Artykuły w czasopismach na temat „Retrieval accuracy”
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Lipponen, Antti, Tero Mielonen, Mikko R. A. Pitkänen, Robert C. Levy, Virginia R. Sawyer, Sami Romakkaniemi, Ville Kolehmainen i Antti Arola. "Bayesian aerosol retrieval algorithm for MODIS AOD retrieval over land". Atmospheric Measurement Techniques 11, nr 3 (19.03.2018): 1529–47. http://dx.doi.org/10.5194/amt-11-1529-2018.
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Abstract. We have developed a Bayesian aerosol retrieval (BAR) algorithm for the retrieval of aerosol optical depth (AOD) over land from the Moderate Resolution Imaging Spectroradiometer (MODIS). In the BAR algorithm, we simultaneously retrieve all dark land pixels in a granule, utilize spatial correlation models for the unknown aerosol parameters, use a statistical prior model for the surface reflectance, and take into account the uncertainties due to fixed aerosol models. The retrieved parameters are total AOD at 0.55 µm, fine-mode fraction (FMF), and surface reflectances at four different wavelengths (0.47, 0.55, 0.64, and 2.1 µm). The accuracy of the new algorithm is evaluated by comparing the AOD retrievals to Aerosol Robotic Network (AERONET) AOD. The results show that the BAR significantly improves the accuracy of AOD retrievals over the operational Dark Target (DT) algorithm. A reduction of about 29 % in the AOD root mean square error and decrease of about 80 % in the median bias of AOD were found globally when the BAR was used instead of the DT algorithm. Furthermore, the fraction of AOD retrievals inside the ±(0.05+15%) expected error envelope increased from 55 to 76 %. In addition to retrieving the values of AOD, FMF, and surface reflectance, the BAR also gives pixel-level posterior uncertainty estimates for the retrieved parameters. The BAR algorithm always results in physical, non-negative AOD values, and the average computation time for a single granule was less than a minute on a modern personal computer.
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Di Noia, A., O. P. Hasekamp, G. van Harten, J. H. H. Rietjens, J. M. Smit, F. Snik, J. S. Henzing, J. de Boer, C. U. Keller i H. Volten. "Use of neural networks in ground-based aerosol retrievals from multi-angle spectropolarimetric observations". Atmospheric Measurement Techniques 8, nr 1 (14.01.2015): 281–99. http://dx.doi.org/10.5194/amt-8-281-2015.
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Abstract. In this paper, the use of a neural network algorithm for the retrieval of the aerosol properties from ground-based spectropolarimetric measurements is discussed. The neural network is able to retrieve the aerosol properties with an accuracy that is almost comparable to that of an iterative retrieval. By using the outcome of the neural network as first guess in the iterative retrieval scheme, the accuracy of the retrieved fine- and coarse-mode optical thickness is further improved, while for the other parameters the improvement is small or absent. The resulting scheme (neural network + iterative retrieval) is compared to the original one (look-up table + iterative retrieval) on a set of simulated ground-based measurements, and on a small set of real observations carried out by an accurate ground-based spectropolarimeter. The results show that the use of a neural-network-based first guess leads to an increase in the number of converging retrievals, and possibly to more accurate estimates of the aerosol effective radius and complex refractive index.
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Frankenberg, C., O. Hasekamp, C. O'Dell, S. Sanghavi, A. Butz i J. Worden. "Aerosol information content analysis of multi-angle high spectral resolution measurements and its benefit for high accuracy greenhouse gas retrievals". Atmospheric Measurement Techniques Discussions 5, nr 2 (16.04.2012): 2857–85. http://dx.doi.org/10.5194/amtd-5-2857-2012.
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Abstract. New generations of space-borne spectrometers for the retrieval of atmospheric abundances of greenhouse gases require unprecedented accuracies as atmospheric variability of long-lived gases is very low. These instruments, such as GOSAT and OCO-2, typically use a high spectral resolution oxygen channel (O2 A-band) in addition to CO2 and CH4 channels to discriminate changes in the photon path-length distribution from actual trace gas amount changes. Inaccurate knowledge of the photon path-length distribution, determined by scatterers in the atmosphere, is the prime source of systematic biases in the retrieval. In this paper, we investigate the combined aerosol and greenhouse gas retrieval using multiple satellite viewing angles simultaneously. We find that this method, hitherto only applied in multi-angle imagery such as from MISR, greatly enhances the ability to retrieve aerosol properties by 2–3 degrees of freedom. We find that the improved capability to retrieve aerosol parameters significantly reduces interference errors introduced into retrieved CO2 and CH4 total column averages. Instead of focussing solely on improvements in spectral and spatial resolution, signal-to-noise ratios or sampling frequency, multiple angles reduce uncertainty in space based greenhouse gas retrievals more effectively and provide a new potential for dedicated aerosols retrievals.
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Frankenberg, C., O. Hasekamp, C. O'Dell, S. Sanghavi, A. Butz i J. Worden. "Aerosol information content analysis of multi-angle high spectral resolution measurements and its benefit for high accuracy greenhouse gas retrievals". Atmospheric Measurement Techniques 5, nr 7 (27.07.2012): 1809–21. http://dx.doi.org/10.5194/amt-5-1809-2012.
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Abstract. New generations of space-borne spectrometers for the retrieval of atmospheric abundances of greenhouse gases require unprecedented accuracies as atmospheric variability of long-lived gases is very low. These instruments, such as GOSAT and OCO-2, typically use a high spectral resolution oxygen channel (O2 A-band) in addition to CO2 and CH4 channels to discriminate changes in the photon path-length distribution from actual trace gas amount changes. Inaccurate knowledge of the photon path-length distribution, determined by scatterers in the atmosphere, is the prime source of systematic biases in the retrieval. In this paper, we investigate the combined aerosol and greenhouse gas retrieval using multiple satellite viewing angles simultaneously. We find that this method, hitherto only applied in multi-angle imagery such as from POLDER or MISR, greatly enhances the ability to retrieve aerosol properties by 2–3 degrees of freedom. We find that the improved capability to retrieve aerosol parameters significantly reduces interference errors introduced into retrieved CO2 and CH4 total column averages. Instead of focussing solely on improvements in spectral and spatial resolution, signal-to-noise ratios or sampling frequency, multiple angles reduce uncertainty in space based greenhouse gas retrievals more effectively and provide a new potential for dedicated aerosols retrievals.
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Tu, Jinsheng, Haohan Wei, Rui Zhang, Lei Yang, Jichao Lv, Xiaoming Li, Shihai Nie, Peng Li, Yanxia Wang i Nan Li. "GNSS-IR Snow Depth Retrieval from Multi-GNSS and Multi-Frequency Data". Remote Sensing 13, nr 21 (26.10.2021): 4311. http://dx.doi.org/10.3390/rs13214311.
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Global navigation satellite system interferometric reflectometry (GNSS-IR) represents an extra method to detect snow depth for climate research and water cycle managing. However, using a single frequency of GNSS-IR for snow depth retrieval is often found to be challenging when attempting to achieve a high spatial and temporal sensitivity. To evaluate both the capability of the GNSS-IR snow depth retrieved by the multi-GNSS system and multi-frequency from signal-to-noise ratio (SNR) data, the accuracy of snow depth retrieval by different frequency signals from the multi-GNSS system is analyzed, and a joint retrieval is carried out by combining the multi-GNSS system retrieval results. The SNR data of the global positioning system (GPS), global orbit navigation satellite system (GLONASS), Galileo satellite navigation system (Galileo), and BeiDou navigation satellite system (BDS) from the P387 station of the U.S. Plate Boundary Observatory (PBO) are analyzed. A Lomb–Scargle periodogram (LSP) spectrum analysis is used to compare the difference in reflector height between the snow-free and snow surfaces in order to retrieve the snow depth, which is compared with the PBO snow depth. First, the different frequency retrieval results of the multi-GNSS system are analyzed. Then, the retrieval accuracy of the different GNSS systems is analyzed through multi-frequency mean fusion. Finally, the joint retrieval accuracy of the multi-GNSS system is analyzed through mean fusion. The experimental shows that the retrieval results of different frequencies of the multi-GNSS system have a strong correlation with the PBO snow depth, and that the accuracy is better than 10 cm. The multi-frequency mean fusion of different GNSS systems can effectively improve the retrieval accuracy, which is better than 7 cm. The joint retrieval accuracy of the multi-GNSS system is further improved, with a correlation coefficient (R) between the retrieval snow depth and the PBO snow depth of 0.99, and the accuracy is better than 3 cm. Therefore, using multi-GNSS and multi-frequency data to retrieve the snow depth has a good accuracy and feasibility.
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Zhou, Minqiang, Bavo Langerock, Mahesh Kumar Sha, Nicolas Kumps, Christian Hermans, Christof Petri, Thorsten Warneke i in. "Retrieval of atmospheric CH<sub>4</sub> vertical information from ground-based FTS near-infrared spectra". Atmospheric Measurement Techniques 12, nr 11 (25.11.2019): 6125–41. http://dx.doi.org/10.5194/amt-12-6125-2019.
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Abstract. The Total Carbon Column Observing Network (TCCON) column-averaged dry air mole fraction of CH4 (XCH4) measurements have been widely used to validate satellite observations and to estimate model simulations. The GGG2014 code is the standard TCCON retrieval software used in performing a profile scaling retrieval. In order to obtain several vertical pieces of information in addition to the total column, in this study, the SFIT4 retrieval code is applied to retrieve the CH4 mole fraction vertical profile from the Fourier transform spectrometer (FTS) spectrum at six sites (Ny-Ålesund, Sodankylä, Bialystok, Bremen, Orléans and St Denis) during the time period of 2016–2017. The retrieval strategy of the CH4 profile retrieval from ground-based FTS near-infrared (NIR) spectra using the SFIT4 code (SFIT4NIR) is investigated. The degree of freedom for signal (DOFS) of the SFIT4NIR retrieval is about 2.4, with two distinct pieces of information in the troposphere and in the stratosphere. The averaging kernel and error budget of the SFIT4NIR retrieval are presented. The data accuracy and precision of the SFIT4NIR retrievals, including the total column and two partial columns (in the troposphere and stratosphere), are estimated by TCCON standard retrievals, ground-based in situ measurements, Atmospheric Chemistry Experiment – Fourier Transform Spectrometer (ACE-FTS) satellite observations, TCCON proxy data and AirCore and aircraft measurements. By comparison against TCCON standard retrievals, it is found that the retrieval uncertainty of SFIT4NIR XCH4 is similar to that of TCCON standard retrievals with systematic uncertainty within 0.35 % and random uncertainty of about 0.5 %. The tropospheric and stratospheric XCH4 from SFIT4NIR retrievals are assessed by comparison with AirCore and aircraft measurements, and there is a 1.0 ± 0.3 % overestimation in the SFIT4NIR tropospheric XCH4 and a 4.0 ± 2.0 % underestimation in the SFIT4NIR stratospheric XCH4, which are within the systematic uncertainties of SFIT4NIR-retrieved partial columns in the troposphere and stratosphere respectively.
