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Super Typhoon Saola (2309) skirted past south-southeast of Hong Kong within 40 km on the night of 1 September 2023, posing a significant storm surge threat to Hong Kong. Given the close proximity of Saola with a peak intensity of about 210 km/h within 300 km of Hong Kong, a close call of the "super typhoon direct-hit" scenario, this case provides valuable insights from a hindcast review of storm surge forecasts and warning operation using the Sea, Lake and Overland Surges from Hurricanes (SLOSH) model, which is the operational storm surge model adopted by the Hong Kong Observatory (HKO). The performance of the HKO's PRobabilistic Inundation Map Evaluation System (PRIMES) using both statistical and model ensemble approaches was also reviewed in this paper. Saola was a challenging case for operational forecasting of a compact TC structure with changes in storm size and intensity when it came close to Hong Kong. With major observations of storm structure using weather radar and dense automatic weather station, tide gauge and water level gauge networks, the high sensitivity of storm surge forecasts to the storm size parameter and the distance of closest approach was clearly revealed in the case of Saola. Even with a circularly symmetric TC parametric model like SLOSH, the hindcast review results illustrated that the model outputs were reasonably accurate during the closest approach of Saola given an accurate storm size and distance of closest approach were input, and using a highly computationally efficient storm surge model made it possible for the nowcasting of storm surges to handle compact and intense TC direct-hit cases in operational TC forecasting. Taking a nowcasting approach not only helps provide more reliable storm tide forecasts, but also facilitates the formulation of a better warning strategy when making final-call decisions in emergency response actions, based on the more frequent real-time analysis of TC position, intensity and storm size and the more accurate prediction of these parameters. A nowcasting workflow for storm surge operation was proposed in this paper. [ABSTRACT FROM AUTHOR]

The ability of the one-dimensional lake model FLake to represent the mixolimnion temperatures for tropical conditions was tested for three locations in East Africa: Lake Kivu, Lake Tanganyika's northern and southern basins. Meteorological observations from surrounding Automatic Weather Stations were corrected and used to drive FLake, whereas a comprehensive set of water temperature profiles served to evaluate the model at each site. Careful forcing data correction and model configuration allowed to reproduce the observed mixed layer seasonality at Lake Kivu and Lake Tanganyika (northern and southern basins), with correct representation of both the mixed layer depth and temperature structure. At Lake Kivu, mixolimnion temperatures predicted by FLake were found sensitive both to minimal variations in the external parameters (lake depth and water transparency) as to small changes in the meteorological driving data, in particular wind velocity. In each case, small modifications may already lead to a regime switch from the correctly represented seasonal mixed layer deepening to either completely mixed (down to the model lake bottom) or permanently stratified (from ~10m downwards) conditions. In contrast, model temperatures are found robust close to the surface, with acceptable predictions of near-surface water temperatures even when the seasonal mixing regime is not reproduced. FLake can thus be a suitable tool to parameterize tropical lake water surface temperatures within atmospheric predictionmodels, but may be less appropriate, in its current form, to study complex limnological processes within tropical lakes. Furthermore, a study of different initial conditions showed that for tropical lakes lacking reliable initial data, a fully mixed, artificially warm initialisation is to be preferred, but only if the model is allowed to spin up until convergence is reached. Finally, FLake was used to attribute the seasonal mixing cycleat Lake Kivu to variations in the near-surface meteorological conditions. It was found that the annual mixing down to 60m during the main dry season is primarily due to enhanced lake evaporation and secondarily due to the decreased incoming long wave radiation, both causing a significant heat loss from the lake surface and associated mixolimnion cooling. [ABSTRACT FROM AUTHOR]

Background: Nature-based solutions (NBS) in urban areas offer an opportunity to improve environmental conditions and to reduce CO2 emissions towards establishing climate-neutral cities in the next few decades. Furthermore, the implementation of NBSs—vertical or horizontal green infrastructures on public facilities—could in particular improve both climate, including outdoor thermal conditions on a micro-scale (especially during the summer season) and the energy demand of buildings as well as save heating energy during the winter period. Results: On both selected buildings, extensive green roofs were implemented as an NBS intervention. The analysed data were obtained using the monitoring systems (from 2019 to 2022) installed on two public buildings in Novi Sad (Republic of Serbia) and Osijek (Republic of Croatia), with a focus on climate/bioclimate characteristics and thermal transmission capacities. Four automatic weather stations (AWS) were used for microclimate monitoring, along with the heat flow meter (HFM) method, to measure the alterations in the thermal transmittance (U value) of a flat concrete roof before and after energy refurbishment and the installation of a green roof. The outcomes of this study show that the air temperatures (Ta) and globe temperatures (Tg) near the green roof are lower by 0–3 °C for Ta and by 0–16.5 °C for Tg than the values captured by the AWSs at other locations. An even more interesting fact is that the green roof has a constant cooling potential during tropical nights, and based upon this research, the cooling value is around 2 °C for Tg (the Ta value is not distinct). The thermal transmittance results show that more savings can be achieved by applying a green roof with an 8 cm thick substrate: U values decreased by 50–69%, as measured by two different heat flux sensors. Conclusions: Nature-based solutions, such as the implementation of an extensive green roof, have positive effects on diverse aspects of urban environments and building energy savings, which are particularly evident in extreme seasons, both summer and winter. Applying the proposed monitoring and assessment system could help local communities in their efforts to reduce carbon-based emissions. This paper provides a good example of the implementation of NBSs on a local- and a micro-scale. [ABSTRACT FROM AUTHOR]

While previous work on the climatology of Northern China has focused on mean wind speed, wind gusts have received comparatively less attention but are equally important to various users. In this paper, an observed hourly maximum gust wind speeds (HMGS) dataset across North China has been created by using time series from 174 meteorological stations. The dataset offers superior quality, high spatiotemporal near-surface HMGS series for North China spanning from 2015 to 2022. The objective of this study is first to improve our understanding of the spatiotemporal gusts climatology in North China by analyzing the observed gust data. Second, we aim to supplement the observational data by using gust analysis and forecast data with a high spatial–temporal resolution from model simulations. The spatial characteristics of the seasonal cycle of the simulated analysis of mean HMGS and the performance in predicting gusts based on the geographical locations and elevations of the validation stations were investigated by comparing it with the observations. Results indicate the following: 1) Wind direction and intensity are affected by the terrain and climate conditions of different weather stations. Stations situated along the Bohai Bay coastal region and at higher-elevation areas of North China exhibit a higher mean HMGS than those located in the coastal and inland plains. 2) The probability density function curves for wind speed and wind direction exhibit notable variations across different elevation intervals. The contribution of moderate and strong gust wind speeds increases gradually with increasing altitude, while the gust directions in mountainous areas exhibit relatively consistent patterns due to the increased exposure to synoptic-scale forcing at higher elevations. 3) The nowcasting prediction system analysis of mean HMGS provides a higher horizontal resolution that is capable of capturing the contrasts between land and sea, as well as the influence of high HMGS associated with large-scale circulations in high-elevation regions. Significance Statement: The purpose of this study is to better understand the spatiotemporal gust climatology in North China and the performance of the model-simulated gust analysis and forecast data. This is important because gusts conditions differ due to varying topographic and climatic conditions of different weather stations. Our results provide a valuable insight into the climatological variations of HMGS, their drivers, and identify the deficiencies in the model-simulation gusts. [ABSTRACT FROM AUTHOR]

