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North East Monsoon (NEM) is the major source of rainfall for the south-eastern parts of peninsular India. Short time rainfall prediction data (i.e., nowcasting) are based on the observations from Doppler weather radars which has a high spatial and temporal resolution. This study focuses on the short-term ensemble prediction system using weather radar data to predict precipitation during the NEM and is the first of its kind in the Indian region to make an assessment of the operational performance of the prediction system. Six rainfall events have been studied for the assessment of short-term prediction system where the precipitation systems are different and include a tropical storm observed over different days during the 2022 NEM season. To assess the performance of the system, Fractional Skill Scores (FSS) at a 1 km window have been computed for a lead time of 0–2 h for all the rainfall events with more than 750 samples using different optical flow methods and ensemble sizes. The best average skill score and maximum skill score obtained at a 2 h lead time is 0.65 and 0.78 for tropical storms, 0.5 and 0.78 for stratiform and 0.15 and 0.38 for convective precipitation. It has found that the performance of the model is best for precipitation systems that are widespread and have a longer life period. [ABSTRACT FROM AUTHOR]

• Middle atmosphere dynamics and coupling using three decades of Indian MST radar and complimentary observations. • Highlighted recent results on waves, tides, planetary waves, tropopause, STE processes and low-latitude mesospheric dynamics. • Gap areas and scope for future study using other complementary techniques are mentioned. The genesis for Indian Mesosphere-Stratosphere-Troposphere (MST) radar came from the Indian Middle Atmospheric Programme (IMAP) to study the middle atmospheric dynamics and its underlying processes. During the early 1990s VHF radar operating at 53 MHz has been set up at Gadanki (13.5°N, 79.2°E), India, which started operating initially in the stratospheric and tropospheric (ST) mode. It is upgraded to MST mode in 1993. Since then, it has served various scientific experiments not only from India but under international collaboration. Numerous focused campaigns that have national and international importance were conducted. Using this unique data in the last 30 years along with complementary measurements, several stimulating results has been reported covering a wide range of topics that includes gravity wave characteristics, tides, planetary waves, oscillations, tropopause dynamics and stratosphere-troposphere exchange processes, monsoon dynamics and circulations, mesospheric structure and dynamics, vertical and lateral coupling and long-term trends. The present communication will highlight the unique results obtained using long-term observations of Indian MST radar and complementary techniques covering the complete middle atmosphere. Unique results include quantification of the momentum carried by the high-frequency waves generated in the troposphere up to the mesosphere, demarcating the complete tropical tropopause layer using MST radar observations alone, indication for the strengthening of the tropical easterly jet in the recent decade, unusual behavior of the Hadley circulation during drought and El Nino years, the revelation of the mechanisms for the occurrence of mesospheric echoes at the tropical latitudes, quantification of the turbulence, evidence for the role of anthropogenic changes on the dynamics of the mesosphere and generation of the empirical model by combing multi-instrument data sets. These data set also served in validating numerous ground-based and space-borne measurements including several re-analysis products over a tropical latitude. Issues remained unresolved and ways to tackle in future experiments are highlighted. [ABSTRACT FROM AUTHOR]

Urban floods are more concerned in recent days due to their substantial effect in loss of human lives and properties. Due to climate change, urban floods are frequently observed in many parts of the world. Flood events in Chennai city are a frequent scenario due to rapid increase in the density of population. Adyar river watershed and surrounding urban cover are focused in the present study. The present study aims at mapping flooded region using Sentinel 1A datasets over Adyar watershed. Series of Sentinel 1A image is collected before, during and after floods for mapping the extent of flood and mapping risk zones in Adyar watershed. Methodologies such as ISODATA Technique, Multi-Temporal Analysis, Thresholding Method, PCA and ICA Analysis and Grey Level Co-Occurrence Matrix are adopted for the extraction of flooded extent from the SAR datasets. Analysis performed over the Adyar watershed provided promising results in the extraction of flooded extent with Thresholding Method and Grey Level Co-Occurrence Matrix being the dominant of all the methods. Though higher accuracy is obtained in the extraction of flooded extent, limitation of layover, foreshortening and shadow is experienced in the built up region for the extraction of flooded pixels., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This study focuses on integrating dual‐frequency precipitation radar (DPR) observations on‐board the Global Precipitation Mission with the storm index from INSAT‐3D for the development of reconstructed radar reflectivity. Performance of the derived product was tested against the independent DPR observations. A correlation coefficient of 0.81 is reported between the reconstructed radar reflectivity and the DPR observations. Derived reconstructed reflectivity is shown to be an excellent product for the weather monitoring over the regions without radar coverage. [ABSTRACT FROM AUTHOR]

Sequences of multi-temporal synthetic aperture radar (SAR) images are routinely used for land-use land-change (LULC) applications, allowing the retrieval of accurate and up-to-date information on the state of the Earth's surface and its temporal variations. Change detection (CD) methods that rely on the exploitation of SAR data are, generally, made of three distinctive steps: (1) pre-processing of the SAR images; (2) comparison of the pairs of SAR images; and (3) the automatic extraction of the "changed areas", employing proper thresholding algorithms. Within this general framework, the reduction in speckle noise effects, which can be obtained by applying spatial multi-looking operations and ad hoc noise filters, is fundamental for the better detecting and classifying of changed regions. Usually, speckle noise filters are singularly and independently applied to every SAR image without the consideration of their inherent temporal relationships. In particular, most use local (spatial) approaches based on determining and averaging SAR backscattered signals related to neighboring SAR pixels. In this work, conversely, we explore the potential of a joint 3D space-time non-local mean filter (NLMF), which relies on the discrimination of similar features in a block of non-local SAR pixels extracted from the same or different SAR images. The theory behind non-local-mean filters is, first, shortly revised. Then, the developed space-time NLMF is applied to a real test case for the purposes of identifying flooded zones due to the massive inundations that hit the Kerala region, India, during the summer of 2018. To this aim, a set of 18 descending SAR images collected from the European (EU) Copernicus Sentinel-1 (S-1) sensor was exploited. The performance of the developed NLMF has also been assessed. It is worth remarking that the proposed method can be applied for the purposes of analyzing a heterogenous set of natural and/or artificial disastrous conditions. Further, it can also be helpful during the pre-processing stages of the sequences of SAR images for the purposes of CD applications. [ABSTRACT FROM AUTHOR]

Analysis of pre-monsoon convective systems over the southern peninsular India has been performed using C-band radar and numerical simulation. Statistics on the radar polarimetric measurements show that the distribution of differential reflectivity (Zdr) and specific differential phase (Kdp) have much higher spread over convective regions. The distribution of Kdp is almost uniform across the vertical over the stratiform regions. The mean profile of Zdr over stratiform regions shows a distinct local maxima near melting level. A comprehensive analysis has been done on an isolated deep convective system on 13 May 2018. Plan position indicator (PPI) diagrams and satellite measured cloud top temperature demonstrate that pre-monsoon deep convective systems can develop very rapidly within a very short span of time over the region. Heavy precipitation near the surface is reflected in the high value of Kdp (>5° km−1). High values of Zdr (>3 dB) were measured at lower levels indicating the oblate shape of bigger raindrops. A fuzzy logic-based hydrometeor identification algorithm has been applied with five variables (Zh, Zdr, ρhv, Kdp, and T) to understand the bulk microphysical properties at different heights within the storm. The presence of bigger graupel particles near the melting layer indicates strong updrafts within the convective core regions. The vertical ice hydrometeor signifies the existence of a strong electric field causing them to align vertically. Numerical simulation with the spectral bin microphysics (SBM) scheme could produce most of the features of the storm reasonably well. In particular, the simulated reflectivity, graupel mixing ratio and rainfall were in good agreement with the observed values. [ABSTRACT FROM AUTHOR]

