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Renno, Anas, Faisal Khateeb, Viviane Kazan, Weikai Qu, Anurekha Gollapudi, Brett Aplin, Jihad Abbas, Gerald Zelenock, and Munier Nazzal. "A single center experience with retrievable IVC filters." Vascular 23, no. 4 (September 2, 2014): 350–57. http://dx.doi.org/10.1177/1708538114546713.
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Objective To evaluate retrievable IVC filters in our institution and assess their retrieval following a well-structured follow up program. Design Retrospective cohort study. Materials The medical records of patients implanted with retrievable IVC filters were reviewed. Methods All retrievable filter insertions between July 2007 and August 2011 at our institution were reviewed. Data was analyzed for age, gender, indication, complications, retrieval rate, and brand of filter inserted. Statistical analysis was done using SPSS software v19. Chi-square was used to compare discrete data and t-test for continuous data. P < 0.05 was significant. Results A total of 484 patients were reviewed of which 258 (53.1%) had a complete medical record. And 96 (37.2%) filters were placed as permanent at the time of insertion. An additional 40 (15.5%) filters were converted to permanent (total permanent filters 136; 52.7%). Death was reported in 26 (10%) patients and 96 (37.2%) out of the remaining 232 patients presented for potential retrieval. Also, 73 (28.2%) had an attempt to retrieve the filters, 69 (94.5%) were successful and 4 (5.4%) failed to retrieve. The remaining 23 (8.9%) patients declined retrieval. Filters studied include Celect (38%), Bard (31.4%), Option (26.2%), Tulip (4.1%), and Recovery (0.2%). Bard was more commonly used as a retrievable filter (80.9%). Retrieval on the first attempt was 90.4% ( n = 66) successful. Of the remaining seven filters, three were successfully retrieved on a second attempt, and four failed to retrieve due to filter tilt. The success rates of retrieval for Celect and Tulip were significantly lower than for Bard ( p = 0.04 and 0.023, respectively). Conclusion Our study showed that a variety of IVC filters can be retrieved successfully with minimal complication rates. In more than half of our patients, IVC filters were used as permanent. Failure of retrieval was most frequently due to filter tilting.
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Jalali, Ali, Shannon Hicks-Jalali, Robert J. Sica, Alexander Haefele, and Thomas von Clarmann. "A practical information-centered technique to remove a priori information from lidar optimal-estimation-method retrievals." Atmospheric Measurement Techniques 12, no. 7 (July 18, 2019): 3943–61. http://dx.doi.org/10.5194/amt-12-3943-2019.
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Abstract. Lidar retrievals of atmospheric temperature and water vapor mixing ratio profiles using the optimal estimation method (OEM) typically use a retrieval grid with a number of points larger than the number of pieces of independent information obtainable from the measurements. Consequently, retrieved geophysical quantities contain some information from their respective a priori values or profiles, which can affect the results in the higher altitudes of the temperature and water vapor profiles due to decreasing signal-to-noise ratios. The extent of this influence can be estimated using the retrieval's averaging kernels. The removal of formal a priori information from the retrieved profiles in the regions of prevailing a priori effects is desirable, particularly when these greatest heights are of interest for scientific studies. We demonstrate here that removal of a priori information from OEM retrievals is possible by repeating the retrieval on a coarser grid where the retrieval is stable even without the use of formal prior information. The averaging kernels of the fine-grid OEM retrieval are used to optimize the coarse retrieval grid. We demonstrate the adequacy of this method for the case of a large power-aperture Rayleigh scatter lidar nighttime temperature retrieval and for a Raman scatter lidar water vapor mixing ratio retrieval during both day and night.
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Ramesh, K., A. P. Kesarkar, J. Bhate, M. Venkat Ratnam, and A. Jayaraman. "Adaptive neuro fuzzy inference system for profiling of the atmosphere." Atmospheric Measurement Techniques Discussions 7, no. 3 (March 20, 2014): 2715–36. http://dx.doi.org/10.5194/amtd-7-2715-2014.
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Abstract. Retrieval of accurate profiles of temperature and water vapor is important for the study of atmospheric convection. However, it is challenging because of the uncertainties associated with direct measurement of atmospheric parameters during convection events using radiosonde and retrieval of remote-sensed observations from satellites. Recent developments in computational techniques motivated the use of adaptive techniques in the retrieval algorithms. In this work, we have used the Adaptive Neuro Fuzzy Inference System (ANFIS) to retrieve profiles of temperature and humidity over tropical station Gadanki (13.5° N, 79.2° E), India. The observations of brightness temperatures recorded by Radiometrics Multichannel Microwave Radiometer MP3000 for the period of June–September 2011 are used to model profiles of atmospheric parameters up to 10 km. The ultimate goal of this work is to use the ANFIS forecast model to retrieve atmospheric profiles accurately during the wet season of the Indian monsoon (JJAS) season and during heavy rainfall associated with tropical convections. The comparison analysis of the ANFIS model retrieval of temperature and relative humidity (RH) profiles with GPS-radiosonde observations and profiles retrieved using the Artificial Neural Network (ANN) algorithm indicates that errors in the ANFIS model are less even in the wet season, and retrievals using ANFIS are more reliable, making this technique the standard. The Pearson product movement correlation coefficient (r) between retrieved and observed profiles is more than 99% for temperature profiles for both techniques and therefore both techniques are successful in the retrieval of temperature profiles. However, in the case of RH the retrieval using ANFIS is found to be better. The comparison of mean absolute error (MAE), root mean square error (RMSE) and symmetric mean absolute percentage error (SMAPE) of retrieved temperature and RH profiles using ANN and ANFIS also indicates that profiles retrieved using ANFIS are significantly better compared to the ANN technique. The error analysis of profiles concludes that retrieved profiles using ANFIS techniques have improved the retrievals substantially; however, retrieval of RH by both techniques (ANN and ANFIS) has limited success.
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Lipponen, Antti, Tero Mielonen, Mikko R. A. Pitkänen, Robert C. Levy, Virginia R. Sawyer, Sami Romakkaniemi, Ville Kolehmainen, and Antti Arola. "Bayesian aerosol retrieval algorithm for MODIS AOD retrieval over land." Atmospheric Measurement Techniques 11, no. 3 (March 19, 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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Zhou, Minqiang, Bavo Langerock, Mahesh Kumar Sha, Nicolas Kumps, Christian Hermans, Christof Petri, Thorsten Warneke, et al. "Retrieval of atmospheric CH<sub>4</sub> vertical information from ground-based FTS near-infrared spectra." Atmospheric Measurement Techniques 12, no. 11 (November 25, 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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Orscheschek, Franziska, Tilo Strobach, Torsten Schubert, and Timothy Rickard. "Two retrievals from a single cue: A bottleneck persists across episodic and semantic memory." Quarterly Journal of Experimental Psychology 72, no. 5 (May 28, 2018): 1005–28. http://dx.doi.org/10.1177/1747021818776818.
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There is evidence in the literature that two retrievals from long-term memory cannot occur in parallel. To date, however, that work has explored only the case of two retrievals from newly acquired episodic memory. These studies demonstrated a retrieval bottleneck even after dual-retrieval practice. That retrieval bottleneck may be a global property of long-term memory retrieval, or it may apply only to the case of two retrievals from episodic memory. In the current experiments, we explored whether that apparent dual-retrieval bottleneck applies to the case of one retrieval from episodic memory and one retrieval from highly overlearned semantic memory. Across three experiments, subjects learned to retrieve a left or right keypress response form a set of 14 unique word cues (e.g., black—right keypress). In addition, they learned a verbal response which involved retrieving the antonym of the presented cue (e.g., black—“white”). In the dual-retrieval condition, subjects had to retrieve both the keypress response and the antonym word. The results suggest that the retrieval bottleneck is superordinate to specific long-term memory systems and holds across different memory components. In addition, the results support the assumption of a cue-level response chunking account of learned retrieval parallelism.
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Torabian, Saba, Zhe Chen, Beth A. Ober, and Gregory K. Shenaut. "Analogical Retrieval of Folktales: A Cross-Cultural Approach." Journal of Cognition and Culture 17, no. 3-4 (October 6, 2017): 281–305. http://dx.doi.org/10.1163/15685373-12340008.
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Abstract This cross-cultural study addressed how individuals retrieve and transfer naturally learned information (i.e., folktales) from long-term memory by analogy with a previously unencountered story, concept, or problem. American and Iranian participants read target stories constructed to be analogous to folktales either familiar or unfamiliar to their culture, all having high structural familiarity and either high or low surface similarity to the source folktales. Participants reported whether targets (analogues) reminded them of any specific folktale they had learned in the past; positive responses plus additional justification (i.e., the folktale’s name or its gist) were interpreted as successful analogical retrievals. The current experiment demonstrated a high overall rate of analogical retrieval for familiar folktales and essentially no retrieval for unfamiliar folktales. There was also reliably more retrieval for analogue stories having higher versus lower surface similarity to target folktales. The high salience of surface similarity was also revealed when participants rated retrieved folktales for similarity to the target. Personal familiarity with folktales increased the retrieval rate, but presenting the folktale’s name as a cue produced mixed effects on retrieval. In summary, individuals readily retrieved culturally familiar folktales from long-term memory when they encountered structurally similar analogues, but retrieval was modulated by surface similarity.
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Garrett, T. J., and C. Zhao. "Ground-based remote sensing of thin clouds in the Arctic." Atmospheric Measurement Techniques Discussions 5, no. 6 (November 30, 2012): 8653–99. http://dx.doi.org/10.5194/amtd-5-8653-2012.
