衛星放射輝度データ同化の観測演算子に対する機械学習アプローチ
観測演算子（OO: Observation Operator）は、データ同化（DA: Data Assimilation）において必要不可欠であり、モデル変数から観測値相当量を導出する。衛星DAでは、衛星マイクロ波輝度温度（BT: Brightness Temperature）のOOは、通常、放射伝達モデル（RTM: Radiative Transfer Model）に基づき、バイアス補正の手続きを用いる。物理ベースのRTMを用いずにOOを得る可能性を探るため、本研究では機械学習（ML: Machine Learning）をOOとして適用し（ML-OO）、晴天条件における海上の改良型マイクロ波...
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| Published in | 気象集誌. 第2輯 Vol. 101; no. 1; pp. 79 - 95 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English Japanese |
| Published |
公益社団法人 日本気象学会
2023
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 0026-1165 2186-9057 |
| DOI | 10.2151/jmsj.2023-005 |
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| Abstract | 観測演算子（OO: Observation Operator）は、データ同化（DA: Data Assimilation）において必要不可欠であり、モデル変数から観測値相当量を導出する。衛星DAでは、衛星マイクロ波輝度温度（BT: Brightness Temperature）のOOは、通常、放射伝達モデル（RTM: Radiative Transfer Model）に基づき、バイアス補正の手続きを用いる。物理ベースのRTMを用いずにOOを得る可能性を探るため、本研究では機械学習（ML: Machine Learning）をOOとして適用し（ML-OO）、晴天条件における海上の改良型マイクロ波探査計（AMSU-A: Advanced Microwave Sounding Unit-A) チャンネル6，7および陸海両上のチャンネル8のBTを同化した。非静力学正二十面体大気モデル（NICAM）と局所アンサンブル変換カルマンフィルタ（LETKF）からなる参照システムを使用した。TOVSのための放射伝達（RTTOV: Radiative Transfer for TOVS）をOOとしてシステムに実装し、独立したバイアス補正手続きを組み合わせた(RTTOV-OO)。参照システムを使って従来型観測とBTを同化するDA実験を1ヶ月間行った。この実験で得られたモデル予報は、MLモデルを学習させML-OOを得るための観測と対にした。さらに、3つのDA実験を行い、ML-OOを用いた従来型観測とBTのDAは、RTTOV-OOのそれと比べて若干劣るが、従来型観測のみに基づく同化よりも良いことを明らかにした。また、ML-OOはバイアスを内部で処理し、それによりシステム全体の枠組みを簡略化した。提案されたML-OOは、(1)衛星特性に大きな変化がある場合にバイアスを現実的に扱えない、(2)多くのチャンネルに適用できない、(3)精度や計算速度においてRTTOV-OOと比較して性能が低下する、(4)物理ベースのRTMを依然として学習用に使用していることによる制限がある。今後の研究により、これらの欠点を軽減し、それにより提案されたML-OOを改善することが可能である。 |
|---|---|
| AbstractList | 観測演算子（OO: Observation Operator）は、データ同化（DA: Data Assimilation）において必要不可欠であり、モデル変数から観測値相当量を導出する。衛星DAでは、衛星マイクロ波輝度温度（BT: Brightness Temperature）のOOは、通常、放射伝達モデル（RTM: Radiative Transfer Model）に基づき、バイアス補正の手続きを用いる。物理ベースのRTMを用いずにOOを得る可能性を探るため、本研究では機械学習（ML: Machine Learning）をOOとして適用し（ML-OO）、晴天条件における海上の改良型マイクロ波探査計（AMSU-A: Advanced Microwave Sounding Unit-A) チャンネル6，7および陸海両上のチャンネル8のBTを同化した。非静力学正二十面体大気モデル（NICAM）と局所アンサンブル変換カルマンフィルタ（LETKF）からなる参照システムを使用した。TOVSのための放射伝達（RTTOV: Radiative Transfer for TOVS）をOOとしてシステムに実装し、独立したバイアス補正手続きを組み合わせた(RTTOV-OO)。参照システムを使って従来型観測とBTを同化するDA実験を1ヶ月間行った。この実験で得られたモデル予報は、MLモデルを学習させML-OOを得るための観測と対にした。さらに、3つのDA実験を行い、ML-OOを用いた従来型観測とBTのDAは、RTTOV-OOのそれと比べて若干劣るが、従来型観測のみに基づく同化よりも良いことを明らかにした。また、ML-OOはバイアスを内部で処理し、それによりシステム全体の枠組みを簡略化した。提案されたML-OOは、(1)衛星特性に大きな変化がある場合にバイアスを現実的に扱えない、(2)多くのチャンネルに適用できない、(3)精度や計算速度においてRTTOV-OOと比較して性能が低下する、(4)物理ベースのRTMを依然として学習用に使用していることによる制限がある。今後の研究により、これらの欠点を軽減し、それにより提案されたML-OOを改善することが可能である。 |
| Author | 三好, 建正 寺崎, 康児 |
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| ContentType | Journal Article |
| DOI | 10.2151/jmsj.2023-005 |
