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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Retrieving microphysical properties of concurrent pristine ice and snow using polarimetric radar observations

Abstract. Ice and mixed-phase clouds play a key role in our climate system because of their strong controls on global precipitation and radiation budget. Their microphysical properties have been characterized commonly by polarimetric radar measurements. However, there remains a lack of robust estimates of microphysical properties of concurrent pristine ice and aggregates because larger snow aggregates often dominate the radar signal and mask contributions of smaller pristine ice crystals. This paper presents a new method that separates the scattering signals of pristine ice embedded in snow aggregates in scanning polarimetric radar observations and retrieves their respective abundances and sizes for the first time. This method, dubbed ENCORE-ice, is built on an iterative stochastic ensemble retrieval framework. It provides the number concentration, ice water content, and effective mean diameter of pristine ice and snow aggregates with uncertainty estimates. Evaluations against synthetic observations show that the overall retrieval biases in the combined total microphysical properties are within 5 % and that the errors with respect to the truth are well within the retrieval uncertainty. The partitioning between pristine ice and snow aggregates also agrees well with the truth. Additional evaluations against in situ cloud probe measurements from a recent campaign for a stratiform cloud system are promising. Our median retrievals have a bias of 98 % in the total ice number concentration and 44 % in the total ice water content. This performance is generally better than the retrieval from empirical relationships. The ability to separate signals of different ice species and to provide their quantitative microphysical properties will open up many research opportunities, such as secondary ice production studies and model evaluations for ice microphysical processes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2008

How Does Mediterranean Basin's Atmosphere Become Weak Moisture Source During Negative Phase of NAO: Use of AIRS, AMSR, TOVS, & TRMM Satellite Datasets Over Last Two NAO Cycles to Examine Governing Controls on E-P

The Mediterranean Sea is a noted 'concentration" basin in that it almost continuously exhibits positive evaporation minus precipitation (E - P ) properties -- throughout the four seasons and from one year to the next. Nonetheless, according to the ECMWF Era-40 48-year (1958-2005) climate reanalysis dataset, for various phases of the North Atlantic Oscillation (NAO) when the pressure gradient between Portugal and Iceland becomes either very relaxed (large negative NAO-Index) or in transition (small positive or negative NAO-Index), the atmospheric moisture source properties of the basin become weak, at times even reversed for several months (i.e., negative E - P). This behavior poses numerous questions concerning how and why these events occur. Moreover, it begs the question of what it would take for the basin to reach its tipping point in which P would exceed E throughout the rainy season (some six months) on an annually persistent basis -- and the sea would possibly transform to a recurring "dilution" basin. This talk investigates these questions by: (1) establishing over a period from 1979 to present, based on detailed analyses of satellite retrieval products from a combination of NASA-AQUA, NOAA-LEO, NASA/JAXA Scatterometer, and NASA-TRMM platforms, plus additional specialized satellite data products and ancillary meteorological datasets, the actual observation-based behavior of E - P, (2) diagnosing the salient physical and meteorological mechanisms that lead to the weaker E - P events during the analysis period, partly based on analyzing surface and upper air data at discrete stations in the western and eastern Mediterranean -- while at the same time evaluating the quality of the ERA-40 data over this same time period, (3) conducting GCM and high-resolution regional modeling experiments to determine if perturbed but realistic meteorological background conditions could maintain Mediterranean as a "dilution" basin through the October to March rainy season on annually recurring basis, and (4) investigating how such conditions might modify important internal and external climatic processes known to be closely related to the dynamical, thermal, and hydrological properties of the basin (e.g., drought frequency over Iberian peninsula, rainfall accumulation within Sahel, alteration of Levantine branch of east-west aligned open thermohaline cell, and modification of warm-salty intermediate flow through Gibraltar straight into North Atlantic).

Smith, Eric A.↗
DOE OSTI2023

Predicting Biomass Yields of Advanced Switchgrass Cultivars for Bioenergy and Ecosystem Services Using Machine Learning

The production of advanced perennial bioenergy crops within marginal areas of the agricultural landscape is gaining interest due to its potential to sustainably produce feedstocks for biofuels and bioproducts while also improving the sustainability and resilience of commodity crop production. However, predicting the biomass yields of this production system is challenging because marginal areas are often relatively small and spread around agricultural fields and are typically associated with various abiotic conditions that limit crop production. Machine learning (ML) offers a viable solution as a biomass yield prediction tool because it is suited to predicting relationships with complex functional associations. The objectives of this study were to (1) evaluate the accuracy of commonly applied ML algorithms in agricultural applications for predicting the biomass yields of advanced switchgrass cultivars for bioenergy and ecosystem services and (2) determine the most important biomass yield predictors. Datasets on biomass yield, weather, land marginality, soil properties, and agronomic management were generated from three field study sites in two U.S. Midwest states (Illinois and Iowa) over three growing seasons. The ML algorithms evaluated in the study included random forests (RFs), gradient boosting machines (GBMs), artificial neural networks (ANNs), K-neighbors regressor (KNR), AdaBoost regressor (ABR), and partial least squares regression (PLSR). Coefficient of determination (R 2 ) and mean absolute error (MAE) were used to evaluate the predictive accuracy of the tested algorithms. Results showed that the ensemble methods, RF (R 2 = 0.86, MAE = 0.62 Mg/ha), GBM (R 2 = 0.88, MAE = 0.57 Mg/ha), and GBM (R 2 = 0.78, MAE = 0.66 Mg/ha), were the most accurate in predicting biomass yields of the Independence, Liberty, and Shawnee switchgrass cultivars, respectively. This is in agreement with similar studies that apply ML to multi-feature problems where traditional statistical methods are less applicable and datasets used were considered to be relatively small for ANNs. Consistent with previous studies on switchgrass, the most important predictors of biomass yield included average annual temperature, average growing season temperature, sum of the growing season precipitation, field slope, and elevation. This study helps pave the way for applying ML as a management tool for alternative bioenergy landscapes where understanding agronomic and environmental performance of a multifunctional cropping system seasonally and interannually at the sub-field scale is critical.

