text:
2. Data collection and analysis
3. ARW-WRF runs
a. Horizontal variability in virtual potential temperature
b. Convective-scale structure and clouds
c. Mesoscale motions
d. Observational evidence of mesoscale circulations
Angevine, W. M., 1997: Errors in mean vertical velocities measured by boundary layer wind profilers. J. Atmos. Oceanic Technol., 14 , 565–569.
Angevine, W. M., 1997: Errors in mean vertical velocities measured by boundary layer wind profilers. J. Atmos. Oceanic Technol., 14, 565–569.)| false
Anthes, R. A., 1984: Enhancement of convective precipitation by mesoscale variations in vegetative covering in semiarid regions. J. Climate Appl. Meteor., 23 , 541–554.
Anthes, R. A., 1984: Enhancement of convective precipitation by mesoscale variations in vegetative covering in semiarid regions. J. Climate Appl. Meteor., 23, 541–554.)| false
Avissar, R., and T. Schmidt, 1998: An evaluation of the scale at which ground surface heat flux patchiness affects the convective boundary layer using large-eddy simulations. J. Atmos. Sci., 55 , 2666–2689.
Avissar, R., and T. Schmidt, 1998: An evaluation of the scale at which ground surface heat flux patchiness affects the convective boundary layer using large-eddy simulations. J. Atmos. Sci., 55, 2666–2689.)| false
Baidya Roy, S., and R. Avissar, 2000: Scales of response of the convective boundary layer to land-surface heterogeneity. Geophys. Res. Lett., 27 , 533–536.
Baidya Roy, S., and R. Avissar, 2000: Scales of response of the convective boundary layer to land-surface heterogeneity. Geophys. Res. Lett., 27, 533–536.)| false
Baidya Roy, S., C. P. Weaver, D. S. Nolan, and R. Avissar, 2003: A preferred scale for landscape forced mesoscale circulations? J. Geophys. Res., 108 , 8854. doi:10.1029/2002JD003097.
Baidya Roy, S., C. P. Weaver, D. S. Nolan, and R. Avissar, 2003: A preferred scale for landscape forced mesoscale circulations? J. Geophys. Res., 108, 8854. doi:10.1029/2002JD003097.)| false
Balaji, V., J-L. Redelsperger, and G. P. Klaasen, 1993: Mechanisms for the mesoscale organization of tropical cloud clusters in GATE Phase III. Part I: Shallow cloud bands. J. Atmos. Sci., 50 , 3571–3589.
Balaji, V., J-L. Redelsperger, and G. P. Klaasen, 1993: Mechanisms for the mesoscale organization of tropical cloud clusters in GATE Phase III. Part I: Shallow cloud bands. J. Atmos. Sci., 50, 3571–3589.)| false
Chen, F., and R. Avissar, 1994: The impact of land-surface wetness heterogeneity on mesoscale heat fluxes. J. Appl. Meteor., 33 , 1323–1340.
Chen, F., and R. Avissar, 1994: The impact of land-surface wetness heterogeneity on mesoscale heat fluxes. J. Appl. Meteor., 33, 1323–1340.)| false
Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129 , 569–585.
Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569–585.)| false
Chen, F., and Y. Zhang, 2009: On the coupling strength between the land surface and the atmosphere. Geophys. Res. Lett., 36 , L10404. doi:10.1029/2009GL037980.
Chen, F., and Y. Zhang, 2009: On the coupling strength between the land surface and the atmosphere. Geophys. Res. Lett., 36, L10404. doi:10.1029/2009GL037980.)| false
Chen, F., and Coauthors, 2007: Description and evaluation of the characteristics of the NCAR high-resolution land data assimilation system. J. Appl. Meteor. Climatol., 46 , 694–713.
Chen, F., and Coauthors, 2007: Description and evaluation of the characteristics of the NCAR high-resolution land data assimilation system. J. Appl. Meteor. Climatol., 46, 694–713.)| false
Clark, T. L., T. Hauf, and J. P. Kuettner, 1986: Convectively forced internal gravity waves: Results from two-dimensional experiments. Quart. J. Roy. Meteor. Soc., 112 , 899–926.
Clark, T. L., T. Hauf, and J. P. Kuettner, 1986: Convectively forced internal gravity waves: Results from two-dimensional experiments. Quart. J. Roy. Meteor. Soc., 112, 899–926.)| false
Conzemius, R. J., and E. Fedorovich, 2006: Dynamics of sheared convective boundary layer entrainment. Part I: Methodological background and large-eddy simulations. J. Atmos. Sci., 63 , 1151–1178.
Conzemius, R. J., and E. Fedorovich, 2006: Dynamics of sheared convective boundary layer entrainment. Part I: Methodological background and large-eddy simulations. J. Atmos. Sci., 63, 1151–1178.)| false
Conzemius, R. J., and E. Fedorovich, 2008: A case study of convective boundary layer development during IHOP_2002: Numerical simulations compared to observations. Mon. Wea. Rev., 136 , 2305–2320.
Conzemius, R. J., and E. Fedorovich, 2008: A case study of convective boundary layer development during IHOP_2002: Numerical simulations compared to observations. Mon. Wea. Rev., 136, 2305–2320.)| false
Coulter, R. L., and D. Holdridge, 1998: A procedure for the automatic estimation of mixed layer height. Proc. Eighth Atmospheric Radiation Measurement (ARM) Program Science Team Meeting, Tucson, AZ, Dept. of Energy Office of Energy Research, 177–180.
Coulter, R. L., and D. Holdridge, 1998: A procedure for the automatic estimation of mixed layer height. Proc. Eighth Atmospheric Radiation Measurement (ARM) Program Science Team Meeting, Tucson, AZ, Dept. of Energy Office of Energy Research, 177–180.)| false
Davis, C., and Coauthors, 2008: Prediction of landfalling hurricanes with the advanced hurricane WRF model. Mon. Wea. Rev., 136 , 1990–2005.
Davis, C., and Coauthors, 2008: Prediction of landfalling hurricanes with the advanced hurricane WRF model. Mon. Wea. Rev., 136, 1990–2005.)| false
Deardorff, J. W., 1972: Numerical investigation of neutral and unstable planetary boundary layers. J. Atmos. Sci., 29 , 91–115.
Deardorff, J. W., 1972: Numerical investigation of neutral and unstable planetary boundary layers. J. Atmos. Sci., 29, 91–115.)| false
Desai, A. R., K. J. Davis, C. J. Senff, S. Ismail, E. V. Browell, D. R. Stauffer, and B. P. Reen, 2006: A case study on the effects of heterogeneous soil moisture on mesoscale boundary-layer structure in the southern Great Plains, USA. Part I: Simple prognostic model. Bound.-Layer Meteor., 119 , 195–238.
Desai, A. R., K. J. Davis, C. J. Senff, S. Ismail, E. V. Browell, D. R. Stauffer, and B. P. Reen, 2006: A case study on the effects of heterogeneous soil moisture on mesoscale boundary-layer structure in the southern Great Plains, USA. Part I: Simple prognostic model. Bound.-Layer Meteor., 119, 195–238.)| false
Ek, M. B., K. E. Mitchell, Y. Lin, E. Rogers, P. Grummann, V. Koren, G. Gayno, and J. D. Tarpley, 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res., 108 , 8851. doi:10.1029/2002JD003296.
Ek, M. B., K. E. Mitchell, Y. Lin, E. Rogers, P. Grummann, V. Koren, G. Gayno, and J. D. Tarpley, 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res., 108, 8851. doi:10.1029/2002JD003296.)| false
Gorska, M., J. Vila-Guerau de Arellano, M. A. LeMone, and C. C. van Heerwaarden, 2008: Mean and flux horizontal variability of virtual potential temperature, moisture, and carbon dioxide: Aircraft observations and LES study. Mon. Wea. Rev., 136 , 4435–4451.
Gorska, M., J. Vila-Guerau de Arellano, M. A. LeMone, and C. C. van Heerwaarden, 2008: Mean and flux horizontal variability of virtual potential temperature, moisture, and carbon dioxide: Aircraft observations and LES study. Mon. Wea. Rev., 136, 4435–4451.)| false
Grossman, R. L., D. Yates, M. A. LeMone, M. L. Wesely, and J. Song, 2005: Observed effects of horizontal radiative surface temperature variations on the atmosphere over a Midwest watershed during CASES 97. J. Geophys. Res., 110 , D06117. doi:10.1029/2004JD004542.
Grossman, R. L., D. Yates, M. A. LeMone, M. L. Wesely, and J. Song, 2005: Observed effects of horizontal radiative surface temperature variations on the atmosphere over a Midwest watershed during CASES 97. J. Geophys. Res., 110, D06117. doi:10.1029/2004JD004542.)| false
Holton, J. R., 1972: An Introduction to Dynamic Meteorology. Academic Press, 319 pp.
Holton, J. R., 1972: An Introduction to Dynamic Meteorology. Academic Press, 319 pp.)| false
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 , 2318–2341.
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, 2318–2341.)| false
Kang, S., 2009: Temporal oscillations in the convective boundary layer forced by mesoscale surface heat-flux variations. Bound.-Layer Meteor., 132 , 59–81. doi:10.1007/s10546-009-9391-5.
Kang, S., 2009: Temporal oscillations in the convective boundary layer forced by mesoscale surface heat-flux variations. Bound.-Layer Meteor., 132, 59–81. doi:10.1007/s10546-009-9391-5.)| false
Kang, S., and K. J. Davis, 2008: The effects of mesoscale surface heterogeneity on the fair-weather convective atmospheric boundary layer. J. Atmos. Sci., 65 , 3197–3213.
Kang, S., and K. J. Davis, 2008: The effects of mesoscale surface heterogeneity on the fair-weather convective atmospheric boundary layer. J. Atmos. Sci., 65, 3197–3213.)| false
Kang, S., K. J. Davis, and M. A. LeMone, 2007: Observations of the ABL structures over a heterogeneous land surface during IHOP_2002. J. Hydrometeor., 8 , 221–244.
Kang, S., K. J. Davis, and M. A. LeMone, 2007: Observations of the ABL structures over a heterogeneous land surface during IHOP_2002. J. Hydrometeor., 8, 221–244.)| false
Klazura, G., and Coauthors, 2006: Atmospheric boundary layer measurements in south-central Kansas, 1997–2004. Bull. Amer. Meteor. Soc., 87 , 1319–1324.
Klazura, G., and Coauthors, 2006: Atmospheric boundary layer measurements in south-central Kansas, 1997–2004. Bull. Amer. Meteor. Soc., 87, 1319–1324.)| false
Knievel, J. C., G. H. Bryan, and J. P. Hacker, 2007: Explicit numerical diffusion in the WRF model. Mon. Wea. Rev., 135 , 3808–3824.
Knievel, J. C., G. H. Bryan, and J. P. Hacker, 2007: Explicit numerical diffusion in the WRF model. Mon. Wea. Rev., 135, 3808–3824.)| false
Krettenauer, K., and U. Schumann, 1992: Numerical simulation of turbulent convection over wavy terrain. J. Fluid Mech., 237 , 261–299.
Krettenauer, K., and U. Schumann, 1992: Numerical simulation of turbulent convection over wavy terrain. J. Fluid Mech., 237, 261–299.)| false
Lafore, J-P., and Coauthors, 1998: The Meso-NH atmospheric simulation system. Part I: Adiabatic formulation and control simulations. Ann. Geophys., 16 , 90–109.
Lafore, J-P., and Coauthors, 1998: The Meso-NH atmospheric simulation system. Part I: Adiabatic formulation and control simulations. Ann. Geophys., 16, 90–109.)| false
LeMone, M. A., 1973: The structure and dynamics of horizontal roll vortices in the planetary boundary layer. J. Atmos. Sci., 30 , 1077–1091.
