1. Introduction
The South China Sea (SCS), a primary source of water vapor for summer rainfall in China (Chow et al. 2008; Lu and Hao 2017), is one of the most sensitive areas for climate change (Ose et al. 1997; Yan et al. 2010). The occurrence frequency of marine disasters (Fang et al. 2017) and extreme weather around SCS raised rapidly in recent years (Huang et al. 2021; Hu et al. 2019; Proag 2021), modulating the climate in East Asia (Yao-Dong et al. 2012). There are two rainy seasons in South China: the early one is from April to June, and the late one is from July to September (Yihui and Zunya 2008). The frontal process dominates the first rainy season (Li et al. 2021), and the precipitation was strongly affected by the pattern of water vapor flux around SCS (Cheng and Lu 2020; Sun et al. 2019). Variables connected with water vapor flux might be the most critical factors that might modulate monsoon precipitation.
Atmospheric reanalysis can provide a sound representation of the regional or global climate and describe multivariate atmospheric conditions by combining a fixed data assimilation system with global observations and satellite data (Hersbach et al. 2020; Kalnay et al. 1996; Kanamitsu et al. 2002; Kistler et al. 2001). After a series of assessments (Fonseca‐Hernandez et al. 2021; Friedrich et al. 2017; Sheridan et al. 2020; Virman et al. 2021), reanalyses have been widely used to describe regional and global climate variation, especially in monsoon regions (Bai et al. 2020; Sheridan et al. 2020). However, the bias from the reanalysis is obvious, especially for monsoon precipitation (Ceglar et al. 2017). NCEP–NCAR and ERA-40 underestimate the monsoon precipitation in Eastern China (Song and Tianjun 2012). NCEP–NCAR tends to give an erroneous precipitation pattern in northeastern China (Liu et al. 2018). ERA-40 has poor performance of precipitation in some tropical seas (Bosilovich et al. 2008). Both ERA-40 and NCEP–NCAR exhibit a significant bias in daily precipitation over the tropical western Pacific (Takahashi et al. 2006).
As a combination of thermal (i.e., atmospheric humidity) and dynamic variables, water vapor flux is one of the most direct and vital controllers on monsoon precipitation (Jones et al. 2017). Because of the considerable uncertainty buried in atmospheric analyses, their water vapor flux needs to be assessed first. Virman et al. (2021) reported that both atmospheric temperature and relative humidity in ERA5 had significant bias over tropical oceans compared with radiosonde data, and the unrealistic entrainment process might be the cause. Kishore et al. (2011) used COSMIC radiosondes to investigate water vapor global distribution (50°S–50°N). They found that ERA-Interim performed better than NCEP–NCAR. The latter heavily underestimated the atmospheric humidity at a low level. Many studies have found that differences in the wind field are significant between observation and reanalyses (Gebremariam 2016; Kozubek et al. 2014; Schafer et al. 2003; Wang et al. 2020). Therefore, an assessment of water vapor flux among different reanalyses might be important not only for understanding the monsoon system but also for promoting assimilation techniques and developing its related observation network.
During the South China Sea Monsoon Experiment (SCSMEX), Xia et al. (2009) studied the differences of potential temperature and specific humidity between NCEP–NCAR and observation. They found that the bias in islands was more significant than inland, illuminating that the different underlying surfaces lead to a different performance of atmosphere profile from reanalysis datasets. Xiang-Shu and Guo (2008) used radiosonde data to study the characteristic of water vapor transport during the SCSMEX and found the water vapor transport would be affected by the change of atmosphere dynamic and thermal field. Two studies did not evaluate water vapor flux from reanalyses directly.
This study compared the water vapor flux from three reanalyses to the radiosonde observations over SCS. Different from previous studies, the subdaily variation of atmospheric profiles would be most concerned. First, a description of methodology and datasets are presented in section 2. Section 3 provided the comparisons between reanalyses and observation. Furthermore, we discuss which variables (thermal or dynamic ones) are most important for water vapor flux patterns from different reanalyses. Finally, section 4 provided conclusions based on the study results.
2. Data and methodology
a. Radiosonde data
The radiosonde data used are from the South China Sea Scientific Expedition organized by Sun Yat-sen University in the summer of 2019 (SCSEX2019). The entire voyage lasted for 31 days, from 4 June to 4 July. A total of 132 profiles from GPS radiosondes (RS41-SGP, Vaisala) were obtained during the experiment, at intervals of 6 h (4 times a day in the first half of the voyage) or 3 h (6 times a day in the second half of the journey). The detection period of the probe is 1 s, thus producing an atmospheric profile with high vertical resolution. The measurements included real-time temperature, (relative) humidity, air pressure, horizontal wind speed, and direction, with a detection accuracy of 0.1°C, 2%, 0.4 hPa, 0.1 m s−1, and 0.1°C, respectively. The observations at 0000, 0600, 1200, and 1800 UTC were selected, and their sample numbers are 24, 25, 26, and 11, respectively.
b. Reanalysis data
Many atmospheric reanalysis datasets have been published worldwide (Hersbach et al. 2020; Kalnay et al. 1996; Kanamitsu et al. 2002; Kobayashi et al. 2015; Wang et al. 2018). Differences among reanalyses have complicated causes, including differences in observations net (Bao and Zhang 2019; Fujiwara et al. 2017), assimilation methods (Jones et al. 2017), understanding of physical processes, and quality control criteria (Schafer et al. 2003; Szot and Kosowski 2013).