The Indian summer monsoon trough (MT) is a dynamically active region of the quasi-stationary feature extending from north-western India towards the Bay of Bengal (BoB). The eastern rim of the MT (EMT) is modulated by the embedded synoptic-scale monsoon low-pressure systems (LPSs) in north BoB. We examine the spatiotemporal variability of the convective storm (CS) in the EMT region, which lies along the pathway of LPSs. A Lagrangian-based objective-tracking method, Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN), was injected into Kolkata S-band radar observations of nine wet seasons (June–September, 2009–2017). CSs frequently occur near the onshore areas and is linked to the formation and propagation of LPSs in the BoB. By examining the relationship of CS top heights with reflectivity lapse rate and fractional volume of 40 dBZ or more, three vertical categories of CS have been identified as shallow (SCS) below 5 km, medium depth (MDCS), and deep CS (DCS). The land–ocean contrast in the spatial distribution of MDCS and DCS and the contoured frequency by altitude diagram shows continental convective vigor. Although short-lived smaller CSs account for most storms in EMT, the bulk of the precipitation (75 % ) contributed from the infrequent largest storms. A weak (strong) linkage between the precipitation flux and CS top (area-time integral) gives a clue on the importance of CS voluminous organization rather than the linkage of heaviest rainfall to tallest CS. While most CSs propagate inland following the mass-weighted mean winds, the largest CS propagate offshore steered by the mid-level wind at 700 hPa. [ABSTRACT FROM AUTHOR]

The key objective of the present study is to estimate the surface displacement and to understand/monitor the active deformation pattern in the Kachchh region post the 2001 Bhuj Earthquake by implementing the Persistent Scatterer Interferometric Synthetic Aperture Radar (PSI) and Differential Interferometric Synthetic Aperture Radar (DInSAR) techniques. We employed the ENVISAT ASAR (15 images), ALOS PALSAR (6 pairs) and SENTINEL-1A (117 images) data sets acquired during the periods 2003–2005, 2007–2009, and 2016–2020 respectively. The PSI results of the Envisat dataset reveals that the Kachchh mainland region has undergone an average surface deformation of ± 22 mm/yr during 2003–2005. The maximum displacement observed from the ALOS PALSAR data sets (Window-1 to 6) during the period 2007–2009 is ∼ ± 1.2 cm. Further, the ground displacement observed from the Sentinel-1A dataset during the period 2016–2020 is ±16 mm/yr for the west-central region and 6 mm/yr uplift and 8 mm/yr subsidence in the eastern Kachchh mainland region. Surprisingly, high rate of deformation is detected towards the Pachham Island, Banni, Rann and the eastern region of the Kachchh after the 2001 Bhuj event. Correlating the results of different data sets, it is concluded that the deformation is high near the vicinity of the fault zones indicating the tectonically active nature of the faults. From the obtained results, we infer that, post the 2001 Bhuj earthquake, the surface displacement in the Kachchh mainland region is escalated till 2009 which is due to continuous aftershock activity and then started declining because of the ongoing seismic settlement. The acquired deformation rates are correlating well with the GPS derived displacement rates. Further, our results will assist in accurately demarcating the extent of the fault zones and also helps in precisely marking the areas undergoing active deformation, which will aid in micro zonation studies, mitigation planning and also for the preparation of an active tectonic map for the region. [ABSTRACT FROM AUTHOR]

Drought is primarily considered as a natural hazard which is a resultant phenomenon of less rainfall. When an agricultural area constantly received low rainfall during the sowing and growing season of crop, the soil moisture of the cropland goes down and the crop began to wilt, the situation is known as agricultural drought. Present study, used the multi-temporal Sentinel 1 SAR and Landsat optical data to assess the agricultural drought in the part of Odisha state. Initially, to establish the methodology, correlation between NDVI and SAR backscattering has been carried out for the demarcated agricultural landscape of MODIS images derived landcover map of 2019. The drought assessment has been carried out for the month of Jun-July of 2020 and 2021 which is the kharif crop growing season. Thereafter, the backscattering vale range has been identified as a threshold value to delineate the agricultural drought affected areas from other SAR images of various vintages. The study area was more affected by the drought in 2020 compared to 2021. In this study, it is observed that although the area is drought affected, received less rainfall, but due to location in coastal region this area is mostly under cloud cover, therefore, it is very difficult assess the agricultural drought using optical remote sensing data. SAR data is also sensitive to soil moisture condition; therefore, it can be potentially to be used for drought assessment for the coastal agricultural area. [ABSTRACT FROM AUTHOR]

A Stratosphere‐Troposphere (ST) Radar operating at 53 MHz is being installed at Ionosphere Field Station (22.94°N, 88.51°E, and 34°N geomagnetic latitude) of University of Calcutta in the eastern part of India adjoining northern Bay of Bengal. This radar is unique, being the only one operational at this frequency in the entire eastern and northeastern part of the country and also in the south‐east Asian longitude sector. Two components of horizontal winds (zonal and meridional) measured by the Pilot version of this radar have been validated with 90 collocated simultaneous balloon‐borne radiosonde observations during July and August 2019. A good correlation of the order of 90%–99% and 75%–95% up to 8 km has been observed between the radar and radiosonde measured zonal winds and meridional winds respectively. This correlation is much stronger between 2 and 6 km altitudes. The correspondence is better for the zonal winds than meridional. However, there have been some data outliers, which may primarily be attributed to different measurement techniques, large beam width of the Pilot array and less antenna aperture. The differences between radar and radiosonde measurements are marginally higher for precipitation events compared to non‐precipitation cases. The zonal and meridional wind patterns exhibit different variations in July and August 2019 while the standard errors in zonal and meridional winds mostly lie within 1 m/s. These results are the first validation of the Pilot version of this radar facility. Plain Language Summary: A 53 MHz radar is being set up by University of Calcutta near Calcutta in the geophysically sensitive transition region from the tropics to the sub‐tropics in the eastern part of India adjoining Bay of Bengal. It is designed to measure atmospheric winds, which could be very severe particularly during tropical cyclones which often affect this region. As an initial step, a Pilot version of the radar has been established and wind measurements compared with simultaneous collocated balloon‐borne radiosondes during July‐August 2019 for precipitation as well as non‐precipitation days up to altitudes of 8 km. Good correspondence have been noted between the two measurements with 90% correlation. Some data outliers have been noted which may be due to large beam width of the Pilot system and less antenna aperture. Key Points: Validation of zonal and meridional wind measurements using a Stratosphere‐Troposphere Radar with radiosondes during July‐August 2019Good correspondence of about 90% is noted between the two measurementsValidation has been classified into precipitation and non‐precipitation cases up to 8 km height [ABSTRACT FROM AUTHOR]

