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- Author or Editor: PAUL T. SCHICKEDANZ x
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Abstract
Rainfall data from a large dense network are being used to study inadvertent rainfall modification in the St. Louis area. Surface raincells are delineated and then analyzed to determine the character of any urban-induced changes in precipitation. Results for comparisons of 605 potential effect cells and 870 non-effect cells from the summer of 1971–72 provide strong evidence that cell characteristics have been sizeably altered by the local urban-industrial environment. For cells occurring in the urban-industrial zone of St. Louis, the average rainfall volume was 176% greater than for cells in the control sample. For cells occurring in the separate industrial region of Wood River, the average volume was 262% greater than the cells in the control sample. The results show that the primary change in St. Louis cells is total rain area, and this and other results suggest that this relates to dynamic effects induced by the urban heat island. The primary change in Wood River cells is in rain intensity, and this and other results suggest that this relates to microphysical effects from the industrial aerosols and additions of moisture into the atmosphere, particularly in dry summers. Importantly the primary causes of observed rain increases in St. Louis and Wood River appear to differ, and additional data must be collected and analyzed to enlarge on the interesting two-summer results.
Abstract
Rainfall data from a large dense network are being used to study inadvertent rainfall modification in the St. Louis area. Surface raincells are delineated and then analyzed to determine the character of any urban-induced changes in precipitation. Results for comparisons of 605 potential effect cells and 870 non-effect cells from the summer of 1971–72 provide strong evidence that cell characteristics have been sizeably altered by the local urban-industrial environment. For cells occurring in the urban-industrial zone of St. Louis, the average rainfall volume was 176% greater than for cells in the control sample. For cells occurring in the separate industrial region of Wood River, the average volume was 262% greater than the cells in the control sample. The results show that the primary change in St. Louis cells is total rain area, and this and other results suggest that this relates to dynamic effects induced by the urban heat island. The primary change in Wood River cells is in rain intensity, and this and other results suggest that this relates to microphysical effects from the industrial aerosols and additions of moisture into the atmosphere, particularly in dry summers. Importantly the primary causes of observed rain increases in St. Louis and Wood River appear to differ, and additional data must be collected and analyzed to enlarge on the interesting two-summer results.
Abstract
The synoptic and subsynoptic atmospheric processes that accompany statistically determined periods of irrigation-induced rainfall increases during the warm season in the Texas Panhandle are examined. Major results are as follows.
Irrigation appears to increase precipitation only when the synoptic condition provides low-level convergence and uplift, such that the additional moisture produced by irrigation (normally confined to the lowest 10–20 m of the atmosphere) is allowed to ascend to cloud base. Stationary fronts are the most favorable such synoptic condition because they fulfill the requirement for longer time durations than moving fronts or surface low pressure centers. The effect of irrigation is more noticeable during generally rainy periods because such periods often contain the types of significant rainfall events that provide sustained low-level convergence over the irrigated region. Because the mean storm track is closer to north Texas in June than in July and August, the irrigation-produced rainfall anomaly in June (which often is >20% in and somewhat downwind of the irrigation core) is the greatest of these three heavily irrigated months.
Irrigation appears to lower the daily surface maximum temperature by ∼2°C during dry, hot conditions and by ∼1°C on damp, cooler days. When combining the temperature anomalies with known increases in surface dewpoint, the lifted index is estimated to decrease by up to 1°C, slightly increasing the probability of convection, even in the absence of convergence.
Other possible mesoscale effects of irrigation are discussed.
Abstract
The synoptic and subsynoptic atmospheric processes that accompany statistically determined periods of irrigation-induced rainfall increases during the warm season in the Texas Panhandle are examined. Major results are as follows.
