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  • View in gallery

    The topography of the STP and the locations of the nine ground-based GPS stations.

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    The elevation differences over the grids of the four reanalyses and AIRS L3 compared with that over GPS stations, and the averaged differences over the footprint of MODIS (including Aqua MODIS and Terra MODIS) and AIRS L2 compared with that over GPS stations.

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    Evaluation of MODIS (a) WVI and (b) WVNI PWV data against the GPS retrievals at the nine stations.

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    Evaluation of PWV estimates based on the AIRS (a) L2 and (b) L3 products against the GPS retrievals at the nine stations.

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    Evaluation of four reanalysis PWV products, (a) MERRA, (b) ERA-Interim, (c) JRA-55, and (d) NCEP-Final, against the GPS PWV retrievals at the nine stations.

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    Comparison of the interannual variations (2007–13), during monsoon (May–September), of the composite PWV for the GPS retrievals and the four reanalysis PWV datasets.

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    Comparison of the seasonal variations (May–September) of the composite PWV between 2007 and 2013 for the GPS retrievals and the four reanalysis PWV datasets.

  • View in gallery

    Comparison of the diurnal variation in the composite PWV during May–September 2007–13 between the GPS retrievals and the MERRA data at the nine stations. The mean PWV value at each station is subtracted from the composite [local standard time (LST)].

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Evaluation of Precipitable Water Vapor from Four Satellite Products and Four Reanalysis Datasets against GPS Measurements on the Southern Tibetan Plateau

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  • 1 Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
  • | 2 University of Chinese Academy of Sciences, Beijing, China
  • | 3 Department of Earth System Science, Tsinghua University, Beijing, China
  • | 4 Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing, China
  • | 5 Regional Climate Group, Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden
  • | 6 State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
  • | 7 Chinese Academy of Meteorological Sciences, Beijing, China
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Abstract

The southern Tibetan Plateau (STP) is the region in which water vapor passes from South Asia into the Tibetan Plateau (TP). The accuracy of precipitable water vapor (PWV) modeling for this region depends strongly on the quality of the available estimates of water vapor advection and the parameterization of land evaporation models. While climate simulation is frequently improved by assimilating relevant satellite and reanalysis products, this requires an understanding of the accuracy of these products. In this study, PWV data from MODIS infrared and near-infrared measurements, AIRS Level-2 and Level-3, MERRA, ERA-Interim, JRA-55, and NCEP final reanalysis (NCEP-Final) are evaluated against ground-based GPS measurements at nine stations over the STP, which covers the summer monsoon season from 2007 to 2013. The MODIS infrared product is shown to underestimate water vapor levels by more than 20% (1.84 mm), while the MODIS near-infrared product overestimates them by over 40% (3.52 mm). The AIRS PWV product appears to be most useful for constructing high-resolution and high-quality PWV datasets over the TP; particularly the AIRS Level-2 product has a relatively low bias (0.48 mm) and RMSE (1.83 mm) and correlates strongly with the GPS measurements (R = 0.90). The four reanalysis datasets exhibit similar performance in terms of their correlation coefficients (R = 0.87–0.90), bias (0.72–1.49 mm), and RMSE (2.19–2.35 mm). The key finding is that all the reanalyses have positive biases along the PWV seasonal cycle, which is linked to the well-known wet bias over the TP of current climate models.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Kun Yang, yangk@itpcas.ac.cn

Abstract

The southern Tibetan Plateau (STP) is the region in which water vapor passes from South Asia into the Tibetan Plateau (TP). The accuracy of precipitable water vapor (PWV) modeling for this region depends strongly on the quality of the available estimates of water vapor advection and the parameterization of land evaporation models. While climate simulation is frequently improved by assimilating relevant satellite and reanalysis products, this requires an understanding of the accuracy of these products. In this study, PWV data from MODIS infrared and near-infrared measurements, AIRS Level-2 and Level-3, MERRA, ERA-Interim, JRA-55, and NCEP final reanalysis (NCEP-Final) are evaluated against ground-based GPS measurements at nine stations over the STP, which covers the summer monsoon season from 2007 to 2013. The MODIS infrared product is shown to underestimate water vapor levels by more than 20% (1.84 mm), while the MODIS near-infrared product overestimates them by over 40% (3.52 mm). The AIRS PWV product appears to be most useful for constructing high-resolution and high-quality PWV datasets over the TP; particularly the AIRS Level-2 product has a relatively low bias (0.48 mm) and RMSE (1.83 mm) and correlates strongly with the GPS measurements (R = 0.90). The four reanalysis datasets exhibit similar performance in terms of their correlation coefficients (R = 0.87–0.90), bias (0.72–1.49 mm), and RMSE (2.19–2.35 mm). The key finding is that all the reanalyses have positive biases along the PWV seasonal cycle, which is linked to the well-known wet bias over the TP of current climate models.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Kun Yang, yangk@itpcas.ac.cn

