North American Regional Reanalysis Research Articles

Gulf Stream Effects on Sea Level Oscillations: Enhancing Performance of a Coastal and Estuarine Model Nested into Global Model through Modified Boundary Conditions

This study investigates the effects of the gulf stream (GS) on sea-level oscillations across various time scales and assesses the performance of a coastal and estuarine model nested within a global model in simulating these variations. It aims to improve boundary conditions to simulate sea-level oscillations more accurately by considering the influence of GS flow. An inverse correlation is observed between observed sea-level oscillation and GS flow, which becomes more pronounced over longer time scales. Using Delft3D, a high-resolution coastal and estuarine model is developed to simulate circulation dynamics in the central Indian River Lagoon (IRL), FL, and adjacent coastal areas on the Florida east coast. The model is nested into the HYCOM (Hybrid Coordinate Ocean Model), and meteorological forcings are derived from the NARR (North American Regional Reanalysis) model. The model demonstrates satisfactory performance across key parameters, including tide, salinity, water temperature, and currents. However, there remains a noticeable difference between the modeled and observed data. To address this, the model is executed with modified flow boundary conditions at eastern boundary nodes, integrating HYCOM tide, and observing low-frequency sea-level variations. The implementation of the new boundary conditions results in an improved simulation of sea-level oscillations. This study presents the conceptual framework and detailed methodologies employed in the creation of a high-resolution model tailored for estuarine and coastal areas nested into global models capable of satisfactorily simulating sea-level oscillations even when the global model does not represent GS effects.

Western Montana Snow Squall Climatology and Synoptic Patterns, 2000–2020

The rapid onset of whiteout conditions, from strong winds, blowing snow, and heavy snow, associated with snow squalls often results in hazardous travel conditions. Snow squalls bring pronounced impacts to the transportation sector, leading to accidents, fatalities, and significant travel delays. Given these impacts, historical frequencies, and the synoptic environments associated with snow squalls in western Montana were examined to develop forecast aides, primarily through synoptic pattern recognition, for meteorologists to identify and communicate snow squalls. A 20-yr climatology was constructed to identify events characterized by wind gusts ≥26 kt (30 mph) and visibility ≤0.8 km (0.5 mi). 74 snow squall events were identified from 2000 through 2020 across three ASOS sites: Missoula (KMSO), Kalispell (KGPI), and Butte (KBTM). The median duration of heavy (moderate) snow during snow squalls was 26 (34) min. A composite study utilizing these events was conducted using the NCEP North American Regional Reanalysis. The composite study along with surface observations, revealed three distinct snow squall patterns and types: orographic snow bands, Pacific cold fronts, and Canadian arctic fronts.

Urban water and electricity demand data for understanding climate change impacts on the water-energy nexus

As the climate crisis intensifies, it is becoming increasingly important to conduct research aimed at fully understanding the climate change impacts on various infrastructure systems. In particular, the water-electricity demand nexus is a growing area of focus. However, research on the water-electricity demand nexus requires the use of demand data, which can be difficult to obtain, especially across large spatial extents. Here, we present a dataset containing over a decade (2007–2018) of monthly water and electricity consumption data for 46 major US cities (2018 population >250,000). Additionally, we include pre-processed climate data from the North American Regional Reanalysis (NARR) to supplement studies on the relationship between the water-electricity demand nexus and the local climate. This data can be used for a number of studies that require water and/or electricity demand data across long time frames and large spatial extents. The data can also be used to evaluate the possible impacts of climate change on the water-electricity demand nexus by leveraging the relationship between the observed values.

