State College, PA, United States
State College, PA, United States
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Xia Y.,National Oceanic and Atmospheric Administration | Mocko D.,NASA | Huang M.,Pacific Northwest National Laboratory | Li B.,University of Maryland College Park | And 4 more authors.
Journal of Hydrometeorology | Year: 2017

To prepare for the next-generation North American Land Data Assimilation System (NLDAS), three advanced land surface models [LSMs; i.e., Community Land Model, version 4.0 (CLM4.0); Noah LSM with multiphysics options (Noah-MP); and Catchment LSM-Fortuna 2.5 (CLSM-F2.5)] were run for the 1979-2014 period within the NLDAS-based framework. Unlike the LSMs currently executing in the operational NLDAS, these three advanced LSMs each include a groundwater component. In this study, the model simulations of monthly terrestrial water storage anomaly (TWSA) and its individual water storage components are evaluated against satellite-based and in situ observations, as well as against reference reanalysis products, at basinwide and statewide scales. The quality of these TWSA simulations will contribute to determining the suitability of these models for the next phase of the NLDAS. Overall, it is found that all three models are able to reasonably capture the monthly and interannual variability and magnitudes of TWSA. However, the relative contributions of the individual water storage components to TWSA are very dependent on the model and basin. A major contributor to the TWSA is the anomaly of total column soil moisture content for CLM4.0 and Noah-MP, while the groundwater storage anomaly is the major contributor for CLSM-F2.5. Other water storage components such as the anomaly of snow water equivalent also play a role in all three models. For each individual water storage component, the models are able to capture broad features such as monthly and interannual variability. However, there are large intermodel differences and quantitative uncertainties, which are motivating follow-on investigations in the NLDAS Science Testbed developed by the NASA and NCEP NLDAS teams. © 2017 American Meteorological Society.

Troccoli A.,University of Reading | Boulahya M.S.,ClimDev Africa | Dutton J.A.,Prescient Weather Ltd. | Furlow J.,USAID | And 2 more authors.
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.

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.

Dutton J.A.,Prescient Weather Ltd. | Dutton J.A.,State College | James R.P.,Prescient Weather Ltd. | James R.P.,State College | And 2 more authors.
Climate Dynamics | Year: 2013

Seasonal probability forecasts produced with numerical dynamics on supercomputers offer great potential value in managing risk and opportunity created by seasonal variability. The skill and reliability of contemporary forecast systems can be increased by calibration methods that use the historical performance of the forecast system to improve the ongoing real-time forecasts. Two calibration methods are applied to seasonal surface temperature forecasts of the US National Weather Service, the European Centre for Medium Range Weather Forecasts, and to a World Climate Service multi-model ensemble created by combining those two forecasts with Bayesian methods. As expected, the multi-model is somewhat more skillful and more reliable than the original models taken alone. The potential value of the multimodel in decision making is illustrated with the profits achieved in simulated trading of a weather derivative. In addition to examining the seasonal models, the article demonstrates that calibrated probability forecasts of weekly average temperatures for leads of 2-4 weeks are also skillful and reliable. The conversion of ensemble forecasts into probability distributions of impact variables is illustrated with degree days derived from the temperature forecasts. Some issues related to loss of stationarity owing to long-term warming are considered. The main conclusion of the article is that properly calibrated probabilistic forecasts possess sufficient skill and reliability to contribute to effective decisions in government and business activities that are sensitive to intraseasonal and seasonal climate variability. © 2013 Springer-Verlag Berlin Heidelberg.

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.

Agency: Department of Energy | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 1.37M | Year: 2015

usiness and industry need to anticipate the potential risks and opportunities that climate change may create in the decades ahead in order to start now to ensure continued success and prosperity. This project is transforming the results of scientific computer climate research into quantitative business-specific formats that provide actionable foresight for adapting to climate change. Climate simulations for the 21st century computed recently for the Intergovernmental Panel on Climate Change (IPCC) by 29 climate modeling centers were aimed at predicting climate change driven by four scenarios of greenhouse forcing. The Climate Change Information System for Business and Industry (ClimBiz) is translating relevant subsets of this huge dataset archived by DOE into forms that will enable private sector executives and their firms to revise their business models and strategies to meet the challenge of climate change. A ClimBiz Phase I data analysis system and website is currently producing information about potential time variations of key climate variables in various regions of the world. Examples of business-specific transformations relevant to agriculture and energy have been developed and demonstrated. Phase II will focus on visualization of spatial patterns of climate change and the associated business impacts and on downscaling the scientific simulations to provide greater regional and local detail. With its own database selected and downloaded from the DOE archive, ClimBiz will offer customers a rapid and flexible facility for creating scenarios relevant to their activities and for estimating the probabilities that industry variables may become favorable or unfavorable in the next few decades or later in the century. Commercial Applications and Other Benefits. ClimBiz information will assist agriculture, the conventional and renewable energy industries, water and coastal management, and insurance and finance, among others, to anticipate and adapt to climate change and thus continue to contribute strongly to the national welfare and economic vitality. ClimBiz will be attractive and valuable to a wide range of customers here and in other nations.

