Recognizes fundamental and continuing contributions to the art and science of weather modification.
While the weather forecast enterprise has dramatically increased its capabilities to provide accurate forecasts of weather hazards at greater lead times and finer scales, a growing body of research demonstrates that people have a difficult time understanding what the impacts of those hazards will be. Even worse, people find it difficult to impossible to conceive of what the impact means for their unique situation. Very few people have the technical background to assess their vulnerability and interpret probabilistic forecasts of hazards to calculate the risks of specific consequences. The HurricaneRiskCalculator® web app is being created to fill these gaps.
The HurricaneRiskCalculator® web app is a public-facing decision support tool based on a probabilistic risk framework that intersects real-time tropical cyclone wind hazard predictions with information from a structural vulnerability assessment. Through this intersection of hazard, vulnerability, and exposure, the tool calculates the risks of various consequences, such as different degrees of structural damage and whether the structure will be habitable following the tropical cyclone.
Learn more about the project: wxrisk.ucar.edu
NCAR & UCAR News: NCAR scientists recruiting the public to help ground-truth hurricane risk app
Since it is a forecast product, the GTG is most useful for route planning, i.e., strategic avoidance of turbulence. However, given the rapidly evolving character of turbulence, for tactical avoidance it is more useful to have a rapidly updated nowcast system. This system, called GTGN™ (N for nowcast), has been developed at RAL and is primarily driven by the most recent available turbulence observations (in situ EDR measurements, turbulence pilot reports, NTDA output, and satellite–based turbulence inferences) merged together with a GTG short–term forecast. The product updates every 15 min.
The GTGN™ procedure uses GTG short–term forecast grids which are modified on a point–by–point basis to provide better agreement with the latest observations. The observations used include PIREPs, in situ EDR data, and NTDA EDR measurements. The gridded output is EDR. An example of the adaptive procedure is shown in the bottom right figure.
A prototype GTGN™ system is now available. To request data access visit the tab above "GTGN™ Data Access".
GTG-N is a gridded turbulence nowcast product developed at the National Center for Atmospheric Research (NCAR/UCAR) and has satisfied the requirements and been approved by the Federal Aviation Administration (FAA) Technical Review Panel (TRP) and the Safety Review Management Panel (SRMP).
NCAR/UCAR is producing GTG-N output that can be made available through a license agreement to interested users. More information about the data feed and GTG-N data output is included in this document: User's Guide. If you are interested in receiving GTG-N data, please submit the contact information form below and we will contact you to set up the license agreement.
NOTE: NCAR/UCAR is not a 24x7 facility. We do not guarantee that GTG-N output is always available.
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VDRAS relies on data assimilation, a technique for combining real-world observations and computer model output to create a more accurate forecast.
VDRAS has a very fast update cycle, providing new data from real-time radar and other sources for assimilation every 6–12 minutes. The mathematical technique it relies on, called 4DVar (Four-Dimensional Variational), is the gold standard for the field of data assimilation. The system uses freely available observations from the National Weather Service's Doppler radar network, so it can be run anywhere in the country without requiring new and costly instrumentation.
VDRAS works with these two other NCAR-based tools: the advanced research version of the Weather Research and Forecasting model, which generates very detailed predictions of areas as small as 3 kilometers (1.8 miles) square; and the Dynamic Integrated ForeCast system, which fine-tunes and optimizes the ongoing forecast.
DART is created by the Data Assimilation Research Section (DAReS), a small group of data assimilation experts, software engineers and physical scientists. DAReS was founded as the result of NCAR's Data Assimilation Initiative, a collaborative process that gauged the needs of researchers across NCAR. The initiative established the necessity of developing methodologies, tools and software to enable data assimilation capabilities within models developed at NCAR.
DAReS staff serve projects at NCAR and within the university community by providing a bridge between teams with existing observational and modeling expertise. This aids model development by describing model error and enables fundamental discoveries by allowing observationalists and modelers to share their expertise by combining models and data using a rigorous assimilation framework.
In the years since DAReS’ founding, DART has grown beyond its original purpose and now supports data assimilation in models developed at NCAR and by the broader geosciences community. DAReS staff have expertise in algorithm development, software design, software portability, large-model implementation and execution, observations and observation operators.
Extreme heat is a leading cause of weather-related human mortality in the United States and in many countries world-wide. Despite the advances in meteorological forecasting capabilities and the widespread prevalence of air conditioning systems across the U.S., extreme heat persists as a threat to human health. As global warming patterns continue, researchers anticipate increases in the severity, frequency and duration of extreme heat events. Recent studies on climate impacts demonstrate that climate change will have differential consequences in the U.S. at the regional and local scales. Research priorities in public health and climate change science communities call for addressing the current impacts of weather hazards on human health, and for preparing for future risks and threats in a warmer climate. The SIMMER project addresses the critical need for information at regional to local scales that are pertinent to public health decision-making in the context of global change.
