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- Author or Editor: Kiran Alapaty x
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Abstract
Two geometrical and two advection-equivalent spatial interpolation schemes were tested in providing lateral boundary conditions to a nested grid domain. Geometric interpolation schemes used in this study are a zeroth- order and a quadratic scheme, while the two advection-equivalent interpolation schemes were based on upwind and Bott’s advection schemes. The test problem involves an initially cone-shaped distribution of a scalar advected from a coarse to a fine grid. Simulation results were compared to the exact solution to study magnitude and phase characteristics of each scheme. Results indicated that Bott’s advection-equivalent interpolation scheme provided better interface conditions and, consequently, a more accurate transition of the signal from a coarse to a fine grid.
Abstract
Two geometrical and two advection-equivalent spatial interpolation schemes were tested in providing lateral boundary conditions to a nested grid domain. Geometric interpolation schemes used in this study are a zeroth- order and a quadratic scheme, while the two advection-equivalent interpolation schemes were based on upwind and Bott’s advection schemes. The test problem involves an initially cone-shaped distribution of a scalar advected from a coarse to a fine grid. Simulation results were compared to the exact solution to study magnitude and phase characteristics of each scheme. Results indicated that Bott’s advection-equivalent interpolation scheme provided better interface conditions and, consequently, a more accurate transition of the signal from a coarse to a fine grid.
Abstract
Two mesoscale circulations, the Sandhills circulation and the sea breeze, influence the initiation of deep convection over the Sandhills and the coast in the Carolinas during the summer months. The interaction of these two circulations causes additional convection in this coastal region. Accurate representation of mesoscale convection is difficult as numerical models have problems with the prediction of the timing, amount, and location of precipitation. To address this issue, the authors have incorporated modifications to the Kain–Fritsch (KF) convective parameterization scheme and evaluated these mesoscale interactions using a high-resolution numerical model. The modifications include changes to the subgrid-scale cloud formulation, the convective turnover time scale, and the formulation of the updraft entrainment rates. The use of a grid-scaling adjustment parameter modulates the impact of the KF scheme as a function of the horizontal grid spacing used in a simulation. Results indicate that the impact of this modified cumulus parameterization scheme is more effective on domains with coarser grid sizes. Other results include a decrease in surface and near-surface temperatures in areas of deep convection (due to the inclusion of the effects of subgrid-scale clouds on the radiation), improvement in the timing of convection, and an increase in the strength of deep convection.
Abstract
Two mesoscale circulations, the Sandhills circulation and the sea breeze, influence the initiation of deep convection over the Sandhills and the coast in the Carolinas during the summer months. The interaction of these two circulations causes additional convection in this coastal region. Accurate representation of mesoscale convection is difficult as numerical models have problems with the prediction of the timing, amount, and location of precipitation. To address this issue, the authors have incorporated modifications to the Kain–Fritsch (KF) convective parameterization scheme and evaluated these mesoscale interactions using a high-resolution numerical model. The modifications include changes to the subgrid-scale cloud formulation, the convective turnover time scale, and the formulation of the updraft entrainment rates. The use of a grid-scaling adjustment parameter modulates the impact of the KF scheme as a function of the horizontal grid spacing used in a simulation. Results indicate that the impact of this modified cumulus parameterization scheme is more effective on domains with coarser grid sizes. Other results include a decrease in surface and near-surface temperatures in areas of deep convection (due to the inclusion of the effects of subgrid-scale clouds on the radiation), improvement in the timing of convection, and an increase in the strength of deep convection.
