In 2018, South Korea recorded its hottest summer since 1973, with 31 heat-wave days. Heatwaves with maximum temperatures above 33 degrees Celsius usually occur after the rainy season in summer. In recent years, not only has the frequency of heatwaves increased, but also their intensity. Summer in South Korea Summer in South Korea (from June to August) is usually hot and humid with a lot of rainfall during the rainy season of the East Asian monsoon (Changma). About 60 percent of precipitation falls during this season. The average temperature in summer was around 24.7 degrees Celsius in 2023. The amount of precipitation in summer that year stood at over 1,000 millimeters, more than four times higher than in winter. Climate change South Korea is known for its four distinct seasons, yet weather patterns have increasingly changed in recent decades, resulting in longer summers and shorter winters. This shows that South Korea is not excluded from the effects of climate change. Changing climate patterns in recent decades have also led to an intensification of precipitation and more heat waves in South Korea. Meanwhile, climate change is taken very seriously by South Koreans: about 48 percent of respondents to a 2019 survey said that global warming or climate change is the most important environmental issue for South Korea.

In 2024, precipitation in Jeju in South Korea was the highest nationwide, with about 1928.9 millimeters. Gyeongnam followed with around 1713.6 millimeters.

In May 2025, the average temperature in Gwangju, South Korea was 18.2 degrees Celsius. August 2024 was the city's hottest month in the past six years, while December 2022 and February 2025 were the coldest, with an average temperature of 1.1 degrees Celsius.

In 2023, the average summer temperature in South Korea was around **** degrees Celsius, up from **** degrees Celsius in the previous year. The highest temperature since 2000 was **** degrees Celsius in 2018, while the lowest temperature was **** degrees Celsius in 2003.

The average temperature in South Korea in 2024 was **** degrees Celsius. The average temperature in South Korea has risen steadily over the years, which is shown in the graph. Extreme weather South Korea has a distinct four-season climate. Generally, summer in South Korea is humid and hot, while winter is dry and cold. However, the summer climate, which usually lasts from June to August, is getting longer and can last from May through to September. Especially in summer, extreme weather such as tropical nights, typhoons, and heatwaves occur. Recently, there was an increase in the consecutive days in which heatwaves reached temperatures above ** degrees. Greenhouse gas emissions South Korea is suffering from air pollution problems, such as yellow dust and fine dust, that have increased rapidly over recent years. In addition, as the carbon dioxide concentration has continued to rise, the average annual temperature has also risen steadily, resulting in abnormal climates, such as heatwaves in summer or extreme cold in winter. South Korea is one of the countries that produces a lot of greenhouse gases. Due to the manufacturing-oriented industrial structure, greenhouse gas emissions from energy use account for a large portion.

In June 2025, the average temperature in South Korea was **** degrees Celsius. August 2024 was the hottest month in the past five years, with a mean of around **** degrees Celsius. In the same period, December 2022 was the coldest month, with an average temperature of minus *** degrees Celsius.

In May 2025, the average temperature in Seoul, South Korea was **** degrees Celsius. August 2024 was the hottest month in the city in the past six years, while December 2022 was the coldest, with an average temperature of minus *** degrees Celsius.

In 2023, the average temperature for summer in South Korea was **** degrees Celsius. South Korea has four distinct seasons, which can be seen in the different average temperatures for each season.

