Using Machine Learning to Calibrate Storm-Scale Probabilistic Guidance of Severe Weather Hazards in the Warn-on-Forecast System
Cooperative Institute for Mesoscale Meteorological Studies（中尺度气象研究合作所）；NOAA/OAR/National Severe Storms Laboratory（美国国家海洋和大气管理局/海洋与大气研究/国家强风暴实验室）；School of Meteorology, University of Oklahoma（俄克拉荷马大学气象学院）；School of Computer Science, University of Oklahoma（俄克拉荷马大学计算机科学学院）
Montgomery Flora, Corey K. Potvin, Patrick S. Skinner, Shawn Handler, Amy McGovern
A primary goal of the National Oceanic and Atmospheric Administration (NOAA) Warn-on-Forecast (WoF) project is to provide rapidly updating probabilistic guidance to human forecasters for short-term (e.g., 0-3 h) severe weather forecasts. Maximizing the usefulness of probabilistic severe weather guidance from an ensemble of convection-allowing model forecasts requires calibration. In this study, we compare the skill of a simple method using updraft helicity against a series of machine learning (ML) algorithms for calibrating WoFS severe weather guidance. ML models are often used to calibrate severe weather guidance since they leverage multiple variables and discover useful patterns in complex datasets. \indent Our dataset includes WoF System (WoFS) ensemble forecasts available every 5 minutes out to 150 min of lead time from the 2017-2019 NOAA Hazardous Weather Testbed Spring Forecasting Experiments (81 dates). Using a novel ensemble storm track identification method, we extracted three sets of predictors from the WoFS forecasts: intra-storm state variables, near-storm environment variables, and morphological attributes of the ensemble storm tracks. We then trained random forests, gradient-boosted trees, and logistic regression algorithms to predict which WoFS 30-min ensemble storm tracks will correspond to a tornado, severe hail, and/or severe wind report. For the simple method, we extracted the ensemble probability of 2-5 km updraft helicity (UH) exceeding a threshold (tuned per severe weather hazard) from each ensemble storm track. The three ML algorithms discriminated well for all three hazards and produced more reliable probabilities than the UH-based predictions. Overall, the results suggest that ML-based calibrations of dynamical ensemble output can improve short term, storm-scale severe weather probabilistic guidance
Developing Machine Learning-Based Watch-to-Warning Severe Weather Guidance from the Warn-on-Forecast System
School of Meteorology, University of Oklahoma, Norman, Oklahoma（俄克拉荷马大学气象学院）；Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma（俄克拉荷马大学严重和高影响天气研究与运作合作研究所）；NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma（国家海洋和大气管理局/海洋学局/严重风暴实验室）；AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography（可信人工智能在气象、气候和沿海海洋学研究中的研究院）；Valparaiso University, Valparaiso, Indiana（瓦尔帕莱索大学）
Information about the hydro-meteorological parameters during the extreme sea storms is of significant importance for the sustainable development in the context of flood risk for the coastal areas. Usually there is a lack of sufficiently long history of instrumental measurements of the extreme winds, waves and storm surges. Simulation of historical storms is an important tool to evaluate the potential coastal hazards. In the absence of measured data hindcasts can satisfy the need for historical data. The wave and storm-surge regional numerical simulations have been carried out for the ten most …
Georgia Institute of Technology（佐治亚理工学院）；Pennsylvania State University（宾夕法尼亚州立大学）
Many systems used by society are extremely vulnerable to space weather events such as solar flares and geomagnetic storms which could potentially cause catastrophic damage. In recent years, many works have emerged to provide early warning to such systems by forecasting these events through some proxy, but these approaches have largely focused on a specific phenomenon. We present a sequence-to-sequence learning approach to the problem of forecasting global space weather conditions at an hourly resolution. This approach improves upon other work in this field by simultaneously forecasting several…
Tide gauge and satellite data indicate that the rate of sea level rise has not increased significantly in response to the last 3/4 century of CO2 emissions, so there is no reason to expect that it will do so in response to the next 3/4 century of CO2 emissions. The best prediction for sea level in the future is simply a linear projection of the history of sea level at the same location in the past. For Long Island, that is about 7-8 inches by 2080.
