Improvement In Forecast Skill Research Articles

Secondary Ice Production Improves Simulations of Freezing Rain

AbstractWeather forecasts and climate projections of precipitation phase and type in winter storms are challenging due to the complicated underlying microphysical and dynamical processes. In the Canadian numerical weather prediction model, explicit freezing rain (FR) at the surface is often overestimated during the winter season for situations in which snow is observed. For a case study simulated using this model with the Predicted Particle Properties (P3) microphysics scheme, the secondary ice production (SIP) process has a major impact on the surface precipitation type. Parameterized SIP substantially reduces FR due to increased collection of supercooled drops with ice particles formed by rime splintering. Hindcast simulations of 40 winter cases show that these results are systematic, and the decreased frequency of FR leads to improved forecast skill relative to observations. Thus, accounting for SIP in the model is critical for accurately simulating precipitation types.

The potential value of seasonal drought forecasts in the context of climate change: A case study of the African elephant conservation sector

AbstractThis study investigates meteorological drought in sub‐Saharan Africa within the context of elephant conservation. Prolonged drought significantly impacts elephants, leading to increased mortality rates and heightened human–elephant conflicts. We assess both the anticipated 21st century changes in impact‐relevant meteorological drought metrics and the efficacy of existing forecasting systems in predicting such droughts on seasonal time scales. The climate change element of our study uses the 6th Coupled Model Intercomparison Project (CMIP6) ensemble to evaluate projected change in 3‐month Standardized Precipitation Index (SPI3). We then carry out a quantitative assessment of seasonal forecast skill, utilizing 110 years of precipitation hindcasts generated by the European Centre for Medium Range Forecasting (ECMWF) system. Our findings indicate that persistent drought is projected to become more frequent over the 21st century in southern Africa, where the majority of elephants reside. Analysis of seasonal hindcasts indicates that, while the forecasts have greater skill than climatology, they remain highly uncertain. Previous work suggests that it may be possible to reduce this uncertainty by contextualizing forecasts within specific climate regimes. However, even with improved forecast skill, effective action hinges on the alignment of forecasts with the practical needs of conservation practitioners. Over the next decades, a co‐production approach will be critical for leveraging seasonal forecasts for climate change adaptation within the conservation sector.

The Influence of Time-Varying Sea Ice Concentration on Antarctic and Southern Ocean Numerical Weather Prediction

Abstract Although operational weather forecasting centers are increasingly using global coupled atmosphere–ocean–ice models to replace atmosphere-only models for short- and medium-range (10 day) weather forecasting, the influence of sea ice on such forecasting has yet to be fully quantified, especially in the Southern Ocean. To address this gap, a polar-specific version of the Weather Research and Forecasting Model is implemented with a circumpolar Antarctic domain to investigate the impact of daily updates of sea ice concentration on short- and medium- range weather forecasting. A statistically significant improvement in near-surface atmospheric temperature and humidity is shown from +24 to +192 h when updating the daily sea ice concentration in the model. The forecast skill improvements for 2-m temperature and dewpoint temperature are enhanced from June to September, which is the period of late sea ice advance. Regionally, model improvement is shown to occur in most sea ice regions, although the improvement is strongest in the Ross Sea and Weddell Sea sectors. The surface heat budget also shows remarkable improvement in outgoing radiative heat fluxes and both sensible and latent heat fluxes. This idealized research demonstrates the nonnegligible effect of including more accurate time-varying sea ice concentration in numerical weather forecasting. Significance Statement The purpose of this study is to understand how a more realistic Antarctic sea ice field may influence the skill of short- and medium-range weather forecasts. Many operational atmospheric numerical weather prediction (NWP) models use a static forecast field through the time frame of the model’s forecast—often 3–10 days. In this study, we updated the sea ice concentration field daily and compared the forecast outcomes with those from model runs using a static sea ice concentration field. We found the forecast skill of near-surface temperature and humidity show the most significant improvements in our idealized experiments. This indicates the importance of incorporating improved dynamic sea ice representation in Antarctic short- to medium-range operational weather forecasting.

