Preprints
https://doi.org/10.5194/amt-2022-49
https://doi.org/10.5194/amt-2022-49
 
16 May 2022
16 May 2022
Status: a revised version of this preprint was accepted for the journal AMT and is expected to appear here in due course.

Latent heating profiles from GOES-16 and its impacts on precipitation forecasts

Yoonjin Lee1, Christian D. Kummerow1,2, and Milija Zupanski2 Yoonjin Lee et al.
  • 1Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, 80521, USA
  • 2Cooperative Institute for Research in the Atmosphere, Fort Collins, Colorado, 80521, USA

Abstract. Latent heating (LH) is an important quantity in both weather forecasting and climate analysis, being the essential factor driving convective systems. Yet, inferring LH rates from our current observing systems is challenging at best. For climate studies, LH has been retrieved from the Precipitation Radar on the Tropical Rainfall Measuring Mission (TRMM) using model simulations in the look-up table (LUT) that relates instantaneous radar profiles to corresponding heating profiles. These radars, first on TRMM and then Global Precipitation Measurement Mission (GPM), provide a continuous record of LH. However, temporal resolution is too coarse to have a significant impacts on forecast models. In operational forecast models such as High-Resolution Rapid Refresh, convection is initiated from LH derived from ground based radar. Despite the high spatial and temporal resolution of ground-based radars, one disadvantage of using these sources is that its data are only available over well observed land areas. This study develops a method to derive LH from the Geostationary Operational Environmental Satellite-16 (GOES-16) in near-real time. Even though the visible and infrared channels on the Advanced Baseline Imager (ABI) provide mostly cloud top information, rapid changes in cloud top visible and infrared properties, when formulated as a LUT similar to those used by the TRMM and GPM radars, can equally be used to derive LH profiles for convective regions based on model simulations with a convective classification scheme and channel 14 (11.2 μm) brightness temperatures. Convective regions detected by GOES-16 are assigned LH from the LUT, and they are compared with LH from the Next Generation Weather Radar (NEXRAD) and one of the Dual-frequency Precipitation Radar (DPR) products, the Goddard Convective-Stratiform Heating (CSH). LH obtained from GOES-16 show similar magnitude with NEXRAD and CSH, and vertical distribution of LH is also very similar with CSH. One month analysis of total LH from convective clouds from GOES-16 and NEXRAD shows good correlation between the two products. Finally LH profiles from GOES-16 and NEXRAD are applied to WRF simulations for convective initiation and their results are compared to investigate their impacts in precipitation forecasts. Results show that LH from GOES-16 have similar impacts as NEXRAD, and improves the forecast significantly.

Yoonjin Lee et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-49', Anonymous Referee #2, 01 Jun 2022
  • RC2: 'Comment on amt-2022-49', Anonymous Referee #1, 08 Jun 2022
  • RC3: 'Comment on amt-2022-49', Anonymous Referee #3, 09 Jun 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-49', Anonymous Referee #2, 01 Jun 2022
  • RC2: 'Comment on amt-2022-49', Anonymous Referee #1, 08 Jun 2022
  • RC3: 'Comment on amt-2022-49', Anonymous Referee #3, 09 Jun 2022

Yoonjin Lee et al.

Yoonjin Lee et al.

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Short summary
Vertical profiles of latent heating are derived from Geostationary Operational Environmental Satellite-16 (GOES-16) to be used in convective initialization. They are compared with other radar products such as NEXRAD and GPM satellite, and the results show that their values are are very similar to radar derived products. Finally, using latent heating derived from GOES-16 in the forecast model shows significant improvements in precipitation forecast comparable to using heating from NEXRAD.