1. Introduction

The Tibetan Plateau (TP) is known as the “Asian water tower” (Xu et al. 2008a), and the TP has undergone a general wetting over the last few decades (Chen et al. 2015; Zhao et al. 2012; Yang et al. 2011, 2014; Lu et al. 2015), which indicates a change in the water cycle, including water vapor transport (Zhang et al. 2016). Water vapor is the most active component in the water cycle, and precipitable water vapor (PWV) is an important parameter in weather forecasting and climate studies. Water vapor transported by the South Asia monsoon winds and westerlies converges above this region (Chen et al. 2012; Feng and Zhou 2012; Tian et al. 2007; Zhang et al. 2016). This convergence means that the region receives high levels of precipitation and runoff, which releases considerable amounts of energy and causes atmospheric heating (Yanai et al. 1992; Ye and Wu 1998). The water vapor that accumulates above the TP gradually moves eastward and strongly influences the occurrence of floods and droughts in eastern Asia (Bin et al. 2013; Xu et al. 2003; Zhang et al. 2014). Additionally, it has been estimated that as much as 75% of the global exchange between the stratosphere and troposphere may occur over the TP and the monsoon regions of southern Asia (Fu et al. 2006; Jiang et al. 2015). Understanding the variation in the transport of water vapor in this region is therefore crucial for weather forecasting, both within the TP and its surrounding.

There are many meridional canyons over the Himalayas and the southern Tibetan Plateau (STP) that can function as channels for water vapor transport (Bookhagen and Burbank 2006, 2010). However, there is a lack of robust experimental data that could be used to study this channeling effect. Current water budget calculations are based on satellite products and reanalyses that combine existing data with numerical modeling. Unfortunately, previously published studies on this region have a number of important deficiencies. First, both global climate models (GCMs) and regional climate models (RCMs) heavily overestimate precipitation and evaporation (Gao et al. 2015; Ma et al. 2015; Maussion et al. 2014; Mueller and Seneviratne 2014; Su et al. 2013; Walther et al. 2013). Second, it is found that most reanalysis PWV datasets exhibit clear wet biases (Gao et al. 2015; Wang and Zeng 2012). Additionally, there are very few ground-based observations, especially over the STP and Himalayas, available to be used to evaluate the quality of satellite products and reanalyses. Consequently, it has not been possible to assess the reliability and uncertainty of satellite and reanalysis data. It is generally assumed that assimilating satellite products will increase the accuracy of water vapor and precipitation simulations, but successful assimilation requires information on the satellite products’ errors.

Some studies have evaluated the reliability and accuracy of satellite and reanalysis vapor products for the TP and its surrounding regions. At present there is a sparsely distributed network of GPS (Xu et al. 2008b; Zhang et al. 2012) and radiosonde stations deployed across the TP (Liu et al. 2015). Lu et al. (2015) showed that major reanalyses, including JRA-55, ERA-40, ERA-Interim, MERRA, and NCEP-2, underestimated the long-term trend in PWV over the TP. (Acronym expansions are available online at http://www.ametsoc.org/PubsAcronymList.) Zhao et al. (2015) found that several reanalysis products underestimate PWV by about 60% when compared to observations from seven radiosonde stations distributed in the eastern TP. Lu et al. (2011) compared MODIS near-infrared water vapor (WVNI) products to ground-based GPS measurements and found that the MODIS WVNI product has a high accuracy under clear-sky conditions. However, it was shown to have a wet bias in the southeastern TP, especially during the summer (June–August). Similar results were found in a later study (Liu et al. 2015) for MODIS WVNI products. However, Liu et al. (2015) found that the MODIS infrared water vapor (WVI) products underestimate PWV over the TP during daytime and nighttime. As another commonly used satellite PWV data, Qin et al. (2012) found that the AIRS PWV product agrees well with GPS retrievals over the southeastern TP after applying a correction. In general, the accuracy of satellite data varies with regions and seasons. For example, the AIRS PWV product has an obvious wet bias when compared to GPS retrievals for India, especially during the summer (Prasad and Singh 2009), whereas it has a dry bias in the contiguous United States during the wet season and a wet bias in the dry season (Raja et al. 2008). Similarly, the accuracy and reliability of reanalysis products also varies between regions. Therefore, satellite and reanalysis products must be carefully evaluated before being used to study weather and climate processes.

The GPS network used in the above-mentioned studies was set up mainly in the eastern region of the Tibetan Plateau, and most of the GPS stations were located in low elevations for monitoring water vapor transfer from South Asia to East Asia. As noted above, the STP is the region in which water vapor passes from South Asia into the TP and is thus very important in the transfer of water vapor. Our objective in this work is to assess the quality of reanalysis data and satellite PWV products for this region. To this end, we retrieved PWV data from nine GPS stations that were recently established on the STP by the Institute of Tibetan Plateau Research, Chinese Academy of Sciences. The PWV data were then used to evaluate current major satellite products and reanalysis datasets, and potential origins of errors in these datasets were identified.

