台灣及南海地區雲的時空特徵: 向日葵8號於夏季觀測之前導研究

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陳曉如(Hsiao-Ju Chen) 查詢紙本館藏

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遙測科技碩士學位學程

論文名稱

台灣及南海地區雲的時空特徵: 向日葵8號於夏季觀測之前導研究
(Spatiotemporal Cloud Characteristics over Taiwan and South China Sea: A Pilot Study from Himawari-8 Observation in Summertime)

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摘要(中)

雲覆蓋全球表面約70%，其消長過程與微物理特性變化，對地球能量收支和水文循環有著重大影響。由於過往研究對雲的特性及其發展過程認知不足，迫使雲迄今為止皆為氣候變化預測模型中最大的不確定性因子。過往研究經常使用繞極軌道衛星作為觀測雲的儀器，然而其有限的時間解析度，無法提供連續性的觀測資料，並充分監測短時天氣系統。因此，本研究嘗試利用具有高時間解析度之地球同步衛星，作為分析工具。
本研究以台灣和南海為研究目標，並特別針對夏季時段，乃因該區域坐落於東印度洋暖池中心及亞洲季風之水氣通道，具有多樣性地貌及複雜的天氣氣候尺度。透過搭載於向日葵八號衛星之儀器-AHI於2017年至2019年夏季(6月至8月)白天(00UTC至08UTC)反演之雲產品，包含雲出現頻率(COF)、雲頂氣壓(CTP)、雲光學厚度(COF)和雲滴有效粒徑(RE)，以及EAR5再分析資料提供的相對濕度(RH)、垂直速度(VV)、空氣溫度(T)及對流可用未能(CAPE)之大氣變量估計值，探討兩者之間的時空分布狀態。研究結果顯示，台灣及南海的低雲、高雲和深對流雲出現頻率，在03UTC至05UTC達最大值，且同時伴隨著COT的增長、相對濕度增加，以及上升氣流的增強。此外，由於台灣地貌複雜，不同雲型出現頻率也有明顯的區域差異，而南海則沒有明顯的空間變化。

摘要(英)

Clouds account for 70% of global coverage, which life cycle and the changes of microphysical properties affect the energy budget and hydrological cycle on Earth. Due to insufficient understanding of the characteristics of clouds and their development process in previous studies, clouds have been the largest uncertainty factor in climate change forecast models so far. Previous studies often used polar-orbiting satellites as the instruments to observe cloud information, but the limitation of temporal resolution cannot provide continuous observation dataset and adequately monitor short-term weather systems. Therefore, this study attempts to use geostationary satellites with high temporal resolution as an analysis instrument. The study areas are Taiwan and South China Sea, which is located in the warm pool center of East Indian Ocean and water vapor path of Asia monsoon, with the various landform and complicated weather and climate scale, especially during summer period. By using the Himawari-8 satellite data and the atmospheric variable estimates provided by ERA5 reanalysis data, to analyze the relationship between cloud occurrence frequency, cloud top pressure, cloud optical thickness, cloud effective radius, and the environmental factors including relative humidity, vertical velocity, air temperature, and convective available potential energy during 2017 to 2019 summer season (June to August), daytime (00UTC to 08UTC). The results show that the occurrence frequency of low cloud, high cloud, and deep convective cloud reached the maximum value during 03 to 05UTC, with increasing COT, RH, and updraft. In addition, due to the complex topography of TW, there are obvious regional differences in frequency and cloud types, while there is no obvious spatial change over SCS.

關鍵字(中)

★ 向日葵8號
★ 雲微物理參數
★ 日變化
★ 海陸差異

關鍵字(英)

