on the weather and climate occurring around the world in
Because weather fascinates many
of us and is experienced by all of us, the report provides good examples of how
data can be analyzed, reported, and visualized.
The first observation I'd make is that the report focuses on
unusual or anomalous events. It tries to
put them in historical context. For
example, I learned that the U.S.
had the wettest October since records were collected 115 years ago. And Toronto
had a snow-free November for the first time in recorded history. In all data analysis - weather data,
financial data, or performance data – it is important to pull out the
significant events from the rest of the data, or the “noise” as we say. NOAA does this by comparing the past year’s
data with their historical data to find out where the year stood in comparison
to all the other years. Similar analysis
can be done by other agencies whether the metric is road-miles constructed or the percent of students receiving student aid that graduated. What is key is comparing the results in light of the historical data and trying to gain insights on what the trend is and what it means.
Because 2009 was the end of the decade, they have also
compiled some data at the decade level rather than at the year level. While the 2009 average global temperature was
the fifth warmest year on record, the 2000-2009 decade was the warmest on
record for the globe. And the decade
before, 1990 – 1999, was the warmest on record at that time. The use of multi-year averages is a good
example of smoothing that can be done to help ferret out significant
information and remove the year-to-year fluctuation in large collections of
time series data. The graph showing the
decade data makes the trend very obvious (Source NOAA report, chapter 2).
Many of the charts in the report show the yearly results as
a delta from the long term average, e.g. last year’s average surface
temperature was .5ºC above the 1961-1990 average using NASA/GISS data. By graphing
the time series data against the long term average, the anomalies standout. Other
charts show the actual values and it is possible to discern trends in the data.
For example, the lower tropospheric temperatures are increasing by
approximately .15ºC per decade. One can
use this information to predict the climate for future decades, which could
have value for policy purposes.
The report also highlights the very strong monthly and seasonal variability in the U.S. surface temperatures in 2009 that would be obscured if one just looked at yearly averages. Another analytical technique - Modeling - can be used to help analyze why the "why" behind the data. Why did 2009 show such strong variability? The report indicates that in 2009 the global climate switched from the La Nina conditions that dominated 2008 to El Nino sea surface temperature (SST) conditions in the tropical Pacific ocean. Was this the cause? NOAA global climate models were subjected to the Pacific SST observed data and the results are show below. While not all of the variability appears to be explained by the model, the warm first quarter over the Great Plains and cold summer seems mostly consistent with the impact of La Nina during the winter and El Nino during the summer.
The State of the Climate report shows good examples of many
data analysis techniques including historical analysis, near-real time
reporting, reanalysis of past data using newer, improved techniques, averaging
of multiple datasets to improve reliability, and drill down capabilities from
decades, to years, to seasons, to months,
and from global to regional to country and state. They also use
interesting visualization techniques.
Those interested in data analysis, as well as weather,
should download this report from the NOAA Website. (Arndt, D.S.,M.O. Baringer, and M.R. Johnson, Eds., 2010: State of the Climate in 2009, Bull. Amer. Meteor. Soc.). Note: While NOAA does use IBM Technology in its Research, the report does not state which technology is used in the reported climate studies and I don't intend to imply any relationship between this report and IBM.)
Those interested in further information on Analytics
including our fall schedule of events, please visit the Analytics Solution
Center website at www.ibm.com/ASCdc.
If your agency uses analytics in interesting and novel ways,
I'd like to hear from you. Please write to me at ASCdc@us.ibm.com.
Frank Stein, Director