Weather Research

UCAR collaboration with The Weather Company to improve weather forecasts worldwide

BOULDER, Colo. — The University Corporation for Atmospheric Research (UCAR) today announced a new collaboration with The Weather Company, an IBM business, to improve global weather forecasting. The partnership brings together cutting-edge computer modeling developed at the National Center for Atmospheric Research (NCAR) with The Weather Company's meteorological science and IBM's advanced compute equipment."This is a major public-private partnership that will advance weather prediction and generate significant benefits for businesses making critical decisions based on weather forecasts," said UCAR President Antonio J. Busalacchi. "We are gratified that taxpayer investments in the development of weather models are now helping U.S. industries compete in the global marketplace."UCAR, a nonprofit consortium of 110 universities focused on research and training in the atmospheric and related Earth system sciences, manages NCAR on behalf of the National Science Foundation.With the new agreement, The Weather Company will develop a global forecast model based on the Model for Prediction Across Scales (MPAS), an innovative software platform developed by NCAR and the Los Alamos National Laboratory.The Model for Prediction Across Scales (MPAS) enables forecasters to combine a global view of the atmosphere with a higher-resolution view of a particular region, such as North America. (@UCAR. This image is freely available for media & nonprofit use.)MPAS offers a unique way of simulating the global atmosphere while providing users with more flexibility when focusing on specific regions of interest. Unlike traditional three-dimensional models that calculate atmospheric conditions at multiple points within a block-shaped grid, it uses a hexagonal mesh resembling a honeycomb that can be stretched wide in some regions and compressed for higher resolution in others. This enables forecasters to simultaneously capture far-flung atmospheric conditions that can influence local weather, as well as small-scale features such as vertical wind shear that can affect thunderstorms and other severe weather.Drawing on the computational power of GPUs — graphics processing units — such as those being used in a powerful new generation of IBM supercomputers, and on the expertise of NCAR and The Weather Company, the new collaboration is designed to push the capabilities of MPAS to yield more accurate forecasts with longer lead times. The results of NCAR's work will be freely available to the meteorological community. Businesses, from airlines to retailers, as well as the general public, stand to benefit.Mary Glackin, head of weather science and operations for The Weather Company, said, "As strong advocates for science, we embrace strong public-private collaborations that understand the value science brings to society, such as our continued efforts with UCAR to advance atmospheric and computational sciences.""As this partnership shows, society is on the cusp of a new era in weather prediction, with more precise short-range forecasts as well as longer-term forecasts of seasonal weather patterns," Busalacchi said. "These forecasts are important for public health and safety, as well as enabling companies to leverage economic opportunities in ways that were never possible before."About The Weather CompanyThe Weather Company, an IBM Business, helps people make informed decisions and take action in the face of weather. The company offers weather data and insights to millions of consumers, as well as thousands of marketers and businesses via Weather’s API, its business solutions division, and its own digital products from The Weather Channel ( and Weather Underground (

Offshore wind turbines vulnerable to Category 5 hurricane gusts

NCAR scientist George Bryan is a co-author of a new study appearing in the journal Geophysical Research Letters. The following is an excerpt from a news release by the University of Colorado Boulder. Offshore wind turbines built according to current standards may not be able to withstand the powerful gusts of a Category 5 hurricane, creating potential risk for any such turbines built in hurricane-prone areas, new University of Colorado Boulder-led research shows.The study, which was conducted in collaboration with the National Center for Atmospheric Research in Boulder, Colorado, and the U.S. Department of Energy’s National Renewable Energy Laboratory in Golden, Colorado, highlights the limitations of current turbine design and could provide guidance for manufacturers and engineers looking to build more hurricane-resilient turbines in the future.Offshore wind-energy development in the U.S. has ramped up in recent years, with projects either under consideration or already underway in most Atlantic coastal states from Maine to the Carolinas, as well as the West Coast and Great Lakes. The country’s first utility-scale offshore wind farm, consisting of five turbines, began commercial operation in December 2016 off the coast of Rhode Island.Turbine design standards are governed by the International Electrotechnical Commission (IEC). For offshore turbines, no specific guidelines for hurricane-force winds exist. Offshore turbines can be built larger than land-based turbines, however, owing to a manufacturer’s ability to transport larger molded components such as blades via freighter rather than over land by rail or truck.Read the full news release.

