• Aberson, S. D., M. L. Black, R. A. Black, J. J. Cione, C. W. Landsea, F. D. Marks, and R. W. Burpee, 2006: Thirty years of tropical cyclone research with the NOAA P-3 aircraft. Bull. Amer. Meteor. Soc., 87, 10391056, https://doi.org/10.1175/BAMS-87-8-1039.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Andersson, E. and M. Masutani, 2010: Collaboration on observing system simulation experiments (joint OSSE). ECMWF Newsletter, No. 123, ECMWF, Reading, United Kingdom, 14–16, https://doi.org/10.21957/62gayq76.

    • Crossref
    • Export Citation
  • Atlas, R., 1997: Atmospheric observations and experiments to assess their usefulness in data assimilation. J. Meteor. Soc. Japan, 75, 111130, https://doi.org/10.2151/jmsj1965.75.1B_111.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Atlas, R., R. N. Hoffman, S. C. Bloom, J. C. Jusem, and J. Ardizzone, 1996: A multiyear global surface wind velocity dataset using SSM/I wind observations. Bull. Amer. Meteor. Soc., 77, 869882, https://doi.org/10.1175/1520-0477(1996)077<0869:AMGSWV>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Atlas, R., S. C. Bloom, R. N. Hoffman, E. Brin, J. Ardizzone, J. Terry, D. Bungato, and J. C. Jusem, 1999: Geophysical validation of NSCAT winds using atmospheric data and analyses. J. Geophys. Res., 104, 11 40511 424, https://doi.org/10.1029/98JC02374.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Atlas, R., and Coauthors, 2001: The effects of marine winds from scatterometer data on weather analysis and forecasting. Bull. Amer. Meteor. Soc., 82, 19651990, https://doi.org/10.1175/1520-0477(2001)082<1965:TEOMWF>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Atlas, R., R. N. Hoffman, J. Ardizzone, S. M. Leidner, J. C. Jusem, D. K. Smith, and D. Gombos, 2011: A cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications. Bull. Amer. Meteor. Soc., 92, 157174, https://doi.org/10.1175/2010BAMS2946.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Atlas, R., L. Bucci, B. Annane, R. Hoffman, and S. Murillo, 2015a: Observing system simulation experiments to assess the potential impact of new observing systems on hurricane forecasting. Mar. Technol. Soc. J., 49, 140148, https://doi.org/10.4031/MTSJ.49.6.3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Atlas, R., V. Tallapragada, and S. Gopalakrishnan, 2015b: Advances in tropical cyclone intensity forecasts. Mar. Tech. Soc. J., 49, 149160, https://doi.org/10.4031/MTSJ.49.6.2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Atlas, R., and Coauthors, 2015c: Observing system simulation experiments (OSSEs) to evaluate the potential impact of an optical autocovariance wind lidar (OAWL) on numerical weather prediction. J. Atmos. Oceanic Technol., 32, 15931613, https://doi.org/10.1175/JTECH-D-15-0038.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Candy, B., S. J. English, and S. J. Keogh, 2009: A comparison of the impact of QuikScat and WindSat wind vector products on Met Office analyses and forecasts. IEEE Trans. Geosci. Remote Sens., 47, 16321640, https://doi.org/10.1109/TGRS.2008.2009993.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Casey, S. P. F., R. Atlas, S. A. Boukabara, R. N. Hoffman, K. Ide, M. Masutani, I. Moradi, and J. S. Woollen, 2016: Geostationary hyperspectral infrared constellation: Global observing system simulation experiments for five Geo-HSS instruments. 20th Conf. on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface (IOAS-AOLS), New Orleans, LA, Amer. Meteor. Soc., J7.4, https://ams.confex.com/ams/96Annual/webprogram/Paper283540.html.

  • Claziria, M. P., and V. Zavorotny, 2015: Algorithm theoretical basis document level 2 wind speed retrieval. University of Michigan Doc. 148-0138, 95 pp.

