Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Reanalysis data”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Improved Stratospheric Temperature Retrievals for Climate Reanalysis

The Data Assimilation Office (DAO) is embarking on plans to generate a twenty year reanalysis data set of climatic atmospheric variables. One of the focus points will be in the evaluation of the dynamics of the stratosphere. The Stratospheric Sounding Unit (SSU), flown as part of the TIROS Operational Vertical Sounder (TOVS), is one of the primary stratospheric temperature sensors flown consistently throughout the reanalysis period. Seven unique sensors made the measurements over time, with individual instrument characteristics that need to be addressed. The stratospheric temperatures being assimilated across satellite platforms will profoundly impact the reanalysis dynamical fields. To attempt to quantify aspects of instrument and retrieval bias we are carefully collecting and analyzing all available information on the sensors, their instrument anomalies, forward model errors and retrieval biases. For the retrieval of stratospheric temperatures, we adapted the minimum variance approach of Jazwinski (1970) and Rodgers (1976) and applied it to the SSU soundings. In our algorithm, the state vector contains an initial guess of temperature from a model six hour forecast provided by the Goddard EOS Data Assimilation System (GEOS/DAS). This is combined with an a priori covariance matrix, a forward model parameterization, and specifications of instrument noise characteristics. A quasi-Newtonian iteration is used to obtain convergence of the retrieved state to the measurement vector. This algorithm also enables us to analyze and address the systematic errors associated with the unique characteristics of the cell pressures on the individual SSU instruments and the resolving power of the instruments to vertical gradients in the stratosphere. The preliminary results of the improved retrievals and their assimilation as well as baseline calculations of bias and rms error between the NESDIS operational product and col-located ground measurements will be presented.

Rokke, L.↗
DOE OSTI2023

A novel data gaps filling method for solar PV output forecasting

This study proposes a modified gaps filling method, expanding the column mean imputation method and evaluated using randomly generated missing values comprising 5%, 10%, 15%, and 20% of the original data on power output. The XGBoost algorithm was implemented as a forecasting model using the original and processed datasets and two sources of solar radiation data, namely, Shortwave Radiation (SWR) from Advanced Himawari Imager 8 (AHI-8) and Surface Solar Radiation Downward (SSRD) from ERA5 global reanalysis data. Further, the accuracy of the two sets of forecasted power output was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Results show that by applying the proposed gap filling method and using SWR in forecasting solar photovoltaic (PV) output, the improvement in the RMSE and MAE values range from 12.52% to 24.30% and from 21.10% to 31.31%, respectively. Meanwhile, using SSRD, the improvement in the RMSE values range from 14.01% to 28.54% and MAE values from 22.39% to 35.53%. To further evaluate the accuracy of the proposed gap-filling method, the proposed method could be validated using different datasets and other forecasting methods. Future studies could also consider applying the said method to datasets with data gaps higher than 20%.

Energy & Fuels↗
NASA NTRS2004

A Mechanism For Solar Forcing of Climate: Did the Maunder Minimum Cause the Little Ice Age?

The mechanism we wish to demonstrate exploits chemical, radiative, and dynamical sensitivities in the stratosphere to affect the climate of the troposphere. The sun, while its variability in total radiative output over the course of the solar cycle is on the order of 0.1%, exhibits variability in the UV output on the order of 5%. We expect to show that a substantially decreased solar UV output lessened the heating of the Earth's stratosphere during the Maunder Minimum, through decreased radiative absorption by ozone and oxygen. These changes in stratospheric heating would lead to major changes in the stratospheric zonal wind pattern which would in turn affect the propagation characteristics of planetary-scale waves launched in the winter hemisphere. Until recently, there was no quantitative data to relate the changes in the stratosphere to those at the surface. There is now empirical evidence from the NCEP Reanalysis data that a definitive effect of the solar cycle on climate in the troposphere exists. Our recent work is summarized as follows (see complete list of publications in later part of this report).

