References

Abbreviations of Journals

  • Atmos. Sci.
  • Atmos. Meas. Tech.
  • Bull. Am. Meteorol. Soc.
  • Clim. Dyn.
  • Earth’s Future
  • Environ. Res. Lett.
  • Fluid Dyn. Res.,
  • Geosci. Model Dev.
  • Geophys. Res. Lett.
  • Hydrol. Earth Syst. Sci.
  • Hydrol. Res. Lett.
  • J. Adv. Modeling Earth Syst.
  • J. Appl. Meteor. Climatol.
  • J. Atmos. Oceanic Technol.
  • J. Atmos. Sci.
  • J. Comput. Phys.
  • J. Geophys. Res.
  • J. Hydrometeoro.
  • J. Meteor. Soc. Japan
  • J. Meteor. Appl.
  • Mon. Wea. Rev.
  • Nature
  • Nat. Clim. Chang.
  • Nat. Hazards Earth Sys. Sci.
  • Nonlin. Processes Geophys.
  • Phys. Rev. Lett.
  • Proc. Natl. Acad. Sci.
  • PLOS ONE
  • Prog. Earth Planet. Sci
  • Q. J. R. Meteorol. Soc.
  • Science
  • Sci. Rep.
  • Wea. and Forecasting

Edits from “APA” of google scholar

  1. “-” –> “–” : for page numbers
  2. & –> and : for list of authors
  3. (YYYY). –> (YYYY): for publication years

Temporal for JSHWR REVIEW

  • Cotterman, K. A., Kendall, A. D., Basso, B., and Hyndman, D. W. (2018): Groundwater depletion and climate change: future prospects of crop production in the Central High Plains Aquifer. Clim. Chang., 146, 187–200. doi: 10.1007/s10584-017-1947-7
  • Elliott, J., and Coauthors (2014): Constraints and potentials of future irrigation water availability on agricultural production under climate change. Proc. Natl. Acad. Sci., 111, 3239–3244. doi: 10.1073/pnas.1222474110
  • Deryng, D., and Cocuthors (2016): Regional disparities in the beneficial effects of rising CO 2 concentrations on crop water productivity. Nat. Clim. Chang., 6, 786. doi: 10.1038/nclimate2995
  • Lobell, D. B., Schlenker, W., and Costa-Roberts, J. (2011): Climate trends and global crop production since 1980. Science, 333, 616–620. doi: 10.1126/science.1204531
  • Müller, C., and Coauthors (2017): Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications. Geosci. Model Dev., 10, 1403–1422. doi: 10.5194/gmd-2016-207.
  • Ray, D. K., Mueller, N. D., West, P. C., and Foley, J. A. (2013): Yield trends are insufficient to double global crop production by 2050. PLOS ONE, 8, e66428. doi: 10.1371/journal.pone.0066428
  • Rosenzweig, C., and Coauthors (2014): Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc. Natl. Acad. Sci., 111, 3268–3273. doi: 10.1158/0008-5472.CAN-14-0155
  • Okada, M., Iizumi, T., Sakamoto, T., Kotoku, M., Sakurai, G., Hijioka, Y., and Nishiori. M. (2018): Varying benefits of irrigation expansion for crop production under a changing climate and competitive water use among crops. Earth’s Future, 6, 1207–1220. doi: 10.1029/2017EF000763
  • Sakurai, G., Iizumi, T., Nishimori, M., and Yokozawa, M. (2014): How much has the increase in atmospheric CO 2 directly affected past soybean production?. Sci. Rep., 4, 4978. doi: 10.1038/srep04978
  • Schleussner, C. F., and Coauthors (2018): Crop productivity changes in 1.5 C and 2 C worlds under climate sensitivity uncertainty. Environ. Res. Lett., 13, 064007. doi: 10.1088/1748-9326/aab63b
  • Zabel, F., Putzenlechner, B., and Mauser, W. (2014): Global agricultural land resources?a high resolution suitability evaluation and its perspectives until 2100 under climate change conditions. PlOS ONE, 9, e107522. doi: 10.1371/journal.pone.0114980

 


References (Based on “APA” of google scholar)

  • Accadia, C., Mariani, S., Casaioli, M., Lavagnini, A., and Speranza, A. (2003): Sensitivity of precipitation forecast skill scores to bilinear interpolation and a simple nearest-neighbor average method on high-resolution verification grids. Wea. and Forecasting, 18, 918–932. doi: 10.1175/1520-0434(2003)018<0918:SOPFSS>2.0.CO;2
  • Akiyama S., Shige S., Yamamoto M., and Iguchi T. (2019):  Heavy Ice Precipitation Band in an Oceanic Extratropical Cyclone Observed by GPM/DPR. Part I: A Case Study. Geophys. Res. Lett. doi: 10.1029/2019GL082896 (in press)
  • Ancell, B., and Hakim, G. J. (2007): Comparing adjoint-and ensemble-sensitivity analysis with applications to observation targeting. Mon. Wea. Rev., 135, 4117–4134. doi: 10.1175/2007MWR1904.1
    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
  • Anderson J. L. (2007): An adaptive covariance inflation error correction algorithm for ensemble filters. Tellus A, 59, 210–224. doi: 10.1111/j.1600-0870.2006.00216.x
  • Anderson J. L. (2009): Spatially and temporally varying adaptive covariance inflation for ensemble filters. Tellus A, 61, 72–83. doi: 10.1111/j.1600-0870.2008.00361.x
  • Anderson J. L., and Anderson S. L. (1999): A Monte Carlo Implementation of the Nonlinear Filtering Problem to Produce Ensemble Assimilations and Forecasts. Mon. Wea. Rev., 127, 2741–2758. doi: 10.1175/1520-0493(1999)127<2741:AMCIOT>2.0.CO;2
  • Aksoy, A., Zhang, F., and Nielsen-gammon, J. W. (2006): Ensemble-based simultaneous state and parameter estimation with MM5. Geophys. Res. Lett., 33, L12801. doi: 10.1029/2006GL026186
  • Annan, J. D. (2005): Dynamic Meteorology and Oceanography Parameter estimation using chaotic time series. Tellus A, 57, 709–714. doi: 10.3402/tellusa.v57i5.14735
  • Arakawa, A., and Schubert, W. H. (1974): Interaction of a Cumulus Cloud Ensemble with the Large-Scale Environment, Part I. J. Atmos. Sci., 31, 674–701. doi: 10.1175/1520-0469(1974)031<0674:IOACCE>2.0.CO;2
  • Beck, H. E., Van Dijk, A. I., Levizzani, V., Schellekens, J., Gonzalez Miralles, D., Martens, B., and De Roo, A. (2017): MSWEP: 3-hourly 0.25 global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data. Hydrol. Earth Syst. Sci., 21, 589-615. doi: 10.5194/hess-21-589-2017
  • Berry, E. (1967): Cloud Droplet Growth by Collection. J. Atmos. Sci., 24, 688–701. doi: 10.1175/1520-0469(1967)024<0688:CDGBC>2.0.CO;2
  • Bishop, C., Etherton, B., and Majumdar, S. (2001): Adaptive Sampling with the Ensemble Transform Kalman Filter . Part I : Theoretical Aspects. Mon. Wea. Rev., 129, 420–436. doi: 10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2
  • Bishop, C. H., and Hodyss D. (2009a): Ensemble covariances adaptively localized with ECO-RAP. Part 1: Tests on simple error models. Tellus A, 61, 84-96. doi: 10.1111/j.1600-0870.2008.00371.x.
