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Comparison of cloud top heights derived from FY-2 meteorological satellites with heights derived from ground-based millimeter wavelength cloud radar
Atmospheric Research 199 (2018) 113–127
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Comparison of cloud top heights derived from FY-2 meteorological satellites with heights derived from ground-based millimeter wavelength cloud radar
MARK
Zhe Wanga,b,c, Zhenhui Wanga,b,⁎, Xiaozhong Caod,e, Fa Taod,e a Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, CMA Key Laboratory for Aerosol-Cloud-Precipitation, Nanjing University of Information Science & Technology, Nanjing 210044, China b School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044, China c CMA Training Center, Beijing 100081, China d CMA Atmosphere Observation Test Bed, Beijing 100081, China e CMA Meteorological Observation Center, Beijing 100081, China
A R T I C L E I N F O
A B S T R A C T
Keywords: FY-2 satellites Cloud radar Cloud detection Cloud top height
Clouds are currently observed by both ground-based and satellite remote sensing techniques. Each technique has its own strengths and weaknesses depending on the observation method, instrument performance and the methods used for retrieval. It is important to study synergistic cloud measurements to improve the reliability of the observations and to verify the different techniques. The FY-2 geostationary orbiting meteorological satellites continuously observe the sky over China. Their cloud top temperature product can be processed to retrieve the cloud top height (CTH). The ground-based millimeter wavelength cloud radar can acquire information about the vertical structure of clouds—such as the cloud base height (CBH), CTH and the cloud thickness—and can continuously monitor changes in the vertical profiles of clouds. The CTHs were retrieved using both cloud top temperature data from the FY-2 satellites and the cloud radar reflectivity data for the same time period (June 2015 to May 2016) and the resulting datasets were compared in order to evaluate the accuracy of CTH retrievals using FY-2 satellites. The results show that the concordance rate of cloud detection between the two datasets was 78.1%. Higher consistencies were obtained for thicker clouds with larger echo intensity and for more continuous clouds. The average difference in the CTH between the two techniques was 1.46 km. The difference in CTH between low- and mid-level clouds was less than that for high-level clouds. An attenuation threshold of the cloud radar for rainfall was 0.2 mm/min; a rainfall intensity below this threshold had no effect on the CTH. The satellite CTH can be used to compensate for the attenuation error in the cloud radar data.
1. Introduction Clouds are important factors in atmospheric science research and influence both dynamic and thermodynamic processes in the atmosphere, such as the water cycle and the radiation balance (Cess et al., 1989). Macroscopic physical parameters, such as the vertical structure of clouds—described by cloud base and cloud top heights (CTHs) — are important in determining the effect of clouds on radiation. This characterizes the thermal properties of clouds and the radiation characteristics of the cloud boundary (Hawkinson et al., 2005). Differences in the vertical structures of clouds produce different radiative forcing effects, which are the key drivers of climate change (Naud et al., 2003). CTH is also a key factor affecting the accurate detection of some cosmic rays (Merino et al., 2015; Rodríguez Frías et al., 2015; Sáez Cano et al., 2015; Soriano et al., 2015). Accurate and timely access to information
⁎
about the parameters of clouds is important, but these data are still uncertain as a result of the complexity of changes over both temporal and spatial scales (Stephens, 2005). Observations of clouds are currently made by both ground- and satellite-based remote sensing techniques. Each method has its own strengths and weaknesses due to differences in the method of observation, instrument performance, and the methods used for retrieval. Satellites have important advantages in the measurement of mid- to high-level clouds and the CTH because they can make large-scale, widecoverage observations of clouds from space. However, their retrieval ability is limited due to the complexity of the underlying surface, especially for atmospheric window channels. Ground-based remote sensing equipment (such as laser ceilometers and millimeter wavelength cloud radar) can be used to observe clouds from the ground surface to the tropopause. The local space–time resolution and
Corresponding author at: School of Atmospheric Physics, Nanjing University of Information Science & Technology, 219, Ningliulu, Nanjing 210044, China. E-mail address: [email protected] (Z. Wang).
http://dx.doi.org/10.1016/j.atmosres.2017.09.009 Received 16 February 2017; Received in revised form 10 August 2017; Accepted 12 September 2017 Available online 15 September 2017 0169-8095/ © 2017 Elsevier B.V. All rights reserved.
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dBZ
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Fig. 1. Contrast of continuous ground clutter (a) before and (b) after removal compared with the all-sky camera images at (c) 12:30 and (d) 16:50 on June 13, 2015.
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(d) vertical structure of clouds over Porto Santo Island in the Atlantic Ocean, and analyzed the difference between four observations. Hollars et al. (2004) compared observations of cloud height from 35 GHz millimeter wavelength cloud radar and GMS-5 geostationary meteorological satellites and concluded that there was no significant difference between the cloud tops of thick clouds, but that the average CTH of thin clouds measured by satellite was about 2 km lower than that from the radar observations. Hawkinson et al. (2005) compared the CTH retrieved by the carbon dioxide absorption spectral channel of the sounder instrument on-board the GOES satellite with cloud boundary
observational precision of cloud radar are both high, but the observations are only local and the spatial coverage is limited by the density distribution of the observation stations. It is therefore important to study synergistic cloud measurements that could improve the observational results and to verify the results of different methods to improve the reliability of cloud observations (Lü et al., 2003). There has been much published research on the comparative verification of synergistic cloud measurements. Wang et al. (1999) used a combination of radiosonde, 8 mm cloud radar, laser cloud gage and geostationary meteorological satellite data to observe changes in the 114
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generate CTH information. FY geostationary orbiting meteorological satellites will become the main force in the future for synergistic cloud measurements in China, but their quantitative detection accuracy for CTH is still unclear. The ground-based millimeter wavelength cloud radar is able to detect small particles such as clouds, fog and dust storms and can penetrate clouds and describe their time-height structure to obtain information on the vertical structure of clouds, such as the heights of cloud base and cloud top and the cloud thickness. It can continuously monitor changes in the vertical profile of clouds. The Meteorological Observation Center of China Meteorological Administration deployed a cloud radar at the Beijing Nanjiao Weather Observatory with the aim of comparing its performance and accuracy in measuring clouds. With the continued improvements in domestic cloud radar detection technology and reductions in cost, its usefulness in operationally measuring clouds gradually became apparent. Wang et al. (2016b) analyzed the consistency of the cloud radar with radiosonde vertical structure observations and concluded that the cloud radar is able to accurately characterize the vertical structure of clouds below 10 km. We obtained CTT data from the FY-2 satellites and the cloud radar reflectivity data from June 2015 to May 2016. CTHs from the two datasets were retrieved and compared in order to evaluate the accuracy of CTH measurement of FY-2 satellites. The difference in CTHs was analyzed and the results can be used as a reference for future research into synergistic cloud measurements.
