scholarly journals Performance of high-resolution X-band weather radar networks – the PATTERN example

2014 ◽  
Vol 7 (12) ◽  
pp. 4151-4166 ◽  
Author(s):  
K. Lengfeld ◽  
M. Clemens ◽  
H. Münster ◽  
F. Ament
Keyword(s):  
High Resolution ◽  
Low Cost ◽  
Weather Radar ◽  
Probability Of Detection ◽  
Small Scale ◽  
Rain Event ◽  
Range Resolution ◽  
Radar Networks ◽  
X Band ◽  
Rain Events

Abstract. This publication intends to prove that a network of low-cost local area weather radars (LAWR) is a reliable and scientifically valuable complement to nationwide radar networks. A network of four LAWRs has been installed in northern Germany within the framework of the Precipitation and Attenuation Estimates from a High-Resolution Weather Radar Network (PATTERN) project observing precipitation with a temporal resolution of 30 s, a range resolution of 60 m and a sampling resolution of 1° in the azimuthal direction. The network covers an area of 60 km × 80 km. In this paper, algorithms used to obtain undisturbed precipitation fields from raw reflectivity data are described, and their performance is analysed. In order to correct operationally for background noise in reflectivity measurements, noise level estimates from the measured reflectivity field are combined with noise levels from the last 10 time steps. For detection of non-meteorological echoes, two different kinds of clutter algorithms are applied: single-radar algorithms and network-based algorithms. Besides well-established algorithms based on the texture of the logarithmic reflectivity field (TDBZ) or sign changes in the reflectivity gradient (SPIN), the advantage of the unique features of the high temporal and spatial resolution of the network is used for clutter detection. Overall, the network-based clutter algorithm works best with a detection rate of up to 70%, followed by the classic TDBZ filter using the texture of the logarithmic reflectivity field. A comparison of a reflectivity field from the PATTERN network with the product from a C-band radar operated by the German Meteorological Service indicates high spatial accordance of both systems in the geographical position of the rain event as well as reflectivity maxima. Long-term statistics from May to September 2013 prove very good accordance of the X-band radar of the network with C-band radar, but, especially at the border of precipitation events, higher-resolved X-band radar measurements provide more detailed information on precipitation structure because the 1 km range gate of C-band radars is only partially covered with rain. The standard deviation within a range gate of the C-band radar with a range resolution of 1 km is up to 3 dBZ at the borders of rain events. The probability of detection is at least 90%, the false alarm ratio less than 10% for both systems. Therefore, a network of high-resolution low-cost LAWRs can give valuable information on the small-scale structure of rain events in areas of special interest, e.g. urban regions, in addition to the nationwide radar networks.

scholarly journals Performance of high-resolution X-band weather radar networks – the PATTERN example

2014 ◽  
Vol 7 (8) ◽  
pp. 8233-8270
Author(s):  
K. Lengfeld ◽  
M. Clemens ◽  
H. Münster ◽  
F. Ament
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Low Cost ◽  
Weather Radar ◽  
Local Area ◽  
Small Scale ◽  
Rain Event ◽  
Range Resolution ◽  
Radar Networks ◽  
X Band

Abstract. This publication intends to proof that a network of low-cost local area weather radars (LAWR) is a reliable and scientifically valuable complement to nationwide radar networks. A network of four LAWRs has been installed in northern Germany within the framework of the project Precipitation and Attenuation Estimates from a High-Resolution Weather Radar Network (PATTERN) observing precipitation with temporal resolution of 30 s, azimuthal resolution of 1° and spatial resolution of 60 m. The network covers an area of 60 km × 80 km. In this paper algorithms used to obtain undisturbed precipitation fields from raw reflectivity data are described and their performance is analysed. In order to correct for background noise in reflectivity measurements operationally, noise level estimates from the measured reflectivity field is combined with noise levels from the last 10 time steps. For detection of non-meteorological echoes two different kinds of clutter filters are applied: single radar algorithms and network based algorithms that take advantage of the unique features of high temporal and spatial resolution of the network. Overall the network based clutter filter works best with a detection rate of up to 70%, followed by the classic TDBZ filter using the texture of the logarithmic reflectivity field. A comparison of a reflectivity field from the PATTERN network with the product from a C-band radar operated by the German Meteorological Service indicates high spatial accordance of both systems in geographical position of the rain event as well as reflectivity maxima. A longterm study derives good accordance of X-band radar of the network with C-band radar. But especially at the border of precipitation events the standard deviation within a range gate of the C-band radar with range resolution of 1 km is up to 3 dBZ. Therefore, a network of high-resolution low-cost LAWRs can give valuable information on the small scale structure of rain events in areas of special interest, e.g. urban regions, in addition the nationwide radar networks.

