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The high complexity of the parameter–simulation problem in land surface models over semiarid areas makes it difficult to reasonably estimate the surface simulation conditions that are important for both weather and climate in different regions. In this study, using the dense site datasets of a typical semiarid region over Tibet and the Noah land surface model with the constrained land parameters of multiple sites, an enhanced Kling–Gupta efficiency criterion comprising multiple objectives, including variable and layer dimensions, was obtained, which was then applied to calibration schemes based on two global search algorithms (particle swarm optimization and shuffled complex evaluation) to investigate the site-scale spatial complexities in soil temperature simulations. The calibrations were then compared and further validated. The results show that the Noah land surface model obtained reasonable simulations of soil moisture against the observations with fine consistency, but the negative fit and huge spatial errors compared with the observations indicated its weak ability to simulate the soil temperature over regional semiarid land. Both calibration schemes significantly improved the soil moisture and temperature simulations, but particle swarm optimization generally converged to a better objective than shuffled complex evaluation, although with more parameter uncertainties and less heterogeneity. Moreover, simulations initialized with the optimal parameter tables for the calibrations obtained similarly sustainable improvements for soil moisture and temperature, as well as good consistency with the existing soil reanalysis. In particular, the soil temperature simulation errors for particle swarm optimization were unbiased, while those for the other method were found to be biased around −3 K. Overall, particle swarm optimization was preferable when conducting soil temperature simulations, and it may help mitigate the efforts in surface forecast improvement over semiarid regions. [ABSTRACT FROM AUTHOR]

Low-cost sensors (LCSs) of Geekcreit PM1/PM2.5/PM10 (based on a PMS5003 sampler) and BOHU BH-1 models A3 and B3 (based on a Pando G7 sampler) were compared for different aerosol size ranges using a research-grade instrument (Grimm 1.109) under controlled laboratory conditions. An aerosol generator was utilized to produce various sizes of monodispersed particulate matter (PM), which was introduced into a laboratory smoke chamber under resistance heating/cooling and/or varying RH conditions. In addition, the accuracy of the air temperature (T) and relative humidity (RH) sensors of the LCSs were assessed against calibrated, laboratory-grade instruments. The study LCSs showed generally accurate readings for PM2.5, irrespectively of the slow T and/or RH changes, which provided apt conditions for accurate calibration slopes (S) and low intercepts/bias (b) of the linear fits. On the other hand, PM1 and PM10 readings slightly deviated from those observed with the reference monitor, likely due to the lower detection efficacy of the LCSs towards fine and coarse PM. Varying RH influenced the S and b values, showing its impact on the detection efficacy of LCSs. Under low/medium RH, homoscedastic calibration curves of PMx were found, whereas rather heteroscedastic calibration plots were observed at high RH. For T calibration, low RH in the smoke chamber provided more reproducible conditions in terms of lower measurement bias for LCSs as recorded against a calibrated, reference-grade thermometer. [ABSTRACT FROM AUTHOR]

The plasma analyzer (PMA) on the Fengyun-3E (FY-3E) meteorological satellite series is a critical sensor of the space environment monitoring package that is capable of the comprehensive in situ detection of the thermal plasma environment and surface discharge effects. In this paper, we conducted a thorough evaluation of the PMA's performance and reliability through a combination of ground-based laboratory calibration and in-orbit testing. During the ground-based calibration, the PMA underwent assessments for the energy range, field of view (FOV), and measurement accuracy, and obtained the detection accuracy and the geometric factors. During the in-orbit testing, the PMA successfully obtained the typical distribution characteristics of low-energy ions and electrons in orbital space regions, as well as the precipitating particles in the middle and high latitudes of both hemispheres. Notably, the PMA observed an expansion of the particle distribution in the high-latitude regions during a moderate geomagnetic storm. The results from both the ground-based calibration and in-orbit testing demonstrated that the PMA met the requirements for thermal plasma detection, with reliable and scientifically valid in-orbit detection data. These results provide a crucial foundation for studying spatial weather variations, improving the accuracy of space environment forecasts and enhancing disaster detection and monitoring capabilities. [ABSTRACT FROM AUTHOR]

A sun photometer is a type of photometer that points at the sun, and it has been playing an increasingly important role in characterizing aerosols across the world. As long as the solar photometer is accurately calibrated, the optical thickness of the aerosol can be obtained from the measured value of this device. When the calibration of a single instrument is not accurate, the inversion quantity varies greatly. The calibration constant of the sun photometer changes during its use process; thus, calibrations are frequently needed in order to ensure the accuracy of the measured value. The calibration constant of the solar photometer is usually determined using the Langley method. Internationally, AERONET has two Langley calibration stations: the Mauna Loa observatory in the United States and the Izaña observatory in Spain. So far, the International Comparison and Calibration System has been established in Beijing, similar to AERONET at GSFC, but the Langley calibration system has not yet been established. Therefore, it is necessary to select a suitable calibration station in China. This paper studies the requirements of the calibration station using the Langley method. We used long-term records of satellite-derived measurements and survey data belonging to the aerosol optical thickness data of SNPP/VIIRS, CERES, MERRA-2, etc., in order to gain a better understanding of whether these stations are suitable for calibration. From the existing astronomical observation stations, meteorological stations, and the Sun–Sky Radiometer Observation Network (SONET) observation stations in China, the qualified stations were selected. According to the statistical data from the Ali observatory, the monthly average of clear sky is 20.21 days, and it is always greater than 15 days. The monthly average of aerosol is not more than 0.15 and is less than 0.3. We believe that the atmosphere above the Ali observatory is stable, and the results show that the Ali observatory has excellent weather conditions. This study can provide a selection of calibration sites for solar photometer calibrations in China that may need to be further characterized and evaluated, and at the same time provide a method to exclude unsuitable calibration sites. [ABSTRACT FROM AUTHOR]

The in-traffic microenvironment can enhance personal exposure to fine particulate matter (PM). With this study, we aimed to calibrate a DustTrak instrument (DustTrak 8533 DRX Aerosol Monitor, TSI Incorporated, Shoreview, MN, USA) and low-cost sensors (AS-LUNG-P sensors) and then assess inhalation exposure to PM2.5 and PM1 for different commuters in central areas of Ho Chi Minh City (HCM). The DustTrak instrument and low-cost sensors were calibrated using a gravimetric method under side-by-side conditions. Relationships between the DustTrak signals and PM concentrations measured by the gravimetric method were identified using simple linear regression models for PM2.5 (R2 = 0.998, p-value < 0.05) and PM1 (R2 = 0.989, p-value < 0.05). Meanwhile, PM concentrations determined by the AS-LUNG-P sensors and the gravimetric method were correlated using two-segmented linear regressions. To obtain the corresponding two-segment regression equations, the response of the AS-LUNG-P sensors was compared with the corrected DustTrak data. The coefficient of variation (CV) evaluated for all sensors was smaller than 10%, indicating that the data were applicable for particle assessment. For inhalation exposure assessment, the results showed that commuters using open transport modes, such as bikes, motorbikes, and walking, were exposed to more PM than those using closed transport modes (e.g., cars). Specifically, the bicyclists had the highest inhaled doses of PM among the open transport groups. PM exposure levels in the morning were higher than in the afternoon. Additionally, exposure levels to PM concentrations rapidly increased when passing through intersections of major roads and moderately decreased when using surgical facemasks. [ABSTRACT FROM AUTHOR]

