Search

In this work we present the main characteristics that derive from the analysis of the luminous part of the trajectory of a meteoroid. Our study is based on applying the α-β algorithm (Gritsevich 2008, Gritsevich 2009, Gritsevich et al. 2012, Lyytinen and Gritsevich 2016, Sansom et al. 2019, Boaca et al. 2022) to the latest detections recorded by the MOROI (Nedelcu et al. 2018) component of the FRIPON network (Colas et al. 2020). Our input parameter are the height and velocity of the meteoroid and the way they change with time. Based on this we determine the ballistic coefficient α and the mass loss parameter β for each analysed meteor event. The α-β algorithm is used in order to decide which of the fireball events produce meteorites and which are fully ablating in the atmosphere. Furthermore, the α and β parameters allow us to determine the mass of the studied meteoroids e.g. at the beginning and at the end of the luminous trajectory.Boaca I., et al. (2022), ApJ, 936, 150.Colas, F., et al. (2020), A&A, 644, A53.Gritsevich, M. I. (2008), DokPh, 53, 97.Gritsevich, M. I. (2009), AdSpR, 44, 323.Gritsevich, M. I., et al., (2012), CosRe, 50, 56.Lyytinen, E., & Gritsevich, M. (2016), P&SS, 120, 35.Nedelcu, D. A., et al. (2018), RoAJ, 28, 57.Sansom, E. K., et al. (2019), ApJ, 885, 115.Acknowledgement.The work of IB and AN was partially supported by a grant of the Ministry of National Education and Scientific Research, PNIII-P2-1214/25.10.2021, program no. 36SOL/2021. MG acknowledges the Academy of Finland project no. 325806 (PlanetS).

Meteor astronomy employs the atmosphere of the Earth as a large area detector for 0.01-1000 mm meteoroids [1]. Monitoring the atmospheres of other planets for meteor activity offers the opportunity to study the parent bodies of as-yet-undetected meteor showers, test ablation models under non-terrestrial conditions and allow spacecraft operators to mitigate the risk of meteoroid impact damage [2]. By adjusting existing techniques to simulate meteoroid ablation in a Venus-like atmosphere [3-8], we show that Venusian meteors are generally brighter but shorter-lived than terrestrial meteors and ablate at a higher altitude, in a predominantly clear region of the atmosphere. These simulations are complemented with a list of cometary bodies and known meteoroid streams that we consider to be prime candidates for producing significant meteor activity at Venus [9,10]. Such predictions may be used in developing future observational campaigns to be carried out from Earth or from Venus orbit.References: [1] Jenniskens, P. (2006) Meteor Showers and their Parent Comets, Cambridge University Press, Cambridge. [2] Christou A. A. et al (2019) In: Meteoroids: Sources of Meteors on Earth and Beyond, Cambridge University Press, p.119-135. [3] Christou A. A. (2004) Icarus 168, 23-33 [4] McAuliffe, J. P., Christou, A. A. (2006) Icarus 180, 8-22 [5] Gritsevich M., Koschny D. (2011) Icarus 212, 877-884 [6] Bouquet A. et al (2012) Planet. Space Sci. 103, 238-249 [7] Gritsevich, M. I. (2009) Adv. Space Res. 44, 323–334 [8] Lyytinen, E., Gritsevich, M. (2016) Planet. Space Sci. 120, 35-42 [9] Christou A. A. (2010) MNRAS 402, 2759-2770 [10] Christou, A. A., Vaubaillon, J. (2011) In: Proc. Meteoroids Conf, NASA/CP-2011-216469, p.2

In the recent paper [1] we introduced the Dust Trail kit model capable of describing the evolution of a cometary dust trail. The model accounts for solar radiation pressure effects, gravitational disturbance caused by Venus, Earth and Moon, Mars, Jupiter and Saturn, and gravitational interaction of the particles in the trail with the parent comet itself. Excellent accuracy of computations is due to their implementation in Orekit Open Source Library for Operational Flight Dynamics, which executes Dormad-Prince numerical integration methods with higher precision. We demonstrate performance of the model by studying the comet 17P/Holmes, which underwent through a massive outburst in October 2007 — the largest documented outburst by a comet thus far. We simulate several particle populations with sizes ranging from 0.001 to 1 mm and by varying assumptions about ejection speed distribution of the particles at the start of the outburst. The model is validated against our earlier observations of the trail obtained in common nodes for 0.5 and 1 revolutions. Using these data, we made predictions for the two-revolution dust trail behavior near the outburst point and the observability from Earth of the cometary material released in the event [1]. We have further developed a set of Python scripts to calculate position of the dust trail for observatory topographical location coordinates [2]. Using these predictions, a set of new observations of the 2007 outburst dust trail was obtained in February, March, October, and December 2022. The trail is still observable by using even moderate ground-based telescopes. The existence of an observable dust trail requires sunlight scattered by a significant number of micron-sized particles produced in the phenomena. Both the surface brightness and the position of the dust trail are within the limits of the published predictions provided by the Dust Trail kit model [1, 2]. References1. Gritsevich M., Nissinen M., Oksanen A., Suomela J., Silber E.A. (2022). Evolution of the dust trail of comet 17P/Holmes, Monthly Notices of the Royal Astronomical Society, 513(2), 2201–2214, https://doi.org/10.1093/mnras/stac8222. Nissinen M., Gritsevich M. (2022). Instructions on where and how to observe the comet 17P/Holmes dust trail. Zenodo. https://doi.org/10.5281/zenodo.6977358

