Preamble

Astronomical Techniques and Instruments, Vol. 2, November 2025, 388–399 Article Open Access Evaluating the regional consistency of astronomical observing con- ditions across Dome A Kaiwen Zheng 1,2,3 , Kun Ma 1,2,4 , Jiali Chen 1,2,3 , Haosi Song , Tiancong Zhang Shiyi Wang , Han Wang , Peng Jiang , Xiaoyan Li 1 Nanjing Institute of Astronomical Optics Technology Chinese Academy of Sciences Nanjing 210042, China 2 CAS Key Laboratory of Astronomical Optics Technology Nanjing Institute of Astronomical Optics Technology Nanjing 210042, China 6 Polar Research Institute of China Shanghai 200136, China *Correspondences:

INTRODUCTION

The highest point of the Antarctic, Dome A, was explored during the Chinese 21 Antarctic Scientific Expe- dition in 2004–2005. Subsequent studies have shown that Dome A provides ideal conditions for terrestrial optical, infrared, and submillimeter astronomical observations Measurements acquired using the Kunlun Differential Image Motion Monitor (KL-DIMM) at Kunlun Station, in Antarctica, show that a telescope mounted on an 8-meter- high tower has a 31% probability of avoiding near-ground turbulence, with a median seeing of 0.31" . The low tem- peratures (−80°C to −60°C) and dry conditions (water vapor column density < 0.1 mm) at Kunlun Station result in extremely low near-infrared background radiation. We measured the sky background intensity at Kunlun Station, using the Near-Infrared Sky Brightness Monitor (NISBM) instrument, for the J, H, and Ks bands as 600–1 100 µJy arcsec 600 µJy arcsec , and 200–900 µJy arc- , respectively . In addition, owing to the orbits of space debris converging over the South Pole, the fre- quency of space debris that transits over Dome A is signifi- cantly higher than at other mid-latitude sites, providing Dome A with a unique advantage for long-term space debris monitoring. In near-Earth orbit, 82.6% of all space debris passes over Kunlun Station during every orbital revo- lution, while 93.7% of debris passes over Kunlun Station during more than half of its orbital revolutions. As the only ground-based site on Earth capable of providing long-term observation near the South Celestial Pole, Antarc- tica fills the observation gap left by Northern Hemi- sphere sites such as Hawaii and Qinghai, serving as a cru- cial strategic node in the global astronomical observation network. In addition, the prolonged polar night, lasting sev- eral months during the Antarctic winter, allows tele- scopes to conduct uninterrupted observations for up to

3 University of Chinese Academy of Sciences , Beijing 100049, China

4 School of Optoelectronic Engineering Changchun University of Science and Technology Changchun 130022, China

5 Bell Honors School

Nanjing University of Posts and Telecommunications Nanjing 210023, China © 2025 Editorial Office of Astronomical Techniques and Instruments, Yunnan Observatories, Chinese Academy of Sciences. This is an open access article under the CC BY 4.0 license ( Citation: Zheng, K. W., Ma, K., Chen, J. L., et al. 2025. Evaluating the regional consistency of astronomical observing conditions across Dome A.

Astronomical Techniques and Instruments (6): 388−399. ati2025061

Abstract

With excellent seeing conditions, ultra-low infrared background noise, high frequency of space debris transits, and continuous polar night coverage, Dome A in Antarctica has become an ideal platform for ground-based astronomy and space situational monitoring. As a crucial observatory site for international deep space, deep Earth, deep sea, and polar exploration, it is very important to evaluate the suitability of Dome A as an observatory site. However, owing to extreme environmental constraints, the evaluation of site conditions is mainly based on single-point measurements, making it challenging to comprehensively evaluate the effective site range and uniformity. This study integrated satellite remote sensing data to develop a cross-comparison framework for diverse indicators across Dome A, to evaluate its spatial uniformity. We find that the area surrounding the Dome A site, defined within a roughly 1° × 1° latitude and longitude range, possesses excellent astronomical observation conditions.

