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DOE OSTI2020

The Boreal Summer Madden–Julian Oscillation and Moist Convective Morphology over the Maritime Continent

Abstract While the boreal summer Madden–Julian oscillation (MJO) is commonly defined as a planetary-scale disturbance, the convective elements that constitute its cloud dipole exhibit pronounced variability in their morphology. We therefore investigate the relationship between the intraseasonal cloud anomaly of the MJO and the convective elements that populate its interior by simulating a boreal summer MJO event over the Maritime Continent using a cloud-resolving model. A progressive relationship between convective cell morphology and the MJO within the convectively enhanced region of the MJO was identified and characterized as follows: anomalously long-lasting cells in the initial phases, followed by an increased number of cells in the intermediate phases, progressing into more expansive cells in the terminal phases. A progressive relationship does not seem to exist within the convectively suppressed region of the MJO within the simulated domain, however. Within the convectively enhanced region of the MJO, the progressive relationship is partially explained by the evolution of bulk atmospheric characteristics, such as instability and wind shear. Positive midlevel moisture anomalies coincide with anomalously long-lasting convective cells, which is hypothesized to further cascade into an increase in convective cell volume, although variability in the number of convective cells seems to be related to an unidentified variable. This intraseasonal relationship between convective cell morphology and the boreal summer MJO within the Maritime Continent may have broader implications for the large-scale structure and evolution of the MJO, related to both convective moistening and cloud-radiative feedbacks.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Resolve the Clear-Sky Continuous Diurnal Cycle of High-Resolution ECOSTRESS Evapotranspiration and Land Surface Temperature

Although numerous evapotranspiration (ET) estimates have been developed from remote sensing measurements, a diurnally resolved product with high spatial resolution (<100 m) is still lacking, which is critically needed for agricultural and ecological monitoring. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) provides, for the first time, ET at 70 m resolution with diurnal samplings, enabling a step toward filling this data gap. This study developed the first framework to resolve the full diurnal cycle of ET and Land Surface Temperature (LST) from sporadic ECOSTRESS measurements. We first constructed 70 m diurnal LST utilizing a diurnal temperature cycle (DTC) model that fused ECOSTRESS and Geostationary Operational Environmental Satellite (GOES) LST. Next, we derived 70 m diurnal ET from the diurnally resolved LST, along with ancillary meteorological and reflectance data sets, using the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) algorithm. This framework was tested in two regions: A relatively homogeneous semiarid grassland landscape (centered at the US-Seg Ameriflux site) and a heterogeneous humid cropland landscape (centered at US-ARM). Our diurnally resolved LST and ET successfully reproduced the spatial patterns in the native ECOSTRESS measurements during overpasses for both landscapes (LST: r > 0.97, RMSE ~2°C; ET: r > 0.99, and RMSE = 1–7 W m -2 ). The constructed time series also well captured the temporal variability of the in situ measurements (especially under clear-sky conditions) at both US-Seg and US-ARM, albeit performance varying with availability and quality of ECOSTRESS samples. This study sets the stage for testing and applying our framework to broader climates, biomes, and landscapes toward eventually generating diurnally resolved 70 m global operational LST/ET products to enhance ecological/agricultural applications.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Evaluation of Mesoscale Convective Systems in Climate Simulations: Methodological Development and Results from MPAS-CAM over the U.S.

In this study, a process-oriented approach is developed to evaluate warm-season mesoscale convective system (MCS) precipitation and their favorable large-scale meteorological patterns (FLSMPs) over the U.S. This approach features a novel observation-driven MCS-tracking algorithm using infrared brightness temperature and precipitation feature at 12, 25 and 50 km resolution and metrics to evaluate the model large-scale environment favorable for MCS initiation. The tracking algorithm successfully reproduces the observed MCS statistics from a reference 4-km radar MCS database. To demonstrate the utility of the new methodologies in evaluating MCS in climate simulations with mesoscale resolution, the process-oriented approach is applied to two climate simulations produced by the Variable-Resolution Model for Prediction Across Scales coupled to the Community Atmosphere Model physics, with refined horizontal grid spacing at 50 km and 25 km over North America. With the tracking algorithm applied to simulations and observations at equivalent resolutions, the simulated number of MCS and associated precipitation amount, frequency and intensity are found to be consistently underestimated in the Central U.S., particularly from May to August. The simulated MCS precipitation shows little diurnal variation and lasts too long, while MCS precipitation area is too large and intensity is too weak. The model is able to simulate four types of observed FLSMP associated with frontal systems and low-level jets (LLJ) in spring, but the frequencies are underestimated because of low-level dry bias and weaker LLJ. Precipitation simulated under different FLSMPs peak during daytime, in contrast to the observed nocturnal peak. Implications of these findings for future model development and diagnostics are discussed.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Relationships among diurnal variations of polar night cloud, precipitation, surface temperatures, and the fair-weather return current of the global electric circuit (GEC)