Impact of precipitation mass sinks on midlatitude storms in idealized simulations across a wide range of climates

Abstract. The combination of precipitation formation and fallout affects atmospheric flows through the release of latent heat and through the removal of mass from the atmosphere, but because the mass of water vapor is only a small fraction of the total mass of Earth's atmosphere, precipitation mass sinks are often neglected in theory and models. However, a small number of modeling studies suggest that water mass sources and sinks can intensify heavily precipitating weather systems. These studies point to a need to more systematically verify the impact of neglecting precipitation mass sinks, particularly for warmer and moister climates in which precipitation rates can be much higher. In this paper, we add precipitation mass sources and sinks to an idealized general circulation model and examine their effects on steady-state midlatitude storm track statistics. The model has several idealizations, including that all condensates immediately fall out of the atmosphere, and is run across a wide range of climates, including very warm climates. We find that modifying the model to include mass sources and sinks has no detectable effect on midlatitude variability or extremes, even in climates much warmer and moister than the modern. However, we find that a 10-fold exaggeration of mass sources and sinks is sufficient to produce more intense midlatitude weather extremes and increase surface pressure variance. This result is consistent with theoretical potential vorticity analysis, which suggests that the dynamical effects of mass sources and sinks are much smaller than the dynamical effects of accompanying latent heating unless mass sinks are artificially amplified by at least a factor of 10. Finally, we use simulations of “tropical cyclone worlds” to attempt to reconcile our results with earlier work showing stronger deepening in a simulation of a tropical cyclone case study when precipitation mass sinks were included. We demonstrate that abruptly “turning on” mass sources and sinks can lead to stronger transient deepening in some individual storms (consistent with results of past work) but weaker transient deepening in other storms, without modifying the steady-state statistics of storms in equilibrium with the large-scale environment (consistent with our other results). Our results provide a firmer foundation for using general circulation models that neglect moist mass sources and sinks in climate simulations, even in climates much warmer than today, while leaving open the possibility that their inclusion might lead to short-term improvements in forecast skill.

Physics-Inspired Adaptions to Low-Parameter Neural Network Weather Forecast Systems

Abstract Recently, there has been a surge of research on data-driven weather forecasting systems, especially applications based on convolutional neural networks (CNNs). These are usually trained on atmospheric data represented on regular latitude–longitude grids, neglecting the curvature of Earth. We assess the benefit of replacing the standard convolution operations with an adapted convolution operation that takes into account the geometry of the underlying data (SphereNet convolution), specifically near the poles. Additionally, we assess the effect of including the information that the two hemispheres of Earth have “flipped” properties—for example, cyclones circulating in opposite directions—into the structure of the network. Both approaches are examples of physics-informed machine learning. The methods are tested on the WeatherBench dataset, at a resolution of ∼1.4°, which is higher than many previous studies on CNNs for weather forecasting. For most lead times up to day +10 for 500-hPa geopotential and 850-hPa temperature, we find that using SphereNet convolution or including hemisphere-specific information individually leads to improvement in forecast skill. Combining the two methods typically gives the highest forecast skill. Our version of SphereNet is implemented flexibly and scales well to high-resolution datasets but is still significantly more expensive than a standard convolution operation. Finally, we analyze cases with high forecast error. These occur mainly in winter and are relatively consistent across different training realizations of the networks, pointing to flow-dependent atmospheric predictability.

Aerosol impacts on summer precipitation forecast over the North China Plain by using Thompson aerosol-aware scheme in WRF: Statistical analysis and significant threat score improvements in polluted condition during June to August 2018