  • Bishop, C. H., and Hodyss, D. (2009b): Ensemble covariances adaptively localized with ECO-RAP. Part 2:Astrategy for the atmosphere. Tellus A, 61, 97-111. doi: 10.1111/j.1600-0870.2008.00372.x.
  • Bormann N, and Bauer P. (2010): Estimates of spatial and interchannel observation-error characteristics for current sounder radiances for numerical weather prediction. I: Methods and application to ATOVS data. Q. J. R. Meteorol. Soc., 136,1036–1050. doi:10.1002/qj.616
  • Bormann, N., Collard, A., and Bauer, P. (2010): Estimates of spatial and interchannel observation-error characteristics for current sounder radiances for numerical weather prediction. II: Application to AIRS and IASI data. Q. J. R. Meteorol. Soc., 136, 1051–1063. doi:10.1002/qj.615
  • Bowler, N. E., Pierce, C. E., and Seed, A. W. (2006): STEPS: A probabilistic precipitation forecasting scheme which merges an extrapolation nowcast with downscaled NWP. Q. J. R. Meteorol. Soc., 132, 2127–2155. doi: 10.1256/qj.04.100
  • Brankart, J. M., Testut, C. E., Brasseur, P., and Verron, J. (2003): Implementation of a multivariate data assimilation scheme for isopycnic coordinate ocean models: Application to a 1993–1996 hindcast of the North Atlantic Ocean circulation. J. Geophys. Res., 108, 3074. doi: 10.1029/2001JC001198
  • Browning, K. A., 1980: Local weather forecasting. Proc. Roy. Soc. London, A371, 179–211.
  • Buehner, M., Du, P., and Bédard, J. (2018): A New Approach for Estimating the Observation Impact in Ensemble–Variational Data Assimilation. Mon. Wea. Rev., 146, 447–465. doi: 10.1175/MWR-D-17-0252.1
  • 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
  • Campbell W. F., C. H. Bishop, and Hodyss, D. (2010): Vertical covariance localization for satellite radiances in ensemble Kalman filters. Mon. Wea. Rev., 138, 282-290, doi: 10.1175/2009MWR3017.1.
  • Campbell W. F., Satterfield E., Ruston B., Baker N. (2016): Accounting for correlated observation error in a dual formulation 4D-variational data assimilation system. Mon. Wea. Rev., 145 doi:10:1175/MWR-D-16-0240.1.
  • Cardinali, C. (2009). Monitoring the observation impact on the short-range forecast. Q. J. R. Meteorol. Soc., 135, 239–250. doi:10.1002/qj.366
  • Cardinali C. (2018). Forecast sensitivity observation impact with an observation-only based objective function. Q. J. R. Meteorol. Soc., 144, 2089–2098. doi: 10.1002/qj.3305
  • Chen, Y., Wang, H., Min, J., Huang, X. Y., Minnis, P., Zhang, R., Haggerty, J., and Palikonda, R. (2015): Variational assimilation of cloud liquid/ice water path and its impact on NWP. J. Appl. Meteor. Climatol., 54, 1809–1825. doi: 10.1175/JAMC-D-14-0243.1
  • Cheng, L., and English, M. (1983): A relationship between hailstone concentration and size. J. Atmos. Sci., 40, 204-213. doi: 10.1175/1520-0469(1983)040<0204:ARBHCA>2.0.CO;2
  • Corazza M., and Coauthors. (2003): Use of the breeding technique to estimate the structure of the analysis “errors of the day.” Nonlin. Processes Geophys., 10, 233–243. doi:10.5194/npg-10-233-2003
  • Daescu D. N., and Todling R. (2009): Adjoint estimation of the variation in model functional output due to the assimilation of data. Mon. Wea. Rev., 137, 1705-1716. doi: 10.1175/2008MWR2659.1
  • Dee D. P. (1995): On-line Estimation of Error Covariance Parameters for Atmospheric Data Assimilation. Mon. Wea. Rev., 123, 1128-1145. doi:10.1175/1520-0493(1995)123<1128:OLEOEC>2.0.CO;2
  • Dee, D. P., and Coauthors (2011): The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc., 137, 553–597. doi: 10.1002/qj.828
  • Desroziers G., Berre L., Chapnik B., and Poli P. (2005): Diagnosis of observation, background and analysis-error statistics in observation space. Q. J. R. Meteorol. Soc., 131, 3385–3396. doi:10.1256/qj.05.108
  • Evensen G. (1994): Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99, 10143–10162. doi:10.1029/94JC00572
  • Evensen G. (2003): The Ensemble Kalman Filter: Theoretical formulation and practical implementation. Ocean Dynamics, 53, 343–367. doi:10.1007/s10236-003-0036-9
  • Fertig, E. J., Harlim, J., and Hunt, B. R. (2007): A comparative study of 4D-VAR and a 4D Ensemble Kalman Filter: Perfect model simulations with Lorenz-96. Tellus A, 59, 96–100. doi: 10.1111/j.1600-0870.2006.00205.x
  • Fielding, M. D., and Stiller, O. (2019): Characterizing the representativity error of cloud profiling observations for data assimilation. J. Geophys. Res., 124, 4086–4103. doi: 10.1029/2018JD029949
  • Furukawa, K. and Coauthors (2014): The orbital checkout status of the dual-frequency precipitation radar on the global precipitation measurement core spacecraft. IEEE Geoscience and Remote Sensing Symposium in 2014, 3750–3753. doi: 10.1109/IGARSS.2014.6947299
  • Gaspari, G., and Cohn, S. (1999): Construction of correlation functions in two and three dimensions. Q. J. R. Meteorol. Soc., 125, 723–757, doi: 10.1002/qj.49712555417.
