Physical Processes

1998 ◽  
Author(s):  
T. N. Krishnamurti ◽  
H. S. Bedi ◽  
V. M. Hardiker
Keyword(s):  
Boundary Layer ◽  
Energy Balance ◽  
Surface Energy ◽  
Surface Energy Balance ◽  
Weather Prediction ◽  
Surface Fluxes ◽  
Physical Processes ◽  
Numerical Weather ◽  
Spectral Models ◽  
Heat And Moisture

In this chapter we present some of the physical processes that are used in numerical weather prediction modeling. Grid-point models, based on finite differences, and spectral models both generally treat the physical processes in the same manner. The vertical columns above the horizontal grid points (the transform grid for the spectral models) are the ones along which estimates of the effects of the physical processes are made. In this chapter we present a treatment of the planetary boundary layer, including a discussion on the surface similarity theory. Also covered is the cumulus parameterization problem in terms of the Kuo scheme and the Arakawa- Schubert sheme. Large-scale condensation and radiative transfer in clear and cloudy skies are the final topics reviewed. There are at least three types of fluxes that one deals with, namely momentum, sensible heat, and moisture. Furthermore, one needs to examine separately the land and ocean regions. In this section we present the socalled bulk aerodynamic methods as well as the similarity analysis approach for the estimation of the surface fluxes. The radiation code in a numerical weather prediction model is usually coupled to the calculation of the surface energy balance. This will be covered later in Section 8.5.6. This surface energy balance is usually carried out over land areas, where one balances the net radiation against the surface fluxes of heat and moisture for the determination of soil temperature. Over oceans, the sea-surface temperatures are prescribed where the surface energy balance is implicit. Thus it is quite apparent that what one does in the parameterization of the planetary boundary layer has to be integrated with the radiative parameterization in a consistent manner.

scholarly journals COSIPY v1.2 – An open-source coupled snowpack and ice surface energy and mass balance model

2020 ◽  
Author(s):  
Tobias Sauter ◽  
Anselm Arndt ◽  
Christoph Schneider
Keyword(s):  
Energy Balance ◽  
Surface Energy ◽  
Mass Balance ◽  
Surface Energy Balance ◽  
Balance Model ◽  
Surface Mass Balance ◽  
Mass Balance Model ◽  
Physical Processes ◽  
Surface Mass ◽  
Ice Surface

Abstract. Glacial changes play a key role both from a socio-economical and political, and scientific point of view. The identification and the understanding of the nature of these changes still poses fundamental challenges for climate, glacier and water research. Many studies aim to identify the climatic drivers behind the observed glacial changes using distributed surface mass and energy balance models. Distributed surface mass balance models, which translate the meteorological conditions on glaciers into local melting rates, thus offer the possibility to attribute and detect glacier mass and volume responses to changes in the climatic forcings. A well calibrated model is a suitable test-bed for sensitivity, detection and attribution analyses for many scientific applications and often serves as a tool for quantifying the inherent uncertainties. Here we present the open-source coupled snowpack and ice surface energy and mass balance model in Python COSIPY, which provides a lean, flexible and user-friendly framework for modelling distributed snow and glacier mass changes. The model has a modular structure so that the exchange of routines or parameterizations of physical processes is possible with little effort for the user. The model has a modular structure so that the exchange of routines or parameterizations of physical processes is possible with little effort for the user. The framework consists of a computational kernel, which forms the runtime environment and takes care of the initialization, the input-output routines, the parallelization as well as the grid and data structures. This structure offers maximum flexibility without having to worry about the internal numerical flow. The adaptive sub-surface scheme allows an efficient and fast calculation of the otherwise computationally demanding fundamental equations. The surface energy-balance scheme uses established standard parameterizations for radiation as well as for the energy exchange between atmosphere and surface. The schemes are coupled by solving both surface energy balance and subsurface fluxes iteratively in such that consistent surface skin temperature is returned at the interface. COSIPY uses a one-dimensional approach limited to the vertical fluxes of energy and matter but neglects any lateral processes. Accordingly, the model can be easily set up in parallel computational environments for calculating both energy balance and climatic surface mass balance of glacier surfaces based on flexible horizontal grids and with varying temporal resolution. The model is made available on a freely accessible site and can be used for non-profit purposes. Scientists are encouraged to actively participate in the extension and improvement of the model code.