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Abstract. This paper describes a method for using interferometer measurements of downwelling thermal radiation to retrieve the properties of single-layer clouds. Cloud phase is determined from ratios of thermal emission in three "micro-windows" where absorption by water vapor is particularly small. Cloud microphysical and optical properties are retrieved from thermal emission in two micro-windows, constrained by the transmission through clouds of stratospheric ozone emission. Assuming a cloud does not approximate a blackbody, the estimated 95% confidence retrieval errors in effective radius, visible optical depth, number concentration, and water path are, respectively, 10%, 20%, 38% (55% for ice crystals), and 16%. Applied to data from the Atmospheric Radiation Measurement program (ARM) North Slope of Alaska – Adjacent Arctic Ocean (NSA-AAO) site near Barrow, Alaska, retrievals show general agreement with ground-based microwave radiometer measurements of liquid water path. Compared to other retrieval methods, advantages of this technique include its ability to characterize thin clouds year round, that water vapor is not a primary source of retrieval error, and that the retrievals of microphysical properties are only weakly sensitive to retrieved cloud phase. The primary limitation is the inapplicability to thicker clouds that radiate as blackbodies.
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Negi, H. S., and A. Kokhanovsky. "Retrieval of snow grain size and albedo of Western Himalayan snow cover using satellite data." Cryosphere Discussions 5, no. 1 (February 16, 2011): 605–53. http://dx.doi.org/10.5194/tcd-5-605-2011.
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Abstract. In the present study we describe the retrievals of snow grain size and spectral albedo (plane and spherical albedo) for Western Himalayan snow cover using Hyperion sensor data. The asymptotic radiative transfer (ART) theory was explored for the snow retrievals. To make the methodology operational only five spectral bands (440, 500, 1050, 1240 and 1650 nm) of Hyperion were used for snow parameters retrieval. The bi-spectral method (440 nm in the visible and 1050/1240 nm in the NIR region) was used to retrieve snow grain size. Spectral albedos were retrieved using satellite reflectances and estimated grain size. A good agreement was observed between retrieved snow parameters and ground observed snow-meteorological conditions. The satellite retrieved grain sizes were compared with field spectroradiometer retrieved grain sizes and close results were found for Lower Himalayan snow. The wavelength 1240 nm was found to be more suitable compared to 1050 nm for grain size retrieval along the steep slopes. The methodology was able to retrieve the spatial variations in snow parameters in different parts of Western Himalaya which are due to snow climatic and terrain conditions of Himalaya. This methodology is of importance for operational snow cover and glacier monitoring in Himalayan region using space-borne and air-borne sensors.
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Negi, H. S., and A. Kokhanovsky. "Retrieval of snow grain size and albedo of western Himalayan snow cover using satellite data." Cryosphere 5, no. 4 (October 14, 2011): 831–47. http://dx.doi.org/10.5194/tc-5-831-2011.
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Abstract. In the present study we describe the retrievals of snow grain size and spectral albedo (plane and spherical albedo) for western Himalayan snow cover using Hyperion sensor data. The asymptotic radiative transfer (ART) theory was explored for the snow retrievals. To make the methodology operational only five spectral bands (440, 500, 1050, 1240 and 1650 nm) of Hyperion were used for snow parameters retrieval. The bi-spectral method (440 nm in the visible and 1050/1240 nm in the NIR region) was used to retrieve snow grain size. Spectral albedos were retrieved using satellite reflectances and estimated grain size. A good agreement was observed between retrieved snow parameters and ground observed snow-meteorological conditions. The satellite retrieved grain sizes were compared with field spectroradiometer retrieved grain sizes and close results were found for lower Himalayan snow. The wavelength 1240 nm was found to be more suitable compared to 1050 nm for grain size retrieval along the steep slopes. The methodology was able to retrieve the spatial variations in snow parameters in different parts of western Himalaya which are due to snow climatic and terrain conditions of Himalaya. This methodology is of importance for operational snow cover and glacier monitoring in Himalayan region using space-borne and air-borne sensors.
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Wang, Ji, Jared R. Kolecki, Jean-Baptiste Ruffio, Jason J. Wang, Dimitri Mawet, Ashley Baker, Randall Bartos, et al. "Retrieving the C and O Abundances of HR 7672 AB: A Solar-type Primary Star with a Benchmark Brown Dwarf." Astronomical Journal 163, no. 4 (March 25, 2022): 189. http://dx.doi.org/10.3847/1538-3881/ac56e2.
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Abstract A benchmark brown dwarf (BD) is a BD whose properties (e.g., mass and chemical composition) are precisely and independently measured. Benchmark BDs are valuable in testing theoretical evolutionary tracks, spectral synthesis, and atmospheric retrievals for substellar objects. Here, we report results of atmospheric retrieval on a synthetic spectrum and a benchmark BD, HR 7672 B, with petitRADTRANS. First, we test the retrieval framework on a synthetic PHOENIX BT-Settl spectrum with a solar composition. We show that the retrieved C and O abundances are consistent with solar values, but the retrieved C/O is overestimated by 0.13–0.18, which is about four times higher than the formal error bar. Second, we perform retrieval on HR 7672 B using high spectral-resolution data (R = 35,000) from the Keck Planet Imager and Characterizer and near-infrared photometry. We retrieve [C/H], [O/H], and C/O to be −0.24 ± 0.05, −0.19 ± 0.04, and 0.52 ± 0.02. These values are consistent with those of HR 7672 A within 1.5σ. As such, HR 7672 B is among only a few benchmark BDs (along with Gl 570 D and HD 3651 B) that have been demonstrated to have consistent elemental abundances with their primary stars. Our work provides a practical procedure of testing and performing atmospheric retrieval, and sheds light on potential systematics of future retrievals using high- and low-resolution data.
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Adhikari, Loknath, Feiqin Xie, and Jennifer S. Haase. "Application of the full spectrum inversion algorithm to simulated airborne GPS radio occultation signals." Atmospheric Measurement Techniques 9, no. 10 (October 18, 2016): 5077–87. http://dx.doi.org/10.5194/amt-9-5077-2016.
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Abstract. With a GPS receiver on board an airplane, the airborne radio occultation (ARO) technique provides dense lower-tropospheric soundings over target regions. Large variations in water vapor in the troposphere cause strong signal multipath, which could lead to systematic errors in RO retrievals with the geometric optics (GO) method. The spaceborne GPS RO community has successfully developed the full-spectrum inversion (FSI) technique to solve the multipath problem. This paper is the first to adapt the FSI technique to retrieve atmospheric properties (bending and refractivity) from ARO signals, where it is necessary to compensate for the receiver traveling on a non-circular trajectory inside the atmosphere, and its use is demonstrated using an end-to-end simulation system. The forward-simulated GPS L1 (1575.42 MHz) signal amplitude and phase are used to test the modified FSI algorithm. The ARO FSI method is capable of reconstructing the fine vertical structure of the moist lower troposphere in the presence of severe multipath, which otherwise leads to large retrieval errors in the GO retrieval. The sensitivity of the modified FSI-retrieved bending angle and refractivity to errors in signal amplitude and errors in the measured refractivity at the receiver is presented. Accurate bending angle retrievals can be obtained from the surface up to ∼ 250 m below the receiver at typical flight altitudes above the tropopause, above which the retrieved bending angle becomes highly sensitive to the phase measurement noise. Abrupt changes in the signal amplitude that are a challenge for receiver tracking and geometric optics bending angle retrieval techniques do not produce any systematic bias in the FSI retrievals when the SNR is high. For very low SNR, the FSI performs as expected from theoretical considerations. The 1 % in situ refractivity measurement errors at the receiver height can introduce a maximum refractivity retrieval error of 0.5 % (1 K) near the receiver, but the error decreases gradually to ∼ 0.05 % (0.1 K) near the surface. In summary, the ARO FSI successfully retrieves the fine vertical structure of the atmosphere in the presence of multipath in the lower troposphere.
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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, and H. Volten. "Use of neural networks in ground-based aerosol retrievals from multi-angle spectropolarimetric observations." Atmospheric Measurement Techniques 8, no. 1 (January 14, 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, and 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, no. 2 (April 16, 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, and 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, no. 7 (July 27, 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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Tokumi, Yuta, Junki Hakamata, and Masataka Tokumaru. "Development of a Nutritional Management System for a Healthy Eating Habits Support System." Journal of Advanced Computational Intelligence and Intelligent Informatics 17, no. 2 (March 20, 2013): 324–34. http://dx.doi.org/10.20965/jaciii.2013.p0324.
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In this study, we propose a Nutritional Management System (NMS) that optimizes nutritional balance using a tabu search. Contemporary recipe retrieval systems generally retrieve a recipe either by using a keyword or by recommending a popular recipe. However, these recipe retrieval systems yield the same retrieval results for different users, and thus, the results do not necessarily reflect an individual user’s tastes. In addition, the search results delivered by many recipe retrieval systems do not always describe the nutritional details of the recipes. Therefore, we developed a Healthy Eating Habits Support System for resolving these issues. This system is composed of an NMS and a Kansei Retrieval System (KRS). The NMS retrieves nutritionally balanced menus using a tabu search. The KRS learns a user’s taste preferences, and selects menus suitable for a user’s tastes from among the menus retrieved by the NMS. The KRS needs multiple nutritionally balanced menus to learn a user’s taste preferences. Thus, in this study, we simulated scenarios to examine whether the NMS can retrieve multiple nutritionally balanced menus in the long-term without duplication, using quasi recipe data and real recipe data. When we used quasi recipe data in a simulation with a very large number of recipes, the NMS could retrieve a sustained quantity of menus in the long-term. In addition, when we used real recipe data, the NMS could quickly retrieve menus over the long-term. Therefore, the effectiveness of the NMS was confirmed.
