An integrated low-cost air quality sensor and a multi-task calibration framework for particulate matter.

With increasing traffic safety risks from winter road icing, predicting road surface temperature (RST) is vital for analyzing icing mechanisms and ensuring road safety. This study built RST prediction models using mobile observational data and compared machine learning methods (Random Forest, XGBoost, and Light GBM) with a deep learning model, the Convolutional Long Short-Term Memory (ConvLSTM) network. Mobile data were collected from Automatic Weather Station (AWS) and non-contact sensors along a 330-km section of the Seohaean Expressway during November-December in 2024. ConvLSTM achieved the lowest RMSE and MAE across all horizons, showing high short-term accuracy (≤ 600 s). In long-term forecasts (≥ 1,800 s), errors increased slightly but overall performance remained superior. Physical and geographical factors, including Solar Zenith Angle (SZA), Sky View Factor (SVF), and bridge/tunnel indicators, improved short-term accuracy but had limited long-term effects. These results highlight ConvLSTM’s potential for road icing prediction and traffic safety management.

Abstract. Mixed-phase clouds are frequently observed in the Arctic and still not well represented in climate and general circulation models. The spatial distribution of cloud thermodynamic phase and its partitioning are important quantities since they affect the radiative effect of clouds as well as cloud life time. In this work, a new quantitative retrieval method of cloud thermodynamic phase partitioning based on multi-angle polarimetry is presented. The polarization signal is sensitive to cloud thermodynamic phase since liquid water and ice particles have different shapes and different optical properties. The basic idea of the retrieval is to fit simulations obtained from a forward operator to measurements in the cloudbow range between 135 and 165° scattering angle and the slope range between 60 and 110° to determine a quantitative ice fraction. Either plane-parallel clouds or three-dimensional (half-spherical) clouds are assumed in the simulations. The retrieval was validated using synthetic data. 3D radiative transfer simulations were performed for different idealized cloud cases as well as for a realistic field of low-level Arctic mixed-phase clouds. As the retrieval is based on polarization, it is sensitive to cloud top. The retrieved ice fraction is here defined as the ratio of the ice optical thickness to the liquid plus ice optical thickness and corresponds to the mean ice fraction of the uppermost cloud layer from cloud top to an optical thickness of about 1 to 2, depending on the solar zenith angle. In addition, the retrieval was applied to measurements of the polarization-resolving cameras of the specMACS instrument during the HALO–(𝒜𝒞)3 campaign, providing two-dimensional fields of cloud thermodynamic phase partitioning with a high spatial resolution of about 100 m.

Abstract. Conductances are key properties of the ionospheric electrodynamics and the difficulty of measuring them directly is a significant limitation to the usefulness of many analysis techniques. We have utilized all available field-aligned observations from the EISCAT incoherent scatter ultra-high frequency (UHF) radar since 2001 and from the 42 m EISCAT Svalbard Radar (ESR) since 1998 to develop a new empirical model for estimating the high-latitude ionospheric Hall and Pedersen conductances. The solar radiation component of the model is parametrized with the solar zenith angle and the F10.7 index, and the auroral precipitation component is parametrized with the magnetic local time and the divergence-free part of the horizontal ionospheric current density, which is obtained from ground-based magnetic field observations. We have also derived a new technique based on spherical elementary current systems that can be used to solve for the ionospheric potential electric field and field-aligned current density from known ionospheric conductances and ground-based magnetic field observations, taking into account induction in the ionosphere and in the ground. The new empirical conductance model and solver were applied to IMAGE magnetometer network observations. Comparison of the results with Swarm and Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE) satellite observations showed reasonable agreement in the electric field profile and direction of the field-aligned current, but in the post-midnight sector the modelled amplitudes tended to be weaker than observations. The combination of the new conductance model and analysis technique allows estimating the key properties of ionospheric electrodynamics from ground-based magnetic field observations.

