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.

Since 2009, the Korea Meteorological Administration (KMA) has participated in ground validation (GV) projects through international partnerships within the framework of the Global Precipitation Measurement (GPM) Mission. The goal of this work is to assess the reliability of ground-based measurements in the Korean Peninsula as a means for validating precipitation products retrieved from satellite microwave sensors, with an emphasis on East Asian precipitation. KMA has a well-developed operational weather service infrastructure composed of meteorological radars, a dense rain gauge network, and automated weather stations. Measurements from these systems, including data from four ground-based radars (GRs), were combined with satellite data from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and used as a proxy for GPM GV over the Korean Peninsula. A time series of mean reflectivity differences (GR 2 PR) for stratiform-only and above-brightband-only data showed that the time-averaged difference fell between 22.0 and 11.0 dBZ for the four GRs used in this study. Site-specific adjustments for these relative mean biases were applied to GR reflectivities, and detailed statistical comparisons of reflectivity and rain rate between PR and bias-adjusted GR were carried out. In rain-rate comparisons, surface rain from the TRMM Microwave Imager (TMI) and the rain gauges were added and the results varied according to rain type. Bias correction has had a positive effect on GR rain rate comparing with PR and gauge rain rates. This study confirmed advance preparation for GPM GV system was optimized on the Korean Peninsula using the official framework.

A fog detection algorithm that uses geostationary satellite data has been developed and tested. This algorithm focuses on continuous fog detection since temporal discontinuities, especially at dawn and dusk, are a major problem with current fog detection algorithms that use satellite imagery data. This is because the spectral radiance at 3.7 µm contains overlapping emissive and reflectance components. In order to determine the radiance at 3.7 µm under fog conditions, radiative transfer model simulations were performed. The results showed that the radiance at 3.7 µm obviously varies with the solar zenith angle, and the brightness temperature differences between 3.7 µm and 10.8 µm are completely dissimilar between day and night (positive and varying with the angle during the daytime, but negative and constant at night). In this algorithm, a dynamic threshold is used as a function of the solar zenith angle. Moreover, additional criteria such as infrared, split-window channels, and a water vapor channel are used to remove high-level clouds. Also, the visible reflectance (0.67 µm) channel is used in the daytime algorithm because visible channel images are very practical for confirming a fog area with the high reflectivity and the smooth texture. The clear-sky visible reflectance for the previous 15 days was also employed to eliminate the surface effect that appeared during dawn and dusk. As the results, fog areas were estimated continuously, allowing the lifecycle of the fog system, from its development to decline, to appear obviously in the resulting images. Moreover, the estimated fog areas matched well with surface observations, except in a high latitude region that was covered by thin cirrus clouds.

2015) Estimating midday near-surface air temperature by weighted consideration of surface and atmospheric moisture conditions using COMS and SPOT satellite data,The measurement of near-surface air temperature (T a ) is critically important for understanding the Earth's energy and water circulation system and for diverse modelling applications. T a data obtained from meteological ground stations are basically available but not suitable for large-scale areas, because of their spatial limitation. Remote-sensing techniques can provide a spatially well-distributed T a map. However, the current remote-sensing methodology for T a mapping has accuracy inferior to common expectations in terms of the region of various terrestrial ecosystems and climatic conditions. Our aim was to develop a midday T a retrieval algorithm with reasonable accuracy over Northeast Asia during one seasonal year. In consideration of the various environmental conditions in our study area, T a was calculated using land surface temperature and the normalized difference vegetation index in the nine cases derived from the combination of surface and atmospheric moisture conditions, and a weighting factor was applied to reduce the bias error among T a results from nine cases. The reasonable pixel window size was established as 13 × 13. The validation process yielded a coefficient of determination (R 2 ), root mean square error, and bias values of 0.9401, 2.8865 K, and 0.4920 K, respectively. Although the study area includes diverse land-cover and climatic conditions, our satellite-derived T a data provided better results compared with a previous study of only four cases with no weighting function in the Korean peninsula. Our suggested methodology will be useful in estimating T a using satellite data, particularly over complex land surfaces.

Abstract. Since the late 1990s, the meteorological observatory established in Anmyeondo (36.5382° N, 126.3311° E, and 30 m above mean sea level) has been monitoring several greenhouse gases such as CO2, CH4, N2O, CFCs, and SF6 as a part of the Global Atmosphere Watch (GAW) Program. A high resolution ground-based (g-b) Fourier transform spectrometer (FTS) was installed at this observation site in 2013 and has been operated within the frame work of the Total Carbon Column Observing Network (TCCON) since August 2014. The solar spectra recorded by the g-b FTS cover the spectral range 3800 to 16 000 cm−1 at a resolution of 0.02 cm−1. In this work, the GGG2014 version of the TCCON standard retrieval algorithm was used to retrieve total column average CO2 and CH4 dry mole fractions (XCO2, XCH4) and from the FTS spectra. Spectral bands of CO2 (at 6220.0 and 6339.5 cm−1 center wavenumbers, CH4 at 6002 cm−1 wavenumber, and O2 near 7880 cm−1 ) were used to derive the XCO2 and XCH4. In this paper, we provide comparisons of XCO2 and XCH4 between the aircraft observations and g-b FTS over Anmyeondo station. A comparison of 13 coincident observations of XCO2 between g-b FTS and OCO-2 (Orbiting Carbon Observatory) satellite measurements are also presented for the measurement period between February 2014 and November 2017. OCO-2 observations are highly correlated with the g-b FTS measurements (r2 = 0.884) and exhibited a small positive bias (0.189 ppm). Both data set capture seasonal variations of the target species with maximum and minimum values in spring and late summer, respectively. In the future, it is planned to further utilize the FTS measurements for the evaluation of satellite observations such as Greenhouse Gases Observing Satellite (GOSAT, GOSAT-2). This is the first report of the g-b FTS observations of XCO2 species over the Anmyeondo station.