  • Entekhabi, D., and Coauthors, 2010: The Soil Moisture Active and Passive (SMAP) mission. Proc. IEEE, 98, 704716, https://doi.org/10.1109/JPROC.2010.2043918.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Errico, R. M., R. Yang, N. C. Privé, K. Tai, R. Todling, M. E. Sienkiewicz, and J. Guo, 2013: Development and validation of observing‐system simulation experiments at NASA’s Global Modeling and Assimilation Office. Quart. J. Roy. Meteor. Soc., 139, 11621178, https://doi.org/10.1002/qj.2027.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gall, R., J. Franklin, F. Marks, E. N. Rappaport, and F. Toepfer, 2013: The Hurricane Forecast Improvement Project. Bull. Amer. Meteor. Soc., 94, 329343, https://doi.org/10.1175/BAMS-D-12-00071.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gopalakrishnan, S. G., S. Goldenberg, T. Quirino, X. Zhang, F. Marks Jr., K.-S. Yeh, R. Atlas, and V. Tallapragada, 2012: Toward improving high-resolution numerical hurricane forecasting: Influence of model horizontal grid resolution, initialization, and physics. Wea. Forecasting, 27, 647666, https://doi.org/10.1175/WAF-D-11-00055.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hock, T. F., and J. L. Franklin, 1999: The NCAR GPS dropwindsonde. Bull. Amer. Meteor. Soc., 80, 407420, https://doi.org/10.1175/1520-0477(1999)080<0407:TNGD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoffman, R. N., 1982: SASS wind ambiguity removal by direct minimization. Mon. Wea. Rev., 110, 434445, https://doi.org/10.1175/1520-0493(1982)110<0434:SWARBD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoffman, R. N., 1984: SASS wind ambiguity removal by direct minimization. Part II: Use of smoothness and dynamical constraints. Mon. Wea. Rev., 112, 18291852, https://doi.org/10.1175/1520-0493(1984)112<1829:SWARBD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoffman, R. N., and R. Atlas, 2016: Future observing system simulation experiments. Bull. Amer. Meteor. Soc., 97, 16011616, https://doi.org/10.1175/BAMS-D-15-00200.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoffman, R. N., S. M. Leidner, J. M. Henderson, R. Atlas, J. V. Ardizzone, and S. C. Bloom, 2003: A two-dimensional variational analysis method for NSCAT ambiguity removal: Methodology, sensitivity, and tuning. J. Atmos. Oceanic Technol., 20, 585605, https://doi.org/10.1175/1520-0426(2003)20<585:ATDVAM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, A., X. Wang, J. R. Carley, L. J. Wicker, and C. Karstens, 2015: A comparison of multiscale GSI-based EnKF and 3DVar data assimilation using radar and conventional observations for midlatitude convective-scale precipitation forecasts. Mon. Wea. Rev., 143, 30873108, https://doi.org/10.1175/MWR-D-14-00345.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knaff, J. A., and R. M. Zehr, 2007: Reexamination of tropical cyclone wind–pressure relationships. Wea. Forecasting, 22, 7188, https://doi.org/10.1175/WAF965.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Komjathy, A., M. Armatys, D. Masters, P. Axelrad, V. Zavorotny, and S. Katzberg, 2004: Retrieval of ocean surface wind speed and wind direction using reflected GPS signals. J. Atmos. Oceanic Technol., 21, 515526, https://doi.org/10.1175/1520-0426(2004)021<0515:ROOSWS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leidner, S. M., L. Isaksen, and R. N. Hoffman, 2003: Impact of NSCAT winds on tropical cyclones in the ECMWF 4DVAR assimilation system. Mon. Wea. Rev., 131, 326, https://doi.org/10.1175/1520-0493(2003)131<0003:IONWOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leidner, S. M., B. Annane, B. McNoldy, R. N. Hoffman, and R. Atlas, 2018: Variational analysis of simulated ocean surface winds from the Cyclone Global Navigation Satellite System (CYGNSS) and evaluation using a regional OSSE. J. Atmos. Oceanic Technol., https://doi.org/10.1175/JTECH-D-17-0136.1, in press,

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McNoldy, B. D., B. Annane, J. Delgado, L. Bucci, R. Atlas, S. J. Majumdar, M. Leidner, and R. N. Hoffman, 2016: Impact of CYGNSS data on tropical cyclone analyses and forecasts in a regional OSSE framework. 20th Conf. on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface (IOAS-AOLS), New Orleans, LA, Amer. Meteor. Soc., J6.6, https://ams.confex.com/ams/96Annual/webprogram/Paper285158.html.

  • McNoldy, B. D., B. Annane, S. Majumdar, J. Delgado, L. Bucci, and R. Atlas, 2017: Impact of assimilating CYGNSS data on tropical cyclone analyses and forecasts in a regional OSSE framework. Mar. Technol. Soc. J., 51, 715, https://doi.org/10.4031/MTSJ.51.1.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nolan, D. S., R. Atlas, K. T. Bhatia, and L. R. Bucci, 2013: Development and validation of a hurricane nature run using the joint OSSE nature run and the WRF Model. J. Adv. Model. Earth Syst., 5, 382405, https://doi.org/10.1002/jame.20031.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • O’Brien, A., 2014: CYGNSS end-to-end simulator technical memo. University of Michigan Doc. 148-0123, 23 pp., http://clasp-research.engin.umich.edu/missions/cygnss/reference/148-0123_CYGNSS_E2ES_EM.pdf.

