scholarly journals The Impact of Satellite-Derived Land Surface Temperatures on Numerical Weather Prediction Analyses and Forecasts

2017 ◽  
Vol 122 (18) ◽  
pp. 9783-9802 ◽  
Author(s):  
B. Candy ◽  
R. W. Saunders ◽  
D. Ghent ◽  
C. E. Bulgin
Keyword(s):  
Numerical Weather Prediction ◽  
Land Surface ◽  
Weather Prediction ◽  
Numerical Weather ◽  
Surface Temperatures ◽  
Land Surface Temperatures ◽  
The Impact

scholarly journals On the Impact of Unmanned Aerial System Observations on Numerical Weather Prediction in the Coastal Zone

2018 ◽  
Vol 146 (2) ◽  
pp. 599-622 ◽  
Author(s):  
David D. Flagg ◽  
James D. Doyle ◽  
Teddy R. Holt ◽  
Daniel P. Tyndall ◽  
Clark M. Amerault ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Research Laboratory ◽  
Weather Prediction ◽  
Ocean Model ◽  
Naval Research ◽  
Unmanned Aerial System ◽  
Numerical Weather ◽  
The Impact

Abstract The Trident Warrior observational field campaign conducted off the U.S. mid-Atlantic coast in July 2013 included the deployment of an unmanned aerial system (UAS) with several payloads on board for atmospheric and oceanic observation. These UAS observations, spanning seven flights over 5 days in the lowest 1550 m above mean sea level, were assimilated into a three-dimensional variational data assimilation (DA) system [the Naval Research Laboratory Atmospheric Variational Data Assimilation System (NAVDAS)] used to generate analyses for a numerical weather prediction model [the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS)] with a coupled ocean model [the Naval Research Laboratory Navy Coastal Ocean Model (NCOM)]. The impact of the assimilated UAS observations on short-term atmospheric prediction performance is evaluated and quantified. Observations collected from 50 radiosonde launches during the campaign adjacent to the UAS flight paths serve as model forecast verification. Experiments reveal a substantial reduction of model bias in forecast temperature and moisture profiles consistently throughout the campaign period due to the assimilation of UAS observations. The model error reduction is most substantial in the vicinity of the inversion at the top of the model-estimated boundary layer. Investigations reveal a consistent improvement to prediction of the vertical position, strength, and depth of the boundary layer inversion. The relative impact of UAS observations is explored further with experiments of systematic denial of data streams from the NAVDAS DA system and removal of individual measurement sources on the UAS platform.

scholarly journals The Impact of Initial Snow Conditions on the Numerical Weather Simulation of a Northern Rockies Atmospheric River

2021 ◽  
Vol 22 (1) ◽  
pp. 155-167
Author(s):  
William Rudisill ◽  
Alejandro Flores ◽  
James McNamara
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Weather Prediction ◽  
Hydrologic Model ◽  
Radiative Properties ◽  
Numerical Weather ◽  
Atmospheric River ◽  
Surface Snow ◽  
Snow Conditions ◽  
The Impact

AbstractSnow’s thermal and radiative properties strongly impact the land surface energy balance and thus the atmosphere above it. Land surface snow information is poorly known in mountainous regions. Few studies have examined the impact of initial land surface snow conditions in high-resolution, convection-permitting numerical weather prediction models during the midlatitude cool season. The extent to which land surface snow influences atmospheric energy transport and subsequent surface meteorological states is tested using a high-resolution (1 km) configuration of the Weather Research and Forecasting (WRF) Model, for both calm conditions and weather characteristic of a warm late March atmospheric river. A set of synthetic but realistic snow states are used as initial conditions for the model runs and the resulting differences are compared. We find that the presence (absence) of snow decreases (increases) 2-m air temperatures by as much as 4 K during both periods, and that the atmosphere responds to snow perturbations through advection of moist static energy from neighboring regions. Snow mass and snow-covered area are both important variables that influence 2-m air temperature. Finally, the meteorological states produced from the WRF experiments are used to force an offline hydrologic model, demonstrating that snowmelt rates can increase/decrease by factor of 2 depending on the initial snow conditions used in the parent weather model. We propose that more realistic representations of land surface snow properties in mesoscale models may be a source of hydrometeorological predictability

scholarly journals Prediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical Forecast

2017 ◽  
Vol 21 (1) ◽  
pp. 37 ◽  
Author(s):  
Hua Deng ◽  
Yan Li ◽  
Yingchao Zhang ◽  
Hou Zhou ◽  
Peipei Cheng ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Numerical Weather Prediction ◽  
Land Surface ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Least Square ◽  
Support Vector ◽  
Surface Model ◽  
Short Term ◽  
Numerical Weather

