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- Author or Editor: H. L. Johnson Jr. x
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Abstract
Thunderstorm radio noise measurements at several frequencies in the range 0.01–74 MHz have been made with specially designed remote recording stations in Iowa. The data were recorded during the spring and summer of 1974 when a series of severe storm systems produced a great number of large hail and tornado reports in Iowa. Computer analyses were made of nearly a billion bits of data, corresponding to 170 h of real-time recordings. Careful compilations of surface severe weather reports, hail damage information from insurance companies, and studies on the Des Moines WSR-57 radar echoes were compared with the analyzed radio noise data. The results include the following:
1) In agreement with earlier work, large‐amplitude radio noise impulse rates were found to he generally good indicators of thunderstorm severity. Although the majority of the radio energy radiated from major lightning strokes occurs in the 0.01 MHz range, this frequency was found to be a poor indicator of storm severity; the higher frequencies (megahertz range) were considerably better. The character of the noise appears similar at 2.5 and 74 MHz.
2) In at least five cases, tornadic events correlated in time with radio noise count rate peaks. One funnel cloud was reported equidistant at 60 km from two recording stations and coincident with count rate peaks at both stations, lending credence to the idea that the peak was associated with the storm occurrence, rather than with corona or other local effects.
3) No unusual radio noise was recorded during the lifetime of a small, verified tornado at 19 km range. In addition, the count rates for its parent thunderstorm would not have indicated severity.
In spite of inherent atmospheric variableness, the radio noise technique is a useful complementary indicator of storm severity.
Abstract
Thunderstorm radio noise measurements at several frequencies in the range 0.01–74 MHz have been made with specially designed remote recording stations in Iowa. The data were recorded during the spring and summer of 1974 when a series of severe storm systems produced a great number of large hail and tornado reports in Iowa. Computer analyses were made of nearly a billion bits of data, corresponding to 170 h of real-time recordings. Careful compilations of surface severe weather reports, hail damage information from insurance companies, and studies on the Des Moines WSR-57 radar echoes were compared with the analyzed radio noise data. The results include the following:
1) In agreement with earlier work, large‐amplitude radio noise impulse rates were found to he generally good indicators of thunderstorm severity. Although the majority of the radio energy radiated from major lightning strokes occurs in the 0.01 MHz range, this frequency was found to be a poor indicator of storm severity; the higher frequencies (megahertz range) were considerably better. The character of the noise appears similar at 2.5 and 74 MHz.
2) In at least five cases, tornadic events correlated in time with radio noise count rate peaks. One funnel cloud was reported equidistant at 60 km from two recording stations and coincident with count rate peaks at both stations, lending credence to the idea that the peak was associated with the storm occurrence, rather than with corona or other local effects.
3) No unusual radio noise was recorded during the lifetime of a small, verified tornado at 19 km range. In addition, the count rates for its parent thunderstorm would not have indicated severity.
In spite of inherent atmospheric variableness, the radio noise technique is a useful complementary indicator of storm severity.
Abstract
Early work attempting to apply GOES rapid scan satellite data to a recently developed simple radar technique used to estimate convective rain volumes over areas in a semiarid environment (the northern Great Plains) is described.
Called the Area-Time-Integral (ATI) technique, it provides a means of estimating total rain volumes over fixed and floating target areas. The basis of the method is the existence of a strong correlation between the radar echo area coverage integrated over the lifetime of the storm and the radar estimated rain volume. The technique does not require the consideration of the structure of the radar intensities to generate rain volumes. but only the area covered by radar echoes. This fact might reduce the source of errors generated by the structure differences between the radar and the satellite signatures above given thresholds.
Satellite and radar data from the 1981 Cooperative Convective Precipitation Experiment (CCOPE) and the North Dakota Cloud Modification Project (NDCMP) are used. Consecutive time steps with both radar reflectivities and satellite (VIS and IR) rapid wan data were considered during the evolution of six convective clusters: three on 12 June, and three on 2 July 1981. Radar echoes with reflectivity values ≥ 25 dBZ were used to define the area of rainfall and the respective digital unit thresholds within the satellite data delineating the rainy part of the cloud area. Correlation of the ATI versus IR digital count values was obtained for every time step and for the storm lifetime, respectively.
A comparison of the stepwise evolution of radar parameters such as echo areas maximum echo heights, maximum reflectivities and satellite parameters such as threshold count values and coldest cloud top temperature is presented graphically and reflects the multicell characteristics of the convective clusters. Also, a comparison of radar and satellite parameters for the cluster lifetime is made. Satellite parameters pertaining to the cluster lifetime were derived both dependently and independently of radar data.
The main purpose of this investigation is to compute convective rain volume of a convective cluster by application of the ATI technique based only on satellite data. As such, the key element is to determine the ATI from satellite data without consideration of radar data. This is possible if trends of satellite products generated independently are similar to those of satellite products based upon radar observations as done here.
A parallel with the two-step techniques generally used to estimate rain volume from satellite data is made. To delineate the rainy part of a cloud area, a regression analysis is used. The regression relate a satellite-independent product to a satellite-dependent product. For a given storm. the satellite-independent product is first computed; then the regression equation gives the ATI, Finally, the rain volume is obtained by using the ATI versus rain volume relationship.
By applying the ATI/rain volume relationship to satellite data, the errors generated by the complicated multiple area-volume transform relations am reduced, as similar errors were reduced when the technique was applied to radar data. In addition, a regression analysis gives more accurate estimates than a single threshold when used to delineate an area covered by rain events from an area covered by clouds. The advantages of the ATI technique are based on the fact that the technique operates on a storm lifetime integrated basis, while the previous techniques operate on a time-step basis. The new technique generates only total rain volume estimates (not rain rates). This indeed is a limitation.
