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The first version of the Taiwan Central Weather Bureau one-tier (TCWB1T) fully coupled global atmospheric and oceanic modeling forecast system had been developed and implemented as a routine operation for seasonal prediction at Central Weather Bureau (CWB) in 2017, with a minor revision in 2020. Based on NCEP CFSv1, the global atmospheric model in NCEP CFSv1 was replaced by CWB's atmospheric global spectral model (GSM) and coupled with the GFDL MOM3. Several parameters have been tested and tuned in the CWB atmospheric GSM, achieving an optimal configuration with better sea surface temperature (SST) predictions for integration more than one year. Using NCEP CFSR as the initial condition, TCWB1T conducted hindcasts from 1982 to 2011 and forecasts from 2012 to 2019 to analyze its performance. The results of these hindcasts and forecasts show that the TCWB1T can make useful predictions as verified against the observations of OISST, ERSST, CFSR, and GPCP based on the methods of EOF, RMSE, anomaly correlation, ranked probability skill score (RPSS), reliability diagram (RD), and relative operating characteristics (ROCs). TCWB1T also has the same level of skill scores as NCEP CFSv2 and/or the ECMWF fifth-generation seasonal forecast system (SEAS5), based on EOF, anomaly pattern correlation, climatological bias, RMSE, temporal correlation, and anomaly correlation percentage of forecast skill. TCWB1T shows forecast skill that is better in winter than in summer. Overall, it indicates that TCWB1T can be used for seasonal ENSO predictions. [ABSTRACT FROM AUTHOR]

The National Centers for Environmental Prediction (NCEP) Global Forecast System version 16 (GFSv16) encountered a few model instability failures during the pre-operational real-time parallel runs. The model forecasts failed when an extremely small thickness depth appeared at the model's lowest layer during the landfall of strong tropical cyclones. A quick solution was to increase the value of minimum thickness depth, an arbitrary parameter introduced to prevent numerical instability. This modification solved the model's numerical instability with a small impact on forecast skills. It was adopted in GFSv16 to implement this version of the operational system as planned. Upon further investigation, it was determined that the extremely thin depth was a result of the advection of geopotential heights at the interfaces of model layers. In the Finite-Volume Cubed-Sphere (FV3) dynamical core, the horizontal winds at interfaces for advection are calculated from the layer-mean values by solving a tridiagonal system of equations in the entire vertical column based on the parabolic spline method (PSM) with high-order boundary conditions (BCs). We replaced the high-order BCs with zero-gradient BCs for the interface-wind reconstruction. The impact of the zero-gradient BCs was investigated by performing sensitivity experiments with GFSv16, idealized mountain ridge tests and the Rapid Refresh Forecast System (RRFS). The results showed that zero-gradient BCs can fundamentally solve the instability and have little impact on the forecast performances and the numerical solutions of idealized mountain tests. This option has been added to FV3 and will be utilized in the GFS (GFSv17/Global Ensemble Forecast System version 13 – GEFSv13) and RRFS for operations. [ABSTRACT FROM AUTHOR]

The National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) version 16 encountered a few model instability failures during the pre-operational real-time parallel runs. The model forecasts failed when an extremely small thickness depth appeared at the model's lowest layer during the landfall of strong tropical cyclones. A quick solution was to increase the value of minimum thickness depth, an arbitrary parameter introduced to prevent the occurrence of extremely thin model layers, thus numerical instability. This modification solved the issue of the model's numerical instability with a small impact on forecast skills. It was adopted in GFSv16 to help implement this version of the operational system as planned. Further investigation showed that the extremely small thickness depth occurred after the advection of geopotential heights at the interfaces of model layers. In the FV3 dynamic core, the horizontal winds at interfaces for advection are calculated from the layer-mean values by solving a tridiagonal system of equations in the entire vertical column based on the Parabolic Spline Method (PSM) with high-order boundary conditions (BCs). We replaced the high-order BCs with zero-gradient BCs for the interface-wind reconstruction. The impact of the zero-gradient BCs was investigated by performing sensitivity experiments with GFSv16, idealized mountain ridge tests, and the Rapid Refresh Forecast System (RRFS). The results showed that zero-gradient BCs can fundamentally solve the instability and have little impact on the forecast performances and the numerical solution of idealized mountain tests. This option has been added to FV3 and will be utilized in the GFS (GFSv17/GEFSv13) and RRFS for operations in 2024. [ABSTRACT FROM AUTHOR]

