Articles | Volume 15, issue 19
https://doi.org/10.5194/gmd-15-7397-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-7397-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
05 Oct 2022
Model evaluation paper |
| 05 Oct 2022
| 05 Oct 2022
A preliminary evaluation of FY-4A visible radiance data assimilation by the WRF (ARW v4.1.1)/DART (Manhattan release v9.8.0)-RTTOV (v12.3) system for a tropical storm case
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China
Precision Regional Earth Modeling and Information Center (PREMIC), Nanjing University of Information Science and Technology, Nanjing, China
Yubao Liu
CORRESPONDING AUTHOR
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China
Precision Regional Earth Modeling and Information Center (PREMIC), Nanjing University of Information Science and Technology, Nanjing, China
Zhaoyang Huo
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China
Precision Regional Earth Modeling and Information Center (PREMIC), Nanjing University of Information Science and Technology, Nanjing, China
Yang Li
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China
Precision Regional Earth Modeling and Information Center (PREMIC), Nanjing University of Information Science and Technology, Nanjing, China
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Cited articles
Albers, S., Saleeby, S. M., Kreidenweis, S., Bian, Q., Xian, P., Toth, Z., Ahmadov, R., James, E., and Miller, S. D.: A fast visible-wavelength 3D radiative transfer model for numerical weather prediction visualization and forward modeling, Atmos. Meas. Tech., 13, 3235–3261, https://doi.org/10.5194/amt-13-3235-2020, 2020.
Anderson, J., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and Avellano, A.: The Data Assimilation Research Testbed: A Community Facility, B. Am. Meteorol. Soc., 90, 1283–1296, https://doi.org/10.1175/2009BAMS2618.1, 2009.
Anderson, J. L.: An Ensemble Adjustment Kalman Filter for Data Assimilation, Mon. Weather Rev., 129, 2884–2903, https://doi.org/10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2, 2001.
Anderson, J. L.: An adaptive covariance inflation error correction algorithm for ensemble filters, Tellus A, 59, 210–224, https://doi.org/10.1111/j.1600-0870.2006.00216.x, 2007.
Anderson, J. L.: Spatially and temporally varying adaptive covariance inflation for ensemble filters, Tellus A, 61, 72–83, https://doi.org/10.1111/j.1600-0870.2008.00361.x, 2009 (code available at: https://github.com/NCAR/DART/archive/refs/tags/v9.8.0.tar.gz, last access: 23 November 2019).
Anderson, J. L.: A Non-Gaussian Ensemble Filter Update for Data Assimilation, Mon. Weather Rev., 138, 4186–4198, https://doi.org/10.1175/2010MWR3253.1, 2010.
Anderson, J. L.: Localization and Sampling Error Correction in Ensemble Kalman Filter Data Assimilation, Mon. Weather Rev., 140, 2359–2371, https://doi.org/10.1175/MWR-D-11-00013.1, 2012.
Baren, A. J., Cotton, R., Furtado, K., Havemann, S., Labonnote, L.-C., Marenco, F., Smith, A., and Thelen. J.-C.: A self-consistent scatteringmodel for cirrus. II: The high and low frequencies, Q. J. Roy. Meteor. Soc., 140, 1039–1057, https://doi.org/10.1002/qj.2193, 2014.
Bauer, P., Geer, J. A., Lopez, P., and Salmond, D.: Direct 4D-Var assimilation of all-sky radiances. Part I: Implementation, Q. J. Roy. Meteor. Soc., 136, 1868–1885, https://doi.org/10.1002/qj.659, 2010.
Bauer, P., Ohring, G., Kummerow, C., and Auligne, T.: Assimilating satellite observations of clouds and precipitation into NWP models, B. Am. Meteorol. Soc., 92, ES25–ES28, https://doi.org/10.1175/2011BAMS3182.1, 2011.
Bretherton, C. S. and Park, S.: A New Moist Turbulence Parameterization in the Community Atmosphere Model, J. Clim., 22, 3422–3448, https://doi.org/10.1175/2008JCLI2556.1, 2009.
Buehner, M., Houtekamer, P. L., Charette, C., Mitchell, H. L., and He, B.: Intercomparison of Variational Data Assimilation and the Ensemble Kalman Filter for Global Deterministic NWP. Part I: Description and Single-Observation Experiments, Mon. Weather Rev., 138, 1550–1566, https://doi.org/10.1175/2009MWR3157.1, 2013.
