NOAA Environmental Modeling Center

In 1997, during the late stages of production of NCEP–NCAR Global Reanalysis (GR), exploration of a regional reanalysis project was suggested by the GR project's Advisory Committee, “particularly if the RDAS [Regional Data Assimilation System] is significantly better than the global reanalysis at capturing the regional hydrological cycle, the diurnal cycle and other important features of weather and climate variability.” Following a 6-yr development and production effort, NCEP's North American Regional Reanalysis (NARR) project was completed in 2004, and data are now available to the scientific community. Along with the use of the NCEP Eta model and its Data Assimilation System (at 32-km–45-layer resolution with 3-hourly output), the hallmarks of the NARR are the incorporation of hourly assimilation of precipitation, which leverages a comprehensive precipitation analysis effort, the use of a recent version of the Noah land surface model, and the use of numerous other datasets that are additional or improved compared to the GR. Following the practice applied to NCEP's GR, the 25-yr NARR retrospective production period (1979–2003) is augmented by the construction and daily execution of a system for near-real-time continuation of the NARR, known as the Regional Climate Data Assimilation System (R-CDAS). Highlights of the NARR results are presented: precipitation over the continental United States (CONUS), which is seen to be very near the ingested analyzed precipitation; fits of tropospheric temperatures and winds to rawinsonde observations; and fits of 2-m temperatures and 10-m winds to surface station observations. The aforementioned fits are compared to those of the NCEP–Department of Energy (DOE) Global Reanalysis (GR2). Not only have the expectations cited above been fully met, but very substantial improvements in the accuracy of temperatures and winds compared to that of GR2 are achieved throughout the troposphere. Finally, the numerous datasets produced are outlined and information is provided on the data archiving and present data availability.

Abstract The second version of the NCEP Climate Forecast System (CFSv2) was made operational at NCEP in March 2011. This version has upgrades to nearly all aspects of the data assimilation and forecast model components of the system. A coupled reanalysis was made over a 32-yr period (1979–2010), which provided the initial conditions to carry out a comprehensive reforecast over 29 years (1982–2010). This was done to obtain consistent and stable calibrations, as well as skill estimates for the operational subseasonal and seasonal predictions at NCEP with CFSv2. The operational implementation of the full system ensures a continuity of the climate record and provides a valuable up-to-date dataset to study many aspects of predictability on the seasonal and subseasonal scales. Evaluation of the reforecasts show that the CFSv2 increases the length of skillful MJO forecasts from 6 to 17 days (dramatically improving subseasonal forecasts), nearly doubles the skill of seasonal forecasts of 2-m temperatures over the United States, and significantly improves global SST forecasts over its predecessor. The CFSv2 not only provides greatly improved guidance at these time scales but also creates many more products for subseasonal and seasonal forecasting with an extensive set of retrospective forecasts for users to calibrate their forecast products. These retrospective and real-time operational forecasts will be used by a wide community of users in their decision making processes in areas such as water management for rivers and agriculture, transportation, energy use by utilities, wind and other sustainable energy, and seasonal prediction of the hurricane season.

We present the impact tests that preceded the most recent operational upgrades to the land surface model used in the National Centers for Environmental Prediction (NCEP) mesoscale Eta model, whose operational domain includes North America. These improvements consist of changes to the “Noah” land surface model (LSM) physics, most notable in the area of cold season processes. Results indicate improved performance in forecasting low‐level temperature and humidity, with improvements to (or without affecting) the overall performance of the Eta model quantitative precipitation scores and upper air verification statistics. Remaining issues that directly affect the Noah LSM performance in the Eta model include physical parameterizations of radiation and clouds, which affect the amount of available energy at the surface, and stable boundary layer and surface layer processes, which affect surface turbulent heat fluxes and ultimately the surface energy budget.

In this paper, the incorporation of a simple atmospheric boundary layer diffusion scheme into the NCEP Medium-Range Forecast Model is described. A boundary layer diffusion package based on the Troen and Mahrt nonlocal diffusion concept has been tested for possible operational implementation. The results from this approach are compared with those from the local diffusion approach, which is the current operational scheme, and verified against FIFE observations during 9–10 August 1987. The comparisons between local and nonlocal approaches are extended to the forecast for a heavy rain case of 15–17 May 1995. The sensitivity of both the boundary layer development and the precipitation forecast to the tuning parameters in the nonlocal diffusion scheme is also investigated. Special attention is given to the interaction of boundary layer processes with precipitation physics. Some results of parallel runs during August 1995 are also presented.

Results are presented from the second phase of the multiinstitution North American Land Data Assimilation System (NLDAS‐2) research partnership. In NLDAS, the Noah, Variable Infiltration Capacity, Sacramento Soil Moisture Accounting, and Mosaic land surface models (LSMs) are executed over the conterminous U.S. (CONUS) in realtime and retrospective modes. These runs support the drought analysis, monitoring and forecasting activities of the National Integrated Drought Information System, as well as efforts to monitor large‐scale floods. NLDAS‐2 builds upon the framework of the first phase of NLDAS (NLDAS‐1) by increasing the accuracy and consistency of the surface forcing data, upgrading the land surface model code and parameters, and extending the study from a 3‐year (1997–1999) to a 30‐year (1979–2008) time window. As the first of two parts, this paper details the configuration of NLDAS‐2, describes the upgrades to the forcing, parameters, and code of the four LSMs, and explores overall model‐to‐model comparisons of land surface water and energy flux and state variables over the CONUS. Focusing on model output rather than on observations, this study seeks to highlight the similarities and differences between models, and to assess changes in output from that seen in NLDAS‐1. The second part of the two‐part article focuses on the validation of model‐simulated streamflow and evaporation against observations. The results depict a higher level of agreement among the four models over much of the CONUS than was found in the first phase of NLDAS. This is due, in part, to recent improvements in the parameters, code, and forcing of the NLDAS‐2 LSMs that were initiated following NLDAS‐1. However, large inter‐model differences still exist in the northeast, Lake Superior, and western mountainous regions of the CONUS, which are associated with cold season processes. In addition, variations in the representation of sub‐surface hydrology in the four LSMs lead to large differences in modeled evaporation and subsurface runoff. These issues are important targets for future research by the land surface modeling community. Finally, improvement from NLDAS‐1 to NLDAS‐2 is summarized by comparing the streamflow measured from U.S. Geological Survey stream gauges with that simulated by four NLDAS models over 961 small basins.

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.

Abstract The Climate Forecast System (CFS), the fully coupled ocean–land–atmosphere dynamical seasonal prediction system, which became operational at NCEP in August 2004, is described and evaluated in this paper. The CFS provides important advances in operational seasonal prediction on a number of fronts. For the first time in the history of U.S. operational seasonal prediction, a dynamical modeling system has demonstrated a level of skill in forecasting U.S. surface temperature and precipitation that is comparable to the skill of the statistical methods used by the NCEP Climate Prediction Center (CPC). This represents a significant improvement over the previous dynamical modeling system used at NCEP. Furthermore, the skill provided by the CFS spatially and temporally complements the skill provided by the statistical tools. The availability of a dynamical modeling tool with demonstrated skill should result in overall improvement in the operational seasonal forecasts produced by CPC. The atmospheric component of the CFS is a lower-resolution version of the Global Forecast System (GFS) that was the operational global weather prediction model at NCEP during 2003. The ocean component is the GFDL Modular Ocean Model version 3 (MOM3). There are several important improvements inherent in the new CFS relative to the previous dynamical forecast system. These include (i) the atmosphere–ocean coupling spans almost all of the globe (as opposed to the tropical Pacific only); (ii) the CFS is a fully coupled modeling system with no flux correction (as opposed to the previous uncoupled “tier-2” system, which employed multiple bias and flux corrections); and (iii) a set of fully coupled retrospective forecasts covering a 24-yr period (1981–2004), with 15 forecasts per calendar month out to nine months into the future, have been produced with the CFS. These 24 years of fully coupled retrospective forecasts are of paramount importance to the proper calibration (bias correction) of subsequent operational seasonal forecasts. They provide a meaningful a priori estimate of model skill that is critical in determining the utility of the real-time dynamical forecast in the operational framework. The retrospective dataset also provides a wealth of information for researchers to study interactive atmosphere–land–ocean processes.