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Friedrich, Martina Michaela, Claudia Rivera, Wolfgang Stremme, Zuleica Ojeda, Josué Arellano, Alejandro Bezanilla, José Agustín García-Reynoso, and Michel Grutter. "NO<sub>2</sub> vertical profiles and column densities from MAX-DOAS measurements in Mexico City." Atmospheric Measurement Techniques 12, no. 4 (May 2, 2019): 2545–65. http://dx.doi.org/10.5194/amt-12-2545-2019.
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Abstract. We present a new numerical code, Mexican MAX-DOAS Fit (MMF), developed to retrieve profiles of different trace gases from the network of MAX-DOAS instruments operated in Mexico City. MMF uses differential slant column densities (dSCDs) retrieved with the QDOAS (Danckaert et al., 2013) software. The retrieval is comprised of two steps, an aerosol retrieval and a trace gas retrieval that uses the retrieved aerosol profile in the forward model for the trace gas. For forward model simulations, VLIDORT is used (e.g., Spurr et al., 2001; Spurr, 2006, 2013). Both steps use constrained least-square fitting, but the aerosol retrieval uses Tikhonov regularization and the trace gas retrieval optimal estimation. Aerosol optical depth and scattering properties from the AERONET database, averaged ceilometer data, WRF-Chem model data, and temperature and pressure sounding data are used for different steps in the retrieval chain. The MMF code was applied to retrieve NO2 profiles with 2 degrees of freedom (DOF = 2) from spectra of the MAX-DOAS instrument located at the Universidad Nacional Autónoma de México (UNAM) campus. We describe the full error analysis of the retrievals and include a sensitivity exercise to quantify the contribution of the uncertainties in the aerosol extinction profiles to the total error. A data set comprised of measurements from January 2015 to July 2016 was processed and the results compared to independent surface measurements. We concentrate on the analysis of four single days and additionally present diurnal and annual variabilities from averaging the 1.5 years of data. The total error, depending on the exact counting, is 14 %–20 % and this work provides new and relevant information about NO2 in the boundary layer of Mexico City.
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Grzegorski, M., M. Wenig, U. Platt, P. Stammes, N. Fournier, and T. Wagner. "The Heidelberg iterative cloud retrieval utilities (HICRU) and its application to GOME data." Atmospheric Chemistry and Physics Discussions 6, no. 2 (March 7, 2006): 1637–78. http://dx.doi.org/10.5194/acpd-6-1637-2006.
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Abstract. Information about clouds, in particular the accurate identification of cloud free pixels, is crucial for the retrieval of tropospheric vertical column densities from space. The Heidelberg Iterative Cloud Retrieval Utilities (HICRU) retrieve effective cloud fraction using spectra of two instruments designed for trace gas retrievals from space: The Global Ozone Monitoring Experiment (GOME) on the European Remote Sensing Satellite (ERS-2) and the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) on ENVISAT. HICRU applies the widely used threshold method to the so-called Polarization Monitoring Devices (PMDs) with higher spatial resolution compared to the channels used for trace gas retrievals. Cloud retrieval and in particular the identification of cloud free pixels is improved by HICRU through a sophisticated, iterative retrieval of the thresholds which takes their dependency on different instrumental and geometrical parameters into account. The lower thresholds, which represent the surface albedo and strongly affect the results of the algorithm, are retrieved accurately through a four stage classification scheme using image sequence analysis. The design and the results of the algorithm applied to GOME data are described and compared to several other cloud algorithms for GOME. The differences to other cloud algorithms are discussed with respect to the particular characteristics of the algorithms.
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Grzegorski, M., M. Wenig, U. Platt, P. Stammes, N. Fournier, and T. Wagner. "The Heidelberg iterative cloud retrieval utilities (HICRU) and its application to GOME data." Atmospheric Chemistry and Physics 6, no. 12 (October 5, 2006): 4461–76. http://dx.doi.org/10.5194/acp-6-4461-2006.
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Abstract. Information about clouds, in particular the accurate identification of cloud free pixels, is crucial for the retrieval of tropospheric vertical column densities from space. The Heidelberg Iterative Cloud Retrieval Utilities (HICRU) retrieve effective cloud fraction using spectra of two instruments designed for trace gas retrievals from space: The Global Ozone Monitoring Experiment (GOME) on the European Remote Sensing Satellite (ERS-2) and the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) on ENVISAT. HICRU applies the widely used threshold method to the so-called Polarization Monitoring Devices (PMDs) with higher spatial resolution compared to the channels used for trace gas retrievals. Cloud retrieval and in particular the identification of cloud free pixels is improved by HICRU through a sophisticated, iterative retrieval of the thresholds which takes their dependency on different instrumental and geometrical parameters into account. The lower thresholds, which represent the surface albedo and strongly affect the results of the algorithm, are retrieved accurately through a four stage classification scheme using image sequence analysis. The design and the results of the algorithm applied to GOME data are described and compared to several other cloud algorithms for GOME. The differences to other cloud algorithms are discussed with respect to the particular characteristics of the algorithms.
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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, and 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, no. 12 (June 18, 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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Garrett, T. J., and C. Zhao. "Ground-based remote sensing of thin clouds in the Arctic." Atmospheric Measurement Techniques 6, no. 5 (May 14, 2013): 1227–43. http://dx.doi.org/10.5194/amt-6-1227-2013.
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Abstract. This paper describes a method for using interferometer measurements of downwelling thermal radiation to retrieve the properties of single-layer clouds. Cloud phase is determined from ratios of thermal emission in three "micro-windows" at 862.5 cm−1, 935.8 cm−1, and 988.4 cm−1 where absorption by water vapour is particularly small. Cloud microphysical and optical properties are retrieved from thermal emission in the first two of these micro-windows, constrained by the transmission through clouds of primarily stratospheric ozone emission at 1040 cm−1. Assuming a cloud does not approximate a blackbody, the estimated 95% confidence retrieval errors in effective radius re, visible optical depth τ, number concentration N, and water path WP are, respectively, 10%, 20%, 38% (55% for ice crystals), and 16%. Applied to data from the Atmospheric Radiation Measurement programme (ARM) North Slope of Alaska – Adjacent Arctic Ocean (NSA-AAO) site near Barrow, Alaska, retrievals show general agreement with both ground-based microwave radiometer measurements of liquid water path and a method that uses combined shortwave and microwave measurements to retrieve re, τ and N. Compared to other retrieval methods, advantages of this technique include its ability to characterise thin clouds year round, that water vapour is not a primary source of retrieval error, and that the retrievals of microphysical properties are only weakly sensitive to retrieved cloud phase. The primary limitation is the inapplicability to thicker clouds that radiate as blackbodies and that it relies on a fairly comprehensive suite of ground based measurements.
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Nadel, Lynn, Jenna Campbell, and Lee Ryan. "Autobiographical Memory Retrieval and Hippocampal Activation as a Function of Repetition and the Passage of Time." Neural Plasticity 2007 (2007): 1–14. http://dx.doi.org/10.1155/2007/90472.
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Multiple trace theory (MTT) predicts that hippocampal memory traces expand and strengthen as a function of repeated memory retrievals. We tested this hypothesis utilizing fMRI, comparing the effect of memory retrieval versus the mere passage of time on hippocampal activation. While undergoing fMRI scanning, participants retrieved remote autobiographical memories that had been previously retrieved either one month earlier, two days earlier, or multiple times during the preceding month. Behavioral analyses revealed that the number and consistency of memory details retrieved increased with multiple retrievals but not with the passage of time. While all three retrieval conditions activated a similar set of brain regions normally associated with autobiographical memory retrieval including medial temporal lobe structures, hippocampal activation did not change as a function of either multiple retrievals or the passage of time. However, activation in other brain regions, including the precuneus, lateral prefrontal cortex, parietal cortex, lateral temporal lobe, and perirhinal cortex increased after multiple retrievals, but was not influenced by the passage of time. These results have important implications for existing theories of long-term memory consolidation.
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Stein, Thorwald H. M., Julien Delanoë, and Robin J. Hogan. "A Comparison among Four Different Retrieval Methods for Ice-Cloud Properties Using Data from CloudSat, CALIPSO, and MODIS." Journal of Applied Meteorology and Climatology 50, no. 9 (September 2011): 1952–69. http://dx.doi.org/10.1175/2011jamc2646.1.
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AbstractThe A-Train constellation of satellites provides a new capability to measure vertical cloud profiles that leads to more detailed information on ice-cloud microphysical properties than has been possible up to now. A variational radar–lidar ice-cloud retrieval algorithm (VarCloud) takes advantage of the complementary nature of the CloudSat radar and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar to provide a seamless retrieval of ice water content, effective radius, and extinction coefficient from the thinnest cirrus (seen only by the lidar) to the thickest ice cloud (penetrated only by the radar). In this paper, several versions of the VarCloud retrieval are compared with the CloudSat standard ice-only retrieval of ice water content, two empirical formulas that derive ice water content from radar reflectivity and temperature, and retrievals of vertically integrated properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) radiometer. The retrieved variables typically agree to within a factor of 2, on average, and most of the differences can be explained by the different microphysical assumptions. For example, the ice water content comparison illustrates the sensitivity of the retrievals to assumed ice particle shape. If ice particles are modeled as oblate spheroids rather than spheres for radar scattering then the retrieved ice water content is reduced by on average 50% in clouds with a reflectivity factor larger than 0 dBZ. VarCloud retrieves optical depths that are on average a factor-of-2 lower than those from MODIS, which can be explained by the different assumptions on particle mass and area; if VarCloud mimics the MODIS assumptions then better agreement is found in effective radius and optical depth is overestimated. MODIS predicts the mean vertically integrated ice water content to be around a factor-of-3 lower than that from VarCloud for the same retrievals, however, because the MODIS algorithm assumes that its retrieved effective radius (which is mostly representative of cloud top) is constant throughout the depth of the cloud. These comparisons highlight the need to refine microphysical assumptions in all retrieval algorithms and also for future studies to compare not only the mean values but also the full probability density function.