The National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) version 16 encountered a few model instability failures during the pre-operational real-time parallel runs. The model forecasts failed when an extremely small thickness depth appeared at the model's lowest layer when strong tropical cyclones made landfall. A quick solution was to increase the value of minimum thickness depth, an arbitrary parameter introduced to prevent the occurrence of extremely thin model layers, thus numerical instability. This modification solved the issue of the model's numerical instability with a small impact on forecast skills. It was adopted in GFSv16 to help implement this version of the operational system as planned. Further investigation showed that the extremely small thickness depth occurred after the advection of geopotential heights at the interfaces of model layers. In the FV3 dynamic core, the horizontal winds at interfaces for advection are calculated from the layer-mean values by solving a tridiagonal system of equations in the entire vertical column based on the Parabolic Spline Method (PSM) with high-order boundary conditions (BCs). We replaced the high-order BCs with zero-gradient BCs for the interface-wind reconstruction. The impact of the zero-gradient BCs was investigated by performing sensitivity experiments with GFSv16, idealized mountain ridge tests, and the Rapid Refresh Forecast System (RRFS). The results showed that zero-gradient BCs can fundamentally solve the instability and have little impact on the forecast performances and the numerical solution of idealized mountain tests. This option has been added to FV3 and will be utilized in the GFS (GFSv17/GEFSv13) and RRFS for operations in 2024. [ABSTRACT FROM AUTHOR]

The article discusses the highlights of the 12th International Regional Spectral Model (RSM) Workshop held on November 5-9, 2012 in California. Topics addressed at the workshop include an overview of RSM dynamics and physics, dynamic downscaling, and numerical modeling. The purpose of the workshop is also is also cited.

A newly developed mass-conserving semi-Lagrangian advection scheme for limited area modelling is illustrated, which requires no initial guess and no iteration for determining trajectories and is different from the traditional semi-Lagrangian scheme. This scheme uses operator splitting to extend one dimension to multi-dimensions to avoid 2D interpolation or remapping. In addition to the mass conservation, the scheme is relatively simple and is competitively accurate as compared to other semi-Lagrangian schemes. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

A new vertical discretization used in the atmospheric dynamics of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) is illustrated, with enthalpy as the thermodynamic prognostic variable to reduce computation in thermodynamic equations while concerning all gas tracers in the model. Mass, energy, entropy, and angular momentum conservations are utilized as constraints to discretize the vertical integration with a finite-difference scheme. A specific definition of a generalized hybrid vertical coordinate, including sigma, isobaric, and isentropic surfaces, is introduced to define pressure at the model levels. Vertical fluxes are obtained by the equation of local changes in variables defined for vertical coordinates at all model layers. The forward-weighting semi-implicit time scheme is utilized to eliminate computational noise for stable integration. Because of time splitting between the dynamic and physics processes, the vertical advection is required both in the model dynamics and model physics, and the semi-implicit time scheme is used both in dynamics and after physics computation. Three configurations--sigma, sigma pressure, and sigma entropy--from the specific hybrid vertical coordinates with layer definition similar to NCEP operational GFS have been implemented in the NCEP GFS. Results from the sigma-isentropic coordinate show the largest anomaly correlation and the smallest root-mean-square error in tropical wind among all results at all layers, especially the upper layers. The scores from a period of daily forecast up to 5 days with the sigma-isentropic coordinate show the same level of skill as compared to the NCEP operational GFS. The results from the hurricane tracks for the fall of 2005 with sigma-isentropic coordinates show better scores compared with the operational GFS. [ABSTRACT FROM AUTHOR]

Regional climate models used to resolve high-resolution local circulation by dynamically downscaling from a coarse-resolution analysis may have difficulty in keeping large-scale information the same as in the analysis. Such difficulty produces large-scale error or bias, mainly due to mathematically ill-posed lateral boundary conditions. To reduce this type of error, in this paper the authors propose a scheme called local mean bias correction (LMBC). LMBC has means in x and in y directions locally over the model domain, but not a single mean over the entire domain. In spectral space, local means are represented by the zero wavenumber in the x direction with all wavenumbers in the y direction, and the zero wavenumber in the y direction with all the wavenumbers in the x direction. The local mean perturbation can be removed to correct the local mean bias where the perturbation is defined as the difference between a regional field and a base field from the analysis. The LMBC constitutes a simple methodology to ameliorate the influence of the lateral boundary conditions (LBCs) on the integration of the regional climate model (RCM); using it in this study improved the regional climate simulation of the 1998 South China Sea (SCS) summer monsoon. Generally speaking, this scheme proved to have a well-simulated circulation during all periods of monsoon activities examined. Circulation errors during the SCS summer monsoon onset can be corrected using this LMBC scheme, though the rainfall amount was always underestimated as compared to the observation. This research demonstrated that shifting the domain north, east, west, and south as well as enlarging domain size produced results that are consistent in different cases over different years. [ABSTRACT FROM AUTHOR]