The short-term effect of ozone on pregnancy loss modified by temperature: Findings from a nationwide epidemiological study in the contiguous United States

BackgroundPregnancy loss, a major health issue that affects human sustainability, has been linked to short-term exposure to ground-surface ozone (O3). However, the association is inconsistent, possibly because of the co-occurrence of O3 and heat episodes, as increased temperature is a risk factor for pregnancy loss. To explain this inconsistency, the effect of O3 on pregnancy loss needs to be examined jointly with that of high temperature. MethodsA total of 247,305 pregnancy losses during the warm season were extracted from fetal death certificates from the 386 counties in contiguous United States from 1989 to 2005. We assessed environmental exposure based on the daily maximum 8 h average of O3 from Air Quality System monitors and the 24 h average temperature from the North American Regional Reanalysis product. We conducted a bidirectional, time-stratified case-crossover study of the association between pregnancy loss and exposures to O3 and temperature and their multiplicative interaction. The main time window for the exposure assessment was the day of case occurrence and the preceding 3 days. To estimate the association, we used conditional logistic regression with adjustment for relative humidity, height of the planetary boundary layer, and holidays. Sensitivity analyses were performed on the lagged structure, nonlinearity, and between-subpopulation heterogeneity of the estimated joint effect. ResultsThe joint effect was first estimated by the regression against categorical exposure by tertile. Compared to the low-low exposure group (O3 ≤ 78 μg/m3 and temperature ≤ 18 °C), the odds of pregnancy loss was significantly higher by 6.0 % (95 % confidence interval [CI] 2.4–9.7 %), 9.8 % (6.1–13.8 %), and 7.5 % (4.7–10.3 %) in the high-low (>104 μg/m3 and ≤18 °C), low-high (≤78 μg/m3 and >23 °C), and high-high (>104 μg/m3 and >23 °C) groups. The model of linear exposure and the multiplicative interaction yielded similar results. Each increment of 10 μg/m3 in O3 and 1 °C in temperature was associated with a 3.0 % (2.0 %–4.0 %) and 3.9 % (3.5 %–4.3 %), respectively, increase in the odds of pregnancy loss. A decrease in odds of 0.2 % (0.1 %–0.2 %) was associated with the temperature × O3 interaction. The finding of an antagonistic interaction between temperature and O3 was confirmed by models parametrizing the joint exposure as alternative nonlinear terms (i.e., a two-dimensional spline term or a varying-coefficient term) and was robust to a variety of exposure lags and stratifications. Therefore, the marginal effect of O3 was estimated to vary by climate zone. A significant association between O3 and pregnancy loss was observed in the northern, but not southern, United States. ConclusionJoint exposure to O3 and high temperature can increase the risk for pregnancy loss. The adverse effect of O3 is potentially modified by ambient temperature. In high-latitude cities, controlling for O3 pollution could protect maternal health.

Evaluating Medium-Range Forecast Performance of Regional-Scale Circulation Patterns

Abstract Accurate subseasonal-to-seasonal (S2S) weather forecasts are crucial to making important decisions in many sectors. However, significant gaps exist between the needs of society and what forecasters can produce, especially at weekly and longer lead times. We hypothesize that by clustering atmospheric states into a number of predefined categories, the noise can be reduced and, consequently, medium-range forecasts can be improved. Self-organizing map (SOM)-based clustering was used on daily mean sea level pressure (MSLP) data from the North American Regional Reanalysis to categorize the synoptic-scale circulation for eastern North America from 1979 to 2016 into 28 discrete patterns. Then, using two goodness-of-fit metrics, the relative skill of four different forecasting methods over a 90-day lead time was studied: 1) a circulation pattern (CP) forecast, 2) raw forecast output from the Climate Forecast System (CFS) operated by the National Centers for Environmental Prediction (NCEP), 3) a simple climatology forecast, and 4) a simple persistence forecast. As expected, forecast skill of both the CP forecast and the raw CFS forecast generally decreased rapidly from the first day, coming to parity with the skill of climatology after 10–12 days when using correlation, and at 7–10 days when using the root-mean-square error (RMSE). Most importantly, this study found that the CP forecast was the most skillful forecast method over the 8–11-day lead time when using RMSE. On a spatial basis, the skill of the CP forecast and the raw CFS decreases latitudinally from north to south. This study thus demonstrates the potential utility of categorical or circulation pattern–based forecasting at 1–2-week lead times.

