Atmospheric Water Vapor Research Articles

Assessing spatiotemporal characteristics of atmospheric water cycle processes over the Tibetan Plateau using the WRF model and finer box model

The Tibetan Plateau (TP) is the highest and one of the most extensive plateaus in the world and serves as a hotspot of climate change. In the context of climate warming, changes in evapotranspiration (ET) and external water vapor transport have a significant impact on assessing atmospheric water cycle processes over the TP. By using the Weather Research and Forecasting (WRF) model for long-term simulations and the finer box model for the calculation of water vapor along the boundary of the TP, the external atmospheric water vapor transport and its spatiotemporal characteristics over the TP are finely described. The simulated precipitation and ET are well-simulated compared with observation. Research results show that: (1) The total water path on the TP decreases from southeast to northwest. Water vapor is mainly transported into the TP from the western and southern boundaries. The net water vapor flux transported from the western boundary to the TP by westerly wind is negative, while the net water vapor flux transported from the southern boundary to the TP by southerly wind is positive. (2) In spring and winter, water vapor is mainly transported into the TP by mid-latitude westerlies from the western boundary. In summer, water vapor transport controlled by mid-latitude westerlies weakens, and water vapor is mainly transported into the TP from the southern boundary. In autumn, water vapor controlled by mid-latitude westerlies gradually strengthens, and water vapor is mainly transported into the TP from the western boundary. In addition, the ratio of ET to precipitation on the TP is about 0.48, and the moisture recycling is about 0.37. Water vapor mainly comes from external water vapor transport.

The Impacts of Regime Shift in Summer Arctic Oscillation on Precipitation in East Asia

Using multiple observational and reanalysis data, this paper investigates the impact of the interdecadal shift in summer Arctic Oscillation (AO) on precipitation in East Asia, by removing ENSO influences. The results indicate that the lower-layer activity center of summer AO in Atlantic shifted eastward after the mid-1980s. This regime shift of summer AO has a significant impact on precipitation in East Asia. Before the mid-1980s, the key regions in which precipitation was affected by AO in East Asia were northern East Asia and Northeastern China and adjacent regions. After the mid-1980s, the key regions in which precipitation was affected by AO in East Asia were central Inner Mongolia and Southern China. The mechanism of precipitation changes can be attributed to changes in atmospheric circulation and water vapor transport related to AO changes. After the mid-1980s, the influence of AO on geopotential height over northern East Asia weakened; meanwhile, the impact of AO on geopotential height over China increased. Consistent with the changes in atmospheric circulation, water vapor transport in East Asia also underwent interdecadal changes before and after the mid-1980s. The differences in atmospheric circulation and water vapor transport in East Asia can be traced back to the North Atlantic. Before the mid-1980s, wave activity flux related to summer AO tended to propagate in high latitudes and subtropics; after the mid-1980s, the wave activity flux changed in its subtropical path and propagated eastward from the North Atlantic through the Middle East to China, significantly affecting the summer precipitation in China.

Exploiting the entire near-infrared spectral range to improve the detection of methane plumes with high-resolution imaging spectrometers

Abstract. Remote sensing emerges as an important tool for the detection of methane plumes emitted by so-called point sources, which are common in the energy sector (e.g., oil and gas extraction and coal mining activities). In particular, satellite imaging spectroscopy missions covering the shortwave infrared part of the solar spectrum are very effective for this application. These instruments sample the methane absorption features at the spectral regions around 1700 and 2300 nm, which enables the retrieval of methane concentration enhancements per pixel. Data-driven retrieval methods, in particular those based on the matched filter concept, are widely used to produce maps of methane concentration enhancements from imaging spectroscopy data. Using these maps enables the detection of plumes and the subsequent identification of active sources. However, retrieval artifacts caused by particular surface components may sometimes appear as false plumes or disturbing elements in the methane maps, which complicates the identification of real plumes. In this work, we use a matched filter that exploits a wide spectral window (1000–2500 nm) instead of the usual 2100–2450 nm window with the aim of reducing the occurrence of retrieval artifacts and background noise. This enables a greater ability to discriminate between surface elements and methane. The improvement in plume detection is evaluated through an analysis derived from both simulated data and real data from areas including active point sources, such as the oil and gas (O&G) industry from San Joaquin Valley (US) and the coal mines from the Shanxi region (China). We use datasets from the Precursore IperSpettrale della Missione Applicativa (PRISMA) and the Environmental Mapping and Analysis Program (EnMAP) satellite imaging spectrometer missions and from the Airborne Visible/Infrared Imaging Spectrometer – Next Generation (AVIRIS-NG) instrument. We find that the interference with atmospheric carbon dioxide and water vapor is generally almost negligible, while co-emission or overlapping of these trace gases with methane plumes leads to a reduction in the retrieved concentration values. Attenuation will also occur in the case of methane emissions situated above surface structures that are associated with retrieval artifacts. The results show that the new approach is an optimal trade-off between the reduction in background noise and retrieval artifacts. This is illustrated by a comprehensive analysis in a PRISMA dataset with 15 identified plumes, where the output mask from an automatic detection algorithm shows an important reduction in the number of clusters not related to CH4 emissions.

