scholarly journals A development of cloud top height retrieval using thermal infrared spectra observed with GOSAT and comparison with CALIPSO data

2016 ◽  
Vol 9 (5) ◽  
pp. 1981-1992 ◽  
Author(s):  
Yu Someya ◽  
Ryoichi Imasu ◽  
Naoko Saitoh ◽  
Yoshifumi Ota ◽  
Kei Shiomi
Keyword(s):  
Measurement Errors ◽  
Thermal Infrared ◽  
Atmospheric Conditions ◽  
Cloud Detection ◽  
Low Cloud ◽  
Atmospheric Profile ◽  
Slicing Method ◽  
Cloud Tops ◽  
Spectral Channels ◽  
Selection Of

Abstract. An algorithm based on CO2 slicing, which has been used for cirrus cloud detection using thermal infrared data, was developed for high-resolution radiance spectra from satellites. The channels were reconstructed based on sensitivity height information of the original spectral channels to reduce the effects of measurement errors. Selection of the reconstructed channel pairs was optimized for several atmospheric profile patterns using simultaneous studies assuming a cloudy sky. That algorithm was applied to data by the Greenhouse gases Observing SATellite (GOSAT). Results were compared with those obtained from the space-borne lidar instrument on-board Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). Monthly mean cloud amounts from the slicing generally agreed with those from CALIPSO observations despite some differences caused by surface temperature biases, optically very thin cirrus, multilayer structures of clouds, extremely low cloud tops, and specific atmospheric conditions. Comparison of coincident data showed good agreement, except for some cases, and revealed that the improved slicing method is more accurate than the traditional slicing method. Results also imply that improved slicing can detect low-level clouds with cloud top heights as low as approximately 1.5 km.

scholarly journals A development of cloud top height retrieval using thermal infrared spectra observed with GOSAT and comparison with CALIPSO data

2016 ◽  
Author(s):  
Y. Someya ◽  
R. Imasu ◽  
N. Saitoh ◽  
Y. Ota ◽  
K. Shiomi
Keyword(s):  
Measurement Errors ◽  
Thermal Infrared ◽  
Atmospheric Conditions ◽  
Cloud Detection ◽  
Low Cloud ◽  
Atmospheric Profile ◽  
Slicing Method ◽  
Cloud Tops ◽  
Spectral Channels ◽  
Good Agreement

Abstract. An algorithm based on CO2 slicing, which has been used for cirrus cloud detection using thermal infrared data, was developed for high-resolution radiance spectra from satellites. The channels were reconstructed based on sensitivity height information of the original spectral channels to reduce the effects of measurement errors. The selections of the reconstructed channel pairs were optimized for several atmospheric profile patterns using simultaneous studies assuming cloudy sky. That algorithm was applied to data by the Greenhouse gases Observing SATellite (GOSAT). The results were compared with those obtained from space-borne lidar instrument onboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). Monthly mean cloud amounts from the slicing generally agreed with those from CALIPSO observations despite some differences caused by surface temperature biases, optically very thin cirrus, multilayer structures of clouds, extremely low cloud tops, and specific atmospheric conditions. Comparison of coincident data showed good agreement except some cases and revealed that the improved slicing method is more accurate than the traditional slicing method. Results also imply that improved slicing can detect low-level clouds with cloud top heights as low as approximately 1.5 km.

scholarly journals A robust threshold-based cloud mask for the HRV channel of MSG SEVIRI

2013 ◽  
Vol 6 (2) ◽  
pp. 2829-2855
Author(s):  
S. Bley ◽  
H. Deneke
Keyword(s):  
High Resolution ◽  
Cloud Fraction ◽  
Small Scale ◽  
Cloud Detection ◽  
Low Resolution ◽  
Test Dataset ◽  
Clear Sky ◽  
Cloud Mask ◽  
Spectral Channels ◽  
Selection Of

Abstract. A robust threshold-based cloud mask for the high-resolution visible (HRV) channel (1 × 1 km2) of the METEOSAT SEVIRI instrument is introduced and evaluated. It is based on operational EUMETSAT cloud mask for the low resolution channels of SEVIRI (3 × 3 km2), which is used for the selection of suitable thresholds to ensure consistency with its results. The aim of using the HRV channel is to resolve small-scale cloud structures which cannot be detected by the low resolution channels. We find that it is of advantage to apply thresholds relative to clear-sky reflectance composites, and to adapt the threshold regionally. Furthermore, the accuracy of the different spectral channels for thresholding and the suitability of the HRV channel are investigated for cloud detection. The case studies show different situations to demonstrate the behaviour for various surface and cloud conditions. Overall, between 4 and 24% of cloudy low-resolution SEVIRI pixels are found to contain broken clouds in our test dataset depending on considered region. Most of these broken pixels are classified as cloudy by EUMETSAT's cloud mask, which will likely result in an overestimate if the mask is used as estimate of cloud fraction.

