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Yong-Keun Lee, Cezar Kongoli, and Jeffrey Key

Abstract

The Advanced Microwave Scanning Radiometer 2 (AMSR2) was launched in 2012 on board the Global Change Observation Mission 1st–Water (GCOM-W1) satellite. This study presents a robust evaluation of AMSR2 algorithms for the retrieval of snow-covered area (SCA) and snow depth (SD) that will be used operationally by the National Oceanic and Atmospheric Administration (NOAA). Quantitative assessment of the algorithms was performed for a 10-yr period with AMSR-E and a 2-yr period with AMSR2 data using the NOAA Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover and in situ SD data as references. AMSR-E SCA showed a monthly overall accuracy rate of about 80% except in May. Accuracy improves significantly to over 90% when wet snow cases are excluded, and accuracy differences between ascending and descending portions of orbits also decrease. Microwave-derived SCA over dry snow areas can therefore be obtained with accuracy close to optically derived SCA. An evaluation of the results for AMSR-E SD showed a low overall bias of 1 cm and a root-mean-square error of 20 cm. Results for AMSR2-based SCA and SD are similar to those from AMSR-E. Biases and root-mean-square errors show dependencies on elevation, forest fraction, the magnitude of snow depth, and snow cover class.

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Yong-Keun Lee, Jason A. Otkin, and Thomas J. Greenwald

Abstract

Synthetic infrared brightness temperatures (BTs) derived from a high-resolution Weather Research and Forecasting (WRF) model simulation over the contiguous United States are compared with Moderate Resolution Imaging Spectroradiometer (MODIS) observations to assess the accuracy of the model-simulated cloud field. A sophisticated forward radiative transfer model (RTM) is used to compute the synthetic MODIS observations. A detailed comparison of synthetic and real MODIS 11-μm BTs revealed that the model simulation realistically depicts the spatial characteristics of the observed cloud features. Brightness temperature differences (BTDs) computed for 8.5–11 and 11–12 μm indicate that the combined numerical model–RTM system realistically treats the radiative properties associated with optically thin cirrus clouds. For instance, much larger 11–12-μm BTDs occurred within thin clouds surrounding optically thicker, mesoscale cloud features. Although the simulated and observed BTD probability distributions for optically thin cirrus clouds had a similar range of positive values, the synthetic 11-μm BTs were much colder than observed. Previous studies have shown that MODIS cloud optical thickness values tend to be too large for thin cirrus clouds, which contributed to the apparent cold BT bias in the simulated thin cirrus clouds. Errors are substantially reduced after accounting for the observed optical thickness bias, which indicates that the thin cirrus clouds are realistically depicted during the model simulation.

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Yong-Keun Lee, Zhenglong Li, Jun Li, and Timothy J. Schmit

Abstract

A physical retrieval algorithm has been developed for deriving the legacy atmospheric profile (LAP) product from infrared radiances of the Advanced Baseline Imager (ABI) on board the next-generation Geostationary Operational Environmental Satellite (GOES-R) series. In this study, the GOES-R ABI LAP retrieval algorithm is applied to the GOES-13 sounder radiance measurements (termed the GOES-13 LAP retrieval algorithm in this study) for its validation as well as for potential transition of the GOES-13 LAP retrieval algorithm for the operational processing of GOES sounder data. The GOES-13 LAP retrievals are compared with five different truth measurements: radiosonde observation (raob) and microwave radiometer–measured total precipitable water (TPW) at the Atmospheric Radiation Measurement Cloud and Radiation Testbed site, conventional raob, TPW measurements from the global positioning system–integrated precipitable water NOAA network, and TPW measurements from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). The results show that with the GOES-R ABI LAP retrieval algorithm, the GOES-13 sounder provides better water vapor profiles than the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) forecast fields at the levels between 300 and 700 hPa. The root-mean-square error (RMSE) and standard deviation (STD) of the GOES-13 sounder TPW are consistently reduced from those of the GFS forecast no matter which measurements are used as the truth. These substantial improvements indicate that the GOES-R ABI LAP retrieval algorithm is well prepared to provide continuity of quality to some of the current GOES sounder products, and the algorithm can be transferred to process the current GOES sounder measurements for operational product generation.

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Jhoon Kim, Ukkyo Jeong, Myoung-Hwan Ahn, Jae H. Kim, Rokjin J. Park, Hanlim Lee, Chul Han Song, Yong-Sang Choi, Kwon-Ho Lee, Jung-Moon Yoo, Myeong-Jae Jeong, Seon Ki Park, Kwang-Mog Lee, Chang-Keun Song, Sang-Woo Kim, Young Joon Kim, Si-Wan Kim, Mijin Kim, Sujung Go, Xiong Liu, Kelly Chance, Christopher Chan Miller, Jay Al-Saadi, Ben Veihelmann, Pawan K. Bhartia, Omar Torres, Gonzalo González Abad, David P. Haffner, Dai Ho Ko, Seung Hoon Lee, Jung-Hun Woo, Heesung Chong, Sang Seo Park, Dennis Nicks, Won Jun Choi, Kyung-Jung Moon, Ara Cho, Jongmin Yoon, Sang-kyun Kim, Hyunkee Hong, Kyunghwa Lee, Hana Lee, Seoyoung Lee, Myungje Choi, Pepijn Veefkind, Pieternel F. Levelt, David P. Edwards, Mina Kang, Mijin Eo, Juseon Bak, Kanghyun Baek, Hyeong-Ahn Kwon, Jiwon Yang, Junsung Park, Kyung Man Han, Bo-Ram Kim, Hee-Woo Shin, Haklim Choi, Ebony Lee, Jihyo Chong, Yesol Cha, Ja-Ho Koo, Hitoshi Irie, Sachiko Hayashida, Yasko Kasai, Yugo Kanaya, Cheng Liu, Jintai Lin, James H. Crawford, Gregory R. Carmichael, Michael J. Newchurch, Barry L. Lefer, Jay R. Herman, Robert J. Swap, Alexis K. H. Lau, Thomas P. Kurosu, Glen Jaross, Berit Ahlers, Marcel Dobber, C. Thomas McElroy, and Yunsoo Choi

Abstract

The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in February 2020 to monitor air quality (AQ) at an unprecedented spatial and temporal resolution from a geostationary Earth orbit (GEO) for the first time. With the development of UV–visible spectrometers at sub-nm spectral resolution and sophisticated retrieval algorithms, estimates of the column amounts of atmospheric pollutants (O3, NO2, SO2, HCHO, CHOCHO, and aerosols) can be obtained. To date, all the UV–visible satellite missions monitoring air quality have been in low Earth orbit (LEO), allowing one to two observations per day. With UV–visible instruments on GEO platforms, the diurnal variations of these pollutants can now be determined. Details of the GEMS mission are presented, including instrumentation, scientific algorithms, predicted performance, and applications for air quality forecasts through data assimilation. GEMS will be on board the Geostationary Korea Multi-Purpose Satellite 2 (GEO-KOMPSAT-2) satellite series, which also hosts the Advanced Meteorological Imager (AMI) and Geostationary Ocean Color Imager 2 (GOCI-2). These three instruments will provide synergistic science products to better understand air quality, meteorology, the long-range transport of air pollutants, emission source distributions, and chemical processes. Faster sampling rates at higher spatial resolution will increase the probability of finding cloud-free pixels, leading to more observations of aerosols and trace gases than is possible from LEO. GEMS will be joined by NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) and ESA’s Sentinel-4 to form a GEO AQ satellite constellation in early 2020s, coordinated by the Committee on Earth Observation Satellites (CEOS).

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