These
multiple-day severe weather forecasts are in the spirit of the
convective outlooks issued by the Storm Prediction Center
(www.spc.noaa.gov/products/outlook;
Edwards 2015) based on predictions of synoptic-scale environments
favorable for severe weather. The advantage of using a dynamical
convective-scale prediction model on medium-range timescales is that
explicit prediction of storms, instead of just environments, potentially
can give forecasts of convective modes and specific hazards.
Figure 13. FSS for surrogate severe predictions at different
lead times for 00Z initializations of C-SHiELD 2019.
3.4 S-SHiELD Subseasonal-to-Seasonal Prediction
We briefly describe the characteristics of the Tier-2 S-SHiELD
configuration, using a 25-km grid designed for climate integrations and
for subseasonal and seasonal predictions. S-SHiELD is configured
similarly to the 13-km SHiELD, although SHiELD’s two-day relaxation
timescale of SSTs in the MLO towards the “frozen anomalies” is
extended to 15 days in S-SHiELD. Unlike the vast majority of climate
models, S-SHiELD is nonhydrostatic and uses a more sophisticated
microphysics which is updated much more frequently. While these features
do make S-SHiELD more expensive than analogous 25-km hydrostatic climate
models (cf. Murakami et al 2016; Roberts et al. 2018), previous
experience with HiRAM (Chen and Lin 2012, 2013; Gao et al 2018) has
shown that nonhydrostatic dynamics and better microphysical-dynamical
coupling yields a better representation of mesoscale convective systems
and in particular of tropical cyclones, a major emphasis of our group’s
research.
The MJO plays a major role in subseasonal variability but has been a
challenge for many models to predict or even simulate reasonably (Kim et
al. 2018). To explore the MJO prediction skill of S-SHiELD we performed
92 40-day predictions, one initialized at 00Z every two days from 1
October 2011 to 31 March 2012, covering the active Dynamics of the MJO
(DYNAMO; Yoneyama et al. 2013) observation period. The Real-time
Multivariate MJO Index (RMM; Wheeler and Hendon 2004) is calculated for
the hindcasts following the methodology of Xiang et al. (2015) and
Vitart et al (2017). For each hindcast we compute daily-mean anomalies
of outgoing longwave radiation (OLR) and zonal wind at 200mb (U200) and
850mb (U850), averaged between 15S-15N. These forecast anomalies are not
bias-corrected since we use observed climatology as reference instead of
model climatology. We then subtract the averaged anomalies of the
previous 120 days from the total anomalies to remove the signals of
interannual and longer time-scale variability; observed anomalies are
appended to the anomalies in the hindcast. The normalized U200, U850 and
OLR anomalies are then projected onto the pre-computed Empirical
Orthogonal Functions (EOFs) from Wheeler and Hendon (2004) to obtain the
two RMM indices.
We find that S-SHiELD with the MLO (Figure 14) has good skill
(correlation > 0.7) out to 19 days and useful skill
(correlation > 0.5) out to 28 days. The RMSE likewise shows
similar skill (RMSE < \(\sqrt{2}\) out to 27 days). This skill
may not be representative of other time periods given that skill is
known to be higher during strong events (cf. Xiang et al 2015) and the
period of evaluation is relatively short. However, this result does give
us confidence that S-SHiELD simulates the MJO well enough for useful S2S
prediction. We plan to expand our evaluation of the MJO in forthcoming
work.