Analysis of the Effect of the Fengyun-3D Satellite Microwave Humidity Sounder (MWHS-II) Data Assimilation on Typhoon “YAGI” Forecast
-
摘要: 2411号台风“摩羯”于2024年9月登陆中国东南沿海地区, 风云三号D星(FY-3D)微波湿度计(MWHS-II)成功监测到该台风. 本研究在天气研究与预报模式数据同化系统(Weather Research and Forecasting model Data Assimilation system, WRFDA)中构建了晴空条件下FY-3D MWHS-II资料三维变分同化框架, 通过设置118 GHz, 183 GHz单频段以及双频段联合同化及预报试验方案, 系统评估了MWHS-II资料同化对台风“摩羯”的强度、路径及降水预报效果. 试验表明, 同化FY-3D MWHS-II资料有效提升了分析场质量, 对台风强度、路径和降水的预报也产生了积极影响. 台风预报方面, 同化118 GHz和183 GHz通道对台风路径预报分别改进了17.18%和13.39%, 洋面区域的183 GHz同化更使得路径预报改进率达到14.59%; 降水预报方面, MWHS-II资料同化显著提升初期24 h内中小雨量级(<25 mm)预报命中率, 其中118 GHz通道对强降水(>50 mm)预报表现出独特优势; 183 GHz通道的改进效应在24 h后逐步显现, 而双频段联合同化方案展现出综合优势, 各量级降水的降水分数技能得分(Fractions Skill Scores, FSS)相比控制试验提升显著. FY-3D MWHS-II的118 GHz和183 GHz频段在不同预报要素上的差异化改进效果, 突显了FY-3D MWHS-II资料在区域台风数值预报中的独特应用价值及应用潜力.Abstract: The Fengyun-3D satellite (FY-3D) Microwave Humidity Sounder (MWHS-II) successfully monitored the Typhoon “YAGI” (2411). In this paper, a three-dimensional variational assimilation framework of FY-3D MWHS-II data in clear sky is constructed in WRFDA. By setting up a single-band and dual-band joint assimilation and prediction experiment scheme of 118 GHz and 183 GHz, the microwave data assimilation and the forecast effect on the intensity, path and precipitation of Typhoon “YAGI” are systematically evaluated. The experiment shows that the assimilation of FY-3D MWHS-II data effectively improves the quality of the analysis field, and also has a positive impact on the forecast of typhoon intensity, track and precipitation. For the typhoon forecast, the assimilation of 118 GHz and 183 GHz channels improved the typhoon path forecast by 17.18% and 13.39% respectively, and the assimilation of 183 GHz in the ocean area made the path forecast improve by 14.59%. For the precipitation forecast, the assimilation of MWHS-II data significantly improves the hit rate of medium and small rainfall levels (< 25 mm) in the initial 24 hours, among which the 118GHz channel has a unique advantage in forecasting heavy rainfall (> 50 mm); The improvement effect of 183 GHz channel gradually appeared after 24 hours, while the dual-band joint scheme showed comprehensive advantages, and the FSS score of precipitation in each magnitude was significantly improved compared with the control experiment. The differential improvement effect of FY-3D MWHS-II’s 118 GHz and 183 GHz frequency bands on different forecast elements highlights the unique application value and potential of FY-3 MWHS-II data in regional typhoon numerical forecast.
-
图 10 循环同化和确定性预报期间台风“摩羯”(2411)预报结果. (a)中心海平面气压变化, (b)台风移动路径. 空心圆表示同化分析后的结果, 实心圆表示预报结果
Figure 10. Forecast results of Typhoon “YAGI” (2411) during cyclic assimilation and deterministic forecast. (a) Central sea level pressure change, (b) typhoon moving path. Hollow circles represent the assimilation analysis results and solid circles represent the forecast results
表 1 FY-3D MWHS-II通道特性参数
Table 1. Channel characteristics of FY-3D MWHS-II
序号 中心频率/GHz 权重函数峰值高度/hPa 极化方式 主要探测目的 1 89.0 — V 背景微波辐射探测、降水检测 2 118.75±0.08 20 H 大气温度和降水参数垂直结构探测 3 118.75±0.2 60 H 4 118.75±0.3 100 H 5 118.75±0.8 250 H 6 118.75±1.1 300 H 7 118.75±2.5 700 H 8 118.75±3.0 — H 9 118.75±5.0 — H 10 150.0 — V 背景微波辐射探测、降水检测 11 183.31±1 350 H 大气湿度垂直结构探测 12 183.31±1.8 400 H 13 183.31±3 500 H 14 183.31±4.5 550 H 15 183.31±7 650 H 表 2 同化试验设置
Table 2. Assimilation experimental settings
试验名称 使用的观测资料 CTRL 仅同化常规观测资料 Exp.118 同化常规观测资料 + MWHS-II Channel 2~9 Exp.183 同化常规观测资料 + MWHS-II Channel 11~15 Exp.118+183 同化常规观测资料 + MWHS-II Channel 2~9 + Channel 11~15 表 3 48 h确定性预报的中心海平面气压和路径的RMSE和预测改进率(Ratio)
