Insights into the Challenges of Modeling the Atmospheric Boundary Layer

This work approaches the topic of modeling the atmospheric boundary layer in four research projects, which are summarized below. i) The diurnal cycles of near-surface meteorological parameters over Antarctic sea ice in six widely used atmospheric reanalyses were validated against observations from Ice Station Weddell. The station drifted from February through May 1992 and provided the most extensive set of meteorological observations ever collected in the Antarctic sea ice zone. For the radiative and turbulent surface fluxes, both the amplitude and shape of the diurnal cycles varied considerably among different reanalyses. Near-surface temperature, specific humidity, and wind speed in the reanalyses all featured small diurnal ranges, which, in most cases, fell within the uncertainties of the observed cycle. A skill score approach revealed the superiority of the ERA-Interim reanalysis in reproducing the observed diurnal cycles. An explanation for the shortcomings in the reanalyses is their failure to capture the diurnal cycle in cloud cover fraction, which leads to errors in other quantities as well. Apart from the diurnal cycles, NCEP-CFSR gave the best error statistics. ii) The accuracy of prediction of stable atmospheric boundary layers depends on the parameterization of the surface layer which is usually derived from the Monin-Obukhov similarity theory. In this study, several surface-layer models in the format of velocity and potential temperature Deacon numbers were compared to observations from CASES-99, Cardington, and Halley datasets. The comparisons were hindered by a large amount of scatter within and among datasets. Tests utilizing R2 demonstrated that the Quasi-Normal Scale Elimination (QNSE) theory exhibits the best overall performance. Further proof of this was provided by 1D simulations with the Weather Research and Forecasting (WRF) model. iii) The increasing number of physics parameterization schemes adopted in numerical weather forecasting models has resulted in a proliferation of inter-comparison studies in recent years. Many of these studies concentrated on determining which parameterization yields results closest to observations rather than analyzing the reasons underlying the differences. In this work, the performance of two 1.5-order boundary layer parameterizations was studied, the QNSE and Mellor-Yamada-Janjić (MYJ) schemes, in the Weather Research and Forecasting (WRF) model. The objectives were to isolate the effect of stability functions on the near-surface values and vertical profiles of virtual temperature, mixing ratio and wind speed. The results demonstrate that the QNSE stability functions yield better error statistics for 2-m virtual temperature but higher up the errors related to QNSE are slightly larger for virtual temperature and mixing ratio. A surprising finding is the sensitivity of the model results to the choice of the turbulent Prandtl number for neutral stratification (Prt0): in the Monin-Obukhov similarity function for heat, the choice of Prt0 is sometimes more important than the functional form of the similarity function itself. There is a stability-related dependence to this sensitivity: with increasing near-surface stability, the relative importance of the functional form increases. In near-neutral conditions, QNSE exhibits too strong vertical mixing attributed to the applied turbulent kinetic energy subroutine and the stability functions including the effect of Prt0. iv) In recent years, many eddy-diffusivity mass flux (EDMF) planetary boundary layer (PBL) parameterizations have been introduced. Yet, most validations are based on idealized setups and/or single column models. To address this gap, this study focused on the effect the mass flux part has on the performance in the QNSE-EDMF PBL scheme in the WRF model by comparing the results to observations from the CASES-97 field campaign. In addition, two refined versions, one introducing the parameterized clouds to the WRF radiation scheme, and the second adding a different entrainment formulation, were evaluated. The introduction of mass flux reduced errors in the average moisture profile but virtual temperature and wind speed profiles did not change as much. The turbulent flux profiles for modeled virtual potential temperature were little affected, with consistent reasonable agreement with observations, if one allows for biases in the observed data and modeled surface fluxes. However, the water vapor flux divergences from QNSE tend to be more negative than observed, while including the mass flux part tends to make the divergences more positive, the latter at least partially due to deeper model PBLs resulting from excessive model surface virtual temperature fluxes. Further, both virtual potential temperature and water vapor flux profiles display spurious spikes attributed to the way the non-local and local terms interact in the model. The influence of the mass flux schemes extends to 60 – 100-km scale circulation features, which were greatly modified by both the inclusion of mass flux and the new entrainment formulation. Adding mass flux based clouds to the radiation calculation improved the time and space averaged modeled incoming shortwave flux. The choice of the representation for entrainment/detrainment often affected the results to the same extent as adding mass flux did

