The present work examines the structure and variability of the tropopause over a high-altitude site in the Western Ghats (WG) and attempts to understand the impact of deep convection on the thermal ...structure of the tropopause. The characteristics of the seasonal variations in the cold-point tropopause (CPT), lapse rate tropopause (LRT), convective tropopause (COT) and tropical tropopause layer (TTL) are studied using high-resolution radiosonde observations from 2012 to 2019 over Mahabaleshwar (17.92° N, 73.66° E; 1.38 km amsl) in the WG. On seasonal time scales, the altitude (temperature) of CPT, LRT and COT vary by ~ 1 km (~ 2 K), 0.5 km (~ 1.8 K) and 1.3 km (~ 6 K), respectively. CPT and LRT show highly similar seasonal behaviour, with higher and colder values during winter and lower and warmer values during monsoon months, whereas COT exhibits a different pattern of seasonal behaviour with higher variability. The thickness of the TTL varies from a minimum of 4.2 km in July to a maximum of 6 km in March. Further, the impact of deep convection on the tropopause was analysed by collocating the radiosonde observations with the deep convection based on infrared brightness temperature (IRBT) data of Indian geostationary satellites, INSAT-3D and Kalpana-1. The anomalies of temperature profiles and tropopause parameters (altitude and temperature) are estimated during deep convection categorized by IRBT values into three groups designated as DC1 (< 220 K), DC2 (220–235 K) and DC3 (235–245 K). The analysis reveals cooling within the TTL and warming in the middle troposphere which are enhanced with the deepening of convection. The TTL cooling (mid-tropospheric warming) peaks near 16 km (10 km) altitude and varies as −1.1 K (1.2 K), −0.8 K (0.9 K) and −0.6 K (0.4 K) for DC1, DC2 and DC3, respectively. The tropopause anomalies show that the CPT and LRT descend while the COT ascends during deep convection. The thickness of the TTL decreases due to the combined effect of the descent of the CPT and ascent of the COT, but the main contribution is the elevation of the COT. The TTL narrows as the convection deepens, and shrinks most (~ 1 km) during deepest convection (DC1).
Cloud radar reflectivity profiles can be an important measurement
for the investigation of cloud vertical structure (CVS). However, extracting
intended meteorological cloud content from the ...measurement often demands an
effective technique or algorithm that can reduce error and observational
uncertainties in the recorded data. In this work, a technique is proposed to
identify and separate cloud and non-hydrometeor echoes using the radar
Doppler spectral moments profile measurements. The point and volume
target-based theoretical radar sensitivity curves are used for removing the
receiver noise floor and identified radar echoes are scrutinized according
to the signal decorrelation period. Here, it is hypothesized that cloud
echoes are observed to be temporally more coherent and homogenous and have a
longer correlation period than biota. That can be checked statistically
using ∼ 4 s sliding mean and standard deviation value of
reflectivity profiles. The above step helps in screen out clouds critically
by filtering out the biota. The final important step strives for the
retrieval of cloud height. The proposed algorithm potentially identifies
cloud height solely through the systematic characterization of Z variability
using the local atmospheric vertical structure knowledge besides to the
theoretical, statistical and echo tracing tools. Thus, characterization of
high-resolution cloud radar reflectivity profile measurements has been done
with the theoretical echo sensitivity curves and observed echo statistics
for the true cloud height tracking (TEST). TEST showed superior performance in
screening out clouds and filtering out isolated insects. TEST constrained with
polarimetric measurements was found to be more promising under high-density biota
whereas TEST combined with linear depolarization ratio and spectral width perform potentially to
filter out biota within the highly turbulent shallow cumulus clouds in the
convective boundary layer (CBL). This TEST technique is promisingly simple
in realization but powerful in performance due to the flexibility in
constraining, identifying and filtering out the biota and screening out the
true cloud content, especially the CBL clouds. Therefore, the TEST algorithm is superior for screening out the low-level clouds that are strongly linked to the
rainmaking mechanism associated with the Indian Summer Monsoon region's
CVS.
We report duration-related properties of rain events at a high-altitude site in the Western Ghats (WG) of India during the summer monsoon (June–September) season. Duration of rain events is inferred ...from disdrometer data collected during the years 2015–18. Vertical profiles of events of different durations are studied using data of an X-band radar available for 2017–18 monsoon seasons. Durations of rain events span from 0.1 to 160 h. Rain intensity (i.e., rain rate), mass weighted drop diameter, and rain liquid water content decrease with duration up to 1 h, remain steady between 1 and 20 h, and then increase for larger durations. Rain drop number concentration shows an increasing trend with duration up to around 10 h and then decreases. Rain events are categorized into short- (< 10 h) and long-duration (> 10 h) events based on rain drop characteristics. The long-duration events account for ~ 15% of the total number of events and contribute ~ 75% to the monsoon rainfall. The long-duration events have association with the monsoon rainfall over the Indian core monsoon zone with a correlation coefficient of 0.77. The frequency distribution of 18 dBZ radar echo top height exhibits bimodal structure (around 2 km and 4 km) for both short and long events with a longer tail in the case of former. The study is primarily focused on the summer monsoon season; however, we also report the rain duration features during the pre-monsoon (March–May) season.
