Closed-form control with spike coding networks

Efficient and robust control using spiking neural networks (SNNs) is still an open problem. Whilst behaviour of biological agents is produced through sparse and irregular spiking patterns, which provide both robust and efficient control, the activity patterns in most artificial spiking neural networks used for control are dense and regular -- resulting in potentially less efficient codes. Additionally, for most existing control solutions network training or optimization is necessary, even for fully identified systems, complicating their implementation in on-chip low-power solutions. The neuroscience theory of Spike Coding Networks (SCNs) offers a fully analytical solution for implementing dynamical systems in recurrent spiking neural networks -- while maintaining irregular, sparse, and robust spiking activity -- but it's not clear how to directly apply it to control problems. Here, we extend SCN theory by incorporating closed-form optimal estimation and control. The resulting networks work as a spiking equivalent of a linear-quadratic-Gaussian controller. We demonstrate robust spiking control of simulated spring-mass-damper and cart-pole systems, in the face of several perturbations, including input- and system-noise, system disturbances, and neural silencing. As our approach does not need learning or optimization, it offers opportunities for deploying fast and efficient task-specific on-chip spiking controllers with biologically realistic activity.Comment: Under review in an IEEE journa

SCIAMACHY Calibration Lessons Learned

The presentation reviews SCIAMACHY calibration and presents the lessons to be learned for future/current instrument

Long-term analysis of GOME in-flight calibration parameters and instrument degradation

Since 1995, the Global Ozone Monitoring Experiment (GOME) has measured solar and backscattered spectra in the ultraviolet and visible wavelength range. Now, the extensive data set of the most important calibration parameters has been investigated thoroughly in order to analyze the long-term stability and performance of the instrument. This study focuses on GOME in-flight calibration and degradation for the solar path. Monitoring the sensor degradation yields an intensity decrease of 70% to 90% in 240–316nm and 35% to 65% in 311–415 nm. The spectral calibration is very stable over the whole period, although a very complex interaction between predisperser temperature and wavelength was found. The leakage current and the pixel-to-pixel gain increased significantly during the mission, which requires an accurate correction of the measured radiance and irradiance signals using proper calibration parameters. Finally, several outliers in the data sets can be directly assigned to instrument and satellite anomalies

The Global Ozone Monitoring Experiment: Review of in-flight performance and new reprocessed 1995-2011 level 1 product

The Global Ozone Monitoring Experiment (GOME) on-board the second European Remote Sensing satellite provided measurements of atmospheric constituents such as ozone or other trace gases for the 16 year period from 1995 to 2011. In this paper we present a detailed analysis of the long-term performance of the sensor and introduce the new homogenized and fully calibrated level 1 product which has been generated using the recently developed GOME Data Processor level-0-to-1b (GDP-L1) Version 5.1. By means of the various in-flight calibration parameters we monitor the behavior and stability of the instrument during the entire mission. Severe degradation of the optical components has led to a significant decrease in intensity in particular in channels 1 and 2 covering the spectral ranges of 240–316 nm and 311–405 nm, respectively. Thus, a soft correction based on using the sun as a stable calibration source is applied. Revision and optimization of other calibration algorithms such as the wavelength assignment, polarization correction, or dark current correction resulted in an improved and homogeneous level 1 product that can be regarded as the European satellite reference data for successor atmospheric composition sensors and that provides an excellent prerequisite for further exploitation of GOME measurements

SCIAMACHY: The new Level 0-1 Processor

SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) is a scanning nadir and limb spectrometer covering the wavelength range from 212 nm to 2386 nm in 8 channels. It is a joint project of Germany, the Netherlands and Belgium and was launched in February 2002 on the ENVISAT platform. After the platform failure in April 2012, SCIAMACHY is now in the postprocessing phase F. SCIAMACHY�s originally specified in-orbit lifetime was double the planned lifetime. SCIAMACHY was designed to measure column densities and vertical profiles of trace gas species in the mesosphere, in the stratosphere and in the troposphere (Bovensmann et al., 1999). It can detect a large amount of atmospheric gases (e.g. O3 , H2CO, CHOCHO, SO2 , BrO, OClO, NO2 , H2O, CO, CH4 , among others ) and can provide information about aerosols and clouds. The operational processing of SCIAMACHY is split into Level 0-1 processing (essentially providing calibrated radiances) and Level 1-2 processing providing geophysical products. The operational Level 0-1 processor has been completely re-coded and embedded in a newly developed framework that speeds up processing considerably. In the frame of the SCIAMACHY Quality Working Group activities, ESA is continuing the improvement of the archived data sets. Currently Version 9 of the Level 0-1 processor is being implemented. It will include An updated degradation correction Several improvements in the SWIR spectral range like a better dark correction, an improved dead & bad pixel characterisation and an improved spectral calibration Improvements to the polarisation correction algorithm Improvements to the geolocation by a better pointing characterisation Additionally a new format for the Level 1b and Level 1c will be implemented. The version 9 products will be available in netCDF version 4 that is aligned with the formats of the GOME -1 and Sentinel missions. We will present the first results of the new Level 0-1 processing in this paper

