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The intensity and frequency of wildfires in California (CA) have increased in recent years, causing significant damage to human health and property. In October 2007, a number of small fire events, collectively referred to as the Witch Creek Fire or Witch Fire started in Southern CA and intensified under strong Santa Ana winds. As a test of current mesoscale modeling capabilities, we use the Weather Research and Forecasting (WRF) model to simulate the 2007 wildfire event in terms of meteorological conditions. The main objectives of the present study are to investigate the impact of horizontal grid resolution and planetary boundary layer (PBL) scheme on the model simulation of meteorological conditions associated with a Mega fire. We evaluate the predictive capability of the WRF model to simulate key meteorological and fire-weather forecast parameters such as wind, moisture, and temperature. Results of this study suggest that more accurate predictions of temperature and wind speed relevant for better prediction of wildfire spread can be achieved by downscaling regional numerical weather prediction products to 1 km resolution. Furthermore, accurate prediction of near-surface conditions depends on the choice of the planetary boundary layer parameterization. The MYNN parameterization yields more accurate prediction as compared to the YSU parameterization. WRF simulations at 1 km resolution result in better predictions of temperature and wind speed than relative humidity during the 2007 Witch Fire. In summary, the MYNN PBL parameterization scheme with finer grid resolution simulations improves the prediction of near-surface meteorological conditions during a wildfire event.
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Downscaling numerical weather models with conditional generative adversarial networks.
Abstract—Numerical simulation of weather is resolution-constrained due to the high computational cost of integrating the coupled PDEs that govern atmospheric motion. For example, the most highly-resolved numerical weather prediction models are limited to approximately 3 km. However many weather and climate impacts occur over much finer scales, especially in urban areas and regions with high topographic complexity like mountains or coastal regions. Thus several statistical methods have been developed in the climate community to downscale numerical model output to finer resolutions. This is conceptually similar to image super-resolution (SR) [1] and in this work we report the results of applying SR methods to the downscaling problem. In particular we test the extent to which a SR method based on a Generative Adversarial Network (GAN) can recover a grid of wind speed from an artificially downsampled version, compared against a standard bicubic upsampling approach and another machine learning based approach, SR-CNN [1]. We use ESRGAN ([2]) to learn to downscale wind speeds by a factor of 4 from a coarse grid. We find that we can recover spatial details with higher fidelity than bicubic upsampling or SR-CNN. The bicubic and SR-CNN methods perform better than ESRGAN on coarse metrics such as MSE. However, the high frequency power spectrum is captured remarkably well by the ESRGAN, virtually identical to the real data, while bicubic and SR-CNN fidelity drops significantly at high frequency. This indicates that SR is considerably better at matching the higher-order statistics of the dataset, consistent with the observation that the generated images are of superior visual quality compared with SR-CNN.
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- Award ID(s):
- 1843103
- PAR ID:
- 10137368
- Date Published:
- Journal Name:
- CLI info
- ISSN:
- 1623-2666
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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