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Performance of the WRF Model for Surface Wind Prediction around QatarAbstract
The performance of the Weather Research and Forecasting (WRF) Model is examined for the region around Qatar in the context of surface winds. The wind fields around this peninsula can be complicated owing to its small size, to a complex pattern of land and sea breezes influenced by the prevailing shamal winds, and to its dry and arid nature. Modeled winds are verified with data from 19 land stations and two offshore buoys. A comparison with these data shows that nonlocal planetary boundary layer (PBL) schemes generally perform better than local schemes over land stations during the daytime, when convective conditions prevail; at nighttime, over land and over water, both schemes yield similar results. Among other parameters, modifications to standard USGS land-use descriptors were necessary to reduce model errors. The RMSE values are comparable to those reported elsewhere. Simulated winds, when used with a wave model, result in wave heights comparable to buoy measurements. Furthermore, WRF results, confirmed by data, show that at times sea breezes develop from both coasts, leading to convergence in the middle of the country; at other times, the large-scale wind impedes the formation of sea breezes on one or both coasts. Simulations also indicate greater land/sea-breeze activity in the summer than in the winter. Differences in the diurnal evolution of surface winds over land and water are found to be related to differences in the boundary layer stability. Overall, the results indicate that the WRF Model as configured here yields reliable simulations and can be used for various practical applications.
Abstract
The performance of the Weather Research and Forecasting (WRF) Model is examined for the region around Qatar in the context of surface winds. The wind fields around this peninsula can be complicated owing to its small size, to a complex pattern of land and sea breezes influenced by the prevailing shamal winds, and to its dry and arid nature. Modeled winds are verified with data from 19 land stations and two offshore buoys. A comparison with these data shows that nonlocal planetary boundary layer (PBL) schemes generally perform better than local schemes over land stations during the daytime, when convective conditions prevail; at nighttime, over land and over water, both schemes yield similar results. Among other parameters, modifications to standard USGS land-use descriptors were necessary to reduce model errors. The RMSE values are comparable to those reported elsewhere. Simulated winds, when used with a wave model, result in wave heights comparable to buoy measurements. Furthermore, WRF results, confirmed by data, show that at times sea breezes develop from both coasts, leading to convergence in the middle of the country; at other times, the large-scale wind impedes the formation of sea breezes on one or both coasts. Simulations also indicate greater land/sea-breeze activity in the summer than in the winter. Differences in the diurnal evolution of surface winds over land and water are found to be related to differences in the boundary layer stability. Overall, the results indicate that the WRF Model as configured here yields reliable simulations and can be used for various practical applications.
Abstract
The performance of the Advanced Research version of the Weather Research and Forecasting (ARW) model in real-time prediction of tropical cyclones (TCs) over the north Indian Ocean (NIO) at 27-km resolution is evaluated on the basis of 100 forecasts for 17 TCs during 2007–11. The analyses are carried out with respect to 1) basins of formation, 2) straight-moving and recurving TCs, 3) TC intensity at model initialization, and 4) season of occurrence. The impact of high resolution (18 and 9 km) on TC prediction is also studied. Model results at 27-km resolution indicate that the mean track forecast errors (skill with reference to persistence track) over the NIO were found to vary from 113 to 375 km (7%–51%) for a 12–72-h forecast. The model showed a right/eastward and slow bias in TC movement. The model is more skillful in track prediction when initialized at the intensity stage of severe cyclone or greater than at the intensity stage of cyclone or lower. The model is more efficient in predicting landfall location than landfall time. The higher-resolution (18 and 9 km) predictions yield an improvement in mean track error for the NIO Basin by about 4%–10% and 8%–24%, respectively. The 9-km predictions were found to be more accurate for recurving TC track predictions by ~13%–28% and 5%–15% when compared with the 27- and 18-km runs, respectively. The 9-km runs improve the intensity prediction by 15%–40% over the 18-km predictions. This study highlights the capabilities of the operational ARW model over the Indian monsoon region and the continued need for operational forecasts from high-resolution models.
