Cloud-Base Height Derived from a Ground-Based Infrared Sensor and a Comparison with a Collocated Cloud Radar

VOLUME 35 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY APRIL 2018

ZHE WANG Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, CMA Key Laboratory for Aerosol–Cloud–Precipitation, and School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, and Training Center, China Meteorological Administration, Beijing, China

ZHENHUI WANG Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, CMA Key Laboratory for Aerosol–Cloud–Precipitation, and School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China

XIAOZHONG CAO,JIAJIA MAO,FA TAO, AND SHUZHEN HU Atmosphere Observation Test Bed, and Meteorological Observation Center, China Meteorological Administration, Beijing, China

(Manuscript received 12 June 2017, in ﬁnal form 31 October 2017)

ABSTRACT

An improved algorithm to calculate cloud-base height (CBH) from infrared temperature sensor (IRT) observations that accompany a microwave radiometer was described, the results of which were compared with the CBHs derived from ground-based millimeter-wavelength cloud radar reﬂectivity data. The results were superior to the original CBH product of IRT and closer to the cloud radar data, which could be used as a reference for comparative analysis and synergistic cloud measurements. Based on the data obtained by these two kinds of instruments for the same period (January–December 2016) from the Beijing Nanjiao Weather Observatory, the results showed that the consistency of cloud detection was good and that the consistency rate between the two datasets was 81.6%. The correlation coefﬁcient between the two CBH datasets reached 0.62, based on 73 545 samples, and the average difference was 0.1 km. Higher correlations were obtained for thicker clouds with a larger echo intensity. A low-level thin cloud cannot be regarded as a blackbody because of its high transmittance, which results in higher CBHs derived from IRT data. Because of a smaller cloud radiation effect for high-level thin cloud above 8 km, the contribution of the atmospheric downward radiation below the cloud base to the IRT cannot be ignored, as it results in lower CBHs derived from IRT data. Owing to the seasonal variation of atmospheric downward radiation reaching the IRT, the difference between the two CBHs also has a seasonal variation. The IRT CBHs are generally higher (lower) than the cloud radar CBHs in winter (summer).

1. Introduction height for climate statistical analysis in an attempt to identify signs of climate change (Ramanathan et al. 1989; Clouds are of great interest to atmospheric science re- Poli et al. 2000). Cloud height is also a key factor, such as search, as they have a signiﬁcant impact on atmospheric in meteorological forecasting, civil aviation, weather dynamics and thermal processes, water vapor cycling, and modiﬁcation, and so on (Zhou and Zhao 2008; Yan et al. the radiation balance at the surface (Cess et al. 1989), 2012). Therefore, accurate and timely access to long-term which is a key driver of climate change (Naud et al. 2003; and continuous cloud height data is very important. Hawkinson et al. 2005; Stephens 2005). Many studies have Observations of clouds are currently made by ground- been conducted using long-term observations of cloud based, sounding, or satellite-based remote sensing techniques. Each method has its own strengths and Corresponding author: Zhe Wang, [email protected] weaknesses related to the method of observation,

