提升數值天氣預報模式之預報力的方式，一直都在精進，其中一種改善方式為提供更合理的初始條件及邊界條件，而若使用星載之高光譜紅外線探空儀及微波探空儀，除了能夠提供三維大氣溫濕度狀態，亦可補足傳統觀測在時間及空間分布之不足。於實務上進行資料同化時，又可區分為使用輻射強度(radiances)資料與反演產品(retrieved products)，使用前者可兼顧即時性之需求，而使用後者與傳統觀測性質相近，原理也較為直觀。
過往研究曾指出，衛星遙測資料不論是輻射強度或反演產品，需考慮可能存在的系統性偏差問題。本篇研究使用美國「聯合繞極軌道衛星系統(JPSS)」中Suomi-NPP衛星上的先進技術微波探空儀(ATMS)與高光譜紅外線探空儀(CrIS)觀測資料，也將其反演產品藉由NCEP再分析資料(FNL)進行大氣熱力參數品質控制與偏差修正，結果可與歐洲中期預報中心(ECMWF)再分析資料比較後，可得到較低系統性偏差的三維大氣溫溼度剖線。
為進一步探討反演產品與輻射強度之差異及修正成效，研究中使用WRF Model及Gridpoint Statistical Interpolation(GSI)資料同化方法，探討尼伯特颱風(2016)之環境、路徑、強度變化與定量降水預報。結果顯示如能透過資料同化技術使用經誤差修正後的溫溼度反演產品於多個實驗組中，將能有較顯著的颱風預報結果，並且提高定量降水預報(QPFs)之技術得分。

Improving numerical weather prediction (NWP) model are discussed uninterruptedly. One of ways to improve the performances of NWP model is providing more reasonable initial conditions and/or boundary conditions. The hyperspetrum infrared sounder and microwave sounder make up conventional observations on special and temporal distribution with three dimensional observations of atmospheric temperature and moisture. Both of radiances and retrieved products can be assimilated into NWP. The radiances usually used in operational center with its immediacy. The use of retrieved products more simple and similar to conventional observations.
Some previous works indicate that systematic bias is found in satellite observed radiance/retrieved products. In this study, we try to reduce the uncertainty of retrieved products from Advanced Technology Microwave Sounder (ATMS) and Cross-track Infrared Sounder (CrIS) onboard NOAA/JPSS Suomi-NPP. The bias correction mothed of moisture by refer to FNL data can makes bias close to zero.
We assimilate radiance and retrieved products in NWP during the period of Typhoon Nepartak (2016). The Weather Research and Forecasting (WRF) Model and Gridpoint Statistical Interpolation (GSI) system are adapted to investigate typhoon track, intensity, environmental fields and Quantitative Precipitation Forecasts (QPFs). The results show it has better forecasts and the skill scores of QPFs after implementing the bias correction.

Atlas, R., 2005: The impact of AIRS data on weather prediction, in Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, Proc. SPIE, 5806, 599-606.
Bjerknes, V., 1911: Dynamic Meteorology and Hydrography. Part II: Kinematics Carnegie Institute. Gibson Bros, New York, USA, 4-6.
Chen, S. H., 2007: The impact of assimilation SSM/I and QuikSCAT satellite winds on Hurricane Isidore simulation, Mon. Weather Rev., 135, 549-566.
Chahine, M. T., Remote sounding of cloudy atmospheres in a single cloud layer, J. Atmos. Sci., 1974, vol. 31, pages 233 - 243.
Dee, D. P., S. Uppala, 2009: Variational bias correction of satellite radiance data in the ERA-Interim reanalysis, Q. J. R. Meteorol. Soc. 135, 1830-1841
Derber, J. C., and W.-S. Wu, 1998: The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system, Mon. Weather Rev., 126, 2287-2299.
Gambacorta, A., C. D. Barnet, 2013: Methodology and Information Content of the NOAA NESDIS Operational Channel Selection for the Cross-Track Infrared Sounder (CrIS), IEEE Trans. Geosci. Remote Sensing, VOL51, NO. 6, 3207-3216.
