Evaluation of different reanalysis precipitation datasets is of great importance to understanding the hydrological processes and water resource management practice in the Qinling-Daba Mountains (QDM), located at the eastern fringe of the Tibetan Plateau. Although the evaluation of satellite precipitation data in this region has been performed, another kind of popular precipitation product-reanalysis dataset has not been assessed in depth. Three popular reanalysis precipitation datasets, including ERA-Interim Reanalysis of European Centre for Medium Forecasts (ERA-Interim), Japanese 55-year Reanalysis (JRA-55), and National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalysis-1 (NCEP/NCAR-1) were evaluated against rain gauge data over the Qinling-Daba Mountains from 2000 to 2014 on monthly, seasonal, and annual scales. Different statistical measures based on the Correlation Coefficient (CC), relative BIAS (BIAS), Root-Mean-Square Error (RMSE), and Mean Absolute Error (MAE) were adopted to determine the performance of the above reanalysis datasets. Results show that ERA-Interim and JRA-55 have good performance on a monthly scale and annual scale. However, the NCEP/NCAR-1 has the least BIAS with the observed precipitation in annual scale in QDM. All reanalysis datasets performed better in spring, summer, and autumn than in winter. The advantages of involving more precipitation observation stations was probably the main reason of the different performance of three precipitation reanalysis products, and the benefit of a four-dimensional variational analysis model over a three-dimensional variational analysis model may be another reason. The evaluation suggested that ERA-Interim is more suitable for study the precipitation and water cycles in the QDM.
As a major component of the hydrological and energy cycle, the spatial and temporal patterns of precipitation greatly impact land surface hydrological fluxes and states [
Over the past several decades, tremendous efforts have been made to measure and monitor precipitation [
Therefore, various research studies have been conducted to explore the performance of the different datasets on regional [
The Qinling-Daba Mountains (QDM), which geographically and climatologically divide northern and southern China with the Huaihe River, serve as an important water source for the middle route of South-to-North Water Diversion Project in China. Meanwhile, the QDMs are located at the eastern fringe of the mountain region of the Qinghai Tibetan Plateau, which is the source of many large rivers and called “Asia Water Tower.” Knowledge of precipitation in the QDM is of great significance to water resources management, hydrological modeling, and climate research in the immediate and surrounding regions. Given the large variations in the terrain, mountain systems develop considerably complex local and regional climate systems [
The development of precipitation datasets provides beneficial conditions to measure precipitation in the QDM. Ren et al. [
The main objective of this work is to evaluate three reanalysis-based datasets on monthly, seasonal, and annual scales through observations in the QDM during 2000–2014. This paper is organized as follows: Section
The QDM, referring to both Qinling Mountain and Daba Mountain, are located in central China with an area of about 222,300 km2, from 30°50′ N to 34°59′ N latitude and from 102°54′ E to 112°40′ E longitude (Figure
The location and the spatial distribution of the rain gauge stations over the Qinling-Daba Mountains in China (Figure
(a) The spatial distribution of annual rainfall; (b) the time distribution of monthly rainfall and air temperature throughout the year over the Qinling-Daba Mountains, China (Figure
ERA-Interim, JRA-55, and NCEP/NCAR-1, which are popular used in many studies [
ERA-Interim is a global reanalysis product created by European Center for Medium-Range Weather Forecasts (ECMWF) [
JRA-55 is a global reanalysis dataset constructed by the Japan Meteorological Agency (JMA) [
NCEP/NCAR-1 is a global reanalysis dataset of atmosphere fields produced by the National Centers for Environmental Prediction and National Center for Atmospheric Research to meet the needs of research and climate monitoring communities [
The China Meteorological Administration (CMA) provided daily in situ observational precipitation data over the QDM during 2000–2014. The precipitation is manually observed at 8 : 00 and 20 : 00 per day by a rain gauges without windproof fences, the area of the collector orifice is 200 cm2, ground stations using the same criteria of CMA in the observation field with 25 m × 25 m with short grass cover, the gauge orifice is 0.7 m above surface. Snow collected in precipitation gauges was melted after each observation and then measured using a standard glass graduated measuring cylinder. Routine maintenance includes the gauge and the field [
A 0.1° buffer both in latitude and longitude direction of the study area boundary was utilized to ensure that the 27 chosen rain gauge stations were able to delineate relatively accurate spatial distribution of precipitation as much as possible [
The basic information of gauge station over the Qinling-Daba Mountains, China.
