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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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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, Geun-Hyeok Ryu, Hwan-Jin Song, and Mi-Lim Ou

Abstract

In contrast to the view that deep convection causes heavy rainfall, Tropical Rainfall Measuring Mission (TRMM) measurements demonstrate that heavy rainfall (ranging from moderate to extreme rain rate) over the Korean peninsula is associated more with low-level clouds (referred to as warm-type clouds in this study) than with conventional deep convective clouds (cold-type clouds). Moreover, it is noted that the low-level warm-type clouds producing heavy rainfall over Korea appear to be closely linked to the atmospheric river, which can form a channel that transports water vapor across the Korean peninsula along the northwestern periphery of the North Pacific high. Much water vapor is transported through the channel and converges on the Korean peninsula when warm-type heavy rain occurs there. It may be possible to produce abundant liquid water owing to the excess of water vapor; this could increase the rate and extent of raindrop growth, primarily below the melting layer, causing heavy rain when these drops fall to the surface. The occurrence of heavy rainfall (also exhibited as medium-depth convection in radar observations over Okinawa, Japan) due to such liquid-water-rich lower warm clouds should induce difficulties in retrieving rainfall from space owing to the lack of scattering-inducing ice crystals over land and the warmer cloud tops. An understanding of the microphysical processes involved in the production of warm-type rain appears to be a prerequisite for better rain retrieval from space and rain forecasting in this wet region.

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Eun-Han Kwon, B. J. Sohn, William L. Smith, and Jun Li

Abstract

Temperature and moisture profiles retrieved from Infrared Atmospheric Sounding Interferometer (IASI) data are evaluated using collocated radiosonde data from September 2008 to August 2009 over East Asia. The level-2 products used in this study were provided by the National Oceanic and Atmospheric Administration/National Environmental Satellite, Data, and Information Service. By using radiosonde observations as a reference, the bias and root-mean-square error (RMSE) of the temperature and water vapor profiles are obtained to examine the performance of the IASI retrievals depending on surface types and the degree of atmospheric moisture. Overall, retrievals over the land or under drier atmospheric conditions show degraded performance for both the temperature and the moisture, especially for the boundary layer temperature. It is further shown that the vertical distributions of the RMSEs and the biases of the IASI retrievals resemble the variability pattern of the radiosonde observations from the mean profiles. These retrieval aspects are thought to be related to the surface emissivity effect on the IASI retrieval and the difficulties of accounting for large atmospheric variability in the retrieval process. Although the retrieval performance appears to degrade under cloudy conditions, cloudy- and clear-sky retrievals show similar statistical behaviors varying with surface type and atmospheric moisture. Furthermore, the similar statistical behaviors between first guess and final retrievals suggest that error characteristics inherent to the first guess were not sufficiently resolved by the physical retrieval process, leaving a need to improve the first guess for the better retrieval.

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Hyojin Han, Jun Li, Mitch Goldberg, Pei Wang, Jinlong Li, Zhenglong Li, B.-J. Sohn, and Juan Li

Abstract

Accurate cloud detection is one of the most important factors in satellite data assimilation due to the uncertainties associated with cloud properties and their impacts on satellite-simulated radiances. To enhance the accuracy of cloud detection and improve radiance assimilation for tropical cyclone (TC) forecasts, measurements from the Advanced Microwave Sounding Unit-A (AMSU-A) on board the Aqua satellite and the Advanced Technology Microwave Sounder (ATMS) are collocated with high spatial resolution cloud products from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Aqua and the Visible Infrared Imager Radiometer Suite (VIIRS) on board the Suomi-National Polar-Orbiting Partnership (Suomi-NPP) satellite. The cloud-screened microwave radiance measurements are assimilated for Hurricane Sandy (2012) and Typhoon Haiyan (2013) forecasts using the Weather Research and Forecasting (WRF) Model and the three-dimensional variational (3DVAR)-based Gridpoint Statistical Interpolation (GSI) data assimilation system. Experiments are carried out to determine the optimal thresholds of cloud fraction (CF) for minimizing track and intensity forecast errors. The results indicate that the use of high spatial resolution cloud products can improve the accuracy of TC forecasts by better eliminating cloud-contaminated microwave sounder field-of-views (FOVs). In conclusion, the combination of advanced microwave sounders and collocated high spatial resolution imagers is able to improve the radiance assimilation and TC forecasts. The methodology used in this study can be applied to process data from other pairs of microwave sounders and imagers on board the same platform.

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B. Soden, S. Tjemkes, J. Schmetz, R. Saunders, J. Bates, B. Ellingson, R. Engelen, L. Garand, D. Jackson, G. Jedlovec, T. Kleespies, D. Randel, P. Rayer, E. Salathe, D. Schwarzkopf, N. Scott, B. Sohn, S. de Souza-Machado, L. Strow, D. Tobin, D. Turner, P. van Delst, and T. Wehr

An intercomparison of radiation codes used in retrieving upper-tropospheric humidity (UTH) from observations in the ν2 (6.3 μm) water vapor absorption band was performed. This intercomparison is one part of a coordinated effort within the Global Energy and Water Cycle Experiment Water Vapor Project to assess our ability to monitor the distribution and variations of upper-tropospheric moisture from spaceborne sensors. A total of 23 different codes, ranging from detailed line-by-line (LBL) models, to coarser-resolution narrowband (NB) models, to highly parameterized single-band (SB) models participated in the study. Forward calculations were performed using a carefully selected set of temperature and moisture profiles chosen to be representative of a wide range of atmospheric conditions. The LBL model calculations exhibited the greatest consistency with each other, typically agreeing to within 0.5 K in terms of the equivalent blackbody brightness temperature (Tb). The majority of NB and SB models agreed to within ±1 K of the LBL models, although a few older models exhibited systematic Tb biases in excess of 2 K. A discussion of the discrepancies between various models, their association with differences in model physics (e.g., continuum absorption), and their implications for UTH retrieval and radiance assimilation is presented.

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