Editorial Type:
Article
Article Type:
Research Article

Evaluating Forecast Impact of Assimilating Microwave Humidity Sounder (MHS) Radiances with a Regional Ensemble Kalman Filter Data Assimilation System

Kathryn M. Newman National Center for Atmospheric Research,* Boulder, Colorado

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Craig S. Schwartz National Center for Atmospheric Research,* Boulder, Colorado

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Zhiquan Liu National Center for Atmospheric Research,* Boulder, Colorado

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Hui Shao National Center for Atmospheric Research,* Boulder, Colorado

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Xiang-Yu Huang Centre for Climate Research Singapore, Meteorological Service, Singapore

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Print Publication:
01 Aug 2015
DOI:
https://doi.org/10.1175/WAF-D-14-00091.1
Page(s):
964–983
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Abstract

This study examines the impact of assimilating Microwave Humidity Sounder (MHS) radiances in a limited-area ensemble Kalman filter (EnKF) data assimilation system. Two experiments spanning 11 August–13 September 2008 were run over a domain featuring the Atlantic basin using a 6-h full cycling analysis and forecast system. Deterministic 72-h forecasts were initialized at 0000 and 1200 UTC for a comparison of forecast impact. The two experiments were configured identically with the exception of the inclusion of the MHS radiances (AMHS) in the second to isolate the impacts of the MHS radiance data. The results were verified against several sources, and statistical significance tests indicate the most notable differences are in the midlevel moisture fields. Both configurations were characterized by high moisture biases when compared to the European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim, also known as ERA-I) specific humidity fields, as well as precipitable water vapor from an observationally based product. However, the AMHS experiment has midlevel moisture fields closer to the ERA-I and observation datasets. When reducing the verification domain to focus on the subtropical and easterly wave regions of the North Atlantic Ocean, larger improvements in midlevel moisture at nearly all lead times is seen in the AMHS simulation. Finally, when considering tropical cyclone forecasts, the AMHS configuration shows improvement in intensity forecasts at several lead times as well as improvements at early to intermediate lead times for minimum sea level pressure forecasts.

The National Center for Atmospheric Research is funded by the National Science Foundation.

Corresponding author address: Kathryn Newman, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307. E-mail: knewman@ucar.edu

Abstract

This study examines the impact of assimilating Microwave Humidity Sounder (MHS) radiances in a limited-area ensemble Kalman filter (EnKF) data assimilation system. Two experiments spanning 11 August–13 September 2008 were run over a domain featuring the Atlantic basin using a 6-h full cycling analysis and forecast system. Deterministic 72-h forecasts were initialized at 0000 and 1200 UTC for a comparison of forecast impact. The two experiments were configured identically with the exception of the inclusion of the MHS radiances (AMHS) in the second to isolate the impacts of the MHS radiance data. The results were verified against several sources, and statistical significance tests indicate the most notable differences are in the midlevel moisture fields. Both configurations were characterized by high moisture biases when compared to the European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim, also known as ERA-I) specific humidity fields, as well as precipitable water vapor from an observationally based product. However, the AMHS experiment has midlevel moisture fields closer to the ERA-I and observation datasets. When reducing the verification domain to focus on the subtropical and easterly wave regions of the North Atlantic Ocean, larger improvements in midlevel moisture at nearly all lead times is seen in the AMHS simulation. Finally, when considering tropical cyclone forecasts, the AMHS configuration shows improvement in intensity forecasts at several lead times as well as improvements at early to intermediate lead times for minimum sea level pressure forecasts.

The National Center for Atmospheric Research is funded by the National Science Foundation.

Corresponding author address: Kathryn Newman, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307. E-mail: knewman@ucar.edu
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  • Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 2884–2903, doi:10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Anderson, J. L., 2003: A local least squares framework for ensemble filtering. Mon. Wea. Rev., 131, 634–642, doi:10.1175/1520-0493(2003)131<0634:ALLSFF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Anderson, J. L., Hoar T. , Raeder K. , Liu H. , Collins N. , Torn R. , and Arellano A. , 2009: The Data Assimilation Research Testbed: A community facility. Bull. Amer. Meteor. Soc., 90, 1283–1296, doi:10.1175/2009BAMS2618.1.

