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Local Climate Analysis Tool: Station Data. Presented by Nicole McGavock National Weather Service Weather Forecast Office Tulsa, OK May 2013. L ocal C limate A nalysis T ool. Analyzing Data is a primary function of LCAT

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local climate analysis tool station data

Local Climate Analysis Tool:Station Data

Presented by

Nicole McGavock

National Weather Service

Weather Forecast Office Tulsa, OK

May 2013

l ocal c limate a nalysis t ool
Local Climate Analysis Tool
  • Analyzing Datais a primary function of LCAT
  • Important to understand the underlying data used in LCAT climate studies

Need to be sure you are comparing apples to apples

  • If not, you will introduce artificial biases or trends
    • Results show a climate signal when one doesn’t exist
    • Results show a different climate signal than actually exists
    • Decision makers and partners will plan,prepare,make decisions, and spend money on incorrect assumptions based on these artificial results
require ments for lcat datasets
Requirements for LCAT Datasets
  • Continuous data record
    • Period of record must be at least 30 years long
      • WMO recommendation
    • Less than 9 days missing per month
      • NCDC rule of thumb
  • Reliable source
    • Data is documented (metadata), published, and accepted in the scientific community
temporal resolution
Temporal Resolution
  • All datasets are available at monthly and seasonal time scales
    • Initially data available through 2012 (DJF2013)
    • Future: data updated monthly
  • Season = average of 3 consecutive months
  • Seasonal average is a weighted average
  • Data available from 1925-Present
  • Range field requires a minimum 30-year period
    • Ensures scientifically sound climate studies
lcat available station datasets
LCAT: Available Station Datasets
  • NCDC Homogenized Dataset
  • ACIS Dataset
  • Custom Datasets
station data
Station Data
  • Point data
    • LCAT results only applicable to that one location
  • Over 5,000 NWS COOP stations
    • Not all COOP sites are available due to missing and/or erroneous data
    • Station availability can be found through LCAT “help” section about data
why use station data
Why Use Station Data?
  • Interest in one specific location
  • Other variables beyond average temperature and total precipitation are available
ncdc homogenized dataset
NCDC Homogenized Dataset
  • What is “Homogenized”?
    • The process of removing systematic changes in the bias of a climate series
  • NCDC performs several quality control, homogeneity, and adjustment procedures to ensure the dataset is 100% complete and homogeneous for the station’s period of record.
  • This technique uses the most recent temperature values to adjust the entire dataset
    • Homogenized data will be DIFFERENT from those found in xmACIS and other sources
homogenized vs raw data
Homogenized vs. Raw Data
  • Inconsistencies in raw data
    • Time of observation
    • Station moves or changes in observational environment
    • Equipment changes
      • transition from liquid-in-glass thermometers to the maximum–minimum temperature system (MMTS) or ASOS
  • Inconsistencies can lead to artificial biases and trends
  • = LCAT results that are not ‘real’
ncdc homogenized dataset1
NCDC Homogenized Dataset

Data homogenization includes:

  • Monthly and daily value internal consistency check
    • Identifies when daily Tmax < Tmin
    • Identifies when daily Tmax < Tmin on preceding and following days as well for Tmin > Tmax during the relevant 3-day window
    • Identifies duplication of data and days with Tmax and Tmin are both zero
    • Identifies temperatures that are at least 10°C warmer or colder that other values for a given month
    • Identifies daily temperatures that exceed the respective 15-day climatological means by at least 6 standard deviations
ncdc homogenized dataset2
NCDC Homogenized Dataset
  • Bias adjustment to a midnight to midnight observation schedule
    • Allows for comparison between COOP stations that report at differing times (7am, 5pm) and ASOS (midnight)
  • Spatial quality control check
    • Identifies daily temperatures whose anomalies differ by more than 10°C from the anomalies at neighboring stations on the preceding, current, and following days
    • Identifies monthly temperatures whose anomalies differ by more than 4°C from concurrent anomalies at the five nearest neighboring stations whose temperature anomalies are well correlated with the target (correlation >0.7 for the corresponding calendar month)
ncdc homogenized dataset3
NCDC Homogenized Dataset
  • Adjustments for sensor changes or station moves
    • Pairwise approach
      • comparisons are made between numerous combinations of temperature series in a region to identify and remove relative inhomogeneities(i.e., abrupt changes in one station series relative to many others)
  • Estimates missing or discarded data
    • Missing values are filled-in with estimated values generated using an optimal interpolation technique known as FILNET (“fill in the network”)
example of artificial bias trend
Example of artificial bias/trend

Analysis courtesy:

Dr. Robert Livezey

ncdc homogenized dataset4
NCDC Homogenized Dataset

Exception: Precipitation data are not homogenized

  • Due to nature of rainfall
    • Can have large gradients temporally and spatially
  • Raw monthly total precipitation
  • No time of observation adjustment
  • Missing values are filled-in with estimate generated using an optimal interpolation technique known as FILNET (“fill in the network”)
    • Uses precipitation values at neighboring COOP stations
ncdc homogenized dataset5
NCDC Homogenized Dataset
  • Available homogenized data:
    • Mean temperature
    • Maximum temperature
    • Minimum temperature
    • Total precipitation
acis dataset
ACIS Dataset

A collection of federal, regional, state, and local weather and climate networks

  • Includes NWS and NCDC quality controlled data
  • Missing data may occur
  • Includes archive-quality historical data
  • Includes near real-time data
  • Same data that is viewed through xmACIS
  • NOT homogenized
acis dataset1
ACIS Dataset
  • Daily and monthly averages and totals are considered raw data and not recommended for LCAT use
    • Reminder: raw data can introduce artificial trends/biases
  • Extremes data CANbe used for LCAT
    • Especially stations with threaded data
    • Extremes can be a valuable source of information and can be used for climate studies
    • ‘Monthly Extremes’ - the hottest (coldest) one day from the month or season
acis dataset2
ACIS Dataset
  • Available ACIS Data - monthly extremes of:
    • Maximum Temperature
    • Minimum Temperature
    • Average Temperature
    • Precipitation
    • Snowfall
    • Snow Depth
    • Heating/Cooling/Growing Degree Days
custom datasets
Custom Datasets
  • Future LCAT capability
  • Custom datasets should meet the requirements for datasets
    • Continuous data record
    • Reliable source
  • Can extend beyond weather variables
  • Examples: # of thunderstorm days, streamflow, tornadoes, number of days above 100°F, wind speed, sea level height, # of mosquitos carrying West Nile Virus, pollen counts, etc.
local climate analysis tool station data1

Local Climate Analysis Tool:Station Data

For more information on Homogenization: