scholarly journals Probabilistic Forecasting of the 500 hPa Geopotential Height over the Northern Hemisphere Using TIGGE Multi-model Ensemble Forecasts

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 253
Author(s):  
Luying Ji ◽  
Qixiang Luo ◽  
Yan Ji ◽  
Xiefei Zhi
Keyword(s):  
Northern Hemisphere ◽  
Geopotential Height ◽  
Prediction Skill ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Height Distribution ◽  
Height Field ◽  
Probabilistic Forecasting ◽  
Ensemble Forecasts ◽  
Geopotential Height Field

Bayesian model averaging (BMA) and ensemble model output statistics (EMOS) were used to improve the prediction skill of the 500 hPa geopotential height field over the northern hemisphere with lead times of 1–7 days based on ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP), and UK Met Office (UKMO) ensemble prediction systems. The performance of BMA and EMOS were compared with each other and with the raw ensembles and climatological forecasts from the perspective of both deterministic and probabilistic forecasting. The results show that the deterministic forecasts of the 500 hPa geopotential height distribution obtained from BMA and EMOS are more similar to the observed distribution than the raw ensembles, especially for the prediction of the western Pacific subtropical high. BMA and EMOS provide a better calibrated and sharper probability density function than the raw ensembles. They are also superior to the raw ensembles and climatological forecasts according to the Brier score and the Brier skill score. Comparisons between BMA and EMOS show that EMOS performs slightly better for lead times of 1–4 days, whereas BMA performs better for longer lead times. In general, BMA and EMOS both improve the prediction skill of the 500 hPa geopotential height field.

scholarly journals Wave Extremes in the Northeast Atlantic from Ensemble Forecasts

2013 ◽  
Vol 26 (19) ◽  
pp. 7525-7540 ◽  
Author(s):  
Øyvind Breivik ◽  
Ole Johan Aarnes ◽  
Jean-Raymond Bidlot ◽  
Ana Carrasco ◽  
Øyvind Saetra
Keyword(s):  
Ensemble Prediction ◽  
Lead Times ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Northeast Atlantic ◽  
The North ◽  
Time Period ◽  
The North Sea ◽  
Mean And Variance ◽  
Buoy Data

Abstract A method for estimating return values from ensembles of forecasts at advanced lead times is presented. Return values of significant wave height in the northeast Atlantic, the Norwegian Sea, and the North Sea are computed from archived +240-h forecasts of the ECMWF Ensemble Prediction System (EPS) from 1999 to 2009. Three assumptions are made: First, each forecast is representative of a 6-h interval and collectively the dataset is then comparable to a time period of 226 years. Second, the model climate matches the observed distribution, which is confirmed by comparing with buoy data. Third, the ensemble members are sufficiently uncorrelated to be considered independent realizations of the model climate. Anomaly correlations of 0.20 are found, but peak events (>P97) are entirely uncorrelated. By comparing return values from individual members with return values of subsamples of the dataset it is also found that the estimates follow the same distribution and appear unaffected by correlations in the ensemble. The annual mean and variance over the 11-yr archived period exhibit no significant departures from stationarity compared with a recent reforecast; that is, there is no spurious trend because of model upgrades. The EPS yields significantly higher return values than the 40-yr ECMWF Re-Analysis (ERA-40) and ECMWF Interim Re-Analysis (ERA-Interim) and is in good agreement with the high-resolution 10-km Norwegian Reanalyses (NORA10) hindcast, except in the lee of unresolved islands where EPS overestimates and in enclosed seas where it has low bias. Confidence intervals are half the width of those found for ERA-Interim because of the magnitude of the dataset.

scholarly journals Subseasonal midlatitude prediction skill following Quasi-Biennial Oscillation and Madden–Julian Oscillation activity

2020 ◽  
Vol 1 (1) ◽  
pp. 247-259
Author(s):  
Kirsten J. Mayer ◽  
Elizabeth A. Barnes
Keyword(s):  
Northern Hemisphere ◽  
Rossby Waves ◽  
Prediction Skill ◽  
Lead Times ◽  
Madden Julian Oscillation ◽  
Quasi Biennial Oscillation ◽  
Weather Forecasts ◽  
The Pacific ◽  
Environmental Prediction ◽  
Biennial Oscillation