Warmer temperatures cause decline in key runoff measure

BOULDER, Colo. — Since the mid-1980s, the percentage of precipitation that becomes streamflow in the Upper Rio Grande watershed has fallen more steeply than at any point in at least 445 years, according to a new study led by the National Center for Atmospheric Research (NCAR).While this decline was driven in part by the transition from an unusually wet period to an unusually dry period, rising temperatures deepened the trend, the researchers said.The study paints a detailed picture of how temperature has affected the runoff ratio — the amount of snow and rain that actually makes it into the river — over time, and the findings could help improve water supply forecasts for the Rio Grande, which is a source of water for an estimated 5 million people.The study results also suggest that runoff ratios in the Upper Rio Grande and other neighboring snow-fed watersheds, such as the Colorado River Basin, could decline further as the climate continues to warm.Sandhill cranes in the San Luis Valley of Colorado. The mountains ringing the valley form the headwaters of the Rio Grande River, which flows south into New Mexico and along the border between Texas and Mexico. (Photo courtesy of the National Park Service.)"The most important variable for predicting streamflow is how much it has rained or snowed," said NCAR scientist Flavio Lehner, lead author of the study. "But when we looked back hundreds of years, we found that temperature has also had an important influence  — which is not currently factored into water supply forecasts. We believe that incorporating temperature in future forecasts will increase their accuracy, not only in general but also in the face of climate change."The study, published in the journal Geophysical Research Letters, was funded by the Bureau of Reclamation, Army Corps of Engineers, National Oceanic and Atmospheric Administration (NOAA), and National Science Foundation, which is NCAR's sponsor.Co-authors of the paper are Eugene Wahl, of NOAA; Andrew Wood, of NCAR; and Douglas Blatchford and Dagmar Llewellyn, both of the Bureau of Reclamation.Over-predicting water supplyBorn in the Rocky Mountains of southern Colorado, the Rio Grande cuts south across New Mexico before hooking east and forming the border between Texas and Mexico. Snow piles up on the peaks surrounding the headwaters throughout the winter, and in spring the snowpack begins to melt and feed the river.The resulting streamflow is used both by farmers and cities, including Albuquerque, New Mexico, and El Paso, Texas, and water users depend on the annual water supply forecasts to determine who gets how much of the river. The forecast is also used to determine whether additional water needs to be imported from the San Juan River, on the other side of the Continental Divide, or pumped from groundwater.Current operational streamflow forecasts depend on estimates of the amount of snow and rain that have fallen in the basin, and they assume that a particular amount of precipitation and snowpack will always yield a particular amount of streamflow.In recent years, those forecasts have tended to over-predict how much water will be available, leading to over-allocation of the river. In an effort to understand this changing dynamic, Lehner and his colleagues investigated how the relationship between precipitation and streamflow, known as the runoff ratio, has evolved over time.Precipitation vs. streamflow: Tree rings tell a new storyThe scientists used tree ring-derived streamflow data from outside of the Upper Rio Grande basin to reconstruct estimates of precipitation within the watershed stretching back to 1571. Then they combined this information with a separate streamflow reconstruction within the basin for the same period. Because these two reconstructions were independent, it allowed the research team to also estimate runoff ratio for each year: the higher the ratio, the greater the share of precipitation that was actually converted into streamflow."For the first time, we were able to take these two quantities and use them to reconstruct runoff ratios over the past 445 years," Wahl said.They found that the runoff ratio varies significantly from year to year and even decade to decade. The biggest factor