A feed-forward neural network is configured to calibrate the bias of a high-resolution probabilistic quantitative precipitation forecast (PQPF) produced by a 12-km version of the NCEP Regional Spectral Model (RSM) ensemble forecast system. Twice-daily forecasts during the 2002–2003 cool season (1 November–31 March, inclusive) are run over four U.S. Geological Survey (USGS) hydrologic unit regions of the southwest United States. Calibration is performed via a cross-validation procedure, where four months are used for training and the excluded month is used for testing. The PQPFs before and after the calibration over a hydrological unit region are evaluated by comparing the joint probability distribution of forecasts and observations. Verification is performed on the 4-km stage IV grid, which is used as “truth.” The calibration procedure improves the Brier score (BrS), conditional bias (reliability) and forecast skill, such as the Brier skill score (BrSS) and the ranked probability skill score (RPSS), relative to the sample frequency for all geographic regions and most precipitation thresholds. However, the procedure degrades the resolution of the PQPFs by systematically producing more forecasts with low nonzero forecast probabilities that drive the forecast distribution closer to the climatology of the training sample. The problem of degrading the resolution is most severe over the Colorado River basin and the Great Basin for relatively high precipitation thresholds where the sample of observed events is relatively small. [ABSTRACT FROM AUTHOR]

The National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM) is used to produce twice-daily (0000 and 1200 UTC), high-resolution ensemble forecasts to 24 h. The forecasts are performed at an equivalent horizontal grid spacing of 12 km for the period 1 November 2002 to 31 March 2003 over the southwest United States. The performance of 6-h accumulated precipitation is assessed for 32 U.S. Geological Survey hydrologic catchments. Multiple accuracy and skill measures are used to evaluate probabilistic quantitative precipitation forecasts. NCEP stage-IV precipitation analyses are used as “truth,” with verification performed on the stage-IV 4-km grid. The RSM ensemble exhibits a ubiquitous wet bias. The bias manifests itself in areal coverage, frequency of occurrence, and total accumulated precipitation over every region and during every 6-h period. The biases become particularly acute starting with the 1800–0000 UTC interval, which leads to a spurious diurnal cycle and the 1200 UTC cycle being more adversely affected than the 0000 UTC cycle. Forecast quality and value exhibit marked variability over different hydrologic regions. The forecasts are highly skillful along coastal California and the windward slopes of the Sierra Nevada Mountains, but they generally lack skill over the Great Basin and the Colorado basin except over mountain peaks. The RSM ensemble is able to discriminate precipitation events and provide useful guidance to a wide range of users over most regions of California, which suggests that mitigation of the conditional biases through statistical postprocessing would produce major improvements in skill. [ABSTRACT FROM AUTHOR]

The National Centers for Environmental Prediction regional spectral model and mesoscale spectral model (NCEP RSM/MSM) use a spectral computation on perturbation. The perturbation is defined as a deviation between RSM/MSM forecast value and their outer model or analysis value on model sigma-coordinate surfaces. The horizontal diffusion used in the models applies perturbation diffusion in spectral space on model sigma-coordinate surfaces. However, because of the large difference between RSM/MSM and their outer model or analysis terrains, the perturbation on sigma surfaces could be large over steep mountain areas as horizontal resolution increases. This large perturbation could introduce systematical error due to artificial vertical mixing from horizontal diffusion on sigma surface for variables with strong vertical stratification, such as temperature and humidity. This nonnegligible error would eventually ruin the forecast and simulation results over mountain areas in high-resolution modeling. To avoid the erroneous vertical mixing on the systematic perturbation, a coordinate transformation is applied in deriving a horizontal diffusion on pressure surface from the variables provided on terrain-following sigma coordinates. Three cases are selected to illustrate the impact of the horizontal diffusion on pressure surfaces, which reduces or eliminates numerical errors of mesoscale modeling over mountain areas. These cases address concerns from all aspects, including unstable and stable synoptic conditions, moist and dry atmospheric settings, weather and climate integrations, hydrostatic and nonhydrostatic modeling, and island and continental orography. After implementing the horizontal diffusion on pressure surfaces for temperature and humidity, the results show better rainfall and flow pattern simulations when compared to observations. Horizontal diffusion corrects the warming, moistening, excessive rainfall, and convergent flow patterns around high mountains under unstable and moist synoptic conditions and corrects the cooling, drying, and divergent flow patterns under stable and dry synoptic settings. [ABSTRACT FROM AUTHOR]