09 BIOMASS FUELS↗
DOE OSTI2025

Inorganic Alterations in Unconventional Shale Reservoirs: Importance of Additive and Base Fluid Chemistry

The effective development of unconventional petroleum systems requires the use of significant water resources. In an effort to reduce the consumption of freshwater resources for hydraulic fracturing, highly saline produced waters are increasingly recycled for use as a base fluid. However, there are significant knowledge gaps regarding potential water–rock interactions resulting from the introduction of produced waters and associated additives into shale reservoirs such as formation and deposition of mineral scale, which can negatively affect hydrocarbon production through wellbore restriction and damage to hydraulically generated fractures. To assess the impacts of field stimulation practices in the subsurface, a series of laboratory experiments were completed using (a) three distinct sedimentary rock formations of the Midland Basin (Texas, USA) and (b) additives with two different base fluids: municipal fresh water and clean brine. The experimental approach used relevant injection sequences and mixing ratios in specialized reactors for 3 weeks. Static pressurized experiments and nonpressurized time-resolved experiments were undertaken. The resulting solids and liquids were analyzed by using a variety of laboratory- and synchrotron-based techniques. The use of an acid spearhead (15% HCl) resulted in texturing of both clay-rich and calcareous shales, which can temporarily enhance porosity but subsequently result in mineral scale deposition. The primary matrix scale was Fe(III)-bearing phases, which occurred in all experiments regardless of base fluid chemistry. Additionally, strontium sulfate (SrSO 4 ) precipitated on shale surfaces when clean brines were used. It was concluded that clean brine was the main source of Sr 2+ species, while persulfate breaker degradation and oxidation of pyrite were the sources of SO 4 2– . Sulfate scaling was more pronounced in clay-rich shales, suggesting that Sr sorption is important for promoting celestite formation. This work demonstrates that mineral scale deposition is a complex phenomenon, whereby the type and proportions of various mineral phases are determined from reservoir alteration processes and coprecipitation of constituents from injection fluids. In conclusion, the experimental results shown here should be considered when evaluating different base fluids and additives in order to mitigate mineral precipitation in unconventional shale reservoirs, which could result in reservoir degradation.

Jew, Adam D. [SLAC National Accelerator Laboratory↗
NASA NTRSDate not supplied

Using Satellite Soil Moisture and Rainfall in the Landslide Hazard Assessment for Situational Awareness System

The Landslide Hazard Assessment for Situational Awareness system(LHASA)gives a global view of landslide hazard in nearly real time. Currently, it is being upgraded from version 1 to version 2, which entails improvements along several dimensions. These include the incorporation of new predictors, machine learning, and new event-based landslide inventories. As a result, LHASA version 2 substantially improves on the prior performanceand introduces a probabilistic element to the global landslide nowcast. Data from the soil moisture active-passive (SMAP) satellite has been assimilated into a globally consistent data product with a latency less than 3 days, known as SMAP Level 4. In LHASA, thesedata representthe antecedent conditions prior to landslide-triggering rainfall. In some cases, soil moisture may have accumulated over aperiod of many months. The model behind SMAP Level 4 also estimates the amount of snow on the ground, which is an important factor in some landslide events. LHASA also incorporates this information as an antecedent condition that modulates the response torainfall. Slope, lithology, and active faults were also used as predictor variables. These factors can have a strong influence on where landslides initiate.LHASA relies on precipitation estimates from the Global Precipitation Measurement mission to identify the locations where landslides are most probable. The low latency and consistent global coverage of these data make them ideal for real-time applications at continental to global scales. LHASA relies primarily on rainfall from the last 24 hours to spothazardous sites, which is rescaled by the local 99thpercentile rainfall.However, the multi-day latency of SMAP requires the use of a 2-day antecedent rainfall variable to represent the accumulation of rain between the antecedent soil moisture and current rainfall. LHASA merges these predictors with XGBoost, a commonly used machine-learning tool, relying on historical landslide inventories to develop the relationship between landslide occurrence and various risk factors. The resulting model relies heavily on current daily rainfall, but other factors also play an important role. LHASA outputsthe probability oflandslide occurrence ona grid of roughly one kilometer over all continents from 60 North to 60 South latitude. Evaluation over the period 2019-2020 showsthat LHASA version 2 doubles the accuracy of the global landslide nowcast without increasing the global false alarm rate. LHASA also identifies the areas where the human exposure to landslide hazard is most intense. Landslide hazard is divided into 4 levels: minimal, low, moderate, and high. Next, the number of persons and the length of major roads (primary and secondary roads)within each of these areas is calculated for every second-level administrative district (county). These results can be viewedthrough a web portal hosted at the Goddard Space Flight Center. In addition, users can download daily hazard and exposure data.LHASAversion 2uses machine learning and satellite data to identify areas of probable landslide hazard within hours of heavy rainfall. Itsglobal maps are significantly more accurate, and it now includes rapid estimates of exposed populations and infrastructure. In addition, a forecast mode will be implemented soon.

