User Manual Sections:
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 globe icon and select an area with a click and drag movement.
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 later 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 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.
The latitude-longitude map shows data values for each grid cell within the user-specified area, averaged (linearly) over the user-specified time range. Fill values do not contribute to the time averages.
The interactive map shows data values for each grid cell within the user-specified area, averaged (linearly) over the user-specified time range as a WMS layer. Fill values do not contribute to the time averages. The map can be zoomed and panned. Color map can be selected when available.
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 thelats4d 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.
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!
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.
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.
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.
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 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.
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.
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.
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)
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.
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).
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.
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.