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Strong, K., B. M. Joseph, R. Dosanjh, I. C. McDade, C. A. McLinden, J. C. McConnell, J. Stegman, D. P. Murtagh i E. J. Llewellyn. "Retrieval of vertical concentration profiles from OSIRIS UVvisible limb spectra". Canadian Journal of Physics 80, nr 4 (1.03.2002): 409–34. http://dx.doi.org/10.1139/p01-153.
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The OSIRIS instrument, launched on the Odin satellite in February 2001, includes an optical spectrograph that will record UVvisible spectra of sunlight scattered from the limb over a range of tangent heights. These spectra will be used to retrieve vertical profiles of ozone, NO2, OClO, BrO, NO3, O2, and aerosols, for the investigation of both stratospheric and mesospheric processes, particularly those related to ozone chemistry. In this work, the retrieval of vertical profiles of trace-gas concentrations from OSIRIS limb-radiance spectra is described. A forward model has been developed to simulate these spectra, and it consists of a single-scattering radiative-transfer model with partial spherical geometry, trace-gas absorption, Mie scattering by stratospheric aerosols, a Lambertian surface contribution, and OSIRIS instrument response and noise. Number-density profiles have been retrieved by using optimal estimation (OE) to combine an a priori profile with the information from sets of synthetic ``measurements''. For ozone, OE has been applied both to limb radiances at one or more discrete wavelengths and to effective-column abundances retrieved over a broad spectral range using differential optical absorption spectroscopy (DOAS). The results suggest that, between 15 and 35 km, ozone number densities can be retrieved to 10% accuracy or better on 1 and 2 km grids and to 5% on a 5 km grid. The combined DOAS-OE approach has also been used to retrieve NO2 number densities, yielding 13% accuracy or better for altitudes from 18 to 36 km on a 2 km grid. Differential optical absorption spectroscopy optimal estimation retrievals of BrO and OClO reproduce the true profiles above 15 km in the noise-free case, but the quality of the retrievals is highly sensitive to noise on the simulated OSIRIS spectra because of the weak absorption of these two gases. The development of inversion methods for the retrieval of trace-gas concentrations from OSIRIS spectra is continuing, and a number of future improvements to the forward model and refinements of the retrieval algorithms are identified. PACS Nos.: 42.68Mj, 94.10Dy
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Matrosov, Sergey Y. "Attenuation-Based Estimates of Rainfall Rates Aloft with Vertically Pointing Ka-Band Radars". Journal of Atmospheric and Oceanic Technology 22, nr 1 (1.01.2005): 43–54. http://dx.doi.org/10.1175/jtech-1677.1.
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Abstract An approach is suggested to retrieve low-resolution rainfall rate profiles and layer-averaged rainfall rates, Ra, from radar reflectivity measurements made by vertically pointing Ka-band radars. This approach is based on the effects of attenuation of radar signals in rain and takes advantage of the nearly linear relation between specific attenuation and rainfall rate at Ka-band frequencies. The variability of this relation due to temperature, details of raindrop size distributions, and the nature of rain (convective versus stratiform) is rather small (∼10%) and contributes little to errors in rainfall rate retrievals. The main contribution to the retrieval errors comes from the uncertainty of the difference in the nonattenuated radar reflectivities in the beginning and the end of the range resolution interval. For 2- and 1-dB uncertainties in this difference, the retrieval errors due to this main contribution are less than 34% and 17%, correspondingly, for rains with Ra ≈ 10 mm h−1 at a 1-km resolution interval. The heavier rain rates are retrieved with a better accuracy since this retrieval error contribution is proportional to 1/Ra. The retrieval accuracy can also be improved but at the expense of more coarse vertical resolutions of retrievals since the main retrieval error contribution is also proportional to the reciprocal of the resolution interval. The Mie scattering effects at Ka band results in less variability in nonattenuated reflectivities (cf. lower radar frequencies), which aids the suggested approach. Given that radar receivers are not saturated, the rainfall rates can be retrieved using cloud radars that were originally designed for measuring only nonprecipitating and weakly precipitating clouds. An important advantage of the attenuation-based retrievals of rainfall is that absolute radar calibration is not required. The inclusion of rainfall information will improve the characterization of the atmospheric column obtained with such radars used for climate research. The applications of the suggested approach are illustrated using the vertically pointing Ka-band radar measurements made during a field experiment in southern Florida. The retrieval results are in good agreement with surface estimates of rainfall rates.
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Roche, Sébastien, Kimberly Strong, Debra Wunch, Joseph Mendonca, Colm Sweeney, Bianca Baier, Sébastien C. Biraud, Joshua L. Laughner, Geoffrey C. Toon i Brian J. Connor. "Retrieval of atmospheric CO<sub>2</sub> vertical profiles from ground-based near-infrared spectra". Atmospheric Measurement Techniques 14, nr 4 (28.04.2021): 3087–118. http://dx.doi.org/10.5194/amt-14-3087-2021.
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Abstract. We evaluate vertical profile retrievals of CO2 from 0.02 cm−1 resolution ground-based near-infrared solar absorption spectra with the GFIT2 algorithm, using improved spectroscopic line lists and line shapes. With these improvements, CO2 profiles were obtained from sequential retrievals in five spectral windows with different vertical sensitivities using synthetic and real spectra. A sensitivity study using synthetic spectra shows that the leading source of uncertainty in the retrieved CO2 profiles is the error in the a priori temperature profile, even with 3-hourly reanalysis a priori profiles. A 2 ∘C error in the temperature profile in the lower troposphere between 0.6 and 0.85 atm causes deviations in the retrieved CO2 profiles that are larger than the typical vertical variations of CO2. To distinguish the effect of errors in the a priori meteorology and trace gas concentration profiles from those in the instrument alignment and spectroscopic parameters, we retrieve CO2 profiles from atmospheric spectra while using an a priori profile built from coincident AirCore, radiosonde, and surface in situ measurements at the Lamont, Oklahoma (USA), Total Carbon Column Observing Network station. In those cases, the deviations in retrieved CO2 profiles are also larger than typical vertical variations of CO2, suggesting that remaining errors in the forward model limit the accuracy of the retrieved profiles. Implementing a temperature retrieval or correction and quantifying and modeling an imperfect instrument alignment are critical to improve CO2 profile retrievals. Without significant advances in modeling imperfect instrument alignment, and improvements in the accuracy of the temperature profile, the CO2 profile retrieval with GFIT2 presents no clear advantage over scaling retrievals for the purpose of ascertaining the total column.
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Leinonen, Jussi, Matthew D. Lebsock, Simone Tanelli, Ousmane O. Sy, Brenda Dolan, Randy J. Chase, Joseph A. Finlon, Annakaisa von Lerber i Dmitri Moisseev. "Retrieval of snowflake microphysical properties from multifrequency radar observations". Atmospheric Measurement Techniques 11, nr 10 (5.10.2018): 5471–88. http://dx.doi.org/10.5194/amt-11-5471-2018.
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Abstract. We have developed an algorithm that retrieves the size, number concentration and density of falling snow from multifrequency radar observations. This work builds on previous studies that have indicated that three-frequency radars can provide information on snow density, potentially improving the accuracy of snow parameter estimates. The algorithm is based on a Bayesian framework, using lookup tables mapping the measurement space to the state space, which allows fast and robust retrieval. In the forward model, we calculate the radar reflectivities using recently published snow scattering databases. We demonstrate the algorithm using multifrequency airborne radar observations from the OLYMPEX–RADEX field campaign, comparing the retrieval results to hydrometeor identification using ground-based polarimetric radar and also to collocated in situ observations made using another aircraft. Using these data, we examine how the availability of multiple frequencies affects the retrieval accuracy, and we test the sensitivity of the algorithm to the prior assumptions. The results suggest that multifrequency radars are substantially better than single-frequency radars at retrieving snow microphysical properties. Meanwhile, triple-frequency radars can retrieve wider ranges of snow density than dual-frequency radars and better locate regions of high-density snow such as graupel, although these benefits are relatively modest compared to the difference in retrieval performance between dual- and single-frequency radars. We also examine the sensitivity of the retrieval results to the fixed a priori assumptions in the algorithm, showing that the multifrequency method can reliably retrieve snowflake size, while the retrieved number concentration and density are affected significantly by the assumptions.
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Foerster, Annette M., i Michael M. Bell. "Thermodynamic Retrieval in Rapidly Rotating Vortices from Multiple-Doppler Radar Data". Journal of Atmospheric and Oceanic Technology 34, nr 11 (listopad 2017): 2353–74. http://dx.doi.org/10.1175/jtech-d-17-0073.1.
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AbstractThermodynamic retrievals can derive pressure and temperature information from kinematic measurements in regions where no in situ observations are available. This study presents a new retrieval technique called SAMURAI-TR (Spline Analysis at Mesoscale Utilizing Radar and Aircraft Instrumentation–Thermodynamic Retrieval) that derives three-dimensional fields of pressure and density potential temperature from multiple-Doppler radar data using a variational approach. SAMURAI-TR advances existing methods by 1) allowing for a horizontal variation in the reference-state definition and 2) representing the retrieved quantities of pressure and temperature as three-dimensional functions consisting of a series of finite-element cubic B-splines. The first advancement enables the retrieval to explicitly account for the large radial gradient of the mean thermodynamic state in tropical cyclones and other rapidly rotating vortices. The second advancement allows for specification of the three-dimensional pressure and temperature gradients as pseudo-observations from Doppler-derived winds, effectively linking the vertical levels without the use of the thermodynamic equation or a microphysical closure. The retrieval uses only the horizontal and vertical momentum equations, their derivatives, and low-pass filters. The accuracy and sensitivity of the retrieval are assessed using a WRF simulation of a tropical cyclone. SAMURAI-TR has good accuracy compared to prior techniques and retrieves pressure to within 0.25 hPa and temperature to within 0.7 K RMSE. The application of the method to real data is demonstrated using multiple-Doppler data from Hurricane Rita (2005).
