Search

This study evaluates the performance of a dynamically downscaled Weather Research and Forecasting (WRF) model by capturing the characteristics of the interseasonal variability in terms of active and break spells of the Indian summer monsoon during the period 1980–2014. We identify the active and break days based on the standard criteria of previous studies. Further, the composite means in the rainfall and different rainfall events, along with the thermal and dynamical features associated with active and break spells, are analyzed and discussed. Our results clearly show that the WRF model reproduces the observed features of monsoon circulation in active spells such as the wider and intensified lower tropospheric winds, the presence of mid-tropospheric cyclonic circulation, and the dominance of strong upper-level tropical easterlies along with the high influx of moisture towards the Indian subcontinent. Even in the break spells, the WRF model effectively simulates the observed features of the ERA, such as weaker monsoon winds at both low and upper levels and decreased strength of northwesterly winds of Shamal, the presence of a strong thermal inversion over the northwestern part of the Arabian Sea, and a significant increase in the African easterly jet. Moreover, our rainfall analysis during active and break spells reveals that the WRF model accurately reproduces the observed characteristics of the regional-scale precipitation. The results also reveal that the spatial distributions of moderate, heavy, and very heavy rainfall events over the monsoon core region are well captured by the WRF model, with only slight intensity variations compared to the observed distributions.

The First author of this work Mr. Podeti Srinivasa Rao is thankful to UGC, New Delhi for providing the Rajiv Gandhi National Fellowship (RGNF) fellow763 ship, grant number: F117.1/201516/RGNF201517SC AND14128/ (SAIII/Website) by UGC New Delhi of India. The authors are thankful to the Indian Mete766 orological Department and National Centre for Environmental Prediction for making available all the observations for validation.

The lockdown measures that were taken to combat the COVID-19 pandemic minimized anthropogenic activities and created natural laboratory conditions for studying air quality. Both observations and WRF-Chem simulations show a 20–50% reduction (compared to pre-lockdown and same period of previous year) in the concentrations of most aerosols and trace gases over Northwest India, the Indo Gangetic Plain (IGP), and the Northeast Indian regions. It is shown that this was mainly due to a 70–80% increase in the height of the boundary layer and the low emissions during lockdown. However, a 60–70% increase in the pollutants levels was observed over Central and South India including the Arabian sea and Bay of Bengal during this period, which is attributed to natural processes. Elevated (dust) aerosol layers are transported from the Middle East and Africa via long-range transport, and a decrease in the wind speed (20–40%) caused these aerosols to stagnate, enhancing the aerosol levels over Central and Southern India. A 40–60% increase in relative humidity further amplified aerosol concentrations. The results of this study suggest that besides emissions, natural processes including background meteorology and dynamics, play a crucial role in the pollution concentrations over the Indian sub-continent.

The abrupt reduction in the human activities during the first lockdown of the COVID-19 pandemic created unprecedented changes in the background atmospheric conditions. Several studies reported the anthropogenic and air quality changes observed during the lockdown. However, no attempts are made to investigate the lockdown effects on the Atmospheric Boundary Layer (ABL) and background instability processes. In this study, we assess the lockdown impacts on the ABL altitude and instability parameters (Convective Available Potential Energy (CAPE) and Convective Inhibition Energy (CINE)) using WRF model simulations. Results showed a unique footprint of COVID-19 lockdown in all these parameters. Increase in the visibility, surface temperature and wind speed and decrease in relative humidity during the lockdown is noticed. However, these responses are not uniform throughout India and are significant in the inland compared to the coastal regions. The spatial variation of temperature (wind speed) and relative humidity shows an increase and decrease over the Indo Gangetic Plain (IGP) and central parts of India by 20% (100%) and 40%, respectively. Increase (80%) in the ABL altitude is larger over the IGP and central parts of India during lockdown of 2020 compared to similar time period in 2015-2019. This increase is attributed to the stronger insolation due to absence of anthropogenic activity and other background conditions. At the same time, CAPE decreased by 98% in the IGP and central parts of India, where it shows an increase in other parts of India. A prominent strengthening of CINE in the IGP and a weakening elsewhere is also noticed. These changes in CAPE and CINE are mainly attributed to the dearth of saturation in lower troposphere levels, which prevented the development of strong adiabatic ascent during the lockdown. These results provide a comprehensive observation and model-based insight for lockdown induced changes in the meteorological and thermo-dynamical parameters.

