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- Author or Editor: Kenneth G. Hubbard x
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Abstract
The physics of ice nucleation as a temperature-dependent process is incorporated into a parameterized equation which simulates the depositional growth of ice at the expense of liquid and vapor phases. The depositional parameterization is examined in the framework of a parcel model and the results compared to a more detailed ice model containing 20 mass categories of ice crystals. Comparisons show that the parametric and detailed models yield nearly identical results. A computational advantage is present with the parameterization because the necessary size distribution information is greatly reduced and the time step is much larger.
Abstract
The physics of ice nucleation as a temperature-dependent process is incorporated into a parameterized equation which simulates the depositional growth of ice at the expense of liquid and vapor phases. The depositional parameterization is examined in the framework of a parcel model and the results compared to a more detailed ice model containing 20 mass categories of ice crystals. Comparisons show that the parametric and detailed models yield nearly identical results. A computational advantage is present with the parameterization because the necessary size distribution information is greatly reduced and the time step is much larger.
Abstract
A method is set forth which provides quality control of any meteorological data exhibiting a seasonal distribution. In-this study the set of all pairs representing a three-month consecutive average of "actual" climate variable and a three-month average derived from the central month are plotted in an x-y coordinate system. Procedures for identifying points which fall off the 1:1 line are discussed with implications for the accuracy of the method. The procedure can be automated for iterative data checking on a digital computer. The method is applicable to all situations where a quality check of average data from a number of stations is desired.
Abstract
A method is set forth which provides quality control of any meteorological data exhibiting a seasonal distribution. In-this study the set of all pairs representing a three-month consecutive average of "actual" climate variable and a three-month average derived from the central month are plotted in an x-y coordinate system. Procedures for identifying points which fall off the 1:1 line are discussed with implications for the accuracy of the method. The procedure can be automated for iterative data checking on a digital computer. The method is applicable to all situations where a quality check of average data from a number of stations is desired.
Abstract
Quality assurance (QA) procedures have been automated to reduce the time and labor necessary to discover outliers in weather data. Measurements from neighboring stations are used in this study in a spatial regression test to provide preliminary estimates of the measured data points. The new method does not assign the largest weight to the nearest estimate but, instead, assigns the weights according to the standard error of estimate. In this paper, the spatial test was employed to study patterns in flagged data in the following extreme events: the 1993 Midwest floods, the 2002 drought, Hurricane Andrew (1992), and a series of cold fronts during October 1990. The location of flagged records and the influence zones for such events relative to QA were compared. The behavior of the spatial test in these events provides important information on the probability of making a type I error in the assignment of the quality control flag. Simple pattern recognition tools that identify zones wherein frequent flagging occurs are illustrated. These tools serve as a means of resetting QA flags to minimize the number of type I errors as demonstrated for the extreme events included here.
Abstract
Quality assurance (QA) procedures have been automated to reduce the time and labor necessary to discover outliers in weather data. Measurements from neighboring stations are used in this study in a spatial regression test to provide preliminary estimates of the measured data points. The new method does not assign the largest weight to the nearest estimate but, instead, assigns the weights according to the standard error of estimate. In this paper, the spatial test was employed to study patterns in flagged data in the following extreme events: the 1993 Midwest floods, the 2002 drought, Hurricane Andrew (1992), and a series of cold fronts during October 1990. The location of flagged records and the influence zones for such events relative to QA were compared. The behavior of the spatial test in these events provides important information on the probability of making a type I error in the assignment of the quality control flag. Simple pattern recognition tools that identify zones wherein frequent flagging occurs are illustrated. These tools serve as a means of resetting QA flags to minimize the number of type I errors as demonstrated for the extreme events included here.