The North China Plain (NCP) is one of the most polluted areas in China, and the impacts of such high-levels of aerosols on clouds and precipitation remain an interesting issue with great uncertainties. From the perspective of regional numerical weather prediction, how to consider the role of aerosols is still a controversial issue considering the balance between the complexities/uncertainties of the model (aerosol-radiation-cloud interactions) and computational costs. To evaluate the reliability of the Thompson aerosol-aware schemes and promote operational applications in the Weather Research and Forecasting model (WRF), summer precipitation forecast with three experiments that considered no aerosol-radiation-cloud interactions (Thomp), aerosol-cloud interactions only (ThompAI) and full aerosol-radiation-cloud interactions (ThompAD) from June to August 2018 were carried out. Detailed statistics of the 3-h and 24-h cumulative precipitation skills were calculated for two groups, which included the entire summer season and selected polluted-precipitation events (26 days) only, were analyzed. The results showed that the overall Threat Scores (TS) for the 26 polluted-precipitation events were lower than those of the other group (the whole summer season)for either Thomp, ThompAI or ThompAD, which revealed that the forecasting abilities for polluted-precipitation cases in the model were obviously lower than those for the nonpolluted precipitation cases, indicating deficiencies in parameterizing the special conditions on polluted days. Nevertheless, for both groups, after using the aerosol-aware Thompson schemes (either ThompAI or ThompAD), the TS scores for most magnitudes (0.1, 1, 5, 10, and 25 mm) were significantly increased compared to the original Thomp experiment, with the exception being for precipitation amounts >50 mm. Meanwhile the BIAS of each magnitude also increased for both ThompAI and ThompAD due to the spatial increase in areas with precipitation compared to the original Thomp experiment. The TS improvements were more significant in the ThompAD experiment than in the ThompAI experiment. Compared with the original Thomp experiment, the ThompAD improvement ratios for the 24-h cumulative precipitation TSs were 3.01%, 4.91%, 2.05%, 13.58%, 7.80% for 0.1 mm, 1 mm, 5 mm, 10 mm, 25 mm precipitation magnitudes respectively; and the improvements were significantly higher when the statistics window was changed from 24-h to 3-h, with 3-h cumulative precipitation TS improvements reaching 10.23%, 5.09%, 11.50%, 17.4%, 14.30%, respectively, indicating the impacts that considering aerosol-radiation-cloud interactions in changing the precipitation patterns (fall zone and amounts) in the model were more effective for 3-h windows compared to 24-h windows. Case studies of selected light-rain, medium-scale rain and heavy rain events further revealed the positive impacts of the Thompson aerosol-aware schemes, and the precipitated areas were much closer to the observations. The reason why ThompAD did not show obvious advantages for large-scale precipitation (50 mm magnitude) may be due to the complex relationship involving aerosol-cold cloud interactions in the model and the randomness caused by inadequate an individual number of cases. The statistics for positive precipitation forecast skill improvements for the NCP region provide confidence in the application of the Thompson aerosol-aware scheme in the WRF model in the future.

Statistical post‐processing of visibility ensemble forecasts

AbstractTo be able to produce accurate and reliable predictions of visibility has crucial importance in aviation meteorology, as well as in water‐ and road transportation. Nowadays, several meteorological services provide ensemble forecasts of visibility; however, the skill and reliability of visibility predictions are far reduced compared with other variables, such as temperature or wind speed. Hence, some form of calibration is strongly advised, which usually means estimation of the predictive distribution of the weather quantity at hand either by parametric or nonparametric approaches, including machine learning‐based techniques. As visibility observations—according to the suggestion of the World Meteorological Organization—are usually reported in discrete values, the predictive distribution for this particular variable is a discrete probability law, hence calibration can be reduced to a classification problem. Based on visibility ensemble forecasts of the European Centre for Medium‐Range Weather Forecasts covering two slightly overlapping domains in Central and Western Europe and two different time periods, we investigate the predictive performance of locally, semi‐locally and regionally trained proportional odds logistic regression (POLR) and multilayer perceptron (MLP) neural network classifiers. We show that while climatological forecasts outperform the raw ensemble by a wide margin, post‐processing results in further substantial improvement in forecast skill, and in general, POLR models are superior to their MLP counterparts.

Multi-model ensemble forecasting of 10-m wind speed over eastern China based on machine learning optimization

Wind substantially impacts human activity and electricity generation. Thus, accurately forecasting the short-term wind speed is of profound societal and economic significance. Based on 100 weather stations in eastern China, the authors first evaluate the performance of the 10-m wind forecast products from five operational forecast models. Among them, the Japan Meteorological Agency (JMA) model performs best in reducing the forecasting errors. Then, the authors establish a 10-m wind speed multimodel ensemble forecast based on the five numerical models’ outputs and machine learning methods, combining dynamic and statistical methods. Feature engineering and machine learning algorithm optimization are conducted for each site separately. The forecast performance of this method is compared to the JMA model and multimodel ensemble forecast by ridge regression at lead times of 24–96 h. The results demonstrate that the multimodel ensemble method based on machine learning optimization can reduce the forecast error of JMA by more than 39%, and the improvement in forecast skill is most evident in November. In addition, it performs better than the ensemble forecast by ridge regression.

