Evaluation of FY-4A Temperature Profile Products and Application to Winter Precipitation Type Diagnosis in Southern China - MDPI
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remote sensing
Article
Evaluation of FY-4A Temperature Profile Products and Application
to Winter Precipitation Type Diagnosis in Southern China
Yang Gao, Dongyan Mao *, Xin Wang and Danyu Qin
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, Innovation Center for
FengYun Meteorological Satellite (FYSIC), National Satellite Meteorological Center, China Meteorological
Administration, Beijing 100081, China; ygao@cma.gov.cn (Y.G.); xinwang@cma.gov.cn (X.W.);
qindy@cma.gov.cn (D.Q.)
* Correspondence: maody@cma.gov.cn
Abstract: FY-4A GIIRS temperature profile products have provided unprecedented information for
studying the atmospheric characteristics of thermal structures since 2020. The main objective of this
paper is to evaluate GIIRS temperature profile products by using radiosonde observations and then
apply them to the diagnosis of winter precipitation types in southern China. GIIRS temperature
profile products for four types (clear sky perfect quality, cloudy sky perfect quality, cloudy sky
good quality and cloudy sky bad quality) show different performances. Relatively, the cloud can
affect the quality and quantity of GIIRS products. At different pressure levels, the perfect flagged
data under the clear or cloudy sky show the best agreement with radiosonde observations, yielding
the highest Pearson correlation coefficient and lowest mean bias as well as root mean square error.
The good flagged data have a slight deviation from the perfect data. The impact on the quantity
of the GIIRS temperature data is greater than that on the quality with an increase in cloud top
height. A case investigation was carried out to analyze the performance of GIIRS temperature profiles
for the diagnosis of precipitation types in a winter storm of 2022. The GIIRS temperature profiles
represent the reasonable atmospheric thermal structures in the rain and snow in Hubei and Hunan
Citation: Gao, Y.; Mao, D.; Wang, X.; provinces. The GIIRS temperature below 700 hPa is an important indictor to precipitation type
Qin, D. Evaluation of FY-4A diagnosis. Furthermore, two critical thresholds for GIIRS temperatures, which are below −2 ◦ C
Temperature Profile Products and at 850 hPa and below 0 ◦ C at 925 hPa, respectively, are proposed for the occurrence of snowfall in
Application to Winter Precipitation this winter storm. In addition, the distribution of GIIRS temperature at different pressure levels is
Type Diagnosis in Southern China. consistent with radiosonde observations in a freezing rain event in Guiyang, all of which show the
Remote Sens. 2022, 14, 2363. https://
warm rain mechanism by combining the cloud top information.
doi.org/10.3390/rs14102363
Academic Editor: Stefania Bonafoni Keywords: GIIRS temperature profile; winter precipitation types; evaluation; application
Received: 8 April 2022
Accepted: 10 May 2022
Published: 13 May 2022
1. Introduction
Publisher’s Note: MDPI stays neutral
The discrimination of winter precipitation types has always been a significant chal-
with regard to jurisdictional claims in
lenge for weather forecasters. Common types of precipitation in winter, such as rain, snow,
published maps and institutional affil-
freezing rain, and sleet, can have very different impacts on human activities. For instance,
iations.
snowfall has a greater impact than rainfall. The time of the rain and snow phase trans-
formation, as well as its geographical location, directly affect the amount of snow, which
is related to road safety. Compared to rain and snow, freezing rain is a severe weather
Copyright: © 2022 by the authors.
