scholarly journals Assessments of Surface Winds and Waves from the NCEP Ensemble Forecast System

2018 ◽  
Vol 33 (6) ◽  
pp. 1533-1546 ◽  
Author(s):  
Ricardo Martins Campos ◽  
Jose-Henrique G. M. Alves ◽  
Stephen G. Penny ◽  
Vladimir Krasnopolsky
Keyword(s):  
Ensemble Forecast ◽  
Lead Times ◽  
Systematic Bias ◽  
Surface Winds ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Error Metrics ◽  
Control Member ◽  
Environmental Prediction ◽  
Extreme Winds

Abstract The error characteristics of surface waves and winds produced by ensemble forecasts issued by the National Centers for Environmental Prediction are analyzed as a function of forecast range and severity. Eight error metrics are compared, separating the scatter component of the error from the systematic bias. Ensemble forecasts of extreme winds and extreme waves are compared to deterministic forecasts for long lead times, up to 10 days. A total of 29 metocean buoys is used to assess 1 year of forecasts (2016). The Global Wave Ensemble Forecast System (GWES) performs 10-day forecasts four times per day, with a spatial resolution of 0.5° and a temporal resolution of 3 h, using a 20-member ensemble plus a control member (deterministic) forecast. The largest errors in GWES, beyond forecast day 3, are found to be associated with winds above 14 m s−1 and waves above 5 m. Extreme percentiles after the day-8 forecast reach 30% of underestimation for both 10-m-height wind (U10) and significant wave height (Hs). The comparison of probabilistic wave forecasts with deterministic runs shows an impressive improvement of predictability on the scatter component of the errors. The error for surface winds drops from 5 m s−1 in the deterministic runs, associated with extreme events at longer forecast ranges, to values around 3 m s−1 using the ensemble approach. As a result, GWES waves are better predicted, with a reduction in error from 2 m to less than 1.5 m for Hs. Nevertheless, under extreme conditions, critical systematic and scatter errors are identified beyond the day-6 and day-3 forecasts, respectively.

Global Assessments of Surface Winds and Waves From an Ensemble Forecast System Using Satellite Data

2019 ◽  
Author(s):  
Ricardo Martins Campos ◽  
C. Guedes Soares
Keyword(s):  
Satellite Data ◽  
Ensemble Forecast ◽  
Altimeter Data ◽  
Forecast System ◽  
Significant Wave ◽  
Error Metrics ◽  
Control Member ◽  
Ensemble Mean ◽  
Wave Heights ◽  
Significant Wave Heights

Abstract This paper evaluates the 10-m wind intensities and significant wave heights from the NCEP Ensemble Forecast System using altimeter data. A total of 20 perturbed members plus a control member (deterministic run) compose the ensemble. The assessment is focused on the comparison between the control run and the ensemble mean, in terms of benefits presented by four error metrics. Four satellite missions are selected for the assessments, obtained from AVISO and NESDIS/NOAA databases. Results show that the scatter components of the errors strongly depends on the latitude, were extra-tropical locations at longer forecast times present large errors. A significant improvement using the ensemble forecast compared to deterministic runs was verified at these locations, where the RMSE of day 10 was reduced from 5 to 3.5 m/s for U10 and from 1.8 to 1.3 meters for Hs.

Bias Correction for Global Ensemble Forecast

2012 ◽  
Vol 27 (2) ◽  
pp. 396-410 ◽  
Author(s):  
Bo Cui ◽  
Zoltan Toth ◽  
Yuejian Zhu ◽  
Dingchen Hou
Keyword(s):  
Bias Correction ◽  
Calibration Method ◽  
Ensemble Forecast ◽  
Main Task ◽  
Physical Sciences ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
The North ◽  
Correction Technique ◽  
Environmental Prediction

Abstract 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.

scholarly journals The Reanalysis for the Global Ensemble Forecast System, version 12.

