Analysis of the Performance of Sea Level Stations at Haiti

Karuska A. Matos Horta

University of Puerto Rico Mayagüez Campus

Caribbean Tsunami Warning Program, Mayaguez P.R

ABSTRACT

Haiti, a Caribbean island with a historical background in tsunamis must be aware in case

of another catastrophe. Sea Level Stations provide the data to be analyzed by tsunami centers to

communicate to the general public if an event of this type occurs. An analysis of the performance

of three Sea Level Stations at Haiti using data from mareographs was made to monitor the data

delivery from these instruments through the years. The economic circumstance can have

implications in the incidence of the maintenance of the stations and can interrupt the

transmission of the data. This happened for one of the station studied and the deficiency of sea

level data affects environmental studies such as climate change, tsunami modeling and marine

services among others.

INTRODUCTION

The study of sea level and other

variables is used in conjunction with

tsunami models to generate flood and

evacuation maps and to send tsunami

watches and warnings. Sea level is the

measurement of the height of the sea at a

given time. The measures of sea level can be

obtained from mareograph by comparing the

sea level to a relative datum, also known as

marigraph or tide gauge (Enouye 2013).

Some countries, like Haiti, can have

economic difficulties to maintain and give

special attention to these tide gauges. This

financial situation can affect the operation of

these instruments and for consequence; can

interrupt the sea level data collection. The

lack of sea level data for a local area affects

environmental studies such as climate

change, tsunami modeling and marine

services.

This work presents how the three sea

level stations located in Haiti (see Figure 1)

have been operating since they started to

function. The first tide gauge was installed

at Cap-Häitien in November 30, 2011. These

stations obtain data through three types of

sensors: bubbler (bub), pressure (prs) and

radar (rad). The bubbler and the pressure

sensor are used to convert measured

pressure directly proportional to the required

sea level. The radar sensor use radar

frequencies to compute distance to the sea

surface (UNESCO 2006). The other two tide

gauges installed in December 2013 situated

at Port-Au-Prince and Jacmel only have

pressure and radar type of sensors. The

measurements provided by these sensors can

be compared between each other to have

more precise data. The monitoring of these

sea level stations includes the maintenance

and replacement of the sensors within each

one.

Tide gauges are very significant in

case of a tsunami. From the sea level

variations observed from the data received,

Tsunami Centers can communicate to local

areas the threat of a tsunami and save

thousands of lives. Haiti has a historical

record of tsunamis. In January 12, 2010 a

tsunami that averaged 10 feet (3 meters)

waves was generated by a magnitude 7.0

earthquake near Port-Au-Prince, Haiti. The

performance ratio of each sensor in the sea

level stations at Haiti were analyzed to

monitor if it sensors were sending data.

Since Haiti have historic background in

tsunamis, this country needs to be aware at

all times in order to get prepare for another

catastrophe.

Figure 1. Map from Haiti with location of Sea

Level Stations.

DATA AND METHODOLOGY

This report analyzes data from Cap-

Häitien (caph), Port-au-Prince (ptpr) and

Jacmel (ptpr) sea level stations for the

periods of November 30, 2011 to May 2016

for caph and December 2013 to May 2016

for ptpr and jaca. To analyze the station

sensor’s operational status, the CTWP office

developed a program in python (see

Appendix IV) that retrieves the raw sea level

data from the IOC website and calculates it

by the number of measurements received

divided by the number of measurements

expected, all multiplied by 100.

To facilitate analysis, all stations’

monthly ratios for a particular sensor are

plotted in graph since the day of their

installation until May 2016, for a total of

three plots. In addition, yearly averages for

each sensor for every station are plotted

within one figure for better comparison (See

Figures 2-4)

Tide predictions were attempted

through the Joint Archive for Sea Level

(JASL) software package for hourly sea

level data processing and quality control.

This software package was designed to

achieve tasks such as tidal analysis and

prediction, quality control, and filtering

hourly into daily and monthly values. The

data was adapted into the format established

in the JASL manual.

As part of the monitoring of the

mareogaphs, the Sea Level Station Report

was updated to keep in record all the

physical aspects of Cap-Haitien, Port-Au-

Prince and Jacmel tide gauges. The

information was obtained from Gerard

Metayer from Service Maritime et de

Navigation d’Haiti (SEMANAH). This

report contains information such as exact

location, the technology employed within

each station and levelling dates and

positions.

RESULTS

PERFORMING RATIOS

This percentage indicates how often

each sensor is sending data per minute. The

oldest tide gauge of Haiti, Cap-Haitien

station, has been working favorably in

comparison with the other two stations,

since it was installed. The performance

ratios per month are not less than 85% in

any of its three sensors (See Appendix V).