scholarly journals Spatiotemporal Variability of Heat Storage in Major U.S. Cities—A Satellite-Based Analysis

2020 ◽  
Vol 13 (1) ◽  
pp. 59
Author(s):  
Joshua Hrisko ◽  
Prathap Ramamurthy ◽  
David Melecio-Vázquez ◽  
Jorge E. Gonzalez
Keyword(s):  
New York ◽  
Energy Balance ◽  
Surface Energy ◽  
Surface Temperature ◽  
Land Surface ◽  
Surface Energy Balance ◽  
Heat Storage ◽  
Numerical Weather ◽  
Numerical Weather Model ◽  
Weather Model

Heat storage, ΔQs, is quantified for 10 major U.S. cities using a method called the thermal variability scheme (TVS), which incorporates urban thermal mass parameters and the variability of land surface temperatures. The remotely sensed land surface temperature (LST) is retrieved from the GOES-16 satellite and is used in conjunction with high spatial resolution land cover and imperviousness classes. New York City is first used as a testing ground to compare the satellite-derived heat storage model to two other methods: a surface energy balance (SEB) residual derived from numerical weather model fluxes, and a residual calculated from ground-based eddy covariance flux tower measurements. The satellite determination of ΔQs was found to fall between the residual method predicted by both the numerical weather model and the surface flux stations. The GOES-16 LST was then downscaled to 1-km using the WRF surface temperature output, which resulted in a higher spatial representation of storage heat in cities. The subsequent model was used to predict the total heat stored across 10 major urban areas across the contiguous United States for August 2019. The analysis presents a positive correlation between population density and heat storage, where higher density cities such as New York and Chicago have a higher capacity to store heat when compared to lower density cities such as Houston or Dallas. Application of the TVS ultimately has the potential to improve closure of the urban surface energy balance.

scholarly journals Surface Energy Balance and Buoyancy Response to Shallow Cumulus Shading

2014 ◽  
Vol 71 (2) ◽  
pp. 665-682 ◽  
Author(s):  
Fabienne Lohou ◽  
Edward G. Patton
Keyword(s):  
Boundary Layer ◽  
Heat Flux ◽  
Energy Balance ◽  
Surface Energy ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Surface Energy Balance ◽  
Surface Model ◽  
Entrainment Rate ◽  
Highly Nonlinear

Abstract The interactions surrounding the coupling between surface energy balance and a boundary layer with shallow cumuli are investigated using the National Center for Atmospheric Research’s large-eddy simulation code coupled to the Noah land surface model. The simulated cloudy boundary layer is based on the already well-documented and previously simulated 21 June 1997 case at the Atmospheric Radiation Measurement Southern Great Plains central facility. The surface energy balance response to cloud shading is highly nonlinear, leading to different partitioning between sensible and latent heat flux compared to the surface not impacted by cloud. The evaporative fraction increases by about 2%–3% in the presence of shallow cumuli at the regional scale but can increase by up to 30% at any individual location. As expected, the cloud’s reduction of solar irradiance largely controls the surface’s response. However, the turbulence and secondary circulations associated with the cloud dynamics increases the surface flux variability. Even though they are less than 1 km in horizontal scale, the cloud-induced surface heterogeneities impact the vertical flux of heat and moisture up to approximately 20% of the height of the subcloud layer zsl, higher than the surface layer’s typical extent. Above 0.2zsl, the cloud root tends to amplify the drying and the cooling of the subcloud layer. Near the entrainment zone, the cloud-induced latent heat flux increase and sensible heat flux decrease compensate each other with respect to total buoyancy and therefore do not significantly modify the subcloud-layer entrainment rate over large time scales.

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