NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999 ds083.2 | DOI: 10.5065/D6M043C6 | |
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Data Citations: | This dataset has been cited 347 times. Published works that cited this dataset Published works that cited this dataset: 2022
Aguilar, C., B. J. Russo, A. Mohebbi, and S. Akbariyeh, 2022: Analysis of factors affecting the frequency of crashes on interstate freeways by vehicle type considering multiple weather variables. Journal of Transportation Safety & Security, 14, 973-1001, https://doi.org/10.1080/19439962.2020.1869875
Ahern, K., R. E. Hart, and M. A. Bourassa, 2022: Asymmetric Hurricane Boundary Layer Structure during Storm Decay. Part II: Secondary Eyewall Formation. Mon. Wea. Rev., 150, 1915-1936, https://doi.org/10.1175/MWR-D-21-0247.1
Bae, M., B. Kim, H. C. Kim, J. H. Woo, and S. Kim, 2022: An observation-based adjustment method of regional contribution estimation from upwind emissions to downwind PM2.5 concentrations. Environment International, 163, 107214, https://doi.org/10.1016/j.envint.2022.107214
Bae, M., S. Kim, and S. Kim, 2022: Quantitative Evaluation on the Drivers of PM2.5 Concentration Change in South Korea during the 1st - 3rd Seasonal PM2.5 Management Periods. KOSAE, 38, 610-623, https://doi.org/10.5572/kosae.2022.38.4.610
Bae, M., and S. Kim, 2022: Adjustment of Foreign Emission Impacts on Provincial PM2.5 Concentrations in South Korea based on Upwind Observations and Estimation of Domestic Emission Uncertainty. KOSAE, 38, 624-636, https://doi.org/10.5572/kosae.2022.38.4.624
Baki, H., S. Chinta, B. Srinivasan, and , 2022: Determining the sensitive parameters of the Weather Research and Forecasting (WRF) model for the simulation of tropical cyclones in the Bay of Bengal using global sensitivity analysis and machine learning. Geosci. Model Dev., 15, 2133-2155, https://doi.org/10.5194/gmd-15-2133-2022
Baki, H., S. Chinta, C. Balaji, and B. Srinivasan, 2022: Parameter Calibration to Improve the Prediction of Tropical Cyclones over the Bay of Bengal Using Machine Learning–Based Multiobjective Optimization. J. Appl. Meteor. Clmatol., 61, 819-837, https://doi.org/10.1175/JAMC-D-21-0184.1
Castesana, P., M. Diaz Resquin, N. Huneeus, E. Puliafito, S. Darras, D. Gómez, C. Granier, M. Osses Alvarado, N. Rojas, and L. Dawidowski, 2022: PAPILA dataset: a regional emission inventory of reactive gases for South America based on the combination of local and global information. Earth Syst. Sci. Data, 14, 271-293, https://doi.org/10.5194/essd-14-271-2022
Chen, C., J. Ge, W. Guo, Y. Cao, Y. Liu, X. Luo, and L. Yang, 2022: The Biophysical Impacts of Idealized Afforestation on Surface Temperature in China: Local and Nonlocal Effects. J. Clim., 35, 4233-4252, https://doi.org/10.1175/JCLI-D-22-0144.1
Chen, J., H. Wang, X. Li, D. Painemal, A. Sorooshian, K. L. Thornhill, C. Robinson, and T. Shingler, 2022: Impact of Meteorological Factors on the Mesoscale Morphology of Cloud Streets during a Cold-Air Outbreak over the Western North Atlantic. , 79, 2863-2879, https://doi.org/10.1175/jas-d-22-0034.1
Cho, H., J. Kug, and S. Jun, 2022: Influence of the recent winter Arctic sea ice loss in short-term simulations of a regional atmospheric model. Sci Rep, 12, https://doi.org/10.1038/s41598-022-12783-4
Fustos-Toribio, I., N. Manque-Roa, D. Vásquez Antipan, M. Hermosilla Sotomayor, and V. Letelier Gonzalez, 2022: Rainfall-induced landslide early warning system based on corrected mesoscale numerical models: an application for the southern Andes. Nat. Hazards Earth Syst. Sci., 22, 2169-2183, https://doi.org/10.5194/nhess-22-2169-2022
Gu, J., J. Feng, X. Hao, T. Fang, C. Zhao, H. An, J. Chen, M. Xu, J. Li, W. Han, C. Yang, F. Li, and D. Chen, 2022: Establishing a non-hydrostatic global atmospheric modeling system at 3-km horizontal resolution with aerosol feedbacks on the Sunway supercomputer of China. Science Bulletin, 67, 1170-1181, https://doi.org/10.1016/j.scib.2022.03.009
Han, Z., J. Ge, X. Chen, X. Hu, X. Yang, and J. Du, 2022: Dust Activities Induced by Nocturnal Low-Level Jet Over the Taklimakan Desert From WRF-Chem Simulation. J. Geophys. Res. Atmos., 127, https://doi.org/10.1029/2021JD036114
He, J., T. V. Loboda, D. Chen, and N. F. French, 2022: Cloud-to-Ground Lightning and Near-Surface Fire Weather Control Wildfire Occurrence in Arctic Tundra. Geophys. Res. Lett., 49, https://doi.org/10.1029/2021GL096814
Heese, B., A. A. Floutsi, H. Baars, D. Althausen, J. Hofer, A. Herzog, S. Mewes, M. Radenz, and Y. Y. Schechner, 2022: The vertical aerosol type distribution above Israel – 2 years of lidar observations at the coastal city of Haifa. Atmos. Chem. Phys., 22, 1633-1648, https://doi.org/10.5194/acp-22-1633-2022
Hu, W., T. Zhao, Y. Bai, S. Kong, L. Shen, J. Xiong, Y. Zhou, Y. Gu, J. Shi, H. Zheng, X. Sun, and K. Meng, 2022: Regulation of Synoptic Circulation in Regional PM 2.5 Transport for Heavy Air Pollution: Study of 5‐year Observation Over Central China. JGR Atmospheres, 127, https://doi.org/10.1029/2021jd035937
Huang, Y., H. Lv, Y. Dong, W. Huang, G. Hu, Y. Liu, H. Chen, Y. Geng, J. Bai, P. Guo, and Y. Cui, 2022: Mapping the Spatio-Temporal Distribution of Fall Armyworm in China by Coupling Multi-Factors. Remote Sensing, 14, 4415, https://doi.org/10.3390/rs14174415
Huang, Y., J. Wei, J. Jin, Z. Zhou, and Q. Gu, 2022: CO Fluxes in Western Europe during 2017–2020 Winter Seasons Inverted by WRF-Chem/Data Assimilation Research Testbed with MOPITT Observations. Remote Sensing, 14, 1133, https://doi.org/10.3390/rs14051133
Ibarra-Espinosa, S., A. Rehbein, E. Dias de Freitas, L. Martins, M. F. Andrade, and E. Landulfo, 2022: Changes in a Bottom-Up Vehicular Emissions Inventory and Its Impact on Air Pollution During COVID-19 Lockdown in São Paulo, Brazil. Frontiers in Sustainable Cities, 4, https://doi.org/10.3389/frsc.2022.883112
