Better weather forecasting: Agricultural and non-agricultural benefits in low- and lower-middle-income countries

By Rethink Priorities, Aisling Leow, jenny_kudymowa, bruce, Tom Hird, JamesHu @ 2023-04-28T16:25 (+41)

This is a linkpost to https://rethinkpriorities.org/publications/better-weather-forecasting

Editorial note

This report is a “shallow” investigation, as described here, and was commissioned by Open Philanthropy and produced by Rethink Priorities. Open Philanthropy does not necessarily endorse our conclusions.

The primary focus of the report was to investigate whether improving weather forecasting could have benefits for agriculture in low- and lower-middle income countries, and evaluate how cost-effective this might be. Note that this means we did not evaluate improvements in weather forecasting against other potential interventions to achieve the same aims, such as the development of climate-resilient crops.

We reviewed the academic and gray literature, and also spoke to seven experts. In our report, we provide a brief description of weather forecasting and the global industry, before evaluating which farmers might most benefit from improved forecasts. We then explore how predictions are currently made in countries of interest, and how accurate they are. We evaluate the cost-effectiveness of one intervention that was often mentioned by experts, and highlight other potential opportunities for grantmaking and further research.

We don’t intend this report to be Rethink Priorities’ final word on this topic and we have tried to flag major sources of uncertainty in the report. We are open to revising our views as more information is uncovered.

Key takeaways

• Weather forecasting consists of three stages.
  • Data assimilation: to understand the current state of the atmosphere, based on observations from satellites and surface-based stations. All forecasts beyond 4-5 days require global observations.
  • Forecasting: to model how the atmosphere will change over time. Limits to supercomputing power necessitates tradeoffs, e.g., between forecast length and resolution.
  • Communication: packaging relevant information and sharing this with potential users.
• The global annual spending on weather forecasting is over $50 billion.
• Around 260-305 million smallholder farms in South Asia, sub-Saharan Africa and Southeast Asia stand to benefit the most. 
  • A wide range of farming decisions benefit from weather forecasts, from strategic seasonal or annual decisions like crop choice, to day-to-day decisions like irrigation timing.
  • There is some evidence that farmers can benefit from forecasts in terms of increased yields and income.
  • For smallholder farmers, cereals are likely the most important crop group, constituting 90% of their agricultural output.
  • Medium-range and seasonal forecasts of rainfall and temperature are most important to these farmers.
• In the lower-middle-income countries and low-income countries¹ of interest, weather forecasting quality remains poor.
  • Global numerical weather prediction (NWP) is a methodology that underlies much of weather forecasting. Seasonal forecasts of temperature seem more accurate than those for precipitation. At shorter timescales, forecasts in the tropics may be useful with a lead time of up to two weeks, and are generally less accurate than forecasts for the mid-latitudes.
  • Public sector forecasting in these LMICs is generally informed by global NWPs, meaning that accuracy and resolution remain low.
  • LMICs do not improve on global NWPs, as they lack resources and access to raw data.
  • We have not found any evidence to suggest that private sector forecasts are better, though Ignitia’s approach targets one of the main issues with global NWPs.
• A small sample of public and private organizations we reviewed spends about $300 million each year on improving forecasting.
• It’s likely that advisories are needed, especially for seasonal forecasts. 
• Improving weather forecasting would also have ~0.75x non-agricultural benefits for LMICs. On top of this, there could be additional DALY benefits from improving disaster risk management.
• We identified twelve potential interventions (captured in a table here), and discuss four of these in our report.
  
  • Our estimate of the cost-effectiveness of funding additional observation stations suggests that this does not cross the Open Philanthropy bar (16x-162x versus a threshold of 1,000x). 
  • Funding research to identify times and places where global NWPs are already performing well could have benefits, and we estimate the costs of a potential research program to be ~$1 million.
  • We also outline two potential tactical grants: extending access to the S2S database, and digitizing paper records from observation stations. 
• We conclude that those pursuing further research on this topic with the aim of identifying the most cost-effective intervention in this area may benefit from focusing on:
  • identifying areas where global NWPs are already accurate enough to provide value for agriculture and working to make these accessible and useful to farmers
  • forecasts with shorter lead times. 

Learn more

Please visit Rethink Priorities' website to read the full report and learn more about our Global Health and Development work.

Acknowledgments

Aisling Leow, Bruce Tsai, and Jenny Kudymowa researched and wrote this report. Aisling also acted as the project lead. James Hu edited the client-facing version of the report to transform it into a public-facing report. Tom Hird supervised and reviewed the report. Thanks to Marcus A. Davis for helpful comments on drafts. Further thanks to Alex Cohen (GiveWell), Christopher Udry (Northwestern University), Douglas Parker (UK National Centre for Atmospheric Science, University of Leeds), Imara Salas (Development Innovation Lab, University of Chicago), Kamoru Lawal (Nigerian Meteorological Agency), Nabansu Chattopadhyay (India Meteorological Department), and Paul Winters (University of Notre Dame) for taking the time to speak with us. Open Philanthropy provided funding for this project, but it does not necessarily endorse our conclusions.

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