Goldberg, M., Y. Qu, L. McMillin, W. Wolf, L. Zhou, and M. Divakarla, 2003: AIRS 179 Near-Real-Time products and algorithms in support of operational numerical weather 180 prediction, IEEE, 41, 379.
Hou, A. Y., S. Q. Zhang, and O. Reale, 2004: Variational continuous assimilation of TMI and SSM/I rain rates: Impact on GEOS-3 hurricane analyses and forecasts, Mon. Weather Rev., 132, 2094-2109.
Holm, E., E. Andersson, A. Beljaars, P. Lopez, J-F. Mahfouf, A. Simmons, J-N. Thepaut, 2002: Assimilation and Modelling of the Hydrological Cycle: ECMWF’s Status and Plans, Research Department/ECMWF.
Hu, M., H. Shao, D. Stark, K. Newman, C. Zhou, 2014: Gridpoint Statistical Interpolation (GSI) Community version 3.3 user’s guide. 108pp.
Joiner, J., and D. Dee, 2000: An error analysis of radiance and suboptimal retrieval assimilation, Q. J. R. Meteorol. Soc., 126, 1495-1514.
Kalnay, E. D., L. T. Anderson, A. F. Bennett, A. J. Busalacchi, S. E. Cohn, P. Courtier, J. Derber, A. C. Lorenc, D. Parrish, J. Purser, N. Sato, and T. Schlatter, 1997: Data assimilation in the ocean and in the atmosphere: What should be next? J. Meteor. Soc. Japan, 75, 489-496.
Kleist, D. T., D. F. Parrish, J. C. Derber, R. Treadon, W.-S. Wu, S. Lord, 2009: Introduction of the GSI into the NCEP Global Data Assimilation System. Weather and Forecasting, 24, 1691–1705.
Le Marshall, J., J. Jung, J. Derber, R. Treadon, S. Lord, M. Goldberg, W. Wolf, H.C. Liu, J. Joiner, J. Woollen, R. Todling, P. van Delst and Y. Tahara, 2005a: AIRS hyperspectral data improves southern hemisphere forecasts, Aust. Meteorol. Mag., 54, 57-60.
Le Marshall, J., J. Jung, S. J. Lord, J. C. Derber, R. Treadon, J. Joiner, M. Goldberg, W. Wolf, H. C. Liu, 2005b: AIRS associated accomplishments at the JCSDA: First use of full spatial resolution hyperspectral data show significant improvements in global forecasts, Proc. SPIE, 5890, 58900O.
Li, J. and H. Liu, 2009: Improved hurricane track and intensity forecast using single field-of-view advanced IR sounding measurements, Geophys. Res. Lett., 36, L11813.
Li, J., P. Wang, H. Han, J. Li, J. Zheng, 2016: On the Assimilation of Satellite Sounder Data in Cloudy Skies in Numerical Weather Prediction Models., J. Meteor. Res., 30(2), 169-182, doi: 10.1007/s13351-016-5114-2.
Liu, C.-Y., J. Li, E. Weisz, T. J. Schmit, S. A. Ackerman, H.-L. Huang, 2008:Synergistic use of AIRS and MODIS radiance measurements for atmospheric profiling, Geophy. Res. Let., 35, L21802.
Liu, C.-Y., S.-C. Kuo, A. Lim, S.-C. Hsu, K.-H. Tseng, N.-C. Yeh, and Y.-C. Yang, 2016: Optimal Use of Space-Borne Advanced Infrared and Microwave Soundings for Regional Numerical Weather Prediction, Remote Sens. 8(10), 816, doi:10.3390/rs8100816
Lim, A. H. N., J. A Jung., H.-L. A. Huang, S. A. Ackerman, J. A. Otkin, 2014: Assimilation of clear sky Atmospheric Infrared Sounder radiances in short-term regional forecast using community models, J. Applies Remote Sensing, 8, 083655-1-27.
McNally, A. P., 2002: A note on the occurrence of cloud in meteorologically sensitive areas and the implications for advanced infrared sounds. Quart. J. Roy. Meteor. Soc., 128, 2551-2556.
Migliorini, S., 2012: On the Equivalence between Radiance and Retrieval Assimilation., Mon. Weath. Rev., 140, 258-265.
