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How to prevent the influence of precipitation's localized and sudden characteristics is the most formidable challenge in the quality control (QC) of precipitation observations. However, with sufficiently high spatiotemporal resolution in observational data, nuanced information can aid us in accurately distinguishing between intense, localized precipitation events, and anomalies in precipitation data. China has deployed over 70,000 automatic weather stations (AWSs) that provide high spatiotemporal resolution surface meteorological observations. This study developed a new method for performing QC of precipitation data based on the high spatiotemporal resolution characteristics of observations from surface AWSs in China. The proposed QC algorithm uses the cumulative average method to standardize the probability distribution characteristics of precipitation data and further uses the empirical orthogonal function (EOF) decomposition method to effectively identify the small-scale spatial structure of precipitation data. Leveraging the spatial correlation characteristics of precipitation, partitioned EOF detection with a 0.5° spatial coverage effectively minimizes the influence of local precipitation on quality control. Analysis of precipitation probability distribution reveals that reconstruction based on the first three EOF modes can accurately capture the organized structural features of precipitation within the detection area. Thereby, based on the randomness characteristics of the residuals, when the residual of a certain observation is greater than 2.5 times the standard deviation calculated from all residuals in the region, it can be determined that the data are erroneous. Although the quality control is primarily aimed at accumulated precipitation, the randomness of erroneous data indicates that 84 continuous instances of error data in accumulated precipitation can effectively trace back to erroneous hourly precipitation observations. This ultimately enables the QC of hourly precipitation data from surface AWSs. Analysis of the QC of precipitation data from 2530 AWSs in Jiangxi Province (China) revealed that the new method can effectively identify incorrect precipitation data under the conditions of extreme weather and complex terrain, with an average rejection rate of about 5%. The EOF-based QC method can accurately detect strong precipitation events resulting from small-scale weather disturbances, thereby preventing local heavy rainfall from being incorrectly classified as erroneous data. Comparison with the quality control results in the Tianqing System, an operational QC system of the China Meteorological Administration, revealed that the proposed method has advantages in handling extreme and scattered outliers, and that the precipitation observation data, following quality control procedures, exhibits enhanced similarity with the CMAPS merged precipitation data. The novel quality control approach not only elevates the average spatial correlation coefficient between the two datasets by 0.01 but also diminishes the root mean square error by 1 mm. [ABSTRACT FROM AUTHOR]

The 18th Workshop on Antarctic Meteorology and Climate was held on 31 May through 2 June 2023. The Antarctic Meteorological Research and Data Center hosted this hybrid workshop at the Pyle Center at the University of Wisconsin-Madison in Madison, WI, USA. Global members of the Antarctic meteorological community gathered to present and discuss topics from scientific research to operational support within Antarctic meteorology and climate. Students and early career professionals chaired various presentations and discussions while all attendees engaged to share results, build collaborative plans, and discuss further developments. The main workshop topics included updates on the current Automatic Weather Station (AWS) networks, challenges, and planning concerning Antarctic forecasting and operational services, Antarctic numerical modeling systems, observational applications and research, and Antarctic community and data sources. Across six sessions, over 25 different presenters elaborated on their work in areas such as field season updates, atmospheric rivers, remote sensing, the Antarctic Mesoscale Prediction System (AMPS), and forecasting challenges due to data scarcity. Workshop discussions resulted in several key outcomes and action items regarding fieldwork impacts, exploration of field techniques, observation coverage, and communication between operations and research scientists. Future workshops will provide opportunities for continued discussion on the evolution of the AWS network and operational needs while providing a venue to promote collaboration and cooperation for Antarctic meteorology and climate activities. [ABSTRACT FROM AUTHOR]

Methods for determining evaporation from water and soil surface are considered. Monthly evaporation values were calculated for experimental grounds in the Crimean Peninsula based on measurement data from automatic meteorological complexes over 2022–2023. The estimates of evaporation from reservoir surfaces were compared with the evaporation values for individual months obtained using ground-based evaporators. Calculations by V.I. Moklyak's formula showed the best agreement with the results derived from field observations of evaporation. The relative error of evaporation estimate by the recommended method is 15%. An estimate of the monthly evaporation from soil, based on calculation methods with the use of dependences on various meteorological factors is given. [ABSTRACT FROM AUTHOR]