Over the past five years (June 2017-current), the vertical electric field (E z ) as well as numerous cloud, precipitation and radiation properties have been monitored at the Department of Energy-Atmospheric Radiation Measurement (DOE ARM) North Slope of Alaska (NSA) field site. Comparisons between the composite diurnal averaged fair-weather E z , and composite ceilometer derived cloud base height during the polar night, reveal a significant correlation between the parameters (r = 0.62), supporting previous studies that there is high correlation between local electric field and cloud properties. With the use of extensive instrumentation at the site, such as the Micro Pulse Lidar (MPL), Ka-band Zenith Radar (KAZR), ceilometer, SKYRAD, Precipitation Imaging Package (PIP), among others, this study provides a more comprehensive examination of the diurnal cycle of cloud and precipitation properties along with the localized fair-weather return current of the larger Global Electric Circuit (GEC) system. Comparisons between the composite diurnal averaged fair-weather E z , and cloud thickness, maximum column backscatter, and precipitation particle counts all show similar diurnal variability during the polar night, indicating that during the largest magnitude fair weather E z time periods, clouds bases tend to be higher, clouds are thicker, have a larger column backscatter, and display more precipitating particles. Furthermore, a slight diurnal variability in the polar night surface temperature was found to be highly correlated (r = 0.87) to the longwave downwelling irradiance measured by SKYRAD, indicating that the variations in the physical properties of local clouds could modulate the diurnal polar night surface temperature variability on the order of 0.5 °C/day. In conclusion, these findings emphasize the importance and global nature of the GEC system, with the global aggregate of thunderstorms and electrified clouds potentially influencing polar night cloud properties as well as diurnal wintertime polar surface temperature variation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Improved constraints on hematite refractive index for estimating climatic effects of dust aerosols

Abstract Uncertainty in desert dust composition poses a big challenge to understanding Earth’s climate across different epochs. Of particular concern is hematite, an iron-oxide mineral dominating the solar absorption by dust particles, for which current estimates of absorption capacity vary by over two orders of magnitude. Here, we show that laboratory measurements of dust composition, absorption, and scattering provide valuable constraints on the absorption potential of hematite, substantially narrowing its range of plausible values. The success of this constraint is supported by results from an atmospheric transport model compared with station-based measurements. Additionally, we identify substantial bias in simulating hematite abundance in dust aerosols with current soil mineralogy descriptions, underscoring the necessity for improved data sources. Encouragingly, the next-generation imaging spectroscopy remote sensing data hold promise for capturing the spatial variability of hematite. These insights have implications for enhancing dust modeling, thus contributing to efforts in climate change mitigation and adaptation.

Environmental Sciences & Ecology↗

DOE OSTI2020

A Computationally Efficient Linear Semi-Lagrangian Scheme for the Advection of Microphysical Variables in Cloud-Resolving Models

An efficient semi-Lagrangian (SL) numerical scheme for the advection of positive-definite microphysical variables in cloud-resolving models is proposed. The scheme is a linear combination of SL schemes of the first and second order. The proposed scheme is compared with two high-order, monotonic, nonlinear Eulerian schemes, in two 2D tests. In the first test, advection of a positive scalar field with large gradients is performed within complicated idealized steering flows. In the second test, a hail storm is simulated using the 2D Hebrew University Cloud Model, describing the interactions between 15 size distributions of different microphysical quantities (each distribution is described by 43 mass bins). The proposed scheme is computationally efficient, has a low numerical diffusion and a high level of mass conservation accuracy, and preserves the sum of multiple advected variables as well as the linear relationships between the advected scalar variables. Although the proposed SL scheme is not exactly positive definite, the negative values and their number, which may appear only in the case of very strong gradients, are negligible. The scheme produces results similar to those of nonlinear Eulerian advection schemes while reducing computation time by approximately a factor of 10.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Intra-Specific Variability in Plant Hydraulic Parameters Inferred From Model Inversion of Sap Flux Data

