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This study assesses the performance of the Weather Research and Forecasting (WRF) model in simulating the Arabian Peninsula summer climate for the period 2001–2016. The European Centre for Medium range Weather Forecast (ECMWF) reanalysis is downscaled using WRF without (CTRL) and with the Spectral Nudging (SPN) method. Our results suggest that the noticeable cold biases in surface temperatures (mean, minimum, and maximum) over the Arabian Peninsula in CTRL are significantly reduced in SPN. The seasonal patterns of surface pressure, cloud cover, lower and upper tropospheric circulation, and mid-tropospheric anticyclone are also simulated more realistically with SPN. The evaluation of mean vertical profiles of dynamical and thermo-dynamical features over the Arabian Peninsula further confirms the enhanced simulations with SPN with respect to CTRL. Though SPN captures better the observed evolution of rainfall compared to that of CTRL, it produces a positive rainfall bias over the Southwestern Arabian Peninsula. Stronger vertical motions associated with the local topography enhance the higher water vapor loading, condenses in the upper layers, and results in excess amount of rainfall in SPN. Furthermore, with SPN, WRF is further able to better simulate the synoptic features of heat waves. Overall, SPN enhances WRF simulation skill of the horizontal structures and vertical profiles of the Arabian Peninsula summer climate by enforcing a better balance between the small and large scale features and associated feedbacks.

The agricultural sector in Saudi Arabia has witnessed rapid growth in both production and area under cultivation over the last few decades. This has prompted some concern over the state and future availability of fossil groundwater resources, which have been used to drive this expansion. Large-scale studies using satellite gravimetric data show a declining trend over this region. However, water management agencies require much more detailed information on both the spatial distribution of agricultural fields and their varying levels of water exploitation through time than coarse gravimetric data can provide. Relying on self-reporting from farm operators or sporadic data collection campaigns to obtain needed information are not feasible options, nor do they allow for retrospective assessments. In this work, a water accounting framework that combines satellite data, meteorological output from weather prediction models, and a modified land surface hydrology model was developed to provide information on both irrigated crop water use and groundwater abstraction rates. Results from the local scale, comprising several thousand individual center-pivot fields, were then used to quantify the regional-scale response. To do this, a semi-automated approach for the delineation of center-pivot fields using a multi-temporal statistical analysis of Landsat 8 data was developed. Next, actual crop evaporation rates were estimated using a two-source energy balance (TSEB) model driven by leaf area index, land surface temperature, and albedo, all of which were derived from Landsat 8. The Community Atmosphere Biosphere Land Exchange (CABLE) model was then adapted to use satellite-based vegetation and related surface variables and forced with a 3 km reanalysis dataset from the Weather Research and Forecasting (WRF) model. Groundwater abstraction rates were then inferred by estimating the irrigation supplied to each individual center pivot, which was determined via an optimization approach that considered CABLE-based estimates of evaporation and TSEB-based satellite estimates. The framework was applied over two study regions in Saudi Arabia: a small-scale experimental facility of around 40 center pivots in Al Kharj that was used for an initial evaluation and a much larger agricultural region in Al Jawf province comprising more than 5000 individual fields across an area exceeding 2500 km2. Total groundwater abstraction for the year 2015 in Al Jawf was estimated at approximately 5.5 billion cubic meters, far exceeding any recharge to the groundwater system and further highlighting the need for a comprehensive water management strategy. Overall, this novel data–model fusion approach facilitates the compilation of national-scale groundwater abstractions while also detailing field-scale information that allows both farmers and water management agencies to make informed water accounting decisions across multiple spatial and temporal scales.

Knowledge about extreme water levels is essential for efficient planning and design of coastal infrastructure. This study uses a high-resolution (~ 60 m) coupled advanced circulation + simulating waves nearshore modeling system to estimate extreme water levels in the coastal waters of King Abdullah Economic City (KAEC), Saudi Arabia, located on the central eastern coast of the Red Sea. High spatial (5 km) and temporal (hourly) resolution meteorological fields are generated to drive the model, along with open ocean tides. The characteristics of extreme water levels in the region are subsequently described based on the validated model simulations. The central Red Sea is characterized by a low-tidal regime, and meteorological events contribute significantly to total water levels: meteorological surges cause water level increases of up to 75 cm inside the KAEC lagoon. An extreme value analysis based on annual maxima of hindcast water level data is conducted and the results suggest that the inferred 100-year water levels are about 80 cm inside the KAEC lagoon. It is also shown that projected sea level rise would reduce the average recurrence intervals of extreme water levels along the KAEC coastline.

This work was supported by the Office of Sponsored Research (OSR) at King Abdullah University of Science and Technology (KAUST) under the Virtual Red Sea Initiative (Grant #REP/1/3268-01-01). The research made use of the KAUST supercomputing facilities.

Summary We propose a feature-oriented ensemble history-matching workflow with a focus on the integration of time-lapse seismic and electromagnetic (EM) data. The developed workflow consists of two main steps. First, the rock cross-properties, such as water saturation and porosity relating to both seismic and EM attributes, are estimated by joint inversion of seismic and EM data using an iterative ensemble smoother (ES). Second, the remaining model parameters of interest, such as permeability, are calibrated using the updated cross-properties. To assimilate the inverted-saturation information efficiently, we take a feature-oriented integration approach in which front positions are identified from the inverted-saturation field. Related model parameters are then conditioned to the interpreted fronts using the iterative ES with a distance parameterization. The novelty of the proposed approach consists of combining the feature-oriented history matching with ensemble-based geophysical inversion to achieve an efficient joint integration of multiple sources of geophysical data. The performance of the proposed history-matching workflow is examined using a 2D channelized reservoir model and a more-realistic 3D reservoir model with a crosswell configuration for seismic and EM surveys. It is demonstrated that the developed workflow provides a novel and effective way to calibrate reservoir models with multiple sources of geophysical data. The experimental results show a positive synergy effect on the characterization of model variables by jointly assimilating seismic and EM data.

