Cloud Cover Data Research Articles

Surviving the cold: Assessing long-term outcomes among Korean CKD patients exposed to low perceived temperature during winter

Perceived temperature (PT), which encompasses meteorological factors such as wind speed, cloud cover, and humidity, reflects the actual effect of temperature on the human body. However, limited data exist on the health implications of prolonged exposure to low temperatures during winter in individuals with chronic kidney disease (CKD). We investigated the association between winter PT and long-term outcomes among CKD patients. A total of 32,870 CKD patients from three tertiary hospitals in Seoul were enrolled in this retrospective study (2001–2018). PT was calculated using Staiger's equation, integrating temperature data from 29 automated weather stations across Seoul, along with dew point temperature, wind velocity, and cloud cover data. Kriging interpolation was utilized to estimate PT values at the patients' locations. Overall mortality and major adverse cardiovascular events (MACEs) were assessed using a time-varying Cox proportional hazards model. Additionally, the Cox regression model evaluated PT corresponding to temperature thresholds for cold surge watches or warnings. Over a median follow-up of 6.14 ± 3.96 years, 6147 deaths (18.7%) were recorded. We found that as the average or minimum PT and Ta decreased by 1 °C, the risk of overall mortality significantly increased. In multivariable analyses, the hazard ratio (HR) for the average PT was 1.049 (95% confidence interval [CI] 1.028–1.071), and that for the minimum PT was 1.038 (CI 1.027–1.052). Furthermore, a cold surge warning at a PT of −25.63 °C indicated an HR of 1.837 (CI 1.764–1.914) and a C-index of 0.792. The increased risk of mortality was more pronounced in patients with low or middle socioeconomic statuses. For MACEs, lower average and minimum PT and Ta were associated with an increased risk, following a similar trend to overall mortality, although not all results reached statistical significance. These findings emphasize the importance of targeted public health policies to mitigate risks among vulnerable CKD patients.

Prediction of Precipitation using Multiscale Geographically Weighted Regression

Prediction of precipitation at locations which lack meteorological measurements is a challenging task in hydrological applications. In this study we aimed to demonstrate potential use of multiscale geographically weighted regression (MGWR) method used to predict precipitation based on relevant meteorological parameters. Geographically weighted regression (GWR) is a regression technique proposed to explore spatial non-stationary relationships. Compared to the linear regression technique, GWR considers the dynamics of local behaviour and, therefore provides an improved representation of spatial variations in relationships. Multiscale geographically weighted regression (MGWR) is a modified version of GWR that examines multiscale processes by providing a scalable and flexible framework. In this study, the MGWR model was used to predict precipitation, which is an essential problem not only in meteorology and climatology, but also in many other disciplines, such as geography and ecology. A meteorological dataset including elevation, precipitation, air temperature, air pressure, relative humidity, and cloud cover data belonging to Türkiye was used, and the performance of the MGWR was assessed in comparison with that of global regression and classical GWR. Experimental evaluations demonstrated that the MGWR model outperformed other approaches in precipitation prediction.

Characterizing land use/land cover change dynamics by an enhanced random forest machine learning model: a Google Earth Engine implementation

Land use and land cover (LULC) analysis is crucial for understanding societal development and assessing changes during the Anthropocene era. Conventional LULC mapping faces challenges in capturing changes under cloud cover and limited ground truth data. To enhance the accuracy and comprehensiveness of the descriptions of LULC changes, this investigation employed a combination of advanced techniques. Specifically, multitemporal 30 m resolution Landsat-8 satellite imagery was utilized, in addition to the cloud computing capabilities of the Google Earth Engine (GEE) platform. Additionally, the study incorporated the random forest (RF) algorithm. This study aimed to generate continuous LULC maps for 2014 and 2020 for the Shrirampur area of Maharashtra, India. A novel multiple composite RF approach based on LULC classification was utilized to generate the final LULC classification maps utilizing the RF-50 and RF-100 tree models. Both RF models utilized seven input bands (B1 to B7) as the dataset for LULC classification. By incorporating these bands, the models were able to influence the spectral information captured by each band to classify the LULC categories accurately. The inclusion of multiple bands enhanced the discrimination capabilities of the classifiers, increasing the comprehensiveness of the assessment of the LULC classes. The analysis indicated that RF-100 exhibited higher training and validation/testing accuracy for 2014 and 2020 (0.99 and 0.79/0.80, respectively). The study further revealed that agricultural land, built-up land, and water bodies have changed adequately and have undergone substantial variation among the LULC classes in the study area. Overall, this research provides novel insights into the application of machine learning (ML) models for LULC mapping and emphasizes the importance of selecting the optimal tree combination for enhancing the accuracy and reliability of LULC maps based on the GEE and different RF tree models. The present investigation further enabled the interpretation of pixel-level LULC interactions while improving image classification accuracy and suggested the best models for the classification of LULC maps through the identification of changes in LULC classes.

Analysis of the summer thermal comfort indices in İstanbul

Thermal indices and thermal comfort maps have great importance in developing health-minded climate action strategies and livable urban layouts. Especially in cities where vulnerability to heatwaves is high, it is necessary to detect the most appropriate indicators for the regional characteristics and action planning with respect to thermal comfort. The aim of the study is to examine thermal indices as indicators of regional climate characteristics by relating to meteorological parameters and spatial features. Atmospheric variables including air temperature, wind speed, cloud cover, and relative humidity data were obtained from 30 meteorological stations located in districts having different climatic features. Heat stress levels for apparent temperature (AT), heat index (HI), wet bulb globe temperature (WBGT), physiological equivalent temperature (PET), universal thermal climate index (UTCI), and perceived temperature (PT) indices were calculated and associated with meteorological parameters. Thermal comfort maps have been created with the daily mean and maximum values of all indices. As a result, the meteorological parameters with the strongest correlation with all thermal indices are air temperature (Ta) with r = 0.89 ± 0.01 and mean radiant temperature (Tmrt) with r = 0.75 ± 0.16. The differences in thermal stress levels over the city have been distinctively observed in the ATmax, PETmax, and PTmax maps, which are generated by the daily maximum values of the indices. Çatalca, where forests cover large areas compared to highly urbanized districts, has the lowest heat stress defined by all indices.

An observational analysis of the eastern tropical Pacific Shallow Meridional Circulation using YOTC data and pilot balloons from Isla del Coco, Costa Rica

The low-level circulation of the atmosphere over Isla del Coco has been studied and the presence of a northerly wind at low levels of the atmosphere in the eastern tropical Pacific, in addition to the deep Hadley circulation cell, has been confirmed. Using data from pilot balloons (May 1997 through January 1999, October 2007 through April 2008), the northern flow is between 1 and 5 km high, depending on the time of year, with a maximum speed located between 2 and 3 km above the surface. The generating mechanism of the surface return flow in the Hadley circulation cell has been formulated as a sea breeze, Ekman pumping of the boundary layer, and it could even be a response to the Rossby wave generated by warming in the Chocó region. These results agree with those obtained from the ERA5 reanalysis (January 1979 through December 2020), which show that the southern return cell varies in position and height during the course of the year, with a poorly organized circulation in March and strengthening from July to February. The incorporation of data of low, medium and high cloud cover from Year of the Tropical Convection (boreal summer 2009) evidenced the presence of high-level clouds in the ITCZ region and low-level clouds to the south of the ITCZ, latitudes where the south surface circulation cell is located.