To achieve high-precision retrieval of water vapor concentration profiles in the near-space region, this study utilizes the high-resolution spectral radiative transfer model SCIATRAN to simulate water vapor observation spectra under different observational geometric parameters and atmospheric aerosol conditions. A comprehensive analysis is conducted on the influence of these parameters on spectral radiance. The results demonstrate that when the tangent height exceeds 40 km, water vapor absorption features significantly weaken. Spectral data acquired under conditions combining small solar zenith angles with large relative azimuth angles exhibit greater stability. Middle and upper atmospheric aerosols, predominantly composed of volcanic ash and particulate matter, induce strong sensitivity of water vapor spectral radiance to stratospheric and mesospheric aerosols. Notably, under extreme volcanic aerosol loading conditions, the differential-to-background ratio of spectral radiance surpasses 2000%. This investigation identifies key sensitive parameters and their mechanistic influences on near-space water vapor observation spectra. The findings provide a theoretical foundation for optimizing the design parameters of near-space sounders, while offering scientific guidance for formulating data screening strategies and conducting error traceability analysis during water vapor concentration retrieval processes.

What determines the Arctic solar radiation energy budget at the surface most strongly: Clouds, surface albedo, or the solar zenith angle?

Aerial craft against the sky are visible in the near ultraviolet (UV) spectral range (300-400nm) as dark objects. The properties of negative UV contrast differ from those of positive thermal contrast. The use of an additional UV channel in observation devices increases their detection capability and quality of target/countermeasure selection. The influence of individual components of UV radiation on UV signatures of airborne objects is discussed and evaluated. The results of calculations of UV background, UV path radiance and negative contrast depending on the object-observer distance are presented. Radiances were calculated using PcModWin. The calculations are performed for the selected geographical locations and times of day and year as well as for the selected visibilities. It was found that UV radiance mostly depends on the Sun's elevation, while the contrast depends almost only on the range of visibility, i.e., the aerosol’s type and density. A case of negative contrast in atmosphere was measured and the measured data were compared with calculated results. The signal-to-noise ratio for the sensor with the defined parameters was also computed. Because scattering is the most significant factor in the near UV (300-400nm) and visible spectral regions, the detection ranges in both regions are similar.

Abstract Bispectral retrievals of the droplet effective radius ( r e ) from instruments such as MODIS are widely utilized to study cloud microphysics in marine boundary layer clouds. These retrievals are known to have systematic errors due to cloud heterogeneity. Here, we develop a neural network regression to retrieve cloud‐top r e at a solar zenith angle of 30° and nadir viewing using MODIS. The neural network regression is trained on 3D radiative transfer simulations of quasi‐adiabatic stochastically generated clouds and corrects relative errors in r e with respect to cloud‐top with an r 2 of 0.88. The neural network regression produces unbiased retrievals of cloud‐top r e against large eddy simulation cloud fields where the bispectral retrieval has biases reaching +100%. The neural network regression reduces retrieval biases against airborne observations of cumulus from CAMP 2 Ex from +100% to +40%, and marginally improves already good consistency against stratocumulus sampled during VOCALS. A cross‐comparison technique for assessing statistical remote sensing retrievals is introduced. The neural network regression explains 63% and 91% of the variance in the differences between MODIS 1.6 and 2.1 μm retrievals for Overcast and partially cloudy pixels (PCL) and 42% and 76% for the 2.1 and 3.7 μm differences, respectively. Residual spectral inconsistency is partially attributed to precipitation‐sized particles using radar observations. Regional averages of the operational MODIS r e exceed the cloud‐top r e predicted by the neural network by +50% for Overcast pixels in the tropics and a consistent +70% for PCL pixels. Errors in bispectral retrievals due to heterogeneity are nonrandom at both the cloud and climate scale.

Abstract. The Carbon Mapper emissions monitoring system contributes to the broader ecosystem of greenhouse gas observations by locating and quantifying CH4 and CO2 super emitters at facility scale across priority regions globally and making the data accessible and actionable. The system includes observing platforms, an operational monitoring strategy optimized for mitigation impact, and a data platform that delivers CH4 and CO2 data products for diverse stakeholders. Operational scale-up of the system is centered around a new constellation of hyperspectral satellites. The Carbon Mapper Coalition (hereafter Tanager) satellites are each equipped with an imaging spectrometer instrument designed by NASA's Jet Propulsion Laboratory that are assembled, launched and operated by Planet Labs. The first Tanager satellite (Tanager-1) was launched 16 August 2024 completed commissioning in January 2025 and continued to improve observational efficiency through summer 2025. Planet is currently working to expand the constellation to four Tanagers. Each imaging spectrometer instrument has a spectral range of about 400–2500 nm, 5 nm spectral sampling, a nadir spatial resolution of 30 m, and nadir swath width of about 19 km at the lowest orbital altitude. Each satellite is capable of imaging 250 000 km2 per day on average. By combining the results of independent controlled release testing with empirical evaluation of the radiometric, spectral, spatial, and retrieval noise performance of the Tanager-1 spectrometer, we predict minimum detection limits of about 64–126 kgCH4 h−1 for CH4 point sources and about 10 078–18 994 kgCO2 h−1 for CO2 point sources for images with 25 % albedo, 45° solar zenith angle, and 3 m s−1 wind speed. A review of the first 11 months of Tanager-1 CH4 and CO2 observations including initial validation with coordinated aircraft under-flights and non-blind controlled release testing indicates that the system is meeting performance requirements and, in many cases, surpassing expectations. We also present early evaluations in challenging onshore and offshore observational conditions and summarize the first use of Tanager data to guide the timely mitigation of a CH4 super emitter.

