Connecting label and point with line/arrow using QGIS

Is it possible in QGIS to connect labels and points with a line/arrow or something?

In my map it is not clear which label goes with its point. I can't bring it closer because there are many more points to come.

  • First, create labeling fields for X and Y position of your labels in your point table.

  • Activate the X and Y positionning in the properties of your layer.

  • Create a new "Generator expression" style in your point style, set it to line type, then use the following formulae :

    make_line(make_point($x,$y),make_point( "LABEL_X" , "LABEL_Y" ))

As an enhancement, you can use various fields (or use a plugin to create them all) to control where the align point will be. You could have the use of a rotation field and a vertical and horizontal alignement field.

The line will move accordingly to your label position, as its arrival point is related to the label positon field.

You could try downloading and installing the EasyCustomLabeling plugin from Plugins > Manage and Install Plugins, this creates a duplicated memory layer of your selected vector layer and contains the same attributes.

(Note that you will also need to download and install the Memory Layer Saver plugin to save the memory layer correctly, this is described in the plugin description).

Once your label layer is created, you can play with the label placement and style of that layer to try and get what you're looking for:

Concerning the method by gisinside: the first step of manually adding x/y attributes for labels can be omitted; (maybe since QGIS3?) there's an auxiliary variable for label-positionings already present. The variables ar called accordingly: "auxiliary_storage_labeling_positionx" and "auxiliary_storage_labeling_positiony".

So in Step Nr. 2 you can use this expression to draw lines:

make_line(make_point($x,$y),make_point( "auxiliary_storage_labeling_positionx" , "auxiliary_storage_labeling_positiony"))

Note however, that the auxiliary variables only get a value assigned, if you touch/move the labels with the move-label-tool. Therefore, you only see lines for labels that have been manually moved around.

If you are using PostGIS for your spatial data, this post by Alexandre Neto explains how leading lines can be added to the points in your database.

Its a well written tutorial worth reading, it also has a movie in the post that shows how it works.

I'm not aware of a way to do so. It might be noteworthy, that the lines you see are actually an interpolated view of the datapoints, so you only have a limited collection of data points available.

You can view the available data points via right click and Mark Data Points

You can navigate your cursor to the next data point using the left and right arrow key on your keyboard.

If you just want to determine a maximum value, you can use a .MEASURE directive. (Use the .op button to place a custom directive on the schematic)

.measure AC res1 MAX mag(V(out))

  • AC is the type of the simulation on which the result has to be taken
  • res1 is the name of the result
  • MAX indicates to find the maximum
  • mag() is for looking for the magnitude (in AC we have complex results)
  • V(out) is the expression on which it should find the maximum

The results are available (oddly) in the Spice Error Log (View -> Spice Error Log or CTRL+L).

res1: MAX(mag(v(out)))=(-0.0171115dB,-3.59527°) FROM 1 TO 100000

Now that doesn't give you the frequency at which this maximum is reached.

To do that, you need another .measure :

.measure AC res2 when mag(V(out))=res1

What's happening here? It measures the time (or frequency) when the condition is true. So basically you ask "at what point is the magnitude of the output voltage equal to the res1 ", res1 contains the maximum of the magnitude, so you ask for the point where it reaches the maximum.

2. Links and their Structures

A transportation network enables flows of people, freight or information, which are occurring along its links. Graph theory must thus offer the possibility of representing movements as linkages, which can be considered over several aspects:

Connection. A set of two nodes as every node is linked to the other. Considers if a movement between two nodes is possible, whatever its direction. Knowing connections makes it possible to find if it is possible to reach a node from another node within a graph.

Path. A sequence of links that are traveled in the same direction. For a path to exist between two nodes, it must be possible to travel an uninterrupted sequence of links. Finding all the possible paths in a graph is a fundamental attribute in measuring accessibility and traffic flows.

Chain. A sequence of links having a connection in common with the other. Direction does not matter.

Length of a Link, Connection or Path. Refers to the label associated with a link, a connection or a path. This label can be distance, the amount of traffic, the capacity or any relevant attribute of that link. The length of a path is the number of links (or connections) in this path.

Cycle. Refers to a chain where the initial and terminal node is the same and that does not use the same link more than once is a cycle.

Circuit. A path where the initial and terminal node corresponds. It is a cycle where all the links are traveled in the same direction. Circuits are very important in transportation because several distribution systems are using circuits to cover as much territory as possible in one direction (delivery route).