Abstract
Current land surface schemes used for mesoscale weather forecast models use the Jarvis-type stomatal resistance formulations for representing the vegetation transpiration processes. The Jarvis scheme, however, despite its robustness, needs significant tuning of the hypothetical minimum-stomatal resistance term to simulate surface energy balances. In this study, the authors show that the Jarvis-type stomatal resistance/transpiration model can be efficiently replaced in a coupled land–atmosphere model with a photosynthesis-based scheme and still achieve dynamically consistent results. To demonstrate this transformative potential, the authors developed and coupled a photosynthesis, gas exchange–based surface evapotranspiration model (GEM) as a land surface scheme for mesoscale weather forecasting model applications. The GEM was dynamically coupled with a prognostic soil moisture–soil temperature model and an atmospheric boundary layer (ABL) model. This coupled system was then validated over different natural surfaces including temperate C4 vegetation (prairie grass and corn field) and C3 vegetation (soybean, fallow, and hardwood forest) under contrasting surface conditions (such as different soil moisture and leaf area index). Results indicated that the coupled model was able to realistically simulate the surface fluxes and the boundary layer characteristics over different landscapes. The surface energy fluxes, particularly for latent heat, are typically within 10%–20% of the observations without any tuning of the biophysical–vegetation characteristics, and the response to the changes in the surface characteristics is consistent with observations and theory. This result shows that photosynthesis-based transpiration/stomatal resistance models such as GEM, despite various complexities, can be applied for mesoscale weather forecasting applications. Future efforts for understanding the different scaling parameterizations and for correcting errors for low soil moisture and/or wilting vegetation conditions are necessary to improve model performance. Results from this study suggest that the GEM approach using the photosynthesis-based soil vegetation atmosphere transfer (SVAT) scheme is thus superior to the Jarvis-based approaches. Currently GEM is being implemented within the Noah land surface model for the community Weather Research and Forecasting (WRF) Advanced Research Version Modeling System (ARW) and the NCAR high-resolution land data assimilation system (HRLDAS), and validation is under way.
Abstract
Current land surface schemes used for mesoscale weather forecast models use the Jarvis-type stomatal resistance formulations for representing the vegetation transpiration processes. The Jarvis scheme, however, despite its robustness, needs significant tuning of the hypothetical minimum-stomatal resistance term to simulate surface energy balances. In this study, the authors show that the Jarvis-type stomatal resistance/transpiration model can be efficiently replaced in a coupled land–atmosphere model with a photosynthesis-based scheme and still achieve dynamically consistent results. To demonstrate this transformative potential, the authors developed and coupled a photosynthesis, gas exchange–based surface evapotranspiration model (GEM) as a land surface scheme for mesoscale weather forecasting model applications. The GEM was dynamically coupled with a prognostic soil moisture–soil temperature model and an atmospheric boundary layer (ABL) model. This coupled system was then validated over different natural surfaces including temperate C4 vegetation (prairie grass and corn field) and C3 vegetation (soybean, fallow, and hardwood forest) under contrasting surface conditions (such as different soil moisture and leaf area index). Results indicated that the coupled model was able to realistically simulate the surface fluxes and the boundary layer characteristics over different landscapes. The surface energy fluxes, particularly for latent heat, are typically within 10%–20% of the observations without any tuning of the biophysical–vegetation characteristics, and the response to the changes in the surface characteristics is consistent with observations and theory. This result shows that photosynthesis-based transpiration/stomatal resistance models such as GEM, despite various complexities, can be applied for mesoscale weather forecasting applications. Future efforts for understanding the different scaling parameterizations and for correcting errors for low soil moisture and/or wilting vegetation conditions are necessary to improve model performance. Results from this study suggest that the GEM approach using the photosynthesis-based soil vegetation atmosphere transfer (SVAT) scheme is thus superior to the Jarvis-based approaches. Currently GEM is being implemented within the Noah land surface model for the community Weather Research and Forecasting (WRF) Advanced Research Version Modeling System (ARW) and the NCAR high-resolution land data assimilation system (HRLDAS), and validation is under way.
Abstract
Stomatal resistance (R s ) forms a pivotal component of the surface energy budget and of the terrestrial biosphere–atmosphere interactions. Using a statistical–graphical technique, the R s -related interactions between different atmospheric and physiological variables are resolved explicitly from observations made during the First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment (FIFE). A similar analysis was undertaken for the R s parameterization schemes, as used in the present models. Three physiological schemes (the Ball–Woodrow–Berry, Kim and Verma, and Jacobs) and one operational Jarvis-type scheme were evaluated in terms of their ability to replicate the terrestrial biosphere–atmosphere interactions.
It was found that all of the R s parameterization schemes have similar qualitative behavior for routine meteorological applications (without carbon assimilation). Compared to the observations, there was no significant difference found in employing either the relative humidity or the vapor pressure deficit as the humidity descriptor in the analysis. Overall, the relative humidity–based interactions were more linear than the vapor pressure deficit and hence could be considered more convenient in the scaling exercises. It was found that with high photosynthesis rates, all of the schemes had similar behavior. It was found with low assimilation rates, however, that the discrepancies and nonlinearity in the interactions, as well as the uncertainties, were exaggerated.
Introduction of CO2 into the analysis created a different dimension to the problem. It was found that for CO2-based studies, the outcome had high uncertainty, as the interactions were nonlinear and the schemes could not converge onto a single interpretive scenario. This study highlights the secondary or indirect effects, and the interactions are crucial prior to evaluation of the climate and terrestrial biosphere–related changes even in the boundary layer perspective. Overall, it was found that direct and indirect effects could lead the system convergence toward different scenarios and have to be explicitly considered for atmospheric applications at all scales.
Abstract
Stomatal resistance (R s ) forms a pivotal component of the surface energy budget and of the terrestrial biosphere–atmosphere interactions. Using a statistical–graphical technique, the R s -related interactions between different atmospheric and physiological variables are resolved explicitly from observations made during the First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment (FIFE). A similar analysis was undertaken for the R s parameterization schemes, as used in the present models. Three physiological schemes (the Ball–Woodrow–Berry, Kim and Verma, and Jacobs) and one operational Jarvis-type scheme were evaluated in terms of their ability to replicate the terrestrial biosphere–atmosphere interactions.
It was found that all of the R s parameterization schemes have similar qualitative behavior for routine meteorological applications (without carbon assimilation). Compared to the observations, there was no significant difference found in employing either the relative humidity or the vapor pressure deficit as the humidity descriptor in the analysis. Overall, the relative humidity–based interactions were more linear than the vapor pressure deficit and hence could be considered more convenient in the scaling exercises. It was found that with high photosynthesis rates, all of the schemes had similar behavior. It was found with low assimilation rates, however, that the discrepancies and nonlinearity in the interactions, as well as the uncertainties, were exaggerated.
Introduction of CO2 into the analysis created a different dimension to the problem. It was found that for CO2-based studies, the outcome had high uncertainty, as the interactions were nonlinear and the schemes could not converge onto a single interpretive scenario. This study highlights the secondary or indirect effects, and the interactions are crucial prior to evaluation of the climate and terrestrial biosphere–related changes even in the boundary layer perspective. Overall, it was found that direct and indirect effects could lead the system convergence toward different scenarios and have to be explicitly considered for atmospheric applications at all scales.
Abstract
The Weather Research and Forecasting Model with Aerosol–Cloud Interactions (WRF-ACI) is developed for studying aerosol effects on gridscale and subgrid-scale clouds using common aerosol activation and ice nucleation formulations and double-moment cloud microphysics in a scale-aware subgrid-scale parameterization scheme. Comparisons of both the standard WRF and WRF-ACI models’ results for a summer season against satellite and reanalysis estimates show that the WRF-ACI system improves the simulation of cloud liquid and ice water paths. Correlation coefficients for nearly all evaluated parameters are improved, while other variables show slight degradation. Results indicate a strong cloud lifetime effect from current climatological aerosols increasing domain average cloud liquid water path and reducing domain average precipitation as compared to a simulation with aerosols reduced by 90%. Increased cloud-top heights indicate a thermodynamic invigoration effect, but the impact of thermodynamic invigoration on precipitation is overwhelmed by the cloud lifetime effect. A combination of cloud lifetime and cloud albedo effects increases domain average shortwave cloud forcing by ~3.0 W m−2. Subgrid-scale clouds experience a stronger response to aerosol levels, while gridscale clouds are subject to thermodynamic feedbacks because of the design of the WRF modeling framework. The magnitude of aerosol indirect effects is shown to be sensitive to the choice of autoconversion parameterization used in both the gridscale and subgrid-scale cloud microphysics, but spatial patterns remain qualitatively similar. These results indicate that the WRF-ACI model provides the community with a computationally efficient tool for exploring aerosol–cloud interactions.
