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Jonathan M. Garner, William C. Iwasko, Tyler D. Jewel, Richard L. Thompson, and Bryan T. Smith

Abstract

A dataset maintained by the Storm Prediction Center (SPC) of 6300 tornado events from 2009–2015, consisting of radar-identified convective modes and near-storm environmental information obtained from Rapid Update Cycle and Rapid Refresh model analysis grids, has been augmented with additional radar information related to the low-level mesocyclones associated with tornado longevity, path-length, and width. All EF2–EF5 tornadoes, in addition to randomly selected EF0–EF1 tornadoes, were extracted from the SPC dataset, which yielded 1268 events for inclusion in the current study. Analysis of that data revealed similar values of the effective-layer significant tornado parameter for the longest-lived (60+ min) tornadic circulations, longest-tracked (≥ 68 km) tornadoes, and widest tornadoes (≥ 1.2 km). However, the widest tornadoes occurring west of –94° longitude were associated with larger mean-layer convective available potential energy, storm-top divergence, and low-level rotational velocity. Furthermore, wide tornadoes occurred when low-level winds were out of the southeast resulting in large low-level hodograph curvature and near-surface horizontal vorticity that was more purely streamwise compared to long-lived and long-tracked events. On the other hand, tornado path-length and longevity were maximized with eastward migrating synoptic-scale cyclones associated with strong southwesterly wind profiles through much of the troposphere, fast storm motions, large values of bulk wind difference and storm-relative helicity, and lower buoyancy.

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Andrew Hazelton, Zhan Zhang, Bin Liu, Jili Dong, Ghassan Alaka, Weiguo Wang, Tim Marchok, Avichal Mehra, Sundararaman Gopalakrishnan, Xuejin Zhang, Morris Bender, Vijay Tallapragada, and Frank Marks

Abstract

NOAA’s Hurricane Analysis and Forecast System (HAFS) is an evolving FV3-based hurricane modeling system that is expected to replace the operational hurricane models at the National Weather Service. Supported by the Hurricane Forecast Improvement Program (HFIP), global-nested and regional versions of HAFS were run in real time in 2019 to create the first baseline for the HAFS advancement. In this study, forecasts from the global-nested configuration of HAFS (HAFS-globalnest) are evaluated and compared with other operational and experimental models. The forecasts by HAFS-globalnest covered the period from July through October during the 2019 hurricane season. Tropical cyclone (TC) track, intensity, and structure forecast verifications are examined. HAFS-globalnest showed track skill superior to several operational hurricane models and comparable intensity and structure skill, although the skill in predicting rapid intensification was slightly inferior to the operational model skill. HAFS-globalnest correctly predicted that Hurricane Dorian would slow and turn north in the Bahamas and also correctly predicted structural features in other TCs such as a sting jet in Hurricane Humberto during extratropical transition. Humberto was also a case where HAFS-globalnest had better track forecasts than a regional version of HAFS (HAFS-SAR) due to a better representation of the large-scale flow. These examples and others are examined through comparisons with airborne tail Doppler radar from the NOAA WP-3D to provide a more detailed evaluation of TC structure prediction. The results from this real-time experiment motivate several future model improvements, and highlight the promise of HAFS-globalnest for improved TC prediction.

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Theodore W. Letcher, Sandra L. LeGrand, and Christopher Polashenski

Abstract

Blowing snow presents substantial risk to human activities by causing severe visibility degradation and snow drifting. Furthermore, blowing snow presents a weather forecast challenge since it is not generally simulated in operational weather forecast models. In this study, we apply a physically based blowing snow model as a diagnostic overlay to output from a reforecast WRF simulation of a significant blowing snow event that occurred over the northern Great Plains of the United States during the winter of 2019. The blowing snow model is coupled to an optics parameterization that estimates the visibility reduction by blowing snow. This overlay is qualitatively evaluated against false color satellite imagery from the GOES-16 operational weather satellite and available surface visibility observations. The WRF-simulated visibility is substantially improved when incorporating blowing snow hydrometeors. Furthermore, the model-simulated plume of blowing snow roughly corresponds to the blowing snow plumes visible in the satellite imagery. Overall, this study illustrates how a blowing snow diagnostic model can aid weather forecasters in making blowing snow visibility forecasts, and demonstrates how the model can be evaluated against satellite imagery.