The forecast of wind energy is closely linked to the prediction of the variation of winds over very short time intervals. Four wind towers located in the Inner Mongolia were selected to understand wind power resources in the compound plateau region. The mesoscale weather research and forecasting combining Yonsei University scheme and Noah land surface model (WRF/YSU/Noah) with 1-km horizontal resolution and 10-min time resolution were used to be as the wind numerical weather prediction (NWP) model. Three statistical techniques, persistence, back-propagation artificial neural network (BP-ANN), and least square support vector machine (LS-SVM) were used to improve the wind speed forecasts at a typical wind turbine hub height (70 m) along with the WRF/YSU/Noah output. The current physical-statistical forecasting techniques exhibit good skill in three different time scales: (1) short-term (day-ahead); (2) immediate-short-term (6-h ahead); and (3) nowcasting (1-h ahead). The forecast method, which combined WRF/YSU/Noah outputs, persistence, and LS-SVM methods, increases the forecast skill by 26.3-49.4% compared to the direct outputs of numerical WRF/YSU/Noah model. Also, this approach captures well the diurnal cycle and seasonal variability of wind speeds, as well as wind direction. Predicción de vientos en una altiplanicie a la altura del eje con el esquema de la Universidad Yonsei/Modelo Superficie Terrestre Noah y la predicción estadísticaResumenLa estimación de la energía eólica está relacionada con la predicción en la variación de los vientos en pequeños intervalos de tiempo. Se seleccionaron cuatro torres eólicas ubicadas al interior de Mongolia para estudiar los recursos eólicos en la complejidad de un altiplano. Se utilizó la investigación climática a mesoscala y la combinación del esquema de la Universidad Yonsei con el Modelo de Superficie Terrestre Noah (WRF/YSU/Noah), con resolución de 1km horizontal y 10 minutos, como el modelo numérico de predicción meteorológica (NWP, del inglés Numerical Weather Prediction). Se utilizaron tres técnicas estadísticas, persistencia, propagación hacia atrás en redes neuronales artificiales y máquina de vectores de soporte-mínimos cuadrados (LS-SVM, del inglés Least Square Support Vector Machine), para mejorar la predicción de la velocidad del viento en una turbina con la altura del eje a 70 metros y se complementó con los resultados del WRF/YSU/Noah. Las técnicas de predicción físico-estadísticas actuales tienen un buen desempeo en tres escalas de tiempo: (1) corto plazo, un día en adelante; (2) mediano plazo, de seis días en adelante; (3) cercano, una hora en adelante. Este método de predicción, que combina los resultados WRF/YSU/Noah con los métodos de persistencia y LS-SVM incrementa la precisión de predicción entre 26,3 y 49,4 por ciento, comparado con los resultados directos del modelo numérico WRF/YSU/Noah. Además, este método diferencia la variabilidad de las estaciones y el ciclo diurno en la velocidad y la dirección del viento.

Initialisation of Land Surface Variables for Numerical Weather Prediction

2012 ◽  
pp. 607-621
Author(s):  
Patricia de Rosnay ◽  
Gianpaolo Balsamo ◽  
Clément Albergel ◽  
Joaquín Muñoz-Sabater ◽  
Lars Isaksen
Keyword(s):  
Numerical Weather Prediction ◽  
Land Surface ◽  
Weather Prediction ◽  
Numerical Weather

Estimates of the Earth surface influence on the accuracy of numerical prediction of air temperature in Belarus using the WRF model

2021 ◽  
Vol 4 ◽  
pp. 50-68
Author(s):  
S.А. Lysenko ◽  
◽  
P.О. Zaiko ◽  
Keyword(s):  
Land Use ◽  
Air Temperature ◽  
Numerical Weather Prediction ◽  
Land Surface ◽  
Lead Time ◽  
Weather Prediction ◽  
Wrf Model ◽  
Surface Model ◽  
Numerical Weather ◽  
Global Land