An investigation of the on-board microwave radiometer of satellite Altimetry for studying the atmosphere variability

Since its first launching, the ability of satellite Altimetry in providing reliable and accurate ocean geophysical information of the sea surface height (SSH), significant wave height (SWH), and wind speed has been proven by numerous researchers, as it was designed for observing the ocean dynamics through nadir range measurement between satellite and the sea surface. However, to achieve high level accuracy, environmental and geophysical effects on the range measurement must be accurately determined and corrected, particularly the effects from the atmospheric water vapor which can divert altimeter range up to 3–45 cm. Thus, satellite Altimetry is originally equipped with the on-board microwave radiometer to measure the water vapour content for correcting the range measurement. To our knowledge, no one has attempted to apply the on-board radiometer for atmospheric studies. In this present work, we attempt to optimize the on-board radiometer data for studying the atmosphere variability due to the El Niño–Southern Oscillation (ENSO) phenomena. We convert the on-board water vapor data into the precipitable water vapour (PWV), and we then investigate whether the derived PWV can capture the variability of ocean–atmosphere phenomena due to ENSO as accurate as the conventional Altimetry-derived sea level anomaly (SLA). Based on our analysis using the empirical orthogonal function (EOF), the results show convincing argument that Altimetry-derived PWV are reliable in examining the atmospheric fluctuation as the correlation of its primary principal component time series (PC1) with Oceanic Nino Index (ONI) is higher (0.87) than SLA (0.80). These results may reinforce the confidence in the ability of satellite Altimetry for ocean–atmospheric studies.Graphical

An Experimental Investigation of the Precipitation Utilization of Plants in Arid Regions.

What represents a water source for the ecological restoration of a plant in an arid region is still up to debate. To address this issue, we conducted an in situ experiment in the Ulan Buh Desert of China, to study desert plants absorbing atmospheric water vapor. We selected Tamarisk, a common drought-salt-tolerant species in the desert, for ecological restoration as our research subject, used a newly designed lysimeter to monitor precipitation infiltration, and a sap flow system to track reverse sap flow that occurred in the shoot, branch, and stem during the precipitation event, and observed the precipitation redistribution process of the Tamarisk plot. The results showed that Tamarisk indeed directly absorbs precipitation water: when precipitation occurs, the main stem, lateral branch, and shoot all show the signs of reversed sap flow, and the reversed sap flow accounted for 21.5% of the annual sap flow in the shoot and branch, and 13.6% in the stem. The precipitation event in the desert was dominated by light precipitation events, which accounted for 81% of the annual precipitation events. It was found that light precipitation can be directly absorbed by the Tamarisk leaves, especially during nighttime or cloudy days. Even when the precipitation is absent, it was found that desert plants can still absorb water from the unsaturated atmospheric vapor; even the absorbed atmospheric water vapor was transported from the leaves to the stem, forming a reversed sap flow, as a reversed sap flow was observed when the atmospheric relative humidity reached 75%. This study indicated that the effect of light precipitation on desert plants was significant and should not be overlooked in terms of managing the ecological and hydrological systems in arid regions.

Detectability of Surface Biosignatures for Directly Imaged Rocky Exoplanets.

