The Rapid Refresh (RAP), an hourly updated assimilation and model forecast system, replaced the Rapid Update Cycle (RUC) as an operational regional analysis and forecast system among the suite of models at the NOAA/National Centers for Environmental Prediction (NCEP) in 2012. The need for an effective hourly updated assimilation and modeling system for the United States for situational awareness and related decision-making has continued to increase for various applications including aviation (and transportation in general), severe weather, and energy. The RAP is distinct from the previous RUC in three primary aspects: a larger geographical domain (covering North America), use of the community-based Advanced Research version of the Weather Research and Forecasting (WRF) Model (ARW) replacing the RUC forecast model, and use of the Gridpoint Statistical Interpolation analysis system (GSI) instead of the RUC three-dimensional variational data assimilation (3DVar). As part of the RAP development, modifications have been made to the community ARW model (especially in model physics) and GSI assimilation systems, some based on previous model and assimilation design innovations developed initially with the RUC. Upper-air comparison is included for forecast verification against both rawinsondes and aircraft reports, the latter allowing hourly verification. In general, the RAP produces superior forecasts to those from the RUC, and its skill has continued to increase from 2012 up to RAP version 3 as of 2015. In addition, the RAP can improve on persistence forecasts for the 1–3-h forecast range for surface, upper-air, and ceiling forecasts.

[1] Thirty-three snowpack models of varying complexity and purpose were evaluated across a wide range of hydrometeorological and forest canopy conditions at five Northern Hemisphere locations, for up to two winter snow seasons. Modeled estimates of snow water equivalent (SWE) or depth were compared to observations at forest and open sites at each location. Precipitation phase and duration of above-freezing air temperatures are shown to be major influences on divergence and convergence of modeled estimates of the subcanopy snowpack. When models are considered collectively at all locations, comparisons with observations show that it is harder to model SWE at forested sites than open sites. There is no universal ''best'' model for all sites or locations, but comparison of the consistency of individual model performances relative to one another at different sites (and vice versa). Calibration of models at forest sites provides lower errors than uncalibrated models at three out of four locations. However, benefits of calibration do not translate to subsequent years, and benefits gained by models calibrated for forest snow processes are not translated to open conditions.

The Rapid Update Cycle (RUC), an operational regional analysis-forecast system among the suite of models at the National Centers for Environmental Prediction (NCEP), is distinctive in two primary aspects: its hourly assimilation cycle and its use of a hybrid isentropic-sigma vertical coordinate. The use of a quasi-isentropic coordinate for the analysis increment allows the influence of observations to be adaptively shaped by the potential temperature structure around the observation, while the hourly update cycle allows for a very current analysis and short-range forecast. Herein, the RUC analysis framework in the hybrid coordinate is described, and some considerations for high-frequency cycling are discussed. A 20-km 50-level hourly version of the RUC was implemented into operations at NCEP in April 2002. This followed an initial implementation with 60-km horizontal grid spacing and a 3-h cycle in 1994 and a major upgrade including 40-km horizontal grid spacing in 1998. Verification of forecasts from the latest 20-km version is presented using rawinsonde and surface observations. These verification statistics show that the hourly RUC assimilation cycle improves short-range forecasts (compared to longer-range forecasts valid at the same time) even down to the 1-h projection.

Twenty-one land surface schemes (LSSs) performed simulations forced by 18 yr of observed meteorological data from a grassland catchment at Valdai, Russia, as part of the Project for the Intercomparison of Land-Surface Parameterization Schemes (PILPS) Phase 2(d). In this paper the authors examine the simulation of snow. In comparison with observations, the models are able to capture the broad features of the snow regime on both an intra-and interannual basis. However, weaknesses in the simulations exist, and early season ablation events are a significant source of model scatter. Over the 18-yr simulation, systematic differences between the models' snow simulations are evident and reveal specific aspects of snow model parameterization and design as being responsible. Vapor exchange at the snow surface varies widely among the models, ranging from a large net loss to a small net source for the snow season. Snow albedo, fractional snow cover, and their interplay have a large effect on energy available for ablation, with differences among models most evident at low snow depths. The incorporation of the snowpack within an LSS structure affects the method by which snow accesses, as well as utilizes, available energy for ablation. The sensitivity of some models to longwave radiation, the dominant winter radiative flux, is partly due to a stability-induced feedback and the differing abilities of models to exchange turbulent energy with the atmosphere. Results presented in this paper suggest where weaknesses in macroscale snow modeling lie and where both theoretical and observational work should be focused to address these weaknesses.