  • Gasperoni, N. A., and Wang, X. (2015): Adaptive localization for the ensemble-based observation impact estimate using regression confidence factors. Mon. Wea. Rev., 143, 1981–2000. doi: 10.1175/MWR-D-14-00272.1
  • Geer, A. J. (2016): Significance of changes in medium-range forecast scores. Tellus A, 68, 30229. doi: 10.3402/tellusa.v68.30229
  • Gelaro, R., Langland, R. H., Pellerin, S., and Todling, R. (2010): The THORPEX observation impact intercomparison experiment. Mon. Wea. Rev., 138, 4009–4025. doi: 10.1175/2010MWR3393.1
  • Golding, B. W. (1998): Nimrod : A system for generating automated very short range forecasts. Meteor. Appl., 16, 1–16. doi: 10.1017/S1350482798000577
  • Greybush, S. J., Kalnay, E., Miyoshi, T., Ide, K., and Hunt, B. R. (2011): Balance and Ensemble Kalman Filter Localization Techniques. Mon. Wea. Rev., 139, 511–522, doi: 10.1175/2010MWR3328.1
  • Hahn, C. J., and S. G. Warren (2007): A gridded climatology of louds over land (1971–96) and ocean (1954–97) from surface observations worldwide. Numeric Data Package NDP-026EORNL/CDIAC-153, CDIAC, Department of Energy, Oak Ridge, TN.
  • Hamill, T. M. (1999): Hypothesis tests for evaluating numerical precipitation forecasts. Wea. and Forecasting, 14, 155-167. doi: 10.1175/1520-0434(1999)014<0155:HTFENP>2.0.CO;2
  • Hamill, T. M., Whitaker, J. S., and Snyder, C. (2001): Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon. Wea. Rev., 129, 2776–2790. doi: 10.1175/1520-0493(2001)129<2776:DDFOBE>2.0.CO;2
  • Hamrud, M., Bonavita, M., and Isaksen, L. (2015): EnKF and hybrid gain ensemble data assimilation. Part I: EnKF implementation. Mon. Wea. Rev., 143, 4847–4864. doi: 10.1175/MWR-D-14-00333.1
  • Hashino, T., Satoh, M., Hagihara, Y., Kubota, T., Matsui, T., Nasuno, T., and Okamoto, H. (2013): Evaluating cloud microphysics from NICAM against CloudSat and CALIPSO. J. Geophys.Res., 118, 7273–7292, doi:10.1002/jgrd.50564.
  • Hobbs, P. V., Chang, S., and Locatelli, J. D. (1974): The dimensions and aggregation of ice crystals in natural clouds. J. Geophys. Res., 79, 2199–2206. doi: 10.1029/JC079i015p02199
  • 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
  • Houtekamer, P., and Mitchell, H. (2001): A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Rev., 129, 123–137, doi: 10.1175/1520-0493(2001)129<0123:ASEKFF>2.0.CO;2
  • Hotta, D., Chen, T.-C., Kalnay, E., Ota Y., and Miyoshi, T. (2017): Proactive QC: a fully flow-dependent quality control scheme based on EFSO. Mon. Wea. Rev., 145, 3331–3354. doi:10.1175/MWR-D-16-0290.1
  • Hotta, D., Kalnay, E., Ota, Y., and Miyoshi, T. (2018): EFSR: Ensemble forecast sensitivity to observation error covariance. Mon. Wea. Rev., 145, 5015-5031. doi: 10.1175/MWR-D-17-0122.1
  • Hou, A. Y., Kakar, R. K., Neeck, S.. Azarbarzin, A. A., Kummerow, C. D., Kojima, M. , Oki, R., Nakamura, K., and Iguchi T. (2014): The GlobalPrecipitation Measurement Mission, Bull. Am. Meteorol. Soc., 95, 701–722, doi:10.1175/BAMS-D-13-00164.1
  • Huffman, G. J., and Coauthors (2007): The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales. J. Hydrometeoro., 8, 38–55. doi: 10.1175/JHM560.1
  • Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., and Xie, P. (2014): GPM Integrated Multi-Satellite Retrievals for GPM (IMERG) Algorithm Theoretical Basis Document (ATBD) Version 4.4. PPS, NASA/GSFC, 30 pp. http://pmm.nasa.gov/sites/default/files/ document_files/IMERG_ATBD_V4.4.pdf
  • Hunt, B. R., Kostelich, E. J., and Szunyogh, I. (2007): Efficient data assimilation for spatiotemporal chaos : A local ensemble transform Kalman filter. Physica D, 230, 112–126. doi: 10.1016/j.physd.2006.11.008
  • Iguchi, T., Seto, S., Meneghini R., Yoshida N., Awaka J., Le M., Chandrasekar V. and Kubota, T. (2017): GPM/DPR Level-2 Algorithm Theoretical Basis Document. Accessed June 6, 2019, https://www.eorc.jaxa.jp/GPM/doc/algorithm/ATBD_DPR_201708_whole_1.pdf
  • Iguchi, T., Kanemaru, K., and Hamada, A. (2018a): Possible improvement of the GPM’s Dual-frequency Precipitation Radar (DPR) algorithm. Proc. SPIE 10776, Remote Sensing of the Atmosphere, Clouds, and Precipitation VII, 107760Q. doi: 10.1117/12.2324290
  • Iguchi, T., Kawamoto, N., and Oki, R. (2018b): Detection of Intense Ice Precipitation with GPM/DPR. J. Atmos. Oceanic Technol., 35, 491–502. doi: 10.1175/JTECH-D-17-0120.1
  • Ishibashi, T. (2010): Optimization of error covariance matrices and estimation of observation data impact in the JMA global 4D-Var system, CAS/JSC WGNE Research Activities in Atmospheric and Oceanic Modelling, 40, 1–11.
  • Japan Meteorological Agency (2013): Outline of the operational numerical weather prediction at the Japan Meteorological Agency. Appendix to WMO Technical Progress Report on the Global Data-processing and Forecasting System (GDPFS) and Numerical Weather Prediction (NWP) research. Japan Meteorological Agency, Tokyo, Japan.
  • JAXA 2017a: User’s guide for Global Satellite Mapping of Precipitation Microwave-IR Combined Product (GSMaP_MVK), Gauge-calibrated Rainfall Product (GSMaP_Gauge) Version 7, Reanalysis Products (GSMaP_RNL), and Gauge-calibrated Reanalysis Product (GSMaP_Gauge_RNL) Version 6. available at http://sharaku.eorc.jaxa.jp/GSMaP/ (needs user registration).
