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Overview and New Opportunities for Multi-Source Data Assimilation

Joint Center of Data Assimilation for Research and Application, Nanjing University of Information Science & Technology, Nanjing 210044
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Abstract

The world witnessed an accelerated development of various types of meteorological observing technology, an evolution of numerical weather prediction (NWP) models from single atmospheric component to coupled multi-components of the earth system, as well as the multi graphics processing unit technology in computer sciences, a new era for rapidly advancing data assimilation science and technology development has arrived. The multi-source data assimilation is important not only for NWP but also for further understanding of global and regional weather changes. This article firstly selectively reviews past methods of multi-source data assimilation. New opportunities are then discussed for future development of data assimilation system framework, for innovative uses of high-resolution observations, and for applications of artificial intelligence machine learning in meteorological data assimilation.

References

 

Alishouse, J., S. Snyder, J. Vongsathorn, et al., 1990: Determinations of oceanic total precipitable water from the SSM/I. IEEE Trans. Geosci. Remote Sens., 28, 811–816, https://doi.org/10.1109/36.58967.

 

Andersson, E., J. Pailleux, J.-N. Thépaut, et al., 1994: Use of cloud-cleared radiances in three/four-dimensional variational data assimilation. Quart. J. Roy. Meteor. Soc., 120, 627–653, https://doi.org/10.1002/qj.49712051707.

 

Anthes, R. A., and T. Rieckh, 2018: Estimating observation and model error variances using multiple data sets. Atmos. Meas. Tech., 11, 4239–4260, https://doi.org/10.5194/amt-11-4239-2018.

 

Aumann, H. H., M. T. Chahine, C. Gautier, et al., 2003: AIRS/AMSU/HSB on the Aqua mission: design, science objectives, data products, and processing systems. IEEE Trans. Geosci. Remote Sens., 41, 253–264, https://doi.org/10.1109/TGRS.2002.808356.

 

Balmaseda, M. A., K. Mogensen, K., and A. T. Weaver, 2013: Evaluation of the ECMWF ocean reanalysis system ORAS4. Quart. J. Roy. Meteor. Soc., 139, 1132–1161, https://doi.org/10.1002/qj.2063.

 

Barsby, J., and R. D. Diab, 1995: Total ozone and synoptic wea-ther relationships over southern Africa and surrounding oceans. J. Geophys. Res. Atmos., 100, 3023–3032, https://doi.org/10.1029/94JD01987.

 

Bevis, M., S. Businger, T. A. Herring, et al., 1992: GPS meteorology: Remote sensing of atmospheric water vapor using the Global Positioning System. J. Geophys. Res. Atmos., 97, 15,787–15,801, https://doi.org/10.1029/92JD01517.

 

Bi, M., and X. Zou, 2023: Comparison of cloud/rain band structures of Typhoon Muifa (2022) revealed in FY-3E MWHS-2 observations with all-sky simulations. J. Geophys. Res. Atmos., 128, e2023JD039410, https://doi.org/10.1029/2023JD039410.

 

Bonavita, M., E. Holm, L. Isaksen, and M. Fisher, 2015: The evolution of the ECMWF hybrid data assimilation system. Quart. J. Roy. Meteor. Soc., 142, 287–303, https://doi.org/10.1002/qj.2652.

 

Bosart, L. F., and J. P. Cussen, 1973: Gravity wave phenomena accompanying east coast cyclogenesis. Mon. Wea. Rev., 101, 446–454, https://doi.org/10.1175/1520-0493(1973)101%3C0446:GWPAEC%3E2.3.CO;2.

 

Brunk, I. W., 1949: The pressure pulsation of April 11, 1949. J. Meteor., 6, 181–187, https://doi.org/10.1175/1520-0469(1949)006%3C0181:TPPOA%3E2.0.CO;2.

 

Charney, J. G., R. Fjørtoft, and J. von Neuman, 1950: Numerical integration of the barotropic vorticity equation. Tellus, 2, 237–254, https://doi.org/10.3402/tellusa.v2i4.8607.

