First Odin sub-mm retrievals in the tropical upper troposphere: ice cloud properties

Abstract. More accurate global measurements of the amount of ice in thicker clouds are needed to validate atmospheric models and sub-mm radiometry can be an important component in this respect. A cloud ice retrieval scheme for the first such instrument in space, Odin-SMR, is presented here. Several advantages of sub-mm observations are shown, such as low influence of particle shape and orientation, and a high dynamic range of the retrievals. In the case of Odin-SMR, only cloud ice above ≈12.5 km can be measured. The present retrieval scheme gives a detection threshold of about 4 g/m2 above 12.5 km and does not saturate even for thickest observed clouds (>500 g/m2). The main retrieval uncertainties are the assumed particle size distribution and cloud inhomogeneity effects. The overall retrieval accuracy is estimated to be ~75%. The retrieval error is judged to have large random components and to be significantly lower than this value for averaged results, but high fixed errors can not be excluded. However, a firm lower value can always be provided. Initial results are found to be consistent with similar Aura MLS retrievals, but show important differences to corresponding data from atmospheric models. This first retrieval algorithm is limited to lowermost Odin-SMR tangent altitudes, and further development should improve the detection threshold and the vertical resolution. It should also be possible to decrease the retrieval uncertainty associated with cloud inhomogeneities by detailed analysis of other data sets.

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First Odin sub-mm retrievals in the tropical upper troposphere: ice cloud properties

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-6-8681-2006 ◽

2006 ◽

Vol 6 (5) ◽

pp. 8681-8712

Author(s):

P. Eriksson ◽

M. Ekström ◽

B. Rydberg ◽

D. P. Murtagh

Keyword(s):

Dynamic Range ◽

Detection Threshold ◽

High Dynamic Range ◽

Retrieval Algorithm ◽

Atmospheric Models ◽

Ice Cloud ◽

Cloud Properties ◽

Retrieval Scheme ◽

Cloud Ice ◽

Further Development

Abstract. There exists today no established satellite technique for measuring the amount of ice in thicker clouds. Sub-mm radiometry is a promising technique for the task, and a retrieval scheme for the first such instrument in space, Odin-SMR, is presented. Several advantages of sub-mm observations are confirmed, such as low influence of particle shape and orientation, and a high dynamic range of the retrievals. In the case of Odin-SMR, cloud ice amounts above ~12.5 km can be determined. The presented retrieval scheme gives a detection threshold of ~4 g/m2 without saturation even for thickest observed clouds. The main retrieval uncertainty is the assumed particle size distribution. Initial results are found to be consistent with similar Aura MLS retrievals. It is then shown that important differences compared to atmospheric models exist. This first retrieval algorithm is limited to lowermost Odin-SMR tangent altitudes, and further development should improve the detection threshold and the vertical resolution.

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Overview and sample applications of SMILES and Odin-SMR retrievals of upper tropospheric humidity and cloud ice mass

Atmospheric Chemistry and Physics ◽

10.5194/acp-14-12613-2014 ◽

2014 ◽

Vol 14 (23) ◽

pp. 12613-12629 ◽

Cited By ~ 9

Author(s):

P. Eriksson ◽

B. Rydberg ◽

H. Sagawa ◽

M. S. Johnston ◽

Y. Kasai

Keyword(s):