The National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM) is used to generate ensemble forecasts over the southwest United States during the 151 days of 1 November 2002 to 31 March 2003. RSM forecasts to 24 h on a 12-km grid are produced from 0000 and 1200 UTC initial conditions. Eleven ensemble members are run each forecast cycle from the NCEP Global Forecast System (GFS) ensemble analyses (one control and five pairs of bred modes) and forecast lateral boundary conditions. The model domain covers two NOAA River Forecast Centers: the California Nevada River Forecast Center (CNRFC) and the Colorado Basin River Forecast Center (CBRFC). Ensemble performance is evaluated for probabilistic forecasts of 24-h accumulated precipitation in terms of several accuracy and skill measures. Differences among several NCEP precipitation analyses are assessed along with their impact on model verification, with NCEP stage IV blended analyses selected to represent “truth.” Forecast quality and potential value are found to depend strongly on the verification dataset, geographic region, and precipitation threshold. In general, the RSM forecasts are skillful over the CNRFC region for thresholds between 1 and 50 mm but are unskillful over the CBRFC region. The model exhibits a wet bias for all thresholds that is larger over Nevada and the CBRFC region than over California. Mitigation of such biases over the Southwest will pose serious challenges to the modeling community in view of the uncertainties inherent in verifying analyses. [ABSTRACT FROM AUTHOR]

A reduced spectral transformation is applied to the NCEP atmospheric global spectral model for operational seasonal forecasts. The magnitude of the associated Legendre coefficient provides a basis for this new transformation, which is a simple modification of a traditional reduced grid spectral transform. This transformation can be called a “reduced spectral” method because its Fourier and Legendre transformations need less computation than the traditional uniform full grid or reduced grid methods. In addition, the reduced spectral method saves an extra 50% on Legendre transformations and is easy to load balance for massively parallel computing under certain decompositions. A comparison, without model physics, among reduced spectral, reduced grid, and full grid transforms indicates that they have negligible differences up to more than a half-month integration and small differences up to a 1- month integration. Extended integrations without physics for up to 4 months show that there is proximity of zonal symmetry between reduced spectral and full grid transforms. When the comparison includes model physics, the results show negligible differences up to 7 days; but the chaotic nature, known as an internal variability, is amplified by physical parameterizations and produces significant differences among these methods after a 1- month integration, which is expected. The seasonally averaged results from 10 years of AMIP-type runs are similar between the reduced spectral method and the full grid method, indicating that they have similar model climatology. These experiments indicate that this reduced spectral transform can be used for short-range as well as seasonal or climate predictions. [ABSTRACT FROM AUTHOR]

This paper illustrates a modified nonhydrostatic version of the National Centers for Environmental Prediction regional spectral model (RSM). This nonhydrostatic version of the RSM can simulate atmospheric motions of all scales, especially mesoscale. For simplicity, it is referred to in this paper as the mesoscale spectral model (MSM). The preliminary results of the previous version of the MSM have been published on the year of 1992, with coarse resolution in three dimensions and no model physics. A fine-resolution two-dimensional version has been tested on classical problems and published on the year of 1994. Instead of an externally determined hydrostatic coordinate as originally designed in 1992 MSM, the internally evolved hydrostatic coordinate as used in the RSM is implemented. This modification makes the MSM closer to the hydrostatic version in model structure and dynamics. Besides the hydrostatic perturbation, related to the external hydrostatic state as perturbation nesting, the nonhydrostatic perturbation related to the internally evolved hydrostatic state is introduced. The same model physics used in the RSM are used in the MSM without the hydrostatic assumption. The major numerical techniques used in the hydrostatic version are used in the MSM as well. They are spectral computation, fourth-order horizontal diffusion, time filter, and semi-implicit adjustment for perturbation. The hydrostatic state, interpolated from the hydrostatic global model, is used as the initial condition without initialization or data assimilation, and it can be integrated up to several days with reasonable predictions. Extended tests of thermal bubbles and mountain waves in very fine resolutions by this revised MSM showed its behavior to be the same as, but not superior to, those of the previous version. These results are compatible to other model results in the literature. Cases using real data with full model physics as used in the RSM show that the revised MSM has... [ABSTRACT FROM AUTHOR]