Understanding plant hydraulic regulation is critical for predicting plant and ecosystem responses to projected increases in drought stress. Plant hydraulic regulation is controlled by observable, diverse plant hydraulic traits that can vary as much across individuals of the same species as they do across different species. Direct measurements of plant hydraulic traits from a range of ecosystems remain limited in comparison to other, more readily measured traits (e.g., specific leaf area). Furthermore, plant hydraulic trait measurements, often made at leaf or branch levels, are not easily scaled to whole-plant values that are typically used to predict plant and ecosystem fluxes. In this study, multiple whole-plant hydraulic parameters are inferred from observations of plant water use (i.e., sap flow), soil properties, and meteorological data. We use a Markov Chain Monte Carlo model inversion approach to obtain the best estimates and uncertainty of plant hydraulic parameters that capture whole-plant effective embolism resistance and stomatal sensitivity to decreasing plant water potential. We then use the inferred values in the model to estimate whole-tree water use and isohydricity. This approach reliably infers whole-plant parameter values with enough specificity to resolve inter- and intra-specific differences, and thus supplements time- and labor-intensive direct measurements of traits.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

What can surface wind observations tell us about interannual variation in wind energy output?

The past decade of wind power growth was supported by capacity factor improvements and associated cost reductions. But are higher capacity factors a technology success story or, as suggested by recent research, has the influence of technology been overstated by ignoring positive surface wind speed trends? The answer could influence estimates of wind energy's cost and even future deployment rates. We find that US surface wind speed observations imply a 2.6% improvement in capacity factors from 2010 to 2019. Yet newer vintages of wind plants have recorded capacity factors that are ~25% larger than plants built close to 2010. It follows that technological factors and improved site quality, not higher wind speeds, drove most of the improvement in capacity factors. Additionally, we match hundreds of meteorological stations to nearby (< 25 km) wind plants and compare annual estimated generation, based on a function of surface wind speed observations, to annual recorded generation. Researchers rely on this publicly available surface data because measurements co-located with wind plants are generally considered proprietary. Our analysis addresses a research gap: interannual variation in observed surface wind speeds is rarely compared to observed data at wind plant locations and turbine heights. We find that despite its common use for this purpose, generation estimates based on publicly available surface observational data provide a poor proxy for interannual variability in recorded wind generation. These findings suggest that caution is generally needed when researchers use surface wind speed measurements to investigate long-term wind energy trends.

17 WIND ENERGY↗

DOE OSTI2026

Understanding aerosol properties in convective outflows during TRACER

Convective outflow boundaries commonly form across the southeastern U.S., often originating from storms initiated along sea-breeze fronts. As outflows spread, merge, and collide, they frequently trigger secondary convection in air masses already modified by primary storm outflow. This resulting air mass carries a distinctive aerosol population, which can influence the development of secondary convective cells. During the U.S. DOE TRACER field campaign in summer 2022, we investigated how outflows affect aerosol size, composition, and hygroscopicity across Greater Houston, Texas. Using a novel detection algorithm, we identified and verified 72 outflow boundaries from surface meteorological observations. In the relatively simple continental environment northwest of Houston, a common relationship emerged between CCN-inferred hygroscopicity (κ) and size-resolved aerosol concentrations. The κ of aerosols typically decreased in size ranges where aerosol concentrations increased following outflow passage and increased where concentrations decreased. In contrast, at the Atmospheric Radiation Measurement’s (ARM) fixed site in La Porte, Texas, the aerosol response depended on outflow direction, shaped by concentrated industrial and shipping activity to the north and south/southeast along the Houston Ship Channel. Concurrent size distribution and composition changes suggested case-specific variability in κ, with either reinforcing or offsetting effects. Outflows passing over industrial/shipping sectors often promoted less hygroscopic aerosol populations while cleaner residential outflows often favored more hygroscopic aerosol populations. These results highlight key aerosol changes following outflow passage, providing a foundation for improved understanding of aerosol-cloud interactions related to secondary convection initiation along or in the wake of outflow boundaries.

