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In this study, we demonstrate the utility of available correlative measurements of carbon species to identify regional and local air mass characteristics as well as their associated source types. In particular, we combine different regression techniques and enhancement ratio algorithms with carbon monoxide (CO), carbon dioxide (CO 2), and methane (CH 4) total column abundance from 11 sites of the Total Carbon Column Observing Network (TCCON) to infer relative contributions of regional and local sources to each of these sites. The enhancement ratios provide a viable alternative to univariate measures of relationships between the trace gases that are insufficient in capturing source-type and transport signatures. Regional enhancements are estimated from the difference between bivariate regressions across a specific time window of observed total abundance of these species (BERr for bulk enhancement regression ratio) and inferred anomalies (AERr for anomaly enhancement regression ratio) associated with a site-specific background. Since BERr and AERr represent the bulk and local species enhancement ratio, respectively, its difference simply represents the site-specific regional component of these ratios. We can then compare these enhancements for CO 2 and CH 4 with CO to differentiate between combustion and non-combustion air masses. Our results show that while the regional and local influences in enhancements vary across sites, dominant characteristics are found to be consistent with previous studies over these sites and with bottom-up anthropogenic and fire emission inventories. The site in Pasadena shows a dominant local influence (> 60 %) across all species enhancement ratios, which appear to come from a mixture of biospheric and combustion activities. In contrast, Anmyeondo shows more regionally influenced (> 60 %) air masses associated with high-temperature and/or biofuel combustion activities. Ascension Island appears to only show a large regional influence (> 80 %) on CO / CO 2 and CO / CH 4 , which is indicative of transported and combustion-related CO from the nearby African region, consistent with a sharp rise in column CO (3.51 ± 0.43 % ppb yr -1) at this site. These methods have important applications to source analysis using spaceborne column retrievals of these species. [ABSTRACT FROM AUTHOR]

This study describes the Korea Meteorological Administration/National Institute of Meteorological Sciences (KMA/NIMS) Atmospheric Research Aircraft (NARA) and its observational data archive. NARA has been performing annual observation flights, which are aimed at reducing the uncertainty of atmospheric observations in the observation data gap area around the Korean peninsula, since January 2018. An online system has also been constructed to provide data management, transmission, quality control and simple visualization. The mission strategy of NARA is subdivided into four individual units, namely observations of severe weather (SW), climate monitoring (CM), environmental monitoring (EM) and cloud physics and weather modification experiments (CP). As of December 2020, NARA has been launched operationally for 325 flights (corresponding to 9.02 flights per month), typically over the West Sea, mid‐inland areas (34.4% and 25.4%, respectively), and below an altitude of 3 km (51.9%). Results of intercomparison tests confirmed that NARA measurements have reasonable offsets (<0.9%) to each other in terms of pressure and temperature. Moreover, the monthly average temperature profile in the East Sea area showed a seasonal variation was detected in monthly variation. From these results, it is evident that NARA data will contribute significantly to enhancing the level of scientific understanding of atmospheric observations and the applications (i.e. long‐term study) thereof as the amount of data accumulated increases. [ABSTRACT FROM AUTHOR]

Dropsonde-based sensible heat flux, latent heat flux, and buoyancy flux were estimated over the sea around the Korean Peninsula in 2021. During a preceding severe weather (SW) mission, a total of 243 dropsondes were released from a National Institute of Meteorological Sciences (NIMS) Atmospheric Research Aircraft (NARA). The heat fluxes were indirectly validated by comparison with model-based heat fluxes. The sensible heat flux calculated by the bulk transfer method depended entirely on the temperature difference between the sea level and atmosphere, whereas the latent heat flux was mainly affected by wind speed. Boundary layer heights above 800 m are closely related to buoyancy flux, which is greater in regions with higher sea surface temperatures. Furthermore, the utility of the dropsonde was confirmed in the marine atmospheric boundary layer (MABL) growth, which is difficult to observe in situ and, a relationship was proposed for estimating MABL based on mean meteorological data over the sea level. [ABSTRACT FROM AUTHOR]

It is evident that evaluating the measurement of greenhouse gases (GHGs) obtained from multi-platform instruments against accurate and precise instrument such as aircraft in-situ is very essential when using remote sensing GHGs results for source/sink estimations with inverse modeling. The results of the inverse models are very sensitive even to small biases in the data (Rayner and O'Brien 2001). In this work, we have evaluated ground-based high resolution Fourier Transform Spectrometer (g-b FTS) and the Greenhouse gases Observing SATellite (GOSAT) column-averaged dry air mole fraction of methane (XCH4) through aircraft in-situ observations over Anmyeondo station (36.538o N, 126.331o E, 30 m above sea level). The impact of the spatial coincidence criteria was assessed by comparing GOSAT data against g-b FTS. We noticed there was no any systematic difference based on the given coincidence criteria. GOSAT exhibited a bias ranging from 0.10 to 3.37 ppb, with the standard deviation from 4.92 to 12.54 ppb, against g-b FTS with the spatial coincidence criteria of ±1, ±3, ±5 degrees of latitude and longitude and ± 1 h time window. Data observed during ascent and descent of the aircraft is considered as vertical profiles within an altitude range of 0.2 to a maximum of 9.0 km so that some assumptions were applied for the construction of the profiles below 0.2 and above 9.0 km. In addition, the suitability of aircraft data for evaluation of remote sensing instruments was confirmed based on the assessment of uncertainties. The spatial coincidence criteria is ±1o latitude and ± 2o longitude and for temporal difference is ±1 h of the satellite observation overpass time were applied, whereas g-b FTS data are the mean values measured within ±30 min of the aircraft observation time. Furthermore, the sensitivity differences of the instruments were taken into account. With respect to aircraft, the g-b FTS data were biased by −0.19 ± 0.69%, while GOSAT data were biased by −0.42 ± 0.84%. These results confirm that both g-b FTS and GOSAT are consistent aircraft observations and assure the reliability of the datasets for inverse estimate of CH4. [ABSTRACT FROM AUTHOR]

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An Atmospheric Emitted Radiance Interferometer (AERI), which measures downwelling radiances, has been in operation at Anmyeon-do, South Korea, since June 2010. Temperature and moisture ( T/ q) profiles with high temporal and vertical resolution can be retrieved from the measured AERI spectrum through the retrieval algorithm AERIPROF. In this work, AERIPROF has been optimized to improve the retrieval performance: 1) a bias spectrum was computed from the coincident radiosondes during the field experiments at Anmyeon-do and 2) regression coefficients were obtained from local radiosondes and associated simulated spectral radiances. An evaluation was performed in the lower troposphere (<700 hPa) with the radiosondes on clear-sky days during the field experiments at Anmyeon-do. The optimized statistical regression results in an improvement of ~0.6 K for temperature and ~0.6 g kg−1 for the mixing ratio on average, in comparison to the original statistical regression. In addition, the optimized AERI T/ q retrievals are compared with the satellite [ Aqua/Atmospheric Infrared Sounder (AIRS), Meteorological Operation (MetOp)/Infrared Atmospheric Sounding Interferometer (IASI)] T/ q retrievals as well as with T/ q profiles analyzed from the regional NWP model, the Korea Local Analysis and Prediction System (KLAPS) analysis. The RMS errors of the AERI retrievals are smaller than those of the satellite retrievals (the KLAPS analysis) by ~1.3 K (~0.2 K) for temperature and ~0.3 g kg−1 (~0.2 g kg−1) for the mixing ratio on average. Significant differences could be found between the AERI retrievals with the KLAPS and the satellite retrievals. The local climatic condition seems to be an important factor to bring about this improvement. Considering the training dataset made with spatially distant radiosondes, this is a significant finding. The AERI could bring new information about the lower troposphere. [ABSTRACT FROM AUTHOR]

The CO2 growth rate is one of the key geophysical quantities reflecting the dynamics of climate change as atmospheric CO2 growth is the primary driver of global warming. As recent studies have shown that TCCON (Total Carbon Column Observing Network) measurement footprints embrace quasi-global coverage, we examined the sensitivity of TCCON to the global CO2 growth. To this end, we used the aggregated TCCON observations (2006-2019) to retrieve Annual Growth Rate of CO2 (AGR) at global scales. The global AGR estimates from TCCON (AGRTCCON) are robust and independent, from (a) the station-wise seasonality, from (b) the differences in time series across the TCCON stations, and from (c) the type of TCCON stations used in the calculation ("background" or "contaminated" by neighboring CO2 sources). The AGRTCCON potential error, due to the irregular data sampling is relatively low (2.4–17.9%). In 2006–2019, global AGRTCCON ranged from the minimum of 1.59 ± 2.27 ppm (2009) to the maximum of 3.27 ± 0.82 ppm (2016), whereas the uncertainties express sub-annual variability and the data gap effects. The global AGRTCCON magnitude is similar to the reference AGR from satellite data (AGRSAT = 1.57–2.94 ppm) and the surface-based estimates of Global Carbon Budget (AGRGCB = 1.57–2.85). The highest global CO2 growth rate (2015/2016), caused by the record El Niño, was nearly perfectly reproduced by the TCCON (AGRTCCON = 3.27 ± 0.82 ppm vs. AGRSAT = 3.23 ± 0.50 ppm). The overall agreement between global AGRTCCON with the AGR references was yet weakened (r = 0.37 for TCCON vs. SAT; r = 0.50 for TCCON vs. GCB) due to two years (2008, 2015). We identified the drivers of this disagreement; in 2008, when only few stations were available worldwide, the AGRTCCON uncertainties were excessively high (AGRTCCON = 2.64 ppm with 3.92 ppm or 148% uncertainty). Moreover, in 2008 and 2015, the ENSO-driven bias between global AGRTCCON and the AGR references were detected. TCCON-to-reference agreement is dramatically increased if the years with ENSO-related biases (2008, 2015) are forfeited (r = 0.67 for TCCON vs. SAT, r = 0.82 for TCCON vs. GCB). To conclude, this is the first study that showed promising ability of aggregated TCCON signal to capture global CO2 growth. As the TCCON coverage is expanding, and new versions of TCCON data are being published, multiple data sampling strategies, dynamically changing TCCON global measurement footprint, and the irregular sensitivity of AGRTCCON to strong ENSO events; all should be analyzed to transform the current efforts into a first operational algorithm for retrieving global CO2 growth from TCCON data. [ABSTRACT FROM AUTHOR]