ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2024, Vol. 21, No. 3, pp. 292-306

Study of Indian summer monsoon in the precipitable water vapor field of the atmosphere based on satellite microwave radiometer data

А.V. Kuzmin 1 , D.M. Ermakov 1, 2 , E.D. Surovyatkina 1 , E.V. Pashinov 1 , S.A. Vturin 1 
1 Space Research Institute RAS, Moscow, Russia
2 Kotelnikov Institute of Radioengineering and Electronics RAS, Fryazino Branch, Fryazino, Moscow Region, Russia
Accepted: 03.05.2024
DOI: 10.21046/2070-7401-2024-21-3-292-306
From SSMIS (Special Sensor Microwave Imager/Sounder) F16-F18 microwave satellite data, daily precipitable water vapor (PWV) fields were obtained from 2012 to 2021 using satellite radiothermovision techniques, both over the World Ocean and over land. The use of satellite radiothermovision made it possible to obtain complete daily PWV fields, eliminating gaps with missing data. From the PWV fields database, to study the characteristics of the summer monsoon near the Hindustan Peninsula, the fragments for two regions were analyzed: in the Bay of Bengal and the Arabian Sea. A zone of maximum water vapor content over the northern Bay of Bengal (centered at 20° N, 87.5° E) has been identified, which plays the key role in formation of the monsoon over the central and north-eastern regions of India. In this region, there is a relatively smooth increase in PWV with an average trend of +0.33 kg/m2 per day from the beginning of March; with the onset of the monsoon it becomes constant, reaching the average value of 62.2 kg/m2 for 2012–2021. A decrease in the PWV value from the average value characterizes the end of the monsoon period. An area has been identified in the Arabian Sea that experiences two peaks in water vapor content: during the onset of the monsoon and before the end of the summer monsoon in India, which is attributed to its distance from the coast and the reversal of the Intertropical Convergence Zone (ITCZ) across the Arabian Sea. Observations in this zone can be useful for monitoring the formation of strong cyclones during the passage of the ITZC through it.
Keywords: global precipitable water vapor fields, summer Indian monsoon, microwave radiometry, satellite radiothermovision
Full text

References:

  1. Boldyrev V. V., Gorobets N. N., Ilgasov P. A., Nikitin O. V., Pantsov V. Yu., Prokhorov Yu. N., Strelnikov N. I., Streltsov A. M., Cherny I. V., Chernyavsky G. M., Yakovlev V. V., Satellite microwave scanner/probe MTVZA-GYa, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2008, Vyp. 5, Vol. 1, pp. 243–248 (in Russian).
  2. Ermakov D. M., Global circulation of latent heat in the Earth’s atmosphere according to data from satellite radiothermovision, Izvestiya, Atmospheric and Oceanic Physics, 2018, Vol. 54, No. 9, pp. 1223–1243, DOI: 10.1134/S000143381809013X.
  3. Ermakov D. M., Polyakov V. D., Polyakova E. V., Development of a new algorithm for the retrieval of total precipitable water of the atmosphere over land from the data of satellite radiothermal monitoring, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 1, pp. 31–41 (in Russian), DOI: 10.21046/2070-7401-2020-17-1-31-41.
  4. Ermakov D. M., Kuzmin A. V., Mazurov A. A. et al., The concept of streaming data processing of Russian satellite microwave radiometers of the MTVZA series based on the IKI-Monitoring Center for collective use, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, Vol. 18, No. 4, pp. 298–303 (in Russian), DOI: 10.21046/2070-7401-2021-18-4-298-303.
  5. Ermakov D. M., Pashinov E. V., Kuzmin A. V. et al., The Concept of Calculating the Elements of the Regional Hydrological Balance with the Use of Satellite Radiothermovision, Hydrometeorology and Ecology, 2023, Vol. 72, pp. 470–492 (in Russian), DOI: 10.33933/2713-3001-2023-72-470-492.
  6. Kramchaninova E. K., Uspensky A. B., Determination of near-surface air temperature over land based on data of microwave sounding from satellite Meteor-M No. 1, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2012, Vol. 9, No. 3, pp. 127–136 (in Russian).
