Insights into the atmospheric state through observations of infrasound from a ground-truth source at regional distance

2021 ◽  
Author(s):  
Karl Koch ◽  
Christoph Pilger
Keyword(s):  
Sound Speed ◽  
Prediction Models ◽  
Sound Propagation ◽  
Middle Atmosphere ◽  
Weather Prediction ◽  
Winter Season ◽  
Ground Truth ◽  
Speed Ratio ◽  
German Aerospace ◽  
Infrasound Signal

<p>From the more than 160 tests of the ARIANE-5 main engine carried out by the German Aerospace Center (DLR) facility near Heilbronn, Germany, a large overall portion was detected at IMS infrasound station IS26 in the Bavarian forest. Located at a distance of about 320 km in an easterly direction (99° east-southeast from North) these observations were mostly made in the winter season between October and April with a detection rate of more than 70% , as stratospheric winds favor infrasound propagating through the atmosphere within the stratospheric duct. Only two exceptions were found for the summer season when stratospheric ducting is not predicted neither by climatologies nor the applied weather prediction models, due to a reversal of the middle atmosphere wind pattern.</p><p>Numerical weather prediction models for summer and winter seasons, or times with detections or non-detections were compared. It is then found that these models differ significantly in the sound speed profiles producing either a strong stratospheric duct for altitudes between 30 and 60 km in the case of detection, i.e. in winter months – or a lack thereof inhibiting regional sound propagation in summer months. It is of course reflected by the effective sound speed ratio, mostly exceeding a value of 1 for detections and less than 1 for non-detections. A significant portion of profiles representing non-detections, however, exhibit a sound speed profile that should enable infrasound signal observations. These cases are analyzed in detail to identify which fine structures within the sound speed profiles could explain the lack of observations.</p>

Infrasound signals from a ground-truth source and implications from atmospheric models: ARIANE engine tests in Southern Germany revisited

2020 ◽  
Author(s):  
Karl Koch ◽  
Christoph Pilger
Keyword(s):  
Winter Season ◽  
Ground Truth ◽  
Forecast Model ◽  
Atmospheric Model ◽  
Summer Season ◽  
Stratospheric Wind ◽  
German Aerospace ◽  
Infrasound Signal ◽  
Main Engine ◽  
The Relationship

<p>Over the past two decades the German Aerospace Center (DLR) facility near Heilbronn, Germany, has conducted a considerable number of tests of the ARIANE-5 main engine. Infrasound signals from many of these tests (~40%) have been observed at IMS station IS26 at a distance of about 320 km in an easterly direction (99° east-southeast from North). Due to the prevailing weather pattern in Central Europe, nearly all detected tests occurred during the winter months from October to April, when the stratospheric wind points in an eastern direction, while it reverses during the summer season. Except for a single event in May 2012, the summer months (May through September) did not yield any infrasound signal detections from the engine tests. On the other hand, not all tests conducted in winter are observed either, while detection in the spring and fall equinox months of April and October must be considered to occur incidentally.<br> <br>The large database of about 160 engine tests enables us to assess how well propagation modelling based on a standard atmospheric specification such as the ECMWF forecast model conforms with observed detections and non-detections.  While reversal of the stratospheric wind pattern in the summer season eliminates the stratospheric duct towards the eastern direction, the case of non-detections in the winter season may be of a more subtle nature. Besides increases in background noise levels due to heavy winds at the station, the fine structure of the stratospheric duct in the atmospheric model should determine the detection capability at IS26, which could be located inside or outside a shadow zone at a specific time. Ultimately, the standard atmospheric model used may not be an accurate description of the atmosphere in such cases either. This work on a controlled ground truth infrasound source will thus increase our understanding on the relationship between infrasound detection capabilities and atmospheric specifications over the seasons.</p>

scholarly journals Multirule Based Diagnostic Approach for the Fog Predictions Using WRF Modelling Tool

