Reply on RC2

ABSTRACTThe present study explores the multi‐decadal variability of Severe Cyclonic Storm (SCS) frequency over the Bay of Bengal (BoB) during post‐monsoon season for the period 1950–2022. The SCS frequency displays noticeable decadal/multi‐decadal variability during the study period, and shows a strong relationship with mid‐tropospheric relative humidity (RH) and sea surface temperature (SST) over BoB. Influence of the large‐scale climate modes, such as Atlantic Multi‐decadal Oscillation (AMO) in modulating the BoB SCS frequency on multi‐decadal time scale is highlighted. It is found that AMO exhibits a significant out‐of‐phase relationship with SCS decadal frequency over the BoB. Detailed analysis suggests that large‐scale circulation changes associated with the negative phase of AMO (strong SCS decade) induced low‐level convergence extending from western Indian Ocean to southern BoB favour increased SCS frequency. A negative AMO (strong SCS period) induced zonally oriented upper‐level convergence and divergence centres, corroborated by the Walker circulation, helped to modulate the low‐level circulation over BoB. High RH and warm SSTs associated with the negative AMO further supported the increased SCS frequency over BoB. In the case of weak SCS decade, positive AMO induced low‐level divergence over southern BoB through atmospheric teleconnections provides unfavourable conditions for SCS formation. In addition to this, changes in the tropospheric temperature over the mid‐latitudes caused by mid‐latitude Rossby waves also contributed to the differences in SCS frequency on the multi‐decadal time scale. This study highlights the role of AMO in modulating the multi‐decadal SCS frequency over the BoB. The results presented here are useful for improving the decadal prediction of cyclone frequency over the BoB.

Cyclones lead to heavy precipitation in a very short period causing severe damage to life and socio-economy along its track. Globally, it is projected that there will be an increase in extreme weather events, which will lead to flooding in places like the Indian subcontinent because of irregular monsoon patterns and cyclonic storms. Extremely rare climatic events occasionally display unexpected phenomena, and cyclone Gulab and Shaheen's formation was one such extraordinary occurrence. Cyclone Gulab developed over the Bay of Bengal on 25th September 2021. The cyclone moved westward and made landfall on the east coast of India in the state of Andhra Pradesh on 26th September. Cyclone Shaheen formed in the North East Arabian sea from the remnants of cyclone Gulab. Although these cyclones were not particularly powerful compared to others in this region, it followed a very unusual track. As the cyclone entered the land, it started losing energy but continued to move across the Indian peninsula as a low-pressure system before emerging into the North Eastern Arabian Sea. Favorable atmospheric and oceanic factors for cyclogenesis in this region caused the system to reintensify on 1st October 2021. The system continued to move westward steadily for two days and intensified into a severe cyclonic storm, Shaheen. On 3rd October, cyclone Shaheen made landfall on the Northeastern coast of Oman and made history as the first severe cyclone to strike the Northern coast of Oman for one and a half-century.After the landfall of cyclone Gulab, the low-pressure system sustained over land and eventually developed into cyclone Shaheen, suggesting that land was a significant source of moisture. Thus, in this study, we quantified the moisture contributed by land in the form of evapotranspiration to the cyclones Gulab and Shaheen. We used an Eulerian water tracking technique incorporated in the state-of-the-art Weather Research and Forecasting (WRF) model to track moisture. The model allows us to specify a source region of moisture originating as evapotranspiration, which can be tracked throughout the atmosphere. This moisture is tracked till it results in precipitation or advects out of the domain. The precipitation associated with this tracked moisture is termed recycled precipitation. ERA5, a fifth-generation ECMWF atmospheric reanalysis data, is used to set up the model's initial and boundary conditions. The microphysical, cumulus, and planetary boundary layer schemes used are WSM6, Kain-Fritsch, and YSU, respectively. Eulerian water tracking being one of the most accurate tracking techniques, will enable us to get accurate contributions of different regions and land use to the cyclonic system. In this study, we mainly focus on the contributions of moisture from the forested areas and understanding the role of antecedent soil moisture in sustaining the low-pressure system across the Indian landmass. Our results showed that Northeast India and Myanmar's dense vegetated regions contributed copious amounts of moisture to the cyclonic systems in the Bay of Bengal.

