1. A comparison between Cloud Top Temperatures observed by satellites and measured by rawinsondes
LT Alban Simon, French Navy
OC 3570 – Summer 2007
Wednesday, September 5th 2007

2. Purpose
Goal: to conduct an analysis of Cloud Top Temperature measurements by different systems: rawinsondes and satellites.
To evaluate the reliability of satellites in getting the right CTT for different environmental conditions.
Because it is of primary interest for meteorological purposes:
- satellites offer global coverage compared to visual observations, rawinsondes or radar,
- CTT helps to determine cloud type and altitude  input for models (COAMPS), meteorological information for pilots (ceiling) and meteorologists (forecasts).
Note: a 2°C error in temperature = a 200 m error (altitude) for an adiabatic profile
= a 300 m error (altitude) for a standard atmosphere
Note: satellites can not resolved multiple layers of clouds  only the cloud top
Note: CTT helps to determine cloud type and altitude  input for models (COAMPS), meteorological information for pilots (ceiling) and meteorologists (forecasts).
Why? This parameter represents the location of the "radiating" top of the clouds; Cloud top pressure is then determined from cloud top temperature using a profile of atmospheric temperature with pressure. It can be considered as equivalent to cloud top height above mean sea level.

3. Scope Review of radiative processes Methodology
Presentation of the rawinsonde RS92 and the different satellite systems
Results and Analysis:
- comparison between different IR channels,
- comparison between different satellite systems,
- comparison between high clouds and low clouds - temperatures,
- comparison daytime/nighttime,
- case studies.

4. Wien’s Displacement Law (derived From Planck’s Law):
Recall:
Wien’s Displacement Law (derived From Planck’s Law):
If temperature varies from -60°C to 40°C, λm is between 9.3 and 13.6μm.
From Kidder and Vonder Haar, Satellite Meteorology
AVHRR
GOES
MODIS
Transmittance
The primary atmospheric absorbers are O2,O3, CH4, CO2 and H2O (see figure).
The spectral windows also are:
Visible: m
Near IR: m (3.7 m)
IR: m (thermal window)
Microwave: (2 - 4 mm, >6mm)
Optically thick clouds, such as most water droplet clouds, are nearly blackbodies in the LW IR portion of the electromagnetic spectrum.
 The observed emission is equivalent to the actual temperature at the top (within the cloud if tenuous).

5. METHODOLOGY Tb=-4.6C - channel4 Tb=-5.5C – channel5
1- determine the satellite passages over the area of operation: NOAA 16, 17 and 18, MODIS Terra and Aqua – rawinsonde schedule complied with the passages note 1
2- identify rawinsondes with clouds: clear sky/low clouds during most of the 1st leg, a few high clouds (cirrus) during the 2nd leg  note 2
3- compare and analyze CTT from rawinsondes and from satellites  satellite passage +/- 30 minutes with respect to rawinsondes:
comparison between IR channels, between satellite systems, comparison high clouds-low clouds, daytime/nighttime, comparison polar orbiter/geostationary satellites, case studies…
Note: an accurate CTT can be calculated but was not used here note 3
Note1: compare and analyze CTT from rawinsondes and from satellites (only NOAA 17, MODIS Terra and Aqua, GOES West – NOAA16 failed, no available data for NOAA18 )
Note2: satellite passage +/- 30 minutes / rawinsondes (wind, solar heating), only 14 rawinsondes could be used among the 32 launches, it corresponds to 10 passages of polar orbiters and 11 pictures by geostationary satellites. On the rawinsondes, RH varies from 94 to 101% for RS92 and could be 80% for RS80. I checked the presence of clouds with visible images.
Note3: an accurate CTT can be calculated using particular algorithms (UCLA). This piece of information is not directly available from satellite data ( level2 data) and must be calculated using different visible and IR channels: cloud detection, aerosol and water vapor calculation, better results with ice clouds/water clouds, better calibration… However, it requires a larger number of pixels (from 9 to 32 pixels) and it generally decreases the spatial resolution. For this project, information from only 2 different channels were thought to be reliable enough.