Thomas Stanley↗
DOE OSTI2025

A Review of Tank 48H Treatment of Tetraphenylborate with Permanganate

Tank 48H contains roughly 270,000 gallons of radioactive waste material. The waste stored in this tank was to be processed in multiple stages utilizing facilities at the Savannah River Site (SRS) almost 30 years ago. The In-Tank Precipitation Process (ITP) was initiated in Tank 48H, which precipitated highly radioactive cesium-137 using sodium tetraphenylborate (NaTPB). While the process succeeded in precipitating the Cs, Sr, and other actinides, it also generated an unexpectedly large amount of benzene. The evolved benzene created a safety concern and made the waste incompatible with further downstream processing. This halted the ITP, and a new method of treatment was required to continue processing the legacy waste contained in Tank 48H. A myriad of treatment options have been proposed with multiple teams of researchers assembled to work on this highly complex issue over the last few decades. This review investigated one potentially viable treatment option, in-tank oxidation with sodium permanganate. Permanganate has been well known in literature as a strong oxidizing agent for organic compounds, as well as being utilized at the Savannah River Site (SRS) in other processes. A small number of studies have been conducted utilizing waste simulants to evaluate the use of permanganate as an oxidant for tetraphenylborate (TPB). A search of the literature and data from these studies indicates that permanganate could be a viable treatment for destruction of TPB in Tank 48H. While the scoping studies had a small number of individual experiments and nonideal conditions, the permanganate decomposed up to 90% of the TPB. A free hydroxide concentration above 1.0 M is required for tank corrosion control. Simulant studies indicate no decrease in TPB decomposition by permanganate until pH 14. The simulant studies show an increase in TPB decomposition as the temperature of the solution is increased to 40 °C. The post-reaction analysis of previous simulant tests did not look at all of the organic degradation products. Investigations into what these organic products are and in what quantity will help guide determinations as to whether the downstream processing facilities are able to handle the material that will be generated. Study on the time frame for the reaction between permanganate and TPB should be investigated as the literature reports only extend out to two weeks reaction time. The permanganate treatment conditions indicate no corrosion control concerns and a longer timescale reaction may be needed for in-tank treatment. In addition, further study would be useful to identify a lower boundary condition for the ratio of TPB and oxidant. The simulant tests applied large excesses of permanganate, and this may be unnecessary. The size constraint of the tank means that there will be practical limitations on the amount of sodium permanganate that can be added to the tank. The amount of permanganate should be minimized as much as possible while still ensuring decomposition of the TPB to minimize the amount of manganese dioxide solids generated.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRSDate not supplied

IMERG and GPCP Seasonality and Response to Climate and Weather Variability

The Integrated Multi-satellitE Retrievals for GPM (IMERG) and the The Global Precipitation Climatology Project (GPCP) are two of the most popular precipitation products. IMERG is a relatively new dataset that targets the needs primarily of the hydrological community by resolving the hydrological cycle of precipitation at fine temporal (30-minutes) and spatial (10-km) scales. IMERG only recently exceeded the user base of the highly successful, but discontinued in 2019, TRMM Multi-satellite Precipitation Analysis (TMPA). GPCP, on the other hand, has traditionally been strong in the climate research community, and recently has been revised under the framework of NASA's Making Earth System Data records for Use in Research Environments (MEaSUREs) program. Both IMERG and GPCP are similar in the underlying approaches to achieve global coverage, in particular using satellite microwave and infrared observations, and adjusting the precipitation retrieval with rain gauge information. While there is a tendency to use both datasets interchangeably, differences remain and some of them limit the usage of IMERG as a climate data record at this point. By applying Principal Component Analysis, we identify the differences and similarities mode-by-mode, and further the guidance on suggested usage of IMERG as a climate data record. As an example in the attached figure, IMERG vs GPSP differences in the explained variance by the two leading seasonal modes can be identified, mainly in the extreme southern latitudes, and over western boundary currents (Gulfstream and Kuroshio). At the preparation time for this presentation, the new version "07"of IMERG was in the works that may resolve the issues presented here. Nevertheless, our analysis can help to gauge the uncertainties of the studies already done using the currently existing IMERG version "06", and evaluate the improvements in the upcoming version "07".