Yung, Yuk L.↗
DOE OSTI2021

Saharan Dust Transport Predictability Utilizing a Subseasonal Experiment (SubX) Model

Prediction of Saharan dust customarily requires complex aerosols models and observations. A previous study of the Miami, Florida dust record in conjunction to reanalysis data discovered a possible source of subseasonal predictability using a dust-transport-efficiency (DTE) index. Development of the Subseasonal Forecast Experiment (SubX) has expanded global forecast products; producing multi-model ensemble forecasts out to 45 days. Retrospective forecast data from the Community Climate System Model version 4.0 (CCSM4) is used in direct comparison to National Centers for Environmental Prediction (NCEP) reanalysis to evaluate the CCSM4 subseasonal forecast and DTE index prediction skill of weekly dust variability. Successful prediction of weekly dust transport using the DTE index is variable year-to-year. The DTE most successfully predicts dust when there is high variability in the tropical winds, likely due to a fluctuating subtropical high, and is not dependent on the overall mean flow or total dust mass transported. The CCSM4 SubX retrospective forecast well represents North Atlantic meteorology out to week-3 in both mean flow and variability. Dust transport can be predicted using the DTE index and CCSM4 SubX retrospective forecasts at week-1 leads to the same success as contemporaneous NCEP reanalysis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Deep Learning Advances Arctic River Water Temperature Predictions

The accelerated warming in the Arctic poses serious risks to freshwater ecosystems by altering streamflow and river thermal regimes. However, limited research on Arctic River water temperatures exists due to data scarcity and the absence of robust methodologies, which often focus on large, major river basins. To address this, we leveraged the newly released, extensive AKTEMP data set and advanced machine learning techniques to develop a Long Short-Term Memory (LSTM) model. By incorporating ERA5-Land reanalysis data and integrating physical understanding into data-driven processes, our model advanced river water temperature predictions in ungauged, snow- and permafrost-affected basins in Alaska. Our model outperformed existing approaches in high-latitude regions, achieving a median Nash-Sutcliffe Efficiency of 0.95 and root mean squared error of 1.0°C. The LSTM model learned air temperature, soil temperature, solar radiation, and thermal radiation—factors associated with energy balance—were the most important drivers of river temperature dynamics. Soil moisture and snow water equivalent were highlighted as critical factors representing key processes such as thawing, melting, and groundwater contributions. Glaciers and permafrost were also identified as important covariates, particularly in seasonal river water temperature predictions. Our LSTM model successfully captured the complex relationships between hydrometeorological factors and river water temperatures across varying timescales and hydrological conditions. This scalable and transferable approach can be potentially applied across the Arctic, offering valuable insights for future conservation and management efforts.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

Using NASA Remotely Sensed Data to Help Characterize Environmental Risk Factors for National Public Health Applications

This project has dual goals in decision ]making activities .. Providing information to decision makers about associations between environmental exposures and health conditions in a large national cohort study. Enriching the CDC Wide ]ranging Online Data for Epidemiologic Research (WONDER) system by integrating environmental exposure data. .. Develop daily high ]quality spatial data sets of environmental variables for the conterminous U.S. for the years 2003-2008 utilizing NASA data (Objective 1). Fine Particulates (PM2.5) (NASA MODIS and EPA AQS). Land Surface Temperature (NASA MODIS). Solar Insolation and Heat ]related Products (Reanalysis Data). Link these environmental variables with public health data from a national cohort study and examine environmental health relationships (Objective 2). Cognitive Function. Hypertension. Make the environmental datasets available to public health professionals, researchers and the general public via the CDC WONDER system (Objective 3).

Al-Hamdan, Mohammad↗
NASA NTRS2002

Configuration and Intraseasonal Duration of Interannual Anomalies of the Great Plains Low-Level Jet