Mesoscale model simulations are presented of a westerly föhn event over the Antarctic Peninsula mountain ridge and onto the Larsen C Ice Shelf, just south of the recently collapsed Larsen B Ice Shelf. Aircraft observations showed the presence of föhn jets descending near to the ice shelf surface with maximum wind speeds at 250-350m in height. Surface flux measurements suggested that melting was occurring. Simulated profiles of wind speed, temperature and wind direction were very similar to the observations. However, the good match only occurred at a model time corresponding to ~ 9 h before the aircraft observations were made since the model föhn jets died down after this. Through comparison to an Automatic Weather Station (AWS) on the ice shelf surface (east side of the ridge) this was attributed to problems with the time evolution of the large scale meteorology of the analysis used to nudge the upper levels of the model. Timing issues aside, the otherwise good comparison between the model and ob servations gave confidence that the model flow structure was similar to that in reality. Details of the model jet structure are explored and discussed and are found to have ramifications for the placement of AWS stations on the ice shelf in order to detect föhn flow. Cross sections of the flow are also examined and were found to compare well to the aircraft measurements. Gravity wave breaking above the mountain crest likely created a situation similar to hydraulic flow and allowed föhn flow and ice shelf surface warming to occur despite strong upwind blocking, which in previous studies of this region has generally not been considered. The surface energy budget of the model during the melting periods showed that the net downwelling shortwave surface flux was the largest contributor to the melting en25 ergy, indicating that the cloud clearing effect of föhn events is likely to be the most important factor for increased melting relative to non-föhn days. The results also indicate that the warmth of the föhn jets through sensible heat flux may not be critical in causing melting beyond boundary layer stabilization effects (which may help to prevent cloud cover and suppress loss of heat by convection) and are actually cancelled by latent heat flux effects (snow ablation). It was found that ground heat flux was likely to be an important factor when considering the changing surface energy budget for the southern regions of the ice shelf as the climate warms. [ABSTRACT FROM AUTHOR]

A prototype of an autonomous system for the retrieval of oceanographic, wave, and meteorologic data was installed and tested in May 2021 on a Portuguese research vessel navigating on the Atlantic Ocean. The system was designed to be installed in fishing vessels that could operate as a distributed network of ocean data collection. It consists of an automatic weather station, a ferrybox with a water pumping system, an inertial measurement unit, a GNSS unit, an onboard desktop computer, and a wave estimator algorithm for wave spectra estimation. Among several parameters collected by this system's sensors are the air temperature, barometric pressure, humidity, wind speed and direction, sea water temperature, pH, dissolved oxygen, salinity, chlorophyll-a, roll, pitch, heave, true heading, and geolocation of the ship. This paper's objectives are the following: (1) describe the autonomous prototype; and (2) present the data obtained during a full-scale trial; (3) discuss the results, advantages, and limitations of the system and future developments. Meteorologic measurements were validated by a second weather station onboard. The estimated wave parameters and wave spectra showed good agreement with forecasted data from the Copernicus database. The results are promising, and the system can be a cost-effective solution for voluntary observing ships. [ABSTRACT FROM AUTHOR]

The dryline, often associated with the development of severe storms in the Southern Great Plains of the United States of America, is a boundary layer phenomenon that occurs when a warm and moist air mass from the Gulf of Mexico meets a hot and dry air mass from the southwest desert area. An accurate knowledge of the water vapor spatio-temporal variability in the lower part of the atmosphere is crucial for a better understanding of the evolution of the dryline. The tropospheric refractivity, directly related to water vapor content, is a proxy for the water vapor content of the troposphere. It has already been demonstrated that the refractivity and the refractivity vertical gradient can be jointly estimated from radar phase measurements. In fact, it has been shown that using kriging interpolation techniques, accurate refractivity maps within the coverage area of the radar can be obtained with high temporal resolution. In this paper, a detailed analysis of the time series of radar-based refractivity maps obtained during a dryline that occurred on the afternoon of 22 May 2002 during the International H 2 O Project ( I H O P _ 2002 ) is presented. Comparisons between the time series of radar refractivity maps, obtained with the NCAR S-Pol radar, and the refractivity measurements derived from automatic ground-based weather stations and the AERI instrument, placed at different locations within the coverage area of the NCAR S-Pol radar, demonstrate the accuracy of radar refractivity estimates even for highly variable conditions, both in time and space, in the troposphere. Correlation coefficients higher than 0.95 are obtained in all weather station locations. Regarding the RMSE, errors less than 6 N-units are obtained for all cases, being even as low as 2.92 N-units at some locations. [ABSTRACT FROM AUTHOR]

This paper compares the daily sunshine duration (SD) measured by the Campbell–Stokes sunshine recorder (CS) with three automatic instruments (Kipp & Zonen pyranometers (CM6 B), pyrheliometer (CHP1) and CSD3 sunshine recorders). The comparisons are essential, since recently, automatic sunshine duration sensors have been replacing the manual measurements, affecting, therefore, the continuity of the CS time series. The comparisons were applied to the daily SD data of three synoptic stations (Larnaca A/P, Pafos A/P and Athalassa) where parallel measurements of manual and automatic instruments are carried out during the periods 2009–2012 and 2012–2015. The CSD3 measurements were obtained from two automatic weather stations (AWSs) which are close to the synoptic station of Larnaca A/P. The comparisons referred to the daily and monthly sums of sunshine duration, as well as to their statistical distributions and their relationships. The daily differences in the pairs of sunshine recorders installed at the same location are mostly within the range of −1.0 to +1.0 h. At Larnaca, in the period 2009–2012, the pyranometric method's (Gl) values were higher than the CS in the summer months. Pafos and Athalassa showed higher values of CS compared to Gl and the pyrheliometric method (Pr), respectively, almost throughout the year. Generally, the annual average daily differences between the different pairs range between 0.3 and 0.5 h. Regression equations were determined allowing the estimation of daily SD under different sky conditions. The coefficient of determination (R2) reached the values of 0.91 and 0.96 under 'all sky' conditions, and between 0.80 and 0.91 under 'clear' sky conditions at the three locations. [ABSTRACT FROM AUTHOR]

Current weather monitoring systems often remain out of reach for small-scale users and local communities due to their high costs and complexity. This paper addresses this significant issue by introducing a cost-effective, easy-to-use local weather station. Utilizing low-cost sensors, this weather station is a pivotal tool in making environmental monitoring more accessible and user-friendly, particularly for those with limited resources. It offers efficient in-site measurements of various environmental parameters, such as temperature, relative humidity, atmospheric pressure, carbon dioxide concentration, and particulate matter, including PM 1, PM 2.5, and PM 10. The findings demonstrate the station's capability to monitor these variables remotely and provide forecasts with a high degree of accuracy, displaying an error margin of just 0.67%. Furthermore, the station's use of the Autoregressive Integrated Moving Average (ARIMA) model enables short-term, reliable forecasts crucial for applications in agriculture, transportation, and air quality monitoring. Furthermore, the weather station's open-source nature significantly enhances environmental monitoring accessibility for smaller users and encourages broader public data sharing. With this approach, crucial in addressing climate change challenges, the station empowers communities to make informed decisions based on real-time data. In designing and developing this low-cost, efficient monitoring system, this work provides a valuable blueprint for future advancements in environmental technologies, emphasizing sustainability. The proposed automatic weather station not only offers an economical solution for environmental monitoring but also features a user-friendly interface for seamless data communication between the sensor platform and end users. This system ensures the transmission of data through various web-based platforms, catering to users with diverse technical backgrounds. Furthermore, by leveraging historical data through the ARIMA model, the station enhances its utility in providing short-term forecasts and supporting critical decision-making processes across different sectors. [ABSTRACT FROM AUTHOR]

