Basic Radar Altimetry Toolbox practical

V. Rosmorduc (CLS)

29/09/2009

1 Introduction Climate prediction, monitoring of mean sea level, lake levels, global warming, El Niño and La Niña events, marine currents and ocean circulation, tides, geoid estimates, wind, wave and marine meteorology models, ice sheet topography and sea ice extend,... radar altimetry can bring such a wealth of information – and more! – from its measurements. Radar altimeters have been flown on satellites for many years and have become a firm part of the climate and ocean observing system. We now have a continuous series of data from 1991, with 7 satellites launched during that time. However, 7 satellites mean 7 data formats, reading routines, etc. – and more than 7 with the future missions planned. Thus the idea of conceiving the Basic Radar Altimetry Toolbox, that provides a tool encompassing all available altimetry data, from all satellites since ERS-1 (1991) and for all level of processing. This document is a step-by-step description of the Altimetry practical training session for the ESA Advanced training course on Ocean remote sensing, Bergen, 2009. It focuses mainly on the sea surface height and its variability, with a few additional lessons.

1.1 Basic Radar Altimetry Toolbox The Basic Radar Altimetry Toolbox is a tool designed to use radar altimetry data. The version that you will use today is 2.0.1. A Radar Altimetry Tutorial is available, to explain altimetry and applications for a user or would-be user of altimetric data. This Radar Altimetry Tutorial describes the applications, how-tos (data use cases) and technique, including the standard data processing, as well as the various satellite missions that carried, carries or will carry a radar altimeter onboard, and a set of altimetry products (data, software and documentation). The Basic Radar Altimetry Toolbox can be divided in four main components: 1- Data reading (also called «ingestion») 2- Processing routine functions 3- Visualisation functions 4- Graphic User Interface (GUI) The structure is onionskin: each layer using the previous ones, and being available to be used without the ones above (e.g. you use the processing routines, which read data with the data ingestion tools, without using either the visualisation or the GUI). The GUI is using the other layers, and is available for Windows (XP and 2000), Linux (Linux Redhat EL4) and MacOS X operating systems. The reading (or “ingestion”) tools are a data dictionary, based on handbooks and data structures. They free the users of looking at each and every data format, byte by byte, in order to be able to read their products. The user can select several data files to work on them at the same time. They can be combined if they are of the same kind (same level, same mission or format). Once a dataset is chosen, the user is able to select a geographical area subset chosen by its minimum and maximum longitude and latitude, and/or a temporal subset by its start and end dates, and this for any type of data. Processing functions are also available, to combine data fields (e.g. addition/subtraction needed to compute sea surface height from satellite altitude, altimetric range and corrections), select them (e.g. data editing to edit out-of-range values), etc. Such formulas can be saved for future use. The toolbox processed outputs are saved in NetCDF. Exports can be made in NetCDF, Ascii, GeoTiff and Google Earth KML. All processing are made through command files where all the parameters are indicated (even when using the GUI, with which the files are automatically generated). Once processed, the BRAT outputs can be visualised, whether one parameter against one other or against two others (typically, classical maps, including several cartographic projections). For all modes, title and comments can be written by the user. The user is able to choose a colour scale among a pre-

defined set. A “do-your-own colour scale” tool is also provided. Plots can be saved in raster (gif, png, tif, jpg) format. The graphic user interface is an interactive interface, to provide the user with an easy-to-use tool. It enables to use all the above components without writing a single line of program or command files. In that frame, users are able to save the data context for future work: they are able to save their set preferences for future uses, under a user-defined name, the area, period, mission, colour scale, type of visualisation and the parameters combinations they might have defined. Here we will work mostly with the Graphic User Interface (GUI), using through it the data reading, some processing and the visualisation functions. Note that other ways of using BRAT exist (in-line command mode, and APIs for Fortran, Matlab, C and IDL). Those can enable, in particular, some automation on the processing.