    • Search Google Scholar
    • Export Citation

  • Andersson, E., Hollingsworth A. , Kelly G. , Lönnberg P. , Pailleux J. , and Zhang Z. , 1991: Global observing system experiments on operational statistical retrievals of satellite sounding data. Mon. Wea. Rev., 119, 1851–1865, doi:10.1175/1520-0493(1991)119<1851:GOSEOO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Aravéquia, J. A., Szunyogh I. , Fertig E. J. , Kalnay E. , Kuhl D. , and Kostelich E. J. , 2011: Evaluation of a strategy for the assimilation of satellite radiance observations with the local ensemble transform Kalman filter. Mon. Wea. Rev., 139, 1932–1951, doi:10.1175/2010MWR3515.1.

    • Search Google Scholar
    • Export Citation

  • Auligné, T., McNally A. P. , and Dee D. P. , 2007: Adaptive bias correction for satellite data in a numerical weather prediction system. Quart. J. Roy. Meteor. Soc.,133, 631–642, doi:10.1002/qj.56.

  • Barker, D., and Coauthors, 2012: The Weather Research and Forecasting Model’s Community Variational/Ensemble Data Assimilation System: WRFDA. Bull. Amer. Meteor. Soc., 93, 831–843, doi:10.1175/BAMS-D-11-00167.1.

    • Search Google Scholar
    • Export Citation

  • Bouttier, F., and Kelly G. , 2001: Observing-system experiments in the ECMWF 4D-Var data assimilation system. Quart. J. Roy. Meteor. Soc., 127, 1469–1488, doi:10.1002/qj.49712757419.

    • Search Google Scholar
    • Export Citation

  • Brown, B. G., Gotway J. H. , Bullock R. , Gilleland E. , and Ahijevych D. , 2009: The Model Evaluation Tools (MET): Community tools for forecast evaluation. Preprints, 25th Conf. on Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, Phoenix, AZ, Amer. Meteor. Soc., 9A.6. [Available online at https://ams.confex.com/ams/pdfpapers/151349.pdf.]

  • Buehner, M., Houtekamer P. L. , Charette C. , Mitchell H. L. , and He B. , 2010a: Intercomparison of variational data assimilation and the ensemble Kalman filter for global deterministic NWP. Part I: Description and single-observation experiments. Mon. Wea. Rev., 138, 1550–1566, doi:10.1175/2009MWR3157.1.

    • Search Google Scholar
    • Export Citation

  • Buehner, M., Houtekamer P. L. , Charette C. , Mitchell H. L. , and He B. , 2010b: Intercomparison of variational data assimilation and the ensemble Kalman filter for global deterministic NWP. Part II: One-month experiments with real observations. Mon. Wea. Rev., 138, 1567–1586, doi:10.1175/2009MWR3158.1.

    • Search Google Scholar
    • Export Citation

  • Burgers, G., van Leeuwen P. J. , and Evensen G. , 1998: Analysis scheme in the ensemble Kalman filter. Mon. Wea. Rev., 126, 1719–1724, doi:10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Cardinali, C., 2009: Monitoring the observation impact on the short-range forecast. Quart. J. Roy. Meteor. Soc., 135, 239–250, doi:10.1002/qj.366.

    • Search Google Scholar
    • Export Citation

  • Casati, B., and Coauthors, 2008: Forecast verification: Current status and future directions. Meteor. Appl., 15, 3–18, doi:10.1002/met.52.

    • Search Google Scholar
    • Export Citation

  • Cavallo, S. M., Dudhia J. , and Snyder C. , 2011: A multilayer upper-boundary condition for longwave radiative flux to correct temperature biases in a mesoscale model. Mon. Wea. Rev., 139, 1952–1959, doi:10.1175/2010MWR3513.1.

