Data Processing of a PolarimetricX-Band Phased Array Weather Radar

A Degree Thesis Submitted to the Faculty ofEscola Tecnica Superior d’Enginyeria de Telecomunicacio de Barcelona

Universitat Politecnica de Catalunya

by

Jezabel Vilardell Sanchez

In partial fulfillmentof the requirements for the degree in

Telecommunications Systems Engineering

supervised by

Dr. Stephen J. Frasier

University of Massachusetts Amherst

Department of Electrical and Computer Engineering

July 2016

A mi querido bebe.

Abstract

DATA PROCESSING OF A POLARIMETRIC

X-BAND PHASED ARRAY WEATHER RADAR

July 2016

Jezabel Vilardell Sanchez

University of Massachusetts Amherst

Supervised by: Professor Stephen J. Frasier

Weather forecast estimation has been a matter of analysis for many years. The arrival

of dual polarization radars meant an improvement due to the incorporation of new vari-

ables. This project illustrates how to obtain the standard and new meteorological variables

(Reflectivity, Doppler Velocity, Spectrum Width, Differential Reflectivity, Co-Polar Corre-

lation Coefficient and Differential Propagation Phase). Reflectivity and Doppler velocity

appeared to be more advantageous because they are more consistent since the Spectrum

Width is easily corrupted. Variables retrieved from the dual polarization radars happen

to be even more reliable than the standard ones because most of them are independent

from miscalibrations and attenuations. This project also analyzes two different noise

identification methods in order to estimate the noise floor. One of them based on the

Co-Polar Correlation Coefficient characteristics that, for most of the cases, show a better

performance than the other method based on three different features that threshold the

noise.

i

Acknowledgments

I would like to thank all the people that have given me support through this journey.

First of all, I would like to thank my advisor Stephen Frasier for granting me the oppor-

tunity to come to UMass and perform my research at MIRSL and Albert Aguasca for

agreeing to be the co-director of my project.

A huge thanks to Krzysztof Orzel for being there every week following my progresses and

solving all my doubts, this project could not have happened without your help and guid-

ance, so thank you for all. I would also like to thank all the people at MIRSL that have

helped me every time I have asked for help.

Furthermore, I also would like to thank all the people that I have met in Amherst, spe-

cially Gerard and Ruben for being there, making me laugh all the time. I was lucky to

have had you both here either for hanging out or for the tips you have given me regarding

the research. I did not just receive support from friends I met here so I would also like to

acknowledge all the loving comfort I have received from my friends in Spain and my host

family in Seattle, thank you all for being there for me via ’0’s and ’1’s.

At last, I really want to thank all the family that have been there for me, asking how I

was doing, giving me support and always believing in me, you have made this possible, I

would not be here if it was not for you, specially my mom, so thank you for allowing me

to experience this. Gracies.

ii

Table of Contents

1 Introduction 1

1.1 Statement of purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 System description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2.1 Radar description . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.2.2 DAT file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.2.3 NetCDF file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2 Noise Floor Estimation 6

2.1 Noise Correction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2 Noise Identification Method I . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.2.1 Standard Deviation of Phase in Range . . . . . . . . . . . . . . . . 9

2.2.2 Standard Deviation of Power in Range . . . . . . . . . . . . . . . . 11

2.2.3 Mean of Normalized Coherent Power . . . . . . . . . . . . . . . . . 12

2.2.4 Decision Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.3 Noise Identification Method II . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3 Standard Meteorological Variables 21

3.1 Reflectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.2 Doppler Velocity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.3 Spectrum Width . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

4 Polarimetric Meterological Variables 31

4.1 Differential Reflectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

4.2 Co-Polar Correlation Coefficient . . . . . . . . . . . . . . . . . . . . . . . . 33

4.3 Differential Propagation phase . . . . . . . . . . . . . . . . . . . . . . . . . 35

5 Conclusions & Future Work 37

5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

iii

List of Figures

1.1 Image of the PTWR. The radar is inside the dome on top of the tower. . . 2

1.2 Example of a netCDF file. . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.1 Noise Floor in dB for beam 3 uncorrected(a) and corrected(b) . . . . . . . 7

2.2 Median of the Noise Floor profiles . . . . . . . . . . . . . . . . . . . . . . . 7

2.3 Phase Field of Beam 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.4 Standard Deviation of Phase in Range of Beam 2 . . . . . . . . . . . . . . 11

2.5 Standard Deviation of Power in Range of Beam 2 . . . . . . . . . . . . . . 12

2.6 Mean of Normalized Coherent Power of Beam 2 . . . . . . . . . . . . . . . 13

2.7 Interest Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.8 Decision matrices of feature fields . . . . . . . . . . . . . . . . . . . . . . . 15

2.9 Final Decision Matrix for Beam 2 . . . . . . . . . . . . . . . . . . . . . . . 15

2.10 Co-Polar Correlation Coefficient for beam 2 . . . . . . . . . . . . . . . . . 16

2.11 Received Power in dB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.12 Received Power in dB of Range Gates with Noise using noise identifica-

tion method I (a) and method II (b) . . . . . . . . . . . . . . . . . . . . . 19

2.13 Noise Floor Estimation using noise identification method I (a) and method

II (b) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.1 Vertical channel reflectivity in dBZ of beams 1(a) and 3(b). . . . . . . . . . 24

3.2 Vertical channel corrected reflectivity in dBZ of beams 1(a) and 3(b). . . . 25

3.3 Example of wind profile pattern. . . . . . . . . . . . . . . . . . . . . . . . . 27

3.4 Doppler Velocity of V Channel for beams 1(a), 2(b) and 3(c). . . . . . . . 28

3.5 Spectrum With of V Channel for beam 3. . . . . . . . . . . . . . . . . . . 30

4.1 Co-Polar Correlation Coefficient for beam 3. . . . . . . . . . . . . . . . . . 34

4.2 Differential propagation phase for beam 1. . . . . . . . . . . . . . . . . . . 36

iv

List of Tables

1.1 Elevation angle of vertical beams. . . . . . . . . . . . . . . . . . . . . . . . 3

1.2 DAT file listing of one beam. . . . . . . . . . . . . . . . . . . . . . . . . . . 4

v

Chapter 1

Introduction

This project is carried out at University of Massachusetts Amherst, in the Electrical

and Computer Engineering department at the Microwave Remote Sensing Laboratory

(MIRSL).