The variability of the water mass exchange between the Arabian Gulf and the Indian Ocean is investigated using a high-resolution (1/36°) ocean model. We focus on the period from December 1996 to March 1998, having as reference in situ measurements at the Strait of Hormuz. Previous studies, based on models and observations, suggested a perpetual deep outflow, mainly in the southern part of the Strait, and a variable flow in the upper layers. In the present study, we confirm that there is a permanent core of a deep outflow in the Strait at depths greater than 40 m, characterised by high-salinity waters. In addition, we show that there is a seasonal signal in the upper layers net flow in the southern part of the Strait, altering from net inflow during winter/spring to net outflow during summer/fall. The mean annual inflow through the Strait is estimated at 0.22 ± 0.01 Sv and the deep outflow at 0.147 ± 0.01 Sv. The water mass exchange through the Strait is controlled by synoptic processes with high variability net transport fields. These processes characterise the structure and the intensity of the transport patterns, exhibiting 2- to 5-day period. On synoptic time scales, winds drive an immediate baroclinic flow at the Strait of Hormuz, affecting mostly the upper layers, and a quasi-barotropic flow that peaks approximately 2 days later.

The city of Jeddah, Saudi Arabia, is characterized by a hot and arid desert climate. On occasion, however, extreme precipitation events have led to flooding that caused extensive damage to human life and infrastructure. This study investigates the effect of incorporating an urban canopy model and urban land cover when simulating severe weather events over Jeddah using the Weather Research and Forecasting (WRF) Model at a convective-permitting scale (1.5-km resolution). Two experiments were conducted for 10 heavy rainfall events associated with the dominant large-scale patterns favoring convection over Jeddah: (i) an “urban” experiment that included the urban canopy model and modern-day land cover and (ii) a “desert” experiment that replaced the city area with its presettlement, natural land cover. The results suggest that urbanization plays an important role in modifying rainfall around city area. The urban experiment enhances the amount of rainfall by 26% on average over the Jeddah city area relative to the desert experiment in these extreme events. The changes in model-simulated precipitation are primarily tied to a nocturnal heat-island effect that modifies the planetary boundary layer and atmospheric instability of the convective events.

Sea surface temperature (SST) is a leading factor impacting coral reefs and causing bleaching events in the Red Sea. A long-term prediction of temperature patterns with an estimate of uncertainty is thus essential for environment management of the Red Sea ecosystem. In this work, we build a data-driven Bayesian structural time series model and show its effectiveness in predicting future SST seasons with a high accuracy, and identifying the main predictive factors of future SST variability among a large number of factors, including regional SST and large-scale climate indices. The modeling scheme proposed here applies an efficient hierarchical clustering to identify interconnected subregions that display distinct SST variability over the Red Sea, and a Markov Chain Monte Carlo algorithm to simultaneously select the main predictors while the time series model is being trained. In particular, numerical results indicate that monthly SST can be reliably predicted for five months ahead.

Research reported in this publication was supported by KingAbdullah University of Science and Technology (KAUST). The authors would like to thank the anonymous reviewers and Franc oisDesbouvries for the fruitful discussions about the resampling in the particle filter.

This research was supported by the King Abdulla University of Science and Technology (KAUST) under the Virtual Red Sea Initiative (Grant # REP/1/3268-01-01),the General Commission for Survey (under award number RGC/3/1612-01-0) and the Saudi ARAMCO-KAUST Marine Environmental Observatory(SAKMEO)at KAUST.All the data we used in this manuscript are publicly available. MERRA2 dataset was downloaded from https://disc.gsfc.nasa.gov/and ERA-interimdataset from https://apps.ecmwf.int/.MODIS, MISR and SeaWIFS satellite data products of AOD were downloaded from https://giovanni.gsfc.nasa.gov/giovanni/. The authors would like to thank three anonymous reviewers for their constructive and helpful comments.

SummaryAn ensemble-based history-matching framework is proposed to enhance the characterization of petroleum reservoirs through the assimilation of crosswell electromagnetic (EM) data. As an advanced technology in reservoir surveillance, crosswell EM tomography can be used to estimate a cross-sectional conductivity map and associated saturation profile at an interwell scale by exploiting the sharp contrast in conductivity between hydrocarbons and saline water. Incorporating this information into reservoir simulation in combination with other available observations is expected to enhance the forecasting capability of reservoir models and to lead to better quantification of uncertainty.The proposed approach applies ensemble-based data-assimilation methods to build a robust and flexible framework in which various sources of available measurements can be integrated. A comparative study evaluates two different implementations of the assimilation of crosswell EM data. The first approach integrates the crosswell EM field components in their original form, which entails forward simulation of the observed EM responses from the simulated reservoir state. In the second approach, formation conductivity is derived from the EM data through inversion and is subsequently assimilated into the reservoir model. An image-oriented distance parameterization of the fluid front assimilates the conductivity field in an efficient and robust manner and overcomes issues with data size, errors, and their correlation. Numerical experiments for different test cases with increasing complexity provide insight into the performance of the two proposed integration schemes. The results demonstrate the efficiency of the developed history-matching workflow and the added value of crosswell EM data in enhancing the reservoir characterization and reliability of dynamic reservoir forecasts.

The southern Red Sea is genetically distinct from the rest of the basin; yet the reasons responsible for this genetic separation remain unclear. Connectivity is a vital process for the exchange of individuals and genes among geographically separated populations, and is necessary for maintaining biodiversity and resilience in coral reef ecosystems. Here, using long-term, high-resolution, 3-D backward particle tracking simulations, we investigate the physical connectivity of coral reefs in the southern Red Sea with neighbouring regions. Overall, the simulation results reveal that the southern Red Sea coral reefs are more physically connected with regions in the Indian Ocean (e.g., the Gulf of Aden) than with the northern part of the basin. The identified connectivity exhibits a distinct monsoon-related seasonality. Though beyond the country boundaries, relatively remote regions of the Indian Ocean may have a substantial impact on the southern Red Sea coral reef regions, and this should be taken into consideration when establishing conservation strategies for these vulnerable biodiversity hot-spots.

