caracterization of the diferential reflectivity of euskalmet polarimetric

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  • ERAD 2012 - THE SEVENTH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY AND HYDROLOGY

    INSERT

    PICTURE

    HERE

    Caracterization of the diferential reflectivity of

    Euskalmet Polarimetric Weather Radar

    Maruri M1,2,3, Romo JA4, Hernaez I5, Etxezarreta A4 , Gaztelumendi S1,2

    1.-Basque Meteorology Agency (EUSKALMET). Parque tecnolgico de lava. Avda. Albert Einstein 44 Ed. 6 Of. 303, 01510 Miano, lava, Spain.

    2.-TECNALIA, Meteo Unit. Parque tecnolgico de lava. Avda. Albert Einstein 44 Ed. 6 Of. 303, 01510 Miano, lava, Spain.

    3.-Matemtica aplicada/Escuela Tcnica Superior de Ingeniera de Bilbao, University of the Basque Country UPV/EHU, Alameda de urquijo sn 48013 Bilbao, Spain

    4.-Electrnica y telecomunicaciones/Escuela Tcnica Superior de Ingeniera de Bilbao, University of the Basque Country UPV/EHU, Alameda de urquijo sn 48013 Bilbao, Spain

    5.- Adasa Sistemas, Environmental Quality Division. C/ Jos Agustn Goytisolo 30-32, 08908, LHospitalet de Llobregat, Barcelona, Spain

    (Dated: 15 April 2012)

    M. Maruri

    1. Introduction

    Differential Reflectivity (ZDR) is a very useful variable to identify different types of hydrometeors such as precipitation or

    hail. Besides, ZDR is very sensitive to system problems (Bringi and Chandrasekar, 2001). The goal of the work is to evaluate

    the information that comes from the analysis of the behaviour of the horizontal reflectivity (Zh) and ZDR under different

    meteorological conditions in the first and half year of operation.

    In this work, the ZDR measurements registered are compared with values from the bibliography. Besides, specific

    meteorological episodes of 2006-2007 are compared with recent episodes to complete the work. The differences found in

    similar episodes of different periods of the operation, are discussed to try to identify discrepancies that must be controlled.

    Special care is recommended with ZDR because it is a noisy variable. However, ZDR and its variation are good

    parameters for monitoring. Some discrepancies from their normal behaviour could be warnings of an arising problem.

    2. Description of the system

    The weather radar of the Basque Country is a Meteor 1500-C band system (Aranda et al, 2006). This is a Doppler

    polarimetric weather radar, where the only polarimetric variable measured is ZDR. The weather radar of the Basque

    Meteorological Agency (Euskalmet) is sited on top of a 1221.2 m mountain (Kapildui mountain) and operates in a complex

    terrain.

    The operational sequence consists on four scans. First of all, two volume scans are performed. The first scan has a range of

    300 km and a radial resolution of 1 km. The main goal of this scan is to get a first look of the weather situation. The second

    scan reaches a range of 100 km with a radial resolution of 250 m. It provides a more detailed view of the Basque Country and

    useful data for Quantitative Precipitation Estimation (QPE). After that, two elevation scans pointing to the west in strategic

    azimuths are performed. They give extra information of the lower layers close to urban areas (Gaztelumendi et al, 2006). All

    this scans use dual polarization.

    The ZDR is monitoring daily, instabilities of the data are correlated with instabilities of the system. Sun measures are

    compared day by day as an operational monitoring tool of the polarimetric variable (Holleman et al, 2010). Routine

    calibration procedures as Single point calibration or Zero check, are revised to evaluate their incidence in the recorded

    polarimetric measurements.

    .3. Methodology

    The methodology looks for the potential use of ZDR in combination with Zh to identify targets. This work is based on

    bibliographic information and on the statistical analysis of the database of the radar itself. In addition to this, several studies

    of hydrometeor classification that combine information from other polarimetric variables are considered. Other experiences

    with similar climatology or systems with similar technologies are considered mainly important

    Once the patterns have been taken from bibliography, they are used in the database. It is important to identify patterns in

    the database in order to define a methodology based on visual inspection. After that a statistical method must be defined for

    the study of the database. In this work, this statistical approach is based on the following ideas:

    1. Descriptive statistics. This is done mainly under clear air conditions and takes into account:

  • ERAD 2012 - THE SEVENTH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY AND HYDROLOGY

    -Diurnal variations.

    -Seasonal variations.

