Atmospheric River Recon
AR Recon Overview
CW3E works directly with water managers in the West to develop science and tools designed to better prepare for the variability inherent in the western US climate. Atmospheric River Reconnaissance campaigns support improved prediction of landfalling atmospheric rivers (ARs) on the US west coast. ARs are a type of storm that are key to the region’s precipitation, flooding and water supply. Forecasts of landfalling ARs are critical to precipitation prediction and yet are in error by +/- 400 km at even just 3-days lead time (Wick et al. 2013). The concept for AR Recon was first recommended in a report to the Western States Water Council that was prepared by a broad cross-disciplinary group in 2013. AR Recon was conducted in 2016 with 3 missions and in 2018 with 6 missions.
Dropsondes on the NOAA G-IV aircraft before getting released through the chute (below left). Each dropsonde is about the size of two soda cans. Photo courtesy Dr. Brian Henn.
During the Atmospheric River Reconnaissance 2018 (AR Recon – 2018) campaign, three aircraft that are normally used for hurricane reconnaissance were deployed over the northeast Pacific to collect observations to support improved AR forecasts. The data were incorporated by global modeling centers. These aircraft included two of the Air Force 53rd Weather Reconnaissance Squadron’s WC-130J Hurricane Hunter aircraft, one based in Hawaii and the other in California, and NOAA’s Gulfstream IV(G-IV), based in Everett, WA. Air Force personnel were stationed at Scripps to help coordinate flight planning. The primary data collected were from the release of dropsondes, which record temperature, wind, and relative humidity at very high resolution throughout the atmosphere. Scripps researchers flew additional instrumentation aboard the NOAA G-IV to characterize the upper atmosphere poleward of the ARs. A simplified version of the GNSS Instrument System for Multistatic and Occultation Sensing (GISMOS) was used to measure profiles of the atmospheric environment to the sides of the aircraft while dropsondes measured profiles directly below the aircraft.
AR Recon – 2018 made use of a model that quantitatively pinpoints locations of greatest sensitivity in the forecast – which is centered typically on the atmospheric river core. This moisture sensitivity is substantially larger than temperature, wind, or any other sensitivity. The field campaign refined how the tool works, and that information was combined with knowledge of dynamically significant meteorological features such as the upper-level jet, cold-air troughs and other features to specify each mission’s detailed flight tracks. The targeted dropsonde profiles collected in the otherwise data sparse ocean may be part of the solution to getting AR right. Analysis of the impact of the data will be carried out over the next couple of years to thoroughly assess this, including development of specialized assimilation methods. Not only were these data assimilated into operational forecast models, but the information collected will be used in research studies to further understand the dynamics and processes that are the main drivers of key AR characteristics such as strength, position, length, orientation, and duration.
Note: Data is also provided on this website for the CalWater campaigns, which were scientific field studies to better understand the dynamics of ARs offshore and aerosol transports.
Ralph, F.M., M.D. Dettinger, A. White, D. Reynolds, D. Cayan, T. Schneider, R. Cifelli, K. Redmond, M. Anderson, F. Gehrke, J. Jones, K. Mahoney, L. Johnson, S. Gutman, V. Chandrasekar, J. Lundquist,, N. Molotch, L. Brekke, R. Pulwarty, J. Horel, L. Schick, A. Edman, P. Mote, J. Abatzaglou, R. Pierce and G. Wick, 2014: A vision for future observations for Western U.S. extreme precipitation and flooding. Journal of Contemporary Water Research and Education, 153, 16-32. https://doi.org/10.1111/j.1936-704X.2014.03176.x
Wick, G.A., P.J. Neiman, F.M. Ralph, and T.M. Hamill, 2013: Evaluation of forecasts of the water vapor signature of atmospheric rivers in operational numerical weather prediction models. Weather and Forecasting, 28, 1337-1352. https://doi.org/10.1175/WAF-D-13-00025.1