This paper is divided into five sections, of which the first is the introduction. Section 2 describes the data and methods used in this study. Section 3 presents a comparison of four satellite products against our GPS retrievals. Section 4 compares four reanalysis-based PWV datasets to the GPS retrievals, with a focus on the seasonal and diurnal variations in the PWV. Finally, section 5 presents the concluding remarks.

2. Data and method

a. GPS station deployment and PWV retrieval

Figure 1 shows the positions of the nine ground-based GPS stations. They are situated at longitudes ranging from 82° to 95°E and at elevations of at least 4200 m MSL (see Fig. 1 and Table 1). Water vapor from the South Asian monsoon region crosses the Himalayas and then passes over this basin before progressing toward the central TP.

Fig. 1.
Fig. 1.

The topography of the STP and the locations of the nine ground-based GPS stations.

Citation: Journal of Climate 30, 15; 10.1175/JCLI-D-16-0630.1

Table 1.

Locations and elevations of the nine GPS stations and some statistical data on the PWV measurements at each one, including the GPS records starting and ending dates, mean summer PWV value (June–August), and the mean monsoon season PWV value and standard deviation (from May to September) between 2007 and 2013.

Table 1.

To derive the PWV from the GPS signals, the zenith total delay (ZTD), which is also called the zenith path delay (ZPD) in some studies (Wang et al. 2005; Wang et al. 2007), is computed using the GPS analysis software package GAMIT10.4 (Herring et al. 2010a, 2010b), which was developed at the Massachusetts Institute of Technology (MIT). The zenith hydrostatic delay (ZHD) is calculated with the model presented by Saastamoinen (1972) using the ERA-Interim pressure data after elevation correction to the GPS stations, because no pressure measurements was available at the GPS sites. The zenith wet delay (ZWD) is obtained by subtracting the ZHD from the ZTD, and the PWV can be determined from the ZWD (Bevis et al. 1994; Wang et al. 2005) using the following equation:
e1
where ZW is the ZWD, which is in units of millimeters; ρω (103 kg m−3) is the liquid water density; Rω is the specific gas constant of water vapor, which equals 461 J kg−1 K−1; k3 and are the refraction coefficient constants, which are approximately 3.776 × 105 K2 hPa−1 and 16.48 K hPa−1, respectively; Tm is the water-vapor-weighted mean temperature, which can be estimated from the surface air temperature Ts (similar to surface pressure, here using the ERA-Interim data after elevation correction to the GPS stations) at the GPS station using the equation below:
e2
where the units of Tm and Ts are in kelvin; the constant parameters a and b are 42.68 and 0.84, respectively, which were determined in Qin et al. (2012) and similar to that in Liu et al. (2005) by fitting radiosonde station data for the TP, where the parameters were considered having no seasonal and/or diurnal variations.

The sampling interval of the GPS receiver is 15 s, and the parameters selected for ZTD retrieval using the GAMIT software package are specified in Table 2. Within GAMIT, the RELAX experiment and LC_AUTCLN observable are common choices for dual-frequency receivers. Wet and dry mapping were performed with the widely used Vienna mapping function (VMF; Bookhagen and Burbank 2006), which is reconstructed from numerical weather prediction model data. A piecewise-linear (PWL) zenith model that updates the zenith delay parameter at 2-h intervals was used. The elevation cutoff for observations in the model is 15° (i.e., PWV data are not retrieved if the angle of elevation is greater than 15°).

Table 2.

Main parameters used in the atmospheric delay model in the GAMIT software package.

Table 2.

Statistical information relating to the PWV data retrieved from the GPS observations at the nine stations is presented in Table 1. The GPS retrievals cover the period from May to September during 2007–13, and their temporal resolution is hourly. Because of the harsh natural environment over the TP and destructions by human activity, it is difficult to have continuous observations from the GPS stations, especially after quality control for the PWV retrievals. These GPS stations were constructed at different times, and some GPS instruments occasionally stopped working in the harsh environment; the sample numbers differ greatly among the nine stations. The mean values of the GPS retrievals from the nine stations also exhibit substantial variation. The mean PWV values during the monsoon season (May–September) range from 6.8 to 11.4 mm, while those for the summer (June–August) range from 8.5 to 13.6 mm. The PWV measured at the northern stations was generally lower than those at the southern ones, and the western stations show lower values than those at the eastern ones. Additionally, there was an apparent seasonal variation in the PWV measured at all of the stations, as demonstrated by the standard deviation (SD) values presented in Table 1.

b. Satellite and reanalysis PWV data

PWV data from four satellite products (MODIS infrared and near-infrared measurements from Aqua and Terra, and AIRS Level-2 and Level-3 on Aqua) and four atmospheric reanalyses [MERRA, JRA-55, NCEP final reanalysis (NCEP-Final), and ERA-Interim] were evaluated against the GPS PWV retrievals. The datasets used are summarized in Table 3 and briefly described below.

Table 3.

Information on the PWV satellite products and reanalysis data evaluated in this work.

Table 3.

