INTERPRETATION OF MICROWAVE REMOTE SENSING OBSERVATIONS OF VOLCANIC ASH USING THE ATHAM SIMULATIONS OF THE ERUPTING PLUMES.

M. Montopoli (1,2), M. Herzog (1), F. S. Marzano (3,2), Domenico Cimini (4,2) M. Lamantea(3), Gianfranco Vulpiani(5) and H. Graf (1)

(1) Dept. of Geography, University of Cambridge. Downing Place CB2 3EN, Cambridge (UK), Email: mm911@cam.ac.uk

(2) CETEMPS, University of L’Aquila, L’Aquila. Via Vetoio 1, 67100 L’Aquila (IT) (3)DIET, Sapienza University of Rome, Rome. Via Eudossiana, 18, 00184, Roma (IT)

(4) IMAA – CNR, Potenza, Italy. C.da S. Loja - Zona Industriale, 85050, Tito scalo, Potenza (IT). (5) Dept. Of Civil Protection, Roma, Italy. Via Vitorchiano 4, 00189 Roma

ABSTRACT

This work analyses the potential features of satellite microwaves sensors to provide quantitative information of near source parameters during an eruptive event. To this aim weather ground radar observations and model simulations are used. The model outputs are based on the volcanic plume simulations (ATHAM) coupled with and Mie routines (SDSU). Comparison between models and observations seems to justify the good agreement found between active ground radar data and satellite passive observations of Grimsvotn eruption in May 2011.

INTRODUCTION

Threats due to volcanic eruptions can be mitigated thanks to a plenty of observational and modeling tools nowadays available. The synergy between remote sensing observations and physical models is probably one of the best ways to fulfill the top level requirements imposed by the civil and scientific communities. These requirements can be summarized into: i) issue timely warnings, ii) monitor the ash plume during its evolution and iii) quantitatively estimate tephra (fragmented material produced by a volcanic eruption). An important aspect to highlight when dealing with ash monitoring is the temporal and spatial scale on which focus the attention. In this respect, can be convenient discriminate among tools able to provide timeliness data during the activity to those that provides information before and after an eruptive event. Thus, the monitoring of geochemical and geodetic precursor signatures have to be distinguished with respect to the real time observations, like those from infrared or microwave sensors either ground or satellite based. When observing close to the volcano vent, observations can be used to estimate near source parameters of eruptions, like plume height, tephra eruption rate and its mass. These

retrievals are fundamental as input information for dispersion models able to quantitatively predict areas likely to be contaminated by given levels of ash concentrations. Remote sensing measurements of tephra can provide not only useful inputs for ash dispersal model initialization, but they can be also used as target reference for model validation purposes. Moreover, volcanic plume models can provide the physical basis to build estimators of ash cloud parameters from remote sensors.

In this study the signatures due to volcanic plumes at microwave wavelengths are preliminarily investigated within a self-consistent simulation framework. The tools used to purse this aim are the ATHAM model coupled with the SDSU simulator. ATHAM stands for Active Tracer High-Resolution Atmospheric Model and it is used as generator of virtual scenarios of volcanic plumes. SDSU is a Satellite Data Simulator Unit, which include Mie routines to simulate the active and passive sensors from microwaves to infrared wavelengths. Section 2 describes the analyzed active and passive measurements; section 3 gives a brief description of the ATHAM model and its output whereas in section 4, the SDSU simulator is introduced. Eventually in section 5 the results are shown before drawing some conclusion in section 6.

GRIMSVOTN ERUPTION OBSERVATIONS

The Grímsvötn eruption has been observed, among others, by a ground based C band weather radar (5.6GHz) located in Keflavik at 260 km far from the volcano vent and from the Special Sensor Microwave Imager/Sounder (SSMIS) aboard the LEO DMSP (United States Air Force Defense Meteorological Satellite Program) platform. The latter is a conically scanning passive microwave radiometer with a swath of about 1700 km and operating at several channels from

about 19 GHz to 189 GHz. For the study of ash we found particularly interesting 7 channels at frequencies and along track filed of views (FOVs), [GHz]/[km] respectively, as follows: [19.35]/[74], [37.0]/[45], [91.6]/[16], [150.0]/[16], [183±1] /[16], [183±3] /[16], [183±6] /[16]. Figure 1 shows an example of temporal collocated SSMIS and radar acquisitions in terms of brightness temperature at horizontal polarization, BTH, in [K] at 183±1 GHz and radar reflectivity factor, Z, in [dBZ] (upper panels). Lower panels of the same figure show the comparison of SSMIS BTHs and the radar derived Total Columnar Content in [kg/m2]. The latter is the result of the VARR inversion technique [8], [9]. The frequencies used for the comparison are those at 91 GHz and 150 GHz (left lower panel) and 186±1 GHz 186±6 GHz (lower right panel). As can be deducted from fig.1 the absolute value of the correlation is quite good varying from -0.37 to -0.63 at 91 GHz and 186±1 GHz, respectively. Table 1 lists the parameters of the linear regressions shown in figure 1. A tentative to explain the BTHs vs TCC behavior, at the considered frequencies, is done in section 4 using an ATHAM-SDSU simulated scenario.

ATHAM VOLCANIC PLUME SIMULATIONS

ATHAM is a non-hydrostatic model with applications across a broad range of atmospheric problems. In the volcanic configuration it has been used to study both the effect of large volcanic eruptions on stratospheric chemistry and the influence of physical processes on plume development [1]. It is designed to simulate the large-scale atmospheric flow and development of a volcanic plume with a given forcing as a lower boundary condition. Differently from many atmospheric models ATHAM uses the concept of active tracers meaning that particles or hydrometeors in the plume can occur in any concentration directly influencing the dynamics through the equation of state. In this study eight incompressible tracers are used. Hydrometeors are split into cloud-based and precipitation, including both liquid and solid phases giving cloud water, cloud ice, rain and graupel. Two classes of gases, water vapor and sulfure dioxide, are foreseen. The dimensional spectrum of tephra particles is represented by two classes designated small (SL) and large lapilli (LL). Figure 2 shows an example of the 2D ATHAM output simulation, taken as reference scenario in terms of species concentration, vertical wind and temperature. The simulation in figure 1 refers to the Grímsvötn eruption in May 2011 in Iceland. It is obtained subjectively setting up, as far as possible, the ATHAM input parameters for reproducing the Grímsvötn event. The nearest radiosounding in Keflavík has been also considered as input. For the analyzed simulations no crosswind effects are taken into account while the

terrain height is modeled through Gaussian horizontal profiles. After a resampling procedure, the vertical and horizontal resolutions of the 2D outputs of ATHAM, are equal to 0.5 and 0.1 km, respectively.

Figure 1. Upper left panel: example of SSMIS acquisition [K] at 183±1 GHz at 8.34UTC. Upper right panel: 5.6 GHz radar vertical cut [dBZ] above the volcano vent. Lower panels: comparison between SSMIS and radar retrievals (see text for details).

BTHlnd =a(TCC)+b,

TCC [kg/m2]; BTHlnd [K]

Freq. [GHz]

a [K�m2/kg]

b [K]

91 -1.5 210

150 -2.5 225

183±1 -1.9 239

183±6 -3.9 249

Table 1. Regression coefficients of the curves shown in the lower panels of figure 1.

Figure 2. Example of an ATHAM simulation of Grímsvötn eruption in Iceland May 2011 in terms of Total concentration [g/m3], Temperature [K], Ice concentration [g/m3] and vertical wind speed [m/s].

SDSU SIMULATOR

SDSU is a Satellite Data Simulator Unit [2]. It is usually used as a forward model tool in the processes of constructing and testing performances of satellite algorithms for meteorological applications. SDSU includes Mie routines to simulate radiances and backscattered electromagnetic echoes from passive radiometers and active radars due to spherical particles. To get benefits derived from SDSU-Meteo potential, some adaptations are needed to be able to manage SDSU-Ash applications. In its default configuration SDSU foresees 6 microphysical species: graupel, hail, rain, cloud water, snow and cloud ice. They are described in terms of vertical profiles of concentrations (Cp) [g/m3] of the respective microphysical species “p”, complex refractive index (np(λ)=np’-jnp”) and Particle Size Distributions (PSD), Np(D), of spherical particles of diameter D.

To adapt SDSU to ingest volcanic plumes, the values of Cp profiles, with the p=snow and p=hail in SDSU, are substituted with profiles of ash from ATHAM. This also implies that nSL(λ) and nLL(λ) are adapted accordingly. In this respect, it has been assumed that nSL(λ) = nLL(λ)=nASH(λ) and its value is 2.48-j0.016 at the considered SSMI frequencies [3]-[5]. For what concerns the ash particle size distributions Nash(D), a Gamma size distribution has been assumed as follows :

Np (D) = Nnp6 ⋅ (3.67+µ)µ+4

3.674Γ(µ + 4)

#

$%

&

'(DDnp

#

$%%

&

'((

µ

exp −(3.67+µ) DDnp

#

$%%

&

'((

*

+,,

-

.//

(1)

where Nnp, Dnp and µ are the intercept parameter,

volume-weighted median diameter and the shape parameter, respectively expressed in [m-4], [mm] and [-]. As a result, in SDSU the PSD Np(D) is expressed in [m-4]. While Dnp and µ are fixed, Nnp is estimated from the knowledge of concentrations Cp and density (ρp) in [kg/m3].

Nnp =Cp10

−3

πρ p

3.67Dnp10

−3

"

#$$

%

&''

4

(2)

Due also to the near source sampling of the available measurements, as evident in figure 1 as well, large and small lapilli gamma PSDs classes have been assumed. They are used to describe in SDSU, the SL and LL ash classes from ATHAM outputs. The LL and SL gamma PSDs parameters (Dnp and µ) are (5 mm, 2) and (1.5 mm, 1.6) respectively with ρLL=1000 kg/m3 and ρSL=1200 kg/m3.

COUPLED ATHAM-SDSU SIMULATIONS

In this section the signatures due to a volcanic plume, as would be seen from a space-borne microwave

radiometer, are shown. The instrument taken as reference is SSMIS introduced in section 2. To perform the SSMI simulations we considered the ATHAM output shown in figure 2. Then, we assumed an SSMIS angle of observation between the nadir and the antenna pointing of 45 deg.

Figure 3 shows the microwave passive Brightness Temperature [K] in Horizonatal polarization over land (BTHlnd) when the FOV intercepts the volcanic plume shown in figure 1 upper left panel. To facilitate the reader in figure 3 the Total Columnar Content (TCC) [kg/m2] is reported as well (grey color). It gives a visual help in correlating the BTH signal and the plume integrals.

Figure 3. Example of a SDSU simulation referred to SSMIS brightness temperatures for various SSMIS channels. Grey curve represents Total Columnar Content [kg/m2] of the simulated volcanic plume.

What is noted is the increasing depression of BTHlnd as the center of the volcano vent is approached. This phenomenon has been already experimentally observed in [6] and it is mostly explained by volume scattering effects of ash and ice particles, which cause the extinction of the upwelling radiation coming from the ground. Due to the larger dynamic of BTHlnd to the volcanic plume, shown at the frequencies above 90 GHz, the BTHlnd at 91.6 GHz, 150.0 GHz, 183±1 GHz, and 183±6 GHz has been related to the TCC. This is shown in panels a)-d) of figure 4 where four values of terrain emissivity (e) have been fixed to e=0.55, e=0.70, e=0.80 e=0.90. In this figure the grey crosses represent the extremes of regression lines in figure 1 (lower panels) derived from observations. As we can see the effect of the terrain emissivity is relevant at 91 GHz and 150 GHz whereas they are less influent close to the water vapour absorption peak (i.e.183 GHz). The portions of the BTHlnd vs. TCC curves marked as green, close to values of TCC=0 refers to the side portions of the ATHAM simulation (upper left panel, figure 2) out off the volcanic plume. In this area the absorption of water vapour (and other gas) for a given terrain

emissivity, drives the BTHlnd response. Excluding these points from the analysis, the linear regression coefficients between BTHlnd vs. TCC have been calculated and listed in table 2. The comparison of these values with those derived from the SSMIS-Radar observations in table 1, gives a good agreement when e is about 0.7 (red curves). A terrain emissivity of 0.7 is compatible with an ice/snow cover as expected in Iceland even in May. The insensitivity of 183±1 GHz to e is due to the water vapour absorption that masks the contribution from the ground. In the whole, figure 4 and tables 1-2, show a good agreement between measurement retrievals and model simulations especially if the slope the curves are compared.

Fig. 4. Panels a)-d): Brightness temperatures at SSMI/S microwave frequencies (91, 150, 183±1, 183±6 GHz) and volcanic plume Total Columnar Content [kg/m2], for various terrain emissivity.

CONCLUSIONS

In this work a preliminary analysis of the volcanic plume simulations as seen by passive microwave satellite sensor, has been carried out. ATHAM volcanic plume model and SDSU sensor simulator have been numerically coupled. Some adaptations have been required for SDSU to ingest volcanic plumes instead of meteorological clouds. A good correlation with the total columnar content has been found both in terms of observed and simulated BTHs. This potentially opens a new scenario for quantitative estimates of near source ash plume from microwave sensors. Future developments will be devoted to consider a set of ATHAM simulations instead of a single realization to consolidate the results shown and to understand under which conditions microwave sensors are less sensitive to volcanic plumes and their synergy with IR radiometric measurements.

ACKNOWLEDGMENT

We are grateful to EC for his support under the Marie

Curie Fellowship within the call FP7-POPLE-2010-IEF, Grant number: 273666. A special thanks is given to Ing. P. Pagliara of the Italian Dept. of Civil protection and Dr. Sigrún Karlsdóttir and Dr. Bolli Palmason of the Iceland Meteorological Office for providing and assisting us in reading the C band radar data.

BTHlnd =a(TCC)+b, TCC [kg/m2]; BTHlnd [K]

Freq. [GHz]

e=0.55

a / b

e=0.70

a / b

e=0.8

a / b

e=0.9

a / b 91 0.3 / 186 -1.7 / 205 -3.0 / 218 4.4 / 231

150 -0.4 / 201 -2.1 / 218 -3.2 / 229 -4.3 / 239

183±1 -2.3 / 261 -2.3 / 261 -2.3 / 261 -2.3 / 261

183±6 -4.7 / 249 -4.9 / 251 -5.0 / 252 -5.7 / 253

Table 2: linear regression parameters derived from figure 4 to be directly compared with those in tab. 1.

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