Modeling the detection of life has never been more opportune. With next-generation space telescopes, such as the currently developing Habitable Worlds Observatory (HWO) concept, we will begin to characterize rocky exoplanets potentially similar to Earth. However, few realistic planetary spectra containing surface biosignatures have been paired with direct imaging telescope instrument models. Therefore, we use a HWO instrument noise model to assess the detection of surface biosignatures affiliated with oxygenic, anoxygenic, and nonphotosynthetic extremophiles. We pair the HWO telescope model to a one-dimensional radiative transfer model to estimate the required exposure times necessary for detecting each biosignature on planets with global microbial coverage and varying atmospheric water vapor concentrations. For modeled planets with 0-50% cloud coverage, we determine pigments and the red edge could be detected within 1000 hr (100 hr) at distances within 15 pc (11 pc). However, tighter telescope inner working angles (2.5 λ/D) would allow surface biosignature detection at further distances. Anoxygenic photosynthetic biosignatures could also be more easily detectable than nonphotosynthetic pigments and the photosynthetic red edge when compared against a false positive iron oxide slope. Future life detection missions should evaluate the influence of false positives on the detection of multiple surface biosignatures.

Joint 1DVar retrievals of tropospheric temperature and water vapor from Global Navigation Satellite System radio occultation (GNSS-RO) and microwave radiometer observations

Abstract. Global Navigation Satellite System radio occultation (GNSS-RO) and microwave radiometry (MWR) are two of the most impactful spaceborne remote sensing techniques for numerical weather prediction (NWP). These two techniques provide complementary information about atmospheric temperature and water vapor structure. GNSS-RO provides high vertical resolution measurements with cloud penetration capability, but the temperature and moisture are coupled in the GNSS-RO retrieval process and their separation requires the use of a priori information or auxiliary observations. On the other hand, the MWR measures brightness temperature (Tb) in numerous frequency bands related to the temperature and water vapor structure but is limited by poor vertical resolution (> 2 km) and precipitation. In this study, we combine these two technologies in an optimal estimation approach, 1D variation method (1DVar), to improve the characterization of the complex thermodynamic structures in the lower troposphere. This study employs both simulated and operational observations. GNSS-RO bending angle and MWR Tb observations are used as inputs to the joint retrieval, where bending can be modeled by an Abel integral and Tb can be modeled by a radiative transfer model (RTM) that takes into account atmospheric absorption, as well as surface reflection and emission. By incorporating the forward operators into the 1DVar method, the strength of both techniques can be combined to bridge individual weaknesses. Applying 1DVar to the data simulated from large eddy simulation (LES) is shown to reduce GNSS-RO temperature and water vapor retrieval biases at the lower troposphere while simultaneously capturing the fine-scale variability that MWR cannot resolve. A sensitivity analysis is also conducted to quantify the impact of the a priori information and error covariance used in different retrieval scenarios. The applicability of 1DVar joint retrieval to the actual GNSS-RO and MWR observations is also demonstrated through combining collocated COSMIC-2 and Suomi-NPP (National Polar-orbiting Partnership) measurements.

Using a tethered balloon to monitor atmospheric water vapour dynamics over the Tibetan Plateau

Using a tethered balloon to monitor atmospheric water vapour dynamics over the Tibetan Plateau

Inter-Calibration of Passive Microwave Satellite Brightness Temperature Observations between FY-3D/MWRI and GCOM-W1/AMSR2

Microwave sensors possess the capacity to effectively penetrate through clouds and fog and are widely used in obtaining soil moisture, atmospheric water vapor, and surface temperature measurements. Long time-series datasets play a pivotal role in climate change studies. Unfortunately, the lifespan of operational satellites often falls short of the needs of these extensive datasets. Hence, comparing and cross-calibrating sensors with similar configurations is paramount. The Microwave Radiation Imager (MWRI) onboard Fengyun-3D (FY-3D) is the latest generation of satellite-based microwave remote sensing instruments in China, and its data quality and application prospects have attracted widespread attention. To comprehensively assess the data quality of MWRI, a comparison of the orbital brightness temperature (TB) data between FY-3D/MWRI and Global Change Observation Mission 1st-Water (GCOM-W1)/Advanced Microwave Scanning Radiometer 2 (AMSR2) is conducted, and then a calibration model is established. The results indicate a strong correlation between the two sensors, with a correlation coefficient exceeding 0.9 across all channels. The mean bias ranges from −1.5 K to 0.15 K. Notably, the bias of vertical polarization is more pronounced than that of horizontal polarization. The TB distribution patterns and temporal evolutions are highly consistent for both sensors, particularly under snow and ice. The small intercepts and close-to-1 slopes obtained during calibration further demonstrate the minor data differences between the two sensors. However, the calibration process effectively reduces the existing errors, and the calibrated FY-3D/MWRI TB data are closer to GCOM-W1/AMSR2, with a mean bias approximately equal to 0 K and a correlation coefficient exceeding 0.99. The excellent consistency of the TB data between the two sensors provides a vital data basis for retrieving surface parameters and establishing long time-series datasets.

