Sensitivity Analysis of the WRF Model: Wind-Resource Assessment for Complex Terrain

Sergio Fernández-González Department of Earth Physics, Astronomy and Astrophysics II, Faculty of Physics, Complutense University of Madrid, Madrid, Spain

Search for other papers by Sergio Fernández-González in
Current site
Google Scholar
PubMed
Close
,
María Luisa Martín Department of Applied Mathematics, Faculty of Computer Engineering, University of Valladolid, Segovia, Spain

Search for other papers by María Luisa Martín in
Current site
Google Scholar
PubMed
Close
,
Eduardo García-Ortega Atmospheric Physics Group, Instituto de Matemática Interdisciplinar, University of León, León, Spain

Search for other papers by Eduardo García-Ortega in
Current site
Google Scholar
PubMed
Close
,
Andrés Merino Atmospheric Physics Group, Instituto de Matemática Interdisciplinar, University of León, León, Spain

Search for other papers by Andrés Merino in
Current site
Google Scholar
PubMed
Close
,
Jesús Lorenzana Supercomputing Center of Castile and León, University of León, León, Spain

Search for other papers by Jesús Lorenzana in
Current site
Google Scholar
PubMed
Close
,
José Luis Sánchez Atmospheric Physics Group, Instituto de Matemática Interdisciplinar, University of León, León, Spain

Search for other papers by José Luis Sánchez in
Current site
Google Scholar
PubMed
Close
,
Francisco Valero Department of Earth Physics, Astronomy and Astrophysics II, Faculty of Physics, Complutense University of Madrid, Madrid, Spain

Search for other papers by Francisco Valero in
Current site
Google Scholar
PubMed
Close
, and
Javier Sanz Rodrigo National Renewable Energy Centre (CENER), Sarriguren, Spain

Search for other papers by Javier Sanz Rodrigo in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Wind energy requires accurate forecasts for adequate integration into the electric grid system. In addition, global atmospheric models are not able to simulate local winds in complex terrain, where wind farms are sometimes placed. For this reason, the use of mesoscale models is vital for estimating wind speed at wind turbine hub height. In this regard, the Weather Research and Forecasting (WRF) Model allows a user to apply different initial and boundary conditions as well as physical parameterizations. In this research, a sensitivity analysis of several physical schemes and initial and boundary conditions was performed for the Alaiz mountain range in the northern Iberian Peninsula, where several wind farms are located. Model performance was evaluated under various atmospheric stabilities and wind speeds. For validation purposes, a mast with anemometers installed at 40, 78, 90, and 118 m above ground level was used. The results indicate that performance of the Global Forecast System analysis and European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim) as initial and boundary conditions was similar, although each performed better under certain meteorological conditions. With regard to physical schemes, there is no single combination of parameterizations that performs best during all weather conditions. Nevertheless, some combinations have been identified as inefficient, and therefore their use is discouraged. As a result, the validation of an ensemble prediction system composed of the best 12 deterministic simulations shows the most accurate results, obtaining relative errors in wind speed forecasts that are <15%.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Sergio Fernández-González, sefern04@ucm.es