Information on the spatial distribution of rainfall is required for many applications, including water and flood management. Weather radars can provide quantitative rainfall estimation over an area. ...Taking the example of an X-band radar installed at Mandhardev in the Western Ghats, we show that radar reflectivity data can have significant bias despite following standard calibration procedures. We report a technique to identify bias in the X-band radar reflectivity factor using collocated disdrometer observations and propose a methodology to correct it. Simultaneous data collected on 83 days during June–September of 2018 are used. Our results show that bias in the radar reflectivity factor reduced from –8.3 to –0.8 dB after correction. The estimated 83-day accumulated rainfall using uncorrected radar reflectivity data is 80% less compared to that of the disdrometer and the difference between the two reduces to 2% with correction. Bias in the estimated rainfall reduces from –30 to –0.7 mm day
–1
and the RMSE reduces by 42%. There are days where daily rainfall from the two instruments agrees well with each other, while large differences exist on some days. We show that the sampling issue is one of the major sources of error, and its contribution depends on the nature/type of precipitating clouds, whereas uncertainties in the
Z–R
relationships account for about 15% difference.
The present study is a first of its kind attempt in exploring the physical features (e.g., height, width, intensity, duration) of tropical Indian bright band using a Ka-band cloud radar under the ...influence of large-scale cyclonic circulation and attempts to explain the abrupt changes in bright band features, viz., rise in the bright band height by ~ 430 m and deepening of the bright band by about 300 m observed at around 14:00 UTC on Sep 14, 2016, synoptically as well as locally. The study extends the utility of cloud radar to understand how the bright band features are associated with light precipitation, ranging from 0 to 1.5 mm/h. Our analysis of the precipitation event of Sep 14–15, 2016 shows that the bright band above (below) 3.7 km, thickness less (more) than 300 m can potentially lead to light drizzle of 0–0.25 mm/h (drizzle/light rain) at the surface. It is also seen that the cloud radar may be suitable for bright band study within light drizzle limits than under higher rain conditions. Further, the study illustrates that the bright band features can be determined using the polarimetric capability of the cloud radar. It is shown that an LDR value of − 22 dB can be associated with the top height of bright band in the Ka-band observations which is useful in the extraction of the bright band top height and its width. This study is useful for understanding the bright band phenomenon and could be potentially useful in establishing the bright band-surface rain relationship through the perspective of a cloud radar, which would be helpful to enhance the cloud radar-based quantitative estimates of precipitation.
The observations of bright band carried out simultaneously with X- and Ka-band radars for the first time over the Indian region have been examined to reveal various contrasting characteristics of ...bright band at the two wavelengths. The study reports the bright band observations on September 12–13, 2015 at millimeter and centimeter wavelengths and brings out a comparative analysis of the bright band features (e.g., intensity, thickness, height, etc.) under three different rain conditions ranging from very light (<0.1 mm/hr) to light (0.1–3 mm/hr) to heavy (3–5 mm/hr). It is seen that the bright band region at Ka-band is always narrower and situated at a higher altitude than at X-band frequency. Our analysis shows that at Ka-band frequency, the polarimetric fields like LDR can be utilized to detect and determine the bright band features using an appropriate selection of a threshold value of LDR, which is found to be −22 dB in this study and could be associated reasonably with the top and bottom heights of the bright band. This study explores the potential of both radars, particularly the Ka-band radar for probing the bright band effect and estimating its features which would be helpful to improve the quantitative estimates of precipitation.
