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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]

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. © 2007 American Meteorological Society.

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. © 2007 American Meteorological Society.

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]

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]