Atmospheric Water Vapor Content Research Articles

Research on Landsat 8 land surface temperature retrieval and spatial–temporal migration capabilities based on random forest model

Land surface temperature (LST) plays a crucial role in characterizing land surface processes and the energy balance. Accurate monitoring of spatial–temporal variations in LST holds great significance for global and regional climate change research. However, with the increasing of multi-source remote sensing data, it has become a challenging task to construct an LST model that reduces dependence on auxiliary data (such as land surface emissivity (LSE) and atmospheric water vapor content (WVC) synchronized with satellite observations. This study proposed an LST retrieval model based on random forest model (RFLSTR). The results of 10-fold cross-validation showed that the RFLSTR model had high modelling accuracy, the coefficient of determination (R2) was 0.99, and the root mean square error (RMSE) and mean absolute error (MAE) were lower than 2 K. Landsat 8 images from the Beijing area and South Korea, were utilized to assess the spatial–temporal migration performance of the RFLSTR model. Additionally, Landsat 9 images from South Korea were employed to analyse the sensor migration performance of the RFLSTR model. In conclusion, the findings highlight that the proposed RFLSTR model achieve high retrieval accuracy without relying on auxiliary data (such as LSE and WVC) synchronized with satellite observations. Moreover, the RFLSTR model exhibits robust spatial–temporal capabilities, enhancing the efficiency of LST retrieval and facilitating the utilization of satellite data.

Monitoring climate change in the marine Arctic

Changes in the temperature regime of the marine Arctic and the influencing factors are considered based on the current knowledge of the causes of climate change and the use of new data sources. In the Arctic, the warming is developing due to such factors as the increase in the transfer of heat and moisture from the low latitudes. This, in turn, drives the feedbacks in the Arctic climate system, increasing the flow of long-wave radiation to the surface due to rising atmospheric water vapor concentrations and slowing down the growth of the sea ice thickness in winter. The increase in the atmospheric heat transfer to the Arctic is associated with changes in atmospheric circulation, in particular, under the influence of ocean surface temperature anomalies, especially in the low latitudes since the bulk of the heat influx from the from solar radiation and anthropogenic forcing is accumulated here. Analyzing the causes of warming in the Arctic in the 1930s and 40s led researchers to the conclusion that the water influx from the North Atlantic is a factor to consider. Therefore, the influx of warm and salty water is also an important influence on the formation of the climate of the marine Arctic today, which should be taken into account when monitoring the temperature and ice regime of this area. Based on the analysis of the characteristics of climate variability in the marine Arctic and its causes, the article examines representative indicators of climate change in the temperature and ice regime of the marine Arctic and the factors influencing them in the present period.

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.

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.

Severe photochemical pollution formation associated with strong HONO emissions from dew and guttation evaporation

The unknown daytime source of HONO has been extensively investigated due to unexplained atmospheric oxidation capacity and current modelling bias, especially during cold seasons. In this study, abrupt morning increases in atmospheric HONO at a rural site in the North China Plain (NCP) were observed almost on daily basis, which were closely linked to simultaneous rises in atmospheric water vapor content and NH3 concentrations. Dew and guttation water formation was frequently observed on wheat leaves, from which water samples were taken and chemically analyzed for the first time. Results confirmed that such natural processes likely governed the daily nighttime deposition and daytime release of HONO and NH3, which have not been considered in the numerous HONO budget studies investigating its large missing daytime source in the NCP. The dissolved HONO and NH3 in leaf surface water droplets reached 1.4 and 23 mg L−1 during the morning on average, resulting in averaged atmospheric HONO and NH3 increases of 0.89 ± 0.61 and 43.7 ± 29.3 ppb during morning hours, with relative increases of 186 ± 212 % and 233 ± 252 %, respectively. The high atmospheric oxidation capacity contained within HONO was stored in near surface liquid water (such as dew, guttation and soil surface water) during nighttime, which prevented its atmospheric dispersion after sunset and protected it from photodissociation during early morning hours. HONO was released in a blast during later hours with stronger solar radiation, which triggered and then accelerated daytime photochemistry through the rapid photolysis of HONO and subsequent OH production, especially under high RH conditions, forming severe secondary gaseous and particulate pollution. Results of this study demonstrate that global ecosystems might play significant roles in atmospheric photochemistry through nighttime dew formation and guttation processes.