 
Charney, J. G., 1951: Dynamic Forecasting by Numerical Process. Compendium of Meteorology. USA, Boston, AMS, 1–2.
 

Courtier, P., E. Andersson, W. Heckley, et al., 1998: The ECMWF implementation of three-dimensional variational assimilation (3D-Var). I: Formulation. Quart. J. Roy. Meteor. Soc., 124, 1783–1807, https://doi.org/10.1002/qj.49712455002.

 
Courtier, P., J.-N. Thépaut, and A. Hollingsworth, 1993: A strategy for operational implementation of 4D-Var. Workshop Proc. of Variational assimilation, with Special Empha-sis on Three-dimensional Aspects, ECMWF, Shinfield Park, Reading, Berkshir, UK, 468 pp.
 
Davis, C., S. Low-Nam, M. A. Shapiro, et al., 1999: Direct retrieval of wind from Total Ozone Mapping Spectrometer (TOMS) data: Examples from FASTEX. Quart. J. Roy. Meteor. Soc., 125 , 3375–3391, https://doi.org/10.1002/qj.49712556113.
 

Davis, J. L., T. A. Herring, I. I. Shapiro, et al., 1986: Geodesy by radio interferometry: Effects of atmospheric modeling errors on estimates of baseline length. Radio Sci., 20, 1593–1607, https://doi.org/10.1029/RS020i006p01593.

 

De Pondeca, M., and X. Zou, 2001a: Moisture retrievals from simulated zenith delay “observations” and their impact on short-range precipitation forecasts. Tellus, 53A, 192–214, https://doi.org/10.3402/tellusa.v53i2.12186.

 

De Pondeca, M., and X. Zou, 2001b: A case study of the variatio-nal assimilation of GPS zenith delay observations into a meso-scale model. J. Appl. Meteor., 40, 1559–1576, https://doi.org/10.1175/1520-0450(2001)040%3C1559:ACSOTV%3E2.0.CO;2.

 
Dee, D. P., and A. M. Da Silva, 2002: The choice of variable for atmospheric moisture analysis. Mon. Wea. Rev., 131 , 155–171, https://doi.org/10.1175/1520-0493(2003)131<0155:TCOVFA>2.0.CO;2.
 

Derber, J. C., D. F. Parrish, and S. J. Lord, 1991: The new global operational analysis system at the National Meteorological Center. Wea. Forecasting, 6, 538–547, https://doi.org/10.1175/1520-0434(1991)006%3C0538:TNGOAS%3E2.0.CO;2.

 

Derber, J. C., and W.-S. Wu, 1998: The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Mon. Wea. Rev., 126, 2287–2299, https://doi.org/10.1175/1520-0493(1998)126%3C2287:TUOTCC%3E2.0.CO;2.

 
De Rosnay, P., G. Balsamo, C. Albergel, et al., 2014: Initialisation of land surface variables for numerical weather prediction. Surveys in Geophysics, 35 , 607–621, https://doi.org/10.1007/s10712-012-9207-x.
 

De Rosnay, P., P. Browne, E. De Boisséson, et al., 2022: Coupled data assimilation at ECMWF: Status, challenges, and future developments. Quart. J. Roy. Meteor. Soc., 148, 2672–2702, https://doi.org/10.1002/qj.4330.

 

Dobson, G. M. B., D. N. Harrison, et al., 1929: Measurement of the amount of ozone in the Earth’s atmosphere and its relation to other geophysical conditions. Proc. R. Soc. London, Ser. A, 122, 456–486, https://doi.org/10.1098/rspa.1929.0034.

 

Donner, L. J., 1988: An initialization for cumulus convection in numerical weather prediction models. Mon. Wea. Rev., 116, 377–385, https://doi.org/10.1175/1520-0493(1988)116%3C0377:AIFCCI%3E2.0.CO;2.

 

Dudhia, J., 1993: A nonhydrostatic version of the Pen State-NCAR Mesoscale Model: Validation tests and simulation of an Atlantic cyclone and cold front. Mon. Wea. Rev., 121, 1493–1513, https://doi.org/10.1175/1520-0493(1993)121%3C1493:ANVOTP%3E2.0.CO;2.

 

Fiorino M., T. T. Warner, 1981: Incorporating surface-winds and rainfall rates into the initialization of a mesoscale hurricane model. Mon. Wea. Rev., 109, 1914–1929, https://doi.org/10.1175/1520-0493(1981)109%3C1914:ISWARR%3E2.0.CO;2.

 
Gray, J. E., and D. W. Allan, 1974: A method for estimating the frequency stability of an individual oscillator. 28th Annual Symposium on Frequency Control, Atlantic City, New Jersey, 29–31 May, IEEE, 243–246, https://doi.org/10.1109/FREQ.1974.200027.
 

Grell, G. A., J. Dudhia, and D. R. Stauffer, 1994: A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note, NCAR/TN-398+STR, USA, Boulder, NCAR, 138 pp.

 

Gustafsson, N., et al., 1999: Three-dimensional variational data assimilation for a high resolution limited area model (HIRLAM). Swedish Meteorological and Hydrological Institute Tech. Rep., 40, 74 pp.

 

Hamill, T. M., and C. Snyder, 2000: A hybrid ensemble Kalman filter–three-dimensional variational (3DVAR) analysis scheme. Mon. Wea. Rev., 128, 2905–2919, https://doi.org/10.1175/1520-0493(2000)128%3C2905:AHEKFV%3E2.0.CO;2.

 

Han, Y., F. Weng, Q. Liu, and P. van Delst, 2007: A fast radiative transfer model for SSMIS upper atmosphere sounding channels. J. Geophys. Res. Atmos., 112, D11121, https://doi.org/10.1029/2006JD008208.

 
Han, Y., H. Revercomb, M. Cromp et al., 2013: Suomi NPP CrIS measurements, sensor data record algorithm, calibration and validation activities, and record data quality. J. Geophys. Res. Atmos., 118 , 12,734–12,748, https://doi.org/10.1002/2013JD020344.
 

Healy, S., J. R. Eyre, M. Hamrud, et al., 2007: Assimi-lating GPS radio occultation measurements with two-dimensional bending angle observation operators. Quart. J. Roy. Meteor. Soc., 133, 1213–1227, https://doi.org/10.1002/qj.63.

 

Healy, S., and J.-N. Thépaut, 2006: Assimilation experiments with CHAMP GPS radio occultation measurements. Quart. J. Roy. Meteor. Soc., 132, 605–623, https://doi.org/10.1256/qj.04.182.

 
Holton, J. R., 2004: An Introduction to Dynamic Meteorology. Elsevier Academic Press, 535 pp.
 

Hu, Y., and X. Zou, 2024: GOES-16 ABI brightness temperature observations capturing vortex rossby wave signals during ra-pid intensification of Hurricane Irma (2017). J. Meteor. Res., 38, 1–16, https://doi.org/10.1007/s13351-024-3229-4.

 

Inverarity, G. W., W. J. Tennant, L. Anton, et al., 2023: Met Office MOGREPS-G initialisation using an ensemble of hybrid four-dimensional ensemble variational (En-4DEnVar) data assimilations. Quart. J. Roy. Meteor. Soc., 149, 1138–1164, https://doi.org/10.1002/qj.4431.

 

Jang, K. I., X. Zou, M. S. F. V. De Pondeca, et al., 2003: Incorporating TOMS ozone data into the prediction of the Washington January 2000 winter storm. J. Appl. Meteor., 42, 797–812, https://doi.org/10.1175/1520-0450(2003)042%3C0797:ITOMIT%3E2.0.CO;2.

 
Janssen, P., J. R. Bidlot, S. Abdalla, et al., 2005: Progress in Ocean Wave Forecasting at ECMWF. UK, Reading, ECMWF, 1–11.
 

Jones, E. A., and X. Wang, 2023: A multiresolution ensemble hybrid 4DEnVar with variable ensemble sizes to improve glo-bal and tropical cyclone track numerical prediction. Mon. Wea. Rev., 151, 1145–1166, https://doi.org/10.1175/MWR-D-22-0186.1.

 
Kalnay, E., 2003: Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press. 341 pp.
 

Klaes, K. D., M. Cohen, Y. Buhler, et al., 2007: An introduction to the EUMETSAT polar system. Bull. Amer. Meteorol. Soc., 88, 1085–1096, https://doi.org/10.1175/BAMS-88-7-1085.

 

Kalnay, E., M. Kanamitsu, R. Kistler, et al., 1996: The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc., 77, 437–472, https://doi.org/10.1175/1520-0477(1996)077%3C0437:TNYRP%3E2.0.CO;2.

 

Kalnay, E., S. Lord, and R. McPherson, 1998: Maturity of operational numerical weather prediction: the medium range. Bull. Amer. Meteor. Soc., 79, 2753–2769, https://doi.org/10.1175/1520-0477(1998)079%3C2753:MOONWP%3E2.0.CO;2.

 

Koch, S. E., and R. E. Golus, 1988: A mesoscale gravity wave event observed during CCOPE: Part I. Multiscale statistical analyses of wave characteristics. Mon. Wea. Rev., 116, 2527–2544, https://doi.org/10.1175/1520-0493(1988)116%3C2527:AMGWEO%3E2.0.CO;2.

 

Krishnamurti, T. N., H. S. Bedi, W. Heckley, et al., 1988: Reduction of the spinup time for evaporation and precipitation in a spectral model. Mon. Wea. Rev., 116, 907–920, https://doi.org/10.1175/1520-0493(1988)116%3C0907:ROTSTF%3E2.0.CO;2.

 

Krishnamurti, T. N., K. Ingles, S. Cocke, et al., 1984: Details of low latitude medium range numerical weather prediction using a global spectral model. Part II. Effects of orography and physical initialization. J. Meteor. Soc. Japan, 62, 613–649, https://doi.org/10.2151/jmsj1965.62.4_613.

 

Kuo, Y.-H., X. Zou, and Y.-R. Guo, 1996: Variational assimilation of precipitable water using a nonhydrostatic mesoscale adjoint model. Part I: Moisture retrieval and sensitivity experiments. Mon. Wea. Rev., 124, 122–147, https://doi.org/10.1175/1520-0493(1996)124%3C0122:VAOPWU%3E2.0.CO;2.

 

Laloyaux, P., M. Balmaseda, D. Dee, K. Mogensen, and P. Janssen, 2016: A coupled data assimilation system for climate reanalysis. Quart. J. Roy. Meteor. Soc., 142, 65–78, https://doi.org/10.1002/qj.2629.

 

LeDimet, F.-X., and O. Talagrand, 1986: Variational algorithms for analysis and assimilation of meteorological observations: Theoretical aspects. Tellus, 38A, 97–110, https://doi.org/10.3402/tellusa.v38i2.11706.

 

Lin, Y. L., and R. C. Goff, 1988: A study of a mesoscale solitary wave in the atmosphere originating near a region of deep convection. J. Atmos. Sci., 45, 194–205, https://doi.org/10.1175/1520-0469(1988)045%3C0194:ASOAMS%3E2.0.CO;2.

 

Lin, L., and X. Zou, 2021: Associations of hurricane intensity changes to satellite total column ozone structural changes within hurricanes. IEEE Trans. Geo. Remote Sens., 60, 4103407, https://doi.org/10.1109/TGRS.2021.3094107.

 

Liu, H., X. Zou, R. A. Anthes, et al., 2001: The impact of 837 GPS/MET bending angle profiles on assimilation and forecasts for the period June 20–30, 1995. J. Geophy. Res., 106, 31,771–31,786

 

Lorenz, E. N., 1963: Deterministic nonperiodic flow. J. Atmos. Sci., 20, 130–141, https://doi.org/10.1175/1520-0469(1963)020%3C0130:DNF%3E2.0.CO;2.

 

Lorenz, E. N., 1965: A study of the predictability of a 28-variable atmospheric model. Tellus, 17, 321–333, https://doi.org/10.1111/j.2153-3490.1965.tb01424.x.

 

Lynch, P., and X.-Y. Huang, 1992: Initialization of the HIRLAM model using a digital filter. Mon. Wea. Rev., 120, 1019–1034, https://doi.org/10.1175/1520-0493(1992)120%3C1019:IOTHMU%3E2.0.CO;2.

 

Lynch, P., D. Giard, and V. Ivanovici, 1997: Improving the efficiency of a digital filtering scheme for diabatic initialization. Mon. Wea. Rev., 125, 1976–1982, https://doi.org/10.1175/1520-0493(1997)125%3C1976:ITEOAD%3E2.0.CO;2.

 

Ma, Y., and X. Zou, 2013: An introduction to satellite-based microwave humidity sounding data. Adv. Meteor. Sci. Tech., 3, 45–51, https://doi.org/10.3969/j.issn.2095-1973.2013.06.006. (in Chinese)

 

Macdonald, N. J., 1968: The evidence for the existence of Rossby-like waves in the hurricane vortex. Tellus, 20, 138–150, https://doi.org/10.3402/tellusa.v20i1.9993.

 

Machenhauer, B., 1977: On the dynamics of gravity oscillations in a shallow water model, with applications to normal mode initialization. Beitr. Phys. Atmos, 50, 210–215

 

Matsumoto, S., and T. Akiyama, 1970: Mesoscale disturbances and related rainfall cells embedded in the “Baiu Front”, with a proposal on the role of convective momentum transfer. J. Meteor. Soc. Japan, 48, 91–102, https://doi.org/10.2151/jmsj1965.48.2_91.

 
Matsumoto, S., K. Ninomiya, and T. Akiyama, 1968: Mesoscale analytical study on a lined-up cumulus row caused by orographic effect under the winter monsoon situation. J. Meteor. Soc. Japan, 46 , 222–223., https://doi.org/10.2151/jmsj1965.46.3_222.
 
McPeters, R. D., P. K. Bhartia, A. J. Krueger, et al., 1998: Earth Probe Total Ozone Mapping Spectrometer (TOMS) Data Products User’s Guide. NASA Tech. Publ. 1998-206895, USA, Maryland, NASA, 1–60.
 

Miller, D., and F. Sanders, 1980: Mesoscale conditions for the severe convection of 3 April 1974 in the East–Central United States. J. Atmos. Sci., 37, 1041–1055, https://doi.org/10.1175/1520-0469(1980)037%3C1041:MCFTSC%3E2.0.CO;2.

 

Montgomery, M. T., and R. J. Kallenbach, 1997: A theory for vortex Rossby-waves and its application to spiral bands and intensity changes in hurricanes. Quart. J. Roy. Meteor. Soc., 123, 435–465, https://doi.org/10.1002/qj.49712353810.

 
Nappo, C. J., 2002: An Introduction to Atmospheric Gravity Waves. Elsevier Academic Press, 276 pp.
 

Niu, Z., and X. Zou, 2024: Improving all-sky simulations of Typhoon cloud/rain band structures of NOAA-20 CrIS window channel observations. J. Geophys. Res. Atmos., 129, e2023JD040622, https://doi.org/10.1029/2023JD040622.

 

Parrish, D. F., and J. C. Derber, 1992: The National Meteorological Center’s spectral statistical-interpolation analysis system. Mon. Wea. Rev., 120, 1747–1763, https://doi.org/10.1175/1520-0493(1992)120%3C1747:TNMCSS%3E2.0.CO;2.

 

Peng, S. Q., and X. Zou, 2002: Assimilation of NCEP multi-sensor hourly rainfall data using 4D-Var approach: A case study of the squall line on 5 April 1999. Meteor. Atmos. Phy., 81, 237–255, https://doi.org/10.1007/s00703-002-0545-y.

 

Peng, S. and X. Zou, 2010: Impact on QPFs from 4D-Var rainfall data assimilation with a modified digital filter in favor of mesoscale gravity waves: A case study. J. Geophys. Res. Atmos., 115, D23111, https://doi.org/10.1029/2010JD013993.

 

Penny, S. G., and T. M. Hamill, 2017: Coupled data assimilation for integrated earth system analysis and prediction. Bull. Amer. Meteor. Soc., 98, ES169–ES172, https://doi.org/10.1175/BAMS-D-17-0036.1.

 

Purser, R. J., W.-S. Wu, D. F. Parrish, et al., 2003a: Numerical aspects of the application of recursive filters to variational statistical analysis. Part I: Spatially homogeneous and isotropic Gaussian covariances. Mon. Wea. Rev., 131, 1524–1535, https://doi.org/10.1175//1520-0493(2003)131%3C1524:NAOTAO%3E2.0.CO;2.

 

Purser, R. J., W.-S. Wu, D. F. Parrish, et al., 2003b: Numerical aspects of the application of recursive filters to variational statistical analysis. Part II: Spatially inhomogeneous and anisotropic general covariances. Mon. Wea. Rev., 131, 1536–1548, https://doi.org/10.1175//2543.1.

 

Qin, Z., and X. Zou, 2019: Impact of AMSU-A data assimilation over high terrains on QPFs downstream of the Tibetan Plateau. J. Meteor. Soc. Japan, 97, 1137–1154, https://doi.org/10.2151/jmsj.2019-064.

 

Qin, Z., Zou, X., Weng, F., 2012: Comparison between linear and nonlinear trends in NOAA-15 AMSU-A brightness temperatures during 1998–2010. Climate Dyn., 39, 1763–1779, https://doi.org/10.1007/s00382-012-1296-1.

 

Rabier, F., H. Järvinen, E. Klinker, et al., 2000: The ECMWF operational implementation of four-dimensional variational assimilation. I: Experimental results with simplified physics. Quart. J. Roy. Meteor. Soc., 126, 1143–1170, https://doi.org/10.1002/qj.49712656415.

 

Reed, R. J., 1950: The role of vertical motions in ozone-weather relationships. J. Meteorol., 7, 263–267, https://doi.org/10.1175/1520-0469(1950)007%3C0263:TROVMI%3E2.0.CO;2.

 
Richardson, L. F., 1922: Weather Prediction by Numerical Process. Cambridge University Press, 236 pp.
 

Riishøjgaard, L. P., and E. Källén, 1997: On the correlation between ozone and potential vorticity for large-scale Rossby waves. J. Geophys. Res. Atmos., 102, 8793–8804, https://doi.org/10.1029/96JD03059.

 

Saunders, R., Matricardi, M., Brunel, P., 1999: An improved fast radiative transfer model for assimilation of satellite radiance observations. Quart. J. Roy. Meteor. Soc., 125, 1407–1425, https://doi.org/10.1002/qj.1999.49712555615.

 
Saunders, R., J. Hocking, E. Turner, et al., 2018: An update on the RTTOV fast radiative transfer model. Geoscientific Model Development Discussions, 1–32, (currently at version 12) [Software] https://nwp-saf.eumetsat.int/site/software/rttov/#Obtaining_RTTOV.
 

Shuman, F. G., 1989: History of numerical weather prediction at the National Meteorological Center. Wea. Forecasting, 4, 286–296, https://doi.org/10.1175/1520-0434(1989)004%3C0286:HONWPA%3E2.0.CO;2.

 

Sela, J. G., 1980: Spectral modeling at the National Meteorologi-cal Center. Mon. Wea. Rev., 108, 1279–292, https://doi.org/10.1175/1520-0493(1980)108%3C1279:SMATNM%3E2.0.CO;2.

 

Shuman, F. G., and J. B. Hovermale, 1968: An Operational Six-Layer Primitive Equation Model. J. Appl. Meteor., 7, 525–547, https://doi.org/10.1175/1520-0450(1968)007%3C0525:AOSLPE%3E2.0.CO;2.

 

Temperton, C., 1989: Implicit normal mode initialization for spectral models. Mon. Wea. Rev., 117, 436–451, https://doi.org/10.1175/1520-0493(1989)117%3C0436:INMIFS%3E2.0.CO;2.