Authors sincerely thank India Meteorological Department, Government of India, for providing the high-resolution gridded rainfall, surface synoptic data, and the number of depressions and cyclones over north Indian Ocean. The third author acknowledges Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India for providing necessary funds (PDF/2016/003854) to carry out this work. This research made use of the Supercomputing Laboratory resources available at King Abdullah University of Science and Technology (KAUST), Kingdom of Saudi Arabia. The authors gratefully acknowledge the anonymous reviewers for constructive suggestions and comments during the review process.

The study was supported by King Abdullah University of Science and Technology (KAUST)under the “Virtual Red Sea Initiative”, Award Number REP/1/3268-01-01 and the Saudi ARAMCO-KAUST Marine Environmental Research Center (SAKMERC). The research made use of the Supercomputing Laboratory resources at KAUST. We thank King Abdullah City for Atomic and Renewable Energy for making the radiometer observations from the Renewable Resource Monitoring and Mapping Solar Measurement Network available online.

The local weather and climate of the Himalayas are sensitive and interlinked with global-scale changes in climate, as the hydrology of this region is mainly governed by snow and glaciers. There are clear and strong indicators of climate change reported for the Himalayas, particularly the Jammu and Kashmir region situated in the western Himalayas. In this study, using observational data, detailed characteristics of long- and short-term as well as localized variations in temperature and precipitation are analyzed for these six meteorological stations, namely, Gulmarg, Pahalgam, Kokarnag, Qazigund, Kupwara and Srinagar during 1980–2016. All of these stations are located in Jammu and Kashmir, India. In addition to analysis of stations observations, we also utilized the dynamical downscaled simulations of WRF model and ERA-Interim (ERA-I) data for the study period. The annual and seasonal temperature and precipitation changes were analyzed by carrying out Mann–Kendall, linear regression, cumulative deviation and Student's t statistical tests. The results show an increase of 0.8 ∘C in average annual temperature over 37 years (from 1980 to 2016) with higher increase in maximum temperature (0.97 ∘C) compared to minimum temperature (0.76 ∘C). Analyses of annual mean temperature at all the stations reveal that the high-altitude stations of Pahalgam (1.13 ∘C) and Gulmarg (1.04 ∘C) exhibit a steep increase and statistically significant trends. The overall precipitation and temperature patterns in the valley show significant decreases and increases in the annual rainfall and temperature respectively. Seasonal analyses show significant increasing trends in the winter and spring temperatures at all stations, with prominent decreases in spring precipitation. In the present study, the observed long-term trends in temperature (∘Cyear-1) and precipitation (mm year−1) along with their respective standard errors during 1980–2016 are as follows: (i) 0.05 (0.01) and −16.7 (6.3) for Gulmarg, (ii) 0.04 (0.01) and −6.6 (2.9) for Srinagar, (iii) 0.04 (0.01) and −0.69 (4.79) for Kokarnag, (iv) 0.04 (0.01) and −0.13 (3.95) for Pahalgam, (v) 0.034 (0.01) and −5.5 (3.6) for Kupwara, and (vi) 0.01 (0.01) and −7.96 (4.5) for Qazigund. The present study also reveals that variation in temperature and precipitation during winter (December–March) has a close association with the North Atlantic Oscillation (NAO). Further, the observed temperature data (monthly averaged data for 1980–2016) at all the stations show a good correlation of 0.86 with the results of WRF and therefore the model downscaled simulations are considered a valid scientific tool for the studies of climate change in this region. Though the correlation between WRF model and observed precipitation is significantly strong, the WRF model significantly underestimates the rainfall amount, which necessitates the need for the sensitivity study of the model using the various microphysical parameterization schemes. The potential vorticities in the upper troposphere are obtained from ERA-I over the Jammu and Kashmir region and indicate that the extreme weather event of September 2014 occurred due to breaking of intense atmospheric Rossby wave activity over Kashmir. As the wave could transport a large amount of water vapor from both the Bay of Bengal and Arabian Sea and dump them over the Kashmir region through wave breaking, it probably resulted in the historical devastating flooding of the whole Kashmir valley in the first week of September 2014. This was accompanied by extreme rainfall events measuring more than 620 mm in some parts of the Pir Panjal range in the south Kashmir.

