Presentation on theme: "Acknowledgements: Collaborators : Dr. Judy Curry, Dr. Hai-Ru Chang, Dr. Jun Jian, Dr. Paula Agudelo, Dr. Carlos Hoyos, James I. Belanger Thanks to collaborators."— Presentation transcript:
Acknowledgements: Collaborators : Dr. Judy Curry, Dr. Hai-Ru Chang, Dr. Jun Jian, Dr. Paula Agudelo, Dr. Carlos Hoyos, James I. Belanger Thanks to collaborators in South Asia especially A.R. Subbiah, and Tom Brennan, Tim Palmer at ECMWF.
Each year, 10s of millions (mostly the poor) face danger and loss due to tropical cyclones and monsoonal flooding Flooding is the greatest cause of death and destruction in the developing world (e.g., India 6 million ha inundated, affecting 35-40 million people) Our advanced prediction system allows these risks to be forecast 10- 14 days and up to 1-3 months ahead. Three examples: 1.Tropical cyclones forecasting 2.Flood forecasting in Bangladesh 3.Flooding in Pakistan OVERVIEW
MAJOR RIVER BASINS IN SOUTH ASIA Major rivers, fertility and high population density Region with catastrophic weather events In: Indus G: Ganges B: Brahmaputra I: Irrawaddy M: Mekong R: Red J: Jiang Y: Yangtze Ye: Yellow
1. Tropical cyclones forecasting Nargis and Myanmar: 2008
Deadliest Tropical Cyclones Almost all of the worst tropical cyclone disasters have occurred in the Bay of Bengal: 17 of the highest 25
The Nargis Anomaly –Bhola Cyclone (1970) no warning or evacuation At least 350,000 people killed –Chittagong Cyclone (1991) limited warning or evacuation At least 138,000 killed; $2.2 billion in economic damage –Cyclone Sidr (2007) 3,000 people killed; $1.7 billion in economic damage –Cyclone Nargis (2008) 138,000 people dead or missing; $11 billion in economic damage So, what went right in Bangladesh in 2007 (Sidr) and what went wrong in Myanmar in 2008? good forecasts (IMD/CFAB), excellent communications on ground, evacuations fairly good forecasts, good IMD communication, no surge forecast, limited/zero response in Myanmar
Forecasting North Indian Ocean Tropical cyclones on 1-15 day time scales Probabilistic forecasts of tropical cyclone are made routinely by CFAN North Indian Ocean-west Pacific particularly predictable. Genesis and landfall of Sidr and Nargis predicted 8.5 and 13.5 days in advance and 7.5 and 11 days, respectively Forecasts shown are lower resolution than current schemes. Nor has there been the statistical rendering such as CFAN does daily in Atlantic
Tropical Cyclone Nargis 2008 First Tropical Storm Advisory: 4/27 12 UTC Landfall: Ayeyarwady Div. of Burma 5/2 12 UTC Maximum Intensity: 115 kts Cyclone Nargis on 5/2 4:40 UTC Image courtesy of MODIS Rapid Response Project at NASA/GSFC
FCST APRIL 19, 2008 -13 DAYS TO LANDFALL -8 DAYS TO IMD TC RECOGNITION
FCST APRIL 21, 2008 -11 DAYS TO LANDFALL -6 DAYS TO IMD TC RECOGNITION
FCST APRIL 23, 2008 -9 DAYS TO LANDFALL -4 DAYS TO IMD TC RECOGNITION
FCST APRIL 25, 2008 -7 DAYS TO LANDFALL -2 DAYS TO IMD TC RECOGNITION
FCST APRIL 27, 2008 -5 DAYS TO LANDFALL 0 DAYS TO IMD TC RECOGNITION
FCST APRIL 29, 2008 -3 DAYS TO LANDFALL 0 DAYS TO IMD TC RECOGNITION
FCST MAY 1, 2008 -1 DAYS TO LANDFALL +2 DAYS TO IMD TC RECOGNITION
SUMMERY OF PREDICTIVE SKILL IN THE NORTH INDIAN OCEAN Northern Indian Ocean tropical cyclones have the greatest predictability of all tropical oceans The probabilistic forecasts have over a week lead on Indian Meteorological Department Forecasts Plans produce probabilistic to 15 days for the NIO eventually extending to 30 days (as per NATL) As deltas are very susceptible to storm surge, we plan to add a storm surge component
Is it possible to make a difference by implementing a robust forecasting system and risk management strategies of extreme events?
