scholarly journals Stable Boundary Layer in Complex Terrain. Part I: Linking Fluxes and Intermittency to an Average Stability Index

2014 ◽  
Vol 53 (9) ◽  
pp. 2196-2215 ◽  
Author(s):  
Luiz E. Medeiros ◽  
David R. Fitzjarrald
Keyword(s):  
Richardson Number ◽  
Prediction Models ◽  
Weather Prediction ◽  
Stability Index ◽  
Bulk Richardson Number ◽  
Hudson Valley ◽  
Continuous Mixing ◽  
Heterogeneous Landscape ◽  
Turbulence Intermittency ◽  
Nearly Continuous

AbstractAverage heat and momentum fluxes observed by a network of surface stations during the Hudson Valley Ambient Meteorology Study (HVAMS) were found as functions of a spatially representative bulk Richardson number Ribr. Preferential sites were identified for the occurrence of strong turbulence under mesoscale stability conditions common to all stations. Locally sensed turbulence intermittency depends on the mesoscale flow stability. Nearly continuous turbulence with few long-lived intermittent events occurs when Ribr < Ricr, the critical gradient Richardson number. Less-continuous mixing associated with a larger number of events occurs when Ricr < Ribr < 5, with the weakest turbulence and fewer events observed for Ribr ≫ Ricr. It was found that the need to allow for extra mixing above the conventional critical bulk Richardson number in numerical weather prediction models is primarily a consequence of spatial averaging in a heterogeneous landscape and is secondarily the result of turbulence above Ricr at locations with “nonideal fetch.”

New modified and extended stability functions for the stable boundary layer based on SHEBA data

2020 ◽  
Author(s):  
Vladimir M. Gryanik ◽  
Andrey Grachev ◽  
Christof Lüpkes ◽  
Dmitry Sidorenko
Keyword(s):  
Boundary Layer ◽  
Sea Ice ◽  
Stable Boundary Layer ◽  
Richardson Number ◽  
Prediction Models ◽  
Weather Prediction ◽  
Bulk Richardson Number ◽  
Transfer Coefficients ◽  
Near Surface ◽  
Stability Functions

&lt;p&gt;The calculation of the near-surface turbulent fluxes of energy and momentum in climate and weather prediction models requires transfer coefficients. Currently used parametrizations of these coefficients are based on stability functions derived from measurements over land and not over sea ice. However, recently, a non-iterative parametrization has been proposed by Gryanik and L&amp;#252;pkes (2018), which can be applied to climate and weather prediction models as well but uses stability functions of Grachev et al. (2007). These functions had been obtained from measurements during the Surface Heat Budget over the Arctic Ocean campaign (SHEBA) and thus from measurements over sea ice. A drawback of the scheme of Gryanik and L&amp;#252;pkes (2018) is that there is still some complexity due to the complexity of the SHEBA based functions.&lt;/p&gt;&lt;p&gt;Thus new stability functions are proposed for the stable boundary layer, which are also based on the SHEBA measurements but avoid the complexity. It is shown that the new functions are superior to the former ones with respect to the representation of the measured relationship between the Obukhov length and the bulk Richardson number. Moreover, the resulting transfer coefficients agree slightly better with the SHEBA observations in the very stable range. Nevertheless, the functions fulfill the same criteria of applicability as the earlier functions and contain furthermore as an extension a dependence on the neutral Prandtl number. Applying the new functions, an efficient non-iterative parametrization of the near-surface turbulent fluxes of momentum and heat is developed where transfer coefficients result as a function of the bulk Richardson number (Ri&lt;sub&gt;b&lt;/sub&gt;) and roughness parameters. The new transfer coefficients, which are recommended for weather and climate models, agree well with the SHEBA data in a large range of stability (0&lt; Ri&lt;sub&gt;b&lt;/sub&gt;&lt;0.5) and with those based on the Dyer-Businger functions in the range Ri&lt;sub&gt;b&lt;/sub&gt; &lt;0.08.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Grachev A.A., Andreas E.L, Fairall C.W., Guest P.S., Persson POG (2007) Boundary-Layer Meteorol., &lt;/span&gt;&lt;span&gt;&lt;strong&gt;124&lt;/strong&gt;&lt;/span&gt;&lt;span&gt;, 315&amp;#8211;333&lt;/span&gt;&lt;span&gt;.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Gryanik, V.M. and L&amp;#252;pkes C. (2018) An Efficient Non-iterative Bulk Parametrization of Surface Fluxes for Stable Atmospheric Conditions Over Polar Sea-Ice,Boundary-Layer Meteorol 166:301-325&lt;/span&gt;&lt;/p&gt;

Forecasting of Tornadoes and Downbursts

2020 ◽  
Author(s):  
Djordje Romanic
Keyword(s):  
Chaos Theory ◽  
Prediction Models ◽  
Weather Prediction ◽  
Small Scale ◽  
Weather Forecasts ◽  
Wind Speeds ◽  
The Past ◽  
Extreme Wind ◽  
Extreme Wind Speeds ◽  
Numerical Weather Prediction Models

Tornadoes and downbursts cause extreme wind speeds that often present a threat to human safety, structures, and the environment. While the accuracy of weather forecasts has increased manifold over the past several decades, the current numerical weather prediction models are still not capable of explicitly resolving tornadoes and small-scale downbursts in their operational applications. This chapter describes some of the physical (e.g., tornadogenesis and downburst formation), mathematical (e.g., chaos theory), and computational (e.g., grid resolution) challenges that meteorologists currently face in tornado and downburst forecasting.