  • JAXA 2017b: Global Precipitation Measurement (GPM) GSMaP Product V04 (Algorithm version 7) Release Note (only in Japanese). Available at http://www.eorc.jaxa.jp/GPM/doc/product_info/release_note_gsmapv04-v7_ja.pdf
  • Jung, B.-J., Kim, H., Zhang, F., and Wu, C.-C. (2012): Effect of targeted dropsonde observations and best track data on the track forecasts of Typhoon Sinlaku (2008) using an ensemble Kalman filter. Tellus A, 64, 14984. doi: 10.3402/tellusa.v64i0.14984
  • Kang, J.-S., Kalnay, E., Liu, J., Fung, I., Miyoshi, T., and Ide, K. (2011): “Variable localization” in an ensemble Kalman filter: Application to the carbon cycle data assimilation. J. Geophys. Res., 116, D09110. doi: 10.1029/2010JD014673
  • Kalnay, E., Ota, Y., Miyoshi, T., and J. Liu, (2012): A simpler formulation of forecast sensitivity to observations: application to ensemble Kalman filters. Tellus A, 64, 18462, doi: 10.3402/tellusa.v64i0.18462
  • Kelly, G., Buizza, R., and Cardinali, C. (2007): The value of observations . I : Data denial experiments for the Atlantic and the Pacific. Q. J. R. Meteorol. Soc., 133, 1803–1815. doi:10.1002/qj.150
  • Kida, S., Shige, S., Kubota, T., Aonashi, K. and Okamoto, K. (2009): Improvement of rain/no-rain classification methods for microwave radiometer observations over ocean using the 37-GHz emission signature. J. Meteor. Soc. Japan, 87, 165–181. doi: 10.2151/jmsj.87A.165
  • Kida, S., Shige, S., Manabe, T., L’ecuyer, T., and Liu, G. (2010): Cloud liquid water path for the rain/no-rain classification method over ocean in the GSMaP algorithm. Trans. JSASS Aerospace Tech. Japan, 8, 19–23. doi: 10.2322/tastj.8.Pn_19
  • Kikuchi, K., Kodama, C., Nasuno, T., Nakano, M., Miura, H., Satoh, M., Noda, A. T., and Yamada, Y., (2017): Tropical intraseasonal oscillation simulated in an AMIP-type experiment by NICAM. Climate Dyn., 48, 2507–2528. doi: 10.1007/s00382-016-3219
  • Kodama, C., Yamada, Y., Noda, A. T., Kikuchi, K., Kajikawa, Y., Nasuno, T., Tomita, T., Yamaura, T., Takahashi, T. G., Hara, M., Kawatani, Y., Satoh, M., and Sugi, M. (2015): A 20-year climatology of a NICAM AMIP-type simulation. J. Meteor. Soc. Japan, 93, 393–424, doi:10.2151/jmsj.2015-024.
  • Kondo, K., Miyoshi, T., and Tanaka, H.L. (2013): Parameter sensitivities of the dual-localization approach in the local ensemble transform Kalman filter. SOLA, 9, 174–178. doi: 10.2151/sola.2013-039
  • Kondo, K., and Miyoshi, T. (2016): Impact of removing covariance localization in an ensemble Kalman filter: Experiments with 10 240 members using an intermediate AGCM. Mon. Wea. Rev., 144, 4849–4865. doi: 10.1175/MWR-D-15-0388.1
  • Kondo, K., and Miyoshi, T. (2019): Non-Gaussian statistics in global atmospheric dynamics: a study with a 10240-member ensemble Kalman filter using an intermediate AGCM. Nonlin. Processes in Geophys., 26, 211−225. doi: 10.5194/npg-26-211-2019
  • Kotsuki S., Terasaki K., and Miyoshi T. (2014): GPM/DPR Precipitation Compared with a 3.5-km-resolution NICAM Simulation. SOLA, 10, 204−209. doi:10.2151/sola.2014-043
  • Kotsuki, S., Greybush, S. G., and Miyoshi, T. (2017a): Can We Optimize the Assimilation Order in the Serial Ensemble Kalman Filter ? A Study with the Lorenz-96 Model. Mon. Wea. Rev., 145, 4977–4995. doi:MWR-D-17-0094.1
  • Kotsuki, S., Miyoshi, T., Terasaki, K., Lien, G.-Y., and Kalnay, E. (2017b): Assimilating the global satellite mapping of precipitation data with the Nonhydrostatic Icosahedral Atmospheric Model (NICAM). J. Geophys. Res., 122, 631–650. doi: 10.1002/2016JD025355
  • Kotsuki, S., Ota. Y., and Miyoshi, T. (2017c): Adaptive covariance relaxation methods for ensemble data assimilation : experiments in the real atmosphere, Q. J. R. Meteorol. Soc., 143, 2001–2015. doi: 10.1002/qj.3060
  • Kotsuki, S., Terasaki K., Yashiro H., Tomita H., Satoh M., and Miyoshi T. (2018): Online Model Parameter Estimation with Ensemble Data Assimilation in the Real Global Atmosphere: A Case with the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) and the Global Satellite Mapping of Precipitation Data. J. Geophys. Res., 123, 7375–7392. doi: 10.1029/2017JD028092
  • Kotsuki, S., Kurosawa, K., and Miyoshi, T. (2019a): On the Properties of Ensemble Forecast Sensitivity to Observations. Q. J. R. Meteorol. Soc., 145, 1897–1914. doi: 10.1002/qj.3534
  • Kotsuki, S., Kurosawa, K., Otsuka, S., Terasaki, K., and Miyoshi, T. (2019b): Global Precipitation Forecasts by Merging Extrapolation-based Nowcast and Numerical Weather Prediction with Locally-optimized Weights. Wea. and Forecasting, 34, 701–714. doi:10.1175/WAF-D-18-0164.1
  • Kotsuki S., Terasaki K., Kanemaru K., Satoh M., Kubota T. and Miyoshi T. (2019c): Predictability of Record-Breaking Rainfall in Japan in July 2018: Ensemble Forecast Experiments with the Near-real-time Global Atmospheric Data Assimilation System NEXRA. SOLA, 15A, 1–7. doi: 10.2151/sola.15A-001
  • Koyama, H., and Watanabe, M. (2010): Reducing Forecast Errors Due to Model Imperfections Using Ensemble Kalman Filtering. Mon. Wea. Rev., 138, 3316–3332. doi: 10.1175/2010MWR3067.1
  • Kubota, T., and Coauthors (2007): Global precipitation map using satelliteborne microwave radiometers by the GSMaP project: Production and validation. International Geoscience and Remote Sensing Symposium (IGARSS), 45, 2584–2587. doi: 10.1109/IGARSS.2006.668
  • Kubota, T., and Coauthors (2014): Evaluation of precipitation estimates by at-launch codes of GPM/DPR algorithms using synthetic data from TRMM/PR observations. IEEE J. Selected Topics in Appl. Earth Observations and Remote Sensing, 7, 3931−3944. doi: 10.1109/JSTARS.2014.2320960
  • Kubota, T., Iguchi, T., Kojima, M., Liao, L., Masaki, T., Hanado, H., Meneghini, R. and Oki, R. (2016): A statistical method for reducing sidelobe clutter for the ku-band precipitation radar on board the GPM core observatory. J. Atmos. Oceanic Technol., 33, 1413–1428. doi: 10.1175/JTECH-D-15-0202.1
  • Kummerow, C., Barnes, W., Kozu, T., Shiue, J., and Simpson J. (1998): The tropical rainfall measuring mission (TRMM) sensor package. J. Atmos. Oceanic Technol., 15, 809–817, doi: 10.1175/1520-0426(1998)015<0809:TTRMMT>2.0.CO;2.
