An Analysis Study of FORMOSAT-7/COSMIC-2 Radio Occultation Data in the Troposphere

Article An Analysis Study of FORMOSAT-7/COSMIC-2 Radio Occultation Data in the Troposphere

Shu-Ya Chen 1 , Chian-Yi Liu 1,2,3,* , Ching-Yuang Huang 1,3, Shen-Cha Hsu 3, Hsiu-Wen Li 1, Po-Hsiung Lin 4, Jia-Ping Cheng 5 and Cheng-Yung Huang 6

1 GPS Science and Application Research Center, National Central University, Taoyuan 32001, Taiwan; [email protected] (S.-Y.C.); [email protected] (C.-Y.H.); [email protected] (H.-W.L.) 2 Center for Space and Remote Sensing Research, National Central University, Taoyuan 32001, Taiwan 3 Department of Atmospheric Sciences, National Central University, Taoyuan 32001, Taiwan; [email protected] 4 Department of Atmospheric Sciences, National Taiwan University, Taipei 10617, Taiwan; [email protected] 5 Central Weather Bureau, Taipei 10048, Taiwan; [email protected] 6 National Space Organization, National Applied Research Laboratories, Hsinchu 30078, Taiwan; [email protected] * Correspondence: [email protected]; Tel.: +886-3-4227151 (ext. 57618)

Abstract: This study investigates the Global Navigation Satellite System (GNSS) radio occultation (RO) data from FORMOSAT-7/COSMIC-2 (FS7/C2), which provides considerably more and deeper profiles at lower latitudes than those from the former FORMOSAT-3/COSMIC (FS3/C). The statistical analysis of six-month RO data shows that the rate of penetration depth below 1 km height within ±45◦ latitudes can reach 80% for FS7/C2, significantly higher than 40% for FS3/C. For verification, FS7/C2 RO data are compared with the observations from chartered missions that provided aircraft dropsondes and on-board radiosondes, with closer observation times and distances from the oceanic Citation: Chen, S.-Y.; Liu, C.-Y.; RO occultation over the South China Sea and near a typhoon circulation region. The collocated Huang, C.-Y.; Hsu, S.-C.; Li, H.-W.; comparisons indicate that FS7/C2 RO data are reliable, with small deviations from the ground- Lin, P.-H.; Cheng, J.-P.; Huang, C.-Y. truth observations. The RO profiles are compared with collocated radiosondes, RO data from other An Analysis Study of FORMOSAT-7/ missions, global analyses of ERA5 and National Centers for Environmental Prediction (NCEP) final COSMIC-2 Radio Occultation Data in (FNL), and satellite retrievals of NOAA Unique Combined Atmospheric Processing System (NCAPS). the Troposphere. Remote Sens. 2021, The comparisons exhibit consistent vertical variations, showing absolute mean differences and 13, 717. https://doi.org/10.3390/ standard deviations of temperature profiles less than 0.5 ◦C and 1.5 ◦C, respectively, and deviations rs13040717 of water vapor pressure within 2 hPa in the lower troposphere. From the latitudinal distributions

Academic Editor: Stefania Bonafoni of mean difference and standard deviation (STD), the intertropical convergence zone (ITCZ) is Received: 11 December 2020 evidentially shown in the comparisons, especially for the NUCAPS, which shows a larger deviation Accepted: 12 February 2021 in moisture when compared to FS7/C2 RO data. The sensitivity of data collocation in time departure Published: 16 February 2021 and spatial distance among different datasets are presented in this study as well.

Publisher’s Note: MDPI stays neutral Keywords: FORMOSAT-7/COSMIC-2; GNSS RO; verification with regard to jurisdictional claims in published maps and institutional affil- iations. 1. Introduction FORMOSAT-3/COSMIC (FS3/C, hereafter FS3), with cross-links to the Global Nav- igation Satellite System (GNSS), has provided more than six million radio occultation Copyright: © 2021 by the authors. (RO) soundings during the past fourteen years. Many studies have demonstrated the Licensee MDPI, Basel, Switzerland. benefits of GNSS RO data on numerical weather predictions and climate change studies, as This article is an open access article overviewed by [1]. The GNSS RO data can provide valuable information about the vertical distributed under the terms and variations in the ionosphere and lower atmosphere, and FS3 has contributed most of the RO conditions of the Creative Commons measurements since launch in 2006. The advantages of GNSS RO data rely on their highly Attribution (CC BY) license (https:// accurate retrieval of the temperature in the upper troposphere and lower stratosphere creativecommons.org/licenses/by/ (UTLS) (e.g., [2–5]). However, a challenge remains the demanding improvement for RO 4.0/).

Remote Sens. 2021, 13, 717. https://doi.org/10.3390/rs13040717 https://www.mdpi.com/journal/remotesensing Remote Sens. 2021, 13, 717 2 of 20

data to be applied to the lower troposphere over the tropical region, mainly due to the difficulty in reliable retrievals under large vertical moisture variations [1]. The FORMOSAT-7/COSMIC-2 (FS7/C2, hereafter FS7) project is the follow-on mission of FS3, with six satellites operated in the orbits of lower inclination (24◦) to provide more RO data over the lower-latitude regions (± 45◦ latitudes). Besides the different data coverage, the FS7 satellites are capable of receiving signals from both the Global Positioning System (GPS) and Globalnaya Navigatsionnaya Sputnikovaya Sistema (GLONASS) [6]. FS7 satellites carry an antenna with a higher signal-to-noise ratio (SNR), which thus allows a deeper penetration of the ray into the lower troposphere and better detection of the atmospheric boundary layer (ABL) top and super-refraction (SR) on top of the ABL [7]. The six satellites of the FS7 constellation were successfully launched on 25 June 2019, and the FS7 neutral atmosphere data were officially released on 10 December 2019. There are 4000–5000 RO soundings per day for FS7, which can be accessed from the two RO data processing centers, i.e., the COSMIC Data Analysis and Archive Center (CDAAC) and the Taiwan Analysis Center for COSMIC (TACC). The RO signal can be retrieved for the upstream data format and bending angle, which then can be reverted to refractivity using Abel inversion. With auxiliary data from other observations or global analysis, atmospheric temperature, and moisture soundings can be retrieved from the RO refractivity (see [8]). Retrievals of both RO bending angle and refractivity can be termed “dry products” (used to obtain the dry temperature) in CDAAC before blending the auxiliary data with the retrievals using 1-D variational minimization to obtain the wet temperature and moisture. The reliability of FS3 RO retrieval was reported in [9]. Applications of FS3 RO data to many aspects of weather prediction and analysis can be found in a number of literatures and have been documented in detail by [1]. The authors of [7] compared the FS7 RO data with different datasets and showed that the FS7 data possess high accuracy and precision. For example, they compared the FS7 RO dry products (bending angle and refractivity) with the European Centre for Medium-Range Weather Forecasts (ECMWF) short-term forecasts and showed that the RO data are quite consistent, with very small biases between 6 and 40 km. However, some larger negative biases are presented below 2 km and smaller positive biases are presented between 2 and 6 km. The negative biases in the lower troposphere may be caused by SR, tracking depth and noise, and fluctuations of refractivity, etc., as discussed in [9]. The authors of [10] compared FS7 RO refractivity with Vaisala RS41 radiosonde data, which is currently the most accurate radiosonde observation and showed a negative difference in the lower troposphere. The negative biases further transfer to the RO wet products (the descendant data) after One-Dimensional Variational (1DVAR) retrieval ( https://cdaac-www.cosmic.ucar.edu/cdaac/doc/documents/1dvar.pdf). The 1DVAR retrieval algorithm derives optimal atmospheric temperature, pressure, and humidity profiles according to the RO refractivity and a priori atmospheric state from the numerical model. As described in [1], quantification of the accuracy for RO-derived water vapor is essential for climate studies. In this study, we aimed to conduct further analyses of the FS7 RO wet retrievals (i.e., wetPf2). For illustrating the properties of FS7 RO retrievals in the troposphere, the wet RO retrievals are compared with multiple datasets, including dropsondes, radiosondes, global analyses, and satellite retrieval data. We also compare the FS3 and FS7 RO retrievals for their data discrepancies in the horizontal and vertical distributions. A brief introduction of the FS7 RO data and other datasets used in this study is given in Section2. Special dropsondes and radiosondes provided by two chartered missions, presented in Section3, are used as ground-truth observations for verification on FS7 RO data. Then, the statistical comparisons with different datasets in a six-month period are discussed. Finally, the conclusions are given in Section4. Remote Sens. 2021, 13, 717 3 of 20

2. Data and Methodology Several satellite missions carried GNSS data receivers, for example, Taiwan–US FS3 and FS7, European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) GRAS (GPS Receiver for Atmospheric Sounding) on the Metop (Meteorological Operational satellites program), and Korea Multi-Purpose Satellite-5 (KOMPSAT-5) provide abundant GNSS RO soundings. To detect the data characteristics of the FS7 RO soundings, the radiosonde observations (RAOB) derived from the National Center for Atmospheric Research (NCAR) Research Data Archive and collocated with the FS7 occultation profiles were derived for the verification. In addition, two special observation missions that provide dropsondes (DROP) and radiosondes observations were chartered for the FS7 verification. Multiple datasets, including global analyses and satellite retrievals, were used for comparison as well. The spatiotemporal windows defined for the collocation were ±3 h and ±100 km of the RO event. The global analyses include the National Centers for Environmental Prediction (NCEP) final (FNL) analysis [11] and the fifth generation of ECMWF atmospheric re-analyses of the global climate (ERA5) ([12]), and both resolutions are in 0.25◦ latitude and longitude grids. The satellite retrievals from NOAA Unique Combined Atmospheric Processing System (NUCAPS) data were used for comparison. The time period for the statistical comparison was from October 2019 to March 2020 (6 months).