Table 1 Number of CTT products obtained. Period
The number of data should be
The actual number of data
No. (%) of missing data
June 2015 July 2015 August 2015 September 2015 October 2015 November 2015 December 2015 January 2016 February 2016 March 2016 April 2016 May 2016 Total
1440 1488 1488 1440 1488 1440 744 744 696 744 720 744 13,176
1179 1264 1305 1079 1225 1387 742 719 659 660 662 719 11,600
261 (18.1) 224 (15.1) 183 (12.3) 361 (25.1) 263 (17.7) 53 (3.7) 2 (0.3) 25 (3.4) 37 (5.3) 84 (11.3) 58 (8.1) 25 (3.4) 1576 (12.0)
parameters acquired by the combined detection of LiDAR and millimeter wavelength cloud radar and found that the average difference was 1772 m. Hamada et al. (2008) used the GMS-5 split window algorithm to retrieve the top heights of tropical upper-tropospheric stratiform cloud and compared the results with cloud radar measurements and found that the estimated CTH of non-precipitating uppertropospheric stratiform clouds tends to rise with decreasing T11 (10.8 μm TB) and increasing ΔT (the brightness temperature difference between the two split windows). The variation in the cloud-top estimates with ΔT reached a few kilometers at T11 of ~250 K. Zhang et al. (2014) compared the CTHs retrieved by the IASI detectors on METOP-A satellites in Europe with ground-based cloud radar (WACR) observations. The results showed that the IASI CTH was lower than that obtained by the WACR observations and that the cloud cover was higher in the field of view; thicker clouds with higher particle concentrations resulted in more similar cloud heights from the two techniques. Oh et al. (2016) compared the cloud base and cloud top heights observed by Ka-band radar installed in Boseong, Korea with the CL51 ceilometer and COMS satellite cloud heights; the error in the cloud radar observations caused by precipitation attenuation were corrected. Most of these studies were based on a comparison of the CTH obtained with retrieval of the geostationary satellite radiance. Compared with the low temporal resolution observations of polar-orbiting satellites, geostationary satellites provide high temporal resolution observation products for a fixed location, which are a powerful detection tool for fast-changing clouds. They are suitable for comparison with ground-based remote sensing observations with high temporal resolution. FY-2 is a geostationary orbiting meteorological satellite system developed by China. It is currently operating three satellites (FY-2E/F/ G) in orbit and can continuously and operationally observe all of China. Its observational data products opened to the public include cloud top temperature (CTT) products, which can be further processed to
2. Data and methods 2.1. Determination of cloud height from cloud radar observation data The data used in this study were obtained from the Ka-band Doppler cloud radar system at Beijing Nanjiao Weather Observatory (39° 48′ 22′′ N, 116° 28′ 10′′ E, 32 m above sea level). This radar system uses pulse compression technology to solve the distance resolution problem and to take account of short-range blind spots. The radar points to the zenith and has a vertical resolution of 30 m. Its temporal resolution is 1 min for a single profile after a series of signal processing steps. The experiment obtained the cloud radar observational data from 0:00 on June 1, 2015 to 24:00 on May 31, 2016 (Beijing time) over a time span of 1 year. We also obtained the all-sky camera observation pictures for the corresponding period which provide verification data for the quality control of the cloud radar data. The cloud height determination method was based on the three-step reflectivity threshold method (Wang et al., 2016b). In the first step of this method, Gaussian filtering is used to filter out random noise and remove continuous non-cloud clutter at 1 km. The cloud boundary was determined in the second step based on reflectivity threshold; the lowest threshold of the reflectivity factor was taken as − 40 dBZ. The third step was a quality control stage used to improve the accuracy of
Table 2 Cloud detection results for satellite and cloud radar. Period
Effective number
Consistent number
Both represent clouds
Both represent clear skies
Consistency rate (%)
June 2015 July 2015 August 2015 September 2015 October 2015 November 2015 December 2015 January 2016 February 2016 March 2016 April 2016 May 2016 Total
997 825 1227 927 1142 1311 514 561 633 563 635 694 10,029
766 581 939 776 837 825 448 460 542 499 559 596 7828
405 196 369 371 249 501 76 72 107 113 182 263 2904
361 385 570 405 588 324 372 388 435 386 377 333 4924
76.8 70.4 76.5 83.7 73.3 62.9 87.2 82.0 85.6 88.6 88.0 85.9 78.1
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Fig. 2. Comparison of the cloud detection differences between the cloud radar and the FY-2 satellites (June 2015 to May 2016). The time series has been split into 18 panels as shown from (a) to (r). The blue stars represent the satellite CTH, the black dots represent the radar CTH and the gray shadows represent missing periods of radar observations. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Fig. 2. (continued)
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Fig. 3. Cloud radar echo (a) after quality control compared with (b) the all-sky camera photo. The black dots present the radar CTH and the open circles represent the satellite CTH. The all-sky camera observation time was 13:00 on July 9, 2015.
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Fig. 4. Comparison of the cloud detection difference between the cloud radar and the FY-2 satellites on November 10, 2015 (a) without filting and (b) with filting. The black dots present the radar CTH and the open circles represent the satellite CTH. (c) The all-sky camera observation time was 12:30 on the same day.
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Table 3 Average CTH observations (unit: km) and correlation coefficients. Period
Satellite cloud top height (CTH)
Radar cloud top height (RTH)
Difference (RTH− CTH)
Correlation coefficient
Sample size
June 2015 July 2015 August 2015 September 2015 October 2015 November 2015 December 2015 January 2016 February 2016 March 2016 April 2016 May 2016 Total
6.99 7.09 5.88 6.47 5.79 6.01 5.11 5.46 5.68 4.64 6.37 7.04 6.04
7.93 8.58 8.18 7.67 8.16 6.47 6.28 5.48 6.76 7.14 8.56 8.82 7.50
0.94 1.49 2.30 1.21 2.37 0.46 1.17 0.02 1.08 2.50 2.18 1.78 1.46
0.49 0.60 0.53 0.53 0.51 0.53 0.45 0.63 0.59 0.53 0.46 0.45 0.52
405 196 369 371 249 501 76 72 107 113 182 263 2904
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16 FY-2 Cloud Radar
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Fig. 5. CTH comparison curves (June 2015 to May 2016). The blue line represents the satellite CTH and the red line represents the radar CTH. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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established by considering the radiation effects of the 11 μm window area and the influence of the surface temperature and optical thickness. The K-distribution method for calculating molecular absorption was combined in the retrieval algorithm with the discrete ordinate method for computing the scattering of atmospheric radiation. This was used to calculate the cloud emissivity in the presence of scattering, molecular absorption and thermal radiation. The CTT error caused by the cloud optical thickness and cloud droplet effective radius was generally < 2 K (Zhao et al., 2002). This method takes into account the influence of the ground temperature and the optical thickness of the clouds in the calculation of radiative transfer—that is, it considers the effect of the interaction between radiation from the ground and the radiation from clouds—and is representative for translucent clouds in the field of view. The CTT data from FY-2F (112°E) and FY-2G (105°E) satellites observed from June 1, 2015 to May 31, 2016 were obtained from the Fengyun Satellite Data Center. The data acquisition time period corresponds to that of the cloud radar. The FY-2F and FY-2G CTT products alternated at half-hour intervals (FY-2G on the hour and FY-2F on the half-hour) during the five month period from June 1 to November 30, 2015. The resolution of the observation time was 0.5 h and therefore 48 data points were obtained each day. From December 1, 2015 to May 31, 2016, FY-2F and FY-2G both provided a CTT product on the hour, so we mainly used the FY-2F CTT products. The FY-2F CTT product was replaced by the FY-2G product when the FY-2F data were unavailable. The time resolution of the observation period was 1 h and 24 data points were acquired each day. Thus 366 days should result in 13,176 data points; however, due to the satellite work status or the product generation status, only 11,600 observations were actually obtained. There were 1576 missing data points, a rate of 12.0% (Table 1).
16 14
Cloud Radar (km)
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FY-2 (km) Fig. 6. Scattergram of the CTH.
the cloud boundary determination. For low-level continuous echo processing, this method directly removes echoes below 1 km, which has little effect in autumn and winter. However, it may not be suitable in the summer when there is more precipitation. Therefore, we have made some improvements after Gaussian filtering, that is, to make a judgment about whether the cloud base of the first layer was earthed and whether the thickness of the cloud was < 2 km after “the cloud boundary searching step”. If these conditions were met, then the boundary information for the layer was deleted. The new cloud boundary information was then re-judged and cloud base heights < 2 km and thicknesses < 600 m were found and removed. We used the all-sky camera pictures to verify the quality control results. As can be seen from Fig. 1c, it was clear sky at 12:30 and a small number of broken clouds were obviously inconsistent with the continuous radar echo shown in Fig. 1a. The cirrus cloud shown in Fig. 1d (taken at 16:50) was consistent with the radar echo at 8 km, but there was no continuous cloud at low altitude. These improvements avoided continuous ground clutter on cloud boundaries when determining overall interference (Fig. 1b).
2.3. Calculation of the FY-2 CTH and space–time matching with the cloud radar The FY-2 CTH was calculated by associating the CTT with the cloud height using the atmospheric temperature profile and then searching for the CTT value in the corresponding atmospheric profile data to find the temperature matching height. The atmospheric profile can be obtained from the regional atmospheric profile sample database, numerical prediction or sounding; each of these methods has advantages and disadvantages. The simplest method is to use the regional atmospheric profile sample, but because this represents the climatic mean of the local area, it has the largest error. Numerical predictions have a good time correspondence with satellite observations, but there is the prediction error of the numerical forecast temperature profile itself. Merino et al. (2015) evaluated model ability to represent temperature profiles in different climatic regions of the globe, and found that the verifications made for the temperature profiles of the WRF model with radiosondes showed RMS of around 1–2 °Celsius for the temperature, and the discrepancies between the WRF model profile and the sounding profile were higher in the mid-high latitudes than in tropical zones. An
2.2. FY-2 satellite observation data and the CTT retrieval method There are many ways for satellite CTH retrieval, including geometric methods (Naud et al., 2006; Lu et al., 2009.), carbon dioxide stratification methods (Platnick et al., 2003), CTT retrieval methods etc. The latter is commonly used to determine the CTH of some geostationary satellites, which usually includes multi-channel method (Szejwach, 1982; Minnis et al., 1995) and single-channel method (Oh et al., 2016). The FY-2 CTT product uses single-channel infrared window area retrieval. A database of CTT look-up tables was
Fig. 7. Cloud heights comparison between cloud radar and satellite observations from 8:00 on June 3, 2015 to 2:00 on June 5, 2015. The open circles represent the satellite CTH and the black dots represent the radar CTH.