The knowledge of type of precipitating cloud is crucial for radar based quantitative estimates of precipitation. We propose a novel model called CloudSense which uses machine learning to accurately ...identify the type of precipitating clouds over the complex terrain locations in the Western Ghats (WGs) of India. CloudSense uses vertical reflectivity profiles collected during July-August 2018 from an X-band radar to classify clouds into four categories namely stratiform,mixed stratiform-convective,convective and shallow clouds. The machine learning(ML) model used in CloudSense was trained using a dataset balanced by Synthetic Minority Oversampling Technique (SMOTE), with features selected based on physical characteristics relevant to different cloud types. Among various ML models evaluated Light Gradient Boosting Machine (LightGBM) demonstrate superior performance in classifying cloud types with a BAC of 0.8 and F1-Score of 0.82. CloudSense generated results are also compared against conventional radar algorithms and we find that CloudSense performs better than radar algorithms. For 200 samples tested, the radar algorithm achieved a BAC of 0.69 and F1-Score of 0.68, whereas CloudSense achieved a BAC and F1-Score of 0.77. Our results show that ML based approach can provide more accurate cloud detection and classification which would be useful to improve precipitation estimates over the complex terrain of the WG.
Ascochyta blight (AB) is an important disease of pea which can cause severe grain yield loss under wet conditions. In our previous study, we identified two quantitative trait loci (QTLs) abIII-1 and ...abI-IV-2 for AB resistance and these QTLs were consistent across locations and/or years in an inter-specific pea population (PR-19) developed from a cross between Alfetta (
) and P651 (
.
). The objectives of this study were to fine map the abIII-1 and abI-IV-2 QTLs using a high density single nucleotide polymorphism (SNP)-based genetic linkage map and analyze identified markers in heterogeneous inbred family (HIF) populations. Selective genotyping of 51 PR-19 recombinant inbred lines was performed using genotyping-by-sequencing (GBS) and the resulting high density genetic linkage map was used to identify eight new SNP markers within the abI-IV-2 QTL, whereas no additional SNPs were identified within the abIII-1 QTL. Two HIF populations HIF-224 (143 lines) and HIF-173 (126 lines) were developed from F
RILs PR-19-224 and PR-19-173, respectively. The HIF populations evaluated under field conditions in 2015 and 2016 showed a wide range of variation for reaction to AB resistance. Lodging score had significant positive (
< 0.001) correlation with AB scores. HIFs were genotyped using SNP markers within targeted QTLs. The genotypic and phenotypic data of the HIFs were used to identify two new QTLs, abI-IV-2.1 and abI-IV-2.2 for AB resistance within the abI-IV-2 QTL. These QTLs individually explained 5.5 to 14% of the total phenotypic variation. Resistance to lodging was also associated with these two QTLs. Identified SNP markers will be useful in marker assisted selection for development of pea cultivars with improved AB resistance.
In this investigation, biodegradable composites were fabricated with polycaprolactone (PCL) matrix reinforced with pine cone powder (15%, 30%, and 45% by weight) and compatibilized with graphite ...powder (0%, 5%, 10%, and 15% by weight) in polycaprolactone matrix by compression molding technique. The samples were prepared as per ASTM standard and tested for dimensional stability, biodegradability, and fracture energy with scanning electron micrographs. Water-absorption and thickness-swelling were performed to examine the dimensional stability and tests were performed at 23 °C and 50% humidity. Results revealed that the composites with 15 wt % of pine cone powder (PCP) have shown higher dimensional stability as compared to other composites. Bio-composites containing 15-45 wt % of PCP with low graphite content have shown higher disintegration rate than neat PCL. Fracture energy for crack initiation in bio-composites was increased by 68% with 30% PCP. Scanning electron microscopy (SEM) of the composites have shown evenly-distributed PCP particles throughout PCL-matrix at significantly high-degrees or quantities of reinforcing.
Folates are important metabolic cofactors essential for human growth and development. Eighty-five accessions from a pea genome wide association study (GWAS) panel were evaluated for folate profile ...using ultra-performance liquid chromatography coupled with mass spectrometry. A GWAS was conducted to identify SNP markers associated with the folate profile of pea. Five folates were quantified and the sum of folates ranged from 14 to 55 µg/100 g dry weight. Significant differences (
P
< 0.001) were observed among the accessions for all folates. The pea accessions were genotyped using genotyping-by-sequencing. After filtering for a read depth of five and minor allele frequency of 0.05, 14,391 SNPs were used for marker-trait association. Five SNP markers were significantly (
P
< 0.01) associated with the sum of folates. Fifteen, eight, and three SNP markers were associated with 5-methyltetrahydrofolate (5-MTHF), 5-formyltetrahydrofolate, and tetrahydrofolate, respectively. Based on associated SNP markers, an additional 24 accessions were evaluated for folate profile, and those predicted to have a relatively high folate concentration had a significantly greater concentration of 5-MTHF and the sum of folates than those predicted to have a lower concentration. In these accessions, Sc_6992_86348 and Sc_3060_11265 were significantly associated with 5-MTHF and the sum of folates in Saskatoon 2016. Six SNP markers were converted into KASP markers, and these can be used for marker-assisted selection in pea breeding.
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