Carminati, F. and Migliorini, S.: All-sky Data Assimilation of MWTS-2 and MWHS-2 in the Met Office Global NWP System, Adv. Atmos. Sci., 38, 1682–1694, https://doi.org/10.1007/s00376-021-1071-5, 2021.
Di, Z., Gong, W., Gan, Y., Shen, C., and Duan, Q.: Combinatorial Optimization for WRF Physical Parameterization Schemes: A Case Study of Three-Day Typhoon Simulations over the Northwest Pacific Ocean, Atmosphere, 10, 233, https://doi.org/10.3390/atmos10050233, 2019.
Dowell, D. C., Wicker, L. J., and Snyder, C.: Ensemble Kalman Filter Assimilation of Radar Observations of the 8 May 2003 Oklahoma City Supercell: Influences of Reflectivity Observations on Storm-Scale Analyses, Mon. Weather Rev., 139, 272–294, https://doi.org/10.1175/2010MWR3438.1, 2011.
Dudhia, J.: A Multi-layer Soil Temperature Model for MM5, Preprints, in: Sixth PSU/NCAR Mesoscale Model Users' Workshop, Boulder, USA, 22–24 July 1996, 49–50, https://www2.mmm.ucar.edu/mm5/lsm/soil.pdf (last access: 23 September 2022), 1996.
Evans, K. F.: SHDOMPPDA: A radiative transfer model for cloudy sky data assimilation, J. Atmos. Sci., 64, 3858–3868, https://doi.org/10.1175/2006JAS2047.1, 2007.
Field, P. R. and Heymsfield, A. J.: Importance of snow to global precipitation, Geophys. Res. Lett., 42, 9512–9520, https://doi.org/10.1002/2015GL065497, 2015.
Gao, J. D., Xue, M., and Stensrud, D. J.: The Development of a Hybrid EnKF-3DVAR Algorithm for Storm-Scale Data Assimilation, Adv. Meteorol., 2013, 512656, https://doi.org/10.1155/2013/512656, 2013.
Geer, A. J., Bauer, P., and O'Dell, C. W.: A revised cloud overlap scheme for fast microwave radiative transfer in rain and cloud, J. Appl. Meteorol. Clim., 48, 2257–2270, https://doi.org/10.1175/2009JAMC2170.1, 2009.
Geer, A. J., Lonitz, K., Weston, P., Kazumori, M., Okamoto, K., Zhu, Y., Liu, H. E., Collard, A., Bell, W., Migliorini, S., Chambon, P., Fourrié, N., Kim, M.-J., Köpken-Watts, C., and Schraff, C.: All-sky satellite data assimilation at operational weather forecasting centres, Q. J. Roy. Meteor. Soc., 144, 1191–1217, https://doi.org/10.1002/qj.3202, 2017.
Geer, A. J., Migliorini, S., and Matricardi, M.: All-sky assimilation of infrared radiances sensitive to mid- and upper-tropospheric moisture and cloud, Atmos. Meas. Tech., 12, 4903–4929, https://doi.org/10.5194/amt-12-4903-2019, 2019.
Hasselmann, K., Barnett, T. P., Bouws, E., Carlson, H., Cartwright, D. E., Enke, K., Ewing, J. A., Gienapp, H., Hasselmann, D. E., Kruseman, P., Meerburg, A., Müller, P., Olbers, D. J., Richter, K., Sell, W., and Walden, H.: Measurements of wind-wave growth and swell during the Joint North Sea Wave Project (JONSWAP), Dtsch. Hydrogr. Z., 8, 1–95, http://resolver.tudelft.nl/uuid:f204e188-13b9-49d8-a6dc-4fb7c20562fc (last access: 23 September 2022), 1973.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1959 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2018a.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1959 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2018b.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., J. Hogan, R., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hu, J., Fu, Y., Zhang, P., Min, Q., Gao, Z., Wu, S., and Li, R.: Satellite Retrieval of Microwave Land Surface Emissivity under Clear and Cloudy Skies in China Using Observations from AMSR-E and MODIS, Remote Sens., 13, 3980, https://doi.org/10.3390/rs13193980, 2021.
Hu, X., Ge, J., Li, W., Du, J., Li, Q., and Mu, Q.: Vertical structure of tropical deep convective systems at different life stages from CloudSat observations, J. Geophys. Res., 126, e2021JD035115, https://doi.org/10.1029/2021JD035115, 2021.
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models, J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008.
