scholarly journals Simulation of seasonal dynamics of the global electric circuit diurnal variation

2019 ◽  
Vol 55 (5) ◽  
pp. 76-84
Author(s):  
N. V. Ilin ◽  
M. V. Shatalina ◽  
N. N. Slyunyaev
Keyword(s):  
Diurnal Variation ◽  
Northern Hemisphere ◽  
Seasonal Dynamics ◽  
Annual Variation ◽  
Winter Season ◽  
Electric Circuit ◽  
Weather Forecast ◽  
Forecast Model ◽  
Global Electric Circuit ◽  
Average Value

Based on the ionospheric potential (IP) parameterization, the seasonal dynamics of the diurnal variation of IP for 20162017 were modeled for the first time using the numerical weather forecast model WRF-ARW. The diurnal variation of the IP, averaged over the annual simulation periods, shows good agreement with the classical Carnegie curve. The proposed parametrization correctly reproduces the basic characteristics of the stationary global electric circuit generators. The annual variation does not show a precise repeatability from year to year, but in the winter season of the Northern Hemisphere a lower IP value was obtained, and in the summer - an increased one. The model diurnal variation demonstrates stable seasonal trends, and in the northern hemisphere, the variation is characterized by only one strongly distinguished maximum IP in the 16-18 UTC area of ~120% of the average value, while in the summer season the daily variation curve has two maxima with smaller value (~ 107% of average): morning at 89 UTC and evening at 1820 UTC. The model annual variation of the diurnal variation agrees with the experimental data of the surface field measuring in Antarctica in the period 20062011. The proposed parametrization and modeling technique made possible the accurate reproduction of the IP variation maximums times, their seasonal variability, and decreasing of the amplitude of variation in the summer period of the Northern Hemisphere.

scholarly journals Diurnal variation of the global electric circuit from clustered thunderstorms

2014 ◽  
Vol 119 (1) ◽  
pp. 620-629 ◽  
Author(s):  
Michael L. Hutchins ◽  
Robert H. Holzworth ◽  
James B. Brundell
Keyword(s):  
Diurnal Variation ◽  
Electric Circuit ◽  
Global Electric Circuit

Modeling Contributions of Continents and Oceans to the Diurnal Variation of the Global Electric Circuit

2019 ◽  
Vol 46 (10) ◽  
pp. 5516-5525 ◽  
Author(s):  
Nikolay N. Slyunyaev ◽  
Nikolay V. Ilin ◽  
Evgeny A. Mareev
Keyword(s):  
Diurnal Variation ◽  
Electric Circuit ◽  
Global Electric Circuit

scholarly journals Statistical downscaling of MM5 model output to better assess avalanche threats

2010 ◽  
Vol 51 (54) ◽  
pp. 14-18 ◽  
Author(s):  
K. Srinivasan ◽  
Ajay Kumar ◽  
Jyoti Verma ◽  
Ashwagosha Ganju
Keyword(s):  
Statistical Downscaling ◽  
Winter Season ◽  
Threat Assessment ◽  
Weather Forecast ◽  
Western Himalaya ◽  
Forecast Model ◽  
Weather Data ◽  
Model Output ◽  
Mm5 Model ◽  
Weather Forecast Model

AbstractIn this study, we use MM5 weather-forecast model output and observed surface weather data from 11 stations in the western Himalaya to develop a statistical downscaling model (SDM) to better predict precipitation, 10 m wind speed and 2 m temperature. The analysis covers three consecutive winters: 2004/05, 2005/06 and 2006/07. The performance of the SDM was assessed using an independent dataset from the 2007/08 winter season. This assessment shows that the SDM technique substantially improves the forecast over specific station locations, which is important for avalanche-threat assessment.

scholarly journals Analysis of the Diurnal Variation of the Global Electric Circuit Obtained From Different Numerical Models

2017 ◽  
Vol 122 (23) ◽  
Author(s):  
Jaroslav Jánský ◽  
Greg M. Lucas ◽  
Christina Kalb ◽  
Victor Bayona ◽  
Michael J. Peterson ◽  
...  
Keyword(s):  
Diurnal Variation ◽  
Numerical Models ◽  
Electric Circuit ◽  
Global Electric Circuit

scholarly journals Dynamical mechanisms linking Indian monsoon precipitation and the circumglobal teleconnection

2021 ◽  
Author(s):  
Jonathan D. Beverley ◽  
Steven J. Woolnough ◽  
Laura H. Baker ◽  
Stephanie J. Johnson ◽  
Antje Weisheimer ◽  
...  
Keyword(s):  
North America ◽  
Northern Hemisphere ◽  
Rossby Waves ◽  
Wave Train ◽  
Forecast Model ◽  
Seasonal Forecast ◽  
The North ◽  
Circumglobal Teleconnection ◽  
Rossby Wave Train ◽  
Ecmwf Model

