During May 1998, the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma coordinated a multi-institution numerical forecast project known as the Storm and Mesoscale Ensemble Experiment (SAMEX). SAMEX involved, for the first time, the real-time operation of four different ensembles of mesoscale models over the same region of the United States. The main purpose of this paper is the evaluation of the ensemble forecasts, performed at a relatively coarse resolution of 30 km. An additional SAMEX goal not discussed here is to compare the value of the ensemble forecasts against single forecasts made over smaller subregions of the Great Plains at both intermediate (10 km) and high (3 km) resolution. The SAMEX '98 ensembles consisted of a single 36-h control forecast from the ARPS (at CAPS), the Penn State-NCAR fifth-generation Mesoscale Model (at NSSL), and the Eta Model and Regional Spectral Model (at NCEP), all with horizontal resolutions of approximately 30 km, and perturbed runs, resulting in a grand ensemble of 25 members. The forecasts of geopotential heights, temperatures, and moisture were verified against the Eta operational analyses, rather than observations. Unlike global ensembles, which tend to be useful in the medium range, the mesoscale SAMEX ensembles provided useful information in the short range. A major result is that the performance of the ensemble of multiple forecast systems is much better than that of each individual ensemble system, probably because it represents more realistically the current uncertainties in both models and initial conditions. A similar advantage from the use of multimodel, multianalysis systems has been observed with global ensembles. The SAMEX results also show that perturbations to model physics parameterizations, as well as the use of consistent perturbations in the boundary conditions, are important for regional ensemble forecasting. Efforts are now under way to compare the ensemble forecasts against those made using higher spatial resolution, and follow-on SAMEX experiments are anticipated in other geographical areas and weather regimes. Although the main results of this paper appear to be very robust, they were based on a small number of cases, and similar experiments carried out during other periods will help to test their significance.
A new version of the Global Ensemble Forecast System (GEFS, v11) is tested and compared with the operational version (v10) in a 2-yr parallel run. The breeding-based scheme with ensemble transformation and rescaling (ETR) used in the operational GEFS is replaced by the ensemble Kalman filter (EnKF) to generate initial ensemble perturbations. The global medium-range forecast model and the Global Forecast System (GFS) analysis used as the initial conditions are upgraded to the GFS 2015 implementation version. The horizontal resolution of GEFS increases from Eulerian T254 (~52 km) for the first 8 days of the forecast and T190 (~70 km) for the second 8 days to semi-Lagrangian T574 (~34 km) and T382 (~52 km), respectively. The sigma pressure hybrid vertical layers increase from 42 to 64 levels. The verification of geopotential height, temperature, and wind fields at selected levels shows that the new GEFS significantly outperforms the operational GEFS up to days 8–10 except for an increased warm bias over land in the extratropics. It is also found that the parallel system has better reliability in the short-range probability forecasts of precipitation during warm seasons, but no clear improvement in cold seasons. There is a significant degradation of TC track forecasts at days 6–7 during the 2012–14 TC seasons over the Atlantic and eastern Pacific. This degradation is most likely a sampling issue from a low number of TCs during these three TC seasons. The results for an extended verification period (2011–14) and the recent two hurricane seasons (2015 and 2016) are generally positive. The new GEFS became operational at NCEP on 2 December 2015.
The main task of this study is to introduce a statistical postprocessing algorithm to reduce the bias in the National Centers for Environmental Prediction (NCEP) and Meteorological Service of Canada (MSC) ensemble forecasts before they are merged to form a joint ensemble within the North American Ensemble Forecast System (NAEFS). This statistical postprocessing method applies a Kalman filter type algorithm to accumulate the decaying averaging bias and produces bias-corrected ensembles for 35 variables. NCEP implemented this bias-correction technique in 2006. NAEFS is a joint operational multimodel ensemble forecast system that combines NCEP and MSC ensemble forecasts after bias correction. According to operational statistical verification, both the NCEP and MSC bias-corrected ensemble forecast products are enhanced significantly. In addition to the operational calibration technique, three other experiments were designed to assess and mitigate ensemble biases on the model grid: a decaying averaging bias calibration method with short samples, a climate mean bias calibration method, and a bias calibration method using dependent data. Preliminary results show that the decaying averaging method works well for the first few days. After removing the decaying averaging bias, the calibrated NCEP operational ensemble has improved probabilistic performance for all measures until day 5. The reforecast ensembles from the Earth System Research Laboratory’s Physical Sciences Division with and without the climate mean bias correction were also examined. A comparison between the operational and the bias-corrected reforecast ensembles shows that the climate mean bias correction can add value, especially for week-2 probability forecasts.
Two widely used precipitation analyses are the Climate Prediction Center (CPC) unified global daily gauge analysis and Stage IV analysis based on quantitative precipitation estimate with multisensor observations. The former is based on gauge records with a uniform quality control across the entire domain and thus bears more confidence, but provides only 24-h accumulation at ⅛° resolution. The Stage IV dataset, on the other hand, has higher spatial and temporal resolution, but is subject to different methods of quality control and adjustments by different River Forecasting Centers. This article describes a methodology used to generate a new dataset by adjusting the Stage IV 6-h accumulations based on available joint samples of the two analyses to take advantage of both datasets. A simple linear regression model is applied to the archived historical Stage IV and the CPC datasets after the former is aggregated to the CPC grid and daily accumulation. The aggregated Stage IV analysis is then adjusted based on this linear model and then downscaled back to its original resolution. The new dataset, named Climatology-Calibrated Precipitation Analysis (CCPA), retains the spatial and temporal patterns of the Stage IV analysis while having its long-term average and climate probability distribution closer to that of the CPC analysis. The limitation of the methodology at some locations is mainly associated with heavy to extreme precipitation events, which the Stage IV dataset tends to underestimate. CCPA cannot effectively correct this because of the linear regression model and the relative scarcity of heavy precipitation in the training data sample.
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.
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