author or Editor: Warren R. Gray x
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Abstract
The high temporal variability of rainfall requires that measurements be taken at a high frequency for that variability to be recorded well, but most conventional gauges do not have this capability. The gauge described has a resolution estimated to be about 6 s and its measurements do capture the variability. Furthermore, the large datasets associated with high-frequency measurements can be avoided and the essential information retained by storing the data as breakpoints. These are a series of data pairs, each of which consists of the rain rate itself and the time when that rate commenced.
A gauge in which the collected rain is formed into a series of drips all of approximately the same known size was a practical choice. The design and calibration of this gauge, in which particular attention was paid to producing a robust instrument incorporating standard components wherever possible, is described. Long-term comparison in the field with a collocated tipping bucket gauge was used in a calibration scheme in which equality between the gauges was sought for both the long-term accumulation and the short-term rain rates. Although the gauge depends on the formation of equisized drips, it was found that drip size increased slowly with rain rate, and so two calibration parameters were required to convert the time interval between drips into the mean rain rate between the drips. After an initial aging period with inconsistent drip formation, the calibration was stable and the onset of the lowest rain rates (0.1 mm h−1) could be determined to within 1 or 2 min and streaming (i.e., the series of drips merging into a continuous stream) did not occur for rain rates less than 100 mm h−1. Some sample applications for the gauges are described: the extraction of breakpoints, the estimation of 1-min rain rates from Dine's tilting siphon data, and their use in a field experiment.
Abstract
The high temporal variability of rainfall requires that measurements be taken at a high frequency for that variability to be recorded well, but most conventional gauges do not have this capability. The gauge described has a resolution estimated to be about 6 s and its measurements do capture the variability. Furthermore, the large datasets associated with high-frequency measurements can be avoided and the essential information retained by storing the data as breakpoints. These are a series of data pairs, each of which consists of the rain rate itself and the time when that rate commenced.
A gauge in which the collected rain is formed into a series of drips all of approximately the same known size was a practical choice. The design and calibration of this gauge, in which particular attention was paid to producing a robust instrument incorporating standard components wherever possible, is described. Long-term comparison in the field with a collocated tipping bucket gauge was used in a calibration scheme in which equality between the gauges was sought for both the long-term accumulation and the short-term rain rates. Although the gauge depends on the formation of equisized drips, it was found that drip size increased slowly with rain rate, and so two calibration parameters were required to convert the time interval between drips into the mean rain rate between the drips. After an initial aging period with inconsistent drip formation, the calibration was stable and the onset of the lowest rain rates (0.1 mm h−1) could be determined to within 1 or 2 min and streaming (i.e., the series of drips merging into a continuous stream) did not occur for rain rates less than 100 mm h−1. Some sample applications for the gauges are described: the extraction of breakpoints, the estimation of 1-min rain rates from Dine's tilting siphon data, and their use in a field experiment.
Abstract
Twelve months of Southern Hemisphere (maritime) midlatitudes Advanced Very High Resolution Radiometer local area coverage data at full radiometric and spatial resolution have been collocated with rain-rate data from three Doppler weather radars.
Using an interactive computing environment, large independent samples of cloudy-altocumulus, cumulonimbus, cirrostratus, cumulus, nimbostratus, stratocumulus, stratus-and cloud-free scenes have been identified (labeled) in the collocated data. Accurate labeling was ensured by providing a supervising-analyst access to appropriate diagnostics, including difference and ratio channels, 3.7-µm reflected and emissive components, spectral histograms, Coakley-Bretherton spatial coherence plots, mean, standard deviation, and gray-level difference (GLD) statistics. This analysis yielded 4323 cloud and no-cloud samples at a spatial resolution of 8 × 8 instantaneous fields of view (IFOV), from 257 NOAA-11 and NOAA-12 orbits.
Bayesian cloud discriminant functions calculated from the labeled samples and utilizing feature vectors including radiometric and GLD spatial characteristics successfully classified scenes into one of the seven cloud and no-cloud classes with significant skill (Kuipers’ performance index 0.63). Utilizing the posterior probability of the classified samples enabled some clouds that were classified erroneously to be identified (and discarded), improving the skill of the discriminant functions by an additional 10% or so. Removing the GLD statistics from the feature vector reduced the skill of the cloud discrimination by about 20% (relative to the nondiscarding discriminant function), while increasing the misclassification of midlevel clouds. However, some cloud classes can only be discriminated from their multispectral signatures. Day and night discriminant functions show similar skill.
Within raining cloud classes, rain rate has been related to the spatial and radiometric characteristics of the cloud. The skill of the rain-rate estimates is dependent on the cloud type. For nimbostratus and altocumulus classes 20%–25% of the rain-rate variation can be explained by predictors that measure the temperature, spatial texture, and degree of isotropy in the sampled clouds. Raining and nonraining samples of altocumulus, cumulus, cirrostratus, and nimbostratus can be delineated with at least 60% accuracy.