Not all weather data are collected by federal agencies. Fueled by the need for more specific meteorological data in real or near-real time, the number of automated weather stations (AWSs) and AWS networks has expanded to the state and private sector over the past decade. This study employed a survey to determine the spatial extent and disposition of these nonfederal AWSs and AWS networks in the United States and Canada, the type of measurements taken, the operating procedures (i.e., maintenance and data-retrieval techniques), and the uses of the data (e.g., research, public service, agency needs). The rapid growth and expansion in the number of AWSs and networks can be viewed as a positive step toward expanding data available for meteorological research and service. As AWS networks continue to grow and expand in the United States and Canada, it is recommended that an AWS climatic database be established. With proper logistical coordination and the cooperation of network operators, development of such a database can become reality.
Not all weather data are collected by federal agencies. Fueled by the need for more specific meteorological data in real or near-real time, the number of automated weather stations (AWSs) and AWS networks has expanded to the state and private sector over the past decade. This study employed a survey to determine the spatial extent and disposition of these nonfederal AWSs and AWS networks in the United States and Canada, the type of measurements taken, the operating procedures (i.e., maintenance and data-retrieval techniques), and the uses of the data (e.g., research, public service, agency needs). The rapid growth and expansion in the number of AWSs and networks can be viewed as a positive step toward expanding data available for meteorological research and service. As AWS networks continue to grow and expand in the United States and Canada, it is recommended that an AWS climatic database be established. With proper logistical coordination and the cooperation of network operators, development of such a database can become reality.
Abstract
Winter wheat yields were simulated by a model requiring climatic data as input for estimating crop evapotranspiration and phenological development. An assumed relationship between the winter wheat yields and the amount and timing of crop water use was optimized to simulate yields for two case studies: a single season, irrigated wheat study and a multi-year, dryland wheat study. The model explained more than 90% of the variance of wheat yields in the irrigated study where total irrigation amounts varied between 0 and 55 cm. About 40% of the variance was explained for annual yields from a 21-year, dryland winter wheat study.
Abstract
Winter wheat yields were simulated by a model requiring climatic data as input for estimating crop evapotranspiration and phenological development. An assumed relationship between the winter wheat yields and the amount and timing of crop water use was optimized to simulate yields for two case studies: a single season, irrigated wheat study and a multi-year, dryland wheat study. The model explained more than 90% of the variance of wheat yields in the irrigated study where total irrigation amounts varied between 0 and 55 cm. About 40% of the variance was explained for annual yields from a 21-year, dryland winter wheat study.
Abstract
Both the spatial regression test (SRT) and inverse distance weighting (IDW) methods have been applied to provide estimates for the maximum air temperature (T max) and the minimum air temperature (T min) in the Applied Climate Information System (ACIS). This is critical to the processes of estimating missing data and identifying suspect data and is undertaken here to ensure quality data in ACIS. The SRT method was previously found to be superior to the IDW method; however, the sensitivity of the performance of both methods to input parameters has not been evaluated. A set of analyses is presented for both methods whereby the sensitivity to the radius of inclusion, the regression time window, the regression time offset, and the number of stations used to make the estimates are examined. Comparisons were also conducted between the SRT and the IDW methods. The performance of the SRT method stabilized when 10 or more stations were applied in the estimates. The optimal number of stations for the IDW method varies from only a few to 30. The results indicate that the best estimates obtained using the IDW method are still inferior to the worst estimates obtained using the SRT method.
Abstract
Both the spatial regression test (SRT) and inverse distance weighting (IDW) methods have been applied to provide estimates for the maximum air temperature (T max) and the minimum air temperature (T min) in the Applied Climate Information System (ACIS). This is critical to the processes of estimating missing data and identifying suspect data and is undertaken here to ensure quality data in ACIS. The SRT method was previously found to be superior to the IDW method; however, the sensitivity of the performance of both methods to input parameters has not been evaluated. A set of analyses is presented for both methods whereby the sensitivity to the radius of inclusion, the regression time window, the regression time offset, and the number of stations used to make the estimates are examined. Comparisons were also conducted between the SRT and the IDW methods. The performance of the SRT method stabilized when 10 or more stations were applied in the estimates. The optimal number of stations for the IDW method varies from only a few to 30. The results indicate that the best estimates obtained using the IDW method are still inferior to the worst estimates obtained using the SRT method.
