scholarly journals A MAX-DOAS aerosol profile retrieval algorithm for high altitude measurements: application to measurements at Schneefernerhaus (UFS), Germany

2019 ◽  
Author(s):  
Zhuoru Wang ◽  
Ka Lok Chan ◽  
Klaus-Peter Heue ◽  
Adrian Doicu ◽  
Thomas Wagner ◽  
...  
Keyword(s):  
High Altitude ◽  
Environmental Research ◽  
Optical Absorption Spectroscopy ◽  
Retrieval Algorithm ◽  
Scaling Factors ◽  
Total Uncertainty ◽  
Look Up Table ◽  
Sun Photometer ◽  
The Look ◽  
The Cost

Abstract. We present a new aerosol profile retrieval algorithm for Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements at high altitude sites. The study is based on the long-term measurement (February 2012 to February 2016) at the Environmental Research Station Schneefernerhaus (UFS), Germany, which is located near the summit of Zugspitze, at an altitude of 2,650 m. Due to the low signal to noise ratio, commonly used MAX-DOAS retrieval algorithms based on the optimal estimation method are not suitable for the retrieval of high altitude measurements. We developed a new retrieval algorithm using an O4 differential slant column density (DSCD) look-up table. The look-up table consists of simulated O4 DSCDs corresponding to numerous possible aerosol profiles. The sensitivities of O4 absorption to several parameters were investigated for the design and parameterization of the look-up table. In the retrieval, the simulated O4 DSCDs for each possible profile are derived by interpolating the look-up table to the observation geometries. The cost functions are calculated for each aerosol profile in the look-up table based on the simulated O4 DSCDs, the O4 DSCD observations as well as the measurement uncertainties. Valid profiles are selected from all the possible profiles according to the cost function, and the optimal solution is defined as the weighted mean of all valid profiles. A comprehensive error analysis is performed to better estimate the total uncertainty. Based on the assumption that the look-up table covers all the possible profiles under clear sky conditions, we determined a set of O4 DSCD scaling factors for different elevation angles and wavelengths. The dependence of the scaling factors on elevation angle might be partly related to the specific properties of the high altitude station, e.g. the highly structured topography, horizontal gradients of the aerosol extinction and the systematic dependence of the surface albedo on altitude. The retrieved aerosol optical depths (AODs) are compared to coincident and co-located sun photometer observations. High correlation coefficients of 0.733 and 0.798 are found for measurements at 360 and 477 nm, respectively. However, especially in summer the sun photometer AODs are systematically higher than the MAX-DOAS retrievals by a factor of 2. The discrepancy might be related to the limited measurement range of the MAX-DOAS, and is probably also related to the decreased sensitivity of the MAX-DOAS measurements at higher altitudes. Our results also show maximum AOD and maximum Ångström exponent in summer which is consistent with observations from an AERONET station located ~ 43 km of the MAX-DOAS.

scholarly journals A multi-axis differential optical absorption spectroscopy aerosol profile retrieval algorithm for high-altitude measurements: application to measurements at Schneefernerhaus (UFS), Germany

2020 ◽  
Vol 13 (4) ◽  
pp. 1835-1866 ◽  
Author(s):  
Zhuoru Wang ◽  
Ka Lok Chan ◽  
Klaus-Peter Heue ◽  
Adrian Doicu ◽  
Thomas Wagner ◽  
...  
Keyword(s):  
Optical Absorption ◽  
High Altitude ◽  
Lookup Table ◽  
Environmental Research ◽  
Retrieval Algorithm ◽  
Aerosol Extinction ◽  
Total Uncertainty ◽  
Sun Photometer ◽  
The Cost

