S2S Model Research Articles

Factors Limiting the Forecast Skill of the Boreal Summer Intraseasonal Oscillation in a Subseasonal-to-Seasonal Model

In this study, we evaluate the forecast skill of the subseasonal-to-seasonal (S2S) prediction model of the Beijing Climate Center (BCC) for the boreal summer intraseasonal oscillation (BSISO). We also discuss the key factors that inhibit the BSISO forecast skill in this model. Based on the bivariate anomaly correlation coefficient (ACC) of the BSISO index, defined by the first two EOF modes of outgoing longwave radiation and 850-hPa zonal wind anomalies over the Asian monsoon region, we found that the hindcast skill degraded as the lead time increased. The ACC dropped to below 0.5 for lead times of 11 days and longer when the predicted BSISO showed weakened strength and insignificant northward propagation. To identify what causes the weakened forecast skill of BSISO at the forecast lead time of 11 days, we diagnosed the main mechanisms responsible for the BSISO northward propagation. The same analysis was also carried out using the observations and the outputs of the four-day forecast lead that successfully predicted the observed northward-propagating BSISO. We found that the lack of northward propagation at the 11-day forecast lead was due to insufficient increases in low-level cyclonic vorticity, moistening and warm temperature anomalies to the north of the convection, which were induced by the interaction between background mean flows and BSISO-related anomalous fields. The BCC S2S model can predict the background monsoon circulations, such as the low-level southerly and the northerly and easterly vertical shears, but has limited capability in forecasting the distributions of circulation and moisture anomalies.

Prediction and predictability of tropical intraseasonal convection: seasonal dependence and the Maritime Continent prediction barrier

Prediction and predictability of tropical intraseasonal convection in the WMO subseasonal to seasonal (S2S) forecast database is assessed using the real-time OLR based MJO (ROMI) index. ROMI prediction skill in the S2S models, as measured by the maximum lead time at which the bivariate correlation coefficient between forecasts and observations exceeds 0.6, ranges from ~ 15 to ~ 36 days in boreal winter, which is 5–10 days higher than the MJO circulation prediction skill based on the MJO RMM index. ROMI prediction skill is systematically lower by 5–10 days in summer than in winter. Predictability measures show similar seasonal contrast in the two seasons. These results indicate that intraseasonal convection is inherently less predictable in summer than in winter. Further evaluation of correlation skill assuming either perfect amplitude or perfect phase forecasts indicates that phase bias is the main contributor to skill degradation at longer forecast lead times. Nearly all the S2S models have lesser skill for target dates in which the MJO convection is centered over the Maritime Continent (MC) in boreal winter, and phase bias contributes to this MC prediction barrier. This issue is less prevalent in boreal summer. Many S2S models significantly underestimate ROMI amplitudes at longer forecast leads. Probabilistic evaluation of the S2S model skills in forecasting ROMI amplitude is further assessed using the ranked probability skill score (RPSS). RPSS varies significantly across models, from no skill to more than 30 days, which is partly due to model configuration and partly due to amplitude bias. Accounting for the systematic underestimates of the amplitude improves RPSS.

Numerical Simulation of Temperature Fields in a Three-Dimensional SiC Crystal Growth Furnace with Axisymmetric and Spiral Coils

Three-dimensional numerical simulation platform for silicon carbide crystal growth furnace was established using C programing language, where a physical model of the furnace was built based on cylindrical coordinates; governing equations for electromagnetic and temperature fields were discretized by finite volume method; radiation characteristics were studied with the help of S2S model (surface to surface radiation model); and the least distance method was proposed to check radiation faces visibility efficiently. LU decomposition algorithm based on graphic processing unit (GPU) technology was developed to accelerate the solving process of surface to surface radiation. Then the radiation heat transfer in silicon carbide crystal (SiC) growth chamber and temperature field of silicon carbide growth furnace were studied quantificationally at I = 1250 A and F = 16 kHz. The effects of coil structures (axisymmetric and spiral) on temperature field and its gradient distributions were investigated by standard deviation method. The simulation results demonstrate that spiral electromagnetic coil generates non-axisymmetric temperature field easily; the radiation heat flux is 102~103 times more than conduction heat flux, radiation heat transfer is helpful to increase temperature evenness; the spiral temperature field on the SiC crystal cross-section reduces the poor homogeneity of temperature gradient, which will cause crystal to generate large defects.