be the shoulder to cry on when they are crying about their ex-boyfriends and how good he was?” And that’s when the coin dropped. When was there an indication that there were an unusual number of respiratory cases? Alpuche also sets the record straight about why it took several weeks to link the outbreak to the first case with symptoms, and abstractionist mythological themes interspersed with the use of the Devanagari script. I haven’t seen for years. There could be another benefit to blurring the lines between weather and climate, J.” says Bill Putman a meteorologist at NASA’s Goddard Space Flight Center in Greenbelt Maryland and another longtime collaborator “But it’s not necessarily a language barrier It’s more a knowledge barrier” Seeing his talent for computational fluid dynamics his adviser suggested Lin switch to Princeton University which with its partnership with GFDL is a hotbed for atmospheric modeling He learned how GFDL scientists divided the air into a 3D grid that spanned the globe and stretched from the surface to the stratosphere following lines of latitude and longitude Along points on the grid they would set initial conditions—the weather or climate for a given moment in time Then point by point the computer would solve equations describing changes in wind air pressure temperature and humidity for successive steps in time Computers were room-sized mainframes at the time and the model grids were huge with a mesh size of 500 kilometers The models could recreate only the largest atmospheric features like jet streams and the Hadley cell the belt that circulates warm air from the equator to the subtropics After graduate school Lin decided to stay in the United States “I’m now more American than I am Taiwanese” he says He drinks whisky but infuses it with ginseng He returned to the University of Oklahoma as a postdoc to work on modeling tornadoes But computers couldn’t yet model events that unfold at such small scales The failure was humbling and Lin says it provided a mantra: “Choose the right level of complexity for the particular problem at the time that you have the resources to do it” Lin soon found the right problem at NASA At high resolution Lin’s model can simulate tornadoes such as the 2013 twister in Moore Oklahoma that killed 24 people VINCENT DELIGNY/AFP/GETTY IMAGES In the late 1980s Rood was working on the problem of the Antarctic ozone hole at Goddard NASA was flying research planes into the hole to measure the chemicals that might be destroying it These flights revealed a drop in several short-lived reactive nitrogen oxides which allowed chlorine from humanmade chemicals to linger priming further reactions that broke down the ozone But Rood’s atmospheric models couldn’t simulate the flows and reactions No matter what he did the nitrogen reactants remained steady How could that happen At the time an elegant mathematical solution had overtaken global modeling called the spectral method Rather than solving at points on a latitude-longitude grid scientists realized that fluid flow in the atmosphere could be represented as the sum of a series of hundreds of sinusoidal crisscrossing waves The code ran faster and the results could be transformed back onto a regular grid The spectral method still powers most global weather forecasts today including at NWS But the speed comes with a cost: When the waves are projected back into physical space mass can gradually grow unbalanced For weather models which only run for days into the future this is not a big deal But for models of atmospheric chemistry and climate which run for much longer periods these distortions were a critical flaw Fortunately for Rood a young Taiwanese scientist had written to him lured by his publications When Lin joined NASA in 1992 as a contractor the two set out to build a model that above all else preserved mass This first meant jettisoning the spectral method It also meant upgrading from finite-difference modeling which solves for points on a grid to a finite-volume model which solves for conditions averaged across each cell or box and is ideally suited for conserving mass because the calculations pass fluxes or volumes of material from one box to the next Others had considered such a solution but thought it too complex or computationally expensive But Lin was a master of computational efficiency Over a furious few years in the mid-1990s he and Rood expanded their model beyond chemical transport—for which it remains the standard—to a fullfledged dynamical core fast enough to be used for climate models Put a mote of dust in the air says Paul Ginoux an aerosol modeler at GFDL who also worked with Lin at Goddard “and this code will transport it at the right place at the right moment And that’s beautiful” The name of the code was far more mundane They called it “FV” for finite-volume and later FV3 Their work soon drew the attention of the National Center for Atmospheric Research (NCAR) in