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Thea N. Sandmæl
,
Brandon R. Smith
,
Anthony E. Reinhart
,
Isaiah M. Schick
,
Marcus C. Ake
,
Jonathan G. Madden
,
Rebecca B. Steeves
,
Skylar S. Williams
,
Kimberly L. Elmore
, and
Tiffany C. Meyer

Abstract

A new probabilistic tornado detection algorithm was developed to potentially replace the operational tornado detection algorithm (TDA) for the WSR-88D radar network. The Tornado Probability algorithm (TORP) uses a random forest machine learning technique to estimate a probability of tornado occurrence based on single-radar data, and is trained on 166,145 data points derived from 0.5°-tilt radar data and storm reports from 2011-2016, of which 10.4% are tornadic. A variety of performance evaluation metrics show a generally good model performance for discriminating between tornadic and non-tornadic points. When using a 50% probability threshold to decide whether the model is predicting a tornado or not, the probability of detection and false alarm ratio are 57% and 50%, respectively, showing high skill by several metrics and vastly outperforming the TDA. The model weaknesses include false alarms associated with poor-quality radial velocity data and greatly reduced performance when used in the western United States. Overall, TORP can provide real-time guidance for tornado warning decisions, which can increase forecaster confidence and encourage swift decision making. It has the ability to condense a multitude of radar data into a concise object-based information read-out that can be displayed in visualization software used by the National Weather Service, core partners, and researchers.

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Jiehong Xie
,
Pang-Chi Hsu
,
Yamin Hu
,
Mengxi Ye
, and
Jinhua Yu

Abstract

The extended-range forecast with a lead time of 10–30 days is the gap between weather (<10 days) and climate (>30 days) predictions. Improving the forecast skill of extreme weather events at the extended range is crucial for risk management of disastrous events. In this study, three deep-learning (DL) models based on the methods of convolutional neural network and gate recurrent unit are constructed to predict the rainfall anomalies and associated extreme events in East China at the lead times of 1–6 pentads. All DL models show skillful prediction of the temporal variation of rainfall anomalies (in terms of temporal correlation coefficient skill) over most regions in East China beyond 4 pentads, outperforming the dynamical models from the China Meteorological Administration (CMA) and the European Centre for Medium Range Weather Forecasts (ECMWF). The spatial distribution of the rainfall anomalies is also better predicted by the DL models than the dynamical models; and the DL models show higher pattern correlation coefficients than the dynamical models at lead times of 3 to 6 pentads. The higher skill of DL models in predicting the rainfall anomalies will help to improve the accuracy of extreme-event predictions. The Heidke skill scores of the extreme rainfall event forecast performed by the DL models are also superior to those of the dynamical models at a lead time beyond about 4 pentads. Heat map analysis for the DL models shows that the predictability sources are mainly the large-scale factors modulating the East Asian monsoon rainfall.

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Joshua Chun Kwang Lee
and
Dale Melvyn Barker

Abstract

A hybrid three-dimensional ensemble-variational (En3D-Var) data assimilation system has been developed to explore incorporating information from an 11-member regional ensemble prediction system, which is dynamically downscaled from a global ensemble system, into the operational 3-hourly cycling convective-scale data assimilation system over the western Maritime Continent. From the ensemble, there exists small-scale ensemble perturbation structures associated with positional differences of tropical convection, but these structures are well-represented only after the downscaled ensemble forecast has evolved for at least 6 hours due to spin-up. There was also a robust moderate negative correlation between total specific humidity and potential temperature background errors, presumably because of incorrect vertical motion in the presence of clouds. Time-shifting of the ensemble perturbations, by using those available from adjacent cycles, helped to ameliorate the sampling error prevalent in their raw auto-covariances. Month-long hybrid En3D-Var trials were conducted using different weights assigned to the ensemble-derived and climatological background error covariances. The forecast fits to radiosonde relative humidity and wind observations were generally improved with hybrid En3D-Var, but in all experiments, the fits to surface observations were degraded compared to the baseline 3D-Var operational configuration. Over the Singapore radar domain, there was a general improvement in the precipitation forecasts, especially when the weighting towards the climatological background error covariance was larger, and with the additional application of time-shifted ensemble perturbations. Future work involves consolidating the ensemble prediction and deterministic system, by centring the ensemble prediction system on the hybrid analysis, to better represent the analysis and forecast uncertainties.

