ISSN 0253-2778

CN 34-1054/N

Open AccessOpen Access JUSTC Earth and Space Sciences 20 April 2022

Characteristics of clouds, precipitation, and latent heat in midlatitude frontal system mixed with dust storm from GPM satellite observations and WRF simulations

Cite this:
https://doi.org/10.52396/JUSTC-2021-0238
More Information
  • Author Bio:

    Renjun Zhou is an associate professor at the School of Earth and Space Sciences (ESS), University of Science and Technology of China (USTC). Admitted to ESS in 1995 as an undergraduate majoring in Atmospheric Sciences with a double major in Electronic Engineering, he finished his undergraduate, master and doctoral studies in ESS, and stayed in the school as a faculty member after graduation. He also went to the City University of Hong Kong and Georgia Institute of Technology as a visiting scholar. His research mainly involves numerical modeling of weather and climate and the development of applications in the field of geosciences. He has been PIs for many scientific research projects such as the National Natural Science Foundation of China, and has published more than 40 academic papers in domestic and foreign journals. He also has many years of experience in the software and hardware development, and has carried out a lot of outreaching cooperation with industries in the fields of meteorology, earthquake, environmental protection, electric power, etc. Website: https://ess.ustc.edu.cn/faculties/detail-144.html

    Rui Li received his PhD degree from the University of Science and Technology of China (USTC) in 2005. Before he joined the School of Earth and Space Sciences (ESS) of USTC in 2013, he was a full research scientist in the State University of New York. He was honored as the “Professor of Hundred-Talent Program of Chinese Academy of Sciences” and the “Specially Recruited Experts of Anhui Province in China” in 2013. He now serves as a full professor and deputy dean of ESS USTC; the professor of the State Key Laboratory of Fire Science; the adjunct professor of University of Quebec in Abitibi, Canada; the member of China National Committee for WMO World Weather Research Programee (WWRP); the member of China National Committee for International Association of Meteorology and Atmospheric Sciences (IAMAS); the member of China National Administry Committee on Teaching Atmospheric Science in Higher Education; and the council member of the Anhui Meteorology Society. His research has been focusing on the water cycle and energy balance processes in the Earth’s climate system, the development of advanced satellite remote sensing methods and datasets and their application in climate system research. Website: https://ess.ustc.edu.cn/faculties/detail-58.html

  • Corresponding author: E-mail: rli7@ustc.edu.cn
  • Received Date: 15 November 2021
  • Accepted Date: 21 January 2022
  • Available Online: 20 April 2022
  • A heavy dust storm originating in Mongolia and Inner Mongolia traveled to Northeast China and met a midlatitude frontal system on May 3, 2017. The potential ice nuclei (IN) effects of mineral dust aerosols on the vertical structure of clouds, precipitation, and latent heat (LH) were studied using Global Precipitation Mission (GPM) satellite observations and Weather Research and Forecasting (WRF) model simulations. The WRF simulations correctly captured the main features of the system, and the surface rain rate distribution was positively correlated with data retrieved from the GPM Microwave Imager. Moreover, the correlation coefficient increased from 0.31 to 0.54 with increasing moving average window size. The WRF-simulated rainfall vertical profiles are generally comparable to the GPM Dual-Frequency Precipitation Radar (DPR) observations, particularly in low layers. The joint probability distribution functions of the rain rate at different altitudes from the WRF simulation and GPM observations show high positive correlation coefficients of ~0.80, indicating that the assumptions regarding the raindrop size distribution in the WRF model and DPR retrieval were consistent. Atmospheric circulation analysis and aerosol optical depth observations from the Himawari-8 satellite indicated that the dust storm entered only a narrow strip of the northwest edge of the frontal precipitation system. The WRF simulations showed that in carefully selected areas of heavy dust, dust can enhance the heterogeneous ice nucleation process and increase the cloud ice, snowfall, high-altitude precipitation rate, and LH rate in the upper layers. This effect is significant at temperatures of −15 °C to −38 °C and requires dust number concentrations exceeding 106 m3. It is important to accurately classify the dusty region in this type of case study. In the selected vertical cross section, the WRF-simulated and DPR-retrieved LH have comparable vertical shapes and amplitudes. Both results reflect the structure of the tilted frontal surface, with positive LH above it and negative LH below it. The simulated area-averaged LH profiles show positive heating in the entire column, which is a convective-dominated region, and this feature is not significantly affected by dust. DPR-based LH profiles show stratiform-dominated or convective-dominated shapes, depending on the DPR retrieval product.

