scholarly journals FRESCO-B: A fast cloud retrieval algorithm using oxygen B-band measurements from GOME-2

2018 ◽  
Author(s):  
Marine Desmons ◽  
Ping Wang ◽  
Piet Stammes ◽  
L. Gijsbert Tilstra
Keyword(s):  
Data Processing ◽  
Satellite Data ◽  
Surface Albedo ◽  
Cloud Fraction ◽  
Mean Difference ◽  
Retrieval Algorithm ◽  
Robust Algorithm ◽  
Retrieval Scheme ◽  
The Difference

Abstract. The FRESCO (Fast Retrieval Scheme for Clouds from the Oxygen A-band) algorithm is a simple, fast and robust algorithm used to retrieve cloud information in operational satellite data processing. It has been applied to GOME-1, SCIAMACHY, GOME-2 and more recently to TROPOMI. FRESCO retrieves effective cloud fraction and cloud pressure from measurements in the oxygen A-band around 761 nm. In this paper, we propose a new version of the algorithm, called FRESCO-B, which is based on measurements in the oxygen B-band around 687 nm. Such a method is interesting for vegetated surfaces where the surface albedo is much lower in the B-band than in the A-band, which limits the ground contribution to the top-of-atmosphere reflectances. In this study we first perform retrieval simulations. These show that the retrieved cloud pressures from FRESCO-B and FRESCO differ only between −10 hPa and +10 hPa, except for high thin clouds over vegetation where the difference is larger, about +15 to +30 hPa, with FRESCO-B yielding higher pressures. Next, inter-comparison between FRESCO-B and FRESCO retrievals over one month of GOME-2B data reveals that the effective cloud fractions retrieved in the O2 A and B bands are very similar (mean difference of 0.003) while the cloud pressures show a mean difference of 11.5 hPa, with FRESCO-B retrieving higher pressures than FRESCO. This agrees with the simulations and is partly due to deeper photons penetrations of O2 B-band in clouds as compared to the O2 A-band photons, and partly due to the surface albedo bias in FRESCO. Finally, validation with ground-based measurements shows that the FRESCO-B cloud pressure represents an altitude within the cloud boundaries for clouds that are not too far from the Lambertian reflector model, which occurs in about 50 % of the cases.

scholarly journals FRESCO-B: a fast cloud retrieval algorithm using oxygen B-band measurements from GOME-2

2019 ◽  
Vol 12 (4) ◽  
pp. 2485-2498 ◽  
Author(s):  
Marine Desmons ◽  
Ping Wang ◽  
Piet Stammes ◽  
L. Gijsbert Tilstra
Keyword(s):  
Satellite Data ◽  
Surface Albedo ◽  
Cloud Fraction ◽  
Mean Difference ◽  
Retrieval Algorithm ◽  
Retrieval Scheme ◽  
Ozone Monitoring ◽  
The Difference ◽  
Scanning Imaging ◽  
Absorption Spectrometer

Abstract. The FRESCO (Fast Retrieval Scheme for Clouds from the Oxygen A band) algorithm is a simple, fast and robust algorithm used to retrieve cloud information in operational satellite data processing. It has been applied to GOME-1 (Global Ozone Monitoring Experiment), SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Chartography), GOME-2 and more recently to TROPOMI (Tropospheric Monitoring Instrument). FRESCO retrieves effective cloud fraction and cloud pressure from measurements in the oxygen A band around 761 nm. In this paper, we propose a new version of the algorithm, called FRESCO-B, which is based on measurements in the oxygen B band around 687 nm. Such a method is interesting for vegetated surfaces where the surface albedo is much lower in the B band than in the A band, which limits the ground contribution to the top-of-atmosphere reflectances. In this study we first perform retrieval simulations. These show that the retrieved cloud pressures from FRESCO-B and FRESCO differ only between −10 and +10 hPa, except for high, thin clouds over vegetation where the difference is larger (about +15 to +30 hPa), with FRESCO-B yielding higher pressure. Next, inter-comparison between FRESCO-B and FRESCO retrievals over 1 month of GOME-2B data reveals that the effective cloud fractions retrieved in the O2 A and B bands are very similar (mean difference of 0.003), while the cloud pressures show a mean difference of 11.5 hPa, with FRESCO-B retrieving higher pressures than FRESCO. This agrees with the simulations and is partly due to deeper photon penetrations of the O2 B band in clouds compared to the O2 A-band photons and partly due to the surface albedo bias in FRESCO. Finally, validation with ground-based measurements shows that the FRESCO-B cloud pressure represents an altitude within the cloud boundaries for clouds that are not too far from the Lambertian reflector model, which occurs in about 50 % of the cases.

