1. Derived Motion Winds Scott Bachmeier, Scott Lindstrom
Cooperative Institute for Meteorological Satellite Studies (CIMSS)
University of Wisconsin - Madison
Contributors: Jaime Daniels (NOAA/NESDIS/STAR)
Steve Wanzong, Dave Stettner, Chris Velden (CIMSS)
June 2016
This is the Satellite Foundation Course for GOES-R Training on Derived Motion Winds, a GOES-R Baseline Product. Subject Matter Experts for this product are Jaime Daniels at NOAA and Chris Velden and coworkers at CIMSS. My name is Scott and I'm guiding you through this training.

2. Lesson Objectives
How Derived Motion Winds (also known as Atmospheric Motion Vectors) are created
Height assignment of Derived Motion Winds
Validation, accuracy and precision of DMW
Uses and examples of Derived Motion Winds
Learning objectives are listed on this slide.

3. Derived Motion Winds Algorithm
Uses a set of 3 sequential images to estimate atmospheric motion
Follows sets of algorithm-identified tracers or “targets” (Visible/Infrared/Water Vapor cloud edges, or clear-sky Water Vapor gradients)
DMW are calculated using 6 ABI bands:
Band 2 (Visible, 0.64 µm)
Band 7 (Shortwave Infrared, 3.9 µm)
Band 9 (Upper-level Water Vapor, 6.2 µm)
Band 10 (Mid-level Water Vapor, 6.9 µm)
Band 11 (Lower-level Water Vapor, 7.3 µm)
Band 14 (Longwave Infrared, 11.2 µm)
Coverage area (time interval): Full disk (60 minutes), CONUS (15 minutes), Mesoscale (5 minutes)
AHI data are used in this training as an excellent proxy for ABI data
The GOES-R Derived Motion Wind algorithm uses three sequential images to estimate motion -- targets within the images are tracked, and those targets include cloud edges and water vapor gradients. Six bands on ABI are used.
WInds are produced in the Full Disk domain every hour, every 15 minutes in the CONUS domain, and every 5 minutes in the MESO domain.
In this training, AHI data from Japan's Himawari-8 satellite is used as a proxy for its very close cousin ABI on GOES-R.

4. Derived Motion Winds Algorithm
A “nested tracking” technique is used for Visible, Infrared, and cloudy Water Vapor images
A “nested tracking” technique is used to process visible, infrared and cloud water vapor derived motion winds. Small 5x5 pixel sub-target scenes are “nested” within a larger NxN Target Scene. “Local motion vectors” are derived for the center pixel of the 5x5 box, then this box is shifted by 1 pixel. A Cluster Analysis program is subsequently run to derive the “Dominant motion vector” for the entire NxN Target Scene. Finally, a height value is assigned to this Dominant motion vector.
Using an image triplet, each NxN (size can vary) Target Scene is searched, both backward and forward in time
A cluster analysis program is then used to derive the “Dominant motion vector” for that entire Target Scene
A height value is assigned to the Dominant motion vector

5. DMW Height Assignment
Uses GOES-R ABI Cloud Height Algorithm (ACHA) products: Cloud Mask, Cloud Type, Cloud Top Pressure, Cloud Top Temperature
The Derived Motion Winds height assignment process requires the following GOES-R ABI Cloud products: Cloud Mask, Cloud Type, Cloud Top Pressure, and Cloud Top Temperature. (A Satellite Foundational Course for GOES-R Training for these Cloud products is available). Keep in mind that any errors in the Cloud height Product will propagate to the Height Assignment Process that is applied to the derived motion winds. (Full details on both are in links at the end of the training)

6. DMW Verification statistics: Himawari-8/AHI vs Aircraft
Verification statistics are derived from studies that have compared Derived Motion Winds to co-located rawinsondes, aircraft data, and model winds. The comparisons on this page are of Derived Motion Winds for longwave IR, visible, shortwave IR and water vapor sources and aircraft data over ocean areas -- the Accuracy (the mean vector difference between the Derived Motion Wind and the aircraft data) was between 3 and 6 meters per second, while the Precision (the standard deviation about the mean vector difference) varied from 2.6 to 4.7 meters per second.
Compares favorably with GOES-R DMW specification:
Mean Vector Difference (DMW vs RAOB/Aircraft/Model)
Standard Deviation about the mean vector difference

7. Himawari-8/AHI Visible and Infrared winds
Operational Himawari-8 AMV
Increase of data quality and quantity ->
Improvement to DMW temporal and spatial resolution
Using 10 min. and 2 km resolution
Himawari-8 Rapid Scan AMV
ABI is very similar to AHI and provides a big increase in spatial resolution (visible: from 1.0 to 0.5 km; infrared: from 4.0 to 2.0 km) The temporal frequency also improves (from 15 minutes to 5 minutes or less) This will result in a dramatic increase in both the areal coverage and the accuracy of Derived Motion Winds.
Using 2.5 min. and 0.5 km (2km for IR)

8. Super Typhoon Soudelor (05 August 2015)
Himawari-8/AHI Visible winds and Water Vapor winds
This example shows the complimentary coverage provided by combining the low-level Visible (using full-resolution 0.5 km images) and cloud-top Water Vapor winds for Super Typhoon Soudelor over the West Pacific
Hurricane force winds (> 75 mph)
Visible winds
Water Vapor winds

9. Several studies have shown that better methods of
DMW assimilation improves NWP model performance
A primary use of the Derived Motion Winds product will be assimilation into Numerical Weather Prediction models. Many studies show that proper assimilation results in improved model forecasts (one example is more accurate tropical cyclone path forecasts). This particular study from the UK Met office shows that as more Derived Motion Winds (or AMVs) are assimilated, their total observational impact is increasing (resulting in better 24-hour forecasts).