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Swagata Payra ◽  
Manju Mohan
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Winter Season ◽  
Wrf Model ◽  
Onset Time ◽  
Northwestern Part ◽  
Model Simulations ◽  
Western Disturbances ◽  
Promising Tool ◽  
Local Perturbations

The prediction of fog onset remains difficult despite the progress in numerical weather prediction. It is a complex process and requires adequate representation of the local perturbations in weather prediction models. It mainly depends upon microphysical and mesoscale processes that act within the boundary layer. This study utilizes a multirule based diagnostic (MRD) approach using postprocessing of the model simulations for fog predictions. The empiricism involved in this approach is mainly to bridge the gap between mesoscale and microscale variables, which are related to mechanism of the fog formation. Fog occurrence is a common phenomenon during winter season over Delhi, India, with the passage of the western disturbances across northwestern part of the country accompanied with significant amount of moisture. This study implements the above cited approach for the prediction of occurrences of fog and its onset time over Delhi. For this purpose, a high resolution weather research and forecasting (WRF) model is used for fog simulations. The study involves depiction of model validation and postprocessing of the model simulations for MRD approach and its subsequent application to fog predictions. Through this approach model identified foggy and nonfoggy days successfully 94% of the time. Further, the onset of fog events is well captured within an accuracy of 30–90 minutes. This study demonstrates that the multirule based postprocessing approach is a useful and highly promising tool in improving the fog predictions.

scholarly journals Comparison of Snow Cover from Satellite and Numerical Weather Prediction Models in the Northern Hemisphere and Northern Europe

2009 ◽  
Vol 48 (6) ◽  
pp. 1199-1216 ◽  
Author(s):  
Otto Hyvärinen ◽  
Kalle Eerola ◽  
Niilo Siljamo ◽  
Jarkko Koskinen
Keyword(s):  
Snow Cover ◽  
Prediction Models ◽  
Weather Prediction ◽  
Ground Truth ◽  
In Situ Measurements ◽  
Lower Atmosphere ◽  
Limited Area ◽  
In Situ Observations ◽  
Using Data

Abstract Snow cover has a strong effect on the surface and lower atmosphere in NWP models. Because the progress of in situ observations has stalled, satellite-based snow analyses are becoming increasingly important. Currently, there exist several products that operationally map global or continental snow cover. In this study, satellite-based snow cover analyses from NOAA, NASA, and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), and NWP snow analyses from the High-Resolution Limited-Area Model (HIRLAM) and ECMWF, were compared using data from January to June 2006. Because no analyses were independent and since available in situ measurements were already used in the NWP analyses, no independent ground truth was available and only the consistency between analyses could be compared. Snow analyses from NOAA, NASA, and ECMWF were similar, but the analysis from NASA was greatly hampered by clouds. HIRLAM and EUMETSAT deviated most from other analyses. Even though the analysis schemes of HIRLAM and ECMWF were quite similar, the resulting snow analyses were quite dissimilar, because ECMWF used the satellite information of snow cover in the form of NOAA analyses, while HIRLAM used none. The differences are especially prominent in areas around the snow edge where few in situ observations are available. This suggests that NWP snow analyses based only on in situ measurements would greatly benefit from inclusion of satellite-based snow cover information.

Winter Precipitation Microphysics Characterized by Polarimetric Radar and Video Disdrometer Observations in Central Oklahoma

2011 ◽  
Vol 50 (7) ◽  
pp. 1558-1570 ◽  
Author(s):  
Guifu Zhang ◽  
Sean Luchs ◽  
Alexander Ryzhkov ◽  
Ming Xue ◽  
Lily Ryzhkova ◽  
...  
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Winter Season ◽  
Winter Precipitation ◽  
Polarimetric Radar ◽  
Particle Size Distributions ◽  
Physical Processes ◽  
Polarimetric Weather Radar ◽  
Numerical Weather Prediction Models ◽  
Precipitation Events