Ikram, F., K. Ullah, and D. Chen, 2022: Evaluation of Three Genesis Potential Indices for Tropical Cyclogenesis in the Arabian Sea: Two Case Studies Using WRF and ERA5. , 150, 3275-3303, https://doi.org/10.1175/mwr-d-22-0048.1
Itahashi, S., and H. Irie, 2022: Surface and aloft NO2 pollution over the greater Tokyo area observed by ground-based and MAX-DOAS measurements bridged by kilometer-scale regional air quality modeling. Prog Earth Planet Sci, 9, https://doi.org/10.1186/s40645-022-00474-7
Kanwal, A., Z. R. Tahir, M. Asim, N. Hayat, M. Farooq, M. Abdullah, and M. Azhar, 2022: Evaluation of Reanalysis and Analysis Datasets against Measured Wind Data for Wind Resource Assessment. Energy & Environment, 0958305X2210840, https://doi.org/10.1177/0958305x221084078
Karim, S. S., Y. Lin, and M. L. Kaplan, 2022: Formation Mechanisms of the Mesoscale Environment Conducive to a Downslope Windstorm over the Cuyamaca Mountains Associated with Santa Ana Wind during the Cedar Fire (2003). , 61, 1797-1818, https://doi.org/10.1175/JAMC-D-22-0025.1
LI, H., X. GE, M. PENG, and L. LI, 2022: The Influences of Monsoon Trough on the Relative Motion of Binary Tropical Cyclones. Journal of the Meteorological Society of Japan, 100, 729-749, https://doi.org/10.2151/jmsj.2022-038
Lan, M., F. Huo, L. Zhou, S. Jiang, R. Cai, and J. Chen, 2022: Mechanisms of Short-Duration Heavy Rainfall in the Western Pacific Subtropical High Area: An Analysis of Two Rainstorms of 2018 in Hunan Province, China. Atmosphere-Ocean, 60, 1-12, https://doi.org/10.1080/07055900.2022.2060177
Li, J., Z. Yue, C. Lu, J. Chen, X. Wu, X. Xu, S. Luo, L. Zhu, S. Wu, F. Wang, and X. He, 2022: Convective Entrainment Rate over the Tibetan Plateau and Its Adjacent Regions in the Boreal Summer Using SNPP-VIIRS. Remote Sensing, 14, 2073, https://doi.org/10.3390/rs14092073
Li, X., N. Bei, J. Wu, S. Liu, Q. Wang, J. Tian, L. Liu, R. Wang, and G. Li, 2022: The Heavy Particulate Matter Pollution During the COVID‐19 Lockdown Period in the Guanzhong Basin, China. JGR Atmospheres, 127, https://doi.org/10.1029/2021jd036191
Li, Y., X. Zhao, X. Deng, and J. Gao, 2022: The impact of peripheral circulation characteristics of typhoon on sustained ozone episodes over the Pearl River Delta region, China. Atmos. Chem. Phys., 22, 3861-3873, https://doi.org/10.5194/acp-22-3861-2022
Liu, Z., Y. Lei, W. Xue, X. Liu, Y. Jiang, X. Shi, Y. Zheng, Q. Zhang, and J. Wang, 2022: Mitigating China’s Ozone Pollution with More Balanced Health Benefits. Environ. Sci. Technol., 56, 7647-7656, https://doi.org/10.1021/acs.est.2c00114
Luo, H., L. Dong, Y. Chen, Y. Zhao, D. Zhao, M. Huang, D. Ding, J. Liao, T. Ma, M. Hu, and Y. Han, 2022: Interaction between aerosol and thermodynamic stability within the planetary boundary layer during wintertime over the North China Plain: aircraft observation and WRF-Chem simulation. Atmos. Chem. Phys., 22, 2507-2524, https://doi.org/10.5194/acp-22-2507-2022
Ma, Y., D. Chen, X. Fu, F. Shang, X. Guo, J. Lang, and Y. Zhou, 2022: Impact of Sea Breeze on the Transport of Ship Emissions: A Comprehensive Study in the Bohai Rim Region, China. Atmosphere, 13, 1094, https://doi.org/10.3390/atmos13071094
Maasakkers, J. D., D. J. Varon, A. Elfarsdóttir, J. McKeever, D. Jervis, G. Mahapatra, S. Pandey, A. Lorente, T. Borsdorff, L. R. Foorthuis, B. J. Schuit, P. Tol, T. A. van Kempen, R. van Hees, and I. Aben, 2022: Using satellites to uncover large methane emissions from landfills. Sci. Adv., 8, https://doi.org/10.1126/sciadv.abn9683
Maharana, P., D. Kumar, R. Kumar, R. Singh, and A. P. Dimri, 2022: Diagnostic of the massive flood event and flood hazard mapping in Tons River basin. Theor Appl Climatol, https://doi.org/10.1007/s00704-022-04008-5
Makosko, A. A., and A. V. Matesheva, 2022: Methodological approach to assessing environmental risk (health risk) from air pollution in the Baikal region in a changing climate. IOP Conference Series: Earth and Environmental Science, Institute of Physics, https://doi.org/10.1088/1755-1315/1040/1/012009
Makosko, A., A. Matesheva, and , 2022: ON THE ASSESSMENT OF ENVIRONMENTAL RISKS FROM AIR POLLUTION IN THE ARCTIC ZONE UNDER A CHANGING CLIMATE IN THE ХХI CENTURY. Arktika: Ekologia i Ekonomika, 12, 34-45, https://doi.org/10.25283/2223-4594-2022-1-34-45
Masunaga, R., and N. Schneider, 2022: Surface Wind Responses to Mesoscale Sea Surface Temperature over Western Boundary Current Regions Assessed by Spectral Transfer Functions. , 79, 1549-1573, https://doi.org/10.1175/jas-d-21-0125.1
Morichetti, M., S. Madronich, G. Passerini, U. Rizza, E. Mancinelli, S. Virgili, and M. Barth, 2022: Comparison and evaluation of updates to WRF-Chem (v3.9) biogenic emissions using MEGAN. Geosci. Model Dev., 15, 6311-6339, https://doi.org/10.5194/gmd-15-6311-2022
Nakata, M., I. Sano, S. Mukai, and A. Kokhanovsky, 2022: Characterization of Wildfire Smoke over Complex Terrain Using Satellite Observations, Ground-Based Observations, and Meteorological Models. Remote Sensing, 14, 2344, https://doi.org/10.3390/rs14102344
Neyestani, S. E., and R. Saleh, 2022: Observationally constrained representation of brown carbon emissions from wildfires in a chemical transport model. Environ. Sci.: Atmos., 2, 192-201, https://doi.org/10.1039/d1ea00059d
Opio, R., I. Mugume, J. Nakatumba-Nabende, J. Nanteza, A. Nimusiima, M. Mbogga, and F. Mugagga, 2022: Evaluation of WRF-chem simulations of NO
Ouyang, S., T. Deng, R. Liu, J. Chen, G. He, J. C. Leung, N. Wang, and S. C. Liu, 2022: Impact of a subtropical high and a typhoon on a severe ozone pollution episode in the Pearl River Delta, China. Atmos. Chem. Phys., 22, 10751-10767, https://doi.org/10.5194/acp-22-10751-2022
Ovchinnikov, M., J. D. Fast, L. K. Berg, W. I. Gustafson, J. Chen, K. Sakaguchi, and H. Xiao, 2022: Effects of Horizontal Resolution, Domain Size, Boundary Conditions, and Surface Heterogeneity on Coarse LES of a Convective Boundary Layer. Mon. Wea. Rev., 150, 1397-1415, https://doi.org/10.1175/MWR-D-21-0244.1
Park, S., U. K. Dash, and J. Yu, 2022: Length Scale Analyses of Background Error Covariances for EnKF and EnSRF Data Assimilation. Atmosphere, 13, 160, https://doi.org/10.3390/atmos13020160