The impact of global navigation satellite system (GNSS) zenith total delay data assimilation on the short-term precipitable water vapor and precipitation forecast over Italy using the Weather Research and Forecasting (WRF) model

Abstract. The impact of assimilating GNSS-ZTD (global navigation satellite system–zenith total delay) on the precipitable water vapor and precipitation forecast over Italy is studied for the month of October 2019, which was characterized by several moderate to intense precipitation events, especially over northwestern Italy. The WRF (Weather Research and Forecasting) model, version 4.1.3, is used with its 3D-Var data assimilation system to assimilate ZTD observations from 388 GNSS receivers distributed over the country. The dataset was built collecting data from all the major national and regional GNSS permanent networks, achieving dense coverage over the whole area. The water vapor forecast is verified for the forecast hours of 1–6 h after the last data assimilation time. Results show that WRF underestimates the atmospheric water vapor content for the period, and GNSS-ZTD data assimilation improves this underestimation. The precipitation forecast is verified in the phases of 0–3 and 3–6 h after the last data assimilation time using more than 3000 rain gauges spread over Italy. The application of GNSS-ZTD data assimilation to a case study improved the precipitation forecast by increasing the rainfall maximum and by better focusing the precipitation pattern over northeastern Italy, with the main drawback being the prediction of false alarms. Considering the study over the whole period, GNSS-ZTD data assimilation had a positive impact on rainfall forecast, with an improvement in the performance up to 6 h and with statistically significant results for moderate to intense rainfall thresholds (25–30 mm (3 h)−1).

The Novel Copernicus Global Dataset of Atmospheric Total Water Vapour Content with Related Uncertainties from GNSS Observations

A novel algorithm has been designed and implemented in the Climate Data Store (CDS) frame of the Copernicus Climate Change Service (C3S) with the main goal of providing high-quality GNSS-based integrated water vapour (IWV) datasets for climate research and applications. For this purpose, the related CDS GNSS datasets were primarily obtained from GNSS reprocessing campaigns, given their highest quality in adjusting systematic effects due to changes in instrumentation and data processing. The algorithm is currently applied to the International GNSS Service (IGS) tropospheric products, which are consistently extended in near real-time and date back to 2000, and to the results of a reprocessing campaign conducted by the EUREF Permanent GNSS Network (EPN repro2), covering the period from 1996 to 2014. The GNSS IWV retrieval employs ancillary meteorological data sourced from ERA5. Moreover, IWV estimates are provided with associated uncertainty, using an approach similar to that used for the Global Climate Observing System Reference Upper-Air Network (GRUAN) GNSS data product. To assess the quality of the newly introduced GNSS IWV datasets, a comparison is made against the radiosonde data from GRUAN and the Radiosounding HARMonization (RHARM) dataset as well as with the IGS repro3, which will be the next GNSS-based extension of IWV time series at CDS. The comparison indicates that the average difference in IWV among the reprocessed GNSS datasets is less than 0.1 mm. Compared to RHARM and GRUAN IWV values, a small dry bias of less than 1 mm for the GNSS IWV is detected. Additionally, the study compares GNSS IWV trends with the corresponding values derived from RHARM at selected radiosonde sites with more than ten years of data. The trends are mostly statistically significant and in good agreement.

Improving HJ-1B/IRS LST Retrieval of the Generalized Single-Channel Algorithm with Refined ERA5 Atmospheric Profile Database

