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The Global Ensemble Forecast System version 12 (GEFSv12) has been implemented into National Centers For Environmental Prediction operations since September 2020, which was uncoupled, but increased the horizontal resolution from 34 to 25 km, increased ensemble members from 21 to 31, and extended forecasts from 16 to 35 days. It significantly improved probabilistic forecast skills in many categories, such as precipitation, tropical storms, Madden‐Julian Oscillation (MJO), etc. The improvements resulted from many aspects, including model resolution increase, dynamical core upgrade, advances in hybrid data assimilation and physical parameterizations, and more importantly, from using new stochastic schemes to improve forecast uncertainty. To further improve GEFS's sub‐seasonal forecast skill, a coupled GEFS was built up on the Unified Forecast System prototype version 5 that fully couples an atmospheric model with a land surface model, ocean model, ice model, and wave model. A set of coupled GEFS experiments were conducted to test different horizontal resolutions at approximately 50 and 25 km while adjusting the stochastic parameterization schemes for the atmosphere to better represent forecast uncertainties. The experiments were run for a 2‐year period from October 2017 to September 2019, with one initialization per week at Wednesday 00 UTC, 11 ensemble members, and were forecasted out to 35 days. The coupled GEFS significantly improves 500 hPa height anomaly correlation in week‐1, week‐2, and MJO skills compared to the current operational forecast. The forecast spread of tropical wind is greatly reduced by improved stochastic schemes and matches well with the forecast root mean square errors. The correlation of forecast error variance and ensemble variance is improved for the coupled GEFSs. Meanwhile, the spread of MJO has been greatly reduced for the coupled GEFSs to improve the MJO forecast uncertainty. Plain Language Summary: The Unified Forecast System is a new community‐based, atmosphere‐land‐ocean‐sea ice‐wave‐aerosol coupled, comprehensive Earth modeling system. A Global Ensemble Forecast System (GEFS; version 12; uncoupled) has been implemented into National Centers For Environmental Prediction operations since September 2020 with 31 members, out to 35 days to cover subseasonal prediction. A new fully coupled GEFS in this investigation has demonstrated an overall better performance around the weather, medium‐range, and weeks 3 and 4 time scales. Forecast uncertainties are quantified by adjusting the coefficients and parameters of the stochastic schemes. Key Points: The coupled Global Ensemble Forecast System has advantages over the uncoupled system for weather and subseasonal predictionsThe forecast uncertainty is quantified by different measures and diagnostic toolsModel resolution is less important for extended range than weather forecasts [ABSTRACT FROM AUTHOR]

This study investigates the refined simulation skill that results when the regional Climate extension of the Weather Research and Forecasting (CWRF) model is nested in the ECMWF Hamburg version 4.5 (ECHAM) atmospheric general circulation model over the United States during 1980-2009, where observed sea surface temperatures are used in both models. Over the contiguous US, for each of the four seasons from winter to fall, CWRF reduces the root mean square error of the ECHAM seasonal mean surface air temperature simulation by 0.19, 0.82, 2.02 and 1.85 °C, and increases the equitable threat score of seasonal mean precipitation by 0.18, 0.11, 0.09 and 0.12. CWRF also simulates much more realistically daily precipitation frequency and heavy precipitation events, typically over the Central Great Plains, Cascade Mountains and Gulf Coast States. These CWRF skill enhancements are attributed to the increased spatial resolution and physics refinements in representing orographic, terrestrial hydrology, convection, and cloud-aerosol-radiation effects and their interactions. Empirical orthogonal function analysis of seasonal mean precipitation and surface air temperature interannual variability shows that, in general, CWRF substantially improves the spatial distribution of both quantities, while temporal evolution (i.e. interannual variability) of the first 3 primary patterns is highly correlated with that of the driving ECHAM (except for summer precipitation), and they both have low temporal correlations against observations. During winter, when large-scale forcing dominates, both models also have similar responses to strong ENSO signals where they successfully capture observed precipitation composite anomalies but substantially fail to reproduce surface air temperature anomalies. When driven by the ECMWF Reanalysis Interim, CWRF produces a very realistic interannual evolution of large-scale precipitation and surface air temperature patterns where the temporal correlations with observations are significant. These results indicate that CWRF can greatly improve mesoscale regional climate structures but it cannot change interannual variations of the large-scale patterns, which are determined by the driving lateral boundary conditions. [ABSTRACT FROM AUTHOR]