Abstract. We present a new aerosol extinction profile retrieval algorithm for multi-axis differential optical absorption spectrometer (MAX-DOAS) measurements at high-altitude sites. The algorithm is based on the lookup table method. It is applied to retrieve aerosol extinction profiles from the long-term MAX-DOAS measurements (February 2012 to February 2016) at the Environmental Research Station Schneefernerhaus (UFS), Germany (47.417∘ N, 10.980∘ E), which is located near the summit of Zugspitze at an altitude of 2650 m. The lookup table consists of simulated O4 differential slant column densities (DSCDs) corresponding to numerous possible aerosol extinction profiles. The sensitivities of O4 absorption to several parameters were investigated for the design and parameterization of the lookup table. In the retrieval, simulated O4 DSCDs for each possible profile are derived by interpolating the lookup table to the observation geometries. The cost functions are calculated for each aerosol profile in the lookup table based on the simulated O4 DSCDs, the O4 DSCD observations, and the measurement uncertainties. Valid profiles are selected from all the possible profiles according to the cost function, and the optimal solution is defined as the weighted mean of all the valid profiles. A comprehensive error analysis is performed to better estimate the total uncertainty. Based on the assumption that the lookup table covers all possible profiles under clear-sky conditions, we determined a set of O4 DSCD scaling factors for different elevation angles and wavelengths. The profiles retrieved from synthetic measurement data can reproduce the synthetic profile. The results also show that the retrieval is insensitive to measurement noise, indicating the retrieval is robust and stable. The aerosol optical depths (AODs) retrieved from the long-term measurements were compared to coinciding and co-located sun photometer observations. High correlation coefficients (R) of 0.733 and 0.798 are found for measurements at 360 and 477 nm, respectively. However, especially in summer, the sun photometer AODs are systematically higher than the MAX-DOAS retrievals by a factor of ∼2. The discrepancy might be related to the limited measurement range of the MAX-DOAS and is probably also related to the decreased sensitivity of the MAX-DOAS measurements at higher altitudes. The MAX-DOAS measurements indicate the aerosol extinction decreases with increasing altitude during all seasons, which agrees with the co-located ceilometer measurements. Our results also show maximum AOD and maximum Ångström exponent in summer, which is consistent with observations at an AERONET station located ∼43 km from the UFS.

scholarly journals The semianalytical cloud retrieval algorithm for SCIAMACHY – I. The validation

2005 ◽  
Vol 5 (2) ◽  
pp. 1995-2015 ◽  
Author(s):  
A. A. Kokhanovsky ◽  
V. V. Rozanov ◽  
T. Nauss ◽  
C. Reudenbach ◽  
J. S. Daniel ◽  
...  
Keyword(s):  
Retrieval Algorithm ◽  
Look Up Table ◽  
Ozone Monitoring ◽  
Scanning Imaging ◽  
Using Data ◽  
The Look ◽  
Along Track ◽  
Spectrometer Data ◽  
Resolution Spectrometer ◽  
Absorption Spectrometer

Abstract. A recently developed cloud retrieval algorithm for the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) is briefly presented and validated using independent and well tested cloud retrieval techniques based on the look-up-table approach for MODeration resolutIon Spectrometer data. The results of the cloud top height retrievals using measurements in the oxygen A-band by an airborne crossed Czerny-Turner spectrograph and the Global Ozone Monitoring Experiment (GOME) instrument are compared with those obtained from airborne dual photography and retrievals using data from Along Track Scanning Radiometer (ATSR-2), respectively.

scholarly journals An improved tropospheric NO<sub>2</sub> column retrieval algorithm for TROPOMI over Europe

2021 ◽  
Author(s):  
Song Liu ◽  
Pieter Valks ◽  
Gaia Pinardi ◽  
Jian Xu ◽  
Ka Lok Chan ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Transport Model ◽  
Estimation Algorithm ◽  
Point Sources ◽  
Optical Absorption Spectroscopy ◽  
Retrieval Algorithm ◽  
Vertical Column ◽  
Chemistry Transport Model ◽  
Total Uncertainty ◽  
Tropospheric No2

Abstract. Launched in October 2017, the TROPOspheric Monitoring Instrument (TROPOMI) aboard Sentinel-5 Precursor provides the potential to monitor air quality over point sources across the globe with a spatial resolution as high as 5.5 km × 3.5 km (7 km × 3.5 km before 6 August 2019). The nitrogen dioxide (NO2) retrieval algorithm for the TROPOMI instrument consists of three steps: the spectral fitting of the slant column, the separation of stratospheric and tropospheric contributions, and the conversion of the slant column to a vertical column using an air mass factor (AMF) calculation. In this work, an improved tropospheric NO2 retrieval algorithm from TROPOMI measurements over Europe is presented. The stratospheric estimation is implemented using the STRatospheric Estimation Algorithm from Mainz (STREAM), which was developed as a verification algorithm for TROPOMI and does not require chemistry transport model data as input. A directionally dependent STREAM (DSTREAM) is developed to correct for the dependency of the stratospheric NO2 on the viewing geometry by up to 2 × 1014 molec/cm2. Applied to synthetic TROPOMI data, the uncertainty in the stratospheric column is 3.5 × 1014 molec/cm2 for polluted conditions. Applied to actual measurements, the smooth variation of stratospheric NO2 at low latitudes is conserved, and stronger stratospheric variation at higher latitudes are captured. For AMF calculation, the climatological surface albedo data is replaced by geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) obtained directly from TROPOMI measurements with a high spatial resolution. Mesoscale-resolution a priori NO2 profiles are obtained from the regional POLYPHEMUS/DLR chemistry transport model with the TNO-MACC emission inventory. Based on the latest TROPOMI operational cloud parameters, a more realistic cloud treatment is provided by a clouds-as-layers (CAL) model, which treats the clouds as uniform layers of water droplets, instead of the clouds-as-reflecting-boundaries (CRB) model, in which clouds are simplified as Lambertian reflectors. For the error analysis, the tropospheric AMF uncertainty, which is the largest source of NO2 uncertainty for polluted scenarios, ranges between 20 % and 50 %, leading to a total uncertainty in the tropospheric NO2 column in the 30–60 % range. From a validation performed with ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements, the improved tropospheric NO2 data shows good correlations for nine European urban/suburban stations with an average correlation coefficient of 0.78. The implementation of the algorithm improvements leads to a decrease of the relative difference from −55.3 % to −34.7 % on average.