Clique. A clique is a maximal complete subgraph where all vertices are connected.

Cluster. Also called community, it refers to a group of nodes having denser relations with each other than with the rest of the network. A wide range of methods are used to reveal clusters in a network, notably they are based on modularity measures (intra- versus inter-cluster variance).

Ego network. For a given node, the ego network corresponds to a sub-graph where only its adjacent neighbors and their mutual links are included.

Nodal region. A nodal region refers to a subgroup (tree) of nodes polarized by an independent node (which largest flow link connects a smaller node) and several subordinate nodes (which largest flow link connects a larger node). Single or multiple linkage analysis methods are used to reveal such regions by removing secondary links between nodes while keeping only the heaviest links.

Dual graph. A method in space syntax that considers edges as nodes and nodes as edges. In urban street networks, large avenues made of several segments become single nodes while intersections with other avenues or streets become links (edges). This method is particularly useful to reveal hierarchical structures in a planar network.

Common neighbor. For two or more nodes, the number of nodes that they are commonly connected two.

In Detail:

Using assign basically turns an integer into a pointer and loads it with the address of the label, not its numeric value.

An Unguarded Assigned GOTO takes the pointer value of the integer and jumps. With a list added it checks if the Integer holds the address of any single label within the list and only jumps when it's among them.

Think of forming a loop with a various cases within, like a state machine. Remember, there was no switch/case like statement in FORTRAN, so it had to be done some other way. And assigned GOTO was the way to go. At the end of each state check the next state was loaded into an integer but control was returned (via unconditional GOTO) to the main loop, which picked the next data and switched accordingly.

All of this ends up with a vast number of targets. In addition storage, and thus variables, was limited, so variables get reused. Using a single variable (like IGO) for all/most Assigned GOTO in a program was quite common. So this variable might contain some value from outside the loop constructions one was in. Quite a good chance to ass programming errors ending up at a GOTO IGO with a leftover target from some prior construct.

Being able to name a list of all (at the point) valid targets seemed like a good idea to catch that and make sure all works as intended.

At that point it's once again important that we talk about a time when everything was barely invented, machines were small and compilers straightforward without much ability or even chance for checking. Not to mention that FORTRAN was on purpose kept simple to have users adopt it. Maybe hard to believe, but scientists were hardcore Assembly users at that time. So many concepts were tried, some of them might look strange from today's orderly landscape.

In addition, FORTRAN, as simple as it may seem today, was considered by many users as bloat. So making it work as straightforward as possible, so users can imagine the Assembly code while writing FORTRAN, was mandatory. There is a very nice interview with Frances Allen talking about this time.

Now, in a perfect world it could end here, but the real world also included implementation specific effects. The FORTRAN description did leave up a few grey areas:

For one it didn't define if and how an integer used to hold an integer is prevented against being used as target, nor if and how one holding a target is protected against being used as integer.

Using a Guarded Assigned GOTO one way to protect against such errors, by checking the values against legal values.

Second, it wasn't stated what happens if the integer is holding a target that is not within the list. Some implementation simply dropped to the next statement, while others threw an exception ending the program - which might be the most safe way.

Well, and some ignored the list at all.

Spaghetti code, implementation dependant behaviour, added, changed or missing instructions - everything we love and hate about BASIC was already present in FORTRAN, but on a much worse level.

With the introduction of additional loop control (WHILE, EXIT, etc.) in many FORTRAN-77 compilers (aka FORTRAN-78) the use of Assigned GOTO as well as computed GOTO or alternate returns became obsolete and finally removed in FORTRAN-90.

First of all, the accepted answer is wrong the statement list was not optional in the "original" FORTRANs (I and II). Here is a listing of the routine used to read GO TO statements:

(See the end of this answer for the source of the code.)

Clearly a comma and a statement list is expected when GO TO is not followed by a number.

But why was it required? Well, the reason is a little complicated. It certainly wasn't for the benefit the programmer or for safety. The concept of a run-time error check generated by a compiler was, after all, a silly idea at the time. The following discussion pertains to IBM's FORTRAN I and II for the 704/709/7090/7094.

