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The Ballynoe barite deposit is a conformable, mineralised horizon of Lower Carboniferous age overlying a diastem and mass faunal extinction demarking the transition from a quiet water environment to one of dynamic sedimentation. The geometry of the barite orebody correlates with the palaeotopography of the footwall, which acted as an important control over the lateral extent, thickness, and nature of the mineralisation. Sedimentary features within the barite horizon suggest that it was precipitated in the form of a cryptocrystalline mud which underwent major diagenetic modification resulting in extensive stylolitisation, recrystallisation, and remobilisation. There is abundant and compelling geological and isotopic evidence for early local exhalation from the presence of a hydrothermal vent fauna consisting of delicately pyritised worm tubes and haematised filaments of apparent microbial origin. The worm tubes are remarkably similar to examples from modern and ancient volcanic-hosted massive sulphide deposits, and the filamentous microfossils have similarities to modern Fe-oxidising bacteria. Strontium in the barite has an 87Sr/86Sr ratio indistinguishable from seawater between 350 and 344 Ma whilst oxygen isotopes from barite and chert suggest a diagenetic origin in equilibrium with such seawater around 60–70 °C. Fluid inclusion studies have shown that, in general, low temperature inclusions are very saline (20%–25%) whilst at higher homogenisation temperatures they are more dilute (9%–12%). [ABSTRACT FROM AUTHOR]

The Ballynoe barite deposit is a conformable, mineralised horizon of Lower Carboniferous age overlying a diastem and mass faunal extinction demarking the transition from a quiet water environment to one of dynamic sedimentation. The geometry of the barite orebody correlates with the palaeotopography of the footwall, which acted as an important control over the lateral extent, thickness, and nature of the mineralisation. Sedimentary features within the barite horizon suggest that it was precipitated in the form of a cryptocrystalline mud which underwent major diagenetic modification resulting in extensive stylolitisation, recrystallisation, and remobilisation. There is abundant and compelling geological and isotopic evidence for early local exhalation from the presence of a hydrothermal vent fauna consisting of delicately pyritised worm tubes and haematised filaments of apparent microbial origin. The worm tubes are remarkably similar to examples from modern and ancient volcanic-hosted massive sulphide deposits, and the filamentous microfossils have similarities to modern Fe-oxidising bacteria. Strontium in the barite has an 87Sr/86Sr ratio indistinguishable from seawater between 350 and 344 Ma whilst oxygen isotopes from barite and chert suggest a diagenetic origin in equilibrium with such seawater around 60–70 °C. Fluid inclusion studies have shown that, in general, low temperature inclusions are very saline (20%–25%) whilst at higher homogenisation temperatures they are more dilute (9%–12%).

An 8‐year climatology of raintype observations from the Global Precipitation Measurement dual‐frequency precipitation radar highlights two stratiform rain producing storm regimes differentiated by the relative importance of convection. The more convectively active regime occurs in the tropics and over warm‐season midlatitude land, where warm‐ and cold‐topped convection accounts for 55% (40%) of the rain (rain area). The less convectively active regime dominates over midlatitude ocean and cold‐season midlatitude land, where convective rain only accounts for 15% (8%) of the rain (rain area). The ratio between cold‐topped convective and stratiform rain area is highly distinct between the two regimes (22% vs. 5.5%), with different precipitation amounts (and thus heating) for similar stratiform rain areas. A third tropics‐only warm‐topped convection regime exists, but is not associated with major stratiform rain production. Plain Language Summary: We separate global precipitation into convective and stratiform types using a spaceborne weather radar, where convective rain is intense and short‐lived and stratiform rain is weaker and more homogeneous in space and time. We show that there are two unique regimes between these precipitation types that are dictated by how important convective processes are to a storm's evolution, that is, cold‐topped convection is either essential to sustaining the storm system or more of a bystander to large‐scale atmospheric processes that prefer to produce stratiform precipitation. The more convective regime is dominant in the tropics but can also occur over land in the midlatitudes during summer. This regime requires strong interaction between the convective and stratiform rainy regions and is more likely to produce large rain amounts. The less convective regime is predominant over higher latitude oceans and over land in the midlatitudes during winter. While convective and stratiform rain occurrence is well correlated in this regime, convective rain does not contribute significantly to the overall precipitation amounts. We believe that these regimes support a reevaluation of how precipitation physics and their interactions are handled in climate models. Key Points: Areal coverage and echo‐top heights of stratiform rain and warm‐ and cold‐topped convection indicate strong geographic variabilityStratiform rain fraction is 45% and 85% in the tropics and midlatitudes, respectively, with a rapid transition in the subtropicsStatistical analysis suggests unique interactions between cold‐top convection and stratiform precipitation in the tropics and extratropics [ABSTRACT FROM AUTHOR]

Recent studies show that the formation of outer rainbands (ORBs) is essence to secondary eyewall formation (SEF) of tropical cyclones (TCs). This study reveals that cloud‐radiation feedback (CRF) facilitates SEF of TCs by promoting the formation of ORBs and their stratiform sectors. In detail, CRF engenders an upper‐level anomalous radiative warming in the outer‐core region, which induces a secondary circulation that accelerates the release of microphysical heating above the freezing level. The enhanced upper‐level latent heating further promotes the outer‐core convection and the top‐down development of the ORBs. Moreover, CRF promotes a broader stratiform precipitation area and stronger stratiform heating profiles of the ORBs, which forces convective updrafts at the radially inward edge of the stratiform precipitation area and eventually leads to SEF. On the contrary, when the anomalous radiative warming is removed, the organization of ORBs and the development of stratiform precipitation are suppressed due to the absence of the radiation‐induced upper‐level secondary circulation, thus inhibiting SEF. Plain Language Summary: Secondary eyewall formation (SEF) is a common phenomenon of intense tropical cyclones (TCs), which significantly influences the intensity and size of TCs. However, forecasting SEF remains a challenge since SEF is dominated by internal dynamical processes but is highly sensitive to environmental forcings. This study probes into the influences of cloud‐radiation feedback (CRF) on SEF with a set of idealized simulations and it turns out that CRF is critical to SEF. The key mechanism lies in an upper‐level anomalous radiative warming associated with CRF in the outer‐core region, which facilitates the development of outer rainbands (ORBs) as well as the stratiform precipitation of ORBs. The broader stratiform precipitation area and stronger stratiform heating profile of ORBs provide favorable conditions for triggering the following convective updrafts associated with SEF at the radially inward side. The results presented in this study highlight the importance of CRF in SEF and imply that the precipitation characteristics of rainbands is an important pathway for CRF affecting the intensity and structure of TCs. Key Points: Cloud‐radiation feedback (CRF) facilitates secondary eyewall formation (SEF) of tropical cyclonesThe anomalous radiative warming caused by clouds facilitates outer‐core convection and the formation of outer rainbandsCRF promotes broader stratiform precipitation with amplified heating profile, favoring SEF [ABSTRACT FROM AUTHOR]

