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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

A Comprehensive Precipitation Data Set for Global Land Areas (TR-051)

An expanded and updated compilation of long-term station precipitation data, together with a new set of gridded monthly mean fields for global land areas, are described. The present data set contains 5328 station records of monthly total precipitation, covering the period from the mid-1800s to the late 1980s. The station data were individually tested and visually inspected for the presence of spurious trends, jumps, and other measurement biases. The quality control procedure which was used to check the station records for nonclimatic discontinuities and other biases is detailed. We also discuss some of the problems which typically contribute to potential inhomogeneities in precipitation records. The station data were interpolated onto a 4° latitude by 5° longitude uniform grid. Comparisons of these data with two other global-scale precipitation climatologies are presented. We find good agreement among the three global-scale climatologies over the common areas in each set. Three different indices of long-term precipitation variations over the global land areas all indicate a general increase of annual precipitation since the 1940s, although a decline is evident over the last decade. There is some indication that the last few decades of the 19th century may have been as wet as the recent ones. An interesting feature of this study is the presence of relatively large differences in seasonal trends, with March-May and September-November becoming wetter in the last few decades. The December-February and June-August seasons exhibit smaller overall trends, although the northern winter season does exhibit large decadal-scale fluctuations.For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/ndps/tr051.htmlThis dataset was transferred from the CDIAC Archive and published on ESS-DIVE in 2018 under the project title "Carbon Dioxide Information Analysis Center (CDIAC), Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (USA); Univ. of Colorado, Boulder, CO (United States). Cooperative Inst. for Research in Environmental Sciences (CIRES)". In 2023, the project title was updated to "Carbon Dioxide Information Analysis Center (CDIAC); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)" to enable consistent management of all datasets previously hosted by the CDIAC Archive that are now published on ESS-DIVE.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Evaluation of precipitation indices in suites of dynamically and statistically downscaled regional climate models over Florida

Abstract The present work evaluates historical precipitation and its indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) in suites of dynamically and statistically downscaled regional climate models (RCMs) against NOAA’s Global Historical Climatology Network Daily (GHCN-Daily) dataset over Florida. The models examined here are: (1) nested RCMs involved in the North American CORDEX (NA-CORDEX) program, (2) variable resolution Community Earth System Models (VR-CESM), (3) Coupled Model Intercomparison Project phase 5 (CMIP5) models statistically downscaled using localized constructed analogs (LOCA) technique. To quantify observational uncertainty, three in situ-based (PRISM, Livneh, CPC) and three reanalysis (ERA5, MERRA2, NARR) datasets are also evaluated against the station data. The reanalyses and dynamically downscaled RCMs generally underestimate the magnitude of the monthly precipitation and the frequency of the extreme rainfall in summer. The models forced with CanESM2 miss the phase of the seasonality of extreme precipitation. All models and reanalyses severely underestimate both the mean and interannual variability of mean wet-day precipitation (SDII), consecutive dry days (CDD), and overestimate consecutive wet days (CWD). Metric analysis suggests large uncertainty across NA-CORDEX models. Both the LOCA and VR-CESM models perform better than the majority of models. Overall, RegCM4 and WRF models perform poorer than the median model performance. The performance uncertainty across models is comparable to that in the reanalyses. Specifically, NARR performs poorer than the median model performance in simulating the mean indices and MERRA2 performs worse than the majority of models in capturing the interannual variability of the indices.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