This study comprehensively evaluates the effects of twelve cumulus parameterization (CUP) schemes on simulations of 1993 and 2008 Central US summer floods using the regional climate-weather research and forecasting model. The CUP schemes have distinct skills in predicting the summer mean pattern, daily rainfall frequency and precipitation diurnal cycle. Most CUP schemes largely underestimate the magnitude of Central US floods, but three schemes including the ensemble cumulus parameterization (ECP), the Grell-3 ensemble cumulus parameterization (G3) and Zhang-McFarlane-Liang cumulus parameterization (ZML) show clear advantages over others in reproducing both floods location and amount. In particular, the ECP scheme with the moisture convergence closure over land and cloud-base vertical velocity closure over oceans not only reduces the wet biases in the G3 and ZML schemes along the US coastal oceans, but also accurately reproduces the Central US daily precipitation variation and frequency distribution. The Grell (GR) scheme shows superiority in reproducing the Central US nocturnal rainfall maxima, but others generally fail. This advantage of GR scheme is primarily due to its closure assumption in which the convection is determined by the tendency of large-scale instability. Future study will attempt to incorporate the large-scale tendency assumption as a trigger function in the ECP scheme to improve its prediction of Central US rainfall diurnal cycle. [ABSTRACT FROM AUTHOR]

This study proposes an end-to-end forecasting framework to incorporate operational seasonal climate forecasts to help farmers improve their decisions prior to the crop growth season, which are vulnerable to unanticipated drought conditions. The framework couples a crop growth model with a decision-making model for rainfed agriculture and translates probabilistic seasonal forecasts into more user-related information that can be used to support farmers' decisions on crop type and some market choices (e.g., contracts with ethanol refinery). The regional Climate-Weather Research and Forecasting model (CWRF) driven by two operational general circulation models (GCMs) is used to provide the seasonal forecasts of weather parameters. To better assess the developed framework, CWRF is also driven by observational reanalysis data, which theoretically can be considered as the best seasonal forecast. The proposed framework is applied to the Salt Creek watershed in Illinois that experienced an extreme drought event during 2012 crop growth season. The results show that the forecasts cannot capture the 2012 drought condition in Salt Creek and therefore the suggested decisions can make farmers worse off if the suggestions are adopted. Alternatively, the optimal decisions based on reanalysis-based CWRF forecasts, which can capture the 2012 drought conditions, make farmers better off by suggesting 'no-contract' with ethanol refineries. This study suggests that the conventional metric used for ex ante value assessment is not capable of providing meaningful information in the case of extreme drought. Also, it is observed that institutional interventions (e.g., crop insurance) highly influences farmers' decisions and, thereby, the assessment of forecast value. [ABSTRACT FROM AUTHOR]

The non-hydrostatic Weather Research and Forecasting model (WRF) was nested into NCEP's operational seasonal forecast model Climate Forecast System (CFS) to downscale seasonal prediction of winter precipitation over continental China. Using the same initial conditions, 16 ensemble downscaling forecasts configured with two alternative schemes of microphysics, cumulus, land surface and radiation in WRF were conducted at 30 km for 27-cold seasons (December-February) during 1982-2008. On average, WRF downscaling forecasts reduced wet bias of seasonal mean precipitation from CFS prediction by 25-71%, decreased errors by up to 33%, and increased equitable threat score by 0.1 for low threshold. With appropriate physical configurations, WRF could improve interannual variations over the region where CFS has correct anomaly signal. The spatial distribution of daily precipitation characteristics such as rainy frequency and extremes highlighted the sensitivity of downscaling forecasts to physical configurations, and the dominant uncertainties were introduced by land surface and radiation schemes. The differences in convective and resolved rainfall between alternative land surface and radiation schemes were consistent with differences of surface downwelling shortwave and longwave radiation through cloud-radiation feedback. Such feedback was strengthened in the land surface sensitivity experiments due to different parameterizations of surface albedo. As compared with CFS ensemble predictions with different initial conditions, the WRF ensemble downscaling forecasts with various physical schemes had larger spread, and some schemes could complement each other in different regions that provided a promising opportunity to enhance the prediction through optimization. The optimized WRF reduced error from the optimized CFS by 30% and increased pattern correlation by 0.12. Moreover, WRF physical configuration ensemble increased percentage of skillful probabilistic forecasts from CFS initial condition ensemble. [ABSTRACT FROM AUTHOR]

