Chakraborty A.,Atmospheric and Oceanic science Group |
Kumar R.,Atmospheric and Oceanic science Group |
Stoffelen A.,Weather Research
Remote Sensing Letters | Year: 2013
Ocean surface winds from the OCEANSAT-2 scatterometer (OSCAT) were validated with equivalent neutral wind observations from 87 global buoys and winds from the European Centre for Medium Range Weather Forecasting (ECMWF) Numerical Weather Prediction (NWP) model using triple collocation for a period of 9 months. Functional relationship analysis (FRA) employing the error-in-variables method is found to be more 'exact' in comparison with classical linear regression analysis for the validation of the OSCAT data. Moreover, using the wind component domain for validation and error assessment rather than the speed and direction domain is confirmed to be favourable. The FRA method applied on the triple-collocated wind components shows that the error standard deviations of the OSCAT and buoy winds are quite similar. The calibration trends and biases for OSCAT, buoys and ECMWF are found to be close to unity and zero, respectively. © 2012 Taylor & Francis.
News Article | August 22, 2016
As the National Oceanic and Atmospheric Administration (NOAA) this month launches a comprehensive system for forecasting water resources in the United States, it is turning to technology developed by the National Center for Atmospheric Research (NCAR) and its university and agency collaborators. WRF-Hydro, a powerful NCAR-based computer model, is the first nationwide operational system to provide continuous predictions of water levels and potential flooding in rivers and streams from coast to coast. NOAA's new Office of Water Prediction selected it last year as the core of the agency's new National Water Model. "WRF-Hydro gives us a continuous picture of all of the waterways in the contiguous United States," said NCAR scientist David Gochis, who helped lead its development. "By generating detailed forecast guidance that is hours to weeks ahead, it will help officials make more informed decisions about reservoir levels and river navigation, as well as alerting them to dangerous events like flash floods." WRF-Hydro (WRF stands for Weather Research and Forecasting) is part of a major Office of Water Prediction initiative to bolster U.S. capabilities in predicting and managing water resources. By teaming with NCAR and the research community, NOAA's National Water Center is developing a new national water intelligence capability, enabling better impacts-based forecasts for management and decision making. Unlike past streamflow models, which provided forecasts every few hours and only for specific points along major river systems, WRF-Hydro provides continuous forecasts of millions of points along rivers, streams, and their tributaries across the contiguous United States. To accomplish this, it simulates the entire hydrologic system — including snowpack, soil moisture, local ponded water, and evapotranspiration — and rapidly generates output on some of the nation's most powerful supercomputers. WRF-Hydro was developed in collaboration with NOAA and university and agency scientists through the Consortium of Universities for the Advancement of Hydrologic Science, the U.S. Geological Survey, Israel Hydrologic Service, and Baron Advanced Meteorological Services. Funding came from NOAA, NASA, and the National Science Foundation, which is NCAR's sponsor. "WRF-Hydro is a perfect example of the transition from research to operations," said Antonio (Tony) J. Busalacchi, president of the University Corporation for Atmospheric Research, which manages NCAR on behalf of the National Science Foundation (NSF). "It builds on the NSF investment in basic research in partnership with other agencies, helps to accelerate collaboration with the larger research community, and culminates in support of a mission agency such as NOAA. The use of WRF-Hydro in an operational setting will also allow for feedback from operations to research. In the end this is a win-win situation for all parties involved, chief among them the U.S. taxpayers." "Through our partnership with NCAR and the academic and federal water community, we are bringing the state of the science in water forecasting and prediction to bear operationally," said Thomas Graziano, director of NOAA’s new Office of Water Prediction at the National Weather Service. The continental United States has more than 3 million miles of rivers and streams, from major navigable waterways such as the Mississippi and Columbia to the remote mountain brooks flowing from the high Adirondacks into the Hudson River. The levels and flow rates of these watercourses have far-reaching implications for water availability, water quality, and public safety. Until now, however, it has not been possible to predict conditions at all points in the nation's waterways. Instead, computer models have produced a limited picture by incorporating observations from about 4,000 gauges, generally on the country's bigger rivers. Smaller streams and channels are largely left out of these forecast models, and stretches of major rivers for tens of miles are often not predicted — meaning that schools, bridges, and even entire towns can be vulnerable to unexpected changes in river levels. To fill in the picture, NCAR scientists have worked for the past several years with their colleagues within NOAA, other federal agencies, and universities to combine a range of atmospheric, hydrologic, and soil data into a single forecasting system. The resulting National Water Model, based on WRF-Hydro, simulates current and future conditions on rivers and streams along points two miles apart across the contiguous United States. Along with an hourly analysis of current hydrologic conditions, the National Water Model generates three predictions: an hourly 0- to 15-hour short-range forecast, a daily 0- to 10-day medium-range forecast, and a daily 0- to 30-day long-range water resource forecast. The National Water Model predictions using WRF-Hydro offer a wide array of benefits for society. They will help local, state, and federal officials better manage reservoirs, improve navigation along major rivers, plan for droughts, anticipate water quality problems caused by lower flows, and monitor ecosystems for issues such as whether conditions are favorable for fish spawning. By providing a national view, this will also help the Federal Emergency Management Agency deploy resources more effectively in cases of simultaneous emergencies, such as a hurricane in the Gulf Coast and flooding in California. "We've never had such a comprehensive system before," Gochis said. "In some ways, the value of this is a blank page yet to be written." WRF-Hydro is a powerful forecasting system that incorporates advanced meteorological and streamflow observations, including data from nearly 8,000 U.S. Geological Survey streamflow gauges across the country. Using advanced mathematical techniques, the model then simulates current and future conditions for millions of points on every significant river, steam, tributary, and catchment in the United States. In time, scientists will add additional observations to the model, including snowpack conditions, lake and reservoir levels, subsurface flows, soil moisture, and land-atmosphere interactions such as evapotranspiration, the process by which water in soil, plants, and other land surfaces evaporates into the atmosphere. Scientists over the last year have demonstrated the accuracy of WRF-Hydro by comparing its simulations to observations of streamflow, snowpack, and other variables. They will continue to assess and expand the system as the National Water Model begins operational forecasts. NCAR scientists maintain and update the open-source code of WRF-Hydro, which is available to the academic community and others. WRF-Hydro is widely used by researchers, both to better understand water resources and floods in the United States and other countries such as Norway, Germany, Romania, Turkey, and Israel, and to project the possible impacts of climate change. "At any point in time, forecasts from the new National Water Model have the potential to impact 300 million people," Gochis said. "What NOAA and its collaborator community are doing is trying to usher in a new era of bringing in better physics and better data into forecast models for improving situational awareness and hydrologic decision making."