★ Himawari-8
★ Cloud Properties
★ Diurnal Variation
★ Land-Sea Difference

論文目次

Chapter 1: Introduction 1
1.1 The Role of the Cloud in the Earth System 1
1.2 The Limitations of Cloud Observation Instruments 2
1.3Motivation 4
1.4Objectives 7
Chapter 2: Data and Methodology 8
2.1 Himawari-8 Satellite Data 8
2.2 AHI Retrieved Cloud Properties 10
2.3 ERA5 Atmospheric Reanalysis Product 10
2.4 Study Area 11
2.5 Temporal Period 12
2.6 Methodology 13
2.6.1 The Method of Satellite and Reanalysis Data Processing 14
2.6.2 Classification Thresholds for Cloud Types 15
2.7 The Condition of Moisture Saturation 17
2.8 The Condition of Updraft 18
2.9 The Definition of Cloud Microphysical Parameters 21
2.9.1 Cloud Top Pressure (CTP) 21
2.9.2 Cloud Optical Thickness (COT) 22
2.9.3 Cloud Droplet Effective Radius (RE) 23
2.10 The Definition of Environmental Parameters 24
2.10.1 Relative Humidity (RH) 24
2.10.2 Temperature (T) 25
2.10.3 Vertical Velocity (VV) 26
2.10.4 Convective Available Potential Energy (CAPE) 27
Chapter 3: Results and Discussion 28
3.1 The Spatial Distribution of Cloud Products and Environmental Factors 28
3.1.1 Summer Mean of Cloud Product Retrieved from MODIS/Terra and Aqua 28
3.1.2 Summer Mean of Cloud Products Retrieved from AHI/Himawari-8 30
3.1.3 Comparison between AHI and MODIS 33
3.1.4 The Spatial Distribution of Environmental Factors 35
3.2 Joint Histogram of CTP with COT and CTP with RE 38
3.3 The Ratio of Different Cloud Phases in the Pure Cloud Pixels 39
3.4 The Characteristics of Ice Cloud and Water Cloud 40
3.5 The Daytime Variation of Cloud Characteristics 43
3.6 The Daytime Variation of Cloud Information 47
3.7 The Daytime Variation of Environment Factors 49
3.8 The Relationship between Daytime Variation of Clouds and Environmental Factors 53
3.8.1 The Relationship between Daytime Variation of Cloud Occurrence Frequency and Environmental Parameters over SCS 53
3.8.2 The Relationship between Daytime Variation of Cloud Occurrence Frequency and Environmental Factors over TW 56
3.8 The Time Maps of Different Cloud Types Occurrence Frequency 60
Chapter 4: Conclusion and Future Work 62
Reference 66

Figure 1. 1 | FAR values for AHI’s and MODIS’s cloud masks 5
Figure 1. 2 | Spatial distribution of the four typical study areas: study area. 6
Figure 2. 1 | The true color composite image of AHI observation 9
Figure 2. 2 | Study area 12
Figure 2. 3 | Flow Chart 13
Figure 2. 4 | The method of daily data processing 14
Figure 2. 5 | The method of Monthly data processing 15
Figure 2. 6 | ISCCP cloud classifications. 16
Figure 2. 7 | The dew point temperature of cloud. 17
Figure 2. 8 | The cooling effect of temperature with height change. 18
Figure 2. 9 | Surface heating 19
Figure 2. 10 | Topography effect 19
Figure 2. 11 | Front 20
Figure 2. 12 | Convergence 20
Figure 2. 13 | Sample plot of cloud top pressure at 04 UTC on July 1, 2018 21
Figure 2. 14 | Sample plot of cloud optical thickness at 04 UTC on July 1, 2018 22
Figure 2. 15 | Sample plot of cloud effective radius at 04 UTC on July 1, 2018 23
Figure 2. 16 | Sample plot of 850hPa relative humidity at 04 UTC on July 1, 2018 24
Figure 2. 17 | Sample plot of 850hPa air temperature at 04 UTC on July 1, 2018 25
Figure 2. 18 | Sample plot of 850hPa Vertical velocity at 04 UTC on July 1, 2018 26
Figure 2. 19 | Sample plot of convective available potential energy at 04 UTC on July 1, 2018 27
Figure 3. 1 | Seasonal mean daytime cloud top pressure from Terra and Aqua 29
Figure 3. 2 | Mean values of cloud priducts. 32
Figure 3. 3 | Comparison of Himawari-8 and MODIS/Aqua retrieved cloud products 34
Figure 3. 4 | The mean values of environmental factors 37
Figure 3. 5 | Occurrence Frequency of COT and RE in different CTP over TW 39
Figure 3. 6 | The ratio of cloud phases during summer season over TW and SCS 40
Figure 3. 7 | CTP, COT, and RE occurrence frequency of ice cloud and water cloud. 42
Figure 3. 8 | The daytime variation of four cloud type occurrence frequency 46
Figure 3. 9 | The daytime variation of CTP, COT, and RE of four cloud types 49
Figure 3. 10 | The value of the daytime variation of environmental factors. 52
Figure 3. 11 | Daytime variation of normalized occurrence frequency of four cloud types and environmental parameters in different layers over SCS. 56
Figure 3. 12 | Daytime variations of normalized occurrence frequency of four cloud types and environmental factors over TW. 58
Figure 3. 13 | The time series of maximum values of cloud occurrence frequency. 61
Figure 3. 14 | The map of terrain over East Asia 61