The spatial structure of land use and biophysical characteristics of land surface (albedo, leaf index, and vegetation cover) are updated using the GLASS (Global Land Surface Satellite) and GLC2019 (Global Land Cover, 2019) modern satellite databases for mesoscale numerical weather prediction with the WRF model for the territory of Belarus. The series of WRF-based numerical experiments was performed to verify the influence of the updated characteristics on the forecast quality for some difficult to predict winter cases. The model was initialized by the GFS (Global Forecast System, NCEP) global numerical weather prediction model. It is shown that the use of high-resolution land use data in the WRF and the consideration of the new albedo and leaf index distribution over the territory of Belarus can reduce the root-mean-square error (RMSE) of short-range (to 48 hours) forecasts of surface air temperature by 16–33% as compared to the GFS. The RMSE of the temperature forecast for the weather stations in Belarus for a forecast lead time of 12, 24, 36, and 48 hours decreased on average by 0.40°С (19%), 0.35°С (10%), 0.68°С (23%), and 0.56°С (15%), respectively. The most significant decrease in RMSE of the numerical forecast of temperature (up to 2.1 °С) was obtained for the daytime (for a lead time of 12 and 36 hours), when positive feedbacks between albedo and temperature of the land surface are manifested most. Keywords: numerical weather prediction, WRF, digital land surface model, albedo, leaf area index, forecast model validation

scholarly journals A fast visible-wavelength 3D radiative transfer model for numerical weather prediction visualization and forward modeling

2020 ◽  
Vol 13 (6) ◽  
pp. 3235-3261
Author(s):  
Steven Albers ◽  
Stephen M. Saleeby ◽  
Sonia Kreidenweis ◽  
Qijing Bian ◽  
Peng Xian ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Radiative Transfer ◽  
Numerical Weather Prediction ◽  
Land Surface ◽  
Weather Prediction ◽  
Lower Boundary ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Numerical Weather ◽  
Visible Wavelength

Abstract. Solar radiation is the ultimate source of energy flowing through the atmosphere; it fuels all atmospheric motions. The visible-wavelength range of solar radiation represents a significant contribution to the earth's energy budget, and visible light is a vital indicator for the composition and thermodynamic processes of the atmosphere from the smallest weather scales to the largest climate scales. The accurate and fast description of light propagation in the atmosphere and its lower-boundary environment is therefore of critical importance for the simulation and prediction of weather and climate. Simulated Weather Imagery (SWIm) is a new, fast, and physically based visible-wavelength three-dimensional radiative transfer model. Given the location and intensity of the sources of light (natural or artificial) and the composition (e.g., clear or turbid air with aerosols, liquid or ice clouds, precipitating rain, snow, and ice hydrometeors) of the atmosphere, it describes the propagation of light and produces visually and physically realistic hemispheric or 360∘ spherical panoramic color images of the atmosphere and the underlying terrain from any specified vantage point either on or above the earth's surface. Applications of SWIm include the visualization of atmospheric and land surface conditions simulated or forecast by numerical weather or climate analysis and prediction systems for either scientific or lay audiences. Simulated SWIm imagery can also be generated for and compared with observed camera images to (i) assess the fidelity and (ii) improve the performance of numerical atmospheric and land surface models. Through the use of the latter in a data assimilation scheme, it can also (iii) improve the estimate of the state of atmospheric and land surface initial conditions for situational awareness and numerical weather prediction forecast initialization purposes.

scholarly journals Sensitivity of Radiative Fluxes to Aerosols in the ALADIN-HIRLAM Numerical Weather Prediction System

Atmosphere ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 205
Author(s):  
Laura Rontu ◽  
Emily Gleeson ◽  
Daniel Martin Perez ◽  
Kristian Pagh Nielsen ◽  
Velle Toll
Keyword(s):  
Optical Properties ◽  
Real Time ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Limited Area ◽  
Prediction System ◽  
Heating Rates ◽  
Radiative Fluxes ◽  
Numerical Weather ◽  
The Impact

The direct radiative effect of aerosols is taken into account in many limited-area numerical weather prediction models using wavelength-dependent aerosol optical depths of a range of aerosol species. We studied the impact of aerosol distribution and optical properties on radiative transfer, based on climatological and more realistic near real-time aerosol data. Sensitivity tests were carried out using the single-column version of the ALADIN-HIRLAM numerical weather prediction system, set up to use the HLRADIA simple broadband radiation scheme. The tests were restricted to clear-sky cases to avoid the complication of cloud–radiation–aerosol interactions. The largest differences in radiative fluxes and heating rates were found to be due to different aerosol loads. When the loads are large, the radiative fluxes and heating rates are sensitive to the aerosol inherent optical properties and the vertical distribution of the aerosol species. In such cases, regional weather models should use external real-time aerosol data for radiation parametrizations. Impacts of aerosols on shortwave radiation dominate longwave impacts. Sensitivity experiments indicated the important effects of highly absorbing black carbon aerosols and strongly scattering desert dust.

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