NNSS Soils Monitoring: Plutonium Valley (CAU 366) FY2013 and FY2014

The Desert Research Institute (DRI) is conducting a field assessment of the potential for contaminated soil transport from the Plutonium Valley Contamination Area (CA) as a result of wind transport and storm runoff in support of Nevada Nuclear Security Administration (NNSA) efforts to complete regulatory closure of the contamination areas. The DRI work is intended to confirm the likely mechanism(s) of transport and determine the meteorological conditions that might cause movement of contaminated soils. Emphasis is given to collecting sediment transported by channelized storm runoff at the Plutonium Valley investigation sites. These data will inform closure plans that are being developed, which will facilitate appropriate closure design and postclosure monitoring. Desert Research Institute installed two meteorological monitoring stations south (station number 1) and north (station number 2) of the Plutonium Valley CA and a runoff sediment sampling station within the CA in 2011. Temperature, wind speed, wind direction, relative humidity, precipitation, solar radiation, barometric pressure, soil temperature, and airborne particulate concentration are collected at both meteorological stations. The maximum, minimum, and average or total (as appropriate) for each of these parameters is recorded for each 10-minute interval. The sediment sampling station includes an automatically activated ISCO sampling pump with collection bottles for suspended sediment, which is activated when sufficient flow is present in the channel, and passive traps for bedload material that is transported down the channel during runoff events. This report presents data collected from these stations during FY2013 and FY2014.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Improving near real-time precipitation estimation using a U-Net convolutional neural network and geographical information

Reliable near real-time precipitation estimates are essential for monitoring and managing of natural disasters such as floods. Quality of inputs and capability of the retrieval algorithm are two important aspects for developing satellite-based precipitation datasets. Most retrieval algorithms utilize infrared (IR) information as their input due to its fine spatiotemporal resolution and near-instantaneous availability. However, their sole reliance on IR information limits their capability to learn different mechanisms of precipitation during training, resulting in less accurate estimates. Moreover, recent advances in the field of machine learning offer attractive opportunities to improve the precipitation retrieval algorithms. This work investigates the effectiveness of adding geographical information (i.e. latitude and longitude) to IR information and the application of a U-Net-based convolutional neural network for improving the accuracy of retrieval algorithms. This research suggests that applying an appropriate CNN architecture on geographical and IR information provides an opportunity to improve the satellite-based precipitation products.

42 ENGINEERING↗
DOE OSTI2019

csapr2cfrmrhi (a1)

The C-Band Scanning ARM Precipitation Radar is a dual-polarization Doppler weather radar that operates in a simultaneous transmit and receive (STAR) mode, splitting the transmit signal so that power is transmitted on both horizontal and vertical polarizations at the same time. The CSAPR can also transmit on a single polarization (horizontal or vertical) and measure linear depolarization ratio (LDR). In addition to the first three Doppler moments (reflectivity, radial velocity, and spectra width), the CSAPR also provides differential reflectivity, correlation coefficient, and specific differential phase. The dual-polarization variables enable the provision of rainfall rate estimates and identification of precipitation types. For the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina, ARM deployed a second-generation CSAPR, known as CSAPR2. CACTI data from the CSAPR2 are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

csapr2cfrrhi (a1)

The C-Band Scanning ARM Precipitation Radar is a dual-polarization Doppler weather radar that operates in a simultaneous transmit and receive (STAR) mode, splitting the transmit signal so that power is transmitted on both horizontal and vertical polarizations at the same time. The CSAPR can also transmit on a single polarization (horizontal or vertical) and measure linear depolarization ratio (LDR). In addition to the first three Doppler moments (reflectivity, radial velocity, and spectra width), the CSAPR also provides differential reflectivity, correlation coefficient, and specific differential phase. The dual-polarization variables enable the provision of rainfall rate estimates and identification of precipitation types. For the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina, ARM deployed a second-generation CSAPR, known as CSAPR2. CACTI data from the CSAPR2 are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

csapr2cfrcrc (a1)

The C-Band Scanning ARM Precipitation Radar is a dual-polarization Doppler weather radar that operates in a simultaneous transmit and receive (STAR) mode, splitting the transmit signal so that power is transmitted on both horizontal and vertical polarizations at the same time. The CSAPR can also transmit on a single polarization (horizontal or vertical) and measure linear depolarization ratio (LDR). In addition to the first three Doppler moments (reflectivity, radial velocity, and spectra width), the CSAPR also provides differential reflectivity, correlation coefficient, and specific differential phase. The dual-polarization variables enable the provision of rainfall rate estimates and identification of precipitation types. For the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina, ARM deployed a second-generation CSAPR, known as CSAPR2. CACTI data from the CSAPR2 are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

csapr2cfrppi (a1)

