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.

2023: Weather and Climate Extremes Hitting the Globe with Emerging Features

Globally,2023 was the warmest observed year on record since at least 1850 and,according to proxy evidence,possibly of the past 100000 years.As in recent years,the record warmth has again been accompanied with yet more extreme weather and climate events throughout the world.Here,we provide an overview of those of 2023,with details and key background causes to help build upon our understanding of the roles of internal climate variability and anthropogenic climate change.We also highlight emerging features associated with some of these extreme events.Hot extremes are occurring earlier in the year,and increasingly simultaneously in differing parts of the world(e.g.,the concurrent hot extremes in the Northern Hemisphere in July 2023).Intense cyclones are exacerbating precipitation extremes(e.g.,the North China flooding in July and the Libya flooding in September).Droughts in some regions(e.g.,California and the Horn of Africa)have transitioned into flood conditions.Climate extremes also show increasing interactions with ecosystems via wildfires(e.g.,those in Hawaii in August and in Canada from spring to autumn 2023)and sandstorms(e.g.,those in Mongolia in April 2023).Finally,we also consider the challenges to research that these emerging characteristics present for the strategy and practice of adaptation.

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.

Impact of mica on geotechnical behavior of weathered granitic soil using macro and micro investigations

The micaceous weathered granitic soil(WGS)is frequently encountered in civil engineering worldwide,unfortunately little information is available regarding how mica affects the physico-mechanical behaviors of WGS.This study prepares reconstituted WGS with different mica contents by removing natural mica in theWGS,and then mixes it with commercial mica powders.The geotechnical behavior as well as the microstructures of the mixtures are characterized.The addition of mica enables the physical indices of WGS to be specific combinations of coarser gradation and high permeability but high Atterberg limits.However,high mica content in WGS was found to be associated with undesirable mechanical properties,including increased compressibility,disintegration,and swelling potential,as well as poor compactability and low effective frictional angle.Microstructural analysis indicates that the influence of mica on the responses of mixtures originates from the intrinsic nature of mica as well as the particle packing being formed withinWGS.Mica exists in the mixture as stacks of plates that form a spongy structure with high compressibility and swelling potential.Pores among the plates give the soil high water retention and high Atterberg limits.Large pores are also generated by soil particles with bridging packing,which enhances the permeability and water-soil interactions upon immersion.This study provides a microlevel understanding of how mica dominates the behavior of WGS and provides new insights into the effective stabilization and improvement of micaceous soils.

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.

Metallurgical performance evaluation of space-weathered Chang’e-5 lunar soil

Space metallurgy is an interdisciplinary field that combines planetary space science and metallurgical engineering.It involves systematic and theoretical engineering technology for utilizing planetary resources in situ.However,space metallurgy on the Moon is challenging because the lunar surface has experienced space weathering due to the lack of atmosphere and magnetic field,making the mi-crostructure of lunar soil differ from that of minerals on the Earth.In this study,scanning electron microscopy and transmission electron microscopy analyses were performed on Chang’e-5 powder lunar soil samples.The microstructural characteristics of the lunar soil may drastically change its metallurgical performance.The main special structure of lunar soil minerals include the nanophase iron formed by the impact of micrometeorites,the amorphous layer caused by solar wind injection,and radiation tracks modified by high-energy particle rays inside mineral crystals.The nanophase iron presents a wide distribution,which may have a great impact on the electromagnetic prop-erties of lunar soil.Hydrogen ions injected by solar wind may promote the hydrogen reduction process.The widely distributed amorph-ous layer and impact glass can promote the melting and diffusion process of lunar soil.Therefore,although high-energy events on the lun-ar surface transform the lunar soil,they also increase the chemical activity of the lunar soil.This is a property that earth samples and tradi-tional simulated lunar soil lack.The application of space metallurgy requires comprehensive consideration of the unique physical and chemical properties of lunar soil.

Digital monitoring of rotary-percussive drilling with down-the-hole hammer for profiling weathered granitic ground

Rock and geotechnical engineering investigations involve drilling holes in ground with or without retrieving soil and rock samples to construct the subsurface ground profile.On the basis of an actual soil nailing drilling for a slope stability project in Hong Kong,this paper further develops the drilling process monitoring(DPM)method for digitally profiling the subsurface geomaterials of weathered granitic rocks using a compressed airflow driven percussive-rotary drilling machine with down-the-hole(DTH)hammer.Seven transducers are installed on the drilling machine and record the chuck displacement,DTH rotational speed,and five pressures from five compressed airflows in real-time series.The mechanism and operations of the drilling machine are elaborated in detail,which is essential for understanding and evaluating the drilling data.A MATLAB program is developed to automatically filter the recorded drilling data in time series and classify them into different drilling processes in sub-time series.These processes include penetration,push-in with or without rod,pull-back with or without rod,rod-tightening and rod-untightening.The drilling data are further reconstructed to plot the curve of drill-bit depth versus the net drilling time along each of the six drillholes.Each curve is found to contain multiple linear segments with a constant penetration rate,which implies a zone of homogenous geomaterial with different weathering grades.The effect from fluctuation of the applied pressures is evaluated quantitatively.Detailed analyses are presented for accurately assess and verify the underground profiling and strength in weathered granitic rock,which provided the basis of using DPM method to confidently assess drilling measurements to interpret the subsurface profile in real time.

