Water Vapor Fields Research Articles

A Numerical Study on Cloud Structure of Typical Summer Precipitation Process over Liupan Mountain Area

Using Weather Research and Forecasting model (WRF), four microphysical schemes (Lin, Goddard, WSM6 and Thompson) were applied for precipitation simulations over southern Ningxia on July 21st, 2024. The simulated results are used to analyze the impact of different microphysical schemes on summertime precipitation processes. The sensitivity experiments show the precipitation process simulated by WSM6 and Thompson schemes are closer to observed precipitation than Lin and Goddard schemes in Southern Ningxia mountainous areas. The evolution characteristics of the dynamic field, water vapor field, and microphysical structures of cloud structures are analyzed. In vertical direction, the cloud system in mountainous areas generally displays a structure of "catalysis-supply". The structure of water condensation in each layer of cloud is different, leading to differences in the contribution of various microphysical processes to precipitation. The super-cooled cloud water is the main source for rain water production. When cloud system grows upward, the abundant cloud water layer simultaneously enhances the microphysical processes within clouds. On the windward slope (eastern part) of the mountain, deep warm cloud layer will contribute to the development of warm cloud precipitation process, which leads to enhanced precipitation under the combined action of cold and warm clouds.

Gibbs states and Brownian models for coexisting haze and cloud droplets.

Cloud microphysics studies include how tiny cloud droplets grow and become rain. This is crucial for understanding cloud properties like size, life span, and impact on climate through radiative effects. Small weak-updraft clouds near the haze-to-cloud transition are especially difficult to measure and understand. They are abundant but hard to capture by satellites. Köhler's theory explains initial droplet growth but struggles with large particle groups. Here, we present a stochastic, analytical framework building on Köhler's theory to account for (monodisperse) aerosols and cloud droplet interaction through competitive growth in a limited water vapor field. These interactions are modeled by sink terms, while fluctuations in supersaturation affecting droplet growth are modeled by nonlinear white noise terms. Our results identify hysteresis mechanisms in the droplet activation and deactivation processes. Our approach allows for multimodal cloud's droplet size distributions supported by laboratory experiments, offering a different perspective on haze-to-cloud transition and small cloud formation.

Application of Satellite Microwave Radiometric Methods to Analyze the Relationship of Tropical Cyclogenesis with Water Vapor Transport in the Atlantic

Some possibilities of using microwave radiometric measurements from the EOS Aqua and GCOM-W1 satellites to study the influence of tropical waves in the Atlantic on cyclogenesis processes in the Gulf of Mexico by monitoring the spatial and temporal variability of water vapor fields in the Gulf are illustrated. Examples of the use of satellite images of atmospheric humidity fields obtained from DMSP and EOS Aqua satellites are given to demonstrate the processes of transformation of tropical waves into Atlantic hurricanes Bonnie (1998), Frances (2004), Ivan (2004).

