Estimation of Saturation Thickness to Initialize the LAMP Moisture Model

Frank Lewis Techniques Development Laboratory, Office of Systems Development, National Weather Service, NOAA, Silver Spring, MD 20910

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David A. Unger Techniques Development Laboratory, Office of Systems Development, National Weather Service, NOAA, Silver Spring, MD 20910

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Joseph R. Bocchieri Techniques Development Laboratory, Office of Systems Development, National Weather Service, NOAA, Silver Spring, MD 20910

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Abstract

The relationship between precipitable water, W; 1000–500 mb thickness, h; station elevation, E, and observed precipitation was examined to obtain an equation to estimate saturation thickness. Radiosonde observations were categorized by values of W, h, and E, and a value for saturation thickness, h3, was determined for each precipitable water category and station elevation group on the basis of the precipitation frequency. A regression equation was then developed that relates h3 to InW and E.

Regression equations were then developed to relate InW to surface observations and the 12-h forecast of W from the LFM model to enable estimation of the saturation thickness at any hour. About 91% of the variance in InW explained by the natural logarithm of the LFM precipitable water forecast. An additional 2–4% was explained by the surface dew point observations. No other variable added significantly to the relationship. An equation relating InW to surface observations was derived to be used in the event the LFM forecast of InW is not available.

Abstract

The relationship between precipitable water, W; 1000–500 mb thickness, h; station elevation, E, and observed precipitation was examined to obtain an equation to estimate saturation thickness. Radiosonde observations were categorized by values of W, h, and E, and a value for saturation thickness, h3, was determined for each precipitable water category and station elevation group on the basis of the precipitation frequency. A regression equation was then developed that relates h3 to InW and E.

Regression equations were then developed to relate InW to surface observations and the 12-h forecast of W from the LFM model to enable estimation of the saturation thickness at any hour. About 91% of the variance in InW explained by the natural logarithm of the LFM precipitable water forecast. An additional 2–4% was explained by the surface dew point observations. No other variable added significantly to the relationship. An equation relating InW to surface observations was derived to be used in the event the LFM forecast of InW is not available.

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