Articles | Volume 13, issue 5
https://doi.org/10.5194/essd-13-2111-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-2111-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
18 May 2021
Data description paper |
| 18 May 2021
| 18 May 2021
A new global gridded sea surface temperature data product based on multisource data
Mengmeng Cao
Hulunbeir Grassland Ecosystem Research station, Institute of
Agricultural Resources and Regional Planning, Chinese Academy of
Agricultural Sciences, Beijing, 100081, China
Hulunbeir Grassland Ecosystem Research station, Institute of
Agricultural Resources and Regional Planning, Chinese Academy of
Agricultural Sciences, Beijing, 100081, China
School of Physics and Electronic-Engineering, Ningxia University,
Yinchuan, 750021, China
Yibo Yan
Hulunbeir Grassland Ecosystem Research station, Institute of
Agricultural Resources and Regional Planning, Chinese Academy of
Agricultural Sciences, Beijing, 100081, China
Jiancheng Shi
National Space Science Center, Chinese Academy of Sciences,
Beijing, 100190, China
Han Wang
Hulunbeir Grassland Ecosystem Research station, Institute of
Agricultural Resources and Regional Planning, Chinese Academy of
Agricultural Sciences, Beijing, 100081, China
Tongren Xu
State Key Laboratory of Remote Sensing Science, Jointly Sponsored
by the Aerospace Information Research Institute of Chinese Academy of
Sciences and Beijing Normal University, Beijing, 100101, China
School of Earth Sciences and Resources, China University of
Geosciences, Beijing, 100083, China
Zijin Yuan
Hulunbeir Grassland Ecosystem Research station, Institute of
Agricultural Resources and Regional Planning, Chinese Academy of
Agricultural Sciences, Beijing, 100081, China
Related authors
Mengmeng Cao, Kebiao Mao, Yibo Yan, Sayed Bateni, and Zhonghua Guo
EGUsphere, https://doi.org/10.5194/egusphere-2025-239, https://doi.org/10.5194/egusphere-2025-239, 2025
Preprint archived
Short summary
Short summary
We proposed a novel SST prediction model based on granular computing and a data-knowledge-driven ConvLSTM framework. Validation against observations and cross-comparisons with baseline models demonstrate that our approach generates consistent and more accurate regional SST predictions, making it highly promising for medium- and long-term monthly SST forecasts.
Shu Fang, Kebiao Mao, Xueqi Xia, Ping Wang, Jiancheng Shi, Sayed M. Bateni, Tongren Xu, Mengmeng Cao, Essam Heggy, and Zhihao Qin
Earth Syst. Sci. Data, 14, 1413–1432, https://doi.org/10.5194/essd-14-1413-2022, https://doi.org/10.5194/essd-14-1413-2022, 2022
Short summary
Short summary
Air temperature is an important parameter reflecting climate change, and the current method of obtaining daily temperature is affected by many factors. In this study, we constructed a temperature model based on weather conditions and established a correction equation. The dataset of daily air temperature (Tmax, Tmin, and Tavg) in China from 1979 to 2018 was obtained with a spatial resolution of 0.1°. Accuracy verification shows that the dataset has reliable accuracy and high spatial resolution.
Yutong Wang, Huazhe Shang, Chenqian Tang, Jian Xu, Tianyang Ji, Wenwu Wang, Lesi Wei, Yonghui Lei, Jiancheng Shi, and Husi Letu
EGUsphere, https://doi.org/10.5194/egusphere-2025-2471, https://doi.org/10.5194/egusphere-2025-2471, 2025
Short summary
Short summary
By analyzing global CloudSat data, we identified that most liquid cloud profiles have triangle-shaped or steadily decreasing structures, and we developed a new method using pattern recognition, fitting techniques, and machine learning to accurately estimate these profiles. This research advances our understanding of cloud life cycle and improves the ability to characterize cloud profiles, which is crucial for enhancing weather forecast and climate change research.
Mengmeng Cao, Kebiao Mao, Yibo Yan, Sayed Bateni, and Zhonghua Guo
EGUsphere, https://doi.org/10.5194/egusphere-2025-239, https://doi.org/10.5194/egusphere-2025-239, 2025
Preprint archived
Short summary
Short summary
We proposed a novel SST prediction model based on granular computing and a data-knowledge-driven ConvLSTM framework. Validation against observations and cross-comparisons with baseline models demonstrate that our approach generates consistent and more accurate regional SST predictions, making it highly promising for medium- and long-term monthly SST forecasts.
