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Tim Cowan, Matthew C. Wheeler, S. Sharmila, Sugata Narsey, and Catherine de Burgh-Day

Abstract

Rainfall bursts are relatively short-lived events that typically occur over consecutive days, up to a week. Northern Australian industries like sugar farming and beef are highly sensitive to burst activity, yet little is known about the multiweek prediction of bursts. This study evaluates summer (December–March) bursts over northern Australia in observations and multiweek hindcasts from the Bureau of Meteorology’s multiweek to seasonal system, the Australian Community Climate and Earth-System Simulator, Seasonal version 1 (ACCESS-S1). The main objective is to test ACCESS-S1’s skill to confidently predict tropical burst activity, defined as rainfall accumulation exceeding a threshold amount over three days, for the purpose of producing a practical, user-friendly burst forecast product. The ensemble hindcasts, made up of 11 members for the period 1990–2012, display good predictive skill out to lead week 2 in the far northern regions, despite overestimating the total number of summer burst days and the proportion of total summer rainfall from bursts. Coinciding with a predicted strong Madden–Julian oscillation (MJO), the skill in burst event prediction can be extended out to four weeks over the far northern coast in December; however, this improvement is not apparent in other months or over the far northeast, which shows generally better forecast skill with a predicted weak MJO. The ability of ACCESS-S1 to skillfully forecast bursts out to 2–3 weeks suggests the bureau’s recent prototype development of a burst potential forecast product would be of great interest to northern Australia’s livestock and crop producers, who rely on accurate multiweek rainfall forecasts for managing business decisions.

Open access
Kathleen F. Jones

Abstract

Freezing rain can cause significant tree damage with fallen trees and branches blocking roads and taking power distribution lines out of service. Power transmission lines are designed for ice loads from freezing rain, using models to estimate equivalent radial ice thicknesses from historical weather data. The conservative simple flux model assumes that all the freezing rain that impinges on a horizontal cylinder, representing vegetation or components of the built infrastructure, freezes. Here I present a simplified heat-balance formulation to calculate the fraction of the impinging precipitation that freezes, using parameters measured at ASOS weather stations and an estimate of solar heating. Radial ice thickness estimates from this approach are compared with the simple model and those generated from the ASOS icing sensor. These estimates can all be tested by comparing to measurements on cylinders at weather stations. A link to an Excel spreadsheet that calculates freezing fraction using user-input weather data is provided. In forecast freezing rain events, this tool could be used by utility crews and emergency response teams to estimate the likely range of equivalent radial ice thicknesses over the affected region and plan their response accordingly.

Significance Statement

Freezing rain can cause significant tree damage with fallen trees and branches blocking roads and taking power distribution lines out of service. Power transmission lines are designed for ice loads from freezing rain calculated from historical weather data. This paper provides an algorithm for computing ice loads on trees and power lines, using weather data to determine the fraction of the precipitation that freezes on them rather than dripping off. This freezing fraction result is compared to estimates reported by weather stations and to a simple model that assumes all the wind-blown freezing rain freezes on the wires, twigs, and branches. A link is provided to an Excel tool that calculates freezing fraction. This could be used with freezing rain forecasts to estimate the likely severity of the event.

Open access
Ha Pham-Thanh, Tan Phan-Van, Roderick van der Linden, and Andreas H. Fink

Abstract

The onset of the rainy season is an important date for the mostly rain-fed agricultural practices in Vietnam. Subseasonal to seasonal (S2S) ensemble hindcasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) are used to evaluate the predictability of the rainy season onset dates (RSODs) over five climatic subregions of Vietnam. The results show that the ECMWF model reproduces well the observed interannual variability of RSODs, with a high correlation ranging from 0.60 to 0.99 over all subregions at all lead times (up to 40 days) using five different RSOD definitions. For increasing lead times, forecasted RSODs tend to be earlier than the observed ones. Positive skill score values for almost all cases examined in all subregions indicate that the model outperforms the observed climatology in predicting the RSOD at subseasonal lead times (∼28–35 days). However, the model is overall more skillful at shorter lead times. The choice of the RSOD criterion should be considered because it can significantly influence the model performance. The result of analyzing the highest skill score for each subregion at each lead time shows that criteria with higher 5-day rainfall thresholds tend to be more suitable for the forecasts at long lead times. However, the values of mean absolute error are approximately the same as the absolute values of the mean error, indicating that the prediction could be improved by a simple bias correction. The present study shows a large potential to use S2S forecasts to provide meaningful predictions of RSODs for farmers.

