博碩士論文 107621019 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:24 、訪客IP:3.236.51.151
姓名 葉浩倫(Hao-Lun Yeh)  查詢紙本館藏   畢業系所 大氣科學學系
論文名稱
(Impact of including observation error correlation for assimilating radar radial wind and its impact on heavy rainfall prediction)
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摘要(中) 在資料同化中,以往會假設觀測誤差彼此間是無相關的。為了避免觀測誤差相關性,在進行同化前,高解析資料通常會經過如超級觀測化(superobbing)或資料稀化(data thinning)策略來降低資料解析度。然而,這些策略犧牲了高時空解析度觀測在提供密集天氣資訊上的優勢。除此之外,在不加入觀測誤差相關性下,同化高解析觀測(例如:雷達徑向風)會低估分析誤差而產生過度擬合,進而降低分析及預報場的準確度。為了能更有效的使用高解析雷達資料以避免過度擬合,本篇研究提出在WRF雷達資料同化系統(WRF-LETKF Radar Assimilation System, WLRAS)中加入都卜勒雷達徑向風誤差相關性,並評估其對短期降水預報的影響。所利用的個案為2017年6月2日台灣北部的梅雨強降水事件,並同時同化雷達徑向風及回波資料。

本研究分為兩階段,第一部分利用觀測增量樣本估計徑向風水平方向上的觀測誤差相關性。結果顯示即便使用不同解析度之超級觀測,徑向風的誤差尺度皆約為25公里。接著,第二部分實驗將徑向風誤差相關性的估計結果加入至雷達資料同化系統中。由同化實驗結果可知,相較於不考慮觀測誤差相關性的實驗,同化徑向風觀測時加入觀測誤差相關性可產生空間上較小尺度的風場修正,而此風場修正會使得分析場在梅雨鋒面區域擁有較強的輻合、激發較多的局部對流,並伴隨較多的水氣量以及較強的西南風,進而提供對流良好的發展條件。風場與水氣場的修正隨後改善了回波及降水的預報。除了更好的單一預報表現之外,在機率定量降水預報(PQPF)當中,可以得知在同化系統中加入徑向風觀測誤差相關性後,系集能有更高機率預報出較為接近觀測的降水強度以及空間分布。另外,由每小時累積雨量預報FSS (Fractions Skill Score)得分之校驗結果得知,加入徑向風觀測誤差相關性之改善的效果可以維持約六小時。
摘要(英) An assumption of uncorrelated observation errors is commonly adopted in conventional data assimilation. For this reason, high-resolution data is re-sampled with strategies like superobbing or data thinning. This also sacrifices the advantage of high temporal and spatial resolution observations that can provide essential detailed structures. However, assimilating the high-resolution data, such as radar radial wind, without considering the observation error correlation can lead to overfitting and thus degrade the performance of data assimilation and forecast. This study uses the radar ensemble data assimilation system, which couples the Weather Research and Forecasting model and Local Ensemble Transform Kalman Filter (WRF-LETKF), to assimilate the radar radial velocity and reflectivity data. We present a strategy to include the error correlation of the Doppler radar radial velocity in the WRF-LETKF radar assimilation system and examine their impact on short-term precipitation prediction based on the heavy rainfall case on 2nd June 2017 in Taiwan.

We first estimate the horizontal error correlation scale for radial velocity based on the innovation statistics. The error correlation scale is approximately 25 km. The introduction of correlated observation error for Doppler radar radial winds exhibits more small-scale features in the wind analysis corrections compared to the experiment using the independent observation assumption. Consequently, the modification on wind corrections leads to stronger convergence accompanied by higher water vapor content, and induces subsequent local convections, resulting in more accurate simulated reflectivity. For probability quantitative precipitation forecast (PQPF), our results show that the experiment using the correlated observation error has higher probability to generate heavy rainfall and agrees better with the observation.
