Climatological Research Research Articles

Comparison of indicators to evaluate the performance of climate models

AbstractThe evaluation of climate models is a crucial step in climate studies. It consists of quantifying the resemblance of model outputs to reference data to identify models with superior capacity to replicate specific climate variables. Clearly, the choice of the evaluation indicator significantly impacts the results, underscoring the importance of selecting an indicator that properly captures the characteristics of a “good model”. This study examines the behaviour of six indicators, considering spatial correlation, distribution mean, variance and shape. Monthly data for precipitation, temperature and teleconnection patterns in Central America were utilized in the analysis. A new multicomponent measure was selected based on these criteria to assess the performance of 32 CMIP6 models in reproducing the annual seasonal cycle of these variables. The top six models were determined using multicriteria methods. It was found that even the best model reproduces one derived climatic variable poorly in this region. The proposed measure and selection method can contribute to enhancing the accuracy of climatological research based on climate models.

A Framework for Measuring Tree Rings Based on Panchromatic Images and Deep Learning.

Tree-ring data are pivotal for decoding the age and growth patterns of trees, reflecting the impact of environmental factors over time. Addressing the significant shortcomings of traditional, labour-intensive and resource-demanding methods, we propose an innovative automated technique that utilizes panchromatic images and deep learning for measuring tree rings. The method utilizes convolutional neural networks to enhance image quality, precisely delineate tree rings through segmentation and perform ring counting and width calculation in the post-processing stage. We compiled an extensive data set from diverse sources, including Beijing Forestry University and the Summer Palace, to train our algorithm. The performance of our method was validated empirically, demonstrating its potential to transform tree-ring analysis and provide deeper insights into ecological and climatological research.

Examining the non-linear relationship between urban form and air temperature at street level: A case of Hong Kong

The relationship between urban form and localized air temperature has been studied extensively using linear regression. However, the findings remain inconsistent, and few studies have explored alternative data modeling techniques. With the rise of machine learning, there is an opportunity to explore new methods in urban climatology research. This study aims to test the hypothesis that machine learning models, rather than linear regression, can better explain and predict urban air temperature fluctuations in space and time. Measurements of air temperature were conducted at street level in Hong Kong. Urban form characteristics surrounding the measurement locations were extracted from geo-spatial databases and street view imagery. The datasets were then used to test the performance of linear regression, Artificial Neural Network (ANN), and Random Forest (RF) in predicting the spatial-temporal temperature fluctuations. The results indicate that the relationships between urban form and air temperature are predominantly non-linear. Both ANN and RF outperformed linear regression in prediction, with an MAE of 0.43 °C and 0.33 °C respectively. This study highlights the potentials of machine learning models in advancing knowledge of the impact of urban form on localized air temperature.

Cyclogenesis: Simulating Hurricanes and Tornadoes

Cyclones are large-scale phenomena that result from complex heat and water transfer processes in the atmosphere, as well as from the interaction of multiple hydrometeors , i.e., water and ice particles. When cyclones make landfall, they are considered natural disasters and spawn dread and awe alike. We propose a physically-based approach to describe the 3D development of cyclones in a visually convincing and physically plausible manner. Our approach allows us to capture large-scale heat and water continuity, turbulent microphysical dynamics of hydrometeors, and mesoscale cyclonic processes within the planetary boundary layer. Modeling these processes enables us to simulate multiple hurricane and tornado phenomena. We evaluate our simulations quantitatively by comparing to real data from storm soundings and observations of hurricane landfall from climatology research. Additionally, qualitative comparisons to previous methods are performed to validate the different parts of our scheme. In summary, our model simulates cyclogenesis in a comprehensive way that allows us to interactively render animations of some of the most complex weather events.

Morphometric analysis of Mogamureru river basin at the YSR Kadapa District, Andhra Pradesh, India using GIS and remote sensing

