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Discussion papers | Copyright
https://doi.org/10.5194/hess-2017-662
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 22 Dec 2017

Research article | 22 Dec 2017

Review status
This discussion paper is a preprint. It has been under review for the journal Hydrology and Earth System Sciences (HESS). The manuscript was not accepted for further review after discussion.

Should radar precipitation depend on incident air temperature? A new estimation algorithm for cold climates

Kuganesan Sivasubramaniam1, Ashish Sharma2, and Knut Alfredsen1 Kuganesan Sivasubramaniam et al.
  • 1Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, 7491 Trondheim, Norway
  • 2School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW2052, Australia

Abstract. In cold climates, the form of precipitation (snow or rain or mixture of snow and rain) results in uncertainty in radar precipitation estimation. Estimation often proceeds without distinguishing the state of precipitation which can be reliably specified as a function of associated air temperature. In the present study, we hypothesise that incident air temperature is related to the phase of the precipitation and ensuing reflectivity measurement, and therefore could be used in prediction models to improve radar precipitation estimates in cold climates. This is the first study to our knowledge that assesses the dependence of radar precipitation on incident air temperature and presents a procedure that can be used for taking it into consideration. We use a data based nonparametric statistical approach for this assessment. A nonparametric predictive model is constructed with radar rain rate and air temperature as predictor variables and gauge precipitation as observed response using a k-nearest neighbour (k-nn) regression estimator. A partial information theoretic technique is used to ascertain the relative importance of the two predictors. Six years (2011–2017) of hourly radar rain rate from the Norwegian national radar network over the Oslo region, hourly gauged precipitation from 88 raingauges and gridded observational air temperature were used to formulate the predictive model and hence evaluate our hypothesis. The predictive model with temperature as an additional covariate reduces root mean squared error (RMSE) up to 15% compared to the predictive model with radar rain rate as the sole predictor. More than 80% of the raingauge locations in the study area showed improvement with the new method. Further, the estimated partial weight for air temperature assumed a zero value for more than 85% of gauge locations when temperature was above 10°C, which indicates that the partial dependence of precipitation on air temperature is most important for colder climates.

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Kuganesan Sivasubramaniam et al.
Kuganesan Sivasubramaniam et al.
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In cold climates, the form of precipitation (rain or snow) results in uncertainty in radar precipitation estimation. This study assesses the relevance of air temperature as an additional factor in deriving radar precipitation. The results show that radar precipitation depends on air temperature especially for cold regions, and that incorporating air temperature as an additional variable during conversion from reflectivity to rain rate improved the radar precipitation estimates significantly.
In cold climates, the form of precipitation (rain or snow) results in uncertainty in radar...
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