2.1. GNSS RO Data from FS3 and FS7 The FS3 constellation is on an orbit of high inclination (72◦) that generates more observations at higher latitudes but still globally distributed. In contrast, the FS7 constellation is on an orbit of low inclination (24◦), and most of the RO data are located within ±45o latitudes. FS7 can provide about 4000–5000 RO soundings per day, which is about twice the FS3 RO data. Figure1 shows the RO data density for one month in 2.5 ◦ × 2.5◦ bins within ±45◦ latitudes for FS3 in March 2009 and FS7 in March 2020. Significant differences in the RO density appear in the tropical region. FS3 shows a nearly homogeneous data density in the subtropical region but with less data in the tropical region. In contrast, the FS7 has a higher data density over the tropical region because of the low-inclination orbit, and some satellites have not moved to the final orbit height yet. Generally, the data density for FS3 is less than 20 RO profiles in the subtropics and even fewer data near the equator. For FS7, there are about 50–60 RO profiles in the tropical region, considerably more than the FS3. FS7 with a higher SNR is expected to increase the capability of the ray, reaching lower altitudes. As one example of the comparison on one day, Figure2 shows the FS3 data on 5 February 2009 and FS7 on 5 February 2020, with their lowest penetrative heights indicated. There are 1018 RO soundings from FS3 and 5419 RO soundings from FS7 in the region of ±45◦ latitudes. Every dot point in Figure2 indicates one RO sounding, and the color indicates the lowest height for each sounding. Note that all of the altitudes for the penetration in the figure were measured above the surface. Figure2 illustrates that most of the FS7 RO data extend deeper into the lower troposphere than FS3. For example, the penetrative depths of FS7 data are below 1 km (Figure2b), compared to about 1–2 km for the FS3 data (Figure2a). The distribution of penetrative depths can be clearly seen from the 6-month statistical histogram (Figure3). The data were collected from October 2008 to March 2009, with 209,709 RO profiles for FS3, and from October 2019 to March 2020, with 599,676 RO profiles for FS7. The data amounts for FS7 are about three times than that for FS3. All the lowest heights were stratified by a 500-m interval until 6 km, and the penetrative depth above 6 km was counted at heights of 5.5–6 km intervals. Figure3 shows that the penetrative depths of RO soundings for FS3 mostly reach 0.5–1.5 km height, while FS7 gives the highest percentage in the altitude below 0.5 km. Statistically, the rate of penetration below 1 km is about 40% for FS3 and 80% for FS7. Similar statistical results are also found in [7,10]. Remote Sens. 2021, 13, x FOR PEER REVIEW 3 of 20

2. Data and Methodology Several satellite missions carried GNSS data receivers, for example, Taiwan–US FS3 and FS7, European Organization for the Exploitation of Meteorological Satellites (EU- METSAT) GRAS (GPS Receiver for Atmospheric Sounding) on the Metop (Meteorological Operational satellites program), and Korea Multi-Purpose Satellite-5 (KOMPSAT-5) provide abundant GNSS RO soundings. To detect the data characteristics of the FS7 RO soundings, the radiosonde observations (RAOB) derived from the National Center for At- mospheric Research (NCAR) Research Data Archive and collocated with the FS7 occultation profiles were derived for the verification. In addition, two special observation missions that provide dropsondes (DROP) and radiosondes observations were chartered for the FS7 verification. Multiple datasets, including global analyses and satellite retrievals, were used for comparison as well. The spatiotemporal windows defined for the collocation were ±3 h and ±100 km of the RO event. The global analyses include the National Centers for Environmental Prediction (NCEP) final (FNL) analysis [11] and the fifth generation of ECMWF atmospheric re-analyses of the global climate (ERA5) ([12]), and both resolutions are in 0.25° latitude and longitude grids. The satellite retrievals from NOAA Unique Combined Atmospheric Processing System (NUCAPS) data were used for comparison. The time period for the statistical comparison was from October 2019 to March 2020 (6 months).

2.1. GNSS RO Data from FS3 and FS7 The FS3 constellation is on an orbit of high inclination (72°) that generates more observations at higher latitudes but still globally distributed. In contrast, the FS7 constella- Remote Sens. 2021, 13, x FOR PEER REVIEW 4 of 20 tion is on an orbit of low inclination (24o), and most of the RO data are located within ±45o latitudes. FS7 can provide about 4000–5000 RO soundings per day, which is about twice the FS3 RO data. Figure 1 shows the RO data density for one month in 2.5° × 2.5° bins within ±45° latitudes for FS3 in March 2009 and FS7 in March 2020. Significant differences in the RO density appear in the tropical region. FS3 shows a nearly homogeneous data density in the subtropical region but with less data in the tropical region. In contrast, the FS7 has a higher data density over the tropical region because of the low-inclination orbit, and some satellites have not moved to the final orbit height yet. Generally, the data density for FS3 is less than 20 RO profiles in the subtropics and even fewer data near the Remote Sens. 2021, 13, 717 equator. For FS7, there are about 50–60 RO profiles in the tropical region, considerably4 of 20 more than the FS3.

(b)

Figure 1. The data density of radio occultation soundings for (a) FORMOSAT-3 in March 2009 and (b) FORMOSAT-7 in March 2020. The data are counted on 2.5° by 2.5° bins.

Remote Sens. 2021, 13, x FOR PEER REVIEW FS7 with a higher SNR is expected to increase the capability of the ray, reaching4 of 20 lower

altitudes. As one example of the comparison on one day, Figure 2 shows the FS3 data on 5 February 2009 and FS7 on 5 February 2020, with their lowest penetrative heights indi- (a) cated. There are 1018 RO soundings from FS3 and 5419 RO soundings from FS7 in the region of ±45° latitudes. Every dot point in Figure 2 indicates one RO sounding, and the color indicates the lowest height for each sounding. Note that all of the altitudes for the penetration in the figure were measured above the surface. Figure 2 illustrates that most of the FS7 RO data extend deeper into the lower troposphere than FS3. For example, the penetrative depths of FS7 data are below 1 km (Figure 2b), compared to about 1–2 km for the FS3 data (Figure 2a). The distribution of penetrative depths can be clearly seen from the 6-month statistical histogram (Figure 3). The data were collected from October 2008 to March 2009, with 209,709 RO profiles for FS3, and from October 2019 to March 2020, with 599,676 RO profiles for FS7. The data amounts for FS7 are about three times than that for FS3. All the lowest heights were stratified by a 500-m interval until 6 km, and the penetrative depth above 6 km was counted at heights of 5.5–6 km intervals. Figure 3 shows that (b) the penetrative depths of RO soundings for FS3 mostly reach 0.5–1.5 km height, while FS7 Figuregives 1. The the data highest density percentage of radio occultation in the altitude soundings below for (0.5a) FORMOSAT-3 km. Statistically, in March the rate 2009 of and penetra-

(b) FORMOSAT-7tion below in 1 Marchkm is 2020.about The 40% data for are FS3 counted and 80% on 2.5 for◦ by FS7. 2.5 ◦Similarbins. statistical results are also Figure 1.found The data in [7,10].density of radio occultation soundings for (a) FORMOSAT-3 in March 2009 and (b) FORMOSAT-7 in March 2020. The data are counted on 2.5° by 2.5° bins.

FS7 with a higher SNR is expected to increase the capability of the ray, reaching lower altitudes. As one example of the comparison on one day, Figure 2 shows the FS3 data on 5 February 2009 and FS7 on 5 February 2020, with their lowest penetrative heights indicated. There are 1018 RO soundings from FS3 and 5419 RO soundings from FS7 in the region of ±45° latitudes. Every dot point in Figure 2 indicates one RO sounding, and the color indicates the lowest height for each sounding. Note that all of the altitudes for the penetration in the figure were measured above the surface. Figure 2 illustrates that most of the FS7 RO data extend deeper into the lower troposphere than FS3. For example, the penetrative depths of FS7 data are below 1 km (Figure 2b), compared to about 1–2 km for the FS3 data (Figure 2a). The distribution of penetrative depths can be clearly seen from the 6-month statistical histogram (Figure 3). The data were collected from October 2008 to March 2009, with 209,709 RO profiles for FS3, and from October 2019 to March 2020, with 599,676 RO profiles for FS7. The data amounts for FS7 are about three times than that for (a) FS3. All the lowest heights were stratified by a 500-m interval until 6 km, and the penetra- tiveFigure depth 2. Cont. above 6 km was counted at heights of 5.5–6 km intervals. Figure 3 shows that the penetrative depths of RO soundings for FS3 mostly reach 0.5–1.5 km height, while FS7 gives the highest percentage in the altitude below 0.5 km. Statistically, the rate of penetration below 1 km is about 40% for FS3 and 80% for FS7. Similar statistical results are also found in [7,10].