How well can persistent contrails be predicted? An update

Abstract. The total aviation effective radiative forcing is dominated by non-CO2 effects. The largest contributors to the non-CO2 effects are contrails and contrail cirrus. There is the possibility of reducing the climate effect of aviation by avoiding flying through ice-supersaturated regions (ISSRs), where contrails can last for hours (so-called persistent contrails). Therefore, a precise prediction of the specific location and time of these regions is needed. But a prediction of the frequency and degree of ice supersaturation (ISS) on cruise altitudes is currently very challenging and associated with great uncertainties because of the strong variability in the water vapour field, the low number of humidity measurements at the air traffic altitude, and the oversimplified parameterisations of cloud physics in weather models. Since ISS is more common in some dynamical regimes than in others, the aim of this study is to find variables/proxies that are related to the formation of ISSRs and to use these in a regression method to predict persistent contrails. To find the best-suited proxies for regressions, we use various methods of information theory. These include the log-likelihood ratios, known from Bayes' theorem, a modified form of the Kullback–Leibler divergence, and mutual information. The variables (the relative humidity with respect to ice, RHiERA5; the temperature, T; the vertical velocity, ω; the divergence, DIV; the relative vorticity, ζ; the potential vorticity, PV; the normalised geopotential height, Z; and the local lapse rate, γ) come from ERA5, and RHiM/I, which we assume as the truth, comes from MOZAIC/IAGOS (Measurement of Ozone and Water Vapour on Airbus In-service Aircraft/In-service Aircraft for a Global Observing System; commercial aircraft measurements). It turns out that RHiERA5 is the most important predictor of ice supersaturation, in spite of its weaknesses, and all other variables do not help much to achieve better results. Without RHiERA5, a regression to predict ISSRs is not successful. Certain modifications of RHiERA5 before the regression (as suggested in recent papers) do not lead to improvements of ISSR prediction. Applying a sensitivity study with artificially modified RHiERA5 distributions points to the origin of the problems with the regression: the conditional distributions of RHiERA5 (conditioned on ISS and non-ISS, from RHiM/I) overlap too heavily in the range of 70 %–100 %, so for any case in that range, it is not clear whether it belongs to an ISSR or not. Evidently, this renders the prediction of contrail persistence very difficult.

A rigorous approach to the specific surface area evolution in snow during temperature gradient metamorphism

Abstract. Despite being one of the most fundamental microstructural parameters of snow, the specific surface area (SSA) dynamics during temperature gradient metamorphism (TGM) have so far been addressed only within empirical modeling. To surpass this limitation, we propose a rigorous modeling of SSA dynamics using an exact equation for the temporal evolution of the surface area, fed by pore-scale finite-element simulations of the water vapor field coupled with the temperature field on X-ray computed tomography images. The proposed methodology is derived from the first principles of physics and thus does not rely on any empirical parameter. Since the calculated evolution of the SSA is highly sensitive to fluctuations in the experimental data, we quantify the impact of these fluctuations within a stochastic error model. In our simulations, the only poorly constrained physical parameter is the condensation coefficient α. We address this problem by simulating the SSA evolution for a wide range of α values and estimate optimal values by minimizing the differences between simulations and experiments. This methodology suggests that α lies in the intermediate range 10-3<α<10-1 and slightly varies between experiments. Also, our results suggest a transition of the value of α in one TGM experiment, which can be explained by a transition in the underlying surface morphology. Overall, we are able to reproduce very subtle variations in the SSA evolution with correlations of R2=0.95 and 0.99, respectively, for the two TGM time series considered. Finally, our work highlights the necessity of including kinetic effects and of using realistic microstructures to comprehend the evolution of SSA during TGM.

Vientos—A New Satellite Mission Concept for 3D Wind Measurements by Combining Passive Water Vapor Sounders with Doppler Wind Lidar

Abstract It is challenging to accurately characterize the three-dimensional distribution of horizontal wind vectors (known as 3D winds). Feature-matching satellite cloud top or water vapor fields have been used for decades to retrieve atmospheric motion vectors, but this approach is mostly limited to a single and uncertain pressure level at a given time. Satellite wind lidar measurements are expected to provide more accurate data and capture the line-of-sight wind for clear skies, within cirrus clouds, and above thick clouds, but only along a curtain parallel to the satellite track. Here we propose Vientos—a new satellite mission concept that combines two or more passive water vapor sounders with Doppler wind lidar to measure 3D winds. The need for 3D wind observations is highlighted by inconsistencies in reanalysis estimates, particularly under precipitating conditions. Recent studies have shown that 3D winds can be retrieved using water vapor observations from two polar-orbiting satellites separated by 50 min, with the help of advanced optical flow algorithms. These winds can be improved through the incorporation of a small number of collocated higher-accuracy measurements via machine learning. The Vientos concept would enable simultaneous measurements of 3D winds, temperature, and humidity, and is expected to have a significant impact on scientific research, weather prediction, and other applications. For example, it can help better understand and predict the preconditions for organized convection. This article summarizes recent results, presents the Vientos mission architecture, and discusses implementation scenarios for a 3D wind mission under current budget constraints.

