1. An Introduction to the IRI Data Library Michael Bell

2. Objectives –Become familiar with the organization of the Data Library –Learn how to find datasets and select spatial and temporal domains –See how to perform simple arithmetic analyses –See how to create customized maps and graphs –Learn how to download data and images –Understand how the Data Library is related to the IRI Map Room

3. Structure of Course 1.Introduction to the organization and primary tools of the Data Library 2.Group examples 3.Individual examples

4. The IRI Data Library is a… Data repository –>300 datasets covering all aspects of climate-related characteristics Data analysis tool –Arithmetic operations  EOF analysis Data visualization tool –Time series, maps, cross-sections Data download resource –Free access to text, binary, GIS- compatible, etc. data files http://iridl.ldeo.columbia.edu

5. Data Library Home Page

7. Finding Datasets

8. Structure of Datasets -Primary Organization -Dataset -Variables -Dataset -Variables -Dataset -Variables -Primary Organization. -NASA -ERBE (Earth Radiation Budget Experiment) -Datasets by instrument -Variables -GES-DAAC (Distributed Active Archive Center) - -GISS -LeGrande_Schmidt2006 (authors) -GPCP (Global Precip Climatology Project) -Datasets by version -Datasets by instrument -Variables. Example

12. Data Browse/Search Map Room Search

14.  Air-Sea Interface  Atmosphere  Climate Indices  Cloud Characteristics and Radiation Budget  Fisheries  Forecasts  Historical Model Simulations  Hydrology  Ice  Oceanography  Topographic and Land Characteristics

17. Dataset Page Contents and Structure Gridded Datasets

18. Function Bar -Access to data manipulation, visualization, download tools

19. Source Bar -Navigational information -Where am I? What operations have I performed? -Documentation access Documentation Access Points

20. Dataset and Variables -Access to variables or lower-level datasets

21. Grids (Independent Variables) -Information about grids on which data is dependent -Latitude (Y) -Longitude (X) -Time (T) -Others (height/depth, ensemble member, etc.)

23. Note new information -Selected variable shown in Source Bar -Datasets and variables heading gone -More information about variable below

24. Important information about variable -Missing value indicator -Units

25. View Links -Access data viewer Function Bar -Access to data manipulation, visualization, download tools

27. Data Selection -Data domain selection

29. Filters -Common data manipulation tools

30. - Monthly Climatology/Anomaly - Average over any ind. variable - Root mean square - Find max/min values over any ind. variable

31. Data Files -Access to data downloads

33. Tables -Access to tabular data for Excel, etc.

35. Expert mode -Manually enter Ingrid code

37. Dataset Page Contents and Structure Station Datasets

38. Three key differences 1. Maps displaying stations

39. Three key differences 1. Map displays station locations 2. Station ids in grid info 3. “Extra” variables provide station information A word of caution… The time grid information represents the full extent of the dataset. This does NOT mean that all of the stations in the dataset have data for the full time period.

40. Selecting Data Domain Gridded Datasets

41. Data Selection -Data domain selection

42. Data Selection Step 1. Change text in Setting Ranges boxes using same syntax as text already there. Step 2. Click Restrict Ranges button. Step 3. When satisfied information in top box represents desired domain, click the Stop Selecting button.

43. Example Time (T) 1 Oct 1996 to 31 Jan 1997 Latitude (Y) 60S to 60N Note: Center of nearest grid point automatically selected.

44. Note: New data domain now represented in Source Bar and grid information.

45. Selecting Data Domain Station Datasets

46. Key difference 1. Selecting station(s)

47. Option 1: Select all stations in an area Step 1. Click and drag a box over area of interest (or manually enter lat/lon limits and click redraw button). Step 2. When satisfied with area selection, click the List of stations in current view link. Step 3. Click the Dataset (and map) all data found in search link.

48. Example Select stations in southern tip of Africa

49. Example Select stations in southern tip of Africa

50. Note: New data domain now represented in Source Bar, map, and grid information.

51. Option 2: Search for a particular station Step 1. Click on the Searches link. Step 2. Enter location of interest and click on the Search [Dataset Name] button. Step 3. To select all matched stations, click the Dataset (and map) with all data found in search link. To select one or more of the matched stations, select the appropriate check boxes and click the Get Marked Stations button.