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the observation on July 9, 2015 as an example (Fig. 3). The radar echo map displayed the weak and thin echo from 8:00 to 19:00, but the satellites did not observe any cloud at the corresponding time. The all-sky camera photos also showed a clear sky. We have reason to believe that the echo may be non-cloud echo which was not completely filtered out. 2. The continuous radar echo the top of which was below 2 km was filtered out by the quality control algorithm (see Fig. 4b). Both satellites and all-sky camera have observed clouds, causing inconsistencies in radar and satellite cloud detection. It was not common in our 1-year observation,
Table 4 Differences in CTHs for different heights of monolayer cloud. Cloud type
Height of cloud base
No. of samples
Average difference (km) (radar CTH minus satellite CTH)
Standard deviation of difference (km)
High Mid Low
RBH ≥ 6 km 2 km ≤ RBH < 6 km 500 m ≤ RBH < 2 km RBH < 500 m
741 773 132 481
3.56 0.79 − 0.67 − 0.43
2.39 2.18 1.98 1.42
3.2. Analysis of CTH averages and correlation
error also occurs if spatial interpolation processing is performed for the low vertical resolution of the profile data. Numerical prediction profile may not meet the demand of the cloud height search. In addition, the amount of data acquisition and processing required for numerical prediction is very high for CTT products with a 1-year time span. This study used the measured L-band sounding data with a balloon placed 158 m west of the cloud radar station. The vertical resolution of the profile was about 8 m. This high vertical resolution facilitated an accurate search and calculation of the cloud height, but because of its low temporal resolution (only twice a day at 07:15 and 19:15), there were some time-matched differences with the high temporal resolution satellite products. To deal with this, the sounding object should be used at a time close to the time of the satellite observations. The sounding data at 08:00 were the target objects for satellite observations at 02:00, 03:00, 04:00, 05:00, 06:00, 07:00, 08:00, 09:00, 10:00, 11:00, 12:00 and 13:00. The sounding data at 20:00 were used as the target of satellite observations at 14:00, 15:00, 16:00, 17:00, 18:00, 19:00, 20:00, 21:00, 22:00 and 23:00. The sounding data at 20:00 the day before were used as the target of the satellite observations at 00:00 and 01:00. This minimized the CTH error due to time differences. To compare these data with the cloud radar observation data from Beijing Nanjiao Weather Observatory, the two datasets were matched in time and space. FY-2 requires nearly 30 min to make a full disk map and therefore the geographical locations were different at specific scanning times, thus satellite positioning data is required to accurately determine the specific time at which the data were obtained over the radar. In this study, the time-matching problem was simplified by extracting the hour and half-hour data from the cloud radar to match the satellite data. In fact, because the satellite scan mode is generally from the north pole to the south pole, the time difference between the satellite and cloud radar observations should not exceed 10 min. Spatial matching was based on the location of the latitude and longitude information of the radar station to extract the corresponding locations of the satellite observations from the CTT products.
The effective observation results were averaged month by month. The monthly averages of satellite CTHs from June 2015 to May 2016 were in the range 4.64–7.09 km and those of radar CTHs were in the range 5.48–8.82 km. The monthly average differences between the two observations were in the range 0.02–2.50 km. Thus, the average height of the cloud top for all valid data was 6.04 km; it was 7.50 km for the cloud radar and the average difference was 1.46 km. Table 3 shows the correlations between the two observations. For a large sample size of 2904, the correlation coefficient was up to 0.52, showing a strong correlation between the two. Correlation analysis was performed in natural months with correlation coefficients ranging from 0.45 to 0.63. Fig. 5 shows plots of the two CTHs; in most cases the two fluctuations were consistent and some very high coincidences in the data were observed, such as in the first half of September 2015. The cloud radar echo map showed the development of more deep cloud in early September 2015, with an echo intensity > 20 dBz, accompanied by precipitation. Clouds with a large optical thickness approximate a black body and the radiant brightness temperature of the cloud top is consistent with the atmospheric temperature, which makes the calculation of the satellite CTH more accurate. For some of the large observed differences at times (such as mid- to late April 2016), the clouds were mostly cirrus. The cloud echoes were weak and the clouds were thin, so they could not be treated as black bodies in the calculations. The cloud top brightness temperature and atmospheric temperature were inconsistent for these clouds, resulting in differences in the calculations. Most of the clouds in the yearlong study (indicated with 2904 open circles) were distributed along and above the 1:1 line in the scattergram of Fig. 6, which is consistent with the analysis above. It is noteworthy that some of the data points for the CTH lie on the vertical axis of the scatter diagram. For example, on October 10, 2015, the cloud radar showed a high-altitude sporadic echo, the cloud layer was thin, the time was discontinuous and the echo was weak, resulting in the cloud top brightness temperature being very different from the atmospheric temperature. However, this was still detected as cloud by the satellite.
3. Comparative verification analysis 3.1. Cloud detection consistency analysis
3.3. Analysis of CTH contrast in a cloud change process
Table 1 shows that during the observed time period of 1 year, there were 11,600 actual observations from the satellite data; there were also missing data in the cloud radar records. A total of 10,029 data points were obtained from the satellite and radar records and these are known as the “effective” observation data. The number of same-cloud detection observations was 7828, which is a consistency rate of 78.1%. The results of the specific cloud tests are given in Table 2. Fig. 2 shows the consistency of the clouds observed by the two methods. The cloud detection results are generally consistent without the missing data from the cloud radar. However, inconsistencies were observed under the following conditions:
Fig. 7 shows the change in persistent cloud for single-spot cloud radar observations from 8:00 on June 3, 2015 to 2:00 on June 5, 2015. No cloud was observed before 10:00 on June 3 and the all-sky camera photo showed blue skies. From 10:00 to 16:00 on June 3, we observed thin, discontinuous clouds with a height > 6 km and the all-sky camera photographed light cirrus clouds with < 10% cloud cover. The CTH reduced gradually after 16:00. The clouds become thicker and the intensity of the radar echo strengthened. The all-sky photographs showed dark clouds over the full visual field. With time, the height of the cloud bottom gradually decreased to below 4 km and virga appeared. Double-layer cloud was observed from 1:00 on June 4 and the upper layer cloud was about 9 km high. At about 6:00, the upper and lower layers of the clouds gradually merged and the virga was connected to the ground, which means that precipitation may have
1. The CTH was < 4 km and the echo intensity was small (usually less than −20 dBZ), but the satellite did not observe any cloud. We took 124
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occurred. By this time, the cloud had become very thick. The observation data from Beijing Nanjiao Weather Observatory automatic station showed that it began to rain at 10:00 and the rain stopped at 16:00, giving 7 h of precipitation with a cumulative 5 mm of rain. There were two short-term cloud events between 18:00 and 23:00. The CTH of the corresponding satellite observations was analyzed. Clouds were observed at 10:30 on June 3 by the satellite. However, the cirrus cloud top was obviously lower than the CTH observed by the cloud radar. The thinner the clouds, the looser the structure and the greater the difference between the two observations. The difference was gradually reduced to 1–2 km for altostratus clouds and the trend of CTH was similar. When the double-layer cloud appeared at 0:00 on June 4, the height of the cloud top was between the CTH of the two-layer cloud observed by the cloud radar. The CTH of the two measurements tended to be consistent when the two layers of cloud merged, the virga was connected to the ground and the cloud was very thick. No cloud was observed on the cloud radar from 15:30 to 18:00; the all-sky camera
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If low cloud down to an RBH of 500 m was used as the boundary for a second classification, the cloud radar echo maps showed that cloud echoes with an RBH < 500 m were usually accompanied by precipitation. Only monolayer clouds were considered in this study. There were a total of 2127 monolayer clouds in 2904 effective cloud observation samples, of which 741, 773 and 613 were high, mid-height and low clouds, respectively. Table 4 shows the statistics for radar and satellite CTH for different types of clouds. The average CTH difference of mid- and low- clouds was < 0.8 km although there were larger differences for the high clouds. The high clouds were dominated by cirrus clouds (cirrostratus and cumulocirrus), which are thinner, more transparent and have a larger space–time discontinuity. The retrieval of CTHs by satellite for high-level cirrus clouds requires further improvement. Fig. 8 shows scatter plots for four types of cloud. The deviation is more obvious for high clouds, which are concentrated above the 1:1 line, whereas the mid-height and low clouds are concentrated near the 1:1 line. The echo-to-ground cloud which is typical of deep rain clouds are concentrated on the 1:1 line. To determine whether precipitation can attenuate the cloud radar echo and thus affect the radar judgment of the CTH, September 4, 2015, when precipitation started after 11:30, can be taken as an example (Fig. 9). The CTH of the cloud radar was in agreement with the satellite observations when the rain intensity was < 0.2 mm/min (< 12 mm/h). When the rain intensity was > 0.2 mm/min (> 12 mm/h), the radar echo was significantly attenuated and was different from the CTH of the satellite observations. This conclusion is consistent with the observations of Hollars et al. (2004), who concluded that the critical attenuation value of radar echo is 10 mm/h. Therefore the automatic station rainfall data can be used to determine whether the cloud radar echo was attenuated. If the rainfall was > 0.2 mm/min, it produced a rainfall attenuation effect. The satellite CTH can be used to compensate for attenuation errors in the cloud radar data. In addition, as can be seen from Fig. 9, the CTHs of the alternate observations with half hour intervals from FY-2F and FY-2G were also consistent with those of the cloud radar. We consider that the consistencies of the time and space matching and the calibration of the two satellites are acceptable.
