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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
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
Qian Zhou, Lei Chen, Wansuo Duan, Xu Wang, Ziqing Zu, Xiang Li, Shouwen Zhang, and Yunfei Zhang

Abstract

Using the latest operational version of the ENSO forecast system from the National Marine Environmental Forecasting Center (NMEFC) of China, ensemble forecasting experiments are performed for El Niño–Southern Oscillation (ENSO) events that occurred from 1997 to 2017 by generating initial perturbations of the conditional nonlinear optimal perturbation (CNOP) and climatically relevant singular vector (CSV) structures. It is shown that when the initial perturbation of the leading CSV structure in the ensemble forecast of the CSVs scheme is replaced by those of the CNOP structure, the resulted ensemble ENSO forecasts of the CNOP+CSVs scheme tend to possess a larger spread than the forecasts obtained with the CSVs scheme alone, leading to a better match between the root-mean-square error and the ensemble spread, a more reasonable Talagrand diagram, and an improved Brier skill score (BSS). All these results indicate that the ensemble forecasts generated by the CNOP+CSVs scheme can improve both the accuracy of ENSO forecasting and the reliability of the ensemble forecasting system. Therefore, ENSO ensemble forecasting should consider the effect of nonlinearity on the ensemble initial perturbations to achieve a much higher skill. It is expected that fully nonlinear ensemble initial perturbations can be sufficiently yielded to produce ensemble forecasts for ENSO, finally improving the ENSO forecast skill to the greatest possible extent. The CNOP will be a useful method to yield fully nonlinear optimal initial perturbations for ensemble forecasting.

Open access
Jinxiao Li, Qing Bao, Yimin Liu, Guoxiong Wu, Lei Wang, Bian He, Xiaocong Wang, Jing Yang, Xiaofei Wu, and Zili Shen

Abstract

There is a distinct gap between tropical cyclone (TC) prediction skill and the societal demand for accurate predictions, especially in the western Pacific (WP) and North Atlantic (NA) basins, where densely populated areas are frequently affected by intense TC events. In this study, seasonal prediction skill for TC activity in the WP and NA of the fully coupled FGOALS-f2 V1.0 dynamical prediction system is evaluated. In total, 36 years of monthly hindcasts from 1981 to 2016 were completed with 24 ensemble members. The FGOALS-f2 V1.0 system has been used for real-time predictions since June 2017 with 35 ensemble members, and has been operationally used in the two operational prediction centers of China. Our evaluation indicates that FGOALS-f2 V1.0 can reasonably reproduce the density of TC genesis locations and tracks in the WP and NA. The model shows significant skill in terms of the TC number correlation in the WP (0.60) and the NA (0.61) from 1981 to 2015; however, the model underestimates accumulated cyclone energy. When the number of ensemble members was increased from 2 to 24, the correlation coefficients clearly increased (from 0.21 to 0.60 in the WP, and from 0.18 to 0.61 in the NA). FGOALS-f2 V1.0 also successfully reproduces the genesis potential index pattern and the relationship between El Niño–Southern Oscillation and TC activity, which is one of the dominant contributors to TC seasonal prediction skill. However, the biases in large-scale factors are barriers to the improvement of the seasonal prediction skill, e.g., larger wind shear, higher relative humidity, and weaker potential intensity of TCs. For real-time predictions in the WP, FGOALS-f2 V1.0 demonstrates a skillful prediction for track density in terms of landfalling TCs, and the model successfully forecasts the correct sign of seasonal anomalies of landfalling TCs for various regions in China.

Open access
Yanshuang Xie, Shaoping Shang, Jinquan Chen, Feng Zhang, Zhigan He, Guomei Wei, Jingyu Wu, Benlu Zhu, and Yindong Zeng

Abstract

Accurate storm surge forecasts provided rapidly could support timely decision-making with consideration of tropical cyclone (TC) forecasting error. This study developed a fast storm surge ensemble prediction method based on TC track probability forecasting and searching optimization of a numerical scenario database (SONSD). In a case study of the Fujian Province coast (China), a storm surge scenario database was established using numerical simulations generated by 93 150 hypothetical TCs. In a GIS-based visualization system, a single surge forecast representing 2562 distinct typhoon tracks and the occurrence probability of overflow of seawalls along the coast could be achieved in 1–2 min. Application to the cases of Typhoon Soudelor (2015) and Typhoon Maria (2018) demonstrated that the proposed method is feasible and effective. Storm surge calculated by SONSD had excellent agreement with numerical model results (i.e., mean MAE and RMSE: 7.1 and 10.7 cm, respectively, correlation coefficient: >0.9). Tide prediction also performed well with MAE/RMSE of 9.7/11.6 cm versus the harmonic tide, and MAE/RMSE of phase prediction for all high waters of 0.25/0.31 h versus observations. The predicted high-water level was satisfactory (MAE of 10.8 cm versus observations) when the forecasted and actual positions of the typhoon were close. When the forecasted typhoon position error was large, the ensemble surge prediction effectively reduced prediction error (i.e., the negative bias of −58.5 cm reduced to −5.2 cm versus observations), which helped avoid missed alert warnings. The proposed method could be applied in other regions to provide rapid and accurate decision-making support for government departments.

Open access
Jörg Steinert, Patrick Tracksdorf, and Dirk Heizenreder

Abstract

The analysis and forecast of precipitation characteristics is a key task for national meteorological services for providing high-quality weather forecasts and warnings. Beside the precipitation amount, the precipitation type is essential to describe and evaluate the recent, ongoing, and future weather situations. This paper introduces a new surface-based hybrid hydrometeor classification algorithm. The presented method combines polarimetric radar observations at radar beam height from the C-band dual-polarization weather radar network of the German Weather Service [Deutscher Wetterdienst (DWD)] with corrected thermodynamical profiles of numerical weather prediction (NWP) model output and extrapolates the hydrometeor classes at radar beam height to a height of 2 m above ground level (AGL). The implemented technique parameterizes the microphysical processes in the lower troposphere based on the appropriate thermodynamical profile that the hydrometeors have to pass along their way from the radar beam height to the surface. Due to errors in NWP output, the NWP vertical profiles of temperature and humidity are adjusted by using several types of surface stations with high spatial and temporal resolution. Verification results show considerable improvements in the hydrometeor classification near the ground compared to the radar sweep height. After an additional positive in-house evaluation, the presented method is integrated into DWD’s operational environment. The topic of this paper is to describe the processing steps for the computation of the near-surface precipitation type. In addition, example cases and a verification study complement the explanations.

Open access