LeMone, M. A., 1973: The structure and dynamics of horizontal roll vortices in the planetary boundary layer. J. Atmos. Sci., 30, 1077–1091.)| false
LeMone, M. A., and Coauthors, 2002: CASES-97: Late-morning warming and moistening of the convective boundary layer over the Walnut River watershed. Bound.-Layer Meteor., 104 , 1–52.
LeMone, M. A., and Coauthors, 2002: CASES-97: Late-morning warming and moistening of the convective boundary layer over the Walnut River watershed. Bound.-Layer Meteor., 104, 1–52.)| false
LeMone, M. A., and Coauthors, 2007: NCAR/CU surface, soil, and vegetation observations during the International H2O Project 2002 field campaign. Bull. Amer. Meteor. Soc., 88 , 65–81.
LeMone, M. A., and Coauthors, 2007: NCAR/CU surface, soil, and vegetation observations during the International H2O Project 2002 field campaign. Bull. Amer. Meteor. Soc., 88, 65–81.)| false
LeMone, M. A., F. Chen, M. Tewari, J. Dudhia, B. Geerts, Q. Miao, R. L. Coulter, and R. L. Grossman, 2010: Simulating the IHOP_2002 fair-weather CBL with the WRF-ARW–Noah modeling system. Part I: Surface fluxes and CBL structure and evolution along the eastern track. Mon. Wea. Rev., 138 , 722–744.
LeMone, M. A., F. Chen, M. Tewari, J. Dudhia, B. Geerts, Q. Miao, R. L. Coulter, and R. L. Grossman, 2010: Simulating the IHOP_2002 fair-weather CBL with the WRF-ARW–Noah modeling system. Part I: Surface fluxes and CBL structure and evolution along the eastern track. Mon. Wea. Rev., 138, 722–744.)| false
Lenschow, D. H., J. C. Wyngaard, and W. T. Pennell, 1980: Mean-field and second-moment budgets in a baroclinic, convective boundary layer. J. Atmos. Sci., 37 , 1313–1326.
Lenschow, D. H., J. C. Wyngaard, and W. T. Pennell, 1980: Mean-field and second-moment budgets in a baroclinic, convective boundary layer. J. Atmos. Sci., 37, 1313–1326.)| false
Letzel, M. O., and S. Raasch, 2003: Large eddy simulation of thermally induced oscillations in the convective boundary layer. J. Atmos. Sci., 60 , 2328–2341.
Letzel, M. O., and S. Raasch, 2003: Large eddy simulation of thermally induced oscillations in the convective boundary layer. J. Atmos. Sci., 60, 2328–2341.)| false
Lothon, M., F. Couvreux, S. Donier, F. Guichard, P. Lacarrere, D. H. Lenschow, J. Noilhan, and F. Said, 2007: Impact of coherent eddies on airborne measurements of vertical turbulent fluxes. Bound.-Layer Meteor., 124 , 425–447. doi:10.1007/s10546-007-9182-9.
Lothon, M., F. Couvreux, S. Donier, F. Guichard, P. Lacarrere, D. H. Lenschow, J. Noilhan, and F. Said, 2007: Impact of coherent eddies on airborne measurements of vertical turbulent fluxes. Bound.-Layer Meteor., 124, 425–447. doi:10.1007/s10546-007-9182-9.)| false
Mahrt, L., J. Sun, D. Vickers, J. I. MacPherson, J. R. Pederson, and R. L. Desjardins, 1994: Observations of fluxes and inland breezes over a heterogeneous surface. J. Atmos. Sci., 51 , 2484–2499.
Mahrt, L., J. Sun, D. Vickers, J. I. MacPherson, J. R. Pederson, and R. L. Desjardins, 1994: Observations of fluxes and inland breezes over a heterogeneous surface. J. Atmos. Sci., 51, 2484–2499.)| false
Miao, Q., and B. Geerts, 2007: Finescale vertical structure and dynamics of some dryline boundaries observed in IHOP. Mon. Wea. Rev., 135 , 4161–4184.
Miao, Q., and B. Geerts, 2007: Finescale vertical structure and dynamics of some dryline boundaries observed in IHOP. Mon. Wea. Rev., 135, 4161–4184.)| false
Miao, S., and F. Chen, 2008: Formation of horizontal convective rolls in urban areas. Atmos. Res., 89 , 298–304. doi:10.1016/j.atmosres.2008.02.013.
Miao, S., and F. Chen, 2008: Formation of horizontal convective rolls in urban areas. Atmos. Res., 89, 298–304. doi:10.1016/j.atmosres.2008.02.013.)| false
Moeng, C-H., and P. P. Sullivan, 1994: A comparison of shear- and buoyancy-driven planetary boundary layer flows. J. Atmos. Sci., 51 , 999–1022.
Moeng, C-H., and P. P. Sullivan, 1994: A comparison of shear- and buoyancy-driven planetary boundary layer flows. J. Atmos. Sci., 51, 999–1022.)| false
Ogura, Y., and Y. Chen, 1977: A life history of an intense mesoscale convective system in Oklahoma. J. Atmos. Sci., 34 , 1458–1476.
Ogura, Y., and Y. Chen, 1977: A life history of an intense mesoscale convective system in Oklahoma. J. Atmos. Sci., 34, 1458–1476.)| false
Patton, E. G., P. P. Sullivan, and C-H. Moeng, 2005: The influence of idealized heterogeneity on wet and dry planetary boundary layers coupled to the land surface. J. Atmos. Sci., 62 , 2078–2097.
Patton, E. G., P. P. Sullivan, and C-H. Moeng, 2005: The influence of idealized heterogeneity on wet and dry planetary boundary layers coupled to the land surface. J. Atmos. Sci., 62, 2078–2097.)| false
Pielke, R. A., G. Dalu, J. Snook, T. Lee, and T. Kittell, 1991: Nonlinear influence of mesoscale land use on weather and climate. J. Climate, 4 , 1053–1069.
Pielke, R. A., G. Dalu, J. Snook, T. Lee, and T. Kittell, 1991: Nonlinear influence of mesoscale land use on weather and climate. J. Climate, 4, 1053–1069.)| false
Reen, B. P., D. R. Stauffer, K. J. Davis, and A. R. Desai, 2006: A case study on the effects of heterogeneous soil moisture on mesoscale boundary-layer structure in the southern Great Plains, USA. Part II: Mesoscale modelling. Bound.-Layer Meteor., 120 , 275–314.
Reen, B. P., D. R. Stauffer, K. J. Davis, and A. R. Desai, 2006: A case study on the effects of heterogeneous soil moisture on mesoscale boundary-layer structure in the southern Great Plains, USA. Part II: Mesoscale modelling. Bound.-Layer Meteor., 120, 275–314.)| false
Segal, M., R. Avissar, M. McCumber, and R. Pielke, 1988: Evaluation of vegetation effects on the generation and modification of mesoscale circulations. J. Atmos. Sci., 45 , 2268–2292.
Segal, M., R. Avissar, M. McCumber, and R. Pielke, 1988: Evaluation of vegetation effects on the generation and modification of mesoscale circulations. J. Atmos. Sci., 45, 2268–2292.)| false
Skamarock, W. C., 2004: Evaluating mesoscale NWP models using kinetic energy spectra. Mon. Wea. Rev., 132 , 3019–3032.
Skamarock, W. C., 2004: Evaluating mesoscale NWP models using kinetic energy spectra. Mon. Wea. Rev., 132, 3019–3032.)| false
Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. G. Powers, 2005: A description of the Advanced Research WRF version 2. NCAR Tech. Note TN-468+STR, 88 pp. [Available from NCAR, P.O. Box 3000, Boulder, CO 80307].
Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. G. Powers, 2005: A description of the Advanced Research WRF version 2. NCAR Tech. Note TN-468+STR, 88 pp. [Available from NCAR, P.O. Box 3000, Boulder, CO 80307].)| false
Souza, E. P., N. O. Renno, and M. A. F. Silva Dias, 2000: Convective circulations induced by surface heterogeneities. J. Atmos. Sci., 57 , 2915–2922.
Souza, E. P., N. O. Renno, and M. A. F. Silva Dias, 2000: Convective circulations induced by surface heterogeneities. J. Atmos. Sci., 57, 2915–2922.)| false
Strassberg, D., M. LeMone, T. Warner, and J. Alfieri, 2008: Comparison of observed 10-m wind speeds to those based on Monin–Obukhov similarity theory using IHOP_2002 aircraft and surface data. Mon. Wea. Rev., 136 , 964–972.
Strassberg, D., M. LeMone, T. Warner, and J. Alfieri, 2008: Comparison of observed 10-m wind speeds to those based on Monin–Obukhov similarity theory using IHOP_2002 aircraft and surface data. Mon. Wea. Rev., 136, 964–972.)| false
Sun, W-Y., and Y. Ogura, 1979: Boundary-layer forcing as a possible trigger to a squall-line formation. J. Atmos. Sci., 36 , 235–254.
Sun, W-Y., and Y. Ogura, 1979: Boundary-layer forcing as a possible trigger to a squall-line formation. J. Atmos. Sci., 36, 235–254.)| false
Taylor, C. M., D. J. Parker, and P. P. Harris, 2007: An observational case study of mesoscale atmospheric circulations induced by soil moisture. Geophys. Res. Lett., 34 , L15801. doi:10.1029/2007GL030572.
Taylor, C. M., D. J. Parker, and P. P. Harris, 2007: An observational case study of mesoscale atmospheric circulations induced by soil moisture. Geophys. Res. Lett., 34, L15801. doi:10.1029/2007GL030572.)| false
Trier, S. B., F. Chen, and K. W. Manning, 2004: A study of convection initiation in a mesoscale model using high-resolution land-surface initial conditions. Mon. Wea. Rev., 132 , 2954–2976.
Trier, S. B., F. Chen, and K. W. Manning, 2004: A study of convection initiation in a mesoscale model using high-resolution land-surface initial conditions. Mon. Wea. Rev., 132, 2954–2976.)| false
Walko, R. L., W. R. Cotton, and R. A. Pielke Sr., 1992: Large-eddy simulations of the effects of hilly terrain on the convective boundary layer. Bound.-Layer Meteor., 58 , 133–150.
Walko, R. L., W. R. Cotton, and R. A. Pielke Sr., 1992: Large-eddy simulations of the effects of hilly terrain on the convective boundary layer. Bound.-Layer Meteor., 58, 133–150.)| false
Weckwerth, T. M., T. W. Horst, and J. W. Wilson, 1999: An observational study of the evolution of horizontal convective rolls. Mon. Wea. Rev., 127 , 2160–2179.
Weckwerth, T. M., T. W. Horst, and J. W. Wilson, 1999: An observational study of the evolution of horizontal convective rolls. Mon. Wea. Rev., 127, 2160–2179.)| false
Weckwerth, T. M., and Coauthors, 2004: An overview of the International H2O Project (IHOP_2002) and some preliminary highlights. Bull. Amer. Meteor. Soc., 85 , 253–277.
Weckwerth, T. M., and Coauthors, 2004: An overview of the International H2O Project (IHOP_2002) and some preliminary highlights. Bull. Amer. Meteor. Soc., 85, 253–277.)| false
Weisman, M. L., W. C. Skamarock, and J. B. Klemp, 1997: The resolution dependence of explicitly modeled convective systems. Mon. Wea. Rev., 125 , 527–548.