2 Altimetry data 2.1.1 Altimetry data processed by the Basic Radar Altimetry Toolbox The following missions are included: • ERS-1 and 2 missions, • Topex/Poseidon, • Geosat Follow-on, • Jason-1, • Envisat, • Jason-2 • And the future Cryosat. In the tutorial, are also mentioned all past, and fore planned missions. The tutorial is built to give general (non mission-specific) information about altimetry, technique and application, as well as an overview of the missions, access to data, software and documentation. The data selected are mainly data from the official data processing and archiving centres, i.e. ESA/F-PAC and Cersat for ERS, Envisat and Cryosat, CNES/Aviso and NASA/Podaac for Topex/Poseidon Jason-1 and Jason-2, NOAA for GFO and Jason-2, Eumetsat for Jason-2. The data product formats read by the Toolbox are: • Jason-1 (I)GDR (Aviso and PoDaac) • Envisat (I)GDR (Esa) • Topex/Poseidon MGDR (Aviso and PoDaac) • Along-track mono-parameter NetCDF data (Aviso) • Along-track multi-parameter NetCDF data (e.g. CorSSH) (Aviso) • Gridded NetCDF data (one parameter) (Aviso) • Gridded vector NetCDF data (two parameters) (Aviso) • River & lake products (ESA) • Jason-1 OSDR (Aviso and PoDaac) • Envisat RA2 WWV (ESA) • AT-SSHA (PoDaac) • ATG-SSHA (PoDaac) • Jason-1 SGDR (Aviso and PoDaac) • Jason-2 O/I/GDR and SSHA (Aviso, Noaa and Eumetsat) • Jason-2 S(I)GDR (Aviso, Noaa) • Envisat SGDR (ESA) • Cryosat SGDR (L1b) (ESA) • Cryosat GDR (L2 data) (ESA) • Cryosat L2 auxiliary files (ESA) • Cryosat IGDR (L2 data) (ESA)

2.1.2 Altimetry data features Altimetry data are available either along-track or gridded, the former being the ‘natural’ format of the data, the latter the result of extensive processing and interpolation. The altimetry data used in this practical are available on request at ESA (eohelp@esa.int) for Envisat GDR/IGDR, or on Aviso web site (http://www.aviso.oceanobs.com) for the gridded data.

3 Radar Altimetry Practical The first thing to use BRAT is to create a new workspace. A workspace enables you to save and re-use all the choices, computation formulas etc. you use during a session. When you open BRATGUI, the software asks for the name and location of the ‘workspace’ you will be working in. If one or more workspace already exists, the last used one is open by default. You can open another one, or create a new one by choosing ‘new’ in the ‘workspace’ menu (leftmost menu).

Figure 1 Create a new workspace window

Getting started: 1. Double-click on the “brat user interface” icon 2. In the “Workspace” menu (leftmost), click on “new”; in the dialogue window,

name it e.g. 'ra_practical', and click on ‘create’, after deciding where you want to save it (‘Browse’).

During the session, you can go in the leftmost menu ('workspace'), and click on 'save', or type 'Crtl+s' to save your work (which is highly recommended). A reminder to save will also open when you close the software. If you do not save, none of your work will be retrievable! Lessons list :

3.1  ................................................................................................................ 4 Along-track ocean data3.1.1  ............................................... 4 Lesson 1: introduction to BRAT – visualise a pass on a map3.1.2  .................................... 13 Lesson 2: compute Sea Surface Height and Sea Level Anomalies3.1.3  .................................................................. 17 Lesson 3: Compute valid Sea Level Anomalies3.1.4  ................................................................................... 19 Lesson 4: Viewing a complete cycle

3.2  .............................................................................................................................. 21 Gridded data3.2.1  ....................................... 21 Lesson 5: Sea Level Anomalies, Absolute Dynamic Topography3.2.2  ........................................................... 22 Lesson 6: Computing mean and standard deviation3.2.3  .................................................................................. 25 Lesson 7: Computing Kinetic Energy

3.3  ................................................................................................. 26 Wind and Waves from altimetry3.3.1  ................................................................................................. 26 Lesson 8: View a hurricane

