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Resource page for Data-enhanced Investigations for Climate Change Education (DICCE)

Supplemental resources for the Data-enhanced Investigations for Climate Change Education (DICCE) project

 

Go to:   DICCE-Giovanni Basic Monthly Climate Data Portal

Go to:  DICCE-Giovanni Basic Daily Data Portal

 

See below for:   (click to go there directly)

 

Giovanni-4 "How-To" Demonstration Presentations 

 

Key terms used in DICCE-Giovanni

  

► NASA Mission
Some missions, but not all, involve launching satellites and using them to gather data. Rather, what ALL NASA missions have in common is that they are specific research projects that groups of scientists have received funding to conduct. The research that these missions conduct involves either gathering data or modeling data. Most of the NASA missions that gather data do so by remote sensing. The remote sensing is carried out on satellites. There are also a smaller number of NASA missions that gather data through other means, such as on the ground or from aircraft. Each time a piece of data is gathered, it is called an “observation.”
 
Additionally, some missions are funded to conduct computer modeling, which usually involves “data assimilation" (i.e., running measurements taken through observations through computer programs that produce quantitative projections and refine output variables of models).
 
 
► Data parameter
A data parameter is a single measured quantity (variable) obtained by observation of the Earth system. For example, the quantity “rainfall rate” is generated through an observation of how quickly rain is falling. Data parameters in the DICCE Giovanni (subsequently referred to as ‘DICCE-G’) monthly data sets are averages of these observations. Some of the daily data are also averages but most others are single measurements taken once a day. Most of these data parameters in DICCE-G are generated by remote sensing on satellites. 
 
► Data product 
A data product (i.e., a data set) is composed of one or more data parameters. The products are sets of data or model projections that are generated by various NASA missions. The scientists decide how to organize their data and projections into these products. Each data product has a name.
 
For example, there is a data product called MODIS Terra Version 5.1. As explained on its web site (http://modis.gsfc.nasa.gov/about/), MODIS (Moderate Resolution Imaging Spectroradiometer) is an instrument aboard a satellite named Terra (but also on a satellite named Aqua).  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. MODIS Terra and MODIS Aqua instruments are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths.
 
The MODIS Terra Version 5.1 data product is Version 5.1 of a set of data taken by the MODIS instrument that is onboard Terra. MODIS Terra Version 5.1 contains numerous data parameters, several of which are available in DICCE-G, including AOD and Deep Blue AOD.
 
In the MODIS Terra Version 5.1 data product, all the data parameters are about various atmospheric phenomena, including AOD and Deep Blue AOD. Frequently, some of the data parameters in a data product/data set are used to calculate other data parameters in the same product/set. 
 
 In the DICCE-G selection tables, each data parameter is identified on the left, and data product information follows to the right, including the product same. For example, in the DICCE-G Basic Monthly data portal (http://gdata1.sci.gsfc.nasa.gov/daac-bin/G3/gui.cgi?instance_id=DICCE-G_Basic), the data product information for the data parameter “Deep Blue AOD at 550 nm” shows that the data product name is “MODIS Terra Version 5.1”, its NASA-assigned ID is “MOD08_M3.051”, and its current time range is from March 2000 to the most recently completed month of data.   Monthly data products are not immediately available at the beginning of the following month, as it takes several days to finish processing the monthly data products.
 
More information about the data products is available at the main Giovanni web site (http://disc.sci.gsfc.nasa.gov/giovanni#maincontent). 

  

In DICCE G, the Start and End dates that appear to the right of each data parameter selection option are, in fact, the Start and End dates of the data product that the parameter belongs to.

 
In most cases, all the data parameters that belong to a particular data product will have the same Start and End dates. There is, however, one significant exception. All of the MODIS Terra Version 5.1 data parameters in DICCE-G have observations that began in March 1, 2000 and continue through today, except for the data parameter known as “Deep Blue AOD at 550 nm (QA-w, Land only)”, which ended in 2007 for reasons having to do with data quality. (For more about this particular data parameter and why it ended in 2007, see its description in the DICCE Enhanced Help.)

