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https://doi.org/10.5194/essd-2020-151
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2020-151
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  14 Oct 2020

14 Oct 2020

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This preprint is currently under review for the journal ESSD.

A high-resolution unified observational data product of mesoscale convective systems and isolated deep convection in the United States for 2004–2017

Jianfeng Li, Zhe Feng, Yun Qian, and L. Ruby Leung Jianfeng Li et al.
  • Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington

Abstract. Deep convection possesses markedly distinct properties at different spatiotemporal scales. We present an original high-resolution (4 km, hourly) unified data product of mesoscale convective systems (MCSs) and isolated deep convection (IDC) in the United States east of the Rocky Mountains and examine their climatological characteristics from 2004 to 2017. The data product is produced by applying an updated FLEXTRKR (Flexible Object Tracker) algorithm to hourly satellite brightness temperature, radar reflectivity, and precipitation datasets. Analysis of the data product shows that MCSs are much larger and longer-lasting than IDC, but IDC occurs about 100 times more frequently than MCSs, with a mean convective intensity comparable to that of MCSs. Hence both MCS and IDC are essential contributors to precipitation east of the Rocky Mountains, although their precipitation shows significantly different spatiotemporal characteristics. IDC precipitation concentrates in summer in the Southeast with a peak in the late afternoon, while MCS precipitation is significant in all seasons, especially for spring and summer in the Great Plains. The spatial distribution of MCS precipitation amounts varies by seasons, while diurnally, MCS precipitation generally peaks during nighttime except in the Southeast. Potential uncertainties and limitations of the data product are also discussed. The data product is useful for investigating the atmospheric environments and physical processes associated with different types of convective systems, quantifying the impacts of convection on hydrology, atmospheric chemistry, and severe weather events, and evaluating and improving the representation of convective processes in weather and climate models. The data product is available at https://doi.org/10.25584/1632005 (Li et al., 2020).

Jianfeng Li et al.

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MCSs and IDC in the US for 2004 – 2017 Jianfeng Li, Zhe Feng, Yun Qian, and L. Ruby Leung https://doi.org/10.25584/1632005

Jianfeng Li et al.

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Short summary
Deep convection has different properties at different scales. We develop a 4 km/hourly observational data product of mesoscale convective systems and isolated deep convection in the United States from 2004–2017. We find that both types of convective systems contribute significantly to precipitation east of the Rocky Mountains but with distinct spatiotemporal characteristics. The data product will be useful for observational analyses and model evaluations of convection events at different scales.
Deep convection has different properties at different scales. We develop a 4 km/hourly...
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