Search for: All records

Abstract The Pico-STRAT Bi Gaz spectrometer provides in situ mixing ratio measurements of water (H₂O) and methane (CH₄) [or carbon dioxide (CO₂)] under balloon. The instrument was flown in the tropical upper troposphere and lower stratosphere in 2019/20 and 2021/22 during the Strateole 2 campaigns for a total of five flights of 20–80 days between 18- and 20-km altitude. In this frame, in situ measurements of water vapor and methane were performed every 4–12 min in the equatorial tropopause layer. On several occasions, water vapor measurements of Pico-STRAT Bi Gaz have been compared with localized measurements from the Fluorescence Lyman-Alpha Stratospheric Hygrometer for Balloon (FLASH-B) Lyman-αhygrometer and vertical profiles of the NOAA Global Monitoring Laboratory (GML) frost point hygrometer over Hilo, Hawaii. Pico-STRAT Bi Gaz measurements agreed with the FLASH-B hygrometer to within 2.2% ± 5.3% between 18.2 and 18.7 km in 2021 and to within 1.3% ± 5.3% near 19 km in December 2019. Pico-STRAT Bi Gaz agreed with NOAA’s frost point hygrometer (FPH) hygrometer to within 1.2% ± 4.1% between 18 and 19 km on four occasions during the two campaigns. These are within both instruments’ uncertainties. Methane measurements from Pico-STRAT Bi Gaz have been compared with in situ measurements from the whole air sampler (WAS) instrument, flown aboard the NASA WB-57 aircraft during the Asian Summer Monsoon Chemical and Climate Impact Project (ACCLIP) 2022 campaign over South Korea, 8 months after the Pico-STRAT Bi Gaz overpass. The relative difference between both instruments is found to be −0.1% ± 0.9% within the altitude range from 17 to 19 km and within the Pico-STRAT measurement uncertainty. 

X-ray fluorescence spectroscopy (XRF) plays an important role for elemental analysis in a wide range of scientific fields, especially in cultural heritage. XRF imaging, which uses a raster scan to acquire spectra pixel-wise across artworks, provides the opportunity for spatial analysis of pigment distributions based on their elemental composition. However, conventional XRF-based pigment identification relies on time-consuming elemental mapping facilitated by the interpretation of measured spectra by experts. To reduce the reliance on manual work, recent studies have applied machine learning techniques to cluster similar XRF spectra in data analysis and to identify the most likely pigments. Nevertheless, it is still challenging to implement automatic pigment identification strategies to directly tackle the complex structure of real paintings, e.g. pigment mixtures and layered pigments. In addition, pigment identification based on XRF on a pixel-by-pixel basis remains an obstacle due to the high noise level. Therefore, we developed a deep-learning based pigment identification framework to fully automate the process. In particular, this method offers high sensitivity to the underlying pigments and to the pigments present in low concentrations, therefore enabling robust mapping of pigments based on single-pixel XRF spectra. As case studies, we applied our framework to lab-prepared mock-up paintings and two 19th-century paintings: Paul Gauguin's Poèmes Barbares (1896) that contains layered pigments with an underlying painting, and Paul Cezanne's The Bathers (1899–1904). The pigment identification results demonstrated that our model achieved comparable results to the analysis by elemental mapping, suggesting the generalizability and stability of our model.