 

DICCE Version 2.0 Instructional Videos

How to make a data map  (MPEG-4)  

How to make a data map (YouTube)

 

How to make a time-series (MPEG-4) 

How to make a time-series (YouTube)

 

How to make a data animation  (MPEG-4) 

How to make a data animation (WMV)

How to make a data animation (YouTube)

 

How to work with color palettes (MPEG-4) 

How to work with color palettes (WMV)

How to work with color palettes (YouTube)

 

How to make a Vertical Profile (MPEG-4)  

How to make a Vertical Profile (WMV)

How to make a Vertical Profile (YouTube)

 

How to put Giovanni Time-Series Data into an Excel Spreadsheet (MPEG-4)

How to put Giovanni Time-Series Data into an Excel Spreadsheet (WMV)

How to put Giovanni Time-Series Data into an Excel Spreadsheet (YouTube)

 

Instructions for how to put Giovanni time-series data into an Excel spreadsheet (also see video directly above)

This tutorial from the Laboratory for Ocean Color Users describes how to import ASCII text data generated for a Giovanni time-series into an Excel spreadsheet, which allows statistical evaluation of possible trends.

LOCUS Tutorial Research Project 7: Time-Series Analysis with Significance Testing Using Giovanni and Excel

 

DICCE Parameter Extended Help

Red Dice  Access the DICCE Parameter Extended Help !

 

 

DICCE Data Parameter Summary Table

Name of data parameter Source of data (remote sensing, ground station, assimilation model, other)

Measurement

units

Approximate spatial resolution

Pre-defined* or dynamic** color palette

Pre-defined color palette maximum and minimum values Time resolution
Euphotic depth  Remote sensing  meters 9 x 9 km  Pre-defined  0-100 log scale  Monthly
Fraction of sea-ice  Assimilation model  Percent  1 x 1 degree+  Dynamic  **  Monthly
Sea Surface Temperature (11 micron day)  Remote sensing  degrees C 9 x 9 km  Pre-defined  0-30  Monthly
Chlorophyll a concentration (SeaWiFS)  Remote sensing  mg/m3 9 x 9 km  Pre-defined  0-30 log scale  Monthly
chlorophyll a concentration 9km (MODIS)  Remote sensing  mg/m3 9 x 9 km  Pre-defined  0-30 log scale  Monthly
Cloud Fraction (Day and Night)  Remote sensing  Unitless (ratio) 1 x 1 degree+  Pre-defined  0.0 - 1.0  Monthly
Relative humidity_descending (RelHumid_D) (12 Levels)  Remote sensing  Percent 1 x 1 degree+  Dynamic  **  Daily & Monthly
Temperature profile_descending (Temperature_D) (24 Levels)  Remote sensing  Kelvin 1 x 1 degree+  Dynamic  **  Daily & Monthly
Aerosol Optical Depth at 550 nm  Remote sensing  Unitless (logarithmic  value) 1 x 1 degree+  Pre-defined  0.0 - 0.9  Daily & Monthly
CO2_fraction §  Remote Sensing  Parts per million 2.5 degree longitude+, 2.0 degree latitude Dynamic  **  Monthly
Deep Blue AOD at 550 nm (QA-w, Land only)  Remote sensing  Unitless (logarithmic value) 1 x 1 degree+  Pre-defined  0.0 - 0.9  Daily & Monthly

