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The skillful prediction of monthly scale rainfall in small regions like Taiwan is one of the challenges of the meteorological scientific community. Taiwan is one of the subtropical islands in Asia. It experiences rainfall extremes regularly, leading to landslides and flash floods in/near the mountains and flooding over low-lying plains, particularly during the summer monsoon season [June–September (JJAS)]. In September 2020, NOAA/NCEP implemented Global Ensemble Forecast System, version 12 (GEFSv12), to support stakeholders for subseasonal forecasts and hydrological applications. In the present study, the performance evaluation of GEFSv12 for monthly rainfall and associated extreme rainfall (ER) events over Taiwan during JJAS against CMORPH has been done. There is a marginal improvement of GEFSv12 in depicting the East Asian summer monsoon index (EASMI) as compared to GEFS-SubX. The GEFSv12 rainfall raw products have been calibrated with a quantile–quantile (QQ) mapping technique for further prediction skill improvement. The results reveal that the spatial patterns of climatological features (mean, interannual variability, and coefficient of variation) of summer monsoon monthly rainfall over Taiwan from QQ-GEFSv12 are very similar to CMORPH than Raw-GEFSv12. Raw-GEFSv12 has an enormous wet bias and overforecast wet days, while QQ-GEFSv12 is close to reality. The prediction skill (correlation coefficient and index of agreement) of GEFSv12 in depicting the summer monsoon monthly rainfall over Taiwan is significantly high (>0.5) in most parts of Taiwan and particularly more during peak monsoon months, September, and August, followed by June and July. The calibration method significantly reduces the overestimation (underestimation) of wet (ER) events from the ensemble mean and probabilistic ensemble forecasts. The predictability of extreme rainfall events (>50 mm day−1) has also improved significantly.

Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface ­temperatures show a lag correlation with summer precipitation in several remote regions, but current global land–atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the “Impact of Initialized Land Temperature and Snowpack on Subseasonal-to-Seasonal Prediction” (LS4P) initiative as a community effort to test the impact of land temperature in high-mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project. After using an innovative new land state initialization approach based on observed surface 2-m temperature over the TP in the LS4P experiment, results from a multimodel ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high-mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global “hotspot” regions identified here; the ensemble means in some “hotspots” produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations.

The frequency of atmospheric blocking has been largely underestimated by general circulation models (GCMs) participating in the Coupled Model Intercomparison Project (CMIP). Errors in the onset, persistence, barotropicity, geographical preference, seasonality, intensity, and moving speed of global blocking were diagnosed in 10 Geophysical Fluid Dynamics Laboratory (GFDL) GCMs for recent CMIP5 and CMIP6 using a detection approach that combines zonal eddies and the reversal of zonal winds. The blocking frequency, similar at 500 and 250 hPa, is underestimated by 50% in the Atlantic–Europe region during December–February but is overestimated by 60% in the Pacific–North America region during that season and by 70% in the southwest Pacific during July–August. These blocking biases at 500 hPa were investigated in the five CMIP6 models that showed improvements over the CMIP5 versions. The Atlantic–Europe underestimate corresponds to lower instantaneous blocking rates, lower persistent blocking rates, and higher persistent stationary ridge rates; the number of blocks with a duration of 4–5 days is only 40%–65% of that in observations. In contrast, the overestimate consists of excessive blocks with a duration longer than 12 days in the Pacific–North America and up to twice as many 4–6-day events in the southwest Pacific. Simulated December–February blocks up to 12 days in the Pacific–North America region tend to be stronger and to move more slowly than those in observations. Diagnostic sensitivity tests indicated that the zonal mean and zonal eddy components of the mean state play a key role, as replacing each with that of observations substantially reduced many of the outstanding biases in these GCMs.

Indian summer monsoon rainfall (ISMR) from June to September (JJAS) contributes 80% of the total annual rainfall in India and controls the agricultural productivity and economy of the country. Extreme rainfall (ER) events are responsible for floods that cause widespread destruction of infrastructure, economic damage, and loss of life. A forecast of the ISMR and associated ER events on an extended range (beyond the conventional one-week lead time) is vital for the agronomic economy of the country. In September 2020, NOAA/NCEP implemented Global Ensemble Forecast System, version 12 (GEFSv12) for various risk management applications. It has generated consistent reanalysis and reforecast data for the period 2000–19. In the present study, the Raw-GEFSv12 with day-1–16 lead-time rainfall forecasts are calibrated using the quantile (QQ) mapping technique against Indian Monsoon Data Assimilation and Analysis (IMDAA) for further improvement. The present study evaluated the prediction skill of Raw and QQ-GEFSv12 for ISMR and ER events over India by using standard skill metrics. The results suggest that the ISMR patterns from Raw and QQ-GEFSv12 with (lead) day 1–16 are similar to IMDAA. However, Raw-GEFSv12 has a dry bias in most parts of prominent rainfall regions. The low- to medium-intensity rainfall events from Raw-GEFSv12 is remarkably higher than the IMDAA, while high- to very-high-intensity rainfall events from Raw-GEFSv12 are lower than IMDAA. The prediction skill of Raw-GEFSv12 in depicting ISMR and associated ER events decreased with lead time, while the prediction skill is almost equal for all lead times with marginal improvement after calibration.

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 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 North America. GEFSv12 was implemented on 23 September 2020.