關鍵字(中) ★ 資料同化
★ WRF-LETKF雷達資料同化系統
★ 觀測誤差相關性
★ 都卜勒雷達徑向風
關鍵字(英) ★ Data Assimilation
★ WRF-LETKF Radar Assimilation System
★ Observation Error Correlation
★ Doppler Radar Radial Wind
論文目次 摘要 i
Abstract ii
Acknowledgement iii
Table of Contents iv
List of Tables vi
List of Figures vi

1. Introduction 1
1.1 Background and Literature Review 1
1.2 Motivation and Objectives 4

2. Methodology 6
2.1 WRF-LETKF Radar Data Assimilation System 6
2.1.1 WRF Model 6
2.1.2 Local Ensemble Transform Kalman Filter (LETKF) 7
2.2 Radar data processing 9
2.3 Superobservation and observation operator 10
2.4 Estimation of error correlations for radial wind 12
2.5 Accounting for correlated observation error in LETKF 13
2.5.1 Adjusted Gradient Updating Method 15
2.5.2 Reconditioning 16
2.5.3 Eigen-decomposition Method 17

3. Estimating Error Correlation Length Scale of Radial Wind 19
3.1 Overview of the heavy rainfall event on 2nd June 2017 19
3.2 Experimental setup 20
3.3 Results of estimation 22

4. Including Error Correlation of Radial Wind in WLRAS 25
4.1 Experimental setup 25
4.2 General performance of radar data assimilation 26
4.3 Impact of using a correlated observation error covariance on analysis and forecast 29
4.4 Sensitivity test 34
4.5 Including error correlation with Eigen method 36
4.6 Summary 38

5. Conclusions and Future Work 40
5.1 Conclusions 40
5.2 Future work 42

References 44
Appendix I 53
Appendix II 54
Tables 55
Figures 57
參考文獻 Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 2884–2903, https://doi.org/10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2.
Bathmann, K., and A. Collard, 2020: Surface-dependent correlated infrared observation errors and quality control in the FV3 framework. Q. J. R. Meteorol. Soc., 1–17, https://doi.org/10.1002/qj.3925.
Bédard, J., and M. Buehner, 2019: A practical assimilation approach to extract smaller-scale information from observations with spatially correlated errors: An idealized study. Q. J. R. Meteorol. Soc., 146, 468–482, https://doi.org/10.1002/qj.3687.
Berenguer, M., and I. Zawadzki, 2008: A study of the error covariance matrix of radar rainfall estimates in stratiform rain. Weather Forecast., 23, 1085–1101, https://doi.org/10.1175/2008WAF2222134.1.
Berry, T., and T. Sauer, 2013: Adaptive ensemble Kalman filtering of non-linear systems. Tellus, Ser. A Dyn. Meteorol. Oceanogr., 65, https://doi.org/10.3402/tellusa.v65i0.20331.
Bick, T., and Coauthors, 2016: Assimilation of 3D radar reflectivities with an ensemble Kalman filter on the convective scale. Q. J. R. Meteorol. Soc., 142, 1490–1504, https://doi.org/10.1002/qj.2751.
Bishop, C. H., B. J. Etherton, and S. J. Majumdar, 2001: Adaptive sampling with the ensemble transform Kalman filter Part I: Theoretical aspects. Mon. Wea. Rev., 129, 420–436, https://doi.org/10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2.
Bormann, N., M. Bonavita, R. Dragani, R. Eresmaa, M. Matricardi, and A. Mcnally, 2016: Enhancing the impact of IASI observations through an updated observation-error covariance matrix. Q. J. R. Meteorol. Soc., 142, 1767–1780, https://doi.org/10.1002/qj.2774.
Campbell, W. F., E. A. Satterfield, B. Ruston, and N. L. Baker, 2017: Accounting for correlated observation error in a dual-formulation 4D variational data assimilation system. Mon. Wea. Rev., 145, 1019–1032, https://doi.org/10.1175/MWR-D-16-0240.1.
Cheng, H.-W., S.-C. Yang, and C.-S. Chen, 2019: Impact of Doppler radar network in Taiwan on the convective-scale data assimilation and precipitation prediction: The extreme precipitation event during 1-3 June 2017. AOGS 16th Annual Meeting, Singapore, Asia Oceanic GeoScience Society, AS30–A029.
——, ——, Y. C. Liou, and C. Sen Chen, 2020: An investigation of the sensitivity of predicting a severe rainfall event in northern Taiwan to the upstream condition with a WRF-based radar data assimilation system. Sci. Online Lett. Atmos., 16, 97–103, https://doi.org/10.2151/SOLA.2020-017.
Chou, M.-D. and Suarez, M. J., 1999: A Solar Radiation Parameterization for Atmospheric Studies, NASA Tech. Memo, NASA/GSFC, 104606, 40.
Clark, P., N. Roberts, H. Lean, S. P. Ballard, and C. Charlton-Perez, 2016: Convection-permitting models: A step-change in rainfall forecasting. Meteorol. Appl., 23, 165–181, https://doi.org/10.1002/met.1538.