Table 2. 1 | The specifications of Himawari-8 / AHI 9
Table 2. 2 | The information of EAR5 11
Table 3. 1 | The value of cloud characteristeice of ice and water cloud 41
Table 3. 2 | Correlation coefficient between the daytime variation of RH, T and VV and cloud over SCS 53
Table 3. 3 | Correlation coefficient between the daytime variation of CAPE and cloud over SCS 53
Table 3. 4 | Correlation coefficient between the daytime variation of of RH, T and VV and cloud over TW 56
Table 3. 5 | Correlation coefficient between the daytime variation of CAPE and cloud over TW 56
Table 3. 6 | The relationship between clouds and environmental factors 59

參考文獻

林博雄、楊穎堅、劉清煌、徐世裴、楊菁華、吳靜軒、游政谷、詹森、隨中興(2016):南海─海洋大陸區對流與大尺度環流交互作用:2016冬季風預實驗。大氣科學，44，329-352。

Barahona, D., Molod, A., & Kalesse, H. (2017). Direct estimation of the global distribution of vertical velocity within cirrus clouds. Scientific reports, 7 (1), 1-11.

Bao, S., Letu, H., Zhao, C., Tana, G., Shang, H., Wang, T., ... & Zhao, J. (2018). Spatiotemporal distributions of cloud parameters and the temperature response over the Mongolian Plateau during 2006–2015 based on MODIS data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 549-558.

Chen, C. S., & Chen, Y. L. (2003). The rainfall characteristics of Taiwan. Monthly Weather Review, 131 (7), 1323-1341.

Cesana, G., Waliser, D. E., Jiang, X., & Li, J. L. (2015). Multimodel evaluation of cloud phase transition using satellite and reanalysis data. Journal of Geophysical Research: Atmospheres, 120, 7871-7892.

Cho, H. M., Zhang, Z., Meyer, K., Lebsock, M., Platnick, S., Ackerman, A. S., ... & Holz, R. E. (2015). Frequency and causes of failed MODIS cloud property retrievals for liquid phase clouds over global oceans. Journal of Geophysical Research: Atmospheres, 120 (9), 4132-4154.

Chen, D., Guo, J., Wang, H., Li, J., Min, M., Zhao, W., & Yao, D. (2018). The cloud top distribution and diurnal variation of clouds over East Asia: Preliminary results from Advanced Himawari Imager. Journal of Geophysical Research: Atmospheres, 123 (7), 3724-3739.

Chepfer, H., Brogniez, H., & Noël, V. (2019). Diurnal variations of cloud and relative humidity profiles across the tropics. Scientific reports, 9 (1), 1-9.

Chen, W. T., Hsu, S. P., Tsai, Y. H., & Sui, C. H. (2019). The influences of convectively coupled Kelvin waves on multiscale rainfall variability over the South China Sea and Maritime Continent in December 2016. Journal of Climate, 32 (20), 6977-6993.

DeMott, C. A., & Randall, D. A. (2004). Observed variations of tropical convective available potential energy. Journal of Geophysical Research: Atmospheres, 109 (D2).

Ding, Y.H., Li, C. Y., & Liu, Y. J. (2004). Overview of the South China Sea monsoon experiment. Advances in Atmospheric Sciences, 21 (3), 343-360.

Ding, Y. H., Li, C. Y., He, J. H., Chen, L., Gan, Z., Qian, Y., ... & Li, L. (2006). South China Sea monsoon experiment (SCSMEX) and the East-Asian monsoon. ACTA METEOROLOGICA SINICA-ENGLISH EDITION, 20 (2), 159.

Dong, X., Xi, B., & Wu, P. (2014). Investigation of the diurnal variation of marine boundary layer cloud microphysical properties at the Azores. Journal of Climate, 27 (23), 8827-8835.

