Relevant bibliographies by topics / Split-window algorithm

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Academic literature on the topic 'Split-window algorithm'

Author: Grafiati
Published: 15 March 2025

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Journal articles on the topic "Split-window algorithm"

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Pinker, Rachel T., Donglian Sun, Meng-Pai Hung, Chuan Li, and Jeffrey B. Basara. "Evaluation of Satellite Estimates of Land Surface Temperature from GOES over the United States." Journal of Applied Meteorology and Climatology 48, no. 1 (January 1, 2009): 167–80. http://dx.doi.org/10.1175/2008jamc1781.1.

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Abstract A comprehensive evaluation of split-window and triple-window algorithms to estimate land surface temperature (LST) from Geostationary Operational Environmental Satellites (GOES) that were previously described by Sun and Pinker is presented. The evaluation of the split-window algorithm is done against ground observations and against independently developed algorithms. The triple-window algorithm is evaluated only for nighttime against ground observations and against the Sun and Pinker split-window (SP-SW) algorithm. The ground observations used are from the Atmospheric Radiation Measurement Program (ARM) Central Facility, Southern Great Plains site (April 1997–March 1998); from five Surface Radiation Budget Network (SURFRAD) stations (1996–2000); and from the Oklahoma Mesonet. The independent algorithms used for comparison include the National Oceanic and Atmospheric Administration/National Environmental Satellite, Data and Information Service operational method and the following split-window algorithms: that of Price, that of Prata and Platt, two versions of that of Ulivieri, that of Vidal, two versions of that of Sobrino, that of Coll and others, the generalized split-window algorithm as described by Becker and Li and by Wan and Dozier, and the Becker and Li algorithm with water vapor correction. The evaluation against the ARM and SURFRAD observations indicates that the LST retrievals from the SP-SW algorithm are in closer agreement with the ground observations than are the other algorithms tested. When evaluated against observations from the Oklahoma Mesonet, the triple-window algorithm is found to perform better than the split-window algorithm during nighttime.
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Meng, Xiangchen, Jie Cheng, Shaohua Zhao, Sihan Liu, and Yunjun Yao. "Estimating Land Surface Temperature from Landsat-8 Data using the NOAA JPSS Enterprise Algorithm." Remote Sensing 11, no. 2 (January 15, 2019): 155. http://dx.doi.org/10.3390/rs11020155.

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Land surface temperature (LST) is one of the key parameters in hydrology, meteorology, and the surface energy balance. The National Oceanic and Atmospheric Administration (NOAA) Joint Polar Satellite System (JPSS) Enterprise algorithm is adapted to Landsat-8 data to obtain the estimate of LST. The coefficients of the Enterprise algorithm were obtained by linear regression using the analog data produced by comprehensive radiative transfer modeling. The performance of the Enterprise algorithm was first tested by simulation data and then validated by ground measurements. In addition, the accuracy of the Enterprise algorithm was compared to the generalized split-window algorithm and the split-window algorithm of Sobrino et al. (1996). The validation results indicate the Enterprise algorithm has a comparable accuracy to the other two split-window algorithms. The biases (root mean square errors) of the Enterprise algorithm were 1.38 (3.22), 1.01 (2.32), 1.99 (3.49), 2.53 (3.46), and −0.15 K (1.11 K) at the SURFRAD, HiWATER_A, HiWATER_B, HiWATER_C sites and BanGe site, respectively, whereas those values were 1.39 (3.20), 1.0 (2.30), 1.93 (3.48), 2.53 (3.35), and −0.35 K (1.16 K) for the generalized split-window algorithm, 1.45 (3.39), 1.08 (2.41), 2.16 (3.67), 2.52 (3.58), and 0.02 K (1.12 K) for the split-window algorithm of Sobrino, respectively. This study provides an alternative method to estimate LST from Landsat-8 data.
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Wang, Lijuan, Ni Guo, Wei Wang, and Hongchao Zuo. "Optimization of the Local Split-Window Algorithm for FY-4A Land Surface Temperature Retrieval." Remote Sensing 11, no. 17 (August 27, 2019): 2016. http://dx.doi.org/10.3390/rs11172016.