A new regional coupled ocean–atmosphere model is developed and its implementation is presented in this paper. The coupled model is based on two open-source community model components: the MITgcm ocean model and the Weather Research and Forecasting (WRF) atmosphere model. The coupling between these components is performed using ESMF (Earth System Modeling Framework) and implemented according to National United Operational Prediction Capability (NUOPC) protocols. The coupled model is named the Scripps–KAUST Regional Integrated Prediction System (SKRIPS). SKRIPS is demonstrated with a real-world example by simulating a 30 d period including a series of extreme heat events occurring on the eastern shore of the Red Sea region in June 2012. The results obtained by using the coupled model, along with those in forced stand-alone oceanic or atmospheric simulations, are compared with observational data and reanalysis products. We show that the coupled model is capable of performing coupled ocean–atmosphere simulations, although all configurations of coupled and uncoupled models have good skill in modeling the heat events. In addition, a scalability test is performed to investigate the parallelization of the coupled model. The results indicate that the coupled model code scales well and the ESMF/NUOPC coupler accounts for less than 5 % of the total computational resources in the Red Sea test case. The coupled model and documentation are available at https://library.ucsd.edu/dc/collection/bb1847661c (last access: 26 September 2019), and the source code is maintained at https://github.com/iurnus/scripps_kaust_model (last access: 26 September 2019).

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.

Internal tides play a crucial role in ocean mixing. To explore the seasonal features of mode-1 M2 internal tides in the Arabian Sea, we analyzed their propagation and energy distribution using along-track sea-level anomaly data collected by satellite altimeters. We identified four primary source regions of internal tides: Abd al Kuri Island, the Carlsberg Ridge, the northeastern Arabian Sea, and the Maldive Islands. The baroclinic signals that originate from Abd al Kuri Island propagate meridionally, whereas those originating from the west coast of India propagate southwestward. The strength and energy flux of the internal tides in the Arabian Sea exhibit significant seasonal and spatial variability. The internal tides generated during winter are more energetic and can propagate further than those generated in summer. Doppler shifting and horizontal variations in stratification can explain the differences in the internal tides’ seasonal distributions.

Uncertainty quantification (UQ) in weather and climate models is required to assess the sensitivity of their outputs to various parameterization schemes and thereby improve their consistency with observations. Herein, we present an efficient UQ and Bayesian inference for the cloud parameters of the NCAR Single Column Atmosphere Model (SCAM6) using surrogate models based on a polynomial chaos expansion. The use of a surrogate model enables to efficiently propagate uncertainties in parameters into uncertainties in model outputs. We investigated eight uncertain parameters: the auto-conversion size threshold for ice to snow (dcs), the fall speed parameter for stratiform cloud ice (ai), the fall speed parameter for stratiform snow (as), the fall speed parameter for cloud water (ac), the collection efficiency of aggregation ice (eii), the efficiency factor of the Bergeron effect (berg_eff), the threshold maximum relative humidity for ice clouds (rhmaxi), and the threshold minimum relative humidity for ice clouds (rhmini). We built two surrogate models using two non-intrusive methods: spectral projection (SP) and basis pursuit denoising (BPDN). Our results suggest that BPDN performs better than SP as it enables to filter out internal noise during the process of fitting the surrogate model. Five out of the eight parameters (namely dcs, ai, rhmaxi, rhmini, and eii) account for most of the variance in predicted climate variables (e.g., total precipitation, cloud distribution, shortwave and longwave cloud radiative effect, ice, and liquid water path). A first-order sensitivity analysis reveals that dcs contributes ~40–80% of the total variance of the climate variables, ai around 15–30%, and rhmaxi, rhmini, and eii around 5–15%. The second- and higher-order effects contribute ~7 and 20%, respectively. The sensitivity of the model to these parameters was further explored using response curves. A Markov chain Monte Carlo (MCMC) sampling algorithm was also implemented for the Bayesian inference of dcs, ai, as, rhmini, and berg_eff using cloud distribution data collected at the Southern Great Plains (USA). The inferred parameters suggest improvements in the global Climate Earth System Model (CESM2) simulations of the tropics and sub-tropics.

The increase of the temperature in the Red Sea basin due to global warming could have a large negative effect on its marine ecosystem. Consequently, there is a growing interest, from the scientific community and public organizations, in obtaining reliable projections of the Red Sea temperatures throughout the 21st century. However, the main tool used to do climate projections, the global climate models (GCM), may not be well suited for that relatively small region. In this work we assess the skills of the CMIP5 ensemble of GCMs in reproducing different aspects of the Red Sea 3D temperature variability. The results suggest that some of the GCMs are able to reproduce the present variability at large spatial scales with accuracy comparable to medium and high-resolution hindcasts. In general, the skills of the GCMs are better inside the Red Sea than outside, in the Gulf of Aden. Based on their performance, 8 of the original ensemble of 43 GCMs have been selected to project the temperature evolution of the basin. Bearing in mind the GCM limitations, this can be an useful benchmark once the high resolution projections are available. Those models project an averaged warming at the end of the century (2080–2100) of 3.3 ±> 0.6°C and 1.6 ±> 0.4°C at the surface under the scenarios RCP8.5 and RCP4.5, respectively. In the deeper layers the warming is projected to be smaller, reaching 2.2 ±> 0.5°C and 1.5 ±> 0.3°C at 300 m. The projected warming will largely overcome the natural multidecadal variability, which could induce temporary and moderate decrease of the temperatures but not enough to fully counteract it. We have also estimated how the rise of the mean temperature could modify the characteristics of the marine heatwaves in the region. The results show that the average length of the heatwaves would increase ~15 times and the intensity of the heatwaves ~4 times with respect to the present conditions under the scenario RCP8.5 (10 time and 3.6 times, respectively, under scenario RCP4.5)., SI

This work combines two auxiliary techniques, namely the one-step-ahead (OSA) smoothing and the hybrid formulation, to boost the forecasting skills of a storm surge ensemble Kalman filter (EnKF) forecasting system. Bayesian filtering with OSA-smoothing enhances the robustness of the ensemble background statistics by exploiting the data twice: first to constrain the sampling of the forecast ensemble with the future observation, and then to update the resulting ensemble. This is expected to improve the behavior of EnKF-like schemes during the strongly nonlinear surges periods, but requires integrating the ensemble with the forecast model twice, which could be computationally demanding. The hybrid flow-dependent/static formulation of the EnKF background error covariance is then considered to enable the implementation of the filter with a small flow-dependent ensemble size, and thus less model runs. These two methods are combined within an ensemble transform Kalman filter (ETKF). The resulting hybrid ETKF with OSA smoothing is tested, based on twin experiments, using a realistic setting of the Advanced Circulation (ADCIRC) model configured for storm surge forecasting in the Gulf of Mexico and assimilating pseudo-observations of sea surface levels from a network of buoys. The results of our numerical experiments suggest that the proposed filtering system significantly enhances ADCIRC forecasting skills compared to the standard ETKF without increasing the computational cost.

