Preprints
https://doi.org/10.5194/gmd-2022-18
https://doi.org/10.5194/gmd-2022-18
Submitted as: model experiment description paper
 | 
06 Apr 2022
Submitted as: model experiment description paper |  | 06 Apr 2022
Status: this preprint was under review for the journal GMD but the revision was not accepted.

Intercomparing radar data assimilation systems for ICE-POP 2018 snowfall cases

Ki-Hong Min, Kao-Shen Chung, Ji-Won Lee, Cheng-Rong You, and Gyuwon Lee

Abstract. Gangwon-do (GWD) has complex terrain and surface characteristics due to its location to the East Sea and the Taebaek Mountain range. This coastal location and rugged terrain can amplify snowfall mechanisms, making it challenging to accurately predict the amount and location. This study compares two methods for assimilating radar data and analyzed snowfall prediction results. The two methods compared are local ensemble transform Kalman filter (LETKF) and three-dimensional variational (3DVAR) data assimilation (DA). LETKF improved the water vapor amount and temperature using the covariance of the ensemble members, but 3DVAR improved the water vapor mixing ratio and temperature through an operator that assumed the atmosphere was saturated when reflectivity was above a certain threshold. In 2018, to understand the snowfall in GWD region and support the Pyeongchang Winter Olympic and Paralympic Games, a long-term heavy snow observation campaign was conducted. The International Collaborative Experiments for the 2018 Pyeongchang Olympic Games Projects (ICE-POP 2018) data are used to study and verify the numerical experiments. From the initial field verification using ICE-POP observation data (radiosonde), wind in LETKF was more accurately simulated than 3DVAR, but it underestimated the water vapor amount and temperature in the lower troposphere due to a lack of a water vapor and temperature observation operator. Snowfall in GWD was less simulated in LETKF, whereas snowfall of 10.0 mm or more was simulated in 3DVAR, resulting in an error of 2.62 mm lower than LETKF. The results signify that water vapor assimilation is important in radar DA and significantly impacts precipitation forecasts, regardless of the DA method used. Therefore, it is necessary to apply observation operators for water vapor and temperature in radar DA.

Ki-Hong Min, Kao-Shen Chung, Ji-Won Lee, Cheng-Rong You, and Gyuwon Lee

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • EC1: 'Comment on gmd-2022-18', Yuefei Zeng, 22 Apr 2022
    • AC1: 'Reply on EC1', Ki-Hong Min, 12 May 2022
  • RC1: 'Comment on gmd-2022-18', Anonymous Referee #1, 16 May 2022
    • AC2: 'Reply on RC1', Ki-Hong Min, 20 Jun 2022
    • AC5: 'Reply on RC1', Ki-Hong Min, 20 Jun 2022
  • RC2: 'Comment on gmd-2022-18', Anonymous Referee #2, 21 May 2022
    • AC3: 'Reply on RC2', Ki-Hong Min, 20 Jun 2022
    • AC6: 'Reply on RC2', Ki-Hong Min, 20 Jun 2022
    • AC7: 'Reply on RC2', Ki-Hong Min, 20 Jun 2022
  • RC3: 'Comment on gmd-2022-18', Anonymous Referee #3, 31 May 2022
    • AC4: 'Reply on RC3', Ki-Hong Min, 20 Jun 2022
    • AC8: 'Reply on RC3', Ki-Hong Min, 20 Jun 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • EC1: 'Comment on gmd-2022-18', Yuefei Zeng, 22 Apr 2022
    • AC1: 'Reply on EC1', Ki-Hong Min, 12 May 2022
  • RC1: 'Comment on gmd-2022-18', Anonymous Referee #1, 16 May 2022
    • AC2: 'Reply on RC1', Ki-Hong Min, 20 Jun 2022
    • AC5: 'Reply on RC1', Ki-Hong Min, 20 Jun 2022
  • RC2: 'Comment on gmd-2022-18', Anonymous Referee #2, 21 May 2022
    • AC3: 'Reply on RC2', Ki-Hong Min, 20 Jun 2022
    • AC6: 'Reply on RC2', Ki-Hong Min, 20 Jun 2022
    • AC7: 'Reply on RC2', Ki-Hong Min, 20 Jun 2022
  • RC3: 'Comment on gmd-2022-18', Anonymous Referee #3, 31 May 2022
    • AC4: 'Reply on RC3', Ki-Hong Min, 20 Jun 2022
    • AC8: 'Reply on RC3', Ki-Hong Min, 20 Jun 2022
Ki-Hong Min, Kao-Shen Chung, Ji-Won Lee, Cheng-Rong You, and Gyuwon Lee
Ki-Hong Min, Kao-Shen Chung, Ji-Won Lee, Cheng-Rong You, and Gyuwon Lee

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Short summary
LETKF underestimated the water vapor mixing ratio and temperature compared to 3DVAR due to a lack of a water vapor mixing ratio and temperature observation operator. Snowfall in GWD was less simulated in LETKF. The results signify that water vapor assimilation is important in radar DA and significantly impacts precipitation forecasts, regardless of the DA method used. Therefore, it is necessary to apply observation operators for water vapor mixing ratio and temperature in radar DA.