Weather Ensembles Research Articles

U.S. West Coast droughts and heat waves exacerbate pollution inequality and can evade emission control policies

Droughts reduce hydropower production and heatwaves increase electricity demand, forcing power system operators to rely more on fossil fuel power plants. However, less is known about how droughts and heat waves impact the county level distribution of health damages from power plant emissions. Using California as a case study, we simulate emissions from power plants under a 500-year synthetic weather ensemble. We find that human health damages are highest in hot, dry years. Counties with a majority of people of color and counties with high pollution burden (which are somewhat overlapping) are disproportionately impacted by increased emissions from power plants during droughts and heat waves. Taxing power plant operations based on each plant’s contribution to health damages significantly reduces average exposure. However, emissions taxes do not reduce air pollution damages on the worst polluting days, because supply scarcity (caused by severe heat waves) forces system operators to use every power plant available to avoid causing a blackout.

Development of interactive guidance for cold exposure using a thermoregulatory model

ABSTRACT For decades, the Wind Chill Temperature Index (WCT) and its various iterations have been used to assess the risk of frostbite on unclothed body parts. This paper presents an innovative knowledge-based Cold Weather Ensemble Decision Aid (CoWEDA) that can be used to guide the selection of the most appropriate cold weather ensemble(s) relative to anticipated mission physical activities and environmental conditions. CoWEDA consists of a validated six-cylinder thermoregulatory model, a database of clothing properties, algorithms for calculating the whole ensemble properties from individual garments and a graphical user interface. The user-friendly CoWEDA allows users to select from an inventory of clothing items to build an ensemble suitable for their needs. CoWEDA predicts the risks of both frostbite and hypothermia and ensures that a selected clothing ensemble will provide adequate protection to prevent cold injury. CoWEDA predictions provide not only estimates of frostbite risk similar to WCT tables but also hypothermia times and clothing required to prevent cold injuries. In addition, a CoWEDA model variant can predict survivability and clothing requirements during cold water immersion. Thus, CoWEDA represents a significant enhancement of the WCT-based guidance for cold weather safety and survival by providing greater individual fidelity in cold injury predictions.

Stochastic Downscaling of Hourly Precipitation Series From Climate Change Projections

AbstractStochastic precipitation generators (SPGs) are often used to produce synthetic precipitation series for water resource management. Typically, an SPG assumes a stationary climate. We present an hourly precipitation generation algorithm for nonstationary conditions informed by the global climate model (GCM) forecasted average monthly temperature (AMT). The physical basis for precipitation formation is considered explicitly in the design of the algorithm using hourly pressure change events (PCE) to define the relationship between hourly precipitation and AMT. The algorithm consists of a multivariable Markov Chain and a moving window driven by time, temperature, and pressure change. We demonstrate the methodology by generating a 100‐year, continuous, synthetic hourly precipitation time series using GCM AMT projections for the Northeast United States. When compared with historical observations, the synthetic results suggest that future precipitation in this region will be more variable, with more frequent mild events and fewer but intensified extremes, especially in warm seasons. The synthetic time series suggests that there will be less precipitation in the summers, while winters will be wetter, consistent with other research on climate change projections for the Northeast United States. This SPG provides physically plausible weather ensembles for water resource studies involving climate change.

Probabilistic load forecasting using post-processed weather ensemble predictions

Probabilistic forecasting of electricity demand (load) facilitates the efficient management and operations of energy systems. Weather is a key determinant of load. However, modelling load using weather is challenging because the relationship cannot be assumed to be linear. Although numerous studies have focussed on load forecasting, the literature on using the uncertainty in weather while estimating the load probability distribution is scarce. In this study, we model load for Great Britain using weather ensemble predictions, for lead times from one to six days ahead. A weather ensemble comprises a range of plausible future scenarios for a weather variable. It has been shown that the ensembles from weather models tend to be biased and underdispersed, which requires that the ensembles are post-processed. Surprisingly, the post-processing of weather ensembles has not yet been employed for probabilistic load forecasting. We post-process ensembles based on: (1) ensemble model output statistics: to correct for bias and dispersion errors by calibrating the ensembles, and (2) ensemble copula coupling: to ensure that ensembles remain physically consistent scenarios after calibration. The proposed approach compares favourably to the case when no weather information, raw weather ensembles or post-processed ensembles without ensemble copula coupling are used during the load modelling.

