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.

Assessing the Variability of Extreme Weather Events and Its Influence on Marine Accidents along the Northern Coast of Tanzania

The marine accidents are among the main components of the Zanzibar Disaster Management Policy (2011) and the Zanzibar Blue Economy Policy (2020). These policies aimed to institute legal frame works and procedures for reducing both the frequency of marine accidents and their associated fatalities. These fatalities include deaths, permanent disabilities and loss of properties which may result into increased poverty levels as per the sustainable development goal one (SDG1) which stipulates on ending the poverty in all its forms everywhere. Thus, in the way to support these Government efforts, the influence of climate and weather on marine accidents along Zanzibar and Pemba Channels was investigated. The study used the 10 years (2013-2022) records of daily rainfall and hourly wind speed acquired from Tanzania Meteorological Authority (TMA) (for the observation stations of Zanzibar, Pemba, Dares Salaam and Tanga), and the significant wave heights data, which was freely downloaded from Globally Forecasting System (GFS-World model of 13 km resolution). The marine accident records were collected from TASAC and Zanzibar Maritime Authority (ZMA), and the anecdotal information was collected from heads of quay and boat captains in different areas of Zanzibar. The Mann Kendal test, was used to determine the slopes and trends direction of used weather parameters, while the Pearson correlations analysis and t-tests were used to understand the significance of the underlying relationship between the weather and marine accidents. The paired t-test was used to evaluate the extent to which weather parameters affect the marine accidents. Results revealed that the variability of extreme weather events (rainfall, ocean waves and wind speed) was seen to be among the key factors for most of the recorded marine accidents. For instance, in Pemba high rainfall showed an increasing trend of extreme rainfall events, while Zanzibar has shown a decreasing trend of these events. As for extreme wind events, results show that Dar es Salaam and Tanga had an increasing trend, while Zanzibar and Pemba had shown a decreasing trend. As for the monthly variability of frequencies of extreme rainfall events, March to May (MAM) season was shown to have the highest frequencies over all stations with the peaks at Zanzibar and Pemba. On the other hand, high frequency of extreme wind speed was observed from May to September with peaks in June to July, and the highest strength was observed during 09:00 to 15:00 GMT. Moreover, results revealed an increasing trend of marine accidents caused by bad weather except during November. Also, results showed that bad weather conditions contributed to 48 (32%) of all 150 recorded accidents. Further results revealed significant correlation between the extreme wind and marine accidents, with the highest strong correlation of r = 0.71 (at p ≤ 0.007) and r = 0.75 (at p ≤ 0.009) at Tanga and Pemba, indicating the occurrence of more marine accidents at the Pemba channel. Indeed, strong correlation of r = 0.6 between extreme rainfall events and marine accidents was shown in Pemba, while the correlations between extremely significant wave heights and marine accidents were r = 0.41 (at p ≤ 0.006) and r = 0.34 (p ≤ 0.0006) for Pemba and Zanzibar Channel, respectively. In conclusion, the study has shown high influence between marine accidents and bad weather events with more impacts in Pemba and Zanzibar. Thus, the study calls for more work to be undertaken to raise the awareness on marine accidents as a way to alleviate the poverty and enhance the sustainable blue economy.

人工影响天气虚拟仿真实验及课程体系建设

在“人工影响天气”的相关课程教学中,构建虚拟仿真实验教学平台有助于学生深入理解基础理论知识,并对实际业务操作及催化方案设计、优化产生感性认识。虚拟仿真实验教学突破了室内教学时空及仪器设备昂贵的局限性,解决了自然界中不能对同一块云反复催化的实际困难,对云降水环境及人工催化过程高度重现,对理论教学体系及教学形式进行补充,完善了课程教学体系。此外,科技成果在教学和本科生科研创新活动的引入,进一步增强学生科技创新意识及学习兴趣,促进了科研创新型人才的培养。

