Plain Language Summary
At many weather forecasting centers where computer weather models are
run, different models are run for different applications. However, each
separate model multiplies the effort needed to maintain and upgrade each
model and makes it difficult to move improvements between models.
We present a new “unified” weather modeling system, SHiELD, able to be
configured for a variety of applications. This system uses a powerful
computer code, FV3, to compute the fluid motion of the atmosphere at any
scale, and also able to zoom in on areas of interest to better “see”
severe storms or intense hurricanes. We show how we started from a
quickly-assembled model for testing FV3 and then gradually improved the
representation of different atmospheric processes and expanded into new
uses for the system, including short-range severe thunderstorm
prediction, hurricane forecasting, and forecasts out to as long as six
weeks. We address some of the challenges that we faced and discuss
prospects for future model improvements. Since many of the parts of
SHiELD are used by models being developed by the National Weather
Service for use by weather forecasters, the advances described here can
be rapidly introduced into those models, eventually improving official
forecasts.
1 Unified Modeling at GFDL
As computing power increases global atmosphere models are now capable of
regular simulation at resolutions that had been the sole domain of
regional atmospheric models. The Integrated Forecast System (IFS; ECMWF
2019a,b) of the European Center for Medium-Range Weather Forecasting
(ECMWF) runs on a 9-km grid, and the Global Forecast System (GFS;
Sela2010) of the US National Centers for Environmental Prediction (NCEP)
runs on a 13-km grid. Some IPCC-class climate models now use grids with
spacings as fine as 25 km (Chen and Lin 2013; Vecchi et al. 2019;
Haarsma et al. 2017). Global atmosphere models lack the lateral boundary
errors that contaminate the solutions of regional models after a few
days of simulation. They thus allow us to extend mesoscale and
storm-scale predictions into the medium range and beyond (Harris and Lin
2013, 2014; Zhou et al. 2019; Harris et al. 2019). Global modeling also
brings many new challenges—one cannot “throw your garbage in the
neighbor’s yard” in global modeling, so to speak. Biases and radiative
imbalances must be minimized, as must errors anywhere in the
atmosphere that could potentially grow and contaminate the entire
domain.
A unified modeling system supports a variety of applications at a wide
range of spatial and temporal scales within a single framework. These
systems promise to simplify operational and research modeling suites and
better exchange improvements and bug fixes between applications. The
Unified Model of the United Kingdom Met Office (UKMO; Brown et al. 2012)
is the most notable unified system. Variable-resolution models (Harris
and Lin 2014, McGregor 2015) are particularly well-suited for unified
modeling as they can efficiently reach very high resolutions over part
of the earth, replacing the highest-resolution regional models (Hazelton
et al. 2018a,b, Zhou et al. 2019) and potentially extending their lead
times.
Here at GFDL a hierarchy of models has been developed for a variety of
time and space scales, from centennial-scale earth-system simulations
(Dunne et al. 2020) to very high-resolution weather prediction. The GFDL
suite is unified around a single dynamical core, the GFDL Finite-Volume
Cubed-Sphere Dynamical Core (FV3, or FV3; Putman and
Lin 2007), and a single framework, the Flexible Modeling System (FMS;
Balaji 2012), and other shared components. We describe one part of this
suite, the System for High Resolution Prediction on Earth-to-Local
Domains, or SHiELD. This model, previously called fvGFS, was developed
as a prototype of the Next-Generation Global Prediction System (NGGPS)
of the National Weather Service, and of the broader Unified Forecast
System (UFS). SHiELD continues GFDL’s high-resolution global modeling
program previously established using the High-Resolution Atmosphere
Model (HiRAM; Zhao et al. 2009; Chen and Lin 2013). SHiELD couples the
nonhydrostatic FV3 dynamical core (Lin et al. 2017) to a physics suite
originally from the GFS (Han et al. 2017, and references therein) and
the Noah Land Surface Model (Ek et al. 2002). SHiELD can be used for a
variety of timescales but has been designed with a particular focus on
short-to-medium range weather (18 hours to 10 days) and into the
subseasonal to seasonal (S2S; several weeks to several months) range.