Forests strongly modify the accumulation, metamorphism and melting of snow in midlatitude and high-latitude regions. Recently, snow routines in hydrological and land surface models were improved to incorporate more accurate representations of forest snow processes, but model intercomparison projects have identified deficiencies, partly due to incomplete knowledge of the processes controlling snow cover in forests. The Snow Under Forest (SnoUF) project was initiated to enhance knowledge of the complex interactions between snow and vegetation. Two field campaigns, during the winters 2016–2017 and 2017–2018, were conducted in a coniferous forest bordering the snow study at Col de Porte (1325 m a.s.l., French Alps) to document the snow accumulation and ablation processes. This paper presents the field site, the instrumentation and the collection and postprocessing methods. The observations include distributed forest characteristics (tree inventory, lidar measurements of forest structure, subcanopy hemispherical photographs), meteorology (automatic weather station and an array of radiometers), snow cover and depth (snow pole transect and laser scan) and snow interception by the canopy during precipitation events. The weather station installed under dense canopy during the first campaign has been maintained since then and has provided continuous measurements throughout the year since 2018. Data are publicly available from the repository of the Observatoire des Sciences de l'Univers de Grenoble (OSUG) data center at 10.17178/SNOUF.2022 (Sicart et al., 2022). [ABSTRACT FROM AUTHOR]

The growing relevance of the use of unmanned aerial vehicles (UAVs) for studying turbulence in the atmospheric boundary layer (ABL) is associated with the need to obtain new observational data at different heights in the boundary layer, as well as over heterogeneous landscapes. Such new data is needed for developing turbulence models for spatially inhomogeneous and unsteady conditions. This paper describes the new Tsimlyanin UAV and its meteorological payload, developed specifically for studying the turbulent structure of the ABL. Tsimlyanin differs from similar vehicles by its hybrid scheme, which combines the possibility of vertical take-off and landing and horizontal flight in airplane mode. The meteorological payload includes specially designed measuring devices, namely, a multihole air pressure probe and a low-inertia resistance thermometer. Also, analog-to-digital converters for several sensors, as well as the onboard data-acquisition system, are newly developed. This paper presents the results of measurements carried out using the UAV during test flights in Tsimlyansk in August 2020. A good agreement between UAV observations with respect to vertical profiles of air temperature, wind speed and direction, dispersion of vertical speed with the data of observations of other complexes (acoustic anemometers, an automatic meteorological station, sodars, and a temperature profiler) was obtained. The results demonstrate the high potential of this UAV for studying turbulence in the ABL. [ABSTRACT FROM AUTHOR]

This paper introduces a unique multiyear dataset and the monitoring capability of the PANDA automatic weather station network, which includes 11 automatic weather stations (AWSs) across the Prydz Bay–Amery Ice Shelf–Dome A area from the coast to the summit of the East Antarctic Ice Sheet. The ∼ 1460 km transect from Zhongshan to Panda S follows roughly along ∼ 77 ∘ E longitude and covers all geographic units of East Antarctica. Initial inland observations, near the coast, started in the 1996/97 austral summer. All AWSs in this network measure air temperature, relative humidity, air pressure, wind speed and wind direction at 1 h intervals, and some of them can also measure firn temperature and shortwave/longwave radiation. Data are relayed in near real time via the Argos system. The data quality is generally very reliable, and the data have been used widely. In this paper, we firstly present a detailed overview of the AWSs, including the sensor characteristics, installation procedure, data quality control protocol and the basic analysis of each variable. We then give an example of a short-term atmospheric event that shows the monitoring capacity of the PANDA AWS network. This dataset, which is publicly available, is planned to be updated on a near-real-time basis and should be valuable for climate change estimation, extreme weather events diagnosis, data assimilation, weather forecasting, etc. The dataset is available at 10.11888/Atmos.tpdc.272721 (Ding et al., 2022b). [ABSTRACT FROM AUTHOR]

Thunderstorm gusts are a common form of severe convective weather in the warm season in North China, and it is of great importance to correctly forecast them. At present, the forecasting of thunderstorm gusts is mainly based on traditional subjective methods, which fails to achieve high-resolution and high-frequency gridded forecasts based on multiple observation sources. In this paper, we propose a deep learning method called Thunderstorm Gusts TransU-net (TG-TransUnet) to forecast thunderstorm gusts in North China based on multi-source gridded product data from the Institute of Urban Meteorology (IUM) with a lead time of 1 to 6 h. To determine the specific range of thunderstorm gusts, we combine three meteorological variables: radar reflectivity factor, lightning location, and 1-h maximum instantaneous wind speed from automatic weather stations (AWSs), and obtain a reasonable ground truth of thunderstorm gusts. Then, we transform the forecasting problem into an image-to-image problem in deep learning under the TG-TransUnet architecture, which is based on convolutional neural networks and a transformer. The analysis and forecast data of the enriched multi-source gridded comprehensive forecasting system for the period 2021–23 are then used as training, validation, and testing datasets. Finally, the performance of TG-TransUnet is compared with other methods. The results show that TG-TransUnet has the best prediction results at 1–6 h. The IUM is currently using this model to support the forecasting of thunderstorm gusts in North China. [ABSTRACT FROM AUTHOR]

This Special Issue of Remote Sensing, which is the third in a series entitled "Remote Sensing of Precipitation", comprises a collection of ten papers devoted to remote sensing applications for measuring precipitation; these include new satellite technologies for the remote sensing of precipitation, the validation of satellite-based precipitation estimates using rain gauge measurements and surface radar estimates, and comparisons between gridded precipitation data. In a comprehensive analysis of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products, Hsu et al. [[4]] evaluated the capability of these precipitation estimates to represent precipitation characteristics over Luzon, which is the largest island in the Philippines. Some satellite precipitation products performed better in summer than in winter; however, the error was larger in seasons with more precipitation. [Extracted from the article]

The present paper addresses investigation of modern morphodynamic processes, which lead to relief modelling on one hand and the formation of new landforms and deposits on the other. Erosional, denudational and gravitational landforms and deposits were observed in three key areas in the Zavalska Mountain (Western Srednogorie), Strazha and Lyubash (Kraishte). The aim of this study is to trace the intensity of exogenous landforming processes, to characterize the deposits and elucidate the connection between the geological base, the topography and the local hydroclimatic conditions for their genesis and dynamics. To achieve this goal, field and laboratory methods were employed, as well as data from an automatic weather station in the territory of the Breznik Municipality. Despite the short observation period of three months, the data reveal significant dynamics, rendering the selected study areas interesting for future research. [ABSTRACT FROM AUTHOR]

This paper proposes an Informer-based temperature prediction model to leverage data from an automatic weather station (AWS) and a local data assimilation and prediction system (LDAPS), where the Informer as a variant of a Transformer was developed to better deal with time series data. Recently, deep-learning-based temperature prediction models have been proposed, demonstrating successful performances, such as conventional neural network (CNN)-based models, bi-directional long short-term memory (BLSTM)-based models, and a combination of both neural networks, CNN–BLSTM. However, these models have encountered issues due to the lack of time data integration during the training phase, which also lead to the persistence of a long-term dependency problem in the LSTM models. These limitations have culminated in a performance deterioration when the prediction time length was extended. To overcome these issues, the proposed model first incorporates time-periodic information into the learning process by generating time-periodic information and inputting it into the model. Second, the proposed model replaces the LSTM with an Informer as an alternative to mitigating the long-term dependency problem. Third, a series of fusion operations between AWS and LDAPS data are executed to examine the effect of each dataset on the temperature prediction performance. The performance of the proposed temperature prediction model is evaluated via objective measures, including the root-mean-square error (RMSE) and mean absolute error (MAE) over different timeframes, ranging from 6 to 336 h. The experiments showed that the proposed model relatively reduced the average RMSE and MAE by 0.25 °C and 0.203 °C, respectively, compared with the results of the CNN–BLSTM-based model. [ABSTRACT FROM AUTHOR]

