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

Samuel T Buisán,Javier Alastrué,Michael E Earle,Craig D Smith,Yves‐Alain Roulet,Roy Rasmussen,Mareile Wolff,Timo Laine,John Kochendorfer,Amber Ross,Rodica Nitu,José Luís Collado

doi:10.1002/asl.976

Abstract

AbstractPrecipitation 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.

Highlights

Winter precipitation forecasts are needed to help address meteorological and hydrological hazards; solid precipitation measurements available for data assimilation and forecast verification are affected by potentially large undercatch errors (Goodison et al, 1998; Rasmussen et al, 2012)
This work illustrates the complexity of Numerical Weather Prediction (NWP) model forecast precipitation verification for winter precipitation using automated gauge measurements
The main conclusions are: i The adjustment of SA-shielded gauge measurements always resulted in precipitation amounts that were closer to the Double Fence Automated Reference (DFAR) measurements

Summary

Introduction

Winter precipitation forecasts are needed to help address meteorological and hydrological hazards; solid precipitation measurements available for data assimilation and forecast verification are affected by potentially large undercatch errors (Goodison et al, 1998; Rasmussen et al, 2012). These errors propagate directly into precipitation forecasts, and affect model climatology, data assimilation, and nowcasting. To minimize false verifications due to known undercatch, Schirmer et al (2015) attempted to verify Numerical Weather Prediction (NWP) model forecasts in mountainous terrain using observations from ultrasonic snow depth measurements and snow pillows instead of gauge measurements. Lopez et al (2013) discarded precipitation gauge measurements during snowfall events to avoid errors in the experimental 4D-Var assimilation of SYNOP rain gauge data at the European Centre for Medium Range Weather Forecast (ECMWF)

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