Retrieval of precipitable water vapor using MFRSR and comparison with other multisensors over the semi-arid area of northwest China

Atmospheric Research 172–173 (2016) 83–94

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Retrieval of precipitable water vapor using MFRSR and comparison with other multisensors over the semi-arid area of northwest China Xia Li a,c, Lei Zhang b,⁎, Xianjie Cao b, Jiannong Quan a,c, Tianhe Wang b, Jiening Liang b, Jinsen Shi b a b c

Beijing Weather Modification Office, Beijing, China Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China Beijing Key Laboratory of Cloud, Precipitation and Atmospheric Water Resources, Beijing, China

a r t i c l e

i n f o

Article history: Received 3 September 2015 Received in revised form 17 December 2015 Accepted 21 December 2015 Available online 28 December 2015 Keywords: MFRSR Precipitable water vapor Retrieval algorithm Comparison

a b s t r a c t Precipitable water vapor (PWV) was retrieved using direct solar irradiance at 938 nm measured by a multifilter rotating shadowband radiometer (MFRSR) at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) located in the semi-arid area of northwest China from August 2007 to June 2010. Measurement also occurred at Zhangye, China, at the Atmosphere Radiation Measurements (ARM) Program's Ancillary Facility during the dust period from April to June 2008. The line-by-line radiative transfer model (LBLRTM) code combined with the HITRAN 2004 spectral database is used to model the water vapor spectral transmittance throughout the 938-nm spectral response of MFRSR in the retrieval algorithm. Gaussian fitting is proposed to determine the daily calibration constant at the top of atmosphere for a long-term series under an obvious annual change in solar radiation. PWV retrieved by MFRSR over SACOL shows that 90% of PWV values are smaller than 1.52 cm, and PWV distribution has a seasonal variation, with maximum in summer and minimum in winter. The comparisons between MFRSR and other measurements show a better agreement between MFRSR and sunphotometer (AERONET's Cimel) PWV retrievals with relative bias of 2.9% and RMS difference of 9.1% than between MFRSR and microwave radiometer (MWR) with relative bias of 10% and RMS difference of 23% over SACOL, and an excellent agreement between MFRSR and sunphotometer with relative bias of 0.56% and RMS difference of 6.1% over Zhangye. To verify satellite PWV products over the semi-arid area of northwest China, the comparisons of PWV from MODIS and AIRS with MFRSR suggest that the agreement between satellite and MFRSR PWV retrievals is not as good as that between MFRSR and other ground-based instruments. MODIS appears to slightly underestimate PWV in a dry atmosphere but overestimate PWV in a moist atmosphere against MFRSR. A method is proposed to correct MODIS PWV products. AIRS PWV products relative to MFRSR show a systematic underestimation. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Water vapor is a key atmospheric constituent, particularly in the lower layers of the troposphere. With large spatial and temporal variability, water vapor is the most active part of atmosphere despite the lower proportion in the atmosphere, which affects the radiation budget of the Earth and atmospheric systems directly by absorbing solar radiation (Chou and Arking, 1981 and long-wave radiation (Stephens and Greenwald, 1991) and indirectly by determining cloud generation and even modifying aerosol optical properties (Haywood et al., 1997; Kay and Box, 2000; Cheng et al., 2008, Huttunen et al., 2014. Also acting as an important greenhouse gas, the influence of water vapor on temperature accounts for approximately 60% of global greenhouse gases, which is far more than the total effect of carbon dioxide and ozone (Held and Soden, 2000; Philipona et al., 2005. Furthermore, water vapor plays an ⁎ Corresponding author. E-mail address: [email protected] (L. Zhang).

http://dx.doi.org/10.1016/j.atmosres.2015.12.015 0169-8095/© 2015 Elsevier B.V. All rights reserved.

important role in weather changes (Makarieva et al., 2013). Water vapor content is also a major parameter in atmospheric correction for satellite remote sensing. Therefore, the estimation of water vapor content is crucial for the predictions of weather and climate change and the application of satellite remote sensing. In particular, the arid and semi-arid areas of northwest China that have water resource shortages are sensitive to climate change. As such, the accurate estimation of water vapor content and its distribution is significant for making use of water resources effectively and for climate research (Zhang et al., 2000; Wang et al., 2003; Wang et al., 2006, Hu et al., 2015. Precipitable water vapor (PWV) as well as vertical profiles of water vapor can be acquired by several techniques, such as in situ measurement, remote sensing measurement from ground sources, aircraft, and satellites. Satellite sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) (Kaufman et al., 1997 on Aqua and Terra platforms and the Atmospheric Infrared Sounder (AIRS) (Aumann et al., 2003 on Aqua can obtain PWV information of global coverage; however, their accuracy still needs to be further verified. Raman lidar

