West African Weather Systems in Development of Tropical Cyclones

Tropical Cyclones have their origins from areas of low atmospheric pressure over warm waters in the tropics or subtropics.We have carefully studied the interconnection between the West African Weather Systems(WAWS)and their subsequent development into Tropical Cyclones.Between 2004 and 2005,we studied the interconnection and the teleconnexion between the WAWS and the various occurrences

Large-Scale Weather Systems: A Future Research Priority

一个简短评价两个被提供盒子对和为分配优先级在大规模天气系统(LSWS ) 上研究的盒子。三方面的盒子对被基于在之上：在环境科学的新贯穿的主题的出现；在大气的科学的另外的学科的分支之上的新鲜强调；并且研究的成熟状态和 LSWS 的预言。盒子为被三争论也支持。首先是 LSWS 研究不应该仅仅是积分的断言但是与新贯穿主题和另外的学科的分支有关的未来研究的一个主要部件。在 LSWS 的第二最近的主要开发研究，作为由在为 LSWS 和潜在的涡度观点的出现的预言策略的范例移动的 epitomized，证明到主题的进行中的活力。第三这个领域的未来开发由新国际 THORPEX 例示观察系统研究和可预测性实验) 计划，表达知性上用社会、经济的利益的指导申请质问基本研究的知觉的 dovetailing。远离在遗赠， LSWS 研究将在在科学和社会之间的新关系的最前线展示，这因此被推断。

Preface to the Special Issue on the 14th International Conference on Mesoscale Convective Systems and High-Impact Weather

A mesoscale convective system(MCS)is an organized cluster of thunderstorms known to be the most important convective mode in causing disastrous high-impact weather,such as heavy rainfall,hail,damaging winds,and tornadoes.The small spatial scale and fast temporal evolution of MCSs make their observation and prediction very challenging.East Asia is home to the world’s most prominent monsoon,setting the stage for various severe convective weather events.MCSs and their associated high-impact weather have long been critical issues of concern;as such,their research efforts are valued by governments in East Asia.

Organizational Modes and Environmental Conditions of the Severe Convective Weathers Produced by the Mesoscale Convective Systems in South China

Composite radar reflectivity data during April-September 2011-2015 are used to investigate and classify storms in south China(18-27°N;105-120°E). The storms appear most frequently in May. They are either linear;cellular or nonlinear systems, taking up 29.45%, 24.51% and 46.04%, respectively, in terms of morphology. Linear systems are subdivided into six morphologies: trailing stratiform precipitation(TS), bow echoes(BE), leading stratiform precipitation(LS), embedded line(EL), no stratiform precipitation(NS) and parallel stratiform precipitation(PS). The TS and NS modes have the highest frequencies but there are only small samples of LS(0.61%) and PS(0.79%) modes.Severe convective wind(≥17m s-1at surface level) accounts for the highest percentage(35%) of severe convective weather events produced by cellular systems including individual cells(IC) and clusters of cells(CC). Short-duration heavy rainfall(≥50 mm h-1) and severe convective wind are the most common severe weather associated with TS and BE modes. Comparison of environmental physical parameters shows that cellular convection systems tend to occur in the environment with favorable thermal condition, substantial unstable energy and low precipitable water from the surface to300 hPa(PWAT). However, the environmental conditions favoring the initiation of linear systems feature strong vertical wind shear, high PWAT, and intense convective inhibition. The environmental parameters favoring the initiation of nonlinear systems are between those of the other two types of morphology.

Weathering and accumulation of trace elements in the soils of the Porali Plain, Balochistan: repercussions in agriculture

This study is thefirst attempt to assess the nature of the soil,especially on the western side of the Porali Plain in Balochistan;a new emerging agriculture hub,using weathering and pollution indices supplemented by multi-variate analysis based on geochemical data.The outcomes of this study are expected to help farmers in soil manage-ment and selecting suitable crops for the region.Twenty-five soil samples were collected,mainly from the arable land of the Porali Plain.After drying and coning-quarter-ing,soil samples were analyzed for major and trace ele-ments using the XRF technique;sieving and hydrometric methods were employed for granulometric analysis.Esti-mated data were analyzed using Excel,SPSS,and Surfer software to calculate various indices,correlation matrix,and spatial distribution.The granulometric analysis showed that 76%of the samples belonged to loam types of soil,12%to sand type,and 8%to silt type.Weathering indices:CIA,CIW,PIA,PWI,WIP,CIX,and ICV were calculated to infer the level of alteration.These indices reflect mod-erate to intense weathering;supported by K_(2)O/AI_(2)O_(3),Rb/K_(2)O,Rb/Ti,and Rb/Sr ratios.Assessment of the geo-ac-cumulation and Nemerow Pollution indices pinpoint rela-tively high concentrations of Pb,Ni,and Cr concentration in the soils.The correlation matrix and Principal Compo-nent Analysis show that the soil in this study area is mainly derived from the weathering of igneous rocks of Bela Ophiolite(Cretaceous age)and Jurassic sedimentary rocks of Mor Range having SEDEX/MVT type mineralization.Weathering may result in the undesirable accumulation of certain trace elements which adversely affects crops.