An unmanned aerial vehicle (UAV) multi-spectral system provides a monitoring platform to rapidly obtain crop spectral information that can reflect crop nitrogen status for the generation of dynamic variable-rate nitrogen (VRN). To improve the accuracy of VRN prescription maps, a method of generating VRN prescription maps on the basis of the vegetation index was proposed, and the effects of UAV flight time and altitude on VRN prescription maps were analyzed. The experimental site was located in Dacaozhuang, Hebei Province, China, and the experimental crop was winter wheat (Lunxuan 145). The flight altitudes of the UAV system were set to 50, 70 and 90 m. The flight times were set to 8:00 a.m., 11:00 a.m., 2:00 p.m. and 5:00 p.m. local time. The flight area was 1.18 ha with a 60° rotation angle under a three-span center pivot irrigation system with an overhang. UAV flight missions were executed during the jointing, heading, and grain filling phases of winter wheat. There were 90 management zones with pie shapes in total, which were composed of a 10° angle in the rotation direction and 4 sprinklers along the lateral direction. The vegetation indices (VIs) which are closely related to crop nutrient status were selected and used to generate distribution maps, which were superimposed with the management zones to generate VRN prescription maps. The results demonstrated that the red-edge soil adjusted vegetation index (RESAVI) was relatively more sensitive to the nitrogen status of winter wheat than the other VIs were. The RESAVI distributions were stable during periods with a solar elevation angle greater than 50° (11:00 a.m.–2:00 p.m. local time), and the VRN prescription maps were similar, with the overlap percentage of the same fertilization grade being greater than 80% and the relative error of the fertilization amount being less than 5%. Compared with that at 2:00 p.m., the overlap percentage of the same fertilization grade was 56.6% in both seasons at 8:00 a.m., whereas flights at 5:00 p.m. exhibited overlaps of 70.9% and 44.6% in the 2023 and 2024 seasons, respectively. Conversely, the flight altitude had little influence on the fertilizer amount and VRN prescription maps. The difference in the amount of fertilizer used was less than 3% at different flight altitudes. The required time is half of that for a 50 m flight when the flight altitude is 70 m and one third of that when the flight altitude is 90 m. Our study recommended operating the UAV multi-spectral system at solar elevation angles greater than 50° when generating VRN prescription maps of winter wheat, and the flight height can be adjusted according to the field area and the endurance time of the UAV.

This study develops and validates a GIS-based, spatio-temporal model to identify and prioritize road segments with high icing susceptibility in the Gümüşhane city center, Türkiye. Icing on urban roadways constitutes a significant threat to vehicular and pedestrian safety, particularly in regions characterized by complex topography and dense urban morphology. A Multi-Criteria Decision Analysis (MCDA) framework, employing the Analytic Hierarchy Process (AHP) with structured expert input, was used to synthesize ten primary causative factors. These include solar radiation, temperature, aspect, object height, precipitation, elevation, pavement material, slope, albedo, and road width — each representing a distinct topographic, climatic, or morphological influence on urban road icing. The AHP quantitatively established the relative influence of these factors, identifying solar radiation (28.5%), temperature (18.2%), and aspect (13.5%) as the most dominant drivers. The model generates monthly susceptibility maps that reveal "urban canyons"—narrow streets flanked by high-rise buildings—consistently exhibit the highest and most persistent susceptibility due to severely limited solar radiation. The accuracy of the model's spatial predictions was quantitatively validated through field observations, with 91.4% of recurring icing locations correctly identified within high or very high susceptibility zones. This research culminates not only in a robust scientific framework but also in a validated decision-support tool that has been successfully integrated into the Gümüşhane City Information System (GCIS), where it is now operationally used to optimize winter maintenance strategies. Ultimately, this work provides a replicable, data-driven approach for enhancing urban resilience and traffic safety in challenging topographical settings.