Abstract

Wind energy requires accurate forecasts for adequate integration into the electric grid system. In addition, global atmospheric models are not able to simulate local winds in complex terrain, where wind farms are sometimes placed. For this reason, the use of mesoscale models is vital for estimating wind speed at wind turbine hub height. In this regard, the Weather Research and Forecasting (WRF) Model allows a user to apply different initial and boundary conditions as well as physical parameterizations. In this research, a sensitivity analysis of several physical schemes and initial and boundary conditions was performed for the Alaiz mountain range in the northern Iberian Peninsula, where several wind farms are located. Model performance was evaluated under various atmospheric stabilities and wind speeds. For validation purposes, a mast with anemometers installed at 40, 78, 90, and 118 m above ground level was used. The results indicate that performance of the Global Forecast System analysis and European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim) as initial and boundary conditions was similar, although each performed better under certain meteorological conditions. With regard to physical schemes, there is no single combination of parameterizations that performs best during all weather conditions. Nevertheless, some combinations have been identified as inefficient, and therefore their use is discouraged. As a result, the validation of an ensemble prediction system composed of the best 12 deterministic simulations shows the most accurate results, obtaining relative errors in wind speed forecasts that are <15%.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Sergio Fernández-González, sefern04@ucm.es
Save
  • Arroyo, R. C., J. S. Rodrigo, and P. Gankarski, 2014: Modelling of atmospheric boundary-layer flow in complex terrain with different forest parameterizations. J. Phys. Conf. Ser., 524, 012119, https://doi.org/10.1088/1742-6596/524/1/012119.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buizza, R., 1997: Potential forecast skill of ensemble prediction and spread and skill distributions of the ECMWF ensemble prediction system. Mon. Wea. Rev., 125, 99119, https://doi.org/10.1175/1520-0493(1997)125<0099:PFSOEP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carvalho, D., A. Rocha, M. Gómez-Gesteira, and C. Silva Santos, 2014: WRF wind simulation and wind energy production estimates forced by different reanalyses: Comparison with observed data for Portugal. Appl. Energy, 117, 116126, https://doi.org/10.1016/j.apenergy.2013.12.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., and J. Dudhia, 2001: Coupling and advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569585, https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chmel, A., 2008: The role of the process heterogeneity in the critical dynamics of fracture. Progress in Statistical Mechanics Research, J. S. Moreno, Ed., Nova Science Publishers, 257–293.

  • Chou, M.-D., M. J. Suárez, X.-Z. Liang, and M. M.-H. Yan, 2001: A thermal infrared radiation parameterization for atmospheric studies. NASA Tech. Rep. Series on Global Modeling and Data Assimilation, Vol. 19, Goddard Space Flight Center, NASA/TM-2001-104606, 56 pp.