ABSTRACT The dataset presented in the paper contains meteorological data from four automatic weather stations (AWS) located in the central and western parts of King George Island (near Arctowski Station and Cape Lions Rump). The dataset includes daily mean, maximum and minimum values of air temperature, relative air humidity, air pressure, wind speed and daily sum of solar radiation. The measurement period ran from 2018.01.01 to 2023.12.31, but it is shorter for two of the stations. Mean values were calculated from measurements taken every 10 min. Direct measurements were used to identify extreme values. The described dataset consists offour files, each for one AWS. It is available in the PANGEA online repository under a non‐restrictive CC BY 4.0 licence for anyone after registration. Despite a strong correlation between the daily mean values of the parameters measured at certain stations, some differences between them were also noticeable. These were due to their location at different altitudes, in a place open to the sea or in a shaded place. Generally, values of wind speed, air humidity, solar radiation and pressure are similar to Arctowski during 2013–2017. The only notable distinction is that the mean annual air temperature and the mean air temperature in the winter months were higher than during 1977–1999 and 2013–2017. The data presented can be used as background for other research projects on King George Island, as well as for analysis of the meteorological conditions themselves. They may also be useful for the evaluation of the management plans of the eight Antarctic Specially Protected Areas or Antarctic Specially Managed Area no. 1 that are located on King George Island.

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control 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 study presents an assessment of the levels of air pollution emanating from natural gas combustion at the Rumaila Combined Cycle Power Plant (CCPP) in Basrah City by using a Gaussian dispersion model, with results at distance of 100, 500 and 1000 m for the pollutants CO, SO2, NO, and particulate matters (PM). Data on atmospheric stability assessments were taken from meteorological stations in Basrah Governorate that belong to the Iraqi Ministry of Agriculture. The study determined the emission rates from the five stacks of the plant and the wind speed, wind direction at stack height, and Turner-Pasquill stability classes for the wet conditions of 2023/2024. The following are the maximum pollutant concentrations emanating from the stacks: CO was 186 μg.m-3 at 100 m, 6 μg.m-3 at 500 m, and 2.5 μg.m-3 at 1000 m; SO₂ was 0.5 μg.m-3 at 100 m, 0.05 μg.m-3 at 500 m, and 0.01 μg.m-3 at 1000 m; NO was 0.07 μg.m-3 at 500 m and 0.03 μg.m-3 at 1000 m; PM was 11 μg.m-3 at 100 m, 0.4 μg.m-3 at 500 m, and 0.15 μg.m-3 at 1000 m. All of these measurements are well below national ambient air quality standards, which means that the Rumaila power plant is not locally deleterious to the air quality.

This work presents an economical IoT weather station with energy-efficient capabilities. It offers a multi-sensor platform, with balanced functionality and power optimization that ensures an extended- wireless range. The developed system features diverse sensors measuring temperature, humidity, pressure and light intensity. Employing power-saving techniques like sleep/wake cycles and controlled sampling rates makes the station economical and power-efficient. This setup allows distant observation via a web browser interface, addressing the challenge of low power and extended-range deployments of embedded systems. The adopted energy optimization strategy enabled autonomous long-range functioning, with real-time data collection and visualization. The recoded peak power consumption is only 2022 A for all the embedded operations.

Introduction: When the forecasted typhoon track differs from the numerical model's prediction, the estimated precipitation and wind from the model may not be reliable. Typically, forecasters receive numerical model forecasts with a delay of 4 h or more in calculation time. However, a more timely reference of precipitation and wind forecasts is required in an emergency with an approaching typhoon. Analyses of the observational data of typhoon-related characteristics, such as heavy rainfall and strong winds, from 1997 to 2021 revealed that their distribution areas are considerably affected by typhoon tracks. In this study, we developed a precipitation and wind prediction system based on the observational data of the typhoons that affected the Korean Peninsula. Methods: Typhoon tracks were categorized into west-coast landfalls, southeast landfalls, and those passing the Korea Strait. Each category affects the Korean Peninsula differently in terms of rainfall and wind. We devised a system that predicts these patterns based on incoming typhoon tracks. We can make forecasts by comparing the approaching typhoons to previous instances and analyzing their center, movement direction, and size. Observations from these past typhoons were averaged to produce a forecast grid for each new typhoon. Results: Our system, validated from 2019 to 2022, showed a wind speed root-mean-square error of 3.37 m/s and a precipitation accuracy index of 0.72. For comparison, traditional numerical models yielded 5.04 m/s and 0.75, respectively. This indicates that our system is comparably efficient and computationally less demanding. Discussion: Our system's strength is its ability to offer real-time typhoon forecasts, often faster than numerical models. However, its dependence on historical data limits its predictive power for atypical weather scenarios. It is essential to consider integrating ensemble models with these observations for enhanced accuracy. Since 2022, this system has been operational at the Korea Meteorological Administration, showing consistent reliability in forecasting. [ABSTRACT FROM AUTHOR]