DOI: 10.1175/JTECH-D-17-0107.1 Ó 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). 689 Unauthenticated | Downloaded 09/27/21 06:47 AM UTC 690 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 35 instrument performance, and the methods used for cal- radiation information. Although the observation band is culation and retrieval. It is therefore important to study in the atmospheric window, the accuracy of cloud mea- synergistic cloud measurements to improve cloud ob- surements is influenced by a number of factors. It is clear servations and to verify the results of different methods that the impact of water vapor is significant. Many to improve their reliability (Lü et al. 2003; Lu et al. studies on water vapor measurements of clear sky show 2012). As observations have gradually become more that water vapor content in the atmosphere is highly automated, the development of ground-based remote correlated with clear-sky infrared radiation, including sensing cloud equipment and technology has gradually the atmospheric window (Shaw et al. 2005; Maghrabi replaced traditional observations, and it has provided and Clay 2010; Mims et al. 2011). The long-wave in- more possibilities for synergistic cloud measurements. frared (LWIR) window has ozone absorption centered The current ground-based cloud remote sensing equip- at 9.6 mm, but the overall envelope (atmospheric trans- ment includes ceilometers, millimeter-wavelength cloud mittance vs wavelength) of the LWIR transmission radars, infrared/visible cloud imagers, and microwave window is governed by the amount of water vapor in the radiometers. Many studies have evaluated and com- atmosphere at any given time and place (Shaw and pared the capabilities of these instruments for cloud Nugent 2013). Thin clouds, which cannot be regarded as height measurement (Lhermitte 1987; Zhong et al. 2009; an equivalent blackbody because of their high trans- Gao et al. 2010; Huang et al. 2013; Costa-Surós et al. parency, also make the accuracy of cloud infrared radi- 2014; Oh et al. 2016). ation measurements questionable (Ahn et al. 2015). Microwave radiometers are mainly used for the Aerosols usually have little influence on the thermal measurement of atmospheric temperature and humidity infrared cloud signature, but water vapor condensed on profiles, water vapor content, and cloud water content sulfate and other hygroscopic aerosols can significantly (Ware et al. 2003). For microwave remote sensing, increase the aerosol optical thickness of the atmosphere clouds have a negative effect on the retrieval of atmo- (Tang 1996). Therefore, it is necessary to evaluate the spheric temperature and humidity profiles (Han and accuracy of IRT CBH measurements. Westwater 1995; Hewison 2007). Therefore, a micro- Millimeter-wavelength cloud radar is able to detect wave radiometer is typically equipped with an infrared small particles, such as cloud, fog, and dust storms. Since temperature sensor (IRT) for obtaining cloud-base the birth of the first millimeter-wavelength radar, sci- height (CBH) information, which, combined with the entists have been constantly testing their cloud obser- liquid water content and cloud optical thickness, is re- vation capabilities (Petrocchi and Paulsen 1966; Hobbs garded as useful for improving the vertical profile re- et al. 1985; Kropfli et al. 1990; Nakamura and Inomata trieval accuracy. Compared with conventional balloon 1992; Sekelsky and Mcintosh 1996). Obviously, cloud sounding, a microwave radiometer has the ability to radar can penetrate clouds to describe their three- obtain atmospheric vertical profiles with high temporal dimensional structure, providing information on the resolution, which can meet the needs of small- and vertical structure and microphysical parameters of medium-scale weather analysis, numerical forecasting, clouds, such as cloud height, cloud thickness, cloud and services related to catastrophic weather. Therefore, particle size, droplet distribution, and so on, which is a its application is expected to become increasingly im- powerful tool for atmospheric cloud and precipitation portant and widespread. Although the IRT is auxiliary research (Kollias et al. 2007). Haper (1966) studied the to a microwave radiometer, it can be used operationally cloud height retrieval methods with an 8.6-mm radar. for CBH measurement, and this can be regarded as a Clothiaux et al. (1995) used a 94-GHz radar to study the supplement to synergistic cloud measurements. characteristics of clouds and retrieved the CBH/cloud- According to the wavelength and width of the band, top height (CTH) using the radar reflectivity data com- the downward infrared radiation measured by a ground- bined with lidar data. Kropfli and Kelly (1996) provided based infrared cloud radiometer can be divided into liquid water profiles in warm marine stratus clouds, wide (Dürr and Philipona 2004) and narrow (Brocard combining radar and microwave radiometer data. et al. 2011; Klebe et al. 2014) widths. Most infrared ra- Moran et al. (1998) used an unattended cloud-profiling diometers work between 8 and 14 mm (wide band). The radar at the Department of Energy’s ARM Cloud atmospheric transmission rate is very high in this band, and Radiation Testbed (CART) sites to examine the so there is no strong absorption of water vapor or carbon radiative impacts of clouds on climate. Hollars dioxide. The IRT installed on top of a ground-based et al. (2004) compared observations of cloud height microwave radiometer works in the 9.6–11.5-mm (nar- from 35-GHz millimeter-wavelength cloud radar and row) band, which further improves the atmospheric Himawari-5 [Geostationary Meteorological Satellite-5 transmittance, and can more accurately receive cloud (GMS-5)] and analyzed the difference between them.

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The Meteorological Observation Center of the China TABLE 1. Number of IRT products (1-min average) obtained. Meteorological Administration deployed cloud radar at Expected number Actual number No. (%) of the Beijing Nanjiao Weather Observatory with the aim Period of data of data missing data of comparing its performance and accuracy in measuring Jan 2016 44 640 39 076 5564 (12.5) clouds. Z. Wang et al. (2016) analyzed the consistency of Feb 2016 41 760 35 848 5912 (14.2) this cloud radar with radiosonde vertical structure ob- Mar 2016 44 640 7440 37 200 (83.3) servations and concluded that cloud radar is able to Apr 2016 43 200 0 43 200 (100) accurately characterize the vertical structure of May 2016 44 640 36 656 7984 (17.9) clouds below 10 km. Jun 2016 43 200 41 304 1896 (4.4) Jul 2016 44 640 43 561 1079 (2.4) The IRT instrument puts out CBH products with a Aug 2016 44 640 43 824 816 (1.8) publicly unknown algorithm, and the difference be- Sep 2016 43 200 41 319 1881 (4.4) tween these data and radar cloud height data is quite Oct 2016 44 640 44 640 0 (0) large. Therefore, this paper presents a double verifica- Nov 2016 43 200 31 170 12 030 (27.8) tion method for cloud detection data derived from IRT Dec 2016 44 640 15 731 28 909 (64.8) Total 527 040 380 569 146 471 (27.8) brightness temperature data and a cloud height search method using the measured sounding temperature profile. We expect the result to be superior to the CBH 3. Algorithm product of IRT and closer to the cloud radar data, and we hope to provide a reference for comparative a. Determination of CBH from cloud radar (CBH_R) analysis and synergistic cloud measurements. The cloud height determination method was based on the three-step reflectivity threshold method 2. Instruments and data (Z. Wang et al. 2016). In the first step, Gaussian filtering was used to filter out random noise and erase the 1-km We obtained IRT brightness temperature data, IRT continuous noncloud clutter. The cloud boundary was CBH product data (CBH_P), and cloud radar reflec- determined in the second step based on the reflectivity tivity data for the same period (January–December threshold, and the lowest threshold of the reflectivity 2016) from Beijing Nanjiao Weather Observatory 0 00 0 00 factor was taken as 240 dBZ. The third step was a (39848 22 N, 116828 10 E; 32 m MSL). quality control stage used to improve the accuracy of Cloud radar reflectivity is measured by a Ka-band the cloud boundary determination. For low-level Doppler cloud radar that points to the zenith with a 0.258 continuous echo processing, this method directly field of view and has a vertical resolution of 30 m and a erases echoes below 1 km, which has little effect in temporal resolution of 1 min. This radar uses a pulse autumn and winter. However, it may be unsuitable for compression technology to solve problems such as the summer, when there are more low clouds and more observation dead zone. A microwave radiometer precipitation. We improved this method by searching equipped with an IRT is located on the northeastern side the boundary of the cloud after Gaussian filtering. of the cloud radar with a distance of 50 m. The IRT After searching, we made a judgment about whether (manufactured by Heitronics) also points to the zenith the first layer of the cloud base was earthed and with a 28 field of view and the spectral coverage spans whether the thickness of the cloud was ,2km.Ifthese from 9.6 to 11.5 mm. The radiant brightness tempera- conditions were met, then the boundary information ture value output is in the range of 2608 to 1008C, for the layer was deleted. The new cloud boundary with a high temporal resolution (1 s). To correspond to information was then rejudged and CBHs , 2kmand the temporal resolution of the cloud radar, 1-min- cloud thicknesses , 600 m were found and removed. averaged IRT data were calculated. Thus, there Through the abovementioned improvements, it was should be 527 040 data points in 366 days. However, possible to avoid the overall interference of continu- because of the IRT work status and other reasons, ous ground clutter on cloud boundary determination only 380 569 observations were actually obtained. (Fig. 1). There were 146 471 missing data points—a rate of 27.8% (Table 1). b. Determination of cloud from IRT We also obtained sounding data measured at the same location and Fengyun-2 (FY-2) satellites cloud-top 1) CLOUD DETECTION METHOD temperature data (which can be further processed into cloud height information) to provide a supplementary The key to obtaining the cloud height from the IRT is reference for this study. to perform cloud detection first, that is, to distinguish

