Evaluation of a Wireless Solar Powered Personal Weather Station

We are evaluating dryland cotton production in Martin County, Texas, measuring cotton lint yield per unit of rainfall. Our goal is to collect rainfall data per 250 - 400 ha. Upon selection of a rainfall gauge, we realized that the cost of using, for example, a tipping bucket-type rain gauge would be too expensive and thus searched for an alternative method. We selected an all-in-one commercially available weather station;hereafter, referred to as a Personal Weather Station (PWS) that is both wireless and solar powered. Our objective was to evaluate average measurements of rainfall obtained with the PWS and to compare these to measurements obtained with an automatic weather station (AWS). For this purpose, we installed four PWS deployed within 20 m of the Plant Stress and Water Conservation Meteorological Tower that was used as our AWS, located at USDA-ARS Cropping Systems Research Laboratory, Lubbock, TX. In addition, we measured and compared hourly average values of short-wave irradiance (Rg), air temperature (Tair) and relative humidity (RH), and wind speed (WS), and calculated values of dewpoint temperature (Tdew). This comparison was done over a 242-day period (1 October 2022-31 May 2023) and results indicated that there was no statistical difference in measurements of rainfall between the PWS and AWS. Hourly average values of Rg measured with the PWS and AWS agreed on clear days, but PWS measurements were higher on cloudy days. There was no statistical difference between PWS and AWS hourly average measurements of Tair, RH, and calculated Tdew. Hourly average measurements of Rg and WS were more variable. We concluded that the PWS we selected will provide adequate values of rainfall and other weather variables to meet our goal of evaluating dryland cotton lint yield per unit rainfall.

Prototyping low-cost automatic weather stations for natural disaster monitoring

Weather events put human lives at risk mostly when people might occupy areas susceptible to natural disasters.Deploying Professional Weather Stations(PWS)in vulnerable areas is key for monitoring weather with reliable measurements.However,such professional instrumentation is notably expensive while remote sensing from a number of stations is paramount.This imposes challenges on the large-scale weather station deployment for broad monitoring from large observation networks such as in Cemaden—The Brazilian National Center for Monitoring and Early Warning of Natural Disasters.In this context,in this paper,we propose a Low-Cost Automatic Weather Station(LCAWS)system developed from Commercial Off-The-Shelf(COTS)and open-source Internet of Things(IoT)technologies,which provides measurements as reliable as a reference PWS for natural disaster monitoring.When being automatic,LCAWS is a stand-alone photovoltaic system connected wirelessly to the Internet in order to provide real-time reliable end-to-end weather measurements.To achieve data reliability,we propose an intelligent sensor calibration method to correct measures.From a 30-day uninterrupted observation with sampling in minute resolution,we show that the calibrated LCAWS sensors have no statistically significant differences from the PWS measurements.As such,LCAWS has opened opportunities for reducing maintenance costs in Cemaden's observational network.

Adaptive Coding Design of Automatic Weather Station Message for"Cloud+End"Networking

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 4000 automatic weather stations across the province show that the networking data acquisition and processing are efficient and stable.

Analysis and Treatment of Faults in the Operation of Rainfall Sensors in Automatic Weather Stations

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.

Quantum Atmospheric Biophysics: A Comparison of Four Weather Stations in India on Average Monthly Temperatures Since 1892 and Forecasts to 2150

The identification method revealed asymmetric wavelets of dynamics, as fractal quanta of the behavior of the surface air layer at a height of 2 m, according to the average monthly temperature at four weather stations in India (Srinagar, Jolhpur, New Delhi and Guvahati). For Srinagar station, the maximum for all years is observed in July, for Jolhpur and New Delhi stations it shifts to June, and for Guvahati it shifts to August. With a high correlation coefficient of 0.9659, 0.8640 and 0.8687, a three-factor model of the form was obtained. The altitude, longitude and latitude of the station are given sequentially. The hottest month for Srinagar over a period of 130 years is in July. At the same time, the temperature increased from 23.4 °C to 24.2 °C (by 3.31%). A noticeable decrease in the intensity of heat flows in June occurred at Jolhpur (over 125 years, a decrease from 36.2 °C to 33.3 °C, or by 8.71%) and New Delhi (over 90 years, a decrease from 35.1 °C to 32.4 °C, or by 7.69%). For almost 120 years, Guvahati has experienced complex climate changes: In 1902, the hottest month was July, but in 2021 it has shifted to August. The increase in temperature at various stations is considered. At Srinagar station in 2021, compared to 1892, temperatures increased in June, September and October. Guvahati has a 120-year increase in December, January, March and April. Temperatures have risen in February, March and April at Jolhpur in 125 years, but have risen in February and March at New Delhi Station in 90 years. Despite the presence of tropical evergreen forests, the area around Guvahati Station is expected to experience strong warming.