The breeding method has been used to generate perturbations for ensemble forecasting at the National Centers for Environmental Prediction (formerly known as the National Meteorological Center) since December 1992. At that time a single breeding cycle with a pair of bred forecasts was implemented. In March 1994, the ensemble was expanded to seven independent breeding cycles on the Cray C90 supercomputer, and the forecasts were extended to 16 days. This provides 17 independent global forecasts valid for two weeks every day.

Monitoring Earth's terrestrial water conditions is critically important to many hydrological applications such as global food production; assessing water resources sustainability; and flood, drought, and climate change prediction. These needs have motivated the development of pilot monitoring and prediction systems for terrestrial hydrologic and vegetative states, but to date only at the rather coarse spatial resolutions (∼10–100 km) over continental to global domains. Adequately addressing critical water cycle science questions and applications requires systems that are implemented globally at much higher resolutions, on the order of 1 km, resolutions referred to as hyperresolution in the context of global land surface models. This opinion paper sets forth the needs and benefits for a system that would monitor and predict the Earth's terrestrial water, energy, and biogeochemical cycles. We discuss six major challenges in developing a system: improved representation of surface‐subsurface interactions due to fine‐scale topography and vegetation; improved representation of land‐atmospheric interactions and resulting spatial information on soil moisture and evapotranspiration; inclusion of water quality as part of the biogeochemical cycle; representation of human impacts from water management; utilizing massively parallel computer systems and recent computational advances in solving hyperresolution models that will have up to 10 9 unknowns; and developing the required in situ and remote sensing global data sets. We deem the development of a global hyperresolution model for monitoring the terrestrial water, energy, and biogeochemical cycles a “grand challenge” to the community, and we call upon the international hydrologic community and the hydrological science support infrastructure to endorse the effort.

Abstract At the National Centers for Environmental Prediction (NCEP), a new three-dimensional variational data assimilation (3DVAR) analysis system was implemented into the operational Global Data Assimilation System (GDAS) on 1 May 2007. The new analysis system, the Gridpoint Statistical Interpolation (GSI), replaced the Spectral Statistical Interpolation (SSI) 3DVAR system, which had been operational since 1991. The GSI was developed at the Environmental Modeling Center at NCEP as part of an effort to create a more unified, robust, and efficient analysis scheme. The key aspect of the GSI is that it formulates the analysis in model grid space, which allows for more flexibility in the application of the background error covariances and makes it straightforward for a single analysis system to be used across a broad range of applications, including both global and regional modeling systems and domains. Due to the constraints of working with an operational system, the final GDAS package included many changes other than just a simple replacing of the SSI with the new GSI. The new GDAS package contained an upgrade to the Global Forecast System model, including a new vertical coordinate, as well as new features in the GSI that were never developed for the SSI. Some of these new features included changes to the observation selection, quality control, minimization algorithm, dynamic balance constraint, and assimilation of new observation types. The evaluation of the new system relative to the SSI-based system was performed for nearly an entire year of analyses and forecasts. The objective and subjective evaluations showed that the new package exhibited superior forecast performance relative to the old SSI-based system. The new system has been shown to improve forecast skill in the tropics and substantially reduce the short-term forecast error in the extratropics. This implementation has laid the groundwork for future scientific advancements in data assimilation at NCEP.

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.