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Ramesh, K., A. P. Kesarkar, J. Bhate, M. Venkat Ratnam, and A. Jayaraman. "Adaptive neuro-fuzzy inference system for temperature and humidity profile retrieval from microwave radiometer observations." Atmospheric Measurement Techniques 8, no. 1 (January 22, 2015): 369–84. http://dx.doi.org/10.5194/amt-8-369-2015.
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Abstract. The retrieval of accurate profiles of temperature and water vapour is important for the study of atmospheric convection. Recent development in computational techniques motivated us to use adaptive techniques in the retrieval algorithms. In this work, we have used an adaptive neuro-fuzzy inference system (ANFIS) to retrieve profiles of temperature and humidity up to 10 km over the tropical station Gadanki (13.5° N, 79.2° E), India. ANFIS is trained by using observations of temperature and humidity measurements by co-located Meisei GPS radiosonde (henceforth referred to as radiosonde) and microwave brightness temperatures observed by radiometrics multichannel microwave radiometer MP3000 (MWR). ANFIS is trained by considering these observations during rainy and non-rainy days (ANFIS(RD + NRD)) and during non-rainy days only (ANFIS(NRD)). The comparison of ANFIS(RD + NRD) and ANFIS(NRD) profiles with independent radiosonde observations and profiles retrieved using multivariate linear regression (MVLR: RD + NRD and NRD) and artificial neural network (ANN) indicated that the errors in the ANFIS(RD + NRD) are less compared to other retrieval methods. The Pearson product movement correlation coefficient (r) between retrieved and observed profiles is more than 92% for temperature profiles for all techniques and more than 99% for the ANFIS(RD + NRD) technique Therefore this new techniques is relatively better for the retrieval of temperature profiles. The comparison of bias, mean absolute error (MAE), RMSE and symmetric mean absolute percentage error (SMAPE) of retrieved temperature and relative humidity (RH) profiles using ANN and ANFIS also indicated that profiles retrieved using ANFIS(RD + NRD) are significantly better compared to the ANN technique. The analysis of profiles concludes that retrieved profiles using ANFIS techniques have improved the temperature retrievals substantially; however, the retrieval of RH by all techniques considered in this paper (ANN, MVLR and ANFIS) has limited success.
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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, no. 1 (January 1, 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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Loveridge, Jesse, Aviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony B. Davis, and Yoav Y. Schechner. "Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 2: Local optimization." Atmospheric Measurement Techniques 16, no. 16 (August 29, 2023): 3931–57. http://dx.doi.org/10.5194/amt-16-3931-2023.
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Abstract. Our global understanding of clouds and aerosols relies on the remote sensing of their optical, microphysical, and macrophysical properties using, in part, scattered solar radiation. Current retrievals assume clouds and aerosols form plane-parallel, homogeneous layers and utilize 1D radiative transfer (RT) models. These assumptions limit the detail that can be retrieved about the 3D variability in the cloud and aerosol fields and induce biases in the retrieved properties for highly heterogeneous structures such as cumulus clouds and smoke plumes. In Part 1 of this two-part study, we validated a tomographic method that utilizes multi-angle passive imagery to retrieve 3D distributions of species using 3D RT to overcome these issues. That validation characterized the uncertainty in the approximate Jacobian used in the tomographic retrieval over a wide range of atmospheric and surface conditions for several horizontal boundary conditions. Here, in Part 2, we test the algorithm's effectiveness on synthetic data to test whether the retrieval accuracy is limited by the use of the approximate Jacobian. We retrieve 3D distributions of a volume extinction coefficient (σ3D) at 40 m resolution from synthetic multi-angle, mono-spectral imagery at 35 m resolution derived from stochastically generated cumuliform-type clouds in (1 km)3 domains. The retrievals are idealized in that we neglect forward-modelling and instrumental errors, with the exception of radiometric noise; thus, reported retrieval errors are the lower bounds. σ3D is retrieved with, on average, a relative root mean square error (RRMSE) < 20 % and bias < 0.1 % for clouds with maximum optical depth (MOD) < 17, and the RRMSE of the radiances is < 0.5 %, indicating very high accuracy in shallow cumulus conditions. As the MOD of the clouds increases to 80, the RRMSE and biases in σ3D worsen to 60 % and −35 %, respectively, and the RRMSE of the radiances reaches 16 %, indicating incomplete convergence. This is expected from the increasing ill-conditioning of the inverse problem with the decreasing mean free path predicted by RT theory and discussed in detail in Part 1. We tested retrievals that use a forward model that is not only less ill-conditioned (in terms of condition number) but also less accurate, due to more aggressive delta-M scaling. This reduces the radiance RRMSE to 9 % and the bias in σ3D to −8 % in clouds with MOD ∼ 80, with no improvement in the RRMSE of σ3D. This illustrates a significant sensitivity of the retrieval to the numerical configuration of the RT model which, at least in our circumstances, improves the retrieval accuracy. All of these ensemble-averaged results are robust in response to the inclusion of radiometric noise during the retrieval. However, individual realizations can have large deviations of up to 18 % in the mean extinction in clouds with MOD ∼ 80, which indicates large uncertainties in the retrievals in the optically thick limit. Using less ill-conditioned forward model tomography can also accurately infer optical depths (ODs) in conditions spanning the majority of oceanic cumulus fields (MOD < 80), as the retrieval provides ODs with bias and RRMSE values better than −8 % and 36 %, respectively. This is a significant improvement over retrievals using 1D RT, which have OD biases between −30 % and −23 % and RRMSE between 29 % and 80 % for the clouds used here. Prior information or other sources of information will be required to improve the RRMSE of σ3D in the optically thick limit, where the RRMSE is shown to have a strong spatial structure that varies with the solar and viewing geometry.
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van Diedenhoven, B., O. P. Hasekamp, and I. Aben. "Surface pressure retrieval from SCIAMACHY measurements in the O<sub>2</sub>A Band: validation of the measurements and sensitivity on aerosols." Atmospheric Chemistry and Physics Discussions 5, no. 2 (March 14, 2005): 1469–99. http://dx.doi.org/10.5194/acpd-5-1469-2005.
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Abstract. We perform surface pressure retrievals from cloud-free Oxygen A band measurements of SCIAMACHY. These retrievals can be well validated because surface pressure is a quantity that is, in general, accurately known from meteorological models. Therefore, surface 5 pressure retrievals and their validation provide important insight into the quality of the instrument calibration. Furthermore, they can provide insight into retrievals which are affected by similar radiation transport processes, for example the retrieval of total columns of H2O, CO, CO2 and CH4. In our retrieval aerosols are neglected. Using synthetic measurements, we show that for low to moderate or high surface albedos this 10 leads to an under- or overestimation of the retrieved surface pressures, respectively. The surface pressures retrieved from the SCIAMACHY measurements indeed show this dependence on surface albedo, when compared to the corresponding pressures from a meteorological database. However, an offset of about 30 hPa was found, which can not be caused by neglecting aerosols in the retrieval. The same offset was found 15 when comparing the retrieved surface pressures to those retrieved from co-located GOME Oxygen A band measurements. This implies a calibration error in the SCIAMACHY measurements. By adding an offset of 1% of the continuum reflectance at 756nm to the SCIAMACHY reflectance measurements, this systematic bias vanishes.
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Lin, J. T., R. V. Martin, K. F. Boersma, M. Sneep, P. Stammes, R. Spurr, P. Wang, M. Van Roozendael, K. Clémer, and H. Irie. "Retrieving tropospheric nitrogen dioxide from the Ozone Monitoring Instrument: effects of aerosols, surface reflectance anisotropy, and vertical profile of nitrogen dioxide." Atmospheric Chemistry and Physics 14, no. 3 (February 7, 2014): 1441–61. http://dx.doi.org/10.5194/acp-14-1441-2014.
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Abstract. Retrievals of tropospheric nitrogen dioxide (NO2) from the Ozone Monitoring Instrument (OMI) are subject to errors in the treatments of aerosols, surface reflectance anisotropy, and vertical profile of NO2. Here we quantify the influences over China via an improved retrieval process. We explicitly account for aerosol optical effects (simulated by nested GEOS-Chem at 0.667° long. × 0.5° lat. and constrained by aerosol measurements), surface reflectance anisotropy, and high-resolution vertical profiles of NO2 (simulated by GEOS-Chem). Prior to the NO2 retrieval, we derive the cloud information using consistent ancillary assumptions. We compare our retrieval to the widely used DOMINO v2 product, using MAX-DOAS measurements at three urban/suburban sites in East China as reference and focusing the analysis on the 127 OMI pixels (in 30 days) closest to the MAX-DOAS sites. We find that our retrieval reduces the interference of aerosols on the retrieved cloud properties, thus enhancing the number of valid OMI pixels by about 25%. Compared to DOMINO v2, our retrieval better captures the day-to-day variability in MAX-DOAS NO2 data (R2 = 0.96 versus 0.72), due to pixel-specific radiative transfer calculations rather than the use of a look-up table, explicit inclusion of aerosols, and consideration of surface reflectance anisotropy. Our retrieved NO2 columns are 54% of the MAX-DOAS data on average, reflecting the inevitable spatial inconsistency between the two types of measurement, errors in MAX-DOAS data, and uncertainties in our OMI retrieval related to aerosols and vertical profile of NO2. Sensitivity tests show that excluding aerosol optical effects can either increase or decrease the retrieved NO2 for individual OMI pixels with an average increase by 14%. Excluding aerosols also complexly affects the retrievals of cloud fraction and particularly cloud pressure. Employing various surface albedo data sets slightly affects the retrieved NO2 on average (within 10%). The retrieved NO2 columns increase when the NO2 profiles are taken from MAX-DOAS retrievals (by 19% on average) or TM4 simulations (by 13%) instead of GEOS-Chem simulations. Our findings are also relevant to retrievals of other pollutants (e.g., sulfur dioxide, ormaldehyde, glyoxal) from UV–visible backscatter satellite instruments.