Andrey Savtchenko↗
NASA NTRSDate not supplied

U.S. IMERG Status

The U.S. Science Team’s Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) product provides estimates of surface precipitation rate and related information on a global 0.1° half-hour grid. It is run three times at increasingly longer latency and higher information input to serve different communities. This presentation will briefly summarize the major upgrades included in Version 07. The Goddard Profiling (GPROF) algorithm (applied to passive microwave sensor data from the GPM virtual constellation) and the Combined Radar-Radiometer Algorithm (CORRA) that provide input to IMERG V07 are both improved, and the new Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Dynamic Infrared Rain Rate (PDIR) algorithm is being applied to infrared data, which employs an additional, warmer infrared temperature threshold than the previous infrared scheme and a deep neural network. V07 has improved bias performance as a result of calibrations that now employ the entire swath widths of CORRA and GPROF GPM Microwave Imager precipitation estimates. As well, time continuity in precipitation features is improved as a result of changes to the Kalman filter that approximately preserve the local histogram of precipitation rates (Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood, or SHARPEN), and that better account for differences in sensor performance. A long-standing bug in the geolocation that shifted grid values 0.1° to the east in the latitude band 70°N-S has been corrected. Other changes in V07 include a hierarchical selection among motion vector sources to address deficiencies in the precipitation propagation near orography, an update to the precipitation phase specification for improved consistency with current inputs, and a renewed effort to implement climatological gauge adjustment to the near-real-time Runs. Early evaluations of V07 IMERG show interesting behavior across the TRMM orbit boost that is highly relevant to the planned orbit boost for the GPM Core observatory.

GPM↗
NASA NTRS2018

An IR Sounding-Based Analysis of the Saharan Air Layer in North Africa

Intense daytime surface heating over barren-to-sparsely vegetated surfaces results in dry convective mixing. In the absence of external forcing such as mountain waves, the dry convection can produce a deep, well-mixed, nearly isentropic boundary layer that becomes a well-mixed residual layer in the evening. These well-mixed layers (WML) retain their unique mid-tropospheric thermal and humidity structure for several days. To detect the SAL and characterize its properties, AIRS Level 2 Ver. 6 temperature and humidity products (2003-Present) are evaluated against rawinsondes and compared to model analysis at each of the 55 rawinsonde stations in northern Africa. To distinguish WML from Saharan air layers (WMLs of Saharan origin), the detection involved a two-step process: 1) algorithm-based detection of WMLs in dry environments (less than 7 g per kilogram mixing ratio) 2) identification of Sahara air layers (SAL) by applying Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) back trajectories to determine the history of each WML. WML occurrence rates from AIRS closely resemble that from rawinsondes, yet rates from model analysis were up to 30% higher than observations in the Sahara due to model errors. Despite the overly frequent occurrence of WMLs from model analysis, HYSPLIT trajectory analysis showed that SAL occurrence rates (given a WML exists) from rawinsondes, AIRS, and model analysis were nearly identical. Although the number of WMLs varied among the data sources, the proportion of WMLs which were classified as SAL was nearly the same. The analysis of SAL bulk properties showed that AIRS and model analysis exhibited a slight warm and moist bias relative to rawinsondes in non-Saharan locations, but model analysis was notably warmer than rawinsondes and AIRS within the Sahara. The latter result is likely associated with the dearth of available data assimilated by model analysis in the Sahara. The variability of SAL thicknesses was reasonably captured by both AIRS and model analysis, but the former favor layers than are thinner than observations. Finally, further analysis of HYSPLIT trajectories revealed that fewer than 10% and 33% of all SAL back trajectories passed through regions with notable precipitation (>100 mm accumulated along the trajectory path) or Aerosol Optical Depth (AOD greater than 0.4, 75th percentile of AOD) on average, respectively. Trajectory analysis indicated that only 57% of Saharan and 24% of non-Saharan WMLs are definitively of Saharan origin (Saharan requirement: Two consecutive days in Sahara and 24 or more of those hours within 72 hours of detection). Non-SAL WMLs either originate from local-to-regionally generated residual layers or from mid-latitude air streams that do not linger over the Sahara for a sufficient time period. Initial analysis shows these non-SAL WMLs tend to be both notably cooler and slightly moister than their SAL counter parts. Continuing analysis will address what role Saharan and non-Saharan air masses characteristics may play on local and regional environmental conditions.

Nicholls, Stephen D.↗
DOE OSTI2023

Investigation of potential causes of production limiting fouling within SWPF due to mercury solids formation during pH swings within the SWPF process

The Salt Waste Processing Facility (SWPF) goal to investigate the use a high sodium Salt Batch 5 Qualification sample as the radioactive salt solution for mixing tests meant to evaluate the propensity for solids formation between the 5.6 M sodium clarified salt solution (CSS) and scrub solutions (0.05 M HNO 3 and 0.025 M NaOH solutions) during pH changes has been completed at SRNL. The key results and conclusions from this investigation to evaluate the propensity for solids formation between CSS and scrub solutions because of pH fluctuations include the following: None of the 8 test matrices, involving the mixing/titrating of scrub solution with CSS and vice versa, showed any visible evidence for the formation of solids with pH changes. The average turbidity measurements for these 8 tests, excluding test 7, was 21.6 ± 0.4 NTU. This average turbidity value is within 2 sigma of the baseline turbidity value of 20.5 NTU for the CSS only without added scrub solutions. However, only in test 7, which involved the titrating of CSS into dilute nitric acid scrub solution (0.05 M HNO 3 solution), was a small measurable change in the turbidity of the mixture observed at 31.9 ºC. On the other hand, the equivalent test (instantaneous addition of CSS into 0.05 M HNO 3 ) gave an average turbidity measurement value of 21.6 ± 0.6 NTU at a temperature of 24.0 ºC. This difference in turbidity measurement has been attributed to either instrument performance errors or the effect of temperature. It is recommended that these CSS and scrub solution mixing tests be performed with corresponding simulant solutions in a non-radioactive environment. A simulant environment will aid in the full integration of a pH and turbidity meter equipment with the experimental set up. A new detection method, preferable a laser-based method, may be needed in detecting and confirming the precipitation of fine particulates at the right blending times and pH fluctuations in the solution mixtures. On the other hand, the differing free OH concentrations (~2 M in CSS), H + concentrations (0.05 M HNO 3 ) for the titrants and the bulk solutions, may make it almost impossible to observe a neutral pH or a pH in the range of 7-9, where the potential for solids formation is maximized and leads to possible precipitation of minerals like aluminum hydroxides.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1998