Particulate matter (PM) pollution is a crucial environmental issue. Considering its adverse health impacts, especially on children's immune systems, Korean regulations require annual PM2.5 measurements in daycare centers. Therefore, we developed a low-cost PM2.5 sensor calibration model for measuring the indoor PM concentrations in daycare centers using long short-term memory (LSTM) algorithms. Moreover, we trained the model to predict the PM2.5 based on temperature and humidity, and optimized its hyperparameters. The model achieved a high accuracy and outperformed traditional calibration methods. The optimal lookback period was 76, which led to a high calibration performance with root mean and mean squared errors, a coefficient of determination, and mean absolute errors of 3.57 and 12.745, 0.962, and 2.7, respectively. The LSTM model demonstrated a better calibration performance than those of the linear (r2 = 0.57) and multiple (r2 = 0.75) linear regression models. The developed calibration model provided precise short-term measurement values for the optimal management of indoor PM concentrations. This methodology can be applied to similar environments to obtain new learning and hyper-parameters. Our results will aid in improving the accuracy of low-cost sensors for measuring indoor PM concentrations, thereby providing cost-effective solutions for enhancing children's health and well-being in daycare centers and other multiuse facilities. [ABSTRACT FROM AUTHOR]

In the Western world, the SO2 concentration in ambient air dropped to low levels, but some emission sources (e.g., merchant ships) and some regions (e.g., low-income countries) still emit substantial amounts of SO2. At those locations, SO2 monitoring is critical. However, low-income countries do not have much access to expensive reference instruments. Low-cost gas sensors might be an alternative, but it is unclear how reliable such measurements are. To evaluate the performance of the low-cost alternative, the same SO2 gas sensor has been subjected to three different calibration methods: (1) low-cost calibration performed in the tropical climate of Cuba; (2) high-end calibration performed in Belgium; (3) a field calibration at an air quality measuring station in Belgium. The first two methods showed similar trends, suggesting that the gas sensor can be calibrated with a low-cost method. The field calibration was hampered by the low SO2 concentrations. For the monitoring campaign in Cienfuegos, Cuba, the low-cost SO2 sensor calibrated by the low-cost method appeared to be sufficiently reliable. The reliability of the sensor increases with the increase in SO2 concentration, so it can be used in Cuba instead of Belgium. [ABSTRACT FROM AUTHOR]

Accurate measurement of low-level thoron gas and high-accuracy calibration of thoron measurement devices are essential for assessing and preventing thoron radiological risks. This study aimed to develop a thoron gas measurement technique using an airflow-through scintillation cell for both low-level measurement and high-accuracy calibration. To achieve this, a compartment model was developed to estimate the influence of progeny deposition and accumulation on the wall of the scintillation cell to prevent an overestimation of thoron. A self-developed scintillation cell was utilised to implement and validate this technique. The lower detection limit and measurement uncertainty were then evaluated to assess the feasibility of the technique for low-level measurement and high-accuracy calibration. The results showed that the compartment model effectively addressed the influence of progeny deposition. The measurement technique achieved a lower detection limit below 100 Bq m−3 even with the coexistence of that of 100 Bq m−3 of radon and attained a measurement uncertainty (k = 2) below 10% when the concentration of thoron exceeded 1000 Bq m−3. In summary, this study developed a reliable and practical thoron gas measurement technique using an airflow-through scintillation cell with a consideration of progeny deposition, and is expected to contribute to the assessment and prevention of thoron radiological risk. [ABSTRACT FROM AUTHOR]

Low-cost air quality sensors have the potential to complement the regulatory network of air quality monitoring stations, with respect to increased spatial density of observations, however, their data quality continues to be of concern. Here we report on our experience with a small network of open low-cost sensor systems for air quality, which was deployed in the region of Stavanger, Norway, under Nordic winter conditions. The network consisted of AirSensEUR sensor systems, equipped with sensors for, among others, nitrogen dioxide and fine particulate matter. The systems were co-located at an air quality monitoring station, for a period of approximately six weeks. A subset of the systems was subsequently deployed at various roadside locations for half a year, and finally co-located at the same air quality monitoring station again, for a post-deployment evaluation. For fine particulate matter, the co-location results indicate a good inter-unit consistency, but poor average out-of-the-box performance (R2 = 0.25, RMSE = 9.6 μ g m − 3 ). While Köhler correction did not significantly improve the accuracy in our study, filtering for high relative humidity conditions improved the results (R2 = 0.63, RMSE = 7.09 μ g m − 3 ). For nitrogen dioxide, the inter-unit consistency was found to be excellent, and calibration models were developed which showed good performance during the testing period (on average R2 = 0.98, RMSE = 5.73 μ g m − 3 ), however, due to the short training period, the calibration models are likely not able to capture the full annual variability in environmental conditions. A post-deployment co-location showed, respectively, a slight and significant decrease in inter-sensor consistency for fine particulate matter and nitrogen dioxide. We further demonstrate, how observations from even such a small network can be exploited by assimilation in a high-resolution air quality model, thus adding value to both the observations and the model, and ultimately providing a more comprehensive perspective of air quality than is possible from either of the two input datasets alone. Our study provides valuable insights on the operation and performance of an open sensor system for air quality, particularly under challenging Nordic environmental conditions. [ABSTRACT FROM AUTHOR]

Low-cost sensors (LCS) have been increasingly deployed to monitor PM2.5 concentrations. More than 1500 LCS have been installed in Thailand to increase public awareness of air quality. However, performance of these sensors has not been systematically investigated. In this study, PM2.5 LCS were co-located next to a PM2.5 federal equivalent method (FEM) reference instrument at three Thai locations—in the north, center and northeast. We evaluated the performance of a PM2.5 LCS (PMS7003, Plantower) to understand the key factors affecting performance, including emission sources, relative humidity, temperature and PM2.5 concentration. Low PM concentration and high humidity levels had a significant impact on performance. Sensors in a high traffic emission area showed low correlation. The unadjusted PM2.5 LCS performance varied with locations. Errors were mainly observed at low concentrations. They significantly underestimated concentrations in congested urban environments. After calibration, accuracy was improved with multiple regression models. The performance of sensors only at Chiang Mai (CM) during the dry season and Ubon Ratchathani (URT) during the dry and wet seasons were acceptable with coefficient of variation: 5.8 ± 4.7–6.8 ± 5.0%, slope: 0.829–0.945, intercept: 1.12–5.49 µg/m3, R2: 0.880–0.934 and RMSE: 4.3–5.1 µg/m3. In the congested area in Bangkok (BKK), they underestimated concentrations of small particles. [ABSTRACT FROM AUTHOR]