Evaluating ensemble post‐processing for wind power forecasts

AbstractCapturing the uncertainty in probabilistic wind power forecasts is challenging, especially when uncertain input variables, such as the weather, play a role. Since ensemble weather predictions aim to capture the uncertainty in the weather system, they can be used to propagate this uncertainty through to subsequent wind power forecasting models. However, as weather ensemble systems are known to be biassed and underdispersed, meteorologists post‐process the ensembles. This post‐processing can successfully correct the biasses in the weather variables but has not been evaluated thoroughly in the context of subsequent forecasts, such as wind power generation forecasts. The present paper evaluates multiple strategies for applying ensemble post‐processing to probabilistic wind power forecasts. We use Ensemble Model Output Statistics (EMOS) as the post‐processing method and evaluate four possible strategies: only using the raw ensembles without post‐processing, a one‐step strategy where only the weather ensembles are post‐processed, a one‐step strategy where we only post‐process the power ensembles and a two‐step strategy where we post‐process both the weather and power ensembles. Results show that post‐processing the final wind power ensemble improves forecast performance regarding both calibration and sharpness whilst only post‐processing the weather ensembles does not necessarily lead to increased forecast performance.

Weather records from recent years performed better than analogue years when merging with real-time weather measurements for dynamic within-season predictions of rainfed maize yield

Within-season crop yield prediction with a dynamic crop model can provide valuable references for field management practices and regional food security. However, weather ensembles containing the unknown future weather conditions occurring after prediction dates are essential for such predictions using crop models. Two strategies were established for selecting analogue weather years as the target growing season based on a five-year maize experiment conducted at eight sites in the Loess Plateau of China. The first strategy tried weather data from different lengths of years ahead the planting year. The second strategy used the k-nearest neighbor (k-NN) algorithm to select analogue weather according to different combinations of weather variables with daily or accumulative values. The results showed that satisfactory predictions could be obtained after maize tasseling (about 50 d prior to maturity). The mean absolute relative error (ARE) and coefficient of variation (CV) of the daily yield predictions after tasseling were 6.6% and 5.7%, respectively, in 2010 at the Yulin site. In the leading-year strategy, the most reliable predictions were obtained by the weather data from the 10 years ahead of planting, with an overall average ARE of 11.7%. In the k-NN strategy, the most reliable predictions were obtained by using the analogue weather selected with only accumulative precipitation, with an overall average ARE of 11.5%. Additionally, both of the two optimal strategies improved the original predictions in most cases. However, the k-NN strategy was more likely to generate worse predictions in the early part of the growing season. Generally, it was more convenient to use the weather data of 10 leading years before the planting year to represent the unknown weather data after the prediction dates. This strategy provided reliable prediction accuracy without complex programming and requirement for long-term weather records.

Technology Pathways Could Help Drive the U.S. West Coast Grid's Exposure to Hydrometeorological Uncertainty

AbstractPrevious studies investigating deep decarbonization of bulk electric power systems and wholesale electricity markets have not sufficiently explored how future grid pathways could affect the grid's vulnerability to hydrometeorological uncertainty on multiple timescales. Here, we employ a grid operations model and a large synthetic weather ensemble to “stress test” a range of future grid pathways for the U.S. West Coast developed by ReEDS, a well‐known capacity planning model. Our results show that gradual changes in the underlying capacity mix from 2020 to 2050 can cause significant “re‐ranking” of weather years in terms of annual wholesale electricity prices (with “good” years becoming bad, and vice versa). Nonetheless, we find the highest and lowest ranking price years in terms of average electricity price remain mostly tied to extremes in hydropower availability (streamflow) and load (summer temperatures), with the strongest sensitivities related to drought. Seasonal dynamics seen today involving spring snowmelt and hot, dry summers remain well‐defined out to 2050. In California, future supply shortfalls in our model are concentrated in the evening and occur mostly during periods of high temperature anomalies in late summer months and in late winter; in the Pacific Northwest, supply shortfalls are much more strongly tied to negative streamflow anomalies. Under our more robust sampling of stationary hydrometeorological uncertainty, we also find that the ratio of dis‐patchable thermal (i.e., natural gas) capacity to wind and solar required to ensure grid reliability can differ significantly from values reported by ReEDS.

