Abstract: Based on the observations of the microwave instruments on satellites, sea surface temperature (SST) can be accurately obtained regardless of clouds, which is helpful to globally monitor changes in SSTs under any conditions. In this study, a new, as well as simple and accurate, algorithm is proposed for retrieving SSTs in the absence of rain by using the Tropical Rainfall Measuring Mission Microwave Imager (TMI) measurements. Applying a loglinear relationship between the brightness temperatures and the main environmental parameters and based on the data from the selected five channels, this algorithm, as a semistatistical technique, was actualized through the radiative transfer model simulations. The unique advantage of this algorithm is that the retrieved SST is able to refrain from the influence by the other environmental parameters, due to the appropriate 5channel loglinear combination. In order to verify the accuracy of the current algorithm, some validations are made in this study. First, the results of case analysis show that there are no unreasonable SST distributions, no matter whether clouds are present or not. On the other hand, the retrievals are also compared against the insitu buoy observations, which showed a good agreement with a bias of 0.116 K and a root mean square error (RMSE) of about 0.665 K. For the different wind speeds, wind directions and or sky conditions, there are high correlation coefficients exceeding 0.95 between these two with, the low bias less than 0.2 K and RMSE of about 0.65 K. Additionally, comparison is also made with the SST retrieved from the Remote Sensing Systems (RSS) products, which showed that there are more than half grids whose differences lower than 1 K, and the variation of the bias between them is not dramatic with changes in other environmental parameters. It is worth noticing that the departure can hold 0.5 K with increasing cloud liquid water path, which implies the influence of clouds can be well eliminated by the current algorithm. However, when high wind speeds the departure is extremely large because of the limitation of the microwave theory on high wind speeds which can produce SST uncertainties not only in the current algorithm, but also in the RSS products. Finally, the similarity of SST global distributions based on this algorithm to those from the other SST data (e.g. the OISST) is shown with the difference less than 1 K in most of the globe, except for few regions having high wind speed related to the error in the current algorithm, or high water vapor content associated with the error in the OISST products. Generally, the above results suggest that this SST algorithm can be well applied to the realtime retrievals, as well as the climatology investigations.