Adjustment of Global Gridded Precipitation for Systematic Bias Jennifer Adam Department of Civil and Environmental Engineering University of Washington

Motivation Systematic bias in gauge-based precipitation measurement Systematic bias in gauge-based precipitation measurement –Can be substantial –Usually results in a net underestimation of precipitation Most global precipitation products are not adjusted for systematic bias Most global precipitation products are not adjusted for systematic bias Model runs forced with unadjusted precipitation estimates will not accurately perform a water balance Model runs forced with unadjusted precipitation estimates will not accurately perform a water balance

Objective To improve gridded precipitation data used to force large-scale hydrology models in order to better represent the global/continental water balance on a mean monthly basis To improve gridded precipitation data used to force large-scale hydrology models in order to better represent the global/continental water balance on a mean monthly basis –Focus effort in areas where winter precipitation occurs mainly as snow Greatest bias Current research interest

Overview of Project Create mean monthly “catch ratios” gridded ½˚ by ½˚ globally Create mean monthly “catch ratios” gridded ½˚ by ½˚ globally Apply to existing gridded precipitation products (time-series or climatologies) during the period of 1979 through 1998 Apply to existing gridded precipitation products (time-series or climatologies) during the period of 1979 through 1998 Use the results of the recent World Meteorological Organization (WMO) Solid Precipitation Measurement Intercomparison Use the results of the recent World Meteorological Organization (WMO) Solid Precipitation Measurement Intercomparison

Background Information

Global Gridded Precipitation (terrestrial) Spatial Interpolation of Gauge Measurements over Land Areas Spatial Interpolation of Gauge Measurements over Land Areas

Precipitation Gauges of the World ~50 types of National Standard gauges ~50 types of National Standard gauges Sevruk et al., 1989 Sevruk et al., 1989

Wind-Induced Undercatch Influencing Factors: Influencing Factors: –Wind speed –Temperature –Gauge type –Gauge height –Windshield –Exposure Nespor and Sevruk, 1999

Wetting Losses Influencing Factors: Influencing Factors: –Gauge type –Climate –Measurement Methodology

Evaporation Losses Influencing Factors: Influencing Factors: –Gauge type –Climate –Measurement Methodology

Wind-Induced Undercatch Snow: 10 to >50% Rain: 2 to 10% Wetting Losses 2 to 10% Evaporation Losses 0 to 4% Treatment of Trace Precipitation as Zero Significant in Cold Arid Regions Splash-out and splash-in 1 to 2% Blowing and Drifting Snow ?? Sevruk, 1982

1998 WMO Solid Precipitation Measurement Intercomparison (Goodison et al. 1998) Goals: Goals: –Introduce reference method for gauge calibration –Determine systematic bias errors in national methods –Derive standard bias adjustment methods 16 participating countries 16 participating countries 1986 to to 1993

Double-Fenced International Reference (DFIR) Encloses the Shielded Tretyakov Gauge Encloses the Shielded Tretyakov Gauge UCAR

Determined Catch Ratio (CR) Regression Equations for the most common National Standard Precipitation Gauges Determined Catch Ratio (CR) Regression Equations for the most common National Standard Precipitation Gauges –Hellmann, US NWS 8”, Tretyakov, Nipher, others CATCH RATIO (CR) = Measured Precipitation True Precipitation True Precipitation Accounts for Wind-Induced Undercatch of Soliid Precipitation Accounts for Wind-Induced Undercatch of Soliid Precipitation WMO Intercomparison Results

Relationship of Wind Speed to Solid Precipitation Undercatch Goodison et al., 1998

Development of Adjustment Model

Wind-Induced Undercatch Wetting Losses Evaporation Losses Adjusted Precipitation Gauge-Measured Precipitation Sevruk, 1982

+ Liquid Solid Legates, 1987

+ Evaporation Losses Ignored Evaporation Losses Ignored

+ 1 CR s Use “Catch Ratio” for Solid Precipitation Use “Catch Ratio” for Solid Precipitation

+ Adjustment Model

Methodology Overview

Step 1: Selection of Correction Domain Solid Precipitation Undercatch: Solid Precipitation Undercatch: –Countries that experience >½ of precipitation as snow during the coldest month of the year. –30 countries in the Northern Hemisphere were selected Liquid Precipitation Undercatch: Worldwide Liquid Precipitation Undercatch: Worldwide Wetting Losses: Worldwide Wetting Losses: Worldwide

NOAA CPC Summary of day Stations (NCAR) NOAA CPC Summary of day Stations (NCAR) 1994 through 1998 daily data 1994 through 1998 daily data Coincident P, T max, T min, Wind Speed measurements Coincident P, T max, T min, Wind Speed measurements 7,878 stations were used (4,647 for snow analysis) 7,878 stations were used (4,647 for snow analysis) Step 2: Choose Meteorological Stations

+ Step 3: Wind-Induced Solid Precipitation Undercatch e.g. CR s = * w h * w h (WMO) e.g. CR s = * w h * w h (WMO) Wind Speed scaled to gauge height Wind Speed scaled to gauge height Apply on a daily basis Apply on a daily basis

+ Step 4: Wind-Induced Liquid Precipitation Undercatch (Legates, 1987) e.g. κ r = μ 2 w hp 2 e.g. κ r = μ 2 w hp 2 Wind Speed scaled to gauge height Wind Speed scaled to gauge height Apply on a monthly basis Apply on a monthly basis

