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2007, SOLA
In this study, we investigate the daily forecast skill of Multi-Center Grand Ensemble (MCGE), consisting of three operational medium-range ensemble forecast data by the Japan Meteorological Agency (JMA), the National Centers for Environmental Prediction (NCEP), and the Canadian Meteorological Center (CMC). The skill is evaluated by comparison among the daily RMSE of ensemble mean forecasts for 500 hPa geopotential height over the Northern Hemisphere (20°N 90°N) from August 2005 to February 2006. It is found that MCGE with the same ensemble size as that of the JMA ensemble is more skillful than JMA ensemble for about 75% in frequency both in autumn and winter. Reduction of error with MCGE has little dependence on the atmospheric flow. The RMSE of MCGE can be reduced up to about 20% whether the atmospheric field is easily-predictable or not. Even for the case that MCGE is not more skillful than JMA, the RMSE is increased at most 10%. We argue that the major benefit of MCGE is to avoid the poorest forecast.
SOLA, 2006
In this study, we investigate the impact of Multi-Center Grand Ensemble (MCGE) forecasts, consisting of three operational ensemble forecasts by the Japan Meteorological Agency (JMA), the National Centers for Environmental Prediction, and the Canadian Meteorological Center. We verified the skill of MCGE forecasts in comparison with that of JMA ensemble forecast using root mean square error, anomaly correlation, and Brier skill score for 500 hPa geopotential height and 850 hPa temperature in the Northern Hemisphere (20°N 90°N) in September 2005. Our results show that MCGE forecasts are more skillful than single-center ensemble forecast without considering weight among ensemble members and bias corrections. This implies that considering weight or bias corrections may result in further improvement of MCGE forecasts, specifically in probabilistic forecasts.
2018
In order to provide ensemble based subseasonal (weeks 3 & 4) forecasts to support NCEP CPC's operational mission, experiments have been designed through the SubX project to investigate potential predictability in both tropical and extratropical regions. The control experiment is the current operational GEFS version 11 extended from 16 days to 35 days. In addition to the control, parallel experiments have been designed to focus on three areas: 1) improving forecast uncertainty representation for the tropics through stochastic physical perturbations; 2) considering the impact of the ocean by using a 2-tiered SST approach; and 3) testing a new scale aware convection scheme to improve model physics for tropical convection and 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. For the experiment with the best overall score, a skill of 23 days could be reached for a strong MJO period. In the extratropics, anomaly correlation (AC) of 500 hPa geopotential height for the ensemble mean improved over weeks 3 & 4. In addition, CRPS of the Northern Hemisphere raw surface temperature (land only) improved as well. A similar result has been found for CONUS precipitation, although forecast skill is extremely low. Our results suggest that calibration may be important and necessary for surface temperature and precipitation forecast for the subseasonal time scale due to the large systematic model errors.
2005
Ensembles provide information on forecast uncertainty, enabling better decision making where weather poses a risk or opportunity, and allowing forecasts to be usefully extended beyond the deterministic range. This paper outlines a strategy for how the Bureau can develop and enhance its ensemble prediction systems to provide accurate deterministic and probabilistic forecasts of surface and upper air fields of interest to forecasters and the public, covering Australia and surrounding waters, on time scales relevant to nowcasting, weather, and seasonal climate prediction.
Geoscientific Model Development, 2017
The 2.5 km convection-permitting (CP) ensemble AROME-EPS (Applications of Research to Operations at Mesoscale – Ensemble Prediction System) is evaluated by comparison with the regional 11 km ensemble ALADIN-LAEF (Aire Limitée Adaption dynamique Développement InterNational – Limited Area Ensemble Forecasting) to show whether a benefit is provided by a CP EPS. The evaluation focuses on the abilities of the ensembles to quantitatively predict precipitation during a 3-month convective summer period over areas consisting of mountains and lowlands. The statistical verification uses surface observations and 1 km × 1 km precipitation analyses, and the verification scores involve state-of-the-art statistical measures for deterministic and probabilistic forecasts as well as novel spatial verification methods. The results show that the convection-permitting ensemble with higher-resolution AROME-EPS outperforms its mesoscale counterpart ALADIN-LAEF for precipitation forecasts. The positive im...