Thompson, Seth A. [Texas A & M Univ., College Stat↗

DOE OSTI2021

A Modeling Tool to Analyze the Performance of Industrial Cooling Towers

Energy efficiency and energy savings have become an important factor as industries look for ways to save energy and minimize their energy consumption while reducing their carbon footprint. Cooling towers are utilized significantly in industries for either serving chillers or process cooling. Depending on the size of the cooling tower, it can use a surprising amount of energy and water, which is why it is crucial to make sure that the facility has optimized their cooling tower.A modeling tool has been developed to perform a thorough analysis of a cooling tower and its various operations to ensure that the facility has optimized its cooling tower. This model analyzes an annual base case in comparison with the revised case of a cooling tower operation. This modeling tool simulates 8,760 hourly calculations for fan power for various fan controls, water consumption, and pumping energy consumption and demand based on the user’s location and its corresponding Typical Meteorological Year 3 (TMY3) weather data. This model is capable to simulate up to five cooling tower cells as one large tower utilizing one pump or parallel pumping. The successful validated cooling tower model should assist industry to save energy at their facility through their cooling tower, whether they use it for process cooling or heating, ventilation and air conditioning (HVAC) applications. The entering/leaving water temperatures based on a monthly operating schedule or a wet-bulb temperature schedule will be used as inputs, and the model can calculate savings in energy for various conditions including variable-frequency drive (VFD) on the fan/pump, drift eliminators, number of cycles of concentration, reduced water flow rate, etc.

Hooper, Josh↗

DOE OSTI2022

Further improvement and evaluation of nudging in the E3SM Atmosphere Model version 1 (EAMv1): simulations of the mean climate, weather events, and anthropogenic aerosol effects

Abstract. A previous study on the use of nudging in E3SM Atmosphere Model version 1 (EAMv1) had an unresolved issue; i.e., a simulation nudged to EAMv1's own meteorology showed non-negligible deviations from the free-running baseline simulation over some of the subtropical marine stratocumulus and trade cumulus regions. Here, we demonstrate that the deviations can be substantially reduced by (1) changing where the nudging tendency is calculated in the time integration loop of a nudged EAM simulation so as to improve consistency with the free-running baseline and (2) increasing the frequency of the constraining data so as to better capture strong sub-diurnal variations. The fact that modification (2) improves the climate representativeness of the nudged simulations has motivated us to investigate whether the use of newer reanalysis products with higher data frequency can help improve nudged hindcast simulations by better capturing the observed weather events. To answer this question, we present simulations conducted at EAMv1's standard horizontal resolution (approximately 1∘) with nudging towards 6-hourly ERA-Interim reanalysis or 6-hourly, 3-hourly, or hourly ERA5 reanalysis. These simulations are evaluated against the climatology of free-running EAMv1 simulations as well as reanalyses, satellite retrievals, and in situ measurements from the Atmospheric Radiation Measurement user facility. For the 1∘ EAMv1 simulations, we recommend using the relocated nudging tendency calculation and the ERA5 reanalysis at 3-hourly or higher frequency. Simulations used for estimating the anthropogenic aerosol effects often use nudging to help discern signal from noise. The sensitivity of such estimates to the configuration of nudging is investigated in EAMv1, again using the standard 1∘ horizontal resolution. We find that, when estimating the global mean effects, the frequency of constraining data has relatively small impacts, while the choice of nudged variables can change the results substantially. The nudging of air temperature (in addition to horizontal winds) has two non-negligible effects. First, when the constraining data come from reanalysis, the nudging-induced mean bias correction can cause significant changes in the simulated clouds and hence substantially different estimates of the aerosol effects. The impact of the mean bias correction on ice cloud formation has been noted in previous studies and is also seen in EAMv1. For applications like ours, where the preferred configurations of nudging are those capable of providing results consistent with the multi-year free-running simulations, the consequence of the mean bias correction is undesirable. The second important impact of temperature nudging is a significant suppression of adjustments to aerosol forcing, which also causes changes in the estimated aerosol effects. This effect can be seen in simulations nudged to either reanalysis or EAM's own meteorology. These results suggest that nudging horizontal winds but not temperature is a better choice for estimating the anthropogenic aerosol effects.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Optimizing Earthquake Nowcasting With Machine Learning: The Role of Strain Hardening in the Earthquake Cycle

