New Investigation of a Tropical Cyclone: Observational and Turbulence Analysis for the Faraji Hurricane
Abstract
:1. Introduction
2. Methods
2.1. Geostationary Instrument Analysis
2.2. Polar Instrument Data Analysis
2.2.1. MODIS Data Processing
- K/m (absolute atmospheric instability), in which the numerical value is the dry adiabatic vertical gradient in stability conditions of the standard atmosphere
- K/m K/m (conditionally atmospheric instability), in which the vertical temperature gradient is between the numerical values of moist adiabatic vertical gradient and dry adiabatic vertical gradient
2.2.2. SLSTR Data Processing
2.2.3. VIIRS Data Processing
3. Results
3.1. Geostationary Data Analysis
3.2. LEO Data Analysis
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
LEO | Low Earth Orbit |
EOS | Earth Observation System |
MODIS | Moderate-Resolution Imaging Spectroradiometer |
MSG | Meteosat Second Generation |
MSG—8 IODC | Meteosat Second Generation 8 Indian Ocean Data Collection |
POD | Proper Orthogonal Decomposition |
SEVIRI | Spinning Enhanced Visible Infra-Red Imager |
SLSTR | Sea and Land Surface Temperature Radiometer |
Suomi NPP | Suomi National Polar-orbiting Partnership |
VIIRS | Visible/Infrared Imager Radiometer Suite |
References
- Dvorak, V.F. Tropical cyclone intensity analysis and forecasting from satellite imagery. Mon. Weather Rev. 1975, 103, 420–430. [Google Scholar] [CrossRef]
- Dvorak, V.F. Tropical Cyclone Intensity Analysis Using Satellite Data; National Oceanic and Atmospheric Administration: Washington, DC, USA, 1984; pp. 46–47. [Google Scholar]
- Piñeros, M.F.; Ritchie, E.A.; Tyo, J.S. Objective measures of tropical cyclone structure and intensity change from remotely sensed infrared image data. IEEE Trans. Geosci. Remote Sens. 2008, 46, 3574–3580. [Google Scholar] [CrossRef]
- Knaff, J.A.; Brown, D.P.; Courtney, J.; Gallina, G.M.; Beven, J.L., II. An Evaluation of Dvorak Technique–Based Tropical Cyclone Intensity Estimates. Weather. Forecast. 2010, 25, 1362–1379. [Google Scholar] [CrossRef]
- Salby, M.L. Fundamental of Atmospheric Physics; Academic Press: London, UK, 1996. [Google Scholar]
- Ahrens, C.D. Meteorology Today: An Introduction to Weather, Climate, and the Environment, 9th ed.; Brooks/Cole, CengageLearning: Boston, MA, USA, 2009. [Google Scholar]
- Andrews, D.G. An Introduction to Atmospheric Physics, 2nd ed.; Cambridge University Press: Cambridge, UK, 2010. [Google Scholar]
- Aminou, D.M.A.; Jacquet, B.; Pasternak, F. Characteristics of the Meteosat Second Generation (MSG) Radiometer/Imager: SEVIRI. In Proceedings of the SPIE 3221, Sensors, Systems, and Next-Generation Satellites, Edinburgh, UK, 26–28 September 2016. [Google Scholar]
- Ciardullo, G.; Primavera, L.; Ferrucci, F.; Carbone, V.; Lepreti, F. Study of Turbulence Associated with the Faraji Cyclone. Climate 2022, 10, 21. [Google Scholar] [CrossRef]
- Vecchio, A.; Carbone, V.; Lepreti, F.; Primavera, L.; Sorriso-Valvo, L.; Straus, T.; Veltri, P. Spatio-Temporal Analysis of Photospheric Turbulent Velocity Fields Using the Proper Orthogonal Decomposition. Sol. Phys. 2008, 251, 163–178. [Google Scholar] [CrossRef]
- Siegel, S.G.; Cohen, K.; Seidel, J.; McLaughlin, T. Proper Orthogonal Decomposition Snapshot Selection for State Estimation of Feedback Controlled Flows. In Proceedings of the 44th AIAA Aerospace Sciences Meeting and Exhibit, Reno, NV, USA, 9–12 January 2006; pp. 1–11. [Google Scholar]
- Vecchio, A.; Carbone, V.; Lepreti, F.; Primavera, L.; Sorriso-Valvo, L.; Veltri, P.; Straus, T.; Alfonsi, G. Proper Orthogonal Decomposition of Solar Photospheric Motions. Phys. Rev. Lett. 2005, 95, 1–4. [Google Scholar] [CrossRef] [PubMed]