Automatic Generation of Artificial Space Weather Forecast Product Based on Sequence-to-sequence Model

Both analyzing a large amount of space weather observed data and alleviating personal experience bias are significant challenges in generating artificial space weather forecast products.With the use of natural language generation methods based on the sequence-to-sequence model,space weather forecast texts can be automatically generated.To conduct our generation tasks at a fine-grained level,a taxonomy of space weather phenomena based on descriptions is presented.Then,our MDH(Multi-Domain Hybrid)model is proposed for generating space weather summaries in two stages.This model is composed of three sequence-to-sequence-based deep neural network sub-models(one Bidirectional Auto-Regressive Transformers pre-trained model and two Transformer models).Then,to evaluate how well MDH performs,quality evaluation metrics based on two prevalent automatic metrics and our innovative human metric are presented.The comprehensive scores of the three summaries generating tasks on testing datasets are 70.87,93.50,and 92.69,respectively.The results suggest that MDH can generate space weather summaries with high accuracy and coherence,as well as suitable length,which can assist forecasters in generating high-quality space weather forecast products,despite the data being starved.

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.

Weathering Characterization and Degradation Analysis of Landscape Wooden Buildings in Semi-Arid and Sandy Area

Wooden buildings play a very important role in China’s construction and landscape architecture industry.In order to explore the weathering characteristics of the surface layer of landscape wooden buildings,the main causes of weathering were analyzed on the basis of summarizing the common types of weathering characterization.The results showed that the weathering characterization was mainly reflected in the surface defects of wood structures,such as cracking,discoloration,peeling,wind erosion wear,and so on.The coating technology on the surface of constructions was the main artificial factor affecting the surface defects of constructions.In the case of similar surface decoration conditions,sunlight and moisture were the main natural factors affecting the weathering of wooden buildings,which will promote the process of weathering.

CHEMICAL BEHAVIOR OF CERIUM ELEMENT IN ROCK WEATHERING SYSTEM

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Weather Prediction With Multiclass Support Vector Machines in the Fault Detection of Photovoltaic System

Since the efficiency of photovoltaic(PV) power is closely related to the weather,many PV enterprises install weather instruments to monitor the working state of the PV power system.With the development of the soft measurement technology,the instrumental method seems obsolete and involves high cost.This paper proposes a novel method for predicting the types of weather based on the PV power data and partial meteorological data.By this method,the weather types are deduced by data analysis,instead of weather instrument A better fault detection is obtained by using the support vector machines(SVM) and comparing the predicted and the actual weather.The model of the weather prediction is established by a direct SVM for training multiclass predictors.Although SVM is suitable for classification,the classified results depend on the type of the kernel,the parameters of the kernel,and the soft margin coefficient,which are difficult to choose.In this paper,these parameters are optimized by particle swarm optimization(PSO) algorithm in anticipation of good prediction results can be achieved.Prediction results show that this method is feasible and effective.

Geochemistry and Sm-Nd systematics of the 1.67 Ga Buanji Group of southwestern Tanzania: Paleo-weathering, provenance and paleo-tectonic setting implications