Jiménez, A., P., Dudhia, J., González-Rouco, J. F., Navarro, J., Montávez, P. J., and García-Bustamante, E.: A Revised Scheme for the WRF Surface Layer Formulation, Mon. Weather Rev., 140, 898–918, https://doi.org/10.1175/MWR-D-11-00056.1, 2012.
Kanji, A. J., Ladino, L. A., Wex, H., Boose, Y., Burkert-Hohn, M., Cziczo, D. J., and Krämer, M.:Overview of Ice Nucleating Particles, Meteor. Monographs, 58, 1.1–1.33, https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0006.1, 2017.
Keat, W. J., Stein, T. H. M., Phaduli, E., Landman, S., Becker, E., Bopape, M.-J. M., Hanley, K. E., Lean, H. W., and Webster, S.: Convective initiation and stormlife cycles in convection-permitting simulations of the Met Office Unified Model over South Africa, Q. J. Roy. Meteor. Soc., 145, 1323–1336, https://doi.org/10.1002/qj.3487, 2019.
Korolev, A., McFarquhar, G., Field, P. R., Franklin, C., Lawson, P., Wang, Z., Williams, E., Abel, S. J., Axisa, D., Borrmann, S., Crosier, J., Fugal, J., Krämer, M., Lohmann, U., Schlenczek, O., Schnaiter, M., and Wendisch, M.: Mixed-Phase Clouds: Progress and Challenges, Meteor. Monographs, 58, 5.1–5.50, https://doi.org/10.1175/AMSMONOGRAPHS-D-17-0001.1, 2017.
Kostka, P. M., Weissmann, M., Buras, R., Mayer, B., and Stiller, O.: Observation operator for visible and near-infrared satellite reflectances, J. Atmos. Ocean. Tech., 31, 1216–1233, https://doi.org/10.1175/JTECH-D-13-00116.1, 2014.
Kubar, L. T. and Hartmann, D. L.: Vertical structure of tropical oceanic convective clouds and its relation to precipitation, Geophys. Res. Lett., 35, L03804, https://doi.org/10.1029/2007GL032811, 2008.
Kurzrock, F., Nguyen, H., Sauer, J., Chane Ming, F., Cros, S., Smith Jr., W. L., Minnis, P., Palikonda, R., Jones, T. A., Lallemand, C., Linguet, L., and Lajoie, G.: Evaluation of WRF-DART (ARW v3.9.1.1 and DART Manhattan release) multiphase cloud water path assimilation for short-term solar irradiance forecasting in a tropical environment, Geosci. Model Dev., 12, 3939–3954, https://doi.org/10.5194/gmd-12-3939-2019, 2019.
Lawson, W. G. and Hansen, J. A.: Implications of Stochastic and Deterministic Filters as Ensemble-Based Data Assimilation Methods in Varying Regimes of Error Growth, Mon. Weather Rev., 132, 1966–1981, https://doi.org/10.1175/1520-0493(2004)132<1966:IOSADF>2.0.CO;2, 2004.
Lei, J., Bikel, P., and Snyder, C.: Comparison of Ensemble Kalman Filters under Non-Gaussianity, Mon. Weather Rev., 138, 1293–1306, https://doi.org/10.1175/2009MWR3133.1, 2010.
Lei, L., Anderson, J. L., and Romine, G. S.: Empirical Localization Functions for Ensemble Kalman Filter Data Assimilation in Regions with and without Precipitation, Mon. Weather Rev., 143, 3664–3679, https://doi.org/10.1175/MWR-D-14-00415.1, 2015.
Li, J., Geer, J. A., Okamoto, K., Otkin. A. J., Liu, Z., Han, W., and Wang, P.: Satellite All-sky Infrared Radiance Assimilation: Recent Progress and Future Perspectives, Adv. Atmos. Sci., 39, 9–21, https://doi.org/10.1007/s00376-021-1088-9, 2022.
Ma, Z., Maddy, E. S., Zhang, B., Zhu, T., and Boukabara, S. A.: Impact Assessment of Himawari-8 AHI Data Assimilation in NCEP GDAS/GFS with GSI, J. Atmos. Ocean. Tech., 34, 797–815, https://doi.org/10.1175/JTECH-D-16-0136.1, 2017.
Matricardi, M.: The generation of RTTOV regression coefficients for IASI and AIRS using a new profile training set and a new line-by-line database, ECMWF, Technical Memorandum, 564, 47 pp., https://doi.org/10.21957/59u3oc9es, 2008.
Mayer, B. and Kylling, A.: Technical note: The libRadtran software package for radiative transfer calculations - description and examples of use, Atmos. Chem. Phys., 5, 1855–1877, https://doi.org/10.5194/acp-5-1855-2005, 2005.
