Weather Research

High-resolution regional modeling (no supercomputer needed)

Annual precipitation over Colorado as modeled by the low-resolution, global Community Earth System Model (top) compared to the high-resolution, regional Weather Research and Forecasting model (below). (Images courtesy Ethan Gutmann, NCAR.) February 13, 2017 | In global climate models, the hulking, jagged Rocky Mountains are often reduced to smooth, blurry bumps. It's a practical reality that these models, which depict the entire planet, typically need to be run at a relatively low resolution due to constraints on supercomputing resources. But the result, a virtual morphing of peaks into hills, affects the ability of climate models to accurately project how precipitation in mountainous regions may change in the future — information that is critically important to water managers.To address the problem, hydrologists have typically relied on two methods to "downscale" climate model data to make them more useful. The first, which uses statistical techniques, is fast and doesn't require a supercomputer, but it makes many unrealistic assumptions. The second, which uses a high-resolution weather model like the Weather Research and Forecasting model (WRF), is much more realistic but requires vast amounts of computing resources.Now hydrologists at the National Center for Atmospheric Research (NCAR) are developing an in-between option: The Intermediate Complexity Atmospheric Research Model (ICAR) gives researchers increased accuracy using only a tiny fraction of the computing resources."ICAR is about 80 percent as accurate as WRF in the mountainous areas we studied," said NCAR scientist Ethan Gutmann, who is leading the development of ICAR. "But it only uses 1 percent of the computing resources. I can run it on my laptop."Drier mountains, wetter plainsHow much precipitation falls in the mountains — and when — is vitally important for communities in the American West and elsewhere that rely on snowpack to act as a frozen reservoir of sorts. Water managers in these areas are extremely interested in how a changing climate might affect snowfall and temperature, and therefore snowpack, in these regions.But since global climate models with low resolution are not able to accurately represent the complex topography of mountain ranges, they are unsuited for answering these questions.For example, as air flows into Colorado from the west, the Rocky Mountains force that air to rise, cooling it and causing moisture to condense and fall to the ground as snow or rain. Once these air masses clear the mountains, they are drier than they otherwise would have been, so there is less moisture available to fall across Colorado's eastern plains.Low-resolution climate models are not able to capture this mechanism — the lifting of air over the mountains — and so in Colorado, for example, they often simulate mountains that are drier than they should be and plains that are wetter. For a regional water manger, these small shifts could mean the difference between full reservoirs and water shortages."Climate models are useful for predicting large-scale circulation patterns around the whole globe, not for predicting precipitation in the mountains or in your backyard," Gutmann said.Precipitation in millimeters over Colorado between Oct. 1 and May 1 as simulated by the Weather Research and Forecasting model (WRF), the Intermediate Complexity Atmospheric Research model (ICAR), and the observation-based Parameter-Elevation Regressions on Independent Slopes Model. (Images courtesy Ethan Gutmann.)A modeling middle groundA simple statistical fix for these known problems may include adjusting precipitation data to dry out areas known to be too wet and moisten areas known to be too dry. The problem is that these statistical downscaling adjustments don't capture the physical mechanisms responsible for the errors. This means that any impact of a warming climate on the mechanisms themselves would not be accurately portrayed using a statistical technique.That's why using a model like WRF to dynamically downscale the climate data produces more reliable results — the model is actually solving the complex mathematical equations that describe the dynamics of the atmosphere. But all those incredibly detailed calculations also take an incredible amount of computing.A few years ago, Gutmann began to wonder if there was a middle ground. Could he make a model that would solve the equations for just a small portion of the atmospheric dynamics that are important to hydrologists — in this case, the lifting of air masses over the mountains — but not others that are less relevant?"I was studying statistical downscaling techniques, which are widely used in hydrology, and I thought, 'We should be able to do better than this,'" he said. "'We know what happens when you lift air up over a mountain range, so why don’t we just do that?'"Gutmann wrote the original code for the model that would become ICAR in just a few months, but he spent the next four years refining it, a process that's still ongoing.100 times as fastLast year, Gutmann and his colleagues — Martyn Clark and Roy Rasmussen, also of NCAR; Idar Barstad, of Uni Research Computing in Bergen, Norway; and Jeffrey Arnold, of the U.S. Army Corps of Engineers — published a study comparing simulations of Colorado created by ICAR and WRF against observations.The authors found that ICAR and WRF results were generally in good agreement with the observations, especially in the mountains and during the winter. One of ICAR's weaknesses, however, is in simulating storms that build over the plains in the summertime. Unlike WRF, which actually allows storms to form and build in the model, ICAR estimates the number of storms likely to form, given the atmospheric conditions, a method called parameterization.Even so, ICAR, which is freely available to anyone who wants to use it, is already being run by teams in Norway, Austria, France, Chile, and New Zealand."ICAR is not perfect; it's a simple model," Gutmann said. "But in the mountains, ICAR can get you 80 to 90 percent of the way there at 100 times the speed of WRF. And if you choose to simplify some of the physics in ICAR, you can get it close to 1,000 times faster."About the articleTitle: The Intermediate Complexity Atmospheric Research Model (ICAR)Authors: Ethan Gutmann, Idar Barstad, Martyn Clark, Jeffrey Arnold, and Roy RasmussenJournal: Journal of Hydrometeorology, DOI: 10.1175/JHM-D-15-0155.1Funders:U.S. Army Corps of EngineersU.S. Bureau of ReclamationCollaborators:Uni Research Computing in NorwayU.S. Army Corps of EngineersWriter/contact: Laura Snider, Senior Science Writer