    • Search Google Scholar
    • Export Citation

  • Chen, F., and Dudhia J. , 2001: Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model description and implementation. Mon. Wea. Rev., 129, 569–585, doi:10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Chen, Y., and Snyder C. , 2007: Assimilating vortex position with an ensemble Kalman filter. Mon. Wea. Rev., 135, 1828–1845, doi:10.1175/MWR3351.1.

    • Search Google Scholar
    • Export Citation

  • Chou, M.-D., and Suarez M. J. , 1994: An efficient thermal infrared radiation parameterization for use in general circulation models. NASA Tech. Memo. 104606, Vol. 3, 85 pp.

  • Collard, A., Hilton F. , Forsythe M. , and Candy B. , 2011: From Observations to Forecasts—Part 8: The use of satellite observations in numerical weather prediction. Weather, 66 (2), 31–36, doi:10.1002/wea.736.

    • Search Google Scholar
    • Export Citation

  • Davis, G., 2007: History of the NOAA satellite program. J. Appl. Remote Sens., 1, 012504, doi:10.1117/1.2642347.

  • Dee, D. P., 2005: Bias and data assimilation. Quart. J. Roy. Meteor. Soc., 131, 3323–3343, doi:10.1256/qj.05.137.

  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, doi:10.1002/qj.828.

    • Search Google Scholar
    • Export Citation

  • Derber, J. C., and Wu W.-S. , 1998: The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Mon. Wea. Rev., 126, 2287–2299, doi:10.1175/1520-0493(1998)126<2287:TUOTCC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Developmental Testbed Center, 2014: Model Evaluation Tools–Tropical cyclone user’s guide. Developmental Testbed Center, accessed 28 July 2014, 29 pp. [Available online at http://www.dtcenter.org/met/users/docs/users_guide/MET-TC_Users_Guide_v4.1.pdf.]

  • Dunion, J. P., and Velden C. S. , 2004: The impact of the Saharan air layer on Atlantic tropical cyclone activity. Bull. Amer. Meteor. Soc., 85, 353–365, doi:10.1175/BAMS-85-3-353.

    • Search Google Scholar
    • Export Citation

  • Ebert, E., and Coauthors, 2013: Progress and challenges in forecast verification. Meteor. Appl., 20, 130–130, doi:10.1002/met.1392.

  • Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99, 10 143–10 162, doi:10.1029/94JC00572.

    • Search Google Scholar
    • Export Citation

  • Fiorino, M., 2009: Record-setting performance of the ECMWF IFS in medium-range tropical cyclone track prediction. ECMWF Newsletter, No. 118, Reading, United Kingdom, 20–27.

  • Guan, L., Zou X. , Weng F. , and Li G. , 2011: Assessments of FY-3A microwave humidity sounder measurements using NOAA-18 microwave humidity sounder. J. Geophys. Res., 116, D10106, doi:10.1029/2010JD015412.

    • Search Google Scholar
    • Export Citation

  • Hamill, T. M., 1999: Hypothesis tests for evaluating numerical precipitation forecasts. Wea. Forecasting, 14, 155–167, doi:10.1175/1520-0434(1999)014<0155:HTFENP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Hamill, T. M., Whitaker J. S. , Fiorino M. , and Benjamin S. G. , 2011: Global ensemble predictions of 2009’s tropical cyclones initialized with an ensemble Kalman filter. Mon. Wea. Rev., 139, 668–688, doi:10.1175/2010MWR3456.1.

    • Search Google Scholar
    • Export Citation

  • Han, Y., van Delst P. , Liu Q. , Weng F. , Yan B. , Treadon R. , and Derber J. , 2006: JCSDA Community Radiative Transfer Model (CRTM)—Version 1. NOAA Tech. Rep. NESDIS 122, 33 pp.

  • Hill, K. A., and Lackmann G. M. , 2009: Influence of environmental humidity on tropical cyclone size. Mon. Wea. Rev., 137, 3294–3315, doi:10.1175/2009MWR2679.1.