Water availability and scarcity are impacted by geomorphological changes that occur within a catchment. As a result, determining the influence of geomorphological processes on the catchment’s hydrology requires a quantitative study of the catchment geometry. Approaches based on remote sensing (RS) and geographic information systems (GIS) have grown in popularity in recent years because they assist strategists and decision-makers in making accurate and effective choices and plans. For this research, the Mogamureru River basin was chosen. The study shows that GIS and RS data can be used to analyse and approximate the period and erosional operations’ risk in a Mogamureru river basin for better design and maintenance. The quickest and most economical method of displaying the hydrologic and physiographic features of the Mogamureru river basin is to assess the morphometric parameters. All of the following parameters such as: linear (7) areal (5), relief (5), and drainage texture (5) characteristics were obtained with hypsometric curve for the Mogamureru river basin utilizing the Shuttle Radar Topography Mission (SRTM) images. According to the linear parameter values, the majority of streams (82 %) belong to the first order, Statistical analysis shows that there is a good relationship between stream order and stream length, as well as stream order and stream number. The circularity ratio (0.55) depicts the young, elongated topography. The basin as a whole is in the mature stage of formation, with good to moderate potential, according to the hypsometric index (0.46). Due to land deterioration, Pulivendula and Vemula have had the most soil loss. This study concludes that morphometric analysis based on GIS & remote sensing techniques is a competent tool for hydrological studies. The research findings will help policymakers for integrated river basin management, agricultural development, and water management. In addition, researchers of morph hydrological, geological and climatological research will be beneficiary.

GWSM4C: A global wave surrogate model for climate simulation based on a convolutional architecture

Accurate and rapid ocean surface wave simulations are essential for navigation safety, marine activity, and climate change research. However, traditional numerical wave models incur substantial computational costs, especially in long-term simulations for climatology research. Deep-learning methods have shown promise in enabling faster simulations with fewer computational resources. However, the cumulative error in current deep-learning wave models limits their use as surrogate component models in climate modeling. In this work, a deep-learning model, named the Global Wave Surrogate Model for Climate simulation (GWSM4C), was developed based on a convolutional architecture, which accounts for the physical processes (wind waves and swells) of waves. Because GWSM4C can automatically generate initial conditions for each simulation moment depending only on historical wind fields, the accumulation of errors from previous simulated wave states can be avoided. The experimental results demonstrated that simulated global significant wave height produced by GWSM4C exhibited a correlation coefficient of 0.94 and a root-mean-square error of 0.21 m compared with the traditional numerical wave model. Furthermore, the generalization test of the GWSM4C indicates that it is feasible to use GWSM4C for long-term wave simulation in climatology-related studies or to integrate it into a coupled climate model as a surrogate wave component model.

Morphometric analysis of Halda River basin, Bangladesh, using GIS and remote sensing techniques

GIS and remote sensing techniques were effectively used to analyse the morphometric parameters including linear, geometric, basin texture (aerial) and relief aspects of the Halda River Basin, Bangladesh. Along with measuring the morphometric parameters using predetermined formulas, advanced geo-computing tools of spatial analysis, cartography, math, geoprocessing and geometric analysis were employed to carry out the spatial distribution of selected parameters, especially aerial parameters. The linear aspect indicates the basin is six-order and oval-shaped. The bifurcation ratio (4.03) and relevant parameters indicate the moderate effect of geology and structural control is evident. The mean stream length (1.27) and Rho value (ranges between 0.11 and 0.20) indicate high runoff in steep areas and hydrologic storage capacity in flat areas. The stream frequency (0.83), drainage density (1.22), drainage intensity (0.68), infiltration ratio (1.02), length of the overland flow (0.41), and constant of channel maintenance (0.82) indicate the presence of moderate hard rock, less structural disturbances and moderate to high surface runoff in the basin. Basin relief (489 m), relative relief (2.02), ruggedness number (400), Melton's ruggedness number (12.43), and mean slope (9.33%) indicate the potential of high erosion and material transfer. The spatial distribution of selected aerial aspects significantly correlated to elevation and slope. The hierarchical pattern and spatial distribution of the morphometric parameters indicate areas with high slopes and lower-order streams have a high potential to be affected by soil erosion, landslides and flash floods, elsewhere, the areas with low slopes are prone to short-duration riverine floods. The research findings will help policymakers for integrated river basin management, agricultural development, and water management. In addition, researchers of morphohydrological, geological and climatological research will be beneficiary.

A Geomedical Survey: Is There an Association Between Climatic Conditions and Leishmania Species Distribution in Iran During the Years 1999–2021?