The regional coverage satellite constellations are in demand for fulfilling earth observation needs of specific user regions of interest as opposed to global coverage thus enhancing the revisit period for the area. In this paper, we present a few key mission aspects of the regional coverage constellation designed and operated by the Indian Space Research Organization (ISRO) in low earth orbit. ISRO developed three X-Band Synthetic Aperture Radar (SAR) Earth Observation satellites and placed them in three inclined orbit planes to enhance the Indian region's coverage. This is India's first inclined orbit constellation designed for remote sensing applications in low earth orbit. The satellites were built around a standard bus carrying a Synthetic Aperture Radar payload with a deployable Radial Rib Antenna (RRA). They were designed to achieve a high agility capability. As SAR sensors are capable of all-weather, day, and night imaging; thus, most suitable in inclined orbits where the illumination conditions vary continuously for imaging. Though the inclined orbits provide more revisit opportunities to the region of interest, their characteristics pose challenges in mission design and operations. Though the mission design and planning involved a host of elements, this paper brings out the details of a few key mission analysis elements, planning aspects and operations that were critical in the course of various mission phases. The mission analysis included mission-specific requirements like launch windows selection for the constellation and reference attitude generation specific to inclined orbit geometry. Mission planning had to take care of Radial Rib Antenna (RRA) deployment requirements which were very critical for the mission, apart from taking care of individual subsystem requirements. The SAR payload data product quality and targeting accuracy are very sensitive to the satellite image point range (vis-a-vis SAR operation data window). Extensive post-launch calibration activity was carried out to meet all these mission requirements and data product specifications. The challenge was in fixing the payload beam alignment and fixing the uncertainties in onboard and ground computations. The paper also describes the methods of fixing all possible error sources affecting the high-resolution SAR payload performance. • The regional coverage satellite constellations are in demand for area targets coverage. • Inclined orbit constellations are more suitable for regional coverage using SAR sensors. • Mission operations plans need to consider all the specific requirements of spacecraft systems. • Pre-launch and post -launch calibrations of sensors is mandatory for achieving system performance. [ABSTRACT FROM AUTHOR]

Ka‐band Micro rain Doppler radar is an effective tool to investigate the profiles of precipitation microstructure in terms of the raindrop size distribution (DSD). The DSD parameters that vary appreciably with height are indicative of the associated atmospheric phenomena. Hence the present investigation endeavors to put light on the underlying physical processes responsible for the evolution of varied rain microstructure profiles using micro rain radar (MRR), and radiometric measurements complemented with re‐analysis outputs over an urban tropical location, Kolkata (22.57°N, 88.37°E), India. MRR unravels the prevalence of significant biases in the typical power law relationship (Dm = aRb) between rain rate (R) and mass‐weighted mean drop diameter (Dm) along the rain height, especially during intense convective rain events, above the atmospheric boundary layer (ABL). Consequently, an alternative empirical relation appropriate to account for the R‐Dm variability above the ABL is proposed. Further, radiometric measurements and re‐analysis outputs reveal that the presence of atmospheric instabilities coupled with wind shear impacts above the ABL contributes to the enhanced breakup of raindrops and the deviations in the usual R‐Dm relationship. Thus, the present study intends to highlight the applicability of ground‐based radar measurements over the tropics to devise quantitative precipitation algorithms for reliable rain estimates. Key Points: Mean drop diameter and parameters of the gamma model show opposite variation with rain rates below and above the boundary layerEnhanced drop breakup due to instabilities and wind shear above the boundary layer during intense convection causes such anomaliesIt is critical that radar quantitative precipitation estimation algorithm accounts for the type of rain namely Convective and Stratiform [ABSTRACT FROM AUTHOR]

Tropical coastal wetlands have been well recognized for their role in mitigating climate change apart from possessing high biodiversity values as well as providing important habitats for fisheries and other ecosystem services. Although these wetlands play key roles in sequestering carbon (C) in the lithosphere and hydrosphere, there is a notable dearth of comprehensive yet spatially explicit accounts of their total blue carbon (TBC) stocks. The anthropogenic factors including development stressors have fundamentally altered the global C dynamics. In this respect, holistic accounting of the total C stocks of the coastal wetlands becomes the foremost task in emphasizing their recognition as integral components of climate change mitigation strategies. This study modeled TBC stocks of an intertidal mudflat of eastern India across different land use/ land covers (LULCs) by quantifying the above ground biomass (AGB), below ground biomass (BGB), and soil organic C (SOC). The distribution of SOC was modeled through spatial interpolation of point-specific C densities (R2 = 0.77; RMSE = 22.91 Mg ha−1). AGB was estimated by establishing allometric relationships between geospatial data, both of active (PALSAR-2) and passive (Pleiades-1B) nature and in-situ dendrometric variables using the artificial neural network (R2 = 0.85; RMSE = 48.91 Mg ha−1). BGB was estimated from the modeled AGB. Spatial integration of these datasets estimated the TBC stock of mudflat as 27,469.30 Mg. Overall, open mangrove represented highest mean TBC density (163.29 Mg ha−1) while herbaceous vegetation recorded largest TBC stock (9818.94 Mg). Mean TBC densities across LULCs were significantly different (p < 0.05) from each other except for the herbaceous vegetation and built-up categories, indicating the inertial effects of paleo-land covers. Accordingly, this study could be inferred as a pioneering one in developing a comprehensive geospatial model of TBC accounting in India towards strengthening the regional and national C inventories. [Display omitted] • Total blue C stocks of an intertidal mudflat including AGB, BGB and SOC estimated • Applying destructive/non-destructive sampling, elemental analysis and RS datasets • Coupling of fine resolution multispectral and SAR datasets through ANN • Effects of land use/land cover patterns on blue C dynamics assessed [ABSTRACT FROM AUTHOR]

The analysis of field observations of thunderclouds in India is provided. The characteristics of the cloud collection obtained from the radar and lightning detection network in the area of the city of Aurangabad (the Maharashtra province, India) are considered. The maximum radar reflectivity of thunderstorm cells, the volume of the supercooled part above the 0°C isotherm, the intensity and precipitation flux, and the frequency of electric discharges are obtained. It is shown that the frequency of electric discharges is linked with some radar parameters of thunderclouds, and this relationship depends on the stage of cloud development. Comparative field data for other regions are given. [ABSTRACT FROM AUTHOR]

Recently, the state of Kerala (India) experienced extreme rainfall events during August 2018. These heavy rains led to major flooding, which was one of the worst natural disasters experienced by the state in the last hundred years. The present study focuses on investigating the spatial and vertical structure of precipitating clouds and their microphysical properties during this extreme precipitation event using C-band polarimetric Doppler Weather Radar (DWR) observations over Thumba (8.5°N, 77°E), India. This is the first time that DWR polarimetric observations have been used for investigating an extreme weather event in India. The DWR observations were carried out during several episodes of extreme rainfall (i.e. 12th to 17th August 2018), and the time evolution of the radar reflectivity structure is examined very closely to understand the structure and dynamics of this unprecedented event. Apart from the DWR observations, prevailing dynamics such as tropical easterly jet (TEJ), low-level jet (LLJ) along with vertical velocity are also investigated, which showed distant signatures leading to the extreme event. It is observed that the low-level convergence during the event played a key role in the development of persistent convection. The weakening of TEJ at the upper troposphere resulted in the decrease of vertical wind shear, which is a favourable condition for the vertical growth of convective clouds. The significance of the present study lies in delineating the structure and dynamics of the extreme precipitation event using C-band polarimetric DWR observations and discussing the potential candidates in triggering the event. [ABSTRACT FROM AUTHOR]

Radar imaging SATellite (RISAT-1A), also known as Earth observation satellite-04 (EOS-04), is a follow-on mission of India’s first indigenously developed spaceborne C-band synthetic aperture radar (SAR) on-board RISAT-1 satellite. This article provides a description of the post-launch calibration and data quality evaluation of EOS-04 launched on 14 February 2022. Calibration devices (corner reflectors) of different shapes and sizes were deployed at Ahmedabad and Amrapur calibration sites in Gujarat, India from 2 to 24 April 2022, and their response in the EOS-04 data was used to assess the radiometric and polarimetric calibration. The results of the analysis showed satisfactory radiometric and polarimetric data quality. Geolocation accuracy was assessed using the ground-surveyed position of the corner reflectors and was found to be in accordance with the specified values of less than 50 m. [ABSTRACT FROM AUTHOR]

