Leveraging the MJO for Multi-Week Predictions: Improving Understanding of MJO - Maritime Continent Interactions
Abstract
ABSTRACT The ultimate goal of this research is to advance global tropical cyclone analysis and prediction through the development of advanced and computationally efficient data assimilation techniques to ingest and integrate satellite observations, as well as other in situ or remotely-sensed data, into cloud-resolving numerical weather prediction models. The satellite observations of particular emphasis here are brightness temperatures from geosynchronous satellites along with satellitederived atmospheric motion winds (AMVs), both of which have high spatial and temporal coverage of the inner-core and storm-scale structure of tropical cyclones. Also considered in this proposal are microwave brightness temperature measurements from polar orbiting satellites, as well as GPS Radio Occultation (RO) observations. Major objectives include: • Examine the structure and dynamics of the covariances between the cloudy/clear-sky brightness temperatures and the model state variables located within and outside tropical cyclones through cloud-resolving ensemble simulations coupled with state-of-the-art community radiative transfer model (CRTM) calculations. These ensemble sensitivity and observation impact analyses will be key to identifying which particular satellite observations will be the most effective if assimilated and to understanding the physical covariance scales. • Conduct extensive and systematic observing system simulation experiments (OSSEs) by assimilating simulated brightness temperatures under varying degrees of model and observation errors. With a known simulated truth, OSSE experiments are critical in identifying the most impactful satellite observations, the most efficient assimilation algorithms, and the most desirable model and ensemble configurations. In particular, we will be examining the successful correlation localization (SCL) technique for effective assimilation of multiscale observations as well as the adaptive covariance relaxation technique for treatment of model and sampling errors in assimilation of the satellite data. • Develop a superobbing (SO) procedure for data thinning and quality control of various satellite brightness temperatures and satellite-derived AMVs to reduce observational bias and noise and to allow for more balanced distributions of observations around the storm. This procedure will be built on the SO technique our team developed for data thinning and quality control of the large volume of airborne Doppler radar data. The procedure will be explored using simulated observations but eventually applied to real-data satellite observations. We will evaluate and develop additional bias correction approaches as necessary. • Evaluate assimilation of real-data satellite observations of selected storms from past field experiments that have extensive airborne inner-core observations, as well as from the upcoming ONR-sponsored OUTFLOW campaign in the Western Pacific basin during which high-resolution AHI-8 (Advanced Himawari-8 Imager) brightness temperatures will be available from the recently launched Japanese geosynchronous satellite Himawari-8. We will conduct systematic Observing System Experiments (OSEs) by withholding or adding different satellite observations to examine the effectiveness of the assimilation procedure and the value of particular satellite instruments and combinations of their brightness temperatures. These experiments will be evaluated in comparison with assimilations of airborne Doppler radar and high-resolution dropsonde observations.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 12, 2016
- Source ID
- N000141512298
Entities
People
- Fuging Zhang
Organizations
- Office of Naval Research
- Pennsylvania State University
- United States Navy