1) MODIS WVNI and WVI products

MODIS is the first space instrument to use near-IR bands together with the traditional IR bands to retrieve PWV (Gao and Kaufman 2003; Seemann et al. 2003). Collection 6 of the MODIS Level-2 PWV from Terra (MOD05_L2) and Aqua (MYD05_L2) provided by NASA, which contains information on column water vapor amounts, was used in this study. The main difference between Terra MODIS and Aqua MODIS is their passing times, which differ by between 3 and 5 h over the STP. There are two Level-2 PWV products: WVNI, which is generated using the MODIS near-infrared (NIR) algorithm at 1 km × 1 km resolution during the day; and WVI, which is generated using the infrared (IR) algorithm at 5 km × 5 km resolution during both day and night.

2) AIRS L2 and L3 products

The AIRS instrument suite was launched onboard the Aqua satellite in 2002. The standard version 6 Level-2 (AIRX2RET) and Level-3 (AIRX3STD) products, which combine AIRS infrared and AMSU microwave radiance data, were used in this study. The spatial resolution of the AIRS Level-2 (L2) products is 45 km × 45 km, and that of the AIRS Level-3 (L3) product is 1° × 1°. In addition, the AIRS L3 gridded products are generated from the L2 products, with quality indicators of “best” or “good” (Olsen et al. 2013; Tian et al. 2013).

3) MERRA data

MERRA (version 1) is a NASA atmospheric data reanalysis for the satellite era that is performed using the three-dimensional variational data assimilation (3DVAR) based on the Goddard Earth Observing System Model, version 5 (GEOS-5). The water vapor observations used in MERRA include the TIROS Operational Vertical Sounder (TOVS), Special Sensor Microwave Imager (SSM/I), and Atmospheric Infrared Sounder (AIRS). It covers the period from 1979 to the present and has a spatial resolution of ½° latitude × ⅔° longitude (Rienecker et al. 2011).

4) ERA-Interim data

ERA-Interim is the latest global atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) covering the data-rich period since 1979. It uses a four-dimensional variational data assimilation (4DVAR) system that is based on the ECMWF Integrated Forecast System released in 2006 (IFS cy31r2). The water vapor observations used in ERA-Interim include clear-sky radiance measurements from polar-orbiting and geostationary sounders, as well as images from TOVS, HIRS, TOMS, and SSM/I. It has a spectral T255 (79 km × 79 km) horizontal resolution (Berrisford et al. 2011; Dee et al. 2011).

5) JRA-55 data

JRA-55 is the first atmospheric global reanalysis dataset and covers a period of more than 50 years, extending back to 1958. It is based on the TL319 (55 km × 55 km) spectral resolution version, with linear Gaussian grid, of the JMA global spectral model (GSM) with 4DVAR and also incorporates TOVS and SSM/I satellite data. Many new kinds of observational data have also been assimilated, including atmospheric motion vector (AMV) and clear-sky radiance (CSR) data (Ebita et al. 2011; Kobayashi et al. 2015).

6) NCEP-Final data

NCEP-Final is based on the Global Data Assimilation System (GDAS) with four-dimensional ensemble variational data assimilation (4DEnVar) and the Global Forecast System (GFS). It is based on continuously collected observational data from the Global Telecommunications System (GTS) since July 1999; the dataset is updated daily, and analytical data are generated with spatial and temporal resolutions of 1° × 1° and 6 h, respectively (NCEP 2000).

The MODIS products have a finer spatial resolution than the others considered in this work; the AIRS L2 product has an intermediate spatial resolution, and the AIRS L3 and NCEP-Final products have the coarsest spatial resolutions. MERRA has the highest temporal resolution (hourly); the other reanalysis datasets provide only four values per day (at 0000, 0600, 1200, and 1800 UTC). Each reanalysis model assimilates a different set of satellite products (see Table 3).

c. Data matchup for elevation differences

All the satellite and reanalysis PWV datasets were evaluated using the GPS PWV measurements acquired at the corresponding time. The GPS retrievals have hourly resolution, so the time difference between the evaluated PWV product and the closest GPS measurement is always less than 30 min. Because of complex topography over the STP, the elevation differences among the reanalysis grids, satellite footprint, and the GPS stations are quite large, as shown in Fig. 2. The elevation differences between the evaluated products and the GPS stations range from 28 to 870 m. The elevation value of the MODIS footprint is closest to that of the GPS stations, because of its fine spatial resolution. On the other hand, elevation differences between the NCEP grids and the GPS stations show a wider range than other reanalysis grids. Because PWV values are highly dependent on the elevation, it is necessary to correct all the PWV data to match the elevation of the GPS observations before they are evaluated against the GPS retrievals. The equation for elevation correction follows Leckner (1978):
e3
where PWV0 is the satellite PWV product or reanalysis PWV data; the PWV value is the PWV after correcting to the GPS station elevation; Δh (meters) is the elevation difference between the satellite footprint or reanalysis grid and the GPS stations (the former minus the latter); and C2 is a constant parameter equal to 0.439, given by Leckner (1978).
Fig. 2.
Fig. 2.