Abstract
The performance of the Advanced Research version of the Weather Research and Forecasting (ARW) model in real-time prediction of tropical cyclones (TCs) over the north Indian Ocean (NIO) at 27-km resolution is evaluated on the basis of 100 forecasts for 17 TCs during 2007–11. The analyses are carried out with respect to 1) basins of formation, 2) straight-moving and recurving TCs, 3) TC intensity at model initialization, and 4) season of occurrence. The impact of high resolution (18 and 9 km) on TC prediction is also studied. Model results at 27-km resolution indicate that the mean track forecast errors (skill with reference to persistence track) over the NIO were found to vary from 113 to 375 km (7%–51%) for a 12–72-h forecast. The model showed a right/eastward and slow bias in TC movement. The model is more skillful in track prediction when initialized at the intensity stage of severe cyclone or greater than at the intensity stage of cyclone or lower. The model is more efficient in predicting landfall location than landfall time. The higher-resolution (18 and 9 km) predictions yield an improvement in mean track error for the NIO Basin by about 4%–10% and 8%–24%, respectively. The 9-km predictions were found to be more accurate for recurving TC track predictions by ~13%–28% and 5%–15% when compared with the 27- and 18-km runs, respectively. The 9-km runs improve the intensity prediction by 15%–40% over the 18-km predictions. This study highlights the capabilities of the operational ARW model over the Indian monsoon region and the continued need for operational forecasts from high-resolution models.
Abstract
Making use of the extensive shipboard and aircraft measurements of aerosol properties over the oceanic regions surrounding the Indian peninsula, under the Integrated Campaign for Aerosols, gases and Radiation Budget (ICARB) field experiment during the premonsoon season (March–May), supplemented with long-term satellite data and chemical transport model simulations, investigations are made of the east–west and north–south gradients in aerosol properties and estimated radiative forcing, over the oceans around India. An eastward gradient has been noticed in most of the aerosol parameters that persisted both within the marine atmospheric boundary layer and above up to an altitude of ~6 km; the gradients being steeper at higher altitudes. It was also noticed that the north–south gradient has contrasting patterns over the Bay of Bengal and the Arabian Sea on the either side of the Indian peninsula. The aerosol-induced atmospheric heating rate increased from a low value of ≤0.1 K day−1 in the southwestern Arabian Sea to as high as ~0.5 K day−1 over the northeastern Bay of Bengal. The simulations of species-resolved spatial gradients have revealed that the observed gradients are the result of the strong modulations by anthropogenic species over the natural gradients, thereby emphasizing the role of human activities in imparting regional forcing. These large spatial gradients in aerosol forcing induced by mostly anthropogenic aerosols over the oceanic regions around the Indian peninsula can potentially affect the regional circulation patterns.
Abstract
Making use of the extensive shipboard and aircraft measurements of aerosol properties over the oceanic regions surrounding the Indian peninsula, under the Integrated Campaign for Aerosols, gases and Radiation Budget (ICARB) field experiment during the premonsoon season (March–May), supplemented with long-term satellite data and chemical transport model simulations, investigations are made of the east–west and north–south gradients in aerosol properties and estimated radiative forcing, over the oceans around India. An eastward gradient has been noticed in most of the aerosol parameters that persisted both within the marine atmospheric boundary layer and above up to an altitude of ~6 km; the gradients being steeper at higher altitudes. It was also noticed that the north–south gradient has contrasting patterns over the Bay of Bengal and the Arabian Sea on the either side of the Indian peninsula. The aerosol-induced atmospheric heating rate increased from a low value of ≤0.1 K day−1 in the southwestern Arabian Sea to as high as ~0.5 K day−1 over the northeastern Bay of Bengal. The simulations of species-resolved spatial gradients have revealed that the observed gradients are the result of the strong modulations by anthropogenic species over the natural gradients, thereby emphasizing the role of human activities in imparting regional forcing. These large spatial gradients in aerosol forcing induced by mostly anthropogenic aerosols over the oceanic regions around the Indian peninsula can potentially affect the regional circulation patterns.
