Transcript Kinematics - University of Alabama

Applied climatology vs.
applied meteorology
 From the AMS glossary:
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
applied meteorology—A 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 climatology—The 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.
Changnon (1995) diagram of applied
climatology
Climatological data
Processing of climatological data
Application and use of climatological data
Climate Data
and Variables
Primary data collection
 Primary data collected via relatively cheap
data loggers or transmitted wirelessly
Secondary data collection
 Most common method
Data Sources
 WMO

http://www.wmo.int/pages/index_en.html
 NOAA
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National Weather Service
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http://www.weather.gov/
National Climatic Data Center
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http://www.noaa.gov/
http://www.ncdc.noaa.gov/oa/ncdc.html
Earth Systems Research Lab

http://esrl.noaa.gov/psd/products/analysis/
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State Climatology offices
 http://nsstc.uah.edu/aosc/
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Regional Climate centers
 http://www.sercc.com/
NCDC
 U.S. Stations

http://lwf.ncdc.noaa.gov/oa/climate/stationlocator.
html
 Climatological Data
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http://www7.ncdc.noaa.gov/IPS/cd/cd.html
Monthly summary by state for all stations
 Local Climatological Data
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http://www7.ncdc.noaa.gov/IPS/lcd/lcd.html
Monthly summary for individual stations
NCDC Climate
Divisions
Divisional means and
anomalies since 1895 for
Temp,Precip,PDSI
Questions about observations and data
 Is the instrument calibrated properly? (accuracy)
 Is the instrument recording representative data?
(validity)
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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?
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
Siting variability

Orland, CA

Marysville, CA
(surfacestations.org)
Issues over time
 Stations move
 Surroundings change
 Instrumentation change
 Observation changes
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Time
Frequency
Time of observation bias
 24-hour observations taken at:
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Midnight (all first-order stations)
Early morning (6am-8am) – especially farm
stations
Evening (6pm-10pm)
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
Automated Surface Observation
System (ASOS)
 Debuted in US in 1990s
 Controls all first-order stations presently
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
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
Coop Stations
Climate Variables
 Temperature
 Actual vs Apparent
 Dew point/humidity
 Precipitation
 Measurement
 Wind direction/speed
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Gauge
Radar
Satellite
Daily, hourly, subhourly
Snow/frozen
 Cloud cover
 Pressure
 Lightning/thunderstorm
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days
Sunshine/radiation
Pan Evaporation
Soil
moisture/temperature
Upper level sounding
SST
Temperature measurement
Other methods?
Stevenson screen/cotton shelter
Precipitation measurement
Weighing gauge (NOAA)
Tipping bucket (Wikipedia)
Standard gauge
(Wikipedia)
Radar (NOAA)
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: Longterm climatological
interpolations done
Dewpoint climatology (PRISM)
Cloud Cover Climatology
January ws/wd climatology
Thundarr Days
Sea surface temperatures
Source: JHUAPL
Pan evaporation / lysimiter
USDA
Upper air observations
 Radiosonde
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Developed in 1928; flourished since WW2
Temperature, humidity, pressure
 Rawinsonde
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Similar, though provides wind speed as well
 Wind profilers

Measure from ground
Upper air observation locations
Storm Data / Storm Reports
 Drought
 Precipitation
 Dust storm
 Snow / Ice
 Flood
 Temperature extremes
 Fog
 Tornado
 Hail
 Wildfire
 Hurricane
 Wind
 Lightning
 Ocean surf
NLDN
 Detect electrical discharge through several sensors
 Triangulate location and polarity
Derived Variables
 HDD, CDD, GDD
 Drought Indices
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http://www.drought.unl.edu/whatis/indices.htm
SPI, PDSI, PHDI, CMI,
 Air Mass Types
 Reanalysis Data
HDD
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
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NCEP / NCAR “NNR” (USA)
ECMWF “ERA” (European Union)
Reanalysis fields produced
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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
Reanalysis examples
US Climate Reference Network
 Set up since 2000 to serve as reference point
for long-term climate records
US Historical Climate Network
 Derived from previously observed data
 Many statistical routines run to attempt to
homogenize datasets
Meteorological Assimilation Data
Ingest System (MADIS)
 35,000+ stations
Levels of aggregation
 Individual station
Levels of aggregation
 Climate division
Levels of aggregation
 State
Levels of aggregation
 Region