Books

  1. Flampouris, S. (2010). On the Wave Field Propagating Over an Uneven Sea Bottom Observed by Ground Based Radar. GKSS-Forschungszentrum Geesthacht.
  2. Flampouris, S. (2006). Investigation of correlations between radar deduced bathymetries due to the outer impact of a storm in the area" Salzsand". In GKSS FORSCHUNGSZENTRUM GEESTHACHT GMBH-PUBLICATIONS-GKSS (Vol. 16). GKSS-FORSCHUNGSZENTRUM GEESTHACHT GMBH. https://publications.hereon.de/id/25059/index.php

Book Chapters

  1. Orzech;, M., Veeramony;, J., Ngodock;, H., & Flampouris, S. (2012). Recent Impulses to Marine Science and Engineering—Proceedings of 3rd International YOUMARES Conference (W. J & E. MH, Eds.; p. 52).
  2. Flampouris, S., Seemann, J., Senet, C., & Ziemer, F. (2012). Storm impact on the coastal geomorphology and current field by wave field image sequences. In Y. H. Ni-Bin Chang (Ed.), Multiscale Hydrologic Remote Sensing: Perspectives and Applications (pp. 33–64). Taylor & Francis Group.
  3. Flampouris, S., Bell, P., & Ziemer, F. (2009). Monitoring of the bathymetry by inverting the wavefield propagation. In K. P. Traub & T. Lüllwitz (Eds.), Symposium Geoinformationen für die Küstenzone, Hamburg 2008. Points Verlag, Norden - Halmstad.
  4. Ziemer, F., & Stylianos, F. (2008). Observing small scale morphodynamic processes during storm events. In Observing the Coastal Sea-an Atlas of Advanced Monitoring Techniques. LOICZ International Project Office.

Journal Articles

  1. Cannon, F., Pfreundschuh, S., Taylor, B., Munchak, S. J., Nelson, E., L’heureux, J., Owens, C., Conibear, L., Flampouris, S., & Chawla, A. (2024). Deep Learning for Multi-Satellite Precipitation Retrievals: Impact of Tomorrow. io’s Microwave Sounders. Authorea Preprints.
  2. Conibear, L., Payne, A. E., Harris, A. E. R., McCandless, T., Brey, S. J., Keshavamurthy, K., Green, M. E., Flampouris, S., & Peffers, L. T. (2023). Post-processing using deep learning to create operational, high-resolution, and probabilistic weather forecasts. Authorea Preprints.
  3. Gasbarro, M., Tolman, H., Achuthavarier, D., Adimi, F., Bernardet, L., Carley, J., Flampouris, S., Garrett, K., Jung, Y., Kleist, D., & others. (2023). Compute and HPC Resource Assessment for the National Weather Service Modeling Program Office.
  4. Sims, J., Lee, T., Koch, D., Gross, B., Stajner, I., Considine, D., Pawson, S., Kleist, D., Gelaro, R., Flampouris, S., & others. (2022). Joint Collaboration on Coupled Data Assimilation and Modeling. Bulletin of the American Meteorological Society, 103(5), E1421–E1425.
  5. The NOAA-NCEP 40 year Reanalysis with the Next Generation Global Ocean Data Assimilation System (NG-GODAS): 1979 to 2019. (2022).
  6. Abdalla, S., Abdeh Kolahchi, A., Ablain, M., Adusumilli, S., Aich Bhowmick, S., Alou-Font, E., Amarouche, L., Andersen, O. B., Antich, H., Aouf, L., Arbic, B., Armitage, T., Arnault, S., Artana, C., Aulicino, G., Ayoub, N., Badulin, S., Baker, S., Banks, C., … Zlotnicki, V. (2021). Altimetry for the future: Building on 25 years of progress. Advances in Space Research, 68(2), 319–363.