Parra, R., 2022: Effect of Global Atmospheric Datasets in Modeling Meteorology and Air Quality in the Andean Region of Ecuador. Aerosol Air Qual. Res., 22, https://doi.org/10.4209/aaqr.210292
Pezzi, L. P., M. L. Quadro, J. A. Lorenzzetti, A. J. Miller, E. B. Rosa, L. N. Lima, and U. A. Sutil, 2022: The effect of Oceanic South Atlantic Convergence Zone episodes on regional SST anomalies: the roles of heat fluxes and upper-ocean dynamics. Clim Dyn, https://doi.org/10.1007/s00382-022-06195-3
Prakash, K. R., V. Pant, T. S. Udaya Bhaskar, and N. Chandra, 2022: What Made the Sustained Intensification of Tropical Cyclone Fani in the Bay of Bengal? An Investigation Using Coupled Atmosphere–Ocean Model. Atmosphere, 13, 535, https://doi.org/10.3390/atmos13040535
Sakaguchi, K., L. K. Berg, J. Chen, J. Fast, R. Newsom, S. Tai, Z. Yang, W. I. Gustafson, B. J. Gaudet, M. Huang, M. Pekour, K. Pressel, and H. Xiao, 2022: Determining Spatial Scales of Soil Moisture—Cloud Coupling Pathways Using Semi‐Idealized Simulations. JGR Atmospheres, 127, https://doi.org/10.1029/2021jd035282
Salata, F., S. Falasca, V. Ciancio, G. Curci, S. Grignaffini, and P. de Wilde, 2022: Estimating building cooling energy demand through the Cooling Degree Hours in a changing climate: A modeling study. Sustainable Cities and Society, 76, 103518, https://doi.org/10.1016/j.scs.2021.103518
Sanchez, K. J., B. Zhang, H. Liu, M. D. Brown, E. C. Crosbie, F. Gallo, J. W. Hair, C. A. Hostetler, C. E. Jordan, C. E. Robinson, A. J. Scarino, T. J. Shingler, M. A. Shook, K. L. Thornhill, E. B. Wiggins, E. L. Winstead, L. D. Ziemba, G. Saliba, S. L. Lewis, L. M. Russell, P. K. Quinn, T. S. Bates, J. Porter, T. G. Bell, P. Gaube, E. S. Saltzman, M. J. Behrenfeld, and R. H. Moore, 2022: North Atlantic Ocean SST-gradient-driven variations in aerosol and cloud evolution along Lagrangian cold-air outbreak trajectories. Atmos. Chem. Phys., 22, 2795-2815, https://doi.org/10.5194/acp-22-2795-2022
Shen, Y., Z. Xiao, Y. Wang, L. Yao, and W. Xiao, 2022: Multisource Remote Sensing Based Estimation of Soil NO x Emissions From Fertilized Cropland at High‐Resolution: Spatio‐Temporal Patterns and Impacts. JGR Atmospheres, 127, https://doi.org/10.1029/2022jd036741
Shestakova, A. A., D. G. Chechin, C. Lüpkes, J. Hartmann, and M. Maturilli, 2022: The foehn effect during easterly flow over Svalbard. Atmos. Chem. Phys., 22, 1529-1548, https://doi.org/10.5194/acp-22-1529-2022
Shi, Y., Q. Zeng, L. Liu, J. Huo, Z. Zhang, W. Ding, and F. Hu, 2022: Observed Evidence That Subsidence Process Stabilizes the Boundary Layer and Increases the Ground Concentration of Secondary Pollutants. J. Geophys. Res. Atmos., 127, https://doi.org/10.1029/2021JD035244
Sun, X., Y. Zhou, T. Zhao, Y. Bai, T. Huo, L. Leng, H. He, and J. Sun, 2022: Effect of Vertical Wind Shear on PM
TATSUMI, K., 2022: Rice yield reductions due to ozone exposure and the roles of VOCs and NO<sub>x</sub> in ozone production in Japan. J. Agric. Meteorol., 78, 89-100, https://doi.org/10.2480/agrmet.d-21-00051
Takeishi, A., and C. Wang, 2022: Radiative and microphysical responses of clouds to an anomalous increase in fire particles over the Maritime Continent in 2015. Atmos. Chem. Phys., 22, 4129-4147, https://doi.org/10.5194/acp-22-4129-2022
Taniguchi, K., K. Kotone, and Y. Shibuo, 2022: Simulation-based assessment of inundation risk potential considering the nonstationarity of extreme flood events under climate change. Journal of Hydrology, 128434, https://doi.org/10.1016/j.jhydrol.2022.128434
Tawinprai, S., S. Polnumtiang, P. Suksomprom, J. Waewsak, and K. Tangchaichit, 2022: Modeling of wind energy potential using a high-resolution grid over Mekong riverside region in the northeastern part of Thailand. Theor Appl Climatol, https://doi.org/10.1007/s00704-022-04235-w
Vinayak, B., H. S. Lee, S. Gedam, and R. Latha, 2022: Impacts of future urbanization on urban microclimate and thermal comfort over the Mumbai metropolitan region, India. Sustainable Cities and Society, 79, 103703, https://doi.org/10.1016/j.scs.2022.103703
Wang, D., W. You, Z. Zang, X. Pan, Y. Hu, and Y. Liang, 2022: A three-dimensional variational data assimilation system for aerosol optical properties based on WRF-Chem v4.0: design, development, and application of assimilating Himawari-8 aerosol observations. Geosci. Model Dev., 15, 1821-1840, https://doi.org/10.5194/gmd-15-1821-2022
Wang, X., C. Zhao, M. Xu, Q. Du, J. Zheng, Y. Bi, S. Lin, and Y. Luo, 2022: The sensitivity of simulated aerosol climatic impact to domain size using regional model (WRF-Chem v3.6). Geosci. Model Dev., 15, 199-218, https://doi.org/10.5194/gmd-15-199-2022
Wang, Y., E. A. Yaluk, H. Chen, S. Jiang, L. Huang, A. Zhu, S. Xiao, J. Xue, G. Lu, J. Bian, M. Kasemsan, K. Zhang, H. Liu, H. Tong, M. G. Ooi, A. Chan, and L. Li, 2022: The Importance of NO x Control for Peak Ozone Mitigation Based on a Sensitivity Study Using CMAQ‐HDDM‐3D Model During a Typical Episode Over the Yangtze River Delta Region, China. JGR Atmospheres, 127, https://doi.org/10.1029/2022JD036555
Wu, J., N. Bei, X. Li, R. Wang, S. Liu, Q. Jiang, X. Tie, and G. Li, 2022: Impacts of Transboundary Transport on Coastal Air Quality of South China. JGR Atmospheres, 127, https://doi.org/10.1029/2021jd036213
Xu, J. Z., H. R. Zhang, Z. Cheng, J. Y. Liu, Y. Y. Xu, and Y. C. Wang, 2022: Approximating Three‐Dimensional (3‐D) Transport of Atmospheric Pollutants via Deep Learning. Earth and Space Science, 9, https://doi.org/10.1029/2022ea002338
Xu, X., X. Feng, H. Lin, P. Zhang, S. Huang, Z. Song, Y. Peng, T. Fu, and Y. Zhang, 2022: Modeling the high-mercury wet deposition in the southeastern US with WRF-GC-Hg v1.0. Geosci. Model Dev., 15, 3845-3859, https://doi.org/10.5194/gmd-15-3845-2022
Yadav, V., M. Sherly, P. Ranjan, V. Prasad, R. O. Tinoco, and A. Laurent, 2022: Risk of plastics losses to the environment from Indian landfills. Resources, Conservation and Recycling, 187, 106610, https://doi.org/10.1016/j.resconrec.2022.106610