2. End-to-end prediction in Bangladesh: Extended probabilistic flood forecasts on medium and long time scales
Seasonal: 1-6 months STRATEGIC Intraseasonal: 15-30 days STRATEGIC/TACTICAL Short-term: 1-15 days TACTICAL Provide: Overlapping forecasts to allow both strategic and tactical decisions for disaster mitigation, water resource management and agricultural optimization: AIM: OVERLAPPING 3-TIERED SYSTEM Produce: o A system that takes developed world technologies and interfaces them with the needs and abilities of developing world infrastructures o Probabilistic forecasts to allow proper risk assessment o A system that is useable and adaptable throughout the developing world
Quantitative information from the user community Combination of probabilistic forecasts of system plus user information produces easy to understand aggregate risk analysis for decision making RENDERING THE FORECAST USEFUL Probability of the occurrence event determined by ensemble forecasts Costs of strategies to mitigate impacts determined by user community Probabilities and costs of occurrence combine to produce an aggregate risk User community makes decisions based on aggregate risk assessment COMBINING PROBABILITIES WITH SPECIFIC PROBLEMS TO DEVELOP A MEASURE OF AGGREGATE RISK Risk = (cost) x (probability)
SITUATION: oGanges and the Brahmaputra exist within two of the largest catchment basins in the world oLarge agrarian populations subject to a host of environmental extreme events
Challenge: Extended stream flow forecasts out to 10-days at entry points of the Ganges (G), Brahmaputra ( B) and Meghna (M) into Bangladesh Probabilistic forecasts for assessment of flood risk Forecast communication to union and village level. Framework: Europe: ECMWF rainfall and meteorological forecasts USA: statistical rendering and hydrological forecasts Bangladesh: Incorporation of forecasts into a national disaster management scheme Bangladesh
DATA ISSUES: oHydrological streamflow data collected at the borders of India and Bangladesh: no upstream Indian data oSatellite data and derived precipitation products from NASA and NOAA Question: Is it possible to produce forecasts with no upstream data from within the Brahmaputra and Ganges catchments? Can ECMWF precipitation forecasts allow the calculation of synthetic hydrological data?
10-day means: 40 members Daily: 51 members DISCHARGE FORECAST SCHEMES: Hopson & Webster 2010 Webster et al. 2010
STATISTICAL RENDERING OF ENSEMBLE PRECIPITATION FORECASTS Using a quantile-to-quantile technique, we adjust each ensemble forecast of precipitation using satellite data as the statistical base. Comparison of the ensemble mean 0-144 hr adjusted precipitation July 20-26 2007 prior (as it turned out) to a major flooding event.
PRELIMINARY 1-10 DAY FORECASTS Real-time experimental forecast were made for 2004 and 2006. If successful, would move on to a fully operational system Opportunity taken to introduce the concepts of probabilistic forecasts and risk to Bangladesh Government institutions and NGOs. During this period, the Ganges has not exceeded flood level. Thus only the Brahmaputra forecasts are shown. Ganges forecasts did not possess any false positives during these years (see http://cafan2.eas.gatech.edu)http://cafan2.eas.gatech.edu
B G 2004 real time 10-day forecasts of the Brahmaputra No flooding of Ganges Early Brahmaputra flooding with double peak
Ensemble member distribution (day +10) Probability of exceeding flood level 10-days in advance calculated from ensemble spread For forecast made July 08 for 17, 2004 Note: If only one forecast had been made, rather than an ensemble of forecasts, any forecast (e.g., a or b would have been equally likely and there would be no information in the forecast ab flood level
During 2007 and 2008, the forecasts were used operationally in Bangladesh In test regions the forecasts were transmitted (via cell phone) to district leaders and from there to the village level Prior training was instigated for evacuation, livestock safety, early harvest, safe drinking water ahead of high probability forecast of floods Forecast for 2008-present were produced
RESPONSE TO BANGLADESH FLOOD FORECASTS Flood Forecasting and Warning Center (FFWC) Water level forecasts throughout Bangladesh Localised flood inundation maps National level Disaster Emergency Response Group Emergency response plans, logistics for preparedness and relief in advance* NGOs 1-10 days forecast Evacuation and response plans to protect lives and livelihoods Local project partners Assessed the risk of flooding based on localised flood inundation maps District level relief and emergency Mobilise resources for relief activities * This is the first time that Bangladesh acted ahead of major flooding!
ECONOMIC ANALYSIS OF THE 2007-2008 MITIGATION The 10 day forecasts were found to permit the following savings : House $130 (4 months labor*) Agricultural $190 (6 months) Household effects $270 (10 months labor) Livestock $500/cattle (2 years labor) * Based on $350/year average pay rural Bangladesh
SEASONAL 1-6 MONTH DISCHARGE FORECASTS In 2007, ECMWF System-3 coupled ocean-atmosphere model became available Jian et al (2009: QJRMS) in a diagnostic study had shown that there was extended predictability, especially for the Ganges. System-3: 110 km resolution, 41 member ensembles each month out by 7 months Experimental 1-6 months forecasts were run in real-time from April, May, June, July for 6 months. System showed extended flood risk at the correct time of the season in 2007 and 2008
DOWNSCALED 2007 FORECASTS Downscaled seasonal forecast predict high probability of July-August flood level exceedance
In: Indus G: Ganges B: Brahmaputra I: Irrawaddy M: Mekong R: Red J: Jiang Y: Yangtze Ye: Yellow Asian river basins where CFAB 1-15 day forecast are applicable and seasonal predictability established
During July and early August, torrential rainfall caused widespread floods in the Indus Valley Many millions of people were affected, 160,000 km 2 inundated, 1600 killed immediately, and water borne diseases occurred 2. WHERE THE JULY/AUGUST PAKISTAN FLOODS PREDICTABLE?
