WRaINfo: An Open Source Library for Weather Radar INformation for FURUNO Weather Radars Based on Wradlib

WRaINfo is a software for real-time weather radar data processing developed by the Helmholtz Innovation Lab FERN.Lab, a technology and innovation platform of the German Research Centre for Geosciences Potsdam (GFZ). WRaINfo is specifically designed for processing X-band weather radar data of FURUNO devices. The modules of the package allow to read and process raw data of the WR2120 and WR2100. For this purpose, many functions of the library wradlib are used and adapted. The processing is controlled by a configuration file, main functionalities include formatting, attenuation correction, clutter detection, georeferencing and gridding of the data. This allows the construction of reproducible, automatic data processing chains. The package is written in the Python programming language. The source code is publicly available on GitLab. Compiled versions are also available on PyPi. The package is distributed under the Apache 2.0 license

Intercomparison and Validation of SAR-Based Ice Velocity Measurement Techniques within the Greenland Ice Sheet CCI Project

Ice velocity is one of the products associated with the Ice Sheets Essential Climate Variable. This paper describes the intercomparison and validation of ice-velocity measurements carried out by several international research groups within the European Space Agency Greenland Ice Sheet Climate Change Initiative project, based on space-borne Synthetic Aperture Radar (SAR) data. The goal of this activity was to survey the best SAR-based measurement and error characterization approaches currently in practice. To this end, four experiments were carried out, related to different processing techniques and scenarios, namely differential SAR interferometry, multi aperture SAR interferometry and offset-tracking of incoherent as well as of partially-coherent data. For each task, participants were provided with common datasets covering areas located on the Greenland ice-sheet margin and asked to provide mean velocity maps, quality characterization and a description of processing algorithms and parameters. The results were then intercompared and validated against GPS data, revealing in several cases significant differences in terms of coverage and accuracy. The algorithmic steps and parameters influencing the coverage, accuracy and spatial resolution of the measurements are discussed in detail for each technique, as well as the consistency between quality parameters and validation results. This allows several recommendations to be formulated, in particular concerning procedures which can reduce the impact of analyst decisions, and which are often found to be the cause of sub-optimal algorithm performance

The glaciers climate change initiative: Methods for creating glacier area, elevation change and velocity products

Glaciers and their changes through time are increasingly obtained from a wide range of satellite sensors. Due to the often remote location of glaciers in inaccessible and high-mountain terrain, satellite observations frequently provide the only available measurements. Furthermore, satellite data provide observations of glacier character- istics that are difficult to monitor using ground-based measurements, thus complementing the latter. In the Glaciers_cci project of the European Space Agency (ESA), three of these characteristics are investigated in detail: glacier area, elevation change and surface velocity. We use (a) data from optical sensors to derive glacier outlines, (b) digital elevation models from at least two points in time, (c) repeat altimetry for determining elevation changes, and (d) data from repeat optical and microwave sensors for calculating surface velocity. For the latter, the two sensor types provide complementary information in terms of spatio-temporal coverage. While (c) and (d) can be generated mostly automatically, (a) and (b) require the intervention of an analyst. Largely based on the results of various round robin experiments (multi-analyst benchmark studies) for each of the products, we suggest and describe the most suitable algorithms for product creation and provide recommendations concerning their practical implementation and the required post-processing. For some of the products (area, velocity) post-processing can influence product quality more than the main-processing algorithm

Estimating Spatial and Temporal Variability in Surface Kinematics of the Inylchek Glacier, Central Asia, using TerraSAR–X Data

We use 124 scenes of TerraSAR–X data that were acquired in 2009 and 2010 to analyse the spatial and temporal variability in surface kinematics of the debris-covered Inylchek Glacier, located in the Tien Shan mountain range in Central Asia. By applying the feature tracking method to the intensity information of the radar data and combining the results from the ascending and descending orbits, we derive the surface velocity field of the glaciated area. Analysing the seasonal variations over the upper part of the Southern Inylchek branch, we find a temperature-related increase in velocity from 25 cm/d up to 50 cm/d between spring and summer, with the peak occurring in June. Another prominent velocity peak is observable one month later in the lower part of the Southern Inylchek branch. This area shows generally little motion, with values of approximately 5–10 cm/d over the year, but yields surface kinematics of up to 25 cm/d during the peak period. Comparisons of the dates of annual glacial lake outburst floods (GLOFs) of the proglacial Lake Merzbacher suggest that this lower part is directly influenced by the drainage, leading to the observed mini-surge, which has over twice the normal displacement rate. With regard to the GLOF and the related response of Inylchek Glacier, we conclude that X–band radar systems such as TerraSAR–X have a high potential for detecting and characterising small-scale glacial surface kinematic variations and should be considered for future inter-annual glacial monitoring tasks

Ground Deformations around the Toktogul Reservoir, Kyrgyzstan, from Envisat ASAR and Sentinel-1 Data—A Case Study about the Impact of Atmospheric Corrections on InSAR Time Series

We present ground deformations in response to water level variations at the Toktogul Reservoir, located in Kyrgyzstan, Central Asia. Ground deformations were measured by Envisat Advanced Synthetic Aperture Radar (ASAR) and Sentinel-1 Differential Interferometric Synthetic Aperture Radar (DInSAR) imagery covering the time periods 2004–2009 and 2014–2016, respectively. The net reservoir water level, as measured by satellite radar altimetry, decreased approximately 60 m (∼13.5 km3) from 2004–2009, whereas, for 2014–2016, the net water level increased by approximately 51 m (∼11.2 km3). The individual Small BAseline Subset (SBAS) interferograms were heavily influenced by atmospheric effects that needed to be minimized prior to the time-series analysis. We tested several approaches including corrections based on global numerical weather model data, such as the European Centre for Medium-RangeWeather Forecasts (ECMWF) operational forecast data, the ERA-5 reanalysis, and the ERA-Interim reanalysis, as well as phase-based methods, such as calculating a simple linear dependency on the elevation or the more sophisticated power-law approach. Our findings suggest that, for the high-mountain Toktogul area, the power-law correction performs the best. Envisat descending time series for the period of water recession reveal mean line-of-sight (LOS) uplift rates of 7.8 mm/yr on the northern shore of the Toktogul Reservoir close to the Toktogul city area. For the same area, Sentinel-1 ascending and descending time series consistently show a subsidence behaviour due to the replenishing of the water reservoir, which includes intra-annual LOS variations on the order of 30mm. A decomposition of the LOS deformation rates of both Sentinel-1 orbits revealed mean vertical subsidence rates of 25 mm/yr for the common time period of March 2015–November 2016, which is in very good agreement with the results derived from elastic modelling based on the TEA12 Earth model