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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.

Real-time production of the second generation CMORPH (CMORPH2) has been migrated to and executed at a NOAA / NWS required operational environment, the NWS/NCEP Central Operation (NCO) and Climate Prediction Center (CPC) Compute Farm (CF) effective December 2022. CMORPH2 real-time production was routinely implemented on a research and development environment at NOAA/CPC since April 2017. Successful migration of the production system to the 7/24 operational environment ensures the production of the high-resolution, high-quality global precipitation analysis at a much higher stability and reduced production latency of one hour, satisfying a requirement for an observational analysis to be infused into operational forecast models and routine field operations.Inputs to the CMORPH2 real-time production include rainfall and snowfall rate retrievals from passive microwave (PMW) measurements aboard more than 10 low earth orbit (LEO) satellites, precipitation estimates derived from infrared (IR) observations of geostationary (GEO) and LEO platforms, and model precipitation forecast from the NCEP operational global forecast system (GFS). These inputs are first inter-calibrated to ensure quantitative consistencies. The inter-calibrated PMW retrievals and IR-based precipitation estimates are then propagated from their respective observation times to the target analysis time along the cloud motion vectors from both the forward and backward directions. The propagated PMW and IR based precipitation estimates are finally integrated into a single field of global precipitation through the Kalman Filter framework. In addition to the total precipitation, fraction of solid precipitation is computed from the surface air temperature and other surface meteorological variables using the algorithm of Sims and Liu (2015).The CMORPH2 satellite precipitation analysis is constructed on a 0.05o latitude/longitude over the entire globe (90oS-90oN) and in a 30-minute temporal resolution. The real-time production is first generated at a very short latency of one hour and then refreshed with any newly available inputs once every 30 minutes up to 12 hours of latency for improved accuracy when inouts from all sources are available in most cases. The CMORPH2 real-time production is utilized by several important users including the NWS Aviation center (AWC), Weather Prediction Center (WPC), and NWS Alaska Office, and pushed to the AWIPS for field applications. Work is under way to examine the CMORPH2 real-time production as a function of region, season, precipitation type, and production latency, and to further improve the CMORPH2 through infusing the PMW precipitation retrievals from the NOAA Direct Broadcast (DB) systems and refining the GEO IR based precipitation estimates. Results will be reported at the 2023 EGU Meetings.

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.

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.

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.

A series of numerical sensitivity experiments is performed to quantify the impact of sea-surface temperature (SST) distribution on offshore surface fluxes and simulated sea-breeze dynamics. The SST simulations of two mid-latitude sea-breeze events over coastal New England are performed using a spatially-uniform SST, as well as spatially-varying SST datasets of 32- and 1-km horizontal resolutions. Offshore surface heat and buoyancy fluxes vary in response to the SST distribution. Local sea-breeze circulations are relatively insensitive, with minimal differences in vertical structure and propagation speed among the experiments. The largest thermal perturbations are confined to the lowest 10% of the sea-breeze column due to the relatively high stability of the mid-Atlantic marine atmospheric boundary layer (ABL) suppressing vertical mixing, resulting in the depth of the marine layer remaining unchanged. Minimal impacts on the column-averaged virtual potential temperature and sea-breeze depth translates to small changes in sea-breeze propagation speed. This indicates that the use of datasets with a fine-scale SST may not produce more accurate sea-breeze simulations in highly stable marine ABL regimes, though may prove more beneficial in less stable sub-tropical environments.

Mesoscale simulations of sea breezes are sensitive to the analysis product used to initialize the simulations, primarily due to the representation of the coastline and the coastal sea surface temperatures (SSTs) in the analyses. The use of spatially coarse initial conditions, relative to the horizontal resolution of the mesoscale model grid, can introduce errors in the representation of coastal SSTs, in part due to the incorrect designation of the land surface. As a result, portions of the coastal ocean are initialized with land surface temperature values and vice versa. The diurnal variation of the sea surface is typically smaller than over land on meso- and synoptic-scale time scales. Therefore, it is common practice to retain a temporally static SST in numerical simulations, causing initial SST errors to persist through the duration of the simulation. These SST errors influence horizontal coastal temperature and humidity gradients and thereby the development of the sea-breeze circulations. The authors developed a technique to modify the initial surface conditions created from a reanalysis product [North American Regional Reanalysis (NARR)] for simulations of two sea-breeze events over the New England coast to more accurately represent the finescale structure of the coastline and the spatial representation of the coastal land surface and SST. Using this technique, the coastal SST (2-m temperature) RMSE is reduced from as much as 25°–1°C (7°–1°C), contributing to a more accurate propagation of the sea-breeze front. Techniques described in this work may be important for mesoscale simulations and forecasts of other coastal phenomena.

The future rate of climate change in mountains has many potential human impacts, including those related to water resources, ecosystem services, and recreation. Analysis of the ensemble mean response of CMIP5 global climate models (GCMs) shows amplified warming in high elevation regions during the cold season in boreal midlatitudes. We examine how the twenty-first century elevation-dependent response in the daily minimum surface air temperature [d(ΔTmin)/dz] varies among 27 different GCMs during winter for the RCP 8.5 emissions scenario. The focus is on regions within the northern hemisphere mid-latitude band between 27.5°N and 40°N, which includes both the Rocky Mountains and the Tibetan Plateau/Himalayas. We find significant variability in d(ΔTmin)/dz among the individual models ranging from 0.16 °C/km (10th percentile) to 0.97 °C/km (90th percentile), although nearly all of the GCMs (24 out of 27) show a significant positive value for d(ΔTmin)/dz. To identify some of the important drivers associated with the variability in d(ΔTmin)/dz during winter, we evaluate the co-variance between d(ΔTmin)/dz and the differential response of elevation-based anomalies in different climate variables as well as the GCMs’ spatial resolution, their global climate sensitivity, and their elevation-dependent free air temperature response. We find that d(ΔTmin)/dz has the strongest correlation with elevation-dependent increases in surface water vapor, followed by elevation-dependent decreases in surface albedo, and a weak positive correlation with the GCMs’ free air temperature response.

We use output from global climate models available from the Coupled Model Intercomparison Project Phase 5 (CMIP5) for three different greenhouse gas emission scenarios to investigate whether the projected warming in mountains by the end of the 21st century is significantly different from that in low elevation regions. To remove the effects of latitudinal variation in warming rates, we focus on seasonal changes in the mid-latitude band of the northern hemisphere between 27.5 N and 40 N, where the two major mountain systems are the Tibetan Plateau/Himalayas in Asia and the Rocky Mountains in the United States. Results from the multi-model ensemble indicate that warming rates in mountains will be enhanced relative to non-mountain regions at the same latitude, particularly during the cold season. The strongest correlations of enhanced warming with elevation are obtained for the daily minimum temperature during winter, with the largest increases found for the Tibetan Plateau/Himalayas. The model projections indicate that this occurs, in part, because of proportionally greater increases in downward longwave radiation at higher elevations in response to increases in water vapor. The mechanisms for enhanced increases in winter and spring maximum temperatures in the Rockies appear to be influenced more by increases in surface absorption of solar radiation owing to a reduced snow cover. Furthermore, the amplification of warming with elevation is greater for a higher greenhouse gas emission scenario.