A Bias-Corrected Precipitation Climatology for China

Baisheng Ye Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, China

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Daqing Yang Water and Environment Research Center, University of Alaska, Fairbanks, Fairbanks, Alaska

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Yongjian Ding Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, China

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Tianding Han Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, China

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Toshio Koike Department of Civil Engineering, School of Engineering, The University of Tokyo, Bunkyo-ku, Tokyo, Japan

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Abstract

This paper presents the results of bias corrections of Chinese standard precipitation gauge (CSPG) measurements for wind-induced undercatch, a trace amount of precipitation, and wetting loss. Long-term daily data of precipitation, temperature, and wind speed during 1951–98 at 710 meteorological stations in China were used for this analysis. It is found that wind-induced gauge undercatch is the greatest error in most regions, and wetting loss and a trace amount of precipitation are important in the low-precipitation regions in northwest China. Monthly correction factors ratio of corrected amount to measured amount of precipitation differ by location and by type of precipitation. Considerable interannual variation of the corrections exists in China due to the fluctuations of wind speed and frequency of precipitation. More importantly, annual precipitation has been increased by 8 to 740 mm with an overall mean of 130 mm at the 710 stations over China because of the bias corrections for the study period. This corresponds to 6%–62% increases (overall mean of 19% at the 710 stations over China) in gauge-measured yearly total precipitation over China. This important finding clearly suggests that annual precipitation in China is much higher than previously reported. The results of this study will be useful to hydrological and climatic studies in China.

Corresponding author address: Dr. Baisheng Ye, Cold and Arid Regions Environmental and Engineering Research Institute (CAREERI), Chinese Academy of Sciences (CAS), 260 Donggang West Road, Lanzhou, Gansu 730000, China. Email: yebs@ns.lzb.ac.cn

Abstract

This paper presents the results of bias corrections of Chinese standard precipitation gauge (CSPG) measurements for wind-induced undercatch, a trace amount of precipitation, and wetting loss. Long-term daily data of precipitation, temperature, and wind speed during 1951–98 at 710 meteorological stations in China were used for this analysis. It is found that wind-induced gauge undercatch is the greatest error in most regions, and wetting loss and a trace amount of precipitation are important in the low-precipitation regions in northwest China. Monthly correction factors ratio of corrected amount to measured amount of precipitation differ by location and by type of precipitation. Considerable interannual variation of the corrections exists in China due to the fluctuations of wind speed and frequency of precipitation. More importantly, annual precipitation has been increased by 8 to 740 mm with an overall mean of 130 mm at the 710 stations over China because of the bias corrections for the study period. This corresponds to 6%–62% increases (overall mean of 19% at the 710 stations over China) in gauge-measured yearly total precipitation over China. This important finding clearly suggests that annual precipitation in China is much higher than previously reported. The results of this study will be useful to hydrological and climatic studies in China.

Corresponding author address: Dr. Baisheng Ye, Cold and Arid Regions Environmental and Engineering Research Institute (CAREERI), Chinese Academy of Sciences (CAS), 260 Donggang West Road, Lanzhou, Gansu 730000, China. Email: yebs@ns.lzb.ac.cn

1. Introduction

Reliable precipitation climatologies of regional to global scales are critical for climatic and hydrological analyses. Traditionally, most existing continental and global precipitation climatologies and maps have been derived from the standard national precipitation gauge records that have long been realized as underestimates of true precipitation amounts and incompatible across national boundaries (Korzun et al. 1978; Legates 1995; Sevruk 1989; Karl et al. 1993; Yang et al. 2001). These climatologies have been extensively used for large-scale hydrological and climatic analyses, including evaluation of climate model simulations, input fields in global hydrological models, and validation of satellite precipitation algorithms. Legates (1995) reviewed the existing global precipitation climatologies and found inconsistencies in some regions. Walsh et al. (1998) recently reported considerable variation between Arctic precipitation estimates from different sources, and this discrepancy implicates the verification of the model simulations of Arctic hydrological variables.

It has been recognized that uncertainties exist in the regional and global precipitation climatologies mainly due to 1) uneven distribution of measurement sites, that is, biased toward coastal and low-elevation areas; 2) spatial and temporal discontinuities of precipitation measurements associated with changes of observational methods and differences of observational techniques used in different countries; and 3) biases of gauge measurements, such as wind-induced gauge undercatch, wetting and evaporation losses, and underestimation of trace precipitation amounts (Korzun et al. 1978; Goodison et al. 1998). Of the above factors, systematic errors in gauge measurements are particularly important, because these biases affect all types of precipitation gauges, especially those used in cold environments.