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FIG. 1. Continuous ground clutter (a) before and (b) after quality control. between cloud radiation and background radiation detection. Brocard et al. (2011) analyzed the downward (representing clear sky). It is possible to study the dif- radiation of cirrus cloud using the detrended fluctuation ference between the received downward radiation and analysis method (DFA). The long-range correlation of the background radiation for cloud detection (Shaw and the brightness temperature sequence for the appropriate Nugent 2013). Marty and Philipona (2000) defined the period was analyzed using the a index in DFA, and it « 5 Y s 4 a , a . ratio of the received radiated emissivity A LW / Ta was found that 0.25 indicates clear sky and 0.25 (where LWY is the downward radiation flux, s is the indicates cloud presence. This method of analyzing the

Stephan–Boltzmann constant, and Ta (K) is the air temporal variation of the downward radiation for cloud temperature near the ground surface) to the clear-sky detection is called the temporal variability method. This 1/8 emissivity «AC 5 «AD 1 k(ea/Ta) (where «AD is the method has high sensitivity to high-level cirrus clouds clear-sky emittance of a completely dry atmosphere, and to clouds that change quickly in a short period, but it ea (Pa) is the water vapor pressure, and k is a location- is not applicable for more uniform clouds. Therefore, dependent coefficient) as the clear-sky index (CSI # 1 Ahn et al. (2015) utilized both the radiation threshold for clear sky and CSI . 1 for cloud). Dürr and Philipona method and the temporal variability method in their (2004) improved the clear-sky emissivity formula to study (termed the double verification method). Clear 1/7 «AC 5 «AD 1 [k(t) 1Dk(t)](ea/Ta) . The adiabatic rate sky is judged when cloud is not detected by both is added to the local coefficient k to avoid the effect of methods; otherwise, it is judged as cloud. The double atmospheric low-level inversion on cloud detection. The verification method not only improves the accuracy of abovementioned cloud detection method distinguishes the cloud detection algorithm but also avoids the effect between clear-sky radiation and cloud radiation by of inversion on the cloud detection results. determining a radiation threshold, and is called the ra- This method (Ahn et al. 2015) has strong operability. diation threshold method. However, this method has its It transforms the theoretical calculation of the threshold limitations, such as low sensitivity for thin cirrus cloud into an empirical formula fitting the measured IRT

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FIG. 3. Relationship between the hourly averaged TbIRT and the FIG. 2. Relationship between simulated clear-sky TbE and hourly averaged of s1min with a s1h value of less than 0.03. measured TbIRT for the conditions of s1min , 0.13. The curve represents an empirical relationship between TbE and the measured TbIRT for clear-sky conditions.

