Assimilating All-sky Infrared Radiances from Himawari-8 Using the 3DVar Method for the Prediction of a Severe Storm over North China
Tóm tắt
Although radar observations capture storm structures with high spatiotemporal resolutions, they are limited within the storm region after the precipitation formed. Geostationary satellites data cover the gaps in the radar network prior to the formation of the precipitation for the storms and their environment. The study explores the effects of assimilating the water vapor channel radiances from Himawari-8 data with Weather Research and Forecasting model data assimilation system (WRFDA) for a severe storm case over north China. A fast cloud detection scheme for Advanced Himawari imager (AHI) radiance is enhanced in the framework of the WRFDA system initially in this study. The bias corrections, the cloud detection for the clear-sky AHI radiance, and the observation error modeling for cloudy radiance are conducted before the data assimilation. All AHI radiance observations are fully applied without any quality control for all-sky AHI radiance data assimilation. Results show that the simulated all-sky AHI radiance fits the observations better by using the cloud dependent observation error model, further improving the cloud heights. The all-sky AHI radiance assimilation adjusts all types of hydrometeor variables, especially cloud water and precipitation snow. It is proven that assimilating all-sky AHI data improves hydrometeor specifications when verified against the radar reflectivity. Consequently, the assimilation of AHI observations under the all-sky condition has an overall improved impact on both the precipitation locations and intensity compared to the experiment with only conventional and AHI clear-sky radiance data.
Tài liệu tham khảo
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.
Barker, D., and Coauthors, 2012: The weather research and forecasting model’s community variational/ensemble data assimilation system: WRFDA. Bull. Amer. Meteor. Soc. 93, 831–843, https://doi.org/10.1175/BAMS-D-11-00167.1.
Barker, D. M, W. Huang, Y. R. Guo, A. J. Bourgeois, and Q. N. Xiao, 2004: A three-dimensional variational data assimilation system for mm5: Implementation and initial results. Mon. Wea. Rev. 132, 897–914, https://doi.org/10.1175/1520-0493(2004)132<0897:ATVDAS>2.0.CO;2.
Bessho, K., and Coauthors, 2016: An introduction to Himawari-8/9- Japan’s new-generation geostationary meteorological satellites. J. Meteor. Soc. Japan 94, 151–183, https://doi.org/10.2151/jmsj.2016-009.
Buehner, M, J. Morneau, and C. Charette, 2013: Four-dimensional ensemble-variational data assimilation for global deterministic weather prediction. Nonlinear Processes in Geophysics 20, 669–682, https://doi.org/10.5194/npg-20-669-2013.
Chen, Y. D, X. Xia, J. Z. Min, X.-Y. Huang, and S. R. H. Rizvi, 2016: Balance characteristics of multivariate background error covariance for rainy and dry seasons and their impact on precipitation forecasts of two rainfall events. Meteorol. Atmos. Phys. 128, 579–600, https://doi.org/10.1007/s00703-016-0434-4.
Chen, Y. D, J. Wang, Y. F. Gao, X. M. Chen, H. L. Wang, and X.-Y. Huang, 2018: Refinement of the use of inhomogeneous background error covariance estimated from historical forecast error samples and its impact on short-term regional numerical weather prediction. J. Meteor. Soc. Japan 96, 429–446, https://doi.org/10.2151/jmsj.2018-048.
Chevallier, F, and G. Kelly, 2002: Model clouds as seen from space: Comparison with geostationary imagery in the 11-μm window channel. Mon. Wea. Rev. 130, 712–722, https://doi.org/10.1175/1520-0493(2002)130<0712:MCASFS>2.0.CO;2.
Chevallier, F, P. Bauer, G. Kelly, C. Jakob, and T. McNally, 2001: Model clouds over oceans as seen from space: Comparison with HIRS/2 and MSU radiances. J. Climate 14, 4216–4229, https://doi.org/10.1175/1520-0442(2001)014<4216:MCOOAS>2.0.CO;2.
Dee, D. P, and S. Uppala, 2009: Variational bias correction of satellite radiance data in the ERA-interim reanalysis. Quart. J. Roy. Meteor. Soc., 135, 1830–1841, https://doi.org/10.1002/qj.493.
Descombes, G, T. Auligné, F. Vandenberghe, D. Barker, and J. Barré, 2014: Generalized background error covariance matrix model (GEN_BE v2.0). Geoscientific Model Development Discussions 7, 4291–4352, https://doi.org/10.5194/gmdd-7-4291-2014.