Station name | Latitude (°) | Longitude (°) | Elevation (m a.s.l.) | Observation years |
---|---|---|---|---|
Minxian | 34.43 | 104.02 | 2315 | 2000–2014 |
Wudu | 33.4 | 104.92 | 1079 | 2000–2014 |
Tianshui | 34.58 | 105.75 | 1142 | 2000–2003, 2007–2008 |
Beidao | 34.57 | 105.87 | 1085 | 2004–2014 |
Baoji | 34.35 | 107.13 | 612 | 2000–2004, 2007–2008 |
Huashan | 34.48 | 110.08 | 2065 | 2000–2014 |
Lushi | 34.05 | 111.03 | 569 | 2000–2014 |
Luanchuan | 33.78 | 111.6 | 750 | 2000–2014 |
Lueyang | 33.32 | 106.15 | 794 | 2000–2014 |
Liuba | 33.65 | 106.95 | 1547 | 2009–2014 |
Hanzhong | 33.07 | 107.03 | 510 | 2000–2014 |
Foping | 33.52 | 107.98 | 827 | 2000–2014 |
Shangzhou | 33.87 | 109.97 | 742 | 2000–2014 |
Zhen’an | 33.43 | 109.15 | 694 | 2000–2014 |
Shangnan | 33.46 | 110.58 | 1137 | 2009–2014 |
Xixia | 33.3 | 111.5 | 250 | 2000–2014 |
Guangyuan | 32.43 | 105.85 | 514 | 2000–2014 |
Ningqiang | 32.84 | 105.95 | 1400 | 2009–2014 |
Shiquan | 33.05 | 108.27 | 485 | 2000–2014 |
Wanyuan | 32.07 | 108.03 | 674 | 2000–2014 |
Zhenba | 32.56 | 107.91 | 1231 | 2009–2014 |
Ankang | 32.72 | 109.03 | 291 | 2000–2014 |
Yunxi | 33 | 110.42 | 249 | 2000–2014 |
Yunxian | 32.85 | 110.82 | 202 | 2007–2008 |
Fangxian | 32.03 | 110.77 | 427 | 2000–2014 |
Zhenping | 31.91 | 109.51 | 1615 | 2009–2014 |
Fengjie | 31.02 | 109.53 | 300 | 2000–2014 |
In terms of temporal resolution, daily gauged precipitation data were accumulated to monthly and annual data. Meanwhile, the precipitation rate data from JRA-55 and NCEP/NCAR-1 were multiplied by corresponding times to obtain rainfall amount which is on the same time scale as gauged rainfall. The monthly total precipitation data of ERA-Interim were obtained by accumulating the daily precipitation. The seasonal total precipitation were summed from monthly precipitation, including winter precipitation (December, January, and February), spring precipitation (March, April, and May), summer precipitation (June, July, and August), and autumn precipitation (September, October, and November).
It is common practice in evaluation studies to compare the point-based rain gauge data against the grid-based precipitation datasets. Given the 18 stations with continuous time series over the QDM, a point-pixel comparison was performed in this study to avoid errors by gridding the rain gauge data [
To quantitatively assess the performance of ERA-Interim, JRA-55, and NCEP/NCAR-1 in the QDM, the following several statistical indices were obtained and compared:
Given the emphasis in multiple related studies on assessment of precipitation products [
The absolute precipitation differences (PD) and percentage of PD (PPD) were adopted as two different methods to quantitatively determine the agreement between precipitation datasets and gauge data during the dry and wet years:
The spatial pattern of the precipitation and its temporal change is one of the characteristics of regional precipitation and hydrological process. Traditional spatial evaluation of precipitation products directly compares the spatial distribution of rainfall, which lack quantitative descriptions. Therefore, this study implemented precipitation centroid movement over a 15-year period from 2000 to 2014 to further explore the effectiveness of ERA-Interim, JRA-55, and NCEP/NCAR-1.
A centroid, which stems from the concept of center of mass (or gravity) in physics, was first introduced in humanities and social fields, such as population, economy, and tourism [
Coordinates of the precipitation centroid were calculated using the following formulas:
The performance of ERA-Interim, JRA-55, and NCEP/NCAR-1 on monthly, seasonal, and annual scales is presented in this section. In this study, the monthly scale was used as the base time scale, and movement of the precipitation centroid was analyzed to further explore the performance of the three datasets over the QDM, China.
