Applied climatology vs. applied meteorology

From the AMS glossary: 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

Application and use of climatological data

Processing of climatological data

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

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/

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

NCDC Climate Divisions

Divisional means and anomalies since 1895 forTemp,Precip,PDSI

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?

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

Time Frequency

Time of observation bias

24-hour observations taken at: 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

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

Temperature measurement

Other methods?

Stevenson screen/cotton shelter

Precipitation measurement

Tipping bucket (Wikipedia) Standard gauge (Wikipedia)

Weighing gauge (NOAA)

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: Long-term climatological interpolations done

Dewpoint climatology (PRISM)

Cloud Cover Climatology

January ws/wd climatology

Thundarr Days

Sea surface temperaturesSource: JHUAPL

Pan evaporation / lysimiter

USDA

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

Upper air observation locations

Storm Data / Storm Reports

Drought Dust storm Flood Fog Hail Hurricane Lightning Ocean surf

Precipitation Snow / Ice Temperature extremes Tornado Wildfire Wind

NLDN

Detect electrical discharge through several sensors Triangulate location and polarity

Derived Variables

HDD, CDD, GDD Drought Indices

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 NCEP / NCAR “NNR” (USA) ECMWF “ERA” (European Union)

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

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