2023年中国气候异常特征及主要天气气候事件

2023年,我国气候主要表现为暖干的特征,全国平均气温10.71℃,较1991—2020年气候平均偏高0.82℃,为1951年以来最暖;全国平均降水量615.0 mm,较常年偏少3.9%,为2012年以来第二少。四季气温均较常年同期偏高,其中夏、秋季分别为历史同期次高和最高;除秋季降水偏多外,其余三季降水均偏少。汛期(5—9月),全国平均降水量较常年同期偏少4.3%,为2012年以来第二少,我国中东部降水总体呈“中间多南北少”的分布。2023年,我国区域性气象干旱多发,西南地区遭遇冬春连旱;春季北方沙尘天气过程偏多;夏季前期,华北和黄淮遭受1961年以来最强高温过程;7月底至8月初,受台风杜苏芮影响,京津冀地区发生历史罕见极端强降水过程,华北地区出现“旱涝急转”;华西秋雨开始早、结束晚、雨量多;1月中旬发生年内最强寒潮过程;秋末冬初冷空气频繁入侵,12月华北和黄淮等地降雪日数偏多、积雪偏深。

安徽省燃气炮人工增雨作业效果综合评估

选取2021—2023年安徽省81次燃气炮作业的双偏振天气雷达、雨量计等多源观测数据,综合评估燃气炮作业增雨效果并分析可能机理。结果显示:在降水开始前作业个例增雨效果较好,并伴有水平反射率因子Z_(H)、差分反射率Z_(DR)的增加和共极化相关系数ρ_(hv)的减少;降水开始后作业增雨效果欠佳。使用携带暖云催化剂的燃气炮作业后云体变化主要在零度层以下,且维持时间较短;使用携带冷云催化剂的燃气炮作业后暖云区和冷云区均有明显变化,且作业影响持续时间更长。燃气炮作业过程中雷达速度谱宽增大,可能是作业引起气流涡旋的增加所导致。统计结果显示:增雨的显著性与作业时长呈负相关,作业时长与Z_(DR)增量呈负相关,过量播撒会导致减雨;增雨的显著性与作业前影响区雨量呈负相关;增雨量与Z_(H)、中低层风速、风切变呈正相关,与高层风速呈负相关。

数值预报AI气象大模型国际发展动态研究

数值预报是研究地球系统的重要工具,有助于加深科学家对大气、海洋、气候和环境等复杂系统之间相互作用和变化过程的理解,在防灾减灾、气候变化和环境治理等方面发挥着不可或缺的作用。随着模式复杂度和分辨率的提高,传统数值模式在气候变化研究和气候预测方面取得了迅速的进展,但也面临一些挑战,需要得到数据同化、集合耦合、高性能计算和不确定性分析等多方面的支持。而近年来,“AI+气象”的交叉研究在气象领域引起了广泛关注。基于多种深度学习架构的人工智能大模型,依托强大的计算资源和海量的数据进行训练,能够以新的科学范式进行高效数值预报。气象大模型不断涌现,一些科技公司如华为、英伟达、DeepMind、谷歌、微软等,以及国内外高校如清华大学、复旦大学、密歇根大学、莱斯大学等发布了多个涵盖临近预报、短时预报、中期预报和延伸期预报等不同领域的气象大模型。这标志着人工智能与气象领域的交叉融合已经达到新的高度。尽管气象大模型在现阶段取得了较大突破,但其发展仍然面临弱可解释性、泛化能力不足、极端事件预报强度偏低、智能预报结果过平滑、深度学习框架能力需要拓展等诸多挑战。

极端天气场景下基于天气衍生品的电价风险管理

极端天气会导致线路跳闸,发电能力损失,负荷激增。市场环境下用户将承受高昂电价冲击,危及社会稳定。此时抑制电价飙升,管理电价风险十分重要。为此,设计了一种基于电价和发电损失容量的电力天气衍生品,可有效管理电价风险。首先,依据风险分解结构原理从源网荷及发电商报价角度分析了极端天气对电价的影响。其次,设计了考虑电价、天气为执行条件和基于损失容量与电价为收益函数的天气衍生品,并从理论层面分析了其效果。最后,结合预期效用最大化目标和电力市场出清模型进行发电商决策与电价模拟,使用条件风险价值(CVaR)指标评估了电价风险及衍生品效果。结果显示,所提电力天气衍生品使极端天气对发电商收入的冲击降低80%以上,用户电价均值降低约0.2%,用户电价风险降低20%以上,说明可有效控制电价风险,助力电力市场建设以及新型电力系统构建。