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, to achieve this goal, it is necessary to identify the local demand for climate information, the relationships between coffee yield and climate variables, and 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 that is 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–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 with 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.

Open access
Kevin J. Dougherty, John D. Horel, and Jason E. Nachamkin

Abstract

Precipitation forecasts from the High-Resolution Rapid Refresh model (HRRR) of the National Centers for Environmental Prediction (NCEP) and the Navy’s Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) are examined during heavy precipitation periods in California. Precipitation forecast discrepancies between the two models are examined during a recent heavy winter precipitation episode in California from 6 to 8 December 2019. The skill of initial 12-h precipitation forecasts is examined objectively from 1 December 2018 to 28 February 2019 from the HRRR, COAMPS, and NCEP’s North American Mesoscale Forecast System (NAM-3km). The HRRR exhibited lower seasonal biases and higher skill based on several metrics applied to a sample of 48 12-h periods during California’s second wettest winter season during the past 20 years. Overall, the NAM-3km and COAMPS exhibited a large wet bias over the interior mountain regions while the HRRR model indicated a dry bias along the northern coastal region. All models tended to underestimate precipitation along the coastal mountains of Northern California. To highlight the regional and localized nature of forecast skill, the fraction skill score (FSS) metric is applied across ranges of spatial scales and precipitation values. For the domain as a whole, the HRRR had higher precipitation forecast skill compared to the other two models, particularly within radial distances of 20–30 km and moderate (10–50 mm) precipitation totals. FSS computed locally highlights the HRRR’s overall higher skill as well as enhanced skill in the southern half of the state.

Open access
Erin E. Thomas, Malte Müller, Patrik Bohlinger, Yurii Batrak, and Nicholas Szapiro

Abstract

Accurately simulating the interactions between the components of a coupled Earth modeling system (atmosphere, sea ice, and wave) on a kilometer-scale resolution is a new challenge in operational numerical weather prediction. It is difficult due to the complexity of interactive mechanisms, the limited accuracy of model components, and scarcity of observations available for assessing relevant coupled processes. This study presents a newly developed convective-scale atmosphere–wave coupled forecasting system for the European Arctic. The HARMONIE-AROME configuration of the ALADIN-HIRLAM numerical weather prediction system is coupled to the spectral wave model WAVEWATCH III using the OASIS3 model coupling toolkit. We analyze the impact of representing the kilometer-scale atmosphere–wave interactions through coupled and uncoupled forecasts on a model domain with 2.5-km spatial resolution. To assess the coupled model’s accuracy and uncertainties we compare 48-h model forecasts against satellite observational products such as Advanced Scatterometer 10-m wind speed, and altimeter-based significant wave height. The fully coupled atmosphere–wave model results closely match both satellite-based wind speed and significant wave height observations as well as surface pressure and wind speed measurements from selected coastal station observation sites. Furthermore, the coupled model contains smaller standard deviation of errors in both 10-m wind speed and significant wave height parameters when compared to the uncoupled model forecasts. Atmosphere and wave coupling reduces the short-term forecast error variability of 10-m wind speed and significant wave height with the greatest benefit occurring for high wind and wave conditions.

