Ground-based infrared retrievals of atmospheric dust properties over Niamey, Niger

A case study: dust storm event (7-10 March 2006)*

ATMS 790 R- Graduate Seminar Spring 2014

PresenterNarayan Adhikari

Physics, UNR

*Turner, D. D. (2008), Ground-based infrared retrievals of optical depth, effective radius, and

composition of airborne mineral dust above the Sahel, J. Geophys. Res., 113, D00E03.

Observation site

Niamey is a part of the Sahara Desert which frequently experiences the world’s largest dust storms.

S A H A R A D E S E R T

Niamey, Niger(2.08°E, 13.48°N)

An example of a dust-storm in the Sahara Desert

Strong heating of the ground produces convective currents which carry dust over large distances.

Saharan dust under a microscope (Observed at Cosmic Dust Lab.)

Saharan dust storm reaching Niamey area in March 2006 (Recorded by ARM Mobile Facility-visibility was reduced by 15% of normal)

Key words Radiation Infrared (IR) : 0.7µm -1mm; Thermal IR: 4 µm – 100 µm; Visible: 0.4 – 0.7 µm

wavenumber vs. wavelength: wavenumber (cm-1)=104/wavelength (µm)

Dust retrievals » dust properties Optical: Optical Depth (OD; τ)-measure of radiation extinction by dust, in our case Physical: effective radius (re) –mean radius of the size distribution of dust particles Chemical: composition of “radiatively significant” minerals such as kaolinite (Al2Si2O5(OH)4) , gypsum (CaSO4·2H2O) and quartz (SiO2)

Instrument AERI: Atmospheric Emitted Radiance Interferometer-operates in the thermal IR (4-20 µm) MFRSR (Multi-Filter Rotating Shadowband Radiometer ), and Cimel sunphotometer - both operates in visible wavelengths

Algorithms MIXCRA: MIXed-phase Cloud Retrieval Algorithm- uses an optimal estimation approach to retrieve dust properties LBLDIS: Line-by-line radiative transfer discrete ordinate solver- the heart of MIXCRA which simulates the AERI-observed spectra

Motivation1. Atmospheric (mineral ) dust composes a significant fraction of the atmospheric

aerosol.

2. The aerosol forcing in the infrared (IR) is generally not included in many climate models, even though atmospheric dust can have a significant impact on the IR radiative fluxes at the surface and top of the atmosphere [Vogelmann et al., 2003].

3. The total radiative forcing due to dust depends on several factors such as the size distribution, optical depth , surface albedo etc. of the dust particles [Tegen et al., 1996].

4. Knowledge of the mineral dust composition is important for properly characterizing the radiative impact of the dust and for the validation of aerosol transport models.

5. Unlike the most common ground-based aerosol remote sensors such as Cimel sunphotometer and MFRSR , the IR retrieval technique can provide the dust properties throughout day and night.

Introduction- what have been studied

• The optical depth, effective radius and mineral composition of the dust over Niamey were retrieved using AERI-observed data followed by the MIXCRA-retrieval algorithm .

• Only three minerals kaolinite (K) , gypsum (G) and quartz (Q) were used in the retrieval process since

- these are the main constituents in the atmospheric dust found in African desert*

- they have distinctly different absorption bands in the thermal IR, and produce unique radiance spectra in that spectral region.

• The mineral combination that yielded the best fit to an AERI-observation, was identified as the ‘‘radiatively significant’’ composition of the dust for that observation.

• Retrievals were compared with the observations from the sunphotometers .

*Sokolik, I. N., and O. B. Toon (1999), Incorporation of mineralogical composition into models of the radiative properties of mineral aerosol from UV to IR wavelengths, J. Geophys. Res., 104(D8), 9423 –9444,doi:10.1029/1998JD200048.

Instrumentation

AERI FTIR Instrument

RadiosondeMFRSR CIMEL Sunphotometer

Black bodies

Interferometer

Detector

Wait, all of these instruments, except radiosonde, are available in our atmospheric lab !