1.1 Statement of purpose

The main purpose of this project is to obtain information about weather phenomena by

computing the data obtained with an X-Band Dual Polarization Phased Array Weather

Radar. A Dual Polarization Radar gives a wider perspective of the obtained data because

the information comes from both horizontal and vertical channels and also the difference

between them, so the main purpose of using Dual Polarization is to be able to predict

more accurately the kind of weather phenomena that the radar is facing.

The following standard and new meteorological variables, that can be obtained by using a

polarimetric radar, are used to study and differentiate the weather phenomena appearing

in the radar’s readings:

1. Reflectivity.

2. Spectrum Width.

3. Doppler velocity.

4. Differential Reflectivity.

1

5. Co-Polar Correlation Coefficient.

6. Differential Propagation Phase.

1.2 System description

This project is carried out with the data acquired from the PTWR (Phase-Tilt Weather

Radar) developed in MIRSL. This radar is on top of a tower placed in Orchard Hill,

Amherst MA. Figure 1.1 shows an image of the radar.

Figure 1.1: Image of the PTWR. The radar is inside the dome on top of the tower.

2

1.2.1 Radar description

The calculation of the previously mentioned variables from the data obtained with the

PTWR is the main goal of this project.

PTWR is a polarimetric radar that operates in the X-Band at 9.36 GHz, it uses both

vertical and horizontal polarizations simultaneously. It is a phased array radar which

enables rapid electronic scans in elevation, providing a refresh rate of less than 1 minute,

which is more than five times faster than current weather radars, this is advantageous for

severe, widely changing weather analysis. The bandwidth of the radar is set on 1.5 MHz

and it can transmit two different pulses, the pulse durations are 50 µs (long pulse) and

5.2 µs (short pulse). The long pulse scans from 351 degrees to -13 degrees counterclockwise

and the short pulse scans from -13 degrees to 351 degrees clockwise in azimuth. The

transmission of the pulses is alternated consecutively. Each individual scan uses one of

the pulses, covers 360 degrees in azimuth and it has 11 vertical beams. The elevation of

the beams is shown in table 1.1. Even though the radar has available 11 beams, the data

analysis of this project focuses on the first three beams.

BEAMS 1 2 3 4 5 6 7 8 9 10 11DEGREES 0 1 3 5 7 9 11 13 15 30 45

Table 1.1: Elevation angle of vertical beams.

3

1.2.2 DAT file

The scans of the radar are stored in a DAT file, each file contains one spin of the radar

(long pulse spin or short pulse spin). An example of the structure of the binary file for

one beam is listed in table 1.2. The first 13 variables are the header of the file (40 bytes

in total). The remaining 8 variables are the body of the DAT file. The body has to be

read once per scan and once per beam. In this case, each file has 128 scans and each scan

has 3 beams so the body has to be read 384 times (128 scans× 3 beams).

Class Bytes Description Typical value

char 1 Pulse type 1char 1 Polarization mode 4 (HVHV)int 2 Number of range gates 2048int 2 Number of scans 128int 2 Pulse compression filter 2 (Hann window)

float 4 Pulse duration [µs] 50 or 5.2float 4 PRF 1st pulse [Hz] 3000float 4 PRF 2nd pulse [Hz] 3000float 4 PRF 3rd pulse [Hz] 3000float 4 PRF 4th pulse [Hz] 3000float 4 Pulse bandwidth [MHz] 1.5float 4 NLFM factor - alfa 1float 4 AM factor - gamma 0.1float 16384 Rhh(0) linear datafloat 16384 Rvv(0) linear datafloat 32768 Rhh(1) complex linear datafloat 32768 Rvv(1) complex linear datafloat 32768 Rvh(1) complex linear datafloat 32768 Rhv(1) complex linear data

Table 1.2: DAT file listing of one beam.

4

1.2.3 NetCDF file

Once all the data is processed and all the variables are calculated, in order to standardize

the results, the Network Common Data Format (NetCDF) is used.

This data format is a standard developed by UCAR (University Corporation for Atmo-

spheric Research) and commonly used in the meteorological field.

The final data has three different 2D matrices for each variable, because there are three

different vertical beams that provide information. In order to not have a heavy netCDF

file, the data is split in multiple netCDF files by vertical beams, so each file just con-

tains the information for one elevation angle. Figure 1.2 shows the information available

in the netCDF file. The name of the file is the date and time (UTC) when the data

was collected, in order to differentiate the different files corresponding to each beam, the

last number of the file name is the elevation angle of the dataset. Depending on which

data is requested the netCDF is going to contain different variables (Z,Zdr, Vr, σv, φdp, ρhv).

Figure 1.2: Example of a netCDF file. This dataset contains the Reflectivity and theDoppler velocity for H polarization. It is the dataset corresponding to beam 3, elevationangle 3 degrees. Screenshot from Panoply version 4.5.1

5

Chapter 2

Noise Floor Estimation

Noise floor (NF) estimation in digital signal processing has many various ways of calcula-

tion. This project carries out two different methods in order to identify the radar gates

containing noise, estimate the noise floor and get rid of it.

2.1 Noise Correction

The noise floor is an unwanted constant signal, however the data obtained from the PTWR

has an uncharacteristic behavior of the noise because it has an increasing tendency (figure

2.1a). There are two different ways to solve this problem, it can be fixed through hardware

or software since the increasing tendency is similar in every scan. In this case it is fixed

by adding a noise correction constant to the processing.

The noise correction is calculated by acquiring data one day without precipitations so

that the radar data only shows noise. Figure 2.1a is an example of data taken during a

clear sky day (05/14/2016) for beam 3 and profile 10. One can appreciate that the noise

increases when it should stay flat. In order to fix this, a collection of data acquired the

same day is used to calculate the median of the noise per each profile. Afterwards, a first

degree polynomial is fitted in that data so that the final result is applicable for all the

cases. Figure 2.2 shows, in blue, the median of the noise per profile for the NF and the

fitted linear polynomial in orange. The value of the fitted line is stored in a MAT file. In

order to apply the correction, the fitted line is loaded and then replicated into a matrix

6

for as many profiles as the data has, to obtain a matrix the same size as Rhh(0), then

divide the Rhh(0) matrix by the new replicated correction matrix to get the flat NF (all

in linear). Each pulse (long and short pulses) and polarization (horizontal and vertical)

has its particular correction line, therefore it has to be calculated separately. Figure 2.1b

shows the final result after applying the noise correction to the data.