This study uses three atmospheric reanalyses, ERA5, JRA55, and NCEP–DOE (NCEP-2). The spatial resolution of the three reanalyses is different, which might cause a spatial mismatch when compared with radiosonde data along the voyage course. To avoid this, we calculate the regional averaged variables over 114°–118°E, 19°–22°N for all reanalyses. Since this region is under a subtropical high (Fig. 1), the spatial distribution of atmospheric variables within it should be pretty homogeneous. There is not any intensive synoptic process traveling across there during the experiment. Therefore, the radiosonde observation represents a relatively large area, just like a grid in reanalysis.
The voyage (blue lines) of SCSEX2019 and temporal averaged specific humidity (shading; g kg−1), temperature (black lines; °C), and wind field (black arrows; m s−1) at 850 hPa from 4 Jun to 4 Jul. The thick black line indicates 5880-m height of 500 hPa.
Citation: Journal of Hydrometeorology 23, 6; 10.1175/JHM-D-21-0210.1
The voyage (blue lines) of SCSEX2019 and temporal averaged specific humidity (shading; g kg−1), temperature (black lines; °C), and wind field (black arrows; m s−1) at 850 hPa from 4 Jun to 4 Jul. The thick black line indicates 5880-m height of 500 hPa.
Citation: Journal of Hydrometeorology 23, 6; 10.1175/JHM-D-21-0210.1
The voyage (blue lines) of SCSEX2019 and temporal averaged specific humidity (shading; g kg−1), temperature (black lines; °C), and wind field (black arrows; m s−1) at 850 hPa from 4 Jun to 4 Jul. The thick black line indicates 5880-m height of 500 hPa.
Citation: Journal of Hydrometeorology 23, 6; 10.1175/JHM-D-21-0210.1
1) ERA5
The European Center for Medium-Range Weather Forecasts (ECMWF) released its latest reanalysis dataset, named ERA5, in 2017 (Hersbach et al. 2020). ERA5 reanalysis, as a constituent part of the EU-funded Copernicus Climate Change Service, uses the Cycle 41r2 Integrated Forecasting System (IFS) and provides data on the global weather and climate. ERA5 uses an ensemble of four-dimensional variational assimilation systems (4D-Var). As an upgraded version of ERA-Interim, ERA5 benefits from many improvements in the observation operators and a decade of developments in model physics, core dynamics, and data assimilation. Compared with ERA-Interim, ERA5 has several new features, including high-resolution (on both temporal and spatial scale) output, advanced parameterization schemes, more assimilated observation, more realistic consideration of forces, etc. The first segment of ERA5 is from 1979 to the present, and it will eventually extend to 1950. The spatial resolution used in the study is 0.25° × 0.25° (Hoffmann et al. 2019).
2) JRA55
The Japan Meteorological Agency (JMA) conducted the second Japanese global atmospheric reanalysis, called the Japanese 55-Year Reanalysis or JRA55. It covers the period from 1958, when regular radiosonde observations began globally, and will be continued for forthcoming years (Ebita et al. 2011; Tsujino et al. 2017). JRA55 is the first comprehensive reanalysis that has covered the last half-century since the ERA-40 (Uppala 2005) and is the first one to apply four-dimensional variational analysis to this period.
3) NCEP-2
The main objective of the NCEP–DOE Reanalysis-2 (NCEP-2 hereafter) project is to correct known errors in NCEP–NCAR and improve the parameterizations of the physical processes. Using a three-dimensional variational assimilation system and an improved forecast model, NCEP-2 proves a 6-hourly global analysis series from 1979 to the present. It provides a better reanalysis and is recommended for users affected by known errors in NCEP-1. Although NCEP-2 might not necessarily provide better analyses than NCEP–NCAR, it can be considered as an updated version of NCEP–NCAR (Kanamitsu et al. 2002; Roads 2003).
c. Water vapor flux
d. Differences and difference ratio
3. Result
The voyage line of the observation experiment is given in Fig. 1. The observation region is north of the subtropical high, with southwesterly as the predominant wind direction. Because of the subtropical high, no significant synoptic process originated or passed during the experiment (figure omitted). Therefore, the finding in this region should represent a large portion of northern SCS in this period.
a. Temperature and specific humidity
The Taylor diagrams (Taylor 2001) of temperature and specific humidity at each level are given in Fig. 2. ERA5 seems to give the best atmospheric temperature (T), especially at 500 and 600 hPa. The daily variability of T is overestimated by NCEP-2 and underestimated by JRA55. All reanalyses have underestimated the daily variability in specific humidity (SH), and NCEP-2 cannot even capture the phase of SH in the lower troposphere. The daily T profile is more representative than SH in the reanalyses over the SCS.
Taylor diagrams of daily (a) temperature, (b) specific humidity, (c) zonal wind, (d) meridional wind, (e) zonal water vapor flux, and (f) meridional water vapor flux at different levels (indicated with numbers) over the South China Sea. The period is from 8 Jun to 3 Jul 2019.