Despite the fact that the low-level jet of the southern Great Plains (the GPLLJ) of the U.S. is primarily a nocturnal phenomenon that virtually vanishes during the daylight hours, it is one of the most persistent and stable climatological features of the low-level continental flow during the warm-season months, May through August. We have used significant-level data to validate the skill of the GEOS-1 Data Assimilation System (DAS) in realistically detecting this jet and inferring its structure and evolution. We have then carried out a 15-year reanalysis with the GEOS-1 DAS to determine its climatology and mean diurnal cycle and to study its interannual variability. Interannual anomalies of the meridional flow associated with the GPLLJ are much smaller than the mean diurnal fluctuations, than random intraseasonal anomalies, and than the mean wind itself. There are three maxima of low-level meridional flow variance over the Great Plains and the Gulf of Mexico: a 1.2 m2 s-2 peak over the southeast Texas, to the east and south of the mean velocity peak, a 1.0 m2 s-2 peak over the western Gulf of Mexico, and a .8 m2 s-2 peak over the upper Great Plains (UGP), near the Nebraska/South Dakota border. Each of the three variance maxima corresponds to a spatially coherent, jet-like pattern of low-level flow interannual variability. There are also three dominant modes of interannual variability corresponding to the three variance maxima, but not in a simple one-to-one relationship. Cross-sectional profiles of mean southerly wind over Texas remain relatively stable and recognizable from year to year with only its eastward flank showing significant variability. This variability, however, exhibits a distinct, biennial oscillation during the first six to seven years of the reanalysis period and only then. This intermittent biennial oscillation (IBO, one of the three modes discussed in the previous paragraph) in the lowlevel flow is restricted to the region surrounding eastern Texas and is also evident in the NCEP/NCAR reanalysis data set from about 1978 to 1985 or 1986 and again from 1995 to 2000. It is evident as well in surface pressure in both the GEOS-1 and NCEP/NCAR sets. The interannual anomalies do not necessarily persist uniformly throughout an entire season, but can fluctuate from one part of the season to the next. To estimate the characteristic sub-seasonal time scales for coherence of these fluctuations, we have taken the weekly anomaly of low-level wind at each point of the domain from the climatological average for that given point and that given week of the season and computed the covariance of its fluctuations over all weeks and over all years with the weekly climatological anomaly of the meridional wind at each of the three reference points discussed above. The typical duration of a coherent interannual anomaly within a given warm season increases with decreasing latitude from 2 to 3 weeks over the UGP, to 6 to 7 weeks over eastern Texas. Coherence over the western Gulf of Mexico is intermediate between the two with a typical duration of 4 to 5 weeks. There appears to be evidence that the interannual anomalies over Texas the Gulf propagate to the UGP after a week and those over the Gulf propagate there after 2 to 3 weeks. There also appears to be some reverse propagation of interannual anomalies over the UGP to Texas and to the Gulf after a period of about one week. The interannual anomalies in southerly flow over eastern Texas seem to correlate well with interannual anomalies of surface temperature and (negative) ground wetness and over western Texas.

Helfand, H. M.↗
DOE OSTI2026

Challenges and alternatives to empirical orthogonal functions for earth system data

Empirical orthogonal functions (EOFs) applied to gridded Earth system data enables users to diagnose modes of variability with relative ease. Yet, many challenges to interpretation exist such that they must be used with awareness and intention when applied to gridded climate data, especially with large ensembles. Utilizing data from two different Earth system modelling large ensemble frameworks, the Energy Exoscale Earth System Model and the Community Earth System Model, as well as reanalysis data, common EOF pitfalls are summarized and discussed. Challenges include erroneous mode swapping, sign flipping, and the temporal variability of the centers of action. For modes of variability with similar contribution to variance, mode swapping is not uncommon. Sign flipping can occur with almost any mode where the pattern is correct, but the sign is arbitrary. Although the variability of the center of action is not necessarily problematic, it potentially complicates interpretation over multi-century timescales. A wide variety of alternative methods to EOFs exist, but fitness-for-purpose must be evaluated. Additionally, illustrations of alternative methods and examples of proper use are provided. Alternative methods fit into three categories: EOF variants, linear methods, and multilinear methods.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2002