Abstract Nowcasting is a term originating from economics, finance, and meteorology. It refers to the process of determining the uncertain state of the economy, markets or the weather at the current time by indirect means. In this paper, we describe a simple two‐parameter data analysis that reveals hidden order in otherwise seemingly chaotic earthquake seismicity. One of these parameters relates to a mechanism of seismic quiescence arising from the physics of strain‐hardening of the crust prior to major events. We observe an earthquake cycle associated with major earthquakes in California, similar to what has long been postulated. An estimate of the earthquake hazard revealed by this state variable time series can be optimized by the use of machine learning in the form of the Receiver Operating Characteristic skill score. The ROC skill is used here as a loss function in a supervised learning mode. Our analysis is conducted in the region of 5° × 5° in latitude‐longitude centered on Los Angeles, a region which we used in previous papers to build similar time series using more involved methods (Rundle & Donnellan, 2020, https://doi.org/10.1029/2020EA001097 ; Rundle, Donnellan et al., 2021, https://doi.org/10.1029/2021EA001757 ; Rundle, Stein et al., 2021, https://doi.org/10.1088/1361-6633/abf893 ). Here we show that not only does the state variable time series have forecast skill, the associated spatial probability densities have skill as well. In addition, use of the standard ROC and Precision (PPV) metrics allow probabilities of current earthquake hazard to be defined in a simple, straightforward, and rigorous way.

58 GEOSCIENCES↗

DOE OSTI2024

Investigating the Relative Contribution from Tropical Indo-Pacific SST to Asian Monsoon Precipitation Variability Using LIM

Abstract A critical issue is determining the factors that control the year-to-year variability in precipitation over southern Asia. In this study, we employ a cyclostationary linear inverse model (CS-LIM) to quantify the relative contribution of tropical Pacific and Indian Ocean sea surface temperature anomalies (SSTAs) to the interannual variability of the Asian monsoon, especially Indian summer monsoon rainfall (ISMR). Through a series of CS-LIM experiments, we isolate the impacts of the direct forcing from Pacific SSTAs, Indian Ocean SSTAs, and their interaction on Asian monsoon rainfall variability. Our results reveal distinct patterns of influence with the direct forcing from the Pacific (Indian) Ocean tending to enhance (reduce) the magnitude of precipitation variability, while the Indo-Pacific interaction acts to strongly damp the variability of Asian monsoon precipitation, especially over India. We further investigate these specific impacts on ISMR by analyzing the relationship between tropical Indo-Pacific SSTAs and the leading three empirical orthogonal functions (EOFs) of ISMR. The results from our CS-LIM experiments indicate that the direct forcing from El Niño–Southern Oscillation (ENSO) enhances the variability of the first and third EOFs, while the Indian Ocean SSTA opposes ENSO’s effects, which is consistent with previous studies. Our new results show that the tropical Indo-Pacific interaction strongly damps ISMR variability, which is due to the ENSO-induced Indian Ocean dipole (IOD) opposing the direct impacts from ENSO on ISMR. Additionally, reduced ENSO amplitude and duration associated with the Indo-Pacific interaction may also contribute to the damping effect on ISMR, but this requires further study to understand the relevant mechanisms.

Meteorology & Atmospheric Sciences↗

DOE OSTI2025

Hourly gap-filled meteorological data from PIE LTER measurements (2004-2023) used as drivers to run ELM PFLOTRAN simulations

This dataset contains continuous gap-filled precipitation, solar radiation, photosynthetically active radiation (PAR), air temperature, relative humidity, wind speed, and barometric pressure data recorded primarily at the Marshview Farm weather station within the Plum Island Long Term Ecosystems Research (PIE LTER) in Newbury Massachusetts (MA) from 2004 to 2023. We compiled the data set from published annual data packages in 15min resolution available on DataOne. Gaps were filled using different statistical techniques or available observations from the vicinity, e.g. the US-PLo and the US-PHM Ameriflux sites, also located within the PIE LTER. Flags are included in this dataset to indicate the origin of each data point. Metadata files ELMPFLOTRAN_met_dd.csv and ELMPFLOTRAN_met_flmd.csv contain more information on site locations, gap filling protocols, data variables, flags, and QA/QC methods. The data set was used in the spin up and simulations of a land surface model coupled to a biogeochemical reaction network (ELM PFLOTRAN) assessing impacts of hydrology and salinity input on methane fluxes in 2022 and 2023 (Sulman et al., 2024).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Climate Change Projection in the Twenty-First Century Simulated by NIMS-KMA CMIP6 Model Based on New GHGs Concentration Pathways