Weisman, M. L., W. C. Skamarock, and J. B. Klemp, 1997: The resolution dependence of explicitly modeled convective systems. Mon. Wea. Rev., 125, 527–548.)| false
Wyngaard, J. C., 2004: Toward numerical modeling in the "Terra Incognita". J. Atmos. Sci., 61 , 1816–1826.
Wyngaard, J. C., 2004: Toward numerical modeling in the "Terra Incognita". J. Atmos. Sci., 61, 1816–1826.)| false
Wyngaard, J. C., L. J. Peltier, and S. Khanna, 1998: LES in the surface layer: Surface fluxes, scaling, and SGS modeling. J. Atmos. Sci., 55 , 1733–1754.
Wyngaard, J. C., L. J. Peltier, and S. Khanna, 1998: LES in the surface layer: Surface fluxes, scaling, and SGS modeling. J. Atmos. Sci., 55, 1733–1754.)| false
Young, G. S., D. A. R. Kristovich, M. R. Hjelmfelt, and R. C. Foster, 2002: Rolls, streets, waves, and more: A review of quasi-two-dimensional structures in the atmospheric boundary layer. Bull. Amer. Meteor. Soc., 83 , 997–1001.
Young, G. S., D. A. R. Kristovich, M. R. Hjelmfelt, and R. C. Foster, 2002: Rolls, streets, waves, and more: A review of quasi-two-dimensional structures in the atmospheric boundary layer. Bull. Amer. Meteor. Soc., 83, 997–1001.)| false
Eastern track, instrumentation, and land use. (top) Instrumentation superimposed on terrain contours (interval = 20 m) in the Walnut River watershed in southeast Kansas. Thick lines: outline of watershed, the Walnut River, and its tributaries; within the watershed, shading = grassland; no shading = mostly cropland. Thick dashed line: eastern flight track. Partial triangle (fine solid lines) connects the three ABLE radar wind profilers at Oxford (OXF; 37.27°N, 97.10°W); Beaumont (BEA; 37.63°N, 96.54°W), and Whitewater (not shown, but on the NW vertex of the triangle at 37.84°N, 96.19°W or 63 km NNW (azimuth 350°) of OXF. (bottom) Central and eastern tracks (dot–dash lines), superimposed on land use/land cover: light gray = grasslands; dark gray = cropland; white = urban; black = open water. Numbers and symbols in both frames correspond to surface flux sites.
(top) Soil type and (bottom) land use/land cover for inner domain (d03; Fig. 3) with state boundaries (red), profiler triangle, and eastern track (solid black lines) included. Soils near the eastern track: 8 = silty clay loam, 4 = silt loam; elsewhere in figure: 1 = sand, 2 = loamy sand, 3 = sandy loam, 6 = loam, 7 = sandy clay loam, 9 = clay loam, 10 = sandy clay, 11 = silty clay, 12 = clay, 13 = organic material, and 14 = water. For vegetation, near the eastern track: 10 = grasslands, 12 = croplands, 13 = urban, and 17 = water; other vegetation in figure: 4 = deciduous broadleaf forests and 16 = barren and sparsely vegetated.
Domains for ARW-WRF runs, with horizontal grid spacing. Outer domain (d01): 9 km; intermediate domain (d02): 3 km; inner domain (d03): 1 km. Innermost domains for sensitivity studies include the two 333-m grid spacing domains for 22 Jun (represented by solid rectangles d04E and d04W), and the rectangle used for the idealized runs using 20 Jun data (labeled "ideal"), which is dashed because (i) it varies in size according to grid spacing, and (ii) there are no outer domains for the idealized runs.
Noah LSM volumetric soil moisture distribution for level 1(0–10 cm) in inner domain d03 (Fig. 3) for coupled run, at (top) 1800 UTC 30 May and (bottom) 1800 UTC 22 Jun 2002. White areas represent open water, thin black lines are state boundaries, and thick black lines represent the profiler triangle and eastern track.
(a) CBL convective patterns, as reflected by (top) mid-CBL (level 5, around 500 m) vertical velocity in ARW-WRF (1800 UTC) for domain d03 (1-km grid) and (bottom) cloud patterns in GOES-8 (1809 UTC) image, for (left) 30 May and (right) 17 June. Model domain d03 and eastern track are included for scale and geographic reference. Values of −zi/L are for regional (grid scale) values of z0 for observations (ARW-WRF) from Table 4. (b) As in (a), but for cloud fields (vertically integrated liquid water) on 20 and 22 June from GOES-8 and ARW-WRF.
CBL convective patterns at 1830 UTC revealed by clear-air return (dBZ) from the WSR-88D radars at Wichita, KS (KICT), Vance Air Force Base, OK (KVNX), and Tulsa, OK (bottom right in each frame, center off the figure). County lines, flight track and profiler triangle are included for reference. The stronger (weaker) returns are associated with clear-air mode (precipitation mode).
Effect of grid spacing on CBL structure, as shown by mid-CBL (level 5, ∼500 m) vertical-velocity fields (m s−1) at 1800 UTC (a) from TS1 (Ts and volumetric soil moisture perturbations at atmospheric grid points, spaced 1 km apart) and (b) from TSmix1_0.2NFs (Ts and volumetric soil moisture perturbations on 1-km grid, atmosphere on 0.2-km grid).
Effect of volumetric soil moisture perturbations on CBL structure, as shown by mid-CBL (level 5, ∼500 m) vertical velocity (m s−1), for runs with no Knievel filter, at (top) 1800 and (bottom) 2100 UTC. (left) TS1NF (Ts and soil moisture perturbations at 1-km atmosphere grid spacing). (right) T1NF (like TS1NF but without soil moisture perturbations).
Aircraft-based profiles of along-track divergence of the along-track wind component, for the four IHOP_2002 days. Solid circles: profiles at 1830 UTC; open circles: profiles at 1930 UTC. Error bars applying to both profiles plotted on 1930 UTC values: lines show standard deviation σ about best-fit straight line to time series of divergence values; arrows show corresponding standard error σ/n, where n is the number of divergence estimates. For comparison, uncertainty estimates from Eq. (A11) of Lenschow et al. (1980) for the low-level legs on the 4 days in chronological order are 0.0075, 0.010, 0.011, and 0.009 m s−1 km−1.
ABLE radar-wind profiler triangle convergence-based estimates of mean vertical velocity W. The CBL depth in meters inferred from the radar wind profilers appears next to each profile. Values are likely too negative since terrain has not been taken into account, with bias increasing with wind speed.
(a) ARW-WRF mesoscale vertical-velocity (shading, color bars to right) and temperature fields (contours, top frames only) for (left) 1800 UTC 30 May and (right) 1800 UTC 17 Jun 2002. (top) Maps of temperature at level 3 (∼300 m, AGL, contour interval 0.2 K, maxima and minima denoted by "+" and "−") and vertical velocity at level 5 (∼500 m AGL); (bottom) vertical cross section of vertical velocity along the northwest–southeast line on the maps. Data filtered using a 121-point (11 km × 11 km) horizontal box filter. The northwest–southeast line extends from 37.73°N, 97.5°W to 37.28°N, 96.25°W. Map grid points are 1 km apart. Model CBL depth at around 50-80 km along cross section: ∼830–850 hPa for 30 May, and ∼830 hPa for 17 June. (b) As in (a), but for 1800 UTC 20 Jun and 1800 UTC 22 Jun 2002. Model CBL depth at around 50–80 km along cross section: ∼830 hPa for 20 June, and ∼800 hPa for 22 June.
Sensible and latent heat flux (W m−2) for 1800 UTC 30 May 2002 for coupled ARW-WRF–Noah run. The area of largest sensible heat flux and smallest latent heat flux just to the west of the profiler triangle is associated with Wichita, KS, and suburbs (see land use/land cover in Figs. 1 and 2, bottom).
Estimating Regional Surface Heat and Moisture Fluxes above Prairie Cropland from Surface and Upper-Air Measurements
Authors: Alan G. Barr and G. S. Strong
Some Tests on Airborne Silver Iodide Burners
Authors: E. J. Smith and K. J. Heffernan
50 years ago . . .
Bulletin of the American Meteorological Society, Volume 70, Number 5, May 1989
Investigations of Temperature Effects on NOAA's Next Generation Water Level Measurement System
Authors: David L. Portep and H. H. Shih
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Simulating the IHOP_2002 Fair-Weather CBL with the WRF-ARW–Noah Modeling System. Part II: Structures from a Few Kilometers to 100 km across
Margaret A. LeMone1, Fei Chen1, Mukul Tewari1, Jimy Dudhia1, Bart Geerts2, Qun Miao2, Richard L. Coulter3, and Robert L. Grossman4
1 National Center for Atmospheric Research,	* Boulder, Colorado
| 2 Department of Atmospheric Sciences, University of Wyoming, Laramie, Wyoming
| 3 Argonne National Laboratory, Chicago, Illinois
| 4 Colorado Research Associates, Boulder, Colorado
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International H2O Project (IHOP) Boundary Layer Processes
https://doi.org/10.1175/2009MWR3004.1
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Fair-weather data along the May–June 2002 International H2O Project (IHOP_2002) eastern track and the nearby Argonne Boundary Layer Experiments (ABLE) facility in southeast Kansas are compared to numerical simulations to gain insight into how the surface influences convective boundary layer (CBL) structure, and to evaluate the success of the modeling system in replicating the observed behavior. Simulations are conducted for 4 days, using the Advanced Research version of the Weather Research and Forecasting (WRF) model coupled to the Noah land surface model (LSM), initialized using the High-Resolution Land Data Assimilation System (HRLDAS). Because the observations focus on phenomena less than 60 km in scale, the model is run with 1-km grid spacing, offering a critical look at high-resolution model behavior in an environment uncomplicated by precipitation.
The model replicates the type of CBL structure on scales from a few kilometers to ∼100 km, but some features at the kilometer scales depend on the grid spacing. Mesoscale (tens of kilometers) circulations were clearly evident on 2 of the 4 days (30 May and 20 June), clearly not evident on 1 day (22 June), with the situation for the fourth day (17 June) ambiguous. Both observed and modeled surface-heterogeneity-generated mesoscale circulations are evident for 30 May. On the other hand, 20 June satellite images show north-northwest–south-southeast cloud streets (rolls) modulated longitudinally, presumably by tropospheric gravity waves oriented normal to the roll axis, creating northeast–southwest ridges and valleys spaced 50–100 km apart. Modeled cloud streets showed similar longitudinal modulation, with the associated two-dimensional structure having maximum amplitude above the CBL and no relationship to the CBL temperature distribution; although there were patches of mesoscale vertical velocity correlated with CBL temperature. On 22 June, convective rolls were the dominant structure in both model and observations.
For the 3 days for which satellite images show cloud streets, WRF produces rolls with the right orientation and wavelength, which grows with CBL depth. Modeled roll structures appeared for the range of CBL depth to Obukhov length ratios (−zi/L) associated with rolls. However, sensitivity tests show that the roll wavelength is also related to the grid spacing, and the modeled convection becomes more cellular with smaller grid spacing.
Corresponding author address: Margaret A. LeMone, NCAR Foothills Laboratory, 3450 Mitchell Lane, Boulder, CO 80301. Email: lemone@ucar.edu
This article included in the International H2O Project (IHOP_2002) special collection.