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.

Fractionation characteristics of magnesium isotope in the ancient weathering crust

Weathering has always been a concerned around the world,as the first and most important step in the global cycle of elements,which leads to the fractionation of isotopes on the scale of geological age.The Middle Ordovician Majiagou Formation in Daniudi area of the Ordos Basin had experienced weathering for>130 Myr.Through thin section observation,major and trace element analysis,carbon,oxygen,and magnesium isotopes composition analysis,the dolomitization modes and weathering of ancient dolo-mite in Daniudi area were analyzed in detail.The results showed that the Sabkha and brine-reflux dolomitization modes had developed,and the Mg isotopes in different layers of the karst crust were fractionated by various factors.The vertical vadose zone was affected by weathering,the Mg isotope of dolomite(δ^(26)Mgdol)showed a downward decreasing trend;the horizontal underflow zone was controlled by diagenesis and formation fluid,δ^(26)Mgdol showed a vertical invariance and negative;the main reason for Mg isotope fractionation in the deep slow-flow zone was the brine-reflux dolomitization mode during early burial period,which showed a vertical downward increase.Finally,the Mg isotope characteristic data of the ancient weathering crust were provided and the process of Mg isotope frac-tionationinthekarstcrust was explained.

Site observations of weathered granitic soils subjected to cementation and partial drainage using SCPTU

Because of the cementation inherited from the parent rock,weathered granitic soil is usually susceptible to disturbance,which poses considerable challenges for laboratory characterization.The cone penetration test with pore pressure measurements has long been known for its reliability in site investigations and stratigraphic profiling.However,although extensive piezocone test results and experience are available for sedimentary soil,similar advances are yet to be made for weathered granitic soil.Moreover,the experience from sedimentary soil may not be directly applicable to weathered profiles because of the essentially different natures of the two types of geomaterials.This study performs seismic piezocone tests in a weathered granitic profile comprising residual granitic soil,completely weathered granite,and highly weathered granite.Pore pressure is measured at both the cone mid-face and the shoulder,and the effects of penetrometer size and penetration rate are considered.A series of updated soil behavior type charts is proposed to interpret the test results,thereby allowing the effect of weathering to be evaluated.This paper offers an important extension to the sparse data on the in situ responses of weathered materials.

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.

Deep convolutional neural network for meteorology target detection in airborne weather radar images

Considering the problem that the scattering echo images of airborne Doppler weather radar are often reduced by ground clutters,the accuracy and confidence of meteorology target detection are reduced.In this paper,a deep convolutional neural network(DCNN)is proposed for meteorology target detection and ground clutter suppression with a large collection of airborne weather radar images as network input.For each weather radar image,the corresponding digital elevation model(DEM)image is extracted on basis of the radar antenna scan-ning parameters and plane position,and is further fed to the net-work as a supplement for ground clutter suppression.The fea-tures of actual meteorology targets are learned in each bottle-neck module of the proposed network and convolved into deeper iterations in the forward propagation process.Then the network parameters are updated by the back propagation itera-tion of the training error.Experimental results on the real mea-sured images show that our proposed DCNN outperforms the counterparts in terms of six evaluation factors.Meanwhile,the network outputs are in good agreement with the expected mete-orology detection results(labels).It is demonstrated that the pro-posed network would have a promising meteorology observa-tion application with minimal effort on network variables or parameter changes.

Middle Eocene terrestrial paleoweathering and climate evolution in the midlatitude Bohai Bay Basin of eastern China

The middle Eocene climatic optimum(MECO,ca.-42 Ma)is a key time period for understanding Cenozoic cooling of the global climate.Still,midlatitude terrestrial records of climate evolution during MEcO epoch are rare.In this study,continuous high-resolution record of shale sediments in mid-Eocene Shahejie Formation(MES shales)in the Bohai Bay Basin were performed with major-element and wavelet analysis.The midlatitude paleoweathering and paleoclimatic evolution during MEcO epoch were analyzed in this study.The MES shales experienced weak-moderate paleoweathering under a subtropical monsoon paleoclimate with mean annual temperature of 8.3-12.9℃ and mean annual precipitation of 685-1100 mm/yr.The MES shales record a mixed provenance involving intermediate igneous rocks,and low compositional maturity.The nutrient-rich environment led to enrichment in organic matter in the MES shales.Wavelet analysis revealed good periodicity about the paleoclimate and weathering during MECO epoch.In the stage I of MES shales depositional process,the paleolake was high in nutrients,and the MES shales experienced high chemical weathering due to a relatively warmer and more humid climate.In contrast,the climate in stage II was relatively cold and dry,and the maturity of the MES shales was relatively high during this stage,suggesting a relatively stable tectonic background.This work provides more terrestrial records of MEco epoch for midlatitude region,and is benefit for better understanding of the palaeoenvironment when MES shales formed.The implication of organic matters enrichment in this study is meaningful for the shale oil/gas exploration in Nanpu Sag.