    • Search Google Scholar
    • Export Citation

  • Holloway, C. E., and Neelin J. D. , 2009: Moisture vertical structure, column water vapor, and tropical deep convection. J. Atmos. Sci., 66, 1665–1683, doi:10.1175/2008JAS2806.1.

    • Search Google Scholar
    • Export Citation

  • Hong, S.-Y., Dudhia J. , and Chen S.-H. , 2004: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Wea. Rev., 132, 103–120, doi:10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Hong, S.-Y., Noh Y. , and Dudhia J. , 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 2318–2341, doi:10.1175/MWR3199.1.

    • Search Google Scholar
    • Export Citation

  • Houtekamer, P. L., and Mitchell H. L. , 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126, 796–811, doi:10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Houtekamer, P. L., Mitchell H. L. , Pellerin G. , Buehner M. , Charron M. , Spacek L. , and Hansen B. , 2005: Atmospheric data assimilation with an ensemble Kalman filter: Results with real observations. Mon. Wea. Rev., 133, 604–620, doi:10.1175/MWR-2864.1.

    • Search Google Scholar
    • Export Citation

  • Janoušek, M., Simmons A. J. , and Richardson D. , 2012: Plots of the long-term evolution of operational forecast skill updated. ECMWF Newsletter, No. 132, ECMWF, Reading, United Kingdom, 11–12.

  • Joo, S., Eyre J. , and Marriott R. , 2013: The impact of MetOp and other satellite data within the Met Office Global NWP system using an adjoint-based sensitivity method. Mon. Wea. Rev., 141, 3331–3342, doi:10.1175/MWR-D-12-00232.1.

    • Search Google Scholar
    • Export Citation

  • Kain, J. S., and Fritsch J. M. , 1990: A one-dimensional entraining/detraining plume model and its application in convective parameterization. J. Atmos. Sci., 47, 2784–2802, doi:10.1175/1520-0469(1990)047<2784:AODEPM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Liu, H., Anderson J. , Kuo Y.-H. , and Raeder K. , 2007: Importance of forecast error multivariate correlations in idealized assimilation of GPS radio occultation data with the ensemble adjustment filter. Mon. Wea. Rev., 135, 173–185, doi:10.1175/MWR3270.1.

    • Search Google Scholar
    • Export Citation

  • Liu, Q., and Weng F. , 2006: Advanced doubling-adding method for radiative transfer in planetary atmosphere. J. Atmos. Sci., 63, 3459–3465, doi:10.1175/JAS3808.1.

    • Search Google Scholar
    • Export Citation

  • Liu, Z., Schwartz C. S. , Snyder C. , and Ha S. Y. , 2012: Impact of assimilating AMSU-A radiances on forecasts of 2008 Atlantic tropical cyclones initialized with a limited-area ensemble Kalman filter. Mon. Wea. Rev., 140, 4017–4034, doi:10.1175/MWR-D-12-00083.1.

    • Search Google Scholar
    • Export Citation

  • Lorenz, C., and Kunstmann J. , 2012: The hydrological cycle in three state-of-the-art reanalyses: Intercomparison and performance analysis. J. Hydrometeor., 13, 1397–1420, doi:10.1175/JHM-D-11-088.1.

    • Search Google Scholar
    • Export Citation

  • Meng, Z., and Zhang F. , 2008a: Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part III: Comparison with 3DVAR in a real-data case study. Mon. Wea. Rev., 136, 522–540, doi:10.1175/2007MWR2106.1.

    • Search Google Scholar
    • Export Citation

  • Meng, Z., and Zhang F. , 2008b: Test of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part IV: Comparison with 3DVAR in a month-long experiment. Mon. Wea. Rev., 136, 3671–3682, doi:10.1175/2008MWR2270.1.

    • Search Google Scholar
    • Export Citation

  • Miyoshi, T., and Sato Y. , 2007: Assimilating satellite radiances with a local ensemble transform Kalman filter (LETKF) applied to the JMA global model (GSM). SOLA, 3, 37–40, doi:10.2151/sola.2007-010.

