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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.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Welcome to NASA's Earth Science Enterprise: Educational CD-ROM Activity Supplement

Since its inception in 1958, NASA has been studying the Earth and its changing environment by observing the atmosphere, oceans, land, ice, and snow, and their influence on weather and climate. We now understand that the key to gaining a better understanding of the global environment is exploring how the Earth's systems of air, land, water, and life interact with each other. This approach-called Earth Systems Science-blends together fields like meteorology, oceanography, geology, and biology. In 1991, NASA launched a more comprehensive program to study the Earth as an integrated environmental system. They call it NASA's Earth Science Enterprise. A major component of the Earth Science Enterprise is the Earth Observing System (EOS). EOS is series of satellites to be launched over the next two decades that will be used to intensively study the Earth, with the hopes of expanding our under- standing of how natural processes affect us, and how we might be affecting them. Such studies will yield improved weather forecasts, tools for managing agriculture and forests, information for fishermen and local planners, and, eventually, the ability to predict how the climate will change in the future. Today's program is laying the foundation for long-term environmental and climate monitoring and prediction. Potentially, this will provide the understanding needed in the future to support difficult decisions regarding the Earth's environment.

Source record↗
NASA NTRS2014

Use of MODIS Cloud Top Pressure to Improve Assimilation Yields of AIRS Radiances in GSI

Radiances from hyperspectral sounders such as the Atmospheric Infrared Sounder (AIRS) are routinely assimilated both globally and regionally in operational numerical weather prediction (NWP) systems using the Gridpoint Statistical Interpolation (GSI) data assimilation system. However, only thinned, cloud-free radiances from a 281-channel subset are used, so the overall percentage of these observations that are assimilated is somewhere on the order of 5%. Cloud checks are performed within GSI to determine which channels peak above cloud top; inaccuracies may lead to less assimilated radiances or introduction of biases from cloud-contaminated radiances.Relatively large footprint from AIRS may not optimally represent small-scale cloud features that might be better resolved by higher-resolution imagers like the Moderate Resolution Imaging Spectroradiometer (MODIS). Objective of this project is to "swap" the MODIS-derived cloud top pressure (CTP) for that designated by the AIRS-only quality control within GSI to test the hypothesis that better representation of cloud features will result in higher assimilated radiance yields and improved forecasts.

Zavodsky, Bradley↗
NASA NTRS2021

Hybrid phenology matching model for robust crop phenological retrieval

Crop phenology regulates seasonal agroecosystem carbon, water, and energy exchanges, and is a key component in empirical and process-based crop models for simulating biogeochemical cycles of farmlands, assessing gross and net primary production, and forecasting the crop yield. The advances in phenology matching models provide a feasible means to monitor crop phenological progress using remote sensing observations, with a priori information of reference shapes and reference phenological transition dates. Yet the underlying geometrical scaling assumption of models, together with the challenge in defining phenological references, hinders the applicability of phenology matching in crop phenological studies. The objective of this study is to develop a novel hybrid phenology matching model to robustly retrieve a diverse spectrum of crop phenological stages using satellite time series. The devised hybrid model leverages the complementary strengths of phenometric extraction methods and phenology matching models. It relaxes the geometrical scaling assumption and can characterize key phenological stages of crop cycles, ranging from farming practice-relevant stages (e.g., planted and harvested) to crop development stages (e.g., emerged and mature). To systematically evaluate the influence of phenological references on phenology matching, four representative phenological reference scenarios under varying levels of phenological calibrations in terms of time and space are further designed with publicly accessible phenological information. The results indicate that the hybrid phenology matching model can achieve high accuracies for estimating corn and soybean phenological growth stages in Illinois, particularly with the year- and region-adjusted phenological reference (R-squared higher than 0.9 and RMSE less than 5 days for most phenological stages). The inter-annual and regional phenological patterns characterized by the hybrid model correspond well with those in the crop progress reports (CPRs) from the USDA National Agricultural Statistics Service (NASS). Compared to the benchmark phenology matching model, the hybrid model is more robust to the decreasing levels of phenological reference calibrations, and is particularly advantageous in retrieving crop early phenological stages (e.g., planted and emerged stages) when the phenological reference information is limited. This innovative hybrid phenology matching model, together with CPR-enabled phenological reference calibrations, holds 3 considerable promise in revealing spatio-temporal patterns of crop phenology over extended geographical regions.