Three reanalysis precipitation datasets were first validated on a monthly scale. To eliminate the influence of the seasonal cycle on CC, the CC of each precipitation dataset was calculated per month (Figure
The line chart of correlation coefficient (CC) between monthly observed precipitation and ERA-Interim, JRA-55, and NCEP/NCAR-1 precipitation during 2000 to 2014 over the Qinling-Daba Mountains, China.
Therefore, the average CCs of the twelve months of a specific year were treated as the overall performance of every single precipitation product on a monthly scale, which are shown in Table
The average of CC, BIAS, RMSE, and MAE between three reanalysis precipitation datasets and gauged precipitation data on a monthly scale during 2000 to 2014 over the Qinling-Daba Mountains, China.
Index | ERA-Interim | JRA-55 | NCEP/NCAR-1 |
---|---|---|---|
CC | 0.64 | 0.58 | 0.22 |
BIAS (%) | 21.78 | 16.57 | −6.31 |
RMSE (mm) | 48.56 | 43.05 | 64.93 |
MAE (mm) | 30.42 | 30.81 | 39.18 |
Overall, ERA-Interim and JRA-55 revealed a similar ability to simulate rainfall for evaluation indices. ERA-Interim had better CC and MAE, and JRA-55 had a better BIAS and RMSE. NCEP/NCAR-1 had the lowest CC and largest RMSE and MAE, suggesting that NCEP/NCAR-1 is the poor performing dataset, even though it had a low BIAS.
The spatial distribution of evaluation indices at individual gauges was obtained to investigate the performance of the three precipitation products, as illustrated in Figures
Correlation coefficient at each precipitation gauge stations over the Qinling-Daba Mountains for monthly precipitation between (a) ERA-Interim and gauges, (b) JRA-55 and gauges, and (c) NCEP/NCAR-1 and gauges.
BIAS (%) at each gauge over the Qinling-Daba Mountains for monthly precipitation between (a) ERA-Interim and gauges, (b) JRA-55 and gauges, and (c) NCEP/NCAR-1 and gauges.
The spatial distribution of correlation coefficients suggests that CCs at most sites were greater than 0.5 for ERA-Interim and JRA-55 (Figure
Furthermore, stations with relatively high CCs were concentrated in the northeastern region of the QDM, which due to the relatively low altitude in the eastern region. The influence of terrain in the eastern region is less than the western and northwestern part of QDM. It is well established that the topographic and orographic influences on precipitation formation and propagation. It is expected that there is less precipitation on the leeward side of the mountain on the western side of the QDM because of the dry-adiabatic decent of air, which leads to lower CCs. It is worth noting that ERA-Interim and JRA-55 had the lowest CC at Wudu station at the same time.
ERA-Interim and JRA-55 had similar distributions of BIAS, which are both overestimated the precipitation at the most stations. Comparatively, NCEP/NCAR-1 underestimated rainfall. ERA-Interim, JRA-55, and NCEP/NCAR-1 all had the largest errors at Wudu station with positive BIAS values of 130.4%, 123.3%, and 223.4%, respectively, which may be attributed to the complex terrain in Bailongjiang Valley. Another interesting phenomenon was the underestimation of precipitation at Huashan station for ERA-Interim, JRA-55, and NCEP/NCAR-1. Considering that the elevation of Huashan station is 2054 m a.s.l., the large wind at such a high elevation leads to lower precipitation gauge capture rate [
In this study, precipitation was greater during summer (June–August) and autumn (September–November) than in spring (March–May) and winter (December–next February) (Figure
CC, BIAS, RMSE, and MAE between three reanalysis precipitation datasets and gauged precipitation data at the seasonal scale over the Qinling-Daba Mountains, China (all products passed the significance test at the 99% confidence level).