Salt-Lake Basin Bedrock Weathered Crust Gas Reservoir in the Altun Mountains Front of the Qaidam Basin,Western China

The bedrock weathered crust in front of the Altun Mountains in the Qaidam Basin,western China,is different from others because this is a salt-lake basin,where saline water fluid infiltrates and is deposited in the overlying strata.A large amount of gypsum infills the bedrock weathered crust,and this has changed the pore structure.Using core observation,polarized light microscopy,electron probe,physical property analysis and field emission scanning electron microscopy experiments,the characteristics of the weathered bedrock have been studied.There are cracks and a small number of dissolved pores in the interior of the weathered crust.Matrix micropores are widely developed,especially the various matrix cracks formed by tectonics and weathering,as well as the stress characteristics of small dissolved pores,and physical properties such as porosity and permeability.This‘dual structure’developed in the bedrock is important for guiding the exploration of the lake basin bedrock for natural gas.

Forecasting solar still performance from conventional weather data variation by machine learning method

Solar stills are considered an effective method to solve the scarcity of drinkable water.However,it is still missing a way to forecast its production.Herein,it is proposed that a convenient forecasting model which just needs to input the conventional weather forecasting data.The model is established by using machine learning methods of random forest and optimized by Bayesian algorithm.The required data to train the model are obtained from daily measurements lasting9 months.To validate the accuracy model,the determination coefficients of two types of solar stills are calculated as 0.935and 0.929,respectively,which are much higher than the value of both multiple linear regression(0.767)and the traditional models(0.829 and 0.847).Moreover,by applying the model,we predicted the freshwater production of four cities in China.The predicted production is approved to be reliable by a high value of correlation(0.868)between the predicted production and the solar insolation.With the help of the forecasting model,it would greatly promote the global application of solar stills.

Effect of surfactant addition on leaching process of weathered crust elution-deposited rare earth ores with magnesium sulfate

Surfactants were proposed to be added into magnesium sulfate solution to improve the leaching process of weathered crust elution-deposited rare earth ores(WREOs).Effects of surfactants and their concentration on the seepage of leaching solutions and the leaching efficiency of rare earth(RE)and aluminum(Al)were investigated,and the leaching kinetics,the mass transfer process,the adhesion work and the adhesion work reduction factor were analyzed to reveal its strengthening leaching mechanism.The results show that cetyltrimethylammonium bromide(CTAB)has a better strengthening effect on the leaching process than dodecyl trimethyl ammonium bromide(DTAB),sodium dodecyl sulfate(SDS),sodium oleate and oleic acid.In the presence of 0.04%CTAB in 0.2 mol/L solution,the permeability coefficient of WREOs increases from 0.945×10^(-5)to 1.640×10^(-5)cm·s^(-1),and the leaching efficiency of RE increases from 80%to 90%,confirming the promotion of surfactants on the leaching process of WREOs.Kinetic analysis shows that the leaching process conforms to the inner diffusion control model,and the leaching kinetics equations of RE and Al related to CTAB content are obtained.Mass transfer discussion shows a smaller height equivalent to theoretical plate(HETP)of RE and Al at CTAB content of 0.04%,suggesting the higher mass transfer efficiency here.According to the interfacial properties of leaching solutions,the calculated adhesion work and the adhesion work reduction factor further demonstrate the strengthening leaching effect of CTAB on the leaching process of WREOs.

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.

Effect of particle gradation on pore structure and seepage law of solution in weathered crust elution-deposited rare earth ores

Both CT and Avizo software were used to explore the effect of particle gradation on the evolution characteristics of pore structure and seepage paths in weathered crust elution-deposited rare earth ores during leaching.The results showed that the pore areas in four kinds of ore samples before leaching were mainly concentrated in 10^(4)–10^(7)μm^(2),whose pore quantities accounted for 96.89%,94.94%,90.48%,and 89.45%,respectively,while the corresponding pore volume only accounted for 30.74%,14.55%,7.58%,and 2.84%of the total pore volume.With the decrease of fractal dimension,the average pore throat length increased,but pore throat quantities,the average pore throat radius and coordination number decreased.Compared with that before leaching,the change degree of pore structure during leaching increased with the fractal dimension decreasing.For example,the reduction rate of the average coordination number of ore samples was 14.36%,21.30%,28.00%,and 32.90%,respectively.Seepage simulation results indicated that seepage paths were uniformly distributed before leaching while the streamline density and seepage velocity increased with the fractal dimension decreasing.Besides,the phenomenon of the streamline interruption gradually reduced during leaching while preferential seepage got more obvious with the decrease of the fractal dimension.