Citation: Journal of Hydrometeorology 23, 6; 10.1175/JHM-D-21-0210.1
Taylor diagrams of daily (a) temperature, (b) specific humidity, (c) zonal wind, (d) meridional wind, (e) zonal water vapor flux, and (f) meridional water vapor flux at different levels (indicated with numbers) over the South China Sea. The period is from 8 Jun to 3 Jul 2019.
Citation: Journal of Hydrometeorology 23, 6; 10.1175/JHM-D-21-0210.1
Taylor diagrams of daily (a) temperature, (b) specific humidity, (c) zonal wind, (d) meridional wind, (e) zonal water vapor flux, and (f) meridional water vapor flux at different levels (indicated with numbers) over the South China Sea. The period is from 8 Jun to 3 Jul 2019.
Citation: Journal of Hydrometeorology 23, 6; 10.1175/JHM-D-21-0210.1
The daily average profiles of T and SH at different moments are given in Fig. 3. The T profile from the reanalyses is close to the observation but with a negative bias. The bias usually appeared at 1000 hPa, and JRA55 gives the largest negative bias, about 1.9°C at 1800 UTC. The temperature T at 850 hPa from the reanalyses seems identical with observation at all moments, except for JRA55 at 1800 UTC. Considering that the top of the atmospheric boundary layer (ABL) over the ocean is close to 850 hPa (∼1500 m MSL), most reanalyses tend to give a much more stably stratified ABL and weak vertical turbulent transport within it. For the whole layers under 500 hPa, ERA5 performs better than JRA55 in T and SH. A possible cause for the cold bias in T is the sea surface temperature (SST). The average SST used in ERA5 is about 1.5°C cooler than our observation (using an infrared radiometer, SI-111, Campbell Scientific Inc.) during the experiment (not shown), which might have caused the cooler and drier atmosphere in the lower troposphere in the reanalyses.
The mean profiles of the regional averaged temperature (°C) and specific humidity (g kg−1) around the area at (a),(c) 0000, (e),(g) 0600, (i),(k) 1200, and (m),(o) 1800 UTC. The difference of T and SH between the reanalyses and the observation at (b),(d) 0000, (f),(h) 0600, (j),(l) 1200, and (n),(p) 1800 UTC. The thick black lines are the observation. The blue, red, and green dashed lines are JRA55, NCEP-2, and ERA5, respectively. The gray shades are the standard deviations for the observation.
Citation: Journal of Hydrometeorology 23, 6; 10.1175/JHM-D-21-0210.1
The mean profiles of the regional averaged temperature (°C) and specific humidity (g kg−1) around the area at (a),(c) 0000, (e),(g) 0600, (i),(k) 1200, and (m),(o) 1800 UTC. The difference of T and SH between the reanalyses and the observation at (b),(d) 0000, (f),(h) 0600, (j),(l) 1200, and (n),(p) 1800 UTC. The thick black lines are the observation. The blue, red, and green dashed lines are JRA55, NCEP-2, and ERA5, respectively. The gray shades are the standard deviations for the observation.
Citation: Journal of Hydrometeorology 23, 6; 10.1175/JHM-D-21-0210.1
The mean profiles of the regional averaged temperature (°C) and specific humidity (g kg−1) around the area at (a),(c) 0000, (e),(g) 0600, (i),(k) 1200, and (m),(o) 1800 UTC. The difference of T and SH between the reanalyses and the observation at (b),(d) 0000, (f),(h) 0600, (j),(l) 1200, and (n),(p) 1800 UTC. The thick black lines are the observation. The blue, red, and green dashed lines are JRA55, NCEP-2, and ERA5, respectively. The gray shades are the standard deviations for the observation.
Citation: Journal of Hydrometeorology 23, 6; 10.1175/JHM-D-21-0210.1
The differences in SH between the reanalyses and observation are closely connected with T, with correlation coefficients of 0.44, 0.48, and 0.75 for ERA5, JRA55, and NCEP-2, respectively. On the one hand, atmospheric temperature and moisture are constrained by the Clausius–Clapeyron equation. On the other hand, turbulence within the ABL simultaneously determines the vertical distribution of both SH and T. As a result, the negative bias in T and SH tends to appear simultaneously. From Fig. 3, the negative bias of SH is also concentrated under 800 hPa and decreases quickly with height, which partly confirms the dominance of turbulent transport within the ABL. JRA55 gives the most significant underestimation of SH at 1800 UTC (0200 LST) at 1000 hPa, about 1.8 g kg−1. The differences for the vertical averaged SH under 500 hPa are −0.70 g kg−1 for ERA5, −0.79 g kg−1 for JRA55, and −0.89 g kg−1 for NCEP-2 (Table 1). Therefore, we suggest that ERA5 has the best performance in representing the SH under 500 hPa in the northern SCS, JRA55 is the second, and NCEP-2 is the worst.