scholarly journals Dust Aerosol Detection by the Modified CO2 Slicing Method

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1615 ◽  
Author(s):  
Yu Someya ◽  
Ryoichi Imasu ◽  
Kei Shiomi
Keyword(s):  
Inversion Layer ◽  
Temperature Inversion ◽  
Dust Aerosol ◽  
Aerosol Layer ◽  
Atmospheric Conditions ◽  
Cloud Detection ◽  
Dust Aerosols ◽  
Surface Skin Temperature ◽  
Slicing Method ◽  
Ground Based Lidar

Dust aerosols, which have diverse and strong influences on the environment, must be monitored. Satellite data are effective for monitoring atmospheric conditions globally. In this work, the modified CO2 slicing method, a cloud detection technique using thermal infrared data from space, was applied to GOSAT data to detect the dust aerosol layer height. The results were compared using lidar measurements. Comparison of horizontal distributions found for northern Africa during summer revealed that both the relative frequencies of the low level aerosol layer from the slicing method and the dust frequencies of CALIPSO are high in northern coastal areas. Comparisons of detected layer top heights using collocated data with CALIPSO and ground-based lidar consistently showed high detection frequencies of the lower level aerosol layer, although the slicing method sometimes produces overestimates. This tendency is significant over land. The main causes of this tendency might be uncertainty of the surface skin temperature and a temperature inversion layer in the atmosphere. The results revealed that obtaining the detailed behavior of dust aerosols using the modified slicing method alone is difficult.

scholarly journals Desarrollo de algoritmos para la selección de canales espectrales en redes inalámbricas de radio cognitiva usando las estrategias rectiva y proactiva - Development of algorithms for the selection of spectral channels in wireless cognitive radio networks using rective and proactive strategies

2017 ◽  
Vol 2 (29) ◽  
pp. 165
Author(s):  
Danilo Alfonso López-Sarmiento ◽  
Jorge Enrique Salamanca- Cespez ◽  
Adriana Patricia Gallego-Torres
Keyword(s):  
Cognitive Radio ◽  
Real Time ◽  
Cognitive Radio Networks ◽  
Radio Networks ◽  
Best Effort ◽  
El Sistema ◽  
Toma De Decisiones ◽  
La Red ◽  
Spectral Channels ◽  
Selection Of

La Radio Cognitiva (CR) es el paradigma que permite optimizar la eficiencia del espectro radio eléctrico a través de una gestión dinámica basado en el sensado, decisión, compartición y movilidad espectral, siendo la toma de decisiones la encargada de la selección del mejor/mejores canales para la transmisión de los datos de los nodos cognitivos (SUs), dependiendo de la calidad de servicio solicitada (Best Effort (BE) o Real Time (RT)). La literatura existente plantea implícitamente la utilización de una estrategia reactiva para la selección de dichas bandas; no obstante, ello implica que las solicitudes de los SUs solo se procesarán una vez arriben a la estación base (BS) de la red cognitiva, generando retrasos adicionales en el envío de los datos. En el presente artículo se plantea el uso de la estrategía proactiva planteada en [1], pretendiendo con ello que el procesamiento de las solicitudes de asignación de espectro para los SUs se realice de manera más rápida o eficiente (tiempo menor de procesamiento en la estación base (BS)), con ello optimizando la etapa de decisión espectral en CR.  Los resultados demuestran que el sistema proactivo reduce el tiempo de asignación de canales a los SUs sin degradar la comunicación del PU.

A model of the distribution and metabolism of corticotropin-releasing factor

1988 ◽  
Vol 254 (1) ◽  
pp. E104-E112
Author(s):  
B. Candas ◽  
J. Lalonde ◽  
M. Normand
Keyword(s):  
Measurement Errors ◽  
Compartment Model ◽  
Corticotropin Releasing Factor ◽  
Data Sets ◽  
Data Set ◽  
Rapid Injection ◽  
Three Compartment Model ◽  
Rival Models ◽  
Sampling Schedule ◽  
Selection Of

The aim of this study is the selection of the number of compartments required for a model to represent the distribution and metabolism of corticotropin-releasing factor (CRF) in rats. The dynamics of labeled rat CRF were measured in plasma for seven rats after a rapid injection. The sampling schedule resulted from the combination of the two D-optimal sampling sets of times corresponding to both rival models. This protocol improved the numerical identifiability of the parameters and consequently facilitated the selection of the relevant model. A three-compartment model fits adequately to the seven individual dynamics and better represents four of them compared with the lower-order model. It was demonstrated, using simulations in which the measurement errors and the interindividual variability of the parameters are included, that his four-to-seven ratio of data sets is consistent with the relevance of the three-compartment model for every individual kinetic data set. Kinetic and metabolic parameters were then derived for each individual rat, their values being consistent with the prolonged effects of CRF on pituitary-adrenocortical secretion.