Quantitative characterization of vertical zonation of Mesozoic granite weathering reservoirs in the coastal area of eastern Fujian Province,China

Weathering crust reservoirs have obvious vertical zonation,which is the focus of weathering crust reservoir research,but there is a lack of quantitative characterization indexes.To achieve the quantitative characterization of granite weathering crust reservoir and provide the basis for oil exploration of granite weathering crust buried hill reservoir,in this paper,the vertical zonation of granite weathering crust reservoir is quantitatively divided by testing and analyzing the uniaxial compressive strength(UCS),magnetic susceptibility(MS),permeability,and chemical index of alteration(CIA)of the Mesozoic granite weathering crust in the coastal area of eastern Fujian.The results show that the granite weathering crust reservoir can be divided into four zones vertically:a soil zone(SZ),weathered and dissolved zone(WDZ),fracture zone(FZ),and bedrock zone(BZ).A cataclastic area is developed in the FZ and BZ,in which structural fractures are well-developed,the fracture surface density is usually greater than 200 m/m^(2),and the contribution to the fractures in the rock mass is up to about 50%,making this the sweet spot of the reservoir.In the SZ,the rocks are loose,and the pores are well-developed.The UCS is less than 10 MPa,and the average rate of change of the UCS(Δ_(σ))is 0.90.The average permeability is 2823.00 mD,and the average rate of change of the permeability(Δ_(κ))is 5.13.The average CIA is 74.9%.The average clay mineral content is 7%.The rocks in the WDZ have been significantly weathered by physical and chemical processes,and the weathering fractures and dissolution pores are well-developed.The average UCS is 18.2 MPa,and the averageΔ_(σ)is 0.70.The average permeability is 143.80 mD,and averageΔ_(κ)is 4.17.The average CIA is 65.3%.The average clay mineral content is 4%.Under the influence of tectonic movement and physical weathering,the rocks in the FZ have developed structural fractures and a few weathered fractures.The average UCS is 57.9 MPa,and the averageΔ_(σ)is 0.18.The average permeability is 5.50 mD,and the averageΔ_(κ)is 2.55.The average CIA is 61.6%.The average clay mineral content is 2%.In the BZ,the rocks are intact and hard.The average UCS is 69.9 MPa,and the average Ds is 0.13.The average permeability is 1.46 mD,and the averageΔ_(κ)is 1.43.The average CIA is 57.8%.The average clay mineral content is less than 1%.The multi-parameter combination of the UCS,Δ_(σ),permeability,Δ_(κ),CIA,and clay mineral content achieved good results in the division of the zones of the weathering crust.The UCS increases gradually from top to bottom,while Ds,permeability,Δ_(κ),CIA,and clay mineral content all decrease gradually.In addition,based on the petrophysical parameters of the rocks,including the density,resistivity,and acoustic velocity,a good division effect was also achieved,which can provide a basis for the vertical zonation of the granite buried-hill weathering crust reservoir.

Convective Storm VIL and Lightning Nowcasting Using Satellite and Weather Radar Measurements Based on Multi-Task Learning Models

Convective storms and lightning are among the most important weather phenomena that are challenging to forecast.In this study,a novel multi-task learning(MTL)encoder-decoder U-net neural network was developed to forecast convective storms and lightning with lead times for up to 90 min,using GOES-16 geostationary satellite infrared brightness temperatures(IRBTs),lightning flashes from Geostationary Lightning Mapper(GLM),and vertically integrated liquid(VIL)from Next Generation Weather Radar(NEXRAD).To cope with the heavily skewed distribution of lightning data,a spatiotemporal exponent-weighted loss function and log-transformed lightning normalization approach were developed.The effects of MTL,single-task learning(STL),and IRBTs as auxiliary input features on convection and lightning nowcasting were investigated.The results showed that normalizing the heavily skew-distributed lightning data along with a log-transformation dramatically outperforms the min-max normalization method for nowcasting an intense lightning event.The MTL model significantly outperformed the STL model for both lightning nowcasting and VIL nowcasting,particularly for intense lightning events.The MTL also helped delay the lightning forecast performance decay with the lead times.Furthermore,incorporating satellite IRBTs as auxiliary input features substantially improved lightning nowcasting,but produced little difference in VIL forecasting.Finally,the MTL model performed better for forecasting both lightning and the VIL of organized convective storms than for isolated cells.