The study illustrates the evidence of dynamical coupling between the high‐latitude sudden stratospheric warming (SSW) events that occurred in three consecutive winter seasons and the concomitant changes in the wind pattern in the lower stratosphere and troposphere observed with a state‐of‐the‐art 205 MHz stratosphere‐troposphere wind profiling Radar, located at Cochin (10.03°N, 77.33°E), a tropical station in southwest peninsular India. Associated with the occurrence of SSW, the tropical region experiences a change in zonal wind speed and direction at upper tropospheric altitudes after the central day of warming. The zonal wind in the troposphere also reversed its prevailing direction. Downward propagation of zonal wind during the SSW period is noticed, which is also supported by the reanalysis data set. In the lower troposphere, vertical wind experienced sudden fluctuations with varying amplitudes soon after the peak day of the SSW event. The surface meteorological parameters in an area (8°–12°N, 74°–78°E), centered at Cochin, from ERA‐Interim are also examined. An abrupt fall in outgoing longwave radiation, followed by convection and unusual rainfall in the tropical region 5–6 days before the peak of SSW events, is a unique and exciting feature noted in the study. Detailed analysis of the past 20 years of SSW events confirmed the observational evidence of unusual rainfall over the low latitude region related with the onset of SSW. Comprehensive observational and numeric modeling studies are needed to understand the mechanism for the SSW events in high latitudes and the associated convective activity and precipitation in the low latitude region. Key Points: Wind profiling Radar observation of impact of stratospheric warming (SSW) over tropicsUpper tropospheric zonal wind decreased or reversed its prevailing direction and Downward propagation of zonal winds during the SSWVertical wind experienced sudden fluctuations and an abrupt fall in OLR, followed by convection and unusual rainfall in the tropical region [ABSTRACT FROM AUTHOR]

Reflectivity measurements from X‐band ground‐based radar (GR) are assessed using observations from the Ku‐band space‐borne radars (SRs) onboard the Tropical Rainfall Measuring Mission and Global Precipitation Measurement satellites as a reference. The GR is at Mandhardev (18.04°N, 73.86°E) in the Western Ghats region, and the evaluation period is from June to September of 2014 and 2018. At X‐band, the rainfall‐induced attenuation leads to significant signal loss. The rain attenuation correction of GR is performed based on the relationship between specific attenuation and reflectivity. Using T‐matrix scattering simulations, the specific attenuation and reflectivity are derived from the disdrometer data. Further, the systematic differences between reflectivity measured by GR and SR are minimized by performing the frequency scaling using scattering simulation. The comparison between corrected reflectivities of GR and SR is achieved by matching the resolution volumes and viewing geometry of both radars. The evaluation between corrected reflectivities of GR and SR shows reasonable agreement between them, indicating that the attenuation correction performs reasonably for GR. In rain regions, the GR bias lies between −2.6 and −1.8 dB for stratiform cases, while for convective samples, the GR bias varies from −2.4 to −0.7 dB relative to SR observations. The GR shows smaller reflectivity compared to SR at all heights. This study aims to implement the attenuation correction to long‐term X‐band radar data available in the Western Ghats region to study the orographic precipitation and convective system during monsoon. Key Points: For the first time, assessment of reflectivities between IITM's X‐band radar and TRMM‐GPM Ku‐band radars is carried outThe bias in GR attenuated corrected reflectivity is within −2.7 dB relative to SR measurementsThis study provides a framework to have attenuated corrected reflectivity for better quantitative application of reflectivity data [ABSTRACT FROM AUTHOR]

Monsoon convection characteristics over land and sea within 150 km of the west coast of India are studied using coastal Mumbai S-band radar. The intraseasonal and interannual monsoon variabilities in cloud characteristics are investigated for the contrasting monsoon seasons of 2013 and 2014. The cloud characteristics studied are frequency of occurrences, cloud top height (CTH), longitudinal distribution, diurnal variation, and scale-wise distribution of cloud cells. The number of cloud cells is about four times higher over land than over sea. The maximum frequency of CTH is found in the cumulus category (3–4 km). The mean CTH varies from 4.49–5.44 km. No significant difference between the CTH over the land and sea regions is found. The contribution of congestus to total cloud cells is found maximum over both land and sea. The longitudinal variation of cloud frequency shows maximum frequency at a distance of 50–60 km from the location of radar over both sea and land. The maximum over the land region is the new feature revealed in the analysis. The diurnal variation of clouds shows a broad structure with maximum in the local noon and minimum in the morning hours. The mean duration of the clouds is 40–44 min both over land and sea. The contribution by the mesoscale convective system (MCS) is dominant (57–63%). The study of cloud distribution over land and sea over the west coast of India using radar data is the first of its kind and has brought out detailed structure of cloud distribution with time and space. [ABSTRACT FROM AUTHOR]

Potentiality of Synthetic Aperture Radar (SAR) based Radar Forest Degradation Index (RFDI) combined with field observations for monitoring spatio-temporal dynamics of intertidal mudflat vegetation was assessed in this study. Five vegetation zones were delineated in the Junput mudflat of eastern India with very high classification accuracy (Kappa coefficient ≥ 0.79). Fragmentation and coalescence patterns of different vegetation zones under two plantation initiatives were also analysed by different spatial metrics. Results reveal gradual degradation of tree dominated vegetation zones and growth of shrub dominated and herbaceous ones from 2007 to 2019. Plantation of exotic species like Eucalyptus globulus and Casuarina equisetifolia along the shoreline had been found to be less effective against storms and sea surges. Conversely, native mangrove plantations and associated herbs had demonstrated remarkable growth in the intertidal areas. Based on the findings, the study pointed out that a zone-wise cum site-specific plantation strategy is needed towards developing effective bio-shields. [ABSTRACT FROM AUTHOR]

Rice is the major food grain for many countries in this world, in particular South Asian countries such as Pakistan, India, Bangladesh, Sri Lanka, Myanmar, Thailand, etc. India is one of the leading producers of rice, with agriculture contributing 14% to 15% to the Gross domestic product (GDP). Detection of paddy field requires constant monitoring of a region throughout the year. Hence, we use dual-polarized Sentinel-1 Synthetic Aperture Radar (SAR) data with 20 m resolutions for our study. In this paper, we propose a semi-supervised algorithm that detects paddy fields in various seasons. The technique is divided into two parts, unsupervised and supervised. Feature extraction layers of the Visual Geometry Group (VGG16) model are used to segregate patches into five clusters using an entropy-based loss function. Depending on the availability of the ground truth, data is sampled (stratified sampling), and only a certain amount of data is further used for the supervised section. Clusters showing higher similarity (Jaccard/Tanimoto test) with the paddy or nonpaddy classes are connected to a three-layer neural network used for the supervised method. This has been tested on two study sites using 10 data sets, five from 2018 to 2019, each. The highest accuracy reported for 2018 and 2019 is 85.61% and 84.92%, respectively. The two sites are selected from different districts of West Bengal, India. Our model has also shown higher generalization capability against the conventional-supervised models. [ABSTRACT FROM AUTHOR]

