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.

An assessment is presented on the relative forecast impact on the performance of a numerical weather prediction model from eight different observation data types: aircraft, profiler, radiosonde, velocity azimuth display (VAD), GPS-derived precipitable water, aviation routine weather report (METAR; surface), surface mesonet, and satellite-based atmospheric motion vectors. A series of observation sensitivity experiments was conducted using the Rapid Update Cycle (RUC) model/assimilation system in which various data sources were denied to assess the relative importance of the different data types for short-range (3-12 h) wind, temperature, and relative humidity forecasts at different vertical levels and near the surface. These experiments were conducted for two 10-day periods, one in November-December 2006 and one in August 2007. These experiments show positive short-range forecast impacts from most of the contributors to the heterogeneous observing system over the RUC domain. In particular, aircraft observations had the largest overall impact for forecasts initialized 3-6 h before 0000 or 1200 UTC, considered over the full depth (1000-100 hPa), followed by radiosonde observations, even though the latter are available only every 12 h. Profiler data (including at a hypothetical 8-km depth), GPS-precipitable water estimates, and surface observations also led to significant improvements in short-range forecast skill.

Since 1994, the NOAA Research-Forecast Systems Laboratory (NOAA /FSL) has been evaluating the utility of ground-based Global Positioning System (GPS) remote sensing techniques for operational weather forecasting, climate monitoring, atmospheric research, and other applications such as satellite calibration and validation. Techniques have been developed to acquire, process, distribute GPS integrated precipitable water vapor (IPW) retrievals and ancillary surface meteorological observations every 30-minutes with less than 15 minute latency. Techniques to assimilate these observations into the research version of the Rapid Update Cycle (RUC) numerical weather prediction assimilation/model system running hourly at NOAA /FSL have been developed, and the impacts of these observations on shortrange weather forecast accuracy have been evaluated since 1998 using a 60-km version of the system. These assessments consist of data denial experiments (parallel runs with and without GPS water vapor observations) to determine the impact that GPS-derived integrated (or total column) precipitable water vapor (IPW) retrievals have on short-range moisture and precipitation forecasts. The experiments have been conducted over a portion of the central United States that, from a meteorological perspective, is one of the best-observed areas on Earth. While this greatly facilitates the impact assessments, it also presents a special challenge to a new observing system under evaluation, such as GPS-Met, since relatively few measurements have to ''compete'' with an enormous number of other (conventional and nonconventional) observations of similar and related parameters. Despite this, five years of experiments inCorresponding author: Seth I. Gutman, Chief, GPS-Met Observing Systems Branch, NOAA Forecast Systems Laboratory, 325 Broadway R/FS3, Boulder CO 80305-3328, USA E-mail: Seth.I.Gutman@noaa.gov ( 2004, Meteorological Society of Japan dicate more or less continuous improvements in 3-hour relative humidity forecasts at pressure levels below 500 hPa. The greatest skill is seen during the cold season when moisture changes are dominated by synoptic-scale weather systems. Perhaps the most significant result is that the impact in improved forecast skill from assimilation of GPS-IPW data has increased each year as the number of stations has increased, suggesting that further increases in the network density over the United States will result in further forecast improvement.

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A time-lagged ensemble forecast system is developed using a set of hourly initialized Rapid Update Cycle model deterministic forecasts. Both the ensemble-mean and probabilistic forecasts from this time-lagged ensemble system present a promising improvement in the very short-range weather forecasting of 1-3 h, which may be useful for aviation weather prediction and nowcasting applications. Two approaches have been studied to combine deterministic forecasts with different initialization cycles as the ensemble members. The first method uses a set of equally weighted time-lagged forecasts and produces a forecast by taking the ensemble mean. The second method adopts a multilinear regression approach to select a set of weights for different time-lagged forecasts. It is shown that although both methods improve short-range forecasts, the unequally weighted method provides the best results for all forecast variables at all levels. The timelagged ensembles also provide a sample of statistics, which can be used to construct probabilistic forecasts.