Scientists take to the skies to test cloud seeding

February 7, 2017 | Does cloud seeding successfully increase snowfall? This winter, scientists with the National Center for Atmospheric Research (NCAR) are taking part in a field project in Idaho that will help answer the question.The project, called SNOWIE (Seeded and Natural Orographic Wintertime Clouds — the Idaho Experiment), is taking place from Jan. 7 to March 17 in the Payette Basin region north of Boise. A public-private partnership, SNOWIE is led by scientists at the University of Wyoming and other universities in collaboration with NCAR, with funding from the National Science Foundation (which is NCAR's sponsor) and the Idaho Power Company.The research team is using airborne and ground-based radars, high-resolution snow gauges, and computer modeling to gain insights into what happens after clouds are seeded with silver iodide. Snow from winter storms develops when ice crystals form on dust and other particles known as "ice nuclei." In cloud seeding, silver iodide is used to make artificial nuclei to encourage snowflakes to form.Silver iodide is released during the SNOWIE field project in such a way that it disperses downwind to the east, with its highest concentrations forming a zigzag pattern (shown in red). This allows scientists to fly a research aircraft from west to east through both seeded and unseeded regions and compare differences in ice crystal formation. (Image by Lulin Xue, ©UCAR. This image is freely available for media & nonprofit use.)NCAR scientists are focusing much of their work on observations taken by a University of Wyoming King Air plane that is flying though plumes of silver iodide released by a seeding aircraft. The silver iodide disperses downwind in a zigzag pattern, enabling the King Air to intercept it multiple times. The scientists will compare the formation of ice crystals in regions of clouds that are seeded with those that are not.The results can also be used to improve the NCAR-based Weather Research and Forecasting model (WRF), especially its simulation of cloud microphysics related to cloud seeding.Although scientists think that cloud seeding and other types of weather modification can increase precipitation in certain circumstances, the effects are difficult to quantify."NCAR's role in these weather modification experiments is to provide an unbiased viewpoint," said NCAR scientist Sarah Tessendorf, a principal investigator on SNOWIE. "The project uses observations and computer models to determine what is happening during a cloud seeding program and whether it is effective as a water augmentation tool."For more about the project, see the NSF news release.Writer/contactDavid Hosansky, Manager of Media RelationsFundersNational Science FoundationIdaho Power CompanyPartnersUniversity of WyomingIdaho Power CompanyUniversity of Colorado BoulderUniversity of Illinois at Urbana-ChampaignBoise State UniversityCenter for Severe Weather ResearchWeather Modification, Inc.

