Abstract
The Microwave Integrated Retrieval System (MiRS) has been the NOAA official operational microwave retrieval algorithm since 2007 and is run operationally on multiple microwave satellite/sensor systems. The algorithm is based on a 1-dimensional variational (1-DVAR) methodology, in which the fundamental physical attributes affecting the microwave observations are retrieved physically, including the profile of atmospheric temperature, water vapor, hydrometeors, as well as surface emissivity and temperature. A description of the mathematical basis and algorithm components are presented here, followed by examples of retrieved hydrometeorological parameters. Examples presented show that global estimates of surface rain rate from different satellites are generally consistent, and that the explicit treatment of both surface (e.g., emissivity) and atmospheric parameters in the forward radiative transfer model allows for accurate and consistent estimates over a variety of surfaces (e.g., ocean, land with different vegetation types, coastal regions). Validation and performance metrics using independent reference data indicate that the rainfall rates meet most NOAA operational requirements. Suggested avenues for future development and enhancements are also presented including an example of one planned operational enhancement that has led to improved light rain detection over land.
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References
Aires, F., Prigent, C., Rossow, W. B., & Rothstein, M. (2001). A new neural network approach including first guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature, and emissivities over land from satellite microwave observations. Journal of Geophysical Research, 106, 14887–14907. https://doi.org/10.1029/2001JD900085.
Baldwin M. E., & Mitchell, K. E. (1998). Progress on the NCEP hourly multisensory U.S. precipitation analysis for operations and GCIP research. Preprints 2nd symposium on integrated observing systems (pp. 10–11), Phoenix, AZ, American Meteorological Society.
Boukabara, S. A., Garrett, K., & Grassotti, C. (2018). Dynamic inversion of global surface microwave emissivity using a 1DVAR approach. Remote Sensing, 10, 679–696. https://doi.org/10.3390/rs10050679.
Boukabara, S. A., Garrett, K., Chen, W., Iturbide-Sanchez, F., Grassotti, C., Kongoli, C., Chen, R., Liu, Q., Yan, B., Weng, F., Ferraro, R., Kleespies, T. J., & Meng, H. (2011). MiRS: An all-weather 1DVAR satellite data assimilation and retrieval system. IEEE Transactions on Geoscience and Remote Sensing, 49, 3249–3272. https://doi.org/10.1109/TGRS.2011.2158438.
Boukabara, S.-A., Garrett, K., Grassotti, C., Iturbide-Sanchez, F., Chen, W., Jiang, Z., Clough, S. A., Zhan, X., Liang, P., Liu, Q., Islam, T., Zubko, V., & Mims, A. (2013). A physical approach for a simultaneous retrieval of sounding, surface, hydrometer, and cryospheric parameters from SNPP/ATMS. Journal of Geophysical Research, 118, 12,600–12,619. https://doi.org/10.1002/2013JD020448.
Chen, Y., Weng, F., Han, Y., & Liu, Q. (2008). Validation of the community radiative transfer model (CRTM) by using CloudSat data. Journal of Geophysical Research, 113, 2156–2202. https://doi.org/10.1029/2007JD009561.
Chirokova, G., DeMaria, M., DeMaria, R., Dostalek, J., & Beven, J. (2015). Use of JPSS ATMS-MiRS retrievals to improve tropical cyclone intensity forecasting. 20th conference on satellite meteorology and oceanography, Phoenix, AZ, American Meteorological Society, P157. Available at https://ams.confex.com/ams/95Annual/webprogram/Paper263652.html, last accessed 7 Nov 2018.
Ding, S., Yang, P., Weng, F., Liu, Q., Han, Y., Delst, P. V., Li, J., & Baum, B. (2011). Validation of the community radiative transfer model. Journal of Quantitative Spectroscopy & Radiative Transfer, 112, 1050–1064. https://doi.org/10.1016/j.jqsrt.2010.11.009.
Doswell, C. A., III, Davies-Jones, R., & Keller, D. L. (1990). On summary measures of skill in rare event forecasting based on contingency tables. Weather and Forecasting, 5, 576–585. https://doi.org/10.1175/1520-0434(1990)005<0576:OSMOSI>2.0.CO;2.