  • Costa, A., A. Crespo, J. Navarro, G. Lizcano, H. Madsen, and E. Feitosa, 2008: A review on the young history of the wind power short-term prediction. Renewable Sustainable Energy Rev., 12, 17251744, https://doi.org/10.1016/j.rser.2007.01.015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and S. M. Uppala, 2009: Variational bias correction of satellite radiance data in the ERA-Interim reanalysis. Quart. J. Roy. Meteor. Soc., 135, 18301841, https://doi.org/10.1002/qj.493.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deppe, A. J., W. A. Gallus Jr., and E. S. Takle, 2013: A WRF ensemble for improved wind speed forecasts at turbine height. Wea. Forecasting, 28, 212228, https://doi.org/10.1175/WAF-D-11-00112.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Draxl, C., A. N. Hahmann, A. Peña, and G. Giebel, 2014: Evaluating winds and vertical wind shear from weather research and forecasting model forecasts using seven planetary boundary layer schemes. Wind Energy, 17, 3955, https://doi.org/10.1002/we.1555.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dudhia, J., 1989: Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 30773107, https://doi.org/10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ellis, N., R. Davy, and A. Troccoli, 2015: Predicting wind power variability events using different statistical methods driven by regional atmospheric model output. Wind Energy, 18, 16111628, https://doi.org/10.1002/we.1779.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Evans, J. P., M. Ekström, and F. Ji, 2012: Evaluating the performance of a WRF physics ensemble over south-east Australia. Climate Dyn., 39, 12411258, https://doi.org/10.1007/s00382-011-1244-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fernández-González, S., F. Valero, J. L. Sánchez, E. Gascón, L. López, E. García-Ortega, and A. Merino, 2015: Numerical simulations of snowfall events: Sensitivity analysis of physical parameterizations. J. Geophys. Res. Atmos., 120, 10 13010 148, https://doi.org/10.1002/2015JD023793.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fernández-González, S., M. L. Martín, A. Merino, J. L. Sánchez, and F. Valero, 2017: Uncertainty quantification and predictability of wind speed over the Iberian Peninsula. J. Geophys. Res. Atmos., 122, 38773890, https://doi.org/10.1002/2017JD026533.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fujita, T., D. J. Stensrud, and D. C. Dowell, 2007: Surface data assimilation using an ensemble Kalman filter approach with initial condition and model physics uncertainties. Mon. Wea. Rev., 135, 18461868, https://doi.org/10.1175/MWR3391.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • García-Ortega, E., J. Lorenzana, A. Merino, S. Fernández-González, L. López, and J. L. Sánchez, 2017: Performance of multi-physics ensembles in convective precipitation events over northeastern Spain. Atmos. Res., 190, 5567, https://doi.org/10.1016/j.atmosres.2017.02.009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gneiting, T., and A. E. Raftery, 2007: Strictly proper scoring rules, prediction, and estimation. J. Amer. Stat. Assoc., 102, 359378, https://doi.org/10.1198/016214506000001437.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gneiting, T., A. E. Raftery, A. H. Westveld, and T. Goldman, 2005: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Wea. Rev., 133, 10981118, https://doi.org/10.1175/MWR2904.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gneiting, T., F. Balabdaoui, and A. E. Raftery, 2007: Probabilistic forecasts, calibration and sharpness. J. Roy. Stat. Soc., 69B, 243268, https://doi.org/10.1111/j.1467-9868.2007.00587.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grimit, E. P., and C. F. Mass, 2002: Initial results of a mesoscale short-range ensemble forecasting system over the Pacific Northwest. Wea. Forecasting, 17, 192205, https://doi.org/10.1175/1520-0434(2002)017<0192:IROAMS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hally, A., E. Richard, and V. Ducrocq, 2014: An ensemble study of HyMeX IOP6 and IOP7a: Sensitivity to physical and initial and boundary condition uncertainties. Nat. Hazards Earth Syst. Sci., 14, 10711084, https://doi.org/10.5194/nhess-14-1071-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., 2001: Interpretation of rank histograms for verifying ensemble forecasts. Mon. Wea. Rev., 129, 550560, https://doi.org/10.1175/1520-0493(2001)129<0550:IORHFV>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., S. L. Mullen, C. Snyder, Z. Toth, and D. P. Baumhefner, 2000: Ensemble forecasting in the short to medium range: Report from a workshop. Bull. Amer. Meteor. Soc., 81, 26532664, https://doi.org/10.1175/1520-0477(2000)081<2653:EFITST>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hari Prasad, K. B. R. R., C. V. Srinivas, T. N. Rao, C. V. Naidu, and R. Baskaran, 2017: Performance of WRF in simulating terrain induced flows and atmospheric boundary layer characteristics over the tropical station Gadanki. Atmos. Res., 185, 101117, https://doi.org/10.1016/j.atmosres.2016.10.020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 23182341, https://doi.org/10.1175/MWR3199.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hou, D., E. Kalnay, and K. K. Droegemeier, 2001: Objective verification of the SAMEX ’98 ensemble forecasts. Mon. Wea. Rev., 129, 7391, https://doi.org/10.1175/1520-0493(2001)129<0073:OVOTSE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jerez, S., J. P. Montavez, P. Jimenez-Guerrero, J. J. Gomez-Navarro, R. Lorente-Plazas, and E. Zorita, 2013: A multi-physics ensemble of present-day climate regional simulations over the Iberian Peninsula. Climate Dyn., 40, 30233046, https://doi.org/10.1007/s00382-012-1539-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, A., and X. Wang, 2012: Verification and calibration of neighborhood and object-based probabilistic precipitation forecasts from a multimodel convection-allowing ensemble. Mon. Wea. Rev., 140, 30543077, https://doi.org/10.1175/MWR-D-11-00356.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Juban, J., N. Siebert, and G. N. Kariniotakis, 2007: Probabilistic short-term wind power forecasting for the optimal management of wind generation. Proc. 2007 Power Tech Conf., Lausanne, Switzerland, IEEE, 683–688, https://doi.org/10.1109/PCT.2007.4538398.