phenomenon that can strain the transport infrastructures and energy networks. Therefore,
Licensee MDPI, Basel, Switzerland. the accurate diagnosis of winter precipitation types can provide important information
This article is an open access article to meteorological services and decision makers. The diagnostic method is an important
distributed under the terms and way to determine precipitation types, which derive statistical relationships between some
conditions of the Creative Commons threshold indictors and precipitation types. Some studies showed that related atmospheric
Attribution (CC BY) license (https:// parameters can be used to determine precipitation types, including surface temperature
creativecommons.org/licenses/by/ and pressure [1,2], vertical temperature profile [3–6], relative humidity [7–9], wet-bulb
4.0/). temperature [10–12]. In recent years, many researchers have also proposed discrimination
Remote Sens. 2022, 14, 2363. https://doi.org/10.3390/rs14102363 https://www.mdpi.com/journal/remotesensing
Remote Sens. 2022, 14, 2363 2 of 15
thresholds and diagnostic methods to determine winter precipitation types in southern
China. Xu et al., 2006 [13] indicated that a temperature below 925 hPa is the key to the
phase of precipitation in southern China. Qi and Zhang 2012 [14] showed that a set of dis-
crimination criteria is proposed according to a temperature below 700 hPa and geopotential
thickness for different precipitation types. Ding et al., 2014 [15] pointed out that there are
significant differences in the temperature thresholds of the rain and snow phase transition
at different altitudes. Our previous diagnostic studies indicated that the temperature at
700 hPa is greater than 4 ◦ C, while being less than 0 ◦ C between 1000 and 850 hPa, which
is conductive to the occurrence of freezing rain [16,17]. These studies all point out that
the atmospheric vertical temperature is one of the critical indicators used to determine
precipitation types.
Radiosonde observations can provide atmospheric temperature data at different pres-
sure layers. However, the distribution of radiosonde stations is sparse. Comparatively,
satellites have the characteristics of high temporal and spatial resolution, which can ef-
fectively make up for the lack of radiostonde observations. The three-axis stabilized
FY-4A represents a new generation of Chinese geostationary meteorological satellite and
was launched on 11 December 2016. It carries the Geostationary Interferometric Infrared
Sounder (GIIRS) which is the first high-spectral resolution advanced IR sounder on board
a geostationary weather satellite [18]. Thus, the FY-4A GIIRS is expected to provide un-
precedented measurements for studying the atmospheric characteristics of dynamic and
thermodynamic structures. Recently, several studies significant improvement on spec-
trum calibration of GIIRS were proposed and operationally implemented since November
2019 [19], and retrieval methods of temperature profiles and humidity profiles have been
carried out and verified [20–25]. A few studies were also performed that used GIIRS data
for weather event applications. Ma et al., 2021 [26] found that four-dimensional wind fields
can be derived from FY4A GIIRS data, and that they can provide dynamic information
during Typhoon Maria. In addition, some results demonstrated that the track and coastal
precipitation forecasts for typhoons from assimilating GIIRS data on numerical models are
better [27–29]. Huang et al. [30,31] found that, relative to observation, the detection and
performance of GIIRS temperature products vary with the intensities of typhoon. Maier
and Knuteson 2022 [32] pointed out that GIIRS profile products are able to capture the
rapid transition from a stable to an unstable atmosphere in a case study.
However, due to its short time in operation, the attempts to apply GIIRS profile prod-
ucts to weather events are relatively limited. To our knowledge, research on the application
of GIIRS temperature profiles to winter precipitation type diagnosis in southern China
has not been carried out. High temporal and spatial resolution temperature profiles can
not only capture changes in the thermodynamic structure of atmosphere, but also provide
unprecedented information for the diagnosing and monitoring of winter precipitation types
in southern China. Therefore, the first objective of this study is to evaluate the suitability
and limitation of GIIRS temperature profile product in southern China. The second objec-
tive is to illustrate the availability of GIIRS temperature profiles in winter precipitation
type diagnosis. It is the first attempt to use temperature profiles from the FY-4A GIIRS to
diagnose and monitor winter precipitation types in southern China, which can provide
new support for weather forecasters.
This paper is arranged as follows. In Section 2, brief descriptions of FY-4A GIIRS tem-
perature profile data and the methodology used are presented. In Section 3, the evaluation
results and their application to winter precipitation types are discussed, while in Section 4,
the conclusions and discussion are presented.
2. Data and Methodology
An important instrument on board the FY-4A for atmospheric temperature measure-
ments is the GIIRS. The GIIRS temperature profile products have 2-h temporal resolution
at 101 pressure levels from 1100 hPa to 0.005 hPa, with a horizontal resolution of 16 km.