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Huang, Pengyu, Qiang Guo, Changpei Han, Chunming Zhang, Tianhang Yang i Shuo Huang. "An Improved Method Combining ANN and 1D-Var for the Retrieval of Atmospheric Temperature Profiles from FY-4A/GIIRS Hyperspectral Data". Remote Sensing 13, nr 3 (29.01.2021): 481. http://dx.doi.org/10.3390/rs13030481.
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In our study, a retrieval method of temperature profiles is proposed which combines an improved one-dimensional variational algorithm (1D-Var) and artificial neural network algorithm (ANN), using FY-4A/GIIRS (Geosynchronous Interferometric Infrared Sounder) infrared hyperspectral data. First, according to the characteristics of the FY-4A/GIIRS observation data using the conventional 1D-Var, we introduced channel blacklists and discarded the channels that have a large negative impact on retrieval, then used the information capacity method for channel selection and introduced a neural network to correct the satellite observation data. The improved 1D-Var effectively used the observation information of 1415 channels, reducing the impact of the error of the satellite observation and radiative transfer model, and realizing the improvement of retrieval accuracy. We subsequently used the improved 1D-Var and ANN algorithms to retrieve the temperature profiles, respectively, from the GIIRS data. The results showed that the accuracy when using ANN is better than using improved 1D-Var in situations where the pressure ranges from 800 hPa to 1000 hPa. Therefore, we combined the improved 1D-Var and ANN method to retrieve temperature profiles for different pressure levels, calculating the error by taking sounding data published by the University of Wyoming as the true values. The results show that the average error of the retrieved temperature profiles is smaller than 2 K when using our method, this method makes the accuracy of the retrieved temperature profiles superior to the accuracy of the GIIRS products from 10 hPa to 575 hPa. All in all, through the combination of the physical retrieval method and the machine learning retrieval method, this paper can certainly provide a reference for improving the accuracy of products.
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Uma, R., i B. Latha. "An efficient voice based information retrieval using bag of words based indexing". International Journal of Engineering & Technology 7, nr 3.3 (8.06.2018): 622. http://dx.doi.org/10.14419/ijet.v7i2.33.14850.
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Data mining is one of the leading and drastically growing researches nowadays. One of the main areas in data mining is Information Retrieval (IR). Information retrieval is a broad job and it is finding information without any structured nature. Infor-mation retrieval retrieves the user required information from a large collection of data. The existing approaches yet to improve the accuracy in terms of relevant accuracy. In this paper, it is motivated to provide an Information Retrieval System (IRS) where it can retrieve information with high relevancy. The proposed IRS is specially designed for physically challenged people like blind people where the input and the output taken/given is voice. The functionality of proposed IRS consists of three stages such as: (i) Voice to Text input, (II). Pattern Matching, and (III). Text to Voice output.In order to improve the accuracy and relevancy the proposed IRS uses an indexing method called Bag of Words (BOW). BOW is like an index-table which can be referred to store, compare and retrieve the information speedily and accurately. Index-table utilization in IRS improves the accuracy with minimized computational complexity. The proposed IRS is simulated in DOTNET software and the results are compared with the existing system results in order to evaluate the performance.
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Pfreundschuh, Simon, Paula J. Brown, Christian D. Kummerow, Patrick Eriksson i Teodor Norrestad. "GPROF-NN: a neural-network-based implementation of the Goddard Profiling Algorithm". Atmospheric Measurement Techniques 15, nr 17 (2.09.2022): 5033–60. http://dx.doi.org/10.5194/amt-15-5033-2022.
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Abstract. The Global Precipitation Measurement (GPM) mission measures global precipitation at a temporal resolution of a few hours to enable close monitoring of the global hydrological cycle. GPM achieves this by combining observations from a spaceborne precipitation radar, a constellation of passive microwave (PMW) sensors, and geostationary satellites. The Goddard Profiling Algorithm (GPROF) is used operationally to retrieve precipitation from all PMW sensors of the GPM constellation. Since the resulting precipitation rates serve as input for many of the level 3 retrieval products, GPROF constitutes an essential component of the GPM processing pipeline. This study investigates ways to improve GPROF using modern machine learning methods. We present two neural-network-based, probabilistic implementations of GPROF: GPROF-NN 1D, which (just like the current GPROF implementation) processes pixels individually, and GPROF-NN 3D, which employs a convolutional neural network to incorporate structural information into the retrieval. The accuracy of the retrievals is evaluated using a test dataset consistent with the data used in the development of the GPROF and GPROF-NN retrievals. This allows for assessing the accuracy of the retrieval method isolated from the representativeness of the training data, which remains a major source of uncertainty in the development of precipitation retrievals. Despite using the same input information as GPROF, the GPROF-NN 1D retrieval improves the accuracy of the retrieved surface precipitation for the GPM Microwave Imager (GMI) from 0.079 to 0.059 mm h−1 in terms of mean absolute error (MAE), from 76.1 % to 69.5 % in terms of symmetric mean absolute percentage error (SMAPE) and from 0.797 to 0.847 in terms of correlation. The improvements for the Microwave Humidity Sounder (MHS) are from 0.085 to 0.061 mm h−1 in terms of MAE, from 81 % to 70.1 % for SMAPE, and from 0.724 to 0.804 in terms of correlation. Comparable improvements are found for the retrieved hydrometeor profiles and their column integrals, as well as the detection of precipitation. Moreover, the ability of the retrievals to resolve small-scale variability is improved by more than 40 % for GMI and 29 % for MHS. The GPROF-NN 3D retrieval further improves the MAE to 0.043 mm h−1; the SMAPE to 48.67 %; and the correlation to 0.897 for GMI and 0.043 mm h−1, 63.42 %, and 0.83 for MHS. Application of the retrievals to GMI observations of Hurricane Harvey shows moderate improvements when compared to co-located GPM-combined and ground-based radar measurements indicating that the improvements at least partially carry over to assessment against independent measurements. Similar retrievals for MHS do not show equally clear improvements, leaving the validation against independent measurements for future investigation. Both GPROF-NN algorithms make use of the same input and output data as the original GPROF algorithm and thus may replace the current implementation in a future update of the GPM processing pipeline. Despite their superior accuracy, the single-core runtime required for the operational processing of an orbit of observations is lower than that of GPROF. The GPROF-NN algorithms promise to be a simple and cost-efficient way to increase the accuracy of the PMW precipitation retrievals of the GPM constellation and thus improve the monitoring of the global hydrological cycle.
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Dr. V. Suma. "A Novel Information retrieval system for distributed cloud using Hybrid Deep Fuzzy Hashing Algorithm". September 2020 02, nr 03 (28.08.2020): 151–60. http://dx.doi.org/10.36548/jitdw.2020.3.003.
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The recent technology development fascinates the people towards information and its services. Managing the personal and pubic data is a perennial research topic among researchers. In particular retrieval of information gains more attention as it is important similar to data storing. Clustering based, similarity based, graph based information retrieval systems are evolved to reduce the issues in conventional information retrieval systems. Learning based information retrieval is the present trend and in particular deep neural network is widely adopted due to its retrieval performance. However, the similarity between the information has uncertainties due to its measuring procedures. Considering these issues also to improve the retrieval performance, a hybrid deep fuzzy hashing algorithm is introduced in this research work. Hashing efficiently retrieves the information based on mapping the similar information as correlated binary codes and this underlying information is trained using deep neural network and fuzzy logic to retrieve the necessary information from distributed cloud. Experimental results prove that the proposed model attains better retrieval accuracy and accuracy compared to conventional models such as support vector machine and deep neural network.
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Herbin, H., L. C. Labonnote i P. Dubuisson. "Multispectral information from TANSO-FTS instrument – Part 2: Application to aerosol effect on greenhouse gas retrievals". Atmospheric Measurement Techniques 6, nr 11 (28.11.2013): 3313–23. http://dx.doi.org/10.5194/amt-6-3313-2013.
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Abstract. This article is the second in a series of studies investigating the benefits of multispectral measurements to improve the atmospheric parameter retrievals. In the first paper, we presented an information content (IC) analysis from the thermal infrared (TIR) and shortwave infrared (SWIR) bands of Thermal And Near infrared Sensor for carbon Observations–Fourier Transform Spectrometer (TANSO-FTS) instrument dedicated to greenhouse gas retrieval in clear sky conditions. This second paper presents the potential of the spectral synergy from TIR to visible for aerosol characterization, and their impact on the retrieved CO2 and CH4 column concentrations. The IC is then used to determine the most informative spectral channels for the simultaneous retrieval of greenhouse gas total columns and aerosol parameters. The results show that a channel selection spanning the four bands can improve the computation time and retrieval accuracy. Therefore, the spectral synergy allows obtaining up to almost seven different aerosol parameters, which is comparable to the most informative dedicated instruments. Moreover, a channel selection from the TIR to visible bands allows retrieving CO2 and CH4 total columns simultaneously in the presence of one aerosol layer with a similar accuracy to using all channels together to retrieve each gas separately in clear sky conditions.
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Di Noia, A., O. P. Hasekamp, G. van Harten, J. H. H. Rietjens, J. M. Smit, F. Snik, J. S. Henzing, J. de Boer, C. U. Keller i H. Volten. "Use of neural networks in ground-based aerosol retrievals from multi-angle spectropolarimetric observations". Atmospheric Measurement Techniques Discussions 7, nr 9 (8.09.2014): 9047–94. http://dx.doi.org/10.5194/amtd-7-9047-2014.
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Abstract. In this paper, the use of a neural network algorithm for the retrieval of the aerosol properties from ground-based spectropolarimetric measurements is discussed. The neural network is able to retrieve the aerosol properties with an accuracy that is almost comparable to that of an iterative retrieval. By using the outcome of the neural network as a first guess of the iterative retrieval scheme, the accuracy of the fine and coarse mode optical thickness are further improved while for the other parameters the improvement is small or absent. The resulting scheme (neural network + iterative retrieval) is compared to the original one (look-up table + iterative retrieval) on a set of simulated ground-based measurements, and on a small set of real observations carried out by an accurate ground-based spectropolarimeter. The results show that the use of a neural network based first guess leads to an increase in the number of converging retrievals, and possibly to more accurate estimates of the aerosol effective radius and complex refractive index.
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Jeong, U., J. Kim, C. Ahn, O. Torres, X. Liu, P. K. Bhartia, R. J. D. Spurr, D. Haffner, K. Chance i B. N. Holben. "An online aerosol retrieval algorithm using OMI near-UV observations based on the optimal estimation method". Atmospheric Chemistry and Physics Discussions 15, nr 12 (18.06.2015): 16615–54. http://dx.doi.org/10.5194/acpd-15-16615-2015.
