Localized atmospheric conditions between multi-reference stations can bring the tropospheric delay irregularity that becomes an error terms affecting positioning accuracy in network RTK environment. Imbalanced network error can affect the network solutions and it can corrupt the entire network solution and degrade the correction accuracy. If an anomaly could be detected before the correction message was generated, it is possible to eliminate the anomalous satellite that can cause degradation of the network solution during the tropospheric delay anomaly. An atmospheric grid that consists of four meteorological stations was used to detect an inhomogeneous weather conditions and tropospheric anomaly applied AWSs (automatic weather stations) meteorological data. The threshold of anomaly detection algorithm was determined based on the statistical weather data of AWSs for 5 years in an atmospheric grid. From the analytic results of anomaly detection algorithm it showed that the proposed algorithm can detect an anomalous satellite with an anomaly flag generation caused tropospheric delay anomaly during localized atmospheric conditions between stations. It was shown that the different precipitation condition between stations is the main factor affecting tropospheric anomalies.

Extreme tropospheric anomalies such as typhoons or regional torrential rain can degrade positioning accuracy of the GPS signal. It becomes one of the main error terms affecting high-precision positioning solutions in network RTK. This paper proposed a detection algorithm to be used during atmospheric anomalies in order to detect the tropospheric irregularities that can degrade the quality of correction data due to network errors caused by inhomogeneous atmospheric conditions between multi-reference stations. It uses an atmospheric grid that consists of four meteorological stations and estimates the troposphere zenith total delay difference at a low performance point in an atmospheric grid. AWS (automatic weather station) meteorological data can be applied to the proposed tropospheric anomaly detection algorithm when there are different atmospheric conditions between the stations. The concept of probability density distribution of the delta troposphere slant delay was proposed for the threshold determination.

The atmospheric responses to a Sea Surface Temperature Anomaly(SSTA) over the equatorial eastern Pacific Ocean have been investigated using the horizontally fine resolution model based on OSU 2-layer Atmospheric General Circulation Model(AGCM). The SSTAs during the peak phase of 1982-83 El Nin∼o have been applied to the model as the boundary conditions of the experiment. The model simulates the eastward movement of the rising branch of the Walker circulation. That is, the major features associated with the El Nin∼no such as the increase of the precipitation rate over the center of the Pacific and decrease over the Indonesia, and the 500hPa geopotential height anomaly in the middle latitude are properly described in the fine resolution model experiment. The model results indicate that this horizontally fine resolution GCM can successfully simulate the ENSO anomalies and be more effectivelly used for the study of the climate and the climate changes.