Abstract The National Institute of Meteorological Sciences-Korea Meteorological Administration (NIMS-KMA) has participated in the Coupled Model Inter-comparison Project (CMIP) and provided long-term simulations using the coupled climate model. The NIMS-KMA produces new future projections using the ensemble mean of KMA Advanced Community Earth system model (K-ACE) and UK Earth System Model version1 (UKESM1) simulations to provide scientific information of future climate changes. In this study, we analyze four experiments those conducted following the new shared socioeconomic pathway (SSP) based scenarios to examine projected climate change in the twenty-first century. Present day (PD) simulations show high performance skill in both climate mean and variability, which provide a reliability of the climate models and reduces the uncertainty in response to future forcing. In future projections, global temperature increases from 1.92 °C to 5.20 °C relative to the PD level (1995–2014). Global mean precipitation increases from 5.1% to 10.1% and sea ice extent decreases from 19% to 62% in the Arctic and from 18% to 54% in the Antarctic. In addition, climate changes are accelerating toward the late twenty-first century. Our CMIP6 simulations are released to the public through the Earth System Grid Federation (ESGF) international data sharing portal and are used to support the establishment of the national adaptation plan for climate change in South Korea.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Assessing shellfish water exposure to fecal bacteria pollution in Salish Sea: three-dimensional modeling and implications for monitoring

Fecal bacteria (FB) contamination poses significant risks to shellfish safety and management in coastal and estuarine waters. Despite extensive pollution identification and correction efforts, FB contamination in shellfish-growing areas persists in the Salish Sea, highlighting the need to identify overlooked sources and better understand FB transport from riverine and shoreline inputs to shellfish beds. To address this, a high-resolution three-dimensional hydrodynamic model coupled with FB kinetics was developed and applied to a case study site in Salish Sea—Portage Bay—to simulate freshwater plume circulation, flushing dynamics, and bacterial transport. Daily FB loading from the major freshwater inflow—Nooksack River was generated by both linear interpolation and integrating a machine learning approach (XGBoost), trained on historical hydrological and meteorological data. The model successfully reproduced both the magnitude and seasonal variation of FB concentrations in Portage Bay for the year of 2021, demonstrating that simplified FB kinetics with first-order decay due to mortality was effective in this dynamic coastal environment with short flushing time. Model results identified the Nooksack River as the dominant far-field FB source, while scenario simulations showed that near-field coastal stormwater outfalls elevated local FB levels following rainfall, particularly under low-flow conditions. The XGBoost prediction provided comparable or superior accuracy to linear interpolation, particularly during periods of missing observational data, by capturing short-term variability and event-driven loading more effectively. Integrating data-driven riverine FB inputs with mechanistic coastal numerical modeling provides a robust framework for operational forecasting of shellfish bed exposure risk and supports adaptive monitoring and management of shellfish growing areas in the Salish Sea and similar coastal systems.

