Abstract

The National Weather Service plays a critical role in alerting the public when dangerous weather occurs. Tornado warnings are one of the most publicly visible products the NWS issues given the large societal impacts tornadoes can have. Understanding the performance of these warnings is crucial for providing adequate warning during tornadic events and improving overall warning performance. This study aims to understand warning performance during the lifetimes of individual storms (specifically in terms of probability of detection and lead time). For example, does probability of detection vary based on if the tornado was the first produced by the storm, or the last? We use tornado outbreak data from 2008 to 2014, archived NEXRAD radar data, and the NWS verification database to associate each tornado report with a storm object. This approach allows for an analysis of warning performance based on the chronological order of tornado occurrence within each storm. Results show that the probability of detection and lead time increase with later tornadoes in the storm; the first tornadoes of each storm are less likely to be warned and on average have less lead time. Probability of detection also decreases overnight, especially for first tornadoes and storms that only produce one tornado. These results are important for understanding how tornado warning performance varies during individual storm life cycles and how upstream forecast products (e.g., Storm Prediction Center tornado watches, mesoscale discussions, etc.) may increase warning confidence for the first tornado produced by each storm.

Abstract

On average, modern numerical weather prediction forecasts for daily tornado frequency exhibit no skill beyond day 10. However, in this extended-range lead window, there are particular model cycles that have exceptionally high forecast skill for tornadoes owing to their ability to correctly simulate the future synoptic pattern. Here, model initial conditions that produced a more skillful forecast for tornadoes over the U.S. were exploited, while also highlighting potential causes for low-skill cycles within the Global Ensemble Forecasting System, version 12 (GEFSv12). Eighty-eight high-skill and 91 low-skill forecasts in which the verifying day-10 synoptic pattern for tornado conditions revealed a western U.S. thermal trough and an eastern U.S. thermal ridge, a favorable configuration for tornadic storm occurrence. Initial conditions for high skill forecasts tended to exhibit warmer sea-surface temperatures throughout the tropical Pacific Ocean and Gulf of Mexico, an active Madden-Julian Oscillation, and significant modulation of Earth-relative atmospheric angular momentum. Low-skill forecasts were often initialized during La Niña and negative Pacific Decadal Oscillation conditions. Significant atmospheric blocking over eastern Russia—in which the GEFSv12 over forecasted the duration and characteristics of the downstream flow—was a common physical process associated with low-skill forecasts. This work helps to increase our understanding of the common causes of high- or low-skill extended-range tornado forecasts and could serve as a helpful tool for operational forecasters.

Abstract

Accurate sub-seasonal to seasonal (S2S) weather forecasts are crucial to making important decisions in many sectors. However, significant gaps exist between the needs of society and what forecasters can produce, especially at weekly and longer lead times. We hypothesize that by clustering atmospheric states into a number of predefined categories, the noise can be reduced, and consequently, medium-range forecasts can be improved. Self-organizing map (SOM)-based clustering was used on daily mean sea-level pressure (MSLP) from the North American Regional Reanalysis to categorize the synoptic-scale circulation for eastern North America from 1979-2016 into 28 discrete patterns. Then, using two goodness-of-fit metrics, this study examined the relative skill of four different forecasting methods over a 90-day lead-time: 1) a circulation pattern (CP) forecast, 2) raw forecast output from the Climate Forecast System (CFS) operated by the National Centers for Environmental Prediction (NCEP) 3) a simple climatology forecast, and 4) a simple persistence forecast. Expectedly, forecast skill in both the CP forecast and the raw CFS forecast generally decreased rapidly from the first day, coming to parity with the skill of climatology after 10 to 12 days when using correlation, and at 7 to 10 days, when using the root mean squared error (RMSE). Most importantly, this study found that the CP forecast was the most skillful forecast method over the 8- to 11-day lead time when using RMSE. On a spatial basis, the skill of the CP forecast and the raw CFS decreases latitudinally from north to south. This study thus demonstrates the potential utility of categorical/circulation pattern-based forecasting at 1 to 2-week lead times.

