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HCWF assembled a team of\nscientists, including monsoon expert\nBoos, as well as economists to turn the\nlatest advances in artificial intelligence\n(AI)-based forecasting into actionable\ninformation for farmers. They have a dual\ngoal: Increase local forecasting capabilities, and develop useful messages that can\nbe disseminated at scale. Last summer,\ntheir efforts culminated in the Indian\ngovernment sending 38 million farmers a first-of-its-kind AI-based forecast on the\nstart of the monsoon rainy season. Behind\nthis watershed moment was a long process\nof taking science into real-world policy.\n\n## Forecasts worth a year's work\n\n“Weather forecasts are a fantastic way\nto improve livelihoods,” says Boos. He\nis not the only one to think so. At the\n2023 UN Climate Change Conference\n(COP28) in Dubai, improved weather\nforecasts were recognized as an instant-impact investment for global agriculture. The COP28 host, the United Arab Emirates, partnered with the Gates Foundation to offer a large funding package for\ninnovation in forecasts. The story of the\nIndian monsoon lays out why forecasts\nare considered such a promising policy\nintervention for agriculture.\n\n**“Weather forecasts\nare a fantastic way to\nimprove livelihoods,”\nsays Boos.**\n\nIn basic terms, the monsoon is like\na giant sea breeze. As land in the Indian\nsubcontinent warms in late spring, air\nabove the land warms and rises, drawing\nin cooler, moisture-laden air from the\nArabian Sea to the west and the Bay of\nBengal to the east. This moist air then\nrises, often forced by India’s high topography, producing prodigious amounts\nof rainfall. These rains bring relief from\nthe scorching heat of April and May, as\nmany Indians agree. But in the words\nof former President Pranab Mukherjee,\nthe monsoon is also “India’s real finance\nminister.” Agriculture employs half of the\npopulation, and the monsoon governs\nthe growing season. Kharif (or autumn)\ncrops—staples like rice and maize and\ncash crops like sugarcane—cannot\ngerminate and grow unless fields flood.\n“The decision of when to plant is consequential,” says Boos. “Plant at the wrong\ntime, and you’ve lost a large fraction of\nyour savings.”\n\nBut predicting the monsoon is a hard,\n150-year-old problem. Monsoon onset\nvaries regionally and exhibits notorious\nyear-to-year variability. And it also has\nfalse starts—an initial spell of heavy\nrainfall can be followed by a dry spell\nbefore the rains begin in earnest. The\nIndian Meteorology Department (IMD)\nhas tried for years to provide forecasts\nbased on average historical trends. It\ncannot afford to run expensive models\nthat capture the underlying physics, let\nalone provide localized forecasts with an\nassigned degree of confidence. The cost\nof running such models lies at the heart\nof why AI can be such a game changer in the weather forecasting space.\n\n**“The decision of when to plant\nis consequential. Plant at the\nwrong time and you’ve lost a\nlarge fraction of your savings,”\nsays Boos.**\n\n## An AI breakthrough\n\nWeather forecasting is hard. Weather\nis governed by very complex interactions\nof many interconnected physical systems,\nlike the ocean, the clouds, and the Earth’s\nsurface. Since the 1960s, atmospheric\nscientists have been hard at work turning\nan increasing portion of the underlying\nphysics into mathematical representations\nof the atmosphere. Scientists call these\ncollections of equations physics-based\nforecasting models. By solving the equations, these models can simulate the\nfuture of the atmosphere. These models\nare impressive feats of science. Today, a\nforecast seven days out is as good as a\nthree-day forecast in the 1980s: truly\nmeteoric progress.\n\nBut there’s a catch: the physical\nsystems governing the atmosphere are\nchaotic. Change the starting conditions\na tiny bit, and you get a wholly different answer—the butterfly effect in action.