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- Author or Editor: Lasse Makkonen x
- Journal of Applied Meteorology and Climatology x
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Abstract
Plotting order-ranked data is a standard technique that is used in estimating the probability of extreme weather events. Typically, observations, say, annual extremes of a period of N years, are ranked in order of magnitude and plotted on probability paper. Some statistical model is then fitted to the order-ranked data by which the return periods of specific extreme events are estimated. A key question in this method is as follows: What is the cumulative probability P that should be associated with the sample of rank m? This issue of the so-called plotting positions has been debated for almost a century, and a number of plotting rules and computational methods have been proposed. Here, it is shown that in estimating the return periods there is only one correct plotting position: P = m/(N + 1). This formula predicts much shorter return periods of extreme events than the other commonly used methods. Thus, many estimates of the weather-related risks should be reevaluated and the related building codes and other related regulations updated.
Abstract
Plotting order-ranked data is a standard technique that is used in estimating the probability of extreme weather events. Typically, observations, say, annual extremes of a period of N years, are ranked in order of magnitude and plotted on probability paper. Some statistical model is then fitted to the order-ranked data by which the return periods of specific extreme events are estimated. A key question in this method is as follows: What is the cumulative probability P that should be associated with the sample of rank m? This issue of the so-called plotting positions has been debated for almost a century, and a number of plotting rules and computational methods have been proposed. Here, it is shown that in estimating the return periods there is only one correct plotting position: P = m/(N + 1). This formula predicts much shorter return periods of extreme events than the other commonly used methods. Thus, many estimates of the weather-related risks should be reevaluated and the related building codes and other related regulations updated.
Abstract
This reply addresses the use of order statistics in extreme value analysis. The author has previously proposed in this journal that the distribution-dependent estimators of plotting position in extreme value analysis should be abandoned and replaced by the Weibull formula. It was also demonstrated that the use of the wrong plotting positions has resulted in underestimation of the probability of extreme-weather events. Cook’s comments challenge these developments and defend the previously presented plotting methods. In this reply it is outlined that the Weibull formula provides the exact probability PI of nonexceedance in order-ranked data. Hence, there is no sampling error related to PI . This renders Cook’s primary arguments invalid. The specific critical comments by Cook are also replied to and are shown to be unfounded.
Abstract
This reply addresses the use of order statistics in extreme value analysis. The author has previously proposed in this journal that the distribution-dependent estimators of plotting position in extreme value analysis should be abandoned and replaced by the Weibull formula. It was also demonstrated that the use of the wrong plotting positions has resulted in underestimation of the probability of extreme-weather events. Cook’s comments challenge these developments and defend the previously presented plotting methods. In this reply it is outlined that the Weibull formula provides the exact probability PI of nonexceedance in order-ranked data. Hence, there is no sampling error related to PI . This renders Cook’s primary arguments invalid. The specific critical comments by Cook are also replied to and are shown to be unfounded.
Abstract
The role of various atmospheric parameters in determining atmospheric ice accretion intensity on structures near the ground is examined theoretically, with an emphasis on glaze formation. Methods are presented for calculating the icing rate on cylindrical objects, and estimates of maximum deposition intensities are made. A relationship between meteorological conditions and the type of ice formation (glaze and rime) is given. The lack of adequate experimental data limits verification of the theory, but some comparisons, mainly qualitative, are promising.
Abstract
The role of various atmospheric parameters in determining atmospheric ice accretion intensity on structures near the ground is examined theoretically, with an emphasis on glaze formation. Methods are presented for calculating the icing rate on cylindrical objects, and estimates of maximum deposition intensities are made. A relationship between meteorological conditions and the type of ice formation (glaze and rime) is given. The lack of adequate experimental data limits verification of the theory, but some comparisons, mainly qualitative, are promising.
Abstract
A time-dependent numerical model of ice accretion on wires, such as overhead conductors, is presented. Simulations of atmospheric icing are made with the model in order to examine the dependence of the accreted ice amount on atmospheric conditions.
The results show that in wet growth (glaze formation) under constant atmospheric conditions, the growth rate increases with time until the process changes to dry growth. In dry growth (rime formation) the growth rate typically increases with time at the beginning of the icing process, but later decreases with time when the ice deposit has grown bigger.
The effect of air temperature on the ice load turns out to be rather small for the first 24 hours of icing in typical dry growth conditions, but it is important for long-term icing. The ultimate ice load may either increase or decrease with decreasing air temperature, depending on the other atmospheric conditions and on the duration of icing. These results largely explain the difficulties encountered in estimating the formation of ice loads by simple methods using the routinely measured meteorological parameters.
Abstract
A time-dependent numerical model of ice accretion on wires, such as overhead conductors, is presented. Simulations of atmospheric icing are made with the model in order to examine the dependence of the accreted ice amount on atmospheric conditions.
The results show that in wet growth (glaze formation) under constant atmospheric conditions, the growth rate increases with time until the process changes to dry growth. In dry growth (rime formation) the growth rate typically increases with time at the beginning of the icing process, but later decreases with time when the ice deposit has grown bigger.