Design, Evaluation and GCM-Performance of a New Parameterization for Microphysics of Clouds with Relaxed Arakawa-Schubert Scheme (McRas)

A prognostic cloud scheme named McRAS (Microphysics of clouds with Relaxed Arakawa-Schubert Scheme) was developed with the aim of improving cloud-microphysics, and cloud-radiation interactions in GCMs. McRAS distinguishes convective, stratiform, and boundary-layer clouds. The convective clouds merge into stratiform clouds on an hourly time-scale, while the boundary-layer clouds do so instantly. The cloud condensate transforms into precipitation following the auto-conversion relations of Sundqvist that contain a parametric adaptation for the Bergeron-Findeisen process of ice crystal growth and collection of cloud condensate by precipitation. All clouds convect, advect, and diffuse both horizontally and vertically with a fully active cloud-microphysics throughout its life-cycle, while the optical properties of clouds are derived from the statistical distribution of hydrometeors and idealized cloud geometry. An evaluation of McRAS in a single column model (SCM) with the GATE Phase III data has shown that McRAS can simulate the observed temperature, humidity, and precipitation without discernible systematic errors. An evaluation with the ARM-CART SCM data in a cloud model intercomparison exercise shows reasonable but not an outstanding accurate simulation. Such a discrepancy is common to almost all models and is related, in part, to the input data quality. McRAS was implemented in the GEOS II GCM. A 50 month integration that was initialized with the ECMWF analysis of observations for January 1, 1987 and forced with the observed sea-surface temperatures and sea-ice distribution and vegetation properties (biomes, and soils), with prognostic soil moisture, snow-cover, and hydrology showed a very realistic simulation of cloud process, incloud water and ice, and cloud-radiative forcing (CRF). The simulated ITCZ showed a realistic time-mean structure and seasonal cycle, while the simulated CRF showed sensitivity to vertical distribution of cloud water which can be easily altered by the choice of time constant and incloud critical cloud water amount regulators for auto-conversion. The CRF and its feedbacks also have a profound effect on the ITCZ. Even though somewhat weaker than observed, the McRAS-GCM simulation produces robust 30-60 day oscillations in the 200 hPa velocity potential. Two ensembles of 4-summer (July, August, September) simulations, one each for 1987 and 1988 show that the McRAS-GCM simulates realistic and statistically significant precipitation differences over India, Central America, and tropical Africa. Several seasonal simulations were performed with McRAS-GEOS II GCM for the summer (June-July- August) and winter (December-January-February) periods to determine how the simulated clouds and CRFs would be affected by: i) advection of clouds; ii) cloud top entrainment instability, iii) cloud water inhomogeneity correction, and (iv) cloud production and dissipation in different cloud-processes. The results show that each of these processes contributes to the simulated cloud-fraction and CRF.

Sud, Y. C.↗
DOE OSTI2023

Evaluation of four ground-based retrievals of cloud droplet number concentration in marine stratocumulus with aircraft in situ measurements

Abstract. Cloud droplet number concentration (Nd) is crucial for understanding aerosol–cloud interactions (ACI) and associated radiative effects. We present evaluations of four ground-based Nd retrievals based on comprehensive datasets from the Atmospheric Radiation Measurement (ARM) Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign. The Nd retrieval methods use ARM ENA observatory ground-based remote sensing observations from a micropulse lidar, Raman lidar, cloud radar, and the ARM NDROP (Droplet Number Concentration) value-added product (VAP), all of which also retrieve cloud effective radius (re). The retrievals are compared against aircraft measurements from the fast cloud droplet probe (FCDP) and the cloud and aerosol spectrometer (CAS) obtained from low-level marine boundary layer clouds on 12 flight days during summer and winter seasons. Additionally, the in situ measurements are used to validate the assumptions and characterizations used in the retrieval algorithms. Statistical comparisons of the probability distribution function (PDF) of the Nd and cloud re retrievals with aircraft measurements demonstrate that these retrievals align well with in situ measurements for overcast clouds, but they may substantially differ for broken clouds or clouds with low liquid water path (LWP). The retrievals are applied to 4 years of ground-based remote sensing measurements of overcast marine boundary layer clouds at the ARM ENA observatory to find that Nd (re) values exhibit seasonal variations, with higher (lower) values during the summer season and lower (higher) values during the winter season. The ensemble of various retrievals using different measurements and retrieval algorithms such as those in this paper can help to quantify Nd retrieval uncertainties and identify reliable Nd retrieval scenarios. Of the retrieval methods, we recommend using the micropulse lidar-based method. This method has good agreement with in situ measurements, less sensitivity to issues arising from precipitation and low cloud LWP and/or optical depth, and broad applicability by functioning for both daytime and nighttime conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Evaluation of Four Ground-based Retrievals of Cloud Droplet Number Concentration in Marine Stratocumulus with Aircraft In Situ Measurements

