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Time
Time is a very important dimension in meteorology, and there are many things to keep in mind. Here we will explore how Metview handles this dimension.
Overlaying Time Steps
Inspect the supplied GRIB files: z500_fc.grib contains geopotential forecasts made in one run, but for six different forecast steps; z500_an.grib contains
Overview
Time is a very important dimension in meteorology, and there are many things to keep in mind. Here we will explore how Metview handles the fourth dimension.
Overlaying Time Steps
Inspect the supplied GRIB files: z500_fc.grib contains geopotential forecasts made in one run, but for six different forecast steps; z500_an.grib contains analysis fields for two times. Visualise the supplied Geographical View icon and drop the forecast GRIB icon along together with its corresponding Contouring icon (cont_fc) into the Display Window, and then drop the analysis GRIB icon along together with its corresponding Contouring icon (cont_an) too. Go through the frames of animation. The fields have been overlaid, but if you look at the times and dates in the title, you will see that they do not match. Metview has simply plotted the first field of each data file together, then the second, and so on. We can make it more intelligent.
Edit the Geographical View icon and set this:
Map Overlay Control | By Date |
Save the icon, visualise it and drop the data with their visdefs in again. Go through the animation steps and look at the Frames tab in the Display Window to see what has happened. Now the fields will be overlaid only if their valid date and time match.
...
Precipitation data provides an interesting challenge. Precipitation fields in MARS are stored as accumulated fields . Visualise the supplied precip.grib icon with the precip_shade visdef. The first field is empty (check using the Cursor Data). The first field has a step of 0, meaning that it contains the total precipitation accumulated between the run time and the run time plus step. Since these are the same, there is no accumulated precipitation! Subsequent steps show more and more precipitation (the amount accumulated over 3, 6, 9, etc hours).
...
We can see from examining the file that the 6 and 9 o-'clock steps are fields 3 and 4 respectively (using 1-based indexing). So the following macro code will compute the difference and return it:
...
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precip = read("precip.grib") n = count(precip) # the number of fields in the fieldset precip_3h = precip[2, n] - precip[1, n-1] return precip_3h |
Visualise the macro.
...
If you drop the precip_shade visdef icon into the plot, it may become blank! There is one more trick: we have created a derived field, and this changes the automatic scaling algorithm used when plotting. Precipitation is stored in metres, but we want to display it in mm. Modify the precip_shade icon and set:
Grib Scaling of Derived Fields | On |
Visualise your macro result again and confirm that you now have precipitation only for the 3-hour periods, which does not accumulate with each frame.
...
midday = date(20150105.5)
Use this syntax to add another variable, d2
, which contains the date and time for 13:00h at 2015-03-13. Print it to check it.
...
Compute and print the difference between your two dates, d2
and d1.
Looping through dates
Three examples (no need to type these in), to get a feel for it, but the code is in a macro called dates in the solutions folder), to get a feel for it:
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for d = 2015-01-01 to 2015-03-01 do print(d) # each step is 1 day end for for d = 2015-01-01 to 2015-03-01 by 2 do print(d) # each step is 2 days end for for d = 2015-01-01 to 2015-03-01 by hour(6) do print(d) # each step is 6 hours end for |
Computing the precipitation rate at a point
As an exercise to put all of this together, we will write a new macro to compute the precipitation rate in mm per hour at a a particular location for each time period. This could be a little complicated, so we'll do it step by step. The steps will be:
...
Compute the 'period' precipitation from precip.grib
...
This is what we already did earlier, so it's done! Just make a copy
...
your
...
earlier macro
...
, compute_precip, and call it precip_rate. Change the result variable name to precip_diff
to make it more generic. Remove the return
line, as we want to use this fieldset, not return it.
Construct a loop to go through the fields
...
- get the date and time of the forecast step
- combine these into a Metview date variable
- add it to a list (which was initialised to
nil
before the loop)
...
Now, create an empty list (dates = nil
). We will add each date variable to it as we loop through the fields.
