PENGARUH ASIMILASI DATA SATELIT HIMAWARI-8 PADA PEMODELAN CUACA WRF-ARW UNTUK PREDIKSI SIKLON TROPIS
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Abstract
Peningkatan akurasi model WRF-ARW untuk prediksi siklon tropis yang berpotensi terjadi di sekitar wilayah Indonesia dan memengaruhi kondisi cuacanya menjadi suatu kajian yang penting dilakukan. Salah satu cara perbaikan prediksi yaitu dengan menerapkan asimilasi data menggunakan data radians satelit Himawari-8. Data radians satelit himawari-8 dengan resolusi spasial dan temporal yang tinggi memiliki banyak keuntungan untuk wilayah Indonesia sehingga dapat dimanfaatkan untuk perbaikan kondisi awal model. Tujuan dari penelitian ini yaitu untuk mengidentifikasi pengaruh asimilasi data satelit Himawari-8 menggunakan teknik 3DVAR pada prediksi siklon tropis. Data satelit yang digunakan untuk asimilasi data yaitu data kanal water vapor dan kanal infra merah lainnya. Prosedur uji parameterisasi fisis pada skema konveksi dan mikrofisis diterapkan sebelum proses asimilasi data. Asimilasi data diterapkan pada prediksi siklon tropis Yvette (2016) dan Veronica (2019). Parameterisasi fisis dengan skema konveksi Kain-Fritsch dan skema mikrofisis WSM3 merupakan skema yang paling baik dalam menghasilkan prediksi siklon tropis. Asimilasi data dari setiap skema yang diujikan memberikan pengaruh dalam proses intensifikasi siklon tropis menjadi lebih kuat dan lebih cepat. Asimilasi data satelit Himawari-8 menggunakan data dari kanal water vapor menghasilkan prediksi siklon tropis yang lebih baik dibandingkan dengan menggunakan semua kanal infra merah. Asimilasi data satelit Himawari-8 menunjukkan adanya perbaikan prediksi yang ditunjukkan dengan pengurangan absolute error mencapai 49,1% pada lintasan siklon tropis, 38,6% pada tekanan udara minimum, 35,4% pada kecepatan angin maksimum dan 10,6% pada parameter curah hujan.
Improving the accuracy of WRF-ARW models for prediction of tropical cyclones potentially occur around Indonesia and affect its weather is an important study to be carried out. One of method to improve predictions is applying data assimilation using Himawari-8 radiance satellite data. Radiance data from Himawari-8 satellite with high spatial and temporal resolution has many advantages for Indonesia so that it can be utilized to improve the initial conditions of the model. The purpose of this study is to identify the effect of the Himawari-8 satellite data assimilation using 3DVAR techniques on tropical cyclone predictions. Satellite data used for data assimilation are radiance data from water vapor channels and other infrared channels. Procedure of physical parameterization test on convection and microphysics scheme are applied before the data assimilation process. Data assimilation is applied on prediction of tropical cyclone Yvette (2016) and Veronica (2019). Physical parameterization with Kain-Fritsch convection scheme and WSM3 microphysics scheme are the best schemes in producing tropical cyclone predictions. Data assimilation from each of the schemes tested has an impact on the intensification process of tropical cyclones becoming stronger and faster. Assimilation of Himawari-8 satellite data using data from water vapor channel produces better tropical cyclones predictions compared to using all infrared channels. The assimilation of Himawari-8 satellite data showed an improvement in predictions as indicated by a reduction in absolute error reaching 49.1% on tropical cyclone track, 38.6% on minimum central pressure, 35.4% on maximum wind speed and 10.6% on rainfall parameters.