  7. Zhu, J., Vernieres, G., Sluka, T., Flampouris, S., Kumar, A., Mehra, A., Cronin, M. F., Zhang, D., Wills, S., Wang, J., & others. (2021). Roles of TAO/TRITON and Argo in Tropical Pacific Observing Systems: An OSSE Study for Multiple Time Scale Variability. Journal of Climate, 34(16), 6797–6817.
  8. Ludeno, G., Flampouris, S., Lugni, C., Soldovieri, F., & Serafino, F. (2017). Corrections to “A Novel Approach Based on Marine Radar Data Analysis for High-Resolution Bathymetry Map Generation”[Jan 14 234-238]. IEEE Geoscience and Remote Sensing Letters, 14(4), 584–584.
  9. Orzech, M., Veeramony, J., & Flampouris, S. (2014). Optimizing spectral wave estimates with adjoint-based sensitivity maps. Ocean Dynamics, 64, 487–505.
  10. Ludeno, G., Flampouris, S., Lugni, C., Soldovieri, F., & Serafino, F. (2013). A novel approach based on marine radar data analysis for high-resolution bathymetry map generation. IEEE Geoscience and Remote Sensing Letters, 11(1), 234–238.
  11. Flampouris, S., Seemann, J., Senet, C., & Ziemer, F. (2010). The influence of the inverted sea wave theories on the derivation of coastal bathymetry. IEEE Geoscience and Remote Sensing Letters, 8(3), 436–440.
  12. Senet, C. M., Seemann, J., Flampouris, S., & Ziemer, F. (2008). Determination of bathymetric and current maps by the method DiSC based on the analysis of nautical X-band radar image sequences of the sea surface (November 2007). IEEE Transactions on Geoscience and Remote Sensing, 46(8), 2267–2279.
  13. Flampouris, S., Ziemer, F., & Seemann, J. (2008). Accuracy of bathymetric assessment by locally analyzing radar ocean wave imagery (February 2008). IEEE Transactions on Geoscience and Remote Sensing, 46(10), 2906–2913.

Conference Proceedings

  1. Pope;, M., Green;, M., Payne, A. E., Chawla, A., & Flampouris, S. (2025). Probabilistic Calibration for ML-Based Extreme Event Prediction. 105th AMS Annual Meeting, New Orleans, LA, Jan 12-16, 2025.
  2. Taylor, B. M., Cannon, F., Pfreundschuh, S., Munchak, & Flampouris, S. (2025). Accelerating the Transition of Satellite Precipitation Retrievals from Research to Operations Using Modern Software Engineering and MLOps Best Practices. 105th AMS Annual Meeting, New Orleans, LA, Jan 12-16, 2025.
  3. Guerrette, J., Honeyager, R., Munchak, S. J., & Flampouris, S. (2025). Progress toward global operational utilization of the Tomorrow Microwave Sounder with the Joint Effort for Data Assimilation. 105th AMS Annual Meeting, New Orleans, LA, Jan 12-16, 2025.
  4. Book;, C., Kim;, J., Snyder;, E., Perlin;, N., Vasic;, R., Flampouris, S., & Adimi, F. (2025). Status of Test Case Integration into UFS Weather Model and Applications. 105th AMS Annual Meeting, New Orleans, LA, Jan 12-16, 2025.
  5. Peña, M., Romero, L., Flampouris, S., Curchitser, E., Pondeca, M., Carley, J. R., Kleist, D. T., Mitsopoulos, P., Akaawase, B., & Anderson, M. (2024). Developing the marine component of the NOAA 3D-Real Time Mesoscale Analysis (RTMA). Ocean Sciences Meeting 2024, New Orleans, LA, Feb 22-27, 2024.
  6. Improving Earth System Models via Hierarchical System Development. (2024). EGU General Assembly 2024.
  7. Tolman, H. L., Bentley, A. M., & Flampouris, S. (2024). Earth System Model Governance and Community Building. 104th AMS Annual Meeting, Baltimore, MD, 28 Jan 1 Feb, 2024.
