WindSat-ground data processing and wind retrieval algorithm

2003 ◽  
Author(s):  
Gaiser ◽  
Richardson ◽  
Smith ◽  
Nai-Yu Wang ◽  
Bevilacqua
Keyword(s):  
Data Processing ◽  
Retrieval Algorithm ◽  
Wind Retrieval

A ‘Track-Wise’ Wind Retrieval Algorithm for the CYGNSS Mission

2019 ◽  
Author(s):  
Faozi Said ◽  
Zorana Jelenak ◽  
Jeonghwang Park ◽  
Seubson Soisuvarn ◽  
Paul S. Chang
Keyword(s):  
Retrieval Algorithm ◽  
Wind Retrieval

Data Processing of the Middle and Upper Atmospheric Wind Retrieval Based on the Fabry-Perot Interferometer

2013 ◽  
Vol 56 (2) ◽  
pp. 129-136
Author(s):  
WANG Hou-Mao ◽  
WANG Yong-Mei ◽  
WANG Ying-Jian
Keyword(s):  
Data Processing ◽  
Wind Retrieval ◽  
Fabry Perot

scholarly journals Improvement of wind retrieval algorithm for Rayleigh Doppler lidar

2012 ◽  
Vol 61 (3) ◽  
pp. 030702
Author(s):  
Shen Fa-Hua ◽  
Shu Zhi-Feng ◽  
Sun Dong-Song ◽  
Wang Zhong-Chun ◽  
Xue Xiang-Hui ◽  
...  
Keyword(s):  
Retrieval Algorithm ◽  
Doppler Lidar ◽  
Wind Retrieval

scholarly journals Profiling wind and greenhouse gases by infrared-laser occultation: results from end-to-end simulations in windy air

2015 ◽  
Vol 8 (7) ◽  
pp. 2813-2825 ◽  
Author(s):  
A. Plach ◽  
V. Proschek ◽  
G. Kirchengast
Keyword(s):  
Wind Speed ◽  
Greenhouse Gases ◽  
Doppler Shift ◽  
Lower Stratosphere ◽  
Infrared Laser ◽  
Retrieval Algorithm ◽  
Upper Troposphere ◽  
Wind Retrieval ◽  
End To End ◽  
Thermodynamic Variables

Abstract. The new mission concept of microwave and infrared-laser occultation between low-Earth-orbit satellites (LMIO) is designed to provide accurate and long-term stable profiles of atmospheric thermodynamic variables, greenhouse gases (GHGs), and line-of-sight (l.o.s.) wind speed with focus on the upper troposphere and lower stratosphere (UTLS). While the unique quality of GHG retrievals enabled by LMIO over the UTLS has been recently demonstrated based on end-to-end simulations, the promise of l.o.s. wind retrieval, and of joint GHG and wind retrieval, has not yet been analyzed in any realistic simulation setting. Here we use a newly developed l.o.s. wind retrieval algorithm, which we embedded in an end-to-end simulation framework that also includes the retrieval of thermodynamic variables and GHGs, and analyze the performance of both stand-alone wind retrieval and joint wind and GHG retrieval. The wind algorithm utilizes LMIO laser signals placed on the inflection points at the wings of the highly symmetric C18OO absorption line near 4767 cm−1 and exploits transmission differences from a wind-induced Doppler shift. Based on realistic example cases for a diversity of atmospheric conditions, ranging from tropical to high-latitude winter, we find that the retrieved l.o.s. wind profiles are of high quality over the lower stratosphere under all conditions, i.e., unbiased and accurate to within about 2 m s−1 over about 15 to 35 km. The wind accuracy degrades into the upper troposphere due to the decreasing signal-to-noise ratio of the wind-induced differential transmission signals. The GHG retrieval in windy air is not vulnerable to wind speed uncertainties up to about 10 m s−1 but is found to benefit in the case of higher speeds from the integrated wind retrieval that enables correction of wind-induced Doppler shift of GHG signals. Overall both the l.o.s. wind and GHG retrieval results are strongly encouraging towards further development and implementation of a LMIO mission.

scholarly journals A Generalized Simulation Capability for Rotating Beam Scatterometers

