Rain is patchy—a new formulation captures that variability in models.
Observations and cloud-resolving models were used to study the spatial variability of rain and develop a new formulation for rain variability in large-scale global climate models.
The grid cells of global climate models are often larger than a single rain storm, so the variability in the cloud and rain properties at smaller grid scales must be parameterized within the model. Using radar observations and cloud-resolving model simulations from the Atmospheric Radiation Measurement (ARM) Tropical Warm Pool-International Cloud Experiment (TWP-ICE), scientists evaluated subgrid-scale variability in rain rate over a variety of scales and formulated a parameterization for the surface variability in rain rate that can be used to improve the prediction of rain in large-scale climate models.
The spatial variability of rain rate R is evaluated by using both radar observations and cloud-resolving model output, focusing on the Tropical Warm Pool-International Cloud Experiment (TWP-ICE) period. In general, the model-predicted rain-rate probability distributions agree well with those estimated from the radar data across a wide range of spatial scales. The spatial variability in R, which is defined according to the standard deviation of R, is found to vary according to both the average of R over a given footprint and the footprint size or averaging scale. There is good agreement between area-averaged model output and radar data at a height of 2.5 km. The model output at the surface is used to construct a scale-dependent parameterization of the spatial variability of rain rate as a function of footprint size and averaging scale that can be readily implemented into large-scale numerical models. The variability in both the rainwater amount and rain rate as a function of height is also explored. From the statistical analysis, a scale- and height-dependent formulation for the spatial variability of both the rainwater amount and rain rate is provided for the analyzed tropical scenario. This research shows how this parameterization can be used to assist in constraining parameters that are often used to describe the surface rain-rate distribution.
Contacts (BER and non-BER)
BER - Sally McFarlane, SC-23.1, 301-903-0943; and Shaima Nasiri, SC-23.1, 301-903-0207
University of Wyoming
This project was supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research.
Lebo, Z. J., C. R. Williams, G. Feingold, and V. E. Larson. 2015. “Parameterization of the Spatial Variability of Rain for Large-Scale Models and Remote Sensing,” Journal of Applied Meteorology and Climatology 54(10), 2027-46. DOI: 10.1175/jamc-d-15-0066.1. (Reference link)
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