Parametric Postprocessing of Dual-Resolution Precipitation Forecasts

Abstract All major weather centers issue ensemble forecasts, which differ both in ensemble size and spatial resolution, even while covering the same domain. These parameters directly determine both the forecast skill of the prediction and the computation cost. In the past few years, the plans for upgrading the configuration of the Integrated Forecast System of the European Centre for Medium-Range Weather Forecasts (ECMWF) from a single forecast with 9-km resolution and a 51-member ensemble with 18-km resolution induced an extensive study of the forecast skill of both raw and postprocessed dual-resolution predictions comprising ensemble members of different horizontal resolutions. We investigate the predictive performance of the censored shifted gamma (CSG) ensemble model output statistic (EMOS) approach for statistical postprocessing with the help of dual-resolution, 24-h, precipitation accumulation ensemble forecasts over Europe with various forecast horizons. We consider the operational 50-member ECMWF ensemble as of high resolution and extend it with a low-resolution (29-km grid), 200-member experimental forecast. The investigated dual-resolution combinations consist of subsets of these two forecast ensembles with equal computational cost, which is equivalent to the cost of the operational ensemble. Our case study verifies that, compared with the raw ensemble combinations, EMOS postprocessing results in a significant improvement in forecast skill and that skill is statistically indistinguishable between any of the analyzed mixtures of dual-resolution combinations. Furthermore, the semilocally trained CSG EMOS provides an efficient alternative to the state-of-the-art quantile mapping without requiring additional historical data.

Sea surface temperature predictability assessment with an ensemble machine learning method using climate model simulations

Ensemble models, statistical analysis and machine learning (ML) can be used to predict novel conditions in a rapidly changing ocean. Traditionally, ML has been understood as a purely data-driven approach and has been used on both observational and model data to forecast Sea Surface Temperature (SST) anomalies. Here we use ML trained only on climate model simulations to predict regional SST variations, thereby suggesting a novel role for ML as an ensemble model interpolator. We propose a measure of the predictability provided by different ML implementations as well as by standard time series analysis methods. Weighting each forecast by this predictability measure computed on model data only, provides a significant improvement in forecast skill. We demonstrate the performance of this approach for regions around Australia, the Nino3.4 region (central-eastern equatorial Pacific) and in the eastern equatorial Pacific. These analyses show that SST predictability varies as a function of geographical location, area size, seasonality, proximity to the coast and model data quality.

Quantifying the Benefits of Altimetry Assimilation in Seasonal Forecasts of the Upper Ocean

AbstractSatellite altimetry measurements of sea surface height provide near‐global ocean state observations on sub‐monthly time scales, which are not always utilized by seasonal climate forecasting systems. As early as the mid‐1990s, attempts were made to assimilate altimetry observations to initialize climate models. These experiments demonstrated improved ocean forecasting skill, especially compared to experiments that did not assimilate subsurface ocean temperature information. Nowadays, some operational climate forecasting models utilize altimetry in their assimilation systems, whereas others do not. Here, we assess the impact of altimetry assimilation on seasonal prediction skill of ocean variables in two climate forecasting systems that are from the European Centre for Medium‐Range Weather Forecasts (SEAS5) and the Australian Bureau of Meteorology (ACCESS‐S). We show that assimilating altimetry improves the initialization of subsurface ocean temperatures, as well as seasonal forecasts of monthly variability in upper‐ocean heat content and sea level. Skill improvements are largest in the subtropics, where there are typically less subsurface ocean observations available to initialize the forecasts. In the tropics, there are no noticeable improvements in forecast skill. The positive impact of altimetry assimilation on forecast skill related to the subsurface ocean does not seem to affect predictions of sea surface temperature. Whether this is because current forecasting systems are close to the potential predictability limit for the ocean surface, or perhaps altimetry observations are not fully exploited, remains a question. In summary, we find that utilizing altimetry observations improves the overall global ocean forecasting skill, at least for upper‐ocean heat content and sea level.

