A Comparison of HWRF, ARW and NMM Models in Hurricane Katrina (2005) Simulation

The life cycle of Hurricane Katrina (2005) was simulated using three different modeling systems of Weather Research and Forecasting (WRF) mesoscale model. These are, HWRF (Hurricane WRF) designed specifically for hurricane studies and WRF model with two different dynamic cores as the Advanced Research WRF (ARW) model and the Non-hydrostatic Mesoscale Model (NMM). The WRF model was developed and sourced from National Center for Atmospheric Research (NCAR), incorporating the advances in atmospheric simulation system suitable for a broad range of applications. The HWRF modeling system was developed at the National Centers for Environmental Prediction (NCEP) based on the NMM dynamic core and the physical parameterization schemes specially designed for tropics. A case study of Hurricane Katrina was chosen as it is one of the intense hurricanes that caused severe destruction along the Gulf Coast from central Florida to Texas. ARW, NMM and HWRF models were designed to have two-way interactive nested domains with 27 and 9 km resolutions. The three different models used in this study were integrated for three days starting from 0000 UTC of 27 August 2005 to capture the landfall of hurricane Katrina on 29 August. The initial and time varying lateral boundary conditions were taken from NCEP global FNL (final analysis) data available at 1 degree resolution for ARW and NMM models and from NCEP GFS data at 0.5 degree resolution for HWRF model. The results show that the models simulated the intensification of Hurricane Katrina and the landfall on 29 August 2005 agreeing with the observations. Results from these experiments highlight the superior performance of HWRF model over ARW and NMM models in predicting the track and intensification of Hurricane Katrina

Air Quality Modeling for the Urban Jackson, Mississippi Region Using a High Resolution WRF/Chem Model

In this study, an attempt was made to simulate the air quality with reference to ozone over the Jackson (Mississippi) region using an online WRF/Chem (Weather Research and Forecasting–Chemistry) model. The WRF/Chem model has the advantages of the integration of the meteorological and chemistry modules with the same computational grid and same physical parameterizations and includes the feedback between the atmospheric chemistry and physical processes. The model was designed to have three nested domains with the inner-most domain covering the study region with a resolution of 1 km. The model was integrated for 48 hours continuously starting from 0000 UTC of 6 June 2006 and the evolution of surface ozone and other precursor pollutants were analyzed. The model simulated atmospheric flow fields and distributions of NO2 and O3 were evaluated for each of the three different time periods. The GIS based spatial distribution maps for ozone, its precursors NO, NO2, CO and HONO and the back trajectories indicate that all the mobile sources in Jackson, Ridgeland and Madison contributing significantly for their formation. The present study demonstrates the applicability of WRF/Chem model to generate quantitative information at high spatial and temporal resolution for the development of decision support systems for air quality regulatory agencies and health administrators

Towards an end-to-end analysis and prediction system for weather, climate, and marine applications in the Red Sea

Author Posting. © American Meteorological Society, 2021. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society 102(1), (2021): E99-E122, https://doi.org/10.1175/BAMS-D-19-0005.1.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 development of the Red Sea modeling system is being supported by the Virtual Red Sea Initiative and the Competitive Research Grants (CRG) program from the Office of Sponsored Research at KAUST, Saudi Aramco Company through the Saudi ARAMCO Marine Environmental Center at KAUST, and by funds from KAEC, NEOM, and RSP through Beacon Development Company at KAUST

Regional scale prediction of the onset phase of the Indian southwest monsoon with a high-resolution atmospheric model

A nonhydrostatic atmospheric model with a resolution of 30 km is used to make predictions of the rainfall during the onset phase of the southwest monsoon (SWM) of 2003. Model predictions of the pentad rainfall time series indicate good predictions up to lead time of 5 days. The correlation coefficients (CCs) between the model-predicted and observed rainfall at different locations, representative of the five homogeneous regions of SWM rainfall, over the Indian subcontinent show correlations significant at 90% level up to 5 days lead time with values above 0.32. The spatial distribution of the model-predicted pentad rainfall show an advancement of the Arabian Sea and the Bay of Bengal branches of SWM over the Indian subcontinent up to 5 days lead time. Copyright © 2008 Royal Meteorological Societ

An assessment of cumulus parameterization schemes in the short range prediction of rainfall during the onset phase of the Indian Southwest Monsoon using MM5 Model