Understanding climatic effect on wildlife is essential to prediction and management of climate change’s impact on the ecosystem. The climatic effect can interact with other environmental factors. This study aimed to determine effects of climate and altitude on Siberian roe deer (Capreolus pygargus) activity in temperate forests of South Korea. We conducted camera trapping to investigate roe deer’s activity level from spring to fall. Logistic regressions were used to determine effects of diel period, temperature, rain, and altitude on the activity level. A negative relationship was noted between temperature and the activity level due to thermoregulatory costs. Roe deer activity exhibited nocturnal and crepuscular patterns during summer and the other seasons, respectively, possibly due to heat stress in summer. In addition, the effect of temperature differed between high- and low-altitude areas. In low-altitude areas, temperature affected negatively the activity level throughout the study period. Conversely, in high-altitude areas, temperature affected activity levels only in summer and early fall. Lower temperatures in higher altitudes favoured roe deer activity, resulting in roe deer’s preference towards higher altitude areas. However, roe deer’s movement toward lower altitudes was observed in summer. Reduced heat stress by changing activity pattern allowed them to access lower altitude areas with greater resource availability during summer. This study revealed how roe deer activity varied across seasons and altitudes, considering the interactions among weather, microclimate and resource availability. It provides insight into how montane species adapt to various climatic conditions, and this could have important implications for wildlife management and conservation efforts. Methods The camera trapping method was used to observe temporal variations in roe deer capture (sampling days: September to October 2021 and April to August 2022). In the study area, a 5 × 6 grid design (interval = 600 m) was established, and one trail camera (Spec Ops Elite HP4; Browning Co., USA) was deployed corresponding to each cell of the grid. The study period was divided into five seasons, and further analyses were performed for each season: spring (15 April to 15 May, 960 trap-days), early summer (16 May to 30 June, 1380 trap-days), summer (1 July to 31 August, 1860 trap-days), early fall (September, 900 trap-days) and fall (October, 810 trap-days). The camera-plot altitudes were categorised into four classes: 600 (600–800 m asl, n = 3), 800 (800–1,000 m asl, n = 10), 1,000 (1,000–1,200 m asl, n = 11) and 1,200 (1,200–1,400 m asl, n = 6). We created a roedeer variable as presence/absence of observation per 2-h in each altitude class. In order to account for sampling effort depending on altitude, a weight variable was created as the total number of plots/the number of plots at the altitude. A temp variable is a predicted value of temperature at an altitude of 1,000 m, based on temperature data from a weather station, located 560 m asl. A rainy variable was established based on precipitation data from the weather station; it determined whether precipitation of sampling day was 0 mm or not. The diel variable explains diel period, such as dawn, day, dusk and night. Dawn and dusk were designated as the time within 2 h before and after sunrise and sunset, respectively.

A service that provides weather forecasts (ultra-short-term forecasts/1-hour intervals, short-term forecasts/3-hour intervals) for beaches nationwide, tide information, wave height information (data from the closest marine observation equipment point to the beach), sunrise and sunset information (based on the point where the beach is located, using data from the Korea Astronomy and Space Science Institute), water temperature information, and high and low tides (based on the closest tide station to the beach, using data from the National Oceanographic Research Institute). To support safe leisure activities for the public during the summer, a weather service is provided for major beaches nationwide, and the target locations are Eulwang-ri, Wangsan, Hanagae, Minmeoru, Janggyeong-ri, Ongam, Sugi, Dongmak, Seopo-ri, Siplipo, Gureop, Ttepuru, Batjireum, Handeul, Keunpulan, Janggol, and Beolan, approximately 330 locations.

Temperature Forecast Project using ML

Problem Statement:

Data Set Information:

This data is for the purpose of bias correction of next-day maximum and minimum air temperatures forecast of the LDAPS model operated by the Korea Meteorological Administration over Seoul, South Korea. This data consists of summer data from 2013 to 2017. The input data is largely composed of the LDAPS model's next-day forecast data, in-situ maximum and minimum temperatures of present-day, and geographic auxiliary variables. There are two outputs (i.e. next-day maximum and minimum air temperatures) in this data. Hindcast validation was conducted for the period from 2015 to 2017.

Attribute Information:

For more information, read [Cho et al, 2020]. 1. station - used weather station number: 1 to 25 2. Date - Present day: yyyy-mm-dd ('2013-06-30' to '2017-08-30') 3. Present_Tmax - Maximum air temperature between 0 and 21 h on the present day (°C): 20 to 37.6 4. Present_Tmin - Minimum air temperature between 0 and 21 h on the present day (°C): 11.3 to 29.9 5. LDAPS_RHmin - LDAPS model forecast of next-day minimum relative humidity (%): 19.8 to 98.5 6. LDAPS_RHmax - LDAPS model forecast of next-day maximum relative humidity (%): 58.9 to 100 7. LDAPS_Tmax_lapse - LDAPS model forecast of next-day maximum air temperature applied lapse rate (°C): 17.6 to 38.5 8. LDAPS_Tmin_lapse - LDAPS model forecast of next-day minimum air temperature applied lapse rate (°C): 14.3 to 29.6 9. LDAPS_WS - LDAPS model forecast of next-day average wind speed (m/s): 2.9 to 21.9 10. LDAPS_LH - LDAPS model forecast of next-day average latent heat flux (W/m2): -13.6 to 213.4 11. LDAPS_CC1 - LDAPS model forecast of next-day 1st 6-hour split average cloud cover (0-5 h) (%): 0 to 0.97 12. LDAPS_CC2 - LDAPS model forecast of next-day 2nd 6-hour split average cloud cover (6-11 h) (%): 0 to 0.97 13. LDAPS_CC3 - LDAPS model forecast of next-day 3rd 6-hour split average cloud cover (12-17 h) (%): 0 to 0.98 14. LDAPS_CC4 - LDAPS model forecast of next-day 4th 6-hour split average cloud cover (18-23 h) (%): 0 to 0.97 15. LDAPS_PPT1 - LDAPS model forecast of next-day 1st 6-hour split average precipitation (0-5 h) (%): 0 to 23.7 16. LDAPS_PPT2 - LDAPS model forecast of next-day 2nd 6-hour split average precipitation (6-11 h) (%): 0 to 21.6 17. LDAPS_PPT3 - LDAPS model forecast of next-day 3rd 6-hour split average precipitation (12-17 h) (%): 0 to 15.8 18. LDAPS_PPT4 - LDAPS model forecast of next-day 4th 6-hour split average precipitation (18-23 h) (%): 0 to 16.7 19. lat - Latitude (°): 37.456 to 37.645 20. lon - Longitude (°): 126.826 to 127.135 21. DEM - Elevation (m): 12.4 to 212.3 22. Slope - Slope (°): 0.1 to 5.2 23. Solar radiation - Daily incoming solar radiation (wh/m2): 4329.5 to 5992.9 24. Next_Tmax - The next-day maximum air temperature (°C): 17.4 to 38.9 25. Next_Tmin - The next-day minimum air temperature (°C): 11.3 to 29.8T

Please note that there are two target variables here:

1) Next_Tmax: Next day maximum temperature

2) Next_Tmin: Next day minimum temperature

This is the Korea Meteorological Administration's Living Weather Index API service, which allows users to view information such as the UV Index, the Air Stagnation Index, and the perceived temperature in summer by target environment. It provides a realistic climate based on seasonal weather conditions, enabling diverse applications such as citizen health management, heat-related illness prevention, outdoor activity planning, and industrial and administrative policy support. Furthermore, it offers significant utility as a reference for education, policymaking, research, and the development of life safety services for various institutions, including schools, local governments, medical institutions, and businesses. It is also a valuable resource for developing policies to prepare for meteorological disasters, promote public safety, and respond to climate change.

Data Set Information:

This data is for the purpose of bias correction of next-day maximum and minimum air temperatures forecast of the LDAPS model operated by the Korea Meteorological Administration over Seoul, South Korea. This data consists of summer data from 2013 to 2017. The input data is largely composed of the LDAPS model's next-day forecast data, in-situ maximum and minimum temperatures of present-day, and geographic auxiliary variables. There are two outputs (i.e. next-day maximum and minimum air temperatures) in this data. Hindcast validation was conducted for the period from 2015 to 2017.

Attribute Information:

For more information, read [Cho et al, 2020]. 1. station - used weather station number: 1 to 25 2. Date - Present day: yyyy-mm-dd ('2013-06-30' to '2017-08-30') 3. Present_Tmax - Maximum air temperature between 0 and 21 h on the present day (°C): 20 to 37.6 4. Present_Tmin - Minimum air temperature between 0 and 21 h on the present day (°C): 11.3 to 29.9 5. LDAPS_RHmin - LDAPS model forecast of next-day minimum relative humidity (%): 19.8 to 98.5 6. LDAPS_RHmax - LDAPS model forecast of next-day maximum relative humidity (%): 58.9 to 100 7. LDAPS_Tmax_lapse - LDAPS model forecast of next-day maximum air temperature applied lapse rate (°C): 17.6 to 38.5 8. LDAPS_Tmin_lapse - LDAPS model forecast of next-day minimum air temperature applied lapse rate (°C): 14.3 to 29.6 9. LDAPS_WS - LDAPS model forecast of next-day average wind speed (m/s): 2.9 to 21.9 10. LDAPS_LH - LDAPS model forecast of next-day average latent heat flux (W/m2): -13.6 to 213.4 11. LDAPS_CC1 - LDAPS model forecast of next-day 1st 6-hour split average cloud cover (0-5 h) (%): 0 to 0.97 12. LDAPS_CC2 - LDAPS model forecast of next-day 2nd 6-hour split average cloud cover (6-11 h) (%): 0 to 0.97 13. LDAPS_CC3 - LDAPS model forecast of next-day 3rd 6-hour split average cloud cover (12-17 h) (%): 0 to 0.98 14. LDAPS_CC4 - LDAPS model forecast of next-day 4th 6-hour split average cloud cover (18-23 h) (%): 0 to 0.97 15. LDAPS_PPT1 - LDAPS model forecast of next-day 1st 6-hour split average precipitation (0-5 h) (%): 0 to 23.7 16. LDAPS_PPT2 - LDAPS model forecast of next-day 2nd 6-hour split average precipitation (6-11 h) (%): 0 to 21.6 17. LDAPS_PPT3 - LDAPS model forecast of next-day 3rd 6-hour split average precipitation (12-17 h) (%): 0 to 15.8 18. LDAPS_PPT4 - LDAPS model forecast of next-day 4th 6-hour split average precipitation (18-23 h) (%): 0 to 16.7 19. lat - Latitude (°): 37.456 to 37.645 20. lon - Longitude (°): 126.826 to 127.135 21. DEM - Elevation (m): 12.4 to 212.3 22. Slope - Slope (°): 0.1 to 5.2 23. Solar radiation - Daily incoming solar radiation (wh/m2): 4329.5 to 5992.9 24. Next_Tmax - The next-day maximum air temperature (°C): 17.4 to 38.9 25. Next_Tmin - The next-day minimum air temperature (°C): 11.3 to 29.8

In May 2025, the average temperature in Daegu, South Korea was 18.1 degrees Celsius. August 2024 was the hottest month in the city in the past six years, while December 2022 was the coldest, with an average temperature of 0.4 degrees Celsius.

The GAME/Tibet project conducted a short-term pre-intensive observing period (PIOP) at the Amdo station in the summer of 1997. From May to September 1998, five consecutive IOPs were scheduled, with approximately one month per IOP. More than 80 scientific workers from China, Japan and South Korea went to the Tibetan Plateau in batches and carried out arduous and fruitful work. The observation tests and plans were successfully completed. After the completion of the IOP in September, 1998, five automatic weather stations (AWS), one Portable Atmospheric Mosonet (PAM), one boundary layer tower and integrated radiation observatory (Amdo) and nine soil temperature and moisture observation stations have been continuously observed to date and have obtained extremely valuable information for 8 years and 6 months consecutively (starting from June 1997). The experimental area is located in Nagqu, in northern Tibet, and has an area of 150 km × 200 km (Fig. 1), and observation points are also established in D66, Tuotuohe and the Tanggula Mountain Pass (D105) along the Qinghai-Tibet Highway. The following observation stations (sites) are set up on different underlying surfaces including plateau meadows, plateau lakes, and desert steppe. (1) Two multidisciplinary (atmosphere and soil) observation stations, Amdo and NaquFx, have multicomponent radiation observation systems, gradient observation towers, turbulent flux direct measurement systems, soil temperature and moisture gradient observations, radiosonde, ground soil moisture observation networks and multiangle spectrometer observations used as ground truth values for satellite data, etc. (2) There are six automatic weather stations (D66, Tuotuohe, D105, D110, Nagqu and MS3608), each of which has observations of wind, temperature, humidity, pressure, radiation, surface temperature, soil temperature and moisture, precipitation, etc. (3) PAM stations (Portable Automated Meso - net) located approximately 80 km north and south of Nagqu (MS3478 and MS3637) have major projects similar to the two integrated observation stations (Amdo and NaquFx) above and to the wind, temperature and humidity turbulence observations. (4) There are nine soil temperature and moisture observation sites (D66, Tuotuohe, D110, WADD, NODA, Amdo, MS3478, MS3478 and MS3637), each of which has soil temperature measurements of 6 layers and soil moisture measurement of 9 layers. (5) A 3D Doppler Radar Station is located in the south of Nagqu, and there are seven encrypted precipitation gauges in the adjacent (within approximately 100 km) area. The radiation observation system mainly studies the plateau cloud and precipitation system and serves as a ground true value station for the TRMM satellite. The GAME-Tibet project seeks to gain insight into the land-atmosphere interaction on the Tibetan Plateau and its impact on the Asian monsoon system through enhanced observational experiments and long-term monitoring at different spatial scales. After the end of 2000, the GAME/Tibet project joined the “Coordinated Enhanced Observing Period (CEOP)” jointly organized by two international plans, GEWEX (Global Energy and Water Cycle Experiment) and CL IVAR (Climate Change and Forecast). The Asia-Australia Monsoon Project (CAMP) on the Tibetan Plateau of the Global Coordinated Enhanced Observation Program (CEOP) has been started. The data set contains POP data for 1997 and IOP data for 1998. Ⅰ. The POP data of 1997 contain the following.