Monthly Weather Review, 2005
The present paper summarizes the methodologies used at the European Centre for Medium-Range Weather Forecasts (ECMWF), the Meteorological Service of Canada (MSC), and the National Centers for Environmental Prediction (NCEP) to simulate the effect of initial and model uncertainties in ensemble forecasting. The characteristics of the three systems are compared for a 3-month period between May and July 2002. The main conclusions of the study are the following:the performance of ensemble prediction systems strongly depends on the quality of the data assimilation system used to create the unperturbed (best) initial condition and the numerical model used to generate the forecasts;a successful ensemble prediction system should simulate the effect of both initial and model-related uncertainties on forecast errors; andfor all three global systems, the spread of ensemble forecasts is insufficient to systematically capture reality, suggesting that none of them is able to simulate all sources o...
Journal of Climate, 2008
This study examines skill of retrospective forecasts using the ECHAM4.5 atmospheric general circulation model (AGCM) forced with predicted sea surface temperatures (SSTs) from methods of varying complexity. The SST fields are predicted in three ways: persisted observed SST anomalies, empirically predicted SSTs, and predicted SSTs from a dynamically coupled ocean-atmosphere model. Investigation of relative skill of the three sets of retrospective forecasts focuses on the ensemble mean, which constitutes the portion of the model response attributable to the prescribed boundary conditions. The anomaly correlation skill analyses for precipitation and 2-m air temperature indicate that dynamically predicted SSTs generally improve upon persisted and empirically predicted SSTs when they are used as boundary forcing in the AGCM predictions. This is particularly the case for precipitation forecasts. The skill differences in these experiments are ascribed to the skill of SST predictions in the tropical ocean basins. The multiscenario forecast by averaging the three retrospective experiments performs, overall, as well as or better than the best of the three individual experiments in specific seasons and regions. The advantage of multiscenario forecast manifests both in the deterministic and probabilistic skill. In particular, the multiscenario precipitation forecast for the December-February season demonstrates better skill than the best of the three scenarios over several regions, such as the western United States and southeastern South America. These results suggest the potential value in producing superensembles spanning different SST prediction scenarios.
2018
NOAA is accelerating its efforts to improve the numerical guidance and prediction capability for extended range (weeks 3 & 4) prediction in its seamless forecast system. Madden Julian Oscillation (MJO) is the dominant mode of sub-seasonal variability in tropics and forecast skill of MJO is investigated in this paper. We used different configurations of the NCEP Global Ensemble Forecast System (GEFS) to perform the experiments. The configurations include: (1) The operational version of the stochastic perturbation forced with operational Sea Surface Temperatures (SSTs); (2) An updated stochastic physics forced with operational SSTs; (3) An updated stochastic physics forced with bias-corrected SSTs that are from Climate Forecast System (Version 2); and (4) As in (3) but with the addition of a scale aware-convection scheme. We evaluated MJO forecast skill from the experiments using Wheeler-Hendon indices and also examined the performance of the forecast system on prediction of key MJO components. We found that using the updated stochastic scheme improved the MJO prediction lead-time by about 4 days. Further updating the underlying SSTs with the bias corrected CFSv2 forecast increased the MJO prediction lead time by another 1.7 days. The best configuration of the four experiments is the last configuration which extends forecast lead time by ~9 days. Further investigation shows that upper and lower level zonal wind has larger contribution to the improvement of the MJO prediction than the outgoing longwave radiation. The improvement of the MJO forecast skill appears to be due primarily to the improvement in the representation of convection and associated circulations over the tropical West Pacific.