      Dust can affect the vertical structure of precipitation and latent heat in frontal raining system.

    A heavy dust storm originating in Mongolia and Inner Mongolia traveled to Northeast China and met a midlatitude frontal system on May 3, 2017. The potential ice nuclei (IN) effects of mineral dust aerosols on the vertical structure of clouds, precipitation, and latent heat (LH) were studied using Global Precipitation Mission (GPM) satellite observations and Weather Research and Forecasting (WRF) model simulations. The WRF simulations correctly captured the main features of the system, and the surface rain rate distribution was positively correlated with data retrieved from the GPM Microwave Imager. Moreover, the correlation coefficient increased from 0.31 to 0.54 with increasing moving average window size. The WRF-simulated rainfall vertical profiles are generally comparable to the GPM Dual-Frequency Precipitation Radar (DPR) observations, particularly in low layers. The joint probability distribution functions of the rain rate at different altitudes from the WRF simulation and GPM observations show high positive correlation coefficients of ~0.80, indicating that the assumptions regarding the raindrop size distribution in the WRF model and DPR retrieval were consistent. Atmospheric circulation analysis and aerosol optical depth observations from the Himawari-8 satellite indicated that the dust storm entered only a narrow strip of the northwest edge of the frontal precipitation system. The WRF simulations showed that in carefully selected areas of heavy dust, dust can enhance the heterogeneous ice nucleation process and increase the cloud ice, snowfall, high-altitude precipitation rate, and LH rate in the upper layers. This effect is significant at temperatures of −15 °C to −38 °C and requires dust number concentrations exceeding 106 m3. It is important to accurately classify the dusty region in this type of case study. In the selected vertical cross section, the WRF-simulated and DPR-retrieved LH have comparable vertical shapes and amplitudes. Both results reflect the structure of the tilted frontal surface, with positive LH above it and negative LH below it. The simulated area-averaged LH profiles show positive heating in the entire column, which is a convective-dominated region, and this feature is not significantly affected by dust. DPR-based LH profiles show stratiform-dominated or convective-dominated shapes, depending on the DPR retrieval product.

    • WRF simulations and GPM observations were combined to study the interactions between dust aerosols and frontal raining system.
    • Greater cloud ice, snowfall, high-altitude precipitation rate, and more latent heat rate at upper layers were found in selected dust laden area.
    • WRF simulated LH and DPR retrieved LH are comparable in terms of both vertical shape and amplitude in selected vertical cross sections.