Yamagami, A., M. Kajino, and T. Maki, 2022: Statistical Evaluation of the Temperature Forecast Error in the Lower‐Level Troposphere on Short‐Range Timescales Induced by Aerosol Variability. JGR Atmospheres, 127, https://doi.org/10.1029/2022jd036595
Yang, Q., X. Wu, Z. Wang, X. Hu, Y. Guo, and C. Qing, 2022: Simulating the night-time astronomical seeing at Dome A using Polar WRF. , 515, 1788-1794, https://doi.org/10.1093/mnras/stac1930
Yang, Y., M. Guo, G. Ren, S. Liu, L. Zong, Y. Zhang, Z. Zheng, Y. Miao, and Y. Zhang, 2022: Modulation of Wintertime Canopy Urban Heat Island (CUHI) Intensity in Beijing by Synoptic Weather Pattern in Planetary Boundary Layer. J. Geophys. Res. Atmos., 127, https://doi.org/10.1029/2021JD035988
Yin, D., Z. G. Xue, D. Bao, A. RafieeiNasab, Y. Huang, M. Morales, and J. C. Warner, 2022: Understanding the role of initial soil moisture and precipitation magnitude in flood forecast using a hydrometeorological modelling system. Hydrological Processes, 36, https://doi.org/10.1002/hyp.14710
Yoshimura, R., K. Suzuki, J. Ito, R. Kikuchi, A. Yakeno, and S. Obayashi, 2022: Large-Eddy and Flight Simulations of a Clear-Air Turbulence Event over Tokyo on 16 December 2014. J. Appl. Meteor. Clmatol., 61, 503-519, https://doi.org/10.1175/JAMC-D-21-0071.1
Yu, J., W. Zhou, J. Wu, X. Li, S. Liu, R. Wang, L. Liu, Q. Jiang, X. Tie, and G. Li, 2022: Impacts of Changes in Land Use and Land Cover Between 2001 and 2018 on Summertime O 3 Formation in North China Plain and Surrounding Areas–A Case Study. JGR Atmospheres, 127, https://doi.org/10.1029/2021jd035956
Zhan, C., and M. Xie, 2022: Land use and anthropogenic heat modulate ozone by meteorology: A perspective from the Yangtze River Delta region. Atmos. Chem. Phys., 22, 1351-1371, https://doi.org/10.5194/acp-22-1351-2022
Zhang, A., Y. Liu, S. Goodrick, and M. D. Williams, 2022: Duff burning from wildfires in a moist region: different impacts on PM<sub>2.5</sub> and ozone. Atmos. Chem. Phys., 22, 597-624, https://doi.org/10.5194/acp-22-597-2022
Zhang, J., C. Lian, W. Wang, M. Ge, Y. Guo, H. Ran, Y. Zhang, F. Zheng, X. Fan, C. Yan, K. R. Daellenbach, Y. Liu, M. Kulmala, and J. An, 2022: Amplified role of potential HONO sources in O<sub>3</sub> formation in North China Plain during autumn haze aggravating processes. Atmos. Chem. Phys., 22, 3275-3302, https://doi.org/10.5194/acp-22-3275-2022
Zhang, M., K. L. Rasmussen, Z. Meng, and Y. Huang, 2022: Impacts of Coastal Terrain on Warm-Sector Heavy-Rain-Producing MCSs in Southern China. Mon. Wea. Rev., 150, 603-624, https://doi.org/10.1175/MWR-D-21-0190.1
Zhang, W., D. Zhao, D. Zhu, J. Li, C. Guan, and J. Sun, 2022: A Numerical Investigation of the Effect of Wave‐Induced Mixing on Tropical Cyclones Using a Coupled Ocean‐Atmosphere‐Wave Model. JGR Atmospheres, 127, https://doi.org/10.1029/2021jd036290
Zhou, Q., L. Cheng, Y. Zhang, Z. Wang, and S. Yang, 2022: Relationships between Springtime PM2.5, PM10, and O3 Pollution and the Boundary Layer Structure in Beijing, China. Sustainability, 14, 9041, https://doi.org/10.3390/su14159041
Zhou, Y., Y. Liu, Z. Huo, and Y. Li, 2022: A preliminary evaluation of FY-4A visible radiance data assimilation by the WRF (ARW v4.1.1)/DART (Manhattan release v9.8.0)-RTTOV (v12.3) system for a tropical storm case. Geosci. Model Dev., 15, 7397-7420, https://doi.org/10.5194/gmd-15-7397-2022
Zhu, S., M. Mac Kinnon, A. Carlos-Carlos, S. J. Davis, and S. Samuelsen, 2022: Decarbonization will lead to more equitable air quality in California. Nature Commun., 13, https://doi.org/10.1038/s41467-022-33295-9
2021
Ahern, K., R. E. Hart, and M. A. Bourassa, 2021: Asymmetric Hurricane Boundary Layer Structure during Storm Decay. Part I: Formation of Descending Inflow. Mon. Wea. Rev., 149, 3851-3874, https://doi.org/10.1175/MWR-D-21-0030.1
Amiot, C. G., S. K. Biswas, T. J. Lang, and D. I. Duncan, 2021: Dual-polarization deconvolution and geophysical retrievals from the advanced microwave precipitation radiometer during olympex/radex. J. Atmos. Oceanic Technol., 38, 607-628, https://doi.org/10.1175/JTECH-D-19-0218.1
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Dayalu, A., J. W. Munger, S. C. Wofsy, Y. Wang, T. Nehrkorn, Y. Zhao, M. B. McElroy, C. P. Nielsen, and K. Luus, 2018: Assessing biotic contributions to CO2 fluxes in northern China using the Vegetation, Photosynthesis and Respiration Model (VPRM-China) and observations from 2005 to 2009. Biogeosciences, 15, 6713-6729, https://doi.org/10.5194/bg-15-6713-2018
Deng, D., and E. A. Ritchie, 2018: Rainfall characteristics of recurving tropical cyclones over the Western North Pacific. J. Clim., 31, 575-592, https://doi.org/10.1175/JCLI-D-17-0415.1
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Gao, X., S. Gao, and Y. Yang, 2018: A Comparison between 3DVAR and EnKF for Data Assimilation Effects on the Yellow Sea Fog Forecast. Atmosphere, 9, 346, https://doi.org/10.3390/atmos9090346
Gibbons, M., Q. Min, and J. Fan, 2018: Investigating the impacts of Saharan dust on tropical deep convection using spectral bin microphysics. Atmos. Chem. Phys., 18, 12161-12184, https://doi.org/10.5194/acp-18-12161-2018
Hai, S., Y. Miao, L. Sheng, L. Wei, and Q. Chen, 2018: Numerical study on the effect of urbanization and coastal change on sea breeze over Qingdao, China. Atmosphere, 9, https://doi.org/10.3390/atmos9090345
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Iguchi, T., T. Matsui, Z. Tao, D. Kim, C. M. Ichoku, L. Ellison, and J. Wang, 2018: NU-WRF aerosol transport simulation over West Africa: Effects of biomass burning on smoke aerosol distribution. J. Appl. Meteor. Clmatol., 57, 1551-1573, https://doi.org/10.1175/JAMC-D-17-0278.1
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Jain, D., A. Chakraborty, and R. S. Nanjundaiah, 2018: A Mechanism for the Southward Propagation of Mesoscale Convective Systems Over the Bay of Bengal. J. Geophys. Res. Atmos., 123, 3893-3913, https://doi.org/10.1002/2017JD027470
