Estimating radar precipitation in cold climates: the role of air temperature within a non-parametric framework

Abstract

Abstract. 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.

Quantitative Precipitation Estimation Model Integrating Meteorological and Geographical Factors at Multiple Spatial Scales

“…The overall performance of their proposed three models is 8.18 mm/h, 8.38 mm/h, 7.91 mm/h for root mean square error (RMSE) values, respectively. Sivasubramaniam et al (2018) developed a nonparametric prediction model, the K-nearest neighbor regression estimator, and demonstrated that the inclusion of air temperature as an additional covariate for model significantly improved prediction results in cold air with an improvement of 15% in RMSE compared to radar precipitation rate as a single predictor in model. Chen et al (2019) designed a two-stage neural network for estimating precipitation intensity and inversion of satellite radar profiles, respectively.…”

“…Inspired by this, we believe that large scale radar reflectivity factors can also provide valid information for rainfall estimation, so we adopt multi-scale rainfall field information as the observations for the model, i.e., covering different ranges of rainfall fields centered on the rainfall collection points. In addition, although previous studies have used additional inputs as covariates, such as temperature (Sivasubramaniam et al, 2018). However, they ignored the influence of the spatial structural characteristics of the covariates on rainfall, so we used meteorological and geographic factors in two dimensions as covariates to establish their association with rainfall at the spatial scale.…”

High-Resolution (2.5 km) Ensemble Precipitation Analysis across Canada

“…Solid precipitation is indeed challenging to measure at gauging sites due to large undercatch during windy conditions (Rasmussen et al 2012). The radar QPEs are discarded as specific adjustments necessary for snow are not currently undertaken for CaPA (e.g., Sivasubramaniam et al 2018), mainly due to the lack of reliable ground observations. The QC process eliminates around 75% of observed data in winter.…”

Radar Reflectivity and Meteorological Factors Merging‐Based Precipitation Estimation Neural Network

“…Sivasubramaniam et al. (2018) indicated that using air temperature as the second covariate in the K‐NN non‐parametric model dramatically reduces the root mean square error (RMSE) and improved the radar precipitation estimation at colder temperatures. Although, the above method used meteorological factor data in the precipitation estimation method, and achieved good results.…”

“…Further, Fassnacht et al (2001) showed that the adjusted radar data provided a more realistic precipitation estimates for the precipitation-runoff model than the calibrated rain gauge precipitation data, which greatly improves the accuracy of the radar precipitation estimation in the cold season. Sivasubramaniam et al (2018) indicated that using air temperature as the second covariate in the K-NN non-parametric model dramatically reduces the root mean square error (RMSE) and improved the radar precipitation estimation at colder temperatures. Although, the above method used meteorological factor data in the precipitation estimation method, and achieved good results.…”