Keywords

Dome A; Cloud cover; Vertical wind shear; Skin temperature; Net solar radiation; Climate stability

670 h, significantly enhancing the observational effi- ciency of time-domain astronomy (for targets such as super- nova explosions and afterglow tracking of gravitational wave events) Owing to technical constraints imposed by extreme environmental conditions, the current site performance eval- uation for Dome A relies mainly on single-point measure- ments. Several key instruments have been deployed at Kun- lun Station, including the Chinese Small Telescope Array (CSTAR) , Antarctic Schmidt Telescopes (AST3) , and the Antarctic 15 cm NIR Telescope , which have already validated the scientific potential of extreme Antarctic astro- nomical sites. A planned J/H/K-band near-infrared tele- scope, with an aperture of 0.5–1 m, will further enhance this astronomical site as a core station for multiband obser- vations. Building on existing single-point site data from Dome A, a further systematic assessment of the observa- tion conditions in the surrounding areas of the single- point can quantitatively analyze the site capacity at Kun- lun Station. This will provide a scientific basis for resource allocation for future large telescope construction, very long baseline interferometry (VLBI) arrays, and large-scale space debris monitoring networks at Dome A.

We selected a 1° × 1° geographic area in latitude and longitude, defined using the geographic coordinate sys- tem, surrounding Dome A as the research region. By using multi-source remote sensing data, meteorological data, and other resources, we obtained evaluation parame- ters such as cloud cover, total precipitation, total column water vapor, skin temperature, vertical wind shear (VWS), and net surface solar radiation within this defined area around Dome A. Using remote sensing and Geographic Information System (GIS) techniques, we analyzed the spa- tiotemporal distribution characteristics of these parame- ters over long time series and their relationship with opti- cal astronomical observations.

OBSERVATIONS AND DATA Using multi-source remote sensing data and high-preci- sion meteorological data, we conducted an in-depth analy- sis of the suitability of Dome A and its surrounding areas for astronomical observations, in terms of cloud cover, total precipitation, total column water vapor, skin tempera- ture, VWS, and net surface solar radiation. The cloud cover analysis in this study used the Cloud, Albedo and Sur- face Radiation dataset from Advanced Very High Resolu- tion Radiometer (AVHRR) data-second edition (CLARA- A2), which is provided by the Climate Monitoring Satel- lite Application Facility (CM SAF) at the European Organi- zation for the Exploitation of Meteorological Satellites (EUMETSAT). This dataset is based on observations from multiple satellites equipped with AVHRR sensors, which have been uniformly processed to generate long- term climate data products. The cloud cover data pro- vided by CLARA-A2 has a spatial resolution of 0.25° × 0.25° and a temporal resolution of 1 h. It can be used to study the distribution of cloud cover on a large scale, together with its evolutionary trends. Here, we used the total cloud cover variable provided in the dataset, which represents the proportion of the observation area covered by cloud at any given time and location.

The fifth-generation reanalysis dataset from the Euro- pean Centre for Medium Range Weather Forecasts (ECMWF), ERA5, was used in this study. The variables, including net surface solar radiation, skin temperature, total precipitation, total column water vapor, and U-compo- nent and V-component of wind data were obtained from dif- ferent sub-products of ERA5. Among them, the net sur- face solar radiation and total column water vapor data were from ERA5 hourly data on single levels, which con- tain data from 1940 to the present. We selected daily net surface solar radiation data for January, April, July, and October 2017, with a time accuracy of 1 h, a spatial resolu- tion of 0.25° × 0.25°, and a data set unit of W m . We used total column water vapor data for the entire year of 2017, with a temporal resolution of 1 h, a spatial resolu- tion of 0.25° × 0.25°, and a dataset unit of kg m . Skin temperature and total precipitation data were obtained from the ERA5-Land monthly averaged dataset, which con- tains data from 1950 to the present. We selected skin tem- perature and total precipitation data for the whole year of 2017, with a time resolution of 1 h and a spatial resolu- tion of 0.1° × 0.1°. Skin temperature is measured in K, and total precipitation is measured in mm. We derived the U-component and V-component of wind data from the ERA5 hourly data on pressure levels, which contains data from 1940 to the present, and selected the U-component and V-component of wind data at the 350 hPa and 600 hPa pressure levels for January, April, July, and October 2017, with a temporal resolution of 1 h, a spatial resolu- tion of 0.25° × 0.25°, and units of m s CLOUD COVER In this study, we used the CLARA-A2 dataset obtained from the American National Oceanic and Atmo- spheric Administration (NOAA) polar-orbiting satellite AVHRR sensor , selecting cloud cover spatial distribu- tion data for January, April, July, and October of 2017.

The cloud cover data for the target region (76.96°E– 77.96°E, 79.88°S–80.88°S) were processed and analyzed to investigate seasonal variations, spatial distribution cha- racteristics, and assess their suitability for astronomical observations.