Abstract An 8‐year climatology of raintype observations from the Global Precipitation Measurement dual‐frequency precipitation radar highlights two stratiform rain producing storm regimes differentiated by the relative importance of convection. The more convectively active regime occurs in the tropics and over warm‐season midlatitude land, where warm‐ and cold‐topped convection accounts for 55% (40%) of the rain (rain area). The less convectively active regime dominates over midlatitude ocean and cold‐season midlatitude land, where convective rain only accounts for 15% (8%) of the rain (rain area). The ratio between cold‐topped convective and stratiform rain area is highly distinct between the two regimes (22% vs. 5.5%), with different precipitation amounts (and thus heating) for similar stratiform rain areas. A third tropics‐only warm‐topped convection regime exists, but is not associated with major stratiform rain production.

This study analyzes the raindrop size distribution (RSD) characteristics over New Delhi by dividing the year into three seasons: PreM (March–May), monsoon (June–September), and PostM (October–February). Data from a Joss-Waldvogel Disdrometer, installed at IITM New Delhi, Rajendra Nagar, was used for three years (2021–2023). The observed raindrop spectra were fitted with three-parameter Gamma functions to obtain the RSD. ERA-5 and satellite data were also employed to establish atmospheric and cloud properties for the three seasons. The RSD for the monsoon season shows the highest concentration of midsize (1–3 mm diameter) drops and the highest mean rain rate. PostM has the least concentration of midsize and large (diameter >3 mm) drops. General statistics of rain integral parameters reveal high variability in rain rate (R) and mass-weighted mean diameter (D m) values during the monsoon season. The mu-lambda scatter plots show considerable differences among the three seasons, indicating slightly distinct rainfall mechanisms in the three seasons. Z - R relations of the form Z = a R b were derived, with the highest coefficient (a) values observed for the PreM precipitation. The exponent (b) is found to be greater than unity in all three seasons. Rainfall was stratified based on rain rate. RSD gets broader with increasing R. Large drops are not found appreciably in the spectrum for R < 20 mm/h. A notable disparity between convective and stratiform RSD is evident. The values of rain integral parameters show considerable differences between the convective and stratiform regimes. A higher fraction of large drops is found for the stratiform rainfall in the PreM season compared to the other two seasons. CAPE, water vapor, surface temperature, and surface winds were higher during PreM and monsoon months compared to PostM. The distribution of differential temperature (δT) indicates that clouds with significant depth are found in PreM and monsoon seasons but are often lacking during PostM. • First attempt to understand the surface rainfall microphysics over Delhi. • Z-R relations derived for every season over Delhi for both stratiform and convective precipitation type. • Results suggest formation of bright bands during stratiform rainfall in PreM season. • Use of reanalysis and satellite data to understand the mean background meteorological conditions during the three seasons. [ABSTRACT FROM AUTHOR]

Rain type classification into convective and stratiform is an essential step required to improve quantitative precipitation estimations by remote sensing instruments. Previous studies with Micro Rain Radar (MRR) measurements and subjective rules have been performed to classify rain events. However, automating this process by using machine learning (ML) models provides the advantages of fast and reliable classification with the possibility to classify rain minute by minute. A total of 20,979 min of rain data measured by an MRR at Das in northeast Spain were used to build seven types of ML models for stratiform and convective rain type classification. The proposed classification models use a set of 22 parameters that summarize the reflectivity, the Doppler velocity, and the spectral width (SW) above and below the so-called separation level (SL). This level is defined as the level with the highest increase in Doppler velocity and corresponds with the bright band in stratiform rain. A pre-classification of the rain type for each minute based on the rain microstructure provided by the collocated disdrometer was performed. Our results indicate that complex ML models, particularly tree-based ensembles such as xgboost and random forest which capture the interactions of different features, perform better than simpler models. Applying methods from the field of interpretable ML, we identified reflectivity at the lowest layer and the average spectral width in the layers below SL as the most important features. High reflectivity and low SW values indicate a higher probability of convective rain. [ABSTRACT FROM AUTHOR]

In this study, the spatiotemporal distribution and characteristics of different precipitation types (stratiform, convective, and snow) over China are analyzed using the radar mosaic images during the summer season over 4 years (from 2018 to 2021). The convective precipitation occurs most frequently along the eastern coast regions. In June, the strong convection center is located in Southern China and moves northward to Eastern China in July, while the lowest frequency occurs in August. Stratiform precipitation dominates summer precipitation over China and mainly distributes in inland regions, with the highest frequency in August. Snowfall primarily presents in the mountains and plateau regions of Western China with the frequency of occurrence around 20%. The snowfall area in July is significantly smaller than that in June and August. The convective, stratiform, and snowfall show strong diurnal variation in terms of solar standard time (LST) especially for snowfall. The convective precipitation demonstrates a bimodal pattern, with the highest peak in the afternoon (15–16 LST) and the secondary peak in the early morning (04–07 LST). Stratiform precipitation is mainly active from the afternoon to the next morning (14–05 LST). Snowfall is significantly more common in the nighttime (around 12%) than in the daytime (around 4%). The occurrence ratio of snowfall at midnight in July is significantly higher than that in June and August. It is expected that this study on summer precipitation over China can be used as a reference to hydrometeorological research and also to improve the understanding of radar precipitation research over China. [ABSTRACT FROM AUTHOR]

The climatology of the vertical profile of raindrops size distribution (DSD) over Sumatra Region (10° S – 10° N, 90° E – 110° E) has been investigated using Global Precipitation Measurement (GPM) level 2 data from January 2015 to June 2018. DSD's vertical profile was observed through a vertical profile of corrected radar reflectivity (Ze) and two parameters of normalized gamma DSD, i.e., mass-weight mean diameter (Dm) and total drops concentration (Nw). Land-ocean contrast and rain type dependence of DSD over Sumatra were clearly observed. The values of Dm and Nw were larger in the land than in the ocean. Negative and positive gradients of Dm toward the surface were dominant during stratiform and convective rains, respectively, consistent with the Z gradient. Moreover, the negative gradient of stratiform rain in the ocean is larger than in land. Thus, the depletion of large drops is dominant over the ocean, which is due to the break-up process that can be observed from the increase of Nw. Raindrop growth of convective rains is more robust over the ocean than land that can be seen from a larger value of Dmgradient. The BB strength is slightly larger over land and coastal region than over the ocean, indicating that the riming process is more dominant over land and coastal regions than the ocean. Doi: 10.28991/esj-2021-01274 Full Text: PDF