Climate Models ◽

Southern Oscillation ◽

A Priori ◽

Global Climate Models ◽

Monte Carlo Integration ◽

Data Sets ◽

Upper Troposphere ◽

Retrieval Scheme ◽

Cloud Ice ◽

Ice Water

Abstract. Retrievals of cloud ice mass and humidity from the Superconducting Submillimeter-Wave Limb-Emission Sounder (SMILES) and the Odin-SMR (Sub-Millimetre Radiometer) limb sounder are presented and example applications of the data are given. SMILES data give an unprecedented view of the diurnal variation of cloud ice mass. Mean regional diurnal cycles are reported and compared to some global climate models. Some improvements in the models regarding diurnal timing and relative amplitude were noted, but the models' mean ice mass around 250 hPa is still low compared to the observations. The influence of the ENSO (El Niño–Southern Oscillation) state on the upper troposphere is demonstrated using 12 years of Odin-SMR data. The same retrieval scheme is applied for both sensors, and gives low systematic differences between the two data sets. A special feature of this Bayesian retrieval scheme, of Monte Carlo integration type, is that values are produced for all measurements but for some atmospheric states retrieved values only reflect a priori assumptions. However, this "all-weather" capability allows a direct statistical comparison to model data, in contrast to many other satellite data sets. Another strength of the retrievals is the detailed treatment of "beam filling" that otherwise would cause large systematic biases for these passive cloud ice mass retrievals. The main retrieval inputs are spectra around 635/525 GHz from tangent altitudes below 8/9 km for SMILES/Odin-SMR, respectively. For both sensors, the data cover the upper troposphere between 30° S and 30° N. Humidity is reported as both relative humidity and volume mixing ratio. The vertical coverage of SMILES is restricted to a single layer, while Odin-SMR gives some profiling capability between 300 and 150 hPa. Ice mass is given as the partial ice water path above 260 hPa, but for Odin-SMR ice water content, estimates are also provided. Besides a smaller contrast between most dry and wet cases, the agreement with Aura MLS (Microwave Limb Sounder) humidity data is good. In terms of tropical mean humidity, all three data sets agree within 3.5 %RHi. Mean ice mass is about a factor of 2 lower compared to CloudSat. This deviation is caused by the fact that different particle size distributions are assumed, combined with saturation and a priori influences in the SMILES and Odin-SMR data.

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A high dynamic range ASK demodulator for passive UHF RFID with automatic over-voltage protection and detection threshold adjustment

2009 IEEE Custom Integrated Circuits Conference ◽

10.1109/cicc.2009.5280804 ◽

2009 ◽

Cited By ~ 4

Author(s):

Ganesh K. Balachandran ◽

Raymond E. Barnett

Keyword(s):

Dynamic Range ◽

Detection Threshold ◽

High Dynamic Range ◽

Uhf Rfid ◽

Passive Uhf Rfid ◽

Threshold Adjustment ◽

High Dynamic

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Classification of Microchannel Flame Regimes Based on Convolutional Neural Networks

10.1115/power2021-64437 ◽

2021 ◽

Author(s):

Seyed Navid Roohani Isfahani ◽

Vinicius M. Sauer ◽

Ingmar Schoegl

Keyword(s):

Data Augmentation ◽

Dynamic Range ◽

High Dynamic Range ◽

Data Sets ◽

Data Set ◽

Testing Data ◽

Experimental Approaches ◽

Transition Points ◽

Combustion Regimes

Abstract Micro-combustion has shown significant potential to study and characterize the combustion behavior of hydrocarbon fuels. Among several experimental approaches based on this method, the most prominent one employs an externally heated micro-channel. Three distinct combustion regimes are reported for this device namely, weak flames, flames with repetitive extinction and ignition (FREI), and normal flames, which are formed at low, moderate, and high flow rate ranges, respectively. Within each flame regime, noticeable differences exist in both shape and luminosity where transition points can be used to obtain insights into fuel characteristics. In this study, flame images are obtained using a monochrome camera equipped with a 430 nm bandpass filter to capture the chemiluminescence signal emitted by the flame. Sequences of conventional flame photographs are taken during the experiment, which are computationally merged to generate high dynamic range (HDR) images. In a highly diluted fuel/oxidizer mixture, it is observed that FREI disappear and are replaced by a gradual and direct transition between weak and normal flames which makes it hard to identify different combustion regimes. To resolve the issue, a convolutional neural network (CNN) is introduced to classify the flame regime. The accuracy of the model is calculated to be 99.34, 99.66, and 99.83% for “training”, “validation”, and “testing” data-sets, respectively. This level of accuracy is achieved by conducting a grid search to acquire optimized parameters for CNN. Furthermore, a data augmentation technique based on different experimental scenarios is used to generate flame images to increase the size of the data-set.

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