Adjustment of global gridded precipitation for systematic bias

Abstract

[1] Systematic biases in gauge-based measurement of precipitation can be substantial. Of the sources of bias, wind-induced undercatch of solid precipitation is by far the largest. A methodology for producing gridded mean monthly catch ratios (CRs) for the adjustment of wind-induced undercatch and wetting losses is developed, which is suitable for application to continental or global gridded precipitation products. The adjustments for wind-induced solid precipitation were estimated using gauge type-specific regression equations from the recent World Meteorological Organization Solid Precipitation Measurement Intercomparison. Wind-induced undercatch of liquid precipitation and wetting losses were estimated using methods employed in previous global bias adjustment efforts. Due to the unique nature of Canada's precipitation measurement network, the Canadian adjustments were determined using more detailed information than for the rest of the domain, and are therefore expected to be more reliable. The gridded gauge adjustment products are designed to be applicable both to climatological estimates and to individual years during the 1979 through 1998 reference period. Application of the CRs to an existing precipitation product yielded an increase in mean annual global terrestrial precipitation of 11.7%. As compared with recent (but more localized) studies that used a similar method to account for wind-induced catch deficiencies, our estimates of wind-induced undercatch are 1.6-7.9% higher on a mean annual basis. Compared to a previous global precipitation bias adjustment effort, our adjusted data set results on average in slightly greater warm season and lower cold season precipitation increases, greater precipitation increases over North America, and lower precipitation increases over Eurasia.

Relevance of Hydro-Climatic Change Projection and Monitoring for Assessment of Water Cycle Changes in the Arctic

“…The most important systematic bias in Arctic precipitation measurements is a wind-induced undercatch of snow (Adam and Lettenmaier 2003;Rawlins et al 2009a). Correcting for this bias requires extensive data on wind speed, gauge type, and height of recording instruments.…”

“…Correcting for this bias requires extensive data on wind speed, gauge type, and height of recording instruments. Several attempts at bias correction for various time periods and regions have recently been performed (e.g., Yang and Ohata 2001;Adam and Lettenmaier 2003;Yang et al 2005), and these studies have generally indicated an underestimation of annual precipitation of about 10-25% over most of the pan-Arctic drainage, with higher values occurring in drainage basin parts that extend far north.…”

An assessment of temperature and precipitation change projections over Italy from recent global and regional climate model simulations

“…This may be an indication of an overestimate of topographic uplift at the 20-km grid spacing. On the other hand, observations over mountainous regions are often characterized by a low-elevation station bias and a gauge under-catch bias, which may account for an underestimate of true precipitation of up to 30% (Adam and Lettenmaier, 2003). This underestimate is maximum in winter, especially in the presence of blowing snow, and minimum in summer, thus showing a pronounced seasonality.…”

Climate change impacts on rainfed agriculture in the Guadiana river basin (Portugal)

“…This dataset was downscaled as described by Maurer et al (2009) using the bias-correction/spatial downscaling method (Wood et al, 2004) to a 0.5 degree grid, based on the 1950-1999 gridded observations of Adam and Lettenmaier (2003). The CMIP3 climate simulations datasets used in this study include the outputs of 16 IPCC Models under 3 future emissions scenarios (A2, A1B, B1,) resulting in a total of 48 different climate simulations containing monthly records of both precipitation and temperature at a 0.5 • spatial resolution for the period from 1950 until 2099.…”