Cloud droplet number concentration (N d ) is crucial for understanding aerosol-cloud interactions (ACI) and associated radiative effects. We present evaluations of four ground-based N d retrievals based on comprehensive datasets from the Atmospheric Radiation Measurements (ARM) Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign. The N d retrieval methods use ARM ENA observatory ground-based remote sensing observations from a Micropulse lidar, Raman lidar, cloud radar, and the ARM NDROP Value-added Product (VAP), all of which also retrieve cloud effective radius (r e ). The retrievals are compared against aircraft measurements from the Fast-Cloud Droplet Probe (FCDP) and the Cloud and Aerosol Spectrometer (CAS) obtained from low-level marine boundary layer clouds on 12 flight days during summer and winter seasons. Additionally, the in situ measurements are used to validate the assumptions and characterizations used in the retrieval algorithms. Statistical comparisons of the probability distribution function (PDF) of the N d and cloud r e retrievals with aircraft measurements demonstrate that these retrievals align well with in situ measurements for overcast clouds, but they may substantially differ for broken clouds or clouds with low liquid water path (LWP). The retrievals are applied to four years of ground-based remote sensing measurements of overcast marine boundary layer clouds at the ARM ENA observatory to find that N d (r e ) values exhibit seasonal variations, with higher (lower) values during the summer season and lower (higher) values during the winter season. The ensemble of various retrievals using different measurements and retrieval algorithms such as those in this paper can help to quantify N d retrieval uncertainties and identify reliable N d retrieval scenarios. Of the retrieval methods, we recommend using the using the Micropulse lidar-based method given its good agreement with in situ measurements, it has less sensitivity to issues arising from precipitation and low cloud LWP/optical depth, and it has broad applicability by functioning for both day and nighttime conditions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1999

Comparison of TRMM and Global Precipitation Climatology Project (GPCP) Precipitation Analyses

This paper describes recent results of using Tropical Rainfall Measuring Mission (TRMM) (launched in November 1997) information as the key calibration tool in a merged analysis on a 1 x 1' latitude/longitude monthly scale based on multiple satellite sources and raingauge analyses. The TRMM-based product is compared with the community-based Global Precipitation Climatology Project (GPCP) results. The long-term GPCP analysis is compared to the new TRMM-based analysis which uses the most accurate TRMM information to calibrate the estimates from the Special Sensor Microwave/Imager (SSM/I) and geosynchronous IR observations and merges those estimates together with the TRMM and gauge information to produce accurate rainfall estimates with the increased sampling provided by the combined satellite information. The comparison with TRMM results on a month-to-month basis should clarify the strengths and weaknesses of the long-term GPCP product in the tropics and point to how to improve the monitoring analysis. Preliminary results from the TRMM merged satellite analysis indicates fairly close agreement with the GPCP estimates. The GPCP analysis is done at 2.5 degree latitude/longitude resolution and interpolated to a 1 degree grid for comparison with the TRMM analysis. As expected the same features are evident in both panels, but there are subtle differences in the magnitudes. Focusing on the Pacific Ocean Inter-Tropical Convergence Zone (ITCZ) one can see the TRMM-based estimates having higher peak values and lower values in the ITCZ periphery. These attributes also show up in the statistics, where GPCP>TRMM at low values (below 10 mm/d) and TRMM>GPCP at high values (greater than 15 mm/d). The area in the Indian Ocean which shows consistently higher values of TRMM over GPCP needs to be examined carefully to determine if the lack of geosynchronous data has led to a difference in the two analyses. By the time of the meeting over a year of TRMM products will be available for comparison. Global tropical and regional values will be compared. Both products will be compared to TRMM validation site data over land and water. The results should begin to determine the use of the TRMM estimates in the evaluation of the GPCP analysis.

Adler, Robert F.↗
DOE OSTI2022

Materials for Advanced Ultra-Supercritical (A-USC) Steam Turbines --- A-USC Component Demonstration