Keywords: Model evaluation/performance; Boundary layer; Convective-scale processes
This paper is the second of a two-part series that uses a combination of numerical simulations and observations to explore the relationship of surface heterogeneity and associated fluxes (W m−2) of sensible heat H and latent heat (LE), to potential temperature Θ (K), mixing ratio Q (g kg−1), depth, and convective structure on scales from 1 to 100 km in the fair-weather convective boundary layer (CBL), while evaluating the numerical simulations. The numerical simulations use the Advanced Research Weather Research and Forecasting (ARW-WRF) model (Skamarock et al. 2005), coupled to the Noah land surface model (LSM), which was initialized using the National Center for Atmospheric Research (NCAR) High-Resolution Land Data Assimilation System (HRLDAS; Chen et al. 2007). The data were collected in southeast Kansas using aircraft, surface flux towers, and three radar wind profilers, during May–June, 2002, as part of the International H2O Project (IHOP_2002; Weckwerth et al. 2004, LeMone et al. 2007). The goal of IHOP_2002 was to improve prediction of convective precipitation in numerical weather prediction (NWP) models by improving the measurement and use of water vapor data, and by improving representation of the evolution of water vapor in numerical weather prediction models. Surface processes were emphasized because of their importance in the initiation and evolution of precipitating convection.
In LeMone et al. (2010, hereafter Part I), we focused on comparisons of observed and simulated surface fluxes, CBL thermodynamics, and CBL depth, and found that
The Noah LSM produced an east–west gradient in H and LE consistent with the modeled soil moisture pattern, but not the observed variation associated with land cover, due to shortcomings in the input land-use and land-cover table.
While modeled mean LE values and horizontal variation in the CBL depth compared reasonably well with observations, H and CBL depth were too high. This is likely due to too low a value of the coefficient C in Zilitinkevich's formulation used in relating the roughness lengths for heat and momentum in the Noah LSM, with too-low simulated soil moisture playing a secondary role.
Here, we evaluate the ARW-WRF–Noah modeling system's ability to simulate convective circulations in the fair-weather CBL on scales from a few kilometers to ∼100 km. The region (the vicinity of the triangle and "eastern track" in Fig. 1) is characterized by a mix of mostly grassland (green) and winter wheat (senescent, harvested by mid-June), with trees bordering many fields and waterways. The track extends across the eastern side of the Walnut River watershed southeast of Wichita, Kansas, and into the watershed to the east.
The effects of surface heterogeneity on fair-weather CBL structure have been the subject of numerous modeling studies, some of which incorporate observational data, and a few observational studies. Mesoscale models have been applied to demonstrate the potential importance of CBL mesoscale circulations (∼10–100 km) generated by surface heterogeneity and to understand the scales favored for such circulations (e.g., Chen and Avissar 1994; Baidya Roy and Avissar 2000; Baidya Roy et al. 2003). Reen et al. (2006) and Desai et al. (2006) combined observations and a mesoscale model to look at the effects of heterogeneous surfaces on CBL structure in real situations. Larger and faster computers have enabled numerous large-eddy simulations (LESs) to study the structure and evolution of mesoscale CBL circulations (e.g., Avissar and Schmidt 1998; Letzel and Raasch 2003; Patton et al. 2005; Kang and Davis 2008). However, observational documentation of mesoscale circulations is typically sketchy (e.g., Mahrt et al. 1994; LeMone et al. 2002; Kang et al. 2007; Taylor et al. 2007), with the significant exception of circulations along the dryline that lead to precipitating convection (Ogura and Chen 1977 and many subsequent papers, recognizing that factors other than surface heterogeneity are also important for the dryline). More traditional LESs have examined CBL-depth scale (∼few kilometers, hereafter referred to as kilometer scale) structure for decades (e.g., Deardorff 1972; Moeng and Sullivan 1994), and there has been limited attention to kilometer-scale structure generated in high-resolution mesoscale model simulations (Trier et al. 2004; Miao and Chen 2008).
Our goal to compare CBL structure and evolution at the scales observed requires high-resolution runs. The choice of an LES to simulate real-world situations is appealing, since large CBL eddies are resolved. Indeed Conzemius and Fedorovich (2008) used LES to simulate of CBL development near the western track, and Gorska et al. (2008) used LES to simulate of mesoscale circulations along the eastern track. However, these studies were hindered by the need for periodic boundary conditions. Moreover, CBL structure can be influenced by interaction with tropospheric gravity waves (e.g., Clark et al. 1986; Balaji et al. 1993), requiring inclusion of a deeper domain than ordinarily used in LES. These factors, along with the convenience of having an NWP model with a reasonably robust surface model, led to the choice to run ARW-WRF run at 1-km resolution. We recognize the potential problems created by "double counting" associated with partially resolving CBL-scale motions and using a CBL scheme, but note the developing literature of high-resolution ARW-WRF runs that appear successful, even at very high resolution (e.g., Davis et al. 2008, Miao and Chen 2008). Furthermore, actual comparisons to data such as done here can help us understand the limitations of stretching ARW-WRF beyond its expected capabilities.
The paper is outlined as follows. Data collection and analysis are discussed in section 2, with model runs and analysis of results summarized in section 3. Section 4 compares the observed and modeled CBL structure from kilometer-scale to mesoscale (∼10–100 km), drawing from some sensitivity runs to help interpret the model CBL convective structure. The results are summarized in section 5.
The four fair-weather days examined had scattered clouds and wind from approximately south-southwest to southeast (Table 1). Winds at 65 m above ground level (AGL) vary from 3.9 m s−1 on 30 May to 9.4 m s−1 on 22 June. Timing of the most recent rainfall ranged from 1.7 to 6.7 days previous. Figure 1 shows a higher density of crops (mainly winter wheat) to the west and mainly grasslands to the east, relative to the eastern track and for at least 100 km to the south. The band of winter wheat extends south to the Oklahoma–Kansas border; south of the border, the winter wheat band extends southwestward into Texas. The dominant soil type along the flight track is silty clay loam (Soil Survey Geographic Database; more information is available online at http://dbwww.essc.psu.edu/dbtop/doc/statsgo/statsgo_info.html; Fig. 2), consistent with the observed near-surface soil type at the IHOP_2002 surface sites.
As in Part I, aircraft data are from the University of Wyoming King Air, which flew straight and level legs along the ∼46-km long eastern track (Fig. 1). While at least 5 passes were flown between 60 and 70 m AGL, there are data collected from at least 1 other altitude range (Table 1). Aircraft-relative winds were measured by a Rosemount 858AJ/1332 differential pressure gust-probe system. Aircraft position and motion relative to the ground were measured by a Honeywell Laseref SM inertial navigation system and corrected using GPS to within 100 m horizontally. Aircraft altitude was based on a King KRA5 radio altimeter for heights below 610 m; and an APN159 radar altimeter for heights above 610 m. Estimates of virtual potential temperature Θυ are based on water vapor densities from a LiCor 6262 gas analyzer,1 flight-level air temperatures from a reverse-flow platinum-resistance thermometer built at the University of Wyoming, and pressure from a Rosemount 1201 sensor. Data were made available at 25 Hz and as 1-s averages.
Statistics on flight-level wind and thermodynamics were based on the 1-s data. Aircraft-based divergences were computed from the along-track wind component using least squares best-fit lines to each flight track. Values at specific times could then be computed from a least squares best fit line to the divergence time series at each level.
To calculate the Obukhov length L, we use leg-averaged flux measurements from the King Air following Strassberg et al. (2008), and fluxes from the three towers along the eastern track, located on grassland sites at positions 7, 8, and 9 in Fig. 1. These measurements are described in detail in Part I and LeMone et al. (2007).
Argonne National Laboratory's Atmospheric Boundary Layer Experiments (ABLE; ABLE is no longer operating; however, the data are available at http://gonzalo.er.anl.gov/ABLE; Klazura et al. 2006) facility radar wind profilers were used to obtain CBL depth and convergence. The locations of the two profilers used for boundary layer (BL) depth—Beaumont and Oxford, Kansas—are shown in Fig. 1. The BL height was defined as the center height of the gate just below the maximum signal-to-noise dropoff rate with height (Coulter and Holdridge 1998). For convergence estimates, the ABLE Whitewater profiler was combined with those at Beaumont and Oxford. The Whitewater profiler lies ∼63 km north-northwest of Oxford (∼350° azimuth) so that the three profiles form a nearly equilateral triangle. The vertical resolution of the profiler data is of the order of 60 m. Radar wind profilers can in principle measure vertical wind directly, but in the CBL this measurement tends to be negatively biased (Angevine 1997).
The model runs are described in detail in Part I, so we provide only essential detail here. Our control runs use ARW-WRF version 2.1.2 with the Yonsei University (YSU) PBL scheme (Hong et al. 2006), coupled to the Noah LSM (Chen and Dudhia 2001; Ek et al. 2003). The simulations, for the four fair-weather days in Table 1, start at 1200 UTC and run for 24 h, using the nested domains shown in Fig. 3. The vertical grid contains 30 full-sigma levels from the surface to 50 hPa, of which the lowest 8 levels are below 1 km AGL (lowest level 28 m AGL) in order to have finer resolution in the CBL. The horizontal grid spacing (number of points) of the three outer domains are 9 km (237 × 201), 3 km (280 × 229), and 1 km (391 × 289), respectively. Initial and boundary conditions are from the National Centers for Environmental Prediction (NCEP) 6-hourly Eta Data Assimilation System (EDAS) on a 40-km grid. In the control ARW-WRF simulation, the Noah LSM is initialized by volumetric soil moisture and temperature profiles and skin temperature obtained from HRLDAS (Chen et al. 2007), which is run offline but on the same nested WRF grid for an 18-month spinup period ending at the initialization time of each ARW-WRF simulation (here, runs start 1 January 2001). The input data at the surface are default values except for the land-use characteristics, which are based on Moderate resolution Imaging Spectroradiometer (MODIS) data from the Boston University Department of Geography (see online at http://www-modis.bu.edu/landcover/; Fig. 2).
We did several sensitivity runs to see whether the modeled CBL-scale convective structure was robust. Two runs tested the effects of horizontal resolution using data from 22 June; and several idealized runs were conducted using data from 20 June to assess the impact of grid spacing, surface properties, and filtering (Table 2). Two higher-resolution runs for 22 June were run using the same version of ARW-WRF (ARW-WRF V2.1.2) as the control runs, with a fourth domain with 333-m grid spacing (Fig. 3), with initial surface conditions interpolated from the 1-km HRLDAS results.
The idealized runs used the public-release ARW-WRF version 3 (ARW-WRF V3), with all points in the domain initially assigned surface characteristics and meteorological data for a typical point on the eastern track (37.4188°N, 96.6472°W), and in domains centered at this point (Fig. 3). Initial surface properties in the idealized runs were varied horizontally by randomly perturbing the initial volumetric soil moisture profile and/or Ts, using a uniform distribution with ranges of ±0.02 and ±0.01 K, respectively. We varied grid spacings for both the surface and atmosphere grid together and separately, ran simulations with and without the Knievel et al. (2007) filter to damp unresolved motions (<6Δx), and changed the domain depth for some runs. The grid points in the innermost domain varied from 226 × 226 for the 200-m runs to 101 × 101 for the remaining runs. Because input data were based on only one point, winds had to be determined using an option in ARW-WRF that maintains the geostrophic part with an implicit pressure gradient, with only the ageostrophic component responding to the Coriolis acceleration. Table 2 will be discussed in greater detail in section 4b.
A final sensitivity run was conducted to assess the effect of the Knievel filter on mesoscale structure. Using the same model setup as for the control runs, we ran ARW-WRF V3 for 30 May with and without the Knievel filter.
As noted in Part I, Θ increased westward along the eastern track in both model results and observations. The largest east–west difference was for 30 May, and the smallest for 22 June. These trends are consistent with the east–west differences in Θυ, which extends through the CBL (Table 3). The differences between these two days are also consistent with the modeled soil moisture patterns, which show stronger contrast along the flight track on 30 May than for 22 June (Fig. 4).