Phenology↗
DOE OSTI2024

Case study on climate change effects and food security in Southeast Asia

Agriculture, a cornerstone of human civilization, faces rising challenges from climate change, resource limitations, and stagnating yields. Precise crop production forecasts are crucial for shaping trade policies, development strategies, and humanitarian initiatives. This study introduces a comprehensive machine learning framework designed to predict crop production. We leverage CMIP5 climate projections under a moderate carbon emission scenario to evaluate the future suitability of agricultural lands and incorporate climatic data, historical agricultural trends, and fertilizer usage to project yield changes. Our integrated approach forecasts significant regional variations in crop production across Southeast Asia by 2028, identifying potential cropland utilization. Specifically, the cropland area in Indonesia, Malaysia, Philippines, and Viet Nam is projected to decline by more than 10% if no action is taken, and there is potential to mitigate that loss. Moreover, rice production is projected to decline by 19% in Viet Nam and 7% in Thailand, while the Philippines may see a 5% increase compared to 2021 levels. Our findings underscore the critical impacts of climate change and human activities on agricultural productivity, offering essential insights for policy-making and fostering international cooperation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1976

EarthSat spring wheat yield system test 1975, appendix 4

A computer system is presented which processes meteorological data from both ground observations and meteorologic satellites to define plant weather aspects on a four time per day basis. Plant growth stages are calculated and soil moisture profiles are defined by the system. The EarthSat system assesses plant stress and prepares forecasts of end-of-year yields. The system was used to forecast spring wheat yields in the upper Great Plains states. Hardware and software documentation is provided.

Source record↗
NASA NTRS2000

Section on Observed Impacts on El Nino

Agricultural applications of El Nino forecasts are already underway in some countries and need to be evaluated or re-evaluated. For example, in Peru, El Nino forecasts have been incorporated into national planning for the agricultural sector, and areas planted with rice and cotton (cotton being the more drought-tolerant crop) are adjusted accordingly. How well are this and other such programs working? Such evaluations will contribute to the governmental and intergovernmental institutions, including the Inter-American Institute for Global Change Research and the US National Ocean and Atmospheric Agency that are fostering programs to aid the effective use of forecasts. As El Nino climate forecasting grows out of the research mode into operational mode, the research focus shifts to include the design of appropriate modes of utilization. Awareness of and sensitivity to the costs of prediction errors also grow. For example, one major forecasting model failed to predict the very large El Nino event of 1997, when Pacific sea-surface temperatures were the highest on record. Although simple correlations between El Nino events and crop yields may be suggestive, more sophisticated work is needed to understand the subtleties of the interplay among the global climate system, regional climate patterns, and local agricultural systems. Honesty about the limitations of an forecast is essential, especially when human livelihoods are at stake. An end-to-end analysis links tools and expertise from the full sequence of ENSO cause-and-effect processes. Representatives from many disciplines are needed to achieve insights, e.g, oceanographers and atmospheric scientists who predict El Nino events, climatologists who drive global climate models with sea-surface temperature predictions, agronomists who translate regional climate connections in to crop yield forecasts, and economists who analyze market adjustments to the vagaries of climate and determine the value of climate forecasts. Methods include historical studies to understand past patterns and to test hindcasts of the prediction tools, crop modeling, spatial analysis and remote sensing. This research involves expanding, deepening, and applying the understanding of physical climate to the fields of agronomy and social science; and the reciprocal understanding of crop growth and farm economics to climatology. Delivery of a regional climate forecast with no information about how the climate forecast was derived limits its effectiveness. Explanation of a region's major climate driving forces helps to place a seasonal forecast in context. Then, a useful approach is to show historical responses to previous El Nino events, and projections, with uncertainty intervals, of crop response from dynamic process crop growth models. Regional ID forecasts should be updated with real-time weather conditions. Since every El Nino event is different, it is important to track, report and advise on each new event as it unfolds. The stability of human enterprises depends on understanding both the potentialities and the limits of predictability. Farmers rely on past experience to anticipate and respond to fluctuations in the biophysical systems on which their livelihoods depend. Now scientists are improving their ability to predict some major elements of climate variability. The improvements in the reliability of El Nino forecasts are encouraging, but seasonal forecasts for agriculture are not, and will probably never be completely infallible, due to the chaotic nature of the climate system. Uncertainties proliferate as we extend beyond Pacific sea-surface temperatures to climate teleconnections and agricultural outcomes. The goal of this research is to shed as a clear light as possible on these inherent uncertainties and thus to contribute to the development of appropriate responses to El Nino and other seasonal forecasts for a range of stakeholders, which, ultimately, includes food consumers everywhere.