When the weather phenomena occur in winter, such as rain and snow, freezing rain, glaze and rime, the rotating parts of the wind sensor of the automatic weather station are easy to freeze, resulting in the lack of wind direction and speed observation data. In order to solve the problem of freezing of the wind sensor, a wind sensor heating device based on the hierarchical heating technology was designed, which adopts the combined control of multi-stage heating elements to realize the adaptive starting of different levels of heating modes. By testing the performance of the wind sensor heating device and processing and analyzing the observed data of wind direction and speed, the feasibility of the application of the wind sensor heating device in the meteorological observation business was evaluated. The results show that the wind sensor with heating device plays a good role in anti freezing and operation is more stable. The wind direction and speed data integrity of the wind sensor with heating device was better than that of the wind sensor without heating device. The two wind speeds have strong correlation and correlation coefficients was above 95% . The consistency of wind direction between the two was significant and both of them were above 80%, and the consistency of wind direction in 10 min was better than that in 2 min. It could be seen that the wind sensor heating device can not only effectively reduce the number of freezing occurrences of the wind sensor and timely eliminate the icing layer, but also reduce the workload of the observer's equipment maintenance, improve the stability of the operation of the wind sensor and the continuity of the data, thus providing decision support for the application of the wind sensor heating device to the meteorological observation business. [ABSTRACT FROM AUTHOR]

Introduction: When the forecasted typhoon track differs from the numerical model’s prediction, the estimated precipitation and wind from the model may not be reliable. Typically, forecasters receive numerical model forecasts with a delay of 4 h or more in calculation time. However, a more timely reference of precipitation and wind forecasts is required in an emergency with an approaching typhoon. Analyses of the observational data of typhoon-related characteristics, such as heavy rainfall and strong winds, from 1997 to 2021 revealed that their distribution areas are considerably affected by typhoon tracks. In this study, we developed a precipitation and wind prediction system based on the observational data of the typhoons that affected the Korean Peninsula.Methods: Typhoon tracks were categorized into west-coast landfalls, southeast landfalls, and those passing the Korea Strait. Each category affects the Korean Peninsula differently in terms of rainfall and wind. We devised a system that predicts these patterns based on incoming typhoon tracks. We can make forecasts by comparing the approaching typhoons to previous instances and analyzing their center, movement direction, and size. Observations from these past typhoons were averaged to produce a forecast grid for each new typhoon.Results: Our system, validated from 2019 to 2022, showed a wind speed root-mean-square error of 3.37 m/s and a precipitation accuracy index of 0.72. For comparison, traditional numerical models yielded 5.04 m/s and 0.75, respectively. This indicates that our system is comparably efficient and computationally less demanding.Discussion: Our system’s strength is its ability to offer real-time typhoon forecasts, often faster than numerical models. However, its dependence on historical data limits its predictive power for atypical weather scenarios. It is essential to consider integrating ensemble models with these observations for enhanced accuracy. Since 2022, this system has been operational at the Korea Meteorological Administration, showing consistent reliability in forecasting.

Based on the local climate zoning theory and the observation data of hourly temperature of 22 automatic weather stations from 2012 to 2021, K-means clustering algorithm was used to analyze the daily, monthly, seasonal, annual and spatial variation characteristics of urban heat island effect in Weihai City in the past 10 years. The results showed that in recent 10 years, the average urban heat island intensity was 1.24 °C, showing a gradual weakening trend of -0.169 3 °C/10 a; the summer average heat island intensity was 0.86 °C, showing a gradual weakening trend of -0.047 5 °C/10 a. The heat island intensity had obvious diurnal variation characteristics, that is, "it was weak in the day and strong at night". In terms of seasonal variation, heat island effect was the weakest in summer, stronger in spring and autumn, and the strongest in winter. The diurnal, seasonal and annual changes of heat island intensity showed a reverse trend to those of temperature. The high-value area of urban heat island was highly consistent with human residential activity areas and industrial and commercial intensive areas, and the extension trend of heat island intensity was the same as the direction of urban development and construction. The "cold island phenomenon" in some offshore areas was related to the geographical location, terrain and the southeast monsoon trend in summer. The adverse effects of urban heat island effect can be reduced by optimizing the types and distribution of vegetation communities, rationally planning and constructing urban water body, promoting green building materials and adjusting shape design, etc. [ABSTRACT FROM AUTHOR]

Generally, solar PV Panel is constructed at fix tilted angle at different locations, such as: street light poles, rooftops, residential buildings, and canopy. The energy production from solar PV generation system is vary and influenced by site (location) and weather parameters. Solar PV generation analyzed in this research is Dual Axis Sun Tracker (DAST) Solar PV Generation. Dual axis sun tracker is a system controller to ensure solar PV panel following the sun path. Furthermore, energy production from DAST-based solar PV generation were measured and collected using energy meter as well as by using automatic weather station for the weather parameters. From these measrument data, it can be correlated the influence of weather parameters to the energy production from DAST PV generation system. The correlation between weather parameters and energy production are analyzed by statistical analysis using SPSS software and multiple linear regression models. From the results and analysis, it can be shown the amount of energy production from DAST-based solar PV Generation are influenced by weather parameters, namely: temperature, humidity, ultraviolet radiation, and solar radiation with significant level up to 98.2%.