Simulations of mesoscale convective systems (MCSs) are conducted using the Weather Research and Forecasting (WRF) model. The considered MCSs occurred on 13 and 14 April 2016 over the west and southwest of Iran and resulted in a heavy flood. To determine the appropriate cumulus parameterization scheme to run the model, simulations of the two MCSs were performed using four nesting domains with 36-, 12-, 4-, and 1.33-km horizontal resolution by examining five cumulus parameterization schemes (CPSs) available in the WRF model for the three outer domains. The five implemented CPSs tested include Kain–Fritsch (KF), Betts–Miller–Janjic (BMJ), Grell–Devenyi (GD), improved Grell–Devenyi (G3D), and Grell–Freitas (GF) schemes. Initial fields for the simulations are taken from the Final Global Analysis (FNL) data with 1-degree horizontal resolution at 6-h time intervals. The simulations were evaluated using two available observational data sets: the Global Precipitation Measurement (GPM) satellite and synoptic station data. Comparison of the simulated 24-hourly precipitation from the second domain of the simulations (12 km) against the GPM data showed that the BMJ and GF schemes have the best performance in the simulation of 24-hourly precipitation based on the minimum values of root-mean-square error (RMSE). The comparison between in situ and the simulated precipitation from the third domain with 4-km resolution shows the great success of the GF scheme in predicting precipitation for 2 consecutive days. Regarding the dynamical analysis of the MCS of 13 April 2016, results of the simulation with 1.33-km resolution (using the GF scheme for the three outer domains) suggest that the model can explain several characteristics such as (1) the horizontal pattern of the MCS, including the convective line and stratiform precipitation region, (2) the formation of an elevated unstable moist layer and its slantwise ascent to the rear of the system, (3) the vertical structure of the MCS, and (4) the formation of a rear inflow jet and its descent to the surface under the influence of cooling processes below the stratiform cloud base. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes an improved algorithm which combines neural networks with diverse 3‐D structural features to partition radar reflectivity into convective and stratiform precipitation types. Radar data used in this work were obtained from three networked X‐band Doppler radars located in Chengdu, China. The proposed algorithm consists of the two sections: six high‐resolution features, which could be extracted from radar volume scanning and expressed the characteristics of the target in many ways, are selected as input to the neural network; systematic self‐diagnosis is implemented; and the optimized model is determined according to analysis of bias and variance of classifier. Three state‐of‐art classification algorithms were implemented as references for algorithm evaluation. Both subjective comparison and statistical results convince that performance of the proposed algorithm is better than performance of traditional classification algorithms in various weather systems. The statistical results show that the proposed algorithm F‐score values are 3%, 24%, and 30% higher than the fuzzy logic, SHY95, and BL algorithms and the recognition speed of the proposed algorithm is 54 times, two times, and four times that of fuzzy logic, SHY95, and BL, respectively. Considering network training is an offline procedure, classification by proposed neural network algorithm has great potential in real‐time weather analysis for the precipitation classification.

Rain type classification into convective and stratiform is an essential step required to improve quantitative precipitation estimations by remote sensing instruments. Previous studies with Micro Rain Radar (MRR) measurements and subjective rules have been performed to classify rain events. However, automating this process by using machine learning (ML) models provides the advantages of fast and reliable classification with the possibility to classify rain minute by minute. A total of 20,979 min of rain data measured by an MRR at Das in northeast Spain were used to build seven types of ML models for stratiform and convective rain type classification. The proposed classification models use a set of 22 parameters that summarize the reflectivity, the Doppler velocity, and the spectral width (SW) above and below the so-called separation level (SL). This level is defined as the level with the highest increase in Doppler velocity and corresponds with the bright band in stratiform rain. A pre-classification of the rain type for each minute based on the rain microstructure provided by the collocated disdrometer was performed. Our results indicate that complex ML models, particularly tree-based ensembles such as xgboost and random forest which capture the interactions of different features, perform better than simpler models. Applying methods from the field of interpretable ML, we identified reflectivity at the lowest layer and the average spectral width in the layers below SL as the most important features. High reflectivity and low SW values indicate a higher probability of convective rain.

Historical data discovery is a challenging task for any study in radar meteorology when the region of interest is recorded in weather observations. Weather radars exist in overlapping networks around the globe and are observing the atmospheric conditions around the clock. The observations are stored according to date, time, and location, as opposed to an indexing scheme based on the phenomena present in the scans themselves. Performing feature-based searches in these voluminous data sets is, at current, impossible. This research seeks to enable users seeking to study such phenomena as a mechanism for locating events of interest by leveraging recent progress from the fields of transfer learning and computer vision. Specifically, this work illustrates a methodology for performing image classification of precipitation regimes on colormapped weather radar scan images, as opposed to the raw data itself. This system reduces the data needed to perform this classification by two orders of magnitude, increasing throughput and democratizing usage of the deep learning tools for this task by allowing training and testing of the models on modest compute systems. [ABSTRACT FROM AUTHOR]

In this study, the spatiotemporal distribution and characteristics of different precipitation types (stratiform, convective, and snow) over China are analyzed using the radar mosaic images during the summer season over 4 years (from 2018 to 2021). The convective precipitation occurs most frequently along the eastern coast regions. In June, the strong convection center is located in Southern China and moves northward to Eastern China in July, while the lowest frequency occurs in August. Stratiform precipitation dominates summer precipitation over China and mainly distributes in inland regions, with the highest frequency in August. Snowfall primarily presents in the mountains and plateau regions of Western China with the frequency of occurrence around 20%. The snowfall area in July is significantly smaller than that in June and August. The convective, stratiform, and snowfall show strong diurnal variation in terms of solar standard time (LST) especially for snowfall. The convective precipitation demonstrates a bimodal pattern, with the highest peak in the afternoon (15–16 LST) and the secondary peak in the early morning (04–07 LST). Stratiform precipitation is mainly active from the afternoon to the next morning (14–05 LST). Snowfall is significantly more common in the nighttime (around 12%) than in the daytime (around 4%). The occurrence ratio of snowfall at midnight in July is significantly higher than that in June and August. It is expected that this study on summer precipitation over China can be used as a reference to hydrometeorological research and also to improve the understanding of radar precipitation research over China.