The sea level station at Port-Au-

Prince (ptpr) has been sending data per

minute frequently as is supposed to be. The

performance ratios per month are mostly

more than 99% (See Appendix V). On the

other hand, Jacmel station (jaca) has the

lowest performance ratio values for both

sensors. Since May 2015 the radar sensor

has been presenting problems in send data.

For May 2016, this station had a

performance ratio of 0.6% for the radar

sensor (See Appendix V). This value is the

lowest performance ratio from the three

stations at Haiti. Also, the pressure sensor

needs to be replaced because is also having

problems for sending data.

Figure 2. Performance Ratio Average for

Pressure Sensor of the three Sea Level

Stations at Haiti.

Figure 3. Performance Ratio Average for

Radar Sensor of the three Sea Level Stations

at Haiti.

Figure 4. Performance Ratio Average for

Bubbler Sensor of the Cap-Haitien Sea

Level Stations at Haiti.

RAW SEA LEVEL DATA PER YEAR

The raw sea level data per year was

extracted from the IOC public website. The

height measurements were plotted into a

graph (see figure 3) to observe the

appearance of the tidal patterns for each sea

level station location. An offset was made to

each graph to evolve it around zero as IOC

do. The off-set is the average (from all the

values in the current start-end timeframe)

subtracted. These graphs showed how the

lack of data affected the analysis of the

measurements. The deficiency of data, as

Jacmel’s radar sensor situation since May

2015, reduces the precision of good tide

predictions. Even though a tide prediction

can be made with a year of data, in order to

make a reliable tide prediction, the data

input must be collected from 19 years.

Figure 4. An example of Raw Sea Level

Data Graph of Port-Au-Prince Station from

2015 (pressure sensor). Problems with the

sensor are the reason of the missing data in

December.

CONCLUSION

The sea level data from tide gauges

is essential. Therefore, its maintenance must

be done regularly in order to provide data at

all times. Sea level stations at Haiti have

been presenting malfunctions which can put

in danger lives in this country. Not

receiving sea level data frequently can

influence in the decision making in the case

of a catastrophe like a tsunami. Monitoring

the performance ratio of these stations

indicates the quality of function of these

instruments and when to pay special

attention to it. Jacmel station had the lowest

performance ratio from the three stations

located in Haiti. Not having enough data to

analyze affects the study of the sea level in

the local area. Also, affects in the precision

of tide predictions.

ACKNOWLEDGMENTS

I want to express my gratitude to

NOAA for the opportunity of my summer

2016 internship at the Caribbean Tsunami

Warning Program (CTWP) at Mayaguez,

Puerto Rico. This project would have not

been completed without the assistance and

support of the CTWP staff.

REFERENCES

1. Intergovernmental Oceanographic

Commission. Revised Edition 2013.

Tsunami Glossary, 2013. Paris,

UNESCO. IOC Technical Series, 85.

(English.) (IOC/2008/TS/85rev)

2. IOC-UNESCO, 2016: Station

details. Accessed in July 2016.

[Available at http://www.ioc-

sealevelmonitoring.org/]

3. Manual on Sea-level Measurements

and Interpretation, Volume IV: An

update to 2006. Paris,

Intergovernmental Oceanographic

Commission of UNESCO. 78 pp.

(IOC Manuals and Guides No.14,

vol. IV ; JCOMM Technical Report

No.31; WMO/TD. No. 1339)

(English)

Appendix I. Performance Ratio Methodology

Description

The performance ratio is a percentage that indicates how often a sea level tide gauge sensor

(pressure (prs), radar (rad), and bubbler (bub)) is sending data to where?. This value provides

information about the tide gauge functionality. Sea level height data values were obtained from

the Intergovernmental Oceanographic Commission (IOC) of the United Nations Educational,

Scientific and Cultural Organization (UNESCO) website, http://www.ioc-

sealevelmonitoring.org. Data was downloaded to the Sea Level Station Analyzer software

developed by Carlos Rivera Lopez from the Caribbean Tsunami Warning Program (CTWP).