    • Search Google Scholar
    • Export Citation

  • Miyoshi, T., Sato Y. , and Kadowaki T. , 2010: Ensemble Kalman filter and 4D-Var intercomparison with the Japanese operational global analysis and prediction system. Mon. Wea. Rev., 138, 2846–2866, doi:10.1175/2010MWR3209.1.

    • Search Google Scholar
    • Export Citation

  • Mlawer, E. J., Taubman S. J. , Brown P. D. , Iacono M. J. , and Clough S. A. , 1997: Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the long- wave. J. Geophys. Res., 102, 16 663–16 682, doi:10.1029/97JD00237.

    • Search Google Scholar
    • Export Citation

  • Mo, K. C., Wang X. L. , Kistler R. , Kanamitsu M. , and Kalnay E. , 1995: Impact of satellite data on the CDAS-Reanalysis system. Mon. Wea. Rev., 123, 124–139, doi:10.1175/1520-0493(1995)123<0124:IOSDAT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • NOAA, 2014: NOAA KLM user’s guide. NOAA/NASA, accessed 25 July 2014. [Available online at http://www.ncdc.noaa.gov/oa/pod-guide/ncdc/docs/klm/index.htm.]

  • Schwartz, C. S., Liu Z. , Chen Y. , and Huang X.-Y. , 2012: Impact of assimilating microwave radiances with a limited-area ensemble data assimilation system on forecasts of Typhoon Morakot. Wea. Forecasting, 27, 424–437, doi:10.1175/WAF-D-11-00033.1.

    • Search Google Scholar
    • Export Citation

  • Simmons, A. J., and Hollingsworth A. , 2002: Some aspects of the improvement in skill of numerical weather prediction. Quart. J. Roy. Meteor. Soc., 128, 647–677, doi:10.1256/003590002321042135.

    • Search Google Scholar
    • Export Citation

  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF, version 3. NCAR Tech. Note NCAR/TN–475+STR, 113 pp. [Available online at http://www2.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf.]

  • Szunyogh, I., Kostelich E. J. , Gyarmati G. , Kalnay E. , Hunt B. R. , Ott E. , Satterfield E. , and Yorke J. A. , 2008: A local ensemble transform Kalman filter data assimilation system for the NCEP global model. Tellus, 60, 113–130, doi:10.1111/j.1600-0870.2007.00274.x.

    • Search Google Scholar
    • Export Citation

  • Tompkins, A. M., 2001: Organization of tropical convection in low vertical wind shears: The role of water vapor. J. Atmos. Sci., 58, 529–545, doi:10.1175/1520-0469(2001)058<0529:OOTCIL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Torn, R. D., 2010: Performance of a mesoscale ensemble Kalman filter (EnKF) during the NOAA high-resolution hurricane test. Mon. Wea. Rev., 138, 4375–4392, doi:10.1175/2010MWR3361.1.

    • Search Google Scholar
    • Export Citation

  • Torn, R. D., and Hakim G. J. , 2008: Performance characteristics of a pseudo-operational ensemble Kalman filter. Mon. Wea. Rev., 136, 3947–3963, doi:10.1175/2008MWR2443.1.

    • Search Google Scholar
    • Export Citation

  • Torn, R. D., Hakim G. J. , and Snyder C. , 2006: Boundary conditions for limited-area ensemble Kalman filters. Mon. Wea. Rev., 134, 2490–2502, doi:10.1175/MWR3187.1.

    • Search Google Scholar
    • Export Citation

  • Trenberth, K., and Fasullo J. T. , 2013: Regional energy and water cycles: Transports from ocean to land. J. Climate, 26, 7837–7851, doi:10.1175/JCLI-D-13-00008.1.

    • Search Google Scholar
    • Export Citation

  • Trenberth, K., Fasullo J. T. , and Mackaro J. , 2011: Atmospheric moisture transports from ocean to land and global energy flows in reanalyses. J. Climate, 24, 4907–4924, doi:10.1175/2011JCLI4171.1.