In this dataset, cloud cover is defined as the percent- age of the atmospheric column in each grid cell that is cov- ered by clouds, averaged over a time scale (such as monthly averages). In other words, it represents the aver- age cloud cover in the entire atmospheric column from the ground to the top of the troposphere during the observa- tion period, which is a spatiotemporal composite average rather than an instantaneous observation or single-layer mea- surement. As shown in , the mean cloud cover

January, 2017 April, 2017 −80.2 −80.3 Dome A Latitude/(°S) −80.4 −80.5 −80.6 −80.7 −80.8 Longitude/(°E) −80.2 −80.3 Dome A Latitude/(°S) −80.4 −80.5 −80.6 −80.7 −80.8 Longitude/(°E) range for Dome A and its surrounding areas in January, April, July, and October 2017 was 25%–35%, 5%–9%, 17%–20%, and 12%–60%, respectively. The monthly mean cloud cover for January, April, July, and October 2017 was 30.5%, 7.38%, 19%, and 41.31%. The cloud cover in this region is generally low, with distinct sea- sonal variation. The spatial resolution of the cloud cover dataset here is 0.25°. We performed precise data crop- ping for the target region, but the image was plotted based on grid points, rather than being reconstructed cen- tered on Dome A, meaning that Dome A is not exactly at the center of the target area. In the visualization stage, we applied a two-dimensional interpolation smoothing pro- cess to the images generated from the raw data, to improve the spatial smoothness of the images without affecting the statistical analysis results of the original data.

Regarding spatial distribution, except for October, there is no significant monthly gradient variation in cloud cover within the 1° × 1° area surrounding Dome A. In October, the cloud cover exhibits a pattern with more in the south and less in the north. On the basis of the cloud cover variation trends in for Dome A and its sur- rounding areas, the mean cloud cover from March to June is relatively low, indicating that this period may provide more favorable observation conditions, especially for obser- vations requiring high atmospheric transparency (e.g., infrared telescopes). By contrast, the high variability in cloud cover during October should be considered when −80.2 −80.3 Dome A Latitude/(°S) −80.4 −80.5 −80.6 −80.7 −80.8 Longitude/(°E) −80.2 −80.3 Dome A Latitude/(°S) −80.4 −80.5 −80.6 −80.7 −80.8 Longitude/(°E) designing scientific equipment for interference resistance.

This seasonal variation trend is strongly consistent with the findings of Saunders et al. , who reported that win- ter cloud cover at multiple Antarctic observation stations is generally low (approximately 20%), while summer cloud cover is relatively high (approximately 50%). This seasonal variation trend not only further validates the ratio- nality of the findings in this study, but also confirms, from a global perspective, the characteristic of extremely high clear-sky rates at Dome A during the Antarctic win- ter. Furthermore, a comparative analysis with the Big Tele- scope Alt-azimuthal (BTA) site of the Special Astrophysi- cal Observatory (SAO) of the Russian Academy of Sci- ences, which hosts the largest optical telescope in Eurasia, further highlights the advantages of Dome A. According to a study by Shikhovtsev et al. , based on reanalysis data from the National Centers for Environmental Predic- tion/National Center for Atmospheric Research (NCEP/ NCAR), the annual mean total cloud cover in the BTA region in 2017 was 38.5%. By contrast, the total annual mean cloud cover over Dome A and its surrounding regions in our study was 20.57% in 2017, indicating significantly lower annual cloud cover and better clear-sky conditions.

Spatial variability can be comprehensively evaluated using coefficients of variation (CV), standard deviation ), and mean ( ), with the results presented in April, at the beginning of polar night, shows an extremely low cloud cover in the region, indicating excellent observa-

2017 July, 2017

October, 2017

Cloud cover (Mean range)

Maximum Minimum

45.7% Cloud cover/(%) Month tional potential. In July, the CV of cloud cover was only 0.05, and the standard deviation was also very low, reflect- ing the high uniformity in the cloud cover distribution of the target area. We found that the low cloud cover levels and their stability in April and July provide compara- tively excellent meteorological conditions for astronomi- cal observations.