We present major and trace and Nd-isotopic data of the^1.67 Ga Buanji Group of southwestern Tanzania in order to constrain the nature of their protolith and the intensity of chemical weathering in the source terranes and make inferences on the nature of climatic conditions during the late Paleoproterozoic in the region. Major and trace element contents of the shales from three stratigraphic formations are comparable to those of the post-Archaean Australian Shale(PAAS) and the average Proterozoic Shale(PS). They are characterized by enrichments in LREE relative to HREE((La/Yb)_(CN)=9.07-13.2;(Gd/Yb)cN = 1.51-1.85) and negative Eu anomalies(Eu/Eu~* = 0.61-0.81); features which are comparable to those of PAAS and PS, indicating upper continental sources. Provenance proxy ratios,together with abundances of Cr, Ni, Co and V that increase up-stratigraphy suggest a decreasing input of felsic detritus up-stratigraphy. Chemical Indices of Alteration(CIA) for the lower, middle and upper Buanji formations are 81. 76 and 79, respectively. These indices largely indicate intermediate(ca. 60-80)to extreme(>80) weathering intensities of the precursor rocks. These observations may suggest the prevalence of warm, humid climates during the late Paleoproterozoic in the region.The lower Buanji Formation yielded a depleted mantle Nd model age(T_(DM)) of 、2100 Ma which indicates an Eburnean parentage. T_(DM) ages of 2486-2155 Ma and 2535-2379 Ma obtained from middle and upper Buanji formations, respectively, suggest a progressive increase of sedimentary input from the Tanzania Craton up-stratigraphy. The Eburnean T_(DM) ages of the lower Buanji rocks are attributed to their derivation through denudation of a decaying topographic high composed predominantly of rocks that were generated during the Palaeoproterozoic Ubendian orogenesis, possibly in the realm of Columbian Supercontinent assembly. Overlapping T_(DM) ages between the middle and upper Buanji formations suggest multiple sources involving mixing of detritus from Archaean cratonic rocks and the Palaeoproterozoic Ubendian belt. However, the Archaean signal is relatively more pronounced in the upper Buanji Formation, suggesting sediments derivation from the craton, to the north of the basin. The middle Buanji Formation suggests more diverse protolith,given the relatively larger spread in the T_(DM) ages. The Nb/Ta, Zr/Sm and Ce/Pb ratios coupled with the negative Nb and Ta anomalies, relative to primitive mantle, suggest that the tectonic setting of the source rocks for the Buanji sediments was a subduction zone akin to that generating modern sland Arc Basalts. Thus, we suggest that the Buanji's palaeogeography is consistent with an extensional continental backarc basin during the late Paleoproterozoic.

Innovation of the double-maize cropping system based on cultivar growing degree days for adapting to changing weather conditions in the North China Plain

Double-maize cropping system is an effective option for coping with climate change in the North China Plain. However, the effects of changes in climate on the growth and yield of maize in the two seasons are poorly understood. Forty-six cultivars of maize with different requirements for growing degree days (GDD), categorized as high (H), medium (M) or low (L), and three cultivar combinations for two seasons as LH (using JD27 and DMY1 from category L in the first season;and YD629 and XD22 from category H in the second season), MM (using JX1 and LC3 from category M in the first season;and ZD958 and JX1 from category M in the second season) and HL (using CD30 and QY9 from category H in the first season;and XK10 and DMY3 from category L in the second season) were tested to examine the eco-physiological determinants of maize yield from 2015 to 2017. The correlations between the combinations of cultivars and grain yield were examined. The combination LH produced the highest annual grain yield and total biomass, regardless of the year. It was followed, in decreasing order, by MM and HL. Higher grain yield and biomass in LH were mainly due to the greater grain yield and biomass in the second season, which were influenced mainly by the lengths of the pre- and post-silking periods and the rate of plant growth (PGR). Temperature was the primary factor that influenced dry matter accumulation. In the first season, low temperatures during pre-silking decreased both the duration and PGR in LH, whereas high temperatures during post-silking decreased the PGR in MM and HL, resulting in no significant differences in biomass being observed among the three combinations. In the second season, high temperatures decreased both the PGR and pre- and post-silking duration in MM and HL, and consequently, the biomass of those two combinations were lower than that in LH. Moreover, because of lower GDD and radiation in the first season and higher grain yield in the second season, production efficiency of temperature and radiation (Ra) was the highest in LH. More importantly, differences in temperature and radiation in the two seasons significantly affected the rate and duration of growth in maize, and thereby affecting both dry matter and grain yield. Our study indicated that the combination of LH is the best for optimizing the double-maize system under changing climatic conditions in the North China Plain.

Selection of Weather Parameters for Air-Conditioning System Design for Buildings with Long Thermal Lag

Two building factors-a longer thermal lag of more than one hour for building envelops and a lag of indoor radiation to convert into cooling load-have impact on the instantaneous heat input and instantaneous cooling load.So the two factors should be taken into account when selecting the weather parameters for air-conditioning system design.This paper developed a new statistic method for the rational selection of coincident solar irradiance,dry-bulb and wet-bulb temperatures.The method was applied to historic weather records of 25 years in Hong Kong to generate coincident design weather data.And the results show that traditional design solar irradiance,dry-bulb and wet-bulb temperatures may be significantly overestimated in many conditions,and the design weather data for the three different constructions is not kept constant.