Investigating the Accuracies in Short-Term Weather Forecasts and Its Impact on Irrigation Practices

The effectiveness of irrigation schedules and crop growth models is largely dependent on the accuracies associated with the most uncertain input parameter, viz. weather forecasts. This research is focused on quantifying the inaccuracies associated with the India Meteorological Department (IMD) issuing short-term weather forecasts (with 1–5 days lead time), and their propagation into irrigation models, with an objective to select the optimal parameters for use with simulation. While precipitation (P) forecasts were directly used, remaining meteorological forecasts were converted to reference evapotranspiration (ET0) forecasts. The effectiveness of the ‘P’ and ‘ET0’ forecasts was found to be low at all lead times. We applied two popular bias correction methods: linear scaling (LS) and empirical quantile mapping (EQM), and observed a marginal improvement in forecast skill. Bias-corrected, forecast-driven irrigation scenarios, along with conventional irrigation (that ignores weather forecast), and a hypothetical perfect 5-day forecast-based irrigation (as reference) system were tested on a water-intensive paddy crop for two growing seasons (S1: monsoon and S2: winter). Conventional irrigation resulted in the highest use of irrigation water (820 mm in S1, 880 mm in S2) and percolation loss (1,140 mm in S1, 680 mm in S2), while achieving a low relative yield (0.88 in S1, 0.87 in S2). LS-corrected forecasts outperformed other scenarios with 20.24%±4.21% and 1.25%±1.51% savings in irrigation costs for S1 and S2, respectively. While IMD forecasts greatly improved irrigation schedules in the monsoon season, their usage for winter crops was found to be trivial. Our findings conclude that LS-corrected IMD forecasts were moderately reliable over multiple lead times and can serve as a valuable addition to irrigation scheduling, provided the contribution of ‘P’ to the total water balance is significant.

Forecast Sensitivity-based Observation Impact (FSOI) in an analysis–forecast system of the California Current Circulation

Forecast Sensitivity-based Observation Impacts (FSOI) in an analysis–forecast​ system of the California Current System (CCS) are quantified using an adjoint-based approach. The analysis–forecast system is based on the Regional Ocean Modeling System (ROMS) and a 4-dimensional variational (4D-Var) data assimilation approach. FSOI was applied to four different metrics of forecast skill that target important features of the CCS circulation along the central California coast. A particular focus of the FSOI analysis is the impact of assimilation of measurements of the radial component of surface currents from a network of high frequency (HF) radars since this is a new data stream in the near-real-time system considered here. On average, ∼50–60% of all observations assimilated into the model yielded improvements in the forecast skill. Conversely, the remaining ∼40–50% of data degrade the forecasts, in line with similar findings in numerical weather prediction systems. Much of the improvement in forecast skill arises from remotely sensed observations, including HF radar data; on average only ∼50% of in situ measurements contribute to a reduction in forecast error. This is partly due to the large volume of remote sensing observations compared to in situ observations. However, in situ observations are an order of magnitude more impactful than remotely sensed data when viewed in terms of the average impact per observation.

Effects of aerosols on the forecasting of Mei-yu frontal storms over the Yangtze–Huai River valley

The evolutions of Mei-yu frontal storms during the Mei-yu season in 2018 were simulated in the Weather Research and Forecasting with Chemistry (WRF/Chem) model to investigate the effects of aerosols on the prediction of clouds and precipitation. Two groups of daily launched forecast experiments were conducted with a duration of 17 days, by turning the chemistry–aerosol module on (WRF/Chem) and off (WRF), and additional cloud droplet number concentration (Nd) experiments were perturbed in WRF. Comparison between the two groups of experiments suggested that activating the chemistry–aerosol module improved the forecast skill for precipitation exceeding 5 mm/h during the Mei-yu season, while had no positive forecast skill on precipitation exceeding 0.1 mm/h in terms of both traditional and neighborhood spatial verifications. In contrast to the uniform Nd distribution adopted in WRF, the inhomogeneous Nd distribution in WRF/Chem agreed better with in-situ aircraft observations collected during the field campaign. As Nd increases in WRF experiments, the warm clouds decreased and deep convective clouds significantly increased with more convective updraft mass flux and net heat budget. Scavenging in Mei-yu frontal storms generally reduced the median Nd in the WRF/Chem experiment. The fewer and larger cloud particles in WRF/Chem promoted the conversion of cloud to rain and the accretion of cloud by rain, thereby leading to invigoration of warm clouds and precipitation efficiency, which explains the improved forecast skill for precipitation exceeding 5 mm/h. Enhanced warm-rain processes caused fewer cloud particles for further ascent and ultimately resulted in suppression of deep convective clouds. The preliminary findings in this study highlights the potential significance of incorporating chemistry–aerosol schemes in the operational forecasting of Mei-yu rainfall.