Irrigation appears to increase precipitation only when the synoptic condition provides low-level convergence and uplift, such that the additional moisture produced by irrigation (normally confined to the lowest 10–20 m of the atmosphere) is allowed to ascend to cloud base. Stationary fronts are the most favorable such synoptic condition because they fulfill the requirement for longer time durations than moving fronts or surface low pressure centers. The effect of irrigation is more noticeable during generally rainy periods because such periods often contain the types of significant rainfall events that provide sustained low-level convergence over the irrigated region. Because the mean storm track is closer to north Texas in June than in July and August, the irrigation-produced rainfall anomaly in June (which often is >20% in and somewhat downwind of the irrigation core) is the greatest of these three heavily irrigated months.
Irrigation appears to lower the daily surface maximum temperature by ∼2°C during dry, hot conditions and by ∼1°C on damp, cooler days. When combining the temperature anomalies with known increases in surface dewpoint, the lifted index is estimated to decrease by up to 1°C, slightly increasing the probability of convection, even in the absence of convergence.
Other possible mesoscale effects of irrigation are discussed.
Abstract
A technique for computing climatological power spectra based on the concept of utilizing non-integer values in the sine and cosine waveforms (NI technique) is developed and applied to climatological rainfall data. This technique provides a powerful alternative to the more common techniques used in the computation of climatological power spectra. The major advantage of this technique is the greatly improved resolution of wavelengths in the 5–25 year region, often a critical region of interest for climatologists. The technique produces spectral density values which are not necessarily independent; however, methods of specifying and then testing the departure from independence (orthogonality) are given. Furthermore, it is shown that the usual equations for the Fourier coefficients are special cases of the more general condition in which the spectral estimates include some degree of non-independence (i.e., lack of orthogonality). It is anticipated that this technique will have wide applicability in climatology, meteorology, hydrology and the other geophysical sciences.
Abstract
A technique for computing climatological power spectra based on the concept of utilizing non-integer values in the sine and cosine waveforms (NI technique) is developed and applied to climatological rainfall data. This technique provides a powerful alternative to the more common techniques used in the computation of climatological power spectra. The major advantage of this technique is the greatly improved resolution of wavelengths in the 5–25 year region, often a critical region of interest for climatologists. The technique produces spectral density values which are not necessarily independent; however, methods of specifying and then testing the departure from independence (orthogonality) are given. Furthermore, it is shown that the usual equations for the Fourier coefficients are special cases of the more general condition in which the spectral estimates include some degree of non-independence (i.e., lack of orthogonality). It is anticipated that this technique will have wide applicability in climatology, meteorology, hydrology and the other geophysical sciences.
Abstract
Storm rainfall data from dense raingage networks in Illinois were employed in a study to determine the length of time required to obtain significance for various increases in storm rainfall due to weather modification efforts. The primary purpose was to evaluate the effect of stratifying the storm data on the detection of seeding effects for a given design using highly accurate measurements of the rainfall parameters. It was also desired to evaluate the efficiency of various rainfall parameters and the efficiency of various statistical designs in detecting various increases. Results indicate that the length of experimentation necessary for detection of seeding effects varies according to weather type, precipitation type, rainfall parameter, and statistical design employed. Results also indicate that as the seeding-induced increase becomes large, the choice of stratification, rainfall parameter, and statistical design becomes less important. An evaluation procedure is recommended which incorporates desirable features from several of the designs, stratifications and rainfall parameters considered in this study. Although it is difficult to verify, a 20% increase in precipitation can be detected in a five-year experiment provided proper choices are made of weather types, statistical designs, data stratifications and rainfall parameters.
Abstract
Storm rainfall data from dense raingage networks in Illinois were employed in a study to determine the length of time required to obtain significance for various increases in storm rainfall due to weather modification efforts. The primary purpose was to evaluate the effect of stratifying the storm data on the detection of seeding effects for a given design using highly accurate measurements of the rainfall parameters. It was also desired to evaluate the efficiency of various rainfall parameters and the efficiency of various statistical designs in detecting various increases. Results indicate that the length of experimentation necessary for detection of seeding effects varies according to weather type, precipitation type, rainfall parameter, and statistical design employed. Results also indicate that as the seeding-induced increase becomes large, the choice of stratification, rainfall parameter, and statistical design becomes less important. An evaluation procedure is recommended which incorporates desirable features from several of the designs, stratifications and rainfall parameters considered in this study. Although it is difficult to verify, a 20% increase in precipitation can be detected in a five-year experiment provided proper choices are made of weather types, statistical designs, data stratifications and rainfall parameters.
