Water Vapor Profiles Research Articles

Design and Verification of a Double-Grating Spectrometer System (DGSS) for Simultaneous Observation of Aerosols, Water Vapor and Clouds

Simultaneous observation of aerosols, water vapor, and clouds is conducive to the analysis of their interactions, and the consistency of observation equipment, instrument performance, and observation time is crucial. Molecular oxygen A-band (758–778 nm) and water vapor absorption band (758–880 nm) are two bands with similar wavelengths, and the hyperspectral remote sensing information of these two bands can be exploited to invert the vertical profile of aerosol and water vapor. In this paper, a double-grating spectrometer system (DGSS) was developed. DGSS uses a telescope system and fiber to introduce multi-angle, double-band sunlight, and it splits light synchronously (non-sequentially) to different positions of the detector through a slit plate and two gratings. The DGSS was calibrated in the laboratory and observed in the external field. The results indicated that the spectral resolution reached 0.06 nm (molecular oxygen A-band, 758–778 nm) and 0.24 nm (water vapor absorption band, 758–880 nm). Meanwhile, the spectra of the two bands (three angles in each band) are not aliased on the detector. Besides, the multi-angle simultaneous observation of the high-resolution spectra of the two bands is realized, which proves the effectiveness of this method. This study will provide a scientific basis for the observation of aerosol, water vapor, and cloud ground-based networks.

Supersaturation Variability from Scalar Mixing: Evaluation of a New Subgrid-Scale Model Using Direct Numerical Simulations of Turbulent Rayleigh–Bénard Convection

Abstract Supersaturation fluctuations in the atmosphere are critical for cloud processes. A nonlinear dependence on two scalars—water vapor and temperature—leads to different behavior than single scalars in turbulent convection. For modeling such multiscalar processes at subgrid scales (SGS) in large-eddy simulations (LES) or convection-permitting models, a new SGS scheme is implemented in CM1 that solves equations for SGS water vapor and temperature fluctuations and their covariance. The SGS model is evaluated using benchmark direct-numerical simulations (DNS) of turbulent Rayleigh–Bénard convection with water vapor as in the Michigan Tech Pi Cloud Chamber. This idealized setup allows thorough evaluation of the SGS model without complications from other atmospheric processes. DNS results compare favorably with measurements from the chamber. Results from LES using the new SGS model compare well with DNS, including profiles of water vapor and temperature variances, their covariance, and supersaturation variance. SGS supersaturation fluctuations scale appropriately with changes to the LES grid spacing, with the magnitude of SGS fluctuations decreasing relative to those at the resolved scale as the grid spacing is decreased. Sensitivities of covariance and supersaturation statistics to changes in water vapor flux relative to thermal flux are also investigated by modifying the sidewall conditions. Relative changes in water vapor flux substantially decrease the covariance and increase supersaturation fluctuations even away from boundaries.

Modeling the complete set of Cassini’s UVIS occultation observations of Enceladus’ plume

The Cassini Ultraviolet Imaging Spectrograph (UVIS) observed a plume of water vapor spewing out from the south polar regions of Enceladus via occultations 7 times during the Cassini mission. Five of them yielded spatially resolved data that allowed fits to a set of individually modeled jets. We created a direct simulation Monte Carlo (DSMC) model to simulate individual water vapor jets with the aim of fitting them to water vapor abundances along the UVIS line of sight during occultation observations. Accurate location and attitude of spacecraft together with positions of Enceladus and Saturn at each observation determine the relationship between the three-dimensional water vapor number density in the plume and the two-dimensional profiles of water vapor abundances along the line of sight recorded by UVIS. By individually fitting observed and modeled jets, every occultation observation of UVIS presented a unique perspective to the physical properties and distribution of the jets. The minimum velocity of water vapor in the jets is determined from the narrowest observed individual jet profile: it ranges from 800 m/s to 1.8 km/s for the UVIS occultation observations. 41 individual jets were required to fit the highest resolution UVIS dataset taken during the Solar occultation however, an alternative larger set of linearly-dependent jets cannot be excluded without invoking additional unrelated data from other instruments. A smaller number of jets is required to fit the stellar occultation data because of their spatial resolution and geometry. We identify a set of 37 jets that were repeatedly present in best fits to several UVIS occultation observations. These jets were probably active through the whole Cassini mission.