A digital tool for prevention and management of cold weather injuries\u2014Cold Weather Ensemble Decision Aid (CoWEDA)

This paper describes a Cold Weather Ensemble Decision Aid (CoWEDA) that provides guidance for cold weather injury prevention, mission planning, and clothing selection. CoWEDA incorporates current science from the disciplines of physiology, meteorology, clothing, and computer modeling. The thermal performance of a cold weather ensemble is defined by endurance times, which are the time intervals from initial exposure until the safety limits are reached. These safety limits correspond to conservative temperature thresholds that provide a warning of the approaching onset of frostbite and/or hypothermia. A validated six-cylinder thermoregulatory model is used to predict human thermal responses to cold while wearing different ensembles. The performance metrics, model, and a database of clothing properties were integrated into a user-friendly software application. CoWEDA is the first tool that allows users to build their own ensembles from the clothing menu (i.e., jackets, footwear, and accessories) for each body region (i.e., head, torso, lower body, hands, feet) and view their selections in the context of physiological strain and the operational consequences. Comparison of predicted values to skin and core temperatures, measured during 17 cold exposures ranging from 0 to −40°C, indicated that the accuracy of CoWEDA prediction is acceptable, and most predictions are within measured mean ± SD. CoWEDA predicts the risk of frostbite and hypothermia and ensures that a selected clothing ensemble is appropriate for expected weather conditions and activities. CoWEDA represents a significant enhancement of required clothing insulation (IREQ, ISO 11079) and wind chill index-based guidance for cold weather safety and survival.

Validation of new method for predicting human skin temperatures during cold exposure: The Cold Weather Ensemble Decision Aid (CoWEDA)

Abstract Purpose The accuracy of the US Army's Cold Weather Ensemble Decision Aid (CoWEDA) was assessed for the prediction of human skin temperatures while exposed to cold environmental conditions. Methods Data from four laboratory cold exposure studies were combined to examine skin temperature responses based on environment, clothing, and activities. This combined dataset included a total of 23 volunteers exposed to cold conditions between −40 and 0 °C during rest and moderate treadmill walking. Observed measures of mean skin temperature and regional skin temperatures were compared to modeled predictions, where predictive accuracy and precision were evaluated by the bias, mean absolute error (MAE), and root mean square error (RMSE). Results Skin temperature predictions were within an acceptable range of accuracy when compared to observed values (bias, −0.71 ± 2.21 °C; MAE, 1.85 ± 1.75 °C; and RMSE, 2.28 ± 1.54 °C) across all conditions. An acceptable level of predictive accuracy was also observed for finger (bias, −0.98 ± 2.50 °C; MAE, 2.68 ± 2.09 °C; RMSE, 2.68 ± 1.71 °C) and mean skin temperatures (bias, −0.03 ± 1.25 °C; MAE, 1.06 ± 0.65 °C; RMSE, 1.25 ± 0.68 °C). Conclusion The CoWEDA acceptably predicts skin temperatures while at rest and exercise during cold exposure in controlled laboratory conditions.

Skill of ensemble flood inundation forecasts at short- to medium-range timescales

We investigate the ability to improve flood inundation forecasts at short- to medium-range (1–7 days) timescales through weather ensembles and statistical water surface elevation (WSE) postprocessing. To generate the flood inundation forecasts, a one-dimensional hydraulic model, namely the Hydrologic Engineering Center’s River Analysis System (HEC-RAS), is coupled to a regional hydrological ensemble prediction system (RHEPS). The RHEPS is comprised of: i) hydrometeorological observations; ii) weather ensembles from the National Centers for Environmental Prediction Global Ensemble Forecast System Reforecast version 2 (GEFSRv2); and iii) Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) as the hydrological model. The coupled RHEPS-hydraulic system is evaluated along the tidal Delaware River near the City of Philadelphia, Pennsylvania, U.S. For the evaluation, emphasis is placed on the tidal-riverine transitional zone of the Delaware River and the downstream propagation of hydrometeorological uncertainty through the flood inundation forecasts. The coupled system is used to generate hourly flood inundation forecasts at lead times from 1 to 7 days, over the period 2008–2013. Additionally, WSEs from the coupled system are statistically postprocessed using quantile regression (QR). Results show that the raw flood inundation ensemble forecasts exhibit higher skill than the deterministic ones. We also find that statistical postprocessing improves the skill of the raw flood inundation ensemble forecasts, with greater improvements at the longer lead times (>3 days). Overall, we find that both weather ensembles and statistical WSE postprocessing can be used to enhance the skill of flood inundation forecasts at short- to medium-range timescales. In turn, this may serve to enhance the spatial representation of flood forecasts several days in advance, which could contribute in the future to making flood forecast communication and warnings more effective.