To assess the national methods of solid precipitation observations, the World Meteorological Organization (WMO) initiated the Solid Precipitation Measurement Intercomparison Project in 1985 (Goodison et al. 1989). The octagonal vertical double fence surrounding a shielded Tretyakov gauge was designated as the intercomparison reference [double fence intercomparison reference (DFIR)]. Thirteen countries including China participated in this project, and the experiments were conducted at 20 selected sites in these countries from 1986 to 1993 (Goodison et al. 1998). The WMO Solid Precipitation Measurement Intercomparison has developed new bias-correction techniques for a number of precipitation gauges commonly used around the world (Goodison et al. 1998), such as the Canadian Nipher snow gauge (Goodison and Metcalfe 1992), the United States National Weather Service (NWS) 8-in. standard gauge (Yang et al. 1998b), the Russian Tretyakov gauge (Yang et al. 1995), the Hellmann gauges (Gunther 1993; Allerup et al. 1997; Yang et al. 1999a), and the Chinese standard gauge (Yang et al. 1989, 1991; Yang 1988). These correction procedures are recommended for test correction of gauge-measured daily precipitation in those countries where national meteorological or hydrological station networks operate these gauges for precipitation observations (Goodison et al. 1998). Applications of the WMO bias-correction methods to the archived national precipitation data in some countries have resulted in significantly higher estimates of precipitation, particularly in the high-latitude regions (Metcalfe et al. 1994; Yang et al. 1998a, 1999b; Yang 1999). Recently, Adam and Lettenmaier (2003) applied bias corrections to global monthly grid precipitation data and focused on reduction of the average errors instead of on individual precipitation events.

Our knowledge of large-scale precipitation patterns is incomplete, and improved climatologies are needed (Legates and Willmott 1990; Xie et al. 1996; Groisman and Legates 1994). This work, as one of the efforts to generate improved precipitation climatologies (Legates and Willmott 1990; Groisman et al. 1996; Xie et al. 1996; Yang 1999), summarizes the methodology of correcting Chinese gauge-measured daily precipitation for biases of wind-induced undercatch, wetting loss, and trace amounts of precipitation. The newly developed bias-correction methodology was applied at 710 climate stations in China during 1951–98, and the magnitude of the biases and their seasonal/spatial variability were quantified. Based on the bias-corrected data, a new precipitation climatology has been developed for China. It is anticipated that bias corrections such as those discussed in this study will significantly improve the accuracy and homogeneity of precipitation data, and the results of corrections will have important impacts on climate monitoring, hydrological modeling, and validation of climate model simulations at regional to global scales.

2. Bias-correction methods

Bias correction of gauge-measured precipitation should be made for trace precipitation, wetting loss, evaporation loss, and wind-induced errors caused by the wind field deformation over the gauge orifice (Sevruk and Hamon 1984). Since the wind field deformation affects the total gauge catch including both the wetting and evaporation losses, the general model for precipitation correction has been modified as follows (Yang et al. 2001):
PcKPgPwPePt
where Pc is the corrected precipitation; Pg is the gauge-measured precipitation; ΔPw and ΔPe are wetting loss and evaporation loss, respectively; ΔPt is the trace precipitation, which is generally a small amount and does not need wind corrections; and K is the correction coefficient (usually K > 1) for wind-induced errors.

The Chinese standard precipitation gauge (CSPG) has been the standard instrument for measuring both solid and liquid precipitation in China climatological and hydrological station networks since the late 1950s (Chinese meteorological Administration 1979). It is a cylinder of galvanized iron, 65 cm long and 20 cm in diameter. The CSPG is placed 0.7 m above the ground without a windshield. To determine the systematic biases in Chinese gauge measurements, a gauge intercomparison study was carried out during 1985–91 at four meteorological stations in the Urumqi River basin, northwest China (Yang 1988; Yang et al. 1991). The method of determining each of the terms in Eq. (1) has been developed (Yang 1988; Yang et al. 1991) and is briefly summarized below.

a. Wetting loss ΔPw

Wetting losses are due to precipitation retaining or sticking to the sides of the gauge and cannot be poured out and measured in precipitation observation. Wetting losses are gauge specific and vary by precipitation type and the number of times the gauge is emptied. According to the methods of observation in China (Chinese Meteorological Administration 1979), the Chinese gauge is used with a funnel and a glass bottle (container) for rain measurements. For solid precipitation measurements the funnel and container are removed. Wetting loss experiments in China show that the average wetting loss of the CSPG per observation was 0.23 mm for rainfall measurement, and 0.30 and 0.29 mm for snow and mixed precipitation, respectively (Yang et al. 1991; Yang 1988). Precipitation is measured twice a day over China, at 0800 and 2000 LT (local, Beijing, time). To be conservative, we correct wetting losses once a day.

b. Trace precipitation ΔPt

A precipitation event of less than 0.10 mm is beyond the resolution of the CSPG and is recorded as trace amount of precipitation. Trace events are counted as precipitation days, but quantitatively they are treated as zero amounts. Considering the wetting loss, the gauge cannot detect the precipitation events that are less than the wetting loss. Precipitation observations in China show that sometimes two trace precipitation events are reported in a single trace precipitation day; thus, it is not unreasonable to assume that a trace event could be a small precipitation amount of 0.05 to 0.15 mm. To be conservative, trace precipitation was corrected on a daily basis in this study; for example, for any given trace day, regardless of the number of the trace observations reported, a value of 0.10 mm was assigned and added to the monthly total.