The elevation differences over the grids of the four reanalyses and AIRS L3 compared with that over GPS stations, and the averaged differences over the footprint of MODIS (including Aqua MODIS and Terra MODIS) and AIRS L2 compared with that over GPS stations.

Citation: Journal of Climate 30, 15; 10.1175/JCLI-D-16-0630.1

Because MODIS PWV is sensitive to the presence of clouds in the field of view (FOV), the MODIS WVI was calculated when at least nine FOVs were cloud free. The MODIS WVNI was evaluated under 99% clear-sky conditions, which were detected using the MODIS cloud mask product. To overcome the problem of misregistration in the comparisons, the MODIS WVNI value for a given location was calculated as the average of the values for a 5 × 5 pixel grid centered on the location in question (Liu et al. 2015; Lu et al. 2011). The elevation correction is applied for each MODIS pixel within a 5 × 5 pixel grid before averaging. Then spatial averaging is performed if there are at least 10 pixels available (under 99% clear-sky condition and with best quality flag). Similarly, only the AIRS L2 PWV product with the best quality (i.e., the quality indicator is 0) was collected in this study.

d. Evaluation metrics

The performances of the four satellite products and four reanalysis datasets were evaluated against the GPS PWV data, and their quality was measured using standard statistical metrics, including the correlation coefficient R, bias, relative bias (Rbias), root-mean-square error (RMSE) and relative root-mean-square error (Rrmse). Equations for computing these quantities are given below:
e4
e5
e6
e7
e8
where PWVgi is the PWV derived from ground-based GPS, is the average GPS PWV value, and N is the total number of samples used for evaluation; PWVki is the satellite PWV product or reanalysis PWV data, and is the average PWV value computed for the satellite PWV product or reanalysis dataset.

3. Evaluation of satellite PWV products

a. Overall evaluation of MODIS PWV products

The Aqua MODIS and Terra MODIS PWV products, which both performed similarly, were evaluated together, as shown in Fig. 3. Because MODIS WVI data are available for both day- and nighttime, whereas WVNI data are only available for the daytime, there were more valid samples for the former (~8330) than the latter (~2613). The correlation coefficient, bias, and RMSE for the MODIS WVI PWV measurements were 0.74, −1.84 mm, and 3.21 mm, respectively, when compared to the GPS retrievals, while the corresponding values for the MODIS WVNI data were 0.93 and 3.52 and 4.06 mm, respectively. The MODIS WVI product thus underestimates the PWV by 26.7% on average, while MODIS WVNI overestimates it by 46.9%. This is consistent with the conclusions of Liu et al. (2015), who found that MODIS WVNI exhibits wet bias when compared to radiosonde observations over the TP, while MODIS WVI exhibits dry biases ranging from 2 to 6 mm. Lu et al. (2011) as well found that MODIS WVNI started to show wet biases after the onset of the monsoon over the southeastern TP. It is also important to recall that the quality of the MODIS WVI product depends on the limited infrared spectral resolution of MODIS (Seemann et al. 2003). The split-window algorithm used to retrieve the MODIS WVI is not sensitive to water vapor in lower atmosphere. Based on above results, both the MODIS WVI and WVNI PWV products do not perform well in the STP region.

Fig. 3.
Fig. 3.

Evaluation of MODIS (a) WVI and (b) WVNI PWV data against the GPS retrievals at the nine stations.

Citation: Journal of Climate 30, 15; 10.1175/JCLI-D-16-0630.1

b. Overall evaluation of AIRS PWV products

Only AIRS L2 data with the best quality flag were evaluated, whereas the AIRS L3 gridded products were generated using AIRS L2 data of both best and good quality. Figure 4 shows that both the AIRS L2 and L3 PWV products have appreciably lower bias and RMSE values than the MODIS PWV products. The R, bias, and RMSE of the AIRS L2 (L3) PWV product are 0.90 (0.84), 0.48 mm (0.40 mm), and 1.83 mm (2.31 mm), respectively.

Fig. 4.
Fig. 4.

Evaluation of PWV estimates based on the AIRS (a) L2 and (b) L3 products against the GPS retrievals at the nine stations.

Citation: Journal of Climate 30, 15; 10.1175/JCLI-D-16-0630.1

The bias values indicate that the AIRS L2 and L3 water vapor products both slightly overestimate PWV (by about 5%). In contrast, Qin et al. (2012) found that the AIRS retrievals over the southeastern TP were slightly lower than experimental observations. We note that the stations included in Qin et al.’s study are within wetter conditions and located at lower elevations than those used in this work. Because the atmospheric humidity affects the deviations of AIRS PWV products (Raja et al. 2008) according to the algorithm used thereof, the different biases between this study and Qin et al.’s work may be attributed to different humidity conditions. The intercepts shown in Fig. 4 indicate that both the AIRS L2 and L3 PWV products overestimate the PWV when the water vapor content is low, which supports this explanation for the difference between our results and those of Qin et al. (2012).