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control 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.)

Climate change is often linked with record-breaking heavy or poor rainfall events, unprecedented storms, extreme day and night time temperatures, etc. It may have a marked impact on climate-sensitive sectors and associated livelihoods. Block-level weather forecasting is a new-fangled dimension of agrometeorological services (AAS) in the country and is getting popularized as a climate-smart farming strategy. Studies on the economic impact of these microlevel advisories are uncommon. Agromet advisory services (AAS) play a critical role as an early warning service and preparedness among the maize farmers in the Parambikulam–Aliyar Basin, as this area still needs to widen and deepen its AWS network to reach the village level. In this article, the responses of the maize farmers of Parambikulam–Aliyar Basin on AAS were analyzed. AAS were provided to early and late Rabi farmers during the year 2020–2022. An automatic weather station was installed at the farmers’ field to understand the real-time weather. Forecast data from the India Meteorological Department (IMD) were used to provide agromet advisory services. Therefore, the present study deserves special focus. Social media and other ICT tools were used for AAS dissemination purposes. A crop simulation model (CSM), DSSAT4.7cereal maize, was used for assessing maize yield in the present scenario and under the elevated GHGs scenario under climate change. Our findings suggest that the AAS significantly supported the farmers in sustaining production. The AAS were helpful for the farmers during the dry spells in the late samba (2021–2022) to provide critical irrigation and during heavy rainfall events at the events of harvest during early and late Rabi (2021–22). Published research articles on the verification of weather forecasts from South India are scanty. This article also tries to understand the reliability of forecasts. Findings from the verification suggest that rainfall represented a fairly good forecast for the season, though erratic, with an accuracy score or HI score of 0.77 and an HK score of 0.60, and the probability of detection (PoD) of hits was found to be 0.91. Verification shows that the forecasted relative humidity observed showed a fairly good correlation, with an R2 value of 0.52. These findings suggest that enhancing model forecast accuracy can enhance the reliability and utility of AAS as a climate-smart adaptation option. This study recommends that AAS can act as a valuable input to alleviate the impacts of hydrometeorological disasters on maize crop production in the basin. There is a huge demand for quality weather forecasts with respect to accuracy, resolution, and lead time, which is increasing across the country. Externally funded research studies such as ours are an added advantage to bridge the gap in AAS dissemination to a great extent.

Abstrak. Pada umumnya data suhu udara diperoleh dari hasil pengamatan pada stasiun iklim Badan Meteorologi, Klimatologi, dan Geofisika (BMKG). Metode ini dapat digunakan untuk merepresentasikan suhu udara suatu wilayah yang berada pada radius ≤ 10 km dari lokasi stasiun iklim, sehingga dibutuhkan sebuah solusi alternatif untuk mendapatkan data suhu udara yang dapat merepresentasikan wilayah yang lebih luas, salah satunya memanfaatkan dataset suhu udara berbasis penginderaan jauh. Penelitian ini bertujuan membandingkan performa sejumlah dataset suhu udara berbasis penginderaan jauh, yaitu Climatic Research Unit gridded Time Series (CRU-TS), Climatologies at High Resolution for the Earth Land Surface Areas (CHELSA), dan TerraClimate dalam mengestimasi curah hujan bulanan di Provinsi Papua Barat. Penelitian ini terdiri atas 6 (enam) tahapan utama, yaitu; (1) inventarisasi data yang bertujuan mengumpulkan dataset suhu udara dan data suhu udara hasil perekaman pada Automatic Weather Station (AWS) tahun 1996 sampai tahun 2019, (2) ekstraksi data, (3) screening data untuk mengganti nilai ekstrim dengan nilai rata-rata, (4) evaluasi data untuk membandingkan dataset dengan data AWS, (5) komparasi data untuk membandingkan performa dataset, dan (6) rekomendasi yang bertujuan untuk menentukan dataset yang paling sesuai untuk digunakan di Provinsi Papua Barat. Hasil penelitian menunjukkan bahwa CHELSA, TerraClimate, dan CRU-TS sangat akurat dalam mengestimasi suhu udara bulanan di Provinsi Papua Barat yang ditunjukkan dengan nilai RBIAS 0,1. Disamping itu CHELSA, TerraClimate, dan CRU-TS memiliki tingkat keeratan hubungan yang sedang terhadap data AWS dengan nilai r = 0,36 – 0,68. Sehingga TerraClimate, CHELSA, dan CRU-TS dapat digunakan sebagai solusi alternatif untuk mendapatkan informasi suhu udara bulanan di Provinsi Papua Barat. Comparison of Remote Sensing-Based Air Temperature Dataset in Estimating Monthly Air Temperature in West Papua Abstract. Air temperature is one of the important components in agriculture. Generally, air temperature data is obtained from climate stations of the Meteorological, Climatological, and Geophysical Agency (BMKG). These methods represented an area within a radius of 10 km from the climate station, therefore an alternative solution is needed for a larger area. The utilization of the air temperature dataset is one alternative solution. This research aims to compare Climatic Research Unit gridded Time Series (CRU-TS), Climatologies at High Resolution for the Earth Land Surface Areas (CHELSA), and TerraClimate as an air temperature dataset in estimating the monthly temperature in West Papua. The main stages in this research are data inventory, data extraction, data screening, data evaluation, data comparison, and data recommendation. The data used in this research are CRU-TS, TerraClimate, CHELSA, and local AWS data recording from 1996 to 2019. The research showed that CRU-TS, TerraClimate, and CHELSA are very accurate in estimating the monthly temperature in West Papua as indicated by RBIAS 0.1. Furthermore, CRU-TS, TerraClimate, and CHELSA have a moderate correlation with AWS data in estimating monthly air temperature with r= 0.36 - 0.68. Therefore, CRU-TS, TerraClimate, and CHELSA can be used as an alternative solution to obtain monthly air temperature information in West Papua.

