1. Winds in the Polar Regions from MODIS: Atmospheric Considerations
Jeff Key1, Dave Santek2 , Chris Velden2, and Paul Menzel1
1Office of Research and Applications, NOAA/NESDIS, Madison, Wisconsin 2Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin

2. Characteristics of the Polar Regions
Condensate over a lead
Greenland ice sheet
SHEBA ship

3. Temperature

4. Water Vapor

5. If it’s not dark, it’s very bright

6. Tropospheric Winds
Raob data are from NCDC/FSL

7. Clouds Properties Arctic Cloud Amount
New data set: The AVHRR Polar Pathfinder has been extended to include surface, cloud, and radiative properties for at 25 km.
Arctic
Cloud
Amount
Near real-time AVHRR retrievals are available at:

10. Joint Frequency of Cloud Pressure and Optical Depth: Arctic Winter and Summer
Frequency of clouds that are thin (OD < 5) and low (P > 600 hPa) – January: 22%, June: 34%

11. Joint Frequency of Cloud Pressure and Optical Depth
Frequency of clouds that are thin (OD < 5) and low (P > 600 hPa) – January: 22%, June: 34%

12. Height Assignment CO2-Slicing
Problems occur when the clear-cloudy radiance difference is small.
Cloud pressures greater than 700 hPa (lower in altitude) are generally not retrievable with this method.
Note difference in horizontal scales.

13. Cloud-Surface Temperature Differences from AVHRR
Summer
Winter
This shows CO2-slicing failures in terms of the percent of time (out of all cloudy cells) that the IR window method is used.
Small cloud-surface temperature differences are common and are not restricted to low clouds. Results are similar for Arctic. Note frequency of warm clouds (warmer than surface), especially in winter.

14. MODIS CO2-Slicing “Failure” Rate in the Polar Regions
No CO2 retrieval attempted below 700 hPa
No CO2 retrieval found
This shows CO2-slicing failures in terms of the percent of time (out of all cloudy cells) that the IR window method is used.

15. IR Window Joint Frequency of Cloud Pressure and Optical Depth
Currently, this approach assumes the cloud is opaque so that the IR brightness temperature is also the cloud temperature. Find the temperature in the profile to get the height.
An adjustment for surface emission should be used with thin clouds, which means optical depth must be calculated. The ISCCP and CASPR methods adjust cloud temperature if the IR optical depth is less than 4.6 (> 1% transmission), which is a larger visible optical depth for water clouds but somewhat smaller for ice clouds.
Joint Frequency of Cloud Pressure and Optical Depth
Frequency of clouds that are thin (OD < 5) and low (P > 600 hPa) – January: 22%, June: 34%

16. Note slope differences for low clouds
H2O-Intercept
Problem: 6.7 m band is insensitive to low clouds. In theory the 7.2 m band, which peaks in the lower troposphere, would be better.
In practice the method is generally not useful for cloud pressures greater than 600 mb for 6.7 m and 750 hPa for 7.2 m.
6.7 m
7.2 m

17. H2O-Intercept: Two Channel Solution?
The idea: The intersection of the lines connecting points at 6.7 m and 7.2 m give a brightness temperature (actually, 3 Tbs) that can be used to estimate the cloud height with a model profile, eliminating the need to compute the opaque cloud temperature (not really the Planck function).
However, the two opaque cloud curves diverge for mid- and low-level clouds.

18. Can the 6.7 m band see the surface?
This figure shows the modeled change in the 6.7 m brightness temperature as a function of total precipitable water (TPW) when the surface temperature is varied by 15 degrees. For a given Arctic/ Antarctic profile, TPW is held constant while the surface temperature is varied.
The figures below show that variations in the surface temperature do affect the 6.7 m Tb when the atmosphere is very dry. So theoretically, this band can see the surface. The problem is more significant for the 7.2 m band.

19. Can the 6.7 m band see the surface? (cont.)
This is a MODIS image covering part of the Arctic (SE Greenland) on 19 March Surface features are clearly seen in the IR window band (left), but are also apparent in the water vapor band (right).
11 m
6.7 m

20. IR Window
Currently, this approach assumes the cloud is opaque so that the IR brightness temperature is also the cloud temperature. Find the temperature in the profile to get the height.
An adjustment for surface emission should be used with thin clouds, which means optical depth must be calculated. The ISCCP and CASPR methods adjust cloud temperature if the IR optical depth is less than 4.6 (> 1% transmission), which is a larger visible optical depth for water clouds but somewhat smaller for ice clouds.

21. Converting the cloud temperature to a cloud pressure (lookup in the profile), the adjustment in summer will generally increase the cloud altitude. In winter the direction of change may be mixed due to inversions.
The point-by-point retrievals, with and without the adjustment for optical depth, are shown above for one summer image. Only clouds with visible optical depths less than 5 are shown.
The relative frequency of the pressure differences is shown at left.

22. Case Study: Infrared Winds
Low Level
Mid Level
High Level
05 March 2001: Daily composite of 11 micron MODIS data over half of the Arctic region. Winds were derived over a period of 12 hours. There are about 4,500 vectors in the image. Vector colors indicate pressure level - yellow: below 700 hPa, cyan: hPa, purple: above 400 hPa.

23. Recommendations
Channel selection: MODIS offers a robust set of spectral information; we currently use only the IR window and (one) WV bands. Other bands may increase the number and clarity of tracking features. For example:
1.6 m will greatly improve snow-cloud discrimination
Channel differences such as , m or m and m will better distinguish thin clouds
H2O intercept height assignment: Surface emission in dry atmospheres will complicate height assignment. There may not be a solution to this problem other than using an estimate of TPW (or an empirical relationship between surface temperature and TPW) to avoid situations where this is a problem.
IR window height assignment: Estimate the cloud optical depth and adjust the cloud temperature for transmission.
Model fields: How good are the model fields of surface temperature and the lower troposphere (i.e., inversions)? We have methods of retrieving clear sky surface temperature and low-level inversion strength with MODIS.