In actuality, the list was necessary for the compiler. During the flow analysis phase, the user's program is divided into basic blocks, and control transfers between the blocks are recorded. Then a simulation of the program is carried out to determine the relative frequency of execution of blocks. (Naturally, any information provided in FREQUENCY specifications is taken into account here.) The knowledge gained in this process is used by the register allocation algorithm, which tries to minimize the use of load/store operations in "hot" portions of the user's code.

Because ASSIGN statements can occur anywhere in a FORTRAN program, the compiler couldn't know where control might be sent by a GO TO statement with a non-numeric operand unless the entire program has been read and all ASSIGN s have been seen. Upon seeing GO TO N, (. ) , the compiler makes a table entry saying "at this point, we might transfer to any one of these statements" a similar thing happens for computed GO TO statements as well. The determination of basic blocks is based on these table entries, and not on accumulated constants in ASSIGN statements, since this way is more straightforward and avoids creating a symbol table exclusively for ASSIGN ed variables. Also, the list of targets lets the compiler produce a diagnostic if an illegal transfer (e.g., one leading into the range of a DO loop that has not yet been entered) might happen.

Consider also how the form of computed and ASSIGN ed GO TO allows for a micro-optimization. Here are excerpts from a real FORTRAN program (from 1965, but these parts use nothing that wasn't available in FORTRAN II regardless, this is just an example for illustration, and the source is linked at the end of this answer):

If the analysis were based on ASSIGN statements, then the compiler would have to treat a GO TO KADD1 as potentially transferring to any of the statements 223, 224, 440, and 442, even though such a four-way transfer isn't actually possible. A "sufficiently smart compiler" could determine this itself, but this was 1958.

Note that the LLVM indirectbr instruction, which performs an indirect transfer of control much like FORTRAN's assigned GO TO , also requires a list of potential locations "so that dataflow analysis has an accurate understanding of the CFG [control-flow graph]".

The level of optimization achieved by IBM's FORTRAN I and FORTRAN II was not matched by another FORTRAN compiler for quite a long time, according to what I've read. Compilers that didn't do sophisticated flow analysis did not have the same need for the target list in ASSIGN ed GO TO statements. Therefore it became optional.

Sources (credit for most of them goes to the Software Preservation Group's fantastic History of FORTRAN and FORTRAN II page):

The next source is a listing of FORTRAN II's source code. The GO TO processing routine C0200 begins at sequence number 4F11844, which is on page 70 of the PDF of Volume I (according to the handwritten page number in the bottom right, this is logically page 66).

The FORTRAN example came from the fabulous B5500 Software repository. The specific source was file BMD02T/T800016, which is headed "AUTOCOVARIANCE AND POWER SPECTRAL ANALYSIS". The dialect of FORTRAN here is B5500 FORTRAN IV a manual for it from 1968 (three years after the quoted code was written) is available here.

Jack Harper's website on the IBM 7090/7094 computers is a great resource when studying old programs like FORTRAN II and the LISP 1.5 interpreter.

Magnetic Fields Lab

Students create and observe ferrofluids to understand magnetic field lines and how they can affect planets.

Astronomy, Experiential Learning

1 Video, 1 Image, 2 PDFs, 1 Link

This lists the logos of programs or partners of NG Education which have provided or contributed the content on this page. Program



1. Activate students’ prior knowledge about magnetic fields.
Ask: What do you already know about magnetic fields? What everyday object can you think of that measures magnetic fields? Elicit from students that a compass uses Earth’s magnetic field to give information about direction. Then show students the NASA video “Magnetometry 101.” Ask them to restate in their own words how magnetic fields can be measured and drawn. Tell students that, in this activity, they will create and observe ferrofluids to understand magnetic field lines and how the field lines can affect planets.

2. Have students investigate magnetic field direction.

Have pairs of students place a bar magnet on a white sheet of paper. Pour a very small amount of filings onto the paper. The filings should form lines. Have students determine the direction of the lines by placing a compass on the lines and drawing an arrow to show which direction is north on the compass. After drawing a few of the arrows, ask students to label which end of the magnet represents north and which represents south. If they are not sure, have them label a few more of the lines created by the filings. Provide support, as needed.