PurposeIran is among the high-risk leishmaniasis regions in the world. WHO recommends the use of GIS as an ideal tool for healthcare authorities to predict the evolution of a disease, delimit the risk of outbreaks and identify critical areas. The aim of this research is to find the association between the main species of Leishmania (L. major, L. tropica, L. infantum) dispersion and climatic variables in Iran.MethodsAll molecular-based reports of leishmaniasis from Iran between 1999 and 2021 were gathered from reliable medical sources. Meteorological data (air and soil temperatures, annual rainfall and humidity) of the country along the study period were obtained from the Iranian Climatological Research Centre. The data concerning species distribution and climatic conditions during this period were moved to a base-map through raster layers using ArcGIS 10.4.1 software. The relationship between parasitological and climatic models was examined using ANOVA.ResultsHigh risk area maps, based on the cut-off thresholds, were generated for Leishmania major, L. tropica and L. infantum. According to the molecular-based reports, the L. major distribution was significantly related to all climatic variables, while L. tropica was merely related to rainfall and humidity, and the L. infantum distribution was significantly associated with rainfall, soil and air temperatures.ConclusionThe association between climatic conditions and Leishmania species distribution in Iran has been confirmed. Consequently, both, the relationship between climatic conditions and the geographical distribution of Leishmania species, and the use of GIS to better understand the spatial epidemiology of leishmaniasis, have been reaffirmed.

Temporal Trend Analysis of Annual, Maximum, and Seasonal Rainfall in Selected Weather Stations in Region IV-A, Philippines

Temporal variability analysis of rainfall is important for planning agricultural crop production activities, designing water resource systems, and conducting climatological research, particularly climate change analysis. This study aimed to analyze the temporal trends of annual (total and 1-d maximum) and seasonal (total) rainfall observed in Region IV-A. Historical rainfall data were collected from seven weather stations scattered all over the region. Each dataset was initially subjected to a series of homogeneity tests (standard normal homogeneity, Pettitt’s, Buishand’s range, and von Neumann ratio), followed by trend analysis using both linear regression analysis and non-parametric methods [Mann-Kendall (MK), modified MK (MMK), and Sen’s slope (SS) estimator]. All data series proved to be homogeneous except for the annual and seasonal (DJF) total rainfall in Sangley Point station. The results of the homogeneity test in Sangley Point in annual and DJF timescale suggest the consideration of the breakpoint on the years of 1984 and 1999, respectively, for trend analysis. Temporal trend based on linear regression showed a weak correlation between rainfall of various time scales and time in years, with only three stations showing a significant trend at a 5% significance level – namely, Los Baños (DJF, 2.86 mm/yr), Tayabas (DJF, 7.99mm/yr), and Sangley Point (JJA, 9.32 mm/yr). On the other hand, both MK and SS indicates a significant increase in the seasonal (DJF) total rainfall in both stations of Ambulong (1.04 mm/yr) and Los Baños (2.68 mm/yr). Overall, the results of trend analysis revealed an increasing trend in the cumulative rainfall across timescales in most stations in the region which has an implication on the timing of the optimum cropping patterns in agricultural areas and on the hydrology of the watersheds in the region. This study has generated new knowledge and information that could prove useful for agricultural production management and for proper planning, design, and management of water resource systems.

A composite approach to produce reference datasets for extratropical cyclone tracks: application to Mediterranean cyclones

Abstract. Many cyclone detection and tracking methods (CDTMs) have been developed in the past to study the climatology of extratropical cyclones. However, all CDTMs have different approaches in defining and tracking cyclone centers. This naturally leads to cyclone track climatologies with inconsistent physical characteristics. More than that, it is typical for CDTMs to produce a non-negligible number of tracks of weak atmospheric features, which do not correspond to large-scale or mesoscale vortices and can differ significantly between CDTMs. Lack of consensus in CDTM outputs and the inclusion of significant numbers of uncertain tracks therein have long prohibited the production of a commonly accepted reference dataset of extratropical cyclone tracks. Such a dataset could allow comparable results on the analysis of storm track climatologies and could also contribute to the evaluation and improvement of CDTMs. To cover this gap, we present a new methodological approach that combines overlapping tracks from different CDTMs and produces composite tracks that concentrate the agreement of more than one CDTM. In this study we apply this methodology to the outputs of 10 well-established CDTMs which were originally applied to ERA5 reanalysis in the 42-year period of 1979–2020. We tested the sensitivity of our results to the spatiotemporal criteria that identify overlapping cyclone tracks, and for benchmarking reasons, we produced five reference datasets of subjectively tracked cyclones. Results show that climatological numbers of composite tracks are substantially lower than the ones of individual CDTMs, while benchmarking scores remain high (i.e., counting the number of subjectively tracked cyclones captured by the composite tracks). Our results show that composite tracks tend to describe more intense and longer-lasting cyclones with more distinguished early, mature and decay stages than the cyclone tracks produced by individual CDTMs. Ranking the composite tracks according to their confidence level (defined by the number of contributing CDTMs), it is shown that the higher the confidence level, the more intense and long-lasting cyclones are produced. Given the advantage of our methodology in producing cyclone tracks with physically meaningful and distinctive life stages, we propose composite tracks as reference datasets for climatological research in the Mediterranean. The Supplement provides the composite Mediterranean tracks for all confidence levels, and in the conclusion we discuss their adequate use for scientific research and applications.