The daily average differences and difference ratio (show in parentheses) of specific humidity (g kg−1) from ERA, JRA55, and NCEP-2.
b. Wind profiles
The three reanalyses’ wind profiles are close to the observation (Fig. 2). ERA5 gives the largest standard deviation and lowest correlation with observation at 1000 hPa. The average profiles of daily zonal (u) and meridional (υ) wind at different moments are given together in Fig. 4. For the u component, all three reanalyses perform well except for an underestimation at most levels. The profiles of u from ERA5 agree well with JRA55, but both are further from the observation than NCEP-2 above 700 hPa. Surprisingly, all reanalyses give a significant underestimation of u at 1800 UTC. At this moment, the most significant bias from ERA5 is at 650 hPa and 1800 UTC, being 2.89 m s−1, and that for NCEP-2 is about −2.15 m s−1 at 925 hPa. It should be noted that the observed u is strongest at 1800 UTC, and all reanalyses show the most significant negative bias at this moment. The underestimation in air temperature under 500 hPa from the reanalyses is also most significant at 1800 UTC (Fig. 3), which may lead to a weaker thermal contrast between the ocean and continent and a weak meridional pressure gradient under 500 hPa.
As in Fig. 3, but for u (left two columns; m s−1) and υ (right two columns).
Citation: Journal of Hydrometeorology 23, 6; 10.1175/JHM-D-21-0210.1
As in Fig. 3, but for u (left two columns; m s−1) and υ (right two columns).
Citation: Journal of Hydrometeorology 23, 6; 10.1175/JHM-D-21-0210.1
As in Fig. 3, but for u (left two columns; m s−1) and υ (right two columns).
Citation: Journal of Hydrometeorology 23, 6; 10.1175/JHM-D-21-0210.1
Compared with u, υ from the three reanalyses all have positive bias under 850 hPa. The υ at 850 hPa seems identical to observation, with a bias of ±1 m s−1. Nevertheless, all reanalyses significantly overestimated υ under 850 hPa, especially at 1800 UTC. At 1000 hPa and 1800 UTC, υ from ERA5 is about 3.06 m s−1 greater than the observation. In general, JRA55 and NCEP-2 give better υ profiles than ERA5. There is a significant bias in the wind direction within the ABL from all the reanalyses from the bias of u and υ.
Previous reports have noted the poor performance of reanalyses in the surface wind over the ocean. Cha et al. (2021) noted that the surface wind from ERA-Interim is about 1.5 m s−1 weaker than buoy observations in the South China Sea. On a global scale, Rivas and Stoffelen (2019) reported that ERA (both ERA5 and ERA-Interim) winds are characterized by too weak mean meridional winds (trades) in the tropics. Due to the discontinuity in surface roughness and solar irradiation, the reanalysis wind owns a large bias near the coastal area (Gualtieri 2021; Pescio et al. 2022). We suggest that the bias in the reanalyses might exist in the mid- to low troposphere over the coastal ocean area.
c. Water vapor flux from monthly mean fields
The three reanalyses give similar performance to the horizontal wind, and it seems that horizontal wind is the decisive factor in water vapor flux. The Taylor diagram of zonal and meridional water vapor flux at each level is given in Figs. 2e and 2f. To be identical with previous sections, we calculate the zonal (Qu) and meridional (Qυ) water vapor flux from mean profiles of wind and humidity (Fig. 5). Since the observation period is close to a month, these results are indicated as the water vapor fluxes from monthly mean fields. The vertical average of Qu (
As in Fig. 3, but for Qu (left two columns; m s−1 g kg−1) and Qυ (right two columns).
Citation: Journal of Hydrometeorology 23, 6; 10.1175/JHM-D-21-0210.1
As in Fig. 3, but for Qu (left two columns; m s−1 g kg−1) and Qυ (right two columns).
Citation: Journal of Hydrometeorology 23, 6; 10.1175/JHM-D-21-0210.1
As in Fig. 3, but for Qu (left two columns; m s−1 g kg−1) and Qυ (right two columns).
Citation: Journal of Hydrometeorology 23, 6; 10.1175/JHM-D-21-0210.1
The most prominent negative bias is given by NCEP-2 at 925 hPa, with −44.88 m s−1 g kg−1 at 1800 UTC, respectively. Compared with NCEP-2, ERA5 has the most prominent positive bias at 1000 hPa, about 40.49 m s−1 g kg−1 at 0000. Like u, Qu at 1800 UTC is underestimated at each level under 500 hPa (Fig. 5n), except for ERA5 and JRA55 at 1000 hPa. The differences and difference ratios of Qu among the reanalyses are listed in Table 2. The averaged Qu under 500 hPa confirms the underestimation from all reanalyses. The difference in mean Qu is most significant at 1000 hPa, with the difference ratio > 100%; ERA5 gives the most prominent differences, being about 18.97 m s−1 g kg−1, and the difference ratios can reach 116.9%. All reanalyses give a negative bias at 925 hPa, and NCEP-2 has the most significant at −26.06 m s−1 g kg−1. At 500 hPa, ERA5 gives the most prominent difference ratio of about −45.6%, much more significant than JRA55 (−30.1%) and NCEP-2 (−22.9%). Even though ERA5 still gives the best performance, JRA55 is the second, NCEP-2 is the worst under 500 hPa (Table 2).