The propagation of electromagnetic waves beyond the line of sight can be caused by atmospheric ducts, which are significant concerns in the fields of radar and communication. This paper utilizes data from seven automatic weather stations and five radio-sounding stations to statistically analyze the characteristics of the atmospheric ducts in the northwest region of the South China Sea (SCS). After verifying the practicality of numerical analysis data from NCEP CFSv2 and ECMWF in studying atmospheric ducts using measured data, we analyzed the spatial–temporal distribution characteristics of the height of the regional evaporation duct and the bottom height of the elevated duct. The study found that the NCEP CFSv2 data accurately capture the evaporation duct height and duct occurrence rate in the study area, and the elevated duct bottom height calculated from ERA5 and the measured data have good consistency. The occurrence rate and height of the evaporation duct in coastal stations in the northwest of the SCS vary significantly by month, demonstrating clear monthly distribution patterns; conversely, changes in the Xisha station are minimal, indicating good temporal uniformity. For lower atmospheric ducts, the difference in occurrence rates between 00:00 and 12:00 (UTC) is negligible. The occurrence probability of elevated ducts in the Beibu Gulf area is relatively high, mainly concentrated from January to April, and the Xisha area is dominated by surface ducts without foundation layers, mainly concentrated from June to August. Monsoons play a critical role in the generation and evolution of atmospheric ducts in the northwest of the SCS, with the height of the evaporation duct increasing and the bottom height of the elevated duct decreasing after the onset of the summer monsoon. In the end, we simulated electromagnetic propagation loss under different frequencies and radiation elevation angles in various duct environments within a typical atmospheric duct structure. [ABSTRACT FROM AUTHOR]

In this paper, the data of Automatic Weather Stations (AWSs), ERA5 reanalysis, sounding, wind profile radar, and dual-polarization radar are used to study an extreme rainfall event in the south China Coast on 11 to 12 May 2022 from the aspects of thermodynamics and microphysical characteristics under the influence of low-level jets (LLJs). Results show that: (1) The extreme rainfall event can be divided into two stages: the first stage (S1) from 0000 to 0600 LST on May 12 and the second stage (S2) from 0700 to 1700 LST on the same day. During S1, the rainfall is mainly caused by the upper-level shortwave trough and the boundary layer jet (BLJ), characterized by strong upward motion on the windward side of mountains. In S2, the combined influence of the BLJ and synoptic-system-related low-level jet (SLLJ) increases the vertical wind shear and vertical vorticity, strengthening the rainstorm. In combination with the effect of topography, a warm and humid southwest flow continuously transports water vapor to farther north, resulting in a significant increase in rainfall over the study area (on the terrain's windward slope). From S1 to S2, the altitude of a divergence center in the upper air decreases obviously. (2) The rainfalls in the two stages are both associated with the mesoscale convergence line (MCL) on the surface, and the wind field from the mesoscale outflow boundary (MOB) in S1 is in the same direction as the environmental winds. Due to a small area of convergence that is left behind the MOB, convection moves eastward quickly and causes a short duration of heavy rainfall. In S2, the convergence along the MOB is enhanced, which strengthens the rainfall and leads to strong outflows, further enhancing the surface convergence near the MOB and forming a positive feedback mechanism. It results in a slow motion of convection and a long duration of heavy rainfall. (3) In terms of microphysics, the center of a strong echo in S1 is higher than in S2. The warm-rain process of the oceanic type characterizes both stages, but the convective intensity in S2 is significantly stronger than that in S1, featuring bigger drop sizes and lower concentrations. It is mainly due to the strengthening of LLJs, which makes small cloud droplets lift to melting levels, enhancing the ice phase process (riming process), producing large amounts of graupel particles and enhancing the melting and collision processes as they fall, resulting in the increase of liquid water content (LWC) and the formation of large raindrops near the surface. [ABSTRACT FROM AUTHOR]

Various methods have been developed to estimate daily crop coefficients, but their performance varies. In this paper, a comprehensive evaluation was conducted to estimate the crop coefficient of winter wheat in four growth stages based on the observed data of weighing-type lysimeters and the high-precision automatic weather station in the Wudaogou Hydrological Experimental Station from 2018 to 2019. The three methods include the temperature effect method, the cumulative crop coefficient method, and the radiative soil temperature method. Our results suggest that the performance of these methods was different in each individual growth stage. The temperature effect method was better in the emergence-branching (RMSE = 0.06, r = 0.80) and heading-maturity stages (RMSE = 0.16, r = 0.94) because the temperature is suitable for crop growth during most of these two periods. The cumulative crop coefficient method was better in the greening-jointing (RMSE = 0.16, r = 0.88) and heading-maturity stages (RMSE = 0.20, r = 0.91) because this method is closely related to crop growth, which is vigorous during these two stages. The radiative soil temperature method was better in the emergence-branching (RMSE = 0.20, r = 0.35) and branch-overwintering stages (RMSE = 0.25, r = 0.52) as the energy balance can be ensured by the relatively high level of the effective energy during these periods. By comparing the estimation accuracy indices of the three methods, we found that the temperature effect method performed the best during the emergence-branching stage (RMSE = 0.06, MAE = 0.06, r = 0.80, dIA = 0.88), branch-overwintering stage (RMSE = 0.13, MAE = 0.11, r = 0.44, dIA = 0.55), and heading-maturity stage (RMSE = 0.16, MAE = 0.13, r = 0.94, dIA = 0.97), while the cumulative crop coefficient method performed best during the greening-jointing stage (RMSE = 0.16, MAE = 0.13, r = 0.88, dIA = 0.89). Based on this result, an integrated modelling procedure was proposed by applying the best method in each growth stage, which provides higher simulation precision than any single method. When the best method was adopted in each growth stage, the estimated accuracy of the whole growth process was RMSE = 0.13, MAE = 0.09, r = 0.98, dIA = 0.99. [ABSTRACT FROM AUTHOR]

This paper presents a validation of outputs from some GCMs of the CMIP6 project when used to assess climate projection and hydrological flows at a catchment scale for the case study area of the Lombardy region (Northern Italy). The modeling chain consists of (i) a choice of climatic scenarios from 10 GCMs of the CMIP6, (ii) the application of a stochastic downscaling procedure to make projections usable at the local scale, and (iii) the use of a semi-distributed physically based hydrological model Poli-Hydro for the generation of hydrological scenarios. Data on observed precipitation and temperature were collected from automatic weather stations, and the hydrological budget of four target catchments within the study area was assessed using Poli-Hydro. An ex-post (back-casting) analysis was performed upon the control data series from the GCMs by comparing statistics of relevant climate variables and model-simulated discharges against observed counterparts during the historical period 2002–2014. Then, during 2015–2021, the goodness of projections was assessed using confidence intervals. Our results show that the accuracy of GCMs in representing regional climate is not always reflected in a credible evaluation of local hydrology. The validation of climate patterns provides somewhat poor results; thus, the interaction among climate and hydrology needs to be explored carefully to warrant the credibility of hydrological scenarios. Overall, the spatial and temporal consistency of GCM projections, as explored here climatically and hydrologically, provides a clue about their dependability for basin scale management. [ABSTRACT FROM AUTHOR]