中国极端天气气候研究——“地球系统与全球变化”重点专项项目简介及最新进展

全球变暖背景下,极端天气气候事件频发,并表现出群发性、持续性、复合性等特点,不可预测性增加;持续性强降水、极端低温、复合型极端高温干旱、群发性热浪和台风等极端天气气候事件对我国经济社会和可持续发展影响巨大。然而,上述极端天气气候事件的新特征、关键过程和机理尚不完全清楚,重大极端事件的预报预测水平亟待提升。文章首先简要介绍“地球系统与全球变化”重点专项项目“中国极端天气气候事件的形成机理及其预测和归因”的基本情况。项目拟在分析全球变化背景下对我国造成重大影响的极端天气气候事件新特征的基础上,深入研究多尺度海-陆-气耦合过程影响极端天气气候事件的机理,挖掘极端天气气候事件次季节-季节预测的前兆信号;发展动力与物理统计相结合的极端事件预测新方法,研制针对中国极端事件的新一代高分辨率数值预报与检测归因系统。文章重点总结了自2022年12月项目立项至今取得的最新研究成果和进展。

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。

四川盆区油菜花期低温阴雨灾损评估及风险区划

花期低温阴雨是四川盆区油菜生产的主要气象灾害之一。本研究利用四川盆区1961-2020年101个气象站的逐日气象资料,1983-2000年油菜产量资料和干旱、低温冷害、连阴雨、大风、冰雹灾情资料,筛选花期低温阴雨灾害年份,采用数理统计方法获取花期低温阴雨产量灾损率和致灾因子,通过相关性分析确定油菜花期低温阴雨致灾指标,并利用2001-2020年灾情资料进行检验;基于筛选出的低温、连阴雨关键致灾因子,采用回归分析法建立油菜花期低温阴雨灾害损失评估模型,并进行回代和预测检验,分析灾害风险区划和变化趋势。结果表明:(1)日平均气温≤7℃、过程持续天数≥1d是四川盆区油菜花期低温致灾指标,日平均气温≤7℃的负积温和≥3d过程持续降水量共同组成低温阴雨灾害的关键致灾因子;选用2001-2020年油菜花期低温冷害发生情况对指标进行检验,与实际情况相符。(2)选用1983-2000年的气象、产量、灾情数据建立四川盆区油菜花期低温阴雨灾损评估模型,模型对轻度、中度灾害损失评估等级与实际等级相同或相差1级的评估准确率在96%以上;重度、特重评估等级与实际等级相同的准确率为0,与实际等级相差1级的准确率为75%和0。选用2001-2020年油菜花期低温阴雨灾害发生情况对模型检验,与实际情况基本相符。(3)1961-2020年四川盆区油菜花期低温阴雨灾害风险偏高区域主要分布于盆区西南部、南部及东北部,以中-高风险为主;灾害风险偏低的区域集中于盆区西北部及中部,以低-次低风险为主。(4)气候变暖背景下,四川盆区油菜花期低温阴雨灾害高风险区域呈减少趋势,低风险区域呈增加趋势。综上分析,四川盆区油菜花期低温冷害指标结果可靠,低温阴雨灾损评估模型能够较好地评估灾害损失,可应用于农业气象业务服务;灾害风险区划结果可为四川油菜生产布局提供参考依据。

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。

灾害天气下计及一二次设备耦合故障的电网短时风险评估

灾害天气下电网发生短时多重故障有可能超出保护系统预设的反应能力,一二次设备耦合影响将助推多重故障风险发展和蔓延。为此,提出了计及一二次设备耦合故障的短时风险评估方法。首先,分析了一次设备与二次设备之间的交互作用及其对电网短时运行风险的影响。其次,构建了一次设备和二次设备的故障概率模型,重点分析了一二次设备耦合故障引发保护拒动的概率。然后,结合保护系统对超预期故障的反应能力,提出了灾害天气下电网短时多重故障风险评估方法。最后,采用改造的IEEE39节点系统对所提方法进行测试。算例结果表明:受外界气象环境影响,保护系统缺陷暴露导致非期望动作使得电网多重故障风险更为严重;综合考虑灾害天气对一二次设备影响下的短时运行风险,对于发现和消除保护系统薄弱环节及制定电网降风险运行策略具有一定参考价值。