  • Kunii, M., Miyoshi T. and Kalnay, E. (2012): Estimating the Impact of Real Observations in Regional Numerical Weather Prediction Using an Ensemble Kalman Filter. Mon. Wea. Rev., 140, 1975–1987. doi:10.1175/MWR-D-11-00205.1
  • Langland, R. H., and Baker, N. L. (2004). Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system. Tellus A, 56, 189–201. doi:10.1111/j.1600-0870.2004.00056.x
  • Li H., Kalnay E., and Miyoshi T. (2009a): Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter. Q. J. R. Meteorol. Soc., 135, 523–533. doi:10.1002/qj371
  • Li, H., Kalney, E., Miyoshi, T., and Dafort, C., (2009b): Accounting for Model Errors in Ensemble Data Assimilation. Mon. Wea. Rev., 137, 3407–3419, doi: 10.1175/2009MWR2766.1
  • Li, H., Liu, J., and Kalnay, E. (2010): Correction of ‘Estimating observation impact without adjoint model in an ensemble Kalman filter’. Q. J. R. Meteorol. Soc., 136, 1652−1654. doi: 10.1002/qj.658
  • Liao, L., and Meneghini, R. (2011): A study on the feasibility of dual-wavelength radar for identification of hydrometeor phases. J. Appl. Meteor. Climatol., 50, 449−456. doi: 10.1175/2010JAMC2499.1
  • Liao, L., Meneghini, R., Tokay, A., and Bliven, L. F. (2016): Retrieval of snow properties for Ku-and Ka-band dual-frequency radar. J. Appl. Meteor. Climatol., 55, 1845−1858. doi: 10.1175/JAMC-D-15-0355.1
  • Lien, G.-Y., Kalnay, E. and Miyoshi, T. (2013): Effective assimilation of global precipitation : simulation experiments. Tellus A, 65, 1–16. doi: 10.3402/tellusa.v65i0.19915
  • Lien, G.-Y., Kalnay, E., Miyoshi, T., and Huffman, G. J. (2016a): Statistical Properties of Global Precipitation in the NCEP GFS Model and TMPA Observations for Data Assimilation. Mon. Wea. Rev., 144, 663–679. doi: 10.1175/MWR-D-15-0150.1
  • Lien, G.-Y., Miyoshi, T., and Kalnay, E. (2016b): Assimilation of TRMM Multisatellite Precipitation Analysis with a low-resolution NCEP Global Forecasting System. Mon. Wea. Rev., 144, 643–661. doi: 10.1175/MWR-D-15-0149.1
  • Lien, G., Hotta, D., Kalnay, E., Miyoshi, T., and Chen, T.-C. (2018): Accelerating assimilation development for new observing systems using EFSO. Nonlin. Processes Geophys., 25, 129–143. doi: 10.5194/npg-25-129-2018
  • Lin, Y. L., Farley, R. D., and Orville, H. D. (1983): Bulk parameterization of the snow field in a cloud model. J. Clim. Appl. Mmeteor., 22, 1065–1092. doi: 10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2
  • Liu Z.-Q., and Rabier F. (2003): The potential of high-density observations for numerical weather prediction: A study with simulated observations. Q. J. R. Meteorol. Soc., 129, 3013–3035. doi: 10.1256/qj.02.170
  • Liu, Y., and Daum, P. H. (2004): Parameterization of the autoconversion process. Part I: Analytical formulation of the Kessler-type parameterizations. J. Atmos. Sci., 61, 1539–1548. doi: 10.1175/1520-0469%282004%29061<1539%3APOTAPI>2.0.CO%3B2
  • Liu, J., and Kalnay, E. (2008): Estimating observation impact without adjoint model in an ensemble Kalman filter. Q. J. R. Meteorol. Soc., 134, 1327–1335. doi: 10.1002/qj.280
  • Lorenc, A. C. (2003): The potential of the ensemble Kalman filter for NWP—a comparison with 4D‐Var. Q. J. R. Meteorol. Soc., 129, 3183–3203. doi: 10.1256/qj.02.132
  • Lorenc, A. C., and Marriott, R. T. (2014): Forecast sensitivity to observations in the Met Office Global numerical weather prediction system. Q. J. R. Meteorol. Soc., 140, 209–224. doi: 10.1002/qj.2122
  • Lorenz, E. (1996): Predictability – A problem partly solved. Proc. Seminar on Predictability, Reading, United Kingdom, ECMWF, 1–18.
  • Lorenz, E. and Emanuel, K. A. (1998): Optimal Sites for Supplementary Weather Observations : Simulation with a Small Model. J. Atmos. Sci., 55, 399–414. doi: 10.1175/1520-0469(1998)055<0399:OSFSWO>2.0.CO;2.
  • Masunaga, H., and Kummerow, C. D. (2005): Combined radar and radiometer analysis of precipitation profiles for a parametric retrieval algorithm. J. Atmos. Oceanic Technol., 22, 909–929. doi: 10.1175/JTECH1751.1
  • Masunaga, H., Satoh, M., and Miura, H. (2008): A joint satellite and global cloud‐resolving model analysis of a Madden‐Julian Oscillation event: Model diagnosis. J. Geophys.Res., 113, D17210. doi: 10.1029/2008JD009986.
  • Mitchell H. L., and Houtekamer P. L. (2000): An adaptive ensemble Kalman filter. Mon. Wea. Rev., 128, 416–433. doi:10.1175/1520-0493(2000)128<0416:AAEKF>2.0.CO;2
  • Miyamoto, Y., Kajikawa, Y., Yoshida, R., Yamaura, T., Yashiro, H., and Tomita, H. (2013): Deep moist atmospheric convection in a subkilometer global simulation. Geophys. Res. Lett., 40, 4922–4926. doi: 10.1002/grl.50944
  • Miyoshi, T. (2005): Ensemble Kalman filter experiments with a primitive-equation global model. Ph.D. dissertation, University of Maryland, College Park, 197pp.