conditions is established using the lower portion of infrared brightness temperature (TbIRT), ground air the data points TbP 5 f (Tb ) From step 2 we know temperature (T ), and humidity (e, water vapor pres- clr E . sfc 5 sure) to obtain the dynamic threshold corresponding to that TbE g(Tsfc, e); thus, the estimation of clear- sky brightness temperature TbP can be derived from the weather background at that time. The procedure is clr as follows: the measured Tsfc and e. The clear-sky brightness P 1 « temperature threshold is set to Tbclr S, where the 1) The atmospheric temperature and humidity proﬁles ﬁtting uncertainty is «S . 0. are inputted into the radiation transmission model to 4) A temporal variability threshold of clear-sky bright-

calculate the theoretical background brightness tem- ness temperature is set for comparison with s1min.In perature (TbE). The atmospheric temperature and fact, the variability and magnitude of s1min depend humidity proﬁles can be obtained from a numerical signiﬁcantly on the season. Here, an hourly average

model or reanalysis data, assuming that all temper- of TbIRT, and an hourly average of s1min and its ature and humidity profiles are cloudless in status. standard deviation (s1h) are calculated. As shown in 2) Through comparison and analysis, it is observed that Fig. 3, the hourly average of TbIRT is highly corre- log(TbE/Tsfc) has a quadratic function relationship lated with the hourly average of s1min when s1h is with e/Tsfc, which is consistent with previous studies small enough (,0.03) to ensure that only the clear- (Marty and Philipona 2000; Dürr and Philipona 2004; sky data are selected. The empirical relationship Carmona et al. 2014). Therefore, an empirical for- s sE between TbIRT and the 1min for clear sky ( clr)is mula is established, sE 5 established as clr h(TbIRT). The hourly averaged s1min decreases with increasing TbIRT, which shows a Tb 5 T exp[a 1 a (e/T ) 1 a (e/T )2], (1) E sfc 0 1 sfc 2 sfc smaller s1min for the summer period (a higher TbIRT) and vice versa. The temporal variability threshold of which is fitted with three coefficients (a , a , and a ). 0 1 2 clear-sky brightness temperature is set to sE 1 « , 3) An empirical relationship between Tb and the clr T E where the fitting uncertainty is « . 0. measured Tb for clear-sky conditions is then estab- T IRT 5) The expressions Tb 2 TbP . « and s 2 sE . « lished. The sample standard deviation of the Tb in a IRT clr S 1min clr T IRT are then determined to be either true or false. If both 1-min period (s ) is used to represent the temporal 1min are false, then it is judged as clear sky; otherwise, it is variability of measured Tb .Whens is small IRT 1min judged as cloudy sky. enough (such as s1min , 0.18), the upper and lower portions of the ‘‘envelope’’ consist of dense data The abovementioned method was used for cloud de- points, which can be seen in Fig. 2. The relationship tection in our studies with some simplification. For steps

between TbE and the measured TbIRT for clear-sky 1 and 2, we used the empirical Eq. (1) and the ﬁtting

Unauthenticated | Downloaded 09/27/21 06:47 AM UTC 694 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 35 coefficients a0 520.5422, a1 5 6.727, and a2 5226.53 regard the cloud as a blackbody. Shaw and Fedor (1993) from Ahn et al. (2015) to estimate TbE with the un- considered that clouds that contain 0.1 mm or more certainty 6.3 K. As different input data and model param- liquid water can be assumed equivalent to a blackbody. eters can lead to differences in the empirical formula and the They also occasionally saw blackbody clouds that had fitting coefficients, which affect the accuracy of the clear-sky very little or no liquid, which were evidently ice clouds. brightness temperature threshold estimation, we carried out The IRT CBH was calculated by associating the cloud- the following analysis: Although TbE is derived from the base temperature with the cloud height using the at- atmospheric temperature and humidity profiles brought mospheric temperature profile and then searching for into the radiation transmission model in step 1, the contri- the temperature value in the corresponding atmospheric bution rate of the low level of the clear sky is the largest profile data to find the matched height. The atmo- according to the weight function of the clear-sky downward spheric profile can be obtained from the regional typ- infrared radiation. The ground humidity e and the ground ical atmospheric profile sample library, numerical air temperature Tsfc can approximate the downward radia- simulation products, or measured sounding. Each has tion information of water vapor and dry air of the whole its own advantages and disadvantages. The use of a level of the atmosphere in the 10-mm window. It is obvious regional typical atmospheric profile sample library re- that TbE is highly correlated with e and Tsfc when the other sults in greater error for this study because it represents parameters in the radiation transmission mode remain un- the climatic mean of the local area, though it is the changed. The exponential form of the fitting function shown simplest. The numerical simulation products could in the Eq. (1) is universal, though the values of the three havearelativelygoodtimecorrespondencewithIRT coefficients (a0, a1, a2) fitted by different observation data observations, but they cannot meet the demand of the maybeslightlydifferent.Infact,TbE is only an intermediate cloud height search as a result of their own forecasting P variable that links Tsfc, e,andTbclr , and this difference can error and the lower profile vertical resolution. In ad- be adjusted by the measured data fitting in step 3. dition, the cost of data acquisition and processing for According to steps 3 and 4, a cubic fit with the actual numerical simulation products is higher for IRT data acquired IRT data was carried out (Figs. 2 and 3). The with a 1-yr time span. expressions are as follows: In this study the local measured sounding data with a vertical resolution of about 8 m (Li et al. 2009)were P 5 : 1 : 3 1 : 3 2 Tbclr 4 158 1 037 TbE 0 010 35 (TbE) used for accurate searching of the CBH. However, the 2 3 lower sounding temporal resolution [only two balloon 1 4:682 3 10 5 3 (Tb ) (R2 5 0:829), (2) E releases per day, at 0715 and 1915 Beijing time (BT)] where the fitting uncertainty is estimated to be 4.2 K; will result in a time match difference with the IRT data and with high temporal resolution. To account for this, the sounding data were matched to the IRT observation sE 5 : 2 : 3 times using a 66-h temporal window. The sounding clr 0 097 89 0 000 887 5 TbIRT data at 0715 BT were used for IRT observations from 2 3:74 3 1026 3 (Tb )2 IRT 0200 to 1359 BT. The sounding data at 1915 BT were 2 : 3 27 3 3 2 5 : used for IRT observations from 1400 to 2359 BT. The 3 883 10 (TbIRT) (R 0 975), (3) sounding data on the day before at 1915 BT were used where the fitting uncertainty with the hourly averaged for the IRT observations from 0000 to 0159 BT. We variability is about 0.008 K. expect this could minimize the cloud height error The goodness of fit was good. Significance tests caused by the time difference. By plotting the com- showed that the results were significant at the 99% sig- parison jCBH_BT 2 CBH_Rj as a function of the time nificance level. For step 5, «S and «T depend on the fitting difference between observation and the most recent uncertainty. The overall uncertaintypffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi in the determination sounding (i.e., the x axis is the time difference from 66 P : 2 1 : 2 5 : 6 of Tbclr is estimated to be 6 3 4 2 7 5K.Weapply to 1h andthe y axis is the average cloud height dif- twice the standard deviation value 15 K as the «S. As the ference), we found that the cloud height difference estimated variabilitypffiffiffiffiffi of Eq. (3) is fairly small, about slightly increased with the growth of the time differ- 0.06 K (0.0083 60), we use 0.18 K (3 3 1s) as our «T. ence, and the average cloud height difference of 66his only 135 m larger than that of 61h (Fig. 4). Another 2) CALCULATION OF CBH FROM IRT issue that needs to be clarified is, because there is only BRIGHTNESS TEMPERATURE (CBH_BT) one IRT value for any given 1-min observation, the The cloud-base brightness temperature is approxi- matched CBH is recognized as the CBH of the lowest mately equal to the actual cloud-base temperature if we layer in multilayer clouds.