Many existing extremal data span only a few decades that result in large uncertainty in the estimated shape parameter of the extreme hazard model. This in turn leads to potentially large bias and variance of extrapolated extreme values at high return levels. This paper illustrates a statistical method that helps obtain hazard models that produce estimates at high return levels with reduced bias and variance. This method makes use of the maximum recorded values of extremal data that are independently recorded from a number of observational sites or generated by heterogeneous mechanisms. The logarithmically transformed average recurrence interval of the maximum recorded value at a site is shown to follow the Gumbel (Type I extreme value) distribution; therefore, multiple, say m , observational sites provide a sample of size m log-transformed average recurrence intervals, each from a distinct site. The sample can be treated as a sample from a Gumbel distribution, irrespective of the underlying hazard-generating mechanisms or the statistical hazard models. Application of the method is illustrated by analysis of the extreme wind gust data from automatic weather stations in South Australia and shown to lead to significant reduction of 2.9 m/s in potential bias of the mean and a reduction of 98.1% in variance of the 500-year gust speed at Adelaide Airport. The results are compared to the specifications in the Australian standard AS/NZS 1170.2:2021 and indicate that the standard may have overestimated the wind gust hazard; hence, the specified design wind speeds may fall on the conservative side for South Australia. [ABSTRACT FROM AUTHOR]

Based on the air temperature, wind speed, humidity, air pressure, etc., of the regional automatic weather station in Chutouling Town, Jizhou from April 2019 to November 2020, and the air temperature of the microclimate observation station receiving data every 10 min in a bailing mushroom greenhouse, this paper analyzed and evaluated a BP (back propagation) neural network and stepwise regression method to establish a prediction model for the temperature in the Bailing mushroom greenhouse for different seasons. The results showed that: (1) The air temperature, wind speed, humidity and air pressure outside the shed were the main factors for building the temperature prediction model for the inside temperature, and the air temperature was the most important factor affecting the temperature inside the shed. After introducing humidity, wind speed and air pressure, the accuracy of the model was significantly improved. (2) The temperature prediction model based on the BP neural network method, for every 10 min interval in the greenhouse, for the Bailing mushroom in different seasons, was more accurate than the stepwise regression model. The simulation results of the two models had the highest accuracy in summer, followed by autumn. (3) The root means square error of the BP neural network and stepwise regression model for inside the greenhouse, simulating the daily temperature variations for different seasons, was 1.25, 1.10, 1.08, 1.31 °C and 1.29, 1.19, 1.11, 1.37 °C, respectively. The BP neural network method performed better for predicting the daily temperature variations in seasons. (4) The specifying data of high temperature (24 July 2020) and strong cold wave (31 December 2019) were selected to test the two model methods; the results showed that the simulation of the BP neural network model was better than the stepwise regression model. [ABSTRACT FROM AUTHOR]

Snow cover is a key element in the water cycle, global heat balance and in the condition of glaciers. Characterised by high temporal and spatial variability, it is subject to short- and long-term changes in climatic conditions. This paper presents a unique dataset of snow measurements on Hansbreen, an Arctic glacier in Svalbard. The dataset includes 79 archived snow profiles performed from 1989 to 2021. It presents all available observations of physical properties for snow cover, such as grain shape and size, hardness, wetness, temperature and density, supplemented with organised metadata. All data has been revised and unified with current protocols and the present International Classification for Seasonal Snow on the Ground, allowing comparison of data from different periods and locations. The information included is essential for estimations of glacier mass balance or snow depth using indirect methods, such as ground-penetrating radar. A wide range of input data makes this dataset valuable to the greater community involved in the study of snow cover evolution and modelling related to glaciology, ecology and hydrology of glacierised areas. Measurement(s) snow cover, snow stratigraphy Technology Type(s) snow pit analyses, automatic weather stations Factor Type(s) Hansbreen Sample Characteristic - Environment seasonal snow cover, cryosphere, polar environment Sample Characteristic - Location glacier, Hansbreen, Hornsund, Spitsbergen, Svalbard, Arctic [ABSTRACT FROM AUTHOR]

In the real world, precipitation data of automatic weather stations are easily influenced by direct thunderstrokes, instrument ageing, electromagnetic interference, human operation errors and other factors. When close to the observation time, if the missing automatic station data cannot be corrected in a timely fashion, the whole quality of the station data will be affected. Thus, correct handling of the missing precipitation data to maintain their integrity has important significance. In this paper, we propose a “from coarse to fine” (FCTF) neural network to fill out the missing blanks and experiments show that this method to solve the problem of meteorological data shortage is effective. [ABSTRACT FROM AUTHOR]

Short-termintense precipitation (SIP; i.e., convective precipitation exceeding 20mmh21) nowcasting is important for urban flood warning and natural hazards management. This paper presents an algorithm for coupling automatic weather station data and single-polarization S-band radar data with a graph model and a random forest for the nowcasting of SIP. Different from the pixel-by-pixel precipitation nowcasting algorithm, this algorithm takes the convective cells as the basic units to consider their interactions and focuses on multicell convective systems. In particular, the following question could be addressed: Will a multicell convective system cause SIP events in the next hour? First, a method based on spatiotemporal superposition between cells is proposed for multicell systems identification. Then, the graph model is used to represent cell physical attributes and the spatial distribution of the entire system. For each graph model, a fusion operation is used to form a 42-dimensional graph feature vector. Finally, combined with the machine learning approaches, a random forest classifier is trained with the graph feature vector to predict the precipitation. In the experiment, this algorithm achieves a probability of detection (POD) of 79.2% and a critical success index (CSI) of 68.3% with the data between 2015 and 2016 in North China. Compared with other precipitation nowcasting algorithms, the graph model and random forest could predict SIP events more accurately and produce fewer false alarms. [ABSTRACT FROM AUTHOR]

Accurate precipitation prediction can help decision makers judge the trend of climate change and formulate more effective measures, and prevent flood and drought disasters. In this paper, we propose a short‐term regional precipitation prediction model based on wind‐improved spatiotemporal convolutional network. Among them, the improved graph convolution network integrates the effects of wind direction and geographic location at past moments to capture the spatial dependence, whilst the gated recurrent unit captures the temporal dependence by learning the dynamic changes of data. The spatio‐temporal memory flow module and attention module are added to capture spatial deformation and temporal variation more accurately, thereby better matching the physical properties of precipitation. The proposed model achieves better prediction results on real data sets. Experiments show that our method is better at extracting the spatio‐temporal information of precipitation data and capturing its time dependence and spatial correlation. Plain Language Summary: Deep‐learning technology has not been fully explored in regional short‐term precipitation prediction. The traditional graph convolution neural network does not consider the practical significance of wind direction in precipitation. Therefore, we introduce a novel short‐term regional precipitation prediction model based on wind improved spatiotemporal convolution network (ASS‐TGCN). Measured data of automatic meteorological station in Jiangsu Province, China have been utilized. Compared with the comparison model, our proposed model has achieved better performance in various indicators. Key Points: Meteorological data are processed to build a data set for automatic stations in Jiangsu provinceThe improved graph convolution network takes considers effects of the wind direction at past moments and geographical location to capture the spatial correlationThe proposed model is more in line with the physical characteristics of precipitation and suitable for precipitation prediction tasks [ABSTRACT FROM AUTHOR]

This paper introduces a unique multiyear dataset and the monitoring capability of the PANDA automatic weather station network which includes eleven automatic weather stations (AWS) across Prydz Bay-Amery Ice Shelf-dome area from the coast to the summit of the East Antarctica ice sheet. The ~1460 km transect from Zhongshan to Panda S station follows roughly along ~77° E longitude and covers all geographic and climatic units of East Antarctica. Initial inland observation, near the coast, started in the 1996/1997 austral summer. All AWSs in this network measure air temperature, relative humidity, air pressure, wind speed and wind direction at 1-hour intervals, and some of them can also measure firn temperature and shortwave/longwave radiation. Data are relayed in near real-time via the ARGOS system. Data quality is generally very reliable and the data have been used widely. In this paper, we firstly present a detailed overview of the AWSs, including the sensor characteristics, installation procedure, data quality control protocol, and the basic analysis of each variable. We then give an example of a short-term atmospheric event that shows the monitoring capacity of the network. This dataset, which is publicly available, is planned to be updated on a near-real time and should be valuable for climate change estimation, extreme weather events diagnosis, data assimilation, weather forecasting, etc. The dataset is available at https://doi.org/10.11888/Atmos.tpdc.272721 (Ding et al., 2022). [ABSTRACT FROM AUTHOR]

