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  • Author or Editor: B. Sohn x
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B-J. Sohn, Seung-Hee Ham, and Ping Yang

Abstract

The authors examined the possible use of deep convective clouds (DCCs), defined as clouds that overshoot the tropical tropopause layer (TTL), for the calibration of satellite measurements at solar channels. DCCs are identified in terms of the Moderate Resolution Imaging Spectroradiometer (MODIS) 10.8-μm brightness temperature (TB11) on the basis of a criterion specified by TB11 ≤ 190 K. To determine the characteristics of these clouds, the MODIS-based cloud optical thickness (COT) and effective radius (re) for a number of identified DCCs are analyzed. It is found that COT values for most of the 4249 DCC pixels observed in January 2006 are close to 100. Based on the MODIS quality-assurance information, 90% and 70.2% of the 4249 pixels have COT larger than 100 and 150, respectively. On the other hand, the re values distributed between 15 and 25 μm show a sharp peak centered approximately at 20 μm. Radiances are simulated at the MODIS 0.646-μm channel by using a radiative transfer model under homogeneous overcast ice cloudy conditions for COT = 200 and re = 20 μm. These COT and re values are assumed to be typical for DCCs. A comparison between the simulated radiances and the corresponding Terra/Aqua MODIS measurements for 6 months in 2006 demonstrates that, on a daily basis, visible-channel measurements can be calibrated within an uncertainty range of ±5%. Because DCCs are abundant over the tropics and can be identified from infrared measurements, the present method can be applied to the calibration of a visible-channel sensor aboard a geostationary or low-orbiting satellite platform.

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B. J. Sohn, Hyo-Jin Han, and Eun-Kyoung Seo

Abstract

Four independently developed high-resolution precipitation products [HRPPs; the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), the Climate Prediction Center Morphing Method (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and the National Research Laboratory (NRL) blended precipitation dataset (NRL-blended)], with a spatial resolution of 0.25° and a temporal resolution of 3 h, were compared with surface rain measurements for the four summer seasons (June, July, and August) from 2003 to 2006. Surface measurements are 1-min rain gauge data from the Automated Weather Station (AWS) network operated by the Korean Meteorological Administration (KMA) over South Korea, which consists of about 520 sites. The summer mean rainfall and diurnal cycles of TMPA are comparable to those of the AWS, but with larger magnitudes. The closer agreement of TMPA with surface observations is due to the adjustment of the real-time version of TMPA products to monthly gauge measurements. However, the adjustment seems to result in significant overestimates for light or moderate rain events and thus increased RMS error. In the other three products (CMORPH, PERSIANN, and NRL-blended), significant underestimates are evident in the summer mean distribution and in scatterplots for the grid-by-grid comparison. The magnitudes of the diurnal cycles of the three products appear to be much smaller than those suggested by AWS, although CMORPH shows nearly the same diurnal phase as in AWS. Such underestimates by three methods are likely due to the deficiency of the passive microwave (PMW)-based rainfall retrievals over the South Korean region. More accurate PMW measurements (in particular by the improved land algorithm) seem to be a prerequisite for better estimates of the rain rate by HRPP algorithms. This paper further demonstrates the capability of the Korean AWS network data for validating satellite-based rain products.

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