The behaviour of planetary boundary layer (PBL) schemes initialized at different life stages of a tropical cyclone (TC) is studied by considering seven Bay of Bengal TC cases. In each TC case, the Advanced Research Weather Research and Forecasting (WRF-ARW) model is initialized at four life stages (depression to very severe cyclone storm) with National Center for Environmental Prediction (NCEP) Global analysis and integrated up to 96 h. A set of six PBL sensitivity experiments are conducted at four stages for all seven TC cases to analyse the impact of the model boundary layer in simulating the TC track and intensity parameters. The model-produced track, intensity and rainfall patterns are evaluated with the best track, intensity and gridded rainfall estimates obtained from the India Meteorological Department (IMD). The spatial and radius/height section simulated fields are evaluated with satellite retrievals. Results depict that the six PBL schemes during model initialization at different stages of a TC have produced sizable differences in the simulation of track and intensity parameters. The local and nonlocal schemes produced different results based on the TC stage at which the model is initialized. The results also suggest that if the model is initialized with a non-organized cyclonic vortex such as depression stage of the storm, PBL schemes exhibit high sensitivity and spread in terms of both track and intensity. While the spread between PBL schemes was significantly reduced and found close to the observed estimates when the model was initialized at the advanced stages of the TC. In addition, the local 1.5-order closure scheme simulated the storm parameters relatively better when the cyclone vortex was not well organized in the model's initial conditions, while the non-local and first-order closure schemes perform better with initial model conditions of a well-defined cyclonic vortex.

In this study, the variability and trends of the outdoor thermal discomfort index (DI) in the Kingdom of Saudi Arabia (KSA) were analyzed over the 39‐year period of 1980–2018. The hourly DI was estimated based on air temperature and relative humidity data obtained from the next‐generation global reanalysis from the European Center for Medium‐Range Weather Forecasts and in‐house high‐resolution regional reanalysis generated using an assimilative Weather Research Forecast (WRF) model. The DI exceeds 28°C, that is, the threshold for human discomfort, in all summer months (June to September) over most parts of the KSA due to a combination of consistently high temperatures and relative humidity. The DI is greater than 28°C for 8–16 h over the western parts of KSA and north of the central Red Sea. A DI of >28°C persistes for 7–9 h over the Red Sea and western KSA for 90% of summer days. The spatial extent and number of days with DI > 30°C, that is, the threshold for severe human discomfort, are significantly lower than those with DI > 28°C. Long‐term trends in the number of days with DI > 28°C indicate a reduced rate of increase or even a decrease over some parts of the southwestern KSA in recent decades (1999–2018). Areas with DI > 30°C, in particular the northwestern regions of the Arabian Gulf and its adjoining regions, also showed improved comfort levels during recent decades. Significant increases in population and urbanization have been reported throughout the KSA during the study period. Analysis of five‐years clinical data suggests a positive correlation between higher temperatures and humidity with heat‐related deaths during the Hajj pilgrimage. The information provided herein is expected to aid national authorities and policymakers in developing necessary strategies to mitigate the exposure of humans to high levels of thermal discomfort in the KSA., Key Points Analyze and investigate variability and trends of the outdoor thermal discomfort index (DI) in the Kingdom of Saudi Arabia (KSA)Reduced rate of increase in DI was reported in the southwestern region during the last 20 yearsDuring this period, except Yanbu, Makkah, Madina, and Taif, most cities had an improved DI