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van Diedenhoven, B., O. P. Hasekamp, and I. Aben. "Surface pressure retrieval from SCIAMACHY measurements in the O<sub>2</sub> A Band: validation of the measurements and sensitivity on aerosols." Atmospheric Chemistry and Physics 5, no. 8 (August 11, 2005): 2109–20. http://dx.doi.org/10.5194/acp-5-2109-2005.
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Abstract. We perform surface pressure retrievals from cloud-free Oxygen A band measurements of SCIAMACHY. These retrievals can be well validated because surface pressure is a quantity that is, in general, accurately known from meteorological models. Therefore, surface pressure retrievals and their validation provide important insight into the quality of the instrument calibration. Furthermore, they can provide insight into retrievals which are affected by similar radiation transport processes, for example the retrieval of total columns of H2O, CO, CO2 and CH4. In our retrieval aerosols are neglected. Using synthetic measurements, it is shown that for low to moderate surface albedos this leads to an underestimation of the retrieved surface pressures. For high surface albedos this generally leads to an overestimation of the retrieved surface pressures. The surface pressures retrieved from the SCIAMACHY measurements indeed show this dependence on surface albedo, when compared to the corresponding pressures from a meteorological database. However, an offset of about 20 hPa was found, which can not be caused by neglecting aerosols in the retrieval. The same offset was found when comparing the retrieved surface pressures to those retrieved from co-located GOME Oxygen A band measurements. This implies a calibration error in the SCIAMACHY measurements. By adding an offset of 0.86% of the continuum reflectance at 756 nm to the SCIAMACHY reflectance measurements, this systematic bias vanishes.
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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, and E. J. Llewellyn. "Retrieval of vertical concentration profiles from OSIRIS UVvisible limb spectra." Canadian Journal of Physics 80, no. 4 (March 1, 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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Lin, J. T., R. V. Martin, K. F. Boersma, M. Sneep, P. Stammes, R. Spurr, P. Wang, M. Van Roozendael, K. Clémer, and H. Irie. "Retrieving tropospheric nitrogen dioxide over China from the Ozone Monitoring Instrument: effects of aerosols, surface reflectance anisotropy and vertical profile of nitrogen dioxide." Atmospheric Chemistry and Physics Discussions 13, no. 8 (August 14, 2013): 21203–57. http://dx.doi.org/10.5194/acpd-13-21203-2013.
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Abstract. Retrievals of tropospheric nitrogen dioxide (NO2) from the Ozone Monitoring Instrument (OMI) are subject to errors in the treatments of aerosols, surface reflectance anisotropy, and vertical profile of NO2. Here we quantify the influences over China via an improved retrieval process. We explicitly account for aerosol optical effects (simulated by nested GEOS-Chem at 0.667° lon × 0.5° lat and constrained by aerosol measurements), surface reflectance anisotropy, and high-resolution vertical profiles of NO2 (simulated by GEOS-Chem). Prior to the NO2 retrieval, we derive the cloud information using consistent ancillary assumptions. We compare our retrieval to the widely used DOMINO v2 product, using as reference MAX-DOAS measurements at three urban/suburban sites in East China and focusing the analysis on the 127 OMI pixels (in 30 days) closest to the MAX-DOAS sites. We find that our retrieval reduces the interference of aerosols on the retrieved cloud properties, thus enhancing the number of valid OMI pixels by about 25%. Compared to DOMINO v2, our retrieval improves the correlation with the MAX-DOAS data in the day-to-day variability of NO2 (R2 = 0.96 vs. 0.72). Our retrieved NO2 columns are about 50% of the MAX-DOAS data on average. This reflects the inevitable spatial inconsistency between the two types of measurement, uncertainties in MAX-DOAS data, and residual uncertainties in our OMI retrievals related to aerosols and vertical profile of NO2. Through a series of tests, we find that excluding aerosol scattering/absorption can either increase or decrease the retrieved NO2, with a mean absolute difference by about 20%. Concentrating aerosols at the boundary layer top enhances the retrieved NO2 by 8% on average with a mean absolute difference by 23%. The aerosol perturbations also affect nonlinearly the retrieved cloud fraction and particularly cloud pressure. Employing various surface albedo datasets alters the retrieved NO2 by 0–7% on average. The retrieved NO2 columns increase when the NO2 profiles are taken from MAX-DOAS retrievals (by 20% on average) or TM4 simulations (by 10%) instead of GEOS-Chem simulations. Our findings are also relevant to retrievals of other pollutants (e.g., sulfur dioxide, formaldehyde, glyoxal) from UV-vis backscatter satellite instruments.
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Kwon, Eun-Han, B. J. Sohn, William L. Smith, and Jun Li. "Validating IASI Temperature and Moisture Sounding Retrievals over East Asia Using Radiosonde Observations." Journal of Atmospheric and Oceanic Technology 29, no. 9 (September 1, 2012): 1250–62. http://dx.doi.org/10.1175/jtech-d-11-00078.1.
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Abstract Temperature and moisture profiles retrieved from Infrared Atmospheric Sounding Interferometer (IASI) data are evaluated using collocated radiosonde data from September 2008 to August 2009 over East Asia. The level-2 products used in this study were provided by the National Oceanic and Atmospheric Administration/National Environmental Satellite, Data, and Information Service. By using radiosonde observations as a reference, the bias and root-mean-square error (RMSE) of the temperature and water vapor profiles are obtained to examine the performance of the IASI retrievals depending on surface types and the degree of atmospheric moisture. Overall, retrievals over the land or under drier atmospheric conditions show degraded performance for both the temperature and the moisture, especially for the boundary layer temperature. It is further shown that the vertical distributions of the RMSEs and the biases of the IASI retrievals resemble the variability pattern of the radiosonde observations from the mean profiles. These retrieval aspects are thought to be related to the surface emissivity effect on the IASI retrieval and the difficulties of accounting for large atmospheric variability in the retrieval process. Although the retrieval performance appears to degrade under cloudy conditions, cloudy- and clear-sky retrievals show similar statistical behaviors varying with surface type and atmospheric moisture. Furthermore, the similar statistical behaviors between first guess and final retrievals suggest that error characteristics inherent to the first guess were not sufficiently resolved by the physical retrieval process, leaving a need to improve the first guess for the better retrieval.
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Li, Dingdong, Yonghua Wu, Barry Gross, and Fred Moshary. "Capabilities of an Automatic Lidar Ceilometer to Retrieve Aerosol Characteristics within the Planetary Boundary Layer." Remote Sensing 13, no. 18 (September 11, 2021): 3626. http://dx.doi.org/10.3390/rs13183626.
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Continuous observation and quantitative retrieval of aerosol backscatter coefficients are important in the study of air quality and climate in metropolitan areas such as New York City. Ceilometers are ideal for this application, but aerosol backscatter coefficient retrievals from ceilometers are challenging and require proper calibration. In this study, we calibrate the ceilometer (Lufft CHM15k, 1064 nm) system constant with the molecular backscatter coefficient and evaluate the calibrated profiles with other independent methods, including the water-phase cloud method and comparison with the NASA Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) attenuated backscatter coefficient profile. Multiple-day calibration results show a stable system constant with a relative uncertainty of about 7%. We also evaluate the overlap correction for the CHM15k ceilometer (provided by Lufft) with a Vaisala CL-31 ceilometer, and the results show good consistency between two ceilometers’ range-corrected signal (RCS) profiles above 200 m. Next, we implement a forward iterative method to retrieve aerosol backscatter coefficients from continuous ceilometer measurements. In the retrieval, the lidar ratio is constrained by the co-located NASA AERONET radiometer aerosol optical depth (AOD) retrieval and agrees with the AERONET lidar-ratio products, derived from aerosol microphysical parameters. The aerosol backscatter coefficient retrievals are validated with co-located elastic-Raman lidar retrievals and indicate a good correlation (R2≥0.95) in the planetary boundary layer (PBL). Furthermore, a case study shows that the ceilometer retrieved aerosol extinction coefficient profiles can be used to estimate the AOD of the PBL and the aloft plumes. Finally, simulations of the uncertainty of aerosol backscatter coefficient retrieval show that a calibration error of 10% results in 10–20% of relative error in the aerosol backscatter coefficient retrievals, while relative error caused by a lidar-ratio error of 10% is less than 4% in the PBL.
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Zheng, Jianyu, Zhibo Zhang, Hongbin Yu, Anne Garnier, Qianqian Song, Chenxi Wang, Claudia Di Biagio, Jasper F. Kok, Yevgeny Derimian, and Claire Ryder. "Thermal infrared dust optical depth and coarse-mode effective diameter over oceans retrieved from collocated MODIS and CALIOP observations." Atmospheric Chemistry and Physics 23, no. 14 (July 25, 2023): 8271–304. http://dx.doi.org/10.5194/acp-23-8271-2023.
