Transcript ICTP_Lidar-02a_Antunax

“Contribution of the synergy of groundand space-borne remote sensing to
atmospheric research. "
Dr. Juan Carlos Antuña Marrero
Senior Researcher,
Grupo de Óptica Atmosférica de Camagüey (GOAC),
Instituto de Meteorología, Cuba
Winter College on Optics: Light: a bridge between Earth and Space
The Abdus Salam
International Centre for Theoretical Physics
February,16th 2015
Winter College on Optics
Light: a bridge between Earth and Space
The Abdus Salam International Centre for Theoretical Physics
Conference:
Contribution of the synergy of ground & spaceborne remote sensing to atmospheric research.
Summary
1. Deriving Backscattering to extinction conversion coefficients.
2. Synergism of Lidar and SAGE II aerosols measurements:
The Mt. Pinatubo Case.
3. Comparing Saharan dust measurements and simulations.
4. Lidar Networking
1. Deriving Backscattering
to extinction conversion
coefficients.
Mie Scattering Theory is the most general solution of the interaction
between light and particles problem.
Based in the solution of the Maxwell equation’s under imposed boundary
and skin conditions.
But the broad spectrum of conditions under which the interaction takes
place (size parameters and refractive index ranging between 0 and  and
1 and  respectively) make the system of equations practically insoluble.
Only particular cases have been solved analytically.
Two well known cases are :
- Single scattering by independent particles (Van de Hulst, 1957)
- Polydispersion scattering (Ddeirmendjian, 1969)
Single scattering by independent particles:
Each particle has its own scattering pattern, not
affected by neighbors particles. Also the incident
beam of light is considered parallel, that is, coming
from a distant source.
Combination of the solution for an individual
particle for a set of identical particles (named also
monodispersers) let to the solution of multiple
particles.
Polydispersion scattering:
A set of particles of the same composition but
different sizes (characterized by its particle size
distribution) is considered, leading to a more
general solution.
General solution for single scattering:
Expressions for the vertical profiles of extinction
(α) and backscattering (β) coefficients as well as
the particle surface area concentration (A) and
particle volume concentration (V) are:
α(z) =   r2 Qα(m, x) n(r,z) dr
[α] = km-1
β(z) =   r2 Qβ(m, x) n(r,z) dr
[β] = sr-1 km-1
A(z) = 4π  n(r,z) r2 dr
V(z)
Where: Qα(m,x) :
Qβ(m,x) :
n(r , z) :
m :
x :
λ :
= 4π/3  n(r,z) r3 dr
extinction efficiency factor
backscattering efficiency factor
particle size distribution
refractive index
size parameter = 2πr/λ
wavelength
Particular Case: Stratospheric Aerosols
Calculations of Qα(m,x) and Qβ(m,x) require:
• the refractive index [m]
• the particle size distribution [n(r)]
m could be calculated combining the values of the
refractive index for H2SO4 - H2O mixtures at
surface temperature (Palmer and Williams, 1975)
with the Lorentz-Lorentz temperature dependence
(Steele and Hamill, 1981). Temperature profiles
are required Assumptions should be made about
the H2SO4 - H2O mixture concentration.
n(r) could be assumed or empirical values from in
situ measurements could be used
Refractive index:
Ratio between the light speed in the two medium
(Geometrical optics).
Theory of molecular optics explains it based in the
scattering properties.
If the distance between particles is small compared to
the , the scattering by the particles can be
characterized using its polarizability.
Then the Lorentz-Lorentz formula is obtained:
n
n
2
2
1

2
( T )
M
where :
: molar refractivity of the
substance
M: molecular weight
 : density at the temperature T
 is practically independent of T and  at the range 190 260 K, it is possible to solve for  at a predetermined T
at which n is well known [ case of Palmer and William data
for T = 300 K]
It conducts to the expression: (Steele and Hamill, 1981)
2 A T   1 1 / 2