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Abstract. An online version of the OMI (Ozone Monitoring Instrument) near-ultraviolet (UV) aerosol retrieval algorithm was developed to retrieve aerosol optical thickness (AOT) and single scattering albedo (SSA) based on the optimal estimation (OE) method. Instead of using the traditional look-up tables for radiative transfer calculations, it performs online radiative transfer calculations with the Vector Linearized Discrete Ordinate Radiative Transfer (VLIDORT) model to eliminate interpolation errors and improve stability. The OE-based algorithm has the merit of providing useful estimates of uncertainties simultaneously with the inversion products. The measurements and inversion products of the Distributed Regional Aerosol Gridded Observation Network campaign in Northeast Asia (DRAGON NE-Asia 2012) were used to validate the retrieved AOT and SSA. The retrieved AOT and SSA at 388 nm have a correlation with the Aerosol Robotic Network (AERONET) products that is comparable to or better than the correlation with the operational product during the campaign. The estimated retrieval noise and smoothing error perform well in representing the envelope curve of actual biases of AOT at 388 nm between the retrieved AOT and AERONET measurements. The forward model parameter errors were analyzed separately for both AOT and SSA retrievals. The surface albedo at 388 nm, the imaginary part of the refractive index at 354 nm, and the number fine mode fraction (FMF) were found to be the most important parameters affecting the retrieval accuracy of AOT, while FMF was the most important parameter for the SSA retrieval. The additional information provided with the retrievals, including the estimated error and degrees of freedom, is expected to be valuable for future studies.
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Cai, Xi, Yansong Bao, George P. Petropoulos, Feng Lu, Qifeng Lu, Liuhua Zhu i Ying Wu. "Temperature and Humidity Profile Retrieval from FY4-GIIRS Hyperspectral Data Using Artificial Neural Networks". Remote Sensing 12, nr 11 (9.06.2020): 1872. http://dx.doi.org/10.3390/rs12111872.
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This study proposes a new technique for retrieving temperature and humidity profiles based on Artificial Neural Networks (ANNs) using data acquired from the GIIRS (Geosynchronous Interferometric Infrared Sounder) L1 and ERA-Interim (European Centre for Medium-Range Weather Forecasts Reanalysis). The approach is also compared against another method that uses simulated data from the radiative transfer model to construct the retrieval network. Furthermore, the two methods of network construction are evaluated in the North China Plain for July and August 2018, for which ground validated observations concurrent to the satellite data were available. In summary, the results showed that: (1) the ANN built with the GIIRS L1 and the EC data is superior to that built with the forward simulation and EC data in retrieval accuracy; (2) the retrieval accuracy for the troposphere exceeds that for the stratosphere; (3) the root mean square errors (RMSEs) of the relative humidity in the troposphere as retrieved by the two ANNs are 6.003% and 10.608%, respectively; (4) a relatively low correlation (R) between the simulated and observed radiance of the GIIRS is found, ranging between 720 and 736.875 cm−1, and the correlation between the simulated and observed radiance of the water vapor channels exceeds that between the temperature channels; (5) compared with Atmospheric Infrared Sounder’s (AIRS’) products, our retrieved temperature profiles exhibit preferable consistency and the humidity retrievals also show an acceptable accuracy. Our study offers important insights towards improving our ability to retrieve atmospheric temperature and humidity profiles from the most sophisticated Earth Observation instruments such as the GIIRS of the FY-4 satellite, which could assist in expanding the use of those products globally.
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Li, Guo, Yunhua Zhang i Xiao Dong. "Approaches for Joint Retrieval of Wind Speed and Significant Wave Height and Further Improvement for Tiangong-2 Interferometric Imaging Radar Altimeter". Remote Sensing 14, nr 8 (16.04.2022): 1930. http://dx.doi.org/10.3390/rs14081930.
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The interferometric imaging radar altimeter (InIRA) adopts a short baseline along with small incidence angles to acquire interferometric signals from the sea surface with high accuracy, thus the wide-swath sea surface height (SSH) and backscattering coefficient (σ0) can be obtained simultaneously. This work presents an approach to jointly retrieve the wind speed and significant wave height (SWH) for the Chinese Tiangong-2 interferometric imaging radar altimeter (TG2-InIRA). This approach utilizes a multilayer perceptron (MLP) joint retrieval model based on σ0 and SSH data. By comparing with the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data, the root mean square errors (RMSEs) of the retrieved wind speed and the SWH are 1.27 m/s and 0.36 m, respectively. Based on the retrieved SWH, two enhanced wind speed retrieval models are developed for high sea states and low sea states, respectively. The results show that the RMSE of the retrieved wind speed is 1.12 m/s when the SWHs < 4 m; the RMSE is 0.73 m/s when the SWHs ≥ 4 m. Similarly, two enhanced SWH retrieval models for relatively larger and relatively smaller wind speed regions are developed based on the retrieved wind speed with corresponding RMSEs of 0.19 m and 0.16 m, respectively. The comparison between the retrieved results and the buoy data shows that they are highly consistent. The results show that the additional information of SWH can be used to improve the accuracy of wind speed retrieval at small incidence angles, and also the additional information of wind speed can be used to improve the SWH retrieval. The stronger the correlation between wind speed and SWH, the greater the improvement of the retrieved results. The proposed method can achieve joint retrieval of wind speed and SWH accurately, which complements the existing wind speed and SWH retrieval methods for InIRA.
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Taha, G., D. F. Rault, R. P. Loughman, A. E. Bourassa i C. von Savigny. "SCIAMACHY stratospheric aerosol extinction profile retrieval". Atmospheric Measurement Techniques Discussions 3, nr 6 (24.11.2010): 5343–74. http://dx.doi.org/10.5194/amtd-3-5343-2010.
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Abstract. The Ozone Mapper and Profiler Suite Limp Profiler (OMPS/LP) algorithm is used to retrieve ozone and aerosol profiles using a series of 120 SCIAMACHY limb measurements collocated with SAGE II solar occultation events. The primary goal of the study is to ascertain the capability of the OMPS/LP retrieval algorithm to accurately retrieve the vertical distribution of stratospheric aerosol extinction coefficient so as to better account for aerosol effects in the ozone profiling retrieval process. Using simulated radiances, we show that the aerosol extinction coefficient can be retrieved from limb scatter measurements within 5% and a standard deviation better than 15%, which is more than sufficient to improve the OMPS/LP ozone products to be used as Environmental Data Records. We also illustrate the ability of SCIAMACHY limb measurements to retrieve stratospheric aerosol profiles with accuracy comparable to other instruments. The retrieved aerosol profiles agree with collocated SAGE II measurements on average to within 25%, with a standard deviation of 35%.
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Li, Quan. "A Partitioning Image Retrieval Method Based on Regional Division and Polymerization". Applied Mechanics and Materials 347-350 (sierpień 2013): 2218–22. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2218.
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in order to further improve the retrieval accuracy of the image retrieval system based on shape feature. A partitioning image retrieval method based on regional division and polymerization is proposed in this paper. Firstly, an image is segmented by the regional division and polymerization method. Secondly, the shape and spatial features of different objects in the image are extracted by the invariant moment. Finally, images are retrieved by calculating the similarity of images, and five different types of images are tested by group. The experimental results show that it is more accurate for the algorithm to retrieve the users interested images.
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Yoshida, Mayumi, Keiya Yumimoto, Takashi M. Nagao, Taichu Y. Tanaka, Maki Kikuchi i Hiroshi Murakami. "Satellite retrieval of aerosol combined with assimilated forecast". Atmospheric Chemistry and Physics 21, nr 3 (10.02.2021): 1797–813. http://dx.doi.org/10.5194/acp-21-1797-2021.
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Abstract. We developed a new aerosol satellite retrieval algorithm combining a numerical aerosol forecast. In the retrieval algorithm, the short-term forecast from an aerosol data assimilation system was used as an a priori estimate instead of spatially and temporally constant values. This method was demonstrated using observation of the Advanced Himawari Imager onboard the Japan Meteorological Agency's geostationary satellite Himawari-8. Overall, the retrieval results incorporated strengths of the observation and the model and complemented their respective weaknesses, showing spatially finer distributions than the model forecast and less noisy distributions than the original algorithm. We validated the new algorithm using ground observation data and found that the aerosol parameters detectable by satellite sensors were retrieved more accurately than an a priori model forecast by adding satellite information. Further, the satellite retrieval accuracy was improved by introducing the model forecast instead of the constant a priori estimates. By using the assimilated forecast for an a priori estimate, information from previous observations can be propagated to future retrievals, leading to better retrieval accuracy. Observational information from the satellite and aerosol transport by the model are incorporated cyclically to effectively estimate the optimum field of aerosol.
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Hu, Hao, i Fuzhong Weng. "Influences of 1DVAR Background Covariances and Observation Operators on Retrieving Tropical Cyclone Thermal Structures". Remote Sensing 14, nr 5 (22.02.2022): 1078. http://dx.doi.org/10.3390/rs14051078.
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Spaceborne passive microwave sounding instruments are important for monitoring tropical cyclones (TCs) over oceans. However, previous studies have found large retrieval errors at TCs’ inner region at the lower troposphere where heavy precipitation occurs. In this study, the background error covariance matrix used in the variational retrieval algorithm is designed to vary with atmospheric conditions. It is found that the errors of retrieved temperature and humidity profiles are significantly reduced under the TC conditions, when they are compared with those from using a static covariance matrix. The retrieval errors of temperature and humidity are about 1.5 K and 10–20%, respectively, in the troposphere. Moreover, the influence of different observation operators on the retrievals are also investigated. It is shown that ARMS (Advanced Radiative Transfer Modeling System) used as an observation operator can produce a higher retrieval accuracy, compared to CRTM (Community Radiative Transfer Model). For the relative humidity profile, the error can be reduced by up to 5% from ARMS. The reason may be attributed to the more comprehensive handling of the scattering from various hydrometeors in ARMS, which results in a higher retrieval accuracy under cloudy conditions.
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R, Dhaya. "Analysis of Adaptive Image Retrieval by Transition Kalman Filter Approach based on Intensity Parameter". Journal of Innovative Image Processing 3, nr 1 (9.03.2021): 7–20. http://dx.doi.org/10.36548/jiip.2021.1.002.