To see whether these along-track Θυ changes can result from horizontal heterogeneity alone (i.e., no synoptic-scale differential advection), we consider two idealized trajectories that start out with the same Θυ, one headed northward toward the western end of the flight track over dormant winter wheat and dry soils, and the second headed northward toward the eastern end over green grasses and wet soils, consistent with southerly winds passing over the vegetation and soil moisture depicted in Figs. 1 and 4. For simplicity, we assume that the surface virtual temperature fluxes along the trajectories are related such that = (1 + f ) , where f is a positive constant, the subscript w denotes the warm western trajectory, and the subscript c denotes the cool eastern trajectory.
We assume2 that , the virtual potential temperature flux at CBL top, is related to the surface flux via ≈ −0.2 . Neglecting direct warming by radiative effects, the along-trajectory time rate of change of the virtual potential temperature averaged over zi, 〈Θυ〉, is given by
Assuming the same zi for both trajectories, and taking the difference between d〈Θυ〉/dt for air along the "warm" and "cool" trajectories:
Integrating (2) and recalling that Δ〈Θυ〉 is initially zero (trajectories start at the same 〈Θυ〉), we obtain the along-trajectory distance DΔΘv required to develop a horizontal temperature difference 〈ΔΘυ〉:
where S is the wind speed along the trajectory. During the morning hours, and zi change together (cf. Figs. 7 and 8 in Part I), keeping their ratio in (1)–(3) roughly constant. This enables us to use noontime values for crude estimates of tΔΘυ and DΔΘv.
Setting f = 0.2 and applying (3) to the four IHOP_2002 days, with S, Δ〈Θυ〉, zi, and from Table 3, we find that DΔΘυ ranges from 83 to 122 km. From Figs. 1 and 2, observed and modeled upstream vegetation has gradients over these scales. The modeled horizontal gradient in soil moisture for 30 May (Fig. 4) and 17 June (not shown) are of sufficient scale; the soil moisture patterns of 20 (not shown) and 22 June (Fig. 4) are more irregular, but still exhibit a broad westward drying. A definitive calculation would have to account for fluxes, BL depths, and land cover along the actual trajectories. Having a deeper BL for the western trajectory would dilute the heating, and thus lengthen the time/distance required to produce a given Δ〈Θυ〉. Moreover, since terrain in the Kansas–Oklahoma region rises toward the west, constant surface air temperatures would mean that east–west convergence could increase the Θ gradient, with upslope easterlies to the east lowering Θ and downslope westerlies to the west raising Θ.
On 3 of the 4 days, the ARW-WRF runs produced roll-type structures aligned with the CBL wind, with structure and orientation similar to that documented in satellite images (Fig. 5). From the figure, the model replicates the observed north–south cloud streets on 22 June, and south-southeast–north-northwest cloud streets that vary longitudinally on 20 June. For 17 June, ARW-WRF produces south-southwest–north-northeast linear roll structures with the same orientation of the cloud streets depicted in the satellite image. The radar composites in Fig. 6 suggest roll alignment corresponding to the cloud streets. The observed and modeled roll wavelength, about 4 km, is consistent with that associated with CBL rolls (∼3zi, e.g., LeMone 1973, land values from Fig. 4 in Young et al. 2002). However, this result could be fortuitous: for these 1-km grid simulations, 4 km is less than the ∼6–10Δx quoted in Skamarock (2004) as the lower limit for properly resolving structure and amplitude.
The ARW-WRF did not produce clear-cut convective patterns on 30 May, with patches of irregular cellular convection and artificial-looking linear structures 2Δx wide near the flight track and profiler triangle (not well resolved in Fig. 5a). The Wichita Weather Surveillance Radar-1988 Doppler (WSR-88D) radar pattern in Fig. 6 also shows a cellular pattern. The 2Δx features probably result from the shallow CBL depth (Table 1), which reduces the expected convection scale (order CBL depth); and a combination of light winds (Table 1) and the numerical diffusion scheme in ARW-WRF version 2.1.2, which is less effective at reducing 2Δx features in light winds (Knievel et al. 2007).
The presence of CBL rolls (as opposed to cellular convection) corresponds to what occurs in nature. In Figs. 5 and 6, the rolls occur for values of −zi/L less than around 25, where L is the Obukhov length, as expected from previous work by Weckwerth et al. (1999) and others. Similarly, ARW-WRF simulations by Miao and Chen (2008) showed rolls for smaller −zi/L ratios and random convection for larger ratios, and Méso-NH (Lafore et al. 1998) simulations by Lothon et al. (2007) showed rolls transitioning to cells as −zi/L increased beyond 25.
For the observations, the "regional"3 −zi/L ratios based on aircraft 46-km averages (Strassberg et al. 2008) seem to have a better correspondence to rolls than those based on the local flux tower data (Table 4). As shown by Strassberg et al. using data from 22 June, these regional values correspond to a momentum roughness length z0 of 0.14 m, close to the value of z0 = 0.12 prescribed for grassland in ARW-WRF. Note that the −zi/L threshold for roll occurrence is significantly exceeded for 20 June (Table 4) when we use u* measured at the surface stations or use model fluxes with the local (0.02 cm) z0 value; thus, additional evidence for the need to use regional values.
The good correspondence between modeled and observed type of convective structure was surprising, given the use of a PBL scheme designed for grid spacings much larger than 1 km (e.g., Wyngaard 2004) and the associated use of grid boxes with smaller width-to-height ratios (Wyngaard et al. 1998). To test the robustness of the convection type and scale in the ARW-WRF simulations, we ran a series of tests to assess the impact of surface perturbations and grid sizes on the characteristics of the CBL convection, summarized in Table 2. As seen from the table, the grid spacings for both the surface and the atmosphere were varied, sometimes separately.4 Volumetric soil moisture (surface temperature Ts) was perturbed randomly using a uniform distribution with a range of ±0.02 (0.01 K) relative to the mean. On some runs, Ts was perturbed and soil moisture left constant. We also varied the depth of the model and horizontal diffusion (not shown). Each run is identified by what is perturbed and at what scale. For example, T1 has Ts at 1-km spacing and a 1-km atmosphere grid, while TS1 has 1-km grids for Ts and soil moisture perturbations and the atmosphere. When the surface and atmosphere grid spacing differ, the suffix "mix" is used. Additional letters indicate the presence or absence of the Knievel et al. (2007) filter and the simulation depth. Thus, TSmix1_0.33NFs has 1-km soil moisture and Ts perturbations and 0.33-km atmosphere grid spacing, no Knievel filter (NF) and a shallow (s) domain.
Roll wavelengths in Table 2 were determined by manually counting vertical-velocity maxima or minima in the x direction (east–west) for direct comparison to grid spacing. However, roll aspect ratios (roll spacing divided by zi) were calculated using the true roll spacing (measured normal to the roll axis). In nested runs, rolls were counted and convective type evaluated far enough downstream of the outer grid to mitigate boundary effects. For example, the convection in Fig. 7b would be considered "cellular."
The data in Table 2 suggest four general tendencies.
First, roll spacing in the x direction decreased with smaller grid size. For 22 June, the 1-km run (control) had rolls ranging from 2 km (2Δx) to ∼4.5 km across, while the 333-m runs (HiResE and HiResW) typically had 2-km rolls. For the idealized runs, the reduction of roll size in the x-direction with smaller grids was clearly associated with the Knievel filter, which damps out perturbations smaller than 6Δx (6.7 km for TS1 versus 3.5 km for TS0.5). As in the case of 22 June, this trend was also evident without the filter (4.0, 2.4, and 1.8 km for TS1NF, TS0.5NFs, and TSmix1_0.33NFs, respectively). At the smallest atmosphere grid spacing with rolls (TSmix1_0.33NFs), their aspect ratio was 1.4, smaller than the expected land value (∼2–3 or slightly greater). There also appear to be secondary effects related to domain top and the presence or absence of nesting. As expected, deactivating the Knievel filter led to smaller-scale rolls (6.7 km for TS1 versus 4.0 km for TS1NF).
Second, reducing the grid size led to more irregular convection. From the table, the two idealized runs with 200-m atmosphere grid spacing (T1mix1_0.2NFs and TS1mix1_0.2NFs) are dominated by cellular convection, rather than rolls; and the high-resolution 22 June runs had a mix of rolls and more three-dimensional structure in the 333-m grid inner domains. This tendency is illustrated by the change in structure between the 1800 UTC mid-CBL vertical-velocity fields for the idealized runs shown in Figs. 7 and 8, for a 1-km grid (TS1 in Fig. 7a, TS1NF in Fig. 8a) versus a 200-m atmosphere grid (TSmix1_0.2NFs in Fig. 7b). The behavior for TSmix1_0.2NFs was not changed significantly when we removed the Smagorinsky diffusion (not shown in Table 2). Trier et al. (2004) similarly found that convection changed from linear to more cellular when their grid spacing was changed from 3.3 to 1.1 km in their ARW-WRF simulations using the Mellor–Yamada–Janjic (MYJ) PBL scheme.
Third, CBL convective structures with Ts perturbations alone developed later than CBL convection with both Ts and soil moisture perturbations, or failed to develop at all, leaving the YSU PBL scheme to do the vertical transport. This is illustrated by the 1-km simulations with and without soil moisture perturbations in Fig. 8, which at 1800 UTC show well-developed rolls for TS1NF but only infinitesimal resolved motions at for T1NF. The onset time for resolved convection in the temperature-perturbation runs is scale dependent. Of the runs at 1, 2, 4, and 10 km (T1NF, T1, T2, T4, T10), temperature perturbations generated CBL convection only for the 1-km unfiltered run (T1NF), which began to form roll-like convection by 2100 UTC (Fig. 8d). For the 200-m atmosphere grid (T1mix1_0.2NFs), convection formed earlier, but was cellular. The increase in convection growth rate with decreasing horizontal scale is consistent with the results of Weisman et al. (1997).
Fourth, CBL rolls tended to grow with time along with the CBL depth (21-UTC roll size under "remarks" in Table 2), indicating ARW-WRF was able to follow this behavior at all resolutions for which rolls were captured.
LeMone et al. (2002) documented ∼60-km circulations on 10 May 1997 linked to the Walnut River Watershed terrain. The repeatable along-track Θυ differences through the mixed layer (Table 3) suggest the possibility of mesoscale circulations in IHOP_2002 as well. To estimate the time required to form a mesoscale circulation, we start with the idealized circulation equation for a sea breeze (Holton 1972), modified by (i) substituting the hydrostatic equation integrated from the surface to zi assuming Tυ ≈ const ≈ 〈Θυ〉 for the layer p0 to pzi, (ii) assuming that Δ〈Tυ〉 ≈ Δ〈Θυ〉, and (iii) assuming that zi ≪ ℓ, the length of the flight track:
where uℓ is the component of the low-level wind along the ℓ direction, R is the gas constant, and p0 and pzi are the air pressure at the bottom and top of the CBL, respectively. Surface friction and Coriolis acceleration are neglected,5 as well as interaction with turbulent eddies. The time tΔuℓ along the air trajectory required for the wind to change by Δuℓ is thus
where ℓ = 45.65 km. Taking Δuℓ as 1 m s−1, and values of Δ〈Θυ〉 and zi from Table 3, tΔuℓ varies from 1.4 h on 30 May to 3.1 h on 22 June, suggesting mesoscale circulations form most easily on 30 May. Multiplying by the along-trajectory wind (assumed equal to the measured values in Table 3), the corresponding along-trajectory distance DΔuℓ required with Δ〈Θυ〉 values from the table ranges from 19 km for 30 May to 107 km for 22 June.