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 authors acknowledge the valuable and constructive comments and suggestions of two anonymous reviewers. This research was funded by King Abdullah University of Science and Technology (KAUST) under the Competitive Research Grants (CRG) Program award URF/1/3408-01-01. The research made extensive use of the supercomputing resources laboratory at KAUST. The data set used in this research can be available via the link https://figshare.com/s/4d2432bd00c7e94ce79e.

The author would like to thank James H Churchill, Larry Pratt, and Jim Thomas for the discussions and help. The topography data are derived from General Bathymetric Chart of the Oceans (GEBCO: https://www.gebco.net/). The initial salinity and temperature is extracted from World Ocean Atlas 2009 (WOA09: https://www.nodc.noaa.gov/OC5/WOA09/pr_woa09.html). The barotropic tidal boundary currents and the spatial distribution of elevation amplitudes and phases are derived from TPXO 7.2 inverse model (http://volkov.oce.orst.edu/tides/global.html). The MITgcm model output data files, sea level data from gauges, and mooring data can be found at Figshare (https://figshare.com/projects/Data_for_B2018JC013970_bc_redsea/57407). The research reported in this manuscript was supported by King Abdullah University of Science and Technology. This research made use of the resources from the Supercomputing Laboratory and/or computer clusters at King Abdullah University of Science and Technology.

Lagrangian tracking of passive tracers in a stochastic velocity field within a sequential ensemble data assimilation framework is challenging due to the exponential growth in the number of particles. This growth arises from describing the behavior of velocity over time as a set of possible combinations of the different realizations, before and after each assimilation cycle. This paper addresses the problem of efficiently advecting particles in stochastic flow fields, whose statistics are prescribed by an underlying ensemble, in a parallel computational framework (openMP). To this end, an efficient algorithm for forward and backward tracking of passive particles in stochastic flow-fields is presented. The algorithm, which employs higher order particle advection schemes, presents a mechanism for controlling the growth in the number of particles. The mechanism uses an adaptive binning procedure, while conserving the zeroth, first and second moments of probability (total probability, mean position, and variance). The adaptive binning process offers a tradeoff between speed and accuracy by limiting the number of particles to a desired maximum. To validate our method, we conducted various forward and backward particles tracking experiments within a realistic high-resolution ensemble assimilation setting of the Red Sea, focusing on the effect of the maximum number of particles, the time step, the variance of the ensemble, the travel time, the source location, and history of transport.

The topography data is derived from General Bathymetric Chart of the Oceans (GEBCO: https://www.gebco.net/). The open boundary conditions are extracted from the Estimating the Circulation and Climate of the Ocean (ECCO) product (https://ecco.jpl.nasa.gov/products/). The data supporting the analysis presented here can be found at Figshare (https://figshare.com/articles/Data for 2018JC014531R/7243184/). The research reported in this manuscript is supported by King Abdullah University of Science and Technology (KAUST) and made use of the resources of the Supercomputing Laboratory and computer clusters at KAUST.

In this paper, an extended SEIR model with a vaccination compartment is proposed to simulate the novel coronavirus disease (COVID-19) spread in Saudi Arabia. The model considers seven stages of infection: susceptible (S), exposed (E), infectious (I), quarantined (Q), recovered (R), deaths (D), and vaccinated (V). Initially, a mathematical analysis is carried out to illustrate the non-negativity, boundedness, epidemic equilibrium, existence, and uniqueness of the endemic equilibrium, and the basic reproduction number of the proposed model. Such numerical models can be, however, subject to various sources of uncertainties, due to an imperfect description of the biological processes governing the disease spread, which may strongly limit their forecasting skills. A data assimilation method, mainly, the ensemble Kalman filter (EnKF), is then used to constrain the model outputs and its parameters with available data. We conduct joint state-parameters estimation experiments assimilating daily data into the proposed model using the EnKF in order to enhance the model’s forecasting skills. Starting from the estimated set of model parameters, we then conduct short-term predictions in order to assess the predicability range of the model. We apply the proposed assimilation system on real data sets from Saudi Arabia. The numerical results demonstrate the capability of the proposed model in achieving accurate prediction of the epidemic development up to two-week time scales. Finally, we investigate the effect of vaccination on the spread of the pandemic.

Phytoplankton phenology and size structure are key ecological indicators that influence the survival and recruitment of higher trophic levels, marine food web structure, and biogeochemical cycling. For example, the presence of larger phytoplankton cells supports food chains that ultimately contribute to fisheries resources. Monitoring these indicators can thus provide important information to help understand the response of marine ecosystems to environmental change. In this study, we apply the phytoplankton size model of Gittings et al. (2019b) to 20-years of satellite-derived ocean colour observations in the northern and central Red Sea, and investigate interannual variability in phenology metrics for large phytoplankton (>2 µm in cell diameter). Large phytoplankton consistently bloom in the winter. However, the timing of bloom initiation and termination (in autumn and spring, respectively) varies between years. In the autumn/winter of 2002/2003, we detected a phytoplankton bloom, which initiated ~8 weeks earlier and lasted ~11 weeks longer than average. The event was linked with an eddy dipole in the central Red Sea, which increased nutrient availability and enhanced the growth of large phytoplankton. The earlier timing of food availability directly impacted the recruitment success of higher trophic levels, as represented by the maximum catch of two commercially important fisheries (Sardinella spp. and Teuthida) in the following year. The results of our analysis are essential for understanding trophic linkages between phytoplankton and fisheries and for marine management strategies in the Red Sea.