A depression formed over south Andaman Sea on 23rd evening and it intensified into a cyclonic storm, Lehar in the early morning of 24 November 2013 near Latitude 10.0° N and longitude 95.0° E. Moving northwestward, it crossed Andaman and Nicobar Islands near Port Blair around 0000 UTC of 25 November as a severe cyclonic storm. On 25th morning, it emerged into southeast Bay of Bengal and moved west-northwestward, intensified into a very severe cyclonic storm in the early hours of 26 November. However, while moving west-northwestwards over westcentral Bay of Bengal, it rapidly weakened from 27th afternoon and crossed Andhra Pradesh coast close to south of Machilipatnam around 0830 UTC of 28 November 2013 as a depression.

Numerical study of coastal hydrodynamics using a coupled model for Hudhud cyclone in the Bay of Bengal

The intensity and frequency variability of cyclones in the North Indian Ocean (NIO) have been amplified over the last few decades. The number of very severe cyclonic storms (VSCSs) over the North Indian Ocean has increased over recent decades. “Phailin”, an extreme severe cyclonic storm (ESCS), occurred during 8–13 October 2013 over the Bay of Bengal and made landfall near the Gopalpur coast of Odisha at 12 UTC on 12 October. It caused severe damage here, as well as in the coastal Odisha, Andhra Pradesh, and adjoining regions due to strong wind gusts (~115 knot/h), heavy precipitation, and devastating storm surges. The fidelity of the WRF model in simulating the track and intensity of tropical cyclones depends on different cloud microphysical parameterization schemes. Thus, four sensitivity simulations were conducted for Phailin using double-moment and single-moment microphysical (MP) parameterization schemes. The experiments were conducted to quantify and characterize the performance of such MP schemes for Phailin. The simulations were performed by the advanced weather research and forecasting (WRF-ARW) model. The model has two interactive domains covering the entire Bay of Bengal and adjoining coastal Odisha on 25 km and 8.333 km resolutions. Milbrandt–Yau (MY) double-moment and WRF single-moment microphysical schemes, with 6, 5, and 3 classes of hydrometeors, i.e., WSM6, WSM5, and WSM3, were used for the simulation. Experiments for Phailin were conducted for 126 h, starting from 00 UTC 08 October to 06 UTC 13 October 2013. It was found that the track, intensity, and structure of Phailin are highly sensitive to the different microphysical parameterization schemes. Further, the precipitation and cloud distribution were studied during the ESCS stage of Phailin. The microphysics schemes (MY, WSM3, WSM5, WSM6), along with Grell–Devenyi ensemble convection scheme predicted landfall of Phailin over the Odisha coast with significant track errors. Supply of moisture remains a more crucial component than SST and wind shear for rapid intensification of the Phailin 12 h before landfall over the Bay of Bengal. Finally, the comparison of cyclone formation between two decades 2001–2010 and 2011–2020 over the Bay of Bengal inferred that the increased numbers of VSCS are attributed to the supply of abundant moisture at low levels in the recent decade 2011–2020.

ABSTRACTPROSAIL is a combination of the leaf optical properties spectra (PROSPECT) model and the scattering by arbitrarily inclined leaves (SAIL) canopy bidirectional reflectance model. When modelling forest canopy reflectance using the PROSAIL radiative transfer model, the sensitivities of parameters can affect the modelling accuracy. Traditionally, sensitivities have been assessed using local sensitivity analysis (LSA); however, drawbacks to this approach include a lack of consideration for coupled effects between different parameters. In this study, parameter sensitivities in the PROSAIL model were calculated using two global sensitivity analysis (GSA) methods (the Extended Fourier Amplitude Sensitivity Test (EFAST) method and the Morris method), field measurements, and Landsat 5 Thematic Mapper (TM) data for a Moso bamboo forest. The results of GSA were compared with those of LSA in order to identify the key parameters impacting the Moso bamboo forest canopy reflectance, and to provide a reference for model optimization and vegetation canopy inversion improvement. The results showed that: (1) the sensitivities of six major input parameters of the PROSAIL model were generally consistent with the sorting orders of the two GSA methods, but were not in accordance with those from the LSA method, especially in the mid-infrared band; (2) coupled effects among parameters acting on reflectance simulation in visible light bands were greater than those in infrared bands; (3) the simulated canopy reflectance was evaluated using Landsat 5 TM data, and the results simulated based on LSA analysis showed higher error than those based on GSA analysis, because the LSA method ignored the influence of some parameters on canopy reflectance, e.g. leaf mesophyll structure (N), average leaf angle (ALA), leaf water content (Cw), and leaf dry matter content (Cm). However, GSA was able to fully consider the coupled effects among parameters, and thus identified the sensitive parameters impacting on reflectance more accurately.