(a)

Remote Sens. 2021, 13, 717 5 of 20 Remote Sens. 2021, 13, x FOR PEER REVIEW 5 of 20

(b)

Figure 2. The radio occultation (RO) soundings within ±45◦ latitudes (a) for FORMOSAT-3 on Figure 2. The radio occultation5 February (RO 2009) soundings and (b) for within FORMOSAT-7 ±45° latitudes on ( 5a) Februaryfor FORMOSAT 2020. The-3 on color 5 February indicates 2009 the and lowest (b) for FORMOSAT-7 on 5 Februarypenetration 2020. height The color (km) ofindicates each RO the sounding lowest penetration above thesurface. height (km) of each RO sounding above the surface.

(a) (b) Figure 3. Data count( aof) RO penetrative depth with a 500-m interval (a) for FORMOSAT(b) -3 from October 2008 to March 2009 and (b) for FORMOSAT-7 from October 2019 to March 2020. All the heights are measured above the surface. Figure 3. Data count of RO penetrative depth with a 500-m interval (a) for FORMOSAT-3 from October 2008 to March 2009 and (b) for FORMOSAT-7 from October2.2. Data 2019 for to MarchVerification 2020. All the heights are measured above the surface.

2.2. Data forAno Veriﬁcationther two RO datasets, i.e., Metop and KOMPSAT-5 RO data, were chosen for inter-comparisons with the FS7 RO data. The Metop RO data were processed by the Radio Another two RO datasets, i.e., Metop and KOMPSAT-5 RO data, were chosen for Occultation Meteorology Satellite Application Facility (ROM SAF) and KOMPSAT-5 by inter-comparisons with the FS7 RO data. The Metop RO data were processed by the Radio the CDAAC. The Metop series includes three polar-orbiting satellites, Metop-A, B, and C, Occultation Meteorology Satellite Application Facility (ROM SAF) and KOMPSAT-5 by operated by the EUMETSAT, and there are about 500–600 RO profiles per day globally the CDAAC. The Metop series includes three polar-orbiting satellites, Metop-A, B, and from each satellite [13]. The Metop RO (hereafter METOP) data show a negative bias in C, operated by the EUMETSAT, and there are about 500–600 RO proﬁles per day globally the bending angle in the lower troposphere that increases with lower latitudes when com- from each satellite [13]. The Metop RO (hereafter METOP) data show a negative bias in the pared to the ECMWF forecast and FS3 [14]. The authors of [15] demonstrated that the bending angle in the lower troposphere that increases with lower latitudes when compared negative differences between FS3 and METOP are related to the different tracking depths to the ECMWF forecast and FS3 [14]. The authors of [15] demonstrated that the negative (FS3 has deeper data) and data retrieval methods. The KOMPSAT-5 RO products are pro- differences between FS3 and METOP are related to the different tracking depths (FS3 has deepercessed data) and by University data retrieval Corporation methods. Thefor Atmospheric KOMPSAT-5 ROResearch products (UCAR are processed) and can by provide Universityabout Corporation 500 RO soundings for Atmospheric daily. A large Research difference, (UCAR) in andthe range can provide of 7 to about13 km 500altitude, RO was soundingsreported daily. when A large comparing difference, results in the from range KOMPSAT of 7 to 13 km-5 rising altitude, occultation was reported and NCEP when fore- comparingcast. The results issue from is still KOMPSAT-5 under investigation, rising occultation and UCAR and recommends NCEP forecast. using The only issue the is setting still underoccultations investigation, by the and moment UCAR [16] recommends. The KOMPSAT using only-5 data the have setting comparable occultations data by quality the to that of FS3 [17]. Since the KOMPSAT-5 rising occultations were flagged as bad, only the

Remote Sens. 2021, 13, 717 6 of 20

moment [16]. The KOMPSAT-5 data have comparable data quality to that of FS3 [17]. Since the KOMPSAT-5 rising occultations were flagged as bad, only the KOMPSAT-5 setting occultations with a good flag of about 200 profiles per day are wesed for the verifications. Hereafter, the KOMPSAT-5 RO data used in this study are indicated as KOMP5. The three-dimensional global analyses included the ERA5 reanalysis and the NCEP FNL operational global analysis. The ERA5 provides data with a horizontal resolution of 30 km and 137 vertical levels from surface up to 80 km height. The NCEP FNL data were obtained from the NCAR Research Data Archive, with a horizontal resolution of 0.25◦. Note that the NCEP FNL data were derived through the Global Data Assimilation System (GDAS) and covered the vertical range from surface to 10 hPa. Both the ERA5 and NCEP FNL possess high horizontal resolution adequate for the comparison. Both the analyses were interpolated into the locations of collocated FS7 RO soundings. ECMWF started assimilating FS7 RO data from 25 March 2020 [18], and only a few days overlapped with the statistical period in this study. For NCEP FNL, there was no overlapped time for the RO data analysis in this study since the FS7 RO data was assimilated at NCEP after 26 May 2020 [19]. Thus, most of the RO data information from FS7 is not repeated in both global analyses. Other than the data mentioned previously, the retrieved atmospheric temperature and moisture sounding profiles from advanced passive microwave and hyperspectral infrared sensors were adopted for the intercomparison as well. NUCAPS is the NOAA operational satellite sounding retrieval algorithm for microwave and infrared sounders onboard Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20 satellites. NUCAPS was also used to retrieve data from the Joint Polar Satellite System-1 (JPSS1) (renamed as NOAA-20 after Nov. 2017). The atmospheric soundings are retrieved from the synesthetic use of the Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS), which have 1305 and 22 channels in infrared and microwave spectral regions, respectively [20]. The sound profiles contain more than 100 pressure levels from surface to the 0.005 hPa, with uncertainties of about 1 K and 20% in temperature and moisture mixing ratio in the troposphere from the NUCAPS Algorithm Theoretical Basis Document (ATBD). These data have been supported for both operational applications and research studies (e.g., [21–23]). Within the six months, Figure4 shows the daily data amounts of different datasets corresponding to the matching criteria for collocated RO points. There are about one hundred data pairs per day for comparison with METOP, and much fewer pairs for the RAOB and KOMP5 RO data (Figure4). Each METOP satellite (A, B, and C) gives about three-thousands RO soundings matched with FS7 in the six months. From temporal and spatial interpolation, the global analyses give the exact same amounts paired with the FS7 RO data. The JPSS1 and SNPP satellite data are the second largest for paring data. Because JPSS1 and SNPP were launched on the same orbit and pass the same location within 50 min, a superposition can be seen in Figure4. Daily data amounts after paring do not considerably vary with time during the six months, except for a larger drop in the first two weeks of November 2019. That was caused by the scheduled orbit adjustment of FS7 satellites in that period. For RAOB and KOMP5, there are some occasions at specific times without collocated RO data as both datasets provide relatively fewer data amounts compared to other datasets. The multiple datasets were evaluated by mean difference (µ) and standard deviation (STD, σ) with respect to the reference data for comparison or verification. The mean difference and STD were calculated, respectively, as

1 num µ = ∑ xi with xi = dataseti − FS7i (1) num i=1

s num 1 2 σ = ∑ (xi − µ) (2) num i=1 Remote Sens. 2021, 13, 717 7 of 20

Remote Sens. 2021, 13, x FOR PEER REVIEW 7 of 20

where dataseti and FS7i indicate multiple datasets and the FS7 RO used for the veriﬁcation or comparison, and i is a data point of total num data during the period.

Figure 4. DailyFigure data 4. Daily amounts data duringamounts October during 2019–March October 2019 2020–March for FS7 2020 (red), for FS7 KOMP5 (red), (orange), KOMP5 METOP(orange), (dash METOP blue), (dash JPSS1 blue), JPSS1 (violet), SNPP(violet), (blue), SNPP RAOB (blue), (black), RAOB ERA5 (black), (dash ERA5 green), (dash and green), FNL (dash and FNL dark (dash green). dark green).