A limited-area spectral model—the Regional Spectral Model—developed at the National Centers for Environmental Prediction is used to prepare daily quantitative precipitation forecasts out to 48 h for the Tennessee and Cumberland River basins in the southeastern United States. One year of these forecasts is evaluated against data from a network of 243 rain gauges and against traditional man-machine forecasts provided under contract to Tennessee Valley Authority river system managers. The intent of this study was to determine whether the model forecasts, made at greater spatial resolution than those typically available from other sources, offered any advantages to water resource managers responsible for making critical day-to-day decisions affecting flood control, navigation, and hydropower production. The model's performance, determined using a variety of statistical measures, was found to be more accurate than the traditional forecasts. In particular, the model had less bias and lower root-mean-square error, and was more accurate in the timing of precipitation events. The model's advantage was especially evident in 24-48-h forecasts and for heavy precipitation events. Three specific case studies of model performance are described to illustrate the model's abilities under conditions that could significantly influence river management decisions. [ABSTRACT FROM AUTHOR]

An optimal model output calibration (MOC) algorithm suitable for surface air temperature forecasts is proposed and tested with the National Centers for Environmental Prediction Regional Spectral Model (RSM). Differing from existing methodologies and the traditional model output statistics (MOS) technique, the MOC algorithm uses forecasts and observations of the most recent 2-4 weeks to objectively estimate and adjust the current model forecast errors and make refined predictions. The MOC equation, a multivariate linear regression equation with forecast error being the predictand, objectively screens as many as 30 candidates of predictors and optimally selects no more than 6. The equation varies from day to day and from site to site. Since it does not rely on long-term statistics of stable model runs, the MOC minimizes the influence of changes in model physics and spatial resolution on the forecast refinement process. Forecast experiments were conducted for six major urban centers in the Tennessee Valley over the period of 27 June to 30 July 1997. Surface air temperature forecasts out to 72 h were produced based upon RSM runs initialized from 0000 UTC observations. Performance of the MOC for minimum and maximum temperature forecasts was assessed by determining mean forecast error (BIAS), mean absolute error (MAE), and root-mean-square errors (rmse) for both the MOC-adjusted and nonadjusted RSM output. The same statistical measures for Nested Grid Model-MOS forecasts over the experiment period were also provided for instruction. A skill score was calculated to demonstrate the improvement of refined forecasts with the MOC over the RSM. On average for the six sites, reduction of forecast errors by the MOC ranged from 58% to 98% in BIAS, 40% to 52% in MAE, and 33% to 46% in rmse. It also showed that the error frequencies of the refined forecasts had Gaussian distributions with the peak centered around zero. The error bands were narrower using the MOC and there were... [ABSTRACT FROM AUTHOR]

Focuses on the National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center regional spectral model (RSM). Improvements made to the (RSM); Tests conducted on the (RSM); Details on how (RSM) is used.

A simple method to employ an orography blending technique near the lateral boundary of the regional model is proposed. The method blends the orography of the regional model on the lateral boundary with that of the global or coarser resolution regional model. This method is tested in the NCEP Regional Spectral Model. A long-range prediction experiment of one month's duration is conducted in the East Asian region. It is found that the proposed method provides regional model forecasts consistent with the global model forecasts without a discernible systematic bias. It is likely that modifying the regional model orography following the values of global model or analysis data is more efficient than elaborating the numerics of a lateral boundary scheme to eliminate systematic error. [ABSTRACT FROM AUTHOR]

An introduction to the special issue of the journal is presented in which the guest editors discuss various reports on topics including forward sensitivity approach for data assimilation, comparison of the observations in the four Global Position System (GPS) Radio Occultation (RO), and the evaluation of the Weather Research and Forecasting (WRF) microphysics scheme.