3.4  .......................................................................................................... 28 Lesson 9: Waveform data3.5  .......................................................................... 30 Lesson 10: Longitude/time (or Hovmuller) plot

3.1 Along-track ocean data Altimetry data are NOT imagery data. The altimeters are taking measurements just beneath the satellite, with a narrow beam. So, once plotted, measurements appear as a narrow thread (“1D-data”). Moreover, the most basic altimetry datasets are a collection of several data fields within which you have to pick those interesting you. Some altimetry data are available as grids, but note those are highly processed data, with interpolation done, among other things (see section 3.2).

3.1.1 Lesson 1: introduction to BRAT – visualise a pass on a map The basic unit of altimetry data files is often the “pass” or half-orbit. We will use such a file to have a look at what altimetry data are really like. Objectives of this lesson In this lesson you will: − gain familiarity with BRAT graphical user interface

− gain familiarity with altimetric GDR along-track data Getting started:

1. The software should open on the “datasets” tab (for a first access). Click on 'new' next to dataset name. A default name is given (dataset_1). Change it for ' envisat_gdr_040_025'. Then click on 'add files' (bottom left of the screen), go in C:\RA\Data\lessons1-4\, and choose 'RA2_GDR_2POF-P20050817_143910_00003017A040_00025_18115_3074.N1' This is the half of the orbit #25 of cycle #40 of Envisat GDR data, measured on 2005-08-17.

Click on the file name in BRAT. You see, right, a list of available fields, with their format and units. Note that each field is part of a record, and you have to know which one you're interested in. You can sort each column by alphabetical order by clicking on the title of the column. You can also go directly to a field you know the name of by typing the first letters. If you click a specific field (let's say 'ku_band_ocean_range'), a brief description is given right.

Figure 2: Create a new dataset

Figure 3: A dataset. Left, the list of files; right (up) the list of available field within the selected file format, (bottom), the description of the selected field as it appears in the data dictionary.

Activity: Now that we have chosen the data we will work with, we will choose one of the fields within this dataset, and visualise it. BRAT is working through intermediate files (NetCDF) to visualise altimetry data. So, you have to select a field, create the file (by executing an “operation”), and only then you can visualise your data. This was done in order to provide the same generic workflow for each and every altimetry data, whatever their format. − Click on the “Operations” tab. Click on 'new'. A default name is given (operation_1). Change it for

'envisat_gdr_040_025_map'.

Figure 4: Creating a new 'operation'.

− You can then begin to define what data field you want to work on. Choose the dataset we will be

working on ('envisat_gdr_040_025' is the only one for now, but you can define as many dataset as you wish within the same workspace). Choose the record within the data: here we will work on ra2_mds, which contains altimetry data fields. You see below the list of available fields, with a short explanation in a tooltip that appears when your mouse goes over a field.

− You can see, in the “Data expressions” box, 4 items: - “X” and “Y” to be filled with “longitude” and “latitude” in order to plot a map; Choose first longitude in the list of field, drag it and drop it in the “X”; do the same with latitude and “Y”. - “Data” will contain the expression(s) you want to compute and/or visualise. Choose 'ku_band_ocean_range' in the list of data field, then drag it to the ‘Data’ expression

You see that the field name now appear below 'Data'. A unit is shown above the box with the details of the expression, right. The default unit defined for 'ku_band_ocean_range' (mm) had been filled in. - The last expression is “Selection criteria”; we will see it later on.

Figure 5: Choose data fields within the Envisat_gdr_040_025 dataset ‘ra2_mds’ record by drag & drop them into either X, Y or Data

Figure 6: an operation filled, with X (longitude), Y (latitude) and only one field (ku_band_ocean_range) as data represented with respect to those X and Y. Note the unit: 'mm’ for ra2_mds/ku_band_ocean_range

Figure 7: 'ku_band_ocean_range' correspond to the h (altimeter range measurement) on the figure.

