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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. Analyzing Data is a primary function of LCAT

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Local Climate Analysis Tool: Station Data

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  1. Local Climate Analysis Tool:Station Data Presented by Nicole McGavock National Weather Service Weather Forecast Office Tulsa, OK May 2013

  2. Local Climate Analysis Tool • Analyzing Datais a primary function of LCAT • Important to understand the underlying data used in LCAT climate studies

  3. 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

  4. 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

  5. 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

  6. LCAT: Available Station Datasets • NCDC Homogenized Dataset • ACIS Dataset • Custom Datasets

  7. 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

  8. Why Use Station Data? • Interest in one specific location • Other variables beyond average temperature and total precipitation are available

  9. 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

  10. 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’

  11. 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

  12. 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)

  13. 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”)

  14. Example of artificial bias/trend Analysis courtesy: Dr. Robert Livezey

  15. 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

  16. NCDC Homogenized Dataset • Available homogenized data: • Mean temperature • Maximum temperature • Minimum temperature • Total precipitation

  17. 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

  18. 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

  19. ACIS Dataset • Available ACIS Data - monthly extremes of: • Maximum Temperature • Minimum Temperature • Average Temperature • Precipitation • Snowfall • Snow Depth • Heating/Cooling/Growing Degree Days

  20. 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.

  21. Local Climate Analysis Tool:Station Data For more information on Homogenization: http://journals.ametsoc.org/doi/pdf/10.1175/2008BAMS2613.1

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