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The National Meteorological Satellite Center/Korean Meteorological Administration (NMSC/KMA) receives data directly from low Earth orbit (LEO) satellites 19;B;, and generates Level 2 products (e.g., temperature and humidity profile) in near real time. Total precipitable water (TPW) and layer precipitable water (LPW) are also generated using the retrieved humidity profiles. Today, forecasters need meteorologically-significant data fields composited from all available data sources, not multiple maps of observations from individual sources. Hence, TPW and LPW are reproduced using the optimal interpolation (OI) method with numerical weather prediction (NWP) data, in order to generate composite precipitable water (PW) products. In the OI procedure, PW data retrieved from the LEO satellites serve as observation data, while PW data from NWP serve as background data. Error covariances are estimated using a new approach, which considers correlations between observation errors to describe the characteristics of the errors better. Both background and observation error covariance matrices may have non-zero off-diagonal components. The composite PW products are validated using radiosonde data. The validation results for optimally-interpolated LPW (OI LPW) are much better than those for optimally-interpolated TPW (OI TPW). Generally, the OI LPW validation results are better than those for observation and background data; OI LPW data are~5-10% more accurate than background data. Optimally-interpolated PW (OI PW) fields are applied to the correction of NWP forecast fields and the prediction of severe weather. , all have 126 km × 126 km resolution. Temperature profiles, humidity profiles, and PW retrieved from Cross-track Infrared Sounder (CrIS) sensor data from the Suomi-NPP satellite, using CSPP, have 41 km × 41 km resolution. The assessment of the temperature and humidity profiles has been carried out in previous studies [3,4,6]. Also, the assessment of the satellite-based PW is carried out in Section 3.The optimal interpolation (OI) method combines background and observation data to generate an analytical product that (1) covers stations without observations and (2) is more accurate than background and observation data, in terms of the root mean square error (RMSE) [7]. There are other methods suitable for the purpose of this study, such as kriging [8] and the cumulative distribution function (CDF) matching method [2,9]. The OI method has advantages over other methods, because it is not necessary to find out independent data [8] and reference data [2,9]. The OI method used in this study differs from other OI methods, in that we use non-zero off-diagonal components in the observation error covariance matrices. These non-zero off-diagonal components are obtained by estimating observation error variances and correlation length scales. Normally, a diagonal matrix is used for the observation error covariance matrix, because there is assumed to be no correlation between the two sets of observations [7,10,11]. This assumption does n...

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This study aims for producing high-quality true-color red-green-blue (RGB) imagery that is useful for interpreting various environmental phenomena, particularly for GK2A. Here we deal with an issue that general atmospheric correction methods for RGB imagery might be breakdown at high solar/viewing zenith angle of GK2A due to erroneous atmospheric path lengths. Additionally, there is another issue about the green band of GK2A of which centroid wavelength (510 nm) is different from that of natural green band (555 nm), resulting in the unrealistic RGB imagery. To overcome those weakness of the RGB imagery for GK2A, we apply the second simulation of the satellite signal in the solar spectrum radiative transfer model look-up table with improved information considering altitude of the reflective surface to reduce the exaggerated atmospheric correction, and a blending technique that mixed the true-color imagery before and after atmospheric correction which produced a naturally expressed true-color image. Consequently, the root mean square error decreased by 0.1–0.5 in accordance with the solar and view zenith angles. The green band signal was modified by combining it with a veggie band to form hybrid green which adjust centroid wavelength of approximately 550 nm. The original composite of true-color RGB imagery is dark; therefore, to brighten the imagery, histogram equalization is conducted to flatten the color distribution. High-temporal-resolution true-color imagery from the GK2A AMI have significant potential to provide scientists and forecasters as a tools to visualize the changing Earth and also expected to intuitively understand the atmospheric phenomenon to the general public.

<p>According to the 6th IPCC report, the concentration of greenhouse gases has increased about 20% since pre-industrial revolution, and 17% of them have increased over the last 10 years. The Korea Metoeorological Administration has analyzed satellite-based greenhouse gases to monitor climate change and support government’s achievement of net zero. The KMA has validated satellite-based greenhouse gases using CO<sub>2</sub>  observed in situ and retrieved TCCON in Anmyeon, the South Korea which is a GAW site. Both ground- and satellite-based CO<sub>2</sub> showed a good agreement in their increasing trends with seasonal variations. However, satellite-based CO2 observed total column appear smaller than in situ observations affected by local sources due to observe near the surface, but agree well with TCCON observed the total column. The RMSD of GOSAT, and OCO2 with in situ and TCCON is estimated about 14.85, 16.93 and 2.81, 2.01 ppmv for a 1.0 degree × 1.0 degree spatial resolution on a daily time scale from January 2014 to December 2018. The results show that satellite-based products could be used greenhouse gases monitoring, but it needs to be verified more validation data.  We will present the detailed methods and results in the conference.</p><p>This work was funded by the Korea Meteorological Administration’s Research and Development Program “Technical Development on Weather Forecast Support and Convergence Service using Meteorological Satellites” under Grant (KMA2020-00120).</p>