Feofilov, A. G., & Stubenrauch, C. J. (2019). Diurnal variation of high-level clouds from the synergy of AIRS and IASI space-borne infrared sounders. Atmospheric Chemistry and Physics, 19 (22), 13957-13972.

Gehlot, S., & Quaas, J. (2012). Convection–climate feedbacks in the ECHAM5 general circulation model: Evaluation of cirrus cloud life cycles with ISCCP satellite data from a Lagrangian trajectory perspective. Journal of climate, 25 (15), 5241-5259.

Gettelman, A., Collins, W. D., Fetzer, E. J., Eldering, A., Irion, F. W., Duffy, P. B., & Bala, G. (2006). Climatology of upper-tropospheric relative humidity from the Atmospheric Infrared Sounder and implications for climate. Journal of climate, 19 (23), 6104-6121.

Gasparini, B., Blossey, P. N., Hartmann, D. L., Lin, G., & Fan, J. (2019). What drives the life cycle of tropical anvil clouds?. Journal of Advances in Modeling Earth Systems, 11 (8), 2586-2605.

Gao, C., Li, Y., & Chen, H. (2019). Diurnal variations of different cloud types and the relationship between the diurnal variations of clouds and precipitation in central and east China. Atmosphere, 10 (6), 304.

Farmer, D. K., Cappa, C. D., & Kreidenweis, S. M. (2015). Atmospheric processes and their controlling influence on cloud condensation nuclei activity. Chemical Reviews, 115 (10), 4199-4217.

Jin, M., & Dickinson, R. E. (1999). Interpolation of surface radiative temperature measured from polar orbiting satellites to a diurnal cycle: 1. Without clouds. Journal of Geophysical Research: Atmospheres, 104 (D2), 2105-2116.

Jin, X., Wu, T., Li, L., & Shi, C. (2009). Cloudiness characteristics over Southeast Asia from satellite FY‐2C and their comparison to three other cloud data sets. Journal of Geophysical Research: Atmospheres, 114 (D17).

Jiang, J. H., Su, H., Zhai, C., Janice Shen, T., Wu, T., Zhang, J., ... & Shiotani, M. (2015). Evaluating the diurnal cycle of upper-tropospheric ice clouds in climate models using SMILES observations. Journal of the Atmospheric Sciences, 72 (3), 1022-1044.

King, M. D., Platnick, S., Menzel, W. P., Ackerman, S. A., & Hubanks, P. A. (2013). Spatial and temporal distribution of clouds observed by MODIS onboard the Terra and Aqua satellites. IEEE transactions on geoscience and remote sensing, 51 (7), 3826-3852.

Katsumata, M., Mori, S., Hamada, J. I., Hattori, M., Syamsudin, F., & Yamanaka, M. D. (2018). Diurnal cycle over a coastal area of the Maritime Continent as derived by special networked soundings over Jakarta during HARIMAU2010. Progress in Earth and Planetary Science, 5 (1), 1-19.

Kikuchi, M., Murakami, H., Suzuki, K., Nagao, T. M., & Higurashi, A. (2018). Improved hourly estimates of aerosol optical thickness using spatiotemporal variability derived from Himawari-8 geostationary satellite. IEEE Transactions on Geoscience and Remote Sensing, 56 (6), 3442-3455.

Luo, Z. J., Jeyaratnam, J., Iwasaki, S., Takahashi, H., & Anderson, R. (2014). Convective vertical velocity and cloud internal vertical structure: An A‐Train perspective. Geophysical Research Letters, 41 (2), 723-729.

Li, J., Mao, J., and Wang, F. (2017): Comparative study of five current reanalyses in characterizing total cloud fraction and top-of-the-atmosphere cloud radiative effects over the Asian monsoon region, Int. J. Climatol., 37.

Letu, H., Nagao, T. M., Nakajima, T. Y., Riedi, J., Ishimoto, H., Baran, A. J., ... & Kikuchi, M. (2018). Ice cloud properties from Himawari-8/AHI next-generation geostationary satellite: Capability of the AHI to monitor the DC cloud generation process. IEEE Transactions on Geoscience and Remote Sensing, 57, 3229-3239.

Letu, H., Yang, K., Nakajima, T. Y., Ishimoto, H., Nagao, T. M., Riedi, J., ... & Shi, J. (2020). High-resolution retrieval of cloud microphysical properties and surface solar radiation using Himawari-8/AHI next-generation geostationary satellite. Remote Sensing of Environment, 239, 111583.