Competing Interests: None

Retrieval of transplantation date (TP) using RISAT-1 medium resolution SCANSAR data for operational pre-harvest crop monitoring has been demonstrated. The methodology used 3-date HH-polarized RISAT data capturing the onset of transplantation (minimal backscatter) and rises as crop grows, for the country scale within 2–4 days of planting and was found to closely agree to ground conditions. Transplantation date ranged from June in north to October in Tamil Nadu. This variation significantly reduced to a month in the summer paddy due to irrigation. Transplantation patterns were different in both the seasons' paddy. Validation exercise for TP in four major states confirmed the feasibility of retrieval datasets for transplantation date monitoring as it matched within 2–4 days in north, south India and MP but 6–8 days in Assam. The technique has reached a level of maturity and is routinely used for monitoring the transplantation date by the Department of Agriculture, India. [ABSTRACT FROM AUTHOR]

Four oil spill events over the Indian Ocean including Chennai, Sharjah, Al Khiran and Mubarak Village are analyzed using Sentinel-1 satellite data. General National Oceanic and Atmospheric Administration (NOAA) Operational Modeling Environment (GNOME) model is utilized for oil spills trajectory production, whereas oil spills weathering processes are modeled using Automated Data Inquiry for Oil Spill (ADIOS). Synthetic Aperture Radar (SAR) based oil spill detection technique provided reliable results at the wind speed between 3 to 9 m/s for all events. Maximum oil spill movement (33 km) from the source point is observed in the Al Khiran, whereas evaporation rate of crude (degraded) oil is observed as high (low). The Near Real Time (NRT) detection of oil spill using SAR imagery needs high computational power, however, provides better results. This study concludes that SAR based oil spill detection is a cost-effective technique and can be utilized for mapping of oil spills., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

As a first of its kind in India, indigenous development of dual-polarization C-Band Doppler weather radar (DWR) is discussed in the manuscript. Radar Development Area (RDA) of ISRO Telemetry Tracking and Command Network (ISTRAC) have developed the system and the DWR is installed at Thumba Equatorial Rocket Launch Station (TERLS) of Vikram Sarabhai Space Centre (VSSC) which is a centre of Indian Space Research Organization (ISRO) in the Kerala state of India. Till now, the DWRs available in India were either imported or were working on single polarization. The purpose of this DWR is to establish the technology based on polarimetric observation for meteorological and hydrological applications like intense weather monitoring and cyclone detection. The current paper mainly aims to show the DWR system-level details and the calibration approaches adopted. Some first cut data validation results are also presented for various events captured by the DWR. [ABSTRACT FROM AUTHOR]

Use of polarimetric SAR data for offshore pollution monitoring is relatively new and shows great potential for operational offshore platform monitoring. This paper describes the development of an automated oil spill detection chain for operational purposes based on C-band (RADARSAT-2) and X-band (TerraSAR-X) fully polarimetric images, wherein we use polarimetric features to characterize oil spills and look-alikes. Numbers of near coincident TerraSAR-X and RADARSAT-2 images have been acquired over offshore platforms. Ten polarimetric feature parameters were extracted from different types of oil and 'look-alike' spots and divided into training and validation dataset. Extracted features were then used to develop a pixel based Artificial Neural Network classifier. Mutual information contents among extracted features were assessed and feature parameters were ranked according to their ability to discriminate between oil spill and look-alike spots. Polarimetric features such as Scattering Diversity, Surface Scattering Fraction and Span proved to be most suitable for operational services., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

The present study aimed to develop a methodology to estimate jute (Corchorus sp.) cultivated areas in the Indian subcontinent using sentinel-1A Synthetic aperture radar (SAR) C band dual polarization data. We developed a hierarchical decision tree (DT) model to estimate jute area and compared it with other supervised and unsupervised methods. The proposed DT model showed improved overall accuracy of 82–83% (Kappa coefficient, k = 0.78–0.79) over supervised or unsupervised methods and validated against Directorate of Economics and Statistics (DES) data, Government of India during 2016–2019. The VV backscatter record improved accuracy over VH backscatter. [ABSTRACT FROM AUTHOR]

Characteristics of developing convective clouds (Cu) in Karnataka State (India) during the thunderstorm formation are analyzed using weather radar and lightning detection network data. It is noted that radar characteristics of Cu which produced lightning, exceed those where lightning does not form. The study has shown that the number of negative cloud-to-ground strokes exceeds the number of positive ones by an order of magnitude. The radar characteristics of clouds in India and the North Caucasus are compared. Significant differences in lightning flash rates over the mentioned regions are registered. A low correlation is found between the supercooled volume and the flash rate of negative lightning. The paper also presents the results of studying the dynamic characteristics of four Cu seeded with a glaciogenic reagent. The thunderstorm risk is estimated for the clouds. It is shown that the seeding increases a probability of lightning events. [ABSTRACT FROM AUTHOR]

In the present communication, C-band polarimetric Doppler Weather Radar (C-DWR) observations are used to investigate the structure and evolution of organized precipitation bands during the Indian summer monsoon (ISM) of 2017, 2018 and 2019 over Thumba (8.50 N, 770 E), a coastal location in south India. The C-DWR observations show organized high radar reflectivity structures of ∼50 dBZ that organized into narrow bands of ∼200 km (North-South) in length and 10–20 km (East-West) in width with vertical extent of ∼6–8 km. These narrow bands of precipitation structures are observed to be formed over the Arabian Sea and subsequently propagating towards the radar site. The observed differential radar reflectivity (Z dr) and the correlation co-efficient values indicate the horizontally oriented droplets and non-uniform hydrometeors. The potential formation mechanisms of the precipitation bands are investigated using reanalysis winds. The results show that blocking effect induced by the Western Ghats around the radar site play a key role in the formation of organized precipitation bands. It is noted that the location of the blocking effect induced convergence (conducive for the formation of precipitating clouds) with respect to the orography is proportional to the intensity of the low-level winds. The significance of the present study lies in understating the structure and evolution of organized precipitation bands and discussing their potential formation mechanisms during the ISM using C-DWR observations. [Display omitted] • Structure and evolution of organized precipitation bands (OPB) are investigated using C-band polarimetric DWR at Thumba. • The study provides observational evidence for the upstream blocking mechanism in the formation of OPBs over the study region. • It is envisaged that the present results will help in modelling the physical processes responsible for the formation of OPBs. [ABSTRACT FROM AUTHOR]

The Normalised Difference Vegetation Index (NDVI) derived from optical satellite images plays a very important role in determining the state of plants' health. Also, it is an important parameter needed in various statistical/process-based models. However, the use of optical images is sometimes limited because of atmospheric conditions and cloud cover. On the other hand, synthetic aperture radar (SAR) remote sensing has been widely used for crop monitoring due to its high-resolution imaging and all-weather data acquisition capabilities. So, if the SAR backscatter response (σ0) and NDVI data could be correlated, it is possible to estimate NDVI (during complete or partial stages of crop development) under overcast situations. In this study, three different experiments have been performed to establish the relationship between NDVI-σ0VV, NDVI-σ0VH, and NDVI-σ0VV/σ0VH. Here, time-series σ0 (in VV and VH polarizations) and NDVI were extracted from Sentinel-1 and Senitnel-2, respectively. Based on the analysis, it is found that the NDVI is more closely correlated with the ratio σ0VV/σ0VH than it is with σ0VV and σ0VH when data points from the start of cropping season up to the start of the maturity stage of the crop, were considered (referred to as experiment 2 and experiment 3). This is opposed to experiment 1, which took into account all data points related to the crop's development i.e. start of cropping season up to the harvesting stage of the crop. The best results were obtained from experiment 3 in which higher-order polynomial regressions were developed between NDVI and σ0VV/σ0VH. A significant correlation ranging from R2 = 0.81 to 0.98 were observed for NDVI-σ0VV/σ0VH. The study was conducted on selected farms located in the same agro-climatic zone during the Rabi season of 2018–19. [ABSTRACT FROM AUTHOR]