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The information changes in image pixel of retrieved records is very common in image process. The image content extraction is containing many parameters to reconstruct the image again for access the information. The intensity level, edge parameters are important parameter to reconstruct the image. The filtering techniques used to retrieve the image from query images. In this research article, the adaptive function kalman filter function performs for image retrieval to get better accuracy and high reliable compared to previous existing method includes Content Based Image Retrieval (CBIR). The kalman filter is incorporated with adaptive feature extraction for transition framework in the fine tuning of kalman gain. The feature vector database analysis provides transparent to choose the images in retrieval function from query images dataset for higher retrieval rate. The virtual connection is activated once in single process for improving reliability of the practice. Besides, this research article encompasses the adaptive updating prediction function in the estimation process. Our proposed framework construct with adaptive state transition Kalman filtering technique to improve retrieval rate. Finally, we achieved 96.2% of retrieval rate in the image retrieval process. We compare the performance measure such as accuracy, reliability and computation time of the process with existing methods.
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He, Bai Qing, i Zi Min Wang. "Comparison of Three retrieval accuracy of image retrieval technology". MATEC Web of Conferences 63 (2016): 05006. http://dx.doi.org/10.1051/matecconf/20166305006.
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Bauer, Peter, Emmanuel Moreau i Sabatino Di Michele. "Hydrometeor Retrieval Accuracy Using Microwave Window and Sounding Channel Observations". Journal of Applied Meteorology 44, nr 7 (1.07.2005): 1016–32. http://dx.doi.org/10.1175/jam2257.1.
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Abstract The retrieval errors of cloud and precipitation hydrometeor contents from spaceborne observations are estimated at microwave frequencies in atmospheric windows between 18 and 150 GHz and in oxygen absorption complexes near 50–60 and 118 GHz. The method is based on a variational retrieval framework using a priori information on the cloud, atmosphere, and surface states from ECMWF short-range forecasts under different weather regimes. This approach was chosen because a consistent description of the model state and its uncertainties is provided, which is unavailable for other methods. The results show that the sounding channels provide more stable, more accurate, and less biased retrievals than window channels—in particular, over land surfaces and with regard to snowfall. Average performance estimates showed that if sounding channels are used, 80% of all retrievals are within 100% error limits and 60% of them are within 50% error limits with regard to rainfall. For snowfall, the sounding channels produce 60% of all retrievals with errors below 100% for rates smaller than 1 mm h−1, and 50%–80% of the cases have errors below 50% for more intense snowfall.
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Newsom, Rob K., David Ligon, Ron Calhoun, Rob Heap, Edward Cregan i Marko Princevac. "Retrieval of Microscale Wind and Temperature Fields from Single- and Dual-Doppler Lidar Data". Journal of Applied Meteorology 44, nr 9 (1.09.2005): 1324–45. http://dx.doi.org/10.1175/jam2280.1.
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Abstract Dual-Doppler lidar observations are used to assess the accuracy of single-Doppler retrievals of microscale wind and temperature fields in a shear-driven convective boundary layer. The retrieval algorithm, which is based on four-dimensional variational data assimilation, is applied by using dual- and single-Doppler lidar data that are acquired during the Joint Urban 2003 field experiment. The velocity field that was retrieved using single-Doppler data is compared directly with radial velocities that were measured by a second noncollocated lidar. Dual-Doppler retrievals are also performed and then compared with the single-Doppler retrieval. The linear correlation coefficient and rms deviation between the single-Doppler retrieval and the observations from the second lidar are found to be 0.94 and 1.2 m s−1, respectively. The high correlation is mainly the result of good agreement in the mean vertical structure as observed by the two lidars. Comparisons between the single- and dual-Doppler retrieval indicate that the single-Doppler retrieval underestimates the magnitude of fluctuations in the crossbeam direction. Vertical profiles of horizontally averaged correlations between the single- and dual-Doppler retrievals also show a marginal correlation (0.4–0.8) between one of the horizontal velocity components. Again, this suggests that the retrieval algorithm has difficulty estimating the crossbeam component from single-Doppler data.
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Li, Zhongjian, Jun Xiang, Lei Wang, Ning Zhang, Ruru Pan i Weidong Gao. "Yarn-Dyed Fabric Image Retrieval Using Colour Moments and the Perceptual Hash Algorithm". Fibres and Textiles in Eastern Europe 27, nr 5(137) (31.10.2019): 60–69. http://dx.doi.org/10.5604/01.3001.0013.2900.
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Due to the variety of yarn colours and arrangement, it is a challenging problem to retrieve a yarn-dyed fabric image. In this paper, yarn-dyed fabric samples are captured by the DigiEye system first, and then pattern images of the fabric images captured are simulated by pattern design software based on extracted structure parameters of the yarn-dyed fabric. For the simulated pattern image, an effective algorithm is proposed to retrieve these kinds of images by combining the colour moments method and perceptual hash algorithm. Then the pattern images retrieved are mapped back to the yarn-dyed fabric image so as to realise the yarn-dyed fabric image retrieval. In the algorithm proposed, the colour moments method is adopted to extract the colour features, and the perceptual hash algorithm is utilised to calculate the spatial features of the simulated pattern images. Then the two kinds of image features are used to compute the similarity between the input original image and each target image based on the Euclidean distance and Hamming distance. Relevant images can be retrieved in dependence on the similarity value, which is determined by calculating the optimum weighted value of the colour features’ similarity and spatial features’ similarity. In order to measure the retrieval efficiency of the method proposed, the accuracy rate and retrieval rate of image retrieval were computed in experiments using a PATTERN image database with 300 images. The experimental results show that the average accuracy rate of the method proposed is 85.30% and the retrieval rate - 53.51% when the weighted value of the colour feature similarity is fixed at 0.45 and the spatial feature similarity is 0.55. It is shown that the method presented is effective to retrieve pattern images of yarn-dyed fabric.
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Pan, Xin, Yuanbo Liu i Xingwang Fan. "Satellite Retrieval of Surface Evapotranspiration with Nonparametric Approach: Accuracy Assessment over a Semiarid Region". Advances in Meteorology 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/1584316.
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Surface evapotranspiration (ET) is one of the key surface processes. Reliable estimation of regional ET solely from satellite data remains a challenge. This study applies recently proposed nonparametric (NP) approach to retrieve surface ET, in terms of latent heat flux (LE), over a semiarid region. The involved input parameters are surface net radiation, land surface temperature, near-surface air temperature, and soil heat flux, all of which are retrievals or products of the Moderate-Resolution Imaging Spectroradiometer (MODIS). Field observations are used as ground references, which were obtained from six eddy covariance (EC) sites with different land covers including desert, Gobi, village, orchard, vegetable field, and wetland. Our results show that the accuracy of LE retrievals varies with EC sites with a determination of coefficient from 0.02 to 0.76, a bias from −221.56 W/m2to 143.77 W/m2, a relative error from 8.82% to 48.35%, and a root mean square error from 67.97 W/m2to 239.55 W/m2. The error mainly resulted from the uncertainties from MODIS products or the retrieval of net radiation and soil heat flux in nonvegetated region. It highlights the importance of accurate retrieval of the input parameters from satellite data, which are the ongoing tasks of remote sensing community.
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Zhang, H., R. M. Hoff, S. Kondragunta, I. Laszlo i A. Lyapustin. "Aerosol Optical Depth (AOD) retrieval using simultaneous GOES-East and GOES-West reflected radiances over the Western US". Atmospheric Measurement Techniques Discussions 5, nr 5 (30.10.2012): 7945–81. http://dx.doi.org/10.5194/amtd-5-7945-2012.
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Abstract. Aerosol Optical Depth (AOD) in the Western United States is observed independently by both the GOES-East and GOES-West imagers. The GASP (GOES Aerosol/Smoke Product) aerosol optical depth retrieval algorithm treats each satellite as a unique sensor and thus NOAA obtains two separate aerosol optical depth values at the same time for the same location. The TOA radiances and the associated derived optical depths can be quite different due to the different viewing geometries with large difference in solar-scattering angles. In order to fully exploit the simultaneous observations and generate consistent AOD retrievals from the two satellites, the authors develop a new aerosol optical depth retrieval algorithm that uses data from both satellites. The algorithm uses combined GOES-East and GOES-West visible channel TOA reflectance and daily average AOD from GOES Multi-Angle Implementation of Atmospheric Correction (GOES-MAIAC) on clear days (AOD less than 0.3), when diurnal variation of AOD is low, to retrieve surface BRDF. The known BRDF shape is applied on subsequent days to retrieve BRDF and AOD. The algorithm is validated at three AERONET sites over the Western US. The AOD retrieval accuracy from the hybrid technique using the two satellites is similar to that from one satellite over UCSB and Railroad Valley. Improvement of the accuracy is observed at Boulder. The correlation coefficients between the GOES AOD and AERONET AOD are in the range of 0.67 to 0.81 over the three sites. The hybrid algorithm has more data coverage compared to the single satellite retrievals over surfaces with high reflectance. The number of coincidences with AERONET observations increases from the use of two-single satellite algorithms by 5–80% for the three sites. With the application of the new algorithm, consistent AOD retrievals and better retrieval coverages can be obtained using the data from the two GOES satellite imagers.
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Herbin, H., L. C. Labonnote i P. Dubuisson. "Multispectral information from TANSO-FTS instrument – Part 1: Application to greenhouse gases (CO<sub>2</sub> and CH<sub>4</sub>) in clear sky conditions". Atmospheric Measurement Techniques 6, nr 11 (28.11.2013): 3301–11. http://dx.doi.org/10.5194/amt-6-3301-2013.
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Abstract. The Greenhouse gases Observing SATellite (GOSAT) mission, and in particular the Thermal And Near infrared Sensor for carbon Observations–Fourier Transform Spectrometer (TANSO-FTS) instrument, has the advantage of being able to measure simultaneously the same field of view in different spectral ranges with a high spectral resolution. These features allow studying the benefits of using multispectral measurements to improve the CO2 and CH4 retrievals. In order to quantify the impact of the spectral synergy on the retrieval accuracy, we performed an information content (IC) analysis from simulated spectra corresponding to the three infrared bands of TANSO-FTS. The advantages and limitations of using thermal and shortwave infrared simultaneously are discussed according to surface type and state vector composition. The IC is then used to determine the most informative spectral channels for the simultaneous retrieval of CO2 and CH4. The results show that a channel selection spanning the three infrared bands can improve the computation time and retrieval accuracy. Therefore, a selection of less than 700 channels from the thermal infrared (TIR) and shortwave infrared (SWIR) bands allows retrieving CO2 and CH4 simultaneously with a similar accuracy to using all channels together to retrieve each gas separately.
