PRELIMINARY STUDY OF A RADIO FREQUENCY INTERFERENCE FILTER FOR NON-POLARIMETRIC C-BAND WEATHER RADAR IN INDONESIA (CASE STUDY: TANGERANG WEATHER RADAR)

C-Band weather radar that operates at a frequency of 5 GHz is very vulnerable to radio frequency interference (RFI) because it is located on a free used frequency. RFI can cause image misinterpretation and precipitation echo distortion. The new allocation for free spectrum recommended by the World Radio Conference 2003 and weather radar frequency protection in Indonesia controlled by the Balai Monitoring Spektrum Frekuensi (BALMON) have not provided permanent protection against weather radar RFI. Several RFI filter methods have been developed for polarimetric radars, but there have been no studies related to RFI filters on non-polarimetric radars in Indonesia. This research aims to conduct an initial study of RFI filters on such radars. Four methods were applied in the initial study. The Himawari 8 cloud mask was used to eliminate interference echo based on VS, IR, and I2 channels, while the nature of false echo interference that does not have a radial velocity value was used as the basis for the application of the Doppler velocity filter. Another characteristic in the form of consistent echo interference up to the maximum range was used as the basis for applying a beam filling analysis filter with reflectivity thresholds of 5 dBZ and 10 dBZ, with beam filling of more than 75%. Finally, supervised learning Random Forest (RF) was also used to identify interference echo based on the characteristics of the sampling results on reflectivity, radial velocity, and spectral width data. The results show that the beam filling analysis method with a threshold of 5 dBZ provides the best RFI filter without eliminating echo precipitation

From agricultural benefits to aviation safety: Realizing the potential of continent-wide radar networks

Migratory animals provide a multitude of services and disservices—with benefits or costs in the order of billions of dollars annually. Monitoring, quantifying, and forecasting migrations across continents could assist diverse stakeholders in utilizing migrant services, reducing disservices, or mitigating human–wildlife conflicts. Radars are powerful tools for such monitoring as they can assess directional intensities, such as migration traffic rates, and biomass transported. Currently, however, most radar applications are local or small scale and therefore substantially limited in their ability to address large-scale phenomena. As weather radars are organized into continent-wide networks and also detect “biological targets,” they could routinely monitor aerial migrations over the relevant spatial scales and over the timescales required for detecting responses to environmental perturbations. To tap these unexploited resources, a concerted effort is needed among diverse fields of expertise and among stakeholders to recognize the value of the existing infrastructure and data beyond weather forecasting

Innovative Visualizations Shed Light on Avian Nocturnal Migration

We acknowledge the support provided by COST–European Cooperation in Science and Technology through the Action ES1305 ‘European Network for the Radar Surveillance of Animal Movement’ (ENRAM) in facilitating this collaboration. We thank ENRAM members and researchers attending the EOU round table discussion ‘Radar aeroecology: unravelling population scale patterns of avian movement’ for feedback on the visualizations. We thank Arie Dekker for his feedback as jury member of the bird migration visualization challenge & hackathon hosted at the University of Amsterdam, 25–27 March 2015. We thank Willem Bouten and Kevin Winner for discussion of methodological design. We thank Kevin Webb and Jed Irvine for assistance with downloading, managing, and reviewing US radar data. We thank the Royal Meteorological Institute of Belgium for providing weather radar data.Globally, billions of flying animals undergo seasonal migrations, many of which occur at night. The temporal and spatial scales at which migrations occur and our inability to directly observe these nocturnal movements makes monitoring and characterizing this critical period in migratory animals’ life cycles difficult. Remote sensing, therefore, has played an important role in our understanding of large-scale nocturnal bird migrations. Weather surveillance radar networks in Europe and North America have great potential for long-term low-cost monitoring of bird migration at scales that have previously been impossible to achieve. Such long-term monitoring, however, poses a number of challenges for the ornithological and ecological communities: how does one take advantage of this vast data resource, integrate information across multiple sensors and large spatial and temporal scales, and visually represent the data for interpretation and dissemination, considering the dynamic nature of migration? We assembled an interdisciplinary team of ecologists, meteorologists, computer scientists, and graphic designers to develop two different flow visualizations, which are interactive and open source, in order to create novel representations of broad-front nocturnal bird migration to address a primary impediment to long-term, large-scale nocturnal migration monitoring. We have applied these visualization techniques to mass bird migration events recorded by two different weather surveillance radar networks covering regions in Europe and North America. These applications show the flexibility and portability of such an approach. The visualizations provide an intuitive representation of the scale and dynamics of these complex systems, are easily accessible for a broad interest group, and are biologically insightful. Additionally, they facilitate fundamental ecological research, conservation, mitigation of human–wildlife conflicts, improvement of meteorological products, and public outreach, education, and engagement.Yeshttp://www.plosone.org/static/editorial#pee

