Journal of the Meteorological Society of Japan. Ser. II
Online ISSN : 2186-9057
Print ISSN : 0026-1165
ISSN-L : 0026-1165
Evaluation of the Near Real-Time Forecasts Using a Global Nonhydrostatic Model during the CINDY2011/DYNAMO
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Supplementary material

2017 Volume 95 Issue 6 Pages 345-368


 By comparison with satellite and field observations, the comprehensive performance and potential utility of near real-time forecasts using Nonhydrostatic Icosahedral Atmospheric Model (NICAM) are demonstrated by exploiting the Cooperative Indian Ocean Experiment on Intraseasonal Variability in the Year 2011 (CINDY2011) / Dynamics of the Madden–Julian Oscillation (DYNAMO) campaign. A week-long forecast was run each day using a regionally stretched version of NICAM, with the finest mesh size of 14 km over the tropical Indian Ocean (IO), throughout the intensive observation period (IOP).

 The simulated precipitation time series fairly represented the evolution and propagation of the observed Madden-Julian Oscillation (MJO) events, although a 30 % overprediction of precipitation over the IO domain (60–90°E, 10°S–10°N) was found on average. Frequencies of strong (> 40 mm day−1) precipitation were overpredicted, while those of weak precipitation were underpredicted against satellite observations. Compared with the field observations at Gan Island, the biases in precipitation frequency were less obvious, whereas the growth of lower to middle tropospheric dry (∼ 1 g kg−1) and warm (∼ 1 K) biases were found. Despite these mean biases, temporal variations of the moisture and zonal wind profiles including the MJO events were reasonably simulated.

 Using the forecast data the moisture and energy budgets during the IOP were investigated. The diagnosis using the 7-day-mean fields captured the observed features of the MJO events. Meanwhile, significant upward transport of moisture by the grid-resolved high-frequency variability was detected throughout the IOP. The relationship between these high-frequency effects and the simulated MJO or mean biases is also discussed.

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© 2017 by Meteorological Society of Japan
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