The primary goals of the SIMMER project are to: 1) advance methodology for assessing current and future urban vulnerability from heat waves through integration of physical and social science models, research results, and NASA data; and 2) develop a System for Integrated Modeling of Metropolitan Extreme Heat Risk (SIMMER) for building local capacity for heat hazard mitigation and climate change adaptation in the public health sector. The study includes the following objectives:
The SIMMER will rely heavily on the use of NASA Earth Science products and will directly support NASA’s objective to promote interdisciplinary research that addresses societal impacts of extreme disturbances. NASA data will be used as inputs into health and climate models, will contribute to creation of new regional and local datasets and innovative research results, and will help validate model outputs. The project will include regional- and local scale analyses. The regional-scale analysis (the study domain will cover the contiguous United States and portions of southern Canada at ~ 25 km2) will focus on the SIMMER objectives 3 and 4. Local, intra-urban scale (1-2 km2; US Census block groups) will focus on the SIMMER objectives 1-4. Houston, Texas will be used for local-scale analysis and will serve as testbed for developing and implementing the SIMMER methodology. We selected Houston as a case study because of the city’s current and projected heat-related health impacts, complex local meteorology and land use types, diverse socio-economic makeup of urban residents, previously-conducted research on the UHI effects, and a strong interest from Houston public health officials in this project.
The methodology developed for Houston is intended to be transferrable to other urban centers. The robustness of the method and its transferability will be validated in Toronto, Canada. With the help of Canadian collaborators we will apply and validate the SIMMER model in assessing heat-related health risks and social vulnerability in Toronto. In addition, involvement of public health sector stakeholders from Houston and Toronto will ensure that relevant application(s) for the model in the context of climate change and human health are included. Involvement of university collaborators and students will facilitate training of current and future researchers in the integration of the physical and social sciences.
APPLICATIONS:
NASA IDS (~ $1.49M)
The Extreme Heat Climate Inspector is an interactive web application which expands GIS mapping and graphing capabilities to visualize projected heat. The data displayed in this app were produced at NCAR for the NASA-funded study, "System for Integrated Modeling of Metropolitan Extreme heat Risk" (SIMMER).
The System for Integrated Modeling of Metropolitan Extreme heat Risk (SIMMER), a NASA-funded study conducted by NCAR scientists in 2010-2014, focused on extreme heat, human health, and urban vulnerability in present and future climates. The project quantified the importance of explicitly characterizing urban properties to improve urban meteorological simulations, and the role of climate change in the future heat stress across the United States and southern Canada. Climate model simulations from SIMMER suggest high heat stress days and nights in cities across the U.S. and in some rural areas will increase substantially by the mid-21st century.
For simulating future climate and extreme heat, we used the Community Land Model (CLM) coupled to an urban canyon model to quantify present-day (PD; 1986–2005) and a projection of one possible manifestation of mid-21st century (MC; 2046–2065) rural and urban heat stress for boreal summer over the U.S. and southern Canada at fine spatial resolution (1/8° in latitude and longitude). The Weather and Research Forecasting model (WRF) was used to downscale a Community Climate System Model (CCSM4) 20th century ensemble member for PD and a CCSM4 RCP8.5 (representative concentration pathway) ensemble member for MC to provide a consistent set of atmospheric forcing variables for CLM.
We implemented five commonly used heat stress indices directly in the model. These are the National Weather Service Heat Index, Apparent Temperature, Simplified Wet Bulb Globe Temperature, Humidex, and Discomfort Index. Heat indices are calculated for both rural (vegetation/soil) and urban areas.
High heat stress days (hot days) are defined as the number of days that the daily maximum air temperature exceeds the 95th percentile of the 1986-2005 rural daily maximum temperature. High heat stress nights (warm nights) are defined similarly using the 1986-2005 rural daily minimum temperature.
The WRF-Solar® model (Jimenez et al. 2016) is a specific configuration and augmentation of the Weather Research and Forecasting (WRF) model. Previous efforts have been largely devoted to enhance the aerosol-cloud-radiation physics. To extend the WRF-Solar capabilities beyond deterministic forecasts, we are developing the WRF-Solar Ensemble Prediction System (WRF-Solar EPS).
WRF-Solar EPS introduces stochastic perturbations in the most relevant variables for solar irradiance forecasts. The variables have been identified with tangent linear models of selected parameterizations (Yang et al. 2020). The model provides a user-friendly configuration to set the characteristics of the perturbations for each variable (in an ascii configuration file) and to select the variables to perturb (in the WRF namelist).
A beta version of the WRF-Solar EPS model is available.
Global horizontal irradiance forecast as a function of the lead time: thin lines) WRF-Solar EPS ensemble members, thicker line) ensemble mean. Observations are also shown (shaded).