A large number of automatic weather stations with different observation elements and gradient configurations are connected for operation, in order to meet the meteorological service needs of different scenes. The station density and observation frequency are encrypted to obtain observation data with higher spatial and temporal resolution. The original message with fixed element data location is the data combination of all observation elements and the maximum observation gradient of each element, which not only has higher invalid data redundancy, but also restricts the efficiency of data collection and processing, and also increases communication costs. An adaptive coding design method for the original message of automatic weather station is proposed. The embedded software coding algorithm of the weather station collector is optimized according to " plug and output" to realize intelligent networking, intelligent identification of observation elements and gradients, and dynamic flexible output of messages with variable length. The intelligent networking and business application of nearly 4 000 automatic weather stations across the province show that the networking data acquisition and processing are efficient and stable. [ABSTRACT FROM AUTHOR]

The use of Small Wind Turbine (SWT) technology is currently being developed in residential and urban areas. The application of SWT can be directly integrated into a building called the Building Integrated Wind Turbine and the industrial area is one area that has the potential to develop the SWT concept as a Building Integrated Wind Turbine. Before designing and manufacturing a wind turbine for a Building Integrated Wind Turbine, data on wind density and direction is required. In areas where wind speed and direction have not been recorded, it is necessary to study wind velocity and direction data in real-time and in real conditions for a certain period. This study aims to map wind density and wind direction with real-time properties in the foundry industry area, Ceper District, Klaten Regency, so the potential value and value of wind density energy can be known. This research method includes data retrieval carried out in real time and in real conditions using the Automatic Weather Station instrument which was carried out from August 6 to September 6, 2021. The data obtained were random data and processed using weibull distribution statistics with Microsoft Excel software. The results of this study indicate an average wind speed of 2,038 m/s in stable conditions and sufficient wind gusts with a power density value of 6,994 Watt/m2. The wind direction tends to be south-east after the wind gusts are hampered by the building profil.