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Cha, Ting-Yu, i Michael M. Bell. "Comparison of single-Doppler and multiple-Doppler wind retrievals in Hurricane Matthew (2016)". Atmospheric Measurement Techniques 14, nr 5 (18.05.2021): 3523–39. http://dx.doi.org/10.5194/amt-14-3523-2021.
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Abstract. Hurricane Matthew (2016) was observed by the ground-based polarimetric Next Generation Weather Radar (NEXRAD) in Miami (KAMX) and the National Oceanic and Atmospheric Administration WP-3D (NOAA P-3) airborne tail Doppler radar near the coast of the southeastern United States for several hours, providing a novel opportunity to evaluate and compare single- and multiple-Doppler wind retrieval techniques for tropical cyclone flows. The generalized velocity track display (GVTD) technique can retrieve a subset of the wind field from a single ground-based Doppler radar under the assumption of nearly axisymmetric rotational wind, but it has been shown to have errors from the aliasing of unresolved wind components. An improved technique that mitigates errors due to storm motion is derived in this study, although some spatial aliasing remains due to limited information content from the single-Doppler measurements. A spline-based variational wind retrieval technique called SAMURAI can retrieve the full three-dimensional wind field from airborne radar fore–aft pseudo-dual-Doppler scanning, but it has been shown to have errors due to temporal aliasing from the nonsimultaneous Doppler measurements. A comparison between the two techniques shows that the axisymmetric tangential winds are generally comparable between the two techniques, and the improved GVTD technique improves the accuracy of the retrieval. Fourier decomposition of asymmetric kinematic and convective structure shows more discrepancies due to spatial and temporal aliasing in the retrievals. The strengths and weaknesses of each technique for studying tropical cyclone structure are discussed and suggest that complementary information can be retrieved from both single- and dual-Doppler retrievals. Future improvements to the asymmetric flow assumptions in single-Doppler analysis and steady-state assumptions in pseudo-dual-Doppler analysis are required to reconcile differences in retrieved tropical cyclone structure.
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Luo, Youmeng, Wei Li, Xiaoyu Ma i Kaiqiang Zhang. "Image Retrieval Algorithm Based on Locality-Sensitive Hash Using Convolutional Neural Network and Attention Mechanism". Information 13, nr 10 (24.09.2022): 446. http://dx.doi.org/10.3390/info13100446.
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With the continuous progress of image retrieval technology, in the field of image retrieval, the speed of a search for a desired image from a great deal of image data becomes a hot issue. Convolutional Neural Networks(CNN) have been used in the field of image retrieval. However, many image retrieval systems based on CNN have a poor ability to express image features, resulting in a series of problems such as low retrieval accuracy and robustness. When the target image is retrieved from a large amount of image data, the vector dimension after image coding is high and the retrieval efficiency is low. Locality-sensitive hash is a method to find similar data from massive high latitude data. It reduces the data dimension of the original spatial data through hash coding and conversion, and can also maintain the similarity between the data. The retrieval time and space complexity are low. Therefore, this paper proposes a locality-sensitive hash image retrieval method based on CNN and the attention mechanism. The steps of the method are as follows: using the ResNet50 network as the feature extractor of the image, adding the attention module after the convolution layer of the model, and using the output of the network full connection layer to retrieve the features of the image database, then using the local-sensitive hash algorithm to hash code the image features of the database to reduce the dimension and establish the index, and finally measuring the features of the image to be retrieved and the image database to get the most similar image, completing the content-based image retrieval task. The method in this paper is compared with other image retrieval methods on corel1k and corel5k datasets. The experimental results show that this method can effectively improve the accuracy of image retrieval, and the retrieval efficiency is significantly improved. It also has higher robustness in different scenarios.
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Löhnert, Ulrich, D. D. Turner i S. Crewell. "Ground-Based Temperature and Humidity Profiling Using Spectral Infrared and Microwave Observations. Part I: Simulated Retrieval Performance in Clear-Sky Conditions". Journal of Applied Meteorology and Climatology 48, nr 5 (1.05.2009): 1017–32. http://dx.doi.org/10.1175/2008jamc2060.1.
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Abstract Two independent ground-based passive remote sensing methods are used to retrieve lower-tropospheric temperature and humidity profiles in clear-sky cases. A simulation study for two distinctly different climatic zones is performed to evaluate the accuracies of a standard microwave profiler [humidity and temperature profiler (HATPRO)] and an infrared spectrometer [Atmospheric Emitted Radiance Interferometer (AERI)] by applying a unified optimal estimation scheme to each instrument. Different measurement modes for each instrument are also evaluated in which the retrieval uses different spectral channels and observational view angles. In addition, both instruments have been combined into the same physically consistent retrieval scheme to evaluate the differences between a combined retrieval relative to the single-instrument retrievals. In general, retrievals derived from only infrared measurements yield superior RMS error and bias to retrievals derived only from microwave measurements. The AERI retrievals show high potential, especially for retrieving humidity in the boundary layer, where accuracies are on the order of 0.25–0.5 g m−3 for a central European climate. In the lowest 500 m the retrieval accuracies for temperature from elevation-scanning microwave measurements and spectral infrared measurements are very similar (0.2–0.6 K). Above this level the accuracies of the AERI retrieval are significantly more accurate (<1 K RMSE below 4 km). The inclusion of microwave measurements to the spectral infrared measurements within a unified physical retrieval scheme only results in improvements in the high-humidity tropical climate. However, relative to the HATPRO retrieval, the accuracy of the AERI retrieval is more sensitive to changes in the measurement uncertainty. The discussed results are drawn from a subset of “pristine” clear-sky cases: in the general case in which clouds and aerosols are present, the combined HATPRO–AERI retrieval algorithm is expected to yield much more beneficial results.
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Norquist, Donald C., Paul R. Desrochers, Patrick J. McNicholl i John R. Roadcap. "A Characterization of Cirrus Cloud Properties That Affect Laser Propagation". Journal of Applied Meteorology and Climatology 47, nr 5 (1.05.2008): 1322–36. http://dx.doi.org/10.1175/2007jamc1756.1.
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Abstract Future high-altitude laser systems may be affected by cirrus clouds. Laser transmission models were applied to measured and retrieved cirrus properties to determine cirrus impact on power incident on a target or receiver. A major goal was to see how well radiosondes and geostationary satellite imagery could specify the required properties. Based on the use of ground-based radar and lidar measurements as a reference, errors in cirrus-top and cirrus-base height estimates from radiosonde observations were 20%–25% of geostationary satellite retrieval errors. Radiosondes had a perfect cirrus detection rate as compared with 80% for satellite detection. Ice water path and effective particle size were obtained with a published radar–lidar retrieval algorithm and a documented satellite algorithm. Radar–lidar particle size and ice water path were 1.5 and 3 times the satellite retrievals, respectively. Radar–lidar-based laser extinction coefficients were 55% greater than satellite values. Measured radar–lidar cirrus thickness was consistently greater than satellite-retrieved thickness, but radar–lidar microphysical retrieval required detection by both sensors at each range gate, which limited the retrievals’ vertical extent. Greater radar–lidar extinction and greater satellite-based cirrus thickness yielded comparable optical depths for the two independent retrievals. Laser extinction–transmission models applied to radiosonde-retrieved cirrus heights and satellite-retrieved microphysical properties revealed a significant power loss by all models as the laser beam transits the cirrus layer. This suggests that cirrus location is more important than microphysics in high-altitude laser test support. Geostationary satellite imagery may be insufficient in cirrus detection and retrieval accuracy. Humidity-sensitive radiosondes are a potential proxy for ground-based remote sensors in cirrus detection and altitude determination.
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Taha, G., D. F. Rault, R. P. Loughman, A. E. Bourassa i C. von Savigny. "SCIAMACHY stratospheric aerosol extinction profile retrieval using the OMPS/LP algorithm". Atmospheric Measurement Techniques 4, nr 3 (16.03.2011): 547–56. http://dx.doi.org/10.5194/amt-4-547-2011.
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Abstract. The Ozone Mapper and Profiler Suite, Limp Profiler (OMPS/LP) algorithm is used to retrieve ozone concentration and aerosol extinction profiles using a series of 120 SCIAMACHY limb measurements collocated with SAGE II solar occultation events. The primary goal of the study is to ascertain the capability of the OMPS/LP retrieval algorithm to accurately retrieve the vertical distribution of stratospheric aerosol extinction coefficient so as to better account for aerosol effects in the ozone profiling retrieval process. Using simulated radiances, we show that the aerosol extinction coefficient can be retrieved from limb scatter measurements within 5% and a standard deviation better than 15%, which is more than sufficient to improve the OMPS/LP ozone products to be used as Environmental Data Records. We also illustrate the ability of SCIAMACHY limb measurements to retrieve stratospheric aerosol extinction profiles with accuracy comparable to other instruments. The retrieved aerosol extinction profiles agree with collocated SAGE II measurements on average to within 25%, with a standard deviation of 35%.
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Zhang, Chunming, Mingjian Gu, Yong Hu, Pengyu Huang, Tianhang Yang, Shuo Huang, Chunlei Yang i Chunyuan Shao. "A Study on the Retrieval of Temperature and Humidity Profiles Based on FY-3D/HIRAS Infrared Hyperspectral Data". Remote Sensing 13, nr 11 (31.05.2021): 2157. http://dx.doi.org/10.3390/rs13112157.