The Red Sea, home to the second-longest coral reef system in the world, is a vital resource for the Kingdom of Saudi Arabia. The Red Sea provides 90% of the Kingdom’s potable water by desalinization, supporting tourism, shipping, aquaculture, and fishing industries, which together contribute about 10%–20% of the country’s GDP. All these activities, and those elsewhere in the Red Sea region, critically depend on oceanic and atmospheric conditions. At a time of mega-development projects along the Red Sea coast, and global warming, authorities are working on optimizing the harnessing of environmental resources, including renewable energy and rainwater harvesting. All these require high-resolution weather and climate information. Toward this end, we have undertaken a multipronged research and development activity in which we are developing an integrated data-driven regional coupled modeling system. The telescopically nested components include 5-km- to 600-m-resolution atmospheric models to address weather and climate challenges, 4-km- to 50-m-resolution ocean models with regional and coastal configurations to simulate and predict the general and mesoscale circulation, 4-km- to 100-m-resolution ecosystem models to simulate the biogeochemistry, and 1-km- to 50-m-resolution wave models. In addition, a complementary probabilistic transport modeling system predicts dispersion of contaminant plumes, oil spill, and marine ecosystem connectivity. Advanced ensemble data assimilation capabilities have also been implemented for accurate forecasting. Resulting achievements include significant advancement in our understanding of the regional circulation and its connection to the global climate, development, and validation of long-term Red Sea regional atmospheric–oceanic–wave reanalyses and forecasting capacities. These products are being extensively used by academia, government, and industry in various weather and marine studies and operations, environmental policies, renewable energy applications, impact assessment, flood forecasting, and more.

The state of Kerala, located on the west coast of India, experienced a record 100‐year flood that resulted in major landslides from unprecedented prolonged and extremely heavy rainfall (50–480 mm·day−1) during August 1–19, 2018, causing extensive damage and about 500 causalities. Rainfall observations indicate that the heavy rainfall occurred over two spells (August 7–10 and 14–18) in association with an offshore trough, and a depression over the Bay of Bengal (BOB). High‐resolution 38‐year climatology data (5 km) and the ERA‐Interim reanalysis dataset show a strong low‐level jet over the Arabian Sea and a depression over the BOB with a southwestward tilt during the heavy rainfall. Very high‐resolution (2‐km) mesoscale model simulations suggest that this high convective instability due to the strong westerly jet along with the formation of offshore vortex, the transport of mid‐tropospheric moisture under the presence of conducive vertical shear of horizontal wind, and transport of mid‐tropospheric moisture from the BOB are the major factors (as shown in the schematic diagram) behind the extreme heavy rainfall over Kerala.

The impact of different surface roughness schemes in air-sea coupling on simulating tropical cyclones (TCs) over the Bay of Bengal is analyzed using Weather Research and Forecasting-Ocean Mixed layer (WRF-OML) modeling system. The sensitivity of three surface roughness schemes is tested by conducting three experiments for seven TCs namely Phailin (2013), Lehar (2013), Hudhud (2014), Vardah (2016), Gaja (2018), Fani (2019) and Amphan (2020). The first experiment (Opt0) is configured with surface drag and moist enthalpy from Garratt formulations, the second (Opt1) uses surface drag from Donelan and constant moist enthalpy formulations and the third experiment (Opt2) employs the modified moist enthalpy from Garratt formulations along with Donelan drag. Results of predicted track, intensities, precipitation and structure of the cyclones are highly sensitive to the surface exchange coefficients formulated through the roughness parameterization. The Opt2 followed by Opt1 experiments captured the deepening and mature phases of the storm close to the observed estimates for both pre- and post-monsoon TCs, by improving the ocean-atmosphere feedback of surface energy fluxes. Comparison of simulated moisture convergence, transport and precipitation with observations for all cyclones suggest that the Opt2 provides an improved representation of surface enthalpy fluxes playing a crucial role in the moisture transport and cloud microphysical processes. The improved simulation of TCs with Opt1 and Opt2 over Opt0 is attributed to the realistic simulation of mixed layer deepening and sea surface temperature cooling effects in the model. An interesting result found from the WRF-OML simulations with three roughness schemes is the maximum moisture transport is mainly concentrated on the right sectors while the moisture convergence and precipitation are distributed on the left sectors of the cyclones.