Impacts of Aeolus horizontal Line‐Of‐Sight (HLOS) wind assimilation on the Korean integrated model (KIM) forecast system

AbstractThe Korean Integrated Model (KIM) forecast system, based on a hybrid four‐dimensional ensemble‐variational method, was extended to assimilate Horizontal Line‐Of‐Sight (HLOS) wind observations from the Atmospheric Laser Doppler Instrument (ALADIN) on board the Atmospheric Dynamic Mission Aeolus satellite. In a global cycling experiment, assimilation of Aeolus HLOS wind observations led to reductions in the average root‐mean‐square error of 0.8 and 0.5% for the zonal and meridional wind analyses when compared against European Center for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) analyses. Even though the observed variable is wind, there was also an overall beneficial impact on analyses of the mass variables. In the Southern Hemisphere (SH), the reduced analysis errors led to forecast skill improvements out to 72 h. In contrast, in the Northern Hemisphere (NH) there was relatively little reduction of analysis errors, but wind forecasts were nevertheless improved, and these positive impacts lasted longer – out to 120 h rather than 72 h. Experiments suggest that the relatively poor long‐range performance in the SH high latitudes was due to problems with the mass increments derived from Aeolus wind increments via the ensemble‐based part of the hybrid background error covariance (B), which eventually led to adverse effects on the wind variables as forecasts progressed in the SH. This study shows that it is necessary to estimate the ensemble B in the Antarctic region with its high elevation more accurately in order to effectively use the Aeolus observation information.

How Predictable is Short-Term Drought in the Northeastern United States?

Abstract We investigated the predictability (forecast skill) of short-term droughts using the Palmer drought severity index (PDSI). We incorporated a sophisticated data training (of decadal range) to evaluate the improvement of forecast skill of short-term droughts (3 months). We investigated whether the data training of the synthetic North American Multi-Model Ensemble (NMME) climate has some influence on enhancing short-term drought predictability. The central elements are the merged information among PDSI and NMME with two postprocessing techniques. 1) The bias correction–spatial disaggregation (BC-SD) method improves spatial resolution by using a refined soil information introduced in the available water capacity of the PDSI calculation to assess water deficit that better estimates drought variability. 2) The ensemble model output statistic (EMOS) approach includes systematically trained decadal information of the multimodel ensemble simulations. Skill of drought forecasting improves when using EMOS, but BC-SD does not increase the forecast skill when compared with an analysis using BC (low spatial resolution). This study suggests that predictability forecast of drought (PDSI) can be extended without any change in the core dynamics of the model but instead by using the sophisticated EMOS postprocessing technique. We pointed out that using NMME without any postprocessing is of limited use in the suite of model variations of the NMME, at least for the U.S. Northeast. From our analysis, 1 month is the most extended range we should expect, which is below the range of the seasonal scale presented with EMOS (2 months). Thus, we propose a new design of drought forecasts that explicitly includes the multimodel ensemble signal.

Improved Simulations of Atmospheric River Climatology and Variability in High‐Resolution CESM

AbstractAtmospheric rivers (ARs), referring to long and narrow filamentary bands of intense water vapor transport in the atmosphere, can cause extreme precipitation, floods, and drought events. Their variability has been linked to various climate modes, such as El Niño/Southern Oscillation, Pacific Decadal Oscillation, and Pacific‐North America pattern. Understanding and improving simulation of this linkage can provide the potential to predict ARs at subseasonal‐to‐decadal timescales. Up to now, the extent to which climate model simulations of ARs are dependent on model resolutions has not been fully assessed. Here, we compare and evaluate ARs in a pair of high‐resolution (HR) and a low‐resolution (LR) Community Earth System Model (CESM) simulations against the observations. The results show that within this CESM framework, climatological AR strength and associated precipitation are severely underestimated by LR compared to the observations, and their relationships with major modes of climate variability are also poorly reproduced. These deficiencies in LR are alleviated to a significant extent in HR. Using a linear regression analysis, we show that HR is able to capture with high fidelity the observed relationships between ARs and major climate modes in the Northern Hemisphere at seasonal‐to‐decadal time scales. These results suggest that the use of HR CESM may potentially lead to an improved forecast skill of ARs at season‐to‐decadal time scales.

