Many satellite-derived products such as the atmospheric wind vector depend their accuracy on the accuracy of the estimated cloud top altitude. The uncertainty in the derived cloud top altitude occurs mainly when there is thin semitransparent cloud where the cloud radiation is contaminated by radiation from the surface and low cloud. Further, validation of the derived cloud top altitude is not easy task, simply due to lack of truth data. Here, we use ground based rawinsonde, radar, and lidar data for the validation of the cloud top altitude derived from GOES-9 satellite data. The preliminary results show that the infrared-water vapor method compares better than the single infrared method for all of the ground truth data.
The direct broadcast Terra/MODIS data has been utilized in Korea Meteorological Administration (KMA) since February 2001. This study introduces utilizations of this data, especially for the derivation of sea surface temperature (SST). For an operational production of the MODIS SST, we have derived a new set of MCSST coefficients by using a simple cloud mask algorithm and by assumption of NOAA daily SST as a true SST. The current NASA’s PFSST and new MCSST algorithms are analyzed by using the collocated buoy observations in the East Asia region. Although the number of collocated data was limited, both algorithms have high correlation with the buoy SST, but somewhat bigger bias and RMS difference than we expected. And PFSST uniformly underestimated the SST. Through more analyzing the archived and future-received data, we plan to derive better MCSST coefficients and apply to MODIS data of Aqua. To use the MODIS cloud mask algorithm to get better SST coefficients is going to be prepared.
Hourly rain rates are estimated operationally in Korea Meteorological Administration (KMA) since last year by the regression method (A_REG), which uses the GMS-5 IR and Automatic Weather Systems (AWS) gauges data over Korean Peninsula. As the results, A_REG method estimates rain rate better than other retrieval methods such as probability matching method and look up table method do. However, this equation can't represent on the whole area including oceans because a number of rain gauges are limited over Korean peninsula. In this study the A_REG method has been improved by using the radar rain rate over Japan instead of AWS over Korea Peninsula. As the statistical verification, the estimated rain rates by R_REG method are improved than existing method (A_REG). The mean errors of R_REG nearly don't appear though A_REG show positive bias. Root mean square errors of R_REG are 2-3 times smaller than A_REG. The correlation coefficients of two methods are similar.
Conference Committee Involvement (2)
Remote Sensing of the Atmosphere and Clouds III
12 October 2010 | Incheon, Korea, Republic of
Passive Optical Remote Sensing of the Atmosphere and Clouds IV
9 November 2004 | Honolulu, Hawai'i, United States