3. Build background about ferrofluids.
Explain to students that one way we can observe magnetic fields is through the use of a material called ferrofluid. A ferrofluid is a liquid that becomes strongly magnetized in the presence of a magnetic field. Display the photo of magnetized ferrofluids for students and allow them to ask questions. Ask: Why do you think something with these properties might be useful on Earth or in space? Explain to students that, on Earth, ferrofluids are used to form liquid seals in electronic devices. In space, ferrofluids are used to control the flow of liquid fuels and the rotation of spacecraft.

4. Introduce the activity.
Tell students that, in this activity, they are going to investigate magnetic fields and use that information to analyze how ferrofluids work. Provide each pair of students with a set of materials and room to work. Distribute one set of worksheets Ferrofluid Investigation and Ferrofluid Observations to each pair. Tell students they will fill in Part 1 of Ferrofluid Observations as they move through the steps of the investigation.

5. Have students complete and discuss the first part of the lab experiment.

Ask students to complete Step 1 in handout Ferrofluid Investigation and then stop. Ask: What do you observe about the filings? Students should notice that the filings form spikes similar in appearance to a porcupine. Explain that this is due to the magnetic field, as well as the fluid suspension. Each spiky point is a subdued representation of the magnetic lines. If the fluid didn't hold the filings back, the spikes would continue to grow into lines like those seen when a bar magnet is placed under a dish of metal filings not suspended in fluid. Ask: What does this tell you about the magnetic field? Students should be able to confirm that the magnetic field shows itself as a series of lines gathered at the top and bottom of the bar magnet.

6. Have students complete and discuss the remaining parts of the lab experiment.
Have students continue with Steps 2-5, filling in Ferrofluid Observations, and then stop. Ask:

  • What do you notice when you move the magnet? (The spikes move with the magnet. As the magnet is pulled away, the spikes are less prominent and smaller. Eventually they are nonexistent and the ferrofluid falls to the bottom of the preform.)
  • What are some possible explanations for that behavior? (The ferrofluid is metallic and is affected by the magnetic attraction of the magnet. The spikes form and reflect the magnetic field, trying to form magnetic circles similar to what is observed with the magnetic field of the Earth and other planets.)
  • What do you think would happen if you repeated the lab, but only used toner or filings and no oil? (Students should understand that they would see the filings or toner flow from the positive to the negative polarity of the magnet.)

7. Have students compare and contrast their observations and USGS data.

Display the USGS: National Geomagnetism Program web page for students. Ask:

  • What do you notice about Earth's magnetic field? (Earth's magnetic field flows from near the north magnetic pole to near the south magnetic pole.)
  • How is it similar to or different from what you saw in your experiment? (The spikes appear to have equal distances and look like they would continue around to the opposite end of the magnet from one pole to the other.)
  • Why do you think your ferrofluid experiment looked more like a porcupine than lines?

Make sure students understand that the section they observed with the ferrofluid would continue to travel around to the opposite side of the magnet, demonstrating the same “lines” as observed with the filings due to the polarity of the magnets. The liquid suspension holds the filings back. The filings not suspended in a liquid will form magnetic lines from one pole of the magnet to the other. We observe this same phenomenon here on Earth. Help students make the connection to magnetic fields on Earth and other planets. Explain that many of the planets have magnetic fields, but some do not have a magnetic field or have a weak magnetic field. Magnetic fields serve as a shield that protects planets from solar radiation. The solar particles are deflected to the polar regions along the magnetic field lines. If a planet has very little or no magnetic field, there is not enough protection for people or vehicles exploring that planet.

Informal Assessment

Check students' completed worksheets to make sure they followed directions and made relevant observations.

Extending the Learning

Have students read the NASA: Science News article “Sickening Solar Flares.” Then have students compare and contrast the planets in our solar system with magnetic fields and what that might mean for the planets and anyone who might explore those planets:

  • Earth—30,000-60,000 Nanotesla (nT)
  • Mercury—100 times weaker than Earth
  • Venus—25,000 times weaker than Earth
  • Mars—5,000 times weaker than Earth
  • Jupiter—20,000 times greater than Earth
  • Saturn—540 times greater than Earth
  • Uranus—40 times greater than Earth
  • Neptune—a quarter that of Earth

Ask: Given what you know about how magnetic fields protect planets from solar radiation, which planets would you rather visit?

If you want to be very pedantic, you could say that the symbols $ p land q o q $ is not an argument at all, but just a formula (which as you mentioned happens to be a tautology). Since it's not an argument, asking whether it is a valid one is strictly speaking meaningless.