The difference and difference ratio (in parentheses) of zonal and meridional water vapor flux (m s−1 g kg−1) from reanalyses under 500 hPa. The maximum absolute value in each column is in bold.
The difference in Qυ from reanalyses is almost all negative under 500 hPa except for 1000 hPa. All three reanalyses have a positive bias at 1000 hPa, and the most significant bias is given by ERA5, being about 50.73 m s−1 g kg−1 with a difference ratio of about 101%. JRA55 and NCEP-2 have a negative bias from 925 to 500 hPa. JRA55 gives the best performance from the vertically averaged Qυ, NCEP-2 is the second, and ERA5 is the worst. Therefore, ERA5 gives the best zonal water vapor flux but the worst meridional one in SCS from the monthly mean fields.
d. Daily water vapor flux
The time series of the daily vertical average of water vapor flux,
The daily series of (a) zonal and (b) meridional vertically averaged water vapor flux (m s−1 g kg−1). The average of each series was given as colored numbers.
Citation: Journal of Hydrometeorology 23, 6; 10.1175/JHM-D-21-0210.1
The daily series of (a) zonal and (b) meridional vertically averaged water vapor flux (m s−1 g kg−1). The average of each series was given as colored numbers.
Citation: Journal of Hydrometeorology 23, 6; 10.1175/JHM-D-21-0210.1
The daily series of (a) zonal and (b) meridional vertically averaged water vapor flux (m s−1 g kg−1). The average of each series was given as colored numbers.
Citation: Journal of Hydrometeorology 23, 6; 10.1175/JHM-D-21-0210.1
The differences in Qυ between reanalyses and the observation are significant on 8–13 June and 21–28 June. The correlation of
The difference between the daily water vapor flux and the water vapor flux from the monthly mean fields should be mainly due to the contribution of the synoptical process. However, it should be noted that the observed average daily water vapor flux shows little difference from the water vapor flux from the monthly mean field, while most reanalyses tend to give a 1/3 to 1 times greater value for the former. Therefore, the contribution of the synoptic process in the horizontal water vapor flux has been significantly overestimated by the reanalyses, especially ERA5. Considering the correlation coefficients and RMSEs, we believe that NCEP-2 gives the most realistic daily horizontal water vapor flux among three reanalyses under 500 hPa in SCS.
e. Source of the water vapor flux bias
The first term on the right-hand side of the equations is the dynamic term (DT), representing the bias mainly due to the unrealistic dynamic field given by the reanalysis. Similarly, the second is the thermal term (TT), and the last is the covariance term (CT). CT is much smaller than the other two terms, so we ignored it in this study. The daily variation of these terms was first calculated, and their averages during the experiment period are listed in Tables 3 and 4, accompanying the difference ratios. A positive ratio indicates a positive contribution to the total bias in water vapor flux. Four pressure levels, 500, 700, 925, and 1000 hPa, are chosen in this section.
Three terms and their ratio (in parentheses) that contributed to ΔQu at different levels. The maximum absolute value in each column is in bold.
At 1000 hPa, ΔQu and ΔQυ are mainly contributed by DT. The difference ratio of DT at 1000 hPa is >100%, and TT is small but negative for all three reanalyses. Therefore, the wind field is the leading cause of the bias in the water vapor flux and the humidity offset part of the bias for the reanalyses. The wind field from ERA5 at 1000 hPa is the most erroneous among the three datasets, making its DT contribute the greatest ΔQu and ΔQυ under 500 hPa. As for JRA55 and NCEP-2, DT contributed most for ΔQυ at 1000 hPa, and for ΔQu at 925 hPa. Therefore, the wind structure within the ABL, especially within the surface layer, needs to be improved over SCS before a reliable horizontal water vapor delivery can be well given by the reanalyses.
At 700 and 500 hPa, DT is comparable to TT, especially for JRA55. At 700 hPa, ERA5 still gave the largest difference ratios of DT, being 92.8% for ΔQu and 85.4% for ΔQυ, respectively. The ratio of DT given by JRA55 is 56.6% for ΔQu and 53.8% for ΔQυ. At 500 hPa, TT is comparable to DT in the three reanalyses. Generally, humidity becomes essential to give a realistic water vapor flux in layers above the ABL in the reanalyses.
The vertical average DT and TT for ΔQu under 500 hPa are all negative and quite close with each other among the reanalyses. The DT ratio is about 60%, and the TT ratio is about 40%. Such results indicate that all reanalyses underestimate the zonal wind and humidity in the lower troposphere, generating an unrealistic weak Qu; ERA5 gives the best ΔQu, as its bias is one-third smaller than those from JRA55 and NCEP-2.
In contrast, the vertical average DT and TT for ΔQυ owns the opposite signs, since all reanalyses underestimate SH (Fig. 3) and overestimate the υ (Fig. 4) under 500 hPa. The
From the discussions above, the most efficient effort might be different to improve the mean water vapor flux of a reanalysis. Taking
4. Conclusions
This study assesses the temperature, specific humidity, horizontal wind, and water vapor flux from the sea surface to 500 hPa on the northern South China Sea with an observation experiment on a research vessel. We find that all three reanalysis datasets captured the observed features of atmospheric profiles under 500 hPa, regardless of slight differences. The main conclusions are as follows:
There is a cold and dry bias in all three reanalyses compared with the radiosonde observation. The bias of the mean specific humidity from reanalyses is less than 10% of observation under 500 hPa. The daily T profile is more representative than SH from reanalyses over the SCS.