c. Evaporation loss ΔPe

Evaporation loss is the water lost by evaporation of the contents in the gauge before the observation is made. Comprehensive assessment of evaporation losses of many national gauges was conducted during the WMO intercomparison project, and the results indicated that average daily losses varied by gauge type and time of the year (Aaltonen et al. 1993; Goodison et al. 1998). Evaporation losses are strongly dependent on weather condition and methods of observations, such as the number of times of daily observations (Sevruk 1982). For the CSPG tested in western China, the annual evaporation loss was estimated to be 18.6 mm (4.4%) in high mountains (3640 m msl with about 300 mm of annual mean potential evaporation and 421 mm of annual precipitation) and 4.4 mm (1.6%) in low elevations (918 m msl with about 2000 mm of annual mean potential evaporation and 277 mm of annual precipitation) (Yang 1988). The funnel and container can greatly reduce evaporation losses. The evaporation losses usually are less than other losses such as wetting loss, trace precipitation, and wind-induced error even in the arid region with high potential evaporation when the funnel and container were used with the gauge in the raining season. The higher evaporation losses in high-mountain regions are due to the CSPG used without a funnel and a container in mountain regions for observations of wet snowfall in the summer season. In these cases, the evaporation loss may be greater than the trace loss.

Daily variation and seasonal change in evaporation losses are great and can be very site dependent. It is difficult to estimate with any confidence the daily evaporation losses at regional climate station networks by using the average evaporation amount obtained from other experimental sites. Therefore evaporation loss in China was not corrected in this study. The annual evaporation loss in China is expected to be small due to the use of a funnel and a container in the high-evaporation period. Neglecting this error in this study will therefore not significantly affect the results of the bias corrections.

d. Wind-induced gauge undercatch

The wind-induced errors are caused by the wind field deformation over the gauge orifice (Sevruk and Hamon 1984). The catch ratio of snow is less than that of rain because the snowfall can be more easily affected by wind than rainfall (Larson and Peck 1974; Goodison et al. 1998; Yang et al. 1995, 1998b). To correct for wind-induced gauge undercatch, wind speed is required. Wind measurements were made at the standard height of 10 m at the observational network in China, and these data were available for this study. It has been suggested that gauge exposure should be considered when reducing wind from the standard height to the gauge level (Sevruk 1982). Gauge exposure depends on the average vertical angle of obstacles around the gauge; it can be directly measured or estimated by a classification system based on metadata archives (Sevruk 1982). The station metadata are not available for this work or most global-scale bias corrections. Site exposure was not accounted for in wind speed estimates at the gauge height. This may introduce some uncertainty in the estimation of gauge catch efficiency.

It has been well documented that for the same wind speed, gauge undercatch of snow is much higher than for rain (Larson and Peck 1974; Goodison et al. 1998; Yang et al. 1995, 1998b). Classifying the type of precipitation is therefore necessary in order to apply the best wind-loss correction. In this work, information of precipitation type was available in the metadata before 1980. After 1980, precipitation type is classified by daily air temperature. The temperature criteria were set at −2° and +2°C, that is, snow and rain for daily temperature below −2° and above +2°C, respectively, and mixed precipitation when daily temperature was in between −2° and +2°C (Yang et al. 2001).

In the WMO intercomparison, catch ratio was defined as the ratio of the amount of precipitation caught by a gauge (including the recorded amount and wetting loss) to the true precipitation (Goodison et al. 1998). Based on the experimental observations in the Urumqi River basin, relation of catch ratio as a function of wind speed has been developed for the CSPG (Yang et al. 1991). It was found in the WMO experiment that wind speed was the most important factor determining gauge catch when precipitation was classified into snow, mixed precipitation, and rain. The results of daily gauge catch ratio (CR; percent) versus daily mean wind speed (Ws; meters per second) at 10-m height are presented below for snow and rain precipitation:
i1525-7541-5-6-1147-e2
For mixed precipitation, the CR is estimated by a liner relationship of snow and rain percentages determined by daily temperature (Td):
i1525-7541-5-6-1147-e4
Once daily wind speed at the 10-m heights was determined, the daily CR for the CSPG was calculated using the regression equations (2)–(4) for snow, rain, or mixed precipitation. The daily catch ratio can be determined according to the precipitation type determined by daily temperature or direct observations. The wind-loss correction coefficient (K) was calculated as K = 1/CR. The daily corrected precipitation amount can be obtained from the daily precipitation data and daily wind-loss coefficient:
i1525-7541-5-6-1147-e5

3. Bias-correction results

Daily meteorological data, such as precipitation, wind speed at 10-m height, and air temperature are needed for the bias corrections. These data are available to this study at 710 stations in mainland China (9.6 million km2 between 15°–55°N and 75°–135°E) during 1951–98 (Fig. 1). The data have been quality controlled by the Climate Data Center, Chinese Meteorological Administration. Most of the stations were set up in the early 1950s. Meteorological station elevations are higher in western China and lower in eastern China. Among the 710 stations, 669 stations have long-term records (over 30 yr), and other stations have relatively short records (less than 30 yr). The stations selected and used for this work generally cover various climate regions over China.

a. Monthly bias correction

The monthly bias correction is the total amount of the daily correction for each month. They vary greatly over China because of different climate characteristics, particularly the amount and type of precipitation and wind conditions. To demonstrate the seasonal cycle of the bias corrections and the associated changes in precipitation amount, monthly trace amounts, wetting loss, and wind-induced errors are presented here for January, April, July, and October.