Impact of Gaussian Transformation on Cloud Cover Data Assimilation for Historical Weather Reconstruction

Abstract Old descriptive diaries are important sources of daily weather conditions before modern instrumental measurements were available. A previous study demonstrated the potential of reconstructing historical weather at a high temporal resolution by assimilating cloud cover converted from descriptive diaries. However, cloud cover often exhibits a non-Gaussian distribution, which violates the basic assumptions of most data assimilation schemes. In this study, we applied a Gaussian transformation (GT) approach to cloud cover data assimilation and conducted observing system simulation experiments (OSSEs) using 20 observation points over Japan. We performed experiments to assimilate cloud cover with large observational errors using the Global Spectral Model (GSM) and a local ensemble transform Kalman filter (LETKF). Without GT, meridional wind and temperature exhibited deteriorations in the lower troposphere compared with the experiment with no observations. In contrast, GT reduced the 2-month root-mean-square errors (RMSEs) by 5%–15% throughout the troposphere for wind, temperature, and specific humidity fields. Significant improvements include zonal wind at 500 hPa and temperature at 850 hPa with 6.4% and 7.3% improvements by GT, respectively, compared with the experiment without GT. We further demonstrate that the additional GT application to the precipitation background field improves precipitation estimation by 12.2%, with pronounced improvements over regions with monthly precipitation of less than 150 mm. We also explored the impact of cloud cover GT on a global scale and confirmed improvements extending from around the observation sites. Our results demonstrate the potential of GT in high-resolution historical weather reconstruction using old descriptive diaries. Significance Statement To reconstruct the historical weather, cloud cover information from old diaries can be used by incorporating high-resolution model simulations. However, cloud cover is not normally distributed and violates an important assumption when combining cloud cover observations with model simulations. Our results demonstrate that transforming the cloud cover distribution into a normal distribution could improve wind speed, temperature, and humidity fields in the model. We demonstrate the critical role of the transformation in a nonnormally distributed variable when combined with models and show the potential of diary-based weather information to reconstruct historical weather.

Determination of the Dynamics of Thunderstorms Through the Dry Adiabatic Lapse Rate and Environmental Lapse Rate

This research aims to determine the types of thunderstorms formed in the thickness of the cloud (determine the Dry adiabatic lapse rate (DALR) and Environmental lapse rate (ELR)) in the case of precipitation during the day. Data were taken by Temperature, Dew point, Atmospheric Pressure, and Height from re-analysis by the (ECMWF) for the heights (0-18000) m, the levels of pressure (1000-100) mbar, low cloud cover data, and the characteristic days ((18, 24, 27) February, 28 April, and 25 November) of the year 2018 for Baghdad station were chosen to obtain the largest possible number of clouds and their diversity to use them in calculating the cloud cover and weather stability in terms of calculating the daily change, temperature, dew point in addition to calculating the low cloud cover with altitude and atmospheric instability. The Sigma Plot program was used in this research to determine the base of clouds and thunderstorms. The change in temperature, Dew point, clouds base, and altitude was determined, then the cloud thickness, types, and classification were calculated. The clouds found are strong thunderstorm clouds characterized by thickness and height, such as the clouds of Nimbostratus (Ns) and Cumulonimbus (Cb).

Deep learning forecasting tool facilitating the participation of photovoltaic systems into day-ahead and intra-day electricity markets

The participation of photovoltaic (PV) power into the energy market requires the development of accurate forecasting models to alleviate the imbalance penalties and ensure the viability of the investments. Several forecasting models have been proposed, focusing on different forecasting horizons. However, their implementation does not consider the energy market rules. Here we present a Day-Ahead Market (DAM)/Intra-Day Market (IDM) forecasting tool which comprises of four individual Deep Learning (DL) forecasters to facilitate the participation of PV power into the DAM and the three auctions of IDM. Different combinations of historical and predicted data of cloud cover and temperature are examined on Convolutional Neural Network and Transformer models for the forecasters’ development. The proposed tool is tested on five PV systems, while the aggregated production is also examined considering their spatial characteristics. The results indicate that the proposed forecasting tool could decrease the Mean Absolute Error up to 31.96% and 32.45% for DAM and IDM respectively.