New pathways for high-resolution weather radar products in the Hamburg metropolitan region

2020 ◽  
Author(s):  
Finn Burgemeister ◽  
Tobias Sebastian Finn ◽  
Tobias Machnitzki ◽  
Marco Clemens ◽  
Felix Ament
Keyword(s):  
Weather Radar ◽  
Real Data ◽  
Potential Method ◽  
Metropolitan Region ◽  
Small Scale ◽  
Radar Observations ◽  
Range Resolution ◽  
Weather Radars ◽  
X Band ◽  
The One

<p>The University of Hamburg operates a single-polarized X-band weather radar to investigate small scale precipitation in Hamburg’s center since 2013. This weather radar provides a temporal resolution of 30 s, a range resolution of 60 m, and a sampling resolution of 1° within a 20 km radius. The X-band observations refine the coarse measurements of the German nationwide C-band radars. On the one hand, the resolution enables new capabilities in research and detection of extreme events, e.g. flash floods or tornadoes in rain events. On the other hand, with the single polarization and small wavelength, attenuation, noise, and non-meteorological echoes become a challenging issue. How can we derive products from disturbed weather radar observations?</p><p>We demonstrate new methods to process X-band weather radar observations effectively using synthetic and real data. Firstly, we present our python package for local weather radars. This package combines all steps of processing our measurements and includes well-established algorithms of image processing and radar meteorology. Secondly, we study machine learning as a new and potential method for our weather radar products. The developed neural network uses raw reflectivity measurements as input and results in data, which is free of noise and non-meteorological echoes. We outline assets and drawbacks of both methods and show possible connections.</p><p>Further X-band weather radar systems are planned for 2020 to monitor precipitation for the Hamburg metropolitan region in a networked environment. The high-quality and -resolution weather radar products will be provided for urban hydrology research within the Cluster of Excellence CLICCS - Climate, Climatic Change, and Society.</p>

A downscaling framework for precipitation nowcasting by merging radar retrievals at different scales and resolutions

2021 ◽  
Author(s):  
Roberto Deidda ◽  
Stefano Farris ◽  
Maria Grazia Badas ◽  
Marino Marrocu ◽  
Luca Massidda ◽  
...  
Keyword(s):  
High Resolution ◽  
Prediction Models ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Weather Radar ◽  
Early Warning Systems ◽  
Small Scale ◽  
Time Step ◽  
Radar Network ◽  
X Band

<p>Convective rainfall events represent one of the most critical issues in urban areas, where numerical weather prediction models are affected by a large uncertainty related to the short temporal and spatial scales involved, thus making early warning systems ineffective. Conversely, radar-based nowcasting models may be a useful tool to guarantee short-term forecasts, through the extrapolation of most recent properties in observed precipitation fields, for lead times ranging from minutes to few hours.</p><p>In this study we develop a procedure for merging relevant information from two radar products with different resolutions and scales: (i) high-resolution observations retrieved by an X-band weather radar in a small domain (the metropolitan area of Cagliari, located in Sardinia, Italy), and (ii) the mosaic data provided by the Italian Civil Protection national radar network (the whole region of Sardinia). Specifically, we here adapt some STEPS procedures to merge the large-scale advection from the latter radar network, and the small-scale statistical properties for the former X-band weather radar. We thus combine the corresponding forecasts preserving the higher resolution scale. In details, for each time step we (i) evaluate the power spectra of the two forecasts (ii) merge the two spectra taking the power of the large (small) frequencies from the high (low) resolution data spectrum and (iii) achieve optimal downscaling by reconstructing the high-resolution nowcast from the blend of the two spectra.</p>