Monsoon intra-seasonal oscillation (MISO) represents a quasi-periodic occurrence of rainfall spells over India during summer monsoon (June–September) associated with large-scale circulation and convection. The connection of MISO with the hydrological cycle has been explored in this study through stable oxygen (δ18O) and hydrogen isotope ratios (δD) in rainwater samples collected from two stations located near the north coast of the Bay of Bengal during 2004–2014. The δ18O and δD values of the samples (total 202) have wide ranges: − 18.2 to 2.8‰ and − 132 to 28‰ (rel. to VSMOW) respectively. Based on northward propagation of negative anomaly in Outgoing Longwave Radiation, 16 convective MISO phases were identified during this period. Out of 69 samples associated with these MISO phases, 46 samples have isotope ratios depleted in heavier isotopes. These ratios are well correlated (R = 0.81) with the MISO propagation speed and enhanced rainfall (R = − 0.71) over the Bay of Bengal. In addition, the isotope ratios also show a significant anti-correlation (R = − 0.94) with satellite-derived stratiform rain fraction. The observed isotopic ratios were compared with the results obtained from an Isotope Enabled Global Spectral Model (IsoGSM). The model simulation reproduces the amplitude of variation in the observed values reasonably well, but on average, the model values are higher in δ18O and δD by about 2‰ and 11‰ respectively. In contrast, the model d-excess values are much lower (by 4.5‰ on average). We speculate that the discrepancies in δ18O, δD and d-excess may arise due to an overestimation of raindrop evaporation effect in the model. Our study thus identifies two major physical processes controlling rain isotope variability in northern Bay of Bengal, namely, the effect of MISO propagation characteristics in inducing isotope depletion through rain out over the ocean and contribution from the stratiform rains. [ABSTRACT FROM AUTHOR]

This article aims to classify precipitation into two categories, namely stratiform and convective. Multiple techniques, such as utilizing the micro rain radar (MRR), electric field monitor (EFM), radiometer, and disdrometer measurements, have been deployed for this purpose, at a tropical location Kolkata, India. A new rain classification technique, using logistic regression modeling of the sixth to third moment ratio ${(M6/M3)}$ , has been proposed. Classification of rain types based on the new technique shows high consistency with that based on radar reflectivity (${Z}$) values obtained from disdrometer measurements. This article also distinguishes mixed rain from stratiform and convective rain. The observations on the bright band structure by MRR and on differential brightness temperature at 31.4 and 22.23 GHz by a radiometer are utilized to classify mixed rain types. Although the EFM measurements do not classify rain types directly, they give a distinct signature of the impending stratiform/convective rain events. A comparative analysis between the percentage of stratiform and convective rain durations shows significant dominance of stratiform rain over convective rain. At the present location, the convective phenomenon shows higher occurrences during the pre-monsoon period compared to the monsoon period. [ABSTRACT FROM AUTHOR]

Idealized numerical simulations are conducted in this study to comparatively investigate the characteristics of the stratiform sector in the outer rainbands of tropical cyclones (TCs) in lower- and upper-layer vertical wind shear (VWS) with moderate magnitude. Consistent with the results in previous studies, the outer rainband stratiform sector of the TCs simulated in both experiments is generally located downshear left. Upper-layer VWS tends to produce stronger asymmetric outflow at upper levels in the downshear-left quadrant than lower-layer shear. This stronger asymmetric outflow transports more water vapor radially outward from the inner core to the outer core at upper levels in the downshear-left quadrant in the upper-layer shear experiment. More depositional growth of both graupel and cloud ice thus occurs downshear left in upper layers in the outer core, yielding more diabatic heating and stronger upward motions, particularly in the stratiformdominated part of the stratiform sector in the upper-layer shear experiment. Resultingly, a better-organized stratiform sector in the outer rainbands is found in the upper-layer VWS experiment than in the lower-layer VWS experiment. The diabatic heating associated with the stratiform sector produces strong midlevel outflow on the radially inward side of, and weak midlevel inflow on the radially outward side of, the heating core, with lower-level inflow beneath the midlevel outflow and upper-level inflow above. The upper-layer VWS tends to produce a deeper asymmetric inflow layer in the outer rainband stratiform sector, with more significant lower-level inflow and tangential jets in the upper-layer VWS experiment. [ABSTRACT FROM AUTHOR]

To understand rainfall dynamics, the characterization of convective and stratiform dominance needs a careful investigation. However, it remains a grey area to date. In this paper, the authors have attempted to differentiate between convective and stratiform events based on vertical profiles of a few upper-air meteorological elements, namely, cloud liquid water (CLW), precipitation water (PW), and latent heat (LH). The parameters have been obtained from the data product 2A12 of the Tropical Microwave Imager (TMI) onboard the Tropical Rainfall Measuring satellite (TRMM). The paper presents the demarcation technique between convective and stratiform dominance on surface rainfall without ambiguity. [ABSTRACT FROM AUTHOR]

This paper proposes an improved algorithm which combines neural networks with diverse 3‐D structural features to partition radar reflectivity into convective and stratiform precipitation types. Radar data used in this work were obtained from three networked X‐band Doppler radars located in Chengdu, China. The proposed algorithm consists of the two sections: six high‐resolution features, which could be extracted from radar volume scanning and expressed the characteristics of the target in many ways, are selected as input to the neural network; systematic self‐diagnosis is implemented; and the optimized model is determined according to analysis of bias and variance of classifier. Three state‐of‐art classification algorithms were implemented as references for algorithm evaluation. Both subjective comparison and statistical results convince that performance of the proposed algorithm is better than performance of traditional classification algorithms in various weather systems. The statistical results show that the proposed algorithm F‐score values are 3%, 24%, and 30% higher than the fuzzy logic, SHY95, and BL algorithms and the recognition speed of the proposed algorithm is 54 times, two times, and four times that of fuzzy logic, SHY95, and BL, respectively. Considering network training is an offline procedure, classification by proposed neural network algorithm has great potential in real‐time weather analysis for the precipitation classification. Plain Language Summary: In recent years, networked radar become the popular way to overcome some of the inherent shortcomings of single radar. There are different scanning strategies for convective and stratiform clouds, and it is difficult to accurately distinguish between convective clouds and stratiform clouds, and when the accuracy is high, there will be a problem of low‐recognition rate. Our algorithm utilizes a variety of features derived from radar products that have a good distinction between the two categories. At the same time, combined with neural network, its powerful nonlinear fitting and high speed make the recognition speed fast and the recognition result is high. The recognition result of our algorithm is higher than the recognition result of the popular fuzzy logic algorithm, and its recognition speed is even several times that of the fuzzy logic algorithm. Key Points: Six distinguishable features are extracted from radar echoes in order to well describe the horizontal and vertical structure of the cloudThe neural network is carefully diagnosed according to analysis of bias and variance of classifier to avoid overfitting and underfittingThe algorithm is evaluated by comparing the classification performance with those of three state‐of‐art algorithms subjectively and statistically [ABSTRACT FROM AUTHOR]