Notably, the cloud cover measured by satellite remote sensing used in this study differs from that measured by ground-based panoramic cameras. In satellite remote sens- ing, cloud cover is mainly determined based on data from the visible light and infrared bands. This is obtained by ana- lyzing parameters such as surface reflectance, brightness temperature, and cloud top characteristics, combined with Precipitation in Antarctica typically falls to the ground in the form of rain, snow, or sleet, thereby carry- ing heat into the snowpack and altering the structure and albedo of the snow. Spatial distribution and variability of precipitation also influence the clear-sky rate, affecting the ability of telescopes to conduct continuous observa- tions. In this study, we used the ERA5 dataset to con- duct a statistical analysis of the total precipitation data for the specified region (76.96°E–77.96°E, 79.88°S–80.88°S) during January, April, July, and October 2017 . This anal- ysis explored the seasonal variations and spatial distribu- tion characteristics of precipitation and provided an assess- ment of the suitability of this region for astronomical obser- cloud detection algorithms (such as thresholding and machine learning classification), to determine whether clouds cover a pixel. All-sky cameras capture 360° all- sky images from the ground upward. By combining human visual interpretation with algorithms that automati- cally identify image differences, this can distinguish between cloud-covered and clear-sky areas in the images.

Because all-sky cameras are suitable for local high-preci- sion, high-frequency cloud quantity monitoring, while remote sensing technology is more suitable for large- scale, multi-parameter cloud characteristic inversion, the two technologies are complementary in terms of astronomi- cal site selection. Given that this study focuses on analyz- ing the large-scale site uniformity of Dome A, we used remote sensing data. According to Yang et al. , based on all-sky camera observations in the Dome A region, approximately 83.3% of the time between January 17, 2017, and May 28, 2018, was cloud-free, providing a good reflection of the frequency of cloud-free conditions over time. Satellite remote sensing, however, provides spa- tial cloud cover, which is the proportion of the area cov- ered by clouds at a given time. Based on satellite remote sensing data, we calculated that the average cloud cover for the region in 2017 was 20.57%. Owing to fundamen- tal differences in statistical methods and data definitions, the two cannot be directly compared quantitatively. Never- theless, both data sources point to the fact that Dome A has an extremely high clear-sky rate during winter nights.

This indirectly reflects the accuracy of satellite remote sens- ing data. vations.

April, July, and October, which was 0.07–0.18 mm, 0.05–0.06 mm, 0.04–0.07 mm, and 0.03–0.05 mm, respec- tively.

Month σ/(%) Range/(%) Spatial distribution January The cloud cover is moderate, with almost no fluctuation.

April The cloud cover is extremely low, with almost no fluctuation.

The cloud cover is low, with almost no fluctuation.

October The cloud cover is relatively high, with significant fluctuation.

−79.9 −80.0 −80.1 −80.2 Latitude/(°N) −80.3 Dome A −80.4 −80.5 −80.6 −80.7 −80.8 Longitude/(°E) −80.2 Latitude/(°N) Dome A −80.4 −80.6 −80.8 −80.2 Latitude/(°N) Dome A −80.4 −80.6 −80.8 Longitude/(°E) In addition to total precipitation, we analyzed the 2017 total column water vapor (TCWV) at Dome A and surrounding areas using the ERA5 reanalysis dataset from the Climate Data Store (CDS) shows the mean TCWV at Dome A and its surrounding areas in 2017. shows the mean TCWV for January, April, July, In terms of spatial distribution, within a 1° × 1° area cen- tered on Dome A (77.45°E, 80.38°S), total precipitation shows a north-to-south gradient distribution along the lati- tude, except in October. refers to the total precipita- tion for the entirety of Dome A and its surrounding areas over 12 months in 2017. It is the sum of the precipita- tion values for all grid cells in the study area and repre- sents the cumulative total for the region, rather than the value for a single grid cell. The total precipitation for Jan- uary, April, July, and October 2017 was 14.25 mm, 6.47 mm, 6.44 mm, and 4.78 mm, respectively. Precipitation remain- ed at relatively low levels from March to December, includ- ing the polar night period (April to July), indicating that the site possesses stable and favorable atmospheric condi- tions. We found that the period from April to July is the optimal time for optical astronomical observations. −80.2 Latitude/(°N) Latitude/(°N) Dome A −80.4 −80.6 −80.8 −80.2 Dome A −80.4 −80.6 −80.8 Longitude/(°E) and October 2017 at Dome A and its surrounding areas, with mean TCWV ranges of 0.542–0.586 kg m , 0.157– 0.165 kg m , 0.136–0.145 kg m , and 0.207–0.215 kg m illustrates the monthly mean TCWV trends for Dome A and its surrounding areas in 2017, with TCWV values of 0.559 kg m , 0.159 kg m , 0.138 kg m , and January, 2017 April, 2017 Longitude/(°E) Longitude/(°E) July, 2017 October, 2017

Monthly total precipitation/mm Total precipitation Month −79.9 −80.0 −80.1 −80.2 Latitude/(°S) Latitud/(°S) −80.3 Dome A −80.4 −80.5 −80.6 −80.7 −80.8 −79.9 −80.0 −80.1 −80.2 −80.3 Dome A −80.4 −80.5 −80.6 −80.7 −80.8 Longitude/(°E) 0.210 kg m for January, April, July, and October 2017, respectively.

