The Global Ensemble Forecast System (GEFS) is upgraded to version 12, in which the legacy Global Spectral Model (GSM) is replaced by a model with a new dynamical core - the Finite Volume Cubed-Sphere Dynamical Core (FV3). Extensive tests were performed to determine the optimal model and ensemble configuration. The new GEFS has cubed-sphere grids with a horizontal resolution of about 25-km and an increased ensemble size from 20 to 30. It extends the forecast length from 16 days to 35 days to support subseasonal forecasts. The stochastic total tendency perturbation (STTP) scheme is replaced by two model uncertainty schemes: the Stochastically Perturbed Physics Tendencies (SPPT) scheme and Stochastic Kinetic Energy Backscatter (SKEB) scheme. Forecast verification is performed on a period of more than two years of retrospective runs. The results show that the upgraded GEFS outperforms the operational-at-the-time version by all measures included in the GEFS verification package. The new system has a better ensemble error-spread relationship, significantly improved skills in large-scale environment forecasts, precipitation probability forecasts over CONUS, tropical cyclone track and intensity forecasts, and significantly reduced 2-m temperature biases over Northern America. GEFSv12 was implemented on September 23, 2020.

Ensembles of numerical weather prediction (NWP) model predictions are used for a variety of forecasting applications. Such ensembles quantify the uncertainty of the prediction because the spread in the ensemble predictions is correlated to forecast uncertainty. For atmospheric transport and dispersion and wind energy applications in particular, the NWP ensemble spread should accurately represent uncertainty in the low-level mean wind. To adequately sample the probability density function (PDF) of the forecast atmospheric state, it is necessary to account for several sources of uncertainty. Limited computational resources constrain the size of ensembles, so choices must be made about which members to include. No known objective methodology exists to guide users in choosing which combinations of physics parameterizations to include in an NWP ensemble, however. This study presents such a methodology. The authors build an NWP ensemble using the Advanced Research Weather Research and Forecasting Model (ARW-WRF). This 24-member ensemble varies physics parameterizations for 18 randomly selected 48-h forecast periods in boreal summer 2009. Verification focuses on 2-m temperature and 10-m wind components at forecast lead times from 12 to 48 h. Various statistical guidance methods are employed for down-selection, calibration, and verification of the ensemble forecasts. The ensemble down-selection is accomplished with principal component analysis. The ensemble PDF is then statistically dressed, or calibrated, using Bayesian model averaging. The postprocessing techniques presented here result in a recommended down-selected ensemble that is about half the size of the original ensemble yet produces similar forecast performance, and still includes critical diversity in several types of physics schemes.

In the event of the release of a dangerous atmospheric contaminant, an atmospheric transport and dispersion (ATD) model is often used to provide forecasts of the resulting contaminant dispersion affecting the population. These forecasts should also be accompanied by accurate estimates of the forecast uncertainty to allow for more informed decisions about the potential hazardous area. This study examines the calculation of uncertainty in the meteorological data as derived from an ensemble, and its effects when used as additional input to drive an ATD model. The first part of the study examines the capability of a linear function to relate ensemble spread to error variance of the ensemble mean given ensemble spread from 24 days of forecasts from the National Centers for Environmental Prediction (NCEP) Short-Range Ensemble Forecast (SREF). This linear function can then be used to calibrate the ensemble spread to produce a more accurate estimate of the meteorological uncertainty. Results for the linear relationship of wind variance are very good, with values of the coefficient of determination R2 generally exceeding 0.94 for forecast lengths of 12 h and greater. The calibration is shown to be more sensitive to forecast hour than vertical level within the lower troposphere. The second part presents a 24-h case study to assess the impact of meteorological uncertainty calculations on Second-Order Closure Integrated Puff (SCIPUFF) ATD model predictions. Both uncalibrated ensemble wind variances and wind variances calibrated based on the results of the first part show improvement in mean concentration forecasts relative to a control experiment using the default hazard mode uncertainty when compared with a baseline SCIPUFF integration based on a high-resolution dynamic analysis of the meteorological conditions. The SCIPUFF experiments that use a wind variance calibration show both qualitative and quantitative improvement in most of the mean concentrations and patterns over the control experiment and the SCIPUFF experiment using uncalibrated wind variances. The SCIPUFF experiments using meteorological ensemble uncertainty information also produce mean concentrations and patterns that compare favorably to those of an explicit SCIPUFF ensemble based on each SREF member. Use of the uncalibrated variance information within a single ATD prediction produces mean ATD predictions most similar to those of the explicit ATD ensemble, and use of calibrated ensemble variance is shown to have some advantages over the explicit ATD ensemble.

