Monday, 11 January 2016
Determination of precipitable water and cloud liquid water over ocean from satellite microwave observation is not only remarkable for a variety of applications to improve the analysis and forecast capabilities of weather systems, but also it is crucial to the data assimilation of satellite microwave observation in numerical weather prediction modeling systems. The cloud and precipitation products are utilized as predictor in bias correction to eliminate the bias implied in satellite observation. In addition, they are used to identify the satellite data contaminated by cloud and precipitation and to control the quality of good radiances assimilated into the numerical prediction models. In general, two window channel measurements may be taken to retrieve the cloud and precipitation products from satellite microwave observation. For example, one channel in window regions (19, 37, and 85 GHz) with the channel centered near the 22 GHz water vapor line are taken in the retrieval from the Special Sensor Microwave Imager (SSM/I). For Advanced Microwave Sounding Unit (AMSU) on board both NOAA and MetOp serial polar orbiting satellites, together with the Advanced Technology Microwave Sounder (ATMS) flown on the NPOESS Preparatory Project (NPP) satellite, the 23.8 and 31.4 GHz channels are utilized to determine the information on precipitable water and cloud liquid water. However, the secondary generation microwave vertical sounding carried on Chinese meteorological polar orbiting satellites does not have the window channels at 23.8, 31.4, and 89 GHz, it presents a great difficult in the assimilation of the satellite microwave observations. The estimation of precipitable water and cloud liquid water from FY-3C microwave observation, by using the additional sounding channels located at 118.75 GHz oxygen absorption band that are carried on operational satellite in first internationally, is conducted in this study. Five issues associated are addressed and will be introduced in our presentation. They are: The first, the cloud algorithm is developed. Among the two channel measurements TB1 and TB2 in the traditional retrieval algorithm, TB2 is the brightness temperature on the water vapor absorption line such as SSM/I 22 GHz or AMSU 23.8 GHz, that is just missing in MWTS. It could be taken by the new humidity sounding channels with frequencies centered near 118.75 GHz and with peak of weighting function at surface. TB1 is the brightness temperature sensitive to the cloud liquid water. For example, SSM/I 19, 37, and 89 GHz or AMSU 31.4, 50.3, and 89 GHz, though AMSU 31.4 GHz is mainly used. MWTS 50.3, 51.76, and 52.8 GHz may be used. The MWHS 118.75±2.5 and MWTS 52.8 GHz are utilized in our algorithm because that the cloud emission and scattering index derived from this paired channels is shown to correspond well with the vertically integrated liquid and ice (total) water path (TWP) from the top of atmosphere to 850 hPa in Han et al.'s study (2015). The algorithms based on other paired channels may be discussed in further works. Secondly, the asymmetry along the scan is corrected through the angle bias by the enhanced bias correction scheme implemented in the National Centers for Environmental Prediction's Gridpoint Statistical Interpolation (GSI) data assimilation system. The third, the MWHS brightness temperature is matched to MWTS observation in that MWTS and MWHS are not at the same locations as ATMS's temperature sounding and water vapor sounding data. The forth, the coefficients in the cloud algorithms are obtained using regression with a training dataset of actual measurements though the simulated one is valid and used in general. The consideration is that it could avoid the noise in simulated measurements. It is tough for the radiative transfer model to handle the radiative effect of cloud in great accuracy. The last, the estimation from FY-3C observation is validated against that of MetOp-B data calculated by the traditional cloud algorithm. The FY-3C and MetOp-B have the close equatorial crossing time to make the result is comparable.
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