Field calibration of low-cost air quality (AQ) monitoring sensors is essential for their successful operation. Low-cost sensors often exhibit non-linear responses to air pollutants and their signals may be affected by the presence of multiple compounds making their calibration challenging. We investigate different approaches for the field calibration of an AQ monitoring device named ENSENSIA, developed in the Institute of Chemical Engineering Sciences in Greece. The present study focuses on the measurements of two of the most important pollutants measured by ENSENSIA: NO2 and O3. The measurement site is located in the center of Patras, the third biggest city in Greece. Reference instrumentation used for regulatory purposes by the Region of Western Greece was used as the evaluation standard. The sensors were installed for two years at the same locations. Measurements from the first year (2021) from seven ENSENSIA sensors (NO2, NO, O3, CO, PM2.5, temperature and relative humidity) were used to train several Machine Learning (ML) and Deep Learning (DL) algorithms. The resulting calibration algorithms were assessed using data from the second year (2022). The Random Forest algorithm exhibited the best performance in correcting O3 and NO2. For NO2 the mean error was reduced from 9.4 ppb to 3 ppb, whilst R2 improved from 0.22 to 0.86. Similar results were obtained for O3, wherein the mean error was reduced from 13 to 4.3 ppb and R2 increased from 0.52 to 0.69. The Long-Short Term Memory Network (LSTM) also showed good performance in correcting the measurements of the two pollutants. [ABSTRACT FROM AUTHOR]

Low-cost sensors provide an affordable alternative to monitor environmental parameters with acceptable performance. There is a substantial amount of literature where low-cost sensors are compared with high-end reference measurements. However, not all organizations have access to such reference infrastructure. We propose low-cost calibration methods for temperature, relative humidity, and CO2 to allow them to collect their own reliable data. These methods are based on simple techniques and procedures that allow temperature calibration to be achieved in the range of 0 to 50 °C, relative humidity from 0 to 90%, and CO2 between 0 and 1100 ppm. The materials used to create the calibration setups can be purchased online, at hardware stores, and in pharmacies. The reliability of the calibration methods was evaluated using several indicators, such as the airtightness of the calibration box, similarity with the factory calibration, similarity with the reference, similarity between different sensors, replicability of the calibration method, and the similarity with a golden standard. In addition, the results of the low-cost calibration methods were compared with the more advanced calibration methods. A short measurement campaign in the city of Santa Clara, Cuba, demonstrated that such calibrations transform in-house developed monitoring systems into valid low-cost scientific instruments for decision-making. This work creates opportunities for institutions and researchers hosted in low- and mid-income countries to build and validate their own equipment to reliably solve local problems. [ABSTRACT FROM AUTHOR]

This paper aims to present simple regressive equations to estimate the parameters of the three-parameter depth–duration–frequency (DDF) curve (3p-DDF), which accurately expresses, for a preassigned return period, the relationship between the rainfall depth and the rainfall duration over large duration ranges, from below 1 h (i.e., tens of minutes) to above 1 h (up to 24 h). These equations are developed to relate their parameters to those of the two-parameter DDF curve (2p-DDF), which can be estimated more easily being based on more readily available data related to rainfall durations above 1 h. In the applications, the regressive equations are first calibrated using recent pluviographic data in northern Italy, Germany, and Sweden. Two validation steps are then carried out to test the equations in terms of estimated rainfall depths using the same data as those used in the calibration step and data of stations from other geographic areas, i.e., Sicily in southern Italy, and from the past century, respectively. The results obtained prove this methodology capable of providing reliable estimation of short-duration rainfalls with various return periods in the absence of measurements with fine temporal resolution. [ABSTRACT FROM AUTHOR]

Radiative transfer models of the Earth's atmosphere play a critical role in supporting Earth Observation applications such as vicarious calibration. In the solar reflective spectral domain, these models usually account for the scattering and absorption processes in the atmosphere and the underlying surface as well as the radiative coupling between these two media. A range of models is available to the scientific community with built-in capabilities making them easy to operate by a large number of users. These models are usually benchmarked in idealised but often unrealistic conditions such as monochromatic radiation reflected by a Lambertian surface. Four different 1D radiative transfer models are compared in actual usage conditions corresponding to the simulation of satellite observations. Observations acquired by six different space-borne radiometers over the pseudo-invariant calibration site Libya-4 are used to define these conditions. The differences between the models typically vary between 0.5 and 3.5% depending on the spectral region and the shape of the sensor spectral response. [ABSTRACT FROM AUTHOR]

In Europe and Asia, vehicle emissions regulations include a number limit for particles larger than 23 nm, which might be reduced to 10 nm in the future. A particle number system (LABS) consists of a volatile particle remover (VPR) and a particle number counter (PNC). However, it is not simple to derive the combined penetration (efficiency), because the parts are calibrated separately at different sizes and with different materials. On the other hand, portable emissions measurement systems (PEMS) for real-driving emissions (RDE) testing or counters for periodical technical inspection (PTI) of vehicle exhaust are calibrated as complete units with soot-like aerosol. The aim of this study is to estimate the efficiency of a LABS using different materials (soot, graphite, salt, silver, emery oil), typically used for the calibration of LABS, PEMS or PTI counters. The results show that appropriate selection of the calibration material is important in order to have representative of the reality efficiencies. The impact is very high for 23 nm systems, but less critical for 10 nm systems. The estimation of a mean size based on the ratio of 23 nm and 10 nm measurements and the correction of the losses in the sub-23 nm region are also discussed. [ABSTRACT FROM AUTHOR]

Particulates from diesel generator operation are a known air pollutant with adverse health effects. In this study, we used low-cost particulate matter (PM) sensors to monitor PM2.5 in a diesel generator plant. We compared the measurement results from a PM sensor and a reference instrument (DustTrak), and we found a high correlation between them. The data overestimation or underestimation of PM sensors implied the need for data calibration. Hence, we proposed a data calibration algorithm based on a nonlinear support vector machines(SVM)model, and we investigated the effect of three calibration factors on the model: humidity, temperature, and total volatile organic compounds (TVOC). It was found that the TVOC correction coefficient has great influence on the model, which should be considered when calibrating the low-cost PM sensor in diesel generator operation sites. A monitoring network with six low-cost sensors was installed in the diesel generator plant to monitor PM2.5 concentration. It was found that normal diesel generator work, diesel generator set handling work, and human activity are the most dominant ways of producing particulate matter at the site, and dispersion is the main cause of increased PM2.5 concentrations in nonworking areas. In this study, PM2.5 emissions from two different diesel generators were tested, and PM2.5 concentrations at monitoring points reached 220 μg/m3 and 120 μg/m3, respectively. This further confirms that diesel generators produce many respirable particles when working. [ABSTRACT FROM AUTHOR]

