COUNTERFLOW VIRTUAL IMPACTOR;
IN-SITU OBSERVATIONS;
MOMENT ESTIMATORS;
WATER-CONTENT;
DISTRIBUTION PARAMETERS;
RADAR MEASUREMENTS;
TROPICAL CIRRUS;
CLOUDS;
PRECIPITATION;
TEMPERATURES;
D O I:
10.1175/JAS-D-17-0145.1
中图分类号:
P4 [大气科学(气象学)];
学科分类号:
0706 ;
070601 ;
摘要:
The possibility is suggested of estimating particle size distributions (PSD) solely based on the bulk quantities of the hydrometeors. The method, inspired by the maximum entropy principle, can be applied to any predefined general PSD form as long as the number of the free parameters is equal to or less than that of the bulk quantities available. As long as an adopted distribution is "physically based,'' these bulk characterizations can recover a fairly accurate PSD estimate. This method is tested for ice particle measurements from the Tropical Composition, Cloud and Climate Coupling Experiment (TC4). The total particle number, total mass, and mean size are taken as bulk quantities. The gamma distribution and two distributions obtained under the maximum entropy principle by taking the size and the particle mass, respectively, as a restriction variable are adopted for fit. The fitting error for the two maximum entropy-based distributions is comparable to that of a standard direct fitting method with the gamma distribution. The same procedure works almost equally well when the mean size is removed from the constraint, especially for an exponential distribution. The results suggest that the total particle number and the total mass of the hydrometeors are sufficient for determining the PSD to a reasonable accuracy when a "physically based'' distribution is assumed. In addition to the in situ cloud measurements, remote sensing measurements such as those from radar as well as satellite can be adopted as physical constraints. Possibilities of exploiting different types of measurements should be further pursued.
机构:
Univ Oklahoma, Cooperat Inst Severe & High Impact Weather & Res O, Norman, OK 73019 USAUniv Oklahoma, Cooperat Inst Severe & High Impact Weather & Res O, Norman, OK 73019 USA
Dzambo, Andrew M. M.
McFarquhar, Greg
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机构:
Univ Oklahoma, Cooperat Inst Severe & High Impact Weather & Res O, Norman, OK 73019 USA
Univ Oklahoma, Sch Meteorol, Norman, OK USAUniv Oklahoma, Cooperat Inst Severe & High Impact Weather & Res O, Norman, OK 73019 USA
McFarquhar, Greg
Finlon, Joseph A. A.
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机构:
Univ Washington, Dept Atmospher Sci, Seattle, WA USAUniv Oklahoma, Cooperat Inst Severe & High Impact Weather & Res O, Norman, OK 73019 USA
机构:
Univ Illinois, Dept Atmospher Sci, Urbana, IL USA
Atmospher & Environm Res Inc, Lexington, MA USAUniv Illinois, Dept Atmospher Sci, Urbana, IL USA
Mascio, Jeana
McFarquhar, Greg M.
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机构:
Univ Illinois, Dept Atmospher Sci, Urbana, IL USA
Univ Oklahoma, Cooperat Inst Mesoscale Meteorol Studies, 120 David L Boren Blvd,Suite 2100, Norman, OK 73072 USA
Univ Oklahoma, Sch Meteorol, Norman, OK 73072 USAUniv Illinois, Dept Atmospher Sci, Urbana, IL USA
McFarquhar, Greg M.
Hsieh, Tsung-Lin
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机构:
Univ Illinois, Dept Atmospher Sci, Urbana, IL USA
Princeton Univ, Princeton, NJ 08544 USAUniv Illinois, Dept Atmospher Sci, Urbana, IL USA
Hsieh, Tsung-Lin
Freer, Matt
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机构:
Univ Illinois, Dept Atmospher Sci, Urbana, IL USA
CloudSci LLC, Boulder, CO USAUniv Illinois, Dept Atmospher Sci, Urbana, IL USA
Freer, Matt
Dooley, Amanda
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机构:
Univ Illinois, Dept Atmospher Sci, Urbana, IL USAUniv Illinois, Dept Atmospher Sci, Urbana, IL USA
Dooley, Amanda
Heymsfield, Andrew J.
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机构:
Natl Ctr Atmospher Res, POB 3000, Boulder, CO 80307 USAUniv Illinois, Dept Atmospher Sci, Urbana, IL USA