On the other hand, $ ext pland q ext< we can conclude >q qquadqquad Bigl( extitfracBigr)$ is a valid rule of inference, and thus constitutes a valid argument, if "argument" means a meaningful combination of one or more rules of inference. (However, some authors use the convention that an argument, in contrast to an inference, is only called "valid" if the inferences in it are valid and the premises are true, which is not the case in your concrete example).

In many contexts, being as pedantic as this is unnecessary and uncalled for, though.

In particular, your tautological formula does become the argument you need if you combine it with the generic inference rule modus ponens.

This inference is still valid in situations where $p$ happens to be false. The design criterion for "valid" inferences is that they must never let you conclude something false from premises that are true -- on the other hand, what they allow us to conclude from false premises is harmless.

About this tutorial

In this tutorial, you will learn how to configure bar, column, line, and pie charts in pop-ups. The examples focus on changes in world population over time and property valuation based on lot and improvements value.

Open the completed map to view the configured pop-up charts. Click any feature to view the bar, column, and line charts. Click the arrow to advance to the next chart.

Use the bookmarks to zoom from World Population to Home Values.

Click any feature to view the pie chart showing lot value and improvements value.

Open the sample map and click Modify Map, or sign in and save the map, to follow the steps below.

3 Answers 3

PHP must be expecting to use the old MySQL authentication plugin algorithms. You do not have to restart mysql. Since old_passwords is a globally dynamic option, all you have to do is run the following:

In addition, please add this to /etc/my.cnf :

to have future restarts retain this setting.

To further verify the need to do this, the next time you login to mysql, run this

If none of the passwords are of length 16, this may explain PHP's reluctance to login.

Sad to say, but the alternative would be to setup 16-character passwords, but you cannot reverse-engineer 41-character passwords. You would have to manually setup the 16-character passwords using the original plain-text values.

For example, if [email protected] had 'helloworld' as the password, it would have convert it using the OLD_PASSWORD function. Here is a comparison:

Once you activate old_passwords, to convert it you would have to do something like this:

2. Background: The MSA/Dialect Distinction in Arabic

Although the Arabic language has an official status in over 20 countries and is spoken by more than 250 million people, the term itself is used rather loosely and refers to different varieties of the language. Arabic is characterized by an interesting linguistic dichotomy: the written form of the language, MSA, differs in a non-trivial fashion from the various spoken varieties of Arabic, each of which is a regional dialect (or a lahjah, lit. “accent” also darjah, lit. “modern”). MSA is the only variety that is standardized, regulated, and taught in schools. This is necessitated because of its use in written communication in formal venues. 1 The regional dialects, used primarily for day-to-day dealings and spoken communication, are not taught formally in schools, and remain somewhat absent from traditional, and certainly official, written communication.

Unlike MSA, a regional dialect does not have an explicit written set of grammar rules regulated by an authoritative organization, but there is certainly a concept of grammatical and ungrammatical. 2 Furthermore, even though they are “spoken” varieties, it is certainly possible to produce dialectal Arabic text, by spelling out words using the same spelling rules used in MSA, which are mostly phonetic. 3

There is a reasonable level of mutual intelligibility across the dialects, but the extent to which a particular individual is able to understand other dialects depends heavily on that person's own dialect and their exposure to Arab culture and literature from outside of their own country. For example, the typical Arabic speaker has little trouble understanding the Egyptian dialect, thanks in no small part to Egypt's history in movie-making and television show production, and their popularity across the Arab world. On the other hand, the Moroccan dialect, especially in its spoken form, is quite difficult to understand by a Levantine speaker. Therefore, from a scientific point of view, the dialects can be considered separate languages in their own right, much like North Germanic languages (Norwegian/Swedish/Danish) and West Slavic languages (Czech/Slovak/Polish). 4

2.1 The Dialectal Varieties of Arabic

Egyptian: The most widely understood dialect, due to a thriving Egyptian television and movie industry, and Egypt's highly influential role in the region for much of the 20th century (Haeri 2003).

Levantine: A set of dialects that differ somewhat in pronunciation and intonation, but are largely equivalent in written form closely related to Aramaic (Bassiouney 2009).

Gulf: Folk wisdom holds that Gulf is the closest of the regional dialect to MSA, perhaps because the current form of MSA evolved from an Arabic variety originating in the Gulf region. Although there are major differences between Gulf and MSA, Gulf has notably preserved more of MSA's verb conjugation than other varieties have (Versteegh 2001).