Sensitivity studies toward the retrieval of ice crystal habit distributions inside cirrus clouds from upwelling far infrared spectral radiance observations

In this paper a retrieval procedure of cirrus ice properties, based on the optimal estimation and the Levenberg–Marquardt iterative formula, is extended with a suitable non linear transformation of the cloud state vector parameters to include the crystal habit distribution in addition to the effective diameter and the optical depth. The cloud optical coefficients are linearly combined using different crystal shapes (solid columns, droxtals, hollow columns, bullet rosettes, aggregates and plates) to build up two different mixtures of habits to be used in the retrieval. This approach allows us to retrieve the coefficients of the habit crystals inside cirrus clouds by using upwelling far infrared spectral radiance measurements. The retrieval capability is shown by performing self-consistency tests using the Simultaneous Atmospheric and Cloud Retrieval (SACR) code and a set of 240 synthetic diverse observations in cloudy-sky generated by using realistic atmospheric scenarios (for mid-latitudes, tropics and poles) and the noise anticipated for the new far infrared sensor Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM), which is planned to be launched as the ninth Earth Explorer (EE-9) by the European Space Agency (ESA) in 2026. The sensitivity of the retrieved crystal habit coefficients to parameters that are not retrieved, such as the surface temperature and emissivity, the cloud top and bottom heights, the ice fraction, the water vapour and temperature profiles, together with the biases caused by the cloud vertical and horizontal inhomogeneity, were also assessed and discussed in the paper.

Measurements of Atmospheric Water Vapor by a 1.316 μm Optical Fiber Laser Heterodyne Radiometer

A passive optical fiber laser heterodyne radiometer prototype with a semiconductor laser near 1.316 μm as the local oscillator was built, parameters of the prototype have been optimized. Using the prototype, the water vapor concentration in the atmospheric column was measured with a spectral resolution of 0.009 cm−1 in late October and early November of 2020, the collection time was approximately 3 min, and the signal-to-noise ratio was better than 120. The water vapor column concentration and profiles were inversed based on the optimal estimation method. Compared with the measurement of the Fourier transform spectrometer (EM27/SUN) which was performed simultaneously, the inversion results deviated by less than 14%, and the variation trend of the water vapor concentration showed good consistency. It is demonstrated that the 1.316 μm optical fiber laser heterodyne radiometer possesses good stability and accuracy in the field measurement of atmospheric water vapor concentration.

Validation and Utility of Satellite Retrievals of Atmospheric Profiles in Detecting and Monitoring Significant Weather Events

Abstract Infrared and microwave sounder measurements from polar-orbiting satellites are used to retrieve profiles of temperature, water vapor, and trace gases utilizing a suite of algorithms called the National Oceanic and Atmospheric Administration (NOAA) Unique Combined Atmospheric Processing System (NUCAPS). Meteorologists operationally use the retrievals similar to radiosonde measurements to assess atmospheric stability and aid them in issuing forecasts and severe weather warnings. Measurements of trace gases by NUCAPS enable detection, tracking, and monitoring of greenhouse gases and emissions from fires that impact air quality. During the polar winters, when ultraviolet measurements of ozone are not possible, absorption features in the infrared spectrum of the sounders enable the assessment of ozone concentration in the stratosphere. These retrievals are used as inputs to monitor the ozone hole over Antarctica. This article illustrates the utility of NUCAPS atmospheric profile retrievals in assessing meteorological events using several examples of severe thunderstorms, tropical cyclones, fires, and ozone maps.