The monthly trace amounts are directly calculated from data of the monthly trace precipitation days. Monthly mean trace amounts are small, usually less than 1 mm over China (Fig. 2). Seasonal and spatial distributions of trace amounts and the correction factors (CFs; ratio of corrected amount to measured amount of precipitation) are different because of the precipitation variation. Generally, the CFs decline from northwest China to southeast China in all months, and the mean CFs are higher in winter months and lower in the summer season. For instance, the CF in January is usually less than 40% in north and west China and as high as 200% in some areas over southwest China because of variability of precipitation. On the other hand, the CF in July is usually less than 4%, with the maximum of 32% in northwest China. The CF of high precipitation in southeast China is less than 1% in every month. The areas with CF less than 1% are very large in July and small in January, indicating that trace corrections are more important in the winter season.

The monthly wetting loss is the sum of daily wetting loss determined by the daily and precipitation type. The monthly mean wetting loss amounts are usually less than 8 mm over China (Fig. 3). In northwest China, the wetting loss corrections are small in all months, that is, less than 2 mm in January, April, and October, and less than 3 mm in July. High wetting losses (over 4 mm) are found in southeast China in January and April and in middle regions of south China in July and October.

The mean monthly CFs for wetting loss range from 0.8% to 120% over China (Fig. 3). The mean CF is large in winter months and small in the summer season. The CFs usually are less than 80% in north and west China. There are several high CF centers in west China that correspond with low precipitation and high trace precipitation corrections. Usually, the CFs are less than 20% in southeast China. In July, the CF is usually less than 15% in northwest China and less than 3% in southeast China of high-precipitation regions. The CFs in April and October are moderate, between those for January and July. Generally, the CFs for wetting losses are larger than those for trace correction, except for some extreme low-precipitation regions.

The monthly mean wind-induced errors vary from 0 to 120 mm over China during 1951–98. Similar to the monthly precipitation, the spatial distribution of monthly mean wind-induced errors are characterized by low corrections in northwest China and high corrections in southeast China (Fig. 4). For instance, in northwest China, the wind-induced errors are less than 1, 3, 5, and 2 mm in January, April, July, and October, respectively. However, over southeast China, the errors are greater, that is, 4, 10, 15, and 5 mm for January, April, July, and October, respectively.

The monthly mean CFs for the wind-induced errors show very similar spatial patterns between months due to similar patterns of monthly mean daily wind speed for precipitation days. They generally correspond to annual mean wind speed for precipitation days and are enhanced by snow percentage due to difference in gauge catch of snow and rain. There is a high CF region in west-central China, in Qinhai-Xizang Plateau. Other high CF areas are in north China where the climate is cold and dry. The monthly CF of wind-induced error is usually 1%–40%, 3%–39%, 2%–31%, and 1%–40% in January, April, July, and October, respectively. The difference is due to the variations in wind speeds and snow percentage.

We also calculated the monthly total correction and its CF for wetting loss, trace, and wind-induced error. Figure 5 presents the results for January, April, July, and October and shows regional patterns over China. The monthly total corrections decreased from northwest China to southeast China. In northwest China, the total corrections are less than 4, 5, 10, and 5 mm in January, April, July, and October, respectively. However, over southeast China, the errors are greater, that is, 4, 10, 15, and 10 mm for January, April, July, and October, respectively. The CF increases from south to north in east China and has high centers around midwest China because of dry and cold climate at the high elevations.

To clearly show the difference between months, several stations have been chosen to represent the regional characteristics. The stations include the northern region with high snow percentage (Fig. 6a), the south region without any snow (Fig. 6b), high elevation with wet snow sometimes in summer months (Fig. 6c), and the extreme dry region (Fig. 6d). Monthly snow, rain, and mixed precipitation, corrected amounts (Fig. 6, left), correction factors (Fig. 6, middle), and wind speed for precipitation days (Fig. 6, right) are shown for the stations. The important common features of the bias corrections can be summarized as follows: 1) In each month, the absolute monthly amounts of wetting losses and wind-induced errors are usually greater than trace precipitation adjustments except for the extreme dry conditions. The trace precipitation events are important in the months with precipitation less than 0.5 mm. 2) Wind-induced errors are generally greater than wetting loss for the months with more than 15–20 mm of precipitation. 3) A clear seasonal variation of the monthly CF: high CF values for the cold season and low CF values for rain in the warm season. The CF in winter in north and west China is higher than in southeast China because of the higher wind loss for snow than for rain and due to the smaller amount of absolute precipitation in the cold season in north and west China. The CF seasonal variation at the stations with high precipitation (>1000 mm yr−1) is small (Fig. 6b).

b. Yearly bias correction

Based on the monthly results, the annual bias correction has been determined and summarized in Fig. 7. The averaged yearly correction for trace precipitation varied from 1.4 to 10 mm with an overall mean of 4.9 mm at the 710 stations, or about 0.1%–26% with an average of 1.4% of the gauge-measured annual precipitation over China. Correction for trace precipitation is thus important especially in northwest China because of low precipitation. The trace amount is several millimeters and the CFs are very small (less than 1%) in most of east China where high precipitation occurs. The yearly mean corrections for wetting loss ranged from 3.1 to 78 mm with an overall mean of 31 mm at the 710 stations, and the CFs are about 1.6%–23.7% with an average of 5.6% of the gauge-measured annual precipitation over China.

