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Numerical Weather Prediction in Indonesia : Demands, Prospects, and Challenges

Numerical Weather Prediction in Indonesia : Demands, Prospects, and Challenges. Tri Wahyu Hadi Atmospheric Science Research Group Bandung Institute of Technology. Outline : Background and motivation Problems with weather in Indonesia and industrial demands for weather information

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Numerical Weather Prediction in Indonesia : Demands, Prospects, and Challenges

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  1. Numerical Weather Prediction in Indonesia :Demands, Prospects, and Challenges Tri Wahyu Hadi Atmospheric Science Research Group Bandung Institute of Technology

  2. Outline : • Background and motivation • Problems with weather in Indonesiaand industrial demands for weather information • Experimental weather prediction at ITB • Current research and some results • More challenges and future plans Acknowledgement : current works have been mainly supported by ITB Research Grant 2006 & 2007, and KAGI 21

  3. 1 2 Background and Motivation  Problems with weather in Indonesiaand industrial demands for weather information

  4. Numerical Weather Prediction :The Success Story Continuous development of NWP models yields reliable weather prediction andinovative delivery of weather information is useful for society Weather information goesto market and becomesweather derivatives Source : http://weathernews.jp/ Good businesses,good income for government, some are fed back to research and operation of weather services Enough research fundingattracts geniuses Contribution to national welfare Source : http://www.climetrix.com/

  5. Numerical (?) Weather Prediction :A not very good story in Indonesia Lack of effort to develop NWPmodel, forecast products are“imported” from Australia (TLAPS) and Japan(?) dull weather information few people care Flood and other weather-related disasters occur again and again without appropriate risk management Source : http://www.bmg.go.id/ Jakarta flooding alone this year cost about USD 4.1 billion to the nation, the government keep spending money foroperational weather services Bad research fundingand research policies lead nowhere Difficulties to alleviate national poverty http://snugroho.wordpress.com/

  6. There is no typhoon nor tornado but… A “small twister” in Yogyakarta destroyed more than 200 houses18 February 2007 (Source : http://www.detik.com and other web sites) Small scale but relatively strong weather disturbance

  7. Flood, land slides, and volcanic mud avalancheJust a routine visitor? Land slide in Flores Island 02 March 2007 Flood in Aceh, December 2006 Volcanic mud avalanche, Januari 2003 (source : http://www.suaramerdeka.com/ Big flood in Jakarta, Jan/Feb 2002, Feb. 2007 Torrential rain due to mesoscale convective system ( source : http://www.mediacenter.or.id/)

  8. Marine and air transportation safety Very often fishermen unable to go to work due to bad weather Several flight accidentsthis year Ferry accident caused by high seas 29/12/2006and several other cases Gekentert: Indonesische Fähre "Senopati Nusantara" (Foto: Reuters) Die Fähre "Senopati Nusantara" war bei schwerer See auf dem Weg von der Hafenstadt Kumai auf Borneo zur Insel Java gesunken. Even for transportations that are known to be vulnarable to weather, risks are not seriously being managed (http://www2.onnachrichten.t-online.de/)

  9. Industrial demands for weather information : Personal experience

  10. Summary : Accurate short and medium range weather prediction is needed by both public and private sectors but high resolution regional or local modelsseem to be necessary for the prediction to be useful Weather risk management must be introduced to the society in order to push the developmen of better weatherservice there should be something we can do

  11. 3 Experimental Weather Prediction at ITB

  12. A downscaling scheme Regional Model domains Global domain (figure by JMA) • Data freely available on the internet,NCEP GFS global model output : • Horizontal resolution : 0.5 & 1 available • Vertical resolution : 24 sigma levels • Time resolution : 3 hr (downloaded at 6 hr interval) • Prediction range : up to 384 hr (var. res.) • Number of output parameters : 128 • GRIB ver. 1 (will be obsolete soon) and ver. 2. (new standard) • Targetted regional model characteristics • Coarse grid res. : 30 kmx 30km • Finer grid res. : 10 km x 10 km • Vertical resolution : 32 sigma levels • Time resolution : 3 hr • Two-way nesting between coarse and finer grid resolution

  13. Targetted Near Real Time Monitoring and Prediction System Block C : PC Cluster Block A :Internet Resources Block B :Download Server Postprocess NOAA GFS Data Model run up to 48-hour lead time prediction Regional model Preprocessing (if data is adequate) Topgraphy and land-use data (fixed) Dept. Atm. Sci. University of Wyoming ? ? Sonde Data Post processing of regional model output Dept. Inf. ScienceKochi University Daily rainfall estimation MTSAT IR mages Space Science and Engineering Center(SSEC) University of Wisconsin -Monitoring -Prediction -Nowcasting -Forcast Verification MTSAT/GOESlatest images Block D : Web Server