Climate change is often linked with record-breaking heavy or poor rainfall events, unprecedented storms, extreme day and night time temperatures, etc. It may have a marked impact on climate-sensitive sectors and associated livelihoods. Block-level weather forecasting is a new-fangled dimension of agrometeorological services (AAS) in the country and is getting popularized as a climate-smart farming strategy. Studies on the economic impact of these microlevel advisories are uncommon. Agromet advisory services (AAS) play a critical role as an early warning service and preparedness among the maize farmers in the Parambikulam–Aliyar Basin, as this area still needs to widen and deepen its AWS network to reach the village level. In this article, the responses of the maize farmers of Parambikulam–Aliyar Basin on AAS were analyzed. AAS were provided to early and late Rabi farmers during the year 2020–2022. An automatic weather station was installed at the farmers' field to understand the real-time weather. Forecast data from the India Meteorological Department (IMD) were used to provide agromet advisory services. Therefore, the present study deserves special focus. Social media and other ICT tools were used for AAS dissemination purposes. A crop simulation model (CSM), DSSAT4.7cereal maize, was used for assessing maize yield in the present scenario and under the elevated GHGs scenario under climate change. Our findings suggest that the AAS significantly supported the farmers in sustaining production. The AAS were helpful for the farmers during the dry spells in the late samba (2021–2022) to provide critical irrigation and during heavy rainfall events at the events of harvest during early and late Rabi (2021–22). Published research articles on the verification of weather forecasts from South India are scanty. This article also tries to understand the reliability of forecasts. Findings from the verification suggest that rainfall represented a fairly good forecast for the season, though erratic, with an accuracy score or HI score of 0.77 and an HK score of 0.60, and the probability of detection (PoD) of hits was found to be 0.91. Verification shows that the forecasted relative humidity observed showed a fairly good correlation, with an R2 value of 0.52. These findings suggest that enhancing model forecast accuracy can enhance the reliability and utility of AAS as a climate-smart adaptation option. This study recommends that AAS can act as a valuable input to alleviate the impacts of hydrometeorological disasters on maize crop production in the basin. There is a huge demand for quality weather forecasts with respect to accuracy, resolution, and lead time, which is increasing across the country. Externally funded research studies such as ours are an added advantage to bridge the gap in AAS dissemination to a great extent. [ABSTRACT FROM AUTHOR]

Based on the observation data of 30 automatic weather stations in Shenzhen, climate change trends in Shenzhen in the past 10 years are analyzed. The results are shown as follows. (1) The average air temperature grows by a rate of about 1.47 ℃/(10 a) in Shenzhen from 2011 to 2020, significantly higher than that in the previous decade, suggesting that climate warming has not eased yet. (2) Due to the effect of urbanization, daily temperature ranges show a decreasing trend over much of Shenzhen, while an abnormal upward trend is witnessed for a few limited areas. (3) Surface wind speeds in Shenzhen generally show a decreasing trend, which is even faster in developed areas. (4) Average relative humidity in Shenzhen shows an increasing trend from 2011 to 2020, with the maximum annual increase at 1.33%. (5) According to the statistics of extreme rainfall data at different spans of time in the districts of Shenzhen, the growth rates of extreme rainfall and annual total precipitation are large in undeveloped areas, with the highest growth rate at 42.41 mm/a, while the maximum value of moving precipitation with duration less than 6 hours shows a decreasing trend in other areas. (6) By using long-time series data of multiple automatic weather stations, a more detailed analysis can be done of the local climate change characteristics for Shenzhen, which is a useful supplement to analyses based on national basic stations data and helpful for deeper exploration of the relationship between urbanization and climate change. [ABSTRACT FROM AUTHOR]

Sea fog often penetrates adjacent coastal areas, a process called sea fog penetration (SFP). SFP can cause traffic accidents and other economic losses. Qingdao, an international port city with a dense population, suffers from SFP originating over the Yellow Sea in the boreal spring (March–May); the process, however, is not well-studied. Based on hourly observations from buoys and automatic weather stations distributed in Qingdao and its adjacent islands, we composite SFP events to reveal their spatiotemporal features and to investigate the mechanisms involved. Results show that these SFP events often penetrate inland areas from southeast to northwest and last 5–8 h at night. We further use reanalysis data to reveal that during the daytime before SFP, strong moisture advection at 925–975 hPa brings sufficient water vapor from the Yellow Sea to Qingdao; the water vapor then transfers downward to the surface via background descending motion and turbulent mixing. The daytime anomalous moistening, together with the following diurnal cooling at night, saturates the surface atmosphere and, hence, facilitates SFP. The strength of SFP depends on the strength of daytime anomalous moistening. Considering that moistening leads SFP by about a day, we use this relationship to predict the intensity of SFP. The accuracy of predicting SFP events could reach 50–80%, which highlights the predictability of intensity of SFP in Qingdao.

Based on the structure and working principle of rain sensors in new automatic weather stations, according to the abnormal precipitation records found in the observation business, the possible faults of rain sensors were analyzed, and treatment methods were discussed. Daily maintenance and management measures were put forward to ensure the normal operation of rain sensors and improve the quality of surface meteorological observation business. [ABSTRACT FROM AUTHOR]

Monitoring and regulating water levels in oil palm swamps has an essential role in providing sufficient water for crops and conserving the land to not easily or quickly deteriorate. Presently, water level is still manual and has weaknesses, one of which is the accuracy of the data taken depending on the observer. Technology such as sensors integrated with artificial neural network is expected to observe and regulate water levels. This study aims to build a prediction model of water levels in oil palm plantations with artificial neural networks based on the rain gauge and ultrasonic sensors installed on Automatic Weather Station (AWS). The obtained results showed that the prediction model runs well with an R2 value of 0,994 and RMSE 1,16 cm. The water level prediction model in this research then tested for accuracy to prove the model's success rate. Testing the water level prediction model's accuracy in the dry season obtained an R2 value of 0,96 and an RMSE of 1,99 cm. Testing the water level prediction model's accuracy in the rainy season obtained an R2 value of 0,85 and an RMSE value of 4,2 cm.