Ju, H., H. C. Kim, B. Kim, Y. S. Ghim, H. J. Shin, and S. Kim, 2018: Long-term Trend Analysis of Key Criteria Air Pollutants over Air Quality Control Regions in South Korea using Observation Data and Air Quality Simulation. KOSAE, 34, 101-119, https://doi.org/10.5572/KOSAE.2018.34.1.101
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Lavender, S. L., R. K. Hoeke, and D. J. Abbs, 2018: The influence of sea surface temperature on the intensity and associated storm surge of tropical cyclone Yasi: A sensitivity study. Nat. Hazards Earth Sys. Sci., 18, 795-805, https://doi.org/10.5194/nhess-18-795-2018
Lee, H., O. Iraqui, Y. Gu, S. H. Yim, A. Chulakadabba, A. Y. Tonks, Z. Yang, and C. Wang, 2018: Impacts of air pollutants from fire and non-fire emissions on the regional air quality in Southeast Asia. Atmos. Chem. Phys., 18, 6141-6156, https://doi.org/10.5194/acp-18-6141-2018
Liang, J., L. Wu, and G. Gu, 2018: Rapid weakening of tropical cyclones in monsoon gyres over the tropical Western North Pacific. J. Clim., 31, 1015-1028, https://doi.org/10.1175/JCLI-D-16-0784.1
Lin, L., and Z. Pu, 2018: Characteristics of background error covariance of soil moisture and atmospheric states in strongly coupled land-atmosphere data assimilation. J. Appl. Meteor. Clmatol., 57, 2507-2529, https://doi.org/10.1175/JAMC-D-18-0050.1
Luiz do Vale Silva, T., D. Veleda, M. Araujo, and P. Tyaquiçã, 2018: Ocean-atmosphere feedback during extreme rainfall events in eastern Northeast Brazil. J. Appl. Meteor. Clmatol., 57, 1211-1229, https://doi.org/10.1175/JAMC-D-17-0232.1
Luo, J., L. L. Pan, S. B. Honomichl, J. W. Bergman, W. J. Randel, G. Francis, C. Clerbaux, M. George, X. Liu, and W. Tian, 2018: Space–time variability in UTLS chemical distribution in the Asian summer monsoon viewed by limb and nadir satellite sensors. Atmos. Chem. Phys., 18, 12511-12530, https://doi.org/10.5194/acp-18-12511-2018
Mauree, D., N. Blond, and A. Clappier, 2018: Multi-scale modeling of the urban meteorology: Integration of a new canopy model in the WRF model. Urban Clim., 26, 60-75, https://doi.org/10.1016/j.uclim.2018.08.002
Mazzeo, A., N. Huneeus, C. Ordoñez, A. Orfanoz-Cheuquelaf, L. Menut, S. Mailler, M. Valari, H. Denier van der Gon, L. Gallardo, R. Muñoz, R. Donoso, M. Galleguillos, M. Osses, and S. Tolvett, 2018: Impact of residential combustion and transport emissions on air pollution in Santiago during winter. Atmos. Environ., 190, 195-208, https://doi.org/10.1016/j.atmosenv.2018.06.043
Meng, X., and J. Cheng, 2018: Evaluating Eight Global Reanalysis Products for Atmospheric Correction of Thermal Infrared Sensor—Application to Landsat 8 TIRS10 Data. Remote Sensing, 10, 474, https://doi.org/10.3390/rs10030474
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Nakano, H., K. Kuroki, Y. Sato, S. Koshita, M. Maeda, and K. Matsuura, 2018: Ship speed loss estimation using wave spectrum of encounter. 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018, Institute of Electrical and Electronics Engineers Inc., https://doi.org/10.1109/OCEANSKOBE.2018.8559122
Nam, K., D. Lee, J. Lee, K. Choi, L. Jang, and K. Choi, 2018: A Study on the Utilization of Air Quality Model to Establish Efficient Air Policies: Focusing on the Improvement Effect of PM2.5 in Chungcheongnam-do due to Coal-fired Power Plants Shutdown. KOSAE, 34, 687-696, https://doi.org/10.5572/KOSAE.2018.34.5.687
Ots, R., M. R. Heal, D. E. Young, L. R. Williams, J. D. Allan, E. Nemitz, C. Di Marco, A. Detournay, L. Xu, N. L. Ng, H. Coe, S. C. Herndon, I. A. Mackenzie, D. C. Green, J. P. Kuenen, S. Reis, and M. Vieno, 2018: Modelling carbonaceous aerosol from residential solid fuel burning with different assumptions for emissions. Atmos. Chem. Phys., 18, 4497-4518, https://doi.org/10.5194/acp-18-4497-2018
Poschlod, B., \. Hodnebrog, R. R. Wood, K. Alterskjær, R. Ludwig, G. Myhre, and J. Sillmann, 2018: Comparison and evaluation of statistical rainfall disaggregation and high-resolution dynamical downscaling over complex terrain. J. Hydrometeor., 19, 1973-1982, https://doi.org/10.1175/JHM-D-18-0132.1
Prakash, K. R., T. Nigam, and V. Pant, 2018: Estimation of oceanic subsurface mixing under a severe cyclonic storm using a coupled atmosphere-ocean-wave model. Ocean Sci., 14, 259-272, https://doi.org/10.5194/os-14-259-2018
Sun, F., Y. Ma, Z. Hu, M. Li, G. Tartari, F. Salerno, T. Gerken, P. Bonasoni, P. Cristofanelli, and E. Vuillermoz, 2018: Mechanism of daytime strong winds on the northern slopes of Himalayas, near Mount Everest: Observation and simulation. J. Appl. Meteor. Clmatol., 57, 255-272, https://doi.org/10.1175/JAMC-D-16-0409.1
Taniguchi, K., 2018: A Simple Ensemble Simulation Technique for Assessment of Future Variations in Specific High-Impact Weather Events. J. Geophys. Res. Atmos., 123, 3443-3461, https://doi.org/10.1002/2017JD027928
Tran Anh, Q., and K. Taniguchi, 2018: Coupling dynamical and statistical downscaling for high-resolution rainfall forecasting: case study of the Red River Delta, Vietnam. Prog Earth Planet Sci, 5, https://doi.org/10.1186/s40645-018-0185-6
Vernon, C. J., R. Bolt, T. Canty, and R. A. Kahn, 2018: The impact of MISR-derived injection height initialization on wildfire and volcanic plume dispersion in the HYSPLIT model. Atmos. Meas. Tech., 11, 6289-6307, https://doi.org/10.5194/amt-11-6289-2018
Wang, C., Z. Wang, and J. Yang, 2018: Cooling Effect of Urban Trees on the Built Environment of Contiguous United States. Earth's Future, 6, 1066-1081, https://doi.org/10.1029/2018EF000891
Xu, Z., A. M. Rhoades, H. Johansen, P. A. Ullrich, and W. D. Collins, 2018: An intercomparison of GCM and RCM dynamical downscaling for characterizing the hydroclimatology of California and Nevada. J. Hydrometeor., 19, 1485-1506, https://doi.org/10.1175/JHM-D-17-0181.1
Yamada, K., and N. Hirasawa, 2018: Analysis of a Record-Breaking Strong Wind Event at Syowa Station in January 2015. J. Geophys. Res. Atmos., 123, 13,643-13,657, https://doi.org/10.1029/2018JD028877