The C-Band Scanning ARM Precipitation Radar is a dual-polarization Doppler weather radar that operates in a simultaneous transmit and receive (STAR) mode, splitting the transmit signal so that power is transmitted on both horizontal and vertical polarizations at the same time. The CSAPR can also transmit on a single polarization (horizontal or vertical) and measure linear depolarization ratio (LDR). In addition to the first three Doppler moments (reflectivity, radial velocity, and spectra width), the CSAPR also provides differential reflectivity, correlation coefficient, and specific differential phase. The dual-polarization variables enable the provision of rainfall rate estimates and identification of precipitation types. For the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina, ARM deployed a second-generation CSAPR, known as CSAPR2. CACTI data from the CSAPR2 are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

csapr2cfrzppi (a1)

The C-Band Scanning ARM Precipitation Radar is a dual-polarization Doppler weather radar that operates in a simultaneous transmit and receive (STAR) mode, splitting the transmit signal so that power is transmitted on both horizontal and vertical polarizations at the same time. The CSAPR can also transmit on a single polarization (horizontal or vertical) and measure linear depolarization ratio (LDR). In addition to the first three Doppler moments (reflectivity, radial velocity, and spectra width), the CSAPR also provides differential reflectivity, correlation coefficient, and specific differential phase. The dual-polarization variables enable the provision of rainfall rate estimates and identification of precipitation types. For the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina, ARM deployed a second-generation CSAPR, known as CSAPR2. CACTI data from the CSAPR2 are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

csapr2cfrmmisc (a1)

The C-Band Scanning ARM Precipitation Radar is a dual-polarization Doppler weather radar that operates in a simultaneous transmit and receive (STAR) mode, splitting the transmit signal so that power is transmitted on both horizontal and vertical polarizations at the same time. The CSAPR can also transmit on a single polarization (horizontal or vertical) and measure linear depolarization ratio (LDR). In addition to the first three Doppler moments (reflectivity, radial velocity, and spectra width), the CSAPR also provides differential reflectivity, correlation coefficient, and specific differential phase. The dual-polarization variables enable the provision of rainfall rate estimates and identification of precipitation types. For the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina, ARM deployed a second-generation CSAPR, known as CSAPR2. CACTI data from the CSAPR2 are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

csapr2cfrhsrhi (a1)

The C-Band Scanning ARM Precipitation Radar is a dual-polarization Doppler weather radar that operates in a simultaneous transmit and receive (STAR) mode, splitting the transmit signal so that power is transmitted on both horizontal and vertical polarizations at the same time. The CSAPR can also transmit on a single polarization (horizontal or vertical) and measure linear depolarization ratio (LDR). In addition to the first three Doppler moments (reflectivity, radial velocity, and spectra width), the CSAPR also provides differential reflectivity, correlation coefficient, and specific differential phase. The dual-polarization variables enable the provision of rainfall rate estimates and identification of precipitation types. For the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina, ARM deployed a second-generation CSAPR, known as CSAPR2. CACTI data from the CSAPR2 are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Investigation on cloud vertical structures based on Ka-band cloud radar observations at Wuhan in Central China

Based on vertically pointing observations of Ka-band frequency-modulated continuous millimeter wave cloud radar (MMCR) in 2020, we investigate cloud vertical structures at Wuhan in central China. Case comparison indicates that the cloud top locations in the MMCR observation are largely consistent with those in satellite data. The precipitation has the occurrence frequency of 9.5% over the year, with the maximum value of 16.3% in July and the submaximal value of 14.7% in January, showing the significant influences of monsoons on precipitation at Wuhan. As the humid, cloudy and rainy climate, the probability of precipitation and non-precipitation clouds increases gradually from 53.9% in January to a large value of 71.5% in July. The non-precipitation clouds have the probability of 44.5% in the year, with two prominent peaks at the reflectivities of -35 to -30 dBZ around 12 km in summer and - 37– -32 dBZ around 10 km in spring. The cloud top can reach the heights of 14.49, 16.62, 13.38 and 11.73 km from spring to winter, respectively. The monthly mean cloud layer thickness is the largest (2.18 km) in May and September, and the smallest (1.36 km) in November. The seasonally averaged base and top heights and thickness of clouds have small diurnal variations. The high cloud is predominant in the warm months from May to September, and in the other cool months, the middle cloud has the most fraction. The quantitative investigation of clouds is helpful in improving the cloud parameterization in climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