Cordoba, M., S. L. Dance, G. A. Kelly, N. K. Nichols, and J. A. Waller, 2017: Diagnosing atmospheric motion vector observation errors for an operational high-resolution data assimilation system. Q. J. R. Meteorol. Soc., 143, 333–341, https://doi.org/10.1002/qj.2925.
Desroziers, G., L. Berre, B. Chapnik, and P. Poli, 2005: Diagnosis of observation, background and analysis-error statistics in observation space. Q. J. R. Meteorol. Soc., 131, 3385–3396, https://doi.org/10.1256/qj.05.108.
Dowell, D. C., L. J. Wicker, and C. Snyder, 2011: Ensemble Kalman filter assimilation of radar observations of the 8 may 2003 Oklahoma city supercell: Influences of reflectivity observations on storm-scale analyses. Mon. Wea. Rev., 139, 272–294, https://doi.org/10.1175/2010MWR3438.1.
Errico, R. M., 1985: Spectra computed from a limited area grid. Mon. Wea. Rev., 113, 1554–1562, https://doi.org/10.1175/1520-0493(1985)113<1554:SCFALA>2.0.CO;2.
Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99, https://doi.org/10.1029/94jc00572.
Fisher, M., M. Leutbecher, and G. A. Kelly, 2005: On the equivalence between Kalman smoothing and weak-constraint four-dimensional variational data assimilation. Quart. J. Roy. Meteor. Soc., 131, 3235–3246.
Fowler, A. M., S. L. Dance, and J. A. Waller, 2018: On the interaction of observation and prior error correlations in data assimilation. Q. J. R. Meteorol. Soc., 144, 48–62, https://doi.org/10.1002/qj.3183.
Gastaldo, T., V. Poli, C. Marsigli, P. Paolo Alberoni, and T. Paccagnella, 2018: Data assimilation of radar reflectivity volumes in a LETKF scheme. Nonlinear Process. Geophys., 25, 747–764, https://doi.org/10.5194/npg-25-747-2018.
Geer, A. J., 2019: Correlated observation error models for assimilating all-sky infrared radiances. Atmos. Meas. Tech., 12, 3629–3657, https://doi.org/10.5194/amt-12-3629-2019.
Golub, G. H. and Van Loan, C. F., 1996: Matrix Computations. (third ed.), Johns Hopkins University Press, Baltimore, MD.
Hollingsworth, A., and P. Lönnberg, 1986: The statistical structure of short-range forecast errors as determined from radiosonde data. Part I: The wind field. Tellus, 38A, 111–136, https://doi.org/10.1111/j.1600-0870.1986.tb00460.x.
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.
Houtekamer, P. L. and Mitchell, H. L. 2005. Ensemble Kalman filtering. Q. J. Roy. Meteorol. Soc. 133, 3260–3289.
Hunt, B. R., E. J. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Phys. D Nonlinear Phenom., 230, 112–126, https://doi.org/10.1016/j.physd.2006.11.008.
Janjić, T., and Coauthors, 2018: On the representation error in data assimilation. Q. J. R. Meteorol. Soc., 144, 1257–1278, https://doi.org/10.1002/qj.3130.
Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170–181, https://doi.org/10.1175/1520-0450(2004)043,0170:TKCPAU.2.0.CO;2.
Keeler, R. J., and S. M. Ellis, 2000: Observational error covariance matrices for radar data assimilation. Phys. Chem. Earth, Part B Hydrol. Ocean. Atmos., 25, 1277–1280, https://doi.org/10.1016/S1464-1909(00)00193-3.
Kotsuki, S., T. Miyoshi, K. Terasaki, G. Y. Lien, and E. Kalnay, 2017: Assimilating the global satellite mapping of precipitation data with the nonhydrostatic icosahedral atmospheric model (NICAM). J. Geophys. Res., 122, 631–650, https://doi.org/10.1002/2016JD025355.
Lindskog, M., K. Salonen, H. Järvinen, and D. B. Michelson, 2004: Doppler radar wind data assimilation with HIRLAM 3DVAR. Mon. Wea. Rev., 132, 1081–1092, https://doi.org/10.1175/1520-0493(2004)132<1081:DRWDAW>2.0.CO;2.
Liu, Z.-Q., and F. Rabier, 2003: The potential of high-density observations for numerical weather prediction: A study with simulated observations. Q. J. R. Meteorol. Soc., 129, 3013–3035, https://doi.org/10.1256/qj.02.170.
Lupo, K. M., R. D. Torn, and S. C. Yang, 2020: Evaluation of stochastic perturbed parameterization tendencies on convective-permitting ensemble forecasts of heavy rainfall events in New York and Taiwan. Weather Forecast., 35, 5–24, https://doi.org/10.1175/WAF-D-19-0064.1.
