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Makenzie J. Krocak and Harold E. Brooks

Abstract

While many studies have looked at the quality of forecast products, few have attempted to understand the relationship between them. We begin to consider whether or not such an influence exists by analyzing storm-based tornado warning product metrics with respect to whether they occurred within a severe weather watch and, if so, what type of watch they occurred within. The probability of detection, false alarm ratio, and lead time all show a general improvement with increasing watch severity. In fact, the probability of detection increased more as a function of watch-type severity than the change in probability of detection during the time period of analysis. False alarm ratio decreased as watch type increased in severity, but with a much smaller magnitude than the difference in probability of detection. Lead time also improved with an increase in watch-type severity. Warnings outside of any watch had a mean lead time of 5.5 min, while those inside of a particularly dangerous situation tornado watch had a mean lead time of 15.1 min. These results indicate that the existence and type of severe weather watch may have an influence on the quality of tornado warnings. However, it is impossible to separate the influence of weather watches from possible differences in warning strategy or differences in environmental characteristics that make it more or less challenging to warn for tornadoes. Future studies should attempt to disentangle these numerous influences to assess how much influence intermediate products have on downstream products.

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Clifford F. Mass, David Ovens, Robert Conrick, and John Saltenberger

Abstract

A series of major fires spread across eastern Washington and western Oregon starting on 7 September 2020, driven by strong easterly and northeasterly winds gusting to ~70 kt (1 kt ≈ 0.51 m s−1) at exposed locations. This event was associated with a high-amplitude upper-level ridge over the eastern Pacific and a mobile trough that moved southward on its eastern flank. The synoptic environment during the event was highly unusual, with the easterly 925-hPa wind speeds at Salem, Oregon, being unprecedented for the August–September period. The September 2020 wildfires produced dense smoke that initially moved westward over the Willamette Valley and eventually covered the region. As a result, air quality rapidly degraded to hazardous levels, representing the worst air quality period of recent decades. High-resolution numerical simulations using the WRF Model indicated the importance of a high-amplitude mountain wave in producing strong easterly winds over western Oregon. The dead fuel moisture levels over eastern Washington before the fires were typical for that time of the year. Along the western slopes of the Oregon Cascades, where the fuels largely comprise a dense conifer forest with understory vegetation, fire weather indices were lower (moister) than normal during the early part of the summer, but transitioned to above-normal (drier) values during August, with a spike to record values in early September coincident with the strong easterly winds. Forecast guidance was highly accurate for both the Washington and Oregon wildfire events. Analyses of climatological data and fuel indices did not suggest that unusual preexisting climatic conditions were major drivers of the September 2020 Northwest wildfires.

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Franziska Hellmuth, Bjørg Jenny Kokkvoll Engdahl, Trude Storelvmo, Robert O. David, and Steven J. Cooper

Abstract

In the winter, orographic precipitation falls as snow in the mid- to high latitudes where it causes avalanches, affects local infrastructure, or leads to flooding during the spring thaw. We present a technique to validate operational numerical weather prediction model simulations in complex terrain. The presented verification technique uses a combined retrieval approach to obtain surface snowfall accumulation and vertical profiles of snow water at the Haukeliseter test site, Norway. Both surface observations and vertical profiles of snow are used to validate model simulations from the Norwegian Meteorological Institute’s operational forecast system and two simulations with adjusted cloud microphysics. Retrieved surface snowfall is validated against measurements conducted with a double-fence automated reference gauge (DFAR). In comparison, the optimal estimation snowfall retrieval produces +10.9% more surface snowfall than the DFAR. The predicted surface snowfall from the operational forecast model and two additional simulations with microphysical adjustments (CTRL and ICE-T) are overestimated at the surface with +41.0%, +43.8%, and +59.2%, respectively. Simultaneously, the CTRL and ICE-T simulations underestimate the mean snow water path by −1071.4% and −523.7%, respectively. The study shows that we would reach false conclusions only using surface accumulation or vertical snow water content profiles. These results highlight the need to combine ground-based in situ and vertically profiling remote sensing instruments to identify biases in numerical weather prediction.

