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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.

Taiwan is highly susceptible to global warming, experiencing a 1.4 °C increase in air temperature from 1911 to 2005, which is twice the average for the Northern Hemisphere. This has potentially led to higher rates of respiratory and cardiovascular mortality. Accurately predicting maximum temperatures during the summer season is crucial, but numerical weather models become less accurate and more uncertain beyond five days. To enhance the reliability of a forecast, post-processing techniques are essential for addressing systematic errors. In September 2020, the NOAA NCEP implemented the Global Ensemble Forecast System version 12 (GEFSv12) to help manage climate risks. This study developed a Hybrid statistical post-processing method that combines Artificial Neural Networks (ANN) and quantile mapping (QQ) approaches to predict daily maximum temperatures (Tmax) and their extremes in Taiwan during the summer season. The Hybrid technique, utilizing deep learning techniques, was applied to the GEFSv12 reforecast data and evaluated against ERA5 reanalysis. The Hybrid technique was the most effective among the three techniques tested. It had the lowest bias and RMSE and the highest correlation coefficient and Index of Agreement. It successfully reduced the warm bias and overestimation of Tmax extreme days. This led to improved prediction skills for all forecast lead times. Compared to ANN and QQ, the Hybrid method proved to be more effective in predicting daily Tmax, including extreme Tmax during summer, on extended-range time-scale deterministic and ensemble probabilistic forecasts over Taiwan. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 Centers for Environmental Prediction (NCEP)-based India Meteorological Department (IMD) operational Global Forecast System (GFS), the European Centre for Medium-Range Weather Forecasts (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 fifth major global reanalysis produced by ECMWF (ERA5) 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 skillful than the deterministic forecasts, as they were able to predict rainfall anomalies (greater than three standard deviations from climatology) beyond day 5 for August 2019 and up to day 3 for 2018. Significance Statement: The purpose of this study is to understand and unravel the large-scale mechanism behind the unprecedented heavy rainfall over Kerala, India, during August 2018 and 2019. The study brings out the importance of probabilistic rainfall predictions for extreme heavy rainfall events. The study reveals that large-scale moisture convergence plays a significant role in the extreme rain of August 2018 and 2019. The extreme rainfall of August is associated with a westward-propagating barotropic Rossby wave. The study also demonstrates that ensemble forecasts of extreme rain by the state-of-the-art prediction systems of GFS, IFS, and NCUM are skillful for longer lead times compared to deterministic models and, therefore, can provide better early warnings to the society. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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; and : for 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 of forecast uncertainty. The relative strengths and weaknesses of the three systems identified in this study can offer guidelines for the future development of ensemble forecasting techniques. [ABSTRACT FROM AUTHOR]

The potential economic benefit associated with the use of an ensemble of forecasts versus anequivalent or higher-resolution control forecast is discussed. Neither forecast systems are post-processed,except a simple calibration that is applied to make them reliable. A simple decision-making model is used where all potential users of weather forecasts are characterized by the ratio between the cost of their action to preventweather-related damages, and the loss that they incur in case they do not protect their operations. It isshown that the ensemble forecast system can be used by a much wider range of users. Furthermore,for many, and for beyond 4-day lead time for all users, the ensemble provides greater potential economicbenefit than a control forecast, even if the latter is run at higher horizontal resolution. It is argued that theadded benefits derive from 1) the fact that the ensemble provides a more detailed forecast probabilitydistribution, allowing the users to tailor their weather forecast-related actions to their particular cost-loss situation, and 2) the ensemble's ability to differentiate between highand low-predictability cases. While single forecasts can statistically be supplemented by more detailed probability distributions, it is not clear whether with more sophisticated postprocessing they can identify more and less predictable forecast cases as successfully asensembles do. [ABSTRACT FROM AUTHOR]