Garcia-Reynoso, A, and M. A. Mora-Ramirez, 2017: Implementation of the unified post processor (UPP) and the model evaluation tools (MET) for WRF-CHEM evaluation performance. Atmósfera 30, 259–273, https://doi.org/10.20937/atm.2017.30.03.06.
Geer, A. J, and P. Bauer, 2011: Observation errors in all-sky data assimilation. Quart. J. Roy. Meteor. Soc. 137, 2024–2037, https://doi.org/10.1002/qj.830.
Geer, A. J, S. Migliorini, and M. Matricardi, 2019: All-sky assimilation of infrared radiances sensitive to mid- and upper-tropospheric moisture and cloud. Atmospheric Measurement Techniques 12, 4903–4929, https://doi.org/10.5194/amt-12-4903-2019.
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.
Harnisch, F, M. Weissmann, and Á. Periáñez, 2016: Error model for the assimilation of cloud-affected infrared satellite observations in an ensemble data assimilation system. Quart. J. Roy. Meteor. Soc. 142, 1797–1808, https://doi.org/10.1002/qj.2776.
Honda, T, S. Takino, and T. Miyoshi, 2019: Improving a precipitation forecast by assimilating all-sky himawari-8 satellite radiances: A case of Typhoon Malakas (2016). SOLA 15, 7–11, https://doi.org/10.2151/sola.2019-002.
Honda, T, S. Kotsuki, G. Y. Lien, Y. Maejima, K. Okamoto, and T. Miyoshi, 2018a: Assimilation of himawari-8 all-sky radiances every 10 minutes: Impact on precipitation and flood risk prediction. J. Geophys. Res. 123, 965–976, https://doi.org/10.1002/2017JD027096.
Honda, T, and Coauthors, 2018b: Assimilating all-sky Himawari-8 satellite infrared radiances: A case of Typhoon Soudelor (2015). Mon. Wea. Rev. 146, 213–229, https://doi.org/10.1175/MWR-D-16-0357.l.
Hong, S.-Y, Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev. 134, 2318–2341, https://doi.org/10.1175/MWR3199.1.
Hunt, B. R, E. J. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D: Nonlinear Phenomena 230, 112–126, https://doi.org/10.1016/j.physd.2006.ll.008.
Kleist, D. T, and K. Ide, 2015: An OSSE-based evaluation of hybrid variational-ensemble data assimilation for the NCEP GFS. Part II: 4DEnVar and hybrid variants. Mon. Wea. Rev. 143, 452–470, https://doi.org/10.1175/MWR-D-13-00350.l.
Köpken, C, G. Kelly, and J.-N. Thépaut, 2004: Assimilation of Meteosat radiance data within the 4D-var system at ECMWF: Assimilation experiments and forecast impact. Quart. J. Roy. Meteor. Soc. 130, 2277–2292, https://doi.org/10.1256/qj.02.230.
Li, J, P. Wang, H. Han, J. L. Li, and J. Zheng, 2016b: On the assimilation of satellite sounder data in cloudy skies in numerical weather prediction models. J. Meteorol. Res. 30, 169–182, https://doi.org/10.1007/s13351-016-5114-2.
Li, X, M. J. Zeng, Y. Wang, W. L. Wang, H. Y. Wu, and H. X. Mei, 2016a: Evaluation of two momentum control variable schemes and their impact on the variational assimilation of radarwind data: Case study of a squall line. Adv. Atmos. Sci. 33, 1143–1157, https://doi.org/10.1007/s00376-016-5255-3.
Liu, C. S, and Q. N. Xiao, 2013: An ensemble-based four-dimensional variational data assimilation scheme. Part III: Antarctic applications with Advanced Research WRF using real data. Mon. Wea. Rev. 141, 2721–2739, https://doi.org/10.1175/MWR-D-12-00130.1.
Liu, Q. H, and F. Z. Weng, 2006: Advanced doubling-adding method for radiative transfer in planetary atmospheres. J. Atmos. Sci. 63, 3459–3465, https://doi.org/10.1175/JAS3808.1.
Liu, Z. Q, C. S. Schwartz, C. Snyder, and S.-Y. Ha, 2012: Impact of assimilating AMSU-A radiances on forecasts of 2008 Atlantic tropical cyclones initialized with a Limited-area Ensemble Kalman Filter. Mon. Wea. Rev. 140, 4017–4034, https://doi.org/10.1175/MWR-D-12-00083.l.