  • Miyoshi, T., and Yamane, S. (2007): Local Ensemble Transform Kalman Filtering with an AGCM at a T159/L48 Resolution. Mon. Wea. Rev., 135, 3841–3861. doi: 10.1175/2007MWR1873.1
  • Miyoshi, T., Yamane S. and Enomoto T. (2007): Localizing the Error Covariance by Physical Distances within a Local Ensemble Transform Kalman Filter (LETKF). SOLA, 3, 89-92. doi:10.2151/sola.2007-023
  • 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
  • Miyoshi T. (2011): The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman Filter. Mon. Wea. Rev., 139, 1519–1535. doi:10.1175/2010MWR3570.1
  • Miyoshi T., and Kunii M. (2012): Using AIRS retrievals in the WRF-LETKF system to improve regional numerical weather prediction. Tellus A, 64, 1–11. doi:10.3402/tellusa.v64i0.18408
  • Miyoshi T., Kalnay E., Li H., (2013): Estimating and including observation-error correlations in data assimilation, Inverse Problems in Science and Engineering, 21, 387–398. doi: 10.1080/17415977.2012.712527
  • Miyoshi, T., Kondo, K., and Imamura, T. (2014). The 10,240‐member ensemble Kalman filtering with an intermediate AGCM. Geophys. Res. Lett., 41, 5264–5271. doi: 10.1002/2014GL060863
  • Molteni, F. (2003): Atmospheric simulations using a GCM with simplified physical parametrizations. I: Model climatology and variability in multi-decadal experiments. Clim. Dyn., 20, 175–191. doi: 10.1007/s00382-002-0268-2
  • Necker, T., Weissmann, M., and Sommer, M. (2018): The importance of appropriate verification metrics for the assessment of observation impact in a convection‐permitting modelling system. Quarterly Journal of the Royal Meteorological Society, 144, 1667–1680. doi: 10.1002/qj.3390
  • Nerger, L., Janjić, T., Schröter, J. and Hiller, W. (2012): A regulated localization scheme for ensemble-based Kalman filters. Q. J. R. Meteorol. Soc., 138, 802–812, doi: 10.1002/qj.945
  • Nerger L. (2015): On serial observation processing in localized ensemble Kalman filters. Mon. Wea. Rev., 143, 1554–1567. doi:10.1175/MWR-D-14-00182.1
  • Nystrom, R. G., Zhang, F., Munsell, E. B., Braun, S. A., Sippel, J. A., Weng, Y., and Emanuel, K. (2018): Predictability and dynamics of Hurricane Joaquin (2015) explored through convection-permitting ensemble sensitivity experiments. J. Atmos. Sci., 75, 401–424. doi: 10.1175/JAS-D-17-0137.1
  • Oczkowski, M., Szunyogh, I., and Patil, D. J. (2005): Mechanisms for the development of locally low-dimensional atmospheric dynamics. J. Atmos. Sci.62, 1135–1156. doi:10.1175/JAS3403.1
  • Ota, Y., Derber, J. C., Miyoshi, T. and Kalnay, E. (2013): Ensemble-based observation impact estimates using the NCEP GFS. Tellus, 65A, 20–38, doi:10.3402/tellusa.v65i0.20038.
  • Ott E., Hunt B. R., Szunyogh I., Zsimin A. V., Kostelich E. J., Corazza M., Kalnay E. Patil D. J., and Y. J. A. (2004): A local ensemble Kalman filter for atmospheric data assimilation. Tellus A, 56, 415–428. doi: 10.1111/j.1600-0870.2004.00076.x
  • Otsuka S., Kotsuki S., and Miyoshi T. (2016): Nowcasting with data assimilation: a case of Global Satellite Mapping of Precipitation. Wea. and Forecasting, 31, 1409–1416. doi:10.1175/WAF-D-16-0039.1
  • Pantillon, F., Knippertz, P., and Corsmeier, U. (2017): Revisiting the synoptic-scale predictability of severe European winter storms using ECMWF ensemble reforecasts. Nat. Hazards Earth Sys. Sci., 17(10), 1795–1810. doi: 10.5194/nhess-17-1795-2017
  • Patil, D. J., Hunt, B. R., Kalnay, E., Yorke, J. A., and Ott, E. (2001): Local Low Dimensionality of Atmospheric Dynamics. Phys. Rev. Lett., 86, 5878–5881. doi: 10.1103/PhysRevLett.86.5878
  • Poterjoy, J., and Zhang, F. (2015): Systematic comparison of four-dimensional data assimilation methods with and without the tangent linear model using hybrid background error covariance: E4DVar versus 4DEnVar. Mon. Wea. Rev., 143, 1601-1621. doi: 10.1175/MWR-D-14-00224.1
  • Poterjoy, J., and Zhang, F. (2016): Comparison of hybrid four-dimensional data assimilation methods with and without the tangent linear and adjoint models for predicting the life cycle of Hurricane Karl (2010): Mon. Wea. Rev., 144, 1449-1468. doi: 10.1175/MWR-D-15-0116.1
  • Potthast, R., and Welzbacher, C. A: (2018). Ultra Rapid Data Assimilation based on Ensemble Filters. Frontiers in Applied Mathematics and Statistics, 4, 45. doi: 10.3389/fams.2018.00045
  • Randall, D. A., Coakley, J. A., Lenschow, D. H., Fairall, C. W., and Kropfli, R. A. (1984): Outlook for Research on Subtropical Marine Stratification Clouds. Bull. Amer. Meteor. Soc., 65, 1290–1301. doi: 10.1175/1520-0477(1984)065<1290:OFROSM>2.0.CO;2
  • Roh, W., and Satoh, M. (2014): Evaluation of precipitating hydrometeor parameterizations in a single-moment bulk microphysics scheme for deep convective systems over the tropical central Pacific. J. Atmos. Sci., 71, 2654–2673, doi:10.1175/JAS-D-13-0252.1.