\nBecause our knowledge of the state of\nthe atmosphere at any given moment is\nimperfect, running even the best model\nwith a single best guess on inputs can\nmean a forecast that is way off. As such, all\nmodern forecasting depends on running\nensembles—using slightly varied starting\npoints to provide a range of outcomes.\nThis is what makes forecasts probabilistic:\nthey come with a degree of confidence.\nBut this comes at a massive cost. A single\nrun of today’s most advanced model takes\nimmense computational power. Running\nensembles is a gargantuan task, possible\nonly for the wealthiest nations with the\nmost powerful supercomputers. And yet,\nit is still only feasible for ensembles to\ncontain tens of alternative simulations.\n\n**“No one had yet demonstrated\nwhether these predictions could\nbe used for a locally specific,\nsocietally consequential problem,” says Boos.**\n\nThis all changed in 2022. A team led\nby computer giant Nvidia that included\nUChicago AI expert and HCWF co-lead\nPedram Hassanzadeh shocked the weather\nforecasting world by presenting an AI\nmodel that was as good at predicting\nglobal atmospheric variables as computationally expensive physics-based models.\nSince then, many others have followed.\nInstead of solving complex equations via\nthe use of numerical approximations, AI\nweather prediction models learn patterns\nfrom observed atmospheric variables. It\ntakes a long time and significant computation for models to train and learn\nthese patterns. But once they acquire the\nknowledge, most can be run on a laptop,\nand, crucially, their developers have made\nthem openly available. This makes running ensemble forecasting with thousands\nof alternative realizations possible, even\nfor organizations like the IMD.\n\nTo Boos and his UChicago colleagues, AI’s “surprising” skill was a\ngolden opportunity for the monsoon\nonset problem. AI models were trained\nby minimizing their average error over\nthe whole planet—they were made to\nexcel at global weather forecasting. “No\none had yet demonstrated whether these\npredictions could be used for a locally\nspecific, societally consequential problem,” says Boos. With the impetus provided by the COP28 funding package,\nHCWF assembled a team of scientists\nto evaluate AI models for the localized\ntask of monsoon onset prediction. In\na careful benchmarking exercise, they\nevaluated all available open-source AI\nmodels and ended up choosing Google’s\nNeuralGCM and the European Centre\nfor Medium-Range Weather Forecasts’\nAIFS models. These were cheapest\nto run and could make precise\nforecasts without missing onsets or giving too many false alarms. The\nteam also found no model was perfect:\neach tended to be overconfident in their\nonset certainty. They therefore developed\na blended strategy using historical trends\nto correct overconfidence. A technical\nsolution was finally in place.\n\n## Monsoon forecasts for Indians, by Indians\n\nArmed with a promising state-of-the-art solution, the HCWF came before the\nstep where most researchers fail: taking\nthe science to the real world. For researchers from high-income countries seeking to\noperate in a low-income country, perhaps\nthe easiest thing to do is to parachute\nin: make all decisions and implement\nthings as they see fit. But this is far from\neffective. In its 2025 progress report, the\nUnited Nations Office for Disaster Risk\nReduction states that any forecasting or\nwarning effort delivers best when locally\nled and user-oriented. Finding the best\nway to do this often proves a hurdle. Boos\nsays the presence of the economists was\nthe “secret sauce” in this regard. “They\nhad gotten the optimal forecast via a\nback-and-forth with the farmers. They\nhad been out there asking, ‘If you have\na prediction, how do you best format it?\nWhat is the best type of information to\nget out?’”\n\nA UChicago team led by economist\nAmir Jina had asked the question: Are\nforecasts better than just having insurance against such failures? They ran trials\nin a few villages in central India. Some\nfarmers were offered an early version of a localized, bespoke probabilistic forecast, while others were offered insurance.