Abstract. The Madden–Julian Oscillation (MJO) is known to force extratropical weather days to weeks following an MJO event through excitation of stationary Rossby waves, also referred to as tropical–extratropical teleconnections. Prior research has demonstrated that this tropically forced midlatitude response leads to increased prediction skill on subseasonal to seasonal (S2S) timescales. Furthermore, the Quasi-Biennial Oscillation (QBO) has been shown to possibly alter these teleconnections through modulation of the MJO itself and the atmospheric basic state upon which the Rossby waves propagate. This implies that the MJO–QBO relationship may affect midlatitude circulation prediction skill on S2S timescales. In this study, we quantify midlatitude circulation sensitivity and prediction skill following active MJOs and QBOs across the Northern Hemisphere on S2S timescales through an examination of the 500 hPa geopotential height field. First, a comparison of the spatial distribution of Northern Hemisphere sensitivity to the MJO during different QBO phases is performed for European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis and ECMWF and the National Centers for Environmental Prediction (NCEP) hindcasts. Secondly, differences in prediction skill in ECMWF and NCEP hindcasts are quantified following MJO–QBO activity. In both hindcast systems, we find that regions across the Pacific, North America, and the Atlantic demonstrate an enhanced MJO impact on prediction skill during strong QBO periods with lead times of 1–4 weeks compared to MJO events during neutral QBO periods.

scholarly journals Subseasonal Midlatitude Prediction Skill Following QBO-MJO Activity

2019 ◽  
Author(s):  
Kirsten J. Mayer ◽  
Elizabeth A. Barnes
Keyword(s):  
Spatial Distribution ◽  
North America ◽  
Northern Hemisphere ◽  
Rossby Waves ◽  
Prediction Skill ◽  
Height Field ◽  
Madden Julian Oscillation ◽  
Quasi Biennial Oscillation ◽  
The Pacific ◽  
Biennial Oscillation

Abstract. The Madden-Julian Oscillation (MJO) is known to force extratropical weather days-to-weeks following an MJO event through excitation of stationary Rossby waves, tropical-extratropical teleconnections. Prior research has demonstrated that this tropically forced midlatitude response leads to increased prediction skill on subseasonal to seasonal (S2S) timescales. Furthermore, the Quasi-Biennial Oscillation (QBO) has been shown to possibly alter these teleconnections through modulation of the MJO itself and the atmospheric basic state upon which the Rossby waves propagate. This implies that the MJO-QBO relationship may affect midlatitude circulation prediction skill on S2S timescales. In this study, we quantify midlatitude circulation sensitivity and prediction skill following active MJOs and QBOs across the Northern Hemisphere on S2S timescales through an examination of the 500 hPa geopotential height field. First, a comparison of the spatial distribution of Northern Hemisphere sensitivity to the MJO during different QBO phases is performed for ERA-Interim reanalysis and ECMWF and NCEP hindcasts. Secondly, differences in prediction skill in ECMWF and NCEP hindcasts are quantified following MJO-QBO activity. We find that regions across the Pacific, North America and the Atlantic exhibit increased prediction skill following MJO-QBO activity, but these regions are not always collocated with the locations most sensitive to the MJO under a particular QBO state. Both hindcast systems demonstrate enhanced prediction skill 7–14 days following active MJO events during strong QBO periods compared to MJO events during neutral QBO periods.

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 Prediction skill of tropical synoptic scale transients from ECMWF and NCEP Ensemble Prediction Systems

2016 ◽  
Vol 2 (1) ◽  
Author(s):  
S. Taraphdar ◽  
P. Mukhopadhyay ◽  
L. Ruby Leung ◽  
Kiranmayi Landu
Keyword(s):  
Random Error ◽  
Velocity Potential ◽  
Horizontal Resolution ◽  
Prediction Skill ◽  
Ensemble Prediction ◽  
Boreal Summer ◽  
Lead Times ◽  
Synoptic Scale ◽  
Planetary Scale ◽  
Prediction Systems

AbstractThe prediction skill of tropical synoptic scale transients (SSTR) such as monsoon low and depression during the boreal summer of 2007–2009 are assessed using high resolution ECMWF and NCEP TIGGE forecasts data. By analyzing 246 forecasts for lead times up to 10 days, it is found that the models have good skills in forecasting the planetary scale means but the skills of SSTR remain poor, with the latter showing no skill beyond 2 days for the global tropics and Indian region. Consistent forecast skills among precipitation, velocity potential, and vorticity provide evidence that convection is the primary process responsible for precipitation. The poor skills of SSTR can be attributed to the larger random error in the models as they fail to predict the locations and timings of SSTR. Strong correlation between the random error and synoptic precipitation suggests that the former starts to develop from regions of convection. As the NCEP model has larger biases of synoptic scale precipitation, it has a tendency to generate more random error that ultimately reduces the prediction skill of synoptic systems in that model. The larger biases in NCEP may be attributed to the model moist physics and/or coarser horizontal resolution compared to ECMWF.

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