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FY-4A is a second generation of geostationary orbiting meteorological satellite, and the successful launch of FY-4A satellite provides a new opportunity to obtain diurnal variation of land surface temperature (LST). In this paper, different underlying surfaces-observed data were applied to evaluate the applicability of the local split-window algorithm for FY-4A, and the local split-window algorithm parameters were optimized by the artificial intelligent particle swarm optimization (PSO) algorithm to improve the accuracy of retrieved LST. Results show that the retrieved LST can efficiently reproduce the diurnal variation characteristics of LST. However, the estimated values deviate hugely from the observed values when the local split-window algorithms are directly used to process the FY-4A satellite data, and the root mean square errors (RMSEs) are approximately 6K. The accuracy of the retrieved LST cannot be effectively improved by merely modifying the emissivity-estimated model or optimizing the algorithm. Based on the measured emissivity, the RMSE of LST retrieved by the optimized local split-window algorithm is reduced to 3.45 K. The local split-window algorithm is a simple and easy retrieval approach that can quickly retrieve LST on a regional scale and promote the application of FY-4A satellite data in related fields.
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Vincent, R. F. "The Case for a Single Channel Composite Arctic Sea Surface Temperature Algorithm." Remote Sensing 11, no. 20 (October 16, 2019): 2393. http://dx.doi.org/10.3390/rs11202393.

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Surface temperatures derived from satellite thermal infrared (TIR) data are critical inputs for assessing climate change in polar environments. Sea and ice surface temperature (SST, IST) are commonly determined with split window algorithms that use the brightness temperature from the 11 μm channel (BT11) as the main estimator and the difference between BT11 and the 12 μm channel (BTD11–12) to correct for atmospheric water vapor absorption. An issue with this paradigm in the Arctic maritime environment is the occurrence of high BTD11–12 that is not indicative of atmospheric absorption of BT11 energy. The Composite Arctic Sea Surface Temperature Algorithm (CASSTA) considers three regimes based on BT11 pixel value: seawater, ice, and marginal ice zones. A single channel (BT11) estimator is used for SST and a split window algorithm for IST. Marginal ice zone temperature is determined with a weighted average between the SST and IST. This study replaces the CASSTA split window IST with a single channel (BT11) estimator to reduce errors associated with BTD11–12 in the split window algorithm. The single channel IST returned improved results in the CASSTA dataset with a mean average error for ice and marginal ice zones of 0.142 K and 0.128 K, respectively.
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Peng, Hong Chun, Hai Ying Li, and Hao Gao. "Study on Methods of Retrieval of Sea Surface Temperature by Using Remote Sensing Data." Advanced Materials Research 610-613 (December 2012): 3742–46. http://dx.doi.org/10.4028/www.scientific.net/amr.610-613.3742.

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This article taking coastal waters of Lianyungang as the research area, and by using MODIS images during 15 April and 1 May, 2012 as source data, and the results of sea surface temperature were extracted by band operation, and by using changes in the different time of SST, spatial variation and comparative analysis to verify the accuracy of the two algorithms. Both the two split-window algorithm can get the sea surface temperature of coastal waters of Lianyungang well, and the result was reliable, and the inversion precision of SST can meet the application requirements in the general ocean applications. Not enough is the two split-window algorithm only worked under clear sky conditions, and can not get the sea surface temperature where the sea under cloud; at the zone of connected to the land and sea, universal SST algorithm was not as good as the Qin Zhihao split-window algorithm, but it has a few parameters, and the advantages of easy operation process of the desired intermediate.
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Pérez-Planells, Lluís, Raquel Niclòs, Jesús Puchades, César Coll, Frank-M. Göttsche, José A. Valiente, Enric Valor, and Joan M. Galve. "Validation of Sentinel-3 SLSTR Land Surface Temperature Retrieved by the Operational Product and Comparison with Explicitly Emissivity-Dependent Algorithms." Remote Sensing 13, no. 11 (June 7, 2021): 2228. http://dx.doi.org/10.3390/rs13112228.