Liu, C-Y., Chiu, C-H., Lin, P-H., & Min, M. (2020). Comparison of Cloud‐Top Property Retrievals From Advanced Himawari Imager, MODIS, CloudSat/CPR, CALIPSO/CALIOP, and Radiosonde. Journal of Geophysical Research: Atmospheres, 125 (15).

Machado, L. A. T., & Rossow, W. B. (1993). Structural characteristics and radiative properties of tropical cloud clusters. Monthly Weather Review, 121 (12), 3234-3260.

Menzel, W. P., Frey, R. A., Zhang, H., Wylie, D. P., Moeller, C. C., Holz, R. E., ... & Gumley, L. E. (2008). MODIS global cloud-top pressure and amount estimation: Algorithm description and results. Journal of Applied Meteorology and Climatology, 47 (4), 1175-1198.

Munneke Kuipers, P., Reijmer, C.H., Van Den Broeke, M.R. (2011): Assessing the retrieval of cloud properties from radiation measurements over snow and ice. International Journal of Climatology. 31, 756-769.

Mercury, M., Green, R., Hook, S., Oaida, B., Wu, W., Gunderson, A., & Chodas, M. (2012). Global cloud cover for assessment of optical satellite observation opportunities: A HyspIRI case study. Remote sensing of environment, 126, 62-71.

Matus, A. V., & L′Ecuyer, T. S. (2017). The role of cloud phase in Earth′s radiation budget. Journal of Geophysical Research: Atmospheres, 122(5), 2559-2578.

Narendra Babu, A., Nee, J. B., & Kumar, K. K. (2010). Seasonal and diurnal variation of convective available potential energy (CAPE) using COSMIC/FORMOSAT‐3 observations over the tropics. Journal of Geophysical Research: Atmospheres, 115(D4).

Noel, V., Chepfer, H., Chiriaco, M., & Yorks, J. (2018). The diurnal cycle of cloud profiles over land and ocean between 51° S and 51° N, seen by the CATS spaceborne lidar from the International Space Station. Atmospheric Chemistry and Physics, 18, 9457-9473.

Petty, G.W. (2006). A First Course in Atmospheric Radiation. Adison, Wisconsin.

Rutledge, S. A., & Houze Jr, R. A. (1987). A diagnostic modelling study of the trailing stratiform region of a midlatitude squall line. Journal of the atmospheric sciences, 44 (18), 2640-2656.

Rossow, W. B., & Schiffer, R. A. (1991). ISCCP cloud data products. Bulletin of the American Meteorological Society, 72 (1), 2-20.

Rossow, W.B., and Schiffer, R.A. (1999). Advances in Understanding Clouds from ISCCP. Bull. Amer. Meteor. Soc., 80.

Rangno, A. L. (2015). CLOUDS AND FOG (Classification of clouds). Encyclopedia of Atmospheric Sciences. 141-160.

Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang, Z., ... & Miller, S. D. (2002). The CloudSat mission and the A-Train: A new dimension of space-based observations of clouds and precipitation. Bulletin of the American Meteorological Society, 83, 1771-1790.

Sekiguchi, M., Nakajima, T., Suzuki, K., Kawamoto, K., Higurashi, A., Rosenfeld, D., ... & Mukai, S. (2003). A study of the direct and indirect effects of aerosols using global satellite data sets of aerosol and cloud parameters. Journal of Geophysical Research: Atmospheres, 108(D22).

Sun, D., Kafatos, M., Pinker, R. T., & Easterling, D. R. (2006). Seasonal variations in diurnal temperature range from satellites and surface observations. IEEE Transactions on Geoscience and Remote Sensing, 44 (10), 2779-2785.

Stubenrauch, C. J., Chédin, A., Rädel, G., Scott, N. A., & Serrar, S. (2006). Cloud properties and their seasonal and diurnal variability from TOVS Path-B. Journal of climate, 19, 5531-5553.

Stubenrauch, C. J., Rossow, W. B., Kinne, S., Ackerman, S., Cesana, G., Chepfer, H., ... & Maddux, B. C. (2013). Assessment of global cloud datasets from satellites: Project and database initiated by the GEWEX radiation panel. Bulletin of the American Meteorological Society, 94, 1031-1049.