Data Fusion of GEO FY-4A GIIRS and LEO Hyperspectral Infrared Sounders with Surface Observations: A Hong Kong Case Study

Abstract Hyperspectral infrared satellite observations from geostationary platforms allow for the retrieval of temperature and water vapor measurements with higher temporal and vertical resolution than was previously available. The Chinese satellite Fengyun-4A (FY-4A) includes the Geostationary Interferometric Infrared Sounder (GIIRS), which has the ability to measure vertical profiles of temperature and water vapor from space at times when ground-based upper-air soundings are not available and can fill an important need in short-range weather prediction. In this study, convective available potential energy (CAPE) and lifted index (LI), which are used for forecasting atmospheric instability, were computed using the SHARPpy algorithm used by the NWS Storm Prediction Center. However, remote infrared and microwave sensing is lacking detailed information in the boundary layer, so the addition of the NOAA Meteorological Assimilation and Data Ingest System (MADIS) surface data may be necessary in order to get accurate temperature and moisture measurement near the surface. This study uses 10–16 May 2019 in the coastal region near Hong Kong for evaluating the use of hourly surface observations combined with satellite soundings from FY-4A GIIRS at 2-h intervals. The GIIRS plus MADIS surface-based CAPE and LI estimates are compared to estimates derived from low-Earth-orbiting (LEO) SNPP and NOAA-20 from NOAA, MetOp from EUMETSAT, NWP reanalysis, and local radiosondes. In the case study, the 2-h sampling interval of the GIIRS geostationary sounder was able to capture the rapid transition (16 h) from a stable to an unstable atmosphere in both CAPE and LI. The use of surface observations with satellite soundings gave mixed results, possibly due to the complex terrain near Hong Kong.

Explaining NOMAD D/H Observations by Cloud‐Induced Fractionation of Water Vapor on Mars

AbstractThe vertical profiles of water vapor and its semi‐heavy hydrogen isotope HDO provided by instruments on ExoMars Trace Gas Orbiter constitute a unique new data set to understand the Martian water cycle including its isotopic composition. As water vapor undergoes hydrogen isotopic fractionation upon deposition (but not sublimation), the D/H isotopic ratio in water is a tracer of phase transitions, and a key quantity to understand the long‐term history of water on Mars. Here, we present 3D global simulations of D/H in water vapor and compare them to the vertically resolved observations of D/H and water ice clouds taken by NOMAD during the second half of Mars year 34. D/H is predicted to be constant with height up to the main cloud level, above which it drops because of strong fractionation, explaining the upper cut‐off in the NOMAD observations when HDO drops below detectability. During the global and regional dust storms of 2018/2019, we find that HDO ascends with H2O, and that the D/H ratio is constant and detectable up to larger heights. The simulations are within the provided observational uncertainties over wide ranges in season, latitude and height. Our work provides evidence that the variability of the D/H ratio in the lower and middle atmosphere of Mars is controlled by fractionation on water ice clouds, and thus modulated by diurnally and seasonally varying cloud formation. We find no evidence of other processes or reservoirs that would have a significant impact on the D/H ratio in water vapor.

In‐Depth Evaluation of MiRS Total Precipitable Water From NOAA‐20 ATMS Using Multiple Reference Data Sets