  8. Hertneky, T. J., Sun, X., Bernardet, L., Ek, M. B., Jensen, T. L., Flampouris, S., & Teng, Y.-C. (2024). Establishing Community Requirements of Hierarchical System Development for Earth System Models. 104th AMS Annual Meeting, Baltimore, MD, 28 Jan 1 Feb, 2024.
  9. Payne, A. E., Keshavamurthy, K., Conibear, L., Harris, A. R., McCandless, T., Green, M. E., & Flampouris, S. (2024). Efficient and Rigorous Data Quality Checking for Training a Machine Learning Postprocessing Algorithm on High-Resolution NWP Data and Surface Observations. 104th AMS Annual Meeting, Baltimore, MD, 28 Jan 1 Feb, 2024.
  10. Green, M. E., McCandless, T., Conibear, L., Taylor, B., Payne, A. E., Harris, A. R., Keshavamurthy, K., & Flampouris, S. (2024). A Machine Learning Based Approach to Predicting Probabilistic Power Generation at Individual Wind Farms in ERCOT. 104th AMS Annual Meeting, Baltimore, MD, 28 Jan 1 Feb, 2024.
  11. Davis, S., Pithani, P., Pattantyus, A., Marchand, M., Flampouris, S., & Peffers, L. (2024). Comprehensive Bespoke Atmospheric Model (CBAM)’s High Resolution Reanalysis Dataset By Tomorrow. Io for Supporting Climate Resilient Crop Varieties in Sub-Saharan Africa. 104th AMS Annual Meeting, Baltimore, MD, 28 Jan 1 Feb, 2024.
  12. Flampouris, S., Peña, M., Pondeca, M., & Tian, X. (2024). Global real-time analysis of surface wind and wave fields. Ocean Sciences Meeting 2024, New Orleans, LA, Feb 22-27, 2024.
  13. Surya, P. P. R. D., Pattantyus, A., Marchand, M. R., Davis, S., Flampouris, S., & Peffers, L. (2024). Virtual Observations to Bridge Meteorological Data Gaps for Airline Operations in the Caribbean Islands. 104th AMS Annual Meeting, Baltimore, MD, 28 Jan 1 Feb, 2024.
  14. Honeyager, R., Tian, X., Guerrette, J., Munchak, S. J., & Flampouris, S. (2024). OSSEs in the cloud: Tomorrow. io’s Observation System Simulation Experiment system on Azure. 104th AMS Annual Meeting, Baltimore, MD, 28 Jan 1 Feb, 2024.
  15. Shrader, Z., Kumar, A., Burrows, D., Pope, M., Potts, M., Trahan, S., Frolov, S., Jensen, A., Booker, K., Blackman, K., & others. (2024). Advancing Atmospheric River Prediction: Development and Implementation of a High-Resolution Forecasting Framework. AGU24, Washington, D.C. Dec 9 – 13, 2024.
  16. Honeyager, R., Guerrette, J., Munchak, J., & Flampouris, S. (2024). The Tomorrow Microwave Sounder program: early observations and forecasting applications. AGU24, Washington, D.C. Dec 9 – 13, 2024.
  17. ‘Impact Assessment of the Tomorrow.io Microwave Sounder (TMS) Constellation. (2024). Unifying Innovations in Forecasting Capabilities Workshop 2024, Jackson, MS July 22-26, 2024.
  18. EPIC Community Infrastructure Supporting Innovation of the Unified Forecast System. (2024). 104th AMS Annual Meeting, Baltimore, MD, 28 Jan 1 Feb, 2024.
  19. Post-Processing High-Resolution Deterministic NWP Model with Machine Learning to Produce Cost-Effective, Operational Probabilistic Forecasts. (2024). Unifying Innovations in Forecasting Capabilities Workshop 2024, Jackson, MS July 22-26, 2024.
