1. UPDATE ON SATELLITE-DERIVED amv RESEARCH AND DEVELOPMENTS
EUMETSAT (Courtesy Régis Borde)
NESDIS (Courtesy Jaime Daniels and Wayne Bresky)
CIMSS (Velden, projects with NRL and UMiami)

2. EUMETSAT
Primary effort to create a better link between features being tracked and height assignment of AMVs using CCC (cross-corr) method which identifies pixels that dominate the optimal motion correlation surface.
Results quite dependent on Cloud Analysis product CTHs.
New scheme gives more good AMVs (QI >80), but slow bias and RMS at high levels are not improved. More work needed.
Preliminary assimilation test at ECMWF gave mainly a neutral impact on forecasts.

3. Prospects
Use this method together with the future Optimal Cloud Analysis (OCA) product.
Estimate and test the potential use of weighted pressure (in hPa) in assimilation. It gives information on the variability within the group of pixels used for HA.
OCA product can also provide information about the reliability of the HA retrieval.
Opportunity to estimate AMV HA quality based on this new information.

4. NESDIS New “Nested Tracking” Method
Experimental: part of new GOES AMV feature tracking algorithm (significant change)
Uses smaller target scenes “nested” within a larger target scene to derive local motions
A clustering analysis algorithm is used to extract the dominant motion in the larger target scene
Cloud-top products (computed upstream of AMV algorithm) from pixels belonging to the largest cluster are used to assign a representative height to the derived motion wind
Significant gains in product accuracy, particularly for upper -level LWIR AMVs.
Near elimination of observed slow speed bias; a significant concern of the NWP user community

5. NESDIS Initial Results from Testing
Statistical comparison (vs. collocated raobs):
Control
15x15 box
Test (19x19 outer box)
Largest cluster from 5x5 sample, new heights
RMSE
7.53
6.63
Avg Difference
5.95
5.28
Speed Bias
-1.97
0.06
Speed
17.46
17.71
Sample
14548
14553
Winds generated using Meteosat μm imagery (15-minute loop interval) for the period Feb , 2007.

6. NESDIS Summary and Future Plans
Nested tracking approach effectively minimizes the slow speed bias
Most speed “adjustments” are small, but some can exceed 10 m/s
Smaller bias a result of lower heights and faster winds
Nested tracking approach also significantly reduces RMSE
Greatest benefit seen at upper levels for IR AMVs
Smaller improvements for cloud-top WV AMVs
Identified opportunities with the nested tracking approach
Additional clusters may contain useful wind information in the target scene
Use pixel level heights from cluster analysis to report layer information
Clustering metrics may enable new quality control to be employed
Number of points in cluster
Number of clusters
mean distance of points in cluster
Extend cluster analysis to include height
Paper in preparation: Bresky, W. and J. Daniels: New Methods Towards Minimizing the Slow Speed Bias Associated with Atmospheric Motion Vectors.
Submitted a proposal to the Joint Center for Satellite Data Assimilation (JCSDA) to perform an NCEP GFS NWP forecast impact study using AMVs derived from new approach

7. Recent efforts to optimize assimilation strategies for AMVs
CIMSS/NRL
Recent efforts to optimize assimilation strategies for AMVs
- Focus on tropical cyclones in global and meso DA/models
Study 1: NRL/FNMOC NAVDAS/NOGAPS data impact studies
Test 4DVAR assimilation of hourly AMVS, and Rapid-Scan AMVs
Results: Positive forecast impact in 3-5 day timeframe for TC tracks
(Berger, Langland, Velden et al. 2011, to appear in JAMC)
FNMOC implemented assimilation of CIMSS NH hourly AMVs from MTSAT-2 and GOES-11 into its operational 4DVAR global NWP system in December Hourly AMVs from Meteosat-7 (NH and SH) were added in February Even with this limited coverage of hourly AMVs, the total number of AMVs assimilated globally increased ~65% and the beneficial impact as measured by the NRL variational adjoint system has increased ~40%.

8. CIMSS/NCAR/UMiami
Study 2: Mesoscale DA/model data impact studies (Tropical Cyclones)
Framework: NCAR Data Assimilation Research Testbed (DART)
Data assimilation: Ensemble Kalman Filter (EnKF)
Model: Advanced Research WRF (WRF-ARW)
Ensemble members: 32; Case: Typhoon Sinlaku (2008)
Assimilation cycle started Sep. 1st, (one week before genesis)
9km moving nest grid with feedback to 27km grid in the forecasts when TC is present.
Deterministic: ECMWF 1.125°x1.125° (Baseline)

9. Analysis Track and Intensity
CIMSS
JMA Best Track
CTL
CIMSS
09/09:00Z
09/10:00Z
09/11:00Z
Upper-lev Div (Above)
Azi-mean Vort (Right)
CTL
CIMSS
Structure