  • Roh, W., Satoh, M., and Nasuno, T. (2017): Improvement of a cloud microphysics scheme for a global nonhydrostatic model using TRMM and a satellite simulator. J. Atmos. Sci., 74, 167−184. doi: 10.1175/JAS-D-16-0027.1
  • Ruiz, J. J., Pulido, M., and Miyoshi, T. (2013a): Estimating Model Parameters with Ensemble-Based Data Assimilation : Parameter Covariance Treatment. J. Meteorol. Soc. Japan, 91, 453–469. doi: 10.2151/jmsj.2013-403
  • Ruiz, J. J., Pulido, M., and Miyoshi, T. (2013b): Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review. J. Meteorol. Soc. Japan, 91, 79–99. doi: 10.2151/jmsj.2013-201
  • Ruiz, J., and Pulido, M. (2015): Parameter Estimation Using Ensemble-Based Data Assimilation in the Presence of Model Error. Mon. Wea. Rev., 143, 1568–1582. doi: 10.1175/MWR-D-14-00017.1
  • Sato, Y., Miyamoto, Y., Nishizawa, S., Yashiro, H., and Kajikawa, Y. (2015): Horizontal Distance of Each Cumulus and Cloud Broadening Distance Determine Cloud Cover, SOLA, 11, 1–4. doi: 10.2151/sola.2015-019
  • Satoh, M., Matsuno, T., Tomita, H., Miura, H., Nasuno, T., and Iga, S. (2008): Nonhydrostatic icosahedral atmospheric model (NICAM) for global cloud resolving simulations. J. Comput. Phys., 227, 3486–3514. doi: 10.1016/j.jcp.2007.02.006
  • Satoh, M., and Coauthors. (2014): The Non-hydrostatic Icosahedral Atmospheric Model: description and development. Prog. Earth Planet. Sci., 1, 18. doi: 10.1186/s40645-014-0018-1
  • Saunders R., Hocking J., Rundle D., Rayer P., Matricardi M., Geer A., Lupu C., Brunel P., and Vidot J. (2013): RTTOV-11: Science and validation report. NWP-SAF report, Met. Office, UK, 62pp.
  • Schirber, S., Klocke, D., Pincus, R., Quaas, J., and Anderson, J. L. (2013): Parameter estimation using data assimilation in an atmospheric general circulation model: From a perfect toward the real world. J. Adv. Modeling Earth Syst., 5, 58–70. doi: 10.1029/2012MS000167
  • Schraff, C., Reich, H., Rhodin, A., Schomburg, A., Stephan, K., Periáñez, A., and Potthast, R. (2016): Kilometre‐scale ensemble data assimilation for the COSMO model (KENDA). Q. J. R. Meteorol. Soc., 142, 1453–1472. doi: 10.1002/qj.2748
  • Schumacher, C., and Houze Jr, R. A. (2003): Stratiform rain in the tropics as seen by the TRMM precipitation radar. J. Clim., 16, 1739–1756. doi: 10.1175/1520-0442(2003)016<1739:SRITTA>2.0.CO;2
  • Seiki, T., and Nakajima, T. (2014): Aerosol effects of the condensation process on a convective cloud simulation. J. Atmos. Sci., 71, 833–853. doi: 10.1175/JAS-D-12-0195.1
  • Shiogama, H. and Coauthors (2012): Perturbed physics ensemble using the MIROC5 coupled atmosphere – ocean GCM without flux corrections : experimental design and results Parametric uncertainty of climate sensitivity, Clim. Dyn., 12, 3041–3056. doi: 10.1007/s00382-012-1441-x
  • Skofronick-Jackson, G. and Coauthors (2017): The Global Precipitation Measurement (GPM) mission for science and society. Bull. Am. Meteorol. Soc., 98, 1679–1695. doi: 10.1175/BAMS-D-15-00306.1
  • Sokol, Z., and Zacharov, P. (2012): Nowcasting of precipitation by an NWP model using assimilation of extrapolated radar reflectivity. Q. J. R. Meteorol. Soc., 138, 1072–1082. doi: 10.1002/qj.970
  • Sommer, M., and Weissmann, M. (2016): Ensemble-based approximation of observation impact using an observation-based verification metric. Tellus A, 68, 1–12. doi: 10.3402/tellusa.v68.27885
  • Sun, J., and Coauthors. (2014): Use of NWP for nowcasting convective precipitation. Bull. Amer. Meteor. Soc., March, 95, 409–426. doi: 10.1175/BAMS-D-11-00263.1
  • Sun, Y. Q., and Zhang, F. (2016): Intrinsic versus practical limits of atmospheric predictability and the significance of the butterfly effect. J. Atmos. Sci., 73, 1419-1438. doi: 10.1175/JAS-D-15-0142.1
  • Suzuki, K., Golaz, J., and Stephens, G. L. (2013): Evaluating cloud tuning in a climate model with satellite observations. Geophys. Res. Lett., 40, 4464–4468. doi: 10.1002/grl.50874
  • Takata K., Emori S., and Watanabe T. (2003): Development of the minimal advanced treatments of surface interaction and runoff. Global and Planetary Change 38, 209–222. doi:10.1016/S0921-8181(03)00030-4
  • Takemura, T., Nozawa, T., Emori, S., and Nakajima, T. Y. (2005): Simulation of climate response to aerosol direct and indirect effects with aerosol transport-radiation model. J. Geophys. Res., 110, D02202. doi: 10.1029/2004JD005029
  • Terasaki K., and Miyoshi T. (2014): Data assimilation with error-correlated and non-orthogonal observations: Experiments with the Lorenz-96 model. SOLA, 10, 210-213. doi: 10.2151/sola.2014-044.