Abstract Drought prediction is of critical importance to early warning for drought managements. This review provides a synthesis of drought prediction based on statistical, dynamical, and hybrid methods. Statistical drought prediction is achieved by modeling the relationship between drought indices of interest and a suite of potential predictors, including large‐scale climate indices, local climate variables, and land initial conditions. Dynamical meteorological drought prediction relies on seasonal climate forecast from general circulation models (GCMs), which can be employed to drive hydrological models for agricultural and hydrological drought prediction with the predictability determined by both climate forcings and initial conditions. Challenges still exist in drought prediction at long lead time and under a changing environment resulting from natural and anthropogenic factors. Future research prospects to improve drought prediction include, but are not limited to, high‐quality data assimilation, improved model development with key processes related to drought occurrence, optimal ensemble forecast to select or weight ensembles, and hybrid drought prediction to merge statistical and dynamical forecasts.

Abstract A new physics package containing revised convection and planetary boundary layer (PBL) schemes in the National Centers for Environmental Prediction’s Global Forecast System is described. The shallow convection (SC) scheme in the revision employs a mass flux parameterization replacing the old turbulent diffusion-based approach. For deep convection, the scheme is revised to make cumulus convection stronger and deeper to deplete more instability in the atmospheric column and result in the suppression of the excessive grid-scale precipitation. The PBL model was revised to enhance turbulence diffusion in stratocumulus regions. A remarkable difference between the new and old SC schemes is seen in the heating or cooling behavior in lower-atmospheric layers above the PBL. While the old SC scheme using the diffusion approach produces a pair of layers in the lower atmosphere with cooling above and heating below, the new SC scheme using the mass-flux approach produces heating throughout the convection layers. In particular, the new SC scheme does not destroy stratocumulus clouds off the west coasts of South America and Africa as the old scheme does. On the other hand, the revised deep convection scheme, having a larger cloud-base mass flux and higher cloud tops, appears to effectively eliminate the remaining instability in the atmospheric column that is responsible for the excessive grid-scale precipitation in the old scheme. The revised PBL scheme, having an enhanced turbulence mixing in stratocumulus regions, helps prevent too much low cloud from forming. An overall improvement was found in the forecasts of the global 500-hPa height, vector wind, and continental U.S. precipitation with the revised model. Consistent with the improvement in vector wind forecast errors, hurricane track forecasts are also improved with the revised model for both Atlantic and eastern Pacific hurricanes in 2008.

A new third-generation ocean wind wave model is presented. This model is based on previously developed input and nonlinear interaction source terms and a new dissipation source term. It is argued that the dissipation source term has to be modeled using two explicit constituents. A low-frequency dissipation term analogous to wave energy loss due to oceanic turbulence is therefore augmented with a diagnostic high-frequency dissipation term. The dissipation is tuned for the model to represent idealized fetch-limited growth behavior. The new model results in excellent growth behavior from extremely short fetches up to full development. For intermediate to long fetches results are similar to those of WAM, but for extremely short fetches the present model presents a significant improvement (although the poor behavior of WAM appears to be related to correctable numerical constraints). The new model furthermore gives smoother results and appears less sensitive to numerical errors. Finally, limitations of the present source terms and possible improvements are discussed.

With improved assimilation techniques, it is now possible to directly assimilate cloud-cleared radiances, rather than temperature and moisture retrievals, in objective analyses. The direct use of the cloud-cleared radiances became the operational technique for using satellite sounding data at the National Centers for Environmental Prediction (NCEP) in October 1995. The methodology for using the data (including bias correction, ozone analysis, skin temperature analysis, and quality control) are described in this paper. The impact of the direct use of the radiances compared to the previously operational use of satellite sounding data shows considerable improvement in NCEP’s forecast skill, especially in the Southern Hemisphere. It is anticipated that additional positive impacts will occur with application of the technique to other remotely sensed data.