Seasonal to decadal predictions and centennial-scale climate projections
coupled to a dynamical ocean are performed at GFDL using the Seamless
System for Prediction and Earth System Research (SPEAR, Delworth et al.
2020), the Coupled Model version 4 (CM4; Held et al. 2020), and the
Earth System Model version 4 (ESM4, Dunne et al. 2020).
Since FV3 is designed to adapt to a variety of purposes and to any scale
of atmospheric motion it is an ideal platform for a unified modeling
system. All of the SHiELD configurations described here, as well as
regional and doubly-periodic applications lying beyond the scope of this
paper, use the same code base, the same executable, the same
preprocessor, the same runscripts, and same post-processing tools,
demonstrating a true unification for modeling on weather-to-S2S
timescales. This approach also suggests how further unification with
GFDL’s climate models, which use a different atmospheric physics (Zhao
et al. 2018), the MOM6 Dynamical Ocean (Adcroft et al. 2019), and the
GFDL LM4 land model, may proceed. Advances in SHiELD can be seamlessly
moved into other UFS models, including the 2019 upgraded GFSv15, and
other FV3-based models. Most notably, advances in SHiELD can migrate
into UFS models slated for operational implementation at NCEP, including
the FV3-based GFSv15. NASA GEOS (Putman and Suarez 2017), NASA/Harvard
GEOS-Chem High-Performance (GHCP), CESM-FV3, and the Chinese Academy of
Sciences’ F-GOALS all also use FV3 as their dynamical core and can
benefit from the advances described below. This diversity of FV3-based
models shows the advantages of using common components to leverage
advances in the dynamical core but while still allowing centers to
tailor their models to their own needs, the freedom to innovate new
model designs, and to encourage the development of models as holistic
integrated systems, rather than clumsily joining independent components.
SHiELD is designed for exploratory research into model design and
development, with a focus on dynamics and physics-dynamics integration,
and for research on prediction and atmospheric processes on timescales
from a few hours to a few months. SHiELD is currently focused on
deterministic prediction although effective S2S prediction will require
the development of a simple ensemble (cf. Chen and Lin 2013). In this
manuscript we use forecast skill as a principal means of establishing
the scientific credibility of SHiELD as a research tool. Further
research will more closely evaluate specific structures and processes
within SHiELD, with some initial results described below (especially
section 3.2) and in prior research (cf. Hazelton 2018a).
The design, evolution, configurations, and simulation characteristics of
SHiELD are the subject of this paper. Section 2 describes the components
of SHiELD and how they work together as a complete modeling system.
Section 3 describes the four configurations of SHiELD for a variety of
applications, including medium-range weather, continental convection,
tropical meteorology and hurricanes, and S2S prediction. Section 4
summarizes the history of SHiELD development and discusses prospects for
future work.
2 SHiELD Components
2.1 Nonhydrostatic FV3 Dynamical Core
All SHiELD simulations use the nonhydrostatic solver within the FV3
Dynamical Core. This core has been described in detail in other papers
(Lin 2004, Putman and Lin 2007, Harris and Lin 2013, and references
therein) and will only be summarized here. FV3 solves the
fully-compressible Euler equations on the gnomonic cubed-sphere grid and
a Lagrangian vertical coordinate. Fast vertically-propagating sound and
gravity waves are solved by the semi‐implicit method; otherwise the
algorithm is fully explicit. FV3 advances sound and gravity wave
processes and advects thermodynamic variables on the shortest
“acoustic” timestep, while sub-cycled tracer advection and vertical
remapping (cf. Lin 2004) are performed on an intermediate “remapping”
timestep, in turn performed multiple times per physics timestep.