Given the rarity of direct observations of CBL mesoscale circulations, it is important to consider the conditions under which they can be observed. Once they are set up, mesoscale circulations should evolve slowly if they are to be observed using aircraft or even radar wind profilers. Under what conditions does this occur? A rapid dynamic response like that for 30 May will weaken the 〈Θυ〉 gradient and lead to a weaker or unsteady mesoscale circulation, unless the horizontal change of vertical flux divergence along the wind is sufficient to maintain the gradient against horizontal advection. Consider an air parcel in a two-dimensional circulation moving from point A on the cool side, with surface flux and virtual potential temperature 〈Θυ〉, to point B on the warm side, with flux (1 + f ) and virtual potential temperature 〈Θυ〉 + Δ〈Θυ〉. We assume that the circulations are truly mesoscale, with the distance between A and B much greater than zi, the scale of CBL large eddies; since the two scales would strongly interact otherwise.
At point M halfway between A and B, the surface buoyancy flux is halfway between point A and point B, namely (1 + 0.5f ) ; which from (1), implies a vertical flux divergence warming within the CBL equal to 1.2(1 + 0.5f ) /zi. For 〈Θv〉M at M to warm at the same rate as at point A, which from (1), is 1.2 /zi, advection un(∂〈Θυ〉/∂x) ≈ un(Δ〈Θυ〉/DAB), where DAB is the distance between A and B, has to be just enough to offset the "extra warming." That is,
where un is the wind from A to B at M, and zi is assumed constant horizontally. Solving (6)
Taking un = 1 m s−1, zi = 1000 m, and = 0.1 K m s−1, the distance DAB corresponding to the steady state is 16.7Δ〈Θυ〉/f km. These idealized calculations with f = 0.2 suggest DAB = 50 km (roughly flight-track scale) for 30 May (Δ〈Θυ〉 = 0.6 K). For mesoscale circulations of this size, warming by vertical flux divergence just offsets cooling from horizontal advection and the Θυ gradient can be maintained.
These arguments about maintenance of a constant 〈Θυ〉 gradient of course apply only under the assumptions that = −0.2 , zi is constant horizontally, friction and Coriolis effects are negligible, and turbulence does not interact with the circulations. Some authors have simulated unsteady mesoscale circulations (Letzel and Raasch 2003), which Kang and Davis (2008) attribute to larger-amplitude horizontal variations.
Kang (2009) suggests that mesoscale circulations become unsteady because of interaction with turbulent eddies when their circulation velocity reaches VC ≈ ut(λC/2λt)0.5, where λC is the wavelength of the circulation (twice our DAB), and ut and λt are the characteristic velocity and scale, respectively, of the turbulent (our kilometer scale) eddies. For typical values of λt ≈ 1.5zi, zi ≈ 1000 m, ut ≈ 1 m s−1, and λC = 100 km, VC = 5.8 m s−1, much greater the 1 m s−1 used to estimate the wavelength for steady state. For the modeled horizontal variability in along the eastern track on 30 May (40 W m−2, Fig. 4 in Part I), the mesoscale eddies modeled by Kang (2009) simply grow linearly at these scales for several hours, making them straightforward to observe. Smaller wavelengths (or larger horizontal flux variations) lead to oscillations developing more rapidly. Nevertheless, in an average sense, surface-heterogeneity-driven smaller-scale circulations are produced in LESs down to kilometer scale by Avissar and Schmidt (1998), Patton et al. (2005), and many others; and Grossman et al. (2005) observed averaged circulations of 10-km scale.
Figure 9 shows that estimates of the along-track divergence of the along-track wind component are consistent with the development of along-track mesoscale circulations in the CBL on 30 May (and possibly 17 June), with convergence (divergence) in the lower CBL and divergence (convergence) in the upper CBL. The divergence pattern on 22 June indicates subsidence. The 20 June divergences are less than the standard error.
We do not expect exact correspondence between the divergence-derived mean vertical velocities6 (W) from the ABLE radar wind profiler triangle in Fig. 10 and the aircraft-derived divergences in Fig. 9; since the latter are only for the wind component parallel to the flight track, and the two measurements are displaced horizontally (Fig. 1). However, the two sets of measurements together reveal some interesting patterns. Figures 9 and 10 both indicate strong subsidence on 22 June. There is evidence for circulations largely confined to the CBL on 30 May, with convergence in the lower CBL and divergence in the upper CBL in Fig. 9 and W extrema within the CBL at 1600 UTC and possibly 2000 UTC in Fig. 10, but not at other times. On 20 June at 1600 UTC, there is subsidence that reaches a maximum at around 1300 m AGL (almost 2zi), suggesting a circulation that extends above the CBL, but aircraft-based convergence is smaller than the expected error and available only below 450 m AGL. On 17 June, the W extremum at 2000 UTC is within the CBL in Fig. 10 and corresponds roughly to the top of the divergence layer at 1930 UTC in Fig. 9; but there are W extrema above the CBL at 1800 and 1900 UTC. We interpret these figures as suggestive of mesoscale circulations on 30 May and possibly 17 June, possible mesoscale features centered above the CBL on 20 June, and no detectable mesoscale circulations in the CBL on 22 June.
To see whether the mesoscale features are replicated in ARW-WRF, we produced horizontal fields in domain d03 of temperature at ARW-WRF level 3 (about 300 m AGL) and the vertical velocity at ARW-WRF level 5 (about 500 m AGL) and applied a bidirectional 121-point box filter (11 km in each direction). The resulting plots appear in Figs. 11a and 11b. At scales of tens of kilometers, we see circulations based in the CBL on 30 May and circulations with maximum amplitude above the CBL on 20 June. Mesoscale structures on 22 June are overwhelmed by the rolls. On 17 June, there are some weak warm upwelling regions and cool downwelling regions in the CBL; but little mesoscale circulation evident in the cross section in Fig. 11a, with maxima and minima above the CBL at 1800 UTC, consistent with the ambiguous observations in Figs. 9 and 10. There is a large response to the Wichita urban heat island (Fig. 12) on 30 May (distance = 20 km in cross section, Fig. 11a), but only a small response on the remaining three days (Figs. 11a,b).
The circulations on 30 May appear to be examples of the classic "inland sea breeze," as studied by Ogura and Chen (1977), Sun and Ogura (1979), Anthes (1984), Segal et al. (1988), Pielke et al. (1991), and many others, with upwelling (downwelling) areas correspond to warmer (cooler) air in the CBL (Fig. 11a). Indeed, Gorska et al. (2008) simulated mesoscale circulations using an LES with the lower boundary conditions idealized from HRLDAS output for 30 May for the eastern track region. The general subsidence over the profiler triangle corresponds roughly to observations (Fig. 10), but the pattern along the flight track is less clear. Exact correspondence is not expected given differences in the observed and modeled fluxes, discussed in Part I. From Fig. 8 in Part I, the modeled PBL top is around 850–830 hPa; thus, the 30 May cross section in Fig. 11a indicates maximum vertical velocities within the CBL, with the largest updraft–downdraft couplet associated with the Wichita urban heat island and its flux anomaly (Fig. 12). All of these features were preserved when this day was rerun using ARW-WRF version 3.0 with the Knievel filter. From the previous discussion, the upstream flux contrast results in strong temperature gradients (Fig. 11a; Table 3), which favor mesoscale circulations. Moreover, the temperature gradient, scale, and flux differences are roughly consistent with near steady state, a condition required for being well observed in a ∼4-h aircraft mission.
While there are localized mesoscale circulations apparently driven by CBL temperature contrast (Fig. 11b, top), there are well-defined linear structures normal to the convective rolls across the region on 20 June (Figs. 5b and 11b) that appear unrelated to the CBL temperature. The vertical-velocity amplitudes in the 20 June cross section in Fig. 11b reach a maximum above the CBL, at about 700 mb. We speculate that the 700-mb extrema are associated with the northeast–southwest linear structures, and that both are due to gravity waves. As obvious from the figures, ARW-WRF replicates both the wavelength and the orientation. However, there is a mismatch in phase speed (14 m s−1 for ARW-WRF, nearly stationary in the observations), so this is speculation at this point.
It is interesting that even on 30 May, when mesoscale circulations were favored; they were not clearly related to the Walnut River Watershed, as was the case in LeMone et al. (2002). We believe that the explanation has to do with the association of vegetation type with elevation in the watershed. The circulation described in LeMone et al. (2002) was in early May, so that the warmth associated with the higher elevations (Walko et al. 1992; Krettenauer and Schumann 1992) was reinforced by higher surface air temperatures associated with dormant grasses there, with lower surface air temperatures above the rapidly growing winter wheat in the lower elevations. However, in the present summer case, the elevated terrain has its surface air temperature cooled due to evapotranspiration from the green grasses, while the lowlands have higher surface air temperatures due to dormant or harvested winter wheat.
Comparisons of satellite images and aircraft and surface observations centered along the IHOP_2002 eastern track to ARW-WRF–Noah modeling results on four fair-weather days with southerly winds and nearly clear skies, show that the model captures both the type of convective-scale structure (a few kilometers across) and influence of surface heterogeneity on CBL mesoscale structure (tens of kilometers across) reasonably well. This is in spite of the model deficiencies discussed in Part I, namely significant overestimates of H and CBL depth and the model's inability to capture variability associated with vegetation because of the model input land-use land-cover table not including enough winter wheat pixels along and upwind the flight track.
While surprising given the probable mismatch of the PBL parameterization to the 1-km grid spacing, modeled two-dimensional CBL structures corresponded well to the cloud streets evident in the 17, 20, and 22 June satellite images, in terms of orientation, spacing, and on 2 out of 3 days, timing. Moreover, evidence from radar as well as satellite images shows that observed as well as modeled rolls occurred for the range of −zi/L < 25 associated with roll occurrence by Weckwerth et al. (1999) and others. For both model and observations, the L values corresponded better to "regional" values of z0 (≈0.1 m), which implicitly account for larger roughness elements (e.g., trees and houses) as well as the vegetation (crops or grassland) specified. However, the "good" roll spacing and aspect ratios were fortuitous: sensitivity studies showed both were influenced by the grid spacing; and with grid spacing less than about 300 m, the rolls broke down into cells. While realistic model convective type is surprising, the change in model behavior at smaller grid sizes is not, given the combined effects of poor horizontal resolution, the potential for double counting created by resolved CBL motions in a PBL scheme designed for larger grid sizes, and the reduction in the aspect ratio of the grid boxes (Wyngaard et al. 1998; Wyngaard 2004).
On scales ≥∼30 km, ARW-WRF again replicates the type of mesoscale structure expected from observations. Mesoscale circulations associated with CBL temperature differences are modeled on 30 May, the day for which they were most likely, given the large along-track Θυ difference and light winds. A sensitivity run using ARW-WRF 3.0 and the Knievel filter produced nearly identical mesoscale structure. On this day, both along-track divergence of the along-track aircraft wind and some radar wind profiler vertical velocity estimates are consistent with CBL circulations, with extrema within the CBL. Idealized trajectory analysis based on model results suggested there was enough horizontal contrast on a large enough scale to create the observed along-track Θυ difference. Idealized analysis of the circulation and effects of horizontal advection suggested that mesoscale circulations could develop fairly rapidly on this day, and be sufficiently steady state to be observable using aircraft, for wavelengths of the order of 100 km.
There is mesoscale variability in both the observations and the model on 20 June, with patches of surface-driven circulations, but, more interestingly, intensity changes in the north-northwest–south-southeast cloud streets marked by northeast–southwest bands of nearly-clear skies and enhanced cloudiness. This modulation is not associated with temperature changes in the CBL and the amplitude of the vertical-velocity variation peaks above the CBL, suggesting gravity waves. A mismatch between observed and modeled phase speeds indicates more work is needed to confirm this hypothesis. No mesoscale CBL variation is evident either in the satellite image or in the ARW-WRF simulations for 22 June, the day with uniform observed subsidence, the least horizontal contrast, and the strongest winds. None of the mesoscale circulations were clearly linked to the topography sampled by the aircraft and profilers, as in the case described in LeMone et al. (2002) for the same region, perhaps because the thermal effects of land cover and elevation were in opposite directions during IHOP_2002, while they reinforced one another in the former case.
