Assessing Long-Range Forecast Impacts of Data Collected during Atmospheric River Reconnaissance Missions
Abstract
Atmospheric Rivers (ARs) are corridors of enhanced water vapor transport that transfer over 90% of water vapor from low latitudes to mid-high latitudes in the atmosphere. In the past decades, ARs have been increasingly recognized as key drivers of extreme weatherand hydrological events in many regions across the world. To fill observation gaps and improve forecast accuracy of landfalling ARsover the US West, Atmospheric River Reconnaissance (AR Recon) campaigns have been launched since 2016 through collaborative effortsbetween universities, federal and state agencies, and operational numerical weather prediction (NWP) centers. While previous studies have focused on short-range (0#2 days) and medium-range (3#7 days) forecast horizons, the impact of AR Recon data beyond one week remains unexplored in the current literature. Given the declining forecast skill observed in global NWP models beyond this timeframe, improving week-2 forecasts is critical for operational planning and risk management. This proposal aims to systematically assess the impact of AR Recon data on weather forecasts, particularly focusing on the long-range (i.e., 7#14-day) timeframe and extending beyond the US West to downstream terrestrial and marine regions. Our methodology involves conducting data denial experiments for AR Recon dropsonde and drifting buoy data using the Joint Effort for Data assimilation Integration (JEDI) system and the Model PredictionAcross Scales # Atmosphere (MPAS-A) forecast model. By utilizing process-oriented diagnostic tools, we will identify key dynamics and physical processes (e.g., planetary boundary layer height, diabatic heating, and upper-level forcing) crucial for enhancing forecast skills in week 2. We will examine the value of assimilating dropsonde data at different altitudes and raw high-vertical resolution data.The proposed research will provide valuable insights for future mission planning and improving NWP models. Furthermore, thiswork will bridge the gap between short-to-medium range and sub-seasonal forecasts in state-of-the-art global forecast models.Approved for public release.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Nov 09, 2024
- Source ID
- N000142412698
Entities
People
- Minghua Zheng
Organizations
- Office of Naval Research
- United States Navy
- University of California, San Diego