WAS 2010 AN EXREME YEAR? Climatologically, monsoon rainfall decreases across India towards Pakistan Pakistan on western edge of monsoon July/early August was an active phase of the monsoon Webster et al. 2010 Climate MJJA Northward propagation: active monsoon phase
WAS 2010 AN EXREME YEAR (cont.)? Monthly Pakistan Rainfall Anomalies Extreme Rain Events Webster et al. 2010 Large interannual variability with rainy and drought seasons In terms of extreme events there are a number of years that are quite similar to 2010. Over northern Pakistan extreme events in 2010 much larger than other years except 2008 There have been other major flood years: 1950, 1973, 1976, 1977, 2001, 2008 Floods in 2010 occurred because of high rain rates over mountainous north. 2009 drought and logging probably exacerbated run-off.
PREDICTABILITY OVER NORTHERN PAKISTAN JULY-AUGUST 2010 ECMWF operational medium range ensemble forecast: Observation Forecast IC 07/27/2010 July 28-29 Event Precipitation forecasts (made 6 and 4 days in advance) of the major northern July precipitation event compare well with satellite observations
PREDICTABILITY OVER NORTHERN PAKISTAN JULY-AUGUST 2010 (cont.) July Rainfall Predictability (2007-2010) Probability that the predicted rainfall>July Mean + 1 StDev Each of the July/August major pulses of rainfall over northern Pakistan is forecasted 6-10 days in advance
Major Weather Phenomena with High Socioeconomic Impact Tropical cyclones – loss of life, severe structural damages, agricultural losses due to storm surge Action Items: 1.Further investigate the value of using ensemble prediction systems a.Produce15-30 days probabilistic forecast based on EPS b.Developed hybrid dynamical-statistic seasonal forecasts c.In regions susceptible to storm surge, a storm surge component should be added 2.Implement plans to disseminate the warning information in timely manner. Local leaders/partners play a key role.
Major Weather Phenomena with High Socioeconomic Impact Sever monsoon flooding/drought – loss of life, agricultural losses, loss of valuables Action Items: 1.Rainfall events are highly predictable on medium range time scale. The performance of a probabilistic forecasting scheme (similar to what was presented earlier) for other regions needs to be assessed 2.Research/improve predictability on intraseasonal time scale. Evaluate if a hybrid dynamical-statistical model is useful 3.Instruct the user community to correctly use and evaluate the probabilistic forecast (e.g., organizing training workshops, providing them with executable modules) 4.For each region, asses the usefulness of a hydrological model (distributed/lumped) to provide information for: a.water management b.water supply forecast c.flood warnings
References: Webster, P. J., Thomas M. Hopson, Carlos D. Hoyos, Jun Jian, Hai-Ru Chang, Paula A. Agudelo, Judith A. Curry, Timothy N. Palmer, A. R. Subbiah, Robert L. Grossman, 2009: Extended-range probabilistic forecasts of river discharge in large basins: A Bangladesh experience, Submitted to Bull Amer. Meteorol. Soc. Hopson, T.M., and P.J. Webster, 2009: A 1-10 day ensemble forecasting scheme for the major river basins of Bangladesh: forecasting severe floods of 2003- 2007. J. Hydromet. In press Webster, P. J., T. Hopson, C. Hoyos, A. Subbiah, H-. R. Chang, R. Grossman, 2006: A three-tier overlapping prediction scheme: Tools for strategic and tactical decisions in the developing world. In Predictability of Weather and Climate, Ed. T. N. Palmer, Cambridge University Press. P 645-673. Webster, P.J., V.E. Toma and H-M Kim, 2010 : Were the 2010 Pakistan floods predictable? Submitted to GRL.
Tropical Cyclone Tracking Tropical cyclone detection uses a modified tracking scheme of Vitart (1997) Variables describe the structure of the system and include: Tropical Cyclone Center: –Local Max 850 hPa Relative Vorticity > 3.5x10 -5 s -1 –Local Min of Sea Level Pressure To Eliminate Extratropical Cyclones: Warm Core Center: –Closest Local Max Temperature in 500-200 hPa layer –Closest Local Max Thickness in 1000-200 hPa layer.
3-days 10-days 15-days SUMMERY OF PREDICTIVE SKILL IN THE NORTH INDIAN OCEAN Northern Indian Ocean tropical cyclones have the greatest predictability of all tropical oceans. CFAN probabilistic forecasts have over a week lead on Indian Meteorological Department Forecasts CFAN proposes to produce probabilistic to 15 days for the NIO eventually extending to 30 days (as per NATL) As deltas are very susceptible to storm surge, we would plan to add a storm surge component