Real-time rainfall monitoring is of great practical importance over the highly populated Indochina area, which is prone to natural disasters, in particular in association with rainfall. With the goal of d etermining near real-time half-hourlyrain estimates from satellite, the three-layer, artificial neural networks (ANN) approach was used to train the brightness temperatures at 6.7, 11, and 12-μm channels of the Japanese geostationary satellite MTSAT against passive microwavebased rain rates from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and TRMM Precipitation Radar (PR) data for the June-September 2005 period. The developed model was applied to the MTSAT data for the June-September 2006 period. The results demonstrate that the developed algorithm is comparable to the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) results and can be used for flood monitoring across the Indochina area on a half-hourly time scale.

In Korea, the ozone profiles have been acquired by using ozonesonde at Pohang station of the Korea Meteorological Administration (KMA) since 1995. These ozone soundings were performed at 0500 UTC on a weekly basis (every Wednesday) in a clear sky. The ozonesonde is equipped with the model 5A ECC sensor, which is one of the most common ozonesonde systems. There have been no attempts to evaluate the Pohang ozonesonde profiles compared with satellite. This paper will provide the first evaluation results for the ozonesonde profiles against HALogen Occultation Experiment (HALOE) measurements over Korea. During 1995-2004 periods, a total of 450 and 188 ozone profiles were obtained from the ozonesonde measurements from HALOE measurements over Korea, respectively. Hence, a total of 34 coincident profile pairs are extracted. Among those total profiles, 26 profiles from ozonesonde are compared against nearly coincident HALOE measurements in time and space. For ozone profiles, the results of statistical analyses showed that the best agreement between two measurements occurs in the 20-25 km and 30-35 km region, where the mean and RMS percent differences are less than ±5 and 14%, respectively. For temperature profiles, the mean and RMS percent differences in 20-25 km region are estimated to be about -0.1 and 1.7%, respectively. According to the scatter plots between two measurements, ozone data are strongly correlated each other above 20 km altitude range with more than 0.8 correlation coefficients. It is found that the altitude (pressure level) differences between two measurements would mainly lead to the discrepancy (over 40% below 18 km) below 20 km in ozone profiles.