associated with this variation was precipitation. When it snows less over the mountains in the headwaters of the Rio Grande, not only is less water available to become streamflow, but the runoff ratio also decreases. In other words, a smaller percentage of the snowpack becomes streamflow during drier years.But the scientists also found that another factor affected the runoff ratio: temperature. Over the last few centuries, the runoff ratio was reduced when temperatures were warmer. And the influence of temperature strengthened during drier years: When the snowpack was shallow, warm temperatures reduced the runoff ratio more than when the snowpack was deep, further exacerbating drought conditions. The low runoff ratios seen in dry years were two and a half to three times more likely when temperatures were also warmer."The effect of temperature on runoff ratio is relatively small compared to precipitation," Lehner said. "But because its greatest impact is when conditions are dry, a warmer year can make an already bad situation much worse."A number of factors may explain the influence of temperature on runoff ratio. When it's warmer, plants take up more water from the soil and more water can evaporate directly into the air. Additionally, warmer temperatures can lead snow to melt earlier in the season, when the days are shorter and the angle of the sun is lower. This causes the snow to melt more slowly, allowing the meltwater to linger in the soil and giving plants added opportunity to use it.The extensive reconstruction of historical runoff ratio in the Upper Rio Grande also revealed that the decline in runoff ratio over the last three decades is unprecedented in the historical record. The 1980s were an unusually wet period for the Upper Rio Grande, while the 2000s and 2010s have been unusually dry. Pair that with an increase in temperatures over the same period, and the decline in runoff ratio between 1986 and 2015 was unlike any other stretch of that length in the last 445 years.The graph shows changes to runoff ratio in the Upper Rio Grande over time. (Image courtesy Flavio Lehner, NCAR.) Upgrading the old approachesThis new understanding of how temperature influences runoff ratio could help improve water supply forecasts, which do not currently consider whether the upcoming months are expected to be hotter or cooler than average. The authors are now assessing the value of incorporating seasonal temperature forecasts into water supply forecasts to account for these temperature influences. The study complements a multi-year NCAR project funded by the Bureau of Reclamation and the Army Corps of Engineers that is evaluating prospects for enhancing seasonal streamflow forecasts for reservoir management.“Forecast users and stakeholders are increasingly raising questions about the reliability of forecasting techniques if climate is changing our hydrology," said Wood, who led the effort. "This study helps us think about ways to upgrade one of our oldest approaches — statistical water supply forecasting — to respond to recent trends in temperature. Our current challenge is to find ways to make sure the lessons of this work can benefit operational streamflow forecasts.” Because the existing forecasting models were calibrated on conditions in the late 1980s and 1990s, it's not surprising that they over-predicted streamflow in the drier period since 2000, Lehner said."These statistical models often assume that the climate is stable," Lehner said. "It's an assumption that sometimes works, but statistical forecasting techniques will struggle with any strong changes in hydroclimatology from decade to decade, such as the one we have just experienced."Lehner is a Postdoc Applying Climate Expertise (PACE) fellow, which is part of the Cooperative Programs for the Advancement of Earth System Science (CPAESS). CPAESS is a community program of the University Corporation for Atmospheric Research (UCAR).About the articleTitle: Assessing recent declines in Upper Rio Grande River runoff efficiency from a paleoclimate perspectiveAuthors: Flavio Lehner, Eugene R. Wahl, Andrew W. Wood, Douglas B. Blatchford, and Dagmar LlewellynJournal: Geophysical Research Letters, DOI: 10.1002/2017GL073253Writer:Laura Snider, Senior Science Writer and Public Information Officer

