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Giovanni User Manual 4.14

Contributors: Keith Bryant, Christopher Lynnes

GIovanni is an on-line environment for the direct statistical intercomparison of geophysical parameters in which the provenance (data lineage) can easily be accessed. This user manual provides assistance on how to use Giovanni and information on Giovanni's data products and services.

User Manual Sections:


Quick Start

Using the Giovanni user interface, it is possible to easily find and display selected data on a plot.  It is also possible to download the plot source files in netCDF format.  While this user interface does not require you to select criteria in any particular order, below is a common sequence of steps you can follow to obtain a plot and data.

Step 1: Select Plot: 

We use "Time Series" for the example.

Step 2: Select Area:

You can either type in longitude and latitude of the edges of your desired box as West, South, East, North, or click on the "Show Map" button and select an area with a click and drag movement. Alternatively, you can click on the "Show Shapes" button and select a shape from a pre-defined list of polyons.

Step 3: Select Variable(s):

To select a variable, you can begin either by:

(a) selecting checkboxes of the desired attributes on the left hand side, or

(b) typing a term into the Search box and pressing Search.  In this latter case, you can then narrow the results by selecting desired attributes on the left hand side.

Note that if you select attributes and then type in a search term, the search will be against the whole collection, not just those matching the selected attributes.

Also, note that all variables have a valid date range and selecting a variable will constrain the valid date ranges presented by the calendar date selector.

Step 3a:  Select "Vertical Slice"

This is the level or layer in a 3-dimensional variable that you want plotted on a map, histogram or time series. This is not necessary for Vertical Profile.

Step 3b:  Select "Destination Units"  (Coming soon...)

Some variables can be converted into alternate units.  See the section on Units Conversion for details on how this works.

Step 4: Select Date Range:

Please note the relationship between date range and selected variables. If a date range is selected that does not intersect the union of the date ranges of the currently selected variables, the user interface will display an error message.


Plot/Service Types


Time-Averaged Map (Interactive)

The time averaged map shows data values for each grid cell within the user-specified area, averaged (linearly) over the user-specified time range as a map layer. Fill values do not contribute to the time averages. The map can be zoomed and panned.  Plot options include setting minimum and maximum values for the color scale, and in some cases selecting other palettes.

Vector magnitude maps, such as those for wind speed magnitude, compute the magnitude of the vector at each time step first before averaging the magnitudes of each grid cell together across time. In contrast, vector maps compute the averages of the latitudinal and longitudinal components of each grid cell across time and display the resulting vectors.


The Animation service shows individual time slice maps of a data variable in an animated sequence.

User-Defined Climatology Map

The User-Defined Climatology maps compute averages for either a specific month or a 3-month group corresponding to the meteorological seasons (DJF=Dec,Jan,Feb, MAM=March,April,May, JJA=June, July, August, SON=Sep, Oct, Nov).  The average is computed over the years specified in the selection screen and displayed in a map. More than one month or season can be selected. We refer to this as a quasi-climatology because a "true" climatology is typically computed over many (e.g., 30) years.  Available only for monthly data.

Accumulation Map

A few variables are available for the Accumulation Map, in which instead of averaging over time, a total is computed over time for a given grid cell. These are typically precipitation-related variables, and are restricted to data variables that are continuous, with few or no gaps. (The reason is that gaps are treated the same as values of 0, resulting in a possibly significant low bias in data with gaps.)

Smoothing option for Maps

When the smoothing option is chosen, two different types of smoothing are applied.  Firstly, spatial interpolation akin to contouring provides the bulk of the smoothing effect, using the GrADS "gxout shaded" algorithm.  On top of that, a bicubic smoothing operator (GrADS smooth option) is applied. This results in eliminating pixels at the edge, so the unsmoothed pixels at the edge are retained as part of the image. 


Correlation Map (and other comparisons)

The correlation map calculates correlation coefficient using simple linear regression between two variables over time within each grid cell, producing two maps: one showing the correlation coefficient (R) and the other displaying the number of contributing (matching) samples in each grid cell.  (Note that the values from both variables must be non-fill in order to contribute to the correlation computation.  If the two variables have different spatial resolutions, the finer resolution is regridded to the coarser resolution using the lats4d application.  Any grid cell that contains less than three matched pairs over time will be assigned a fill value.

An additional product of the correlation computation is an average at each grid cell of the differences between the two variables at each timestep for that grid cell. This map may contain more values than the correlation map, as the differences will be computed for as few as one non-fill matched time step in a grid cell.

Static Scatter Plot

The scatter plot produces a (static) scatter plot of all data pairs from two selected variables.  The data pairs are matched in both space (grid cell) and time. The plot shows both the scatter and the parameters of the simple linear regression, i.e., slope, offset and correlation coefficient (R). If the two variables have different spatial resolutions, the finer resolution is regridded to the coarser resolution using the lats4d application. Caveat:  the averaging that occurs within regridding may produce an artificially high correlation coefficient; interpret with care!

