Optical Depth Distribution of Optically Thin Clouds
and Surface Elevation Variability Derived from
CALIPSO Lidar Measurements
Zhaoyan Liu (1), Bing Lin (2), Michael Obland (2), and Joel Campbell (2)
(1) Science Systems and Applications Inc (SSAI)
(2) NASA Langley Research Center
Special thanks to Drs. Y. Hu and X. Lu at LaRC for providing their surface elevation data
and to the LaRC CALIPSO team for providing the CALIOP data
Background – ASCENDS
ASCENDS - Active Sensing of CO2 Emissions over Nights, Days, and Seasons
Primary Objective:
1. Make integrated path differential absorption (IPDA) lidar CO2 measurements during
day and night, over all latitudes, all seasons, and in the presence of thin or
scattered clouds
Measurement Requirements for CO2 Volume Mixing Ratio (XCO2):
1. Precision: ≤ 0.5 ppm (~0.1%) for clear sky with a 10-s averaging
2. Bias: < 0.5 ppm within 1 year after launch.
Very challenging requirement for the high precision and low bias!
In this talk, using the CALIPSO lidar measurements to study:
1. Variability of surface elevation
2. Distribution of optically thin clouds (below the detection limit of passive sensors)
2
Needs for Ranging Capability
Field of View
∆R
Surface
Typical for passive satellite sensors
Lowest 1000 m of the atmosphere: ~ 114 hPa
• 10 m error  1.14 hPa error  ~ 0.44 ppm (or ~ 0.1%) CO2 error
Range precision < 5 meter, corresponding to 0.22 ppm (~0.05%) error in CO2 measurement
Importance of thin cirrus clouds
Observed - Modeled
GOSAT measurements
Relatively large differences may be due to imperfect The presence of clouds limits CO2 measurements
screening of thin clouds?
Oshchepkov et al., JGR, 2012
CALIPSO Lidar Measurements
Thin clouds
High dense clouds
Dust
Surface
Smoke
Low dense clouds
The CALIPSO lidar is a satellite borne backscatter lidar and can measure the vertical distribution of clouds and aerosols and the surface.
High sensitivity allows detection of thin clouds and aerosol that generally cannot be detected by passive satellite sensors.
Surface Elevation
2007 – 2012 CALIPSO Data
(b) Mean Std Dev (1.3-km Footprint) [m]
[km]
3
0
2
-50
1
0
30
0
20
-50
-100
Longitude
1.3-km
2.3-km
60
40
20
1
2
3
0
4
Surface Elevation (km)
5
6
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
10
0
100
0
Longitude
(f) Histograms
1.3-km
2.3-km
5
20
-100
(e) Histograms
Cummulative Frequency
Mean Standard Deviation (m)
(d) Statistics
0
0
100
Longitude
100
80
0
30
0
-50
10
0
100
40
50
10 15 20 25 30 35 40 45 50 55 60
Surface Elevation Standard Deviation (m)
Cummulative Frequency
-100
50
40
50
Latitude
Latitude
50
(c) Mean Std Dev (2.3-km Footprint) [m]
50
4
Latitude
(a) Mean CALIPSO Elevation
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
1.3-km
0.1
2.3-km
0
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
XCO 2 Relative Error (%)
The higher the surface elevation, the larger its variability (standard deviation).
Over land between 60S – 60N, ~20% of the land surface has a elevation variability of ~ 10m that can potentially cause an error in XCO2 of
~0.1%, for the footprints considered
Surface Elevation
40
0.4
35
0.35
30
0.3
25
0.25
20
0.2
15
0.15
10
0.1
Precision requirement for range
5
Relative Error in XCO2 (%)
Mean Elevation Standard Deviation (m)
CALIPSO 2008 - 2009
0.05
0
0
0
2
4
6
8
10
12
14
16
18
20
Footprint (km)
The mean standard deviation of the surface elevation as well as its variability generally increase as increasing the footprint size.
For a footprint size of 1 km, the mean elevation standard deviation is ~7 meter, which can potentially cause an error of ~0.07% in XCO2.
Thin Cloud Distributions
(a) Freq (0.0=< COD < 0.01)
(b) Freq (0.01< COD <=0.1)
0.3
0
0.2
-50
0.1
0
100
0.3
0
0.2
-50
0.1
0
-100
(d) Freq (OD = 0)
0
100
0.3
0
0.2
-50
0.1
Longitude
100
0
0.2
-50
0.1
-100
0
0
100
50
15
0
10
5
-50
-100
0
Longitude
100
0
0
(f) Cloud Top (0.01< COD <=0.3) [km]
20
Latitude
Latitude
50
0
0.3
(e) Cloud Top (0.01< COD <=0.1) [km]
0.4
-100
50
0
20
Latitude
-100
50
0.4
Latitude
50
(c) Freq (0.1< COD <=0.3)
0.4
Latitude
Latitude
0.4
50
15
0
10
5
-50
-100
0
100
0
Longitude
In general, the clear air condition does not occur frequently (<20%), except over high mountains or in the polar regions.
The occurrence frequency for 0.01 < COD < 0.1 which is below the detection limit of passive sensors is quite high especially over the tropics.
These thin clouds are generally high cirrus clouds.
These thin clouds can be undetected by passive sensors and hence bias the CO2 retrieval. These thin clouds have smaller impacts on CO2
measurements using lidars with ranging capability
Range-Resolved Lidar CO2 Measurement
MFLL CO2 Column Measurements Through Thin Cirrus
• MFLL is an IPDA lidar for CO2
measurement developed by NASA
Langley Research Center and Excelis
(see Dobler et al., AO 2013)
• Pseudo random modulation can
provide range info
• Allows retrievals in the presence of
tenuous clouds
Lin et al. Optics Express, 2015
Two Airborne IPDA Systems for CO2 Measurement
Multifunctional Fiber Laser Lidar (MFLL)
• Developed by Exelis in 2004, and Exelis/Langley
since 2005
• 14 Proof-of-concept and field campaigns
MFLL integrated on DC-8
ASCENDS CarbonHawk Experiment Simulator (ACES)
• Developed at Langley with support from Exelis
• Advancing key technologies for spaceborne
measurements of CO2 column mixing ratio
ACES integrated on HU-25
Summary
• The variability of the surface elevation in a footprint can cause errors in the CO2 measurement from space
• The presence of optically thin clouds can also impact the CO2 retrieval
• In the study presented in this talk, we examined the surface elevation variability and optical depth
distribution of optically thin clouds using the CALIPSO lidar measurement, to support evaluation of space
based CO2 measurements such as ASCENDS
‒ In general, the larger the footprint or the higher the elevation, the larger the variability of the surface
elevation
‒ A footprint of 1km, typical for passive sensors, can potentially cause a relative error of ~0.07%
‒ A lidar typically has a footprint of ~ 0.1km and therefore yields much smaller errors compared with
passive sensors
‒ Clear and clean air condition typically occurs over high mountains or high latitudes in the polar regions
and its occurrence frequency is small (<20%) over the other regions
‒ Optically thin cirrus clouds that can be missed by passive sensor detection can occur quite frequently in
the tropical and subtropical regions, which can cause biases in the passive CO2 retrieval, while for
lidars with ranging capability, accurate CO2 measurement is possible even in the presence of thin cirrus
clouds