scholarly journals The semianalytical cloud retrieval algorithm for SCIAMACHY I. The validation

2006 ◽  
Vol 6 (7) ◽  
pp. 1905-1911 ◽  
Author(s):  
A. A. Kokhanovsky ◽  
V. V. Rozanov ◽  
T. Nauss ◽  
C. Reudenbach ◽  
J. S. Daniel ◽  
...  
Keyword(s):  
Retrieval Algorithm ◽  
Modis Data ◽  
Look Up Table ◽  
Ozone Monitoring ◽  
Scanning Imaging ◽  
Using Data ◽  
The Look ◽  
Along Track ◽  
Resolution Spectrometer ◽  
Absorption Spectrometer

Abstract. A recently developed cloud retrieval algorithm for the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) is briefly presented and validated using independent and well tested cloud retrieval techniques based on the look-up-table approach for MODeration resolutIon Spectrometer (MODIS) data. The results of the cloud top height retrievals using measurements in the oxygen A-band by an airborne crossed Czerny-Turner spectrograph and the Global Ozone Monitoring Experiment (GOME) instrument are compared with those obtained from airborne dual photography and retrievals using data from Along Track Scanning Radiometer (ATSR-2), respectively.

scholarly journals An improved TROPOMI tropospheric NO<sub>2</sub> research product over Europe

2021 ◽  
Vol 14 (11) ◽  
pp. 7297-7327
Author(s):  
Song Liu ◽  
Pieter Valks ◽  
Gaia Pinardi ◽  
Jian Xu ◽  
Ka Lok Chan ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Transport Model ◽  
A Priori ◽  
Relative Difference ◽  
Point Sources ◽  
Optical Absorption Spectroscopy ◽  
Retrieval Algorithm ◽  
Chemistry Transport Model ◽  
Total Uncertainty ◽  
Tropospheric No2

Abstract. Launched in October 2017, the TROPOspheric Monitoring Instrument (TROPOMI) aboard Sentinel-5 Precursor provides the potential to monitor air quality over point sources across the globe with a spatial resolution as high as 5.5 km × 3.5 km (7 km × 3.5 km before 6 August 2019). The DLR nitrogen dioxide (NO2) retrieval algorithm for the TROPOMI instrument consists of three steps: the spectral fitting of the slant column, the separation of stratospheric and tropospheric contributions, and the conversion of the slant column to a vertical column using an air mass factor (AMF) calculation. In this work, an improved DLR tropospheric NO2 retrieval algorithm from TROPOMI measurements over Europe is presented. The stratospheric estimation is implemented using the STRatospheric Estimation Algorithm from Mainz (STREAM), which was developed as a verification algorithm for TROPOMI and does not require chemistry transport model data as input. A directionally dependent STREAM (DSTREAM) is developed to correct for the dependency of the stratospheric NO2 on the viewing geometry by up to 2×1014 molec./cm2. Applied to synthetic TROPOMI data, the uncertainty in the stratospheric column is 3.5×1014 molec./cm2 in the case of significant tropospheric sources. Applied to actual measurements, the smooth variation of stratospheric NO2 at low latitudes is conserved, and stronger stratospheric variation at higher latitudes is captured. For AMF calculation, the climatological surface albedo data are replaced by geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) obtained directly from TROPOMI measurements with a high spatial resolution. Mesoscale-resolution a priori NO2 profiles are obtained from the regional POLYPHEMUS/DLR chemistry transport model with the TNO-MACC emission inventory. Based on the latest TROPOMI operational cloud parameters, a more realistic cloud treatment is provided by a Clouds-As-Layers (CAL) model, which treats the clouds as uniform layers of water droplets, instead of the Clouds-As-Reflecting-Boundaries (CRB) model, in which clouds are simplified as Lambertian reflectors. For the error analysis, the tropospheric AMF uncertainty, which is the largest source of NO2 uncertainty for polluted scenarios, ranges between 20 % and 50 %, leading to a total uncertainty in the tropospheric NO2 column in the 30 %–60 % range. From a validation performed with ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements, the new DLR tropospheric NO2 data show good correlations for nine European urban/suburban stations, with an average correlation coefficient of 0.78. The implementation of the algorithm improvements leads to a decrease of the relative difference from −55.3 % to −34.7 % on average in comparison with the DLR reference retrieval. When the satellite averaging kernels are used to remove the contribution of a priori profile shape, the relative difference decreases further to ∼ −20 %.

