Effects of delayed weather radar images on pilots’ spatial awareness

The US weather radar archive holds detailed information about biological phenomena in the atmosphere over the last 20 years. Communally roosting birds congregate in large numbers at nighttime roosting locations, and their morning exodus from the roost is often visible as a distinctive pattern in radar images. This paper describes a machine learning system to detect and track roost signatures in weather radar data. A significant challenge is that labels were collected opportunistically from previous research studies and there are systematic differences in labeling style. We contribute a latent-variable model and EM algorithm to learn a detection model together with models of labeling styles for individual annotators. By properly accounting for these variations we learn a significantly more accurate detector. The resulting system detects previously unknown roosting locations and provides comprehensive spatio-temporal data about roosts across the US. This data will provide biologists important information about the poorly understood phenomena of broad-scale habitat use and movements of communally roosting birds during the non-breeding season.

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Gust front detection in weather radar images by entropy matched functional template

Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004. ◽

10.1109/acssc.2004.1399077 ◽

2005 ◽

Author(s):

O. Alkhouli ◽

V. DeBrunner

Keyword(s):

Weather Radar ◽

Radar Images ◽

Front Detection ◽

Gust Front

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Aerosol indirect effects on continental low-level clouds over Sweden and Finland

Atmospheric Chemistry and Physics ◽

10.5194/acp-14-12167-2014 ◽

2014 ◽

Vol 14 (22) ◽

pp. 12167-12179 ◽

Cited By ~ 4

Author(s):

M. K. Sporre ◽

E. Swietlicki ◽

P. Glantz ◽

M. Kulmala

Keyword(s):

Boundary Layer ◽

Radiative Forcing ◽

Remote Sensing Data ◽

Cloud Droplet ◽

Weather Radar ◽

Number Concentration ◽

Effective Radius ◽

Distribution Data ◽

Low Level ◽

Radar Images

Abstract. Aerosol effects on low-level clouds over the Nordic Countries are investigated by combining in situ ground-based aerosol measurements with remote sensing data of clouds and precipitation. Ten years of number size distribution data from two aerosol measurement stations (Vavihill, Sweden and Hyytiälä, Finland) provide aerosol number concentrations in the atmospheric boundary layer. This is combined with cloud satellite data from the Moderate Resolution Imaging Spectroradiometer and weather radar data from the Baltic Sea Experiment. Also, how the meteorological conditions affect the clouds is investigated using reanalysis data from the European Centre for Medium-Range Weather Forecasts. The cloud droplet effective radius is found to decrease when the aerosol number concentration increases, while the cloud optical thickness does not vary with boundary layer aerosol number concentrations. Furthermore, the aerosol–cloud interaction parameter (ACI), a measure of how the effective radius is influenced by the number concentration of cloud active particles, is found to be somewhere between 0.10 and 0.18 and the magnitude of the ACI is greatest when the number concentration of particles with a diameter larger than 130 nm is used. Lower precipitation intensity in the weather radar images is associated with higher aerosol number concentrations. In addition, at Hyytiälä the particle number concentrations is generally higher for non-precipitating cases than for precipitating cases. The apparent absence of the first indirect effect of aerosols on low-level clouds over land raises questions regarding the magnitude of the indirect aerosol radiative forcing.

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An analysis of anomalous propagation parameters and its effect on the intensity of clutter in weather radars

MAUSAM ◽

10.54302/mausam.v71i1.2 ◽

2021 ◽

Vol 71 (1) ◽

pp. 11-20

Author(s):

BIBRAJ R ◽

KANNAN B. ARUL MALAR ◽

RAO K. RAMACHANDRA ◽

SAIKRISHNAN K. C.

Keyword(s):

Refractive Index ◽

Electromagnetic Waves ◽

Inversion Layer ◽

Weather Radar ◽

Temperature Inversion ◽

Radiosonde Data ◽

Radio Waves ◽

Radar Images ◽

Propagation Parameters ◽

Anomalous Propagation

Weather radar is used by forecasters for identifying storms and estimating its corresponding precipitation. Anomalous propagation of the radar beam may lead to misinterpretation of the weather events and associated errors in precipitation estimates. As the weather radar transmits electromagnetic waves, it is affected by the refractive index of the atmosphere which depends on the temperature, pressure and water vapor content. It is important to understand the refractive index of the atmosphere and how it affects the beam propagation of the radar to interpret the echoes better. Meteorological conditions causing anomalous propagation is well described in literature by Battan (1973), Doviak and Zrnik (2006) and Rinehart (2001). The vertical refractivity gradient (VRG) affects the propagation of radio waves in the atmosphere (Gossard, 1977). These anomalous propagation cause clutter to be displayed in the radar images. The intensity of the clutter was differentiated into various groups by the amount of clutter present in the radar image. Refractivity parameters at various heights and the height of the temperature inversion layer were calculated using radiosonde observational data at the Visakhapatnam (VSK) station. The observed values from the radiosonde data were compared with the intensity groups and it was found that three parameters were influential in determining the intensity of the clutter which is the presence of the temperature inversion layer above the radar, the VRG of the temperature inversion layer above the radar and the VRG from the radar to a height of 1 km from sea level.

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Prediction of Weather Radar Images via a Deep LSTM for Nowcasting

2020 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn48605.2020.9206889 ◽

2020 ◽

Author(s):

Guang Yao ◽

Zongxuan Liu ◽

Xufeng Guo ◽

Chaoshi Wei ◽

Xinfeng Li ◽

...

Keyword(s):

Weather Radar ◽

Radar Images

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Precipitation Nowcasting with Weather Radar Images and Deep Learning in São Paulo, Brasil

Atmosphere ◽

10.3390/atmos11111157 ◽

2020 ◽

Vol 11 (11) ◽

pp. 1157

Author(s):

Suzanna Maria Bonnet ◽

Alexandre Evsukoff ◽

Carlos Augusto Morales Rodriguez

Keyword(s):

Deep Learning ◽

Weather Radar ◽

Sao Paulo ◽

São Paulo ◽

Additional Tool ◽

Radar Images ◽

Radar Echoes ◽

Meteorological Systems ◽

Weather Changes ◽

Time Required

Precipitation nowcasting can predict and alert for any possibility of abrupt weather changes which may cause both human and material risks. Most of the conventional nowcasting methods extrapolate weather radar echoes, but precipitation nowcasting is still a challenge, mainly due to rapid changes in meteorological systems and time required for numerical simulations. Recently video prediction deep learning (VPDL) algorithms have been applied in precipitation nowcasting. In this study, we use the VPDL PredRNN++ and sequences of radar reflectivity images to predict the future sequence of reflectivity images for up to 1-h lead time for São Paulo, Brazil. We also verify the feasibility for the continuous use of the VPDL model, providing the meteorologist with trends and forecasts in precipitation edges regardless of the weather event occurring. The results obtained confirm the potential of the VPDL model as an additional tool to assist nowcasting. Even though meteorological systems that trigger natural disasters vary by location, a general solution can contribute as a tool to assist decision-makers and consequently issue efficient alerts.

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