Shaomin Liu, Ziwei Xu, Tao Che, Xin Li, Tongren Xu, Zhiguo Ren, Yang Zhang, Junlei Tan, Lisheng Song, Ji Zhou, Zhongli Zhu, Xiaofan Yang, Rui Liu, and Yanfei Ma
Earth Syst. Sci. Data, 15, 4959–4981, https://doi.org/10.5194/essd-15-4959-2023, https://doi.org/10.5194/essd-15-4959-2023, 2023
Short summary
Short summary
We present a suite of observational datasets from artificial and natural oases–desert systems that consist of long-term turbulent flux and auxiliary data, including hydrometeorological, vegetation, and soil parameters, from 2012 to 2021. We confirm that the 10-year, long-term dataset presented in this study is of high quality with few missing data, and we believe that the data will support ecological security and sustainable development in oasis–desert areas.
Xinlei He, Yanping Li, Shaomin Liu, Tongren Xu, Fei Chen, Zhenhua Li, Zhe Zhang, Rui Liu, Lisheng Song, Ziwei Xu, Zhixing Peng, and Chen Zheng
Hydrol. Earth Syst. Sci., 27, 1583–1606, https://doi.org/10.5194/hess-27-1583-2023, https://doi.org/10.5194/hess-27-1583-2023, 2023
Short summary
Short summary
This study highlights the role of integrating vegetation and multi-source soil moisture observations in regional climate models via a hybrid data assimilation and machine learning method. In particular, we show that this approach can improve land surface fluxes, near-surface atmospheric conditions, and land–atmosphere interactions by implementing detailed land characterization information in basins with complex underlying surfaces.
Ping Wang, Kebiao Mao, Fei Meng, Zhihao Qin, Shu Fang, and Sayed M. Bateni
Geosci. Model Dev., 15, 6059–6083, https://doi.org/10.5194/gmd-15-6059-2022, https://doi.org/10.5194/gmd-15-6059-2022, 2022
Short summary
Short summary
In order to obtain the key parameters of high-temperature spatial–temporal variation analysis, this study proposed a daily highest air temperature (Tmax) estimation frame to build a Tmax dataset in China from 1979 to 2018. We found that the annual and seasonal mean Tmax in most areas of China showed an increasing trend. The abnormal temperature changes mainly occurred in El Nin~o years or La Nin~a years. IOBW had a stronger influence on China's warming events than other factors.
Peilin Song, Yongqiang Zhang, Jianping Guo, Jiancheng Shi, Tianjie Zhao, and Bing Tong
Earth Syst. Sci. Data, 14, 2613–2637, https://doi.org/10.5194/essd-14-2613-2022, https://doi.org/10.5194/essd-14-2613-2022, 2022
Short summary
Short summary
Soil moisture information is crucial for understanding the earth surface, but currently available satellite-based soil moisture datasets are imperfect either in their spatiotemporal resolutions or in ensuring image completeness from cloudy weather. In this study, therefore, we developed one soil moisture data product over China that has tackled most of the above problems. This data product has the potential to promote the investigation of earth hydrology and be extended to the global scale.
Ming Li, Husi Letu, Yiran Peng, Hiroshi Ishimoto, Yanluan Lin, Takashi Y. Nakajima, Anthony J. Baran, Zengyuan Guo, Yonghui Lei, and Jiancheng Shi
Atmos. Chem. Phys., 22, 4809–4825, https://doi.org/10.5194/acp-22-4809-2022, https://doi.org/10.5194/acp-22-4809-2022, 2022
Short summary
Short summary
To build on the previous investigations of the Voronoi model in the remote sensing retrievals of ice cloud products, this paper developed an ice cloud parameterization scheme based on the single-scattering properties of the Voronoi model and evaluate it through simulations with the Community Integrated Earth System Model (CIESM). Compared with four representative ice cloud schemes, results show that the Voronoi model has good capabilities of ice cloud modeling in the climate model.
Shu Fang, Kebiao Mao, Xueqi Xia, Ping Wang, Jiancheng Shi, Sayed M. Bateni, Tongren Xu, Mengmeng Cao, Essam Heggy, and Zhihao Qin
Earth Syst. Sci. Data, 14, 1413–1432, https://doi.org/10.5194/essd-14-1413-2022, https://doi.org/10.5194/essd-14-1413-2022, 2022
Short summary
Short summary
Air temperature is an important parameter reflecting climate change, and the current method of obtaining daily temperature is affected by many factors. In this study, we constructed a temperature model based on weather conditions and established a correction equation. The dataset of daily air temperature (Tmax, Tmin, and Tavg) in China from 1979 to 2018 was obtained with a spatial resolution of 0.1°. Accuracy verification shows that the dataset has reliable accuracy and high spatial resolution.