Evaluation of global teleconnections in CMIP6 climate projections using complex networks

Abstract. In climatological research, the evaluation of climate models is one of the central research subjects. As an expression of large-scale dynamical processes, global teleconnections play a major role in interannual to decadal climate variability. Their realistic representation is an indispensable requirement for the simulation of climate change, both natural and anthropogenic. Therefore, the evaluation of global teleconnections is of utmost importance when assessing the physical plausibility of climate projections. We present an application of the graph-theoretical analysis tool δ-MAPS, which constructs complex networks on the basis of spatio-temporal gridded data sets, here sea surface temperature and geopotential height at 500 hPa. Complex networks complement more traditional methods in the analysis of climate variability, like the classification of circulation regimes or empirical orthogonal functions, assuming a new non-linear perspective. While doing so, a number of technical tools and metrics, borrowed from different fields of data science, are implemented into the δ-MAPS framework in order to overcome specific challenges posed by our target problem. Those are trend empirical orthogonal functions (EOFs), distance correlation and distance multicorrelation, and the structural similarity index. δ-MAPS is a two-stage algorithm. In the first place, it assembles grid cells with highly coherent temporal evolution into so-called domains. In a second step, the teleconnections between the domains are inferred by means of the non-linear distance correlation. We construct 2 unipartite and 1 bipartite network for 22 historical CMIP6 climate projections and 2 century-long coupled reanalyses (CERA-20C and 20CRv3). Potential non-stationarity is taken into account by the use of moving time windows. The networks derived from projection data are compared to those from reanalyses. Our results indicate that no single climate projection outperforms all others in every aspect of the evaluation. But there are indeed models which tend to perform better/worse in many aspects. Differences in model performance are generally low within the geopotential height unipartite networks but higher in sea surface temperature and most pronounced in the bipartite network representing the interaction between ocean and atmosphere.

Remote sensing strategies to monitoring land use maps with AVHRR and MODIS data over the South Asia regions.

In South Asia, annual land use and land cover (LULC) is a severe issue in the field of earth science because it affects regional climate, global warming, and human activities. Therefore, it is vitally essential to obtain correct information on the LULC in the South Asia regions. LULC annual map covering the entire period is the primary dataset for climatological research. Although the LULC annual global map was produced from the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset in 2001, this limited the perspective of the climatological analysis. This study used AVHRR GIMMS NDVI3g data from 2001 to 2015 to randomly forests classify and produced a time series of the annual LULC map of South Asia. The MODIS land cover products (MCD12Q1) are used as data from reference for trained classifiers. The results were verified using the annual map of the LULC time series, and the space-time dynamics of the LULC map were shown in the last 15years, from 2001 to 2015. The overall precision of our 15-year land cover map simplifies 16 classes, which is 1.23% and 86.70% significantly maximum as compared to the precision of the MODIS data map. Findings of the past 15years show the changing detection that forest land, savanna, farmland, urban and established land, arid land, and cultivated land have increased; by contrast, woody prairie, open shrublands, permanent ice and snow, mixed forests, grasslands, evergreen broadleaf forests, permanent wetlands, and water bodies have been significantly reduced over South Asia regions.