Table 3. RMSE and ratio of central sea level pressure and path during the 48-hour deterministic forecast
试验名称 中心海平面气压 路径 洋面+陆地 仅洋面 洋面+陆地 仅洋面 RMSE/hPa Ratio/(%) RMSE/hPa $ \mathrm{R}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o} $/(%) RMSE/km Ratio/(%) RMSE/km Ratio/(%) CTRL 40.5418 — 31.3296 — 116.9672 — 106.4815 — Exp.118 39.4163 2.78 33.3405 –6.42 96.8727 17.18 97.9256 8.04 Exp.183 40.0193 1.29 31.3021 0.087 101.3016 13.39 90.9416 14.59 Exp.118+183 40.6933 –0.37 30.2557 3.43 117.5214 –0.47 98.2435 7.74 -
[1] ZHANG P, HU X Q, LU Q F, et al. FY-3E: the first operational meteorological satellite mission in an early morning orbit[J]. Advances in Atmospheric Sciences, 2022, 39(1): 1-8 doi: 10.1007/s00376-021-1304-7 [2] HE J Y, GUO Y, XIE X X, et al. Updates of microwave humidity sounder from FengYun-3A to 3F satellites[J]. IEEE Geoscience and Remote Sensing Letters, 2024, 21: 4501405 doi: 10.1109/LGRS.2024.3432068 [3] 张升伟, 李靖. “风云三号”卫星微波湿度计[J]. 高科技与产业化, 2013, 19(11): 79-80ZHANG Shengwei, LI Jing. “FY-3” satellite MWHS[J]. High-Technology :Times New Roman;">& Commercialization, 2013, 19(11): 79-80 [4] (何杰颖, 张升伟, 王振占, 等. 风云气象卫星微波大气探测回顾与展望[J]. 空间科学学报, 2023, 43(6): 1025-1035 doi: 10.11728/cjss2023.06.yg16HE Jieying, ZHANG Shengwei, WANG Zhenzhan, et al. Prospects for microwave atmospheric sounding of the new generation of Fengyun meteorological satellites[J]. Chinese Journal of Space Science, 2023, 43(6): 1025-1035 doi: 10.11728/cjss2023.06.yg16 [5] DUNCAN D I, BORMANN N. On the Addition of Microwave Sounders and NWP Skill, Including Assessment of FY-3D Sounders[R]. Reading: European Centre for Medium-Range Weather Forecasts, 2020 [6] CARMINATI F, MIGLIORINI S. All-sky data assimilation of MWTS-2 and MWHS-2 in the Met Office global NWP system[J]. Advances in Atmospheric Sciences, 2021, 38(10): 1682-1694 doi: 10.1007/s00376-021-1071-5 [7] CARMINATI F, ATKINSON N, CANDY B, et al. Insights into the microwave instruments onboard the Fengyun 3D Satellite: data quality and assimilation in the met office NWP system[J]. Advances in Atmospheric Sciences, 2021, 38(8): 1379-1396 doi: 10.1007/s00376-020-0010-1 [8] BORMANN N, DUNCAN D, ENGLISH S, et al. Growing operational use of FY-3 data in the ECMWF system[J]. Advances in Atmospheric Sciences, 2021, 38(8): 1285-1298 doi: 10.1007/s00376-020-0207-3 [9] YANG Y M, DU M B, ZHANG J. FY-3A satellite microwave data assimilation experiments in tropical cyclone forecast[J]. Journal of Tropical Meteorology, 2013, 19(3): 297-304 [10] XU D M, MIN J Z, SHEN F F, et al. Assimilation of MWHS radiance data from the FY-3B satellite with the WRF Hybrid-3DVAR system for the forecasting of binary typhoons[J]. Journal of Advances in Modeling Earth Systems, 2016, 8(2): 1014-1028 doi: 10.1002/2016MS000674 [11] CHEN K Y, CHEN Z X, XIAN Z P, et al. Impacts of the all-sky assimilation of FY-3C and FY-3D MWHS-2 radiances on analyses and forecasts of typhoon Hagupit[J]. Remote Sensing, 2023, 15(9): 2279 doi: 10.3390/rs15092279 [12] XU D M, SHU A Q, LI H, et al. Effects of assimilating clear-sky FY-3D MWHS2 radiance on the numerical simulation of tropical storm ampil[J]. Remote Sensing, 2021, 13(15): 2873 doi: 10.3390/rs13152873 [13] XIAN Z P, CHEN K Y, ZHU J. All-sky assimilation of the MWHS-2 observations and evaluation the impacts on the analyses and forecasts of binary typhoons[J]. Journal of Geophysical Research: Atmospheres, 2019, 124(12): 6359-6378 doi: 10.1029/2018JD029658 [14] HUANG L Z, XU D M, LI H, et al. Assimilating FY-3D MWHS2 radiance data to predict typhoon Muifa based on different initial background conditions and fast radiative transfer models[J]. Remote Sensing, 2023, 15(13): 3220 doi: 10.3390/rs15133220 [15] XIAO H Y, HAN W, ZHANG P, et al. Assimilation of data from the MWHS-II onboard the first early morning satellite FY-3E into the CMA global 4D-Var system[J]. Meteorological Applications, 2023, 30(3): e2133 doi: 10.1002/met.2133 [16] SHEN F F, YUAN X L, LI H, et al. Improving Typhoon Muifa (2022) forecasts with FY-3D and FY-3E MWHS-2 satellite data assimilation under clear sky conditions[J]. Remote Sensing, 2024, 16(14): 2614 doi: 10.3390/rs16142614 [17] 李娜, 张升伟, 何杰颖. 