NCAR-based climate model joins seasonal forecasting effort

January 12, 2017 | An NCAR-based computer model known for global climate projections decades into the future recently joined a suite of other world-class models being used to forecast what may lie just a few months ahead.The Community Earth System Model has long been an invaluable tool for scientists investigating how the climate may change in the long term — decades or even centuries into the future. Last summer, CESM became the newest member of the North American Multi-Model Ensemble (NMME), an innovative effort that combines some techniques typically used in weather forecasting with those used in climate modeling to predict temperature and precipitation seasons in advance. The result is a bridge that helps span the gap between two-week forecasts and decades-long projections.The forecasted temperature anomalies (departures from average) over North America made by the entire NMME suite (top) and by CESM (middle). Observed temperature anomalies for the same period (bottom). Click to enlarge. (Images courtesy NOAA.) But NMME also builds another bridge: this one between operational forecasters, who issue the forecasts society depends on, and researchers. Now a collection of nine climate models, the NMME has proven it produces more accurate seasonal forecasts than any one model alone. It was adopted in May by the National Oceanic and Atmospheric Administration (NOAA) as one of the agency's official seasonal forecasting tools."What is so important about NMME is that it's bringing research to bear on operational forecasts," said Ben Kirtman, a professor of atmospheric sciences at the University of Miami who leads the NMME project. "The marriage between real-time prediction and research has fostered new understandings, identified new problems that we hadn't thought about before, and really opened up new lines of research."A new way to start a climate model runWeather models and climate models have a lot of things in common; for one, they both use mathematical equations to represent the physical processes going on in the atmosphere. Weather models, which are concerned with what’s likely to happen in the immediate future, depend on being fed accurate initial conditions to produce good forecasts. Even if a weather model could perfectly mimic how the atmosphere works, it would need to know what the atmosphere actually looks like now — the temperature and pressure at points across the country, for example — to determine what the atmosphere will look like tomorrow.Climate modelers, on the other hand, are often interested in broad changes over many decades, so the exact weather conditions at the beginning of a simulation are usually not as important. In fact, their impact is quickly drowned out by larger-scale trends that unfold over long time periods.In recent years, however, scientists have become interested in whether climate models — which simulate changes in ocean circulation patterns, sea surface temperatures, and other large-scale phenomena that have lingering impacts on weather patterns — could be initialized with accurate starting conditions and then used to make skillful seasonal forecasts.The NMME project is exploring this question. The global climate models that make up NMME project are all being initialized monthly to create multiple forecasts that stretch a year in advance. Along with CESM, those models include the NCAR-based Community Climate System Model, Version 4, which is being initialized by Kirtman's team at the University of Miami. (See a full list of models below.)Taken together, the individual model forecasts reveal information to forecasters about the amount of uncertainty in the seasonal forecast. If individual forecasts vary substantially, the future is less certain. If they agree, forecasters can have more confidence.The forecasted precipitation anomalies (departures from average) over North America made by the entire NMME suite (top) and by CESM (middle). Observed precipitation anomalies for the same period (bottom). Click to enlarge. (Images courtesy NOAA.)A valuable collection of dataCESM's first seasonal forecast as part of NMME, which was issued for July, August, and September 2016, was perhaps the most accurate of any in the ensemble. The forecast — which called for conditions to be warmer and drier than average across most of the United States — was issued after more than a year of work by NCAR scientists Joseph Tribbia and Julie Caron.All of the models in the NMME suite must be calibrated by running "hindcasts." By comparing the model's prediction of a historical season with what actually happened, the scientists can identify if the model is consistently off in some areas. For example, the model might generally predict that seasons will be wetter or cooler than they actually are for certain regions of the country. These tendencies can then be statistically corrected in future forecasts."We ran 10 predictions every month for a 33-year period and ran each prediction out for one year," Tribbia said. "You can learn a lot about how your model performs when you have so many runs."Once CESM was calibrated, it joined the NMME operational suite of models. But the data generated by the rigorous hindcasting process wasn't cast aside once the calibration was finished. Instead, every modeling group has saved not only monthly data, but also high-frequency daily data that are being stored at NCAR.The trove of historical predictions, along with the new predictions being generated in real-time, are an incredible resource for scientists interested in improving the techniques for initializing climate models and exploring what types of things can, and cannot, be predicted in advance."Predictability research can be a challenge. The NMME dataset allows you to check yourself in a robust way," Kirtman said. "If you think you've found a source of predictability in the hindcast mode, you can then try to do it in real time. It's really exciting — and it really holds your feet to the fire."This year, as much as 18.5 terabytes of NMME data were downloaded from NCAR monthly, according to NCAR's Eric Nienhouse, who oversees the data archive.Now that CESM is an active part of NMME, Tribbia and Caron will also be diving into the data."Now the fun begins," Caron said. "We get to start looking at the data to see how we're doing, and what we might change in the future to make our seasonal forecasts better."Models that make up NMME:NCEP CFSv2: National Centers for Environmental Prediction Climate Forecast System Version 2 (NOAA)CMC1 CanCM3: Canadian Meteorological Centre/Canadian Centre for Climate Modeling and AnalysisCMC2 CanCM4: Canadian Meteorological Centre/Canadian Centre for Climate Modeling and AnalysisGFDL FLOR: Geophysical Fluid Dynamics Laboratory Forecast-oriented Low Ocean Resolution (NOAA)GFDL CM2.1: Geophysical Fluid Dynamics Laboratory Coupled Climate Model Version 2.1 (NOAA)NCAR CCSM4: National Center for Atmospheric Research Community Climate System Model Version 4NASA GEOS5: NASA Goddard Earth Observing System Model Version 5NCAR CESM: National Center for Atmospheric Research Community Earth System ModelIMME: National Centers for Environmental Prediction International Multi-Model Ensemble (NOAA)Writer/contact:Laura Snider, Senior Science Writer  

