Global Model Data Research Articles

Bias correction and ensemble techniques in statistical downscaling model for rainfall prediction using Tweedie-LASSO in West Java, Indonesia

Rainfall is a climate element with high variations in space and time scales, so it is not easy to predict. One way to predict rainfall is statistical downscaling (SD). SD can predict local rainfall based on Global Circulation Model (GCM) data. The Decadal Climate Prediction Project (DCPP), one of the GCMs, originates from adjacent grids and experiences multicollinearity problems. Rainfall as a response variable is Tweedie Compound Poisson Gamma (TCPG) distribution data because it has a discrete component (rainfall events) and a continuous component (rainfall intensity), so SD modelling will be carried out using Tweedie-LASSO. This research aims to compare the performance of bias correction and ensemble methods in SD in predicting rainfall in West Java, Indonesia. Bias correction uses Empirical Quantile Mapping (EQM) with CHIRPS data, and the ensemble method uses a stacking technique with Random Forest (Stacking-RF) due to the varied characteristics of DCPP model sources. Evaluation results using Root Mean Square Error Prediction (RMSEP) and correlation coefficient show that bias correction improves single-model performance but not ensemble models. Besides that, ensemble models outperform single models both before and after bias correction. The combination of bias correction and ensemble modelling can be recommended when conducting SD to enhance the prediction capability of rainfall at stations and other areas.

Predictability of Hurricane Storm Surge: An Ensemble Forecasting Approach Using Global Atmospheric Model Data

Providing storm surge risk information at multi-day lead times is critical for hurricane evacuation decisions, but predictability of storm surge inundation at these lead times is limited. This study develops a method to parameterize and adjust tropical cyclones derived from global atmospheric model data, for use in storm surge research and prediction. We implement the method to generate storm tide (surge + tide) ensemble forecasts for Hurricane Michael (2018) at five initialization times, using archived operational ECMWF ensemble forecasts and the dynamical storm surge model ADCIRC. The results elucidate the potential for extending hurricane storm surge prediction to several-day lead times, along with the challenges of predicting the details of storm surge inundation even 18 h before landfall. They also indicate that accurately predicting Hurricane Michael’s rapid intensification was not needed to predict the storm surge risk. In addition, the analysis illustrates how this approach can help identify situationally and physically realistic scenarios that pose greater storm surge risk. From a practical perspective, the study suggests potential approaches for improving real-time probabilistic storm surge prediction. The method can also be useful for other applications of atmospheric model data in storm surge research, forecasting, and risk analysis, across weather and climate time scales.

Assessment of surface ozone products from downscaled CAMS reanalysis and CAMS daily forecast using urban air quality monitoring stations in Iran

Abstract. Tropospheric ozone time series consist of the effects of various scales of motion, from meso-scales to large timescales, which are often challenging for global models to capture. This study uses two global datasets, namely the reanalysis and the daily forecast of the Copernicus Atmosphere Monitoring Service (CAMS), to assess the capability of these products in presenting ozone's features on regional scales. We obtained 16 relevant meteorological and several pollutant species, such as O3, CO, NOx, etc., from CAMS. Furthermore, we employed a comprehensive set of in situ measurements of ozone at 27 urban stations in Iran for the year 2020. We decomposed the time series into three spectral components, i.e., short (S), medium (M), and long (L) terms. To cope with the scaling issue between the measured data and the CAMS' products, we developed a downscaling approach based on a long short-term memory (LSTM) neural network method which, apart from modeled ozone, also assimilated meteorological quantities as well as lagged O3 observations. Results show the benefit of applying the LSTM method instead of using the original CAMS products for providing O3 over Iran. It is found that lagged O3 observation has a larger contribution than other predictors in improving the LSTM. Compared with the S, the M component shows more associations with observations, e.g., correlation coefficients larger than 0.7 for the S and about 0.95 for the M in both models. The performance of the models varies across cities; for example, the highest error is for areas with high emissions of O3 precursors. The robustness of the results is confirmed by performing an additional downscaling method. This study demonstrates that coarse-scale global model data, such as CAMS, need to be downscaled for regulatory purposes or policy applications at local scales. Our method can be useful not only for the evaluation but also for the prediction of other chemical species, such as aerosols.

