A new approach to simulating the impact of turbulence on electric processes in mesoscale numerical weather prediction models

2021 ◽  
Author(s):  
Svetlana Dementyeva ◽  
Nikolai Ilin ◽  
Mikhail Kulikov ◽  
Evgeny Mareev
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
New Approach ◽  
Numerical Weather ◽  
The Impact ◽  
Numerical Weather Prediction Models ◽  
Mesoscale Numerical Weather Prediction

scholarly journals Operational Assimilation of GPS Zenith Total Delay Observations into the Met Office Numerical Weather Prediction Models

2012 ◽  
Vol 140 (8) ◽  
pp. 2706-2719 ◽  
Author(s):  
Gemma V. Bennitt ◽  
Adrian Jupp
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Three Dimensional ◽  
Total Delay ◽  
Numerical Weather ◽  
Zenith Total Delay ◽  
Nwp Model ◽  
The Impact ◽  
Numerical Weather Prediction Models

Abstract Zenith total delay (ZTD) observations derived from ground-based GPS receivers have been assimilated operationally into the Met Office North Atlantic and European (NAE) numerical weather prediction (NWP) model since 2007. Assimilation trials were performed using the Met Office NAE NWP model at both 12- and 24-km resolution to assess the impact of ZTDs on forecasts. ZTDs were found generally to increase relative humidity in the analysis, increasing the humidity bias compared to radiosonde observations, which persisted through the forecasts at some vertical levels. Improvements to cloud forecasts were also identified. Assimilation of ZTDs using both three-dimensional and four-dimensional variational data assimilation (3D-Var/4D-Var) was investigated, and it is found that assimilation at 4D-Var does not deliver any clear benefit over 3D-Var in the periods studied with the NAE model. This paper summarizes the methods used to assimilate ZTDs at the Met Office and presents the results of impact trials performed prior to operational assimilation. Future improvements to the assimilation methods are discussed.

Calibrating the Tropospheric Delays of VLBI Observations using Numerical Weather Prediction Models

2021 ◽  
Author(s):  
Tobias Nilsson ◽  
Kyriakos Balidakis
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
External Information ◽  
Local Networks ◽  
Numerical Weather ◽  
Vlbi Data ◽  
The Impact ◽  
Tropospheric Delays ◽  
Numerical Weather Prediction Models

<p>The observations of geodetic Very Long Baseline Interferometry (VLBI) are affected by the troposphere, and this effect needs to be considered in the VLBI data analysis. The normal way of doing this is to estimate the zenith tropospheric delays and tropospheric gradients as additional parameter in the analysis. However, due to the poor geometric distributions of the observations in some VLBI sessions, like the Intensives, the tropospheric parameters cannot be estimated with a high accuracy. An alternative is to use external information on the tropospheric delay from Numerical Weather Prediction Models (NWM). Due to the increasing accuracy of the NWM, this alternative is becoming more and more interesting. In this work, we use tropospheric delays from the fifth ECMWF reanalysis, ERA5, in the analysis of VLBI data and evaluate the impacts on the results. We study the impact of different types of VLBI sessions, like Intensives, local networks, and global networks. The results of this study will show to what extent ERA5 data can be used to correct the tropospheric delays in geodetic VLBI. Furthermore, the results also give information on the accuracy of the tropospheric delays from NMW.</p>

scholarly journals Convectively Induced Secondary Circulations in Fine-Grid Mesoscale Numerical Weather Prediction Models

2014 ◽  
Vol 142 (9) ◽  
pp. 3284-3302 ◽  
Author(s):  
J. Ching ◽  
R. Rotunno ◽  
M. LeMone ◽  
A. Martilli ◽  
B. Kosovic ◽  
...  
Keyword(s):  
Rayleigh Number ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Grid Spacing ◽  
Fine Grid ◽  
Numerical Weather ◽  
Secondary Circulations ◽  
Numerical Weather Prediction Models ◽  
Mesoscale Numerical Weather Prediction

Mesoscale numerical weather prediction models using fine-grid [O(1) km] meshes for weather forecasting, environmental assessment, and other applications capture aspects of larger-than-grid-mesh size, convectively induced secondary circulations (CISCs) such as cells and rolls that occur in the convective planetary boundary layer (PBL). However, 1-km grid spacing is too large for the simulation of the interaction of CISCs with smaller-scale turbulence. The existence of CISCs also violates the neglect of horizontal gradients of turbulent quantities in current PBL schemes. Both aspects—poorly resolved CISCs and a violation of the assumptions behind PBL schemes—are examples of what occurs in Wyngaard’s “terra incognita,” where horizontal grid spacing is comparable to the scale of the simulated motions. Thus, model CISCs (M-CISCs) cannot be simulated reliably. This paper describes how the superadiabatic layer in the lower convective PBL together with increased horizontal resolution allow the critical Rayleigh number to be exceeded and thus allow generation of M-CISCs like those in nature; and how the M-CISCs eventually neutralize the virtual temperature stratification, lowering the Rayleigh number and stopping their growth. Two options for removing M-CISCs while retaining their fluxes are 1) introducing nonlocal closure schemes for more effective removal of heat from the surface and 2) restricting the effective Rayleigh number to remain subcritical. It is demonstrated that CISCs are correctly handled by large-eddy simulation (LES) and thus may provide a way to improve representation of them or their effects. For some applications, it may suffice to allow M-CISCs to develop, but account for their shortcomings during interpretation.

scholarly journals Parametrizations of Liquid and Ice Clouds’ Optical Properties in Operational Numerical Weather Prediction Models

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 89
Author(s):  
Harel. B. Muskatel ◽  
Ulrich Blahak ◽  
Pavel Khain ◽  
Yoav Levi ◽  
Qiang Fu
Keyword(s):  
Optical Properties ◽  
Numerical Weather Prediction ◽  
Radiation Transfer ◽  
Prediction Models ◽  
Weather Prediction ◽  
Effective Size ◽  
Asymmetry Factor ◽  
Numerical Weather ◽  
Numerical Weather Prediction Models ◽  
Spectral Averaging

Parametrization of radiation transfer through clouds is an important factor in the ability of Numerical Weather Prediction models to correctly describe the weather evolution. Here we present a practical parameterization of both liquid droplets and ice optical properties in the longwave and shortwave radiation. An advanced spectral averaging method is used to calculate the extinction coefficient, single scattering albedo, forward scattered fraction and asymmetry factor (bext, v, f, g), taking into account the nonlinear effects of light attenuation in the spectral averaging. An ensemble of particle size distributions was used for the ice optical properties calculations, which enables the effective size range to be extended up to 570 μm and thus be applicable for larger hydrometeor categories such as snow, graupel, and rain. The new parameterization was applied both in the COSMO limited-area model and in ICON global model and was evaluated by using the COSMO model to simulate stratiform ice and water clouds. Numerical weather prediction models usually determine the asymmetry factor as a function of effective size. For the first time in an operational numerical weather prediction (NWP) model, the asymmetry factor is parametrized as a function of aspect ratio. The method is generalized and is available on-line to be readily applied to any optical properties dataset and spectral intervals of a wide range of radiation transfer models and applications.

Forecasting wave height probabilities with numerical weather prediction models

2005 ◽  
Vol 32 (14-15) ◽  
pp. 1841-1863 ◽  
Author(s):  
Mark S. Roulston ◽  
Jerome Ellepola ◽  
Jost von Hardenberg ◽  
Leonard A. Smith
Keyword(s):  
Wave Height ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Numerical Weather ◽  
Numerical Weather Prediction Models
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