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Cloud Cover and Its Relationship to Relative Humidity during a Springtime Midlatitude Cyclone

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  • 1 Atmospheric Sciences Research Center, State University of New York, Albany, New York
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Abstract

Vertical distributions of fractional cloud coverage derived from the U.S. Air Force 3DNEPH satellite, aircraft, and surface-based analysis are compared with related standard meteorological observations over the eastern United States. Cloud cover and related observations are interpolated onto the identical three-dimensional grid consisting of 15 tropospheric levels at various horizontal resolutions ranging from (80 km)2 to (800 km)2 for five local noon periods during a springtime midlatitude cyclone. During the period analyzed, cloud cover maximizes near 900 mb at 35% cloud cover and decreases to near-zero cover at the surface. Above 900 mb, fractional cloudiness gradually decreases to 10%–20% cover at 200 mb. Cloud cover is positively correlated with relative humidity and large-scale vertical velocity, and negatively correlated with wind shear and temperature lapse rate, except in the lowest 100 mb, where cloud cover is weakly correlated with relative humidity, vertical velocity, wind shear, and temperature lapse rate. Mean fractional cloud coverage observed at various relative humidities and pressures is derived from these observations, and resolution-dependent algorithms for estimating cloud coverage from relative humidity are suggested. This analysis suggests that there is considerable uncertainty in measuring or calculating cloud cover and other meteorological factors averaged over large areas, especially in the upper troposphere. However, cloud cover appears to decrease exponentially as humidity falls below 100%, and relative to other layers in the troposphere, the layers 2.5–5 km above the surface contain the highest cloud amounts at the lowest relative humidities, with mean cloud amounts of 30% near 50% humidity at 650 mb. In the upper troposphere, cloud amounts are greater under convectively unstable conditions relative to stable conditions at high relative humidities. In the lower troposphere, high humidity environments where convection is possible contain lower cloud amounts relative to stable conditions at the same relative humidity, which may result from cumulus-induced subsidence of dry air into the lower troposphere under convectively unstable conditions. Using relative humidity alone as an indicator of cloud coverage, cloud amount can be assessed only to within a root-mean-square difference of 15%–30% from the 3DNEPH cloud cover, depending on the resolution at which calculations are performed. Many meteorological, climate, and chemical models of the atmosphere specify cloud amounts less than reported by these observations when relative humidities are less than 90%–95% during this analysis period. This is especially true in the midtroposphere (850–600 mb), where most algorithms specify zero cloud amounts at relative humidities below 60%–80%, while observed cloud amounts range from 20% to 60% at these height and humidity ranges. At humidities close to saturation, current algorithms probably overestimate cloud coverage.

Abstract

Vertical distributions of fractional cloud coverage derived from the U.S. Air Force 3DNEPH satellite, aircraft, and surface-based analysis are compared with related standard meteorological observations over the eastern United States. Cloud cover and related observations are interpolated onto the identical three-dimensional grid consisting of 15 tropospheric levels at various horizontal resolutions ranging from (80 km)2 to (800 km)2 for five local noon periods during a springtime midlatitude cyclone. During the period analyzed, cloud cover maximizes near 900 mb at 35% cloud cover and decreases to near-zero cover at the surface. Above 900 mb, fractional cloudiness gradually decreases to 10%–20% cover at 200 mb. Cloud cover is positively correlated with relative humidity and large-scale vertical velocity, and negatively correlated with wind shear and temperature lapse rate, except in the lowest 100 mb, where cloud cover is weakly correlated with relative humidity, vertical velocity, wind shear, and temperature lapse rate. Mean fractional cloud coverage observed at various relative humidities and pressures is derived from these observations, and resolution-dependent algorithms for estimating cloud coverage from relative humidity are suggested. This analysis suggests that there is considerable uncertainty in measuring or calculating cloud cover and other meteorological factors averaged over large areas, especially in the upper troposphere. However, cloud cover appears to decrease exponentially as humidity falls below 100%, and relative to other layers in the troposphere, the layers 2.5–5 km above the surface contain the highest cloud amounts at the lowest relative humidities, with mean cloud amounts of 30% near 50% humidity at 650 mb. In the upper troposphere, cloud amounts are greater under convectively unstable conditions relative to stable conditions at high relative humidities. In the lower troposphere, high humidity environments where convection is possible contain lower cloud amounts relative to stable conditions at the same relative humidity, which may result from cumulus-induced subsidence of dry air into the lower troposphere under convectively unstable conditions. Using relative humidity alone as an indicator of cloud coverage, cloud amount can be assessed only to within a root-mean-square difference of 15%–30% from the 3DNEPH cloud cover, depending on the resolution at which calculations are performed. Many meteorological, climate, and chemical models of the atmosphere specify cloud amounts less than reported by these observations when relative humidities are less than 90%–95% during this analysis period. This is especially true in the midtroposphere (850–600 mb), where most algorithms specify zero cloud amounts at relative humidities below 60%–80%, while observed cloud amounts range from 20% to 60% at these height and humidity ranges. At humidities close to saturation, current algorithms probably overestimate cloud coverage.

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