scholarly journals Supplementary material to "A Global Ozone Profile Climatology for Satellite Retrieval Algorithms Based on Aura MLS Measurements and the MERRA-2 GMI Simulation"

2021 ◽  
Author(s):  
Jerald R. Ziemke ◽  
Gordon J. Labow ◽  
Natalya A. Kramarova ◽  
Richard D. McPeters ◽  
Pawan K. Bhartia ◽  
...  
Keyword(s):  
Satellite Retrieval ◽  
Ozone Profile ◽  
Retrieval Algorithms ◽  
Supplementary Material

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 A Global Multilayer Cloud Identification with POLDER/PARASOL

2017 ◽  
Vol 56 (4) ◽  
pp. 1121-1139 ◽  
Author(s):  
M. Desmons ◽  
N. Ferlay ◽  
F. Parol ◽  
J. Riédi ◽  
F. Thieuleux
Keyword(s):  
Threshold Level ◽  
Satellite Retrieval ◽  
Retrieval Algorithms ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Confidence Index ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Discrete Values ◽  
Level 2 ◽  
Risk Of Misclassification

AbstractThe detection of multilayer cloud situations is important for satellite retrieval algorithms and for many climate-related applications. In this paper, the authors describe an algorithm based on the exploitation of the Polarization and Directionality of the Earth’s Reflectance (POLDER) observations to identify monolayered and multilayered cloudy situations along with a confidence index. The authors’ reference comes from the synergy of the active instruments of the A-Train satellite constellation. The algorithm is based upon a decision tree that uses a metric from information theory and a series of tests on POLDER level-2 products. The authors obtain a multilayer flag as the final result of a tree classification, which takes discrete values between 0 and 100. Values closest to 0 (100) indicate a higher confidence in the monolayer (multilayer) character. This indicator can be used as it is or with a threshold level that minimizes the risk of misclassification, as a binary index to distinguish between monolayer and multilayer clouds. For almost fully covered and optically thick enough cloud scenes, the risk of misclassification ranges from 29% to 34% over the period 2006–10, and the average confidences in the estimated monolayer and multilayer characters of the cloud scenes are 74.0% and 58.2%, respectively. With the binary distinction, POLDER provides a climatology of the mono–multilayer cloud character that exhibits some interesting features. Comparisons with the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) multilayer flag are given.

scholarly journals Supplementary material to "Ozone Profile Retrieval from nadir TROPOMI measurements in the UV range"

2021 ◽  
Author(s):  
Nora Mettig ◽  
Mark Weber ◽  
Alexei Rozanov ◽  
Carlo Arosio ◽  
John P. Burrows ◽  
...  
Keyword(s):  
Ozone Profile ◽  
Supplementary Material ◽  
Uv Range

scholarly journals Copula-Derived Observation Operators for Assimilating TMI and AMSR-E Retrieved Soil Moisture into Land Surface Models

2007 ◽  
Vol 8 (3) ◽  
pp. 413-429 ◽  
Author(s):  
Huilin Gao ◽  
Eric F. Wood ◽  
Matthias Drusch ◽  
Matthew F. McCabe
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Qualitative Difference ◽  
Surface Model ◽  
Land Surface Models ◽  
Satellite Retrieval ◽  
Surface Models ◽  
Model Predictions ◽  
Satellite Retrievals ◽  
Retrieval Algorithms

Abstract Assimilating soil moisture from satellite remote sensing into land surface models (LSMs) has potential for improving model predictions by providing real-time information at large scales. However, the majority of the research demonstrating this potential has been limited to datasets based on either airborne data or synthetic observations. The limited availability of satellite-retrieved soil moisture and the observed qualitative difference between satellite-retrieved and modeled soil moisture has posed challenges in demonstrating the potential over large regions in actual applications. Comparing modeled and satellite-retrieved soil moisture fields shows systematic differences between their mean values and between their dynamic ranges, and these systematic differences vary with satellite sensors, retrieval algorithms, and LSMs. This investigation focuses on generating observation operators for assimilating soil moisture into LSMs using a number of satellite–model combinations. The remotely sensed soil moisture products come from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and the NASA/Earth Observing System (EOS) Advanced Microwave Scanning Radiometer (AMSR-E). The soil moisture model predictions are from the Variable Infiltration Capacity (VIC) hydrological model; the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40); and the NCEP North American Regional Reanalysis (NARR). For this analysis, the satellite and model data are over the southern Great Plains region from 1998 to 2003 (1998–2002 for ERA-40). Previous work on observation operators used the matching of cumulative distributions to transform satellite-retrieved soil moisture into modeled soil moisture, which implied perfect correlations between the ranked values. In this paper, a bivariate statistical approach, based on copula distributions, is employed for representing the joint distribution between retrieved and modeled soil moisture, allowing for a quantitative estimation of the uncertainty in modeled soil moisture when merged with a satellite retrieval. The conditional probability distribution of model-based soil moisture conditioned on a satellite retrieval forms the basis for the soil moisture observation operator. The variance of these conditional distributions for different retrieval algorithms, LSMs, and locations provides an indication of the information content of satellite retrievals in assimilation. Results show that the operators vary by season and by land surface model, with the satellite retrievals providing more information in summer [July–August (JJA)] and fall [September–November (SON)] than winter [December–February (DJF)] or spring [March–May (MAM)] seasons. Also, the results indicate that the value of satellite-retrieved soil moisture is most useful to VIC, followed by ERA-40 and then NARR.

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