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References
Adler, R. F., & Rodgers, E. B. (1977). Satellite-observed latent heat release in a tropical cyclone. Monthly Weather Review, 105(8), 956-963. doi:10.1175/1520-0493(1977)105<0956:SOLHRI>2.0.CO;2
Barker, D. M., Huang, W., Guo, Y.-R., Bourgeois, A., & Xiao, Q. (2004). A three-dimensional variational data assimilation system for MM5: Implementation and initial results. Monthly Weather Review, 132(4), 897-914. doi:10.1175/1520-0493(2004)132<0897:ATVDAS>2.0.CO;2
Bauer, P., Thorpe, A., & Brunet, G. (2015). The quiet revolution of numerical weather prediction. Nature, 525(7567), 47. doi: 10.1038/nature14956
Benjamin, S. G., Brown, J. M., Brunet, G., Lynch, P., Saito, K., & Schlatter, T. W. (2019). 100 years of progress in forecasting and NWP applications. Meteorological Monographs, 59, 13.11-13.67. doi: 10.1175/AMSMONOGRAPHS-D-18-0020.1
Bessho, K., Date, K., Hayashi, M., Ikeda, A., Imai, T., Inoue, H., Kumagai, Y., Miyakawa, T., Murata, H., & Ohno, T. (2016). An introduction to Himawari-8/9—Japan’s new-generation geostationary meteorological satellites. Journal of the Meteorological Society of Japan. Ser. II, 94(2), 151-183. doi: 10.2151/jmsj.2016-009
Biswas, M. K., Bernardet, L., & Dudhia, J. (2014). Sensitivity of hurricane forecasts to cumulus parameterizations in the HWRF model. Geophysical Research Letters, 41(24), 9113-9119. doi: 10.1002/2014GL062071
BMKG. (2009). Dampak Siklon Tropis. Retrieved from http://meteo.bmkg.go.id/siklon/learn/07/id
Chen, G., Yu, H., & Cao, Q. (2015). Evaluation of Tropical Cyclone Forecasts from Operational Global Models Over the Western North Pacific in 2013. Tropical Cyclone Research and Review, 4(1), 18-26.
Chen, L., Li, Y., & Cheng, Z. (2010). An overview of research and forecasting on rainfall associated with landfalling tropical cyclones. Advances in Atmospheric Sciences, 27(5), 967-976. doi: 10.1007/s00376-010-8171-y
Choudhury, D., & Das, S. (2017). The sensitivity to the microphysical schemes on the skill of forecasting the track and intensity of tropical cyclones using WRF-ARW model. Journal of Earth System Science, 126(4), 57. doi: 10.1007/s12040-017-0830-2
CIMSS. (2019). Community Satellite Processing Package for Geostationary Data. Retrieved from http://cimss.ssec.wisc.edu/csppgeo
Emanuel, K. A. (1986). An air-sea interaction theory for tropical cyclones. Part I: Steady-state maintenance. Journal of the atmospheric sciences, 43(6), 585-605. doi: 10.1175/1520-0469(1986)043<0585:AASITF>2.0.CO;2
Gallus Jr, W. A. (1999). Eta simulations of three extreme precipitation events: Sensitivity to resolution and convective parameterization. Weather and Forecasting, 14(3), 405-426. doi: 10.1175/1520-0434(1999)014<0405:ESOTEP>2.0.CO;2
Gopalakrishnan, D., & Chandrasekar, A. (2018). On the improved predictive skill of WRF model with regional 4DVar initialization: a study with North Indian Ocean tropical cyclones. IEEE Transactions on Geoscience and Remote Sensing, 56(6), 3350-3357. doi: 10.1109/TGRS.2018.2798623
Heming, J. (2017). Tropical cyclone tracking and verification techniques for Met Office numerical weather prediction models. Meteorological Applications, 24(1), 1-8. doi: 10.1002/met.1599
Hong, S.-Y., Dudhia, J., & Chen, S.-H. (2004). A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Monthly Weather Review, 132(1), 103-120. doi: 10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2
Ide, K., Courtier, P., Ghil, M., & Lorenc, A. C. (1997). Unified Notation for Data Assimilation: Operational, Sequential and Variational (gtSpecial IssueltData Assimilation in Meteology and Oceanography: Theory and Practice). Journal of the Meteorological Society of Japan. Ser. II, 75(1B), 181-189. doi: 10.2151/jmsj1965.75.1B_181
Jung, J. A., Zapotocny, T. H., Le Marshall, J. F., & Treadon, R. E. (2008). A two-season impact study of NOAA polar-orbiting satellites in the NCEP global data assimilation system. Weather and Forecasting, 23(5), 854-877. doi: 10.1175/2008WAF2007065.1
Kain, J. S. (2004). The Kain–Fritsch convective parameterization: an update. Journal of Applied Meteorology, 43(1), 170-181. doi: 10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2
Kiki, & Hendriadi, R. (2017). Siklon Tropis Yvette dan Dampaknya Terhadap Kondisi Cuaca di Indonesia (19-23 Desember 2016). Pusat Meteorologi Publik. Badan Meteorologi Klimatologi dan Geofisika. Jakarta.