We will obtain the date for each field of the original precipitation fieldset and add it to the list. We need to loop through the fields - we should already have n
defined as the number of fields from the previous exercise:
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dates = nil
for i = 1 to n do
print(i) # we will put proper code here in the next step!
end for |
Extract the date and time from each field
You can get the valid date (including its time) of a field like this, inside the loop, where i
is the field index
Here are some hints to help.
You can get the date and time of a field as numbers like this:
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ddt = gribvalid_get_longdate(precip_diff[i], 'validityDate') t = grib_get_long(precip_diff[i], 'validityTime') |
then combine those numbers into proper date variables.
)
|
Print the result to see what's being returned.
Add the date to the list
We add it to the list like this (inside the loop)A list is built up like this:
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dates = nil fordates i = .... do dt = ..... # construct a date/time variable dates = dates & [dt] end for |
& [dt] |
Compute the differences between consecutive dates
This is very similar to computing the precipitation data earlier (ok, we know it's 3 hours, but in theory it could be anything). We do this after the previous loop:
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date_diffs = dates[2, n] - dates[1, n-1] |
Now you have a list of time differences in days. You can multiply by 24 to get them in hours.The nearest_gridpoint()
function can be called in a number of ways, but we will use it like this:
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valuesdate_diffs_in_hours = nearest_gridpoint(precipdate_diffs, lat, lon) |
The result is a list of values, a value for each field. You can directly multiply a list variable by a number to obtain a new list where each element has been multiplied.
The final calculation requires converting the time intervals into hours (because if the time difference between two steps is 7 hours, then the rate of precip per hour is the mean precip value divided by 7).
Computing a climatology
The supplied GRIB file era_t2m_jan_2009_2013.grib contains 2 metre temperature fields from the ERA Interim data set, interpolated onto a low-resolution 5x5 degree grid. The data are from years 2009 to 2013 and only include the month of January. The data are also from two times: 00:00 and 12:00. Check that all of this is true!
We will compute a small climatology dataset, which will simply be the mean of all these fields. Write a small macro to do this - it should be just 2 lines long: one to read the GRIB file, and one to compute the mean (simply the mean()
function). Return or plot the result to confirm that it looks sensible.
Info |
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Remember that the result is a derived field, and so the default temperature scaling from Kelvin to Celcius will not be applied unless Grib Scaling of Derived Fields is set to On in the Contouring icon. |
Often, these climatological averages are computed individually for each time step. So in our case, we want to now produce two means: one for all the fields at 00:00 and one for all the fields at 12:00. Hint: use the GRIB Filter icon (and its equivalent Macro code) to extract all the fields where Time = 0 and compute their mean. Do the same with all the 12:00 fields. Concatenate the two mean fields into a 2-field fieldset and plot it.
Extracting dates from other data types
Geopoints
To extract dates from a geopoints file/variable, use the dates()
Macro function. Try it on the supplied geopoints file to see what it returns.
BUFR
The easiest way to extract dates from a BUFR file is to convert it to geopoints using the Observation Filter and then extract the dates from the resulting geopoints.
Other formats
Extracting dates from other formats can be more tricky and will not be covered here.
Extra Work
If you have time, try the following.
Computing monthly anomalies
Continuing from the section "Computing a climatology", we will now take some data from 2014 and compute its difference from the climatology data we produced.
Examine the supplied GRIB file era_t2m_jan_2014.grib. It contains low-resolution temperature fields (4x4 degree) over an area of Europe for each day in January 2014 at time steps 00:00 and 12:00. Try the following in a new macro:
- compute the mean temperature field for the whole month
- separate the data into the two different time steps and compute the mean field for each. The end result should be two fields - one is the mean of all the 00:00 fields and the other is the mean of all the 12:00 fields.
- for each time step, compute the difference between the 2014 mean and the climatological mean computed earlier (you may wish to combine both macros into a single macro at this point)
- note that the two data sets are on different grids - you will need to change one of them to the other's grid
- plot the result (it should be two fields) with Contouring icons appropriate for showing temperature anomalies.
