Articles | Volume 17, issue 10
https://doi.org/10.5194/essd-17-5137-2025
© Author(s) 2025. 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-17-5137-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
02 Oct 2025
Data description paper |
| 02 Oct 2025
| 02 Oct 2025
A new dataset of rain cells based on observations of Tropical Rainfall Measuring Mission (TRMM) precipitation radar, visible/infrared scanner and microwave imager
Zhenhao Wu
School of Earth and Space Sciences, CMA-USTC Laboratory of Fengyun Remote Sensing, University of Science and Technology of China, Hefei, 230026, China
Jian Shang
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center, China Meteorological Administration, Beijing, 100081, China
Chunguan Cui
Institute of Heavy Rain, China Meteorological Administration, Wuhan, 430205, China
Peng Zhang
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center, China Meteorological Administration, Beijing, 100081, China
Songyan Gu
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center, China Meteorological Administration, Beijing, 100081, China
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center, China Meteorological Administration, Beijing, 100081, China
Yunfei Fu
CORRESPONDING AUTHOR
School of Earth and Space Sciences, CMA-USTC Laboratory of Fengyun Remote Sensing, University of Science and Technology of China, Hefei, 230026, China
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WindRAD (Wind Radar) is the first dual-frequency rotating fan-beam scatterometer in orbit. We observe non-linearity in the backscatter distribution. Therefore, higher-order calibration (HOC) is proposed, which removes the non-linearities per incidence angle. The combination of HOC and NOCant is discussed. It can remove not only the non-linearity but also the anomalous harmonic azimuth dependencies caused by the antenna rotation; hence the optimal winds can be achieved with this combination.
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Atmos. Chem. Phys., 23, 9265–9285, https://doi.org/10.5194/acp-23-9265-2023, https://doi.org/10.5194/acp-23-9265-2023, 2023
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Ying Chen, Ruibo Lei, Xi Zhao, Shengli Wu, Yue Liu, Pei Fan, Qing Ji, Peng Zhang, and Xiaoping Pang
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Earth Syst. Sci. Data, 14, 1433–1445, https://doi.org/10.5194/essd-14-1433-2022, https://doi.org/10.5194/essd-14-1433-2022, 2022
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Yungang Wang, Liping Fu, Fang Jiang, Xiuqing Hu, Chengbao Liu, Xiaoxin Zhang, Jiawei Li, Zhipeng Ren, Fei He, Lingfeng Sun, Ling Sun, Zhongdong Yang, Peng Zhang, Jingsong Wang, and Tian Mao
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Far-ultraviolet (FUV) airglow radiation is particularly well suited for space-based remote sensing. The Ionospheric Photometer (IPM) instrument carried aboard the Feng Yun 3-D satellite measures the spectral radiance of the Earth FUV airglow. IPM is a tiny, highly sensitive, and robust remote sensing instrument. Initial results demonstrate that the performance of IPM meets the designed requirement and therefore can be used to study the thermosphere and ionosphere in the future.
Wengang Zhang, Ling Wang, Yang Yu, Guirong Xu, Xiuqing Hu, Zhikang Fu, and Chunguang Cui
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Global precipitable water vapor (PWV) derived from MERSI-II (Medium Resolution Spectral Imager) is compared with PWV from the Integrated Global Radiosonde Archive (IGRA). Our results show a good agreement between PWV from MERSI-II and IGRA and that MERSI-II PWV is slightly underestimated on the whole, especially in summer. The bias between MERSI-II and IGRA grows with a larger spatial distance between the footprint of the satellite and the IGRA station, as well as increasing PWV.
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The result shows dust aerosols from the Taklimakan Desert have higher aerosol scattering during dust storm cases of this paper, and this caused higher negative direct radiative forcing efficiency (DRFEdust) than aerosols from the Sahara.
The microphysical properties and particle shapes of dust aerosol significantly influence DRFEdust. The satellite-based equi-albedo method has a unique advantage in DRFEdust estimation: it could validate the results derived from the numerical model directly.
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Multi-source dataset use is hampered by use of different spatial and temporal resolutions. We merged Tropical Rainfall Measuring Mission precipitation radar and visible and infrared scanner measurements with ERA5 reanalysis. The statistical results indicate this process has no unacceptable influence on the original data. The merged dataset can help in studying characteristics of and changes in cloud and precipitation systems and provides an opportunity for data analysis and model simulations.