Note that you could also have

- “created an expression” using the button, then either dragged & dropped your field, or typed its name (in which case you’d have to define the unit yourself)

- Used the right-click menu with “latitude” selected, then chosen “set as Y”, and so on for longitude as X and ku_band_ocean_range as Data.

Now, click on “Execute”, The log tab opens, to monitor the operation. For a single pass, it should be pretty quick to execute (a whole Envisat cycle is 1002 passes, it is quite longer – between half an hour and two hours, depending on your computer).

Figure 8: The log tab once the above operation is finished.

When the operation is finished ('ENDED'), the software switches automatically to the Operations tab (the tab from which last operation was launched).

- Go to the 'Views' tab, and click on 'new'. A default name is given (display_1). Change it for 'En_GDR_map'. Click on 'Z=F(lon, lat)'. A list of available fields is given, left (here, only one); drag & drop it or use the arrows to send it right (selected fields). Then click on 'execute'. The visualisation window open, and you should see a map like the one in Figure 12 below.

Figure 9: the 'Views' tab

Figure 10: the 'View' with the one operation executed

Figure 11: The 'Views' tab with a field selected

Figure 12: Resulting plot

Figure 13: the same plot, rotated to better show the data

hat you should see is a coloured line. Altimetry is a nadir technique, different from imagery. The

.1.2 Lesson 2: compute Sea Surface Height and Sea Level Anomalies

eight and Sea Level Anomalies from Envisat GDR

etme Envisat GDR pass than in the previous lesson.

nd change the unit to 'km' (mm is in fact a

ctivity

click on 'execute' now, and see the curve. However, we will refine a bit, and add another 'data

Wmeasurements are taken just below the satellite and only there. The line you see on the map shows you where Envisat RA2 really took its measurements (and the colours show what these measurements are). Note that it crosses continents. This particular pass also crosses the Gulf Stream.

3Objectives of this lesson In this lesson you will:

ce H− compute Sea Surfa− gain familiarity with altimetric along-track data − visualise data as a curve

uts − visually compare two outp

ting started: GYou will use the sa

1. Go back to the 'Operations' menu, click on 'new'. curve'. 2. Change the default name for 'envisat_gdr_040_025_

3. Choose 'envisat_gdr_040_025' as dataset, as earlier. 4. Within the ra2_mds record,

ge' as Data expression, achoose 'ku_band_ocean_ranbit small for a value of about 800 km!), and choose 'latitude' as X (and leave Y empty).

: AYou could expression'. You can only have one 'X' expression (and one 'Y' in the Z=F(X,Y) case), but as many data expressions as you wish.

- Sea Surface Height in altimetry is defined as:

ions list, then either click on the “insert expression” button,

-defined formula in the

SSH= (altitude – range) – corrections So, click on 'Data' in the Data expressor by using the right-click menu. Name it 'SSH' (for Sea Surface Height). Click on the 'formulas' button, and choose the 'SSH_Envisat_GdrA' prelist.

Figure 14: Use of the 'Formulas' box to insert pre-defined expressions

d click on OK. You ellite altitude, minus the

uestion: Look at the SSH formula you have as data expression. s needed for altimetry. Identify each

l corrections

(wet, dry)

SSH includes geoid, mean dynamic topography and Sea Level Anomalies (also called Sea Surface

ography + geoid hy + geoid

several years. Sea Level Anomalies is considered as the variable

ow, re-do the exactly the same, except that you will call the expression 'SLA' (for Sea level anomalies).

- Now un-click 'as alias', just below the ‘formulas box’, an now see as data expression a difference between several fields: the sat

altimetric range minus all the needed corrections. QCompare the fields used with what was said about the correctionfield in the formula with the following categories:

corrections = instrumentasea state bias correctionsionospheric correction tropospheric correctionstides (ocean, earth, pole) Inverse barometer

Height Anomalies). SSH = Dynamic Top = SLA + Mean Dynamic Topograp = SLA + Mean Sea Surface Mean Sea Surface is a mean overpart of the SSH.

