First evaluation of the GEMS formaldehyde product against TROPOMI and ground-based column measurements during the in-orbit test period

Abstract

Abstract. The Geostationary Environment Monitoring Spectrometer (GEMS) on board GEO-KOMPSAT-2B was launched in February 2020 and has been monitoring atmospheric chemical compositions over Asia. We present the first evaluation of the operational GEMS formaldehyde (HCHO) vertical column densities (VCDs) during and after the in-orbit test (IOT) period (August–October 2020) by comparing them with the products from the TROPOspheric Monitoring Instrument (TROPOMI) and Fourier-transform infrared (FTIR) and multi-axis differential optical absorption spectroscopy (MAX-DOAS) instruments. During the IOT, the GEMS HCHO VCDs reproduced the observed spatial pattern of TROPOMI VCDs over the entire domain (r= 0.62) with high biases (10 %–16 %). We found that the agreement between GEMS and TROPOMI was substantially higher in Northeast Asia (r= 0.90), encompassing the Korean Peninsula and east China. GEMS HCHO VCDs captured the seasonal variation in HCHO, primarily driven by biogenic emissions and photochemical activities, but showed larger variations than those of TROPOMI over coastal regions (Kuala Lumpur, Singapore, Shanghai, and Busan). In addition, GEMS HCHO VCDs showed consistent hourly variations with MAX-DOAS (r= 0.77) and FTIR (r= 0.86) but were 30–40 % lower than ground-based observations. Different vertical sensitivities of GEMS and ground-based instruments caused these biases. Utilizing the averaging kernel smoothing method reduces the low biases by approximately 10 % to 15 % (normalized mean bias (NMB): −47.4 % to −31.5 % and −38.6 % to −26.7 % for MAX-DOAS and FTIR, respectively). The remaining discrepancies are due to multiple factors, including spatial collocation and different instrumental sensitivities, requiring further investigation using inter-comparable datasets.

Advantages of Inter‐Calibration for Geostationary Satellite Sensors Onboard Twin Satellites

“…Kim et al, 2020). While overall systematic bias across spectra may have minimal impact on retrieval under such approaches, it still has the potential to influence specific retrieval processes relying on theoretical irradiance for radiance scaling (Cho et al, 2023) or using an air mass factor (G. Lee et al, 2024).…”