Kuo, H.-L. (1974). Further studies of the parameterization of the influence of cumulus convection on large-scale flow. Journal of the atmospheric sciences, 31(5), 1232-1240. doi: 10.1175/1520-0469(1974)031<1232:FSOTPO>2.0.CO;2
Kushardono, D. (2012). Kajian Satelit Penginderaan Jauh Cuaca Generasi Baru Himawari 8 dan 9. Jurnal Inderaja, 3(5).
Li, X., & Zou, X. (2017). Bias characterization of CrIS radiances at 399 selected channels with respect to NWP model simulations. Atmospheric research, 196, 164-181. doi: 10.1016/j.atmosres.2017.06.007
Liu, Z., & Barker, D. (2006). Radiance assimilation in WRF-Var: implementation and initial results. Paper presented at the 7th WRF users workshop.
Lu, J., Feng, T., Li, J., Cai, Z., Xu, X., Li, L., & Li, J. (2019). Impact of Assimilating Himawari?8?Derived Layered Precipitable Water With Varying Cumulus and Microphysics Parameterization Schemes on the Simulation of Typhoon Hato. Journal of Geophysical Research: Atmospheres, 124(6), 3050-3071. doi: 10.1029/2018JD029364
Ma, Z., Maddy, E. S., Zhang, B., Zhu, T., & Boukabara, S. A. (2017). Impact assessment of Himawari-8 AHI data assimilation in NCEP GDAS/GFS with GSI. Journal of Atmospheric and Oceanic Technology, 34(4), 797-815. doi: 10.1175/JTECH-D-16-0136.1
Mahala, B. K., Mohanty, P. K., & Nayak, B. K. (2015). Impact of Microphysics Schemes in the Simulation of Cyclone Phailinusing WRF Model. Procedia Engineering, 116, 655-662. doi: 10.1016/j.proeng.2015.08.342
Mohapatra, M. (2014). Tropical cyclone forecast verification by India Meteorological Department for north Indian Ocean: A review. Tropical Cyclone Research and Review, 3(4), 229-242.
Montmerle, T., Rabier, F., & Fischer, C. (2007). Relative impact of polar?orbiting and geostationary satellite radiances in the Aladin/France numerical weather prediction system. Quarterly Journal of the Royal Meteorological Society: A journal of the atmospheric sciences, applied meteorology and physical oceanography, 133(624), 655-671. doi: 10.1002/qj.34
NCEP. (2008). NCEP ADP Global Upper Air and Surface Weather Observations (PREPBUFR format). Retrieved from: https://doi.org/10.5065/Z83F-N512
NCEP. (2009). NCEP GDAS Satellite Data 2004-continuing. Retrieved from: https://doi.org/10.5065/DWYZ-Q852
NCEP. (2015). NCEP GFS 0.25 Degree Global Forecast Grids Historical Archive. Retrieved from: https://doi.org/10.5065/D65D8PWK
Neumann, C. J. (2017a). Global Guide to Tropical Cyclone Forecasting Overview. In WMO (Ed.), Global guide to tropical cyclone forecasting (pp. 11-27). Geneva, Switzerland: World Meteorological Organization.
Neumann, C. J. (2017b). A Global Tropical Cyclone Climatology. In WMO (Ed.), Global guide to tropical cyclone forecasting (pp. 28-62). Geneva, Switzerland: World Meteorological Organization.
Ngo-Duc, T., Mai, H. T., & Kieu, C. (2013). A study of the connection between tropical cyclone track and intensity errors in the WRF model. Meteorology and Atmospheric Physics, 122(1-2), 55-64. doi: 10.1007/s00703-013-0278-0
Osuri, K. K., Mohanty, U., Routray, A., Kulkarni, M. A., & Mohapatra, M. (2012). Customization of WRF-ARW model with physical parameterization schemes for the simulation of tropical cyclones over North Indian Ocean. Natural Hazards, 63(3), 1337-1359. doi: doi.org/10.1007/s11069-011-9862-0
Potter, H., DiMarco, S. F., & Knap, A. H. (2019). Tropical Cyclone Heat Potential and the Rapid Intensification of Hurricane Harvey in the Texas Bight. Journal of Geophysical Research: Oceans, 124(4), 2440-2451. doi: doi.org/10.1029/2018JC014776
Qin, Z., Zou, X., & Weng, F. (2017). Impacts of assimilating all or GOES-like AHI infrared channels radiances on QPFs over Eastern China. Tellus A: Dynamic Meteorology and Oceanography, 69(1), 1345265. doi: 10.1080/16000870.2017.1345265
Raju, P., Potty, J., & Mohanty, U. (2011). Sensitivity of physical parameterizations on prediction of tropical cyclone Nargis over the Bay of
Bengal using WRF model. Meteorology and Atmospheric Physics, 113(3-4), 125. doi: 10.1007/s00703-011-0151-y
Rosendal, H. E., & Shaw, S. L. (1982). Relationship of maximum sustained winds to minimum sea level pressure in central North Pacific tropical cyclones.