This approach, whereby cloud classes are identified then rain rates estimated as a function of cloud type, would seem to resolve some of the usual problems associated with rain-rate analyses from midlatitudes infrared and visible satellite data. It also extends rain-rate diagnosis to nonconvective (frontal) cloud systems.
Abstract
Twelve months of Southern Hemisphere (maritime) midlatitudes Advanced Very High Resolution Radiometer local area coverage data at full radiometric and spatial resolution have been collocated with rain-rate data from three Doppler weather radars.
Using an interactive computing environment, large independent samples of cloudy-altocumulus, cumulonimbus, cirrostratus, cumulus, nimbostratus, stratocumulus, stratus-and cloud-free scenes have been identified (labeled) in the collocated data. Accurate labeling was ensured by providing a supervising-analyst access to appropriate diagnostics, including difference and ratio channels, 3.7-µm reflected and emissive components, spectral histograms, Coakley-Bretherton spatial coherence plots, mean, standard deviation, and gray-level difference (GLD) statistics. This analysis yielded 4323 cloud and no-cloud samples at a spatial resolution of 8 × 8 instantaneous fields of view (IFOV), from 257 NOAA-11 and NOAA-12 orbits.
Bayesian cloud discriminant functions calculated from the labeled samples and utilizing feature vectors including radiometric and GLD spatial characteristics successfully classified scenes into one of the seven cloud and no-cloud classes with significant skill (Kuipers’ performance index 0.63). Utilizing the posterior probability of the classified samples enabled some clouds that were classified erroneously to be identified (and discarded), improving the skill of the discriminant functions by an additional 10% or so. Removing the GLD statistics from the feature vector reduced the skill of the cloud discrimination by about 20% (relative to the nondiscarding discriminant function), while increasing the misclassification of midlevel clouds. However, some cloud classes can only be discriminated from their multispectral signatures. Day and night discriminant functions show similar skill.
Within raining cloud classes, rain rate has been related to the spatial and radiometric characteristics of the cloud. The skill of the rain-rate estimates is dependent on the cloud type. For nimbostratus and altocumulus classes 20%–25% of the rain-rate variation can be explained by predictors that measure the temperature, spatial texture, and degree of isotropy in the sampled clouds. Raining and nonraining samples of altocumulus, cumulus, cirrostratus, and nimbostratus can be delineated with at least 60% accuracy.
This approach, whereby cloud classes are identified then rain rates estimated as a function of cloud type, would seem to resolve some of the usual problems associated with rain-rate analyses from midlatitudes infrared and visible satellite data. It also extends rain-rate diagnosis to nonconvective (frontal) cloud systems.
Abstract
Rain gauge, radar, and atmospheric observations during a prolonged northwesterly storm in November 1994 have been used to study factors influencing the distribution of precipitation across the Southern Alps. Despite the persistent northwesterly flow, the location and intensity of precipitation varied markedly during this storm, providing an excellent dataset for these investigations. Data from 36 recording gauges in the northern half of the Alps were supplemented by data from 57 daily gauges, which were partitioned into 6-h values. These data were grouped according to distance from the alpine divide, and best-fit transect curves, normalized for rainfall intensity, were established every 6 h. The fraction of the total transect precipitation falling in leeside catchments varied between 0.11 and 0.70, while a “spillover distance” index varied between 6 and 29 km. Comparison with atmospheric profiles of temperature and wind from Hokitika on the west coast of New Zealand and with European Centre for Medium-Range Weather Forecasts analyses revealed that precipitation was confined upwind of the divide during a period of blocked flow near the start of the storm, and only extended into leeside catchments with the onset of stronger flow and reduced static stability. Regression equations involving these factors explained up to 93% of the spillover variations. It is suggested that ascent and precipitation maxima are shifted upstream during blocked flow, while spillover is enhanced during stronger and/or unstable flow as the upstream influence lessens and snow and ice particles drift farther downwind before falling below the freezing level. Further case and modeling studies are needed to demonstrate the wider applicability of these findings.