Comparison of Methods for Estimating Crop Water Use: Sap Flow, FAO-56 Penman-Monteith, and Weather Parameters

Knowing crop water uptake each day is useful for developing irrigation scheduling. Many technologies have been used to estimate daily crop water use. Sap flow is one of the technologies that measure water flow through the stem of a plant and estimate daily crop water uptake. Sap flow sensor is an effective direct method for measuring crop water use, but it is relatively expensive and requires frequent maintenance. Therefore, alternative methods, such as evapotranspiration based on FAO 56 Penman-Monteith equation and other weather parameters were evaluated to find the correlation with sap flow. In this study, Dynamax Flow 32-1K sap flow system was utilized to monitor potato water use. The results show sap flow has a strong correlation with evapotranspiration (RMSE = 1.34, IA = 0.89, MBE = -0.83), solar radiation (RMSE = 2.25, IA = 0.72, MBE = -1.80), but not with air temperature, relative humidity, wind speed, and vapor pressure. It is worth noting that the R2 between sap flow and relative humidity was 0.55. This study has concluded that daily evapotranspiration and solar radiation can be used as alternative methods to estimate sap flow.

基于陆气耦合模式的降雨径流模拟研究进展

基于陆气耦合模式的降雨径流模拟是变化环境下水文循环过程研究的重要内容之一。围绕陆气耦合降雨径流模拟,分析基于陆气耦合模式的降雨径流模拟框架,对比基础输入资料、天气模式和陆面水文模型各方面的优缺点,回顾基于该框架的发展历程,从集合预报、数据驱动、陆面水文模型与天气模式3个方面介绍了陆气耦合降雨径流模拟技术的发展,提出多尺度转换、双向耦合、不确定性问题及误差修正4个热点关键技术问题,并对未来研究进行展望,以期为推进基于陆气耦合模式的降雨径流模拟研究提供参考。

Real-Time Crop Prediction Based on Soil Fertility and Weather Forecast Using IoT and a Machine Learning Algorithm

The aim of this article is to assist farmers in making better crop selection decisions based on soil fertility and weather forecast through the use of IoT and AI (smart farming). To accomplish this, a prototype was developed capable of predicting the best suitable crop for a specific plot of land based on soil fertility and making recommendations based on weather forecast. Random Forest machine learning algorithm was used and trained with Jupyter in the Anaconda framework to achieve an accuracy of about 99%. Based on this process, IoT with the Message Queuing Telemetry Transport (MQTT) protocol, a machine learning algorithm, based on Random Forest, and weather forecast API for crop prediction and recommendations were used. The prototype accepts nitrogen, phosphorus, potassium, humidity, temperature and pH as input parameters from the IoT sensors, as well as the weather API for data forecasting. The approach was tested in a suburban area of Yaounde (Cameroon). Taking into account future meteorological parameters (rainfall, wind and temperature) in this project produced better recommendations and therefore better crop selection. All necessary results can be accessed from anywhere and at any time using the IoT system via a web browser.

Clothing Sales Forecast Considering Weather Information: An Empirical Study in Brick-and-Mortar Stores by Machine-Learning

Reliable sales forecasts are important to the garment industry. In recent years, the global climate is warming, the weather changes frequently, and clothing sales are affected by weather fluctuations. The purpose of this study is to investigate whether weather data can improve the accuracy of product sales and to establish a corresponding clothing sales forecasting model. This model uses the basic attributes of clothing product data, historical sales data, and weather data. It is based on a random forest, XGB, and GBDT adopting a stacking strategy. We found that weather information is not useful for basic clothing sales forecasts, but it did improve the accuracy of seasonal clothing sales forecasts. The MSE of the dresses, down jackets, and shirts are reduced by 86.03%, 80.14%, and 41.49% on average. In addition, we found that the stacking strategy model outperformed the voting strategy model, with an average MSE reduction of 49.28%. Clothing managers can use this model to forecast their sales when they make sales plans based on weather information.