In this paper, we used FY-4A GIIRS level 2 temperature profile products during the winter
Remote Sens. 2022, 14, 2363 3 of 15
(December–February) of 2021–2022. The level 2 GIIRS temperature products are processed
by the retrieval algorithm based on the level 1 data, which are the calibrated and navigation
data from GIIRS onboard the FY-4A. The GIIRS temperature products consist of latitude,
longitude, pressure level, temperature profile, quality flags and cloud mask. The quality
flags represent the retrieval accuracy of temperature data, which are classified into four
categories: perfect (QFlag = 0), good (QFlag = 1), bad (QFlag = 2) and very bad or do not
use (QFlag = 3 and QFlag = 4). When the QFlag is equal to 4, it indicates that the level 1
data from GIIRS is invalid. Each vertical point of every profile is marked by a quality flag.
In addition, the cloud masks are classified into clear sky and cloudy sky.
The radiosonde observations were collected from 18 stations in southern China, which
are provided by the National Meteorological Information Center, China Meteorological
Administration. The temporal resolution is twice a day (00:00 UTC and 12:00 UTC), and the
temperature profiles are at 10 levels from 925 hPa to 100 hPa. The study in this paper
evaluates the GIIRS data above 925 hPa, because the altitude of the radiosonde stations in
southwestern China is higher due to the local topography.
The hourly surface observations of precipitation types and 2 m temperature were
obtained from 583 ground-based stations in southern China, which are also provided by
the National Meteorological Information Center, China Meteorological Administration.
Figure 1 shows the spatial distribution of radiosonde stations and ground-based
stations. Taking into account the resolution of GIIRS product, we set the pairing criterion
that GIIRS grid matches with a radiosonde station or ground-based station at better than
0.16◦ in both latitude and longitude. On the other hand, if a radiosonde station or ground-
based station matches with more than one GIIRS grid, the nearest profile data are selected.
Figure 1. The distribution of radiosonde stations (top panel, red dots) and ground-based stations
(bottom panel, blue dots) in southern China.
Remote Sens. 2022, 14, 2363 4 of 15
The cloud top information used in this study comes from the Advanced Geosyn-
chronous Radiation Imager (AGRI), which is another important instrument on board the
FY-4A. The horizontal resolution of cloud top product is 4 km, and the temporal interval is
5 min.
In order to compare GIIRS products and sounding observations, three widely used
statistical metrics were applied to evaluate the GIIRS temperature profile, the Pearson corre-
lation coefficient (CC), root mean square error (RMSE) and mean bias (MB). The equations
of the indicators are as follows:
n
∑ Si − S Ri − R
CC = s i=1 s
n 2 n 2
∑ Si − S ∑ Ri − R
i=1 i=1
v
u n
u ∑ (Si − Ri )2
u
t i=1
RMSE =
n
n
∑ (Si − Ri )
i=1
MB =
n
where n is the number of matched samples, Si is the FY-4A GIIRS temperature profile, Ri
represents radiosonde temperature profile, and S and R are their mean values, respectively.
3. Results
3.1. Evaluation of GIIRS Temperature Profiles under Clear and Cloudy Sky
In order to demonstrate whether the atmospheric temperature profile product re-
trieved by GIIRS can be applied to diagnose and monitor winter precipitation types in
southern China, detailed evaluation is the basis of their application.
As mentioned above, based on the cloud mask results of GIIRS temperature profile
product, all of the matched samples are divided into “clear sky” and “cloudy sky” to
evaluate the product with different quality flags. Figure 2 shows the frequency distribution
of the matched samples. The occurrence frequency of missing data is 1% under clear sky,
while it is almost 22.5% under the cloudy sky. The frequencies of perfect flagged data under
clear sky and cloudy sky are 95.7% and 22.6%, respectively. Meanwhile, the temperature
data has 16.8% of good samples and 14.3% of bad samples under the cloudy sky. Thus,
the amount and quality of GIIRS temperature data will be affected by the cloud. Under
the clear sky, the data only have two kinds of quality flags, which represent perfect data
(QFlag = 0) and unavailable data (QFlag = 4). Therefore, this study focuses on four types of
data, such as the perfect data (QFlag = 0) under clear sky, the data under cloudy sky whose
qualities are perfect (QFlag = 0), good (QFlag = 1) and bad (QFlag = 2).