To improve NOAA’s seasonal forecasting capabilities, a new coupled system within the Unified Forecast System (UFS) framework is being developed through a community-wide effort led by NOAA’s Environmental Modeling Center targeting the configuration of a future operational Seasonal Forecast System (SFS v1). An experimental version of this ensemble seasonal forecasting system is tested on forecasting the strong El Niño of 2015/16. The then-operational systems and NCEP real-time seasonal forecasts (CFSv2) underestimated its strength towards the end of 2015 and beginning of 2016. In addition to perturbing the atmospheric initial conditions, run-time stochastic physics-based perturbations are applied in both atmosphere and ocean components of this new coupled system to represent the model uncertainties. The UFS ensembles are initialized on June 1st, 2015 and run through a 9-month period. Compared to CFSv2, the forecast of Niño 3.4 SST and intra-seasonal zonal windstress for the 2015/16 El Niño in the UFS system are improved, as is the ensemble spread. A cold SST forecast error develops in the central equatorial Pacific, likely from excess evaporative cooling, shallower thermocline, and an excessively strong vertical current shear driven cooling. Near the eastern equatorial Pacific coast, on the other hand, warm surface and cool subsurface errors persist from initialization until the end of the forecast. The results suggest that further improvement in the seasonal forecast may be achieved by a combination of factors, including, but not limited to, improving the coupled system initialization, along with the atmospheric physics.

For the newly implemented Global Ensemble Forecast System, version 12 (GEFSv12), a 31-yr (1989–2019) ensemble reforecast dataset has been generated at the National Centers for Environmental Prediction (NCEP). The reforecast system is based on NCEP’s Global Forecast System, version 15.1, and GEFSv12, which uses the Finite Volume 3 dynamical core. The resolution of the forecast system is ∼25 km with 64 vertical hybrid levels. The Climate Forecast System (CFS) reanalysis and GEFSv12 reanalysis serve as initial conditions for the Phase 1 (1989–99) and Phase 2 (2000–19) reforecasts, respectively. The perturbations were produced using breeding vectors and ensemble transforms with a rescaling technique for Phase 1 and ensemble Kalman filter 6-h forecasts for Phase 2. The reforecasts were initialized at 0000 (0300) UTC once per day out to 16 days with 5 ensemble members for Phase 1 (Phase 2), except on Wednesdays when the integrations were extended to 35 days with 11 members. The reforecast dataset was produced on NOAA’s Weather and Climate Operational Supercomputing System at NCEP. This study summarizes the configuration and dataset of the GEFSv12 reforecast and presents some preliminary evaluations of 500-hPa geopotential height, tropical storm track, precipitation, 2-m temperature, and MJO forecasts. The results were also compared with GEFSv10 or GEFS Subseasonal Experiment reforecasts. In addition to supporting calibration and validation for the National Water Center, NCEP Climate Prediction Center, and other National Weather Service stakeholders, this high-resolution subseasonal dataset also serves as a useful tool for the broader research community in different applications.

NOAA has created a global reanalysis dataset, 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 2000–19. 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 versus GEFSv12. Skills of reforecasts initialized from the new reanalyses are similar but slightly lower than skills initialized from a preoperational 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.

Tortoise shell is not only an armor, but also an important structure for storing nutrients for tortoise. The shell from one tortoise strain, Chinemys reevesii (CR), has long been used as a traditional Chinese medicine (TCM). Nowadays, massive CR shells are also processed into healthy food in Asian countries. Despite large-scale culture of CR in nowadays, there still exist massive counterfeit CR shells derived from other strains of tortoises in the market of TCM. However, it is usually difficult to identify the strain of shell only with the naked eyes, which leads to uncertainties for the healthcare efficacy. In this context, the present study aims to analyze both metabolites and inorganic elements contained in the shell from CR and two other common tortoise strains in the market, using gas chromatography-mass spectrometry (GC-MS) and inductively coupled plasma mass spectrometry (ICP-MS). We report that comprehensive metabolic and elemental differences have been revealed in the shell of three analyzed tortoise strains. Higher levels of various amino acids, fatty acids, and salutary trace elements and lower levels of lead and arsenic were found in CR shell, in comparison to the other two strains. These metabolic and elemental hallmarks found in CR shell are in accordance with its predominant healthcare effect described in TCM. A combination between leucine and lead was determined to be a potential marker to predict the strain of CR. The top discriminant metabolites and elements, as well as the combinational marker identifying the strain of CR in the training set, were further validated for their predictability by external test samples. Our study demonstrates that analyses of metabolites and inorganic elements contained in tortoise shell are useful to identify the strain of the tortoise.

Operational and research applications generally use the consensus approach for forecasting the track and intensity of tropical cyclones (TCs) due to the spatial displacement of the TC location and structure in ensemble member forecasts. This approach simply averages the location and intensity information for TCs in individual ensemble members, which is distinct from the traditional pointwise arithmetic mean (AM) method for ensemble forecast fields. The consensus approach, despite having improved skills relative to the AM in predicting the TC intensity, cannot provide forecasts of the TC spatial structure. We introduced a unified TC ensemble mean forecast based on the feature-oriented mean (FM) method to overcome the inconsistency between the AM and consensus forecasts. FM spatially aligns the TC-related features in each ensemble field to their geographical mean positions before the amplitude of their features is averaged.We select 219 TC forecast samples during the summer of 2017 for an overall evaluation of the FM performance. The results show that the TC track consensus forecasts can differ from AM track forecasts by hundreds of kilometers at long lead times. AM also gives a systematic and statistically significant underestimation of the TC intensity compared with the consensus forecast. By contrast, FM has a very similar TC track and intensity forecast skill to the consensus approach. FM can also provide the corresponding ensemble mean forecasts of the TC spatial structure that are significantly more accurate than AM for the low- and upper-level circulation in TCs. The FM method has the potential to serve as a valuable unified ensemble mean approach for the TC prediction.

