Abstract
In preparation for the 2nd geostationary multi-purpose satellite of Korea with a 16-channel Advanced Meteorological Imager; an algorithm has been developed to retrieve clear-sky vertical profiles of temperature (T) and humidity (Q) based on a nonlinear optimal estimation method. The performance and characteristics of the algorithm have been evaluated using the measured data of the Advanced Himawari Imager (AHI) on board the Himawari-8 of Japan, launched in 2014. Constraints for the optimal estimation solution are provided by the forecasted T and Q profiles from a global numerical weather prediction model and their error covariance. Although the information contents for temperature is quite low due to the limited number of channels used in the retrieval; the study reveals that useful moisture information (2~3 degrees of freedom for signal) is provided from the three water vapor channels; contributing to the increase in the moisture retrieval accuracy upon the model forecast. The improvements are consistent throughout the tropospheric atmosphere with almost zero mean bias and 9% (relative humidity) of root mean square error between 100 and 1000 hPa when compared with the quality-controlled radiosonde data from 2016 August.
Highlights
Information on the vertical distribution of temperature (T) and moisture (Q) is critical to the prediction of weather phenomena
It is important to have atmospheric profile information with high spatial resolution in a timely manner. Such atmospheric information is provided through prediction or measurement from Numerical Weather Prediction (NWP) models or hyper-spectral sounders onboard polar orbiters, both are provided in lower temporal and spatial resolution compared to that from the generation high-performance geostationary imagers such as the operational Advanced Himawari Imager (AHI) on board the Himawari-8, Advanced Baseline Imager (ABI) on board the Geostationary Operational Environmental Satellite-R (GOES-R) series, or the planned Advanced Meteorological Imager (AMI) on board the Geostationary Korea multipurpose Satellite-2A (GK-2A)
The results showed that the retrieved moisture profile significantly improves upon the Global Forecasting System (GFS) forecast from National Centers for Environmental Prediction (NCEP) between 300 and 700 hPa while the retrieved temperature profile shows similar accuracy to the model forecast field when compared with radiosonde measurements
Summary
Information on the vertical distribution of temperature (T) and moisture (Q) is critical to the prediction of weather phenomena. To see the impact of B-matrix on the retrieval, a sensitivity test was performed with differently scaled B-matrix In this test, the two terms in the inverse-weight in Equation (2), i.e., B-matrix (Sa) converted to observation space (KnSaKTn) and the observation error covariance (S ) are compared. KSaKT is much more sensitive to the change in the Q error covariance than in T and this change is more prominent in the water vapor channels (Ch. 8–10) than in the window (Ch. 13–15) or CO2 absorption (Ch. 16) channels This implies that if mis-scaled Sa is used and the magnitude of KSaKT is not balanced out with S in Equation (2), the weight given to the satellite measurement becomes too large or small, which may prevent the convergence to the optimal solution. T and Q: B-matrix used in KMA OPS 1DVar O3: B-matrix used in ECMWF 1DVar Monthly climatology generated from CIMSS global LSE database RTM RTTOV v.11.2
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