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This dataset contains meteorological measurements including temperature, pressure, and relative humidity at multiple heights deployed on tethered balloon system at AWAKEN site G. Z05 refers to the fifth height position of the iMet-XQ2 mounted on the tethered balloon.
This dataset contains meteorological measurements including temperature, pressure, and relative humidity at multiple heights deployed on tethered balloon system at AWAKEN site G. Z04 refers to the fourth height position of the iMet-XQ2 mounted on the tethered balloon.
This dataset contains meteorological measurements including temperature, pressure, and relative humidity at multiple heights deployed on tethered balloon system at AWAKEN site G. Z03 refers to the third height position of the iMet-XQ2 mounted on the tethered balloon.
This dataset contains meteorological measurements including temperature, pressure, and relative humidity at multiple heights deployed on tethered balloon system at AWAKEN site G. Z02 refers to the second height position of the iMet-XQ2 mounted on the tethered balloon.
This dataset contains meteorological measurements including temperature, pressure, and relative humidity at multiple heights deployed on tethered balloon system at AWAKEN site G. Z01 refers to the first height position of the iMet-XQ2 mounted on the tethered balloon.
This dataset contains meteorological measurements including temperature, pressure, and relative humidity at multiple heights deployed on tethered balloon system at AWAKEN site A1. Z06 refers to the sixth height position of the iMet-XQ2 mounted on the tethered balloon.
This dataset contains meteorological measurements including temperature, pressure, and relative humidity at multiple heights deployed on tethered balloon system at AWAKEN site A1. Z01 refers to the first height position of the iMet-XQ2 mounted on the tethered balloon.
This dataset contains meteorological measurements including temperature, pressure, and relative humidity at multiple heights deployed on tethered balloon system at AWAKEN site A1. Z04 refers to the fourth height position of the iMet-XQ2 mounted on the tethered balloon.
This dataset contains meteorological measurements including temperature, pressure, and relative humidity at multiple heights deployed on tethered balloon system at AWAKEN site A1. Z05 refers to the fifth height position of the iMet-XQ2 mounted on the tethered balloon.
This dataset contains meteorological measurements including temperature, pressure, and relative humidity at multiple heights deployed on tethered balloon system at AWAKEN site A1. Z03 refers to the third height position of the iMet-XQ2 mounted on the tethered balloon.
This dataset contains meteorological measurements including temperature, pressure, and relative humidity at multiple heights deployed on tethered balloon system at AWAKEN site A1. Z02 refers to the second height position of the iMet-XQ2 mounted on the tethered balloon.
Many current gridded surface meteorological datasets are inadequate for quantifying near surface spatiotemporal variability because they do not fully represent the impacts of land surface heterogeneity. Of note, explicit representation of the spatial structure and magnitude of local urban warming are usually lacking. Here we enhance the representation of spatial meteorological variability over urban areas in the conterminous United States (CONUS) by employing the High-Resolution Land Data Assimilation System (HRLDAS), which accounts for the fine-scale impacts of spatiotemporally varying land surfaces on weather. We also synthesize in situ meteorological data including local mesonets to create a 1 km grid spacing model-observation fusion product spanning 1981-2018 over the CONUS. Daily maximum, minimum, and mean values for a variety of temperature estimates, humidity, and surface energy budget terms, among others, are included. This High-resolution Urban Meteorology for Impacts Dataset (HUMID) will be useful for studies examining spatial variability of near surface meteorology and the impacts of urban heat islands across many disciplines including epidemiology, ecology, and climatology.
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.
This repository presents HydroSMADE—Hydropower Site-level Monthly Availability Data Ensemble, a new open dataset that provides monthly hydropower availability for 1,593 existing and 124,333 potential sites worldwide over the period 1950–2100. The dataset is generated by using a global hydrologic model (Xanthos) with explicit representation of hydropower operation. Specifically, HydroSMADE distinguishes between storage and diversion sites, applies optimized operating rules, and incorporates site-specific characteristics such as generation capacity, maximum turbine flow, and reservoir storage. Driven by bias-corrected meteorological inputs, the data is provided for 30 alternative future scenarios. The scenarios consist of the full factorial combination of three standard CMIP6 atmospheric forcing pathways (SSP1-2.6, SSP3-7.0, and SSP5-8.5) and ten CMIP6 General Circulation Models (GCMs): GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, EC-Earth3, CanESM5, MIROC6, CNRM-ESM2-1, UKESM1-0-LL, and CNRM-CM6-1. The repository contains a total of 122 files: a text file (readme.txt) containing a brief description of the included data, a CSV file containing site attributes, and the remaining 120 files (in CSV) containing site-level monthly hydropower availability. Example Jupyter Notebooks to explore the HydroSMADE dataset are available on GitHub at https://github.com/kamal0013/HydroSMADE More details on the methods and technical validation of HydroSMADE are available in the following paper by the same authors: Chowdhury, A. K., Abeshu, G. W., Zhao, M., Wild, T. B., Hassan, N., Ying, Z., Kim, G. J., Matthew, B., Jonathan, L., & Li, H.-Y. (Submitted). Hydropower Site-level Monthly Availability Data Ensemble for 1950-2100 at Existing and Potential Global Sites.
