Uncrewed aerial vehicles (UAVs) offer versatile platforms for low-altitude (1 to 120 m) trace-gas measurements that can fill spatio-temporal measurement gaps between ground-based mobile platforms and conventional aircraft measurements. Small UAVs (under 25 kg) are easier to transport and typically face fewer regulatory restrictions than larger ones. However, they have significant payload limitations, underscoring the importance of developing lightweight, high-performance gas sensors for UAV deployment. In this work, we developed an ultra-lightweight methane sensor based on mid-infrared spectroscopy for precise, rapid in-situ measurements aboard small UAVs. Weighing just 1.2 kg, including the dedicated battery, the stand-alone, power efficient instrument is 75 % lighter than our previous design, making it one of the lightest methane sensors reported to date. The design is simple and compact, featuring a Herriott-type multi-pass cell in an open-path configuration. We report the sensor performance in detail in both laboratory experiments and field deployments, including the response to variable environmental conditions. The measurement precision for a 1 s averaging time is 3.7 ppb in the laboratory and 6.7 ppb in-flight. The 0.1 s in-flight precision, which is more suitable for monitoring rapidly varying methane from a fast-moving platform, is 27 ppb. We deployed the sensor in a controlled-release experiment with a methane point source emitting 0.48 kg h−1. Using a direct mass balance flux quantification approach and wind measurements recorded by an on-board anemometer, estimated emissions were within 9 % of the true emission rate. The results demonstrate the instrument's potential for in-situ measurement of fugitive emissions from anthropogenic and natural point sources. Further systematic controlled-release studies are needed to fully assess the detection probability and quantification accuracy of this UAV-based measurement system.
Droplet-freezing assays underpin immersion-mode ice-nucleation research yet approaches to uncertainty quantification for fraction-frozen curves and derived active-site densities (ns(T)) are inconsistent. Further, there is currently no rigorous method for significance testing the difference between fraction-frozen curves. To address these issues, we recast droplet-freezing measurements as survival data and apply analysis techniques typically used in medical statistics. Using the Kaplan–Meier estimator, we derive nonparametric pointwise confidence intervals for droplet fraction-frozen without binning or model assumptions. These intervals can also be propagated to cumulative ice-nucleation activity spectra such as K(T), ns(T) or nm(T). Simulations based on fitted literature freezing curves show that Kaplan–Meier intervals have close to nominal coverage over the central portion of fraction-frozen curves, but coverage deteriorates near the ends of the curve where finite experiments contain little information. We also compare several statistical tests for distinguishing between droplet-freezing curves. These tests quantify whether the observed separation between two finite-droplet curves is larger than expected from sampling variability alone. The log-rank test, often used alongside Kaplan–Meier confidence intervals in medical statistics, can be useful for approximately proportional-hazards-like alternatives, but can have low power when curves differ in shape or reflect multiple ice-active populations. We therefore introduce an integrated squared fraction-frozen distance permutation test, which we refer to as the Integrated Squared Difference (ISD) test. This test compares the accumulated squared separation between two empirical fraction-frozen curves directly and does not require proportional hazards. Simulations comparing the ISD test, log-rank and several standard empirical-distribution-function tests show that no single test is most powerful in all situations: performance depends on droplet number, curve shape, and the form of the difference being tested. We recommend reporting fraction-frozen curves with Kaplan–Meier pointwise confidence intervals and droplet numbers, and using the ISD test for general curve comparison. We note finally that a significant global test indicates that two curves differ somewhere over the analysed temperature interval, but does not identify where they differ or why; this still requires inspection of the curves and potentially of normalised spectra.
The Meteosat Third Generation Lightning Imager (MTG-LI) is the first European space-based geostationary mission devoted to the monitoring and characterization of lightning activity over hemispheric scales. The present paper provides the first comprehensive summary of MTG-LI data during its first year of continuous observations. From July 2024 to June 2025, the instrument detected a total of 595 × 106 flashes within its field-of-view, encompassing Europe, Africa, and part of the Atlantic Ocean, South America, middle East and Indian Ocean. The highest observed lightning flash density of 189 flashes km−2 yr−1 was found in the east of The Democratic Republic of the Congo. The MTG-LI is capable of capturing both global and local lightning features. In addition, the instrument's high sensitivity to intra-cloud lightning activity translates into flash density values notably higher than ground-based lightning statistics. These results demonstrate that the MTG-LI is an excellent instrument for the continuous monitoring of total lightning activity across its entire field of view on both operational and climatological scales.
As the world's first geostationary satellite equipped with a passive microwave payload, China's FengYun-4M (FY-4M) is planned to be launched at the end of 2026, ushering in a new era of continuous observation of various geophysical parameters associated with weather processes. To better understand the observational characteristics of this satellite's more than a hundred channels, especially the potential application of its unique temperature hyperspectral channels (52.6–57.3 GHz) and several high-frequency channels (380 and 424 GHz) in the high-precision detection of atmospheric temperature profiles over ocean, this paper proposes a complete retrieval algorithm with a channel optimization scheme, based on information entropy theory and the Bayesian technique. Using degrees of freedom as an indicator, the ranking results of information contribution show that when hyperspectral channels are included, water vapor absorption channels and window channels used to obtain auxiliary information such as water vapor and hydrometeors are more important for the quantitative extraction of temperature profile information than traditional oxygen absorption channels at 50 and 118 GHz. Based on this, a corresponding channel configuration was constructed for all-weather temperature profile retrieval. The results of retrieval experiments show that the root mean square error (RMSE) remains below 0.5 K under clear-sky and cloudy conditions, and is within 0.8 K during precipitation. Additionally, the computational time is reduced by 14 % relative to the full-channel configuration. This suggests that the presented algorithm with this channel configuration scheme is able to achieve a favorable balance between retrieval accuracy and computational efficiency, making it a preferred choice for future operational retrieval systems.
At the atmospheric monitoring station Schauinsland (Black Forest, Germany), high-precision long-term measurements of atmospheric CO2 and CH4 have been conducted since 1972 and 1991, respectively. An additional air intake at 35 m a.g.l. (above ground level) has been installed alongside the existing intake at 12 m a.g.l. in September 2021. To ensure consistency and continuity of the historic CO2 and CH4 time series, a three-year comparison between the two intake heights has been carried out. This revealed systematic differences in CO2 mole fractions between the two levels: During summer, daytime diurnal mean CO2 mole fractions at 35 m are up to 0.5 ppm higher, while night-time values are up to 1.1 ppm lower, compared to 12 m, resulting in smaller diurnal amplitudes at 35 m. Seasonal mean CO2 mole fractions are lowered by up to 0.36 ppm at 35 m relative to 12 m in summer, whereas no significant differences are observed in winter. The diurnal and seasonal mean CH4 mole fraction differences between the two intake heights are negligible. However, highly frequent CH4 spikes originating from nearby grazing cows, which are present only during the summer months, increase hourly mean mole fractions at 12 m by up to 38 ppb, while no significant influence is measured at 35 m. The REBS (Robust Extraction of the Baseline Signal) algorithm is applied to the minutely CH4 summer data at 12 m and proved to be an effective approach for identification of data affected by very local influences.