The patterns of the trace and wetting losses reflected the patterns of trace and measured precipitation days, respectively. The CF patterns are opposite to the patterns of the annual precipitation over China (Fig. 8). The CFs for both the trace and wetting losses decrease from northwest to southeast. It is important to note the annual corrected amounts of the wetting losses are generally larger than trace corrections except for some extremely dry climate regions in northwest China.

The annual average corrections for wind-induced errors have a much larger variation from 1.4 to 690 mm with an overall mean of 93 mm at the 710 stations over China. The amounts increased from less than 25 mm in northwest China to over 100 mm in southeast China. This difference from the annual precipitation patterns is due to variations in wind speed and the difference in the snow percentage. The annual CFs of the wind-induced errors are about 1.7%–32.1% with an average being 12.0% of the gauge-measured annual precipitation at the 710 stations over China. The mean annual total corrections ranged from 8 to 740 mm with an average of 130 mm at the 710 stations over China (Fig. 7). The amount varies from 8–50 mm in northwest China to 100–740 mm in southeast China. The amount is less then 100 mm in majority of China with annual precipitation less than 500–1000 mm. The mean annual CFs for total correction vary from 30%–62% in northwest China to 5%–15% in southeast China mainly due to the lower temperature, lower precipitation, higher snowfall proportion, and higher winds on precipitation days in the northern regions (Fig. 8). The overall mean annual CF at the 710 stations is 19%, which is higher than the global mean CF of 11.7% (Adam and Letternmaier 2003) and 11% (Legates and Willmott 1990).

Generally, the CF pattern for total correction is very similar to the CF pattern for the wind-induced error, although it has been enhanced by wetting loss and trace precipitation in west China, especially in the high CF regions in west-central China. The results show that the wind-induced errors are the largest bias, and trace and wetting losses are important particularly in the dry region of west China, that is, with yearly precipitation less than 250 mm. It is important to note that because of the effect of high elevation such as the Tianshan Mountains and Qinhai-Xizang Plateau in west China, the CF distribution over China shows not only poleward increase, which was found for the continental United States, Greenland, and northern Canada (Legates and DeLiberty 1993; Yang et al. 1998a, 1999b; Metcalfe et al. 1994), but also a westward increase due to the high-altitude region in west China. Similar results were found in the global monthly precipitation bias corrections (Adam and Lettenmaier 2003).

Maximum and minimum CF values are also provided in Fig. 9 to illustrate the interannual and interstation variation. The CF variation (5%–30% in east China and 15%–250% over the arid region in northwest China) is induced by year-to-year fluctuation of wind speed, frequency, and amounts of precipitation. This is particularly accentuated in west regions of China, where yearly precipitation is low (the annual total from 14 to 300 mm) and the trace and wetting loss are important components of the corrections. The annual CFs there can reach up to 60% or higher because of the low precipitation. The trace precipitation days are greater than the measurable precipitation days and mean daily precipitation is less than 3 mm. Similar results have been found in precipitation bias corrections for arctic regions such as northern Canada (Metcalfe and Goodison 1993) and Alaska (Yang et al. 1998a, 2001).

Observation data show that annual precipitation varies from 14 to 2800 mm over China (Fig. 8a). The long-term annual mean precipitation ranges from less 200 mm over northwest China to more than 1500 mm in the southern coastal regions, with mean snow percentage between 0% in the southeast regions to 46% in north and west region of China (Fig. 8c). Based on the bias-corrected results, yearly corrected precipitation maps were developed in this study (Fig. 10). Relative to the yearly precipitation map derived from the observed data (Fig. 8a), the bias-corrected yearly precipitation map (Fig. 10) shows much higher values over China. The area with less than 50 mm of precipitation is smaller, and the 500-m contour in the corrected precipitation map is similar to the 400-m contour in the gauge-measured precipitation map, although the precipitation distribution pattern has not changed much due to the bias corrections.

4. Conclusions and discussion

Long-term daily data of precipitation, temperature, and wind speed during 1951–98 at 710 meteorological stations in China were used to generate reliable precipitation datasets and climatology. Temperature data were used to determine precipitation types at some stations when direct observations were not available. Wind speeds at the gauge level were estimated from the wind data measured at the standard height of 10 m above the ground. The gauge catch ratio was calculated for the snow, mixed, and rain classes, and wind-induced error was quantified for each precipitation day. Trace precipitation and wetting losses were also estimated on a daily basis for all the stations. Based on this, bias corrections of Chinese standard precipitation gauge (CSPG) measurements for wind-induced undercatch, a trace amount of precipitation, and wetting loss have been completed in this study.

