caracterization of the diferential reflectivity of euskalmet polarimetric

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

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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 tecnológico de Álava. Avda. Albert Einstein 44 Ed. 6 Of. 303, 01510 Miñano, Álava, Spain.

2.-TECNALIA, Meteo Unit. Parque tecnológico de Álava. Avda. Albert Einstein 44 Ed. 6 Of. 303, 01510 Miñano, Álava, Spain.

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

4.-Electrónica y telecomunicaciones/Escuela Técnica 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/ José Agustín Goytisolo 30-32, 08908, L’Hospitalet 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:


-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 0.5º 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.


Fig. 3. Example of a clear air cold day (2006/12/27) case study. PPI at 0.5º 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 (-0.5º, 0º and 0.5º).


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 (Gaztelumendi et al 2007) and the second is

on May 30, 2011 (Gaztelumendi et al 2012). The differences in the horizontal reflectivity can be justified with the

meteorological episode itself. This is because is not the exactly the same. The main problem arises in the interpretation of the

ZDR. In 2006, the recorded values of ZDR are consistent with those found in the literature, whereas the behavior during

2011 shows patterns of ZDR caused by the meteorological event, but with an observable deviation of 2 to 3 dB. Due to this

deviation, it is difficult to identify the hydrometeors without additional tools.

Fig. 7. Zh and ZDR 0.5º PPIs of the 2006/06/04 event (up) and of the 2011/05/30 (down).

Fig. 8. NW to SE vertical cut of the 2011/05/31 hail episode.


For the precipitation identification, a software tool that analyses the hydrometeor type of a certain region of the scan based

on bibliography is under test in the university. The results are given in percentage of probability of a certain type of

precipitation.

Fig. 9. Example of the hydrometeor classification tool developed at the university (up) and its correlation with actual METAR information (down).

4.4 Monitoring tools

The sun monitoring tool is an easy way for monitoring the diurnal evolution of ZDR (Holleman et al., 2010). This simple

idea is used routinely in the Basque weather radar. Differences in colors in the operation palette which are in a range of 1.5

dB are considered for further studies. With the PPI plots that are below it is very easy to understand this idea. These are

typical patterns of the sun signature, first during 2006-2007 and second during the 2010-2012, where a clear offset can be

seen.

Apart from this idea, another visual monitoring tool is used to check differences in consecutive scans that have no

meteorological explanation and to flag them. Special care is taken with procedures that involve calibrations such as Single

Point Calibration and Zero Check. Some results are presented in Maruri et al. in this conference.

Fig. 10. Sun monitoring patterns of 2007/02/01 (left) and 2010/12/31 (right). A clear offset of about 1.5 dB can be seen.


5. Conclusion

ZDR is a very sensitive variable. Several types of modifications can introduce offsets that must be corrected. Thus, special

care must be taken when applying any change to the system. ZDR has been commonly used for hydrometeor classification, as

it is explained in this paper (see 4.3). Apart from this, ZDR is a very useful variable for identifying blocked areas and non

meteorological echoes. For this porpoise an azimuthal pattern study is proposed.

To correct any variations, ZDR needs a daily monitoring. Studies of meteorological events and the identification of

different meteorological structures (horizontal and vertical) are catalogued to detect systematic deviations in real time. There

are several bibliographical experiences for correcting the observed offsets through a zenithal scan under round stratiform

precipitation. Other experiences offer offset correction models through horizontal scans or using the sun. The calibration of

this variable must be done systematically and under the same conditions in order to have comparable information. In this

case, different offsets are obtained from different statistical studies and, therefore, it is difficult to set a unique bias

correction.

The behavior of ZDR is correlated with the radar temperature. Daily visual inspection of data has shown that an increasing

room temperature leads to ZDR mean values 1 to 2 dB above expected. Thus, monitoring the radar indoor temperature,

specially that of the transmitter, is very important. In general, increasing temperature produces decreasing transmitted power.

These fluctuations must be carefully controlled with an air conditioning system.

Acknowledgment

The authors would like to acknowledge funding support from the Basque Government-ETORTEK 2010 and Selex

Gematronik.

References

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