Anatomy of a storm: A review of shape analysis research that fuses form and function in weather forecasting and analysis

Features in the natural and built environment can be viewed as objects, and an object’s shape provides valuable information about the physical processes that generate those features. Formally, shape is defined as an object’s characteristics independent of rotation, translation, and scale. Shape analysis involves quantification of an object’s form. Shape metrics, or indices, are mathematical quantities that characterize the object’s size and shape. Shape analysis has a rich history in geography and, more specifically, in meteorology and climatology research, with early examples in the identification of comma-shaped clouds in satellite imagery and “hook echoes” in radar reflectivity imagery. At its basis, shape analysis can be characterized as image analysis, which involves processing an image to extract meaningful information. Shape analysis usually involves image segmentation to isolate objects of interest and region analysis to calculate statistical data about these object(s). Current shape analysis research in meteorology and climatology can be split into two broad themes: (1) verification studies to compare model forecasts with observations and (2) process studies that provide information about the dynamical structure of a particular weather or climate phenomenon. In this report, I provide examples of emerging research that uses shape analysis to study tropical cyclones, mesoscale weather phenomena, and atmospheric rivers. Thus far, most of the process studies have been related to TC structure. Future research should also consider innovative approaches to image segmentation, new spatial verification methods for ensemble forecasting products, and more shaped-based process studies in mesoscale meteorology.

Assessment of the capability of modern reanalyses to simulate precipitation in warm months using adjusted radar precipitation

Study regionCentral Europe Study focusPrecipitation fields from reanalyses have been widely used for hydrological applications, meteorological extreme analyses or climatological research. Modern reanalyses provide all characteristics in regular grids with high resolutions and thus can substitute for gridded in situ, radar and satellite observations. Here, we assess the ability of three modern reanalyses with different spatial resolutions and different rates of assimilation to reproduce precipitation in Central Europe. Two global (ERA-5 and ERA-5 Land) and one regional (Mescan-Surfex) reanalysis products are evaluated against adjusted radar-derived precipitation totals in the warm parts of the 17-year period. New hydrological insights for the regionA comparison of daily precipitation totals generally shows good agreement with the observations in all reanalyses. However, the detailed analysis of climatological indices shows a general overestimation of precipitation sums and the number of wet days in all tested months but an underestimation of high precipitation on monthly or seasonal sums and a lower occurrence of days with sums above 10 mm. Mescan-Surfex seems to be the most accurate of all three reanalyses, with the exception of the years between 2009 and 2012, with very low precipitation in southern Bohemia. The largest differences between radar and reanalysis precipitation characteristics are detected in the summer months when convective precipitation prevails. The obtained results can be used for further applications in hydrological modelling.

IGS ROTI Maps: Current Status and Its Extension towards Equatorial Region and Southern Hemisphere

The International GNSS Service (IGS) diurnal ROTI maps ionospheric product was developed to characterize ionospheric irregularities occurrence over the Northern hemisphere and has been available for the community since 2014. Currently, the diurnal ROTI maps database hosted by NASA CDDIS covers the period from 2010 to now. Here, we report the ROTI maps product operational status and important changes in the product availability and access. Apart from actual ROTI maps product production, we work on the extension of ROTI maps to cover not only the Northern hemisphere but also the area of the Southern hemisphere and equatorial/low latitude region. Such extended ROTI maps are important for ionospheric irregularities climatology research and ionospheric responses to space weather. We present recent development toward the new ROTI maps product and the updated data format. To evaluate extended the ROTI maps performance, we analyzed the ability to represent key features of ionospheric irregularity occurrence over the Southern hemisphere and low latitudes. For auroral and midlatitudes, we present the cross-comparison of ROTI-derived irregularities patterns over the Northern and Southern hemispheres. For low latitudes, we examined the sensitivity of the resulted ROTI maps to detect plasma irregularities associated with equatorial plasma bubbles development for low, middle, and high solar activity periods.

CHARACTERISTICS OF JOINT DISTRIBUTION MODELS AND RETURN PERIOD OF EXTREME RAINFALL VOLUME AND INTENSITY

This article highlights a new approach to finding a copula model that most closely matches extreme rainfall characteristics in South Sulawesi, Indonesia. In climatology research, data that does not meet normal assumptions are often found due to extreme observations; therefore, a method is needed to overcome the dependency of variables that do not meet the normality assumption. The copula approach is a method that can determine the relationship between such variables. Copula can describe the dependency structure between variables with different margins and model its tail dependencies. This study discusses the application of Archimedean copula in modeling the dependency structure of both variables, namely the intensity of the 75th percentile (I75) and volume of the 75th percentile (P75). For explaining the dependencies of both variables, the best copula model was selected using the empirical copula method. The results showed that 16 stations followed the Clayton copula model, 23 stations followed the Gumbel copula model, and 14 stations followed the Frank copula model. After knowing the distribution model characteristics and the intensity and volume of extreme rainfall, the return period value on the intensity and volume of extreme rainfall was calculated. The return period value can be useful as information and evaluation valuable material for preparing natural hazard mitigation programs.

