SO 254Spring 2017
SO254 Lab 3
Upper-air observations and analysis
Objectives:
-Become familiar with web sites that provide griddedupper-air observations
-Write MATLAB code toanalyze temperature and height at different vertical levels
-Identify important features on the objectively analyzed upper-air map (i.e., troughs, ridges, low-level fronts)
-Interpret the objective analysis (written paragraphs) describing the country’s weather
Introduction to reanalyses
One of the most important repositories for weather data is the reanalysis. Atmospheric reanalyses have been produced periodically, with a ground-breaking product developed by NOAA in 1996 that has been cited over 22000 times (per Google Scholar). An analysis is a detailed representation of the state of the atmosphere based on observations (satellite, radar, upper-air, surface, buoy, aircraft, etc). Analyses are often gridded in both the horizontal and vertical, with the spacing between grid points getting better over time (in 1996, 2.5° x 2.5° latitude-longitude spacing was standard; now it is 0.5° x 0.5°). A reanalysis is similar to an analysis, with two key differences: first, a reanalysis is not real-time. That means that a radiosonde intended for a 1200 UTC analysis that was launched a few minutes late, and thus wasn’t available for the analysis at 1200 UTC, can be included in the reanalysis after-the-fact. Second, a reanalysis includes output from a numerical weather prediction model, which allows for a complete grid (e.g., grid points over the oceans are not left blank because there are no nearby observations).
The major weather prediction centers all now maintain updated atmospheric reanalyses, and each center’s reanalysis product differs slightly from the other centers: NOAA, the ECMWF, the Japanese Meteorological Agency, etc. In 2017, one of the best sources for downloading reanalysis data in near real-time is maintained by NOAA: Using the Internet Explorer browser (the only one that allows passive FTP; details below), take a few minutes to read the brief description at that link.
NOAA maintains several reanalyses, each designed with different users in mind. The near real-time product, which is also one of the original products(so its horizontal resolution has not improved over time), has lots of variables. They tend to be grouped by vertical level (so “Pressure level” would contain variables like temperature, wind, and geopotential height on pressure surfaces like 500 mb and 850 mb, while “Surface” would contain many of the same variables, but for their values at the Earth’s surface). Click on “Pressure level” and explore the information on that page.We note that the pressure level data are available at different times: monthly averages (so one value per month), daily averages (so one value per day), and then synoptic times of 0000, 0600, 1200, and 1800 UTC (so instantaneous values four times per day).
Question 1a: at what horizontal resolution (e.g., grid spacing and grid range) are these data available?
Question 1b: at what vertical resolution (e.g., pressure levels) are these data available?
Tasks
The variables available on the different pressure levels include Air Temperature, Geopotential Height, Relative Humidity, etc. We are interested in two of those: air temperature and geopotential height, and we are interested in those data at 31 January 2017 at 0000 UTC (which is the date and time of your hand analyses in Lab 2). Notice there are two options to get the data: you can download the raw data files, or you can create a plot or subset (from which to then download a raw data file).
Click the image for “Create plot / subset” for Geopotential Height. Since we want a specific hour, and not the daily mean, choose “4-times Daily” option.
In the 3 options that are presented, only the first one allows us to access data at 0000 UTC on 31 January 2017. So, select the link for “Make plot or subset” for the first option in the table. Take some time to look over the web form that shows on the next page. Notice you can choose the date, time, pressure level, variable, latitude/longitude range, and then some options for how to control the image.
Go ahead and make a plot of the 500-mb height at 0000 UTC 31 January 2017 [note that you will have to enter the date/time twice (begin and end)]. If you don’t change any of the plotting options, you get an image that looks like this one:
Figure 1: geopotential height at 500 mb at 0000 UTC 31 January 2017, from NCEP/NCAR Reanalysis 1.
At 0000 UTC 31 January 2017, a ridge (orange colors) was located over western North America and Alaska, with a located trough over eastern North America and Canada (Fig. 1). The plot is fairly crude, and not very eye-pleasing. You could play around with the options on the web page to improve it. However, the best way to improve the plot is to make it yourself. MATLAB has graphics options to give you control over the figure you create.