2021 ◽  
Author(s):  
Thomas M. Hamill ◽  
Jeffrey S. Whitaker ◽  
Anna Shlyaeva ◽  
Gary Bates ◽  
Sherrie Fredrick ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Reanalysis Data ◽  
Ensemble Forecast ◽  
Time Data ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Data Set ◽  
Control Member ◽  
Data Assimilation System ◽  
Assimilation System

AbstractNOAA has created a global reanalysis data set, intended primarily for initialization of reforecasts for its Global Ensemble Forecast System, version 12 (GEFSv12), which provides ensemble forecasts out to +35 days lead time. The reanalysis covers the period 2000-2019. It assimilates most of the observations that were assimilated into the operational data assimilation system used for initializing global predictions. These include a variety of conventional data, infrared and microwave radiances, Global Positioning System radio occultations, and more. The reanalysis quality is generally superior to that from NOAA’s previous-generation Climate Forecast System Reanalysis (CFSR), demonstrated in the fit of short-term forecasts to the observations and in the skill of 5-day deterministic forecasts initialized from CFSR vs. GEFSv12. Skills of reforecasts initialized from the new reanalyses are similar but slightly lower than skills initialized from a pre-operational version of the real-time data assimilation system conducted at the higher, operational resolution. Control member reanalysis data on vertical pressure levels are made publicly available.

scholarly journals Medium-Range Convection-Allowing Ensemble Forecasts with a Variable-Resolution Global Model

2019 ◽  
Vol 147 (8) ◽  
pp. 2997-3023 ◽  
Author(s):  
Craig S. Schwartz
Keyword(s):  
High Resolution ◽  
Ensemble Forecast ◽  
The Other ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Variable Resolution ◽  
Cell Spacing ◽  
Medium Range ◽  
Potential Benefits ◽  
Convective Mode

Abstract Two sets of global, 132-h (5.5-day), 10-member ensemble forecasts were produced with the Model for Prediction Across Scales (MPAS) for 35 cases in April and May 2017. One MPAS ensemble had a quasi-uniform 15-km mesh while the other employed a variable-resolution mesh with 3-km cell spacing over the conterminous United States (CONUS) that smoothly relaxed to 15 km over the rest of the globe. Precipitation forecasts from both MPAS ensembles were objectively verified over the central and eastern CONUS to assess the potential benefits of configuring MPAS with a 3-km mesh refinement region for medium-range forecasts. In addition, forecasts from NCEP’s operational Global Ensemble Forecast System were evaluated and served as a baseline against which to compare the experimental MPAS ensembles. The 3-km MPAS ensemble most faithfully reproduced the observed diurnal cycle of precipitation throughout the 132-h forecasts and had superior precipitation skill and reliability over the first 48 h. However, after 48 h, the three ensembles had more similar spread, reliability, and skill, and differences between probabilistic precipitation forecasts derived from the 3- and 15-km MPAS ensembles were typically statistically insignificant. Nonetheless, despite fewer benefits of increased resolution for spatial placement after 48 h, 3-km ensemble members explicitly provided potentially valuable guidance regarding convective mode throughout the 132-h forecasts while the other ensembles did not. Collectively, these results suggest both strengths and limitations of medium-range high-resolution ensemble forecasts and reveal pathways for future investigations to improve understanding of high-resolution global ensembles with variable-resolution meshes.

scholarly journals Verification of Multimodel Ensemble Forecasts of North Atlantic Tropical Cyclones

2017 ◽  
Vol 32 (6) ◽  
pp. 2083-2101 ◽  
Author(s):  
Nicholas M. Leonardo ◽  
Brian A. Colle
Keyword(s):  
North Atlantic ◽  
Ensemble Prediction ◽  
Laboratory Model ◽  
Lead Times ◽  
Forecast System ◽  
Deterministic Models ◽  
Ensemble Forecasts ◽  
The Cross ◽  
Cross Track ◽  
Along Track