Full-year evaluation of nonmeteorological Echo removal with dual-polarization fuzzy logic for two C-band radars in a temperate climate

The Royal Netherlands Meteorological Institute (KNMI) operates two dual-polarization C-band weather radars in simultaneous transmission and reception (STAR; i.e., horizontally and vertically polarized pulses are transmitted simultaneously) mode, providing 2D radar rainfall products. Despite the application of Doppler and speckle filtering, remaining nonmeteorological echoes (especially sea clutter) mainly due to anomalous propagation still pose a problem. This calls for additional filtering algorithms, which can be realized by means of polarimetry. Here we explore the effectiveness of the open-source wradlib fuzzy echo classification and clutter identification based on polarimetric moments. Based on our study, this has recently been extended with the depolarization ratio and clutter phase alignment as new decision variables. Optimal values for weights of the different membership functions and threshold are determined employing a 4-h calibration dataset from one radar. The method is applied to a full year of volumetric data from the two radars in the Dutch temperate climate. The verification focuses on the presence of remaining nonmeteorological echoes by mapping the number of exceedances of radar reflectivity factors for given thresholds. Moreover, accumulated rainfall maps are obtained to detect unrealistically large rainfall depths. The results are compared to those for which no further filtering has been applied. Verification against rain gauge data reveals that only a little precipitation is removed. Because the fuzzy logic algorithm removes many nonmeteorological echoes, the practice to composite data from both radars in logarithmic space to hide these echoes is abandoned and replaced by linearly averaging reflectivities.</p

Rainfall Monitoring Using Microwave Links from Cellular Communication Networks : The Dutch Experience

Microwave links from commercial cellular communication networks have been used for rainfall monitoring in The Netherlands since 2003. Here we report on the start of our work on this topic using a dedicated microwave link in 1999, our first trails with commercial microwave links (CMLs) from Vodafone in 2003, our first published results in 2007, our work on sources of error and uncertainties in rainfall retrievals using microwave links of different lengths and frequencies, and finally the fruitful collaboration with T-Mobile NL, which lead to areal rainfall estimation for the Rotterdam metropolitan area in 2009 and 2010 and country-wide rainfall mapping for the entire land surface area of The Netherlands since 2012. Our current work on this topic follows three lines of research: (1) further refinement of our rainfall retrieval and mapping algorithm; (2) further quantification of sources of error and uncertainties using a dedicated experimental setup of multiple microwave links and a line configuration of disdrometers for ground validation; (3) promotion of the international replication of this method for rainfall monitoring through collaboration with partners in Europe and beyond.</p

F4. Country-Wide Rainfall Maps from a Commercial Cellular Telephone Network

Accurate rainfall observations with high spatial and temporal resolutions are needed for many applications, for instance, as input for hydrological models. Weather radars often provide data with sufficient spatial and temporal resolution, but usually need adjustment. In general, only few rain gauge measurements are available to adjust the radar data in real-time, for example, each hour. Physically based methods, such as a Vertical Profile of Reflectivity (VPR) correction, can be valuable and hold a promise. However, they are not always performed in real-time yet and can be difficult to implement. The estimation of rainfall using microwave links from commercial cellular telephone networks is a new and potentially valuable source of information. Such networks cover large parts of the land surface of the earth and have a high density. The data produced by the microwave links in such networks is essentially a by-product of the communication between mobile telephones. Rainfall attenuates the electromagnetic signals transmitted from one telephone tower to another. By measuring the received power at one end of a microwave link as a function of time, the path-integrated attenuation due to rainfall can be calculated. Previous studies have shown that average rainfall intensities over the length of a link can be derived from the pathintegrated attenuation. A recent study of us shows that urban rainfall can be estimated from commercial microwave link data for the Rotterdam region, a densely-populated delta city in the Netherlands. A data set from a commercial microwave link network over the Netherlands is analyzed, containing approximately 1500 links covering the land surface of the Netherlands (35500 km2). This data set consists of several days with extreme rainfall in June, July and August 2011. A methodology is presented to derive rainfall intensities and daily rainfall depths from the microwave link data, which have a temporal resolution of 15 min. The magnitude and dynamics of these rainfall intensities is compared with those obtained from weather radar. Rainfall maps are derived from the microwave link data and are verified against rainfall maps based on gaugeadjusted weather radar data. Although much more work needs to be done, the first results look promising. Since cellular telephone networks are used worldwide, data from such networks could also become a valuable source of rainfall information in countries which do not have continuously operating weather radars, and no or few rain gauges. Apart from rainfall maps which are solely based on microwave link data, a preliminary analysis will be presented to assess whether commercial microwave link data can be used to adjust radar rainfall accumulations

