A Discussion on the Flexible Regulation Capacity Requirements of China’s Power System

A high proportion of variable renewable energy(VRE)is one of the most significant characteristics of China’s future power system under the"dual carbon"target.However,wind and solar power units are more uncontrollable and less supportive for power system stability than traditional thermal power units,due to their susceptibility to the weather and the grid connection of power electronics.Therefore,as the capacity and generation of VRE grow rapidly and even dominate the power structure,the power system’s ability to deal with disturbances will continue to decrease.

A Deep Learning Approach for Forecasting Thunderstorm Gusts in the Beijing–Tianjin–Hebei Region

Thunderstorm gusts are a common form of severe convective weather in the warm season in North China,and it is of great importance to correctly forecast them.At present,the forecasting of thunderstorm gusts is mainly based on traditional subjective methods,which fails to achieve high-resolution and high-frequency gridded forecasts based on multiple observation sources.In this paper,we propose a deep learning method called Thunderstorm Gusts TransU-net(TGTransUnet)to forecast thunderstorm gusts in North China based on multi-source gridded product data from the Institute of Urban Meteorology(IUM)with a lead time of 1 to 6 h.To determine the specific range of thunderstorm gusts,we combine three meteorological variables:radar reflectivity factor,lightning location,and 1-h maximum instantaneous wind speed from automatic weather stations(AWSs),and obtain a reasonable ground truth of thunderstorm gusts.Then,we transform the forecasting problem into an image-to-image problem in deep learning under the TG-TransUnet architecture,which is based on convolutional neural networks and a transformer.The analysis and forecast data of the enriched multi-source gridded comprehensive forecasting system for the period 2021–23 are then used as training,validation,and testing datasets.Finally,the performance of TG-TransUnet is compared with other methods.The results show that TG-TransUnet has the best prediction results at 1–6 h.The IUM is currently using this model to support the forecasting of thunderstorm gusts in North China.

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.

Dynamic response and failure process of horizontal-layered fractured structure rock slope under strong earthquake

Rock slope with horizontal-layered fractured structure(HLFS)has high stability in its natural state.However,a strong earthquake can induce rock fissure expansion,ultimately leading to slope failure.In this study,the dynamic response,failure mode,and spectral characteristics of rock slope with HLFS under strong earthquake conditions were investigated based on the large-scale shaking table model test.On this basis,multiple sets of numerical calculation models were further established by UDEC discrete element program.Five influencing factors were considered in the parametric study of numerical simulations,including slope height,slope angle,bedding-plane spacing and secondary joint spacing as well as bedrock dip angle.The results showed that the failure process of rock slope with HLFS under earthquake action is mainly divided into four phases,i.e.,the tensile crack of the slope shoulder joints and shear dislocation at the top bedding plane,the extension of vertical joint cracks and increase of shear displacement,the formation of step-through sliding surfaces and the instability,and finally collapse of fractured rock mass.The acceleration response of slopes exhibits elevation amplification effect and surface effect.Numerical simulations indicate that the seismic stability of slopes with HLFS exhibits a negative correlation with slope height and angle,but a positive correlation with bedding-plane spacing,joint spacing,and bedrock dip angle.The results of this study can provide a reference for seismic stability evaluation of weathered rock slopes.

Stress-assisted corrosion mechanism of 3Ni steel by using gradient boosting decision tree machining learning method

Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend.The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method.The information on the corrosion process was recorded using the galvanic corrosion current monitoring method.The gradi-ent boosting decision tree(GBDT)machine learning method was used to mine the corrosion mechanism,and the importance of the struc-ture factor was investigated.Field exposure tests were conducted to verify the calculated results using the GBDT method.Results indic-ated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel.Dif-ferent mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion.When the corrosion reached a stable state,the in-crease in Mn element content increased the corrosion rate of 3Ni steel,while Cu reduced this rate.In the presence of stress,the increase in Mn element content and Cu addition can inhibit the corrosion process.The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology,verifying the reliability of the big data evaluation method and data prediction model selection.