Season | Dataset | CC | BIAS (%) | RMSE (mm) | MAE (mm) |
---|---|---|---|---|---|
Spring | ERA-Interim | 0.67 | 23.61 | 69.45 | 55.79 |
JRA-55 | 0.51 | 35.61 | 89.65 | 71.88 | |
NCEP/NCAR-1 | 0.07 | −5.55 | 120.12 | 88.11 | |
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Summer | ERA-Interim | 0.57 | 27.80 | 187.92 | 154.01 |
JRA-55 | 0.66 | 1.86 | 120.62 | 95.39 | |
NCEP/NCAR-1 | 0.12 | 1.99 | 211.08 | 170.28 | |
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Autumn | ERA-Interim | 0.66 | 10.38 | 87.10 | 67.36 |
JRA-55 | 0.70 | 8.49 | 83.21 | 62.20 | |
NCEP/NCAR-1 | 0.05 | −14.92 | 171.11 | 128.61 | |
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Winter | ERA-Interim | 0.49 | 109.77 | 36.03 | 31.26 |
JRA-55 | 0.17 | 209.67 | 65.95 | 58.16 | |
NCEP/NCAR-1 | 0.05 | 16.03 | 29.43 | 21.06 |
All reanalysis datasets displayed higher CC values in spring, summer, and autumn than winter and had lower BIAS values in summer and autumn than spring and winter. Thus, precipitation datasets performed better in warmer and wetter seasons (summer and autumn), which may be conducive to monitoring and predicting geologic hazards caused by heavy rain in a short time period in the QDM. The larger errors in RMSE and MAE in summer and autumn may be due to the fact that rainfall concentrated during those seasons over the QDM (Figure
It is worth mentioning that JRA-55 coincided worse performing with observed rainfall in spring and winter but performed better in summer and autumn than ERA-Interim, which indicates that it may be better to use JRA-55 for simulating abundant precipitation than ERA-Interim over the QDM. The CCs of NCEP/NCAR-1 were too low to simulate true rainfall, making NCEP/NCAR-1 the worst performance dataset of the three.
In summary, all evaluated datasets displayed higher accuracy in summer and autumn than spring and, especially winter, when the performance of the datasets was much worse than the other seasons. ERA-Interim and JRA-55 had similar performance and good agreement with observed precipitation, while NCEP/NCAR-1 showing the poorest performance.
The average annual precipitation of each dataset was calculated and compared with the in situ observed precipitation on an annual scale (Figure
The line chart of regional average annual precipitation from rain gauges and the three reanalysis datasets.
The annual precipitation was in continuous fluctuation from 2000 to 2014, and the overall trend of reanalysis datasets was consistent with the precipitation from rain gauges. However, some deviations were found for certain years: the observed rainfall reached a maximum and minimum value in 2011 and 2001, respectively, while ERA-Interim, JRA-55, and NCEP/NCAR-1 were not in agreement: ERA-Interim and JRA-55 peaked in 2003, which meant the rainfall simulation ability should be further enhanced. Meanwhile, changes in the performance of the reanalysis datasets may be related to improvements in algorithms and additional data in recent years.
A quantitative evaluation on an annual scale in the QDM is based on the overall performance of the three reanalysis precipitation datasets in Table
CC, BIAS, RMSE, and MAE between three reanalysis precipitation datasets and gauged rainfall at annual scale over the Qinling-Daba Mountains, China (all products passed the significance test at the 99% confidence level).
Index | ERA-Interim | JRA-55 | NCEP/NCAR-1 |
---|---|---|---|
CC | 0.54 | 0.56 | −0.19 |
BIAS | 21.78 | 16.57 | −6.31 |
RMSE | 298.21 | 265.40 | 437.87 |
MAE | 243.93 | 214.38 | 340.83 |
From the evaluation indices, JRA-55 had higher accuracy than ERA-Interim at annual scale with slight advantages. NCEP/NCAR-1 had best BIAS value and worst CC, RMSE, and MAE. Considering the possible mutual cancellation in BIAS, NCEP/NCAR-1 was also regard as the worst product all in all.
The spatial distribution of CC for annual precipitation at each gauge over the QDM (Figure
Correlation coefficient at each gauge over the Qinling-Daba Mountains for annual precipitation between (a) ERA-Interim and gauges, (b) JRA-55 and gauges, and (c) NCEP/NCAR-1 and gauges.
Comprehensively, the values of RMSE and MAE varied with the precipitation accumulation on monthly, seasonal, and annual scales. ERA-Interim and JRA-55 performed better on a monthly scale than an annual scale. On a monthly scale, ERA-Interim had a higher CC and lower MAE values than JRA-55, while JRA-55 had a lower BIAS and RMSE than ERA-Interim. The performance of ERA-Interim and JRA-55 is equally matched. However, JRA-55 was better than ERA-Interim for all indices on an annual scale. Further, ERA-Interim and JRA-55 exhibit a better ability to simulate precipitation in the spring, summer, and autumn than in winter.
To further explore the performance of reanalysis precipitation datasets, based on whether the gauges average annual precipitation in the year is greater or less than the average during 2000 to 2014, the 15-year period was further divided into two groups: wet years and dry years. The wet years include 2000, 2003, 2005, 2009, 2010, 2011, and 2014; the other years between 2001 and 2014 belong to the dry years over the QDM. The PDs and PPDs during the wet and dry years are shown in Tables
The PDs and PPDs between ERA-Interim, JRA-55, and NCEP/NCAR-1 and observed precipitation for the wet years.