2018 ◽  
Author(s):  
Zhen Li ◽  
Ad Stoffelen ◽  
Anton Verhoef
Keyword(s):  
Incidence Angle ◽  
Retrieval Algorithm ◽  
Pencil Beam ◽  
Wind Vector ◽  
Response Functions ◽  
Rotating Beam ◽  
Retrieval Performance ◽  
Wind Retrieval ◽  
Spatial Response

Abstract. Rotating-beam wind scatterometers exist in two types: rotating fan-beam and rotating pencil-beam. In our study, a generic simulation frame is established and verified to assess the wind retrieval skill of the three different scatterometers: SCAT on CFOSAT, WindRad on FY-3E and SeaWinds on QuikScat. Besides the comparison of the so-called 1st rank-solution retrieval skill of the input wind field, other Figure of Merits (FoMs) are applied to statistically characterize the associated wind retrieval performance from three aspects: wind vector root mean square error, ambiguity susceptibility, and wind biases. The evaluation shows that, overall, the wind retrieval quality of the three instruments can be ranked from high to low as WindRad, SCAT, and SeaWinds, where the wind retrieval quality strongly depends on the Wind Vector Cell (WVC) location across the swath. Usually, the higher the number of views, the better the wind retrieval, but the effect of increasing the number of views reaches saturation, considering the fact that the wind retrieval quality at the nadir and sweet swath parts stays relatively similar for SCAT and WindRad. On the other hand, the wind retrieval performance in the outer swath of WindRad is improved substantially as compared to SCAT due to the increased number of views. The results may be generally explained by the different incidence angle ranges of SCAT and WindRad, mainly affecting azimuth diversity around nadir and number of views in the outer swath. This simulation frame can be used for optimizing the Bayesian wind retrieval algorithm, in particular to avoid biases around nadir, but also to investigate resolution and accuracy through incorporating and analysing the spatial response functions of the simulated Level-1B data for each WVC.

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 Carbon monoxide column retrieval for clear-sky and cloudy atmospheres: a full-mission data set from SCIAMACHY 2.3 μm reflectance measurements

2017 ◽  
Author(s):  
Tobias Borsdorff ◽  
Joost aan de Brugh ◽  
Haili Hu ◽  
Philippe Nédélec ◽  
Ilse Aben ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Data Processing ◽  
Pearson Correlation ◽  
Retrieval Algorithm ◽  
Vertical Column ◽  
Validation Data ◽  
Data Set ◽  
Airborne Measurements ◽  
Clear Sky ◽  
Infrared Measurements

Abstract. We discuss the retrieval of carbon monoxide (CO) vertical column densities from clear-sky and cloud contaminated 2311–2338 nm reflectance spectra measured by the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) from January 2003 until the end of the mission in April 2012. These data was processed with the Shortwave Infrared CO Retrieval algorithm SICOR that we developed for the operational data processing of the Tropospheric Monitoring Instrument (TROPOMI) that will be launched on ESA’s Sentinel-5 Precursor (S5P) mission. This study complements previous work that was limited to clear-sky observations over land. Over the oceans, CO is estimated from cloudy-sky measurements only, which is an important addition to the SCIAMACHY clear-sky CO data set as shown by NDACC and TCCON measurements at coastal sites. For Ny-Ålesund, Lauder, Mauna Loa, and Reunion, a validation of SCIAMACHY clear-sky retrievals is not meaningful because of the high retrieval noise and the few collocations at these sites. This improves significantly when considering cloudy-sky observations, where we find a low mean bias b = ±6.0 ppb and a strong correlation between the validation data set and the SCIAMACHY data sets with a mean Pearson correlation coefficient r = 0.7. Also for land observations, cloudy-sky CO retrievals present an interesting complement to the clear-sky data set, which is less sensitive to the spatial representativeness of the satellite and validation measurement. For example, at the cities Teheran and Beijing the agreement of SCIAMACHY clear-sky CO observations with MOZAIC/IAGOS airborne measurements is poor with a mean bias of b = 171.2 ppb and 57.9 ppb because of local CO pollution, which cannot be captured by SCIAMACHY. The validation improves significantly for cloudy sky retrievals with b = 52.3 ppb and 5.0 ppb, respectively. This is due to a reduced retrieval sensitivity to CO below the cloud and so to the altitude range, which is mostly affected by strong local surface emissions. At the less urbanized region around the airportWindhoek, local CO pollution is less prominent and so MOZAIC/IAGOS measurements agree well with SCIAMACHY clear-sky retrievals with a mean bias of b = 15.5 ppb, but can be even further improved considering cloudy SCIAMACHY observations with a mean CO bias of b = 0.2 ppb. Overall the cloudy-sky CO retrievals from SCIAMACHY short wave infrared measurements present a valuable addition to the clear-sky only data set. Moreover, the study represents the first application of the S5P algorithm for operational CO data processing on cloudy observations prior to the launch of the S5P mission.