Non-crossing Quantile Regression Neural Network as a Calibration Tool for Ensemble Weather Forecasts

Despite the maturity of ensemble numerical weather prediction(NWP),the resulting forecasts are still,more often than not,under-dispersed.As such,forecast calibration tools have become popular.Among those tools,quantile regression(QR)is highly competitive in terms of both flexibility and predictive performance.Nevertheless,a long-standing problem of QR is quantile crossing,which greatly limits the interpretability of QR-calibrated forecasts.On this point,this study proposes a non-crossing quantile regression neural network(NCQRNN),for calibrating ensemble NWP forecasts into a set of reliable quantile forecasts without crossing.The overarching design principle of NCQRNN is to add on top of the conventional QRNN structure another hidden layer,which imposes a non-decreasing mapping between the combined output from nodes of the last hidden layer to the nodes of the output layer,through a triangular weight matrix with positive entries.The empirical part of the work considers a solar irradiance case study,in which four years of ensemble irradiance forecasts at seven locations,issued by the European Centre for Medium-Range Weather Forecasts,are calibrated via NCQRNN,as well as via an eclectic mix of benchmarking models,ranging from the naïve climatology to the state-of-the-art deep-learning and other non-crossing models.Formal and stringent forecast verification suggests that the forecasts post-processed via NCQRNN attain the maximum sharpness subject to calibration,amongst all competitors.Furthermore,the proposed conception to resolve quantile crossing is remarkably simple yet general,and thus has broad applicability as it can be integrated with many shallow-and deep-learning-based neural networks.

Influence of vapor pressure deficit on vegetation growth in China

Vapor pressure deficit(VPD)plays a crucial role in determining plant physiological functions and exerts a substantial influence on vegetation,second only to carbon dioxide(CO_(2)).As a robust indicator of atmospheric water demand,VPD has implications for global water resources,and its significance extends to the structure and functioning of ecosystems.However,the influence of VPD on vegetation growth under climate change remains unclear in China.This study employed empirical equations to estimate the VPD in China from 2000 to 2020 based on meteorological reanalysis data of the Climatic Research Unit(CRU)Time-Series version 4.06(TS4.06)and European Centre for Medium-Range Weather Forecasts(ECMWF)Reanalysis 5(ERA-5).Vegetation growth status was characterized using three vegetation indices,namely gross primary productivity(GPP),leaf area index(LAI),and near-infrared reflectance of vegetation(NIRv).The spatiotemporal dynamics of VPD and vegetation indices were analyzed using the Theil-Sen median trend analysis and Mann-Kendall test.Furthermore,the influence of VPD on vegetation growth and its relative contribution were assessed using a multiple linear regression model.The results indicated an overall negative correlation between VPD and vegetation indices.Three VPD intervals for the correlations between VPD and vegetation indices were identified:a significant positive correlation at VPD below 4.820 hPa,a significant negative correlation at VPD within 4.820–9.000 hPa,and a notable weakening of negative correlation at VPD above 9.000 hPa.VPD exhibited a pronounced negative impact on vegetation growth,surpassing those of temperature,precipitation,and solar radiation in absolute magnitude.CO_(2) contributed most positively to vegetation growth,with VPD offsetting approximately 30.00%of the positive effect of CO_(2).As the rise of VPD decelerated,its relative contribution to vegetation growth diminished.Additionally,the intensification of spatial variations in temperature and precipitation accentuated the spatial heterogeneity in the impact of VPD on vegetation growth in China.This research provides a theoretical foundation for addressing climate change in China,especially regarding the challenges posed by increasing VPD.

Preface to the Special Issue:AI Applications in Atmospheric and Oceanic Science:Pioneering the Future(Part I)

As AI continues to establish itself as a cornerstone technology across various industries and scientific disciplines,its profound impact on atmospheric and oceanic science is becoming increasingly apparent.The advantages of AI in surmounting obstacles within our field are undeniable,as evidenced by breakthroughs in weather forecasting(e.g.,Bi et al.,2023),climate prediction(e.g.,Ham et al.,2019),AI-based parameterization schemes(e.g.,Rasp et al.,2018;Wang and Tan,2023),and beyond.Recognizing the transformative potential of AI in atmospheric and oceanic science,this special issue endeavors to explore the extensive applications of AI in our domain.