Stand-alone low-cost sensor network in the inner city of Munich for modeling urban air pollutants

2021 ◽  
Author(s):  
Adrian Wenzel ◽  
Jia Chen ◽  
Florian Dietrich ◽  
Sebastian T. Thekkekara ◽  
Daniel Zollitsch ◽  
...  
Keyword(s):  
Air Quality ◽  
High Resolution ◽  
Sensor Network ◽  
Air Pollutants ◽  
Low Cost ◽  
Air Pollutant ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Small Scale ◽  
Urban Air

<p>Modeling urban air pollutants is a challenging task not only due to the complicated, small-scale topography but also due to the complex chemical processes within the chemical regime of a city. Nitrogen oxides (NOx), particulate matter (PM) and other tracer gases, e.g. formaldehyde, hold information about which chemical regime is present in a city. As we are going to test and apply chemical models for urban pollution – especially with respect to spatial and temporally variability – measurement data with high spatial and temporal resolution are critical.</p><p>Since governmental monitoring stations of air pollutants such as PM, NOx, ozone (O<sub>3</sub>) or carbon monoxide (CO) are large and costly, they are usually only sparsely distributed throughout a city. Hence, the official monitoring sites are not sufficient to investigate whether small-scale variability and its integrated effects are captured well by models. Smart networks consisting of small low-cost air pollutant sensors have the ability to provide the required grid density and are therefore the tool of choice when it comes to setting up or validating urban modeling frameworks. Such sensor networks have been established and run by several groups, achieving spatial and temporal high-resolution concentration maps [1, 2].</p><p>After having conducted a measurement campaign in 2016 to create a high-resolution NO<sub>2</sub> concentration map for Munich [3], we are currently setting up a low-cost sensor network to measure NOx, PM, O<sub>3</sub> and CO concentrations as well as meteorological parameters [4]. The sensors are stand-alone, so that they do not demand mains supply, which gives us a high flexibility in their deployment. Validating air quality models not only requires dense but also high-accuracy measurements. Therefore, we will calibrate our sensor nodes on a weekly basis using a mobile reference instrument and apply the gathered sensor data to a Machine Learning model of the sensor nodes. This will help minimize the often occurring drawbacks of low-cost sensors such as sensor drift, environmental influences and sensor cross sensitivities.</p><p> </p><p>[1] Bigi, A., Mueller, M., Grange, S. K., Ghermandi, G., and Hueglin, C.: Performance of NO, NO2 low cost sensors and three calibration approaches within a real world application, Atmos. Meas. Tech., 11, 3717–3735, https://doi.org/10.5194/amt-11-3717-2018, 2018</p><p>[2] Kim, J., Shusterman, A. A., Lieschke, K. J., Newman, C., and Cohen, R. C.: The BErkeley Atmospheric CO2 Observation Network: field calibration and evaluation of low-cost air quality sensors, Atmos. Meas. Tech., 11, 1937–1946, https://doi.org/10.5194/amt-11-1937-2018, 2018</p><p>[3] Zhu, Y., Chen, J., Bi, X., Kuhlmann, G., Chan, K. L., Dietrich, F., Brunner, D., Ye, S., and Wenig, M.: Spatial and temporal representativeness of point measurements for nitrogen dioxide pollution levels in cities, Atmos. Chem. Phys., 20, 13241–13251, https://doi.org/10.5194/acp-20-13241-2020, 2020</p><p>[4] Zollitsch, D., Chen, J., Dietrich, F., Voggenreiter, B., Setili, L., and Wenig, M.: Low-Cost Air Quality Sensor Network in Munich, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19276, https://doi.org/10.5194/egusphere-egu2020-19276, 2020</p>

scholarly journals Retrieval of High-Resolution Wind Fields over the Southern Korean Peninsula Using the Doppler Weather Radar Network