Two NCAR scientists honored by American Geophysical Union

BOULDER, Colo. — Martyn Clark, senior scientist at the National Center for Atmospheric Research (NCAR), will be honored next week as a Fellow of the American Geophysical Union (AGU) for his exceptional contribution to Earth science.Clark is an expert in the numerical modeling and prediction of hydrologic processes. His current research includes developing new modeling methods to improve streamflow forecasts and better understand climate change impacts on regional water resources. Clark, who grew up in Christchurch, New Zealand, has authored or co-authored 135 journal articles since receiving his Ph.D. from the University of Colorado in 1998.NCAR Senior Scientist Martyn Clark (©UCAR. Photo by Carlye Calvin. This image is freely available for media & nonprofit use.)"This well-deserved honor reflects Martyn's eminent work in the increasingly critical area of water-resource prediction and management," said NCAR Director James W. Hurrell.Clark said he was delighted to see NCAR's hydrologic modeling recognized. "Hydrology is beginning to play a much stronger role in addressing important interdisciplinary science questions about Earth System change, such as how changes in the terrestrial water cycle affect biological productivity and how groundwater can buffer water stress in ecosystems and human societies. It's exciting to advance modeling capabilities in these areas."NCAR Senior Scientist Bette Otto-Bliesner. (©UCAR. Photo by Carlye Calvin. This image is freely available for media & nonprofit use.)Clark is among 60 individuals from eight countries recognized as Fellows this year; only one in one thousand AGU members receive this recognition in any given year. Nearly 40 percent of this year's fellows are from the 110 member colleges and universities of the University Corporation for Atmospheric Research (UCAR), which manages NCAR. This year's class will be honored next Wednesday at the 2016 AGU Fall Meeting in San Francisco.NCAR Senior Scientist Bette Otto-Bliesner, who was named an AGU Fellow last year, is being honored by her peers in the Paleoceanography and Paleoclimatology Focus Group and Ocean Sciences Section by being asked to give the 2016 Emiliani Lecture. She will give the lecture next Wednesday at the AGU Fall Meeting on the topic of "Resolving Some Puzzles of Climate Evolution Since the Last Glacial Maximum: A Melding of Paleoclimate Modeling and Data."The AGU, dedicated to advancing Earth and space sciences for the benefit of society, is a not-for-profit, professional organization representing 60,000 members in more than 140 countries. 

Extreme downpours could increase fivefold across parts of the U.S.

BOULDER, Colo. — At century's end, the number of summertime storms that produce extreme downpours could increase by more than 400 percent across parts of the United States — including sections of the Gulf Coast, Atlantic Coast, and the Southwest — according to a new study by scientists at the National Center for Atmospheric Research (NCAR).The study, published today in the journal Nature Climate Change, also finds that the intensity of individual extreme rainfall events could increase by as much as 70 percent in some areas. That would mean that a storm that drops about 2 inches of rainfall today would be likely to drop nearly 3.5 inches in the future."These are huge increases," said NCAR scientist Andreas Prein, lead author of the study. "Imagine the most intense thunderstorm you typically experience in a single season. Our study finds that, in the future, parts of the U.S. could expect to experience five of those storms in a season, each with an intensity as strong or stronger than current storms."The study was funded by the National Science Foundation (NSF), NCAR's sponsor, and the Research Partnership to Secure Energy for America.“Extreme precipitation events affect our infrastructure through flooding, landslides and debris flows,” said Anjuli Bamzai, program director in NSF’s Directorate for Geosciences, which funded the research.  “We need to better understand how these extreme events are changing. By supporting this research, NSF is working to foster a safer environment for all of us.”The figure shows the expected increase in the number of summertime storms that produce extreme precipitation at century's end compared to the period 2000 - 2013. (©UCAR. Courtesy Andreas Prein, NCAR. This image is freely available for media & nonprofit use.)A year of supercomputing timeAn increase in extreme precipitation is one of the expected impacts of climate change because scientists know that as the atmosphere warms, it can hold more water, and a wetter atmosphere can produce heavier rain. In fact, an increase in precipitation intensity has already been measured across all regions of the U.S. However, climate models are generally not able to simulate these downpours because of their coarse resolution, which has made it difficult for researchers to assess future changes in storm frequency and intensity.For the new study, the research team used a new dataset that was created when NCAR scientists and study co-authors Roy Rasmussen, Changhai Liu, and Kyoko Ikeda ran the NCAR-based Weather Research and Forecasting (WRF) model at a resolution of 4 kilometers, fine enough to simulate individual storms. The simulations, which required a year to run, were performed on the Yellowstone system at the NCAR-Wyoming Supercomputing Center.Prein and his co-authors used the new dataset to investigate changes in downpours over North America in detail. The researchers looked at how storms that occurred between 2000 and 2013 might change if they occurred instead in a climate that was 5 degrees Celsius (9 degrees Fahrenheit) warmer — the temperature increase expected by the end of the century if greenhouse gas emissions continue unabated.Prein cautioned that this approach is a simplified way of comparing present and future climate. It doesn't reflect possible changes to storm tracks or weather systems associated with climate change. The advantage, however, is that scientists can more easily isolate the impact of additional heat and associated moisture on future storm formation."The ability to simulate realistic downpours is a quantum leap in climate modeling. This enables us to investigate changes in hourly rainfall extremes that are related to flash flooding for the very first time," Prein said. "To do this took a tremendous amount of computational resources."Impacts vary across the U.S.The study found that the number of summertime storms producing extreme precipitation is expected to increase across the entire country, though the amount varies by region. The Midwest, for example, sees an increase of zero to about 100 percent across swaths of Nebraska, the Dakotas, Minnesota, and Iowa. But the Gulf Coast, Alabama, Louisiana, Texas, New Mexico, Arizona, and Mexico all see increases ranging from 200 percent to more than 400 percent.The study also found that the intensity of extreme rainfall events in the summer could increase across nearly the entire country, with some regions, including the Northeast and parts of the Southwest, seeing particularly large increases, in some cases of more than 70 percent.A surprising result of the study is that extreme downpours will also increase in areas that are getting drier on average, especially in the Midwest. This is because moderate rainfall events that are the major source of moisture in this region during the summertime are expected to decrease significantly while extreme events increase in frequency and intensity. This shift from moderate to intense rainfall increases the potential for flash floods and mudslides, and can have negative impacts on agriculture.The study also investigated how the environmental conditions that produce the most severe downpours might change in the future. In today's climate, the storms with the highest hourly rainfall intensities form when the daily average temperature is somewhere between 20 and 25 degrees C (68 to 77 degrees F) and with high atmospheric moisture. When the temperature gets too hot, rainstorms become weaker or don't occur at all because the increase in atmospheric moisture cannot keep pace with the increase in temperature. This relative drying of the air robs the atmosphere of one of the essential ingredients needed to form a storm.In the new study, the NCAR scientists found that storms may continue to intensify up to temperatures of 30 degrees C because of a more humid atmosphere. The result would be much more intense storms."Understanding how climate change may affect the environments that produce the most intense storms is essential because of the significant impacts that these kinds of storms have on society," Prein said.About the articleTitle: The future intensification of hourly precipitation extremesAuthors: Andreas F. Prein, Roy M. Rasmussen, Kyoko Ikeda, Changhai Liu, Martyn P. Clark, and Greg J. HollandJournal: Nature Climate Change, DOI: 10.1038/NCLIMATE3168Writer:Laura Snider, Senior Science Writer and Public Information Officer

