DOI: 10.1016/j.atmosres.2019.01.024
Scopus记录号: 2-s2.0-85060704332
论文题名: Prediction of heat waves in Pakistan using quantile regression forests
作者: Khan N. ; Shahid S. ; Juneng L. ; Ahmed K. ; Ismail T. ; Nawaz N.
刊名: Atmospheric Research
ISSN: 1698095
出版年: 2019
卷: 221 起始页码: 1
结束页码: 11
语种: 英语
英文关键词: Extreme temperature
; Heat waves
; Pakistan
; Quantile regression forest
; Synoptic climate
Scopus关键词: Atmospheric humidity
; Climate models
; Decision trees
; Global warming
; Regression analysis
; Extreme temperatures
; Heat waves
; Pakistan
; Quantile regression
; Synoptic climate
; Forecasting
; climate prediction
; climate variation
; computer simulation
; extreme event
; heat wave
; high temperature
; numerical model
; performance assessment
; regression analysis
; synoptic meteorology
; weather forecasting
; Pakistan
英文摘要: The rising temperature due to global warming has caused an increase in frequency and severity of heat waves across the world. A statistical model known as Quantile Regression Forests (QRF) has been proposed in this study for the prediction of heat waves in Pakistan for different time-lags using synoptic climate variables. The gridded daily temperature data of Princeton's Global Meteorological Forcing (PGF) was used for the reconstruction of historical heat waves and the National Centers for Environmental Prediction (NCEP) reanalysis data was used to select the appropriate set of predictors to forecast the heat waves using QRF. The performance of QRF in prediction of heat waves was compared with classical random forest (RF). The results showed superior performance of QRF in detecting heat waves compared to RF. The QRF model was able to predict the triggering and departure dates of heat waves with 1 to 10 days lead times at various levels of accuracy. The model was able to predict the triggering dates of 2 to 3 out of 3 heat waves in the month of May, 8 to 12 out of 13 heat waves in June and 2 out of 2 in July and the departure dates of 3 out of 3 in May, 10 out of 13 in June and 2 out of 2 in July with an accuracy of up to ±5 days. The evaluation of different atmospheric variables revealed that wind and relative humidity are the major factors that define the heat waves in Pakistan. The research proved the advantage of QRF model to predict the conditional quantiles that help to explain some extreme behaviors of temperature. © 2019 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/122293
Appears in Collections: 气候变化事实与影响
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作者单位: Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, 81310, Malaysia; Faculty of Science and Technology, University Kebangsaan Malaysia, UKM, Bangi, Selangor 43600, Malaysia; Faculty of Water Resource Management, Lasbela University of Agriculture Water and Marine Sciences (LUAWMS), Uthal, Balochistan 90150, Pakistan
Recommended Citation:
Khan N.,Shahid S.,Juneng L.,et al. Prediction of heat waves in Pakistan using quantile regression forests[J]. Atmospheric Research,2019-01-01,221