Brahmaputra is one of the perennial rivers in India which causes floods every year in the north-east state of Assam causing hindrance to normal life and damage to crops. The availability of temporal Remote Sensing (RS) data helps to study the periodical changes caused by flood event and its eventual effect on natural environment. Integrating RS and GIS methods paved a way for effective flood mapping over a large spatial extent which helps to assess the damage accurately for mitigation. In the present study, multitemporal Sentinel-1A data is exploited to assess the 2017 flood situation of Brahmaputra River in Assam state. Five data sets that are taken during flood season and one reference data taken during the non-monsoon season are used to estimate the area inundated under floods for the quantification of damage assessment. A visual interpretation map is produced using colour segmentation method by estimating the thresholds from histogram analysis. A new method is developed to identify the optimum value for threshold from statistical distribution of Synthetic Aperture Radar (SAR) data that separates flooded water and non-flooded water. From this method, the range of backscatter values for normal water are identified as - 18 to - 30 dB and the range is identified as - 19 to - 24 dB for flooded water. The results showed that the method is able to separate the flooded and non-flooded region on the microwave data set, and the derived flood extent using this method shows the inundated area of 3873.14 Km 2 on peak flood date for the chosen study area.

A stratosphere–troposphere (ST) wind profiler radar operating at a high VHF range (205 MHz) installed at Cochin (10.03°N, 76.33°E), India, provides high‐resolution winds in time and altitude extending from 315 m to 20 km. We evaluated the horizontal and vertical winds obtained from atmospheric global reanalyses (ERA5, ERA‐Interim, MERRA‐2 and NCEP) data and the high‐resolution regional model (WRF) output winds with the ST radar (STR) wind observations at Cochin. The data for one whole year, from 1 August 2018 to 31 July 2019, for 0600 UTC and 1200 UTC were used. Wind data appraisal has been carried out at 24 levels from 315 m to 18.5 km altitude for the reanalyses datasets and 29 levels for model‐simulated winds in different seasons. Vertical profiles of horizontal winds from reanalysis and model‐simulated data somewhat catch the major changes observed from STR. The zonal winds match very well with the STR, whereas meridional winds show moderate similarity. Higher correlations in zonal winds are found during the summer monsoon, whereas meridional winds show apparent likeness during winter. The ERA5, ERA‐Interim, and model‐simulated winds have better agreement with the STR winds. Observed vertical winds from STR displays large fluctuations, while the estimated vertical winds in reanalysis are highly smoothed. Vertical profiles of vertical winds from the reanalysis datasets are not uniform. The reliability of vertical winds in reanalysis and WRF model output values are dubious compared to that of the directly observed winds from STR. The study indicates that improved quality of vertical and meridional winds in reanalysis data is needed by assimilating directly observed data from the available advanced facilities. [ABSTRACT FROM AUTHOR]

Identifying hitherto unknown palaeo-channels, especially in the arid regions of the Thar Desert, is crucial since these channels may form excellent aquifers, and are also associated with valuable ore deposits of many precious minerals. This study employed integrated C-band Synthetic Aperture Radar (SAR) of Sentinel-1A and high-resolution multispectral Sentinel-2A data of pre- and post-monsoon seasons (June and November) to delineate playas and palaeo-channels. This approach is the first of its kind for this area. The palaeo-channels were delineated through a detailed visual inspection of colour composite (CC) images of Sentinel-2A data, SAR backscatter (VH) images and fused SAR and optical images. Then, we studied the topographic profiles generated from the Shuttle Radar Topography Mission - Digital Elevation Model (SRTM-DEM) across the identified palaeo-channels, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) to further confirm the existence of a palaeo-channel's course and playas. As a result, several playas and palaeo-channels in the area were successfully identified, some of which were previously unmapped and undetected. The results indicate that the post-monsoon datasets are more useful for the precise delineation of palaeo-channels due to the presence of relatively higher moisture along the palaeo-channels' courses., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Rainfall-induced landslide (RFIL) disasters have become a major concern within the Indian Himalayan region. On August 14, 2023, at around 7:20 AM (India Standard Time), an excessive rainfall event triggered a landslide in the Summer Hill area of Shimla, Himachal Pradesh, India, named locally as the Shiv Bawdi landslide. The abrupt occurrence of this landslide claimed 20 lives and caused damage to the Upper and Lower Summer Hill Roads and the Kalka-Shimla Railway track. Therefore, a preliminary analysis was carried out to understand the landslide characteristics and possible failure mechanism. This includes field investigation, Interferometric Synthetic Aperture Radar (InSAR) analysis, unmanned aerial vehicle (UAV)-based mapping, and pore water pressure (PWP) simulations. The obtained results indicate that the landslide affected an area of approximately 7.52 × 103 m2 and had a volume of about 3.71 × 104 m3. Similarly, the estimated maximum velocity of this landslide was approximately 9.27 m/s, with a maximum momentum of 3.44 × 105 kg m/s. The time-series InSAR analysis also revealed pre-failure deformation signatures at the landslide site. Further, using the recorded rainfall, transient seepage analysis was performed to assess the pre-failure PWP condition. The analysis suggests that continuous antecedent rainfall resulted in the formation of positive PWP, leading to the development of localized perched aquifers by saturating the slope material. Moreover, the high-intensity rainfall on the day of failure ultimately triggered the landslide in the form of a debris flow. [ABSTRACT FROM AUTHOR]

The distribution and magnitude of forces driving lithospheric deformation in the India‐Eurasia collision zone have been debated over many decades. Here we test a two‐dimensional (2‐D) Thin Viscous Shell approach that has been adapted to explicitly account for displacement on major faults and investigate the impact of lateral variations in depth‐averaged lithospheric strength. We present a suite of dynamic models to explain the key features from new high‐resolution Sentinel‐1 Interferometric Synthetic Aperture Radar as well as Global Navigation Satellite System velocities. Comparisons between calculated and geodetically observed velocity and strain rate fields indicate: (a) internal buoyancy forces from Gravitational Potential Energy acting on a relatively weak region of highest topography (>2,000 m) contribute to dilatation of the high plateau and contraction on the margins; (b) a weak central Tibetan Plateau (∼1021 Pa s compared to far‐field depth‐averaged effective viscosity of at least 1022–1023 Pa s) is required to explain the observed long‐wavelength eastward velocity variation; (c) localized displacement on fault systems enables strain concentration and clockwise rotation around the Eastern Himalayan Syntaxis. We discuss the tectonic implications for rheology of the lithosphere, distribution of geodetic strain, and partitioning of active faulting and seismicity. Plain Language Summary: The collision of the Indian Plate with Eurasia has created the Tibetan Plateau, one of the largest deforming regions in the continents. The mode of deformation has been a focus for heated debate and has inspired two contrasting tectonic models: (a) The deformation is localized on major faults separating "blocks" or (b) the strain is distributed throughout a "continuum." We approximate the India‐Eurasia collision by treating the continent as a thin viscous shell with regional variations in strength, explicitly accounting for displacements on selected major faults. We present a suite of models to explain the key features of new geodetic measurements from satellites. The best‐fit model involves a weak Tibetan Plateau, a particularly weaker central plateau, and four strong regions outside the plateau, and allows displacements on major faults. This represents the deformation field of the India‐Eurasia collision zone as a combination of continuous distributed deformation and focused strain on major faults. Key Points: A suite of faulted viscous shell models testing key parameters explain new observations from geodesy for the India‐Asia collisionThe India‐Asia collision is explained by the balance between buoyancy and boundary forces, slip on faults, and internal strength variationsCentral Tibetan Plateau has a vertically‐averaged effective viscosity of ∼1021 Pa s, 1–2 orders lower than the surrounding area [ABSTRACT FROM AUTHOR]