Weather and climate conditions drive the evolution of tropical glaciers which play an important role as water reservoirs for Peruvian inhabitants in the arid coast and semi-arid Andean region. The scarcity of long-term high-quality observations over Peruvian glaciers has motivated the extensive use of reanalysis data to describe the climatic evolution of these glaciers. However, the representativeness and uncertainties of these reanalysis products over these glaciers are still poorly constrained. This study evaluates the ability of the ERA5-Land reanalysis (ERA5L) to reproduce hourly and monthly 2 m air temperature and relative humidity (T2m and Rh2m, respectively) over several Peruvian glaciers. We compared the ERA5L with data from four on-glacier automatic weather stations (AWS), whose hourly time series were completed with nearby stations, for the period January 2017 to December 2019. Results indicates a better performance of the reanalysis for T2m (r >0.80) than for Rh2m (∼0.4< r <∼0.6) in all four glaciers. Concerning the observations, both parameters show a daily cycle influenced by the presence of the glacier. This influence is more prominent during the dry months when the so-called glacier damping and cooling effects are stronger. On a monthly time scale, the ERA5L validation for both parameters are better in wet outer tropical sites (RMSE between ±0.2°C for T2m and between 3%–7% for Rh2m) rather than in dry outer tropical sites (RMSE between ±0.2°C for T2m and between 3%–7% for Rh2m). Among all sites considered in the study, the Rh2m bias is the highest in the Cavalca glacier (correlation of 0.81; RMSE 13%, MAE 11% and bias 8.3%) and the lowest in Artesonraju glacier (correlation of 0.96; RMSE 3%; MAE 2.3% and bias — 0.8%). Based on certain considerations outlined in this paper, it is appropriate to use ERA5L to characterize T2m and Rh2m conditions on Peruvian glaciers, particularly in the wet outer tropics. [ABSTRACT FROM AUTHOR]

The validation of in-orbit instrument performance requires both stability in calibration source and also calibration corrections to compensate for instrument changes. Unlike internal calibrations, the Moon offers an external source whose signal variance is predictable and nondegrading. This paper describes a method of validation using lunar observations scanning near full moon by the Clouds and the Earth's Radiant Energy System (CERES) Flight Model (FM)-1 and FM-2 aboard the Terra satellite, FM-3 and FM-4 aboard the Aqua satellite, and, as of 2012, FM-5 aboard Suomi National Polar-orbiting Partnership. Given the stability of the source, adjustments within the data set are based entirely on removing orbital effects. Lunar observations were found to require a consistent data set spanning at least two to three years in length to examine instrument stability due to the final step when lunar libration effects are addressed. Initial results show a 20% annual variability in the data set. Using this method, however, results show trends per data channel of 1.0% per decade or less for FM-1 through FM-4. Results for FM-5 are not included in this paper because a sufficient data record has not yet been collected. [ABSTRACT FROM PUBLISHER]

Fatty acids are the major compounds in olive oils, and the determination of their profile is very useful in order for the authentication of the high quality of extra virgin olive oil (EVOO) and the evaluation of its purity and traceability. This work considers the estimations of saturated (SAFA), monounsaturated (MUFA) and polyunsaturated (PUFA) fatty acids obtained by gas chromatography (GC), 13C (carbonyl, C‐16, and aliphatic carbons regions), and 1H NMR spectra. The aim of this paper is to analyze the correlation structure being between the abovementioned fatty acids estimations and some long‐run agroclimatic measurements (temperature, humidity, wind speed and direction, radiation, precipitation, and evapotranspiration), which were downloaded from the official website of the Andalusian Automatic Weather Stations (AWSs). The versatile graphical possibilities of the free software R‐project allow the design of computational programs providing interesting conclusions about the described correlations as a function of month, variety of olive, and protected designation of origin (PDO). The statistically significant correlations could determine relevant agroclimatic information to be included, in a further research, among the explanatory variables in regression models in order to improve the estimation of fatty acid profile of EVOOs, considered as a quality parameter in their authentication. Fatty acids are the major compounds in olive oils and very useful in order to the authentication of the high quality of extra virgin olive oil (EVOO). This paper analyzes the effect of agroclimatic big fata, instead of a meteorological factor, on EVOO fatty acid profile determined by GC and NMR spectra. The power of the free software R‐project and its versatile graphical possibilities allows the design of computational programs providing interesting conclusions about the described correlations as a function of month, variety of olive, and protected designation of origin (PDO). [ABSTRACT FROM AUTHOR]

This paper provides a spatial distribution characteristic of precipitation and water-logging disaster in Xi'an based on hourly precipitation at 23 automatic meteorological observatory stations and water-logging information from 2009 to 2015. The overall spatial distribution of precipitation and intensity is higher and stronger in the north and south of the city but weaker and less in the east and west. The peak precipitation in the south and north appears at 18:00, while in the north also at 20:00–21:00. There is no obvious diurnal variation in the precipitation in the east and west, and higher precipitation frequency was recorded in the east. The water-logging frequency is highest in the Baqiao District, but the water-logged area and depth are smaller and shallower. The water-logged areas are largest in Yanta, Xinchen and Beilin Districts, where frequency is less and the water are shallow. There are more water-logged sites in Lianhu and Weiyang Districts than in other regions. Precipitation intensity and underlying surface properties are the main two factors for water-logging. By using Shanghai Urban Flooding Assessment Model, the area, depth and location of water-logging were simulated through inputting the different magnitude hourly precipitation. The guidance information provided by this paper could help city planning to provide a long-term water-logging disaster management in Xi'an. [ABSTRACT FROM AUTHOR]

The negative surface mass balance of glaciers and ice caps under a warming climate impacts local ecosystems, influencing the volume and timing of water flow in local catchments, while also contributing to global sea level rise. Peripheral glaciers distinct to the Greenland ice sheet respond faster to climate change than the main ice sheet. Accurate assessment of surface mass balance depends on in-situ observations of near-surface climate and ice ablation, but very few in-situ observations of near-surface climate and ice ablation are freely available for Greenland’s peripheral glaciers. The transect of three automated weather and ablation stations on the peripheral A. P. Olsen ice cap in northeast Greenland is an example of this much needed data. The transect has been monitored since 2008, and in 2022 the old weather and ablation stations were replaced by a new standardized setup. In order to ensure comparable data quality from the old and new monitoring station setups, it was necessary to re-evaluate the data collected between 2008 and 2022. This paper presents the fully reprocessed near-surface climate and ablation data from the A. P. Olsen ice cap transect from 2008 to 2022, with a focus on data quality and the usability for ice ablation process studies. The usability and some quality issues are exemplified by using the data in an energy balance melt model for two different years. We showed that the inherent uncertainties of the data resulted in an accurate reproduction of ice ablation for just one of the two years. A transect of three automatic ablation and weather stations of this length is unique for Greenland’s peripheral glaciers and it has a broad scale of usage from input to climate reanalysis to detailed surface ablation studies. The dataset can be downloaded here: https://doi.org/10.22008/FK2/X9X9GN (Larsen and Citterio, 2023). Future refinements will be uploaded as new versions and the continuation of the transect time series are available via. [ABSTRACT FROM AUTHOR]

