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Jarmo Koistinen
and
Heikki Pohjola

Abstract

An operational method is presented that corrects the bias of radar-based quantitative precipitation estimations (QPE) in radar networks that is due to the vertical profile of reflectivity (VPR) factor. It is used in both rain and snowfall. Measured average VPRs are obtained from the volume scans of each radar at ranges of 2–40 km. At each radar, two time ensembles of the bias estimates are made use of: the first ensemble contains 0–24 members at each range gate, calculated by beam convolution from the measured VPRs at 15-min intervals during the most recent 6 h. The second ensemble similarly contains 24 members calculated from parameterized climatological VPRs. In each scan the precipitation type classification and the climatological VPR are matched with the freezing level obtained from a numerical weather prediction model. The members of the two ensembles are weighted for both time lapse and quality and are then combined. At each composite grid point, the value of the networked VPR correction is then determined as a distance-weighted mean of the time ensembles of biases from all radars located closer than 300 km. In the absence of calibration errors, the resulting estimate of the reflectivity factor at ground level Z e is a seamless continuous field. As verified by radar–radar and radar–gauge comparisons in the Finnish network of eight C-band Doppler radars, the method efficiently reduces the range-dependent bias in QPE. For example, at radar ranges of 141–219 km, the average bias in the ground level Z e was −8.7 and 1.2 dB before and after the VPR correction, respectively.

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Tuomo Lauri
,
Jarmo Koistinen
, and
Dmitri Moisseev

Abstract

When making radar-based precipitation products, a radar measurement is commonly taken to represent the geographical location vertically below the contributing volume of the measurement sample. However, when wind is present during the fall of the hydrometeors, precipitation will be displaced horizontally from the geographical location of the radar measurement. Horizontal advection will introduce discrepancies between the radar-measured and ground level precipitation fields. The significance of the adjustment depends on a variety of factors related to the characteristics of the observed precipitation as well as those of the desired end product. In this paper the authors present an advection adjustment scheme for radar precipitation observations using estimated hydrometeor trajectories obtained from the High-Resolution Limited-Area Model (HIRLAM) MB71 NWP model data. They use the method to correct the operational Finnish radar composite and evaluate the significance of precipitation advection in typical Finnish conditions. The results show that advection distances on the order of tens of kilometers are consistently observed near the edge of the composite at ranges of 100–250 km from the nearest radar, even when using a low elevation angle of 0.3°. The Finnish wind climatology suggests that approximately 15% of single radar measurement areas are lost on average when estimating ground level rainfall if no advection adjustment is applied. For the Finnish composite, area reductions of approximately 10% have been observed, while the measuring area is extended downstream by a similar amount. Advection becomes increasingly important at all ranges in snowfall with maximum distances exceeding 100 km.

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Timo Puhakka
,
Kirsti Jylhä
,
Pirkko Saarikivi
,
Jarmo Koistinen
, and
Janne Koivukoski

Abstract

After the accident at the Chernobyl nuclear plant on 26 April 1986, much of Europe was affected by radioactive pollution. The first releases were transported toward Scandinavia, where most of the fallout was attributable to wet deposition. This study analyzes the synoptic scale and mesoscale meteorological conditions influencing the transport, and the meteorological factors related to the observed fallout in southern Finland. The study focuses on the role of rainfall in the final deposition onto the ground, studied using weather radar data. The results demonstrate that, although the large scale transport from Chernobyl could be roughly estimated by simple methods using routine synoptic data, sonic essential smaller-scale features could not be understood before an isentropic trajectory analysis, together with the conceptual model of a cyclone and its related conveyor belts, was applied. The main result of the study was the good correlation between the radioactive fallout and the corresponding areal distribution of rainfall measured by a weather radar.

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Terhi Mäkinen
,
Jenna Ritvanen
,
Seppo Pulkkinen
,
Nadja Weisshaupt
, and
Jarmo Koistinen

Abstract

The latest established generation of weather radars provides polarimetric measurements of a wide variety of meteorological and nonmeteorological targets. While the classification of different precipitation types based on polarimetric data has been studied extensively, nonmeteorological targets have garnered relatively less attention beyond an effort to detect them for removal from meteorological products. In this paper we present a supervised learning classification system developed in the Finnish Meteorological Institute (FMI) that uses Bayesian inference with empirical probability density distributions to assign individual range gate samples into 7 meteorological and 12 nonmeteorological classes, belonging to five top-level categories of hydrometeors, terrain, zoogenic, anthropogenic, and immaterial. We demonstrate how the accuracy of the class probability estimates provided by a basic naive Bayes classifier can be further improved by introducing synthetic channels created through limited neighborhood filtering, by properly managing partial moment nonresponse, and by considering spatial correlation of class membership of adjacent range gates. The choice of Bayesian classification provides well-substantiated quality estimates for all meteorological products, a feature that is being increasingly requested by users of weather radar products. The availability of comprehensive, fine-grained classification of nonmeteorological targets also enables a large array of emerging applications, utilizing nonprecipitation echo types and demonstrating the need to move from a single, universal quality metric of radar observations to one that depends on the application, the measured target type, and the specificity of the customers’ requirements.