Figure 3 illustrates the density scatterplots for the GIIRS temperature products and
radiosonde observations. The perfect flagged samples perform better than good and bad
samples, with perfect data having a higher percentage of points near the 1:1 line than
others. The distribution of perfect data under cloudy sky is similar to that of perfect data
under the clear sky, and the RMSE is 1.81 ◦ C and 1.82 ◦ C, respectively. As shown in
Figure 3, the deviation of GIIRS temperature data from radiosonde observation increases
with the degradation of data quality. The RMSE of good data or bad data is higher, even
if the CC is very close to that of perfect data. A deeper statistical analysis is conducted
with respect to different pressure levels. In Figure 4a–c, all perfect flagged data show
a higher CC and smaller MB as well as RMSE at different pressure levels. The CC of
good data is slightly lower than that of perfect data, while the RMSE is obviously larger.
Comparatively, the RMSE of perfect data is around 2 ◦ C below 500 hPa, while the RMSE
of good data is above 3 ◦ C. The MB of good and bad data has a noticeable change from
Remote Sens. 2022, 14, 2363 5 of 15
positive to negative between 100–250 hPa. This phenomenon is mainly caused by the fact
that the number of outliers and uncertainty of the temperature are greater in the tropopause
layer (100–300 hPa) than the troposphere layer due to the instrument sensitivity [18,25].
With decreasing pressure level, the MB of good and bad data changes from negative to
positive between 500 and 700 hPa. This fact is related to the cloud top height in winter,
which can reach to 4–6 km in southern China. The retrieval of vertical profile is affected
by cloud height, resulting in the variation of MB above and below cloud. Comparatively,
the MB of perfect flagged data under cloudy sky also has a similar positive and negative
change. However, the smaller variation in perfect flagged data is attributed to the higher
accuracy. Meanwhile, the MB of good data at 850 hPa and 925 hPa is below 1.5 ◦ C, which is
very close to that of perfect data. The bad flagged data of different levels obtain a larger
deviation not only in RMSE but also in MB.
Figure 2. The occurrence frequency (%) with different quality flags (QFlag) from FY-4A GIIRS
temperature profile product. The frequency is calculated under the cloudy sky (red bar) and clear sky
(blue bar) separately.
Figure 5 shows the occurrence frequency of bias for GIIRS temperature products
in comparison with radiosonde observations. The perfect flagged data have the smallest
systematic deviation under the clear and cloudy sky, the percentage frequency of occurrence
peaked around a bias of −1 ◦ C, and the occurrence frequency is above 20%. The peak
frequencies of good flagged data occur at biases around 2 ◦ C and −3 ◦ C, whereas the peak
frequencies for bad flagged data occur at biases around 4 ◦ C and −5 ◦ C. Therefore, they all
indicate the characteristics of double peaks.
3.2. Evaluation of GIIRS Temperature Profiles at Different Cloud Top Height
As mentioned above, cloudy sky, when compared against clear sky, not only had an
impact on the amount of GIIRS temperature profile but also exhibited more bad flagged
data. Considering the spectral characteristics of GIIRS instrument, we continue to study
the relationship between GIIRS temperature profiles and cloud top height. In Figure 6,
four types of data illustrate a similar distribution that the frequencies of occurrence peaked
around a cloud top height of 4 km, and the occurrence frequency is above 8%. This
phenomenon is mainly due to the climatic characteristics of winter in southern China,
which is dominated by middle and low level clouds. However, the occurrence frequency of
missing data reaches 11% when the cloud top height is 6 km. With increasing cloud top
height, the trend of missing data lags behind the other four types of data, which shows the
Remote Sens. 2022, 14, 2363 6 of 15
cloud top height having a more significant impact on the data quantity than quality. In other
words, it will directly lead to the failure of retrieval with an increase in the cloud top height,
showing more missing values under the cloud. These missing values are due to the fact
that high thick clouds can block the infrared sounder from observing the atmosphere under
the cloud.
Figure 3. Density scatterplots for GIIRS atmospheric temperature profile products vs. the radiosonde
observations. (a) the perfect flagged samples under the clear sky; (b) the perfect flagged samples
under the cloudy sky; (c) the good flagged samples under the cloudy sky; (d) the bad flagged samples
under the cloudy sky. The occurrence frequency represents the percentage with respect to the total
samples of the number (perfect flagged samples under the clear sky, good flagged samples and bad
flagged samples under the cloudy sky) lying in each grid area with an interval of 0.5 ◦ C. The color
bar indicates the occurrence frequency (%) lying with in each grid area. The black line is the 1:1 line.