(a) (b)

Figure 2.1: Noise Floor in dB for beam 3 uncorrected(a) and corrected(b). The abscis-sas represent the range gates and the ordinates the logarithmic value of Rhh lag 0. Datafrom PTWR, 05/30/2016, time: 12:01 UTC.

Figure 2.2: Median of profiles for the Noise Floor. The X axis represent the range gatesand the Y axis the median value of Rhh lag 0. The blue line represents the median valueof the profiles for the NF. In orange is represented the linear polynomial fitting the NFcurve. Data from PTWR, 05/30/2016, time: 12:01 UTC.

7

2.2 Noise Identification Method I

The first method applied is based on [1].

The main idea of this method is to calculate three different features which aim to have a

threshold at which the noise can be differentiated from the desired signal. After calculating

all of them, a binary decision matrix can be generated. This binary matrix determines

when a particular gate will be considered noisy or not, by having at least 2 out of 3 features

classifying the gate as noisy.

The three features used to identify the noise are the following ones:

1. Standard Deviation of Phase in Range

2. Standard Deviation of Power in Range

3. Mean of Normalized Coherent Power

The figures in this chapter are taken from a particular dataset, that shows a good amount

of data, they show horizontal channel in beam 2.

8

2.2.1 Standard Deviation of Phase in Range

Feature 1 is based on the random nature of the Doppler velocity of noise [1]. This can be

appreciated in figure 2.3 around gates 800-1400. The Standard Deviation (SD) of phase

in range tend to change wildly in the range gates with noise presence.

When there is signal present at a certain gate, the radial velocity remains rather constant

in the adjacent gates so, we can compute the SD of the gates and select which gates

contain noise.

However, this feature goes a little bit further, and instead of using directly the Doppler

velocity, it uses the Doppler’s phase.

Velocity is the phase scaled by Nyquist velocity, and the phase has a range of -180 degrees

to +180 degrees. In [1], they postulate that noise is presented as random in phase and

this leads to the fact that for a uniformly-distributed random variable the expected value

of the variance can be shown by the following formula.

σ =

√Range2

12(2.1)

In this case the range is 360 degrees so the expected value for the SD is:

σ =

√3602

12' 104 deg (2.2)

This feature computes the standard deviation of adjacent gates. In order to do that, we

run a sliding window of 9 samples through our matrix of gates in range and calculate the

SD for those 9 samples each time.

The Standard deviation of phase in range is calculated by equation 2.3 for the horizontal

channel and equation 2.4 for the vertical channel. The angle has to be in degrees.

PHASE SDEV = std(6 (Rhh(1))) (2.3)

PHASE SDEV = std(6 (Rvv(1))) (2.4)

9

Figure 2.3 shows the received signal’s Doppler phase. A neighbor of gates with rather con-

stant values is indicative of desirable signal and regions with non-uniform rapidly changing

values, are indicating noisy gates.

Figure 2.4 displays the Standard Deviation of figure 2.3, it is observable that SD for gates

mainly ranging from 1 to 800 is wandering around zero, indicating low variance of the

phase field in adjacent gates. As postulated [1], noise has a tendency of having a random

nature thus SD for gates with presence of noise is going to be rather high, by this we can

clearly differentiate noise from signal in the range gates.

In figure 2.4, regions with predominance of blue mean low and constant SD values, point-

ing out gates with signal, and regions with non-uniform and abruptly changing values of

SD suggest noisy gates. After several simulations it is concluded that range gates with

values above 50 are considered to contain signal.

Figure 2.3: Phase Field of Beam 2, the X and Y axes represent the range gates and pro-files of the radar respectively. In yellow positive values of the phase, in turquoise val-ues ranging around zero and in blue the negative values of the phase. Pixels changingabruptly represent noisy gates. Data from PTWR, 05/30/2016, time: 12:01 UTC.

10

Figure 2.4: Standard Deviation of Phase in Range of Beam 2, the horizontal and ver-tical axes represent the range gates and profiles of the radar respectively. In yellow thehigh values of SD and in blue SD values around zero. Low constant values of SD indi-cate gates with signal present. Data from PTWR, 05/30/2016, time: 12:01 UTC.

2.2.2 Standard Deviation of Power in Range

The power in range does not vary significantly in regions of noise. However, for echoes,

the variability in power is considerable. [2] This variable behavior can be analyzed by

computing the SD of power in log (dB) units, again, over a number of gates (in this case

9). This feature field is calculated by equation 2.5 (horizontal channel) and equation 2.6

(vertical channel).

POWER SDEV = std(10log10(|Rhh(0)|)) (2.5)

POWER SDEV = std(10log10(|Rvv(0)|)) (2.6)

Figure 2.5 shows the SD of Power in Range. Gates from 800 to 1400, show low values

of SD, between 0 and 0.5, meaning non significance variance of power thus indicative of

noisy gates. On the other hand, the high values of this plot are around 2 which is not

a considerably high value for SD but in this case it is quiet enough to differentiate noise

from signal. After several simulations, in this particular feature field, it is considered that

11

range gates with values below 0.5 are noisy gates.

Figure 2.5: Standard Deviation of Power in Range of Beam 2, abscissa and ordinaterepresent the range gates and profiles of the radar respectively. In yellow, values of SDnearby 2 and in blue, SD values near zero. Low SD, ranging from 0 to 0.5 show gateswith noise. Data from PTWR, 05/30/2016, time: 12:01 UTC.

2.2.3 Mean of Normalized Coherent Power

The normalized coherent power (NCP) is the ratio of power calculated at lag one (Rhh(1))

to the total received power(Rhh(0)). It gives an indication of how coherent is the signal.

NCP is scaled from 0 to 1 and when there is noise present it has low values. Nonetheless, to

improve the reliability of this feature, we compute the mean over the previously mentioned

9 samples window [1]. The final equation to calculate feature 3 is equation 2.7 for the

horizontal channel and equation 2.8 for the vertical channel.

NCP =|Rhh(1)||Rhh(0)|

(2.7)

NCP =|Rvv(1)||Rvv(0)|

(2.8)

12

Figure 2.6 shows the NCP for beam 2. It is visible that regions with noisy gates have low

values, ranging between 0 and 0.15. So in this case, after several simulations, it is decided

that values above 0.15 are considered to point out range gates with signal.