Construction of a Real-Time Forecast Model for Coastal Engineering and Processes Nested in a Basin Scale Model

A numerical model was developed using Delft3D to simulate the circulation dynamics in Port Everglades, FL, and the adjacent coastal area. The model was nested within the HYCOM (Hybrid Coordinate Ocean Model), while meteorological data were obtained from the NARR (North American Regional Reanalysis) model. To evaluate the model, model outputs were compared with observed data from the NOAA. Calibration experiments were conducted on the model parameters, including the bottom friction, wind forcings, and vertical layer specification. These experiments revealed that implementing a 10-layer model slightly improved the vertical stratification, while the utilization of 2-D wind data resulted in more pronounced surface layer characteristics in temperature and velocity profiles and employing moderate values of the Chezy coefficient produced optimal outcomes for the bottom roughness parameter. The model demonstrated satisfactory performance across major parameters, including water level, salinity, temperature, and currents. A real-time forecast system has been constructed with this nested model, providing up to 3-day forecasts that are updated daily. To facilitate automated forecasting without manual intervention, an automation system has been developed using a combination of bash, MATLAB, and Python scripts. This study provides a comprehensive documentation of the concepts and detailed methods involved in developing a real-time forecast model for estuarine and coastal regions.

Meteorological data source comparison—a case study in geospatial modeling of potential environmental exposure to abandoned uranium mine sites in the Navajo Nation

Meteorological (MET) data is a crucial input for environmental exposure models. While modeling exposure potential using geospatial technology is a common practice, existing studies infrequently evaluate the impact of input MET data on the level of uncertainty on output results. The objective of this study is to determine the effect of various MET data sources on the potential exposure susceptibility predictions. Three sources of wind data are compared: The North American Regional Reanalysis (NARR) database, meteorological aerodrome reports (METARs) from regional airports, and data from local MET weather stations. These data sources are used as inputs into a machine learning (ML) driven GIS Multi-Criteria Decision Analysis (GIS-MCDA) geospatial model to predict potential exposure to abandoned uranium mine sites in the Navajo Nation. Results indicate significant variations in results derived from different wind data sources. After validating the results from each source using the National Uranium Resource Evaluation (NURE) database in a geographically weighted regression (GWR), METARs data combined with the local MET weather station data showed the highest accuracy, with an average R2 of 0.74. We conclude that local direct measurement-based data (METARs and MET data) produce a more accurate prediction than the other sources evaluated in the study. This study has the potential to inform future data collection methods, leading to more accurate predictions and better-informed policy decisions surrounding environmental exposure susceptibility and risk assessment.

Cut-cell Eta Model: History and Challenges Overcome

Incentive for writing a limited area weather prediction model stemmed from the author’s several years stay at the University of California in Los Angeles, at the end of the sixties. Exposed to what he refers to as the Akio Arakawa approach, having had an idea for a scheme that was an improvement to what Arakawa was using, and being aware of the importance of topography for the weather of the country he was to continue his career in, led in 1973 to his first limited area 3D code, the forerunner of what was to become the Eta model. Refinements and enhancements introduced by the author in subsequent years and of the collaborator he acquired, Zaviša Janjić, resulted in the code that when installed at the then U.S. National Meteorological Center, attracted attention. Hallmarks of the model were Mesinger’s eta vertical coordinate, and Janjić’s transformation of the Arakawa horizontal advection scheme to the model’s semi-staggered B/E grid. In 1993 the Eta became the primary regional forecasting model of the U.S. Weather Bureau, and in 1998 its precipitation accuracy of 24-48 h forecasts became higher across all intensity thresholds than that of its predecessor, the Nested Grid Model (NGM) for its 00-24 h forecasts. Lately, the Eta is extensively used also as a regional climate model (RCM), mostly over the South American domain, and in near-real time as a tool for the North American Regional Reanalysis (NARR), run by the U.S. National Centers for Environmental Prediction/Climate Prediction Center. Several later unique numerical refinements of the Eta addressing problems noticed are summarized in a “before and after” fashion, and results are mentioned of its ensemble skill compared to that of its highly acclaimed driver European Centre for Medium-Range Weather Forecasts (ECMWF) model.