    -Azimuthal variations.

    2. Identification of patterns. It is divided into the following areas:

    -ZDR-behavior in situations of no precipitation (clear air). Identification of patterns associated with clutter.

    -ZDR-behavior in situations of no precipitation. Patterns associated with previous situations.

    -ZDR-behavior in situations of precipitation. Look for patterns in the precipitation area and define the most likely type

    of precipitation.

    4. Results and discussion

    4.1 Descriptive statistic.

    The descriptive analysis was is for clear air conditions, because precipitation can mask part of the behavior of the variables

    whish is really an effect of the environment.

    1) Diurnal variations

    Visual inspection of data shows a diurnal pattern of Zh and ZDR on some specific days. These days were correlated with

    clear and warm days.

    Fig. 1. Example of a clear air warm day (2006/06/03) case study. PPI at 0.5 elevation of Zh and ZDR at 10:02 (left). Diurnal evolution of ZDR (right).

    ZDR under clear air conditions reaches values between 0 and 10 dB (a 5 dB-thresold is used in the frequency plots), while

    horizontal reflectivity is mainly below 10 dBZ. The diurnal behaviour has a correlation with the sun. The two minimum

    values are correlated with sunrise and sunset. In Europe, clear air echoes are detected in a 50-60 km range and in 1-2 km

    altitude. Such echoes are rarely seen in winter (Meischnner, 1995). Humidity gradients (bragg scattering-refractive index

    unhomogeneities), biological targets (insects and birds) and residual clutter close to the radar could be the reasons of this

    behaviour.

    Fig. 2. Scatter plot of Zh (x-axis, in dBZ) against ZDR (y-axis, in dB) for a clear air warm day (2006/06/03).

    2) Seasonal variations

    The diurnal behavior shown above has also an evolution throughout the year. In winter, under clear air conditions, the

    frequency plots of bins with a ZDR over 5 dB decrease drastically.

  • ERAD 2012 - THE SEVENTH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY AND HYDROLOGY

    Fig. 3. Example of a clear air cold day (2006/12/27) case study. PPI at 0.5 elevation of Zh and ZDR at 11:32 (left). Diurnal evolution of ZDR (right).

    The differences between summer and winter Zh-ZDR scatter plots show a decrease in the number of bins with high ZDR

    values during winter, as well as narrower clouds of points, which mean lower values of Zh. These two seasonal features are

    very common in the Kaplidui weather radar.

    Fig. 4. Scatter plot of Zh (x-axis, in dBZ) against ZDR (y-axis, in dB) for a clear air warm day (2006/12/27).

    3) Azimuthal variations

    The lower elevations of the Kapildui radar are used in two ways. On one hand, they give information of the lower layers

    over one of the most populated cities in the region. On the other hand, they are used for monitoring different things such as

    bias problems caused by wrong pointing, bad calibration of the variables or by transmitter or receiver problems. Ground

    targets are fixed at one point, what makes them useful for monitoring tools. The study of azimuthal variations is used for

    monitoring.

    Azimuth studies reveal a specific pattern. Nowadays, this is used only as extra information and further study is needed.

    However, one can see that the minimum values of mean differential reflectivity are correlated with topographic barriers.

    Fig. 5. Example of the azimuthal evolution of ZDR. Lower values are correlated with the main topographic barriers.

    Fig. 6. System monitoring using the three lowest ZDR PPIs (-0.5, 0 and 0.5).

  • ERAD 2012 - THE SEVENTH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY AND HYDROLOGY

    4.2 Identification of patterns

    Through the study of the database of 2006 and 2007 different patterns of ZDR are identified. In these episodes the patterns

    are correlated with hydrometeor information from other sources (metar information, images, etc). Clear air patterns,

    precipitation patterns and mixed patterns of ZDR and Zh were also compared with bibliographic resources (Cremonini et al.,

    2004; Ryzhkov et al., 2005; Park, 2009; Straka et al., 2000).

    In this paper two hail episodes are presented in the next section, one from 2006 and other from 2011, in order to compare

    the differences in the behavior. Horizontal reflectivities are comparable but the range of the measurements of ZDR is

    suspicious and it shows and offset around 2 dB. The graphs shown here have additional quality problems associated with

    beam blockings and attenuations, which are not discussed.

    4.3 Variations of the ZDR in operation

    The data recorded under extreme meteorological events, and with an impact on the population, are analyzed

    systematically. This is the case of these two situations, the first is on July 4, 2006