To further assess the observational conditions at Dome A and its surrounding areas, we compared them with those from other well-known astronomical observa- tory sites. According to Deng et al. , at the Lenghu site, 55% of the total nights throughout the year showed precip- itable water vapor (PWV) below 2 mm, while at Mauna Kea, 54% of the total nights throughout the year had PWV below 2 mm. By contrast, the TCWV at Dome A and its surrounding areas was even lower, especially dur- ing polar night. The mean annual TCWV for Dome A (kg m −79.9 −80.0 −80.1 −80.2 Latitude/(°S) −80.3 Dome A −80.4 −80.5 −80.6 −80.7 −80.8 Longitude/(°E) April, 2017 −79.9 −80.0 −80.1 −80.2 Latitude/(°S) −80.3 Dome A −80.4 −80.5 −80.6 −80.7 −80.8 −79.9 −80.0 −80.1 −80.2 Latitude/(°S) −80.3 Dome A −80.4 −80.5 −80.6 −80.7 −80.8 Longitude/(°E) and its surrounding areas in 2017 was 0.254 kg m (equiv- alent to 0.254 mm). Notably, the statistical methods for TCWV used in this study differed from those for PWV used in previously published literature. However, this study’s finding still reflects the extremely low atmo- spheric water vapor at Dome A. This is advantageous for infrared and submillimeter astronomical observations, fur- ther validating the suitability of this site for astronomy.

SKIN TEMPERATURE In addition to precipitation factors, skin temperature January, 2017 (kg m (kg m Longitude/(°E) Longitude/(°E) (kg m (kg m July, 2017 October, 2017

0.60 TCWV (Mean ± range)

Mean TCWV/(kg m is a significant meteorological variable that influences astro- nomical observation conditions. To enhance the meteorolog- ical suitability assessment for astronomical observation site selection, we included the incorporation of skin temper- ature as an evaluation factor derived from cloud cover and precipitation analysis. We conducted a statistical analy- sis of the target region (76.96°E–77.96°E, 79.88°S–80.88°S) using data from the ERA5 dataset for four representative months in 2017 Figs. 9 show the mean skin tem- peratures of Dome A and its surrounding areas in 2017, as well as the mean skin temperatures in January, April, July, and October 2017. The ranges of mean skin tempera- tures were 230.37–236.77 K, 208.26–214.01 K, 205.89– 208.86 K, and 217.42–221.93 K, with corresponding mean skin temperatures for each month of 237.85 K, 210.72 K, 206.86 K, and 219.19 K, respectively. The skin temperature of Dome A and its surrounding areas was observed to be generally low, with April and July show- ing lower temperatures than those in the other two months. This finding suggests that atmospheric radiative cooling is significant during polar night. shows a summary of the monthly mean skin temperature trends for Dome A and its surrounding areas in 2017. To verify the reliability of the ERA5 reanalysis data used in this study, the mean skin temperatures from ERA5 for January, Febru- ary, and March 2017 were compared with the ground- level (0 m) temperature monitored by the temperature sen- sor equipped on the AST3-2 telescope at Dome A during the same period. The results show that the mean skin tem- perature derived from the ERA5 reanalysis data for these three months was 227.2 K (−45.95°C), while the observed ground-level temperature averaged −45.65°C. The close agreement between these values indicates that the ERA5 reanalysis data reliably represents surface temperature in this region.

In terms of spatial distribution, skin temperature showed a longitudinal gradient across the Dome A region, with the lowest temperature recorded in the southeastern region within the 1° × 1° area surrounding Dome A (cen- tered on 77.45°E, 80.38°S). We evaluated the spatial vari- ability within this area using a combination of the CV, −79.9 −80.0 −80.1 −80.2 Latitude/(°S) −80.3 Dome A −80.4 −80.5 −80.6 −80.7 −80.8 Longitude/(°E) . As shown in , the results of this analysis indicate that skin temperatures were at their lowest in April and July, with a smaller and more evenly dis- tributed temperature difference in July. Accordingly, the eastern region is recommended as ideal for astronomical observations in July.

VWS is an important atmospheric parameter that influ- ences the stability of the atmosphere and, consequently, the quality of astronomical observations. Although it is related to atmospheric seeing, this section focuses specifi- cally on quantifying and analyzing VWS over Dome A.