Xiangjin Meng, Kebiao Mao, Fei Meng, Jiancheng Shi, Jiangyuan Zeng, Xinyi Shen, Yaokui Cui, Lingmei Jiang, and Zhonghua Guo
Earth Syst. Sci. Data, 13, 3239–3261, https://doi.org/10.5194/essd-13-3239-2021, https://doi.org/10.5194/essd-13-3239-2021, 2021
Short summary
Short summary
In order to improve the accuracy of China's regional agricultural drought monitoring and climate change research, we produced a long-term series of soil moisture products by constructing a time and depth correction model for three soil moisture products with the help of ground observation data. The spatial resolution is improved by building a spatial weight decomposition model, and validation indicates that the new product can meet application needs.
Bing Zhao, Kebiao Mao, Yulin Cai, Jiancheng Shi, Zhaoliang Li, Zhihao Qin, Xiangjin Meng, Xinyi Shen, and Zhonghua Guo
Earth Syst. Sci. Data, 12, 2555–2577, https://doi.org/10.5194/essd-12-2555-2020, https://doi.org/10.5194/essd-12-2555-2020, 2020
Short summary
Short summary
Land surface temperature is a key variable for climate and ecological environment research. We reconstructed a land surface temperature dataset (2003–2017) to take advantage of the ground observation site through building a reconstruction model which overcomes the effects of cloud. The reconstructed dataset exhibited significant improvements and can be used for the spatiotemporal evaluation of land surface temperature and for high-temperature and drought-monitoring studies.
Cited articles
Ackerman, S. A., Holz, R. E., Frey, R., Eloranta, E. W., Maddux, B. C., and
McGill, M.: Cloud detection with MODIS. Part ii: validation, J. Atmos.
Ocean. Tech., 25, 1073–1086, https://doi.org/10.1175/2007JTECHA1053.1,
2008.
Alerskans, E., Høyer, J. L., Gentemann, C. L., Pedersen, L. T.,
Nielsen-Englyst, P., and Donlon, C.: Construction of a climate data record
of sea surface temperature from passive microwave measurements, Remote Sens.
Environ., 236, 11485, https://doi.org/10.1016/j.rse.2019.111485, 2020.
Banzon, V., Smith, T. M., Steele, M., Huang, B., and Zhang, H.-M.: Improved
estimation of proxy sea surface temperature in the Arctic, J. Atmos. Ocean.
Tech., 37, 341–349, https://doi.org/10.1175/jtech-d-19-0177.1, 2020.
Banzon, V. F. and Reynolds, R. W.: Use of windsat to extend a
microwave-based daily optimum interpolation sea surface temperature time
series, J. Climate, 26, 2557–2562, https://doi.org/10.1175/jcli-d-12-00628.1,
2013.
Barton, I. and Pearce, A.: Validation of GLI and other satellite-derived sea
surface temperatures using data from the Rottnest Island ferry, Western
Australia, J. Oceanogr., 62, 303–310,
https://doi.org/10.1007/s10872-006-0055-5, 2006.
Benali, A., Carvalho, A. C., Nunes, J. P., Carvalhais, N., and Santos, A.:
Estimating air surface temperature in Portugal using MODIS LST data, Remote
Sens. Environ., 124, 108–121, https://doi.org/10.1016/j.rse.2012.04.024,
2012.
Bretherton, F. P., Davis, R. E., and Fandry, C. B.: A technique for
objective analysis and design of oceanographic experiments applied to
MODE-73, Deep-Sea Res. Oceanogr. Abstr., 23, 559–582, https://doi.org/10.1016/0011-7471(76)90001-2, 1976.
Burnett, W., Harper, S., Preller, R., Jacobs, G., and LaCroix, K.: Overview
of operational ocean forecasting in the US navy past, present, and future,
Oceanography, 27, 24–31, https://doi.org/10.5670/oceanog.2014.65, 2014.
Cao, M., Mao, K., Yan, Y., Shi, J., Wang, H., Xu, T., Fang, S., and Yuan,
Z.: A new global gridded sea surface temperature data product based on
multisource data (Version 1.0) [Dataset], Zenodo,
https://doi.org/10.5281/zenodo.4419804, 2021a.