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Abstract. In this study, we developed a novel algorithm based on the collocated Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared (TIR) observations and dust vertical profiles from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) to simultaneously retrieve dust aerosol optical depth at 10 µm (DAOD10 µm) and the coarse-mode dust effective diameter (Deff) over global oceans. The accuracy of the Deff retrieval is assessed by comparing the dust lognormal volume particle size distribution (PSD) corresponding to retrieved Deff with the in situ-measured dust PSDs from the AERosol Properties – Dust (AER-D), Saharan Mineral Dust Experiment (SAMUM-2), and Saharan Aerosol Long-Range Transport and Aerosol–Cloud-Interaction Experiment (SALTRACE) field campaigns through case studies. The new DAOD10 µm retrievals were evaluated first through comparisons with the collocated DAOD10.6 µm retrieved from the combined Imaging Infrared Radiometer (IIR) and CALIOP observations from our previous study (Zheng et al., 2022). The pixel-to-pixel comparison of the two DAOD retrievals indicates a good agreement (R∼0.7) and a significant reduction in (∼50 %) retrieval uncertainties largely thanks to the better constraint on dust size. In a climatological comparison, the seasonal and regional (2∘×5∘) mean DAOD10 µm retrievals based on our combined MODIS and CALIOP method are in good agreement with the two independent Infrared Atmospheric Sounding Interferometer (IASI) products over three dust transport regions (i.e., North Atlantic (NA; R=0.9), Indian Ocean (IO; R=0.8) and North Pacific (NP; R=0.7)). Using the new retrievals from 2013 to 2017, we performed a climatological analysis of coarse-mode dust Deff over global oceans. We found that dust Deff over IO and NP is up to 20 % smaller than that over NA. Over NA in summer, we found a ∼50 % reduction in the number of retrievals with Deff>5 µm from 15 to 35∘ W and a stable trend of Deff average at 4.4 µm from 35∘ W throughout the Caribbean Sea (90∘ W). Over NP in spring, only ∼5 % of retrieved pixels with Deff>5 µm are found from 150 to 180∘ E, while the mean Deff remains stable at 4.0 µm throughout eastern NP. To the best of our knowledge, this study is the first to retrieve both DAOD and coarse-mode dust particle size over global oceans for multiple years. This retrieval dataset provides insightful information for evaluating dust longwave radiative effects and coarse-mode dust particle size in models.
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Cha, Ting-Yu, and Michael M. Bell. "Comparison of single-Doppler and multiple-Doppler wind retrievals in Hurricane Matthew (2016)." Atmospheric Measurement Techniques 14, no. 5 (May 18, 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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Shi, Chong, Makiko Hashimoto, and Teruyuki Nakajima. "Remote sensing of aerosol properties from multi-wavelength and multi-pixel information over the ocean." Atmospheric Chemistry and Physics 19, no. 4 (February 26, 2019): 2461–75. http://dx.doi.org/10.5194/acp-19-2461-2019.
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Abstract. In this study, we investigate the feasibility of a multi-pixel scheme in the inversion of aerosol optical properties for multispectral satellite instruments over the ocean. Different from the traditional satellite aerosol retrievals conducted pixel by pixel, we derive the aerosol optical thickness (AOT) of multiple pixels simultaneously by adding a smoothness constraint on the spatial variation of aerosols and oceanic substances, which helps the satellite retrieval, with higher consistency from pixel to pixel. Simulations are performed for two representative oceanic circumstances, open and coastal waters, as well as the land–ocean interface region. We retrieve the AOT for fine, sea spray, and dust aerosols simultaneously using synthetic spectral measurements, which are from the Greenhouse Gases Observing Satellite and Thermal and Near Infrared Sensor for Carbon Observation – Cloud and Aerosol Imager (GOSAT∕TANSO-CAI), with four wavelengths ranging from the ultraviolet to shortwave infrared bands. The forward radiation calculation is performed by a coupled atmosphere–ocean radiative transfer model combined with a three-component bio-optical oceanic module, where the chlorophyll a concentration, sediment, and colored dissolved organic matter are considered. Results show that accuracies of the derived AOT and spectral remote-sensing reflectance are both improved by applying smoothness constraints on the spatial variation of aerosol and oceanic substances in homogeneous or inhomogeneous surface conditions. The multi-pixel scheme can be effective in compensating for the retrieval biases induced by measurement errors and improving the retrieval sensitivity, particularly for the fine aerosols over the coastal water. We then apply the algorithm to derive AOTs using real satellite measurements. Results indicate that the multi-pixel method helps to polish the irregular retrieved results of the satellite imagery and is potentially promising for the aerosol retrieval over highly turbid waters by benefiting from the coincident retrieval of neighboring pixels. A comparison of retrieved AOTs from satellite measurements with those from the Aerosol Robotic Network (AERONET) also indicates that retrievals conducted by the multi-pixel scheme are more consistent with the AERONET observations.
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Lu, Shijun, Ruru Deng, Yeheng Liang, Longhai Xiong, Xianjun Ai, and Yan Qin. "Remote Sensing Retrieval of Total Phosphorus in the Pearl River Channels Based on the GF-1 Remote Sensing Data." Remote Sensing 12, no. 9 (April 30, 2020): 1420. http://dx.doi.org/10.3390/rs12091420.
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Total phosphorus (TP) concentration is one of the indicators for surface water quality evaluation. In this study, an indirect algorithm was proposed to retrieve TP concentration. This algorithm retrieves the TP concentration in urban waters based on Gaofen-1 (GF-1) remote sensing data. The algorithm uses the correlation between remote-sensing reflectance, optically significant constituents of water (chlorophyll, suspended sediment, and organic matter (excluding algae)), and TP to establish a retrieval model. First, the concentrations of optically active components are retrieved using a semi-analytical model. Second, the correlation between TP and optically active components is used to retrieve the TP concentration in waters. The GF-1 remote sensing data for 7 August 2015 were used to perform remote sensing retrieval of TP concentration in the Pearl River channels in Guangzhou, China. The results show that the TP concentration in most areas of the Front Channel, Western Channel, Guangzhou Channel, and the western part of the Back Channel was higher than 0.2 mg/L, while the TP concentration in the middle and eastern parts of the Back Channel was generally lower than 0.2 mg/L. The mean absolute percentage error of the retrieval is 24.18%. The experimental results show that the model is suitable for remote sensing retrieval of TP in urban waters in Guangzhou.
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Dubovik, O., M. Herman, A. Holdak, T. Lapyonok, D. Tanré, J. L. Deuzé, F. Ducos, A. Sinyuk, and A. Lopatin. "Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations." Atmospheric Measurement Techniques Discussions 3, no. 6 (November 16, 2010): 4967–5077. http://dx.doi.org/10.5194/amtd-3-4967-2010.
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Abstract. The proposed development is an attempt to enhance aerosol retrieval by emphasizing statistical optimization in inversion of advanced satellite observations. This optimization concept improves retrieval accuracy relying on the knowledge of measurement error distribution. Efficient application of such optimization requires pronounced data redundancy (excess of the measurements number over number of unknowns) that is not common in satellite observations. The POLDER imager on board of the PARASOL micro-satellite registers spectral polarimetric characteristics of the reflected atmospheric radiation at up to 16 viewing directions over each observed pixel. The completeness of such observations is notably higher than for most currently operating passive satellite aerosol sensors. This provides an opportunity for profound utilization of statistical optimization principles in satellite data inversion. The proposed retrieval scheme is designed as statistically optimized multi-variable fitting of the all available angular observations of total and polarized radiances obtained by POLDER sensor in the window spectral channels where absorption by gaseous is minimal. The total number of such observations by PARASOL always exceeds a hundred over each pixel and the statistical optimization concept promises to be efficient even if the algorithm retrieves several tens of aerosol parameters. Based on this idea, the proposed algorithm uses a large number of unknowns and is aimed on retrieval of extended set of parameters affecting measured radiation. The algorithm is designed to retrieve complete aerosol properties globally. Over land, the algorithm retrieves the parameters of underlying surface simultaneously with aerosol. In all situations, the approach is anticipated to achieve a robust retrieval of complete aerosol properties including information about aerosol particle sizes, shape, absorption and composition (refractive index). In order to achieve reliable retrieval from PARASOL observations even over very reflective desert surfaces, the algorithm was designed as simultaneous inversion of a large group of pixels within one or several images. Such, multi-pixel retrieval regime takes advantage from known limitations on spatial and temporal variability in both aerosol and surface properties. Specifically the variations of the retrieved parameters horizontally from pixel-to-pixel and/or temporary from day-to-day are enforced to be smooth by additional appropriately set a priori constraints. This concept is expected to provide satellite retrieval of higher consistency, because the retrieval over each single pixel will be benefiting from co-incident aerosol information from neighboring pixels, as well, from the information about surface reflectance (over land) obtained in preceding and consequent observations over the same pixel. The paper provides in depth description of the proposed inversion concept, illustrates the algorithm performance by a series of numerical tests and presents the examples of preliminary retrieval results obtained from actual PARASOL observations. It is should be noted that many aspects of the described algorithm design considerably benefited from experience accumulated in the preceding effort on developments of currently operating AERONET and PARASOL retrievals, as well as, several core software components were inherited from those earlier algorithms.
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Dubovik, O., M. Herman, A. Holdak, T. Lapyonok, D. Tanré, J. L. Deuzé, F. Ducos, A. Sinyuk, and A. Lopatin. "Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations." Atmospheric Measurement Techniques 4, no. 5 (May 31, 2011): 975–1018. http://dx.doi.org/10.5194/amt-4-975-2011.