Ziyu Huang, Lei Zhong, Yaoming Ma, and Yunfei Fu
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Spectral nudging is an effective dynamical downscaling method used to improve precipitation simulations of regional climate models (RCMs). However, the biases of the driving fields over the Tibetan Plateau (TP) would possibly introduce extra biases when spectral nudging is applied. The results show that the precipitation simulations were significantly improved when limiting the application of spectral nudging toward the potential temperature and water vapor mixing ratio over the TP.
Cited articles
Austin, P. M. and Houze, R. A.: Analysis of the structure of precipitation patterns in New England, J. Appl. Meteorol., 11, 926–935, https://doi.org/10.1175/1520-0450(1972)011<0926:Aotsop>2.0.Co;2, 1972.
Awaka, J.: A three-dimensional rain cell model for the study of interference due to hydrometeor scattering, J. Commun. Res. Lab., 36, 13–44, 1989.
Awaka, J., Iguchi, T., Kumagai, H., and Okamoto, K.: Rain type classification algorithm for TRMM precipitation radar, IEEE International Geoscience and Remote Sensing Symposium Proceedings, Remote Sensing – A Scientific Vision for Sustainable Development, Singapore, 3–8 August 1997, https://doi.org/10.1109/IGARSS.1997.608993, 1997.
Begum, S. and Otung, I. E.: Rain cell size distribution inferred from rain gauge and radar data in the UK, Radio. Sci., 44, RS2015, https://doi.org/10.1029/2008RS003984, 2009.
Cai, H. K., Mao, Y. Q., Zhu, X. H., Fu, Y. F., and Zhou, R. J.: Comparison of the minimum bounding rectangle and minimum circumscribed ellipse of rain cells from TRMM, Adv. Atmos. Sci., 41, 484–499, https://doi.org/10.1007/s00376-023-2281-9, 2024.
Capsoni, C., Fedi, F., Magistroni, C., Paraboni, A., and Pawlina, A.: Data and theory for a new model of the horizontal structure of rain cells for propagation applications, Radio. Sci., 22, 395–404, https://doi.org/10.1029/RS022i003p00395, 1987.
Chen, Y. L., Zhang, A. Q., Fu, Y. F., Chen, S. M., and Li, W. B.: Morphological characteristics of precipitation areas over the Tibetan Plateau measured by TRMM PR, Adv. Atmos. Sci., 38, 677–689, https://doi.org/10.1007/s00376-020-0233-1, 2021.
Han, Q., Rossow, W. B., and Lacis, A. A.: Near-global survey of effective droplet radii in liquid water clouds using ISCCP data, J. Climate, 7, 465–497, https://doi.org/10.1175/1520-0442(1994)007<0465:NGSOED>2.0.CO;2, 1994.
Feral, L., Mesnard, F., Sauvageot, H., Castanets, L., and Lemorton, J.: Rain cells shape and orientation distribution in south-west of France, Phys. Chem. Earth B: Hydrol. Oceans Atmos., 25, 1073–1078, https://doi.org/10.1016/s1464-1909(00)00155-6, 2000.
Fu, Y. F.: Cloud parameyers retrieved by the bispectral reflectance algorithm and associated applications, J. Meteorol. Res-Prc., 28, 965–982, https://doi.org/10.1007/s13351-014-3292-3, 2014 (in Chinese).
Fu, Y. F.: Research progress on retrieval algorithms of cloud liquid water over ocean based on remote sensing by satellite-borne passive microwave instruments, Torrential Rain and Disasters, 40, 217–230, https://doi.org/10.3969/j.issn.1004-9045.2021.03.001, 2021 (in Chinese).
Fu, Y. F. and Liu, G. S.: The variability of tropical precipitation profiles and its impact on microwave brightness temperatures as inferred from TRMM data, J. Appl. Meteorol., 40, 2130–2143, https://doi.org/10.1175/1520-0450(2001)040<2130:Tvotpp>2.0.Co;2, 2001.