Based on a recently developed approach that can recognize both persistent blocking and ridge events effectively, the contributions of the frequency of these persistent events (FOPE) over different regions in Eurasia to precipitation over eastern China were investigated. The results reveal that, the FOPE over the longitudinal range of 110°–130°E, near the Stanovoy Mountains and the Okhotsk Sea, is significantly correlated with precipitation over the middle and lower reaches of the Yangtze River (MLRYR) during summer, particularly in August. The preceding full July (or 1–20 July) mean Balkhash Lake–Caucasus geopotential height index, which measures the combined effect of the Balkhash Lake and Caucasus geopotential height anomalies, is closely related to the August geopotential height anomaly around the Stanovoy Mountains and the Okhotsk Sea, and can therefore reflect the August 110°–130°E FOPE. The predictability based on this preceding atmospheric signal seems to be attributable to slow-varying atmospheric processes on a subseasonal (20-day mean) time scale. On this time scale, the Balkhash Lake and Caucasus geopotential height anomalies occur prior to, and seem to modulate, the geopotential height anomaly around the Stanovoy Mountains and the associated 110°–130°E FOPE through an eastward extension and through exciting a positive–negative–positive pattern in 500-hPa geopotential heights, respectively. As a result of the slow-varying atmospheric processes, this preceding atmospheric signal performs well in predicting the August 110°–130°E FOPE, which also facilitates the prediction of the MLRYR precipitation.

The National Centers for Environmental Prediction have generated an 18-yr (1999–2016) subseasonal (weeks 3 and 4) reforecast to support the Climate Prediction Center’s operational mission. To create this reforecast, the subseasonal experiment version of the GEFS was run every Wednesday, initialized at 0000 UTC with 11 members. The Climate Forecast System Reanalysis (CFSR) and Global Data Assimilation System (GDAS) served as the initial analyses for 1999–2010 and 2011–16, respectively. The analysis of 2-m temperature error demonstrates that the model has a strong warm bias over the Northern Hemisphere (NH) and North America (NA) during the warm season. During the boreal winter, the 2-m temperature errors over NA exhibit large interannual and intraseasonal variability. For NA and the NH, weeks 3 and 4 errors are mostly saturated, with initial conditions having a negligible impact. Week 2 errors (day 11) are ~88.6% and 86.6% of their saturated levels, respectively. The 1999–2015 reforecast biases were used to calibrate the 2-m temperature forecasts in 2016, which reduces (increases) the systematic error (forecast skill) for NA, the NH, the Southern Hemisphere, and the tropics, with a maximum benefit for NA during the warm season. Overall, analysis adjustment for the CFSR period makes bias characteristics more consistent with the GDAS period over the NH and tropics and substantially improves the corresponding forecast skill levels. The calibration of the forecast using week 2 bias provides similar skill to using weeks 3 and 4 bias, promising the feasibility of using week 2 bias to calibrate the weeks 3 and 4 forecast. Our results also demonstrate that 10-yr reforecasts are an optimal training period. This is particularly beneficial considering limited computing resources.

During August 2018 and 2019 the southern state of India, Kerala received unprecedented heavy rainfall which led to widespread flooding. We aim to characterize the convective nature of these events and the large-scale atmospheric forcing, while exploring their predictability by three state of the art global prediction systems, the National Centre for Environmental Prediction (NCEP) based India Meteorological Department (IMD) operational Global Forecast System (GFS), the European Centre for Medium Range Weather Forecast (ECMWF) integrated forecast system (IFS) and the Unified Model based NCUM being run at the National Centre for Medium Range Weather Forecasting (NCMRWF).Satellite, radar and lightning observations suggest that these rain events were dominated by cumulus congestus and shallow convection with strong zonal flow leading to orographically enhanced rainfall over the Ghats mountain range, sporadic deep convection was also present during the 2019 event. A moisture budget analyses using the ERA5 (ECMWF Reanalyses version 5) reanalyses and forecast output revealed significantly increased moisture convergence below 800 hPa during the main rain events compared to August climatology. The total column integrated precipitable water tendency, however is found to be small throughout the month of August, indicating a balance between moisture convergence and drying by precipitation. By applying a Rossby wave filter to the rainfall anomalies it is shown that the large-scale moisture convergence is associated with westward propagating barotropic Rossby waves over Kerala, leading to increased predictability of these events, especially for 2019.Evaluation of the deterministic and ensemble rainfall predictions revealed systematic rainfall differences over the Ghats mountains and the coastline. The ensemble predictions were more skilful than the deterministic forecasts, as they were able to predict rainfall anomalies (>3 standard deviations from climatology) beyond day 5 for August 2019 and up to day 3 for 2018.

With the successful upgrade of its deterministic model GFS (v15) on June 12, 2019, NCEP has scheduled the implementation of its next Global Ensemble Forecast System (GEFS v12) in the summer of 2020. These two model upgrades on deterministic and ensemble forecast systems are substantially different from previous upgrades. A new dynamical core (FV3) is adopted for the first time in the NCEP operational models, replacing the previous spectral dynamical core. The previous 3-category Zhao-Carr microphysics scheme is also being replaced by a more advanced 6-category GFDL microphysics scheme. From an ensemble model perspective, the previous GEFS has already demonstrated great success in past decades for weather and week-2 prediction by providing reliable probabilistic forecasts. Recently, there has been a large demand for subseasonal prediction, and GEFS v12 forecasts will be extended to 35 days to cover this time range. To better represent large uncertainties associated with this time scale, SPPT (stochastic physics perturbed tendency) and SKEB (stochastic kinetic energy backscatter) stochastic schemes are taking the place of the original STTP (stochastic total tendency perturbation), and a prescribed SST generated from combination of NSST and bias corrected CFS forecasts is also applied to simulate the sub-seasonal variation of SST forcing. As a major system upgrade, a 2.5-year retrospective run of GEFS v12 is carried out to evaluate the model performance. A 30-year reforecast will be provided to stakeholders and the public to calibrate the forecast. The improvement of predictability and prediction skill will be studied through various measurements across tropical to extratropical areas in terms of deterministic (ensemble mean) and probabilistic (ensemble distribution) forecasts. The characteristics of model systematic error will be identified from comparing the major changes of the two state-of-art ensemble systems. As GEFS serves the most crucial model guidance for 5-7 day hurricane forecasts in support of the NHC (National Hurricane Center) and other customers, model capability in predicting tropical cyclone track and intensity is also examined from the retrospective runs. The results show there are significant improvements for tropical cyclone track forecast in North Atlantic and the western North Pacific, in particular, the intensity forecast is improved remarkably in all the basins.