Lorenc, A. C, N. E. Bowler, A. M. Clayton, S. R. Pring, and D. Fairbairn, 2014: Comparison of hybrid-4DEnVar and Hybrid-4DVar Data assimilation methods for global NWP. Mon. Wea. Rev. 143, 212–229, https://doi.org/10.1175/MWR-D-14-00195.1.
Lu, J. Z, T. Feng, J. Li, Z. L. Cai, X. J. Xu, L. Li, and J. L. Li, 2019: Impact of assimilating Himawari-8-derived layered precipitable water with varying cumulus and microphysics parameterization schemes on the simulation of Typhoon Hato. J. Geophys. Res. 124, 3050–3071, https://doi.org/10.1029/2018JD029364.
McNally, A. P, J. C. Derber, W.-S. Wu, and B. B. Katz, 2000: The use of TOVS level-1B radiances in the NCEP SSI analysis system. Quart. J. Roy. Meteor. Soc. 126, 689–724, https://doi.org/10.1002/qj.49712656315.
Minamide, M, and F. Q. Zhang, 2017: Adaptive observation error inflation for assimilating all-sky satellite radiance. Mon. Wea. Rev. 145, 1063–1081, https://doi.org/10.1175/MWR-D-16-0257.1.
Minamide, M, and F. Q. Zhang, 2018: Assimilation of all-sky infrared radiances from Himawari-8 and impacts of moisture and hydrometer initialization on convection-permitting tropical cyclone prediction. Mon. Wea. Rev. 146, 3241–3258, https://doi.org/10.1175/MWR-D-17-0367.l.
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, 16663–16682, https://doi.org/10.1029/97JD00237.
Okamoto, K., 2017: Evaluation of IR radiance simulation for all-sky assimilation of himawari-8/AHI in a mesoscale NWP system. Quart. J. Roy. Meteor. Soc. 143, 1517–1527, https://doi.org/10.1002/qj.3022.
Okamoto, K, Y. Sawada, and M. Kunii, 2019: Comparison of assimilating all-sky and clear-sky infrared radiances from Himawari-8 in a mesoscale system. Quart. J. Roy. Meteor. Soc. 145, 745–766, https://doi.org/10.1002/qj.3463.
Otkin, J. A, R. Potthast, and A. S. Lawless, 2018: Nonlinear bias correction for satellite data assimilation using Taylor series polynomials. Mon. Wea. Rev. 146, 263–285, https://doi.org/10.1175/MWR-D-17-0171.1.
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<1747:TNMCSS>2.0.CO;2.
Qin, Z. K, X. L. Zou, and F. Z. Weng, 2013: Evaluating added benefits of assimilating goes imager radiance data in GSI for coastal QPFs. Mon. Wea. Rev. 141, 75–92, https://doi.org/10.1175/MWR-D-12-00079.1.
Roberts, N. M, and H. W. Lean, 2008: Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon. Wea. Rev. 136, 78–97, https://doi.org/10.1175/2007MWR2123.l.
Sawada, Y, K. Okamoto, M. Kunii, and T. Miyoshi, 2019: Assimilating every-10-minute himawari-8 infrared radiances to improve convective predictability. J. Geophys. Res. 124, 2546–2561, https://doi.org/10.1029/2018JD029643.
Schmetz, J, P. Pili, S. Tjemkes, D. Just, J. Kerkmann, S. Rota, and A. Ratier, 2002: An introduction to meteosat second generation (MSG). Bull. Amer. Meteor. Soc. 83, 977–992, https://doi.org/10.1175/1520-0477(2002)083<0977:AITMSG>2.3.CO;2.
Schmit, T. J, M. M. Gunshor, W. P. Menzel, J. J. Gurka, J. Li, and A. S. Bachmeier, 2005: Introducing the next-generation advanced baseline imager on GOES-R. Bull. Amer. Meteor. Soc. 86, 1079–1096, https://doi.org/10.1175/BAMS-86-8-1079.
Schmit, T. J, P. Griffith, M. M. Gunshor, J. M. Daniels, S. J. Goodman, and W. J. Lebair, 2017: A closer look at the ABI on the GOES-R series. Bull. Amer. Meteor. Soc. 98, 681–698, https://doi.org/10.1175/BAMS-D-15-00230.l.
Shen, F. F, and J. Z. Min, 2015: Assimilating AMSU — a radiance data with the WRF hybrid En3DVAR system for track predictions of Typhoon Megi (2010). Adv. Atmos. Sci. 32, 1231–1243, https://doi.org/10.1007/s00376-014-4239-4.