Towards Increasing Data Availability for Meteorological Services: Inter-Comparison of Meteorological Data from a Synoptic Weather Station and Two Automatic Weather Stations in Kenya

Meteorological data is useful for varied applications and sectors ranging from weather and climate forecasting, landscape planning to disaster management among others. However, the availability of these data requires a good network of manual meteorological stations and other support systems for its collection, recording, processing, archiving, communication and dissemination. In sub-Saharan Africa, such networks are limited due to low investment and capacity. To bridge this gap, the National Meteorological Services in Kenya and few others from African countries have moved to install a number of Automatic Weather Stations (AWSs) in the past decade including a few additions from private institutions and individuals. Although these AWSs have the potential to improve the existing observation network and the early warning systems in the region, the quality and capacity of the data collected from the stations are not well exploited. This is mainly due to low confidence, by data users, in electronically observed data. In this study, we set out to confirm that electronically observed data is of comparable quality to a human observer recorded data, and can thus be used to bridge data gaps at temporal and spatial scales. To assess this potential, we applied the simple Pearson correlation method and other statistical tests and approaches by conducting inter-comparison analysis of weather observations from the manual synoptic station and data from two Automatic Weather Stations (TAHMO and 3D-PAWS) co-located at KMD Headquarters to establish existing consistencies and variances in several weather parameters. Results show there is comparable consistency in most of the weather parameters between the three stations. Strong associations were noted between the TAHMO and manual station data for minimum (r = 0.65) and maximum temperatures (r = 0.86) and the maximum temperature between TAHMO and 3DPAWS (r = 0.56). Similar associations were indicated for surface pressure (r = 0.99) and RH (r > 0.6) with the weakest correlations occurring in wind direction and speed. The Shapiro test for normality assumption indicated that the distribution of several parameters compared between the 3 stations were normally distributed (p > 0.05). We conclude that these findings can be used as a basis for wider use of data sets from Automatic Weather Stations in Kenya and elsewhere. This can inform various applications in weather and climate related decisions.

Fault Analysis and Maintenance of DZZ Series of Automatic Weather Stations

The instruments of regional automatic weather station are placed outside to measure daily changes of meteorological factors. Due to common influence of sun,wind,rain and other factors,it is very easy to cause various faults and damages of the instrument. The construction of regional automatic weather station network plays an important role in improving forecast accuracy,servicing local government and ecological civilization construction,providing scientific disaster prevention and relief decision-making basis for government department. In this paper,based on daily operation situation of regional automatic weather station in Shaoyang region,combining communication,hardware and software,fault situation of collector,temperature and humidity sensor,wind sensor and rainfall sensor in automatic weather station is analyzed in detail. Moreover,some common fault cases are listed and analyzed,some troubleshooting methods are summarized,and daily maintenance measures are proposed.

Detecting Climate Change in Using Extreme Data from Two Surface Weather Stations: Case Study Valle of Comitan and La Esperanza, Chiapas, Mexico