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is only 53.5%. The results for the speciﬁc cloud detection are given in Table 2 (note: no IRT data were available during the whole of April 2016). It can be seen that the cloud detection consistency rate shows seasonal changes, with the highest values in winter, followed by spring and autumn, and the lowest in summer. Using the radar detection results as a ‘‘true value,’’ the number of IRT false alarms (IRT indicates cloud when the radar indicates clear) is 36 812 (12.9%) and the number of IRT misses (IRT indicates clear when the radar indicates cloud) is 15 891 (5.5%). The number of false alarms is more than 2 times higher than the number of misses, indicating that the IRT cloud detection sensitivity is FIG. 4. Relationship between the temporal matching difference and higher. The false alarms are also possibly true clouds the cloud height difference. that are not detected by the radar. With the help of sounding and satellite observations, we analyzed the inconsistencies between the two cloud 4. Comparative analysis detection methods by comparing more than 300 days of a. Consistency of cloud detection observations. As the comparative graphs are numerous, they are not shown here. The number of IRT data points obtained in a 1-yr The IRT false alarms mainly occurred in the following period (1 January–31 December 2016) was 380 569, as cases: shown in Table 1. In the same period, there were still missing data points in the cloud radar data records, 1) When there were weak thin cirrus clouds at high especially in March, July, August, and December. altitude. Figure 5a compares the cloud radar and IRT Therefore, a total of 286 333 data points were obtained data on 5 June 2016. The cloud radar began to detect from the concurrent IRT and radar records, and these cirrus cloud with a height above 6 km at 1000 BT, are termed the ‘‘effective’’ observation data. The num- which was consistent with the IRT cloud detection. ber of consistent cloud detection observations (both The difference was that the cloud radar echo was cloudy and clear sky) was 233 630, with a consistency weak and discontinuous, while the IRT detection rate (for IRT brightness temperature and cloud radar results showed a relatively continuous CBH. By an- data) of 81.6%. A cloud detection consistency rate of alyzing the sounding humidity proﬁle at 0800, 1400, more than 80% in such a large number of data samples and 2000 BT on that day, we can see a wet layer with shows that the abovementioned cloud detection algo- relative humidity greater than 70% at high altitude at rithm is good. In contrast, the cloud detection consis- 0800 BT (Fig. 5b), which increased to 100% at 1400 tency rate for the IRT CBH product and the cloud radar BT (Fig. 5c) and was still maintained up to 80% at

TABLE 2. Cloud detection comparison results for IRT brightness temperature and cloud radar data.

Both Both Effective Consistent represent represent IRT IRT false Consistency Period number number clouds clear skies misses alarms rate (%) Jan 2016 30 490 27 337 4647 22 690 965 2188 89.7 Feb 2016 33 964 31 165 6531 24 634 965 1834 91.8 Mar 2016 7305 6204 2508 3696 136 965 84.9 Apr 2016 0 — — — — — — May 2016 35 391 29 634 11 420 18 214 2659 3098 83.7 Jun 2016 38 607 27 849 12 609 15 240 1155 9603 72.1 Jul 2016 14 824 11 181 3508 7673 763 2880 75.4 Aug 2016 8633 7017 1497 5520 1356 260 81.3 Sep 2016 37 831 28 612 10 453 18 159 3583 5636 75.6 Oct 2016 32 934 26 248 11 901 14 347 1729 4957 79.7 Nov 2016 30 636 24 809 6125 18 684 2148 3679 81.0 Dec 2016 15 718 13 574 2346 11 228 432 1712 86.4 Total 286 333 233 630 73 545 160 085 15 891 36 812 81.6

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FIG. 5. (a) Cloud detection results for IRT and cloud radar on 5 Jun 2016, and the relative humidity proﬁle at (b) 0800, (c) 1400, and (d) 2000 BT on that day. Shown are the IRT cloud base (red dots), radar cloud base (black dots), and the radar reﬂectivity (color bar).