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    [2]
    Huang J, Minnis P, Lin B, et al. Possible influences of Asian dust aerosols on cloud properties and radiative forcing observed from MODIS and CERES. Geophysical Research Letters, 2006, 33 (6): L06824. doi: 10.1029/2005GL024724
    [3]
    Li R, Dong X, Guo J, et al. The implications of dust ice nuclei effect on cloud top temperature in a complex mesoscale convective system. Scientific Reports, 2017, 7 (1): 13826. doi: 10.1038/s41598-017-12681-0
    [4]
    Kaufman Y J, Koren I, Remer L A, et al. The effect of smoke, dust, and pollution aerosol on shallow cloud development over the Atlantic Ocean. Proceedings of the National Academy of Sciences, 2005, 102 (32): 11207–11212. doi: 10.1073/pnas.0505191102
    [5]
    Li R, Min Q-L, Harrison L C. A case study: The indirect aerosol effects of mineral dust on warm clouds. Journal of the Atmospheric Sciences, 2010, 67 (3): 805–816. doi: 10.1175/2009JAS3235.1
    [6]
    Rosenfeld D, Rudich Y, Lahav R. Desert dust suppressing precipitation: A possible desertification feedback loop. Proceedings of the National Academy of Sciences, 2001, 98 (11): 5975–5980. doi: 10.1073/pnas.101122798
    [7]
    Min Q-L, Li R, Lin B, et al. Evidence of mineral dust altering cloud microphysics and precipitation. Atmospheric Chemistry and Physics, 2009, 9 (9): 3223–3231. doi: 10.5194/acp-9-3223-2009
    [8]
    Li R, Min Q-L. Impacts of mineral dust on the vertical structure of precipitation. Journal of Geophysical Research, 2010, 115: 09203. doi: 10.1029/2009JD011925
    [9]
    Schaefer V J. The detection of ice nuclei in the free atmosphere. Journal of Atmospheric Sciences, 1949, 6 (4): 283–285.
    [10]
    Dong X, Li R, Wang Y, et al. Potential impacts of Sahara dust aerosol on rainfall vertical structure over the Atlantic Ocean as identified from EOF analysis. Journal of Geophysical Research: Atmospheres, 2018, 123 (16): 8850–8868. doi: 10.1029/2018JD028500
    [11]
    Villanueva D, Heinold B, Seifert P, et al. The day-to-day co-variability between mineral dust and cloud glaciation: A proxy for heterogeneous freezing. Atmos. Chem. Phys., 2020, 20 (4): 2177–2199. doi: 10.5194/acp-20-2177-2020
    [12]
    Zhao C, Lin Y, Wu F, et al. Enlarging rainfall area of tropical cyclones by atmospheric aerosols. Geophysical Research Letters, 2018, 45 (16): 8604–8611. doi: 10.1029/2018GL079427
    [13]
    Rosenfeld D, Lohmann U, Raga G B, et al. Flood or drought: How do aerosols affect precipitation? Science, 2008, 321 (5894): 1309–1313. doi: 10.1126/science.1160606
    [14]
    Fan J, Rosenfeld D, Zhang Y, et al. Substantial convection and precipitation enhancements by ultrafine aerosol particles. Science, 2018, 359 (6374): 411–418. doi: 10.1126/science.aan8461
    [15]
    Zhou S, Yang J, Wang W-C, et al. An observational study of the effects of aerosols on diurnal variation of heavy rainfall and associated clouds over Beijing–Tianjin–Hebei. Atmos. Chem. Phys., 2020, 20: 5211–5229. doi: 10.5194/acp-20-5211-2020
    [16]
    Sun Y, Zhao C. Distinct impacts on precipitation by aerosol radiative effect over three different megacity regions of eastern China. Atmos. Chem. Phys., 2021, 21: 16555–16574. doi: 10.5194/acp-21-16555-2021
    [17]
    Rosenfeld D. TRMM observed first direct evidence of smoke from forest fires inhibiting rainfall. Geophysical Research Letters, 1999, 26 (20): 3105–3108. doi: 10.1029/1999GL006066
    [18]
    Guo J, Liu H, Li Z, et al. Aerosol-induced changes in the vertical structure of precipitation: A perspective of TRMM precipitation radar. Atmos. Chem. Phys., 2018, 18 (18): 13329–13343. doi: 10.5194/acp-18-13329-2018
    [19]
    Naeger A. Impact of dust aerosols on precipitation associated with atmospheric rivers using WRF-Chem simulations. Results in Physics, 2018, 10: 217–221. doi: 10.1016/j.rinp.2018.05.027
    [20]
    Li R, Min Q, Fu Y. 1997/98 El Niño-induced changes in rainfall vertical structure in the East Pacific. Journal of Climate, 2011, 24 (24): 6373–6391. doi: 10.1175/JCLI-D-11-00002.1
    [21]