n (T ) 
1  2 A T 
2
Where:
n (300)  1
A 



2




n (300)  2  (300)
Stele and Hamill, 1981
Several approaches to infer backscattering to extinction
conversion coefficients:
1. Sun photometer measurements [Russell et al., 1993]
2. particle measurements aboard the ER-2 aircraft
[Brock et al., 1993]
3. SAGE II satellite extinction profiles [Thomason and
Osborn, 1992]
4. Multi wavelength lidar systems [Ansmann et al., 1998]
5. Particle size and concentration data from balloon
flights
[Jäger and Hofmann, 1991] [Jäger and Deshler, 2002]
Converting extinction & backscattering coefficients
between wavelengths:
[Jäger and Hofmann, 1991]
[Jäger and Deshler, 2002]
Period 1979 - 1990
Period 1991 – 1999
Method to convert particle backscatter measurements,
at the widely used lidar wavelength of 532 nm, to
particle extinction, and particle mass and surface area
wavelength dependences of backscatter and extinction in
the range 355 to 1064 nm are offered, comprising the
classical lidar wavelengths of conventional Nd:YAG and
ruby lasers.
The method utilizes particle size and concentration data
from balloon flights over Laramie, Wyoming at 41ºN
Particle
Size
Distribution
wavelength exponents of particle backscatter (kb)
B2  B1 2 / 1 
b
wavelength exponents of particle extinction (ke)
E2  E1 2 / 1 
e
Wavelength
Exponents:
Backscatter
Wavelength
Exponents:
Extinction
2. Synergism of Lidar
and SAGE II aerosols
measurements:
The Mt. Pinatubo Case
Synergy of Optical Instruments:
The Pinatubo case
Mount Pinatubo Eruption
June 12, 1991
The most intense of the
20th Century
Injected 20 Mt of SO2
into the stratosphere
Better documented ever
But still gaps in datasets
SAGEII
II
SAGE
Instruments operational at
the time of the
Mt. Pinatubo
Eruption
·
Aerosol Measurements:
vertical profiles of aerosol extinction every 0.5 km
from the surface to 40 km
at four wavelengths, 0.386, 0.452, 0.525, and 1.020
m
Main orbiting parameters:
Non-sun synchronous orbit
Altitude : 650 km
Inclination : 57º
Nodal period : 96.8 minutes
Also profiles of:
ozone (O3) at 0.6 m
nitrogen dioxide (NO2) 0.453 and 0.448 m
water vapor at 0.94 m.
SAGE II : Stratospheric Aerosol and Gas Experiment
On Board ERBS October 1984
Designed for 2 years, it lasted
LIDAR : LIght Detection And Ranging
25 km
SAGE II Derived Aerosol Surface Area
(Thomason, 1998)
“GAPS” in time and space are normally filled
by interpolation
Lidar measurements of the S. A. from the
Mt. Pinatubo broadly used for studying its
local features,
20 km
But never had been used for filling the
SAGE II gaps. No quantitative
comparisons for the whole post-Pinatubo
period held.
No information about the magnitudes:
15 km
• of the differences between both
instruments.
•of the daily variability of the S. A.
To explain how volcanic eruptions could cause
climatic effects, we need to know precisely the amount
of aerosols, their physical properties, and their
evolution both in space and time.
Satellites provide better geographical coverage than
any ground-based instruments. SAGE II provided the best
coverage of all the satellite instruments for the Pinatubo
eruption. But…...
It has spotty spatial and temporal sampling.
It cannot observe in regions of dense aerosols.
Lidar measurements, being active and vertically
pointing, provide vertical profiles with a greater vertical
resolution than satellite limb measurements. Time series
of lidar measurements are only constrained by weather
conditions (cloud free sky). But…. They only have local
coverage.
Synergy between AOD
Measured at Mauna Loa
by:
SAGE II
Lidar
Sun-photometer
Evaluating the results of filling
gaps in the SAGE II vertical
aerosol extinction profiles
using lidar measurements in
the core of the Mt Pinatubo
aerosol cloud.
The results using the lidar filling method show better agreement with the
available lidar profiles (and with the sunphotometer data at Mauna Loa)
than the ones filled with downward extrapolation, and produce AOD time
series that are less variable in time.
Evaluating the aerosol natural variability for the Mt Pinatubo aerosol
cloud using both lidar and SAGE II datasets.
Lidar Measurements
SAGE II Measurements
For the first time ever, magnitudes of the aerosol variability for the 1991
Mount Pinatubo stratospheric cloud have been determined. The variability
reaches values between 50 and 150% of the absolute percent differences,
for time lapses of 12 to 48 hours, at the core of the cloud.
Subvisible cirrus clouds radiative effects
Using GFDL Column
Radiative
Transfer Code
PhD Dissertation (2010):
Barja, B., (2009). Characterizing
subvisible cirrus cloud in the Wider
