Convective and large-scalemass flux profiles over tropical oceans determined fromsynergistic analysis of a suite of satellite observations

Journal of Geophysical Research

Volumn 121, Issue 13, 2016, Pages 7958-7974

  Masunaga, Hirohiko ^a   Luo, Zhengzhao Johnny ^b  

^a NAGOYA UNIVERSITY   (Japan)

^b CITY UNIVERSITY OF NEW YORK   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AMBIENT NOISE; ATMOSPHERIC PLUME; BUOYANCY; CLIMATE MODELING; CLOUD COVER; CONVECTIVE CLOUD; CONVECTIVE SYSTEM; MASS TRANSFER; REMOTE SENSING; SATELLITE DATA; SYNERGISM; TROPICAL REGION; VERTICAL PROFILE;

EID: 84978878010     PISSN: 01480227     EISSN: 21562202     Source Type: Journal    
DOI: 10.1002/2016JD024753     Document Type: Article

References (38)

1. Arakawa, A., and W. H. Schubert (1974), Interaction of a cumulus cloud ensemble with the large-scale environment. Part I., J. Atmos. Sci., 31, 674-701.
2. Asai, T., and A. Kasahara (1967), A theoretical study of the compensating downward motions associated with cumulus clouds, J. Atmos. Sci., 24, 487-496.
3. Deser, C. (1994), Daily surface wind variations over the equatorial Pacific Ocean, J. Geophys. Res., 99, 23,071-23,078.
4. Grant, A. L. M. (2001), Cloud-base fluxes in the cumulus-capped boundary layer, Q. J. R. Meteorol. Soc., 127, 407-421.
5. Gregory, D. (2001), Estimation of entrainment rate in simple models of convective clouds, Q. J. R. Meteorol. Soc., 127, 53-72.
6. Heymsfield, G. M., L. Tian, A. J. Heymsfield, L. Li, and S. Guimond (2010), Characteristics of deep tropical and subtropical convection from nadir-viewing high-altitude airborne Doppler radar, J. Atmos. Sci., 67, 285-308.
7. Houghton, H. G., and H. E. Cramer (1951), A theory of entrainment in convective currents, J. Meteorol., 8, 95-102.
8. Houze, R. A. J. (1982), Cloud clusters and large-scale vertical motions in the tropics, J. Meteorol. Soc. Jpn., 60, 396-410.
9. Imaoka, K., and R. W. Spencer (2000), Diurnal variation of precipitation over the tropical oceans observed by TRMM/TMI combined with SSM/I, J. Clim., 13, 4149-4158.
10. Johnson, R. H., T. M. Rickenbach, S. A. Rutledge, P. E. Ciesielski, and W. H. Schubert (1999), Trimodal characteristics of tropical convection, J. Clim., 12, 2397-2418.
11. Kessler, E. (1969), On the Distribution and Continuity of Water Substance in Atmospheric Circulations, Am. Meteorol. Soc., pp. 1-84, Boston.
12. Kumar, V. V., C. Jakob, A. Protat, C. R. Williams, and P. T. May (2015), Mass-flux characteristics of tropical cumulus clouds from wind profiler observations at Darwin, Australia, J. Atmos. Sci., 72, 1837-1855, doi:10.1175/JAS-D-14-0259.1.
13. Kummerow, C. D., S. Ringerud, J. Crook, D. Randel, andW. Berg (2011), An observationally generated a-priori database for microwave rainfall retrievals, 28, 113-130.
14. LeMone, M. A., and E. J. Zipser (1980), Cumulusnimbus vertical velocity events in GATE. Part I: Diameter, intensity and mass flux, J. Atmos. Sci., 37, 2444-2457.
15. Liu, C., E. J. Zipser, and S. W. Nesbitt (2007), Global distribution of tropical deep convection: Different perspectives from TRMM infrared and radar data, J. Clim., 20, 489-503.
16. Lorenc, A. C. (1986), Analysis methods for numerical weather prediction, Q. J. R. Meteorol. Soc., 112, 1177-1194.
17. Luo, Z. J., G. Y. Liu, and G. L. Stephens (2010), Use of A-Train data to estimate convective buoyancy and entrainment rate, Geophys. Res. Lett., 37, L09804, doi:10.1029/2010GL042904.
18. Luo, Z. J., J. Jeyaratnam, S. Iwasaki, H. Takahashi, and R. Anderson (2014), Convective vertical velocity and cloud internal vertical structure:An A-Train perspective, Geophys. Res. Lett., 41, 723-729, doi:10.1002/2013GL058922.