scholarly journals Large Scale Hotel Resource Retrieval Algorithm Based on Characteristic Threshold Extraction

2022 ◽  
Vol 16 ◽  
pp. 26-31
Author(s):  
Min Fang
Keyword(s):  
Energy Consumption ◽  
Data Processing ◽  
Large Scale ◽  
High Energy ◽  
Error Component ◽  
Retrieval Algorithm ◽  
Data Detection ◽  
Redundant Data ◽  
Variable Window ◽  
Resource Data

At present, the hotel resource retrieval algorithm has the problem of low retrieval efficiency, low accuracy, low security and high energy consumption, and this study proposes a large scale hotel resource retrieval algorithm based on characteristic threshold extraction. In the large-scale hotel resource data, the mass sequence is decomposed into periodic component, trend component, random error component and burst component. Different components are extracted, the singular point detection is realized by the extraction results, and the abnormal data in the hotel resource data are obtained. Based on the attribute of hotel resource data, the data similarity is processed with variable window, the total similarity of data is obtained, and the abnormal detection of redundant resource data is realized. The abnormal data detection results and redundant data detection results are substituted into the space-time filter, and the data processing is completed. The retrieval problem is identified, and the data processing results are replaced in the hotel resource retrieval based on the characteristic threshold extraction to achieve the normalization of data source and rule knowledge. The characteristic threshold and retrieval strategy are determined, and data fusion reasoning is carried out. After repeated iteration, effective solutions are obtained. The effective solution is fused to get the best retrieval result. Experimental results showed that the algorithm has higher retrieval accuracy, efficiency and security coefficient, and the average search energy consumption is 56n J/bit.

scholarly journals Evaluation of a Support Vector Machine–Based Single-Doppler Wind Retrieval Algorithm

2017 ◽  
Vol 34 (8) ◽  
pp. 1749-1761 ◽  
Author(s):  
Nan Li ◽  
Ming Wei ◽  
Yongjiang Yu ◽  
Wengang Zhang
Keyword(s):  
Support Vector Machine ◽  
High Resolution ◽  
Condition Number ◽  
Condition Numbers ◽  
Retrieval Algorithm ◽  
Support Vector ◽  
Small Scale ◽  
Wind Retrieval ◽  
Original Algorithm ◽  
Retrieval Accuracy

AbstractWind retrieval algorithms are required for Doppler weather radars. In this article, a new wind retrieval algorithm of single-Doppler radar with a support vector machine (SVM) is analyzed and compared with the original algorithm with the least squares technique. Through an analysis of coefficient matrices of equations corresponding to the optimization problems for the two algorithms, the new algorithm, which contains a proper penalization parameter, is found to effectively reduce the condition numbers of the matrices and thus has the ability to acquire accurate results, and the smaller the analysis volume is, the smaller the condition number of the matrix. This characteristic makes the new algorithm suitable to retrieve mesoscale and small-scale and high-resolution wind fields. Afterward, the two algorithms are applied to retrieval experiments to implement a comparison and a discussion. The results show that the penalization parameter cannot be too small, otherwise it may cause a large condition number; it cannot be too large either, otherwise it may change the properties of equations, leading to retrieved wind direction along the radial direction. Compared with the original algorithm, the new algorithm has definite superiority with the appropriate penalization parameters for small analysis volumes. When the suggested small analysis volume dimensions and penalization parameter values are adopted, the retrieval accuracy can be improved by 10 times more than the traditional method. As a result, the new algorithm has the capability to analyze the dynamical structures of severe weather, which needs high-resolution retrieval, and the potential for quantitative applications such as the assimilation in numerical models, but the retrieval accuracy needs to be further improved in the future.

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