All Time Past Year Past 30 Days
Abstract Views 243 243 42
Full Text Views 108 108 8
PDF Downloads 113 113 9

The Impact of Dropsonde Data on the Performance of the NCEP Global Forecast System During the 2020 Atmospheric Rivers Observing Campaign. Part 1: Precipitation

Stephen J. LordaUniversity Corporation for Atmospheric Research/CPAESS, Boulder, Colorado and NOAA/NCEP Environmental Modeling Center, College Park, Maryland

Search for other papers by Stephen J. Lord in
Current site
Google Scholar
PubMed
Close
,
Xingren WubI. M. Systems Group, Inc., Rockville, and NOAA/NCEP Environmental Modeling Center, College Park, Maryland

Search for other papers by Xingren Wu in
Current site
Google Scholar
PubMed
Close
,
Vijay TallapragadacNOAA/NCEP Environmental Modeling Center, College Park, Maryland

Search for other papers by Vijay Tallapragada in
Current site
Google Scholar
PubMed
Close
, and
F. M. RalphdCW3E, Scripps Institution of Oceanography, UC San Diego, California

Search for other papers by F. M. Ralph in
Current site
Google Scholar
PubMed
Close
Restricted access

The impact of assimilating dropsonde data from the 2020 Atmospheric River (AR) Reconnaissance (ARR) field campaign on operational numerical precipitation forecasts was assessed. Two experiments were executed for the period from 24 January to 18 March 2020 using the NCEP Global Forecast System version 15 (GFSv15) with a four-dimensional hybrid ensemble-variational (4DEnVar) data assimilation system. The control run (CTRL) used all the routinely assimilated data and included ARR dropsonde data, whereas the denial run (DENY) excluded the dropsonde data. There were 17 Intensive Observing Periods (IOPs) totaling 46 Air Force C-130 and 16 NOAA G-IV missions to deploy dropsondes over targeted regions with potential for downstream high-impact weather associated with the ARs. Data from a total of 628 dropsondes were assimilated in the CTRL. The dropsonde data impact on precipitation forecasts over U.S. West Coast domains is largely positive, especially for day 5 lead time, and appears driven by different model variables on a case-by-case basis. These results suggest that data gaps associated with ARs can be addressed with targeted ARR field campaigns providing vital observations needed for improving U.S. West Coast precipitation forecasts.

Corresponding author: Vijay Tallapragada, Vijay.Tallapragada@noaa.gov

The impact of assimilating dropsonde data from the 2020 Atmospheric River (AR) Reconnaissance (ARR) field campaign on operational numerical precipitation forecasts was assessed. Two experiments were executed for the period from 24 January to 18 March 2020 using the NCEP Global Forecast System version 15 (GFSv15) with a four-dimensional hybrid ensemble-variational (4DEnVar) data assimilation system. The control run (CTRL) used all the routinely assimilated data and included ARR dropsonde data, whereas the denial run (DENY) excluded the dropsonde data. There were 17 Intensive Observing Periods (IOPs) totaling 46 Air Force C-130 and 16 NOAA G-IV missions to deploy dropsondes over targeted regions with potential for downstream high-impact weather associated with the ARs. Data from a total of 628 dropsondes were assimilated in the CTRL. The dropsonde data impact on precipitation forecasts over U.S. West Coast domains is largely positive, especially for day 5 lead time, and appears driven by different model variables on a case-by-case basis. These results suggest that data gaps associated with ARs can be addressed with targeted ARR field campaigns providing vital observations needed for improving U.S. West Coast precipitation forecasts.

Corresponding author: Vijay Tallapragada, Vijay.Tallapragada@noaa.gov
Save