Hide table of contents

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.

If you are interested in Rethink Priorities' work, please consider subscribing to our newsletter. You can explore all of our completed public research here.

Comments6
Sorted by Click to highlight new comments since: Today at 3:29 AM

Hi,

This is a little tangential to the topic you have researched, but I wonder whether improving forecasting capacity with an eye on abrupt sunlight reduction scenarios (like nuclear winter) would be cost-effective. I have heard that the current models are good to predict global variations (e.g. of temperature), but not so much to get the local variations right (apart from relatively large local phenomena like monsoons). I suppose it would be quite important to figure out in the 1st few months after the nuclear war how the wheather would evolve in the next few years in order to minitigate the food shortfall.

Hi Vasco - sorry for the slow response! I don't think I have a particularly satisfactory answer for you on this topic, but I think my instinctive response is that the potential could be limited, depending on whether we think the patterns that underly the algorithms for forecasting now will be the same/ be able to be quickly adapted for these scenarios. My understanding is that forecasting depends on predicting the physics in the atmosphere (which may not change), as well as interactions with the land/oceans (which may be affected, e.g. changing landscapes due to death of flora/fauna). If this scenario is one that we haven't experienced before, maybe we could simply update the parameters based on new observations and the models would work, but maybe we would need to do research to improve the methods that are in use, which could take time. Another limitation could be that I'm not sure how well models could predict the evolution over several years, as you mention - our focus was on <6 months and we found limited accuracy.

Perhaps just one other thing to flag is that doing forecasting at somewhere like ECMWF uses a huge amount of energy, so we'd want to think about the trade-offs with other needs!

Thanks for the feedback!

Super interesting, thanks! Any clue why accuracy declined for the tropics over 2004-2006 in figure 4? Just an anomaly?

And out of curiousity, do any authors or experts have a sense for how likely it is that machine learning will significantly change the situation? (I would expect rich countries to drive this innovation so I doubt it's of philanthropic interest)

Hey Ben - thanks for your question. Unfortunately we didn't come across anything in our research that would quickly explain the decrease for 2004-2006 (or the increase in 2012-2016). It could have been differences in the atmospheric conditions at that time that the model was less able to handle, or changes to the ECMWF forecasting methods.

Re. machine learning, it does seem likely that there's scope for that here. While we didn't look into this in detail, we mention two ideas briefly in this spreadsheet of potential interventions

  1. Use machine learning to downscale (i.e. get better resolution) of existing forecasts: Tim Palmer at Oxford is a proponent of this.
  2. Use machine learning to improve forecasts: this seems plausible and interesting, and I don't know that we should expect that rich countries will drive improvements that close the gap in forecast quality between the tropics and NH. It also seems likely that the quality/ quantity of data from the tropics will put some limits on how successful ML can be for some locations.

Would be interested to hear if you have any further thoughts on either topic!

Thanks! That all makes sense. I think I was imagining ML-based improvements to drive accuracy in absolute terms - so it wouldn't close the NH-tropic gap, but could raise tropic accuracy overall. But provided there are incentives for improved accuracy in the NH, I'd expect private investment to pursue it. 

I agree - the data quality/quantity seems like larger bottlenecks to improving tropic accuracy. It seems possible that ML-approaches that work better with poor quality/quantity data may be sufficiently different to NH problems such that the expected private investment wouldn't translate into improvements for the tropics, maybe opening up potential for philanthropy to have an impact... but that's a long chain and I don't anywhere near enough about ML/weather forecasting to make a good guess.

Curated and popular this week
Relevant opportunities