A Binary Logistic Regression Model for Severe Convective Weather with Numerical Model Data

Guqian, Pang; He, Jian Wen; Huang, Yuming; Zhang, Liuhong

doi:10.1155/2019/6127281

Cited by 7 publications

(7 citation statements)

References 8 publications

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“…Where, P i is the probability of Y equals to 1 if the sample respondents' willingness to pay for crop insurance, and 0 if otherwise; [25,26,27,28,29,30,31].…”

Section: Binary Logistic Regression Modelmentioning

confidence: 99%

Socio-economic Impact of Climate Change, Adaptation and Determinants of Willingness to Pay for Crop Insurance in Central Agro-climatic Zone of Afghanistan

Sarwary

Senthil‐Nathan

Vidhyavathi

et al. 2020

CJAST

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Afghanistan is frequently affected by drought and other climate change related hazards due to increased temperature and reduced precipitation over most parts of the country. The present study was undertaken to analyze the climate change preparedness measures for adaptation and willingness to pay for crop insurance to mitigate the climate change impacts. This study was conducted in central agro-climatic zone of Afghanistan to collect the primary data through the random sampling technique with a total sample of 105 respondents from the seven provinces located in this zone. Likert type responses were used to assess the climate change adaptation and binary log it regression model was used to identify the factors affecting the willingness to pay for crop insurance to mitigate the climate change impacts on agriculture. The results of the study indicated that changing cropping pattern (74%) followed by receiving technical information from experts (70%) and keeping the land as fallow (60%) were rated as very high to high preparedness measures to mitigate the climate change impacts. Logit regression model revealed that the education and technical information received from experts were positively significant and their marginal effects implied that the probabilities of willing to pay for crop insurance were increased by 1.3 and 15.9 percent, respectively. The establishment of crop insurance agencies at province level may help reducing the impact of climate change and increasing the income and livelihood of the farmers.

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“…Where, P i is the probability of Y equals to 1 if the sample respondents' willingness to pay for crop insurance, and 0 if otherwise; [25,26,27,28,29,30,31].…”

Section: Binary Logistic Regression Modelmentioning

confidence: 99%

Socio-economic Impact of Climate Change, Adaptation and Determinants of Willingness to Pay for Crop Insurance in Central Agro-climatic Zone of Afghanistan

Sarwary

Senthil‐Nathan

Vidhyavathi

et al. 2020

CJAST

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“…More than ten indices with similar representative properties are used for that method, and the area that fulfills the multi-thresholds is considered as the target area. Logistic regression models are used for intense convection prediction [25,26], but with similar problems as [24]. Most of them do not have a solid physical basis, and even worse, they do not provide SDHR forecasts.…”

Section: Introductionmentioning

confidence: 99%

Probability Forecasting of Short-Term Short-Duration Heavy Rainfall Combining Ingredients-Based Methodology and Fuzzy Logic Approach

et al. 2022

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Highly convection-related short-duration heavy rainfall (SDHR), defined as rainfall greater than 20 mm h−1 of a whole hour, causes severe damage every year in China. An objective forecasting method is developed to provide guidance products for the short-term probability of SDHR. Representative parameters of environmental moisture content, instability, and dynamical forcing are selected as predictors based on the ingredients-based methodology. The predictors are selected by comparing their ability to discriminate between SDHR and both no rainfall and ordinary rainfall with hourly rainfall records and the NCEP reanalysis dataset during the warm seasons of 2002 and 2009. A fuzzy logic approach is obtained for the calculation of SDHR probability. Intervals of intensities are obtained based on specific percentiles and various weight settings examined. The probabilistic SDHR forecasts during the 2015 warm seasons with the NCEP GFS dataset are obtained, and forecasts are evaluated by using an operational used spatial verification method. Results show that the reference operational SDHR forecasts are surpassed by the 00–12 h period objective SDHR forecasts measured with the maximum critical success index (CSI), and even the average CSI (CSIave) for the top groups is better than the reference. The guidance SDHR products are skillful within 60 h. Although the weights vary significantly, the short-term patterns of the SDHR probability are mainly determined by the environmental conditions. The objective forecasting method is ingredients-based but is combined with fuzzy logic algorithms. The new approach provides a feasible exploration of the convective weather phenomenon.

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“…complementary to case studies or weather forecasting of individual events). For example, binary logistic regression has been applied in relation to hail occurrence in the United States (Allen et al 2015), Germany (Mohr et al 2015) and Spain (Gascón et al 2015), lightning activity in Australia (Bates et al 2018) as well as convective hazards in China (Pang et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Severe convection-related winds in Australia and their associated environments

Brown

Dowdy

2021

J. South. Hemisph. Earth Syst. Sci.

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Severe surface wind gusts produced by thunderstorms have the potential to damage infrastructure and are a major hazard for society. Wind gust data are examined from 35 observing stations around Australia, with lightning observations used to indicate the occurrence of deep convective processes in the vicinity of the observed wind gusts. A collation of severe thunderstorm reports is also used to complement the station wind gust data. Atmospheric reanalysis data are used to systematically examine large-scale environmental measures associated with severe convective winds. We find that methods based on environmental measures provide a better indication of the observed severe convective winds than the simulated model wind gusts from the reanalysis data, noting that the spatial scales on which these events occur are typically smaller than the reanalysis grid cells. Consistent with previous studies in other regions and idealised modelling, the majority of severe convective wind events are found to occur in environments with steep mid-level tropospheric lapse rates, moderate convective instability and strong background wind speeds. A large proportion of events from measured station data occur with relatively dry environmental air at low levels, although it is unknown to what extent this type of environment is representative of other severe wind-producing convective modes in Australia. The occurrence of severe convective winds is found to be well represented by a number of indices used previously for forecasting applications, such as the weighted product of convective available potential energy (CAPE) and vertical wind shear, the derecho composite parameter and the total totals index, as well as by logistic regression methods applied to environmental variables. Based on the systematic approach used in this study, our findings provide new insight on spatio-temporal variations in the risk of damaging winds occurring, including the environmental factors associated with their occurrence.

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A Binary Logistic Regression Model for Severe Convective Weather with Numerical Model Data

Cited by 7 publications

References 8 publications

Socio-economic Impact of Climate Change, Adaptation and Determinants of Willingness to Pay for Crop Insurance in Central Agro-climatic Zone of Afghanistan

Socio-economic Impact of Climate Change, Adaptation and Determinants of Willingness to Pay for Crop Insurance in Central Agro-climatic Zone of Afghanistan

Probability Forecasting of Short-Term Short-Duration Heavy Rainfall Combining Ingredients-Based Methodology and Fuzzy Logic Approach

Severe convection-related winds in Australia and their associated environments

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