In-Flight Validation of the ECOSTRESS, Landsats 7 and 8 Thermal Infrared Spectral Channels Using the Lake Tahoe CA/NV and Salton Sea CA Automated Validation Sites

2020 ◽  
Vol 58 (2) ◽  
pp. 1294-1302 ◽  
Author(s):  
Simon J. Hook ◽  
Kerry Cawse-Nicholson ◽  
Julia Barsi ◽  
Robert Radocinski ◽  
Glynn C. Hulley ◽  
...  
Keyword(s):  
Lake Tahoe ◽  
Thermal Infrared ◽  
Salton Sea ◽  
Infrared Spectral ◽  
Spectral Channels

scholarly journals An empirical method to correct for temperature-dependent variations in the overlap function of CHM15k ceilometers

2016 ◽  
Vol 9 (7) ◽  
pp. 2947-2959 ◽  
Author(s):  
Maxime Hervo ◽  
Yann Poltera ◽  
Alexander Haefele
Keyword(s):  
Boundary Layer ◽  
Planetary Boundary Layer ◽  
Correction Method ◽  
Empirical Method ◽  
Cloud Base ◽  
Atmospheric Conditions ◽  
Cloud Detection ◽  
Temperature Dependent ◽  
Night Time ◽  
Gradient Based

Abstract. Imperfections in a lidar's overlap function lead to artefacts in the background, range and overlap-corrected lidar signals. These artefacts can erroneously be interpreted as an aerosol gradient or, in extreme cases, as a cloud base leading to false cloud detection. A correct specification of the overlap function is hence crucial in the use of automatic elastic lidars (ceilometers) for the detection of the planetary boundary layer or of low cloud. In this study, an algorithm is presented to correct such artefacts. It is based on the assumption of a homogeneous boundary layer and a correct specification of the overlap function down to a minimum range, which must be situated within the boundary layer. The strength of the algorithm lies in a sophisticated quality-check scheme which allows the reliable identification of favourable atmospheric conditions. The algorithm was applied to 2 years of data from a CHM15k ceilometer from the company Lufft. Backscatter signals corrected for background, range and overlap were compared using the overlap function provided by the manufacturer and the one corrected with the presented algorithm. Differences between corrected and uncorrected signals reached up to 45 % in the first 300 m above ground. The amplitude of the correction turned out to be temperature dependent and was larger for higher temperatures. A linear model of the correction as a function of the instrument's internal temperature was derived from the experimental data. Case studies and a statistical analysis of the strongest gradient derived from corrected signals reveal that the temperature model is capable of a high-quality correction of overlap artefacts, in particular those due to diurnal variations. The presented correction method has the potential to significantly improve the detection of the boundary layer with gradient-based methods because it removes false candidates and hence simplifies the attribution of the detected gradients to the planetary boundary layer. A particularly significant benefit can be expected for the detection of shallow stable layers typical of night-time situations. The algorithm is completely automatic and does not require any on-site intervention but requires the definition of an adequate instrument-specific configuration. It is therefore suited for use in large ceilometer networks.

Assessment of the Robustness of Gas Turbine Diagnostics Tools Based on Neural Networks

2006 ◽  
Author(s):  
R. Bettocchi ◽  
M. Pinelli ◽  
P. R. Spina ◽  
M. Venturini ◽  
G. A. Zanetta
Keyword(s):  
Neural Networks ◽  
Gas Turbine ◽  
Measurement Errors ◽  
Health State ◽  
Artificial Intelligence Technique ◽  
Boundary Load ◽  
Set Up ◽  
Operating Points ◽  
Range Of Variation ◽  
Selection Of

The paper deals with the set-up and the application of an Artificial Intelligence technique based on Neural Networks (NNs) to gas turbine diagnostics, in order to evaluate its capabilities and its robustness. The data used for both training and testing the NNs were generated by means of a Cycle Program, calibrated on a Siemens V94.3A gas turbine. Such data are representative of operating points characterized by different boundary, load and health state conditions. The analyses carried out are aimed at the selection of the most appropriate NN structure for gas turbine diagnostics, by evaluating NN robustness with respect to: • interpolation capability and accuracy in the presence of data affected by measurement errors; • extrapolation capability in the presence of data lying outside the range of variation adopted for NN training; • accuracy in the presence of input data corrupted by bias errors; • accuracy when one input is not available. This situation is simulated by replacing the value of the unavailable input with its nominal value.

The Selection of Thermistors for the Temperature Measurement Gear

1970 ◽  
Vol 111 (5) ◽  
pp. 59-62
Author(s):  
J. Leskauskaite ◽  
A. Dumcius
Keyword(s):  
Temperature Measurement ◽  
Measurement Errors ◽  
Numerical Data ◽  
Optimum Selection ◽  
Selection Of

The parameters represented on manufacturer datasheet are usually insufficient for the optimum selection of thermistors. Some manufacturers give generalized numerical data of R(T) dependences. Using this data it is possible to select successfully the most suitable thermistor and equation for describing of T(R) dependence. It is shown that by selecting four points in the generalized characteristic it is possible to calculate the coefficients of the approximation equation. By the application of these coefficients in the calculations the temperature measurement errors can be decreased by an order. The results of calculations and experiment are given. Ill. 4, bibl. 8, tabl. 5 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.111.5.357

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