  14. A practical approach using MM5 MM5 modeling system : • Non-hydrostatic • Primitive equations • Developed by PSU/NCAR • Free software • Parallel version with MPI • Model Setup - boundary-layer parameterization : MRF - cumulus parameterization : Grell (30 km) & KF (10 km) Downscaling without additional input except refinement of tropography

  15. Numerical forecast system implementation MM5 Forcast Run MM5 Regional Prediction effective forecast lead time Download Time Data downloaded at 6 hr fcst interval 1200 UTC 1200 UTC GFS Forcast Run at 1200 UTC PC Cluster assembledinhouse : 8 nodes AMD Athlon 64; 10 MB total memory 600 GB data storage (a much more powerful one is badly needed) 0300 UTC 0530 UTC

  16. Experimental prediction output at http://weather.geoph.itb.ac.id/(screen shots)

  17. Summary : We have set up a simple but fully automated numerical weather prediction system, which provides basic infrastructure for research on mesoscale numerical wether prediction in Indonesia at least we are doing something !

  18. 4 Current Research and Some Results

  19. Big Floods in Jakarta 1998/99 (La Nina, no big flood?), 2001/02 (normal, big flood), 2005/06 (normal, no big flood)2006/2007 (weak El Nino, big flood)  Big floods this year and before are related to specific weather pattern? Monsoon Migration(Lau & Chan, 1983) onset break July 2005 – June 2006 July 2001 – June 2002 July 1998 – June 1999 retreat OLR over Java

  20. Daily Accumulated Rainfall over Jakarta Area 1-4 Feb. 07 Source : A. Sasmito et al. (BMG)  Extreme torrential rainfall on 1 Feb. 2007

  21. Daily accumulated rainfall during similar flood event in 2002 All stations recordedlarge rainfall Inland station recorded more rainfallduring 27-28 Jan 2002 Concentration of large rainfall over Jakarta area duringseveral days and extreme torrential rainfall triggered big flood

  22. Synoptic scale conditionrelated to the flood event of Jan/Feb 2002 • Southward migration of monsoon trough • Appearance of cyclonic vortex over the Indian Oceansouth-west of Jakarta • Data : NCEP global tropospheric analysis and GMS IR imageries(daily averaged TBB)

  23. Comparison with the cases of 2006 and 2007(note : there was a long break in January 2007) Confluence Flood in Central Java Cyclonic vortex Big flood in Jakarta (again) Did NWP model predict this event clearly?

  24. Regional NWP Model Output (using GFS output of 30 Jan 2007 as input) re-run

  25. Regional NWP Model Output (using GFS output of 30 Jan 2007 as input) re-run

  26. Summary : Torrential rainfall that triggered big floodin Jakarta seems to be related to the passage of southward migrating monsoon trough over Java Island and the appearance of cyclonic vortex in the Indian Ocean southwest of Jakarta. Although it might not be very accurate, 24-12 lead time regional NWP model output should be useful for early warning more in-depth study?

  27. Rc (mm) High (3 km) Resolution Simulation of Convective Rainfall (Daily accumulated rainfall, Jan/Feb 2002 case) Rainfall was concentrated over Jakarta area (northern coast of West Java)

  28. Clw (g/kg) Cold pool advection and blocking by topography 27 Jan 2002, 0200 LT 27 Jan 2002, 0300 LT High moisture 27 Jan 2002, 0400 LT 27 Jan 2002, 0500 LT (South-North section over latitude of Jakarta, colorized contour is equavelnt pototential temperature, vertical winds are exaggerated)

  29. Clw (g/kg) Mixed layer blocking Subsequent cold pool advection did not yield deep convection because moisture is too low

  30. Summary : We have studied the Jakarta flood event of Jan/Feb 2002 using high resolution model and it is found that complex mesoscale processes involving the roles of topography, cold pool advection, and mixing layer development were responsible for the observed rainfall concentration Cyclonic vortex seems to play rolein strengthening the southward componentof the monsoon flow over West Java, which was important for the cold pool advection

  31. 5 More challenges and future plans

  32. Other Recent, Ongoing, and Planned Works

  33. MM5 – WRF convective rainfall comparison Back

  34. 24 hr lead time prediction of daily rainfall Rainfall (mm) Time (days) Back Not too prospective yet…?

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