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Zhao, N., S. Iwasaki, M. Yamamoto, and A. Isobe, 2018: Modulation of Extratropical Cyclones by Previous Cyclones via the Sea Surface Temperature Anomaly Over the Sea of Japan in Winter. J. Geophys. Res. Atmos., 123, 6312-6330, https://doi.org/10.1029/2017jd027503
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2017
Acosta, R. P., and M. Huber, 2017: The neglected Indo‐Gangetic Plains low‐level jet and its importance for moisture transport and precipitation during the peak summer monsoon. Geophys. Res. Lett., 44, 8601-8610, https://doi.org/10.1002/2017GL074440
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Kim, B., J. Hong, S. Jun, X. Zhang, H. Kwon, S. Kim, J. Kim, S. Kim, and H. Kim, 2017: Major cause of unprecedented Arctic warming in January 2016: Critical role of an Atlantic windstorm. Sci Rep, 7, https://doi.org/10.1038/srep40051
Kim, H. C., S. Kim, B. Kim, C. Jin, S. Hong, R. Park, S. Son, C. Bae, M. Bae, C. Song, and A. Stein, 2017: Recent increase of surface particulate matter concentrations in the Seoul Metropolitan Area, Korea. Sci Rep, 7, https://doi.org/10.1038/s41598-017-05092-8
Kosmopoulos, P. G., S. Kazadzis, M. Taylor, E. Athanasopoulou, O. Speyer, P. I. Raptis, E. Marinou, E. Proestakis, S. Solomos, E. Gerasopoulos, V. Amiridis, A. Bais, and C. Kontoes, 2017: Dust impact on surface solar irradiance assessed with model simulations, satellite observations and ground-based measurements. Atmos. Meas. Tech., 10, 2435-2453, https://doi.org/10.5194/amt-10-2435-2017
Kutty, G., and K. Gohil, 2017: The role of mid-level vortex in the intensification and weakening of tropical cyclones. J. Earth Sys. Sci., 126, https://doi.org/10.1007/s12040-017-0879-y
León-Cruz, J. F., N. Carbajal, and L. F. Pineda-Martínez, 2017: Meteorological analysis of the tornado in Ciudad Acuña, Coahuila State, Mexico, on May 25, 2015. Nat. Hazards, 89, 423-439, https://doi.org/10.1007/s11069-017-2972-6
Li, Y., K. Szeto, R. E. Stewart, J. M. Thériault, L. Chen, B. Kochtubajda, A. Liu, S. Boodoo, R. Goodson, C. Mooney, and S. Kurkute, 2017: A numerical study of the June 2013 flood-producing extreme rainstorm over Southern Alberta. J. Hydrometeor., 18, 2057-2078, https://doi.org/10.1175/JHM-D-15-0176.1
Lin, L., A. M. Ebtehaj, A. N. Flores, S. Bastola, and R. L. Bras, 2017: Combined assimilation of satellite precipitation and soil moisture: A case study using TRMM and SMOS data. Mon. Wea. Rev., 145, 4997-5014, https://doi.org/10.1175/MWR-D-17-0125.1
Lu, L., K. Sasa, W. Sasaki, D. Terada, T. Kano, and T. Mizojiri, 2017: Rough wave simulation and validation using onboard ship motion data in the Southern Hemisphere to enhance ship weather routing. Ocean Eng., 144, 61-77, https://doi.org/10.1016/j.oceaneng.2017.08.037
Michaelis, A. C., J. Willison, G. M. Lackmann, and W. A. Robinson, 2017: Changes in winter North Atlantic extratropical cyclones in high-resolution regional pseudo-global warming simulations. J. Clim., 30, 6905-6925, https://doi.org/10.1175/JCLI-D-16-0697.1
Ramakrishna, S. S., V. Brahmananda Rao, B. R. Srinivasa Rao, D. Hari Prasad, N. Nanaji Rao, and R. Panda, 2017: A study of 2014 record drought in India with CFSv2 model: role of water vapor transport. Clim Dyn, 49, 297-312, https://doi.org/10.1007/s00382-016-3343-9
Shepherd, T. J., and K. J. Walsh, 2017: Sensitivity of hurricane track to cumulus parameterization schemes in the WRF model for three intense tropical cyclones: impact of convective asymmetry. Meteor. Atmos. Phys., 129, 345-374, https://doi.org/10.1007/s00703-016-0472-y
Wang, Y., K. Yang, Z. Pan, J. Qin, D. Chen, C. Lin, Y. Chen, Lazhu, W. Tang, M. Han, N. Lu, and H. Wu, 2017: Evaluation of precipitable water vapor from four satellite products and four reanalysis datasets against GPS measurements on the Southern Tibetan Plateau. J. Clim., 30, 5699-5713, https://doi.org/10.1175/JCLI-D-16-0630.1
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2016
Aleynik, D., A. C. Dale, M. Porter, and K. Davidson, 2016: A high resolution hydrodynamic model system suitable for novel harmful algal bloom modelling in areas of complex coastline and topography. Harmful Algae, 53, 102-117, https://doi.org/10.1016/j.hal.2015.11.012
Bourikas, L., P. B. James, A. S. Bahaj, M. F. Jentsch, T. Shen, D. C. Chow, and J. Darkwa, 2016: Transforming typical hourly simulation weather data files to represent urban locations by using a 3D urban unit representation with micro-climate simulations. , 2, 7, https://doi.org/10.1186/s40984-016-0020-4
Brown, T., G. Mills, S. Harris, D. Podnar, H. Reinbold, and M. Fearon, 2016: A bias corrected WRF mesoscale fire weather dataset for Victoria, Australia 1972-2012. J. Southern Hemisphere Earth Sys. Sci., 66, 281-313, https://doi.org/10.22499/3.6603.004
Ferreyra, M. G., G. Curci, and M. Lanfri, 2016: First Implementation of the WRF-CHIMERE-EDGAR Modeling System Over Argentina. IEEE J. Selected Topics Appl. Earth Observations Remote Sens., 9, 5304-5314, https://doi.org/10.1109/JSTARS.2016.2588502
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Ots, R., D. E. Young, M. Vieno, L. Xu, R. E. Dunmore, J. D. Allan, H. Coe, L. R. Williams, S. C. Herndon, N. L. Ng, J. F. Hamilton, R. Bergström, C. Di Marco, E. Nemitz, I. A. Mackenzie, J. P. Kuenen, D. C. Green, S. Reis, and M. R. Heal, 2016: Simulating secondary organic aerosol from missing diesel-related intermediate-volatility organic compound emissions during the Clean Air for London (ClearfLo) campaign. Atmos. Chem. Phys., 16, 6453-6473, https://doi.org/10.5194/acp-16-6453-2016
Ots, R., M. Vieno, J. D. Allan, S. Reis, E. Nemitz, D. E. Young, H. Coe, C. Di Marco, A. Detournay, I. A. Mackenzie, D. C. Green, and M. R. Heal, 2016: Model simulations of cooking organic aerosol (COA) over the UK using estimates of emissions based on measurements at two sites in London. Atmos. Chem. Phys., 16, 13773-13789, https://doi.org/10.5194/acp-16-13773-2016