Our statistical analysis suggests that the occurrence frequency of perfect flagged
data under the cloud sky is significantly lower than that under the clear sky. However,
the perfect flagged data under the cloudy sky still show the best agreement with radiosonde
observations, and the good flagged data have a slight deviation from the perfect data. On
the whole, the MB of perfect data is between −1 ◦ C and 1 ◦ C, whereas the MB of good data is
between −2 ◦ C and 2 ◦ C. Considering that the cloud top height of winter in southern China
is mainly below 6 km, only perfect and good flagged data below 500 hPa are considered in
the next part of this study. For instance, if a vertical temperature profile does not contain
missing values below 500 hPa, and the quality flags are perfect or good below 500 hPa, this
profile is considered to be reliable. This kind of profile can be used for the diagnosis of rain
and snow events.
Remote Sens. 2022, 14, 2363 7 of 15
Figure 4. Statistical indicators of GIIRS atmospheric temperature profile products with respect to
radiosonde observations in different pressure levels (100 hPa, 150 hPa, 200 hPa, 250 hPa, 300 hPa,
400 hPa, 500 hPa, 700 hPa, 850 hPa and 925 hPa). (a) Pearson correlation coefficient; (b) mean bias;
(c) root mean square error. The four types of GIIRS data are the perfect flagged under the clear sky
(red line), the perfect flagged under the cloudy sky (orange line), the good (purple line) and bad (blue
line) flagged under the cloudy sky.
3.3. Application of Winter Precipitation Type Diagnosis in Southern China
The advantage of GIIRS temperature profile is that it has high temporal and spatial
resolution, which is useful to diagnose the precipitation types in some areas without ra-
diosonde observations. In 2022, a winter storm occurred in southern China, this weather
event persisted from 25 to 29 January. With the cold air moving to the southeast, the trans-
formation of rain and snow occurred in many provinces in southern China. In addition,
some areas also experienced freezing rain in Guizhou province. Figure 7 shows two atmo-
spheric temperature profiles from GIIRS in Yichang in this winter storm. It features that the
temperature throughout the atmospheric column is below freezing at 20:00 on 27 January,
which is propitious for the formation of snow. However, the temperature profile below
700 hPa is greater than 0 ◦ C at 12:00 on 25 January, so the diagnosis of the precipitation
type should be rain. Compared with surface observations, there was agreement between
the GIIRS temperature profiles and precipitation types. Therefore, the transition of rain
and snow events in Yichang was described by a GIIRS temperature below 700 hPa. When
rainfall occurs, a warm layer with the temperature above 0 ◦ C below 700 hPa is important.
Remote Sens. 2022, 14, 2363 8 of 15
Figure 5. The occurrence frequency (%) of bias for GIIRS temperature profile products in comparison
with radiosonde observations. The interval of bias is 1.0°C on the horizontal axis. The four types of
GIIRS data are the perfect flagged under the clear sky (red line), the perfect flagged under the cloudy
sky (orange line), the good (purple line) as well as bad (blue line) flagged under the cloudy sky.
Figure 6. The occurrence frequency of GIIRS temperature profile products with the different cloud
top heights under the cloudy sky. The black line represents the missing data. The different quality
flags are as follows: perfect (orange line), good (purple line), bad (blue line), very bad or do not use
(green line).
Remote Sens. 2022, 14, 2363 9 of 15
Figure 7. GIIRS temperature profiles in Yichang, Hubei province. The purple line was the GIIRS
profile at 12:00 (Beijing time) on 25 January 2022. The black line was the GIIRS profile at 20:00 (Beijing
time) on 27 January. The surface observation of the precipitation type was rain at 12:00 on 25 January,
while it was snow at 20:00 on 27 January. The quality flags of purple profile are 3 (100 hPa), 3 (150 hPa),
0 (200 hPa), 1 (250 hPa), 2 (300 hPa), 2 (400 hPa), 0 (500 hPa), 0 (700 hPa), 1 (850 hPa), 1 (925 hPa),
0 (1000 hPa). The quality flags of black profile are 3 (100 hPa), 3 (150 hPa), 2 (200 hPa), 1 (250 hPa),
3 (300 hPa), 0 (400 hPa), 0 (500 hPa), 1 (700 hPa), 0 (850 hPa), 0 (925 hPa), 0 (1000 hPa). The quality
flags are 0, 1, 2 and 3, which represent perfect data, good data, bad data and very bad data.