Abstract
Rain gauge, radar, and atmospheric observations during a prolonged northwesterly storm in November 1994 have been used to study factors influencing the distribution of precipitation across the Southern Alps. Despite the persistent northwesterly flow, the location and intensity of precipitation varied markedly during this storm, providing an excellent dataset for these investigations. Data from 36 recording gauges in the northern half of the Alps were supplemented by data from 57 daily gauges, which were partitioned into 6-h values. These data were grouped according to distance from the alpine divide, and best-fit transect curves, normalized for rainfall intensity, were established every 6 h. The fraction of the total transect precipitation falling in leeside catchments varied between 0.11 and 0.70, while a “spillover distance” index varied between 6 and 29 km. Comparison with atmospheric profiles of temperature and wind from Hokitika on the west coast of New Zealand and with European Centre for Medium-Range Weather Forecasts analyses revealed that precipitation was confined upwind of the divide during a period of blocked flow near the start of the storm, and only extended into leeside catchments with the onset of stronger flow and reduced static stability. Regression equations involving these factors explained up to 93% of the spillover variations. It is suggested that ascent and precipitation maxima are shifted upstream during blocked flow, while spillover is enhanced during stronger and/or unstable flow as the upstream influence lessens and snow and ice particles drift farther downwind before falling below the freezing level. Further case and modeling studies are needed to demonstrate the wider applicability of these findings.
Abstract
A rain gauge is described that quantizes rainwater collected by a funnel into equal-sized drops. Using a funnel of 150-mm diameter, the quantization corresponds to 1/160 mm of rainfall, enabling the measurement of low rainfall rates and the attainment of a fine temporal resolution on the order of 15 s without unduly large sampling errors. Two drop-producing units are compared and an operational rain gauge design is presented. Field comparisons with conventional rain gauges are made, showing excellent correlations for daily rain totals, and intercomparisons between clusters of dropper gauges are also given. Examples of highly resolved rainfall events are shown demonstrating the ability to measure low rainfall accumulations and also coherent high intensity events of short duration, which are not detectable with conventional rain gauges.
Abstract
A rain gauge is described that quantizes rainwater collected by a funnel into equal-sized drops. Using a funnel of 150-mm diameter, the quantization corresponds to 1/160 mm of rainfall, enabling the measurement of low rainfall rates and the attainment of a fine temporal resolution on the order of 15 s without unduly large sampling errors. Two drop-producing units are compared and an operational rain gauge design is presented. Field comparisons with conventional rain gauges are made, showing excellent correlations for daily rain totals, and intercomparisons between clusters of dropper gauges are also given. Examples of highly resolved rainfall events are shown demonstrating the ability to measure low rainfall accumulations and also coherent high intensity events of short duration, which are not detectable with conventional rain gauges.
Abstract
This paper reports on the first application of a multispectral textural Bayesian cloud classification algorithm (“SRTex”) to the general problem of the determination of high–spatial resolution cloud-amount and cloud-type climatological distributions. One year of NOAA-14 daylight passes over a region of complex topography (the South Island of New Zealand and adjacent ocean areas) is analyzed, and exploratory cloud-amount and -type climatological distributions are developed. When validated against a set of surface observations, the cloud-amount distributions have no significant bias at seasonal and yearly timescales, and explain between 70% (seasonal) and 90% (annual) of the spatial variance in the surface observations.
The cloud-amount distributions show strong land/sea contrasts. Lowest cloud frequencies are found in the lee of the major alpine feature in the analysis domain (the Southern Alps) and over mountain-sheltered valleys and adjacent sea areas. Over the oceans, cloud frequencies are highest over sub-Antarctic water masses, and range from 90% to 95%. However, over the sea adjacent to the coast on the western side of the Southern Alps, there is a distinct minimum in cloud amount that appears to be related to the orography.
The cloud-type climatological distributions are analyzed in terms of both simple frequency of occurrence and conditional frequency of occurrence, which is the frequency of occurrence as a fraction of the total number of times that the cloud type could have been observed. These distributions reveal the presence of preferred locations for some cloud types. There is strong evidence that uplift over major mountain ranges is a source of transmissive cirrus (enhancing occurrence by a factor of 2) and that the resulting cirrus coverage is most extensive and frequent in spring. Over the ocean areas, SST-related effects may determine the spatial distributions of stratocumulus, with higher frequencies observed over sub-Antarctic waters than over subtropical waters. Also, there is a positive correlation between mean cloud-top height and SST, but no similar relationship is found for other cloud types.
Abstract
This paper reports on the first application of a multispectral textural Bayesian cloud classification algorithm (“SRTex”) to the general problem of the determination of high–spatial resolution cloud-amount and cloud-type climatological distributions. One year of NOAA-14 daylight passes over a region of complex topography (the South Island of New Zealand and adjacent ocean areas) is analyzed, and exploratory cloud-amount and -type climatological distributions are developed. When validated against a set of surface observations, the cloud-amount distributions have no significant bias at seasonal and yearly timescales, and explain between 70% (seasonal) and 90% (annual) of the spatial variance in the surface observations.