Figure 2.6: Mean of Normalized Coherent Power of Beam 2, x-axis and y-axis representthe range gates and profiles of the radar respectively. In yellow, values of NCP wander-ing around 1 and in blue, values near zero. High NCP values show range gates contain-ing targeted signal. Data from PTWR, 05/30/2016, time: 12:01 UTC.

2.2.4 Decision Matrix

Once the features are calculated, a binary decision matrix is created for each feature. The

binary matrices contain the decision whether a certain gate is considered to be a noisy

gate (zero) or contain signal (one).

Figure 2.7 represents three different interest maps, each of one for a determined feature

field. By applying this thresholding the binary decision matrices can be obtained. Each

feature field has a constant threshold nevertheless the case, 50 for feature 1, 0.5 for feature

2 and 0.15 for feature 3.

13

Figure 2.7: Interest Maps for converting the feature matrices into binary matrices. TheX axis corresponds to the feature fields values and the Y axis is the conversion value forthe decision matrix at a certain threshold.

After applying the interest mapping, the binary decision matrices are obtained. Figure

2.8 shows the three different decision matrices corresponding to each of the feature fields.

Figures 2.8a, 2.8b, 2.8c are the decision matrices for the features 1, 2 and 3 respectively.

For example, the blue area in figure 2.8c resemblances the yellow area in figure 2.6, which

is the area where gates are considered to contain signal.

The last step is to obtain the final decision matrix by combining the decision matrices of

the features.

For each particular gate, every decision matrix has a value, either 0, for noise, or 1, for

signal. So, if two or more decision matrices coincide on the decision value, as in 0 or 1,

then that gate it is considered as so.

Figure 2.9 shows the final decision matrix of this dataset for beam 2, in the horizontal

channel. The vertical channel will have its own decision matrix too, which will differ from

the horizontal one but is calculated the same way. Afterwards, this matrix it is used to

locate the noisy gates in order to calculate the noise floor.

14

Figure 2.8: Decision matrices of feature fields. X-Y axes are the range gates and theprofiles of the radar respectively. Blue color is 1, which highlights range gates with sig-nal and yellow color is 0, which points out noisy gates. Decision Matrix of feature 1(a).Decision Matrix of feature 2 (b). Decision Matrix of feature 3 (c). Data from PTWR,05/30/2016, time: 12:01 UTC.

Figure 2.9: Final Decision Matrix for Beam 2. Abcissa and ordinate are the range gatesand the profiles of the radar respectively. Blue areas are the range gates with signal (1)and yellow areas are the range gates with noise (0). Data from PTWR, 05/30/2016,time: 12:01 UTC.

15

2.3 Noise Identification Method II

The second method applied is based on the characteristics of the Co-Polar Correlation

Coefficient (ρhv). It is a unit-less variable which ranges between 0 and 1. ρhv indicates the

diversity of how each scatterer in the sampling volume contributes to the overall horizontal

and vertical polarization signal. For further explanation see chapter 4.

This second method it is simpler than the first one and requires less computational power.

This method identifies the noisy gates by applying a threshold to the ρhv. This threshold

value is decided based on ρhv characteristics.

In general, pure rain produces values of ρhv > 0.98 [3]. However, after numerous simula-

tions, the threshold for this radar is set on 0.4 because the main goal is to identify the

range gates with noise and not the ones with rain, hail or other meteorological events, so

by choosing 0.4 as the thresholding value, it is easier to assure not to miss any gate with

targeted signal. Figure 2.10 shows the ρhv for beam 2. Darker regions, pointed out in blue

and gray colors are range gates containing noise.

Figure 2.10: Co-Polar Correlation Coefficient for beam 2. Abcissas and ordinates are therange gates and profiles of the radar, respectively. The red areas have values wanderingaround 0.9 and are indicative of rain. Data from PTWR, 05/30/2016, time: 12:01 UTC.

16

2.4 Results

This section presents de analysis of the resulting noise floor retrieved by the two different

methods explained in sections 2.2 and 2.3. The following plots are obtained from the same

dataset used in the mentioned sections.

Both methods are applied over the received power in logarithmic units (dB) 10log10(Rhh(0))

(Figure 2.11).

The main goal is to calculate the noise floor following the next steps:

1. Locate the range gates with signal presence in order to eliminate them.

2. Get rid of the range gates containing signal.

3. Obtain the mean value of the noise floor for each profile.

The first step it is achieved by using the noise identification methods previously explained

(decision matrix or ρhv thresholding). After that, the aim is to work with the noisy gates

so the gates considered to have signal need to be removed. (step 2)

The final step is to obtain a mean value of the noise floor for each of the profiles. In this

case, instead of using the mean, the median is used to avoid uncommon values caused by

outliers.

Figure 2.11 is the received power signal. At first sight, green areas could be highlights

of rain regions, however it is difficult to distinguish signal from noise in the blue areas.

Therefore, the noise identification methods are needed to identify the noisy range gates

more accurately.

Figure 2.12 is the resulting received power in dB after applying the noise identification

methods. The remaining data it represents the range gates that contain noise. Figures

2.12a and 2.12b show the results of Method I (decision matrix) and Method II (ρhv) apiece.

The results are similar but at first sight it looks like method I it is stricter in the central

area than Method II since figure 2.12b has more gates remaining in between gates 200 and

800. However, in the blue area, Method II seems to be more uncompromising since the

area is thicker thus it is implying that there are no gates with signal in that sector.

17

Figure 2.11: Received Power of the radar in dB. The X-Y axes are the range gates andthe profiles of the radar respectively. Green areas represent precipitations and clear blueareas tend to be noisy gates. Data from PTWR, 05/30/2016, time: 12:01 UTC.

Once the noisy gates are located it is possible to calculate the noise floor of each profile.

However, there might be profiles where there is a lot of rain so there might be not enough

noisy gates to estimate the noise floor. Therefore, in order to avoid bad noise floor esti-

mations, a minimum number of noisy range gates is required. As explained in section 1.3,

this radar has around 200 profiles thus the threshold of minimum range gate is set in 50

gates because a percentage of noisy gates higher than 20% has proven to be enough to

have a good estimation of the noise floor.