AbstractThe National Oceanic and Atmospheric Administration (NOAA) Microwave Integrated Retrieval System (MiRS) algorithm has been producing retrieval products from NOAA‐20 Advanced Technology Microwave Sounder (ATMS) data since shortly after launch in late 2017. Retrieval products from the Suomi National Polar‐orbiting Partnership (SNPP) ATMS data have been similarly available since 2012. Among the products routinely generated is Total Precipitable Water (TPW), based on the vertical integral of the MiRS retrieved water vapor profile. In this study, TPW data from NOAA‐20 ATMS have been the subject of an in‐depth evaluation. The evaluation included characterization of dependence on orbital node (ascending/descending), season, surface type, scan angle, and meridional (zonally averaged) variation. Due to the similarity between NOAA‐20 and SNPP derived TPW, the emphasis here is on the NOAA‐20 evaluation. The globally averaged values of TPW for 2019 are 25.41 and 25.39 mm from NOAA‐20 and SNPP ATMS, respectively, for combined orbits (ascending and descending). For combined orbits, the bias and standard deviation of MiRS NOAA‐20 ATMS TPW with respect to European Centre for Medium‐Range Weather Forecasts/Global Data Assimilation System (ECMWF/GDAS) analyses are 0.79/1.76 mm and 3.65/3.55 mm, respectively, at 10°N where part of annual mean Intertropical Convergence Zone (ITCZ) is located, while at 40°N, corresponding bias and standard deviation values are 0.84/0.67 mm and 3.00/2.55 mm, respectively. The yearly averaged horizontal distribution of MiRS TPW retrieved from NOAA‐20 ATMS is highly correlated with that of ECMWF and GDAS analyses data, with a correlation coefficient of ∼1.

Improving thermodynamic profile retrievals from microwave radiometers by including radio acoustic sounding system (RASS) observations

Abstract. Thermodynamic profiles are often retrieved from the multi-wavelength brightness temperature observations made by microwave radiometers (MWRs) using regression methods (linear, quadratic approaches), artificial intelligence (neural networks), or physical iterative methods. Regression and neural network methods are tuned to mean conditions derived from a climatological dataset of thermodynamic profiles collected nearby. In contrast, physical iterative retrievals use a radiative transfer model starting from a climatologically reasonable profile of temperature and water vapor, with the model running iteratively until the derived brightness temperatures match those observed by the MWR within a specified uncertainty. In this study, a physical iterative approach is used to retrieve temperature and humidity profiles from data collected during XPIA (eXperimental Planetary boundary layer Instrument Assessment), a field campaign held from March to May 2015 at NOAA's Boulder Atmospheric Observatory (BAO) facility. During the campaign, several passive and active remote sensing instruments as well as in situ platforms were deployed and evaluated to determine their suitability for the verification and validation of meteorological processes. Among the deployed remote sensing instruments were a multi-channel MWR as well as two radio acoustic sounding systems (RASSs) associated with 915 and 449 MHz wind profiling radars. In this study the physical iterative approach is tested with different observational inputs: first using data from surface sensors and the MWR in different configurations and then including data from the RASS in the retrieval with the MWR data. These temperature retrievals are assessed against co-located radiosonde profiles. Results show that the combination of the MWR and RASS observations in the retrieval allows for a more accurate characterization of low-level temperature inversions and that these retrieved temperature profiles match the radiosonde observations better than the temperature profiles retrieved from only the MWR in the layer between the surface and 3 km above ground level (a.g.l.). Specifically, in this layer of the atmosphere, both root mean square errors and standard deviations of the difference between radiosonde and retrievals that combine MWR and RASS are improved by mostly 10 %–20 % compared to the configuration that does not include RASS observations. Pearson correlation coefficients are also improved. A comparison of the temperature physical retrievals to the manufacturer-provided neural network retrievals is provided in Appendix A.

TROPOspheric Monitoring Instrument observations of total column water vapour: Algorithm and validation

