October 2, 2019

Closing the Gaps in Modeling Rain in Large Storms

Researchers identified why stratiform precipitation properties can vary across simulations.

: Researchers simulated the convective clouds studied in a DOE-NASA field campaign in Oklahoma to examine model biases and variability.

Researchers simulated the convective clouds studied in a DOE-NASA field campaign in Oklahoma to examine model biases and variability. 

Image courtesy of the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility.

The Science
Underestimating the amount of widespread, light to moderate stratiform rainfall within large convective storms is a long-standing problem in atmospheric models. However, the factors that contribute to this issue are not well understood. Researchers at the U.S. Department of Energy's (DOE’s) Pacific Northwest National Laboratory led a study comparing model results with observations to characterize bias and across simulations to characterize model variability in simulated stratiform precipitation from a squall line convective system. They identified major factors and processes that contribute to those differences. Results showed that, contrary to real-life observations, simulated ice water content decreased as it fell toward the melting level, and this was a primary cause of the underestimated stratiform rainfall rate. Researchers also found that the variability of stratiform rain properties across simulations using different microphysics schemes was primarily caused by different condensate properties detrained, or pushed out, from the convective area of the squall line system.
The Impact
The study addresses a long-standing problem in atmospheric models—failing to simulate stratiform rain area and amount within large convective storms. The findings provide guidance and insights for observational needs and model improvements to reduce model biases.  
In this model intercomparison study, researchers focused on model biases—offsets from observations—and variabilities (differences among simulations) of stratiform precipitation properties for a midlatitude squall line mesoscale convective system (MCS) observed from the Midlatitude Continental Convective Clouds Experiment (MC3E), a DOE-NASA field campaign in Oklahoma. They simulated the squall line MCS, employing a cloud-resolving model with eight different cloud microphysics schemes. Simulations with most of the microphysics schemes underestimated the total stratiform precipitation, mainly due to an underestimation of stratiform precipitation area. All schemes underestimated the frequency of moderate stratiform rain rates and rain water content below the melting level, largely because the ice water content decreased approaching the melting level, which is contrary to the observed trend. The researchers also found that the variability of stratiform precipitation area across the simulations positively correlated with condensate amount detrained from the convective cores, but was also affected by hydrometeor type, size, and fall speed. Stratiform precipitation, rain rate, and area across the simulations each varied by a factor of 1.5. This significant variability primarily resulted from downward stratiform ice mass flux variability, which was highly correlated with condensate amounts detrained from convective regions. This illustrates the key role of convective microphysics in determining stratiform properties and highlights the need for more measurements of convective kinematics and microphysics in order to improve parameterizations and reduce model biases.
Contacts (BER PM)
Sally McFarlane
Atmospheric Radiation Measurement (ARM) user facility
Shaima Nasiri 
Atmospheric System Research

Revised: October 2, 2019 | Published: April 17, 2019

(PI Contact)
Jiwen Fan 
Pacific Northwest National Laboratory 

This study was supported by the U.S. Department of Energy (DOE) Atmospheric System Research (ASR) program and the Climate Model Development and Validation (CMDV) program. This research used PNNL Institutional Computing resources and also resources at the National Energy Research Scientific Computing Center, which is supported by the DOE Office of Science under contract DE‐AC02‐05CH1123. Bin Han and Dr. Chen were supported by the National Basic Research Program of China (2013CB430105) and the National Natural Science Foundation of China (41575130 and 41775132). Dr. Varble was supported by DOE ASR grant DE‐SC0008678. Dr. Morrison was supported by DOE ASR grant DE‐SC0008648. Drs. Morrison and Varble were also supported by DOE ASR grant DE‐SC0016476. Dr. Dong was supported by the DOE CMDV project at the University of Arizona with award number DE‐SC0017015. Dr. Giangrande is an employee of Brookhaven Science Associates, LLC, under contract DE‐AC02‐98CH10886 with DOE. Prof. Alexander Khain and Jacob Shpund are supported by grant DE‐SC008811 and the Israel Science Foundation grant 2027/17. Researchers also acknowledge the Atmospheric Radiation Measurement (ARM) Climate Research Facility, a DOE user facility, as well as the MC3E team for the field data.
Han B, J Fan, A Varble, H Morrison, CR Williams, B Chen, X Dong, SE Giangrande, A Khain, E Mansell, JA Milbrandt, J Shpund, and G Thompson. 2019. “Cloud-Resolving Model Intercomparison of an MC3E Squall Line Case: Part II. Stratiform Precipitation Properties.” Journal of Geophysics Research: Atmospheres 124(2):1090−1117, https://doi.org/10.1029/2018JD029596.