Land surface temperature (LST) is a fundamental variable of environmental monitoring and surface equilibrium. Although the HJ-1B infrared scanner (IRS) has accumulated many observations, further application of HJ-1B/IRS is limited by the lack of LST products. This study refined the ERA5 atmospheric profile database, instead of the widely used traditional TIGR atmospheric profile database, and simulated the coefficients of the generalized single-channel (GSCs) algorithms to improve LST retrieval. GSCs can be divided into the GSCw and GSCwT algorithms, depending on whether the input is atmospheric water vapor content (WVC) or in situ near-surface air temperature and WVC. Land surface emissivity (LSE) was obtained from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) and vegetation/snow cover products. Then, the retrieved LSTs were evaluated using the LSTs from the RTE algorithm, TIGRw/TIGRwT profiles, and in situ near-surface air temperature from the HiWATER experiment in China from 2012 to 2014. The bias (root mean square error (RMSE)) values are displayed as ERA5wT < RTE < ERA5w < TIGRwT < TIGRw. The accuracy of ERA5wT, with a bias (RMSE) of 0.02 K (2.30 K), is higher than that of RTE, with a bias (RMSE) of 0.74 K (2.47 K). The accuracy of RTE is preferable to that of ERA5w, with a bias (RMSE) of 0.89 K (2.48 K), followed by TIGRwT, with a bias (RMSE) of −1.18 K (2.50 K), and then, TIGRw, with a bias (RMSE) of 1.60 K (2.77 K). In summary, the accuracy of LST obtained by GSC from the refined ERA5 atmospheric profiles is higher than that obtained from the TIGR profiles. The accuracy of LST obtained by GSCwT is greater than that obtained by GSCw. The accuracy of LST obtained using in situ near-surface air temperature is higher than that obtained using ERA5 air temperature. The accuracy of LSEASTER is slightly better than that of LSEMOD21. The aforementioned conclusions can provide scientific support to generate HJ-1B/IRS LST products.

Dominant modes and mechanisms of the interannual variability of atmospheric water vapor content during winter over Central Asia

Atmospheric water vapor content (WVC)—a key component of the water cycle—plays a crucial role as an indicator of precipitation changes and directly influences precipitation in the arid areas of Central Asia. These areas are extremely sensitive to global climate change because of their dry climates and fragile water resource systems, especially during winter. This study analyzes the main interannual variation patterns of the atmospheric WVC during winter over Central Asia and reveals the possible physical mechanism behind the changes in the dominant modes (EOF1 and EOF2) of water vapor. The results show that EOF1 (49.4%)—responsible for the interannual variation of the winter atmospheric WVC in Central Asia—has a uniform monopole mode, while EOF2 (23.7%) shows a reverse north–south dipole distribution (with the boundary being at approximately 44° N). The negative geopotential height anomaly over the high latitudes of Europe and the positive geopotential height anomaly over Central Asia are the major impact factors of EOF1, whereas Central Asia has a prevalence of westerly airflow during winter. By influencing the geopotential height anomaly in Central Asia, the winter Niño 3.4 index intensifies the convergence of Atlantic Ocean moisture with tropical Indian Ocean moisture and their eastward transport. In contrast, EOF2 is mainly affected by the positive geopotential height anomaly over the high latitudes of Europe and the negative geopotential height anomaly over the middle latitudes of this region. In particular, the negative anomaly center of the geopotential height on the eastern side of Central Asia extends westward into Central Asia, resulting in reverse moisture transport by westerly and easterly airflows in the southern and northern parts of Central Asia, respectively. This anomaly is correlated with the formation of anomalous cyclonic circulation over North Africa and Europe because of the negative phase of the Arctic Oscillation, leading to prevailing southerly winds in the south of Central Asia, which correspond to abnormal vertical upward movement; however, the situation in the northern part of Central Asia is the opposite. Therefore, the distribution of winter precipitation in Central Asia shows the following pattern: more in the south and less in the north. This study provides scientific and technological support for the formation of research mechanisms and the prediction of winter precipitation in Central Asia.