The demarcation of linear geomorphological features due to fracture systems, underlying or exposed faults, drainage lines etc. from satellite images has been one of the most widely used applications in geological studies. Synthetic Aperture Radar (SAR) data has been recognized as an effective tool for lineament extraction due to its unique sensitivity to subtle topography, soil moisture and texture. However, the typical SAR sensor parameters such as frequency, polarization and look direction plays a vital role in enhancing the performance for a specific application. Therefore, the study aims to analyse the multi-frequency (C- and L-band), multi-polarization (like and cross) and multi-look direction SAR data for geological lineaments extraction. In this paper, Sentinel-1 and Advanced Land Observation Satellite (ALOS)-2 Phased Array type L-band Synthetic Aperture Radar (PALSAR)-2 datasets acquired over a part of the Indo-Burma ranges of Manipur region (northeastern part of India) are considered for analysis. The lineaments extracted from SAR datasets are compared with reference to lineaments provided by the Geological Survey of India (GSI) through their portal. The study observes that the like-polarization C-band SAR data provide optimal results for this application. It is also observed that the integration of lineaments extracted from multi-look SAR data can significantly improve the performance than that derived from single-look data. Overall, the study also focuses on the spatial distribution of lineaments with respect to the local structural regime for a better understanding of the regional tectonic setup. [ABSTRACT FROM AUTHOR]

Coal is the primary source of energy for the production of electricity as well as other industrial purposes. Despite being one of the largest coal producers in the world, India still imports coal to meet domestic demand. Coal is mainly extracted from opencast mines with high mechanization and deeper depths to meet the required coal demand. However, there are many problems with deeper opencast projects, like slope stability and its failure, which can be caused by slope geometry and rock properties. These failures result in equipment damage and human fatalities. It is necessary to monitor the stability of the slope to keep the working conditions safe. Traditional instrument techniques are currently deployed in opencast mines for regular slope monitoring. These traditional instruments usually rely on humans to collect and analyze data offline, which involves huge costs for manpower utilization. Moreover, it only works during daylight, and reliability is also not up to the mark. On the other hand, the latest instruments like Light Detection and Ranging (LiDAR) and Radio Detection and Ranging (RADAR) can efficiently monitor slope movements; those are more expensive and require highly skilled manpower. To eliminate this uncertainty and to obtain real-time data for studying the dynamic behavior of workings, Wireless Sensor Networks (WSN) are introduced. This study uses a node with a sensor and a receiver to collect data based on slope displacement measured in millimeters in a selected opencast mine. Data obtained using a WSN-based monitoring system was observed to be close to the existing total station monitoring method. ANSYS finite element-based numerical modeling software was used for simulating the field conditions and validated the results obtained by the WSN-based sensor's real-time monitoring and existing traditional field monitoring system. [ABSTRACT FROM AUTHOR]

This study examined a typical case of deep convective storm that formed over southwest India on October 12, 2011, using ground-based X-band radar measurements and Weather Research and Forecasting (WRF) model simulations. The radar observation showed isolated pockets of convective storm, which merged later to form a convective cluster. The observed storms were tall, extending well into the mixed-phase region. Few storms even extended up to the tropopause height. Three different WRF cloud microphysics schemes (WRF Double-Moment 6-Class, Morrison Double-Moment, and Milbrandt–Yau Double-Moment) were used to simulate the observed deep convective storm to examine the vertical structure of hydrometeors. All the cloud microphysics schemes were able to reproduce the convective storm event with a lag time of almost two and a half hours. The WRF Double-Moment 6-Class scheme better simulates the vertical structure of storm compared to the other two microphysics schemes. The WRF model reasonably simulated the observed patterns of convective storm when the WRF cloud microphysics scheme better simulate the graupel and snow. The differences in simulated storm structure obtained by different microphysics schemes compared to observation highlight the deficiency involved in the simulations in capturing the microphysics that is guiding the intensity of convective storms. The present study thus underscores the importance of microphysics in different parameterization schemes of WRF simulation over southwest India, which has an implication in the forecasting of convective storms. [ABSTRACT FROM AUTHOR]

Microphysical evolution of tropical clouds in the core monsoon region of India is examined for the first time using ground-based cloud radar measurements. Combining high-resolution radar reflectivity (Z) profiles with empirical relations, cloud microphysical profiles in terms of cloud ice/liquid water content (IWC/LWC) during Indian summer monsoon (ISM) season are retrieved. Though the study is carried out using a point observation, it is shown that it represents the large-scale monsoon flow over the radar site. Cloud radar measurements during ISM period are classified into active and break ISM days. Radar-derived IWC profiles are validated against CloudSat, whereas LWC profiles are validated using the collocated microwave radiometer and microphysical observations from in situ aircraft measurements. The validated IWC and LWC profiles show significant differences between active and break ISM phases including their diurnal evolution. Larger (smaller) IWC values observed during active (break) days reveal the microphysical activity associated with the contrasting cloud vertical structure in the respective ISM phases. Observed discontinuity in the cloud vertical structure during break ISM days is attributed to the lack of moist convection. The significance of the present study lies in reporting the first ground-based radar measurements of cloud microphysical properties during active and break ISM period and discussing their distinctiveness. [ABSTRACT FROM AUTHOR]

Ten-year climatology of physical properties of convective echoes during pre-monsoon season over south peninsular India and neighborhood are studied using Precipitation Radar dataset onboard Tropical Rainfall Measuring Mission satellite. Attenuation corrected radar reflectivity (Ze) is used to define an intense convective echo (ICE) which is a group of two or more contiguous convective pixels with Ze exceeding 30 dBZ. Height distribution of ICE is right skewed, single modal with mean and median as 7.8 and 7.3 kms. The ICE with area in range of more than 102 to 103 km2 (C) are most frequent (58.5%) followed by smallest scale (D) having area ≤ 102 km2 (37.2%). Large ICE's (B/C scale) are less frequent (4.2%). Mean areas of ICE's in D, C and B/C scales are 72.7, 279 and 1932 km2, respectively. The relation between height and area is linear indicating that taller ICE's are broader. The mean top heights of D, C and B/C scales are 5.5, 8.7 and 14.2 km, respectively. Frequency distributions of the height of 30 and 40 dBZ show single peaks at 5.5 and 4.75 km. Mean heights of 30 dBZ and 40 dBZ are 5.7 and 4.8 km while median heights are 5.5 and 4.7 km. Their cumulative frequency distribution shows that 6 and 3% of ICE cross 10 km height. Reflectivity structures of ICE show that systems over land are intense compared to that over ocean. [ABSTRACT FROM AUTHOR]

Glaciers are found largely in remote and inhospitable terrains around the world, and it is generally known that the ocean has transformed into glaciers, according to a long-standing study. As a result, remote sensing technology is critical for remotely monitoring the region to detect any potential threats or mass changes in the near future. We used C-band Synthetic Aperture Radar data from the Sentinel-1 satellite to estimate the surface velocity of the Gangotri glacier in the Himalayan region in this paper. Using SAR offset tracking methodology, the maximum intensity correlation provides the two-dimensional movement of the Gangotri glacier and its tributaries in the Gharwal Himalayas in India. The repeat-pass SAR image subpixel offset tracking approach was also used to create velocity vectors from Sentinel-1 seasonal datasets. Based on 40 years of daily reports, we also used the Artificial Neural Network (ANN) Algorithm to forecast future trends in precipitation, temperature, and insolation index for the region. [ABSTRACT FROM AUTHOR]