Drones need aviation forecasts, too

UAS Weather ForumWhat: An opportunity for stakeholders from the UAS community -- including manufacturers, operators, regulators, and researchers -- to come together to discuss weather effects on drones and the support needed to mitigate those impacts.When: 9 a.m. - 12 p.m., Monday, May 8, 2017Where: XPONENTIAL, Kay Bailey Hutchison Convention Center, Dallas Click here for more information.April 13, 2017 | The possible future uses for drones are spectacularly diverse. Unmanned aircraft systems (UAS) could make door-to-door deliveries, search for a lost hiker, survey agricultural crops, inspect infrastructure, or collect scientific data from difficult-to-reach places, among other things. Already Amazon is experimenting with drone delivery of packages, for example, and BNSF Railway is testing the use of drones to inspect hundreds of miles of railroad tracks.Yet the ultimate success of efforts like these may hinge on a good weather forecast. The National Center for Atmospheric Research (NCAR), long a trusted provider of critical weather information to the aviation industry, is beginning to lend its expertise to the UAS community as well.Staff in NCAR's Research Applications Laboratory are already working with NASA to provide low-level turbulence forecasts for NASA's project to create a UAS Traffic Management (UTM) system, which would be similar to the air traffic control system for crewed airplanes. And in May, the NCAR team is hosting a UAS Weather Forum in Dallas. The forum will be co-located with XPONENTIAL, a conference on "all things unmanned" that is organized by the Association of Unmanned Vehicle Systems International."As the aircraft get smaller and smaller, the challenges of providing the needed weather information increase," said NCAR scientist Matthias Steiner, deputy director of RAL's Aviation Applications Program. "These small UAS's are more sensitive to winds, temperature, turbulence, precipitation — essentially the full range of weather — than larger planes flying at higher altitudes."NASA engineers prepare to launch a remotely piloted aircraft during practice runs for an Unmanned Aircraft Systems Traffic Management test. (Image courtesy NASA.)Weather impacts on dronesDrones, at least the small ones allowed under current Federal Aviation Administration rules, fly in the lowest few hundred feet of the atmosphere, where weather can be highly dynamic and less predictable.This layer of the atmosphere is heavily affected by land surface and topography. Consider, for example, wind as it blows through a city. The buildings force the wind to speed through "urban canyons" and swirl into tight eddies behind structures. Uneven heating — the sunny side of the street warming more than the shady side, for example — can create circulating downdrafts and updrafts.Piloting a drone through a built-up area could be tricky without a detailed understanding of the local atmospheric circulation patterns. And even with that information, it's important to understand how different drones will be affected. The tinier and lighter the drone, the more vulnerable it is to the vagaries of the weather, just as a small Cessna is more vulnerable to turbulence than a giant 747. And the type of drone, such as a fixed wing or a quadcopter, matters as well because each has a different ability to respond.The concern is not just crashing on the ground; severe weather conditions could also lead to a collision in the sky. NASA's UTM project is exploring the possibility of managing a high volume of drones by essentially assigning individual UAS's to a lane of airspace. But weather will affect the ability of a drone to stay in its lane. An abrupt updraft, for instance, could force a drone that is supposed to fly at a lower altitude into the higher-altitude lane assigned to another UAS (or a crewed aircraft in mixed airspace), increasing the possibility of a collision between the two.Weather can have less obvious impacts on drone operation as well. Extremely cold weather, headwinds, or turbulence that requires a lot of flight control adjustments could drain the aircraft's battery more quickly, reducing its range and, potentially, its ability to return home. Facilitating a community dialogueThese kinds of weather challenges would likely not surprise a seasoned aviator. But many of the organizations interested in using drones today come to the UAS community from the technology side, not the aviation side, and may lack a full understanding of the impacts that atmospheric conditions can have on flight.This is where NCAR has expertise to offer. For decades, NCAR has been providing the aviation industry with the tools they need to increase flight safety, including wind shear alerts, turbulence forecasts, and information on inflight icing potential.In an effort to stay on top of the latest weather challenges facing the aviation industry, NCAR launched the Friends and Partners in Aviation Weather Forum in 1997. The meeting, now held twice yearly, is an opportunity for stakeholders from the operational, regulatory, and research sectors to come together."We created these meetings as a means of fostering dialogue," Steiner said. "We want to know: 'What are your operational sensitivities? How can we help you?' Now we are emulating these forums with the UAS community. "The UAS Weather Forum at the XSPONENTIAL conference on May 8 is the first effort at starting a similar meeting—and fostering the dialogue needed to advance drone safety, even in the face of challenging weather conditions."We want drone operators to know NCAR is a partner that can help them address their weather impacts," Steiner said.Writer/contact: Laura Snider, Senior Science Writer and Public Information Officer