Atmospheric Water Vapor Transport between Ocean and Land under Climate Warming

Abstract Global warming intensifies atmospheric water vapor transport between ocean and land, which increases the likelihood of extreme precipitation and floods. However, accurate estimations of water vapor exchange between ocean and land are difficult due to the lack of available data and effective methods. This study developed a novel eight-direction-vector decomposition algorithm for calculating water vapor flux between ocean and land based on the ERA5 dataset, and the results showed that global water vapor exchange between ocean and land had significantly increased in the past 40 years, except for Antarctica. During 1980–2018, the average annual net water vapor inflow from ocean to land (Qnet) was 44.68 × 1015 kg yr−1, and Qnet increased at a rate of 1.48 × 1015 kg yr−1 decade−1. The intensified atmospheric water vapor exchange between ocean and land was directly caused by the increase of atmospheric water vapor content, which largely depended on the rising air temperature, and it was found that water vapor flux between ocean and land increased by over 8% K−1 with the increasing air temperature at the global average. This study also identified El Niño–Southern Oscillation (ENSO) as an important contributor to the global ocean–land water vapor exchange anomalies. A strong El Niño event (MEI = 1) can result in a 1.36 × 1015 kg yr−1 (3.03%) decrease in Qnet, and a strong La Niña event (MEI = −1) can increase Qnet by 1.38 × 1015 kg yr−1 (3.09%). The eight-direction-vector decomposition algorithm was effective in ocean–land water vapor flux estimations at different spatial and temporal scales, which could provide great insights into the mechanisms of extreme precipitation events. Significance Statement This study developed a novel approach on water vapor flux estimation (i.e., the eight-direction-vector decomposition algorithm) and achieved a high-temporal–spatial-resolution estimation of water vapor flux between ocean and land. It was found that water vapor flux between ocean and land was intensified by increasing air temperature at a rate of 8% K−1, and El Niño yielded an anomaly low net water vapor input from ocean to land at the global scale. The algorithm developed in this study can be used for estimating water vapor fluxes at different spatial and temporal scales, which is crucial for evaluating the role of water vapor flux on formations for extreme weather (e.g., torrential rainstorms and heat waves) and climatic extremes (e.g., droughts and floods).

Reproducing High Spatiotemporal Resolution Precipitable Water Distributions Using Numerical Prediction Data

Water vapor is an important greenhouse gas that affects regional climatic and weather processes. Atmospheric water vapor content is highly variable spatially and temporally, and continuous quantification over a wide area is problematic. However, existing methods for measuring precipitable water (PW) have advantages and disadvantages in terms of spatiotemporal resolution. This study uses high temporal resolution numerical prediction data and high spatial resolution elevation to reproduce PW distributions with high spatiotemporal resolution. This study also focuses on the threshold for elevation correction, improving temporal resolution, and reproducing PW distributions in near real time. Results show that using the water vapor content in intervals between the ground surface and 1000-hPa isobaric surface as the threshold value for elevation correction and generating hourly numerical prediction data using the Akima spline interpolation method enabled the reproduction of hourly PW distributions for 75% of the global navigation satellite system observation stations in the target region throughout the year with a root mean square error of 3 mm or less. These results suggest that using the mean value of monthly correction coefficients for the past years enables the reproduction of PW distributions in near real time following the acquisition of numerical prediction data.

Improvement of research methodology mesostructures of atmospheric fronts on the basis of ground­based microwave radiometry

The paper considers the some results of experimental studies of the mesoscale structure of atmospheric fronts using a ground-based microwave water vapor radiometer. A methodology for studying the mesoscale structure of atmospheric fronts is described, which includes two stages: the stage of syn- optic (frontological) analysis and the stage of mesoscale analysis based on measurements of atmospheric moisture and cloud liquid water content using a microwave water vapor radiometer. The application of the methodology is demonstrated by the example of the analysis of the results of observation of the case of the passage of warm and cold fronts through the Leningrad region on September 9—14, 2020. Based on the analysis of the results of previous studies, the features of changes in the characteristics of moisture content during the passage of atmospheric fronts are generalized. The growth of the atmospheric water vapor and cloud liquid water content is delayed relative to the appearance of harbingers of a warm cloud front of the upper tier. The growth of moisture content characteristics has a wave-like character, which can be explained by the manifestation of the strip mesostructure of the front. Estimates of the characteristic scales of spatial heterogeneities in the structure of atmospheric fronts for the area under consideration differ from similar characteristics obtained by P. Hobbs for the northeastern coast of North America. In the course of mesoscale analysis, the type of atmospheric front crossing the observation point can be clarified. The results obtained determine the prospects for improving the methods of diagnostics and current forecasting of dangerous weather phenomena during the passage of atmospheric fronts using ground-based microwave radiometers

Percentile-Based Relationship between Daily Precipitation and Surface Air Temperature over the Qinghai–Tibet Plateau