Open access
Florian Dupuy, Olivier Mestre, Mathieu Serrurier, Valentin Kivachuk Burdá, Michaël Zamo, Naty Citlali Cabrera-Gutiérrez, Mohamed Chafik Bakkay, Jean-Christophe Jouhaud, Maud-Alix Mader, and Guillaume Oller

Abstract

Cloud cover provides crucial information for many applications such as planning land observation missions from space. It remains, however, a challenging variable to forecast, and numerical weather prediction (NWP) models suffer from significant biases, hence, justifying the use of statistical postprocessing techniques. In this study, ARPEGE (Météo-France global NWP) cloud cover is postprocessed using a convolutional neural network (CNN). CNN is the most popular machine learning tool to deal with images. In our case, CNN allows the integration of spatial information contained in NWP outputs. We use a gridded cloud cover product derived from satellite observations over Europe as ground truth, and predictors are spatial fields of various variables produced by ARPEGE at the corresponding lead time. We show that a simple U-Net architecture (a particular type of CNN) produces significant improvements over Europe. Moreover, the U-Net outclasses more traditional machine learning methods used operationally such as a random forest and a logistic quantile regression. When using a large number of predictors, a first step toward interpretation is to produce a ranking of predictors by importance. Traditional methods of ranking (permutation importance, sequential selection, etc.) need important computational resources. We introduced a weighting predictor layer prior to the traditional U-Net architecture in order to produce such a ranking. The small number of additional weights to train (the same as the number of predictors) does not impact the computational time, representing a huge advantage compared to traditional methods.

Open access
Ying Wang and Zhaoxia Pu

Abstract

The benefits of assimilating Next Generation Weather Radar (NEXRAD) radial velocity data for convective systems have been demonstrated in previous studies. However, impacts of assimilation of such high spatial and temporal resolution observations on hurricane forecasts have not been demonstrated with the National Centers for Environmental Prediction (NCEP) Hurricane Weather and Research Forecasting (HWRF) system. This study investigates impacts of NEXRAD radial velocity data on forecasts of the evolution of landfalling hurricanes with different configurations of data assimilation. The sensitivity of data assimilation results to influencing parameters within the data assimilation system, such as the maximum range of the radar data, superobservations, horizontal and vertical localization correlation length scale, and weight of background error covariances, are examined. Two hurricane cases, Florence and Michael, which occurred in the summer of 2018 are chosen to conduct a series of experiments. Results show that hurricane intensity, asymmetric structure of inland wind and precipitation, and quantitative precipitation forecasting are improved. Suggestions for implementation of operational configurations are provided.

Open access
Hailiang Du

Abstract

The evaluation of probabilistic forecasts plays a central role both in the interpretation and in the use of forecast systems and their development. Probabilistic scores (scoring rules) provide statistical measures to assess the quality of probabilistic forecasts. Often, many probabilistic forecast systems are available while evaluations of their performance are not standardized, with different scoring rules being used to measure different aspects of forecast performance. Even when the discussion is restricted to strictly proper scoring rules, there remains considerable variability between them; indeed strictly proper scoring rules need not rank competing forecast systems in the same order when none of these systems are perfect. The locality property is explored to further distinguish scoring rules. The nonlocal strictly proper scoring rules considered are shown to have a property that can produce “unfortunate” evaluations, particularly the fact that the continuous rank probability score prefers the outcome close to the median of the forecast distribution regardless of the probability mass assigned to the value at/near the median raises concern to its use. The only local strictly proper scoring rule, the logarithmic score, has direct interpretations in terms of probabilities and bits of information. The nonlocal strictly proper scoring rules, on the other hand, lack meaningful direct interpretation for decision support. The logarithmic score is also shown to be invariant under smooth transformation of the forecast variable, while the nonlocal strictly proper scoring rules considered may, however, change their preferences due to the transformation. It is therefore suggested that the logarithmic score always be included in the evaluation of probabilistic forecasts.

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Carlo Cafaro, Beth J. Woodhams, Thorwald H. M. Stein, Cathryn E. Birch, Stuart Webster, Caroline L. Bain, Andrew Hartley, Samantha Clarke, Samantha Ferrett, and Peter Hill

Abstract

Convection-permitting ensemble prediction systems (CP-ENS) have been implemented in the midlatitudes for weather forecasting time scales over the past decade, enabled by the increase in computational resources. Recently, efforts are being made to study the benefits of CP-ENS for tropical regions. This study examines CP-ENS forecasts produced by the Met Office over tropical East Africa, for 24 cases in the period April–May 2019. The CP-ENS, an ensemble with parameterized convection (Glob-ENS), and their deterministic counterparts are evaluated against rainfall estimates derived from satellite observations (GPM-IMERG). The CP configurations have the best representation of the diurnal cycle, although heavy rainfall amounts are overestimated compared to observations. Pairwise comparisons between the different configurations reveal that the CP-ENS is generally the most skillful forecast for both 3- and 24-h accumulations of heavy rainfall (97th percentile), followed by the CP deterministic forecast. More precisely, probabilistic forecasts of heavy rainfall, verified using a neighborhood approach, show that the CP-ENS is skillful at scales greater than 100 km, significantly better than the Glob-ENS, although not as good as found in the midlatitudes. Skill decreases with lead time and varies diurnally, especially for CP forecasts. The CP-ENS is underspread both in terms of forecasting the locations of heavy rainfall and in terms of domain-averaged rainfall. This study demonstrates potential benefits in using CP-ENS for operational forecasting of heavy rainfall over tropical Africa and gives specific suggestions for further research and development, including probabilistic forecast guidance.