Cao, M., Mao, K., Yan, Y., Shi, J., Wang, H., Xu, T., Fang, S., and Yuan, Z.: A New Global Gridded Sea Surface Temperature Data Product Based on Multisource Data (Version 1.0) [Code], Zenodo, https://doi.org/10.5281/zenodo.4762067, 2021b.
Carton, J. A. and Giese, B. S.: A Reanalysis of Ocean Climate Using Simple
Ocean Data Assimilation (SODA), Mon. Weather Rev., 136, 2999–3017,
https://doi.org/10.1175/2007mwr1978.1, 2008.
Carton, J. A., Chepurin, G. A., and Chen, L.: SODA3: A new ocean climate
reanalysis, J. Climate, 31, 6967–6983,
https://doi.org/10.1175/JCLI-D-18-0149.1, 2018.
Castro, S. L., Emery, W. J., and Wick, G. A.: Skin and bulk sea surface
temperature estimates from passive microwave and thermal infrared satellite
imagery and their relationships to atmospheric forcing, Gayana
(Concepción), 68, 96–101,
https://doi.org/10.4067/S0717-65382004000200018, 2004.
Castro, S. L., Wick, G. A., and Steele, M.: Validation of satellite sea
surface temperature analyses in the Beaufort Sea using UpTempO buoys, Remote
Sens. Environ., 187, 458–475, https://doi.org/10.1016/j.rse.2016.10.035,
2016.
Chao, Y., Li, Z., Farrara, J. D., and Hung, P.: Blending sea surface
temperatures from multiple satellites and in situ observations for coastal
oceans, J. Atmos. Ocean. Tech., 26, 1415–1426,
https://doi.org/10.1175/2009jtecho592.1, 2009a.
Chao, Y., Li, Z., Farrara, J., McWilliams, J. C., Bellingham, J., Capet, X.,
Chavez, F., Choi, J.-K., Davis, R., Doyle, J., Fratantoni, D. M., Li, P.,
Marchesiello, P., Moline, M. A., Paduan, J., and Ramp, S.: Development,
implementation and evaluation of a data-assimilative ocean forecasting
system off the central California coast, Deep-Sea Res. Pt. II, 56, 100–126, https://doi.org/10.1016/j.dsr2.2008.08.011, 2009b.
Chassignet, E. P., Hurlburt, H. E., Metzger, E. J., Smedstad, O. M.,
Cummings, J. A., Halliwell, G. R., Bleck, R., Baraille, R., Wallcraft, A.
J., Lozano, C., Tolman, H. L., Srinivasan, A., Hankin, S., Cornillon, P.,
Weisberg, R., Barth, A., He, R., Werner, F., and Wilkin, J.: US GODAE Global
Ocean Prediction with the HYbrid Coordinate Ocean Model (HYCOM),
Oceanography, 22, 64–75, https://doi.org/10.5670/oceanog.2009.39, 2009.
Dash, P., Ignatov, A., Martin, M., Donlon, C., Brasnett, B., Reynolds, R.,
Banzon, V., Helen, B., Cayula, J.-F., Chao, Y., Grumbine, R., Maturi, E.,
Harris, A., Mittaz, J., Sapper, J., Chin, T., Vazquez, J., Armstrong, E.,
Gentemann, C., and Poulter, D.: Group for High Resolution SST (GHRSST)
Analysis Fields Inter Comparisons: Part2. Near real-time web-based Level 4
SST Quality Monitor (L4-SQUAM), Deep-Sea Res. Pt. II,
7, 31–43, 2011.
Donlon, C. J., Minnett, P. J., Gentemann, C., Nightingale, T. J., Barton, I.
J., Ward, B., and Murray, M. J.: Toward improved validation of satellite sea
surface skin temperature measurements for climate research, J. Climate, 15,
353–369, https://doi.org/10.1175/1520-0442(2002)015<0353:TIVOSS>2.0.CO;2, 2002.
Fairall, C. W., Bradley, E. F., Godfrey, J. S., Wick, G. A., Edson, J. B.,
and Young, G. S.: Cool-skin and warm-layer effects on sea surface
temperature, J. Geophys. Res.-Oceans, 101, 1295–1308,
https://doi.org/10.1029/95jc03190, 1996.
Gentemann, C. L.: Microwave sea surface temperatures for climate,
available at: http://www.wcrp-climate.org/conference2011/posters/C14/C14_Gentemann_T45B.pdf (last access: 2 March 2020), 2011.
Gentemann, C. L.: Three way validation of MODIS and AMSR-E sea surface
temperatures, J. Geophys. Res.-Oceans, 119, 2583–2598,
https://doi.org/10.1002/2013jc009716, 2014.