Distribution and transformation characteristics of water vapor field in the fissured rock mass and its ecological significance

Opencast mining has produced a large number of high and steep rock slopes, leading to serious safety and environmental challenges. Extreme water scarcity conditions severely impede the survival and development of natural and artificial vegetation when carrying out ecological restoration work. The water vapor field within rocks is a significant but often neglected component of the terrestrial hydrologic cycle and can provide a hidden but critical source of water for plants. However, there is a lack of understanding of the distribution characteristics of the water vapor field within rocks and the pattern of liquid water formation as well as whether it is quantitatively significant and ecologically relevant. This study conducted monitoring experiments on the water vapor field of fissured rock mass and studied the distribution and transformation characteristics of water vapor field in the fissured rock mass and its ecological significance. The results showed temperature and absolute humidity gradually decreased with horizontal depth in spring and summer, and the opposite in autumn and winter. Relative humidity increased rapidly and stabilized with horizontal depth in the spring and summer, and increased rapidly in the autumn and winter, before decreasing slightly. The driving force of water vapor migration is the water vapor partial pressure gradient. In spring and summer, water vapor migrated from air along the fissures to the deep part of the rock mass, while the opposite was true in autumn and winter. While the water vapor migrated through the fissured rock mass, water vapor saturation zones would be produced locally, where potential condensate is generated. The fissure network functioned as a sustained system of water supply pipelines, and the plant roots that grew into the fissures could absorb the condensate, continuously supplying the growth and development of the rock slope plants. This study provides a comprehensive understanding of the rock water vapor field and its effects on ecosystems in arid and semi-arid areas, which can guide the restoration of ecosystems on rock slopes.

Responsive soft actuator: harnessing multi-vapor, light, and magnetic field stimuli.

Bioinspired soft actuators, capable of undergoing shape deformation in response to external triggers, hold great potential in fields such as soft robotics, artificial muscles, drug delivery, and smart switches. However, their widespread application is hindered by limitations in responsiveness, durability, and complex fabrication processes. In this study, we propose a new approach to tackle these challenges by developing a single-layer soft actuator that responds to multiple stimuli using a straightforward solution-casting method. This actuator comprises bio-polymer gelatin, bio-compatible PEDOT:PSS, and iron oxide (Fe3O4) nanoparticles. Our actuator exhibits responsiveness to a range of organic solvent vapors, including water vapor, light, and magnetic fields. Notably, it exhibits rapid and reversible bending in distinct directions in response to different vapors, bending upwards in the presence of water vapor and downwards in the presence of alcohol vapor. Moreover, exposure to infrared (IR) light induces a bending toward the light source. The incorporation of magnet-responsive Fe3O4 nanoparticles induces multi-functionality in the actuator. The actuation characteristics of the actuator are controlled by leveraging its responsiveness to dual stimuli, such as water vapor and magnetic fields, as well as light and magnetic fields. For the proof of concept, we showcase several potential applications of our multi-stimuli responsive soft actuator, including magnet-triggered electrical switches, cargo transportation, soft grippers, targeted drug delivery, energy harvesting, and bio-mimicry.

Estimation of the water vapor field by fusing GPS and surface meteorological observations on the Loess Plateau of China

Estimation of the water vapor field by fusing GPS and surface meteorological observations on the Loess Plateau of China

The dehydration carousel of stratospheric water vapor in the Asian summer monsoon anticyclone

Abstract. During the StratoClim Geophysica campaign, air with total water mixing ratios up to 200 ppmv and ozone up to 250 ppbv was observed within the Asian summer monsoon anticyclone up to 1.7 km above the local cold-point tropopause (CPT). To investigate the temporal evolution of enhanced water vapor being transported into the stratosphere, we conduct forward trajectory simulations using both a microphysical and an idealized freeze-drying model. The models are initialized at the measurement locations and the evolution of water vapor and ice is compared with satellite observations of MLS and CALIPSO. Our results show that these extremely high water vapor values observed above the CPT are very likely to undergo significant further freeze-drying due to experiencing extremely cold temperatures while circulating in the anticyclonic “dehydration carousel”. We also use the Lagrangian dry point (LDP) of the merged back-and-forward trajectories to reconstruct the water vapor fields. The results show that the extremely high water vapor mixed with the stratospheric air has a negligible impact on the overall water vapor budget. The LDP mixing ratios are a better proxy for the large-scale water vapor distributions in the stratosphere during this period.