Mass Concentration- Land 

Mass Concentration-Ocean

Remote Sensing µg/cm2 1 x 1 degree+ Dynamic ** Daily & Monthly
Total Column Ozone  Remote sensing  Dobson Units 1 x 1 degree+ Dynamic  **  Daily & Monthly
GPCP precipitation Combined remote sensing and ground station  millimeters/day 2.5 x 2.5 degree+  Pre-defined  0-30 mm/day  Monthly
Observed Ground Station Precipitation Ground station  millimeters/hour 0.5 x 0.5 degree+  Dynamic  **  Monthly
Rainfall rate  Assimilation model  kg/m2/second 1 x 1 degree+  Dynamic  **  Monthly
Net longwave radiation  Assimilation model  Watts/m2 1 x 1 degree+  Dynamic  **  Monthly
Net shortwave radiation  Assimilation model  Watts/m2 1 x 1 degree+  Dynamic  **  Monthly
Photosynthetically Available Radiation   Remote sensing  Einstein/m2/day  9 x 9 km  Pre-defined  0 - 70  Monthly
GLDAS Soil Moisture Assimilation model kg/m3 1 x 1 degree+ Dynamic ** Monthly
Fractional snow-covered area  Assimilation model  Unitless (fractional value)  0.66 degree longitude+, 0.5 degree latitude  Dynamic  **  Monthly
Land Surface Temperature (daytime)  Remote sensing  Kelvin  1 x 1 degree+  Pre-defined  210-320 K  Daily (AIRS) & Monthly (MODIS)
Land Surface Temperature (nighttime)  Remote sensing  Kelvin  1 x 1 degree+  Pre-defined  210-320 K  Daily (AIRS) & Monthly (MODIS)
Near surface air temperature  Assimilation model  Kelvin 1 x 1 degree+  Dynamic  **  Monthly
Near surface wind magnitude  Assimilation model  meters/second 1 x 1 degree+  Dynamic  **  Monthly
Snow depth  Assimilation model  meters  0.66 degree longitude+, 0.5 degree latitude  Dynamic  **  Monthly
Snowfall rate  Assimilation model  kg/m2/second  0.66 degree longitude+, 0.5 degree latitude  Dynamic  **  Monthly
Snow mass  Assimilation model  kg/m2 1 x 1 degree+  Dynamic  **  Monthly

Normalized Difference Vegetation Index (NDVI) &

Enhanced Vegetation Index (EVI)

 Remote sensing  Unitless (higher values indicate increased "greenness") 1 x 1 degree+  Pre-defined  0.0 - 0.9  Monthly
Methane, CH4 Remote sensing Unitless, as volume mixing ratio 1 x 1 degree+ Dynamic **  Daily
Carbon monoxide, CO Remote sensing Unitless, as volume mixing ratio 1 x 1 degree+ Dynamic **  Daily
Nitrogen dioxide, NO2 Remote sensing molecules/cubic cm 0.25 x 0.25 degree
Pre-defined 0 - 10  x 1015  Daily
Sulfur dioxide, SO2 Remote sensing Dobson Units 0.125 x 0.125 degree
Pre-defined 0 - 5  Daily

 

* Pre-defined:  The initial color palette used to plot the data map uses default maximum and minimum values, and either a linear or logarithmic numerical scale, which are assigned to that specific data parameter.  Only pre-defined color palettes can have a logarithmic scale.  Because ocean color data has a logarithmic distribution, pre-defined logarithmic scales are used for these data.

** Dynamic:  To define the initial color palette numerical range used to plot the data map, the maximum and minimum data values that occur in the region and time period that is being plotted are used.  Dynamic color palettes are always linear.

§ To replot CO2 fraction data, the data range minimum and maximum values must be expressed with three zeroes after the decimal point, i.e., .000xx.   CO2 fraction data are in units of parts per million, so the numerical data value is a decimal number.  A scaling factor is used to display the values for the color palette.  Thus, 380 ppm is actually .00038, and is shown on the palette as 3.8 (the scale factor is 1 x 10-4, displayed above the map plot).