PERSIANN-CNN: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Convolutional Neural Networks

Abstract Accurate and timely precipitation estimates are critical for monitoring and forecasting natural disasters such as floods. Despite having high-resolution satellite information, precipitation estimation from remotely sensed data still suffers from methodological limitations. State-of-the-art deep learning algorithms, renowned for their skill in learning accurate patterns within large and complex datasets, appear well suited to the task of precipitation estimation, given the ample amount of high-resolution satellite data. In this study, the effectiveness of applying convolutional neural networks (CNNs) together with the infrared (IR) and water vapor (WV) channels from geostationary satellites for estimating precipitation rate is explored. The proposed model performances are evaluated during summer 2012 and 2013 over central CONUS at the spatial resolution of 0.08° and at an hourly time scale. Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)–Cloud Classification System (CCS), which is an operational satellite-based product, and PERSIANN–Stacked Denoising Autoencoder (PERSIANN-SDAE) are employed as baseline models. Results demonstrate that the proposed model (PERSIANN-CNN) provides more accurate rainfall estimates compared to the baseline models at various temporal and spatial scales. Specifically, PERSIANN-CNN outperforms PERSIANN-CCS (and PERSIANN-SDAE) by 54% (and 23%) in the critical success index (CSI), demonstrating the detection skills of the model. Furthermore, the root-mean-square error (RMSE) of the rainfall estimates with respect to the National Centers for Environmental Prediction (NCEP) Stage IV gauge–radar data, for PERSIANN-CNN was lower than that of PERSIANN-CCS (PERSIANN-SDAE) by 37% (14%), showing the estimation accuracy of the proposed model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

High-energy synchrotron radiation X-ray diffraction measurements during in situ aging of a NiTi-15 at. % Hf high temperature shape memory alloy

NiTiHf high temperature shape memory alloys (HTSMAs) have begun to show considerable progress in their ability to be manufactured and are now on the cusp of becoming viable alloys for use in the aerospace industry, mainly as actuation devices. By aging the alloys at intermediate temperatures between 450 and up to 700 °C, Ni-rich NiTiHf HTSMAs form the H-phase, a Ni-rich nanoscale precipitate which can be used to strengthen the alloy and improve the shape memory response; however, precipitate phase evolution and growth has not yet been studied in-situ nor have the effects of previous thermo-mechanical processing on their formation. In this study, H-phase precipitate formation is observed experimentally in situ during aging of a Ni-rich NiTiHf alloy using high energy synchrotron radiation X-ray diffraction (SR-XRD) measurements. The effects of prior thermo-mechanical treatments, i.e. hot and cold rolling, on its formation rate and coarsening rate are compared. It is shown that growth of the H-phase can be observed in situ using SR-XRD and the H-phase formation rate seems to exhibit linear growth when aging at 550 °C, while increasing the temperature to 650 °C shows a logarithmic trend that plateaus after approximately 1 h of aging. Other properties such as incubation time, austenite lattice strains, texture evolution, and overall comparison between hot and cold rolling are discussed. New potential peak age conditions are suggested based on the diffraction data and evaluated using three-point bending and TEM imaging.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Unraveling the mechanisms underlying pulse dynamics of soil respiration in tropical dry forests