Case study on a novel approach to determining initial values for 3D water vapor tomography in the a priori water vapor field

Insufficient prior information in GNSS water vapor tomographic models can lead to instability in the structure of the design matrix. The addition of high-precision initial values for water vapor density can effectively solve this problem, but common methods for obtaining water vapor density are relatively crude and not fully utilizing the original accuracy of the a priori water vapor field. To address this issue, a new method is proposed that takes into account the influence of the a priori information weight ratio in the tomographic model on tomographic analysis results, based on the negative exponential decrease of atmospheric water vapor density in the vertical direction with height. Moreover, the 3D water vapor tomographic technique based on the ray-tracing fast voxel traversal algorithm has been improved and implemented in the C++ programming language. To verify the feasibility, accuracy, and stability of the improved 3D water vapor tomographic technique and the proposed method, the experiment using actual data from the CORS station in Hong Kong in January and May and the corresponding grid data provided by ERA5 was conducted. As benchmarks, radio-sounding data from the experimental area was utilized. In May, the proposed method exhibited an average daily improvement in the root mean square error of 0.88% (UTC0) and 0.97% (UTC12), and an improvement in the interquartile range of the difference between the water vapor density values at each altitude layer (excluding the top and bottom layers) and the corresponding sounding data of 5.08% (UTC0) and 3.37% (UTC12). In January, the average daily improvement in the root mean square error was −13.78% (UTC0) and −4.84% (UTC12), and the improvement in the interquartile range of the difference between the water vapor density values at each altitude level and those calculated from the corresponding sounding data was −14.49% (UTC0) and −6.31% (UTC12). The results show that the proposed method and the commonly used method maintain high consistency in the accuracy of the water vapor density values solved by the system of tomographic equations and improve stability, especially in May when the water vapor density near the surface is high. The feasibility of the improved 3D water vapor tomographic technique based on the ray-tracing fast voxel traversal algorithm is also demonstrated.

Evaluation of the surface air temperature over the Tibetan Plateau among different reanalysis datasets

The surface air temperature (SAT) over the Tibetan Plateau (TP) not only affects the physical processes such as local evaporation, snow melting, and glacier ablation, but also has a great impact on the downstream regions and even the global climate change. The development of reanalysis data has gradually overcome the problem of sparse stations over the TP, but there are still some deficiencies. Therefore, the distance between indices of simulation and observation (DISO) method is used to calculate the distance between five reanalysis datasets (ERA5, JRA-55, ERA-Interim, MERRA2, NCEP2) and the CMFD to evaluate the abilities of different reanalysis datasets to capture the SAT over the TP in different seasons. The results indicate that ERA-Interim has a higher ability to reproduce the SAT over the TP in spring and summer, while it is ERA5 in autumn and winter. It should be noted that although the optimal reanalysis has a better performance in capturing the SAT of the TP, there are still a certain degree of deviations in their spatial fields. We further show the spatial deviation fields of SAT over the TP corresponding to the optimal reanalysis data in different seasons, and analyze the possible reasons. The result implies that the SAT deviation field is mainly related to the snow in winter and spring, while the summer SAT deviation field is mainly related to the water vapor, and the autumn is related to both the snow and the water vapor fields. Overall, the quality of reanalysis data needs to be further improved in the future.