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Satellite infrared hyperspectral instruments can obtain a wealth of atmospheric spectrum information. In order to obtain high-precision atmospheric temperature and humidity profiles, we used the traditional One-Dimensional Variational (1D-Var) retrieval algorithm, combined with the information capacity-weight function coverage method to select the spectrum channel. In addition, an Artificial Neural Network (ANN) algorithm was introduced to correct the satellite observation data error and compare it with the conventional error correction method. Finally, to perform the temperature and humidity profile retrieval calculation, we used the FY-3D satellite HIRAS (Hyperspectral Infrared Atmospheric Sounder) infrared hyperspectral data and combined the RTTOV (Radiative Transfer for TOVS) radiative transfer model to build an atmospheric temperature and humidity profile retrieval system. We used data on the European region from July to August 2020 to carry out the training and testing of the retrieval system, respectively, and used the balloon-retrieved sounding data of temperature and humidity published by the University of Wyoming as standard truth values to evaluate the retrieval accuracy. Our preliminary research results show that, compared with the retrieval results of conventional deviation correction, the introduction of ANN algorithm error correction can improve the retrieval accuracy of the retrieval system effectively and the RMSE (Root-Mean-Square Error) of the temperature and humidity has a maximum accuracy of improvement of about 0.5 K (The K represents the thermodynamic temperature unit) and 5%, respectively. The temperature and humidity results obtained by the retrieval system were compared with Global Forecast System (GFS) forecast data. The retrieved temperature RMSE was less than 1.5 K on average, which was better than that for the GFS; the humidity RMSE was less than 15% as a whole, and better than the forecast profile between 100 hpa (1 hpa is 100 pa, the pa represents the air pressure unit) and 600 hpa. Compared with AIRS (Atmospheric Infrared Sounder) products, the result of the retrieval system also had a higher accuracy. The main improvement of the temperature was at 200 hpa and 800 hpa, with maximum accuracy improvements of 2 K and 1.5 K, respectively. The RMSE of the humidity retrieved by the system was also better than the AIRS humidity products at most pressure levels, and the error of maximum difference could reach 15%. After combining the two algorithms, the FY-3D/HIRAS infrared hyperspectral retrieval system could obtain higher-precision temperature and humidity profiles, and relevant results could provide a reference for improving the accuracy of business products.
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Tan, Jiancan, Nusseiba NourEldeen, Kebiao Mao, Jiancheng Shi, Zhaoliang Li, Tongren Xu i Zijin Yuan. "Deep Learning Convolutional Neural Network for the Retrieval of Land Surface Temperature from AMSR2 Data in China". Sensors 19, nr 13 (6.07.2019): 2987. http://dx.doi.org/10.3390/s19132987.
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A convolutional neural network (CNN) algorithm was developed to retrieve the land surface temperature (LST) from Advanced Microwave Scanning Radiometer 2 (AMSR2) data in China. Reference data were selected using the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product to overcome the problem related to the need for synchronous ground observation data. The AMSR2 brightness temperature (TB) data and MODIS surface temperature data were randomly divided into training and test datasets, and a CNN was constructed to simulate passive microwave radiation transmission to invert the surface temperature. The twelve V/H channel combinations (7.3, 10.65, 18.7, 23.8, 36.5, 89 GHz) resulted in the most stable and accurate CNN retrieval model. Vertical polarizations performed better than horizontal polarizations; however, because CNNs rely heavily on large amounts of data, the combination of vertical and horizontal polarizations performed better than a single polarization. The retrievals in different regions indicated that the CNN accuracy was highest over large bare land areas. A comparison of the retrieval results with ground measurement data from meteorological stations yielded R2 = 0.987, RMSE = 2.69 K, and an average relative error of 2.57 K, which indicated that the accuracy of the CNN LST retrieval algorithm was high and the retrieval results can be applied to long-term LST sequence analysis in China.
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Shupe, Matthew D., Taneil Uttal i Sergey Y. Matrosov. "Arctic Cloud Microphysics Retrievals from Surface-Based Remote Sensors at SHEBA". Journal of Applied Meteorology 44, nr 10 (1.10.2005): 1544–62. http://dx.doi.org/10.1175/jam2297.1.
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Abstract An operational suite of ground-based, remote sensing retrievals for producing cloud microphysical properties is described, assessed, and applied to 1 yr of observations in the Arctic. All measurements were made in support of the Surface Heat Budget of the Arctic (SHEBA) program and First International Satellite Cloud Climatology Project Regional Experiment (FIRE) Arctic Clouds Experiment (ACE) in 1997–98. Retrieval techniques and cloud-type classifications are based on measurements from a vertically pointing 35-GHz Doppler radar, microwave and infrared radiometers, and radiosondes. The retrieval methods are assessed using aircraft in situ measurements from a limited set of case studies and by intercomparison of multiple retrievals for the same parameters. In all-liquid clouds, retrieved droplet effective radii Re have an uncertainty of up to 32% and liquid water contents (LWC) have an uncertainty of 49%–72%. In all-ice clouds, ice particle mean sizes Dmean can be retrieved with an uncertainty of 26%–46% while retrieved ice water contents (IWC) have an uncertainty of 62%–100%. In general, radar-only, regionally tuned empirical power-law retrievals were best suited among the tested retrieval algorithms for operational cloud monitoring at SHEBA because of their wide applicability, ease of use, and reasonable statistical accuracy. More complex multisensor techniques provided a moderate improvement in accuracy for specific case studies and were useful for deriving location-specific coefficients for the empirical retrievals but were not as frequently applicable as the single sensor methods because of various limitations. During the yearlong SHEBA program, all-liquid clouds were identified 19% of the time and were characterized by an annual average droplet Re of 6.5 μm, LWC of 0.10 g m−3, and liquid water path of 45 g m−2. All-ice clouds were identified 38% of the time with an annual average particle Dmean of 73 μm, IWC of 0.014 g m−3, and ice water path of 30 g m−2.
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Gao, Meng, Kirk Knobelspiesse, Bryan A. Franz, Peng-Wang Zhai, Andrew M. Sayer, Amir Ibrahim, Brian Cairns i in. "Effective uncertainty quantification for multi-angle polarimetric aerosol remote sensing over ocean". Atmospheric Measurement Techniques 15, nr 16 (25.08.2022): 4859–79. http://dx.doi.org/10.5194/amt-15-4859-2022.
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Abstract. Multi-angle polarimetric (MAP) measurements can enable detailed characterization of aerosol microphysical and optical properties and improve atmospheric correction in ocean color remote sensing. Advanced retrieval algorithms have been developed to obtain multiple geophysical parameters in the atmosphere–ocean system. Theoretical pixel-wise retrieval uncertainties based on error propagation have been used to quantify retrieval performance and determine the quality of data products. However, standard error propagation techniques in high-dimensional retrievals may not always represent true retrieval errors well due to issues such as local minima and the nonlinear dependence of the forward model on the retrieved parameters near the solution. In this work, we analyze these theoretical uncertainty estimates and validate them using a flexible Monte Carlo approach. The Fast Multi-Angular Polarimetric Ocean coLor (FastMAPOL) retrieval algorithm, based on efficient neural network forward models, is used to conduct the retrievals and uncertainty quantification on both synthetic HARP2 (Hyper-Angular Rainbow Polarimeter 2) and AirHARP (airborne version of HARP2) datasets. In addition, for practical application of the uncertainty evaluation technique in operational data processing, we use the automatic differentiation method to calculate derivatives analytically based on the neural network models. Both the speed and accuracy associated with uncertainty quantification for MAP retrievals are addressed in this study. Pixel-wise retrieval uncertainties are further evaluated for the real AirHARP field campaign data. The uncertainty quantification methods and results can be used to evaluate the quality of data products, as well as guide MAP algorithm development for current and future satellite systems such as NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission.
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Blumberg, W. G., T. J. Wagner, D. D. Turner i J. Correia. "Quantifying the Accuracy and Uncertainty of Diurnal Thermodynamic Profiles and Convection Indices Derived from the Atmospheric Emitted Radiance Interferometer". Journal of Applied Meteorology and Climatology 56, nr 10 (październik 2017): 2747–66. http://dx.doi.org/10.1175/jamc-d-17-0036.1.
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AbstractWhile radiosondes have provided atmospheric scientists an accurate high-vertical-resolution profile of the troposphere for decades, they are unable to provide high-temporal-resolution observations without significant recurring expenses. Remote sensing technology, however, has the ability to monitor the evolution of the atmosphere in unprecedented detail. One particularly promising tool is the Atmospheric Emitted Radiance Interferometer (AERI), a passive ground-based infrared radiometer. Through a physical retrieval, the AERI can retrieve the vertical profile of temperature and humidity at a temporal resolution on the order of minutes. The synthesis of these two instruments may provide an improved diagnosis of the processes occurring in the atmosphere. This study provides a better understanding of the capabilities of the AERI in environments supportive of deep, moist convection. Using 3-hourly radiosonde launches and thermodynamic profiles retrieved from collocated AERIs, this study evaluates the accuracy of AERI-derived profiles over the diurnal cycle by analyzing AERI profiles in both the convective and stable boundary layers. Monte Carlo sampling is used to calculate the distribution of convection indices and compare the impact of measurement errors from each instrument platform on indices. This study indicates that the nonintegrated indices (e.g., lifted index) derived from AERI retrievals are more accurate than integrated indices (e.g., CAPE). While the AERI retrieval’s vertical resolution can inhibit precise diagnoses of capping inversions, the high-temporal-resolution nature of the AERI profiles overall helps in detecting rapid temporal changes in stability.
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Lindholm, Torun, Fredrik U. Jönsson i Marco Tullio Liuzza. "Retrieval effort cues predict eyewitness accuracy." Journal of Experimental Psychology: Applied 24, nr 4 (grudzień 2018): 534–42. http://dx.doi.org/10.1037/xap0000175.
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Alsubhi, Kholoud, Amani Jamal i Areej Alhothali. "Deep learning-based approach for Arabic open domain question answering". PeerJ Computer Science 8 (4.05.2022): e952. http://dx.doi.org/10.7717/peerj-cs.952.
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Open-domain question answering (OpenQA) is one of the most challenging yet widely investigated problems in natural language processing. It aims at building a system that can answer any given question from large-scale unstructured text or structured knowledge-base. To solve this problem, researchers traditionally use information retrieval methods to retrieve the most relevant documents and then use answer extractions techniques to extract the answer or passage from the candidate documents. In recent years, deep learning techniques have shown great success in OpenQA by using dense representation for document retrieval and reading comprehension for answer extraction. However, despite the advancement in the English language OpenQA, other languages such as Arabic have received less attention and are often addressed using traditional methods. In this paper, we use deep learning methods for Arabic OpenQA. The model consists of document retrieval to retrieve passages relevant to a question from large-scale free text resources such as Wikipedia and an answer reader to extract the precise answer to the given question. The model implements dense passage retriever for the passage retrieval task and the AraELECTRA for the reading comprehension task. The result was compared to traditional Arabic OpenQA approaches and deep learning methods in the English OpenQA. The results show that the dense passage retriever outperforms the traditional Term Frequency-Inverse Document Frequency (TF-IDF) information retriever in terms of the top-20 passage retrieval accuracy and improves our end-to-end question answering system in two Arabic question-answering benchmark datasets.
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Geddes, A., i H. Bösch. "Tropospheric aerosol profile information from high-resolution oxygen A-band measurements from space". Atmospheric Measurement Techniques 8, nr 2 (20.02.2015): 859–74. http://dx.doi.org/10.5194/amt-8-859-2015.