Accurately forecasting solar radiation distribution at both spatial and temporal dimensions simultaneously with fully-convolutional deep neural network model

Accurately forecasting solar radiation is of great significance to solar energy utilization. To forecast the spatial and temporal distributions of solar radiation simultaneously, a deep neural network model named MRE-UNet is proposed, the solar radiation data in Heilongjiang province is taken as an example to test the forecasting performance in different periods ahead forecasting cases. According to the evaluation results, an 16 h historical solar radiation data was determined to be the best choice for input, and the minimum of MSE can reach 6.47 × 10−4, 1.38 × 10−3 and 2.69 × 10−3 for 1 h, 3 h and 6 h ahead forecasting cases, respectively. The transferability of the MRE-UNet is tested by performing the solar radiation nowcasting in Hubei province, China using the pre-trained MRE-UNet trained by the solar radiation data in Heilongjiang province. The robustness of MRE-UNet is tested by monitoring the effects of adding different level of noise, and MSE keeps to be 6.27 × 10−4 even though the measuring noise increase to be 50%. For further demonstration on the effectiveness of MRE-UNet in spatiotemporal forecasting, the performance in total cloud cover forecasting is also tested, and satisfactory forecasting results are obtained. Finally, spatiotemporal correlation analysis on solar radiation and total cloud cover data is carried out, a potential reason for satisfying forecasting performance of MRE-UNet is given. From this work, MRE-UNet proposed can be provided as an efficient tool for dealing with further solar radiation spatiotemporal forecasting problem, and the spatiotemporal correlation characteristics can be employed as the basis for further developing effective solar radiation forecasting approach to a degree.

Availability, outage, and capacity of spatially correlated, Australasian free-space optical networks

Network capacity and reliability for free space optical communication (FSOC) is strongly driven by ground station availability, which is dominated by local cloud cover causing an outage. Here, we combine remote sensing data and novel methods to provide a generalized framework for assessing and optimizing optical ground station networks. This work is guided by an example network of eight Australian and New Zealand optical communication ground stations that span approximately 60° in longitude and 20° in latitude. Utilizing time-dependent cloud cover data from five satellites, we present a detailed analysis that determines the network availability and diversity, which showed that the Australasian region is well-suited for an optical network with a 69% average site availability and low spatial cloud cover correlations. Employing methods from computational neuroscience, we provide a Monte Carlo method for sampling the joint probability distribution of site availabilities for an arbitrarily sized and point-wise correlated network of ground stations. Furthermore, we develop a general heuristic for site selection under availability and correlation optimizations and combine it with orbital propagation simulations to compare the data capacity between optimized networks and the example network. We show that the example network may be capable of providing tens of terabits per day to a low Earth orbit satellite and up to 99.97% reliability to geostationary satellites. We therefore used the Australasian region to demonstrate, to the best of our knowledge, novel, generalized tools for assessing and optimizing FSOC ground station networks, as well as the suitability of the region for hosting such a network.

Analysis of sunshine duration and cloud cover trends in Lisbon for the period 1890–2018

Sunshine duration (SD) represents a valuable parameter for early years when few or none measurements of surface solar radiation (SSR) are available. In the present work, daily and monthly SD records registered in Lisbon (Portugal) for the period 1890–1940 have been digitized to expand the data series available in electronic format, which starts in 1941. The resulting series for the period 1890–2018 can be considered as the earliest one in Portugal and the second one in the Iberian Peninsula. Cloud cover (CC) data for the same period have also been digitized. The SD series exhibits a weak negative trend (without statistical significance) from the 1890s to the 1910s, which is in line with the early dimming period in SSR reported in some regions. Subsequently, no trends are obtained for the period 1910s–1950s, which indicates that the early brightening is not observed in Lisbon unlike other locations in the Iberian Peninsula. After that, two strong statistically significant trends are found for the periods 1950s–1980s and 1980s–2010s in line with the well-known global dimming and brightening periods in SSR, respectively. On the other hand, the CC series presents an increase from 1890 to the 1980s, followed by a decrease up to 2018 (both being statistically significant), which may partially explain the reported SD trends. An analysis of SD under cloudless conditions proved the utility of this quantity to track long-term changes in atmospheric aerosol load. In addition, this analysis and a seasonal one pointed out that aerosols seem to play a relevant role in SD long-term variability.