Copyright of Plateau Meteorology is the property of Plateau Meteorology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article presents a comprehensive hydrometeorological dataset collected over the past two decades throughout the Cordillera Blanca, Peru. The data-recording sites, located in the upper portion of the Rio Santa valley, also known as the Callejon de Huaylas, span an elevation range of 3738–4750 m a.s.l. As many historical hydrological stations measuring daily discharge across the region became defunct after their installation in the 1950s, there was a need for new stations to be installed and an opportunity to increase the temporal resolution of the streamflow observations. Through inter-institutional collaboration, the hydrometeorological network described in this paper was deployed with the goal of evaluating how progressive glacier mass loss was impacting stream hydrology, and understanding better the local manifestation of climate change over diurnal to seasonal and interannual time scales. The four automatic weather stations supply detailed meteorological observations and are situated in a variety of mountain landscapes, with one on a high-mountain pass, another next to a glacial lake, and two in glacially carved valleys. Four additional temperature and relative humidity loggers complement the weather stations within the Llanganuco valley by providing these data across an elevation gradient. The six streamflow gauges are located in tributaries to the Rio Santa and collect high-temporal-resolution runoff data. The datasets presented here are available freely from 10.4211/hs.35a670e6c5824ff89b3b74fe45ca90e0 (Mateo et al., 2021). Combined, the hydrological and meteorological data collected throughout the Cordillera Blanca enable detailed research of atmospheric and hydrological processes in tropical high-mountain terrain. [ABSTRACT FROM AUTHOR]

Accurate and high‐resolution air temperature prediction is important in many different applications. Hourly air temperature forecasting in mountainous areas is necessary and important because mountainous areas are becoming increasingly important areas of human activities. At present, scientists successfully employ numerical weather prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model, to achieve reliable forecasts. However, air temperature forecasting and modeling over complex geographical zones are still difficult tasks. The WRF model is a mesoscale model and does not adequately account for the influence of terrain on the air temperature. It is important to downscale larger‐scale models to a much finer scale. In this paper, a statistical temperature downscaling method based on geographically weighted regression (GWR) and diurnal temperature cycle (DTC) models is proposed. A statistical downscaling scheme of WRF simulation data is designed to forecast the hourly air temperature from 1‐km spatial resolution to 30 m, up to 24 hr in advance. The combined downscaling model's root‐mean‐square error (RMSE) decreased by 0.87°C at the automatic weather station (AWS) level and 0.62°C over the domain when compared to WRF simulations, and the mean absolute error (MAE) decreased by 0.71°C and 0.51°C, respectively, at these two levels. The results reveal that the combined downscaling model performs very well in correcting and downscaling the air temperature in WRF simulations in the study areas. Key Points: A statistical downscaling scheme was designed for weather research and forecasting (WRF) simulationsTo downscale the hourly air temperature from 1‐km spatial resolution to 30‐m, up to 24 hr in advance in a mountainous areaCompared to WRF simulation results, root‐mean‐square error (RMSE) and mean absolute error (MAE) of WRF‐downscaled results decreased by 0.87°C and 0.71°C, respectively, at the automatic weather station (AWS) level [ABSTRACT FROM AUTHOR]

Hydrological studies of glaciers in the Indian Himalaya are very important for understanding the melting processes and assessing the influences of climate change. The diurnal variation in the melt-runoff is controlled by the glacial drainage system. To understand the response of such systems continuous monitoring of hydrological and meteorological data is essential. In the present paper, we have studied the high-resolution data for the assessment of hydrological response of Dokriani Glacier in the upper Ganga basin of Garhwal Himalaya. The data were collected for a period of two years (2011–2012) by establishing an Automatic Weather Station (AWS) and discharge gauging site with the provision of automatic water level recorder (AWLR) near the snout of the glacier. A considerable amount of runoff has been observed at nighttime during the glacial ablation with maximum discharge in the evening and minimum in the morning. The depletion of snow from the glacier surface results into exposure of glacier surface ice and reduction in the holding capacity of water in the glacier. Such variations in the physical condition of a glacier attribute to the changes in the hydrological response of the glacier over time. The effect on the hydrological response has also been studied by analyzing diurnal hydrographs for each ablation month. The hydrological response of the glacier becomes faster with the advancement of the ablation season. Significant changes in the hydrological response of the Glacier are observed over a decade. [ABSTRACT FROM AUTHOR]

Arctic glaciers have been, and are predicted to remain, an important sea‐level rise contributor over the 20th and 21st centuries. However, multi‐annual observations of their basic meteorological characteristics, which drive melt processes, are very sparse, contributing to uncertainties in sea‐level rise projections. This paper presents a methodology to process and analyse 6 years (September 2011–September 2017) of glacier meteorology data collected by an automatic weather station on the ablation zone of a small valley glacier in Svalbard (~79°N). The study focuses on the microclimate of the study glacier and its differences with that of large glaciers in the region, namely slightly increased summer air temperature with steep near‐surface lapse rates, frequently superadiabatic, reduced wind speed and incoming shortwave radiation flux. These differences are likely related to the adjacent complex relief and non‐ice‐covered surfaces within the basin of the study glacier and, as such, pose a challenge for accurate simulation of alpine glacier microclimates in regional melt assessments in the Arctic. The processing routine applied in this paper might serve as a reference for future similar studies. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning model that integrates a convolutional neural network and a gated recurrent unit with groups of neighboring stations to accurately predict PM2.5 concentrations at 25 stations in Seoul, South Korea. The deep learning model uses observations obtained from one Korea Meteorological Administration (KMA) station, 25 National Institute of Environmental Research (NIER) stations, and 28 automatic weather stations (AWSs) throughout Seoul. To train the deep learning model, we use all available meteorological and air quality data observed between 2015 and 2017. With the trained model, we predict PM2.5 concentrations at all 25 NIER stations in Seoul for 2018. This study also proposes a geographical polygon group model that determines the optimal number of neighboring NIER stations required to increase the accuracy of PM2.5 concentration predictions at the target station. Comparing the model measures for each of the 25 monitoring sites in 2018, we find that the geographical polygon group model achieves an index of agreement of 0.82–0.89 and a Pearson correlation coefficient of 0.70–0.83. Compared to the method using only meteorological and air quality data from one target station (average IOA = 0.77) to predict PM2.5 concentrations at the 25 stations in Seoul, the proposed method using geographical correlation-based neighboring NIER stations as polygonal groups (average IOA = 0.85) improves the PM2.5 prediction accuracy by an average of about 10%. This approach, based on deep learning, can be updated to predict air pollution or air quality indices up to several days in advance. [ABSTRACT FROM AUTHOR]

The Programme for Monitoring of the Greenland Ice Sheet (PROMICE) has been measuring climate and ice sheet properties since 2007. Currently, the PROMICE automatic weather station network includes 25 instrumented sites in Greenland. Accurate measurements of the surface and near-surface atmospheric conditions in a changing climate are important for reliable present and future assessment of changes in the Greenland Ice Sheet. Here, we present the PROMICE vision, methodology, and each link in the production chain for obtaining and sharing quality-checked data. In this paper, we mainly focus on the critical components for calculating the surface energy balance and surface mass balance. A user-contributable dynamic web-based database of known data quality issues is associated with the data products at https://github.com/GEUS-Glaciology-and-Climate/PROMICE-AWS-data-issues/ (last access: 7 April 2021). As part of the living data option, the datasets presented and described here are available at 10.22008/promice/data/aws. [ABSTRACT FROM AUTHOR]