Maldonado, P., J. Ruiz, and C. Saulo, 2020: Parameter sensitivity of the WRF-LETKF system for assimilation of radar observations: Imperfect-model observing system simulation experiments. Weather Forecast., 35, 1345–1362, https://doi.org/10.1175/WAF-D-19-0161.1.
Maree, S. C. 2012: Correcting non positive definite correlation matrices. Bachelor thesis, Delft University of Technology, 68 pp.
Miyoshi T., Kalnay E., Li H., 2013: Estimating and including observation‐error correlations in data assimilation. Inverse Problems in Science and Engineering, 21, 387–398, https://doi.org/10.1080/17415977.2012.712527.
——, and Coauthors, 2016: Big data assimilation revolutionizing severe weather prediction. Bull. Am. Meteorol. Soc., 97, 1347–1354, https://doi.org/10.1175/BAMS-D-15-00144.1.
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. Atmos., 102, 16663–16682, https://doi.org/10.1029/97jd00237.
Montmerle, T., and C. Faccani, 2009: Mesoscale assimilation of radial velocities from Doppler radars in a preoperational framework. Mon. Wea. Rev., 137, 1939–1953, https://doi.org/10.1175/2008MWR2725.1.
Ott E., B. R. Hunt, I. Szunyogh, A. V. Zimin, E. J. Kostelich, M. Corrazza, E. Kalnay, D. J. Patil, and J. A. Yorke, 2004: A local ensemble Kalman filter for atmospheric data assimilation. Tellus, 56A, 415–428.
Putnam, B. J., M. Xue, Y. Jung, N. Snook, and G. Zhang, 2014: The Analysis and Prediction of Microphysical States and Polarimetric Radar Variables in a Mesoscale Convective System Using Double-Moment Microphysics, Multinetwork Radar Data, and the Ensemble Kalman Filter. Mon. Wea. Rev., 142, 141–162, https://doi.org/10.1175/MWR-D-13-00042.1.
Rainwater, S., C. H. Bishop, and W. F. Campbell, 2015: The benefits of correlated observation errors for small scales. Q. J. R. Meteorol. Soc., 141, 3439–3445, https://doi.org/10.1002/qj.2582.
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/10.1175/2007MWR2123.1.
Shao, Y.-M., 2015: Applying the local ensemble transform Kalman filter radar data assimilation system to improve short-term quantitative rainfall forecast: A case study with SoMEX IOP8. M. S. dissertation. Department of Atmospheric Physics, National Central University, Taoyuan, Taiwan, 95 pp.
Simonin, J. A. Waller, S. P. Ballard, S. L. Dance, and N. K. Nichols, 2019: A pragmatic strategy for implementing spatially correlated observation errors in an operational system: An application to Doppler radial winds. Q. J. R. Meteorol. Soc., 145, 2772–2790, https://doi.org/10.1002/qj.3592.
Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-4751STR, 113 pp., https://doi.org/10.5065/D68S4MVH.
Stewart, L. M., S. L. Dance, N. K. Nichols, J. R. Eyre, and J. Cameron, 2014: Estimating interchannel observation-error correlations for IASI radiance data in the Met Office system. Q. J. R. Meteorol. Soc., 140, 1236–1244, https://doi.org/10.1002/qj.2211.
Tabeart, J. M., S. L. Dance, A. S. Lawless, S. Migliorini, N. K. Nichols, F. Smith, and J. A. Waller, 2020: The impact of using reconditioned correlated observation-error covariance matrices in the Met Office 1D-Var system. Q. J. R. Meteorol. Soc., 146, 1372–1390, https://doi.org/10.1002/qj.3741.
Tao, W.–K., J. Simpson, and M. McCumber, 1989: An Ice–Water Saturation Adjustment. Mon. Wea. Rev., 117, 231-235.
——, and Coauthors, 2003: Microphysics, radiation and surface processes in the Goddard Cumulus Ensemble (GCE) model. Meteorol. Atmos. Phys., 82, 97–137, https://doi.org/10.1007/s00703-001-0594-7.
Tewari, M., and Coauthors, 2004: Implementation and verification of the unified Noah land surface model in the WRF model. 20th Conf. on Weather Analysis and Forecasting/16th Conf. on Numerical Weather Prediction, Seattle, WA, Amer. Meteor. Soc., 14.2a, https://ams.confex.com/ams/84Annual/techprogram/paper_69061.htm.
