This study evaluates the quality of surface air temperature, relative humidity, and precipitation detection observed by 22 internet of thing (IoT)-based mini-weather stations in Seoul in 2020 summer. The automatic weather station (AWS) closest to each IoT-based station is used as reference. The IoT-based observations show surface air temperature and relative humidity are about 0.2-4.0°C higher and about -1--22% lower than the AWS observations, respectively. However, they exhibit temporal variability similar to the AWS observations on both diurnal and daily time scales, with daily correlations greater than 0.90 for temperature and 0.82 for relative humidity. Given these strong linear relationships, it show that temperature and relative humidity biases can be effectively corrected by applying a simple bias correction method. For IoT-based precipitation detection, we found that precipitation conductivity value (PCV) during precipitation events is well separated from that during non-precipitation events, providing a basis for distinguishing precipitation events from non-precipitation events. When the PCV threshold is set to 250 for precipitation detection, the highest critical success index and the bias score index close to one, suitable for operational precipitation detection, are obtained. These results demonstrate that IoT-based mini-weather stations can successfully measure surface air temperature, relative humidity, and precipitation detection with appropriate bias corrections.

The relationship between air temperature and sea surface temperature are studied using the daily air temperature and sea surface temperature data for 25 years (1970∼1994) at 9 coastal stations in Korea. Seasonal variations of air temperature have larger amplitudes than those of sea surface temperature. The seasonal variations of air temperature leads those of sea surface temperature by 2 to 3 weeks. The anomalies of sea surface temperature and air temperature are positively correlated. The long term anomalies of sea surface temperature and air temperature with time scales more than 1 month are more highly correlated than those of short term, with time scales less than 1 month. Accumulated monthly anomalies of sea surface temperature and air temperature for 6 months showed higher correlation than the anomalies of each month. The magnitudes of sea surface temperature and air temperature anomalies are related with the duration of anomalies. Their magnitudes are large when the durations of anomalies are long.

The present study intends to investigate the transient response of an atmosphere/ocean general circulation model to a gradual increase of atmospheric carbon dioxide. To detect the climatic change of the surface air temperature due to gradual increasing carbon dioxide for 100 years, two runs of GFDL CGCM for 1 % CO_2 run with increasing CO_2 and the control run with fixed CO_2 are compared. From results it is noted that the transient response of surface air temperature is more increased over the Northern Hemisphere than the Southern Hemisphere. However, in Northern Hemisphere the transient response of the surface air temperature due to the gradual increase of atmospheric carbon dioxide is slowly increased with latitudes and is clearly larger over continents than oceans. The annual global mean temperature is continuously increased with 0.03552 per one year with strong S/N ratio and distinguished from the natural variability. The time dependent response of the gradual increasing CO_2 has the strong seasonal variability with small change in summer and large change in winter, and the strong regionality in the Asian and the American continents. It has been suggested that the direct and the feedback processes in the climate systems should be investigated by the detailed sensitivity runs to get the meaningful estimate of the CO_2 forced variability.