A favorable forecast for Kenyan students

November 30, 2016 | As scientists expand a program to provide critically needed weather observations in developing countries, they are forging a partnership with local schoolchildren and their teachers.The students and teachers are helping to oversee and maintain innovative weather stations, built largely with 3D-printed parts, at four schools in Kenya. By transmitting information about temperature, rainfall, and other weather parameters, the stations can help alert communities to floods and other potential disasters, as well as provide improved weather forecasts to local farmers, who are deciding when to plant and fertilize crops.NCAR scientist Paul Kucera describes the various components of the 3D-PAWS at the Sirua Aulo Maasai High School. (©UCAR. Photo by Kristin Wegner. This image is freely available for media & nonprofit use.) The weather stations, known as 3D-PAWS (for 3D-Printed Automated Weather Stations), are built with components that can be easily replaced if they wear out in the field. They were designed by weather experts at the National Center for Atmospheric Research (NCAR) and its managing entity, the University Corporation for Atmospheric Research (UCAR)."In my 30 years of doing fieldwork, this is one of the best deployments I've ever had," said NCAR scientist Paul Kucera. "At every school, we were joined by hundreds of students and dozens of teachers who wanted to learn more about the weather stations and the value of these forecasts."The weather stations were installed as a partnership with the Global Learning and Observations to Benefit the Environment (GLOBE) program, an international science and education initiative that encompasses tens of thousands of schools. This approach means that 3D-PAWS serves the dual purpose of educating students and improving forecasts."This is a great partnership to now extend our weather stations to schools," said Kristin Wegner, a project manager with the GLOBE Implementation Office, based at UCAR. "There is so much enthusiasm among the teachers and students because it's such a great learning tool as well as helping their communities."Students will learn about local weather and climate by comparing their weather observations to those taken at other schools using science protocols established by GLOBE. They can also assess the impacts of climate change on society and the environment, as well as see how the observations help with farming, flood prediction, and other applications.The installments took place during GLOBE's biannual Lake Victoria Learning Expedition, in which students and scientists from around the world explore the environment around the lake and discuss potential research collaborations. The expedition was coordinated by GLOBE Africa Regional Coordinator Mark Brettenny and  GLOBE Kenya Assistant Country Coordinator Charles Mwangi. Schools also received equipment donated from Youth Learning as Citizen Environmental Scientists.Needed: more stationsLike many developing countries, Kenya does not have detailed forecasts, partly because weather stations are scarce. The density of stations in Africa is eight times lower than recommended by the World Meteorological Organization. Building out a network can be prohibitively expensive, with a single commercial weather station often costing $10,000 to $20,000, plus ongoing funding for maintenance and replacing worn-out parts.To fill this need, UCAR and NCAR scientists have worked for years to come up with a weather station that is inexpensive and easy to fix and can be adapted to the needs of the host country. The resulting 3D-PAWS are constructed out of plastic parts that are custom designed and can be run off a 3D printer, along with off-the-shelf sensors and a basic, credit card-sized computer developed for schoolchildren.The total cost is about $300 per station. As the stations age, the host country can easily have replacement parts printed.Funding for the project comes from the U.S. Agency for International Development's Office of Foreign Disaster Assistance and the U.S. National Weather Service.Scientists installed the 3D-PAWS in Zambia earlier this year. Kenya is the second country to receive them."We're looking forward to installing more stations," Wegner said. "Additional schools are already asking about them."FundersU.S. Agency for International Development's Office of Foreign Disaster Assistance U.S. National Weather Service.PartnerGlobal Learning and Observations to Benefit the Environment (GLOBE)Writer/contact:David Hosansky, Manager of Media Relations