Tropical Convection's Roles in Tropical Tropopause Cirrus

Remote sensing observations reveal the frequent occurrence of tropopause cirrus, thin cirrus layers located near the tropical cold-point tropopause. Here, we present a theory in which tropical convection plays several important roles in tropopause cirrus formation. First, tropical convection is the primary means by which the moisture required for tropopause cirrus formation is transported into the upper troposphere. However, previous studies suggest that this convection rarely penetrates to the altitudes at which tropopause cirrus layers are observed, suggesting that additional vertical moisture transport is required to explain tropopause cirrus formation. We propose a mechanism for explaining this transport in which tropical convection plays the key role. According to this hypothesis, the transport is accomplished by meridional circulations that develop within the tropopause transition layer (TTL) in response to momentum transport by Rossby waves generated by tropical convection. Results of a series of global scale model runs designed to test this hypothesis will be presented. In addition, reanalyses vertical velocity data will be examined for evidence of the expected correlation between large-scale rising motion within the TTL and tropical convection. Once moisture is present near the cold-point tropopause, large-scale cooling is required to initiate tropopause cirrus formation. One source of this cooling is stratospheric tropical waves induced by tropical convection, as we will show using a time series of radiosonde temperature data superimposed with data on cloud occurrence from the DOE ARM Nauru99 field experiment. Observations of the global characteristics of these waves from a longer time series of reanalysis data will also be presented.

Boehm, Matthew T.↗
NASA NTRSDate not supplied

A Long-Term GOES Satellite Overshooting Cloud Top and Anvil Cloud Climatology Over South America

The modern-era GOES satellite series began in 1994 with the GOES-8 satellite, and was augmented in 2018 with higher spatial resolution and more frequent imaging when GOES-16 became operational. GOES imagery has provided forecasters and researchers new perspectives into cloud top patterns associated with severe convection, and the ability to better forecast convection in regions without adequate ground-based weather radar coverage. While much attention has been given to convection over North America, convection over South America can be equally, if not more, intense and frequent. Recent studies have demonstrated that overshooting cloud tops (OT) and surrounding anvil clouds can be detected within infrared satellite imagery. Relative storm updraft intensity metrics such as the tropopause-relative infrared brightness temperature, the prominence of an OT relative to its surrounding anvil, and cloud top height can also be derived using automated methods combined with reanalysis data. These automated OT detection and intensity estimation methods have recently been applied to all GOES images collected over South America, in combination with the MERRA-2 reanalysis, from 1995 to 2022 at NASA Langley within a project supported by the NASA Applied Sciences Disasters program. Innovative aggregation methods have merged these products into daily, monthly, annual, and multi-annual composites, with hourly time bins, at ~4 km pixel spacing to enable researchers a new opportunity to study South American convective processes throughout the diurnal cycle. These products have recently become publicly available from NASA. This presentation will overview this new dataset, and novel insights into South American convection depicted by the data.

Kristopher Bedka↗
DOE OSTI2022

Evaluation of obstacle modelling approaches for resource assessment and small wind turbine siting: case study in the northern Netherlands

Abstract. Growth in adoption of distributed wind turbines for energy generation is significantly impacted by challenges associated with siting and accurate estimation of the wind resource. Small turbines, at hub heights of 40 m or less, are greatly impacted by terrestrial obstacles such as built structures and vegetation that can cause complex wake effects. While some progress in high-fidelity complex fluid dynamics (CFD) models has increased the potential accuracy for modelling the impacts of obstacles on turbulent wind flow, these models are too computationally expensive for practical siting and resource assessment applications. To understand the efficacy of available models in situ, this study evaluates classic and commonly used methods alongside new state-of-the-art lower-order models derived from CFD simulations and machine learning approaches. This evaluation is conducted using a subset of an extensive original dataset of measurements from more than 300 operational wind turbines in the northern Netherlands. The results show that data-driven methods (e.g. machine learning and statistical modelling) are most effective at predicting production at real sites with an average error in annual energy production of 2.5 %. When sufficient data may not be available de novo to support these data-driven approaches, models derived from high-fidelity simulations show promise and reliably outperform classic methods. On average these models have 6.3 %–11.5 % error compared with 26 % for classic methods and 27 % baseline error for reanalysis data without obstacle correction. While more performant on average, these methods are also sensitive to the quality of obstacle descriptions and reanalysis inputs.