Fu, Y. F., Liu, P., Liu, Q., Ma, M., Sun, L., and Wang, Y.: Climatological Characteristics of VIRS Channels for Precipitating Cloud in Summer Over the Tropics and Subtropics, J. Atmos. Environ. Optics., 6, 129–140, https://doi.org/10.3969/j.issn.1673-6141.2011.02.009, 2011 (in Chinese).
Fu, Y. F., Chen, Y., Zhang, X., Wang, Y., Li, R., Liu, Q., Zhong, L., Zhang, Q., and Zhang, A.: Fundamental characteristics of tropical rain cell structures as measured by TRMM PR, J. Meteorol. Res.-Prc., 34, 1129–1150, https://doi.org/10.1007/s13351-020-0035-5, 2020.
Gagin, A., Rosenfeld, D., Woodley, W. L., and Lopez, R. E.: 1986: Results of seeding for dynamic effects on rain-cell properties in FACE-2, J. Appl. Meteor. Climatol., 25, 3–13, https://doi.org/10.1175/1520-0450(1986)025<0003:ROSFDE>2.0.CO;2, 1986.
Goldhirsh, J. and Musiani, B.: Rain cell size statistics derived from radar observations at Wallops Island, Virginia, IEEE T. Geosci. Remote. Sens., GE-24, 947–954, https://doi.org/10.1109/TGRS.1986.289711, 1986.
Grody, N. C.: Remote sensing of atmospheric water content from satellites using microwave radiometry, IEEE T. Antenn. Propag., 24, 155–162, https://doi.org/10.1109/TAP.1976.1141324, 1976.
Grody, N. C., Grubel, A., and Shen, W. C.: Atmospheric water content over the tropical Pacific derived from the Nimbus-6 scanning microwave spectrometer, J. Appl. Meteor., 19, 986–996, https://doi.org/10.1175/1520-0450(1980)019<0986:AWCOTT>2.0.CO;2, 1980.
He, H. Z., Cheng, M. H., and Zhou, F. X.: 3D structure of rain and cloud hydrometeors for Typhoon Kujira (0302), Chinese J. Atmos. Sci., 30, 491–503, https://doi.org/10.3878/j.issn.1006-9895.2006.03.12, 2006 (in Chinese).
Houze, R. A.: Structures of atmospheric precipitation systems: A global survey, Radio. Sci., 16, 671–689, https://doi.org/10.1029/RS016i005p00671, 1981.
Houze, R. A.: Stratiform Precipitation in Regions of Convection: A Meteorological Paradox?, B. Am. Meteorol. Soc., 78, 2179–2196, https://doi.org/10.1175/1520-0477(1997)078<2179:SPIROC>2.0.CO;2, 1997.
Konrad, T. G.: Statistical models of summer rainshowers derived from fine-scale radar observations, J. Appl. Meteorol., 17, 171–188, https://doi.org/10.1175/1520-0450(1978)017<0171:SMOSRD>2.0.Co;2, 1978.
Kozu, T., Kawanishi, T., Kuroiwa, H., Oikawa, M., Kumagai, H., Okamoto, K., Okumura, M., Nakatsuka, H., and Nishikawa, K.: Development of precipitation radar onboard the Tropical Rainfall Measuring Mission (TRMM) satellite, IEEE T. Geosci. Remote. Sens., 39, 102–116, https://doi.org/10.1109/36.898669, 2001.
Kummerow, C., Barnes, W., Kozu, T., Shiue, J., and Simpson, J.: The Tropical Rainfall Measuring Mission (TRMM) Sensor Package, J. Atmos. Ocean. Tech., 15, 809–817, https://doi.org/10.1175/1520-0426(1998)015<0809:TTRMMT>2.0.Co;2, 1998.
Kummerow, C., Simpson, J., Thiele, O., Barnes, W., Chang, A. T. C., Stocker, E., Adler, R. F., Hou, A., Kakar, R., Wentz, F., Ashcroft, P., Kozu, T., Hong, Y., Okamoto, K., Iguchi, T., Kuroiwa, H., Im, E., Haddad, Z., Huffman, G., Ferrier, B., Olson, W. S., Zipser, E., Smith, E. A., Wilheit, T. T., North, G., Krishnamurti, T., and Nakamura, K.: The Status of the Tropical Rainfall Measuring Mission (TRMM) after two years in orbit, J. Appl. Meteorol. Climatol., 39, 1965–1982, https://doi.org/10.1175/1520-0450(2001)040<1965:Tsottr>2.0.Co;2, 2000.