Hazmat risk area assessment by atmospheric dispersion modelling using Latin hypercube sampling with weather ensemble

Atmospheric dispersion modelling is always encumbered by errors and uncertainties originating from different aspects of the weather description as well as the source and dispersion models. Even so, the typical results from these kinds of studies are limited to one realization with no measure of uncertainties in either the temporal or spatial dimensions. This result is then to be interpreted as the most probable outcome given the current information. However, in many situations this limited result and the presentation thereof are far from satisfying and in the worst case even dangerously misleading. To address this shortcoming, this work presents a well‐established method for uncertainty investigation in simulations called Latin hypercube sampling in combination with a weather ensemble which results in an alternative way of estimating the resulting risk area as a function of weather forecast time from a statistical perspective. The main idea with this approach is to use the entire probability distribution of the simulation parameters instead of only one value as is the case in traditional methodology. This is a useful concept that provides additional and valuable information for decision makers.

Long-term ensemble forecast of snowmelt inflow into the Cheboksary Reservoir under two different weather scenarios

Abstract. A long-term forecasting ensemble methodology, applied to water inflows into the Cheboksary Reservoir (Russia), is presented. The methodology is based on a version of the semi-distributed hydrological model ECOMAG (ECOlogical Model for Applied Geophysics) that allows for the calculation of an ensemble of inflow hydrographs using two different sets of weather ensembles for the lead time period: observed weather data, constructed on the basis of the Ensemble Streamflow Prediction methodology (ESP-based forecast), and synthetic weather data, simulated by a multi-site weather generator (WG-based forecast). We have studied the following: (1) whether there is any advantage of the developed ensemble forecasts in comparison with the currently issued operational forecasts of water inflow into the Cheboksary Reservoir, and (2) whether there is any noticeable improvement in probabilistic forecasts when using the WG-simulated ensemble compared to the ESP-based ensemble. We have found that for a 35-year period beginning from the reservoir filling in 1982, both continuous and binary model-based ensemble forecasts (issued in the deterministic form) outperform the operational forecasts of the April–June inflow volume actually used and, additionally, provide acceptable forecasts of additional water regime characteristics besides the inflow volume. We have also demonstrated that the model performance measures (in the verification period) obtained from the WG-based probabilistic forecasts, which are based on a large number of possible weather scenarios, appeared to be more statistically reliable than the corresponding measures calculated from the ESP-based forecasts based on the observed weather scenarios.

Visualization of Time-Varying Weather Ensembles across Multiple Resolutions.

Uncertainty quantification in climate ensembles is an important topic for the domain scientists, especially for decision making in the real-world scenarios. With powerful computers, simulations now produce time-varying and multi-resolution ensemble data sets. It is of extreme importance to understand the model sensitivity given the input parameters such that more computation power can be allocated to the parameters with higher influence on the output. Also, when ensemble data is produced at different resolutions, understanding the accuracy of different resolutions helps the total time required to produce a desired quality solution with improved storage and computation cost. In this work, we propose to tackle these non-trivial problems on the Weather Research and Forecasting (WRF) model output. We employ a moment independent sensitivity measure to quantify and analyze parameter sensitivity across spatial regions and time domain. A comparison of clustering structures across three resolutions enables the users to investigate the sensitivity variation over the spatial regions of the five input parameters. The temporal trend in the sensitivity values is explored via an MDS view linked with a line chart for interactive brushing. The spatial and temporal views are connected to provide a full exploration system for complete spatio-temporal sensitivity analysis. To analyze the accuracy across varying resolutions, we formulate a Bayesian approach to identify which regions are better predicted at which resolutions compared to the observed precipitation. This information is aggregated over the time domain and finally encoded in an output image through a custom color map that guides the domain experts towards an adaptive grid implementation given a cost model. Users can select and further analyze the spatial and temporal error patterns for multi-resolution accuracy analysis via brushing and linking on the produced image. In this work, we collaborate with a domain expert whose feedback shows the effectiveness of our proposed exploration work-flow.

Advances in Space Weather Ensemble Forecasting

Key Points Space Weather forecasting is advancing Ensemble forecasting is an important development More than 15 ensemble forecasting papers have published in SWE

Impact of Ensemble Size on Forecasting Occurrence of Rainfall Using TIGGE Precipitation Forecasts

AbstractHighly uncertain precipitation input greatly reduces the overall reliability of rainfall-runoff modeling for estimating stream flows, which in turn forms the basis for decisions leading towards sustainable management of water resources. Probabilistic weather forecasting using multiple numerical weather prediction (NWP) models known as ensemble prediction system (EPS), has gained popularity amongst meteorologists in recent years. However, keeping in mind the computational burden and complexities of hydrologic modeling, scientists are constrained to use a limited number of ensemble members to drive the operational river flow forecasting systems. In this paper, 12 weather ensembles of different size are evaluated for their performance in forecasting occurrence of precipitation over a wet month in a large New Zealand catchment by comparing them against point observations of the corresponding events at rain gauge stations. The authors investigate various configurations of 12-h accumulated precipitation...