    Li R, Shao W, Guo J, et al. A simplified algorithm to estimate latent heating rate using vertical rainfall profiles over the Tibetan Plateau. Journal of Geophysical Research: Atmospheres, 2019, 124 (2): 942–963. doi: 10.1029/2018JD029297
    [22]
    Tao W-K, Lang S, Olson W S, et al. Retrieved vertical profiles of latent heat release using TRMM rainfall products for February 1998. Journal of Applied Meteorology, 2001, 40 (6): 957–982. doi: https://doi.org/10.1175/1520-0450(2004)043<1095:SROLHP>2.0.CO;2
    [23]
    Shige S, Takayabu Y N, Tao W-K, et al. Spectral retrieval of latent heating profiles from TRMM PR data. Part I: Development of a model-based algorithm. Journal of Applied Meteorology, 2004, 43 (8): 1095–1113. doi: 10.1175/1520-0450(2004)043<1095:SROLHP>2.0.CO;2
    [24]
    Fan J, Leung L R, Rosenfeld D, et al. Microphysical effects determine macrophysical response for aerosol impacts on deep convective clouds. Proceedings of the National Academy of Sciences, 2013, 110 (48): E4581–E4590. doi: 10.1073/pnas.1316830110
    [25]
    Tao W-K, Smith E A, Adler R F, et al. Retrieval of latent heating from TRMM measurements. Bulletin of the American Meteorological Society, 2006, 87 (11): 1555–1572. doi: 10.1175/BAMS-87-11-1555
    [26]
    Tao W-K, Houze R Jr, Smith E A. The fourth TRMM latent heating workshop. Bulletin of the American Meteorological Society, 2007, 88: 1255–1259. doi: 10.1175/BAMS-88-8-1255
    [27]
    Yoshida M, Kikuchi M, Nagao T M, et al. Common retrieval of aerosol properties for imaging satellite sensors. Journal of the Meteorological Society of Japan. Ser. II, 2018, 96B: 193–209. doi: 10.2151/jmsj.2018-039
    [28]
    Thompson G, Field P R, Rasmussen R M, et al. Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Monthly Weather Review, 2008, 136 (12): 5095–5115. doi: 10.1175/2008MWR2387.1
    [29]
    Iacono M J, Delamere J S, Mlawer E J, et al. Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. Journal of Geophysical Research: Atmospheres, 2008, 113: D13103. doi: 10.1029/2008JD009944
    [30]
    Jiménez P A, Dudhia J, González-Rouco J F, et al. A revised scheme for the WRF surface layer formulation. Monthly Weather Review, 2012, 140 (3): 898–918. doi: 10.1175/MWR-D-11-00056.1
    [31]
    Tewari M, Chen F, Wang W, et al. Implementation and verification of the unified Noah land surface model in the WRF model (Formerly Paper Number 17.5). In: 20th Conference on Weather Analysis and Forecasting/16th Conference on Numerical Weather Prediction. Seattle, WA: American Meteorological Society, 2004: 11-15.
    [32]
    Hong S-Y, Noh Y, Dudhia J. A new vertical diffusion package with an explicit treatment of entrainment processes. Monthly Weather Review, 2006, 134 (9): 2318–2341. doi: 10.1175/MWR3199.1
    [33]
    Grell G A, Freitas S R. A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling. Atmos. Chem. Phys., 2014, 14 (10): 5233–5250. doi: 10.5194/acp-14-5233-2014
    [34]
    Ginoux P, Chin M, Tegen I, et al. Sources and distributions of dust aerosols simulated with the GOCART model. Journal of Geophysical Research: Atmospheres, 2001, 106 (D17): 20255–20273. doi: 10.1029/2000JD000053
    [35]
    Demott P J, Prenni A J, Liu X, et al. Predicting global atmospheric ice nuclei distributions and their impacts on climate. Proceedings of the National Academy of Sciences, 2010, 107 (25): 11217–11222. doi: 10.1073/pnas.0910818107
    [36]
    Saha S, Moorthi S, Wu X, et al. The NCEP Climate Forecast System Version 2. Journal of Climate, 2014, 27 (6): 2185–2208. doi: 10.1175/JCLI-D-12-00823.1
    [37]
    Iguchi T, Seto S, Meneghini R, et al. GPM/DPR Level-2 algorithm theoretical basis document. Washington, DC: NASA, 2019.
    [38]
    Houze R A. Stratiform precipitation in regions of convection: A meteorological paradox? Bulletin of the American Meteorological Society, 1997, 78 (10): 2179–2196. doi: 10.1175/1520-0477(1997)078<2179:SPIROC>2.0.CO;2
    [39]
    Stevens B, Feingold G. Untangling aerosol effects on clouds and precipitation in a buffered system. Nature, 2009, 461 (7264): 607–613. doi: 10.1038/nature08281
  • 加载中