Given the case, if there is a profile with not enough range gates, then instead of estimating

the noise floor in that particular profile, the noise floor is obtained from the nearest profile

with enough range to estimate it correctly.

Figure 2.13 shows the estimated noise floor by the two noise identification methods. The

red circles denote a group of consecutive profiles that do not have 50 or more noisy range

gates thus they utilize the estimated value of the nearest profile with enough gates to have

a good estimation. In this dataset, there are two zones where the noise floor estimation

has a constant value. This event is caused by areas where the precipitation is present all

18

(a) (b)

Figure 2.12: Received Power in dB of Range Gates with Noise using noise identifica-tion method I (a) and method II (b). X-Y axes represent the range gates and profiles ofthe radar respectively. Blue areas represent the noisy range gates. Red circles highlightzones where profiles do not have range gates with noise. Data from PTWR, 05/30/2016,time: 12:01 UTC.

over the profile, leaving no range gate with noise.

The constant noise floor values zones in figure 2.13a, which corresponds to the noise floor

estimation obtained using the first noise identification method, are recognizable in figure

2.12a (red circles). This is also visible in figures 2.12b and 2.13b.

One can observe in figures 2.13a and 2.13b that the noise behavior in the two graphics is

similar. However, the two zones highlighted with the red circles differ to one another. The

first difference between the two plots is located around profiles 20 and 40, in figure 2.13a

the constant zone between these gates is larger that in figure 2.13b. Figure 2.12a shows

less noisy range gates whereas figure 2.12b shows more in the red circle area. In the area

between range gates 90 and 120 marked with the second red circle in figure 2.13, can be

observed the same problem as in the area between range gates 20 and 40.

The power levels in the red circle zones of figure 2.13a are lower than the power levels in

the red circle zones of figure 2.13b, because in this particular case the nearest profile with

enough noisy range gates to estimate the noise floor is located in the previous profiles,

while in the case of figure 2.13b the nearest profile is found after the flat area.

19

In conclusion, noise identification method I appears to be stricter than noise iden-

tification method II because it uses three different arguments to locate the range gates

with noise, whereas method II only makes use of one so by this, method I is more likely

to be more accurate. However, method II happens to be more flexible when it comes

to dense storms that do not allow to estimate the noise properly in a significant area of

precipitation.

(a) Method I (b) Method II

Figure 2.13: Noise Floor Estimation using noise identification method I (a) and methodII (b). The X axis represents the profiles of the radar and the Y axis is the receivednoise power in V/m. The red circles indicate the profiles that do not have any noisyrange gate. Data from PTWR, 05/30/2016, time: 12:01 UTC.

20

Chapter 3

Standard Meteorological Variables

Conventional weather radars (single polarization) measure three moments:

1. Reflectivity (Z)

2. Doppler Velocity (Vr)

3. Spectrum Width (σv)

Single polarization radars send and receive signals at horizontal or vertical polarization,

therefore these three moments are going to be measured just for the polarization the signal

has been sent [4].

Even though these moments are estimated for the single polarization radars, they can also

be obtained for the dual polarization ones, giving the opportunity to get the moments for

either the horizontal and vertical channel simultaneously.

These three products are moments of the Doppler Spectrum. The reflectivity is the 0th

moment, Doppler velocity is the 1st moment and the Spectrum Width is the 2nd moment.

21

3.1 Reflectivity

Reflectivity is the amount of transmitted power returned to the radar receiver, each channel

(horizontal or vertical) has a different reflectivity.

This product is a function of:

1. Size (Radar cross section).

2. Shape (round, oblate, flat, etc).

3. State (liquid, frozen, mixed, dry, wet).

4. Concentration (number of particles in a volume).

Given the fact that reflectivity covers a wide range of signal it is more adequate for

calculations and comparisons to use a logarithmic scale so, the standard unit used for

reflectivity is the [dBZ]. The dBZ values are directly proportional to the returned signal

power. Z has values of -20 to 20 dBZ in clear air and in precipitation mode the reflectivity

ranges from 5 to 75 dBZ [4], rain with values aroung 55-60 dBZ can cause severe flooding.

The scale of dBZ is also related to the intensity of rainfall so low values of reflectivity

mean light rain, thus the higher the dBZ the stronger the rain rate.

There are different ways of calculating the reflectivity [5]. In this case, it is obtained in

a logarithmic scale through the received power. Equation 3.1 is the formula used in this

project to calculate the reflectivity in dBZ. Channel H has a different SNR than channel

V.

Z(dBZ) = C + (SNR · PN)(dBm) + 20log10(r(km)) (3.1)

Where r is the range in [Km], PN is an estimate of the noise floor calculated by equation

3.2 and C is the radar constant and is calculated by the equation 3.3.

PN = kTFB (3.2)

22

Where k is the Boltzmann constant (1.38 ·10−23 J/K), T is the room temperature (300K),

F is the noise figure of the system and B is the bandwidth.

C = 10log10(1024ln(2)λ2LrPtG2θφcπ3|K|2

) (3.3)

Where Lr = attenuation (usually a factor about 1.6).

θ = azimuth beamwidth (degrees).

φ = elevation beamwidth (degrees).

c = velocity of light [m/s].

τ = radar pulse width [s].

|K|2 ' 0.93 (constant for water phase).

Figure 3.1 is an example of reflectivity for the vertical polarization, beams 1 and 3. This

representation is a radar plan position indicator (PPI). The radar is set in the center of

the plot with the origin of coordinates. The white circle in the middle is the blind distance

of the radar calculated by the equation 3.4. The data is plotted in a circle and the axes

represent the distance in meters from the radar thus some quadrants have negative values

because the origin of ordinates is set at the center where the radar is.

Rb =cτ

2(3.4)

One can notice that there is a major area in green in both plots, values between 30-40

dBZ denote rain. The blue area shows zones with light rain. In figure 3.1a there are zones

colored in red, close to the blind zone with values ranging between 50-60 dBZ, these are

not zones with precipitation, these areas represent clutter but it is hard to differentiate

because of the attenuation.

23

(a) (b)

Figure 3.1: Vertical channel reflectivity in dBZ of beams 1(a) and 3(b). Circular plot,axes indicate distance of the data relative to the radar, set in th middle. Green areasdenote zones with moderate rain, blue areas point out light rain and red zones highlightclutter. Data from PTWR, 05/30/2016, time: 12:01 UTC.