Boulder Colorado one of the country’s leading institutes for weather and climate science which incorporated FV into its influential climate model NASA’s climate laboratory in New York City adopted it as well And in 2003 GFDL lured Lin away to upgrade FV and fold it into its global simulation The results of these models some of the top US contributions to the United Nations panel on climate change have informed much of what the public hears about global warming And they’ve all had Lin’s innovations at their heart There’s a term of art at NOAA for the reactive way Congress finances weather research: “budgeting by disaster” It’s rarely pretty and it’s why the coming merger in atmospheric modeling will at its root be thanks to the calamities of Hurricane Katrina and Hurricane Sandy In 2005 after NWS failed to forecast Katrina’s direct hit on New Orleans Louisiana until 2 days out Congress set aside money to improve predictions of Atlantic hurricanes As it happened it was around this time that Lin walked into the office of his boss at GFDL Isaac Held and declared: “I’m going to revolutionize weather prediction” Computers were now capable of processing boxes small enough to render hurricanes More important Lin had developed a key bit of physics needed for FV3 to forecast realistic hurricanes Many global forecasting models operate using an assumption called the hydrostatic principle—where the gravity of the air in any box is exactly balanced by the upward force of the air pressure in the box below it This works for coarse models which cannot directly simulate the fine upward and downward flows in the real atmosphere But recreating weather events like hurricanes and thunderstorms where updrafts are important requires breaking this hydrostatic principle After a decade of mulling Lin finally had an efficient way of incorporating nonhydrostatic flows into his code He needed to test it Frank Marks who leads hurricane research at NOAA’s Atlantic Oceanographic and Meteorological Laboratory in Miami Florida was overseeing improvements for the regional hurricane model for the Atlantic basin With a smaller area to model Marks can afford to have fine-scale boxes Lin convinced him to use Katrina dollars to buy extra supercomputer time Run FV3 at a 1-kilometer resolution Lin promised and the finest details of cyclones would arise Sure enough the violent walls of a hurricane’s eye opened in his code Zooming in on storms The FV3 model divides the atmosphere into boxes and simulates conditions in each one To avoid problems at the poles its coordinates are based on a cubed sphere The program can also nest grids to simulate weather at different scales IMAGES: (CLOCKWISE FROM LEFT) LUCAS HARRIS NOAA/GFDL; C BICKEL/SCIENCE; XI CHEN (PRINCETON UNIVERSITY); NASA’S GODDARD SPACE FLIGHT CENTER In 2014 when NOAA announced a competition to choose the “core” of the agency’s next-generation weather forecast system Lin was ready Five models were entered including FV3 And by the summer of 2015 FV3 was one of two frontrunners along with the Model for Prediction Across Scales (MPAS) the globalized version of a long-standing system produced by NCAR and used by many researchers They would be judged on their speed and accuracy in mimicking the atmosphere’s flows For 6 months Lin’s placid office turned frenetic as his team worked nights and weekends to embed FV3 within the weather service’s system “There was never a time where I thought we were losing the battle on scientific ground” Lin says One advantage of his model was efficiency It is Lin’s obsession—and not just at work: When Hurricane Sandy knocked out power at Lin’s modest home he refused to use a normal generator and instead rigged his Prius up to his home wiring Its battery he explained would make certain any extra electricity the car’s generator churned out wouldn’t go to waste So that FV3 could make efficient use of limited computing power Lin and his team had written the code to work in parallel This is hard for global models where the weather in one box can influence another box a hemisphere away But this interconnectedness isn’t as big a problem in the vertical dimension so Lin enabled FV3’s layers to be detached from each other and be processed in parallel He won additional efficiencies by changing the shape of the grid Climate models are plagued by the so-called pole problem the result of the strangely squished and stretched boxes near the poles So Lin and Putman his former NASA colleague abandoned the latitudelongitude system in favor of a cubed sphere Picture a six-sided die inflated like a balloon There were no more poles to handle just six square panels with tricky interactions at the seams The net result: compared with MPAS FV3 took a third as many computer processors to run at operational standards It also outperformed MPAS when run on a vast number of processors and it could zoom in to model one part of the globe at high resolution without skewing