N

When you have inserted the SSH pre-defined formula, type in '-' (minus), go in the field list, and find the 'm_sea_surf_ht' field and drop it in your formula (you can sort the fields by alphabetical order by right-clicking and choosing “sort ascending” in the menu). The field is inserted where your cursor was (hopefully, at the end, after the minus). You can also type in 'm_sea_surf_ht' after the ‘-‘ using your keyboard. Save your fre-use it. When you save, you can add comments, which can be useful later on (Note that a pre-defined SLA formula also exists), and the unit.

ormula by clicking on 'save as formula' button (above the units box). You will then be able to

Figure 15: the box opening when saving a formula, where you can add comments and information on it.

ow, click on execute, bottom right. The log tab open, to monitor the operation.

hen the operation is finished, go to the 'Views' menu, and click on 'new'. A default name is given

ow, ku_band_ocean_range, SSH and SLA).

N W(display_2). Change it for 'envisat_gdr_040_025_curve'. A list of available fields is given, left (here, three should sh

Choose SSH, send it right using the arrow, and click on 'execute'. The visualisation window open, and you should see a curve like the one below:

Figure 16: Sea Surface Height plotted vs latitude (Envisat descending ground track #25, cycle 040, 17 August 2005), with no data editing applied.

You can zoom in between e.g. 0 and 25° lat in the visualisation window, either in the ‘X’ tab of the ‘Properties’, or using mouse middle-button to select an area (the middle roll can be used, too, by clicking on it as if it was a button). You can also close the window, go to the 'Views' tab and type in '0' and '25' as X min and X max in the Zoom box, and re-click on 'execute': this has the distinct advantage that your selection will be saved for future use.

Visualise also the SLA.

3.1.3 Lesson 3: Compute valid Sea Level Anomalies Objectives of this lesson In this lesson you will: − compute Sea Level Anomalies from Envisat GDR − select valid (open ocean) data from Envisat GDR (a process also called “data editing”) − gain awareness of the existence of invalid data Getting started:

1. You will use the same Envisat GDR pass than in the previous lessons 2. Go in the “Operations” menu, choose “duplicate”. This will copy the previous operation. Call the

duplicate “Envisat_sla_edited”.

Activity:

− If you look at the pass we’re working on, you will see that it goes over land. The first thing to be sure of is that the data we are using are taken over ocean only. In the “Selection criteria” expression, add in the field altim_landocean_flag. This data field is a flag, which can be equal to 0, 1, 2 or 3, depending on the surface the measurement was taken on (other flags exists, for data quality in particular). Find in the data field’s description (in the tooltip) which value corresponds to ocean, and type “== (this value)” after altim_landocean_flag in the “Selection criteria” expression. You should have: altim_landocean_flag == 0, 1, 2 or 3 Execute the operation, visualise it. Compare to the previous output: you should have less very high peeks on your curve.

− Click on the “insert formula” button. Unclick the “As alias” box below “formula”, select the “Ocean_data_editing_Envisat_GdrA” formula and insert it in the “Selection criteria” expression.

Questions: Examine the formula now in the “Selection criteria” expression. This formula includes all the thresholds considered as normal for altimetry data over the ocean. Comment on them. Do some of them seem too low (or too high)?

− Compare the SLA plotted in the previous lesson with the new ones. − If you have time, you can make two more datasets with the same Envisat track taken during

previous cycles (#38 and 39), and execute duplicate operations to compute SLA with them. Compare them with the edited SLA measured during cycle 40. This should give you an idea of the sea surface height variability in this region (Gulf Stream current)

Figure 17: SLA without (red) and with (green) editing.

Now, if you constrain the minimum and maximum of the SLA between -1.5 and 1.5 m, and zoom on the 25-45°N part of the track, you’ll have:

Figure 18: SLA without (red) and with (green) editing: zoom on 25-45°N (crossing the Gulf Stream)

You will note in the SLA curve two rather high, and one rather low, values that has not been edited. Those are in fact real signal of ocean eddies within the Gulf Stream.