Harmful algal blooms (HABs) are one of the leading causes of biodiversity loss and alterations to ecosystem services. The Red Sea is one of the least studied large marine ecosystems (LMEs), and knowledge on the large-scale spatiotemporal distribution of HABs remains limited. We implemented the recently developed remote sensing algorithm of Gokul et al. (2019) to produce a high-resolution atlas of HAB events in the Red Sea and investigated their spatiotemporal variability between 2003 and 2017. The atlas revealed that (i) the southern part of the Red Sea is subject to a higher occurrence of HABs, as well as long-lasting and large-scale events, in comparison to the northern part of the basin, and (ii) the Red Sea HABs exhibited a notable seasonality, with most events occurring during summer. We further investigated the potential interactions between identified HAB events and the National Aquaculture Group (NAQUA), Al-Lith (Saudi Arabia)&mdash, the largest aquaculture facility on the Red Sea coast. The results suggest that the spatial coverage of HABs and the elevated chlorophyll-a concentration (Chl-a) (&gt, 1 mg m&minus, 3, a proxy for high nutrient concentration), in the coastal waters of Al-Lith during summer, increased concurrently with the local aquaculture annual production over a nine-year period (2002&ndash, 2010). This could be attributed to excessive nutrient loading from the NAQUA facility&rsquo, s outfall, which enables the proliferation of HABs in an otherwise oligotrophic region during summer. Aquaculture is an expanding, high-value industry in the Kingdom of Saudi Arabia. Thus, a wastewater management plan should ideally be implemented at a national level, in order to prevent excessive nutrient loading. Our results may assist policy-makers&rsquo, efforts to ensure the sustainable development of the Red Sea&rsquo, s coastal economic zone.

The present study aims to find the best location for installing wave energy converters (WEC) in the NEOM area, located in the Red Sea northern region, and to determine the most suitable converter system for harvesting wave energy using available data provided by KAUST. The Red Sea region represents a challenge for wave modulization and analysis due to its two distinct and opposing wave structures induced by reverse winds that converge at its middle. By incorporating wind and wave data series from 1985 to 2015 in the Advanced Research Weather and Forecasting model and WAVEWATCH III. In the present study, the highest peak period found in the selected area is 4 seconds based on the wave hindcast generated on a 1-km resolution grid, and the highest wave found was 0.79 m. A total of 8 points were selected and analyzed to test the potential of wave energy at NEOM coastlines along the Gulf of Aqaba and NEOM Bay. Based on the results, the Gulf of Aqaba, with a mean wave power of 1.98 kW/m at P#2 is a good candidate for a WEC system. Possible installation of wave energy converters in the selected areas is discussed in this thesis, including farms of point absorbers with the integration of wave and solar sources (DEIM). Based on preliminary information regarding the NEOM region, potential environmental and social challenges were identified in this study for the viability of wave energy exploitation.

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.

Oil spills at sea pose a serious threat to coastal environments. Identifying oil pollution sources could help to investigate unreported spills, and satellite imagery can be an effective tool for this purpose. We present a Bayesian approach to estimate the source parameters of a spill from contours of oil slicks detected by remotely sensed images. Five parameters of interest are estimated: the 2D coordinates of the source of release, the time and duration of the spill, and the quantity of oil released. Two synthetic experiments of a spill released from a fixed point source are investigated, where a contour is fully observed in the first case, while two contours are partially observed at two different times in the second. In both experiments, the proposed method is able to provide good estimates of the parameters along with a level of confidence reflected by the uncertainties within.

Bayesian inference with coordinate transformations and polynomial chaos for a Gaussian process with a parametrized prior covariance model was introduced in Sraj et al. (Comput Methods Appl Mech Eng 298:205–228, 2016a) to enable and infer uncertainties in a parameterized prior field. The feasibility of the method was successfully demonstrated on a simple transient diffusion equation. In this work, we adopt a similar approach to infer a spatially varying Manning’s n field in a coastal ocean model. The idea is to view the prior on the Manning’s n field as a stochastic Gaussian field, expressed through a covariance function with uncertain hyper-parameters. A generalized Karhunen-Loeve (KL) expansion, which incorporates the construction of a reference basis of spatial modes and a coordinate transformation, is then applied to the prior field. To improve the computational efficiency of the method proposed in Sraj et al. (Comput Methods Appl Mech Eng 298:205–228, 2016a), we propose to use two polynomial chaos expansions to (i) approximate the coordinate transformation and (ii) build a cheap surrogate of the large-scale advanced circulation (ADCIRC) numerical model. These two surrogates are used to accelerate the Bayesian inference process using a Markov chain Monte Carlo algorithm. Water elevation data are inverted within an observing system simulation experiment framework, based on a realistic ADCIRC model, to infer the KL coordinates and hyper-parameters of a reference 2D Manning’s field. Our results demonstrate the efficiency of the proposed approach and suggest that including the hyper-parameter uncertainties greatly enhances the inferred Manning’s n field, compared with using a covariance with fixed hyper-parameters.

This study analyses the connection between extreme rainfall events in Jeddah, Saudi Arabia, and synoptic‐scale weather patterns over the Arabian Peninsula. Mean rainfall follows a decreasing trend; however, the number of rainy days has increased. Interestingly, extreme rainfall is becoming less frequent but shows an increased intensity. Here we utilize self‐organizing maps (SOMs) to identify the weather patterns of the most intense rainy days and the synoptic systems causing extreme rainfall in the Jeddah region. Three main weather patterns that cause heavy rainfall events over Jeddah during the cooler months (November–April) are identified, all reflect tropical‐extratropical interactions. Extreme events in the early period (1979–1998) are characterized by a stronger tropical influence and local precipitation patterns, while a stronger extratropical forcing and higher extreme rainfall amounts are spotted in the late period (1999–2018). Our results suggest that in recent decades, the mechanism causing extreme rainfall over the city of Jeddah has shifted toward a weather regime with stronger extratropical influence.