  7. Kutuza B. G., Danilychev M. V., Yakovlev O. I., Sputnikovyi monitoring Zemli: Mikrovolnovaya radiometriya atmosfery i poverkhnosti (Satellite monitoring of the Earth: Microwave radiometry of the atmosphere and surface), Moscow: Lenand, 2016, 336 p. (in Russian).
  8. Mitnik L. M., Mitnik M. L., Gurvich I. A., Vykochko A. V., Kuzlyakina Yu. A., Cherny I. V., Chernyavsky G. M., Investigation of tropical cyclone evolution in the Northwest Pacific Ocean from Aqua AMSR-E and Meteor-M No. 1 MTVZA-GYa data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2012, Vol. 9, No. 4, pp. 121–128 (in Russian).
  9. Pashinov E. V., Retrieval of integrated water vapor content of the atmosphere over the ocean using MTVZA-GY (Meteor-M No. 2) data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, No. 4, pp. 225–235 (in Russian), DOI: 10.21046/2070-7401-2018-15-4-225-235.
  10. Sadovsky I. N., Sazonov D. S., Geographical Reference of MTVZA-GYa’s Radiometric Remote Sensing Data, Izvestiya, Atmospheric and Oceanic Physics, 2022, No. 6, pp. 101–112 (in Russian), DOI: 10.31857/S0205961422060100.
  11. Sadovsky I. N., Sazonov D. S., Correction Procedure for MTVZA-GYa Georeference, Izvestiya, Atmospheric and Oceanic Physics, 2023, No. 6, pp. 73–85 (in Russian), DOI: 10.31857/S0205961423060076.
  12. Sazonov D. S., Algorithm for reconstructing ocean surface temperature, near-surface wind speed and integral vapor content from MTVZA-GYa data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 1, pp. 50–64 (in Russian), DOI: 10.21046/2070-7401-2022-19-1-50-64.
  13. Sazonov D. S., Study the Possibility of Precipitation Intensity Recovery from MTVZA-GYa Measurements, Izvestiya, Atmospheric and Oceanic Physics, 2023, Vol. 5, pp. 23–35 (in Russian), DOI: 10.31857/S020596142305007X, EDN: XQPADE.
  14. Filei A. A., Andreev A. I., Kuchma M. O., Uspensky A. B., Retrieval of Total Precipitable Water from Meteor-M No. 2-2 MTVZA-GYa Data Using a Neutral Network Algorithm, Meteorology and Hydrology, 2022, Vol. 4, pp. 34–45 (in Russian), DOI: 10.52002/0130-2906-2022-4-34-45
  15. Chernyavsky G. M., Mitnik L. M., Kuleshov V. P. et al., Microwave sensing of the ocean, atmosphere and land surface from Meteor-M No. 2 data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, No. 4, pp. 78–100 (in Russian), DOI: 10.21046/2070-7401-2018-15-4-78-100.
  16. Sharkov E. A., Passive Microwave Remote Sensing of the Earth: Physical Foundations, Berlin: Springer/PRAXIS, 2003, 613 p.
  17. Bollasina M. A., Hydrology: Probing the monsoon pulse, Nature Climate Change, 2014, Vol. 4, pp. 422–423, https://doi.org/10.1038/nclimate2243.
  18. Divakarla M. G., Barnet C. D., Goldberg M. D. et al., Validation of Atmospheric Infrared Sounder temperature and water vapor retrievals with matched radiosonde measurements and forecasts, J. Geophysical Research, 2006, Vol. 111, Issue D9, Article D09S15, https://doi.org/10.1029/2005JD006116.
  19. Du J., Kimball J. S., Jones L. A., Satellite microwave retrieval of total precipitable water vapor and surface air temperature over land from AMSR 2, IEEE Trans. Geoscience and Remote Sensing, 2015, Vol. 53, Issue 5, pp. 2520–2531, DOI: 10.1109/TGRS.2014.2361344.
  20. Du J., Kimball J. S., Jones L. A. et al., A global satellite environmental data record derived from AMSR-E and AMSR 2 microwave Earth observations, Earth System Science Data, 2017, Vol. 9, Issue 2, pp. 791–808, https://doi.org/10.5194/essd-9-791-2017.