Santhi, Y. D., Ratnam, M. V., Dhaka, S. K., & Rao, S. V. (2014). Global morphology of convection indices observed using COSMIC GPS RO satellite measurements. Atmospheric research, 137, 205-215.

Seela, B. K., Janapati, J., Lin, P. L., Wang, P. K., & Lee, M. T. (2018). Raindrop size distribution characteristics of summer and winter season rainfall over north Taiwan. Journal of Geophysical Research: Atmospheres, 123 (20), 11-602.

Shang, H., Letu, H., Nakajima, T. Y., Wang, Z., Ma, R., Wang, T., ... & Shi, J. (2018). Diurnal cycle and seasonal variation of cloud cover over the Tibetan Plateau as determined from Himawari-8 new-generation geostationary satellite data. Scientific reports, 8, 1-8.

Sun, G., Li, Y., & Lu, J. (2019). Cloud vertical structures associated with northward advance of the East Asian summer monsoon. Atmospheric Research, 215, 317-325.

Sui, C-H., Lin, P-H., Chen, W-T., Jan, S., Liu, C-Y., Yang, Y-J., ... & Tseng, L. S. (2020). The South China Sea Two Islands Monsoon Experiment for studying convection and subseasonal to seasonal variability. Terr. Atmos. Ocean. Sci, 31, 103-129.

Tian, B., Fetzer, E. J., Kahn, B. H., Teixeira, J., Manning, E., & Hearty, T. (2013). Evaluating CMIP5 models using AIRS tropospheric air temperature and specific humidity climatology. Journal of Geophysical Research: Atmospheres, 118 (1), 114-134.

Van Diedenhoven, B., Fridlind, A. M., Cairns, B., Ackerman, A. S., & Yorks, J. E. (2016). Vertical variation of ice particle size in convective cloud tops. Geophysical research letters, 43 (9), 4586-4593.

Wang, B. (2002). Rainy season of the Asian–Pacific summer monsoon. Journal of Climate, 15 (4), 386-398.

With, L. M., Robert, A. H. (2006). Chapter 8: Weather Systems. Atmospheric Science, 313-373.

Wang, S. Y., & Chen, T. C. (2008). Measuring East Asian summer monsoon rainfall contributions by different weather systems over Taiwan. Journal of applied meteorology and climatology, 47 (7), 2068-2080.

Wang, H., Luo, Y. L., & Zhang, R. H. (2011). Analyzing seasonal variation of clouds over the Asian monsoon regions and the Tibetan Plateau region using CloudSat/CALIPSO data. Chinese Journal of Atmospheric Sciences, 35, 1117-1131.

Wang, Y., & Zhao, C. (2017). Can MODIS cloud fraction fully represent the diurnal and seasonal variations at DOE ARM SGP and Manus sites? Journal of Geophysical Research: Atmospheres, 122 (1), 329-343.

Xu, W. (2013). Precipitation and convective characteristics of summer deep convection over East Asia observed by TRMM. Monthly weather review, 141 (5), 1577-1592.

Yang, Y., Zhao, C.F., Dong, X.B., Fan, G.C., Zhou, Y.Q., Wang, Y., Zhao, L.J., Lv, F., Yan, F. (2019): Toward understanding the process-level impacts of aerosols on microphysical properties of shallow cumulus cloud using aircraft observations. Atmos.Res. 221, 27-33.

Yang, Y., Zhao, C., Fan, H. (2020): Spatiotemporal distribution of cloud properties over China based on Himawari-8 advanced Himawari imager data. Atmospheric Research. 240, 104927.

Zhao, W., Marchand, R., Fu, Q. (2017): The Diurnal Cycle of Clouds and Precipitation at the ARM SGP Site: An Atmospheric State-Based Analysis and Error Decomposition of a Multiscale Modeling Framework Simulation. JGR Atmospheres. 122, 13387-13403.

Zhuge, X., Zou, X., Li, X., Tang, F., Yao, B., & Yu, L. (2021). Seasonal and Diurnal Variations in Cloud-Top Phase over the Western North Pacific during 2017–2019. Remote Sensing, 13 (9), 1687.

指導教授

劉千義(Chian-Yi Liu)

審核日期

2021-7-28

推文