c. Evaluation at the individual stations

To understand whether the evaluation metrics are consistent across these stations, Table 4 presents the R, Rbias (%), and Rrmse (%) for each satellite PWV product relative to the GPS retrievals at the individual stations. The MODIS WVNI product has a higher correlation with the GPS PWV data than other satellite products at eight of the nine stations (R > 0.90) (the following is AIRS L2), while the MODIS WVI products have the weakest correlation (R < 0.80 at seven out of nine stations) at all the stations. Meanwhile, the Rrmse values for AIRS L2 at all nine stations are lower than those for the other satellite PWV products. With the exception of the JRGR and YARE stations, the Rbias of the AIRS L2 and L3 PWV products are all below 10%. Moreover, the AIRS L3 and L2 products have the smallest Rbias values at six and two stations, respectively, and AIRS L2 has the smallest Rrmse value at all nine stations. The MODIS WVI or WVNI datasets have the largest Rbias and Rrmse values at the nine stations. Furthermore, there is a clear dry bias in the MODIS WVI product and a wet bias in the MODIS WVNI at all nine stations. These results indicate that the better performance of AIRS products and worse performance of MODIS products are spatially consistent. The poor performance of the MODIS PWV products (section 3a) may be due to several factors. First, few channels are used to retrieve atmospheric vapor; second, there are defects in the algorithms used to identify clouds; and, third, there are flaws in the parameters and algorithms used to retrieve vapor levels.

Table 4.

Correlation coefficient R, Rbias (%), and Rrmse (%) for the satellite PWV data relative to the GPS retrievals at each station. The best performance figures for each station (i.e., the highest R, lowest Rbias, and lowest Rrmse) are highlighted in boldface.

Table 4.

4. Evaluation of reanalysis PWV datasets

The four reanalysis datasets considered in this work (MERRA, ERA-Interim, JRA-55, and NCEP-Final) are frequently used in weather and climate studies for this region (e.g., Feng and Zhou 2012; Lin et al. 2016; Liu et al. 2013; Qian et al. 2015; Su et al. 2015; Wright et al. 2011), but the accuracy of their PWV products over the STP is unknown. In this section, seasonal and diurnal variations of the reanalysis water vapor data are compared with the GPS retrievals.

a. Overall evaluation of reanalysis PWV data

Figure 5 shows scatterplots for each of the reanalysis PWV datasets relative to the GPS retrievals. All four datasets exhibit similar performance over the STP, showing good correlations with the GPS PWV measurements (their R values range from 0.87 to 0.90), similar wet biases (ranging from 0.72 to 1.49 mm), and similar RMSE values (ranging from 2.19 to 2.35 mm). The slopes of the fitting lines are approximately equal to 1. The ERA-Interim PWV data have the strongest correlation with the GPS retrievals, but also have a slightly higher mean bias than the others. The JRA-55 PWV data have the lowest bias among the reanalysis datasets, but their correlation with the GPS retrievals is slightly lower than those of the other three. In general, there are no decisive differences in the evaluation metrics for the reanalysis water vapor datasets, so we cannot conclude that any one of them is better than the others. On the basis of their bias values, the MERRA, ERA-Interim, JRA-55, and NCEP-Final overestimate the PWV by 9.8%, 16.7%, 8.2%, and 10%, respectively. Previous studies found that most reanalysis datasets overestimate the precipitation over the TP when compared to in situ observations (Gao et al. 2015; Wang and Zeng 2012). The overestimated precipitation for the TP may be linked to the overestimation of PWV in these models. The impact of the PWV overestimation on precipitation simulations merits further investigation, although this is beyond the scope of this study.

Fig. 5.
Fig. 5.

Evaluation of four reanalysis PWV products, (a) MERRA, (b) ERA-Interim, (c) JRA-55, and (d) NCEP-Final, against the GPS PWV retrievals at the nine stations.

Citation: Journal of Climate 30, 15; 10.1175/JCLI-D-16-0630.1

b. Evaluation at the individual stations

Evaluation metrics for each of the reanalysis PWV datasets at the nine individual GPS stations are presented in Table 5. The performances of the reanalysis data are more spatially consistence across the stations than those of the satellite products. Their correlation coefficients R relative to the GPS retrievals at most stations range from 0.85 to 0.90, while their Rrmse values are 20%–30% at most of the stations. These RMSE values are greater than those for AIRS L2, comparable to those for AIRS L3, and much lower than those for the MODIS products. There are no distinct differences in the evaluation metrics between the individual stations or across all sets of stations. The Rbias of the MERRA and ERA-Interim PWV data when compared to the GPS retrievals at all nine stations are positive, and the relative biases of the JRA-55 and NCEP-Final datasets are also positive at eight of nine stations. This indicates that the wet biases in the four reanalysis PWV datasets are systematic but not coincident. Moreover, the reanalysis PWV datasets yield particularly wetter biases at stations JRGR and YARE stations than at the other stations. These two stations are located in the southwestern TP, where more than half of the precipitation is due to convective storms (Dong et al. 2016). The larger wet biases for the reanalysis datasets at these two stations may occur because the reanalysis models cannot capture the abundant convections. However, further studies and a more rigorous analysis would be required to draw a firm conclusion on this issue.

