Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 1, pp. 78-86
Correction of cloud water estimates from satellite monitoring data
1 Kotelnikov Institute of Radioengineering and Electronics RAS, Fryazino Branch, Fryazino, Moscow Region, Russia
Accepted: 14.03.2022
DOI: 10.21046/2070-7401-2022-19-1-78-86
One of the most significant systematic errors in determining the parameters of the cloud layer is associated with the inhomogeneity of the cloud layer in the field of view of microwave sensors. The resulting discrepancies in the estimates of the optical path of radiation in clouds, the so-called CLWP, require correction, since these discrepancies can reach two times, for example, in areas of the atmosphere with shallow cumulus convection. This determines the significance and relevance of correcting the CLWP values, since these values determine the water content of clouds and, as a result, probabilistic estimates of the predicted volumes and intensities of rainfall from them. The paper proposes and implements a method for correcting cloud water content estimates based on microwave monitoring data using cloud mask data obtained from the results of synchronous observations of the cloud layer from geostationary platforms in the visible and IR ranges. A general methodological approach has been developed, and the capabilities of modern observation tools are discussed, emphasizing their high temporal resolution contributing to timely detection and monitoring of rapidly developing dangerous atmospheric phenomena such as mesoscale convective complexes (MCCs). Asymptotic solutions are obtained for small values of CLWP in the lower layers of the atmosphere (under clouds) and for the limiting, under terrestrial conditions, values of the reflection of microwave radiation from the Earth’s surface.
Keywords: microwave radiometer, brightness temperature, water content of clouds, relative area of clouds
Full textReferences:
- Savorskiy V. P., Kutuza B. G., Akvilonova A. B., Kibardina I. N., Panova O. Yu., Danilychev M. V., Shirokov S. V., Enhancing the efficiency of the reconstruction of the temperature and humidity profiles of the cloud atmosphere by the data of satellite microwave spectrometers, J. Communications Technology and Electronics, 2020, Vol. 65, No. 7, pp. 792–799, https://doi.org/10.1134/S1064226920070104/.
- ARTS User Guide. Version 2.2.66, Eriksson P., Buhler S. (eds.), 2020, 169 p., acsessed: https://arts.mi.uni-hamburg.de/misc/arts-doc-stable/uguide/arts_user.pdf.
- Bauer P., Thorpe A., Brunet G., The quiet revolution of numerical weather prediction, Nature, 2015, No. 525, pp. 47–55, https://doi.org/10.1038/nature14956.
- Bony S., Stevens B., Frierson D., Jakob C., Kageyama M., Pincus R., Shepherd T., Sherwood S., Siebesma A., Sobel A., Watanabe M., Webb M., Clouds, circulation and climate sensitivity, Nature Geoscience, 2015, No. 8(4), pp. 261–268, https://doi.org/10.1038/NGEO2398.
- Bremen L. V., Ruprecht E., Macke A., Errors in liquid water path retrieval arising from cloud inhomogeneities: The beam-filling effect, Meteorologische Zeitschrift, 2002, No. 11, pp. 13–19, DOI: 10.1127/0941-2948/2002/0011-0013.
- Christopher S. A., Chou J., Cloud liquid water path comparisons from passive microwave and solar reflectance satellite measurements: Assessment of subfield-of-view cloud effects in microwave retrievals, J. Geophysical Research, 1997, No. 102, pp. 19585–19596.
- Elsaesser G. S., O’Dell C. W., Lebsock M. D., Bennartz R., Greenwald T. J., Wentz F. J., The multi-sensor advanced climatology of liquid water path (MAC-LWP), J. Climate, 2017, No. 30, pp. 10193–10210, https://doi.org/10.1175/JCLI-D-16-0902.1.
- Hilburn K. A., Wentz F. J., Intercalibrated passive microwave rain products from the Unified Microwave Ocean Retrieval Algorithm (UMORA), J. Applied Meteorology and Climatology, 2008, No. 47, pp. 778–794, DOI: 10.1175/2007JAMC1635.1.
- Lebsock M. D., Su H., Application of active spaceborne remote sensing for understanding biases between passive cloud waterpath retrievals, J. Geophysical Research Atmospheres, 2014, No. 119, pp. 8962–8979, DOI: 10.1002/2014JD021568.
- O’Dell C. W., Wentz F. J., Bennartz R., Cloud liquid water path from satellite-based passive microwave observations: Anew climatology over the global oceans, J. Climate, 2008, No. 21, pp. 1721–1739, DOI: 10.1175/2007JCLI1958.1.
- Stephens G. L., Kummerow C., The remote sensing of clouds and precipitation from space: a review, J. Atmospheric Sciences, 2007, No. 64, pp. 3742–3765.
- Stephens G., Christensen M., Andrews T., Haywood J., Malavelle F., Suzuki K., Jing X., Lebsock M., Li J.-L. F., Takahashi H., Sy O., Cloud physics from space, Quarterly J. Royal Meteorological Society, 2019, Vol. 145, No. 724, pp. 2854–2875, https://doi.org/10.1002/qj.3589.
- Wentz F. J., Meissner T., AMSR Ocean Algorithm, Algorithm Theoretical Basis Document, Version 2, AMSR Ocean Algorithm, RSS Tech. Proposal 121599A-1, 2000, 67 p.
- Wentz F. J., Spencer R. W., SSM/I rain retrievals within a unified all-weather ocean algorithm, J. Atmospheric Sciences, 1998, No. 55, pp. 1613–1627.
- Werner F., Deneke H., Increasing the spatial resolution of cloud property retrievals from Meteosat SEVIRI by use of its high-resolution visible channel: evaluation of candidate approaches with MODIS observations, Atmospheric Measurement Techniques, 2020, No. 13, pp. 1089–1111, https://doi.org/10.5194/amt-13-1089-2020.