\nFarmers offered the forecast changed their\nbehavior and abandoned prior beliefs\nabout the season. Those who thought\nthe monsoon season would be longer\nthan forecasted scaled back investment,\nwhile those who were pessimistic about\nthe season increased their investments,\nresulting in an average 22 percent increase\nin productivity.\n\nBy early 2025, HCWF took the case\nto the Indian Ministry of Agriculture\nand Farmers Welfare, and together they\nformed a plan. HCWF worked with a\nnon-profit, Precision Development, to\ndevelop messaging in 13 local languages.\nThe Ministry would employ m-Kisan, a\nmessage delivery system running since\n2013 that can tap into vast registries\nof farmer contacts. m-Kisan ultimately\ndelivered week-by-week probabilistic\nforecasts on monsoon onset to 38 million farmers. The 2025 monsoon had\na dry spell followed by an initial rainy\nperiod—the ultimate litmus test for a\nforecasting framework. But the AI framework developed by the HCWF scientific\nteam caught it, and farmers received a\ncorrect forecast four weeks in advance.\n\nFarmers welcomed the forecast with open arms. In a post-message survey,\nfarmer Parasnath Tiwari, from the central state of Madhya Pradesh, said that\nhe found the information “useful, and\nshared it with others.” Fellow farmer\nArvindkumar Haridas added, “Everyone\nwas farming according to the forecast.\nThe monsoon arrived right on time, and\neveryone was prosperous.” It is not hard\nto see why this was a success. The ministry made the final go call and used its\nown infrastructure to deliver millions of\nmessages. The messages did not come\nfrom some faceless black box in a foreign\ncountry, but from an agency farmers can\njudge with their vote. Moving forward,\nIndian scientists are being trained by the\nHCWF to run and evaluate the models\nfor themselves, paving the way to full\nIndian ownership of the process.\n\n**“The effort to democratize access\nto ensemble weather prediction\nis really in its infancy, perhaps\nnot even born yet,” Boos says\nexcitedly.**\n\nAfter last summer’s success, Boos\nand the HCWF crew have big plans.\nImproving their monsoon AI framework’s\nprecision and rolling it out to all Indian farmers are immediate next steps. But\nHCWF scientists know that there is a\nlot of untapped potential in AI-based\nforecasting. “The effort to democratize\naccess to ensemble weather prediction\nis really in its infancy, perhaps not even\nborn yet,” Boos says excitedly, adding\n“this is what really drew” him to this\nwork. The HCWF and partners from\nthe UK plan to expand efforts to Africa,\nworking with locals to build similar rainy\nseason forecasting capabilities there. As\nfor their next scientific endeavor: predicting extreme weather events, like the individual storm and flood events that form\npart of a monsoon season. Could the AI\nmodels be as good as traditional models\nonce again? Boos remains confident. In\nthe end, science is all about making a real\ndifference. The HCWF looks to have\nfound a great way to do just that.","image":{"publicURL":"/static/f8a4923043b054cd1e972c4da212c28e/c5850deda96224cd2379429077ac4146.jpg"},"authors":[{"id":"684f9826a1914505ee622dcb","name":"Savvas Marcou"}],"designers":[{"id":"692fe50ba1914505ee622dd8","name":"Chunzi Liu"}],"categories":[{"id":"6030b07d7782326a48b058bc","title":"Climate Change"},{"id":"61fdc5cd1e0c2a0a6f3372c1","title":"Science and Society"}],"magazine":{"id":"6a1b3daba1914505ee622e6e","title":"Spring 2026","issue":50}},"recent":{"edges":[{"node":{"id":"Article_6a1b7b70a1914505ee622e8a","title":"A quarter-century of BSR","authors":[{"id":"6930d4c5a1914505ee622e05","name":"Eleanor Wang"}],"categories":[],"image":{"publicURL":"/static/e2535987c5657a0ccb90aefc70d390fa/d0381637f7e57d0c526bc22c6b39f421.png"},"published_at":"2026-05-31T19:32:44.843Z"}},{"node":{"id":"Article_6a1c7dfda1914505ee622e93","title":"A taste of Ohlone culture","authors":[{"id":"6a1b3e7ea1914505ee622e74","name":"Jack 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