Erythemal ultraviolet (EUV) irradiance, with a wavelength range of 280–400 nm, is associated with both health risks and physiological benefits. While moderate EUV exposure stimulates vitamin D synthesis—essential for bone health and immune function—excessive exposure can cause skin damage, ocular complications, and increased risk of skin cancer, highlighting the need for accurate UV monitoring. However, ground-based measurements remain limited due to the high cost of instrumentation. This study introduces a semi-empirical model for estimating hourly EUV irradiance in Thailand using meteorological and satellite data. The model was developed using cloud index, visibility, total column ozone, and the cosine of the solar zenith angle across four stations: Chiang Mai, Ubon Ratchathani, Nakhon Pathom, and Songkhla. The baseline model, constructed using data from 2016 to 2019, achieved a mean bias difference (MBD) of 3.57%, a root mean square difference (RMSD) of 21.80%, and an R² of 0.81. However, its performance declined in areas with high aerosol loading and low visibility, particularly in Chiang Mai, where seasonal biomass burning is prevalent. To improve accuracy, a modified model was developed by incorporating aerosol optical depth (AOD) at stations where such data were available. The enhanced model yielded an MBD of 6.18%, an RMSD of 15.16%, and an R² of 0.93. These results highlight the critical role of aerosols in UV attenuation and demonstrate the model’s potential for scalable, cost-effective applications in UV risk assessment, especially in regions lacking high-resolution ground monitoring infrastructure.

Abstract. Station observations of surface Arctic aerosol have long shown a pronounced seasonal cycle, with burdens characteristically peaking in the late winter/early spring. Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol optical depth (AOD) products replicate this seasonality, but passive sensor and reanalysis data products do not. We find that the sub- and low-Arctic seasonality of gridded AOD products from six passive sensors diverges from that of CALIOP data products during the months of September–April, even when controlling for sampling biases. Using colocated CALIOP and Moderate Resolution Imaging Spectroradiometer (MODIS) (Aqua) retrievals, we find that for colocations characterized by low-quality MODIS retrievals, the bias between MODIS and CALIOP strongly depends on the solar zenith angle (SZA), with MODIS AODs showing a 132 % reduction relative to the instrument-mean over a theoretical 0–90° SZA domain. As the fraction of MODIS retrievals flagged as “low-quality” increases with higher SZAs, retrieval quality mediates the relationship between the SZA and dataset biases in gridded products. The dependency may be the result of cloud adjacency effects, and likely also affects midlatitude AOD seasonality. Though additional sources of uncertainty in high latitude retrievals remain, the observed dependency impacts passive sensor data products' representations of (sub-)Arctic aerosol burdens in boreal spring and autumn, which are important for understanding aerosol processes in a highly sensitive yet understudied region. This work also contributes to improved understanding and quantification of the effects of viewing geometry on satellite AOD retrievals, which can help constrain aerosol observations and associated forcings, globally.

Abstract The standalone non‐local thermodynamic equilibrium (NLTE) bias correction scheme, developed by Li et al. ( Journal of Geophysical Research , 125, 2020 and Quarterly Journal of the Royal Meteorological Society , 10.1002/qj.5020 , 2025), was tested within a data assimilation framework. The bias correction scheme utilizes a look‐up table and a linear regression method. Together with quality control procedures, this enables the assimilation of short‐wave infrared (SWIR) radiances. Initial evaluations revealed that significant residual biases remained in the polar region (latitude greater than 60° N/S) after applying the developed schemes to SWIR channels. This resulted in SWIR radiances that were not assimilated in the polar region. In the non‐polar region (latitude less than 60° N/S), there remained slight day/night discrepancies after applying the bias correction scheme. In this case, these biases arose from using different forecast models to determine the regression coefficients for the bias correction scheme and those employed by the data assimilation system. To address these biases, two additional quality control procedures were introduced. Six‐week experiments were conducted, assimilating 140 stratospheric SWIR channels over the non‐polar regions. Results were compared against a control, without SWIR assimilation. Three scenarios were examined: the assimilation of SWIR radiances, the assimilation of only solar‐day SWIR radiances and the assimilation of only solar‐night SWIR radiances. Observations were classified solar‐day radiances when the solar zenith angle was less than 90°, and solar‐night when it exceeded 120°. Overall, results were promising. The inclusion of SWIR channels did not degrade existing observations, and no significant temperature biases were found at levels where SWIR channels were sensitive over the course of the experiment. SWIR radiances benefited forecasts by improving 250‐ and 500‐hPa geopotential height anomaly correlations and reducing forecast temperature root‐mean‐squared error beyond 48 hours between 50 hPa and surface. Assimilating only solar‐day and only solar‐night SWIR radiances separately yielded better results than their combined assimilation.