In this study, methods from the field of deep learning are used to calibrate a metal oxide semiconductor (MOS) gas sensor in a complex environment in order to be able to predict a specific gas concentration. Specifically, we want to tackle the problem of long calibration times and the problem of transferring calibrations between sensors, which is a severe challenge for the widespread use of MOS gas sensor systems. Therefore, this contribution aims to significantly diminish those problems by applying transfer learning from the field of deep learning. Within the field of deep learning, transfer learning has become more and more popular. Nowadays, building a model (calibrating a sensor) based on pre-trained models instead of training from scratch is a standard routine. This allows the model to train with inherent information and reach a suitable solution much faster or more accurately. For predicting the gas concentration with a MOS gas sensor operated dynamically using temperature cycling, the calibration time can be significantly reduced for all nine target gases at the ppb level (seven volatile organic compounds plus carbon monoxide and hydrogen). It was possible to reduce the calibration time by up to 93% and still obtain root-mean-squared error (RMSE) values only double the best achieved RMSEs. In order to obtain the best possible transferability, different transfer methods and the influence of different transfer data sets for training were investigated. Finally, transfer learning based on neural networks is compared to a global calibration model based on feature extraction, selection, and regression to place the results in the context of already existing work. [ABSTRACT FROM AUTHOR]

Currently, more and more emphasis is being placed on reducing energy consumption in buildings to reduce greenhouse gases in the atmosphere. Building performance simulation is very useful to predict energy demand and indoor environment quality. An indispensable element of the simulation is the validation and calibration of the model, which is an arduous process. The aim of the study was to present a four-level validation (using measurement results) and calibration of a thermal model of a naturally ventilated single-family house. Numerical calculations using co-simulation between EnergyPlus and Contam were performed. The results of the one-year simulation measurements of the indoor temperature and ventilation airflows were compared. After the calibration was performed, a high convergence of the results was found. The normalized mean bias error for hourly and monthly values did not exceed 1% and the coefficient of variation of the root mean squared error was a maximum of 7% with a simultaneous high correlation of the results in the range from 0.85 to 0.89. It was found that the final results were significantly influenced by the appropriate modeling of air exchange in the building, including the opening of windows. [ABSTRACT FROM AUTHOR]

Scattered light polarization serves as an indicator and a characteristic of various processes in the atmosphere. The polarization measurements of all scattering matrix elements provide an adequate description of the optical and morphological parameters and orientation of particles in clouds. In this article, we consider the problem of the calibration of matrix polarization lidar (MPL) parameters. Calibration by air is an effective alternative to the technique for correcting optical element parameters and among the calibration parameters of the MPL optical path are the relative transmission coefficient of a two-channel receiver, the angular positions of the transmission axes of the optical elements of the transmitter and receiver units, including the polarizers and wave plates, and the retardance of wave plates. For the first time, the method of calibration by air was partially implemented in the MPL to study Asian dust in the atmosphere. We considered the calibration problem more generally and this was due to the need to calibrate different MPL modifications from stationary to mobile ones. The calibration equations have been derived in terms of instrumental vectors, and the method of their solution by the generalized least squares method has been proposed. The method has been verified on a numerical MPL model and validated using MPL measurements in Daejeon, Republic of Korea. [ABSTRACT FROM AUTHOR]

(1) Background: This work evaluated the usability of commercial "low-cost" air quality sensor systems to substantiate evidence-based policy making. (2) Methods: Two commercially available sensor systems (Airly, Kunak) were benchmarked at a regulatory air quality monitoring station (AQMS) and subsequently deployed in Kampenhout and Sint-Niklaas (Belgium) to address real-world policy concerns: (a) what is the pollution contribution from road traffic near a school and at a central city square and (b) do local traffic interventions result in quantifiable air quality impacts? (3) Results: The considered sensor systems performed well in terms of data capture, correlation and intra-sensor uncertainty. Their accuracy was improved via local re-calibration, up to data quality levels for indicative measurements as set in the Air Quality Directive (Uexp < 50% for PM and <25% for NO2). A methodological setup was proposed using local background and source locations, allowing for quantification of the (3.1) maximum potential impact of local policy interventions and (3.2) air quality impacts from different traffic interventions with local contribution reductions of up to 89% for NO2 and 60% for NO throughout the considered 3 month monitoring period; (4) Conclusions: Our results indicate that commercial air quality sensor systems are able to accurately quantify air quality impacts from (even short-lived) local traffic measures and contribute to evidence-based policy making under the condition of a proper methodological setup (background normalization) and data quality (recurrent calibration) procedure. The applied methodology and learnings were distilled in a blueprint for air quality sensor networks for replication actions in other cities. [ABSTRACT FROM AUTHOR]

Accurate hydrological simulation plays an important role in the research of hydrological problems; the accuracy of the watershed hydrological model is seriously affected by model-parameter uncertainty and model-input uncertainty. Thus, in this study, different calibration methods and rainfall inputs were introduced into the SWAT (Soil and Water Assessment Tool) model for watershed hydrological simulation. The Chengbi River basin, a typical karst basin in Southwest China, was selected as the target basin. The indicators of the NSE (Nash efficiency coefficient), Re (relative error) and R2 (coefficient of determination) were adopted to evaluate the model performance. The results showed that: on the monthly and daily scales, the simulated runoff with the single-site method calibrated model had the lowest NSE value of 0.681 and highest NSE value of 0.900, the simulated runoff with the multi-site method calibrated model had the lowest NSE value of 0.743 and highest NSE value of 0.953, increased correspondingly, indicating that adopting the multi-site method could reduce the parameter uncertainty and improve the simulation accuracy. Moreover, the NSE values with IMERG (Integrated Multisatellite Retrievals for Global Rainfall Measurement) satellite rainfall data were the lowest, 0.660 on the monthly scale and 0.534 on the daily scale, whereas the NSE values with fusion rainfall data processed by the GWR (geographical weighted regression) method greatly increased to 0.854 and 0.717, respectively, and the NSE values with the measured rainfall data were the highest, 0.933 and 0.740, respectively, demonstrating that the latter two rainfall inputs were more suitable sources for hydrological simulation. [ABSTRACT FROM AUTHOR]