Abstract An attempt has been made to classify stratiform and convective rain using an impact type disdrometer at a tropical coastal site based on (Testud et al., 2001) method. Using this classification scheme, all rain events were analysed and computed their duration (minutes) and accumulated water (mm) in convective, stratiform, transition and mixed rain types. For the first time, in this paper an atmospheric electric field mill was used for rain type classification. The classification results were confirmed with disdrometer, Micro Rain Radar (MRR), and atmospheric electric field mill (EFM) observations. In convective rain type, non-bright band and high electric field were correspondingly observed in MRR and EFM records. Whereas bright band and shallow electric fields were detected during stratiform rain in the above observations of instruments. Other supporting evidences were observed from disdrometer also in corresponding rain types. Seasonal variations in rain duration and associated accumulated water were investigated in the four seasons (winter, pre-monsoon, summer monsoon and post-monsoon) for different rain types. General and mixed rain events were identified depending on the presence of stratiform or convective type in them. Percentage of rain duration and events for the study period were analysed with respect to (a) mean monthly rain water availability and (b) the intensity pattern in the total rainfall encountered in each month. Among the four seasons, summer monsoon received maximum rainfall followed by post-monsoon. A bimodal variation in the rainfall with a primary peak in June and secondary peak in October was observed in the annual cycle. Rainfall was intense during the pre- and post-monsoon seasons and less intense during the summer monsoon season. Also, shorter duration rain events were repeated in summer monsoon and fewer rain events of longer duration are resulted during pre- and post-monsoon seasons. In this duration, mixed rain accounts second higher in all the seasons. It was more evident in summer monsoon and least in post-monsoon. Rainfall contribution from convective rain was dominant in all the seasons except in winter. In summer monsoon, mixed and transition rain types have contributed much water among the four seasons. In mixed and transition rain types, mixed rain contributed more in duration and transition contributed more to water. From day-night variation analysis, it was observed that there was day-high and night-low in pre-monsoon and its reversal in post-monsoon season, with almost same occurrence during the summer monsoon. In the case of number of rain events, general (mixed) events were less (more) during the summer monsoon. From statistical analyses, an increase was observed in the duration of rain events from winter to post-monsoon. About 20% of duration was aggregated by longer duration events in all the seasons except during the winter. Highlights • Developed a classification scheme for tropical rain into stratiform, convective, transition and mixed types. • Transition and mixed rain together observed ~50% of the summer monsoon accumulated water. • Night rain is more intense than day rain with an inverse relation between duration and accumulated water during day & night. • Pre- and post-monsoons had intense rain from few long interval events while summer monsoon had more short interval events. [ABSTRACT FROM AUTHOR]

The Kömürlükdere and Göçükdibi Cu-Zn ores are located in the Central Pontide orogenic belt, where Besshi-type deposits (such as Hanönü, Zeybek, Sayyayla [Kastamonu]) have been discovered in last two decades. The Göçükdibi and Kömürlükdere ores are hosted in metabasites and quartz schist that belong to the Middle Jurassic Kunduz metamorphic rocks in the accretionary complex in the Central Pontides. The ore bodies show stratiform features, parallel to schistosity, within an alternation of metabasite and quartz schist succession. The average thicknesses of the ore zone and ore levels within the ore zone are 12 m and 3 cm, respectively. The ore bodies contain chalcopyrite, sphalerite, and magnetite, with dominant pyrite formed during the ore formation phase. Meanwhile, hematite, covellite, malachite, and goethite minerals formed during the supergene processes. Microprobe studies showed that the Fe and Cd content of sphalerite varies from 0.08 to 1.12 wt%, indicating Fe-poor sphalerite, and 0.08–0.27 wt%, respectively. The Zn/Cd ratios (average 274.4 for Kömürlükdere, 288.6 for Göçükdibi) of the sphalerite are comparable to those of the worldwide volcanogenic-massive sulfide (VMS) ore system related to andesitic-basaltic source rocks. The Co/Ni ratio (mean 3.2) of pyrite shows volcanogenic pyrite. The average δ34S of the stratiform pyrites is 3.9 ‰ (ranging from 2.06 ‰ to 5.34 ‰), indicating that the sulfur comes from a large homogeneous source, possibly magmatic. The average homogenization temperature (Th) and salinity of fluid inclusions obtained from quartz and sphalerite are 313 °C and 7.7 % equiv. NaCl, respectively, and similar to those of the global VMS deposits. The metabasites contain an average of 164 ppm Cu, 127 ppm Zn, and 2.2 ppm Sb, which shows enrichment several times greater than the background value of the basalts. Meanwhile the ore levels contain an average 0.23 % and 0.83 % Cu and Zn values, respectively. The trace metal enrichment of associated metabasites and the ore zones is evident for elements such as Cu, Zn, Cd, As, and Sb, indicating that they both precipitated in the same basin as syngenetic, and that there was repeated pulses of metal-rich fluids exhaled into the basin. Field observations and analytical data (mineral chemistry, sulfur isotope, fluid inclusion, etc.) show that the Kömürlükdere and Göçükdibi Cu-Zn ore bodies occurred as Besshi-type deposits within the Middle Jurassic Central Pontide Supercontinent, which developed in the marine environment appropriate to the accretionary complex located along the southern margin of Eurasia. • It was revealed for the first time that Gökçedoğan stratiform Cu-Zn mineralization occurred in the early Jurassic period within the Kunduz metamorphic, • Cu and Zn values of metabasites accompanying stratiform ore levels are enriched up to 5 times than the Clark value, • Gökçedoğan Cu-Zn mineralization exhibits similar characteristics to Besshi-type deposits. [ABSTRACT FROM AUTHOR]

Quantitative precipitation estimation (QPE) through remote sensing has to take rain microstructure into consideration, because it influences the relationship between radar reflectivity Z and rain intensity R. For this reason, separate equations are used to estimate rain intensity of convective and stratiform rain types. Here, we investigate whether incorporating synoptic scale meteorology could yield further QPE improvements. Depending on large-scale weather types, variability in cloud condensation nuclei and the humidity content may lead to variation in rain microstructure. In a case study for Bavaria, we measured rain microstructure at ten locations with laser-based disdrometers, covering a combined 18,600 h of rain in a period of 36 months. Rain was classified on a temporal scale of one minute into convective and stratiform based on a machine learning model. Large-scale wind direction classes were on a daily scale to represent the synoptic weather types. Significant variations in rain microstructure parameters were evident not only for rain types, but also for wind direction classes. The main contrast was observed between westerly and easterly circulations, with the latter characterized by smaller average size of drops and a higher average concentration. This led to substantial variation in the parameters of the radar rain intensity retrieval equation Z–R. The effect of wind direction on Z–R parameters was more pronounced for stratiform than convective rain types. We conclude that building separate Z–R retrieval equations for regional wind direction classes should improve radar-based QPE, especially for stratiform rain events.