This is the second part of a study on continental‐scale water and energy flux analysis and validation conducted in phase 2 of the North American Land Data Assimilation System project (NLDAS‐2). The first part concentrates on a model‐by‐model comparison of mean annual and monthly water fluxes, energy fluxes and state variables. In this second part, the focus is on the validation of simulated streamflow from four land surface models (Noah, Mosaic, Sacramento Soil Moisture Accounting (SAC‐SMA), and Variable Infiltration Capacity (VIC) models) and their ensemble mean. Comparisons are made against 28‐years (1 October 1979–30 September 2007) of United States Geological Survey observed streamflow for 961 small basins and 8 major basins over the conterminous United States (CONUS). Relative bias, anomaly correlation and Nash‐Sutcliffe Efficiency (NSE) statistics at daily to annual time scales are used to assess model‐simulated streamflow. The Noah (the Mosaic) model overestimates (underestimates) mean annual runoff and underestimates (overestimates) mean annual evapotranspiration. The SAC‐SMA and VIC models simulate the mean annual runoff and evapotranspiration well when compared with the observations. The ensemble mean is closer to the mean annual observed streamflow for both the 961 small basins and the 8 major basins than is the mean from any individual model. All of the models, as well as the ensemble mean, have large daily, weekly, monthly, and annual streamflow anomaly correlations for most basins over the CONUS, implying strong simulation skill. However, the daily, weekly, and monthly NSE analysis results are not necessarily encouraging, in particular for daily streamflow. The Noah and Mosaic models are useful (NSE > 0.4) only for about 10% of the 961 small basins, the SAC‐SMA and VIC models are useful for about 30% of the 961 small basins, and the ensemble mean is useful for about 42% of the 961 small basins. As the time scale increases, the NSE increases as expected. However, even for monthly streamflow, the ensemble mean is useful for only 75% of the 961 small basins.

Abstract Real-data experiments with an ensemble data assimilation system using the NCEP Global Forecast System model were performed and compared with the NCEP Global Data Assimilation System (GDAS). All observations in the operational data stream were assimilated for the period 1 January–10 February 2004, except satellite radiances. Because of computational resource limitations, the comparison was done at lower resolution (triangular truncation at wavenumber 62 with 28 levels) than the GDAS real-time NCEP operational runs (triangular truncation at wavenumber 254 with 64 levels). The ensemble data assimilation system outperformed the reduced-resolution version of the NCEP three-dimensional variational data assimilation system (3DVAR), with the biggest improvement in data-sparse regions. Ensemble data assimilation analyses yielded a 24-h improvement in forecast skill in the Southern Hemisphere extratropics relative to the NCEP 3DVAR system (the 48-h forecast from the ensemble data assimilation system was as accurate as the 24-h forecast from the 3DVAR system). Improvements in the data-rich Northern Hemisphere, while still statistically significant, were more modest. It remains to be seen whether the improvements seen in the Southern Hemisphere will be retained when satellite radiances are assimilated. Three different parameterizations of background errors unaccounted for in the data assimilation system (including model error) were tested. Adding scaled random differences between adjacent 6-hourly analyses from the NCEP–NCAR reanalysis to each ensemble member (additive inflation) performed slightly better than the other two methods (multiplicative inflation and relaxation-to-prior).

