1. 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.

2. Changnon (1995) diagram of applied climatology Climatological data Processing of climatological data Application and use of climatological data

3. Climate Data and Variables

4. Primary data collection Primary data collected via relatively cheap data loggers or transmitted wirelessly

5. Secondary data collection Most common method

6. 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/ http://nsstc.uah.edu/aosc/ Regional Climate centers  http://www.sercc.com/ http://www.sercc.com/

7. NCDC U.S. Stations http://lwf.ncdc.noaa.gov/oa/climate/stationlocator. html 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

8. NCDC Climate Divisions Divisional means and anomalies since 1895 for Temp,Precip,PDSI

9. 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?

10. 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

11. Siting variability Orland, CA Marysville, CA (surfacestations.org)

12. Issues over time Stations move Surroundings change Instrumentation change Observation changes Time Frequency

13. Time of observation bias 24-hour observations taken at: Midnight (all first-order stations) Early morning (6am-8am) – especially farm stations Evening (6pm-10pm)

14. 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

15. Automated Surface Observation System (ASOS) Debuted in US in 1990s Controls all first-order stations presently

16. 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

17. 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

18. Coop Stations

19. 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

20. Temperature measurement Other methods? Stevenson screen/cotton shelter

21. Precipitation measurement Tipping bucket (Wikipedia)Standard gauge (Wikipedia) Weighing gauge (NOAA) Radar (NOAA)

22. 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

23. Dewpoint climatology (PRISM)

24. Cloud Cover Climatology

26. January ws/wd climatology

27. Thundarr Days

28. Sea surface temperatures Source: JHUAPL

29. Pan evaporation / lysimiter USDA

30. 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

31. Upper air observation locations

32. Storm Data / Storm Reports Drought Dust storm Flood Fog Hail Hurricane Lightning Ocean surf Precipitation Snow / Ice Temperature extremes Tornado Wildfire Wind

33. NLDN Detect electrical discharge through several sensors Triangulate location and polarity

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

35. HDD

36. 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)

37. 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

38. Reanalysis examples

39. US Climate Reference Network Set up since 2000 to serve as reference point for long-term climate records

40. US Historical Climate Network Derived from previously observed data Many statistical routines run to attempt to homogenize datasets

41. Meteorological Assimilation Data Ingest System (MADIS) 35,000+ stations

42. Levels of aggregation Individual station

44. Levels of aggregation Climate division

45. Levels of aggregation State

47. Levels of aggregation Region