In this paper, we present the total column water vapour (TCWV) retrieval for the TROPOspheric Monitoring Instrument (TROPOMI) observations in the visible blue spectral band. The TROPOMI TCWV algorithm is being optimized and validated in the framework of the Sentinel 5 Precursor Product Algorithm Laboratory (S5P-PAL) project from the European Space Agency (ESA). The retrieval was first developed to retrieve TCWV from the Global Ozone Monitoring Experiment 2 (GOME-2). We have optimized the settings of the retrieval to adapt it for TROPOMI observations. The TROPOMI TCWV algorithm follows the typical two step approach, using spectral fit retrieval of slant columns, and conversion of the slant columns to vertical columns using air mass factors (AMFs). An iterative optimization algorithm is developed to dynamically find the optimal a priori water vapour profile for the AMF calculation. Further optimizations on the spectral retrieval, air mass factor calculations as well as a new surface albedo retrieval approach are implemented.The TCWV retrieval algorithm is applied to TROPOMI observations from May 2018 to May 2021. The results are validated by comparing them to ERA5 reanalysis data, GOME-2, MODerate resolution Imaging Spectroradiometer (MODIS) and Special Sensor Microwave Imager Sounder (SSMIS) satellite observations. TCWV derived from TROPOMI observations agree well with the other data sets with Pearson correlation coefficient (R) ranging from 0.96 to 0.99. The mean bias between TROPOMI and ERA5 data is −1.24 kg m−2 for measurements over land and 0.73 kg m−2 for measurements over water. The comparison to MODIS observations show similar results with small dry bias of 1.51,kg m−2 for measurements over land and a small wet bias of 1.25 kg m−2 for measurements over water. Slightly larger dry bias of 1.98 kg m−2 for measurements over land and 1.74 kg m−2 for measurements over water are found when compared to GOME-2 obserations. Compared to SSMIS data over water, TROPOMI observations are bias low by 3.25 kg m−2. The small discrepancies found between TROPOMI and reference data sets are related to the differences in measurement technique, measurement time, surface albedo issue, as well as cloud and aerosol contamination. This study demonstrates that the algorithm can provide stable and consistent results on a global scale and can be applied to generate operational TCWV products from TROPOMI and the forthcoming Copernicus missions Sentinel-4 and Sentinel-5. We have also demonstrated the capability of retrieving fine scale water vapour structures in a case study over the Amazon. This indicates that the TROPOMI data set is also useful for local and regional climate studies.

Radiometric Noise Assessment of the Cross-Track Infrared Sounder on the NOAA-20 Satellite

The Cross-track Infrared Sounder (CrIS) is a Michelson-type Fourier Transform Spectrometer. The CrIS flight module 2 instrument was launched into orbit on November 18, 2017, onboard the NOAA-20 satellite as part of the United States (US) Joint Polar Satellite System (JPSS). The CrIS instrument measures the top-of-atmosphere upwelling spectral radiance in the thermal infrared (IR) spectrum. These measurements provide critical information for medium-range weather forecasting, and the retrieval of atmospheric profiles of temperature, water vapor, and other trace gases. The instrument noise equivalent radiance differential (NEdN) estimates are used by the weather forecasting systems, the trace gas atmospheric retrieval algorithms, and for trending the health and stability of the instrument over time. The current operational NEdN estimate is calculated using instrument observations from the deep space (DS) view and the internal calibration target (ICT). Two alternative methodologies are described here based on the principal component analysis (PCA) of an ensemble of calibrated Earth scene (ES) spectra. The NEdN calculation methods show that the instrument meets the specifications with a margin for all 27 detectors with an exception of one midwave IR (MWIR) field-of-view (FOV) 9, which is borderline. The PCA analysis shows that warmer ES spectra have higher noise, known as scene shot, for the short-wave IR (SWIR) band. Using the PCA analysis, the NEdN for the long-wave IR FOV 5 is 30% higher than the NEdN calculated by the operational algorithm. Correlated noise is also found due to the effect of the instrument self-apodization correction.