Investigating the Applicability of a Global Average Calibration Line for Ambient Size-Resolved Cloud Condensation Nuclei (CCN) Measurements: A Technical NoteAbstract
Aerosol–cloud–precipitation interaction represents the largest uncertainty in climate change’s current and future understanding. Therefore, aerosol properties affecting the cloud and precipitation formation and their accurate estimation is a first step in developing improved parameterizations for the prognostic climate models. Over the last couple of decades, a commercially available Cloud Condensation Nuclei Counter (CCNC) has been deployed in the field and laboratory for characterizing CCN properties of ambient or atmospherically relevant laboratory-generated aerosols. However, most of the CCN measurements performed in the field are often compounded with the erroneous estimation of CCN concentration and other parameters due to a lack of robust and accurate CCNC calibration. CCNC is not a plug-and-play instrument and requires prudent calibration and operation, to avoid erroneous data and added parameterization uncertainties. In this work, we propose and demonstrate the usability of a global calibration equation derived from CCNC calibration experiments from 8 contrasting global environments. Significant correlation was observed between the global calibration and each of the 16 individual experiments. A significant improvement in the correlation was observed when the calibration experiments were separated for high-altitude measurements. Using these equations, we further derived the effective hygroscopicity parameter and found lower relative uncertainty in the hygroscopicity parameter at higher effective supersaturation. Our results signify that altitude-based pressure change could be of importance for accurate calibration at high-altitude locations. Our results are consistent with previous studies emphasizing the criticality of the accurate CCN calibration for the lower supersaturations.
Abstract
Aerosol–cloud–precipitation interaction represents the largest uncertainty in climate change’s current and future understanding. Therefore, aerosol properties affecting the cloud and precipitation formation and their accurate estimation is a first step in developing improved parameterizations for the prognostic climate models. Over the last couple of decades, a commercially available Cloud Condensation Nuclei Counter (CCNC) has been deployed in the field and laboratory for characterizing CCN properties of ambient or atmospherically relevant laboratory-generated aerosols. However, most of the CCN measurements performed in the field are often compounded with the erroneous estimation of CCN concentration and other parameters due to a lack of robust and accurate CCNC calibration. CCNC is not a plug-and-play instrument and requires prudent calibration and operation, to avoid erroneous data and added parameterization uncertainties. In this work, we propose and demonstrate the usability of a global calibration equation derived from CCNC calibration experiments from 8 contrasting global environments. Significant correlation was observed between the global calibration and each of the 16 individual experiments. A significant improvement in the correlation was observed when the calibration experiments were separated for high-altitude measurements. Using these equations, we further derived the effective hygroscopicity parameter and found lower relative uncertainty in the hygroscopicity parameter at higher effective supersaturation. Our results signify that altitude-based pressure change could be of importance for accurate calibration at high-altitude locations. Our results are consistent with previous studies emphasizing the criticality of the accurate CCN calibration for the lower supersaturations.
Abstract
The very severe cyclonic storm (VSCS) “Phailin (2013)” was the strongest cyclone that hit the eastern coast of the India Odisha state since the supercyclone of 1999. But the same story of casualties was not repeated as that of 1999 where approximately 10 000 fatalities were reported. In the case of Phailin, a record 1 million people were evacuated across 18 000 villages in both the Odisha and Andhra Pradesh states to coastal shelters following the improved operational forecast guidance that benefited from highly skillful and accurate numerical model guidance for the movement, intensity, rainfall, and storm surge. Thus, the property damage and death toll were minimized through the proactive involvement of three-tier disaster management agencies at central, state, and district levels.
Abstract
The very severe cyclonic storm (VSCS) “Phailin (2013)” was the strongest cyclone that hit the eastern coast of the India Odisha state since the supercyclone of 1999. But the same story of casualties was not repeated as that of 1999 where approximately 10 000 fatalities were reported. In the case of Phailin, a record 1 million people were evacuated across 18 000 villages in both the Odisha and Andhra Pradesh states to coastal shelters following the improved operational forecast guidance that benefited from highly skillful and accurate numerical model guidance for the movement, intensity, rainfall, and storm surge. Thus, the property damage and death toll were minimized through the proactive involvement of three-tier disaster management agencies at central, state, and district levels.