McCarty, W., Jedlovec, G., and Timothy L. M.: Impact of the assimilation of Atmospheric Infrared Sounder radiance measurements on short-term weather forecasts, J. Geophys. Res., 144, D18122, https://doi.org/10.1029/2008JD011626, 2009.
Migliorini, S. and Candy, B.: All-sky satellite data assimilation of microwave temperature sounding channels at the Met Office, Q. J. Roy. Meteor. Soc., 145, 867–883, https://doi.org/10.1002/qj.3470, 2019.
Mittermaier, M., Roberts, N., and Thompson, S. A.: A long-term assessment of precipitation forecast skill using the Fractions Skill Score, Meteorol. Appl., 20, 176–186, https://doi.org/10.1002/met.296, 2013.
Mülmenstädt, J., Sourdeval, O., Delanoë, J., and Quaas, J.: Frequency of occurrence of rain from liquid-, mixed-, and ice-phase clouds derived from A-Train satellite retrievals, Geophys. Res. Lett., 42, 6502–6509, https://doi.org/10.1002/2015GL064604, 2015.
Nakajima T. and King, M. D.: Determination of the optical thickness and effective particle radius of clouds from reflected solar radiation measurements. Part I: Theory, J. Atmos. Sci., 47, 1878–1893, https://doi.org/10.1175/1520-0469(1990)047<1878:DOTOTA>2.0.CO;2, 1990.
National Centers for Environmental Prediction, National Weather Service, NOAA, and U.S. Department of Commerce: NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/D6M043C6, 2000.
Pinheiro, F. R., van Leeuwen, P. J., and Geppert, G.: Efficient nonlinear data assimilation using synchronization in a particle filter, Q. J. Roy. Meteor. Soc., 145, 2510–2523, https://doi.org/10.1002/qj.3576, 2019.
Polkinghorne, R. and Vukicevic, T.: Data assimilation of cloud-affected radiances in a cloud-resolving model, Mon. Weather Rev., 139, 755–773, https://doi.org/10.1175/2010MWR3360.1, 2011.
Poterjoy, J.: A Localized Particle Filter for High-Dimensional Nonlinear Systems, Mon. Weather Rev., 144, 59–76, https://doi.org/10.1175/MWR-D-15-0163.1, 2016.
Prates, C., Migliorini, S., English, S., and Pavelinc, E.: Assimilation of satellite infrared sounding measurements in the presence of heterogeneous cloud fields, Q. J. Roy. Meteor. Soc., 140, 2062–2077, https://doi.org/10.1002/qj.2279, 2014.
Saunders, R., Hocking, J., Turner, E., Rayer, P., Rundle, D., Brunel, P., Vidot, J., Roquet, P., Matricardi, M., Geer, A., Bormann, N., and Lupu, C.: An update on the RTTOV fast radiative transfer model (currently at version 12), Geosci. Model Dev., 11, 2717–2737, https://doi.org/10.5194/gmd-11-2717-2018, 2018 (code available at: https://nwp-saf.eumetsat.int/site/software/rttov/rttov-v12/, last access: 5 March 2019).
Scheck, L.: A neural network based forward operator for visible satellite images and its adjoint, J. Quant. Spectrosc. Ra., 274, 107841, https://doi.org/10.1016/j.jqsrt.2021.107841, 2021.
Scheck, L., Frèrebeau, P., Buras-Schnell, R., and Mayer, B.: A fast radiative transfer method for the simulation of visible satellite imagery, J. Quant. Spectrosc. Ra., 175, 54–67, https://doi.org/10.1016/j.jqsrt.2016.02.008, 2016a.
Scheck, L., Hocking, J., and Saunders, R.: A comparison of MFASIS and RTTOV-DOM, Report of Visiting Scientist mission NWP_VS16_01 (Document ID, NWPSAF-MO-VS-054), EUMETSAT, https://nwpsaf.eu/vs_reports/nwpsaf-mo-vs-054.pdf (last access: 22 September 2022), 2016b.
Scheck, L., Weissmann, M., and Bernhard, M.: Efficient Methods to Account for Cloud-Top Inclination and Cloud Overlap in Synthetic Visible Satellite Images, J. Atmos. Ocean. Tech., 35, 665–685, https://doi.org/10.1175/JTECH-D-17-0057.1, 2018.