    • Crossref
    • Export Citation
  • Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kieu, C., P. T. Minh, and H. T. Mai, 2014: An application of the multi-physics ensemble Kalman filter to typhoon forecast. Pure Appl. Geophys., 171, 14731497, https://doi.org/10.1007/s00024-013-0681-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Komaromi, W. A., and S. J. Majumdar, 2014: Ensemble-based error and predictability metrics associated with tropical cyclogenesis. Part I: Basinwide perspective. Mon. Wea. Rev., 142, 28792898, https://doi.org/10.1175/MWR-D-13-00370.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kunz, M., S. Mohr, M. Rauthe, R. Lux, and C. Kottmeier, 2010: Assessment of extreme wind speeds from regional climate models—Part 1: Estimation of return values and their evaluation. Nat. Hazards Earth Syst. Sci., 10, 907922, https://doi.org/10.5194/nhess-10-907-2010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, J. A., W. C. Kolczynski, T. C. McCandless, and S. E. Haupt, 2012: An objective methodology for configuring and down-selecting an NWP ensemble for low-level wind prediction. Mon. Wea. Rev., 140, 22702286, https://doi.org/10.1175/MWR-D-11-00065.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Y., and Coauthors, 2011: Wind energy forecasting with the NCAR RTFDDA and ensemble RTFDDA systems. Second Conf. on Weather, Climate, and the New Energy Economy, Seattle, WA, Amer. Meteor. Soc., 9B.6, http://ams.confex.com/ams/91Annual/webprogram/Paper186591.html.

  • Mass, C. F., D. Ovens, K. Westrick, and B. A. Colle, 2002: Does increasing horizontal resolution produce more skillful forecasts? Bull. Amer. Meteor. Soc., 83, 407430, https://doi.org/10.1175/1520-0477(2002)083<0407:DIHRPM>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McMurdie, L. A., and B. Ancell, 2014: Predictability characteristics of landfalling cyclones along the North American west coast. Mon. Wea. Rev., 142, 301319, https://doi.org/10.1175/MWR-D-13-00141.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • MEASNET, 2009: Cup anemometer calibration procedure. Version 2, 9 pp., http://www.measnet.com/wp-content/uploads/2011/06/measnet_anemometer_calibration_v2_oct_2009.pdf.

  • Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102, 16 66316 682, https://doi.org/10.1029/97JD00237.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nakanishi, M., and H. Niino, 2006: An improved Mellor–Yamada level-3 model: Its numerical stability and application to a regional prediction of advection fog. Bound.-Layer Meteor., 119, 397407, https://doi.org/10.1007/s10546-005-9030-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Outten, S. D., and I. Esau, 2013: Extreme winds over Europe in the ENSEMBLES regional climate models. Atmos. Chem. Phys., 13, 51635172, https://doi.org/10.5194/acp-13-5163-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petersen, E. L., I. Troen, H. E. Jørgensen, and J. Mann, 2014: The new European wind atlas. Energy Bull., 17, 3439.