Go back to the web interface and download the data (make a subset without making a plot). Because of USNA security restrictions, the FTP link to this plot is unavailable in either Chrome or Firefox. You must use Explorer, then click the wheel-like icon in the far upper-right, then choose Internet Options, and under the Advanced tab, untick the box for “Use Passive FTP (for firewall and DSL modem capability)”. Rename the file you save as “hgt500_00z_31jan2017.nc”.
Download the 850 mb height, and 500 mb and 850 mb temperature data, in the same way. You will have four netCDF files upon completion of your downloads:
hgt500_00z_31jan2017.nchgt850_00z_31jan2017.nc
tmp500_00z_31jan2017.nctmp850_00z_31jan2017.nc
The netCDF format seems to be the community standard in 2017 for gridded meteorological data sets. You can read more about netCDF at
Now that you have the four netCDF data files downloaded, we can begin writing a script in MATLAB to extract the meteorology data from them, then write a series of code segments to plot the data. Start your script with standard header (see Lab 1). Here are some hints on how to do that.
Hint 1: look at the variable names, dimensions, structures, and units by using ncinfo.
var_info = ncinfo('hgt500_00z_31jan2017.nc');
Once you run that line of code, you get a variable called var_info. Double-click onvar_info, then double click on “variables” and then double click on the various <1x1 struct> cells that appear in the five columns. We see that there are 5 variables: hgt, lat, level, lon, and time. There are 144 longitudes and 73 latitudes, which makes sense if the resolution is 2.5°x2.5° from 0°-360° and 90°S-90°N.
Hint 2: Now that you know the names of the longitude and latitude variables, extract them and look at them in MATLAB.
lat = ncread('hgt500_00z_31jan2017.nc','lat');
lon = ncread('hgt500_00z_31jan2017.nc','lon');
The first variable, lat, is latitude, with the first latitude 90°, and the values then decrease by 2.5° (southern hemisphere latitudes are negative). The first longitude is 0°, which then increases by 2.5°.
Hint 3: extract the height variable and look at itin MATLAB(double-click hgt500)
hgt500 = ncread('hgt500_00z_31jan2017.nc','hgt');
It appears that the height at 90°N latitude (column 1) and 0° longitude (row 1) is 5129 m (how did we know the unit is meters? Hint: the output from ncinfo told us!) What if we forgot the level was 500 mb, or if we wanted to double-check our work? We could look at the level variable:
level = ncread('hgt500_00z_31jan2017.nc','level');
Now, let’s make a contour plot of the 500 mb height data. Hint: the command in MATLAB to plot contour plots is
figure(1)
clf
contour(hgt500);
The resulting figure window looks like Figure 2.
In Figure 2, notice the x and y axes. What do the values represent? What is missing? Well, we have not told MATLAB anything about the lat and lon values. Remember, the columns increase from 1 to 73, with column 1 representing the North Pole, and the rows increase from 1 to 144, with row 1 representing the Prime Meridian. We need to give MATLAB information on the latitude and longitude coordinates of these data. To do that, we will use the meshgridcommand, then re-plot the data.
[y,x]=meshgrid(lat,lon);
figure(1)
clf
holdon
axisequal
contour(x,y,hgt500);
colorbar
Notice how we have improved the contour command, by passing it information on the x and y coordinates. You should notice that the meshgrid command gridded the lat and lon values, and stored the result in two new variables: y and x. Double-click on variables x and y, and look at them. We also used “hold on”, which is a command to tell MATLAB to stack, or overlay, figure commands on one another (the default is for MATLAB to erase the figure window each time something new is plotted. In our code above, axis equal would be erased by contour(x,y,hgt500) without the hold on command). The resulting figure looks much more like Figure 3 than Figure 2. It also has a colorbar, which gives context to the colors. The axis equal command helps account for the map projection of a spherical Earth onto a 2-dimensional figure.
Figure 3: Second attempt to contour 500 mb heights at 0000 UTC 31 January 2017.
