scholarly journals Combining citizen science and weather radar data to study large‐scale bird movements

Ibis ◽  
2020 ◽  
Author(s):  
Nadja Weisshaupt ◽  
Teemu Lehtiniemi ◽  
Jarmo Koistinen
Keyword(s):  
Citizen Science ◽  
Large Scale ◽  
Weather Radar ◽  
Radar Data

scholarly journals The large-scale spatio-temporal variability of precipitation over Sweden observed from the weather radar network

2013 ◽  
Vol 6 (6) ◽  
pp. 10699-10730
Author(s):  
A. Devasthale ◽  
L. Norin
Keyword(s):  
Temporal Variability ◽  
High Intensity ◽  
Large Scale ◽  
Weather Radar ◽  
Radar Data ◽  
The Arctic ◽  
Northern Sweden ◽  
Late Night ◽  
Spatio Temporal ◽  
Precipitation Events

Abstract. Using measurements from the national network of 12 weather radar stations for the last decade (2000–2010), we investigate the large-scale spatio-temporal variability of precipitation over Sweden. These statistics provide useful information to evaluate regional climate models as well as for hydrology and energy applications. A strict quality control is applied to filter out noise and artifacts from the radar data. We focus on investigating four distinct aspects namely, the diurnal cycle of precipitation and its seasonality, the dominant time scale (diurnal vs. seasonal) of variability, precipitation response to different wind directions, and the correlation of precipitation events with the North Atlantic Oscillation (NAO) and the Arctic Oscillation (AO). When classified based on their intensity, moderate to high intensity events (precipitation > 0.34 mm (3 h)−1) peak distinctly during late afternoon over the majority of radar stations in summer and during late night or early morning in winter. Precipitation variability is highest over the southwestern parts of Sweden. It is shown that the high intensity events (precipitation > 1.7mm (3 h)−1) are positively correlated with NAO and AO (esp. over northern Sweden), while the low intensity events are negatively correlated (esp. over southeastern parts). It is further observed that southeasterly winds often lead to intense precipitation events over central and northern Sweden, while southwesterly winds contribute most to the total accumulated precipitation for all radar stations. Apart from its operational applications, the present study demonstrates the potential of the weather radar data set for studying climatic features of precipitation over Sweden.

Web‐based real‐time visualization of large‐scale weather radar data using 3D tiles

2020 ◽  
Author(s):  
Mingyue Lu ◽  
Xinhao Wang ◽  
Xintao Liu ◽  
Min Chen ◽  
Shuoben Bi ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Weather Radar ◽  
Radar Data ◽  
Web Based ◽  
Real Time Visualization

scholarly journals Bird strikes at commercial airports explained by citizen science and weather radar data

2021 ◽  
Vol 58 (10) ◽  
pp. 2029-2039 ◽  
Author(s):  
Cecilia Nilsson ◽  
Frank A. La Sorte ◽  
Adriaan Dokter ◽  
Kyle Horton ◽  
Benjamin M. Van Doren ◽  
...  
Keyword(s):  
Citizen Science ◽  
Weather Radar ◽  
Radar Data

scholarly journals The large-scale spatio-temporal variability of precipitation over Sweden observed from the weather radar network

2014 ◽  
Vol 7 (6) ◽  
pp. 1605-1617 ◽  
Author(s):  
A. Devasthale ◽  
L. Norin
Keyword(s):  
Temporal Variability ◽  
High Intensity ◽  
Large Scale ◽  
Weather Radar ◽  
Radar Data ◽  
The Arctic ◽  
Northern Sweden ◽  
Late Night ◽  
Spatio Temporal ◽  
Precipitation Events

Abstract. Using measurements from the national network of 12 weather radar stations for the 11-year period 2000–2010, we investigate the large-scale spatio-temporal variability of precipitation over Sweden. These statistics provide useful information to evaluate regional climate models as well as for hydrology and energy applications. A strict quality control is applied to filter out noise and artifacts from the radar data. We focus on investigating four distinct aspects: the diurnal cycle of precipitation and its seasonality, the dominant timescale (diurnal versus seasonal) of variability, precipitation response to different wind directions, and the correlation of precipitation events with the North Atlantic Oscillation (NAO) and the Arctic Oscillation (AO). When classified based on their intensity, moderate- to high-intensity events (precipitation > 0.34 mm/3 h) peak distinctly during late afternoon over the majority of radar stations in summer and during late night or early morning in winter. Precipitation variability is highest over the southwestern parts of Sweden. It is shown that the high-intensity events (precipitation > 1.7 mm/3 h) are positively correlated with NAO and AO (esp. over northern Sweden), while the low intensity events are negatively correlated (esp. over southeastern parts). It is further observed that southeasterly winds often lead to intense precipitation events over central and northern Sweden, while southwesterly winds contribute most to the total accumulated precipitation for all radar stations. Apart from its operational applications, the present study demonstrates the potential of the weather radar data set for studying climatic features of precipitation over Sweden.

scholarly journals Animal Migration Patterns Extraction Based on Atrous-Gated CNN Deep Learning Model