System of Multigrid Nonlinear Least-squares Four-dimensional Variational Data Assimilation for Numerical Weather Prediction(SNAP):System Formulation and Preliminary Evaluation

A new forecasting system-the System of Multigrid Nonlinear Least-squares Four-dimensional Variational(NLS-4DVar)Data Assimilation for Numerical Weather Prediction(SNAP)-was established by building upon the multigrid NLS-4DVar data assimilation scheme,the operational Gridpoint Statistical Interpolation(GSI)−based data-processing and observation operators,and the widely used Weather Research and Forecasting numerical model.Drawing upon lessons learned from the superiority of the operational GSI analysis system,for its various observation operators and the ability to assimilate multiple-source observations,SNAP adopts GSI-based data-processing and observation operator modules to compute the observation innovations.The multigrid NLS-4DVar assimilation framework is used for the analysis,which can adequately correct errors from large to small scales and accelerate iteration solutions.The analysis variables are model state variables,rather than the control variables adopted in the conventional 4DVar system.Currently,we have achieved the assimilation of conventional observations,and we will continue to improve the assimilation of radar and satellite observations in the future.SNAP was evaluated by case evaluation experiments and one-week cycling assimilation experiments.In the case evaluation experiments,two six-hour time windows were established for assimilation experiments and precipitation forecasts were verified against hourly precipitation observations from more than 2400 national observation sites.This showed that SNAP can absorb observations and improve the initial field,thereby improving the precipitation forecast.In the one-week cycling assimilation experiments,six-hourly assimilation cycles were run in one week.SNAP produced slightly lower forecast RMSEs than the GSI 4DEnVar(Four-dimensional Ensemble Variational)as a whole and the threat scores of precipitation forecasts initialized from the analysis of SNAP were higher than those obtained from the analysis of GSI 4DEnVar.

The Need of Incorporating Indigenous Knowledge Systems into Modern Weather Forecasting Methods

The study was aimed to examine the need of incorporating traditional weather forecasting renowned indigenous knowledge system (IKS) into modern weather forecasting methods to be used for planning farming activities. In addition, not only gap that is not infused by current weather forecasting system with their advanced studies to understand why it is incorporated into existing technical frameworks was regarded, but also the limitation of advanced weather forecasting approach and strength to be elicited by indigenous knowledge system are crucial. Perspicuously, forms and onsite interrogates have been conducted to assess people’s beliefs, understanding, and attitudes on the indigenous knowledge system significance on weather forecasting. Therefore, atmospheric and biological conditions, astronomic, as well as relief characteristics were used to predict the weather over short and long periods. Usually, in assessing weather conditions, the conduct of animals and insects were listed as essential. Obviously, in order to predict weather particularly from rain within about short period of time, astronomical characteristics were used. Commonly, there are few peers who know conventional weather prediction approaches. This lowers the reliability of conventional weather prediction. The findings revealed some variables that impact meteorological inaccuracy by scientific methods and help to recognize and evaluate the gap that current meteorological technologies do not achieve and new particulars anticipated to be filled with conventional methods to attain accurate weather prediction. Additionally, the study indicated that both modern and conventional processes have certain positive and limitations, which means that they can be coupled to generate more accurate weather prediction reports for end users.

Design and Development of Weather Monitoring and Controlling System for a Smart Agro (Farm)

Weather plays an important role in our farming system. In greenhouse or internal farming system, weather monitoring is important. For better production and maintenance, it is important to monitor. This project is developed for forecasting weather parameters like humidity, temperature, soil moisture, and raid detection. Humidity and temperature are monitored for internal temperature. The soil is the most important part of a greenhouse. In this project, soil moisture level is monitored and controlled for maintaining soil moisture level. Rain detection is used in outside of the farm. It detects rainwater and sends a message to the server. It is monitored by using a local server. In remote routing area, it also can be monitored and controlled without physical existence. Also, it is a low-cost weather monitoring system for the agro farm. The Raspberry Pi is a low cost, credit-card sized computer that plugs into a computer monitor or TV, and uses a standard keyboard and mouse. The monitoring system could be designed by using the sensor. It is useful for forecasting and data analysis process. In this project weather forecasting system is designed by using a sensor. In this project, Raspberry Pi work like a remote monitoring and controlling system for the agro farm.