Abstract
Soil moisture (SM) plays an important role in land surface and atmosphere interactions. It modifies energy balance near the surface and the rate of water cycling between land and atmosphere. The lack of observed SM data prohibits understanding of SM variations at climate scales under varying land uses. However, with simulation models it is possible to develop a long-term SM dataset and study these issues.
In this paper a water balance model is used to provide a quantitative assessment of SM climatologies for three land uses, namely, irrigated corn, rain-fed corn, and grass, grown under three hydroclimatic regimes in Nebraska. These regimes are stops along an east–west decreasing precipitation gradient of the Great Plains. The simulated SM climatologies are provided for the root zone as a whole and for the five layers of the soil profile to a depth of 1.2 m. As expected, the soil water content in the root zone of irrigated corn was higher than rain-fed corn or grass. The lowest levels of soil water depletion were found under rain-fed corn cultivation due to its complete reliance on naturally available SM. The annual total evapotranspiration (ET) was 34% and 36% higher for irrigated corn than for rain-fed corn and grass, respectively. The study suggests that due to interannual variability the SM variability is higher for deeper depths, as compared to near-surface depths. Growing season SM depletion and prevailing soil water content at various depths of the soil profile varies with crops, soils, and prevailing hydroclimatic conditions.
The results show that land use affects the magnitude of SM variability at all time scales. At a daily temporal scale, SM variability is less under irrigated land use and sharply increases under rain-fed land uses. At the monthly scale, SM variability largely follows the trend of the daily time scale. Year-to-year SM variability is significant. Extremely dry or wet conditions enhance and reduce, respectively, the forcing of land use on SM variability at an annual time scale. Thus, large-scale interannual climate variations and land use jointly affect SM variability at this scale.
Abstract
Soil moisture (SM) plays an important role in land surface and atmosphere interactions. It modifies energy balance near the surface and the rate of water cycling between land and atmosphere. The lack of observed SM data prohibits understanding of SM variations at climate scales under varying land uses. However, with simulation models it is possible to develop a long-term SM dataset and study these issues.
In this paper a water balance model is used to provide a quantitative assessment of SM climatologies for three land uses, namely, irrigated corn, rain-fed corn, and grass, grown under three hydroclimatic regimes in Nebraska. These regimes are stops along an east–west decreasing precipitation gradient of the Great Plains. The simulated SM climatologies are provided for the root zone as a whole and for the five layers of the soil profile to a depth of 1.2 m. As expected, the soil water content in the root zone of irrigated corn was higher than rain-fed corn or grass. The lowest levels of soil water depletion were found under rain-fed corn cultivation due to its complete reliance on naturally available SM. The annual total evapotranspiration (ET) was 34% and 36% higher for irrigated corn than for rain-fed corn and grass, respectively. The study suggests that due to interannual variability the SM variability is higher for deeper depths, as compared to near-surface depths. Growing season SM depletion and prevailing soil water content at various depths of the soil profile varies with crops, soils, and prevailing hydroclimatic conditions.
The results show that land use affects the magnitude of SM variability at all time scales. At a daily temporal scale, SM variability is less under irrigated land use and sharply increases under rain-fed land uses. At the monthly scale, SM variability largely follows the trend of the daily time scale. Year-to-year SM variability is significant. Extremely dry or wet conditions enhance and reduce, respectively, the forcing of land use on SM variability at an annual time scale. Thus, large-scale interannual climate variations and land use jointly affect SM variability at this scale.
Abstract
In this study, daily temperature and precipitation amounts that are observed by the Cooperative Observer Program (COOP) were compared among geographically close stations. Hourly observations from nearby Automatic Weather Data Network (AWDN) stations were utilized to resolve the discrepancies between the observations during the same period. The statistics of maximum differences in temperature and precipitation between COOP stations were summarized. In addition, the quantitative measures of the deviations between COOP and AWDN stations were expressed by root-mean-square error, mean absolute error, and an index of agreement. The results indicated that significant discrepancies exist among the daily observations between some paired stations because of varying observation times, observation error, sensor error, and differences in microclimate exposure. The purpose of this note is to bring attention to the problem and offer guidance on the use of daily observations in the comparison and creation of weather maps. In addition, this study demonstrates approaches for identifying the sources of the discrepancies in daily temperature and precipitation observations. The findings will be useful in the quality assurance (QA) procedures of climate data.