Iraqi: Sometimes considered to be one of the Gulf dialects, though it has distinctive features of its own in terms of prepositions, verb conjugation, and pronunciation (Mitchell 1990).

Maghrebi: Heavily influenced by the French and Berber languages. The Western-most varieties could be unintelligible by speakers from other regions in the Middle East, especially in spoken form. The Maghreb is a large region with more variation than is seen in other regions such as the Levant and the Gulf, and could be subdivided further (Mohand 1999).


Historically, the surface weather map was the first weather map produced, dating back to the early 19th century. Even today, it remains the one of the most useful charts for ascertaining current weather conditions just above the surface of the earth for a large geographic region. These maps are called surface analysis charts if they contain fronts and analyzed pressure fields, with the solid lines representing isobars.


Because many of the surface weather maps display weather conditions at a particular time, these charts can be considered one of the varieties of synoptic charts. The word " synoptic " is derived from the Greek words syn meaning the same or together and " optic " meaning visible hence, seen together. Synoptic weather analysis requires the simultaneous observation of the weather at many widely located sites using standardized instruments and techniques. By international agreement all meteorological observations are taken at the same time according to Universal Coordinated Tim e (UTC) or Z time

You should look at the title of the chart to determine the time of the chart. Most of the charts will list the UTC or Z time when the observations were made. The weather data that are plotted on many of these surface weather maps are based upon the hourly surface observations that are made at many airport weather stations. These observations are made within 5 minutes of the top of the hour. The frontal analyses that may appear on the surface chart are usually produced at 3 hourly intervals (0000 UTC, 0300 UTC and so forth).


The simplest weather chart would represent a plot of one weather element (such as air temperature), using the observations of that element made at many locations at the same time. An operational synoptic weather chart of the surface weather conditions often is a composite chart that includes the spatial distribution of several weather elements that have been concurrently observed. Some of these weather elements that are displayed on surface weather maps include the air temperature, dewpoint temperature, air pressure and wind information (wind speed and direction).

Some of the surface weather maps that are presently available may contain an overlay of the current radar or satellite imagery.


A display of all this information for many locations at one given time would be difficult to make and interpret unless a uniform system of plotting were adopted. The pictorial presentation and weather data together with an analysis can be determined at a glance. The location of each reporting station has been printed on the base maps as a small circle. The weather data from each reporting station are plotted around these circles on these base maps in a particular systematic fashion called a "station model". A sample station model for a weather observation station, complete with the proper position of the weather data:

The following discussion is intended to help you decode and interpret a surface weather map that contain abridged station models.


In the United States, the current near-surface air temperature and dewpoint temperature are reported in whole (or integer) Fahrenheit degrees. These temperatures are measured by instruments located in a standard instrument shelter at a height of approximately 5 feet above the ground. The air temperature is plotted on the chart to the upper left of the station model, while the dewpoint value is placed below the temperature entry, or to the lower left of the station circle. A negative sign is included when the air temperature or dewpoint is less than 0 degrees F. The value of the dewpoint may never exceed the air temperature.


The observed near-surface wind speed and wind direction , are represented on the map by a combination of a wind arrow shaft and wind barbs around the station model. These wind data are obtained from a standard "anemometer height" of approximately 30 feet above the ground. The "wind arrow" is a symbolic back portion of an arrow that "flies with the wind"- the wind barbs located on the tail of the arrow are upwind, while the small circle at the head of the arrow is located at the station. Thus, the orientation of these wind arrows on the map indicates the wind direction to the nearest 10 degrees, measured clockwise from true north (defined as 360 degrees, and located toward the top of the chart). By meteorological convention, the winds are named for the direction from which they are blowing. Hence, a south wind is from the south.

The number and length of the barbs on the tail of the arrow indicate the near-surface wind speed in knots (nautical miles per hour, which are 15% larger than the familiar statute miles per hour) using the following convention:


The current sea level corrected air pressure is plotted on the map to the upper right of the station model. The numeric pressure entries are in units of tenths of millibars. The barometric pressure measured by a barometer at the station is adjusted (or corrected) to sea level conditions to eliminate the variations in reported pressure due to the altitude of the station.