Forests strongly modify the accumulation, metamorphism and melting of snow in mid and high-latitude regions. 17 Recently, snow routines in hydrological and land surface models have been improved to incorporate more accurate 18 representations of forest snow processes, but model inter-comparison projects have identified deficiencies, partly due to incomplete knowledge of the processes controlling snow cover in forests. The Snow Under Forest (SnoUF) project was initiated to enhance knowledge of the complex interactions between snow and vegetation. Two field campaigns, during the winters 2016-17 and 2017-18, were conducted in a coniferous forest bordering the snow study at Col de Porte (1325 m a.s.l, French Alps) to document the snow accumulation and ablation processes. This paper presents the field site, instrumentation, and collection methods. The observations include distributed forest characteristics (tree inventory, LIDAR measurements of forest structure, sub-canopy hemispherical photographs), meteorology (automatic weather station and radiometers array), snow cover and depth (snow poles transect and laser scan), and snow interception by the canopy during precipitation events. The weather station installed under dense canopy during the first campaign has been maintained since then and provides continuous measurements throughout the year since 2018. Data are publicly available from the repository of the Observatoire des Sciences de l'Univers de Grenoble (OSUG) data center at http://dx.doi.org/10.17178/SNOUF.2022 (Sicart et al., 2022). [ABSTRACT FROM AUTHOR]

Accurate surface meteorological (MET) observations reported reliably and in near–real time remain a critical component of on-scene environmental observation systems. Presented is a system developed by Scripps Institution of Oceanography that allows for rapid, global deployment of ground-based weather observations to support both timely decision-making and collection of high-quality weather time series for science or military applications in austere environments. Named the Expeditionary Meteorological (XMET), these weather stations have been deployed in extreme conditions devoid of infrastructure ranging from tropical, polar, maritime, and desert environments where near continuous observations were reported. To date, over 1 million weather observations have been collected during 225 deployments around the world with a data report success rate of 99.5%. XMET had its genesis during Operation Iraqi Freedom (OIF), when the U.S. Marine Corps 3rd Marine Aircraft Wing identified an immediate capability gap in environmental monitoring of their operation area due to high spatiotemporal variability of dust storms in the region. To address the sensing gap, XMET was developed to be a portable, expendable, ruggedized, self-contained, bidirectional, weather observation station that can be quickly deployed anywhere in the world to autonomously sample and report aviation weather observations. This paper provides an overview of the XMETs design, reliability in different environments, and examples of unique meteorological events that highlight both the unit's reliability and ability to provide quality time series. The overview shows expeditionary MET sensing systems, such as the XMET, are able to provide long-term continuous observational records in remote and austere locations essential for regional spatiotemporal MET characterization. [ABSTRACT FROM AUTHOR]

Copyright of Journal of the Japanese Society of Snow & Ice / Seppyo is the property of Japanese Society of Snow & Ice and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The New York State Mesonet (NYSM) is a network of 126 standard environmental monitoring stations deployed statewide with an average spacing of 27 km. The primary goal of theNYSMis to provide high-quality weather data at high spatial and temporal scales to improve atmospheric monitoring and prediction, especially for extreme weather events. As compared with other statewide networks, the NYSM faced considerable deployment obstacles with New York's complex terrain, forests, and very rural and urban areas; its wide range of weather extremes; and its harsh winter conditions. To overcome these challenges, the NYSM adopted a number of innovations unique among statewide monitoring systems, including 1) strict adherence to international siting standards and metadata documentation; 2) a hardened system design to facilitate continued operations during extreme, high-impact weather; 3) a station design optimized to monitor winter weather conditions; and 4) a camera installed at every site to aid situational awareness. The network was completed in spring of 2018 and provides data and products to a variety of sectors including weather monitoring and forecasting, emergency management, agriculture, transportation, utilities, and education. This paper focuses on the standard network of the NYSM and reviews the network siting, site configuration, sensors, site communications and power, network operations and maintenance, data quality control, and dissemination. A few example analyses are shown that highlight the benefits of the NYSM. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) plays a critical role in influencing regional and global climate, via both thermal and dynamical mechanisms. Meanwhile, as the largest high-elevation part of the cryosphere outside the polar regions, with vast areas of mountain glaciers, permafrost and seasonally frozen ground, the TP is characterized as an area sensitive to global climate change. However, meteorological stations are biased and sparsely distributed over the TP, owing to the harsh environmental conditions, high elevations, complex topography and heterogeneous surfaces. Moreover, due to the weak representation of the stations, atmospheric conditions and the local land–atmosphere coupled system over the TP as well as its effects on surrounding regions are poorly quantified. This paper presents a long-term (2005–2016) in situ observational dataset of hourly land–atmosphere interaction observations from an integrated high-elevation and cold-region observation network, composed of six field stations on the TP. These in situ observations contain both meteorological and micrometeorological measurements including gradient meteorology, surface radiation, eddy covariance (EC), soil temperature and soil water content profiles. Meteorological data were monitored by automatic weather stations (AWSs) or planetary boundary layer (PBL) observation systems. Multilayer soil temperature and moisture were recorded to capture vertical hydrothermal variations and the soil freeze–thaw process. In addition, an EC system consisting of an ultrasonic anemometer and an infrared gas analyzer was installed at each station to capture the high-frequency vertical exchanges of energy, momentum, water vapor and carbon dioxide within the atmospheric boundary layer. The release of these continuous and long-term datasets with hourly resolution represents a leap forward in scientific data sharing across the TP, and it has been partially used in the past to assist in understanding key land surface processes. This dataset is described here comprehensively for facilitating a broader multidisciplinary community by enabling the evaluation and development of existing or new remote sensing algorithms as well as geophysical models for climate research and forecasting. The whole datasets are freely available at the Science Data Bank (10.11922/sciencedb.00103; Ma et al., 2020) and additionally at the National Tibetan Plateau Data Center (10.11888/Meteoro.tpdc.270910, Ma 2020). [ABSTRACT FROM AUTHOR]

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The shift from manual weather measurements to automation is almost inevitable. When switching to AWS (Automatic Weather Station), WMO requires parallel data testing between automatic and manual measurements to be performed. The purpose of this paper is to conduct a parallel test of AWS data using a simple statistical test that has been applied to three main weather parameters, namely temperature, pressure, humidity, rainfall, and wind direction and speed. The months of January and June were used as samples to represent the character of the wet and dry seasons in the Makassar monsoon area. The results of the analysis show that during the rainy season, only pressure and temperature are identical and homogeneous. Meanwhile, in the dry season, apart from these two parameters, humidity and wind speed are also homogeneous and rainfall is a non-homogeneous parameter in January and June. Both AWS and manual observations show that the influence of land-sea winds in Makassar is very strong. Considering that there are inhomogeneous parameters, it is highly recommended to test for a longer time, taking into account the season, the influence of other global phenomena, the effect of missing data and incorrect data testing various methods of homogeneity and characteristics in each place and their effect on forecasts. [ABSTRACT FROM AUTHOR]

Typical model evaluation strategies evaluate models over large periods of time (months, seasons, years, etc.) or for single case studies such as severe storms or other events of interest. The weather-pattern-based model evaluation technique described in this paper uses self-organizing maps to create a synoptic climatology of the weather patterns present over a region of interest, the Ross Ice Shelf for this analysis. Using the synoptic climatology, the performance of the model, the Weather Research and Forecasting Model run within the Antarctic Mesoscale Prediction System, is evaluated for each of the objectively identified weather patterns. The evaluation process involves classifying each model forecast as matching one of the weather patterns from the climatology. Subsequently, statistics such as model bias, root-mean-square error, and correlation are calculated for each weather pattern. This allows for the determination of model errors as a function of weather pattern and can highlight if certain errors occur under some weather regimes and not others. The results presented in this paper highlight the potential benefits of this new weather-pattern-based model evaluation technique. [ABSTRACT FROM AUTHOR]