Figure 8a illustrates the temperature profile in Xiangyang, Hubei province at 08:00 on
28 January. The atmospheric temperature of the whole layer of Xiangyang is relatively low,
at less than 0 ◦ C, which can lead to the occurrence of snowfall. Meanwhile, the ground-
based observation shows that the precipitation type is snow in Xiangyang. The temperature
profile in Xiangtan is shown in Figure 8b, there is an inversion layer, warm air with the
temperature above 0 ◦ C between 700 hPa and 925 hPa. In addition, a layer colder than
0 ◦ C exists below 925 hPa. However, this vertical thermal structure over Xiangtan will
not lead to freezing rain, because the cold layer in the lower level is relatively shallow
and the 2 m temperature is higher than 0 ◦ C, so the liquid water from the deep warm
layer falls into the shallow cold layer, they cannot be turned into supercooled water and
frozen on the ground. Therefore, although there is an inversion layer over Xiangtan at
08:00 on 28 January, the precipitation type should be rain, which is also consistent with the
ground-based observation.
These snow and rain examples in Hubei and Hunan, GIIRS temperature profiles
represent the reasonable thermal structures. The precipitation type diagnosis by GIIRS
temperature profile is also consistent with the surface observations. In addition, results also
show that the GIIRS temperature below 700 hPa is an important indictor to determine rain
or snow. This indictor is in agreement with the results obtained by others, who diagnose
precipitation types by using radiosonde observations [13,14]. In order to verify which layer
of temperature dominates the rainfall and snowfall, more detailed statistics were performed.
According to the matched principle, we match the GIIRS temperature below 700 hPa with
the surface observation of precipitation types from 25 to 29 January. All precipitation
matched samples from this winter storm are divided into snowfall and rainfall (excluding
freezing rain) samples, the perfect or good flagged temperature at 700 hPa, 850 hPa, 925 hPa
and 1000 hPa are selected to make statistics. Figure 9 shows the perfect or good flagged
Remote Sens. 2022, 14, 2363 10 of 15
GIIRS temperature for different precipitation types. The medians at 700 hPa are 0 ◦ C for
rain and −6 ◦ C for snow, respectively, while there is considerable overlap in temperature
for snow and rain events. Comparatively, there are fewer overlaps in temperature for snow
and rain events at 850 hPa and 925 hPa. When the temperature at 850 hPa is below −2 ◦ C,
the probability of snowfall events is greater. In addition, the probability of snowfall events
is greater when the temperature is below 0 ◦ C at 925 hPa. Therefore, T850hPa < −2 ◦ C and
T925hPa < 0 ◦ C are two critical thresholds for the occurrence of snowfall in this winter storm.
Figure 8. GIIRS temperature profiles at 08:00 (Beijing time) on 28 January 2022, in (a) Xiangyang,
Hubei province; (b) Xiangtan, Hunan province. The surface observation of precipitation type was
snow in Xiangyang, and the precipitation type was rain in Xiangtan at 08:00 on 28 January 2022.
The quality flags of profile in Figure 8a are 3 (100 hPa), 3 (150 hPa), 0 (200 hPa), 3 (250 hPa), 3 (300 hPa),
2 (400 hPa), 1 (500 hPa), 0 (700 hPa), 1 (850 hPa), 1 (925 hPa), 0 (1000 hPa). The quality flags in Figure 8b
are 0 (100 hPa), 0 (150 hPa), 0 (200 hPa), 0 (250 hPa), 0 (300 hPa), 0 (400 hPa), 0 (500 hPa), 0 (700 hPa),
0 (850 hPa), 0 (925 hPa), 1 (1000 hPa). The quality flags are 0, 1, 2 and 3, which represent perfect data,
good data, bad data and very bad data.