Temporal and spatial variation of cloud cover in arid regions of Central Asia in the last 40 years

Water resources are one of the key factors restricting the development of arid areas, and cloud water resources is an important part of water resources. The arid region of central Asia is the core region of the current national green silk road construction, and is the largest arid region in the world. Based on cloud cover data of ECMWF, the current study analyzed temporal and spatial characteristics of cloud properties in arid regions of Central Asia between 1980 and 2019. Our findings show that: (1) From the point of view of spatial distribution, total cloudiness in arid regions of Central Asia was low in the south and high in the north. The distribution of high cloud frequency and medium cloud frequency was higher in the south and lower in the north, while low cloud frequency distribution was low in the south and high in the north. (2) In terms of time, the variation of cloud cover and cloud type frequency had obvious seasonal characteristics. From winter to spring, cloud cover increased, and the change of cloud type frequency increased. From spring to summer, cloud cover continued to increase and the change of cloud type frequency increased further. Cloud cover began to decrease from summer to autumn, and the change of cloud type frequency also decreased. (3) Generally, average total cloud cover decreased in most of central Asia, and high and medium cloud cover increased while low cloud cover decreased. This study provides a reference for the rational development of cloud resources in the region.

Approximately linear INGARCH models for spatio-temporal counts

Abstract Existing integer-valued generalised autoregressive conditional heteroskedasticity (INGARCH) models for spatio-temporal counts do not allow for negative parameter and autocorrelation values. Using approximately linear INGARCH models, the unified and flexible spatio-temporal (B)INGARCH framework for modelling unbounded (bounded) counts is proposed. These models combine negative dependencies with kinds of a long memory. They are easily adapted to special marginal features or cross-dependencies: When modelling precipitation data (counts of rainy hours), we account for zero-inflation, while for cloud-coverage data (counts of okta), we deal with missing data and additional cross-correlation. A copula related to the spatial error model shows an appealing performance.

Developing a near Real-Time Cloud Cover Retrieval Algorithm Using Geostationary Satellite Observations for Photovoltaic Plants

Clouds can block solar radiation from reaching the surface, so timely and effective cloud cover test and forecasting is critical to the operation and economic efficiency of photovoltaic (PV) plants. Traditional cloud cover algorithms based on meteorological satellite observation require many auxiliary data and computing resources, which are hard to implement or transplant for applications at PV plants. In this study, a portable and fast cloud mask algorithm (FCMA) is developed to provide near real-time (NRT) spatial-temporally matched cloud cover products for PV plants. The geostationary satellite imager data from the Advanced Himawari Imager aboard Himawari-8 and the related operational cloud mask algorithm (OCMA) are employed as benchmarks for comparison and validation. Furthermore, the ground-based manually observed cloud cover data at seven quintessential stations at 08:00 and 14:00 BJT (Beijing Time) in 2017 are employed to verify the accuracy of cloud cover data derived from FCMA and OCMA. The results show a high consistency with the ground-based data, and the average correlation coefficient (R) is close to 0.85. Remarkably, the detection accuracy of FCMA is slightly higher than that of OCMA, demonstrating the feasibility of FCMA for providing NRT cloud cover at PV plants.