Gentemann, C. L., Meissner, T., and Wentz, F. J.: Accuracy of satellite sea
surface temperatures at 7 and 11 GHz, IEEE Trans. Geosci. Remote Sensing,
48, 1009–1018, https://doi.org/10.1109/tgrs.2009.2030322, 2010.
Guan, L. and Kawamura, H.: SST availabilities of satellite infrared and
microwave measurements, J. Oceanogr., 59, 201–209,
https://doi.org/10.1023/A:1025543305658, 2003.
Guan, L. and Kawamura, H.: Merging satellite infrared and microwave SSTs:
Methodology and evaluation of the new SST, J. Oceanogr., 60, 905–912,
https://doi.org/10.1007/s10872-004-5782-x, 2004.
Han, G., Li, W., Zhang, X., Li, D., He, Z., Wang, X., Wu, X., Yu, T., and
Ma, J.: A regional ocean reanalysis system for coastal waters of China and
adjacent seas, Adv. Atmos. Sci., 28, 682,
https://doi.org/10.1007/s00376-010-9184-2, 2011.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, j., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, D., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková,
M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay,
P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J. N.: The ERA5 global
reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049,
https://doi.org/10.1002/qj.3803,2020.
Hosoda, K. and Sakaida, F.: Global daily high-resolution satellite-based
foundation sea surface temperature dataset: development and validation
against two definitions of foundation SST, Remote Sens., 8, 962,
https://doi.org/10.3390/rs8110962, 2016.
Hosoda, K., Kawamura, H., and Sakaida, F.: Improvement of New Generation Sea
Surface Temperature for Open ocean (NGSST-O): a new sub-sampling method of
blending microwave observations, J. Oceanogr., 71, 205–220,
https://doi.org/10.1007/s10872-015-0272-x, 2015.
Høyer, J. L., Karagali, I., Dybkjær, G., and Tonboe, R.: Multi sensor
validation and error characteristics of Arctic satellite sea surface
temperature observations, Remote Sens. Environ., 121, 335–346,
https://doi.org/10.1016/j.rse.2012.01.013, 2012.
Huang, B., Wang, W., Liu, C., Banzon, V., Zhang, H., and Lawrimore, J.: Bias
adjustment of AVHRR SST and its impacts on two SST analyses, J. Atmos.
Ocean. Tech., 32, 372–387, https://doi.org/10.1175/jtech-d-14-00121.1,
2015.
Huang, S., Cheng, L., and Sheng, Z.: A method of making up the satellite
retrieval data of sea surface temperature, Scientia Meteorologica Snica, 28,
237–243, https://doi.org/10.3969/j.issn.1009-0827.2008.03.001, 2008 (in
Chinese).
Karagali, I., Høyer, J. L., and Donlon, C. J.: Using a 1-D model to
reproduce the diurnal variability of SST, J. Geophys. Res.-Oceans, 122,
2945–2959, https://doi.org/10.1002/2016JC012542, 2017.
Kawai, Y. and Wada, A.: Diurnal sea surface temperature variation and its
impact on the atmosphere and ocean: A review, J. Oceanogr., 63, 721–744,
https://doi.org/10.1007/s10872-007-0063-0, 2007.
Kilpatrick, K. A., Podesta, G. P., and Evans, R.: Overview of the NOAA/NASA
advanced very high resolution radiometer Pathfinder algorithm for sea
surface temperature and associated matchup database, J. Geophys.
Res.-Oceans, 106, 9179–9197, https://doi.org/10.1029/1999jc000065, 2001.
Kilpatrick, K. A., Podestá, G., Walsh, S., Williams, E., Halliwell, V.,
Szczodrak, M., Brown, O. B., Minnett, P. J., and Evans, R.: A decade of sea
surface temperature from MODIS, Remote Sens. Environ., 165, 27–41,
https://doi.org/10.1016/j.rse.2015.04.023, 2015.
Li, A., Bo, Y., Zhu, Y., Guo, P., Bi, J., and He, Y.: Blending
multi-resolution satellite sea surface temperature (SST) products using
Bayesian maximum entropy method, Remote Sens. Environ., 135, 52–63,
https://doi.org/10.1016/j.rse.2013.03.021, 2013.
Li, W., Xie, Y., He, Z., Han, G., Liu, K., Ma, J., and Li, D.: Application
of the multigrid data assimilation scheme to the China seas' temperature
forecast, J. Atmos. Ocean. Technol., 25, 2106–2116,
https://doi.org/10.1175/2008jtecho510.1, 2008.