Promising Results Predict Role for Artificial Intelligence in Weather Forecasting

Artificial intelligence(AI)has already demonstrated its proficiency at difficult scientific tasks like predicting how proteins will fold and identifying new astronomical objects in masses of observational data[1].Now,recent results suggest that AI also excels at weather forecasting.For global predictions,GraphCast,an AI system developed by Google subsidiary DeepMind(London,UK),outperforms the state-of-the-art model from the European Centre for Medium-Range Weather Forecasts(ECMWF),providing more accurate projections of variables such as temperature and humidity 90%of the time[2,3].Other AI systems,including Pangu-Weather from the Chinese tech company Huawei(Shenzhen,China)[4],can also match or beat traditional global forecasting models.

Quantifying the Temperature Effects on the Morbidity of Digestive Diseases In an Inland Plateau-Valley City

Extreme air temperature and increased weather oscillations caused by climate change have been threatening global health.Meteorological conditions are external inducers that may trigger the onset of gastrointestinal diseases,in addition to bacterial infection or behavioral factors including smoking,alcohol consumption,and hot food consumption[1,2].

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.

Is the Cape Stone Forest in Shantou City formed by wave erosion? A comparison of the pedestal rocks on the coasts and the mountains of Queshi in eastern Guangdong, China

Cape Stone Forest is a group of granite rock pillars(pedestal rocks) towering over Shilin Lake, on the southern shore of Shantou Bay in eastern Guangdong, China. The rock pillars were previously identified as sea stacks because they have marine notch-like concave sidewalls at their base, and more importantly, the lake is immediately adjacent to the bay, which is exposed to the open sea. However, rock pillars similar in shape and size can also be found at the top of Queshi Mountain, which is only about 300 meters northwest of the lake and about 85 meters above sea level. Therefore, the marine origin of Cape Stone Forest is seriously questioned. In this study, 3D imagery and drone technology were used to collect data in the investigations without direct manual measurements in the water or on the mountain. It shows that the concave sidewalls of the rock pillars in the lake and on the mountains occur at different heights and are exposed to different directions, while a natural sea stack on Mayu Island at the mouth of Shantou Bay has a horizontal notch parallel to the sea level, although the granite rock of the sea stack is the same as that of the lake and the mountains. The eastern side of the island, where the sea stack is located, is exposed to the open sea but blocks large waves for the rock pillars in the lake. Therefore, the origin of Cape Stone Forest cannot be explained by wave-based mechanisms. The only satisfactory explanation that takes into account all the field evidence is that the narrow rock pillars of the lake and mountain were formed by chemical weathering that penetrated closely the spaced joints of the granite rock, and the notch-like concave sidewalls were formed by more effective chemical weathering at the base of the pillars.

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.

Formation and growth of nanophase iron particles on the surface of Mercury revealed by experimental study

Space weathering is a primary factor in altering the composition and spectral characteristics of surface materials on airless planets.However,current research on space weathering focuses mainly on the Moon and certain types of asteroids.In particular,the impacts of meteoroids and micrometeoroids,radiation from solar wind/solar flares/cosmic rays,and thermal fatigue due to temperature variations are being studied.Space weathering produces various transformation products such as melted glass,amorphous layers,iron particles,vesicles,and solar wind water.These in turn lead to soil maturation,changes in visible and near-infrared reflectance spectra(weakening of characteristic absorption peaks,decreased reflectance,increased near-infrared slope),and alterations in magnetism(related to small iron particles),collectively termed the“lunar model”of space weathering transformation.Compared to the Moon and asteroids,Mercury has unique spatial environmental characteristics,including more intense meteoroid impacts and solar thermal radiation,as well as a weaker particle radiation environment due to the global distribution of its magnetic field.Therefore,the lunar model of space weathering may not apply to Mercury.Previous studies have extensively explored the eff ects of micrometeoroid impacts.Hence,this work focuses on the eff ects of solar-wind particle radiation in global magnetic-field distribution and on the weathering transformation of surface materials on Mercury under prolonged intense solar irradiation.Through the utilization of highvalence state,heavy ion implantation,and vacuum heating simulation experiments,this paper primarily investigates the weathering transformation characteristics of the major mineral components such as anorthite,pyroxene,and olivine on Mercury’s surface and compares them to the weathering transformation model of the Moon.The experimental results indicate that ion implantation at room temperature is insufficient to generate np-Fe^(0)directly but can facilitate its formation,while prolonged exposure to solar thermal radiation on Mercury’s surface can lead directly to the formation of np-Fe^(0).Therefore,intense solar thermal radiation is a crucial component of the unique space weathering transformation process on Mercury’s surface.