Implementing urban canopy height derived from a TanDEM-X-DEM: An expert survey and case study

Three-dimensional urban vegetation data provide crucial ecological information, but generally feature poor coverage and availability. The spaceborne radar interferometer TanDEM-X delivers a digital elevation model that can be processed to produce urban Canopy Height Models (CHMs), including vegetation height and area per 12-metre cell. To date, these CHMs are underutilised in urban research. This study therefore explores the potential of urban TanDEM-X CHM by combining an expert survey on potential directions for CHM in urban research and a case study conducting and integrating a CHM for climatological research in the city of Yazd, Iran. We complement these empirical analyses with a detailed literature section of the current state of application of TanDEM-X-CHM. The expert survey highlights key research directions and interests for urban CHM implementation. The case study provides an example of implementation based on findings from the expert survey, revealing effects of vegetation height and volume on land surface temperature. Our results underscore the unique characteristics of a TanDEM-X-based CHM, asserting their potential for producing three-dimensional vegetation information crucial for effective research and policy efforts in urban ecology.

Whither physical geography Redux: Revisiting the place of physical geography in the United States

The differentiation of human and physical geography is a fundamental distinction in Geography. This commentary examines the past, present, and possible future places of physical geography within Geography, focusing on the situation in the United States. Physical geography played a major role in the establishment of Geography as an academic discipline, but became marginalized soon thereafter. For the past 80 years, Geography in the United States has largely been a social science that has uneasily accommodated a natural-science component within it. Although somewhat healthy during the latter part of the 20th century, physical geography appears to be on the decline. In part, this situation reflects the social-science identity of Geography, a lack of adequate training of physical geographers in basic science and mathematics, as well as growing emphasis on research in geomorphology, climatology, and biogeography, the major subfields of physical geography, by cognate disciplines. The future of physical geography is in doubt: it could keep struggling to compete with better-trained scientists in cognate disciplines, evolve into a geospatial methods branch of the earth and environmental sciences, or pursue integrated research with human geographers to address Anthropocene concerns. Any of these paths forward has important implications for trying to sustain the process approach to inquiry that has underpinned research in physical geography since the 1950s. Regardless of how physical geography evolves, it needs to seriously address issues of equity, diversity, and inclusion. Doing so will only enhance whatever it becomes.

Wind kinetic energy climatology and effective resolution for the ERA5 reanalysis

ERA5 represents the state of the art for atmospheric reanalyses and is widely used in meteorological and climatological research. In this work, this dataset is evaluated using the wind kinetic energy spectrum. Seasonal climatologies are generated for 30° latitudinal bands in the Northern Hemisphere (periodic domain) and over the North Atlantic area (limited-area domain). The spectra are also assessed to determine the effective resolution of the reanalysis. The results present notable differences between the latitudinal domains, indicating that ERA5 is properly capturing the synoptic conditions. The seasonal variability is adequate too, being winter the most energetic, and summer the least energetic season. The limited area domain results introduce a larger energy density and range. Despite the good results for the synoptic scales, the reanalysis’ spectra are not able to properly reproduce the dissipation rates at mesoscale. This is a source of uncertainties which needs to be taken into account when using the dataset. Finally, a cyclone tropical transition is presented as a case study. The spectrum generated shows a clear difference in energy density at every wavelength, as expected for a highly-energetic status of the atmosphere.

A 30 m-resolution land use-land cover product for the Colombian Andes and Amazon using cloud-computing

Land use-land cover (LULC) data are critical inputs for policy and scientific research in hydrology, climatology, territory planning and conservation. Colombia, a megadiverse country and deforestation hotspot on Earth, critically needs recent and high-resolution information on land use and land cover. Using Landsat OLI 8 data, we here present a new LULC product for the Colombian Amazon and Andes at 30 m × 30 m resolution for year 2018, using classification and validation procedures based on the Google Earth Engine cloud-based platform. The novel products show high overall accuracy (>90%), are achieved with high automatization and improve current coarser LULC datasets for Colombia. All the processing procedures used are open access and distributable to the scientific community.