The use of dual polarization radars gives the advantage of being able to correct the at-

tenuation that appears in the reflectivity by using the differential propagation phase (φdp,

section 4.2). Equation 3.5 shows the modification that has to be added to the reflectivity

in order to get rid of the attenuation.

Zcorrected(dBZ) = Z(dBZ) + 0.28φdp (3.5)

Figure 3.2 is the representation of the same that as figure 3.1 but calculated with equation

3.5 which has a correction factor (0.28φdp) added to the reflectivity (equation 3.1). The

difference is observable, for instance, the clutter is easier to spot since in figure 3.2 has

values over 70 dBZ instead of values around 40dBZ. After adding the correction factor,

in figure 3.2a the clutter is denoted with purple. Furthermore, in figure 3.2b, near the

blind zone, appears some purple area pointing out zones with clutter that do not appear

in figure 3.1b. Apart from the highlight of the clutter, it is also denotable that the rain

area has higher values since figure 3.2 has less blue zone and more yellow zone meaning

that precipitation is more intense than what the first reflectivity calculation estimates.

So the addition of this correction not only solves the attenuation problem but helps identify

the clutter zones that previously may have been interpreted as precipitation data.

24

(a) (b)

Figure 3.2: Vertical channel corrected reflectivity in dBZ of beams 1(a) and 3(b). PPI,axes indicate distance of the data relative to the radar, set in the middle. Green areasdenote zones with moderate rain, yellow areas point out zones with stronger rain andpurple areas are clutter. Data from PTWR, 05/30/2016, time: 12:01 UTC.

3.2 Doppler Velocity

The Doppler Velocity (Vr) is a function of the mean component of scatterer motion in the

radial direction from the radar [7]. It measures the radial speed of hydrometeors and the

motion of wind relative to the observation point, the radar. This variable is only capable to

see motion along the radial direction, movement that involve a change in distance relative

to the radar so, motions perpendicular to the radar beam are not appreciable.

The Doppler velocity is different depending on the polarization so it is calculated with

different values for each channel (horizontal and vertical). Equations 3.6 and 3.7 are used

to calculate Vrh and Vrv respectively.

Vrh =−λ

4π1

PRFh

· 6 (Rhh(1)) (3.6)

Vrv =−λ

4π1

PRFv

· 6 (Rvv(1)) (3.7)

In weather radar applications the Doppler velocity is a good asset when estimating the

wind direction. When large-scale weather produces vertically sheared but horizontal ho-

25

mogeneous flow field wind over the area of observation of the radar, the Doppler velocity

appears to have interpretable patterns to determine the speed and direction profiles of

the wind [8]. Winds that go away from the radar, outbound, have positive values of Vr

whereas those going towards the radar, inbound, have negative values.

There are six different patterns of the wind [7]:

1. Patterns associated with vertical profiles having constant wind direction.

2. Patterns associated with nonuniform horizontal wind fields.

3. Patterns associated with vertical profiles having constant wind speed.

4. Patterns associated with vertical profiles of varying wind speed and direction.

5. Patterns associated with vertical discontinuities in the wind field.

6. Patterns associated with horizontal discontinuities in the wind field.

Figure 3.3 shows an example of a wind pattern associated with vertical profiles having

constant wind speed. Figure 3.3a illustrates with arrows the wind direction, 3.3b shows the

Doppler velocity of that particular event and 3.3c shows how to understand the direction

of the wind using the zero value zone of Vr. The zero band is where the wind surpasses

the radar thus the observation point changes.

Figure 3.4b shows the Doppler velocity of V Channel for beam 2 of the data used in this

chapter. By comparing it with the wind patterns, this figure can be classified as type 2

(nonuniform horizontal wind fields). The wind comes from the East (inbound, negative

values) and goes to the West (outbound, positive values).

Another aspect of the Doppler velocity is that it has a limited range of observable radial

velocities. Velocities beyond that range (in this case [-12,12] [m/s]) will be folded back into

the range such that a strong outbound velocity will be interpreted as a strong inbound

velocity within the observable range.

This effect can be appreciated in figure 3.4c which depicts the Doppler velocity of V Chan-

nel for beam 3. It is observable near the zero band that the winds is advancing towards

South-East however, there is a positive zone at the edge of the radar range (yellow area)

26

Figure 3.3: Example of wind profile pattern. Plan view of enviromental wind field (a),Doppler velocity pattern (b) and illustration of how wind direction in a horizontally ho-mogeneous flow field can be interpreted using the zero Doppler velocity band (c). Uni-form arrow lenght in (a) indicates constant wind speed with heigh. Colors in (b) indi-cate vr values: the red-orange area denotes outbound and the bue area inbound.(From[7].)

following the negative values zone, even though it is shown as a positive value, it is not,

it is the effect of folding back a value that is beyond the established range.

Apart from the wind direction, in the Doppler velocity non-moving clutter, such as build-

ings, is easily spotted because it has value 0. However it is ambiguous when it comes to

differentiating clutter in the zero band zone. Figure 3.4a is the Vrv for beam 1, it shows a

zone of clutter mostly on the 4th quadrant (grey areas).

27

(a)

(b)

(c)

Figure 3.4: Doppler Velocity of V Channel for beams 1(a), 2(b) and 3(c). Grey col-ored band going through the blind zone is the zero band, where the point of observa-tion changes. Positive values indicate outbound winds and negative values are inboundwinds. Grey zones in (a) that are not zero band denote non-moving clutter. Data fromPTWR, 05/30/2016, time: 12:01 UTC.

28

3.3 Spectrum Width

Spectrum width (σv) is a measure of the velocity dispersion (shear or turbulence within

the resolution volume) [8]. It has the potential to improve the interpretation of the radar

data in severe weather, specially in storm turbulence. σv is a principal mean to detect

regions dangerous for safe flights [9].

The spectrum width provides a measure of the variability of the mean radial velocity

estimated due to wind shear, turbulence and/or quality of the velocity samples.

Nevertheless, the use of the spectrum width is belittled by the reflectivity and the Doppler

velocity because σv values are easily corrupted (e.g., by overlaid echoes) thus, less reliable

[10].

Spectrum width is calculated by equations 3.8 and 3.9 for the horizontal and vertical

channel, respectively.