Abstract
The method of maximum likelihood is used to develop a statistical test for the scale parameters of two gamma distributions with common shape factors. A simple method of determining the power of the test using the non-central chi square distribution is also presented. The results of applying the test to gamma-scale parameters are compared with results obtained by applying the “t” test to normal and log-normal means. The likelihood ratio test for differences in gamma-scale parameters is more powerful than the “t” test applied to log-normal means. The power is nearly equal for the likelihood test of gamma-scale parameters and the “t” test for non-transformed means, although the latter test may not be as robust as is the likelihood ratio test. Since many meteorological variables are known to be gamma distributed, this test should have several applications in meteorology.
Abstract
The method of maximum likelihood is used to develop a statistical test for the scale parameters of two gamma distributions with common shape factors. A simple method of determining the power of the test using the non-central chi square distribution is also presented. The results of applying the test to gamma-scale parameters are compared with results obtained by applying the “t” test to normal and log-normal means. The likelihood ratio test for differences in gamma-scale parameters is more powerful than the “t” test applied to log-normal means. The power is nearly equal for the likelihood test of gamma-scale parameters and the “t” test for non-transformed means, although the latter test may not be as robust as is the likelihood ratio test. Since many meteorological variables are known to be gamma distributed, this test should have several applications in meteorology.
Abstract
The desigu of a field experiment in rainfall augmentation requires prior estimates of the duration of the experiment and the density of raingages. A “Monte Carlo” method was developed to generate synthetic climatological rainfall data for various time periods and densities of raingages. The method was applied to a hypothetical cloud seeding experiment. Rainfall data for reporting networks were simulated and the resulting data were used to estimate the change in error variance induced by varying the density in a raingage network and the length of the experiment. The “t” test was applied to the simulated nontransformed data which were skewed and to data normalized by a transformation. In addition, the generalized likelihood ratio test was used to test for differences in location parameters of the seeded and nonseeded gamma distributions having a common shape factor.
The applicability and limitations of the method are discussed. With proper consideration of the limitations and with additional research on the problems encountered, it should be possible to obtain a preliminary estimate of the error variance of a proposed experimental design for many areas and various conditions.
Abstract
The desigu of a field experiment in rainfall augmentation requires prior estimates of the duration of the experiment and the density of raingages. A “Monte Carlo” method was developed to generate synthetic climatological rainfall data for various time periods and densities of raingages. The method was applied to a hypothetical cloud seeding experiment. Rainfall data for reporting networks were simulated and the resulting data were used to estimate the change in error variance induced by varying the density in a raingage network and the length of the experiment. The “t” test was applied to the simulated nontransformed data which were skewed and to data normalized by a transformation. In addition, the generalized likelihood ratio test was used to test for differences in location parameters of the seeded and nonseeded gamma distributions having a common shape factor.
The applicability and limitations of the method are discussed. With proper consideration of the limitations and with additional research on the problems encountered, it should be possible to obtain a preliminary estimate of the error variance of a proposed experimental design for many areas and various conditions.
Abstract
Historical hail-day records of U.S. Weather Bureau first-order stations and cooperative substations are the only long, objective records of hail occurrence available throughout the United States. Although hail-day data are limited in areal density and are not necessarily the most desired measure of seeding effects, they are the only data available to obtain a measure of the areal-temporal variability of hail for most areas of the United States. Consequently, hail-day data from Illinois have been employed in a pilot project to determine the time required to obtain statistically significant changes in hail-day frequencies over various sized areas. Four statistical designs were investigated using the historical hail-day data for five areas in Illinois. The results show that the optimum design for hail-day data is the continuous seeding (seeding on all days likely to have hail) over an area. The optimum test is the sequential test involving the Poisson and Negative Binomial distributions. Detection of a 20-percent reduction in summer hail days would require, on the average, a continuous seeding program ranging from 13 to 37 yr, depending on the level of precision desired, and the size and location of the seeded area. Major reductions, those in excess of 60 percent, would require experiments of only 1- to 3-yr length.