As South West Western Australia (WA) residents count the cost of last week’s devastating bushfires, Murdoch University scientists are working on a model to help predict future bushfire threats in the region. The work’s aim is to inform preparation and help assess the risks of catastrophes, such as the Yarloop tragedy in which two elderly men died and 143 properties were razed. “Using state-of-the-art regional climate models, we are investigating future changes in fire weather by focusing on the key contributing climate factors, which are temperature, rainfall, wind speed and relative humidity,” researcher Alyce Sala-Tenna says. The end result will be bushfire risk ratings projected over time consistent with the McArthur Forest Fire Danger Index — the same index WA’s Department of Fire and Emergency Services (DFES) currently uses to produce daily Fire Danger Ratings in regional WA. “We’re aiming that our work can show how bushfires are going to change and get a better understanding of where more frequent occurrences and intensities, longer fire seasons and shifts are likely to occur,” Sala-Tenna says. To develop the models, the Murdoch team is drawing on the Weather Research and Forecast Model (WRF), developed in America. Through WRF, they’ve downscaled global climate models from their original 100- to 250-kilometer resolutions to five-kilometer grids of WA’s South West, each grid covering an area slightly larger than Kings Park. The grid model incorporates data from the Intergovernmental Panel on Climate Change (IPCC)’s A2 scenario. The resulting 30-year climate simulations will assess future changes in fire weather, including the impact of flammable fuel loads. To make the whole project work, the Murdoch team is relying on the power of the Pawsey Supercomputing Centre’s petascale machine Magnus. Magnus is the most powerful supercomputer in the southern hemisphere with its processing power equivalent to six million iPads. “Our simulations require significant computing power and data storage, so without Pawsey, the research would not be possible,” Sala-Tenna says. The research could also assist in developing effective policies on bushfire management into the future and provide agencies and the public with a better understanding of how climate change affects the natural environment. “You can never prevent bushfires — they’re part of the natural cycle and are part of how Australia has developed — but we can be better prepared,” Sala-Tenna says. Individuals concerned about bushfires in their area can consult the DFES Map of Bush Fire Prone Areas. This article was originally published on ScienceNetwork WA. Read the original article.
Vatvani D.,Deltares |
Zweers N.C.,Weather Research |
Van Ormondt M.,Deltares |
Smale A.J.,Deltares |
And 2 more authors.
Natural Hazards and Earth System Science | Year: 2012
To simulate winds and water levels, numerical weather prediction (NWP) and storm surge models generally use the traditional bulk relation for wind stress, which is characterized by a wind drag coefficient. A still commonly used drag coefficient in those models, some of them were developed in the past, is based on a relation, according to which the magnitude of the coefficient is either constant or increases monotonically with increasing surface wind speed (Bender, 2007; Kim et al., 2008; Kohno and Higaki, 2006). The NWP and surge models are often tuned independently from each other in order to obtain good results. Observations have indicated that the magnitude of the drag coefficient levels off at a wind speed of about 30 m s -1, and then decreases with further increase of the wind speed. Above a wind speed of approximately 30 m s -1, the stress above the air-sea interface starts to saturate. To represent the reducing and levelling off of the drag coefficient, the original Charnock drag formulation has been extended with a correction term. In line with the above, the Delft3D storm surge model is tested using both Charnock's and improved Makin's wind drag parameterization to evaluate the improvements on the storm surge model results, with and without inclusion of the wave effects. The effect of waves on storm surge is included by simultaneously simulating waves with the SWAN model on identical model grids in a coupled mode. However, the results presented here will focus on the storm surge results that include the wave effects. The runs were carried out in the Gulf of Mexico for Katrina and Ivan hurricane events. The storm surge model was initially forced with H*wind data (Powell et al., 2010) to test the effect of the Makin's wind drag parameterization on the storm surge model separately. The computed wind, water levels and waves are subsequently compared with observation data. Based on the good results obtained, we conclude that, for a good reproduction of the storm surges under hurricane conditions, Makin's new drag parameterization is favourable above the traditional Charnock relation. Furthermore, we are encouraged by these results to continue the studies and establish the effect of improved Makin's wind drag parameterization in the wave model. The results from this study will be used to evaluate the relevance of extending the present towards implementation of a similar wind drag parameterization in the SWAN wave model, in line with our aim to apply a consistent wind drag formulation throughout the entire storm surge modelling approach. © 2012 Author(s). CC Attribution 3.0 License.