Spectrally Resolved Raman Lidar to Measure Backscatter Spectra of Atmospheric Three-Phase Water and Fluorescent Aerosols Simultaneously: Instrument, Methodology, and Preliminary Results

This work presents a spectrally resolved Raman lidar (SRRL) for simultaneous measurement of volume backscattering coefficient spectra (backscatter spectra for short) of atmospheric three-phase water and fluorescent aerosols. The SRRL emits 354.8-nm laser light and records both N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Raman echoes around 386.7 nm and spectral signals in a specially modified spectral range of ~393–424 nm. By defining normalized spectra, a unique spectra decomposition approach is developed for successive retrieval of normalized spectra components of fluorescent aerosols, water vapor, ice water, and liquid water. Furthermore, the backscatter spectra of each component are obtained by referencing the N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Raman signals. Three typical measurement cases are provided. For the clear-day case, the lidar-measured normalized water vapor Raman spectra are found to be nearly invariant in shape and can serve as background spectra reference for decomposition of mixed-phase water Raman spectra. Besides, fluorescence backscatter intensities are usually weak enough to be neglected in clear areas. For the fluorescence case, the fluorescence backscatter intensities become much stronger to prevent accurate measurements of water vapor. For the mixed-phase water virga case, the decomposed backscatter spectra of condensed ice and liquid water indicate the coexistence of ice water and less amount of liquid water in the falling water virga. In conclusion, this kind of SRRL is believed to have provided the potential for simultaneous and accurate profiling of atmospheric water vapor and cloud liquid/ice water and opened up new perspectives for studies of cloud–aerosol interaction.

Geostationary Hyperspectral Infrared Sounder Channel Selection for Capturing Fast-Changing Atmospheric Information

Various methodologies have been developed for selecting a subset of channels from a hyperspectral infrared (IR) sounder for assimilation. The information entropy iterative method was considered optimal for channel selection. However, this method only considers the decrease in uncertainty in the atmospheric state caused by measurements at a single time, without considering the dynamic effect of measurements over a period of time; therefore, it might not be optimal for hyperspectral IR sounders onboard geosynchronous satellites that mainly aim to observe rapidly changing weather events. An alternative channel selection method is developed by adding an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> index, which reflects the Jacobian variance over time; the adjusted algorithm is ideal for the Geosynchronous Interferometric Infrared Sounder (GIIRS), which is the first high-spectral-resolution advanced IR sounder onboard a geostationary weather satellite. Comparisons between the conventional algorithm (information entropy iterative method) and the adjusted algorithm show that the channels selected from GIIRS by the adjusted algorithm will have larger brightness temperature diurnal variations and better information content than the conventional algorithm, based on the same background error covariance matrix, the observational error covariance matrix, and the channel blacklist. The adjusted algorithm is able to select the channels for monitoring atmospheric temporal variation while retaining the information content from the conventional method. The 1-D variational (1Dvar) retrieval experiment also verifies the superiority of this adjusted algorithm; it indicates that using the channel selected by the adjusted algorithm could enhance the water vapor profile retrieval accuracy, especially for the lower and middle troposphere atmosphere.

First Airborne Measurements With a G-Band Differential Absorption Radar

Observations and analysis from the first airborne deployment of a G-band atmospheric radar, the Jet Propulsion Laboratory&#x2019;s vapor in-cloud profiling radar (VIPR), are presented with a focus on ocean surface scatterometry and water vapor retrievals using the differential absorption radar (DAR) technique. We assess transceiver cross-calibration stability between the two DAR frequency channels using a combination of ground-based calibration measurements and airborne cloud observations that are validated by independent sensors. By comparing calibrated, unattenuated ocean surface backscatter measurements with existing models, we demonstrate the need for improvements in treating ocean surface scattering at G-band frequencies for future in-flight radar calibration applications. During science-oriented flights off the coast of California, we demonstrate partial column integrated water vapor (IWV) measurement capabilities with 0.3-mm precision in clear sky by performing a descent maneuver over an operational radiosonde (RS) launching site and comparing with the coincident <i>in situ</i> profiles to find a root mean square error (RMSE) of 0.5 mm. Furthermore, we present water vapor profiling and IWV measurements in the presence of clouds and precipitation when flying through a frontal weather system in the vicinity of Bodega Bay. By comparing with coincident RS profiles, we find an RMSE for in-cloud humidity profile retrievals of 1.1 <inline-formula> <tex-math notation="LaTeX">${\rm gm}^{-3}$ </tex-math></inline-formula> and precision of 0.6 <inline-formula> <tex-math notation="LaTeX">${\rm gm}^{-3}$ </tex-math></inline-formula> while the IWV retrievals reveal a bias from differential hydrometeor extinction of 5 mm. We provide a critical assessment of cloud-scattering-induced biases in the water vapor retrievals, as well as discuss a potential method for eliminating systematic error from the DAR measurements.

