DOI: 10.5194/hess-22-6533-2018
论文题名: Estimating radar precipitation in cold climates: The role of air temperature within a non-parametric framework
作者: Sivasubramaniam K. ; Sharma A. ; Alfredsen K.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2018
卷: 22, 期: 12 起始页码: 6533
结束页码: 6546
语种: 英语
Scopus关键词: Atmospheric temperature
; Atmospheric thermodynamics
; Gages
; Information theory
; Mean square error
; Nearest neighbor search
; Parameter estimation
; Radar
; Rain
; Ground based precipitation
; K nearest neighbours (k-NN)
; Near surface air temperature
; Precipitation estimation
; Precipitation rates
; Predictor variables
; Regression estimators
; Root mean square errors
; Radar measurement
; air temperature
; climate
; covariance analysis
; estimation method
; gauge
; ground-based measurement
; precipitation (climatology)
; radar
; rainfall
; snow
; Norway
; Oslo [Norway]
英文摘要: The use of ground-based precipitation measurements in radar precipitation estimation is well known in radar hydrology. However, the approach of using gauged precipitation and near-surface air temperature observations to improve radar precipitation estimates in cold climates is much less common. In cold climates, precipitation is in the form of snow, rain or a mixture of the two phases. Air temperature is intrinsic to the phase of the precipitation and could therefore be a possible covariate in the models used to ascertain radar precipitation estimates. In the present study, we investigate the use of air temperature within a non-parametric predictive framework to improve radar precipitation estimation for cold climates. A non-parametric predictive model is constructed with radar precipitation rate and air temperature as predictor variables and gauge precipitation as an observed response using a k nearest neighbour (k-nn) regression estimator. The relative importance of the two predictors is ascertained using an information theory-based weighting. Four years (2011-2015) of hourly radar precipitation rates from the Norwegian national radar network over the Oslo region, hourly gauged precipitation from 68 gauges and gridded observational air temperatures were used to formulate the predictive model, hence making our investigation possible. Gauged precipitation data were corrected for wind-induced under-catch before using them as true observed response. The predictive model with air temperature as an added covariate reduces root-mean-square error (RMSE) by up to 15% compared to the model that uses radar precipitation rate as the sole predictor. More than 80% of gauge locations in the study area showed improvement with the new method. Further, the associated impact of air temperature became insignificant at more than 85% of gauge locations when the near-surface air temperature was warmer than 10°C, which indicates that the partial dependence of precipitation on air temperature is most useful for colder temperatures. © 2018 Author(s). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/163103
Appears in Collections: 气候变化与战略
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作者单位: Sivasubramaniam, K., Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Trondheim, 7491, Norway; Sharma, A., School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia; Alfredsen, K., Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Trondheim, 7491, Norway
Recommended Citation:
Sivasubramaniam K.,Sharma A.,Alfredsen K.. Estimating radar precipitation in cold climates: The role of air temperature within a non-parametric framework[J]. Hydrology and Earth System Sciences,2018-01-01,22(12)