2021 ◽  
Vol 13 (24) ◽  
pp. 4998
Author(s):  
Shuaihang Wang ◽  
Cheng Hu ◽  
Kai Cui ◽  
Rui Wang ◽  
Huafeng Mao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Bird Migration ◽  
Weather Radar ◽  
Weather Conditions ◽  
Radar Data ◽  
Shape Information ◽  
Biological Targets ◽  
Radar Images ◽  
Biological Target

Weather radar data can capture large-scale bird migration information, helping solve a series of migratory ecological problems. However, extracting and identifying bird information from weather radar data remains one of the challenges of radar aeroecology. In recent years, deep learning was applied to the field of radar data processing and proved to be an effective strategy. This paper describes a deep learning method for extracting biological target echoes from weather radar images. This model uses a two-stream CNN (Atrous-Gated CNN) architecture to generate fine-scale predictions by combining the key modules such as squeeze-and-excitation (SE), and atrous spatial pyramid pooling (ASPP). The SE block can enhance the attention on the feature map, while ASPP block can expand the receptive field, helping the network understand the global shape information. The experiments show that in the typical historical data of China next generation weather radar (CINRAD), the precision of the network in identifying biological targets reaches up to 99.6%. Our network can cope with complex weather conditions, realizing long-term and automated monitoring of weather radar data to extract biological target information and provide feasible technical support for bird migration research.

scholarly journals Technical a Gaussian Mixture Model to Separate Birds and Insects in Single-Polarization Weather Radar Data

2021 ◽  
Vol 13 (10) ◽  
pp. 1989
Author(s):  
Raphaël Nussbaumer ◽  
Baptiste Schmid ◽  
Silke Bauer ◽  
Felix Liechti
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Doppler Radar ◽  
Animal Movement ◽  
Weather Radar ◽  
Gaussian Mixture ◽  
Radar Data ◽  
Volume Data ◽  
Simple Method ◽  
Single Polarization

Recent and archived data from weather radar networks are extensively used for the quantification of continent-wide bird migration patterns. While the process of discriminating birds from weather signals is well established, insect contamination is still a problem. We present a simple method combining two Doppler radar products within a Gaussian mixture model to estimate the proportions of birds and insects within a single measurement volume, as well as the density and speed of birds and insects. This method can be applied to any existing archives of vertical bird profiles, such as the European Network for the Radar surveillance of Animal Movement repository, with no need to recalculate the huge amount of original polar volume data, which often are not available.

scholarly journals Analyzing indoor mycobiomes through a large‐scale citizen science study in Norway

2021 ◽  
Author(s):  
Pedro M. Martin‐Sanchez ◽  
Eva‐Lena F. Estensmo ◽  
Luis N. Morgado ◽  
Sundy Maurice ◽  
Ingeborg B. Engh ◽  
...  
Keyword(s):  
Citizen Science ◽  
Large Scale ◽  
Science Study

scholarly journals The MJO in a Coarse-Resolution GCM with a Stochastic Multicloud Parameterization

2015 ◽  
Vol 72 (1) ◽  
pp. 55-74 ◽  
Author(s):  
Qiang Deng ◽  
Boualem Khouider ◽  
Andrew J. Majda
Keyword(s):  
General Circulation ◽  
Large Scale ◽  
Deterministic Model ◽  
Weather Prediction ◽  
General Circulation Models ◽  
Radar Data ◽  
Equation Model ◽  
Death Process ◽  
Relevant Parameter ◽  
Convective Systems

Abstract The representation of the Madden–Julian oscillation (MJO) is still a challenge for numerical weather prediction and general circulation models (GCMs) because of the inadequate treatment of convection and the associated interactions across scales by the underlying cumulus parameterizations. One new promising direction is the use of the stochastic multicloud model (SMCM) that has been designed specifically to capture the missing variability due to unresolved processes of convection and their impact on the large-scale flow. The SMCM specifically models the area fractions of the three cloud types (congestus, deep, and stratiform) that characterize organized convective systems on all scales. The SMCM captures the stochastic behavior of these three cloud types via a judiciously constructed Markov birth–death process using a particle interacting lattice model. The SMCM has been successfully applied for convectively coupled waves in a simplified primitive equation model and validated against radar data of tropical precipitation. In this work, the authors use for the first time the SMCM in a GCM. The authors build on previous work of coupling the High-Order Methods Modeling Environment (HOMME) NCAR GCM to a simple multicloud model. The authors tested the new SMCM-HOMME model in the parameter regime considered previously and found that the stochastic model drastically improves the results of the deterministic model. Clear MJO-like structures with many realistic features from nature are reproduced by SMCM-HOMME in the physically relevant parameter regime including wave trains of MJOs that organize intermittently in time. Also one of the caveats of the deterministic simulation of requiring a doubling of the moisture background is not required anymore.

scholarly journals Assessment of severe hailstorms and hail risk using weather radar data

2015 ◽  
Vol 22 (4) ◽  
pp. 746-753 ◽  
Author(s):  
Roxana Cică ◽  
Sorin Burcea ◽  
Roxana Bojariu
Keyword(s):  
Weather Radar ◽  
Radar Data
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