Insights into the Challenges of Modeling the Atmospheric Boundary Layer

This work approaches the topic of modeling the atmospheric boundary layer in four research projects, which are summarized below. i) The diurnal cycles of near-surface meteorological parameters over Antarctic sea ice in six widely used atmospheric reanalyses were validated against observations from Ice Station Weddell. The station drifted from February through May 1992 and provided the most extensive set of meteorological observations ever collected in the Antarctic sea ice zone. For the radiative and turbulent surface fluxes, both the amplitude and shape of the diurnal cycles varied considerably among different reanalyses. Near-surface temperature, specific humidity, and wind speed in the reanalyses all featured small diurnal ranges, which, in most cases, fell within the uncertainties of the observed cycle. A skill score approach revealed the superiority of the ERA-Interim reanalysis in reproducing the observed diurnal cycles. An explanation for the shortcomings in the reanalyses is their failure to capture the diurnal cycle in cloud cover fraction, which leads to errors in other quantities as well. Apart from the diurnal cycles, NCEP-CFSR gave the best error statistics. ii) The accuracy of prediction of stable atmospheric boundary layers depends on the parameterization of the surface layer which is usually derived from the Monin-Obukhov similarity theory. In this study, several surface-layer models in the format of velocity and potential temperature Deacon numbers were compared to observations from CASES-99, Cardington, and Halley datasets. The comparisons were hindered by a large amount of scatter within and among datasets. Tests utilizing R2 demonstrated that the Quasi-Normal Scale Elimination (QNSE) theory exhibits the best overall performance. Further proof of this was provided by 1D simulations with the Weather Research and Forecasting (WRF) model. iii) The increasing number of physics parameterization schemes adopted in numerical weather forecasting models has resulted in a proliferation of inter-comparison studies in recent years. Many of these studies concentrated on determining which parameterization yields results closest to observations rather than analyzing the reasons underlying the differences. In this work, the performance of two 1.5-order boundary layer parameterizations was studied, the QNSE and Mellor-Yamada-Janjić (MYJ) schemes, in the Weather Research and Forecasting (WRF) model. The objectives were to isolate the effect of stability functions on the near-surface values and vertical profiles of virtual temperature, mixing ratio and wind speed. The results demonstrate that the QNSE stability functions yield better error statistics for 2-m virtual temperature but higher up the errors related to QNSE are slightly larger for virtual temperature and mixing ratio. A surprising finding is the sensitivity of the model results to the choice of the turbulent Prandtl number for neutral stratification (Prt0): in the Monin-Obukhov similarity function for heat, the choice of Prt0 is sometimes more important than the functional form of the similarity function itself. There is a stability-related dependence to this sensitivity: with increasing near-surface stability, the relative importance of the functional form increases. In near-neutral conditions, QNSE exhibits too strong vertical mixing attributed to the applied turbulent kinetic energy subroutine and the stability functions including the effect of Prt0. iv) In recent years, many eddy-diffusivity mass flux (EDMF) planetary boundary layer (PBL) parameterizations have been introduced. Yet, most validations are based on idealized setups and/or single column models. To address this gap, this study focused on the effect the mass flux part has on the performance in the QNSE-EDMF PBL scheme in the WRF model by comparing the results to observations from the CASES-97 field campaign. In addition, two refined versions, one introducing the parameterized clouds to the WRF radiation scheme, and the second adding a different entrainment formulation, were evaluated. The introduction of mass flux reduced errors in the average moisture profile but virtual temperature and wind speed profiles did not change as much. The turbulent flux profiles for modeled virtual potential temperature were little affected, with consistent reasonable agreement with observations, if one allows for biases in the observed data and modeled surface fluxes. However, the water vapor flux divergences from QNSE tend to be more negative than observed, while including the mass flux part tends to make the divergences more positive, the latter at least partially due to deeper model PBLs resulting from excessive model surface virtual temperature fluxes. Further, both virtual potential temperature and water vapor flux profiles display spurious spikes attributed to the way the non-local and local terms interact in the model. The influence of the mass flux schemes extends to 60 – 100-km scale circulation features, which were greatly modified by both the inclusion of mass flux and the new entrainment formulation. Adding mass flux based clouds to the radiation calculation improved the time and space averaged modeled incoming shortwave flux. The choice of the representation for entrainment/detrainment often affected the results to the same extent as adding mass flux did