- Veltri, P.; Vecchio, A.; Carbone, V. Proper orthogonal decomposition analysis of spatio-temporal behavior of renal scintigraphies. Phys. Med. 2009, 26, 57–70. [Google Scholar] [CrossRef] [PubMed]
- Lumley, J.L. Coherent Structures in Turbulence. Transition and Turbulence; Academic Press: New York, NY, USA, 1981. [Google Scholar]
- Alfonsi, G.; Restano, C.; Primavera, L. Coherent structures of the flow around a surface-mounted cubic obstacle in turbulent channel flow. J. Wind Eng. Ind. Aerodyn. 2003, 91, 495–511. [Google Scholar] [CrossRef]
- Alfonsi, G.; Primavera, L.; Felisari, R. On the behavior of POD modes of the flow past a perforated plate. J. Flow Vis. Image Process. 2003, 10, 105–117. [Google Scholar] [CrossRef]
- Alfonsi, G.; Primavera, L. The structure of turbulent boundary layersin the wall region of plane channel flow. Proc. R. Soc. A 2007, 463, 593–612. [Google Scholar] [CrossRef]
- MODIS Characterization Support Team (MCST). MODIS 1 km Calibrated Radiances Product. NASA MODIS Adaptive Processing System; Goddard Space Flight Center: Greenbelt, MD, USA, 2017. [Google Scholar]
- Román, M.O.; Wang, Z.; Sun, Q.; Kalb, V.; Miller, S.D.; Molthan, A.; Schultz, L.; Bell, J.; Stokes, E.C.; Pandey, B.; et al. NASA’s Black Marble nighttime lights product suite. Remote Sens. Environ. 2018, 210, 113–143. [Google Scholar] [CrossRef]
- Cao, C.; Xiong, X.; Wolfe, R.; DeLuccia, F.; Liu, Q.; Blonski, S.; Lin, G.; Nishihama, M.; Pogorzala, D.; Oudrari, H.; et al. Visible iNfrared Imaging Radiometer Suite (VIIRS) Sensor Data Record (SDR) User’s Guide; Version 1.2; National Oceanic and Atmospheric Administration: Washington, DC, USA, 2013. [Google Scholar]
- Donlon, C.; Berruti, B.; Buongiorno, A.; Ferreira, M.-H.; Féménias, P.; Frerick, J.; Goryl, P.; Klein, U.; Laur, H.; Mavrocordatos, C.; et al. The Global Monitoring for Environment and Security (GMES) Sentinel-3 mission. Remote Sens. Environ. 2012, 120, 37–57. [Google Scholar] [CrossRef]
- Malguzzi, P.; Buzzi, A.; Drofa, O. The meteorological global model GLOBO at the ISAC-CNR of Italy: Assessment of 1.5 years of experimental use for medium range weather forecast. Weather. Forecast. 2011, 26, 1045–1055. [Google Scholar] [CrossRef]
- Knapp, K.R.; Kruk, M.C.; Levinson, D.H.; Diamond, H.J.; Neumann, C.J. The International Best Track Archive for Climate Stewardship (IBTrACS) Unifying Tropical Cyclone Data. Bull. Am. Meteorol. Soc. 2010, 91, 363–376. [Google Scholar] [CrossRef] [Green Version]
- Hoarau, P. Category 4, 19S(FARAJI) Annular-like Cyclone Has Probably Peaked, Invest 92P on the Map, 08/09utc Utpdates; Joint Typhoon Warning Center: Pearl Harbor, HI, USA, 2021. [Google Scholar]
- Météo France La Réunion, Very Intense Tropical Cyclone 10 (Faraji); Meteo France: Paris, France, 2021.
- Pili, P. Calibration of SEVIRI. In Proceedings of the 2000 EUMETSAT Meteorological Satellite Data Users Conference, EUMETSAT EUM, Bologna, Italy, 29 May–2 June 2000; Volume 29, pp. 33–39. [Google Scholar]
- Schmetz, J.; Pili, P.; Tjemkes, S.; Just, D.; Kerkmann, J.; Rota, S.; Ratier, A. SEVIRI Calibration. Bull. Am. Meteorol. Soc. 2002, 83, 977–992. [Google Scholar] [CrossRef]
- Bradbury, T. Meteorology and Flight, 3rd ed.; A and C Black: London, UK, 2000. [Google Scholar]
- Orlanski, I. A rational subdivision of scales for atmospheric processes. Bull. Am. Meteorol. Soc. 1975, 56, 527–530. [Google Scholar]
- Kolmogorov, A.N. The Local Structure of Turbulence in Incompressible Viscous Fluid for Very Large Reynolds Numbers. Dokl. Ak. Nauk SSSR 1941, 30, 301–304. [Google Scholar]
- Observing Systems Capability Analysis and Review Tool. Available online: https://space.oscar.wmo.int/ (accessed on 7 February 2023).