1. IntroductionThe Fireball Recovery and Inter Planetary Observation Network (FRIPON) network [1] uses all-sky cameras in order to detect fireballs. The FRIPON network comprises over 150 cameras installed all over Europe [1]. In this work we focus on the results obtained by the Meteorite Orbits Reconstruction by Optical Imaging (MOROI) [2] component of the FRIPON network in Romania. As of May 2022, the MOROI network detects the events with the use of 13 all-sky cameras.2. MethodsThe method for computing the fireball trajectory used by FRIPON is presented in [3].The height, velocity and slope γ of the meteoroid are the input data for computing the ballistic coefficient α and the mass-loss parameter β. We select the candidates that are likely to produce meteorites of the ground using the α-β algorithm presented in [4], [5], [6], [7]. 3. Results Starting from January 2021 (the starting moment of data fusion of the MOROI network into the FRIPON network) until the present time (May 2022) over 100 meteors were detected. We present the most spectacular events that are likely to result on a meteorite production on the surface of the Earth. Figure 1: The outcome of the FRIPON (MOROI) detections in Romania In Figure 1 are represented the coordinates of the meteoroids with noticeable deceleration in the (ln(αsinγ),lnβ) coordinates system. The values of the shape parameter correspond to the cases when the meteoroid doesn’t rotate (µ=0) or rotates uniformly (µ=2/3). The boundaries (‘likely fall’, ‘possible fall’, ‘unlikely fall’) are represented for meteoroids with final mass of 50 g. We processed the 100 detections of the FRIPON (MOROI) network in Romania. From this amount of data, we found 15 fireball events with noticeable deceleration. We found one event in the ‘likely fall’ area and three events in the ‘possible fall’ area. The fireball that is likely to produce meteorites was detected by the MOROI network on 24.11.2021 at 19:20:57 UT. We model the dark flight trajectory of the meteoroids with the ‘likely fall’ and ‘possible fall’ outcomes and determine their strewn field with the model presented in [8], [9]. We use the wind model from the European Centre for Medium-Range Weather Forecasts (ECMWF). A meteorite recovery campaign will be organised to identify the strewn field area. Acknowledgement. The work of IB and MB was partially supported by a grant of the Romanian Ministry of Education and Research, CNCS-UEFISCDI, project number PN-III-P1-1.1-PD-2019-0784, within PNCDI III. The work of IB, MB, AN was partially supported by a grant of the Ministry of National Education and Scientific Research, PNIII-P2-1214/25.10.2021, program no. 36SOL/2021. JM and MG acknowledge the Academy of Finland project no. 325806 (PlanetS). References: [1] Colas F., Zanda B., Bouley S., Jeanne S., Malgoyre A., Birlan M., Blanpain C., Gattacceca J., Jorda L., Lecubin J., et al. (385 more) FRIPON: a worldwide network to track incoming meteoroids. Astronomy &. Astrophys. 644, A53. doi:10.1051/0004-6361/202038649. 2020. [2] Nedelcu D.A., Birlan M., Turcu V., Boaca I., Badescu O., Gornea A., Sonka A.B., Blagoi O., Danescu C., Paraschiv P. Meteorites Orbits Reconstruction by Optical Imaging (MOROI) Network. Romanian Astronomical Journal 28(1), 57 – 65. 2018. [3] Jeanne, S., Colas, F., Zanda, B., Birlan, M., Vaubaillon, J., Bouley, S., Vernazza, P., Jorda, L., Gattacceca, J., Rault, J. L., Carbognani, A., Gardiol, D., Lamy, H., Baratoux, D., Blanpain, C., Malgoyre, A., Lecubin, J., Marmo, C., Hewins, P. Calibration of fish-eye lens and error estimation on fireball trajectories: application to the FRIPON network. Astronomy and Astrophysics, 627:A78. 2019. [4] Gritsevich, M. I. The Pribram, Lost City, Innisfree, and Neuschwanstein falls: An analysis of the atmospheric trajectories. Solar System Research.42, 372–390. 2008. [5] Gritsevich, M.I., Stulov, V.P., Turchak, L.I. Consequences of collisions of natural cosmic bodies with the Earth's atmosphere and surface. Cosmic Research vol.50, no.1, 56-64. 2012. [6] Sansom, E.K., Gritsevich, M., Devillepoix, H.A.R., Jansen-Sturgeon, T., Shober, P., Bland, P.A., Towner, M.C., Cupák, M., Howie, R.M., Hartig, B.A.D. Determining Fireball Fates Using the α-β Criterion. Astrophysical Journal 885(2):115. 2019. [7] Boaca I., Gritsevich M., Birlan M., Nedelcu, A., Boaca, T., Colas, F., Malgoyre, A., Zanda, B., Vernazza P., to be submitted. 2022. [8] Moilanen, J., Gritsevich, M., Lyytinen, E., Determination of strewn fields for meteorite falls, Monthly Notices of the Royal Astronomical Society 503, 3337–3350. 2021. [9] Boaca I., Nedelcu A., Birlan M., Boaca T., Anghel S. Mathematical model for the dark-flight trajectory of a meteoroid, Romanian Astronomical Journal, Vol. 31, No. 2. 2021.

The comet 17P/Holmes underwent an enormous outburst in October 2007 leaving vast amount of dust particles, which spread into elliptic orbits around the Sun [1, 2]. During February-March 2022 dust trail observations were forecasted to be possible with amateur sized telescope when outburst dust particles were in second revolution orbiting the Sun [1]. Observations were compared to the new dust trail particle model ‘Dust Trail kit’ implemented in Orekit. 1. Introduction Cometary Dust Trail kit model was developed between 1999-2013 and latest dust trail observations were referenced to model at 2015 [1]. More recent observations were needed to ensure and validate model accuracy and these new observations were part of the announced comet 17P dust trail observation campaign that started in 2013 [1]. 2. Observations 2.1 February 26th and 28th 2022 The 17P/Holmes dust trail was observed 26.2.2022 18:26UT and 28.8.2022 18:31UT based to the modeled position reported in [1]. 40 x 90 second exposures were taken at both nights with 305mm aperture telescope and clear filter using CCD bin2 mode and tracking stars. Stacked images were astrometrically plate solved and then subtracted and inverted to highlight the dust trails. Figure 1 shows the dust trails with their positions. The upper dark dust trail was observed on 26.2.2022 and lower light dust trail on 28.2.2022. 2.2 March 1st and 2nd 2022 The 17P/Holmes dust trail was observed on 1.3.2022 18:25UT and 2.3.2022 18:35UT based to the model position. CCD bin3 mode was used to get more sensitivity with exposure times 50 x 45 second at both nights. Images were then stacked, plate solved and subtracted. Figure 2 shows dust trails with positions, the upper dark dust trail is an observation made on 1.3.2022 and the lower light dust trail on 2.3.2022. 3. Comparison to the model Observations 2.1 and 2.2 were compared with the results of the ‘Dust Trail kit’ model implemented in Orekit by Markku Nissinen. Figure 3 show a good correlation between the model calculations and the four observations. 4. Equipment and software 4.1 Equipment The telescope used was 305mm aperture and 1205mm focal length F4 Newton with coma corrector. Mount was iOptron CEM60 and autoguider was configured to track stars during each exposure. The telescope was placed in Viestikallio Remote Controlled Observatory at Artjärvi, Finland under Bortle 3 rural sky conditions. CCD-camera was cooled QSI690wsg having Sony ICX814 CCD-sensor. Exposures 90s and 45s were used with CCD binning 2x2 and 3x3, giving 1.3 and 1.9 arcsec/px resolution. Baader Clear filter was used. 4.2 Software Image processing software used was PixInsight (PI), images were registered and stacked with PI Star registration and PI Integration functionality. PI PixelMath was used in image subtraction. ASTAP software was used for plate solving and astrometric analysis. 5. Figures Figure 1: Subtracted image of 17P/Holmes dust trails with measured positions, upper dust trail 28.2.2022, lower trail 26.2.2022, Jorma Ryske. Observations separation is 48 hours. Figure 2: Subtracted image of 17P/Holmes dust trails with measured positions, upper dark dust trail 1.3.2022, lower light dust trail 2.3.2022, Jorma Ryske. Observations separation is 24 hours. Two satellite tracks in image. Figure 3: 17P Dust Trail kit model positions (markers with no circles as small, medium, big and toward the Sun particles) [1] and observations positions (square and triangle markers with circles) for dates 26.2. & 28.2.2022 and 1.3. & 2.3.2022. The X-axis shows RA and the Y-axis DEC. Model calculations M. Nissinen, M. Gritsevich, observations J. Ryske. 6. Summary and Conclusions The particles released by the comet 17P/Holmes during the 2007 outburst formed a dust trail which is still possible to observe with relatively small aperture amateur sized telescopes using modern CCD-camera and image processing technology giving useful research data by amateur astronomers. Observations ensure that the developed dust particle model is producing the realistic results and can be used to forecast dynamics of meteoroid streams. Acknowledgements Special thanks to Arto Oksanen, Jyväskylän Sirius ry, image verification and Harry Lehto, University of Turku, image photometric guidance. References [1] Evolution of the Dust Trail of Comet 17P/Holmes, Maria Gritsevich, Markku Nissinen, Arto Oksanen, Jari Suomela, Elizabeth A. Silber. MNRAS Monthly Notices of the Royal Astronomical Society, 513(2), pp. 2201–2214, https://doi.org/10.1093/mnras/stac822 [2] Dust Trail Observations of Comet 17P/Holmes and Predictions for 2021-2022, Nissinen, Markku; Gritsevich, Maria; Oksanen, Arto; Suomela, Jari. Europlanet Science Congress 2021, EPSC2021-86.

In this work we present the main results of the project named ‘Meteor mathematical modelling of dark flight’ (MeMATH), and current state and future work related to our project. The MeMATH project started in September 2020.The main objectives of the MeMATH project are:i) numerical simulation of the dark-flight trajectory;ii) determining the search area for meteorite fragments;iii) the study of the ablation of large bodies.In the first stage of the project, we developed a mathematical model for the dark flight trajectory of a meteoroid. The novelty of our model is that it considers the ellipsoidal shape of the Earth, the Coriolis effect and the centrifugal force.In the current stage of the project, we are determining the ballistic coefficient α and the mass loss parameter β based on the meteoroid height and deceleration.The α and β parameters have a great impact in the study of meteoroids from the identification of the parent body to determining the initial and final mass and finding out weather the remnant matter after ablation could result in a meteorite on the ground.Acknowledgement.The work of IB and MB was supported by a grant of the Romanian Ministry of Education and Research, CNCS-UEFISCDI, project number PN-III-P1-1.1-PD-2019-0784, within PNCDI III.

All-sky camera systems such as the AMOS network, record a large number of fireball events. By using multi-station triangulation method obtain information about trajectory of the body in the description change in altitude and velocity over time. Based on the characteristic of the object trajectory can determine important physical properties as the input mass of the meteoroid or the final mass of meteorites, and thus the probability of the formation and impact of particles on the Earth's surface. For this purpose, we used the method of dimensionless coefficients α (ballistic coefficient) and β (mass loss coefficient), which define the impact of the dynamical and physical properties of meteoroids on the searched input/final masses. Large number of recorded fireballs requires automatic data processing and their effective reduction. For this purpose, we have created a program with a user interface that works with data from all-sky fireballs cameras (in our case we focus on data from the Slovak AMOS system), defines the values of α-β coefficients and evaluates the probability of the meteorite formation with specific mass during the flight through the atmosphere. The program gives an interactive settings of physical parameters of the body and thus defines impact on the required values of body input/final masses. This algorithms was created for the purpose of user-friendly processing of scientific data, and the same time serves for the selecting suitable candidates for the formation and impact of dust particles and meteorites on the Earth's surface.

On November 7, 2020, a bright fireball was observed over Sweden, and 13.8 kg iron meteorite was later recovered. Multiple observations of the fireball were conducted from Denmark, Finland, and Norway, making it the first instrumentally documented fall of an iron meteorite.We used the instrumental recordings of the bolide to reconstruct its preatmospheric orbit, and studied the past orbital evolution of the meteoroid. We found no close affinity of the orbit of the meteoroid with any near-Earth asteroid. The long YORP timescale suggests that the meteoroid could have arrived intact from the main asteroid belt. Our analysis of the orbit shows that the meteoroid probably entered its near-Earth orbit via either the 𝜈6 secular resonance with Saturn or the 3:1 mean motion resonance with Jupiter.The work was partially funded by the National Research Foundation of Ukraine (project N2020.02/0371).

Introduction In Spain, the Spanish Meteor Network (SPMN) has been operating for 25 years, recording meteoric events and re-entries over the Iberian Peninsula, Morocco, and the insular territory [1]. This is a pro-am project involving a scientific team specialized in areas such as astronomy, geology, geophysics, and chemistry. Obtaining the trajectory of meteoroids impacting the atmosphere is crucial both for the recovery of possible meteorites and for studying their origin in the Solar System. Some of these objects can be dynamically associated with their parent bodies, being part of meteoroid streams [2]. Herein lies the importance of monitoring the sky constantly and completely from multiple monitoring stations. The SPMN network has 34 stations equipped with all-sky cameras or wide-angle lenses, and we recently upgraded the software to reduce the ever-increasing amount of data. Here we present our automated Python (called 3D-FireTOC) pipeline for meteor detection from digital systems, astrometric measurements, photometry, atmospheric trajectory reconstruction and heliocentric orbit computation, all in all quantifying the error measurements in each step [3]. Analytical procedures of the 3D-FireTOC software A key step to achieve proper reduction is the development of automated astrometry to ensure the measurement of meteors appearing in the field of view of video-detection systems. To do this, we use computer vision techniques to obtain the pixel coordinates corresponding to the moving meteor in each frame. Each image is processed and compared with a reference image (without detection) allowing us to extract the pixels that have been activated by the meteor. In this way, the centroid of the detected pixel area corresponds to the position in the image of the meteoroid (see Figure 1). Due to the changing nature of this type of recordings as well as possible light reflections and obstacles in the field of view, we have implemented three methods to avoid false positives: 1) discriminating by the size of the detected area excluding excessively small and large contours, 2) predicting the next position of the meteor with a Kalman filter, and 3) post-processing the detected points and applying clustering algorithms to check if the trajectory is consistent with a more or less straight line. Figure 2 shows an example of false positive avoidance. To transform the pixels into real coordinates is necessary to identify stars in the image to obtain their position in the sky for the date of the event. To do this, we use corner detection algorithms since the stars appear randomly distributed in the sky and far from each other. Again, we use clustering algorithms but this time selecting the points identified as noise, as can be seen in Figure 2. Once the stars have been identified, thanks to JPL's Horizons ephemerides, we can model the deformation produced by the lens by finding the correspondence between pixel and real position. In particular, we apply a polynomial variant [4] of the method proposed by [5] for all-sky camera astrometry. Fig.1 Fig. 2 The result we obtain from each observation is an apparent trajectory projected on the celestial sphere. Naturally, two or more observations sufficiently far apart are required to triangulate the real position of the meteoroid. Because these fragments reach the Earth at very high velocities, air resistance practically does not bend their trajectories so that they can approximate a straight line. This allows the plane intersection method to be applied to reconstruct the atmospheric flight [6]. Finally, the trajectory is projected backwards to obtain the radiant, i.e. the position of origin in the sky. From the atmospheric flight, the α-β criterion can be applied approximating the chances that the event produced meteorites [7,8]. Recent example: SPM010521 event On May 1, 2021, a bolide flew over Aragón reaching a magnitude of -11. It was recorded by 6 stations of the SPMN network (Table 1 and Figure 3). The luminous phase started at 107 km altitude and ended at 43 km. The flight angle with respect to the horizontal was 24º degrees. With a geocentric velocity of 30 km/s, its orbital parameters indicate a possible association with a IAU working list of meteor showers called Southern May Ophiuchids. Unfortunately, it was not a meteorite-dropper event as reveals the α-β criterion result: α=315.4, β=1.2, an estimated initial mass of 0.1 kg and an estimated final mass less than one gram. Figure 4 shows the 3D representation and scale of the reconstructed atmospheric trajectory, as well as the calculated heliocentric orbit. Fig. 3 Fig. 4 Conclusions With the implementation of this new software, the SPMN increases its capacity to rapidly generate new knowledge about the origin of large meteoroids, and their capacity to generate hazard. Multi-station analyses also provide valuable information about their bulk physical properties and, from their heliocentric orbits, the dynamic association with comets, asteroids, or even planetary bodies can be inferred [3]. In addition, the automation of the meteor detection and the entire analysis process allows immediate preparation of meteorite search campaigns. Our software developments will be soon applied to increase our close cooperation with FRIPON [9]. As an example of application, we present the results obtained on SPMN010521, a recent fireball recorded and analyzed by the SPMN, which did not produce meteorites and seems to be dynamically associated with an unestablished meteor shower. Acknowledgements This research has been funded by the research project PGC2018-⁠097374-⁠B-⁠I00, (MCI-⁠AEI-⁠FEDER, UE). Funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme for the project “Quantum Chemistry on Interstellar Grains” (QUANTUMGRAIN, grant agreement No. 865657) is acknowledged. References [1] Trigo-Rodríguez J.M. et al. (2006) Astronomy & Geophysics 47, 6.26 [2] Hughes, D. W.(1990) MNRAS 245, 198-203. [3] Peña-Asensio, E., Trigo-Rodríguez, J. M., Gritsevich, M., & Rimola, A. (2021) MNRAS 504(4), 4829-4840. [4] Bannister, S. M., Boucheron, L. E., & Voelz, D. G. (2013) ASP125(931), 1108. [5] Borovička, J. (1992) AICAS, 79. [6] Ceplecha, Z. (1987) BAIC, 38, 222-234. [7] Gritsevich M., 2009, Advances in Space Research, 44, 323. [8] Sansom E. K., et al., 2019, The Astrophysical Journal, 885, 115. [9] Colas, F. et al. (2020) Astronomy & Astrophysics 644, id.A53, 23 pp.

1. Introduction When the comet 17P/Holmes' outburst [1] took place on 2007 October 23-24 [2] a large number of dust particles and gas were ejected from the comet [7]. The dust particles ended up on elliptic orbits around the Sun and seemingly vanished. However, there are two common nodes of the orbits, where dust particles converge [6]. 2. Observations Our first observations of the dust were made in February 2013. The second observation was made in August 2013. (Fig. 1 & 2) [3]. Particle radius 1 mm corresponds to β value 0.001, particle radius 0.1 mm corresponds to β value 0.01 and particle radius 0.03 mm corresponds to β value 0.03. Figure 1. Modeling and observation 2013 August 24. Figure 2. Modeling of dust trail 2013 August 24. Observations were continued in September 2014 (Fig. 3) [4]. Figure 3. Comet 17P/Holmes pictured when traveling on top of the dust trail. In February 2015 dust trail was visible without image subtraction (Fig. 4) [5]. Figure 4. Observation 2015 February 14 in Hankasalmi Observatory. 3. Predictions The dust trail particles are modelled using our software named the ‘Dust Trail kit’. This model can be used also for calculating predictions for meteor streams that hit Earth’s atmosphere [8]. In Fig. 5 Comet is plotted on top of the modeled trail section for September 6 2021. Figure 5. Comet 17P/Holmes plotted on top of the modeled trail. The density of small particles is not increasing significantly until well into 2022 (Fig. 6). The physical dust trail will move towards the original explosion point. Width of the trail seen from Earth is comparatively similar with 2015 February observations in 2022 February and March. Particle density is comparatively similar also. All particle sizes are present in the trail near the explosion point. Big particles are located in large abundance at the physical center of the trail. Figure 6. Prediction of the dust trail in February 2022 near the explosion point. Marked in the picture is 17P/Holmes orbit at explosion event, modeled 2015 February trail and 0.01 AU further away modeled 2022 February trail. Density in the model is 15000 particles for each beta for 2015 and 4000 particles for each beta for 2022. 4. Conclusions According to our theoretical results the dust trail will be detectable in visible light even when observed by modest aperture telescopes, although it may require the use of image subtraction. Interplanetary dust at the predicted time and coordinates will also be bright in mid infrared. Acknowledgements We express deep gratitude to Esko Lyytinen for initiating this research and for putting in place effective collaboration under the umbrella of the Ursa Astronomical Association and the Finnish Fireball Network. We thank Salli and Olli Lyytinen for sharing the material for this research from Esko Lyytinen’s personal archive and computers. We believe this allowed us to provide a comprehensive representation of the dust trail evolution research ideas that were earlier described to us by Esko in the form of personal communications, emails and notes. We are grateful to Pekka Lehtikoski for his contribution to the programming of the mathematical model. This work was supported, in part, by the Academy of Finland project no. 325806 (PlanetS). Dedication This presentation is dedicated to the memory of mastermind Esko Lyytinen who did a tremendous amount of original research, modeling, and predictions of meteor streams for the scientific community. References [1] Lin Z. Y., Lin C. S., Ip W. H. and Lara L. M. (2009). “The Outburst of Comet 17P/Holmes”. The Astronomical Journal, Volume 138, Number 2. [2] Sekanina Z. (2009). “Comet 17P/Holmes: A Megaburst Survivor”, International Comet Quarterly, pp. 5-23. [3]Lyytinen E., Nissinen M. and Oksanen A. (2015). “Dust Trail of Comet 17P/Holmes”. ATel 7062. [4] Lyytinen E., Nissinen M., Lehto H. J. and Suomela J. (2014). “Dust Trail of Comet 17P/Holmes”. CBET 3969. [5] Lyytinen E., Lehto H. J., Nissinen M., Jenniskens P. and Suomela J. (2013). “Comet 17P/Holmes Dust Trail”. CBET 3633 #1. [6] Lyytinen E., Nissinen M. and Lehto H. J. (2013). “Comet 17P/Holmes: originally widely spreading dust particles from the 2007 explosion converge into an observable dust trail near the common nodes of the meteoroids' orbits”. WGN, Journal of the International Meteor Organization, vol. 41, no. 3, pp. 77–83. [7] Reach W. T., Vaubaillon J., Lisse C. M., Holloway M. and Rho J. (2010). “Explosion of Comet 17P/Holmes as revealed by the Spitzer Space Telescope”. Icarus 208, Issue 1, pp. 276-292. [8] Lyytinen E., Nissinen M. and Van Flandern T. (2001). “Improved 2001 Leonid Storm Predictions from a Refined Model”. WGN, Journal of the International Meteor Organization, vol. 29, no. 4, pp. 110–118.

Meteoroids entering the Earth's atmosphere are associated with a number of phenomena including ablation, ambipolar diffusion, plasma transport, chemical reactions, shock waves, and plasma turbulence. A bright daylight fireball observed on 2020-12-04 13:30 UTC with two meteor cameras located in Skibotn and Sørreisa allowed the precise entry trajectory of the fireball to be determined. The path of the entering object is approximately between Angeli Finland and Pajala Sweden. Based on the brightness and entry trajectory, it is possible to estimate the approximate mass of the object, and associate it with a meteor shower (Northern Taurids). The effects of the fireball on the atmosphere were detected with a number of radar and radio instruments within the region, including ionosondes, meteor radars, an all-sky VHF imaging system, and an infrasound sensor. These observations allow a detailed study of the atmospheric interaction of a large meteoric body with the Earth's atmosphere to be made. In this talk, we will describe the observations of this fireball and discuss preliminary findings.

As of today, instrumentally observed meteorite falls account for only 37 recovered meteorite cases, with derived Solar System orbit, out of 65098 registered meteorite names. To bridge this knowledge gap, a number of fireball networks have been set up around the globe. These networks regularly obtain thousands of records of well-observed meteor phenomena, some of which may be classified as a likely meteorite fall (Sansom et al. 2019). A successful recovery of a meteorite from the fireball event often requires that the science team can be promptly directed to a well-defined search area. Here we present a neat Monte Carlo model, which comprises adequate representation of the processes occurring during the luminous trajectory coupled together with the dark flight (Moilanen et al. 2021). In particular, the model accounts for fragmentation and every generated fragment may be followed on its individual trajectory. Yet, the algorithm accounts only for the mass constrained by the observed deceleration, so that the model does not overestimate the total mass of the fragments on the ground (and this mass may also be retrieved as zero). We demonstrate application of the model using historical examples of well-documented meteorite falls, which illustrate a good match to the actual strewn field with the recovered meteorites, both, in terms of fragments’ masses and their spatial distribution on the ground. Moreover, during its development, the model has already assisted in several successful meteorite recoveries including Annama, Botswana (asteroid 2018 LA), and Ozerki (Trigo-Rodríguez et al. 2015, Lyytinen and Gritsevich 2016, Maksimova et al. 2020, Jenniskens et al. 2021).ReferencesJenniskens P. et al. (2021). Asteroid 2018 LA, impact, recovery and origin on Vesta. Submitted to Science.Lyytinen E., Gritsevich M. (2016). Implications of the atmospheric density profile in the processing of fireball observations. Planetary and Space Science, 120, 35-42 http://dx.doi.org/10.1016/j.pss.2015.10.012Maksimova A.A., Petrova E.V., Chukin A.V., Karabanalov M.S., Felner I., Gritsevich M., Oshtrakh M.I. (2020). Characterization of the matrix and fusion crust of the recent meteorite fall Ozerki L6. Meteoritics and Planetary Science 55(1), 231–244, https://doi.org/10.1111/maps.13423 Moilanen J., Gritsevich M., Lyytinen E. (2021). Determination of strewn fields for meteorite falls. Monthly Notices of the Royal Astronomical Society, in revision.Sansom E.K., Gritsevich M., Devillepoix H.A.R., Jansen-Sturgeon T., Shober P., Bland P.A., Towner M.C., Cupák M., Howie R.M., Hartig B.A.D. (2019). Determining fireball fates using the α-β criterion. The Astrophysical Journal 885, 115, https://doi.org/10.3847/1538-4357/ab4516Trigo-Rodríguez J.M., Lyytinen E., Gritsevich M., Moreno-Ibáñez M., Bottke W.F., Williams I., Lupovka V., Dmitriev V., Kohout T., Grokhovsky V. (2015). Orbit and dynamic origin of the recently recovered Annama’s H5 chondrite. Monthly Notices of the Royal Astronomical Society, 449 (2): 2119-2127, http://dx.doi.org/10.1093/mnras/stv378

Introduction At present, the main database of asteroid absolute magnitudes is the Minor Planet Center (MPC). The MPC receives asteroid magnitudes from many observatories in different spectral bands of different photometric systems, but calculates absolute magnitudes ​​for V band of the Johnson photometric system using the HG-function [1]. A comparison of the MPC absolute magnitudes (HMPC) with the dataset of high-quality absolute magnitudes obtained by Pravec et al. [2] revealed systematical differences between these datasets. The new HG1G2 function was recommended by Muinonen et al. [3] to be used for more precise calculating the asteroid absolute magnitudes. This function was calibrated according to good measured magnitude-phase dependences, and the average parameters ​​for major taxonomic classes of asteroids were obtained [4, 5]. Using these average values, it is possible to calculate the absolute magnitudes of asteroids from individual observations at different phase angles. For sparse photometric data, it was suggested to use the two-parameter HG12 system [3, 4, 6]. Recently, large-scale survey programs such as Pan-STARRS and Palomar Transient Factory (PTF) [7, 8] performed new observations, within which absolute magnitudes of asteroids have also been derived. Both datasets are homogeneous, but they were obtained mostly in the Sloan system and then transformed to the Johnson system. Comparison of these datasets of absolute magnitudes ​​with the MPC data showed systematical deviations [7, 8]. It should be noted that the absolute magnitudes of asteroids in the Pan-STARRS and PTF surveys [7, 8] were obtained in the new HG12 system. In addition, the work is continued to obtain the asteroid absolute magnitudes in the new system for PTF and Pan-STARRS datasets [9]. To check the reliability of these datasets and identify systematic deviations, an independent set of high-quality data on absolute magnitudes is required. Thus, we initiated an observational program to determine the absolute magnitudes of several hundred asteroids. Here we present preliminary results of our program. Results For our dataset, we used first the data of the asteroid magnitude-phase dependences collected at the Institute of Astronomy of V. N. Karazin Kharkiv National University [5, 10–12]. We also used observational data of asteroids from our other programs and performed new observations of some asteroids [13–14]. We calculated our absolute magnitude dataset in the Johnson V band with a correction for lightcurve variations using the HG1G2 system according to its extension to low-accuracy data [4]. In such manner, we obtained a homogenous dataset of absolute magnitudes of about 300 asteroids in the range from 6.5 to 16 mag. Fig. 1 shows the correlations of the absolute magnitudes from the MPC, Pan-STARRS (HPS), and PTF (HPTF) datasets with those of the Kharkiv dataset (HKH). Figure 1: Absolute magnitudes of MPC (HMPC), Pan-STARRS (HPS), and PTF (HPTF) vs. those of the Kharkiv dataset (HKH). We observe a high correlation between our dataset and the other three datasets. There are small differences in the constant terms and slopes that point out some systematical deviations especially for the MPC and the PTF datasets. Figure 2: Histogram of the absolute magnitude deviations between the Kharkiv and Pan-STARRS datasets. Figure 3: Histogram of the absolute magnitude deviations between the Kharkiv and MPC datasets. For the MPC dataset, we found a systematical deviation of about 0.1 mag (Fig. 3). It is less than that obtained in [2] and close to that obtained in [7]. The smallest deviations were found for the Pan-STARRS dataset. This is confirmed by the histogram of absolute magnitude deviations between the Kharkiv and Pan-STARRS datasets presented in Fig. 2. The solid line is a fit to the data using the Gaussian function. Conclusions We prepared a preliminary comparative analysis of the asteroid absolute magnitudes between the Kharkiv dataset and the MPC, Pan-STARRS, and PTF datasets. The analysis has shown that the absolute magnitude dataset obtained from the Pan-STARRS survey project is closest to our dataset and can be the most suitable for the determination of diameters or albedos of asteroids. Acknowledgments This research has made use of data and/or services provided by the International Astronomical Union's Minor Planet Center. References [1] Bowell, E., Hapke, B., Domingue, D., et al. In Asteroids II. Tucson. Univ. Arizona Press. P. 524−556, 1989. [2] Pravec, P., Harris, A.W., Kušnirák, P., et al. Icarus, 221, 365-387, 2012. [3] Muinonen, K., Belskaya, I.N., Cellino, A., et al. Icarus, 209, 542–555, 2010. [4] Penttilä, A. Shevchenko, V.G., Wilkman, O., Muinonen, K. PSS, 123, 117–125, 2016. [5] Shevchenko, V.G., Belskaya, I.N., Muinonen, K., et al. PSS, 123, 101−116, 2016. [6] Oszkiewicz, D.A., Muinonen, K., Bowell, E., et al. JQSRT, 112, 1919-1929, 2011. [7] Veres, P., Jedicke, R., Firzsimmons, A., et al. Icarus, 261, 34-47, 2015. [8] Waszczak, A., Chang, C.-K., Ofeck, E.O., et al. Astron. J., 150, A75, 2015. [9] Penttilä, A., Cellino A., Lu X., et al. DPS, 48, No 7, 228, 2016. [10] Shevchenko, V.G., Belskaya I.N., Lupishko D.F., et al. EAR-A-COMPIL-3-MAGPHASE-V1.0. NASA PDS, 2010. [11] Shevchenko, V.G., Belskaya, I.N., Slyusarev, I.G., et al. Icarus, 217, 202–208, 2012. [12] Slyusarev, I.G., Shevchenko, V.G., Belskaya, I.N., et al. LPSC 43, 1885, 2012. [13] Chiorny, V.G., Shevchenko V.G., Krugly Yu.N., et al. PSS, 55, 986−997, 2007. [14] Shevchenko, V.G., Mikhalchenko O.I., Belskaya I.N., et al. LPSC 50, Abstract #1771, 2019.

We introduce a new atmospheric refraction correction method which allows to retrieve fireball position with high accuracy without the need to consider at which distance from the observer or height above the Earth's surface the fireball is situated.Traditional refraction correction is valid for objects positioned at infinite distance and it overcompensates when an object is situated inside the atmosphere. In this numerical study the overcompensated correction is reduced by artificially increasing the observing site height above the sea level, called the delta-z correction. We use analytically derived formula for the delta-z correction with different refraction models and compare these results to the numerical solution where light ray is traced through the atmosphere. Ray tracing technique is implemented on the finely meshed atmospheric layers in order to derive value of the correction.We parametrize the viewing angle and the fireball height above sea level in order to define whether this delta-z correction is significant or negligible. Significance is defined by studying the errors caused by the observed horizontal altitude, height of the fireball above the sea-level, and height of the observing site. We find that the delta-z correction should be performed if a fireball is observed within 20 degrees altitude above the horizon or with negative altitudes. We also find that delta-z correction is always accurate if fireball is situated 20km or higher above the sea level and hence it can be safely applied in processing of all observational cases of fireballs.

Introduction: On January 24th, 2016, 15:27 UTC, a bright daytime fireball was observed in Florida and registered by weather radar and a dash-board camera. Eight fusion-crusted stones with a total mass of 1.1 kg were found [1]. Osceola is now the 33rd meteorite (and the 9th L chondrite) with a published orbit [2]. Methods. Orbit. The atmospheric trajectory was reconstructed based on a dash-cam video recording with account for atmospheric conditions, using the methods described in [3,4]. The orbit (a=1.486 au; e=0.3406, i=13.20°, W=303.9°, w=169.0) was calculated based on this trajectory with the open source software Meteor Toolkit [5]. Noble gases. Four chips (total mass = 118.2 mg) were analyzed at ETH Zurich after problems were encountered during the analysis of the first two chips. Pending detailed analysis, we only report here the results for the second two chips (Os-3 and Os-4). Extraction was done by total fusion in a single temperature step (Table 1), and each measurement was bracketed by blanks. We measured 3,4He, 20,21,22Ne, 36,38,40Ar, 84Kr, 129,132Xe and some potentially interfering (isobaric) species [6]. We calculate meteoroid radius, shielding depth and cosmic-ray exposure (CRE) ages using [7,8] and the bulk chemistry for L chondrites given therein. Radiogenic gas retention (RGR) ages are based on typical abundances of 0.013, 0.043, 825 ppm adopted for U, Th, K, respectively [9]. Cosmogenic radionuclides. Results are forthcoming and will be presented and discussed at the conference. Results & Discussion. Orbit. The aphelion of Osceola’s barely Earth-crossing orbit is located just inside the inner edge of the asteroid belt, at ca. 2 au, i.e. the orbit is dynamically evolved (Figure 1). In that respect, it is comparable, among L chondrites, only to Creston [10]. Noble gas inventory. Unsurprising for an equilibrated L chondrite, the noble gases in Osceola are cosmogenic and radiogenic for He and Ne, with only minor trapped Ar, and Q-like 84Kr/36Ar, 132Xe/36Ar. Meteoroid size and cosmic history. The cosmogenic 22Ne/21Ne ratio of ~1.07 in both chips suggests irradiation in a meteoroid with a radius >50 cm, at a depth of >40 cm [7], consistent with the size estimate based on the fireball deceleration using the mass calculation method detailed in [11]. Under these shielding conditions, we expect a cosmogenic 3He/21Ne ratio of ~4.5, which fits well with the values measured in Os-3 and -4 (4.60 and 4.40), suggesting no significant loss of He relative to Ne during cosmic-ray exposure. Using 22Ne/21Ne-based production rates from [8], 3He and 21Ne in both chips give a consistent CRE age o 18±2 Ma, while 38Ar yields ~22 Ma. The former is our preferred age, since 38Ar can be more affected by sample inhomogeneities (i.e., distribution of Ca, which is the primary target for production of 38Ar) than 3He and 21Ne. This CRE age does not fall on any prominent peak in the CRE-age-histogram of the L chondrites (e.g., [12]). The CRE age is compatible with the expected collisional lifetime of a R >50 cm meteoroid in the asteroid belt (>14 Ma [13]). The U,Th-He retention age (4He corrected for the contribution of cosmogenic 4He = ~6 × 3He) of the two chips is 460 and 420 Ma, respectively, close to the age of the L chondrite parent body disruption event ca. 470 Ma ago [14]. Given the uncertainties inherent in these ages, it seems at least plausible that Osceola, like a large fraction of the L chondrites falling today, was affected by this shock event. The K-Ar retention ages for the two chips is are 1.5 and 1.1 Ga. The higher age from the K-Ar system might either be due to a contribution of atmospheric 40Ar (given that the measured 40Ar/36Ar ratios of the two chips are lower than the atmospheric value), or incomplete degassing of radiogenic 40Ar during the shock event 470 Ma ago. Dissimilar shock-degassing patterns for the U,Th-He and K-Ar systems are frequently observed for meteorites with shock stages S3 and S4 [15], consistent with the shock classification of Osceola of S4 [1]. Table 1: He, Ne, Ar in Osceola All concentrations given in units of 10-8 cm3STP/g (uncertainty in amounts Chip (mg) 3He=3Hecos 4He 20Ne 21Ne 22Ne 36Ar 38Ar 40Ar = 40Arrad 4Herad 21Necos 38Arcos Os-3 (21.5) 34.9 342 7.20 7.58 8.21 4.39 2.04 714 133 7.58 1.38 Os-4 (15.8) 35.9 337 7.00 8.18 8.73 3.55 2.11 498 122 8.18 1.64 References. [1] Bouvier et al., 2017, M&PS 52:2411. [2] www.meteoriteorbits.info, acc. 7.3.20. [3] Lyytinen & Gritsevich, 2016, Proc. Int. Meteor Conf., 159-163. [4] Lyytinen & Gritsevich, 2016, Planet. Space Sci. 120:35. [5] Dmitriev et al., 2015, Planet. Space Sci. 117:223. [6] Meier et al., 2017, M&PS 52:1561. [7] Leya & Masarik, 2009, M&PS 44:1061. [8] Dalcher et al., 2013, M&PS 48:1841. [9] Wasson & Kallemeyn, 1988, Phil. Trans. Roy. Soc. 325:535. [10] Jenniskens et al., 2019 M&PS 54:699. [11] Gritsevich, 2009, Adv. Space Res. 44:323. [12] Marti & Graf, 1992, Ann. Rev. Earth Planet. Sci. 20:221. [13] Farinella et al., 1998, Icarus 132:378. [14] Korochantseva et al., 2007 M&PS 42:113. [15] Stöffler et al., 1991 Geochim. Cosmochim. Acta 55:3845.

The optical properties of a sample are important in, e.g., astrophysics and space science. The samples can be unique or available in limited quantities, implying that sample preservation is important. Established single-particle light-scattering measurements involve attaching the sample to a holder or measuring a laminar flow of particles, neither of which allows for a controlled and non-destructive measurement. Acoustic levitation is a state-of-the-art approach applicable to scattering measurements. We present an instrument capable of performing a full 4π scattering measurement, i.e., a measurement from any direction on the full 4π solid angle. We measure scattering properties of millimeter-sized samples at any direction. This is enabled by introducing a novel non-contacting sample holder based on acoustic levitation, which allows a disturbance-free measurement of an orientation-controlled sample. The instrument is scalable and currently employs polarized visible light (400–700 nm). It also measures beam and sample stability as well as temperature and humidity, to ensure consistency of measurements. We demonstrate a measurement of a sample consisting of agglomerated 500-nm silica spheres. The upper left 2×2 submatrix of the Mueller scattering matrix is measured from the sample along the scattering angle. 1. Introduction Experimental measurements of light scattering by a small-particle sample in a controlled position and orientation (six degrees of freedom) has remained open. Scattering characteristics for single particles have been earlier measured as ensemble averages over the size, shape, and orientation distribution (e.g., [1]) by introducing a particle flow through the measurement volume. Scattering characteristics for single particles in levitation have been carried out using, for example, electrodynamic levitation [2] and optical tweezers [3-5]. In the present manuscript, a novel experimental method based on ultrasonic levitation with full control of the sample position and orientation is offered [6-8]. 2. Levitator-scatterometer: results and discussion Figure 1 shows our levitator scatterometer with detailed information about its components. Figure 1: Levitator-scatterometer with a photograph (above) and top view schematic (below). The components are as follows: (1) fiber-coupled light source with collimator, (2) focusing lens (optional), (3) bandpass filter for wavelength selection, (4) adjustable aperture for beam shaping, (5) motorized linear polarizer, (6) high-speed camera, (7) high-magnification objective, (8) acoustic levitator for sample trapping, (9) measurement head, comprising an IR filter, motorized shutter, motorized linear polarizer, and a photomultiplier tube (PMT), (10) motorized rotation stage for adjusting measurement head angle, (11) optical flat for Fresnel reflection, (12) neutral density filter, and (13) reference PMT, for monitoring beam intensity. The system is divided into three enclosed compartments to eliminate stray light. For the experiment, an aggregate consisting of densely-packed, nominally 0.5-µm diameter spherical SiO2 particles was selected and polished further, to approximate a spherical shape, after which it was characterized by weighing and measuring its dimensions (Fig. 2). The nearly spherical aggregate had a diameter of 1.16 mm and a volume density of 0.47. The light beam was filtered to 488±5 nm, with a Gaussian spectrum. The measured phase function, degree of linear polarization for unpolarized incident light, as well as the depolarization are shown in Figs. 3-5, respectively, as a function of the phase angle (source-sample-detector angle). Figure 2: The sample of densely-packed spherical SiO2 grains. Figure 3: The measured and modeled phase functions normalized to unity when integrated from 15.1° to 165.04°. Figure 4: Degree of linear polarization for unpolarized incident light (Mueller scattering-matrix-element ratio -M12/M11 in %) from the measuements and modeling. Figure 5: Depolarization (M22/M11) from the measurements and modeling. Figures 3-5 further include the first modeling based on the R2T2 and SIRIS4 software (Radiative Transfer with Reciprocal Transactions; see [6] and references therein). The phase function is accurately modeled, whereas there are challenges remaining for modeling the degree of polarization and depolarization. There is compelling need for high-precision validation samples with accurate documentation: the size and shape distributions of the silica samples remain too uncertain to be utilized in model validation. Conclusion We have described novel experimental instrumentation for measuring light scattering by isolated samples in levitation. As it is extremely difficult to accurately characterize random media composed of sub-wavelength-scale particles or inhomogeneities, controlled Mueller-matrix measurements can offer a tool for retrieving information on the volume density and particle size and shape distribution in the medium. Acknowledgments. Research by KM, AP, HH, and MG supported, in part, by the Academy of Finland (grants 325805 and 325806). References [1] Muñoz, O., Volten, H., de Haan, J. F., Vassen, W., and Hovenier, J. W.: Experimental determination of scattering matrices of olivine and Allende meteorite particles, Astron. Astrophys., Vol. 360, pp. 777–788, 2000. [2] Sasse, C., Muinonen, K., Piironen, J., and Dröse, G.: Albedo measurements on single particles, J. Quantitat. Spectrosc. Radiat. Transf., Vol. 55, pp. 673—681, 1996. [3] Gong, Z., Pan, Y.-L., Videen, G., and Wang, C.: Optical trapping and manipulation of single particles in air: Principles, technical details, and applications, J. Quantitat. Spectrosc. Radiat. Transf., Vol. 214, pp. 94–119, 2018. [4] Nieminen, T. A., du Preez-Wilkinson, N., Stilgoe, A. B., Loke, V. L. Y., Bui, A. A. M., and Rubinsztein-Dunlop, H. Optical tweezers: Theory and modelling. J. Quantitat. Spectrosc. Radiat. Transf., Vol. 146, pp. 59–80, 2014. [5] Herranen, J., Markkanen, J., and Muinonen, K. Dynamics of interstellar dust particles in electromagnetic radiation fields: A numerical solution. Radio Sci., Vol. 52(8), pp. 1016–1029, 2017. [6] Muinonen, K., Väisänen, T., Martikainen, J., Markkanen, J., Penttilä, A., Gritsevich, M., Peltoniemi, J., Blum, J., Herranen, J., Videen, G., Maconi, G., Helander, P., Salmi, A., Kassamakov, I., and Haeggström, E.: Scattering and absorption of light in planetary regoliths. J. Visual. Exper., Vol. 149, e59607, doi:10.3791/59607, 2019. [7] Maconi, G., Helander, P., Gritsevich, M., Salmi, A., Penttilä, A., Kassamakov, I., Haeggström, E., and Muinonen, K.: 4π scatterometer, J. Quantitat. Spectrosc. Radiat. Transf., Vol. 246, 106910, 1-7, 2020. [8] Helander, P., Puranen, T., Meriläinen, A., Maconi, G., Penttilä, A., Gritsevich, M., Kassamakov, I., Salmi, A., Muinonen, K., and Haeggström, E.: Omnidirectional microscopy by ultrasonic sample control, Appl. Phys. Lett., Vol. 116, 194101, 1-5, 2020.

