1. Jennifer M. Adams and Brian Doty IGES/COLA
TIGGE data via OPeNDAP
Strike a balance between information of interest to data providers and data users.
Jennifer M. Adams and Brian Doty IGES/COLA

2. What is GrADS?
GrADS is an interactive tool that integrates data access, analysis, and visualization
Handles many data formats: Binary, NetCDF, HDF, GRIB, BUFR
Two data models for gridded and in situ data
Expression handling is flexible, compact, recursive
Programmable interface for scripting
Written in C; code is open source (GPL)
Active users forum with subscribers
3200 downloads of our latest version (2.0.a7) in the month since its release on August 10

3. GOES VIS Image with Radar, 500mb Heights, and SLP
Some examples of GrADS graphical output:
GOES vis image in background, with radar overlaid, sea level pressure in colored contours, and 500mb heights contoured in black. Nice way to see how the meteorological fields relate to the cloud cover and precipitation patterns.
18z 4 June 2009

4. Analysis of Surface METAR Observations with Radar
Plotted surface observations showing a frontal passage over the Mid-Atlantic Radar mosaic GrADS’ surface temperature analysis in shaded colors GrADS’ sea level pressure analysis in black contours
18z 29 May 2009

5. Another Example of GrADS Graphics Output
QuikSCAT Winds (HDF), Surface Obs (BUFR), and ETA Model SLP (GRIB)
00z 6 Dec 2003

6. What is the GrADS Data Server?
GDS is a stable, secure, OPeNDAP data server that provides subsetting and server-side analysis services over the internet
GDS can serve any GrADS-readable dataset, and unifies all data formats into a NetCDF framework
Open a data set with instead of /disk/filename
GrADS and the GDS are a coupled software system.
GDS has been running stably on heavily used public data servers at NASA, NCEP, and NCDC for about six years. NOAA recently upgraded GDS to “operational” status at NCEP.

7. News from GrADS/GDS Team
GrADS has a 5th grid dimension for ensembles set X, Y, Z, T, or E set lon, lat, lev, time, or ens
GrADS has an interface for HDF5 and GRIB2
GDS can serve any GrADS data set
GrADS is a client for any OPeNDAP data set
GrADS supports GIS-compatible formats GeoTIFF, KML, and ESRI Shapefiles
GrADS 2.0 and GDS 2.0 were both released in 2008.

8. GrADS Plot in Google Earth

9. Palmer Drought Severity Index July 2009

10. Ensemble Forecast Time Series (Longitude, Latitude, and Level are fixed)
Consider a forecast time series from 21 ensemble members, each drawn with a different color. The members all agree for the first 2-3 days, then begin to diverge in the 3-7 day period, and after that there is little coherence to the forecast.
Forecast Time --->

11. Ensemble Forecast Grid (Longitude, Latitude, and Level are fixed)
Ensemble Member
Here is the same data drawn as a 2-D Time v. Ensemble plot Each row in the grid represents one of the colored lines drawn in the previous plot. Pixels are colored according to the data values. I can draw it this way because the ensembles are handled as the 5th dimension in my gridded forecast data set. The well-defined purple stripe is the event all members agree on at the beginning of the forecast, and the image gets noisier as the forecast evolves.
Forecast Time --->

12. Ten Ensemble Forecasts (Longitude, Latitude, and Level are fixed)
Ensemble Member
Now we expand our ensemble set to include the same data from ten previous forecasts initialized at 12 hour intervals. Note the time axis has expanded to accommodate the shift in the temporal coverage of additional ensemble forecasts. Ensemble members are characterized by their start time and length, the 5D grid’s time axis is an envelope that spans all members. You can see how the purple stripe feature has gradually coalesced over the five-day period from something incoherent and noisy into a well-defined event.
Forecast Time --->

13. Ensemble Forecast Time Series (Longitude, Latitude, and Level are fixed)
Back to a plot I showed earlier.
It takes a long time to draw this because you would need to download 21 time series.
This spaghetti-style drawing can be improved by exploiting GrADS analysis capabilities in the E dimension …
Forecast Time --->