Scheck, L., Weissmann, M., and Bach, L.: Assimilating visible satellite images for convective-scale numerical weather prediction: A case-study, Q. J. Roy. Meteor. Soc., 146, 3165–3186, https://doi.org/10.1002/qj.3840, 2020.
Schröttle, J., Weissmann, M., Scheck, L., and Hutt, A.: Assimilating Visible and Infrared Radiances in Idealized Simulations of Deep Convection, Mon. Weather Rev., 148, 4357–4375, https://doi.org/10.1175/MWR-D-20-0002.1, 2020.
Shen, F., Xu, D., Min, J., Chu, Z., and Li, X.: Assimilation of radar radial velocity data with the WRF hybrid 4DEnVar system for the prediction of hurricane Ike (2008), Atmos. Res., 234, 104771, https://doi.org/10.1016/j.atmosres.2019.104771, 2020.
Shen, Z. and Tang, Y.: A modified ensemble Kalman particle filter for non-Gaussian systems with nonlinear measurement functions, J. Adv. Model. Earth Sy., 7, 50–66, https://doi.org/10.1002/2014MS000373, 2015.
Short, C. J. and Petch, J.: Reducing the spin-up of a regional NWP system without data assimilation, Q. J. Roy. Meteor. Soc., 148, 1623–1643, https://doi.org/10.1002/qj.4268, 2022.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., Wang, X.-Y., Wang, W., and Power, J. G.: A Description of the Advanced Research WRF Version 3 (No. NCAR/TN-475+STR), University Corporation for Atmospheric Research, https://doi.org/10.5065/D68S4MVH, 2008 (code available at: https://github.com/wrf-model/WRF/archive/refs/tags/v4.1.1.tar.gz, last access: 22 June 2019).
Stengel, M., Lindskog, M., Undén, P., and Gustafsson, N.: The impact of cloud-affected IR radiances on forecast accuracy of a limited-area NWP model, Q. J. Roy. Meteor. Soc., 139, 2081–2096, https://doi.org/10.1002/qj.2102, 2013.
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: implementation of a new snow parameterization, Mon. Weather Rev., 136, 5095–5115, https://doi.org/10.1175/1520-0493(2004)132<0519:EFOWPU>2.0.CO;2, 2008.
Tiedtke, M.: A comprehensive mass flux scheme for cumulus parameterization in large-scale models, Mon. Weather Rev., 117, 1779–1800, https://doi.org/10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2, 1989.
Vidot, J. and Borbás, É.: Land surface VIS/NIR BRDF atlas for RTTOV-11: model and validation against SEVIRI land SAF albedo product, Q. J. Roy. Meteor. Soc., 140, 2186–2196, https://doi.org/10.1002/qj.2288, 2014.
Vidot, J., Brunel, P., Dumont, M., Carmagnola, C., and Hocking J.: The VIS/NIR Land and Snow BRDF Atlas for RTTOV: Comparison between MODIS MCD43C1 C5 and C6, Remote Sens., 10, 21, https://doi.org/10.3390/rs10010021, 2018.
Vukicevic, T., Greenwald, T., Zupanski, M., Zupanski, D., Vondar Harr, T., and Jones, A. S.: Mesoscale cloud state estimation from visible and infrared satellite radiance. Mon. Weather Rev., 132, 3066–3077, https://doi.org/10.1175/MWR2837.1, 2004.
Várnai T. and Marshak A.: Statistical Analysis of the Uncertainties in Cloud Optical Depth Retrievals Caused by Three-Dimensional Radiative Effects, J. Atmos. Sci., 58, 1540–1548, https://doi.org/10.1175/1520-0469(2001)058<1540:SAOTUI>2.0.CO;2, 2001.
White, A. T., Pour-Biazar, A., Doty, K., Dornblaser, B., and McNider, R. T.: Improving Cloud Simulation for Air Quality Studies through Assimilation of Geostationary Satellite Observations in Retrospective Meteorological Modeling, Mon. Weather Rev., 146, 29–48, https://doi.org/10.1175/MWR-D-17-0139.1, 2018.
Xu, D., Min, J., Shen, F., Ban, J., and Chen, P.: Assimilation of MWHS radiance data from the FY-3B satellite with the WRF Hybrid-3DVAR system for the forecasting of binary typhoons, J. Adv. Model. Earch Sy., 8, 1014–1028, https://doi.org/10.1002/2016MS000674, 2016.