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Abstract. Aerosols are an important factor in the Earth climatic system and they play a key role in air quality and public health. Observations of the oxygen A-band at 760 nm can provide information on the vertical distribution of aerosols from passive satellite sensors that can be of great interest for operational monitoring applications with high spatial coverage if the aerosol information is obtained with sufficient precision, accuracy and vertical resolution. To address this issue, retrieval simulations of the aerosol vertical profile retrieval from O2 A-band observations by GOSAT, the upcoming Orbiting Carbon Observatory-2 (OCO-2) and Sentinel 5-P missions, and the proposed CarbonSat mission have been carried out. Precise retrievals of aerosol optical depth (AOD) within the boundary layer were found to favour low-resolution, high signal-to-noise instruments such as Sentinel-5 P, whereas higher-resolution instruments such as OCO-2 showed greater performance at higher altitudes and in information content above the boundary layer. Retrieval of the AOD in the 0–2 km range with precision appears difficult from all studied instruments and the retrieval errors typically exceed a value of 0.05 for AODs up to 0.3. Constraining the surface albedo is a promising and effective way of improving the retrieval of aerosol, but the accuracy of the required prior knowledge is very high. Due to the limited information content of the aerosol profile retrieval, the use of a parameterised aerosol distribution is assessed, and we show that the AOD and height of an aerosol layer can be retrieved well if the aerosol layer is uplifted to the free troposphere; however, errors are often large for aerosol layers in the boundary layer. Additional errors are introduced by incorrect assumptions on surface pressure and aerosol mixture, which can both bias retrieved AOD and height by up to 45%. In addition, assumptions of the boundary layer temperature are found to yield an additional error of up to 8%. We conclude that the aerosol profile retrievals from O2 A-band using existing or upcoming satellite sensors will only provide limited information on aerosols in the boundary layer but such observations can be of great value for observing and mapping aerosol plumes in the free troposphere.
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Fathi, Sepehr, Mark Gordon, Paul A. Makar, Ayodeji Akingunola, Andrea Darlington, John Liggio, Katherine Hayden i Shao-Meng Li. "Evaluating the impact of storage-and-release on aircraft-based mass-balance methodology using a regional air-quality model". Atmospheric Chemistry and Physics 21, nr 20 (18.10.2021): 15461–91. http://dx.doi.org/10.5194/acp-21-15461-2021.
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Abstract. We investigate the potential for aircraft-based top-down emission rate retrieval over- and under-estimation using a regional chemical transport model, the Global Environmental Multiscale-Modeling Air-Quality and CHemistry (GEM-MACH). In our investigations we consider the application of the mass-balance approach in the Top-down Emission Rate Retrieval Algorithm (TERRA). Aircraft-based mass-balance retrieval methodologies such as TERRA require relatively constant meteorological conditions and source emission rates to reliably estimate emission rates from aircraft observations. Avoiding cases where meteorology and emission rates change significantly is one means of reducing emissions retrieval uncertainty, and quantitative metrics that may be used for retrieval accuracy estimation are therefore desirable. Using these metrics has the potential to greatly improve emission rate retrieval accuracy. Here, we investigate the impact of meteorological variability on mass-balance emission rate retrieval accuracy by using model-simulated fields as a proxy for real-world chemical and meteorological fields, in which virtual aircraft sampling of the GEM-MACH output was used for top-down mass balance estimates. We also explore the impact of upwind emissions from nearby sources on the accuracy of the retrieved emission rates. This approach allows the state of the atmosphere used for top-down estimates to be characterized in time and 3D space; the input meteorology and emissions are “known”, and thus potential means for improving emission rate retrievals and determining the factors affecting retrieval accuracy may be investigated. We found that emissions retrieval accuracy is correlated with three key quantitative criteria, evaluated a priori from forecasts and/or from observations during the sampling period: (1) changes to the atmospheric stability (described as the change in gradient Richardson number), (2) variations in the direction of transport, as a result of plume vertical motion and in the presence of vertical wind shear, and (3) the combined effect of the upwind-to-downwind concentration ratio and the upwind-to-downwind concentration standard deviations. We show here that cases where these criteria indicate high temporal variability and/or high upwind emissions can result in “storage-and-release” events within the sampled region (control volume), which decrease emission rate retrieval accuracy. Storage-and-release events may contribute the bulk of mass-balance emission rate retrieval under- and over-estimates, ranging in the tests carried out here from −25 % to 24 % of the known (input) emissions, with a median of −2 %. Our analysis also includes two cases with unsuitable meteorological conditions and/or significant upwind emissions to demonstrate conditions which may result in severe storage, which in turn cause emission rate under-estimates by the mass-balance approach. We also introduce a sampling strategy whereby the emission rate retrieval under- and over-estimates associated with storage-and-release are greatly reduced (to −14 % to +5 %, respectively, relative to the magnitude of the known emissions). We recommend repeat flights over a given facility and/or time-consecutive upwind and downwind (remote) vertical profiling of relevant fields (e.g., tracer concentrations) in order to measure and account for the factors associated with storage-and-release events, estimate the temporal trends in the evolution of the system during the flight/sampling time, and partially correct for the effects of meteorological variability and upwind emissions.
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Sarath, Devika, i M. Sucharitha. "A Study on Image Retrieval Based on Tetrolet Transform". International Journal of Engineering & Technology 7, nr 3.27 (15.08.2018): 321. http://dx.doi.org/10.14419/ijet.v7i3.27.17964.
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Retrieving images from the large databases has always been one challenging problem in the area of image retrieval while maintaining the higher accuracy and lower computational time. Texture defines the roughness of a surface. For the last two decades due to the large extent of multimedia database, image retrieval has been a hot issue in image processing. Texture images are retrieved in a variety of ways. This paper presents a survey on various texture image retrieval methods. It provides a brief comparison of various texture image retrieval methods on the basis of retrieval accuracy and computation time. Image retrieval techniques vary with feature extraction methods and various distance measures. In this paper, we present a survey on various texture feature extraction methods by applying tertrolet transform. This survey paper facilitates the researchers with background of progress of image retrieval methods that will help researchers in the area to select the best method for texture image retrieval appropriate to their requirements.
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Ernst, F., C. von Savigny, A. Rozanov, V. Rozanov, K. U. Eichmann, L. A. Brinkhoff, H. Bovensmann i J. P. Burrows. "Global stratospheric aerosol extinction profile retrievals from SCIAMACHY limb-scatter observations". Atmospheric Measurement Techniques Discussions 5, nr 4 (21.08.2012): 5993–6035. http://dx.doi.org/10.5194/amtd-5-5993-2012.
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Abstract. Stratospheric aerosol extinction profiles are retrieved from SCIAMACHY/Envisat limb-scatter observations in the visible spectral range. The retrieval algorithm is based on a colour-index approach using the normalized limb-radiance profiles at 470 nm and 750 nm wavelength. The optimal estimation approach in combination with the radiative transfer model SCIATRAN is employed for the retrievals. This study presents a detailed description of the retrieval algorithm, and a sensitivity analysis investigating the impact of the most important parameters that affect the aerosol extinction profile retrieval accuracy. It is found that the parameter with the largest impact is surface albedo, particularly for SCIAMACHY observations in the Southern Hemisphere where the error in stratospheric aerosol extinction can be up to 50% if the surface albedo is not well known. The effect of errors in the assumed ozone and neutral density profiles on the aerosol profile retrievals is with generally less than 6% relatively small. The aerosol extinction profiles retrieved from SCIAMACHY are compared with co-located SAGE II solar occultation measurements of stratospheric aerosol extinction during the period 2003–2005. The mean aerosol extinction profiles averaged over all co-locations agree to within 20% between 15 and 35 km altitude. However, larger differences are observed at specific latitudes.
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Anandh, A., K. Mala i R. Suresh Babu. "Combined global and local semantic feature–based image retrieval analysis with interactive feedback". Measurement and Control 53, nr 1-2 (12.12.2019): 3–17. http://dx.doi.org/10.1177/0020294018824122.
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Nowadays, user expects image retrieval systems using a large database as an active research area for the investigators. Generally, content-based image retrieval system retrieves the images based on the low-level features, high-level features, or the combination of both. Content-based image retrieval results can be improved by considering various features like directionality, contrast, coarseness, busyness, local binary pattern, and local tetra pattern with modified binary wavelet transform. In this research work, appropriate features are identified, applied and results are validated against existing systems. Modified binary wavelet transform is a modified form of binary wavelet transform and this methodology produced more similar retrieval images. The proposed system also combines the interactive feedback to retrieve the user expected results by addressing the issues of semantic gap. The quantitative evaluations such as average retrieval rate, false image acceptation ratio, and false image rejection ratio are evaluated to ensure the user expected results of the system. In addition to that, precision and recall are evaluated from the proposed system against the existing system results. When compared with the existing content-based image retrieval methods, the proposed approach provides better retrieval accuracy.
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Chang, Yuyang, Qiaoyun Hu, Philippe Goloub, Igor Veselovskii i Thierry Podvin. "Retrieval of Aerosol Microphysical Properties from Multi-Wavelength Mie–Raman Lidar Using Maximum Likelihood Estimation: Algorithm, Performance, and Application". Remote Sensing 14, nr 24 (7.12.2022): 6208. http://dx.doi.org/10.3390/rs14246208.
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Lidar plays an essential role in monitoring the vertical variation of atmospheric aerosols. However, due to the limited information that lidar measurements provide, ill-posedness still remains a big challenge in quantitative lidar remote sensing. In this study, we describe the Basic algOrithm for REtrieval of Aerosol with Lidar (BOREAL), which is based on maximum likelihood estimation (MLE), and retrieve aerosol microphysical properties from extinction and backscattering measurements of multi-wavelength Mie–Raman lidar systems. The algorithm utilizes different types of a priori constraints to better constrain the solution space and suppress the influence of the ill-posedness. Sensitivity test demonstrates that BOREAL could retrieve particle volume size distribution (VSD), total volume concentration (Vt), effective radius (Reff), and complex refractive index (CRI = n − ik) of simulated aerosol models with satisfying accuracy. The application of the algorithm to real aerosol events measured by LIlle Lidar AtmosphereS (LILAS) shows it is able to realize fast and reliable retrievals of different aerosol scenarios (dust, aged-transported smoke, and urban aerosols) with almost uniform and simple pre-settings. Furthermore, the algorithmic principle allows BOREAL to incorporate measurements with different and non-linearly related errors to the retrieved parameters, which makes it a flexible and generalized algorithm for lidar retrieval.