The October 23-29 print edition of The Economist features a cover story entitled Instant Economics: the real time revolution. The story is good news for the Weather (Water, and Climate) Enterprise, and good news for the larger world.
(To grossly simplify), the article notes that throughout the history of economics, the relevant data – levels of economic activity, employment, money flows, etc. – have been developed and made available only months (in some cases many months) after the fact. This may not have been too injurious to the advance of economic theory on timescales of decades, but it has limited its practical application. This has been particularly vexing to governments attempting to adjust fiscal and monetary policy to the needs of time and circumstance.
According to The Economist, the exigencies of the pandemic have combined with increasingly muscular and capable information technology to change all that. The result was that governments worldwide were able to see the economic impacts of the global and national shutdowns at the time of the pandemic’s onset. They were able to institute quick-fix policies, track their effects, detect emerging consequences (both good and bad), and adjust and adapt accordingly. They could identify needs; target and distribute stimulus checks; recommend government pledges to buy vaccines, etc. early – well before shortfalls in economic activity and sector-by-sector impacts would have shown up in traditional data. The article explores in some detail the emergence of new digital proxies for economic activity, based on novel private-sector data sets – tracking the mobility of mobile phone pings, or interpreting OpenTable real-time estimates of restaurant use, and myriad others – that make nimble reaction possible. They contrast this present-day government agility with the slow response and missteps that characterized government interventions as recently as the 2007-2008 global financial crisis.
This story should sound familiar to meteorologists. A similar shift occurred in our field – only it began a bit earlier – actually, 180 years ago. Prior to that time, meteorologists exchanged weather information by letter. By this means, it was just barely possible, and only long after the fact, to piece together occasional vague pictures of patterns in weather and their movements. The invention of the telegraph changed all that. Telegraph operators began sharing local weather data over their lines. Weather patterns and their movements came more sharply into focus. Worldwide, national weather services came into being.
Initially, those services were confined to nowcasts – depictions of prevailing conditions. But not long after, weather forecasting became a thing. And if we “forecast by analogy” in view of the vibrancy of the new data-based economic research (also documented in the article), it doesn’t take too much imagination to envision near-to-intermediate term improvements over the next decade in the quality, number, and specificity of economic forecasts.
That matters, because weather forecasts are about more than public safety in the face of weather hazards. They are also vital to wringing the last bit of economic value out of weather benefits to sensitive sectors. And these touch virtually every aspect of the economy – from the obvious, such as agriculture, water resources, and energy use and renewable-energy availability, to the more subtle, such as ground-, air-, sea-, and riverine transportation or public buying preferences in the face of weather or even election turnouts.
To be fair, weather predictions have always been used to guide economic activity as well as protect people and property from harm. This goes back long before the recent impact-based decision support services (IDSS) label was applied. Business and governments have sought and received such service from the National Weather Service and the larger Weather Enterprise for decades. But climate change, and new vulnerabilities of communities and economies to disruption of critical infrastructure are driving growing complexity, urgency, and stakes of such work. They’re also making it more important to take into account not just the connections between the big-picture weather patterns and what goes on in local weather details, but the corresponding connections between macro-economics and the outlook and proper strategies for the micro-economic sphere – individual corporations and small business. The new interest in and understanding of these economic connections, and new analytic capabilities for teasing them out, make it likely that the weather (and climate) information will be wielded more adeptly across all walks of life, and that value of weather (and climate) forecasts will grow commensurately.
The full Economist article notes that this creative destruction of economics (to use Schumpeter’s term) is creating new winners (“collaborators”) and losers (“lone wolves”) among economic researchers, with “lab leaders” somewhere in between.
A word, especially to early-career scientists in our field: anticipate that something similar will happen across meteorology – and at the nexus of these two major disciplines. Embrace this opportunity! Supplement your meteorological nous with enough background in business and economics to pull your weight in multi-disciplinary collaboration; enhance your usefulness (and personal brand) further by acquiring some familiarity with artificial intelligence. AMS journals can help – including both the now-venerable Weather and Society and the newbie, Artificial Intelligence for the Earth Systems. Learn more about related topics at the upcoming 2022 AMS Annual Meeting (including the 38th Conference on Environmental Information Technologies and the 21st Conference on Artificial Intelligence for Environmental Science and so much more).
Do this and you can write your own ticket over the course of your career. And help a needy, challenged world at the same time.
What are you waiting for?