VWS is primarily influenced by three factors. First, free-atmosphere turbulence arises from instabilities in the airflow of the upper atmosphere, causing scintillation of starlight, with VWS being the primary driving mecha- nism behind this turbulence. Another factor is boundary- layer turbulence, associated with near-surface wind speed.

This includes both mechanical turbulence and thermal con- vection near the ground. Finally, the height of the bound- ary layer also plays an important role, with a lower bound- ary layer generally being more favorable because it helps to minimize the influence of ground-layer turbulence on astronomical observations.

To effectively analyze the VWS conditions over Dome A, we employed the ERA5 reanalysis dataset, released by the CDS platform , as the primary data source for evaluating the net surface solar radiation in the study area. We selected the U-component and V-compo- nent of wind data for January, April, July, and October 2017 over Dome A and its surrounding regions. The data have a temporal resolution of 1 h (accumulated values), and we chose pressure levels of 600 hPa, representing the lower troposphere, and 350 hPa, representing the upper tro- posphere, to construct vertical wind profiles. These profi- les were then used to further analyze the structure of VWS.

We adopted the 600 hPa pressure level (approxi- mately 4 370 m above sea level) as a representative lower-atmospheric height for estimating near-surface wind

January, 2017 −79.9 −80.0 −80.1 −80.2 Latitude/(°S) −80.3 Dome A −80.4 −80.5 −80.6 −80.7 −80.8 Longitude/(°E) July, 2017 −79.9 −80.0 −80.1 −80.2 Latitude/(°S) −80.3 −80.4 −80.5 −80.6 −80.7 −80.8 Mean ± range Skin temperature/K A and its surrounding areas in 2017. speed. Owing to the vertical resolution of 50 hPa in the ERA5 dataset, we selected 600 hPa as the closest avail- able pressure level to the surface elevation of Dome A.

The scalar wind speed at 600 hPa (denoted as , in April, 2017 −79.9 −80.0 −80.1 −80.2 Latitude/(°S) −80.3 Dome A −80.4 −80.5 −80.6 −80.7 −80.8 Longitude/(°E) October, 2017 −79.9 −80.0 −80.1 −80.2 Latitude/(°S) −80.3 Dome A −80.4 −80.5 −80.6 −80.7 −80.8

units of m s −1 ) at each spatial grid point is calculated as

WS 600 = √

where represent the zonal and meridional wind components at the 600 hPa level, respectively.

Similarly, the upper-level wind speed is represented by the wind speed at the 350 hPa pressure level (approxi- mately 7 370 m), which characterizes the upper tropo- spheric flow. Owing to the limitations of the standard pres- sure levels in the ERA5 dataset, higher vertical resolu- tion data that accurately cover the boundary layer height (with a typical median of only 15 m) are not available. Con- sequently, within the accessible vertical levels, we selected the 350–600 hPa wind shear index as an indirect representation of the upper-atmosphere wind structure, aim- ing to explore potential variations in large-scale dynamic patterns rather than directly describing turbulence character- istics within the boundary layer. The scalar wind speed at Longitude/(°E) Longitude/(°E) January, April, July, and October 2017.

Month Range/K Interquartile range (IQR)/K Spatial distribution January Higher and uniform skin temperatures April Extremely low and uniform skin temperatures Extremely low and uniform skin temperatures October Lower and uniform skin temperatures

350 hPa (denoted as WS 350 , in units of m s −1 ) at each spa- tial grid point is calculated as

WS 350 = √

where denote the zonal and meridional wind components at the 350 hPa level, respectively.

VWS is an important metric used to quantify the varia- tion in wind speed between different atmospheric layers, in units of m/s/km. It has a significant impact on atmo- spheric turbulence and astronomical seeing, and is given by

VWS = WS 350 − WS 600 ∆ h × 1 000 , (3)

where represents the difference in altitude, in units of m.

On the basis of ERA5 reanalysis data, our analysis indi- cates that VWS and near-surface wind speed in the tar- get region show pronounced seasonal variation. As shown , the mean near-surface wind speed over Dome A and its surrounding region during January, April, July, and October of 2017 falls in ranges of 3.89–4.96 m s 5.39–7.06 m s , 5.39–5.99 m s , and 5.28–8.16 m s respectively. As shown in , the mean VWS over Do- me A and its surrounding region during January, April, July, and October of 2017 is in the ranges 1.73–2.06 m/s/km, January, 2017 −79.9 −80.0 −80.1 −80.2 Latitude/(°S) −80.3 Dome A −80.4 −80.5 −80.6 −80.7 −80.8 Longitude/(°E) −79.9 −80.0 −80.1 −80.2 Latitude/(°S) −80.3 Dome A −80.4 −80.5 −80.6 −80.7 −80.8 Longitude/(°E) 1.51–1.91 m/s/km, 1.93–2.13 m/s/km, and 1.41–2.39 m/s/km, respectively.