Abstract
The Weather Research and Forecasting Model with Aerosol–Cloud Interactions (WRF-ACI) is developed for studying aerosol effects on gridscale and subgrid-scale clouds using common aerosol activation and ice nucleation formulations and double-moment cloud microphysics in a scale-aware subgrid-scale parameterization scheme. Comparisons of both the standard WRF and WRF-ACI models’ results for a summer season against satellite and reanalysis estimates show that the WRF-ACI system improves the simulation of cloud liquid and ice water paths. Correlation coefficients for nearly all evaluated parameters are improved, while other variables show slight degradation. Results indicate a strong cloud lifetime effect from current climatological aerosols increasing domain average cloud liquid water path and reducing domain average precipitation as compared to a simulation with aerosols reduced by 90%. Increased cloud-top heights indicate a thermodynamic invigoration effect, but the impact of thermodynamic invigoration on precipitation is overwhelmed by the cloud lifetime effect. A combination of cloud lifetime and cloud albedo effects increases domain average shortwave cloud forcing by ~3.0 W m−2. Subgrid-scale clouds experience a stronger response to aerosol levels, while gridscale clouds are subject to thermodynamic feedbacks because of the design of the WRF modeling framework. The magnitude of aerosol indirect effects is shown to be sensitive to the choice of autoconversion parameterization used in both the gridscale and subgrid-scale cloud microphysics, but spatial patterns remain qualitatively similar. These results indicate that the WRF-ACI model provides the community with a computationally efficient tool for exploring aerosol–cloud interactions.
Abstract
Many convective parameterization schemes define a convective adjustment time scale τ as the time allowed for dissipation of convective available potential energy (CAPE). The Kain–Fritsch scheme defines τ based on an estimate of the advective time period for deep convective clouds within a grid cell, with limits of 1800 and 3600 s, based on practical cloud-lifetime considerations. In simulations from the Weather Research and Forecasting (WRF) Model using 12-km grid spacing, the value of τ often defaults to the lower limit, resulting in relatively rapid thermodynamics adjustments and high precipitation rates. Herein, a new computation for τ in the Kain–Fritsch scheme is implemented based on the depth of the buoyant layer and the convective velocity scale. This new τ formulation is applied using 12- and 36-km model grid spacing in conjunction with a previous modification that takes into account the radiation effects of parameterized convective clouds. The dynamically computed convective adjustment time scale is shown to reduce the precipitation bias by approximately 15% while also providing improved simulations of inland rainfall from tropical storms.
Abstract
Many convective parameterization schemes define a convective adjustment time scale τ as the time allowed for dissipation of convective available potential energy (CAPE). The Kain–Fritsch scheme defines τ based on an estimate of the advective time period for deep convective clouds within a grid cell, with limits of 1800 and 3600 s, based on practical cloud-lifetime considerations. In simulations from the Weather Research and Forecasting (WRF) Model using 12-km grid spacing, the value of τ often defaults to the lower limit, resulting in relatively rapid thermodynamics adjustments and high precipitation rates. Herein, a new computation for τ in the Kain–Fritsch scheme is implemented based on the depth of the buoyant layer and the convective velocity scale. This new τ formulation is applied using 12- and 36-km model grid spacing in conjunction with a previous modification that takes into account the radiation effects of parameterized convective clouds. The dynamically computed convective adjustment time scale is shown to reduce the precipitation bias by approximately 15% while also providing improved simulations of inland rainfall from tropical storms.
