2 3 4 5 6 7

Correction Band

Correction of Global Precipitation Products for Systematic Bias and Orographic Effects Jennifer C. Adam1, Dennis P. Lettenmaier1, Elizabeth A. Clark1, and Eric F. Wood2

1. Department of Civil and Environmental Engineering, Box 352700, University of Washington, Seattle, WA 981952. Department of Civil Engineering, Princeton University, Princeton, NJ, 08544

85th AMS Annual Meeting (January, 2005) San Diego, California

ABSTRACTGauge-based global gridded precipitation products often have two problems: (1) there can be large systematic biases in precipitation measurement, especially for solid precipitation, and (2) precipitation is often underestimated in topographically complex regions due to the prevalence of low elevation valley stations. Precipitation is the main driver of the land surface hydrologic system and therefore the most important input variable to hydrology models. We describe an approach we have taken to correct global gridded precipitation data for the two above-mentioned sources of bias. The final product is a (1979 - 1999) gridded precipitation climatology for the global land areas that is adjusted for systematic biases on a monthly basis and for orographic effects on an annual basis. Both adjustments are designed to be applied to the existing 0.5 degree precipitation product developed by Cort Willmott and others at the University of Delaware. Adjustments for wind-induced under-catch of solid precipitation were estimated using gauge type-specific regression equations from the recent (1998) World Meteorological Organization Solid Precipitation Measurement Intercomparison. Wind-induced undercatch of liquid precipitation and wetting losses were estimated using the methods employed in previous global bias adjustment efforts. In an attempt to develop a globally consistent correction for the underestimation of gridded precipitation in mountainous regions, we used a hydrologic water balance approach. The precipitation in orographically-influenced drainage basins was adjusted using a combination of water balance and variations of the Budyko ET/P vs. PET/P curve. The method is similar to other methods in which streamflow measurements are distributed back onto the watershed and a water balance is performed to determine true precipitation. Rather than relying on modeled runoff ratios, we estimated evaporation using Budyko ET/P vs. PET/P curves. Combination of the gauge catch deficiency and orographic adjustments resulted in a net increase of 15.1% of estimated global terrestrial mean annual precipitation (11.7% and 3.4%, respectively). We also estimated the effects of the adjustments on mean annual and monthly precipitation for large continental-scale river basins. In general, river basins with considerable orography (e.g. the Brahmaputra, Columbia, and Yukon) experienced the greatest precipitation increases due to correction for orographic effects, while river basins in colder climates (e.g. the Lena, Ob, and Yukon) experienced the greatest precipitation increases due to adjustment for systematic bias (especially in the winter).

3 Analysis of Corrections

2 Correction for Orographic Effects

1Correction for Gauge Undercatch (Adam and Lettenmaier, 2003)

CONCLUDING REMARKS• Important Features include: the use of an existing gridded precipitation dataset (Willmott and Matsuura, 2001); adjustment for gauge undercatch is closely tied to the results of the most recent WMO precipitation measurement intercomparison (Goodison et al., 1998); in situ gauge-based data sources.• Net increase in mean annual precipitation for 1997 to 1999 is 15.1% with variation both spatially and temporally (mean monthly).• Potential for improvement given extended and increased availability of data and other information.

Photo: http://philler.scs.gmu.edu/vaccess

REFERENCESAdam, J.C. and D.P. Lettenmaier, 2003: Adjustment of global gridded precipitation for systematic bias. J. Geophys. Res., 108 (D9), 1-14.Budyko, M.I., 1974: Climate and Life, 508 pp., Academic, San Diego, Calif.Daly, C., R.P. Neilson, and D.L. Phillips, 1994: A Statistical-topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140-158.Goodison, B.E, P.Y.T. Louie, and D. Yang, 1998: WMO solid precipitation intercomparison, final report, WMO/TD-872, 212 pp World Meteorol. Organ., Geneva.Groisman, P.Y., 1998: National Climatic Data Center Data Documentation for TD-9816, Canadian Monthly Precipitation, 21 pp, Natl. Clim. Data Cent., Asheville, N.C., 1998.Legates, D.R., 1987: A climatology of global precipitation, Publ. Climatol., 40(1), 86 pp.Mekis, E., and W.D. Hogg, Rehabilitation and analysis of Canadian daily precipitation time series, Atm. Ocean, 37(1), 53-85, 1999.Sankarasubramanian, A. and R.M. Vogel, 2002: Annual hydroclimatology of the United States. Water Resour. Res., 38 (6), 1083.Willmott, C.J. and K. Matsuura, 2001: Terrestrial air temperature and precipitation: monthly and annual time series (1950 – 1999) (version 1.02), Cent. For Clim. Res., Univ. of Del., Newark, DE.