Atmospheric dust intrusions from the peninsular region into the Northern Gulf of California: case study

Through atmospheric transport, coming from the mainland, an important amount of dust arrives to surface waters of open-ocean regions and many coastal zones. Semi-arid and desert areas tend to act as important sources of dust. The Gulf of California (GC) is one of the most productive marginal seas of the world, and it is surrounded by the semi-arid deserts located in Baja California Peninsula, United States, and Sonora (Mexico). Dust fluxes coming from the BCP during the summer are relevant due to biological impact on the California Gulf. In the northern region of the BCP, meteorological events have not been well studied. These events are known in popular slang as "El Torito". The characterization of a dust event that originated in the Baja California Peninsula (BCP) on June 14, 2016, is presented. Meteorological variables (from North American Regional Reanalysis -NARR), daily sea surface temperature, and aerosol optical thickness (from MODIS) are used to explain the dust event genesis, are used. Results suggest that sea surface temperature fluctuations in the Pacific Ocean off the coast of the BCP generate changes in hydrometeorological variables (wind, relative humidity, and atmospheric pressure) that can give rise to dust events. The June 14 dust storm originates in the San Felipe desert and disperses aeolian to the northern Gulf of California, as shown by aerosol optical thickness images and trajectories simulated with the HYSPLIT model, which establishes vertical distributions with predominant heights between 0 and 500 m.

Interannual, sub-seasonal and spatial variations in growing season surface heat fluxes for the U.S. Corn Belt

Spatiotemporal variations in surface latent (LE) and sensible (H) heat fluxes from croplands and patches of remnant deciduous forest in the Midwest U.S. Corn Belt have been proposed to trigger “nonclassical mesoscale circulations” (NCMCs) in the atmosphere's boundary layer and moist convection, under suitable synoptic conditions. However, prior to evaluating the link between land use and land cover (LULC), NCMCs, and precipitation for the Corn Belt, it is necessary to better understand the interannual, sub-seasonal, and spatial variations in surface heat fluxes. Here, a nine-year warm season “climatology” (1999–2007) of daily afternoon and early evening averages of LE and H from two flux towers, representative of cropland (Bondville, IL) and deciduous forest (Morgan Monroe State Forest, IN), is analyzed for sub-seasonal and interannual variations in the convective fluxes. Wilcoxon signed rank tests show significant growing season and sub-seasonal anomalies in LE and H at both flux tower sites. Structural equation modeling relates these inter-growing season anomalies in LE and H to variations in net radiation, vapor pressure deficit, and temperature, while MODIS Normalized Difference Vegetation Index data link some of the sub-seasonal variations in the surface energy balance to phenology at the start of the growing season. Additional composite analysis of LE and H from the North American Regional Reanalysis shows characteristic spatial heterogeneities and significant differences in LE and H for different synoptic circulation patterns related to the variations in net radiation and LULC. These results highlight the importance of considering the sub-seasonal and spatial variations in surface heat fluxes of the Corn Belt for the range of synoptic conditions, within the context of this region's recent interannual climate variations and will help inform the search for LULC-influenced moist convection.