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Land surface temperature (LST) is an essential climate variable (ECV) for monitoring the Earth climate system. To ensure accurate retrieval from satellite data, it is important to validate satellite derived LSTs and ensure that they are within the required accuracy and precision thresholds. An emissivity-dependent split-window algorithm with viewing angle dependence and two dual-angle algorithms are proposed for the Sentinel-3 SLSTR sensor. Furthermore, these algorithms are validated together with the Sentinel-3 SLSTR operational LST product as well as several emissivity-dependent split-window algorithms with in-situ data from a rice paddy site. The LST retrieval algorithms were validated over three different land covers: flooded soil, bare soil, and full vegetation cover. Ground measurements were performed with a wide band thermal infrared radiometer at a permanent station. The coefficients of the proposed split-window algorithm were estimated using the Cloudless Land Atmosphere Radiosounding (CLAR) database: for the three surface types an overall systematic uncertainty (median) of −0.4 K and a precision (robust standard deviation) 1.1 K were obtained. For the Sentinel-3A SLSTR operational LST product, a systematic uncertainty of 1.3 K and a precision of 1.3 K were obtained. A first evaluation of the Sentinel-3B SLSTR operational LST product was also performed: systematic uncertainty was 1.5 K and precision 1.2 K. The results obtained over the three land covers found at the rice paddy site show that the emissivity-dependent split-window algorithms, i.e., the ones proposed here as well as previously proposed algorithms without angular dependence, provide more accurate and precise LSTs than the current version of the operational SLSTR product.
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Na, Yin Tai. "Study on the Application and Comparative Analysis of Land Surface Temperature Retrieval Method Based on Multi-Sensor Remote Sensing Data." Advanced Materials Research 1010-1012 (August 2014): 1276–79. http://dx.doi.org/10.4028/www.scientific.net/amr.1010-1012.1276.

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The three commonly used remote sensing land surface temperature retrieval methods are described, namely single-window algorithm, split window algorithm and multi-channel algorithm, which have their advantages and disadvantages. The land surface temperature (LST) of study area was retrieved with multi-source remote sensing data. LST of study area was retrieved with the split window algorithm on January 10, 2003 and November 19, 2003 which is comparatively analyzed with the LST result of ETM+data with the single-window algorithm and the LST result of ASTER data with multi channel algorithm in the same period. The results show that land surface temperature of different land features are significantly different, where the surface temperature of urban land is the highest, and that of rivers and lakes is the lowest, followed by woodland. It is concluded that the expansion of urban green space and protection of urban water can prevent or diminish the urban heat island.
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Gerace, Aaron, Tania Kleynhans, Rehman Eon, and Matthew Montanaro. "Towards an Operational, Split Window-Derived Surface Temperature Product for the Thermal Infrared Sensors Onboard Landsat 8 and 9." Remote Sensing 12, no. 2 (January 9, 2020): 224. http://dx.doi.org/10.3390/rs12020224.

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The split window technique has been used for over thirty years to derive surface temperatures of the Earth with image data collected from spaceborne sensors containing two thermal channels. The latest NASA/USGS Landsat satellites contain the Thermal Infrared Sensor (TIRS) instruments that acquire Earth data in two longwave infrared bands, as opposed to a single band with earlier Landsats. The United States Geological Survey (USGS) will soon begin releasing a surface temperature product for Landsats 4 through 8 based on the single spectral channel methodology. However, progress is being made toward developing and validating a more accurate and less computationally intensive surface temperature product based on the split window method for Landsat 8 and 9 datasets. This work presents the progress made towards developing an operational split window algorithm for TIRS. Specifically, details of how the generalized split window algorithm was tailored for the TIRS sensors are presented, along with geometric considerations that should be addressed to avoid spatial artifacts in the final surface temperature product. Validation studies indicate that the proposed algorithm is accurate to within 2 K when compared to land-based measurements and to within 1 K when compared to water-based measurements, highlighting the improved accuracy that may be achieved over the current single-channel methodology being used to derive surface temperature in the Landsat Collection 2 surface temperature product. Surface temperature products using the split window methodologies described here can be made available upon request for testing purposes.
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Sun, Donglian, Yunyue Yu, Li Fang, and Yuling Liu. "Toward an Operational Land Surface Temperature Algorithm for GOES." Journal of Applied Meteorology and Climatology 52, no. 9 (September 2013): 1974–86. http://dx.doi.org/10.1175/jamc-d-12-0132.1.