Accurate Height Determination in Uneven Terrains with Integration of Global Navigation Satellite System Technology and Geometric Levelling: A Case Study in Lebanon

The technology for determining a point’s coordinates on the earth’s surface using the global navigation satellite system (GNSS) is becoming the norm along with ground-based methods. In this case, determining coordinates does not cause any particular difficulties. However, to identify normal heights using this technology with a given accuracy, special research is required. The fact is that satellite determinations of geodetic heights (h) over an ellipsoid surface differ from ground-based measurements of normal height (HN) over a quasi-geoid surface by a certain value called quasi-geoid height or height anomaly (ζ). In relation to determining heights of a certain territory, the concept of geoid height (N) is usually operated when dealing with a geoid model. In this work, geodetic and normal heights are determined for five control points in three different regions in Lebanon, where measurements are carried out using GNSS technology and geometric levelling. The obtained quasi-geoid heights are compared with geoid heights derived from the global Earth model EGM2008. The results obtained showed that, in the absence of gravimetric data, the combination of global Earth model data, geometric levelling for selected areas, and satellite determinations allows for the creation of a highly accurate altitude network for mountainous areas.

Assessment of WRF-CO2 simulated vertical profiles of CO2 over Delhi region using aircraft and global model data

High-resolution regional model simulation of CO2 may be more beneficial to reduce the uncertainty in estimation of CO2 source and sink via inverse modeling. However, the study of atmospheric CO2 transport with regional models is rare over India. Here, weather research and forecasting chemistry model adjusted for CO2 (WRF-CO2) is used for simulating vertical profile of CO2 and its assessment is performed over Delhi, India (27.4–28.6° N and 77–96° E) by comparing aircraft observations (CONTRAIL) and a global model (ACTM) data. During August and September, the positive vertical gradient (~ 13.4 ppm) within ~ 2.5 km height is observed due to strong CO2 uptake by newly growing vegetation. A similar pattern (~ 4 ppm) is noticed in February due to photosynthesis by newly growing winter crops. The WRF-CO2 does not show such steep increasing slope (capture up to 5%) during August and September but same for February is estimated ~ 1.7 ppm. Generally, CO2 is quite well mixed between ~ 2.5 and ~ 8 km height above ground which is well simulated by the WRF-CO2 model. During stubble burning period of 2010, the highest gradient within 2.5 km height above ground was recorded in October (− 9.3 ppm), followed by November (− 7.6 ppm). The WRF-CO2 and ACTM models partially capture these gradients (October − 3.3 and − 2.7 ppm and November − 3.8 and − 4.3 ppm respectively). A study of the seasonal variability of CO2 indicates seasonal amplitudes decrease with increasing height (amplitude is ~ 21 ppm at the near ground and ~ 6 ppm at 6–8 km altitude bin). Correlation coefficients (CC) between the WRF-CO2 model and observation are noted to be greater than 0.59 for all the altitude bins. In contrast to simulated fossil CO2, the biospheric CO2 is in phase with observed seasonality, having about 80% at the lowest level and gradually declines with height due to mixing processes, reaching around 60% at the highest level. The model simulation reveals that meteorology plays a significant role of the horizontal and vertical gradient of CO2 over the region.

With super SDMs (machine learning, open access big data, and the cloud) towards more holistic global squirrel hotspots and coldspots

Species-habitat associations are correlative, can be quantified, and used for powerful inference. Nowadays, Species Distribution Models (SDMs) play a big role, e.g. using Machine Learning and AI algorithms, but their best-available technical opportunities remain still not used for their potential e.g. in the policy sector. Here we present Super SDMs that invoke ML, OA Big Data, and the Cloud with a workflow for the best-possible inference for the 300 + global squirrel species. Such global Big Data models are especially important for the many marginalized squirrel species and the high number of endangered and data-deficient species in the world, specifically in tropical regions. While our work shows common issues with SDMs and the maxent algorithm (‘Shallow Learning'), here we present a multi-species Big Data SDM template for subsequent ensemble models and generic progress to tackle global species hotspot and coldspot assessments for a more inclusive and holistic inference.