Jimenez, P. A., J. P. Hacker, J. Dudhia, S. E. Haupt, J. A. Ruiz-Arias, C. A. Gueymard, G. Thompson, T. Eidhammer and A. Deng, 2016a: WRF-Solar: Description and Clear-Sky Assessment of an Augmented NWP Model for Solar Power Prediction. Bull. Amer. Met. Soc., 97, 1249-1264. doi:10.1175/BAMS-D-14-00279.1
Yang, J., J. H. Kim, P. A. Jimenez, M. Sengupta, J. Dudhia, Y. Xie, A. Golnas and R. Giering, 2020: An efficient method to identify uncertainties of WRF-Solar variables in forecasting solar irradiance using a tangent linear sensitivity analysis. Solar Energy (In press)
The variables to perturb were identified using six parameterizations responsible for radiation and cloud formation and dissipation:
The most relevant input variables for each module were selected using tangential linear analysis. With this aim, we developed tangent linear models (TLM) for each of the selected modules. The TLMs were used to analyze uncertainties of the output variables to uncertainties in the input variables in order to select the most sensitive variables controlling radiative transfer and cloud processes. We identified 14 variables (Yang et al. 2020): surface albedo, aerosol optical depth, Ångström exponent, asymmetry factor, water vapor mixing ratio, cloud/ice/snow mixing ratios, ice number concentration, potential temperature, turbulent kinetic energy, soil moisture content, soil temperature, and vertical velocity. Table 1 shows a complete list of these variables (column 2) and their associated parameterizations (column 3).
Table 1 also shows the characteristics of the stochastic perturbations (columns, 4, 5 and 6). The perturbations are obtained through sampling an isotropic Gaussian distribution. The perturbations are characterized by the standard deviation of the Gaussian distribution (σ, column 4), the wavelength (λ, column 5), and the decorrelation time (τ, column 6) between consecutive perturbations (Berner et al. 2009 and Jankov et al. 2017). WRF-Solar EPS adds the stochastic perturbations inside each parameterization every model time step.
Table 1. Characteristics of the 14 stochastic perturbations in WRF-Solar EPS.
Berner, J., G. Shutts, M. Leutbecher, and T. Palmer, 2009: A spectral stochastic kinetic energy backscatter scheme and its impact on flow-dependent predictability in the ECMWF en- semble prediction system. J. Atmos. Sci., 66, 603–626.
Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity, Mon. Weather Rev.,129, 569–585.
Deng, A., B. J. Gaudet, J. Dudhia, and K. Alapaty, 2014: Implementation and evaluation of a new shallow convection scheme in WRF. 26th Conf. on Weather Analysis and Forecasting/22nd Conf. on Numerical Weather Prediction, Atlanta, GA, Amer. Meteor. Soc., 12.5. [Available online at https:// ams.confex.com/ams/94Annual/webprogram/Paper236925.html.]
Jankov, I., and Coauthors, 2017: A performance comparison between multiphysics and stochastic approaches within a North American RAP ensemble. Mon. Wea. Rev., 145, 1161–1179.
Jimenez, P.A., S. Alessandrini, S. E. Haupt, A. Deng, B. Kosovic, J. A. Lee, L. Delle Monache, 2016: The role of unresolved clouds on short-range global horizontal irradiance predictability. Mon. Wea. Rev., 144, 3099 - 3107. doi:10.1175/MWR-D-16-0104.1.
Nakanishi, M., and H. Niino, 2009: Development of an improved turbulence closure model for the atmospheric boundary layer. J. Meteor. Soc. Japan, 87, 895–912.
Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall, 2008: Explicit forecasts of winter precipitation using an improved bulk mi- crophysics scheme. Part II: Implementation of a new snow parameterization. Mon. Wea. Rev., 136, 5095–5115.
Xie, Y., M. Sengupta, and J. Dudhia, 2016: A Fast All-sky Radiation Model for Solar applications (FARMS): Algorithm and performance evaluation. Sol. Energy, 135, 435-445.
Yang, J., J. H. Kim, P. A. Jimenez, M. Sengupta, J. Dudhia, Y. Xie, A. Golnas and R. Giering, 2020: An efficient method to identify uncertainties of WRF-Solar variables in forecasting solar irradiance using a tangent linear sensitivity analysis. Solar Energy (In press.)
WRF-Solar EPS requires to describe the characteristic of the stochastic perturbations for each variable and to select the variables to perturb. These two steps are summarized as follows:
Yang, J., J.H. Kim, P.A. Jimenez, M. Sengupta, J. Dudhia, Y. Xie, A. Golnas and R. Giering, 2020: An efficient method to identify uncertainties of WRF-Solar variables in forecasting solar irradiance using a tangent linear sensitivity analysis. Solar Energy (In press.)
Yang, J., Sengupta, M., Xie, Y., Jimenez, P.A. and Kim, J.H., 2019. Adjoint Sensitivity of FARMS to the Forecasting Variables of WRF-Solar. In 36th European Photovoltaic Solar Energy Conference and Exhibition.
Kim, J.H., Jimenez, P.A., Dudhia, J., Yang, J., Sengupta, M., Xie, Y., 2020, “Probabilistic Forecast of All-sky Solar Radiation Using Enhanced WRF-Solar”, In 37th European Photovoltaic Solar Energy Conference and Exhibition.
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