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Abstract. The proposed development is an attempt to enhance aerosol retrieval by emphasizing statistical optimization in inversion of advanced satellite observations. This optimization concept improves retrieval accuracy relying on the knowledge of measurement error distribution. Efficient application of such optimization requires pronounced data redundancy (excess of the measurements number over number of unknowns) that is not common in satellite observations. The POLDER imager on board the PARASOL micro-satellite registers spectral polarimetric characteristics of the reflected atmospheric radiation at up to 16 viewing directions over each observed pixel. The completeness of such observations is notably higher than for most currently operating passive satellite aerosol sensors. This provides an opportunity for profound utilization of statistical optimization principles in satellite data inversion. The proposed retrieval scheme is designed as statistically optimized multi-variable fitting of all available angular observations obtained by the POLDER sensor in the window spectral channels where absorption by gas is minimal. The total number of such observations by PARASOL always exceeds a hundred over each pixel and the statistical optimization concept promises to be efficient even if the algorithm retrieves several tens of aerosol parameters. Based on this idea, the proposed algorithm uses a large number of unknowns and is aimed at retrieval of extended set of parameters affecting measured radiation. The algorithm is designed to retrieve complete aerosol properties globally. Over land, the algorithm retrieves the parameters of underlying surface simultaneously with aerosol. In all situations, the approach is anticipated to achieve a robust retrieval of complete aerosol properties including information about aerosol particle sizes, shape, absorption and composition (refractive index). In order to achieve reliable retrieval from PARASOL observations even over very reflective desert surfaces, the algorithm was designed as simultaneous inversion of a large group of pixels within one or several images. Such multi-pixel retrieval regime takes advantage of known limitations on spatial and temporal variability in both aerosol and surface properties. Specifically the variations of the retrieved parameters horizontally from pixel-to-pixel and/or temporary from day-to-day are enforced to be smooth by additional a priori constraints. This concept is expected to provide satellite retrieval of higher consistency, because the retrieval over each single pixel will be benefiting from coincident aerosol information from neighboring pixels, as well, from the information about surface reflectance (over land) obtained in preceding and consequent observations over the same pixel. The paper provides in depth description of the proposed inversion concept, illustrates the algorithm performance by a series of numerical tests and presents the examples of preliminary retrieval results obtained from actual PARASOL observations. It should be noted that many aspects of the described algorithm design considerably benefited from experience accumulated in the preceding effort on developments of currently operating AERONET and PARASOL retrievals, as well as several core software components were inherited from those earlier algorithms.
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Duncan, David Ian, Patrick Eriksson, and Simon Pfreundschuh. "An experimental 2D-Var retrieval using AMSR2." Atmospheric Measurement Techniques 12, no. 12 (December 3, 2019): 6341–59. http://dx.doi.org/10.5194/amt-12-6341-2019.
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Abstract. A two-dimensional variational retrieval (2D-Var) is presented for a passive microwave imager. The overlapping antenna patterns of all frequencies from the Advanced Microwave Scanning Radiometer 2 (AMSR2) are explicitly simulated to attempt retrieval of near-surface wind speed and surface skin temperature at finer spatial scales than individual antenna beams. This is achieved, with the effective spatial resolution of retrieved parameters judged by analysis of 2D-Var averaging kernels. Sea surface temperature retrievals achieve about 30 km resolution, with wind speed retrievals at about 10 km resolution. It is argued that multi-dimensional optimal estimation permits greater use of total information content from microwave sensors than other methods, with no compromises on target resolution needed; instead, various targets are retrieved at the highest possible spatial resolution, driven by the channels' sensitivities. All AMSR2 channels can be simulated within near their published noise characteristics for observed clear-sky scenes, though calibration and emissivity model errors are key challenges. This experimental retrieval shows the feasibility of 2D-Var for cloud-free retrievals and opens the possibility of stand-alone 3D-Var retrievals of water vapour and hydrometeor fields from microwave imagers in the future. The results have implications for future satellite missions and sensor design, as spatial oversampling can somewhat mitigate the need for larger antennas in the push for higher spatial resolution.
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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, and Brian J. Connor. "Retrieval of atmospheric CO<sub>2</sub> vertical profiles from ground-based near-infrared spectra." Atmospheric Measurement Techniques 14, no. 4 (April 28, 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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Torres, Benjamin, and David Fuertes. "Characterization of aerosol size properties from measurements of spectral optical depth: a global validation of the GRASP-AOD code using long-term AERONET data." Atmospheric Measurement Techniques 14, no. 6 (June 17, 2021): 4471–506. http://dx.doi.org/10.5194/amt-14-4471-2021.
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Abstract. A validation study is conducted regarding aerosol optical size property retrievals from measurements of the direct sun beam only (without the aid of diffuse radiation). The study focuses on using real data to test the new GRASP-AOD application, which uses only spectral optical depth measurements to retrieve the total column aerosol size distributions, assumed to be bimodal lognormal. In addition, a set of secondary integral parameters of aerosol size distribution and optical properties are provided: effective radius, total volume concentration and fine-mode fraction of aerosol optical depth (AOD). The GRASP-AOD code is applied to almost 3 million observations acquired over 20 years (1997–2016) at 30 AERONET (Aerosol Robotic Network) sites. These validation sites have been selected based on known availability of an extensive data record, significant aerosol load variability throughout the year, wide worldwide coverage and diverse aerosol types and source regions. The output parameters are compared to those coming from the operational AERONET retrievals. The retrieved fine-mode fractions at 500 nm (τf(500)) obtained by the GRASP-AOD application are compared to those retrieved by the spectral deconvolution algorithm (SDA) and by the AERONET aerosol retrieval algorithm. The size distribution properties obtained by the GRASP-AOD are compared to their equivalent values from the AERONET aerosol retrieval algorithm. The analysis showed the convincing capacity of the GRASP-AOD approach to successfully discriminate between fine- and coarse-mode extinction to robustly retrieve τf(500). The comparisons of 2 million results of τf(500) retrieval by the GRASP-AOD and SDA showed high correlation with a root mean square error (RMSE) of 0.015. Also, the analysis showed that the τf(500) values computed by the AERONET aerosol retrieval algorithm agree slightly better with the GRASP-AOD (RMSE = 0.018, from 148 526 comparisons) than with the SDA (RMSE = 0.022, from 127 203 comparisons). The comparisons of the size distribution retrieval showed agreement for the fine-mode median radius between the GRASP-AOD and AERONET aerosol retrieval algorithm results with an RMSE of 0.032 µm (or 18.7 % in relative terms) for the situations when τ(440)>0.2 occur for more than 80 000 pairs of the study. For the cases where the fine mode is dominant (i.e., α>1.2), the RMSE is only of 0.023 µm (or 13.9 % in relative terms). Major limitations in the retrieval were found for the characterization of the coarse-mode details. For example, the analysis revealed that the GRASP-AOD retrieval is not sensitive to the small variations of the coarse-mode volume median radius for different aerosol types observed at different locations. Nonetheless the GRASP-AOD retrieval provides reasonable agreement with the AERONET aerosol retrieval algorithm for overall coarse-mode properties with with RMSE = 0.500 µm (RMSRE = 20 %) when τ(440)>0.2. The values of effective radius and total volume concentration computed from the GRASP-AOD retrieval have been compared to those estimated by the AERONET aerosol retrieval algorithm. The RMSE values of the correlations were 30 % for the effective radius and 25 % for the total volume concentration when τ(440)>0.2. Finally, the study discusses the importance of employing the assumption of bimodal lognormal size distribution. It also evaluates the potential of using ancillary data, in particular aureole measurements, for improving the characterization of the aerosol coarse-mode properties.
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Herrera, Milagros E., Oleg Dubovik, Benjamin Torres, Tatyana Lapyonok, David Fuertes, Anton Lopatin, Pavel Litvinov, et al. "Estimates of remote sensing retrieval errors by the GRASP algorithm: application to ground-based observations, concept and validation." Atmospheric Measurement Techniques 15, no. 20 (October 21, 2022): 6075–126. http://dx.doi.org/10.5194/amt-15-6075-2022.
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Abstract. Understanding the uncertainties in the retrieval of aerosol and surface properties is very important for an adequate characterization of the processes that occur in the atmosphere. However, the reliable characterization of the error budget of the retrieval products is a very challenging aspect that currently remains not fully resolved in most remote sensing approaches. The level of uncertainties for the majority of the remote sensing products relies mostly on post-processing validations and intercomparisons with other data, while the dynamic errors are rarely provided. Therefore, implementations of fundamental approaches for generating dynamic retrieval errors and the evaluation of their practical efficiency remains of high importance. This study describes and analyses the dynamic estimates of uncertainties in aerosol-retrieved properties by the GRASP (Generalized Retrieval of Atmosphere and Surface Properties) algorithm. The GRASP inversion algorithm, described by Dubovik et al. (2011, 2014, 2021), is designed based on the concept of statistical optimization and provides dynamic error estimates for all retrieved aerosol and surface properties. The approach takes into account the effect of both random and systematic uncertainties propagations. The algorithm provides error estimates both for directly retrieved parameters included in the retrieval state vector and for the characteristics derived from these parameters. For example, in the case of the aerosol properties, GRASP directly retrieves the size distribution and the refractive index that are used afterwards to provide phase function, scattering, extinction, single scattering albedo, etc. Moreover, the GRASP algorithm provides full covariance matrices, i.e. not only variances of the retrieval errors but also correlations coefficients of these errors. The analysis of the correlation matrix structure can be very useful for identifying less than obvious retrieval tendencies. This appears to be a useful approach for optimizing observation schemes and retrieval set-ups. In this study, we analyse the efficiency of the GRASP error estimation approach for applications to ground-based observations by a sun/sky photometer and lidar. Specifically, diverse aspects of the error generations and their evaluations are discussed and illustrated. The studies rely on a series of comprehensive sensitivity tests when simulated sun/sky photometer measurements and lidar data are perturbed by random and systematic errors and inverted. Then, the results of the retrievals and their error estimations are analysed and evaluated. The tests are conducted for different observations of diverse aerosol types, including biomass burning, urban, dust and their mixtures. The study considers observations of AErosol RObotic NETwork (AERONET) sun/sky photometer measurements at 440, 675, 870 and 1020 nm and multiwavelength elastic lidar measurements at 355, 532 and 1064 nm. The sun/sky photometer data are inverted alone or together with lidar data. The analysis shows overall successful retrievals and error estimations for different aerosol characteristics, including aerosol size distribution, complex refractive index, single scattering albedo, lidar ratios, aerosol vertical profiles, etc. Also, the main observed tendencies in the error dynamic agree with known retrieval experience. For example, the main accuracy limitations for retrievals of all aerosol types relate to the situations with low optical depth. Also, in situations with multicomponent aerosol mixtures, the reliable characterization of each component is possible only in limited situations, for example, from radiometric data obtained for low solar zenith angle observations or from a combination of radiometric and lidar data. At the same time, the total optical properties of aerosol mixtures are always retrieved satisfactorily. In addition, the study includes an analysis of the detailed structure of the correlation matrices for the retrieval errors in mono- and multicomponent aerosols. The conducted analysis of error correlation appears to be a useful approach for optimizing observation schemes and retrieval set-ups. The application of the approach to real data is provided.
