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

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

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)

statistical methods for downscaling daily sequences

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

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

Hindcast skill of KNN downscalingin terms of seasonal rainfall amount

Anomaly correlations

for 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

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

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 ...