Abstract Climate changes significantly impact the hydrological cycle. Precipitation is one of the most important atmospheric inputs to the terrestrial hydrologic system, and its variability considerably influences environmental and socioeconomic development. Atmospheric warming intensifies the hydrological cycle, increasing both atmospheric water vapor concentration and global precipitation. The relationship between heavy precipitation and temperature has been extensively investigated in literature. However, the relationship in different percentile ranges has not been thoroughly analyzed. Moreover, a percentile-based regression provides a simple but effective framework for investigation into other factors (precipitation type) affecting this relationship. Herein, a comprehensive investigation is presented on the temperature dependence of daily precipitation in various percentile ranges over the Qinghai–Tibet Plateau. The results show that 1) most stations exhibit a peaklike scaling structure, while the northeast part and south margin of the plateau exhibit monotonic positive and negative scaling structures, respectively. The scaling structure is associated with the precipitation type, and 2) the positive and negative scaling rates exhibit similar spatial patterns, with stronger (weaker) sensitivity in the south (north) part of the plateau. The overall increase rate of daily precipitation with temperature is scaled by Clausius–Clapeyron relationship. 3) The higher percentile of daily precipitation shows a larger positive scaling rate than the lower percentile. 4) The peak-point temperature is closely related to the local temperature, and the regional peak-point temperature is roughly around 10°C. Significance Statement This study aims to better understand the relationship between precipitation and surface air temperature in various percentile ranges over the Qinghai–Tibet Plateau. This is important because percentile-based regression not only accurately describes the response of precipitation to warming temperature but also provides a simple but effective framework for investigating other factors (precipitation type) that may be affecting this relationship. Furthermore, the sensitivity and peak-point temperature are evaluated and compared among different regions and percentile ranges; this study also attempts to outline their influencing factors. To our knowledge, this study is the first integration of percentile-based analysis of the dependence of daily precipitation on surface air temperature.

Northwestward advance of the northern boundary of the East Asian summer monsoon over the 21st century in CMIP6 projections

AbstractThe East Asian summer monsoon (EASM) projection has attracted much attention, whereas there are few investigations on the future changes of the EASM northern boundary. The boundary migration would influence the distribution of precipitation and the related vegetation, and its projection is important for policy development of climate change adaptation. In this study, based on the median of 22 selected Coupled Model Intercomparison Project Phase 6 (CMIP6) models, the linear trends of meridional movement of the EASM northern boundary are found to be 0.45−1.39° of latitude during 2015–2099 based on the three precipitation‐based metrics under three shared socioeconomic pathway scenarios. Spatially, the multimetric climatological EASM northern boundary displays a 70–170 km northwestward advance during 2080–2099 compared to 1981–2010. Such an advance also holds true for most individual models, but the migration magnitudes vary with metrics, scenarios and models. The strengthened EASM in association with the intensified land–sea thermal contrast and the enhanced atmospheric water vapour content in response to global warming account for the northwestward advance of the EASM northern boundary, which is related to the increased possibility of the negative phase of the Pacific decadal oscillation in the future. Additionally, the thermodynamic component due to the increased moisture content contributes more than the dynamic term arising from the reinforced EASM circulations to the intensified precipitation and northwestward migration of the EASM northern boundary. The future advance of the EASM northern boundary favours a “northern flood and southern drought” precipitation pattern over eastern China, which would partly mitigate drought conditions in northern arid regions.

Asymmetries of the lag between air temperature and insolation in gauge observations and reanalyses over China

The annual cycle of near-surface air temperature should receive more attention in climate studies because it is especially important to crop growth and to human comfort. The phase of the annual temperature cycle generally lags behind that of insolation due to the time required for the Earth's surface to absorb the energy from solar radiation and then warm the near-surface air. This study decomposes the daily maximum, minimum, and mean air temperatures using Fourier transforms and examines the lag in number of days between the phases of temperature and insolation. It was found that the lags between air temperature and insolation were asymmetrical during the summer and winter seasons. The mean summer lags were 30.8, 32.4, and 30.6 days from gauge observations over China, which were longer than the 21.0, 21.5, and 16.7 days of winter lags for daily maximum, minimum, and mean temperature respectively. Spatially, these lags showed wider climatological and interannual variability in southern than in northern areas because higher atmospheric water vapor content and greater cloud cover caused a longer response time of air temperature to solar radiation. Decreasing variations were derived for the winter lag, at about −1.11 days/decade, −0.68 days/decade, and − 0.78 days/decade for the maximum, minimum, and mean temperature respectively. Currently, reanalysis data can accurately simulate the climatological, long-term, and interannual variability of the summer and winter lags over most of the study area, except for southwestern China, although they have negative biases in characterizing the annual cycle.

