Search

Sea surface temperature (SST) is an essential climate variable, that is directly used in climate monitoring. Although satellite measurements can offer continuous global coverage, obtaining a long‐term homogeneous satellite‐derived SST data set suitable for climate studies based on a single instrument is still a challenge. In this work, we assess a homogeneous SST data set derived from reprocessed Infrared Atmospheric Sounding Interferometer (IASI) level‐1 (L1C) radiance data. The SST is computed using Planck's Law and simple atmospheric corrections. We assess the data set using the ERA5 reanalysis and the EUMETSAT‐released IASI level‐2 SST product. Over the entire period, the reprocessed IASI SST shows a mean global difference with ERA5 close to zero, a mean absolute bias under 0.5°C, with a SD of difference around 0.3°C and a correlation coefficient over 0.99. In addition, the reprocessed data set shows a stable bias and SD, which is an advantage for climate studies. The interannual variability and trends were compared with other SST data sets: ERA5, Hadley Centre's SST (HadISST), and NOAA's Optimal Interpolation SST Analysis (OISSTv2). We found that the reprocessed SST data set is able to capture the patterns of interannual variability well, showing the same areas of high interannual variability (>1.5°C), including over the tropical Pacific in January corresponding to the El Niño Southern Oscillation. Although the period studied is relatively short, we demonstrate that the IASI data set reproduces the same trend patterns found in the other data sets (i.e., cooling trend in the North Atlantic, warming trend over the Mediterranean)., Key Points First IASI algorithm focused on sea surface temperature (SST) suitable for climate studiesThe IASI‐derived SST data set is compared with other available data setsClimate variability and trends are shown and compared to other data sets

TanSat is the 1st Chinese carbon dioxide (CO2) measurement satellite, launched in 2016. In this study, the University of Leicester Full Physics (UoL‐FP) algorithm is implemented for TanSat nadir mode XCO2 retrievals. We develop a spectrum correction method to reduce the retrieval errors by the online fitting of an 8th order Fourier series. The spectrum‐correction model and its a priori parameters are developed by analyzing the solar calibration measurement. This correction provides a significant improvement to the O2 A band retrieval. Accordingly, we extend the previous TanSat single CO2 weak band retrieval to a combined O2 A and CO2 weak band retrieval. A Genetic Algorithm (GA) has been applied to determine the threshold values of post‐screening filters. In total, 18.3% of the retrieved data is identified as high quality compared to the original measurements. The same quality control parameters have been used in a footprint independent multiple linear regression bias correction due to the strong correlation with the XCO2 retrieval error. Twenty sites of the Total Column Carbon Observing Network (TCCON) have been selected to validate our new approach for the TanSat XCO2 retrieval. We show that our new approach produces a significant improvement on the XCO2 retrieval accuracy and precision when compared to TCCON with an average bias and RMSE of −0.08 ppm and 1.47 ppm, respectively. The methods used in this study can help to improve the XCO2 retrieval from TanSat and subsequently the Level‐2 data production, and hence will be applied in the TanSat operational XCO2 processing., Key Points First using O2 A and 1.61 um CO2 band approaching TanSat XCO2 retreivalDevelopment a method on radiometric correction of TanSat L1B data in O2 A and 1.61 um CO2 Validation of new TanSat retrieval against TCCON and recived significant improved results compare to previously retrieval

Raman spectrometers will form a key component of the analytical suite of future planetary rovers intended to investigate geological processes on Mars. In order to expand the applicability of these spectrometers and use them as analytical tools for the investigation of silicate glasses, a database correlating Raman spectra to glass composition is crucial. Here we investigate the effect of the chemical composition of reduced silicate glasses on their Raman spectra. A range of compositions was generated in a diffusion experiment between two distinct, iron‐rich end‐members (a basalt and a peralkaline rhyolite), which are representative of the anticipated compositions of Martian rocks. Our results show that for silica‐poor (depolymerized) compositions the band intensity increases dramatically in the regions between 550–780 cm−1 and 820–980 cm−1. On the other hand, Raman spectra regions between 250–550 cm−1 and 1000–1250 cm−1 are well developed in silica‐rich (highly polymerized) systems. Further, spectral intensity increases at ~965 cm−1 related to the high iron content of these glasses (~7–17 wt % of FeOtot). Based on the acquired Raman spectra and an ideal mixing equation between the two end‐members we present an empirical parameterization that enables the estimation of the chemical compositions of silicate glasses within this range. The model is validated using external samples for which chemical composition and Raman spectra were characterized independently. Applications of this model range from microanalysis of dry and hydrous silicate glasses (e.g., melt inclusions) to in situ field investigations and studies under extreme conditions such as extraterrestrial (i.e., Mars) and submarine volcanic environments., Key Points Raman spectra of glasses are presented for a large range of chemical compositions, analogous to Martian compositionsSpectral intensities are parameterized to derive a model for estimation of the glass composition from Raman spectraThis enables in situ estimations of glass compositions in extraterrestrial or submarine volcanic settings