Opportunistic remote sensing of rainfall using microwave links from cellular communication networks

Microwave backhaul links from cellular communication networks provide a valuable “opportunistic” source of high‐resolution space–time rainfall information, complementing traditional in situ measurement devices (rain gauges, disdrometers) and remote sensors (weather radars, satellites). Over the past decade, a growing community of researchers has, in close collaboration with cellular communication companies, developed retrieval algorithms to convert the raw microwave link signals, stored operationally by their network management systems, to hydrometeorologically useful rainfall estimates. Operational meteorological and hydrological services as well as private consulting firms are showing an increased interest in using this complementary source of rainfall information to improve the products and services they provide to end users from different sectors, from water management and weather prediction to agriculture and traffic control. The greatest potential of these opportunistic environmental sensors lies in those geographical areas over the land surface of the Earth where the densities of traditional rainfall measurement devices are low: mountainous and urban areas and the developing world. This article provides a nonexpert summary of the history, theory, challenges, and opportunities toward continental‐scale rainfall monitoring using microwave links from cellular communication networks

The effect of differences between rainfall measurement techniques on groundwater and discharge simulations in a lowland catchment

Several rainfall measurement techniques are available for hydrological applications, each with its own spatial and temporal resolution and errors. When using these rainfall datasets as input for hydrological models, their errors and uncertainties propagate through the hydrological system. The aim of this study is to investigate the effect of differences between rainfall measurement techniques on groundwater and discharge simulations in a lowland catchment, the 6.5-km2 Hupsel Brook experimental catchment. We used five distinct rainfall data sources: two automatic raingauges (one in the catchment and another one 30km away), operational (real-time and unadjusted) and gauge-adjusted ground-based C-band weather radar datasets and finally a novel source of rainfall information for hydrological purposes, namely, microwave link data from a cellular telecommunication network. We used these data as input for the, a recently developed rainfall-runoff model for lowland catchments, and intercompared the five simulated discharges time series and groundwater time series for a heavy rainfall event and a full year. Three types of rainfall errors were found to play an important role in the hydrological simulations, namely: (1) Biases, found in the unadjusted radar dataset, are amplified when propagated through the hydrological system; (2) Timing errors, found in the nearest automatic raingauge outside the catchment, are attenuated when propagated through the hydrological system; (3) Seasonally varying errors, found in the microwave link data, affect the dynamics of the simulated catchment water balance. We conclude that the hydrological potential of novel rainfall observation techniques should be assessed over a long period, preferably a full year or longer, rather than on an event basis, as is often done

Quality Control for Crowdsourced Personal Weather Stations to Enable Operational Rainfall Monitoring

Automatic personal weather stations owned and maintained by weather enthusiasts provide spatially dense in situ measurements that are often collected and visualized in real time on online weather platforms. While the spatial and temporal resolution of this data source is high, its rainfall observations are prone to typical errors, currently preventing its large‐scale, real‐time application. This study proposes a quality control methodology consisting of four modules targeting these errors, applicable in real time without requiring auxiliary measurements. The quality control improves the overall accuracy of a year of hourly rainfall depths in Amsterdam to a bias of −11.3% (0.2% when a proxy for overall rainfall underestimation by personal weather stations is used), a Pearson correlation coefficient of 0.82, and a coefficient of variation of 2.70, while maintaining 88% of the original data set. Application on a national scale (average 1 station per ∼10 km2) yields high‐resolution nationwide rainfall maps, hence showing the great potential of personal weather stations for complementing existing often sparse traditional rain gauge network