1. Precipitation Gauge Network (PGN)

2. Radiosonde Observation at Naqu

3. Analysis of Stable Isotope for Water Cycle Studies

4. Doppler radar observation

5. Large-Scale Hydrological Cycle in Tibet

(Link to Numaguchi's home page)

1. Portable Automated Mesonet (PAM) [Japanese]

2. Ground Truth Data Collection (GTDC) for Satellite Remote Sensing

3. Tanggula AWS (D105 station in Tibet)

4. Syamboche AWS (GEN/GAME AWS in Nepal)

Ⅱ. The IOP data of 1998 contain the following.

1. Anduo

（1） PBL Tower, 2） Radiation, 3） Turbulence

SMTMS

1. D66 （1） AWS （2） SMTMS （3） GTDC （4) Precipitation

2. Toutouhe （1） AWS （2） SMTMS （3 ）GTDC

3. D110 （1） AWS （2） SMTMS (3) GTDC (4) SMTMS

4. MS3608 （1） AWS （2） SMTMS （3） Precipitation

5. D105 （1） Precipitation (2) GTDC

6. MS3478(NPAM) （1） PAM （2） Precipitation

7. MS3637 （1） PAM （2） SMTMS （3） Precipitation

8. NODAA （1） SMTMS (2) Precipitation

9. WADD （1） SMTMS （2） Precipitation （3） Barometricmd

10. AQB （1） Precipitation

11. Dienpa (RS2) （1） Precipitation

12. Zuri （1） Precipitation （2） Barometricmd

13. Juze （1） Precipitation

14. Naqu hydrological station （1） Precipitation

15. MSofNaqu （1） Barometricmd

16. Naquradarsite

（1）Radar system （2） Precipitation

1. Syangboche [Nepal] （1） AWS

2. Shiqu-anhe （1） AWS （2） GTDC

3. Seqin-Xiang （1） Barometricmd

4. NODA （1）Barometricmd （2） Precipitation （3) SMTMS

5. NaquHY （1） Barometricmd （2） Precipitation

6. NaquFx(BJ) （1） GTDC（2) PBLmd

Global Upper Air CARDS Synoptic Sort is digital data set DSI-6306, archived at the National Climatic Data Center (NCDC). It is meteorological upper air data. CARDS stands for Comprehensive Aerological Data Set. The goal of the CARDS project has been to produce global upper air data sets based on daily (up to four per day) radiosonde observations suitable for use in evaluating climate models and detecting tropospheric and stratospheric climate change. A major task of the CARDS project, which began in 1991, has been to build observational databases and make this data available to the research community and to NCDC customers. Data from over 27 sources has been collected, converted to a like format, and processed through the Comprehensive Hydrostatic Quality Control (CHQC). This data was then merged, based upon add/replace algorithms that took into consideration the number of mandatory and significant pressure levels and the number of errors detected during the CHQC, selecting the best observations from the available data sets. The data has then been further quality controlled via an advanced Complex Quality Control (CQC) system. The CARDS database contains some 27 million observations beginning in 1948, comprising a data set of approximately 64GB. A metadata record for each observation contains information related to the number of levels in total and by type, as well as a summary of the observation. A full suite of inventories and reports is available based upon the metadata. Building of future CARDS data sets will be based upon the detection and identification or removal of systematic errors (biases). Also, CARDS will provide an in-depth upper air station history for 2400+ stations. CARDS data includes the following purely meteorological parameters: clouds and obscuration, pressure, geopotential height, temperature, relative humidity, dew point depression, and wind speed and direction. CARDS data also includes a large number of technical specifications and quality control parameters. DSI-6306 is a quality controlled compilation of many upper air data sets. DSI-6306 