3.1.4 Lesson 4: Viewing a complete cycle Objectives of this lesson In this lesson you will: − See the result of SSH and/or SLA computation for a full Envisat cycle Activity We will switch back to maps now. You can do the same kind of operation for SLA than the first one we did to visualize the descending half orbit #25. Once you’re happy with your operations and choice of field, you can add a complete cycle (or part of it) in the dataset, instead of the one pass (an ‘add directory’ button is available for such long list of files). If you keep the same dataset name, the only thing you will have to do is to click on ‘execute’. And to wait (a complete Envisat cycle is about 1000 such passes). We won’t do it ‘live’ today, but use a previously made computation. Create a new dataset, and put in it the Envisat_gdr_040.nc file (in C:\RA\Data\ folder). This file is an output file from BRAT, made from the full cycle 40, with computation of SSH and SLA with respect to longitude (X) and latitude (Y). Define an operation, with longitude,latitude as X,Y and either (or both) SSH or SLA as data expressions. Then visualise the result. Execute, and you should see something like the figure below for SLA (with the min and max of the scale defined as -0.5 m, + 1.0 m):

Figure 19: SLA computed from the Envisat’s cycle 040 (August 2005)

You can change projection, for e.g. ‘plate carree’, and zoom in a region.

Figure 20: Use of the zoom to show the Atlantic Ocean (here the SLA; you can see the numerous eddies in the Gulf Stream)

You can have a map completely filled by colour by using the ‘field options’ in the Operation tab, where there is the possibility of using a Loess filter to smooth and / or roughly interpolate the data between existing measurements. However, you have to do this when building your operation.

Figure 21: Example of field option for interpolating data between measurements.

3.2 Gridded data

3.2.1 Lesson 5: Sea Level Anomalies, Absolute Dynamic Topography Objectives of this lesson In this lesson you will: − use pre-computed Sea Level Anomalies − examine sea level anomalies; note the distribution over the whole ocean − zoom on a region − choose the right scale (minimum-maximum) for the region zoomed on. Activity Create a new dataset, name it ‘gridded_SLA’. Go to C:\RA\Data\lessons5-7\, and choose the dt_upd_global_merged_msla_h_20050831_20050831_20060206.nc file. Create the operations: Choose ‘gridded_SLA’ as dataset. Put 'grid_0001' as data expression, name it 'msla', longitude as X and latitude as Y. Click on the “set resolution/filter button” Put: - in Longitude min = -100, Max = 0, and Step = 1/3 - in Latitude, min = 0, Max = 70 and Step also at 1/3. The latter gives you the resolution of the map; here the maps are originally at 1/3°.

No selection is needed on those data (extensive data editing is part of the processing of those data). Execute the operation. Create a new 'view' (say 'gridded maps'), choose your data in the Z=F(lon, lat) list. Select the ‘msla’ field Then Execute. If you wish to save the image, for future use, go in the ‘file’ menu of the Display Window, select ‘save image’, name it and choose a folder to store it. You can do the same with the “MADT” file (dt_upd_global_merged_madt_h_20050831_20050831_20060206.nc). Compare the two.

3.2.2 Lesson 6: Computing mean and standard deviation Objectives of this lesson In this lesson you will: − use a series of pre-computed Sea Level Anomalies − compute mean and standard deviation of Sea Level Anomalies over one year − visualise ocean variability Getting started:

1. Create a new dataset; click on “Add dir” (this enables to include a whole directory in a dataset. choose the msla directory (in C:\RA\Data\lessons5-7\ ). The data available in this directory correspond to MSLA data taken 3 months apart between July 2006 and April 2007.

2. Create a new operation similar to the previous one with the new dataset, or duplicate previous one and change the dataset for the one you’ve just created. You should have: Longitude, Latitude as X and Y, grid_0001 as data expression.

Activity: − Check that you have “MEAN” selected in the bottom left rolling menu of the “Operations” tab, below

the box where the expressions are detailed. This will ensure that when the software encounters two data at the same (X,Y), it will average them.