Aerosol–cloud interactions at the four candidate sites of the ANAtOLIA project

Abstract ANAtOLIA (Atmospheric monitoring to Assess the availability of Optical Links through the Atmosphere) is a European Space Agency project aimed at selecting sites for optical communication in the atmosphere. The main monitored parameters are cloud cover, aerosol in relation to atmospheric turbulence aimed at monitoring and forecasting the influence of aerosol and cloud cover in reducing optical communication through the atmosphere in selected sites by ESA. In this work, a novel algorithm that uses both the Pearson correlation coefficient and Fourier analysis is used to assess such influences. Aerosol and cloud cover data are obtained from ground stations and satellite over Calern (France), Catania (Italy), Cebreros (Spain), and Lisbon (Portugal). The novel algorithm provides a preliminary long-, medium-, and short-term aerosol–cloud interaction for these four candidate sites, obtaining respectively the variability, the seasonal, and hourly trend of the aerosol concentration; the main medium-term periodicities of aerosols as clouds precursors; the short-term correlation between morning-afternoon aerosol concentration. The use of aerosols as a precursor parameter of cloud cover through a Fourier analysis, makes the algorithm versatile and usable for all sites of optical communication and astronomical importance in which optical transparency is a fundamental requirement, and therefore it is a potential tool to be developed to implement forecasting models.

Preliminary Investigation of Total Ozone Column and Its Relationship with Atmospheric Variables

This study attempts to investigate the interaction between lower and upper atmosphere, employing daily data of Total Ozone Column (TOC) and atmospheric parameter (cloud cover) over Nigeria from 1998-2012; in order to study the dynamic effect of ozone on climate and vice versa. This is due to the fact that ozone and climate influence each other and the understanding of the dynamic effect of the interconnectivity is still an open research area. Monthly mean daily TOC and cloud cover data were obtained from the Earth Probe Total Ozone Mass Spectroscopy (EPTOMS) and the International Satellite Cloud Climatology Project (ISCCP)-D2 datasets respectively. Bivariate analysis and Mann Kendall trend tests were used in data analysis. MATLAB and ArcGIS software were employed in analyzing the data. Results reveal that TOC increased spatially from the coastal region to the north eastern region of the country. Seasonally, the highest value of TOC was observed at the peak of rainy season when cloud activity is very high, while the lowest value was recorded in dry season. These variations were attributed to rain producing mechanisms and atmospheric phenomena which influence the transport and distribution of ozone. Furthermore, the statistical analysis reveals significant relationship between TOC and low and middle cloud covers in contrast to high cloud cover. This relationship is consistent with previous studies using other atmospheric variables. This study has given scientific insight which is useful in understanding the coupling of the lower and upper atmosphere.

Comparing methods to estimate cloud cover over the Baikal Natural Territory in December 2020

The paper addresses the issue of how much cloud cover data obtained using satellite and model-interpolation techniques are suitable for monitoring the transparency of the atmosphere and determining conditions for airglow observations at a local geophysical observatory. For this purpose, we compared the temporal dynamics of cloud cover from ECMWF’s ERA5 reanalysis and NOAA satellites with the night atmosphere transparency according to a digital camera. We considered the dynamics of the addressed parameters at the Geophysical Observatory of the Institute of Solar-Terrestrial Physics, located in the Baikal Natural Territory near the village of Tory (Republic of Buryatia, Russia), during December 2020. The comparative analysis showed a generally good agreement between cloud cover data from ECMWF’s ERA5 climate reanalysis and those observed with the camera. Disadvantages are the lack of information on rapid variations in cloud cover in the reanalysis and positive and negative delays in the dynamics of cloud fields that last about two hours. Due to irregular satellite data, large time gaps between passes and difficulties in estimating cloud cover at night, we could not come to reliable conclusions concerning the applicability of satellite data.

Сопоставление методов определения облачного покрова над Байкальской природной территорией в декабре 2020 г.