Significance Statement

In addition to meteorological echoes, weather radars observe a wide variety of nonmeteorological phenomena including birds, insects, and human-made objects like ships and aircraft. Conventionally, these data have been rejected as undesirable disturbance, but lately their value for applications like aeroecological monitoring of bird and insect migration has been understood. The utilization of these data, however, has been hampered by a lack of comprehensive classification of nonmeteorological echoes. In this paper we present a comprehensive, fine-grained, probabilistic classifier for all common types of nonmeteorological echoes which enables the implementation of a wide range of novel weather radar applications.

Open access
Pekka J. Rossi
,
Vesa Hasu
,
Kalle Halmevaara
,
Antti Mäkelä
,
Jarmo Koistinen
, and
Heikki Pohjola

Abstract

Convective storms cause several types of damage, including economic and ecological losses, every year. This paper focuses on an automatic hazard-level determination of convective storms based on a largely unused information source: real-time emergency report data. In addition to the location of the report, the emergency response centers classify cases into different emergency types and deliver a free-form verbal description of the incident for online use. This study uses archived weather-related emergency reports to determine hazard levels for convective storms detected by the weather radar. To develop an algorithm for estimating the hazard level of convective storms, a weather radar–databased convective storm-tracking algorithm was applied with a method that links reported emergency events to individually tracked convective storms. Based on the relationship between each convective storm track and an emergency report, the algorithm determines the hazard level of the storms automatically. Moreover, the developed algorithm takes into account the population density at the location of the report because, in densely populated areas, the flow of emergency reports is more intense. The proposed algorithm with case studies shows the potential use of real-time emergency call data in operational severe weather nowcasting and warning tools. This study demonstrates that supplementing storms with emergency information is advantageous, especially with long-lasting storms such as supercell storms or mesoscale convective systems.

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Judy Shamoun-Baranes
,
Silke Bauer
,
Jason W. Chapman
,
Peter Desmet
,
Adriaan M. Dokter
,
Andrew Farnsworth
,
Hans van Gasteren
,
Birgen Haest
,
Jarmo Koistinen
,
Bart Kranstauber
,
Felix Liechti
,
Tom H. E. Mason
,
Cecilia Nilsson
,
Raphael Nussbaumer
,
Baptiste Schmid
,
Nadja Weisshaupt
, and
Hidde Leijnse

Abstract

Weather radar networks have great potential for continuous and long-term monitoring of aerial biodiversity of birds, bats, and insects. Biological data from weather radars can support ecological research, inform conservation policy development and implementation, and increase the public’s interest in natural phenomena such as migration. Weather radars are already used to study animal migration, quantify changes in populations, and reduce aerial conflicts between birds and aircraft. Yet efforts to establish a framework for the broad utilization of operational weather radar for biodiversity monitoring are at risk without suitable data policies and infrastructure in place. In Europe, communities of meteorologists and ecologists have made joint efforts toward sharing and standardizing continent-wide weather radar data. These efforts are now at risk as new meteorological data exchange policies render data useless for biodiversity monitoring. In several other parts of the world, weather radar data are not even available for ecological research. We urge policy makers, funding agencies, and meteorological organizations across the world to recognize the full potential of weather radar data. We propose several actions that would ensure the continued capability of weather radar networks worldwide to act as powerful tools for biodiversity monitoring and research.

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Jarkko T. Koskinen
,
Jani Poutiainen
,
David M. Schultz
,
Sylvain Joffre
,
Jarmo Koistinen
,
Elena Saltikoff
,
Erik Gregow
,
Heikki Turtiainen
,
Walter F. Dabberdt
,
Juhani Damski
,
Noora Eresmaa
,
Sabine Göke
,
Otto Hyvärinen
,
Leena Järvi
,
Ari Karppinen
,
Janne Kotro
,
Timo Kuitunen
,
Jaakko Kukkonen
,
Markku Kulmala
,
Dmitri Moisseev
,
Pertti Nurmi
,
Heikki Pohjola
,
Pirkko Pylkkö
,
Timo Vesala
, and
Yrjö Viisanen

Abstract

The Finnish Meteorological Institute and Vaisala have established a mesoscale weather observational network in southern Finland. The Helsinki Testbed is an open research and quasi-operational program designed to provide new information on observing systems and strategies, mesoscale weather phenomena, urban and regional modeling, and end-user applications in a high-latitude (~60°N) coastal environment. The Helsinki Testbed and related programs feature several components: observing system design and implementation, small-scale data assimilation, nowcasting and short-range numerical weather prediction, public service, and commercial development of applications. Specifically, the observing instrumentation focuses on meteorological observations of meso-gamma-scale phenomena that are often too small to be detected adequately by traditional observing networks. In particular, more than 40 telecommunication masts (40 that are 120 m high and one that is 300 m high) are instrumented at multiple heights. Other instrumentation includes one operational radio sounding (and occasional supplemental ones), ceilometers, aerosol-particle and trace-gas instrumentation on an urban flux-measurement tower, a wind profiler, and four Doppler weather radars, three of which have dual-polarimetric capability. The Helsinki Testbed supports the development and testing of new observational instruments, systems, and methods during coordinated field experiments, such as the NASA Global Precipitation Measurement (GPM). Currently, the Helsinki Testbed Web site typically receives more than 450,000 weekly visits, and more than 600 users have registered to use historical data records. This article discusses the three different phases of development and associated activities of the Helsinki Testbed from network development and observational campaigns, development of the local analysis and prediction system, and testing of applications for commercial services. Finally, the Helsinki Testbed is evaluated based on previously published criteria, indicating both successes and shortcomings of this approach.

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