Lau, K. M. and Wu, H. T.: Characteristics of precipitation, cloud, and latent heating associated with the Madden-Julian oscillation, J. Climate, 23, 504–518, https://doi.org/10.1175/2009jcli2920.1, 2010.
Li, R. and Fu, Y. F.: Tropical precipitation estimated by GPCP and TRMM PR observations, Adv. Atmos. Sci., 22, 852–864, https://doi.org/10.1007/BF02918685, 2005.
Liu, C. T. and Zipser, E. J.: Regional variation of morphology of organized convection in the tropics and subtropics, J. Geophys. Res.-Atmos., 118, 453–466, https://doi.org/10.1029/2012JD018409, 2013.
Liu, C. T., Zipser, E. J., and Nesbitt, S. W.: Global distribution of tropical deep convection: different perspectives from TRMM infrared and radar data, J. Climate, 20, 489–503, https://doi.org/10.1175/JCLI4023.1, 2007.
Liu, C. T., Zipser, E. J., Cecil, D. J., Nesbitt, S. W., and Sherwood, S.: A cloud and precipitation feature database from nine years of TRMM observations, J. Appl. Meteorol. Climatol., 47, 2712–2728, https://doi.org/10.1175/2008jamc1890.1, 2008.
Liu, G. S. and Curry, J. A.: Determination of characteristic features of cloud liquid water from satellite microwave measurements, J. Geophys. Res., 98, 5069–5092, https://doi.org/10.1029/92JD02888, 1993.
Liu, G. S. and Fu, Y. F.: The characteristics of tropical precipitation profiles as inferred from satellite radar measurements, J. Meteor. Soc. Japan, 79, 131–143, https://doi.org/10.2151/jmsj.79.131, 2001.
Liu, Q. and Fu, Y. F.: Comparison of radiative signals between precipitating and non-precipitating clouds in frontal and typhoon domains over East Asia, Atmos. Res., 96, 436–446, https://doi.org/10.1016/j.atmosres.2010.02.003, 2010.
Nakajima, T. and King, M. D.: Determination of the optical thickness and effective particle radius of clouds from reflected solar radiation measurements. Part I: Theory, J. Atmos. Sci., 47, 1878–1893, https://doi.org/10.1175/1520-0469(1990)047<1878:DOTOTA>2.0.CO;2, 1990.
Nesbitt, S. W., Zipser, E. J., and Cecil, D. J.: A census of precipitation features in the tropics using TRMM: Radar, ice scattering, and lightning observations, J. Climate, 13, 4087–4106, https://doi.org/10.1175/1520-0442(2000)013<4087:ACOPFI>2.0.Co;2, 2000.
Nesbitt, S. W., Cifelli, R., and Rutledge, S. A.: Storm morphology and rainfall characteristics of TRMM precipitation features, Mon. Wea. Rev., 134, 2702–2721, https://doi.org/10.1175/MWR3200.1, 2006.
Ni, X., Liu, C., Zhang, Q., and Cecil, D. J.: Properties of hail storms over China and the United States from the Tropical Rainfall Measuring Mission, J. Geophys. Res.-Atmos., 121, 12031–12044, https://doi.org/10.1002/2016JD025600, 2016.
Oki, T. and Kanae, S.: Global hydrological cycles and world water resources, Science, 313, 1068–1072, https://doi.org/10.1126/science.1128845, 2006.
Petty, G. W.: Physical retrievals of over-ocean rain rate from multichannel microwave imagery. Part I: Theoretical characteristics of normalized polarization and scattering indices, Meteorl. Atmos. Phys., 54, 79–99, https://doi.org/10.1007/BF01030053, 1994a.
Petty, G. W.: Physical retrievals of over-ocean rain rate from multichannel microwave imagery. Part II: Algorithm implementation, Meteorl. Atmos. Phys., 54, 101–121, https://doi.org/10.1007/BF01030054, 1994b.