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placed on the ground continuously measures vertical profiles of water vapor above blind ozone (Turner and Goldsmith, 1999, Wang et al., 2015. Microwave radiometer (MWR) (Ware et al., 2003 provides continuous observation of water vapor only within a limited height region. Sunphotometer provides simultaneous measurements of direct solar radiation and sky radiation, and multifilter rotating shadowband radiometer (MFRSR) provides simultaneous measurements of total horizontal and diffuse horizontal irradiances at multiple channels (Harrison et al., 1994. Both are widely applied to field experiments to retrieve PWV and aerosol optical properties (Michalsky et al., 1995; Alexandrov et al., 2002a, 2002b, 2008, Yin et al., 2015. There have been some studies engaged in PWV retrieval in the northwest region of China using in situ and remote sensing measurements. Wang et al. (2006a, 2006b) estimated the regional distribution of water vapor using the NCEP/NCAR reanalysis data in northwest China; however, Yang et al. (2008) used operational in situ measurements to calculate PWV over the Taklimakan region and suggested a large error of PWV from NCEP/NCAR reanalysis data compared with in situ measurements. In addition, Jin et al. (2008) monitored the lower PWV in northwest China using the Global Positioning System (GPS) constellation satellite. Gong et al. (2011) utilized the images of MODIS offering a global perspective to analyze the seasonal variation of PWV in China; however, little verification of MODIS PWV products has been carried out. In this paper, PWV over the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) is retrieved using MFRSR, and its long-term distribution characteristics are analyzed. Then the accuracy and sensitivity of MFRSR PWV retrieval are evaluated by comparisons with a sunphotometer and MWR. At last, the validation of MODIS and AIRS PWV products is presented based on comparison with MFRSR. PWV retrieved using MFRSR is also carried out over Zhangye during the dust period in 2008. 2. Experiment and data acquisition 2.1. Sites This study uses the observation data over the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) and Zhangye, in which SACOL is situated on the semi-arid area of northwest China (35.57°N, 104.08°E, 1965.8 m a.s.l.) (Huang et al., 2008). Zhangye (39.082°N, 100.276°E, 1461 m a.s.l.) is one of the sites used during the 2008 China–US joint dust storm field campaign carried out by Lanzhou University, Chinese Academy of Sciences, China Meteorological Administration, the U.S. Department of Energy Atmospheric Radiation Measurement (DOE/ARM) Program and University of Maryland from late April to mid-June 2008. SACOL, designed according to international standards, makes several measurements of atmospheric composition and meteorological elements by advanced climate and environment observation instruments and has accumulated a lot of valuable observations since its establishment in 2005. The measurements over Zhangye are carried out by the ARM ancillary facility (AAF) with a subset of AMF instruments, the so-called SMART-COMMIT (Ge et al., 2010). 2.2. Instrumentation 2.2.1. MFRSR MFRSR activities started in August 2007 over SACOL. MFRSR simultaneously measures the total horizontal irradiances and diffuse horizontal irradiances at six narrow wavelengths (413.8, 495.7, 613.4, 670.1, 869.5, and 938.0 nm) and at a broadband of 300–1100 nm per minute. MFRSR measures the irradiances from four different angles in which the effective fields of sky are blocked by rotating shadowband for completing an observation: one angle is a band with a stay at nadir position, the other is a band rotated to the position of the sun wherein it is completely blocked, and the remaining two are bands rotated 9° to

either side of the sun (Harrison et al., 1994, 2003). The direct normal irradiance is derived by subtracting diffuse irradiance from total horizontal irradiance. The 938-nm channel is selected for PWV retrieval using the algorithm introduced in the next section. 2.2.2. Sunphotometer The Cimel sunphotometer is an automated sun and sky scanning filter radiometer and measures direct solar irradiances with a field of view of 1.2° at 8 spectral channels ranging from 340 to 1020 nm, of which the 936-nm channel is employed to retrieve PWV over SACOL. The direct solar measurement is made typically every 15 min, while the sky is scanned many times at different angles relative to the sun. This instrument is also part of AERONET (Aerosol Robotic Network) (Holben et al., 1998. The level 2.0 data of AERONET in 2008, which automatically implemented a cloud screening algorithm and quality controlled (Smirnov et al., 2000, are used in this paper. 2.2.3. Microwave radiometer Microwave radiometer (MWR) provides continuous water vapor and temperature profiles from the surface to 10-km height with 47 layers per minute and one layer cloud liquid profile. MWR measures brightness temperature at selected frequencies with 12 channels, 5 of which from 22 to 30 GHz are employed to acquire water vapor profile and the other 7 from 51 to 59 GHz are for the temperature profile Rowe et al., 2008; Xu et al., 2014). The MWR (TP/WVP 3000) at SACOL uses a neural network algorithm of gradual improvement proposed by Solheim et al. (1998) to derive PWV. Huang et al. (2010) utilized the microwave radiometer data and another statistical method proposed by Liljegren et al. (2001) to derive PWV. They found a good agreement between this method and the neural network approach, with a correlation coefficient of 0.99 and relative bias of 3.58%. The neural network algorithm for MWR PWV retrieval is used in our analysis. 2.2.4. MODIS The Moderate Resolution Imaging Spectroradiometer (MODIS) is a scanning spectroradiometer with 36 wavelengths at visible, nearinfrared, and infrared bands from 553 to 14235 nm. There are two MODIS on board the NASA Terra and Aqua platform launched on 18 December 1999 and 4 May 2002, respectively. The products of MODIS include atmospheric humidity and temperature distributions, total ozone, aerosol properties, and PWV. PWV is retrieved by two types of MODIS infrared (IR) and near-infrared (NIR) retrieval algorithms. The MODIS IR algorithm calculates PWV by integrating the water vapor mixing ratio retrieved from measurements of infrared radiation over land and ocean for both day and night Seemann et al., 2003. Gao and Kaufman (2003) proposed a separate PWV retrieval algorithm, which employs MODIS NIR radiation of five channels centered between 865 and 1240 nm, which consists of three water vapor absorption channels and two atmospheric window channels. The PWV products (archived in MOD05_L2 for Terra MODIS and MYD05_L2 for Aqua MODIS) retrieved by the NIR algorithm are generated over clear land areas, over extended oceanic areas with sun glint, and above clouds over both land and ocean. As PWV retrieval using the MODIS IR algorithm relative to the NIR algorithm shows much uncertainty Chen et al., 2008), we employ the PWV products from the latter with 1-km resolution under clear skies from both satellites. 2.2.5. AIRS The Atmospheric Infrared Sounder (AIRS), the Advance Microwave Sounding Unit (AMSU), and the Humidity Sounder for Brazil (HSB) are onboard the NASA Aqua satellite. AIRS is the first hyper-spectral infrared radiometer designed to support the operational requirement for medium-range weather forecasting of the National Ocean and Atmospheric Administration's National Centers for Environmental Prediction (NCEP) and other numerical weather forecasting centers (Pagano et al., 2003. AIRS monitors temperature and humidity profiles using 2382