  21. Ermakov D., Satellite radiothermovision of atmospheric processes: method and applications, Cham: Springer, 2021, 226 p., https://doi.org/10.1007/978-3-030-57085-9.
  22. Fan J., Meng J., Ludescher J. et al., Network-based approach and climate change benefits for forecasting the amount of Indian monsoon rainfall, J. Climate, 2022, Vol. 35, Issue 3, pp. 1009–1020, https://doi.org/10.1175/JCLI-D-21-0063.1
  23. Gadgil S., The Indian monsoon and its variability, Annual Review of Earth and Planetary Sciences, 2003, Vol. 31, pp. 429–467, DOI: 10.1146/annurev.earth.31.100901.141251.
  24. Hersbach H., Bell B., Berrisford P. et al., The ERA5 global reanalysis, Quarterly J. Royal Meteorological Society, 2020, Vol. 146, Issue 730, pp. 1999–2019, https://doi.org/10.1002/qj.3803.
  25. Hollinger J. P., DMSP Special Sensor Microwave/Imager Calibration/Validation, Final Report, Vol. 1, Space Sensing Branch, Naval Research Laboratory, Washington, DC, 1988, 190 p.
  26. Imaoka K., Maeda T., Kachi M. et al., Status of AMSR 2 instrument on GCOM-W1, Proc. SPIE, Vol. 8528, Earth Observing Missions and Sensors: Development, Implementation, and Characterization II, 2012, Article 852815, https://doi.org/10.1117/12.977774.
  27. Koike T., Nakamura Y., Kaihotsu I. et al., Development of an Advanced Microwave Scanning Radiometer (AMSR-E) algorithm of soil moisture and vegetation water content, Proc. Hydraulic Engineering, 2004, Vol. 48, pp. 217–222, DOI: https://doi.org/10.2208/prohe.48.217.
  28. Kunkee D. B., Poe G. A., Boucher D. J. et al., Design and Evaluation of the First Special Sensor Microwave Imager/Sounder, IEEE Trans. Geoscience and Remote Sensing, 2008, Vol. 46, pp. 863–883, DOI: 10.1109/tgrs.2008.917980.
  29. Ludescher J., Martin M., Boers N. et al., Network-based Forecasting of Climate Phenomena, Proc. National Academy of Sciences (PNAS), 2021, Vol. 118, No. 47, Article e1922872118, https://doi.org/10.1073/pnas.1922872118.
  30. Sivira R. G., Brogniez H., Mallet C., Oussar Y., A layer-averaged relative humidity profile retrieval for microwave observations: design and results for the Megha-Tropiques payload, Atmospheric Measurement Techniques, 2015, Vol. 8, pp. 1055–1071, https://doi.org/10.5194/amt-8-1055-2015.
  31. Soman M. K., Krishna Kumar K., Space-time evolution of the meteorological features associated with the onset of the Indian summer monsoon, Monthly Weather Review, 1993, Vol. 121, pp. 1177–1194.
  32. Stolbova V., Surovyatkina E., Bookhagen B., Kurths J., Tipping elements of the Indian monsoon: Prediction of onset and withdrawal, Geophysical Research Letters, 2016, Vol. 43, Issue 8, pp. 3982–3990, https://doi.org/10.1002/2016GL068392.
  33. Surovyatkina E., Forecasting of Indian Monsoon, 2023, https://www.pik-potsdam.de/members/elenasur/forecasting-indian-monsoon
  34. Turner A. G., Annamalai H., Climate change and the South Asian summer monsoon, Nature Climate Change, 2012, Vol. 2, pp. 587–595, DOI: 10.1038/nclimate1495.
  35. Wentz F., A well-calibrated ocean algorithm for Special Sensor Microwave/Imager, J. Geophysical Research, 1997, Vol. 102, Issue C4, pp. 8703–8718, https://doi.org/10.1029/96JC01751.
  36. Zhang T, Jiang X., Yang S., Chen J. et al., A predictable prospect of the South Asian summer monsoon. Nature Communications, 2022, No. 13, Article 7080, https://doi.org/10.1038/s41467-022-34881-7.