Improving model-data mismatch for photon-counting detector model using global and local model parameters.

An energy-discriminating capability of a photon counting detector (PCD) can provide many clinical advantages, but several factors, such as charge sharing (CS) and pulse pileup (PP), degrade the capability by distorting the measured x-ray spectrum. To fully exploit the merits of PCDs, it is important to characterize the output of PCDs. Previously proposed PCD output models showed decent agreement with physical PCDs; however, there were still scopes to be improved: a global model-data mismatch and pixel-to-pixel variations. In this study, we improve a PCD model by using count-rate-dependent model parameters to address the issues and evaluate agreement against physical PCDs. The proposed model is based on the cascaded model, and we made model parameters condition-dependent and pixel-specific to deal with the global model-data mismatch and the pixel-to-pixel variation. The parameters are determined by a procedure for model parameter estimation with data acquired from different thicknesses of water or aluminum at different x-ray tube currents. To analyze the effects of having proposed model parameters, we compared three setups of our model: a model with default parameters, a model with global parameters, and a model with global-and-local parameters. For experimental validation, we used CdZnTe-based PCDs, and assessed the performance of the models by calculating the mean absolute percentage errors (MAPEs) between the model outputs and the actual measurements from low count-rates to high count-rates, which have deadtime losses of up to 24%. The outputs of the proposed model visually matched well with the PCD measurements for all test data. For the test data, the MAPEs averaged over all the bins were 49.2-51.1% for a model with default parameters, 8.0-9.8% for a model with the global parameters, and 1.2-2.7% for a model with the global-and-local parameters. The proposed model can estimate the outputs of physical PCDs with high accuracy from low to high count-rates. We expect that our model will be actively utilized in applications where the pixel-by-pixel accuracy of a PCD model is important.

Geological Structure Model for Recharge Area in Patuha Geothermal Field

Geothermal sustainability requires geological structure analysis using gravity methods for groundwater management. Geological structure analysis reviews fault data. Geological structure as a pathway for fluid movement for groundwater management in the research area, especially the Patuha Geothermal Field, West Java. This study uses global gravity model, terrestrial gravity, DEM, and geological data. The gravity method applied in this research involves calculations for latitude correction, free air correction, bouguer correction and terrain correction. The final results of this research are free air anomaly, complete Bouguer anomaly, and model evaluation (MRSE, MRE, and MAE). Free air anomaly results from free air correction of global gravity model data. DEM topography analyzes free air anomaly. The FAA data model evaluated RMSE at 1.27, MRE at 0.004, and MAE at 0.83. 138.02–280.26 mgal is the FAA range. The research area’s elevation range is 836–2431 m. Topography and free air anomaly correlate 0.75. Positive high connection. Mountains and plateaus may have positive gravity anomalies. Free air, bouguer, terrain, and CBA are applied to global gravity model data. The CBA model evaluation indicates quality. RMSE is 1.47, MRE is 0.012, and MAE is 1.11. CBA fluctuation implies fault spread. Faults serve as water flow conduits and channels, increasing the risk of infiltration. Meanwhile, cracks are being distributed as conduits and for infiltration. This research forms the basis for developing and increasing geothermal production so that further studies are needed regarding the influence of geological structures for reservoir in geothermal areas.