its performance in coarser regions It was a slaughter NCAR withdrew its model before NOAA anointed FV3 as the winner in July 2016 “There was just never any conclusive evidence that MPAS had an advantage that was worth the cost” says Michalakes who led the computing comparisons During the competition Lin had complained that NOAA was biased in favor of MPAS; now he crows about his victory “Most people in that discipline paid no respect to what we had been doing” he says “They found out the hard way” With NCAR toppled Lin now faces far bigger rivals: the United Kingdom’s Met Office which since the early 1990s has been the only center to have merged its weather and climate forecasts and the European Centre for Medium-Range Forecasts which has long run the top-rated weather model This time around he’ll need help European modelers start with the same set of balloon satellite and ground measurements as everyone else But they cleverly inject randomness into these initial conditions then do multiple runs to come up with a “consensus” forecast Getting the United States up to those standards will require winning over US researchers to provide innovative techniques that Lin and his colleagues can adapt for their model Yet there’s a risk that academic weather scientists will avoid using FV3 and instead stick with MPAS more comfortable with its origins and documentation says Cliff Mass an atmospheric scientist at the University of Washington in Seattle Lin’s reluctance to break down his code in the past has heightened concerns “Lin is a brilliant modeler” Mass says “He’s not big on community support” But Putman believes Lin will embrace true improvements “If he sees something that will push this code beyond where it is now I’m sure he’s willing to adapt” At a workshop next week NWS will lay out its aggressive timetable for turning on FV3 By this May FV3 ought to be fully wired into the service’s data assimilation And by the first half of 2018 if all goes well NOAA will flip the switch making it the standard forecast that feeds into all of our phones Meanwhile Lin’s team continues to tinker with FV3 They’re honing a more powerful zooming technique: allowing the grid to create nests of high-resolution boxes 2 to 3 kilometers a side over regions of interest This could allow high-resolution hurricane forecasts to be run at the same time as global predictions with no need to wait for the global run to finish And it could capture tornado outbreaks and severe storms weather that has been too finegrained for existing global models “We’re kind of ambitious” Lin says “We’re trying to cover everything” On a screen at GFDL Lucas Harris Lin’s deputy zooms in on Oklahoma where a nested FV3 grid is recreating the events of May 2013 It was that month that a severe twister plowed through Moore Oklahoma killing 24 As the model runs scattered storms organize into a line of squalls Then anvil clouds form—the thunderstorm cells from which tornadoes would touch down on Moore Next Harris changes the place and time to the eastern United States in June 2012 when a bow of thunderstorms—a so-called derecho—caught forecasters off guard and in some areas knocked out power for a week The model sees traces of the storm nearly 3 days in advance “Previously” Harris says “it was believed there was only 12 hours of predictability to this event” So far these results have stayed in the lab but Lin is doing his best to spread the gospel For the 2017 hurricane season his prototype will run alongside existing regional hurricane models And next month Lin will return to Oklahoma for the “Spring Experiment” a research jamboree of severe storm scientists to test how the zooming technique could help local forecasters All this collaboration this dependence on outside contributions makes Lin nervous His model is moving out of the lab into the messy real world Will it become the bedrock of all weather and climate prediction from tornadoes next week to temperature rises next decade “I’m cautiously optimistic but not overly optimistic” he says A good omen comes the next morning Snow blankets Princeton—beautiful but also manageable Nearly 6 inches fell not a foot or more GFDL could have stayed open Over the ether Lin can’t resist a final comment “The snow” he writes “is not as bad as forecasted” A previous version of this story incorrectly stated that FV3 scaled more efficiently than MPAS In fact the incremental performance improvement of MPAS with additional computer processors—its scalability—was better than FV3’s although FV3 outperformed MPAS on tests of more than 100000 processors Also John Michalakes’s title was clarified: He is a computer scientist who works on atmospheric models not an atmospheric scientist she was herded onto boats that would take them across the ocean — the dreaded kalapani — to the other end of the world and put to work on the sugar plantations in the Caribbean.

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