Follow-up studies should further analyze the contribution of the spatiotemporal matching error to the difference in the height of the cloud top. The cloud radar also has its limitations especially for high-level cirrus observations. Compared with the ground-based lidar, the cloud radar may not detect very small liquid water drops or ice crystals far away from itself, while the lidar has a corresponding sensitivity (Uttal et al., 1995; Comstock et al., 2002). Therefore, the joint use of these two kinds of instruments can get more comprehensive cirrus information, and can better characterize the macro-physical properties of cirrus. In addition, the filtering method in this study needs to be further improved to better distinguish low-cloud from non-cloud echoes. As analyzed in this study, the satellite observation could be used as an effective validation for echo filtering. The transparency of a cloud in infrared channel will cause the CTH from the satellite data to be lower than that of the radar data. Adjustment of the satellite observations from the perspective of the optical thickness of the cloud to narrow the difference between the two, particularly for the height difference of high transparent cirrus clouds, is also worthy of further study. Other studies have shown that the optical thickness and water content of clouds are correlated (Nakajima et al., 2005). The radar reflectivity factor Z and the cloud liquid (ice) water content are also statistically related (Atlas, 1954; Wang et al., 2016a). The radar reflectivity data could therefore be further applied to the calculation of the optical thickness of clouds to make the corresponding radiation correction to the CTH from satellite observations.
4. Discussion and conclusions
Atlas, D., 1954. The estimation of cloud parameters by radar. J. Meteorol. 11, 309–317. Cess, R.D., Potter, G.L., Blanchet, J.P., Boer, G.J., Ghan, S.J., Kiehl, J.T., Treut, H.Le, Li, Z.-X., Liang, X.-Z., Mitchell, J.F.B., Morcrette, J.-J., Randall, D.A., Riches, M.R., Roeckner, E., Schlese, U., Slingo, A., Taylor, K.E., Washington, W.M., Wetherald, R.T., Yagai, I., 1989. Interpretation of cloud-climate feedback as produced by 14 atmospheric general circulation models. Science 245, 513–516. Comstock, J.M., Ackerman, T.P., Mace, G.G., 2002. Ground-based lidar and radar remote sensing of tropical cirrus clouds at Nauru Island: cloud statistics and radiative impacts. J. Geophys. Res. 107, 4714. Hamada, A., Nishi, N., Iwasaki, S., Ohno, Y., Kumagai, H., Okamoto, H., 2008. Cloud type and top height estimation for tropical upper-tropospheric clouds using GMS-5 splitwindow measurements combined with cloud radar measurements. Sci. Online Lett. Atmos. 4, 57–60. Hawkinson, J.A., Feltz, W., Ackerman, S.A., 2005. A comparison of GOES sounder- and cloud lidar- and radar-retrieved cloud-top heights. J. Appl. Meteorol. 44, 1234–1242. Hollars, S., Fu, Q., Comstock, J., Ackerman, T., 2004. Comparison of cloud-top height retrievals from ground-based 35 GHz MMCR and GMS-5 satellite observations at ARM TWP Manus site. Atmos. Res. 72, 169–186. Lü, D.R., Wang, P.C., Qiu, J.H., Tao, S.Y., 2003. An overview on the research progress of atmospheric remote sensing and satellite meteorology in China. Chin. J. Atmos. Sci. 27, 552–566. Lu, F., Xu, J., Menzel, W.P., Velden, C.S., 2009. Geometric cloud top height assignment by geosynchronous meteorological satellite images. In: Geoscience and Remote Sensing Symposium, 2009 IEEE International, IGARSS. vol. 25. pp. III-605–III-608 (IEEE). Merino, A., Sánchez, J.L., López, L., Gascón, E., García-Ortega, E., Hermida, L., Fernández-González, S., Soriano, J.F., Sáez-Cano, G., Del Peral, L., Reyes, M., Joven, E., Martín, Y., Licandro, J., Franchini, S., Rodríguez Frías, M.D., 2015. Cloud top height estimation from WRF model: application to the infrared camera onboard EUSO-BALLOON. Proc. Sci (30-July-2015, art. no. 377). Minnis, P., Smith, J.W.L., Garber, D.P., Ayers, J.K., Doelling, D.R., 1995. Cloud properties derived from GOES-7 for spring 1994 ARM intensive observing period using version 1.0.0 of ARM satellite data analysis program. NASA Ref. Publ. 1366, 1–58. Nakajima, T.Y., Uchiyama, A., Takamura, T., Tsujioka, N., Takemura, T., Nakajima, T.,
Acknowledgments We thank the CMA Atmosphere Observation Test Bed and the National Satellite Meteorological Center of the CMA for providing the observational data support. This work is jointly supported by the National Natural Science Foundation of China (41675028, 61531019, 41275043) and a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). References
Cloud top heights were calculated from the CTT data retrieved by the FY-2F/G stationary meteorological satellites and the cloud radar reflectivity data from June 1, 2015 to May 31, 2016. The corresponding all-sky camera pictures and precipitation data from the automatic station were acquired for auxiliary analysis. The cloud detection results and the CTHs of the cloud radar and FY-2 satellites were compared with each other. It was concluded that the consistency rate of cloud detection was 78.1%. The average height difference in the cloud top measured by the two methods was 1.46 km. The thicker clouds gave a larger echo intensity and more continuous clouds gave a better consistency for the CTH. The difference in the CTH between low and mid-height clouds was less than that for high clouds, which is similar to other satellites using the infrared channel for CTH retrieval. The CTH retrieval for high-level cirrus clouds by the FY-2 satellites needs to be improved. The attenuation threshold of the cloud radar for rainfall was 0.2 mm/min; a rainfall intensity below this threshold had no effect on the CTH. The satellite CTH can be used to compensate for attenuation errors in the cloud radar data. By comparing the results of the alternate observations of FY-2F and FY-2G with the cloud radar, it is considered that the consistencies of the time and space matching and the calibration of the two satellites are acceptable. It is inevitable that the method of time matching to find the height of the satellite cloud top and the method of space–time matching between the cloud radar and the satellite cloud top will produce errors.