σv =

λ[−0.5 · ln |Rhh(1)||Rhh(0)|

]1/2

4π · 1

PRFh

(3.8)

σv =

λ[−0.5 · ln |Rvv(1)||Rvv(0)|

]1/2

4π · 1

PRFv

(3.9)

Low values of σv indicate smooth flow whereas high values depict variability in movement,

turbulence and chaotic flow. The general scale for the spectrum width ranges from 0 to

20 kt (knots), anything beyond 20kt is truncated.

Figure 3.5 is the representation of σv of the vertical channel for beam 3. Figure 3.5a shows

σv in the general scale, in this case, the values are low, denoting smooth flow, so in order

to appreciate the plot better figure 3.5b shows the same data but with a shorter scale

(0-7kt). It is observable in figure 3.5b that even though the values of σv are low, there are

zones with lower flow at the center of the precipitation, pointed out in darker blue.

29

(a) (b)

Figure 3.5: Spectrum With of V Channel for beam 3 for general scale(a) and shorterscale(b). σv units in [kt]. In (a), blue denotes smooth flow. In (b), zones in green pointout regions with a higher flow than the center of the precipitation highlighted in blue.Data from PTWR, 05/30/2016, time: 12:01 UTC.

30

Chapter 4

Polarimetric Meterological Variables

Meteorological radars using dual polarization are those that transmit and receive both in

horizontal and vertical polarization in order to estimate additional characteristics of the

weather phenomena [5].

Transmitting the two polarizations allows the estimation of the differential quantities

between echoes from the two polarizations.

The most common polarimetric parameters are:

1. Differential Reflectivity (Zdr)

2. Co-Polar Correlation Coefficient (ρhv)

3. Differential Propagation Phase (φdp)

Polarimetric radars mean an improvement to rainfall estimation, precipitation classifi-

cation, data quality and weather hazard detection therefore, a more accurate weather

estimation.

31

4.1 Differential Reflectivity

The Differential Reflectivity (Zdr) is the difference in returned energy between the hor-

izontal and vertical pulses of the radar so, it is calculated as the difference between the

horizontal and vertical reflecitivies in dBZ, equation 4.1. It ranges from -7.9 to 7.9 dB

[3]. If the φdp is calculated, an attenuation correction factor is added to Zdr as shown in

equation 4.2.

Zdr(dB) = Zh(dBZ)− Zv(dBZ) (4.1)

Zdr = Zdr + 0.04φdp (4.2)

Positive values indicate that the targets are larger horizontally than they are vertically,

whereas negative values indicate targets larger vertically than horizontally. Values near

zero suggest that the target is spherical. Usually values of Zdr are positive since the rain

drops are wider in the horizontal direction.

Zdr is biased towards larger particles. The larger the particle, the more it contributes to

the reflectivity factor. It is also affected by the physical composition and/or the density

of particles. Zdr is enhanced as the complex refractive index increases, thus the Zdr of an

oblate water drop is larger than the Zdr of an ice pellet the same size and shape, because

the complex refractive index of water is greater than that of ice.

However, Zdr is independent of particle concentration because it is a ratio of the backscat-

tered power at H and V polarizations and it is also not affected by absolute miscalibration

of the radar transmitter or receiver.

Even so, after trying to estimate this variable, it has been observed that the radar has a

miscalibration and the Vertical Channel always has more power than the Horizontal one,

which is rather odd since the rain drops tend to be wider in the horizontal polarization

[3]. Because of this miscalibration, the radar differential reflectivity has not been able to

be estimated.

32

4.2 Co-Polar Correlation Coefficient

The Co-Polar Correlation Coefficient (CC or ρhv) is a measure of the diversity of how each

scatterer in the sampling volume contributes to the overall H and V polarization signals

[3]. This diversity includes any physical characteristic of the scatterers that affects the

returned signal amplitude and phase. When it exists a large variety in the types, shapes

and/or orientations of particles within the radar sampling volume, ρhv decreases.

ρhv is not affected by the diversity of sizes unless the shape of the particles varies across

the size spectrum. It is also immune to particle concentration, radar miscalibrations,

attenuations or differential attenuations and beam blockage.

It is a unit less variable ranging from 0 to 1. More uniform scatterers tend to produce ρhv

near 1.0. Spherical particles of any size, produce ρhv = 1.0, because they contribute equally

to the signals at H and V polarizations. Pure rain produces high values, ρhv > 0.98. It

does not reach 1.0 because rain drops change shape across the spectrum. Pure dry hail

produce high values of ρhv too, however, wet hail has values of ρhv under 0.95 and very

large hail has values under 0.85. Dry snow aggregates produce high values, ρhv > 0.97

because they have low density.

The Co-Polar correlation coefficient is calculated by first obtaining ρhv(Ts), and then

applying a correction factor, ρhv(2Ts). This factor is needed because ρhv is calculated

between H and V at lag zero (ρhv(0)), however simultaneous H and V samples are not

available thus an estimation of ρhv(Ts) is used, but this depends on the spread of radial

velocities [8]. Equation 4.3 shows the complete formula. The numerator is |ρhv(Ts)| and

the denominator is ρhv(2Ts).

ρhv(0) =

|Rhv(1)|√|Rhh(0)| · |Rvv(0)|

4

√|Rhh(1)||Rhh(0)|

(4.3)

Basically, ρhv with higher values indicates similar behavior in the region and low values

convey dissimilar behavior. The ρhv will be high as long as the magnitude or angle of the

33

radar’s H and V pulses undergo similar changes from pulse to pulse, otherwise it will be

low. This is the reason why ρhv is a good estimator of noise, because non-meteorological

echoes produce complex scattering pattern which causes the H and V pulses to vary widely

from pulse to pulse (ρhv < 0.4). Meteorological echoes have values ranging from 0.8 to

0.98. However, accuracy of ρhv is degraded by the distance from the radar and also when

multiple types of hydrometeors are present within a pulse volume, thus volume with rain

and hail will yield a lower ρhv than the same volume with just rain.

Figure 4.1 shows the Co-Polar Correlation Coefficient for beam 3. It is observable that

regions with rain have high values wandering 1.0 and areas with predominance of noise

are below 0.4.

Figure 4.1: Co-Polar Correlation Coefficient for beam 3. Values around 1.0 denote pre-cipitation and values under 0.4 are pointing out noise. Data from PTWR, 05/30/2016,time: 12:01 UTC.

