Wu Jie, Ren Hongli, Zhao Chongbo, et al. Research and application of operational MJO monitoring and prediction products in Beijing Climate Center. J Appl Meteor Sci, 2016, 27(6): 641-653. DOI:  10.11898/1001-7313.20160601.
Citation: Wu Jie, Ren Hongli, Zhao Chongbo, et al. Research and application of operational MJO monitoring and prediction products in Beijing Climate Center. J Appl Meteor Sci, 2016, 27(6): 641-653. DOI:  10.11898/1001-7313.20160601.

Research and Application of Operational MJO Monitoring and Prediction Products in Beijing Climate Center

DOI: 10.11898/1001-7313.20160601
  • Received Date: 2016-05-19
  • Rev Recd Date: 2016-09-15
  • Publish Date: 2016-11-30
  • Both Madden-Julian oscillation (MJO) and the boreal summer intra-seasonal oscillation (BSISO) have great impacts on the global weather and climate events, which are the most important predictability source of sub-seasonal to seasonal (S2S) prediction. However, the monitoring of MJO/BSISO in China National Climate Center/Beijing Climate Center (NCC/BCC) entirely depends on external data, and the prediction skill of the introduced statistical forecast model is also much lower than dynamic mode, and the practical real-time operation ability has not been established. Therefore, based on CMA (China Meteorological Administration) global analysis data of T639 model, OLR (outgoing long-wave radiation) data of FY-3B satellite and the real-time forecast data of BCC atmospheric general circulation model system (BCC_AGCM2.2), applying the real-time multivariate MJO (RMM) index and BSISO index, BCC develops the MJO real-time monitoring and forecast technology, and establishes the trial ISV (intra-seasonal variability)/MJO prediction system (IMPRESS1.0).In comparison, monitoring results based on T639 wind analysis and FY-3B OLR data is generally consistent with the operational products from other centers, suggesting the capability of characterizing the oscillation and evolution of MJO/BSISO index accurately. Case study for the typical strong MJO event in March 2015 indicates that the amplitude peak of RMM index based on T639 and FY-3B OLR data is weaker than monitoring results based on NCEP and NOAA OLR data. Further analysis for three variables U850 (zonal wind at 850 hPa), U200 (zonal wind at 200 hPa) and OLR show that the convection monitored by FY-3B satellite is more consistent with NOAA's result, while the projection amplitude of the U850 based on T639 analysis against MJO mode is slightly weaker than NCEP/NCAR reanalysis data, which leads to weaker RMM index amplitude. The forecast skill verification shows the IMPRESS1.0 is able to provide correct evolution and intensity information of MJO at least 16 days in advance, and the skill of operational forecast in 2015 reach 18 days. The rolling prediction skill could be improved continually as the evolution of MJO event, and the predicted RMM index phase space track is closer to reality. Meanwhile, the verification of hindcasts by using correlation skill (COR), root mean square error (RMSE) and mean square skill score (MSSS) shows that the IMPRESS1.0 has useful prediction skill for about 12 days for MJO index and 8 days for BSISO1 and BSISO2 index, respectively. The case study for BSISO event in July 2015 also shows prediction skill, the reconstructed anomaly circulation and convection against BSISO index clearly demonstrate the dominant mode and northward propagation of BSISO. Therefore, the unified monitoring and forecast productions based on IMPRESS1.0 can provide important references for extended-range prediction, and offer certain help for operation and research.
  • Fig. 1  Time series of the amplitude of RMM index from Jan to Jul in 2015

    (the shaded is based on Australian Bureau of Meteorology monitoring)

    Fig. 2  The RMM index phase space diagram plots for Mar 2015,where the RMM indices calculated by all three variables (a), OLR only (b), U850 only (c) and U200 only (d)

    Fig. 3  The prediction correlation skill verification of RMM indices based on the real-time operational forecast of BCC_AGCM2.2 in 2015

    Fig. 4  The phase space diagram of RMM indices evolution for the monitoring and the forecast of three major MJO events in 2015 (the red solid line is based on T639 analysis and FY-3B OLR monitoring, the dash lines of different colors are forecasts based on BCC_AGCM2.2 for 5 start days and show the first 20 days of each forecast)

    (a) from 1 Mar to 15 Apr in 2015,(b) from 1 Jun to 15 Jul in 2015,(c) form 1 Dec 2015 to 13 Jan 2016

    Fig. 5  Time series of the amplitude of BSISO indices from May to Oct in 2015

    (the shaded is based on APCC monitoring)

    Fig. 6  The prediction skill of BSISO indices based on BCC_AGCM2.2 for 1991-2010

    Fig. 7  The RMM index phase space diagram (a) for the latest 30-day monitoring and 30 d forecast (taking 16 Mar 2015 as example, the purple point represent forecast time, the solid line is monitoring and the dash line is forecast), (b) for the latest 45-day monitoring (the solid line) and 10-day forecast verification (the dash line)

    (taking 26 Mar 2015 as example, the monitoring is based on T639 analysis and FY-3B OLR data and the forecast is based on BCC_AGCM2.2 data)

    Fig. 8  The time-longitude plot of anomaly U850 (a) and OLR (b) for the latest 120-day monitoring and 50-day forecast (taking 16 Mar 2015 as example), averaged from 15°S to 15°N

    (the shaded and contour represent original anomaly and the reconstruction from RMM indices)

    Fig. 9  Time series of the BSISO indices for monitoring and 50-day forecast

    (taking 16 Jul 2015 as example) (the solid line represent monitoring and the dash line represent forecast, the monitoring is based on T639 analysis and FY-3B OLR data and the forecast is based on BCC_AGCM2.2 data, the purple vertical line shows forecast start time, the horizontal coordinate is calendar month and the vertical coordinate is the value of BSISO indices)

    Fig. 10  The phase space diagram of latest 30-day monitoring and 30-day forecasts

    (taking 16 Jul 2015 as example) for 3-day running mean BSISO1 (a) and BSISO2 (b) index (the purple point represents forecast time, the solid line represents monitoring which is based on T639 analysis and FY-3B OLR data and the dash line represents forecast which is based on BCC_AGCM2.2 data)

    Fig. 11  The reconstruction patterns of anomalous wind at 850 hPa

    (the vector, unit:m·s-1), OLR (the contour, unit:W·m-2) and precipitation (the shaded) for the first pentad of monitoring and the following four pentads of forecasts

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    • Received : 2016-05-19
    • Accepted : 2016-09-15
    • Published : 2016-11-30

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