 

+ Information on the spatial resolution of data in Giovanni

1 degree is approximately 111 x 111 km at the Equator. The longitudinal (vertical) dimension decreases with increasing latitude; length of a degree of longitude = cos (latitude) x 111.325 km. For example, at the latitude of Mount McKinley (Denali) in Alaska, 63 degrees North, the expression would be cos(63°) x 111.325 km = .454 x 111.325 km, which is approximately 50.4 km.

Some plotting projections use an equal area grid, in which the length of the longitudinal dimension increases closer to the poles, so that each data element represents data for an approximately equal area on the Earth's surface.


More explanation about resolution

NASA missions that produce data parameters with a coarse spatial resolution typically report the resolution in terms of geographical degrees, or fractions of degrees. The size of a degree or fraction of a degree varies depend on how close the measured area is to the equator and the poles. The spatial area of a 1 x 1 degree square (i.e., 1 degree of latitude multiplied by 1 degree of longitude) gets smaller the closer you get to the poles.  The area hence gets smaller because longitudinal degrees decrease in length the closer you get to the poles, whereas latitudinal degrees always stays the same.

NASA missions that produce data parameters with finer resolution often are reported in terms of square kilometers rather than in terms of degrees. For example, there is a land surface temperature data parameter with a 5.6 kilometer resolution. This is because the area captured in an individual data value is so small that it is going to be consistent not matter what the longitude is of the focal geographical area.

Sometimes we report resolution in terms of pixels. Pixels are the smallest discrete visible element of an image. In a high resolution image, an area represented by a single data value is smaller than for images with lower resolution.  [ This is also true for your computer monitor. A high resolution screen has more discrete picture elements (pixels) than a low resolution screen, which makes the graphics on the high resolution screen appear sharper. ]  The resolution that is optimal for looking at different types of Earth system/geophysical variables varies depending on the characteristics of the variable. Parameters with less spatial variability can be legitimately studied at coarser resolution (for example, atmospheric ozone) than others than need finer resolution (ocean chlorophyll concentration)

If you zoom in on an image of a data parameter with a coarser resolution, it will appear blockier whereas those with finer resolution appear smoother. However, any image will end up with square pixels visible at the maximum level of zooming in because all digital images are drawn pixel by pixel, and all pixels are approximately square in shape when rendered on a map.

REALLY IMPORTANT NOTE: When you attempt to plot a map showing a particular data parameter, if the area you've selected for your plot is smaller than the area captured by the resolution of the data, the plot will not be able to be rendered, and you will get an error message. (Example: Each value captured for CO2 fraction covers a 2.5 degree longitude by 2.0 degree latitude area, so the area you then select for your CO2 fraction map plot needs to be at least 2.5 degrees longitude by 2.0 degrees latitude.)

 

Climate Change Schema

The Climate Change Schema is a brief summary of the interconnections and uncertainties in climate change science.

Climate Change Schema (PDF)

 

Data Parameter Trend Guides

The Data Parameter Trend Guides discuss what time-series trends for data parameters in DICCE-G can indicate.

Physical Atmosphere Trend Guide (PDF)

Physical Land Trend Guide (PDF)

Atmospheric Gases Trend Guide (PDF)

Energy and Radiation Trend Guide (PDF)

Land Biosphere Trend Guide (PDF)

Ocean Biosphere Trend Guide (PDF)

Physical Ocean Trend Guide (PDF)

Precipitation Trend Guide (PDF)

How to identify potential climate trends in short-term satellite (and related) data sets available in DICCE-Giovanni (PDF)

 

Troubleshooting Guide

A guide to inconsistencies, oddities, and vagaries that may show up in Giovanni data visualizations

DICCE Troubleshooting Guide (PDF)

 

Guide to differences between similar data products

 