Li, Y. and He, R.: Spatial and temporal variability of SST and ocean color
in the Gulf of Maine based on cloud-free SST and chlorophyll reconstructions
in 2003–2012, Remote Sens. Environ., 144, 98–108,
https://doi.org/10.1016/j.rse.2014.01.019, 2014.
Liu, M., Guan, L., Zhao, W., and Chen, G.: Evaluation of sea surface
temperature from the HY-2 scanning microwave radiometer, IEEE T. Geosci.
Remote, 55, 1372–1380, https://doi.org/10.1109/TGRS.2016.2623641,
2017.
Liu, Y., Chin, T. M., and Minnett, P. J.: Sampling errors in
satellite-derived infrared sea-surface temperatures. Part II: Sensitivity
and parameterization, Remote Sens. Environ., 198, 297–309,
https://doi.org/10.1016/j.rse.2017.06.011, 2017.
Luo, B., Minnett, P. J., Gentemann, C., and Szczodrak, G.: Improving
satellite retrieved night-time infrared sea surface temperatures in aerosol
contaminated regions, Remote Sens. Environ., 223, 8–20,
https://doi.org/10.1016/j.rse.2019.01.009, 2019.
Luo, B., Minnett, P. J., Szczodrak, M., Kilpatrick, K., and Izaguirre, M.:
Validation of Sentinel-3A SLSTR derived sea-surface skin temperatures with
those of the shipborne M-AERI, Remote Sens. Environ., 244, 111826,
https://doi.org/10.1016/j.rse.2020.111826, 2020.
Mao, K., Yuan, Z., Zuo, Z., Xu, T., Shen, X., and Gao, C.: Changes in global
cloud cover based on remote sensing data from 2003 to 2012, Chinese Geogr.
Sci., 29, 306–315, https://doi.org/10.1007/s11769-019-1030-6, 2019.
Martin, A. J., Hines, A., and Bell, M. J.: Data assimilation in the FOAM
operational short-range ocean forecasting system: A description of the
scheme and its impact, Q. J. Roy. Meteor. Soc., 133, 981–995,
https://doi.org/10.1002/qj.74, 2007.
McCoy, D. T., Eastman, R., Hartmann, D. L., and Wood, R.: The change in low
cloud cover in a warmed climate inferred from AIRS, MODIS, and ERA-Interim,
J. Climate, 30, 3609–3620, https://doi.org/10.1175/JCLI-D-15-0734.1, 2017.
Minnett, P. J.: Consequences of sea surface temperature variability on the
validation and applications of satellite measurements, J. Geophys.
Res.-Oceans, 96, 18475–18489, https://doi.org/10.1029/91JC01816, 1991.
Minnett, P. J.: Radiometric measurements of the sea-surface skin
temperature: the competing roles of the diurnal thermocline and the cool
skin, Int. J. Remote Sens., 24, 5033–5047,
https://doi.org/10.1080/0143116031000095880, 2003.
Minnett, P. J., Smith, M., and Ward, B.: Measurements of the oceanic thermal
skin effect, Deep-Sea Res. Pt. II, 58, 861–868,
https://doi.org/10.1016/j.dsr2.2010.10.024, 2011.
Minnett, P. J., Alvera-Azcárate, A., Chin, T. M., Corlett, G. K.,
Gentemann, C. L., Karagali, I., Li, X., Marsouin, A., Marullo, S., Maturi,
E., Santoleri, R., Saux Picart, S., Steele, M., and Vazquez-Cuervo, J.: Half
a century of satellite remote sensing of sea-surface temperature, Remote
Sens. Environ., 233, 111366, https://doi.org/10.1016/j.rse.2019.111366,
2019.
Ng, H. G., MatJafri, M. Z., Abdullah, K., and Othman, N.: Merging infrared
and microwave SST data at south China sea, Proceedings of the 6th
International Conference on Computer Graphics, Imaging and Visualization
(CGIV 2009), Tianjin, China, 11–14 August 2009, 530–535, 2009.
Oke, P. R., Brassington, G. B., Griffin, D. A., and Schiller, A.: The
Bluelink ocean data assimilation system (BODAS), Ocean Model., 21, 46–70,
https://doi.org/10.1016/j.ocemod.2007.11.002, 2008.