In this paper, we were careful to compare the type rather than the details of CBL and lower-tropospheric structure on scales from kilometers to tens of kilometers, because either the model or the data were not adequate for a closer comparison. The major model shortcomings—the inability to represent the impact of the dormant/harvested winter wheat in the mixed grass-winter wheat regions, and the overestimate of surface sensible heat flux—can be dealt with in a straightforward way. Indeed, a new formulation for C in the Noah LSM based on Chen and Zhang (2009) should improve modeled surface fluxes. The impact on this change on ARW-WRF is currently being tested (S. Trier 2009, personal communication). To deal with observational shortcomings, we will simulate the well-documented mesoscale circulations observed during the Cooperative Atmosphere-Surface Exchange Study-97 (CASES-97) and documented in LeMone et al. (2002). Like the present work, however, the future work will require finer grid spacing than optimum for present ARW-WRF PBL schemes.
This work would not have been possible without the commitment, enthusiasm, and expertise of the Wyoming King Air aircrew, the staff maintaining the Wyoming Cloud Radar, the National Center for Atmospheric Research (NCAR) staff maintaining the surface flux instruments, and the students taking the manual observations at the flux sites. We gratefully acknowledge the four reviewers whose thorough reviews improved the manuscript and Stan Trier of NCAR for supplying filtering software for the ARW-WRF data, and for reviewing the manuscript, as well as discussions with George Bryan and Chin-Hoh Moeng in designing and interpreting our sensitivity studies. The NCAR portion of this research was supported by USWRP Grant NSF 01, the NCAR Water Cycle Initiative, and NASA-THP (Grant NNG06GH17G) and NOAA/JCSDA (Grant NA06NES4400013), as well as NCAR base funding. RLG's participation in IHOP_2002 was supported by NSF Grant ATM-0296159. RC's work was supported by the U.S. Department of Energy, Office of Biological and Environmental Research, under Contract W-31-109-Eng-38.
Citation: Monthly Weather Review 138, 3; 10.1175/2009MWR3004.1
Boundary layer King Air flights and environmental conditions along the eastern track (zi = CBL depth).
Effects of resolution on CBL structure. All idealized runs use ARW-WRF V3, with uniform vegetation (grassland) and soil texture (silty clay loam), and surface, subsurface, and meteorological data from 37.4188°N, 96.6472°W on 20 June, with initial Ts and/or volumetric soil moisture varied using random perturbations. 22 Jun runs use ARW-WRF V2.1.2. Y = "yes," N = "no," hWRF = CBL depth from ARW-WRF, averaged over domain unless otherwise indicated. "Filt" refers to the Knievel et al. (2007) filter used in ARW-WRF to damp out unresolved motions; "Nest" refers to whether the finest resolution was a result of nesting.
Observed parameters related to development of mesoscale circulations, interpolated to 1830 UTC unless otherwise indicated.
Criterion −zi/L for convective-roll occurrence from model and observations at 1830 UTC.
There is a small bias (∼1 g m−3) between the LiCor, and the Lyman α, which was used in Part I because it was referenced to an EG and G chilled-mirror dewpointer. Since only horizontal changes in Θυ are considered, the bias should not influence the results.
Conzemius and Fedorovich (2006) suggest the relationship ≈ −0.2 applies if the shear at PBL top is small. Strassberg et al. (2008) found the shear to be small for the June days, and "small to moderate" for 30 May, with a zi to surface flux ratio less than 0.2, so the relationship should apply reasonably well for the 4 days discussed here.
The "regional" fluxes, being based on momentum fluxes computed from fluctuations relative to a 46-km flight-leg linear trend, include the effects of trees, buildings, etc.; while the "local" fluxes, being based on surface flux sites that are in open areas, represent the surface sampled (in this case, grasses).
The two grid sizes were always such that no interpolation was necessary. For example, for the larger grid 1 km, the smaller grid might be 500 m, so that the four 500-m squares exactly overlap with the 1-km squares. In this case, the 1-km grid square responds to the average of the values for the four 500-m squares; and the four 500-m squares respond to the 1-km square values.
Accounting for the retarding effects of friction would increase the time required to generate a circulation in (5). However, as pointed out in Holton and elsewhere, the frictional force increases as the square of the velocity. We neglect friction and Coriolis accelerations since our arguments are qualitative and we allow uℓ to increase to only 1 m s−1. For more intense circulations, such as those considered in Miao and Geerts (2007) and Souza et al. (2000) or in considering how a steady state is reached, these effects are important.
The unusually large negative values are related to the ground rising northward by about 200 m within the profiler triangle in Fig. 1. To obtain a true vertical wind relative to the center of the earth, a correction related to the wind vector and the gradient in terrain along that vector would be needed.
* The National Center for Atmospheric Research is sponsored by the National Science Foundation.
https://journals.ametsoc.org/view/journals/mwre/138/3/2009mwr3004.1.xml


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One of Charleston's premier golf communities, Wild Dunes has two stellar golf courses for home owners, visiting guests, and even the public. Condos and villas are usually for sale overlooking one of the golf courses as well, so if you're looking for beachfront property in Wild Dunes or a home on the green of the ocean course Wild Dunes won't disappoint. This Charleston gated community although private is still open to the public for golfers and diners who wish to enjoy one of the three restaurants in the resort.


Part-time Lecturer and Teaching Assistant: Sebastien read political science at Kyoto University on the Japanese Government (Monbushò) Scholarship and received his LLB in 1994. He then obtained an M.Phil. in international relations from the University of Cambridge in the following year. His teaching and research interests include Sino-Japanese relations, international relations of East Asia, and domestic politics and foreign policy of Japan. He formerly taught at City University of Hong Kong between 1997 and 2000.
"The Role of Tanaka Kakuei in the Sino-Japanese Normalisation of 1972", Social Science Japan No. 26 (May 2003): 13-18.


For a number of years, Kansas legislators have been working to repair the state's damaged credit rating. In 2016, S&P downgraded Kansas State's rating from AA to AA-minus, a demotion for which many blame years of tax cuts under the previous governor, among other factors. A recent article from Bond Buyer takes a look at Governor Laura Kelly's new budget proposal, as well as S&P's take on it.
According to S&P calculations, Kansas faces a large—and growing—budget gap, measuring in the tens to hundreds of million dollars. Though this is a relatively moderate gap, it does indicate that the state continues to struggle fiscally.
Governor Kelly's proposed fiscal 2020 budget includes a number of strategies for stabilizing the state's fiscal health. One method put forward is the repayment of a $317.2 million loan five years ahead of schedule. Kelly also proposes increasing general-fund expenditures by drawing on revenue growth from 2017 tax increases, re-amortizing the state's pension fund schedule, utilizing new revenue from economic growth, and decreasing the fund balance slightly.
Kansas has dealt with a lot of fiscal volatility in the last decade. While Governor Kelly is optimistic about her proposed budget, S&P is relatively more cautious.
For more details, read the article in full at Bond Buyer.


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Big Boi could release his sophomore album, Daddy Fat Sax: Soul Funk Crusader, if he wanted to. So why hasn't he? Well, according to an allhiphop.com, he's gonna let it marinate for a second.
"I'mma marinate on it because I'm on the road. I feel good about it, all the music's sounding great," he said. The Southern rap great claims that he's already completed 17 songs for his upcoming sophomore solo album, which will feature Janelle Monae. "She's got a creative mind, she's got a great spirit. She's a good girl and I'm proud of her. She's making me very proud," he says of his protégé.
As far as the release date for his album goes, Daddy Fat Sax should come sometime before the end of the year. About the project, he reveals, "S-A-X-X-X triple assassin, and the beats are going to be as hard hitting as always. I'mma let everything else be a surprise, and just let it speak for itself."
(Photo: Mark Davis/PictureGroup)
Written by Kim Osorio

redpajama_set_name: RedPajamaCommonCrawl


text:
Cold Storage Safety Fatality File
Home/Cold Storage Safety Fatality File
Two workers killed in ammonia gas leak in cold-storage unit
Two labourers working in a potato cold-storage unit died and two were seriously injured after inhaling ammonia gas that leaked following an explosion in a pipeline late on Saturday night. The incident occurred in the Itaunja police station area.
The injured were admitted to the intensive care unit (ICU) of King George's Medical University (KGMU) Trauma Centre. Their condition is improving gradually, according to the police.
Sources said the control valve of a pipeline carrying ammonia to the Vindeshwari cold-storage facility exploded, causing panic. Four labourers working in the unit at the time inhaled the ammonia. The deceased were identified as Sitapur residents Mishrilal (28) and Dharmendra (27), while Paramanda and Vinod were injured and moved to KGMU. About half-a-kilometre of the area around the facility was cordoned off as a precautionary measure as the fire department brought the gas leak under control.
Cold Storage Safety – Spanish
Farm Workshop Safety Meeting Kit
Farm Workshop Safety Stats and Facts


text:
Q: Join attributes from point into lines (but only lines with certain attribute value) I have point objects with attributes and line objects, where the attributes are missing.
The only connection between these geometries is an angle I calculated. (the labeling of the points has the same angle than the lines)
So I want to join the attributes from the points Into the line object, but only in the line object with the same angle value as the point. (There is a lot of overlap because I have over 10,000 line objects and each point belongs to a specific line)
My idea is it to generate a buffer around the points which hits the lines. Next step would be to check if the angle value matches before joining the attributes and this part is where I'm struggling.
Is there a possibility to do this?
I'm new to GIS.

redpajama_set_name: RedPajamaStackExchange


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text:
Gaming Discussion - Do You Own a VR Device? - View Post
Bristow9091
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Bristow9091 on 09 October 2018
Landale_Star said:
Bristow9091 said:
I don't own a headset, and for the most part I'm still in the "No" camp, as in I don't see myself getting one, but there's been some game releases that have tempted me more than a few times, I just have my worries about space, sound, wires etc. not to mention the price, I think even with a good Black Friday sale the price would still be too high for me to buy into it, but maybe if I get to experience it myself it'll change my mind on it. I love the fact that Dreams will be fully playable in VR, I can imagine some really cool levels/games being made, and I think that game will become a "big" draw for the VR audience too, but again, with how expensive the headsets are, and with new models seemingly coming out every year (PSVR is on its second headset already, right?) making me feel like my purchase will become inferior before I get any worth out of it, I don't know if it's for me yet.
TL;DR No I don't have VR, I'm sort of interested and have considered it a few times, but don't think I'll be biting yet unless I can actually try it beforehand and see if it's for me.
Dreams being in VR does sound cool, but I just realised what the reality might be. I'm sure a few decent games will come from it, but given that VR is a completely different way of playing games, I can see amateurs (like me) making absolutely horrible games that make people sick. Maybe Dreams will restrict the VR creation mode to enforce certain comfort settings, which would then limit the potential. I'm still looking forward to the game though and trying out both the VR and "flat" modes.
Basically, I think there's more that can go wrong when making VR games and have suddenly got less excited about Dreams in VR.
Yeah but those "horrible" games will basically become free advertisements for Dreams, I mean, think of the potential they'd bring. YouTube for example, there's Markiplier (amongst others) that basically scours the internet for the "worst" games he can find, they're fucking horrendous, yet some are so bad they're funny, and it makes for some pretty entertaining videos. I can imagine content creators basically making videos of them dream surfing and just reacting to whatever horrors pop up in the Dreams VR world.