2000 BT (Fig. 5d). Apparently, the continuous strong 2016, the radar cloud echo began to appear at about wetting layer at high altitude makes the downward 1900 BT and showed a cross-sectional shape, but the radiation increase higher than the clear-sky radiation IRT cloud began to appear at about 1500 BT, threshold, which causes the IRT false alarm. In consistent with the satellite cloud observations, addition, maybe there are actual thin cirrus clouds which means that the cloud had lasted for some time that are missed by the radar but are detected by the before 1900 BT. This phenomenon may have been IRT. Even cloud radars fail to detect the smallest because the radar transmitter was off but the product liquid water drops and ice crystals above the radar if generation system was still running. they are sufﬁciently far from it (Uttal et al. 1995; Comstock et al. 2002). When the cloud base is high and ﬂatter, there may be 2) When there was a strong wet layer in the mid- or low- more ‘‘IRT misses.’’ As shown in Fig. 8, on 20 May 2016, level layers. As shown in Fig. 6a, on 22 June 2016, the the CBH of the radar was above 8 km but the IRT did not cloud radar detection results were inconsistent with detect cloud between 0500 and 0900 BT. IRT misses are

the IRT results before 1200 BT. The sounding profile related to the threshold selection. The threshold «S is de- at 0800 BT on that day showed the existence of a rived statistically and represents only the general situation, strong wet layer in the mid- and low-level layers, so there must be a situation when it is actually cloudy but 2 P , « resulting in larger downward infrared radiation. In TbIRT Tbclr S. If the cloud base is flat at this time (i.e., s 2 sE contrast to the abovementioned example, the IRT 1min clr is lower than the temporal variability threshold ‘‘false CBHs’’ showed a large fluctuation range, parameter 0.18), then the double verification detection is which may be due to large variability of water vapor ‘‘clear sky,’’ which causes IRT cloud detection ‘‘misses.’’ in the air. But, on the whole, IRT misses are far fewer in number than 3) When there was a problem with generation of the the ‘‘false alarms,’’ indicating that the IRT cloud detection radar product. Figure 7 shows that on 15 January algorithm is highly sensitive.

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FIG. 6. (a) Symbols as in Fig. 5a. Cloud detection results for IRT and cloud radar on 22 Jun 2016 and (b) the relative humidity proﬁle at 0800 on that day.

Moreover, the field-of-view difference of the two in- increasing echo intensity. The cloud body thickness be- struments may also result in cloud detection in- gan to thin from 0300 BT, but the CBH was still main- consistency. The solid angle view of the IRT is 28, tained at about 2 km. Then, the cloud dissipated for a which is 8 times larger than that of cloud radar (0.258). short while until 0500 BT, after which the cloud once The radar beamwidth is about 50 m at a height of again thickened. The CBH gradually descended below 10 km, while the IRT can reach 400 m at the same 1 km at 0600 BT and the maximum echo intensity in the height. If the cloud size is small, it is possible that the cloud was higher than 20 dBZ. The CBH began to IRT could observe a cloud when the radar does not, gradually increase at 0800 BT and the cloud body be- resulting in a disagreement even when both retrievals came increasingly thinner after 1000 BT. The clouds had are correct. completely dissipated by 1200 BT 19 February. The relationship between time and CBH corresponded well b. Case analysis for CBH comparison between the IRT and cloud radar data. At 2130 BT Here, we use two cases to compare the CBH derived 18 February, a difference in CBH existed when the cloud from IRT brightness temperature and from ground- radar CBH fell below 3.5 km, but the variation trend was based cloud radar reflectivity. In case 1 (Fig. 9a), the consistent. At 1400 BT 19 February, the difference be- development of mid- and low-level cloud can be seen tween the two became gradually obvious, which was from 1900 BT 18 February to 1200 BT 19 February in mainly due to the following areas (as shown by the three Fig. 9a. From the cloud radar echo map, we can see that red boxes in Fig. 9a): first, there were gaps between the high clouds with a CBH up to 6 km began to appear at clouds; second, the CBH was low (,4 km); and third, the 1800 BT 18 February. Over time, the cloud body con- cloud thickness was thin. Figure 10 is a quantitative tinued to develop and the CBH became lower and analysis for case 1 in which the relationships between lower. The CBH was below 2 km at 0000 BT 19 Febru- the cloud-base height difference of two instruments ary, and the cloud thickness was growing along with and cloud thickness (Fig. 10a) and maximum radar

FIG. 7. Cloud detection results for IRT and cloud radar on 15 Jan 2016. Satellite-derived cloud top (open circles).

