The mesoscale numerical weather prediction model (MM4) in which the computations of the turbulent exchange coefficient in the boundary layer and surface fluxes are improved, is used to study the influences of boundary...The mesoscale numerical weather prediction model (MM4) in which the computations of the turbulent exchange coefficient in the boundary layer and surface fluxes are improved, is used to study the influences of boundary layer parameterization schemes on the predictive results of the mesoscale model. Seven different experiment schemes (including the original MM4 model) designed in this paper are tested by the observational data of several heavy rain cases so as to find an improved boundary layer parameterization scheme in the mesoscale meteorological model. The results show that all the seven different boundary layer parameterization schemes have some influences on the forecasts of precipitation intensity, distribution of rain area, vertical velocity, vorticity and divergence fields, and the improved schemes in this paper can improve the precipitation forecast. Key words Boundary layer parameterization - Mesoscale numerical weather prediction (MNWP) - Turbulent exchange coefficient - Surface fluxes - Heavy rain This paper was supported by the National Natural Science Foundation of China (Grant No. 49875005 and No. 49735180).展开更多
Turbulent fluxes were measured by an eddy covariance system at three levels over an intricate land surface on the southern part of the Loess Plateau, consisting of heterogeneous flat terrain and a large valley 500 maw...Turbulent fluxes were measured by an eddy covariance system at three levels over an intricate land surface on the southern part of the Loess Plateau, consisting of heterogeneous flat terrain and a large valley 500 maway from the observation site to the southeast. The surface roughness length, the seasonal variation of bulk transfer coefficient for sensible heat (CH), and the seasonal variation of surface moisture availability (β) were also analyzed based on the observation. The flux footprint was carefully considered in this study. A relatively dry period of the experimental area existed from June to the first week of July 2004 when the land surface offered turbulent energy to the atmospheric surface layer mainly by sensible heat flux with a maximum value of around 230 Wm-2. A wet duration lasted from the second week of July to the end of September 2004 with very frequent rainfall events in conditions when the winds were mainly from the southeast;latent heat flux was dominant during the wet season and reached a peak value of around 280 Wm-2. The surface parameters of CH and β were calculated when the mean winds coming from the flat terrain, i.e., from the northwest direction. The values of CH ranged between 0.004 and 0.006 during the observational year of June 2004 to June 2005. The surface moisture availability β changed with seasons as anticipated with high values during June and July 2004 and lowest values around0.03 inFebruary 2005. Its peak value of 0.91 occurred in July;the mean value of β during the wet season was 0.29. Furthermore, the relationship between the surface soil water content and β indicated that changes in soil water content contributed much to variations of surface moisture availability β.展开更多
文摘The mesoscale numerical weather prediction model (MM4) in which the computations of the turbulent exchange coefficient in the boundary layer and surface fluxes are improved, is used to study the influences of boundary layer parameterization schemes on the predictive results of the mesoscale model. Seven different experiment schemes (including the original MM4 model) designed in this paper are tested by the observational data of several heavy rain cases so as to find an improved boundary layer parameterization scheme in the mesoscale meteorological model. The results show that all the seven different boundary layer parameterization schemes have some influences on the forecasts of precipitation intensity, distribution of rain area, vertical velocity, vorticity and divergence fields, and the improved schemes in this paper can improve the precipitation forecast. Key words Boundary layer parameterization - Mesoscale numerical weather prediction (MNWP) - Turbulent exchange coefficient - Surface fluxes - Heavy rain This paper was supported by the National Natural Science Foundation of China (Grant No. 49875005 and No. 49735180).
文摘Turbulent fluxes were measured by an eddy covariance system at three levels over an intricate land surface on the southern part of the Loess Plateau, consisting of heterogeneous flat terrain and a large valley 500 maway from the observation site to the southeast. The surface roughness length, the seasonal variation of bulk transfer coefficient for sensible heat (CH), and the seasonal variation of surface moisture availability (β) were also analyzed based on the observation. The flux footprint was carefully considered in this study. A relatively dry period of the experimental area existed from June to the first week of July 2004 when the land surface offered turbulent energy to the atmospheric surface layer mainly by sensible heat flux with a maximum value of around 230 Wm-2. A wet duration lasted from the second week of July to the end of September 2004 with very frequent rainfall events in conditions when the winds were mainly from the southeast;latent heat flux was dominant during the wet season and reached a peak value of around 280 Wm-2. The surface parameters of CH and β were calculated when the mean winds coming from the flat terrain, i.e., from the northwest direction. The values of CH ranged between 0.004 and 0.006 during the observational year of June 2004 to June 2005. The surface moisture availability β changed with seasons as anticipated with high values during June and July 2004 and lowest values around0.03 inFebruary 2005. Its peak value of 0.91 occurred in July;the mean value of β during the wet season was 0.29. Furthermore, the relationship between the surface soil water content and β indicated that changes in soil water content contributed much to variations of surface moisture availability β.