As the offshore wind industry continues to gain momentum both in the U.S. and globally, reanalysis products remain essential tools for the lifecycle of a wind farm. From project siting and production estimates to determining construction and maintenance windows, reanalysis products play an important role. These regional and global long-term models also serve as inputs to higher resolution models. With such wide-ranging impacts on the offshore wind industry, the need for validation of reanalysis products is strong, particularly at typical turbine hub heights. Pacific Northwest National Laboratory (PNNL) operates two AXYS WindSentinelTM buoys for the U.S. Department of Energy (DOE) in order to collect meteorological and oceanographic data, including hub height wind speed, in areas of interest for offshore wind development. This work uses DOE buoy observations off the coasts of New Jersey and Virginia to evaluate the performance of Modern Era Retrospective Analysis for Research and Applications-2 (MERRA-2), the North American Regional Reanalysis (NARR), the European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th generation (ERA5), and the National Oceanic and Atmospheric Administration (NOAA) assimilation system Rapid Refresh (RAP). Biases and degrees of correspondence are determined for each reanalysis product in order to provide insights on the performance and uncertainty for long-term wind resource characterization. With the baseline metrics determined, this work proceeds with an investigation into the sources of large deviations between the modeled and observed hub height wind speeds during the East Coast lidar buoy deployments. Consistent sources of reanalysis model error include stably stratified flow conditions, high wind shear, flow parallel to the coastline, and tropical and winter storms. For the near shore New Jersey location, offshore winds (i.e. blowing from land to water) tend to be associated with model overestimation of observed hub height wind speed, while winds parallel to the coastline are correlated with model underestimation of wind speed. Similarly, near the Virginia buoy, during southerly winds (parallel to the coastline) models underestimate the observed wind speed due to coastal upwelling. Reanalysis model wind speeds underestimate the hub height winds with increasing stable atmospheric stratification. Reanalysis model biases are seen when the observed wind speeds approach or exceed typical turbine cut-out speeds (around 20 m s-1). Large model errors during winter and tropical storm events, high wind speed events, and ramp events are often the result of wind speed timing and magnitude offsets. Overall, ERA5 provides the most successful representation of observed offshore hub height wind speeds at the U.S. East Coast buoy locations and is therefore best suited as an input boundary condition for model case studies. ERA5 performs well for a variety of atmospheric phenomena, including storms and sea breezes, however RAP, the next most successful reanalysis model, performs best in capturing the frequency of ramp events.
Aerosol-cloud-precipitation interactions (ACPI) remain a major uncertainty in understanding the Earth’s radiation budget and water cycle (including extremes). After decades of active research, various observationally based metrics have been developed to constrain ACPI in Earth System Models (ESMs), but direct comparison of model and data estimates can confound scientific understanding because limitations and uncertainties in sampling and retrieval procedures may combine with model deficiencies in process representations of ACPI to obstruct understanding. Furthermore, conventional ACPI metrics often vary from one regime to another, and the ACPI process representation in ESMs is also typically derived based on only a limited area/regime (even though the parameterization applies globally). To bridge the gap between models and data and to correctly describe ACPI in all regimes, we propose to construct a new CALIPSO-CloudSat merged dataset that is produced by the same algorithms used in satellite simulators in ESMs, and to use machine learning techniques to derive new ACPI metrics that can be accurately estimated by satellites and can provide meaningful constraints on cloud microphysical process representations in ESMs. The dataset will include measured and retrieved variables for aerosol, cloud, and precipitation from CALIPSO and CloudSat, and environmental variables from meteorological reanalysis. The data will be used to train a neural network to construct the ACPI metrics as a function of environmental conditions. The new ACPI formula will be used to constrain the ACPI in the Energy Exascale Earth System Model (E3SM), and to augment/reformulate the ACPI process representation in the E3SM to improve the simulation of the evolution of the atmosphere under different environmental conditions.
This dataset contains meteorological, oceanographic, and sensor-depth data collected near Block Island during the 2016 and 2017 RODEO field seasons to provide environmental context for concurrent acoustic and survey operations. It includes buoy-derived wind and wave time series, in-situ water temperature logger records, and depth (pressure) records from a sensor mounted on the vertical line array.
A typical meteorological year (TMY) data set essentially represents an hourly compilation of median months constructed using multi-year datasets. Although TMY data sets are generated from irradiance data in the horizontal plane, they are used in photovoltaic (PV) modeling for systems inclined to various angles. This paper demonstrates that a POA TMY (plane of array TMY) generated by selecting median months from a multi-year POA irradiance timeseries dataset produces significantly different results than a POA TMY generated by transposing a TMY dataset constructed from horizontal data. In some months the differences can be more than 3%. These results point to the need for generating TMY's using POA irradiance timeseries representing the orientation at which PV panels will be deployed.