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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.
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IRI Net Assessment Precipitation Forcecast for Nov-Dec-Feb
(NDJ)issued Oct 2006
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Indramayu
Paddy damages:60% of national damages
from West Java80% of West Java
damages from Indramayu & Cirebone
districts
BMG station data NDJ 1981/2 - 2001/2
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RegCM3 Forecast: Based on Persisted SST (from Oct 2006) Ensemble Mean - Climatology
J. Qian
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RegCM3 Forecast: Based on Persisted SST (from Oct 2006) Ensemble Mean - Climatology
J. Qian
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Seasonality of Predictability: RegCM3 skill over Java is high in dry and transitioning seasons and low in the peak rainy season
J. Qian
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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
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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)
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statistical methods for downscaling daily sequences
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KNN downscaling: 39-station seasonal rainfall amount
Obs
box-and-whiskers show KNN forecast/hindcast
distributionKNN 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) V. Moron
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NHMM downscaling: 39-station seasonal rainfall amount
NHMMObs
Forecast Median
(asterisks show 100 ensemble members of
forecast distribution)
NHMM was driven by PCs of GCM
precip, winds and Sept Nino 3.4
index.
Cross-validated hindcast skill:
r=0.42
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Hindcast skill of KNN downscalingin terms of seasonal rainfall amount
Anomaly correlations
for each station (%)
V. Moron
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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
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IRI Net Assessment for grid box over Indramayu: 50%-35%-15%Note that the station values are quite close to the Net Assessment!
Forecast Probability of Below-Normal and Above-Normal Categories of Seasonal Average
NDJ Rainfall Amount
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Beyond Seasonal Averages: Forecasted Dry-Spell Risk (NHMM)
(risk of dry spells >= 10 days)
Historical Risk Forecasted Risk
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Historical station-averaged
daily rainfall amount
21 stochastic NHMM
simulations of station-averaged
daily rainfall amount for Nov-
Jan 2006/7
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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 ...