Level 1-to-2 Data Processor Version 3.0: A Major Upgrade of the GOME/ERS-2 Total Ozone Retrieval Algorithm,

Abstract The Global Ozone Monitoring Experiment (GOME) was launched in April 1995, and the GOME Data Processor (GDP) retrieval algorithm has processed operational total ozone amounts since July 1995. GDP Level 1-to-2 is based on the two-step Differential Optical Absorption Spectroscopy (DOAS) approach, involving slant column fitting followed by Air Mass Factor (AMF) conversions to vertical column amounts. We present a major upgrade of this algorithm to Version 3.0. GDP 3.0 was implemented in July 2002, and the 9-year GOME data record from July 1995 to December 2004 has been processed using this algorithm. The key component in GDP 3.0 is an iterative approach to AMF calculation, in which AMFs and corresponding vertical column densities are adjusted to reflect the true ozone distribution as represented by the fitted DOAS effective slant column. A neural networkensemble is used to optimize the fast and accurate parameterization of AMFs. We describe results of a recent validation exercise for the operational version of the total ozone algorithm; in particular, seasonal and meridian errors are reduced by a factor of two. On a global basis, GDP 3.0 ozone total column results lie between -2% and +4% of ground-based values for moderate solar zenith angles lower than 70°. A larger variability of about +5% and -8% is observed for higher solar zenith angles up to 90°. Copyright OCIS codes 01

The FDR4ATMOS Project

The FDR4ATMOS project has two main tasks. The focus of task A is to update the SCIAMACHY processing chain for better Ozone total columns. After the full re-processing of the SCIAMACHY mission with processor versions 9 (Level 1) and version 7 (Level 2), the comparison with ground-based data showed that the total Ozone column showed a downward trend of nearly 2% from the beginning of the time series to its end. This trend is an artefact and is likely caused by changes made to the calibration algorithms in the Level 1 processor (the DOAS retrieval algorithm for Ozone was not changed). The most likely reason are changes in the degradation correction that lead to subtle changes in the spectral structures that in the retrieval are interpreted as an atmospheric signature. In task A we will update the Level 0-1 processor with the final aim of a mission re-processing. The second task in the FDR4ATMOS project is to develop a cross-instrument Level 1 product for GOME-1 and SCIAMACHY for the UV, VIS and NIR spectral range with a focus on the spectral windows used for O3, SO2, NO2 total column retrieval and the determination of cloud properties. Contrary to other projects, FDR4ATMOS does not aim to build a harmonised time series on Level 2 products but on Level 1 products, i.e. radiances and reflectances. The GOME-1 and SCIAMACHY instrument together span 17 years of spectrally highly resolved data. The goal of the FDR4ATMOS project is to generate harmonised data sets that allow the user to use it directly in long term trend analysis, independent of the instrument. Since this was never done for highly resolved spectrometers, new methods have to be developed that e.g. take into account the different observation geometries and observation times of the instrument without impacting the spectral structures that are used for the retrieval of the atmospheric species. The resulting algorithms and the processor should also be as generic as possible to be able to transfer the methodology easily to other instruments (e.g. GOME-2, Sentinel-5p) for a future extension of the time series. The FDR4ATMOS started in October 2019 and is currently in phase 1. We will present the goals of the project and first results