Abstract
Ocean state forecast (OSF) along ship routes (OAS) is an advisory service of the Indian National Centre for Ocean Information Services (INCOIS) of the Earth System Science Organization (ESSO) that helps mariners to ensure safe navigation in the Indian Ocean in all seasons as well as in extreme conditions. As there are many users who solely depend on this service for their decision making, it is very important to ensure the reliability and accuracy of the service using the available in situ and satellite observations. This study evaluates the significant wave height (Hs) along the ship track in the Indian Ocean using the ship-mounted wave height meter (SWHM) on board the Oceanographic Research Vessel Sagar Nidhi, and the Cryosat-2 and Jason altimeters. Reliability of the SWHM is confirmed by comparing with collocated buoy and altimeter observations. The comparison along the ship routes using the SWHM shows very good agreement (correlation coefficient > 0.80) in all three oceanic regimes, [the tropical northern Indian Ocean (TNIO), the tropical southern Indian Ocean (TSIO), and extratropical southern Indian Ocean (ETSI)] with respect to the forecasts with a lead time of 48 h. However, the analysis shows ~10% overestimation of forecasted significant wave height in the low wave heights, especially in the TNIO. The forecast is found very reliable and accurate for the three regions during June–September with a higher correlation coefficient (average = 0.88) and a lower scatter index (average = 15%). During other months, overestimation (bias) of lower Hs is visible in the TNIO.
Abstract
Ocean state forecast (OSF) along ship routes (OAS) is an advisory service of the Indian National Centre for Ocean Information Services (INCOIS) of the Earth System Science Organization (ESSO) that helps mariners to ensure safe navigation in the Indian Ocean in all seasons as well as in extreme conditions. As there are many users who solely depend on this service for their decision making, it is very important to ensure the reliability and accuracy of the service using the available in situ and satellite observations. This study evaluates the significant wave height (Hs) along the ship track in the Indian Ocean using the ship-mounted wave height meter (SWHM) on board the Oceanographic Research Vessel Sagar Nidhi, and the Cryosat-2 and Jason altimeters. Reliability of the SWHM is confirmed by comparing with collocated buoy and altimeter observations. The comparison along the ship routes using the SWHM shows very good agreement (correlation coefficient > 0.80) in all three oceanic regimes, [the tropical northern Indian Ocean (TNIO), the tropical southern Indian Ocean (TSIO), and extratropical southern Indian Ocean (ETSI)] with respect to the forecasts with a lead time of 48 h. However, the analysis shows ~10% overestimation of forecasted significant wave height in the low wave heights, especially in the TNIO. The forecast is found very reliable and accurate for the three regions during June–September with a higher correlation coefficient (average = 0.88) and a lower scatter index (average = 15%). During other months, overestimation (bias) of lower Hs is visible in the TNIO.
Abstract
The U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) user facility recently initiated the Large-Eddy Simulation (LES) ARM Symbiotic Simulation and Observation (LASSO) activity focused on shallow convection at ARM’s Southern Great Plains (SGP) atmospheric observatory in Oklahoma. LASSO is designed to overcome an oft-shared difficulty of bridging the gap from point-based measurements to scales relevant for model parameterization development, and it provides an approach to add value to observations through modeling. LASSO is envisioned to be useful to modelers, theoreticians, and observationalists needing information relevant to cloud processes. LASSO does so by combining a suite of observations, LES inputs and outputs, diagnostics, and skill scores into data bundles that are freely available, and by simplifying user access to the data to speed scientific inquiry. The combination of relevant observations with observationally constrained LES output provides detail that gives context to the observations by showing physically consistent connections between processes based on the simulated state. A unique approach for LASSO is the generation of a library of cases for days with shallow convection combined with an ensemble of LES for each case. The library enables researchers to move beyond the single-case-study approach typical of LES research. The ensemble members are produced using a selection of different large-scale forcing sources and spatial scales. Since large-scale forcing is one of the most uncertain aspects of generating the LES, the ensemble informs users about potential uncertainty for each date and increases the probability of having an accurate forcing for each case.
