Weather forecasting by interactive analysis of radar and satellite imagery

At the Meteorological Office, Bracknell, quantitative rainfall maps from a network of ground-based radars, augmented by cloud images from Meteosat , are used to produce analyses and very-short-period forecasts of precipitation. These remotely sensed images provide the only way of presenting the current weather situation quickly enough and with the required spatial resolution and areal coverage. The processing of the radar and satellite data is highly automated, but there are some tasks that require judgements based upon many strands of information and an understanding of meteorological processes. To this end, forecasters use a specially developed display system to interact with the imagery. The facilities for interacting with the pictures have been optimized so that the forecaster, who is kept very busy in active weather situations, can keep pace with the flow of real-time data. Even so, as more radars are added to the network, ways must be found of reducing the burden of the forecaster’s interpretive and judgemental functions by automating some of them and making others easier to perform.

2003 ◽  
Author(s):  
Frank Fell ◽  
Phelim Burgess ◽  
Alexander Gruenewald ◽  
Mia V. Meyer ◽  
Richard P. Santer ◽  
...  

2010 ◽  
Vol 2010 (6) ◽  
pp. 429-438 ◽  
Author(s):  
Danielle Hoja ◽  
Maximilian Schwinger ◽  
Anna Wendleder ◽  
Peter Löwe ◽  
Harald Konstanski ◽  
...  

2013 ◽  
Vol 34 ◽  
pp. 5-8 ◽  
Author(s):  
P. L. Bragato ◽  
D. Pesaresi ◽  
A. Saraò ◽  
P. Di Bartolomeo ◽  
G. Durì

Abstract. The Centro di Ricerche Sismologiche (CRS, Seismological Research Center) of the Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS (Italian National Institute for Oceanography and Experimental Geophysics) in Udine (Italy) after the strong earthquake of magnitude Mw = 6.4 occurred in 1976 in the Italian Friuli-Venezia Giulia region, started to operate the Northeastern Italy Seismic Network: it currently consists of 12 very sensitive broad band and 21 simpler short period seismic stations, all telemetered to and acquired in real time at the OGS-CRS data centre in Udine. Real time data exchange agreements in place with other Italian, Slovenian, Austrian and Swiss seismological institutes lead to a total number of 93 seismic stations acquired in real time, which makes the OGS the reference institute for seismic monitoring of Northeastern Italy, as shown in Fig. 1 (Bragato et al., 2011; Saraò et al., 2010). Since 2002 OGS-CRS is using the Antelope software suite as the main tool for collecting, analyzing, archiving and exchanging seismic data, initially in the framework of the EU Interreg IIIA project "Trans-national seismological networks in the South-Eastern Alps" (Bragato et al., 2010; Pesaresi et al., 2008). SeisComP is also used as a real time data exchange server tool. In order to improve the seismological monitoring of the Northeastern Italy area, at OGS-CRS we tuned existing programs and created ad hoc ones like: a customized web server named PickServer to manually relocate earthquakes, a script for automatic moment tensor determination, scripts for web publishing of earthquake parametric data, waveforms, state of health parameters and shaking maps, noise characterization by means of automatic spectra analysis, and last but not least scripts for email/SMS/fax alerting. A new OGS-CRS real time seismological website (http://rts.crs.inogs.it/) has also been operative since several years.


2018 ◽  
Vol 10 (8) ◽  
pp. 1216 ◽  
Author(s):  
Jonathan Dash ◽  
Grant Pearse ◽  
Michael Watt

The development of methods that can accurately detect physiological stress in forest trees caused by biotic or abiotic factors is vital for ensuring productive forest systems that can meet the demands of the Earth’s population. The emergence of new sensors and platforms presents opportunities to augment traditional practices by combining remotely-sensed data products to provide enhanced information on forest condition. We tested the sensitivity of multispectral imagery collected from time-series unmanned aerial vehicle (UAV) and satellite imagery to detect herbicide-induced stress in a carefully controlled experiment carried out in a mature Pinus radiata D. Don plantation. The results revealed that both data sources were sensitive to physiological stress in the study trees. The UAV data were more sensitive to changes at a finer spatial resolution and could detect stress down to the level of individual trees. The satellite data tested could only detect physiological stress in clusters of four or more trees. Resampling the UAV imagery to the same spatial resolution as the satellite imagery revealed that the differences in sensitivity were not solely the result of spatial resolution. Instead, vegetation indices suited to the sensor characteristics of each platform were required to optimise the detection of physiological stress from each data source. Our results define both the spatial detection threshold and the optimum vegetation indices required to implement monitoring of this forest type. A comparison between time-series datasets of different spectral indices showed that the two sensors are compatible and can be used to deliver an enhanced method for monitoring physiological stress in forest trees at various scales. We found that the higher resolution UAV imagery was more sensitive to fine-scale instances of herbicide induced physiological stress than the RapidEye imagery. Although less sensitive to smaller phenomena the satellite imagery was found to be very useful for observing trends in physiological stress over larger areas.


2015 ◽  
Vol 30 (Suppl 1) ◽  
pp. S12 ◽  
Author(s):  
Byong Sop Lee ◽  
Wi Hwan Moon ◽  
Eun Ae Park

2019 ◽  
Vol 11 (21) ◽  
pp. 2538 ◽  
Author(s):  
Joanna Suliga ◽  
Joy Bhattacharjee ◽  
Jarosław Chormański ◽  
Ann van Griensven ◽  
Boud Verbeiren

The processing tool TREX, standing for ‘Tool for Raster data EXploration’ is presented and evaluated in the Biebrza wetlands in northeastern Poland. TREX was designed for the automatization of processing satellite data from the Proba-V satellite into maps of NDVI or LAI in any defined by the user projection, spatial resolution, or extent. The open source and access concept of TREX encourages the potential community of users to collaborate, develop, and integrate the tool with other satellite imagery and models. TREX reprojects, shifts, and resamples original data obtained from the Proba-V satellite to deliver reliable maps of NDVI and LAI. Validation of TREX in Biebrza wetlands resulted in correlations between 0.79 and 0.92 for NDVI data (measured with ASD Field Spec 4) and 0.92 for LAI data (measured with LiCOR—LAI-2000 Plant Canopy Analyzer).


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