scholarly journals FRESCO+: an improved O<sub>2</sub> A-band cloud retrieval algorithm for tropospheric trace gas retrievals

2008 ◽  
Vol 8 (3) ◽  
pp. 9697-9729 ◽  
Author(s):  
P. Wang ◽  
P. Stammes ◽  
R. van der A ◽  
G. Pinardi ◽  
M. van Roozendael
Keyword(s):  
Rayleigh Scattering ◽  
Cloud Fraction ◽  
Retrieval Algorithm ◽  
Minor Effect ◽  
Vertical Column ◽  
Cloud Parameters ◽  
Tropospheric No2 ◽  
Retrieval Scheme ◽  
The Difference ◽  
A Minor

Abstract. The FRESCO (Fast Retrieval Scheme for Clouds from the Oxygen A-band) algorithm has been used to retrieve cloud information from measurements of the O2 A-band around 760 nm by GOME, SCIAMACHY and GOME-2. The cloud parameters retrieved by FRESCO are the effective cloud fraction and cloud pressure, which are used for cloud correction in the retrieval of trace gases like O3 and NO2. To improve the cloud pressure retrieval for partly cloudy scenes, single Rayleigh scattering has been included in an improved version of the algorithm, called FRESCO+. We compared FRESCO+ and FRESCO effective cloud fractions and cloud pressures using simulated spectra and one month of GOME measured spectra. As expected, FRESCO+ gives more reliable cloud pressures over partly cloudy pixels. Simulations and comparisons with ground-based radar/lidar measurements of clouds shows that the FRESCO+ cloud pressure is about the optical midlevel of the cloud. Globally averaged, the FRESCO+ cloud pressure is about 50 hPa higher than the FRESCO cloud pressure, while the FRESCO+ effective cloud fraction is about 0.01 larger. The effect of FRESCO+ cloud parameters on O3 and NO2 vertical column densities (VCD) is studied using SCIAMACHY data and ground-based DOAS measurements. We find that the FRESCO+ algorithm has a significant effect on tropospheric NO2 retrievals but a minor effect on total O3 retrievals. The retrieved SCIAMACHY tropospheric NO2 VCDs using FRESCO+ cloud parameters (v1.1) are lower than the tropospheric NO2 VCDs which used FRESCO cloud parameters (v1.04), in particular over heavily polluted areas with low clouds. The difference between SCIAMACHY tropospheric NO2 VCDs v1.1 and ground-based MAXDOAS measurements performed in Cabauw, The Netherlands, during the DANDELIONS campaign is about −2.12×1014 molec cm−2.

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.

scholarly journals Surface solar irradiance from SCIAMACHY measurements: algorithm and validation

2011 ◽  
Vol 4 (5) ◽  
pp. 875-891 ◽  
Author(s):  
P. Wang ◽  
P. Stammes ◽  
R. Mueller
Keyword(s):  
Standard Deviation ◽  
Mean Difference ◽  
Surface Radiation ◽  
Mean Bias Error ◽  
Retrieval Algorithm ◽  
Bias Error ◽  
Retrieval Scheme ◽  
The Mean ◽  
Scanning Imaging ◽  
One Year