Open access
Michelle L. L’Heureux, Michael K. Tippett, and Emily J. Becker

Abstract

The relation between the El Niño–Southern Oscillation (ENSO) and California precipitation has been studied extensively and plays a prominent role in seasonal forecasting. However, a wide range of precipitation outcomes on seasonal time scales are possible, even during extreme ENSO states. Here, we investigate prediction skill and its origins on subseasonal time scales. Model predictions of California precipitation are examined using Subseasonal Experiment (SubX) reforecasts for the period 1999–2016, focusing on those from the Flow-Following Icosahedral Model (FIM). Two potential sources of subseasonal predictability are examined: the tropical Pacific Ocean and upper-level zonal winds near California. In both observations and forecasts, the Niño-3.4 index exhibits a weak and insignificant relationship with daily to monthly averages of California precipitation. Likewise, model tropical sea surface temperature and outgoing longwave radiation show only minimal relations with California precipitation forecasts, providing no evidence that flavors of El Niño or tropical modes substantially contribute to the success or failure of subseasonal forecasts. On the other hand, an index for upper-level zonal winds is strongly correlated with precipitation in observations and forecasts, across averaging windows and lead times. The wind index is related to ENSO, but the correlation between the wind index and precipitation remains even after accounting for ENSO phase. Intriguingly, the Niño-3.4 index and California precipitation show a slight but robust negative statistical relation after accounting for the wind index.

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Yang Lyu, Xiefei Zhi, Shoupeng Zhu, Yi Fan, and Mengting Pan

Abstract

In this study, two pattern projection methods, i.e., the stepwise pattern projection method (SPPM) and the newly proposed neighborhood pattern projection method (NPPM), are investigated to improve forecast skills of daily maximum and minimum temperatures (Tmax and Tmin) over East Asia with lead times of 1–7 days. Meanwhile, the decaying averaging method (DAM) is conducted in parallel for comparison. These postprocessing methods are found to effectively calibrate the temperature forecasts on the basis of the raw ECMWF output. Generally, the SPPM is slightly inferior to the DAM, while its insufficiency decreases with increasing lead times. The NPPM shows manifest superiority for all lead times, with the mean absolute errors of Tmax and Tmin decreased by ~0.7° and ~0.9°C, respectively. Advantages of the two pattern projection methods are both mainly concentrated on the high-altitude areas such as the Tibetan Plateau, where the raw ECMWF forecasts show the most conspicuous biases. In addition, aiming at further assessments of these methods on extreme event forecasts, two case experiments are carried out toward a heat wave and a cold surge, respectively. The NPPM is retained as the optimal with the highest forecast skills, which reduces most of the biases to <2°C for both Tmax and Tmin over all the lead days. In general, the statistical pattern projection methods are capable of effectively eliminating spatial biases in forecasts of surface air temperature. Compared with the initial SPPM, the NPPM not only produces more powerful forecast calibrations, but also provides more pragmatic calculations and greater potential economic benefits in practical applications.

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Christopher J. Nowotarski, Justin Spotts, Roger Edwards, Scott Overpeck, and Gary R. Woodall

Abstract

Tropical cyclone tornadoes pose a unique challenge to warning forecasters given their often marginal environments and radar attributes. In late August 2017 Hurricane Harvey made landfall on the Texas coast and produced 52 tornadoes over a record-breaking seven consecutive days. To improve warning efforts, this case study of Harvey’s tornadoes includes an event overview as well as a comparison of near-cell environments and radar attributes between tornadic and nontornadic warned cells. Our results suggest that significant differences existed in both the near-cell environments and radar attributes, particularly rotational velocity, between tornadic cells and false alarms. For many environmental variables and radar attributes, differences were enhanced when only tornadoes associated with a tornado debris signature were considered. Our results highlight the potential of improving warning skill further and reducing false alarms by increasing rotational velocity warning thresholds, refining the use of near-storm environment information, and focusing warning efforts on cells likely to produce the most impactful tornadoes.

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Dian Nur Ratri, Kirien Whan, and Maurice Schmeits

Abstract

The seasonal precipitation forecast is one of the essential inputs for economic and agricultural activities and has significant impact on decision-making. Large-scale modes of climate variability have strong relationships with seasonal rainfall in Java and are natural candidates for use as potential predictors in a statistical postprocessing application. We explore whether using climate indices as additional predictors in the statistical postprocessing of ECMWF Seasonal Forecast System 5 (SEAS5) precipitation can improve skill. We use parametric statistical postprocessing by applying a logistic distribution-based ensemble model output statistics (EMOS) technique. We add a variety of potential predictors in the analysis, namely SEAS5 raw and empirical quantile mapping (EQM) bias-corrected precipitation, Niño-3.4 index, dipole mode index (DMI), Madden–Julian oscillation (MJO) indices, sea surface temperature (SST) around Java, and several other predictors. We analyze the period of 1981–2010, focusing on July, August, September, and October. We use the continuous ranked probability skill score (CRPSS) and Brier skill score (BSS) in a comparative verification of raw, EQM, and EMOS seasonal precipitation forecasts. We have found that it is essential to use EQM-corrected precipitation as a predictor instead of raw precipitation in the latter. Besides, Niño-3.4 and DMI forecasts are not needed as extra predictors to improve monthly precipitation forecasts for the first lead month, except for September. However, for somewhat longer lead months, in September and October when there is more skill than climatology, the model that includes only Niño-3.4 and DMI forecasts as potential predictors performs about the same compared to the model that uses only EQM-corrected precipitation as a predictor.