3. Analysis ResultsThe multiple and Discussions datasets were evaluated by mean difference (휇) and standard deviation 3.1. Spatiotemporal(STD, 휎) sensitivitywith respect to the reference data for comparison or verification. The mean dif- For theference comparison and STD between were calculated, FS7 RO data respectively, and other datasets, as the effect of spatiotemporal 푛푢푚 (1) distance on the analysis should1 be examined for collocated RO points. Figure5 shows the statistical spatial휇 difference= ∑ between푥 with FS7 푥 and= 푑푎푡푎푠푒푡 different− 퐹푆 datasets,7 including radiosonde 푛푢푚 푖 푖 푖 푖 (RAOB), both global analyses (ERA5푖=1 and FNL), other RO missions (METOP and KOMP5), and satellite retrievals (JPSS1 and SNPP). The absolute differences were averaged(2) from surface to 200 hPa. For both temperature and vapor푛푢푚 pressure, the analysis is not sensitive to temporal 1 departure between any data pairs휎 = within√ 3∑ h( (Figure푥 − 휇)52a,b). The magnitude of the absolute 푛푢푚 푖 mean difference in temperature is largest for푖= RAOB,1 followed by satellite retrievals, other RO missions, and then the global analyses (Figure5a), but with smaller absolute mean differences in vapor pressurewhere 푑푎푡푎푠푒푡 for satellite푖 and retrievals 퐹푆7푖 indicate and KOMP5 multiple (Figure datasets5b).The and spatial the FS7 comparison RO used for for the verifi- KOMP5 mightcation haveor comparison, high uncertainty and i is because a data ofpoint the fewerof total data num collocations data during (Figure the period.4). The magnitude and ranking of the absolute mean difference in temperature for different datasets are similar,3. Analysis regardless Results of temporal and Discussions departure and spatial distance. The ranking for different datasets is3.1. also Spatiotemporal the same in comparison sensitivity for water vapor pressure, except for the METOP and KOMP5. METOP and KOMP5 have the maximum and minimum differences, respectively, For the comparison between FS7 RO data and other datasets, the effect of spatiotem- in vapor pressure. The temperature difference increases with the spatial distance for all the poral distance on the analysis should be examined for collocated RO points. Figure 5 datasets, especially at a larger tendency of about 0.05 K per 100 km for both global analyses shows the statistical spatial difference between FS7 and different datasets, including radi- (NCEP FNL and ERA5). In contrast, there is only a small tendency for satellite retrievals (JPSS1 osonde (RAOB), both global analyses (ERA5 and FNL), other RO missions (METOP and and SNPP). The tendency in vapor pressure difference for all the datasets is similar to that in KOMP5), and satellite retrievals (JPSS1 and SNPP). The absolute differences were aver- temperature, but both satellite retrievals give nearly the same smallest deviations from the RO aged from surface to 200 hPa. For both temperature and vapor pressure, the analysis is data. Generally, the value of the absolute mean difference in the 100 km range is comparable not sensitive to temporal departure between any data pairs within 3 hours (Figure 5a,b). to the results of time departure, which is insensitive. To reduce the influences from temporal The magnitude of the absolute mean difference in temperature is largest for RAOB, fol- and spatial departures on the comparison, we adopted a spatiotemporal window of ±3 h and lowed by satellite retrievals, other RO missions, and then the global analyses (Figure 5a), ±100 km for the collocated RO points. The wet retrieval from RO measurement in principle but with smaller absolute mean differences in vapor pressure for satellite retrievals and is the averaged state along the ray point and, in general, will be closer to satellite retrievals and globalKOMP5 analyses (Fig ature a relatively 5b). The coarserspatial comparison resolution but for deviated KOMP5 moremight much have fromhigh RAOBuncertainty be- (as point measurement).cause of the fewer Since data water collocations vapor is generally(Figure 4). less The homogeneous magnitude and than ranking temperature of the absolute in the lowermean troposphere, difference in the temperature larger deviation for different from the datasets RO vapor are pressuresimilar, regardless is present forof temporal radiosondes,departure as seen and in Figure spatial5d. distance. As discussed The ranking in [ 24], thefor METOPdifferen refractivityt datasets is has also a negativethe same in com- bias in theparison lower troposphere for water vapor and reaches pressure, a maximum except infor the the tropical METOP region, and which KOMP5. is where METOP and KOMP5 have the maximum and minimum differences, respectively, in vapor pressure. The temperature difference increases with the spatial distance for all the datasets, especially at a larger tendency of about 0.05 K per 100 km for both global analyses (NCEP FNL

Remote Sens. 2021, 13, x FOR PEER REVIEW 8 of 20

and ERA5). In contrast, there is only a small tendency for satellite retrievals (JPSS1 and SNPP). The tendency in vapor pressure difference for all the datasets is similar to that in temperature, but both satellite retrievals give nearly the same smallest deviations from the RO data. Generally, the value of the absolute mean difference in the 100 km range is comparable to the results of time departure, which is insensitive. To reduce the influences from temporal and spatial departures on the comparison, we adopted a spatiotemporal window of ±3h and ±100 km for the collocated RO points. The wet retrieval from RO measurement in principle is the averaged state along the ray point and, in general, will be closer to satellite retrievals and global analyses at a relatively coarser resolution but deviated more much from RAOB (as point measurement). Since water vapor is generally less ho- Remote Sens. 2021, 13, 717 mogeneous than temperature in the lower troposphere, the larger deviation from the8 of RO 20 vapor pressure is present for radiosondes, as seen in Figure 5d. As discussed in [24], the METOP refractivity has a negative bias in the lower troposphere and reaches a maximum in the tropical region, which is where the FS7 covers. The larger difference in the lower the FS7 covers. The larger difference in the lower troposphere could translate to the retrieved troposphere could translate to the retrieved vapor pressure, which might be the reason vapor pressure, which might be the reason for the larger difference for METOP in Figure5d. for the larger difference for METOP in Figure 5d.

(a) (b)

Figure 5. The mean absolute differencedifference betweenbetween FS7FS7 andand differentdifferent datasets, datasets, including including RAOB RAOB (black), KOMP5(black), KOMP5 (orange), (orange), METOP(light METOP blue), (light ERA5 blue), (green), ERA5 FNL (green), (dark green),FNL (dark JPSS1 green), (violet), JPSS1 and (violet), SNPP (blue), and inSNPP (a) temperature (blue), in (a (K)) temperature and (b) vapor (K) pressure and (b) (hPa)vapor versus pressure the time(hPa) (departure versus the hours time from(departure the RO hours event) duringfrom the October RO event) 2019–March during October 2020. The 2019 differences–March are2020. averaged The differences from surface are averaged to 200 hPa. from (c,d surface) are as into (a,b), respectively, but versus the horizontal distance (in km) from the collocated RO point.

3.2. Ground-Truth Verifications Before the comparisons with multiple datasets, more accurate independent observations should be used to quantify the data quality of FS7 wet RO retrievals. RAOB as a ground-truth measurement is useful for verification as described in [10], despite few being available over the ocean. For verification on oceanic RO soundings, the Central Weather Bureau (CWB) took two flights to obtain the in situ observations with DROP, which were released at times closer to the occurrence of RO soundings. The DROP was released east of Taiwan on 18 and 20 October 2019, as indicated in Figure6. There are five FS7 RO profiles closer to the nine DROP, which are chosen for comparison. To assess the data quality of FS7, RAOB and DROP were taken as ground-truth observations. The reference data in Equation (1) were changed to RAOB or DROP for the FS7 RO data verification. Remote Sens. 2021, 13, x FOR PEER REVIEW 9 of 20

200 hPa. (c) and (d) are as in (a) and (b), respectively, but versus the horizontal distance (in km) from the collocated RO point.

3.2. Ground-Truth Verifications

Before the comparisons with multiple datasets, more accurate independent observations should be used to quantify the data quality of FS7 wet RO retrievals. RAOB as a ground-truth measurement is useful for verification as described in [10], despite few being available over the ocean. For verification on oceanic RO soundings, the Central Weather Bureau (CWB) took two flights to obtain the in situ observations with DROP, which were released at times closer to the occurrence of RO soundings. The DROP was released east of Taiwan on 18 and 20 October 2019, as indicated in Figure 6. There are five FS7 RO profiles closer to the nine DROP, which are chosen for comparison. To assess the data Remote Sens. 2021, 13, 717 9 of 20 quality of FS7, RAOB and DROP were taken as ground-truth observations. The reference data in Equation (1) were changed to RAOB or DROP for the FS7 RO data verification.

Figure 6. Locations of FS7 RO soundings (blue) and DROP on 18 (red) and 20 (green) October 2019. Figure 6. Locations of FS7 RO soundings (blue) and DROP on 18 (red) and 20 (green) October 2019.The redThe and red greenand green solid solid dots aredots the are released the released DROP DROP in the in mission the mission collocated collocated with the with ﬁve the RO five points, ROrespectively, points, respectively, and the other and DROP the other locations DROP (open locations circles) (open were circles) not used. were The not released used. The time released (UTC) of timeeach (UTC) collocated of each DROP collocated is indicated DROP by is theindicated numbers by nearthe numbers the symbol. near the symbol.