scholarly journals HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity

2021 ◽  
Vol 13 (11) ◽  
pp. 2179
Author(s):  
Pedro Mateus ◽  
Virgílio B. Mendes ◽  
Sandra M. Plecha
Keyword(s):  
Water Vapor ◽  
Relative Humidity ◽  
Real Time ◽  
Prediction Models ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Global Navigation Satellite Systems ◽  
International Gnss Service ◽  
Weighted Mean Temperature

The neutral atmospheric delay is one of the major error sources in Space Geodesy techniques such as Global Navigation Satellite Systems (GNSS), and its modeling for high accuracy applications can be challenging. Improving the modeling of the atmospheric delays (hydrostatic and non-hydrostatic) also leads to a more accurate and precise precipitable water vapor estimation (PWV), mostly in real-time applications, where models play an important role, since numerical weather prediction models cannot be used for real-time processing or forecasting. This study developed an improved version of the Hourly Global Pressure and Temperature (HGPT) model, the HGPT2. It is based on 20 years of ERA5 reanalysis data at full spatial (0.25° × 0.25°) and temporal resolution (1-h). Apart from surface air temperature, surface pressure, zenith hydrostatic delay, and weighted mean temperature, the updated model also provides information regarding the relative humidity, zenith non-hydrostatic delay, and precipitable water vapor. The HGPT2 is based on the time-segmentation concept and uses the annual, semi-annual, and quarterly periodicities to calculate the relative humidity anywhere on the Earth’s surface. Data from 282 moisture sensors located close to GNSS stations during 1 year (2020) were used to assess the model coefficients. The HGPT2 meteorological parameters were used to process 35 GNSS sites belonging to the International GNSS Service (IGS) using the GAMIT/GLOBK software package. Results show a decreased root-mean-square error (RMSE) and bias values relative to the most used zenith delay models, with a significant impact on the height component. The HGPT2 was developed to be applied in the most diverse areas that can significantly benefit from an ERA5 full-resolution model.

scholarly journals Fengyun Meteorological Satellite Products for Earth System Science Applications

2021 ◽  
Author(s):  
Di Xian ◽  
Peng Zhang ◽  
Ling Gao ◽  
Ruijing Sun ◽  
Haizhen Zhang ◽  
...  
Keyword(s):  
Data Sharing ◽  
Satellite Data ◽  
Prediction Models ◽  
Weather Forecasting ◽  
Numerical Models ◽  
Weather Prediction ◽  
Vegetation Indices ◽  
Open Data ◽  
Earth System ◽  
Inversion Algorithm

AbstractFollowing the progress of satellite data assimilation in the 1990s, the combination of meteorological satellites and numerical models has changed the way scientists understand the earth. With the evolution of numerical weather prediction models and earth system models, meteorological satellites will play a more important role in earth sciences in the future. As part of the space-based infrastructure, the Fengyun (FY) meteorological satellites have contributed to earth science sustainability studies through an open data policy and stable data quality since the first launch of the FY-1A satellite in 1988. The capability of earth system monitoring was greatly enhanced after the second-generation polar orbiting FY-3 satellites and geostationary orbiting FY-4 satellites were developed. Meanwhile, the quality of the products generated from the FY-3 and FY-4 satellites is comparable to the well-known MODIS products. FY satellite data has been utilized broadly in weather forecasting, climate and climate change investigations, environmental disaster monitoring, etc. This article reviews the instruments mounted on the FY satellites. Sensor-dependent level 1 products (radiance data) and inversion algorithm-dependent level 2 products (geophysical parameters) are introduced. As an example, some typical geophysical parameters, such as wildfires, lightning, vegetation indices, aerosol products, soil moisture, and precipitation estimation have been demonstrated and validated by in-situ observations and other well-known satellite products. To help users access the FY products, a set of data sharing systems has been developed and operated. The newly developed data sharing system based on cloud technology has been illustrated to improve the efficiency of data delivery.

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.

Impact of Consistent Semi-Lagrangian Trajectory Calculations on Numerical Weather Prediction Performance

2017 ◽  
Vol 145 (10) ◽  
pp. 4127-4150 ◽  
Author(s):  
Syed Zahid Husain ◽  
Claude Girard
Keyword(s):  
Numerical Weather Prediction ◽  
Numerical Experiments ◽  
Prediction Models ◽  
Weather Prediction ◽  
Forecast Accuracy ◽  
Multiscale Model ◽  
Dynamical Equations ◽  
Numerical Weather ◽  
Trajectory Calculation ◽  
Trajectory Calculations

Inconsistencies may arise in numerical weather prediction models—that are based on semi-Lagrangian advection—when the governing dynamical and the kinematic trajectory equations are discretized in a dissimilar manner. This study presents consistent trajectory calculation approaches, both in the presence and absence of off-centering in the discretized dynamical equations. Both uniform and differential off-centering in the discretized dynamical equations have been considered. The proposed consistent trajectory calculations are evaluated using numerical experiments involving a nonhydrostatic two-dimensional theoretical mountain case and hydrostatic global forecasts. The experiments are carried out using the Global Environmental Multiscale model. Both the choice of the averaging method for approximating the velocity integral in the discretized trajectory equations and the interpolation scheme for calculating the departure positions are found to be important for consistent trajectory calculations. Results from the numerical experiments confirm that the proposed consistent trajectory calculation approaches not only improve numerical consistency, but also improve forecast accuracy.

Sign in / Sign up

Export Citation Format

Share Document