6. Radiosonde Vaisala RS 92 specifications:
Temperature sensor type: capacitive wire
Measurement range +60 °C to -90 °C
Resolution 0.1 °C
Humidity sensor type: thin-film capacitor, heated twin sensor
Measurement range 0 to 100% RH
Resolution 1% RH
Pressure sensor type: silicon
Measurement range 1080 hPa to 3 hPa
Resolution 0.1 hPa
Frequency band: 403 MHz (tuning range MHz)
GPS receiver: Positioning uncertainty<10m and Velocity uncertainty<1.5 m/s.
From
From

7. POLAR ORBITERS MODIS specifications:
- Orbit: 705 km, sun-synchronous, near-polar, circular
Swath Dimensions: 2330 km (across track) by 10 km (along track at nadir)
- Spatial Resolution: 250 m (bands 1-2)
(at nadir): 500 m (bands 3-7), 1000 m (bands 8-36)
- Carried by MODIS Aqua and Terra
Surface/Cloud Temperature:
Channel 31: μm – 0.05K
Channel 32: μm – 0.05K
From
AVHRR 3 specifications:
- Orbit: 833 km , sun-synchronous, near-polar, circular
Swath Dimensions: ± 55.4° from nadir (>2600 km)
Spatial Resolution: 1.09 km
- Carried by NOAA 15, 16, 17 and 18 (started 1998)
Surface/Cloud Temperature:
Channel 4: μm – 0.12K
Channel 5: μm – 0.12K
From

8. GEOSTATIONARY GOES West specifications:
- Orbit: km, geostationary
- Spatial Resolution: 4km
- Carried by GOES West
Surface/Cloud Temperature:
Channel I4: μm
Channel I5: μm
From

9. Results: general
satellite
AVHRR channel 4 A/D
AVHRR channel 5 A/D
MODIS channel31 A/D
MODIS channel32 A/D
GOES channel 4 A/D
GOES channel 5 A/D
Mean
(Tr-Ts)
-0.9/0.4
-3.8/-2.5
0.9/0.6
-2.7/-2.1
-1.8/-1.2
Standard deviation
4.3/1.6
6.8/6.6
1.8/1.5
1.9/1.6
6.3/4.4
5.7/4.1
Number of events 5 11
Temperatures observed with descending rawinsondes are thought to be the true ones and are closer to the measured temperature from the satellites  see note1
Using all the available rawinsondes, MODIS channel 31,AVHRR channel 4 appear to give the best approximations for the actual temperatures  references for the analysis (polar orbiters better than geostationary)  see note2
Why did we find negative and positive differences?  calibration, cloud thickness, moist elevated layer, resolution ???
Note: the satellite specifications claim a 0.1K resolution
Note1: we can analyze with different prospective the observed difference between temperature and humidity measurements by ascending and descending rawinsondes:
- Technical prospective: as the ascending sonde goes through a humid layer, the box and the sensors collect water particles which bias the measurements (higher cloud top). On the contrary, the descending sonde is thought to come from a dryer layer and overcome this issue  problem: some sondes gave inversed results.
Meteorological prospective: it takes around 30 to 45’ minutes to complete a sounding, during that period of time, the balloon can drift a lot due to the wind (winds can different at different levels) and/or the advection of different air mass can lead to dramatic differences in the vertical profile.
Note2: as exposed in the review, the transmission is better between 10 and 11μm than between 11 and 12 μm, it is not surprising to find smaller differences for the brightness temperatures in this latter range (water vapor absorption). However, the results are still pretty good for AVHRR channel 5 and MODIS channel 32. GOES channel 5 was found to be better than channel 4, however we will use channel 4 for the analysis it is known to be the reference channel for CTT and its band corresponds to the bands of MODIS and AVHRR.
Based on our theoretical background, we would expect the difference to be always positive since the water vapor and the aerosols would tend decrease the brightness temperature but it was not always the case: about 50% positive and 50% negative. This may occur as the data are calibrated on SST. See example.
Note3: looking at MODIS channels, we could have expected worse results compared to AVHRR  but because of more recent technology
Note4: for the following analysis, we will only work with channels 4 and 32.

