1. 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.

2. IRI Net Assessment Precipitation Forcecast for Nov-Dec-Feb (NDJ)
issued Oct 2006

3. Indramayu BMG station data NDJ 1981/2 - 2001/2 Paddy damages:
60% of national damages from West Java
80% of West Java damages from Indramayu & Cirebone districts
BMG station data NDJ 1981/ /2

4. RegCM3 Forecast: Based on Persisted SST (from Oct 2006) Ensemble Mean - Climatology
J. Qian

5. RegCM3 Forecast: Based on Persisted SST (from Oct 2006) Ensemble Mean - Climatology
J. Qian

6. Seasonality of Predictability: RegCM3 skill over Java is high in dry and transitioning seasons and low in the peak rainy season
J. Qian

7. RegCM Summary
Thirty year ( ) 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

8. Statistical downscaling
39 stations of daily rainfall 1981/ /02 over Indramayu from BMG
set of GCM retrospective forecasts, started in October of each year , 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)

9. statistical methods for downscaling daily sequences

10. KNN downscaling: 39-station seasonal rainfall amount
Obs
The ordinate units are standard deviations, with zero being long term mean. Thus, 2006 fcst is only slightly dry. Note we don’t have data for years , so those years are also true forecasts. How good were they?
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

11. NHMM downscaling: 39-station seasonal rainfall amount
Obs
Forecast Median
NHMM was driven by PCs of GCM precip, winds and Sept Nino 3.4 index.
Cross-validated hindcast skill: r=0.42
Forecast for NDJ 2006/07 is on the right. There is considerable spread in the forecast. There is a 50% chance of being between the 2 small black squares (the quartiles).
(asterisks show 100 ensemble members of forecast distribution)

12. Hindcast skill of KNN downscaling in terms of seasonal rainfall amount
Anomaly correlations for each station (%)
Correlation values are plotted at each station’s location.
The value at each station is the correlation between the timeseries of observed Nov-Jan rainfall total, and the timeseries of forecasted values 1981/ /02
V. Moron

13. Indramayu Stations Anomaly Correlation Skills (%)
Nov-Jan season
Retrospective fcsts, downscaling with KNN from ECHAM4 winds, with September SST anomalies 1981/ /2
V. Moron

14. Forecast Probability of Below-Normal and Above-Normal Categories of Seasonal Average NDJ Rainfall Amount
For an equal-odds “climatological” forecast (white areas on global map), the circles would all be 33% on the left and right. The forecast is dry because the circles are larger on the left (below-normal cat.) and smaller on the right (above-normal cat.).
IRI Net Assessment for grid box over Indramayu: 50%-35%-15% Note that the station values are quite close to the Net Assessment!

15. Beyond Seasonal Averages: Forecasted Dry-Spell Risk (NHMM)
(risk of dry spells >= 10 days)
Dry-spell risk (per NDJ season) in the NHMM simulations (left) , and 2006/07 forecast. Risks above 100% mean some chance of more than one dry spell. Note that the NHMM usually underestimates the lengths of dry spells, so the historical risks are probably underestimated. But the change between left and right panels is not affected by this bias.
Historical Risk
Forecasted Risk

16. Historical station-averaged daily rainfall amount
Top: historical weather sequences (left to right)
Bottom: NHMM simulated weather (21 realizations)
Note that it is hard to tell the difference! No systematic delay in onset is seen in the fcst, beyond Nov 1 (starting in October would help).
21 stochastic NHMM simulations of station-averaged daily rainfall amount for Nov-Jan 2006/7

17. 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 ...