This work was funded by the Saudi ARAMCO-KAUST Marine Environmental Observatory (SAKMEO) and the KAUST Office of Sponsored Research (OSR) under the Collaborative Research Grant (CRG) program (Grant # URF/1/2979-01-01). The authors would like to thank Dr. Hari Dasari for providing the atmospheric forcing, and Dr. Nikolaos Zarokanellos and Michel Campbell for valuable advices on various technical issues with the gliders. The authors are also grateful for the NASA Jet Propulsion Laboratory Our Ocean group for providing the G1SST dataset.

The Ensemble Adjustment Kalman Filter (EAKF) of the Data Assimilation Research Testbed (DART) is implemented to assimilate observations of satellite sea surface temperature, altimeter sea surface height and in situ ocean temperature and salinity profiles into an eddy-resolving 4 km Massachusetts Institute of Technology general circulation model (MITgcm) of the Red Sea. We investigate the impact of three different ensemble generation strategies (1) Iexp, uses ensemble of ocean states to initialize the model on 1st January, 2011 and inflates filter error covariance by 10 percent, (2) IAexp, adds ensemble of atmospheric forcing to Iexp, and (3) IAPexp, adds perturbed model physics to IAexp. The assimilation experiments are run for one year, starting from the same initial ensemble and assimilating data every three days. Results demonstrate that the Iexp mainly improved the model outputs with respect to assimilation free MITgcm run in the first few months, before showing signs of dynamical imbalances in the ocean estimates, particularly in the data-sparse subsurface layers. The IAexp yielded substantial improvements throughout the assimilation period with almost no signs of imbalances, including the subsurface layers. It further well preserved the model mesoscale features resulting in an improved forecasts for eddies, both in terms of intensity and location. Perturbing model physics in IAPexp slightly improved the forecast statistics and also the placement of basin scale eddies. Increasing hydrographic coverage further improved the results of IAPexp compared to IAexp in the subsurface layers. Switching off multiplicative inflation in IAexp and IAPexp leads to further improvements, especially in the subsurface layers., Comment: 23 pages, 13 figures, 2 tables. Under Revision in JGR-Oceans

Aerosol abundance from widespread deserts over the Arabian Peninsula (AP) plays an important role in the regional climate and air quality. This study investigates the spatio-temporal variability of Aerosol Optical Depth (AOD) ranging from seasonal to inter-annual time scales during the period 2003–2016 using satellite retrievals from MISR and MODIS, together with ground-based observations from the Aerosol Robotic Network (AERONET) available during the period 2003–2012 over the AP. The MODIS AOD (MISR AOD) observations exhibit 0.81 and 0.85 (0.71 and 0.76) correlations with ground-based observations during the wet and dry seasons, respectively. The correlations were also found to be significant with respect to the surface monitoring station at Solar village in Saudi Arabia. Our observational analysis reveals higher (lower) concentrations of AOD during dry (wet) seasons over the AP. The observed AODs indicate similar spatial distributions over annual and seasonal time scales, with high AODs over the Southern Red sea (SR), and the Northeast AP, and lower AODs over the central and southern end of the AP. It is also observed that the AOD exhibits year-to-year variations over different sub-regions of the AP. Analyses of the inter-annual variability over the AP reveals a significant relationship between the AOD and El Nino-Southern Oscillation (ENSO) during the summertime. Furthermore, positive AOD anomalies over the AP can be closely linked with the intensification of the westerly jet at the Tokar Gap during La Nina phases. Enhanced monsoon associated heat low over the AP during La Nina phases further favors greater uplift and saltation of dust from desert regions.

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.

Data assimilation, which relies on explicit knowledge of dynamical models, is a well‐known approach that addresses models' limitations due to various reasons, such as errors in input and forcing data sets. This approach, however, requires intensive computational efforts, especially for high‐dimensional systems such as distributed hydrological models. Alternatively, data‐driven methods offer comparable solutions when the physics underlying the models are unknown. For the first time in a hydrological context, a nonparametric framework is implemented here to improve model estimates using available observations. This method uses Takens delay coordinate method to reconstruct the dynamics of the system within a Kalman filtering framework, called the Kalman‐Takens filter. A synthetic experiment is undertaken to fully investigate the capability of the proposed method by comparing its performance with that of a standard assimilation framework based on an adaptive unscented Kalman filter (AUKF). Furthermore, using terrestrial water storage (TWS) estimates obtained from the Gravity Recovery And Climate Experiment mission, both filters are applied to a real case scenario to update different water storages over Australia. In situ groundwater and soil moisture measurements within Australia are used to further evaluate the results. The Kalman‐Takens filter successfully improves the estimated water storages at levels comparable to the AUKF results, with an average root‐mean‐square error reduction of 37.30% for groundwater and 12.11% for soil moisture estimates. Additionally, the Kalman‐Takens filter, while reducing estimation complexities, requires a fraction of the computational time, that is, ∼8 times faster compared to the AUKF approach.

To understand the dynamics and health of marine ecosystems such as lagoons and coral reefs as well as to understand the impact of human activities on these systems, it is imperative to predict the residence times of water masses and connectivity between ocean domains. In the present work, we consider the pristine lagoons and coral reefs of the Red Sea as an example of such sensitive ecosystems, with a large number of marine species, many of which are unique to the region. To study the residence times and connectivity patterns, we make use of recent advances in dynamic three-dimensional Lagrangian analyses using partial differential equations. Specifically, we extend and apply our novel efficient flow map composition scheme to predict the time needed for any particular water parcel to leave the domain of interest (i.e. a lagoon) as well as the time for any particular water parcel to enter that domain. These spatiotemporal residence time fields along with four-dimensional Lagrangian metrics such as finite time Lyapunov exponent (FTLE) fields provide a quantitative description of the Lagrangian pathways and connectivity patterns of lagoons in the Red Sea.