Salish Sea↗

DOE OSTI2021

Combined Mesonet and Tracker

Title: Combined Mesonet and Trackers (UNL Mobile Mesonets) Authors University of Nebraska PI: Adam Houston, UNL Professor (ahouston2@unl.edu) Mailing Address: 126 Bessey Hall P.O. Box 880340 Lincoln, NE 68588-0340 CoMeT Overview The University of Nebraska-Lincoln operates three Combined Mesonet and Tracker (CoMeTs). CoMeTs are Ford Explorers (model years 2015, 2017, and 2019) with forward-mounted suites of meteorological sensors and dual moonroofs, combining the capability of a mobile mesonet to collect near-surface observations with the capability of an unmanned aircraft systems (UAS) tracker vehicle, which enables an observer in the second row of seats to see the aircraft and maintain compliance with Federal Aviation Administration policies on UAS operation. The CoMeTs collect observations of slow temperature and humidity at ~2 m above ground level (AGL) using a Vaisala HMP155A, fast temperature at ~2 m AGL using a Campbell Scientific 109SS-L thermistor, pressure at ~2.5 m AGL using a Vaisala PTB210, wind speed and direction at ~3.25 m AGL using an R.M. Young 05103 propeller anemometer, and vehicle heading using a KVH Industries C-100 fluxgate compass (Barbieri et al. 2019). The HMP155A and 109SS-L thermistor are shielded and aspirated within a U-tube (Waugh and Frederickson 2010; Houston et al. 2016). This list of sensors is also included in the CoMeT data file metadata. Manufacturer specifications for these instruments are given in Table 1 of Hanft and Houston (2018). The reported measured quantities are summarized below.CoMeT-3 was funded through an equipment allocation included in the NSF TORUS award (AGS-1824649). Instrument Description The specific sensors included on each CoMeT are summarized in the table at the end of this section. In general each CoMeT collects observations of slow temperature and humidity at ~2 m above ground level (AGL) using a Vaisala HMP155, fast temperature at ~2 m AGL using a Campbell Scientific 109SS-L thermistor, pressure at ~2.5 m AGL using a Vaisala PTB210 barometer with a Gill pressure port, wind speed and direction at ~3.25 m AGL using an R.M. Young 05103 propeller anemometer, position using a Garmin 19x HVS receiver, and vehicle heading using a KVH Industries C-100 fluxgate compass. The HMP155 and 109SS are shielded and aspirated within a U-tube (Waugh and Frederickson 2010; Houston et al. 2016). Fast temperature and corrected RH measurements (using sensors housed within the U-tube) have a time constant of 10-12 s based on data collected across a temperature and RH shock during the CLOUD-MAP 2017 calibration/validation tests on June 26, 2017. Vehicle speed was < 10 kts for this test. CoMeT-1 CoMeT-2 CoMeT-3 Slow Temperature Slow RH Vaisala HMP155A-L20-PT Part #: 22280-7 Vaisala HMP155E Part #: E1AA11A0B1A1A0A Vaisala HMP155E Part #: E1AA11A0B1A1A0A Fast temperature Campbell Scientific 109SS-L20-PT Part #: 21448-3 Campbell Scientific 109SS-L12-PW Part #: 21448-109 Campbell Scientific 109SS-L12-PW Part #: 21448-150 Pressure Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Wind RM Young 05103-L20-PT Part #: 18435-310 RM Young 05103-L20-PW Part #: 18435-244 RM Young 05103-L20-PW Part #: 18435-244 GPS Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Compass KVH C-100 Part #: 01-0177-15 KVH C-100 Part #: 01-0177-15 KVH C-100 Part #: 01-0177-15 Logger Campbell Scientific CR6-NA-XT-SW Part #: 28385-9 Campbell Scientific CR6-WIFI-XT-SW Part #: 28385-6 Campbell Scientific CR6-WIFI-XT-SW Part #: 28385-6 Data Collection and Real-Time Processing The reported measured quantities are summarized in the table below. Quantity Units Source Epoch time Seconds GPS Latitude and longitude Degrees GPS Altitude m GPS Pressure hPa PTB210 Temperature (fast) deg C 109SS-L Temperature (slow) deg C HMP155 RH (slow) % HMP155 Vehicle speed m/s GPS Vehicle heading deg C-100 and GPS In addition to the measured variables, several derived variables are calculated. Corrected/fast relative humidity (%) Relative humidity is adjusted to the fast temperature following Richardson et al. (1998) and Houston et al. (2016). Water vapor mixing ratio (g/kg) Dew point temperature (°C) Potential temperature (Kelvin) Virtual potential temperature (Kelvin) Equivalent potential temperature (Kelvin) Regular intercomparisons between all three CoMeTs were performed during TORUS 2019. Comparisons were also conducted between CoMeT-1 and CoMeT-2 during LAPSE-RATE (2018) on 14 July. In these intercomparisons, the vehicles were parked adjacent to each other aligned perpendicular to (and facing into) the wind. To minimize engine heating effects, intercomparisons were only conducted when the wind speed was >10 kts. Data Format Original data files for each deployment are saved as text files and then converted to NetCDF. NetCDF versions have units that are CF compliant and may not match the original units in the txt files. The naming convention for the NetCDF files is as follows: UNL.CoMeT3.{deployment date YYYYMMDD}.{start time of observation collection in UTC HHMM}.L2.