We found that trace corrections were from 1.4 to 10 mm with an overall mean of 4.9 mm, or about 0.1%– 26% (average 1.4%) of gauge-measured yearly amount at the 710 stations over China. The wetting losses ranged from 3.1 to 78 mm with an overall mean of 31 mm at the 710 stations over China, and the relative amounts are about 1.6%–23.7% with an average of 5.6% of the yearly total precipitation. The wind-induced errors vary from 1.4 to 690 mm with an overall mean of 93 mm at the 710 stations over China; the annual changes are about 1.4%–32.1% increases with an average of 12.0% at the 710 station. Among the errors, wind-induced gauge undercatch is the greatest in most regions, and wetting loss and trace amount of precipitation are important in the low-precipitation regions of the northwest China. Monthly correction factors differ by location and by type of precipitation. Considerable interannual variation of the corrections exists in China due to the fluctuations of wind speed and frequency of precipitation.

As a result of the corrections, mean annual precipitation has been increased by 8 to 740 mm with an overall mean of 130 mm at the 710 stations over China for the period 1951–98. This change corresponds to 6% to 62% increases (mean of 19% at the 710 stations over China) in gauge-measured yearly total precipitation. This result clearly shows that that annual precipitation in China is much higher than previously reported.

The bias corrections are based on some assumptions such as mean amounts for trace and wetting losses. All the assumptions are conservative and the evaporation loss is not considered in the paper, so the biases are not overestimated. We hope to better define wetting loss and the trace amount over China when data and information of precipitation duration and observation time become available. Our efforts are currently ongoing to assess the impacts of the bias corrections on regional climate change analysis and hydrological investigation over China.

Acknowledgments

This study was supported by Natural Science Foundation of China (NSFC) Grant 49971022, the Innovation Project of Chinese Academy of Sciences (KZCX1-10-06), and NSF Grant 0230083. We appreciate the constructive comments and suggestions by the three anonymous reviewers. We also thank the National Climate Center of the Chinese Meteorological Administration that provided the meteorological data. Part of this study was carried out in the Department of Civil Engineering of the University of Tokyo and was supported by the JSPS.

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    • Export Citation
  • Legates, D. R., and DeLiberty T. L. , 1993: Precipitation measurement biases in the United States. Water Resour. Bull, 29 , 854861.

  • Metcalfe, J. R., and Goodison B. E. , 1993: Correction of Canadian winter precipitation data. Preprints, Eighth Symp. on Meteorological Observations and Instrumentation, Anaheim, CA, Amer. Meteor. Soc., 338–343.

    • Search Google Scholar
    • Export Citation
  • Metcalfe, J. R., Ishida S. , and Goodison B. E. , 1994: A corrected precipitation archive for the Northwest Territories. Proc. Sixth Biennial AES/ DIAND Meeting on Northern Climate, Yellowknife, NT, Canada, AES/DIAND, Mackenzie Basin Impact Study Interim Rep. 2, 110–117.

    • Search Google Scholar
    • Export Citation
  • Sevruk, B., 1982: Method of correction for systematic error in point precipitation measurement for operational use. WMO Tech. Doc. 589, 91 pp.

    • Search Google Scholar
    • Export Citation
  • Sevruk, B., 1989: Reliability of precipitation measurement. Proc. Int. Workshop on Precipitation Measurement, St. Moritz, Switzerland, WMO/IAHS/ETH, 13–19.

    • Search Google Scholar
    • Export Citation
  • Sevruk, B., and Hamon W. R. , 1984: International comparison of national precipitation gauges with a reference pit gauge. WMO Instrument and Observing Methods Rep. 17, WMO, 111 pp.

    • Search Google Scholar
    • Export Citation
  • Walsh, J. E., Kattsov V. , Portis D. , and Meleshko V. , 1998: Arctic precipitation and evaporation: Model results and observational estimates. J. Climate, 11 , 7287.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, P., Rudolf B. , Schneider U. , and Arkin P. A. , 1996: Gauge-based monthly analysis of global precipitation from 1971 to 1994. J. Geophys. Res, 101 (D14) 1902319034.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, D., 1988: Research on analysis and correction of systematic errors in precipitation measurement in Urumqi River basin, Tianshan. Ph.D. dissertation, Lanzhou Institute of Glaciology and Geocryology, Chinese Academy of Sciences, 169 pp.

    • Search Google Scholar
    • Export Citation
  • Yang, D., 1999: An improved precipitation climatology for the Arctic Ocean. Geophys. Res. Lett, 26 , 16251628.

  • Yang, D., Shi Y. , Kang E. , and Zhang Y. , 1989: Research on analysis and correction of systematic errors in precipitation measurement in Urumqi River basin, Tianshan. Proc. Int. Workshop on Precipitation Measurement, St. Moritz, Switzerland, WMO/IAHS/ ETH, 173–179.

    • Search Google Scholar
    • Export Citation
  • Yang, D., Shi Y. , Kang E. , Zhang Y. , and Yang X. , 1991: Results of solid precipitation measurement intercomparison in the Alpine area of Urumqi River basin. Chin. Sci. Bull, 36 , 11051109.