High-res model captures explosive increase in hurricane strength

Nov. 1, 2016 | Last fall, Hurricane Patricia exploded from a Category 1 to a record-breaking Category 5 storm in just 24 hours.Patricia's rapid intensification off the coast of Mexico blindsided forecasters, whose models vastly underestimated how strong the hurricane would become. Patricia — and more recently Hurricane Matthew, which also jumped from Category 1 to Category 5 in less than a day — highlight a weakness in predictive capabilities. While we've made great strides in forecasting a hurricane's track, forecasting its intensity remains a challenge.New research using a sophisticated weather model based at the National Center for Atmospheric Research (NCAR) offers some clues about how these forecasts can be improved.The scientists — Ryder Fox, an undergraduate researcher at the New Mexico Institute for Mining and Technology, and Falko Judt, an NCAR postdoctoral researcher — found that an advanced version of the Weather Research and Forecasting model (WRF-ARW) could accurately forecast Hurricane Patricia's rapid intensification when run at a high enough resolution."Because Patricia was so out of bounds — the hurricane broke records for high wind speed and low pressure — we didn't think our model would actually be able to capture its peak intensity," Judt said. "The fact that the model nailed it took us by surprise."Hurricane Patricia approaches the west coast of Mexico on Oct. 23, 2015. (Image courtesy NASA.)   Judt and Fox think that the model's resolution was one important key to its success. The scientists ran WRF-ARW with a 1-kilometer (0.6-mile) resolution on the Yellowstone system at the NCAR-Wyoming Supercomputing Center. The models being used to actually forecast Patricia at the time had resolutions between 3 and 15 kilometers."Going to 1-kilometer resolution may be especially important for very strong storms, because they tend to have an eyewall that's really small," Judt said. "Patricia's eye was just 13 kilometers across at its most intense."Still, the researchers caution that more simulations are needed to be sure that the model's ability to capture Hurricane Patricia's intensity wasn't a fluke."We're not sure yet that, if we ran the same model for Hurricane Matthew, we would forecast that storm correctly," Judt said. "There are so many things that can go wrong with hurricane forecasting."To address this uncertainty, Judt and Fox have begun running the model additional times, each with slightly tweaked starting conditions. The preliminary results show that while each model run is distinct, each one also captures the rapid intensification of the storm. This relative harmony among the ensemble of model runs suggests that WRF-ARW does a good job of reproducing the storm-friendly environmental conditions that Patricia formed in."The set-up that nature created may have allowed for a storm to intensify no matter what," Judt said. "The sea surface was downright hot, the air was really moist, and the wind shear, at times, was virtually zero. It was a very ripe environment."Fox began working with Judt through SOARS, the Significant Opportunities in Atmospheric Research program, which pairs young researchers with NCAR mentors. An undergraduate-to-graduate bridge program, SOARS is designed to broaden participation in the atmospheric and related sciences."The SOARS program means everything — not just to my ability to do this type of research, but also to grow as a scientist and to find my place within the scientific community," said Fox, who published the research results as an article in Physics Today.Fox hopes the research on accurate modeling of Hurricane Patricia may lead to improved early warning systems that could help prevent loss of life."My personal passion regarding severe weather research lies in improved early warning systems," Fox said, "which optimally lead to lower death counts."

