The New ISU Time Scale, TCB: Nothing New
Explore the source record for details and available documents.
j2000 IAU 1991 TCB astronomical measurements plane↗
SEARCH · Engineering Papers
Results for “TcB”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
TCB operation supply inventory system /TCBSYS/
System produces inventory report for each updated period and special report for long term inventory information summary. Report summarizes consumption, outstanding orders, and balance of each inventory item. System generates, corrects, and adjusts inventory tapes. Restrictions of system are listed.
Tu, H.-Y.↗
Mechanism of cyanoacetylene photochemistry at 185 and 254 nm
The role of cyanoacetylene (HC3N) in the atmospheric photochemistry of Titan and its relevance to polymer formation are discussed. Investigation of the relative light absorption of HC3N, acetylene (C2H2), and diacetylene (C4H2) revealed that HC3N is an important absorber of UV light in the 205- to 225-nanometer wavelength region in Titan's polar regions. Laboratory studies established that photolysis of C2H2 initiates the polymerization of HC3N even though the HC3N is not absorbing the UV light. Quantum yield measurements establish that HC3N is 2-5 times as reactive as C2H2 for polymer formation. Photolysis of HC3N with 185-nanometer light in the presence of N2, H2, Ar, or CF4 results in a decrease in the yield of 1,3,5-tricyanobenzene (1,3,5-tcb), while photolysis in the presence of CH4, C2H6, or n-C4H10 results in an increase in 1,3,5-tcb. The rate of loss of HC3N is increased by all gases except H2, where it is unchanged. It was not possible to detect 1,3,5-tcb as a photoproduct when the partial pressure of HC3N was decreased to 1 torr. Photolysis of HC3N with 254-nanometer light in the presence of H2 or N2 results in the formation of 1,2,4-tcb, while photolysis in the presence of CH4, C2H6, or n-C4H10 results in the formation of increasing amounts of 1,3,5-tcb. Mechanisms for the formation of polymers are presented.
Non-NASA Center↗
Objectively Identifying Transverse Cirrus Bands in Tropical Cyclones using a Convolutional Neural Network
Transverse cirrus bands (TCBs) are bands of upper-level clouds regularly seen in mesoscale and synoptic-scale weather systems. In tropical cyclones, their appearance has been subjectively linked to intensification and the diurnal cycle. However, these hypothesized relationships have not been rigorously tested due to the subjective nature of TCBs in satellite images. A machine learning technique that successfully identifies TCBs objectively in imagery from the GOES-16 Advanced Baseline Imager (ABI) has been developed to solve this problem. The technique uses a U-Net convolutional neural network (CNN) that assigns a probability to each pixel in an image based on the likelihood of the pixel being associated with a TCB. Using the U-Net CNN, a database of TCBs from 2019 to 2022 was developed for the Atlantic tropical cyclone basin by defining an appropriate probability threshold that defines the difference between TCB and non-TCB pixels. This threshold is where the Jaccard score, calculated using manually identified TCBs and model identified TCBs, is maximized. Statistics for TCB occurrence will also be presented, including the relationships between TCBs and storm relative motion, shear relative direction, cardinal direction, tropical cyclone intensity, tropical cyclone intensification rates, and time of day.
John Mark Mayhall↗
Objectively Identifying Transverse Cirrus Bands in Tropical Cyclones using a Convolutional Neural Network
Transverse cirrus bands (TCBs) are bands of upper-level clouds that are regularly seen in mesoscale and synoptic-scale weather systems. In tropical cyclones, their appearance has been subjectively linked to intensification and the diurnal cycle, but these hypothesized relationships have not been rigorously tested due to the difficulty of objectively identifying TCBs in satellite images. This presentation describes a machine learning technique that successfully identifies TCBs in imagery from the GOES-16 Advanced Baseline Imager. The technique uses a convolutional neural network (CNN) that assigns a probability of each pixel in the image being associated with a TCB. Using the CNN, a database of TCBs from 2018 to 2022 was developed for the Atlantic basin. Statistics using this database will be presented, including the relationship between TCBs and deep-layer vertical wind shear, intensity change, and the TC diurnal cycle.
John Mark Mayhall↗
Time Scales in the JPL and CfA Ephemerides
Over the past decades, the IAU has repeatedly attempted to correct its definition of the basic fundamental argument used in the emphemerides. Finally, they have defined a time system which is physically possible, according to the accepted standard theory of gravitation.
time CfA ephemerides ephemerides time ET-TDB-TCB↗