基于FY-3C MWHTS的台风降水反演算法研究[J]. 遥感技术与应用, 2019, 34(5): 1091-1100LI Na, ZHAGN Shengwei, HE Jieying. Research on typhoon precipitation retrieval algorithm based on FY-3C MWHTS[J]. Remote Sensing Technology and Application, 2019, 34(5): 1091-1100 [18] JU Y L, HE J Y, MA G, et al. Impact of the detection channels added by Fengyun Satellite MWHS-II at 183 GHz on global numerical weather prediction[J]. Remote Sensing, 2023, 15(17): 4279 doi: 10.3390/rs15174279 [19] LIU K W, HE J Y, CHEN H N. Precipitation retrieval from Fengyun-3D microwave humidity and temperature sounder data using machine learning[J]. Remote Sensing, 2022, 14(4): 848 doi: 10.3390/rs14040848 [20] 秦璐瑶. 云环境下风云三号卫星微波资料同化及对数值预报影响的研究[D]. 南京: 南京信息工程大学, 2023QIN Luyao. Study on Microwave Data Assimilation of FY-3 Satellite in Cloud Environment and Its Influence on Numerical Prediction[D]. Nanjing: Nanjing University of Information Science & Technology, 2023 [21] MATRICARDI M, CHEVALLIER F, KELLY G, et al. An improved general fast radiative transfer model for the assimilation of radiance observations[J]. Quarterly Journal of the Royal Meteorological Society, 2004, 130(596): 153-173 doi: 10.1256/qj.02.181 [22] ZOU X L. Atmospheric Satellite Observations: Variation Assimilation and Quality Assurance[M]. Beijing: Science Press, 2023 [23] BONAVITA M, ISAKSEN L, HÓLM E. On the use of EDA background error variances in the ECMWF 4D-Var[J]. Quarterly Journal of the Royal Meteorological Society, 2012, 138(667): 1540-1559 doi: 10.1002/qj.1899 [24] ZHANG P, LU Q F, HU X Q, et al. Latest progress of the Chinese meteorological satellite program and core data processing technologies[J]. Advances in Atmospheric Sciences, 2019, 36(9): 1027-1045 doi: 10.1007/s00376-019-8215-x [25] (朱国富. 数值天气预报中分析同化基本方法的历史发展脉络和评述[J]. 气象, 2015, 41(8): 986-996 doi: 10.7519/j.issn.1000-0526.2015.08.008ZHU Guofu. Remarks on development of basic methods of atmospheric data assimilation for numerical weather prediction[J]. Meteorological Monthly, 2015, 41(8): 986-996 doi: 10.7519/j.issn.1000-0526.2015.08.008 [26] 杨寅, 韩威, 董佩明. AMSU微波探测资料同化的质量控制方法概述[J]. 气象, 2011, 37(11): 1395-1401 doi: 10.7519/j.issn.1000-0526.2011.11.010Yang Yin, Han Wei, DONG Peiming. Overview on the Quality Control in Assimilation of AMSU Microwave Sounding Data[J]. Meteorological Monthly, 2011, 37(11): 1395-1401 doi: 10.7519/j.issn.1000-0526.2011.11.010 [27] WENG F Z, GRODY N C, FERRARO R, et al. Cloud liquid water climatology from the special sensor microwave/imager[J]. Journal of Climate, 1997, 10(5): 1086-1098 doi: 10.1175/1520-0442(1997)010<1086:CLWCFT>2.0.CO;2 [28] LIN B, MINNIS P, FAN A. Cloud liquid water path variations with temperature observed during the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment[J]. Journal of Geophysical Research: Atmospheres, 2003, 108(D14): 4427 doi: 10.1029/2002JD002851 [29] WENG F Z, ZHAO L M, FERRARO R R, et al. Advanced microwave sounding unit cloud and precipitation algorithms[J]. Radio Science, 2003, 38(4): 8068 [30] 钱小立. 基于微波温度计的温度日变化特征及云水路径气候变化趋势研究[D]. 