Agency: Department of Commerce | Branch: National Oceanic and Atmospheric Administration | Program: SBIR | Phase: Phase I | Award Amount: 94.69K | Year: 2015

The value of the subseasonal and seasonal probability forecasts of the National Weather Service (NWS) will be enhanced when the information about standard meteorological variables is converted into information about business impact variables such as degree days, wind and solar power potential, and end-of-season crop yield. Toward that goal, in Phase I we will identify a suite of such action variables for a broad range of industries and activities, develop algorithms to obtain probabilities about a representative sample of such variables from the NWS Climate Forecast System Version 2 (CSF2) forecasts, and develop methods for verifying forecasts about action variables. Computing the forecasts and verification over a historical period will provide an estimate of the skill of the forecasts of the impact variables. The ongoing CFS2 forecasts can then be combined with forecast skill to show decision makers the expected consequences of acting at various predicted probabilities to seize opportunity or mitigate advedse events. This will lead in Phase II to web-based interactive decision advisory systems tailored to industries such as energy, agriculture, transportation, and insurance and finance that will allow their decision makers to assess alternative actions, reduce climate variability risk, and increase profits.

Agency: Department of Commerce | Branch: National Oceanic and Atmospheric Administration | Program: SBIR | Phase: Phase I | Award Amount: 94.97K | Year: 2011

Prescient Weather proposes four Phase I tasks to increase the value of the new NOAA Climate Forecast System to commercial customers. The capabilities demonstrated and explored in Phase I will be developed and integrated in Phase II as components of a new Seasonal Information and Decision Support System (SIDSS) for our World Climate Service customers. The Phase I tasks are: • Improve season forecast calibration with a new climate-conserving calibration algorithm that produces relatively flat rank probability diagrams; • Convert calibrated forecasts of meteorological variables into forecasts of impact and decision variables such as degree days or wind power availability’ • Explore calibration of two-to-four week forecasts with conditioning on expected flow patterns; • Explore the potential of model output statistics (MOS) to calibrate and improve weekly and monthly forecasts of seasonal variability. Several of the tasks will explore the use of principal component methods to project forecasts on the historical verification data and to define flow regimes for conditional calibration. The SIDSS development in Phase II will focus on the client decision context, present historical and predicted information including numerical and analog forecast, and facilitate client development of individualized forecasts. It is a key part of our commercialization strategy.

Agency: Department of Commerce | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 336.38K | Year: 2012

Prescient Weather process five integrated Phase II tasks to increase the value of the NOAA Climate Forecast System and to assist the private sector in managing weather and climate risk and opportunity: The Phase II tasks are: (1) Develop an optimal WCS seasonal multi-model ensemble by calibrating and combining the NWS CFSv2, the ECMWF SFSv4, and the new National Multi-Model Ensemble (NMME) to create more skillful operational seasonal forecasts; (2) Develop an optimal WCS weekly forecast ensemble from the same models and then create an operational multi-model probability forecast; (3) Develop probability forecasts for impact variables critical in agriculture, energy, and renewable energy on the weekly, monthly, and seasonal scale; (4) Develop effective methods for combining probability forecasts, business models, and forecast performance statistics to enable users to act on the forecast with confidence in the consequences; (5) Complete and implement the Internet-based Seasonal and Subseasonal Prediction, Information, and Decision Support System (SSPIDSS) as the interactive workspace to support decision-making. The SSPIDSS implementation will focus on the client decision context, presenting a tier of probabilistic forecasts of meteorological and industry variables on the scale of seasons for long-range strategy, months and weeks for tactical adjustments, and days for immediate action.

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