The cloud-amount distributions show strong land/sea contrasts. Lowest cloud frequencies are found in the lee of the major alpine feature in the analysis domain (the Southern Alps) and over mountain-sheltered valleys and adjacent sea areas. Over the oceans, cloud frequencies are highest over sub-Antarctic water masses, and range from 90% to 95%. However, over the sea adjacent to the coast on the western side of the Southern Alps, there is a distinct minimum in cloud amount that appears to be related to the orography.
The cloud-type climatological distributions are analyzed in terms of both simple frequency of occurrence and conditional frequency of occurrence, which is the frequency of occurrence as a fraction of the total number of times that the cloud type could have been observed. These distributions reveal the presence of preferred locations for some cloud types. There is strong evidence that uplift over major mountain ranges is a source of transmissive cirrus (enhancing occurrence by a factor of 2) and that the resulting cirrus coverage is most extensive and frequent in spring. Over the ocean areas, SST-related effects may determine the spatial distributions of stratocumulus, with higher frequencies observed over sub-Antarctic waters than over subtropical waters. Also, there is a positive correlation between mean cloud-top height and SST, but no similar relationship is found for other cloud types.
Abstract
Bayesian methods are used to develop a cloud mask classification algorithm for use in an operational sea surface temperature (SST) retrieval processing system for Advanced Very High Resolution Radiometer (AVHRR) local area coverage (LAC) resolution data. Both radiative and spatial features are incorporated in the resulting discriminant functions, which are determined from a large training sample of cloudy and clear observations. This approach obviates the need to specify the arbitrary thresholds used by hierarchical cloud-clearing methods, provides an estimate of the probability that an instantaneous field of view is cloudy (clear), and allows the skill of different cloud discriminant models to be objectively analyzed.
Results show that spatial information is of particular importance in reducing the false alarm rate of the cloudy class. However, while the use of complex textural measures such as gray-level difference statistics—as opposed to simple statistics such as the standard deviation—improves the skill of nighttime cloud-masking algorithms, they are of little advantage during daytime hours.
Cloud mask discriminant models having similar high Kuipers’ performance index scores (i.e., 0.935) are developed for both day and night satellite data from the Southern Hemisphere midlatitudes. Applied to LAC orbital (i.e., operational) data, the characteristics of the cloud masks appear to be similar to those derived from analysis of the training sample data. However, in this case, to enhance processing performance, a hybrid algorithm is employed—obviously cloudy instantaneous fields of view (IFOVs) are first removed via a gross threshold check and the Bayesian method applied only to the remaining IFOVs. This same (hybrid) algorithm is also applied to an ensemble of 30 days of AVHRR LAC data from the New Zealand region. Analysis of the resulting time-composited SST data (means and standard deviations) shows there is little evidence of a day–night bias in the performance of the Bayesian cloud-masking algorithm and that the resulting SST data may be used to determine the variability of oceanographic features.
Although this paper uses AVHRR data to demonstrate the principles of the Bayesian cloud-masking algorithm, there is no reason why the approach could not be used with other instruments.
Abstract
Bayesian methods are used to develop a cloud mask classification algorithm for use in an operational sea surface temperature (SST) retrieval processing system for Advanced Very High Resolution Radiometer (AVHRR) local area coverage (LAC) resolution data. Both radiative and spatial features are incorporated in the resulting discriminant functions, which are determined from a large training sample of cloudy and clear observations. This approach obviates the need to specify the arbitrary thresholds used by hierarchical cloud-clearing methods, provides an estimate of the probability that an instantaneous field of view is cloudy (clear), and allows the skill of different cloud discriminant models to be objectively analyzed.
Results show that spatial information is of particular importance in reducing the false alarm rate of the cloudy class. However, while the use of complex textural measures such as gray-level difference statistics—as opposed to simple statistics such as the standard deviation—improves the skill of nighttime cloud-masking algorithms, they are of little advantage during daytime hours.
Cloud mask discriminant models having similar high Kuipers’ performance index scores (i.e., 0.935) are developed for both day and night satellite data from the Southern Hemisphere midlatitudes. Applied to LAC orbital (i.e., operational) data, the characteristics of the cloud masks appear to be similar to those derived from analysis of the training sample data. However, in this case, to enhance processing performance, a hybrid algorithm is employed—obviously cloudy instantaneous fields of view (IFOVs) are first removed via a gross threshold check and the Bayesian method applied only to the remaining IFOVs. This same (hybrid) algorithm is also applied to an ensemble of 30 days of AVHRR LAC data from the New Zealand region. Analysis of the resulting time-composited SST data (means and standard deviations) shows there is little evidence of a day–night bias in the performance of the Bayesian cloud-masking algorithm and that the resulting SST data may be used to determine the variability of oceanographic features.
Although this paper uses AVHRR data to demonstrate the principles of the Bayesian cloud-masking algorithm, there is no reason why the approach could not be used with other instruments.