All three reanalyses failed to give a reliable wind direction under 850 hPa, especially at 1800 UTC. The underestimation in u and overestimation in υ are similar in all reanalyses.
During the observation experiment, the primary bias of the water vapor flux from reanalyses is concentrated under the 925 hPa. The observation indicates that the contribution of the synoptic process, which all reanalyses have overestimated, especially the ERA5, is small for the water vapor flux. NCEP-2 performed best in giving a realistic horizontal water vapor in the South China Sea.
All three reanalyses agree that the u bias contributed to two-thirds of the underestimation in zonal water vapor flux under 500 hPa, and the atmospheric humidity bias contributed to the rest. In contrast, the effects of the υ and humidity bias offset each other, reducing the water vapor flux bias, especially for JRA55.
From our discussions, the bias in water vapor flux from reanalyses needs to be treated with caution in scientific research. The bias in water vapor flux is not only from the bias of wind and humidity profiles but also from the accumulated effect from the submonth-scale process. For a certain period in a given direction (zonal or meridional), these terms might work in different roles, thus needing to be dealt with specifically. To efficiently improve the water vapor flux in an atmospheric product (like a reanalysis), besides implementing more observations in core regions (like the upstream ocean area), understanding the bias source is critical.
Acknowledgments.
This study was supported by the National Key R&D Program of China (2020YFA0608804, 2019YFA0607004), the Guangdong Basic and Applied Basic Research Foundation (2020A1515110675, 2019A1515111041), and the National Natural Science Foundation of China (U1901209). We appreciate the great work of the crew members who participated in the observation expedition in 2019. We thank three reanalysis (ERA5, JRA55, and NCEP-2) developers and their managers and funding agencies, whose work and support were essential for obtaining the datasets.
Data availability statement.
The ERA5 were provided by the ECMWF at (https://apps.ecmwf.int/data-catalogues/era5/?type=an&class=ea&stream=oper&expver=1). The JRA55 was obtained from Japanese Meteorological Agency (JMA) and is available at (http://doi.org/10.5065/D6HH6H41). And the NCEP-2 was obtained from National Oceanic and Atmospheric Administration (NOAA) Physical Sciences Laboratory (PSL) at (https://psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html). Selected radiosonde data archiving for supporting this paper is underway and can be found online (https://doi.org/10.6084/m9.figshare.16895296.v1).
REFERENCES
Bai, L., Y. Wen, C. Shi, Y. Yang, and J. Meng, 2020: Which precipitation product works best in the Qinghai-Tibet Plateau, multi-source blended data, global/regional reanalysis data, or satellite retrieved precipitation data? Remote Sens., 12, 683, https://doi.org/10.3390/rs12040683.
Bao, X., and F. Zhang, 2019: How accurate are modern atmospheric reanalyses for the data-sparse Tibetan Plateau region? J. Climate, 32, 7153–7172, https://doi.org/10.1175/JCLI-D-18-0705.1.
Bosilovich, G. M., J. Chen, and F. Robert, 2008: Evaluation of global precipitation in reanalyses. J. Appl. Meteor. Climatol., 47, 2279–2299, https://doi.org/10.1175/2008JAMC1921.1.
Ceglar, A., A. Toreti, G. Balsamo, and S. Kobayashi, 2017: Precipitation over monsoon Asia: A comparison of reanalyses and observations. J. Climate, 30, 465–476, https://doi.org/10.1175/JCLI-D-16-0227.1.
Cha, J., X. Y. Lin, X. G. Guo, X. F. Wan, and D. W. You, 2021: Evaluation of reanalysis surface wind products with quality-assured buoy wind measurements along the north coast of the South China Sea. Acta Oceanol. Sin., 40, 58–69, https://doi.org/10.1007/s13131-021-1746-x.
Cheng, T. F., and M. Lu, 2020: Moisture source–receptor network of the east Asian summer monsoon land regions and the associated atmospheric steerings. J. Climate, 33, 9213–9231, https://doi.org/10.1175/JCLI-D-19-0868.1.
Chow, K. C., H. W. Tong, and J. Chan, 2008: Water vapor sources associated with the early summer precipitation over China. Climate Dyn., 30, 497–517, https://doi.org/10.1007/s00382-007-0301-6.
Ebita, A., S. Kobayashi, Y. Ota, M. Moriya, and T. Ishimizu, 2011: The Japanese 55-year reanalysis “JRA-55”: An interim report. Sci. Online Lett. Atmos., 7, 149–152, https://doi.org/10.2151/sola.2011-038.
Fang, J., and Coauthors, 2017: Spatial-temporal changes of coastal and marine disasters risks and impacts in Mainland China. Ocean Coast. Manage., 139, 125–140, https://doi.org/10.1016/j.ocecoaman.2017.02.003.