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| References | Chantry, M., S. Hatfield, P. Dueben, I. Polichtchouk, and T. Palmer, 2021: Machine learning emulation of gravity wave drag in numerical weather forecasting. J. Adv. Model. Earth Syst., 13, e2021MS002477, doi:10.1029/2021MS002477. Rodríguez-Fernández, N., P. de Rosnay, C. Albergel, P. Richaume, F. Aires, C. Prigent, and Y. Kerr, 2019: SMOS neural network soil moisture data assimilation in a land surface model and atmospheric impact. Remote Sens., 11, 1334, doi:10.3390/rs11111334. Harris, B. A., and G. Kelly, 2001: A satellite radiance-bias correction scheme for data assimilation. Quart. J. Roy. Meteor. Soc., 127, 1453-1468. Pal, A., S. Mahajan, and M. R. Norman, 2019: Using deep neural networks as cost-effective surrogate models for super-parameterized E3SM radiative transfer. Geophys. Res. Lett., 46, 6069-6079. Satoh, M., H. Tomita, H. Yashiro, H. Miura, C. Kodama, T. Seiki, A. T. Noda, Y. Yamada, D. Goto, M. Sawada, T. Miyoshi, Y. Niwa, M. Hara, T. Ohno, S.-i Iga, T. Arakawa, T. Inoue, and H. Kubokawa, 2014: The Nonhydrostatic Icosahedral Atmospheric Model: Description and development. Prog. Earth Planet. Sci., 1, 18, doi:10.1186/s40645-014-0018-1. O'Malley, T., E. Bursztein, J. Long, F. Chollet, H. Jin, and L. Invernizzi, 2019: KerasTuner. [Available at https://github.com/keras-team/keras-tuner.] Krasnopolsky, V. M., M. S. Fox-Rabinovitz, and A. A. Belochitski, 2008: Decadal climate simulations using accurate and fast neural network emulation of full, longwave and shortwave, radiation. Mon. Wea. Rev., 136, 3683-3695. Kingma, D. P., and J. Ba, 2014: Adam: A method for stochastic optimization. [Available at https://arxiv.org/abs/1412.6980#.] Grody, N., J. Zhao, R. Ferraro, F. Weng, and R. Boers, 2001: Determination of precipitable water and cloud liquid water over oceans from the NOAA 15 advanced microwave sounding unit. J. Geophys. Res., 106, 2943-2953. Boukabara, S. A., V. Krasnopolsky, S. G. Penny, J. Q. Stewart, A. McGovern, D. Hall, J. E. Ten Hoeve, J. Hickey, H.-L. A. Huang, J. K. Williams, K. Ide, P. Tissot, S. E. Haupt, K. S. Casey, N. Oza, A. J. Geer, E. S. Maddy, and R. N. Hoffman, 2021: Outlook for exploiting artificial intelligence in the Earth and environmental sciences. Bull. Amer. Meteor. Soc., 102, E1016-E1032. Kwon, Y., B. A. Forman, J. A. Ahmad, S. V. Kumar, and Y. Yoon, 2019: Exploring the utility of machine learning-based passive microwave brightness temperature data assimilation over terrestrial snow in High Mountain Asia. Remote Sens., 11, 2265, doi:10.3390/rs11192265. Miyoshi, T., Y. Sato, and T. Kadowaki, 2010: Ensemble Kalman filter and 4D-Var intercomparison with the Japanese operational global analysis and prediction system. Mon. Wea. Rev., 138, 2846-2866. Okamoto, K., M. Kazumori, and H. Owada, 2005: The assimilation of ATOVS radiances in the JMA GIobal Analysis System. J. Meteor. Soc. Japan, 83, 201-217. Terasaki, K., and T. Miyoshi, 2017: Assimilating AMSU-A radiances with the NICAM-LETKF. J. Meteor. Soc. Japan, 95, 433-446. Bormann, N., A. Fouilloux, and W. Bell, 2013: Evaluation and assimilation of ATMS data in the ECMWF system. J. Geophys. Res.: Atmos., 118, 12970-12980. Glorot, X., A. Bordes, and Y. Bengio, 2011: Deep sparse rectifier neural networks. Proceeding of the 14th International Conference on Artificial Intelligence