In order to provide ensemble-based subseasonal (weeks 3 and 4) forecasts to support the operational mission of the Climate Prediction Center, National Centers for Environmental Prediction, experiments have been designed through the Subseasonal Experiment (SubX) project to investigate the predictability in both tropical and extratropical regions. The control experiment simply extends the current operational Global Ensemble Forecast System (GEFS; version 11) from 16 to 35 days. In addition to the control, the parallel experiments will be mainly designed to focus on three areas: (1) improving model uncertainty representation for the tropics through stochastic physical perturbations; (2) considering the impact of the ocean by using a two-tiered sea surface temperature approach; and (3) testing a new scale-aware convection scheme to improve the model physics for tropical convection and Madden-Julian Oscillation (MJO) forecasts. All experiments are initialized every 5 days at 0000 UTC during the period of May 2014-May 2016 (25 months). In the tropics, MJO forecast skill has been improved from an average of 12.5 days (control) to nearly 22 days by combining all three modifications to GEFS. In the extratropics, the ensemble mean anomaly correlation of 500-hPa geopotential height improved over weeks 3 and 4. In addition, the Continuous Ranked Probability Score (of the Northern Hemisphere raw surface temperature (land only) is improved as well. A similar result is found in the Contiguous United States precipitation, although forecast skill is extremely low. Our results imply that calibration may be important and necessary for surface temperature and precipitation forecast for the subseasonal timescale due to the large systematic model errors.Subseasonal forecasts span the time period between weather and seasonal (and/or climate) forecasts. Two of the leading systems (the European Centre for Medium-Range Weather Forecasts global ensemble system and the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFS v2)) are extending weather forecast to cover subseasonal timescale but having limited value, such as

The uncertainty in meteorological predictions is of interest for applications ranging from economic to recreational to public safety. One common method to estimate uncertainty is by using meteorological ensembles. These ensembles provide an easily quantifiable measure of the uncertainty in the forecast in the form of the ensemble variance. However, ensemble variance may not accurately reflect the actual uncertainty, so any measure of uncertainty derived from the ensemble should be calibrated to provide a more reliable estimate of the actual uncertainty in the forecast. A previous study introduced the linear variance calibration (LVC) as a simple method to determine the ensemble variance to error variance relationship and demonstrated this technique on real ensemble data. The LVC parameters, the slopes, and y intercepts, however, are generally different from the ideal values.This current study uses a stochastic model to examine the LVC in a controlled setting. The stochastic model is capable of simulating underdispersive and overdispersive ensembles as well as perfectly reliable ensembles. Because the underlying relationship is specified, LVC results can be compared to theoretical values of the slope and y intercept. Results indicate that all types of ensembles produce calibration slopes that are smaller than their theoretical values for ensemble sizes less than several hundred members, with corresponding y intercepts greater than their theoretical values. This indicates that all ensembles, even otherwise perfect ensembles, should be calibrated if the ensemble size is less than several hundred.In addition, it is shown that an adjustment factor can be computed for inadequate ensemble size. This adjustment factor is independent of the stochastic model and is applicable to any linear regression of error variance on ensemble variance. When applied to experiments using the stochastic model, the adjustment produces LVC parameters near their theoretical values for all ensemble sizes. Although the adjustment is unnecessary when applying LVC, it allows for a more accurate assessment of the reliability of ensembles, and a fair comparison of the reliability for differently sized ensembles.