The Hydrometeorology of Extreme Floods in the Lower Mississippi River

Abstract The Lower Mississippi River has experienced a cluster of extreme floods during the past two decades. The Bonnet Carré spillway, which is located on the Mississippi River immediately upstream of New Orleans, has been operated 15 times since its completion in 1931, with 7 occurrences after 2008. In this study, we examine rainfall and atmospheric water balance components associated with Lower Mississippi River flooding in 2008, 2011, and 2015–19. We focus on multiple time scales—1, 3, 7, and 14 days—reflecting contributions from individual storm systems and the aggregate contributions from a sequence of storm systems. Atmospheric water balance variables—integrated water vapor flux (IVT) and precipitable water—are central to our assessment of the storm environment for Lower Mississippi flood events. We find anomalously large IVT corridors accompany the critical periods of heavy rainfall and are organized in southwest–northeast orientation over the Mississippi domain. Atmospheric rivers play an important role as agents of extremes in water vapor flux and rainfall. We conduct climatological analyses of IVT and precipitable water extremes across the four time scales using 40 years of North American Regional Reanalysis (NARR) fields from 1979 to 2018. We find significant increasing trends in both variables at all time scales. Increases in IVT especially cover large regions of the Mississippi domain. The findings point to increased vulnerability faced by the Mississippi flood control system in the current and future climate.

Estimates of the spatially complete, observational-data-driven planetary boundary layer height over the contiguous United States

Abstract. This study aims to generate a spatially complete planetary boundary layer height (PBLH) product over the contiguous United States (CONUS). An eXtreme Gradient Boosting (XGB) regression model was developed using selected meteorological and geographical data fields as explanatory variables to fit the PBLH values derived from Aircraft Meteorological DAta Relay (AMDAR) reports hourly profiles at 13:00–14:00 LST (local solar time) during 2005–2019. A preprocessing step was implemented to exclude AMDAR data points that were unexplainable by the predictors, mostly under stable conditions. The PBLH prediction by this work as well as PBLHs from three reanalysis datasets (the fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis of the global climate – ERA5; the Modern-Era Retrospective analysis for Research and Applications, Version 2 – MERRA-2; and the North American Regional Reanalysis – NARR) were compared to reference PBLH observations from spaceborne lidar (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations, CALIPSO), airborne lidar (High Spectral Resolution Lidar, HSRL), and in situ research aircraft profiles from the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) campaigns. Compared with PBLHs from reanalysis products, the PBLH prediction from this work shows closer agreement with the reference observations, with the caveat that different PBLH products and estimates have different ways of identifying the PBLH; thus, their comparisons should be interpreted with caution. The reanalysis products show significant high biases in the western CONUS relative to the reference observations. One direct application of the dataset generated by this work is that it enables sampling of the PBLH at the sounding locations and times of sensors aboard satellites with an overpass time in the early afternoon, e.g., the Afternoon Train (A-train), the Suomi National Polar-orbiting Partnership (Suomi NPP), the Joint Polar Satellite System (JPSS), and the Sentinel-5 Precursor (Sentinel-5P) satellite sensors. As both AMDAR and ERA5 are continuous at hourly resolution, the observational-data-driven PBLHs may be extended to other daytime hours.

Random Forest Approach for Improving Nonconvective High Wind Forecasting across Southeast Wyoming

Abstract High winds are one of the key forecast challenges across southeast Wyoming. The complex mountainous terrain across the region frequently results in strong gap winds in localized areas, as well as more widespread bora and chinook winds in the winter season (October–March). The predictors and general weather patterns that result in strong winds across the region are well understood by local forecasters. However, no single predictor provides notable skill by itself in separating warning-level events from others. Random forest (RF) classifier models were developed to improve upon high wind prediction using a training dataset constructed of archived observations and model parameters from the North American Regional Reanalysis (NARR). Three locations were selected for initial RF model development, including the city of Cheyenne, Wyoming, and two gap regions along Interstate 80 (Arlington) and Interstate 25 (Bordeaux). Verification scores over two winters suggested the RF models were beneficial relative to current operational tools when predicting warning-criteria high wind events. Three case studies of high wind events provide examples of the RF models’ effectiveness to forecast operations over current forecast tools. The first case explores a classic, widespread high wind scenario, which was well anticipated by local forecasters. A more marginal scenario is explored in the second case, which presented greater forecast challenges relating to timing and intensity of the strongest winds. The final case study carefully uses Global Forecast System (GFS) data as input into the RF models, further supporting real-time implementation into forecast operations.