52. Example Select station(s) in Windhoek, Namibia

53. Note: New data domain now represented in Source Bar, map, and grid information.

54. Visualizing Data: Making maps and graphs

56. Redraw  Zoom To Full  Help 

57. Change Time -Change text and redraw -Select button with adjacent date Make Animation -Enter range of dates (date1 “to” date2) and redraw

58. Change Region -Click and drag box over area -Change lat/lon limits and redraw

59. Change Color Scale Limits -Enter new limits and redraw

60. Change Map Options -Use pull down menus to: -Change axis selection -Draw admin boundaries -Draw contours, etc. -Use Edit plot button to add more customizations

61. Change Map Options -Select a new color scale -Add admin boundaries Return to Viewer -Click more options

62. Download map -Select desired format for map and color scale

63. Return to dataset page -To maintain domain selections from viewer, click data in view button. -To return without saving selections, click Entire Dataset link or dataset link at top of page.

64. Downloading Data Files

65. Download Data File Step 1. Select Data Files link. Step 2. Select link for desired format.

67. Download Tables Step 1. Select Tables link. Step 2. Select link for desired format.

69. Note: If planning to import into Excel, select tsv format from columnar tables with options page.

70. Analysis Options… http://iridl.ldeo.columbia.edu/dochelp/StatTutorial/

71. IRI Map Room

72. Climate Information in Context http://iridl.ldeo.columbia.edu/maproom/.IFRC/.Forecasts/

73. Climate Information in Context http://iri.columbia.edu/~mbell/MDG/

74. Flying Solo Tip: Bookmark analyses you might like to use again What to do when you have a question: 1.Look for similar example in the Tutorials and Documentation resources 2.Send email to help@iri.columbia.eduhelp@iri.columbia.edu ** Copy the url of the page in question into the email

75. Location of slides: http://iri.columbia.edu/~mbell/ciph09/

76. Group Examples Domain selections (spatial and temporal) Calculate… –Climatologies –Anomalies –Spatial averages –Seasonal averages Customize maps/graphs Create data masks Prep data files for analysis in CPT

77. Group Example 1 Use Datasets by Category catalog to find a data set with the following characteristics: 1. Includes observed sea surface temperatures 2. Monthly temporal resolution 3. Spatial resolution at least 1ºx1º 4. Includes 60ºS-60ºN in spatial domain 5. Includes 1985-2005 in temporal domain

78. Group Example 1: Result

79. Group Example 2: Prepare spatially averaged monthly SSTs in the Tropical Atlantic region for 1986-2005 for use in Excel From the Reyn_SmithOIv2 monthly data… –Select the Sea Surface Temperature variable –Select Jan 1986 – Dec 2005 time period –Select region in Tropical Atlantic (10ºS-10ºN, 330ºE-350ºE) –Calculate spatial average (XY link on Filters page) –View Ingrid in Expert Mode –View data in data viewer –Download for use in Excel START HERE

80. Group Example 2: Result To download data: Click on Tables, select tsv or csv file type, and click Get Table button. VIEW RESULT

81. Group Example 3: Make a map of seasonal global SSTAs for Jan 1982 – Dec 2005 From the Reyn_SmithOIv2 monthly data… –Select the Sea Surface Temperature variable (Ignore the existing SSTA variable – we’re going calculate it) –Select the Jan 1982-Dec 2005 time period –Select anomalies link from Filters page –View Ingrid in Expert Mode –In Expert Mode enter the following text, then click OK. T 3 runningAverage –View data in data viewer –Select a color scale appropriate for SSTA START HERE

82. Group Example 3: Result VIEW RESULT

83. Group Example 4: Make a time series of monthly station-observed precipitation in Dakar, Senegal From the NOAA NCDC GHCN v2beta dataset… –Search for a station in Dakar –Select precipitation variable –View data in data viewer –Adjust time period in data viewer to focus on available data

84. Group Example 4: Result VIEW RESULTS

85. Group Example 5: Download ECHAM4.5 ensemble-averaged precipitation from forecasts made Jan 1968 – 2003 and valid for FMA 1968- 2003 in CPT format Dataset location in Library: IRI FD ECHAM4.5 Forecast psst ensemble12 MONTHLY surface Hint: The L grid describes the lead time. A lead time of 0.5 indicates that the forecast is valid for the same month in which it started. Example: S= Aug 1970, L=2.5  Forecast data valid for Oct 1970 Select 1 Jan 1968-2003 start times and lead times for FMA Calculate ensemble and lead time averages from Filters page View Ingrid in Expert Mode Download data in CPT format