Temperature, water vapor and tropopause characteristics over the Tibetan Plateau in summer based on the COSMIC, ERA-5 and IGRA datasets

We established a set of atmospheric standard temperature and water vapor profiles and examined the characteristics of the tropopause over the Tibetan Plateau in summer using IGRA, COSMIC and ERA-5 datasets. We studied the temperature and water vapor profiles under clear sky, cloudy and “other” conditions and their diurnal variation. The atmosphere over the Central Tibetan Plateau is warmest and wettest in the lower to mid-troposphere, whereas the Western Tibetan Plateau is warmest and driest in the mid to high troposphere. The coldest and wettest air near the tropopause is over the Eastern Tibetan Plateau. The height of the tropopause decreases gradually from west to east over the plateau, although the difference in height is ≤0.5 km. Relative to clear sky conditions, cloudy conditions are characterized by warming and wetting anomalies in the lower to mid-troposphere and cooling and wetting anomalies in the tropopause and lower stratosphere. The largest vertical diurnal differences in temperature are at the surface and the difference decreases with increasing altitude from the surface to ~12 km. The difference between the six time periods of early morning, morning, noon, afternoon, evening and midnight are within −0.5 to 0.5 K above 12 km. The difference in water vapor between the six time periods is ≤0.2 g m−3. The lowest tropopause is at early morning. The height of the tropopause then increases followed by a decrease, with a sharp peak at noon. This establishment of a set of atmospheric standard profiles over Tibetan Plateau provides more accurate inputs for climate models.

Water vapor saturation and ice cloud occurrence in the atmosphere of Mars

We investigate water vapor saturation in the martian atmosphere in the presence or proximity of water ice clouds. We evaluate mixing ratio profiles of ambient water vapor derived from measurements by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) and the Nadir and Occultation for MArs Discovery (NOMAD) instrument in comparison with saturated vapor mixing ratio profiles derived from co-located temperature measurements by the Mars Climate Sounder (MCS). We find that during the aphelion season the average saturation state of the atmosphere is close to saturation in the presence of clouds, with supersaturation ratios reaching values of no more than two to three towards the top of the cloud layer. During the perihelion season, subseasonally averaged water vapor is close to saturation or somewhat subsaturated in the presence of clouds, with some supersaturation at southern high latitudes towards the top of the clouds. Measurements at northern mid- to high latitudes during a Global Dust Storm suggest a shift of the cloud occurrence to higher altitudes but exhibit a similar saturation structure, with supersaturations not exceeding a ratio of five. The atmosphere above cloud layers is largely subsaturated, suggesting that cloud formation is fairly instantaneous upon the temperature dropping below the frost point and high levels of supersaturation are not required to form water ice clouds. We propose a schematic model of cloud evolution in which small cloud particles are formed rapidly in slightly supersaturated regions, and then fall and grow such that ice opacity is still observed even in the subsaturated regions below.