The performance of cumulus parameterization schemes in the short range prediction of rainfall during onset phase of the Indian Southwest Monsoon (ISM) was evaluated using Fifth-Generation Pennsylvania State University / National Center for Atmospheric Research Mesoscale Model (MM5). MM5 model was used to predict rainfall at 30 km resolution up to 72 h over the Indian subcontinent for each day during the period 1–30 June 2002, which corresponds to the onset phase of the ISM. Experiments were performed with 5 different cumulus parameterization schemes of Anthes–Kuo (AK), Grell (GR), Betts–Miller (BM), Kain–Fritsch (KF) and Kain–Fritsch2 (KF2). Rainfall prediction assessment was made over five zones through comparison with corresponding APHRODITE gridded precipitation data and for selected location with station observations by analyzing the statistical parameters of correlation coefficient, mean absolute error and Hanssen–Kuipers score. Monthly mean zone-wise rainfall was well predicted by GR and AK schemes up to 48 hours and slight overestimation beyond. GR scheme predicted higher rainfall over west coast, central parts of India and low rainfall over southeast peninsula. BM and KF schemes showed overestimation with prediction of rainfall over dry southeast peninsula. All the schemes underestimated the coefficient of variability (CV) over all the five zones. AK and GR schemes had the mean and CV nearer to the APHRODITE observations, with AK scheme slightly better than GR scheme over Zones 1, 2 and 3 while GR scheme had the best agreement over Zones 4 and 5. GR scheme had also shown higher CC values and lower MAE over most of the zones up to 72 h, while BM had the least predictability with lower CC and HK scores and higher MAE over most of the zones. Over Western Ghats, the uncertainty limits could be higher than shown due to dominant heavy rains. Of the ten stations selected for verification, GR scheme had shown better prediction with significant positive CC values at nine of the ten stations and consistently lower MAE values and higher HK scores. Further analysis has shown that GR scheme predicted higher grid-scale and nighttime rainfall agreeing with earlier studies concerning monsoon rainfall. All other schemes predicted the features contrarily with higher convective and daytime rainfall. GR scheme alone was found to have provided the best prediction considering the mean monthly, daily zone-wise and station rainfall predictions. The present study concludes that GR cumulus parameterization scheme is the most suitable at 30 km resolution

Inter-annual variability and skill of tropical rainfall and SST in APCC seasonal forecast models

International audienceThe present study explored the performance of the current coupled models obtained from the Asia Pacific Economic Cooperation (APEC) Climate Centre (APCC) in representing the tropical Indo-Pacific sea surface temperature (SST) and rainfall during boreal summer season (June through September; JJAS). We have used the retrospective/hindcast runs for 28 years from 1983 to 2010 initialized in May. The mean SST bias in the tropical Indo-Pacific Oceans showed large diversity among the models in JJAS. In the case of the rainfall, most of the models displayed a strong dry bias over the major continental regions and wet biases over the tropical oceans. The majority of the models simulated the Inter-annual variability (IAV) of JJAS rainfall and SST reasonably well over the equatorial Pacific region, where the models are close to observed IAV and maximum signal to noise ratio (SNR). It is found that the models display, low IAV of rainfall and SST over the Indian Ocean with low SNR values, resulting in less predictive skill as compared to the tropical Pacific region. Similarly, all models showed a higher skill in summer rainfall prediction over the oceanic regions compared to the Asian land region, where SNR is very low. Further analysis suggested that the models have greater skill in predicting El Niño-Southern Oscillation (ENSO). The category wise analysis showed that models could predict 60–70% of the extreme ENSO events, but the normal events are represented only by 50%. It is noted that the models predict many false alarms for El Niño resulting in a higher frequency of El Niño occurrence. This is mainly responsible for stronger ENSO and the Asian Monsoon teleconnections in the models than in the observations. Meanwhile, the category wise rainfall skill for extended Indian monsoon region (EMR) displayed 50–60% accuracy for the extreme monsoon years and is around 50% for normal years. However, models such as CCSM3, CFSV2, and CANCM3 have displayed higher rainfall skills over EMR as compared to the other models possibly due to better representation of teleconnections spatial patterns between EMR rainfall and SST anomalies over Indo-Pacific Oceans

Multi-Mission Satellite Detection and Tracking of October 2019 Sabiti Oil Spill in the Red Sea

A multi-mission satellite remote sensing (MSRS) approach is explored to detect and track leaked oil from the Sabiti oil tanker accident that occurred in the central Red Sea on 11 October 2019 (RSOS-2019). The spilled oil spread rapidly and reached the coastal land near Jeddah, the second largest city of KSA, on 17 October. Different oil spill detection algorithms were implemented on SAR and optical sensor-based satellite images to track the oil spill. Sentinel-1 SAR images were most efficient at detecting the spread and thickness of RSOS-2019, but their spatio-temporal coverage greatly limits their use for tracking the oil movement. The spread and propagation of oil were well captured by Sentinel-2 images up to three weeks after the accident day, in agreement with the SAR images. MODIS successfully detected the narrow patch of oil that was leaked on the incident day and the widespread oil patches two days after. Landsat-8 RGB composite and thermal infrared images captured the oil spill on 13 October. By filtering clouds from the Meteosat images through sequential analysis, the spread and movement of the oil patches were efficiently tracked on 13 October. PlanetScope images available between 12 and 17 October enabled tracking of the oil near the coastal waters. The inferred oil spill movements are consistent with the ocean currents as revealed by a high-resolution regional ocean reanalysis. Our results demonstrate the potential of the MSRS approach to detect and track oil spills in the open and coastal waters of the Red Sea in near real-time

Towards an End-to-End Analysis and Prediction System for Weather, Climate, and Marine Applications in the Red Sea

International audienceThe Red Sea, home to the second-longest coral reef systemin 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 Seacoast, and global warming, authorities are working on optimizing the harnessing of environmental resources, including renewable energy, rainwater harvesting, etc. All these require high-resolution weather and climate information. Toward this end, we have undertaken a multi-pronged R&D activity in which we are developingan integrated data-driven regional coupled modeling system. The telescopically-nested components include 5km-600m resolution atmospheric models to address weather and climate challenges, 4km-50m resolution ocean models with regional and coastal configurations to simulate and predict the general and mesoscale circulation; 4km-100m ecosystem models to simulate the biogeochemistry; and 1km-50m 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/industry in various weather and marine studies and operations, environmental policies, renewable energy applications, impact assessment, flood-forecasting, etc