and Statistics (AISTAT), Fort Lauderdale, FL, USA, 315-323. [Available at http://proceedings.mlr.press/v15/glorot11a/glorot11a.pdf.] Janjić, T., N. Bormann, M. Bocquet, J. A. Carton, S. E. Cohn, S. L. Dance, S. N. Losa, N. K. Nichols, R. Potthast, J. A. Waller, and P. Weston, 2018: On the representation error in data assimilation. Quart. J. Roy. Meteor. Soc., 144, 1257-1278. Ott, J., M. Pritchard, N. Best, E. Linstead, M. Curcic, and P. Baldi, 2020: A Fortran-Keras deep learning bridge for scientific computing. Sci. Programming, 2020, 8888811, doi:10.1155/2020/8888811. Bengio, Y., 2012: Practical recommendations for gradient-based training of deep architectures. Neural Networks: Tricks of the Trade. Montavon, G., G. B. Orr, and K. R Müller (eds.), Lecture Notes in Computer Science, vol. 7700, Springer, Berlin, Heidelberg, 437-478. Kalnay, E., 2002: Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press, 368 pp. Zou, C.-Z., and W. Wang, 2011: Intersatellite calibration of AMSU-A observations for weather and climate applications: AMSU-A intersatellite calibration. J. Geophys. Res., 116, D23113, doi:10.1029/2011JD016205. Alvarado, M. J., V. H. Payne, E. J. Mlawer, G. Uymin, M. W. Shephard, K. E. Cady-Pereira, J. S. Delamere, and J.-L. Moncet, 2013: Performance of the Line-By-Line Radiative Transfer Model (LBLRTM) for temperature, water vapor, and trace gas retrievals: Recent updates evaluated with IASI case studies. Atmos. Chem. Phys., 13, 6687-6711. Hunt, B. R., E. J. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Phys. D, 230, 112-126. Dee, D. P., 2004: Variational bias correction of radiance data in the ECMWF system. Proceedings of the ECMWF Workshop on Assimilation of High spectral resolution sounders in NWP, Reading, UK, 97-112. Derber, J. C., and W.-S. Wu, 1998: The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Mon. Wea. Rev., 126, 2287-2299. Zhou, Y., and C. Grassotti, 2020: Development of a machine learning-based radiometric bias correction for NOAA's Microwave Integrated Retrieval System (MiRS). Remote Sens., 12, 3160, doi:10.3390/rs12193160. Eyre, J. R., S. J. English, and M. Forsythe, 2020: Assimilation of satellite data in numerical weather prediction. Part I: The early years. Quart. J. Roy. Meteor. Soc., 146, 49-68. Saunders, R., M. Matricardi, and P. Brunel, 1999: An improved fast radiative transfer model for assimilation of satellite radiance observations. Quart. J. Roy. Meteor. Soc., 125, 1407-1425. Saunders, R., J. Hocking, E. Turner, P. Rayer, D. Rundle, P. Brunel, J. Vidot, P. Roquet, M. Matricardi, A. Geer, N. Bormann, and C. Lupu, 2018: An update on the RTTOV fast radiative transfer model (currently at version 12). Geosci. Model Dev., 11, 2717-2737. Kotsuki, S., Y. Ota, and T. Miyoshi, 2017: Adaptive covariance relaxation methods for ensemble data assimilation: Experiments in the real atmosphere. Quart. J. Roy. Meteor. Soc., 143, 2001-2015. Scheck, L., 2021: A neural network based forward operator for visible satellite images and its adjoint. J. Quant. Spectrosc. Radiat. Transfer, 274, 