PyTroll: An Open-Source, Community-Driven Python Framework to Process Earth Observation Satellite Data

Raspaud, Martin; Hoese, David; Dybbroe, Adam; Lahtinen, Panu; Devasthale, Abhay; Itkin, Mikhail; Hamann, Ulrich; Rasmussen, Lars Ørum; Nielsen, Esben Stigård; Leppelt, Thomas; Maul, Alexander; Kliche, Christian; Thorsteinsson, Hrobjartur

doi:10.1175/bams-d-17-0277.1

Cited by 34 publications

(30 citation statements)

References 10 publications

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“…Since its first release in the mid-1990s [47,48] Python has become increasingly popular for scientific computing [49,50]. Thanks to its numerous extension packages, in particular NumPy/SciPy [51], the "Python-based ecosystem" ( [52], https://scipy.org/) is widely considered to be the language of choice in many areas, e.g., atmospheric science and astrophysics [53][54][55].…”

Section: Introductionmentioning

confidence: 99%

Py4CAtS—PYthon for Computational ATmospheric Spectroscopy

et al. 2019

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Radiation is a key process in the atmosphere. Numerous radiative transfer codes have been developed spanning a large range of wavelengths, complexities, speeds, and accuracies. In the infrared and microwave, line-by-line codes are crucial esp. for modeling and analyzing high-resolution spectroscopic observations. Here we present Py4CAtS—PYthon scripts for Computational ATmospheric Spectroscopy, a Python re-implemen-tation of the Fortran Generic Atmospheric Radiation Line-by-line Code GARLIC, where computationally-intensive code sections use the Numeric/Scientific Python modules for highly optimized array processing. The individual steps of an infrared or microwave radiative transfer computation are implemented in separate scripts (and corresponding functions) to extract lines of relevant molecules in the spectral range of interest, to compute line-by-line cross sections for given pressure(s) and temperature(s), to combine cross sections to absorption coefficients and optical depths, and to integrate along the line-of-sight to transmission and radiance/intensity. Py4CAtS can be used in three ways: in the (Unix/Windows/Mac) console/terminal, inside the (I)Python interpreter, or Jupyter notebook. The basic design of the package, numerical and computational aspects relevant for optimization, and a sketch of the typical workflow are presented. In conclusion, Py4CAtS provides a versatile environment for “interactive” (and batch) line-by-line radiative transfer modeling.

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Section: Introductionmentioning

confidence: 99%

Py4CAtS—PYthon for Computational ATmospheric Spectroscopy

et al. 2019

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“…The MERRA-2 model global winds (tavg1_2d_ocn_Nx product) were available at 7-km spatial resolution through the UHR process. RAP winds were available at a native 13-km resolution horizontal grid and 3-h temporal resolution, and therefore were resampled at a 7-km prior to ingest them in the ambiguity removal process using the pyresample module [55]. This Python module is a fast and generalized interface to resample (reproject) Earth-observing satellite data, both gridded and swath types, by the implementation of a (custom) KD-tree approach.…”

Section: Resampling Of Background Windsmentioning

confidence: 99%

Evaluating the Detection of Mesoscale Outflow Boundaries Using Scatterometer Winds at Different Spatial Resolutions

et al. 2021

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Outflow boundaries induced by cold-pools are a key characteristic of convective systems related to microphysical and kinematic processes during the mature stage of their lifecycle. Over the ocean, such kinematic processes are associated with low-level wind modulations that are captured by scatterometers. This study investigates the ability of the Advanced Scatterometer (ASCAT) wind retrievals to detect the outflow boundary associated with an oceanic mesoscale convective system (MCS). Leveraging a new technique to identify cold pools that is based on features that enclose elevated magnitude of the gradient of the wind, termed as `Gradient Feature’ (GF), wind retrievals at 50-, 25- and 7-km spatial resolution were utilized to explore how the characteristics of the outflow boundary vary with resolution. Ground-based radar retrievals were also implemented to assess and correct, when possible, the performance of the ASCAT retrievals. The magnitude of the gradient of the wind for the coarser resolution was an order of magnitude smaller (10−4 s−1) than the finer ones (10−3 s−1). An increase in the magnitude of the gradient wind field associated with the outflow boundary was captured by all resolutions and a respective feature was identified by the GF method. The location of the features relative to the distance from the front edge of the MCS decreased with resolution, indicating the importance of the high resolution ASCAT product to capture their extent, as well as additional smaller scale features. The effect of the background wind field on the selection of the final wind field during the ambiguity removal process for the high-resolution product is also discussed.

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“…"Nearest-point" algorithms are computationally efficient and correctly handle the quantities that need to be conserved. To remap the data from one spatial grid to another we used the pyresample package, which is part of the PyTroll suite [36].…”

Section: Combining Marine Currents and Tsm Upstream Datamentioning

confidence: 99%

Synergy of Satellite Remote Sensing and Numerical Ocean Modelling for Coastal Geomorphology Diagnosis

et al. 2019

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Sediment dynamics is the primary driver of the evolution of the coastal geomorphology and of the underwater shelf clinoforms. In this paper, we focus on mesoscale and sub-mesoscale processes, such as coastal currents and river plumes, and how they shape the sediment dynamics at regional or basin spatial scales. A new methodology is developed that combines observational data with numerical modelling: the aim is to pair satellite measurements of suspended sediment with velocity fields from numerical oceanographic models, to obtain an estimation of the sediment flux. A numerical divergence of this flux is then computed. The divergence field thus obtained shows how the aforementioned mesoscale processes distribute the sediments. The approach was applied and discussed on the Adriatic Sea, for the winter of 2012, using data provided by the ESA Coastcolour project and the output of a run of the MIT General Circulation Model.

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PyTroll: An Open-Source, Community-Driven Python Framework to Process Earth Observation Satellite Data

Cited by 34 publications

References 10 publications

Py4CAtS—PYthon for Computational ATmospheric Spectroscopy

Py4CAtS—PYthon for Computational ATmospheric Spectroscopy

Evaluating the Detection of Mesoscale Outflow Boundaries Using Scatterometer Winds at Different Spatial Resolutions

Synergy of Satellite Remote Sensing and Numerical Ocean Modelling for Coastal Geomorphology Diagnosis

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