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channels centered between 400 and 15,500 nm. The AMSU is a microwave radiometer with 15 channels to measure the temperature and evaporation at different heights in cloud and cloud-free areas. The HSB is a microwave sounder with 4 channels designed to obtain cloud and humidity data. The AIRS generates the products of water vapor and temperature with 45-km horizontal resolution, as well as water vapor and temperature profiles with 1- to 2-km vertical resolution. The level 2 standard products of PWV with a resolution of 45 km are used in this paper. 2.3. PWV retrieval algorithm for MFRSR The retrieval algorithm of MFRSR consists of two steps: retrieval of aerosol optical depth (AOD) and then PWV in which the AOD retrieval is based on the Beer–Lambert–Bouguer attenuation law using MFRSR observation in atmospheric window channels (all channels except 938 nm) without strong molecular absorption, such as O2 and H2O. Atmospheric transmission in a specific narrow wavelength band can be expressed as " EðλÞ ¼ E0 ðλÞ exp −

X

# mi τi ðλÞ ;

ð1Þ

i

where E(λ) is the radiation output, here replaced by instrument output voltage V(λ); E0(λ) is the radiation at the top of the atmosphere (TOA), here replaced by V0 (λ) as the instrument calibration constant; and τi (λ) is the spectral optical depth of the ith extinction component. Several contributors to radiance attenuation include Raleigh scattering by air molecules, absorption of ozone, and absorption and scattering of aerosol particles. Raleigh scattering optical depth is derived using the method mentioned by Hansen and Travis, 1974, and the optical depth of ozone attenuation is determined by ozone total column amounts from TOMS and the absorption coefficient of ozone. In Eq. (1), the only unknown parameter in retrieval of AOD is the instrument calibration constant. The calibration constant is determined using the Langley plot technique (Schmid and Wehrli, 1995 for all atmospheric window channels. Two aspects of note are the choice of ‘stable day’ and the determination of the range of air mass, which yields an unbiased Langley plot result to be used in the robust estimation of the calibration constant. The terms for a cloud-free sky, stable atmosphere condition, and aerosol loading remaining less and little change need to be taken into account for the ‘stable day’, and the calibration time should be controlled properly within 1.5 or 2 hours (Michalsky et al., 2001. Considering the above

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conditions, the cloud-free sky is distinguished using the measurements of the total sky imager and the range of air mass is chose between two and five to reduce atmospheric instability and the impact of atmospheric refraction (Michalsky et al., 1995). Given the strict restrictions of ‘stable day’, only a few days may be selected to derive the calibration constant. The traditional approach is to filter and average V0(λ) values from these days as the calibration constant for a period Schmid et al., 2001; Harrison et al., 2003; Alexandrov et al., 2009; however, this average cannot truly reflect the annual variation of radiation, especially for a long-term series of data. A new method of Gaussian fitting mentioned by Mavromatakis et al. (2007) is exploited to obtain the daily calibration constant instead of a mean value. The actual meaning of calibration constant reflects the downward solar radiation at the TOA, which theoretically shows a significant annual variation with the day of the year, an obvious property of total solar radiation. Therefore, the calibration constant can be expressed as a function of the day of the year and then is calculated by Gaussian function fitting using the selected calibration constants of ‘stable day’. The consequent daily calibration constant at the 870-nm channel shown in Fig. 1a may be more realistic. The second step is to retrieve PWV using direct solar irradiance measurement at 938 nm, a water vapor absorption band. The attenuation law in the band of strong spectral variation of water vapor absorption needs to consider the extinction of water vapor. The band-weighted water vapor absorption transmittance Tw(λ) of a particular instrument is calculated by integrating the water vapor spectral transmittance T(λ) with the laboratory-measured spectral response function f(λ) of the 938-nm filter over wavelength λ: Z T w ðλÞ ¼

T ðλÞf ðλÞdλ Z : f ðλÞdλ

ð2Þ

Usually, Tw(λ) can also be expressed by an exponential function with two parameters (Ingold et al., 2000) h i T w ðλÞ ¼ exp −aðmuÞb ;

ð3Þ

where u is PWV in units of cm and a and b are constants assessed by fitting accurate water vapor absorption transmittance and slant column water vapor mu. In our retrieval algorithm, the LBLRTM_V11.7 (Clough et al., 2005 running for a wide range of wavelengths and solar zenith angles, together with an updated HITRAN 2004 spectral absorption database (Gordon et al., 2007, is used to model the water vapor spectral

Fig. 1. (a) Daily total solar radiation at TOA calculated using a theoretical method (Liou, 2002 (black line) and logarithm of the instrument calibration constant for 870-nm channel derived using Gaussian fitting (blue line). (b) Spectral transmittance of water vapor corresponding to a 1-cm column amount calculated using the updated HITRAN 2004 database (gray line) and the MFRSR spectral response function at the 938-nm channel normalized to unity at the maximum (black line).

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Table 1 Summary of available PWV data from MFRSR, sunphotometer and MWR over SACOL. Instrument

Period

Frequency

Number

Sunphotometer MFRSR

January 2008–October 2008 August 2007–June 2010 January 2008–June 2008 January 2009–June 2010

Several minutes 1 min

14506 142240

1 min

929425

MWR

transmittance shown in Fig. 1b. LBLRTM utilizes the Voigt line shape at all atmospheric levels with an algorithm based on linear approximation functions and combines the continuum model MT-CKD, including self and foreign-broadened water vapor and continuum absorption of other components (such as oxygen, carbon dioxide, and ozone) (Shephard et al., 2009. The calibration constant in the water vapor absorption channel can be determined using the modified Langley plot technique (Michalsky et al., 1995, which is similar to the Langley analysis mentioned in the retrieval of AOD; however, the stability of column water vapor instead of aerosol loading is required. In addition, the relationship between wavelength and AOD proposed by Ångström (1929) is used to extrapolate the instantaneous value of AOD at 938 nm with the least square method based on the AOD of atmospheric window channels. Consequently, PWV can be calculated using the equation

