Applied climatology vs. applied meteorology

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Applied climatology vs. applied
meteorology

• From the AMS glossary
• applied meteorologyA field of study where
  weather data, analyses, and forecasts are put to
  practical use. Examples of applications include
  environmental health, weather modification, air
  pollution meteorology, agricultural and forest
  meteorology, transportation, value-added product
  development and display, and all aspects of
  industrial meteorology.
• applied climatologyThe scientific analysis of
  climatic data in the light of a useful
  application for an operational purpose.
  Operational is interpreted as any specialized
  endeavor within such as industrial,
  manufacturing, agricultural, or technological
  pursuits This is the general term for all such
  work and includes agricultural climatology,
  aviation climatology, bioclimatology, industrial
  climatology, and others.

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Changnon (1995) diagram of applied climatology
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Climate Data and Variables
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Primary data collection

• Primary data collected via relatively cheap data
  loggers or transmitted wirelessly

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Secondary data collection

• Most common method

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Data Sources

• WMO
• http//www.wmo.int/pages/index_en.html
• NOAA
• http//www.noaa.gov/
• National Weather Service
• http//www.weather.gov/
• National Climatic Data Center
• http//www.ncdc.noaa.gov/oa/ncdc.html
• Earth Systems Research Lab
• http//esrl.noaa.gov/psd/products/analysis/
• State Climatology offices
• http//nsstc.uah.edu/aosc/
• Regional Climate centers
• http//www.sercc.com/

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NCDC

• U.S. Stations
• http//lwf.ncdc.noaa.gov/oa/climate/stationlocator
  .html
• Climatological Data
• http//www7.ncdc.noaa.gov/IPS/cd/cd.html
• Monthly summary by state for all stations
• Local Climatological Data
• http//www7.ncdc.noaa.gov/IPS/lcd/lcd.html
• Monthly summary for individual stations

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NCDC Climate Divisions
Divisional means and anomalies since 1895
for Temp,Precip,PDSI
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Questions about observations and data

• Is the instrument calibrated properly? (accuracy)
• Is the instrument recording representative data?
  (validity)
• Spatial anomalies?
• What is the potential for bias?
• Is the instrument properly sited?
• Is the instrument recording too coarse data?
  (precision)
• How are observations interpolated?
• Is the data appropriate for your research
  purposes?

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Ideal siting

• Open location with low vegetation
• Horizontal distance of 2 x vertical height of
  nearest object
• No nearby artificial heat sources
• Not in unusual microclimate
• Anemometer at 10 m elevation
• Other instruments at 1.5-2 m elevation

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Siting variability

• Orland, CA
• Marysville, CA
• (surfacestations.org)

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Issues over time

• Stations move
• Surroundings change
• Instrumentation change
• Observation changes
• Time
• Frequency

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Time of observation bias

• 24-hour observations taken at
• Midnight (all first-order stations)
• Early morning (6am-8am) especially farm
  stations
• Evening (6pm-10pm)

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Types of stations

• First-order station measures primary weather
  variables more or less continuously, reporting
  hourly (at least)
• Second-order station same as first-order, though
  usually less than 24 hour coverage
• Cooperative station usually takes observations
  one time per day

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Automated Surface Observation System (ASOS)

• Debuted in US in 1990s
• Controls all first-order stations presently

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ASOS first-order stations

• Report hourly values
• Report sub-hourly only if conditions
  significantly change
• Report maximum/minimum temperature every six
  hours and every day
• Are geared towards aviation purposes

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Things ASOS measures

• YES
• Clouds on vertical to 12,000
• Surface visibility and obstructions
• Present weather
• Temperature / dew point
• Pressure / altimeter
• Wind
• Precipitation accumulation
• Significant weather changes

• NO
• Clouds off-vertical or above 12,000
• Variable visibility
• Mixed precipitation
• Lightning
• Tornado
• Snowfall
• Snow on ground

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Coop Stations
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Climate Variables

• Temperature
• Actual vs Apparent
• Precipitation
• Measurement
• Gauge
• Radar
• Satellite
• Daily, hourly, sub-hourly
• Snow/frozen

• Dew point/humidity
• Cloud cover
• Wind direction/speed
• Pressure
• Lightning/thunderstorm days
• Sunshine/radiation
• Pan Evaporation
• Soil moisture/temperature
• Upper level sounding
• SST

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Temperature measurement
Other methods?
Stevenson screen/cotton shelter
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Precipitation measurement
Weighing gauge (NOAA)
Tipping bucket (Wikipedia)
Standard gauge (Wikipedia)
Radar (NOAA)
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Radar estimates of precipitation

• Produced in 1 hour and storm total maps
• hail and sleet may reduce accuracy
• Eastern US Radar estimates corrected by ground
  observations
• Western US Long-term climatological
  interpolations done

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Dewpoint climatology (PRISM)
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Cloud Cover Climatology
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January ws/wd climatology
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Thundarr Days
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Sea surface temperatures
Source JHUAPL
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Pan evaporation / lysimiter
USDA
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Upper air observations

• Radiosonde
• Developed in 1928 flourished since WW2
• Temperature, humidity, pressure
• Rawinsonde
• Similar, though provides wind speed as well
• Wind profilers
• Measure from ground

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Upper air observation locations
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Storm Data / Storm Reports

• Drought
• Dust storm
• Flood
• Fog
• Hail
• Hurricane
• Lightning
• Ocean surf

• Precipitation
• Snow / Ice
• Temperature extremes
• Tornado
• Wildfire
• Wind

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NLDN

• Detect electrical discharge through several
  sensors
• Triangulate location and polarity

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Derived Variables

• HDD, CDD, GDD
• Drought Indices
• http//www.drought.unl.edu/whatis/indices.htm
• SPI, PDSI, PHDI, CMI,
• Air Mass Types
• Reanalysis Data

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HDD
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Reanalysis data

• Combination of weather forecast model
  initialization and analysis, and short-term
  forecast
• Project started in 1990s to reproduce synoptic
  maps back to 1948 extrapolation to 1908 coming
  soon
• Two significant programs
• NCEP / NCAR NNR (USA)
• ECMWF ERA (European Union)

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Reanalysis fields produced

• Class A the most reliable class of variables
  "analysis variable is strongly influenced by
  observed data"
• Class B the next most reliable class of
  variables "although some observational data
  directly affect the value of the variable, the
  model also has a very strong influence on the
  output values."
• Class C the least reliable class of variables
  NO observations directly affect the variable and
  it is derived solely from the model computations
  forced by the model's data assimilation process,
  not by any real data.
• Class D a mean field that is obtained from
  climatological values and does not depend on the
  model

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Reanalysis examples
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US Climate Reference Network

• Set up since 2000 to serve as reference point for
  long-term climate records

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US Historical Climate Network

• Derived from previously observed data
• Many statistical routines run to attempt to
  homogenize datasets

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Meteorological Assimilation Data Ingest System
(MADIS)

• 35,000 stations

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Levels of aggregation

• Individual station

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Levels of aggregation

• Climate division

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Levels of aggregation

• State

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Levels of aggregation

• Region

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