Xu, K.-M. and Randall, A. D.: A Semiempirical Cloudiness Parameterization for Use in Climate Models, J. Atmos. Sci., 53, 3084–3102, https://doi.org/10.1175/1520-0469(1996)053<3084:ASCPFU>2.0.CO;2, 1996.
Xue, J. S.: Scientific issues and perspective of assimilation of meteorological satellite data, Acta Meteorol. Sin., 67, 903–911, https://doi.org/10.11676/qxxb2009.088, 2009 (in Chinese with English abstract).
Yan, Y. and Liu, Y.: Vertical Structures of Convective and Stratiform Clouds in Boreal Summer over the Tibetan Plateau and Its Neighboring Regions, Adv. Atmos. Sci., 36, 1089–1102, https://doi.org/10.1007/s00376-019-8229-4, 2019.
Yang, C., Liu, Z., Bresch, J., Rizvi, S. R. H., Huang, X.-Y., and Min, J.: AMSR2 all-sky radiance assimilation and its impact on the analysis and forecast of Hurricane Sandy with a limited-area data assimilation system, Tellus A, 68, 30917, https://doi.org/10.3402/tellusa.v68.30917, 2016.
Yang, J., Zhang, Z., Wei, C., Lu, F., and Guo, Q.: Introducing the New Generation of Chinese Geostationary Weather Satellites, Fengyun-4, B. Am. Meteorol. Soc., 98, 1737–1658, https://doi.org/10.1175/BAMS-D-16-0065.1, 2017.
Zhang, A. and Fu. Y.: Life Cycle Effects on the Vertical Structure of Precipitation in East China Measured by Himawari-8 and GPM DPR, Mon. Weather Rev., 146, 2183–2199, https://doi.org/10.1175/MWR-D-18-0085.1, 2018.
Zhang, C., Wang, Y., and Hamilton, K.: Improved Representation of Boundary Layer Clouds over the Southeast Pacific in ARW-WRF Using a Modified Tiedtke Cumulus Parameterization Scheme, Mon. Weather Rev., 139, 3489–3513, https://doi.org/10.1175/MWR-D-10-05091.1, 2011.
Zhang, M., Zupanski, M., Kim, M.-J., and Knaff, J. A.: Assimilating AMSU-A Radiances in the TC Core Area with NOAA Operational HWRF (2011) and a Hybrid Data Assimilation System: Danielle (2010), Mon. Weather Rev., 141, 3889–2907, https://doi.org/10.1175/MWR-D-12-00340.1, 2013.
Zhang, P., Zhu, L., Tang, S., Gao, L., Chen, L., Zheng, W., Han, X., Chen, J., and Shao, J.: General Comparison of FY-4A/AGRI With Other GEO/LEO Instruments and Its Potential and Challenges in Non-meteorological Applications, Front. Earth Sci., 6, 224, https://doi.org/10.3389/feart.2018.00224, 2019.
Zhang, T., Sun, J., and Yang, L.: A Numerical Study of Effects of Radiation on Deep Convective Warm Based Cumulus Cloud Development with a 3-D Radiative Transfer Model, Atmosphere, 11, 1187, https://doi.org/10.3390/atmos11111187, 2020.
Zhou, Y. B., Liu, Y. B., and Liu, C.: A machine learning-based method to account for 3D Short-Wave radiative effects in 1D satellite observation operators, J. Quant. Spectrosc. Ra., 275, 107891, https://doi.org/10.1016/j.jqsrt.2021.107891, 2021.
Zhou, Y. B., Liu, Y. B., Huo, Z. Y., and Li, Y.: WRF-DART/RTTOV input and (processed) output files for GMD-2022-30, Zenodo [data set], https://doi.org/10.5281/zenodo.7028828, 2022.
Zhu, Y., Liu, E., Mahajan, R., Thomas, C., Groff, D., Van Delst, P., Collard, A., Treadon, R., and Derber, C. J.: All-Sky Microwave Radiance Assimilation in NCEP's GSI Analysis System, Mon. Weather Rev., 144, 4709–4735, https://doi.org/10.1175/MWR-D-15-0445.1, 2016.
Short summary
The study evaluates the performance of the Data Assimilation Research Testbed (DART), equipped with the recently added forward operator Radiative Transfer for TOVS (RTTOV), in assimilating FY-4A visible images into the Weather Research and Forecasting (WRF) model. The ability of the WRF-DART/RTTOV system to improve the forecasting skills for a tropical storm over East Asia and the Western Pacific is demonstrated in an Observing System Simulation Experiment framework.
The study evaluates the performance of the Data Assimilation Research Testbed (DART), equipped...