Abstract. Broadband surface solar irradiances (SSI) are, for the first time, derived from SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY) satellite measurements. The retrieval algorithm, called FRESCO (Fast REtrieval Scheme for Clouds from the Oxygen A band) SSI, is similar to the Heliosat method. In contrast to the standard Heliosat method, the cloud index is replaced by the effective cloud fraction derived from the FRESCO cloud algorithm. The MAGIC (Mesoscale Atmospheric Global Irradiance Code) algorithm is used to calculate clear-sky SSI. The SCIAMACHY SSI product is validated against globally distributed BSRN (Baseline Surface Radiation Network) measurements and compared with ISCCP-FD (International Satellite Cloud Climatology Project Flux Dataset) surface shortwave downwelling fluxes (SDF). For one year of data in 2008, the mean difference between the instantaneous SCIAMACHY SSI and the hourly mean BSRN global irradiances is −4 W m−2 (−1 %) with a standard deviation of 101 W m−2 (20 %). The mean difference between the globally monthly mean SCIAMACHY SSI and ISCCP-FD SDF is less than −12 W m−2 (−2 %) for every month in 2006 and the standard deviation is 62 W m−2 (12 %). The correlation coefficient is 0.93 between SCIAMACHY SSI and BSRN global irradiances and is greater than 0.96 between SCIAMACHY SSI and ISCCP-FD SDF. The evaluation results suggest that the SCIAMACHY SSI product achieves similar mean bias error and root mean square error as the surface solar irradiances derived from polar orbiting satellites with higher spatial resolution.

scholarly journals FRESCO+: an improved O<sub>2</sub> A-band cloud retrieval algorithm for tropospheric trace gas retrievals

2008 ◽  
Vol 8 (21) ◽  
pp. 6565-6576 ◽  
Author(s):  
P. Wang ◽  
P. Stammes ◽  
R. van der A ◽  
G. Pinardi ◽  
M. van Roozendael
Keyword(s):  
Rayleigh Scattering ◽  
Cloud Fraction ◽  
Retrieval Algorithm ◽  
Minor Effect ◽  
Vertical Column ◽  
Cloud Parameters ◽  
Tropospheric No2 ◽  
Vertical Column Density ◽  
The Difference ◽  
A Minor

Abstract. The FRESCO (Fast Retrieval Scheme for Clouds from the Oxygen A-band) algorithm has been used to retrieve cloud information from measurements of the O2 A-band around 760 nm by GOME, SCIAMACHY and GOME-2. The cloud parameters retrieved by FRESCO are the effective cloud fraction and cloud pressure, which are used for cloud correction in the retrieval of trace gases like O3 and NO2. To improve the cloud pressure retrieval for partly cloudy scenes, single Rayleigh scattering has been included in an improved version of the algorithm, called FRESCO+. We compared FRESCO+ and FRESCO effective cloud fractions and cloud pressures using simulated spectra and one month of GOME measured spectra. As expected, FRESCO+ gives more reliable cloud pressures over partly cloudy pixels. Simulations and comparisons with ground-based radar/lidar measurements of clouds show that the FRESCO+ cloud pressure is about the optical midlevel of the cloud. Globally averaged, the FRESCO+ cloud pressure is about 50 hPa higher than the FRESCO cloud pressure, while the FRESCO+ effective cloud fraction is about 0.01 larger. The effect of FRESCO+ cloud parameters on O3 and NO2 vertical column density (VCD) retrievals is studied using SCIAMACHY data and ground-based DOAS measurements. We find that the FRESCO+ algorithm has a significant effect on tropospheric NO2 retrievals but a minor effect on total O3 retrievals. The retrieved SCIAMACHY tropospheric NO2 VCDs using FRESCO+ cloud parameters (v1.1) are lower than the tropospheric NO2VCDs which used FRESCO cloud parameters (v1.04), in particular over heavily polluted areas with low clouds. The difference between SCIAMACHY tropospheric NO2 VCDs v1.1 and ground-based MAXDOAS measurements performed in Cabauw, The Netherlands, during the DANDELIONS campaign is about −2.12×1014molec cm−2.

scholarly journals Improving cloud information over deserts from SCIAMACHY Oxygen A-band measurements

2006 ◽  
Vol 6 (1) ◽  
pp. 163-172 ◽  
Author(s):  
N. Fournier ◽  
P. Stammes ◽  
M. de Graaf ◽  
R. van der A ◽  
A. Piters ◽  
...  
Keyword(s):  
Recognition Algorithm ◽  
Cloud Fraction ◽  
Retrieval Algorithm ◽  
Retrieval Scheme ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Scanning Imaging ◽  
Cloud Top Pressure ◽  
Atmospheric Trace Gas