- Van Ha, P.; Thanh, N.T.N.; Hung, B.Q.; Klein, P.; Jourdan, A.; Laffly, D. Assessment of georeferencing methods on MODIS Terra/Aqua and VIIRS NPP satellite images in Vietnam. Presented at the 10th International Conference on Knowledge and Systems Engineering (KSE), Ho Chi Minh City, Vietnam, 1–3 November 2018; pp. 282–287. [Google Scholar]
- Quartly, G.D.; Guymer, T.H. Realizing Envisat’s potential for rain cloud studies. Geophys. Res. Lett. 2007, 34. [Google Scholar] [CrossRef] [Green Version]
- Scharroo, R.; Smith, W.F.H.; Lillibridge, J.L. Satellite altimetry and the intensification of Hurricane Katrina. EOS 2005, 86, 365–370. [Google Scholar] [CrossRef] [Green Version]
Satellite Platform | Sensor | Short Name of the Type of Image | Bands Spatial Resolution | Time Resolution | TOTAL Bands Number |
---|---|---|---|---|---|
EOS-Aqua | MODIS | MYD021KM | 0.25 km (1–2); 0.5 km (3–7); 1 km (8–36) | Daily | 36 |
EOS-Terra | MODIS | MOD021KM | 0.25 km (1–2); 0.5 km (3–7); 1 km (8–36) | Daily | 36 |
Suomi NPP | VIIRS | VNP46A1 | 0.75 km (M1–M16); 0.375 km (I1-I5 and DNB) | Daily | 22 |
Sentinel-3 | SLSTR | SL1RBT | 0.5 km (S1–S6); 1 km (S7–S11) | Daily | 11 |
Type of Image | Sensor | Band | Date (d-m-y) | Sensing Time (h UTC) |
---|---|---|---|---|
MYD021KM | MODIS | 32 | 07-02-2021 | 19.55 * |
MYD021KM | MODIS | 32 | 08-02-2021 | 08.10 |
MYD021KM | MODIS | 32 | 09-02-2021 | 19.45 * |
MYD021KM | MODIS | 32 | 10-02-2021 | 07.55 |
MYD021KM | MODIS | 32 | 11-02-2021 | 08.40 |
MYD021KM | MODIS | 32 | 12-02-2021 | 20.15 * |
MOD021KM | MODIS | 32 | 06-02-2021 | 17.45 * |
MOD021KM | MODIS | 32 | 07-02-2021 | 16.50 * |
MOD021KM | MODIS | 32 | 08-02-2021 | 05.20 |
MOD021KM | MODIS | 32 | 08-02-2021 | 17.35 * |
MOD021KM | MODIS | 32 | 09-02-2021 | 04.25 |
MOD021KM | MODIS | 32 | 09-02-2021 | 16.40 * |
MOD021KM | MODIS | 32 | 10-02-2021 | 17.20 * |
MOD021KM | MODIS | 32 | 11-02-2021 | 04.15 |
MOD021KM | MODIS | 32 | 12-02-2021 | 04.55 |
S3ASL1RBT | SLSTR | S9 | 04-02-2021 | 14.58 |
S3ASL1RBT | SLSTR | S9 | 07-02-2021 | 04.35 |
S3ASL1RBT | SLSTR | S9 | 07-02-2021 | 17.04 * |
S3ASL1RBT | SLSTR | S9 | 08-02-2021 | 04.09 |
S3ASL1RBT | SLSTR | S9 | 08-02-2021 | 16.38 * |
S3ASL1RBT | SLSTR | S9 | 09-02-2021 | 03.43 |
S3ASL1RBT | SLSTR | S9 | 11-02-2021 | 16.57 * |
S3ASL1RBT | SLSTR | S9 | 12-02-2021 | 04.05 |
VNP46A1 | VIIRS | M16 | 04-02-2021 | 00.00 * |
VNP46A1 | VIIRS | M16 | 05-02-2021 | 00.00 * |
VNP46A1 | VIIRS | M16 | 06-02-2021 | 00.00 * |
VNP46A1 | VIIRS | M16 | 07-02-2021 | 00.00 * |
VNP46A1 | VIIRS | M16 | 08-02-2021 | 00.00 * |
VNP46A1 | VIIRS | M16 | 09-02-2021 | 00.00 * |
VNP46A1 | VIIRS | M16 | 10-02-2021 | 00.00 * |
VNP46A1 | VIIRS | M16 | 11-02-2021 | 00.00 * |
VNP46A1 | VIIRS | M16 | 12-02-2021 | 00.00 * |
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Ciardullo, G.; Primavera, L.; Ferrucci, F.; Lepreti, F.; Carbone, V. New Investigation of a Tropical Cyclone: Observational and Turbulence Analysis for the Faraji Hurricane. Remote Sens. 2023, 15, 1383. https://doi.org/10.3390/rs15051383
Ciardullo G, Primavera L, Ferrucci F, Lepreti F, Carbone V. New Investigation of a Tropical Cyclone: Observational and Turbulence Analysis for the Faraji Hurricane. Remote Sensing. 2023; 15(5):1383. https://doi.org/10.3390/rs15051383
Chicago/Turabian StyleCiardullo, Giuseppe, Leonardo Primavera, Fabrizio Ferrucci, Fabio Lepreti, and Vincenzo Carbone. 2023. "New Investigation of a Tropical Cyclone: Observational and Turbulence Analysis for the Faraji Hurricane" Remote Sensing 15, no. 5: 1383. https://doi.org/10.3390/rs15051383