Retrieval of Atmospheric Water Vapor Content in the Environment from AHI/H8 Using Both Physical and Random Forest Methods—A Case Study for Typhoon Maria (201808)

The advanced imagers onboard the new generation of geostationary satellites could provide multilayer atmospheric moisture information with unprecedented high spatial and temporal resolutions, while the physical retrieval algorithm (One-Dimensional Variational, 1DVAR) is performed for operational atmospheric water vapor products with reduced resolutions, which is due to the limited computational efficiency of the physical retrieval algorithm. In this study, a typical cost-efficient machine learning (Random Forecast, RF) algorithm is adopted and compared with the physical retrieval algorithm for retrieving the atmospheric moisture from the measurements of Advance Himawari Imager (AHI) onboard the Himawari-8 satellite during the typhoon Maria (201808). It is found that the accuracy of the RF-based algorithm has much high computational efficiency and provides moisture retrievals with accuracy 35–45% better than that of 1DVAR, which means the retrieval process can be conducted at full spatial resolution for potential operational application. Both the Global Forecast System (GFS) forecasts and the AHI measurements are necessary information for moisture retrievals; they provide added value for each other.

Impact of High Concentrations of Saharan Dust Aerosols on Infrared-Based Land Surface Temperature Products

An analysis of three operational satellite-based thermal-infrared land surface temperature (LST) products is presented for conditions of heavy dust aerosol loading. The LST products are compared against ERA5's skin temperature (SKT) across the Sahara Desert and Sahel region, where high concentrations of dust aerosols are prevalent. Large anomalous differences are found between satellite LST and ERA5's SKT during the periods of highest dust activity, and satellite–ERA5 differences are shown to be strongly related to dust aerosol optical depth at 550 nm (DuAOD), indicating an underestimation of LST in conditions of heavy dust aerosol loading. In situ measurements from two ground stations in the Sahel region provide additional evidence of this underestimation, showing increased biases of satellite LST with DuAOD, and no significant dependence of ERA5's SKT biases on dust aerosol concentrations. The impact of atmospheric water vapor content on LST and SKT is also examined, but dust aerosols are shown to be the primary driver of the inaccurate LSTs observed. Based on comparisons with in situ data, we estimate an aerosol-induced underestimation of LST of approximately 0.9 K for every 0.1 increase in DuAOD. Analysis of brightness temperatures (BTs) in the thermal infrared atmospheric window reveals that dust aerosols have the opposite effect on BT differences compared to water vapor, leading to an underestimation of atmospheric correction by the LST retrieval algorithms. This study highlights a shortcoming of current operational LST retrieval algorithms that must be addressed.

Classification of Ground-Based Cloud Images by Contrastive Self-Supervised Learning

Clouds have an enormous influence on the hydrological cycle, Earth’s radiation budget, and climate changes. Accurate automatic recognition of cloud shape based on ground-based cloud images is beneficial to analyze the atmospheric motion state and water vapor content, and then to predict weather trends and identify severe weather processes. Cloud type classification remains challenging due to the variable and diverse appearance of clouds. Deep learning-based methods have improved the feature extraction ability and the accuracy of cloud type classification, but face the problem of lack of labeled samples. In this paper, we proposed a novel classification approach of ground-based cloud images based on contrastive self-supervised learning (CSSL) to reduce the dependence on the number of labeled samples. First, data augmentation is applied to the input data to obtain augmented samples. Then contrastive self-supervised learning is used to pre-train the deep model with a contrastive loss and a momentum update-based optimization. After pre-training, a supervised fine-tuning procedure is adopted on labeled data to classify ground-based cloud images. Experimental results have confirmed the effectiveness of the proposed method. This study can provide inspiration and technical reference for the analysis and processing of other types of meteorological remote sensing data under the scenario of insufficient labeled samples.