FV3’s discretization along Lagrangian surfaces uses the
piecewise-parabolic method, which previously used a monotonicity
constraint to ensure positivity and to dissipate energy cascading to
grid scale. In nonhydrostatic FV3 dynamical quantities (vorticity,
potential temperature, and air mass) are advected by a non-monotonic
scheme to reduce dissipation of resolved-scale modes. Previous work with
nonhydrostatic FV3 had continued to use a monotonic advection scheme to
avoid unphysical negative values. In this manuscript we present results
using a new positive-definite but non-monotonic scheme to advect
tracers, which greatly improves the representation of
marginally-resolved and discontinuous features without creating
computational noise at sharp gradients. This scheme is described in
detail in Appendix A and applications to the representation of tropical
cyclones in section 3d.
2.2 GFS/SHiELD Physics and Noah LSM
SHiELD inherits the
GFS
suite of physical parameterizations developed by the Environmental
Modeling Center (EMC) of NCEP (2020) . The initial 2016 version
of SHiELD, implemented for dynamical core testing during Phase II of
NGGPS, used physics largely identical to the then-operational GFSv13:
The Simplified Arakawa-Schubert (SAS) shallow and deep convection
schemes described in Han and Pan (2011); the hybrid Eddy-diffusivity
Mass-flux (EDMF) scheme (Han et al. 2016); the Rapid Radiative Transfer
Model (RRTM; Clough et al. 2005); the microphysics of Zhao and Carr
(1997) and cloud-fraction scheme of Xu and Randall (1996); the Navy’s
simplified ozone scheme (McCormack et al. 2006); the GFS orographic
gravity wave drag and mountain blocking schemes (Alpert 2002); and the
convective gravity wave drag scheme of Chun and Baik (1998).
We have since made many changes to the physics to be able to support new
applications, especially for convective scale prediction and marine
phenomena, or to take advantage of new capabilities within the FV3
dynamical core. We first introduced the six-category GFDL microphysics
and cloud fraction scheme (Zhou et al. 2019) with the fast microphysical
processes split out of the physics driver and taking place on the
shorter remapping timestep. Later, the GFDL microphysics was fully
in-lined within FV3 (appendix B). Several new PBL schemes have also been
used in SHiELD, including a modified hybrid EDMF PBL as per Zhang et al.
(2015), and the Yonsei University scheme (YSU; Hong et al. 2006, Hong
2010, Wilson and Fovell 2018). We have also adopted the Scale-Aware SAS
(Han et al. 2017) convection scheme in more recent versions of SHiELD.
The land surface model (LSM) is the Noah land-surface model (Ek et al.
2003), integrated within the physics and paired to the GFS surface-layer
scheme. In 2017 Noah was upgraded to use the high-resolution land
surface data (Wei et al. 2017), which greatly improves the appearance of
land-surface fields in convective-scale simulations.
2.3 Mixed-layer Ocean
Initially sea-surface temperatures (SSTs) were prescribed as the
climatological SST plus an SST anomaly from initial conditions which
gradually decays to zero, without influence from the atmosphere.
However, air-sea interactions are critical for several phenomena of
interest to us, especially tropical cyclones and the Madden-Julian
Oscillation (MJO) and may impact large-scale skill as well. To
incorporate atmosphere-ocean interaction, we have implemented a
modification of the mixed-layer ocean (MLO) of Pollard et al. 1973. This
simple ocean computes the mixed layer depth and heat within that mixed
layer as prognostic variables, with tendencies computed from the net
surface heat flux. The SST is nudged towards the NCEP Real-Time Global
Sea Surface Temperature (RTGSST; Thiébaux et al. 2003) climatology plus
a fixed initial anomaly which decays with a fixed timescale. The ocean
mixed layer depth is also nudged toward observed climatology (de Boyer
Montégut et al., 2004). While considerably simpler than the
three-dimensional dynamical oceans in CM4 (Held et al. 2020) and in the
GFDL Hurricane Model (Bender et al. 2019), the MLO still represents the
thermodynamic and dynamic ocean interactions of greatest significance on
the timescales for which SHiELD is used (Hazelton et al. 2018b), without
incurring the complexity of a three-dimensional dynamical ocean.
