Snow Height and Surface Temperature Variations from Ground GPS Receivers in Greenland

N. Najibi, S. Jin

Abstract:	The continental ice sheets are frozen fresh-water reservoirs with volume change related to the temperature and snowfall, which affect on and are affected by changes in the Earth's climate. It also plays an important role in the study of global water cycle and understanding water exchange and energy balance. For example, the melting of ice sheets and snow in Antarctic and Greenland is causing the sea level rising with fresh water input from continental fresh water due to human-induced global warming. The snow height and surface temperature play a critical role in the climate change and hydrologic cycle as well as energy exchange between the terrestrial surface, snowpack and the atmosphere. However, snow height and surface temperature are traditionally measured by specific in-situ instruments and sensors, while it is very difficult to monitor global high temporal-spatial snow depth and temperature variability due to high cost and hard labor intensity. For example, the precipitation reaches the ground surface and begins to form as snow in high atmosphere. These snowflakes form in the above layers of atmosphere where the air temperature is less than 0 ?C and thus start to fall toward the earth surface as snow. If the falling snow passes through the freezing level into the warmer air or arrives at snow surface with higher temperature than snowflake itself, it would melt to rain or runoff before reaching the ground or touching the snow surface. Therefore, it is very necessary to have sufficient information about snow surface features such as temperature, characteristics, albedo, and density and so on. Meanwhile, snow surface temperature is also playing a critical role in the snow melting and snow accumulating. Nowadays, the remote sensing techniques can measure snow surface temperature for hydrology and glaciology studies accurately. Recently the studies showed that the snow depth can be retrieved from GPS signal to noise ratio (SNR) and multipath signals, especially L4 values, while L4 and SNR are different completely since L4 is extracting from the GPS received signals while SNR is computing based on the phases of the received signals. The most important challenge of the climate variation data analysis is to separate many effective parameters and their interactions. This issue is becoming more important when the understanding and measurement of the variability of the earth climate is complex where there is no enough information about the certainty, accuracy and reasonability of the current models. In this paper, measuring snow height and snow surface temperature is investigated using reflected signals from ground Global Positioning System (GPS) receivers in Greenland with widely snowpack, ice sheets and permafrost. The GPS reflected signals in the frequencies of L-Bands (1.2276 and 1.5754 GHz) on the frozen water (e.g. snowpack, ice caps, glaciers, etc) allow us to detect the reflected surface characteristics. In order to obtain the reflected surface features and estimate snow depth and surface temperature, the multipath signals are extracted from a free GPS linear combination (L4). Here two GPS stations SMM1 (72.58N and 38.46W) and MARG (77.19N and 65.69W) from January 1, 2010 to August 1, 2010 with two co-located Greenland Climate Network Automated Weather Stations (GC-Net AWS) Summit (72.57N and 38.50W) and GITS (77.14N and 61.04W) sites are tested respectively. Results show that variations of snow height and snow surface temperature coincide with L4 fluctuations. However, due to the lack of information about the type of accurate model, the nonparametric fitting into the function of L4 versus snow height and snow surface temperature as bootstrapping models in the direct and inverse solutions are proposed. In general, increasing temperature results in snow melting in form of reduction in snow height, but it doesn’t occur in the inverse way since temperature’s decreasing makes the snow height larger, which depends critically on snow falling as snow accumulating. The results indicate that the proposed model is applicable in a minimum bias since the mean bias during these 210 days from January 1, 2010 to August, 1 2010 at SMM1 and MARG station is 1.64 and 2.13 ?C with standard deviation of 4.45 and 3.73 ?C, respectively. The proposed bootstrapping model builds a sampling distribution for a statistic estimator by resampling the available data, which performs empirical distribution function (EDF) and probability density function (PDF) of nonparametric bootstrapping. Although EDF and PDF are the most critical functions in construction of the model, particularly for the inverse direction of bootstrapping, but EDF estimates the unknown cumulative distribution function (CDF) by giving equal probabilities to the original values (GPS L4 variations) and each output value (snow surface temperature), while each one is independently modeled (sampled in direct model or resampled in inverse model). Therefore the bootstrap modeled output’s trend is an empirical normalized sample with replacement from the original trend. The accuracy of proposed model has a very good agreement when compared to AWS values with mean bias of 1.64 and 2.13 ?C and standard deviation of 4.45 and 3.73 ?C at SMM1 and MARG stations respectively.
Published in:	Proceedings of the 26th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2013) September 16 - 20, 2013 Nashville Convention Center, Nashville, Tennessee Nashville, TN
Pages:	62 - 68
Cite this article:	Najibi, N., Jin, S., "Snow Height and Surface Temperature Variations from Ground GPS Receivers in Greenland," Proceedings of the 26th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2013), Nashville, TN, September 2013, pp. 62-68.
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