The potential for uncertainty in Numerical Weather Prediction model verification when using solid precipitation observations

• Main Authors: Samuel T. Buisán, Craig D. Smith, Amber Ross, John Kochendorfer, José Luís Collado, Javier Alastrué, Mareile Wolff, Yves‐Alain Roulet, Michael E. Earle, Timo Laine, Roy Rasmussen, Rodica Nitu
• Format: Article
• Language: English
• Published: Wiley 2020-07-01
• Series: Atmospheric Science Letters
• Subjects: NWP verification; snow; solid precipitation; SPICE
• Online Access: https://doi.org/10.1002/asl.976

Abstract Precipitation forecasts made by Numerical Weather Prediction (NWP) models are typically verified using precipitation gauge observations that are often prone to the wind‐induced undercatch of solid precipitation. Therefore, apparent model biases in solid precipitation forecasts may be due in part to the measurements and not the model. To reduce solid precipitation measurement biases, adjustments in the form of transfer functions were derived within the framework of the World Meteorological Organization Solid Precipitation Inter‐Comparison Experiment (WMO‐SPICE). These transfer functions were applied to single‐Alter shielded gauge measurements at selected SPICE sites during two winter seasons (2015–2016 and 2016–2017). Along with measurements from the WMO automated field reference configuration at each of these SPICE sites, the adjusted and unadjusted gauge observations were used to analyze the bias in a Global NWP model precipitation forecast. The verification of NWP winter precipitation using operational gauges may be subject to verification uncertainty, the magnitude and sign of which varies with the gauge‐shield configuration and the relation between model and site‐specific local climatologies. The application of a transfer function to alter‐shielded gauge measurements increases the amount of solid precipitation reported by the gauge and therefore reduces the NWP precipitation bias at sites where the model tends to overestimate precipitation, and increases the bias at sites where the model underestimates the precipitation. This complicates model verification when only operational (non‐reference) gauge observations are available. Modelers, forecasters, and climatologists must consider this when comparing modeled and observed precipitation.