The present study attempts to identify the flood footprint of river Keleghai, a tributary of river Haldi, in West Bengal, India. Keleghai basin is known for recurrent flooding that causes severe damage to the socioeconomic infrastructure. So far, no attempt has been made for the identification of flood inundation footprints and flood risk zones in Keleghai river basin through remote sensing and multi-criteria decision-making process. Initially, a flood susceptibility or vulnerability map has been prepared, and secondly, flood footprints have been identified in the said river basin. For the preparation of flood vulnerability map with the help of the analytical hierarchy process (AHP), the elevation, slope, rainfall, normalised difference vegetation index (NDVI), land use and land cover (LULC) and distance from river and topographical wetness index (TWI) of the concerned river basin have been used. To prepare the flood footprints synthetic aperture radar (SAR), data have been processed on Google Earth Engine (GEE) platform. The result shows that more than 50% of the basin area belongs to high risk zone, and the other 40% comes under the moderate risk category. The central, northern and eastern parts of the basin present the highest susceptibility to flood hazard. This area is characterised by moderate-to-low elevation, gentle slope, moderate rainfall and less vegetative cover. This outcome can effectively be utilised in hazard management purpose for Keleghai as well as other river basins. This study will help in identifying the most vulnerable zones of the basin in terms of flood hazard assessment. On the other hand, correlating the empirical model with the real world data will provide excellent opportunity to testify the applicability of the model in decision-making purpose that could lead to a way of resilience. [ABSTRACT FROM AUTHOR]

Remote sensing is widely used for monitoring reservoir capacities. The relationship between two water levels (WLs) and the corresponding satellite-derived water spread areas provides the reservoir volume between the two WLs. However, the accuracy of this method depends on the ability to capture the water spread areas at fine increments of WLs so that the cross section of a reservoir is represented in adequate detail. In a monsoon climate, persistent cloud cover during the rainy season limits the number of usable optical satellite images. Hence, the time interval between two successive WLs for which cloud-free images are available is generally large. Using only a few WLs increases the likelihood of missing a significant break of slope in the reservoirs that may lead to gross error in the storage measurement. We showed that the combination of freely available optical (e.g., Landsat 8) and cloud-penetrating microwave [e.g., European Space Agency (ESA) Sentinel-1] images may improve storage estimation and mitigate the inherent uncertainty of using remote sensing data for monitoring reservoir sedimentation. Three reservoirs in the Upper Godavari Basin in India constituted the study area. Findings show that the average annual sedimentation rate was overestimated by ∼10% when using only Landsat-8 images compared to combined radar and optical remote sensing databases (2016 and 2017). The suggested method made significant improvements in the estimation of reservoir capacities for narrow-bottomed and convex-edged reservoirs. The capacity of a reservoir is typically monitored on a periodic basis as a means of determining the reservoir's overall health. This is a common application of satellite imagery. Satellite images are used to determine the volume of water stored in a reservoir by analyzing the relationship between two water levels and the area covered by the water. The greater the difference between the two water levels, the less precise this measurement will be. Under monsoon climate, cloud cover prevents the use of optical images during the rainy season when reservoir levels fluctuate greatly. Because there are fewer water levels to choose from due to the lack of images, the accuracy of the estimate suffers. Using three reservoirs in the monsoon region of peninsular India, this study shows that combining optical data from satellites like Landsat-8 with publicly available cloud-penetrating Sentinel-1 radar images greatly increases the number of water spread area–water level data points and decreases the error and uncertainty in the storage estimation. Based on our findings, it appears that the reported rate of capacity loss, derived from limited optical images, is likely to be inaccurate. The authors also found that the storage estimates of the narrow-bottomed conical-shaped reservoirs will be greatly improved by increasing the number of water levels in volume calculation. [ABSTRACT FROM AUTHOR]

Variations in raindrop size distribution (DSD) during the southwest monsoon (SWM) season over different climatic regions in the Indian subcontinent and adjoining seas are studied in this paper using five years (2014 - 2018) of global precipitation measurement dual-frequency precipitation radar derived DSDs. The rain rate (R) stratified DSD measurements show clearly that land, sea, and orography differ in their mass-weighted mean diameter (Dm) values. Irrespective of R, Dm values of deep rain were found to be larger in continental rain than in maritime and orographic rain. However, for shallow storms, the Dm values were smaller for continental rain than for orographic and maritime rain. Based on the Dm values and their variations with R of the deep systems, the regions could be categorized into four groups, within which the Dm values were nearly equal: (1) the northwest India (NWI) and the southeast peninsular India (SEPI); (2) the foothills of the Himalayas (FHH) and the central India (CI); (3) the northeast India (NEI) and the Bay of Bengal (BOB); and (4) the Arabian Sea (AS), the Western Ghats (WG), and the Myanmar coast (MC). Compared to other geographical regions of the Indian subcontinent, the Dm values of the deep systems were the largest over NWI and SEPI and the smallest over the WG, MC, and AS; while for shallow systems, the Dm values were the largest over the BOB and AS and the smallest over the SEPI and NWI regions. Though the cloud drops were smaller over the continental regions, the raindrops were larger than in the maritime and orographic rain regions. The microphysical and dynamical processes that occur during precipitation play a vital role in altering the DSDs of continental rain. [ABSTRACT FROM AUTHOR]

Skillful quantitative precipitation forecast using the numerical weather prediction model relies on an accurate estimate of the atmospheric state as an initial condition. Variational assimilation methods (VAR) have the potential to provide improved initial state estimation to the numerical weather prediction model using observations, prior data (background), and their respective error covariance. The quality of variational assimilation hinges on the background error statistics (BES) as it weights the error in prior state and determines the spread of assimilated observations in model space. Traditional approaches used to model stationary BES in a three‐dimensional variational assimilation system often fail to represent the model error in BES. In this study, we have proposed an ensemble method using Stochastically Perturbed Parameterization Tendency to represent the model error in BES. The characteristics of the proposed BES are compared with the traditional approaches using the National Meteorological Centre method for different control variables choices. We have further tested the performance of the proposed method in improving the skill of precipitation forecast for an extreme rainfall event, which caused devastating flood over Chennai city, India, on December 2015. Results demonstrate that the use of the proposed method results in better forecast skill of convective precipitation in terms of both position and intensity than traditional National Meteorological Centre‐based BES. Best results are obtained when zonal and meridional momentum control variables are used for modeling ensemble‐based BES. Key Points: New method is proposed to represent model error of background error statisticsPerformance of proposed method improves forecast skill for an extreme eventBest results are obtained when using zonal and meridional momentum control variables [ABSTRACT FROM AUTHOR]

Periodic crop condition monitoring is of prime importance in cotton belt of western India for water stress management. In this article, vegetation water content (VWC) is assessed using Radar Vegetation Index (RVI) derived from the RISAT-1 data during July to September, vegetative to first picking phase, for utilizing its potential for large area cotton condition assessment. The RVI estimation from dual-polarized data has been demonstrated for regional applications. Prediction models of VWC for cotton crop using RVI and in situ ground measurements depicts significant relationship, with R2 varying from 0.5 to 0.6 and RMSE of 0.3–0.7 kg m−2. High correlation exists between RVI with crop age and crop biomass with R2 varying from 0.55 to 0.7, this proves useful for sowing date prediction. The results showed good validation (R2 = 0.8) for operational applications. The estimated VWC was found with 30–35% error above 4 kg m−2 biomasses as compared to 20–25% in lower ranges. [ABSTRACT FROM AUTHOR]