Table 5.

Correlation coefficient R, Rbias (%), and Rrmse (%) for the PWV reanalysis datasets relative to the GPS retrievals at each station. The best performance figures for each station (i.e., the highest R, lowest Rbias, and lowest Rrmse) are highlighted in boldface.

Table 5.

c. Temporal variation in the PWV

Because of the complexity of the Asian summer monsoon, climate and weather forecasting over the TP is very challenging. GCMs generally predict too much precipitation over the TP, which is attributed to their coarse spatial resolutions that cannot adequately reproduce the region’s grand and complex topography (Ma et al. 2015). However, few studies have strictly evaluated each component of the water cycle in models for this region because of a lack of observations. The seasonal and diurnal variations in the PWV can reveal the ability of the reanalysis models to describe large-scale and mesoscale weather processes. Therefore, they are evaluated below.

1) Interannual and seasonal variations of PWV

Figure 6 shows the interannual variations of monsoon season PWV from GPS retrievals and the four reanalyses using the matched samples with the GPS retrievals. Overall, the four reanalyses exhibit similar interannual variations but with appreciable wet biases. Among the four reanalyses, MERRA performs best with a correlation coefficient of 0.97 with GPS retrievals at interannual scales. Figure 7 shows the seasonal variations of the composite PWV over all the stations. There is an apparent seasonal variation in the PWV over the STP. The water vapor content in summer is more than doubled than that outside summer. The PWV rises rapidly in the middle of June, when the summer monsoon starts in the STP, and carries large quantities of water vapor from the Bay of Bengal and Indian Ocean. In addition, the water vapor content decreases from the end of August, just before the monsoon’s retreat. All four reanalysis datasets accurately capture the seasonal variations in PWV and the moisture changes before and after the summer monsoon over the STP. However, all of them have obvious wet biases, which are consistent with the results presented in section 4a. The wet biases consistently show up for each year during 2007–13, as Fig. 6 shows. In addition, the wet biases are larger before August than afterward. The ERA-Interim dataset shows a greater wet bias than the others before August, but this bias decreases from mid-August onward.

Fig. 6.
Fig. 6.

Comparison of the interannual variations (2007–13), during monsoon (May–September), of the composite PWV for the GPS retrievals and the four reanalysis PWV datasets.

Citation: Journal of Climate 30, 15; 10.1175/JCLI-D-16-0630.1

Fig. 7.
Fig. 7.

Comparison of the seasonal variations (May–September) of the composite PWV between 2007 and 2013 for the GPS retrievals and the four reanalysis PWV datasets.

Citation: Journal of Climate 30, 15; 10.1175/JCLI-D-16-0630.1

2) Diurnal variations of PWV

Diurnal variations in PWV closely relate to variations in precipitation, surface evaporation, and other weather processes (Dai and Deser 1999; Dai et al. 2002). In addition, they depend on valley depth and width (Kuwagata et al. 2001). Information on this variation is essential for accurate numerical weather forecasting (Zhang et al. 2014). The STP is among the regions most heavily affected by the South Asian monsoon (Xu et al. 2003). However, because of the lack of high-temporal-resolution data, the diurnal variation in the PWV over the STP is currently unknown. In this work we analyzed the diurnal cycles in the MERRA PWV data and the GPS retrievals by taking each measurement recorded on a given day at a given station and subtracting the daily mean for that day from each one. Only the MERRA data were considered in this phase of the study because the temporal resolutions of the other reanalysis datasets are too low to enable a meaningful evaluation of their diurnal variation. Figure 8 indicates that the abnormal GPS PWV ranges from −1.5 to 1.5 mm (~17%). The variation in the PWV at the southern stations (JISG, YARE, and ZHXZ) is greater than that at the northern stations (CUOM, JIAW, JRGR, and NCRS). There are also significant differences in the diurnal variation of PWV between the MERRA products and GPS retrievals. First, the peak in the diurnal variation for the MERRA PWV data occurs before that in the GPS data at most of the stations. It probably causes a systemic error in the simulated phase of the diurnal variation of precipitation. It is known that most climate models predict peak precipitation over land to occur at an earlier point than it actually does (Dai 2006; Dai and Trenberth 2004; Walther et al. 2013; Yang and Slingo 2001). This is consistent with the phase error of the reanalysis PWV data. Again, this demonstrates the importance of error diagnosis in simulated water vapor datasets. Second, the MERRA data shows a greater diurnal range than the GPS observations at most of the stations. This indicates that the diurnal water cycle in the MERRA model may be too strong. For example, it may assume greater levels of land evaporation than actually occur, as indicated in Mueller and Seneviratne (2014). The oversmoothed terrain in the model may also result in too-strong diurnal water vapor transport from South Asia to the STP. In addition, the GPS retrievals suggest three types of diurnal cycles of PWV over STP: night maximum (at CUOM, JRGR, YARE, and ZHXZ stations), bimode (at DANA, JIAW, and NCRS stations), and late afternoon maximum (at JISG and CUIJ stations). But these features are poorly reproduced in the corresponding MERRA data. For example, at the DANA station, the GPS observations feature minima occurring at 0600 and 1500 LST, with peaks at 1000 and 2300 LST. Similar patterns, albeit with weaker amplitudes, are observed at the JIAW and NCRS stations. However, the small peaks at around 1200 LST in the GPS retrievals for these stations are not observed in the MERRA product, possibly because MERRA cannot accurately model mesoscale circulation (e.g., mountain–valley winds and lake–land breezes).