Air temperature is one of the most important parameters for assessing and monitoring the changing weather and climate patterns. Measurement of air temperature is done from a limited number of automatic weather stations distributed in certain parts of the country, with a lot of discontinuities in space and time because of various operational constraints. Contrary to it, satellites provide seamless observations of Land Surface Temperature in space and time globally, only obscured by the cloud cover. Though Land Surface Temperature and Air Temperature have different physical interpretations, nevertheless some previous studies have shown some correlation between them, which varies according to elevation, land cover type, time of observation (day or night). Hence, land surface temperature can be one of the means to derive air temperature in the regions where the availability of automatic weather stations is limited. The present study attempts to develop a deep neural network-based model to estimate air temperature from land surface temperature based on elevation and land cover. The model results were evaluated by comparing them with ground observations using statistical indices viz., Nash and Sutcliffe's coefficient of efficiency, Legates and McCabe coefficient of efficiency, Coefficient of determination, and Index of agreement. The values obtained for these indices are 0.74, 0.85, 0.85, and 0.97 respectively, which reflect the predictive capability of the model. [ABSTRACT FROM AUTHOR]

Bureau of Meteorology automatic weather stations (AWS) are employed to record 1-min air temperature data in accord with World Meteorological Organization recommendations. These 1-min values are logged as the value measured for the last second in each minute. The Bureau explains that this is appropriate because the inherent measurement system time constant means the 1-s data are not instantaneous, but are an average smoothed over the previous 40–80s. To test this proposition in the field air temperature data were measured at 1-Hz at two Bureau AWS sites between April and June 2018. The frequency distribution of the differences between each 1-s value and the 60-s average centred on that value provided information on the overall measurement system response time constant. Expressed in terms of an e-folding measurement system response time, the data from the two measurement systems studied yielded response times in the range 50–150s, largely consistent with the Bureau's explanation.

When looking at installing a renewable energy generator, you need to be confident of the resource (solar, wind) at your particular location as this affects the energy generated at the selected site. With solar photovoltaic (PV) systems, this can be done by looking at historic data, generally from satellite readings, for the particular latitude. This will yield pretty accurate resource data. However, the wind resource is incredibly variable and depends upon the exact topology of the area. Houses, trees, and valleys all can affect the local wind resource. For this reason, wind speed data is collected at a potential wind turbine installation site. This gives real data which can be used to assess the wind speed. When installing a number of very expensive large wind turbines, one must be very confident about the wind speed data. The data must be robust and reliable and the developer will be willing to spend a lot of money on accurate industrial equipment to have lots of confidence in the data. This project intends to overcome this barrier by providing a low-cost, reusable, open-source wind speed recording unit, which can be left at high altitude in a remote location to record data and help improve the site’s wind speed assessment. We have proposed and developed a low-cost hardware module based on Arduino open source platform, which measures the meteorological data, including air, temperature, relative humidity, wind speed and solar radiation, with two options: The first is the wireless option at which it sends the measured information to Excel spreadsheet running on a PC through wireless link. The second is the data logger option at which it records the measured data to SD card as Excel file with date and time every 10 seconds.

The Indian Himalayan Region (IHR) has experienced accelerated changes in climatic seasonality and land use--land cover. Researchers envision understanding the process controls of water fluxes and their hydrogeological and ecological implications but scarce in situ data in the IHR hinders scientific research. Our work discusses an integrative technique for springshed monitoring for insights into hydrological processes and understanding integrated measurement strategies. Initial emphasis was on instrumentation and data collection from two headwater watersheds, instrumented with advanced field-based monitoring platforms in Pauri-Garhwal and Almora districts of Uttarakhand. We describe the observatory setup and discuss the pilot sites' general characteristics followed by the monitoring concept, infrastructure and initial datasets of the pilot sites. The social dimension of the spring water use and governance is also analysed in brief. Such a springshed management approach is the way forward for water security in the IHR. [ABSTRACT FROM AUTHOR]