Catalog

    Figure  1.  The spatial pattern of air temperature (left column), specific humidity (central column) and geopotential height (right column) at 850 hPa (top row), 500 hPa (middle row) and 300 hPa (bottom row) at 8:00 UTC on 2017-05-03 derived from Climate Forecast System Reforecast product.

    Figure  2.  Satellite observations and model simulations of precipitation and aerosol distribution. (a) GPM GMI observed surface rain rate; (b) GPM DPR observed surface rain rate; (c) Himawari-8 observed aerosol optical depth; (d) WRF simulated surface rain rate without dust; (e) WRF simulated surface rain rate with dust; (f) WRF simulated dust column content.

    Figure  5.  The dust column density (gray shaded) and ice water path (rainbow shaded) simulated by WRF and five selected lines for making cross-section analysis.

    Figure  3.  Scatterplot between GMI observed surface rain rate against WRF simulated surface rain rate in sector G1 at (a) original resolution of GMI; (b) 0.1° grid; (c) 0.25° grid; and (d) 0.5° grid.

    Figure  4.  The intercomparison of rainfall vertical structure between DPR retrievals and WRF simulations. (a) The area means vertical profiles of precipitation rate area. The joint probability function (JPDF) of height and precipitation rate derived from (b) DPR observation, (c) WRF-ND simulation and (d) WRF-DS simulation, where red lines are 99.5 percentiles of DPR observation, and black lines are 99.5 percentiles of WRF simulations (ND, DS, respectively).

    Figure  6.  WRF DS and ND cross sections along line 1 in Fig. 5 (perpendicular to the front line). The first column is from WRF-DS simulation, the second column is from the WRF-ND simulation, and the third column is the difference between these two simulations. The fourth column is the mean profile averaged over the whole cross section. Different rows represent different variables as denoted at top left of every row. Brown contour lines in the 1st column represent dust concentrations. Vectors are wind fields (vertical winds are multiplied by 5).

    Figure  8.  Same as Fig. 6, but along line 3 in Fig. 5.

    Figure  7.  Same as Fig. 6, but along line 2 in Fig. 5 (perpendicular to the front line).

    Figure  9.  The difference of cloud ice (up row), snow (mid row) and latent heating (bottom row) between WRF-DS and WRF-ND at temperature colder than –38 ℃, –38 – –15 ℃ and warmer than –15 ℃ as functions of dust number concentration.

    Figure  10.  DPR vs. WRF cross section along line 4 in Fig. 5. (a) DPR precipitation rate; (b) WRF-ND precipitation rate; (c) WRF-DS precipitation rate; (d) SLH latent heating product; (e) CSH latent heating product; (f) USTC-VPH latent heating product; (g) WRF-ND latent heating; (h) WRF-DS latent heating;(i) mean latent heating profile of SLH, CSH, USTC-VPH, WRF-ND and WRF-DS.

    Figure  11.  Same as Fig. 10, but along line 5 in Fig. 5.

    Figure  12.  Average vertical profile of latent heat retrieved by JAXA SLH, NASA CSH, USTC VPH, WRF-ND and WRF-DS in the whole study area.