Let’s add a global coastline to this figure. Download the coastline data from Load the coastline.mat into MATLAB:
loadcoastline.mat
Look at the resulting variable, coastline, by double-clicking on it once it appears in your workspace. What data do you expect are in column 1? What about column 2? If we want to draw a line indicating the coastline, how will these two columns of data be useful? One syntax could be:
figure(1)
clf
holdon
axisequal
contour(x,y,hgt500);
colorbar
plot(coastline(:,1),coastline(:,2),'k','linewidth',1.5);
The ‘k’ tells MATLAB to plot a black line, and ‘linewidth’ tells it to plot 1.5 width (1.0 is default). The contour function is quite powerful. One of the things contour allows is to control for the values of the isolines being contoured. Remember, for 500 mb, the convention is to plot the 5400 m line with interval spacing of +/- 60 m. Read online and in the help command for how to setup plotting in this format. You’ll need to contour starting, at least, at 4800 m, and ending at 6000 m. The help command suggests one way to do that is to give the contour command a vector of contour intervals. Thus, if you wanted to contour values at 1, 2, 3, 5, and 10, you could use contour(x,y,hgt500,[1 2 3 5 10]). If you wanted regularly spaced contours, like between -5 and 5 and interval 0.25, the syntax could be contour(x,y,hgt500,-5:0.25:5).
The last task is to zoom the figure to match the latitude and longitude coordinates from your hand analyses from Lab 2. Use the xlim and ylim commands to set the longitude and latitude ranges accordingly.
Summary of deliverables for this lab:
Figure with 500-mb heights contoured every 60 m from 4800 m to 6000 m
Figure with 850-mb heights contoured every 30 m from 900 m to 1800 m
Figure with 500-mb temperatures contoured every 5C from -60C to 30C
Figure with 850-mb temperatures contoured every 5C from -60C to 30C
Clean, commented MATLAB code
All figures should be zoomed appropriately (including to match the lat/lon of the figures from Lab 2), with appropriate titles, axis labels, and colorbar. You should write a detailed lab report, and guidance on lab report format is given below.
Format for lab report
Your completed lab report should include:
1)A cover page with text centered and in the following format:
Lab 3: Upper-air observations and analysis
Rank First Name Last Name
SO 254: Intro to Meteorology (Section #)
DD Month YYYY
2)An introduction section. In the introduction, write a few sentences about pressure levels, upper-air analyses, and the value of such analyses (e.g., what information do you get by analyzing 500 and 850 mb levels? What weather features can be seen at those levels?)
3)A data section. In the data section, write a few sentences about the NCEP/NCAR Reanalysis data. What temporal and spatial resolution does it have? Include a web link for downloading the data.
4)A results section. The results section will largely be a “revised” version of your discussion submitted with Lab 2, wherein you take into account comments provided by your instructor on your Lab 2. However, you will also need to reference the four MATLAB generated charts by figure number as you discuss them. Remember, the proper format to reference a figure by number is at the end of a sentence. E.g., do not say “Figure 1 shows a trough over Michigan”. Instead say, “A 500-mb trough, with minimum heights of 5130 m and temperatures of -15C, was centered over Michigan at 0000 UTC 31 January 2017 (Figure 1).” Place the figures at the end of the writeup, with appropriate captions: e.g., “Figure 1: Height at 500-mb at 0000 UTC on 31 January 2017, from NCEP/NCAR Reanalysis I.”
While the data for this lab is from the same time and day as in Lab 2, the analysis may reveal new or different features. Thus, include any new meteorological insights gained from your MATLAB analysis. This should be 1 to 2 double spaced pages.
5)A conclusion section. Very briefly summarize the main points of the results section. Tie those main points back to whatever you mention in the introduction (but don’t say “As mentioned in the introduction …”).
6)A references section. If you cite any references anywhere in the lab, include them here.
7)A page (or pages) containing your clean and commented (explain what each section of code is doing) MATLAB script.
A sample of the 500-mb figure, with appropriate axis labels and title, is below.
Figure 4: Example 500-mb heights.
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