+ Step 5: Wetting Losses (Legates, 1987) Assume one measurement per day at each station Assume one measurement per day at each station 0.02 < ΔP wr < 0.30 mm/day 0.02 < ΔP wr < 0.30 mm/day ΔP ws = ½ ΔP wr ΔP ws = ½ ΔP wr

+ Step 6: Apply Model R determined based on daily air temperature R determined based on daily air temperature Apply on a daily basis Apply on a daily basis

Step 7: Determine Mean Monthly Catch Ratios for each station Step 8: Interpolate Catch Ratios to ½ ° x ½ ° globally to ½ ° x ½ ° globally Step 9: Apply to an existing Gridded Precipitation Product Mean Monthly Observed Mean Monthly Adjusted

Canada Unique Precipitation Gauge Network Unique Precipitation Gauge Network –Liquid Precipitation: AES Type B –Solid Precipitation: ~125 Nipher Gauges ~2500 Snow Ruler Stations Previous Bias Adjustment Efforts over Canada Previous Bias Adjustment Efforts over Canada –Groisman (1998) –Mekis and Hogg (1999)

6,692 stations 6,692 stations Monthly analysis Monthly analysis Assumed CR = 90% Assumed CR = 90% 495 stations 495 stations Daily analysis Daily analysis Utilized WMO Results Utilized WMO Results

Groisman ÷ Mekis and Hogg (1979 – 1990) Ratios applied to Groisman station data Ratios applied to Groisman station data Mean Monthly Catch Ratios calculated Mean Monthly Catch Ratios calculated

Results

Gridded Catch Ratios Catch Ratio (%)

Adjusted Gridded Precipitation Catch Ratios Applied to Willmott and Matsuura (2001) Monthly Time-Series from 1979 through 1998 Catch Ratios Applied to Willmott and Matsuura (2001) Monthly Time-Series from 1979 through 1998 Precipitation (mm/month)

Adjustment Effects Global Mean Annual Increase of 11.2% Global Mean Annual Increase of 11.2% All Adjustments Wind-Induced Snow Undercatch Wetting LossesWind-Induced Rain Undercatch

Limitations in Methodology

Wind-Induced Undercatch Gauge Representation Gauge Representation –Gauge type or shield uniform over country –Gauge height uniform, wind sensor height at 10 m Regression Equation Application Regression Equation Application –N and r 2 –Equation developed for what gauge? Interpolation Interpolation –Station density and uniformity –Are selected stations representative of network?

Scoring System – Solid Precipitation

Data Set Comparisons

Comparison Against Yang et al. Greenland: Yang 2.5% lower (wind sensor height, rain undercatch eqn.) Greenland: Yang 2.5% lower (wind sensor height, rain undercatch eqn.) Siberia: Yang 1.6% lower (rain undercatch eqn.) Siberia: Yang 1.6% lower (rain undercatch eqn.) Alaska: Yang 3.5% lower (shielding,gauge height, wind sensor height, rain undercatch eqn.) Alaska: Yang 3.5% lower (shielding,gauge height, wind sensor height, rain undercatch eqn.)

Gridded Global Dataset Comparisons Adjusted Willmott 2001Legates1987 Original Willmott 2001 CRU GPCC 1994 Series Climatol Series Series Climatol Bias- Adjusted NoAdjustmentAttemptedNoAdjustmentAttemptedNoAdjustmentAttempted

Legates (1987) Global Precipitation Product ½° by ½° monthly precipitation climatology (global land areas) ½° by ½° monthly precipitation climatology (global land areas) Accounts for: Accounts for: –Wind-Induced Undercatch (Liquid and Solid) –Wetting Losses –Evaporation Losses Adjustments determined from mean monthly meteorological data Adjustments determined from mean monthly meteorological data

Legates higher: June – Sep. Legates higher: June – Sep. Legates lower: Oct. - May Legates lower: Oct. - May Mean Monthly Precipitation

Mean Annual Precipitation Vs. Latitude Legates up to 10% lower above 30° North Legates up to 10% lower above 30° North Legates higher over Eurasia at some latitudes Legates higher over Eurasia at some latitudes

Summary Adjusts existing gridded precipitation products for wind- induced undercatch and wetting losses on a mean monthly basis Adjusts existing gridded precipitation products for wind- induced undercatch and wetting losses on a mean monthly basis Effort focused on snow-dominated regions and solid precipitation undercatch - Utilizes the recent WMO Solid Precipitation Measurement Intercomparison results Effort focused on snow-dominated regions and solid precipitation undercatch - Utilizes the recent WMO Solid Precipitation Measurement Intercomparison results Wind-induced (station-specific) catch ratios from 1.6 to 3.5% higher than Yang et al. Wind-induced (station-specific) catch ratios from 1.6 to 3.5% higher than Yang et al. Results in more cold season precipitation and less warm season precipitation than Legates adjustment effort Results in more cold season precipitation and less warm season precipitation than Legates adjustment effort

Acknowledgements: Dennis Lettenmaier, Steve Burges, Bart Nijssen and the Land Surface Hydrology Research Group Supported by NASA grant NAG to the University of Washington.