scholarly journals Review of « Ozone Profile Climatology for Remote Sensing Retrieval Algorithms » by Kai Yang and Xiong Liu.

2019 ◽  
Author(s):  
Anonymous
Keyword(s):  
Remote Sensing ◽  
Ozone Profile ◽  
Retrieval Algorithms

scholarly journals Deriving the slit functions from OMI solar observations and its implications for ozone-profile retrieval

2017 ◽  
Vol 10 (10) ◽  
pp. 3677-3695 ◽  
Author(s):  
Kang Sun ◽  
Xiong Liu ◽  
Guanyu Huang ◽  
Gonzalo González Abad ◽  
Zhaonan Cai ◽  
...  
Keyword(s):  
Atmospheric Composition ◽  
Half Maximum ◽  
Ozone Monitoring Instrument ◽  
Gaussian Functions ◽  
Solar Observations ◽  
Ozone Profile ◽  
Retrieval Algorithms ◽  
Ozone Monitoring ◽  
Cross Track ◽  
Over Time

Abstract. The Ozone Monitoring Instrument (OMI) has been successfully measuring the Earth's atmospheric composition since 2004, but the on-orbit behavior of its slit functions has not been thoroughly characterized. Preflight measurements of slit functions have been used as a static input in many OMI retrieval algorithms. This study derives on-orbit slit functions from the OMI irradiance spectra assuming various function forms, including standard and super-Gaussian functions and a stretch to the preflight slit functions. The on-orbit slit functions in the UV bands show U-shaped cross-track dependences that cannot be fully represented by the preflight ones. The full widths at half maximum (FWHM) of the stretched preflight slit functions for detector pixels at large viewing angles are up to 30 % larger than the nadir pixels for the UV1 band, 5 % larger for the UV2 band, and practically flat in the VIS band. Nonetheless, the on-orbit changes of OMI slit functions are found to be insignificant over time after accounting for the solar activity, despite of the decaying of detectors and the occurrence of OMI row anomaly. Applying the derived on-orbit slit functions to ozone-profile retrieval shows substantial improvements over the preflight slit functions based on comparisons with ozonesonde validations.

scholarly journals Correction: Oveisgharan, S., et al. Evaluating the Preconditions of Two Remote Sensing SWE Retrieval Algorithms over the US, Remote Sensing 2020, 12, 2021

2020 ◽  
Vol 12 (16) ◽  
pp. 2592
Author(s):  
Shadi Oveisgharan ◽  
Daniel Esteban-Fernandez ◽  
Duane E. Waliser ◽  
Randall R Friedl ◽  
Son V. Nghiem ◽  
...  
Keyword(s):  
Remote Sensing ◽  
The Us ◽  
Retrieval Algorithms

The authors would like to add Radar to the title for more clarification [...]

scholarly journals Multi-sensor cloud and aerosol retrieval simulator and remote sensing from model parameters – Part 2: Aerosols

2016 ◽  
Vol 9 (7) ◽  
pp. 2377-2389 ◽  
Author(s):  
Galina Wind ◽  
Arlindo M. da Silva ◽  
Peter M. Norris ◽  
Steven Platnick ◽  
Shana Mattoo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Weather Prediction ◽  
Retrieval Algorithm ◽  
Model Parameters ◽  
Aerosol Retrieval ◽  
Model Aerosol ◽  
Numerical Experimentation ◽  
Retrieval Algorithms ◽  
Sensor Cloud ◽  
Aerosol Properties

Abstract. The Multi-sensor Cloud Retrieval Simulator (MCRS) produces a “simulated radiance” product from any high-resolution general circulation model with interactive aerosol as if a specific sensor such as the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of the atmospheric column and land–ocean surface at a specific location. Previously the MCRS code only included contributions from atmosphere and clouds in its radiance calculations and did not incorporate properties of aerosols. In this paper we added a new aerosol properties module to the MCRS code that allows users to insert a mixture of up to 15 different aerosol species in any of 36 vertical layers.This new MCRS code is now known as MCARS (Multi-sensor Cloud and Aerosol Retrieval Simulator). Inclusion of an aerosol module into MCARS not only allows for extensive, tightly controlled testing of various aspects of satellite operational cloud and aerosol properties retrieval algorithms, but also provides a platform for comparing cloud and aerosol models against satellite measurements. This kind of two-way platform can improve the efficacy of model parameterizations of measured satellite radiances, allowing the assessment of model skill consistently with the retrieval algorithm. The MCARS code provides dynamic controls for appearance of cloud and aerosol layers. Thereby detailed quantitative studies of the impacts of various atmospheric components can be controlled.In this paper we illustrate the operation of MCARS by deriving simulated radiances from various data field output by the Goddard Earth Observing System version 5 (GEOS-5) model. The model aerosol fields are prepared for translation to simulated radiance using the same model subgrid variability parameterizations as are used for cloud and atmospheric properties profiles, namely the ICA technique. After MCARS computes modeled sensor radiances equivalent to their observed counterparts, these radiances are presented as input to operational remote-sensing algorithms.Specifically, the MCARS-computed radiances are input into the processing chain used to produce the MODIS Data Collection 6 aerosol product (M{O/Y}D04). The M{O/Y}D04 product is of course normally produced from M{O/Y}D021KM MODIS Level-1B radiance product directly acquired by the MODIS instrument. MCARS matches the format and metadata of a M{O/Y}D021KM product. The resulting MCARS output can be directly provided to MODAPS (MODIS Adaptive Processing System) as input to various operational atmospheric retrieval algorithms. Thus the operational algorithms can be tested directly without needing to make any software changes to accommodate an alternative input source.We show direct application of this synthetic product in analysis of the performance of the MOD04 operational algorithm. We use biomass-burning case studies over Amazonia employed in a recent Working Group on Numerical Experimentation (WGNE)-sponsored study of aerosol impacts on numerical weather prediction (Freitas et al., 2015). We demonstrate that a known low bias in retrieved MODIS aerosol optical depth appears to be due to a disconnect between actual column relative humidity and the value assumed by the MODIS aerosol product.