17 WIND ENERGY↗
NASA NTRS2016

Role of Surface Wind and Vegetation Cover in Multi-decadal Variations of Dust Emission in the Sahara and Sahel

North Africa, the world's largest dust source, is non-uniform, consisting of a permanently arid region (Sahara), a semi-arid region (Sahel), and a relatively moist vegetated region (Savanna), each with very different rainfall patterns and surface conditions. This study aims to better understand the controlling factors that determine the variation of dust emission in North Africa over a 27-year period from 1982 to 2008, using observational data and model simulations. The results show that the model-derived Saharan dust emission is only correlated with the 10-m winds (W10m) obtained from reanalysis data, but the model-derived Sahel dust emission is correlated with both W10m and the Normalized Difference Vegetation Index (NDVI) that is obtained from satellite. While the Saharan dust accounts for 82 of the continental North Africa dust emission (1340-1570 Tg year(exp -1) in the 27-year average, the Sahel accounts for 17 with a larger seasonal and inter-annual variation (230-380 Tg year(exp -1), contributing about a quarter of the transatlantic dust transported to the northern part of South America. The decreasing dust emission trend over the 27-year period is highly correlated with W10m over the Sahara (R equals 0.92). Over the Sahel, the dust emission is correlated with W10m (R 0.69) but is also anti-correlated with the trend of NDVI (R equals 0.65). W10m is decreasing over both the Sahara and the Sahel between 1982 and 2008, and the trends are correlated (R equals 0.53), suggesting that Saharan Sahelian surface winds are a coupled system, driving the inter-annual variation of dust emission.

Inter-annual variation↗
NASA NTRS2017

Importance of a Priori Vertical Ozone Profiles for TEMPO Air Quality Retrievals

Ozone (O3) is a toxic pollutant which plays a major role in air quality. Typically, monitoring of surface air quality and O3 mixing ratios is conducted using in situ measurement networks. This is partially due to high-quality information related to air quality being limited from space-borne platforms due to coarse spatial resolution, limited temporal frequency, and minimal sensitivity to lower tropospheric and surface-level O3. The Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite is designed to address the limitations of current space-based platforms and to improve our ability to monitor North American air quality. TEMPO will provide hourly data of total column and vertical profiles of O3 with high spatial resolution to be used as a near-real-time air quality product. TEMPO O3 retrievals will apply the Smithsonian Astrophysical Observatory profile algorithm developed based on work from GOME (Global Ozone Monitoring Experiment), GOME-2, and OMI (Ozone Monitoring Instrument). This algorithm is suggested to use a priori O3 profile information from a climatological data-base developed from long-term ozone-sonde measurements (tropopause-based (TB-Clim) O3 climatology). This study evaluates the TB-Clim dataset and model simulated O3 profiles, which could potentially serve as a priori O3 profile information in TEMPO retrievals, from near-real-time data assimilation model products (NASA GMAO's (Global Modeling and Assimilation Office) operational GEOS-5 (Goddard Earth Observing System, Version 5) FP (Forecast Products) model and reanalysis data from MERRA2 (Modern-Era Retrospective analysis for Research and Applications, Version 2)) and a full chemical transport model (CTM), GEOS-Chem. In this study, vertical profile products are evaluated with surface (0-2 kilometers) and tropospheric (0-10 kilometers) TOLNet (Tropospheric Ozone Lidar Network) observations and the theoretical impact of individual a priori profile sources on the accuracy of TEMPO O3 retrievals in the troposphere and at the surface are presented. Results indicate that while the TB-Clim climatological dataset can replicate seasonally-averaged tropospheric O3 profiles, model-simulated profiles from a full CTM resulted in more accurate tropospheric and surface-level O3 retrievals from TEMPO when compared to hourly and daily-averaged TOLNet observations. Furthermore, it is shown that when large surface O3 mixing ratios are observed, TEMPO retrieval values at the surface are most accurate when applying CTM a priori profile information compared to all other data products.