NOAA/NCEP, 2000: NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999 (updated daily). NCAR Computational and Information Systems Laboratory Research Data Archive, accessed 14 October 2014, doi:10.5065/D6M043C6.
Parrish, D. F., and J. C. Derber, 1992: The National Meteorological Center’s spectral statistical-interpolation system. Mon. Wea. Rev., 120, 1747–1763.
Pavelin, E., S. English, and J. Eyre, 2008: The assimilation of cloud affected infrared satellite radiances for numerical prediction, Q. J. R. Meteorol. Soc., 134, 737-749.
Price, J. F., 1981: Upper Ocean Response to a Hurricane. J. Phy. Oceanogr., 11, 153–175.
Price, J. F., T. B. Sanford, G. Z. Forristall, 1994: Forced stage response to a moving hurricane. J. Phy. Oceanogr., 24, 233–260.
Pu, Z., W.-K. Tao, S. A. Braun, J. Simpson, Y. Jia, J. Halverson, A. Hou, and W. Olson, 2002: The impact of TRMM data on mesoscale numerical simulation of supertyphoon Paka, Mon. Weather Rev., 130, 2448-2458.
Pu, Z., X. Li, C. Velden, S. Aberson, and W. T. Liu, 2008: Impact of aircraft dropsonde and satellite wind data on the numerical simulation of two landfalling tropical storms during TCSP, Weather Forecast., 23, 62-79.
Pu, Z., L. Zhang, 2010: Validation of Atmospheric Infrared Sounder temperature and moisture profiles over tropical oceans and their impact on numerical simulations of tropical cyclones, J. Geophys. Res., 115, D24114, doi:10.1029/2010JD014258.
Purser, J., Wan-Shu Wu, David F. Parrish, and Nigel M. Roberts, 2003: Numerical Aspects of the Application of Recursive Filters to Variational Statistical Analysis. Part II: Spatially Inhomogeneous and Anisotropic General Covariances. Monthly Weather Review, 131, 1536-1548.
Rabier, F., A. McNally, E. Anderson, P. Courtier, P. Unden, J. Eyre, A. Hollingsworth, and F. Bouttier, 1998: The ECMWF implementation of three-dimensional variational assimilation (3D-Var). II: Structure functions. Quart. J. Roy. Meteor. Soc., 124, 1809–1829.
Reale, O., J. Susskind, R. Rosenberg, E. Brin, E. Liu, L. P. Riishojgaard, J. Terry, and J. C. Jusem, 2008: Imroving forecast skill by assimilation of quality –controlled AIRS temperature retrievals under partially cloudy conditions, Geophys. Res. Lett., 35, L08809.
Rosenkranz, P., 2000: Retrieval of temperature and moisture proles from AMSU-A and 198 AMSU-B measurements, IEEE, 39, 2429.
Soden, B. J., C. S. Velden, and E. E. Tuleya, 2001: The impact of satellite winds on experimental GFDL hurricane model forecasts, Mon. Weather Rev., 129, 835-852.
Susskind, Barnet, and Blaisdell, 2003: Retrieval of atmospheric and surface parameters fromAIRS/AMSU/HSB data in the presence of clouds, IEEE Trans. Geosci. Remote Sensing, 41 (2),390–409.
Velden, C. S., T. L. Olander, and S. Wamzonng, 1998: The impact of multispectral GOES-8 wind information on Atlantic tropical cyclone track forecasts in 1995. Part 1: Dataset methodology, description and case analysis, Mon. Weather Rev., 126, 1202-1218
Wu, L., S. A. Braun, J. J. Qu, and X. Hao, 2006: Simulating the formation of Hurricane Isabel (2003) with AIRS data, Geophys. Res. Lett., 33, L04804.
Zhang, X., Q. Xiao, and P. J. Fitzpatrick, 2007: The impact of multi-satellite data on the initialization and simulation of Hurricane Lili’s (2002) rapid weakening phase, Mon. Weather Rev., 135, 526-548
Zheng J., J. Li, T. J. Schmit, J. L. Li, Z. Q. Liu , 2015: The Impact of AIRS Atmospheric Temperature and Moisture Profiles on Hurricane Forecasts: Ike (2008) and Irene (2011). Adv. Atmos. Sci., 32, 319-335.