Copula-based methods for global sensitivity analysis with correlated random variables and stochastic processes under incomplete probability information

Weather Research and Forecasting (WRF-ARW) model and its three-dimensional variational data assimilation (3D-Var) system are used to investigate the impact of the Quick Scatterometer (QuikSCAT) near surface winds, Special Sensor Microwave/Imager (SSM/I)-derived Total Precipitable Water (TPW), and Meteosat-7-derived Atmospheric Motion Vectors (AMVs) on the track and intensity prediction of tropical cyclones over the North Indian Ocean. The case of tropical cyclone, Gonu (June 2007; Arabian Sea), is first tested and the results show significant improvements particularly due to the assimilation of QuikSCAT winds. Three other cases, cyclone Mala (April 2006; Bay of Bengal), Orissa super cyclone (October 1999; Bay of Bengal), and Very Severe Cyclonic storm (October 1999; Bay of Bengal), are then examined. The prediction of cyclone tracks improved significantly with the assimilation of QuikSCAT winds. The track improvement resulted from the relocation of the initial cyclonic vortices after the assimilation of QuikSCAT wind vectors. After the assimilation of QuikSCAT winds, the mean (for four cyclone cases) track errors for first, second, and third day forecasts are reduced to 72, 101, and 166 km, respectively, from 190, 250, and 381 km of control (without QuikSCAT winds) runs. The assimilation of QuikSCAT winds also shows positive impact on the intensity (in terms of maximum surface level winds) prediction particularly for those cyclones, which are at their initial stages of the developments at the time of data assimilation. The assimilation of SSM/I TPW has significant influence (negative and positive) on the cyclone track. In three of the four cases, the assimilation of the SSM/I TPW resulted in drying of lower troposphere over cyclonic region. This decrease of moisture in TPW assimilation experiment resulted in reduction of cyclonic intensity. In three of the four cyclones, the assimilation of Meteosat-7 AMVs shows negative impact on the track prediction.

The objective of the present study is to investigate in detail the impact of different physical parameterization schemes on track and intensity of two severe cyclonic storms, AILA and JAL, which formed over Bay of Bengal, using a WRF mesoscale model. Three 2-way interactive nested domains with horizontal resolutions of 60, 20 and 6.6 km are used with initial and boundary conditions from NCEP-FNL data. Three sets of experiments include sensitivity to cumulus, microphysics and planetary boundary layer parameterization schemes, respectively. From cumulus parameterization experiments, Betts–Miller–Janjic is found to be better in the group. The strength of mid-latitude trough and presence of southward wind surge for cyclone AILA, the strength of the cross-equatorial flow as well as stronger easterly wind fields in the mid-tropospheric levels for cyclone JAL, and the amount of potential vorticity for both cyclones are some of the factors, which affect the large-scale flow and, hence, the track of both storms. WSM6 Microphysics scheme is able to produce a realistic feature of the cyclones as compared to the other schemes. The realistic representation of mid-tropospheric heating contributed by snow and graupel hydrometeors may be one of the reasons for better intensity simulation by WSM6. The higher values of relative humidity in and above the boundary layer favor the deep vertical mixing in YSU and thus contribute towards the better intensity simulation.