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Xiping Zhang
,
Juan Fang
, and
Zifeng Yu

Abstract

Tropical cyclone (TC) genesis forecasts during 2018–20 from two operational global ensemble prediction systems (EPSs) are evaluated over three basins in this study. The two ensembles are from the European Centre for Medium-Range Weather Forecasts (ECMWF-EPS) and the MetOffice in the United Kingdom (UKMO-EPS). The three basins include the northwest Pacific, northeast Pacific, and the North Atlantic. It is found that the ensemble members in each EPS show a good level of agreement in forecast skill, but their forecasts are complementary. Probability of detection (POD) can be doubled by taking all the member forecasts in the EPS into account. Even if an ensemble member does not make a hit forecast, it may predict the presence of cyclonic vortices. Statistically, a hit forecast has more nearby disturbance forecasts in the ensemble than a false alarm. Based on the above analysis, we grouped the nearby forecasts at each model initialization time to define ensemble genesis forecasts, and verified these forecasts to represent the performance of the ensemble system. The PODs are found to be more than twice that of the individual ensemble members at most lead times, which is about 59% and 38% at the 5-day lead time in UKMO-EPS and ECMWF-EPS, respectively; while the success ratios are smaller compared with that of the ensemble members. In addition, predictability differs in different basins, and genesis events in the North Atlantic basin are the most difficult to forecast in EPS, and its POD at the 5-day lead time is only 46% and 23% in UKMO-EPS and ECMWF-EPS, respectively.

Significance Statement

Operational forecasting of tropical cyclone (TC) genesis relies greatly on numerical models. Compared with deterministic forecasts, ensemble prediction systems (EPSs) can provide uncertainty information for forecasters. This study examined the predictability of TC genesis in two operational EPSs. We found that the forecasts of ensemble members complement each other, and the detection ratio of observed genesis will be doubled by considering the forecasts of all members, as multiple simulations conducted by the EPS partially reflect the inherent uncertainties of the genesis process. Successful forecasts are surrounded by more cyclonic vortices in the ensemble than false alarms, so the vortex information is used to group the nearby forecasts at each model initialization to define ensemble genesis forecasts when evaluating the ensemble performance. The results demonstrate that the global ensemble models can serve as a valuable reference for TC genesis forecasting.

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Patrik Benáček
,
Aleš Farda
, and
Petr Štěpánek

Abstract

Producing an accurate and calibrated probabilistic forecast has high social and economic value. Systematic errors or biases in the ensemble weather forecast can be corrected by postprocessing models whose development is an urgent challenge. Traditionally, the bias correction is done by employing linear regression models that estimate the conditional probability distribution of the forecast. Although this model framework works well, it is restricted to a prespecified model form that often relies on a limited set of predictors only. Most machine learning (ML) methods can tackle these problems with a point prediction, but only a few of them can be applied effectively in a probabilistic manner. The tree-based ML techniques, namely, natural gradient boosting (NGB), quantile random forests (QRF), and distributional regression forests (DRF), are used to adjust hourly 2-m temperature ensemble prediction at lead times of 1–10 days. The ensemble model output statistics (EMOS) and its boosting version are used as benchmark models. The model forecast is based on the European Centre for Medium-Range Weather Forecasts (ECMWF) for the Czech Republic domain. Two training periods 2015–18 and 2018 only were used to learn the models, and their prediction skill was evaluated in 2019. The results show that the QRF and NGB methods provide the best performance for 1–2-day forecasts, while the EMOS method outperforms other methods for 8–10-day forecasts. Key components to improving short-term forecasting are additional atmospheric/surface state predictors and the 4-yr training sample size.

Significance Statement

Machine learning methods have great potential and are beginning to be widely applied in meteorology in recent years. A new technique called natural gradient boosting (NGB) has been released and used in this paper to refine the probabilistic forecast of surface temperature. It was found that the NGB has better prediction skills than the traditional ensemble model output statistics in forecasting 1 and 2 days in advance. The NGB has similar prediction skills with lower computational demands compared to other advanced machine learning methods such as the quantile random forests. We showed a path to employ the NGB method in this task, which can be followed for refining other and more challenging meteorological variables such as wind speed or precipitation.