Roy, C., & Kovordanyi, R. (2018). Tropical cyclone and track forecasting. In Exploring Natural Hazards (pp. 1-48): Chapman and Hall/CRC.
Shen, F., & Min, J. (2015). Assimilating AMSU-A radiance data with the WRF hybrid En3DVAR system for track predictions of Typhoon Megi (2010). Advances in Atmospheric Sciences, 32(9), 1231-1243. doi: 10.1007/s00376-014-4239-4
Simpson, J., Ritchie, E., Holland, G., Halverson, J., & Stewart, S. (1997). Mesoscale interactions in tropical cyclone genesis. Monthly Weather Review, 125(10), 2643-2661. doi: 10.1175/1520-0493(1997)125<2643:MIITCG>2.0.CO;2
Stengel, M., Undén, P., Lindskog, M., Dahlgren, P., Gustafsson, N., & Bennartz, R. (2009). Assimilation of SEVIRI infrared radiances with HIRLAM 4D?Var. Quarterly Journal of the Royal Meteorological Society: A journal of the atmospheric sciences, applied meteorology and physical oceanography, 135(645), 2100-2109. doi: 10.1002/qj.501
Stensrud, D. J. (2009). Parameterization schemes: keys to understanding numerical weather prediction models: Cambridge University Press.
Sun, Y., Zhong, Z., & Lu, W. (2015). Sensitivity of tropical cyclone feedback on the intensity of the western Pacific subtropical high to microphysics schemes. Journal of the atmospheric sciences, 72(4), 1346-1368. doi: 10.1175/JAS-D-14-0051.1
Tjasyono, B. (2007). Meteorologi Indonesia Volume 1, Karakteristik dan Sirkulasi Atmosfer. Badan Meteorologi dan Geofisika. Jakarta.
Tofallis, C. (2014). Add or multiply? A tutorial on ranking and choosing with multiple criteria. INFORMS Transactions on education, 14(3), 109-119. doi: 10.1287/ited.2013.0124
Tuleya, R. E., & Kurihara, Y. (1982). A note on the sea surface temperature sensitivity of a numerical model of tropical storm genesis. Monthly Weather Review, 110(12), 2063-2069. doi: 10.1175/1520-0493(1982)110<2063:ANOTSS>2.0.CO;2
Wang, W., & Seaman, N. L. (1997). A comparison study of convective parameterization schemes in a mesoscale model. Monthly Weather Review, 125(2), 252-278. doi: 10.1175/1520-0493(1997)125<0252:ACSOCP>2.0.CO;2
Wang, Y., Liu, Z., Yang, S., Min, J., Chen, L., Chen, Y., & Zhang, T. (2018). Added value of assimilating Himawari?8 AHI water vapor radiances on analyses and forecasts for “7.19” severe storm over north China. Journal of Geophysical Research: Atmospheres, 123(7), 3374-3394. doi: 10.1002/2017JD027697
Warner, T. T. (2011). Numerical weather and climate prediction: Cambridge University Press.
WMO. (2013). Verification methods for tropical cyclone forecasts. WMO
WMO. (2016). Purpose and Objectives. Retrieved from http://www.wmo.int/pages/prog/www/tcp/purpose.html
Xu, D., Liu, Z., Huang, X.-Y., Min, J., & Wang, H. (2013). Impact of assimilating IASI radiance observations on forecasts of two tropical cyclones. Meteorology and Atmospheric Physics, 122(1-2), 1-18. doi: 10.1007/s00703-013-0276-2
Zou, X., Qin, Z., & Weng, F. (2011). Improved coastal precipitation forecasts with direct assimilation of GOES-11/12 imager radiances. Monthly Weather Review, 139(12), 3711-3729. doi: 10.1175/MWR-D-10-05040.1