Effect of Adding Global Geopotential Model Generated Data to Terrestrial Data on Geoid Model (case study at Bekasi, Indonesia)

Geoid modeling requires accurate and evenly distributed gravity data throughout the study area. Although airborne gravity measurements are getting more massive, some areas are politically restricted areas. Therefore, terrestrial measurements are still needed, such as in Bekasi City and its surroundings. In terrestrial geoid modeling, ideal conditions are often difficult to achieve, because terrestrial measurements can only be made in accessible, safe, and permitted places. These non-ideal conditions result in an uneven distribution of data which can decrease in the accuracy of the resulting geoid. One way to overcome this data acquisition problem is to fill in the data using synthetic data calculated from the global geopotential model data. This study aimed to analyze the effect of using EGM data on geoid accuracy. The data needed for this research were terrestrial gravity data, EGM2008, and SRTM30 Plus, and GEBCO. The geoid was calculated using several calculation modules from Gravsoft. Specific modifications were made using Simply Fortran to facilitate geoid modelling with integral of Hotine. This research was conducted with two scenarios, namely geoid calculations using only terrestrial gravity data and geoid calculations using a combination of terrestrial data and EGM2008. The accuracy of the geoid was analyzed based on the magnitude of the deviation between the local Bekasi geoid and the national geoid represented by INAGEOID2020 2020 version 2. Free Air gravity anomaly provide by Badan Informasi Geospasial were converted to be gravity disturbance data on the earth’s surface. The secondary data gravity disturbance was calculated from EGM2008 with n=360 and n=2190. The geoid calculated from terrestrial geoid data alone produced the largest offset to the national geoid. Combination of terrestrial gravity data and generated data from EGM2008 n=360 performed higher precision geoid.

The optimization of marine geoid model from altimetry data using Least Squares Stokes modification approach with additive corrections across Malaysia

ABSTRACT Due to the high cost and time constraints, geoid and gravity surveys utilizing airborne and shipborne surveys can only be implemented in marine areas with limited coverage. Satellite altimeter has become an essential tool for global geoid and gravity field recovery. With approximately 60% observation coverage of the Earth’s surface for ocean height, researchers can replace conventional marine geoid models by conducting faster surveys over massive areas at a minimal cost. This study attempts to develop a marine geoid model from multi-mission satellite altimetry along-track data using the Least Squares Modification of Stokes with Additive Corrections. The gravity anomaly has been computed using Gravity Software, and the planar Fast Fourier Transformation method has been applied. The evaluation, selection, blunder detection, combination, and re-gridding of the altimetry-derived gravity anomalies and Global Geopotential Model data are demonstrated. The cross-validation approach has been employed in the cleaning and quality control the data using the Kriging interpolation method. The optimal condition modification parameters of the spherical cap, terrestrial gravity data error, and correlation length have also been applied. Then, the additive corrections based on Downward Continuation, Atmospheric Effects, and Ellipsoidal corrections were combined with the estimated geoid to provide a precise marine geoid over the Malaysian seas. The findings have been evaluated with the MyGeoid17 model from the Department of Survey and Mapping Malaysia. The validation yields satisfactory results with accuracy up to a centimetre level. Hence, the marine geoid model can be utilized for orthometric height determination in marine areas over the Malaysian seas.

Appraisal of lineaments patterns and crustal architectures around the Owen fracture zone, Arabian Sea, using global gravity model data

Appraisal of lineaments patterns and crustal architectures around the Owen fracture zone, Arabian Sea, using global gravity model data

Evaluation of Spectral Wave Models Physics as Applied to a 100‐Year Southern Hemisphere Extra‐Tropical Cyclone Sea State

AbstractExtreme significant wave height estimates, and their probability of exceedance, are fundamental offshore and coastal engineering design parameters. These estimates are characterized by uncertainty due to an incomplete understanding of the atmosphere‐ocean energy and momentum exchanges during intense storms. This particularly affects extreme wave statistics of ocean regions exposed to large and frequent synoptic disturbances such as Extra‐Tropical Cyclones (ETCs). In this work, we assessed the performance of global phase‐averaged spectral wave models in representing the 1 in 100‐year sea state generated by a Southern Ocean ETC in April 2021. We collected in situ and remote sensing observations, from the storm generation region to its decaying phase and the impact on South‐East Australian coastlines. We compared the observations with a suite of reanalysis and hindcast global wave model data sets. While comparing well for wind speed up to 20 m/s, the models presented differences in solving the air‐sea momentum exchange between the atmosphere and the ocean for wind speed velocities between 20 and 35 m/s, which are a distinctive characteristic of ETCs. Despite marked differences in the storm generation region, the models converged to a similar representation of the swell systems impacting the South‐East Australian coastlines, as demonstrated by a comparison with deep‐water buoy observations close to the coastlines. Furthermore, we found that the energy of the ERA5 reanalysis, which assimilates satellite wave height measurements is quickly dispersed and, as such, of little advantage in representing the 9.9 m significant wave heights that impacted the South‐East Australian coastlines on 10 April 2021.