2.4 Interoperability with other UFS models
SHiELD was designed to work with other models that use FV3, FMS, the GFS
Physics Driver, and/or the Interoperable Physics Driver
(IPD). The
IPD is the interface between FV3 and the GFS Physics Driver, although it
can support other physics suites. Innovations within SHiELD can then be
seamlessly exchanged with other models using these same components. The
UFS Atmosphere led by NCEP (https://github.com/NOAA-EMC/fv3atm/)
analogous to SHiELD. For example, the transition of FV3 and the GFDL
Microphysics into the operational GFSv15 was accelerated by the IPD.
Conversely, schemes which have been introduced into the GFS Physics
Driver by the broader community can then be integrated into SHiELD,
including the numerous schemes implemented by Zhang et al. (2018).
3 SHiELD Configurations
SHiELD leverages the flexibility of FV3 to be able to make accurate and
efficient simulations at a variety of spatial and temporal scales. Much
of the development of SHiELD (and previously, of HiRAM) has been driven
by a desire to improve the simulation quality at the
convection-permitting resolutions covered by the range of SHiELD
configurations.
We present four different configurations of SHiELD. All configurations
are global domains using either a uniform grid or a locally refined grid
using nesting or stretching (Harris and Lin 2013; Harris et al. 2016;
Zhou et al. 2019). SHiELD can also run on FV3’s doubly-periodic domain
(Held and Zhou, 2006, Arnold and Putman, 2018) or on a regional domain
using any regular quadrilateral grid (Dong et al., 2020), at spatial
resolutions down to a few tens of meters (Jeevanjee 2017). These
applications lie beyond the scope of this paper.
The four configurations can be fit within two “tiers”; Tier-1
configurations are the most well-tested, having originally been
developed as prototypes to replace legacy NCEP models by FV3-based UFS
systems, and having been run in near-real time for several years. These
configurations demonstrate the capabilities of SHiELD, allow direct
comparison to existing operational models, and provide robust tests of
the forecast skill and reliability of SHiELD. Current real-time
configurations are run twice daily and displayed at
https://shield.gfdl.noaa.gov/.
The Tier-1 configurations are our flagship 13-km SHiELD, a prototype for
the now-operational GFSv15 and for future upgrades of the GFS;
(Tropical) T-SHiELD with a static, 3-km nest spanning the tropical North
Atlantic, a prototype of the Hurricane Analysis and Forecast System
(HAFS); and (Continental) C-SHiELD with a 3-km nest over the contiguous
United States (CONUS), a prototype of the Regional Forecast System
(RFS). Each of the Tier-1 configurations are usually refreshed every
year with a new version, indicated by the year of the upgrade.
Our Tier-2 configurations address new challenges for numerical
prediction and are still under development. Our 25-km (Subseasonal)
S-SHiELD addresses the challenging domain of S2S prediction. Another
configuration not discussed in this paper is the SHiELD global
cloud-resolving model (GCRM) and addresses the frontier computational
and data challenges of such simulations. This configuration was
submitted to the DYnamics of the Atmospheric general circulation Modeled
On Non-hydrostatic Domains (DYAMOND) intercomparison (Stevens et al.
2019, Satoh et al. 2019). Both configurations inspire the development of
new functionality and capabilities within SHiELD and readily expose
instabilities, climate drift, conservation issues, and other
shortcomings. The advances driven by work on these frontier challenges
help improve the Tier-1 configurations, demonstrating the value of a
seamless prediction system. The domains for each of the four
configurations plus the GCRM configuration are depicted schematically in
Figure 1.