A North American Hourly Assimilation and Model Forecast Cycle: The Rapid Refresh

Abstract

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.

Forecasting Low-Visibility Procedure States with Tree-Based Statistical Methods

“…Many physically-based models for visibility and fog forecasts were developed in the past, e.g. HRRR (Benjamin et al 2016), The London Model (Boutle et al 2016), and PAFOG (Bott and Trautmann 2002). Generally, these models are computationally expensive and special end-user-related variables such as lvp have to be derived afterwards from their output.…”

“…An operational visibility forecasting system for several lead times and locations was developed by Ghirardelli and Glahn (2010), using again multiple linear regression. Glahn et al (2017) combined this system with the physically-based forecasts of Benjamin et al (2016) to improve the performance. Other statistical techniques to forecast visibility are, for example, neural networks (e.g., Pasini et al 2001;Marzban et al 2007), Bayesian model averaging (e.g., Roquelaure et al 2009), or decision trees (e.g., Bartoková et al 2015;Dutta and Chaudhuri 2015).…”

Characterizing severe weather potential in synoptically weakly forced thunderstorm environments

“…Lateral bound- ary conditions are provided by the Global Forecast System (GFS). Additional information regarding the RAP assimilation system and model physics can be found in Benjamin et al (2016). The model has output available at 37 vertical levels spaced at 25 hPa intervals between 1000 and 100 hPa and 10 hPa intervals above 100 hPa.…”

A Nowcasting Model for Tropical Cyclone Precipitation Regions Based on the TREC Motion Vector Retrieval with a Semi-Lagrangian Scheme for Doppler Weather Radar

“…In contrast, radar-assimilated model nowcasting does not suffer from this problem because it uses physical (deterministic) equations to solve clouds' thermodynamic processes to predict their future status, and that prediction is expected to be more reliable [8]. For example, the recently-developed RAPid refresh model (RAP) digests weather radar observations through a data-assimilation system, and it can produce a fast prediction up to 18 h into the future nationwide with updates every 60 min [9]. Although theoretically, using an NWP model that assimilates radar observations to issue forecasts should surpass using radar observations only, it cannot replace radar-based nowcasting yet.…”