The Tropical cyclone (TC) forecast skill of the eight global medium-range forecast models which are participating in the DIMOSIC (DIfferent Models, Same Initial Conditions) project is investigated in this study. Each model was used to generate 10-day forecasts from the same initial conditions provided by the European Centre for Medium-Range Weather Forecasts. There are a total of 123 initial dates spanning in one year from June 2018 to June 2019 with a 3-day interval. The TC track and intensity forecasts are evaluated against the best track dataset. TC-related precipitation and tropical cyclogenesis forecasts are also compared to explore the differences and similarities of TC forecasts across the models. This comparison of TC forecasts allows model developers in different centers to benchmark their model against other models, with the impact of the initial condition quality removed. The verifications reveal that most models show slow-moving and right-of-track biases in their TC track forecasts. Also, a common dry bias in TC-related precipitation indicates a general deficiency in TC intensity and convection in the models which should be related to insufficient model resolution. These findings provide important references for future model developments.

We present the System for High-resolution prediction on Earth-to-Local Domains (SHiELD), an atmosphere model coupling the nonhydrostatic FV3 Dynamical Core to a physics suite originally taken from the Global Forecast System. SHiELD is designed to demonstrate new capabilities within its components, explore new model applications, and to answer scientific questions through these new functionalities. A variety of configurations are presented, including short-to-medium-range and subseasonal-to-seasonal (S2S) prediction, global-to-regional convective-scale hurricane and contiguous US precipitation forecasts, and global cloud-resolving modeling. Advances within SHiELD can be seamlessly transitioned into other Unified Forecast System (UFS) or FV3-based models, including operational implementations of the UFS. Continued development of SHiELD has shown improvement upon existing models. The flagship 13-km SHiELD demonstrates steadily improved large-scale prediction skill and precipitation prediction skill. SHiELD and the coarser-resolution S-SHiELD demonstrate a superior diurnal cycle compared to existing climate models; the latter also demonstrates 28 days of useful prediction skill for the Madden-Julian Oscillation. The global-to-regional nested configurations T-SHiELD (tropical Atlantic) and C-SHiELD (contiguous United States) shows significant improvement in hurricane structure from a new tracer advection scheme and promise for medium-range prediction of convective storms, respectively.

This paper documents the third version of the GFDL cloud microphysics scheme (GFDL MP v3) used in the System for High-resolution prediction on Earth-to-Local Domains (SHiELD) model. Compared to the GFDL MP v2, the GFDL MP v3 is entirely reorganized, optimized, and modularized by functions. In addition, the particle size distribution (PSD) of all cloud categories is redefined to mimic the latest observations, and the cloud condensation nuclei (CCNs) are calculated from the MERRA2 aerosol data. The GFDL MP has been redesigned so all processes use the redefined PSD to ensure overall consistency and easily permit introductions of new PSDs and microphysical processes. Analyses gathered from simulations by SHiELD with selected configurations are examined. Compared to the GFDL MP v2, the GFDL MP v3 significantly improves the predictions of geopotential height, air temperature, and specific humidity in the Troposphere, as well as the high, middle and total cloud fractions and the liquid water path. With the more realistic PSD implemented in GFDL MP v3, the predictions of geopotential height in the Troposphere, low and total cloud fractions are further improved. Furthermore, using climatological aerosol data to calculate CCNs leads to even better predictions of geopotential height, air temperature, and specific humidity in the Troposphere, high and middle cloud fractions, as well as the liquid and ice water paths. However, the upgrade of the GFDL MP shows little impact on the precipitation prediction. Degradation due to the scheme upgrade is also addressed and discussed to guide the future GFDL MP development.

In this study, we used the potential vorticity (PV) diagnosis technique to investigate the key factors that affect the track forecasts of Hurricane Maria (2017) in the NCEP GFS v14, ECMWF IFS and GFDL SHiELD models. In Chen et al. (2019), it showed that a slow bias of Maria’s translation speed in the IFS 5-day forecasts was significantly improved by GFDL SHiELD with IFS initial conditions (SHiELD_IFS). Our results found that the slow moving bias in the IFS is mainly due to a strong, westerly steering flow contribution from a cutoff low from the northeast quadrant and another low system from the southwest quadrant of Maria. On the other hand, the SHiELD_IFS improves on the IFS by better simulating the strength of the Bermuda High, and low systems in the southwest, northwest, and northeast quadrants allowing for better track alignment with observations. We also found that the northward track bias of Maria in the legacy GFS and SHiELD with the GFS initial conditions (SHiELD_GFS) was associated with a weaker Continental High which contributed less northerly steering flow compared to that in the IFS and SHiELD_IFS. Furthermore, the Bermuda High was relatively weak in the SHiELD_GFS, while the two low systems in the northwest and northeast quadrants contributed steering flow opposing Maria’s moving direction, causing a slowdown of translation speed of Maria in the SHiELD_GFS. By performing this piecewise potential vorticity diagnosis on all of the storms in the 2017 North Atlantic Hurricane Season, we could possibly identify the key elements that generate the biases in TC track forecasts in these models.