107841, doi:10.1016/j.jqsrt.2021.107841. Hocking, J., 2019: RTTOV v12 quick start guide. NWP SAF, 11 pp. [Available at https://nwp-saf.eumetsat.int/site/download/documentation/rtm/docs_rttov12/rttovquick-start.pdf.] Whitaker, J. S., and T. M. Hamill, 2012: Evaluating methods to account for system errors in ensemble data assimilation. Mon. Wea. Rev., 140, 3078-3089. Hatfield, S., M. Chantry, P. Düeben, P. Lopez, A. Geer, and T. Palmer, 2021: Building tangent-linear and adjoint models for data assimilation with neural networks. J. Adv. Model. Earth Syst., 13, e2021MS002521, doi:10.1029/2021MS002521. Rivera, J. P., J. Verrelst, J. Gómez-Dans, J. Muñoz-Marí, J. Moreno, and G. Camps-Valls, 2015: An emulator toolbox to approximate radiative transfer models with statistical learning. Remote Sens., 7, 9347-9370. |
| References_xml | – reference: Okamoto, K., M. Kazumori, and H. Owada, 2005: The assimilation of ATOVS radiances in the JMA GIobal Analysis System. J. Meteor. Soc. Japan, 83, 201-217. – reference: Hunt, B. R., E. J. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Phys. D, 230, 112-126. – reference: Ott, J., M. Pritchard, N. Best, E. Linstead, M. Curcic, and P. Baldi, 2020: A Fortran-Keras deep learning bridge for scientific computing. Sci. Programming, 2020, 8888811, doi:10.1155/2020/8888811. – reference: Boukabara, S. A., V. Krasnopolsky, S. G. Penny, J. Q. Stewart, A. McGovern, D. Hall, J. E. Ten Hoeve, J. Hickey, H.-L. A. Huang, J. K. Williams, K. Ide, P. Tissot, S. E. Haupt, K. S. Casey, N. Oza, A. J. Geer, E. S. Maddy, and R. N. Hoffman, 2021: Outlook for exploiting artificial intelligence in the Earth and environmental sciences. Bull. Amer. Meteor. Soc., 102, E1016-E1032. – reference: Bengio, Y., 2012: Practical recommendations for gradient-based training of deep architectures. Neural Networks: Tricks of the Trade. Montavon, G., G. B. Orr, and K. R Müller (eds.), Lecture Notes in Computer Science, vol. 7700, Springer, Berlin, Heidelberg, 437-478. – reference: Kalnay, E., 2002: Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press, 368 pp. – reference: Miyoshi, T., Y. Sato, and T. Kadowaki, 2010: Ensemble Kalman filter and 4D-Var intercomparison with the Japanese operational global analysis and prediction system. Mon. Wea. Rev., 138, 2846-2866. – reference: Dee, D. P., 2004: Variational bias correction of radiance data in the ECMWF system. Proceedings of the ECMWF Workshop on Assimilation of High spectral resolution sounders in NWP, Reading, UK, 97-112. – reference: Derber, J. C., and W.-S. Wu, 1998: The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Mon. Wea. Rev., 126, 2287-2299. – reference: Terasaki, K., and T. Miyoshi, 2017: Assimilating AMSU-A radiances with the NICAM-LETKF. J. Meteor. Soc. Japan, 95, 433-446. – reference: Zou, C.