This study examines skill of retrospective forecasts using the ECHAM4.5 atmospheric general circulation model (AGCM) forced with predicted sea surface temperatures (SSTs) from methods of varying complexity. The SST fields are predicted in three ways: persisted observed SST anomalies, empirically predicted SSTs, and predicted SSTs from a dynamically coupled ocean-atmosphere model. Investigation of relative skill of the three sets of retrospective forecasts focuses on the ensemble mean, which constitutes the portion of the model response attributable to the prescribed boundary conditions. The anomaly correlation skill analyses for precipitation and 2-m air temperature indicate that dynamically predicted SSTs generally improve upon persisted and empirically predicted SSTs when they are used as boundary forcing in the AGCM predictions. This is particularly the case for precipitation forecasts. The skill differences in these experiments are ascribed to the skill of SST predictions in the tropical ocean basins. The multiscenario forecast by averaging the three retrospective experiments performs, overall, as well as or better than the best of the three individual experiments in specific seasons and regions. The advantage of multiscenario forecast manifests both in the deterministic and probabilistic skill. In particular, the multiscenario precipitation forecast for the December-February season demonstrates better skill than the best of the three scenarios over several regions, such as the western United States and southeastern South America. These results suggest the potential value in producing superensembles spanning different SST prediction scenarios.
Journal of Climate, 2000
In this paper the performance of a multimodel ensemble forecast analysis that shows superior forecast skills is illustrated and compared to all individual models used. The model comparisons include global weather, hurricane track and intensity forecasts, and seasonal climate simulations. The performance improvements are completely attributed to the collective information of all models used in the statistical algorithm. The proposed concept is first illustrated for a low-order spectral model from which the multimodels and a ''nature run'' were constructed. Two hundred time units are divided into a training period (70 time units) and a forecast period (130 time units). The multimodel forecasts and the observed fields (the nature run) during the training period are subjected to a simple linear multiple regression to derive the statistical weights for the member models. The multimodel forecasts, generated for the next 130 forecast units, outperform all the individual models. This procedure was deployed for the multimodel forecasts of global weather, multiseasonal climate simulations, and hurricane track and intensity forecasts. For each type an improvement of the multimodel analysis is demonstrated and compared to the performance of the individual models. Seasonal and multiseasonal simulations demonstrate a major success of this approach for the atmospheric general circulation models where the sea surface temperatures and the sea ice are prescribed. In many instances, a major improvement in skill over the best models is noted.
Geophysical Research Letters, 2014
Geoscientific Model Development Discussions, 2016
The 2.5 km convection-permitting (CP) ensemble AROME-EPS (Applications of Research to Operations at Mesoscale – Ensemble Prediction System) is evaluated by comparison with the regional 11 km ensemble ALADIN-LAEF (Aire Limitée Adaption dynamique Développement InterNational – Limited Area Ensemble Forecasting) to show whether a benefit is provided by a CP EPS. The evaluation focuses on the abilities of the ensembles to quantitatively predict precipitation during a 3-month convective summer period over areas consisting of mountains and lowlands. The statistical verification uses surface observations and 1 km × 1 km precipitation analyses, and the verification scores involve state-of-the-art statistical measures for deterministic and probabilistic forecasts as well as novel spatial verification methods. The results show that the convection-permitting ensemble with higher resolution AROME-EPS outperforms its mesoscale counterpar...
2019
Correspondence to: Yuejian Zhu, Environmental Modeling Center, NOAA/NWS/NCEP, 5830 University Research Court, College Park, MD 20740; E-mail: [email protected] An Investigation of Prediction and Predictability of NCEP Global Ensemble Forecast System (GEFS) Yuejian Zhu1, Wei Li2, Eric Sinsky2, Hong Guan3, Xiaqiong Zhou2, and Bing Fu2 1Environmental Modeling Center, NOAA/NWS/NCEP 2IMSG at Environmental Modeling Center, NOAA/NWS/NCEP 3SRG at Environmental Modeling Center, NOAA/NWS/NCEP
Climate, 2016
Ensembles of general circulation model (GCM) integrations yield predictions for meteorological conditions in future months. Such predictions have implicit uncertainty resulting from model structure, parameter uncertainty, and fundamental randomness in the physical system. In this work, we build probabilistic models for long-term forecasts that include the GCM ensemble values as inputs but incorporate statistical correction of GCM biases and different treatments of uncertainty. Specifically, we present, and evaluate against observations, several versions of a probabilistic forecast for gridded air temperature 1 month ahead based on ensemble members of the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2). We compare the forecast performance against a baseline climatology based probabilistic forecast, using average information gain as a skill metric. We find that the error in the CFSv2 output is better represented by the climatological variance than by the distribution of ensemble members because the GCM ensemble sometimes suffers from unrealistically little dispersion. Lack of ensemble spread leads a probabilistic forecast whose variance is based on the ensemble dispersion alone to underperform relative to a baseline probabilistic forecast based only on climatology, even when the ensemble mean is corrected for bias. We also show that a combined regression based model that includes climatology, temperature from recent months, trend, and the GCM ensemble mean yields a probabilistic forecast that outperforms approaches using only past observations or GCM outputs. Improvements in predictive skill from the combined probabilistic forecast vary spatially, with larger gains seen in traditionally hard to predict regions such as the Arctic.