Semi-volatile organic compounds (SVOCs) are important pollutants in indoor environments. Quantification of gaseous SVOC concentrations is essential to assess the pollution levels. Solid-phase microextraction (SPME) is considered to be an attractive sampling technique with merits, including simplicity of use, rapid sampling, and solvent free. However, the applications of SPME for sampling gaseous SVOCs are often limited by the fluctuating velocity of indoor air (leading to an unstable sampling rate) and the uncertainties associated with the traditional calibration of SPME. Therefore, we established an SPME-based active sampler to ensure the stable sampling of SVOCs in fluctuating air and developed a two-step calibration method based on the sampling principle of SPME. The presented method and a traditional method (sorbent tubes packed with Tenax TA) were simultaneously used to measure SVOC concentrations in an airstream generated in experiments. Three typical indoor SVOCs, diisobutyl phthalate (DiBP), tris (1-chloro-2-propyl) phosphate (TCPP), and benzyl butyl phthalate (BBzP) were chosen as the analytes. Mean concentrations measured by SPME agreed well with the sorbent tubes (relative deviations <12%), supporting the feasibility of the presented method. Further studies are expected to facilitate the application of the presented method (especially the problem associated with the sampling-tube loss of low volatile SVOCs). [ABSTRACT FROM AUTHOR]

Poor air quality is a major environmental concern worldwide, but people living in low- and middle-income countries are disproportionately affected. Measurement of PM2.5 is essential for establishing regulatory standards and developing policy frameworks. Low-cost sensors (LCS) can construct a high spatiotemporal resolution PM2.5 network, but the calibration dependencies and subject to biases of LCS due to variable meteorological parameters limit their deployment for air-quality measurements. This study used data collected from June 2019 to April 2021 from a PurpleAir Monitor and Met One Instruments' Model BAM 1020 as a reference instrument at Alberta, Canada. The objective of this study is to identify the relevant meteorological parameters for each season that significantly affect the performance of LCS. The meteorological features considered are relative humidity (RH), temperature (T), wind speed (WS) and wind direction (WD). This study applied Multiple Linear Regression (MLR), k-Nearest Neighbor (kNN), Random Forest (RF) and Gradient Boosting (GB) models with varying features in a stepwise manner across all the seasons, and only the best results are presented in this study. Improvement in the performance of calibration models is observed by incorporating different features for different seasons. The best performance is achieved when RF is applied but with different features for different seasons. The significant meteorological features are PM2.5_LCS in Summer, PM2.5_LCS, RH and T in Autumn, PM2.5_LCS, T and WS in Winter and PM2.5_LCS, RH, T and WS in Spring. The improvement in R2 for each season (values in parentheses) is Summer (0.66–0.94), Autumn (0.73–0.96), Winter (0.70–0.95) and Spring (0.70–0.94). This study signifies selecting the right combination of models and features to attain the best results for LCS calibration. [ABSTRACT FROM AUTHOR]

The paper presents the results of a 1.5-year evaluation study of low- and medium-cost ozone sensors. The tests covered electrochemical sensors: SensoriC O3 3E 1 (City Technology) and semiconductor gas sensors: SM50 OZU (Aeroqual), SP3-61-00 (FIS) and MQ131 (Winsen). Three copies of each sensor were enclosed in a measurement box and placed near the reference analyser (MLU 400). In the case of SensoriC O3 3E 1 sensors, the R2 values for the 1-h data were above 0.90 for the first 9 months of deployment, but a performance deterioration was observed in the subsequent months (R2 ≈ 0.6), due to sensor ageing processes. High linear relationships were observed for the SM50 devices (R2 > 0.94), but some periodic data offsets were reported, making regular checking and recalibration necessary. Power-law functions were used in the case of SP3-61-00 (R2 = 0.6–0.7) and MQ131 (R2 = 0.4–0.7). Improvements in the fittings were observed for models that included temperature and relative humidity data. In the case of SP3-61-00, the R2 values increased to above 0.82, while for MQ131 they increased to above 0.86. The study also showed that the measurement uncertainty of tested sensors meets the EU Directive 2008/50/EC requirements for indicative measurements and, in some cases, even for fixed measurements. [ABSTRACT FROM AUTHOR]

Deployment of an air quality low-cost sensor network (AQLCSN), with proper calibration of low-cost sensors (LCS), offers the potential to substantially increase the ability to monitor air pollution. However, to leverage this potential, several drawbacks must be ameliorated, thus the calibration of such sensors is becoming an essential component in their use. Commonly, calibration takes place in a laboratory environment using gasses of known composition to measure the response and a linear calibration is often reached. On site calibration is a promising complementary technique where an LCS and a reference instrument are collocated with the former being calibrated to match the measurements of the latter. In a scenario where an AQLCSN is already operational, both calibration approaches are resource and time demanding procedures to be implemented as frequently repeated actions. Furthermore, sensors are sensitive to the local meteorology and adaptation is a slow process making relocation a complex and expensive option. We concentrate our efforts in keeping the LCS positions fixed and propose to blend a genetic algorithm (GA) with a hybrid stacking (HS) ensemble into the GAHS framework. GAHS employs a combination of batch machine learning algorithms and regularly updated online machine learning calibration function(s) for the whole network when a small number of reference instruments are present. Furthermore, we introduce the concept of spatial online learning to achieve better spatial generalization. The frameworks are tested for the case of Thessaloniki where a total of 33 devices are installed. The AQLCSN is calibrated on the basis of on-site matching with high quality observations from three reference station measurements. The O3 LCS are successfully calibrated for 8–10 months and the PM10 LCS calibration is evaluated for 13–24 months showing a strong seasonal dependence on their ability to correctly capture the pollution levels. [ABSTRACT FROM AUTHOR]

To reduce inaccuracies in the measurement of air pollutants by portable monitors it is necessary to establish quantitative calibration relationships against their respective reference analyser. This is usually done under controlled laboratory conditions or one-off static co-location alongside a reference analyser in the field, neither of which may adequately represent the extended use of portable monitors in exposure assessment research. To address this, we investigated ways of establishing and evaluating portable monitor calibration relationships from repeated intermittent deployment cycles over an extended period involving stationary deployment at a reference site, mobile monitoring, and completely switched off. We evaluated four types of portable monitors: Aeroqual Ltd. (Auckland, New Zealand) S500 O3 metal oxide and S500 NO2 electrochemical; RTI (Berkeley, CA, USA) MicroPEM PM2.5; and, AethLabs (San Francisco, CA, USA) AE51 black carbon (BC). Innovations in our study included: (i) comparison of calibrations derived from the individual co-locations of a portable monitor against its reference analyser or from all the co-location periods combined into a single dataset; and, (ii) evaluation of calibrated monitor estimates during transient measurements with the portable monitor close to its reference analyser at separate times from the stationary co-location calibration periods. Within the ~7 month duration of the study, 'combined' calibration relationships for O3, PM2.5, and BC monitors from all co-locations agreed more closely on average with reference measurements than 'individual' calibration relationships from co-location deployment nearest in time to transient deployment periods. 'Individual' calibrations relationships were sometimes substantially unrepresentative of the 'combined' relationships. Reduced quantitative consistency in field calibration relationships for the PM2.5 monitors may have resulted from generally low PM2.5 concentrations that were encountered in this study. Aeroqual NO2 monitors were sensitive to both NO2 and O3 and unresolved biases. Overall, however, we observed that with the 'combined' approach, 'indicative' measurement accuracy (±30% for O3, and 50% for BC and PM2.5) for 1 h time averaging could be maintained over the 7-month period for the monitors evaluated here. [ABSTRACT FROM AUTHOR]