Analysis of Extreme Precipitation Weather Process in Henan during July 17-22, 2021

To learn more about the unusually heavy rainfall in central China, this research uses the monthly climatic data, weather map information and US NCEP re-analysis data to analyze the atmospheric circulation, precipitation and weather situation of this extreme precipitation weather process in Henan during July 17-22, 2021. The results show that the precipitation process is affected by the joint action of the subtropical high, the continental high, the low vortex, the low-level jet, the typhoon “In-fa” and other multi-scale systems in the middle and low latitudes. This precipitation process was also affected by the topographic uplift and blocking of Taihang Mountain and Funiu Mountain.

Analysis of Weather Anomalies to Assess the 2021 Flood Events in Yaounde, Cameroon (Central Africa)

Extreme weather anomalies such as rainfall and its subsequent flood events are governed by complex weather systems and interactions between them. It is important to understand the drivers of such events as it helps prepare for and mitigate or respond to the related impacts. In line with the above statements, quarter-hourly data for the year 2021 recorded in the Yaounde meteorological station were synthesized to come out with daily and dekadal (10-day averaged) anomalies of six climate factors (rainfall, temperature, insolation, relative humidity, dew point and wind speed), in order to assess the occurrences and severity of floods to changing weather patterns in Yaounde. In addition, Precipitation Concentration Index (PCI) was computed to evaluate the distribution and analyse the frequency and intensity of precipitation. Coefficient of variation (CV) was used to estimate the seasonal and annual variation of rainfall patterns, while Mann-Kendall (MK) trend test was performed to detect weather anomalies (12-month period variation) in quarter-hourly rainfall data from January 1st to December 31st 2021. The Standard Precipitation Index (SPI) was also used to quantify the rainfall deficiency of the observed time scale. Results reveal that based on the historical data from 1979 to 2018 in the bimodal rainfall forest zone, maximum and minimum temperature averages recorded in Yaounde in 2021 were mostly above historical average values. Precipitations were rare during dry seasons, with range value of 0 - 13.6 mm for the great dry season and 0 - 21.4 mm for the small dry season. Whereas during small and great rainy seasons, rainfalls were regular with intensity varying between 0 and 50 mm, and between 0 and 90.4 mm, respectively. The MK trend test showed that there was a statistical significant increase in rainfall trend for the month of August at a 5% level of significance, while a significant decreasing trend was observed in July and December. There was a strong irregular rainfall distribution during the months of February, July and December 2021, with a weather being mildly wetted during all the dry seasons and extremely wetted in August. Recorded flooding days within the year of study matched with heavy rainy days including during dry seasons.

基于危险天气不确定性的最小风险路径规划方法

为降低飞行过程中遭遇危险天气的概率,同时避免大范围绕飞导致的路径与耗油增加,针对航路中的雷暴、积冰和颠簸天气,使用数值预报和概率预报,面向航前飞行计划,提出一种基于危险天气不确定性的最小风险路径规划方法.首先,基于概率预报数据使用配料法和C-F模型计算雷暴发生概率,基于数值预报数据计算积冰预测指数和颠簸预测指数;然后,融合多类型危险天气,提出一种具备风险标识的栅格化地图;在此基础上,改进传统路径最短的规划算法,构建以风险最小化为目标的Dijkstra和A^(*)算法;最后,使用2023年4月3日华中地区强对流天气预测数据建立风险地图,使用上述改进算法与传统Dijkstra、A^(*)和RRT算法进行路径规划并对比分析.结果表明,传统Dijkstra和A^(*)算法可计算得到最短飞行路径,而改进的A^(*)算法可计算得到总风险最小路径;若综合考虑飞行风险与路径长度,改进的Dijkstra算法最为适合.

改进黑猩猩算法的光伏发电功率短期预测

针对晴空、非晴空条件下光伏出力预测精度不高等问题,提出一种改进K均值(K-means++)算法和黑猩猩优化算法CHOA(chimpanzee optimization algorithm)相结合,优化最小二乘支持向量机LSSVM(least squares support vector machine)的模型,进行光伏功率预测。首先,利用密度聚类和混合评价函数改进K-means++对原始数据进行自适应类别划分。其次,通过相关性分析和随机森林特征提取构建模型的输入特征集。最后,根据特征集建立基于DK-PCHOA-LSSVM的短期光伏发电预测模型。结合实际算例,结果表明:该模型在恶劣天气下预测精度明显优于其他模型,验证了其有效性和优越性。