Download - Development of Bias-Corrected Precipitation Database and Climatology for the Arctic Regions
Development of Bias-Corrected Precipitation Database and
Climatology for the Arctic Regions
Daqing Yang, Principal Investigator
Douglas L. Kane, Co-Investigator
Institute of Northern Engineering University of Alaska Fairbanks
David R. Legates, Co-Investigator
Center for Climatic Research
Department of Geography University of Delaware
Outline
• Background /Goals
• Methods
- Results and applications of WMO gauge intercomparison project
• Data Sources
• Major Tasks
• Results/Products/Impacts
Uncertainties of Precipitation Records and Climatology in the Arctic regions
• Sparseness of the precipitation observation networks;
• Uneven distribution of measurement sites, i.e. biased toward coastal and the low-elevation areas;
• Spatial and temporal discontinuities of precipitation measurements induced by changes in observation methods and by different observation techniques used across national borders; and
• Biases of gauge measurements, such as wind-induced undercatch, wetting and evaporation losses, and underestimate of trace amount of precipitation.
Research Goals
• Evaluate and define the accuracy of precipitation measurements in the Arctic regions.
• Implement the consistent bias-correction methods over the pan-Arctic, i.e. Alaska, northern Canada, Siberia, northern Europe, Greenland, and the Arctic Ocean.
• Develop biased-corrected and compatible precipitation database (including grid products) and climatology for the Arctic regions as a whole.
US Wyoming snow system in Barrow, AK WMO double fence intercomparison reference (DFIR)
in Barrow, AK
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)Canadian Nipher NWS 8" AlterNWS 8" unsh Hellmann unshTretyakov
Summary of bias correction in NWT, Canada, 1961-1990
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Whitehorse, Yukon, 1948-1994
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Months
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measuredOverall mean for the NP drifting stations, 1957-90 (Yang, 1999)
Overall mean for 61 climate stations in Siberia, 1986-92 (Yang and Ohata, 2001)0
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Comparison of Bias Corrections in the High Latitudes
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Siberia Alaska NWT Greenland Arctic basin
Regions
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a) DIKSON, 73.30N, 80.24E
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cipi
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trace amount
wind loss
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a) Dikson (73.30N, 80.24E)
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Daily mean w ind speed at 2m (m/s)
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Data SourcesDaily precipitation, temperature and wind speed are needed for this
research. • National Climatic Data Center (NCDC), global daily surface data
archive (1994-present) for over 8,000 stations around the world, http://www.ncdc.noaa.gov/cgi-bin/res40.pl
• WMO GTS, the Global Climate Observing System (GCOS) Surface Network (GSN), http://www.wmo.ch/web/gcos/gcoshome.htlm
• Arctic Precipitation Data Archive (APDA) at the Global Precipitation Data Center (GPDC), http://www.dwd.de/research/gpcc/acsys
• Arctic Ocean (6 hourly and daily) met data collected at the Russian NP drifting station, National Snow and Ice Data Center (NSIDC)
http://www-nsidc.colorado.edu/index.html
Station and gauge info: – type of precipitation gauge– height of gauge and wind sensor– wind shield
WMO and national weather services:– USA, Canada – Russia, Finland, Denmark, ...
Synoptic/climate stations on land above 45N and the Arctic Ocean drifting stations will be used for this research
R ussia
Mongolia
K azakhstan
G reenland
C hina
C anada
Major Task 1: Evaluation and Implementation of the WMO
Bias Correction Methods - threshold wind 6.5m/s
• Analysis of wind regimes over the arctic regions • Focus on winter season and on snowfall days
• Define regions where the WMO bias correction methods may not be appropriate and therefore alternative approaches or further experimental studies should be considered
Major Task 2: Development of Bias-Corrected Arctic
Precipitation Database and Climatology
• Implement the WMO methods to all the stations in the
Arctic regions for last 30 years, 1970-2000???
• Create bias-corrected daily precipitation dataset- an
important basis for analyses of Arctic regional
precipitation, i.e. long-term mean, seasonal cycle, year-
to-year variation, and trend
• Develop improved precipitation climatology for the Arctic regions
–Consider terrain and the orographic effect on precipitation distribution, use
high-resolution digital elevation models (DEM) to determine elevation, slope,
and aspect of the topography
–Apply PRISM (Daly et al., 1994) and the High-Resolution Weather Data
System (HRWxDS) (Legates et al,. 1999) to generate regional maps of
monthly/yearly bias-corrected precipitation
–Develop gridded precipitation data, use equal-area EASE grid system,
compatible with ACSYS/Arctic Precipitation Data Archive (APDA),
hydrological model intercomparison project, and RS snowcover (SCE/SWE)
products
Major Task 3: Comparison and Validation of the Results
• Compare our results with other precipitation datasets/products, such as Legates and Willmott (1990), Jaeger (1983), UNESCO (1978), Adam and Lettenmaier (2003), and others?
• Compare gauge measurements/corrections with snowcover accumulation in selected regions/basins
• Assess the impact of precipitation bias corrections to regional hydrologic model analyses (Zhang et al., 2000)
• Compare GCM/RCM precipitation simulation with the observed and bias-corrected precipitation fields in selected regions, i.e. Alaska and central Siberia – model simulation agrees better to bias-corrected precipitation
fields particularly in winter months and over windy areas !?
Results/Products/Impacts• Practical procedures for correcting gauge-measured precipitation data in the
high latitude regions
• Bias-corrected daily/monthly/yearly (station) precipitation records/correction factors (CF, %) for arctic regions across national boundaries
• Bias-corrected, gridded monthly/yearly regional precipitation data/climatology for the arctic regions
• Impacts: – water balance calculations of both the Arctic Ocean and terrestrial systems – climate change analysis and hydrologic modeling – validation of GCM/RCM simulations – calibration of remote sensing data/products