The U.S. Advanced Ultra-Supercritical (A-USC) Consortium was formed in 2001 as a government/industry program, sponsored by the U.S. Department of Energy (DOE) and the Ohio Coal Development Office (OCDO) and cost shared by industrial and not-for-profit partners. The purpose of the consortium was to advance the state of the art for power generation by evaluating and developing materials that allow the use of advanced steam cycles in coal-based power plants. These advanced cycles, with steam temperatures up to 1400°F (760°C), can increase the efficiency of coal-fired boilers from an average of 35% (current U.S. fleet) to more than 45% higher heating value (HHV) (>49% lower heating value [LHV]). The increase in a plant’s efficiency is limited unless new materials able to withstand these higher operating temperatures and pressures are identified and approved for use. The A-USC Consortium identified these needed materials during earlier phases of the program. It developed the welding and joining techniques along with manufacturing processes for casting and wrought products made from these new high-nickel alloys. It subjected these materials to extensive laboratory and steam loop testing. It then obtained ASME code approval for their use in U.S. boiler systems. The program’s successes leave this last remaining activity (ComTest Phase 2) that the U.S. utility industry has recommended to be accomplished prior to commercialization. The focus of the activity is the evaluation and demonstration of commercial readiness for “full scale” components to be made from these nickel-based alloy materials and provided by a U.S. domestic supply chain that is new to working with these alloys. According to studies completed by the Electric Power Research Institute (EPRI), the cost of an A-USC plant is approximately 20% higher than a non-A-USC plant because of its use of nickel-based alloys needed for the high temperature operating conditions. However, CO 2 reductions of approximately 30% from the current fleet average provide a strong incentive for its consideration. The actual costs and perceived value for CO 2 abatement will determine whether new or retrofitted plants are undertaken, although decisions to build A-USC plants in India would indicate its economic feasibility while also being part of a global carbon emissions strategy. The work by the A-USC Consortium, prior to the start of the ComTest project, has included lab scale and pilot scale materials testing, both in air and oxy-combustion. This testing has included air-cooled and steam-cooled “loops” that were installed into existing operating utility boilers to gain exposure of these materials to realistic conditions of high temperature and corrosion caused by the constituents in the coal ash. The A-USC Consortium also gained ASME Code approval of the Inconel 740 material, has cast and extruded the largest high nickel precipitation hardened alloys, and developed unique welding techniques to avoid problems identified by the competing European program. However, as valuable as these material test loops and accomplishments have been for obtaining information, their scale is below that required to minimize the risk associated for a U.S. utility to build a multibillion-dollar A-USC power plant. To reduce the final identified risk barrier to full-scale commercialization of these advanced materials and systems, the A-USC Consortium (guided by a utility industry advisory committee) has identified the key areas of the technology they desire to see as being capable of full-scale manufacturing and/or fabrication from an identified, capable U.S. domestic supplier base. A significant amount of work was accomplished during Phase 1 to identity the components, as well as the component size, that would be manufactured from advanced alloys such as Inconel 740H or Haynes 282 alloys. Pathways to supply these components for ComTest have been identified, as well as any further development that would be required. The Phase 2 effort used Phase 1 findings for designing these key full-scale components for A-USC boilers and turbines to include large castings; extrusions, forgings, fabrication of water walls and steam loops with headers from advanced materials, raw material (such as pipe extrusion billets) are at the commercial readiness level to permit advancement to a demonstration project. The Phase 2 work scope was addressed by a diverse team, including government, industry, and not-for-profit partners. The work scope under Phase 2 addressed fabrication of components identified as being outside of the proven capabilities of the existing supply chain, including the following: Steam turbine rotor forging and Haynes 282 nozzle carrier casting Superheater and reheater header and tube assemblies Large-diameter pipe extrusions and forgings Test valve articles to support ASME Code approval. In addition, key fabrication steps were completed, including boiler weld overlays and simulated field repairs. Throughout, extensive inspection and quality assurance testing of the components were performed. The team worked to advance ASME Code approval for key components and processes. Although much of the focus of ComTest Phase 2 was the high-temperature nickel-based alloy materials, a broader range of materials were incorporated, which would be representative of the materials used in full-scale A-USC power plant applications and have cross-cutting applicability on other high-temperature power generation options, such as advanced nuclear, supercritical CO 2 cycles, and central solar receivers. This report that has been submitted is organized in the following manner: Section 1 contains an Executive Summary. Section 2 discusses the ComTest project background and organization. Section 3 discusses project management and reporting. Section 4 discusses the procurement of nickel-based alloy and other A-USC materials and components. Section 5 discusses the fabrication of procurement of nickel-based alloy and other A-USC materials and components. Section 6 discusses the fabrication of cast nickel-based A-USC steam turbine components. Section 7 discusses the fabrication of forged nickel-based A-USC steam turbine piping and steam pipe components. Section 8 discusses the qualification of pressure relieve valves (PRVs) for A-USC power plants. Section 9 discusses proposed plans for future evaluation of A-USC components. Section 10 contains the summary and conclusion.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2026

Data, model inputs, and analysis scripts associated with a manuscript on stream intermittency controls across spatial scales in Pacific Northwest watersheds