Coastal planners and decision makers design risk management strategies based on hazard projections. However, projections can differ drastically. What causes this divergence and which projection(s) should a decision maker adopt to create plans and adaptation efforts for improving coastal resiliency? Using Norfolk, Virginia, as a case study, we start to address these questions by characterizing and quantifying the drivers of differences between published sea-level rise and storm surge projections, and how these differences can impact efforts to improve coastal resilience. We find that assumption…
Improving Predictions of Convective Storm Wind Gusts through Statistical Post-Processing of Neural Weather Models
Expertise Center for Climate Extremes (ECCE), University of Lausanne（气候极端专家中心（ECCE）、洛桑大学）；École polytechnique, Institut Polytechnique de Paris（巴黎高等理工学院）；Institute of Geography - Oeschger Centre for Climate Change Research, University of Bern（地理研究所—奥施格气候变迁研究中心、伯恩大学）；Federal Office of Meteorology and Climatology MeteoSwiss（联邦气象与气候办公室梅托斯瓦尔）；Faculty of Geosciences and Environment, University of Lausanne（地球科学与环境学院、洛桑大学）
The paper describes the existing operational sea state forecast system of NIMH- BAS for sea state in the Black Sea and our current progress on the implementation of an additional component for the forecasts of wind waves in the Bulgarian coastal zone. Wind Waves and especially the extreme ones, occurring during severe storms are a major hazard for the coastal zone, causing significant damages to the infrastructure, threat for the human lives and also causing significant damages to the protected areas around the coast. The numerical model WAVEWATCH III is in use for wind waves forecasts for the…
Convective storms are one of the severe weather hazards found during the warm season. Doppler weather radar is the only operational instrument that can frequently sample the detailed structure of convective storm which has a small spatial scale and short lifetime. For the challenging task of short-term convective storm forecasting, 3-D radar images contain information about the processes in convective storm. However, effectively extracting such information from multisource raw data has been problematic due to a lack of methodology and computation limitations. Recent advancements in deep learni…
Severe storms, tropical cyclones, and associated tornadoes, floods, lightning, and microbursts threaten life and property. Reliable, precise, and accurate alerts of these phenomena can trigger defensive actions and preparations. However, these crucial weather phenomena are difficult to forecast. The objective of this paper is to demonstrate the potential of 4d-VAR (four dimensional variational data assimilation) for exigent forecasting (XF) of severe storm precursors and to thereby characterize the probability of a worst-case scenario. 4d-VAR is designed to adjust the initial conditions (IC) o…
Pixels and Predictions: Potential of GPT-4V in Meteorological Imagery Analysis and Forecast Communication
Bingham Research Center, Utah State University（犹他州立大学宾厄姆研究中心）；Utah State University（犹他州立大学）；NOAA/OAR/National Severe Storms Laboratory（美国国家海洋和大气管理局/海洋与大气研究办公室国家强风暴实验室）；Cooperative Institute for Severe and High-Impact Research and Operations, The University of Oklahoma（俄克拉荷马大学严重与高影响研究及合作运营研究所）；Department of Communication, The University of Oklahoma（俄克拉荷马大学传播系）；Centre for Crisis Studies and Mitigation, University of Manchester（曼彻斯特大学危机研究与减缓中心）；Department of Earth and Environmental Sciences, University of Manchester（曼彻斯特大学地球与环境科学系）；NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography（美国国家科学基金会天气、气候与沿海海洋学可信人工智能研究院）；Department of Geography and Meteorology, Valparaiso University（瓦尔帕莱索大学地理与气象系）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Department of Communication（传播系）