NCEP has implemented the first version of the Finite Volume Cubed Sphere (FV3) dynamic core based Global Forecast System (GFS v15) into operations in June 2019, replacing the spectral model-based GFS. This is the first instantiation of NOAA's Unified Forecast System (UFS), which is being built as a comprehensive coupled Earth system model using modern tools and software infrastructure (e.g., NEMS, NUOPC, and ESMF) to support research and operations. Advancements in model physics and data assimilation are in development using CCPP and JEDI frameworks. Testing and evaluation of UFS are facilitated through the development of unified workflow and METplus capabilities. All these initiatives involve significant engagement with the research community, with emphasis on more efficient and streamlined transition of research advances to operations (R2O).The next major operational upgrade towards UFS is for the Global Ensemble Forecast System (GEFSv12) planned for implementation later this year. Compared to the currently operational GEFS, the next GEFS version 12 incorporates the following advances: the same FV3 based global model and UFS infrastructure as in GFS, higher resolution (~25km), increased membership (31), GFSv15 physics, advanced stochastic physics perturbations (SKEB and SPPT), and 2-tiered SST approach using SST anomalies from CFSv2 as input. For the first time, GEFSv12 will provide ensemble based operational weather predictions for sub-seasonal scales with daily 00z forecasts going out to 35 days. GEFSv12 also comes with 20-year reanalysis, 30-year reforecasts and 3-year retrospective forecasts to support stakeholder needs for calibration and validation. In addition, GEFSv12 absorbs the global wave ensembles and aerosol capabilities (control member only) through one-way coupling, taking major steps towards building a unified system and simplifying NCEP’s production suite.This presentation describes the design and development of GEFSv12 and discusses results from the evaluation of the retrospective and reforecast experiments. Significant improvements were noted in both deterministic and probabilistic forecast metrics for several variables including 500 hPa geopotential height anomaly correction, 850 hPa temperature and winds, near surface variables, precipitation, tropical cyclone tracks and intensity, and modes of variability including MJO and NAO. Substantial improvements were also noted in the performance of wave ensemble and aerosol predictions.This presentation also describes NOAA’s efforts towards accelerating further development of fully coupled UFS consisting of six component models of the Earth system: the FV3 dynamical core for the atmosphere, MOM6 for the ocean, Noah MP for the land surface, GOCART for aerosols, CICE5/CICE6 for sea ice and WW3 for ocean surface waves. Combined with data assimilation advances, an ambitious goal of unifying both the high-resolution deterministic (GFSv17) and probabilistic (GEFSv13) predictions for global medium range and sub-seasonal time scales is planned to significantly advance the global prediction capabilities at NCEP.

This paper proposes a method for multi-model ensemble forecasting based on Bayesian model averaging (BMA), aiming to improve the accuracy of tropical cyclone (TC) intensity forecasts, especially forecasts of minimum surface pressure at the cyclone center (Pmin). The multi-model ensemble comprises three operational forecast models: the Global Forecast System (GFS) of NCEP, the Hurricane Weather Research and Forecasting (HWRF) models of NCEP, and the Integrated Forecasting System (IFS) of ECMWF. The mean of a predictive distribution is taken as the BMA forecast. In this investigation, bias correction of the minimum surface pressure was applied at each forecast lead time, and the distribution (or probability density function, PDF) of Pmin was used and transformed. Based on summer season forecasts for three years, we found that the intensity errors in TC forecast from the three models varied significantly. The HWRF had a much smaller intensity error for short lead-time forecasts. To demonstrate the proposed methodology, cross validation was implemented to ensure more efficient use of the sample data and more reliable testing. Comparative analysis shows that BMA for this three-model ensemble, after bias correction and distribution transformation, provided more accurate forecasts than did the best of the ensemble members (HWRF), with a 5%–7% decrease in root-mean-square error on average. BMA also outperformed the multi-model ensemble, and it produced “predictive variance” that represented the forecast uncertainty of the member models. In a word, the BMA method used in the multi-model ensemble forecasting was successful in TC intensity forecasts, and it has the potential to be applied to routine operational forecasting.

NOAA is accelerating its efforts to improve the numerical guidance and prediction capability for extended range (weeks 3 and 4) prediction in its seamless forecast system. Madden Julian Oscillation (MJO) is the dominant mode of sub-seasonal variability in tropics and prediction 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 prediction 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 over the tropics has larger improvement than the outgoing longwave radiation (OLR). The improvement of the MJO prediction skill appears to be related primarily to the improvement in the representation associated circulations and OLR over the tropical West Pacific.