China's rainfall interannual predictability is generally believed to depend upon the accurate representation of its annual cycle as well as teleconnections with planetary surface anomalies, including tropical east Pacific sea surface temperature and Eurasian snow and soil moisture. A suite of general circulation model (GCM) simulations is used to ascertain the existence of these relationships. First, a comparison of thirty 1980-88 Atmospheric Model Intercomparison Project (AMIP) GCM simulations shows no clear correspondence between model skill to reproduce observed rainfall annual cycle and interannual variability. Thus, accurate representation of either component does not ensure the realistic simulation of the other. Second, diagnosis of the 1903-94 and 195097 National Center for Atmospheric Research (NCAR) Community Climate Model, version 3 (CCM3), ensemble integrations indicates the existence of teleconnections in which spring planetary surface anomalies lead China's summer rainfall variations. These teleconnections, however, are sensitive to initial conditions, which define distinct dynamic regimes during the integration period. In addition, analysis of the NCAR Climate System Model (CSM) 300-yr equilibrium simulation reveals that the teleconnections display decadal variations. These results cast doubt on the traditional physical mechanisms that explain China's rainfall teleconnections and, hence, emphasize the need to incorporate interactions between planetary surface anomalies and specific dynamic regimes. [ABSTRACT FROM AUTHOR]

This study investigates the role of convection representation across the gray zone in the multigrid nesting Weather Research and Forecasting (WRF) model based on two extreme precipitation events in Shanghai during the warm season. The precipitation forecast sensitivity and physical processes are examined using three convective treatments: (1) the traditional Kain-Fritsch (KF), (2) scale-aware Grell-Freitas (GF) cumulus parameterization, and (3) explicit convection (EC) solution and different nesting configurations consisting of grid spacings of 27, 15, 9, 5, 3, and 1 km using single, double, and triple nestings. At coarser grids (> 10 km), GF produces wet biases with delayed rainfall peaks, while KF produces dry biases with largely underestimated rainfall peaks. At a 9-km grid spacing, these biases were largely reduced, and using cumulus parameterizations (CUPs) shows specific advantages over the EC in the diurnal variation in extreme precipitation. GF performs better in simulating primary rainfall peak intensity, but KF performs better in simulating diurnal timing, albeit with underestimated intensity. The utilization of CUPs is beneficial at the 5-km grid in simulating both extreme intensity and diurnal variations, but it shows mixed effects at 3 km. When the 1-km grid with explicit convection was nested directly into their outermost 15-km or 9-km grids using KF CUP, avoiding treatment in the gray zone (5 km or 3 km), the primary rainfall peak at noon was best reproduced, but a secondary peak with a weak forcing was not detected. This peak was identified with local moisture divergence and a stable atmosphere. • Cumulus parameterization is beneficial with 9- and 5-km grids. • Kain-Fritsch can predict the diurnal rainfall peak better than the Grell-Freitas scheme for extreme rainfall in Shanghai. • Double nesting with a large ratio outperforms traditional triple nesting when forecasting the extreme rainfall in Shanghai. [ABSTRACT FROM AUTHOR]

The weak and sustained tropical cyclone (TC) Rumbia (2018) and its associated heavy precipitation after landfall are simulated by the Weather Research and Forecasting model using three microphysics parameterizations (MP) and two planetary boundary layer (PBL) schemes. Key factors regulating the heavy rainfall distribution of landed TC at three stages (landfall, inland slow-moving and recurving stage) and their sensitivities to multi-physics parameterizations are examined. Results show that heavy rainfall distribution is largely regulated by the intensity of TC itself at landfall, but more affected by environmental factors at the two inland stages, including the environmental vertical wind shear, the associated upper-level wind divergence and low-level moisture convergence. Different MP and PBL schemes mainly affect the simulation of TC thermodynamic structure and key environmental factors, leading to their different forecast skills at three stages. Specifically, the Yonsei University (YSU) PBL scheme systematically outperforms the Mellor-Yamada-Nakanishi-Niino (MYNN) scheme in simulating stronger TC and heavy rainfall intensity. With the advantageous YSU PBL scheme, the Ferrier MP scheme only shows a slight advantage in simulating the intensity of TC and heavy rainfall at landfall. However, the WRF single-moment 6-class (WSM6) and Thompson scheme produce more graupel or snow, and better simulate the key environmental factors, showing their respective advantages in simulating the heavy rainfall structure and location at the two inland stages. This implies that the use of nonlocal YSU PBL scheme and the MP schemes with sophisticated ice processes shows superiority in simulating the postlandfall heavy rainfall induced by weak TCs. • Postlandfall rainfall of weak TC is highly affected by environmental factors. • Ice species and radial distribution of diabatic heating affect TC structures and rainfall. • The MYNN PBL scheme underestimates TC intensity and heavy rainfall compared to YSU. [ABSTRACT FROM AUTHOR]