Todling, R., 2015: A lag-1 smoother approach to system-error estimation: Sequential method. Q. J. R. Meteorol. Soc., 141, 1502–1513, https://doi.org/10.1002/qj.2460.
Tsai, C. C., S. C. Yang, and Y. C. Liou, 2014: Improving quantitative precipitation nowcasting with a local ensemble transform Kalman filter radar data assimilation system: Observing system simulation experiments. Tellus, Ser. A Dyn. Meteorol. Oceanogr., 66, https://doi.org/10.3402/tellusa.v66.21804.
——, ——, C.-Y. Lin, and J.-C. Liou, 2016: The development of WLRAS and its very short-term QPF performance in multiple heavy rainfall events. Seventh EnKF Data Assimilation Workshop, State College, PA, The Pennsylvania State University, http://www.adapt.psu.edu/2016EnKFWorkshop/ABSTRACTS/Chih-Chien_Tsai_Poster.html.
Tu, C.-C., Y.-L. Chen, P.-L. Lin, and P.-H. Lin, 2020: The relationship between the boundary layer moisture transport from the South China Sea and heavy rainfall over Taiwan. Terr. Atmos. Ocean. Sci., 31, 159–176, https://doi.org/10.3319/tao.2019.07.01.01.
Waller, J. A., S. L. Dance, A. S. Lawless, and N. K. Nichols, 2014: Estimating correlated observation error statistics using an ensemble transform Kalman filter. Tellus, Ser. A Dyn. Meteorol. Oceanogr., 66, https://doi.org/10.3402/tellusa.v66.23294.
——, S. P. Ballard, S. L. Dance, G. Kelly, N. K. Nichols, and D. Simonin, 2016a: Diagnosing horizontal and inter-channel observation error correlations for SEVIRI observations using observation-minus-background and observation-minus-analysis statistics. Remote Sens., 8, https://doi.org/10.3390/rs8070581.
——, D. Simonin, S. L. Dance, N. K. Nichols, and S. P. Ballard, 2016b: Diagnosing observation error correlations for Doppler radar radial winds in the met office UKV model using observation-minus-background and observation-minus-analysis statistics. Mon. Wea. Rev., 144, 3533–3551, https://doi.org/10.1175/MWR-D-15-0340.1.
——, S. L. Dance, and N. K. Nichols, 2017: On diagnosing observation-error statistics with local ensemble data assimilation. Q. J. R. Meteorol. Soc., 143, 2677–2686, https://doi.org/10.1002/qj.3117.
——, E. Bauernschubert, S. L. Dance, N. K. Nichols, R. Potthast, and D. Simonin, 2019: Observation error statistics for Doppler radar radial wind superobservations assimilated into the DWD COSMO-KENDA system. Mon. Wea. Rev., 147, 3351–3364, https://doi.org/10.1175/MWR-D-19-0104.1.
Weston, P.P., 2011: Progress towards the implementation of correlated observation errors in 4D-Var. Forecasting Research Technical Report 560, Met Office, Exeter, UK.
——, W. Bell, and J. R. Eyre, 2014: Accounting for correlated error in the assimilation of high-resolution sounder data. Q. J. R. Meteorol. Soc., 140, 2420–2429, https://doi.org/10.1002/qj.2306.
Wu, P. Y., S. C. Yang, C. C. Tsai, and H. W. Cheng, 2020: Convective-scale sampling error and its impact on the ensemble radar data assimilation system: A case study of a heavy rainfall event on 16 June 2008 in Taiwan. Mon. Wea. Rev., 148, 3631–3652, https://doi.org/10.1175/MWR-D-19-0319.1.
Yang, S. C., Z. M. Huang, C. Y. Huang, C. C. Tsai, and T. K. Yeh, 2020: A case study on the impact of ensemble data assimilation with GNSS-zenith total delay and radar data on heavy rainfall prediction. Mon. Wea. Rev., 148, 1075–1098, https://doi.org/10.1175/MWR-D-18-0418.1.
Yin J. and Zhang Y., 2013: Alternative gradient algorithms for computing the nearest correlation matrix. Appl. Math. Comput., 219, pp. 7591-7599, https://doi.org/10.1016/j.amc.2013.01.045.
Zhang, F., Y. Weng, J. A. Sippel, Z. Meng, and C. H. Bishop, 2009: Cloud-resolving hurricane initialization and prediction through assimilation of Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 137, 2105–2125, https://doi.org/10.1175/2009MWR2645.1.
指導教授 楊舒芝(Shu-Chih Yang) 審核日期 2021-1-5
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