 

Uccellini, L. W., 1975: A case study of apparent gravity wave initiation of severe convective storms. Mon. Wea. Rev., 103, 497–513, https://doi.org/10.1175/1520-0493(1975)103%3C0497:ACSOAG%3E2.0.CO;2.

 

Uccellini, L. W., and S. E. Koch, 1987: The synoptic setting and possible energy sources for mesoscale wave disturbances. Mon. Wea. Rev., 115, 721–729, https://doi.org/10.1175/1520-0493(1987)115%3C0721:TSSAPE%3E2.0.CO;2.

 

Wu, W., R. J. Purser, and D. F. Parrish, 2002: Three-dimensional variational analysis with spatially inhomogeneous covariances. Mon. Wea. Rev., 130, 2905–2916, https://doi.org/10.1175/1520-0493(2002)130%3C2905:TDVAWS%3E2.0.CO;2.

 

Wu, Y.-H., and X. Zou, 2008: Numerical test of a simple approach for using total ozone data in hurricane environment. Quart. J. Roy. Meteor. Soc., 134, 1397–1408, https://doi.org/10.1002/qj.299.

 

Xiao, Q., X. Zou, and Y.-H. Kuo, 2000: Incorporating the SSM/I-derived precipitable water and rainfall rate into a numerical model: A case study for the ERICA IOP-4 cyclone. Mon. Wea. Rev., 128, 87–108, https://doi.org/10.1175/1520-0493(2000)128%3C0087:ITSIDP%3E2.0.CO;2.

 

Xu, X., and X. Zou, 2020: Estimating GPS RO observation error variances over China using the three-cornered hat method. Quart. J. Roy. Meteor. Soc., 147, 647–659, https://doi.org/10.1002/qj.3938.

 

Zeng, Z., and X. Zou, 2006: Application of principle component analysis to CHAMP radio occultation data for quality control and a diagnostic study. Mon. Wea. Rev., 134, 3263–3282, https://doi.org/10.1175/MWR3233.1.

 

Zhang, L., Y. Liu, J. Gong, et al., 2019: The operational global four-dimensional variational data assimilation system at the China Meteorological Administration. Quart. J. Roy. Meteor. Soc., 145, 1882–1896, https://doi.org/10.1002/qj.3533.

 

Zhang, X., T. S. Quirino, K.-S. Yeh, et al., 2011: HWRFx: Improving hurricane forecast with high-resolution modeling. Comput. Sci. Eng., 13, 13–21, https://doi.org/10.1109/MCSE.2010.121.

 

Zhang, C., X. Zou, and Z. Tan, 2023: Eye and eyewall radii and track of Typhoon Trami (2018) derived from Advanced Himawari Imager (AHI) brightness temperature observations. IEEE Trans. Geosci. Remote Sens., 61, 1–11, https://doi.org/10.1109/TGRS.2023.3302337.

 

Zhu, S., B. Wang, L. Zhang, et al., 2022: A four-dimensional ensemble-variational (4DEnVar) data assimilation system based on GRAPES-GFS: System description and primary tests. J. Adv. Model. Earth Sys., 14, e2021MS002737, https://doi.org/10.1029/2021MS002737.

 
Zhou, L, S.-J. Lin, J.-H. Chen, et al., 2019: Toward convective-scale prediction within the next generation global prediction system. Bull. Amer. Meteor. Soc., 1225–1243, https://doi.org/10.1175/BAMS-D-17-0246.2.
 
Zou, X., 2020: Atmospheric Satellite Observations: Variational Assimilation and Quality Assurance. Academic Press, 324 pp.
 

Zou, X., and Y.-H. Kuo, 1996: Rainfall assimilation through an optimal control of initial and boundary conditions in a limited-area mesoscale model. Mon. Wea. Rev., 124, 2859–2882, https://doi.org/10.1175/1520-0493(1996)124%3C2859:RATAOC%3E2.0.CO;2.