Open access
Vittorio A. Gensini, Cody Converse, Walker S. Ashley, and Mateusz Taszarek

Abstract

Previous studies have identified environmental characteristics that skillfully discriminate between severe and significant-severe weather events, but they have largely been limited by sample size and/or population of predictor variables. Given the heightened societal impacts of significant-severe weather, this topic was revisited using over 150 000 ERA5 reanalysis-derived vertical profiles extracted at the grid point nearest—and just prior to—tornado and hail reports during the period 1996–2019. Profiles were quality controlled and used to calculate 84 variables. Several machine learning classification algorithms were trained, tested, and cross validated on these data to assess skill in predicting severe or significant-severe reports for tornadoes and hail. Random forest classification outperformed all tested methods as measured by cross-validated critical success index scores and area under the receiver operating characteristic curve values. In addition, random forest classification was found to be more reliable than other methods and exhibited negligible frequency bias. The top three most important random forest classification variables for tornadoes were wind speed at 500 hPa, wind speed at 850 hPa, and 0–500-m storm-relative helicity. For hail, storm-relative helicity in the 3–6 km and −10° to −30°C layers, along with 0–6-km bulk wind shear, were found to be most important. A game theoretic approach was used to help explain the output of the random forest classifiers and establish critical feature thresholds for operational nowcasting and forecasting. A use case of spatial applicability of the random forest model is also presented, demonstrating the potential utility for operational forecasting. Overall, this research supports a growing number of weather and climate studies finding admirable skill in random forest classification applications.

Open access
Marvin Kähnert, Harald Sodemann, Wim C. de Rooy, and Teresa M. Valkonen

Abstract

Forecasts of marine cold air outbreaks critically rely on the interplay of multiple parameterization schemes to represent subgrid-scale processes, including shallow convection, turbulence, and microphysics. Even though such an interplay has been recognized to contribute to forecast uncertainty, a quantification of this interplay is still missing. Here, we investigate the tendencies of temperature and specific humidity contributed by individual parameterization schemes in the operational weather prediction model AROME-Arctic. From a case study of an extensive marine cold air outbreak over the Nordic seas, we find that the type of planetary boundary layer assigned by the model algorithm modulates the contribution of individual schemes and affects the interactions between different schemes. In addition, we demonstrate the sensitivity of these interactions to an increase or decrease in the strength of the parameterized shallow convection. The individual tendencies from several parameterizations can thereby compensate each other, sometimes resulting in a small residual. In some instances this residual remains nearly unchanged between the sensitivity experiments, even though some individual tendencies differ by up to an order of magnitude. Using the individual tendency output, we can characterize the subgrid-scale as well as grid-scale responses of the model and trace them back to their underlying causes. We thereby highlight the utility of individual tendency output for understanding process-related differences between model runs with varying physical configurations and for the continued development of numerical weather prediction models.

Open access
Michael Maier-Gerber, Andreas H. Fink, Michael Riemer, Elmar Schoemer, Christoph Fischer, and Benedikt Schulz

Abstract

While previous research on subseasonal tropical cyclone (TC) occurrence has mostly focused on either the validation of numerical weather prediction (NWP) models, or the development of statistical models trained on past data, the present study combines both approaches to a statistical–dynamical (hybrid) model for probabilistic forecasts in the North Atlantic basin. Although state-of-the-art NWP models have been shown to lack predictive skill with respect to subseasonal weekly TC occurrence, they may predict the environmental conditions sufficiently well to generate predictors for a statistical model. Therefore, an extensive predictor set was generated, including predictor groups representing the climatological seasonal cycle (CSC), oceanic, and tropical conditions, tropical wave modes, as well as extratropical influences, respectively. The developed hybrid forecast model is systematically validated for the Gulf of Mexico and central main development region (MDR) for lead times up to 5 weeks. Moreover, its performance is compared against a statistical approach trained on past data, as well as against different climatological and NWP benchmarks. For subseasonal lead times, the CSC models are found to outperform the NWP models, which quickly lose skill within the first two forecast weeks, even in case of recalibration. The statistical models trained on past data increase skill over the CSC models, whereas even greater improvements in skill are gained by the hybrid approach out to week 5. The vast majority of the additional subseasonal skill in the hybrid model, relative to the CSC model, could be attributed to the tropical (oceanic) conditions in the Gulf of Mexico (central MDR).

Open access
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