SOLA, 2018, Vol. 14, 203−209, doi:10.2151/sola.2018-036
Analysis of Uncertainties in Forecasts of Typhoon Soudelor (2015) from Ensemble Prediction Models
Deqiang Liu1, 2, 3, Xubin Zhang4, Yerong Feng4, Ning Pan1, and Chuanrong Huang5 1 Fujian Meteorological Observatory, Fuzhou, China 2 Laboratory of Straits Meteorology, Xiamen Meteorological Bureau, Xiamen, China 3 Wuyishan National Park Meteorological Observatory, Wuyishan, China 4 Institute of Tropical and Marine Meteorology, Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction, CMA, Guangzhou, China 5 Fujian Meteorological Service, Fuzhou, China
Text S1. Typhoon, Datasets, and Methodology
S1.1. Overview of Typhoon Soudelor
Typhoon Soudelor (2015) formed as a tropical depression over the northwest
Pacific Ocean 1600 km east of Guam on 30 July 2015. It intensified gradually into a super typhoon and made landfall in Taiwan at around 2040 UTC on 07 August and then Fujian at around 1410 UTC on 08 August 2015. Although several main operational prediction centers provided successful TCT forecasts for Soudelor, the typhoon still had a large impact on those living in the regions surrounding the Taiwan
Strait. Long-duration, large-area, and high-intensity TC-related heavy rainfall resulted in tremendous damage to agriculture, industry, and human life.
S1.2. Datasets
Uncertainties for key parameters characterizing Soudelor (2015) were compared among nine ensemble forecast datasets; seven are from global models, with data made available by the TIGGE project http://apps.ecmwf.int/datasets/data/tigge/levtype=sfc/type=cf/), and two are from
China Meteorological Administration (CMA) regional models. Details of the nine
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Liu et al., Uncertainties of Soudelor (2015) through Ensemble Models
EPSs are given in Table S1. The best track data for Soudelor have been archived by the Shanghai Typhoon Institute (Ying et al., 2014), CMA. The automatic weather station observations used for verifying 24-h precipitation estimates were collected by the Fujian Meteorological Observatory.
S1.3. Methodology for calculating spread
Ensemble spread is commonly used as a measure of model uncertainty and is formulated as follows (Elsberry and Carr III, 2000; Goerss, 2000; Yamaguchi et al.,
2009):
1 N SPREAD() M M 2 (1) N i1 i
where M i is the model forecast value for the ith ensemble member, M is the ensemble mean, and N is the number of ensemble members.
Text S2. Relationship between TCT forecast error and rainfall forecasting skill
We investigated the relationship of group-averaged TCT forecast error with ETS and Brier Score (BS) for 24-h rainfall (> 50 mm) during Soudelor’s second landfall.
The average TCT forecast error for regional models was 39.6 km, smaller than that for global models (Table S2). Likewise, ETS for regional models was 0.30, compared with 0.22 for global models. The probability forecasting skill was also analyzed. BS for regional models (0.21) was smaller than that for global models (0.24), indicating higher forecasting skill in regional models. In addition, the multi-physics models were found to have a smaller ETS (0.20) than the single-physics models (0.26), which is consistent with TCT error in the two types of models. Differences in results between SOLA, 2018, Vol. 14, 203−209, doi:10.2151/sola.2018-036 the groups may indirectly demonstrate the crucial role of TCT error in TCHR forecasts.
Text S3. Differences in the cloud microphysics and cumulus schemes for
GZ-GRAPES and NMC-GRAPES
GZ-GRAPES employs a random combination of the microphysics of WSM6
(Hong and Lim, 2006) with either the Kain-Fritsch (KF, Kain and Fritsch, 1990) or
Simplied Arakawa-Schubert (SAS, Pan and Wu, 1995) cumulus schemes, whereas
NMC-GRAPES utilizs WSM6 as the microphysics scheme in combination with the
KF, SAS, Kain-Fritsch-eta (Kain, 2004) and Betts-Miller-Janjic (BMJ, Janjic, 1994) cumulus schemes.