    [1]
    Boucher O, Randall D, Artaxo P, et al. Clouds and Aerosols. In: Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press, 2013: 571 – 658.
    [2]
    Huang J, Minnis P, Lin B, et al. Possible influences of Asian dust aerosols on cloud properties and radiative forcing observed from MODIS and CERES. Geophysical Research Letters, 2006, 33 (6): L06824. doi: 10.1029/2005GL024724
    [3]
    Li R, Dong X, Guo J, et al. The implications of dust ice nuclei effect on cloud top temperature in a complex mesoscale convective system. Scientific Reports, 2017, 7 (1): 13826. doi: 10.1038/s41598-017-12681-0
    [4]
    Kaufman Y J, Koren I, Remer L A, et al. The effect of smoke, dust, and pollution aerosol on shallow cloud development over the Atlantic Ocean. Proceedings of the National Academy of Sciences, 2005, 102 (32): 11207–11212. doi: 10.1073/pnas.0505191102
    [5]
    Li R, Min Q-L, Harrison L C. A case study: The indirect aerosol effects of mineral dust on warm clouds. Journal of the Atmospheric Sciences, 2010, 67 (3): 805–816. doi: 10.1175/2009JAS3235.1
    [6]
    Rosenfeld D, Rudich Y, Lahav R. Desert dust suppressing precipitation: A possible desertification feedback loop. Proceedings of the National Academy of Sciences, 2001, 98 (11): 5975–5980. doi: 10.1073/pnas.101122798
    [7]
    Min Q-L, Li R, Lin B, et al. Evidence of mineral dust altering cloud microphysics and precipitation. Atmospheric Chemistry and Physics, 2009, 9 (9): 3223–3231. doi: 10.5194/acp-9-3223-2009
    [8]
    Li R, Min Q-L. Impacts of mineral dust on the vertical structure of precipitation. Journal of Geophysical Research, 2010, 115: 09203. doi: 10.1029/2009JD011925
    [9]
    Schaefer V J. The detection of ice nuclei in the free atmosphere. Journal of Atmospheric Sciences, 1949, 6 (4): 283–285.
    [10]
    Dong X, Li R, Wang Y, et al. Potential impacts of Sahara dust aerosol on rainfall vertical structure over the Atlantic Ocean as identified from EOF analysis. Journal of Geophysical Research: Atmospheres, 2018, 123 (16): 8850–8868. doi: 10.1029/2018JD028500
    [11]
    Villanueva D, Heinold B, Seifert P, et al. The day-to-day co-variability between mineral dust and cloud glaciation: A proxy for heterogeneous freezing. Atmos. Chem. Phys., 2020, 20 (4): 2177–2199. doi: 10.5194/acp-20-2177-2020
    [12]
    Zhao C, Lin Y, Wu F, et al. Enlarging rainfall area of tropical cyclones by atmospheric aerosols. Geophysical Research Letters, 2018, 45 (16): 8604–8611. doi: 10.1029/2018GL079427
    [13]
    Rosenfeld D, Lohmann U, Raga G B, et al. Flood or drought: How do aerosols affect precipitation? Science, 2008, 321 (5894): 1309–1313. doi: 10.1126/science.1160606
    [14]
    Fan J, Rosenfeld D, Zhang Y, et al. Substantial convection and precipitation enhancements by ultrafine aerosol particles. Science, 2018, 359 (6374): 411–418. doi: 10.1126/science.aan8461
    [15]
    Zhou S, Yang J, Wang W-C, et al. An observational study of the effects of aerosols on diurnal variation of heavy rainfall and associated clouds over Beijing–Tianjin–Hebei. Atmos. Chem. Phys., 2020, 20: 5211–5229. doi: 10.5194/acp-20-5211-2020
    [16]
    Sun Y, Zhao C. Distinct impacts on precipitation by aerosol radiative effect over three different megacity regions of eastern China. Atmos. Chem. Phys., 2021, 21: 16555–16574. doi: 10.5194/acp-21-16555-2021
    [17]
    Rosenfeld D. TRMM observed first direct evidence of smoke from forest fires inhibiting rainfall. Geophysical Research Letters, 1999, 26 (20): 3105–3108. doi: 10.1029/1999GL006066
    [18]
    Guo J, Liu H, Li Z, et al. Aerosol-induced changes in the vertical structure of precipitation: A perspective of TRMM precipitation radar. Atmos. Chem. Phys., 2018, 18 (18): 13329–13343. doi: 10.5194/acp-18-13329-2018
    [19]
    Naeger A. Impact of dust aerosols on precipitation associated with atmospheric rivers using WRF-Chem simulations. Results in Physics, 2018, 10: 217–221. doi: 10.1016/j.rinp.2018.05.027
    [20]
    Li R, Min Q, Fu Y. 1997/98 El Niño-induced changes in rainfall vertical structure in the East Pacific. Journal of Climate, 2011, 24 (24): 6373–6391. doi: 10.1175/JCLI-D-11-00002.1