Freezing rain is a more complex weather event, which often occurs in southern China.
Freezing rain can be caused by an ice-phase mechanism and warm rain mechanism [33,34].
The ice-phase mechanism is the typical thermal structure: the ice particles fall into a warm
layer, and then they can be melted to liquid water, then supercool and freeze up in the
lower cold layer where the temperature is below 0 ◦ C. Some studies have found that
the warm rain mechanism can also have a warm layer, but the precipitation particles
have always existed in form of liquid [35–37]. Therefore, the difference between the ice-
phase mechanism and the warm rain mechanism is the process of melting ice particles.
Although the vertical temperature profile is most critical indictor, freezing rain diagnosis
must take account of related atmospheric parameters, including 2 m temperature and cloud
particle information. Therefore, a single threshold of vertical temperature is not suitable
to determine the occurrence of freezing rain. On the other hand, freezing rain event only
occurs in Guizhou province from 25 to 29 January, so we did not make statistics to explore
the GIIRS temperature threshold for freezing rain.
In this winter storm event, freezing rain occurred in central Guizhou at 08:00 on
28 January. Guizhou is a region with a high altitude and complex terrain, the distribution of
atmospheric temperature data is above 850 hPa. Therefore, the diagnosis of freezing rain is
a significant challenge for weather forecasters. Figure 10 shows the cloud top information
and temperature profiles in Guiyang when the precipitation type is freezing rain.Remote Sens. 2022, 14, 2363 11 of 15
Figure 9. Box-whisker plot of the perfect and good flagged GIIRS temperature for different precipita-
tion types. The upper and lower boundaries of the box indicate the 90 and 10 percentiles. The median
is represented by the horizontal line inside the box. The maximum and minimum temperature values
are indicated by the top and bottom ends of the whiskers, respectively. (a) 700 hPa; (b) 850 hPa;
(c) 925 hPa; (d) 1000 hPa.
According to the matched principle in Section 2, a GIIRS temperature profile closest to
a radiosonde station in Guiyang is selected. The quality flag is perfect at every pressure
level on this profile. In Figure 10d, the distribution of the GIIRS temperature profile is very
similar to that of radiosonde observation, all showing cold layers with the temperature less
than 0 ◦ C above 700 hPa. However, it can be found that there is a shallow warm layer below
700 hPa. The temperature from GIIRS and observation at 700 hPa is 1.45 ◦ C and 1.4 ◦ C,
respectively. In Figure 10a–c, the cloud top height in central Guizhou is relatively low,
about 3–4 km, the cloud top temperature is around 0 ◦ C and the cloud top phase is mainly
supercooled cloud and warm water cloud. In addition, the 2 m temperature is −1.2 ◦ C. This
shows that liquid particles fall into a shallow warm layer, then supercooling in the lower
cold layer, and eventually freezing up on surface where the 2 m temperature is below 0 ◦ C.
By combining the cloud top information, we can see that the phase of precipitation particles
did not change. Therefore, in this winter storm, freezing rain in Guiyang on 28 January is
caused by warm-rain mechanism.Remote Sens. 2022, 14, 2363 12 of 15
Figure 10. FY-4A AGRI cloud top information (a) cloud top height (km), (b) cloud top temperature
(◦ C), (c) cloud phase, (d) GIIRS temperature profile vs. radiosonde observation at 08:00 on 28 January
2022 (black dot: the location of Guiyang). The quality flags of GIIRS profile in Figure 10d are
0 (100 hPa), 0 (150 hPa), 0 (200 hPa), 0 (250 hPa), 0 (300 hPa), 0 (400 hPa), 0 (500 hPa), 0 (700 hPa),
0 (850 hPa). The quality flags are 0, 1, 2 and 3, which represent perfect data, good data, bad data and
very bad data.
4. Discussion and Conclusions
The discrimination of winter precipitation types in southern China is difficult for
weather forecasters, and skills need to be improved. In this study, we first evaluated the FY-
4A GIIRS temperature profiles at different pressure levels, using radiosonde observations
in southern China. In addition, we also illustrated the availability of GIIRS temperature
profiles in winter precipitation type diagnosis for the first time in a winter storm event
of 2022.
