Neural Network Estimation of Atmospheric Thermodynamic State for Weather Forecasting Applications

  • William J. Blackwell
  • Adam B. Milstein
  • Bradley Zavodsky
  • Clay B. Blankenship
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8534)


We present recent work using neural network estimation techniques to process satellite observation of the Earth’s atmosphere to improve weather forecasting performance. A novel statistical method for the retrieval of atmospheric temperature and moisture (relative humidity) profiles has been developed and evaluated with sounding data from the Atmospheric InfraRed Sounder (AIRS) and the Advanced Microwave Sounding Unit (AMSU) on the NASA Aqua satellite and the Infrared Atmospheric Sounding Interferometer (IASI) and AMSU on the EUMETAT MetOp-A satellite. The present work focuses on the cloud impact on the AIRS and IASI radiances and explores the use of stochastic cloud clearing mechanisms together with neural network estimation. The algorithm outputs are ingested into a numerical model, and forecast information and decision support tools are then presented to a meteorologist. We discuss the underlying physical problem, the algorithmic framework, and the interaction with forecaster.


Neural networks numerical weather prediction weather forecasting 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ashley, W.S., Black, A.W.: Fatalities Associated with Nonconvective High Wind Events in the United States. J. of Applied Meteor. and Climatology 47, 717–725 (2008)CrossRefGoogle Scholar
  2. 2.
    Aumann, H.H., Manning, E., Barnet, C., Maddy, E., Blackwell, W.: An anomaly correlation skill score for the evaluation of the performance of hyperspectral infrared sounders. In: Atmospheric and Environmental Remote Sensing Data Processing and Utilization V: Readiness for GEOSS III. Proceedings of the SPIE, vol. 7456, pp. 74 560T–74 560T–7 (2009)Google Scholar
  3. 3.
    Blackwell, W.J., Pieper, M., Jairam, L.G.: Neural network estimation of atmospheric profiles using AIRS/IASI/AMSU data in the presence of clouds. In: Proc. SPIE Asia Pacific Remote Sensing Symposium, Noumea, New Caledonia (November 2008)Google Scholar
  4. 4.
    Blackwell, W.J., Chen, F.W.: Recent Progress in Neural Network Estimation of Atmospheric Profiles Using Microwave and Hyperspectral Infrared Sounding Data in the Presence of Clouds. In: Proc. SPIE Defense and Security Symposium (April 2007)Google Scholar
  5. 5.
    Blackwell, W.J.: Neural Network Retrievals of Atmospheric Temperature and Moisture Profiles from High-Resolution Infrared and Microwave Sounding Data. In: Signal and Image Processing for Remote Sensing, pp. 205–232. Taylor and Francis (October 2006)Google Scholar
  6. 6.
    Blackwell, W.J., Chen, F.W.: Neural Network Retrieval of Atmospheric Temperature and Moisture Profiles from AIRS/AMSU Data in the Presence of Clouds. In: Proc. SPIE, vol. 6233 (October 2006)Google Scholar
  7. 7.
    Blackwell, W.J., Chen, F.W.: Combined Infrared and Microwave Retrievals of Atmospheric Profiles in the Presence of Clouds using Nonlinear Stochastic Methods: The SCENE Algorithm. In: Proc. IGARSS (August 2006)Google Scholar
  8. 8.
    Blackwell, W.J.: A Neural-Network Technique for the Retrieval of Atmospheric Temperature and Moisture Profiles from High Spectral Resolution Sounding Data. IEEE Trans. Geosci. Remote Sensing 43(11), 2535–2546 (2005)CrossRefGoogle Scholar
  9. 9.
    Blackwell, W.J.: Validation of Neural Network Atmospheric Temperature and Moisture Retrievals using AIRS/AMSU Radiances. In: Proc. SPIE, vol. 5806, pp. 607–617 (October 2005)Google Scholar
  10. 10.
    Blackwell, W.J., Chen, F.W.: Neural Networks in Atmospheric Remote Sensing. Artech House (2009)Google Scholar
  11. 11.
    Blackwell, W.J.: Neural Network Jacobian Analysis for High-Resolution Profiling of the Atmosphere. EURASIP Journal on Advances in Signal Processing (71) (March 2012)Google Scholar
  12. 12.