The accurate production of a rainfall environment similar to natural rainfall by a rainfall simulator (RS) is a crucial and challenging task in rainfall instrument testing or calibration. Although the spatial uniformity of rainfall accumulation is a key parameter of an RS, the spatial uniformity comparison between simulated rainfall and natural rainfall, and the spatial uniformity improvements for an RS are scant in the literature. In this study, a fine-scale natural rainfall experiment was studied using the same testing methods of an RS and the rainfall uniformity was evaluated using the Christiansen Uniformity Coefficient (CU). Simultaneously, factors influencing the spatial uniformity of natural rainfall, including the average rainfall accumulation (RA), the deviation of RA, and the area of the test zone, were analyzed. The results successfully reproduced some of the behaviors observed in natural rainfall experiments, showing that CU is dependent on these parameters. Based on these studies, we developed a rainfall simulator with a rotary platform (RSRP) and found that although spatial uniformity of the RSRP was greatly improved using an appropriate rotary speed, it was not consistent with the spatial uniformity of natural rainfall. Furthermore, we tested four tipping-bucket rain gauges using this imperfect RSRP, and found that the RSRP might acquire the instrumental errors associated with RA for a tested rainfall instrument. [ABSTRACT FROM AUTHOR]

The electromagnetic power that comes from the Sun has been proved to be an effective reference for checking the quality of dual-polarization weather radar receiver. Operational monitoring methods have been developed and implemented for determining the electromagnetic antenna pointing, assessing the receiver stability, and the differential reflectivity offset. So far, the focus has been on relative calibration: horizontal and vertical polarization have been mutually compared and evaluated versus the reference mainly in terms of standard deviation of the error. Radar receivers have been able to capture and describe the monthly variability (slowly varying component) of the microwave signal emitted by the Sun. In this paper, we present results from a novel Sun-based method aiming at the absolute calibration of dual-polarization weather radar receivers. To obtain best results, the radar receiver has to be off-line for a few minutes during the tracking of the Sun in order to have the antenna beam axis pointing at the center of the Sun. Among the five polarimetric weather radar receivers of the Swiss network, radar "WEI" located at an altitude of 2850 m next to Davos shows the best absolute agreement with the Dominion Radio Astrophysical Observatory (DRAO) reference for both horizontal (H) and vertical (V) polarization. Albis radar, which is located at an altitude of 938 m near Zurich, shows the largest difference: the radar receiver is too low compared to the Sun reference by -1.62 (-1.25) dB for the H (V) channel. Interestingly, the standard deviation of the error is smaller than ±0.17 dB for all Swiss radar receivers. With a standard deviation of ±0.04 dB Albis radar shows the best relative agreement between H and V. These results are encouraging and MeteoSwiss is planning to repeat off-line Sun-tracking measurements in the future on a regular basis. [ABSTRACT FROM AUTHOR]

Topography is a significant factor influencing the spatial distribution of precipitation. This study developed a new methodology to evaluate and calibrate the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) products by merging geographic and topographic information. In the proposed method, firstly, the consistency rule was introduced to evaluate the fitness of satellite rainfall with measurements on the grids with and without ground gauges. Secondly, in order to improve the consistency rate of satellite rainfall, genetic programming was introduced to mine the relationship between the gauge rainfall and location, elevation and TMPA rainfall. The proof experiment and analysis for the mean annual satellite precipitation from 2001-2012, 3B43 (V7) of TMPA rainfall product, was carried out in eight mountainous areas of China. The result shows that the proposed method is significant and efficient both for the assessment and improvement of satellite precipitation. It is found that the satellite rainfall consistency rates in the gauged and ungauged grids are different in the study area. In addition, the mined correlation of location-elevation-TMPA rainfall can noticeably improve the satellite precipitation, both in the context of the new criterion of the consistency rate and the existing criteria such as Bias and RMSD. The proposed method is also efficient for correcting the monthly and mean monthly rainfall of 3B43 and 3B42RT. [ABSTRACT FROM AUTHOR]

An approach based on the Penman-Monteith equation was used to estimate the actual evapotranspiration from local meteorological data over non-homogeneous land cover in a Mediterranean site in the south-east of Italy, with two six month data sets from two different years of measurements (2006 and 2009). The "critical resistance" formulation was used in different forms to model the surface resistance, together with some modifications to take into account the soil moisture content. One, two, or three model parameters were estimated, one of them related to the atmospheric resistance and the others to the surface resistance, and the calibration was made by either linear regression or nonlinear minimization of a proper cost function, depending on the applicability. Two kinds of cost functions were tested, the first depending on both the latent heat flux and the difference between screen air temperature and surface radiometric temperature, and the second depending on the temperature difference only. In all cases the calculated fluxes give better results with respect to both a flux-gradient approach and a complementarity based method, that require comparable data inputs. However the calibration by the temperature differences only, that requires no turbulent flux measurements, considerably increases the statistical uncertainty of the calibration parameters. The inclusion of the soil moisture did not significantly improve the model results in the considered site. [ABSTRACT FROM AUTHOR]

The Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR) being developed for airborne measurements will offer retrievals of aerosol microphysical and optical properties from multi-angular and multi-spectral measurements of sky radiance and direct-beam sun transmittance. In this study, we assess the expected accuracy of the 4STAR-based aerosol retrieval and its sensitivity to major sources of anticipated perturbations in the 4STAR measurements. The major anticipated perturbations are (1) an apparent enhancement of sky radiance at small scattering angles associated with the necessarily compact design of the 4STAR and (2) an offset (i.e., uncertainty) of sky radiance calibration independent of scattering angle. The assessment is performed through application of the operational AERONET aerosol retrieval and constructed synthetic 4STAR-like data. Particular attention is given to the impact of these perturbations on the broadband fluxes and the direct aerosol radiative forcing. The results from this study suggest that limitations in the accuracy of 4STAR-retrieved particle size distributions and scattering phase functions have diminished impact on the accuracy of retrieved bulk microphysical parameters, permitting quite accurate retrievals of properties including the effective radius (up to 10%, or 0.03), and the radiatively important optical properties, such as the asymmetry factor (up to 4%, or ±0.02) and single-scattering albedo (up to 6%, or ±0.04). Also, the obtained results indicate that the uncertainties in the retrieved aerosol optical properties are quite small in the context of the calculated fluxes and direct aerosol radiative forcing (up to 15%, or 3 W·m-2). [ABSTRACT FROM AUTHOR]

