Auligné, T., A. P. McNally, and D. P. Dee, 2007: Adaptive bias correction for satellite data in a numerical weather prediction system. Quart. J. Roy. Meteor. Soc., 133, 631−642, https://doi.org/10.1002/qj.56.
Bénard, P., J. Vivoda, J. Mašek, K. Smolíková, P. Yessad, C. Smith, R. Brožková, and J.-F. Geleyn, 2010: Dynamical kernel of the Aladin-NH spectral limited-area model: Revised formulation and sensitivity experiments. Quart. J. Roy. Meteor. Soc., 136, 155−169, https://doi.org/10.1002/qj.522.
Bengtsson, L., and Coauthors, 2017: The Harmonie-AROME model configuration in the ALADIN-HIRLAM NWP system. Mon. Wea. Rev., 145, 1919−1935, https://doi.org/10.1175/MWR-D-16-0417.1.
Berre, L., 2000: Estimation of synoptic and mesoscale forecast error covariances in a limited-area model. Mon. Wea. Rev., 128, 664−667, https://doi.org/10.1175/1520-0493(2000)128<0644:EOSAMF>2.0.CO;2.
Bonsignori, R., 2007: The microwave humidity sounder (MHS): In-orbit performance assessment. Proc. SPIE 6744, Sensors, Systems, and Next-Generation Satellites XI, Florence, Italy, SPIE, 67440A, https: //doi.org/10.1117/12.737986.
Bormann, N., and P. Bauer, 2010: Estimates of spatial and interchannel observation-error characteristics for current sounder radiances for numerical weather prediction. I: Methods and application to ATOVS data. Quart. J. Roy. Meteor. Soc., 136, 1036−1050, https://doi.org/10.1002/qj.616.
Brousseau, P., L. Berre, F. Bouttier, and G. Desroziers, 2012: Flow-dependent background-error covariances for a convective-scale data assimilation system. Quart. J. Roy. Meteor. Soc., 138, 310−322, https://doi.org/10.1002/qj.920.
Bubnová, R., G. Hello, P. Bénard, and J.-F. Geleyn, 1995: Integration of the fully elastic equations cast in the hydrostatic pressure terrain-following coordinate in the framework of the ARPEGE/ALADIN NWP system. Mon. Wea. Rev., 123, 515−535, https://doi.org/10.1175/1520-0493(1995)123<0515:IOTFEE>2.0.CO;2.
Carminati, F., N. Atkinson, B. Candy, and Q. F. Lu, 2020: Insights into the microwave instruments onboard the Fengyun-3D satellite: Data quality and assimilation in the met office NWP system. Adv. Atmos. Sci., https://doi.org/10.1007/s00376-020-0010-1.
Chapnik, B., G. Desroziers, F. Rabier, and O. Talagrand, 2006: Diagnosis and tuning of observational error in a quasi-operational data assimilation setting. Quart. J. Roy. Meteor. Soc., 132, 543−565, https://doi.org/10.1256/qj.04.102.
de Rooy, W. C., and A. P. Siebesma, 2008: A simple parameterization for detrainment in shallow cumulus. Mon. Wea. Rev., 136, 560−576, https://doi.org/10.1175/2007MWR2201.1.
Dee, D. P., 2005: Bias and data assimilation. Quart. J. Roy. Meteor. Soc., 131, 3323−3343, https://doi.org/10.1256/qj.05.137.
English, S. J., 2008: The importance of accurate skin temperature in assimilating radiances from satellite sounding instruments. IEEE Trans. Geosci. Remote Sens., 46, 403−408, https://doi.org/10.1109/TGRS.2007.902413.
ESA, 2021: Arctic weather satellite. [Available online at http://www.esa.int/Applications/Observing_the_Earth/Meteorological_missions/Arctic_Weather_Satellite.]