The paper addresses the issue of how much cloud cover data obtained using satellite and model-interpolation techniques are suitable for monitoring the transparency of the atmosphere and determining conditions for airglow observations at a local geophysical observatory. For this purpose, we compared the temporal dynamics of cloud cover from ECMWF’s ERA5 reanalysis and NOAA satellites with the night atmosphere transparency according to a digital camera. We considered the dynamics of the addressed parameters at the Geophysical Observatory of the Institute of Solar-Terrestrial Physics, located in the Baikal Natural Territory near the village of Tory (Republic of Buryatia, Russia), during December 2020. The comparative analysis showed a generally good agreement between cloud cover data from ECMWF’s ERA5 climate reanalysis and those observed with the camera. Disadvantages are the lack of information on rapid variations in cloud cover in the reanalysis and positive and negative delays in the dynamics of cloud fields that last about two hours. Due to irregular satellite data, large time gaps between passes and difficulties in estimating cloud cover at night, we could not come to reliable conclusions concerning the applicability of satellite data.

Evaluation of the Effects of Thermal Comfort Conditions on Cardiovascular Diseases in Amasya City, Turkey.

Studies fall short when it comes to determining the relationship between thermal comfort and cardiovascular diseases. Studies examining the relationship between thermal comfort conditions and human health in Turkey, located in the transition zone of air masses at mid-latitudes, are quite limited. This is the first study conducted in Turkey that deals with thermal comfort conditions and CVDs, which is the leading cause of death. This study aimed to examine the relationship between thermal comfort conditions and CVDs of Amasya, a medium-sized exemplary Turkish city. To determine the thermal comfort conditions in the study area between 2014-2019, the physiologically equivalent temperature (PET) index obtained from the Rayman model, which uses hourly air temperature (ºC), relative humidity (%), wind speed (m/s), and cloud cover (octa) data, was used. The relationship between PET values and CVDs was determined by Pearson correlation analysis and linear regression analysis. The study indicated a negative, high, and moderate correlation between PET values and cardiovascular diseases (p < 0.001). The results show that when PET values increase by 1 ºC, patient admissions will decrease by about 104 to 108 patients (-104.737 to -108.619 units.). These results can be informative and guiding for both the protection of public health and studies on climate change and human health.

Characterizing the Long-Term Landscape Dynamics of a Typical Cloudy Mountainous Area in Northwest Yunnan, China

Detailed knowledge of landscape dynamics is crucial for many applications, from resource management to ecosystem service assessments. However, identifying the spatial distribution of the landscape using optical remote sensing techniques is difficult in mountainous areas, primarily due to cloud cover and topographic relief. Our study uses stable classification samples from mountainous areas to investigate an integrated approach that addresses large volumes of cloud-cover data (with associated data gaps) and extracts landscape time series (LTS) with a high time–frequency resolution. We applied this approach to map LTS in a typical cloudy mountainous area (Erhai watershed in northwestern Yunnan, China) using dense Landsat stacks, and then we also used the classified results to investigate the spatial–temporal landscape changes in the study area at biennial intervals. The overall accuracy of the landscape classification ranged from 81.75% to 88.18%. The results showed highly dynamic processes in the landscape throughout the study period. Forest was the main land cover type, covering approximately 39.19% to 41.68% of the total study area. Alpine meadow showed fluctuating trends, with a net loss of 11.22% and an annual reduction rate of −0.4%. Shrub cover increased by 1.26%, and water bodies showed a small decrease in area, resulting in an overall net change of −0.03%. Built-up land and farmland areas continued to expand, and their annual growth rates were 1.52% and 1.06%, respectively. Bare land showed the highest loss, with a net change of 228.97 km2. In the Erhai watershed, all the landscape classes changed or transitioned into other classes, and a substantial decrease in bare land occurred. The biennial LTS maps allow us to fully understand the spatially and temporally complex change processes occurring in landscape classes; these changes would not be observable at coarse temporal intervals (e.g., 5–10 years). Our study highlights the importance of increasing the temporal resolution in landscape change studies to support sustainable land resource management strategies and integrate landscape planning for environmental conservation.