Details
| Original language | English |
|---|---|
| Title of host publication | Remote Sensing of Clouds and the Atmosphere XXVIII |
| Editors | Adolfo Comeron, Evgueni I. Kassianov, Klaus Schafer, Richard H. Picard, Konradin Weber |
| Publisher | SPIE |
| ISBN (electronic) | 9781510666894 |
| Publication status | Published - 2023 |
| Event | Remote Sensing of Clouds and the Atmosphere XXVIII 2023 - Amsterdam, Netherlands Duration: 5 Sept 2023 → 6 Sept 2023 |
Publication series
| Name | Proceedings of SPIE - The International Society for Optical Engineering |
|---|---|
| Volume | 12730 |
| ISSN (Print) | 0277-786X |
| ISSN (electronic) | 1996-756X |
Abstract
Understanding the tropospheric diabatic heating is essential for predicting Earth’s weather and climate. To reach that goal, information on water vapor (WV) in the atmosphere is mandatory. Unfortunately, monitoring WV is challenging, because of its high variability in both space and time. Particularly, advanced knowledge and modeling of its fine scale behavior would be beneficial to improve forecasting applications. In this contribution, we propose to compare and discuss the spectral content of dataset from two instruments recording WV content of the atmosphere, focusing on small scales: Ocean Land Color Instrument (OLCI) on board of Copernicus Sentinel-3 and Zenith wet delay (ZWD) retrieved from Global Navigation Satellite System (GNSS) observations. Kolmogorov’s theory states that the structure function of passive scalar, or equivalently the temporal power spectrum (assuming taylor frozen turbulence), should follow a given power law within the inertial range. Using the von Karman modelling, it is possible to assess the outer scale length of turbulence defined as the frequency where the spectrum saturates. For our analysis on WV small scales, we have selected a region around Lindenberg in Germany. We will discuss the spectral content of the retrieved observations and their specificity. We will highlight the potential of ZWD from GNSS observations to study daily variations of turbulence parameters.
Keywords
- atmospheric turbulence, GNSS, OLCI, outer scale length, Remote sensing water vapor, von Karman power spectrum, Zenith wet delay
ASJC Scopus subject areas
- Materials Science(all)
- Electronic, Optical and Magnetic Materials
- Physics and Astronomy(all)
- Condensed Matter Physics
- Computer Science(all)
- Computer Science Applications
- Mathematics(all)
- Applied Mathematics
- Engineering(all)
- Electrical and Electronic Engineering
Sustainable Development Goals
Cite this
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Remote Sensing of Clouds and the Atmosphere XXVIII. ed. / Adolfo Comeron; Evgueni I. Kassianov; Klaus Schafer; Richard H. Picard; Konradin Weber. SPIE, 2023. 127300F (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 12730).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Retrieval of water vapor in the atmosphere and its spectral content
T2 - Remote Sensing of Clouds and the Atmosphere XXVIII 2023
AU - Kermarrec, G.
AU - Calbet, X.
AU - Deng, Z.
AU - Carbajal Henken, C.
AU - Preusker, R.
N1 - Funding information: This study is supported by the Deutsche Forschungsgemeinschaft under the project KE2453/2-1.
PY - 2023
Y1 - 2023
N2 - Understanding the tropospheric diabatic heating is essential for predicting Earth’s weather and climate. To reach that goal, information on water vapor (WV) in the atmosphere is mandatory. Unfortunately, monitoring WV is challenging, because of its high variability in both space and time. Particularly, advanced knowledge and modeling of its fine scale behavior would be beneficial to improve forecasting applications. In this contribution, we propose to compare and discuss the spectral content of dataset from two instruments recording WV content of the atmosphere, focusing on small scales: Ocean Land Color Instrument (OLCI) on board of Copernicus Sentinel-3 and Zenith wet delay (ZWD) retrieved from Global Navigation Satellite System (GNSS) observations. Kolmogorov’s theory states that the structure function of passive scalar, or equivalently the temporal power spectrum (assuming taylor frozen turbulence), should follow a given power law within the inertial range. Using the von Karman modelling, it is possible to assess the outer scale length of turbulence defined as the frequency where the spectrum saturates. For our analysis on WV small scales, we have selected a region around Lindenberg in Germany. We will discuss the spectral content of the retrieved observations and their specificity. We will highlight the potential of ZWD from GNSS observations to study daily variations of turbulence parameters.
AB - Understanding the tropospheric diabatic heating is essential for predicting Earth’s weather and climate. To reach that goal, information on water vapor (WV) in the atmosphere is mandatory. Unfortunately, monitoring WV is challenging, because of its high variability in both space and time. Particularly, advanced knowledge and modeling of its fine scale behavior would be beneficial to improve forecasting applications. In this contribution, we propose to compare and discuss the spectral content of dataset from two instruments recording WV content of the atmosphere, focusing on small scales: Ocean Land Color Instrument (OLCI) on board of Copernicus Sentinel-3 and Zenith wet delay (ZWD) retrieved from Global Navigation Satellite System (GNSS) observations. Kolmogorov’s theory states that the structure function of passive scalar, or equivalently the temporal power spectrum (assuming taylor frozen turbulence), should follow a given power law within the inertial range. Using the von Karman modelling, it is possible to assess the outer scale length of turbulence defined as the frequency where the spectrum saturates. For our analysis on WV small scales, we have selected a region around Lindenberg in Germany. We will discuss the spectral content of the retrieved observations and their specificity. We will highlight the potential of ZWD from GNSS observations to study daily variations of turbulence parameters.
KW - atmospheric turbulence
KW - GNSS
KW - OLCI
KW - outer scale length
KW - Remote sensing water vapor
KW - von Karman power spectrum
KW - Zenith wet delay
UR - http://www.scopus.com/inward/record.url?scp=85180013218&partnerID=8YFLogxK
U2 - 10.1117/12.2678381
DO - 10.1117/12.2678381
M3 - Conference contribution
AN - SCOPUS:85180013218
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Remote Sensing of Clouds and the Atmosphere XXVIII
A2 - Comeron, Adolfo
A2 - Kassianov, Evgueni I.
A2 - Schafer, Klaus
A2 - Picard, Richard H.
A2 - Weber, Konradin
PB - SPIE
Y2 - 5 September 2023 through 6 September 2023
ER -