Tomoko Matsuo DAI/GSP *in collaboration with Jeff Anderson(DAI),

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Presentation transcript:

Data Assimilation of MLT (~50-110 km) observations using a 3d chemical-dynamical model in DART Tomoko Matsuo DAI/GSP *in collaboration with Jeff Anderson(DAI), Dan Marsh(ACD), Anne Smith(ACD)

Mesosphere and lower thermosphere http://www.timed.jhuapl.edu

Scientific Objectives climatological global tidal structure day-to-day synoptic scale tidal variability roles of non-migrating tides and planetary waves in creating or modulating the tidal variability.

TIMED-SABER/TIDI TIDI Dayside Measurements Vector Wind O2 (0-0) P15 60 - 100 km O2 (0-0) P9 70 - 115 km OI 557.7 nm 100 - 180 km TIDI Nightside Measurements Vector Wind O2 (0-0) P9 80 - 105 km OI 557.7 nm 90 - 110 km

Data Availability http://www.timed.jhuapl.edu Horizontal Axis # of ground-based radars Total # of observations (50K) per Given time scale, assimilation window: one orbit a time (90-100min) http://www.timed.jhuapl.edu

ROSE 3-D chemical dynamical model [Rose and Brasseur, 1983; 1989] Model Resolution 38 levels (pressure coordinate) 17.5 to 110 km by 2.5 km 5º latitude x 11.25º longitude 7.5 min time step Chemistry 27 species, 101 gas-phase rxns (JPL-2000) Semi-lagrangian transport scheme Airglow package Offline D-region ion chemistry Photolysis rates based on T U V Dynamics Primitive equations Hines gravity wave parameterization NCEP and GSWM forcing at lower boundary Tidal amplitude comparisons Chemical and dynamical time scale. Chemistry has traditionally been a primary diagnosis of the region. Size of state vector (400K) Tn (K) Local time

Preliminary Results from synthetic observation experiments. MODEL no natural error growth large uncertainty in forcing Ensemble Spread Reduction Ensemble mean and spread MODEL (no error growth & large uncertainty in forcing) OBSERVATIONS (the huge variability) COVARIANCE DART facilitation how quickly a given model can be implemented into DART

Summary Need of DA system for MLT region (~50-110km) is timely. With the DART facilitation, a prototype ensemble filter assimilation system for synthetic observations with ACD's ROSE model is being constructed. Future Work: Assimilation of the ground-based and satellite observations (15 k scalar observations per TIMED orbit). Estimation of forcing and model parameters Challenges and Open Questions: How to cast a DA problem in strongly forced and dissipative systems when models do not have natural error growth? Model Error v.s. Observation Error: Observed day-to-day variability is significantly higher than the variability reproduced by numerical models. Large uncertainty in forcing pilot study for WACCAM_DART CAM’s gravity breaking parameterization,