Reanalysis | ERA-Interim | JRA-55 | NCEP/NCAR-1 |
---|---|---|---|
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2000 | 405.25 | 259.75 | 94.72 |
2003 | 371.56 | 192.93 | −55.51 |
2005 | 125.74 | 60.4 | −48.49 |
2009 | 103.6 | 69.75 | −121.62 |
2010 | 178.22 | 97.56 | −43.38 |
2011 | 39.93 | 68.77 | −242.83 |
2014 | 52.86 | 83.53 | −84.66 |
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2000 | 46.18 | 29.6 | 10.79 |
2003 | 37.61 | 19.53 | −5.62 |
2005 | 13.76 | 6.61 | −5.31 |
2009 | 11.94 | 8.04 | −14.01 |
2010 | 19.32 | 10.58 | −4.7 |
2011 | 3.99 | 6.88 | −24.28 |
2014 | 6.29 | 9.93 | −10.07 |
The PDs and PPDs between ERA-Interim, JRA-55, and NCEP/NCAR-1 and observed precipitation for the dry years.
Reanalysis | ERA-Interim | JRA-55 | NCEP/NCAR-1 |
---|---|---|---|
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2001 | 234.99 | 233.97 | −170.66 |
2002 | 269.71 | 215.85 | 40.56 |
2004 | 283.36 | 135.59 | 39.84 |
2006 | 196.41 | 143.75 | 85.85 |
2007 | 320.53 | 183.57 | 92.53 |
2008 | 167.6 | 118.2 | −9.86 |
2012 | 187.36 | 159.27 | −72.32 |
2013 | 148.97 | 119.31 | 36.18 |
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2001 | 35.4 | 35.25 | −25.71 |
2002 | 37.42 | 29.95 | 5.63 |
2004 | 36.4 | 17.42 | 5.12 |
2006 | 27.42 | 20.07 | 11.98 |
2007 | 39.62 | 22.69 | 11.44 |
2008 | 21.2 | 14.95 | −1.25 |
2012 | 24.89 | 21.16 | −9.61 |
2013 | 20.03 | 16.05 | 4.87 |
The PDs and PPDs for reanalysis precipitation datasets varied in the wet (Table
The precipitation centroids of rain gauges and ERA-Interim, JRA-55, and NCEP/NCAR-1 reanalysis products were all located in the central region of the QDM and presented an east-west spatial distribution pattern (Figure
The precipitation centroids over the Qinling-Daba Mountains during 2000 to 2014 of (a) rain gauge, (b) ERA-Interim, (c) JRA-55, and (d) NCEP/NCAR-1, and (e) the black box represents the data range in a–d.
The centroid movement distance is accumulated to estimate the magnitude of correspondence between reanalyzed and gauged data as an evaluation indicator (Table
The migration distance (km) of precipitation centroid of gauge and precipitation products.
Year | Gauge | ERA-Interim | JRA-55 | NCEP/NCAR-1 |
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2000 | ||||
2001 | 19.6 | 40.3 | 7.2 | 55.7 |
2002 | 19.6 | 19.1 | 8.6 | 30.3 |
2003 | 8.9 | 12.0 | 11.1 | 22.5 |
2004 | 7.6 | 6.8 | 13.0 | 2.8 |
2005 | 11.4 | 12.2 | 10.7 | 12.3 |
2006 | 12.2 | 2.4 | 6.2 | 3.6 |
2007 | 8.9 | 8.0 | 15.2 | 9.6 |
2008 | 4.9 | 5.7 | 5.2 | 9.2 |
2009 | 12.6 | 3.8 | 7.4 | 25.7 |
2010 | 8.1 | 4.5 | 10.4 | 28.6 |
2011 | 17.7 | 14.2 | 21.2 | 33.8 |
2012 | 16.4 | 3.5 | 14.0 | 7.1 |
2013 | 17.5 | 20.6 | 5.0 | 26.5 |
2014 | 39.2 | 24.8 | 24.1 | 28.4 |
Sum | 204.6 | 177.9 | 159.2 | 296.1 |
In terms of time, all three reanalysis precipitation datasets showed poor performance in winter on a seasonal and monthly scale. Standard Chinese rain gauges lack windproof and automatic heating devices; the solid precipitation measured is artificially melted into water immediately after the snow events. Considering the complexity of mountainous areas such as QDM, the interference by wind may cause only half of the actual precipitation to be represented by observed solid precipitation in rain gauges without a windproof device [
The three datasets evaluated in this work display bigger errors and larger deviations at Wudu station than the other stations, for which the complex terrain may be responsible. Wudu station is located in the Bailongjiang River valley with an altitude of 1079 m a.s.l., while the mountains on both sides are all above this elevation, and the peak of the mountains are above 2500 m a.s.l. Although the concave terrain allows the rain gauge at Wudu station to obtain accurate rainfall in the valley, it cannot reflect the true precipitation information in the surrounding areas to some extent. Some precipitation recorded at Wudu station was probably caused by local convection, but not over a large range. On the other hand, the difference between the actual altitude and the altitude in different reanalysis data probably contributes to the relatively large errors at Wudu station, where the complex terrain cannot be represented by coarse resolution reanalysis data.