  • Terasaki, K., Sawada, M., and Miyoshi, T. (2015): Local Ensemble Transform Kalman Filter Experiments with the Nonhydrostatic Icosahedral Atmospheric Model NICAM. SOLA, 11, 23–26. doi: 10.2151/sola.2015-006
  • Terasaki, K. and Miyoshi, T. (2017): Assimilating AMSU-A Radiances with the NICAM-LETKF. J. Meteorol. Soc. Japan, 95, 433–446. doi: 10.2151/jmsj.2017-028
  • Terasaki, K., Kotsuki, S., and Miyoshi, T. (2019): Multi-year analysis using the NICAM-LETKF data assimilation system. SOLA, 15, 41–46. doi: 10.2151/sola.2019-009
  • Tippett M. K., Anderson J. L., Bishop C. H., and Whitaker J. S. (2003): Ensemble square root filters. Mon. Wea. Rev., 131, 1485–1490. doi: 10.1175/1520-0493(2003)131<1485:ESRF>2.0.CO;2
  • Tochimoto, E., and Kawano, T. (2012): Development processes of Baiu frontal depressions. SOLA, 8, 9-12. doi: 10.2151/sola.2012-003
  • Todling, R. (2013): Comparing two approaches for assessing observation impact. Mon. Wea. Rev., 141, 1484-1505. doi: 10.1175/MWR-D-12-00100.1
  • Tomita, H., and Satoh, M. (2004): A new dynamical framework of nonhydrostatic global model using the icosahedral grid. Fluid Dyn. Res., 34, 357-400. doi: 10.1016/j.fluiddyn.2004.03.003
  • Tomita, H. (2008): New microphysical schemes with five and six categories by diagnostic generation of cloud ice. J. Meteor. Soc. Japan, 86, 121-142. doi: 10.2151/jmsj.86A.121
  • Tong, M., and M. Xue (2008): Simultaneous estimation of microphysical parameters and atmospheric state with simulated radar data and ensemble square root Kalman filter. Part II: Parameter estimation experiments, Mon. Wea. Rev., 136, 1649–1668. doi: 10.1175/2007MWR2071.1
  • Torn, R. D., and Hakim, G. J. (2008): Ensemble-based sensitivity analysis. Mon. Wea. Rev, 136, 663-677. doi:
  • Toth Z., and Kalnay E. (1993): Ensemble Forecasting at NMC: The Generation of Perturbations. Bull. Amer. Meteor. Soc., 74, 2317-2330. doi:10.1175/1520-0477(1993)074<2317:EFANTG>2.0.CO;2
  • Toyoshima, K., Masunaga, H., and Furuzawa, F. A. (2015): Early evaluation of Ku-and Ka-band sensitivities for the global precipitation measurement (GPM) dual-frequency precipitation radar (DPR). SOLA, 11, 14−17. doi: 10.2151/sola.2015-004
  • Ushio T., Sasashige K., Kubota T., Shige S., Okamoto K.-I., Aonashi K., Inoue T., Takahashi N., Iguchi T., Kachi M., Oki R., Morimoto T., and Kawasaki Z.-I. (2009): A Kalman filter approach to the Global Satellite Mapping of Precipitation (GSMaP) from combined passive microwave and infrared radiometric data. J. Meteorol. Soc. Japan, 87, 137–151. doi: 10.2151/jmsj.87A.137
  • Weyn J. A., Durran D. R., Caruana R. (2019): Can machines learn to predict weather? Using deep learning to predict gridded 500‐hPa geopotential height from historical weather data. J. Adv. Modeling Earth Syst. doi: 10.1029/2019MS001705 (in press)
  • Weston P. P., Bell W., and Eyre J. R. (2014): Accounting for correlated error in the assimilation of high-resolution sounder data. Q. J. R. Meteorol. Soc., 140, doi:10.1002/qj.2306.
  • Whitaker J. S., and Hamill T. M. (2002): Ensemble Data Assimilation without Perturbed Observations. Mon. Wea. Rev., 130, 1913–1924. doi:10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2
  • Whitaker, J. S., and Hamill, T. M. (2012): Evaluating Methods to Account for System Errors in Ensemble Data Assimilation. Mon. Wea. Rev., 140, 3078–3089. doi: 10.1175/MWR-D-11-00276.1
  • Wielicki, B. A., Barkstrom, B. R., Harrison, E. F., Lee III, R. B., Smith, G. L., and Cooper, J. E. (1996): Clouds and the Earth’s Radiant Energy System (CERES): An earth observing system experiment. Bull. Am. Meteorol. Soc., 77, 853–868. doi: 10.1175/1520-0477(1996)077<0853:CATERE>2.0.CO;2
  • Wilcoxon, F. (1945): Individual Comparisons by Ranking Methods. Biometrics Bulletin, 1, 80–83. doi: 10.2307/3001968.
  • Wood, R. (2012): Stratocumulus clouds. Mon. Wea. Rev., 140, 2373–2423. doi: 10.1175/MWR-D-11-00121.1
  • Wu, C.-C., Lien, G.-Y., Chen, J.-H., and Zhang, F. (2010): Assimilation of tropical cyclone track and structure based on the ensemble Kalman filter (EnKF). J. Atmos. Sci., 67, 3806–3822. doi: 10.1175/2010JAS3444.1
  • Xie, B., Zhang, F., Zhang, Q., Poterjoy, J., and Weng, Y. (2013): Observing strategy and observation targeting for tropical cyclones using ensemble-based sensitivity analysis and data assimilation. Mon. Wea. Rev., 141, 1437–1453. doi: 10.1175/MWR-D-12-00188.1
  • Yang, S. C., Kalnay, E., Hunt, B., and Bowler, N. E. (2009): Weight interpolation for efficient data assimilation with the Local Ensemble Transform Kalman Filter. Q. J. R. Meteorol. Soc., 135, 251–262. doi: 10.1002/qj.353
  • Yashiro H., Terasaki K., Miyoshi T., and H. Tomita (2016): Performance evaluation of a throughput-aware framework for ensemble data assimilation: the case of NICAM-LETKF. Geosci. Model Dev., 9, 2293–2300, doi:10.5194/gmd-9-2293-2016.
  • Ying Y., and Zhang F. (2015): An adaptive covariance relaxation method for ensemble data assimilation. Q. J. R. Meteorol. Soc., 141, 2898–2906. doi:10.1002/qj.2576
  • Yoshimura K., Miyoshi T., and Kanamitsu M. (2014): Observation system simulation experiments using water vapor isotope information. J. Geophys. Res., 119, 7842–7862. doi:10.1002/2014JD021662
  • Yuter, S. E., and Houze Jr, R. A. (1995): Three-dimensional kinematic and microphysical evolution of Florida cumulonimbus. Part II: Frequency distributions of vertical velocity, reflectivity, and differential reflectivity. Mon. Wea. Rev., 123, 1941-1963. doi: 10.1175/1520-0493(1995)123<1941:TDKAME>2.0.CO;2
  • Zhang F., Snyder C., and Sun J. (2004): Impacts of Initial Estimate and Observation Availability on Convective-Scale Data Assimilation with an Ensemble Kalman Filter. Mon. Wea. Rev., 132, 1238–1253. doi:10.1175/1520-0493(2004)132<1238:IOIEAO>2.0.CO;2
  • Zhang, F., Bei, N., Rotunno, R., Snyder, C., and Epifanio, C. C. (2007): Mesoscale predictability of moist baroclinic waves: Convection-permitting experiments and multistage error growth dynamics. J. Atmos. Sci., 64, 3579-3594. doi: 10.1175/JAS4028.1
  • Zhang, F., Weng, Y., Kuo, Y.-H., Whitaker, J. S., and Xie, B. (2010): Predicting Typhoon Morakot’s catastrophic rainfall with a convection-permitting mesoscale ensemble system. Wea. and Forecasting, 25, 1816-1825. doi: 10.1175/2010WAF2222414.1
  • Zhu, Y., and Gelaro, R. (2008): Observation Sensitivity Calculations Using the Adjoint of the Gridpoint Statistical Interpolation (GSI) Analysis System. Mon. Wea. Rev., 136, 335–351. doi: 10.1175/2007MWR2046.1