Abstract

An evaluation of the quantitative precipitation forecasts (QPF) from the Global Forecast System (GFS) and the North American Mesoscale (NAM) operational models was performed by the Developmental Testbed Center (DTC) in order to quantify the differences in QPF produced by two modeling systems that... Show moreAn evaluation of the quantitative precipitation forecasts (QPF) from the Global Forecast System (GFS) and the North American Mesoscale (NAM) operational models was performed by the Developmental Testbed Center (DTC) in order to quantify the differences in QPF produced by two modeling systems that vary significantly in horizontal resolution. There are several common traditional verification metrics used to evaluate precipitation forecasts using a grid-to-grid approach. However, it is widely acknowledged that such metrics tend to unfairly penalize higher resolution models with a finer level of detail. Thus, more advanced spatial verification techniques were also applied to assess whether they could provide more meaningful insight in to the performance of the higher resolution NAM model. Accumulation periods of 3-h and 24-h were evaluated out to 84-h for model output initialized at 00 UTC daily between 18 December 2008 – 15 December 2009. Both the NAM and GFS models, along with the National Centers for Environmental Prediction (NCEP) Stage II precipitation analyses for the 3-h accumulation and the NCEP/Climate Prediction Center (CPC) precipitation analyses for the 24-h accumulations, were interpolated to a 4-, 15- and 60-km verification domain and compared. Objective model verification statistics were generated using version 3.0 of the Model Evaluation Tools (MET) software package, which offers a wide variety of state-of-the-art verification methods. For this test, the traditional metrics computed included frequency bias and Gilbert Skill Score (GSS). The object-based verification approach referred to as the Method for Object-based Diagnostic Evaluation (MODE) and the neighborhood method Fraction Skill Score (FSS) were used for the advanced spatial techniques. Show less