Priori↗
DOE OSTI2023

Recent streamflow trends across permafrost basins of North America: Datasets

Climate change impacts, including changing temperatures, precipitation, and vegetation, are widely anticipated to cause major shifts to the permafrost with resulting impacts to hydro-ecosystems across the high latitudes of the globe. However, it is challenging to examine streamflow shifts in these regions owing to a paucity of data, discontinuity of records, and other issues related to data consistency and accuracy. We looked at recent changes in streamflow over 1976-2021 in watersheds affected by varying degrees of permafrost coverage to characterize trends and drivers for a range of watersheds across North America. Data sets are described in detail in the paper associated with this data set, Bennett et al. 2023, Front. Water - Water and Critical Zone, DOI: 10.3389/frwa.2023.1099660.These data contain CSV files of the streamflow, climate, and land surface characteristics for several sites located across the high latitude regions of North America. Both observed and reanalysis data products are provided. These files can be opened using Excel or a text editor, or they can be read, and analyzed in software tools such as Python or R. A brief description of the files is below, and more details can be found in the Methods section.rabpro_stats_north_select_74_55m.csv - This file describes the observed gages used in the analysis.GF31_23_metadat.csv - This file describes the 23 permafrost systems. Columns are as described in rabpro_stats_north_select_74_55m.csv above, with rabpro_id, the id used for the timeseries file mapping in GF31_23_time_series.csv.GF31_23_time_series.csv - This file contains the time series data for the stations described in GF31_23_metadat.csv.GF31_random_reaches_1583.csv - This file describes the 1583 randomly selected permafrost-dominant sites for machine learning analysis.era5_GF31_monthly_vars_random_reaches_1583.csv - This file contains the monthly ERA5 land data for the 1583 randomly selected permafrost-dominant sites.observed.zip: USGS and Hydat station data for the 74 gages analyzed in this study. Monthly, seasonal, and annual streamflow observations for minimum streamflow, mean streamflow, and maximum streamflow. Units are m3/sec. 1975-2022.streamflow_daily_GF31_infilled_1979_2022.csv - infilled daily streamflow data (infilled using GloFAS v 3.1) for 55 gages. Units are m3/sec. 1979-2022.glofas_23.zip - Glofas v3.1 file for the 23 permafrost-dominant gages in the study. Monthly, seasonal, and annual streamflow observations for minimum streamflow, mean streamflow, and maximum streamflow. Units are m3/sec. 1979-2021.glofas_1583.zip - Glofas v3.1 file for the 1583 randomly selected permafrost-dominant gages in the study. Monthly, seasonal, and annual streamflow observations for minimum streamflow, mean streamflow, and maximum streamflow. Units are m3/sec. 1979-2021.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

The Earth System Grid Federation : an Open Infrastructure for Access to Distributed Geospatial Data

The Earth System Grid Federation (ESGF) is a multi-agency, international collaboration that aims at developing the software infrastructure needed to facilitate and empower the study of climate change on a global scale. The ESGF's architecture employs a system of geographically distributed peer nodes, which are independently administered yet united by the adoption of common federation protocols and application programming interfaces (APIs). The cornerstones of its interoperability are the peer-to-peer messaging that is continuously exchanged among all nodes in the federation; a shared architecture and API for search and discovery; and a security infrastructure based on industry standards (OpenID, SSL, GSI and SAML). The ESGF software is developed collaboratively across institutional boundaries and made available to the community as open source. It has now been adopted by multiple Earth science projects and allows access to petabytes of geophysical data, including the entire model output used for the next international assessment report on climate change (IPCC-AR5) and a suite of satellite observations (obs4MIPs) and reanalysis data sets (ANA4MIPs).

search,↗
DOE OSTI2025

Monitoring and modeling hydrologic conditions in Ukraine for hydropower generation