10. Small negative ΔT Shallow layer (influence of the surface)
Sonde 11
Small negative ΔT Shallow layer (influence of the surface)
Sonde 11
Sonde 31
Small positive ΔT thicker layer+ elevated moist layer
Sonde 31

11. Positive ΔT for GOES  elevated moist layer
Sonde 27
Strong negative ΔT for GOES  semi-transparent clouds+ spatial resolution
Sonde 27
Sonde 24
Positive ΔT for GOES  elevated moist layer
Sonde 24

12. The Perfect Case! Extended and thick cloud layer
ΔT~ 0°C for any satellite system and for any IR frequency
Sonde 17
Extended and thick cloud layer
Strong subsidence with relatively air above
No wind

13. Results : High and low clouds
satellite
AVHRR channel 4
MODIS channel31
GOES channel 4
High clouds
Mean (Tr-Ts)
1.0 /
-9.8
Standard deviation
2.4 (2)
/ (0)
0.7(2)
Low clouds
0.1
0.6
-0.1
1.2 (3)
1.5 (4)
1.9(9)
ΔT~0 for low clouds – very good!
Variability much more important for higher clouds  note1
Results much better for sonde 6 and 17 than for 2 and 31
No high clouds information from MODIS.
AVHRR seems to be better than MODIS and GOES for low clouds.
Note1: many reasons may explain this variability: observed high clouds were cirrus which are not really blackbodies and tend to be semi-transparent, the observed temperature is the one of the mid-cloud layer. The results would have probably been different with thick clouds such as cumulonimbus. Moreover, high clouds are generally made of ice water with a smaller emissivity (sonde 27:-11.2°C and sonde 29: -5.3°C). The temperatures are also further in the spectrum and the transmission is also more affected by the atmosphere. Because of its spectral definition, MODIS channel 31 would have probably been better for this kind of temperature.

14. Observed Temperature (descending)
Results: satellites vs. temperature
From 0°C to 15°C, ΔT was less than 2°C for any of the satellites, it corresponds to the working window for the systems. However, it is still a 200m error in the elevation.
As expected (transmissivity diagram), channel 5 and 32 seem better than channel 4 and 31 for T<15.5°C while it is the contrary for T> 15.5°C
None of the systems seem to be very good for high clouds (AVHRR>MODIS>>GOES)
Observed Temperature (descending)
AVHRR3 channel 4
AVHRR3 channel 5
MODIS channel 31
MODIS channel 32
GOES channel 4
GOES channel 5
16.1
1.4
2.7
1.7
1.9
0.5
2.8
15.9 /
-2.9
-2.0
15.6
2.2
2.6
0.9
0.0
15.5
1.3
15.4 -1
-0.6
-1.4
-1.2
-1.1
14.5
-0.2
0.7
12.6
-0.3
0.2
-5.3
-0.7
-10.3
-8.9
-11.2
-14
-9.3
-7.6

15. Results: nighttime vs. daytime
satellite
AVHRR channel 4
MODIS channel31
GOES channel 4
Daytime
Mean (Tr-Ts)
0.7
0.8
-1.7
Standard deviation
1.7 (4)
1.6 (4)
3.8(8)
Nightime
-0.7
-0.3
-2.9
0.0 (1)
6.4(3)
AVHRR and MODIS give similar results during nighttime and daytime >> GOES
Results seem to be better during nighttime but only one observations (MODIS>AVHRR)  quieter, solar radiation

16. References:
Kidder S. Q., and T. H. Vonder Harr, Satellite Meteorology, Academic Press, 1995.
2) Huang H.-L., S. C. Ou and S. Vibert, Cloud Top Parameters – Visible/Infrared imager/radiometer suite algorithm theoretical basis document, version5, Raytheon System Company, 2002.
3) Joro S. and A. Dybbroe, Validating the AVHRR cloud top temperature and jeight product using weather radra data,
4) Derrien, M., L. Lavanant and H. Le Gleau, Retrieval of the cloud top temperature of semitransparent clouds with AVHRR. Proceedings of the IRS'88, Lille, France, , 1999.
5) J. R. Schott, Thermal Infrared Calibration of Aerial and Satellite Images Over Land, IEEE, 1994. 6) 7) 8)

17. QUESTIONS?

18. Satellite resolution
Pressure