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Indramayu experimental downscaled forecasts Nov–Jan 2006/7, made Oct 2006. With special thanks to Prof. V. Moron (U. Aix-Marseilles, France) for the KNN downscaling results. IRI Net Assessment Precipitation Forcecast for Nov-Dec-Feb (NDJ) issued Oct 2006. Paddy damages:
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Indramayu experimental downscaled forecastsNov–Jan 2006/7, made Oct 2006 • With special thanks to Prof. V. Moron (U. Aix-Marseilles, France) for the KNN downscaling results.
IRI Net Assessment Precipitation Forcecast for Nov-Dec-Feb (NDJ) issued Oct 2006
Paddy damages: 60% of national damages from West Java 80% of West Java damages from Indramayu & Cirebone districts Indramayu BMG station data NDJ 1981/2 - 2001/2
RegCM3 Forecast: Based on Persisted SST (from Oct 2006) Ensemble Mean - Climatology J. Qian
RegCM3 Forecast: Based on Persisted SST (from Oct 2006) Ensemble Mean - Climatology J. Qian
Seasonality of Predictability: RegCM3 skill over Java is high in dry and transitioning seasons and low in the peak rainy season J. Qian
RegCM Summary • Thirty year (1971-2000) simulation with 25km-grid RegCM3 has been carried out over Java. The predictability skill is high in the dry and transitioning seasons but low in the peak rainy season. The correlation skill over Indramayu is only slightly positive in NDJ. • Preliminary dynamical downscaling forecast by RegCM3 indicates tendency of negative rainfall anomalies in the coming season over Java, with probabilities of severe drought near the northern coast and a hint of less severe drought near the southern coast (in Dec). J. Qian
Statistical downscaling • 39 stations of daily rainfall 1981/82 - 2001/02 over Indramayu from BMG • set of GCM retrospective forecasts, started in October of each year 1981 - 2001, with SST anomaly field from September persisted through the November-January period (NDJ); each forecast consists of 12 ECHAM 4.5 simulations with different atmospheric initial conditions • NHMM downscaling method: non-homogeneous hidden Markov model • KNN downscaling method: K-nearest neighbors approach (Moron et al. 2006)
Obs KNN downscaling: 39-station seasonal rainfall amount KNN is based on GCM precip, winds and Sept Nino 3.4 index Cross-validated hindcast skill: r=0.44(increases to r=0.58 if Oct-Jan season is used) box-and-whiskers show KNN forecast/hindcast distribution V. Moron
NHMM Obs Forecast Median NHMM downscaling: 39-station seasonal rainfall amount NHMM was driven by PCs of GCM precip, winds and Sept Nino 3.4 index. Cross-validated hindcast skill: r=0.42 (asterisks show 100 ensemble members of forecast distribution)
Hindcast skill of KNN downscalingin terms of seasonal rainfall amount Anomaly correlationsfor each station (%) V. Moron
Indramayu Stations Anomaly Correlation Skills (%) Nov-Jan season Retrospective fcsts, downscaling with KNN from ECHAM4 winds, with September SST anomalies1981/2 - 2001/2 V. Moron
Forecast Probability of Below-Normal and Above-Normal Categories of Seasonal Average NDJ Rainfall Amount IRI Net Assessment for grid box over Indramayu: 50%-35%-15%Note that the station values are quite close to the Net Assessment!
Beyond Seasonal Averages: Forecasted Dry-Spell Risk (NHMM) (risk of dry spells >= 10 days) Historical Risk Forecasted Risk
Historical station-averaged daily rainfall amount 21 stochastic NHMM simulations of station-averaged daily rainfall amount for Nov-Jan 2006/7
In summary ... • This is a first forecast experiment, enabled by BMG daily data for 39 stations over Indramayu. • Statistically downscaled rainfall skill is only moderate for NDJ season. It is higher for Oct-Jan season. It is also higher for rainfall occurrence frequency than for seasonal rainfall total. • Forecast is moderately dry, with probability shifts at the station level similar to the IRI Net Assessment. • Slightly increased dry spell risk is indicated at many stations. • Shiv, Neil & Esther are in Indramayu as we speak ...