Abstract
The U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) user facility recently initiated the Large-Eddy Simulation (LES) ARM Symbiotic Simulation and Observation (LASSO) activity focused on shallow convection at ARM’s Southern Great Plains (SGP) atmospheric observatory in Oklahoma. LASSO is designed to overcome an oft-shared difficulty of bridging the gap from point-based measurements to scales relevant for model parameterization development, and it provides an approach to add value to observations through modeling. LASSO is envisioned to be useful to modelers, theoreticians, and observationalists needing information relevant to cloud processes. LASSO does so by combining a suite of observations, LES inputs and outputs, diagnostics, and skill scores into data bundles that are freely available, and by simplifying user access to the data to speed scientific inquiry. The combination of relevant observations with observationally constrained LES output provides detail that gives context to the observations by showing physically consistent connections between processes based on the simulated state. A unique approach for LASSO is the generation of a library of cases for days with shallow convection combined with an ensemble of LES for each case. The library enables researchers to move beyond the single-case-study approach typical of LES research. The ensemble members are produced using a selection of different large-scale forcing sources and spatial scales. Since large-scale forcing is one of the most uncertain aspects of generating the LES, the ensemble informs users about potential uncertainty for each date and increases the probability of having an accurate forcing for each case.
Abstract
Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop series. From this workshop, a critical conclusion that the DOE BER and ASCR community came to is the requirement to develop a new paradigm for Earth system predictability focused on enabling artificial intelligence (AI) across the field, laboratory, modeling, and analysis activities, called model experimentation (ModEx). BER’s ModEx is an iterative approach that enables process models to generate hypotheses. The developed hypotheses inform field and laboratory efforts to collect measurement and observation data, which are subsequently used to parameterize, drive, and test model (e.g., process based) predictions. A total of 17 technical sessions were held in this AI4ESP workshop series. This paper discusses the topic of the AI Architectures and Codesign session and associated outcomes. The AI Architectures and Codesign session included two invited talks, two plenary discussion panels, and three breakout rooms that covered specific topics, including 1) DOE high-performance computing (HPC) systems, 2) cloud HPC systems, and 3) edge computing and Internet of Things (IoT). We also provide forward-looking ideas and perspectives on potential research in this codesign area that can be achieved by synergies with the other 16 session topics. These ideas include topics such as 1) reimagining codesign, 2) data acquisition to distribution, 3) heterogeneous HPC solutions for integration of AI/ML and other data analytics like uncertainty quantification with Earth system modeling and simulation, and 4) AI-enabled sensor integration into Earth system measurements and observations. Such perspectives are a distinguishing aspect of this paper.
Significance Statement
This study aims to provide perspectives on AI architectures and codesign approaches for Earth system predictability. Such visionary perspectives are essential because AI-enabled model-data integration has shown promise in improving predictions associated with climate change, perturbations, and extreme events. Our forward-looking ideas guide what is next in codesign to enhance Earth system models, observations, and theory using state-of-the-art and futuristic computational infrastructure.
Abstract
Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop series. From this workshop, a critical conclusion that the DOE BER and ASCR community came to is the requirement to develop a new paradigm for Earth system predictability focused on enabling artificial intelligence (AI) across the field, laboratory, modeling, and analysis activities, called model experimentation (ModEx). BER’s ModEx is an iterative approach that enables process models to generate hypotheses. The developed hypotheses inform field and laboratory efforts to collect measurement and observation data, which are subsequently used to parameterize, drive, and test model (e.g., process based) predictions. A total of 17 technical sessions were held in this AI4ESP workshop series. This paper discusses the topic of the AI Architectures and Codesign session and associated outcomes. The AI Architectures and Codesign session included two invited talks, two plenary discussion panels, and three breakout rooms that covered specific topics, including 1) DOE high-performance computing (HPC) systems, 2) cloud HPC systems, and 3) edge computing and Internet of Things (IoT). We also provide forward-looking ideas and perspectives on potential research in this codesign area that can be achieved by synergies with the other 16 session topics. These ideas include topics such as 1) reimagining codesign, 2) data acquisition to distribution, 3) heterogeneous HPC solutions for integration of AI/ML and other data analytics like uncertainty quantification with Earth system modeling and simulation, and 4) AI-enabled sensor integration into Earth system measurements and observations. Such perspectives are a distinguishing aspect of this paper.
Significance Statement
This study aims to provide perspectives on AI architectures and codesign approaches for Earth system predictability. Such visionary perspectives are essential because AI-enabled model-data integration has shown promise in improving predictions associated with climate change, perturbations, and extreme events. Our forward-looking ideas guide what is next in codesign to enhance Earth system models, observations, and theory using state-of-the-art and futuristic computational infrastructure.
Abstract
Repeat XBT transects made at near-fortnightly intervals in the Lakshadweep Sea (southeastern Arabian Sea) and ocean data assimilation products are examined to describe the year-to-year variability in the observed near-surface thermal inversions during the winter seasons of 2002–06. Despite the existence of a large low-salinity water intrusion into the Lakshadweep Sea, there was an unusually lower number of near-surface thermal inversions during the winter 2005/06 compared to the other winters. The possible causative mechanisms are examined. During the summer monsoon of 2005 and the following winter season, unusually heavy rainfall occurred over the southwestern Bay of Bengal and the Lakshadweep Sea compared to other years in the study. Furthermore, during the winter of 2005, both the East India Coastal Current and the Winter Monsoon Current were stronger compared to the other years, transporting larger quantities of low salinity waters from the Bay of Bengal into the Lakshadweep Sea where a relatively cooler near-surface thermal regime persisted owing to prolonged upwelling until November 2005. In addition, the observed local surface wind field was relatively stronger, and the net surface heat gain to the ocean was weaker over the Lakshadweep Sea during the postmonsoon season of 2005. Thus, in winter 2005/06, the combination of prolonged upwelling and stronger surface wind field resulting in anomalous net surface heat loss caused weaker secondary warming of the near-surface waters in the Lakshadweep Sea. This led to a weaker horizontal sea surface temperature (SST) gradient between the Lakshadweep Sea and the intruding Bay of Bengal waters and, hence, a reduced number of thermal inversions compared to other winters despite the presence of stronger vertical haline stratification.
Abstract
Repeat XBT transects made at near-fortnightly intervals in the Lakshadweep Sea (southeastern Arabian Sea) and ocean data assimilation products are examined to describe the year-to-year variability in the observed near-surface thermal inversions during the winter seasons of 2002–06. Despite the existence of a large low-salinity water intrusion into the Lakshadweep Sea, there was an unusually lower number of near-surface thermal inversions during the winter 2005/06 compared to the other winters. The possible causative mechanisms are examined. During the summer monsoon of 2005 and the following winter season, unusually heavy rainfall occurred over the southwestern Bay of Bengal and the Lakshadweep Sea compared to other years in the study. Furthermore, during the winter of 2005, both the East India Coastal Current and the Winter Monsoon Current were stronger compared to the other years, transporting larger quantities of low salinity waters from the Bay of Bengal into the Lakshadweep Sea where a relatively cooler near-surface thermal regime persisted owing to prolonged upwelling until November 2005. In addition, the observed local surface wind field was relatively stronger, and the net surface heat gain to the ocean was weaker over the Lakshadweep Sea during the postmonsoon season of 2005. Thus, in winter 2005/06, the combination of prolonged upwelling and stronger surface wind field resulting in anomalous net surface heat loss caused weaker secondary warming of the near-surface waters in the Lakshadweep Sea. This led to a weaker horizontal sea surface temperature (SST) gradient between the Lakshadweep Sea and the intruding Bay of Bengal waters and, hence, a reduced number of thermal inversions compared to other winters despite the presence of stronger vertical haline stratification.