Neural Network Load Forecasting with Weather Ensemble Predictions

In recent years, a large literature has evolved on the use of artificial neural networks (NNs) for electric load forecasting. NNs are particularly appealing because of their ability to model an unspecified non-linear relationship between load and weather variables. Weather forecasts are a key input when the NN is used for forecasting. This study Investigates the use of weather ensemble predictions in the application of NNs to load forecasting for lead times from 1 to 10 days ahead. A weather ensemble prediction consists of multiple scenarios for a weather variable. We use these scenarios to produce multiple scenarios for load. The results show that the average of the load scenarios is a more accurate load forecast than that produced using traditional weather forecasts. We use the load scenarios to estimate the uncertainty in the NN load forecast This compares favourably with estimates based solely on historical load forecast errors.

Comparison of traditional and manufactured cold weather ensembles

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 05:83-90 (1995) - DOI: https://doi.org/10.3354/cr005083 Comparison of traditional and manufactured cold weather ensembles Oakes J, Wilkins H, Riewe R, Kelker D, Forest T ABSTRACT: Inuit elders stress the importance of wearing caribou skin clothing for winter trips. Although laboratory tests for thermal insulation values of clothing are extensive, caribou skin clothing is rarely tested. The purpose of this research was to compare the thermal comfort of caribou skin clothing, military winter issue clothing, and northern expedition clothing using experimental and ethnographic methods. To collect the experimental data, each subject wore the ensembles in an environmental chamber designed to simulate travelling on a sled pulled by a snowmobile in the Arctic winter. Skin temperatures and comfort ratings were analyzed using analysis of variance and multiple regression methods. Ethnographic methods were used to collect 'traditional' knowledge from Inuit elders. Findings indicate that the average skin temperature and comfort ratings dropped significantly less (p < 0.05) when wearing the caribou skin ensemble compared to changes observed when wearing the military or expedition clothing ensembles. There were no significant differences between the military and expedition clothing ensembles. Inuit elders use years of field experiences as a base for their recommendations to use caribou skin clothing for long-term protection for sedentary individuals exposed to a cold winter climate. They also point out the advantages of specific style features and materials for cold weather clothing. In conclusion, the combination of laboratory tests and traditional knowledge has given 2 distinct perspectives which provide a more holistic answer to cold weather clothing needs for people travelling during the Arctic winter. KEY WORDS: Cold weather clothing · Inuit clothing · Skin clothing . Caribou clothing · Contemporary cold weather fabrics · Scientific and traditional knowledge · Thermal comfort Full text in pdf format PreviousNextExport citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 05, No. 1. Publication date: February 23, 1995 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 1995 Inter-Research.

Problems and prospects in long and medium range weather forecasting

We investigate the ability to improve flood inundation forecasts at short- to medium-range (1–7 days) timescales through weather ensembles and statistical water surface elevation (WSE) postprocessing. To generate the flood inundation forecasts, a one-dimensional hydraulic model, namely the Hydrologic Engineering Center’s River Analysis System (HEC-RAS), is coupled to a regional hydrological ensemble prediction system (RHEPS). The RHEPS is comprised of: i) hydrometeorological observations; ii) weather ensembles from the National Centers for Environmental Prediction Global Ensemble Forecast System Reforecast version 2 (GEFSRv2); and iii) Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) as the hydrological model. The coupled RHEPS-hydraulic system is evaluated along the tidal Delaware River near the City of Philadelphia, Pennsylvania, U.S. For the evaluation, emphasis is placed on the tidal-riverine transitional zone of the Delaware River and the downstream propagation of hydrometeorological uncertainty through the flood inundation forecasts. The coupled system is used to generate hourly flood inundation forecasts at lead times from 1 to 7 days, over the period 2008–2013. Additionally, WSEs from the coupled system are statistically postprocessed using quantile regression (QR). Results show that the raw flood inundation ensemble forecasts exhibit higher skill than the deterministic ones. We also find that statistical postprocessing improves the skill of the raw flood inundation ensemble forecasts, with greater improvements at the longer lead times (>3 days). Overall, we find that both weather ensembles and statistical WSE postprocessing can be used to enhance the skill of flood inundation forecasts at short- to medium-range timescales. In turn, this may serve to enhance the spatial representation of flood forecasts several days in advance, which could contribute in the future to making flood forecast communication and warnings more effective.