International audience; A new approach is developed for data assimilation into adaptive mesh models with the ensemble Kalman filter (EnKF). The EnKF is combined with a wavelet-based multiresolution analysis (MRA) scheme to enable robust and efficient assimilation in the context of reduced-complexity adaptive spatial discretization. The wavelet representation of the solution enables the use of different meshes that are individually adapted to the corresponding members of the EnKF ensemble. The analysis step of the EnKF is then performed by involving coarsening, refinement, and projection operations on its ensemble members. Depending on the choice of these operations, five variants of the MRA-EnKF are introduced, and tested on the one-dimensional Burgers equation with periodic boundary condition. The numerical results suggest that, given an appropriate tolerance value for the coarsening operation, four out of the five proposed schemes significantly reduce the computational complexity of the assimilation system, with marginal accuracy loss compared to the reference, full resolution, and EnKF solution. Overall, the proposed framework offers the possibility of capitalizing on the advantages of adaptive mesh techniques, and the flexibility of choosing suitable context-oriented criteria for efficient data assimilation.

Continuous data assimilation (CDA) is successfully implemented for the first time for efficient dynamical downscaling of a global atmospheric reanalysis. A comparison of the performance of CDA with the standard grid and spectral nudging techniques for representing long- and short-scale features in the downscaled fields using the Weather Research and Forecast (WRF) model is further presented and analyzed. The WRF model is configured at 25km horizontal resolution and is driven by 250km initial and boundary conditions from NCEP/NCAR reanalysis fields. Downscaling experiments are performed over a one-month period in January, 2016. The similarity metric is used to evaluate the performance of the downscaling methods for large and small scales. Similarity results are compared for the outputs of the WRF model with different downscaling techniques, NCEP reanalysis, and Final Analysis. Both spectral nudging and CDA describe better the small-scale features compared to grid nudging. The choice of the wave number is critical in spectral nudging; increasing the number of retained frequencies generally produced better small-scale features, but only up to a certain threshold after which its solution gradually became closer to grid nudging. CDA maintains the balance of the large- and small-scale features similar to that of the best simulation achieved by the best spectral nudging configuration, without the need of a spectral decomposition. The different downscaled atmospheric variables, including rainfall distribution, with CDA is most consistent with the observations. The Brier skill score values further indicate that the added value of CDA is distributed over the entire model domain. The overall results clearly suggest that CDA provides an efficient new approach for dynamical downscaling by maintaining better balance between the global model and the downscaled fields., Comment: 27 Pages, 13 figures

This study investigates the influence of the Indian Summer Monsoon (ISM) on the atmospheric circulation over the Arabian Peninsula (AP) using the European Centre for Medium Range Weather Forecasts’ twentieth century reanalysis (ERA-20C) for the period 1901–2010. After describing the summer climate of the AP using various dynamic and thermodynamic parameters, we investigate the link between extreme ISMs and atmospheric circulation over the AP on inter-annual time scale. Analysis of composites of different parameters during extreme monsoon (strong and weak) years reveals that the ISM plays an important role in the summer circulation over the AP and adjoining regions. The major noticeable changes in modulating circulation during extreme monsoons are: (1) a strengthening of lower tropospheric northerly winds, westerly winds passing through the Tokar Gap, Shamal winds, and the upper tropospheric easterly jet stream during strong ISM; (2) a northward (southward) shift of the subtropical westerly jet stream during strong (weak) monsoon years; (3) the development of strong upper level ridge above the surface thermal low during strong ISM years, which result in a baroclinic structure over the AP and adjoining regions; (4) an increase in adiabatic warming, and hence aridity, over the AP during strong monsoon years, caused by intense subsidence of the middle to upper troposphere due to zonal overturning circulation; and (5) convective instability during strong monsoon years caused by an intensification of the upward motion over the southern AP. Furthermore, during strong monsoons, the availability of excess moisture leads to atmospheric instability, which in turn triggers the formation of clouds that lead to more rainfall over the southwestern AP. Finally, the westward propagation of a Gill-type Rossby waves induced by the ISM play an important role in the variations of the AP summer climate by enhancing the warm core structure over the AP and through their interaction with the midlatitude westerlies during strong monsoons.

The wind and wave climatology of the Red Sea is derived from a validated 30-yr high-resolution model simulation. After describing the relevant features of the basin, the main wind and wave systems are identified by using an innovative spectral partition technique to explain their genesis and characteristics. In the northern part of the sea, wind and waves of the same intensity are present throughout the year, while the central and southern zones are characterized by a marked seasonality. The partition technique allows the association of a general decrease in the energy of the different wave systems with a specific weather pattern. The most intense decrease is found in the northern storms, which are associated with meteorological pulses from the Mediterranean Sea.

Hydrological hazards such as storm surges, tsunamis, and rainfall-induced flooding are physically complex events that are costly in loss of human life and economic productivity. Many such disasters could be mitigated through improved emergency evacuation in real-time and through the development of resilient infrastructure based on knowledge of how systems respond to extreme events. Data-driven computational modeling is a critical technology underpinning these efforts. This investigation focuses on the novel combination of methodologies in forward simulation and data assimilation. The forward geophysical model utilizes adaptive mesh refinement (AMR), a process by which a computational mesh can adapt in time and space based on the current state of a simulation. The forward solution is combined with ensemble based data assimilation methods, whereby observations from an event are assimilated into the forward simulation to improve the veracity of the solution, or used to invert for uncertain physical parameters. The novelty in our approach is the tight two-way coupling of AMR and ensemble filtering techniques. The technology is tested using actual data from the Chile tsunami event of February 27, 2010. These advances offer the promise of significantly transforming data-driven, real-time modeling of hydrological hazards, with potentially broader applications in other science domains.