Forsythe, J. M., Kidder, S. Q., Fuell, K. K., LeRoy, A., Jedlovec, G. J., & Jones, A. S. (2015). A multisensor, blended, layered water vapor product for weather analysis and forecasting. Journal of Operational Meteorology, 3, 41–58. https://doi.org/10.15191/nwajom.2015.0305.
Guardian. (2015). Typhoon Soudelor: Death toll rises as storm crosses Taiwan. Available at https://www.theguardian.com/world/2015/aug/08/taiwan-hit-by-typhoon-soudelor, last accessed 7 Nov 2018.
Han, Y., Van Delst, P., Liu, Q., Weng, F., Yan, B., Treadon, R., & Derber, J. (2006). Community Radiative Transfer Model (CRTM) – Version 1 (NOAA technical report 122). 33 pp. Available at https://repository.library.noaa.gov/view/noaa/1157, last accessed 7 Nov 2018.
Iturbide-Sanchez, F., Boukabara, S.-A., Chen, R., Garrett, K., Grassotti, C., Chen, W., & Weng, F. (2011). Assessment of a variational inversion system for rainfall rate over land and water surfaces. IEEE Transactions on Geoscience and Remote Sensing, 49, 3311–3333. https://doi.org/10.1109/TGRS.2011.2119375.
Jones, A. S., & Vonder Haar, T. H. (1990). Passive microwave remote sensing of cloud liquid water over land regions. Journal of Geophysical Research, 95, 16673–16683. https://doi.org/10.1029/JD095iD10p16673.
Joyce, R. J., & Xie, P. (2011). Kalman filter based CMORPH. Journal of Hydrometeorology, 12, 1547–1563. https://doi.org/10.1175/JHM-D-11-022.1.
Joyce, R. J., Janowiak, J. E., Arkin, P. A., & Xie, P. (2004). CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. Journal of Hydrometeorology, 5, 487–503. https://doi.org/10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2.
Lin, Y., & Mitchell, K. E. (2005). The NCEP Stage II/IV hourly precipitation analyses: Development and applications. 19th conference on hydrology, San Diego, CA, American Meteorological Society, 1.2. Available at https://ams.confex.com/ams/pdfpapers/83847.pdf, last accessed 7 Nov 2018.
Liu, Q., & Lu, S. (2016). Community radiative transfer model for air quality studies (chapter 2). In Light scattering reviews (Vol. 11, pp. 67–115). Berlin/Heidelberg: Springer Praxis Books/Springer.
Liu, Q., & Weng, F. (2005). One-dimensional retrieval algorithm of temperature, water vapor, and cloud water profiles from advanced microwave sounding unit (AMSU). IEEE Transactions on Geoscience and Remote Sensing, 43, 1087–1095. https://doi.org/10.1109/TGRS.2004.843211.
Liu, S., Grassotti, C., Chen, J., & Liu, Q. (2017). GPM products from the microwave-integrated retrieval system. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 2565–2574. https://doi.org/10.1109/JSTARS.2017.2716356.
NOAA. (2015). JPSS NESDIS ESPC requirements document volume 2: Science Requirements Document (JERD), JPSS-REQ1004, 49 pp. Available at https://www.star.nesdis.noaa.gov/jpss/documents/Requirements/JERDV2_JPSS-REQ-1004_Version2.0.pdf, last accessed 7 Nov 2018.
Rodgers, C. D. (2000). Inverse methods for atmospheric sounding: Theory and practice. Singapore, 238 pp: World Scientific. https://doi.org/10.1142/3171.
Weng, F., Han, Y., Van Delst, P., Liu, Q, Kleespies, T., Yan, B., & LeMarshall, J. (2005). JCSDA Community Radiative Transfer Model (CRTM). In Procedings of 14th TOVS conference (pp. 217–222), Beijing.
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Grassotti, C. et al. (2020). Precipitation Estimation from the Microwave Integrated Retrieval System (MiRS). In: Levizzani, V., Kidd, C., Kirschbaum, D.B., Kummerow, C.D., Nakamura, K., Turk, F.J. (eds) Satellite Precipitation Measurement. Advances in Global Change Research, vol 67. Springer, Cham. https://doi.org/10.1007/978-3-030-24568-9_9
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