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Holz, Robert E., Steve Ackerman, Paolo Antonelli, Fred Nagle, Robert O. Knuteson, Matthew McGill, Dennis L. Hlavka, and William D. Hart. "An Improvement to the High-Spectral-Resolution CO2-Slicing Cloud-Top Altitude Retrieval." Journal of Atmospheric and Oceanic Technology 23, no. 5 (May 1, 2006): 653–70. http://dx.doi.org/10.1175/jtech1877.1.
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Abstract An improvement to high-spectral-resolution infrared cloud-top altitude retrievals is compared to existing retrieval methods and cloud lidar measurements. The new method, CO2 sorting, determines optimal channel pairs to which the CO2 slicing retrieval will be applied. The new retrieval is applied to aircraft Scanning High-Resolution Interferometer Sounder (S-HIS) measurements. The results are compared to existing passive retrieval methods and coincident Cloud Physics Lidar (CPL) measurements. It is demonstrated that when CO2 sorting is used to select channel pairs for CO2 slicing there is an improvement in the retrieved cloud heights when compared to the CPL for the optically thin clouds (total optical depths less than 1.0). For geometrically thick but tenuous clouds, the infrared retrieved cloud tops underestimated the cloud height, when compared to those of the CPL, by greater than 2.5 km. For these cases the cloud heights retrieved by the S-HIS correlated closely with the level at which the CPL-integrated cloud optical depth was approximately 1.0.
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Benavent-Oltra, Jose A., Roberto Román, María J. Granados-Muñoz, Daniel Pérez-Ramírez, Pablo Ortiz-Amezcua, Cyrielle Denjean, Anton Lopatin, et al. "Comparative assessment of GRASP algorithm for a dust event over Granada (Spain) during ChArMEx-ADRIMED 2013 campaign." Atmospheric Measurement Techniques 10, no. 11 (November 17, 2017): 4439–57. http://dx.doi.org/10.5194/amt-10-4439-2017.
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Abstract. In this study, vertical profiles and column-integrated aerosol properties retrieved by the GRASP (Generalized Retrieval of Atmosphere and Surface Properties) algorithm are evaluated with in situ airborne measurements made during the ChArMEx-ADRIMED field campaign in summer 2013. In the framework of this campaign, two different flights took place over Granada (Spain) during a desert dust episode on 16 and 17 June. The GRASP algorithm, which combines lidar and sun–sky photometer data measured at Granada, was used to retrieve aerosol properties. Two sun-photometer datasets are used: one co-located with the lidar system and the other in the Cerro Poyos station, approximately 1200 m higher than the lidar system but at a short horizontal distance. Column-integrated aerosol microphysical properties retrieved by GRASP are compared with AERONET products showing a good agreement. Differences between GRASP retrievals and airborne extinction profiles are in the range of 15 to 30 %, depending on the instrument on board the aircraft used as reference. On 16 June, a case where the dust layer was coupled to the aerosol layer close to surface, the total volume concentration differences between in situ data and GRASP retrieval are 15 and 36 % for Granada and Cerro Poyos retrievals, respectively. In contrast, on 17 June the dust layer was decoupled from the aerosol layer close to the surface, and the differences are around 17 % for both retrievals. In general, all the discrepancies found are within the uncertainly limits, showing the robustness and reliability of the GRASP algorithm. However, the better agreement found for the Cerro Poyos retrieval with the aircraft data and the vertical homogeneity of certain properties retrieved with GRASP, such as the scattering Ångström exponent, for cases with aerosol layers characterized by different aerosol types, shows that uncertainties in the vertical distribution of the aerosol properties have to be considered. The comparison presented here between GRASP and other algorithms (i.e. AERONET and LIRIC) and with airborne in situ measurements shows the potential to retrieve the optical and microphysical profiles of the atmospheric aerosol properties. Also, the advantage of GRASP versus LIRIC is that GRASP does not assume the results of the AERONET inversion as a starting point.
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Löhnert, Ulrich, D. D. Turner, and 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, no. 5 (May 1, 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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Leinonen, Jussi, Matthew D. Lebsock, Simone Tanelli, Ousmane O. Sy, Brenda Dolan, Randy J. Chase, Joseph A. Finlon, Annakaisa von Lerber, and Dmitri Moisseev. "Retrieval of snowflake microphysical properties from multifrequency radar observations." Atmospheric Measurement Techniques 11, no. 10 (October 5, 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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LEE, SUH-YIN, and MAN-KWAN SHAN. "ACCESS METHODS OF IMAGE DATABASE." International Journal of Pattern Recognition and Artificial Intelligence 04, no. 01 (March 1990): 27–44. http://dx.doi.org/10.1142/s0218001490000034.
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The perception of spatial relationships among objects in a picture is one of the important selection criteria to discriminate and retrieve images in an image database system. The data structure called 2-D string, proposed by Chang et al., is adopted to represent the symbolic pictures. When there are a large number of images in the image database and each image contains many objects, the processing time for image retrievals is tremendous. It is essential to develop efficient access methods for these retrievals. In this paper, the efficient methods for retrieval by objects, retrieval by pairwise spatial relationships and retrieval by subpicture are proposed. All the methods are based on the superimposed coding technique.
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Gonzales, Eileen C., Ben Burningham, Jacqueline K. Faherty, Nikole K. Lewis, Channon Visscher, and Mark Marley. "A Comparative L-dwarf Sample Exploring the Interplay between Atmospheric Assumptions and Data Properties." Astrophysical Journal 938, no. 1 (October 1, 2022): 56. http://dx.doi.org/10.3847/1538-4357/ac8f2a.
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Abstract Comparisons of atmospheric retrievals can reveal powerful insights on the strengths and limitations of our data and modeling tools. In this paper, we examine a sample of five L dwarfs of similar effective temperature (T eff) or spectral type to compare their pressure–temperature (P-T) profiles. Additionally, we explore the impact of an object’s metallicity and the signal-to-noise ratio (S/N) of the observations on the parameters we can retrieve. We present the first atmospheric retrievals: 2MASS J15261405+2043414, 2MASS J05395200−0059019, 2MASS J15394189−0520428, and GD 165B increasing the small but growing number of L dwarfs retrieved. When compared to the atmospheric retrievals of SDSS J141624.08+134826.7, a low-metallicity d/sdL7 primary in a wide L+T binary, we find that similar T eff sources have similar P-T profiles with metallicity differences impacting the relative offset between their P-T profiles in the photosphere. We also find that for near-infrared spectra, when the S/N is ≳80 we are in a regime where model uncertainties dominate over data measurement uncertainties. As such, S/N does not play a role in the retrieval’s ability to distinguish between a cloud-free and cloudless model, but may impact the confidence of the retrieved parameters. Lastly, we also discuss how to break cloud model degeneracies and the impact of extraneous gases in a retrieval model.
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Karthikeyan, Lanka, Ming Pan, Dasika Nagesh Kumar, and Eric F. Wood. "Effect of Structural Uncertainty in Passive Microwave Soil Moisture Retrieval Algorithm." Sensors 20, no. 4 (February 24, 2020): 1225. http://dx.doi.org/10.3390/s20041225.
Full textAbstract:
Passive microwave sensors use a radiative transfer model (RTM) to retrieve soil moisture (SM) using brightness temperatures (TB) at low microwave frequencies. Vegetation optical depth (VOD) is a key input to the RTM. Retrieval algorithms can analytically invert the RTM using dual-polarized TB measurements to retrieve the VOD and SM concurrently. Algorithms in this regard typically use the τ-ω types of models, which consist of two third-order polynomial equations and, thus, can have multiple solutions. Through this work, we find that uncertainty occurs due to the structural indeterminacy that is inherent in all τ-ω types of models in passive microwave SM retrieval algorithms. In the process, a new analytical solution for concurrent VOD and SM retrieval is presented, along with two widely used existing analytical solutions. All three solutions are applied to a fixed framework of RTM to retrieve VOD and SM on a global scale, using X-band Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) TB data. Results indicate that, with structural uncertainty, there ensues a noticeable impact on the VOD and SM retrievals. In an era where the sensitivity of retrieval algorithms is still being researched, we believe the structural indeterminacy of RTM identified here would contribute to uncertainty in the soil moisture retrievals.