The elevation of Huashan station is 2064 m a.s.l., which almost reaches the elevation of peak of Huashan Mountain (2154 m a.s.l.). The three reanalysis precipitation datasets underestimated the precipitation at Huashan station at the same time, which also can be explained by the large difference between the actual altitude and the altitude in different reanalysis data. This also indicates that there are precipitation gradients around Huashan stations, although they are difficult to be detected in current in situ observation and satellite precipitation products. More intense in situ precipitation observation network will help to obtain the local precipitation gradients.
It needs to note that various reanalysis datasets may have varied performance on different scales and for each evaluation index. Harada et al. [
ERA-Interim and JRA-55 merged precipitation data from observed stations, while no rain observations were included for assimilation of the NCEP/NCAR-1 model. However, how many and which stations in the QDM were involved in ERA-Interim and JRA-55 are difficult to obtain and cannot be excluded in the evaluation. It seems unfair to compare ERA-Interim, JRA-55, and NCEP/NCAR-1 by the same observation data. On the other hand, the coarsest spatial resolution may be another reason for the poor performance of NCEP/NCAR-1. Meanwhile, the advantages of the four-dimensional variational analysis over three-dimensional variational analysis model is potentially one of reasons why ERA-Interim and JRA-55 showed higher agreement than NCEP/NCAR-1 [
It should also be noted that the observed precipitation also probably has some uncertainty. The general problem of representativeness is particularly acute in the measurement of precipitation, and precipitation measurements are particularly sensitive to exposure, wind, and topography. Although both the meteorological stations are observed with the manually standard process in the standard field with grass land cover, the local factors such as terrain and wind are still different to considered and corrected. Many different studies [
Based on this study and Wang’s previous study [
QDM is a unique region where precipitation can significantly be impacted by the terrain which has serious impacts to the local community by environmental, ecological, and biological processes. Three reanalysis precipitation datasets, including ERA-Interim, JRA-55, and NCEP/NCAR-1, were evaluated over the QDM against rain gauge data from 2000 to 2014 on monthly, seasonal, and annual scales. Based on all results, some conclusions can be made:
Overall, the performance of ERA-Interim is close to that of JRA-55 with higher CC above 0.5 and lower RMSE less than 50 mm in monthly scale, while NCEP/NCAR-1 has the worst performance on a monthly scale and annual scale. However, the NCEP/NCAR-1 has the least BIAS with the observed precipitation in an annual scale in QDM.
All reanalysis datasets performed better in spring, summer, and autumn than in winter. JRA-55 had a better agreement with rain gauge data in summer and autumn, while ERA-Interim exhibited a higher agreement in spring and winter in QDM.
The advantages of involving more precipitation observation stations are probably the main reason of the different performance of three precipitation reanalysis products, and the benefit of a four-dimensional variational analysis model over a three-dimensional variational analysis model may be another reason.
The evaluation on different precipitation products is very important to understanding the spatial-temporal distribution of precipitation in QDM, which is critical to simulation the hydrological processes and water resource management in QDM, where is the main water source of Xian city. Enhancing the precipitation measuring accuracy, especially in winter, and increasing the measuring stations are still needed to further evaluation the different precipitation products in QDM.
All relevant data can be obtained from the following links. ERA-Interim can be obtained from
The authors declare that they have no conflicts of interest.
This work was supported by the National Key Research and Development Plan (2017YFC1502501) and China National Natural Science Foundation (nos. 41671056 and 41730751).