南京: 南京信息工程大学, 2022: 26-28QIAN Xiaoli. Characteristics of Daily Temperature Variation and Trend of c Cloud Liquid Water Path Climate Change Based on Microwave Thermometer[D]. Nanjing: Nanjing University of Information Science and Technology, 2022: 26-28 [31] AULIGNÉ T, MCNALLY A P, DEE D P. Adaptive bias correction for satellite data in a numerical weather prediction system[J]. Quarterly Journal of the Royal Meteorological Society, 2007, 133(624): 631-642 doi: 10.1002/qj.56 [32] 台风摩羯-百科[EB/OL]. (2024-09-11)[2024-12-03]. https://baike.weixin.qq.com/v220167141.htm?scene_id=134&sid=10351723090268142305&ch=s1sSevere Typhoon Yagi[EB/OL]. (2024-09-11)[2024-12-03]. https://baike.weixin.qq.com/v220167141.htm?scene_id=134&sid=10351723090268142305&ch=s1s [33] HONG S Y, LIM J, OCK J. The WRF single-moment 6-class microphysics scheme (WSM6)[J]. Asia-Pacific Journal of Atmospheric Sciences, 2006, 42(2): 129-151 [34] DUDHIA J. Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model[J]. Journal of the Atmospheric Sciences, 1989, 46(20): 3077-3107 doi: 10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2 [35] MLAWER E J, TAUBMAN S J, BROWN P D, et al. Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave[J]. Journal of Geophysical Research: Atmospheres, 1997, 102(D14): 16663-16682 doi: 10.1029/97JD00237 [36] HONG S Y, NOH Y, DUDHIA J. A new vertical diffusion package with an explicit treatment of entrainment processes[J]. Monthly Weather Review, 2006, 134(9): 2318-2341 doi: 10.1175/MWR3199.1 [37] KAIN J S. The Kain–fritsch convective parameterization: an update[J]. Journal of Applied Meteorology and Climatology, 2004, 43(1): 170-181 doi: 10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2 [38] COURTIER P, THÉPAUT J N, HOLLINGSWORTH A. A strategy for operational implementation of 4D-Var, using an incremental approach[J]. Quarterly Journal of the Royal Meteorological Society, 1994, 120(519): 1367-1387 [39] DERBER J, BOUTTIER F. A reformulation of the background error covariance in the ECMWF global data assimilation system[J]. Tellus A: Dynamic Meteorology and Oceanography, 1999, 51(2): 195-221 doi: 10.3402/tellusa.v51i2.12316 [40] SUN J Z, WANG H L, TONG W X, et al. Comparison of the impacts of momentum control variables on high-resolution variational data assimilation and precipitation forecasting[J]. Monthly Weather Review, 2016, 144(1): 149-169 doi: 10.1175/MWR-D-14-00205.1 [41] PARRISH D F, DERBER J C. The national meteorological center’s spectral statistical-interpolation analysis system[J]. Monthly Weather Review, 1992, 120(8): 1747-1763 doi: 10.1175/1520-0493(1992)120<1747:TNMCSS>2.0.CO;2 [42] ERSBACH H, BELL B, BERRISFORD P, et al. The ERA5 global reanalysis[J]. Quarterly Journal of the Royal Meteorological Society, 2020, 146(730): 1999-2049 doi: 10.1002/qj.3803 [43] QIN L Y, CHEN Y D, MA G, et al. Assimilation of FY-3D MWTS-II radiance with 3D precipitation detection and the impacts on typhoon forecasts[J]. Advances in Atmospheric Sciences, 2023, 40(5): 900-919 doi: 10.1007/s00376-022-1252-x [44] LI J, LIU G Q. Direct assimilation of Chinese FY-3C Microwave Temperature Sounder-2 radiances in the global GRAPES system[J]. Atmospheric Measurement Techniques, 2016, 9(7): 3095-3113 doi: 10.5194/amt-9-3095-2016 [45] HUFFMAN G J, STOCKER E F, BOLVIN D T, et al. GPM IMERG Final Precipitation L3 Day 0.1 Degree x 0.1 DegreeV06[R]. Greenbelt: Goddard Earth Sciences Data and Information Services Center (GES DISC), 2019 [46] ROBERTS N M, LEAN H W. Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events[J]. Monthly Weather Review, 2008, 136(1): 78-97 doi: 10.1175/2007MWR2123.1 -
-
冯雨萱 女, 现为中国科学院国家空间科学中心博士研究生, 主要从事微波遥感探测与定量信息处理技术研究. E-mail:
下载: