第四章中尺度模式的物理过程1_2009

atmospheric processes leads to greater forecast sensitivity to physical parameters more realistic-looking forecast detail but also more complicated model error characteristics greater reliance on model diagnostics to make adjustments to the model forecast fields changes in one parameterization affect the behavior parameterizations through a complex web of interactions the need to test complex interactions together of other

The largest impact of using parameterization schemes is usually on predictions of sensible weather at the surface.
4. Outlook

As computer power increases and the number and complexity of schemes grows, it is important to remember the following:

3. Impacts of Parameterizing


Problems associated with using parameterizations can result from Interactions between parameterization schemes, where each scheme contains its own set of errors and assumptions (for example, a soil model and radiation scheme passing back and forth information about heating the boundary layer) The increasing complexity and interconnectedness of parameterizations, which result in forecast errors that are more difficult to trace back to specific processes



(11)Condensation (12)Evaporation (13)Soil water/snow melt (14)Snow/ice/water cover (15)Vegetation (16)Soil properties (17)Surface Roughness (18)Turbulence (19)Topography
第三章 中尺度模式的物理过 程参数化
一、物理过程参数化 Parameterization of Physical Processes

Baidu Nhomakorabea

1. Why parameterize? 2. Accounting for the Effects of Physical Processes 3. Impacts of Parameterizing 4. Outlook


Several types of assumptions are used to "create" information.



Empirical/statistical: This assumes that a given relationship holds in every case (for example, surface layer wind speed variations with height for PBL processes and surface wind forecasts). 通量廓线关系 Dynamical/thermodynamical constraining assumption: A complex process is summarized through a simplified relationship, for example, equilibrium of instability for Arakawa-Schubert convective parameterization. Arakawa-Schubert积云参数化 Model within a model: Although the use of nested models (for example, one-dimensional cloud models and soil models) pushes the assumption back to a finer detail, assumptions must still be made. Running a model within a model requires far more development by modelers and takes longer to run.
The key problem of numerical parameterization is

trying to predict with incomplete information, for example, the effects of sub grid-scale processes with information at the grid scale.
Parameterization is necessary for several reasons:

Computers are not yet powerful enough to treat many physical processes explicitly because they are either too small or complex to be resolved Many other physical processes cannot be explicitly modeled because they are not sufficiently understood to be represented in equation format or there are no appropriate data
2. Accounting for the Effects of Physical Processes

Each important physical process that cannot be directly predicted requires a parameterization scheme based on reasonable physical (for example, radiation) or statistical (for example, inferring cloudiness from relative humidity) representations. The scheme must derive information about the processes from the variables in the forecast equations using a set of assumptions. Closure refers to the link between the assumptions in the parameterization and the forecast variables. (It closes the loop between the parameterization and forecast equations.)
Imagine using the wind forecast in a grid box to predict boundary-layer turbulence without knowing topography details, vegetation characteristics, or the details of structures at the surface.





model output will still require human interpretation and adjustment
二、雷诺平均 Reynolds averaging



The ensemble properties of all time fluctuations in the flow are described by a turbulence closure. In this approach, the subgrid-scale motions and processes as well as the larger-scale environment is parameterized. The parameterization approach gives a less detailed representation than the explicit representation, but it is more practical in terms of computing time and it may be sufficiently accurate for many mesoscale models, in considering grid size, initial data, etc. Unresolved turbulent eddies of various scales smaller than the grid interval often fluctuate rapidly in time, so that their behavior can only be described statistically.
1. Why parameterize?
Resolve 分辨 Unresolved 不可分辨

Implicitly
隐式 Explicitly 显式


NWP models cannot resolve weather features and/or processes that occur within a single model grid box. The model must account for the aggregate effect of the unresolved features or processes. The method of accounting for such effects without directly forecasting them is called parameterization. Parameterization is how we include the effects of physical processes implicitly when we cannot include the processes themselves explicitly. Parameterization can be thought of as emulation (modeling the effects of a process) rather than simulation (modeling the process itself)

There are many complex processes that need to be parameterized:


(1)Incoming solar radiation (2)Scattering by aerosols (3)Absorption by the Atmosphere (4)Reflection/Absorption by clouds (5)Reflection/Absorption at the Earth Surface (6)Emission from the earth’s surface (7)Emission from the clouds (8)Rain (cooling) (9)Deep convection (warming) (10)Snow