Shen, F. F, D. M. Xu, and J. Z. Min, 2019: Effect of momentum control variables on assimilating radar observations for the analysis and forecast for Typhoon Chanthu (2010). Atmospheric Research 230, 104622, https://doi.org/10.1016/j.atmosres.2019.104622.
Stengel, M, P. Undén, M. Lindskog, P. Dahlgren, N. Gustafsson, and R. Bennartz, 2009: Assimilation of SEVIRI infrared radiances with HIRLAM 4D-Var. Quart. J. Roy. Meteor. Soc. 135, 2100–2109, https://doi.org/10.1002/qj.501.
Sun, J. Z, H. L. Wang, W. X. Tong, Y. Zhang, C.-Y. Lin, and D. M. Xu, 2016: Comparison of the impacts of momentum control variables on high-resolution variational data assimilation and precipitation forecasting. Mon. Wea. Rev. 144, 149–169, https://doi.org/10.1175/MWR-D-14-00205.l.
Tewari, M., and Coauthors, 2004: Implementation and verification of the unified NOAH land surface model in the WRF model. In: Paper 14.2A, 20th conference on Weather Analysis and Forecasting/16th conference on numerical weather prediction, Vol 1115, pp 6.
Thompson, G, R. M. Rasmussen, and K. Manning, 2004: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: Description and sensitivity analysis. Mon. Wea. Rev. 132, 519–542, https://doi.org/10.1175/1520-0493(2004)132<0519:EFOWPU>2.0.CO;2.
Tiedtke, M., 1989: A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon. Wea. Rev. 117, 1779–1800, https://doi.org/10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2.
Wang, P, J. Li, B. Lu, T. J. Schmit, J. Z. Lu, Y.-K. Lee, J. L. Li, and Z. Q. Liu, 2018a: Impact of moisture information from advanced Himawari imager measurements on heavy precipitation forecasts in a regional NWP model. J. Geophys. Res. 123, 6022–6038, https://doi.org/10.1029/2017JD028012.
Wang, Y. B, Z. Q. Liu, S. Yang, J. Z. Min, L. Q. Chen, Y. D. Chen, and T. Zhang, 2018b: Added value of assimilating himawari-8 AHI water vapor radiances on analyses and forecasts for “7.19” severe storm over North China. J. Geophys. Res. 123, 3374–3394, https://doi.org/10.1002/2017JD027697.
Wu, Y. L, Z. Q. Liu, and D. Q. Li, 2020: Improving forecasts of a record-breaking rainstorm in Guangzhou by assimilating every 10-min AHI radiances with WRF 4DVAR. Atmospheric Research 239, 104912, https://doi.org/10.1016/j.atmosres.2020.104912.
Xu, D. M, Z. Q. Liu, X.-Y. Huang, J. Z. Min, and H. L. Wang, 2013: Impact of assimilating IASI radiance observations on forecasts of two tropical cyclones. Meteorol. Atmos. Phys. 122, 1–18, https://doi.org/10.1007/s00703-013-0276-2.
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.
Yang, C, Z. Q. Liu, F. Gao, P. P. Childs, and J. Z. Min, 2017a: Impact of assimilating GOES imager clear-sky radiance with a rapid refresh assimilation system for convection-permitting forecast over Mexico. J. Geophys. Res. 122, 5472–5490, https://doi.org/10.1002/2016JD026436.
Yang, J, Z. Q. Zhang, C. Y. Wei, F. Lu, and Q. Guo, 2017b: Introducing the new generation of Chinese geostationary weather satellites, fengyun-4. Bull. Amer. Meteor. Soc. 98, 1637–1658, https://doi.org/10.1175/BAMS-D-16-0065.l.
Zhang, Y. J, F. Q. Zhang, and D. J. Stensrud, 2018: Assimilating all-sky infrared radiances from GOES-16 ABI using an Ensemble Kalman filter for convection-allowing severe thunderstorms prediction. Mon. Wea. Rev. 146, 3363–3381, https://doi.org/10.1175/MWR-D-18-0062.l.
Zhuge, X, and X. Zou, 2016: Test of a modified infrared-only ABI cloud mask algorithm for ahi radiance observations. J. Appl. Meteorol. Climatol. 55, 2529–2546, https://doi.org/10.1175/JAMC-D-16-0254.1.
Zou, X. L, Z. K. Qin, and F. Z. Weng, 2011: Improved coastal precipitation forecasts with direct assimilation of GOES-11/12 imager radiances. Mon. Wea. Rev. 139, 3711–3729, https://doi.org/10.1175/MWR-D-10-05040.1.