by Judith Curry
Global prediction partnerships would cost little and reduce the regional carnage caused by floods, droughts and tropical cyclones. – Peter Webster
Peter Webster has a commentary article in Nature, entitled Meteorology: Improve Weather Forecasts for the Developing World. Unfortunately this article is behind paywall, but here are some liberal excerpts:
Hurricane Sandy hit the northeast coast of the United States with ample warning. The storm caused widespread damage but only around 100 people died, thanks to planning made possible by extended and accurate weather forecasts.
However, in the developing world, such storms take a much greater toll, some examples:
- Very Severe Storms Sidr in 2007 and Nargis in 2008 caused over 10,000 and 138,000 fatalities in Bangladesh and Myanmar, respectively.
- Flooding in the Ganges and Brahmaputra basins has displaced over 40 million people in each of the past few years.
- A three week break in rainfall just after seasonal planting, following what seemed to be a normal monsoon onset, caused a disastrous crop failure in India in 2002
Because the resilience of poor populations is low and falls with each crisis, the cumulative effects are relentlessly impoverishing.
Advances in prediction science mean that such catastrophes can be forecast anywhere in the world with as long a lead time as Hurricane Sandy. The problem is how to tailor complex global forecasts to a country or region and communicate them effectively to local populations.
In most developing countries hazard warnings are issued only a few days in advance, if at all. Yet, for a flood or cyclone, at least a week of forewarning is necessary to allow the slowest member of a society (perhaps a farmer and his cattle) to evacuate. For short droughts, several weeks’ notice would allow planting and harvesting schedules to be adjusted. Long droughts require warnings months ahead, so that resistant crops can be chosen and fodder and water stored.
Regional forecasts with long lead times must take global atmospheric circulations into account, because the local weather is influenced by distant events.
Taking decades to develop and expensive to build and maintain, global weather forecast models are run by only a few national or multinational government organizations, including the European Centre for Medium-Range Weather Forecasts (ECMWF), the United Kingdom Meteorological Office and the U.S. National Center for Environmental Prediction (NCEP).
In theory, developing countries can access these data streams. [However] less developed countries have small budgets and slow internet connections.
Bangladesh offers one success story that could be emulated. Following the 1998 floods, the ECMWF and Bangladeshi government authorities, together with Georgia Institute of Technology, developed a 1-10 day flood forecasting system and created the Climate Forecast Applications Network (CFAN) to distribute it. Since 2004 CFAN has produced daily forecasts of the Brahmaputra and Ganges river flows, and transmitted them to the Bangladesh Flood Forecast and Warning Centre.
[LINK to Webster’s paper describing the Bangladesh project]
Before the 2007 flood season, village and community leaders in six unions of Bangladesh were trained to interpret the data and take action if flooding was likely. Local leaders might tell farmers to harvest crops, shelter animals, store clean water and secure food, household and farming effects.
Bangladesh experienced three major floods in 2007 and 2008. Each was forecast successfully 10 days in advance and mitigation steps taken. A World Bank report concluded that about $40 was saved for every dollar invested in the regional forecasting and warning system. Global forecasts produced in Europe and turned into flood forecasts in the United States were, within 6 hours, integrated into Bangladesh’s disaster management protocol by local experts.
In 2009, to help facilitate technological transfer and capacity building, CFAN handed over its flood forecast modules to the Bangladesh Flood Forecasting and Warning Centre (FFWC), as part of the capacity building commitment. However, it proved difficult for FFWC to handle the large volume of data transfer and the responsibility was taken over by an international non-government entity funded to some degree by contributions from member states , the Regional Integrated and Multi-Hazard Early Warning System (RIMES). The RIMES framework is innovative but suffers from the funding pressures that plague forecasting and warning groups in the developing world. Limited funding also means that the necessary cadre of scientists to tackle specific problems cannot easily be maintained. It also makes it difficult to perform one essential process: the updating of the modules as the global forecast systems and satellite systems change.
