Snow Layer Prediction Model using Back Scattering Co-efficient

Abstract

In this study, the objective is to estimate the physical parameters of each snow layer at a given certain altitude, which is according to the problem will always be between other altitudes, whose profile data is entirely provided. The provided data for the target profile will also consist of the number of layers, height of each layer, the back-scattering coefficient and the altitude. The goal is to estimate the interval [y_min, y_max] for each parameter, which are sphericity, diameter and density of the snowflakes. We are using here a multilayer snow profile and then applying the back scattering coefficients to calculate the snow layer altitude and height. Further, by correct prediction we can preserve the snow layers by continuously monitoring the snow model at the correct altitude throughout the year. Our aim demonstrates the comparatively better way to predict the best snow profile at right altitudes.

Introduction

I. INTRODUCTION

The Arctic region has undergone a serious climate change in the near decade. A lot of ice region has melted due to global warming. Unlike Antarctic Sea ice arctic region is highlighted for decline due to serious change in sea ice thickness [1]. Also, a huge loss of multi-year ice is observed and replaced by first year ice which is a lot of concern [2].  It has a resulted in decline of the Spring snow depth [3]. These changes have significantly affected the entire environment of eco system and marine environment with navigation system for water animals.[4][5] Similarly, NoSREx campaign was conducted near the Arctic Region to the Sodankylä?Pallas testbed for measuring the ever-declining ice bed of Sodankylä region. The landscape is generally flat and there are small hilly parts where the ice beds appear to be getting extinct in the recent years (NoSREx Data Report).[6][7]

II. RESEARCH OBJECTIVES

In this project we will give the context of the problem, describe how we determine the boundaries of the target profiles through interpolation and the ideas about increasing the performance of the model. We will conclude with the main challenges encountered, the methods that were tried and the possible extensions. We are given the data of 7 altitudes for 4 different dates. Each profile consists of assimilated data and results of snow evolution model (crocus). Therefore, we have the following parameters: (h, g1, g2, g3, ρ, σ). So, if we consider a snow-pack with k layers of snow, where k is known:

V. ACKNOWLEDGMENT

We thank UGA Grenoble and GIPSA Lab for providing immense help during this project.  Also we are very much obliged to UGA School of Mathematics and School of Computer Science for guiding usus through our project work. 

Conclusion

One of the main challenges was to find the way to approach the problem. In the beginning we were looking for the ways to map the pixel intensities with the physical parameters of the snow layer. We came up with the graphical approach, but since the conditions changed where the images were not a concern, the approach gave a near perfect estimation of snowflakes. Also, to improve the estimated parameters, we provided all together with the back-scattering coefficients and the heights.

References

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Copyright

Copyright © 2023 Tushar Debnath, Dibakar Saha, Sumit Bhowmik. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.