The SARS-CoV virus spreads in the atmosphere mainly in the form of aerosols. Particle air filters are widely used in indoor heating, ventilation, and air-conditioning (HVAC) systems and filtration equipment to reduce aerosol concentration and improve indoor air quality. Requirements arise to rate filters according to their mass-based filtration efficiency. The size distribution of test aerosol greatly affects the measurement results of mass-based filtration efficiency and dust loading of filters, as well as the calibration of optical instruments for fine-particle (PM2.5) mass concentration measurement. The main objective of this study was to find a new method to generate a chemically nontoxic aerosol with a similar particle size distribution to atmospheric aerosol. We measured the size distribution of aerosols generated by DEHS (di-ethyl-hexyl-sebacate), PSL (poly-styrene latex), olive oil, and 20% sucrose solution with a collision nebulizer in a wide range of 15 nm–20 μm. Individually, none of the solutions generated particles that share a similar size distribution to atmospheric aerosol. We found that the 20% sucrose solution + olive oil mixture solution (Vss:Voo = 1:2) could be used to generate a chemically nontoxic aerosol with similar particle number/volume size distribution to the atmospheric aerosol (t-test, p < 0.05). The differences in the mass-base filtration efficiency measured by the generated aerosol and the atmospheric aerosol were smaller than 2% for MERV 7, 10, 13, and 16 rated filters. The aerosol generated by the new method also performed well in the calibration of optical-principle-based PM2.5 concentration measurement instruments. The average relative difference measured by a tapered element oscillating microbalance (TEOM) and a Dusttrak Model 8530 (calibrated by aerosol generated by the new method) was smaller than 5.8% in the real-situation measurement. [ABSTRACT FROM AUTHOR]

The objective calibration method originally performed on regional climate models is applied to a fine horizontal resolution Numerical Weather Prediction (NWP) model over a mainly continental domain covering the Alpine Arc. The method was implemented on the MeteoSwiss COSMO (consortium for a small-scale modeling) model with a resolution of 0.01° (approximately 1 km). For the model calibration, five tuning parameters of the parameterization schemes affecting turbulence, soil-surface exchange and radiation were chosen. A full year was simulated, with the history of the soil included (hindcast) to find the optimal parameter value. A different year has been used to give an independent assessment of the impact of the optimization process. Although the operational MeteoSwiss model is already a well-tuned configuration, the results showed that a slight model performance gain is obtained by using the Calibration of COSMO (CALMO) methodology. [ABSTRACT FROM AUTHOR]

Commercially available low-cost air quality sensors have low accuracy. The improved accuracy of low-cost PM2.5 sensors allows the use of low-cost sensor systems to reasonably investigate PM2.5 emissions from industrial activities or to accurately estimate individual exposure to PM2.5. In this work, we developed a new PM2.5 calibration model (HybridLSTM) by combining a deep neural network (DNN) optimized in calibration problems and a long short-term memory (LSTM) neural network optimized in time-dependent characteristics to improve the performance of conventional calibration algorithms of low-cost PM sensors. The PM2.5 concentrations, temperature and humidity by low-cost sensors and gravimetric-based PM2.5 measuring instrument were sampled for a sufficiently long time. The proposed model was compared with benchmarks (multiple linear regression model (MLR), DNN model) and low-cost sensor results. The gravimetric measurements were used as reference data to evaluate sensor accuracy. For root-mean-square error (RMSE) for PM2.5 concentrations, the proposed model reduced 41–60% of error when compared with the raw data of low-cost sensors, reduced 30–51% of error when compared with the MLR model and reduced 8–40% of error when compared with the MLR model. R2 of HybridLSTM, DNN, MLR and raw data were 93, 90, 80 and 59%, respectively. HybridLSTM showed the state-of-the-art calibration performance for a low-cost PM sensor. In other words, the proposed ML model has state-of-the-art calibration performance among the tested calibration algorithms. [ABSTRACT FROM AUTHOR]

The implementation of statistical postprocessing of ensemble forecasts is increasingly developed among national weather services. The so-called Ensemble Model Output Statistics (EMOS) method, which consists of generating a given distribution whose parameters depend on the raw ensemble, leads to significant improvements in forecast performance for a low computational cost, and so is particularly appealing for reduced performance computing architectures. However, the choice of a parametric distribution has to be sufficiently consistent so as not to lose information on predictability such as multimodalities or asymmetries. Different distributions are applied to the postprocessing of the European Centre for Medium-range Weather Forecast (ECMWF) ensemble forecast of surface temperature. More precisely, a mixture of Gaussian and skewed normal distributions are tried from 3- up to 360-h lead time forecasts, with different estimation methods. For this work, analytical formulas of the continuous ranked probability score have been derived and appropriate link functions are used to prevent overfitting. The mixture models outperform single parametric distributions, especially for the longest lead times. This statement is valid judging both overall performance and tolerance to misspecification. [ABSTRACT FROM AUTHOR]

As low-cost sensors have become ubiquitous in air quality measurements, there is a need for more efficient calibration and quantification practices. Here, we deploy stationary low-cost monitors in Colorado and Southern California near oil and gas facilities, focusing our analysis on methane and ozone concentration measurement using metal oxide sensors. In comparing different sensor signal normalization techniques, we propose a z-scoring standardization approach to normalize all sensor signals, making our calibration results more easily transferable among sensor packages. We also attempt several different physical co-location schemes, and explore several calibration models in which only one sensor system needs to be co-located with a reference instrument, and can be used to calibrate the rest of the fleet of sensor systems. This approach greatly reduces the time and effort involved in field normalization without compromising goodness of fit of the calibration model to a significant extent. We also explore other factors affecting the performance of the sensor system quantification method, including the use of different reference instruments, duration of co-location, time averaging, transferability between different physical environments, and the age of metal oxide sensors. Our focus on methane and stationary monitors, in addition to the z-scoring standardization approach, has broad applications in low-cost sensor calibration and utility. [ABSTRACT FROM AUTHOR]