    • Search Google Scholar
    • Export Citation

  • United Nations Economic Commission for Europe, 2010: Making data meaningful. Part 2: A guide to presenting statistics. UNECE, 58 pp. [Available online at http://www.unece.org/fileadmin/DAM/stats/documents/writing/MDM_Part2_English.pdf.]

  • Vonder Haar, T. H., Bytheway J. L. , and Forsythe J. M. , 2012: Weather and climate analyses using improved global water vapor observations. Geophys. Res. Lett., 39, L15802, doi:10.1029/2012GL052094.

    • Search Google Scholar
    • Export Citation

  • Warner, T. T., Peterson R. A. , and Treadon R. E. , 1997: A tutorial on lateral boundary conditions as a basic and potentially serious limitation to regional numerical weather prediction. Bull. Amer. Meteor. Soc., 78, 2599–2617, doi:10.1175/1520-0477(1997)078<2599:ATOLBC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Whitaker, J. S., Hamill T. M. , Wei X. , Song Y. , and Toth Z. , 2008: Ensemble data assimilation with the NCEP Global Forecast System. Mon. Wea. Rev., 136, 463–482, doi:10.1175/2007MWR2018.1.

    • Search Google Scholar
    • Export Citation

  • WMO, 2010: Guide to Meteorological Instruments and Methods of Observation. WMO. [Available online at http://library.wmo.int/pmb_ged/wmo_8_en-2012.pdf.]

  • Xu, J., Rugg S. , Byerle L. , and Liu Z. , 2009: Weather forecasts by the WRF-ARW Model with the GSI data assimilation system in the complex terrain areas of southwest Asia. Wea. Forecasting, 24, 987–1008, doi:10.1175/2009WAF2222229.1.

    • Search Google Scholar
    • Export Citation

  • Yan, B., Weng F. , and Derber J. , 2010: Assimilation of satellite microwave water vapor sounding channel data in NCEP Global Forecast System (GFS). 17th Int. TOVS Study Conf., Monterrey, CA, International ATOVS Working Group.

  • Yang, F., 2014: Historical performances of global NWP models. NCEP/EMC/Global Climate and Weather Modeling Branch, accessed 6 April 2014. [Available online at http://www.emc.ncep.noaa.gov/gmb/STATS_vsdb/longterm/.]

  • Zapotocny, T. H., Menzel W. P. , Jung J. A. , and Nelson J. P. III, 2005: A four-season impact study of rawinsonde, GOES, and POES data in the Eta Data Assimilation System. Part II: Contribution of the components. Wea. Forecasting, 20, 178–198, doi:10.1175/WAF838.1.

    • Search Google Scholar
    • Export Citation

  • Zapotocny, T. H., Jung J. A. , Le Marshall J. F. , and Treadon R. E. , 2007: A two-season impact study of satellite and in situ data in the NCEP Global Data Assimilation System. Wea. Forecasting, 22, 887–909, doi:10.1175/WAF1025.1.

    • Search Google Scholar
    • Export Citation

  • Zhang, F., Zhang M. , and Poterjoy J. , 2013: E3DVar: Coupling an ensemble Kalman filter with three-dimensional variational data assimilation in a limited-area weather prediction model and comparison to E4DVar. Mon. Wea. Rev., 141, 900–917, doi:10.1175/MWR-D-12-00075.1.

    • Search Google Scholar
    • Export Citation

  • Zhang, M., Zhang F. , Huang X.-Y. , and Zhang X. , 2011: Intercomparison of an ensemble Kalman filter with three- and four-dimensional variational data assimilation methods in a limited-area model over the month of June 2003. Mon. Wea. Rev., 139, 566–572, doi:10.1175/2010MWR3610.1.

    • Search Google Scholar
    • Export Citation

  • Zou, X., Qin Z. , and Weng F. , 2013: Improved quantitative precipitation forecasts by MHS radiance data assimilation with a newly added cloud detection algorithm. Mon. Wea. Rev., 141, 3203–3221, doi:10.1175/MWR-D-13-00009.1.

    • Search Google Scholar
    • Export Citation
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