34

4.3 Differential Propagation phase

The Differential Propagation phase (φdp) is the resulting difference in phase shift between

H and V polarizations. As the EM radiation propagates through the precipitation, it

acquires an additional phase shift compared to a signal that travels through clear air.

When the precipitation is not spherical, the amount of phase shift acquired is different for

the H and V channel [3].

φdp is proportional to the concentration number of particles and increases with the size

of the particles. A high concentration of smaller rain drops could yield higher φdp than

a smaller concentration of larger rain drops. It is also not affected by the presence of

hail and shifts on snow and ice are near 0 degrees. Since φdp is a phase measurement

it is not affected by attenuation, partial beam blockage or radar miscalibrations and it

is not biased by noise. These characteristics make the differential propagation phase an

attractive variable for attenuation correction, quantitative precipitation estimation or even

radar reflectivity calibration [11].

It is a positive variable that measures in degrees. Equation 4.4 is the formula to obtain

φdp, however, a correction factor is added into this equation because at the beginning

of the precipitation, φdp has to start with zero. This way if the plot does not show the

beginning of the storm it can be estimated by seeing the first value of φdp visible. In order

to obtain that, a constant, system φdp, is added, thus the φdp is going to be estimated by

equation 4.5.

φdp =1

26 Rhv(1)− 1

26 Rvh(1) (4.4)

φdp = φdp + refφdp (4.5)

To obtain this constant it is need a dataset that sees the storm approaching the radar but

it is not on top of it yet thus the beginning of the storm is visible in the plot.

Figure 4.2 is an example of an approaching storm that has not yet reached the radar or

the blind zone thus the margins of the storm are visible, marked with a red circle in the

plot. Taking these samples from the storm’s margin, the median can be calculated and

that is the refφdp value that it is added to the φdp equation.

35

Apart from the storm’s margin, in this figure it is also observable the increasing nature of

φdp along with the range.

Figure 4.2: Differential propagation phase for beam 1. Scale colored zone from green tored highlights area with precipitation. Non uniform areas are noise. Red circle denotesthe beginning of the incoming storm. Data from PTWR, 05/30/2016, time: 12:01 UTC.

36

Chapter 5

Conclusions & Future Work

5.1 Conclusions

The calculations of the weather variables for the analysis of the meteorological events have

been explained. The use of Dual polarization radars means an improvement to weather

forecast making it possible to determine more accurately the meteorological events present

in the radar observation zone.

The first step taken in this project was to get rid of the noise floor that came with the

data. There are multiple ways of identifying and eliminating the noise floor, however,

the case of study in this project was based on two different methods. The first method

applied was based on a paper wrote by M.Dixon and J.C. Hubbert (2012) and the sec-

ond one was based on the general characteristics of the Co-Polar Correlation Coefficient.

The method developed by Dixon and Hubbert was more complex than the second one,

thus it implied a higher computational power to process it, nevertheless, it was supposed

to be more trustworthy as it analyzed three different features that processed noise. The

conclusion obtained in this chapter was the following. Method I is stricter and effective

than method II when there are enough noisy gates in each profile to estimate the noise

floor. However, when the precipitation is much denser and there are large areas where

there are no profiles available with enough noisy gates to estimate the noise floor then,

method I can not estimate properly the noise floor because it adopts a noise estimation

value taken from the nearest profile with a good estimation of the noise but, since the

37

region with precipitation is large, the nearest profile happens to be too far to represent a

good estimation and it ends up attenuating or eliminating values that are not noise. So

in conclusion, method II is not as strict as method I but it does a good estimation of the

noise floor in any meteorological situation and it is also less complex thus it needs less

computational power so to avoid attenuating the valuable signal method II is the one used.

After deleting the noise, this project focused on the conventional variables of weather

analysis (Z, Vr and σv). Each of these three moments give a certain information about the

weather phenomena, however, Reflectivity and Doppler velocity are more valuable when it

comes to weather forecast because they are more reliable than the Spectrum width since

this variable is easily corrupted by overlaid echoes. Furthermore, after analyzing multiple

datasets, it has been observed that the reflectivity calculated for beam 1 (elevation angle

0 degrees) is really affected by ground clutter (e.g., trees and/or buildings) although after

applying the differential propagation phase correction, this clutter became an easy target

to identify. Apart from the corrected reflectivity, the Doppler velocity also appeared to

be a good variable to identify this ground clutter since the clutter is immobile thus it is

displayed as a non moving target, however, it can be confused with the zero band velocity

where the point of view of the radar changes.

The use of a dual polarization radars adds a new dimension of variables to the weather fore-

cast and this project analyses three of them (Zdr, ρhv and φdp). Zdr depends on the single

polarization reflecitivies (Zh and Zv) since there is a miscalibration and the Zv appears to

have more returned power than Zh, the differential reflectivity does not show proper value.

On the other hand, ρhv appeared to be very useful because with this variable the range

gates with noise are easily identified. Furthermore ρhv is a very robust variable because it

is not affected by miscalibrations nor attenuations thus the information obtained is reli-

able. The last variable, φdp is also very reliable since it is also not affected by attenuations,

beam blockage or miscalibrations thus it is a useful variable when it comes to attenuation

correction, quantitative rainfall estimation or calibration of the radar reflectivity.

38

5.2 Future Work

This project shows how to calculate the meteorological variables that a dual polarization

radar can provide, however, there are improvements that can be added.

It has been pointed out that many of the variables presented are a good asset regarding

ground clutter identification. One future approach for this weather radar could be to

properly identify this ground clutter and eliminate it.

The miscalibration of the radar between the V and H channel. Because of this fact, the

differential reflectivity it is not properly estimated. In order to solve this problem, a cer-

tain dataset it is needed, where there is rain approaching the radar but with low values

of reflectivity (around 20 dBZ). With those values obtained from the first profiles with

precipitation, a correction factor can be calculated and applied to Zv.

The Doppler velocity is defined to have 6 different patterns of the wind. It might be

interesting to perform a weather characterization of the area analyzing the wind patterns,

obtained for several months, and compare it with modeled back-trajectories, in order to

define the origin of the meteorological events.

It could also be interesting to compare the processed data of this radar with other obser-

vation systems, such as Vaisala Ceilometer, in order to determine which systems might

estimate better like the size distribution.

39

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