Scientists link recent California droughts and floods to distinctive atmospheric waves

BOULDER, Colo. — The crippling wintertime droughts that struck California from 2013 to 2015, as well as this year's unusually wet California winter, appear to be associated with the same phenomenon: a distinctive wave pattern that emerges in the upper atmosphere and circles the globe.Scientists at the National Center for Atmospheric Research (NCAR) found in a recent study that the persistent high-pressure ridge off the west coast of North America that blocked storms from coming onshore during the winters of 2013-14 and 2014-15 was associated with the wave pattern, which they call wavenumber-5. Follow-up work showed that wavenumber-5 emerged again this winter but with its high- and low-pressure features in a different position, allowing drenching storms from the Pacific to make landfall. "This wave pattern is a global dynamic system that sometimes makes droughts or floods in California more likely to occur," said NCAR scientist Haiyan Teng, lead author of the California paper. "As we learn more, this may eventually open a new window to long-term predictability." The high- and low-pressure regions of wavenumber-5 set up in different locations during January 2014, when California was enduring a drought, and January 2017, when it was facing floods. The location of the high and low pressure regions (characterized by anticylonic vs. cyclonic upper-level air flow) can act to either suppress or enhance precipitation and storms. The black curves illustrate the jet streams that trap and focus wavenumber-5. (Image by Haiyan Teng and Grant Branstator, ©UCAR. This image is freely available for media & nonprofit use.)  The finding is part of an emerging body of research into the wave pattern that holds the promise of better understanding seasonal weather patterns in California and elsewhere. Another new paper, led by NCAR scientist Grant Branstator, examines the powerful wave pattern in more depth, analyzing the physical processes that help lead to its formation as well as its seasonal variations and how it varies in strength and location.The California study was published in the Journal of Climate while the comprehensive study into the wave patterns is appearing in the Journal of the Atmospheric Sciences. Both papers were funded by the National Science Foundation, which is NCAR's sponsor, as well as by the Department of Energy, the National Oceanic and Atmospheric Administration, and NASA.The new papers follow a 2013 study by Teng and Branstator showing that a pattern related to wavenumber-5 tended to emerge about 15-20 days before major summertime heat waves in the United States.Strong impacts on local weather systemsWavenumber-5 consists of five pairs of alternating high- and low-pressure features that encircle the globe about six miles (10 kilometers) above the ground. It is a type of atmospheric phenomenon known as a Rossby wave, a very large-scale planetary wave that can have strong impacts on local weather systems by moving heat and moisture between the tropics and higher latitudes as well as between oceanic and inland areas and by influencing where storms occur.The slow-moving Rossby waves at times become almost stationary. When they do, the result can be persistent weather patterns that often lead to droughts, floods, and heat waves. Wavenumber-5 often has this stationary quality when it emerges during the northern winter, and, as a result, is associated with a greater likelihood of