scholarly journals Ozone profile climatology for remote sensing retrieval algorithms

2019 ◽  
Vol 12 (9) ◽  
pp. 4745-4778 ◽  
Author(s):  
Kai Yang ◽  
Xiong Liu
Keyword(s):  
Remote Sensing ◽  
A Priori ◽  
Uv Spectra ◽  
Empirical Orthogonal Functions ◽  
Ozone Monitoring Instrument ◽  
Orthogonal Functions ◽  
Monitoring Instrument ◽  
Retrieval Algorithms ◽  
Ozone Monitoring ◽  
Priori Information

Abstract. New ozone (O3) profile climatologies are created from the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) O3 record between 2005 and 2016, within the period of Aura Microwave Limb Sounder (MLS) and Aura Ozone Monitoring Instrument (OMI) assimilation. These two climatologies consist of monthly mean O3 profiles and the corresponding covariances dependent on the local solar time, longitude (15∘), and latitude (10∘), which are parameterized by tropopause pressure and total O3 column. They are validated through comparisons, which show good agreements with previous O3 profile climatologies. Compared to a monthly zonal mean climatology, both tropopause- and column-dependent climatologies provide improved a priori information for profile and total O3 retrievals from remote sensing measurements. Furthermore, parameterization of the O3 profile with total column O3 usually reduces the natural variability of the resulting climatological profile in the upper stratosphere further than the tropopause parameterization, which usually performs better in the upper troposphere and lower stratosphere (UTLS). Therefore tropopause-dependent climatology is more appropriate for profile O3 retrieval for complementing the vertical resolution of backscattered ultraviolet (UV) spectra, while the column-dependent climatology is more suited for use in total O3 retrieval algorithms, with an advantage of complete profile specification without requiring ancillary information. Compared to previous column-dependent climatologies, the new MERRA-2 column-dependent climatology better captures the diurnal, seasonal, and spatial variations and dynamical changes in O3 profiles with higher resolutions in O3, latitude, longitude, and season. The new MERRA-2 climatologies contain the first quantitative characterization of O3 profile covariances, which facilitate a new approach to improve O3 profiles using the most probable patterns of profile adjustments represented by the empirical orthogonal functions (EOFs) of the covariance matrices. The MERRA-2 daytime column-dependent climatology is used in the combo O3 and SO2 algorithm for retrieval from the Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) satellite, the Ozone Mapping and Profiler Suite Nadir Mapper (OMPS-NM) on the Suomi National Polar Partnership (SNPP), and the Ozone Monitoring Instrument (OMI) on the Aura spacecraft.

Using Machine Learning for processing Big Data of Copernicus Satellite Sensors at the Example of the TROPOMI / Sentinel-5 Precursor and Sentinel-4 Cloud Product

2020 ◽  
Author(s):  
Fabian Romahn ◽  
Athina Argyrouli ◽  
Ronny Lutz ◽  
Diego Loyola ◽  
Victor Molina Garcia
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Neural Networks ◽  
General Procedure ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Critical Parameters ◽  
Observation Data ◽  
Computational Performance ◽  
Retrieval Algorithms

<p>The satellites of the Copernicus program show the increasing relevance of properly handling the huge amount of Earth observation data, nowadays common in remote sensing. This is further challenging if the processed data has to be provided in near real time (NRT), like the cloud product from TROPOMI / Sentinel-5<!-- The slash has spaces before “TROPOMI” and after “Sentinel-5”, which is inconsistent with the lack of spaces in the title. --> Precursor (S5P) or the upcoming Sentinel-4 (S4) mission.</p><p>In order to solve the inverse problems that arise in the retrieval of cloud products, as well as in similar remote sensing problems, usually complex radiative transfer models (RTMs) are used. These are very accurate, however also computationally very expensive and therefore often not feasible in combination with NRT requirements. With the recent significant breakthroughs in machine learning, easier application through better software and more powerful hardware, the methods of this field have become very interesting as a way to improve the classical remote sensing algorithms.</p><p>In this presentation we show how artificial neural networks (ANNs) can be used to replace the original RTM in the ROCINN (Retrieval Of Cloud Information using Neural Networks) algorithm with sufficient accuracy while increasing the computational performance at the same time by several orders of magnitude.</p><p>We developed a general procedure which consists of smart sampling, generation and scaling of the training data, as well as training, validation and finally deployment of the ANN into the operational processor. In order to minimize manual work, the procedure is highly automated and uses latest technologies such as TensorFlow. It is applicable for any kind of RTMs and thus can be used for many retrieval algorithms like it is already done for ROCINN in S5P and will be soon for ROCINN in the context of S4. Regarding the final performance of the generated ANN, there are several critical parameters which have a high impact (e.g. the structure of the ANN). These will be evaluated in detail. Furthermore, we also show general limitations of ANNs in comparison with RTMs, how this can lead to unexpected side effects and ways to cope with these issues.</p><p>With the example of ROCINN, as part of the operational S5P and upcoming S4 cloud product, we show the great potential of machine learning techniques in improving the performance of classical retrieval algorithms and thus increasing their capability to deal with much larger data quantities. However, we also highlight the importance of a proper configuration and possible limitations.</p>

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