Abstract Microclimatic conditions and activities in outdoor areas are dynamic in time and space. Such variability needs to be considered when assessing outdoor thermal comfort (OTC) and heat stress. This work proposes a methodology to analyse the spatio-temporal profiles of thermal comfort along pedestrian pathways. The analysis is based on simulated Physiological Equivalent Temperature (PET) data using the SOLWEIG model for two adjacent neighbourhoods in Barcelona, characterised by different urban form and vegetation cover. The thermal sensation along six representative pathways is compared. The results show a large variability of thermal profiles due to variations in urban morphology, vegetation and solar elevation. The heat stress severity of each pathway and time of the day is characterised in terms of magnitude (mean PET along the path) and exposure (total time of exposure to heat stress according to PET levels). The results show that the critical time of the day for heat stress along a pathway can change depending on the chosen criteria. In many cases, the critical time for exposure does not correspond to the time of maximum solar radiation. The profiles also highlight a great variability of patterns in terms of intermittency of thermal conditions and continuous exposure duration to heat stress conditions. The proposed methodology can provide valuable insights into the heat stress severity along pedestrian pathways, supporting the strategic placement of shelters to mitigate heat-related health risks and enhance OTC in urban areas.

Due to the effect of changing the angle of incidence of solar radiation on the surface of the photovoltaic panel in determining the amount of electrical production, the world is heading today to study this effect and work to achieve the optimal position of the panel to increase its efficiency. This includes studying and analyzing the angles of PV panel tilting, taking into consideration the change in the position of the sun (altitude angle). In this article, a system has been designed using MATLAB/SIMULINK software to simulate the work of a home canopy that exploits photovoltaic panels to generate electrical energy. The system is designed to process multiple data at the same time, ten tilt angles of panels are adopted, in addition to taking into account the change in the position of the sun (from 40ᵒ to 80ᵒ) in winter and summer respectively, every second over a whole year. The curves of the proposed model showed that the high solar elevation angle in summer of Iraq (which did not require significant tilting of the panels to obtain vertical radiation (only 10ᵒ-20ᵒ) allows for the benefit of panels installed on a flat surface, at least if the panels cannot be oriented at the required tilt angle. However, flat panels are not effective in the winter due to the need to orient them at a relatively high tilt angle (50ᵒ) to obtain vertical radiation. It is also possible to design a fixed canopy throughout the year with panels tilted at an angle close to the latitude of the station's geographic area, given the relatively high radiation values in Iraq throughout the four seasons. these results show realistic behavior with excellent accuracy, by reaching the required monthly and seasonally tilt angles, which confirm the importance of having accurate models that enable the users to estimate angles and production quantities under different conditions and locations

Abstract Solar simulation accuracy is challenged by shading and solar reflections within built environments. Thus, identifying these events in observational datasets is essential for targeted validations and model refinements. This study proposes a clustering-based method that couples feature engineering with k-means clustering to group pyranometer measurements based on physical conditions related to shading and cloudiness. Three engineered features are used: (i) binned global tilted irradiance (GTI) that distinguishes permanent shadows from transient shading from clouds, (ii) the difference between measured GTI and clear-sky GTI to capture the impact of cloudiness and shading from surroundings, and (iii) the difference between measured and modelled GTI to reveal solar reflections. Two case studies have been used to demonstrate the robustness of the methodology: a dense urban canyon in Geneva (Switzerland) and a stand-alone building in Trondheim (Norway). These differentiate for the urban context, climates, and solar geometry. Findings demonstrate that the model can effectively separate cloudy from clear-sky conditions and identify shading by surroundings, self-shading, and noshading. Nonetheless, performance is worse in Trondheim than in Geneva due to lower irradiance variability. Additionally, shading conditions with low solar elevations and solar reflections in open urban settings are poorly detected.