Modelling snowpack bulk density using snow depth, cumulative degree‐days, and climatological predictor variables

AbstractSnowpack water equivalent (SWE) is a key variable for water resource management in snow‐dominated catchments. While it is not feasible to quantify SWE at the catchment scale using either field surveys or remotely sensed data, technologies such as airborne LiDAR (light detection and ranging) support the mapping of snow depth at scales relevant to operational water management. To convert snow depth to water equivalent, models have been developed to predict SWE or snowpack density based on snow depth and additional predictor variables. This study builds upon previous models that relate snowpack density to snow depth by including additional predictor variables to account for (1) long‐term climatologies that describe the prevailing conditions influencing regional snowpack properties, and (2) the effect of intra‐ and inter‐year variability in meteorological conditions on densification through a cumulative degree‐day index derived from North American Regional Reanalysis products. A non‐linear model was fit to 114 506 snow survey measurements spanning 41 years from 1166 snow courses across western North America. Under spatial cross‐validation, the predicted densities had a root‐mean‐square error of 47.1 kg m−3, a mean bias of −0.039 kg m−3, and a Nash‐Sutcliffe Efficiency of 0.70. The model developed in this study had similar overall performance compared to a similar regression‐based model reported in the literature, but had reduced seasonal biases. When applied to predict SWE from simulated depths with random errors consistent with those obtained from LiDAR or Structure‐from‐Motion, 50% of the SWE estimates for April and May fell within −45 to 49 mm of the observed SWE, representing prediction errors of −15% to 20%.

Analysis of the critical components of flash drought using the standardized evaporative stress ratio

Flash droughts develop rapidly (∼1 month timescale) and produce significant ecological, agricultural, and socioeconomical impacts. Recent advances in our understanding of flash droughts have resulted in methods to identify and quantify flash drought events. However, few studies have been done to isolate the individual rapid intensification and drought components of flash drought, which could further determine their causes, evolution, and predictability. This study utilized the standardized evaporative stress ratio (SESR) to quantify individual components of flash drought from 1979 – 2019, using evapotranspiration (ET) and potential evapotranspiration (PET) data from the North American Regional Reanalysis (NARR) dataset. The temporal change in SESR was utilized to quantify the rapid intensification component of flash drought. The drought component was also determined using SESR and compared to the United States Drought Monitor. The results showed that SESR was able to represent the spatial coverage of drought well for regions east of the Rocky Mountains. Furthermore, the rapid intensification component agreed well with previous flash drought studies, with the overall climatology of rapid intensification events showing similar hotspots to the flash drought climatology east of the Rocky Mountains. The rapid intensification climatology suggested areas west of the Rocky Mountains experience rapid drying more often than east of the Rocky Mountains.

Assessment of Model-Derived Mass Flux Relative to Satellite Gravimetry via a Water Balance in North America

Abstract GRACE and GRACE Follow-On (GRACE-FO) mission data are utilized to assess mass flux derived from the North American Regional Reanalysis (NARR) and the NLDAS-2 Noah land surface model via multiple water balance formulations. Water balances are computed for 18 medium size basins in North America at the USGS Watershed Boundary Dataset HU2 level over the span of the GRACE and GRACE-FO missions (2002–21). Performance of model-derived mass flux is presented in the context of statistical agreement to changes in terrestrial water storage (ΔTWS) derived from Center for Space Research (CSR) GRACE RL06 mass concentrations (mascons), and GRACE and NARR uncertainty is estimated against comparable datasets. The land surface water balance method utilizing NLDAS-2 Noah consistently outperforms the total column method utilizing NARR, which is likely due to enhanced precipitation forcing and an updated Noah model version used in NLDAS-2. The surface approach to the calculation of atmospheric moisture flux divergence is carried through the presented analyses and is demonstrated to be comparable in performance to the more common volume approach. Mass balance methodology, basin characteristics, and ΔTWS signal characteristics are assessed to quantify effects on model performance and while factors such as basin size, basin average topography gradient, and ΔTWS annual amplitude are shown to have a measurable effect on model performance, no single factor exhibited a dominant or consistent effect. Drought conditions are shown to have a significant temporally localized effect on model-derived mass flux accuracy, with NARR being particularly susceptible to this effect. Significance Statement Measurements of Earth’s gravity field from the GRACE and GRACE-FO satellite missions are utilized to create estimates of water storage changes in 18 North American river basins that are compared to changes in water storage calculated from an atmospheric model reanalysis (NARR) and a land surface model (NLDAS-2 Noah). The resulting comparison demonstrates that certain basin characteristics can have a slight effect on model accuracy, while climatic conditions such as drought can have a major impact on model accuracy. This work provides useful quantification of when and where modeled water transport loses accuracy, which is integral to our understanding of the present and future distribution of this crucial resource and the natural processes that affect it.