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Soriano, J.F., Del Peral, L., Morales De Los Ríos, J.A., Prieto, H., Sáez-Cano, G., Joven, E., Reyes, M., Martín, Y., Licandro, J., Merino, A., López, L., Sánchez, J.L., Franchini, S., Roibás, E., Rodríguez Frías, M.D., 2015. Performance of the Spanish infrared camera onboard the EUSO-Balloon (CNES) flight on August 25, 2014. Proc. Sci (30-July2015, art. no. 653). Stephens, G.L., 2005. Review article-cloud feedbacks in the climate system: a critical review. J. Clim. 18, 237–273. Szejwach, G., 1982. Determination of semi-transparent cirrus cloud temperature from infrared radiances: application to Meteosat. J. Appl. Meteorol. 21, 384–393. Uttal, T., Intrieri, J.M., Eberhard, W.L., Clothiaux, E.E., Ackerman, T.P., 1995. Cloud boundary statistics during FIRE II. J. Atmos. Sci. 52, 4276–4284. Wang, J., Rossow, W.B., Uttal, T., Rozendaal, M., 1999. Variability of cloud vertical structure during ASTEX observed from a combination of rawinsonde, radar, ceilometer, and satellite. Mon. Weather Rev. 127, 2484–2502. Wang, J.H., Ge, J.X., Wei, M., Gu, S.S., Yang, Z.X., 2016a. Study of the relationship between IWC and Z for nonspherical ice particles at millimeter wavelength. J. Trop. Meteorol. 2016 (S1), 78–88. Wang, Z., Wang, Z.H., Cao, X.Z., 2016b. Consistency analysis for cloud vertical structure derived from millimeter cloud radar and radiosonde profiles. Acta. Meteor. Sin. 74, 815–826. Zhang, Y.P., Zhang, W.X., Lü, D.R., Bi, Y.H., 2014. Cloud top heights measured by METOP-A IASI instrument compared with ground-based cloud radar. Chin. J. Atmos. Sci. 38, 874–884. Zhao, F.S., Ding, Q., Sun, T.M., Kong, Q.X., Hu, W., Xun, S.P., Shao, H.F., 2002. An iterative algorithm for the retrieval of cloud properties from NOAA-AVHRR imagery. Acta. Meteor. Sin. 60, 594–601.
2005. Comparisons of warm cloud properties obtained from satellite, ground, and aircraft measurements during APEX intensive observation period in 2000 and 2001. J. Meteorol. Soc. Jpn. 83, 1085–1095. Naud, C.M., Muller, J.P., Clothiaux, E.E., 2003. Comparison between active sensor and radiosonde cloud boundaries over the ARM Southern Great Plains Site. J. Geophys. Res. 108, 291–302. Naud, C.M., Muller, J.P., Clothiaux, E.E., 2006. Assessment of multispectral ATSR2 stereo cloud-top height retrievals. Remote Sens. Environ. 104, 337–345. Oh, S.B., Kim, Y.H., Kim, K.H., Cho, C.H., Lim, E., 2016. Verification and correction of cloud base and top height retrievals from Ka–band cloud radar in Boseong, Korea. Adv. Atmos. Sci. 33, 73–84. Platnick, S., King, M.D., Ackerman, S.A., Menzel, W.P., Baum, B.A., Riedi, J.C., Frey, R.A., 2003. The MODIS cloud products: algorithms and examples from Terra. IEEE Trans. Geosci. Remote Sens. 41, 459–473. Rodríguez Frías, M.D., Licandro, J., Sabau, M.D., Sánchez, J.L., Franchini, S., López, L., Ramírez, L., Joven, E., Reyes, M., González-Alvarado, C., Belenguer, T., Meseguer, J., Pérez-Grande, I., Sáez-Cano, G., Soriano, J.F.H., Carretero, J., Prieto, H., Morales De Los Ríos, J.A., Martín, Y., Merino, A., Sanz-Palomino, M., García-Ortega, E., Gascón, E., Fernández-González, S., Alonso, G., Roibas, E., Sanz-Andrés, A., Pindado, S., Maroto, O., Díez-Merino, L., Tomás, A., Carbonell, J., Echeandía, C., Pérez-Cano, S., Del Peral, L., 2015. The Spanish infrared camera onboard the EUSO-BALLOON (CNES) flight on August 24, 2014. Proc. Sci (30-July-2015, art. no. 687). Sáez Cano, G., Del Peral, L., Mustafa, M., Soriano, J.F., Adams Jr., J.H., Wiencke, L., López, L., Merino, A., Reyes, M., Joven, E., Martín, Y., Roibas, E., Sanz, A., Sánchez, J.L., Franchini, S., Licandro, J., Rodríguez Frías, M.D., 2015. Cloud optical depth obtained from the infrared camera data and the UV flashers mounted on a helicopter flying under the euso-balloon during its flight. Proc. Sci (30-July-2015, art. no. 658).
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Comparison of cloud top heights derived from FY-2 meteorological satellites with heights derived from ground-based millimeter wavelength cloud radar
MARK
Zhe Wanga,b,c, Zhenhui Wanga,b,⁎, Xiaozhong Caod,e, Fa Taod,e a Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, CMA Key Laboratory for Aerosol-Cloud-Precipitation, Nanjing University of Information Science & Technology, Nanjing 210044, China b School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044, China c CMA Training Center, Beijing 100081, China d CMA Atmosphere Observation Test Bed, Beijing 100081, China e CMA Meteorological Observation Center, Beijing 100081, China
A R T I C L E I N F O
A B S T R A C T
Keywords: FY-2 satellites Cloud radar Cloud detection Cloud top height
Clouds are currently observed by both ground-based and satellite remote sensing techniques. Each technique has its own strengths and weaknesses depending on the observation method, instrument performance and the methods used for retrieval. It is important to study synergistic cloud measurements to improve the reliability of the observations and to verify the different techniques. The FY-2 geostationary orbiting meteorological satellites continuously observe the sky over China. Their cloud top temperature product can be processed to retrieve the cloud top height (CTH). The ground-based millimeter wavelength cloud radar can acquire information about the vertical structure of clouds—such as the cloud base height (CBH), CTH and the cloud thickness—and can continuously monitor changes in the vertical profiles of clouds. The CTHs were retrieved using both cloud top temperature data from the FY-2 satellites and the cloud radar reflectivity data for the same time period (June 2015 to May 2016) and the resulting datasets were compared in order to evaluate the accuracy of CTH retrievals using FY-2 satellites. The results show that the concordance rate of cloud detection between the two datasets was 78.1%. Higher consistencies were obtained for thicker clouds with larger echo intensity and for more continuous clouds. The average difference in the CTH between the two techniques was 1.46 km. The difference in CTH between low- and mid-level clouds was less than that for high-level clouds. An attenuation threshold of the cloud radar for rainfall was 0.2 mm/min; a rainfall intensity below this threshold had no effect on the CTH. The satellite CTH can be used to compensate for the attenuation error in the cloud radar data.
1. Introduction Clouds are important factors in atmospheric science research and influence both dynamic and thermodynamic processes in the atmosphere, such as the water cycle and the radiation balance (Cess et al., 1989). Macroscopic physical parameters, such as the vertical structure of clouds—described by cloud base and cloud top heights (CTHs) — are important in determining the effect of clouds on radiation. This characterizes the thermal properties of clouds and the radiation characteristics of the cloud boundary (Hawkinson et al., 2005). Differences in the vertical structures of clouds produce different radiative forcing effects, which are the key drivers of climate change (Naud et al., 2003). CTH is also a key factor affecting the accurate detection of some cosmic rays (Merino et al., 2015; Rodríguez Frías et al., 2015; Sáez Cano et al., 2015; Soriano et al., 2015). Accurate and timely access to information
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about the parameters of clouds is important, but these data are still uncertain as a result of the complexity of changes over both temporal and spatial scales (Stephens, 2005). Observations of clouds are currently made by both ground- and satellite-based remote sensing techniques. Each method has its own strengths and weaknesses due to differences in the method of observation, instrument performance, and the methods used for retrieval. Satellites have important advantages in the measurement of mid- to high-level clouds and the CTH because they can make large-scale, widecoverage observations of clouds from space. However, their retrieval ability is limited due to the complexity of the underlying surface, especially for atmospheric window channels. Ground-based remote sensing equipment (such as laser ceilometers and millimeter wavelength cloud radar) can be used to observe clouds from the ground surface to the tropopause. The local space–time resolution and
Corresponding author at: School of Atmospheric Physics, Nanjing University of Information Science & Technology, 219, Ningliulu, Nanjing 210044, China. E-mail address: [email protected] (Z. Wang).
http://dx.doi.org/10.1016/j.atmosres.2017.09.009 Received 16 February 2017; Received in revised form 10 August 2017; Accepted 12 September 2017 Available online 15 September 2017 0169-8095/ © 2017 Elsevier B.V. All rights reserved.