Taniguchi, K., 2016: Future changes in precipitation and water resources for Kanto Region in Japan after application of pseudo global warming method and dynamical downscaling. J. Hydrol. Regional Stud., 8, 287-303, https://doi.org/10.1016/j.ejrh.2016.10.004
Tolentino, J. T., M. V. Rejuso, L. C. Inocencio, M. C. Ang, and G. Bagtasa, 2016: Effect of horizontal and vertical resolution for wind resource assessment in Metro Manila, Philippines using Weather Research and Forecasting (WRF) model. Proceedings of SPIE - The International Society for Optical Engineering, SPIE, https://doi.org/10.1117/12.2241952
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Yang, J. X., D. S. McKague, and C. S. Ruf, 2016: Boreal, Temperate, and Tropical Forests as Vicarious Calibration Sites for Spaceborne Microwave Radiometry. IEEE Transactions on Geoscience Remote Sens., 54, 1035-1051, https://doi.org/10.1109/TGRS.2015.2472532
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2015
Aouizerats, B., G. R. van der Werf, R. Balasubramanian, and R. Betha, 2015: Importance of transboundary transport of biomass burning emissions to regional air quality in Southeast Asia during a high fire event. Atmos. Chem. Phys., 15, 363-373, https://doi.org/10.5194/acp-15-363-2015
Campos, E., and J. Wang, 2015: Numerical simulation and analysis of the April 2013 Chicago Floods. Journal of Hydrology, 531, 454-474, https://doi.org/10.1016/j.jhydrol.2015.09.004
Huang, H., H. Liu, J. Huang, W. Mao, and X. Bi, 2015: Atmospheric boundary layer structure and turbulence during sea fog on the Southern China Coast. Mon. Wea. Rev., 143, 1907-1923, https://doi.org/10.1175/MWR-D-14-00207.1
Krusche, N., C. Peralta, C. Chang, and B. Stoevesandt, 2015: Wind Power Energy in Southern Brazil: Evaluation using a Mesoscale Meteorological Model. Energy Procedia, Elsevier Ltd, 164-168, https://doi.org/10.1016/j.egypro.2015.07.890
Marín, J. C., D. Pozo, and M. Curé, 2015: Estimating and forecasting the precipitable water vapor from GOES satellite data at high altitude sites. Astronomy and Astrophysics, 573, https://doi.org/10.1051/0004-6361/201424460
Meredith, E. P., V. A. Semenov, D. Maraun, W. Park, and A. Chernokulsky, 2015: Crucial role of Black Sea warming in amplifying the 2012 Krymsk precipitation extreme. Nature Geosci, 8, 615-619, https://doi.org/10.1038/ngeo2483
Nakamura, R., O. Takahiro, T. Shibayama, E. Miguel, and H. Takagi, 2015: Evaluation of Storm Surge Caused by Typhoon Yolanda (2013) and Using Weather - Storm Surge - Wave - Tide Model. Procedia Engineering, 116, 373-380, https://doi.org/10.1016/j.proeng.2015.08.306
Pohl, E., R. Gloaguen, and R. Seiler, 2015: Remote Sensing-Based Assessment of the Variability of Winter and Summer Precipitation in the Pamirs and Their Effects on Hydrology and Hazards Using Harmonic Time Series Analysis. Remote Sensing, 7, 9727-9752, https://doi.org/10.3390/rs70809727
Srinivas, C. V., D. Hari Prasad, D. V. Bhaskar Rao, R. Baskaran, and B. Venkatraman, 2015: Simulation of the Indian summer monsoon onset-phase rainfall using a regional model. Ann. Geophys., 33, 1097-1115, https://doi.org/10.5194/angeo-33-1097-2015
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Abstract: |
These NCEP FNL (Final) Operational Global Analysis data are on 1-degree by 1-degree grids prepared operationally every six hours. This product is from the Global Data Assimilation System (GDAS), which continuously collects observational data from the Global Telecommunications System (GTS), and other sources, for many analyses. The FNLs are made with the same model which NCEP uses in the Global Forecast System (GFS), but the FNLs are prepared about an hour or so after the GFS is initialized. The FNLs are delayed so that more observational data can be used. The GFS is run earlier in support of time critical forecast needs, and uses the FNL from the previous 6 hour cycle as part of its initialization. The analyses are available on the surface, at 26 mandatory (and other pressure) levels from 1000 millibars to 10 millibars, in the surface boundary layer and at some sigma layers, the tropopause and a few others. Parameters include surface pressure, sea level pressure, geopotential height, temperature, sea surface temperature, soil values, ice cover, relative humidity, u- and v- winds, vertical motion, vorticity and ozone. The archive time series is continuously extended to a near-current date. It is not maintained in real-time. |
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Temporal Range: |
1999-07-30 18:00 +0000 to 2022-12-31 18:00 +0000 (Entire dataset)
Period details by dataset productPeriod details by dataset product: 1999-07-30 18:00 +0000 to 1999-12-31 18:00 +0000 (GRIB1 6 HOURLY FILES for 1999)
2000-01-01 00:00 +0000 to 2000-12-31 18:00 +0000 (GRIB1 6 HOURLY FILES for 2000)
2001-01-01 00:00 +0000 to 2001-12-31 18:00 +0000 (GRIB1 6 HOURLY FILES for 2001)
2002-01-01 00:00 +0000 to 2002-12-31 18:00 +0000 (GRIB1 6 HOURLY FILES for 2002)
2003-01-01 00:00 +0000 to 2003-12-31 18:00 +0000 (GRIB1 6 HOURLY FILES for 2003)
2004-01-01 00:00 +0000 to 2004-12-31 18:00 +0000 (GRIB1 6 HOURLY FILES for 2004)
2005-01-01 00:00 +0000 to 2005-12-31 18:00 +0000 (GRIB1 6 HOURLY FILES for 2005)
2006-01-01 00:00 +0000 to 2006-12-31 18:00 +0000 (GRIB1 6 HOURLY FILES for 2006)
2007-01-01 00:00 +0000 to 2007-12-06 06:00 +0000 (GRIB1 6 HOURLY FILES for 2007)
2007-12-06 12:00 +0000 to 2007-12-31 18:00 +0000 (GRIB2 6 HOURLY FILES for 2007)
2008-01-01 00:00 +0000 to 2008-12-31 18:00 +0000 (GRIB2 6 HOURLY FILES for 2008)
2009-01-01 00:00 +0000 to 2009-12-31 18:00 +0000 (GRIB2 6 HOURLY FILES for 2009)
2010-01-01 00:00 +0000 to 2010-12-31 18:00 +0000 (GRIB2 6 HOURLY FILES for 2010)
2011-01-01 00:00 +0000 to 2011-12-31 18:00 +0000 (GRIB2 6 HOURLY FILES for 2011)
2012-01-01 00:00 +0000 to 2012-12-31 18:00 +0000 (GRIB2 6 HOURLY FILES for 2012)
2013-01-01 00:00 +0000 to 2013-12-31 18:00 +0000 (GRIB2 6 HOURLY FILES for 2013)
