BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.staging.osgeo.org//foss4g-europe-2025//talk//JNM3A C BEGIN:VTIMEZONE TZID:CET BEGIN:STANDARD DTSTART:20001029T040000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:CET TZOFFSETFROM:+0200 TZOFFSETTO:+0100 END:STANDARD BEGIN:DAYLIGHT DTSTART:20000326T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:CEST TZOFFSETFROM:+0100 TZOFFSETTO:+0200 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-europe-2025-JNM3AC@talks.staging.osgeo.org DTSTART;TZID=CET:20250716T120000 DTEND;TZID=CET:20250716T123000 DESCRIPTION:Wind is a natural movement of air caused by variations in air p ressure due to the uneven heating of the Earth. Wind speed and direction a re dynamic variables that fluctuate over both time and space. These variab les are crucial for urban and spatial planning\, agriculture and crop mana gement\, sports event organization\, aerial navigation\, air pollution mod eling\, and fire management. The latter is especially important\, as fire behavior and spread are significantly influenced by wind conditions at the exact location. Thus\, accurately determining wind conditions at a given location is essential. Typically\, wind measurement is performed at sparse locations using weather instruments. Although these measurements are cond ucted according to strict standards—typically at an altitude of 10 meter s above ground\, on grassy terrain\, and without nearby obstacles—captur ing wind speed and direction at a single point in space and time does not fully represent the broader conditions. Thus\, various spatial methods are utilized for wind interpolation. Wind interpolation refers to the process of estimating wind speed and direction at locations where no direct measu rements are available.\nThis paper investigates the effectiveness of selec ted interpolation methods for estimating wind speed and direction at unkno wn locations\, using measurements from a network of weather stations. Four well-established methods were considered: Natural Neighbor (NN)[1]\, Inve rse Distance Weighting (IDW)\, Kriging (K)\, and Ordinary Kriging (OK)[2]. The study focuses on the Split-Dalmatia County region.\n\nMETHODOLOGY\nFo r this purpose\, wind measurement data from 28 weather stations with conti nuous data availability was utilized. Data from weather stations distribut ed across Split-Dalmatia County were collected throughout 2024 from the We ather Underground website[3]. This service integrates meteorological data from both public and privately owned weather stations. The data was prepro cessed\, and scenarios representing simultaneous measurements were selecte d and included in the analysis. These scenarios corresponded to three main wind directions (Bora\, Sirocco\, and Mistral)\, four seasons (Winter\, S pring\, Summer\, and Autumn)\, and different times of day (morning\, after noon\, and evening). Since some wind directions are uncommon in certain se asons or times of the day\, a total of 28 unique scenarios were used in th is study.\nOf the 28 stations\, data from 24 stations was used for wind in terpolation across the study area\, while four were selected as "unknown" locations for comparison with the interpolated values. In two experiments\ , the four unknown stations were chosen to represent: (1) locations with d istinct geographical challenges (land\, coast\, canyon\, and island locati ons) and (2) a station spatially surrounded by known measurements. For eac h experiment\, scenario\, and interpolation method\, we calculated and ana lyzed the Root Mean Squared Error (RMSE)\, Mean Absolute Error in the zona l u-direction (MAE u)\, and Mean Absolute Error in the meridional v-direct ion (MAE v) for the unknown stations\, using actual measurements as the gr ound truth and comparing them with interpolated values.\nInterpolation was performed using the Python packages Rasterio\, PyKrige\, and Delaunay\, a s well as some custom code\, while the visualization of interpolated value s was conducted using QGIS software. The analysis was carried out in Pytho n\, utilizing the Seaborn and Matplotlib libraries to generate a series of charts that revealed noteworthy findings.\n\nRESULTS\nThe evaluation of i nterpolation methods demonstrated that Ordinary Kriging achieved the lowes t interpolation errors\, likely due to its ability to account for spatial autocorrelation and incorporate data from multiple nearby stations. Despit e utilizing a significant number of measurements\, distance-based weightin g methods\, such as IDW and Natural Neighbors\, exhibited higher errors\, with RMSE values reaching up to 5 m/s. This highlights the impact of terra in complexity on accurate wind interpolation.\nWhen analyzing the median v alues of exhibited errors\, IDW methods showed the lowest RMSE vector and MAE v (meridional) direction\, while Ordinary Kriging produced the lowest median MAE in the u (zonal) direction.\nSpatial analysis revealed that int erpolation accuracy varied significantly by location\, with the station si tuated in a river canyon displaying the highest errors across all methods. This underscores the difficulty of wind interpolation in complex terrains . Wind type also played a crucial role\, with Bora producing the highest R MSE values (up to 8 m/s) across all methods due to its turbulent nature. I n contrast\, Jugo and Maestral\, with their steadier patterns\, resulted i n lower errors (below 3 m/s).\nA separate analysis of the u (zonal) and v (meridional) wind components indicated no significant difference in interp olation accuracy between them\, as both components contribute equally to w ind variability. However\, certain locations exhibited elevated errors dur ing Maestral wind scenarios. Upon closer examination\, this can be attribu ted to their positioning relative to the coastline and surrounding topogra phy.\nA comparison of errors between both wind components showed that Maes tral exhibited significant errors in eastward-oriented directions\, affect ing both rural and urban areas.\nVisualization of interpolation across the entire study area revealed that the examined methods struggled to adapt t o local conditions. While Kriging produced wind field maps with expected v ariations in wind speed and direction\, it statistically resulted in less accurate predictions. However\, maps resulting from other methods do not e xhibit expected spatial patterns of the wind field.\n\nCONCLUSIONS\nThis s tudy underscores the critical role of terrain complexity\, wind type\, and station placement in determining interpolation accuracy\, particularly in challenging environments such as canyons\, where conventional methods str uggle to capture abrupt wind variations.\nFuture research on wind interpol ation should focus on integrating high-resolution topographic and land cov er data to improve accuracy\, especially in complex terrains. Machine lear ning techniques\, utilizing historical data\, could enhance predictive ca pabilities by capturing intricate spatial patterns. Expanding the study to include time-series analysis and temporal interpolation would provide bet ter wind forecasting insights. Additionally\, leveraging higher-resolution datasets from remote sensing and hybrid approaches that combine statistic al and physics-based models could refine wind field predictions. Validatin g these methods across diverse geographic regions and developing real-time applications for fire management and disaster response would further enha nce the practical utility of wind interpolation techniques. DTSTAMP:20260527T212630Z LOCATION:PA01 (Quarticle) SUMMARY:Evaluation of Spatial Interpolation Methods for Wind Speed and Dire ction: A Case Study in Split-Dalmatia County - Mateo Radić URL:https://talks.staging.osgeo.org/foss4g-europe-2025/talk/JNM3AC/ END:VEVENT END:VCALENDAR