Reply on RC1

Question

1. What is the WRF model's grid type?

2. What advection schemes used?

3. What is the purpose of introducing random potential temperature perturbations during model initialization?

4. How are initial thermodynamic profiles in simulations designed?

Accepted Answer

The WRF model uses a horizontally and vertically staggered grid, specifically an Arakawa-C type grid. This grid configuration is designed for spatial discretization, where the horizontal grid is based on a preprint published on 6 April 2023. The Arakawa-C type grid allows for efficient representation of meteorological variables and their interactions within the model. The staggered grid arrangement helps in accurately capturing the dynamics of atmospheric processes, such as advection and diffusion, by placing different variables at different grid points. This arrangement enhances the model's ability to simulate complex atmospheric phenomena, including turbulence and convection, which are crucial for accurate weather forecasting and climate research.

Accepted Answer

The model uses a positive-definite advection scheme for scalars and Weighted Essentially Non-Oscillatory (WENO) advection for momentum variables. WENO is a high-order numerical scheme that is designed to handle discontinuities and sharp gradients in the flow field. It is particularly useful for simulating turbulent flows and capturing the complex dynamics of atmospheric phenomena. The positive-definite advection scheme ensures that the physical properties of the flow are conserved and that the solution remains stable and accurate over time. Together, these advection schemes enable the model to accurately simulate the moist airflow over a mountain ridge and the associated turbulence and precipitation processes.

Accepted Answer

Random potential temperature perturbations are introduced during model initialization to perturb the initial state and initiate convection. These perturbations are uniformly distributed with a maximum amplitude of 0.1 K and applied equally to the lowest 12 model levels (~ 260 m). The purpose of using vertically uniform initial perturbations, as suggested by Kealy et al. (2019), is to be less susceptible to numerical dissipation during model spin-up and more effective at triggering convective circulations. This approach ensures that the surface sensible heat flux at the beginning of the simulation is equal to zero at all grid points, avoiding the development of spurious thermal circulations.

Accepted Answer

Initial thermodynamic profiles in simulations are designed based on an idealized version of a radiosounding from the IOP8b of COPS. The radiosonde was launched on 15 July 2007, characterizing the pre-convective environment. The profiles are modified to ensure comparable properties across all simulations, despite differences in terrain geometry. The profiles include a cosine profile for orography, with mountain heights of 500 m and 1000 m, and average slope angles of 10% and 20%. The profiles aim to have roughly equal CAPE, CIN, and LFC height above the mountaintop in all simulations. A ground-based stable layer extends up to 500 m above the ridge, followed by a near-neutral layer up to 3000 m above the ridge, and a pseudo-adiabatic layer up to the tropopause at 11.5 km AMSL. The initial temperature and dewpoint profiles are almost identical up to the tropopause for all simulations. Dry simulations with reduced moisture content were also conducted to study the full diurnal cycle of the cross-valley circulation without interference from clouds and precipitation.

Accepted Answer

The relationship between convective indices and precipitation onset in the simulations is not well correlated. Convective indices such as CAPE, CIN, LCL above ground level, and LFC above ground level are initially similar in all simulations. However, the onset of precipitation does not follow the values of these indices closely. The time when CIN reaches values close to zero is not well correlated with the precipitation onset, with a lag between 3 hours (for the h500s10 simulation) and 5 hours (for the two high-mountain simulations). The maximum value of CAPE is not well correlated with the total amount of precipitation. An exception is the h500s20 simulation, which produces almost no precipitation, has the lowest CAPE values, and CIN drops the latest to zero. Therefore, convective indices do not provide a clear understanding of the differences between the simulations in terms of precipitation onset and amount.

Accepted Answer

The relationship between updraft width and precipitation onset in the moisture budget section is that broader updrafts, which form preferentially over the less sharp orographic profile, are more likely to evolve into precipitating cumulonimbus clouds. This is observed in the s10 runs, where the mean updraft size is larger, leading to a higher rate of advective moisture transport over the ridge and weaker turbulent dilution. As a result, the earlier onset and higher amount of precipitation observed in the s10 runs are linked with the mean updraft size. The narrower updrafts over steeper mountains make them more susceptible to the entrainment of dryer, non-cloudy air, which weakens them and delays precipitation onset.

Accepted Answer

Convective inhibition (CIN) impacts mountain geometries by influencing the onset and intensity of convection. In simulations with less stable stratification, initial CIN is reduced, leading to earlier precipitation onset. Higher and steeper mountain ranges result in later and weaker convection. The accumulated precipitation varies based on the mountain height and slope, with steeper mountains producing later and weaker convection. The circulation intensity in dry simulations is similar, with stronger but narrower updrafts for steeper runs. Overall, the relationship between CIN and mountain geometries affects the timing and strength of convection and precipitation.

Accepted Answer

Orographic convection over mountains of moderate steepness is connected with an earlier onset and a higher accumulation of rainfall. This is due to a more favorable balance between moisture advection and moisture diffusion, resulting in a stronger accumulation of water vapor at the mountaintop. Recent research emphasizes that dry air entrainment plays a key role in determining the fate of moist updrafts. Updraft dilution ultimately reduces precipitation efficiency, which is the fraction of condensed water mass that actually precipitates to the ground. The cloud water budget analysis complements the water vapor budget analysis, providing insights into the total water (vapor, liquid, and solid) budget within the cloud control volume. The cloud control volume is defined based on the average water vapor tendency within the last hour, excluding inactive cloud parts. The vertical boundaries are defined along e-levels to avoid numerical inaccuracies. The cloud volume grows in time horizontally and vertically, and the total water tendency includes the change in total water content due to the change of the size of the cloud volume alone. The budget components are computed from the output of the budget analysis tool WRFlux and standard WRF output. The temporally accumulated cloud water budget components are shown in Fig. 14, with all components defined as positive when they are a gain for total water in the cloud volume.

Accepted Answer

The impact of moisture flux on precipitation efficiency is significant. According to Demko and Geerts (2010), the precipitation efficiency can be estimated as P/VVF b, where VVF b represents the condensation without evaporation in the cloud. Using accumulated values at the end of the simulation, the precipitation efficiency for different simulations was found to be 11.5%, 0.04%, 7.7%, and 1.2% for h500s10, h500s20, h1000s10, and h1000s20 simulations, respectively. The h500s10 simulation showed the highest precipitation efficiency, making it the most efficient in terms of precipitation production. On the other hand, the s20 simulations were found to be very inefficient at producing precipitation. The differences in precipitation efficiency can be attributed to the amount of water vapor accumulated in the active cloud volume and the fraction of water vapor converted into precipitation. Overall, the cloud water budget provides an explanation for the delayed and reduced precipitation over steep mountains, as less water vapor accumulates in the cloud and a smaller fraction is converted into precipitation.

Accepted Answer

In LCIN simulations, there is a monotonic relationship between precipitation and terrain volume, which is close to linear when the smallest mountain is excluded. Doubling the volume leads to an increase in precipitation with factors 1.25 and 1.77, similar to the findings of Imamovic et al. (2019). However, this relationship does not hold in strongly inhibited HCIN simulations. The s10 simulations, with V/V0 values of 0.5 and 2, produce more precipitation than the h1000s20 mountain (V/V0 = 1). Imamovic et al. (2019) observed significant precipitation amounts for low mountain volumes and even for flat terrain, while our smallest mountain (V/V0 = 0.25, h500s20) resulted in hardly any precipitation in both LCIN and HCIN simulations.

Reply on RC1

Chat with Paper

AI Agents for this Paper

Most frequently asked questions

1. What is the WRF model's grid type?

2. What advection schemes used?

3. What is the purpose of introducing random potential temperature perturbations during model initialization?

4. How are initial thermodynamic profiles in simulations designed?

5. What is the relationship between convective indices and precipitation onset in the simulations?

6. What is the relationship between updraft width and precipitation onset in the moisture budget section?

7. How does convective inhibition affect mountain geometries?

8. How does orographic convection affect rainfall accumulation over mountains?

9. What is the impact of moisture flux on precipitation efficiency?

10. What is the relationship between precipitation and terrain volume in LCIN simulations?

References

Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity

Weighted essentially non-oscillatory schemes

A time-split nonhydrostatic atmospheric model for weather research and forecasting applications

Stratocumulus-capped mixed layers derived from a three-dimensional model

A Revised Scheme for the WRF Surface Layer Formulation

Related Papers (5)

Boundary layer control on convective available potential energy: Implications for cumulus parameterization

A Numerical Investigation of the Effects of Dry Air Aloft on Deep Convection

Evolution of Convective Energy and Inhibition before Instances of Large CAPE

Analysis of a local rain storm in beijing associated with two convective lines

Comment on egusphere-2023-648