2018
For example, regarding the percentage variance about the seasonal norm for Day-1 forecasts explained, minimum temperature predictive skill has lifted from about 55% to 85%, maximum temperature forecast skill has lifted from about 55% to 90%, Quantitative Precipitation Forecast (QPF) skill has lifted from 30% to 60%, whilst Probability of Precipitation (PoP) forecast skill has lifted from 35% to 50%.
Meteorological Applications
Nowadays, major advances have been made in meteorological forecasts. For instance, ensemble forecast systems have been developed to quantify prediction uncertainty. In this research, sub-daily ensemble precipitation forecasts of five THORPEX Interactive Grand Global Ensemble (TIGGE) models from 2014 to 2018 were evaluated in 10 major basins located in north and west Iran. Furthermore, Bayesian model averaging (BMA) was used to combine five prediction models and construct a grand ensemble. The results indicate that the models had the best performance in the Karun and Western Border basins to the southwest and west, with average performance in the Sefidrood basin in the north. In terms of the prediction of precipitation depth, the European Centre for Medium-Range Weather Forecasts (ECMWF) and the UK Met Office (UKMO) models, and in terms of prediction of precipitation occurrence and non-occurrence, the National Centers for Environmental Prediction (NCEP) model, performed best. Overall, the Japan Meteorological Agency (JMA) and the China Meteorological Administration (CMA) models acquired medium scores. The BMA technique greatly improved the probability forecasts, reducing uncertainties in the numerical models. Moreover, the models' forecasts were weaker with a 6 hr lead time compared with those with 24 hr, which may be attributable to the inaccurate detection of the initiation time of precipitation by the models. In addition, the performance of the UKMO (ECMWF) models with increasing basin elevation increased (decreased), while all models better forecasted precipitation in wet years/seasons than they did in dry years/seasons. Overall, the evaluations showed that the ECMWF, UKMO and NCEP models performed well in the majority of the northern and western basins of Iran.
Hydrology and Earth System Sciences, 2009
Hydrological forecasting consists in the assessment of future streamflow. Current deterministic forecasts do not give any information concerning the uncertainty, which might be limiting in a decision-making process. Ensemble forecasts are expected to fill this gap. In July 2007, the Meteorological Service of Canada has improved its ensemble prediction system, which has been operational since 1998. It uses the GEM
Advances in Geosciences, 2007
Spatial interpolation of precipitation data is uncertain. How important is this uncertainty and how can it be considered in evaluation of high-resolution probabilistic precipitation forecasts? These questions are discussed by experimental evaluation of the COSMO consortium's limitedarea ensemble prediction system COSMO-LEPS. The applied performance measure is the often used Brier skill score (BSS). The observational references in the evaluation are (a) analyzed rain gauge data by ordinary Kriging and (b) ensembles of interpolated rain gauge data by stochastic simulation. This permits the consideration of either a deterministic reference (the event is observed or not with 100% certainty) or a probabilistic reference that makes allowance for uncertainties in spatial averaging. The evaluation experiments show that the evaluation uncertainties are substantial even for the large area (41 300 km 2 ) of Switzerland with a mean rain gauge distance as good as 7 km: the one-to three-day precipitation forecasts have skill decreasing with forecast lead time but the one-and two-day forecast performances differ not significantly.
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