• What is the Seasonal Prediction?
  • The seasonal prediction (or seasonal forecast) predicts the departures from the average climate coming seasons. In other words, the seasonal forecast is to identify the future occurrence of large departures or anomalies from the average, namely climate. The seasonal forecast also bases on the anticipated weather for the lead time (lead-month) span.
    The skill of the seasonal prediction system depends on the ability of the AOGCMs (Atmosphere-Ocean Global Circulation Models). The skill of current seasonal prediction models is affected in mid-latitudes. The seasonal prediction system is operated by the physics of climate variability, numerical experimentation, and statistics based on physical understanding.
  • <1month lead time>
  • <3month lead time>
    ECMWF -> http://www.ecmwf.int/products/forecasts/seasonal/
  • Why do we need the Seasonal Prediction System?
  • Many non-modeling methods have been proposed over the years. Some of these methods have focused on analyzing historical weather patterns and statistical evaluation. It might seem plausible that we can find similar pattern between today's weather patterns and the past patterns. However, the weather never exactly repeats itself because chaos (the “butterfly effect”) will ensure that the weather will evolve into quite different patterns over a few days. Thus, in order to predict (or forecast) seasonal weather, we use seasonal prediction (or forecast) models and ensemble forecast techniques.
  • <mean annual precipitation departure for interior stations>
    http://sgst.wr.usgs.gov/alaska/research/climate/
  • The current situation of the Seasonal Prediction System in Korea
  • The Meteorological Administration (KMA) is testing the Global Seasonal Prediction System version 4 (GloSea4, 2012) and 5 (GloSea5, 2013) joint with the UK Met Office based on HadGEM3. This system is the fully-coupled, atmosphere-ocean-land-sea ice global model for the sub-seasonal to seasonal prediction.
    The KMA has conducted long period Hindcast experiments for 14-year (from 1996-2009) to verify predictability of GloSea4 and GloSea5. The forecast are based on the average of 12-member ensembles of Hindcasts produced by the GloSea.
  • <correlation skill map of surface air in May> <correlation between predicted and observed SST anomalies for Nino 3, Nino 3.4, Nino 4 regions>
  • Seasonal prediction of Tropical Storm
  • A statistical?dynamical tropical storm (TS) forecasting system, based on a statistical TS model, with explicit uncertainty estimates, and built from a suite of high-resolution global atmospheric dynamical model integrations spanning a broad range of climate states is described [Vecchi et al., 2011]. The choice of predictors, sea surface temperature (SST), is motivated by physical considerations, as well as the results of high-resolution hurricane modeling and statistical modeling of the observed record. The statistical TS model is applied to a suite of initialized dynamical global climate model forecasts of SST to predict TS frequency in main develop regions.
  • References
  • Smith, N.,J.E. Blomley and G. Meyers. A univariate statistical interpolation scheme for subsurface thermal analyses in the tropical oceans. Prog. Oceanog. 28, 219-256 .(1991).
    Vecchi, Gabriel A., Ming Zhao, Hui Wang, Gabriele Villarini, Anthony Rosati, Arun Kumar, Isaac M. Held, Richard Gudgel, 2011: Statistical?Dynamical Predictions of Seasonal North Atlantic Hurricane Activity. Mon. Wea. Rev., 139, 1070?1082.
    Updated at June 24. 2013
    Contact : Eunkyo Seo (ekseo90@unist.ac.kr)
  • Seasonal prediction of Arctic Oscillation
  • In this research, AO prediction performances from two operational seasonal ensemble forecasting systems, UK Met Office Global Seasonal forecasting system version 4 (GloSea4) [Arribas et al., 2011] and The National Centers for Environmental Prediction (NCEP) coupled forecast system model version 2 (CFSv2) [Saha et al., 2012], are evaluated. AO predictability is marginally sensitive to initialization and time frequency of model. We examine the relationship to AO predictability from various initializations of the forecast systems.
  • AO Index Time Series
  • References
  • Arribas A., et al. (2011), The GloSea4 ensemble prediction system for seasonal forecasting, Mon. Wea. Rev., 139, 1891?1910.
    Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Pan, H.-L., Behringer, D., Hou, Y.-T., Chuang, H.-Y., Iredell, M., Ek, M., Meng, J., Yang, R., van den Dool, H., Zhang, Q., Wang, W., and Chen, M. (2012), The NCEP Climate Forecast System Version 2, J. Clim., submitted.
    Updated at June 24. 2013
    Contact : Daehyun Kang (dhkang@unist.ac.kr)
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