  • Powell, M. D., and T. A. Reinhold, 2007: Tropical cyclone destructive potential by integrated kinetic energy. Bull. Amer. Meteor. Soc., 88, 513526, https://doi.org/10.1175/BAMS-88-4-513.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rappaport, E. N., and Coauthors, 2009: Advances and challenges at the National Hurricane Center. Wea. Forecasting, 24, 395419, https://doi.org/10.1175/2008WAF2222128.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rogers, R., P. Reasor, and S. Lorsolo, 2013: Airborne Doppler observations of the inner-core structural differences between intensifying and steady-state tropical cyclones. Mon. Wea. Rev., 141, 29702991, https://doi.org/10.1175/MWR-D-12-00357.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ruf, C. S., and Coauthors, 2016a: New ocean winds satellite mission to probe hurricanes and tropical convection. Bull. Amer. Meteor. Soc., 97, 385395, https://doi.org/10.1175/BAMS-D-14-00218.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ruf, C. S., and Coauthors, 2016b: CYGNSS Handbook. University of Michigan, 154 pp.

  • Schulz, E. W., J. D. Kepert, and D. J. M. Greenslade, 2007: An assessment of marine surface winds from the Australian Bureau of Meteorology numerical weather prediction systems. Wea. Forecasting, 22, 613636, https://doi.org/10.1175/WAF996.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tallapragada, V., and Coauthors, 2013: Hurricane Weather Research and Forecasting (HWRF) Model: 2013 scientific documentation. Developmental Testbed Center Tech. Rep., 99 pp., https://dtcenter.org/HurrWRF/users/docs/scientific_documents/HWRFv3.5a_ScientificDoc.pdf.

  • Uhlhorn, E. W., and D. S. Nolan, 2012: Observational undersampling in tropical cyclones and implications for estimated intensity. Mon. Wea. Rev., 140, 825840, https://doi.org/10.1175/MWR-D-11-00073.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Uhlhorn, E. W., P. G. Black, J. L. Franklin, M. Goodberlet, J. Carswell, and A. S. Goldstein, 2007: Hurricane surface wind measurements from an operational stepped frequency microwave radiometer. Mon. Wea. Rev., 135, 30703085, https://doi.org/10.1175/MWR3454.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Willoughby, H. E., E. N. Rappaport, and F. D. Marks, 2007: Hurricane forecasting: The state of the art. Nat. Hazards Rev., 8, https://doi.org/10.1061/(ASCE)1527-6988(2007)8:3(45).

    • Crossref
    • Search Google Scholar
    • Export Citation
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A Study of the HWRF Analysis and Forecast Impact of Realistically Simulated CYGNSS Observations Assimilated as Scalar Wind Speeds and as VAM Wind Vectors

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  • 1 NOAA/Atlantic Oceanographic and Meteorological Laboratory, Miami, Florida
  • | 2 Cooperative Institute for Marine and Atmospheric Studies, University of Miami, Miami, Florida
  • | 3 Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida
  • | 4 Atmospheric and Environmental Research, Lexington, Massachusetts
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Abstract

In preparation for the launch of the NASA Cyclone Global Navigation Satellite System (CYGNSS), a variety of observing system simulation experiments (OSSEs) were conducted to develop, tune, and assess methods of assimilating these novel observations of ocean surface winds. From a highly detailed and realistic hurricane nature run (NR), CYGNSS winds were simulated with error characteristics that are expected to occur in reality. The OSSE system makes use of NOAA’s HWRF Model and GSI data assimilation system in a configuration that was operational in 2012. CYGNSS winds were assimilated as scalar wind speeds and as wind vectors determined by a variational analysis method (VAM). Both forms of wind information had positive impacts on the short-term HWRF forecasts, as shown by key storm and domain metrics. Data assimilation cycle intervals of 1, 3, and 6 h were tested, and the 3-h impacts were consistently best. One-day forecasts from CYGNSS VAM vector winds were the most dynamically consistent with the NR. The OSSEs have a number of limitations; the most noteworthy is that this is a case study, and static background error covariances were used.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Bachir Annane, bachir.annane@noaa.gov

Abstract

In preparation for the launch of the NASA Cyclone Global Navigation Satellite System (CYGNSS), a variety of observing system simulation experiments (OSSEs) were conducted to develop, tune, and assess methods of assimilating these novel observations of ocean surface winds. From a highly detailed and realistic hurricane nature run (NR), CYGNSS winds were simulated with error characteristics that are expected to occur in reality. The OSSE system makes use of NOAA’s HWRF Model and GSI data assimilation system in a configuration that was operational in 2012. CYGNSS winds were assimilated as scalar wind speeds and as wind vectors determined by a variational analysis method (VAM). Both forms of wind information had positive impacts on the short-term HWRF forecasts, as shown by key storm and domain metrics. Data assimilation cycle intervals of 1, 3, and 6 h were tested, and the 3-h impacts were consistently best. One-day forecasts from CYGNSS VAM vector winds were the most dynamically consistent with the NR. The OSSEs have a number of limitations; the most noteworthy is that this is a case study, and static background error covariances were used.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Bachir Annane, bachir.annane@noaa.gov
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