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.

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.

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.

Application of RIA Technology in WEB Platform in the Regional Automatic Meteorological Station

Started from the business reality of automatic weather station,the basic idea that the information platform(system) of regional automatic weather station was designed and realized by using RIA technology was put forward.Based on that RIA technology and the client-side development technology FLEX were expounded,'Regional Automatic Weather Station Information Platform' which had the non-refresh operation,the good user experience and data processing capability was designed and realized by introducing ArcGIS API for Flex,and the technology advantage of RIA was fully embodied.

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.

Primary Analysis of the Causes of Temperature Changes after Songjiang Observation Station Transferring

Temperatures of the old station and the new one in Songjiang were compared with each other,and data via automatic weather station instead of manual observation after transferring were also analyzed.The results showed that the average temperature,the minimum temperature,the maximum temperature and the extreme temperature in the new observation station were all higher than those in the old one.The average temperature difference of the new observation and the old one from 20:00 to 08:00 of the next day was bigger than that from 08:00 to 20:00.There were two causes of temperature changes after Songjiang station transferring:one was that two stations were affected by the surrounding environment and the nature of underlying surface,and the other was that theory of sensing part,observation time and process were different between the automatic weather station and the manual observation.

Adjustment of precipitation measurements using Total Rain weighing Sensor(TRwS)gauges in the cryospheric hydrometeorology observation(CHOICE)system of the Qilian Mountains,Northwest China

Precipitation is one of the most important indicators of climate data,but there are many errors in precipitation measurements due to the influence of climatic conditions,especially those of solid precipitation in alpine mountains and at high latitude areas.The measured amount of precipitation in those areas is frequently less than the actual amount of precipitation.To understand the impact of climatic conditions on precipitation measurements in the mountainous areas of Northwest China and the applicability of different gauges in alpine mountains,we established a cryospheric hydrometeorology observation(CHOICE)system in 2008 in the Qilian Mountains,which consists of six automated observation stations located between 2960 and 4800 m a.s.l.Total Rain weighing Sensor(TRwS)gauges tested in the World Meteorological Organization-Solid Precipitation Intercomparison Experiment(WMO-SPICE)were used at observation stations with the CHOICE system.To study the influence of climatic conditions on different types of precipitation measured by the TRwS gauges,we conducted an intercomparison experiment of precipitation at Hulu-1 station that was one of the stations in the CHOICE system.Moreover,we tested the application of transfer functions recommended by the WMO-SPICE at this station using the measurement data from a TRwS gauge from August 2016 to December 2020 and computed new coefficients for the same transfer functions that were more appropriate for the dataset from Hulu-1 station.The new coefficients were used to correct the precipitation measurements of other stations in the CHOICE system.Results showed that the new parameters fitted to the local dataset had better correction results than the original parameters.The environmental conditions of Hulu-1 station were very different from those of observation stations that provided datasets to create the transfer functions.Thus,root-mean-square error(RMSE)of solid and mixed precipitation corrected by the original parameters increased significantly by the averages of 0.135(353%)and 0.072 mm(111%),respectively.RMSE values of liquid,solid and mixed precipitation measurements corrected by the new parameters decreased by 6%,20% and 13%,respectively.In addition,the new parameters were suitable for correcting precipitation at other five stations in the CHOICE system.The relative precipitation(RP)increment of different types of precipitation increased with rising altitude.The average RP increment value of snowfall at six stations was the highest,reaching 7%,while that of rainfall was the lowest,covering 3%.Our results confirmed that the new parameters could be used to correct precipitation measurements of the CHOICE system.

Performance of surface radiation products of Greenland Ice Sheet using in-situ measurements

Radiation is the direct energy source of the surface natural environment and the main driving force of climate change.It has increasingly become an important meteorological factor affecting the surface heat exchange and glacier mass balance,especially in the glacier changes of the Greenland Ice Sheet(Gr IS).Due to the harsh climatic conditions of Gr IS and sparse observed data,it has become an important way to obtain radiation data from reanalysis datasets.However,the applicability of these radiation data on Gr IS is uncertain and worth exploring.In this work,we evaluate five reanalysis datasets(the fifth generation of European Centre for Medium-Range Weather Forecasts(ERA5),European Centre for Medium-Range Weather Forecasts Interim Reanalysis(ERA-Interim),Japanese 55-year Reanalysis(JRA55),National Centers for Environmental Prediction Reanalysis II(NCEP2)and Modern-Era Retrospective analysis for Research and Applications,Version 2(MERRA-2))during 1997-2022 using observations from 26 Program for Monitoring the Greenland Ice Sheet(PROMICE)automatic weather stations(AWSs)and 3 K-transect AWSs on Gr IS.The conclusions are as follows:ERA5 has the best performances in downward shortwave radiation(SWD)as well as downward and upward longwave radiation(LWD and LWU),but the performance is not the best in upward shortwave radiation(SWU).Based on the radiation budget analysis with ERA5 during 1979-2022,the fluctuation of longwave radiation is greater than that of shortwave radiation.The seasonal variation of shortwave radiation is obvious,while that of longwave radiation is small.The increasing trend of longwave radiation may result from global warming,in which ice sheets absorb more solar radiation and the surface heats up significantly,emitting more LWU.