    Blankenship, C.B., Zavodsky, B.T., Jedlovec, G.J., Wick, G.A., Neiman, P.J.: Impact of AIRS thermodynamic profiles on precipitation forecasts for atmospheric river cases affecting the western United States. Preprints, Ninth Annual Symposium on Future Operational Environmental Satellite Systems, Austin, TX. Amer. Met. Soc., P291 (2013),
  13. 13.
    Brown, B.G., Gotway, J.H., Bullock, R., Gilleland, E., Fowler, T., Ahijevych, D., Jensen, T.: The Model Evaluation Tools (MET): Community tools for forecast evaluation. Preprints, 25th Conf. on International Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, Phoenix, AZ, Amer. Meteor. Soc., 9A.6 (2009),
  14. 14.
    Cho, C., Staelin, D.H.: Cloud clearing of Atmospheric Infrared Sounder hyperspectral infrared radiances using stochastic methods. J. of Geophys. Res. 111(D9) (April 2006)Google Scholar
  15. 15.
    Folmer, M.J., et al.: The Use of the RGB Products at the HPC, OPC, NHC, and SAB Proving Grounds During the 2011 Atlantic Hurricane Season. In: 30th Conference on Hurricanes and Tropical Meteorology, Ponte Vedra Beach, FL, Amer. Met. Soc., 7C.8 (2012),
  16. 16.
    Kidder, S.Q., Jones, A.S.: A Blended Satellite Total Precipitable Water Product for Operational Forecasting. J. Atmos. Oceanic Technol. 24, 74–81 (2007)CrossRefGoogle Scholar
  17. 17.
    Knox, J.A., Frye, J.D., Durkee, J.D., Fuhrmann, C.M.: Non-Convective High Winds Associated with Extratropical Cyclones. Geography Compass 5(2), 63–89 (2011)CrossRefGoogle Scholar
  18. 18.
    Iwasaki, S., Shibata, T., Nakamoto, J., Okamoto, H., Ishimoto, H., Kubota, H.: Characteristics of deep convection measured by using the A-train constellation. J. Geophys. Res. 115, D06207 (2010), doi:10.1029/2009JD013000Google Scholar
  19. 19.
    Lin, Y., Mitchell, K.E.: The NCEP Stage II/IV hourly precipitation analyses: Development and applications. Preprints, 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor. Soc., 1.2 (2005),
  20. 20.
    Lin, Y., Mitchell, K.E., Rogers, E., DiMego, G.J.: Using hourly and daily precipitation analyses to improve model water budget. In: Ninth Symp. on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface, Amer. Meteor. Soc, San Diego (2005), (preprints)
  21. 21.
    Parkinson, C.L.: Aqua: An Earth-Observing Satellite Mission to Examine Water and Other Climate Variables. IEEE Trans. on Geoscience and Remote Sensing 41(2), 173–183 (2003)CrossRefGoogle Scholar
  22. 22.
    Ralph, F.M.: coauthors, 2011: Research aircraft observations of water vapor transport in atmospheric rivers and evaluation of reanalysis products. American Geophysical Union Fall Meeting, A11A-046 (2011)Google Scholar
  23. 23.
    Skamarock, W.C., et al.: A description of the Advanced Research WRF version 3, NCAR Tech. Note NCAR/TN-475+STR, 123 (2008),
  24. 24.
    Tao, Z., Blackwell, W.J., Staelin, D.H.: Error Variance Estimation for Individual Geophysical Parameter Retrievals. IEEE Trans. Geosci. Remote Sens. 51(3), 1718–1727 (2013)CrossRefGoogle Scholar
  25. 25.
    Wu, L., Su, H., Fovell, R.G., Wang, B., Shen, J.T., Kahn, B.H., Hristova-Veleva, S.M., Lambrigtsen, B.H., Fetzer, E.J., Jiang, J.H.: Relationship of environmental relative humidity with North Atlantic tropical cyclone intensity and intensification rate. Geophys. Res. Lett. 39, L20809 (2012), doi:10.1029/2012GL053546Google Scholar
  26. 26.
    Zavodsky, B.T., Molthan, A.L., Folmer, M.J.: Multispectral Imagery for Detecting Stratospheric Air Intrusions Associated with Mid-Latitude Cyclones. J. Operational Meteor. (2013) (in press)Google Scholar
  27. 27.
    Zavodsky, B.T., Chou, S.-H., Jedlovec, G.J.: Improved Regional Analyses and Heavy Precipitation Forecasts with Assimilation of Atmospheric Infrared Sounder Retrieved Thermodynamic Profiles. IEEE Trans. Geoscience and Remote Sensing 50, 4243–4251 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • William J. Blackwell
    • 1
  • Adam B. Milstein
    • 1
  • Bradley Zavodsky
    • 2
  • Clay B. Blankenship
    • 3
  1. 1.Lincoln LaboratoryMassachusetts Institute of TechnologyLexingtonU.S.A.
  2. 2.SPoRTNASA Marshall Space Flight CenterHuntsvilleUSA
  3. 3.SPoRTUniversities Space Research AssociationHuntsvilleUSA

Personalised recommendations