 
Zou, X., and Y.-H. Wu, 2005: On the relationship between TOMS ozone and hurricanes. J. Geophys. Res. Atmos., 110 , D06109, https://doi.org/10.1029/2004JD005019.
 

Zou, X., and Q. Xiao, 2000: Studies on the initialization and simulation of a mature hurricane using a variational bogus data assimilation scheme. J. Atmos. Sci., 57, 836–860, https://doi.org/10.1175/1520-0469(2000)057%3C0836:SOTIAS%3E2.0.CO;2.

 
Zou, X., and Z. Zeng, 2006: A quality control procedure for GPS RO data. J. Geophys. Res. Atmos., 111 , D02112, https://doi.org/10.1029/2005JD005846.
 

Zou, X., Y.-H. Kuo, and Y.-R. Guo, 1995: Assimilation of atmospheric radio refractivity using a nonhydrostatic adjoint mo-del. Mon. Wea. Rev., 123, 2229–2249, https://doi.org/10.1175/1520-0493(1995)123%3C2229:AOARRU%3E2.0.CO;2.

 

Zou, X., H. Liu, and R. A. Anthes, 2002: A statistical estimate of errors in the calculation of radio occultation bending angles caused by a 2D approximation of raytracing and the assumption of spherical symmetry of the atmosphere. J. Atmos. Oceanic Technol., 19, 51–64, https://doi.org/10.1175/1520-0426(2002)019%3C0051:ASEOEI%3E2.0.CO;2.

 

Zou, X., H. Liu, J. Derber, et al., 2001: Four-dimensional variational data assimilation with a full-physics version of the NCEP spectral model: System development and preliminary results. Quart. J. Roy. Meteor. Soc., 127, 1095–1122, https://doi.org/10.1002/qj.49712757321.

 

Zou, X., I. M. Navon, and J. Sela, 1993: Control of gravity oscillations in variational data assimilation. Mon. Wea. Rev., 121, 272–289, https://doi.org/10.1175/1520-0493(1993)121%3C0272:COGOIV%3E2.0.CO;2.

 

Zou, X., Z. Qin, and Y. Zheng, 2015a: Improved tropical storm forecasts with GOES-13/15 imager radiance assimilation and asymmetric vortex initialization in HWRF. Mon. Wea. Rev., 143, 2485–2505, https://doi.org/10.1175/MWR-D-14-00223.1.

 

Zou, X., Z. Qin, and F. Weng, 2017: Impacts from assimilation of one data stream of AMSU-A and MHS radiances on quantitative precipitation forecasts. Quart. J. Roy. Meteor. Soc., 143, 731–743, https://doi.org/10.1002/qj.2960.

 
Zou, X., F. Vandenberghe, M. De Pondeca, et al., 1997: Introduction to adjoint techniques and the MM5 adjoint modeling system. NCAR Technical Note NCAR/TN-435-STR, USA, Boulder, NCAR, 1–122.
 

Zou, X., F. Vandenberghe, B. Wang, et al., 1999: A raytracing operator and its adjoint for the use of GPS/MET refraction angle measurements. J. Geophys. Res. Atmos., 104, 301–318, https://doi.org/10.1029/1999JD900450.

 

Zou, X., F. Weng, V. Tallapragada, et al., 2015b: Satellite data assimilation of upper-level sounding channels in HWRF with two different model tops. J. Meteor. Res., 29, 1–27, https://doi.org/10.1007/s13351-015-4108-9.

 
Zou, X., S. Yang, and P. Ray, 2012: Impacts of ice clouds on GPS radio occultation measurements. J. Atmos. Sci., 69 , 3670–3682, https://doi.org/10.1175/JAS-D-11-0199.1.
 

Zuo, H., M. A. Balmaseda, S. Tietsche, et al., 2019: The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment. Ocean Sci., 15, 779–808, https://doi.org/10.5194/os-15-779-2019.

Journal of Meteorological Research
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Cite this article:
ZOU X. Overview and New Opportunities for Multi-Source Data Assimilation. Journal of Meteorological Research, 2025, 39(1): 1-25. https://doi.org/10.1007/s13351-025-4140-3
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