Abstract
Efforts to improve the prediction accuracy of high-resolution (1–10 km) surface precipitation distribution and variability are of vital importance to local aspects of air pollution, wet deposition, and regional climate. However, precipitation biases and errors can occur at these spatial scales due to uncertainties in initial meteorological conditions and/or grid-scale cloud microphysics schemes. In particular, it is still unclear to what extent a subgrid-scale convection scheme could be modified to bring in scale awareness for improving high-resolution short-term precipitation forecasts in the WRF Model. To address these issues, the authors introduced scale-aware parameterized cloud dynamics for high-resolution forecasts by making several changes to the Kain–Fritsch (KF) convective parameterization scheme in the WRF Model. These changes include subgrid-scale cloud–radiation interactions, a dynamic adjustment time scale, impacts of cloud updraft mass fluxes on grid-scale vertical velocity, and lifting condensation level–based entrainment methodology that includes scale dependency.
A series of 48-h retrospective forecasts using a combination of three treatments of convection (KF, updated KF, and the use of no cumulus parameterization), two cloud microphysics schemes, and two types of initial condition datasets were performed over the U.S. southern Great Plains on 9- and 3-km grid spacings during the summers of 2002 and 2010. Results indicate that 1) the source of initial conditions plays a key role in high-resolution precipitation forecasting, and 2) the authors’ updated KF scheme greatly alleviates the excessive precipitation at 9-km grid spacing and improves results at 3-km grid spacing as well. Overall, the study found that the updated KF scheme incorporated into a high-resolution model does provide better forecasts for precipitation location and intensity.
Abstract
Efforts to improve the prediction accuracy of high-resolution (1–10 km) surface precipitation distribution and variability are of vital importance to local aspects of air pollution, wet deposition, and regional climate. However, precipitation biases and errors can occur at these spatial scales due to uncertainties in initial meteorological conditions and/or grid-scale cloud microphysics schemes. In particular, it is still unclear to what extent a subgrid-scale convection scheme could be modified to bring in scale awareness for improving high-resolution short-term precipitation forecasts in the WRF Model. To address these issues, the authors introduced scale-aware parameterized cloud dynamics for high-resolution forecasts by making several changes to the Kain–Fritsch (KF) convective parameterization scheme in the WRF Model. These changes include subgrid-scale cloud–radiation interactions, a dynamic adjustment time scale, impacts of cloud updraft mass fluxes on grid-scale vertical velocity, and lifting condensation level–based entrainment methodology that includes scale dependency.
A series of 48-h retrospective forecasts using a combination of three treatments of convection (KF, updated KF, and the use of no cumulus parameterization), two cloud microphysics schemes, and two types of initial condition datasets were performed over the U.S. southern Great Plains on 9- and 3-km grid spacings during the summers of 2002 and 2010. Results indicate that 1) the source of initial conditions plays a key role in high-resolution precipitation forecasting, and 2) the authors’ updated KF scheme greatly alleviates the excessive precipitation at 9-km grid spacing and improves results at 3-km grid spacing as well. Overall, the study found that the updated KF scheme incorporated into a high-resolution model does provide better forecasts for precipitation location and intensity.
Abstract
Objective analysis and diagnostic methods are used to provide hourly meteorological fields to many air quality simulation models. The viability of using predictions from the Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model version 4 (MM4) together with four-dimensional data assimilation, technique to provide meteorological information to the U.S. EPA Regional Oxidant Model (ROM) was studied. Two numerical simulations were performed for eight days using the ROM for a domain covering the eastern United States. In the first case, diagnostically analyzed data were used to provide meteorological conditions, while in the second case the MM4's prognostic data were used. Comparisons of processed diagnostic and prognostic meteorological data indicated differences in dynamical, thermodynamical, and other derived variables. Uncertainties and forecast errors present in the predicted vertical temperature profiles led to estimation of lower mixed-layer heights (∼ 30%–50%) and a smaller diurnal range of atmospheric temperatures (∼ 2 K) compared with those obtained from the diagnostic data. Comparison of area-averaged horizontal winds for four subdomains indicated minor differences (∼ 1–2 m s−1). These differences systematically affected the estimation of other derived meteorological parameters, such as friction velocity and sensible heat flux. Processed emission data also showed some differences (∼ 1–5 ppb h−1) that resulted from the differing characteristics of the diagnostic and prognostic meteorological data.