The study area is located between the cities of Comitan (16°10'43"N and 92°04'20''W) a city with 150,000 inhabitants and La Esperanza (16°9'15''N and 91°52'5''W) a town with 3000 inhabitants. Both weather stations are 30 km from each other in the Chiapas State, México. 54 years of daily records of the series of maximum (tmax) and minimum temperatures (tmin) of the weather station 07205 Comitan that is on top of a house and 30 years of daily records of the weather station 07374 La Esperanza were analyzed. The objective is to analyze the evidence of climate change in the Comitan valley. 2.07% and 19.04% of missing data were filled, respectively, with the WS method. In order to verify homogeneity three methods were used: Standard Normal Homogeneity Test (SNHT), the Von Neumann method and the Buishand method. The heterogeneous series were homogenized using climatol. The trends of tmax and tmin for both weather stations were analyzed by simple linear regression, Sperman’s rho and Mann-Kendall tests. The Mann-Kendal test method confirmed the warming trend at the Comitan station for both variables with ZMK statistic values equal to 1.57 (statistically not significant) and 4.64 (statistically significant). However, for the Esperanza station, it determined a cooling trend for tmin and a slight non-significant warming for tmax with a ZMK statistic of -2.27 (statistically significant) and 1.16 (statistically not significant), for a significance level α = 0.05.

Quality Control System of Meteorological Real-time Data from Automatic Weather Stations in Shandong

The quality control system for meteorological real-time data from automatic weather stations in Shandong realized integration of communi- cation system and provincial quality control system, and an interaction platform which was mainly created by Web was set up. The system not only was fully guaranteed for the funning of basic business, also improved the reliability of the data.

Digital Weather Stations as a Part of Wind Power Station

This paper mainly studies Weather Stations part of the wind power station. The use of wind energy in practice is carried out using the facilities of the wind in which the kinetic energy of the windscreen flow is converted into mechanical energy wind speed, then electrical energy alternator. The effective operation of the wind turbine is dependent on the direction of the wind. Speed air density, which in turn depends on the temperature and humidity. Thus, the speed of the wind worked effectively in its composition must include the weather. Meteorological station also performs the role of prevention. When the sharp wind speed or increase wind speed above the maximum value, it sends a signal to the lock assembly wind to prevent wind turbine technology from damage. The work of the meteorological stations design as part of the Wind Energy Station is considered. The complex technical devices are used for its implementation. A set of technical means used to its implementation and designed system consists of a temperature, humidity, wind speed, wind direction and rain gauge sensors that are connected to PIC16f876A microcontroller.

Benchmarking the Robustness of Cellular Up-Links in Automatic Weather Station Networks

We present a problem for benchmarking the robustness of cellular up-links, in an automatic weather station (AWS) testbed. Based on the problem, we conduct a small-scale measurement study of robustness, where the AWS is equipped with four (4) cellular modems for weather data delivery. The effectiveness of up-links is challenging because of overlapping spatial-temporal factors such as the presence of good reflectors that lead to multi-path effects, interference, network load or other reasons. We argue that, there is a strong need for independent assessments of their robustness, to perform end-to-end network measurement. However, it is yet difficult to go from a particular measurement to an assessment of the entire network. We extensively measure the variability of Radio Signal Strength (RSSI) as link metric on the cellular modems. The RSSI is one of the important link metrics that can determine the robustness of received RF signals, and explore how they differed from one another at a particular location and instant time. We also apply the statistical analysis that quantifies the level of stability by considering the robustness, referring short-term variation, and determines good up-link in comparison to weak one. The results show that the robustness of cellular up-links exists for an unpredictable period of time and lower than one could hope. More than 50% of up-links are intermittent. Therefore, we plan to extend our work by exploring RSSI thresholds, to develop a classification scheme supporting a decision whether a link is either intermittent or not. This will help in normalizing the level of stability, to design the RSSI estimation metric for the robust routing protocol in weather data networks.

QA/QC Procedures for <i>in-Situ</i>Calibration of a High Altitude Automatic Weather Station: The Case Study of the AWS Pyramid, 5050 m asl, Khumbu Valley, Nepal

In-situ calibrations of weather stations are usually performed by positioning standard instruments close to the station under calibration and comparing the obtained results. This procedure could be useful to evaluate the proper functioning of the monitoring equipments, but do not allowed the determination of a calibration curve that allow the corrections of the acquired parameters. Thus, the development of a dedicated facility for in-situ calibration of weather stations, enabling simultaneous generation of a wide range of temperatures and pressures could offer important improvements in this framework, particularly if this facility is applied to high mountains monitoring stations where the weather stations calibrations could be very difficult. This paper will present the calibration chamber developed in the framework of the EMRP-METEOMET (Metrology for Meteorology) Project, which aims is to bring metrological traceability to high altitude meteorological instruments and through this experience will provide a general overview on the importance of the application of this methodology at different levels.