Building upon the Bangladesh model, it is envisioned that partnerships can be established in other regions to deal with a range of weather hazards. A partnership plan needs to be developed that bridges the gap between the producers of the global forecasts and the user community. The boundary organization or group that forms the bridge depends on the type of hazard being addressed. But the aim of each team is the same: to produce hazard forecast modules that utilize global forecasts to be used to provide hazard warnings for the developing country or region. The boundary team is also responsible for the updating of the module to keep up-to-date with changes in the satellite systems used for regional calibration and also in the global forecasting systems themselves. Depending on the resources and capabilities of the developing country, the production of the forecasts can be either transferred to an agency within the country or the boundary organization can provide continuing support.
Such partnerships can be facilitated by sustained funding from intergovernmental organizations, such the United Nation, the World Bank and USAID. The cost is relatively small and it has been estimated that flood forecasting modules for the entire South and East Asian region could be developed and implemented for less than $1M/year.
Asia and Africa stand on the threshold of immense economic advancement and can build resilience through the effective use of extended range weather forecasts. Faced with possible climate change, societies that learn to cope with and mitigate hazards in the current era will be most adept at dealing with more frequent and intense hazards in the future.
What Peter Webster proposes could have enormous humanitarian and socioeconomic benefits in the developing world. The financial resources required to implement such a proposal is minimal. So, what are the roadblocks to accomplishing this?
After the devastating Pakistani floods in 2010, Peter Webster wrote a paper [link] that demonstrated that the Pakistani floods were predictable at least 10 days in advance using the ECMWF forecast system. ECMWF was actually criticized for not warning Pakistan in some way. Since then, the major global weather forecasting centers (ECMWF, NCEP, UKMO) have been paying more attention to extreme events in the developing world, and presumably communicating to some extent with the meteorological agencies in these countries.
However there is a big gap between a global model forecast of say a major blocking pattern developing, and a location specific flood forecast. You need a dedicated team that is focused on the specific region, while at the same time having the capability to process the global weather forecast stream. The boundary organizations (e.g. a university team, a private company, a NGO or a multigovernmental agency such as RIMES) have a critical role to play, but without funding for the boundary organizations, this just doesn’t happen.
How to fund the boundary organizations here? Webster proposes funding from agencies such as UN, WorldBank, USAID. RIMES has attempted a model whereby the funding comes from the developing countries themselves, but their experience has been that they cannot obtain funding to pay scientists from say the U.S. or Europe to facilitate this process. It seems possible that a social business model could be developed to pay for this.
The other impediment to this model is the governments of the recipient countries. Developing countries that do have meteorological agencies may not want to admit to their government that they do not have the tools or the capabilities to handle their nation’s weather forecasting needs. Peter Webster was told by one meteorological agency in a South Asian country that they only need a two day forecast, and that probabilistic forecasts don’t work for their country. Weather information is regarded by some countries as national security information: India does not make available its geostationary weather satellite data nor its streamflow data. Further, there are ancillary political considerations involved, such as when the Myanmar government did not inform its population of the coming cyclone for political purposes (link).
Even if all of the above impediments have been successfully managed, there is a further challenge particularly if funding from USAID and other government aid agencies has been involved. A key objective to their funding is build capacity in the developing countries, rather than have the project continuing to support the developing country. A laudable goal, but idealistic in many instances. Surely no one is proposing that each developing country develop a global weather forecast capability such as ECMWF. However, for example, Peter Webster’s grant from USAID in Bangladesh required the transfer of the flood forecasting technology to Bangladesh. After purchasing computer equipment and extensive training of personnel, the Bangladeshis were not capable of sustaining this project, for reasons that are outlined in Webster’s previous paper. The project was salvaged by RIMES, but RIMES continues to require support from Webster’s research group to maintain this project.
What Webster is proposing is a ‘flat world’ approach that takes advantage of expertise and resources around the world to tackle a problem in an efficient and economical way. The political impediments for governments to manage this may be insurmountable; NGOs, private companies and social business may be the way to go. But given the large amount of funds that the U.S. and European countries spend on humanitarian aid in response to these disasters, one would hope that the UN or the U.S. could figure out some way to support this idea that has such great humanitarian and economic potential for the developing world at so little cost.
Private weather forecasting companies are starting to develop in Asia: the first one to my knowledge is Skymet. We have also spoken with a group in China that is trying to develop a private company along these lines. It will be interesting to see how this develops, and to what extent these market driven developments from within the region can help countries such as Myanmar and Bangladesh.