Rossow, W. B. and Garder, L. C.: Cloud detection using satellite measurements of infrared and visible radiances for ISCCP, J. Climate, 6, 2341–2369, https://doi.org/10.1175/1520-0442(1993)006<2341:CDUSMO>2.0.CO;2, 1993.
Rossow, W. B. and Schiffer, R. A.: Advances in understanding clouds from ISCCP, Bull. Amer. Meteor. Soc., 80, 2261–2288, https://doi.org/10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2, 1999.
Sauvageot, H., Mesnard, F., and Tenório, R. S.: The relation between the area-average rain rate and the rain cell size distribution parameters, J. Atmos. Sci., 56, 57–70, https://doi.org/10.1175/1520-0469(1999)056<0057:TRBTAA>2.0.Co;2, 1999.
Schumacher, C. and Houze, R. A.: The TRMM precipitation radar's view of shallow, isolated rain, J. Appl. Meteorol., 42, 1519–1524, https://doi.org/10.1175/1520-0450(2003)042<1519:TTPRVO>2.0.CO;2, 2003.
Simpson, J., Kummerow, C., Tao, W.-K., and Adler, R. F.: On the Tropical Rainfall Measuring Mission (TRMM), Meteorol. Atmos. Phys., 60, 19–36, https://doi.org/10.1007/BF01029783, 1996.
Sun, L. and Fu, Y.: A new merged dataset for analyzing clouds, precipitation and atmospheric parameters based on ERA5 reanalysis data and the measurements of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar and visible and infrared scanner, Earth Syst. Sci. Data, 13, 2293–2306, https://doi.org/10.5194/essd-13-2293-2021, 2021.
Viltard, N., Kummerow, C., Olson, W. S., and Hong, Y.: Combined use of the radar and radiometer of TRMM to estimate the influence of drop size distribution on rain retrievals, J. Appl. Meteorol., 39, 2103–2114, https://doi.org/10.1175/1520-0450(2001)040<2103:Cuotra>2.0.Co;2, 2000.
Wang, Y., Fu, Y. F., Liu, G. S., Liu, Q., and Sun, L.: A new water vapor algorithm for TRMM Microwave Imager (TMI) measurements based on a log linear relationship, J. Geophys. Res., 114, D21304, https://doi.org/10.1029/2008JD011057, 2009.
Wu, Z. H. and Fu, Y. F.: A new dataset of rain cell generated from observations of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar and visible and infrared scanner and microwave imager, Zenodo [data set], https://doi.org/10.5281/zenodo.15387988, 2025.
Yokoyama, C., Zipser, E. J., and Liu, C.: TRMM-Observed shallow versus deep convection in the eastern pacific related to large-scale circulations in reanalysis datasets, J. Climate, 27, 5575–5592, https://doi.org/10.1175/JCLI-D-13-00315.1, 2014.
Yuter, S. E. and Houze, R. A.: Three-Dimensional kinematic and microphysical evolution of florida cumulonimbus. Part II: frequency distributions of vertical velocity, reflectivity, and differential reflectivity, Mon. Weather Rev., 123, 1941–1963, https://doi.org/10.1175/1520-0493(1995)123<1941:Tdkame>2.0.Co;2, 1995.
Zhou, Y., Lau, W. K. M., and Liu, C. T.: Rain characteristics and large-scale environments of precipitation objects with extreme rain volumes from TRMM observations, J. Geophys. Res.-Atmos., 118, 9673–9689, https://doi.org/10.1002/jgrd.50776, 2013.
Zipser, E. J. and Lutz, K. R.: The vertical profile of radar reflectivity of convective cells: A strong indicator of storm intensity and lightning probability?, Mon. Weather Rev., 122, 1751–1759, https://doi.org/10.1175/1520-0493(1994)122<1751:Tvporr>2.0.Co;2, 1994.
Short summary
We have established a new dataset of rain cell precipitation parameters and visible-infrared and microwave signals by combining multi-instrument observation data from the Tropical Rainfall Measuring Mission (TRMM). The purpose of this dataset is to promote the three-dimensional studies of rain cell precipitation systems and to reveal the spatial and temporal variations in their scale, morphology, and intensity.
We have established a new dataset of rain cell precipitation parameters and visible-infrared and...
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