Comparison of predicted concentrations of primary (emitted) chemical species such as NO x and reactive organic gases in the two numerical simulations indicated higher values (1–5 and 1–25 ppb, respectively) when the prognostic meteorological data were used. This result was consistent with the lower estimated values of the ROM's layer 1 and layer 2 heights (planetary boundary layer) with the prognostic meteorology. However, comparison of predicted ozone concentrations did not indicate similar features. Area averages of predicted concentrations of ozone for four subdomains indicated both increases and decreases (+1 5 to −10 ppb) over the area averages predicted by the ROM using diagnostic meteorological data. These results indicate that the prediction of trace gas concentrations and the nonlinearity in the model's chemistry are sensitive to the type of meteorological input used.
Abstract
Objective analysis and diagnostic methods are used to provide hourly meteorological fields to many air quality simulation models. The viability of using predictions from the Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model version 4 (MM4) together with four-dimensional data assimilation, technique to provide meteorological information to the U.S. EPA Regional Oxidant Model (ROM) was studied. Two numerical simulations were performed for eight days using the ROM for a domain covering the eastern United States. In the first case, diagnostically analyzed data were used to provide meteorological conditions, while in the second case the MM4's prognostic data were used. Comparisons of processed diagnostic and prognostic meteorological data indicated differences in dynamical, thermodynamical, and other derived variables. Uncertainties and forecast errors present in the predicted vertical temperature profiles led to estimation of lower mixed-layer heights (∼ 30%–50%) and a smaller diurnal range of atmospheric temperatures (∼ 2 K) compared with those obtained from the diagnostic data. Comparison of area-averaged horizontal winds for four subdomains indicated minor differences (∼ 1–2 m s−1). These differences systematically affected the estimation of other derived meteorological parameters, such as friction velocity and sensible heat flux. Processed emission data also showed some differences (∼ 1–5 ppb h−1) that resulted from the differing characteristics of the diagnostic and prognostic meteorological data.
Comparison of predicted concentrations of primary (emitted) chemical species such as NO x and reactive organic gases in the two numerical simulations indicated higher values (1–5 and 1–25 ppb, respectively) when the prognostic meteorological data were used. This result was consistent with the lower estimated values of the ROM's layer 1 and layer 2 heights (planetary boundary layer) with the prognostic meteorology. However, comparison of predicted ozone concentrations did not indicate similar features. Area averages of predicted concentrations of ozone for four subdomains indicated both increases and decreases (+1 5 to −10 ppb) over the area averages predicted by the ROM using diagnostic meteorological data. These results indicate that the prediction of trace gas concentrations and the nonlinearity in the model's chemistry are sensitive to the type of meteorological input used.
Abstract
Large errors in atmospheric boundary layer (ABL) simulations can be caused by inaccuracies in the specification of surface characteristics in addition to assumptions and simplifications made in boundary layer formulations or other model deficiencies. For certain applications, such as air quality studies, these errors can have significant effects. To reduce such errors, a continuous surface data assimilation technique is developed. In this technique, surface-layer temperature and water vapor mixing ratio are directly assimilated by using the analyzed surface data. Then, the difference between the observations and model results is used to calculate adjustments to the surface fluxes of sensible and latent heat. These adjustments are then used to calculate a new estimate of the ground temperature, thereby affecting the simulated surface fluxes on the subsequent time step. This indirect data assimilation is applied simultaneously with the direct assimilation of surface data in the model's lowest layer, thereby maintaining greater consistency between the ground temperature and the surface-layer mass-field variables. A one-dimensional model was used to study the improvements that result from applying this technique for ABL simulations in two cases. It was found that application of the new technique led to significant reductions in ABL modeling errors.
