Atmospheric River Reconnaissance 2021: A Review

Weather and Forecasting · 2022 · 23 citations

• Computer Science
• Meteorology
• Environmental science

Abstract Atmospheric River Reconnaissance (AR Recon) is a targeted campaign that complements other sources of observational data, forming part of a diverse observing system. AR Recon 2021 operated for ten weeks from January 13 to March 22, with 29.5 Intensive Observation Periods (IOPs), 45 flights and 1142 successful dropsondes deployed in the northeast Pacific. With the availability of two WC-130J aircraft operated by the 53 rd Weather Reconnaissance Squadron (53 WRS), Air Force Reserve Command (AFRC) and one National Oceanic and Atmospheric Administration (NOAA) Aircraft Operations Center (AOC) G-IVSP aircraft, six sequences were accomplished, in which the same synoptic system was sampled over several days. The principal aim was to gather observations to improve forecasts of landfalling atmospheric rivers on the U.S. West Coast. Sampling of other meteorological phenomena forecast to have downstream impacts over the U.S. was also considered. Alongside forecast improvement, observations were also gathered to address important scientific research questions, as part of a Research and Operations Partnership. Targeted dropsonde observations were focused on essential atmospheric structures, primarily atmospheric rivers. Adjoint and ensemble sensitivities, mainly focusing on predictions of U.S. West Coast precipitation, provided complementary information on locations where additional observations may help to reduce the forecast uncertainty. Additionally, Airborne Radio Occultation (ARO) and tail radar were active during some flights, 30 drifting buoys were distributed, and 111 radiosondes were launched from four locations in California. Dropsonde, radiosonde and buoy data were available for assimilation in real-time into operational forecast models. Future work is planned to examine the impact of AR Recon 2021 data on model forecasts.

Modeling the Morphodynamics of Coastal Responses to Extreme Events: What Shape Are We In?

Annual Review of Marine Science · 2021 · 103 citations

• Environmental science
• Geology
• Meteorology

This review focuses on recent advances in process-based numerical models of the impact of extreme storms on sandy coasts. Driven by larger-scale models of meteorology and hydrodynamics, these models simulate morphodynamics across the Sallenger storm-impact scale, including swash,collision, overwash, and inundation. Models are becoming both wider (as more processes are added) and deeper (as detailed physics replaces earlier parameterizations). Algorithms for wave-induced flows and sediment transport under shoaling waves are among the recent developments. Community and open-source models have become the norm. Observations of initial conditions (topography, land cover, and sediment characteristics) have become more detailed, and improvements in tropical cyclone and wave models provide forcing (winds, waves, surge, and upland flow) that is better resolved and more accurate, yielding commensurate improvements in model skill. We foresee that future storm-impact models will increasingly resolve individual waves, apply data assimilation, and be used in ensemble modeling modes to predict uncertainties.

West Coast Forecast Challenges and Development of Atmospheric River Reconnaissance

Bulletin of the American Meteorological Society · 2020 · 90 citations

• Environmental science
• Meteorology
• Climatology

Abstract Water management and flood control are major challenges in the western United States. They are heavily influenced by atmospheric river (AR) storms that produce both beneficial water supply and hazards; for example, 84% of all flood damages in the West (up to 99% in key areas) are associated with ARs. However, AR landfall forecast position errors can exceed 200 km at even 1-day lead time and yet many watersheds are <100 km across, which contributes to issues such as the 2017 Oroville Dam spillway incident and regularly to large flood forecast errors. Combined with the rise of wildfires and deadly post-wildfire debris flows, such as Montecito (2018), the need for better AR forecasts is urgent. Atmospheric River Reconnaissance (AR Recon) was developed as a research and operations partnership to address these needs. It combines new observations, modeling, data assimilation, and forecast verification methods to improve the science and predictions of landfalling ARs. ARs over the northeast Pacific are measured using dropsondes from up to three aircraft simultaneously. Additionally, airborne radio occultation is being tested, and drifting buoys with pressure sensors are deployed. AR targeting and data collection methods have been developed, assimilation and forecast impact experiments are ongoing, and better understanding of AR dynamics is emerging. AR Recon is led by the Center for Western Weather and Water Extremes and NWS/NCEP. The effort’s core partners include the U.S. Navy, U.S. Air Force, NCAR, ECMWF, and multiple academic institutions. AR Recon is included in the “National Winter Season Operations Plan” to support improved outcomes for emergency preparedness and water management in the West.

Observation of Jet Stream Winds during NAWDEX and Characterization of Systematic Meteorological Analysis Errors

Monthly Weather Review · 2020 · 63 citations

• Environmental science
• Climatology
• Atmospheric sciences

Abstract Observations across the North Atlantic jet stream with high vertical resolution are used to explore the structure of the jet stream, including the sharpness of vertical wind shear changes across the tropopause and the wind speed. Data were obtained during the North Atlantic Waveguide and Downstream Impact Experiment (NAWDEX) by an airborne Doppler wind lidar, dropsondes, and a ground-based stratosphere–troposphere radar. During the campaign, small wind speed biases throughout the troposphere and lower stratosphere of only −0.41 and −0.15 m s−1 are found, respectively, in the ECMWF and Met Office analyses and short-term forecasts. However, this study finds large and spatially coherent wind errors up to ±10 m s−1 for individual cases, with the strongest errors occurring above the tropopause in upper-level ridges. ECMWF and Met Office analyses indicate similar spatial structures in wind errors, even though their forecast models and data assimilation schemes differ greatly. The assimilation of operational observational data brings the analyses closer to the independent verifying observations, but it cannot fully compensate for the forecast error. Models tend to underestimate the peak jet stream wind, the vertical wind shear (by a factor of 2–5), and the abruptness of the change in wind shear across the tropopause, which is a major contribution to the meridional potential vorticity gradient. The differences are large enough to influence forecasts of Rossby wave disturbances to the jet stream with an anticipated effect on weather forecast skill even on large scales.