Open access
Akila Sampath, Uma S. Bhatt, Peter A. Bieniek, Robert Ziel, Alison York, Heidi Strader, Sharon Alden, Richard Thoman, Brian Brettschneider, Eugene Petrescu, Peitao Peng, and Sarah Mitchell

Abstract

In this study, seasonal forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), are compared with station observations to assess their usefulness in producing accurate buildup index (BUI) forecasts for the fire season in Interior Alaska. These comparisons indicate that the CFSv2 June–July–August (JJA) climatology (1994–2017) produces negatively biased BUI forecasts because of negative temperature and positive precipitation biases. With quantile mapping (QM) correction, the temperature and precipitation forecasts better match the observations. The long-term JJA mean BUI improves from 12 to 42 when computed using the QM-corrected forecasts. Further postprocessing of the QM-corrected BUI forecasts using the quartile classification method shows anomalously high values for the 2004 fire season, which was the worst on record in terms of the area burned by wildfires. These results suggest that the QM-corrected CFSv2 forecasts can be used to predict extreme fire events. An assessment of the classified BUI ensemble members at the subseasonal scale shows that persistently occurring BUI forecasts exceeding 150 in the cumulative drought season can be used as an indicator that extreme fire events will occur during the upcoming season. This study demonstrates the ability of QM-corrected CFSv2 forecasts to predict the potential fire season in advance. This information could, therefore, assist fire managers in resource allocation and disaster response preparedness.

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Nathan A. Wendt and Israel L. Jirak

Abstract

The Multi-Radar Multi-Sensor (MRMS) system generates an operational suite of derived products in the National Weather Service useful for real-time monitoring of severe convective weather. One such product generated by MRMS is the maximum estimated size of hail (MESH) that estimates hail size based on the radar reflectivity properties of a storm above the environmental 0°C level. The MRMS MESH product is commonly used across the National Weather Service (NWS), including the Storm Prediction Center (SPC), to diagnose the expected hail size in thunderstorms. Previous work has explored the relationship between the MRMS MESH product and severe hail (≥25.4 mm or 1 in.) reported at the ground. This work provides an hourly climatology of severe MRMS MESH across the contiguous United States from 2012 to 2019, including an analysis of how the MESH climatology differs from the severe hail reports climatology. Results suggest that the MESH can provide beneficial hail risk information in areas where population density is low. Evidence also shows that the MESH can provide potentially beneficial information about severe hail occurrence during the night in locations that are climatologically favored for upscale convective growth and elevated convection. These findings have important implications for the use of MESH as a verification dataset for SPC probabilistic hail forecasts as well as severe weather watch decisions in areas of higher hail risk but low population density.

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Benjamin A. Schenkel, Michael Coniglio, and Roger Edwards

Abstract

This work investigates how the relationship between tropical cyclone (TC) tornadoes and ambient (i.e., synoptic-scale) deep-tropospheric (i.e., 850–200-hPa) vertical wind shear (VWS) varies between coastal and inland environments. Observed U.S. TC tornado track data are used to study tornado frequency and location, while dropsonde and radiosonde data are used to analyze convective-scale environments. To study the variability in the TC tornado–VWS relationship, these data are categorized by both 1) their distance from the coast and 2) reanalysis-derived VWS magnitude. The analysis shows that TCs produce coastal tornadoes regardless of VWS magnitude primarily in their downshear sector, with tornadoes most frequently occurring in strongly sheared cases. Inland tornadoes, including the most damaging cases, primarily occur in strongly sheared TCs within the outer radii of the downshear-right quadrant. Consistent with these patterns, dropsondes and coastal radiosondes show that the downshear-right quadrant of strongly sheared TCs has the most favorable combination of enhanced lower-tropospheric near-surface speed shear and veering, and reduced lower-tropospheric thermodynamic stability for tornadic supercells. Despite the weaker intensity farther inland, these kinematic conditions are even more favorable in inland environments within the downshear-right quadrant of strongly sheared TCs, due to the strengthened veering of the ambient winds and the lack of changes in the TC outer tangential wind field strength. The constructive superposition of the ambient and TC winds may be particularly important to inland tornado occurrence. Together, these results will allow forecasters to anticipate how the frequency and location of tornadoes and, more broadly, convection may change as TCs move inland.

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