Abstract

Eyewall replacement cycles (ERCs) in tropical cyclones (TCs) are generally associated with rapid changes in TC wind intensity and broadening of the TC wind-field, both of which can create unique forecasting challenges. As part of the NOAA Joint Hurricane Testbed Project, a new model was developed to provide operational probabilistic guidance on ERC onset. The model is based on the time evolution of TC wind-intensity and passive satellite microwave imagery, and is named “M-PERC” for Microwave-based Probability of Eyewall Replacement Cycle. The model was initially developed in the Atlantic basin, but is found to be globally applicable and skillful. The development of M-PERC and its performance characteristics are described here, as well as a new intensity prediction model that extends previous work. Application of these models is expected to contribute to a reduction of TC intensity forecast error.

Abstract

This study focused on developing a consensus machine learning (CML) model for tropical cyclone (TC) intensity-change forecasting, especially for rapid intensification (RI). This CMLmodelwas built upon selected classical machine learning models with the input data extracted from a high-resolution hurricane model, the HurricaneWeather Research and Forecasting (HWRF) system. The input data contained 21 or 34 RI-related predictors extracted from the 2018 version of HWRF (H218). This study found that TC inner-core predictors can be critical for improving RI predictions, especially the inner-core relative humidity. Moreover, this study emphasized that the importance of performing resampling on an imbalanced input dataset. Edited Nearest Neighbor and Synthetic Minority Oversampling Technique improved the Probability of Detection (POD) by ∼10% for the RI class. This paper also showed that the CML model has satisfactory performance on RI predictions compared to the operational models. CML reached 56% POD and 46% False Alarm Ratio (FAR), while the operational models had only 10 to 30% POD but 50 to 60% FAR. The CML performance on the non-RI classes was comparable to the operational models. The results indicated that, with proper and sufficient training data and RI-related predictors, CML has the potential to provide reliable probabilistic RI forecasts during a hurricane season.

Abstract

This work set out to assess the performance of four forecast systems (the Short-Range Ensemble Forecast (SREF), High-Resolution Rapid Refresh Ensemble (HRRRE), the National Blend of Models (NBM), and the Probabilistic Snow Accumulation product (PSA) from the National Weather Service (NWS) Boulder, CO Weather Forecast Office) when predicting snowfall events around the Intermountain West to advise winter weather decision-making processes at Denver International Airport. The goal was to provide airport personnel and the Boulder NWS Forecast Office with operationally-relevant verification results on the timing and severity of these events so they are able to make better-informed decisions to minimize negative impacts of storms. Forecasts of snow events using various probability thresholds and a climatological snow-to-liquid ratio of 15:1 were evaluated against Meteorological Aerodrome Reports (METARs) for 24-hour periods following four decision-making times spaced equally throughout the day. For the ensembles, a frequentist approach was used: the forecast probability equaled the percentage of ensemble members that predicted a snow event. The results show that the NBM had the best timing of snow events out of the products while all the products tended to over-forecast snow amount. Additionally, NBM had fewer snow events and rarely had high probabilities of snow, unlike the other forecast products.

Abstract

Developed as part of a larger effort by the National Weather Service (NWS) Radar Operations Center to modernize their suite of single-radar severe weather algorithms for the WSR-88D radar network, the Tornado Probability algorithm (TORP) and the New Mesocyclone Detection Algorithm (NMDA) were evaluated by operational forecasters during the 2021 National Oceanic and Atmospheric Administration (NOAA) Hazardous Weather Testbed (HWT) Experimental Warning Program Radar Convective Applications experiment. Both TORP and NMDA leverage new products and advances in radar technology to create rotation-based objects that interrogate single-radar data, providing important summary and trend information that aids forecasters in issuing time-critical and potentially life-saving weather products. Utilizing virtual resources like Google Workspace and cloud instances on Amazon Web Services, 18 forecasters from the NOAA NWS and the United States Air Force participated remotely over three weeks during the spring of 2021, providing valuable feedback on the efficacy of the algorithms and their display in an operational warning environment, serving as a critical step in the research-to-operations process for the development of TORP and NMDA. This article will discuss the details of the virtual HWT experiment and the results of each algorithm’s evaluation during the testbed.