Mitigation of bias sources for atmospheric temperature and humidity in the mobile Raman Weather and Aerosol Lidar (WALI)

Abstract. Lidars using vibrational and rotational Raman scattering to continuously monitor both the water vapor and temperature profiles in the low and middle troposphere offer enticing perspectives for applications in weather prediction and studies of aerosol–cloud–water vapor interactions by simultaneously deriving relative humidity and atmospheric optical properties. Several heavy systems exist in European laboratories, but only recently have they been downsized and ruggedized for deployment in the field. In this paper, we describe in detail the technical choices made during the design and calibration of the new Raman channels for the mobile Weather and Aerosol Lidar (WALI), going over the important sources of bias and uncertainty on the water vapor and temperature profiles stemming from the different optical elements of the instrument. For the first time, the impacts of interference filters and non-common-path differences between Raman channels, and their mitigation, in particular are investigated, using horizontal shots in a homogeneous atmosphere. For temperature, the magnitude of the highlighted biases can be much larger than the targeted absolute accuracy of 1 ∘C defined by the WMO (up to 6 ∘C bias below 300 m range). Measurement errors are quantified using simulations and a number of radiosoundings launched close to the laboratory. After de-biasing, the remaining mean differences are below 0.1 g kg−1 on water vapor and 1 ∘C on temperature, and rms differences are consistent with the expected error from lidar noise, calibration uncertainty, and horizontal inhomogeneities of the atmosphere between the lidar and radiosondes.

Comparison of different machine learning approaches for tropospheric profiling based on COSMIC-2 data

Precise and reliable information on the tropospheric temperature and water vapor profiles play a key role in weather and climate studies. Among the sensors supporting the observations of the troposphere, one can distinguish the Global Navigation Satellite System Radio Occultation (RO) technique, which provides accurate and high-quality meteorological profiles. However, external knowledge about temperature is essential to estimate other physical atmospheric parameters. To overcome this constraint, I trained and evaluated four different machine learning models comprising Artificial Neural Network (ANN) and Random Forest regression algorithms, where no auxiliary meteorological data is needed. To develop the models, I employed 150,000 globally distributed (45°S–45°N) RO profiles between October 2019 and December 2020. Input vectors consisted of bending angle or refractivity profiles from the Formosa Satellite-7/Constellation Observing System for Meteorology, Ionosphere, and Climate-2 mission together with the month, hour, and latitude of the RO event. While temperature, pressure, and water vapor profiles derived from the modern ERA5 reanalysis and interpolated to the RO location served as the models’ targets. Evaluation on the testing data set revealed a good agreement between all model outputs and ERA5 targets, where slightly better statistics were noted for ANN and refractivity inputs. Vertically averaged root mean square error (RMSE) did not exceed 1.7 K for the temperature and reached around 1.4 hPa and 0.45 hPa for the total and water vapor pressures. Additional validation with 477 co-located radiosonde observations and the operational one-dimensional variational product showed slightly larger discrepancies with the mean RMSE of around 1.9 K, 1.9 hPa, and 0.5 hPa for the temperature, pressure, and water vapor, respectively.Graphical

Evaluation of a Prototype Broadband Water-Vapour Profiling Differential Absorption Lidar at Cardington, UK

For a one-month period in summer 2020, a prototype Vaisala broadband differential absorption lidar (BB-DIAL) was deployed at a Met Office research site. It was compared with in-situ observations of humidity (93 radiosonde ascents and 27 of uncrewed aerial vehicle flights) and the Met Office 1.5 km resolution numerical weather prediction (NWP) model: UK Variable resolution model (UKV). The BB-DIAL was able to collect data up to the cloud base, in all-weather situations including rain, when it was possible to reach 3 km. The average maximum height was 1300 m, with 75% of the data reaching 1000 m and 35% extending to 1500 m. Compared with radiosondes, the standard deviation for the water vapour is between 5% and 10%. The comparison with the UKV is very encouraging, with a correlation of 0.90. The error against the radiosonde is smaller than against the UKV, which is encouraging for assimilation the BB-DIAL data in UKV. Some data quality issues, such as an increase in error and variable bias in the region of overlap between the far field and close field, spurious oscillations and an unrealistic dry layer above fog are identified. Despite these issues, the overall results from this assessment are promising in terms of potential benefit, instrument reliability and capturing significant humidity changes in the boundary layer.