Short-lived and intense rainfall is common in Hong Kong during the wet season and often causes disruption to daily lives. As global numerical weather prediction (NWP) models are progressively improved, representation of sub-synoptic or mesoscale systems associated with intense rainfall is better parametrized and resolved than ever before, but their quantitative precipitation forecasts (QPFs) still tend to underestimate the magnitude of intense rainfall. This study calibrated model QPFs over the region of Hong Kong by two frequency-matching methods. In both methods, conversion schemes between the direct model output (DMO) and calibrated forecasts were first established by matching the cumulative distribution to that of the observed data. The "Adaptive Table" method updated the conversion scheme whenever the latest observation fell out of its expected range in the existing scheme, whereas the "Sliding Window" method reconstructed the conversion scheme using data from the most recent two years. The calibration methods had been verified against actual rainfall events with different thresholds, and it was found that both methods could improve model performance for moderate and heavy rainfall in short-range forecasts with similar effectiveness. They were also able to reduce the systematic bias of precipitation forecasts for significant rainfall throughout the verification period. [ABSTRACT FROM AUTHOR]

Continued developments in instrumentation and modeling have driven progress in monitoring methane (CH4) emissions at a range of spatial scales. The sites that emit CH4 such as landfills, oil and gas extraction or storage infrastructure, intensive livestock farms account for a large share of global emissions, and need to be monitored on a continuous basis to verify the effectiveness of reductions policies. Low cost sensors are valuable to monitor methane (CH4) around such facilities because they can be deployed in a large number to sample atmospheric plumes and retrieve emission rates using dispersion models. Here we present two tests of three different versions of Figaro® TGS tin-oxide sensors for estimating CH4 concentrations variations, at levels similar to current atmospheric values, with a sought accuracy of 0.1 to 0.2 ppm. In the first test, we characterize the variation of the resistance of the tin-oxide semi-conducting sensors to controlled levels of CH4, H2O and CO in the laboratory, to analyze cross-sensitivities. In the second test, we reconstruct observed CH4 variations in a room, that ranged from 1.9 and 2.4 ppm during a three month experiment from observed time series of resistances and other variables. To do so, a machine learning model is trained against true CH4 recorded by a high precision instrument. The machine-learning model using 30% of the data for training reconstructs CH4 within the target accuracy of 0.1 ppm only if training variables are representative of conditions during the testing period. The model-derived sensitivities of the sensors resistance to H2O compared to CH4 are larger than those observed under controlled conditions, which deserves further characterization of all the factors influencing the resistance of the sensors. [ABSTRACT FROM AUTHOR]

The accuracy and utility of low-cost PM2.5 sensors was evaluated for measuring spatial variation and modeling population exposure to PM2.5 pollution from domestic wood-heating (DWH) in Armidale, a regional town in New South Wales (NSW), Australia, to obtain estimates of health costs and mortality. Eleven 'PurpleAir' (PA) monitors were deployed, including five located part of the time at the NSW government station (NSWGov) to derive calibration equations. Calibrated PA PM2.5 were almost identical to the NSWGov tapered element oscillating microbalance (TEOM) and Armidale Regional Council's 2017 DustTrak measurements. Spatial variation was substantial. National air quality standards were exceeded 32 times from May–August 2018 at NSWGov and 63 times in one residential area. Wood heater use by about 50% of households increased estimated annual PM2.5 exposure by over eight micrograms per cubic meter, suggesting increased mortality of about 10% and health costs of thousands of dollars per wood heater per year. Accurate real-time community-based monitoring can improve estimates of exposure and avoid bias in estimating dose-response relationships. Efforts over the past decade to reduce wood smoke pollution proved ineffective, perhaps partly because some residents do not understand the health impacts or costs of wood-heating. Real-time Internet displays can increase awareness of DWH and bushfire pollution and encourage governments to develop effective policies to protect public health, as recommended by several recent studies in which wood smoke was identified as a major source of health-hazardous air pollution. [ABSTRACT FROM AUTHOR]

The success of the Montreal Protocol in curbing increases in harmful solar ultraviolet (UV) radiation at the Earth's surface has recently been demonstrated. This study also provided evidence that the UV Index (UVI) measured by SUV-100 spectroradiometers at three Antarctic sites (South Pole, Arrival Heights, and Palmer Station) is now decreasing. For example, a significant (95% confidence level) downward trend of −5.5% per decade was reported at Arrival Heights for summer (December through February). However, it was also noted that these measurements are potentially affected by long-term drifts in calibrations of approximately 1% per decade. To address this issue, we have reviewed the chain of calibrations implemented at the three sites between 1996 and 2018 and applied corrections for changes in the scales of spectral irradiance (SoSI) that have occurred over this period (Method 1). This analysis resulted in an upward correction of UVI data measured after 2012 by 1.7% to 1.8%, plus smaller adjustments for several shorter periods. In addition, we have compared measurements during clear skies with model calculations to identify and correct anomalies in the measurements (Method 2). Corrections from both methods reduced decadal trends in UVI on average by 1.7% at the South Pole, 2.1% at Arrival Heights, and 1.6% at Palmer Station. Trends in UVI calculated from the corrected dataset are consistent with concomitant trends in ozone. The decadal trend in UVI calculated from the corrected dataset for summer at Arrival Heights is −3.3% and is significant at the 90% level. Analysis of spectral irradiance measurements at 340 nm suggests that this trend is partially caused by changes in sea ice cover adjacent to the station. For the South Pole, a significant (95% level) trend in UVI of −3.9% per decade was derived for January. This trend can partly be explained by a significant positive trend in total ozone of about 3% per decade, which was calculated from SUV-100 and Dobson measurements. Our study provides further evidence that UVIs are now decreasing in Antarctica during summer months. Reductions have not yet emerged during spring when the ozone hole leads to large UVI variability. [ABSTRACT FROM AUTHOR]

The Maïdo high-altitude observatory located in Reunion Island (21° S, 55.5° E) is equipped with the Lidar1200, an innovative Raman lidar designed to measure the water vapor mixing ratio in the troposphere and the lower stratosphere, to perform long-term survey and processes studies in the vicinity of the tropopause. The calibration methodology is based on a GNSS (Global Navigation Satellite System) IWV (Integrated Water Vapor) dataset. The lidar water vapor measurements from November 2013 to October 2015 have been calibrated according to this methodology and used to evaluate the performance of the lidar. The 2-year operation shows that the calibration uncertainty using the GNSS technique is in good agreement with the calibration derived using radiosondes. During the MORGANE (Maïdo ObservatoRy Gaz and Aerosols NDACC Experiment) campaign (Reunion Island, May 2015), CFH (Cryogenic Frost point Hygrometer) radiosonde and Raman lidar profiles are compared and show good agreement up to 22 km asl; no significant biases are detected and mean differences are smaller than 9% up to 22 km asl. [ABSTRACT FROM AUTHOR]