{post-processing codes}.cdf example: UNL.CoMeT3.20190627.1931.L2.g1.f1.cdf Post-processing codes are included to track modifications to the raw data. These codes are closely connected to error flags associated with each record. Each letter corresponds to a particular instrument: g: GPS p: Barometer tf: Fast temperature ts: Slow temperature rh: Relative humidity f: Compass w: Wind monitor a: All instruments Each number corresponds to a particular post-processing action described more below. Measured and derived variables are included in the following table. Variable Heading Standard Name Units time Time seconds since 00:00:00, 01-01-1970 Alt Altitude meters lat Latitude degrees north lon Longitude degrees east fast_temp Air Temperature kelvin slow_temp Air Temperature kelvin pressure Air Pressure pascals logger_RH Relative Humidity percent calc_corr_RH Relative Humidity percent wind_speed Wind Speed meters per second wind_dir Wind From Direction degrees vehicle_dir Vehicle Direction degrees dewpoint Dew Point Temperature kelvin mixing_ratio Humidity Mixing Ratio g/g theta Air Potential Temperature kelvin theta_v Virtual Potential Temperature kelvin theta_e Equivalent Potential Temperature Kelvin error_flag The error_flag variable is a string that matches the post-processing codes listed above. All instruments will have an associated code, but will have a “0” if the datum is unchanged from the initial processed value. Error Codes The following table summarizes the error codes for data collected before 2020: Error Code Relevant CoMeT Description g1 1,2,3 Exact correction. GPS position and time reprocessed from raw data g2 1 As far as we can tell this is an exact correction to an error in the GPS time. During the correct time periods the time suddenly went backwards ~250s and stayed at this offset for 750s when it corrected itself. The offset was applied to the “time warp” period. p1 2 Approximate correction. Hole in the pressure tube connecting the pressure port to the barometer. Resulted in erroneously low air pressure measurements when the vehicle was in motion. Derived variables recalculated (dew point temperature [e depends on qv and p], water vapor mixing ratio, potential temperature, virtual potential temperature, equivalent potential temperature) a1 3 Exact correction. Missing data reprocessed from raw data a2 1 Bug fix to bias correction for ts1, ts2, and rh1: water vapor mixing ratio was off by a factor of 10 and virtual potential temperature was wrong because of this. f1 3 No correction, missing data. Fluxgate compass inoperable. Wind speed and direction calculated using GPS-derived vehicle heading instead. rh1 1 Approximate correction. Constant bias of +1.7% removed from relative humidity. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) ts1 1 Approximate correction. Constant bias of +0.6 K removed from slow temperature. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) ts2 1 Approximate correction. Constant bias of +1.0 K removed. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) References Bolton, D., 1980: The Computation of Equivalent Potential Temperature. Mon. Wea. Rev., 108, 1046–1053, https://doi.org/10.1175/1520-0493(1980)108<1046:TCOEPT>2.0.CO;2. Hanft, W., and A. L. Houston, 2018: An Observational and Modeling Study of Mesoscale Air Masses with High Theta-E. Mon. Wea. Rev., 146, 2503–2524, https://doi.org/10.1175/MWR-D-17-0389.1.Wexler Houston, A. L., R. J. Laurence III, T. W. Nichols, S. Waugh, B. Argrow, and C. L. Ziegler, 2016: Intercomparison of unmanned aircraft-borne and mobile mesonet atmospheric sensors. Journal of Atmospheric and Oceanic Technology. 33, 1569-1582, doi: 10.1175/JTECH-D-15-0178.1. Lowe, P. R., 1977: An Approximating Polynomial for the Computation of Saturation Vapor Pressure. J. Applied Meteorology, 16, 100–103. Richardson, S. J., S. E. Frederickson, F. V. Brock, and J. A. Brotzge, 1998: Combination temperature and relative humidity probes: Avoiding large air temperature errors and associated relative humidity errors. Preprints, 10th Symp. On Meteorological Observations and Instrumentation, Phoenix, AZ, Amer. Meteor. Soc., 278–283. Waugh, S., and S. E. Frederickson, 2010: An improved aspirated temperature system for mobile meteorological observations, especially in severe weather. 25th Conf. on Severe Local Storms, Denver, CO, Amer. Meteor. Soc., P5.2. [Available online at https://ams.confex.com/ams/25SLS/techprogram/paper_176205.htm.]

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Evaluation of a high-resolution regional climate simulation for surface and hub-height wind climatology over North America

Assessing the availability of key wind resources requires augmenting observations to support the implementation of wind energy infrastructure. However, observations are limited, necessitating the development of high-resolution, long-term gridded datasets. This study presents a robust, dynamically downscaled climatological dataset, offering 20 years of hourly wind data at a 4 km spatial resolution across North America, and evaluates its performance against observations, including meteorological towers and automated surface-observing system (ASOS) stations, as well as coarse-resolution reanalysis data (the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5)). Results demonstrate that the downscaled high-resolution wind data outperform ERA5 in regions of complex terrain and coastal areas, with improved overlap coefficients for wind data distributions and reduced root mean square errors (RMSEs) for hub-height and near-surface diurnal wind patterns. The downscaled simulation also captures the synoptic drivers of seasonal wind direction patterns reasonably well, indicated by high wind rose similarity indices. This study also provides an analysis of interannual variability, utilizing the dataset's full 20-year period, and model uncertainty, generated by varying model initial conditions and physics parameterizations across 1-year ensemble members, which are key considerations for wind resource assessment in wind farm development.

17 WIND ENERGY↗