  20. J. Kim, C. B., S. Flampouris, & Booker, K. (2024). Hierarchical Decomposition of the UFS Test Cases and DevOps Test Framework Infrastructures. Unifying Innovations in Forecasting Capabilities Workshop 2024, Jackson, MS July 22-26, 2024.
  21. Blackman, K., Potts, M. A., Kim, J., Booker, K., & Stylianos, F. (2024). Epic Community Infrastructure and Application Tools Supporting Innovation of the Unified Forecast System. 104th AMS Annual Meeting, Baltimore, MD, 28 Jan 1 Feb, 2024.
  22. Ek, M., Hertneky, T. J., Xue, L. ; Jensen, T. L., X., S., Bernardet, L., S.;, F., Teng, Y.-C., & W., L. (2024). Improving Earth System Models via Hierarchical System Development. 104th AMS Annual Meeting, Baltimore, MD, 28 Jan 1 Feb, 2024.
  23. Ek, M., Hertneky, T., Xue, L., Jensen, T., Sun, X., Bernardet, L., Flampouris, S., Teng, Y.-C., & Li, W. (2023, November). Improving Earth System Models via Hierarchical System Development. AGU23, San Francisco, CA, Dec 11 – 15, 2023.
  24. Blackman, K., Flampouris, S., Potts, M., & Jones, A. (2023). EPIC Community Engagement supporting community modeling innovation. AGU23, San Francisco, CA, Dec 11 – 15, 2023.
  25. Teng, Y.-C., & Flampouris, S. (2023). The Hierarchical Testing Framework for the Unified Forecast System (UFS). 103rd AMS Annual Meeting, Denver, CO, Jan 9 - 12, 2023.
  26. Blackman, K., Potts, M., Flampouris, S., & Kim, J. (2023). EPIC Community Infrastructure supporting innovation of the Unified Forecast System. AGU23, San Francisco, CA, Dec 11 – 15, 2023.
  27. Payne, A. E., Conibear, L., Reed Harris, A. E., McCandless, T., Keshavamurthy, K., Green, M., Brey, S. J., Flampouris, S., & Peffers, L. (2023). Post-Processing High-Resolution NWP using Deep Learning to Create Operational Probabilistic Forecasts. AGU23, San Francisco, CA, Dec 11 – 15, 2023.
  28. Flampouris, S., Kim, J., McFarland, J., Teng, Y.-C., Chin, S., Shrader, Z., Delponte, M., & Gutierrez, J. M. (2023). EPIC: Providing the Infrastructure for Open Development of the UFS Weather Model. 103rd AMS Annual Meeting, Denver, CO, Jan 9 - 12, 2023.
  29. Flampouris, S., Honeyager, R., Tian, X., & Guerrette, J. (2023). Open Source Science and Innovation, the example of Tomorrow. io’s Global Forecasting System. AGU23, San Francisco, CA, Dec 11 – 15, 2023.
  30. Flampouris, S., & EPIC, infrastructure team. (2022). Building the Community Infrastructure for the Unified Forecast System. AGU Fall Meeting Abstracts, 2022, GC15H–0541.
  31. Kim, J., Curtis, B., Worthen, D., Wang, J., McFarland, J., Shrader, Z., & Flampouris, S. (2022). Continuous Integration Using Jenkins for the UFS Weather Model Development. AGU Fall Meeting Abstracts, 2022, GC14B–02.
  32. Xue, Y., Kondragunta, C. R., Koch, D., Smith, S. B., Sims, J. D., Kinter, J. L., Whitaker, J., Tallapragada, V. S., Adimi, F., & Flampouris, S. (2022). NOAA’s Unified Forecast System Research to Operations Project. 102nd American Meteorological Society Annual Meeting.
  33. Tolman, H. L., Chawla, A., Flampouris, S., & Huang, M. (2022). Evolving the NOAA Production Suite on Available Compute Resources: A Tool to Assess What Is Feasible. 102nd American Meteorological Society Annual Meeting.