The spatial variability of both near-surface wind speed and vertical wind shear was further evaluated using statistical indicators. The results are summarized in Tables 3 , respectively. As shown in Table , near-surface wind speeds were generally higher in October, with the strongest spatial fluctuations occurring in October, whereas July exhibited the most uniform distribution. Simi- larly, indicates that vertical wind shear was rela- tively stable in July, suggesting that this period features a more homogeneous atmospheric structure and provides favorable conditions for astronomical observations.

Regarding spatial distribution, VWS in the northern part of the study area was generally low, while near-sur- face wind speed tended to be relatively high. Notably, near-surface wind speeds in April and October were com- paratively higher. October showed the greatest variability, characterized by a standard deviation of 0.94 m s and a CV of 0.14. By contrast, near-surface wind speed in July showed low variability and moderate mean values, with the most uniform spatial distribution, indicating a rela- tively stable atmospheric state. At the same time, VWS in July was also relatively stable, with a low coefficient of variation. These data suggest that July features a more homogeneous atmospheric structure and provides prefer- April, 2017 −79.9 −80.0 −80.1 −80.2 Latitude/(°S) Latitude/(°S) −80.3 Dome A −80.4 −80.5 −80.6 −80.7 −80.8 Longitude/(°E) −79.9 −80.0 −80.1 −80.2 −80.3 Dome A −80.4 −80.5 −80.6 −80.7 −80.8 Longitude/(°E) July, 2017 October, 2017 October 2017.

(m/s/km) (m/s/km) January, 2017 April, 2017 −79.9 −80.0 −80.1 −80.2 Latitude/(°S) −80.3 Dome A −80.4 −80.5 −80.6 −80.7 −80.8 Longitude/(°E) −79.9 −80.0 −80.1 −80.2 Latitude/(°S) −80.3 Dome A −80.4 −80.5 −80.6 −80.7 −80.8 Longitude/(°E) Month (m/s/km) (m/s/km) Range/ (m/s/km) able conditions for astronomical observations.

To further assess the radiative characteristics of the astronomical observation environment, we utilized the ERA5 reanalysis dataset released by the CDS platform Surface net solar radiation data were selected for January, April, July, and October 2017, with a temporal resolution of one-hour accumulated values. shows the analy- sis and calculation results of surface net solar radiation over the target region (76.96°E–77.96°E, 79.88°S–80.88°S) −79.9 −80.0 −80.1 −80.2 Latitude/(°S) −80.3 Dome A −80.4 −80.5 −80.6 −80.7 −80.8 Longitude/(°E) −79.9 −80.0 −80.1 −80.2 Latitude/(°S) −80.3 Dome A −80.4 −80.5 −80.6 −80.7 −80.8 Longitude/(°E) for January, April, July, and October of 2017, and presents the data characteristics for different seasonal months. We used these data to systematically examine the seasonal variation patterns and spatial distribution character- istics of surface net solar radiation, to evaluate their poten- tial impact on the suitability for astronomical observations.

The spatial variability of surface net solar radiation in the target region was comprehensively evaluated using σ , µ , and CV. In January, the CV was only 0.41, and the mean value reached 61.96 W m −2 , indicating that the region experiences relatively high solar radiation inten- sity with a uniform spatial distribution and low spatial vari-

July, 2017 October, 2017 (m/s/km) (m/s/km) Month /(m s /(m s Range/(m s IQR/(m s Spatial distribution January Relatively low overall wind speed April Wind speed strengthens overall, especially in the northwest Uniform wind speed with minimal spatial variation October Large fluctuations, with higher wind speeds in the northwest (m/s/km) Spatial distribution January Small wind speed fluctuations between the upper and lower levels April Small wind speed fluctuations between the upper and lower levels Small wind speed fluctuations between the upper and lower levels October Large wind speed fluctuations between the upper and lower levels