In general, the GIIRS temperature products for four types (clear sky perfect quality,
cloudy sky perfect quality, cloud sky good quality, and cloudy sky bad quality) show the
different performances compared to radiosonde observations. Relatively, the cloud can
affect the quality and quantity of GIIRS temperature profile products. Under clear and
cloudy skies, the frequencies of missing data are 1% and 22.5%. Meanwhile, the frequencies
of perfect flagged data are 95.7% and 22.6%, respectively. The perfect flagged data show
the best agreement with radiosonde observations, the RMSE is 1.81 ◦ C and 1.82 ◦ C under
clear and cloudy sky, respectively. The RMSE of good data and bad data is 3.13 ◦ C and
4.57 ◦ C under the cloudy sky, respectively. At different pressure levels, statistical indicatorsRemote Sens. 2022, 14, 2363 13 of 15
always show that the perfect data has the highest CC, lower MB and RMSE. The good data
has a slightly lower CC, higher MB and RMSE than perfect data. The MB of good data
below 700 hPa is very close to perfect data. On the whole, the perfect flagged data have the
smallest systematic deviation under clear or cloudy sky when compare with the other flags
of data. We also found that the cloud top height having a more significant impact on the
amount of data.
A case study was carried out to analyze FY-4A GIIRS temperature profile’s ability to
diagnose precipitation types in a winter storm in southern China. The distribution of GIIRS
temperature below 700 hPa can represent the transformation process of rain and snow in
Yichang. Furthermore, in the snowfall and rainfall events in Xiangyang and Xiangtan, GIIRS
products also show reasonable atmospheric thermal structures. The GIIRS temperature
below 700 hPa is an important indicator to precipitation type diagnosis. According to
the statistical results of all rain and snow events from 25 to 29 January in southern China,
T850hPa < −2 ◦ C and T925hPa < 0 ◦ C are two critical thresholds for the occurrence of snowfall.
In the freezing rain event in Guiyang, GIIRS temperature profile performed very
well, the temperature distribution at different pressure levels is consistent with radiosonde
observation. This shows that freezing rain is a warm rain mechanism by combining the
cloud top information of FY-4A AGRI. Therefore, Guizhou is a region with a high altitude in
southern China, determined by not only using FY-4A GIIRS temperature profile to diagnose
precipitation types but also combined with the cloud top information of FY-4A AGRI. In the
future, more freezing rain events are necessary in order to obtain the diagnosis method of
freezing rain.
FY-4A GIIRS temperature profiles provide unprecedented measurements for studying
the atmospheric characteristics of thermal structure. This study assessed FY-4A GIIRS
temperature profile products in southern China. In addition, it is the first attempt to apply
GIIRS profiles to the diagnosis of winter precipitation types, and the results can provide
a new perspective for weather forecasters. The relative humidity profiles of FY-4A GIIRS
after May 2019 are not available due to some contamination effects on its mid-wave infrared
measurements [19]. Therefore, we did not analyze the effect of relative humidity on those
types in this winter storm. However, FY-4B was launched on 3 June 2021, the GIIRS
temperature and relative humidity profile products will be released in the near future. We
will combine FY-4B temperature with relative humidity profile to explore the dual threshold
methodology of winter precipitation type diagnosis.
Author Contributions: Conceptualization, D.M., D.Q., X.W. and Y.G.; methodology, Y.G.; formal
analysis, Y.G.; investigation, Y.G., D.M., X.W. and D.Q.; writing—original draft preparation, Y.G.;
writing—review and editing, Y.G., D.M., X.W. and D.Q.; All authors have read and agreed to the
published version of the manuscript.
Funding: This research was funded by the Second Tibetan Plateau Scientific Expedition and Research
(STEP) program (grant number 2019QZKK0105).
Data Availability Statement: The FY-4A products were downloaded from the FENGYUN Satellite
Data Center (http://satellite.nsmc.org.cn/, accessed on 7 April 2022).
Acknowledgments: The authors thank the National Meteorological Information Center of China
Meteorological Administration for providing radiosonde and surface observations.
Conflicts of Interest: The authors declare no conflict of interest.
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