Structure of the lithosphere beneath Cameroon using global gravity model data: implications for the setting of the Cameroon Volcanic Line

Structure of the lithosphere beneath Cameroon using global gravity model data: implications for the setting of the Cameroon Volcanic Line

Utilization of ASTER data in lithological and lineament mapping of the southern flank of the Central High Atlas in Morocco

Geological mapping undoubtedly plays an important role in several studies and remote sensing data are of great significance in geological mapping, particularly in poorly mapped areas situated in inaccessible regions. In the present study, Principal Component Analysis (PCA), Band Rationing (BR) and Minimum Noise Fraction (MNF) algorithms are applied to map lithological units and extract lineaments in the Amezri-Amassine area, by using multispectral ASTER image and global digital elevation model (GDEM) data for the first time. Following preprocessing of ASTER images, advanced image algorithms such as PCA, BR and MNF analyses are applied to the 9ASTER bands. Validation of the resultant maps has relied on matching lithological boundaries and faults in the study area and on the basis of pre-existing geological maps. In addition to the PCA image, a new band-ratio image, 4/6–5/8–4/5, as adopted in the present work, provides high accuracy in discriminating lithological units. The MNF transformation reveals improvement over previous enhancement techniques, in detailing most rock units in the area. Hence, results derived from the enhancement techniques show a good correlation with the existing litho-structural map of the study area. In addition, the present results have allowed to update this map by identifying new lithological units and structural lineaments. Consequently, the methodology followed here has provided satisfactory results and has demonstrated the high potential of multispectral ASTER data for improving lithological discrimination and lineament extraction.

Regional terrain-based VS30 prediction models for China

Time-averaged shear-wave velocity to 30 m (VS30) is commonly used in ground motion models as a parameter for evaluating site effects. This study used a collection of boreholes in Beijing, Tianjin, Guangxi, Guangdong, and three other municipalities and provinces, which were divided into three regions with reference to the seismic ground motion parameter zonation map of China, to establish VS30 prediction models based on terrain categories. Regional effects were verified by comparing morphometric parameter (topographic slope, surface texture, and local convexity) thresholds and terrain classification maps obtained from global digital elevation model (DEM) data and regional DEM data of the three regions. Additionally, VS30 prediction models for the three regions using both types of terrain classification maps were established and analyzed comparatively to provide credible regional VS30 models for China. Through analysis of the correlations between the measured VS30 values and the predicted VS30 values, calculation of the mean squared error and mean absolute percentage error in each region, and with consideration of the geological characteristics of the boreholes, the VS30 prediction models based on terrain classification maps from regional data were finally applied in developing regional VS30 models for China. Intercomparison of the VS30 prediction models for the three regions indicated that subregional consideration is necessary in terrain classification. Finally, a spatial analysis method adopting inverse distance weighting of the residuals was used to update the initial VS30 models. The developed VS30 models could be used both in developing regional ground motion models and in the construction of earthquake disaster scenarios.Graphical

Improvement of GOCI-II Water Vapor Absorption Correction through Fusion with GK-2A/AMI Data