  34. Adimi, F., Sims, J. D., & Flampouris, S. (2022). Management and Governance of the Unified Forecast System Research to Operations (UFS-R2O) Project. 102nd American Meteorological Society Annual Meeting.
  35. Sims, J. D., Adimi, F., Koch, D. M., Flampouris, S., Kondragunta, C. R., & Huang, M. (2022). NOAA’s Investment and Vision for the Unified Forecast System Research to Operations (UFS-R2O) Project: A Programmatic Overview. 102nd American Meteorological Society Annual Meeting.
  36. Koch, D., Rood, R. B., Alves, J.-H. G. M., Tolman, H. L., Xue, Y., Huang, M., Jung, Y., Adimi, F., & Flampouris, S. (2021). NOAA’s Path to Earth System Prediction using the Unified Forecast System. 101st American Meteorological Society Annual Meeting.
  37. Flampouris, S., Jung, Y., Kleist, D. T., Tallapragada, V. S., Carley, J. R., Koch, D., & Mahajan, R. (2021). The NWS Data Assimilation Strategy within the Unified Forecast System. 101st American Meteorological Society Annual Meeting.
  38. Koch, D., Carlis, D. N. L., Kondragunta, C. R., Adimi, F., Flampouris, S., & Kumar, K. V. (2021). Accelerating the UFS implementation through the integrated and collaborative UFS R20 project. 101st American Meteorological Society Annual Meeting.
  39. Flampouris, S. (2021). Real Time Global Wave Analysis. The 4th Taiwan West Pacific Global Forecast System Development Workshop.
  40. Vernieres, G., Sluka, T., Ebrahimi, H., Mahajan, R. B., Flampouris, S., Kim, J., Meixner, J., Kuang, J., & Paturi, S. (2020). Sea–Ice Ocean Coupled Assimilation at the JCSDA: Preliminary Results of a JEDI-Based Data Assimilation System for the Marine Component of the NOAA/EMC Coupled Model. 100th American Meteorological Society Annual Meeting.
  41. Vernieres, G., Sluka, T. C., Mahajan, R., Menetrier, B., Holdaway, D., Ebrahimi, H., Flampouris, S., & Kim, J. (2020). Interfacing the Modular Ocean Model version 6 (MOM6) with the Joint Effort for Data assimilation Integration (JEDI). Ocean Sciences Meeting 2020.
  42. Kim, J., Vernires, G., & Flampouris, S. (2020). Computational Performance Analysis of JEDI-based Sea Ice Ocean Coupled Assimilation (SOCA) System: CICE6. Ocean Sciences Meeting 2020.
  43. Carley, J. R., Pondeca, M., Levine, S., Zhang, X., Morris, M. T., Flampouris, S., Gibbs, A. M., Lin, Y., Luo, Y., Zhao, G., & others. (2020). A Description of the v2. 8 RTMA/URMA Upgrade and Progress toward 3D RTMA. 100th American Meteorological Society Annual Meeting.
  44. Flampouris, S., Vernires, G., Sluka, T. C., Meixner, J., Kim, J., Kuang, J., & Paturi, S. (2020). New Generation of Global Ocean Data Assimilation System at NCEP. Ocean Sciences Meeting 2020.
  45. Roles of TAO/TRITON and Argo in Tropical Pacific Observing Systems: An OSSE Study for Multiple Time Scale Variability. (2020). AGU Fall Meeting 2020.
  46. Flampouris, S., & Penny, S. (2018). The Local Ensemble Transform Kalman Filter for the Global Wave Prediction System at NWS. 2018 Ocean Sciences Meeting.
  47. Carley, J. R., Pondeca, M., Levine, S., Yang, R., Lin, Y., Flampouris, S., Whiting, J., Melchior, S., Gibbs, A. M., Purser, R. J., & others. (2018). Ongoing Upgrades to NOAA’s Real Time Mesoscale Analysis System. 29th Conference on Weather Analysis and Forecasting/25th Conference on Numerical Weather Prediction.