u¼

( " #)1=b 1 1 E0 ðλÞ X ln − mi τi ðλÞ : m a EðλÞ i

ð4Þ

2.4. Uncertainties analysis of the retrieval algorithm The major error of PWV retrieved by MFRSR is related to the process of determining aerosol optical depth in the water vapor absorption channel. According to error propagation theory, the uncertainty is caused by the uncertainties of instrument calibration, AOD, Rayleigh scattering optical depth and artificial errors (such as instrument tilt and the time shift of the shadow band) induced by various technical problems. The uncertainty of inferring the calibration constant using the Langley plot technique and Gaussian function fitting is about 3%, leading to PWV retrieval with an error of approximately 5%. The Rayleigh scattering optical depth in the visible and near-infrared bands is very small with a magnitude of approximately 10− 3, so it exhibits a negligible influence in PWV retrieval. The error induced by AOD will not exceed 5% (Alexandrov et al., 2009), which is less than that caused by instrument calibration. Another uncertainty in PWV retrieval is associated with the accuracy of modeling water vapor spectral transmittance throughout the 938-nm

spectral response of MFRSR, which heavily depends on the model and the resolution of water vapor spectroscopy. Furthermore, the imperfect spectral response function of the filter, which obviously varies from one instrument to another, has a significant influence on PWV retrieval. The line-by-line radiative transfer code such as LBLRTM is more accurate than moderate-resolution (MODTRAN) or low-resolution (LOWTRAN) radiative transfer code (Bokoye et al., 2003) in modeling water vapor transmittance, while the HITRAN 2004 spectral absorption database upgraded in 2006 is used to minimize the impact of the resolution of water vapor spectroscopy on modeling (Rothman et al., 2005; Gordon et al., 2007). The instrument spectral response function measured in the laboratory changes with time, wherein erroneous estimation may be induced by spectral shifts and out-of-band leaks. The spectral shift usually happens after long-term field deployment, and a 1-nm shift can induce error within ± 5% in PWV retrieval (Alexandrov et al., 2009. Out-of-band leak in the spectral response can be transferred into the water vapor absorption transmittance calculation and causes the overestimation of PWV by a few percentages, with the larger PWV values having greater impact. SACOL is located in a semi-arid area with 95% of the PWV values below 3 cm (Huang et al., 2010. Thus, the impact of out-of-band leak is relatively small. In reality, a variety of error sources have different impacts, which may lead to the overestimation or underestimation of PWV retrieval. As to the total retrieval error, all of the errors may be offset, and its quantification is relatively difficult. The best way is to compare the result of MFRSR with other independent ground-based measurements to evaluate the reliability of PWV retrieval from MFRSR. Another uncertainty in PWV retrieval is associated with the accuracy of modeling water vapor spectral transmittance throughout the 938-nm spectral response of MFRSR, which heavily depends on the model and the resolution of water vapor spectroscopy. Furthermore, the imperfect spectral response function of the filter, which obviously varies from one instrument to another, has a significant influence on PWV retrieval. The line-by-line radiative transfer code such as LBLRTM is more accurate than moderate-resolution (MODTRAN) or low-resolution (LOWTRAN) radiative transfer code (Bokoye et al., 2003 in modeling water vapor transmittance, while the HITRAN 2004 spectral absorption database upgraded in 2006 is used to minimize the impact of the resolution of water vapor spectroscopy on modeling (Rothman et al., 2005; Gordon et al., 2007). The instrument spectral response function measured in the laboratory changes with time, wherein erroneous estimation may be induced by spectral shifts and out-of-band leaks. The spectral shift usually happens after long-term field deployment, and a 1-nm shift can induce error within ± 5% in PWV retrieval (Alexandrov et al., 2009. Out-of-band leak in the spectral response can be transferred into the water vapor absorption transmittance calculation and causes the overestimation of PWV by a few percentages, with the larger PWV

Fig. 2. Comparison between PWV retrievals from MFRSR and sunphotometer from January 2008 to October 2008. (a) Scatterplot for all matching points. (b) Biases of MFRSR minus sunphotometer PWV versus sunphotometer PWV. Error bars show the mean biases and standard deviation with PWV segmentation of 0.2 cm.

X. Li et al. / Atmospheric Research 172–173 (2016) 83–94

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Fig. 3. Same as Fig. 2 but for the comparison between MFRSR and MWR from January 2008 to June 2008 and January 2009 to June 2010.

values having greater impact. SACOL is located in a semi-arid area with 95% of the PWV values below 3 cm (Huang et al., 2010. Thus, the impact of out-of-band leak is relatively small. In reality, a variety of error sources have different impacts, which may lead to the overestimation or underestimation of PWV retrieval. As to the total retrieval error, all of the errors may be offset, and its quantification is relatively difficult. The best way is to compare the result of MFRSR with other independent ground-based measurements to evaluate the reliability of PWV retrieval from MFRSR. 3. Results 3.1. Comparison of MFRSR and other ground-based measurements over SACOL PWV retrieved by MFRSR using the presented algorithm is compared with that from other instruments. The comparisons help to evaluate the reliability of the presented retrieval algorithm for MFRSR over the semiarid area and to analyze the possible causes that lead to inconsistency among their retrievals. The sunphotometer and MWR are considered to be consistent with MFRSR in space, but the time intervals of comparison are within ±1 min. Table 1 lists the detailed information about covered period, measurement frequency, and total number of available measurements of each instrument over SACOL. MFRSR has continuous measurements since August 2007, with only a short period without data in 2008. The sunphotometer collected measurements since 2007, but the data of level 2.0 from January 2008 to October 2008 are selected in this paper. PWV is retrieved by MWR from January 2008 to June 2010 except the period from July 2008 to December 2008. The MFRSR and sunphotometer retrieve PWV whenever it is a cloudfree sky during the day. MFRSR and MWR produce PWV with a high temporal resolution of 1 min, which explains the large number of available measurements. 3.1.1. Comparison between PWV retrievals from the MFRSR and sunphotometer Fig. 2a is a scattering diagram of MFRSR versus sunphotometer PWV retrievals, which shows remarkable linear agreement, with a correlation