Abstract North Atlantic tropical cyclone (TC) forecasts from four ensemble prediction systems (EPSs) are verified using the National Hurricane Center’s (NHC) best tracks for the 2008–15 seasons. The 1–5-day forecasts are evaluated for the 21-member National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS), the 23-member UKMO ensemble (UKMET), and the 51-member European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble, as well as a combination of these ensembles [Multimodel Global (MMG)]. Several deterministic models are also evaluated, such as the Global Forecast System (GFSdet), Hurricane Weather Research and Forecasting Model (HWRF), the deterministic ECMWF model (ECdet), and the Geophysical Fluid Dynamical Laboratory model (GFDL).The ECdet track errors are the smallest on average at all lead times, but are not significantly different from the GEFS and ECMWF ensemble means. All models have a slow bias (90–240 km) in the along-track direction by 120 h, while there is little bias in the cross-track direction. Much of this slow bias is attributed to TCs undergoing extratropical transition (ET). All EPSs are underdispersed in the along-track direction, while the ECMWF is slightly overdispersed in the cross-track direction. The MMG and ECMWF track forecasts have more probabilistic skill than the ECdet and comparable skill to the NHC climatology-based cone forecast. TC intensity errors for the HWRF and GFDL are lower than the coarser models within the first 24 h, but are comparable to the ECdet at longer lead times. The ECMWF and MMG have comparable or better probabilistic intensity forecasts than the ECdet, while the GEFS’s weak bias limits its skill.

scholarly journals Short-Range Ensemble Forecasts of Precipitation Type

2005 ◽  
Vol 20 (4) ◽  
pp. 609-626 ◽  
Author(s):  
Matthew S. Wandishin ◽  
Michael E. Baldwin ◽  
Steven L. Mullen ◽  
John V. Cortinas
Keyword(s):  
Short Range ◽  
Ensemble Forecast ◽  
Ensemble Forecasting ◽  
Precipitation Forecast ◽  
Initial Condition ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Probabilistic Forecasts ◽  
Precipitation Type ◽  
Forecast Quality

Abstract Short-range ensemble forecasting is extended to a critical winter weather problem: forecasting precipitation type. Forecast soundings from the operational NCEP Short-Range Ensemble Forecast system are combined with five precipitation-type algorithms to produce probabilistic forecasts from January through March 2002. Thus the ensemble combines model diversity, initial condition diversity, and postprocessing algorithm diversity. All verification numbers are conditioned on both the ensemble and observations recording some form of precipitation. This separates the forecast of type from the yes–no precipitation forecast. The ensemble is very skillful in forecasting rain and snow but it is only moderately skillful for freezing rain and unskillful for ice pellets. However, even for the unskillful forecasts the ensemble shows some ability to discriminate between the different precipitation types and thus provides some positive value to forecast users. Algorithm diversity is shown to be as important as initial condition diversity in terms of forecast quality, although neither has as big an impact as model diversity. The algorithms have their individual strengths and weaknesses, but no algorithm is clearly better or worse than the others overall.

Applying Conditional Nonlinear Optimal Perturbation (CNOP) in the ensemble ENSO forecast system

2020 ◽  
Author(s):  
Qian Zhou ◽  
Yunfei Zhang ◽  
Junya Hu ◽  
Wansuo Duan
Keyword(s):  
Ensemble Forecast ◽  
Error Growth ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Vector Method ◽  
Optimal Perturbation ◽  
Initial Errors ◽  
Prediction Time ◽  
Forecasting System ◽  
Marine Environmental

<p><span>      Considering the effects of initial uncertainty on the ENSO forecast, ensemble forecasts method is applied in the latest version of ENSO forecast system in National Marine Environmental Forecasting Center (NMEFC, China). The currently operational ENSO forecasts system of NMEFC is established based on the CESM model, with initialization and data assimilation. </span></p><p><span>      First, leading five Singular Vectors (SV) are obtained using the climatological SST empirical singular vector method, and a SV based ensemble forecasts system is . However, the SVs can only present the initial errors that have the fasted error growth rates in a linear assumption, while ENSO and its forecasting system both are nonlinear. So, Conditional Nonlinear Optimal Perturbations (CNOP), which is has the largest error growth at the prediction time in a nonlinear scenario, is used to replace the leading SV, while other 4 SVs are kept to construct a CNOP-SV based ensemble forecast system. The hindcasts of ENSO from 1982 to 2017 shows that, the ENSO prediction skills of both SV based and CNOP-SV based ENSO ensemble forecasts are improved when compared with the old forecasting system, moreover, the CNOP-SV based ensemble forecast system has a much larger spread, showing higher prediction skills.</span></p>

scholarly journals Land Surface Parameter and State Perturbations in the Global Ensemble Forecast System