Caribbean and its effects on solar
radiation. (In Spanish).
Adapting the GFDL Radiative Transfer Code:
to the resources available at GOAC
to the local atmospheric conditions
validation with local radiation data clear sky (CS)
Using cirrus lidar measurements taken at Camagüey:
• derive cirrus extinction from lidar backscattering
• parameterize lidar profiles for feeding RT code
Running RT code under CS and in presence of cirrus
a) Measured extinction profile.
a)
b)
b) Parameterized optical depth
profile.
c) HR for homogeneous and nonhomogeneous cloud.
d) Fluxes for homogeneous and nonhomogeneous cloud.
c)
d)
Diurnal cycle
Heating Rate
and
Radiative
Forcing
A) Heating Rate
B) Radiative Forcing
August 11th 1998
Optical Depth 2.74,
Base: 9.39 km ( 320 hPa)
Top: 15.24 km (120 hPa)
“Mt Pinatubo stratospheric aerosol radiative effects”
PhD Dissertation (2010):
Estevan, Rene (2009). Radiative effects
of the Mt. Pinatubo aerosols over the
Wider Caribbean, (In Spanish).
Using GFDL Column
Radiative Transfer Code
Severe & catastrophic reductions of solar
radiation possible by 3 different events:
•Impact of an extraterrestrial object on the earth
surface [Chapman, 2004; McGuire, 2006]
•Nuclear War [Sagan, 1985; Robock, 1989; Robock
et al., 2006]
•Volcanic Eruptions [McGuire, 2006]
Time (months) from the eruption to recover to 75 %
of the solar radiation levels before the eruption.
Magnitude
10x
50x
100x
150x
200x
Months
19
23
28
32
34
x : magnitude of the Mount Pinatubo eruption
3. Comparing Saharan dust
measurements & simulations
Saharan dust
AOD (532 nm) CALIOP
Summer
AOD (550 nm) MODIS
Summer
Winter
Winter
GEOS-Chem (Goddard Earth Observing
System –Chem) model
Generalized Retrieval of Aerosol and
Surface Properties (GRASP)
Unified algorithm
based on the synergy
of atmospheric
properties gathered
from a variety
of remote sensing
observations.
(Under development)
http://www.youtube.com/watch?v=PcDeqwDF15A
4. Lidar Networking
Lidar Networks
• EARLINET: European Aerosol Research Lidar Network
http://www.earlinet.org/
• CIS-LiNET: Community of Independent States Lidar
Network
http://www.cis-linet.basnet.by/
• AD-NET: Asian Dust and aerosol researchers Network
http://www-lidar.nies.go.jp/AD-Net/
• REALM: Regional East Atmospheric Lidar Mesonet
http://alg.umbc.edu/REALM/
• MPLNET: Micro Pulse Lidar NETwork (GSFC, NASA)
http://mplnet.gsfc.nasa.gov/
• LALINET: Latin American Lidar Network
http://lalinet.org/
Courtesy Dr. Anatoli
Chaikovsky
CREST: Center for Remote Sensing Science and TechnologyCourtesy Prof.
Raymond Hoff
GAW: Global Atmospheric
Watch
•WMO/GAW was established 1989
•GAW focuses on global networks for GHGs, ozone, UV,
aerosols, selected reactive gases, and precipitation
chemistry
•GAW partnership with contributors from 80 countries
•GAW coordinated by Environ. Division of WMO/AREP
•GAW coordinates activities and data from 24 Global, ~200
Regional, and ~many Contributing stations
http://www.wmo.int/pages/prog/arep/gaw/gaw_home_en.html
Extracts from new GAW Strategic Plan
Goals
•Establishing a GAW aerosol lidar network in cooperation
with existing networks and interested research groups.
Products and Services
•Easily accessible data.
•Climatology of GAW aerosol variables.
•Calibration and comparison of aerosol instruments.
•Standard operating procedures for aerosol instruments.
•Global coordination of aerosol optical depth and aerosol
profiling networks.
Future Products and Services
•Near real-time data (selected variables) for assimilation
and verification of numerical weather and air quality
forecast models.
Lidars and models
Lidar data complementary to in-situ AOD
Lidar data provide vertical aerosol structure:
dust storms
biomass burning
pollution
volcanic ash
Lidars particular importance for aerosol models use for:
climate assessments
model validation
probable first ingredient to be incorporated in
operational chemical weather models
Models assisting lidars
EARLINET already implemented dust predictions
warning lidar operators on incoming dust
GALION
GAW Aerosol Lidar Observation Network
Objective:
Long-term monitoring program on a global scale of aerosol
vertical distribution combining simultaneous different types of
data from lidar instruments
Work plan:
• GALION will be based on an agreed cooperation between
existing aerosol lidar networks and individual lidar stations.
• GALION will support and coordinate global coverage by
ground-based aerosol lidar through the implementation of
standardized instruments at selected observatories in
cooperation with experienced research groups.
Muchas Gracias.