Interactive Scatter Plot

The interactive scatter plot produces a scatter plot and a map showing the location of data pairs in the scatter plot. Users can select data pairs of interest by selecting data pairs (click and drag on the scatter plot). Users can also select locations of interest by selecting region of interest in the map.

Time-Averaged Scatter Plot

The Time-Averaged Scatter Plot produces a scatter plot of all co-located points averaged over time and a map showing the location of data pairs in the scatter plot. Only values that are non-fill for both data fields at a given time-step are used in the computation of the averages over time for each grid cell.  

Users can select data pairs of interest by selecting data pairs (click and drag on the scatter plot). Users can also select locations of interest by selecting region of interest in the map.

Area-Averaged Scatter Plot

The Area-Averaged Scatter Plot computes an average over the selection area for each time step of two separate variables. The resulting values are matched up by time and plotted as an X-Y scatter plot. All cells whose center point falls within the selection box are included.

Difference of Time-Averaged Maps

The Difference of Time-Averaged Maps computes the time average for each grid cell for two variables being compared. The differences between the two resultant maps are then computed and plotted on a map.  Only variables with the same Measurement and Units can be differenced in this way.  Fill values in either resultant map are not included in the final difference.

Time Series

Time Series (Area-Averaged)

The standard Giovanni time-series plot is produced by computing spatial averages over the user-selected area of a given variable for each time step within the user's range.  Fill values do not contribute to the spatial averages. Each average value is then plotted against time to create the time-series output.

When a shape is selected, the shape is rasterized at a resolution four times higher than that of the data. The high resolution raster array is then regridded to an array at the data’s resolution, with weights proportional to the amount of shape coverage in each cell. These weights are used in the area averaging computation to enforce that cells with lower shape coverage have a smaller influence on the resultant average.

Seasonal (Interannual) Time Series

The Seasonal Time Series computes an area averaged time series for each year in the user's selection for a given month or 3-month meteorological season, To avoid biasing the results, partial seasons (i.e., missing one or months) are not plotted. Meteorological winter (Dec-Jan-Feb) is labeled with the year in which January falls, so DJF for 2007 goes from Dec 2006 to Feb 2007.  This service is available for monthly data only. 

Hovmoller plots

The Hovmoller plot averages over either latitude or longitude at each time step and creates a two-dimensional color slice plot for the remaining horizontal dimension vs. time.  Lat - time Hovmoller plots show latitude on the vertical axis.  Lon - time Hovmoller plots show longitude on the  horizontal axis.

Time Series of Area-Averaged Differences

This service compares two variables over time by first differencing the first variable from the second at each grid cell, and then computing the average difference over the user-selected area. The area averaged difference is computed over a geographic (Cartesian) map, with values weighted as a function of latitude (i.e., by multiplying by cosine(lat)).  Fill values are not included in the calculations. All cells whose center point falls within the selection box are included.

Vertical Plots

Vertical Profile (Time and Space Averaged)

Several of the variables in Giovanni have a vertical dimension in addition to the horizontal dimensions of Longitude and Latitude.  For example, The Atmospheric Infrared Sounder (AIRS) Temperature, water vapor, and relative humidity have vertical dimension of atmospheric pressure.  The vertical profile plot option displays a profile of the given variable which is first averaged over the user selected region and then over the selected period. Fill values do not contribute to the averages. All cells whose center point falls within the selection box are included.

Other Plots


The histogram service computes a histogram over the values present in the given temporal and spatial selection. No averaging is done over any dimensions. Fill values in the data are dropped and not considered in the results. The sample mean, sample standard deviation, and median are also presented in a box in the top right hand corner.



Aerosol Optical Depth or Thickness or Aerosol Extinction

Aerosol optical depth or thickness is a measure of radiation extinction at the encounter of aerosol particles in the atmosphere.

The extinction or total aerosol optical depth is a measure of radiation extinction due to aerosol scattering and absorption. Aerosol Total Optical Depth is available through Giovanni at 550 nm from MODIS.
Read more red dice Information for Educators (DICCE Project)

Giovanni includes several measurements of Total Aerosol Optical Depth from different instruments and platforms, often measured at different wavelengths. 


Component Aerosol Optical Depth

In addition, the optical depth of several different species of aerosol is available from the GOCART model, and the Optical Depth related to Absorption only is available from the Ozone Monitoring Instrument.


Angstrom Exponent

The Angstrom Exponent describes the spectral dependence of aerosol optical thickness (τ) on the wavelength of incident light (λ).  This provides additional information on the particle size (larger the exponent, the smaller the particle size), aerosol phase function and the relative magnitude of aerosol radiances at different wavelengths.  The spectral dependence of aerosol optical thickness can be approximated (depending on size distribution) by, 

τa = β λα   where   α   is Angstrom exponent (β = aerosol optical thickness at 1 μm)

Angstrom exponent (computed from τ measurements on two different wavelengths) can be used to find τ on another wavelength using the relation.


Pixel Count

Level 3 gridded products are often produced by averaging multiple pixels from the Level 2 orbital products in a given grid cell.  For such algorithms, it is sometimes useful to know how many level 2 pixels, or Pixel Count, were used in the average.  Note however, that while low pixel counts typically indicates a lack of representativeness, medium pixel counts are often obtained when pixels cluster into one portion of the cell, improving representativeness only marginally.