M2‐SCREAM: A Stratospheric Composition Reanalysis of Aura MLS Data With MERRA‐2 Transport

AbstractThe MERRA‐2 Stratospheric Composition Reanalysis of Aura Microwave Limb Sounder (M2‐SCREAM) is a new reanalysis of stratospheric ozone, water vapor, hydrogen chloride (HCl), nitric acid (HNO3) and nitrous oxide (N2O) between 2004 and the present (with a latency of several months). The assimilated fields are provided at a 50‐km horizontal resolution and at a three‐hourly frequency. M2‐SCREAM assimilates version 4.2 Microwave Limb Sounder (MLS) profiles of the five constituents alongside total ozone column from the Ozone Monitoring Instrument. Dynamics and tropospheric water vapor are constrained by the MERRA‐2 reanalysis. The assimilated species are in excellent agreement with the MLS observations, except for HNO3 in polar night, where data are not assimilated. Comparisons against independent observations show that the reanalysis realistically captures the spatial and temporal variability of all the assimilated constituents. In particular, the standard deviations of the differences between M2‐SCREAM and constituent mixing ratio data from The Atmospheric Chemistry Experiment Fourier Transform Spectrometer are much smaller than the standard deviations of the measured constituents. Evaluation of the reanalysis against aircraft data and balloon‐borne frost point hygrometers indicates faithful representations of small‐scale structures in the assimilated water vapor, HNO3 and ozone fields near the tropopause. Comparisons with independent observations and a process‐based analysis of the consistency of the assimilated constituent fields with the MERRA‐2 dynamics and with large‐scale stratospheric processes demonstrate the utility of M2‐SCREAM for scientific studies of chemical and transport variability on time scales ranging from hours to decades. Analysis uncertainties and guidelines for data usage are provided.

Mechanistic and Kinetic Investigations on Decomposition of Trifluoromethanesulfonyl Fluoride in the Presence of Water Vapor and Electric Field

As a potential insulation gas to replace sulfur hexafluoride (SF6) due to environmental concerns, trifluoromethanesulonyl fluoride (CF3SO2F) has attracted great interests in various high-voltage electric applications. Thermal stability of CF3SO2F plays an important role in the rational design of the gas-insulated electric equipment. Unimolecular decomposition of CF3SO2F was investigated using high-level ab initio methods including the explicitly correlated RCCSD(T)-F12, the composite ROCBS-QB3, and the multireference RS2 extrapolated to complete basis set limit on the basis of M06-2X-, B2PLYPD3-, and CCSD-optimized geometrical parameters. Rate coefficients and decomposition temperatures were simulated using master equations. CF3SO2F decomposes predominantly via a simple C-S bond cleavage to form CF3 and SO2F, accompanied by a roaming induced F-abstraction detour to release CF4 and SO2, or isomerizes via CF3 migration to the more stable CF3OSFO followed by the production of CF2O and SOF2. Various characteristic decomposition products (e.g., CF4, C2F6, CF2O, SO2, SOF2, SO2F2, CF3H, and so on.) have been identified theoretically through secondary reactions and hydrolysis of CF3SO2F in the presence of water vapor. Electronic structures and stability of CF3SO2F could be affected significantly by the external electric field orientated along the S-C bond. The field-dependent electron-molecule capture rates support that CF3SO2F is superior to SF6 in dielectric strength. The present computational findings shed light on the practical use of CF3SO2F as the replacement gas for SF6.

An Improved Tropospheric Tomographic Model Based on Artificial Neural Network

Global navigation satellite systems (GNSS) tropospheric tomography can be used to build a three-dimensional water vapor field. In traditional tomography, the signals crossing from the four sides of the tomographic region are not utilized. To make the best use of these valuable side-crossing signals, an improved tomographic model based on back propagation artificial neural network (BP-ANN) is proposed. In the new tomographic model, the inside part of the slant wet delay (SWD) of the side-crossing signal is divided into two sections: the isotropic and anisotropic components. The former is estimated by the zenith wet delay multiplied by the mapping function multiplied by an isotropic scale factor using a BP-ANN model, and the latter is estimated by horizontal gradients of the SWD multiplied by an anisotropic scale factor using an empirical model. The new tomographic model is experimentally evaluated using the HK CORS network measurements for the period of 21 days from 1 to 21 August 2019. Statistical results show that the root mean square error (RMSE) of slant water vapor reconstructed from the improved model is reduced to 1.35 from 2.85 mm of the traditional model. Compared with the traditional/height factor models, the percentages of the reduction in the RMSE of the tomographic result derived from the new model are 16%/9% and 22%/16%, respectively, using radiosonde and ERA5 data as references. These results suggest a good performance of the new model for GNSS tropospheric tomography.