AbstractThe circumglobal teleconnection (CGT) is an important mode of circulation variability, with an influence across many parts of the northern hemisphere. Here, we examine the excitation mechanisms of the CGT in the ECMWF seasonal forecast model, and the relationship between the Indian summer monsoon (ISM), the CGT and the extratropical northern hemisphere circulation. Results from relaxation experiments, in which the model is corrected to reanalysis in specific regions, suggest that errors over northwest Europe are more important in inhibiting the model skill at representing the CGT, in addition to northern hemisphere skill more widely, than west-central Asia and the ISM region, although the link between ISM precipitation and the extratropical circulation is weak in all experiments. Thermal forcing experiments in the ECMWF model, in which a heating is applied over India, suggest that the ISM does force an extratropical Rossby wave train, with upper tropospheric anticyclonic anomalies over east Asia, the North Pacific and North America associated with increased ISM heating. However, this eastward-propagating branch of the wave train does not project into Europe, and the response there occurs largely through westward-propagating Rossby waves. Results from barotropic model experiments show a response that is highly consistent with the seasonal forecast model, with similar eastward- and westward-propagating Rossby waves. This westward-propagating response is shown to be important in the downstream reinforcement of the wave train between Asia and North America.

Fusion of rain radar images and wind forecasts in adeep learning model applied to rain nowcasting

2021 ◽  
Author(s):  
Anastase Charantonis ◽  
Vincent Bouget ◽  
Dominique Béréziat ◽  
Julien Brajard ◽  
Arthur Filoche
Keyword(s):  
Deep Learning ◽  
Optical Flow ◽  
Weather Forecast ◽  
Forecast Model ◽  
Radar Data ◽  
Learning Model ◽  
Rainfall Forecasting ◽  
Radar Images ◽  
Deep Learning Model ◽  
Rain Radar

<p>Short or mid-term rainfall forecasting is a major task with several environmental applications such as agricultural management or flood risks monitoring. Existing data-driven approaches, especially deep learning models, have shown significant skill at this task, using only rainfall radar images as inputs. In order to determine whether using other meteorological parameters such as wind would improve forecasts, we trained a deep learning model on a fusion of rainfall radar images and wind velocity produced by a weather forecast model. The network was compared to a similar architecture trained only on radar data, to a basic persistence model and to an approach based on optical flow. Our network outperforms by 8% the F1-score calculated for the optical flow on moderate and higher rain events for forecasts at a horizon time of 30 minutes. Furthermore, it outperforms by 7% the same architecture trained using only rainfall radar images. Merging rain and wind data has also proven to stabilize the training process and enabled significant improvement especially on the difficult-to-predict high precipitation rainfalls. These results can also be found in Bouget, V., Béréziat, D., Brajard, J., Charantonis, A., & Filoche, A. (2020). Fusion of rain radar images and wind forecasts in a deep learning model applied to rain nowcasting. arXiv preprint arXiv:2012.05015</p>

scholarly journals Nimbus-7 SMMR Derived Global Snow Cover Parameters

1987 ◽  
Vol 9 ◽  
pp. 39-44 ◽  
Author(s):  
A.T.C. Chang ◽  
J.L. Foster ◽  
D.K. Hall
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Snow Water Equivalent ◽  
Theoretical Calculations ◽  
Winter Season ◽  
Western China ◽  
High Plains ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water

Snow covers about 40 million km2of the land area of the Northern Hemisphere during the winter season. The accumulation and depletion of snow is dynamically coupled with global hydrological and climatological processes. Snow covered area and snow water equivalent are two essential measurements. Snow cover maps are produced routinely by the National Environmental Satellite Data and Information Service of the National Oceanic and Atmospheric Administration (NOAA/NESDIS) and by the US Air Force Global Weather Center (USAFGWC). The snow covered area reported by these two groups sometimes differs by several million km2, Preliminary analysis is performed to evaluate the accuracy of these products.Microwave radiation penetrating through clouds and snowpacks could provide depth and water equivalent information about snow fields. Based on theoretical calculations, snow covered area and snow water equivalent retrieval algorithms have been developed. Snow cover maps for the Northern Hemisphere have been derived from Nimbus-7 SMMR data for a period of six years (1978–1984). Intercomparisons of SMMR, NOAA/NESDIS and USAFGWC snow maps have been conducted to evaluate and assess the accuracy of SMMR derived snow maps. The total snow covered area derived from SMMR is usually about 10% less than the other two products. This is because passive microwave sensors cannot detect shallow, dry snow which is less than 5 cm in depth. The major geographic regions in which the differences among these three products are the greatest are in central Asia and western China. Future study is required to determine the absolute accuracy of each product.Preliminary snow water equivalent maps have also been produced. Comparisons are made between retrieved snow water equivalent over large area and available snow depth measurements. The results of the comparisons are good for uniform snow covered areas, such as the Canadian high plains and the Russian steppes. Heavily forested and mountainous areas tend to mask out the microwave snow signatures and thus comparisons with measured water equivalent are poorer in those areas.

Sign in / Sign up

Export Citation Format

Share Document