This study examines the impact of assimilation of the surface winds obtained from SCATterometer SATellite-1 (SCATSAT-1) in predicting the tropical cyclones over the Bay of Bengal using a coupled ocean-atmospheric model. Three sets of numerical experiments are conducted for six cyclones during post-monsoon (VARDAH, GAJA, PHETHAI) and pre-monsoon cyclones (MORA, FANI, AMPHAN). The first experiment, ‘CONTROL’, is conducted with the Weather Research and Forecasting - Ocean Mixed Layer (WRF-OML) model initialized using Global Forecasting System analysis and ocean initial conditions obtained from the HYbrid Coordinated Ocean Model (HYCOM) model. The second experiment, ‘PREPBUFR’, is conducted by assimilating the National Center for Environmental Prediction (NCEP) prepared BUFR observations with the WRF-OML model and three-dimensional variational assimilation method. Further, the 'SCATSAT' experiment is conducted as PREPBUFR, but additionally, the SCATSAT-1 surface wind vectors are assimilated. Our results of the simulated tracks from three experiments suggest that CONTROL and PREPBUFR simulations exhibit faster translation speed and more track deviations than the India Meteorological Department (IMD) observations. The realistic representation of low-level cyclonic vortex through the assimilation of SCATSAT-1 winds seems to produce positive feedback to both track and intensity, producing a significant improvement in predicting intensity and marginal enhancement on simulation of track and translation speed. The assimilation of winds further improved the representation of different life cycles of the storms as seen in IMD. The analysis of air-sea parameters, in terms of mixed layer deepening, sea surface temperatures, and air-sea flux exchanges, suggests that the response of air-sea feedback is strong in SCATSAT compared to PREPBUFR and CONTROL. Overall, the assimilation of SCATSAT surface winds improved the WRF-OML performance on the prediction of track and intensity, upper ocean response, primary and secondary circulations of tropical cyclones, and the rainfall distributions.

India Meteorological Department (IMD) acknowledged for providing online access of the synoptic and automatic weather station data, Doppler Weather Radar products and the multi satellite-rain gauge merged product of rainfall. Radiosonde data for Kochi is downloaded the university of Wyoming, USA. The authors acknowledge ECMWF and Copernicus Climate Change Service for providing the ERA5 reanalysis data. The authors also acknowledge GES DISC, NASA for providing the rainfall estimates of TRMM3B42V7. The 0.25ox 0.25o GFS data used for the WRF model initialization is downloaded from NOMADS, NOAA. Authors wish to thank the anonymous reviewers for their technical comments which greatly helped to improve the content of the paper.

The Red Sea convergence zone (RSCZ) is formed by opposite surface winds blowing from northwest to southeast directions at around 18°–19°N between October and January. A reverse-oriented, low-level monsoon trough at 850 hPa, known as the Red Sea trough (RST), transfers moisture from the southern Red Sea to RSCZ. The positions of the RSCZ and RST and the intensity of the RST have been identified as important factors in modulating weather and climatic conditions across the Middle East. Here, we investigate the influence of the El Nino southern oscillation (ENSO) on the interannual variability of RSCZ, RST, and regional rainfall during winter months. Our results indicate that El Nino (warm ENSO phase) favours a shift of the RSCZ to the north and a strengthening of the RST in the same direction. Conversely, during November and December of La Nina periods (cold ENSO phase), the RSCZ shift to the south and the RST strengthens in the same direction. During El Nino periods, southeasterly wind speeds increase (20–30%) over the southern Red Sea and northwesterly wind speeds decrease (10–15%) over the northern Red Sea. Noticeable increases in the number of rainy days and the intensity of rain events are observed during El Nino phases. These increases are associated with colder than normal air intrusion at lower levels from the north combined with warm air intrusion from the south over the RSCZ. Our analysis suggests that during El Nino winters, warmer sea surface temperatures and higher convective instability over the Red Sea favour local storms conditions and increase rainfall over the Red Sea and adjoining regions.

This study presents an assessment of the potential for harvesting wind and wave energy from the Red Sea based on an 18-year high-resolution regional atmospheric reanalysis recently generated using the Advanced Weather Research Forecasting model. This model was initialized with ERA-Interim global data and the Red Sea reanalysis was generated using a cyclic three-dimensional variational approach assimilating available data in the region. The wave hindcast was generated using WAVEWATCH III on a 5-km resolution grid, forced by the Red Sea reanalysis surface winds. The wind and wave products were validated against data from buoys, scatterometers and altimeters. Our analysis suggests that the distribution of wind and wave energy in the Red Sea is inhomogeneous and is concentrated in specific areas, characterized by various meteorological conditions including weather fronts, mesoscale vortices, land and sea breezes and mountain jets. A detailed analysis of wind and wave energy variation was performed at three hotspots representing the northern, central and southern parts of the Red Sea. Although there are potential sites for harvesting wind energy from the Red Sea, there are no potential sites for harvesting wave energy because wave energy in the Red Sea is not strong enough for currently available wave energy converters. Wave energy should not be completely ignored, however, at least from the perspective of hybrid wind-wave projects.