Introduction Boulders, meters-size blocks of rock, are seen in great numbers in high-resolution images of the Moon, Mars, and small bodies. Boulders and their geological associations provide information about surface processes on these bodies. Here we report on rarity of boulders on Mercury, and discuss possible implications of this. Rarity of boulders on Mercury We studied boulders on Mercury with high-resolution images acquired by MDIS NAC camera onboard MESSENGER. We screened all ~3000 images of the highest resolution ( To compare Mercury with the Moon, we extracted similar random 0.25 Mpix samples from LROC NAC images that have the same sampling, the same solar illumination incidence angle and are situated in highlands. We artificially degraded image quality to mimic MDIS NAC quality. Then we screened these image samples searching for boulders and found them at ~15% of the random sample images. Thus, boulders on Mercury are extremely rare in comparison to the Moon (~ 30x less abundant). Possible causes Boulder population on the Moon is dynamic: the characteristic boulder lifetime is ~100 Ma [2], and the observed population represents equilibrium between boulder formation and obliteration. The same obviously occurs on Mercury. Therefore, much sparce boulder population can occur due to a lower production rate, a shorter lifetime, or both. Production rate. Boulders on the Moon occur in association with (1) fresh impact craters of different sizes and (2) hilltops, upper edges of rilles, and other convex relief forms. Respectively, there are two mechanisms of boulder formation: (1) bedrock fragmentation and excavation by impacts, and (2) progressive exposure of pre-existing blocks and fractured bedrock by removal of regolith layer from convex relief by diffusive creep. On Mercury we observe boulders in the first type of settings (craters) only. The intercrater plains on Mercury are significantly smoother than the lunar highlands [3], therefore suitable hilltops may be less abundant and might not be sampled in our limited image set. Thus, the general smoothness of Mercury in comparison to the Moon [3] may contribute to a lower boulder production rate. Regolith on Mercury is thought to be thicker than on the Moon [3,4]. The smallest craters are forming in regolith and cannot excavate boulders. Due to the thicker regolith, the onset crater size for boulder formation is larger. Since the crater formation rate on Mercury is on the same order of magnitude, the larger onset crater size means a lower rate of boulder-forming impacts, and therefore, a lower boulder formation rate. However, these two factors seem insufficient to explain the huge difference in boulder occurrence between the Moon and Mercury. Lifetime. Boulders are destroyed by small meteoritic impacts, ground by micrometeoritic impacts [2], and cracked and disintegrated by thermal stresses [5,6]. The relative role of these processes on the Moon is controversial. Since meteoritic flux is on the same order of magnitude on both bodies, it cannot account for the observed huge difference. Peak daytime temperature at a flat monolith bedrock surface induces tensile stress equal to E α ΔT/(1-ν), where E is the Young’s modulus (~60 GPa for basalts [7]), ν is Poisson ratio (~0.25), α is linear thermal expansion coefficient (~10-5 K-1), and ΔT is the peak temperature excess above the mean temperature. For Mercury, this stress would regularly exceed 100 MPa. This value gives an order of magnitude estimate for peak tensile stress experienced by a boulder, if it is larger or comparable to the diurnal thermal skin depth for solid rock, which is ~3 m for the long day on Mercury. For smaller boulders the peak stress is lower; detailed modeling has been done in [5]. The boulders we observe are larger than 3 m, therefore, the peak stress is on the order of 100 MPa, much higher than a typical tensile strength of intact basalts (~14 MPa [7]). Therefore, all boulders acquire boulder-scale fractures at their first exposure at the surface. The difference Δα between thermal expansion coefficients of rock-forming mineral grains leads to thermal stress at grain scale [6]. They are scaled as C E Δα ΔT, where C depending on ν and grain geometry is on the order of unity. These stresses would also routinely exceed tensile strength; they would lead to formation of microfractures at grain scale. Thus, thermal stresses cause extensive fracturing of rocks on Mercury, which likely contributes to the short boulder life time. The micrometeoritic flux on Mercury is significantly higher than on the Moon [8,9]. If micrometeoritic grinding is the dominant boulder obliteration mechanism on the Moon, then the difference in the micrometeoritic flux is likely to be sufficient to account for the entire observed difference in boulder abundance. References [1] Zharkova A. et al. (2019) LPSC 50, 1162. [2] Basilevsky A.T. et al. (2013) PSS 89, 118-126. [3] Kreslavsky M. et al. (2014) GRL 41, 8245-8251. [4] Zharkova A. et al. (2015) AGU Fall, P53A-2099. [5] Molaro J. et al. (2017) Icarus 294, 247-261. [6] Molaro J. et al. (2015) JGR 120, 255-277. [7] Schultz R. (1993) JGR 98, 10883-10895. [8] Cintala M. (1992) JGR 97, 947. [9] Borin P. et al. (2009) Astron. Astrophys. 503, 259.

We have recently investigated the use of SAR interferometry for the mapping of Snow Water Equivalent (SWE) temporal variations using Sentinel-1 data [1]. Maps of temporal changes of SWE, measured with a sub-centimetre accuracy and updated every six days have been obtained over a study area in Finland. This methodology relies on the shift in the interferometric phase caused by the refraction of the microwave signal penetrating the snow layer. In this work, we investigate phase inconsistencies [2] of a sets of three interferograms obtained from three Sentinel-1 images acquired along the same orbit at different acquisition times to study the snow melt. We find that while phase inconsistencies are not expected to be present in case of examining surfaces covered with frozen snow, the scattering mechanism of microwave in the snow layer during the melting phase affects both the interferometric phase and coherence. This work was supported, in part, by the Academy of Finland project no. 325806. References:[1] V. Conde, G. Nico, P. Mateus, J. Catalão, A. Kontu, M. Gritsevich, On the estimation of temporal changes of snow water equivalent by spaceborne SAR interferometry: a new application for the Sentinel-1 mission, J. Hydrol. Hydromech., 67, 2019, 1, 93–100. DOI: 10.2478/johh-2018-0003[2] F. De Zan, M. Zonno, P. López-Dekker, Phase inconsistencies and multiple scattering in SAR interferometry, IEEE Transactions on Geoscience and Remote Sensing, 53(12), 6608-6616, 2015

Many natural land surfaces, such as sand or snow, consist of densely packed grains, often covered by dust, water droplets, contaminated with other materials such as possible oil leaks, hoar frost, and can also be internally cracked, porous, and heterogeneous. Most scattering models ignore these complications, but here a more detailed approach is taken to test all these effects. The current model is composed of three techniques: 1) Monte Carlo-based electromagnetic volume integral equation technique for non-spherical wavelength scale dust particles, 2) Monte Carlo ray tracing for stochastic-shaped grains much larger than the wavelength, with optional point scattering from dust cover, internal inclusions, and liquid surface layer, in a layer of an optical depths of few units, and 3) adding-doubling to combine smaller layers into an arbitrary, thick and vertically inhomogeneous medium. The model allows the medium to be built in a modular way, and after initialisation, rather complicated layered structures can be computed quickly and flexibly. The computed results are compared against experimental measurements of snow and sand. The model agrees with measurements usually within the measurement accuracy (∼ 0:05). The scattering is observed to depend significantly on grain size, shape, orientation, composition, fine structures, dust, and some other properties that need to be defined. Both, measurement and modelling, require much deeper attention to these properties. © 2020 Copernicus GmbH. All rights reserved.

Light-absorbing impurities affect snow and ice via a decrease in albedo and a consequent disturbance to the radiative energy balance. Experimentally, these matters have only been examined in a few studies. Here we present results from a series of experiments in which we deposited different soot concentrations onto natural snow in different regions of Finland, and thereafter monitored the changes of the snowpack through the melting season. Measurements of the particulates in the snow indicated concentrations in the range of thousands of ppb to have clear effects on the snow properties, including the albedo, the physical snow characteristics, and an increased melt rate. For soot concentrations in the hundreds of ppb range, the effects were not as clearly visible, and it was more difficult to attribute the effects solely to the soot on the snow. Comparisons between our experimental data and the widely used Snow, Ice and Aerosol Radiation (SNICAR) model showed a general agreement when the model was specifically tuned to our measurements. This study highlights the importance of additional experimental studies, to further articulate and quantify the effects of light-absorbing impurities on snow.