Soil moisture, snowpack data could help predict 'flash droughts'

BOULDER, Colo. — New research suggests that "flash droughts" — like the one that unexpectedly gripped the Southern Rockies and Midwest in the summer of 2012 — could be predicted months in advance using soil moisture and snowpack data. Researchers at the National Center for Atmospheric Research (NCAR) analyzed the conditions leading up to the 2012 drought, which ultimately caused $30 billion in economic losses, looking for any warning signs that a drought was on the way. In a study funded by the National Science Foundation and published in the Journal of Geophysical Research-Atmospheres, the scientists find that observations of snowmelt and soil moisture could have predicted the ensuing drought up to four months in advance."The 2012 drought over the Midwest was one of the most severe and extensive U.S. droughts since the 1930s Dust Bowl, but it was also extremely challenging to predict," said Debasish PaiMazumder, lead author of the study. "This study demonstrated the potential to improve seasonal drought outlooks in the future, giving farmers, water planners, and others more time to prepare."The official U.S. Drought Monitor issued on Aug. 21, 2012. The map shows the exceptionally severe drought across the middle of the country. Just three months before, drought forecasts failed to predict that a drought was on the way. Click to enlarge. (Image courtesy National Drought Mitigation Center.)Seasonal drought forecasts issued in May 2012 for the upcoming summer did not foresee a drought forming in the country's midsection. But by the end of August, a drought that had started in the Southern Rockies had spread across the Midwest, parching Oklahoma, Kansas, Nebraska, and Missouri.These flash droughts — which form and intensify rapidly — can catch forecasters off guard because they are not preceded by any large-scale climate patterns that could act as a warning signal. For example, one contributor to the recent California drought was a persistent high-pressure system parked off the west coast of Canada that deflected storms away from the state. Because forecasters could identify the high-pressure system, they could also accurately predict fewer storms and a worsening of the drought.Previous research has shown that looking at soil moisture alone could improve the lead-time of drought predictions by one to two months. PaiMazumder and NCAR colleague James Done were interested in whether they could extend this further by adding snowpack into the equation.“Advance knowledge of a drought even a month or two ahead of time can greatly minimize the effects on society,” said Anjuli Bamzai, program director in NSF’s Division of Atmospheric and Geospace Sciences, which funded the research.  “This study highlights the role of snowpack and soil moisture conditions in predicting the sudden onset of drought.”To explore the physical connections among snowpack, soil moisture, and drought, the researchers analyzed data collected between 1980-2012. To supplement those observations, they also explored the physical connections in a new NCAR-based community Weather Research and Forecasting (WRF) model dataset comprising 24 simulations of the period 1990-2000 and 2012. Because each simulation was run with small tweaks to the way the model represents atmospheric physics, the result was a broad look at different climate scenarios that could have plausibly unfolded during the study period."The model helped us get a handle on how robust the relationships between snowpack, soil moisture, and drought are," Done said. "The stronger the relationship, the better a predictor is."While observations of snowpack and soil moisture could have helped predict the 2012 drought, the method does not replace other drought prediction measures that identify large-scale phenomena that frequently lead to drought conditions."This is another ingredient that could be used when making seasonal drought forecasts," Done said. "But it's not the only ingredient, and for many droughts that are tied to large-scale precursors, it may not be the most important one." About the articleTitle: Potential Predictability Sources of the 2012 US Drought in Observations and a Regional Model EnsembleAuthors: Debasish PaiMazumder and James DoneJournal: Journal of Geophysical Research – Atmospheres, DOI: 10.1002/2016JD025322Writer:Laura Snider, Senior Science Writer and Public Information Officer   

Advanced computer model focuses on Hurricane Matthew

Oct. 6, 2016 | As Hurricane Matthew churns toward the southeastern U.S. coast, scientists at the National Center for Atmospheric Research (NCAR) are testing an advanced research computer model to see how well it can predict the powerful storm's track and intensity.The Model for Prediction Across Scales (MPAS) uses an innovative software approach that allows scientists to focus on regional conditions while still capturing far-flung atmospheric processes that can influence the storm in question. This is a contrast to the forecast models typically used to track hurricanes today, which cannot simultaneously capture both global and local atmospheric processes.The experimental MPAS model simulates Hurricane Matthew hitting the Southeast. To see a range of model output, visit the MPAS tropical cyclone website. MPAS is able to do both because it uses a flexible mesh that allows it to zoom into higher resolution in some areas — over hurricane breeding grounds, for example — while zooming out over the rest of Earth. This ability to vary resolution across the globe requires a small fraction of the computer power needed to have high resolution everywhere.By testing MPAS during hurricane season, the research team can determine the adjustments that need to be made to the model while gaining insights into how to improve hurricane forecasting in the future."This is an experimental effort," said Chris Davis, a senior scientist and director of NCAR's Mesoscale and Microscale Meteorology Laboratory. "We're doing this to see if we can find systematic biases in the model so we can improve simulations of the tropics in general and hurricanes in particular."Davis and the other members of the research team, including NCAR scientists David Ahijevych, Sang-Hun Park, Bill Skamarock, and Wei Wang, are running MPAS once a day on NCAR's Yellowstone supercomputer, inputting various ocean and atmospheric conditions to see how it performs. The work is supported by the National Science Foundation and the Korea Institute of Science and Technology Information.Even though they are just tests, Davis said the MPAS simulations are often comparable with official forecast models such as those run by the National Hurricane Center and the European Centre for Medium-Range Weather Forecasts. As Matthew was in its early stages, in fact, MPAS did a better job than other models in simulating the northward movement of the storm from the Caribbean Sea toward the Florida coast.The scientists will analyze how MPAS performed and share the results with colleagues in the meteorological community. It's a step in an ongoing research effort to better predict the formation and behavior of hurricanes."We run the model even when the tropics are quiet, but an event like Matthew gives us a special opportunity to see what contributes to errors in tropical cyclone prediction," Davis said. "While a major hurricane can have catastrophic impacts, we hope to learn from it and make computer models even better in the future."Funders:National Science FoundationKorea Institute of Science and Technology InformationWriter/contact:David Hosansky, Manager of Media Relations