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0.2

0.4

0.6

0.8

1.0

1.2

0 1 2 3PET/P

E/P

Budyko (1974) S&V, gamma=1.5S&V, gamma=1.0 S&V, gamma=0.5Physical Limits

EnergyLimited

MoistureLimited

UpslopeDownslopeCross-Wind

Bias Adjustment Model

)()()1( wsgs

wrgra PPCR

RPPRP

• Pg = gauged precipitation (mm day-1)• Pa = adjusted precipitation (mm day-1)• R = snowfall fraction, based on daily air temperature• Kr = correction ratio for wind-induced undercatch,

liquid precipitation (Legates, 1987)• CRs = catch ratio for wind-induced undercatch,

solid precipitation (uses WMO regressions)• ΔPw = wetting loss (mm day-1; Legates, 1987)

1

2

Daily Correction of Gauged Precipitation

Calculate Monthly Catch Ratios

a

gall

P

PCR

3 Interpolate to 0.5° global land areas

Mean Monthly ObservedMean Monthly Adjusted

Global

Columbia

Lena

Mississippi

Ob

Yukon

Percent Increase in Precipitation

Percent Increase due to Gauge Bias

Correction

Percent Increase due to both Gauge Bias and

Orographic Corrections

Percent Increase due to Gauge Bias

Correction

Percent Increase due to both Gauge Bias and

Orographic Corrections

We determined the affects of the corrections by first correcting the Willmott and Matsuura (2001) dataset for gauge undercatch bias (box 1) for the mean monthly climatology of 1979 to 1999. Following this we applied the corrections for orographic effects (box 2). The bar graphs show precipitation before and after corrections globally and for six watersheds, and the spatial plots show the spatial distribution of the percent increase in precipitation for each of these watersheds. The gauge bias corrections have the largest effects on basins in the northern latitudes (although the Mississippi basin has large corrections because of the tendency of the US gauge to have large biases). Mountainous regions have the largest corrections for orographic effects.

Uncorrected PrecipitationGauge Bias CorrectedBoth Corrections Applied

Pre

cipi

tati

on,

mm

Annual Increase: (gauge corr.) 11.7%

(net) 15.5%

Annual Increase: (gauge corr.) 5.9%

(net) 24.8%

Annual Increase: (gauge corr.) 13.0%

(net) 58.1%

Annual Increase: (gauge corr.) 9.3%

(net) 31.9%

Annual Increase: (gauge corr.) 17.6%

(net) 19.4%

Annual Increase: (gauge corr.) 13.7%

(net) 17.4%

Annual Increase: (gauge corr.) 16.6%

(net) 58.8%

Brahmaputra

Step 1Definition of Correction Domain: Slopes from a 5min DEM were used to select the 0.5 degree cells for correction. All 5min cells within the correction domain were assigned to a correction band ranging from 2

Average Correction Ratios for Gauged Basins: The Budyko Method

Spatial Distribution of Correction Ratios within Gauged Basins

Interpolation of Correction Ratios to Ungauged Basins

Step 2

Step 3

Step 4

GQEPdt

dS QEP

γφ,fP

E

P

PETφ Where = Aridity Index

P

bγ Where = Soil Moisture Storage Index

γφ,fP

Q-P

The Budyko Method

1

2

We solve for basin-average precipitation using two equations: (1) water balance, and (2) The ET/P vs. PET/P curves of Budyko (1974) and Sankarasubramanian and Vogel (2000).

(lowest elevations) to 7 (highest elevations).

Gauged basin were selected using the datasets of RivDIS v1.1, GRDC, and HCDN. Mean annual basin average precipitation for these basins was determined using the Budyko Method (see box). Basin average precipitation correction ratios were calculated by dividing the Budyo P by our gauge-corrected P.

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)(bandrCbandBbandA 2

Equation

Constraints:1. r=1 for band=12. Rave is conserved

From PRISM:

Spatial variability of the correction ratio across each of the gauged basins was constructed by developing a relationship between the correction ratio and the 5min correction bands (discussed in box). PRISM (Daly et al. 1994) for the US was used to determine the form of this relation by assuming that the variability of PRISM precipitation with elevation is correct. 33 basins in the US were used for this regression. A quadratic expression was used in which two constraints were imposed (see box).

Correction ratios were interpolated the 5min gridded data of slope types from 5min grid cells in gauged basins to grid cells in the rest of the correction domain using the 5min gridded data of slope types (see image) and dominant wind direction.

The correction ratio patterns are realistic for many of the continents (e.g. N. and S. America), although other continents (e.g. Europe and Africa) have large regions of low ratios probably because of problematic streamflow records.

• Station observations were obtained from the NOAA Climate Prediction Center Summary of the Day dataset (see image at right)

• Stations having coincident precipitation, temperature, and windspeed during the years of 1994-1998 were chosen

• Blue stations had corrections for both solid and liquid precipitation and red stations had corrections for liquid precipitation only.

• Adjustments were made for: (1) wind-induced undercatch and (2) wetting losses (see box)

• Wind-induced undercatch of solid precipitation: We use the results from the WMO Solid Precipitation Measurement Intercomparison (Goodison et al., 1998) (see figure to left showing catch ratio as a function of windspeed).

• Wind-induced undercatch of liquid precipitation: This follows the method

• Special treatment for Canada using the corrected data of Groisman (1998) and Mekis and Hogg (1999)

Catch Ratio (aggregated to seasonal for this plot)

of Legates (1987).

• Wetting losses: this follows the method of Legates (1987).

• We made assumptions for each country regarding prevalent gauge type, presence of gauge shields, gauge height, and anemometer height.

• Adjustment for the wind-induced undercatch of solid precipitation yielded the greatest increases, especially in winter.

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