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FIG. 8. Cloud detection results for IRT and cloud radar on 20 May 2016. reflectivity (Fig. 10b) are showed as two scatterplots. cloud began to develop rapidly at 2200 BT and became The scatter points in the two panels are linearly fitted increasingly thicker. The CBH fell to about 4 km and was separately, and both fitting curves are decreasing func- maintained. At 1300 BT 23 May, the CBH from the radar tions. A second cloud variation case is shown in Fig. 9b quickly dropped to 0.5 km but the CBH of IRT descended (case 2). From 1300 BT 22 May, high cloud up to 10 km relatively slowly. At 2100 BT, the cloud dissipated and the appeared, which had weak radar echo intensity and was CBH from the IRT increased rapidly and became unstable. not continuous. The IRT CBH was lower than that of Through the abovementioned qualitative and quan- the cloud radar and exhibited a fluctuating form. The titative analysis, we believe that thicker clouds with a

FIG. 9. Two case studies for the comparison of CBH_BT and CBH_R: (a) 18–19 Feb (case 1) and (b) 21–23 May (case 2).

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FIG. 10. Relationship between CBH_BT–CBH_R and (a) cloud thickness and (b) radar reflectivity of case 1. larger echo intensity show better CBH consistency. The temperature measured by IRT. Figure 6b showed a wet IRT CBH was higher than that of cloud radar for low- layer in the low-level air, making the IRT cloud bases level thin cloud. The abovementioned conclusions are in appear .2 km lower than the radar cloud bases from line with the principle of radiation transmission: the 1400 to 2200 BT (Fig. 6a). greater the optical thickness, the closer the cloud is to a c. Statistical and correlation analysis of CBH blackbody, resulting in better consistency between the two datasets. If the clouds are thin and not continuous, The CBH from the two instruments were averaged on a then the IRT is susceptible to other factors, resulting in a natural monthly basis (Table 3). The average CBH_BT fluctuating CBH. The higher transmittance of the low- was 3.87 km, which was very close to CBH_R (3.97 km). level thin cloud means that it cannot be regarded as a However, the difference between CBH_P (5.26 km) and blackbody, resulting in a higher IRT CBH, as can be CBH_R was quite large. A correlation analysis was carried seen in Fig. 9a (second box) and Fig. 7 (2100–2400 BT). out on the observational results with a large sample size For the high-level thin cloud above 8 km, as a result of (73 545). The correlation coefficient between CBH_BT the smaller cloud radiation effect, the contribution of and CBH_R ranged from 0.23 to 0.84, with an average of atmospheric downward radiation below the cloud base 0.62; however, the correlation coefficient between CBH_P to IRT cannot be neglected, and the combination of the and CBH_R was only 0.31. The abovementioned analysis cloud radiation and atmospheric downward radiation shows that the CBH derived from IRT brightness tem- causes the IRT CBH to be lower. As mentioned in the perature is superior to the IRT CBH product and is closer introduction, infrared remote sensing is susceptible to to the cloud radar data. atmospheric water vapor even in the atmospheric win- Figure 11 shows CBH comparison curves (January– dow, which can lead to the higher cloud brightness December 2016) and their scatterplots. It can be seen

TABLE 3. Average CBH observations (km) and correlation coefﬁcients. (CBH_R: CBH from cloud radar; CBH_P: IRT CBH product data; CBH_BT: CBH from IRT brightness temperature).

Difference Difference Correlation Correlation CBH_P CBH_BT coefﬁcient coefﬁcient Sample Period CBH_R CBH_P CBH_BT 2 CBH_R 2 CBH_R (CBH_P) (CBH_BT) size Jan 2016 2.47 4.90 3.68 2.43 1.21 0.09 0.68 4647 Feb 2016 2.77 4.64 3.84 1.87 1.07 0.32 0.77 6531 Mar 2016 3.67 6.64 4.98 2.97 1.31 0.20 0.32 2508 Apr 2016 — — — — — — — 0 May 2016 4.86 6.49 4.28 1.63 20.58 0.33 0.79 11 420 Jun 2016 4.91 5.26 3.58 0.35 21.33 0.25 0.60 12 609 Jul 2016 3.78 5.42 3.28 1.64 20.50 0.33 0.23 3508 Aug 2016 6.01 7.49 5.78 1.48 20.22 0.13 0.44 1497 Sep 2016 4.32 4.88 3.78 0.56 20.55 0.04 0.52 10 453 Oct 2016 3.26 4.73 3.62 1.47 0.37 0.30 0.75 11 901 Nov 2016 2.95 3.66 3.35 0.71 0.40 0.39 0.84 6125 Dec 2016 4.85 7.14 5.31 2.29 0.46 0.00 0.63 2346 Total 3.97 5.26 3.87 1.29 20.10 0.31 0.62 73 545