UCAR congressional briefing highlights flood, drought prediction

WASHINGTON — The nation is poised to make major advances in "water intelligence" with more detailed forecasts of floods, streamflow, and potential drought conditions, a panel of experts said at a congressional briefing today.The briefing, sponsored by the University Corporation for Atmospheric Research (UCAR), highlighted the new National Water Model, a comprehensive system for forecasting water resources from coast to coast. The technology underpinning the model, launched last month by the National Oceanic and Atmospheric Administration (NOAA), was developed by the National Center for Atmospheric Research (NCAR) and its collaborators at universities, the National Science Foundation and other federal agencies, and the private sector."The new forecast model is really a quantum leap forward and will help safeguard Americans from major floods and other precipitation events," said UCAR President Antonio J. Busalacchi, who introduced the panel. "It bridges the gap between research and operations, generating real-time forecasts to help vulnerable communities and protect lives and property."UCAR manages NCAR on behalf of the National Science Foundation."Through a series of partnerships, it's possible to provide consistent, high-resolution, integrated water analyses, predictions, and data to address critical unmet information and service gaps," said Edward Clark, director of the Geo-Intelligence Office of Water Prediction at the NOAA National Water Center.Scientists generated this inundation forecast during Houston-area flooding earlier this year in a demonstration of  advanced computer modeling technology. (©UCAR. Image by David Gochis, NCAR. This image is freely available for media & nonprofit use.)Unlike past streamflow models, which provided forecasts every few hours and only for specific points along major river systems, the new system continuously simulates conditions at 2.7 million locations along rivers, streams, and their tributaries across the contiguous United States. It paves the way for the biggest improvement in flood forecasting in the nation's history."The National Water Model provides a different way of thinking about continental hydrology by providing a view of a connected plumbing network from the mountains to the ocean," said panelist Richard Hooper, executive director of the Consortium of Universities for the Advancement of Hydrologic Science (CUAHSI). "Previously, hydrologists had considered river basins as discrete units rather than this river-continuum approach. This change in view opens up new areas of research that will improve our ability to predict not just floods but other aspects of water resources, including water quality and the impacts of droughts."Thanks to ongoing research, the National Water Model is expected to provide increasingly detailed street-level forecasts, inundation maps, and additional features such as water quality forecasts. Scientists are working on incorporating more processes, such as soil saturation and the amount of water drawn up by vegetation."By dramatically increasing the geographic coverage as well as the lead times for forecasts, the National Water Model is ushering in a new era in flood and flash flood forecasting," said John McHenry, chief scientist of advanced meteorological systems for Baron Services. "Business, industry, and the general public will benefit through reduction in lost lives and property."The panelists emphasized the importance of water resources to the major sectors of the U.S. economy. They warned that the nation is facing myriad water-related challenges ranging from growing demand to increasingly costly floods and droughts. Meeting those challenges will require continued coordination among research organizations, universities, the private sector, and federal, state, and local agencies."Beyond developing a new computer model, we're building a community by sharing resources, tools, and ideas," said NCAR scientist David Gochis. "The scientists are engaging with practitioners and decision makers to make the system as usable as possible."The development team at NCAR worked with scientists at NOAA, the U.S. Geological Survey, and universities to adapt WRF-Hydro to serve as the first version of the National Water Model.The panelists also discussed the need for better water intelligence among diverse communities across the country. For example, Ryan Emanuel, associate professor at North Carolina State University's Department of Forestry and Environmental Resources, noted that indigenous tribes across the nation are particularly vulnerable to drought and flooding for a range of cultural, historical, and economic reasons."Indigenous peoples across the United States are diverse, but one common theme is that water is sacred," said Emanuel, a member of the Lumbee Tribe of North Carolina. "It's not only critical for life, but it is life itself. Beyond the tools, the models, and the management lies the knowledge of the original inhabitants of this nation that water binds us all to a common fate."The event is the latest in a series of UCAR congressional briefings about critical topics in the Earth system sciences. Past briefings have focused on predicting space weather, aviation weather safety, the state of the Arctic, hurricane prediction, and potential impacts of El Niño.

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