This research work was supported by King Abdullah University of Science and Technology (KAUST), Saudi Arabia and the Saudi ARAMCO-KAUST Marine Environmental Research Center (SAKMERC). This research made use of the resources of the Supercomputing Laboratory and/or computer clusters at KAUST. The NCEP FNL, prepbufr global observational datasets were obtained from http://rda.ucar.edu. TRMM-3B42 rainfall estimates were downloaded from the TRMM NASA GFSC server. CCMP datasets were downloaded from podaac.jpl.nasa.gov. CMORPH rainfall estimates were downloaded from the Climate Prediction Center.

Although wind and wave modeling is rather successful in the open ocean, modeling enclosed seas, particularly seas with small basins and complex orography, presents challenges. Here, we use data assimilation to improve wind and wave simulations in the Red Sea. We generated two sets of wind fields using a nested, high-resolution Weather Research and Forecasting model implemented with (VARFC) and without (CTL) assimilation of observations. Available conventional and satellite data were assimilated using the consecutive integration method with daily initializations over one year (2009). By evaluating the two wind products against in-situ data from synoptic stations, buoys, scatterometers, and altimeters, we found that seasonal patterns of wind and wave variability were well reproduced in both experiments. Statistical scores for simulated winds computed against QuikSCAT, buoy, and synoptic station observations suggest that data assimilation decreases the root-mean-square error to values between 1 and 2 m s −1 and reduces the scatter index by 30% compared to the CTL. Sensitivity clearly increased around mountain gaps, where the channeling effect is better described by VARFC winds. The impact of data assimilation is more pronounced in wave simulations, particularly during extreme winds and in the presence of mountain jets.

The Red Sea is a narrow, elongated basin that is more than 2000 km long. This deceivingly simple structure offers very interesting challenges for wind and wave modeling, not easily, if ever, found elsewhere. Using standard meteorological products and local wind and wave models, this study explores how well the general and unusual wind and wave patterns of the Red Sea could be reproduced. The authors obtain the best results using two rather opposite approaches: the high-resolution Weather Research Forecasting (WRF) local model and the slightly enhanced surface winds from the global European Centre for Medium-Range Weather Forecasts model. The reasons why these two approaches produce the best results and the implications on wave modeling in the Red Sea are discussed. The unusual wind and wave patterns in the Red Sea suggest that the currently available wave model source functions may not properly represent the evolution of local fields. However, within limits, the WAVEWATCH III wave model, based on Janssen’s and also Ardhuin’s wave model physics, provides very reasonable results in many cases. The authors also discuss these findings and outline related future work.

An integrated modeling system is developed to simulate the far-field dispersions of concentrate discharges along the Saudi coast of the Red Sea. It comprises the Weather Research and Forecast (WRF) model for simulating the atmospheric circulations, the MIT general circulation model (MITgcm) for simulating the large-scale ocean conditions, and the Connectivity Modeling System (CMS) for tracking particle pathways. We use the system outputs and remote sensing altimetry data to study and analyze the atmospheric and oceanic conditions along the Saudi coast of the Red Sea and to conduct particle tracking experiments. The model simulations show distinctive patterns of seasonal variations in both the atmospheric conditions and the large-scale ocean circulation in the Red Sea, which are also reflected in the salinity and temperature distributions along the Saudi coast. The impact of this seasonality on the far-field dispersion of concentrate discharges are illustrated in seasonal dispersion scenarios with discharging outfalls located at the northern, central and southern Saudi coasts of the Red Sea.

The Red Sea is a challenge for wave modeling because of its unique two opposed wave systems, forced by opposite winds and converging at its center. We investigate the different physical aspects of wave evolution and propagation in the convergence zone. The two opposing wave systems have similar amplitude and frequency, each driven by the action of its own wind. Wave patterns at the centre of the Red Sea, as derived from extensive tests and intercomparison between model and measured data, suggest that the currently available wave model source functions may not properly represent the evolution of the local fields that appear to be characterized by a less effective wind input and an enhanced white-capping. We propose and test a possible simple solution to improve the wave-model simulation under opposing winds and waves condition. This article is protected by copyright. All rights reserved.