-Z., and W. Wang, 2011: Intersatellite calibration of AMSU-A observations for weather and climate applications: AMSU-A intersatellite calibration. J. Geophys. Res., 116, D23113, doi:10.1029/2011JD016205. – reference: Harris, B. A., and G. Kelly, 2001: A satellite radiance-bias correction scheme for data assimilation. Quart. J. Roy. Meteor. Soc., 127, 1453-1468. – reference: Whitaker, J. S., and T. M. Hamill, 2012: Evaluating methods to account for system errors in ensemble data assimilation. Mon. Wea. Rev., 140, 3078-3089. – reference: Kwon, Y., B. A. Forman, J. A. Ahmad, S. V. Kumar, and Y. Yoon, 2019: Exploring the utility of machine learning-based passive microwave brightness temperature data assimilation over terrestrial snow in High Mountain Asia. Remote Sens., 11, 2265, doi:10.3390/rs11192265. – reference: Bormann, N., A. Fouilloux, and W. Bell, 2013: Evaluation and assimilation of ATMS data in the ECMWF system. J. Geophys. Res.: Atmos., 118, 12970-12980. – reference: Glorot, X., A. Bordes, and Y. Bengio, 2011: Deep sparse rectifier neural networks. Proceeding of the 14th International Conference on Artificial Intelligence and Statistics (AISTAT), Fort Lauderdale, FL, USA, 315-323. [Available at http://proceedings.mlr.press/v15/glorot11a/glorot11a.pdf.] – reference: Zhou, Y., and C. Grassotti, 2020: Development of a machine learning-based radiometric bias correction for NOAA's Microwave Integrated Retrieval System (MiRS). Remote Sens., 12, 3160, doi:10.3390/rs12193160. – reference: O'Malley, T., E. Bursztein, J. Long, F. Chollet, H. Jin, and L. Invernizzi, 2019: KerasTuner. [Available at https://github.com/keras-team/keras-tuner.] – reference: Grody, N., J. Zhao, R. Ferraro, F. Weng, and R. Boers, 2001: Determination of precipitable water and cloud liquid water over oceans from the NOAA 15 advanced microwave sounding unit. J. Geophys. Res., 106, 2943-2953. – reference: Scheck, L., 2021: A neural network based forward operator for visible satellite images and its adjoint. J. Quant. Spectrosc. Radiat. Transfer, 274, 107841, doi:10.1016/j.jqsrt.2021.107841. – reference: Satoh, M., H. Tomita, H. Yashiro, H. Miura, C. Kodama, T. Seiki, A. T. Noda, Y. Yamada, D. Goto, M. Sawada, T. Miyoshi, Y. Niwa, M. Hara, T. Ohno, S.-i Iga, T. Arakawa, T. Inoue, and H. Kubokawa, 2014: The Nonhydrostatic Icosahedral Atmospheric Model: Description and development. Prog. Earth Planet. Sci., 1, 18, doi:10.1186/s40645-014-0018-1. – reference: Saunders, R., J. Hocking, E. Turner, P. Rayer, D. Rundle, P. Brunel, J. Vidot, P. Roquet, M. Matricardi, A. Geer, N. Bormann, and C. Lupu, 2018: An update on the RTTOV fast radiative transfer model (currently at version 12). Geosci. Model Dev., 11, 2717-2737. – reference: Hatfield, S., M. Chantry, P. Düeben, P. Lopez, A. Geer, and T. Palmer, 2021: Building tangent-linear and adjoint models for data assimilation with neural networks. J. Adv. Model. Earth Syst., 13, e2021MS002521, doi:10.1029/2021MS002521. – reference: Saunders, R., M. Matricardi, and P. Brunel, 1999: An improved fast radiative transfer model for assimilation of satellite radiance observations. Quart. J. Roy. Meteor. Soc., 125, 1407-1425. – reference: Eyre, J. R., S. J. English, and M. Forsythe, 2020: Assimilation of satellite data in numerical weather prediction. Part I: The early years. Quart. J. Roy. Meteor. Soc., 