Reconstructing Missing Hourly Real-Time Precipitation Data Using a Novel Intermittent Sliding Window Period Technique for Automatic Weather Station Data

Precipitation is the most discontinuous atmospheric parameter because of its temporal and spatial variability. Precipitation observations at automatic weather stations（AWSs） show different patterns over different time periods. This paper aims to reconstruct missing data by finding the time periods when precipitation patterns are similar, with a method called the intermittent sliding window period（ISWP） technique—a novel approach to reconstructing the majority of non-continuous missing real-time precipitation data. The ISWP technique is applied to a 1-yr precipitation dataset（January 2015 to January 2016）, with a temporal resolution of 1 h, collected at 11 AWSs run by the Indian Meteorological Department in the capital region of Delhi. The acquired dataset has missing precipitation data amounting to 13.66%, of which 90.6% are reconstructed successfully. Furthermore, some traditional estimation algorithms are applied to the reconstructed dataset to estimate the remaining missing values on an hourly basis. The results show that the interpolation of the reconstructed dataset using the ISWP technique exhibits high quality compared with interpolation of the raw dataset. By adopting the ISWP technique, the root-mean-square errors（RMSEs）in the estimation of missing rainfall data—based on the arithmetic mean, multiple linear regression, linear regression,and moving average methods—are reduced by 4.2%, 55.47%, 19.44%, and 9.64%, respectively. However, adopting the ISWP technique with the inverse distance weighted method increases the RMSE by 0.07%, due to the fact that the reconstructed data add a more diverse relation to its neighboring AWSs.

2013-2020年海河流域农田水热碳通量及气象要素观测数据集

本研究以海河流域官厅水库旁农田生态系统为研究对象,采用蒸渗仪、涡动相关仪、大孔径闪烁仪和自动气象站/气象要素梯度等观测系统,开展农田生态系统多尺度通量和气象要素的长期定位观测。本数据集由多尺度通量(米,百米,公里级)和气象要素数据组成,观测项目包括生态系统净碳交换量、潜热通量/蒸散发、感热通量、空气温度、空气相对湿度、风速、风向、向下/上短波辐射、向下/上长波辐射、净辐射、大气压、降水、红外辐射温度、光合有效辐射、土壤温度、土壤水分、土壤热通量、平均土壤温度等。本数据集经过了严格的处理和质量控制,可用于研究水库消涨对周边生态系统碳水等物质和能量的循环带来的影响,也可为相关遥感模型或过程模型等研究提供有力的数据基础。

天气雷达站电磁环境影响监测研究

天气雷达站产生的超标电磁辐射会对人体造成严重的损伤。为此,提出天气雷达站电磁环境影响监测与分析。分析天气雷达站电磁影响因素,结合发射天线的方向函数,构建雷达功率密度模型。测试结果表明,对于S波、C波段、X波段,真实值与计算值呈现随距离增加而降低的趋势。对于三个波的频段,当接近天线的远场区时,计算值与真实值逐渐接近,相对误差均较小,说明模型在远场区的适用性更强;研究算法与实测值的拟合程度较高;在模型的应用中,真实反映了某市区的电磁辐射情况。

ON CHEMICAL WEATHERING OF WEATHERING CRUST AT THE GREAT WALL STATION REGION, ANTARCTICA

The chief aim of this study is to investigate the chemical weathering process of the weathering crust in Great Wall Station region of China (in Fildes Peninsula), Antarctica by the method of sedimentology.CW,SW,GW,TW,WE weathering crusts developed on volcanic clastic rock, gray aptitic basalt with tuff or basaltic bedrock. On change of minerals, geochemical behaviors of elements, migration and enrichment regularities of elements, Correlation between element geochemical behaviors, change of weathering potential of rocks in chemical weathering process are studied by us.We can see that the sequence fo weathering strengths of the abovementioned sections, from high to low, should reflected in TW, GW, CW and SWT and basical correspond with that calculated from the enrichment and differentiation indexes.