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AbstractFor most land surface temperature (LST) regression algorithms, a set of optimized coefficients is determined by manual separation of the different subdivisions of atmospheric and surface conditions. In this study, a machine-learning technique, the regression tree (RT) technique, is introduced with the aim of automatically finding these subranges and the thresholds for the stratification of regression coefficients. The use of RT techniques in LST retrieval has the potential to contribute to the determination of optimal regression relationships under different conditions. Because of the lack of split-window channels for the Geostationary Operational Environmental Satellite (GOES) M–Q series (GOES-12–GOES-15, plus GOES-Q), a dual-window LST algorithm was developed by combining the infrared 11-μm channel with the shortwave-infrared (SWIR) 3.9-μm channel, which presents lower atmospheric absorption than does the infrared split-window channels (11 and 12 μm). The RT technique was introduced to derive the regression models under different conditions. The algorithms were used to derive the LST product from GOES observations and were evaluated against the 2004 Surface Radiation budget network. The results indicate that the RT technique outperforms the traditional regression method.
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Heidinger, Andrew K., and Michael J. Pavolonis. "Gazing at Cirrus Clouds for 25 Years through a Split Window. Part I: Methodology." Journal of Applied Meteorology and Climatology 48, no. 6 (June 1, 2009): 1100–1116. http://dx.doi.org/10.1175/2008jamc1882.1.

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Abstract This paper demonstrates that the split-window approach for estimating cloud properties can improve upon the methods commonly used for generating cloud temperature and emissivity climatologies from satellite imagers. Because the split-window method provides cloud properties that are consistent for day and night, it is ideally suited for the generation of a cloud climatology from the Advanced Very High Resolution Radiometer (AVHRR), which provides sampling roughly four times per day. While the split-window approach is applicable to all clouds, this paper focuses on its application to cirrus (high semitransparent ice clouds), where this approach is most powerful. An optimal estimation framework is used to extract estimates of cloud temperature, cloud emissivity, and cloud microphysics from the AVHRR split-window observations. The performance of the split-window approach is illustrated through the diagnostic quantities generated by the optimal estimation approach. An objective assessment of the performance of the algorithm cloud products from the recently launched space lidar [Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation/Cloud-Aerosol Lidar with Orthogonal Polarization (CALIPSO/CALIOP)] is used to characterize the performance of the AVHRR results and also to provide the constraints needed for the optimal estimation approach.
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Dissertations / Theses on the topic "Split-window algorithm"

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Zhang, Shuting. "Angular effects of surface brightness temperature observed from Sentinel-3A/SLSTR data." Electronic Thesis or Diss., Strasbourg, 2024. http://www.theses.fr/2024STRAD055.

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Ce travail de thèse utilise les données TIR de SLSTR comme source principale pour extraire la température de brillance de la surface (SBT) en appliquant l’algorithme split-window, afin d’analyser l’effet angulaire sur la SBT. En se basant sur une base de données de simulation, une méthode d’extraction de la SBT a été développée et appliquée aux observations à double angle de SLSTR. L’étude a ensuite examiné l’amplitude et les caractéristiques des différences de SBT entre les vues nadir et obliques, en tenant compte de facteurs tels que l’occupation du sol /la couverture terrestre, la saison, la latitude et le climat. Enfin, l’outil GeoDetector a été utilisé pour effectuer une analyse d’attribution des effets angulaires sur la SBT
This study adopts SLSTR TIR data as the main data source and retrieves surface brightness temperature using split-window algorithm to analyze the angular effect of surface brightness temperature (SBT). Based on the simulation database, SBT retrieval method is developed and applied to SLSTR dual-angle SBT extraction. Then the magnitude and characteristics of SBT differences between nadir and oblique views were observed, considering factors such as land use/land cover, season, latitude and climate. Finally, GeoDetector tool was used to perform attribution analysis of SBT angular effects
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Book chapters on the topic "Split-window algorithm"

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Thakur, Pawan Kumar, Manish Kumar, R. B. Singh, Vaibhav E. Gosavi, Bhim Chand, and Sarika Sharma. "Land Surface Temperature Retrieval of Landsat-8 Data Using Split-Window Algorithm Over Delhi City, India." In Remote Sensing and Geographic Information Systems for Policy Decision Support, 191–218. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7731-1_9.

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Conference papers on the topic "Split-window algorithm"

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Heidarian, Peyman, Hua Li, Zelin Zhang, Ruibo Li, Qinhuo Liu, and Tan Yumin. "High-Resolution Land Surface Temperature Retrieval from GF5-02 VIMI Data using an Operational Split-Window Algorithm." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 2713–16. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10641359.