Assessing the sensitivity of land-atmosphere coupling strength to boundary and surface layer parameters in the WRF model over Amazon

India received a lot of rain from the two main rain-bearing systems (i.e., Monsoon Depressions (MDs) and Deep Depressions (DDs)) during the monsoon season. The MDs and DDs contributed roughly 60% of the seasonal rainfall and primarily originated over the Bay of Bengal (BoB) before moving northwest to the mainland. As they migrate from the BoB to the mainland, they have significantly impacted the eastern coast of India, especially Odisha. Due to its heavy reliance on intricate, non-linear processes at a smaller scale, rainfall forecasting is very challenging to predict with a reasonable degree of accuracy, particularly at the district level. For the first time, we attempted to employ high-resolution real-time forecast outputs (i.e., WRF) as the input feature to district-scale DL models to reduce prediction error rather than relying on observation or reanalysis datasets. In this study, we used input data from the Weather Research and Forecast (WRF) forecast at high resolution (3km), which was initialized using GFS real-time forecast, to enhance the spatial and categorical rainfall prediction (intensity) at the district scale by introducing two DL-based architectures: U-Net (+A) (attention-based U-Net architecture) and KU-Net (+A) (Attention-based Kernelized U-Net Architecture). The model was trained using 24 cases of monsoon LPS (12 MDs and 12 DDs) spanning between 2007 and 2018 and tested for two cases, July 2023 and August 2023, over Odisha in real-time.Our suggested model (KU-Net (+A)) significantly enhanced the Odisha district-level rainfall prediction. While WRF shows the MAE of more than 25 mm and 36 mm for both instances, respectively, the DL models decreased the MAE values to less than 8 mm up to Day 4 for case 1 and less than 15 mm for case 2. The rainfall distribution for case 1 demonstrates that the suggested DL model better captures the spatial rainfall, whereas the WRF underestimates it by less than 10 mm. In the second scenario, WRF underestimates the HREs, while the suggested DL model accurately predicts them in terms of size, intensity, and dispersion. The confusion matrix indicates that there were no HREs in Case 1, and the DL model's TPR (True Positive Rate) for the LR (Light Rain) and MR (Moderate Rain) classes is greater (>80%) than WRF's (52.5%). Only the HR (Heavy Rain), VHR (Very Heavy Rain), and HER (Extremely Heavy Rain) categories' TPRs of 85.7%, 87%, and 100%, respectively, are captured by the suggested DL model in the second real-time scenario. The study's conclusions directly impact how early warning systems can improve using the DL model.

<p>The Indian subcontinent is prone to tropical cyclones that originate in the Indian Ocean and cause widespread destruction to life and property. Accurate prediction of cyclone track, landfall, wind, and precipitation are critical in minimizing damage. The Weather Research and Forecast (WRF) model is widely used to predict tropical cyclones. The accuracy of the model prediction depends on initial conditions, physics schemes, and model parameters. The parameter values are selected empirically by scheme developers using the trial and error method, implying that the parameter values are sensitive to climatological conditions and regions. The number of tunable parameters in the WRF model is about several hundred, and calibrating all of them is highly impossible since it requires thousands of simulations. Therefore, sensitivity analysis is critical to screen out the parameters that significantly impact the meteorological variables. The Sobol’ sensitivity analysis method is used to identify the sensitive WRF model parameters. As this method requires a considerable amount of samples to evaluate the sensitivity adequately, machine learning algorithms are used to construct surrogate models trained using a limited number of samples. They could help generate a vast number of required pseudo-samples. Five machine learning algorithms, namely, Gaussian Process Regression (GPR), Support Vector Machine, Regression Tree, Random Forest, and K-Nearest Neighbor, are considered in this study. Ten-fold cross-validation is used to evaluate the surrogate models constructed using the five algorithms and identify the robust surrogate model among them. The samples generated from this surrogate model are then used by the Sobol’ method to evaluate the WRF model parameter sensitivity.</p>