19. Masunaga, H. (2012), A satellite study of the atmospheric forcing and response to moist convection over tropical and subtropical oceans, J. Atmos. Sci., 69, 150-167.
20. Masunaga, H. (2013), A satellite study of tropical moist convection and environmental variability: A moisture and thermal budget analysis, J. Atmos. Sci., 70, 2443-2466.
21. Masunaga, H. (2015), Assessment of a satellite-based atmospheric budget analysis method using CINDY2011/DYNAMO/AMIE and TOGA COARE sounding array data, J. Meteorol. Soc. Jpn, 93A, 21-40.
22. Masunaga, H., and T. S. L’Ecuyer (2014), A mechanism of tropical convection inferred from observed variability in the moist static energy budget, J. Atmos. Sci., 71, 3747-3766.
23. May, P. T., and D. K. Rajopathyaya (1999), Vertical velocity characteristics of deep convection over Darwin, Australia, Mon. Weather Rev., 127, 1056-1071.
24. Mohr, K. I., and E. J. Zipser (1996), Defining mesoscale convective systems by their 85 GHz ice-scattering signatures, Bull. Am. Meteorol. Soc., 77, 1179-1189.
25. Morita, J., Y. N. Takayabu, S. Shige, and Y. Kodama (2006), Analysis of rainfall characteristics of the Madden-Julian Oscillation using TRMM satellite data, Dyn. Atmos. Oceans, 42, 107-126.
26. Perry, K. L. (2001), SeaWinds on QuikSCAT level 3 daily, gridded ocean wind vectors (JPL SeaWinds project) version 1.1, JPL document D-20335, Jet Propulsion Laboratory. [Available at ftp://podaac.jpl.nasa.gov/OceanWinds/quikscat/L3/jpl/v2/docs/qscat_L3.pdf.]
27. Short, D. A., and K. Nakamura (2000), TRMM radar observations of shallow precipitation over the tropical oceans, J. Clim., 13, 4107-4124.
28. Simpson, J., and V. Wiggert (1969), Models of precipitating cumulus towers, Mon. Weather Rev., 97, 471-489.
29. Susskind, J., C. D. Barnet, and J. M. Blaisdell (2003), Retrieval of atmospheric and surface parameters from AIRS/AMSU/HSB data in the presence of clouds, IEEE Trans. Geosci. Remote Sens., 41, 390-409.
30. Susskind, J., J. M. Blaisdell, L. Iredell, and F. Keita (2011), Improved temperature sounding and quality control methodology using AIRS/AMSU data: The AIRS science team version 5 retrieval algorithm, IEEE Trans. Geosci. Remote Sens., 49, 883-907.
31. Takahashi, H., and Z. J. Luo (2012), Where is the level of neutral buoyancy for deep convection?, Geophys. Res. Lett., 39, L15809, doi:10.1029/2012GL052638.
32. Tromeur, E., and W. B. Rossow (2010), Interaction of tropical deep convection with the large-scale circulation in the MJO, J. Clim., 23, 1837-1853.
33. Wang, C., Z. J. Luo, X. Cheng, X. Zeng, W.-K. Tao, and X. Huang (2014), A physically based algorithm for non-blackbody correction of cloud-top temperature and application to convection study, J. Appl. Meteorol. Climatol., 53, 1844-1857.
34. Wang, Z., and K. Sassen (2001), Cloud type and macrophysical property retrieval using multiple remote sensors, J. Appl. Meteorol., 40, 1665-1682.
35. Wentz, F. J., and T. Meissner (2000), AMSR Ocean Algorithm, Algorithm Theoretical Basis Document, Remote Sensing Systems, Santa Rosa, Calif. [Available at http://www.ssmi.com/.]
36. Yanai, M., S. Esbensen, and J.-H. Chu (1973), Determination of bulk properties of tropical cloud clusters from large scale heat and moisture budgets, J. Atmos. Sci., 30, 611-627.
37. Zipser, E. J. (1977), Mesoscale and convective-scale downdrafts as distinct components of squall-line structure, Mon. Weather Rev., 105, 1568-1589.
38. Zipser, E. J. (2003), Some views on “Hot Towers” after 50 years of tropical field programs and two years of TRMM data, in Cloud Systems, Hurricanes, and the Tropical Rainfall Measuring Mission (TRMM), Meteor. Monogr., vol. 29, pp. 49-58, Am. Meteorol. Soc.