Abstract
Historical hail-day records of U.S. Weather Bureau first-order stations and cooperative substations are the only long, objective records of hail occurrence available throughout the United States. Although hail-day data are limited in areal density and are not necessarily the most desired measure of seeding effects, they are the only data available to obtain a measure of the areal-temporal variability of hail for most areas of the United States. Consequently, hail-day data from Illinois have been employed in a pilot project to determine the time required to obtain statistically significant changes in hail-day frequencies over various sized areas. Four statistical designs were investigated using the historical hail-day data for five areas in Illinois. The results show that the optimum design for hail-day data is the continuous seeding (seeding on all days likely to have hail) over an area. The optimum test is the sequential test involving the Poisson and Negative Binomial distributions. Detection of a 20-percent reduction in summer hail days would require, on the average, a continuous seeding program ranging from 13 to 37 yr, depending on the level of precision desired, and the size and location of the seeded area. Major reductions, those in excess of 60 percent, would require experiments of only 1- to 3-yr length.
Abstract
A statistical methodology involving the analysis of three basic types of historical hail data on an areal approach is presented for the planning and evaluation of hail suppression experiments in Illinois. The methodology was used to generate nomograms relating the number of years required to detect significant results to 1) type I error, 2) type II error, and 3) power of the test for various statistical tests and experimental designs. These nomograms were constructed for various area sizes and geographical locations within the State.
Results indicate that, for an Illinois experiment, insurance crop-loss data are the optimum hail measurement if the study area has more than 60 percent insurance coverage. The optimum experimental design is the random-historical design in which all potential storms are seeded on a particular day, and 80 percent of the forecasted hail days are chosen at random to be “seeded days.” The recommended statistical analysis is the sequential analytical approach. If, however, conditions for the sequential analytical approach are not fulfilled by the data sample, the nonsequential approach utilizing a one-sample test with the historical record as the control (random-historical design) should be employed.
For a significance level of 0.05 and a beta error of 0.3, the average detection time in an area of approximately 1,500 sq mi would be 11 yr for a 20 percent reduction in the number of acres damaged, 2 yr for a 40 percent reduction, and 1 yr for a 60 and 80 percent reduction. If the nonsequential analyses were required, the number of years would be 25, 5, and 1, respectively.
Abstract
A statistical methodology involving the analysis of three basic types of historical hail data on an areal approach is presented for the planning and evaluation of hail suppression experiments in Illinois. The methodology was used to generate nomograms relating the number of years required to detect significant results to 1) type I error, 2) type II error, and 3) power of the test for various statistical tests and experimental designs. These nomograms were constructed for various area sizes and geographical locations within the State.
Results indicate that, for an Illinois experiment, insurance crop-loss data are the optimum hail measurement if the study area has more than 60 percent insurance coverage. The optimum experimental design is the random-historical design in which all potential storms are seeded on a particular day, and 80 percent of the forecasted hail days are chosen at random to be “seeded days.” The recommended statistical analysis is the sequential analytical approach. If, however, conditions for the sequential analytical approach are not fulfilled by the data sample, the nonsequential approach utilizing a one-sample test with the historical record as the control (random-historical design) should be employed.
For a significance level of 0.05 and a beta error of 0.3, the average detection time in an area of approximately 1,500 sq mi would be 11 yr for a 20 percent reduction in the number of acres damaged, 2 yr for a 40 percent reduction, and 1 yr for a 60 and 80 percent reduction. If the nonsequential analyses were required, the number of years would be 25, 5, and 1, respectively.
