The National Blend of Models (NBM) is the culmination of an effort to develop a nationally consistent set of foundational gridded guidance products based on well-calibrated National Weather Service (NWS) and non-NWS model information. These guidance products are made available to the National Centers for Environmental Prediction centers and NWS Weather Forecast Offices for use in their forecast process. As the NWS continues to shift emphasis from production of forecast products to impact-based decision support services for core partners, the deterministic and probabilistic output from the NBM will become increasingly important as a starting point to the forecast process. The purpose of this manuscript is to document the progress of NBM versions 3.1 and 3.2 and what techniques are used to blend roughly 30 individual models and ensembles for a number of forecast elements and regions. Focus will be on the core elements such as (1) temperature and dew point temperature, (2) winter weather, fire weather, thunderstorm probabilities, and (3) wind speed and gusts.

In an effort to support aviation forecasting, the National Weather Service's Meteorological Development Laboratory (MDL) has recently redeveloped the Localized Aviation Model Output Statistics (MOS) Program (LAMP) system. LAMP is designed to run hourly in NWS operations and produce short-range aviation forecast guidance at 1-h projections out to 25 h. This paper compares and contrasts LAMP ceiling height and visibility forecasts with forecasts produced by the 20-km Rapid Update Cycle model (RUC20), the Weather Research and Forecasting Nonhydrostatic Mesoscale Model (WRF-NMM), and the Short-Range Ensemble Forecast system (SREF). RUC20 and WRF-NMM forecasts of continuous ceiling height and visibility were interpolated to stations and converted into categorical forecasts. These interpolated forecasts were also categorized into instrument flight rule (IFR) or lower conditions and verified against LAMP forecasts at stations in the contiguous United States. LAMP and SREF probabilistic forecasts of ceiling height and visibility from LAMP and the SREF system were also verified. This study demonstrates that for the 0000 and 1200 UTC cycles over the contiguous United States, LAMP station-based categorical forecasts of ceiling height, visibility, and IFR conditions or lower are more accurate than the RUC20 and WRF-NMM ceiling height and visibility forecasts interpolated to stations. Moreover, for the 0900 and 2100 UTC forecast cycles and verification periods studied here, LAMP ceiling height and visibility probabilities exhibit better reliability and skill than the SREF system.

Winter storms are disruptive to society and the economy, and they often cause significant injuries and deaths. Innovations in winter storm forecasting have occurred across the value chain over the past two decades, from physical understanding, to observations, to model forecasts, to post-processing, to forecaster knowledge and interpretation, to products and services, and ultimately to decision support. These innovations enable more accurate and consistent forecasts, which are increasingly being translated into actionable information for decision makers. This paper reviews the current state of winter storm forecasting in the context of the U.S. National Weather Service operations and describes a potential future state. Given predictability limitations, a key challenge of winter storm forecasting has been characterizing uncertainty and communicating the forecast in ways that are understandable and useful to decision makers. To address this challenge, particular focus is placed on establishing a probabilistic framework, with probabilistic hazard information serving as a foundation for winter storm decision support services. The framework is guided by social science research to ensure effective communication of risk to meet users’ needs. Solutions to gaps impeding progress in winter storm forecasting are highlighted, including better understanding of mesoscale phenomenon, the need for better ensemble calibration, a rigorous and consistent database of observed impacts, and linking multi-parameter probabilities (e.g., probability of intense snowfall rates at rush hour) with users’ information needs and decisions.

Table 1 outlines the ceiling and visibility conditions that define each one of these flight categories. Since IFR, LIFR, and VLIFR conditions adversely affect the aviation industry, they comprise a common group of hazardous conditions. As such, it is convenient to delineate the five flight groups into two categories: 1) IFR, LIFR, and VLIFR (hereafter referred to as IFR or worse), and 2) MVFR and VFR. Understanding the variations in the relative frequency distributions of visibility, ceiling height, and aviation flight categories just prior to and shortly after precipitation begins is crucial for generating accurate aviation forecasts. BACkground. The Meteorological Development Laboratory (MDL)'s Localized Aviation Model Output Statistics (MOS) Program (LAMP) forecast system is designed to update the Global Forecast System (GFS)-based MOS by running every hour and generate hourly resolution forecasts of weather elements that directly affect aviation interests for projections out to 25 hours in advance (see Ghirardelli 2005 for a comprehensive discussion of the GFS-based Aviation Weather Observations vs. LAMP Forecasts with the Onset of Precipitation by david E. Rudack