Quantifying the chemical composition of weathering products of Hainan basalts with reflectance spectroscopy and its implications for Mars

With the development of the hyperspectral remote sensing technique,extensive chemical weathering profiles have been identified on Mars.These weathering sequences,formed through precipitation-driven leaching processes,can reflect the paleoenvironments and paleoclimates during pedogenic processes.The specific composition and stratigraphic profiles mirror the mineralogical and chemical trends observed in weathered basalts on Hainan Island in south China.In this study,we investigated the laboratory reflectance spectra of a 53-m-long drilling core of a thick basaltic weathering profile collected from Hainan Island.We established a quantitative spectral model by combining the genetic algorithm and partial least squares regression(GA-PLSR)to predict the chemical properties(SiO2,Al2O3,Fe2O3)and index of laterization(IOL).The entire sample set was divided into a calibration set of 25 samples and a validation set of 12 samples.Specifically,the GA was used to select the spectral subsets for each composition,which were then input into the PLSR model to derive the chemical concentration.The coefficient of determination(R2)values on the validation set for SiO2,Al2O3,Fe2O3,and the IOL were greater than 0.9.In addition,the effects of various spectral preprocessing techniques on the model accuracy were evaluated.We found that the spectral derivative treatment boosted the prediction accuracy of the GA-PLSR model.The improvement achieved with the second derivative was more pronounced than when using the first derivative.The quantitative model developed in this work has the potential to estimate the contents of similar weathering basalt products,and thus infer the degree of alteration and provide insights into paleoclimatic conditions.Moreover,the informative bands selected by the GA can serve as a guideline for designing spectral channels for the next generation of spectrometers.

Changes in the adult sex ratio of six duck species breeding populations over two decades

Despite all efforts,long-term changes in the adult sex ratios of breeding duck populations are still unclear;this uncertainty is especially true for male-bias populations,which are often under the scrutiny of researchers lacking convenient results for the active protection of endangered species.Species with male-bias populations are usually strongly affected by a decline in population size that leads to a higher extinction risk.In this study,we examined our long-term data of the abundance of breeding populations in six duck species(Mallard Anas platyrhynchos,Gadwall Mareca strepera,Red-crested Pochard Netta rufina,Common Pochard Aythya ferina,Tufted Duck Aythya fuligula,and Common Goldeneye Bucephala clangula)from fishponds in South Bohemia,Czechia,between 2004 and 2022.This evidence was used to assess long-term changes in the adult sex ratio in these breeding populations and investigate the possible effects of the NAO index(North Atlantic Oscillation index)on them,indicating climate conditions in winter.We determined a long-term decrease of the proportion of females in the breeding season in two of the six examined species:Common Pochard and Red-crested Pochard,which is driven by the long-term increase in the number of males in contrast to the decreasing or stable number of females likely caused by different migration behaviours between females and males.In the case of Common Pochard,in breeding populations,we estimated 60-65%of males in the early 2000s rising to 75-80%in the early 2020s.However,we establish no significant effects linked to climate conditions of the previous winter in these species as a crucial cause of the changes of the proportion of females in the breeding population.