And for that reason I actually hope there are no restrictions on making VR levels, I know (From the quickfire interview with Alex Evans) that you need to be able to complete a VR level in order to publish it, like you can't just have a level without an end, there needs to be something there, so that could eliminate some fears you have, but other than that, they should totally retain level/game creation and not hold anything back from creators, more tools means more experiences, for better or worse! :P
Check out the Definitive Edition of Playstation Nation!


text:
States ranked from first to worst on retirement
By Chris Kahn Chris Kahn's Twitter profile
Mar. 23, 2015 /
Bankrate's "Best and Worst States to Retire" list ranks all 50 states according to their cost of living, crime rate (violent and property crimes), health care quality, state and local tax burden, personal well-being for seniors and weather (temperature, percentage of sunshine, humidity). The overall ranks of 1 to 50 go from best to worst. For more on how we ranked the states, check out our story "How we ranked Best & Worst States to Retire."
Check out how your state ranks for retirement
1 Wyoming 19 5 20 37 1 8
2 Colorado 30 25 6 14 19 3
3 Utah 7 22 19 7 23 6
4 Idaho 3 2 27 21 27 7
5 Virginia 22 4 15 13 21 10
6 Iowa 11 12 4 5 22 39
7 Montana 27 19 8 24 13 9
8 South Dakota 26 11 31 15 3 29
9 Arizona 32 41 2 22 17 5
10 Nebraska 9 20 16 11 26 21
11 Minnesota 33 15 5 1 45 48
12 Maine 38 3 28 4 37 27
13 North Dakota 29 10 23 16 15 43
14 Kansas 10 32 13 25 25 17
15 Vermont 41 1 3 10 42 35
16 New Hampshire 39 7 17 6 7 49
17 Wisconsin 25 13 21 3 46 46
18 Massachusetts 43 21 22 2 40 11
19 Delaware 37 42 7 8 36 18
20 Michigan 18 29 14 17 30 45
21 Pennsylvania 34 16 36 23 41 22
22 Washington 36 36 9 19 24 40
23 Texas 14 38 37 41 4 23
24 North Carolina 28 33 30 30 34 19
25 South Carolina 24 48 26 35 (tie) 9 16
26 Illinois 21 24 32 32 38 36
27 Nevada 35 44 45 43 8 4
28 Florida 31 39 18 35 (tie) 20 28
29 Indiana 5 30 34 40 29 34
30 Tennessee 2 47 40 38 (tie) 6 24
31 California 46 31 10 34 47 2
32 Maryland 40 34 11 27 44 13
33 Georgia 15 35 33 42 16 20
34 Ohio 17 27 41 31 33 37
35 Alabama 12 43 35 33 10 41
36 Mississippi 1 23 44 47 11 42
37 New Mexico 13 50 38 48 14 1
38 Rhode Island 42 18 46 9 43 12
39 Connecticut 48 6 24 12 48 14
40 Oklahoma 4 40 29 49 12 26
41 Oregon 44 28 12 29 35 31
42 Missouri 16 37 39 38 (tie) 18 38
43 Kentucky 6 9 49 45 (tie) 28 33
44 Hawaii 50 26 1 20 31 32
45 New Jersey 45 8 43 18 49 15
46 Louisiana 20 49 48 45 (tie) 5 44
47 West Virginia 23 14 50 50 32 47
48 Alaska 49 46 25 28 2 50
49 New York 47 17 42 26 50 25
50 Arkansas 8 45 47 44 39 30
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Chris Kahn, a research and statistics editor, previously spent 13 years in daily news. He was a national energy writer at The Associated Press from 2008 to 2012. Before that, he covered a variety of subjects at AP, including state and federal courts, legislatures, immigration, sports, and arts and culture in Phoenix, Richmond, Va., and Roanoke, Va. Chris covered the 2010 Gulf of Mexico oil spill and the 2007 Virginia Tech massacre as a member of AP's national breaking news team. He also worked as an education writer at The South Florida Sun-Sentinel from 2004 to 2006. Chris has a master's degree from the Walter Cronkite School of Journalism and Mass Communication at Arizona State University. He also has undergraduate degrees in physics and anthropology from the University of Arizona. He lives in New York City.
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text:
Systemic Factors in Organisational Failures: A Review of some recent high-profile cases
David H. Slater
Carey Dene, Carey, Herefordshire HR2 6NG, United Kingdom
Ben Ale
Technical University Delft, Mekelweg 5, 2628 CD Delft, The Netherlands
This paper explores the various options for organisational structures by examining some high-profile incidents in the light of a number of theories that have been put forward to explain behaviours.
On first inspection, it might be concluded that a common theme emerging from studying the results of such organisational behaviours might be the slavish conformance to "acceptable" or expected "institutionalised" structures which stipulate impenetrable layers of middle management between the "sharp end" teams and the responsible executives and their governing Boards. This so called "clay layer" has been highlighted as a major factor in high profile incidents such as Challenger, Columbia, Chernobyl, Longford, Macondo blow out and many others .
However, on closer examination, there are other examples of corporate failures, (such as the Post Office computerisation scandal) which suggest that the "clay layer" in fact serves another purpose; to allow credible "deniability" for the controlling minds in difficult areas. The Health and Safety at Work Act 1974 maintains that the" person who was the 'controlling mind' of the organisation is personally responsible for the offence. This paper sets out to scrutinise whether these are borne out in practice. and urges that the design and application of organisational structures should be tailored to effective and ethical operation.
SLATER, David H.; ALE, Ben. Systemic Factors in Organisational Failures: A Review of some recent high-profile cases. Medical Research Archives, [S.l.], v. 10, n. 12, dec. 2022. ISSN 2375-1924. Available at: <https://esmed.org/MRA/mra/article/view/3454>. Date accessed: 29 jan. 2023. doi: https://doi.org/10.18103/mra.v10i12.3454.
. Slater DH, Ale BJM. Organisations: Drifting or Dysfunctional? In Leva M, Patelli E, Podofillini P, Wilson S. Eds. Proceedings of the 32nd European Safety and Reliability Conference (ESREL 2022): Singapore: Research Publishing; 2022. ISBN: 978-981-18-5183-4. doi: 10.3850/978-981-18-5183-4_S32-02-197-cd.
. www.legislation.gov.uk/ukpga/1974/37/contents (as per 19/11/2022).
. Deming WE. Quality, productivity and competitive position. Cambridge: Massachusetts Institute of Technology; 1982. ISBN 0911379002.
. Wallis N. The Great Post Office Scandal: the fight to expose a multimillion pound IT disaster which put innocent people in jail. Bath: Bath Publishing; 2021.
. https://www.birminghammail.co.uk/news/midlands-news/calls-urgent-investigation-culture-toxicity-25660367 (as per 03/12/2022)
Slater D, Hollnagel E, MacKinnon R, Sujan M, Carson-Stevens A, Ross, A, Bowie P. A systems analysis of the COVID-19 pandemic response in the United Kingdom, Part 1, The overall context. Safety Science: 2022; 146: 105535. Doi.org/10.1016/j/ssci.2021.105525
Slater D. A Systems Analysis of the UK COVID 19 Pandemic Response: Part 2, Work as imagined vs Work as done. Safety Science: 2021; 146(8):105526. DOI: 10.1016/j.ssci.2021.105526
. Swuste P, Van Gulijk C, Zwaard W. Safety metaphors and theories, a review of the occupational safety literature of the US, UK and The Netherlands, till the first part of the 20th century. Safety Science: 2010;48: 1000–1018.
. LeCoze JC. Crisis Development: Normal Accidents and Beyond. Oxford Research Encyclopedias: 2022: https://oxfordre.com/politics/view/10.1093/acrefore/9780190228637.001.0001/acrefore-9780190228637-e-1557 (as per 18/11/2022).
. Perrow C. Normal Accidents: Living with High-Risk Technologies. New York: Basic Books; 1984.
. Hopkins A. The Limits of Normal Accident Theory. Safety Science: 1999; 2: 93 – 102.
. Bonneuil C, Fressoz JB. The Shock of the Anthropocene: The Earth, History and Us. London: Verso; 2013.
. Turner B.. The Organizational and Interorganizational Development of Disasters. Administrative Science Quarterly: 1976;21(3):378–397. CiteSeerX 10.1.1.393.67. doi:10.2307/2391850. JSTOR 2391850.
. Perrow Charles. Normal Accidents: Living with High-Risk Technologies. New York: Basic Books; 1984.
. La Porte TR, Consolini PM. Working in Practice But Not in Theory. Journal of Public Administration Research and Theory: 1991; J-PART; 1: 19-48.
. Sagan S. The Limits of Safety. Princeton: Princeton University Press; 1993.
. Vaughan D. The Challenger Launch Decision: Risky Technology, Culture, and Deviance at NASA. University Of Chicago Press: 1996. ISBN 978 0226851761.
. Hopkins A. Lessons from Longford. London: Blackwells; 2002. ISBN 9781864686883.
. Le Coze JC. Post Normal Accident. Revisiting Perrow's classic. Boca Raton: FL CRC Press, Taylor & Francis; 2020.
. Downer J. 737-Cabriolet: The Limits of Knowledge and the Sociology of Inevitable Failure. American Journal of Sociology. 2011;117(3):725-762.
. Pritchard S. An Environmental Disaster: Nature, Technology and Politics at Fukushima. Environmental History: 2012;17:219 – 243.
. Starbuck WH, Farjoun M. Organization at the limit: Lessons from the Columbia Disaster. Oxford: Blackwell Publishing; 2005.
. Ale BJM, Slater DH, Hartford DND. The ethical dilemmas of risky decisions. Risk Analysis: 2022;1–15. https://doi.org/10.1111/risa.13893.
. Moore-Bick M. Grenfell Tower Inquiry Phase 1 Report. REPORT of the PUBLIC INQUIRY into the FIRE at GRENFELL TOWER on 14 JUNE 2017. Crown Copyright; 2019. ISBN 978-1-5286-1602-7.
. https://www.infectedbloodinquiry.org.uk/: infected blood inquiry (as per 03/12/2022).
. Peter LJ, Hull R. The Peter Principle. New York: William Morrow & Co Inc; 1969. ISBN: 0062092065
. Peter LJ. The Peter Pyramid: Or, Will We Ever Get the Point? New York: William Morrow and Company; 1986. ISBN 978-0688053802.
. Syed M. Clapped out: Is the NHS broken? Channel 4 Documentary, https://www.channel4.com/programmes/clapped-out-is-the-nhs-broken-dispatches/on-demand/72600-001 (as per 03/12/2022)
. Rezvani Z. Breaking the clay layer: The role of middle managers in safety management. PhD Thesis. Delft: TU Delft; 2018. ISBN 9789461868855, https://doi.org/10.4233/uuid:2e988560-de75-4933-8b6b-f52b31289423
. Heinrich H, Cranniss E. Industrial Accident Prevention, a Scientific Approach. New York: Graw Hill Book Company; 1959.
. Mason R, Mitroff I. Complexity: The nature of real world problems. In Bob de Wit B. Ron Meyer R. Eds. Strategy Synthesis. Stamford: Thomson Publishing; 2003: 26-36.
. Rittel HWJ; Webber MM. Dilemmas in a General Theory of Planning. Policy Sciences: 1973; 4 (2) 155–169. doi:10.1007/bf01405730. S2CID 18634229. Archived from the original on 30 September 2007.Reprinted in Cross, N., ed. Developments in Design Methodology. Chichester, England: John Wiley & Sons; 1984: 135–144.