coefficient of 0.99 and a root mean square (RMS) difference of 0.09 cm (equivalent to 9.1% of the average sunphotometer PWV). The MFRSR PWV presents a wet bias of 0.03 cm (2.9%) compared to sunphotometer retrieval. More than 80% of relative biases between the MFRSR and sunphotometer are less than 10%; furthermore, approximately 62% of relative biases show that MFRSR PWV values are larger than sunphotometer values. A high correlation and a small RMS difference between MFRSR and sunphotometer retrievals show the accuracy of MFRSR PWV retrieval using the presented algorithm over the semiarid area compared to the precision of ±10% for sunphotometer PWV retrieval. Fig. 2b shows scatter plots of MFRSR minus sunphotometer PWV as a function of sunphotometer PWV in which the mean values of MFRSR minus sunphotometer PWV and error bars are computed with the PWV interval of 0.2 cm. Most of the positive bias appears when the sunphotometer PWV value is less than 1.0 cm in which the maximum is approximately 0.23 cm, corresponding to a sunphotometer PWV value of 0.6 cm. Then the positive bias gradually decreases to near zero while PWV increases, whereas the bias transforms from a positive to a negative value until PWV reaches 1.7 cm. The difference between MFRSR and sunphotometer PWV retrievals may be attributed to the following factors: the difference in observational field of view (MFRSR for the entire sky and sunphotometer for the sun direction), the difference in sensitivity of the instrument in dry atmosphere, and calibration error of MFRSR. The results of comparison agree with the conclusion by other authors (Alexandrov et al., 2009; Schneider et al., 2010 in which Alexandrov et al. (2009) compared PWV between a C1 MFRSR and sunphotometer operating at a Southern Great Plains site and reported a mean difference of 3%, and Schneider et al. (2010) observed the systematic overestimation of MFRSR PWV versus the sunphotometer with a mean difference of 0.37 cm in Izaña, Spain. In addition, Sapucci et al. (2007) calculated the mean difference and RMS difference of − 0.21 cm and 0.27 cm, respectively, for a comparison between sunphotometer level 2.0 and radiosonde PWV and noted that sunphotometer level 2.0 data presented nearly all values were drier than the radiosonde values in the Amazonian region. From another point of view, there is a similar result of sunphotometer underestimation relative to other instruments in the above studies.

Table 2 Linear regression for comparison between PWV retrievals from MFRSR (Y) and the other two remote sensing instruments (X). Instrument

Correlative coefficient

Slope

Intercept (cm)

Standard deviation (cm)

Mean bias (cm)

RMS difference (cm)

Number of coincidences

Sunphotometer MWR

0.988 0.968

0.941 0.948

0.088 0.090

0.088 0.129

0.028 0.057

0.087 0.139

5242 67547

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Fig. 4. PWV retrievals over Zhangye from April 22 to June 9, 2008. Time series of daily average values of PWV by MFRSR and sunphotometer. The internal panel presents a scatterplot for all matching points between MFRSR and sunphotometer.

3.1.2. Comparison between PWV retrievals from the MFRSR and MWR Fig. 3a shows the scattering diagram between PWV retrievals from MFRSR and MWR, containing 67,547 matched data. A slight overestimation of PWV from MFRSR relative to MWR with a mean difference of 0.06 cm (equivalent to 10% of the average MWR PWV) and RMS difference of 0.14 cm (23%) is presented. The biases of MFRSR minus MWR PWV (Fig. 3b) show that the larger disagreement between the two measurements appears in the range of − 0.77 to 0.52 cm, corresponding to the MWR PWV values ranging from 0.5 to 1.5 cm. In addition, their relative biases (not shown here) reveal a cutoff point of an MWR PWV value of 0.65 cm, below which the relative bias changes from − 60% to 229%, whereas the relative bias decreases significantly within ± 67% when the MWR PWV value becomes larger than 0.65 cm. Different observation geometry, wherein MFRSR observes direct solar radiation and MWR points to the zenith, is likely to cause the above difference. In addition, the inconsistent height of PWV retrievals (MFRSR for the full atmospheric column and MWR from the surface to 10-km height) may be another reason for the wet bias of MFRSR. Table 2 depicts the parameters of comparison between PWV retrievals from MFRSR and other measurements, including the sunphotometer and MWR. The agreement between MFRSR and sunphotometer PWV retrievals is better than that between MFRSR and MWR retrievals. MFRSR always shows an overestimation of PWV with its value of 0.03 cm compared with the sunphotometer and 0.06 cm compared with MWR. Nevertheless, the

results of the comparisons verify the reliability of PWV retrieved by MFRSR. 3.2. Comparison of MFRSR and sunphotometer over Zhangye The China–US joint dust field campaign was synchronously carried out in Zhangye from April to June 2008, and in this paper, PWV is also retrieved from MFRSR and sunphotometer. In the retrieval algorithm, the calibration constants are calculated by average values of each month from 11 relatively cloud-free skies determined by a total sky imager using standard Langley analysis in atmospheric window channels and a modified Langley plot at the 938-nm channel. Fig. 4 gives the comparison between PWV retrievals from MFRSR and sunphotometer and shows the trend of MFRSR PWV presenting overestimation then turning into underestimation over the number of days. Under the influence of adjustment of atmospheric circulation and the beginning of the East Asian monsoon, the daily average PWV increases from spring to summer. The scattering diagram at the internal panel of Fig. 4 shows the matching measurements of MFRSR to PWV derived from sunphotometer and indicates the agreement of PWV retrievals between MFRSR and sunphotometer, with a correlation coefficient of 0.99 and an RMS difference of 0.03 cm (equivalent to 6.12% of the average sunphotometer PWV). MFRSR slightly overestimates by 0.56%. This result is similar to that over SACOL, but MFRSR over SACOL shows a higher wet bias of 2.9%. Therefore, the result of this comparison also illustrates the reliability of MFRSR PWV retrieval over Zhangye.

Fig. 5. Daily average PWV retrieved by MFRSR from August 2007 to June 2010 over SACOL.