  • Ritter, M., and L. Deckert, 2017: Site assessment, turbine selection, and local feed-in tariffs through the wind energy index. Appl. Energy, 185, 10871099, https://doi.org/10.1016/j.apenergy.2015.11.081.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saha, S., and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 10151057, https://doi.org/10.1175/2010BAMS3001.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sanz Rodrigo, J., F. Borbón Guillén, P. Gómez Arranz, M. S. Courtney, R. Wagner, and E. Dupont, 2013: Multi-site testing and evaluation of remote sensing instruments for wind energy applications. Renewable Energy, 53, 200210, https://doi.org/10.1016/j.renene.2012.11.020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shin, H. H., and S. Hong, 2011: Intercomparison of planetary boundary-layer parametrizations in the WRF model for a single day from CASES-99. Bound.-Layer Meteor., 139, 261281, https://doi.org/10.1007/s10546-010-9583-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Siuta, D., G. West, and R. Stull, 2017a: WRF hub-height wind forecast sensitivity to PBL scheme, grid length, and initial condition choice in complex terrain. Wea. Forecasting, 32, 493509, https://doi.org/10.1175/WAF-D-16-0120.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Siuta, D., G. West, R. Stull, and T. Nipen, 2017b: Calibrated probabilistic hub-height wind forecasts in complex terrain. Wea. Forecasting, 32, 555577, https://doi.org/10.1175/WAF-D-16-0137.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sivillo, J. K., J. E. Ahlquist, and Z. Toth, 1997: An ensemble forecasting primer. Wea. Forecasting, 12, 809818, https://doi.org/10.1175/1520-0434(1997)012<0809:AEFP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and J. B. Klemp, 2008: A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. J. Comput. Phys., 227, 34653485, https://doi.org/10.1016/j.jcp.2007.01.037.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smirnova, T. G., J. M. Brown, and S. G. Benjamin, 1997: Performance of different soil model configurations in simulating ground surface temperature and surface fluxes. Mon. Wea. Rev., 125, 18701884, https://doi.org/10.1175/1520-0493(1997)125<1870:PODSMC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Staid, A., P. Pinson, and S. D. Guikema, 2015: Probabilistic maximum-value wind prediction for offshore environments. Wind Energy, 18, 17251738, https://doi.org/10.1002/we.1787.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., and J. M. Fritsch, 1994: Mesoscale convective systems in weakly forced large-scale environments. Part II: Generation of a mesoscale initial condition. Mon. Wea. Rev., 122, 20682083, https://doi.org/10.1175/1520-0493(1994)122<2068:MCSIWF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall, 2008: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon. Wea. Rev., 136, 50955115, https://doi.org/10.1175/2008MWR2387.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torralba, V., F. J. Doblas-Reyes, D. MacLeod, I. Christel, and M. Davis, 2017: Seasonal climate prediction: A new source of information for the management of wind energy resources. J. Appl. Meteor. Climatol., 56, 12311247, https://doi.org/10.1175/JAMC-D-16-0204.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Toth, Z., 2001: Ensemble forecasting in WRF. Bull. Amer. Meteor. Soc., 82, 695697, https://doi.org/10.1175/1520-0477(2001)082<0695:MSEFIW>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Toth, Z., Y. Zhu, and T. Marchok, 2001: The use of ensembles to identify forecasts with small and large uncertainty. Wea. Forecasting, 16, 463477, https://doi.org/10.1175/1520-0434(2001)016<0463:TUOETI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Warner, T., 2011: Numerical Weather and Climate Predictions. Cambridge University Press, 526 pp.

  • Weigel, A. P., M. A. Liniger, and C. Appenzeller, 2008: Can multi-model combination really enhance the prediction skill of probabilistic ensemble forecasts? Quart. J. Roy. Meteor. Soc., 134, 241260, https://doi.org/10.1002/qj.210.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. 3rd ed. Elsevier, 676 pp.

    • Crossref
    • Export Citation
  • Zhong, S., and C. D. Whiteman, 2008: Downslope flows on a low-angle slope and their interactions with valley inversions. Part II: Numerical modeling. J. Appl. Meteor. Climatol., 47, 20392057, https://doi.org/10.1175/2007JAMC1670.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, Y., Z. Toth, R. Wobus, D. Richardson, and K. Mylne, 2002: The economic value of ensemble-based weather forecasts. Bull. Amer. Meteor. Soc., 83, 7383, https://doi.org/10.1175/1520-0477(2002)083<0073:TEVOEB>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 2517 699 98
PDF Downloads 2171 595 86