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Use of Satellite Observations for Analog Ensemble Predictions to Contribute to Decision Advantage


OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Integrated Network System of Systems, Trusted AI and Autonomy OBJECTIVE: Develop an innovative methodology to utilize satellite observations of weather-related atmospheric variables within the analog ensemble (AnEn) technique for environmental predictions, when no in-situ observations (i.e. field data) are available. DESCRIPTION: Uncertainty in weather prediction affects Army mission preparation and planning degrading decision advantage. Numerical Weather Prediction (NWP) models generate atmospheric forecasts to provide a deterministic weather forecast, but present inherent uncertainty. A number of factors cause uncertainty associated with Numerical Weather Prediction (NWP) models; including but not limited to, errors in initial conditions, quality of the model initialization field, model physics, and various parameterization schemes [1, 2, 3, 4, 5]. Understanding the uncertainty in forecast predictions will address problems in weather support that cause impediments to the Army’s mission preparation and planning. PHASE I: Determine the scientific, technical merit, and feasibility for developing an AnEn framework using satellite observations (potentially also using hybrid in-situ and satellite observations, required in Phase II) for continuous and discontinuous atmospheric variables. Develop a conceptual methodology providing multiple weather and environmental conditions with their associated uncertainty. Deliver a report documenting the research and development efforts along with a detailed description of the proposed final methodology, implementation, and impacts upon uncertainty quantification results. PHASE II: The methodology will be fully implemented, using the programming language python, enabling straightforward integration with the Army’s geospatial software baseline used by geospatial engineers. The code will allow users, whether civilian or Army, to make weather and environmental predictions based on either satellite data or a combination of satellite and in situ observations. A methodology and implementation for hybrid use of satellite and other observational datasets within the AnEn techniques shall be set forth. A report will be delivered that provides an understanding of the AnEn techniques strengths and weakness when utilizing satellite and/or hybrid observational datasets, along with implementation recommendations. PHASE III DUAL USE APPLICATIONS: The AnEn prediction geospatial tool can be integrated into baseline software on the Geospatial Workstation (GWS) used by Army geospatial engineers, leading to a DoD commercialization potential. The geospatial engineers will benefit from this tool by having the new capability to predict several potential weather and environmental related impacts to mission planning quickly and capture weather-related mission risks caused by prediction uncertainty. Non-DoD commercialization potential exists within the civilian sector. The technology has many potential applications outside of the military to address weather-related forecasting challenges, and topics. For non-DoD sectors, the python based development fosters access and integration opportunities due to the popular adoption of python in many development practices. Furthermore, the ease of integration with geospatial (i.e. ArcGIS) software will facilitate the potential use within the non-DoD sector for those with existing ArcGIS licensure. REFERENCES: 1. E. N. Lorenz, "Atmospheric predictability as revealed by naturally occurring analogs," Journal of Atmospheric Science, vol. 26, pp. 639-646, 1969. 2. J. Berner, M. L. Shutts and T. Palmer, "A spectral stochastic kinetic enery backscatter scheme and its impact on flow-dependent predictability in the ECMWF ensemble prediction system," Journal of Atmospheric Science, vol. 66, pp. 603-626, 2009. 3. S. Haupt, P. Jimenez, J. Lee and B. Kosovic, "Principles of meteorology and numerical weather prediction," Renewable Energy Forecasting, no. Elsevier, pp. 3-28, 2017. 4. J. Berner, S.-Y. Ha, A. Hacker, A. Fournier and C. Snyder, "Model uncertainty in a mesoscale ensemble prediction system: Stochastic versus multiphysics representations," Monthly lWeather REview, vol. 139, pp. 1972-1995, 2011. 5. J. Slingo and T. Palmer, "Uncertainty in weather and climate prediction," Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 369, pp. 4751-4767, 2011. 6. L. Delle Monache, T. Nipen, Y. Liu, G. Roux and R. Stull, "Kalman filter and analog schemes to post-process numerical weather predictions," Monthly Weather Review, vol. 139, p. 35543570, 2011. 7. L. Delle Monache, T. Eckel, D. Rife, B. Nagarajan and K. Searight, "Probabilistic weather predictions with an analog ensemble," Monthly Weather Review, vol. 131, pp. 3498-3516, 2013. 8. S. Alessasndrini, L. Delle Monache, L. Sperati and G. Cervone, "An analog ensemble for short-term probabilistic solar power forecast," Applied Energy, vol. 157, pp. 95-110, 2015. 9. J. Zhang, C. Draxl, T. Hopson, L. Delle Monache, E. Vanvyve and B. M. Hodge, "Comparison of numerical weather prediction based deterministic and probabilistic wind resource assessment methods," Applied Energy, vol. 156, pp. 528-541, 2015. 10. G. Cervone, L. Clemente-Harding, S. Alessandrini and L. Delle Monache, "Photovoltaic Power Forecasts Using Artificial Neural Networks and an Analog Ensemble," Renewable & Sustainable Energy Reviews, 2017. 11. E. Vanvyve, L. Delle Monache, A. J. Monaghan and J. O. Pinto, "Wind resource estimates with an analog ensemble approach," Renewable Energy, vol. 74, pp. 761-773, 2015. 12. H. M. van den Dool, "A new look at weather forecasting through analogs," Monthly Weather Review, vol. 117, pp. 2230-2247, 1989. 13. S. Alessandrini, L. Delle Monache, C. Rozoff and W. Lewis, "Probabilistic prediction of tropical cyclone intensity with an analog ensemble," Monthly Weather Review, vol. 146, no. 6, pp. 1723-1744, 2018. 14. A. Badreddine, D. Bari, T. Bergot and Y. Ghabbar, "Analog Ensemble Forecasting System for Low-Visibility Conditions over the Main Airports of Morocco," Atmosphere, vol. 13, p. 1704, 2022. 15. M. Shahriari, G. Cervone, L. Clemente-Harding and L. Delle Monache, "Using the analog ensemble method as a proxy measurement for wind power predictability," Renewable Energy, vol. 146, pp. 789-801, 2020. 16. L. Clemente-Harding, "Extension of the Analog Ensemble Technique to the Spatial Domain," The Pennsylvania State University, University Park , 2019. 17. E. Kalnay, Atmospheric modeling, data assimilation and predictability, Cambridge University Press, 2003. KEYWORDS: uncertainty quantification, data analytics, geoinformatics, analog ensemble, prediction, atmospheric science, machine learning
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