Combined assimilation of radar and lightning data for the short-term forecast of severe convection system

In this study, a lightning data assimilation (LDA) method adjusting dynamical fields was examined in the rapid cycle with Weather Research and Forecasting (WRF) model and WRF Data Assimilation (WRFDA). This method retrieves pseudo vertical velocity profiles from total lightning observations and promotes wind convergence over lightning regions. This newly developed LDA scheme is compared with radar data assimilation (RDA) that has been routinely applied in severe storm nowcasting models. Depending on whether the radar or lightning data is assimilated, four experiments were designed to evaluate the positive impacts of dynamical adjustment from LDA. Using a typical severe mesoscale convection system occurred in Beijing, we found that the effect of RDA and LDA are different. The assimilation of radar radial velocity mainly improves the forecast in a longer time, while assimilation of pseudo-vertical-velocity from lightning data mainly corrects intensity and location of the forecasted precipitation. When both radar and lightning data are assimilated, the small-scale wind convergence are promoted and contributes to the intensified updrafts. Thermal and water vapor fields are also adjusted indirectly. Consequently, the convective precipitation is significantly improved and the positive impacts from the combined assimilation scheme persisted over a longer period (at least 3 h). It can be concluded that a combined assimilation of convective data from multiple sources such as radar and lightning data enhance prominently the accuracy of short-term convection forecasts.

Tropospheric water vapor: a comprehensive high-resolution data collection for the transnational Upper Rhine Graben region

Abstract. Tropospheric water vapor is one of the most important trace gases of the Earth's climate system, and its temporal and spatial distribution is critical for the genesis of clouds and precipitation. Due to the pronounced dynamics of the atmosphere and the nonlinear relation of air temperature and saturated vapor pressure, it is highly variable, which hampers the development of high-resolution and three-dimensional maps of regional extent. With their complementary high temporal and spatial resolutions, Global Navigation Satellite Systems (GNSS) meteorology and Interferometric Synthetic Aperture Radar (InSAR) satellite remote sensing represent a significant alternative to generally sparsely distributed radio sounding observations. In addition, data fusion with collocation and tomographical methods enables the construction of detailed maps in either two or three dimensions. Finally, by assimilation of these observation-derived datasets with dynamical regional atmospheric models, tropospheric water vapor fields can be determined with high spatial and continuous temporal resolution. In the following, a collection of basic and processed datasets, obtained with the above-listed methods, is presented that describes the state and course of atmospheric water vapor for the extent of the GNSS Upper Rhine Graben Network (GURN) region. The dataset contains hourly 2D fields of integrated water vapor (IWV) and 3D fields of water vapor density (WVD) for four multi-week, variable season periods between April 2016 and October 2018 at a spatial resolution of (2.1 km)2. Zenith total delay (ZTD) from GNSS and collocation and refractivities are provided as intermediate products. InSAR (Sentinel-1A/B)-derived double differential slant total delay phases (ddSTDPs) and GNSS-based ZTDs are available for March 2015 to July 2019. The validation of data assimilation with five independent GNSS stations for IWV shows improving Kling–Gupta efficiency (KGE) scores for all seasons, most notably for summer, with collocation data assimilation (KGE = 0.92) versus the open-cycle simulation (KGE = 0.69). The full dataset can be obtained from https://doi.org/10.1594/PANGAEA.936447 (Fersch et al., 2021).

