What is the economic justification for government production of weather forecasts? The question has gained particular relevance during the first and second Trump administrations, and this is my place to collect and clarify economic reasoning related to the question, focusing on “classic” economic rationales for public policy.
I’ll divide the government’s possible contributions into four main areas: (1) gathering and sharing weather and climate data, (2) basic research on weather forecasting, (3) production of operational forecasts, and (4) dissemination and outreach.
My impression is that people widely agree that (1) should be done by the government in conjunction with international organizations like the WMO. Global weather data coverage crucially underpins modern meteorology. International cooperative agreements are important for ensuring consistent and continuous data inputs. As evidence that this view – that data gathering should be done by the public sector – has wide support, note that even though Project 2025 calls for privatization of many parts of NOAA it does not call for privatizing NOAA’s weather data gathering. There are subtleties though. Project 2025 does call for shutting down many climate-related data gathering efforts which would likely have the effect of impairing weather-forecast-relevant data gathering. And private weather forecasting companies might primarily rely on government data but might also gather their own data.
For (2), the standard arguments for government involvement in basic research also apply to weather forecasting. Matt Clancy has an excellent FAQ on government involvement in R&D. These arguments strike me as especially strong for climate modeling, an area of research that the private sector might be less likely to invest in due to time scales, coordination challenges, and all the other reasons it is challenging for the private sector to tackle climate change. Thus it is an area where government R&D would be especially unlikely to crowd out private sector R&D.
Outreach, (4), is the area where, historically, the U.S. government has played an intentionally smaller role, leaving more to the private sector, though the boundary had evolved over time (for a history, see Henson (2010)). It isn’t obvious to me where this boundary should be drawn, and I would need to think more about it. Maybe an economist who specializes in media can chime in.
The locus of the current debate is on area (3), the production of operational forecasts. There are three central and related economic arguments for government production of operational forecasts that I see: non-rivalry, private-sector under provision of information, and public safety concerns with market-based allocations.
The argument from non-rivalry
A common argument for government issuance is that weather forecasts are public goods (see, for instance, here, here, here). Weather forecasts indeed have one characteristic of public goods – they are non-rival – but they are in principle excludable (which relates to public safety arguments for government provision, discussed below). Given this, forecasts are more accurately characterized as club goods. When thinking about government provision of weather forecasts, however, non-rivalry is, in a certain sense, the more crucial of the two characteristics. An efficient market sets price equal to marginal cost. The marginal cost of one additional person consuming a weather forecast is zero. So the price should be zero. Plenty of private sector companies give away forecasts for a financial price of zero, but free goods aren’t free.
The argument from information economics
Weather and climate data are information, and Grossman and Stiglitz (1980) arguments caution that the private market will tend to underinvest in gathering information. This general result applies pretty directly to weather data: gathering weather information is costly and trading on the information would reveal the information, so incentives for gathering the information are reduced. In practice, there are firms that need weather data and firms that obtain and supply weather data, but that just means we need to iterate on the argument by saying that the end user of the data (say, a natural gas trading firm) won’t have as strong of demand for the output of a weather data firm so the weather data firm won’t have as much incentive to gather costly data.
There is a bit more nuance to the application of this argument to weather forecasts. I am partial to the Timmermann and Granger (2004) view of information which includes the model used to produce forecasts. In that sense, weather forecasts themselves are information, so the same argument applies.
The argument from public safety
There are many ways to think about the economic justification for government weather forecasts as it relates to public safety. Like the post office, there can be a societal objective for universal coverage for a service regardless of willingness to pay. This is in keeping with the National Weather Service mission to protect lives and property.
Having a single entity responsible for issuing extreme weather warnings helps with coordination. Issuing too many warnings risks a “cry wolf” effect where people stop paying attention. And conflicting warnings might make it hard to understand, an issue that the NWS already spends considerable effort grappling with.
There are many markets that are disallowed because we find them repugnant. During the times when we might most want forecasts to be available for moral reasons (risk of disaster for instance), the market value would be highest. We have plenty of examples of cases where we don’t allow firms to gouge during times like these. Even if we had private forecasts, we would probably enact similar policies, which would lower the price forecasters could charge and which would disincentivize private investment. This also relates back to the idea of forecasts as public goods. Even if forecasts are in principle excludable, we might to disallow exclusion in the interest of public safety, a point memorably made in the Last Week Tonight episode on weather. Come to think of it, that LWT episode did a better job laying out all of these issues than this post, so just go watch that.
References
Grossman, SJ, and JE Stiglitz. 1980. “On the Impossibility of Informationally Efficient Markets.” American Economic Review 70 (3): 393–408.
Henson, Robert. 2010. “The Invisible Weather Team: How Public and Private Meteorologists Shape the Weathercast.” In Weather on the Air, by Robert Henson, 45–63. Boston, MA: American Meteorological Society. https://doi.org/10.1007/978-1-935704-00-3_3.
Timmermann, Allan, and Clive W. J. Granger. 2004. “Efficient Market Hypothesis and Forecasting.” International Journal of Forecasting 20 (1): 15–27. https://doi.org/10.1016/S0169-2070(03)00012-8.
Version history
2025-06-10: First version
2025-06-12: Expanded intro, edited all points
2025-07-25: Word choice edits, expanded discussion of data gathering, references added