地面自动气象站数据流式处理设计与实现

针对观测密度和频次日益增加的海量地面自动气象站数据,在气象大数据云平台(天擎)中设计了基于Storm的实时流式处理,利用大规模并行处理的优势提高地面自动气象站数据的处理时效。在流式处理中,设计处理拓扑直接解码标准格式的数据消息;消息确认采用手工确认的方式,将数据解码组件锚定数据接入组件,实现每条数据的可靠处理;数据解码时进行字节校验和时间检查等,过滤异常数据;应用批量加定时的发送策略,解决海量监控信息发送气象综合业务实时监控系统(天镜)的问题;集群部署时保留部分剩余资源,有效应对单节点异常。应用效果表明:国家气象站小时数据的服务时效由全国综合气象信息共享系统(CIMISS)的175 s提高至天擎的78 s,约6×10^(4)个区域气象站小时数据的服务时效由CIMISS的5 min提高至天警的2 min,实况分析系统将数据源切换至天擎后,相同时间检索可获取的站点数量较CIMISS增加1倍。2021年12月基于Storm的流式处理与天擎一同在国省业务化运行,实现了长期稳定运行,为MICAPS4、SWAN2.0、实况分析系统等用户提供高效稳定的地面自动气象站数据。

月球空间天气探测与研究进展

稳定的天体地质构造、与地球合适的距离、无稠密大气和全球磁场,使得月球成为日地月空间天气监测和研究的天然优良实验室。回顾了国内外月球空间天气探测和研究进展,梳理了月球空间天气关键科学问题及预报应用问题,提出了建立月球空间天气监测站的构想,明确了监测站具体科学目标,并介绍了监测站三大系统构成及相互关系,包括监测系统、科学研究系统以及建模与预报系统。建立月球空间天气监测站对研究太阳爆发活动、日地月耦合关系以及月球局地环境的变化具有重要的科学意义,也有利于促进月球空间天气建模和预报技术的发展,提升未来月球科研探索任务空间环境保障能力。

基于置信规则库气象站故障检测专家系统

针对气象站故障检测的需求,研制了基于置信规则库的气象站故障检测专家系统。专家系统利用前期建立起来的气象站的气象数据库,根据当前实时气象数据,通过改进的置信规则库模型结合专家经验知识自动完成数据分析和处理、是否有故障判断并对故障程度进行等级划分。工作人员在进行现场故障检测之前,只需通过气象站传感器故障专家系统对异常传感器进行辅助诊断,确定故障等级后依据故障等级携带相应检修设备或需更换的零部件进入现场实施检查和维修。实验测试表明系统对各个设备的总体平均检测准确率从69.8%提高到85.7%,极大提升了检测的效率和准确性。

Extreme Antarctic Cold of Late Winter 2023

Extreme cold temperatures were observed in July and August 2023,coinciding with the WINFLY(winter fly-in)period of mid to late August into September 2023,meaning aircraft operations into McMurdo Station and Phoenix Airfield were adversely impacted.Specifically,with temperatures below−50℃,safe flight operation was not possible because of the risk of failing hydraulics and fuel turning to gel onboard the aircraft.The cold temperatures were measured across a broad area of the Antarctic,from East Antarctica toward the Ross Ice Shelf,and stretching across West Antarctica to the Antarctic Peninsula.A review of automatic weather station measurements and staffed station observations revealed a series of sites recording new record low temperatures.Four separate cold phases were identified,each a few days in duration and occurring from mid-July to the end of August 2023.A brief analysis of 500-hPa geopotential height anomalies shows how the mid-tropospheric atmospheric environment evolves in relation to these extreme cold temperatures.The monthly 500-hPa geopotential height anomalies show strong negative anomalies in August.Examination of composite geopotential height anomalies during each of the four cold phases suggests various factors leading to cold temperatures,including both southerly off-content flow and calm atmospheric conditions.Understanding the atmospheric environment that leads to such extreme cold temperatures can improve prediction of such events and benefit Antarctic operations and the study of Antarctic meteorology and climatology.