    [21]
    Li R, Shao W, Guo J, et al. A simplified algorithm to estimate latent heating rate using vertical rainfall profiles over the Tibetan Plateau. Journal of Geophysical Research: Atmospheres, 2019, 124 (2): 942–963. doi: 10.1029/2018JD029297
    [22]
    Tao W-K, Lang S, Olson W S, et al. Retrieved vertical profiles of latent heat release using TRMM rainfall products for February 1998. Journal of Applied Meteorology, 2001, 40 (6): 957–982. doi: https://doi.org/10.1175/1520-0450(2004)043<1095:SROLHP>2.0.CO;2
    [23]
    Shige S, Takayabu Y N, Tao W-K, et al. Spectral retrieval of latent heating profiles from TRMM PR data. Part I: Development of a model-based algorithm. Journal of Applied Meteorology, 2004, 43 (8): 1095–1113. doi: 10.1175/1520-0450(2004)043<1095:SROLHP>2.0.CO;2
    [24]
    Fan J, Leung L R, Rosenfeld D, et al. Microphysical effects determine macrophysical response for aerosol impacts on deep convective clouds. Proceedings of the National Academy of Sciences, 2013, 110 (48): E4581–E4590. doi: 10.1073/pnas.1316830110
    [25]
    Tao W-K, Smith E A, Adler R F, et al. Retrieval of latent heating from TRMM measurements. Bulletin of the American Meteorological Society, 2006, 87 (11): 1555–1572. doi: 10.1175/BAMS-87-11-1555
    [26]
    Tao W-K, Houze R Jr, Smith E A. The fourth TRMM latent heating workshop. Bulletin of the American Meteorological Society, 2007, 88: 1255–1259. doi: 10.1175/BAMS-88-8-1255
    [27]
    Yoshida M, Kikuchi M, Nagao T M, et al. Common retrieval of aerosol properties for imaging satellite sensors. Journal of the Meteorological Society of Japan. Ser. II, 2018, 96B: 193–209. doi: 10.2151/jmsj.2018-039
    [28]
    Thompson G, Field P R, Rasmussen R M, et al. Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Monthly Weather Review, 2008, 136 (12): 5095–5115. doi: 10.1175/2008MWR2387.1
    [29]
    Iacono M J, Delamere J S, Mlawer E J, et al. Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. Journal of Geophysical Research: Atmospheres, 2008, 113: D13103. doi: 10.1029/2008JD009944
    [30]
    Jiménez P A, Dudhia J, González-Rouco J F, et al. A revised scheme for the WRF surface layer formulation. Monthly Weather Review, 2012, 140 (3): 898–918. doi: 10.1175/MWR-D-11-00056.1
    [31]
    Tewari M, Chen F, Wang W, et al. Implementation and verification of the unified Noah land surface model in the WRF model (Formerly Paper Number 17.5). In: 20th Conference on Weather Analysis and Forecasting/16th Conference on Numerical Weather Prediction. Seattle, WA: American Meteorological Society, 2004: 11-15.
    [32]
    Hong S-Y, Noh Y, Dudhia J. A new vertical diffusion package with an explicit treatment of entrainment processes. Monthly Weather Review, 2006, 134 (9): 2318–2341. doi: 10.1175/MWR3199.1
    [33]
    Grell G A, Freitas S R. A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling. Atmos. Chem. Phys., 2014, 14 (10): 5233–5250. doi: 10.5194/acp-14-5233-2014
    [34]
    Ginoux P, Chin M, Tegen I, et al. Sources and distributions of dust aerosols simulated with the GOCART model. Journal of Geophysical Research: Atmospheres, 2001, 106 (D17): 20255–20273. doi: 10.1029/2000JD000053
    [35]
    Demott P J, Prenni A J, Liu X, et al. Predicting global atmospheric ice nuclei distributions and their impacts on climate. Proceedings of the National Academy of Sciences, 2010, 107 (25): 11217–11222. doi: 10.1073/pnas.0910818107
    [36]
    Saha S, Moorthi S, Wu X, et al. The NCEP Climate Forecast System Version 2. Journal of Climate, 2014, 27 (6): 2185–2208. doi: 10.1175/JCLI-D-12-00823.1
    [37]
    Iguchi T, Seto S, Meneghini R, et al. GPM/DPR Level-2 algorithm theoretical basis document. Washington, DC: NASA, 2019.
    [38]
    Houze R A. Stratiform precipitation in regions of convection: A meteorological paradox? Bulletin of the American Meteorological Society, 1997, 78 (10): 2179–2196. doi: 10.1175/1520-0477(1997)078<2179:SPIROC>2.0.CO;2
    [39]
    Stevens B, Feingold G. Untangling aerosol effects on clouds and precipitation in a buffered system. Nature, 2009, 461 (7264): 607–613. doi: 10.1038/nature08281

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