FTIR (Fourier Transform Infrared ) spectrometer

Hot BBMirror

Cold BB

Sample spectra measured with FTIR (black)and modeled (red)

The FTIR spectrometer outputs the radiance of the thermal IR radiation emitted by the atmosphere.

FTIR spectrometer

MIXCRA-retrieval algorithm

MIXCRA was run for 6 separate times for each cloud-free AERI observation- three times in single-mineral mode (only K, only G & only Q) and three times in dual-mineral mode (K+G, K+Q, G+Q) , and the mode that yielded the best statistical fit for that observation was determined as the best retrieval.

Mineral parameters(τ1 ,τ2 , re1, re2 )

LBLDIS calculations

Scattering propertiesusing sphere (Mie theory)

Refractive indices of the minerals (mineral composition)

Atmospheric state from radiosonde (T, P, H, RH etc.)

Simulated radiance spectra

RMS difference betweenobservation and simulation

Save the best fitted spectra, and mineral parameters and composition

Fig. Schematic diagram of MIXCRA

Radiative properties of dust minerals

Simulated downwelling radiances for four different cases with AOD=1.0 at 11.0 µm (AOD=6 at 0.5 µm), re=1.4 µm & aerosol layer from surface to 1 km. 1 RU = 1 mW /( m2 sr cm-1)

The refractive indices of these three minerals result in unique infrared radiance spectra such as quartz (780 cm-1), kaolinite (920 cm-1) and gypsum (1120 cm-1), which allows us to determine the ‘‘radiatively significant’’ mineral composition from the AERI data.

QK

G

Q

KQ

Results: radiative effect of mineral dust in the thermal IR region

For AOD = 1.0, the differences in the downwelling LW flux at surface is about 80 W/m2, while the differences in the upwelling flux at TOA approach 20 W/m2 , with respect to AOD=0.0 i.e., clear sky.

Fig. Sensitivity of the longwave radiative fluxes to mineral dust composition and 11-µm AOD with re=1.5 μm

Question:Will the dust produce warming, or cooling effect at the surface in the thermal IR region ?

Some examples of AERI radiance observations and LBLDIS calculations using both the MIXCRA-retrieved dust properties (red) and assuming no aerosol (gray) : τ1 and τ2 are 11-µm AOD of kaolinite and gypsum.

Results: quality of fit with dust retrievals

The mineral dust properties were retrieved from the cloud-free AERI data during dust- storm event from 7-10 March 2006.

In each case, kaolinite+gypsum was retrieved as the best mineral composition of the dust.

Spectral sensitivity to aerosol size distribution

The aerosol size distribution is typically bimodal , with a fine and coarse mode.

The thermal IR (4-50 µm) radiations are primarily sensitive to only the coarse mode.

Size parameters are indicated along horizontal bars.

λ

r2πparametersize

r is the aerosol radius in μm

Results: comparison among various measurements

μm1.02

μm11.0logμm1.02atAODμm11.0atAOD

Evolution of total AOD(blue) and effective radius in µm (red) of the kaolinite component during dust storm in March 2006, derived from MIXCRA retrievals.

Conclusions

• The dust’s properties (optical depth, effective radius , and mineral composition) can be investigated, throughout the day and night, from the emission signature in the thermal IR, using AERI-based IR retrievals. Keep in mind that most ground-based aerosol remote sensors operate in the visible and near IR (<1.05 µm), and do not provide any information at night.

• From a radiatively significant point of view, the mineral composition (Kaolinite+ gypsum) was retrieved as the most common minerals (69%) and quartz as a relatively insignificant mineral (<10 %) in the dust above Niamey.

• Limitation: This technique cannot sense some of the important mineral components such as hematite (Fe2O3) , which

-do not have any absorption bands in the thermal IR radiation -have similar absorption bands in the thermal IR.

• Remedy : To fully characterize the dust properties , IR retrievals must be combined with the shortwave retrievals such as MFRSR.

Finally…

Questions, comments, and feedback are welcome.

Thank You !