Assessment of the look-up table using the tubular and bundle CHF data and modification of the bundle correction factor

Kerntechnik ◽  
2006 ◽  
Vol 71 (4) ◽  
pp. 192-202
Author(s):  
D. K. Chandraker ◽  
P. K. Vijayan ◽  
D. Saha ◽  
R. K. Sinha
Keyword(s):  
Correction Factor ◽  
Look Up Table ◽  
The Look

scholarly journals Development of Multilayer-Based Map Matching to Enhance Performance in Large Truck Fleet Dispatching

2021 ◽  
Vol 10 (2) ◽  
pp. 79
Author(s):  
Ching-Yun Mu ◽  
Tien-Yin Chou ◽  
Thanh Van Hoang ◽  
Pin Kung ◽  
Yao-Min Fang ◽  
...  
Keyword(s):  
Data Structures ◽  
Spatial Data ◽  
Spatial Information ◽  
Data Retrieval ◽  
Spatial Data Mining ◽  
Map Matching ◽  
Matching Algorithm ◽  
Table Method ◽  
Look Up Table ◽  
The Look

Spatial information technology has been widely used for vehicles in general and for fleet management. Many studies have focused on improving vehicle positioning accuracy, although few studies have focused on efficiency improvements for managing large truck fleets in the context of the current complex network of roads. Therefore, this paper proposes a multilayer-based map matching algorithm with different spatial data structures to deal rapidly with large amounts of coordinate data. Using the dimension reduction technique, the geodesic coordinates can be transformed into plane coordinates. This study provides multiple layer grouping combinations to deal with complex road networks. We integrated these techniques and employed a puncture method to process the geometric computation with spatial data-mining approaches. We constructed a spatial division index and combined this with the puncture method, which improves the efficiency of the system and can enhance data retrieval efficiency for large truck fleet dispatching. This paper also used a multilayer-based map matching algorithm with raster data structures. Comparing the results revealed that the look-up table method offers the best outcome. The proposed multilayer-based map matching algorithm using the look-up table method is suited to obtaining competitive performance in identifying efficiency improvements for large truck fleet dispatching.

scholarly journals A Fast Retrieval of Cloud Parameters Using a Triplet of Wavelengths of Oxygen Dimer Band around 477 nm

2021 ◽  
Vol 13 (1) ◽  
pp. 152
Author(s):  
Haklim Choi ◽  
Xiong Liu ◽  
Gonzalo Gonzalez Abad ◽  
Jongjin Seo ◽  
Kwang-Mog Lee ◽  
...  
Keyword(s):  
Scattered Light ◽  
Trace Gas ◽  
Retrieval Algorithm ◽  
The Novel ◽  
Cloud Parameters ◽  
Retrieval Scheme ◽  
The Difference ◽  
The Look ◽  
Time Required ◽  
Cloud Top Pressure

Clouds act as a major reflector that changes the amount of sunlight reflected to space. Change in radiance intensity due to the presence of clouds interrupts the retrieval of trace gas or aerosol properties from satellite data. In this paper, we developed a fast and robust algorithm, named the fast cloud retrieval algorithm, using a triplet of wavelengths (469, 477, and 485 nm) of the O2–O2 absorption band around 477 nm (CLDTO4) to derive the cloud information such as cloud top pressure (CTP) and cloud fraction (CF) for the Geostationary Environment Monitoring Spectrometer (GEMS). The novel algorithm is based on the fact that the difference in the optical path through which light passes with regard to the altitude of clouds causes a change in radiance due to the absorption of O2–O2 at the three selected wavelengths. To reduce the time required for algorithm calculations, the look-up table (LUT) method was applied. The LUT was pre-constructed for various conditions of geometry using Vectorized Linearized Discrete Ordinate Radiative Transfer (VLIDORT) to consider the polarization of the scattered light. The GEMS was launched in February 2020, but the observed data of GEMS have not yet been widely released. To evaluate the performance of the algorithm, the retrieved CTP and CF using observational data from the Global Ozone Monitoring Experiment-2 (GOME-2), which cover the spectral range of GEMS, were compared with the results of the Fast Retrieval Scheme for Clouds from the Oxygen A band (FRESCO) algorithm, which is based on the O2 A-band. There was good agreement between the results, despite small discrepancies for low clouds.

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