ThereThere is is a a total total of nine pairspairs forfor comparison comparison after after selecting selecting both both data data that that satisfy satisfy the timethe timeand and distance distance criteria. criteria. Figure Figure7 show 7 show each comparisoneach comparison for vapor for pressurevapor pressure and temperature. and temperature.The spatial The distances spatial betweendistances the between locations the of thelocations compared of the RO compared and DROP RO at different and DROP vertical at differentlevels are vertical also presented. levels are Most also of presented. the FS7 RO Most information of the FS7 extended RO information to near-surface. extended As can to be nearseen,-surface. both data As exhibitcan be veryseen, close both temperaturesdata exhibit very in some close pairs temperature (Figure7a–e)s in whilesome alsopairs giving (Fig- uredistinguishable 7a–e) while also differences giving distinguishable in some pairs (Figure differences7f–i) that in aresome located pairs over(Figure the 7f region–i) that where are Typhoon Neoguri is active near Okinawa at the time. The data pair in Figure7e provides the located over the region where Typhoon Neoguri is active near Okinawa at the time. The smallest spatial distance between the DROP and FS7 RO soundings, generally less than 70 km. data pair in Figure 7e provides the smallest spatial distance between the DROP and FS7 For this pair, the temperature and vapor pressure between RO and DROP are quite small, RO soundings, generally less than 70 km. For this pair, the temperature and vapor pres- with small deviations within ±1 ◦C and ±1 hPa above 3 km, respectively. The statistical mean sure between RO and DROP are quite small, with small deviations within ±1 °C and ±1 difference and STD for the nine data pairs are very small above 6 km height (Figure7j). It hPa above 3 km, respectively. The statistical mean difference and STD for the nine data shows a maximum temperature difference of 2 ◦C near 4 km height and a maximum positive pairs are very small above 6 km height (Figure 7j). It shows a maximum temperature dif- vapor pressure difference of 2 hPa below 2 km. The temperature difference near the level of ference of 2 °C near 4 km height and a maximum positive vapor pressure difference of 2 4 km is larger than the 6-monthly statistics, which could be caused by the sample size and larger atmospheric variations associated with the typhoon system. In general, the temperature deviations are rather small in the upper troposphere for FS7 as in similar comparisons for FS3 (e.g., [7]), and the moisture deviations are about 2 hPa in the boundary layer regardless of the pair comparison. Remote Sens. 2021, 13, x FOR PEER REVIEW 10 of 20

hPa below 2 km. The temperature difference near the level of 4 km is larger than the 6- monthly statistics, which could be caused by the sample size and larger atmospheric var- Remote Sens. 2021, 13, 717 iations associated with the typhoon system. In general, the temperature deviations10 of 20 are rather small in the upper troposphere for FS7 as in similar comparisons for FS3 (e.g., [7]), and the moisture deviations are about 2 hPa in the boundary layer regardless of the pair comparison.

(a) (b)

(e) (f)

(g) (h)

Figure 7. Cont. Remote Sens. 2021, 13, x FOR PEER REVIEW 11 of 20

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(i) (j)

Figure 7. (a–i) The comparison between(i) FS7 (blue) and DROP sounding (red), and their( differencej) (green) in vapor pres-

sureFigure (hPa) 7. (anda–i) temperature The comparison (°C) between on the left FS7 and (blue) middle and DROPof each sounding panel. The (red), right and column their difference on each panel (green) shows in vapor the pressurespatial distance between FS7 and DROP. (j) shows the mean difference and standard deviation (STD) averaged from the nine Figure(hPa) 7. and (a–i temperature) The comparison (◦C) on between the left and FS7 middle (blue) ofand each DROP panel. sounding The right (red), column and on their each paneldifference shows (green) the spatial in vapor distance pres- data pairs in (a–i). surebetween (hPa) and FS7 andtemperature DROP. (j) shows(°C) on the the mean left differenceand middle and of standard each panel. deviation The (STD)right averagedcolumn on from each the panel nine data shows pairs the in (spatiala–i). distance between FS7 and DROP. (j) shows the mean difference and standard deviation (STD) averaged from the nine Figure 8 shows similar comparisons with the radiosonde soundings that were re- data pairs in (a–i). Figure8 shows similar comparisons with the radiosonde soundings that were released leasedat different at different times times in 2019 in 2019 and 2020and 2020 from from the Researchthe Research Vessel Vessel Legend. Legend. The The route route of of the the Legend vessel travels between Dongsha Island and Taiping Island in the SCSTIMX LegendFigure vessel 8 shows travels similar between comparisons Dongsha Island with and the Taiping radiosonde Island soundings in the SCSTIMX that (Southwere re- (South China Sea Two Island Monsoon Experiment). There were two trips southwest of leasedChina at Sea different Two Island times Monsoon in 2019 and Experiment). 2020 from There the Research were two Vessel trips southwest Legend. The of Taiwan, route of Taiwan, one from 24 to 26 August 2019 and the other from 14 to 16 April 2020, to release theone Legend from 24vessel to 26 travels August between 2019 and Dongsha the other Island from and 14 to Taiping 16 April Island 2020, in to the release SCSTIMX the the radiosondes for comparison with FS7 RO data. There were in total eight pairs selected (Southradiosondes China forSea comparisonTwo Island with Monsoon FS7 RO Experiment). data. There were There in totalwere eight two pairstrips selectedsouthwest for of forthe the collocated collocated comparison comparison indicated indicated in thein the ﬁgure. figure. The The temperature temperature differences differences of FS7 of FS7 data Taiwan, one from 24 to 26 August 2019 and the other from 14 to 16 April 2020, to release datawere we furtherre further reduced reduced in these in these pairs pairs compared compared to Figure to Fig7,ure with 7, anwith average an average of about of about 0.5 ◦C the radiosondes for comparison with FS7 RO data. There were in total eight pairs selected 0.5deviation °C deviation and STDand STD throughout throughout the tropospherethe troposphere (Figure (Fig9urei). On9i). theOn otherthe other hand, hand, FS7 FS7 RO forROdata the data givecollocated give less less moisture moisture comparison than than radiosondeindicated radiosonde in below below the figure. 22 kmkm in inThe most temperature pairs and and a a differencesnegative negative mean mean of FS7 datadifferencedifference were furtherof of about about reduced 1 1hPa hPa within within in these the the tropospherepairs troposphere compared (Fig (Figureure to Fig9i).9i). ureHowever, However, 7, with the thean negative negativeaverage meanof mean about 0.5differencedifference °C deviation exte extendednded and to STD to −−4 hPa4 throughout hPa near near the the surfacethe surface troposphere over over this this region, (Fig region,ure which 9i). which On is is somewhatthe somewhat other hand, larger larger FS7 ROthanthan data the the givecorresponding corresponding less moisture value value than found found radiosonde to to the the east east below of of Taiwan Taiwan 2 km in (Fig (Figure mosture pairs7j).7j). and a negative mean difference of about 1 hPa within the troposphere (Figure 9i). However, the negative mean difference extended to −4 hPa near the surface over this region, which is somewhat larger than the corresponding value found to the east of Taiwan (Figure 7j).

FigureFigure 8. 8. TheThe same same as as that that in in Fig Figureure 66 but but for for the the locations locations of of FS7 FS7 RO RO soundings soundings (blue) (blue) and and RAOB RAOB releasedreleased fr fromom the the Research Research Vessel Vessel Legend Legend on on 24 24–26–26 August August 2019 2019 (red) (red) and and 14 14–16–16 April April 2020 2020 (green). (green).

Figure 8. The same as that in Figure 6 but for the locations of FS7 RO soundings (blue) and RAOB released from the Research Vessel Legend on 24–26 August 2019 (red) and 14–16 April 2020 (green).

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(a) (b) (c)

(d) (e) (f)

(g) (h) (i)

FigureFigure 9. The 9. sameThe same as that as that in Fig in Figureure 7 but7 but for for the the comparison comparison betweenbetween FS7 FS7 and and RAOB RAOB released released from from the Researchthe Research Vessel Vessel Legend.Legend. (i) is (thei) is mean the mean difference difference and and STD STD averaged averaged from from the the eighteight datadata pairs pairs in in (a –(ah–).h).

BesidesBesides the the individual individual comparisons comparisons fromfrom the the two two chartered chartered missions, missions, the six-monththe six-month data verification against RAOB was examined. The vertical distribution of the collocated data verification against RAOB was examined. The vertical distribution of the collocated data amounts of RAOB show no change between 2 km and 15 km and a quick decrease databelow amounts about of 2 kmRAOB (Figure show 10), no and change the volume between gradually 2 km decreasesand 15 km above and 16a kmquick (figure decrease belownot shown).about 2 Inkm the (Fig troposphere,ure 10), and the six-monththe volume RAOB gradually verifications decreases show above slightly 16 positive km (figure notdifferences shown). In in the temperature troposphere, and vapor the six pressure-month above RAOB 4 km verifications but larger negative show slightly differences positive differencesin vapor pressurein temperature in the lower and troposphere. vapor pressure In the above lower troposphere,4 km but larger RAOB negative has maximum differences in vapordeviations pressure of about in 1.5the◦ Clower and 2.2troposphere. hPa from the In FS7 the RO lower temperature troposphere, and vapor RAOB pressure, has maxi- mumrespectively. deviations A of negative about 1.5 difference °C and up2.2 tohPa− fromhPa in the the FS7 lower RO tropospheretemperature is and present vapor for pressure,RAOB, respectively. and the negative A negative moisture difference deviation up below to − 2 kmhPa is in in the agreement lower withtroposphere the verification is present in [10], which may be associated with the tracking uncertainty due to the super-refraction for RAOB, and the negative moisture deviation below 2 km is in agreement with the ver- from FS7 [7,9,10]. Generally, the absolute magnitudes of temperature and vapor pressure ificationdifferences in [10], are which less than may 0.5 be◦C associated and 1 hPa, with respectively, the tracking in the uncertainty troposphere. due The to vertical the super- refractionaverage from of the FS7 difference [7,9–10 and]. Generally, STD from surfacethe absolute to 15 km magnitudes altitude are of 0.14 temperature◦C and 0.12 and◦C va- porfor pressur temperaturee differences and are are−0.03 less hPa than and 0.5 0.24 °C hPa and for 1 vapor hPa, pressure.respectively, in the troposphere. The vertical average of the difference and STD from surface to 15 km altitude are 0.14 °C and 0.12 °C for temperature and are −0.03 hPa and 0.24 hPa for vapor pressure.