Improving the estimation of atmospheric water vapor pressure using interpretable long short-term memory networks

Atmospheric water vapor pressure is an essential meteorological control on land surface and hydrologic processes. As it is not as frequently observed as other meteorologic conditions, it is often inferred through the August–Roche–Magnus formula by simply assuming dew point and daily minimum temperatures are equivalent or by empirically correlating the two temperatures using an aridity correction. The performance of both methods varies considerably across different regions and during different time periods; obtaining consistently accurate estimates across space and time remains a great challenge. Here, an interpretable Long Short-Term Memory (iLSTM) network conditioned on static, location specific attributes is proposed to estimate the daily vapor pressure. This approach allows for training a single transferable model using ensemble data from multiple sites and exploring the quantitative dependency of vapor pressure prediction on multiple environmental variables and their histories. To evaluate this approach, three iLSTM model configurations were developed, each considering different site attributes as static variables. For each configuration, multiple model realizations were trained using 83 FLUXNET sites in the United States and Canada, where each realization corresponds to different withheld groups of sites used for model evaluation. Results show that the iLSTM networks noticeably improve the estimation accuracy in comparison with the two assumption-based methods for most sites, reducing the failure rate from 32 % to 10.9 % for the best iLSTM model configuration. Additionally, this network provides reasonable insights into both the relative importance of the time-series input variables and their temporal importance. This method is found to be effective for imputing vapor pressure across space and time.

Collaborative Utilization of Sentinel-1/2 and DEM Data for Mapping the Soil Organic Carbon in Forested Areas Based on the Random Forest

Optical remote sensing data are widely used for constructing soil organic carbon (SOC) mapping models. However, it is challenging to map SOC in forested areas because atmospheric water vapor affects the results derived from optical remote sensing data. To address this issue, we utilized Sentinel-1, Sentinel-2, and digital elevation model (DEM) data to obtain a comprehensive feature set (including S1-based textural indices, S2-based spectral indices, and DEM-derived indices) to map the SOC content in forested areas. The features set were the predictor variables, and the measured SOC content was the dependent variable. The random forest algorithm was used to establish the SOC model. The ratio of performance to inter-quartile range (RPIQ) was 2.92 when the S2-based spectral indices were used as predictor variables. When the comprehensive feature set was utilized as the model input, the model achieved an RPIQ of 4.13 (R2 = 0.91, root mean square error (RMSE) = 9.18), representing a 41.44% improvement in model accuracy. The average SOC content in the Greater Khingan Mountains was 43.75 g kg−1. The northern and southwestern parts had higher SOC contents (>54.93 g kg−1), while the southeastern and northwestern parts had lower contents (<39.83 g kg−1). This discrepancy was primarily attributed to agricultural activities. The results indicate that using a comprehensive feature set and the random forest algorithm is a reliable approach for estimating the spatial distribution of the SOC content in forested areas and is suitable for forest ecology and carbon management studies.

Estimation of Land Surface Temperature from Chinese ZY1-02E IRS Data

The role of land surface temperature (LST) is of the utmost importance in multiple academic disciplines, such as climatology, hydrology, ecology, and meteorology. To date, many methods have been proposed to estimate LST from satellite thermal infrared data. The single-channel (SC) algorithm can provide an accurate result in retrieving LST based on prior knowledge of known land surface emissivity (LSE). The SC algorithm is extensively employed for retrieving LST from Landsat series data due to its simplicity and its reliance on just one thermal infrared channel. The Thermal Infrared Sensor (IRS) on the Chinese ZY1-02E satellite is a pivotal instrument employed for gathering thermal infrared (TIR) data of land surfaces. The objective of this research is to evaluate the feasibility of a single-channel approach based on water vapor scaling (WVS) for deriving LST from ZY1-02E IRS data because of its wide spectrum range, i.e., 7~12 μm, which is affected strongly by both atmospheric water vapor and ozone. Three study areas, namely the Baotou, Heihe River Basin, and Yantai Sea sites, were selected as validation sites to evaluate the LST inversion accuracy. This evaluation was also conducted via cross-comparison between the retrieved LST and MODIS LST products. The results revealed that the WVS-based method exhibited an average bias of 0.63 K and an RMSE of 1.62 K compared to the in situ LSTs. The WVS-based method demonstrated reasonable accuracy through cross-validation with the MODIS LST product, with an average bias of 0.77 K and an RMSE of 2.0 K. These findings indicate that the WVS-based method is effective in estimating LST from ZY1-02E IRS data.