Tropical dry forests are already undergoing changes in the quantity and timing of rainfall, but there is great uncertainty over how these shifts will affect belowground carbon (C) cycling. While it has long been known that dry soils quickly release carbon dioxide (CO 2 ) upon rewetting, the mechanisms underlying the so-called 'Birch effect' are still debated. Here, we quantified soil respiration pulses and their biotic predictors in response to simulated precipitation events in a regenerating tropical dry forest in Costa Rica. We also simulated the observed rewetting CO 2 pulses with two soil carbon models: a conventional model assuming first-order decay rates of soil organic matter, and an enzyme-catalyzed model with Michaelis–Menten kinetics. We found that rewetting of dry soils produced an immediate and dramatic pulse of CO 2 , accompanied by rapid immobilization of nitrogen into the microbial biomass. However, the magnitude of the rewetting CO 2 pulse was highly variable at fine spatial scales, and was well correlated with the size of the dissolved organic C pool prior to rewetting. Both the enzyme-catalyzed and conventional models were able to reproduce the Birch effect when respiration was coupled directly to microbial C uptake, although models differed in their ability to yield realistic estimates of SOC and microbial biomass pool sizes and dynamics. Lastly, our results suggest that changes in the timing and intensity of rainfall events in tropical dry forests will exert strong influence on ecosystem C balance by affecting the dynamics of microbial biomass growth.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Solidification behavior and cracking mechanisms of Ru-containing BCC-B2 superalloys

Many precipitation-strengthened alloys are highly susceptible to cracking during additive manufacturing (AM), which often requires compositional adjustments to mitigate. Recently, it has been demonstrated that refractory BCC alloys can be precipitation strengthened with ordered Ru-containing B2 precipitates to temperatures exceeding 1200 °C. This motivates a study of the potential fabricability of quaternary and quinary refractory BCC-B2 alloys by AM using single track laser experiments. Alloys with greater than 9 at.% Ru exhibit crack formation upon cooling. Solidification cracking was observed in all Zr-containing alloys, which can be mitigated by substituting Hf. Furthermore, this cracking is attributed to the strong partitioning of Zr during solidification, as revealed by characterization of alloy solidus, liquidus, and partition coefficients. Hf 5 Ru 4 -Nb 63 V 28 is identified as a promising BCC-B2 composition with a high B2 solvus (>1300 °C) and solidus (1695 °C) along with a high resistance to cracking.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Susceptibility of new soil organic carbon to mineralization during dry-wet cycling in soils from contrasting ends of a precipitation gradient

The persistence of soil organic carbon (SOC) is influenced by soil physicochemical properties, organic matter quality, and climatic conditions that govern its vulnerability to microbial activity. We compared the susceptibility of newly formed SOC to mineralization in two soils (Andosols) that developed under contrasting precipitation regimes. Soil from the high rainfall region (‘high rain ’) had higher SOC and lower iron concentrations than soils exposed to less rainfall (‘low rain ’). We amended soils with 13 C-labeled carbohydrates and measured the amount of substrate-derived SO 13 C mineralized when exposed to dry-wet cycling following months-long incubations. We hypothesized that susceptibility would differ due to iron content and mineralogy, initial SOC, substrate solubility (cellulose versus glucose amendment), and microbial substrate use efficiency (SUE). We found that SO 13 C was less susceptible to dry-wet cycling when more 13 C was assimilated into microbial biomass and co-localized with mineral surfaces than when co-localized with existing organo-mineral surfaces, according to microscale NanoSIMS imaging. Considerably less SO 13 C was susceptible to mineralization in the ferrihydrite-rich (low SOC) low rain soil than the leached (high SOC) high rain soil when C was added as either glucose (7.3-fold less C mineralized) or cellulose (15.2-fold less). Additionally, the SUE of glucose was comparable to cellulose in low rain soil where SO 13 C was less water soluble and coprecipitated with ferrihydrite, and used half as efficiently as cellulose in high rain soil. Our results show that the susceptibility of newly formed SOC to mineralization is modified by the effects of bioavailability on microbial metabolism and the availability of mineral surfaces for forming new organo-mineral complexes.

21 NanoSIMS↗
DOE OSTI2014

Atmospheric Rivers Coming to a Cloud Near You

Learn about the ARM Cloud Aerosol Precipitation Experiment (ACAPEX) field campaign in this short video. Ruby Leung, PNNL's lead scientist on this campaign's observational strategy to monitor precipitation.

54 ENVIRONMENTAL SCIENCES↗