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Table S1. Descriptions of the ensemble prediction systems (EPSs). BV (Bred Vector), SV (Singular Vector), EnKF (Ensemble Kalman Filter), ETKF (Ensemble Transform Kalman Filter), LETKF (Local Ensemble Transform Kalman Filter), ETR (Ensemble Transform with Rescaling), and DEIP (Downscaling from ECMWF Initial Perturbations) are initial perturbation strategies for models. SPPT (Stochastic Perturbation of Physics Tendency), SKEB (Stochastic Kinetic Energy Backscatter), RP (Random Parameters), and SPRME (Stochastic Perturbation to account for Random Model Errors) are methods for generating model uncertainty perturbations. Horizontal Initial Model Physical Model Datasets name EPS provider resolution perturbation uncertainty parameterizations for domain (lat ╳ lon) Strategy perturbations members China Meteorological CMA Administration, 0.28° ╳ 0.28° BV Global SPPT Same Beijing, China Meteorological Service ECCC of Canada, Montreal, 0.9° ╳ 0.9° EnKF Global SPPT & SKEB Different Canada European Centre for N200 (Reduced Medium-Range Gaussian) , ECMWF SV Global SPPT Same Weather Forecasts, N128 after day Reading, Europe 10 Institute of Tropical and Marine Meteorology, Guangdong Provincial GZ-GRAPES Key Laboratory of 0.09° ╳ 0.09° SV&LETKF Regional SPPT Same Regional Numerical Weather Prediction, CMA, Guangzhou, China
Japan Meteorological JMA 0.38° ╳ 0.38° ETKF Global RP & SKEB Same Agency, Tokyo, Japan
Korea Meteorological KMA Administration, Seoul, 0.56° ╳ 0.38° ETR Global SPRME Same Korea National Centres for Environmental NCEP 1.0° ╳ 1.0° ETKF Global SKEB& RP Same Prediction, Washington, DC, USA National NMC-GRAPES Meteorological Center, 0.15° ╳ 0.15° DEIP Regional STTP Different CMA, Beijing, China
MetOffice, Exeter, UKMO 0.28° ╳ 0.19° ETKF Global RP Different United Kingdom
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Table S2. Group-average TCT and TCI spread during Soudelor’s passage over the CMR, the group-average ETS and BS for 24-h rainfall (> 50 mm), group-average TCT forecast error during Soudelor’s second landfall, and group-average spread of perturbed steering flows (PSF) at 1200 UTC on 09 August 2015, the period during Soudelor’s recurvature after the landfall in mainland China. Groups
Single-phyiscs ensemble multi-physics ensemble Global models Regional models models models
Average TCT spread 34.8 41.7 33.8 41.4 from 6 to 12 h (km)
Average TCI spread 3.4 7.4 4.0 4.7 from 6 to 12 h (hPa)
Average ETS for 24-h
rainfall (> 50 mm) from 0.22 0.30 0.26 0.20
24 to 48 h
Average BS for 24-h
rainfall (> 50 mm) from 0.24 0.21 0.23 0.23
24 to 48 h
Average TCT forecast 44.2 39.6 38.4 52.7 error at 48 h (km)
Average spread of PSF 0.393 0.420 0.390 0.417
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Fig. S1. (a) Evolution of the spread of TC track with lead time for the nine EPSs, averaged by the ensemble forecasts initialized at 1200 UTC 06, 0000 UTC 07, and 1200 UTC 07 August 2015. 0 h indicates 1200 UTC 07 August 2015. MEAN SPREAD is the average of the nine models. (b) Same as in (a) but for TC intensity spread (hPa).
SOLA, 2018, Vol. 14, 203−209, doi:10.2151/sola.2018-036
Fig. S2. As in Fig. 2, but for the spread of perturbed steering flows.
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Liu et al., Uncertainties of Soudelor (2015) through Ensemble Models
Fig. S3. Temporal evolution of the spread of 850-200-hPa vertical wind shear at the location of TC center for 9 EPSs, averaged by the ensemble forecasts initialized at 1200 UTC 06, 0000 UTC 07, and 1200 UTC 07 August 2015. SOLA, 2018, Vol. 14, 203−209, doi:10.2151/sola.2018-036
Fig. S4. Horizontal vector wind fields at 850 hPa for nine EPSs, validated at 1200 UTC 09 August 2015, averaged by the ensemble forecasts initialized at 1200 UTC 06, 0000 UTC 07, and 1200 UTC 07 August 2015.
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Fig. S5. As in Fig. S4, but for 850-hPa divergence. Dashed lines indicate convergence areas. SOLA, 2018, Vol. 14, 203−209, doi:10.2151/sola.2018-036
Fig. S6. (a) Temporal variation in mean ensemble forecast error (MEFE) and mean ensemble spread (MES) for TCT forecasts (km) from regional models (REG), global models (GLB), models whose ensemble members have different parameterizations (DP), and models whose ensemble members share the same parameterization (SP), over the period 1200 UTC 07 August (0 h) to 1200 UTC 9 August 2015. (b) Same as in (a) but for TCI forecasts (hPa).
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