  48. Gibbs, A., Van der Westhuysen, A., Flampouris, S., Caldwell, P., Santos, P., & Padilla-Hernandez, R. (2018). Evaluation and Challenges Using the Nearshore Wave Prediction System on Oahu, Hawaii, through the 2016–2017 Winter Season. 16th Symposium on the Coastal Environment, 98th AMS Annual Meeting.
  49. Flampouris, S., & Carley, J. R. (2018). Operational High Resolution Analysis of Significant Wave Height for CONUS. AGU Fall Meeting Abstracts.
  50. Carley, J. R., Pondeca, M., Levine, S., Yang, R., Lin, Y., Flampouris, S., Alves, J.-H., Whiting, J., Melchior, S., Gibbs, A. M., & others. (2018). The Continued Development of the NOAA RTMA/URMA Systems. 98th American Meteorological Society Annual Meeting.
  51. Flampouris, S. (2017). Global Observations of the Wave Field Direction: Revisiting an old idea. EGU General Assembly Conference, 11834.
  52. Flampouris, S., Penny, S., & Alves, H. (2017). Towards the Operational Ensemble-based Data Assimilation System for the Wave Field at the National Weather Service. EGU General Assembly Conference Abstracts, 11977.
  53. Flampouris, S., Alves, H., & Pondeca, M. (2016). Development of a GSI-Based, 2D-VAR Data Assimilation System for Operational Wave Guidance at the National Weather Service. American Geophysical Union, PO54D–3285.
  54. Flampouris, S., Veeramony, J., Orzech, M., & Ngodock, H. (2014). Development of the error covariance function for assimilation of wave spectra – the example of SWAN. Ocean SciencesMeeting, Honolulu, Hawaii USA.
  55. Serafino, F., Ludeno, G., Lugni, C., Flampouris, S., Natale, A., Arturi, D., & Soldovieri, F. (2013). Generation of bathymetric maps with high resolution through the analysis of nautical X-band radar images. 2013 IEEE International Geoscience and Remote Sensing Symposium-IGARSS, 2278–2280.
  56. Seemann, J., Ziemer, F., Wu, L.-C., Cysewski, M., & Flampouri, S. (2013). The analysis of sea surface dynamics using a dopplerized X-band radar. 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, 2428–2430.
  57. Flampouris, S., Seemann, J., & Ziemer, F. (2013). Wave heave spectra from radar Doppler velocities at extreme low grazing angles. 15th EGU General Assembly Conference Abstracts, 12483.
  58. Validationof a wave data assimilation system based on SWAN. (2013). Geophys Research Abstracts, EGU General Assembly.
  59. Serafino, F., Ludeno, G., Flampouris, S., & Soldovieri, F. (2012). Analysis of nautical X-band radar images for the generation of bathymetric map by the NSP method. 2012 IEEE International Geoscience and Remote Sensing Symposium, 2829–2832.
  60. Veeramony, J., Orzech, M., Ngodock, H. E., & Flampouris, S. (2012). Towards an Operational Nearshore Wave Data Assimilation System. AGU Fall Meeting.
  61. Seemann, J., Ziemer, F., Cysewski, M., Flampouris, S., Gurgel, K.-W., & Schlick, T. (2011). HF radar based current observation system in the German Bight. OCEANS 2011 IEEE - Spain, 1–7.
  62. Flampouris, S., Seemann, J., & Ziemer, F. (2010). Radar observations of wave field in littoral zone. 2010 IEEE International Geoscience and Remote Sensing Symposium, 948–951.
  63. Flampouris, S., Seemann, J., & Ziemer, F. (2010). Observation of littoral hydrodynamics by ground based Dopplerized X-band Radar. 2010 Ocean Sciences Meeting.
  64. Flampouris, S., Seemann, J., & Ziemer, F. (2009). Sharing our experience using wave theories inversion for the determination of the local depth. OCEANS 2009-EUROPE, 1–7.