persistent extreme events.To determine the degree to which the wave pattern influenced the California drought, Teng and Branstator used three specialized computer models, as well as California rainfall records and 20th century data about global atmospheric circulation patterns. The different windows into the atmosphere and precipitation patterns revealed that the formation of a ridge by the California coast is associated with the emergence of the distinctive wavenumber-5 pattern, which guides rain-producing low-pressure systems so that they travel well north of California.Over the past winter, as California was lashed by a series of intense storms, wavenumber-5 was also present, the scientists said. But the pattern had shifted over North America, replacing the high-pressure ridge off the coast with a low-pressure trough. The result was that the storms that were forced north during the drought winters were, instead, allowed to make landfall.Clues to seasonal weather patternsForecasters who predict seasonal weather patterns have largely looked to shifting sea surface temperatures in the tropical Pacific, especially changes associated with El Niño and La Niña. But during the dry winters of 2013-14 and 2014-15, those conditions varied markedly: one featured the beginning of an El Niño while the sea surface temperatures during the other were not characteristic of either El Niño or La Niña.The new research indicates that the wave pattern may provide an additional source of predictability that sometimes may be more important than the impacts of sea surface temperature changes. First, however, scientists need to better understand why and when the wave pattern emerges.In the paper published in Journal of the Atmospheric Sciences, Branstator and Teng explored the physics of the wave pattern. Using a simplified computer model of the climate system to identify the essential physical processes, the pair found that wavenumber-5 forms when strong jet streams act as wave guides, tightening the otherwise meandering Rossby wave into the signature configuration of five highs and five lows."The jets act to focus the energy," Branstator said. "When the jets are present, the energy is trapped and cannot escape." But even when the jets are present, the wavenumber-5 pattern does not always form, indicating that other forces requiring study are also at play.The scientists also searched specifically for what might have caused the wave pattern linked to the severe California drought to form. In the paper published in the Journal of Climate, the pair found that extremely heavy rainfall from December to February in certain regions of the tropical Pacific could double the probability that the extreme ridge associated with wavenumber-5 will form. The reason may have to do with the tropical rain heating parts of the upper atmosphere in such a way that favors the formation of the wavenumber-5 pattern.But the scientists cautioned that many questions remain."We need to search globally for factors that cause this wavenumber-5 behavior," Teng said, "Our studies are just the beginning of that search."About the articlesTitle: Causes of Extreme Ridges That Induce California DroughtAuthors: Haiyan Teng and Grant BranstatorJournal: Journal of Climate, DOI: 10.1175/JCLI-D-16-0524.1
Title: Tropospheric Waveguide Teleconnections and Their SeasonalityAuthors: Grant Branstator and Haiyan TengJournal: Journal of the Atmospheric Sciences, DOI: 10.1175/JAS-D-16-0305.1Writer:David Hosansky, Manager of Media Relations