Validation of the Diurnal Cycles in Atmospheric Reanalyses Over Antarctic Sea Ice

The diurnal cycles of near-surface meteorological parameters over Antarctic sea ice in six widely used atmospheric reanalyses are validated against observations from Ice Station Weddell. The station drifted from February through May 1992 and provided the most extensive set of meteorological observations ever collected in the Antarctic sea ice zone. For the radiative and turbulent surface fluxes, both the amplitude and shape of the diurnal cycles vary considerably among different reanalyses. Near-surface temperature, specific humidity, and wind speed in the reanalyses all feature small diurnal ranges, which, in most cases, fall within the uncertainties of the observed cycle. A skill score approach revealed the superiority of the ERA-Interim reanalysis in reproducing the observed diurnal cycles. An explanation for the shortcomings in the reanalyses is their failure to capture the diurnal cycle in cloud cover fraction, which leads to errors in other quantities as well. Apart from the diurnal cycles, NCEP-CFSR gave the best error statistics

The Importance of Surface Layer Parameterization in Modeling of Stable Atmospheric Boundary Layers

The accuracy of prediction of stable atmospheric boundary layers depends on the parameterization of the surface layer which is usually derived from the Monin–Obukhov similarity theory. In this article, several surface-layer models in the format of velocity and potential temperature Deacon numbers are compared with observations from CASES99, Cardington, and Halley datasets. The comparisons were hindered by a large amount of scatter within and among datasets. Tests utilizing R2 demonstrated that the quasi-normal scale elimination (QNSE) theory exhibits the best overall performance. Further proof of this was provided by 1D simulations with the Weather Research and Forecasting (WRF) model

Methodical Assessment of the Differences Between the QNSE and MYJ PBL Schemes for Stable Conditions

The increasing number of physics parametrization schemes adopted in numerical weather forecasting models has resulted in a proliferation of intercomparison studies in recent years. Many of these studies concentrated on determining which parametrization yields results closest to observations rather than analyzing the reasons underlying the differences. In this work, we study the performance of two 1.5-order boundary layer parameterizations, the quasi-normal scale elimination (QNSE) and Mellor–Yamada–Janjić (MYJ) schemes, in the weather research and forecasting model. Our objectives are to isolate the effect of stability functions on the near-surface values and vertical profiles of virtual temperature, mixing ratio and wind speed. The results demonstrate that the QNSE stability functions yield better error statistics for 2 m virtual temperature but higher up the errors related to QNSE are slightly larger for virtual temperature and mixing ratio. A surprising finding is the sensitivity of the model results to the choice of the turbulent Prandtl number for neutral stratification (Prt0): in the Monin–Obukhov similarity function for heat, the choice of Prt0 is sometimes more important than the functional form of the similarity function itself. There is a stability-related dependence to this sensitivity: with increasing near-surface stability, the relative importance of the functional form increases. In near-neutral conditions, QNSE exhibits too strong vertical mixing attributed to the applied turbulent kinetic energy subroutine and the stability functions, including the effect of Prt0