Peres, L. F., Franca, G. B., Paes, R. C. O. V., Sousa, R. C., and Oliveira,
A. N.: Analyses of the positive bias of remotely sensed SST retrievals in
the coastal waters of Rio de Janeiro, IEEE T. Geosci. Remote,
55, 6344–6353, https://doi.org/10.1109/tgrs.2017.2726344, 2017.
Pimentel, S., Tse, W.-H., Xu, H., Denaxa, D., Jansen, E., Korres, G.,
Mirouze, I., and Storto, A.: Modeling the near-surface diurnal cycle of sea
surface temperature in the Mediterranean Sea, J. Geophys. Res.-Oceans, 124,
171–183, https://doi.org/10.1029/2018JC014289, 2018.
Pisano, A., Buongiorno Nardelli, B., Tronconi, C., and Santoleri, R.: The
new Mediterranean optimally interpolated pathfinder AVHRR SST Dataset
(1982–2012), Remote Sens. Environ., 176, 107–116, https://doi.org/10.1016/j.rse.2016.01.019, 2016.
Purdy, W. E., Gaiser, P. W., Poe, G. A., Uliana, E. A., Eissner, T., and
Wentz, F. J.: Geolocation and pointing accuracy analysis for the WindSat
sensor, IEEE T. Geosci. Remote, 44, 496–505,
https://doi.org/10.1109/tgrs.2005.858415, 2006.
Reynolds, R. W. and Smith, T. M.: Improved global sea surface temperature
analyses using optimum interpolation, J. Climate, 7, 929–948,
https://doi.org/10.1175/1520-0442(1994)007<0929:IGSSTA>2.0.CO;2, 1994.
Reynolds, R. W. and Smith, T. M.: A high-resolution global sea surface
temperature climatology, J. Climate, 8, 1571–1583,
https://doi.org/10.1175/1520-0442(1995)008<1571:Ahrgss>2.0.Co;2, 1995.
Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C., and Wang, W.
Q.: An improved in situ and satellite SST analysis for climate, J. Climate,
15, 1609–1625, https://doi.org/10.1175/1520-0442(2002)015<1609:Aiisas>2.0.Co;2, 2002.
Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S., and
Schlax, M. G.: Daily high-resolution-blended analyses for sea surface
temperature, J. Climate, 20, 5473–5496,
https://doi.org/10.1175/2007JCLI1824.1, 2007.
Sakalli, A. and Basusta, N.: Sea surface temperature change in the Black Sea
under climate change: A simulation of the sea surface temperature up to
2100, Int. J. Climatol., 38, 4687–4698, https://doi.org/10.1002/joc.5688,
2018.
Satyamurty, P. and Rosa, M. B.: Synoptic climatology of tropical and
subtropical South America and adjoining seas as inferred from Geostationary
Operational Environmental Satellite imagery, Int. J. Climatol., 40, 378–399,
https://doi.org/10.1002/joc.6217, 2020.
Saunders, P. M.: The temperature at the ocean-air interface, Asia-Pac, J.
Atmos. Sci., 24, 269–273,
https://doi.org/10.1175/1520-0469(1967)024<0269:TTATOA>2.0.CO;2, 1967.
Shi, Y., Zhou, X., Yang, X., Shi, L., and Ma, S.: Merging satellite ocean
color data with bayesian maximum entropy method, IEEE J. Sel. Top. Appl.
Earth Observ., 8, 3294–3304, https://doi.org/10.1109/JSTARS.2015.2425691,
2015.
Smith, T. M. and Reynolds, R. W.: Extended reconstruction of global sea
surface temperatures based on COADS data (1854–1997), J. Climate, 16,
1495–1510, https://doi.org/10.1175/1520-0442-16.10.1495, 2003.
Storkey, D., Blockley, E. W., Furner, R., Guiavarc'h, C., Lea, D., Martin,
M. J., Barciela, R. M., Hines, A., Hyder, P., and Siddorn, J. R.:
Forecasting the ocean state using NEMO:The new FOAM system, J. Oper.
Oceanogr., 3, 3–15, https://doi.org/10.1080/1755876X.2010.11020109, 2010.
Sun, W., Wang, J., Zhang, J., Ma, Y., Meng, J., Yang, L., and Miao, J.: A
new global gridded sea surface temperature product constructed from infrared
and microwave radiometer data using the optimum interpolation method, Acta
Oceanol. Sin., 37, 41–49, https://doi.org/10.1007/s13131-018-1206-4, 2018.
Taylor, K. E.: Summarizing multiple aspects of model performance in a single
diagram, J. Geophys. Res.-Atmos., 106, 7183–7192,
https://doi.org/10.1029/2000jd900719, 2001.