This study analyzes the predictability of the persistent maxima of 500-hPa geopotential height (Z500; PMZ) zonal eddies over the Northern Hemisphere in the long-term forecast datasets of the Global Ensemble Forecast System (GEFS) version 10. PMZ patterns, which potentially extending the predictability of severe weather events, include not only closed blocking anticyclones that occur more frequently in the Euro-Atlantic-Asia sector (EAAS) but also persistent open ridges and omega-shape blockings that prevail more often over the Pacific-North America sector (PNAS). The predicted PMZ occurrence frequencies in both the EAAS and the PNAS generally decrease with the lead time, which is consistent with classical blockings in early studies but different from the nearly invariant frequencies of blockings in a recent relevant diagnosis by Hamill and Kiladis. The Brier skill score associated with PMZ frequencies is generally higher in the PNAS than in the EAAS, indicating better predictions in the former. The forecast reliability decreases with the lead time in both sectors, particularly at the tails of probability distributions, suggesting some limitations of the GEFS. PMZ events longer than 1 week with anomaly correlation coefficients (ACCs) exceeding 0.6 in the Northern Hemisphere have a mean useful skill of nearly 10 lead days, which is approximately 0.5–1 day more than the average skill of all cases. Among these events, 50% extend useful ACC skills up to 12 days, and 25% extend the useful skill even further. A discussion is provided regarding how the better PMZ prediction skill in the PNAS can help improve 2 to 3-week predictions over North America.

Six sets of hindcasts conducted with the NCEP GFS have been used to study the SST-feedback processes and assess the relative contributions of atmospheric internal dynamics and SST feedback on the October and November MJO events observed during the DYNAMO IOP (Oct- and Nov-MJO). The hindcasts are carried out with three variants of the Arakawa–Shubert cumulus scheme under TMI and climatological SST conditions. The positive intraseasonal SST anomaly along with its convergent Laplacian produces systematic surface disturbances, which include enhanced surface convergence, evaporation, and equivalent potential temperature no matter which cumulus scheme is used. Whether these surface disturbances can grow into a robust response of MJO convection depends on the characteristics of the cumulus schemes used. If the cumulus scheme is able to amplify the SST-initiated surface disturbances through a strong upward–downward feedback, the model is able to produce a robust MJO convection response to the underlying SST anomaly; otherwise, the model will not produce any significant SST feedback. A new method has been developed to quantify the “potential” and “practical” contributions of the atmospheric internal dynamics and SST feedback on the MJOs. The present results suggest that, potentially, the SST feedback could have larger contributions than the atmospheric internal dynamics. Practically, the contributions to the Oct- and Nov-MJO events are, respectively, dominated by atmospheric internal dynamics and SST feedback. Averaged over the entire period, the contributions from the atmospheric internal dynamics and SST feedback are about half and half.

Sea surface temperature (SST) variability has been shown to have predictive value for land precipitation, although SSTs are unable to fully predict intraseasonal to interannual hydrologic extremes. The possible remote effects of large-scale land surface temperature (LST) and subsurface temperature (SUBT) anomalies in geographical areas upstream and closer to the areas of drought/flood have largely been ignored. Here evidence from climate observations and model simulations addresses these effects. Evaluation of observational data using Maximum Covariance Analysis identifies significant correlations between springtime 2-m air temperature (T2 m) cold (warm) anomalies in both the western U.S. and the Tibetan Plateau and downstream drought (flood) events in late spring/summer. To support these observational findings, climate models are used in sensitivity studies, in which initial LST/SUBT anomaly is imposed to produce observed T2 m anomaly, to demonstrate a causal relationship for two important cases: between spring warm T2 m/LST/SUBT anomalies in western U.S. and the extraordinary 2015 flood in Southern Great Plains and adjacent regions and between spring cold T2 m/LST/SUBT anomalies in the Tibetan Plateau and the severe 2003 drought south of the Yangtze River region. The LST/SUBT downstream effects in North America are associated with a large-scale atmospheric stationary wave extending eastward from the LST/SUBT anomaly region. The effects of SST in these cases are also tested and compared with the LST/SUBT effects. These results suggest that consideration of LST/SUBT anomalies has the potential to add value to intraseasonal prediction of dry and wet conditions, especially extreme drought/flood events. The results suggest the importance of developing land data and models capable of preserving observed soil memory. This work was supported by the grants from U.S. National Science Foundation AGS-1346813, AGS-1419526, and AGS-1540518 (J. D. N.). This work is also supported by the Jiangsu Collaborative Innovation Center for Climate Change, China AGS-1346813 AGS-1419526 AGS-1540518 (J. D. N.)

The Global Ensemble Forecasting System (GEFS) is being extended from 16 to 35 days to cover the subseasonal period, bridging weather and seasonal forecasts. In this study, the impact of SST forcing on the extended-range land-only global 2-m temperature, continental United States (CONUS) accumulated precipitation, and MJO skill are explored with version 11 of the GEFS (GEFSv11) under various SST forcing configurations. The configurations consist of 1) the operational GEFS 90-day e-folding time of the observed real-time global SST (RTG-SST) anomaly relaxed to climatology, 2) an optimal AMIP configuration using the observed daily RTG-SST analysis, 3) a two-tier approach using the CFSv2-predicted daily SST, and 4) a two-tier approach using bias-corrected CFSv2-predicted SST, updated every 24 h. The experimental period covers the fall of 2013 and the winter of 2013/14. The results indicate that there are small differences in the ranked probability skill scores (RPSSs) between the various SST forcing experiments. The improvements in forecast skill of the Northern Hemisphere 2-m temperature and precipitation for weeks 3 and 4 are marginal, especially for North America. The bias-corrected CFSv2-predicted SST experiment generally delivers superior performance with statistically significant improvement in spatially and temporally aggregated 2-m temperature RPSSs over North America. Improved representation of the SST forcing (AMIP) increased the forecast skill for MJO indices up through week 2, but there is no significant improvement of the MJO forecast skill for weeks 3 and 4. These results are obtained over a short period with weak MJO activity and are also subject to internal model weaknesses in representing the MJO. Additional studies covering longer periods with upgraded model physics are warranted.