Fig. 8.
Fig. 8.

Comparison of the diurnal variation in the composite PWV during May–September 2007–13 between the GPS retrievals and the MERRA data at the nine stations. The mean PWV value at each station is subtracted from the composite [local standard time (LST)].

Citation: Journal of Climate 30, 15; 10.1175/JCLI-D-16-0630.1

Attention is also paid to a possible error in the retrieved diurnal cycle of PWV. As indicated by Wang et al. (2005), the linear relationship between Tm and Ts [i.e., Eq. (2)] implies a strong diurnal cycle of Tm. The amplitude of the diurnal cycle of Tm estimated by the linear TmTs relationship is about 2–4 K over the STP (not shown), greater than the one (about 2 K over the Himalayas and the STP) derived from reanalysis datasets (see Wang et al. 2005). Because the retrieved PWV is positively correlated to Tm, the bias in the diurnal cycle of Tm may cause a spurious PW diurnal cycle with amplitude of 0.2 mm (positive bias in the day and negative bias in the night). Thus, the bias in the PWV diurnal cycle from MERRA is even greater than that shown in Fig. 8.

Briefly, the four reanalysis datasets perform similarly in terms of the three evaluation metrics used, and all of them overestimate the PWV during the seasonal progression of the monsoon. Moreover, the diurnal peak in the MERRA PWV product is too early, and the diurnal amplitude of the MERRA PWV is greater than that observed in the GPS retrievals.

5. Concluding remarks

High-temporal-resolution PWV data were retrieved from nine new GPS stations in the STP. These water vapor measurements reveal significant seasonal and diurnal variation: water vapor levels rise rapidly in late June and then decrease from early September; on a daily scale, water vapor levels usually peak between late afternoon and midevening and are lower between morning and midday. This unique GPS dataset was used to evaluate the accuracy of four satellite products (MODIS infrared and near-infrared and AIRS L2 and L3) and four reanalysis (MERRA, ERA-Interim, JRA-55, and NCEP-Final) PWV datasets.

The evaluation revealed that the AIRS L2 and L3 PWV products are more accurate than the MODIS products, achieving higher correlation coefficients and smaller discrepancies relative to the GPS data for most stations. The relative biases of the AIRS PWV products at most stations are within 10%, whereas those of the MODIS products are above 20%. Additionally, the MODIS WVNI exhibits large wet biases (above 30% at most stations), while the MODIS WVI shows large dry biases (above 20% at most stations). Therefore, AIRS products are more suitable than MODIS products for assimilation into models for the STP.

All four reanalysis-based PWV datasets were found to offer similar performance. Their relative biases were all within 20% for most stations, and their spatial differences were less pronounced than the two satellite PWV products. All of the reanalysis datasets capture the seasonal and intraseasonal variations in PWV over the STP. Because the MERRA dataset has a high temporal resolution, we also evaluated the quality of its information on the diurnal PWV cycle. The MERRA dataset cannot realistically reproduce the diurnal variation in PWV observed in the GPS data, which suggests that the PWV level peaks earlier than it actually does and that the diurnal amplitude is greater than it actually is. The MERRA product also fails to reflect the observed bimodality of the measured daily PWV values, indicating that mesoscale processes (which cannot be simulated in the reanalysis model) have important effects on the diurnal variation in PWV over the complex terrain of the STP. Most importantly, all of the reanalysis products exhibit obvious wet biases over the STP during the seasonal progression of the summer monsoon, which are observed at most of the stations. These biases may be closely related to the wet bias of current climate models when applied to the TP and their tendency to predict an early diurnal peak. Because these stations are affected by water vapor transport from South Asia, this wet bias may indicate that current reanalysis models have significant deficiencies when applied to this topographically complex region.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grants 91537210 and 41325019), the International Partnership Program of Chinese Academy of Sciences (Grant 131C11KYSB20160061), the Strategic Priority Research Program (B) of the Chinese Academy of Sciences (Grant XDB03030300) and Swedish VR, STINT, BECC, and MERGE. The GPS stations used in the study were set up by the Institute of Tibetan Plateau Research, Chinese Academy of Sciences. The authors thank Dr. Liu Jing’s Group for providing the GPS raw data.

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