Thiebaux, J., Rogers, E., Wang, W. Q., and Katz, B.: A new high-resolution
blended real-time global sea surface temperature analysis, B. Am.
Meteorol. Soc., 84, 645–656, https://doi.org/10.1175/bams-84-5-645, 2003.
Varela, R., Costoya, X., Enriquez, C., Santos, F., and Gomez-Gesteira, M.:
Differences in coastal and oceanic SST trends north of Yucatan Peninsula, J.
Mar. Syst., 182, 46–55, https://doi.org/10.1016/j.jmarsys.2018.03.006, 2018.
Vincent, R. F., Marsden, R. F., Minnett, P. J., Creber, K. A. M., and
Buckley, J. R.: Arctic waters and marginal ice zones: A composite Arctic sea
surface temperature algorithm using satellite thermal data, J. Geophys.
Res.-Atmos., 113, C04021, https://doi.org/10.1029/2007jc004353, 2008.
Wang, Y., Guan, L., and Qu, L.: Merging sea surface temperature observed by
satellite infrared and microwave radiometers using kalma, Periodical of
Ocean University of China, 40, 126–130, https://doi.org/10.16441/j.cnki.hdxb.2010.12.019, 2010 (in Chinese).
Wentz, F. J., Gentemann, C., Smith, D., and Chelton, D.: Satellite
measurements of sea surface temperature through clouds, Science, 288,
847–850, https://doi.org/10.1126/science.288.5467.847, 2000.
Wick, G. A., Jackson, D. L., and Castro, S. L.: Production of an enhanced
blended infrared and microwave sea surface temperature product, IGARSS 2004.
2004 IEEE International Geoscience and Remote Sensing Symposium, 20–24 September 2004, Anchorage,
AK, USA, 2004.
Xie, J., Zhu, J., and Li, Y.: Assessment and inter-comparison of five
high-resolution sea surface temperature products in the shelf and coastal
seas around China, Cont. Shelf Res., 28, 1286–1293,
https://doi.org/10.1016/j.csr.2008.02.020, 2008.
Xu, F. and Ignatov, A.: In situ SST Quality Monitor (iQuam), J. Atmos.
Ocean. Tech., 31, 164–180, https://doi.org/10.1175/JTECH-D-13-00121.1,
2014.
Xu, S. and Cheng, J.: A new land surface temperature fusion strategy based
on cumulative distribution function matching and multiresolution Kalman
filtering, Remote Sens. Environ., 254, 112256,
https://doi.org/10.1016/j.rse.2020.112256, 2021.
Yan, Y., Mao, K., Shi, J., Piao, S., Shen, X., Dozier, J., Liu, Y., Ren, H.,
and Bao, Q.: Driving forces of land surface temperature anomalous changes in
North America in 2002–2018, Sci. Rep.-UK, 10, 6931,
https://doi.org/10.1038/s41598-020-63701-5, 2020.
Zabolotskikh, E. V., Mitnik, L. M., Reul, N., and Chapron, B.: New
possibilities for geophysical parameter retrievals opened by GCOM-W1 AMSR2,
IEEE J. Sel. Top. Appl. Earth Observ., 8, 4248–4261,
https://doi.org/10.1109/JSTARS.2015.2416514, 2015.
Zhao, B., Mao, K., Cai, Y., Shi, J., Li, Z., Qin, Z., Meng, X., Shen, X., and Guo, Z.: A combined Terra and Aqua MODIS land surface temperature and meteorological station data product for China from 2003 to 2017, Earth Syst. Sci. Data, 12, 2555–2577, https://doi.org/10.5194/essd-12-2555-2020, 2020.
Zhu, J., Zhou, G., Yan, C., Fu, W., and You, X.: A three-dimensional
variational ocean data assimilation system: Scheme and preliminary results,
Science in China Series D: Earth Sciences, 49, 1212–1222,
https://doi.org/10.1007/s11430-006-1212-9, 2006 (in Chinese).
Short summary
We constructed a temperature depth and observation time correction model to eliminate the sampling depth and temporal differences among different data. Then, we proposed a reconstructed spatial model that filters and removes missing pixels and low-quality pixels contaminated by clouds from raw SST images and retrieves real sea surface temperatures under cloud coverage based on multisource data to generate a high-quality unified global SST product with long-term spatiotemporal continuity.
We constructed a temperature depth and observation time correction model to eliminate the...
Altmetrics
Final-revised paper
Preprint