Persistent open ridges and blocking highs (maxima) of 500-hPa geopotential height (Z500; PMZ) adjacent in space and time are identified and tracked as one event with a Lagrangian objective approach to derive their climatological statistics with some dynamical reasoning. A PMZ starts with a core that contains a local eddy maximum of Z500 and its neighboring grid points whose eddy values decrease radially to about 20 geopotential meters (GPMs) smaller than the maximum. It connects two consecutive cores that share at least one grid point and are within 10° of longitude of each other using an intensity-weighted location. The PMZ ends at the core without a successor. On each day, the PMZ impacts an area of grid points contiguous to the core and with eddy values decreasing radially to 100 GPMs. The PMZs identified and tracked consist of persistent ridges, omega blockings and blocked anticyclones either connected or as individual events. For example, the PMZ during 2–13 August 2003 corresponds to persistent open ridges that caused the extreme heatwave in Western Europe. Climatological statistics based on the PMZs longer than 3 days generally agree with those of blockings. In the Northern Hemisphere, more PMZs occur in DJF season than in JJA and their duration both exhibit a log-linear distribution. Because more omega-shape blocking highs and open ridges are counted, the PMZs occur more frequently over Northeast Pacific than over Atlantic-Europe during cool seasons. Similar results are obtained using the 200-hPa geopotential height (in place of Z500), indicating the quasi-barotropic nature of the PMZ.

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.

This article introduces a method for objectively separating and validating forecast scenarios within a large multimodel ensemble for the medium-range (3–7 day) forecasts of extratropical cyclones impacting the U.S. East Coast. The method applies fuzzy clustering to the principal components (PCs) of empirical orthogonal function (EOF) analysis on mean sea level pressure (MSLP) from a 90-member combination of the global ensembles from the National Centers for Environmental Prediction, the Canadian Meteorological Center, and the European Centre for Medium-Range Weather Forecasts. Two representative cases are presented to illustrate the applications of this method. Application to the 26–28 January 2015 event demonstrates that the forecast scenarios determined by the fuzzy clustering method are well separated and consistent in different state variables (i.e., MSLP, 500-hPa geopotential height, and total precipitation). The fuzzy clustering method and an existing ensemble sensitivity method are applied to the 26–28 December 2010 event to investigate forecast uncertainty, which demonstrates that these two methods are complementary to each other and can be used in the operations together to track the evolution of forecast uncertainty. For past cases one can define a cluster close to the analysis based on the projection of the analysis onto the PC base of clustering. This analysis group is validated using conventional validation metrics for both cases examined, and this analysis group has fewer errors than the other groups as well as the multimodel ensemble mean and individual model means.

In 2006, the statistical postprocessing of the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) and North American Ensemble Forecast System (NAEFS) was implemented to enhance probabilistic guidance. Anomaly forecasting (ANF) is one of the NAEFS products, generated from bias-corrected ensemble forecasts and reanalysis climatology. The extreme forecast index (EFI), based on a raw ensemble forecast and model-based climatology, is another way to build an extreme weather forecast. In this work, the ANF and EFI algorithms are applied to extreme cold temperature and extreme precipitation forecasts during the winter of 2013/14. A highly correlated relationship between the ANF and EFI allows the determination of two sets of thresholds to identify extreme cold and extreme precipitation events for the two algorithms. An EFI of −0.78 (0.687) is approximately equivalent to a −2σ (0.95) ANF for the extreme cold event (extreme precipitation) forecast. The performances of the two algorithms in forecasting extreme cold events are verified against analysis for different model versions, reference climatology, and forecasts. The verification results during the winter of 2013/14 indicate that ANF forecasts more extreme cold events with a slightly higher skill than EFI. The bias-corrected forecast performs much better than the raw forecast. The current upgrade of the GEFS has a beneficial effect on the extreme cold weather forecast. Using the NCEP Climate Forecast System Reanalysis and Reforecast (CFSRR) as a climate reference gives a slightly better score than the 40-yr reanalysis. The verification methodology is also extended to an extreme precipitation case, showing a broad potential use in the future.

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.

The National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) has been in daily operation to provide probabilistic guidance for public since December 1992. Since July 2017, the GEFS was extended from 16 days to 35 days forecast to support NCEP Climate Prediction Center (CPC)’s sub-seasonal forecast. The latest GEFS version was upgraded in three areas to improve sub-seasonal forecast: (1) introducing a new set of stochastic physical perturbations to improve model uncertainty representation for the tropics; (2) a 2-tiered SST approach to consider ocean impact; and (3) a new scale-aware convection scheme to improve model physics for tropical convection and MJO forecasts. The new set of stochastic physical perturbations include stochastic kinetic energy backscatter to make up subscale energy lost during model integration; stochastic physics perturbation tendency with five different spatial and temporal scales to perturb physical tendency; and stochastic perturbed humidity on the model lower level. After upgraded to new set of stochastic physical perturbations, the MJO forecast skill has been improved from 12.5 days of a 25-month period to nearly 22 days by combining all three modifications include stochastic physics. In the extratropics, the 500-hPa geopotential height; surface temperature and precipitation are improved for sub-seasonal timescale as well. However, the raw forecast skills of surface temperature and precipitation are extremely low, and the results imply that calibration may be important and necessary for surface temperature and precipitation forecast for the sub-seasonal timescale due to the large systematic model errors.

