Applicability analysis of MEOFIS platform for the fine forecasting of temperature and wind-speed on northern Bohai Bay
Long Qian · Journal of Applied Oceanography · 2014
The MEOFIS(meteorological element objective forecast integrated system)platform is based on the statistical dynamical forecasting method. According to the model prediction and historical observational data,the refined forecasting elements are obtained. The T639 model products and data from buoy stations of Bohai Bay,coastal automatic weather stations of the year 2009 ~ 2011 are used to build the equations. The temperature and wind speed on the bay in the year 2012 ~ 2013 is predicted and used to analyze the applicability of the platform of the northern Bohai Bay forecasting. Objective verification of the forecast elements shows that within 1℃ error range,the sea surface temperature is forecasted better than that of onshore within 1℃ error range. MEOFIS grasps the trend of sea surface temperature better. The accuracy rates of daily maximum,daily minimum and 3h temperature are all over 68%. The forecasting of daily maximum,3h temperature in autumn and minimum temperature in winter is ideal with accuracy rate of 86. 8% 、75. 2% and 78. 9%,respectively. The temperature forecasting in spring is unsatisfactory overall. The significance test shows that compared with the results of T639,the temperature-correction capability of MEOFIS is significant in 4 seasons. Within 1℃ error range,the maximum wind speed accuracy rate in transitional seasons, spring and autumn,is over 75. 0%. the worst forecasting occurs in summer though the 3h forecasting accuracy rate is the biggest(78. 0%). The wind speed forecasting in winter is unsatisfactory overall. EEMD( Ensemble Empirical Mode Decomposition) is utilized to filter 3h temperature and wind speed forecasting error range. The results show that temperature and wind speed forecasting errors from MEOFIS have remarkable biweekly oscillation waves. Filtering can improve the prediction accuracy and has better result for temperature forecast. The accuracy rates for forecast seasons of small deviation and variance are improved better.