Abstract Solar simulation accuracy is challenged by shading and solar reflections within built environments. Thus, identifying these events in observational datasets is essential for targeted validations and model refinements. This study proposes a clustering-based method that couples feature engineering with k-means clustering to group pyranometer measurements based on physical conditions related to shading and cloudiness. Three engineered features are used: (i) binned global tilted irradiance (GTI) that distinguishes permanent shadows from transient shading from clouds, (ii) the difference between measured GTI and clear-sky GTI to capture the impact of cloudiness and shading from surroundings, and (iii) the difference between measured and modelled GTI to reveal solar reflections. Two case studies have been used to demonstrate the robustness of the methodology: a dense urban canyon in Geneva (Switzerland) and a stand-alone building in Trondheim (Norway). These differentiate for the urban context, climates, and solar geometry. Findings demonstrate that the model can effectively separate cloudy from clear-sky conditions and identify shading by surroundings, self-shading, and noshading. Nonetheless, performance is worse in Trondheim than in Geneva due to lower irradiance variability. Additionally, shading conditions with low solar elevations and solar reflections in open urban settings are poorly detected.

Multi-angular remote sensing plays a critical role in the study domains of ecological monitoring, climate change, and energy balance. The successful retrieval of the surface Bidirectional Reflectance Distribution Function (BRDF) and albedo from multi-angular remote sensing observations for various applications relies on the sensitivity of an appropriate BRDF model to both the number and the sampling distribution of multi-angular observations. In this study, based on selected high-quality multi-angular datasets, we designed three representative angular sampling schemes to systematically analyze different illuminating–viewing configurations of the retrieval results in a kernel-driven BRDF model framework. We first proposed an angular information index (AII) by incorporating a weighting mechanism and information effectiveness to quantify the angular information content for the angular sampling distribution schemes. In accordance with the principle that observations on the principal plane (PP) provide the most representative anisotropic scattering features, the assigned weight gradually decreases from the PP towards the cross-principal plane (CPP). The information effectiveness is determined based on the cosine similarity between the observations, effectively reducing the information redundancy. With such a method, we assess the AII of the different sampling schemes and further analyze the impact of angular distribution on both BRDF inversion and the estimation of snow surface albedo, including White-Sky Albedo (WSA) and Black-Sky Albedo (BSA) based on the RossThick-LiSparseReciprocal-Snow (RTLSRS) BRDF model. The main conclusions are as follows: (1) The AII approach can serve as a robust indicator of the efficiency of different sampling schemes in BRDF retrieval, which indicates that the RTLSRS model can provide a robust inversion when the AII value exceeds a threshold of −2. (2) When the AII value reaches such a reliable level, different sampling schemes can reproduce the BRDF shapes of snow across different bands to somehow varying degrees. Specifically, observations with smaller view zenith angle (VZA) ranges can reconstruct a BRDF shape that amplifies the anisotropic effect of snow; in addition, the forward scattering tends to be more pronounced at larger solar zenith angles (SZAs), while the variations in BRDF shape reconstructed from off-PP observations depend on both wavelength and SZAs. (3) The relative differences in both BSA and WSA grow with increasing wavelength for all these sampling schemes, mostly within 5% for short bands but up to 30% for longer wavelengths. With this novel AII method to quantify the information contribution of multi-angular sampling distributions, this study offers valuable insights into several main multi-angular BRDF sampling strategies in satellite sensor missions, which relate to most of the fields of multi-angular remote sensing applications in engineering.

The use of small-scale solar energy is one of the strategic solutions for electricity needs in remote areas and outdoor activities. As the need for portable electrification increases, the development of solar backpack systems is becoming a practical and sustainable alternative. This research aims to design and test a portable backpack system based on a thin film solar panel with a capacity of 20 Wp, equipped with a 12V solar charge controller and a 7.2 Ah battery. The system is designed with two outputs, namely 12V DC and 5V USB, to support lightweight devices such as LED lights and smartphones. The test was carried out at two different locations, namely Mount Lorokan and Mount Penanggungan, with data taken which included latitude, longitude, solar elevation angle, PV voltage, PV current, and output power. The measurement results show that there is a variation in the performance of solar panels according to geographical conditions and solar elevation angles. On Mount Lorokan, the peak power reaches 8.55 W at an angle of 75°, while on Mount Penanggungan the maximum power is slightly higher, which is 8.6 W at an angle of 78°. Mount Lorokan excels at maximum power, Penanggungan shows more stable performance at low to medium angles. This research contributes to the development of a portable electrification system based on renewable energy that supports power continuity in locations without access to electricity.