X. Li et al. / Atmospheric Research 172–173 (2016) 83–94 Table 3 Basic statistics on the daily average values of PWV over SACOL from August 2007 to June 2010. Year

Average ± SD (cm)

Maximum (cm)

Minimum (cm)

Median (cm)

2007 2008 2009 2010

0.636 ± 0.396 0.791 ± 0.560 0.587 ± 0.468 0.414 ± 0.259

0.851 2.594 1.690 1.215

0.161 0.175 0.125 0.163

0.582 0.649 0.316 0.296

3.3. PWV characteristics over SACOL The result of the time series during August 2007 to June 2010 composed of the remaining 269 daily average values of PWV after excluding cloud cover and implementing quality control is shown in Fig. 5. The trend of annual variation indicates that PWV increases with the day of the year with the maximum at the 200th day and is then reduced until the end of the year. The seasonal cycle can also be seen clearly with higher values in summer (daily average values of 1.05-2.59 cm) and lower values in winter (daily average values of 0.12-0.35 cm), which is possibly attributable to the variation of the atmospheric humidity and temperature. The occasional extreme high value indicates wet atmosphere over SACOL in summer. The basic statistics are given in Table 3, which shows that all daily average values of PWV are less than 3 cm and that most of the values are less than 0.5 cm in winter. PWV with an average value of 0.79 cm in 2008 is higher than that of other years, while the difference between maximum and minimum daily average values can be up to 2.42 cm in 2008. Fig. 6 further shows the characteristics of the monthly average PWV, from which it can be seen that PWV increases gradually from January to August and then decreases slowly. PWV in summer is markedly higher than that of other seasons, especially in July and August, followed by September and October. The two maximum values of monthly average PWV appear in summer, with peaks of 1.77 ± 0.29 cm in July 2008 and 1.46 ± 0.22 cm in August 2009, while the two minima are found in January 2009 and December 2007, with values of 0.20 ± 0.05 cm and 0.22 ± 0.04 cm, respectively. Note that the seasonal variation of PWV agrees well with that of regional temperature. Fig. 6 also presents that the lower the water vapor, the smaller are the differences in each year such as in winter. On the contrary, in the case of high water vapor, there are larger differences such as in summer. The daily PWV shows the smoothest distribution in winter, corresponding to the smaller standard deviation of the monthly average value; however, daily average values in summer are accompanied by the largest temporal variation, with PWV increasing intensively, especially in summer of 2008. Typical seasonal variation of PWV over SACOL can be attributed to its geographical position, which is located at the edge of the East Asian monsoon climate region where there are four distinctive seasons.

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The amount of water vapor over a specific region depends on not only the local generation of water vapor but also the advection. The latter may display a particularly important role. The advection of water vapor in northwest China is complicated and affected by the summer monsoon of East Asian, topography, and glacier snow Shi, 1995. Wang et al., 2006 reported that convergence and divergence of water vapor are caused by advection in the East Asian monsoon region through atmospheric transportation and convergence (or divergence), which also affects the regional water vapor budget. Thus, the observed seasonal variation of PWV over SACOL depicts the significant role of monsoon circulation and other factors during summer, which brings considerable amount of water vapor, and possibly influences regional precipitation. The frequency distribution of PWV is shown in Fig. 7. The average PWV value for the period from August 2007 to June 2010 is 0.72 ± 0.53 cm, with 1st and 3rd quartiles of 0.26 cm and 1.07 cm, respectively. Furthermore, 90% of the values are smaller than 1.52 cm, and the maximum frequency appears in the range of 0.2 to 0.3 cm. 3.4. Verification of PWV retrievals from satellite sensors Satellites measure the characteristics of the atmosphere and Earth's surface and provide a variety of products; however, the accuracies of satellite products need to be verified. In this paper, the PWV products from MODIS and AIRS are verified based on PWV retrieval from MFRSR. To avoid additional measurement from the effect of space, the matching criteria of instruments under comparison select the satellite measurements covering SACOL and Zhangye. Specifically, MODIS measurement cannot be chosen to compare with MFRSR unless at least 10 ground pixels are valid values and are within a radius of 2 km from the location of SACOL and Zhangye. If so, then the distance-weighted average value of all valid values within 2 km is calculated as the final value for comparison. The time intervals of comparison are also within ± 10 min. AIRS measurement compared with MFRSR is similar to MODIS but has at least 2 ground pixels instead of 10 ground pixels and has a 50-km radius instead of 2 km. 3.4.1. Comparison between PWV retrievals from satellite sensors and MFRSR over SACOL The comparisons of PWV retrievals from MODIS on Terra and Aqua and MFRSR are given in Fig. 8. Because of the temporal restriction of satellite overpass (only once or twice during the daytime) and the cloud-free constraint in PWV retrieval from MFRSR, 206 data pairs for Terra and 201 data pairs for Aqua are used in the comparison with MFRSR PWV. Fig. 8a and b show the linear correlation and the difference between Terra MODIS and MFRSR. The regression gives the correlation coefficient of 0.97 and RMS difference of 0.25 cm, whereas MODIS yields an obvious overestimation with a wet bias of 0.07 cm. Fig. 8b further

Fig. 6. Monthly average PWV retrieved by MFRSR with color bars and standard deviation with black error bars from August 2007 to June 2010 over SACOL

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Fig. 7. Frequency distribution of PWV from August 2007 to June 2010 over SACOL. Red solid line corresponds to the median and the red dash box delimits the 1st (left) and 3rd (right) quartile. Blue dot represents the average value of PWV and is accompanied by its standard deviation with blue line.

indicates that the positive bias of MODIS minus MFRSR decreases until the PWV value increases to 0.4 cm with a maximum positive bias of 0.41 cm. Then the bias transferring into the negative value is extended to the PWV value up to 1.0 cm, after which the negative bias gradually decreases into positive bias. However, despite alternating from positive to negative bias, the overall result still displays a mean overestimation of Terra MODIS relative to MFRSR PWV, and the largest positive bias reaches 0.78 cm, corresponding to an MFRSR PWV value of 2.5 cm.