By convention, the lead "9" or "10" is dropped from the reported value and the decimal point omitted when this value is plotted. A sea level pressure report of 995.8 mb would be plotted as "958", a report of 1002.8 mb would be plotted as "028", and 1025.8 mb would be "258". Since the sea level pressure usually ranges between 980 and 1040 mb, you should have no problem in determining whether the plotted value is preceded by a "9" or "10". If in doubt, check the pressure values at neighboring reporting stations.


A set of unique and international standard symbols would be plotted directly to the left of the station model (between the air and dewpoint temperatures) as necessary. These symbols indicate the observation of a particular significant current weather event such as precipitation or a reduction in visibility. The following abridged list represents several of the common symbols that you should recognize:

Precipitation intensities , as ascertained from measured precipitation rates or a reduction in visibility, are indicated on the chart by the repetition of symbols for rain, snow and drizzle. For example, the rain symbols can be used to represent the rain intensity:

The amount of shading inside the station location circle is used to depict sky cover, or the total fraction of the local sky hemisphere that is covered by clouds at the observation time. The following abridged cloud cover symbols include:


At first glance, the array of data plotted on the map may appear unorganized and overwhelming. However, large scale organized weather systems can be discerned through map analysis. In meteorology, the term weather analysis usually refers to the sequence of operations involved with the organization of the plotted information on the weather map. This logical portrayal of the data leads to interpretation of the spatial distribution of more than one weather element. Typically, a major part of the analysis phase involves drawing of isopleths , a generic term referring to lines connecting points of equal values. Map analysis increases the visual communication value of the chart. For example, once isobars , or lines of equal barometric pressure, have been drawn upon a surface chart, one can immediately locate regions of high and low atmospheric pressure across the region.

While weather elements, such as temperature and pressure, are observed only at particular locations, we can assume that these elements are continuously distributed in the horizontal direction. Such a distribution is called a meteorological field in other words, for any latitude and longitude, some value of that variable exists without any voids or discontinuities.


Many of the surface analysis charts may contain thin solid lines to depict the features of the horizontal pressure field at mean sea level. These lines are called isobars and connect all points having the same sea level corrected barometric pressure. By meteorological tradition, the isobar spacing is at 4 mb intervals, centered upon 1000 mb that is, 996, 1000, 1004 mb, and so forth.

High and low pressure centers are indicated by a large block H and L , respectively, together with a set of digits identifying the estimated value of the central pressure. On some charts, the H is colored blue, while the L is drawn in red.

A trough of low pressure that contains significant weather phenomena (such as precipitation and distinct wind shifts) may be identified on the map by a thick brown dashed line running along the axis of the trough. On some maps this trough line may have the abbreviation, " TROF ".


The surface analysis may include one or more color coded lines to identify a front . A front is defined as the transition zone between air masses having dissimilar thermal and moisture properties. Usually, these transition zones are only 50 to 100 km wide, a sufficiently small horizontal distance to permit their representation as lines on a large-scale surface analysis chart. Fronts are classified according to their movement and can be represented on a Surface Analysis chart as follows:


The surface analysis permits one to identify and locate the large scale features of the sea level pressure field and the surface fronts. Isobars with the lowest value will encircle the region with the lowest point in the pressure field, while the closed isobar with the largest value isolates the highest sea level pressure. The packing of the isobars reveals how rapidly the pressure varies with distance in the horizontal direction. A tighter packing indicates a much more rapid horizontal variation of air pressure.

The isobar pattern is also useful for visualizing the near surface wind regimes. The winds tend to parallel the isobars, with low pressure to the left of the wind flow in the Northern Hemisphere a slight cross-isobar deflection of the winds toward lower pressure is often seen. As a result, winds appear to spiral in toward a surface low pressure center in a counterclockwise fashion, and spiral around a high pressure cell in a clockwise outflow regime. Additionally, where the isobars are packed more closely, the wind speed tends to be greater.

If previous surface charts are available for the last day or two, you will be able to judge the movement of weather systems over time, based upon continuity principles. You can make a reasonable short range weather forecast based upon the movement of the low and high pressure centers.

A radar image overlay on the surface analysis permits a more additional information about regions of precipitation. The radar echoes provide information about the extent of the precipitation in regions between the normal surface observation network. The intensity of the precipitation can also be estimated from the radar reflectivity and displayed as a series of 6 color codes.