Abstract

Tornadoes produced by right-moving supercells (RM) and quasi-linear convective systems (QLCS) are compared across the contiguous United States for the period 2003–2021, based on the maximum F/EF-scale rating per hour on a 40-km horizontal grid. The frequency of QLCS tornadoes has increased dramatically since 2003 while the frequency of RM tornadoes has decreased during that same period. Prior work noting that the most common damage rating for QLCS tornadoes at night is EF1 persists in this larger, independent sample. A comparison of WSR-88D radar attributes between RM and QLCS tornadoes shows no appreciable differences between EF0 tornadoes produced by either convective mode. Differences become apparent for EF1–2 tornadoes, where rotational velocity is larger and velocity couplet diameter is smaller for RM tornadoes compared to QLCS tornadoes. The frequency of tornadic debris signatures (TDS) in dual polarization data is also larger for EF1–2 RM tornadoes when controlling for tornadoes sampled relatively close to the radar sites, and during daylight versus overnight. The weaker rotational velocities, broader velocity couplet diameters, and lower frequencies of TDSs both close to the radar and at night for QLCS EF1 tornadoes suggest that a combination of inadequate radar sampling and occasional misclassification of wind damage may be responsible for the irregularities in the historical record of QLCS tornado reports.

Abstract

Low Earth orbit (LEO) hyper-spectral infrared (IR) sounders have significant yet untapped potential for characterizing thermodynamic environments of convective initiation and ongoing convection. While LEO soundings are of value to weather forecasters, the temporal resolution needed to resolve the rapidly evolving thermodynamics of the convective environment is limited. We have developed a novel nowcasting methodology to extend snapshots of LEO soundings forward in time up to six hours to create a product available within National Weather Service systems for user assessment. Our methodology is based on parcel forward-trajectory calculations from the satellite observing time to generate future soundings of temperature (T) and specific humidity (q) at regularly gridded intervals in space and time. The soundings are based on NOAA-Unique Combined Atmospheric Processing System (NUCAPS) retrievals from the Suomi NPP and NOAA-20 satellite platforms. The tendencies of derived convective available potential energy (CAPE) and convective inhibition (CIN) are evaluated against gridded, hourly accumulated rainfall obtained from the Multi-Radar Multi-Sensor (MRMS) observations for 24 hand-selected cases over the Contiguous United States. Areas with forecast increases in CAPE (reduced CIN) are shown to be associated with areas of precipitation. The increases in CAPE and decreases in CIN are largest for areas that have the heaviest precipitation and are statistically significant compared to areas without precipitation. These results imply that adiabatic parcel advection of LEO satellite sounding snapshots forward in time are capable of identifying convective initiation over an expanded temporal scale compared to soundings used only during the LEO satellite overpass time.

Abstract

This study introduces a novel method for comparing vertical thermodynamic profiles, focusing on the atmospheric boundary layer, across a wide range of meteorological conditions. This method is developed using observed temperature and dewpoint temperature data from 31,153 soundings taken at 0000 UTC and 32,308 soundings taken at 1200 UTC between May 2019 – March 2020. Temperature and dewpoint temperature vertical profiles are first interpolated onto a height above-ground-level (AGL) coordinate, after which the temperature of the dry adiabat defined by the surface-based parcel’s temperature is subtracted from each quantity at all altitudes. This allows for common sounding features, such as turbulent mixed layers and inversions, to be similarly depicted regardless of temperature and dewpoint-temperature differences resulting from altitude, latitude, or seasonality.

The soundings that result from applying this method to the observed sounding collection described above are then clustered to identify distinct boundary-layer structures in the data. Specifically, separately at 0000 and 1200 UTC, a k-means clustering analysis is conducted in the phase space of the leading two empirical orthogonal functions of the sounding data. As compared to clustering based on the original vertical profiles, which results in clusters that are dominated by seasonal and latitudinal differences, clusters derived from transformed data are less latitudinally and seasonally stratified and better represent boundary-layer features such turbulent mixed layers and pseudoadiabatic profiles. The sounding-comparison method thus provides an objective means of categorizing vertical thermodynamic profiles with wide-ranging applications, as demonstrated by using the method to verify short-range Global Forecast System model forecasts.