10. Jet Streak Intensity (Underestimated By RUC)
Model Verification:
Jet Streak Intensity (Underestimated By RUC)
Derived Motion Winds can be used for model verification. In this case, the RUC model (the display here is of max wind speed from the RUC in cyan) under-estimated the intensity of the core of an upper-tropospheric jet streak over the southwestern US - several wind targets displayed velocity values of knots (much higher than the 140+ knot core forecast by the model)

11. Cloud-Top (200-400 MB) Water Vapor Winds (Large Differences)
Model Verification:
Cloud-Top ( MB) Water Vapor Winds (Large Differences)
This image shows significant differences between the GFS winds in red and the Himawari-8 Derived Motion Winds in blue - in some cases, the directional difference was as much as 180 degrees.
How would this affect your interpretation of GFS model results?
Himawari-8/AHI Winds
GFS Winds

12. Low-level (850-950 MB) Shortwave and Longwave IR Winds
Model Verification:
Low-level ( MB) Shortwave and Longwave IR Winds
Himawari-8/AHI Winds ( mb)
GFS Forecast Winds (900mb)
This example shows low-level Derived Motion Winds from AHI were in agreement with the GFS in terms of the location of the center of a deep mid-latitude cyclone south of Kamchatka; however, east-southeast of the storm center the Derived Motion Winds showed a backing of winds that highlighted a region of stronger low-level warm air advection that was missed by the GFS.

13. Application: Identification of a favorable vertical wind shear profile in the pre-storm environment
Derived Motion Winds can also help diagnose mesoscale features and/or boundaries to aid in situational awareness. In this example from the Hazardous Weather Testbed Blog, winds helped to verify that a favorable vertical wind profile existed ahead of a severe thunderstorm that had just developed over North Texas. Low-level winds (dark blue barbs) were south to southwesterly whereas upper level winds (red barbs) were from the west and northwest.

14. Application: Identification of a favorable vertical wind shear profile in the pre-storm environment
(optional slide) Here is another view of that same case over North Texas, showing the Visible, Infrared, and Water Vapor Derived Motion Winds computed during GOES-14 SRSO-R operations. Remember that winds in GOES-R MESO Sectors will be produced only every 5 minutes. That's similar to the production cadence during SRSO-R.

15. Application: identification of a pre-frontal trough axis across Arkansas, Texas
Another example highlighted on the Hazardous Weather Testbed Blog was a pre-frontal trough axis that was oriented from northeast to southwest across Arkansas and Texas; the visible Derived Motion Winds added confidence in the location and movement of this boundary.

16. Himawari-8/AHI Winds Displayed in AWIPS
Low Level (P > 700 mb) Winds derived using
Shortwave IR - Band 7 (3.9 µm) – used at night
Visible - Band 2 (0.64 µm) - used in daytime
How will Derived Motion Winds be displayed in AWIPS? Low-level winds (shown here) are calculated for targets having a pressure of 700 mb or greater. Vectors are computed from 3.9 micron Shortwave Infrared (Band 7) images during the night, and from 0.64 micron Visible (Band 2) images during the day.
> 48 kts
34 – 48 kts
< 34 kts

17. Himawari-8/AHI Winds Displayed in AWIPS
Upper Level ( mb) Winds derived using Longwave Infrared - Band 14 (11.2 µm) - day/night
Upper-level Derived Motion Winds can be calculated using targets from 11.2 micron Longwave Infrared (Band 14) images - as seen here - or by using 6.2 micron Water Vapor (Band 8) images
> 48 kts
34 – 48 kts
< 34 kts

18. Summary Derived Motion Winds calculated using 6 ABI bands:
Band 2 (Visible, 0.64 µm)
Band 7 (Shortwave Infrared, 3.9 µm)
Band 9 (Upper-level Water Vapor, 6.2 µm)
Band 10 (Mid-level Water Vapor, 6.9 µm)
Band 11 (Lower-level Water Vapor, 7.3 µm)
Band 14 (Longwave Infrared, 11.2 µm)
Coverage area/time: Full Disk (60 minutes), CONUS (15 minutes), Mesoscale (5 minutes)
Accuracy compares well with RAOB/Aircraft/Models
Proper assimilation improves NWP model performance
Applications: model verification, mesoscale feature identification/tracking
This slide should remind you of what you just learned

19. Internet Resources ATBD on Derived Motion Wind Vectors
ATBD on Cloud Heights
HWT Blog Post on Pre-Frontal Trough in DMWs
HWT Blog Post on Vertical Wind Shear in DMWs
Case over Texas referred to in training
Case over Missouri
All HWT Blogs on Derived Motion Wind Vectors
GOES-R Website
You can find additional information and examples about DMWs at the links on this slide. Two of the HWT Blog Posts were references already in this training, but there is plenty of other information there. In addition, the ATBD (Advanced Theoretical Basis Document) gives all the technical information on the Derived Motion Wind Product.
This concludes the Satellite Foundation Course for GOES-R on Derived Motion Winds, a GOES-R Baseline Product. Thanks for listening!