%0 Journal Article %1 Wasko.Sharma2014 %A Wasko, Conrad %A Sharma, Ashish %D 2014 %J Water Resources Research %K ClausiusClapeyron Regression Regression:quantile precipitation temperature %N 4 %P 3608--3614 %R 10.1002/2013WR015194 %T Quantile regression for investigating scaling of extreme precipitation with temperature %U http://dx.doi.org/10.1002/2013WR015194 %V 50 %X The consensus in the scientific community is that the intensity of extreme precipitation will increase in a warmer climate. However, as there is limited observational evidence to this effect, there is a growing body of research which focuses on directly investigating the relationship between temperature and precipitation. This is currently performed by binning precipitation data in temperature bins and then investigating the trend in the precipitation percentiles in each bin with temperature. In this paper, we highlight limitations in the binning approach and present quantile regression as an alternative to the above process. Quantile regression allows estimation of this scaling directly and, unlike binning, is unbiased with sample size. Moreover, quantile regression presents a natural framework for investigation into other factors (covariates) that may be affecting the nature of the scaling relationship. Results using subdaily rainfall data for Australia show the efficacy of the proposed quantile regression method, as well as the presence of season indicators as significant covariates that affect the scaling relationship of precipitation with temperature. A general increase in the scaling coefficient in winter versus summer is observed.

@article{Wasko.Sharma2014, abstract = {The consensus in the scientific community is that the intensity of extreme precipitation will increase in a warmer climate. However, as there is limited observational evidence to this effect, there is a growing body of research which focuses on directly investigating the relationship between temperature and precipitation. This is currently performed by binning precipitation data in temperature bins and then investigating the trend in the precipitation percentiles in each bin with temperature. In this paper, we highlight limitations in the binning approach and present quantile regression as an alternative to the above process. Quantile regression allows estimation of this scaling directly and, unlike binning, is unbiased with sample size. Moreover, quantile regression presents a natural framework for investigation into other factors (covariates) that may be affecting the nature of the scaling relationship. Results using subdaily rainfall data for Australia show the efficacy of the proposed quantile regression method, as well as the presence of season indicators as significant covariates that affect the scaling relationship of precipitation with temperature. A general increase in the scaling coefficient in winter versus summer is observed.}, added-at = {2014-04-28T11:21:45.000+0200}, author = {Wasko, Conrad and Sharma, Ashish}, biburl = {https://www.bibsonomy.org/bibtex/2157bbda458cca3352ff23f6c873b6cc9/marsianus}, description = {Quantile regression for investigating scaling of extreme precipitation with temperature - Wasko - 2014 - Water Resources Research - Wiley Online Library}, doi = {10.1002/2013WR015194}, interhash = {969ede2f7babfd435fe0291d941a19ca}, intrahash = {157bbda458cca3352ff23f6c873b6cc9}, issn = {1944-7973}, journal = {Water Resources Research}, keywords = {ClausiusClapeyron Regression Regression:quantile precipitation temperature}, number = 4, pages = {3608--3614}, timestamp = {2014-06-26T11:12:25.000+0200}, title = {Quantile regression for investigating scaling of extreme precipitation with temperature}, url = {http://dx.doi.org/10.1002/2013WR015194}, volume = 50, year = 2014 }