Abstract. The retrieval of column densities and concentration profiles of atmospheric trace gas species from satellites is sensitive to light scattered by clouds. The SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument on the Envisat satellite, principally designed to retrieve trace gases in the atmosphere, is also capable of detecting clouds. FRESCO (Fast Retrieval Scheme for Clouds from the Oxygen A-band) is a fast and robust algorithm providing cloud information from the O2 A-band for cloud correction of ozone. FRESCO provides a consistent set of cloud products by retrieving simultaneously effective cloud fraction and cloud top pressure. The FRESCO retrieved values are compared with the SCIAMACHY Level 2 operational cloud fraction of OCRA (Optical Cloud Recognition Algorithm) but, also, with cloud information from HICRU (Heidelberg Iterative Cloud Retrieval Utilities), SACURA (SemiAnalytical CloUd Retrieval Algorithm) and the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument. The results correlate well, but FRESCO overestimates cloud fraction over deserts. Thus, to improve retrievals at these locations, the FRESCO surface albedo databases are decontaminated from the presence of desert dust aerosols. This is achieved by using the GOME Absorbing Aerosol Index. It is shown that this approach succeeds well in producing more accurate cloud information over the Sahara.

scholarly journals Surface solar irradiance from SCIAMACHY measurements: algorithm and validation

2011 ◽  
Vol 4 (1) ◽  
pp. 873-912 ◽  
Author(s):  
P. Wang ◽  
P. Stammes ◽  
R. Mueller
Keyword(s):  
Standard Deviation ◽  
Mean Difference ◽  
Surface Radiation ◽  
Mean Bias Error ◽  
Retrieval Algorithm ◽  
Bias Error ◽  
Retrieval Scheme ◽  
The Mean ◽  
Scanning Imaging ◽  
One Year

Abstract. Broadband surface solar irradiances (SSI) are, for the first time, derived from SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY) satellite measurements. The retrieval algorithm, called FRESCO (Fast REtrieval Scheme for Clouds from Oxygen A band) SSI, is similar to the Heliosat method. In contrast to the standard Heliosat method, the cloud index is replaced by the effective cloud fraction derived from the FRESCO cloud algorithm. The MAGIC (Mesoscale Atmospheric Global Irradiance Code) algorithm is used to calculate clear-sky SSI. The SCIAMACHY SSI product is validated against the globally distributed BSRN (Baseline Surface Radiation Network) measurements and compared with the ISCCP-FD (International Satellite Cloud Climatology Project Flux Dataset) surface shortwave downwelling fluxes (SDF). For one year of data in 2008, the mean difference between the instantaneous SCIAMACHY SSI and the hourly mean BSRN global irradiances is −4 W m−2(−1%) with a standard deviation of 101 W m−2 (20%). The mean difference between the globally monthly mean SCIAMACHY SSI and ISCCP-FD SDF is less than −12 W m−2 (−2%) for every month in 2006 and the standard deviation is 62 W m−2 (12%). The correlation coefficient is 0.93 between SCIAMACHY SSI and BSRN global irradiances and is greater than 0.96 between SCIAMACHY SSI and ISCCP-FD SDF. The evaluation results suggest that the SCIAMACHY SSI product achieves similar mean bias error and root mean square error as the surface solar irradiances derived from polar orbiting satellites with higher spatial resolution.

scholarly journals Retrieval methods of effective cloud cover for the GOME instrument: an intercomparison

2002 ◽  
Vol 2 (3) ◽  
pp. 623-668 ◽  
Author(s):  
O. N. E. Tuinder ◽  
R. de Winter-Sorkina ◽  
P. J. H. Builtjes
Keyword(s):  
Recognition Algorithm ◽  
Cloud Fraction ◽  
Trace Gas ◽  
Retrieval Algorithm ◽  
Fitting Algorithm ◽  
Retrieval Scheme ◽  
Ozone Monitoring ◽  
Radiative Scattering ◽  
Atmospheric Trace Gas ◽  
Surface Observations