Atmospheric variability is driven not only by internal dynamics, but also by external forcing, such as soil states, SST, snow, sea-ice cover, and so on. To investigate the forecast uncertainties and effects of land surface processes on numerical weather prediction, we added modules to perturb soil moisture and soil temperature into NCEP’s Global Ensemble Forecast System (GEFS), and compared the results of a set of experiments involving different configurations of land surface and atmospheric perturbation. It was found that uncertainties in different soil layers varied due to the multiple timescales of interactions between land surface and atmospheric processes. Perturbations of the soil moisture and soil temperature at the land surface changed sensible and latent heat flux obviously, as compared to the less or indirect land surface perturbation experiment from the day-to-day forecasts. Soil state perturbations led to greater variation in surface heat fluxes that transferred to the upper troposphere, thus reflecting interactions and the response to atmospheric external forcing. Various verification scores were calculated in this study. The results indicated that taking the uncertainties of land surface processes into account in GEFS could contribute a slight improvement in forecast skill in terms of resolution and reliability, a noticeable reduction in forecast error, as well as an increase in ensemble spread in an under-dispersive system. This paper provides a preliminary evaluation of the effects of land surface processes on predictability. Further research using more complex and suitable methods is needed to fully explore our understanding in this area.

This study investigates why OLR plays a small role in the Real-time Multivariate (Madden–Julian oscillation) MJO (RMM) index and how to improve it. The RMM index consists of the first two leading principal components (PCs) of a covariance matrix, which is constructed by combined daily anomalies of OLR and zonal winds at 850 (U850) and 200 hPa (U200) in the tropics after being normalized with their globally averaged standard deviations of 15.3 W m−2, 1.8 m s−1, and 4.9 m s−1, respectively. This covariance matrix is reasoned mathematically close to a correlation matrix. Both matrices substantially suppress the overall contribution of OLR and make the index more dynamical and nearly transparent to the convective initiation of the MJO. A covariance matrix that does not use normalized anomalies leads to the other extreme where OLR plays a dominant role while U850 and U200 are minor. Numerous tests indicate that a simple scaling of the anomalies (i.e., 2 W m−2, 1 m s−1, and 1 m s−1) can better balance the roles of OLR and winds. The revised PCs substantially enhance OLR over the eastern Indian and western Pacific Oceans and change it less notably in other locations, while they reduce U850 and U200 only slightly. Comparisons with the original RMM in spatial structure, power spectra, and standard deviation demonstrate improvements of the revised RMM index.

A postprocessing technique is employed to correct model bias for precipitation fields in real time based on a comparison of the frequency distributions of observed and forecast precipitation amounts. Essentially, a calibration is made by defining an adjustment to the forecast value in such a way that the adjusted cumulative forecast distribution over a moving time window dynamically matches the corresponding observed distribution accumulated over a domain of interest, for example, the entire conterminous United States (CONUS), or different River Forecast Center (RFC) regions in the cases examined herein. In particular, the Kalman filter method is used to catch the flow dependence and bias information. Calibration is done on a pointwise basis for a specified domain. Using this unique technique, the calibration of precipitation forecasts for the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) was implemented in May 2004. To further satisfy various users, a recent upgrade to the May 2004 implementation has been made for higher resolution with better analyses. From this study, it was found that this method has a positive impact on the intensity-dominated errors but has some common limitations with extreme events and dry bias elimination like other precipitation calibration methods. Overall, the frequency-matching algorithm substantially improves NCEP Global Forecast System (GFS) and GEFS systematic precipitation forecast errors (or biases) over a wide range of forecast amounts and produces more realistic precipitation patterns. Moreover, this approach improves the deterministic forecast skills measured by most verification scores through applying this method to GFS and GEFS ensemble means.

The National Oceanic and Atmospheric Administration/Earth System Research Laboratory (NOAA/ESRL) generated a multidecadal (from 1985 to present) ensemble reforecast database for the 2012 version of the Global Ensemble Forecast System (GEFS). This dataset includes 11-member reforecasts initialized once per day at 0000 UTC. This GEFS version has a strong cold bias for winter and warm bias for summer in the Northern Hemisphere. Although the operational decaying average bias-correction approach performs well in winter and summer, it sometimes fails during the spring and fall transition seasons at long lead times (>~5 days). In this paper, 24- (1985–2008) and 25-yr (1985–2009) reforecast biases are used to calibrate 2-m temperature forecasts in 2009 and 2010, respectively. The reforecast-calibrated forecasts for both years are more accurate than those adjusted by the decaying average method during transition seasons. A long training period (>5 yr) is necessary to help avoid a large impact on bias correction from an extreme year case and keep a broader diversity of weather scenarios. The improvement from using the full 25-yr, 31-day window, weekly training dataset is almost equivalent to that from using daily training samples. This provides an option to reduce computational expenses while maintaining a desired accuracy. To provide the potential to improve forecast accuracy for transition seasons, reforecast information is added into the current operational bias-correction method. The relative contribution of the two methods is determined by the correlation between the ensemble mean and analysis. This method improves the forecast accuracy for most of the year with a maximum benefit during April–June.

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.

During the presummer rainy season (April–June), southern China often experiences frequent occurrences of extreme rainfall, leading to severe flooding and inundations. To expedite the efforts in improving the quantitative precipitation forecast (QPF) of the presummer rainy season rainfall, the China Meteorological Administration (CMA) initiated a nationally coordinated research project, namely, the Southern China Monsoon Rainfall Experiment (SCMREX) that was endorsed by the World Meteorological Organization (WMO) as a research and development project (RDP) of the World Weather Research Programme (WWRP). The SCMREX RDP (2013–18) consists of four major components: field campaign, database management, studies on physical mechanisms of heavy rainfall events, and convection-permitting numerical experiments including impact of data assimilation, evaluation/improvement of model physics, and ensemble prediction. The pilot field campaigns were carried out from early May to mid-June of 2013–15. This paper: i) describes the scientific objectives, pilot field campaigns, and data sharing of SCMREX; ii) provides an overview of heavy rainfall events during the SCMREX-2014 intensive observing period; and iii) presents examples of preliminary research results and explains future research opportunities.