    • Search Google Scholar
    • Export Citation
  • Yang, D., and Coauthors, 1995: Accuracy of Tretyakov precipitation gauge: Results of WMO intercomparison. Hydrol. Processes, 9 , 877895.

  • Yang, D., Benson C. B. , and Ishida S. , and Coauthors, 1998a: Adjustment of daily precipitation at 10 climate stations in Alaska: Application of WMO intercomparison results. Water Resour. Res, 34 , 241256.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, D., Metcalfe J. R. , Golubev V. S. , Bates R. , Pangburn T. , and Hanson C. L. , and Coauthors, 1998b: Accuracy of NWS 8′′ standard nonrecording precipitation gauge: Results and application of WMO intercomparison. J. Atmos. Oceanic Technol, 15 , 5468.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, D., and Coauthors, 1999a: Wind-induced precipitation undercatch of the Hellmann gauges. Nordic Hydrol, 30 , 5780.

  • Yang, D., Ishida S. , Goodison B. E. , and Gunther T. , 1999b: Bias correction of daily precipitation measurements for Greenland. J. Geophys. Res, 105 (D6) 61716182.

    • Search Google Scholar
    • Export Citation
  • Yang, D., and Coauthors, 2001: Compatibility evaluation of national precipitation gauge measurements. J. Geophys. Res, 106 (D2) 14811492.

Fig. 1.
Fig. 1.

Climate station distribution and elevation information over China

Citation: Journal of Hydrometeorology 5, 6; 10.1175/JHM-366.1

Fig. 2.
Fig. 2.

Contour map of (left) monthly mean correction (mm) and (right) correction factor (%) for trace precipitation in Jan, Apr, Jul, and Oct over China

Citation: Journal of Hydrometeorology 5, 6; 10.1175/JHM-366.1

Fig. 3.
Fig. 3.

Contour map of (left) monthly mean correction (mm) and (right) correction factor (%) for wetting loss in Jan, Apr, Jul, and Oct over China

Citation: Journal of Hydrometeorology 5, 6; 10.1175/JHM-366.1

Fig. 4.
Fig. 4.

Contour map of (left) monthly mean correction (mm) and (right) correction factor (%) for wind-induced errors in Jan, Apr, Jul, and Oct over China

Citation: Journal of Hydrometeorology 5, 6; 10.1175/JHM-366.1

Fig. 5.
Fig. 5.

Contour map of (left) monthly total correction (mm) and (right) correction factor (%) in Jan, Apr, Jul, and Oct over China

Citation: Journal of Hydrometeorology 5, 6; 10.1175/JHM-366.1

Fig. 6.
Fig. 6.

Monthly (left) observed precipitation and corrected amount, (middle) correction factors, and (right) wind speed at selected stations: (a) northern region with high snow percentage; (b) south region without any snow; (c) high elevation with wet snow sometimes in summer months; and (d) extreme dry region

Citation: Journal of Hydrometeorology 5, 6; 10.1175/JHM-366.1

Fig. 7.
Fig. 7.

Contour maps of (left) annual mean corrections (mm) and (right) correction factors (%) for trace precipitation, wetting loss, wind-induced errors, and total correction during 1951–98 over China

Citation: Journal of Hydrometeorology 5, 6; 10.1175/JHM-366.1

Fig. 8.
Fig. 8.

Contour maps of (a) mean annual measured precipitation (mm), (b) mean annual temperature (°C), (c) mean snow percentage (%), and (d) mean daily wind speed for precipitation days

Citation: Journal of Hydrometeorology 5, 6; 10.1175/JHM-366.1

Fig. 9.
Fig. 9.

Contour maps of annual maximum and minimum correction factor over China (%)

Citation: Journal of Hydrometeorology 5, 6; 10.1175/JHM-366.1

Fig. 10.
Fig. 10.

Contour maps of corrected mean annual precipitation over China during 1951–98

Citation: Journal of Hydrometeorology 5, 6; 10.1175/JHM-366.1

Save
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  • Karl, T. R., Quayle R. G. , and Groisman P. Y. , 1993: Detecting climate variations and change: New challenges for observing and data management systems. J. Climate, 6 , 14811494.

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  • Legates, D. R., and DeLiberty T. L. , 1993: Precipitation measurement biases in the United States. Water Resour. Bull, 29 , 854861.

  • Metcalfe, J. R., and Goodison B. E. , 1993: Correction of Canadian winter precipitation data. Preprints, Eighth Symp. on Meteorological Observations and Instrumentation, Anaheim, CA, Amer. Meteor. Soc., 338–343.

    • Search Google Scholar
    • Export Citation
  • Metcalfe, J. R., Ishida S. , and Goodison B. E. , 1994: A corrected precipitation archive for the Northwest Territories. Proc. Sixth Biennial AES/ DIAND Meeting on Northern Climate, Yellowknife, NT, Canada, AES/DIAND, Mackenzie Basin Impact Study Interim Rep. 2, 110–117.