2014-01-01 00:00 +0000 to 2014-12-31 18:00 +0000 (GRIB2 6 HOURLY FILES for 2014)
2015-01-01 00:00 +0000 to 2015-12-31 18:00 +0000 (GRIB2 6 HOURLY FILES for 2015)
2016-01-01 00:00 +0000 to 2016-12-31 18:00 +0000 (GRIB2 6 HOURLY FILES for 2016)
2017-01-01 00:00 +0000 to 2017-12-31 18:00 +0000 (GRIB2 6 HOURLY FILES for 2017)
2018-01-01 00:00 +0000 to 2018-12-31 18:00 +0000 (GRIB2 6 HOURLY FILES for 2018)
2019-01-01 00:00 +0000 to 2019-12-31 18:00 +0000 (GRIB2 6 HOURLY FILES for 2019)
2020-01-01 00:00 +0000 to 2020-12-31 18:00 +0000 (GRIB2 6 HOURLY FILES for 2020)
2021-01-01 00:00 +0000 to 2021-12-31 18:00 +0000 (GRIB2 6 HOURLY FILES for 2021)
2022-01-01 00:00 +0000 to 2022-12-31 18:00 +0000 (GRIB2 6 HOURLY FILES for 2022)
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Updates: | Daily | ||||||||||||||||||||||||||||
Variables: |
Variables by dataset productVariables by dataset product:
Variable list for GRIB1 6 HOURLY FILES 1999.07.30 to 2007.12.06Variable list for GRIB1 6 HOURLY FILES 1999.07.30 to 2007.12.06: 5-wave geopotential height 5-wave geopotential height anomaly Absolute vorticity Best (4 layer) lifted index Cloud water Cloud water mixing ratio Convective available potential energy Convective inhibition Geopotential height Geopotential height anomaly Ice concentration Ice cover Land cover Maximum temperature Minimum temperature Ozone mixing ratio Planetary boundary layer height Potential temperature Precipitable water Pressure Pressure reduced to MSL Relative humidity Specific humidity Surface lifted index Temperature Total cloud cover Total ozone u-component of wind v-component of wind Vertical speed shear Vertical velocity (pressure) Volumetric soil moisture content Water equivalent of accumulated snow depth Variable list for GRIB2 6 HOURLY FILES 2007.12.06 to currentVariable list for GRIB2 6 HOURLY FILES 2007.12.06 to current: 5-wave geopotential height 5-wave geopotential height anomaly Absolute vorticity Apparent temperature Best (4 layer) lifted index Categorical freezing rain (yes=1; no=0) Categorical ice pellets (yes=1; no=0) Categorical rain (yes=1; no=0) Categorical snow (yes=1; no=0) Cloud water Cloud water mixing ratio Convective available potential energy Convective inhibition Derived radar reflectivity Dewpoint temperature Field capacity Frictional velocity Geopotential height Geopotential height anomaly Graupel Haines index High cloud cover ICAO standard atmosphere reference height Ice cover Ice growth rate Ice temperature Ice thickness Ice water mixing ratio Land cover (0=sea, 1=land) Land-sea coverage (nearest neighbor) [land=1, sea=0] Liquid volumetric soil moisture (non-frozen) Low cloud cover Maximum/Composite radar reflectivity Medium cloud cover MSLP (Eta model reduction) Ozone mixing ratio Percent frozen precipitation Planetary boundary layer height Plant canopy surface water Potential evaporation rate Potential temperature Precipitable water Precipitation rate Pressure Pressure of level from which parcel was lifted Pressure reduced to MSL Rain water mixing ratio Relative humidity Snow depth Snow water mixing ratio Soil temperature Soil type Specific humidity Storm relative helicity Sunshine duration Surface lifted index Surface roughness Temperature Total cloud cover Total ozone u-component of storm motion u-component of wind v-component of storm motion v-component of wind Vegetation Ventilation rate Vertical speed shear Vertical velocity (geometric) Vertical velocity (pressure) Visibility Volumetric soil moisture content Water equivalent of accumulated snow depth Wilting point Wind speed (gust) |
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Vertical Levels: | |||||||||||||||||||||||||||||
Data Types: | Grid | ||||||||||||||||||||||||||||
Spatial Coverage: |
Longitude Range: Westernmost=180W Easternmost=180E Latitude Range: Southernmost=90S Northernmost=90N Detailed coverage informationDetailed coverage information: 1° x 1° from 0E to 1W and 90N to 90S (360 x 181 Longitude/Latitude) 1° x 1° from 0E to 359E and 90N to 90S (360 x 181 Longitude/Latitude)
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Data Contributors: |
DOC/NOAA/NWS/NCEP National Centers for Environmental Prediction, National Weather Service, NOAA, U.S. Department of Commerce
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Related Resources: |
NMC EMC Model Documentation NCEP Global Parallel System Documentation MRF/AVN/GDAS CHANGES SINCE 1991 THE NATIONAL WEATHER SERVICE GATEWAY ... HISTORY ... NCAR Command Language GRIB to NetCDF file structure description Ask NCEP science questions about the FNL dataset through this link NCEP Grid-Point Statistical Interpolation (GSI) homepage WRF User Support |
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WRF Preprocessing System (WPS): | The GRIB-formatted data in this dataset can be used to initialize the Weather Research and Forecasting (WRF) Model. WRF Vtables | ||||||||||||||||||||||||||||
How to Cite This Dataset:
RIS BibTeX
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National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce. 2000, updated daily. NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. https://doi.org/10.5065/D6M043C6. Accessed† dd mmm yyyy.
Get a customized data citation (must be signed in)†Please fill in the "Accessed" date with the day, month, and year (e.g. - 5 Aug 2011) you last accessed the data from the RDA. Bibliographic citation shown in style |
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Total Volume: |
708.74 GB (Entire dataset) Volume details by dataset productVolume details by dataset product: GRIB1 6 HOURLY FILES 1999.07.30 to 2007.12.06: 277.08 GB GRIB2 6 HOURLY FILES 2007.12.06 to current: 431.66 GB
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Metadata Record: | Display in
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Data License: | This work is licensed under a Creative Commons Attribution 4.0 International License. |