Abstract
In this study, daily temperature and precipitation amounts that are observed by the Cooperative Observer Program (COOP) were compared among geographically close stations. Hourly observations from nearby Automatic Weather Data Network (AWDN) stations were utilized to resolve the discrepancies between the observations during the same period. The statistics of maximum differences in temperature and precipitation between COOP stations were summarized. In addition, the quantitative measures of the deviations between COOP and AWDN stations were expressed by root-mean-square error, mean absolute error, and an index of agreement. The results indicated that significant discrepancies exist among the daily observations between some paired stations because of varying observation times, observation error, sensor error, and differences in microclimate exposure. The purpose of this note is to bring attention to the problem and offer guidance on the use of daily observations in the comparison and creation of weather maps. In addition, this study demonstrates approaches for identifying the sources of the discrepancies in daily temperature and precipitation observations. The findings will be useful in the quality assurance (QA) procedures of climate data.
Abstract
The search for precipitation quality control (QC) methods has proven difficult. The high spatial and temporal variability associated with precipitation data causes high uncertainty and edge creep when regression-based approaches are applied. Precipitation frequency distributions are generally skewed rather than normally distributed. The commonly assumed normal distribution in QC methods is not a good representation of the actual distribution of precipitation and is inefficient in identifying the outliers. This paper first explores the use of a single gamma distribution, fit to all precipitation data, in a quality assurance test. A second test, the multiple intervals gamma distribution (MIGD) method, is introduced. It assumes that meteorological conditions that produce a certain range in average precipitation at surrounding stations will produce a predictable range at the target station. The MIGD bins the average of precipitation at neighboring stations; then, for the events in a specific bin, an associated gamma distribution is derived by fit to the same events at the target station. The new gamma distributions can then be used to establish the threshold for QC according to the user-selected probability of exceedance. This paper also explores a test (Q test) for precipitation, which uses a metric based on comparisons with neighboring stations. The performance of the three approaches is evaluated by assessing the fraction of “known” errors that can be identified in a seeded error dataset. The single gamma distribution and Q-test approach were found to be relatively efficient at identifying extreme precipitation values as potential outliers. However, the MIGD method outperforms the other two QC methods. This method identifies more seeded errors and results in fewer type I errors than the other methods. It will be adopted in the Applied Climatic Information System (ACIS) for precipitation quality control.
Abstract
The search for precipitation quality control (QC) methods has proven difficult. The high spatial and temporal variability associated with precipitation data causes high uncertainty and edge creep when regression-based approaches are applied. Precipitation frequency distributions are generally skewed rather than normally distributed. The commonly assumed normal distribution in QC methods is not a good representation of the actual distribution of precipitation and is inefficient in identifying the outliers. This paper first explores the use of a single gamma distribution, fit to all precipitation data, in a quality assurance test. A second test, the multiple intervals gamma distribution (MIGD) method, is introduced. It assumes that meteorological conditions that produce a certain range in average precipitation at surrounding stations will produce a predictable range at the target station. The MIGD bins the average of precipitation at neighboring stations; then, for the events in a specific bin, an associated gamma distribution is derived by fit to the same events at the target station. The new gamma distributions can then be used to establish the threshold for QC according to the user-selected probability of exceedance. This paper also explores a test (Q test) for precipitation, which uses a metric based on comparisons with neighboring stations. The performance of the three approaches is evaluated by assessing the fraction of “known” errors that can be identified in a seeded error dataset. The single gamma distribution and Q-test approach were found to be relatively efficient at identifying extreme precipitation values as potential outliers. However, the MIGD method outperforms the other two QC methods. This method identifies more seeded errors and results in fewer type I errors than the other methods. It will be adopted in the Applied Climatic Information System (ACIS) for precipitation quality control.