Research of Deformation and Soil Moisture in Loess Landslide Simultaneous Retrieved with Ground-Based GNSS

The Loess Plateau is one of the three most severely affected geological disaster areas in China. Water sensitivity is the most significant feature of the loess. Under the action of continuous heavy rainfall, rainwater infiltrates the loess, resulting in a rapid increase in soil saturation and changes in soil moisture. This affects the shear strength of the soil and induces shallow loess landslides. Therefore, it is significant to our country’s disaster prevention and mitigation efforts to effectively monitor the deformation and inducement of such landslides. At present, the global navigation satellite system (GNSS) is widely used in the field of landslide disaster monitoring as a technical means to directly obtain real-time three-dimensional vector deformation of the surface. At the same time, GNSS can also provide a steady stream of L-band microwave signals to obtain surface environmental information, such as soil moisture around the station. In past landslide disaster monitoring research, GNSS was only used to provide three-dimensional deformation information, and its ability to provide environmental information around the station was almost completely ignored. This study proposes a ground-based GNSS remote sensing comprehensive monitoring system integrating “three-dimensional deformation and soil moisture content” combined with a rainfall-type shallow loess landslide event in Linxia City. The ability of ground-based GNSS to comprehensively monitor shallow loess landslide disasters was analysed. Experiments show that GNSS can provide high-precision deformation time series characteristics and monitor the changes in soil moisture content around the station at the same time; the two have a certain response relationship, which can comprehensively evaluate the stability of shallow loess landslides. As heavy rainfall is a key factor affecting the change in soil water content, this study adds the atmospheric water vapour content calculated by ground-based GNSS refraction remote sensing in the discussion chapter and analyses the relationship between precipitable water vapour and rainfall in this area to give full play to ground-based GNSS remote sensing. In the role of landslide disaster monitoring, we hope to build a more comprehensive ground-based GNSS remote sensing monitoring system to better serve the monitoring of landslide disasters.

Climate Disruption Caused by a Decline in Marine Biodiversity and Pollution

The world has focused on carbon mitigation as the only solution for climate change. This discussion paper considers how marine biodiversity regulates the climate, and the factors that control marine biodiversity.
 The main Greenhouse Gas (GHG) is water vapor, which accounts for 75% of all GHGs; the second most important is carbon dioxide, followed by methane and particulates such as black carbon (BC) soot. The concentration of water vapor in the atmosphere is regulated by air temperature; warmer conditions lead to higher evaporation, which in turn increases the concentration of water vapor, the Clausius-Clapeyron relation. This means that as the oceans and atmosphere warm, a self-reinforcing feedback loop accelerates the evaporation process to cause further warming.
 It is not considered possible to directly regulate atmospheric water vapor. This explains why climate change mitigation strategies have focussed primarily on reducing carbon dioxide emissions as the means to reduce water vapor. This report concludes that the current climate change mitigation strategy will not work on its own because it depends on decreasing the concentration of atmospheric carbon dioxide and on the assumption that water vapor is only regulated by temperature.
 71% of planet Earth is covered by an ocean that has a surface microlayer (SML) between 1 µm and 1000µm deep, composed of lipids and surfactants produced by marine phytoplankton. This SML layer is known to promote the formation of aerosols and clouds; it also reduces the escape of water molecules and slows the transfer of thermal energy to the atmosphere. The concentration of water vapor is increasing in our atmosphere, and 100% of this increase is evaporation from the ocean surface; water vapour from land systems is decreasing. This means that the oceans are almost entirely responsible for climate change.
 The SML layer attracts toxic forever, lipophilic chemicals, microplastics and hydrophobic black carbon soot from the incomplete combustion of fossil fuels. Concentrations of toxic chemicals are 500 times higher in this SML layer than in the underlying water. Toxic forever chemicals combined with submicron and microplastic particles and black carbon particulates are known to be toxic to plankton. Marine primary productivity or phytoplankton photosynthesis may have declined by as much as 50% since the 1950s. Reduced phytoplankton plant growth equates to a degraded SML membrane, reduced carbon assimilation, and higher concentrations of dissolved carbon dioxide in ocean surface water, which accelerates the decline in ocean pH. The key phytoplankton species responsible for the production of the SML layer are the first to suffer from pH decline, a process called “ocean acidification”.
 Ocean acidification will lead to a regime shift away from the key carbonate-based species and diatoms below pH 7.95 which will be reached by 2045. The SML layer will decrease, allowing evaporation and atmospheric water vapor concentrations to increase. A reduced SML layer will lead to fewer aerosols, cloud formation and precipitation, as well as increased humidity and temperature. When clouds form under these conditions, the higher humidity will cause torrential downpours and flooding. The result could be catastrophic climate change, even if we achieve net zero by 2050. In parallel, ocean acidification and the collapse of the marine ecosystem could also lead to the loss of most seals, birds, whales, fish, and food supply for 3 billion people.