Abstract: The properties of stratiform and the convective precipitation of tropical cyclones (TCs) over the northwest Pacific are examined using the Tropical Rainfall Measuring Mission data for 1998–2013. TCs are classified into inner core (IC), inner rainband (IB), and outer rainband (OB) regions, and the results show that TCs are dominated by stratiform precipitation, which accounts for more than 78% of the total raining area. The highest fraction of the stratiform raining area exists in the IB region and increases as the TC intensity increases (from 80% to 93%). Strong convective signatures generally occur in the IC region, less often in the IB region, and least often in the OB region. Stratiform precipitation in the IC region generally has comparable or even stronger ice scattering signatures and higher 20 dBZ radar echo heights than the convective precipitation in the IB and OB regions. Weak convection decreases significantly as the TC intensity increases, which leads to increased convective intensity. Stratiform (convective) precipitation accounts for 61% (39%) of the total TC volumetric rain and 25% (75%) of the total TC lightning flash, respectively. Moreover, stratiform precipitation's contribution to the total TC volumetric rain and lightning flash increases as the TC intensity increases, which indicates that stronger TCs are favorable for maintaining more stratiform precipitation. The stratiform and convective precipitation properties in different TC regions and intensities cooperatively change with the enhanced ascending branch in the IC region and the radial outflow at the upper levels of the secondary circulation. [ABSTRACT FROM AUTHOR]

Introduction The Mn mineralization occurs in the northeastern segment of the Sabzevar zone (SZ), north of the Central Iranian Microcontinent (CIM). This Zone (SZ) is located between the CIM fragmentation in the south and the Kopeh dagh sedimentary sequence in the north. The ore deposits of the northeastern segment of the Sabzevar zone can be divided into three groups, each with different metal association and spatial distribution and each related to a major geodynamic event. The first mineralization with associated Ordovician host rock is characterized by Taknar polymetallic (Fe-rich) massive sulfide deposit. The Cretaceous mineralization consists of Cr deposits associated with serpentinized peridotites, Cyprus type VMS, Mn deposit in pillow lava, volcano-sedimentary hosted Besshi type VMS and Mn deposit. Paleogene mineralization in eastern segment of the Sabzevar zone began with porphyry deposits, Cu Red Bed mineralization occurs in the Paleogene sandy red marl. Materials and methods A field study and sampling was performed during the autumn of 2012. To assess the geochemical characteristics of 48 systematic samples (least fractured and altered) of ore-bearing layers and host rocks were collected from the deposit for polished thin section examination. In order to correctly characterize their chemical compositions, 15 least-altered and fractured samples were chosen for major elements analysis. Results The Late Cretaceous volcano-sedimentary sequence in south-southwest of Sabzevar hosts numerous manganese mineralization. The sequence based on the stratigraphic position, age and composition of the rocks, can be divided into two lower and upper parts. The lower part or K2tv unit mainly formed from marine sediments interbedded with volcanic rocks. The sedimentary rocks of this part include silicified tuff, chert, shale and sandstone, and the volcanic rocks involve pyroclastic rocks of various composition, rhyolite, dacite and andesitic lava. The upper part or LMV unit comprised of limestone, marl and volcanic rocks, overlies concordantly on the lower part (K2tv). The manganese mineralization within the host volcano-sedimentary sequence, based on stratigraphic position, relative age and type of host rocks involved the two horizons: the first horizon (Mn Ia, Ib) consisting of Benesbourd (Masoudi, 2008), Nudeh (Nasrolahi et al., 2012), Homaie (Nasiri et al., 2010), Goft and Manganese Gostar Khavar Zamin deposits, occurred in the lower part of the sequence (K2tv unit) and is hosted by red tuffs. The second horizon (Mn II) comprising of Zakeri (Taghizadeh et al., 2012), Cheshmeh Safeid, Mohammad Abad Oryan and Chah Setareh deposits, is hosted by marly-carbonate tuffs and locates within the upper part of the sequence (LMV unit) (Maghfouri, 2012). Geometry and shape of the ore bodies in various deposits are as stratiform, layered, parallel and concordant with layering of the host rocks. Textures of the ores include massive, lenticular, banded, laminated and disseminated. Mineralogy of the ores in the two ore horizons is simple and similar and is dominated by pyrolusite, psilomelane and braunite. Gangue minerals are predominantly the host rock-forming minerals including quartz, chlorite and feldspar. Discussion Geochemical data, structures and textures, stratigraphic position and lithologic characteristics of the host rocks represent that manganese reserves in south-southwest Sabzevar were formed as sedimentary-exhalative. Acknowledgements The authors are grateful to the Tarbiat Modares University Grant Commission for research funding. References Maghfouri, S., 2012. Geology, Mineralogy, Geochemistry and Genesis of Cu Mineralization within Late Cretaceous Volcano-Sedimentary Sequence in Southwest of Sabzevar, with emphasis on the Nudeh Deposit. M.Sc. Thesis, Tarbiat Modares University, Tehran, Iran, 312 pp. (in Persian) Masoudi, M., 2008. Geology, mineralogy, geochemistry and genesis of Benesbourd Mn deposit in the Southwest Sabzevar. M.Sc. Thesis, Tehran Islamic Azad University, Iran, 100 pp. (in Persian) Nasiri, F., Lotfi, M. and Jafari, M., 2010. Mineralogical studies on the Homaei manganese deposit in southwest of Sabzevar. 30th Symposium on Geosciences, Geological Survey of Iran, Tehran, Iran. (in Persian) Nasrollahi, S., Mousivand, F. and Ghasemi, H., 2012. Nudeh Mn deposit in the upper Cretaceous volcano- sedimentary sequence, Sabzevar subzone. 31th Symposium on Geosciences, Geological Survey of Iran, Tehran, Iran. (in Persian) Taghizadeh, S., Mousivand, F. and Ghasemi, H., 2012. Zakeri Mn deposit, example of exhalative mineralization in the southwest Sabzevar. 31th Symposium on Geosciences, Geological Survey of Iran, Tehran, Iran. (in Persian)

