NAM-NMM Temperature Downscaling Using Personal Weather Stations to Study Urban Heat Hazards
Rising temperatures worldwide pose an existential threat to people, properties, and the environment. Urban areas are particularly vulnerable to temperature increases due to the heat island effect, which amplifies local heating. Throughout the world, several megacities experience summer temperatures...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | GeoHazards |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-795X/2/3/14 |
| _version_ | 1797519149263486976 |
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| author | Martina Calovi Weiming Hu Guido Cervone Luca Delle Monache |
| author_facet | Martina Calovi Weiming Hu Guido Cervone Luca Delle Monache |
| author_sort | Martina Calovi |
| collection | DOAJ |
| description | Rising temperatures worldwide pose an existential threat to people, properties, and the environment. Urban areas are particularly vulnerable to temperature increases due to the heat island effect, which amplifies local heating. Throughout the world, several megacities experience summer temperatures that stress human survival. Generating very high-resolution temperature forecasts is a fundamental problem to mitigate the effects of urban warming. This paper uses the Analog Ensemble technique to downscale existing temperature forecast from a low resolution to a much higher resolution using private weather stations. A new downscaling approach, based on the reuse of the Analog Ensemble (AnEn) indices, resulted by the combination of days and Forecast Lead Time (FLT)s, is proposed. Specifically, temperature forecasts from the NAM-NMM Numerical Weather Prediction model at 12 km are downscaled using 83 Private Weather Stations data over Manhattan, New York City, New York. Forecasts for 84 h are generated, hourly for the first 36 h, and every three hours thereafter. The results are dense forecasts that capture the spatial variability of ambient conditions. The uncertainty associated with using non-vetted data is addressed. |
| first_indexed | 2024-03-10T07:38:59Z |
| format | Article |
| id | doaj.art-bc81250df0b24c9b80fe879c0bb0b09c |
| institution | Directory Open Access Journal |
| issn | 2624-795X |
| language | English |
| last_indexed | 2024-03-10T07:38:59Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | GeoHazards |
| spelling | doaj.art-bc81250df0b24c9b80fe879c0bb0b09c2023-11-22T13:15:33ZengMDPI AGGeoHazards2624-795X2021-08-012325727610.3390/geohazards2030014NAM-NMM Temperature Downscaling Using Personal Weather Stations to Study Urban Heat HazardsMartina Calovi0Weiming Hu1Guido Cervone2Luca Delle Monache3Department of Geography, Norwegian University of Science and Technology, 7491 Trondheim, NorwayDepartment of Geography, Institute for Computational and Data Sciences and Earth and Environmental Systems Institute, The Pennsylvania State University, University Park, PA 16801, USADepartment of Geography, Institute for Computational and Data Sciences and Earth and Environmental Systems Institute, The Pennsylvania State University, University Park, PA 16801, USAScripps Institution of Oceanography, University of California, La Jolla, CA 92093, USARising temperatures worldwide pose an existential threat to people, properties, and the environment. Urban areas are particularly vulnerable to temperature increases due to the heat island effect, which amplifies local heating. Throughout the world, several megacities experience summer temperatures that stress human survival. Generating very high-resolution temperature forecasts is a fundamental problem to mitigate the effects of urban warming. This paper uses the Analog Ensemble technique to downscale existing temperature forecast from a low resolution to a much higher resolution using private weather stations. A new downscaling approach, based on the reuse of the Analog Ensemble (AnEn) indices, resulted by the combination of days and Forecast Lead Time (FLT)s, is proposed. Specifically, temperature forecasts from the NAM-NMM Numerical Weather Prediction model at 12 km are downscaled using 83 Private Weather Stations data over Manhattan, New York City, New York. Forecasts for 84 h are generated, hourly for the first 36 h, and every three hours thereafter. The results are dense forecasts that capture the spatial variability of ambient conditions. The uncertainty associated with using non-vetted data is addressed.https://www.mdpi.com/2624-795X/2/3/14ensemble modelingtemperature forecasturban environmentsvolunteered geographic informationspatial downscalingprivate weather station |
| spellingShingle | Martina Calovi Weiming Hu Guido Cervone Luca Delle Monache NAM-NMM Temperature Downscaling Using Personal Weather Stations to Study Urban Heat Hazards GeoHazards ensemble modeling temperature forecast urban environments volunteered geographic information spatial downscaling private weather station |
| title | NAM-NMM Temperature Downscaling Using Personal Weather Stations to Study Urban Heat Hazards |
| title_full | NAM-NMM Temperature Downscaling Using Personal Weather Stations to Study Urban Heat Hazards |
| title_fullStr | NAM-NMM Temperature Downscaling Using Personal Weather Stations to Study Urban Heat Hazards |
| title_full_unstemmed | NAM-NMM Temperature Downscaling Using Personal Weather Stations to Study Urban Heat Hazards |
| title_short | NAM-NMM Temperature Downscaling Using Personal Weather Stations to Study Urban Heat Hazards |
| title_sort | nam nmm temperature downscaling using personal weather stations to study urban heat hazards |
| topic | ensemble modeling temperature forecast urban environments volunteered geographic information spatial downscaling private weather station |
| url | https://www.mdpi.com/2624-795X/2/3/14 |
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