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Jadwiga H. Richter, Anne A. Glanville, James Edwards, Brian Kauffman, Nicholas A. Davis, Abigail Jaye, Hyemi Kim, Nicholas M. Pedatella, Lantao Sun, Judith Berner, Who M. Kim, Stephen G. Yeager, Gokhan Danabasoglu, Julie M. Caron, and Keith W. Oleson

Abstract

Prediction systems to enable Earth system predictability research on the subseasonal time scale have been developed with the Community Earth System Model, version 2 (CESM2) using two configurations that differ in their atmospheric components. One system uses the Community Atmosphere Model, version 6 (CAM6) with its top near 40 km, referred to as CESM2(CAM6). The other employs the Whole Atmosphere Community Climate Model, version 6 (WACCM6) whose top extends to ∼140 km, and it includes fully interactive tropospheric and stratospheric chemistry [CESM2(WACCM6)]. Both systems are utilized to carry out subseasonal reforecasts for the 1999–2020 period following the Subseasonal Experiment’s (SubX) protocol. Subseasonal prediction skill from both systems is compared to those of the National Oceanic and Atmospheric Administration CFSv2 and European Centre for Medium-Range Weather Forecasts (ECMWF) operational models. CESM2(CAM6) and CESM2(WACCM6) show very similar subseasonal prediction skill of 2-m temperature, precipitation, the Madden–Julian oscillation, and North Atlantic Oscillation to its previous version and to the NOAA CFSv2 model. Overall, skill of CESM2(CAM6) and CESM2(WACCM6) is a little lower than that of the ECMWF system. In addition to typical output provided by subseasonal prediction systems, CESM2 reforecasts provide comprehensive datasets for predictability research of multiple Earth system components, including three-dimensional output for many variables, and output specific to the mesosphere and lower-thermosphere (MLT) region from CESM2(WACCM6). It is shown that sudden stratosphere warming events, and the associated variability in the MLT, can be predicted ∼10 days in advance. Weekly real-time forecasts and reforecasts with CESM2(CAM6) and CESM2(WACCM6) are freely available.

Significance Statement

We describe here the design and prediction skill of two subseasonal prediction systems based on two configurations of the Community Earth System Model, version 2 (CESM2): CESM2 with the Community Atmosphere Model, version 6 [CESM2(CAM6)] and CESM 2 with Whole Atmosphere Community Climate Model, version 6 [CESM2(WACCM6)] as its atmospheric component. These two systems provide a foundation for community-model based subseasonal prediction research. The CESM2(WACCM6) system provides a novel capability to explore the predictability of the stratosphere, mesosphere, and lower thermosphere. Both CESM2(CAM6) and CESM2(WACCM6) demonstrate subseasonal surface prediction skill comparable to that of the NOAA CFSv2 model, and a little lower than that of the ECMWF forecasting system. CESM2 reforecasts provide a comprehensive dataset for predictability research of multiple aspects of the Earth system, including the whole atmosphere up to 140 km, land, and sea ice. Weekly real-time forecasts, reforecasts, and models are publicly available.

Open access
Wei Sun, Zhiquan Liu, Guiting Song, Yangyang Zhao, Shan Guo, Feifei Shen, and Xiangming Sun

Abstract

To improve the wind speed forecasts at turbine locations and at hub height, this study develops the WRFDA system to assimilate the wind speed observations measured on the nacelle of turbines (hereafter referred as turbine wind speed observations) with both 3DVAR and 4DVAR algorithms. Results exhibit that the developed data assimilation (DA) system helps in greatly improving the analysis and the forecast of wind turbine speed. Among three experiments with no cycling DA, with 2-h cycling DA, and with 4-h cycling DA, the last experiment generates the best analysis, improving the averaged forecasts (from T + 9 to T + 24) of wind speed over all wind farms by 32.5% in the bias and 6.3% in the RMSE. After processing the turbine wind speed observations into superobs, even bigger improvements are revealed when validating against either the original turbine wind speed observations or the superobs. Taken the results validated against the superobs as an example, the bias and RMSE of the forecasts (from T + 9 to T + 24) averaged over all wind farms are reduced by 38.8% and 12.0%, respectively. Compared to the best-performed 3DVAR experiment (4-h cycling and superobs), the experiment following the same DA strategy but using 4DVAR algorithm exhibits further improvements, especially for the averaged bias in the forecasts of all wind farms, and the changing amount in the forecasts of the enhanced wind farms. Compared to the control experiment, the 4DVAR experiment reduces the bias and RMSE in the forecasts (from T + 9 to T + 24) by 54.6% (0.66 m s−1) and 12.7% (0.34 m s−1).