Reduced error and uncertainty in analysis and forecasting in the Southern Hemisphere through assimilation of PANSY radar observations from Syowa Station: A midlatitude extreme cyclone case

AbstractThe sparse distribution of the observing network over the high latitudes of the Southern Hemisphere reduces the accuracy of regional atmospheric circulation (re)analysis and weather forecasting. Using operational medium‐range ensemble forecasts, we compared the forecast skill among forecast centres regarding a midlatitude cyclone close to western Australia on 17 December 2017. At forecast day 4.5, a large ensemble spread in an upper‐level trough over western Australia, which had travelled from coastal Antarctica, caused large uncertainty and error in the predicted position of the midlatitude cyclone. Using an ensemble data assimilation system and the Atmospheric General Circulation Model for the Earth Simulator and the Local Ensemble Transform Kalman Filter Experimental Ensemble Reanalysis version 2 (ALERA2), we conducted an operational forecast experiment to investigate the impact on ALERA2 and its forecast result of assimilation of 1‐hourly horizontal wind speed observational data from the PANSY radar located at the Japanese Syowa Station in Antarctica. An observing system experiment revealed that incorporation of PANSY radar data improved the reproducibility of atmospheric parameters in the troposphere and lower stratosphere as initial conditions for forecasting. To investigate the impact of PANSY radar data on forecast skill regarding the midlatitude cyclone, 63‐member ensemble forecast experiments were conducted using analysis data both with and without PANSY data as initial conditions. Comparison of the forecast results from the ensemble forecast experiments confirmed that incorporation of PANSY radar data reduced the ensemble spread and error in the upper troposphere over the Antarctic region as initial conditions, which enhanced the accuracy of the prediction of the positions of the upper‐level trough and surface cyclone over western Australia at forecast day 4.5. These experiments suggest that the PANSY radar represents an observing system suitable for continuous improvement in forecast skill of the atmospheric circulation in the Southern Hemisphere.

Operational Assimilation of Spectral Wave Data From the Sofar Spotter Network

AbstractHistorically, the sparseness of in situ open‐ocean wave and weather observations has severely limited the forecast skill of weather over the ocean with major social and economic consequences for coastal communities and maritime industries. Ocean surface waves, specifically, are important for the interaction between atmosphere and ocean, and thus key in modeling weather and climate processes. Here, we investigate the improvements achievable from a large distributed sensor network combined with advances in assimilation strategies. Wave spectra from a global network of over 600 Sofar Spotter buoys are assimilated into an operational global wave forecast via optimal interpolation to update model spectra to best fit observations. We demonstrate end‐to‐end improvements in forecast skill of significant wave height of 38%, and up to 45% for other bulk parameters. This shows distributed observations of the air‐sea interface, with advances in assimilation strategies, can reduce uncertainty in forecasts to dramatically improve earth system modeling.

Establishing Opportunities and Limitations of Forecast Use in the Operational Management of Highly Constrained Multiobjective Water Systems

The potential utility of forecast information to improve reservoir control is well established, but physical, operational, and institutional constraints inhibit the full realization of these benefits. To help integrate forecast technology into operational water management, it is necessary to establish the opportunities and limitations of forecast use within the current system management framework, so that water managers and planners can identify in practice what constraints or aspects of control policy design most limit forecast utility. In this work, we explore the value of forecasts in the current outflow management plan of the Lake Ontario-St. Lawrence River basin, which is the first regulation policy of this system to use water supply forecasts to guide release decisions. We assess the value of forecasts for many-objective system management (e.g., upstream and downstream flood risk reduction, hydropower production, commercial navigation costs, recreational boating benefits, and wetland restoration), as a function of forecast accuracy, timing, and interactive effects, with system constraints and control rule structures. Results show that a 20%–30% reduction in forecast error over current statistical forecasts leads to observable improvements in most objectives, which grow significantly with additional forecast accuracy. Realizing these benefits is possible by increasing forecast accuracy, particularly in the springtime when tradeoffs between objectives are the greatest. However, we also identify one objective (wetland restoration) that declines with improved forecast skill, as well as operational constraints that limit forecast utility under the current outflow management plan. Overall, these results suggest that key aspects of the current control policy could be revised to better utilize forecast information, especially as forecasts improve over time.