Remote Sens. 2021, 13, x FOR PEER REVIEW 13 of 20 Remote Sens. 2021, 13, 717 13 of 20

Figure 10. 10.The The mean mean differences differences (solid line)(solid and line) standards and deviationstandards (STD, deviation dashed line) (STD, between dashed FS7 line) between and RAOB in (a) temperature (◦C, red) and (b) water vapor pressure (hPa, blue). The right panel FS7 and RAOB in (a) temperature (°C, red) and (b) water vapor pressure (hPa, blue). The right shows vertical variations in the total counted data in comparison. panel shows vertical variations in the total counted data in comparison. 3.3. Veriﬁcation against FS7 3.3. VerificationThe analysis aboveagainst showed FS7 the precision of FS7 with RAOB, and then, the FS7 data were derived as the reference data for multiple datasets. The vertical distribution of the collocatedThe analysis data amounts, above show METOPed and the KOMP5, precision are of similar FS7 withwith no RAOB, change and between then, the FS7 data we2 kmre andderived 40 km as but the with reference the smallest data amounts for multiple (several datasets. hundred to The one vertical thousand) distribution for of the collocatedKOMP5 (Figure data 11 amounts,). The comparisons METOP with and the KOMP5, two datasets are similar exhibit similarwith no patterns change in between 2 km andthe troposphere, 40 km but with with negative the smallest differences amounts in temperature (several and hundred vapor pressure to one above thousand) 4 km. for KOMP5 In the lower troposphere, both METOP and KOMP5 show positive differences in vapor (Figure 11). The comparisons with the two datasets exhibit similar patterns in the tropo- pressure. Relatively larger differences are given by METOP in both temperature and spheremoisture,, with with maximanegative of − differences0.5 ◦C and 2 hPa,in temperature respectively. For and the temperaturevapor pressure comparison above 4 km. In the lower(Figure troposphere,11a), KOMP5 shows both theMETOP differences andwithin KOMP5 0.25 show◦C, which positive is about differences half of that in vapor pressure.for METOP. Relatively In the larger lower troposphere,differences KOMP5are given also by shows METOP a small in both deviation temperature from the and moisture, withFS7 RO maxim temperature.a of −0.5 On ° theC and other 2 hand, hPa, therespectively. differences in F vaporor the pressure temperature increase comparison from (Figure KOMP5 to Metop (Figure 11b). The mean difference between FS7 and KOMP5 shows 11a)very, small KOMP5 magnitudes showsat the all differences vertical levels, within except 0.25 with ° someC, which amounts is about less than half 0.5 of hPa that for METOP. Inbelow the 2lower km. In troposphere, contrast, METOP KOMP5 gives a larger also vaporshows pressure a small difference deviation when from compared the FS7 RO temperature.with FS7, On which the may other associate hand, with the the differences larger refractivity in vapor bias in pressure lower troposphere increase for from KOMP5 to MetopMETOP, (Fig as discussedure 11b). in SectionThe mean 3.1. Ondifference the other hand,between the KOMP5 FS7 and data KOMP5 downloaded shows from very small mag- TACC, which use the same retrieval algorithm as used for FS7, thus has generally smaller nitudes at all vertical levels, except with some amounts less than 0.5 hPa below 2 km. In mean differences and STDs for both temperature and vapor pressure, which is in better contrast,agreement with METOP FS7. gives a larger vapor pressure difference when compared with FS7, whichFigure may 12 associate shows the with differences the larger in the refractivity analyses between bias in FS7 lower and troposphere other datasets, for METOP, as discussedincluding ERA5, in Section FNL, JPSS1, 3.1. On and the SNPP. other The amountshand, the of dataKOMP5 pairs fordata comparison downloaded are from TACC, whichalmost constant use the with same height retrieval but are graduallyalgorithm reduced as used from for 700 FS7, hPa to thus 1000 hPa.has Comparedgenerally smaller mean to both FNL and ERA5, the temperature differences show negative differences for most of differencesthe proﬁles from and surface STDs to for 200 both hPa (Figuretemperature 12a). However, and vapor the comparison pressure, with which satellite is in better agree- mentretrievals with (JPSS1 FS7. and SNPP) show positive temperature differences below about 4 km (600 hPa) and larger negative differences relative to the global analyses above this height. The maximum temperature bias is about −0.6 K at about 450 hPa. The comparisons with global analyses exhibit smaller mean temperature differences and STDs, about half of those for both the satellite data. Even though FS7 RO analysis shows larger mean temperature differences and STD in comparison with satellite retrievals (Figure 12a), their absolute magnitudes are still comparable with those from other RO soundings or radiosondes, as shown in Figures 10 and 11a. For vapor pressure (Figure 12b), both FNL and ERA5

Remote Sens. 2021, 13, 717 14 of 20

show positive differences within 1 hPa near the surface. FNL gives a relatively smaller mean difference than ERA5, which might be associated with the fact that the NCEP short-term forecast is used as the model ﬁrst gauss for the 1DVAR retrieval [25]. Thus, the veriﬁcations against FNL show the smallest STDs in both temperature and vapor pressure. In comparison with JPSS1 and SNPP in moisture, negative mean differences of about −0.5 hPa in 600–900 hPa are present in Figure 12b. The mean differences in both temperature and vapor pressure for both satellite retrievals are opposite to those for both global analyses below about 4 km (600 hPa) (Figure 12). As shown in Figures5 and 12, the Remote Sens. 2021, 13, x FOR PEER REVIEW 14 of 20 JPSS1 and SNPP have similar patterns when compared to FS7, and the two datasets are combined together as NUCAPS for the later comparison.

(a) (b)

FigureFigure 11. The 11. meanThe mean differences differences (solid (solid line) line) and standardstandard deviations deviations (STD, (STD, dashed dashed line) betweenline) between FS7 and FS7 different and different datasets, da- Remotetasets, Sens.including 2021including, 13, x METOP FOR METOP PEER (red) (red) REVIEW and and KOMP5 KOMP5 (blue), (blue), in (a) in temperature (a) temperature (◦C) and (°C) (b) and water (b vapor) water pressure vapor (hPa).pressure The (hPa). right panel The right15 of 20

panel showsshows the the vertical vertical variations variations of theof the total total counted counted data indata comparison. in comparison.

Figure 12 shows the differences in the analyses between FS7 and other datasets, including ERA5, FNL, JPSS1, and SNPP. The amounts of data pairs for comparison are al- most constant with height but are gradually reduced from 700 hPa to 1000 hPa. Compared to both FNL and ERA5, the temperature differences show negative differences for most of the profiles from surface to 200 hPa (Figure 12a). However, the comparison with satellite retrievals (JPSS1 and SNPP) show positive temperature differences below about 4 km (600 hPa) and larger negative differences relative to the global analyses above this height. The maximum temperature bias is about −0.6 K at about 450 hPa. The comparisons with global analyses exhibit smaller mean temperature differences and STDs, about half of those for both the satellite data. Even though FS7 RO analysis shows larger mean temperature differences and STD in comparison with satellite retrievals (Figure 12a), their absolute magnitudes are still comparable with those from other RO soundings or radiosondes, as shown in Figures 10 and 11a. For vapor pressure (Figure 12b), both FNL and ERA5 show positive(a) differences within 1 hPa near the surface. FNL(b) gives a relatively smaller mean difference than ERA5, which might be associated with the fact that the NCEP short-term FigureFigure 12. The 12. sameThe same as that as that in Fig in Figureure 11 11but but between between FS7 FS7 and and different different datasets,datasets, including including ERA5 ERA5 (green), (green), FNL FNL (dark (dark green), green), forecast is used as the model first gauss for the 1DVAR retrieval [25]. Thus, the verifica- JPSS1JPSS1 (violet), (violet), and andSNPP SNPP (blue) (blue) along along the the pressure pressure coordinate. coordinate. tions against FNL show the smallest STDs in both temperature and vapor pressure. In comparisonTheThe latitudinal latitudinal with JPSS1 distributions distributions and SNPP ofof in thethe moisture, mean mean differences differences negative between betweenmean FS7 differences FS7 RO dataRO data and of ERA5,aboutand ERA5, −0.5 hPaFNL,FNL, in and 600 and –NUCAPS900 NUCAPS hPa a reare are present sho shownwn inin FigureFig. Fig ure12b. 13 13.. The The The mean mean differencesdifferences differences and in STDsbothand STDs aretemperature longitudi- are longitu- and nally averaged for their latitudinal distributions. For FNL and ERA5, the mean temperature vapordinally pressure averaged for for both their satellite latitudinal retrievals distributions. are opposite For FNL to those and forERA5, both the global mean analyses temper- belowdifferences about 4 arekm roughly (600 hPa) similar, (Figure with 12). slightly As shown larger in negative Figure valuess 5 and in 12, the the tropical JPSS1 region and SNPP aturebelow differences about 1 km are altitude roughly (900 similar hPa) as, with well slightly as comparable larger negativenegative values values throughout in the tropical haveregion similar below patterns about when1 km compar altitudeed (900to FS7, hPa) and as the well two asdatasets comparable are combined negative together values asthroughout NUCAPS thefor thelatitudinal later comparison. bins in the upper troposphere. Most of the temperature differences are within −0.2 K for ERA5 and FNL and exhibit much smaller magnitudes roughly in the middle troposphere (Figure 13a,c). The comparison indicates that ERA5 may be impacted with larger cold biases in the boundary layer near the equator than FNL if FS7 RO wet temperature is deemed more reliable. For vapor pressure, it is noted that positive mean differences appear throughout the latitude and below about 5 km (500 hPa) (Figure 13b,d), and the deviations from ERA5 in the tropical region are somewhat larger than from FNL. For NUCAPS, the temperature and moisture deviations from FS7 are quite different from those obtained with both global analyses (Figure 13e,f). For example, there are significant temperature differences of around −0.5 K in 300–600 hPa, while presenting positive differences mainly in 600–700 hPa and 900–1000 hPa. Furthermore, most of the moisture differences are negative below about 6 km altitude (400 hPa) throughout the latitude. Since the larger moisture differences for the satellite retrievals are opposite to those for both global analyses, FS7 RO data may suggest a bias possibly existing in the water vapor retrieval of NUCAPS [26]. A dry bias of water vapor retrieval from NUCAPS was revealed in [27,28] due to the absorption of clouds in the infrared spectrum.