The daytime interaction of the land surface with the atmospheric boundary layer (ABL) is studied using a coupled one-dimensional (column) land surface–ABL model. This is an extension of earlier work that focused on modeling the ABL for 31 May 1978 at Cabauw, Netherlands; previously, it was found that coupled land–atmosphere tests using a simple land surface scheme did not accurately represent surface fluxes and coupled ABL development. Here, findings from that earlier study on ABL parameterization are utilized, and include a more sophisticated land surface scheme. This land surface scheme allows the land–atmosphere system to respond interactively with the ABL. Results indicate that in coupled land–atmosphere model runs, realistic daytime surface fluxes and atmospheric profiles are produced, even in the presence of ABL clouds (shallow cumulus). Subsequently, the role of soil moisture in the development of ABL clouds is explored in terms of a new relative humidity tendency equation at the ABL top where a number of processes and interactions are involved. Among other issues, it is shown that decreasing soil moisture may actually lead to an increase in ABL clouds in some cases.The daytime interaction of the land surface with the atmospheric boundary layer (ABL) is studied using a coupled one-dimensional (column) land surface-ABL model. This is an extension of earlier work that focused on modeling the ABL for 31 May 1978 at Cabauw, Netherlands; previously, it was found that coupled land atmosphere tests using a simple land surface scheme did not accurately represent surface fluxes and coupled ABL development. Here, findings from that earlier study on ABL parameterization are utilized, and include a more sophisticated land surface scheme. This land surface scheme allows the land-atmosphere system to respond interactively with the ABL. Results indicate that in coupled land-atmosphere model runs, realistic daytime surface fluxes and atmospheric profiles are produced, even in the presence of ABL clouds (shallow cumulus). Subsequently, the role of soil moisture in the development of ABL clouds is explored in terms of a new relative humidity tendency equation at the ABL top where a number of processes and interactions are involved. Among other issues, it is shown that decreasing soil moisture may actually lead to an increase in ABL clouds in some cases.

Abstract The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model with hourly data assimilation that covers the conterminous United States and Alaska and runs in real time at the NOAA/National Centers for Environmental Prediction (NCEP). Implemented operationally at NOAA/NCEP in 2014, the HRRR features 3-km horizontal grid spacing and frequent forecasts (hourly for CONUS and 3-hourly for Alaska). HRRR initialization is designed for optimal short-range forecast skill with a particular focus on the evolution of precipitating systems. Key components of the initialization are radar-reflectivity data assimilation, hybrid ensemble-variational assimilation of conventional weather observations, and a cloud analysis to initialize stratiform cloud layers. From this initial state, HRRR forecasts are produced out to 18 h every hour, and out to 48 h every 6 h, with boundary conditions provided by the Rapid Refresh system. Between 2014 and 2020, HRRR development was focused on reducing model bias errors and improving forecast realism and accuracy. Improved representation of the planetary boundary layer, subgrid-scale clouds, and land surface contributed extensively to overall HRRR improvements. The final version of the HRRR (HRRRv4), implemented in late 2020, also features hybrid data assimilation using flow-dependent covariances from a 3-km, 36-member ensemble (“HRRRDAS”) with explicit convective storms. HRRRv4 also includes prediction of wildfire smoke plumes. The HRRR provides a baseline capability for evaluating NOAA’s next-generation Rapid Refresh Forecast System, now under development. Significance Statement NOAA’s operational hourly updating, convection-allowing model, the High-Resolution Rapid Refresh (HRRR), is a key tool for short-range weather forecasting and situational awareness. Improvements in assimilation of weather observations, as well as in physics parameterizations, have led to improvements in simulated radar reflectivity and quantitative precipitation forecasts since the initial implementation of HRRR in September 2014. Other targeted development has focused on improved representation of the diurnal cycle of the planetary boundary layer, resulting in improved near-surface temperature and humidity forecasts. Additional physics and data assimilation changes have led to improved treatment of the development and erosion of low-level clouds, including subgrid-scale clouds. The final version of HRRR features storm-scale ensemble data assimilation and explicit prediction of wildfire smoke plumes.

A brief historical overview of numerical wind wave forecast modeling efforts at the National Centers for Environmental Prediction (NCEP) is presented, followed by an in-depth discussion of the new operational National Oceanic and Atmospheric Administration (NOAA) “WAVEWATCH III” (NWW3) wave forecast system. This discussion mainly focuses on a parallel comparison of the new NWW3 system with the previously operational Wave Model (WAM) system, using extensive buoy and European Remote Sensing Satellite-2 (ERS-2) altimeter data. The new system is shown to describe the variability of the wave height more realistically, with similar or smaller random errors and generally better correlation coefficients and regression slopes than WAM. NWW3 outperforms WAM in the Tropics and in the Southern Hemisphere, and they both show fairly similar behavior at northern high latitudes. Dissemination of NWW3 products, and plans for its further development, are briefly discussed.