Weakened Increase in Global Near‐Surface Water Vapor Pressure During the Last 20 Years

AbstractIt is well known that global warming increases the atmospheric water vapor content, which results in substantial changes in the hydrological cycle. Using five observational data sets, the results show that an increasing trend of near‐surface water vapor pressure (AVP) over land and ocean was significant from 1975 to 1998, while such an increasing trend in AVP subsequently weakened from 1999 to 2019. This phenomenon is associated with decreased oceanic evaporation and land surface evapotranspiration in response to recent climate variations. One consequence of such a phenomenon is a large increase in near‐surface vapor pressure deficit (VPD), which in turn increases atmospheric demand for water vapor and thus aridity and drought over land. This result emphasizes the importance of water vapor change under global warming.

Measuring Turbulent Water Vapor Fluxes Using a Tunable Diode Laser-Based Open-Path Gas Analyzer

The reliable observation and accurate estimates of land–atmosphere water vapor (H2O) flux is essential for ecosystem management and the development of Earth system models. Currently, the most direct measurement method for H2O flux is eddy covariance (EC), which depends on the development of fast-response H2O sensors. In this study, we presented a cost-efficient open-path H2O analyzer (model: HT1800) based on the tunable diode laser absorption spectroscopy (TDLAS) technique, and investigated its applicability for measuring atmospheric turbulent flux of H2O using the EC method. We prepared two HT1800 analyzers with lasers that operate at wavelengths of 1392 nm and 1877 nm, respectively. The field performance of the two analyzers was evaluated through inter-comparative experiments with LI-7500RS and IRGASON, two of the most commonly used H2O analyzers in the EC community. Water vapor densities measured by the three types of analyzers had high overall agreement with the reference sensor; however, they all experienced drift. The mean density drifts of HT1800, LI-7500 and IRGASON were 3.7–5.2%, 4.0% and 3.8%, respectively. Even so, the half-hourly H2O fluxes measured by HT1800 were highly consistent with those by LI-7500RS and IRGASON (with a difference of less than 2%), suggesting that HT1800 can obtain H2O fluxes with high confidence. The HT1800 was also proved to be suitable for EC application in terms of data availability, flux detection limit and response to the high-frequency turbulent variation. Furthermore, we investigated how the spectroscopic effect influences the measurements of H2O density and flux. Despite the fact that the 1392 nm laser was much more susceptible to the spectroscopic effect, the fluxes after correcting for this bias showed excellent agreement with the IRGASON fluxes. Considering the cost advantage in laser and photodetector, the HT1800 analyzer using a 1392 nm infrared laser is a promising and economical solution for EC measurement studies of water vapor.

Improved water vapor corrosion resistance of SiC bricks via in situ formation of microporous Yb2Si2O7 bonding phase

AbstractSiC‐based lining refractories used in incinerators faced serious hot water vapor corrosion. The pores in SiC‐based refractories played a crucial role in corrosion resistance because both the penetration of water vapor and the expulsion of corrosion products depended on diffusion channels provided by pores. The in situ microporous Yb2Si2O7 bonding phase formed in the SiC bricks after sintering at 1550°C under air atmosphere, which simultaneously decreased the apparent porosity and pore size. The optimization of pore structures reduced the permeability to gases of SiC bricks by 37.5%, which prevented the penetration of hot water vapor into SiC bricks. The microporous Yb2Si2O7‐bonded SiC bricks presented only .15% permanent linear change and showed good thermal shock resistance after corrosion at 1000°C for 100 h under hot water vapor atmosphere. Some SiC particles in the refractories could be adopted as high‐temperature pore‐forming agents; the gaseous oxidation products of SiC bricks, including SiO(g) and CO(g), contributed to the generation of microporous Yb2Si2O7 (pore size ≤5 μm) and SiC whiskers during sintering, which obviously improved mechanical properties and water vapor corrosion resistance of SiC bricks.