Fischer, C., T. Montmerle, L. Berre, L. Auger, and S. E. Ştefănescu, 2005: An overview of the variational assimilation in the ALADIN/France numerical weather-prediction system. Quart. J. Roy. Meteor. Soc., 131, 3477−3492, https://doi.org/10.1256/qj.05.115.
Fouquart, Y., and B. Bonnel, 1980: Computation of solar heating of the earth’s atmosphere: A new parameterization. Beitr. Phys. Atmos., 53, 35−62.
Frolov, S., W. Campbell, B. Ruston, C. H. Bishop, D. Kuhl, M. Flatau, and J. McLay, 2020: Assimilation of low-peaking satellite observations using the coupled interface framework. Mon. Wea. Rev., 148, 637−654, https://doi.org/10.1175/MWR-D-19-0029.1.
Geer, A. J., and Coauthors, 2017: The growing impact of satellite observations sensitive to humidity, cloud and precipitation. Quart. J. Roy. Meteor. Soc., 143, 3189−3206, https://doi.org/10.1002/qj.3172.
Geer, A. J., and Coauthors, 2018: All-sky satellite data assimilation at operational weather forecasting centres. Quart. J. Roy. Meteor. Soc., 144, 1191−1217, https://doi.org/10.1002/qj.3202.
Giard, D., and E. Bazile, 2000: Implementation of a new assimilation scheme for soil and surface variables in a global NWP model. Mon. Wea. Rev., 128, 997−1015, https://doi.org/10.1175/1520-0493(2000)128<0997: IOANAS>2.0.CO;2.
Goldberg, M. D., and F. Z. Weng, 2006: Advanced technology microwave sounder. Earth Science Satellite Remote Sensing, J. J. Qu et al., Eds. Springer, 243−253, https: //doi.org/10.1007/528978-3-540-37293-6-13.
Gustafsson, N., X.-Y. Huang, X.-H. Yang, K. Mogensen, M. Lindskog, O. Vignes, T. Wilhelmsson, and S. Thorsteinsson, 2012: Four-dimensional variational data assimilation for a limited area model. Tellus A: Dynamic Meteorology and Oceanography, 64, 14985, https://doi.org/10.3402/tellusa.v64i0.14985.
Gustafsson, N., J. Bojarova, and O. Vignes, 2014: A hybrid variational ensemble data assimilation for the high resolution limited area model (HIRLAM). Nonlinear Processes in Geophysics, 21, 303−323, https://doi.org/10.5194/npg-21-303-2014.
Bonavita, M., L. Isaksen, and E. Hólm, 2012: On the use of EDA background error variances in the ECMWF 4D‐Var. Quarterly journal of the royal meteorological society 138(667), 1540−1559, https://doi.org/10.1002/qj.1899.
Jiang, L. P., C. X. Shi, T. Zhang, Y. Guo, and S. Yao, 2020: Evaluation of assimilating FY-3C MWHS-2 radiances using the GSI global analysis system. Remote Sensing, 12, 2511, https://doi.org/10.3390/rs12162511.
Karbou, F., C. Prigent, L. Eymard, and J. R. Pardo, 2005: Microwave land emissivity calculations using AMSU measurements. IEEE Trans. Geosci. Remote Sens., 43(5), 948−959, https://doi.org/10.1109/TGRS.2004.837503.
Klaes, K. D., and Coauthors, 2007: An introduction to the EUMETSAT Polar system. Bull. Amer. Meteor. Soc., 88(7), 1085−1096, https://doi.org/10.1175/BAMS-88-7-1085.
Lawrence, H., N. Bormann, A. J. Geer, Q. F. Lu, and S. J. English, 2018: Evaluation and assimilation of the microwave sounder MWHS-2 onboard FY-3C in the ECMWF numerical weather prediction system. IEEE Trans. Geosci. Remote Sens., 56, 3333−3349, https://doi.org/10.1109/TGRS.2018.2798292.
Lenderink, G., and A. A. M. Holtslag, 2004: An updated length-scale formulation for turbulent mixing in clear and cloudy boundary layers. Quar. J. Roy. Meteor. Soc., 130, 3405−3427, https://doi.org/10.1256/qj.03.117.
Li, J., and G. Q. Liu, 2016: Direct assimilation of Chinese FY-3C microwave temperature sounder-2 radiances in the global GRAPES system. Atmospheric Measurement Techniques, 9, 3095−3113, https://doi.org/10.5194/amt-9-3095-2016.
Lorenc, A. C., 1986: Analysis methods for numerical weather prediction. Quart. J. Roy. Meteor. Soc., 112, 1177−1194, https://doi.org/10.1002/qj.49711247414rg/10.1256/qj.03.117.
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.
Masson, V., and Coauthors, 2013: The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes. Geoscientific Model Development, 6, 929−960, https://doi.org/10.5194/gmd-6-929-2013.
Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102, 16 663−16 682, https://doi.org/10.1029/97JD00237.
Müller, M., and Coauthors, 2017: AROME-MetCoOP: A Nordic convective-scale operational weather prediction model. Wea. Forecasting, 32, 609−627, https://doi.org/10.1175/WAF-D-16-0099.1.
Neggers, R. A. J., M. Köhler, and A. C. M. Beljaars, 2009: A dual mass flux framework for boundary layer convection. Part I: Transport. J. Atmos. Sci., 66, 1465−1487, https://doi.org/10.1175/2008JAS2635.1.
Randriamampianina, R., 2006: Impact of high resolution observations in the ALADIN/HU model. Időjárás, 110, 329−349.
Randriamampianina, R., T. Iversen, and A. Storto, 2011: Exploring the assimilation of IASI radiances in forecasting polar lows. Quart. J. Roy. Meteor. Soc., 137, 1700−1715, https://doi.org/10.1002/qj.838.
Saunders, R., and Coauthors, 2018: An update on the RTTOV fast radiative transfer model (currently at version 12). Geoscientific Model Development, 11, 2717−2737, https://doi.org/10.5194/gmd-11-2717-2018.
Saunders, R. W., 1993: Note on the advanced microwave sounding unit. Bull. Amer. Meteor. Soc., 74(11), 2211−2212, https://doi.org/10.1175/1520-0477-74.11.2211.
Schwartz, C. S., Z. Q. Liu, Y. S. Chen, and X.-Y. Huang, 2012: Impact of assimilating microwave radiances with a limited-area ensemble data assimilation system on forecasts of typhoon morakot. Wea. Forecasting, 27, 424−437, https://doi.org/10.1175/WAF-D-11-00033.1.
Seity, Y., P. Brousseau, S. Malardel, G. Hello, P. Bénard, F. Bouttier, C. Lac, and V. Masson, 2011: The AROME-France convective-scale operational model. Mon. Wea. Rev., 139, 976−991, https://doi.org/10.1175/2010MWR3425.1.
Storto, A., and R. Randriamampianina, 2010: The relative impact of meteorological observations in the norwegian regional model as determined using an energy norm-based approach. Atmospheric Science Letters, 11, 51−58, https://doi.org/10.1002/asl.257.
Taillefer, F., 2002: CANARI—technical documentation—based on ARPEGE cycle CY25T1 (AL25T1 for ALADIN). Météo-France. Available from https://netfam.fmi.fi/HMS07/canaridoc.pdf.
Xu, D. M., J. Z. Min, F. F. Shen, J. M. Ban, and P. Chen, 2016: Assimilation of MWHS radiance data from the FY-3B satellite with the WRF hybrid-3DVAR system for the forecasting of binary typhoons. Journal of Advances in Modeling Earth Systems, 8, 1014−1028, https://doi.org/10.1002/2016MS000674.
Zhang, P., and Coauthors, 2019: Latest progress of the Chinese meteorological satellite program and core data processing technologies. Adv. Atmos. Sci., 36, 1027−1045, https://doi.org/10.1007/s00376-019-8215-x.
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.