Fonseca-Hernandez, M., C. Turrent, Y. G. Mayor, and I. Tereshchenko, 2021: Using observational and reanalysis data to explore the southern Gulf of California boundary layer during the North American monsoon onset. J. Geophys. Res. Atmos., 126, e2020JD033508, https://doi.org/10.1029/2020JD033508.
Friedrich, L. S., A. J. McDonald, G. E. Bodeker, K. E. Cooper, J. Lewis, and A. J. Paterson, 2017: A comparison of Loon balloon observations and stratospheric reanalysis products. Atmos. Chem. Phys., 17, 855–866, https://doi.org/10.5194/acp-17-855-2017.
Fujiwara, M., and Coauthors, 2017: Introduction to the SPARC Reanalysis Intercomparison Project (S-RIP) and overview of the reanalysis systems. Atmos. Chem. Phys., 17, 1417–1452, https://doi.org/10.5194/acp-17-1417-2017.
Gebremariam, S. T., 2016: Wind speed comparison of climate models, reanalysis and observations over North America. Ph.D. thesis, Howard University, 102 pp.
Gualtieri, G., 2021: Reliability of ERA5 reanalysis data for wind resource assessment: A comparison against tall towers. Energies, 14, 4169, https://doi.org/10.3390/en14144169.
Hersbach, H., B. Bell, P. Berrisford, S. Hirahara, and J. Thépaut, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803.
Hoffmann, L., and Coauthors, 2019: From ERA-Interim to ERA5: The considerable impact of ECMWF’s next-generation reanalysis on Lagrangian transport simulations. Atmos. Chem. Phys., 19, 3097–3124, https://doi.org/10.5194/acp-19-3097-2019.
Hu, P., Y. Liu, and Y. Hou, 2019: Studies on marine dynamic disasters. J. Oceanol. Limnol., 37, 1791–1794, https://doi.org/10.1007/s00343-019-1791-x
Huang, Y., X. Huang, H. Zhao, and J. Jin, 2021: The influence of environmental factors on the intensity of typhoon Mirinae in the South China Sea and its forecast analysis. IOP Conf. Ser.: Earth Environ. Sci., 658, 012019, https://doi.org/10.1088/1755-1315/658/1/012019.
Jones, P. D., C. Harpham, A. Troccoli, B. Gschwind, and S. Dorling, 2017: Using ERA-Interim reanalysis for creating datasets of energy-relevant climate variables. Earth Syst. Sci. Data, 9, 471–495, https://doi.org/10.5194/essd-9-471-2017.
Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437–472, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.
Kanamitsu, M., W. Ebisuzaki, J. Woollen, S. K. Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter, 2002: NCEP–DOE AMIP-II Reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 1631–1644, https://doi.org/10.1175/BAMS-83-11-1631.
Kishore, P., and Coauthors, 2011: Global (50°S–50°N) distribution of water vapor observed by COSMIC GPS RO: Comparison with GPS radiosonde, NCEP, ERA-Interim, and JRA-25 reanalysis data sets. J. Atmos. Sol.-Terr. Phys., 73, 1849–1860, https://doi.org/10.1016/j.jastp.2011.04.017.
Kistler, R., and Coauthors, 2001: The NCEP-NCAR 50-year reanalysis: Monthly means CD-ROM and documentation. Bull. Amer. Meteor. Soc., 82, 247–268, https://doi.org/10.1175/1520-0477(2001)082<0247:TNNYRM>2.3.CO;2.
Kobayashi, S., and Coauthors, 2015: The JRA-55 reanalysis: General specifications and basic characteristics. J. Meteor. Soc. Japan, 93, 5–48, https://doi.org/10.2151/jmsj.2015-001.
Kozubek, M., J. Laštovička, and P. Križan, 2014: Differences in mid-latitude stratospheric winds between reanalysis data and versus radiosonde observations at Prague. Ann. Geophys., 32, 353–366, https://doi.org/10.5194/angeo-32-353-2014.
Li, X., C. Wang, and J. Lan, 2021: Role of the South China Sea in Southern China rainfall: Meridional moisture flux transport. Climate Dyn., 56, 2551–2568, https://doi.org/10.1007/s00382-020-05603-w.
Liu, P., and Coauthors, 2018: Comparison the Adaptability of CFSR, MERRA, NCEP Reanalysis Precipitation Data and Observation in Northeast China. Res. Soil Water Conserv., 25, 215–221, https://doi.org/10.13869/j.cnki.rswc.2018.04.032.
Lu, M., and X. Hao, 2017: Diagnosis of the tropical moisture exports to the mid-latitudes and the role of atmospheric steering in the extreme precipitation. Atmosphere, 8, 256, https://doi.org/10.3390/atmos8120256.
Ose, T., Y. Song, and A. Kitoh, 1997: Sea surface temperature in the South China Sea: An index for the Asian monsoon and ENSO system. J. Meteor. Soc. Japan, 75, 1091–1107, https://doi.org/10.2151/jmsj1965.75.6_1091.
Pescio, A. E., W. C. Dragani, and P. B. Martin, 2022: Performance of surface winds from atmospheric reanalyses in the southwestern South Atlantic Ocean. Int. J. Climatol., 42, 2368–2383, https://doi.org/10.1002/joc.7371.