India Meteorological Department (IMD) introduced the objective tropical cyclone (TC) intensity forecast valid for next 24 h over the north Indian Ocean (NIO) in 2003 and extended up to 72 h in 2009. In this study, an attempt is made to evaluate the TC intensity forecast issued by IMD during 2005–2011 (7 years) by calculating the absolute error (AE), root mean square error (RMSE) and skill in intensity forecast in terms of maximum sustained surface wind (MSW). The accuracy of TC intensity forecast has been analysed with respect to basin of formation (Bay of Bengal, Arabian Sea and NIO as whole), season of formation (pre-monsoon and post-monsoon seasons), intensity of TCs (cyclonic storm and severe cyclonic storm or higher intensities) and type of track of TCs (climatological/straight moving and recurving/looping type). The study shows that the average AE (RMSE) in intensity forecast is about 11(14), 14(19) and 20(26) knots, respectively, for 24-, 48- and 72-h forecasts over the NIO as a whole during 2009–2011. The skill of intensity forecast is about 44 %(48 %), 60 %(58 %) and 60 %(65 %) for 24-, 48- and 72-h forecasts during 2009–2011 with respect to AE (RMSE). There is no significant improvement in terms of reduction in AE and RMSE of MSW forecast over the NIO like that over the northwest Pacific and northern Atlantic Oceans during 2005–2011. However, the skill in intensity forecast compared to persistence method has significantly improved by about 6 %(10 %) and 9 %(8 %) per year, respectively, for 12- and 24-h forecasts considering the AE (RMSE) during 2005–2011. There is also significant increasing trend in percentage of 24-h intensity forecasts with error of 10 knots or less during 2005–2011.

The chief feature of the pa per is the study of. the composite structure of the surface wind field in severe cyclonic storms in the Bay of Bengal and the Arabian Sea by analysis of ships' reports received from the storm field, The storm field has been divided into octants, with respect to the direction of movement of the storm and from available ships' reports in the fields of alls3vere cyclonic storms in the Indian Seas during the period 1925-54, the variation of total tangential and radial velocities with distance from centre in each octant analysed. From these, composite pictures giving the total, tangential and radial wind field have been constructed. The mean fields of incurvature, divergence and vorticity have been obtained from the above and discussed. Precipitation associated with the above wind field has also been computed, Rainfall associated with individual storms has also been analysed and compared with computed values.
 
 In the initial paragraphs, the distribution of pressure in the storm field and Dear the centre of storm has been analysed and discussed. The distribution of free air temperature and sea surface temperature has also been studied for a few storms for which data were available.

<p><strong><em>Abstract:</em></strong> Vulnerability not only establishes the relationships people have with their environment but also links the social forces and institutions and the cultural values that sustain them. Physiographically, a deltaic plain, the Sundarban region is a dynamic ecosystem which frequently witnesses physical and social vulnerability brought about by natural hazards like tropical cyclones resulting in saltwater flooding. The Bay of Bengal basin records the highest number of tropical cyclones globally. About 8 storms with sustained wind speeds more than 63 km/hr form in the Bay of Bengal, of which, 2 become tropical cyclones. Cyclone Yaas was identified as a very severe cyclonic storm, originally formed as a tropical disturbance identified by the India Meteorological Department (IMD) on 23<sup>rd</sup> May 2021. Yaas made it’s landfall at Dhamra of Orissa state of India, on 26<sup>th</sup> May 2021, coinciding with spring tide. Coastal flooding was reported from Maipith, Bhubaneswari village panchayats along the rivers Matla, Thakuran and Nimania of Kultali community development block, Dhaspara, Sumatinagar, Kastala, Kochuberia, Mahishamari, Muriganga, Shilpara village panchayats of Sagar community development block, Amrabati, Lakshmipur, Fraserganj of Namkhana community development block, Gobardhanpur, Mridangabhanga, Krishnadaspur, East Chintamanipur, Kumarpur of Patharpratima community development block. Within the last 7 years, capacity building in the form of concrete houses under Indira Awas Yojana, cyclone shelters, metalled roads has increased. However, in the time of disasters even the settled portions get submerged in water as the settled portions are located at a lower height than the high water level. The local demand of concrete embankments is debatable and in some places the use of brick pitching, brick block pitching, porcupine mesh and Aila bandh have not been successful in preventing breaching and consequent saltwater inundation. Vulnerability is severe in case of Basanti, Gosaba, Kultali, Namkhana, Patharpratima community development blocks and high in case of Kakdwip, Sagar and Hingalganj community development blocks (Sahana et al, 2019). This paper takes all these phenomena into account and provides certain probable mitigation strategies which include the mapping of erosion accretion sites by comparative analyses of toposheets and satellite images, site specific mangrove aforestation, improved construction of embankments following expert and local opinions, revival of the decayed creeks to prevent waterlogging at times of disaster and rehabilitation of people from sites of continuous erosion to community shelters located at elevated areas alongwith tidal river management as practiced in Bangladesh where the rivers are temporarily allowed their spill areas.</p><p> </p><p> </p>