Research Project Entitled “The Dynamics and Physical Processes in The Weather and Climate System”——Part Ⅰ: A Brief Introduction

In the beginning of the 21st century, the Tenth Five-Year Priority Research Projects of the Earth Sci- ences of the National Natural Science Foundation of China (NSFC) were initiated. After nearly a two-year long process to prepare, the ?rst version of six Priority Research Projects of Earth Sciences was pub- lished in October 2001 by NSFC, viz., Local Response to Global Changes, Life Process and Environment, Dynamics and Physical Processes in the Weather and Climate System, Continental Dynamics, Regional Sustainable Development, Solar-Terrestrial Environment and Space Weather. The process involved more than 200 renowned Chinese scientists and many departments and agencies in China. The six Priority Research Projects guide the research e?ort of the earth sciences for the NSFC from year 2001 to 2005. This paper provides a brief introduction to the Third Priority Research Project of the Department of Earth Sciences of NSFC—-Dynamics and Physical Processes in the Weather and Climate System (DPWCS).

An Observing System Simulation Experiment to Assess the Potential Impact of a Virtual Mobile Communication Tower–based Observation Network on Weather Forecasting Accuracy in China. Part 1: Weather Stations with a Typical Mobile Tower Height of 40 m

The importance of a national or regional network of meteorological stations for improving weather predictions has been recognized for many years.Ground-based automatic weather stations typically observe weather at a height of 2-10 m above ground level(AGL);however,these observations may have two major shortcomings.Large portions of data cannot be used if the station height is significantly lower than the model surface level;and such observations may contain large representativity errors as near-surface observations are often affected by the local environment,such as nearby buildings and tall trees.With the recent introduction of a significant number of mobile communication towers that are typically over40 m AGL in China,a campaign has been proposed to use such towers to build a future observing system with an observing height of 40 m.A series of observing system simulation experiments has been conducted to assess the potential utility of such a future observing system as part of a feasibility study.The experiments were conducted using the Weather Research and Forecasting model and its Rapid Update Cycle data assimilation system.The results revealed the possibility of improving weather forecasting by raising present weather stations to a height of 40 m;this would not only enable more observations to pass the terrain check,but should also reduce interpolation errors.Additionally,improvements for temperature,humidity and wind forecasting could be achieved as the accuracy of the initial conditions increases.

A New X-band Weather Radar System with Distributed Phased-Array Front-ends: Development and Preliminary Observation Results

A novel weather radar system with distributed phased-array front-ends was developed. The specifications and preliminary data synthesis of this system are presented, which comprises one back-end and three or more front-ends. Each front-end, which utilizes a phased-array digital beamforming technology, sequentially transmits four 22.5°-width beams to cover the 0°–90° elevational scan within about 0.05 s. The azimuthal detection is completed by one mechanical scan of0°–360° azimuths within about 12 s volume-scan update time. In the case of three front-ends, they are deployed according to an acute triangle to form a fine detection area(FDA). Because of the triangular deployment of multiple phased-array front-ends and a unique synchronized azimuthal scanning(SAS) rule, this new radar system is named Array Weather Radar(AWR). The back-end controls the front-ends to scan strictly in accordance with the SAS rule that assures the data time differences(DTD) among the three front-ends are less than 2 s for the same detection point in the FDA. The SAS can maintain DTD < 2 s for an expanded seven-front-end AWR. With the smallest DTD, gridded wind fields are derived from AWR data, by sampling of the interpolated grid, onto a rectangular grid of 100 m ×100 m ×100 m at a 12 s temporal resolution in the FDA. The first X-band single-polarized three-front-end AWR was deployed in field experiments in 2018 at Huanghua International Airport, China. Having completed the data synthesis and processing, the preliminary observation results of the first AWR are described herein.

Updating of the hierarchical rock mass rating(HRMR) system and a new subsystem developed for weathered granite formations

The RMR system is still very much applied in rock mechanics engineering context. It is based on the evaluation of six weights to obtain a final rating. To obtain the final rating a considerable amount of information is needed concerning the rock mass which can be difficult to obtain in some projects or project stages at least with accuracy. In 2007 an alternative classification scheme based on the RMR, the Hierarchical Rock Mass Rating(HRMR) was presented. The main feature of this system was the adaptation to the level of knowledge existent about the rock mass to obtain the classification of the rock mass since it followed a decision tree approach. However, the HRMR was only valid for hard rock granites with low fracturing degrees. In this work, the database was enlarged with approximately 40% more cases considering other types of granite rock masses including weathered granites and based on this increased database the system was updated. Granite formations existent in the north of Portugal including Porto city are predominantly granites. Some years ago a light rail infrastructure was built in the city of Porto and surrounding municipalities which involved considerable challenges due to the high heterogeneity levels of the granite formations and the difficulties involved in their geomechanical characterization. In this work it is intended to provide also a contribution to improve the characterization of these formations with special emphasis to the weathered horizons. A specific subsystem applicable to the weathered formations was developed. The results of the validation of these systems are presented and show acceptable performances in identifying the correct class using less information than with the RMR system.