Open access
Zied Ben Bouallègue and David S. Richardson

Abstract

The relative operating characteristic (ROC) curve is a popular diagnostic tool in forecast verification, with the area under the ROC curve (AUC) used as a verification metric measuring the discrimination ability of a forecast. Along with calibration, discrimination is deemed as a fundamental probabilistic forecast attribute. In particular, in ensemble forecast verification, AUC provides a basis for the comparison of potential predictive skill of competing forecasts. While this approach is straightforward when dealing with forecasts of common events (e.g., probability of precipitation), the AUC interpretation can turn out to be oversimplistic or misleading when focusing on rare events (e.g., precipitation exceeding some warning criterion). How should we interpret AUC of ensemble forecasts when focusing on rare events? How can changes in the way probability forecasts are derived from the ensemble forecast affect AUC results? How can we detect a genuine improvement in terms of predictive skill? Based on verification experiments, a critical eye is cast on the AUC interpretation to answer these questions. As well as the traditional trapezoidal approximation and the well-known binormal fitting model, we discuss a new approach that embraces the concept of imprecise probabilities and relies on the subdivision of the lowest ensemble probability category.

Open access
R. R. Burton, A. M. Blyth, Z. Cui, J. Groves, B. L. Lamptey, J. K. Fletcher, J. H. Marsham, D. J. Parker, and A. Roberts

Abstract

The ability to predict heavy rain and floods in Africa is urgently needed to reduce the socioeconomic costs of these events and increase resilience as climate changes. Numerical weather prediction in this region is challenging, and attention is being drawn to observationally based methods of providing short-term nowcasts (up to ∼6-h lead time). In this paper a freely available nowcasting package, pySTEPS, is used to assess the potential to provide nowcasts of satellite-derived convective rain rate for West Africa. By analyzing a large number of nowcasts, we demonstrate that a simple approach of “optical flow” can have useful skill at 2-h lead time on a 10-km scale and 4-h lead time at larger scales (200 km). A diurnal variation in nowcast skill is observed, with the worst-performing nowcasts being those that are initialized at 1500 UTC. Comparison with existing nowcasts is presented. Such nowcasts, if implemented operationally, would be expected to have significant benefits.

Significance Statement

A freely available, easy-to-use nowcasting package has been applied to satellite-retrieved rainfall rates for West Africa, and extrapolations have useful skill at up to 4 h of lead time.

Open access
Jinyoung Rhee and Boksoon Myoung

Abstract

We propose the objective long-range forecasting model based on Gaussian processes (OLRAF-GP), focusing on summertime near-surface air temperatures in June (1-month lead), July (2-month lead), and August (3-month lead). The predictors were objectively selected based on their relationships with the target variables, either from observations (GP-OBS) or from observations and dynamical climate model results from APEC Climate Center multimodel ensemble (APCC MME) for the period with no observed data (GP-MME). The performances of the OLRAF-GP models were compared with the model with predetermined predictors from observations (GP-PD). Both GP-MME and GP-OBS outperformed GP-PD in June [Heidke skill score (HSS); HSS = 0.46, 0.72, and 0.16 for mean temperature] and July (HSS = 0.53, 0.3, and 0.07 for mean temperature). Furthermore, GP-MME mostly outperformed GP-OBS and GP-PD in August (HSS = 0.52, 0.28, and 0.5, respectively, for mean temperature), implying larger contributions of the additional predictors from MME. OLRAF-GP models, especially GP-MME, are expected to better forecast summertime temperatures in regions where existing models have been struggling. We find that the physical processes associated with the notable predictors are aligned with those in previous studies, such as the attribution of the La Niña conditions in the previous winter, the related Indian Ocean capacitor effect, and the impacts of wintertime Polar/Eurasia pattern. These results imply that the mechanisms of the objectively selected predictors can be physically meaningful, and their inclusion can improve model performance and efficiency.

Significance Statements

This study aims to improve the long-range probabilistic forecasting of summertime near-surface temperatures for regions where the climate variability is not sufficiently explained by well-known key predictors. We propose objective and probabilistic forecasting models that use objectively selected predictors either from observations or from observations and results of the dynamical climate model. The overall skill scores of the proposed models (overall HSS = 0.33, 0.39) for the case study site of South Korea are higher than the model with predetermined predictors (overall HSS = 0.19). We also find that the mechanisms of the objectively selected predictors can be physically meaningful, and their inclusion can improve model performance and efficiency.