Comparison of PWV obtained from Aqua MODIS and MFRSR shows a better agreement than that from Terra MODIS and MFRSR, with a wet bias of 0.05 cm and RMS difference of 0.23 cm (Fig. 8c). The increasing trend of difference between Aqua MODIS and MFRSR with MFRSR PWV is similar to Terra MODIS (Fig. 8d). Consequently, MODIS compared with MFRSR shows an underestimation of PWV, with mean differences of 0.11 cm and 0.10 cm for Terra and Aqua, respectively, in a dry atmosphere (PWV value ranges from 0.4 cm to 1.0 cm) and an overestimation of PWV by up to 0.78 cm and 0.84 cm for Terra and Aqua, respectively, in a moist and quite dry atmosphere (PWV value is larger than 1.0 cm and less than 0.4 cm), while the overestimation increases with PWV. Most of the work on the verification of MODIS NIR PWV products has focused on comparison of MODIS and GPS, but rarely on MODIS and MFRSR, especially on verification over the semi-arid area of northwest China. The overestimation of PWV from MODIS has been confirmed, and our analysis is consistent with former research. Li et al. (2003) tested MODIS NIR PWV products and showed overestimation of MODIS PWV against GPS in east Sussex, UK. Liu et al. (2006) calculated the difference in PWV from the MODIS NIR algorithm and GPS in Beijing, China, and indicated that MODIS overestimated PWV with an RMS difference of 0.28 cm. Chen et al. (2008) reported that MODIS NIR PWV was moister than GPS PWV, with a bias of 0.18 cm and RMS difference of 0.33 cm. Meanwhile, MODIS underestimated in a dry atmosphere and overestimated in a moist atmosphere, and the overestimation increased with PWV over the United States and Australia. Kumar et al. (2010) noted that the MODIS NIR algorithm overestimated PWV over the Indo-Gangetic plains, in particular under moist atmosphere conditions.

Fig. 8. Same as Fig. 2 but (a) and (b) provide comparison between Terra MODIS and MFRSR, (c) and (d) for Aqua MODIS and MFRSR from January 2008 to June 2010.

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Fig. 9. Same as Fig. 2 but for the comparison between AIRS and MFRSR from January 2008 to June 2010.

The comparison of PWV retrievals between AIRS and MFRSR is illustrated in Fig. 9. Note that the linear regression is obtained using 176 data pairs which represent the time of 176 satellite overpasses simultaneously with MFRSR measurement within 10 min. Fig. 9 shows a systematic underestimation of AIRS PWV compared to MFRSR, with a mean difference of 0.13 cm and RMS difference of 0.22 cm. The comparison reveals that AIRS PWV tends to be relatively dry in low PWV (less than 2 cm in Fig. 9b), which is consistent with that of Ye et al. (2007) and Liu et al. (2011), but different than that of Livingston et al. (2007), who showed a wet bias of AIRS compared to AATS-14 (NASA Ames Airborne Tracking sunphotometer), and that of Rama varma raja et al. (2008), who showed that AIRS PWV was wetter in dry cases than GPS when the PWV value was less than 1 cm. Fig. 9b further shows higher dry bias of AIRS PWV under lower PWV conditions, especially in the vicinity of 1 cm. This observation does not agree with that of Tobin et al. (2006) who analyzed a wet bias of AIRS PWV at an SGP site. Therefore, the comparison for AIRS PWV is extremely sensitive to the sampling location and the instruments. As a high spatial coverage instrument for PWV retrieval, further verification of AIRS PWV in wider regions is urgently needed. Table 4 depicts the linear regression parameters between satellite sensors and MFRSR. Aqua MODIS PWV shows a smaller mean difference of 0.05 cm compared to MFRSR than that of Aqua MODIS and AIRS. Whether on Terra or Aqua, MODIS overestimates PWV relative to MFRSR, but AIRS PWV is just the opposite, with a systematic underestimation of 0.13 cm compared to MFRSR. 3.4.2. Comparison between PWV retrievals from satellite sensors and MFRSR over Zhangye Fig. 10a presents the matching measurements of MFRSR to PWV products derived from the MODIS NIR algorithm for 27 Terra and 21 Aqua overpasses. MODIS PWV is wet biased relative to MFRSR by approximately 0.19 cm and 0.27 cm, with RMS differences of 0.28 cm and 0.33 cm for Terra and Aqua, respectively. Thus, the agreement between Terra MODIS and MFRSR is substantially better than that between Aqua MODIS and MFRSR, which is contrary to the comparison over SACOL. Due to the reduced matching measurements over Zhangye, the possibility of random error generated in the linear regression increases markedly.

The comparison of PWV retrievals between AIRS and MFRSR is shown in Fig. 10b. The matching measurements of AIRS to MFRSR for 18 Aqua overpasses show a systematic underestimation by AIRS, with a mean difference of 0.04 cm and RMS difference of 0.15 cm, which is better than the comparison over SACOL. Note that the linear regression using 18 data pairs over Zhangye is inadequate statistically. 3.4.3. Error analysis of MODIS NIR PWV The accuracy of PWV retrieved by satellite sensors such as MODIS and AIRS depends on the calibration of the spectral response, assumptions in the retrieval algorithm, and impact of cloud contamination in the field of view Seemann et al., 2003. The algorithm of PWV retrieval for MODIS NIR channels utilizes a radiative transfer model and certain assumptions, one of which is that the variation of surface spectral reflectance is considered to be linear with wavelength in NIR channels and another is that single and multiple scattering effects of aerosol are negligible under low aerosol concentrations. The uncertainty of surface spectral reflectance for PWV retrievals is the largest source of error (Wang et al., 2005. SACOL is affected significantly by dust storms in spring and is located in the semi-arid region of Loess Plateau, China, where surface reflectance changes clearly with the seasons. Loess and hay exist in early spring and withered vegetation in late autumn, and winter has snow cover and summer has native vegetation cover. Complex aerosol composition may be affected by local sources, nearby biomass burning in the heating period, and long distance transportation of mineral dust from the Taklimakan Desert or Northwest Gobi Li and Zhang, 2012), Therefore, it may cause large error over SACOL such that the retrieval algorithm for PWV using NIR channels ignores the effects of aerosol scattering, especially in spring and winter. How to introduce the effects of aerosol is still an open question, and more efforts are needed, one of which is to consider the aerosol scattering effects by setting the threshold of aerosol optical depth. The MODIS NIR PWV products compared with MFRSR over SACOL are shown in Fig. 8a and c. If setting the MFRSR PWV as the reference values to calibrate MODIS PWV, the corrected MODIS NIR PWV (PWVC) values are calculated as follows: PWVCT ¼ 0:746  PWVOT þ 0:149;

