Hyo-Jin Han

Validation of Satellite-Based High-Resolution Rainfall Products over the Korean Peninsula Using Data from a Dense Rain Gauge Network

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 t...

On the Assimilation of Satellite Sounder Data in Cloudy Skies in Numerical Weather Prediction Models

Satellite measurements are an important source of global observations in support of numerical weather prediction (NWP). The assimilation of satellite radiances under clear skies has greatly improved NWP forecast scores. However, the application of radiances in cloudy skies remains a significant challenge. In order to better assimilate radiances in cloudy skies, it is very important to detect any clear field-of-view (FOV) accurately and assimilate cloudy radiances appropriately. Research progress on both clear FOV detection methodologies and cloudy radiance assimilation techniques are reviewed in this paper. Overview on approaches being implemented in the operational centers and studied by the satellite data assimilation research community is presented. Challenges and future directions for satellite sounder radiance assimilation in cloudy skies in NWP models are also discussed.

Evaluating the calibration of MTSAT-1R infrared channels using collocated Terra MODIS measurements

The calibration of four MTSAT‐1R infrared channels was evaluated by comparing MTSAT measurements with Terra/MODIS inferred MTSAT‐equivalent brightness temperatures during August 2005 and August 2006. Theoretical relationships converting MODIS brightness temperatures to MTSAT‐equivalent values were obtained and used for the comparison. Results indicate that MTSAT two split window channels are well calibrated, and no serious systematic errors or biases are found; and the MTSAT water‐vapour channel shows a good linear relationship but with a warm bias up to 2 K. The significant cold bias of MTSAT 3.7 µm channel about −6.7 K in August 2005 is found to be much removed in August 2006, after correction of the electrical crosstalk between MTSAT‐1R SWIR channel and WV channel starting from March 2006. Since then, calibration performances of MTSAT‐1R split window channels and shortwave IR channel seem to be comparable with MODIS calibration, while the water‐vapour channel shows more uncertainties up to 2 K of bias.

Microwave Sounder Cloud Detection Using a Collocated High-Resolution Imager and Its Impact on Radiance Assimilation in Tropical Cyclone Forecasts

AbstractAccurate 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...

Simulation of spectral effects of Asian dusts on the AIRS radiances and its application to retrieval of dust properties

In order to examine the effect of Asian dusts and apply to retrieval of dust properties, radiances measured by AIRS were simulated using the RTTOV-9 model. The model has been implemented with new optical properties for Asian dusts; refractive indices of mineral dust in the OPAC library and size distribution of Asian dusts retrieved from 10 years of skyradiometer measurements at Dunhuang, China. The simulations were performed using the implemented model, but with specification of AOT and height of dust layers obtained from CALIOP measurements. In the simulations, surface and atmospheric temperatures are from AIRS level 2 products while surface emissivity is specified with UW/CIMSS monthly mean global infrared surface emissivity data. Results show that effect of Asian dusts on AIRS spectra is substantial over infrared window regions (i.e.: 3.7 - 4.1 μm, 8.8 - 9.3 μm, 10 - 13 μm) for moderate and strong dust cases (AOT ≥ 0.5), while surface effect is dominant for weak dust cases (AOT < 0.5). Over 10 - 13 μm and 3.6 - 4.1 μm ranges, the simulation performances are improved when the dust effect is added. However, on the spectral range of 8.8 - 9.3 μm, the simulation overestimates radiances in comparison with AIRS measurements, probably because the mineral dust composition of OPAC does not coincide with the Asian dust. The comparison of simulated radiances with AIRS measurements shows a comparable quality for both clear and dusty conditions on the 10 - 13 μm and 3.6 - 4.1 μm ranges, suggesting that results can be incorporated for developing dust retrieval algorithm from hyperspectral images such as AIRS and IASI.