The ensemble Kalman filter (EnKF) is widely used for sequential data assimilation. It operates as a succession of forecast and analysis steps. In realistic large-scale applications, EnKFs are implemented with small ensembles and poorly known model error statistics. This limits their representativeness of the background error covariances and, thus, their performance. This work explores the efficiency of the one-step-ahead (OSA) smoothing formulation of the Bayesian filtering problem to enhance the data assimilation performance of EnKFs. Filtering with OSA smoothing introduces an updated step with future observations, conditioning the ensemble sampling with more information. This should provide an improved background ensemble in the analysis step, which may help to mitigate the suboptimal character of EnKF-based methods. Here, the authors demonstrate the efficiency of a stochastic EnKF with OSA smoothing for state estimation. They then introduce a deterministic-like EnKF-OSA based on the singular evolutive interpolated ensemble Kalman (SEIK) filter. The authors show that the proposed SEIK-OSA outperforms both SEIK, as it efficiently exploits the data twice, and the stochastic EnKF-OSA, as it avoids observational error undersampling. They present extensive assimilation results from numerical experiments conducted with the Lorenz-96 model to demonstrate SEIK-OSA’s capabilities.

The research was supported by the Office of Sponsor Research (OSR) at King Abdullah University of Science and Technology (KAUST) under the Virtual Red Sea Initiative (grant no. REP/1/3268-01-01), by the Saudi Aramco Marine Environment Research Center at KAUST, and the Beacon Development Company . All model runs are conducted on the KAUST supercomputing facility SHAHEEN.

Ensemble optimal interpolation (EnOI) is a variant of the ensemble Kalman filter (EnKF) that operates with a static ensemble to drastically reduce its computational cost. The idea is to use a pre-selected ensemble to parameterize the background covariance matrix, which avoids the costly integration of the ensemble members with the dynamical model during the forecast step of the filtering process. To better represent the pronounced time-varying circulation of the Red Sea, we propose a new adaptive EnOI approach in which the ensemble members are adaptively selected at every assimilation cycle from a large dictionary of ocean states describing the Red Sea variability. We implement and test different schemes to select the ensemble members (i) based on the similarity to the forecast state according to some criteria, or (ii) in term of best representation of the forecast in an ensemble subspace using an Orthogonal Matching Pursuit (OMP) algorithm. The relevance of the schemes is first demonstrated with the Lorenz 63 and Lorenz 96 models. Then results of numerical experiments assimilating real remote sensing data into a high resolution MIT general circulation model (MITgcm) of the Red Sea using the Data Assimilation Research Testbed (DART) system are presented and discussed.

The dynamic and thermodynamic characteristics of a severe weather event that caused heavy wind and rainfall over Mecca, Kingdom of Saudi Arabia, on 11 September 2015 were investigated using available observations and the Weather Research and Forecasting model configured at 1 km resolution. Analysis of surface, upper air observations and model outputs reveals that the event was initiated by synoptic scale conditions that intensified by interaction with the local topography, triggering strong winds and high convective rainfall. The model predicted the observed characteristics of both rainfall and winds well, accurately predicting the maximum wind speed of 20–25 m s−1 that was sustained for about 2 h. A time series analysis of various atmospheric variables suggests a sudden fall in pressure, temperature and outgoing long wave radiation before the development of the storm, followed by a significant increase in wind speed, latent and moisture fluxes and change in wind direction during the mature stage of the storm. The model outputs suggest that the heavy rainfall was induced by a low-level moisture supply from the Red Sea combined with orographic lifting. Latent heat release from microphysical processes increased the vertical velocities in the mid-troposphere, further increasing the low-level convergence that strengthened the event.

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.

Bayesian estimation/inversion is commonly used to quantify and reduce modeling uncertainties in coastal ocean model, especially in the framework of parameter estimation. Based on Bayes rule, the posterior probability distribution function (pdf) of the estimated quantities is obtained conditioned on available data. It can be computed either directly, using a Markov chain Monte Carlo (MCMC) approach, or by sequentially processing the data following a data assimilation approach, which is heavily exploited in large dimensional state estimation problems. The advantage of data assimilation schemes over MCMC-type methods arises from the ability to algorithmically accommodate a large number of uncertain quantities without significant increase in the computational requirements. However, only approximate estimates are generally obtained by this approach due to the restricted Gaussian prior and noise assumptions that are generally imposed in these methods. This contribution aims at evaluating the effectiveness of utilizing an ensemble Kalman-based data assimilation method for parameter estimation of a coastal ocean model against an MCMC polynomial chaos (PC)-based scheme. We focus on quantifying the uncertainties of a coastal ocean ADvanced CIRCulation (ADCIRC) model with respect to the Manning’s n coefficients. Based on a realistic framework of observation system simulation experiments (OSSEs), we apply an ensemble Kalman filter and the MCMC method employing a surrogate of ADCIRC constructed by a non-intrusive PC expansion for evaluating the likelihood, and test both approaches under identical scenarios. We study the sensitivity of the estimated posteriors with respect to the parameters of the inference methods, including ensemble size, inflation factor, and PC order. A full analysis of both methods, in the context of coastal ocean model, suggests that an ensemble Kalman filter with appropriate ensemble size and well-tuned inflation provides reliable mean estimates and uncertainties of Manning’s n coefficients compared to the full posterior distributions inferred by MCMC.

We present our efforts to build an ensemble data assimilation and forecasting system for the Red Sea. The system consists of the high-resolution Massachusetts Institute of Technology general circulation model (MITgcm) to simulate ocean circulation and of the Data Research Testbed (DART) for ensemble data assimilation. DART has been configured to integrate all members of an ensemble adjustment Kalman filter (EAKF) in parallel, based on which we adapted the ensemble operations in DART to use an invariant ensemble, i.e., an ensemble Optimal Interpolation (EnOI) algorithm. This approach requires only single forward model integration in the forecast step and therefore saves substantial computational cost. To deal with the strong seasonal variability of the Red Sea, the EnOI ensemble is then seasonally selected from a climatology of long-term model outputs. Observations of remote sensing sea surface height (SSH) and sea surface temperature (SST) are assimilated every 3 days. Real-time atmospheric fields from the National Center for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF) are used as forcing in different assimilation experiments. We investigate the behaviors of the EAKF and (seasonal-) EnOI and compare their performances for assimilating and forecasting the circulation of the Red Sea. We further assess the sensitivity of the assimilation system to various filtering parameters (ensemble size, inflation) and atmospheric forcing.