Design and Data Analysis of a New Type of Antifreezing Cup-Type Wind Velocity Sensor

In most areas of China, affected by the environment of low temperature and high humidity, the wind speed sensor and wind direction sensor are frozen and cannot output data in autumn, winter or the alternation of winter and spring. In order to solve the freezing situation of the wind sensor, this paper designs a new type of antifreeze wind speed sensor. After meteorology performance testing and field observation tests, the correlation coefficient of the observation data is demonstrated, and the data curve is fitted. The result shows the sensor is stable, and has a good antifreeze effect, the data output is reliable.

Changing Characteristics of Urban Heat Island Effect in Weihai City

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 ℃, showing a gradual weakening trend of -0.169 3 ℃/10 a;the summer average heat island intensity was 0.86 ℃, showing a gradual weakening trend of -0.047 5 ℃/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.

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.

Research on Model of Flood Disaster's Monitoring and Its Application Based on DEM

In order to assess the flood damage rapidly and accurately,this paper proposed a practical method of flood disaster monitoring based on meso-scale automatic weather stations rainfall data and 1:5 million high-precision DEM (digital elevation model) data.It can predict roughly areas by the automatic weather station rainfall analysis and processing when the floods happen.Using partitions 'horizontal' approximation methods,the model of DEM flooding disaster's monitoring has been constructed based on 1:5 million high-precision DEM.And the technical methods applied to the analysis of experimental area.The result of flood disaster's monitoring is carried on comparison and the analysis through the verification by CBERS-02B.It finds that the area of floods is very consistent by the model of DEM and CBERS-02B flooding disaster's monitoring.So the method of flood disaster's motoring based on DEM can be real-time,dynamic,and can monitor the flood zone accurately and effectively.It also can provide the decision making department with present and assisting scheme of policy making.

Climate and meteorological processes of the East Antarctic ice sheet between Zhongshan and Dome-A

The 1228 km over-snow traverse route between the Chinese Zhongshan Station, on the coast of Prydz Bay, and Dome-A, at 4091 m elevation the highest point of the East Antarctic ice sheet, has been the focus of CHINARE surface meteorological and climate studies since 2002. A network of seven Automatic Weather Stations has been deployed along this section, including at Dome-A itself, and some of these have now provided nearly-hourly data for over a decade. Atmospheric boundary layer turbulence and radiation observations have been made over the near-coastal ice sheet inland of Zhongshan and surface turbulence measurements using an ultrasonic anemometer system have also been made in the deep interior of the ice sheet. Summer GPS radiosonde soundings of the atmospheric boundary layer have been made at Kunlun Station, near Dome-A. In this paper these observations are combined to provide a comprehensive overview of the meteorological regime of this region of the ice sheet, its climate variability, and as a reference for future study of climate change. This includes investigation of the variation of surface climate features with elevation and distance from the coast, the height and structure of the boundary layer over the ice sheet, and seasonal and regional changes in ice/snow-air interactions, including turbulent and radiative energy fluxes. The air temperature and snow temperature between the coastal Zhongshan and Dome-A on the inland plateau have not changed significantly in the past decade compared with the inter-annual variability.

EVALUATION OF TEMPERATURE RECORDS OBTAINED BY AWS IN HAINAN PROVINCE AND ADJUSTMENT OF CLIMATOLOGICAL TIME SERIES

Based on the parallel air temperature data of automatic sounding and manual observations at 16 weather stations in Hainan province from 2004 to 2005, a comparative analysis and evaluation is made for validity according to relevant standards. The results indicate that there are daily and seasonal differences between temperature observations recorded by automatic weather stations (AWSs) and with conventional methods. The reasons for the differences are the systematic error, the sensitivity of the two types of instruments to the environmental temperature change, the difference of the observation time and the effect of solar radiation. Because the long-range data were obtained from manual observation, an empirical conversion formula between the temperature records obtained by the instruments is provided for continuous use of the climate data after the changes in instruments.

Comparative Analysis of Climate Characteristics of Extremely Short-Time Severe Precipitation in Guizhou Based on Two Types of Rainfall Data

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 99.5 th 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.

The 18th Workshop on Antarctic Meteorology and Climate

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.

Assessment of air temperatures from different meteorological reanalyses for the East Antarctic region between Zhongshan and Dome A

The accuracy of daily mean 2 meter air temperatures from five reanalyses are assessed against in-situ observations from Automatic Weather Stations in East Antarctica for 2005 to 2008. The five reanalyses all explain more than 70% of the average variance, and have annual root mean square errors （RMSE） between 3.4 and 6.9℃. The NOAA reanalyses, NCEP-1, NCEP-2 and 20CRv2, have cool biases of 2.5, 1.4 and 1.5℃, respectively. The ERA Interim and JCDAS reanalyses have warm biases of 1.7 and 2.0℃. All reanalyses generally perform better in the austral spring and worse in winter and autumn. They also show the best performance at an inland plateau site at 2800 m elevation, but are worst at Dome A, the summit of the East Antarctic ice sheet. In general, ERA Interim is superior to the other reanalyses, probably because of its 4D assimilation scheme. The three NOAA reanalyses perform worst; Their assimilation scheme is more constrained by limited observations and 20CRy2 has less input data, assimilating only surface pressure observations. Despite deficiencies and limitations, the reanalyses are still powerful tools for climate studies in the Antarctic region. However, more in-situ observations are required, especially from the vast interior of Antarctica.