2019 ◽  
Vol 147 (4) ◽  
pp. 1319-1340
Author(s):  
Maria Gehne ◽  
Thomas M. Hamill ◽  
Gary T. Bates ◽  
Philip Pegion ◽  
Walter Kolczynski
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Initial Conditions ◽  
Ensemble Forecast ◽  
Ensemble Prediction ◽  
Systematic Bias ◽  
Perturbation Scheme ◽  
Forecast System ◽  
Vegetation Fraction ◽  
Land Surface Parameter

Abstract The National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) is underdispersive near the surface, a common characteristic of ensemble prediction systems. Here, several methods for increasing the spread are tested, including perturbing soil initial conditions, soil tendencies, and surface parameters, with physically based perturbations. Perturbations are applied to the soil initial conditions based on empirical orthogonal functions (EOFs) of differences between normalized soil moisture states from two land surface models (LSMs). Perturbations to roughness lengths for heat and momentum, soil hydraulic conductivity, stomatal resistance, vegetation fraction, and albedo are applied, with the amplitude and perturbation scales based on previous research. Soil moisture and temperature tendencies are also perturbed using a stochastic perturbation scheme. The results show that surface perturbations, through their impact on 2-m temperature spread, have a modest positive impact on the skill of short-range ensemble forecasts. However, adjusting the forecasts using an estimate of the systematic bias shows that bias correction has a greater impact on the forecast reliability than surface perturbations, indicating that systematic bias in the model needs to be addressed as well.

scholarly journals A Comparison of Perturbations from an Ensemble Transform and an Ensemble Kalman Filter for the NCEP Global Ensemble Forecast System

2016 ◽  
Vol 31 (6) ◽  
pp. 2057-2074 ◽  
Author(s):  
Xiaqiong Zhou ◽  
Yuejian Zhu ◽  
Dingchen Hou ◽  
Daryl Kleist
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Forecast Model ◽  
Skill Score ◽  
Ensemble Forecast ◽  
Boreal Summer ◽  
Lead Times ◽  
Operational Environment ◽  
Forecast System ◽  
Ensemble Mean

Abstract Two perturbation generation schemes, the ensemble transformation with rescaling (ETR) and the ensemble Kalman filter (EnKF), are compared for the NCEP operational environment for the Global Ensemble Forecast System (GEFS). Experiments that utilize each of the two schemes are carried out and evaluated for two boreal summer seasons. It is found that these two schemes generally have comparable performance. Experiments utilizing both perturbation methods fail to generate sufficient spread at medium-range lead times beyond day 8. In general, the EnKF-based experiment outperforms the ETR in terms of the continuous ranked probability skill score (CRPSS) in the Northern Hemisphere (NH) for the first week. In the SH, the ensemble mean forecast is more skillful from the ETR perturbations. Additional experiments are performed with the stochastic total tendency perturbation (STTP) scheme, in which the total tendencies of all model variables are perturbed to represent the uncertainty in the forecast model. An improved spread–error relationship is found for the ETR-based experiments, but the STTP increases the ensemble spread for the EnKF-based experiment that is already overdispersive at early lead times, especially in the SH. With STTP employed, an increase in the EnKF-based CRPSS in the NH is reduced with a larger degradation in both the probability and ensemble-mean forecast skills in the SH. The results indicate that a rescaling of the EnKF initial perturbations and/or tuning of the STTP scheme is required when STTP is applied using the EnKF-based perturbations. This study provided guidance for the replacement of ETR with EnKF perturbations as part of the 2015 GEFS implementation.

scholarly journals Toward the Improvement of Subseasonal Prediction in the National Centers for Environmental Prediction Global Ensemble Forecast System

2018 ◽  
Vol 123 (13) ◽  
pp. 6732-6745 ◽  
Author(s):  
Yuejian Zhu ◽  
Xiaqiong Zhou ◽  
Wei Li ◽  
Dingchen Hou ◽  
Christopher Melhauser ◽  
...  
Keyword(s):  
Ensemble Forecast ◽  
Forecast System ◽  
Environmental Prediction
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