The CO2Image mission: retrieval studies and performance analysis

The CO2Image satellite mission, led by the German Aerospace Center (DLR), aims to demonstrate the feasibility of quantifying carbon dioxide (CO2) and methane (CH4) emissions from medium-size point sources. Several DLR institutes are currently working on the reliminary design phase (Phase B) of the mission. Here we present a performance analysis based on the current instrument specifications. The Beer InfraRed Retrieval Algorithm (BIRRA), the line-by-line radiative transfer model Py4CAtS (Python for Computational ATmospheric Spectroscopy) and a COSIS (Carbon dioxide Sensing Imaging Spectrometer) instrument model are employed to infer CO2 and CH4 concentrations from synthetic COSIS spectra. We evaluate the instrument's performance and determine if it meets the intended requirements. The study assesses uncertainties in the retrieved concentrations as well as errors in point source emission estimates caused by instrument noise. First results suggest that the detection and quantification limits stated in the mission requirements document are justified. The analysis also demonstrates that retrieval errors tend to increase when the signal-to-noise ratio is low, complicating the distinction between emission sources and background concentrations. Furthermore, we discuss non-instrumental effects and demonstrate that the fit quality significantly improves if a low-level plume is scaled instead of a background reference profile that covers the atmosphere's full vertical extent. The analysis on heterogeneous scenes (high albedo contrast) reveals that the various instrument setups perform similarly for both molecules

CO2Image retrieval studies and performance analysis

Current and planned satellite missions such as the Japanese GOSAT (Greenhouse Gases Observing Satellite) and NASA's OCO (Orbiting Carbon Observatory) series and the upcoming Copernicus Carbon Dioxide Monitoring (CO2M) mission aim to constrain national and regional-scale emissions down to scales of urban agglomerations and large point sources. The CO2Image demonstrator mission of the German Aerospace Center (DLR) is specifically designed to detect and quantify carbon dioxide (CO2) and methane (CH4) emissions from medium-size point sources. To this end its COSIS (Carbon dioxide Sensing Imaging Spectrometer) push-broom grating spectrometer measures reflected solar radiation with a high spatial resolution of 50x50 m2, covering tiles of ~50x50 km2 extent. The instrument has a moderate spectral resolution of approximately ~1 nm and observes in a single spectral window in the 2 µm region. Here we present and discuss the impact of the expected COSIS performance on the retrieved level-2 data. The level-1 data (spectra) are generated using the Py4CAtS (Python for Computational ATmospheric Spectroscopy) line-by-line radiative transfer model and the COSIS SIMulator (COSIS-SIM). Based on the COSIS instrument parameters the analysis examines the retrieval errors related to noise which allows to estimate the detection and quantification limit of CO2 and CH4 emission rates at the instrument's spatial and spectral resolution. We further discuss the effect of heterogeneous scenes, i.e. high contrast surfaces that cause an effective distortion of the spectral response function by non-uniform illumination of the entrance slit. Finally, we assess the influence of initial guess values for the plume's vertical extent and shape on the retrieval

SCIAMACHY: Level 0-1 Processor V9 and Phase F Re-processing

SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) is a scanning nadir and limb spectrometer covering the wavelength range from 212 nm to 2386 nm in 8 channels. It is a joint project of Germany, the Netherlands and Belgium and was launched in February 2002 on the ENVISAT platform. After the platform failure in April 2012, SCIAMACHY is now in the postprocessing phase F. Its originally specified in-orbit lifetime was double the planned lifetime. SCIAMACHY was designed to measure column densities and vertical profiles of trace gas species in the mesosphere, in the stratosphere and in the troposphere (Bovensmann et al., 1999). It can detect a large amount of atmospheric gases (e.g. O3 , H2CO, CHOCHO, SO2 , BrO, OClO, NO2 , H2O, CO, CH4 , among others ) and can provide information about aerosols and clouds. The operational processing of SCIAMACHY is split into Level 0-1 processing (essentially providing calibrated radiances) and Level 1-2 processing providing geophysical products. The operational Level 0-1 processor has been completely re-coded and embedded in a newly developed framework that speeds up processing considerably. In the frame of the SCIAMACHY Quality Working Group activities, ESA is continuing the improvement of the archived data sets. Version 9 of the Level 0-1 processor includes - An updated degradation correction - Improvements to the polarisation correction algorithm - Improvements to the geolocation by a better pointing characterisation - Several improvements in the SWIR spectral range like a better dark correction, an improved dead & bad pixel characterisation and an improved spectral calibration The new format for the Level 1b and Level 1c will be netCDF V4. We will present the verification results and the results of the mission re-processing