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript "Hydroclimatic Memory and Watershed Template Shape Stream Intermittency: Multi-scale Attribution Using Process-based Simulation and Explainable ML" by Niroula et al. (2026), submitted to Water Resources Research (WRR). The study investigates the dominant controls on stream intermittency across local, reach, and watershed scales using a coupled process-based simulation and explainable machine-learning framework. Long-term daily simulations from the Advanced Terrestrial Simulator (ATS) were used to generate wetness states and ponded-depth responses over river-corridor cells. These ATS outputs were then aggregated across scales and used to train XGBoost (eXtreme Gradient Boosting) models. SHAP (SHapley Additive exPlanations) was applied to quantify the relative importance of hydroclimatic forcings, watershed template attributes, and antecedent-memory effects in shaping intermittency behavior. The analysis is carried out for three contrasting Pacific Northwest watersheds: Oak Creek (OCW), American River Watershed (ARW), and H.J. Andrews (HJA). Across these testbeds, the package contains ATS-ready watershed inputs, ATS run configuration and selected output files, model-evaluation data products, intermittency-analysis datasets, machine-learning target-feature tables, SHAP outputs, and notebooks used to organize, analyze, and visualize results. At a high level, the package documents a workflow in which ATS provides the physically based simulation backbone and explainable machine learning is used as a post-processing attribution tool. The contents are intended to support interpretation of the manuscript figures and results, provide context for how intermittency metrics were generated at multiple scales, and preserve the key artifacts needed to understand and reuse the analysis workflow. The package contains a high-level directory summary file (`summary.txt`) and four main content folders (1) `evaluation_plots` contains evaluation figures and supporting evaluation datasets; (2) `intermittency_plots` contains intermittency-focused analysis notebook and prepared datasets; (3) `ml-training-and-shap_values_plots` contains ML training inputs, SHAP outputs, and figure-generation notebooks; and (4) `watershed_mesh_and_ats_input` contains ATS model setup materials, forcing inputs, geometry, and selected run files. More specifically, the `evaluation_plots` folder contains the notebook used for ATS evaluation plotting and site-specific evaluation datasets. These include evapotranspiration and water-balance products for three watersheds, as well as an Oak Creek field-measurement discharge file. The `intermittency_plots` folder contains the notebook used for intermittency analysis and the prepared datasets used to analyze intermittent and non-intermittent wetness behavior across the study watersheds. The `ml-training-and-shap_values_plots` folder contains notebooks and outputs for the machine-learning and explainability workflow. This includes the main XGBoost and SHAP notebook(s), a beeswarm plotting notebook, target-feature tables for machine-learning training, SHAP summary tables, and per-sample SHAP value archives. The `watershed_mesh_and_ats_input` folder contains ATS-related watershed inputs and supporting materials. This includes mesh and shape products, ATS-readable LAI and meteorological forcing inputs, selected ATS spinup and transient-run files, and a watershed workflow example notebook. Subdirectories are organized by watershed where applicable.All files are .cpg (codepage files), .csv (comma-separated values), .dbf (database files), .exo (Exodus mesh format), .h5 (HDF5 format), .ipynb (Jupyter notebooks), .pkl (Python pickle), .prj (projection files), .sh (shell scripts), .shp (shapefile geometry), .shx (shapefile index), .txt (text files), or .xml (markup data).

Advanced Terrestrial Simulator↗
NASA NTRS2012

Evaluating the Impacts of NASA/SPoRT Daily Greenness Vegetation Fraction on Land Surface Model and Numerical Weather Forecasts

The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Greenness Vegetation Fraction (GVF) dataset, which is updated daily using swaths of Normalized Difference Vegetation Index data from the Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA EOS Aqua and Terra satellites. NASA SPoRT began generating daily real-time GVF composites at 1-km resolution over the Continental United States (CONUS) on 1 June 2010. The purpose of this study is to compare the National Centers for Environmental Prediction (NCEP) climatology GVF product (currently used in operational weather models) to the SPoRT-MODIS GVF during June to October 2010. The NASA Land Information System (LIS) was employed to study the impacts of the SPoRT-MODIS GVF dataset on a land surface model (LSM) apart from a full numerical weather prediction (NWP) model. For the 2010 warm season, the SPoRT GVF in the western portion of the CONUS was generally higher than the NCEP climatology. The eastern CONUS GVF had variations both above and below the climatology during the period of study. These variations in GVF led to direct impacts on the rates of heating and evaporation from the land surface. In the West, higher latent heat fluxes prevailed, which enhanced the rates of evapotranspiration and soil moisture depletion in the LSM. By late Summer and Autumn, both the average sensible and latent heat fluxes increased in the West as a result of the more rapid soil drying and higher coverage of GVF. The impacts of the SPoRT GVF dataset on NWP was also examined for a single severe weather case study using the Weather Research and Forecasting (WRF) model. Two separate coupled LIS/WRF model simulations were made for the 17 July 2010 severe weather event in the Upper Midwest using the NCEP and SPoRT GVFs, with all other model parameters remaining the same. Based on the sensitivity results, regions with higher GVF in the SPoRT model runs had higher evapotranspiration and lower direct surface heating, which typically resulted in lower (higher) predicted 2-m temperatures (2-m dewpoint temperatures). Portions of the Northern Plains states experienced substantial increases in convective available potential energy as a result of the higher SPoRT/MODIS GVFs. These differences produced subtle yet quantifiable differences in the simulated convective precipitation systems for this event.

Bell, Jordan R.↗
DOE OSTI2024

Bulk cloud microphysical properties as seen from numerical simulation and remote sensing products: case study of a hailstorm event over the La Plata Basin

Hailstorms develop over the La Plata Basin, in south-eastern South America, more often during later winter and early austral spring, between September and October. These systems have significant socioeconomic impacts over the region. Thus, a better understanding of how atmospheric drivers modulate the formation of hailstorms is important to improve the forecast of such phenomena. In this study, we selected a hailstorm event observed over the eastern La Plata Basin during 14–15 July 2016 to evaluate the performance of the Brazilian developments on the Regional Atmospheric Modelling System (BRAMS) model. The ability of the model in simulating cloud microphysical properties was evaluated by comparing simulations driven by different global forcings against in situ and remote sensing observations. The model results showed good skill in capturing the basic characteristics of the thunderstorm, particularly in terms of the spatial distribution of hydrometeors. The simulated spatial distribution of hail covers locations where hail fall was reported. The BRAMS simulations suggest that, despite relatively low values of the convective available potential energy (CAPE) (700–1000 J kg -1 ), environments with strong 0–8-km bulk shear (60–70 kt, ~30.9–36.0 m s –1 ) can promote the formation of ice clouds and hail fall over the eastern La Plata Basin. To be more conclusive, however, further research is needed to understand how different combinations of CAPE and shear affect hail formation over the region.

54 ENVIRONMENTAL SCIENCES↗