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Tan, Mingming, Hua Li, Xiangrong Xin, Ruibo Li, Yifan Lu, and Qing Xiao. "High-Resolution Sea Surface Temperature Retrieval from GF5-02 VIMI Data Using A Nonlinear Split-Window Algorithm." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 5865–69. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10642876.

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Li, Fengguang, Huazhong Ren, Baozhen Wang, Jinshun Zhu, Songyi Lin, Wenjie Fan, Zian Wang, and Qiming Qin. "An Angle-Dependent Non-Linear Split-Window Algorithm for Estimating Sea Surface Temperature from Chinese HY-1D Satellite." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 5855–59. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10641508.

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Mito, C. O., Giovanni Laneve, and Marco M. Castronuovo. "General split window algorithm for land surface temperature estimation." In International Symposium on Remote Sensing, edited by Manfred Owe and Guido D'Urso. SPIE, 2002. http://dx.doi.org/10.1117/12.454192.

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V., Ionca, Bogliolo M. P., Laneve G., Liberti G., Palombo A., and Pignatti S. "Split Window Algorithm Calibration and Validation for TASI Sensor." In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019. http://dx.doi.org/10.1109/igarss.2019.8898750.

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Na-na, Liu, Li Jing-wen, and Cui Yan-feng. "Cluster-Based Split-Window Radon Transform Algorithm for Ship Wake Detection." In 2009 WRI World Congress on Computer Science and Information Engineering. IEEE, 2009. http://dx.doi.org/10.1109/csie.2009.521.

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Saad, Sameh M., and Ramin Bahadori. "Pollution routing problem with time window and split delivery." In The 7th International Workshop on Simulation for Energy, Sustainable Development & Environment. CAL-TEK srl, 2019. http://dx.doi.org/10.46354/i3m.2019.sesde.004.

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In most classic vehicle routing problems, the main goal is to minimise the total travel time or distance while, the green vehicle routing problem, in addition to the stated objectives, also focuses on minimising fuel costs and greenhouse gas emissions, including carbon dioxide emissions. In this research, a new approach in Pollution Routing Problem (PRP) is proposed to minimise the CO2 emission by investigating vehicle weight fill level in length of each route. The PRP with a homogeneous fleet of vehicles, time windows, considering the possibility of split delivery and constraint of minimum shipment weight that must be on the vehicle in each route is investigated simultaneously. The mathematical model is developed and implemented using a simulated annealing algorithm which is programmed in MATLAB software. The generated results from all experiments demonstrated that the application of the proposed mathematical model led to the reduction in CO2 emission.
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Chen Du, Huazhong Ren, Qiming Qin, Jinjie Meng, and Jing Li. "Split-Window algorithm for estimating land surface temperature from Landsat 8 TIRS data." In IGARSS 2014 - 2014 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2014. http://dx.doi.org/10.1109/igarss.2014.6947256.

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Zhao, Shaohua, Qiming Qin, Yujiu Xiong, Guoyu Qiu, and Yonghui Yang. "Application of split window algorithm to retrieve land surface temperature over northwestern China." In Second International Conference on Earth Observation for Global Changes, edited by Xianfeng Zhang, Jonathan Li, Guoxiang Liu, and Xiaojun Yang. SPIE, 2009. http://dx.doi.org/10.1117/12.836766.

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Guillory, Anthony R., Henry E. Fuelberg, and Gary J. Jedlovec. "A Physical Split Window Technique for Deriving Precipitable Water Utilizing Vas Data." In Optical Remote Sensing of the Atmosphere. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/orsa.1991.omb3.

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A new algorithm, developed by Jedlovec (1987), is examined which uses Visible Infrared Spin Scan Radiometer (VISSR) Atmospheric Sounder (VAS) 11 and 12 μm (split window) data to derive precipitable water (PW) at mesoscale resolution. The algorithm is physically based and derives its first guess information from radiosonde data. It has several advantages: 1) it can be applied to multispectral imaging (MSI) data, which are available half hourly, 2) it uses only limited spatial averaging, and 3) it can be applied to instruments which lack sounding channels (e.g., GOES-Next imager).
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