Offshore reanalysis wind speed assessment across the wind turbine rotor layer off the United States Pacific coast

Abstract. The California Pacific coast is characterized by considerable wind resource and areas of dense population, propelling interest in offshore wind energy as the United States moves toward a sustainable and decarbonized energy future. Reanalysis models continue to serve the wind energy community in a multitude of ways, and the need for validation in locations where observations have been historically limited, such as offshore environments, is strong. The U.S. Department of Energy (DOE) owns two lidar buoys that collect wind speed observations across the wind turbine rotor layer along with meteorological and oceanographic data near the surface to characterize the wind resource. Lidar buoy data collected from recent deployments off the northern California coast near Humboldt County and the central California coast near Morro Bay allow for validation of commonly used reanalysis products. In this article, wind speeds from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), the Climate Forecast System version 2 (CFSv2), the North American Regional Reanalysis (NARR), the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5), and the analysis system of the Rapid Refresh (RAP) are validated at heights within the wind turbine rotor layer ranging from 50 to 100 m. The validation results offer guidance on the performance and uncertainty associated with utilizing reanalyses for offshore wind resource characterization, providing the offshore wind energy community with information on the conditions that lead to reanalysis error. At both California coast locations, the reanalyses tend to underestimate the observed rotor-level wind resource. Occasions of large reanalysis error occur in conjunction with stable atmospheric conditions, wind speeds associated with peak turbine power production (> 10 m s−1), and mischaracterization of the diurnal wind speed cycle in summer months.

Application of Artificial Intelligence Models for Aeolian Dust Prediction at Different Temporal Scales: A Case with Limited Climatic Data

Accurately predicting ambient dust plays a crucial role in air quality management and hazard mitigation. Dust emission is a complex, non-linear response to several climatic variables. This study explores the accuracy of Artificial Intelligence (AI) models: an adaptive-network-based fuzzy inference system (ANFIS) and a multi-layered perceptron artificial neural network (mlp-NN), over the Southwestern United States (SWUS), based on the observed dust data from IMPROVE stations. The ambient fine dust (PM2.5) and coarse dust (PM10) concentrations on monthly and seasonal timescales from 1990–2020 are modeled using average daily maximum wind speed (W), average precipitation (P), and average air temperature (T) available from the North American Regional Reanalysis (NARR) dataset. The model’s performance is measured using correlation (r), root mean square error (RMSE), and percentage bias (% BIAS). The ANFIS model generally performs better than the mlp-NN model in predicting regional dustiness over the SWUS region, with r = 0.77 and 0.83 for monthly and seasonal fine dust, respectively. AI models perform better in predicting regional dustiness on a seasonal timescale than the monthly timescale for both fine dust and coarse dust. AI models better predict fine dust than coarse dust on both monthly and seasonal timescales. Compared to precipitation, air temperature is the more important predictor of regional dustiness on both monthly and seasonal timescales. The relative importance of air temperature is higher on the monthly timescale than the seasonal timescale for PM2.5 and vice versa for PM10. The findings of this study demonstrate that the AI models can predict monthly and seasonal fine and coarse dust, based on the limited climatic data, with good accuracy and with potential implications for research in data sparse regions.