Impact of Assimilating Radar Data with 3D Thermodynamic Fields in an Ensemble Kalman Filter: Proof of Concept and Feasibility

Abstract This study examined the impact of assimilating 3D temperature and water vapor information in addition to radar observations in a multiscale weather system. A frontal system with extremely heavy rainfall over northern Taiwan was selected. Using the WRF–LETKF Radar Assimilation System, we performed three sets of observing system simulation experiments to assimilate radar observations with or without thermodynamic variables obtained using different methods. First, assimilating the radar data for 2 h showed better structure and short-term forecast than 1 h. Second, we assimilated radar data and thermodynamic variables from a perfect model simulation. The results of the analysis revealed that when a precipitation position error occurred in the background field, assimilating thermodynamic information with the radar data could correct the dynamic structure and shorten the spinup assimilation period, resulting in substantial improvements to the quantitative precipitation forecast. Third, we applied a thermodynamics retrieval algorithm for a feasibility study. With a warm and wet bias of the retrieved fields, assimilating the temperature data had significant impact on the midlevel of stratiform areas and the forecast of the heavy rainfall was consequently improved. Assimilating the water vapor information helped reconstruct the range and intensity of the cold pool, but the improvement of rainfall forecast was limited. The optimal results of analysis and short-term forecast were achieved when both retrieved temperature and water vapor fields were assimilated. In conclusion, assimilating thermodynamic variables in the precipitation system is feasible for shortening the spinup period of data assimilation and improving the analysis and short-term forecast.

Improved Modeling of Mars' HDO Cycle Using a Mars' Global Climate Model

AbstractHDO and the D/H ratio are essential to understand Mars past and present climate, in particular with regard to the evolution through ages of the Martian water cycle. We present here new modeling developments of the HDO cycle with the Laboratoire de Météorologie Dynamique Mars Global Climate Model (GCM). The present study aims at exploring the behavior of the D/H ratio cycle and its sensitivity to the modeling of water ice clouds and the formulation of the fractionation by condensation. Our GCM simulations are compared with observations provided by the Atmospheric Chemistry Suite (ACS) on board the ESA/Roscosmos Trace Gas Orbiter (TGO), and reveal that the model quite well reproduces the temperature and water vapor fields, which offers a good basis for representing the D/H ratio cycle. The comparison also emphasizes the importance of modeling the state of supersaturation, resulting from the microphysical processes of water ice clouds, to correctly account for the water vapor and the D/H ratio of the middle‐to‐upper atmosphere. This work comes jointly with a detailed comparison of the measured D/H profiles by TGO/ACS and the model outputs, conducted in the companion paper of Rossi et al. (2022, https://doi.org/10.1029/2022JE007201) (this issue).

Using InSAR Data to Improve the Water Vapor Distribution Downstream of the Core of the South American Low‐Level Jet

AbstractTwo and half years of Interferometric Synthetic Aperture Radar (InSAR) images obtained by Sentinel‐1 near Santa Cruz de la Sierra, Bolivia, before August 2019 are here analyzed and assimilated in the Weather Research and Forecast model (WRF) to assess the quality of the water vapor field at the core of the South American Low‐level Jet, and the downstream propagation of the implied water vapor anomalies into a large sector of South America. Due to the topographic locking of the Jet near the edge of the Andes cordillera at Santa Cruz, this experiment allows an assessment of the extension of the spatial impact of InSAR data assimilation in varying circulation conditions. That data improves the model skill at varying locations thousands of kilometers downstream, in both the distribution of water vapor assessed at a large number of well‐distributed GNSS observations and precipitation assessed against the Global Precipitation dataset and ERA5 reanalysis. Gains in the precipitation forecast skill are found to have a larger impact on the forecast of light rain, and lead to an almost cancellation of forecasts of no rain in cases with moderate to heavy rain. Because the distribution of water vapor is a main driver of weather, and InSAR is one of the few sources of data that can look down to mesoscale resolution regardless of daytime or weather, it is suggested that it may have a significant positive impact on short‐range weather forecasting and, maybe more importantly, on the quality of the climate of the forecast model.