The effect of air temperature on the ice load turns out to be rather small for the first 24 hours of icing in typical dry growth conditions, but it is important for long-term icing. The ultimate ice load may either increase or decrease with decreasing air temperature, depending on the other atmospheric conditions and on the duration of icing. These results largely explain the difficulties encountered in estimating the formation of ice loads by simple methods using the routinely measured meteorological parameters.
Abstract
Atmospheric ice loads are a major design criterion of tall structures in cold regions. In this paper the possibility to derive the design ice loads using analysis of meteorological observations made routinely at a weather station is studied. Ice loads calculated by extrapolating weather station data and using simplistic ice loading and unloading models are compared with those measured on a 323-m-height lattice TV tower. The comparison is made cumulatively in 3-h intervals over seven winter periods. The results show reasonable agreement in the time of the icing events and in overall loads. In the cases where the cumulative ice loads differ, the discrepancies are mostly due to incorrectly predicted unloading events. This study points out the importance of on-site temperature data for successfully estimating cumulative ice loads over long cold periods.
Abstract
Atmospheric ice loads are a major design criterion of tall structures in cold regions. In this paper the possibility to derive the design ice loads using analysis of meteorological observations made routinely at a weather station is studied. Ice loads calculated by extrapolating weather station data and using simplistic ice loading and unloading models are compared with those measured on a 323-m-height lattice TV tower. The comparison is made cumulatively in 3-h intervals over seven winter periods. The results show reasonable agreement in the time of the icing events and in overall loads. In the cases where the cumulative ice loads differ, the discrepancies are mostly due to incorrectly predicted unloading events. This study points out the importance of on-site temperature data for successfully estimating cumulative ice loads over long cold periods.
Abstract
The theory of Langmuir and Blodgett for the droplet collision efficiency was verified by growing rime ice accretions on rotating cylinders in a wind tunnel. The results show that the theory is in excellent agreement with the experimental data in the studied range of mean cylinder collision efficiency from 0.07 to 0.63.
Abstract
The theory of Langmuir and Blodgett for the droplet collision efficiency was verified by growing rime ice accretions on rotating cylinders in a wind tunnel. The results show that the theory is in excellent agreement with the experimental data in the studied range of mean cylinder collision efficiency from 0.07 to 0.63.
Abstract
In-cloud icing on aircraft and ground structures can be observed every winter in many countries. In extreme cases ice can cause accidents and damage to infrastructure such as power transmission lines, telecommunication towers, wind turbines, ski lifts, and so on. This study investigates the potential for predicting episodes of in-cloud icing at ground level using a state-of-the-art numerical weather prediction model. The Weather Research and Forecasting (WRF) model is applied, with attention paid to the model’s skill to explicitly predict the amount of supercooled cloud liquid water content (SLWC) at the ground level at different horizontal resolutions and with different cloud microphysics schemes. The paper also discusses how well the median volume droplet diameter (MVD) can be diagnosed from the model output. A unique dataset of direct measurements of SLWC and MVD at ground level on a hilltop in northern Finland is used for validation. A mean absolute error of predicted SLWC as low as 0.08 g m−3 is obtained when the highest model resolution is applied (grid spacing equal to 0.333 km), together with the Thompson microphysics scheme. The quality of the SLWC predictions decreases dramatically with decreasing model resolution, and a systematic difference in predictive skill is found between the cloud microphysics schemes applied. A comparison between measured and predicted MVD shows that when prescribing the droplet concentration equal to 250 cm−3 the model predicts MVDs ranging from 12 to 20 μm, which corresponds well to the measured range. However, the variation from case to case is not captured by the current cloud microphysics schemes.
Abstract
In-cloud icing on aircraft and ground structures can be observed every winter in many countries. In extreme cases ice can cause accidents and damage to infrastructure such as power transmission lines, telecommunication towers, wind turbines, ski lifts, and so on. This study investigates the potential for predicting episodes of in-cloud icing at ground level using a state-of-the-art numerical weather prediction model. The Weather Research and Forecasting (WRF) model is applied, with attention paid to the model’s skill to explicitly predict the amount of supercooled cloud liquid water content (SLWC) at the ground level at different horizontal resolutions and with different cloud microphysics schemes. The paper also discusses how well the median volume droplet diameter (MVD) can be diagnosed from the model output. A unique dataset of direct measurements of SLWC and MVD at ground level on a hilltop in northern Finland is used for validation. A mean absolute error of predicted SLWC as low as 0.08 g m−3 is obtained when the highest model resolution is applied (grid spacing equal to 0.333 km), together with the Thompson microphysics scheme. The quality of the SLWC predictions decreases dramatically with decreasing model resolution, and a systematic difference in predictive skill is found between the cloud microphysics schemes applied. A comparison between measured and predicted MVD shows that when prescribing the droplet concentration equal to 250 cm−3 the model predicts MVDs ranging from 12 to 20 μm, which corresponds well to the measured range. However, the variation from case to case is not captured by the current cloud microphysics schemes.