Abstract. The radiative scattering by clouds leads to errors in the retrieval of column densities and concentration profiles of atmospheric trace gas species from satellites. Moreover, the presence of clouds changes the UV actinic flux and the photo-dissociation rates of various species significantly. The Global Ozone Monitoring Experiment (GOME) instrument on the ERS-2 satellite, principally designed to retrieve trace gases in the atmosphere is also capable of detecting clouds. Four cloud fraction retrieval methods for GOME data that have been developed are discussed in this paper (the Initial Cloud Fitting Algorithm, the PMD Cloud Retrieval Algorithm, the Optical Cloud Recognition Algorithm and the Fast Retrieval Scheme for Cloud Observables). Their results of cloud fraction retrieval are compared to each-other and also to synoptic surface observations. It is shown that all studied retrieval methods calculate an effective cloud fraction that is related to a cloud with a high optical thickness. Generally, we found ICFA to produce the lowest cloud fractions, followed by OCRA, then FRESCO and PC2K along four processed tracks (+2%, +10% and +15% compared to ICFA respectively). Synoptical surface observations gave the highest absolute cloud fraction when compared with individual PMD sub-pixels of roughly the same size.

scholarly journals Improving cloud information over deserts from SCIAMACHY O<sub>2</sub> A-band

2005 ◽  
Vol 5 (4) ◽  
pp. 6013-6039 ◽  
Author(s):  
N. Fournier ◽  
P. Stammes ◽  
M. de Graaf ◽  
R. van der A ◽  
A. Piters ◽  
...  
Keyword(s):  
Surface Albedo ◽  
Recognition Algorithm ◽  
Cloud Fraction ◽  
Trace Gas ◽  
Retrieval Scheme ◽  
Scanning Imaging ◽  
Cloud Top Pressure ◽  
Atmospheric Trace Gas ◽  
Aerosol Index ◽  
Level 2

Abstract. The retrieval of column densities and concentration profiles of atmospheric trace gas species from satellites is sensitive to light scattered by clouds. The SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument on the Envisat satellite, principally designed to retrieve trace gases in the atmosphere, is also capable of detecting clouds. FRESCO (Fast Retrieval Scheme for Clouds from the Oxygen A-band) is a fast and robust algorithm providing cloud information from the O2 A-band for cloud correction of ozone. FRESCO provides a consistent set of cloud products by retrieving simultaneously effective cloud fraction and cloud top pressure. The FRESCO retrieved values are compared with both the SCIAMACHY Level 2 operational cloud fraction of OCRA (Optical Cloud Recognition Algorithm) and cloud information deduced from the MODIS instrument. The results correlate well, but FRESCO overestimates cloud fraction over deserts. Thus, to improve retrievals at these locations, the FRESCO surface albedo databases are decontaminated from the presence of desert dust aerosols. This is achieved by using a GOME Absorbing Aerosol Index. It is shown that this approach succeeds well in producing more accurate cloud information over the Sahara.

Implementasi Sistem Informasi Monitoring Pengolahan Data Inventory Gudang Pada PT.Talaga Mulya Indah

2020 ◽  
Vol 6 (2) ◽  
pp. 187-197
Author(s):  
Nurlaila Suci Rahayu Rais ◽  
Dedeh Apriyani ◽  
Gito Gardjito
Keyword(s):  
Data Processing ◽  
Integrated Control ◽  
Unified Modeling Language ◽  
Review Literature ◽  
Analysis Method ◽  
Inventory Data ◽  
Literature Study ◽  
Unified Modeling ◽  
The Difference ◽  
The Right

Monitoring of warehouse inventory data processing is an important thing for companies. PT Talaga mulya indah is still manual using paper media, causing problems that have an effect on existing information, namely: problems with data processing of incoming and outgoing goods. And the difference between data on the amount of stock of goods available with physical data, often occurs inputting data more than once for the same item, searching for available data, and making reports so that it impedes companies in monitoring inventory of existing stock of goods. Which aims to create a system that can provide updated information to facilitate the warehouse admin in making inventory reports, and reduce errors in input by means of integrated control. In this study, the authors used the data collection method used in this analysis using the method of observation, interviews, and literature review (literature study). For analysis using the PIECES analysis method. Furthermore, the system design used is UML (Unified Modeling Language). The results of this study are expected to produce the right data in the process of monitoring inventory data processing, also can provide the right information and make it easier to control the overall availability of goods.

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