January, 2017 April, 2017 −79.9 −80.0 −80.1 −80.2 Latitude/(°S) −80.3 Dome A −80.4 −80.5 −80.6 −80.7 −80.8 Longitude/(°E) −79.9 −80.0 −80.1 −80.2 Latitude/(°S) −80.3 Dome A −80.4 −80.5 −80.6 −80.7 −80.8 Longitude/(°E) ability. In April, the solar elevation angle decreased signifi- cantly, resulting in a mean net surface solar radiation of only 0.52 W m . Concurrently, the CV reached 3.78, indi- cating that although the overall solar radiation over Dome A and its surrounding region was very low, a degree of spa- tial variability existed. Nevertheless, the overall condi- tions remained suitable for conducting optical astronomi- cal observations. In July, which corresponds to the polar night period in Antarctica, , and CV of net surface solar radiation were all zero, indicating a complete absence of solar illumination and variability. This finding reflects the natural characteristics of the Antarctic polar night, making it a valuable period for astronomical regional observations. In October, the mean net surface solar radiation reached 28.63 W m , indicating that solar radiation in Antarctica begins to increase during this period. However, the radiation level remained relatively low, with moderate variability, reflecting a low mean value and relatively stable distribution characteristics. Nev- ertheless, considering the increased and uneven cloud −79.9 −80.0 −80.1 −80.2 Latitude/(°S) −80.3 Dome A −80.4 −80.5 −80.6 −80.7 −80.8 Longitude/(°E) −79.9 −80.0 −80.1 −80.2 Latitude/(°S) −80.3 Dome A −80.4 −80.5 −80.6 −80.7 −80.8 Longitude/(°E)

cover during this time, observing conditions become more complex, and opportunities for astronomical observation are relatively limited.

Our comprehensive analysis indicates that the sea- sonal variation of net surface solar radiation has a deci- sive impact on the astronomical observation conditions in the target region. January falls in the Antarctic summer period with high solar radiation, making it suitable primar- ily for specific solar physics studies. Surface net solar radia- tion reaches its minimum in April and July, resulting in the lowest background brightness and minimal thermal dis- turbances. Coupled with the low cloud cover levels dur- ing this period, these environmental characteristics indi- cate favorable conditions for optical astronomical observa- tions in this timeframe.

In this study, we systematically assessed the suitabil- ity of Dome A and its surrounding region in Antarctica July, 2017 October, 2017 October 2017.

October 2017 Month /(W m Spatial distribution January High values are uniformly distributed with low variability April Very low solar radiation across the entire region, with large variability No solar radiation across the entire region October Low mean levels exhibiting moderate fluctuations

for astronomical observations. Using multisource remote sensing data and high-precision meteorological reanalysis datasets, we focused on key observational parameters, including cloud cover, precipitation, atmospheric water vapor, skin temperature, vertical wind shear, and net sur- face solar radiation, during four representative months of 2017: January, April, July, and October. The results indi- cate that Dome A not only shows extremely low cloud cover and precipitation characteristics but also has highly stable climatic features in terms of skin temperature and wind field structure. Notably, April and July are character- ized by low cloud cover, reduced net solar radiation, and favorable vertical wind shear conditions, making them exceptionally suitable periods for optical astronomical obser- vations. Dome A and its surrounding region possess excel- lent conditions for astronomy, with climatic and environ- mental stability and spatial homogeneity, making it a key strategic node within both Antarctic and global astronomi- cal research networks. This study provides important scien- tific foundations for the expanded use of Dome A and the development of future astronomical infrastructure, while also offering strong support for the rational allocation and optimization of global astronomical observation resources.

ACKNOWLEDGEMENTS This work was supported by the Space Debris Research Project, China (KJSP2020010102) and the National Key R&D Program of China (2022YFC2807 300).

AI DISCLOSURE STATEMENT ChatGPT (GPT-4, OpenAl) was employed for code error checking during the calculations in this paper and for language and grammar checking of the article. The authors carefully reviewed, edited, and revised the Chat- GPT-generated texts to their own preferences, assuming ultimate responsibility for the content of the publication.

AUTHOR CONTRIBUTIONS Peng Jiang and Xiaoyan Li supervised the project, con- tributed to the conceptualization, and oversaw the techni- cal workflow. Kaiwen Zheng and Kun Ma were responsi- ble for data analysis and processing. Haosi Song, Tian- cong Zhang, Shiyi Wang, and Han Wang acquired and orga- nized the relevant datasets. Kaiwen Zheng and Jiali Chen jointly drafted the manuscript and edited it for language.

All authors read and approved the final manuscript.

DECLARATION OF INTERESTS The authors declare no competing interests.

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