This software pulls the data directly from the html code of the IOC sea level data public

webpage. Once the data is downloaded it goes through a pre-processing that allows the data to be

stored in the computer memory and accessed through the Python programming language (Sea

Level Station Analyzer User’s Manual). The program then analyzes the data of each sensor per

station and calculates its performance ratio with the following formula,

𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 𝑅𝑎𝑡𝑖𝑜 =# 𝑜𝑓 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑚𝑒𝑛𝑡𝑠 𝑟𝑒𝑐𝑒𝑖𝑣𝑒𝑑

# 𝑜𝑓𝑚𝑒𝑎𝑢𝑠𝑟𝑒𝑚𝑒𝑛𝑡𝑠 𝑒𝑥𝑝𝑒𝑐𝑡𝑒𝑑 (100%)

The Sea Level Station Analyzer software produces a .txt file that includes the sampling rate in

seconds for each sensor, the web address from where the data was downloaded, provides the type

of sensors analyzed, the amount of data received per sensor, the calculated performance ratio,

and the number of gaps. After calculating all the performance ratios, the percentages are graphed

as a scatter plot. In this report, Microsoft Office Excel 2010 Windows version was used, but any

other plotting program can be used.

Steps to prepare the data spreadsheet

When setting up the spreadsheet, the first columns will have the performance ratio data for each

sensor (depends on station) and the last column will have the Month/Year.

Figure 2. Organizing monthly data for plotting performance ratio.

Steps to develop performance ratio yearly scatter plots

1. Go to tab name Insert.

2. In the chart tools bar select the Scatter option.

3. Select Scatter with only markers.

4. Right click on top of the chart area and go to Select Data.

5. In the Select Data Source window click Add to enter a series.

6. Enter Series name.

7. Press on the red arrow icon on the Series X values (x axis) and select data from the

Month/Year column in the current data spreadsheet.

8. Select the red arrow icon on the Series Y values (y axis) and select the data specific to

the sensor you want to plot and click OK.

9. Right click on top of an axis and select Format Axis, this option allows to customize the

scales.

a. To remove decimal points:

i. Select Number on the left toolbar.

ii. Change decimal places to zero and click close.

b. To add axis name and chart title:

i. Click Layout on the Chart tools bar.

ii. Go to the Chart Title tab.

iii. Click in the Above Chart option and modify title.

iv. Click Layout on the Chart tools bar.

v. Go to the Axis Titles tab.

vi. Place mouse over the Primary Horizontal Axis Title option.

vii. Click in Title Below Axis.

viii. Go back to the plot and click on the horizontal Axis title label and modify it.

ix. Go to the Axis Titles tab.

x. Place mouse over the Primary Vertical Axis Title option.

xi. Click over Rotated Title.

xii. Go back to the plot and click on the Axis title label and modify it

c. To add gridlines:

i. Click Layout on the Chart Tools bar.

ii. Click on the Gridlines tab.

iii. Place the mouse over the Primary Vertical Gridlines.

iv. Click on the Major Gridlines option.

Figure 3: After following all the step above, the graph should look like this.

Appendix II. CSV Format for JASL Software

Appendix III. Development of hourly tide gauge data for plotting in

JASL

Appendix IV. Setup procedure for the JASL software - Ubuntu Linux

16.04:

In order to be able to run the JASL program on Ubuntu Linux 16.04 some pre-installation

actions must be performed. The JASL software manual states that the software required for the

JASL program to run are the following:

Matplotlib 1.3 or higher. Author homepage: http://matplotlib.org/

Python 2.6 or higher. Author homepage: https://www.python.org/

The software has tested in RHEL 5 and Ubuntu Linux 14.04. The software runs virtually on any

operating system (even in Virtual Machines) that can meet the minimum system requirements

mentioned above. However, when dealing with very old operating systems some problems may

arise. In the case of old operating systems (especially for Linux, in general) the user may

experience trouble if the system’s Python Interpreter is not Python 2.7 or newer. Proceeding to

use the JASL software without having the system’s Python Interpreter 2.6 or below will cause

problems when installing the Matplotlib software, which will requires Python 2.7 or higher,

otherwise it won’t work. If the user can manage to upgrade the system Python Interpreter to

version 2.7 or higher the user can then proceed to install the Matplotlib software. Because of this

it is very important to have a Python 2.7 Interpreter, to avoid problems with other software

dependencies required by the JASL software.

Installation instructions for setting up the minimal system requirements for Ubuntu 14.04

onwards:

To install Python 2.7 or higher, open a “Terminal” window.

Type: python --version

The command should print to the screen which Python version it

has.

If the previous command didn’t work (“python: no such file or directory”,

etc), type: sudo apt-get install python , provide user password if

necessary. In recent Ubuntu versions (14.04 onwards) the default python

is 2.7.

To install Matplotlib 1.3 or higher, open a “Terminal” window.

Type: sudo apt-get install python-matplotlib

This command should prompt you to install Matplotlib and all

other software dependencies (additional software) required to use

Matplotlib, provide user password if necessary.

This should suffice the system requirements that the JASL software requires.

Supporting Information