MONTHLY  DATA

Data

Product

1

Data

Product

2

Data

Product

3

Attributes of

Data Product 1

Attributes of

Data Product 2

Attributes of

Data Product 3

 MODIS-Aqua chlorophyll a  SeaWiFS chlorophyll a    MODIS chlorophyll a is slightly more accurate in turbid waters;  the MODIS chlorophyll data is current to the present day  The SeaWiFS chlorophyll a data extend back to September 1997, and include the large El Nino event of 1997-1998.  
 Aerosol optical depth at 550 nm  Deep Blue AOD at 550 nm    Aerosol optical depth at 550 nm is calculated by the standard MODIS algorithm.  This algorithm does not work over bright Earth surface areas.  Deep Blue AOD is calculated using the MODIS blue wavelength band, and does allow estimation over bright surface areas, primarily desert regions.  
 Mass concentration (Land)  Mass concentration (Ocean)    The aerosol mass concentration algorithm has two parallel data products, calculated over land and ocean.   Mass concentration for land can be used with either AOD data product.  The aerosol mass concentration algorithm has two parallel data products, calculated over land and ocean.   Mass concentration for ocean can be used with either AOD data product.  
 GPCP Precipitation  Observed ground station precipitation Rainfall rate  GPCP precipitation is calculated by combining data from several data sources (satellites and ground stations), providing accurate comprehensive coverage.  GPCP precipitation and rainfall rate cover the same time period.  Observed ground station data is only derived from rain gauges on weather stations.  The data does extend back to 1950, allowing trend analysis, but ends in 1999.  Rainfall rate is a model-derived quantity.  It gives rainfall data in terms of mass, rather than accumulation (i.e. inches or centimeters). The model can fill areas where actual data coverage was sparse.
 Land surface temperature (day)  Land surface temperature (night)  Land surface temperature (day and night) 5.6 km  Land surface temperature (day) is affected by solar heating and cloud cover, and thus may be more variable than nighttime temperatures.  Land surface temperature (night) is less affected by solar heating and cloud cover, which can be considered to give a truer indication of surface temperature and trends.  The 5.6 km data products are at higher spatial resolution, improving ability to distinguish surface features and boundaries (such as between urban and rural areas).
 Near surface air temperature      Near surface air temperature is a model-derived data product based on surface and satellite data inputs.  It extends back to 1979, unlike the land surface temperature data products, which are from satellites and begin the year 2000.    
 Snow depth  Snow mass  Snowfall rate  Snow depth is the most familiar snow variable, expressing the amount of snow in terms of depth (inches or centimeters).  Snow depth can vary depending on the type of snow crystals present and the water content of the snow pack.  Snow mass expresses the amount of snow in terms of mass (grams or kilograms), which is more indicative of actual water content than snow depth.  Snowfall rate expresses the amount of snow falling as precipitation, and does not indicate how much snow is actually present on the ground.
 Enhanced Vegetation Index (EVI)  Normalized Difference Vegetation Index (NDVI)    EVI seeks to be an improved variable over NDVI by reducing the influence of the atmosphere on the satellite data, and providing a better way to assess variability in high biomass regions (i.e. dense forests and grasslands).  NDVI has been a  standard way to assess the greenness of the land surface, i.e. the presence and health of plants, since it was first used for satellite observations.  

 

Guide to differences between similar data products

 

