The Model Evaluation Tools (MET): More than a decade of community-supported forecast verification

Abstract

MET is a community-based package of state-of-the-art tools to evaluate predictions of weather, climate, and other phenomena, with capabilities to display and analyze verification results via the METplus system.Forecast verification and evaluation is a critical aspect of forecast development and improvement, day-to-day forecasting, and the interpretation and application of forecasts. In recent decades, the verification field has rapidly matured, and many new approaches have been developed. However, until recently, a stable set of modern tools to undertake this important component of forecasting has not been available. The Model Evaluation Tools (MET) was conceived and implemented to fill this gap. MET (https://dtcenter.org/community-code/model-evaluation-tools-met) was developed by the National Center for Atmospheric Research (NCAR), the National Oceanic and Atmospheric Administration (NOAA), and the United States Air Force (USAF) and is supported via the Developmental Testbed Center (DTC) and collaborations with operational and research organizations.MET incorporates traditional verification methods, as well as modern verification capabilities developed over the last two decades. MET stands apart from other verification packages due to its inclusion of innovative spatial methods, statistical inference tools, and a wide range of approaches to address the needs of individual users, coupled with strong community engagement and support. In addition, MET is freely available, which ensures that consistent modern verification capabilities can be applied by researchers and operational forecasting practitioners, enabling the use of consistent and scientifically-meaningful methods by all users.This article describes MET and the expansion of MET to an umbrella package (METplus) that includes a database and display system and Python wrappers to facilitate the wide use of MET. Examples of MET applications illustrate some of the many ways that the package can be used to evaluate forecasts in a meaningful way.