Introduction

This paper presents a new method to quickly estimate SSR by combining signals of polar-orbit and geostationary satellites. This method includes two steps. The fifirst step is to estimate hourly cloud parameters by combining high-accuracy cloud products of MODIS and high-temporal-resolution top of-the-atmosphere (TOA) radiance data of all MTSAT channels. The second step is to use the cloud information and other auxiliary information in an effificient parameterization model to retrieve SSR at a high spatiotemporal resolution.

Conclusions and remarks

To obtain high-resolution SSR data, this study developed an ANN-based algorithm to estimate cloud parameters (cloud mask, effective particle radius, and liquid/ice water path) from MTSAT imagery. The algorithm was built by the combination of MODIS cloud products and MTSAT data. The estimated cloud parameters and other information (such as aerosol, ozone, PW) were put into a parameterization model to estimate SSR. The estimated SSR was validated against both experimental data and operational station data in China, with an RMSE of 98.5 W m ^-2 for hourly SSR, 34.2 W m ^-2 for daily SSR, and 22.1 W m ^-2 for monthly SSR, as well as an MBE of about 10 W m ^-2.

Compared with two satellite radiation products (GLASS and ISCCP-FD), the SSR estimate presented in this study has comparable accuracy in terms of RMSE. GLASS underestimates the peak values of SSR, while it overestimates the low values. Our algorithm generally overestimates the SSR, which might be attributed to the underestimation of the cloud water path. The combining of CLOUDSAT and MTSAT in the future may be an alternative method to further improve the accuracy of cloud parameters, because CLOUDSAT has a greater advantage in retrieving cloud parameters than MODIS.