    • Search Google Scholar
    • Export Citation
  • Sevruk, B., 1982: Method of correction for systematic error in point precipitation measurement for operational use. WMO Tech. Doc. 589, 91 pp.

    • Search Google Scholar
    • Export Citation
  • Sevruk, B., 1989: Reliability of precipitation measurement. Proc. Int. Workshop on Precipitation Measurement, St. Moritz, Switzerland, WMO/IAHS/ETH, 13–19.

    • Search Google Scholar
    • Export Citation
  • Sevruk, B., and Hamon W. R. , 1984: International comparison of national precipitation gauges with a reference pit gauge. WMO Instrument and Observing Methods Rep. 17, WMO, 111 pp.

    • Search Google Scholar
    • Export Citation
  • Walsh, J. E., Kattsov V. , Portis D. , and Meleshko V. , 1998: Arctic precipitation and evaporation: Model results and observational estimates. J. Climate, 11 , 7287.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, P., Rudolf B. , Schneider U. , and Arkin P. A. , 1996: Gauge-based monthly analysis of global precipitation from 1971 to 1994. J. Geophys. Res, 101 (D14) 1902319034.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, D., 1988: Research on analysis and correction of systematic errors in precipitation measurement in Urumqi River basin, Tianshan. Ph.D. dissertation, Lanzhou Institute of Glaciology and Geocryology, Chinese Academy of Sciences, 169 pp.

    • Search Google Scholar
    • Export Citation
  • Yang, D., 1999: An improved precipitation climatology for the Arctic Ocean. Geophys. Res. Lett, 26 , 16251628.

  • Yang, D., Shi Y. , Kang E. , and Zhang Y. , 1989: Research on analysis and correction of systematic errors in precipitation measurement in Urumqi River basin, Tianshan. Proc. Int. Workshop on Precipitation Measurement, St. Moritz, Switzerland, WMO/IAHS/ ETH, 173–179.

    • Search Google Scholar
    • Export Citation
  • Yang, D., Shi Y. , Kang E. , Zhang Y. , and Yang X. , 1991: Results of solid precipitation measurement intercomparison in the Alpine area of Urumqi River basin. Chin. Sci. Bull, 36 , 11051109.

    • Search Google Scholar
    • Export Citation
  • Yang, D., and Coauthors, 1995: Accuracy of Tretyakov precipitation gauge: Results of WMO intercomparison. Hydrol. Processes, 9 , 877895.

  • Yang, D., Benson C. B. , and Ishida S. , and Coauthors, 1998a: Adjustment of daily precipitation at 10 climate stations in Alaska: Application of WMO intercomparison results. Water Resour. Res, 34 , 241256.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, D., Metcalfe J. R. , Golubev V. S. , Bates R. , Pangburn T. , and Hanson C. L. , and Coauthors, 1998b: Accuracy of NWS 8′′ standard nonrecording precipitation gauge: Results and application of WMO intercomparison. J. Atmos. Oceanic Technol, 15 , 5468.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, D., and Coauthors, 1999a: Wind-induced precipitation undercatch of the Hellmann gauges. Nordic Hydrol, 30 , 5780.

  • Yang, D., Ishida S. , Goodison B. E. , and Gunther T. , 1999b: Bias correction of daily precipitation measurements for Greenland. J. Geophys. Res, 105 (D6) 61716182.

    • Search Google Scholar
    • Export Citation
  • Yang, D., and Coauthors, 2001: Compatibility evaluation of national precipitation gauge measurements. J. Geophys. Res, 106 (D2) 14811492.

  • Fig. 1.

    Climate station distribution and elevation information over China

  • Fig. 2.

    Contour map of (left) monthly mean correction (mm) and (right) correction factor (%) for trace precipitation in Jan, Apr, Jul, and Oct over China

  • Fig. 3.

    Contour map of (left) monthly mean correction (mm) and (right) correction factor (%) for wetting loss in Jan, Apr, Jul, and Oct over China

  • Fig. 4.

    Contour map of (left) monthly mean correction (mm) and (right) correction factor (%) for wind-induced errors in Jan, Apr, Jul, and Oct over China

  • Fig. 5.

    Contour map of (left) monthly total correction (mm) and (right) correction factor (%) in Jan, Apr, Jul, and Oct over China

  • Fig. 6.

    Monthly (left) observed precipitation and corrected amount, (middle) correction factors, and (right) wind speed at selected stations: (a) northern region with high snow percentage; (b) south region without any snow; (c) high elevation with wet snow sometimes in summer months; and (d) extreme dry region

  • Fig. 7.

    Contour maps of (left) annual mean corrections (mm) and (right) correction factors (%) for trace precipitation, wetting loss, wind-induced errors, and total correction during 1951–98 over China

  • Fig. 8.

    Contour maps of (a) mean annual measured precipitation (mm), (b) mean annual temperature (°C), (c) mean snow percentage (%), and (d) mean daily wind speed for precipitation days

  • Fig. 9.

    Contour maps of annual maximum and minimum correction factor over China (%)

  • Fig. 10.

    Contour maps of corrected mean annual precipitation over China during 1951–98

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