86. Group Example 5: Result To download data: Click on Data Files and select CPT. VIEW RESULTS

87. Locate the UEA CRU TS2.1 dataset –Select the monthly mean temperature variable –Select a climatology base period (1971-2000) –Select Monthly Climatology link from Filters page –View Ingrid in Expert Mode –View data in data viewer –Select region around your country –Select a color scale for temperature and add state and river overlays –Animate map by entering “Jan to Dec” in time text box Group Example 6: Make an animated map of monthly climatological temperature in your country, including provincial boundaries and major rivers

88. Group Example 6: Result First frame of animation:

89. Locate the UEA CRU TS2.1 dataset –Select the 1971-2000 climatology dataset –Select the precipitation variable –In Expert Mode enter the following text, then click OK. [T] sum –In Expert Mode enter the following text, then click OK. 125 maskle –View data in data viewer –Add desired administrative boundaries, color scale, etc. Group Example 7: Make a global map of climatological annual rainfall for areas that receive more than 5 inches/year.

90. Group Example 7: Result VIEW RESULTS

91. Results to Example 5 URL for Example 5 Results: http://iridl.ldeo.columbia.edu/expert/SOURCES/.UEA/.CRU/.TS2p0/.monthly/.mean/.temp/T/%28Jan%201971%29%28 Dec%202000%29RANGEEDGES/yearly- climatology/figviewer.html?my.help=more+options&map.T.plotvalue=Jan+to+Dec&map.Y.units=degree_north&ma p.Y.plotlast=58.5N&map.here.x=0&map.here.y=0&map.url=temp_colors+X+Y+fig%3A+colors+verythin+solid+stat es_gaz+medium+countries_gaz+blue+thin+rivers_gaz+%3Afig&map.domain=+%7B+%2FT+0.5+plotvalue+X+68. 333321+141.3333+plotrange+Y+14.5+58.5+plotrange+%7D&map.domainparam=+%2Fplotaxislength+432+psdef +%2Fplotborder+72+psdef+%2FXOVY+null+psdef&map.zoom=Zoom&map.Y.plotfirst=14.5N&map.X.plotfirst=68. 33332E&map.X.units=degree_east&map.X.modulus=360&map.X.plotlast=141.3333E&map.temp.plotfirst=- 40.&map.temp.units=Celsius_scale&map.temp.plotlast=40.&map.plotaxislength=432&map.plotborder=72&map.fnt =Helvetica&map.fntsze=12&map.XOVY=auto&map.color_smoothing=1&map.iftime=25&map.mftime=25&map.ffti me=200

92. Individual Example 1: Make a map of global climatological SSTs during July based on the 1971-2000 base period. Getting Started… Dataset location in Library: NOAA NCDC ERSST Hint: Calculate 1971-2000 climatology before selecting July dates

93. Individual Example 2: Create a time series of monthly precipitation which has been spatially averaged over the Sahel for 1951-2000 Getting Started… Dataset location in Library: DEKLIM VASClimO PrcpClim Res-1x1 Approx Sahel region: 7 º -22 º N, 17 º W-28 º E

94. Individual Example 3: Make animated map of April soil moisture anomalies in Afghanistan during 1990-2006 Getting Started… Dataset location in Library: NOAA NCEP CPC GMSM Hint: Calculate anomalies before selecting April dates

95. Individual Example 4: Download ECHAM4.5 ensemble-averaged surface temperature from forecasts made 1 May 1968 – 2003 and valid for JAS 1968-2003 in CPT format Getting Started… Dataset location in Library: IRI FD ECHAM4.5 Forecast psst ensemble12 MONTHLY surface

96. Individual Example 5: Create a time-longitude plot of weekly SSTA in the Tropical Pacific for Jan 1982-Dec 2005. Use data from 5ºS-5ºN. Getting Started… Hint: There are two datasets that include weekly sea- air interface data. Use the one that has an ssta variable so you do not have to calculate it yourself.

97. Individual Example 1: Result VIEW RESULT

98. Individual Example 2: Result VIEW RESULT

99. Individual Example 3: Result VIEW RESULT First frame of animation: To animate map: Enter Apr 1990 to Apr 2006 above map and click Redraw button.

100. Individual Example 4: Result VIEW RESULT To download data: Click on Data Files and select CPT.

101. Individual Example 5: Result VIEW RESULTS