  65. Flampouris, S., Seemann, J., & Ziemer, F. (2009). Observing littoral waves by doppler radar. 2009 IEEE International Geoscience and Remote Sensing Symposium, 3, III–757.
  66. Observing litteral waves by doppler waves. (2009). Online available at: \urlhttps://doi.org/10.1109/IGARSS.2009.5417875 (DOI). Flampouris, S.; Seemann, J.; Ziemer, F.: Observing litteral waves by doppler waves. In: Proceedings, 2009 IEEE International Geoscience and Remote Sensing Symposium. Cape Town (ZA). IEEE. 2009. III-760. DOI: 10.1109/IGARSS.2009.5417875.
  67. Flampouris, S., Ziemer, F., & Seemann, J. (2007). Survey of bathymetry and current fields by radar image series acquired by shore based X-band radar. 2007 IEEE International Geoscience and Remote Sensing Symposium, 3579–3582.
  68. Flampouris, S., & Ziemer, F. (2006). Comparison of bathymetric changes observed by radar on different time scales. SeaTech Week - Operational Coastal Oceanography Conference - IFREMER, Brest 2006.

Technical Reports

  1. Sun, X., Hertneky, T., Bernardet, L., Jensen, T., Teng, Y.-C., Flampouris, S., Xue, L., Li, W., Nance, L., & Ek, M. (2023). Hierarchical System Development for the Unified Forecast System. In Developmental Testbed Center.
  2. Kim, J., Vernieres, G., & Flampouris, S. (2020). Overview of Sea Ice Data Assimilation Activities and System Development at NOAA-NCEP (A. E., Ed.; Techreport No.50; Number 50, pp. 807–808). World Meteorological Organization. https://wgne.net/bluebook/index.php?year=2020&ch_=1
  3. Flampouris, S., De Pondeca, M. S. F. V., Padilla-Hernandez, R., & Carley, J. R. (2020). Upgrades to the NCEP’s Real-Time and UnRestricted Mesoscale Analysis Systems for the Significant Wave Height (E. Astakhova, Ed.; Techreport No.50; Number 50, pp. 803–804). World Meteorological Organization. https://wgne.net/bluebook/uploads/2020/sections/BB_20_S8.pdf
  4. Veeramony, J., Flampouris, S., Orzech, M., & Ngodock, H. (2018). Covariance Function for Nearshore Wave Assimilation Systems (Techreport No.18-9753; Numbers 18-9753). Naval Research Laboratory. https://apps.dtic.mil/sti/tr/pdf/AD1048549.pdf
  5. Flampouris, S., Pondeca, M., Whiting, J., Carley, J., Alves, J., Yang, R., & Levine, S. (2018). Modular Data Assimilation System for Significant Wave Height: The Example of Local Ensemble Transform Kalman Filter for the National Weather Service (E. Astakhova, Ed.; pp. 8.03–805). World Meteorological Organization.
  6. Flampouris, S., & Penny, S. G. (2017). Modular Data Assimilation System for Significant Wave Height: The Example of Local Ensemble Transform Kalman Filter for the National Weather Service (E. Astakhova, Ed.; pp. 803–805). World Meteorological Organization.
  7. Orzech, M., Veeramony, J., Ngodock, H., Flampouris, S., & Souopgui, I. (2016). Recent Updates to SWANFAR, a 5DVAR Data Assimilation System for SWAN (Techreport No.16-9705; Numbers 16-9705). Naval Research Laboratory. https://apps.dtic.mil/sti/pdfs/ADA640877.pdf
  8. Flampouris, S., Alves, J.-H., Pondeca, M., & Whiting, J. (2016). Inclusion of Significant Wave Height Analysis to NCEP’s UnRestricted Mesoscale Analysis (URMA) (A. E., Ed.; pp. 803–805). World Meteorological Organization.