Execute the operation (this will take a little time; in the meanwhile, you can begin to prepare next operation) and visualise the result. It will do a very rough ‘annual’ mean from the 4 files (a whole year with 52 files will take a little to much time to be tried here, but it can be done all the same; even a 15-year mean is feasible).

− Now change “MEAN” in “STDDEV”: this will compute the standard deviation of the files you have in

your dataset (you can also define two expressions, one being a mean, the other a standard deviation and execute them in the same operation). Execute the operation with this new statistical function. Visualise the result.

Figure 22: Sea Level Anomalies mean over 2006-2007

Figure 23: Standard deviation over 2006-2007

Area of maximum standard deviation are in the major currents (Gulf Stream, Kuroshio, Antarctic Circumpolar Current, …).

3.2.3 Lesson 7: Computing Kinetic Energy Geostrophic currents are available as gridded datasets, derived from the altimetric ocean topography. We can compute Kinetic Energy using BRAT. Objectives of this lesson In this lesson you will: − use a pre-computed geostrophic velocity dataset − compute kinetic energy − visualise it Getting started:

1. Create a new dataset; choose the dt_upd_global_merged_msla_uv_20050817_20050817_20060206.nc file (in C:\RA\Data\lessons5-7\ ) Those data include U (zonal velocity) in grid_0001 and V (meridional velocity) in grid_0002 data fields, corresponding to the geostrophic velocities that can be computed from the SLA.

Activity: − compute a kinetic energy from these data using the “functions” for square. − Visualise the result.

Questions: Localise the regions of stronger kinetic energy. Compare with the regions of high standard deviations.

Enter in the Data expression: (1/2)*(sqr(Grid_0001)+sqr(Grid_0002)) (you can use the “function” button to insert the ‘sqr’ function) The unit is cm^2/s^2. Execute the operation. To visualise it, min is 0, max can be about 1000.

Here you can see the major currents, but also the outline of currents circling around eddies. Note in particular the eddy shed from the Loop Current in the Gulf of Mexico. About 10 days later, the Hurricane Katrina crossed it (see lesson “View a hurricane”), and intensification of the hurricane seems to match its crossing the Loop current and this eddy shed from it.

3.3 Wind and Waves from altimetry

3.3.1 Lesson 8: View a hurricane Objectives of this lesson In this lesson you will: − Visualise data taken over a hurricane − Use the wind speed and significant wave height data fields available within the GDRs; compare also

with a corresponding gridded dataset. Getting started:

1. Create a new dataset; choose the nrt_mswh_en_20329.nc file (in C:\RA\Data\lesson8\ ). This file is a gridded dataset (on a 1°x1° grid) merging the Significant Wave Height measured by Envisat on 2005-08-29, here expressed in Julian day.

2. Create another dataset using the file: RA2_GDR_2POF-P20050828_025917_00003017A040_00175_18265_3759.N1 This file is a Geophysical Data Record from Envisat.

Activity: - Prepare an operation for the gridded data. Execute it. You will see a grid of points separated by

blanks. This comes from the fact that the default resolution for the maps is 1/3°, whereas this dataset is 1°x1°. Come back to the “Operations” tab, and Click on the “set resolution/filter button” and change in both 1/3 by 1 as “Step”. Re-execute. You should now have a filled map. If you look in the Gulf of Mexico, you will see an area of high waves (although quite smoothed low); this corresponds to the hurricane Katrina that struck USA end of August 2005.

− You can also view this hurricane in Envisat GDR data. Create a new operation of the Y=F(X) type. Put “latitude” as X field, and choose ku_sig_wv_ht as a data expression; create another data expression and put in ra2_wind_sp; those are the Significant wave height as measured in Ku-band, and the wind speed modulus coming from the RA2 altimeter. To better view the hurricane effect, you will have to select data over ocean only. Execute the operation, visualise the plots between 17 and 30°N.