(a) (b)

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the latitudinal bins in the upper troposphere. Most of the temperature differences are within −0.2 K for ERA5 and FNL and exhibit much smaller magnitudes roughly in the middle troposphere (Figure 13a,c). The comparison indicates that ERA5 may be impacted with larger cold biases in the boundary layer near the equator than FNL if FS7 RO wet temperature is deemed more reliable. For vapor pressure, it is noted that positive mean differences appear throughout the latitude and below about 5 km (500 hPa) (Figure 13b,d), and the deviations from ERA5 in the tropical region are somewhat larger than from FNL. For NUCAPS, the temperature and moisture deviations from FS7 are quite different from those obtained with both global analyses (Figure 13e,f). For example, there are signiﬁcant temperature differences of around −0.5 K in 300–600 hPa, while presenting positive differences mainly(a) in 600–700 hPa and 900–1000 hPa. Furthermore,(b) most of the moisture Figure 12. The samedifferences as that in are Fig negativeure 11 but below between about FS7 and 6 km different altitude datasets, (400 hPa) including throughout ERA5 (green), the latitude. FNL (dark Since green), JPSS1 (violet), andthe SNPP larger (blue) moisture along the differences pressure coordinate. for the satellite retrievals are opposite to those for both global analyses, FS7 RO data may suggest a bias possibly existing in the water vapor retrieval of NUCAPSThe latitudinal [26]. A dry distributions bias of water of vaporthe mean retrieval differences from NUCAPS between FS7 was RO revealed data and ERA5, in [27,28] dueFNL, to and the absorptionNUCAPS are of cloudsshown inin theFigure infrared 13. The spectrum. mean differences and STDs are longitu- Figuredinally 14 shows averaged the mean for their differences latitudinal and distributions. STDs within For±45 FNL◦ latitudes and ERA5, for different the mean temper- datasets, whichature aredifferences vertically are averaged roughly between similar, 200with and slightly 1000 hPa.larger Negative negative temperature values in the tropical differencesregion are present below over about most 1 km of thealtitude plotted (900 regions, hPa) withas well weaker as comparable magnitudes negative over values different regionsthroughout for different the latitudinal datasets thatbins arein the not upper revealed troposphere. from the latitudinal Most of the distributions temperature differ- in Figure 13ences. Larger are deviationswithin −0.2 are K foundfor ERA5 near and the FNL tropical and region exhibit where much contains smaller more magnitudes active roughly convectionin in the the middle intertropical troposphere convergence (Figure zone 13a,c). (ITCZ) The (Figure comparison 14a,c). indicates Differing that from ERA5 the may be comparisonimpacted with ERA5 with and larger FNL, cold the biases temperature in the boundary differences layer for NUCAPSnear the equator are relatively than FNL if FS7 smaller nearRO the wet equator temperature but somewhat is deemed larger more outside reliable. (Figure For vapor 13e). pressure, The deviations it is noted have that positive larger variationsmean differences along the latitude appear for throughout NUCAPS, the with latitude a dramatic and below change about to a maximum5 km (500 ofhPa) (Figure ◦ −0.05 K near13b,d), the equator and the and deviations a minimum from of ERA5−0.2 Kin inthe some tropical regions region near are±45 somewhat. The apparent larger than from deviation withFNL. colderFor NUCAPS, temperature the temperature for FNL is contributed and moisture by deviations larger magnitudes from FS7 extended are quite different along the equatorialfrom thoseregions, obtained mainly with both from global Africa analyses to the Western (Figure Pacific.13e,f). For For example, both ERA5 there are sig- and FNL, thenificant STDs temperature of their temperature differences differences of around are −0.5 below K in 0.2 300 K–600 in the hPa, tropical while regionpresenting posi- (Figure 14b,d),tive differences significantly mainly smaller inthan 600– those700 hPa for and NUCAPS, 900–1000 which hPa. is Furthermore, also evident inmost their of the mois- zonal averageture and differences meridional are average.negative Thebelow temperature about 6 km differences altitude (400 over hPa) the tropicalthroughout region the latitude. are generallySince larger the larger over the moisture land than differences over the for ocean the satellite for the threeretrievals datasets. are opposite There are to those for particularlyboth large global deviations analyses, in STD FS7 over RO Tibet data for may NUCAPS. suggest All athree bias datasets possibly exhibit existing smaller in the water STDs in lowervapor latitudes, retrieval with of NUCAPS no common [26]. in-phase A dry signalbias of in water longitudes. vapor retrieval from NUCAPS was revealed in [27,28] due to the absorption of clouds in the infrared spectrum.

(a) (b)

Figure 13. Cont. Remote Sens. 2021, 13, 717 16 of 20 Remote Sens. 2021, 13, x FOR PEER REVIEW 16 of 20

(e) (f)

Figure 13. Pressure–latitude cross section of mean differences between FS7 and ERA5 averaged in a Figure 13. Pressure10◦–bylatitude 10◦ bin cross in ( asection) temperature of mean (K) differences and (b) vapor between pressure FS7 and (hPa). ERA5 (c,d averaged) are as in in (a ,ab 10) but° by for 10 FNL,° bin in (a) temperature (K) respectively.and (b) vapor (e ,pressuref) are as in(hPa). (a,b ),(c respectively,) and (d) are butas in for (a) NUCAPS and (b) but (JPSS1 for FNL, and SNPP), respectively. respectively. (e) and (f) are as in (a) and (b), respectively, but for NUCAPS (JPSS1 and SNPP), respectively. For vapor pressure, as shown in Figure 15, positive mean differences are produced when comparedFigure to both 14 shows ERA5 andthe mean FNL, differences with somewhat and STDs larger within magnitudes ±45° latitudes mainly infor different the lowerdatasets, latitudes which for the are former vertically (Figure averaged 15a). between For FNL, 200 the and deviations 1000 hPa. areNegative reduced temperature below 0.2differences hPa in the tropical are present region over (Figure most 15 ofc) the compared plotted toregions, those ofwith ERA5 weaker with 0.3magnitudes hPa over (Figure 15a),different which regions is also indicated for different by their datasets zonal that average. are not The revealed water pressure from the differences latitudinal distribu- for NUCAPStions show in Fig signiﬁcantlyure 13. Larger larger deviations positive values are found with anear minimum the tropical of −0.5 region hPa, mainly where contains near the equatormore active (Figure convection 15e) in opposition in the intertrop to thoseical of both convergence global datasets. zone (ITCZ) In terms (Fig ofure STD, 14a,c). Dif- ERA5 alsofering gives largerfrom deviationsthe comparison than FNL, with but ERA5 mainly and over FNL, the the southern temperature hemisphere; differences both for NU- are much smallerCAPS are than relatively that for smaller NUCAPS near (Figure the equator 15f). More but consistencysomewhat larger with bothoutside global (Figure 13e). analyses tendThe todeviations suggest that have some larger biases variations may be associatedalong the latitude with NUCAPS for NUCAPS retrievals,, with and a dramatic FS7 retrievalschange may to provide a maximum a reference of −0.05 to K the near bias the correction equator and for NUCAPS.a minimum In of this −0.2 study, K in some re- only overallgions salient near differences ±45°. The apparent in the comparisons deviation with with colder three temperature datasets are focusedfor FNL andis contributed without furtherby larger detailing magnitudes irregular extended variations along with the latitude equatorial or longitude. regions, mainly from Africa to the Western Pacific. For both ERA5 and FNL, the STDs of their temperature differences are below 0.2 K in the tropical region (Figure 14b,d), significantly smaller than those for NU- CAPS, which is also evident in their zonal average and meridional average. The temperature differences over the tropical region are generally larger over the land than over the ocean for the three datasets. There are particularly large deviations in STD over Tibet for

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Remote Sens. 2021, 13, 717 NUCAPS. All three datasets exhibit smaller STDs in lower latitudes, with no common17 in of- 20 phase signal in longitudes.