AbstractThe study of precipitation in different phases is important to understanding the physical processes that occur in storms, as well as to improving their representation in numerical weather prediction models. A 2D video disdrometer was deployed about 30 km from a polarimetric weather radar in Norman, Oklahoma, (KOUN) to observe winter precipitation events during the 2006/07 winter season. These events contained periods of rain, snow, and mixed-phase precipitation. Five-minute particle size distributions were generated from the disdrometer data and fitted to a gamma distribution; polarimetric radar variables were also calculated for comparison with KOUN data. It is found that snow density adjustment improves the comparison substantially, indicating the importance of accounting for the density variability in representing model microphysics.

scholarly journals A Deep Learning Method for Bias Correction of ECMWF 24–240 h Forecasts

2021 ◽  
Author(s):  
Lei Han ◽  
Mingxuan Chen ◽  
Kangkai Chen ◽  
Haonan Chen ◽  
Yanbiao Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Relative Humidity ◽  
Wind Direction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Ground Truth ◽  
Learning Method ◽  
Weather Variables ◽  
Medium Range Weather Forecast ◽  
Forecast Lead Time

AbstractCorrecting the forecast bias of numerical weather prediction models is important for severe weather warnings. The refined grid forecast requires direct correction on gridded forecast products, as opposed to correcting forecast data only at individual weather stations. In this study, a deep learning method called CU-net is proposed to correct the gridded forecasts of four weather variables from the European Centre for Medium-Range Weather Forecast Integrated Forecasting System global model (ECMWF-IFS): 2-m temperature, 2-m relative humidity, 10-m wind speed, and 10-m wind direction, with a forecast lead time of 24 h to 240 h in North China. First, the forecast correction problem is transformed into an image-to-image translation problem in deep learning under the CU-net architecture, which is based on convolutional neural networks. Second, the ECMWF-IFS forecasts and ECMWF reanalysis data (ERA5) from 2005 to 2018 are used as training, validation, and testing datasets. The predictors and labels (ground truth) of the model are created using the ECMWF-IFS and ERA5, respectively. Finally, the correction performance of CU-net is compared with a conventional method, anomaly numerical correction with observations (ANO). Results show that forecasts from CU-net have lower root mean square error, bias, mean absolute error, and higher correlation coefficient than those from ANO for all forecast lead times from 24 h to 240 h. CU-net improves upon the ECMWF-IFS forecast for all four weather variables in terms of the above evaluation metrics, whereas ANO improves upon ECMWF-IFS performance only for 2-m temperature and relative humidity. For the correction of the 10-m wind direction forecast, which is often difficult to achieve, CU-net also improves the correction performance.

Calibration of solar radiation ensemble forecasts using convolutional neural network

2021 ◽  
Author(s):  
Florian Dupuy ◽  
Yen-Sen Lu ◽  
Garrett Good ◽  
Michaël Zamo
Keyword(s):  
Neural Network ◽  
Solar Radiation ◽  
Convolutional Neural Network ◽  
Prediction Models ◽  
Weather Prediction ◽  
Special Kind ◽  
Ground Truth ◽  
Weather Forecast ◽  
Ensemble Forecasts ◽  
Processing Techniques

<p><span>E</span><span>nsemble </span><span>forecast </span><span>approaches have become state-of-the-art for the quantification of weather forecast uncertainty. </span><span>However</span><span>, ensemble forecasts </span><span>from</span><span> numerical weather prediction models (NWPs) still tend to be biased and underdispersed, </span>hence justifying the use of statistical post-processing techniques <span>to improve forecast skill. </span></p><p>In this study, ensemble forecasts are post-processed using a convolutional neural network (CNN). CNNs are the most popular machine learning tool to deal with images. In our case, CNNs allow to integrate information from spatial patterns contained in NWP outputs.</p><p>We focus on solar radiation forecasts for 48 hours ahead over Europe from the 35-members ARPEGE (Météo-France global NWP) and a 512-members WRF (Weather Research and Forecasting) ensembles. We used a U-Net (a special kind of CNN) designed to produce a probabilistic forecast (quantiles) using as ground truth the CAMS (Copernicus Atmosphere Monitoring System) radiation service dataset with a spatial resolution of 0.2°.</p>

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.

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