Accounting for the Evaporation of Rain

The Science

Light rain is an important part of the water cycle, but is difficult to measure globally. The Cloud Profiling Radar on the CloudSat satellite provides the first global estimates of light rainfall from warm marine clouds. Rainfall is not directly measured by the radar, but must be derived using an algorithm that attempts to include all of the relevant physics to convert from the measured parameters to rainfall. One physical process that needs to be included in the algorithm is evaporation of rainfall between cloud base and the ground. Scientists use measurements from the DOE Atmospheric Radiation Measurement (ARM) “MAGIC” (Marine ARM GPCI Investigation of Clouds) field campaign in the subtropical Pacific Ocean to evaluate and improve the treatment of evaporation of rainfall in the CloudSat algorithm.

The Impact

The ARM surface-based cloud radars provide important information on cloud base droplet size distribution and evaporation of rain between the cloud and the surface that cannot be observed by the downward-looking CloudSat radar. Comparison to ARM radar data indicate that CloudSat light rain rates over the Pacific have a mean relative error of 57%. A bias correction developed using the ARM data was applied to the CloudSat algorithm and evaluated using an independent aircraft dataset in the Southeast Pacific. The bias correction reduced the mean bias by a factor of 20 for profiles with maximum reflectivity of 15 dBZ. The corrected rainfall estimates will enable improved scientific understanding of light rain over remote regions.

Summary

In this paper, scientists used the upward-looking W-band radar from the ship-based MAGIC campaign in the northeast subtropical Pacific basin to quantify the error budget in the CloudSat 2C-RAIN-PROFILE algorithm’s evaporation- sedimentation model, and to perform an empirical bias correction. They found that in this region of light, warm, marine rain, the 2C-RAIN-PROFILE algorithm’s choice of mean drop size radius near the cloud base in combination with the model’s parameterization constants caused significant bias. These factors were responsible for an overestimation of near-surface (128 m) conditional rain rate of between 0.01 and 0.06 mm/h along the MAGIC transect between Los Angeles and Honolulu, corresponding to a mean relative error of 57%.

A bias correction was designed to minimize the mean bias in near-surface rain rate in the evaporation- sedimentation model using data from the MAGIC campaign in the northeast subtropical Pacific stratocumulus region. This correction was found to be valid in both the stratocumulus and trade cumulus cloud regimes. The bias correction, derived with data from the northeast Pacific region, was evaluated with data from the Southeast Pacific stratocumulus-to-cumulus transition region using data from the VOCALS airplane-based W-band radar. The bias correction reduced the mean bias in this data by a factor of about 20 for profiles with a maximum reflectivity of 15 dBZ, and by a factor of about 2 for profiles with maximum reflectivity greater than 15 dBZ, providing evidence that the correction is generally applicable to these marine low cloud regimes.

Principal Investigator(s)

Peter Kalmus
Jet Propulsion Laboratory, California Institute of Technology
[email protected]

Funding

The research described in this publication was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Data were obtained from the Atmospheric Radiation Measurement Program sponsored by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Climate and Environmental Sciences Division. Data were also obtained from the NASA CloudSat Project and from National Center for Atmospheric Research/Earth Observing Laboratory under sponsorship of the National Science Foundation.

References

Kalmus, P. and M. Lebsock. “Correcting Biased Evaporation in CloudSat Warm Rain.” IEEE Transactions on Geoscience and Remote Sensing, 55(11), (2017). [DOI: 10.1109/TGRS.2017.2722469]