146, 49-68. – reference: Hocking, J., 2019: RTTOV v12 quick start guide. NWP SAF, 11 pp. [Available at https://nwp-saf.eumetsat.int/site/download/documentation/rtm/docs_rttov12/rttovquick-start.pdf.] – reference: Kotsuki, S., Y. Ota, and T. Miyoshi, 2017: Adaptive covariance relaxation methods for ensemble data assimilation: Experiments in the real atmosphere. Quart. J. Roy. Meteor. Soc., 143, 2001-2015. – reference: Alvarado, M. J., V. H. Payne, E. J. Mlawer, G. Uymin, M. W. Shephard, K. E. Cady-Pereira, J. S. Delamere, and J.-L. Moncet, 2013: Performance of the Line-By-Line Radiative Transfer Model (LBLRTM) for temperature, water vapor, and trace gas retrievals: Recent updates evaluated with IASI case studies. Atmos. Chem. Phys., 13, 6687-6711. – reference: Kingma, D. P., and J. Ba, 2014: Adam: A method for stochastic optimization. [Available at https://arxiv.org/abs/1412.6980#.] – reference: Rivera, J. P., J. Verrelst, J. Gómez-Dans, J. Muñoz-Marí, J. Moreno, and G. Camps-Valls, 2015: An emulator toolbox to approximate radiative transfer models with statistical learning. Remote Sens., 7, 9347-9370. – reference: Rodríguez-Fernández, N., P. de Rosnay, C. Albergel, P. Richaume, F. Aires, C. Prigent, and Y. Kerr, 2019: SMOS neural network soil moisture data assimilation in a land surface model and atmospheric impact. Remote Sens., 11, 1334, doi:10.3390/rs11111334. – reference: Chantry, M., S. Hatfield, P. Dueben, I. Polichtchouk, and T. Palmer, 2021: Machine learning emulation of gravity wave drag in numerical weather forecasting. J. Adv. Model. Earth Syst., 13, e2021MS002477, doi:10.1029/2021MS002477. – reference: Pal, A., S. Mahajan, and M. R. Norman, 2019: Using deep neural networks as cost-effective surrogate models for super-parameterized E3SM radiative transfer. Geophys. Res. Lett., 46, 6069-6079. – reference: Janjić, T., N. Bormann, M. Bocquet, J. A. Carton, S. E. Cohn, S. L. Dance, S. N. Losa, N. K. Nichols, R. Potthast, J. A. Waller, and P. Weston, 2018: On the representation error in data assimilation. Quart. J. Roy. Meteor. Soc., 144, 1257-1278. – reference: Krasnopolsky, V. M., M. S. Fox-Rabinovitz, and A. A. Belochitski, 2008: Decadal climate simulations using accurate and fast neural network emulation of full, longwave and shortwave, radiation. Mon. Wea. Rev., 136, 3683-3695. |
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| Snippet | 観測演算子（OO: Observation Operator）は、データ同化（DA: Data Assimilation）において必要不可欠であり、モデル変数から観測値相当量を導出する。衛星DAでは、衛星マイクロ波輝度温度（BT: Brightness Temperature）のOOは、通常、放射伝達モデル（RTM:... |
| SourceID | jstage |
| SourceType | Publisher |
| StartPage | 79 |
| SubjectTerms | forward operator machine learning neural network observation operator satellite radiance data assimilation |
| Title | 衛星放射輝度データ同化の観測演算子に対する機械学習アプローチ |
| URI | https://www.jstage.jst.go.jp/article/jmsj/101/1/101_2023-005/_article/-char/ja |
| Volume | 101 |
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| ispartofPNX | 気象集誌. 第2輯, 2023, Vol.101(1), pp.79-95 |
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