Open access
Andrew Brown, Andrew Dowdy, and Elizabeth E. Ebert

Abstract

Epidemic asthma events represent a significant risk to emergency services as well as the wider community. In southeastern Australia, these events occur in conjunction with relatively high amounts of grass pollen during the late spring and early summer, which may become concentrated in populated areas through atmospheric convergence caused by a number of physical mechanisms including thunderstorm outflow. Thunderstorm forecasts are therefore important for identifying epidemic asthma risk factors. However, the representation of thunderstorm environments using regional numerical weather prediction models, which are a key aspect of the construction of these forecasts, have not yet been systematically evaluated in the context of epidemic asthma events. Here, we evaluate diagnostics of thunderstorm environments from historical simulations of weather conditions in the vicinity of Melbourne, Australia, in relation to the identification of epidemic asthma cases based on hospital data from a set of controls. Skillful identification of epidemic asthma cases is achieved using a thunderstorm diagnostic that describes near-surface water vapor mixing ratio. This diagnostic is then used to gain insights on the variability of meteorological environments related to epidemic asthma in this region, including diurnal variations, long-term trends, and the relationship with large-scale climate drivers. Results suggest that there has been a long-term increase in days with high water vapor mixing ratio during the grass pollen season, with large-scale climate drivers having a limited influence on these conditions.

Significance Statement

We investigate the atmospheric conditions associated with epidemic thunderstorm asthma events in Melbourne, Australia, using historical model simulations of the weather. Conditions appear to be associated with high atmospheric moisture content, which relates to environments favorable for severe thunderstorms, but also potentially pollen rupturing as suggested by previous studies. These conditions are shown to be just as important as the concentration of grass pollen for a set of epidemic thunderstorm asthma events in this region. This means that weather model simulations of thunderstorm conditions can be incorporated into the forecasting process for epidemic asthma in Melbourne, Australia. We also investigate long-term variability in atmospheric conditions associated with severe thunderstorms, including relationships with the large-scale climate and long-term trends.

Open access
William E. Line, Lewis Grasso, Don Hillger, Carl Dierking, Aaron Jacobs, and Samuel Shea

Abstract

Sea spray presents a significant hazard to vessels in the high latitudes. At issue is the accumulation of ice, which can destabilize, and at times, sink a ship. Many studies have focused on icing prediction systems, but a knowledge gap exists in the detection of sea spray using remote sensing data. The recent availability of data from new and advanced imagers on board NOAA satellites, specifically the GOES-R series Advanced Baseline Imager (ABI) and JPSS Visible Infrared Imaging Radiometer Suite (VIIRS), offers new tools for the detection and tracking of sea spray for forecasters. While ABI provides superior temporal coverage in order to capture the near-real-time evolution of sea spray, VIIRS contributes higher spatial detail, allowing for improved analysis of sea spray extent, particularly within smaller bodies of water. Forecasters can implement these detection techniques to help verify sea spray–related forecast products, and to pass along potentially life-saving information to their mariner core partners. This paper discusses the freezing sea spray hazard, and introduces newly identified methods for detecting and tracking sea spray using NOAA satellite data.

Open access
Bjørg Jenny Kokkvoll Engdahl, Tim Carlsen, Morten Køltzow, and Trude Storelvmo

Abstract

In-cloud icing is a major hazard for aviation traffic and forecasting of these events is an important task for weather agencies worldwide. A common tool utilized by aviation forecasters is an icing intensity index based on supercooled liquid water from numerical weather prediction models. We seek to validate the modified microphysics scheme, ICE-T, in the HARMONIE-AROME numerical weather prediction model with respect to aircraft icing. Icing intensities and supercooled liquid water derived from two 3-month winter season simulations with the original microphysics code, CTRL, and ICE-T are compared with pilot reports of icing and satellite retrieved values of liquid and ice water content from CloudSat–CALIPSO and liquid water path from AMSR-2. The results show increased supercooled liquid water and higher icing indices in ICE-T. Several different thresholds and sizes of neighborhood areas for icing forecasts were tested out, and ICE-T captures more of the reported icing events for all thresholds and nearly all neighborhood areas. With a higher frequency of forecasted icing, a higher false alarm ratio cannot be ruled out, but is not possible to quantify due to the lack of no-icing observations. The increased liquid water content in ICE-T shows a better match with the retrieved satellite observations, yet the values are still greatly underestimated at lower levels. Future studies should investigate this issue further, as liquid water content also has implications for downstream processes such as the cloud radiative effect, latent heat release, and precipitation.

Open access
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