(a) (b)

(e) (f)

Figure 14. The mean differences between FS7 and different datasets including (a) ERA5, (c) FNL, and (e) NUCAPS in Figure 14. The mean differences between FS7 and different datasets including (a) ERA5, (c) FNL, and (e) NUCAPS in temperature (K). (b,d,f) are as in (a,c,e), respectively, but for STD. Both mean differences and STDs are averaged in the temperature (K). (b), (d), and (f) are as in (a), (c), and (e), respectively, but for STD. Both mean differences and STDs are Remotesurfaceaveraged Sens. 2021 to 200 ,in 13 the hPa., x FOR surface Curves PEER to REVIEW to200 the hPa. right Curves and bottomto the right of each and bottom panel show of each the panel zonal show average the zonal and meridionalaverage and average meridional of18 the of 20

analysis,average respectively. of the analysis, respectively.

For vapor pressure, as shown in Figure 15, positive mean differences are produced when compared to both ERA5 and FNL, with somewhat larger magnitudes mainly in the lower latitudes for the former (Figure 15a). For FNL, the deviations are reduced below 0.2 hPa in the tropical region (Figure 15c) compared to those of ERA5 with 0.3 hPa (Figure 15a), which is also indicated by their zonal average. The water pressure differences for NUCAPS show significantly larger positive values with a minimum of −0.5 hPa, mainly near the equator (Figure 15e) in opposition to those of both global datasets. In terms of STD,(a )ERA5 also gives larger deviations than FNL, (butb) mainly over the southern hemisphere; both are much smaller than that for NUCAPS (Figure 15f). More consistency with both global analyses tend to suggest that some biases may be associated with NUCAPS retrievals, and FS7 retrievals may provide a reference to the bias correction for NUCAPS. In this study, only overall salient differences in the comparisons with three datasets are focused and without further detailing irregular variations with latitude or longitude.

(e) (f)

Figure 15. The same as that in Figure 14 but for vapor pressure (hPa), which is averaged from the surface to 500 hPa. Figure 15. The same as that in Figure 14 but for vapor pressure (hPa), which is averaged from the surface to 500 hPa.

4. Conclusions FS7 project is the follow-on mission of FS3, consisting of six satellites in the low inclination of 24° that were launched in June 2019. The RO observation data of FS7 was officially released at the end of 2019. With the ability to receive signals from both GPS and GLONASS, and the high SNR, FS7 can provide more RO data amounts (up to 5000 per day) and further penetrative depth into the lower troposphere than FS3. The analysis results show that the rate of penetration below 1 km is about 40% for FS3 but can reach 80% for FS7. Two chartered missions providing airborne and ship soundings from dropsondes and radiosondes are used for verification on FS7 RO data retrievals. The comparisons with chartered sounding observations over two oceanic regions east and southwest of Taiwan present a good consistency between FS7 and soundings from dropsondes and radiosondes when data pairs are collocated with each other. The mean differences and STDs for FS7 RO data are in agreement with the 6-month (Oct. 2019–Mar. 2020) statistics, which were verified against operational radiosondes. These comparisons support the small deviations in FS7 temperature in the troposphere, generally less than 0.5 °C. Negative biases in vapor pressure are present in the lower troposphere with a typical magnitude near 2 hPa and may double near the surface for some comparisons. The negative biases at the lower troposphere may be associated with RO refractivity. In this study, we also compared the RO data retrievals of FS7 with multiple datasets, including the RO products from the EUMETSAT Metop-A/B/C and Korean KOMPSAT-5, global analyses from ERA5 and NCEP FNL, and satellite retrievals from NUCAPS. With a spatiotemporal window of ±3 h and ±100 km, the comparisons with multiple datasets against FS7 within ±45° latitude in the selected period of six months exhibit consistent patterns in the troposphere with negative differences in temperature and positive differences on vapor pressure except for the NUCAPS data. Generally, the deviations and STDs of temperature for FS7 are less than 0.5 °C and 1.0 °C, respectively. For vapor pressure,

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4. Conclusions FS7 project is the follow-on mission of FS3, consisting of six satellites in the low inclination of 24◦ that were launched in June 2019. The RO observation data of FS7 was officially released at the end of 2019. With the ability to receive signals from both GPS and GLONASS, and the high SNR, FS7 can provide more RO data amounts (up to 5000 per day) and further penetrative depth into the lower troposphere than FS3. The analysis results show that the rate of penetration below 1 km is about 40% for FS3 but can reach 80% for FS7. Two chartered missions providing airborne and ship soundings from dropsondes and radiosondes are used for veriﬁcation on FS7 RO data retrievals. The comparisons with chartered sounding observations over two oceanic regions east and southwest of Taiwan present a good consistency between FS7 and soundings from dropsondes and radiosondes when data pairs are collocated with each other. The mean differences and STDs for FS7 RO data are in agreement with the 6-month (Oct. 2019–Mar. 2020) statistics, which were veriﬁed against operational radiosondes. These comparisons support the small deviations in FS7 temperature in the troposphere, generally less than 0.5 ◦C. Negative biases in vapor pressure are present in the lower troposphere with a typical magnitude near 2 hPa and may double near the surface for some comparisons. The negative biases at the lower troposphere may be associated with RO refractivity. In this study, we also compared the RO data retrievals of FS7 with multiple datasets, including the RO products from the EUMETSAT Metop-A/B/C and Korean KOMPSAT- 5, global analyses from ERA5 and NCEP FNL, and satellite retrievals from NUCAPS. With a spatiotemporal window of ±3 h and ±100 km, the comparisons with multiple datasets against FS7 within ±45◦ latitude in the selected period of six months exhibit consistent patterns in the troposphere with negative differences in temperature and positive differences on vapor pressure except for the NUCAPS data. Generally, the deviations and STDs of temperature for FS7 are less than 0.5 ◦C and 1.0 ◦C, respectively. For vapor pressure, they are generally within ±2 hPa. Comparisons with NUCAPS retrievals show mean differences opposite to those from both global analyses and other sounding data. From the latitudinal distributions of mean difference and STD, the ITCZ is evident from the comparisons, especially for NUCAPS, which shows a larger deviation in moisture when compared to FS7/C2 RO data. In this study, the comparisons completely rely on the RO wet products at TACC, which are retrieved by the 1DVAR algorithm. It is interesting to identify whether the moisture at lower levels may be better retrieved with data assimilation of the RO bending angle in the future. On the other hand, the comparisons from the statistics and two chartered observational missions highlighted the potential usefulness of the RO-retrieved moisture from FS7 for analysis of the marine boundary layer. Future study might consider the impacts due to the clouds (e.g., [29,30]) so that the uncertainties could be further consolidated for additional factors.

Author Contributions: Conceptualization, S.-Y.C., C.-Y.L. and C.-Y.H. (Ching-Yuang Huang); Data curation, P.-H.L., J.-P.C. and C.-Y.H. (Cheng-Yung Huang); Formal analysis, S.-Y.C., C.-Y.L. and C.-Y.H. (Ching-Yuang Huang); Project administration, S.-Y.C., C.-Y.L. C.-Y.H. (Ching-Yuang Huang) and C.-Y.H. (Cheng-Yung Huang); Software, C.-Y.L., S.-C.H. and H.-W.L.; Validation, C.-Y.L, S.-Y.C., S.-C.H. and H.-W.L.; Writing—original draft, S.-Y.C.; Writing—review & editing, S.-Y.C., C.-Y.L. and C.-Y.H. (Ching-Yuang Huang). All authors have read and agreed to the published version of the manuscript. Funding: This research was funded jointly by the Ministry of Science and Technology (MOST) (grant No. MOST 109-2111-M-008-026-) and National Space Organization (NSPO) (grant No. NSPO-S- 109138) in Taiwan. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Remote Sens. 2021, 13, 717 19 of 20

Data Availability Statement: Publicly available datasets were analyzed in this study. The reposito- ries of the datasets have been indicated in the text. Acknowledgments: The authors would like to thank Shu-Hui Ho, Jyun-Ying Huang, and Ching- Chieh Lin at Taiwan Analysis Center for COSMIC (TACC) for providing FS7/C2 RO data, and Kang-Ning Huang at Central Weather Bureau (CWB) in Taiwan for providing dropsonde data. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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