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Wenwei Xu, Karthik Balaguru, Andrew August, Nicholas Lalo, Nathan Hodas, Mark DeMaria, and David Judi

Abstract

Reducing tropical cyclone (TC) intensity forecast errors is a challenging task that has interested the operational forecasting and research community for decades. To address this, we developed a deep learning (DL)-based multilayer perceptron (MLP) TC intensity prediction model. The model was trained using the global Statistical Hurricane Intensity Prediction Scheme (SHIPS) predictors to forecast the change in TC maximum wind speed for the Atlantic basin. In the first experiment, a 24-h forecast period was considered. To overcome sample size limitations, we adopted a leave one year out (LOYO) testing scheme, where a model is trained using data from all years except one and then evaluated on the year that is left out. When tested on 2010–18 operational data using the LOYO scheme, the MLP outperformed other statistical–dynamical models by 9%–20%. Additional independent tests in 2019 and 2020 were conducted to simulate real-time operational forecasts, where the MLP model again outperformed the statistical–dynamical models by 5%–22% and achieved comparable results as HWFI. The MLP model also correctly predicted more rapid intensification events than all the four operational TC intensity models compared. In the second experiment, we developed a lightweight MLP for 6-h intensity predictions. When coupled with a synthetic TC track model, the lightweight MLP generated realistic TC intensity distribution in the Atlantic basin. Therefore, the MLP-based approach has the potential to improve operational TC intensity forecasts, and will also be a viable option for generating synthetic TCs for climate studies.

Open access
Ivana Aleksovska, Laure Raynaud, Robert Faivre, François Brun, and Marc Raynal

Abstract

Agriculture is a highly weather-dependent activity, and climatic conditions impact both directly crop growth and indirectly diseases and pest developments causing yield losses. Weather forecasts are now a major component of various decision-support systems that assist farmers to optimize the positioning of crop protection treatments. However, properly accounting for weather uncertainty in these systems still remains a challenge. In this paper, three global and regional ensemble prediction systems (EPSs), covering different spatiotemporal scales, are coupled to a temperature-driven developmental model for grapevine moths in order to provide probabilistic forecasts of treatment dates. It is first shown that a parametric postprocessing of the EPSs significantly improves the prediction of treatment dates. Anticipating the need for phytosanitary treatments also requires seamless weather forecasts from the next hour to subseasonal time scales. An approach is presented to design seamless ensemble forecasts from the combination of the three EPSs used. The proposed method is able to leverage the increased performance of high-resolution EPS at short ranges, while ensuring a smooth transition toward larger-scale EPSs for longer ranges. The added value of this seamless integration on agronomic predictions is, however, difficult to assess with the current experimental setup. Additional simulations over a larger number of locations and years may be required.

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Hung Ming Cheung, Chang-Hoi Ho, Minhee Chang, Dasol Kim, Jinwon Kim, and Woosuk Choi

Abstract

Despite tremendous advancements in dynamical models for weather forecasting, statistical models continue to offer various possibilities for tropical cyclone (TC) track forecasting. Herein, a track-pattern-based approach was developed to predict a TC track for a lead time of 6–8 days over the western North Pacific (WNP), utilizing historical tracks in conjunction with dynamical forecasts. It is composed of four main steps: 1) clustering historical tracks similar to that of an operational 5-day forecast in their early phase into track patterns, and calculating the daily mean environmental fields (500-hPa geopotential height and steering flow) associated with each track; 2) deriving the two environmental variables forecasted by dynamical models; 3) evaluating pattern correlation coefficients between the two environmental fields from step 1 and those from dynamical model for a lead times of 6–8 days; and 4) producing the final track forecast based on relative frequency maps obtained from the historical tracks in step 1 and the pattern correlation coefficients obtained from step 3. TCs that formed in the WNP and lasted for at least 7 days, during the 9-yr period 2011–19 were selected to verify the resulting track-pattern-based forecasts. In addition to the performance comparable to dynamical models under certain conditions, the track-pattern-based model is inexpensive, and can consistently produce forecasts over large latitudinal or longitudinal ranges. Machine learning techniques can be implemented to incorporate nonlinearity in the present model for improving medium-range track forecasts.

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