Your result shows the monthly anomalies for January 2014 compared with the previous 5 years.
*24 |
Extract the point value for each field in precip_diff
Use the nearest_gridpoint()
function on the precip_diff
fieldset. It returns a list of values, one for each field. Choose a location with some high precipitation.
The nearest_gridpoint()
function can be called in a number of ways, but we will use it like this (giving actual numbers for lat
and lon
) :
Code Block | ||
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values = nearest_gridpoint(precip_diffs, lat, lon) |
The result is a list of values, a value for each field. You can directly multiply a list variable by a number to obtain a new list where each element has been multiplied - do this to scale from metres to mm.
Compute precipitation rate in mm per hour
The final calculation requires converting the data values into mm per hour - divide the list of precipitation values by the list of time differences, which are in hours.
Print the result - it will be a list of numbers, one for each time period.
Computing a climatology
The supplied GRIB file era_t2m_jan_2009_2013.grib contains 2 metre temperature fields from the ERA Interim data set, interpolated onto a low-resolution 5x5 degree grid. The data are from years 2009 to 2013 and only include the month of January. The data are also from two times: 00:00 and 12:00. Check that all of this is true!
We will compute a small climatology dataset, which will simply be the mean of all these fields. Write a small macro to do this - it should be just 2 lines long: one to read the GRIB file, and one to compute the mean (simply the mean()
function). Return or plot the result to confirm that it looks sensible.
Info |
---|
Remember that the result is a derived field, and so the default temperature scaling from Kelvin to Celcius will not be applied unless Grib Scaling of Derived Fields is set to On in the Contouring icon. |
Often, these climatological averages are computed individually for each time step. So in our case, we want to now produce two means: one for all the fields at 00:00 and one for all the fields at 12:00. Hint: use the GRIB Filter icon (and its equivalent Macro code) to extract all the fields where Time = 0, and compute their mean. Do the same with all the 12:00 fields. Concatenate the two mean fields into a 2-field fieldset and plot it.
Extracting dates from other data types
Geopoints
To extract dates from a geopoints file/variable, use the dates()
Macro function. Try it on the supplied geopoints file to see what it returns.
BUFR
The easiest way to extract dates from a BUFR file is to convert it to geopoints using the Observation Filter and then extract the dates from the resulting geopoints.
GRIB
For GRIB, we also have the base_date()
function, which returns the model run time for each field.
NetCDF
The values()
function will return a list of dates when the current variable is a time variable - see Data Part 2.
Extra Work
If you have time, try the following.
Computing monthly anomalies
Continuing from the section "Computing a climatology", we will now take some data from 2014 and compute its difference from the climatology data we produced.
Examine the supplied GRIB file era_t2m_jan_2014.grib. It contains low-resolution temperature fields (4x4 degree) from the ERA Interim data set for each day in January 2014 at time steps 00:00 and 12:00. Try the following in a new macro:
- separate the data into the two different time steps and compute the mean field for each. The end result should be two fields - one is the mean of all the 00:00 fields and the other is the mean of all the 12:00 fields.
- for each time step, compute the difference between the 2014 mean and the climatological mean computed earlier (you may wish to combine both macros into a single macro at this point)
- note that the two data sets are on different grids - you will need to change one of them to the other's grid
- plot the result (it should be two fields) with Contouring icons appropriate for showing temperature anomalies.
Your result shows the monthly anomalies for January 2014 compared with the previous 5 years.
Finding points with large anomalies
See if you can find the points which have anomalies over a certain threshold (e.g. 4 degrees). Create a geopoints variable with the result.
One possible way to do it:
- convert the anomaly field to geopoints (conversion to geopoints only works with one field at a time)
- use the
filter()
andabs()
functions to find just the absolute values greater than 4 - plot with customised Symbol Plotting icons (we could take the ones used in the Processing Data tutorial)
- these points could be written to a file
In Missing Values and Masks, we will see how we could do this sort of thing directly with the GRIB fields