DAILY  DATA

Also see the explanation of ascending and descending nodes

Data Product 1 Data Product 2 Attributes of Data Product 1 Attributes of Data Product 2
 MODIS-Aqua Aerosol Optical Depth (AOD)  MODIS-Terra Aerosol Optical Depth (AOD)  MODIS-Aqua crosses the equator on the lighted side of the Earth at approximately 1:30 PM local time. The MODIS-Aqua mission began in 2002, so the data record is shorter than for MODIS-Terra.  MODIS-Terra crosses the equator on the lighted side of the Earth at approximately 10:30 AM local time.  Because the MODIS-Terra mission began in 2000, this data record is longer than for MODIS-Aqua.
 MODIS-Aqua Deep Blue AOD  MODIS-Terra Deep Blue AOD  The MODIS-Aqua Deep Blue AOD data record covers the period 2002-present.  The MODIS-Terra Deep Blue AOD data record covers the period 2000-2007. MODIS-Terra Deep Blue AOD data is not available after 2007 due to the loss of polarization correction.
 Mass Concentration (Land)  Mass Concentration (Ocean)  The aerosol mass concentration algorithm has two parallel data products, calculated over land and ocean.   Mass concentration for land can be used with either AOD data product.  The aerosol mass concentration algorithm has two parallel data products, calculated over land and ocean.   Mass concentration for ocean can be used with either AOD data product.
 CH4 Volume Mixing Ratio (ascending)  CH4 Volume Mixing Ratio (descending)  The ascending data product is collected on the night (unlit) side of the Earth, and may be less influenced by daytime solar radiation and heating effects.  The descending data product is collected on the day (illuminated) side of the Earth, and may thus be affected by solar radiation and diurnal atmospheric heating.  CH4 may be released from the Earth's surface as it is warmed by sunlight.  CH4 can also be broken down by sunlight to produce CO2.
 CO Volume Mixing Ratio (ascending)  CO Volume Mixing Ratio (descending)  The ascending data product is collected on the night (unlit) side of the Earth, and may be less influenced by daytime solar radiation and heating effects.  The descending data product is collected on the day (illuminated) side of the Earth, and may thus be affected by solar radiation and diurnal atmospheric heating. CO can be produced by photochemical reaction of sunlight with organic matter in surface waters.
 Total Column Ozone (ascending)  Total Column Ozone (descending)  The ascending data product is collected on the night (unlit) side of the Earth, and may be less influenced by daytime solar radiation and heating effects.  The descending data product is collected on the day (illuminated) side of the Earth, and may thus be affected by solar radiation and diurnal atmospheric heating.
Outgoing longwave radiation flux (ascending)  Outgoing longwave radiation flux (descending)  The ascending data product is collected on the night (unlit) side of the Earth, and may be less influenced by daytime solar radiation and heating effects.  The descending data product is collected on the day (illuminated) side of the Earth, and may thus be affected by solar radiation and diurnal atmospheric heating.
 Relative humidity (ascending)  Relative humidity (descending)  The ascending data product is collected on the night (unlit) side of the Earth, and may be less influenced by daytime solar radiation and heating effects.  The descending data product is collected on the day (illuminated) side of the Earth, and may thus be affected by solar radiation and diurnal atmospheric heating.  Relative humidity varies with temperature.
 Surface air temperature (ascending)  Surface air temperature (descending)  The ascending data product is collected on the night (unlit) side of the Earth, and may be less influenced by daytime solar radiation and heating effects.  The descending data product is collected on the day (illuminated) side of the Earth, and may thus be affected by solar radiation and diurnal atmospheric heating.  Surface air temperature usually increases due to solar heating of the ground surface.
 Temperature profile (ascending)  Temperature profile (descending)  The ascending data product is collected on the night (unlit) side of the Earth, and may be less influenced by daytime solar radiation and heating effects.  The descending data product is collected on the day (illuminated) side of the Earth, and may thus be affected by solar radiation and diurnal atmospheric heating.  Surface air temperatures are usually warmer during the day due to solar heating of the ground surface.
 NO2 Column  NO2 Tropospheric Column  The NO2 column amount data product indicates the total amount of NO2 from the surface to the top of the atmosphere.  The NO2 tropospheric column data product indicates near-surface NO2 concentrations.  This is the estimated tropospheric contribution to total NO2 column amount.

 

 

DICCE Giovanni Plotting Tips

Link to the DICCE Giovanni Plotting Tips page, providing information on color palettes for animations, Y-axis customization, and more.

 

Giovanni-4 "How-To" Demonstration Presentations

The following are demonstration presentations showing how to do many different functions with the Giovanni-4 interface.  They are useful for both users new to Giovanni, and those who may be familiar with Giovanni-3, but not Giovanni-4.

 

 

 

 

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