In order to fill the gaps of the research on the data of automatic weather stations (referred to as automatic stations) not used for the climate characteristics of extremely short-time severe precipitation in Guizhou Province, the climate characteristics of extremely short-time severe precipitation in Guizhou Province were compared and analyzed based on the hourly precipitation data of the automatic stations and the national weather stations (referred to as the national stations) from April to September during 2010-2019. The results show that the average state of maximum hourly precipitation of all stations the automatic stations and the national stations) and national stations both are representative, but the data of all stations are more representative when the maximum hourly precipitation is extreme. The 99.5th quantile is the most reasonable threshold of extremely short-time severe precipitation in each station. The spatial distribution of extremely short-time severe precipitation intensity in all stations and national stations is generally that the southern region is stronger than the northern region, and the intensity values are concentrated in the range of 40 - 50 mm/h. All stations data can better reflect the distribution characteristics of < 40 and ≥ 50 mm/h. The national stations data underestimates the precipitation intensity in the southern and northeastern marginal areas of Guizhou, and slightly exaggerates the precipitation intensity in the northern part of Guizhou. The monthly and diurnal variations of the frequency of extremely short-time severe precipitation in all stations and national stations are very obvious and the variation trend is the same, but the intensity of extremely short-time severe precipitation has no obvious monthly variation characteristics. There is no significant diurnal variation in the intensity of extremely short-time severe precipitation in all stations, but the diurnal variation in the data of national stations is significant. Since the frequency of extremely short-time severe precipitation in national stations is less, the diurnal variation in the intensity of extremely short-time severe precipitation in all stations is more statistically significant. [ABSTRACT FROM AUTHOR]

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The article presents the soil temperature data obtained using two automatic weather stations located in the Mordovia State Nature Reserve (Russia). Measurements were carried out at the soil surface and at depths of 20 cm, 40 cm and 60 cm. The meteorological stations are located 15 km apart, in general, in similar landscapes. This caused similar results of meteorological measurements. Differences in the average of the daily temperature at corresponding depths are less than 2°C. The average annual temperature differs less than 0.5°C, i.е. within a sensor's error. The annual temperature trend is typical for Central Russia. And it is characterised by well warming in summer and stagnation in winter. The diurnal amplitudes are small. This can be explained by the location of both weather stations under the forest canopy and a well-developed ground vegetation cover.

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]

Automatic weather station (AWS) observations are used to select tropical cyclones (TCs) affecting the Taiwan Strait during 2007–2016. Applying these data, the influence of TCs on the Taiwan Strait is measured and TCs with weak or no impact are removed. Objective statistical properties of the remaining TCs are analysed by clustering them into three groups using the fuzzy C‐means method. Clusters are differentiated as (a) TCs affecting the Taiwan Strait but recurving at the edge of the TC alert area, (b) TCs moving straight across The Philippines and the South China Sea, and (c) TCs striking Taiwan and eastern China and recurving after landfall. Differences among the clusters include in the spatial range and duration of gales monitored by AWS, and the track, intensity, and genesis location of TCs. Also analysed are the influence of large‐scale atmospheric environments on TC motion, sea‐surface temperature responses to TCs, and the influence of El Niño‐Southern Oscillation on TC classifications. [ABSTRACT FROM AUTHOR]

Weather is affected by a complex interplay of factors, including topography, location, and time. For the prediction of temperature in Korea, it is necessary to use data from multiple regions. To this end, we investigate the use of deep neural-network-based temperature prediction model time-series weather data obtained from an automatic weather station and image data from a regional data assimilation and prediction system (RDAPS). To accommodate such different types of data into a single model, a bidirectional long short-term memory (BLSTM) model and a convolutional neural network (CNN) model are chosen to represent the features from the time-series observed data and the RDAPS image data. The two types of features are combined to produce temperature predictions for up to 14 days in the future. The performance of the proposed temperature prediction model is evaluated by objective measures, including the root mean squared error and mean bias error. The experiments demonstrated that the proposed model combining both the observed and RDAPS image data is better in all performance measures for all prediction periods compared with the BLSTM-based model using observed data and the CNN-BLSTM-based model using RDAPS image data alone.

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Abstract: One of the most dramatic indicators of climate change is the reduction in the extent and thickness of Arctic sea ice that has resulted in an increase in wind‐driven sea ice mobility. During April and May 2017, satellite observations indicated that the ice arch that forms between Nares Strait and the Lincoln Sea collapsed. Typically, this collapse occurs in July or August allowing multiyear ice to exit the Arctic through Nares Strait. Here we show that the period of the collapse was associated with the presence of a polynya in northern Nares Strait, thin ice in the Lincoln Sea, and an unusual wind regime characterized by strong northerly flow. We propose that these anomalous conditions resulted in the arch's early collapse. If the ice in the region continues to thin, early collapses may occur more frequently with implications for the regional as well as the downstream climate and ecosystems. [ABSTRACT FROM AUTHOR]