is in synoptic order. The following is a partial list of the data sets that were used in the construction of DSI-6306. (These data sets should not be used separately; they contain original data as it was received at NCDC, prior to NCDC quality control.) DSI-Number Name FGDC Number DSI-6308 MIT Global Upper Air (CARDS) C00551 DSI-6309 NCAR-NMC Global Upper Air (CARDS) C00552 DSI-6310 Global Upper Air (from TDF-54) (CARDS) C00553 DSI-6311 Global Upper Air (from TDF-56) (CARDS) C00554 DSI-6314 USSR GTS Global Upper Air (CARDS) C00555 DSI-6315 Peoples Republic of China Upper Air (CARDS) C00556 DSI-6316 Argentina Upper Air (CARDS) C00557 DSI-6318 Hong Kong Upper Air (CARDS) C00558 DSI-6319 Republic of Korea Upper Air (CARDS) C00559 DSI-6320 Hungary Upper Air (CARDS) C00560 DSI-6321 Netherlands Upper Air (CARDS) C00561 DSI-6322 Australia GTS Upper Air (CARDS) C00562 DSI-6323 Australia Upper Air Thermo/Winds Merged (CARDS) C00563 DSI-6324 Brazil Upper Air (CARDS) C00564 DSI-6325 USSR Upper Air (CARDS) C00565 DSI-6326 Global Upper Air (from TDF-5683) (CARDS) C00566 DSI-6355 Russian Ice Island Upper Air (CARDS) C00567 DSI-6380 Global Aircraft Reports (CARDS) C00568 MIT = Massachusetts Institute of Technology NCAR = National Center for Atmospheric Research NMC = old National Meteorological Center, now National Centers for Environmental Prediction (NCEP) GTS = Global Telecommunications System TDF = Tape Deck Format USSR = old Union of Soviet Socialist Republics The names of the data sets indicate their source and where observations were taken. For example, DSI-6324 is Brazilian data received from Brazil; DSI-6314 USSR GTS Global Upper Air was received from the old USSR, who received it over GTS, and it is global data. NCDC maintains this data set in archive but no longer updates nor actively distributes it. It has been superseded by the Integrated Global Radiosonde Archive (IGRA) (C00616).

Dataset Overview:

This dataset contains daily time series data collected in 2023 using a Digital Water Level Recorder (DWLR). The data provides insights into the impacts of global warming, seasonal weather patterns, and environmental changes on village well near Incheon, South Korea.

Key features of the dataset include:

• Water Level: A steady rise throughout the year with abrupt increases during the summer and rainfall periods, capturing potential effects of global warming.
• Temperature: Data reflects the seasonal temperature trends of Incheon, from cold winters to hot summers. Rainfall: Shows typical monsoon trends with heavy precipitation in the summer months.
• pH Levels: Fluctuations between 6.9 and 7.4, with higher values observed during winter, and 10 outliers indicating potential environmental disturbances.
• Dissolved Oxygen: Data includes random missing values (20), simulating real-world gaps in environmental data collection.

Note: This dataset has been altered for learning purposes in time series analysis, forecasting, and environmental studies, and could be useful in climate research, hydrological modeling, and machine learning applications related to water resource management.

The number of rainy days recorded for the summer of 2024 in South Korea was **. The summer of 2020 marked a five-year high in terms of recorded rainy days in the country.

In May 2025, the average temperature in Daejeon, South Korea was 18 degrees Celsius. August 2024 was the city's hottest month in the past five years, while December 2022 was the coldest, with an average temperature of minus two degrees Celsius.