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(d) vertical structure of clouds over Porto Santo Island in the Atlantic Ocean, and analyzed the difference between four observations. Hollars et al. (2004) compared observations of cloud height from 35 GHz millimeter wavelength cloud radar and GMS-5 geostationary meteorological satellites and concluded that there was no significant difference between the cloud tops of thick clouds, but that the average CTH of thin clouds measured by satellite was about 2 km lower than that from the radar observations. Hawkinson et al. (2005) compared the CTH retrieved by the carbon dioxide absorption spectral channel of the sounder instrument on-board the GOES satellite with cloud boundary
observational precision of cloud radar are both high, but the observations are only local and the spatial coverage is limited by the density distribution of the observation stations. It is therefore important to study synergistic cloud measurements that could improve the observational results and to verify the results of different methods to improve the reliability of cloud observations (Lü et al., 2003). There has been much published research on the comparative verification of synergistic cloud measurements. Wang et al. (1999) used a combination of radiosonde, 8 mm cloud radar, laser cloud gage and geostationary meteorological satellite data to observe changes in the 114
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generate CTH information. FY geostationary orbiting meteorological satellites will become the main force in the future for synergistic cloud measurements in China, but their quantitative detection accuracy for CTH is still unclear. The ground-based millimeter wavelength cloud radar is able to detect small particles such as clouds, fog and dust storms and can penetrate clouds and describe their time-height structure to obtain information on the vertical structure of clouds, such as the heights of cloud base and cloud top and the cloud thickness. It can continuously monitor changes in the vertical profile of clouds. The Meteorological Observation Center of China Meteorological Administration deployed a cloud radar at the Beijing Nanjiao Weather Observatory with the aim of comparing its performance and accuracy in measuring clouds. With the continued improvements in domestic cloud radar detection technology and reductions in cost, its usefulness in operationally measuring clouds gradually became apparent. Wang et al. (2016b) analyzed the consistency of the cloud radar with radiosonde vertical structure observations and concluded that the cloud radar is able to accurately characterize the vertical structure of clouds below 10 km. We obtained CTT data from the FY-2 satellites and the cloud radar reflectivity data from June 2015 to May 2016. CTHs from the two datasets were retrieved and compared in order to evaluate the accuracy of CTH measurement of FY-2 satellites. The difference in CTHs was analyzed and the results can be used as a reference for future research into synergistic cloud measurements.
Table 1 Number of CTT products obtained. Period
The number of data should be
The actual number of data
No. (%) of missing data
June 2015 July 2015 August 2015 September 2015 October 2015 November 2015 December 2015 January 2016 February 2016 March 2016 April 2016 May 2016 Total
1440 1488 1488 1440 1488 1440 744 744 696 744 720 744 13,176
1179 1264 1305 1079 1225 1387 742 719 659 660 662 719 11,600
261 (18.1) 224 (15.1) 183 (12.3) 361 (25.1) 263 (17.7) 53 (3.7) 2 (0.3) 25 (3.4) 37 (5.3) 84 (11.3) 58 (8.1) 25 (3.4) 1576 (12.0)
parameters acquired by the combined detection of LiDAR and millimeter wavelength cloud radar and found that the average difference was 1772 m. Hamada et al. (2008) used the GMS-5 split window algorithm to retrieve the top heights of tropical upper-tropospheric stratiform cloud and compared the results with cloud radar measurements and found that the estimated CTH of non-precipitating uppertropospheric stratiform clouds tends to rise with decreasing T11 (10.8 μm TB) and increasing ΔT (the brightness temperature difference between the two split windows). The variation in the cloud-top estimates with ΔT reached a few kilometers at T11 of ~250 K. Zhang et al. (2014) compared the CTHs retrieved by the IASI detectors on METOP-A satellites in Europe with ground-based cloud radar (WACR) observations. The results showed that the IASI CTH was lower than that obtained by the WACR observations and that the cloud cover was higher in the field of view; thicker clouds with higher particle concentrations resulted in more similar cloud heights from the two techniques. Oh et al. (2016) compared the cloud base and cloud top heights observed by Ka-band radar installed in Boseong, Korea with the CL51 ceilometer and COMS satellite cloud heights; the error in the cloud radar observations caused by precipitation attenuation were corrected. Most of these studies were based on a comparison of the CTH obtained with retrieval of the geostationary satellite radiance. Compared with the low temporal resolution observations of polar-orbiting satellites, geostationary satellites provide high temporal resolution observation products for a fixed location, which are a powerful detection tool for fast-changing clouds. They are suitable for comparison with ground-based remote sensing observations with high temporal resolution. FY-2 is a geostationary orbiting meteorological satellite system developed by China. It is currently operating three satellites (FY-2E/F/ G) in orbit and can continuously and operationally observe all of China. Its observational data products opened to the public include cloud top temperature (CTT) products, which can be further processed to
2. Data and methods 2.1. Determination of cloud height from cloud radar observation data The data used in this study were obtained from the Ka-band Doppler cloud radar system at Beijing Nanjiao Weather Observatory (39° 48′ 22′′ N, 116° 28′ 10′′ E, 32 m above sea level). This radar system uses pulse compression technology to solve the distance resolution problem and to take account of short-range blind spots. The radar points to the zenith and has a vertical resolution of 30 m. Its temporal resolution is 1 min for a single profile after a series of signal processing steps. The experiment obtained the cloud radar observational data from 0:00 on June 1, 2015 to 24:00 on May 31, 2016 (Beijing time) over a time span of 1 year. We also obtained the all-sky camera observation pictures for the corresponding period which provide verification data for the quality control of the cloud radar data. The cloud height determination method was based on the three-step reflectivity threshold method (Wang et al., 2016b). In the first step of this method, Gaussian filtering is used to filter out random noise and remove continuous non-cloud clutter at 1 km. The cloud boundary was determined in the second step based on reflectivity threshold; the lowest threshold of the reflectivity factor was taken as − 40 dBZ. The third step was a quality control stage used to improve the accuracy of
Table 2 Cloud detection results for satellite and cloud radar. Period
Effective number
Consistent number
Both represent clouds
Both represent clear skies
Consistency rate (%)
June 2015 July 2015 August 2015 September 2015 October 2015 November 2015 December 2015 January 2016 February 2016 March 2016 April 2016 May 2016 Total
997 825 1227 927 1142 1311 514 561 633 563 635 694 10,029
766 581 939 776 837 825 448 460 542 499 559 596 7828
405 196 369 371 249 501 76 72 107 113 182 263 2904
361 385 570 405 588 324 372 388 435 386 377 333 4924
76.8 70.4 76.5 83.7 73.3 62.9 87.2 82.0 85.6 88.6 88.0 85.9 78.1
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Fig. 3. Cloud radar echo (a) after quality control compared with (b) the all-sky camera photo. The black dots present the radar CTH and the open circles represent the satellite CTH. The all-sky camera observation time was 13:00 on July 9, 2015.
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Table 3 Average CTH observations (unit: km) and correlation coefficients. Period
Satellite cloud top height (CTH)
Radar cloud top height (RTH)
Difference (RTH− CTH)
Correlation coefficient
Sample size
June 2015 July 2015 August 2015 September 2015 October 2015 November 2015 December 2015 January 2016 February 2016 March 2016 April 2016 May 2016 Total
6.99 7.09 5.88 6.47 5.79 6.01 5.11 5.46 5.68 4.64 6.37 7.04 6.04
7.93 8.58 8.18 7.67 8.16 6.47 6.28 5.48 6.76 7.14 8.56 8.82 7.50
0.94 1.49 2.30 1.21 2.37 0.46 1.17 0.02 1.08 2.50 2.18 1.78 1.46
0.49 0.60 0.53 0.53 0.51 0.53 0.45 0.63 0.59 0.53 0.46 0.45 0.52
405 196 369 371 249 501 76 72 107 113 182 263 2904
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established by considering the radiation effects of the 11 μm window area and the influence of the surface temperature and optical thickness. The K-distribution method for calculating molecular absorption was combined in the retrieval algorithm with the discrete ordinate method for computing the scattering of atmospheric radiation. This was used to calculate the cloud emissivity in the presence of scattering, molecular absorption and thermal radiation. The CTT error caused by the cloud optical thickness and cloud droplet effective radius was generally < 2 K (Zhao et al., 2002). This method takes into account the influence of the ground temperature and the optical thickness of the clouds in the calculation of radiative transfer—that is, it considers the effect of the interaction between radiation from the ground and the radiation from clouds—and is representative for translucent clouds in the field of view. The CTT data from FY-2F (112°E) and FY-2G (105°E) satellites observed from June 1, 2015 to May 31, 2016 were obtained from the Fengyun Satellite Data Center. The data acquisition time period corresponds to that of the cloud radar. The FY-2F and FY-2G CTT products alternated at half-hour intervals (FY-2G on the hour and FY-2F on the half-hour) during the five month period from June 1 to November 30, 2015. The resolution of the observation time was 0.5 h and therefore 48 data points were obtained each day. From December 1, 2015 to May 31, 2016, FY-2F and FY-2G both provided a CTT product on the hour, so we mainly used the FY-2F CTT products. The FY-2F CTT product was replaced by the FY-2G product when the FY-2F data were unavailable. The time resolution of the observation period was 1 h and 24 data points were acquired each day. Thus 366 days should result in 13,176 data points; however, due to the satellite work status or the product generation status, only 11,600 observations were actually obtained. There were 1576 missing data points, a rate of 12.0% (Table 1).