Latent Heat Flux

The heat/energy transfer involving evaporation of water at the sea surface, dependent on difference of sea and air surface specific humidity, wind speed, and sea surface roughness.


Sensible Heat Flux

The heat/energy transfer involving conduction and convection at the sea surface, dependent on difference of sea and air surface temperature, wind speed, and sea surface roughness.


Wind Stress

The momentum transfer (downward from atmosphere to ocean) involving shear stress exerted by the wind on the sea surface, depending on wind speed and sea surface roughness.

i)                   Wind Stress Magnitude (scalar)

ii)                 Wind Stress Vector (vector expressed via latitudinal and longitudinal components)



AIRS Products

The Atmospheric Infrared Sounder (AIRS) is an infrared spectrometer with 2378 channels in the 3.7–15.4 micron spectral range.

It was launched in May 2002 aboard the Aqua spacecraft. The AIRS primary products include atmospheric profiles of temperature and humidity on global scales, day and night. The temperature profiles cover the atmospheric column from the surface to the stratosphere (0.1 mb) and the humidity profiles are accurate only in the troposphere, below about 200-300 mb. In addition, AIRS products include global surface temperatures, total column precipitable water vapor, cloud properties and outgoing long-wave radiances which are of significant value in studies of Earth's radiation budget and climate variability.

GOCART Products

The Goddard Chemistry Aerosol Radiation and Transport (GOCART) model simulation provides global daily and monthly aerosol optical depths data at seven wavelengths (350, 450, 550, 650, 900, 1000, and 1500 nm) for major tropospheric aerosol components, including sulfate, dust, black carbon (BC), organic carbon (OC), and sea-salt aerosols at a horizontal resolution of 2.5x2.0 deg. The GOCART model uses the assimilated meteorological fields of the Goddard Earth Observing System Data Assimilation System (GEOS DAS), generated by the Goddard Global Modeling and Assimilation Office (GMAO). The data provided from the GES DISC are from the GOCART Simulation Experiment ID G4P0.

GSSTF3 Products

The air-sea turbulent fluxes of GSSTF3, involving heat/energy and momentum transfer between the atmosphere and ocean facilitated by turbulent motion, consist of three major components: latent heat flux, sensible heat flux and wind stress.

MODIS Products

MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS view the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications).

The aerosol variables in Giovanni are extracted from Level-3 gridded atmospheric daily products for both MODIS on Aqua and MODIS on Terra.  The MODIS Aerosol Product monitors the ambient aerosol optical thickness over the oceans globally and over a portion of the continents.

OMI Products

The Ozone Monitoring Instrument (OMI) measures primarily in the ultraviolet and near-UV part of the spectrum. OMI supplies an Aerosol Extinction Optical Depth (the same as Total Aerosol Optical Depth) as well as an Aerosol Optical Depth due only to radiation absorption (Aerosol Absorption Optical Depth).

SeaWiFS Deep Blue Products

The Sea-viewing Wide-Field-of-view Sensor was designed primarily with ocean color in mind. However, it has several wavelengths that are similar to those of MODIS, making it possible to retrieve Aerosol Optical Depth using a variant of the MODIS Deep Blue Algorithm. Under NASA's MEaSUREs program. the Consistent Long Term Aerosol Data Records over Land and Ocean from SeaWiFS project has produced Total Aerosol Optical Depth at a variety of wavelengths and two spatial resolutions, 0.5° and 1°.

N.B.:  The Long Term Aerosol Data Records project plans to put out a release 4 as a final release.


Other Features

Units Conversion

Many of the variables can be converted from the current units to different units, such as mm/hr to inch/day.  This capability is indicated by an option menu in the Units column for that variable (coming soon...) For efficiency's sake, this conversion is usually applied to the output data from a given service.  However, there are two cases where the conversion must be done before the processing algorithm runs. The first is for comparison services that require identical units to be sensible, i.e., the services with the word "Difference" in them.  The second set of cases are those where the service algorithm aggregates (e.g., averages) the data over the time dimension.  In these case, if the destination units is a monthly rate (e.g., inch/month), then the conversion must be done before the algorithm runs in order to account for the varying number of days in each month.


Service requests for variables specified in monthly rate units (e.g., mm/month) will give plots bias relative to plots that use variables with daily or hourly rate units (e.g., inch/day).  Plots that display the time dimension will tend to show higher values for longer months (i.e., months with more days). For example, suppose a user requests a time series of precipitation in mm/month. The March data points will show the total precipitation for 31 days while the April data points will show the total precipitation for only 30 days.   Plots that average over the time dimension will have similar problems. Longer months will tend to have larger values, which will pull the average up. Shorter months will tend to have smaller values, which will pull the average down.  Histogram plots will be slightly skewed by monthly units conversion. Values from longer months will tend to end up in higher-valued bins and values from shorter months will end up in lower-valued bins.



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Last updated: Apr 06, 2016 04:23 PM ET