The ensemble transform with rescaling (ETR) method has been used to produce fast-growing components of analysis error in the NCEP Global Ensemble Forecast System (GEFS). The rescaling mask contained in the ETR method constrains the amplitude of perturbations to reflect regional variations of analysis error. However, because of a lack of suitable three-dimensional (3D) analysis error estimation, in the operational GEFS the mask is based on the estimated analysis error at 500 hPa and is not flow dependent but changes monthly. With the availability of an ensemble-based data assimilation system at NCEP, a 3D mask can be computed. This study generates initial perturbations by the ensemble transform with 3D rescaling (ET_3DR) and compares the performance with the ETR. Meanwhile, the ET_3DR is also applied within the ensemble Kalman filter (EnKF) method (hereafter EnKF_3DR). Results from a set of experiments indicate that the 3D mask suppresses perturbations less in unstable regions. Relative to the ETR, the large amplitudes of the ET_3DR initial perturbations at 500 hPa better reflect areas of baroclinic instability over the extratropics and deep convection over the tropics. Furthermore, the maxima of the vertical distribution for the ET_3DR initial perturbations correspond to the heights of the subtropical westerly and tropical easterly jet regions. Such perturbations produce faster spread growths. Results with EnKF_3DR also show benefits from an orthonormalization by the ensemble transform algorithm and amplitude constraint by the 3D mask rescaling. Thus, the EnKF_3DR forecasts outperform the EnKF.

The ensemble mean quantitative precipitation forecasts (QPFs) and probabilistic QPFs (PQPFs) from six operational global ensemble prediction systems (EPSs) in The Observing System Research and Predictability Experiment Interactive Grand Global Ensemble (TIGGE) data set are evaluated against the Tropical Rainfall Measuring Mission observations using a series of area-weighted verification metrics during June to August 2008–2012 in the Northern Hemisphere (NH) midlatitude and tropics. Results indicate that generally the European Centre for Medium-Range Weather Forecasts performs best while the Canadian Meteorological Centre (CMC) is relatively good for short-range QPFs and PQPFs at light precipitation thresholds. The overall forecast skill is better in the NH midlatitude than in the NH tropics. QPFs and PQPFs from China Meteorological Administration (CMA) have very little discrimination ability of different observed rain events in the NH tropics. The day +1 QPFs from Japan Meteorological Agency have remarkably large moist biases in the NH tropics, which leads to the discontinuity of forecast performance with the lead times. Performance changes due to the major EPS upgrades during the five summers are also examined using the forecasts from CMA as the reference to eliminate the interannual variation. After the EPS upgrade, CMC improves the PQPF skill at light precipitation threshold while its excessively enlarged ensemble spread increases the overall QPF and PQPF errors.

A multidecadal ensemble reforecast database is now available that is approximately consistent with the operational 0000 UTC cycle of the 2012 NOAA Global Ensemble Forecast System (GEFS). The reforecast dataset consists of an 11-member ensemble run once each day from 0000 UTC initial conditions. Reforecasts are run to +16 days. As with the operational 2012 GEFS, the reforecast is run at T254L42 resolution (approximately 1/2° grid spacing, 42 levels) for week +1 forecasts and T190L42 (approximately 3/4° grid spacing) for the week +2 forecasts. Reforecasts were initialized with Climate Forecast System Reanalysis initial conditions, and perturbations were generated using the ensemble transform with rescaling technique. Reforecast data are available from 1985 to present. Reforecast datasets were previously demonstrated to be very valuable for detecting and correcting systematic errors in forecasts, especially forecasts of relatively rare events and longer-lead forecasts. What is novel about this reforecast dataset relative to the first-generation NOAA reforecast is that (i) a modern, currently operational version of the forecast model is used (the previous reforecast used a model version from 1998); (ii) a much larger set of output data has been saved, including variables relevant for precipitation, hydrologic, wind energy, solar energy, severe weather, and tropical cyclone forecasting; and (iii) the archived data are at much higher resolution. The article describes more about the reforecast configuration and provides a few examples of how this second-generation reforecast data may be used for research and a variety of weather forecast applications.

Two important questions are addressed in this paper using the Global Ensemble Forecast System (GEFS) from the National Centers for Environmental Prediction (NCEP): (1) How many ensemble members are needed to better represent forecast uncertainties with limited computational resources? (2) What is the relative impact on forecast skill of increasing model resolution and ensemble size? Two-month experiments at T126L28 resolution were used to test the impact of varying the ensemble size from 5 to 80 members at the 500-hPa geopotential height. Results indicate that increasing the ensemble size leads to significant improvements in the performance for all forecast ranges when measured by probabilistic metrics, but these improvements are not significant beyond 20 members for long forecast ranges when measured by deterministic metrics. An ensemble of 20 to 30 members is the most effective configuration of ensemble sizes by quantifying the tradeoff between ensemble performance and the cost of computational resources. Two representative configurations of the GEFS—the T126L28 model with 70 members and the T190L28 model with 20 members, which have equivalent computing costs—were compared. Results confirm that, for the NCEP GEFS, increasing the model resolution is more (less) beneficial than increasing the ensemble size for a short (long) forecast range.

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.