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Matthew D. Brothers
and
Christopher L. Hammer

Abstract

High winds are one of the key forecast challenges across southeast Wyoming. The complex mountainous terrain across the region frequently results in strong gap winds in localized areas, as well as more widespread bora and chinook winds in the winter season (October–March). The predictors and general weather patterns that result in strong winds across the region are well understood by local forecasters. However, no single predictor provides notable skill by itself in separating warning-level events from others. Random forest (RF) classifier models were developed to improve upon high wind prediction using a training dataset constructed of archived observations and model parameters from the North American Regional Reanalysis (NARR). Three locations were selected for initial RF model development, including the city of Cheyenne, Wyoming, and two gap regions along Interstate 80 (Arlington) and Interstate 25 (Bordeaux). Verification scores over two winters suggested the RF models were beneficial relative to current operational tools when predicting warning-criteria high wind events. Three case studies of high wind events provide examples of the RF models’ effectiveness to forecast operations over current forecast tools. The first case explores a classic, widespread high wind scenario, which was well anticipated by local forecasters. A more marginal scenario is explored in the second case, which presented greater forecast challenges relating to timing and intensity of the strongest winds. The final case study carefully uses Global Forecast System (GFS) data as input into the RF models, further supporting real-time implementation into forecast operations.

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Stephen J. Lord
,
Xingren Wu
,
Vijay Tallapragada
, and
F. M. Ralph

Abstract

The impact of assimilating dropsonde data from the 2020 Atmospheric River (AR) Reconnaissance (ARR) field campaign on operational numerical precipitation forecasts was assessed. Two experiments were executed for the period from 24 January to 18 March 2020 using the NCEP Global Forecast System, version 15 (GFSv15), with a four-dimensional hybrid ensemble–variational (4DEnVar) data assimilation system. The control run (CTRL) used all the routinely assimilated data and included ARR dropsonde data, whereas the denial run (DENY) excluded the dropsonde data. There were 17 intensive observing periods (IOPs) totaling 46 Air Force C-130 and 16 NOAA G-IV missions to deploy dropsondes over targeted regions with potential for downstream high-impact weather associated with the ARs. Data from a total of 628 dropsondes were assimilated in the CTRL. The dropsonde data impact on precipitation forecasts over U.S. West Coast domains is largely positive, especially for day-5 lead time, and appears driven by different model variables on a case-by-case basis. These results suggest that data gaps associated with ARs can be addressed with targeted ARR field campaigns providing vital observations needed for improving U.S. West Coast precipitation forecasts.

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Matthew B. Switanek
,
Thomas M. Hamill
,
Lindsey N. Long
, and
Michael Scheuerer

Abstract

Tropical cyclones are extreme events with enormous and devastating consequences on life, property, and our economies. As a result, large-scale efforts have been devoted to improving tropical cyclone forecasts with lead times ranging between a few days to months. More recently, subseasonal forecasts (e.g., 2-6 weeks lead time) of tropical cyclones have received greater attention. Here, we study whether bias-corrected, subseasonal tropical cyclone reforecasts of the GEFS and ECMWF models are skillful in the Atlantic basin. We focus on the peak hurricane season, July-November, using the reforecast years 2000-2019. Model reforecasts of accumulated cyclone energy (ACE) are produced, and validated, for lead times of 1-2 weeks and 3-4 weeks. Weeks 1-2 forecasts are substantially more skillful than a 31-day moving-window climatology, while weeks 3-4 forecasts still exhibit positive skill throughout much of the hurricane season. Furthermore, the skill of the combination of the two models is found to be an improvement with respect to either individual model. In addition to the GEFS and ECMWF model reforecasts, we develop a statistical modeling framework which solely relies on daily sea surface temperatures. The reforecasts of ACE from this statistical model is capable of producing better skill than the GEFS or ECMWF model, individually, and it can be leveraged to further enhance the model combination reforecast skill for the 3-4 week lead time.

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Qin Xu
,
Kang Nai
,
Li Wei
,
Nathan Snook
,
Yunheng Wang
, and
Ming Xue

Abstract

A time-space shift method is developed for relocating model-predicted tornado vortices to radar observed locations to improve the model initial conditions and subsequent predictions of tornadoes. The method consists the following three steps: (i) Use the vortex center location estimated from radar observations to sample the best ensemble member from tornado-resolving ensemble predictions. Here, the best member is defined in terms of the predicted vortex center track that has a closest point, say at the time of t = t*, to the estimated vortex center at the initial time t 0 (when the tornado vortex signature is first detected in radar observations). (ii) Create a time-shifted field from the best ensemble member in which the field within a circular area of about 10 km radius around the vortex center is taken from t = t*, while the field outside this circular area is transformed smoothly via temporal interpolation to the best ensemble member at t 0. (iii) Create a time-space-shifted field in which the above time-shifted circular area is further shifted horizontally to co-center with the estimated at t 0, while the field outside this circular area is transformed smoothly via spatial interpolation to the non-shifted field at t 0 from the best ensemble member. The method is applied to the 20 May 2013 Oklahoma Newcastle–Moore tornado case, and is shown to be very effective in improving the tornado track and intensity predictions.

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