A coupled strain softening and hardening model for completely weathered granite in a fault zone

his study focused on exploring the specificity of mechanical behavior for completely weathered granite,as a special soil,by consolidated drained triaxial tests.The influences of dry density(1.60,1.70,1.80 and 1.90 g/cm^(3)),confining pressure(100,200,400 and 600 kPa),and moisture content(13.0%,that is,natural moisture content)were investigated in the present work.A newly developed Duncan-Chang model was established based on the experimental data and Duncan-Chang model.The influence of each parameter on the type of the proposed model curve was also evaluated.The experimental results revealed that with varying dry density and confining pressure,the deviatoric stress–strain curves have diversified characteristics including strain-softening,strain-stabilization and strain-hardening.Under high confining pressure condition,specimens with different densities all showed strain-hardening characteristic.Whereas at the low confining pressure levels,specimens with higher densities gradually transform into softening characteristics.Except for individual compression shear failure,the deformation modes of the specimens all showed swelling deformation,and all the damaged specimens maintained good integrity.Through comparing the experiment results,the strain-softening or strain-hardening behavior of CWG specimens could be predicted following the proposed model with high accuracy.Additionally,the proposed model can accurately characterize the key mechanical indicators,such as tangent modulus,peak value and residual strength,which is simple to implement and depends on fewer parameters.

The Response of Carbonate System to Watershed Urbanization Process in a Semi-Arid River

Different from rivers in humid areas,the variability of riverine CO_(2) system in arid areas is heavily impacted by anthropogenic disturbance with the increasing urbanization and water withdrawals.In this study,the water chemistry and the controls of carbonate system in an urbanized river(the Fenhe River)on the semi-arid Loess Plateau were analyzed.The water chemistry of the river water showed that the high dissolved inorganic carbon(DIC)concentration(about 37 mg L^(-1))in the upstream with a karst land type was mainly sourced from carbonate weathering involved by H_(2)CO_(3) and H_(2)SO_(4),resulting in an oversaturated partial pressure of CO_(2)(pCO_(2))(about 800μatm).In comparison,damming resulted in the widespread appearance of non-free flowing river segments,and aquatic photosynthesis dominated the DIC and pCO_(2) spatiality demonstrated by the enriched stable isotope of DIC(δ^(13)CDIC).Especially in the mid-downstream flowing through major cities in warm and low-runoff August,some river segments even acted as an atmospheric CO_(2) sink.The noteworthy is wastewater input leading to a sudden increase in DIC(>55 mg L^(-1))and pCO_(2)(>4500μatm)in the downstream of Taiyuan City,and in cold November the increased DIC even extended to the outlet of the river.Our results highlight the effects of aquatic production induced by damming and urban sewage input on riverine CO_(2) system in semi-arid areas,and reducing sewage discharge may mitigate CO_(2) emission from the rivers.

Assimilating FY-4A AGRI Radiances with a Channel-Sensitive Cloud Detection Scheme for the Analysis and Forecasting of Multiple Typhoons

This paper presents an attempt at assimilating clear-sky FY-4A Advanced Geosynchronous Radiation Imager(AGRI)radiances from two water vapor channels for the prediction of three landfalling typhoon events over the West Pacific Ocean using the 3DVar data assimilation(DA)method along with the WRF model.A channel-sensitive cloud detection scheme based on the particle filter(PF)algorithm is developed and examined against a cloud detection scheme using the multivariate and minimum residual(MMR)algorithm and another traditional cloud mask–dependent cloud detection scheme.Results show that both channel-sensitive cloud detection schemes are effective,while the PF scheme is able to reserve more pixels than the MMR scheme for the same channel.In general,the added value of AGRI radiances is confirmed when comparing with the control experiment without AGRI radiances.Moreover,it is found that the analysis fields of the PF experiment are mostly improved in terms of better depicting the typhoon,including the temperature,moisture,and dynamical conditions.The typhoon track forecast skill is improved with AGRI radiance DA,which could be explained by better simulating the upper trough.The impact of assimilating AGRI radiances on typhoon intensity forecasts is small.On the other hand,improved rainfall forecasts from AGRI DA experiments are found along with reduced errors for both the thermodynamic and moisture fields,albeit the improvements are limited.