ð5Þ

Table 4 Linear regression for comparison between PWV retrievals from satellite sensors (Y) and MFRSR (X). Sensor

Correlative coefficient

Slope

Intercept (cm)

Standard deviation (cm)

Mean bias (cm)

RMS difference (cm)

Number of coincidences

Terra MODIS Aqua MODIS AIRS

0.965 0.957 0.949

1.248 1.196 0.988

−0.127 −0.114 −0.116

0.193 0.198 0.186

0.069 0.047 −0.127

0.248 0.229 0.224

206 201 176

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Fig. 10. Comparison between PWV retrievals from satellite sensors and MFRSR from April 22 to June 9, 2008, (a) for MODIS and (b) for AIRS.

PWVCA ¼ 0:766  PWVOA þ 0:156;

ð6Þ

where PWVOT and PWVOA are the original MODIS NIR PWV from Terra and Aqua, respectively. Eqs. (5) and (6) are used to correct MODIS PWV products over SACOL from January 2008 to June 2010. Note that Eqs. (5) and (6) are derived by the linear correlation between MODIS and MFRSR. The mean differences of corrected MODIS NIR PWV from Terra and Aqua and MFRSR PWV are decreased to 0 cm, and the RMS differences are improved to 0.14 cm and 0.15 cm, respectively. Finally, this corrected method is applied to the field campaign in Zhangye. The mean differences of corrected MODIS PWV for Terra and Aqua and MFRSR are reduced to 0.12 cm and 0.18 cm, and RMS differences are improved to 0.22 cm and 0.25 cm, respectively, which shows obvious exaltation relative to comparisons before correction. However, the corrected method is derived from only one site and does not represent the characteristics for the semi-arid area of northwest China. Therefore, as an effective instrument for PWV measurement, the evaluation and application of satellites over the semi-arid area of northwest China needs to carefully consider the characteristics of the sensor and the applicability of the retrieval algorithm in this area, and more work about the verification of MODIS NIR PWV is needed for the broader semi-arid area using future field campaigns. 4. Conclusion and discussion MFRSR PWV retrieval based on the Beer–Lambert–Bouguer attenuation law utilizes the LBLRTM and updated HITRAN 2004 to model the water vapor spectral transmittance. A new method of Gaussian fitting based on the standard and modified Langley analysis technique extrapolating the daily calibration constant is proposed, which is considered to be a more realistic reflection of the dramatic theoretical changes in solar radiation at the TOA. PWV retrieved from MFRSR shows that the average value over SACOL is 0.72 ± 0.53 cm and that 90% of PWV values are less than 1.52 cm, with maximum frequency in the range of 0.2 to 0.3 cm. To verify the accuracy of the PWV retrieval from MFRSR and satellite PWV products, a series of comparisons between MFRSR and other measurements, including an AERONET sunphotometer, MWR, MODIS, and AIRS, are made. Measurements over SACOL from January 2008 to June 2010 and Zhangye during the China–US joint dust field campaign from April 2008 to June 2010 are chosen. MFRSR PWV over SACOL shows a better agreement with the sunphotometer than other measurements. PWV from MFRSR exhibits a wet bias of 0.03 cm (2.9%) and RMS difference of 0.09 cm (9.1%) relative to the sunphotometer, while MFRSR yields a slight overestimation of 0.06 cm (10%) and RMS difference of 0.14 cm (23%) relative to

MWR. The comparison between MFRSR and the sunphotometer over Zhangye shows that MFRSR overestimates PWV by 0.56% with an RMS difference of 6.12%. All of the comparisons adequately indicate the reliability of PWV retrieval from MFRSR using the presented algorithm over the semi-arid area. The small difference between MFRSR and the sunphotometer may be attributed to the differences in the field of view and directly observed radiation, as well as calibration error of MFRSR. The disagreement between MFRSR and MWR is likely to be associated with the difference in the observation geometric path and the integrated height of water vapor; however, the latter seems to be minor. MODIS PWV exhibits wet biases of 0.07 cm for Terra and 0.05 cm for Aqua, with corresponding RMS differences of 0.25 cm and 0.23 cm, respectively, compared to MFRSR over SACOL. There are similar results over Zhangye. The major error of the MODIS NIR PWV retrieval algorithm over this region is very likely due to the uncertainty of surface spectral reflectance and the neglect of scattering effects of aerosol, especially in spring and winter. A method based on MFRSR PWV as the reference measurement to calibrate the MODIS PWV products is presented; consequently, the agreement between MODIS and MFRSR after correction is improved relative to the previous comparison. AIRS PWV shows a systematic underestimation by 0.13 cm compared to MFRSR over SACOL, but with dry bias of 0.04 cm over Zhangye. Subsequently, comparisons from this paper and other studies show that AIRS PWV is extremely sensitive to the sampling location and the instruments. This paper verified the reliability of PWV retrieved by MFRSR using the above algorithm over the semi-arid area of northwest China compared with other measurements. It is certainly believable that the analysis has provided useful correlative measurements and a comparison for evaluating the accuracy of MODIS and AIRS PWV products. This work will accelerate the future application of MFRSR measurement for climate research in this region. Acknowledgments This work was partially supported by the National Science and Technology Support Program (2014BAC16B04), the Major National Scientific Research Projects (2012CB955302), and partially supported by the National Natural Science Foundation of China (41405127) and the Science and Technology Projects of Beijing Meteorological Bureau (BMBKJ201504006). The measurements are provided by SACOL and the first China–US joint dust field campaign, and LBLRTM 11.7 is provided by the Atmospheric and Environmental Research (AER) Radiative Transfer Working Group. We would like to sincerely thank Jennifer Delamere for the guidance on the LBLRTM and all members involved in the measurements.

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