In remote sensing of the ocean color, in particular, in coarse-resolution global model simulations, atmospheric trace gases including water vapor are generally treated as auxiliary data, which create uncertainties in atmospheric correction. The second Korean geostationary satellite mission, Geo-Kompsat 2 (GK-2), is unique in combining visible and infrared observations from the second geostationary ocean color imager (GOCI-II) and the advanced meteorological imager (AMI) over Asia and the Pacific Ocean. In this study, we demonstrate that AMI total precipitable water (TPW) data to allow realistic water vapor absorption correction of GOCI-II color retrievals for the ocean. We assessed the uncertainties of two candidate TPW products for GOCI-II atmospheric correction using atmospheric sounding data, and then analyzed the sensitivity of four ocean-color products (remote sensing reflectance [Rrs], chlorophyll-a concentration [CHL], colored dissolved organic matter [CDOM], and total suspended sediment [TSS]) for GOCI-II water vapor transmittance correction using AMI and global model data. Differences between the TPW sources increased the mean absolute percentage error (MAPE) of Rrs from 2.97% to 6.43% in the blue to green bands, higher than the global climate observing system requirements (<5%) at 412 nm. By contrast, MAPE values of 3.53%, 6.18%, and 7.71% were increased to 6.63%, 13.53%, and 16.14% at high sun and sensor zenith angles for CHL, CDOM, and TSS, respectively. Uncertainty analysis provided similar results, indicating that AMI TPW produced approximately 3-fold lower error rates in ocean-color products than obtained using TPW values from the National Centers for Environmental Prediction. These results imply that AMI TPW can improve the accuracy and ability of GOCI-II ocean-color products to capture diurnal variability.

COASTAL INUNDATION SIMULATION DUE TO SEA LEVEL RISE IN TERENGGANU, MALAYSIA

Abstract. Coastal Inundation is the amount of water levels that reach above normal level due to flooding in coastal areas. This natural phenomenon is due to the rising of local sea level. This study aims to develop a coastal inundation simulation along the coastline of Terengganu. To ensure the success of this study, the rate and magnitude of sea level rise are calculated using Radar Altimetry Database System (RADS) from multi-mission satellite altimetry data. The rate of sea level rise is computed using robust fit regression technique and overlaid with global Digital Elevation Model (DEM) data, known as TanDEM-X. The overlaid data are utilised to simulate the probable areas to be affected by inundation from year 2040 to 2100 using ArcGIS software. The results obtained from the period of 1993 to 2015 show that the average rate of sea level rise along the Terengganu shoreline is 4.84 mm/year with a standard deviation of 0.49 mm/year. By 2100, the estimated average regional sea level rise is projected to increase by 0.412 m. This study could help coastal development planning, defence, and safety monitoring.

FFM: Flood Forecasting Model Using Federated Learning

Floods are one of the most common natural disasters that occur frequently causing massive damage to property, agriculture, economy and life. Flood prediction offers a huge challenge for researchers struggling to predict floods since long time. In this article, flood forecasting model using federated learning technique has been proposed. Federated Learning is the most advanced technique of machine learning (ML) that guarantees data privacy, ensures data availability, promises data security, and handles network latency trials inherent in prediction of floods by prohibiting data to be transferred over the network for model training. Federated Learning technique urges for onsite training of local data models, and focuses on transmission of these local models on the network instead of sending huge data set towards central server for local model aggregation and training of global data model at the central server. In this article, the proposed model integrates locally trained models of eighteen clients, investigates at which station flooding is about to happen and generates flood alert towards a specific client with five days lead time. A local feed forward neural network (FFNN) model is trained at the client station where the flood has been expected. Flood forecasting module of local FFNN model predicts the expected water level by taking multiple regional parameters as input. The dataset of five different rivers and barrages has been collected from 2015 to 2021 considering four aspects including snow melting, rainfall-runoff, flow routing and hydrodynamics. The proposed flood forecasting model has successfully predicted previous floods happened in the selected zone during 2010 to 2015 with 84 % accuracy.

