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Kevin Bachmann and Ryan D. Torn

Abstract

Tropical cyclones are associated with a variety of significant social hazards, including wind, rain, and storm surge. Despite this, most of the model validation effort has been directed toward track and intensity forecasts. In contrast, few studies have investigated the skill of state-of-the-art, high-resolution ensemble prediction systems in predicting associated TC hazards, which is crucial since TC position and intensity do not always correlate with the TC-related hazards, and can result in impacts far from the actual TC center. Furthermore, dynamic models can provide flow-dependent uncertainty estimates, which in turn can provide more specific guidance to forecasters than statistical uncertainty estimates based on past errors. This study validates probabilistic forecasts of wind speed and precipitation hazards derived from the HWRF ensemble prediction system and compares its skill to forecasts by the stochastically-based operational Monte Carlo Model (NHC), the IFS (ECMWF), and the GEFS (NOAA) in use 2017-2019. Wind and Precipitation forecasts are validated against NHC best track wind radii information and the National Stage IV QPE Product. The HWRF 34 kn wind forecasts have comparable skill to the global models up to 60 h lead time before HWRF skill decreases, possibly due to detrimental impacts of large track errors. In contrast, HWRF has comparable quality to its competitors for higher thresholds of 50 kn and 64 kn throughout 120 h lead time. In terms of precipitation hazards, HWRF performs similar or better than global models, but depicts higher, although not perfect, reliability, especially for events over 5 in120h−1. Post-processing, like Quantile Mapping, improves forecast skill for all models significantly and can alleviate reliability issues of the global models.

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Jing Zhang, Jie Feng, Hong Li, Yuejian Zhu, Xiefei Zhi, and Feng Zhang

Abstract

Operational and research applications generally use the consensus approach for forecasting the track and intensity of tropical cyclones (TCs) due to the spatial displacement of the TC location and structure in ensemble member forecasts. This approach simply averages the location and intensity information for TCs in individual ensemble members, which is distinct from the traditional pointwise arithmetic mean (AM) method for ensemble forecast fields. The consensus approach, despite having improved skills relative to the AM in predicting the TC intensity, cannot provide forecasts of the TC spatial structure. We introduced a unified TC ensemble mean forecast based on the feature-oriented mean (FM) method to overcome the inconsistency between the AM and consensus forecasts. FM spatially aligns the TC-related features in each ensemble field to their geographical mean positions before the amplitude of their features is averaged.

We select 219 TC forecast samples during the summer of 2017 for an overall evaluation of the FM performance. The results show that the TC track consensus forecasts can differ from AM track forecasts by hundreds of kilometers at long lead times. AM also gives a systematic and statistically significant underestimation of the TC intensity compared with the consensus forecast. By contrast, FM has a very similar TC track and intensity forecast skill to the consensus approach. FM can also provide the corresponding ensemble mean forecasts of the TC spatial structure that are significantly more accurate than AM for the low- and upper-level circulation in TCs. The FM method has the potential to serve as a valuable unified ensemble mean approach for the TC prediction.

Open access
Diego Pons, Ángel G. Muñoz, Ligia M. Meléndez, Mario Chocooj, Rosario Gómez, Xandre Chourio, and Carmen González Romero

Abstract

The provision of climate services has the potential to generate adaptive capacity and help coffee farmers become or remain profitable by integrating climate information in a risk-management framework. Yet, in order to achieve this goal, it is necessary to identify the local demand for climate information, the relationships between coffee yield and climate variables, farmers’ perceptions, and to examine the potential actions that can be realistically put in place by farmers at the local level. In this study, we assessed the climate information demands from coffee farmers and their perception on the climate impacts to coffee yield in the Samalá watershed in Guatemala. After co-identifying the related candidate climate predictors, we propose an objective, flexible forecast system for coffee yield based on precipitation. The system, known as NextGen, analyzes multiple historical climate drivers to identify candidate predictors, and provides both deterministic and probabilistic forecasts for the target season. To illustrate the approach, a NextGen implementation is conducted in the Samalá watershed in southwestern Guatemala. The results suggest that accumulated June-July-August precipitation provides the highest predictive skill associated with coffee yield for this region. In addition to a formal cross-validated skill assessment, retrospective forecasts for the period 1989-2009 were compared to agriculturalists’ perception on the climate impacts to coffee yield at the farm level. We conclude with examples of how demand-based climate service provision in this location can inform adaptation strategies like optimum shade, pest control, and fertilization schemes months in advance. These potential adaptation strategies were validated by local agricultural technicians at the study site.

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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.

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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 September 7, 2020, driven by strong easterly and northeasterly winds gusting to ~70 kt 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 are largely comprised of 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 pre-existing climatic conditions were major drivers of the September 2020 Northwest wildfires.

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Callie McNicholas and Clifford F. Mass

Abstract

With over a billion smartphones capable of measuring atmospheric pressure, a global mesoscale surface pressure network based on smartphone pressure sensors may be possible if key technical issues are solved, including collection technology, privacy and bias correction. To overcome these challenges, a novel framework was developed for the anonymization and bias correction of smartphone pressure observations (SPOs) and was applied to billions of SPOs from The Weather Company (IBM). Bias correction using machine learning reduced the errors of anonymous (ANON) SPOs and uniquely identifiable (UID) SPOs by 43% and 57%, respectively. Applying multi-resolution kriging, gridded analyses of bias-corrected smartphone pressure observations were made for an entire year (2018), using both anonymized (ANON) and non-anonymized (UID) observations. Pressure analyses were also generated using conventional (MADIS) surface pressure networks. Relative to MADIS analyses, ANON and UID smartphone analyses reduced domain-average pressure errors by 21% and 31%. The performance of smartphone and MADIS pressure analyses was evaluated for two high-impact weather events: the landfall of Hurricane Michael and a long-lived mesoscale convective system. For these two events, both anonymized and non-anonymized smartphone pressure analyses better captured the spatial structure and temporal evolution of mesoscale pressure features than the MADIS analyses.

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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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