Study region: The Dnieper and Dniester Rivers of Ukraine. Study focus: The ongoing conflict in Ukraine has caused disruptions to electricity generation, of which hydroelectric sources contribute approximately 9 % to the country’s needs. With the takeover of the Zaporizhzhia nuclear power plant by enemy forces, the loss of the Kakhovka hydroelectric dam, and the future impacts of the conflict on electricity generation unclear, it may be valuable for the Ukrainian government to better understand how it could leverage hydroelectric power sources in the near future. Unfortunately, measurements of river discharge throughout Ukraine ceased data collection in the late 1980’s to early 1990’s. To address this data gap, we developed a protocol that combined satellite-based time-series measurements of river width at seven locations throughout Ukraine from 2013 to 2023 with reanalysis data, climate-model predictions, and hydrologic models to both provide a means of monitoring a proxy for near-real-time discharge and also predict near-term (i.e., 2023–2030) hydrologic patterns for the region. New hydrological insights for the region: We ran new algorithms on 144 WorldView-2 and WorldView-3 satellite images to map rivers and extract width, one of which was validated against river gauge data located along the same river but in a neighboring country. Hydrologic models using two climate scenarios found minimal change in annual discharge at all sites, but magnitude and timing of peak discharge showed a moderate trend. The results suggest that hydropower is underutilized in Ukraine.

13 HYDRO ENERGY↗
DOE OSTI2022

High-Resolution Wind Resource Data Set of the Greater Puerto Rico Region

In February 2022, the U.S. Department of Energy and six national laboratories launched the Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100). PR100 aims to provide a comprehensive analysis of possible pathways for Puerto Rico's energy future, with a goal of 100% renewable energy by 2050. As a part of the renewable energy potential assessment in this project, we developed 20 years (2001-2020) of data using a numerical weather prediction (NWP) model for onshore and offshore wind resource assessment for the Puerto Rico. The research steps in developing the long-term wind resource data sets based on the NWP model were: 1. Model wind resource based on the Weather Research and Forecasting (WRF) model. 2. Develop WRF model configurations for Puerto Rico. 3. Test WRF with 11 different physics parameterizations for planetary boundary layer (PBL). 4. Assess WRF output from the different PBL schemes against observations. 5. Select a final model configuration which can produce the modeled wind speed with sufficient accuracy. 6. Produce 20 years wind resource data sets for Puerto Rico region. In the first stage of our framework for developing wind resource data, we developed a WRF model configuration using two nested domains (9 km and 3 km) to cover Puerto Rico and U.S. Virgin Islands and downscale the ERA5 reanalysis data (0.25 degrees x 0.25 degrees; hourly interval) to a 3-km domain. For the second stage, we implemented one-year simulations focused on using 11 different PBL physics parameterizations to find a combination of WRF physics parameterizations that could provide accurately modeled wind speed for Puerto Rico. We also analyzed the sensitivity of the modeled wind speed to PBL schemes for onshore and offshore locations. The WRF output resulting from the 11 WRF experiments using different PBL parameterizations were evaluated against observations obtained from the National Data Buoy Center (NDBC) as well as at hub height for a location for which measurements were available. A final model setup selected through the validation with observational data was used to produce 20 years of data with 3-km spatial and 5-minute temporal resolution. The WRF model output was post-processed to include wind profiles and basic atmospheric variables in a format that can be easily used for downstream modeling. The 20 years of wind resource data will be made available through NREL and support the estimation of wind energy development costs for the PR100 study.

17 WIND ENERGY↗
NASA NTRS2002

Extreme Weather Events and Their Relationship to Low Frequency Teleconnection Patterns

A new method for identifying the structure and other characteristics of extreme weather events is introduced and applied to both model simulations and observations. The approach is based on a linear regression model that links daily extreme precipitation amounts for a particular point on the globe to precipitation and related quantities at all other points. We present here some initial results of our analysis of extreme precipitation events over the United States, including how they are influenced by ENSO and various large-scale teleconnection patterns such as the PNA. The results are based on simulations made with the NASA/NCAR AGCM (Lin and Rood 1996). The quality of the simulated climate for the NASA/NCAR AGCM forced with observed SSTs is described in Chang et al. (2001). The runs analyzed here consist of three 20-year runs forced with idealized cold, neutral and warm ENSO SST anomalies (superimposed on the mean seasonal cycle of SST). The idealized warm or cold SST anomalies are fixed throughout each 20- year simulation and consist of the first EOF (+/- 3 standard deviations) of monthly SST data. Comparisons are made with the results obtained from a similar analysis that uses daily NOAA precipitation observations (Higgins et al. 1996) over the United States and NCEP/NCAR reanalysis data for the period 1949-1998.

Chang, Yehui↗