Continuation of Characteristics and Predictability of Arctic Cyclones
Abstract
Funds are provided to conduct research to understand the structure, dynamics, and predictability of major Arctic cyclones that are thought to play a major role in the evolution of the Arctic sea ice cover, as suspected for the Great Arctic Cyclone of August 2012. Exploration of the linkage between development of these summer cyclones and the upper tropospheric atmospheric circulation is also a key goal. Symptomatic of the challenges facing Arctic weather prediction is the spread of forecasts from operational ensemble prediction systems and the spread of basic variables seen in different global reanalyses.With current ONR funding, the WRF MRI-4DVAR data assimilation approach has been adapted to the Arctic environment. It has been applied to produce regional forecasts of the extreme Arctic cyclone of August 2016 that had a significant impact on the sea ice cover. Very accurate position and intensity forecasts 5 days into the future were obtained with the forecast skill continuing through at least forecast day 7. The assimilation of satellite microwave radiances was central to the success achieved. These forecasts roughly doubled the accuracy duration of the NCEPGFS and other ensemble prediction systems. The goal is to further extend and improve the forecast skill of major summer cyclones that disrupt the Arctic sea ice cover. How much longer than 5-7 days into the future can accurate forecasts be extended? Does such extended forecast skill occur for other extreme cyclones and in other summers? We will use Polar WRF for the forecast model and MRI-4DVAR to derive the initial conditions for the forecasts of several extreme summer cyclones. Satellite radiances from the MHS and IASI sounders will additionally be assimilated. We will investigate whether the detailed cyclone structure and governing dynamics are captured by our forecasts, especially using the observations obtained during the ONR THINICE airborne field campaign scheduled for August 2021. We will transfer our knowledge to Naval Research Laboratory (NRL) to enhance the Arctic forecast skill of their regional Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS) model.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 05, 2021
- Source ID
- N000142112650
Entities
People
- David H. Bromwich
Organizations
- Office of Naval Research
- Ohio State University
- United States Navy