Proag, V., 2021: Climate change and infrastructure. Infrastructure Planning and Management: An Integrated Approach, Springer, 279–304, https://doi.org/10.1007/978-3-030-48559-7_10.
Rivas, M. B., and A. Stoffelen, 2019: Characterizing ERA-Interim and ERA5 surface wind biases using ASCAT. Ocean Sci., 15, 831–852, https://doi.org/10.5194/os-15-831-2019.
Roads, J., 2003: The NCEP NCAR, NCEP DOE, and TRMM tropical atmosphere hydrologic cycles. J. Hydrometeor., 4, 826–840, https://doi.org/10.1175/1525-7541(2003)004<0826:TNNATT>2.0.CO;2.
Schafer, R., S. K. Avery, and K. S. Gage, 2003: A comparison of VHF wind profiler observations and the NCEP–NCAR reanalysis over the tropical Pacific. J. Appl. Meteor., 42, 873–889, https://doi.org/10.1175/1520-0450(2003)042<0873:ACOVWP>2.0.CO;2.
Sheridan, S. C., C. C. Lee, and E. T. Smith, 2020: A comparison between station observations and reanalysis data in the identification of extreme temperature events. Geophys. Res. Lett., 47, e2020GL088120, https://doi.org/10.1029/2020GL088120.
Song, F., and A. Z. Tianjun, 2012: Reliability of the 20CR reanalysis data in measuring the east Asian summer monsoon variability. Chin. J. Atmos. Sci., 36, 1207–1219, https://doi.org/10.3878/j.issn.1006-9895.2012.12005.
Sun, B., H. Wang, and B. Zhou, 2019: Interdecadal variation of the relationship between east Asian water vapor transport and tropical Pacific sea surface temperatures during January and associated mechanisms. J. Climate, 32, 7575–7594, https://doi.org/10.1175/JCLI-D-19-0290.1.
Szot, S., and M. Kosowski, 2013: A comparison of the radiosonde and NCEP-NCAR Reanalysis data over Central Europe. Proc. Int. Scientific Conf. Environmental Changes and Adaptation Strategies, Skalica, Slovakia, Slovak University of Agriculture, 4 pp., http://cbks.cz/SbornikSkalice2013/pdf/Szot.pdf.
Takahashi, K., N. Yamazaki, and H. Kamahori, 2006: Trends of heavy precipitation events in global observation and reanalysis datasets. SOLA, 2, 96–99, https://doi.org/10.2151/sola.2006-025.
Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106, 7183–7192, https://doi.org/10.1029/2000JD900719.
Tsujino, H., and Coauthors, 2017: JRA-55 based surface dataset for driving ocean–sea-ice models (JRA55-do). Ocean Modell., 130, 79–139, https://doi.org/10.1016/j.ocemod.2018.07.002.
Uppala, S. M., 2005: The ERA-40 re-analysis. Quart. J. Roy. Meteor. Soc., 131, 2961–3012, https://doi.org/10.1256/qj.04.176.
Virman, M., M. Bister, J. Räisänen, V. A. Sinclair, and H. Järvinen, 2021: Radiosonde comparison of ERA5 and ERA-Interim reanalysis datasets over tropical oceans. Tellus, 73A, 1–7, https://doi.org/10.1080/16000870.2021.1929752.
Wang, G., X. Wang, H. Wang, M. Hou, Y. Li, W. Fan, and Y. Liu, 2020: Evaluation on monthly sea surface wind speed of four reanalysis data sets over the China seas after 1988. Acta Oceanol. Sin., 39, 83–90, https://doi.org/10.1007/s13131-019-1525-0.
Wang, M., and Coauthors, 2018: Collection and pre-processing of satellite remote-sensing data in CRA-40 (CMA’s global atmospheric reanalysis) (in Chinese). Adv. Meteor. Sci. Technol., 8, 158–163, https://doi.org/10.3969/j.issn.2095-1973.2018.01.021.
Xia, X., Z. Guo, W. Zhang, and F. Zheng, 2009: Preliminary error analysis of the NCAR analysis datasets in the atmospheric boundary layer. Climatic Environ. Res., 14 (5), 475 − 483.
Xiang-Shu, L. I., and X. L. Guo, 2008: Atmospheric structure and characteristics of convection transport of heat and moisture during monsoon onset over South China Sea. Climatic Environ. Res., 13, 93–101.
Yan, Y., Y. Qi, and Z. Wen, 2010: Interannual heat content variability in the South China Sea and its response to ENSO. Dyn. Atmos. Oceans, 50, 400–414, https://doi.org/10.1016/j.dynatmoce.2010.07.002.
Yao-Dong, L., S. Xiao-Kang, Z. Shao-Hong, and H. Liang, 2012: A study on the change of precipitation characteristics before and after east Asian summer monsoon onset. Geophysical Research Abstracts, Vol. 14, EGU2012-1542, https://meetingorganizer.copernicus.org/EGU2012/EGU2012-1542.pdf.
Yihui, D., and W. Zunya, 2008: A study of rainy seasons in China. Meteor. Atmos. Phys., 100, 121–138, https://doi.org/10.1007/s00703-008-0299-2.
