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State College, PA, United States

Troccoli A.,CSIRO | Audinet P.,The World Bank | Bonelli P.,Research on Energy Systems Spa | Buontempo C.,UK Met Office | And 16 more authors.
Bulletin of the American Meteorological Society | Year: 2013

The growing body of knowledge and experience in weather and climate risk management in the energy industry has driven a rapidly growing research interest in establishing links between weather, climate, and energy. Weather and climate information is also critical to managing the energy supply from other energy sectors along with better understanding and estimation of energy demand, despite increased attention being stimulated by a renewed and fervent interest in renewable energy sources. The International Conference Energy and Meteorology (ICEM) 2011 has been held in Queensland, Australia, to take advantage of the substantial overlap between these energy activities and their use of weather and climate information. The objective of the event has been to provide a forum where scientists, engineers, economists, policymakers, and other specialists and practitioners involved in research or implementation activities at the intersection between weather, climate, and energy, can discuss relevant research findings and emerging practices.

Troccoli A.,University of Reading | Dutton J.A.,Prescient Weather Ltd. | Furlow J.,USAID | Gurney R.J.,University of Reading | Harrison M.,University of Oxford
Bulletin of the American Meteorological Society | Year: 2010

Weather and climate risk management has been considered significantly while formulating policies and strategies for the energy sector. The methods for converting traditional weather and climate charts or data presentations into forms that depict the opportunity and risk for all aspects of the life cycle for individual components of the energy industry would also be effective and would provide educational resources. The guidelines for using weather and climate information in energy projects covering their life cycles and project structure and design, data requirements, and science issues, would be beneficial to address environmental issues. The reliable access to the data and forecasts of various weather services should be implemented using readily accessible servers and grid computing technology. Energy projects should be examined for weather and climate sensitivities, and such sensitivities should be accommodated within project designs, and management.

James R.P.,Prescient Weather Ltd. | Arguez A.,National Oceanic and Atmospheric Administration
Journal of Atmospheric and Oceanic Technology | Year: 2015

The climatological daily variance of temperature is sometimes estimated from observed temperatures within a centered window of dates. This method overestimates the true variance of daily temperature when the rate of seasonal temperature change is large, because the seasonal change within the date window introduces additional variance. The contribution of the seasonal change may be removed by performing the variance calculation using daily temperature anomalies, leading to a bias-free estimate of variance. The difference between the variance estimation methods is illustrated using both idealized simulations of temperature variability and observed historical temperature data. The simulation results confirm that removing the climatological temperature cycle eliminates bias in the variance estimates. For several U.S. midlatitude locations, the difference in estimated standard deviation of daily mean temperature is on the order of a few percent near the seasonal peaks in climatological temperature change, but the maximum difference is larger in highly continental climates. These differences are shown to be significant when estimating the probability of temperature extremes under the assumption of a Gaussian distribution. © 2015 American Meteorological Society.

Xia Y.,EMC | Cosgrove B.A.,National Weather Service - NWS | Mitchell K.E.,Prescient Weather Ltd. | Peters-Lidard C.D.,NASA | And 6 more authors.
Journal of Geophysical Research: Atmospheres | Year: 2016

This paper compares the annual and monthly components of the simulated energy budget from the North American Land Data Assimilation System phase 2 (NLDAS-2) with reference products over the domains of the 12 River Forecast Centers (RFCs) of the continental United States (CONUS). The simulations are calculated from both operational and research versions of NLDAS-2. The reference radiation components are obtained from the National Aeronautics and Space Administration Surface Radiation Budget product. The reference sensible and latent heat fluxes are obtained from a multitree ensemble method applied to gridded FLUXNET data from the Max Planck Institute, Germany. As these references are obtained from different data sources, they cannot fully close the energy budget, although the range of closure error is less than 15% for mean annual results. The analysis here demonstrates the usefulness of basin-scale surface energy budget analysis for evaluating model skill and deficiencies. The operational (i.e., Noah, Mosaic, and VIC) and research (i.e., Noah-I and VIC4.0.5) NLDAS-2 land surface models exhibit similarities and differences in depicting basin-averaged energy components. For example, the energy components of the five models have similar seasonal cycles, but with different magnitudes. Generally, Noah and VIC overestimate (underestimate) sensible (latent) heat flux over several RFCs of the eastern CONUS. In contrast, Mosaic underestimates (overestimates) sensible (latent) heat flux over almost all 12 RFCs. The research Noah-I and VIC4.0.5 versions show moderate-to-large improvements (basin and model dependent) relative to their operational versions, which indicates likely pathways for future improvements in the operational NLDAS-2 system. ©2015. American Geophysical Union. All Rights Reserved.

Xia Y.,EMC | Cosgrove B.A.,National Water Center National Weather Service Silver Spring | Mitchell K.E.,Prescient Weather Ltd. | Peters-Lidard C.D.,NASA | And 7 more authors.
Journal of Geophysical Research: Atmospheres | Year: 2016

The purpose of this study is to evaluate the components of the land surface water budget in the four land surface models (Noah, SAC-Sacramento Soil Moisture Accounting Model, (VIC) Variable Infiltration Capacity Model, and Mosaic) applied in the newly implemented National Centers for Environmental Prediction (NCEP) operational and research versions of the North American Land Data Assimilation System version 2 (NLDAS-2). This work focuses on monthly and annual components of the water budget over 12 National Weather Service (NWS) River Forecast Centers (RFCs). Monthly gridded FLUX Network (FLUXNET) evapotranspiration (ET) from the Max-Planck Institute (MPI) of Germany, U.S. Geological Survey (USGS) total runoff (Q), changes in total water storage (dS/dt, derived as a residual by utilizing MPI ET and USGS Q in the water balance equation), and Gravity Recovery and Climate Experiment (GRACE) observed total water storage anomaly (TWSA) and change (TWSC) are used as reference data sets. Compared to these ET and Q benchmarks, Mosaic and SAC (Noah and VIC) in the operational NLDAS-2 overestimate (underestimate) mean annual reference ET and underestimate (overestimate) mean annual reference Q. The multimodel ensemble mean (MME) is closer to the mean annual reference ET and Q. An anomaly correlation (AC) analysis shows good AC values for simulated monthly mean Q and dS/dt but significantly smaller AC values for simulated ET. Upgraded versions of the models utilized in the research side of NLDAS-2 yield largely improved performance in the simulation of these mean annual and monthly water component diagnostics. These results demonstrate that the three intertwined efforts of improving (1) the scientific understanding of parameterization of land surface processes, (2) the spatial and temporal extent of systematic validation of land surface processes, and (3) the engineering-oriented aspects such as parameter calibration and optimization are key to substantially improving product quality in various land data assimilation systems. © 2016. American Geophysical Union. All Rights Reserved.

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