Ocean air masses from the East Asian monsoon dominate the land‐surface atmospheric water cycles in the coastal areas of Liaodong Bay, Northeast China

AbstractLong‐term atmospheric water vapour hydrogen (δ2H), oxygen (δ18O) and deuterium excess (d‐excess) can provide unique insights into the land‐atmosphere coupling processes. The in‐situ measurements of atmospheric water vapour δ2H, δ18O and d‐excess were conducted above a reed wetland of Liaodong Bay (2019–2020). We found significant inter‐annual variations in atmospheric water vapour isotopes between the two growing (May–September) seasons. The δ2H, δ18O and d‐excess of atmospheric water vapour exhibited different seasonal and diurnal cycles concerning the vertical measurement heights, especially in 2019. The isotopic differences of atmospheric water vapour among vertical measurement heights were more evident in the daytime. Rainfall events directly impacted the diurnal patterns of water vapour isotopes, and the influences depended on rainfall intensities. However, only weak correlations existed between water vapour isotopes and local meteorological factors (R2 = 0.01–0.16, p < 0.001), such as water vapour concentration (w), Relative Humidity (RH) and surface air temperature (Ta). Based on the back‐air trajectory analyses, the spatial–temporal dynamics of atmospheric water vapour isotopes are highly synchronized with monsoon activities. Different water vapour sources influence the water vapour isotope in this region and the higher d‐excess value is related to the intense convection brought by the monsoon. High‐resolution measurements of atmospheric water vapour isotopes will improve our understanding of the hydrological cycles in coastal areas.

Water vapor effect on the physico-geometrical reaction pathway and kinetics of the multistep thermal dehydration of calcium chloride dihydrate.

This study investigated how water vapor influences the reaction pathway and kinetics of the multistep thermal dehydration of inorganic hydrates, focusing on CaCl2·2H2O (CC-DH) transforming into its anhydride (CC-AH) via an intermediate of its monohydrate (CC-MH). In the presence of atmospheric water vapor, the thermal dehydration of CC-DH stoichiometrically proceeded through two distinct steps, resulting in the formation of CC-AH via CC-MH under isothermal conditions and linear nonisothermal conditions at a lower heating rate (β). Irrespective of atmospheric water vapor pressure (p(H2O)), these reaction steps were kinetically characterized by a physico-geometrical consecutive process involving the surface reaction and phase boundary-controlled reaction, which was accompanied by three-dimensional shrinkage of the reaction interface. In addition, a significant induction period was observed for the second reaction step, that is, the thermal dehydration of CC-MH intermediate to form CC-AH. With increasing p(H2O), a systematic increase in the apparent Arrhenius parameters was observed for the first reaction step, that is, the thermal dehydration of CC-DH to form CC-MH, whereas the second reaction step exhibited unsystematic variations of the Arrhenius parameters. At a larger β in the presence of atmospheric water vapor, the first and second reaction steps partially overlapped; moreover, an alternative reaction step of the thermal dehydration of CC-MH to form CaCl2·0.3H2O was observed between these reaction steps. The physico-geometrical phenomena influencing the reaction pathway and kinetics of the multistep thermal dehydration were elucidated by considering the effects of atmospheric and self-generated water vapor in a geometrically constrained reaction scheme.

Comparison of Nighttime Land Surface Temperature Retrieval Using Mid-Infrared and Thermal Infrared Remote Sensing Data Under Different Atmospheric Water Vapor Conditions

Comparison of Nighttime Land Surface Temperature Retrieval Using Mid-Infrared and Thermal Infrared Remote Sensing Data Under Different Atmospheric Water Vapor Conditions

Accurate Measurements of Atmospheric Water Vapor Using Lateral Scanning Vibrational Raman Scattering Lidar with Continuous-Wave Laser

Accurate Measurements of Atmospheric Water Vapor Using Lateral Scanning Vibrational Raman Scattering Lidar with Continuous-Wave Laser

Compressed Sensing of Field-Resolved Molecular Fingerprints Beyond the Nyquist Frequency

Ultrashort time-domain spectroscopy and field-resolved spectroscopy of molecular fingerprints are gold standards for detecting samples’ constituents and internal dynamics. However, they are hindered by the Nyquist criterion, leading to prolonged data acquisition, processing times, and sizable data volumes. In this work, we present the first experimental demonstration of compressed sensing on field-resolved molecular fingerprinting by employing random scanning. Our measurements enable pinpointing the primary absorption peaks of atmospheric water vapor in response to terahertz light transients while sampling beyond the Nyquist limit. By drastically undersampling the electric field of the molecular response at a Nyquist frequency of 0.8 THz, we could successfully identify water absorption peaks up to 2.5 THz with a mean squared error of 12 × 10 −4 . To our knowledge, this is the first experimental demonstration of time-domain compressed sensing, paving the path toward real-time field-resolved fingerprinting and acceleration of advanced spectroscopic techniques.