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the cloud boundary determination. For low-level continuous echo processing, this method directly removes echoes below 1 km, which has little effect in autumn and winter. However, it may not be suitable in the summer when there is more precipitation. Therefore, we have made some improvements after Gaussian filtering, that is, to make a judgment about whether the cloud base of the first layer was earthed and whether the thickness of the cloud was < 2 km after “the cloud boundary searching step”. If these conditions were met, then the boundary information for the layer was deleted. The new cloud boundary information was then re-judged and cloud base heights < 2 km and thicknesses < 600 m were found and removed. We used the all-sky camera pictures to verify the quality control results. As can be seen from Fig. 1c, it was clear sky at 12:30 and a small number of broken clouds were obviously inconsistent with the continuous radar echo shown in Fig. 1a. The cirrus cloud shown in Fig. 1d (taken at 16:50) was consistent with the radar echo at 8 km, but there was no continuous cloud at low altitude. These improvements avoided continuous ground clutter on cloud boundaries when determining overall interference (Fig. 1b).
2.3. Calculation of the FY-2 CTH and space–time matching with the cloud radar The FY-2 CTH was calculated by associating the CTT with the cloud height using the atmospheric temperature profile and then searching for the CTT value in the corresponding atmospheric profile data to find the temperature matching height. The atmospheric profile can be obtained from the regional atmospheric profile sample database, numerical prediction or sounding; each of these methods has advantages and disadvantages. The simplest method is to use the regional atmospheric profile sample, but because this represents the climatic mean of the local area, it has the largest error. Numerical predictions have a good time correspondence with satellite observations, but there is the prediction error of the numerical forecast temperature profile itself. Merino et al. (2015) evaluated model ability to represent temperature profiles in different climatic regions of the globe, and found that the verifications made for the temperature profiles of the WRF model with radiosondes showed RMS of around 1–2 °Celsius for the temperature, and the discrepancies between the WRF model profile and the sounding profile were higher in the mid-high latitudes than in tropical zones. An
2.2. FY-2 satellite observation data and the CTT retrieval method There are many ways for satellite CTH retrieval, including geometric methods (Naud et al., 2006; Lu et al., 2009.), carbon dioxide stratification methods (Platnick et al., 2003), CTT retrieval methods etc. The latter is commonly used to determine the CTH of some geostationary satellites, which usually includes multi-channel method (Szejwach, 1982; Minnis et al., 1995) and single-channel method (Oh et al., 2016). The FY-2 CTT product uses single-channel infrared window area retrieval. A database of CTT look-up tables was
Fig. 7. Cloud heights comparison between cloud radar and satellite observations from 8:00 on June 3, 2015 to 2:00 on June 5, 2015. The open circles represent the satellite CTH and the black dots represent the radar CTH.
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the observation on July 9, 2015 as an example (Fig. 3). The radar echo map displayed the weak and thin echo from 8:00 to 19:00, but the satellites did not observe any cloud at the corresponding time. The all-sky camera photos also showed a clear sky. We have reason to believe that the echo may be non-cloud echo which was not completely filtered out. 2. The continuous radar echo the top of which was below 2 km was filtered out by the quality control algorithm (see Fig. 4b). Both satellites and all-sky camera have observed clouds, causing inconsistencies in radar and satellite cloud detection. It was not common in our 1-year observation,
Table 4 Differences in CTHs for different heights of monolayer cloud. Cloud type
Height of cloud base
No. of samples
Average difference (km) (radar CTH minus satellite CTH)
Standard deviation of difference (km)
High Mid Low
RBH ≥ 6 km 2 km ≤ RBH < 6 km 500 m ≤ RBH < 2 km RBH < 500 m
741 773 132 481
3.56 0.79 − 0.67 − 0.43
2.39 2.18 1.98 1.42
3.2. Analysis of CTH averages and correlation
error also occurs if spatial interpolation processing is performed for the low vertical resolution of the profile data. Numerical prediction profile may not meet the demand of the cloud height search. In addition, the amount of data acquisition and processing required for numerical prediction is very high for CTT products with a 1-year time span. This study used the measured L-band sounding data with a balloon placed 158 m west of the cloud radar station. The vertical resolution of the profile was about 8 m. This high vertical resolution facilitated an accurate search and calculation of the cloud height, but because of its low temporal resolution (only twice a day at 07:15 and 19:15), there were some time-matched differences with the high temporal resolution satellite products. To deal with this, the sounding object should be used at a time close to the time of the satellite observations. The sounding data at 08:00 were the target objects for satellite observations at 02:00, 03:00, 04:00, 05:00, 06:00, 07:00, 08:00, 09:00, 10:00, 11:00, 12:00 and 13:00. The sounding data at 20:00 were used as the target of satellite observations at 14:00, 15:00, 16:00, 17:00, 18:00, 19:00, 20:00, 21:00, 22:00 and 23:00. The sounding data at 20:00 the day before were used as the target of the satellite observations at 00:00 and 01:00. This minimized the CTH error due to time differences. To compare these data with the cloud radar observation data from Beijing Nanjiao Weather Observatory, the two datasets were matched in time and space. FY-2 requires nearly 30 min to make a full disk map and therefore the geographical locations were different at specific scanning times, thus satellite positioning data is required to accurately determine the specific time at which the data were obtained over the radar. In this study, the time-matching problem was simplified by extracting the hour and half-hour data from the cloud radar to match the satellite data. In fact, because the satellite scan mode is generally from the north pole to the south pole, the time difference between the satellite and cloud radar observations should not exceed 10 min. Spatial matching was based on the location of the latitude and longitude information of the radar station to extract the corresponding locations of the satellite observations from the CTT products.
The effective observation results were averaged month by month. The monthly averages of satellite CTHs from June 2015 to May 2016 were in the range 4.64–7.09 km and those of radar CTHs were in the range 5.48–8.82 km. The monthly average differences between the two observations were in the range 0.02–2.50 km. Thus, the average height of the cloud top for all valid data was 6.04 km; it was 7.50 km for the cloud radar and the average difference was 1.46 km. Table 3 shows the correlations between the two observations. For a large sample size of 2904, the correlation coefficient was up to 0.52, showing a strong correlation between the two. Correlation analysis was performed in natural months with correlation coefficients ranging from 0.45 to 0.63. Fig. 5 shows plots of the two CTHs; in most cases the two fluctuations were consistent and some very high coincidences in the data were observed, such as in the first half of September 2015. The cloud radar echo map showed the development of more deep cloud in early September 2015, with an echo intensity > 20 dBz, accompanied by precipitation. Clouds with a large optical thickness approximate a black body and the radiant brightness temperature of the cloud top is consistent with the atmospheric temperature, which makes the calculation of the satellite CTH more accurate. For some of the large observed differences at times (such as mid- to late April 2016), the clouds were mostly cirrus. The cloud echoes were weak and the clouds were thin, so they could not be treated as black bodies in the calculations. The cloud top brightness temperature and atmospheric temperature were inconsistent for these clouds, resulting in differences in the calculations. Most of the clouds in the yearlong study (indicated with 2904 open circles) were distributed along and above the 1:1 line in the scattergram of Fig. 6, which is consistent with the analysis above. It is noteworthy that some of the data points for the CTH lie on the vertical axis of the scatter diagram. For example, on October 10, 2015, the cloud radar showed a high-altitude sporadic echo, the cloud layer was thin, the time was discontinuous and the echo was weak, resulting in the cloud top brightness temperature being very different from the atmospheric temperature. However, this was still detected as cloud by the satellite.
3. Comparative verification analysis 3.1. Cloud detection consistency analysis
3.3. Analysis of CTH contrast in a cloud change process
Table 1 shows that during the observed time period of 1 year, there were 11,600 actual observations from the satellite data; there were also missing data in the cloud radar records. A total of 10,029 data points were obtained from the satellite and radar records and these are known as the “effective” observation data. The number of same-cloud detection observations was 7828, which is a consistency rate of 78.1%. The results of the specific cloud tests are given in Table 2. Fig. 2 shows the consistency of the clouds observed by the two methods. The cloud detection results are generally consistent without the missing data from the cloud radar. However, inconsistencies were observed under the following conditions:
Fig. 7 shows the change in persistent cloud for single-spot cloud radar observations from 8:00 on June 3, 2015 to 2:00 on June 5, 2015. No cloud was observed before 10:00 on June 3 and the all-sky camera photo showed blue skies. From 10:00 to 16:00 on June 3, we observed thin, discontinuous clouds with a height > 6 km and the all-sky camera photographed light cirrus clouds with < 10% cloud cover. The CTH reduced gradually after 16:00. The clouds become thicker and the intensity of the radar echo strengthened. The all-sky photographs showed dark clouds over the full visual field. With time, the height of the cloud bottom gradually decreased to below 4 km and virga appeared. Double-layer cloud was observed from 1:00 on June 4 and the upper layer cloud was about 9 km high. At about 6:00, the upper and lower layers of the clouds gradually merged and the virga was connected to the ground, which means that precipitation may have
1. The CTH was < 4 km and the echo intensity was small (usually less than −20 dBZ), but the satellite did not observe any cloud. We took 124
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Fig. 8. Scatterplot of cloud height for different cloud types. (a) High clouds, (b) mid-height clouds, (c) low clouds (RBH ≥ 500 m) and (d) low clouds (RBH < 500 m).
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3.4. Contrast analysis of CTHs for different types of cloud The consistency of the CTH observations for different cloud heights was studied. Using the radar bottom height (RBH) as the classification standard, a RBH ≥ 6 km was defined as high cloud; < 6 km but ≥ 2 km was defined as mid-height cloud and < 2 km was defined as low cloud.
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photo showed that the cloud began to dissipate at 17:00 and formed cirrus and cumulus clouds after 30 min. We have reason to suspect that the cloud radar was not working properly during this time period. During this period, the cloud was detected by the satellite observations, but the CTH gradually decreased and reached the same level by 18:00. This example shows that the CTH is more consistent for thicker, continuous clouds with a larger echo intensity and without voids. For stratiform clouds with thin layers, the cloud top measurements are different, but the trend is similar. For thin, high-level cirrus clouds, the CTH is different and the consistency of the trend is not obvious.
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occurred. By this time, the cloud had become very thick. The observation data from Beijing Nanjiao Weather Observatory automatic station showed that it began to rain at 10:00 and the rain stopped at 16:00, giving 7 h of precipitation with a cumulative 5 mm of rain. There were two short-term cloud events between 18:00 and 23:00. The CTH of the corresponding satellite observations was analyzed. Clouds were observed at 10:30 on June 3 by the satellite. However, the cirrus cloud top was obviously lower than the CTH observed by the cloud radar. The thinner the clouds, the looser the structure and the greater the difference between the two observations. The difference was gradually reduced to 1–2 km for altostratus clouds and the trend of CTH was similar. When the double-layer cloud appeared at 0:00 on June 4, the height of the cloud top was between the CTH of the two-layer cloud observed by the cloud radar. The CTH of the two measurements tended to be consistent when the two layers of cloud merged, the virga was connected to the ground and the cloud was very thick. No cloud was observed on the cloud radar from 15:30 to 18:00; the all-sky camera
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Fig. 9. Rain intensity and attenuation of radar echo observations. The black line represents the rainfall intensity measured by rain gage and the open circles are the satellite cloud top.
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If low cloud down to an RBH of 500 m was used as the boundary for a second classification, the cloud radar echo maps showed that cloud echoes with an RBH < 500 m were usually accompanied by precipitation. Only monolayer clouds were considered in this study. There were a total of 2127 monolayer clouds in 2904 effective cloud observation samples, of which 741, 773 and 613 were high, mid-height and low clouds, respectively. Table 4 shows the statistics for radar and satellite CTH for different types of clouds. The average CTH difference of mid- and low- clouds was < 0.8 km although there were larger differences for the high clouds. The high clouds were dominated by cirrus clouds (cirrostratus and cumulocirrus), which are thinner, more transparent and have a larger space–time discontinuity. The retrieval of CTHs by satellite for high-level cirrus clouds requires further improvement. Fig. 8 shows scatter plots for four types of cloud. The deviation is more obvious for high clouds, which are concentrated above the 1:1 line, whereas the mid-height and low clouds are concentrated near the 1:1 line. The echo-to-ground cloud which is typical of deep rain clouds are concentrated on the 1:1 line. To determine whether precipitation can attenuate the cloud radar echo and thus affect the radar judgment of the CTH, September 4, 2015, when precipitation started after 11:30, can be taken as an example (Fig. 9). The CTH of the cloud radar was in agreement with the satellite observations when the rain intensity was < 0.2 mm/min (< 12 mm/h). When the rain intensity was > 0.2 mm/min (> 12 mm/h), the radar echo was significantly attenuated and was different from the CTH of the satellite observations. This conclusion is consistent with the observations of Hollars et al. (2004), who concluded that the critical attenuation value of radar echo is 10 mm/h. Therefore the automatic station rainfall data can be used to determine whether the cloud radar echo was attenuated. If the rainfall was > 0.2 mm/min, it produced a rainfall attenuation effect. The satellite CTH can be used to compensate for attenuation errors in the cloud radar data. In addition, as can be seen from Fig. 9, the CTHs of the alternate observations with half hour intervals from FY-2F and FY-2G were also consistent with those of the cloud radar. We consider that the consistencies of the time and space matching and the calibration of the two satellites are acceptable.
Follow-up studies should further analyze the contribution of the spatiotemporal matching error to the difference in the height of the cloud top. The cloud radar also has its limitations especially for high-level cirrus observations. Compared with the ground-based lidar, the cloud radar may not detect very small liquid water drops or ice crystals far away from itself, while the lidar has a corresponding sensitivity (Uttal et al., 1995; Comstock et al., 2002). Therefore, the joint use of these two kinds of instruments can get more comprehensive cirrus information, and can better characterize the macro-physical properties of cirrus. In addition, the filtering method in this study needs to be further improved to better distinguish low-cloud from non-cloud echoes. As analyzed in this study, the satellite observation could be used as an effective validation for echo filtering. The transparency of a cloud in infrared channel will cause the CTH from the satellite data to be lower than that of the radar data. Adjustment of the satellite observations from the perspective of the optical thickness of the cloud to narrow the difference between the two, particularly for the height difference of high transparent cirrus clouds, is also worthy of further study. Other studies have shown that the optical thickness and water content of clouds are correlated (Nakajima et al., 2005). The radar reflectivity factor Z and the cloud liquid (ice) water content are also statistically related (Atlas, 1954; Wang et al., 2016a). The radar reflectivity data could therefore be further applied to the calculation of the optical thickness of clouds to make the corresponding radiation correction to the CTH from satellite observations.
4. Discussion and conclusions
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Acknowledgments We thank the CMA Atmosphere Observation Test Bed and the National Satellite Meteorological Center of the CMA for providing the observational data support. This work is jointly supported by the National Natural Science Foundation of China (41675028, 61531019, 41275043) and a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). References
Cloud top heights were calculated from the CTT data retrieved by the FY-2F/G stationary meteorological satellites and the cloud radar reflectivity data from June 1, 2015 to May 31, 2016. The corresponding all-sky camera pictures and precipitation data from the automatic station were acquired for auxiliary analysis. The cloud detection results and the CTHs of the cloud radar and FY-2 satellites were compared with each other. It was concluded that the consistency rate of cloud detection was 78.1%. The average height difference in the cloud top measured by the two methods was 1.46 km. The thicker clouds gave a larger echo intensity and more continuous clouds gave a better consistency for the CTH. The difference in the CTH between low and mid-height clouds was less than that for high clouds, which is similar to other satellites using the infrared channel for CTH retrieval. The CTH retrieval for high-level cirrus clouds by the FY-2 satellites needs to be improved. The attenuation threshold of the cloud radar for rainfall was 0.2 mm/min; a rainfall intensity below this threshold had no effect on the CTH. The satellite CTH can be used to compensate for attenuation errors in the cloud radar data. By comparing the results of the alternate observations of FY-2F and FY-2G with the cloud radar, it is considered that the consistencies of the time and space matching and the calibration of the two satellites are acceptable. It is inevitable that the method of time matching to find the height of the satellite cloud top and the method of space–time matching between the cloud radar and the satellite cloud top will produce errors.
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