Modelling glacier mass balance and climate sensitivity in the context of sparse observations: application to Saskatchewan Glacier, western Canada

Abstract. Glacier mass balance models are needed at sites with scarce long-term observations to reconstruct past glacier mass balance and assess its sensitivity to future climate change. In this study, North American Regional Reanalysis (NARR) data were used to force a physically based, distributed glacier mass balance model of Saskatchewan Glacier for the historical period 1979–2016 and assess its sensitivity to climate change. A 2-year record (2014–2016) from an on-glacier automatic weather station (AWS) and historical precipitation records from nearby permanent weather stations were used to downscale air temperature, relative humidity, wind speed, incoming solar radiation and precipitation from the NARR to the station sites. The model was run with fixed (1979, 2010) and time-varying (dynamic) geometry using a multitemporal digital elevation model dataset. The model showed a good performance against recent (2012–2016) direct glaciological mass balance observations as well as with cumulative geodetic mass balance estimates. The simulated mass balance was not very sensitive to the NARR spatial interpolation method, as long as station data were used for bias correction. The simulated mass balance was however sensitive to the biases in NARR precipitation and air temperature, as well as to the prescribed precipitation lapse rate and ice aerodynamic roughness lengths, showing the importance of constraining these two parameters with ancillary data. The glacier-wide simulated energy balance regime showed a large contribution (57 %) of turbulent (sensible and latent) heat fluxes to melting in summer, higher than typical mid-latitude glaciers in continental climates, which reflects the local humid “icefield weather” of the Columbia Icefield. The static mass balance sensitivity to climate was assessed for prescribed changes in regional mean air temperature between 0 and 7 ∘C and precipitation between −20 % and +20 %, which comprise the spread of ensemble Representative Concentration Pathway (RCP) climate scenarios for the mid (2041–2070) and late (2071–2100) 21st century. The climate sensitivity experiments showed that future changes in precipitation would have a small impact on glacier mass balance, while the temperature sensitivity increases with warming, from −0.65 to −0.93 m w.e. a−1 ∘C−1. The mass balance response to warming was driven by a positive albedo feedback (44 %), followed by direct atmospheric warming impacts (24 %), a positive air humidity feedback (22 %) and a positive precipitation phase feedback (10 %). Our study underlines the key role of albedo and air humidity in modulating the response of winter-accumulation type mountain glaciers and upland icefield-outlet glacier settings to climate.

A Comprehensive Approach for Floodplain Mapping through Identification of Hazard Using Publicly Available Data Sets over Canada

Quantifying flood inundation and hazards over large regions is paramount for gaining critical information on flood risk over the vulnerable population and environment. Readily available global data and enhancement in computational simulations have made it easier to simulate flooding at a large scale. This study explores the usability of publicly available datasets in flood inundation and hazard mapping, and ensures the flood-related information reaches the end-users efficiently. Runoff from the North American Regional Reanalysis and other relevant inputs are fed to the CaMa-Flood model to generate flooding patterns for 1 in 100 and 1 in 200-year return period events over Canada. The simulated floodplain maps are overlaid on the property footprints of 34 cities (falling within the top 100 populated cities of Canada) to determine the degree of exposure during 1991, 2001 and 2011. Lastly, Flood Map Viewer—a web-based public tool, is developed to disseminate extensive flood-related information. The development of the tool is motivated by the commitment of the Canadian government to contribute $63 M over the next three years for the development of flood maps, especially in high-flood risk areas. The results from the study indicate that around 80 percent of inundated spots belong to high and very-high hazard classes in a 200-year event, which is roughly 4 percent more than observed during the 100-year event. We notice an increase in the properties exposed to flooding during the last three decades, with a signature rise in Toronto, Montreal and Edmonton. The flood-related information derived from the study can be used along with vulnerability and exposure components to quantify flood risk. This will help develop appropriate pathways for resilience building for long-term sustainable benefits.