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FIG. 11. CBH comparison curves (January–December 2016) and their scatterplots: CBH_R (blue lines) and CBH_BT (red lines). Series has been split into 13 panels to show seasonal variations. Abundance of data points for June and September led to dividing the data into two panels for clarity. that the two datasets are fairly consistent, especially in statistical analysis of data drawn from the scatterplots in January, February, November, and December, in which Fig. 11, the proportion of ‘‘CBH_BT overestimate’’ the average temperature is relatively low. The accuracy (CBH_BT-CBH_R . 0) is 63.73% in winter but only of the IRT CBH is greatly inﬂuenced by water vapor. 31.66% in summer. In September, the proportion of The air in winter is drier than that in summer, making CBH_BT overestimate is 48.44%, which is almost the winter IRT CBH more accurate. The scatterplots identical to the proportion of ‘‘CBH_BT un- roughly show a 1:1 linear distribution, but the seasonal derestimate’’ (51.56%). For year-round data, it can be variation of the CBH between the two is reﬂected. The seen from Fig. 12 that the proportions of CBH_BT scatter distribution for January, February, March, No- overestimate decrease with CBH_R increasing. These vember, and December (winter) is above the 1:1 line; that analyses show that CBH_BT will overestimate (un- is, the CBH_BT is generally higher than the CBH_R. derestimate) the cloud-base height for low-level (upper In May, June, July, and August (summer), the overall level) clouds. Figure 13 shows the monthly variation of distribution of the scatter is reversed, mostly below the the average CBH difference between the two datasets, 1:1 line; that is, the CBH_BT is generally lower than the which is consistent with the abovementioned analysis. In CBH_R. When the season changes from summer to addition, many studies on cloud seasonal statistical winter (September), the scatter distribution is on both characteristics have shown that a seasonal variation sides of the 1:1 line and more dispersed. Through the of cloud height is noticeable (S. J. Wang et al. 2016;

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FIG. 12. Proportion of the data amount of CBH_BT under- and overestimates in different CBH_R.

Liu et al. 2016). The average CBH in spring and summer affected by cloud type, geography, and annual climate is higher than the annual average, and the average CBH characteristics. When there is more precipitation in July in autumn and winter is lower than the annual average, and more high clouds in December, the seasonal variation which reﬂects the seasonal variation of the height of the of the CBH will be different from that of the general condensation layer. Figure 13 generally reﬂects the seasonal statistical characteristics shown in Fig. 11.How- seasonal variation of the CBH_R. The CBH_R showed ever, these aberrations may also be related to the lack of an upward trend in spring and summer and a gradual data in these months. It appears from Table 3 that the decline in autumn and winter. However, it simply does months with the least data are August (1497), December not appear to be supported by the red line (CBH_BT) in (2346), March (2508), and July (3508). August and March Fig. 13. The statistical characteristics of the CBH are are essential months for making these conclusions.

FIG. 13. Comparison of monthly average CBH_R and CBH_BT.

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5. Discussion and conclusions study is necessary to precisely consider the contribution of the spatiotemporal matching error to the CBH differences As the calculation method for the IRT CBH output between the two methods. The transparency of thin clouds product is not clear, and the difference between this in the mid- and low-level layers causes a higher IRT CBH product and the CBH of cloud radar is large, we im- compared with the cloud radar CBH. For IR measurement proved the algorithm to calculate CBHs with IRT of optically thin clouds, the cloud emissivity should be es- brightness temperature data and the results were com- timated and accounted for. Shaw et al. (2012) showed the pared with the CBHs derived from cloud radar reflec- IR cloud emissivity versus visible cloud optical depth, tivity data for the same period (January 2016–December which was correlated with the water content of clouds 2016) from Beijing Nanjiao Weather Observatory. (Shaw and Fedor 1993; Nakajima et al. 2005). The radar The corresponding L-band sounding data and FY-2 reflectivity factor Z and the cloud liquid (ice) water con- cloud observation products were obtained to provide a tent are also statistically related (Atlas 1954; J. H. Wang supplementary reference. The cloud detection capabil- et al. 2016). Moreover, the influence of water vapor on IRT ity and CBH of the two instruments were compared. The cloud height derivation cannot be neglected. Further study results showed that the consistency of cloud detection is required to comprehensively consider these factors, to was good and the consistency rate between the two da- revise the IRT brightness temperature data, and to im- tasets was 81.6%. The correlation coefficient between prove the accuracy of the CBH derived from IRT bright- the two CBHs reached 0.62, based on 73 545 samples, ness temperature. and the average difference was 0.1 km. The results were superior to the original CBH product of IRT and closer Acknowledgments. We thank the Atmosphere Ob- to the cloud radar data, which could be used as a refer- servation Test Bed and the National Satellite Meteo- ence for comparative analysis and synergistic cloud rological Center of the CMA for providing support for measurements. Higher consistencies were obtained for the observational data. This work was jointly supported thicker clouds with a larger echo intensity. Low-level by the National Natural Science Foundation of China thin cloud cannot be regarded as a blackbody because of (41675028, 61531019, 41275043) and a project funded by its high transmittance, resulting in a higher IRT CBH. the Priority Academic Program Development (PAPD) Because of the smaller cloud radiation for the high-level of Jiangsu Higher Education Institutions. thin cloud above 8 km, the contribution of the atmospheric downward radiation below the clouds to the IRT cannot be ignored, as it results in a lower IRT CBH. REFERENCES Owing to the seasonal variation of atmospheric down- Ahn, M.-H., D. Han, H. Y. Won, and V. Morris, 2015: A cloud ward radiation contribution to the IRT, the difference detection algorithm using the downwelling infrared radiance between the two CBH datasets shows a seasonal varia- measured by an infrared pyrometer of the ground-based mi- tion. The IRT CBH is generally higher than that of cloud crowave radiometer. Atmos. Meas. 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