Satellite observations of gravity wave momentum flux in the mesosphere and lower thermosphere (MLT): feasibility and requirements

Abstract. In the recent decade it became evident that we need to revise our picture of how gravity waves (GWs) reach the mesosphere and lower thermosphere (MLT). This has consequences for our understanding not just of the properties of the GWs themselves, but in particular of the global circulation in the MLT. Information on spectral distribution, direction, and zonal mean GW momentum flux is required to test the theoretical and modeling findings. In this study, we propose a constellation of two CubeSats for observing mesoscale GWs in the MLT region by means of temperature limb sounding in order to derive such constraints. Each CubeSat deploys a highly miniaturized spatial heterodyne interferometer (SHI) for the measurement of global oxygen atmospheric band emissions. From these emissions, the 3-D temperature structure can be inferred. We propose obtaining four independent observation tracks by splitting the interferograms in the center and thus gaining two observation tracks for each satellite. We present a feasibility study of this concept based on self-consistent, high-resolution global model data. This yields a full chain of end-to-end (E2E) simulations incorporating (1) orbit simulation, (2) airglow forward modeling, (3) tomographic temperature retrieval, (4) 3-D wave analysis, and (5) GW momentum flux (GWMF) calculation. The simulation performance is evaluated by comparing the retrieved zonal mean GWMF with that computed directly from the model wind data. A major question to be considered in our assessment is the minimum number of tracks required for the derivation of 3-D GW parameters. The main result from our simulations is that the GW polarization relations are still valid in the MLT region and can thus be employed for inferring GWMF from the 3-D temperature distributions. Based on the E2E simulations for gaining zonal mean climatologies of GW momentum flux, we demonstrate that our approach is robust and stable, given a four-track observation geometry and the expected instrument noise under nominal operation conditions. Using phase speed and direction spectra we show also that the properties of individual wave events are recovered when employing four tracks. Finally, we discuss the potential of the proposed observations to address current topics in the GW research. We outline for which investigations ancillary data are required to answer science questions.

Investigation of Forest Fire Characteristics in North Korea Using Remote Sensing Data and GIS

Forest fires cause damage to property and the environment around the world every year. North Korea has suffered from fires every year. Fires may lead to temporary or permanent damage to forest ecosystems, long-term site degradation, and alteration of hydrological regimes, producing detrimental impacts on economies, human health, and safety. In North Korea, fires cause serious damage to the affected mountainous environment. However, it is very difficult to obtain ground information or perform field checks because of the political isolation of North Korea. Thus, there are few studies that have investigated North Korean fires. In this situation, remote sensing techniques and digital topographic data can be used to investigate fire characteristics in North Korea. In this study, fire trends were analyzed using Moderate Resolution Imaging Spectroradiometer (MODIS) data from the Land Processes Distributed Active Archive Center (LPDAAC) from 2004 to 2015, and Landsat data were processed to estimate burned areas in South Hamgyong Province (SHP) and Gangwon Province (GWP) in North Korea. The burn severity of large fires in elevation, slope, and landform features was also analyzed to investigate large fire-burned areas using 30-m-resolution Global Digital Elevation Model (DEM) data from the United States National Aeronautics and Space Administration (NASA). After the results were compared and discussed, the following conclusions were derived. (1) In terms of location, fires in SHP were relatively concentrated along BaekDu-DaeGan (BDDG), while fires in GWP were scattered throughout the province. (2) In terms of size, the large fire-burned areas with an area greater than 1000 ha are significantly more frequent in SHP than in GWP. In brief, large fires occurred more frequently and were more serious in SHP than in GWP. (3) In terms of forest type, coniferous areas were more susceptible to damage from fires and large fires than deciduous areas in both GWP and SHP. This is attributed to the combustible resin within the coniferous trees. Particularly, when a crown fire occurs, it tends to spread rapidly throughout the coniferous forest. (4) Regarding landforms, most large fires occurred along windward-side open slopes, while there were very few fires in shallow valleys, high ridges, or U-shaped valleys. It is believed that cultivation in high-elevation terrain and a lack of fire-extinguishing equipment and systems allow large fires to spread quickly. North Korea is very susceptible to large fire damage and must develop preparation measures against such situations.