Precipitation, especially convective precipitation, is highly associated with hydrological disasters (e.g., floods and drought) that have negative impacts on agricultural productivity, society, and the environment. To mitigate these negative impacts, it is crucial to monitor the precipitation status in real time. The new Advanced Baseline Imager (ABI) onboard the GOES-16 satellite provides such a precipitation product in higher spatiotemporal and spectral resolutions, especially during the daytime. This research proposes a deep neural network (DNN) method to classify rainy and non-rainy clouds based on the brightness temperature differences (BTDs) and reflectances (Ref) derived from ABI. Convective and stratiform rain clouds are also separated using similar spectral parameters expressing the characteristics of cloud properties. The precipitation events used for training and validation are obtained from the IMERG V05B data, covering the southeastern coast of the U.S. during the 2018 rainy season. The performance of the proposed method is compared with traditional machine learning methods, including support vector machines (SVMs) and random forest (RF). For rainy area detection, the DNN method outperformed the other methods, with a critical success index (CSI) of 0.71 and a probability of detection (POD) of 0.86. For convective precipitation delineation, the DNN models also show a better performance, with a CSI of 0.58 and POD of 0.72. This automatic cloud classification system could be deployed for extreme rainfall event detection, real-time forecasting, and decision-making support in rainfall-related disasters.

Rain microstructure parameters assessed by disdrometers are commonly used to classify rain into convective and stratiform. However, different types of disdrometer result in different values for these parameters. This in turn potentially deteriorates the quality of rain type classifications. Thies disdrometer measurements at two sites in Bavaria in southern Germany were combined with cloud observations to construct a set of clear convective and stratiform intervals. This reference dataset was used to study the performance of classification methods from the literature based on the rain microstructure. We also explored the possibility of improving the performance of these methods by tuning the decision boundary. We further identified highly discriminant rain microstructure parameters and used these parameters in five machine-learning classification models. Our results confirm the potential of achieving high classification performance by applying the concepts of machine learning compared to already available methods. Machine-learning classification methods provide a concrete and flexible procedure that is applicable regardless of the geographical location or the device. The suggested procedure for classifying rain types is recommended prior to studying rain microstructure variability or any attempts at improving radar estimations of rain intensity.

Cheshmeh-Konan area is located ~15 km northwest of Tasuj, East-Azarbaidjan province. The most important lithologic units of the area are red sandstones, green-gray sandstones, shale, and marl (Miocene). Based on field evidence and petrographical investigations, the host sediments were deposited in a peritidal setting associated with a sabkha-type environment. Copper mineralization in the area occurred as stratiform within the green-gray coarse-grained sandstones and includes minerals such as chalcocite, covellite, bornite, chalcopyrite, malachite, and azurite. Considering the obtained results, control of the mineralization in the area is in relation to sandstone sedimentary facies and concentration of organic matters. Combination of the obtained results from field, petrographical and mineralographical investigations indicate that factors such as accessibility to chloride ligands, presence of high quantities of organic matters, diagenetic processes, function of marls and shales as geochemical barriers, and the existence of fault systems have played a major role in copper mineralization at Chesmeh-Konan area. The obtained results show that copper mineralization in the area is mostly similar to Red Bed type sedimentary copper deposits.

• Ka-band radar polarimetric signatures of Melting region were investigated in WG. • Radar reflectivity is corrected for different attenuations to improve data quality. • Characterization of seeder-feeder mechanism in the WG. During the Indian summer monsoon, high temporal and spatial resolution observations from Ka-band dual-polarization radar deployed in the Western Ghats (WG), India are utilized to investigate the vertical structure of the stratiform precipitating system. First, the radar reflectivity (Z) measurements are corrected for rain attenuation, based on the relationship between specific attenuation (A) and Z. The disdrometer dataset is used to calculate the expected value of A at Ka-band for a given Z using microwave scattering simulation. The radar data are also corrected for wet radome transmission loss, gaseous, and melting layer (ML) attenuation. Within the ML, a change in Z and other radar polarimetric variables suggest hydrometeors phase change. In addition to Z , polarimetric measurements such as differential reflectivity (ZDR), linear depolarization ratio (LDR), and copolar correlation coefficient between horizontal and vertical channel ( ρ HV ) are used to determine the ML height. Ka-band polarimetric radar signatures near the ML are characterized by a low ρ HV (∼ 0.93), high Z (∼ 25–30 dBZ), ZDR (∼ 3 dB), and LDR (∼ −15 dB) values. In the stratiform precipitation region, well-defined fall streaks are observed above ML, which descends through 0 °C isotherm into the rain region. When the fall streaks are present above the ML, Z increases below the ML, indicating a seeder-feeder mechanism. Because there is little observational evidence concerning the vertical distribution of stratiform clouds over the Indian subcontinent, the current study may help to understand stratiform clouds and the ice processes that occur within them. [ABSTRACT FROM AUTHOR]

The aim of this study is to investigate the potential of cloud classification by means of support vector machines using high resolution images from northern Algeria. The images were taken from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board of the Meteosat Second Generation (MSG) satellite. An automatic system was developed to operate during both day and nighttime by following two steps of data processing. The first aims to detect rainy areas in cloud systems, whereas the second delineates convective cells from stratiform ones. A set of 12 spectral parameters was selected to extract information about cloud properties, which are different from day to night. The training and validation steps of this study were performed by in-situ rainfall measurement data, collected during the rainy season of years 2011 and 2012 via automatic rain gauge stations distributed in northern Algeria. Artificial neural networks (ANNs) and support vector machine (SVM) were explored, by combining spectral parameters derived from MSG images. Better performances were obtained by the SVM classifier, in terms of Critical Success Index and Probability of Detection for rainy areas detection (CSI = 0.81, POD = 91%), and also for convective/stratiform delineation (CSI = 0.55, POD = 74%). [ABSTRACT FROM AUTHOR]

Stratiform rain situations are generally associated with the presence of a melting layer characterized by a strong signature in polarimetric radar variables. This layer is an important feature as it indicates the transition from solid to liquid precipitation. The melting layer remains poorly characterized, particularly from a polarimetric radar point of view. In this work a new algorithm to automatically detect the melting layer on polarimetric RHI radar scans using gradients of reflectivity and copolar correlation is first proposed. The algorithm was applied to high-resolution X-band polarimetric radar data and validated by comparing the height of the detected layer with freezing-level heights obtained from radiosoundings and was shown to give both small errors and bias. The algorithm was then used on a large selection of precipitation events (more than 4000 RHI scans) from different seasons and climatic regions (South of France, Swiss Alps and plateau, and Iowa, USA) to characterize the geometric and polarimetric signatures of the melting layer. The melting layer is shown to have a very similar geometry on average, independent of the topography and climatic conditions. Variations in the thickness of the melting layer during and between precipitation events was shown to be strongly related to the presence of rimed particles, to the vertical velocity of hydrometeors and to the intensity of the bright band. [ABSTRACT FROM AUTHOR]