2009 ◽  
Vol 24 (1) ◽  
pp. 87-103 ◽  
Author(s):  
S-G. Park ◽  
Dong-Kyou Lee
Keyword(s):  
High Resolution ◽  
Korean Peninsula ◽  
General Structure ◽  
Three Dimensional ◽  
Small Scale ◽  
Wind Fields ◽  
Entire Area ◽  
Radar Network ◽  
Strong Winds ◽  
Rain Events

Abstract The performance of a radar network for retrieving high-resolution wind fields over South Korea is examined. The network consists of a total of 18 operational radars. All of the radars possess the Doppler capability and carry out plan position indicator (PPI) volume scans comprising 6–15 elevation steps at every 6 or 10 min. An examination of the coverage of the radar network reveals that the radar network allows the retrieval of three-dimensional high-resolution wind fields over the entire area of the southern Korean Peninsula as well as nearby oceans above a height of approximately 3 km. After the quality control procedures of the radar measurements, the high-resolution wind fields (a few kilometers) are extracted using multiple-Doppler wind synthesis in the Custom Editing and Display of Reduced Information in Cartesian Space (CEDRIC) package developed by NCAR. The radar-retrieved winds are evaluated using the following two rain events: 1) Typhoon Ewiniar in 2006, which resulted in strong winds and heavy rainfall over the entire southern Korean Peninsula, and 2) a well-developed hook echo with a relatively small-scale diameter of about 30 km. The wind fields retrieved from the radar network exhibit counterclockwise rotation around the typhoon center and a general structure around a hook echo such as a cyclonically rotating updraft (i.e., mesocyclone). Comparisons with the wind measurements from four UHF wind profilers for the typhoon case reveal that the u- and υ-wind components retrieved from the radar network deviate by standard deviations of 3.6 and 4.5 m s−1 over ranges from −30 to 20 m s−1 and from 0 to 40 m s−1, respectively. Therefore, it is concluded that the operational radar network has the potential to provide three-dimensional high-resolution wind fields within the mesoscale precipitation systems over almost the entire area of the southern Korean Peninsula.

X-band compact low cost multi-channel radar prototype for short range high resolution 3D-InISAR

2014 ◽  
Author(s):  
Stefano Lischi ◽  
Riccardo Massini ◽  
Daniele Stagliano ◽  
Luca Musetti ◽  
Fabrizio Berizzi ◽  
...  
Keyword(s):  
High Resolution ◽  
Short Range ◽  
Low Cost ◽  
X Band

Incorporating emergency management needs in the development of weather radar networks

2009 ◽  
Vol 7 (1) ◽  
pp. 45 ◽  
Author(s):  
Ellen J. Bass, PhD ◽  
Leigh Baumgart, MS ◽  
Brenda Philips, MBA ◽  
Kevin Kloesel, PhD ◽  
Kathleen Dougherty, MA ◽  
...  
Keyword(s):  
High Resolution ◽  
Emergency Management ◽  
Low Cost ◽  
High Resolution Data ◽  
Product Usage ◽  
Adaptive Sensing ◽  
Emergency Managers ◽  
Radar Networks ◽  
Network Concept ◽  
Resolution Data

The Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) is developing networks of lowpower, low-cost radars that adaptively collect, process, and visualize high-resolution data in the lowest portion of the atmosphere. CASA researchers are working with emergency managers (EM) to ensure that the network concept is designed with EMs’ needs in mind. Interviews, surveys, analysis of product usage logs, and simulated scenarios are being used to solicit EM input. Results indicate the need for products for both high- and low-bandwidth end users, visualizations for velocity products that are more easily interpreted, and enhanced training. CASA researchers are developing interventions to address these needs.

scholarly journals A High-resolution Aerial System of a New Type

1953 ◽  
Vol 6 (3) ◽  
pp. 272 ◽  
Author(s):  
BY Mills ◽  
AG Little
Keyword(s):  
High Resolution ◽  
Radio Astronomy ◽  
Small Area ◽  
Low Cost ◽  
Scale Model ◽  
Small Scale ◽  
New Type ◽  
Small Scale Model

A method of constructing an aerial system of high resolution but small area and low cost is described. Its application to the production of narrow pencil beams at metre wavelengths for investigations in radio astronomy is discussed. A small-scale model has been constructed to test the principle.

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