The analyses of six convective clusters suggest that the extension of the ATI technique using satellite data holds promise.
Abstract
Early work attempting to apply GOES rapid scan satellite data to a recently developed simple radar technique used to estimate convective rain volumes over areas in a semiarid environment (the northern Great Plains) is described.
Called the Area-Time-Integral (ATI) technique, it provides a means of estimating total rain volumes over fixed and floating target areas. The basis of the method is the existence of a strong correlation between the radar echo area coverage integrated over the lifetime of the storm and the radar estimated rain volume. The technique does not require the consideration of the structure of the radar intensities to generate rain volumes. but only the area covered by radar echoes. This fact might reduce the source of errors generated by the structure differences between the radar and the satellite signatures above given thresholds.
Satellite and radar data from the 1981 Cooperative Convective Precipitation Experiment (CCOPE) and the North Dakota Cloud Modification Project (NDCMP) are used. Consecutive time steps with both radar reflectivities and satellite (VIS and IR) rapid wan data were considered during the evolution of six convective clusters: three on 12 June, and three on 2 July 1981. Radar echoes with reflectivity values ≥ 25 dBZ were used to define the area of rainfall and the respective digital unit thresholds within the satellite data delineating the rainy part of the cloud area. Correlation of the ATI versus IR digital count values was obtained for every time step and for the storm lifetime, respectively.
A comparison of the stepwise evolution of radar parameters such as echo areas maximum echo heights, maximum reflectivities and satellite parameters such as threshold count values and coldest cloud top temperature is presented graphically and reflects the multicell characteristics of the convective clusters. Also, a comparison of radar and satellite parameters for the cluster lifetime is made. Satellite parameters pertaining to the cluster lifetime were derived both dependently and independently of radar data.
The main purpose of this investigation is to compute convective rain volume of a convective cluster by application of the ATI technique based only on satellite data. As such, the key element is to determine the ATI from satellite data without consideration of radar data. This is possible if trends of satellite products generated independently are similar to those of satellite products based upon radar observations as done here.
A parallel with the two-step techniques generally used to estimate rain volume from satellite data is made. To delineate the rainy part of a cloud area, a regression analysis is used. The regression relate a satellite-independent product to a satellite-dependent product. For a given storm. the satellite-independent product is first computed; then the regression equation gives the ATI, Finally, the rain volume is obtained by using the ATI versus rain volume relationship.
By applying the ATI/rain volume relationship to satellite data, the errors generated by the complicated multiple area-volume transform relations am reduced, as similar errors were reduced when the technique was applied to radar data. In addition, a regression analysis gives more accurate estimates than a single threshold when used to delineate an area covered by rain events from an area covered by clouds. The advantages of the ATI technique are based on the fact that the technique operates on a storm lifetime integrated basis, while the previous techniques operate on a time-step basis. The new technique generates only total rain volume estimates (not rain rates). This indeed is a limitation.
The analyses of six convective clusters suggest that the extension of the ATI technique using satellite data holds promise.
Abstract
The Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) was designed to be a land surface model (LSM) benchmarking intercomparison. Unlike the traditional methods of LSM evaluation or comparison, benchmarking uses a fundamentally different approach in that it sets expectations of performance in a range of metrics a priori—before model simulations are performed. This can lead to very different conclusions about LSM performance. For this study, both simple physically based models and empirical relationships were used as the benchmarks. Simulations were performed with 13 LSMs using atmospheric forcing for 20 sites, and then model performance relative to these benchmarks was examined. Results show that even for commonly used statistical metrics, the LSMs’ performance varies considerably when compared to the different benchmarks. All models outperform the simple physically based benchmarks, but for sensible heat flux the LSMs are themselves outperformed by an out-of-sample linear regression against downward shortwave radiation. While moisture information is clearly central to latent heat flux prediction, the LSMs are still outperformed by a three-variable nonlinear regression that uses instantaneous atmospheric humidity and temperature in addition to downward shortwave radiation. These results highlight the limitations of the prevailing paradigm of LSM evaluation that simply compares an LSM to observations and to other LSMs without a mechanism to objectively quantify the expectations of performance. The authors conclude that their results challenge the conceptual view of energy partitioning at the land surface.
Abstract
The Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) was designed to be a land surface model (LSM) benchmarking intercomparison. Unlike the traditional methods of LSM evaluation or comparison, benchmarking uses a fundamentally different approach in that it sets expectations of performance in a range of metrics a priori—before model simulations are performed. This can lead to very different conclusions about LSM performance. For this study, both simple physically based models and empirical relationships were used as the benchmarks. Simulations were performed with 13 LSMs using atmospheric forcing for 20 sites, and then model performance relative to these benchmarks was examined. Results show that even for commonly used statistical metrics, the LSMs’ performance varies considerably when compared to the different benchmarks. All models outperform the simple physically based benchmarks, but for sensible heat flux the LSMs are themselves outperformed by an out-of-sample linear regression against downward shortwave radiation. While moisture information is clearly central to latent heat flux prediction, the LSMs are still outperformed by a three-variable nonlinear regression that uses instantaneous atmospheric humidity and temperature in addition to downward shortwave radiation. These results highlight the limitations of the prevailing paradigm of LSM evaluation that simply compares an LSM to observations and to other LSMs without a mechanism to objectively quantify the expectations of performance. The authors conclude that their results challenge the conceptual view of energy partitioning at the land surface.