Seasonal variability of storm top altitudes for convective and stratiform precipitation in the tropics and subtropics are investigated based on measurements of the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) from 1998 to 2011. Statistically, the spatial distribution of mean convective storm top altitudes shows a large variation between land and ocean, while the stratiform storm tops exhibit insignificant land-ocean differences. Seasonal variances of tropical convective and stratiform storm top altitudes are small, with their means are approximately at 5 km (6 km) and 5.5 km (6 km) over the ocean (land) in each season. In the subtropics, the difference of the storm top altitudes between summer and winter reaches ~ 4 km and ~ 2 km for convective and stratiform precipitation, respectively. The zonal mean storm top altitudes of stratiform precipitation are highly correlated with the zonal averaged air temperature and sea surface temperature. Additionally, the mean storm tops of higher altitudes correspond with larger mean rain rates for both convective and stratiform precipitation at the seasonal scale. Such relationship satisfies the quadratic functions with a correlation coefficient of 0.9. On the basis of this relationship, the summer mean rain rates are retrieved from storm top altitudes, which are 1-3 mm/h and 0.3-0.9 mm/h smaller than the observed ones, for convective and stratiform precipitation, respectively. These results suggest that the quadratic function between storm top altitudes and rain rates have potential applications in precipitation parameterization of models and climatic studies. [ABSTRACT FROM AUTHOR]

Zinc-lead mineralisation in Besti Gol, Chitral is located within the Hindu Kush terrane, 60 km to the northwest of Northern suture that marks the suture zone between the Karakoram plate and the Kohistan island arc. A small suture zone, Tirich Boundary Zone, is located 20 km to the northeast of the investigated area that separates the Karakoram from East Hindu Kush. The zinc-lead mineralisation is mainly confined to a marble horizon within the Jurassic Arkari Formation. The conformability of the sulphide-mineralised zone with the host marble indicates that it has been precipitated in synsedimentary environment and can be classed as a stratiform deposit. The salinities of hydrothermal fluids at Besti Gol range from 2 to 14 wt % NaCl equivalent, indicating that brines generated in a sedimentary basin were responsible for the precipitation of these deposits during diagenesis. Subsequent metamorphism induced distinct textural changes in ore minerals during recrytallisation. Coarse grain size, euhedral morphology and triple junction in the calcite also specify metamorphism. The variation of FeS content in sphalerite indicates crytallisation during sedimentation or a low-pressure-temperature environment followed by high temperature, sulphur and oxygen fugacity during the metamorphism. This was followed by the hydrothermal activities where sphalerite was converted into smithsonite and hydrozincite, and galena into cerussite and plattnerite. The enrichment of the F, Li, As, Rb, Ba, Sc and Zr as compared to average carbonate rocks, coupled with the positive correlation among trace elements such as As with Ba, Sc and Sr within the marble beds, further establishes hydrothermal alteration. The study area has undergone at least two phases of deformation in a collisional environment. It is concluded that the early phases of stratiform zinc-lead mineralisation may have been deposited during the closure of the suture zone, Tirich Boundary Zone, which lies to the SW of the mineralised zone. [ABSTRACT FROM AUTHOR]

Application of 2-Dimensional Video Disdrometer (2DVD) data, collected in central Oklahoma, to the problem of convective-stratiform rain separation is presented. The partition into convective (CO) and stratiform (ST) periods is achieved by applying a multi-variable Bayesian classification algorithm to the 2DVD dataset. It turns out that the CO-ST separation methods developed for measurements with one type of disdrometer may not work optimally on measurements with a different type of disdrometer. Similarly, single/dual parameter, or simple threshold separation methods may not be able to adequately separate CO and ST rain types. The corresponding shape-slope (μ-Λ) relations of the constrained gamma distribution are derived for these two rain classes. These constrained gamma relations are then used for rain drop size distribution (DSD) retrievals, and the results are compared with those obtained from the exponential distribution and the unified μ-Λ constraint previously proposed. It is demonstrated that the results based on the convective-stratiform separation yield more accurate DSD retrievals with respect to the exponential distribution and moderate improvements in comparison to unified μ-Λ constraint. [ABSTRACT FROM AUTHOR]

Abstract: Kafang is one of the main ore deposits in the world-class Gejiu polymetallic tin district, SW China. There are three main mineralization types in the Kafang deposit, i.e., skarn Cu–Sn ores, stratiform Cu ores hosted by basalt and stratiform Cu–Sn ores hosted by carbonate. The skarn mainly consists of garnet and pyroxene, and retrograde altered rocks. These retrograde altered rocks are superimposed on the skarn and are composed of actinolite, chlorite, epidote and phlogopite. Major ore minerals are chalcopyrite, pyrrhotite, cassiterite, pyrite and scheelite. Sulfur and Pb isotopic components hint that the sources of different types of mineralization are distinctive, and indicate that the skarn ore mainly originated from granitic magma, whereas the basalt-hosted Cu ores mainly derived from basalt. Microthermometry results of fluid inclusions display a gradual change during the ore-forming process. The homogenization temperature of different types of inclusions continuously decreases from early to late mineralization stages. The salinities and freezing temperatures exhibit similar evolutionary tendencies with the T homogenization, while the densities of the different types keep constant, the majority being less than 1. Oxygen and hydrogen isotopic values (δ18O and δD) of the hydrothermal fluids fall within ranges of 3.1 to 7.7‰ with an average of 6.15‰, calculated at the corresponding homogenization temperature, and −73 and −98‰ with an average of −86.5‰, respectively. Microthermometry data and H–O isotopes indicate that the ore-forming fluid of the Kafang deposit is mainly derived from magma in the early stage and a mixture of meteoric and magmatic water in late stage. Molybdenite Re–Os age of the skarn type mineralization is 83.4±2.1Ma, and the stratiform ores hosted by basalt is 84.2±7.3Ma, which are consistent with the LA-ICP-MS zircon age of the Xinshan granite intrusion (83.1±0.4Ma). The evidence listed above reflects the fact that different ore styles in the Kafang deposit belong to the same mineralization system. [Copyright &y& Elsevier]