ARM Data Help Improve Precipitation in Global Climate Models

Cloud, radiation, and drizzle measurements lead to better simulations.

Image courtesy of the Atmospheric Radiation Measurement (ARM) Climate Research Facility via a Creative Commons license.
Clouds drift over the coastline outcroppings of Graciosa Island where Atmospheric Radiation Measurement observations were collected for 19 months and used to improve precipitation errors in global climate models.

The Science

Global climate models commonly overestimate the frequency of light precipitation events while underestimating the occurrence of rarer but intense precipitation events. To assess and improve systematic model errors in the simulation of cloud and precipitation properties, Department of Energy researchers leveraged observational data from a 19-month deployment of the Atmospheric Radiation Measurement (ARM) Mobile Facility to Graciosa Island in the Azores. The synergy and colocation of cloud and radiation observations together with vertically resolved observations of cloud and drizzle droplets provide deeper insights into the model errors than can be gained from a satellite perspective alone.

The Impact

These results illustrate how high-resolution ARM observations of cloud and precipitation processes in important climatic regions provide critical information to improve global climate model simulations.


Researchers identified three specific model issues that contributed to the error in simulated precipitation at Graciosa: (1) the triggering of cloud formation in the boundary layer, (2) rate of conversion from cloud droplets to rain, and (3) evaporation of drizzle. New formulations for each of these processes were developed and implemented in the model. Comparison to the ARM observations illustrates that the new process formulations improve the frequency of occurrence of overcast low clouds in the model, increase their liquid water path, and reduce the overestimation of precipitation occurrence at the cloud base and the surface. Global simulations with the improved model indicate that the changes reduce the mean absolute error in reflected sunlight over large areas of the globe.


Maike Ahlgrimm
European Centre for Medium-Range Weather Forecasts, Reading, UK


This study was funded by the Atmospheric System Research program of the Office of Biological and Environmental Research within the U.S. Department of Energy's Office of Science under grant no. DE-SC0005259.


Ahlgrimm, M., and R. Forbes. “Improving the representation of low clouds and drizzle in the ECMWF model based on ARM observations from the Azores,” Mon. Wea.Rev. 142, 668–685 (2014). [DOI: 10.1175/MWR-D-13-00153.1].

Highlight Categories

Program: BER , CESD

Performer: University , SC User Facilities , BER User Facilities , ARM