Questions: Try to apply the Ocean_data_editing_Envisat_GdrA formula. What do you notice? Why?

Data editing criteria given in the Basic Radar Altimetry Toolbox are not made for extreme weather. So they tend to consider data measured during such extreme events as hurricane as invalid.

Try to plot Wind speed (without Selection criteria except the land/ocean flag). Look at the highest values. What do you think?

Wind speed retrieval algorithms are made for a range of wind speed. They thus tend to level the highest wind speed values to a given value (note that the algorithms change from mission to mission; they can be improved and thus changed for re-processing of the datasets). At that time, Envisat wind speed algorithms limited it at 21 m/s

You can try and repeat the along-track operation with all the altimetry satellites available at that time: - Jason-1, cycle 134, pass #026 - Topex/Poseidon, cycle 477, pass #026 - GFO, cycle 157, pass #409 (note that ERS-2 was also measuring, but so close to Envisat as not to bring much more information)

3.4 Lesson 9: Waveform data Objectives of this lesson In this lesson you will: − Use the altimetric waveform data − Visualise series of waveforms Waveforms are the return echoes from the radar after reflection on the surface. They are the basic information that enables to compute all the altimetry parameters (range, backscatter coefficient and wind speed from it, SWH, etc.). However, they are only used for the specific applications where the standard processing of waveforms into range, SWH and wind speed are not adapted. Activity

- Create a new dataset with the JA2_IPS_2PTP008_187_20080926_172543_20080926_182156 file (in fact, you can even create a new workspace, since we will do completely different things than previously) included in the C:\RA\Data\lesson9\ folder. This is Jason-2 IGDR pass file for pass 187, cycle 008.

- Define an operation with the field “lat_20hz” as X, and “waveforms_20hz_ku” as Data. You can

select only data between 38.75°N and 40°N (is_bounded(38.75,lat_20hz,40)). This is over the Mediterranean Sea, near the Balearic Islands. Waveform data are not like the data previously used. They are stored as arrays of values, not as one value for each and every point. So, for each latitude you will have 104 values, corresponding to the (normalised) power variations of the return echo of the altimeter after reflection on the surface.

- Execute the operation. You will see the result in two categories of the Views tab. One is Y=F(X):

the waveforms will be seen as curves with respect to the array index (0 to 104), for each latitude. The second one is Z=F(X,Y), which is a kind of “3D” visualisation, the amplitude being colour-coded, with respect to latitude and array index.

Figure 24: Examples of a waveform plotted, as a curve within a series, over open water

Figure 25: Examples of a waveform plotted, as amplitude vs latitude and waveform index.

3.5 Lesson 10: Longitude/time (or Hovmuller) plot Objectives of this lesson In this lesson you will: − Use pre-computed data from BRAT − Visualise Sea level anomalies along time at a given latitude The dataset you will use was computed using Aviso Jason-1 along-track sea level anomalies from 2002-08-11 (day 19215) to 2008-12-03 (day 21521). Those are preferred to gridded data since time is a variable in them (it is only an attribute in the gridded data). In this Operation, longitude was defined as X, time as Y (unit as ‘days since 1950’) and SLA as data expression. The Operation was done with a selection in Latitude between -26 and -24°, and in Longitude between 20 and 120°E (to have only the Indian Ocean). Resolution was defined as 1° in longitude between 20 and 120°E, and as 10 (days) in time. Processing of such an operation is relatively fast, but there are 329 files, so it takes a little time to process them all (about 20 minutes) – but doing a longitude-time diagram on a long period is usually more interesting. Result of this Operation is available as ‘hovmuller.nc’ in the C:\RA\Data\Lesson10\ folder. Activity Create a new dataset with the hovmuller.nc file. Define Longitude as X, Time (in days since 1950) as Y, and SLA as Data expression Execute, and view the result.

Figure 26: Display window with an Hovmuller plot. Number of digits of the Y axis has been set to 5 (4 is default), and the axis title shortened to “days”. Color scale is set to -0.25,0.25 m.