14. Ensemble Mean = tloop(ave(Z,e=1,e=21)) Ensemble Min/Max = tloop(min(Z,ens=c00,ens=p20)) +/- StdDev of Ensemble Mean = tloop(sqrt(ave(pow(Z-Zave,2),e=1,e=21)))
You can get an even better display by doing 4 relatively simple calculations: the ensemble mean, the standard deviation of the ensemble mean, and the min/max of all the members.
For GDS data sets, these derived quantities are calculated on the server side. 17 Mb operated on, 1 Kb downloaded: data requirements reduced by 5 orders of magnitude.
Forecast Time --->

15. Ensemble Data Sets Behind GDS
File aggregation
Format translation
Subset over all dimensions
Server-side analysis
Data become more usable and accessible
Putting 5-D data sets behind GDS makes the data even more usable and accessible for a variety of reasons (listed above).

16. TIGGE Data Behind GDS at NCAR
Perfect testbed for ensemble handling and GRIB2 interface
Boost to usage of TIGGE data
Forecasts organized by date and by provider
Time series of analyses
Nearly unbearable load on old hardware
48-hour data embargo
Int’l agreement requires password protection
At dataportal.ucar.edu:
~250 Gb/day 5 Tb online 2-3 week window

17. TIGGE Multi-Member Multi-Model Ensemble 500mb Geopotential Height valid August 30, 2008
7-day Lead
5-day Lead
3-day Lead
1-day Lead
Here’s an example of what can be done with TIGGE data behind GDS --
Each color is an ensemble mean forecast of 500mb Height from 8 different TIGGE providers. The white contours show the multi-model ensemble average (198 members). The four panels show the forecasts with the same valid time but with lead times of 1, 3, 5, and 7 days.
In this example, we operated on 6.0 Gb of grib2 data, downloaded 3.3 Mb.

18. All-India Precipitation (mm/day)
2008 Indian Monsoon
Comparison of surface observations, TRMM satellite estimates, and TIGGE forecasts
Station Observations
TRMM Estimate
ECMWF 1.5-day Forecast
NCEP 1.5-day Forecast
An analysis of precipitation data for the 2008 Indian Monsoon season, 1 June through 30 September. We compared station observations of daily precip, TRMM satellite estimates, and TIGGE forecast model output from ECMWF and NCEP. Top four panels show example snapshots of daily precip (mm/day) from a single day (June 23, 2008). Bottom panel shows time series of daily area averaged precip (over India) for the entire monsoon season.
All-India Precipitation (mm/day)

19. TIGGE Forecasts of Hurricane Ike in the Gulf of Mexico, September 2008
00z 8 Sep
12z 8 Sep
00z 9 Sep
Here’s a schematic to facilitate interpretation of the following multi-panel slide.
There are four forecast cycles, initialized at 12-hour intervals, one in each row.
We are interested in the period (highlighted by the white box) during which the Hurricane crosses the Gulf of Mexico, a 3.5-day span leading up to the Hurricane’s landfall.
The next slide will show the tracks of the SLP minima during the period of interest, for each ensemble member of all four forecast cycles from four different models.
For each forecast cycle, the lead time before the period of interest shrinks.
12z 9 Sep

20. TIGGE Forecasts of Hurricane Ike valid: 12z 9 Sep - 00z 13 Sep 2008
Init: 00z 8 Sep
Init: 12z 8 Sep
Init: 00z 9 Sep
These are predicted tracks for hurricane Ike from four different TIGGE models (one color per model). The tracks are created by connecting the dots that mark the location of the sea level pressure minimum at each time step within the valid date range (a 3.5-day period). Multiple tracks in each panel are from individual ensemble members. All 16 panels have the same valid time, a 3.5 day period that ends just before the hurricane’s landfall Each column represents a different TIGGE Model Each row represents a different initialization date, with later dates towards the bottom. Tracks in the top row begin at a 1.5 day lead time; second row start at a 1.0 day lead; third row starts at a 12hr lead; tracks in the bottom row begin at analysis time.
Models: RED = China CMA ORANGE = Canada CMC GREEN = ECMWF BLUE = NCEP
Init: 12z 9 Sep

21. TIGGE GDS http://cdp.ucar.edu:9090
Jennifer Miletta Adams