UCAR praises passage of Weather Research and Forecasting Innovation Act

Update: April 18, 2017Today President Donald Trump signed H.R. 353, the "Weather Research and Forecasting Innovation Act of 2017," into law.BOULDER, Colo. — With the unanimous passage of legislation to improve weather research and prediction, Congress has taken a major step today toward strengthening the nation's resilience to severe weather and boosting U.S. economic competitiveness."This landmark legislation will save lives and property while providing business leaders with critical intelligence," said Antonio J. Busalacchi, president of the University Corporation for Atmospheric Research (UCAR). "Today's bipartisan vote underscores the enduring value of scientific research to our nation."The Weather Research and Forecasting Innovation Act is the first major weather legislation since the early 1990s. It calls for more research into subseasonal to seasonal prediction, a priority for business and community leaders who need more reliable predictions of weather patterns weeks to months in advance. The bill also will strengthen short-term weather forecasts and smooth the way for research findings to be adopted by forecasters and commercial weather companies.Antonio J. Busalacchi. (©UCAR. Photo by Carlye Calvin. This image is freely available for media & nonprofit use.)Improved short- and long-term weather predictions have major implications for public safety and the economy. The nation experienced 15 weather and climate disasters last year that cost $1 billion dollars or more, including tornadoes and widespread flooding that left dozens dead. Even routine weather events can affect transportation, supply chain management, consumer purchasing, and other sectors, with a collective impact of hundreds of billions of dollars on the U.S. economy.Scientists at the National Center for Atmospheric Research, which is managed by UCAR on behalf of the National Science Foundation, have estimated that weather forecasts provide an annual benefit to the American public of more than $30 billion, compared with about $5 billion spent on generating U.S. weather forecasts."Research into the atmosphere provides an enormous return on investment," Busalacchi said. "Weather affects all of us, and being able to make plans based on forecasts of likely weather conditions is literally worth many billions of dollars to households and businesses."Decades of investments by federal agencies in weather research, observing systems, computer models, and supercomputing resources are dramatically advancing our understanding of how our atmosphere works. Five-day weather forecasts now are as reliable as two-day forecasts used to be, hurricane forecasts will soon extend out to seven days, and scientists are starting to find ways to project certain events, such as droughts and heat waves, a month or longer in advance.The Weather Research and Forecasting Innovation Act is designed to strengthen:forecasts of tornadoes, hurricanes, and other severe stormslong-range prediction of weather patterns, from two weeks to two years aheadcommunication of forecasts, which influences subsequent decisions by public safety officials, businesses, and the publictsunami warningsthe process of moving research into operations and commercializationThe legislation (HR 353) was introduced by Rep. Frank Lucas of Oklahoma and Sen. John Thune of South Dakota. Co-sponsors include Sen. Brian Schatz and Reps. Jim Bridenstine, Lamar Smith, Dana Rohrabacher, Chris Stewart, Aumua Amata Coleman Radewagen, and Suzanne Bonamici.The bipartisan bill authorizes spending increases at the National Oceanic and Atmospheric Administration (NOAA) for weather research focused on observations, models, and more powerful computing resources. It authorizes spending for COSMIC-2 an innovative suite of micro-satellites that will provide critical atmospheric observations, with multiagency support provided by UCAR, NOAA, the U.S. Air Force, the National Science Foundation, and Taiwan's National Space Organization. The legislation also expands commercial opportunities to provide weather data while increasing the efficiency of NOAA's weather satellite programs."We are very appreciative of the work by Senator Thune, Representative Lucas, and the many co-sponsors in the House and Senate," Busalacchi said."As the United States faces an increasingly competitive global marketplace, it needs more accurate and longer-term weather forecasts," he added. "At UCAR we look forward to working with NOAA, the Department of Defense, and the other federal agencies; the private sector; and the university community to build off of the National Science Foundation investment in basic research in this essential area."

UCAR/NCAR statement on the passing of Matthew J. Parker

The National Center for Atmospheric Research (NCAR) and the University Corporation for Atmospheric Research (UCAR) join American Meteorological Society (AMS) colleagues and those in the broader meteorological community in mourning the passing of AMS President Matthew J. Parker, who died on March 15.This past January, Parker took over as AMS president during the society’s annual meeting in Seattle having been elected as president-elected in November 2015. He had spent much of his career, since 1989, at Savannah River National Laboratory in South Carolina. During that time, Parker rose through the ranks and was most recently senior fellow meteorologist in the Atmospheric Technologies Group.Matthew Parker (Photo courtesy of the American Meteorological Society.)“Matt was a true leader in the community who advocated for an analysis to show the value and return on investment in the weather enterprise,” said UCAR President Antonio J. Busalacchi. “Matt was a strong supporter of a more diverse and inclusive weather enterprise and while at the Department of Energy, worked to integrate all parts of the community, including the public, private, and academic sectors. This loss will be deeply felt.”NCAR Director James W. Hurrell expressed a similar sentiment, noting that Parker’s passing “is an enormous loss for the entire scientific community. Matt was a tremendous leader who was deeply committed to our field, and to AMS in particular. He will be sorely missed.”  William Mahoney, interim director of NCAR’s Research Applications Laboratory and Commissioner of AMS’s Commission on the Weather, Water, and Climate Enterprise, added: “Matt understood that creating collaboration among government, private, and academic sectors could be a powerful and effective strategy for advancing our scientific and operational capabilities. We will miss Matt’s leadership but the Commission will continue to work on implementing his vision.”See AMS’s statement here.

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  


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