A comparison of model-simulated relative humidity with satellite-derived cloudiness
- North Carolina State Univ., Raleigh, NC (United States)
One of the unresolved problems in climate and numerical weather prediction is the treatment of cloudiness. In the real atmosphere, clouds are interactively linked to dynamical, hydrological, turbulent, and radiative processes over scales ranging from the microphysics of cloud particles to the synoptic and planetary scales of extensive multi-layered clouds. In current general circulation models (GCMs), clouds are not related to the hydrological cycle; they exist only for their direct impact on the radiation fields, and their characteristics (horizontal cover, height, optical properties) are either held constant or diagnosed from a limited set of parameters. Many modelers have relied on resolvable-scale relative humidity for diagnosing non-convective cloudiness. Methods for linking clouds to the hydrological cycle or to the turbulent processes in GCMs are still in their infancy, although it is possible that liquid water will be explicitly predicted in the next generation of GCMs. Few comparisons of observed clouds and related relative humidity fields have actually been performed so far because of the lack of adequate cloud climatologies and atmospheric data sets allowing one to make verifications. The recent availability of the International Satellite Cloud Climatology Project (ISCCP) data sets, a consistent and global cloud climatology, is of great interest for climate research; however, the resulting cloud climatology (consisting of 30-day means of cloud characteristics averaged over approximately [250 km{sup 2}]) may not be sufficient for validating cloud generation schemes. The comparison of model-generated parameters associated with observed cloudiness requires a more detailed approach: for example, model-generated relative humidity must be compared with observed cloudiness for a variety of meteorological situations, and the results must be examined in terms of physically significant and predictable parameters.
- Research Organization:
- USDOE Office of Energy Research, Washington, DC (United States). Environmental Sciences Div.
- OSTI ID:
- 83156
- Report Number(s):
- CONF-940277-; ON: DE95009951; CNN: Contract 091575-A-Q1; TRN: 95:003626-0015
- Resource Relation:
- Conference: Atmospheric radiation measurement science team meeting, Charleston, SC (United States), 28 Feb - 3 Mar 1994; Other Information: PBD: Apr 1995; Related Information: Is Part Of Proceedings of the Fourth Atmospheric Radiation Measurement (ARM) Science Team Meeting; PB: 373 p.
- Country of Publication:
- United States
- Language:
- English
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