Proceedings Article | 14 December 2016
Junye Chen, Quanhua Liu, Mohar Chattopadhyay, Kevin Garrett, Christopher Grassotti, Shuyan Liu, Sid Boukabara
KEYWORDS: Matrices, Meteorology, Microwave radiation, System integration, Temperature metrology, Clouds, Data modeling, Liquids, Skin, Satellites
The NOAA Microwave Integrated Retrieval System (MiRS) retrieves the atmospheric profiles of state variables (temperature, moisture, liquid cloud, hydrometeors, surface emissivity spectrum and skin temperature) simultaneously and coherently with one-dimension Variational (1DVar) method based on passive microwave radiance observation from multiple polar-orbiting operational satellites, including NOAA18, NOAA19, MetOp (A and B), DMSP (F16 and F18) and SNPP. In MiRS, the geophysical consistency between the retrieved state variables is obtained through the constraint from the state variable background covariance matrices, which define the variability of each state variable and the relationship between them. The current atmospheric covariance matrices were mainly built based on the ECMWF 60 layer sample data set (EC60). Because no rain data available in EC60, the MM5 model output rain was used as a substitute. Although MiRS performs well based on current matrices, obviously, there is room for improvement if the matrices could be rebuilt based more representative, higher resolution dataset, and most importantly, all variables are from one datasets, so the cross relationship between parameters could be properly represented. The ECMWF IFS-137 dataset (EC137) is the most recent ECMWF sample data set. Besides several major modifications and improvements in ECMWF forecasting system, comparing with EC60, the profiles in EC137 have more than doubled vertical resolution. The hydrometeor variables are included in EC137, rather than in EC60. With upgraded sampling method, the profiles in EC137 are more evenly distributed in both temporal and spatial domains. And the profile population in EC137 shows significant different statistic characters than its precedents. To generate the new matrices, EC137 data is interpolated from 137 layers to 100 layers. The variable units are transferred to be used for radiation transfer calculation and match with those used in MiRS. For example, the unit of rain and snow is transferred from mass flux (Kg/(m2*s)) to water content (Kg/m2) with the assumption of rain (snow) drop speed as 4.0 (1.0) m/s. Comparing the matrix based on EC137 and that of current MiRS, except the variation in details, there are two significant distinct features are noticeable. First, the snow variable becomes effective in EC137 matrix because the snow variable is provided in EC137 rather than in EC60 and MM5. Second, the covariance between rain and other variables becomes meaningful in EC137 matrix. In the old matrix, rain is not well correlated with other variables, as rain data is from MM5, but other parameters are from EC60, two unrelated datasets. All these differences imply different, most likely better performance of MiRS system if employing the background covariance matrices based on EC137. Tuning is conducted after implementation of the new EC137 matrices. And the impact of the new matrices is assessed by comparing the retrieval products based on the new and old matrices, and with colloated dropsonde, radiosonde and ECMWF analysis profiles.