Abstract
Large errors in atmospheric boundary layer (ABL) simulations can be caused by inaccuracies in the specification of surface characteristics in addition to assumptions and simplifications made in boundary layer formulations or other model deficiencies. For certain applications, such as air quality studies, these errors can have significant effects. To reduce such errors, a continuous surface data assimilation technique is developed. In this technique, surface-layer temperature and water vapor mixing ratio are directly assimilated by using the analyzed surface data. Then, the difference between the observations and model results is used to calculate adjustments to the surface fluxes of sensible and latent heat. These adjustments are then used to calculate a new estimate of the ground temperature, thereby affecting the simulated surface fluxes on the subsequent time step. This indirect data assimilation is applied simultaneously with the direct assimilation of surface data in the model's lowest layer, thereby maintaining greater consistency between the ground temperature and the surface-layer mass-field variables. A one-dimensional model was used to study the improvements that result from applying this technique for ABL simulations in two cases. It was found that application of the new technique led to significant reductions in ABL modeling errors.
Abstract
The flux-adjusting surface data assimilation system (FASDAS) is developed to provide continuous adjustments for initial soil moisture and temperature and for surface air temperature and water vapor mixing ratio for mesoscale models. In the FASDAS approach, surface air temperature and water vapor mixing ratio are directly assimilated by using the analyzed surface observations. Then, the difference between the analyzed surface observations and model predictions of surface layer temperature and water vapor mixing ratio are converted into respective heat fluxes, referred to as adjustment heat fluxes of sensible and latent heat. These adjustment heat fluxes are then used in the prognostic equations for soil temperature and moisture via indirect assimilation in the form of several new adjustment evaporative fluxes. Thus, simulated surface fluxes for the subsequent model time step are affected such that the predicted surface air temperature and water vapor mixing ratio conform more closely to observations. The simultaneous application of indirect and direct data assimilation maintains greater consistency between the soil temperature–moisture and the surface layer mass-field variables. The FASDAS is coupled to a land surface submodel in a three-dimensional mesoscale model and tests are performed for a 10-day period with three one-way nested domains. The FASDAS is applied in the analysis nudging mode for two coarse-resolution nested domains and in the observational nudging mode for a fine-resolution nested domain. Further, the effects of FASDAS on two different initial specifications of a three-dimensional soil moisture field are also studied. Results indicate that the FASDAS consistently improved the accuracy of the model simulations.
Abstract
The flux-adjusting surface data assimilation system (FASDAS) is developed to provide continuous adjustments for initial soil moisture and temperature and for surface air temperature and water vapor mixing ratio for mesoscale models. In the FASDAS approach, surface air temperature and water vapor mixing ratio are directly assimilated by using the analyzed surface observations. Then, the difference between the analyzed surface observations and model predictions of surface layer temperature and water vapor mixing ratio are converted into respective heat fluxes, referred to as adjustment heat fluxes of sensible and latent heat. These adjustment heat fluxes are then used in the prognostic equations for soil temperature and moisture via indirect assimilation in the form of several new adjustment evaporative fluxes. Thus, simulated surface fluxes for the subsequent model time step are affected such that the predicted surface air temperature and water vapor mixing ratio conform more closely to observations. The simultaneous application of indirect and direct data assimilation maintains greater consistency between the soil temperature–moisture and the surface layer mass-field variables. The FASDAS is coupled to a land surface submodel in a three-dimensional mesoscale model and tests are performed for a 10-day period with three one-way nested domains. The FASDAS is applied in the analysis nudging mode for two coarse-resolution nested domains and in the observational nudging mode for a fine-resolution nested domain. Further, the effects of FASDAS on two different initial specifications of a three-dimensional soil moisture field are also studied. Results indicate that the FASDAS consistently improved the accuracy of the model simulations.