Rosenzweig, Cynthia↗
NASA NTRS2013

Prediction of Seasonal Climate-induced Variations in Global Food Production

Consumers, including the poor in many countries, are increasingly dependent on food imports and are therefore exposed to variations in yields, production, and export prices in the major food-producing regions of the world. National governments and commercial entities are paying increased attention to the cropping forecasts of major food-exporting countries as well as to their own domestic food production. Given the increased volatility of food markets and the rising incidence of climatic extremes affecting food production, food price spikes may increase in prevalence in future years. Here we present a global assessment of the reliability of crop failure hindcasts for major crops at two lead times derived by linking ensemble seasonal climatic forecasts with statistical crop models. We assessed the reliability of hindcasts (i.e., retrospective forecasts for the past) of crop yield loss relative to the previous year for two lead times. Pre-season yield predictions employ climatic forecasts and have lead times of approximately 3 to 5 months for providing information regarding variations in yields for the coming cropping season. Within-season yield predictions use climatic forecasts with lead times of 1 to 3 months. Pre-season predictions can be of value to national governments and commercial concerns, complemented by subsequent updates from within-season predictions. The latter incorporate information on the most recent climatic data for the upcoming period of reproductive growth. In addition to such predictions, hindcasts using observations from satellites were performed to demonstrate the upper limit of the reliability of crop forecasting.

variations↗
NASA NTRS2017

Intercomparison of Soil Moisture, Evaporative Stress, and Vegetation Indices for Estimating Corn and Soybean Yields Over the U.S.

This paper presents an intercomparative study of 12 operationally produced large-scale datasets describing soil moisture, evapotranspiration (ET), and or vegetation characteristics within agricultural regions of the contiguous United States (CONUS). These datasets have been developed using a variety of techniques, including, hydrologic modeling, satellite-based retrievals, data assimilation, and survey in-field data collection. The objectives are to assess the relative utility of each dataset for monitoring crop yield variability, to quantitatively assess their capacity for predicting end-of-season corn and soybean yields, and to examine the evolution of the yield-index correlations during the growing season. This analysis is unique both with regards to the number and variety of examined yield predictor datasets and the detailed assessment of the water availability timing on the end-of-season crop production during the growing season. Correlation results indicate that over CONUS, at state-level soil moisture and ET indices can provide better information for forecasting corn and soybean yields than vegetation-based indices such as normalized difference vegetation index. The strength of correlation with corn and soybean yields strongly depends on the interannual variability in yield measured at a given location. In this case study, some of the remotely derived datasets examined provide skill comparable to that of in situ field survey-based data further demonstrating the utility of these remote sensing-based approaches for estimating crop yield.

evapotranspiration↗
DOE OSTI2025

Intercomparison of Deep Learning Model Architectures for Atmospheric River Prediction

With a rapid surge in the application of machine learning (ML) for a diverse range of tasks in climate science, the present study addresses a challenge for climate scientists when selecting the optimal ML or deep learning (DL) architecture for a given application. In particular, a DL intercomparison study was performed with a focus on forecasting the position of atmospheric rivers (ARs) on short-range time scales (up to 5-day lead times). AR predictions from multiple DL architectures, including various types of convolutional autoencoders and a vision transformer (ViT), were compared against ECMWF ERA5 reanalysis and hindcasts from a global climate model. DL models with similar trainable parameters were trained on ERA5 reanalysis data and AR positions derived from a thresholding algorithm to ensure a fair comparison among the DL models. Each model’s performance and accuracy in forecasting AR location and key input fields within a 5-day window were assessed using metrics of root-mean-square error, anomaly correlation, and mean intersection over union. The ViT architecture outperformed other autoencoder models in most of the metrics. Incorporating additional meteorological fields only yielded slight improvements in forecasting certain fields at longer lead times. The results also suggest that a smaller number of input time steps or smaller number of autoregressive steps can achieve better prediction skills, while also improving the overall computational efficiency. This research offers valuable insights into the strengths and weaknesses of different DL techniques for AR forecasting, hopefully guiding the development of improved models for forecasting this phenomenon.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2008

Use of High-Resolution WRF Simulations to Forecast Lightning Threat

Recent observational studies have confirmed the existence of a robust statistical relationship between lightning flash rates and the amount of large precipitating ice hydrometeors aloft in storms. This relationship is exploited, in conjunction with the capabilities of cloud-resolving forecast models such as WRF, to forecast explicitly the threat of lightning from convective storms using selected output fields from the model forecasts. The simulated vertical flux of graupel at -15C and the shape of the simulated reflectivity profile are tested in this study as proxies for charge separation processes and their associated lightning risk. Our lightning forecast method differs from others in that it is entirely based on high-resolution simulation output, without reliance on any climatological data. short [6-8 h) simulations are conducted for a number of case studies for which three-dmmensional lightning validation data from the North Alabama Lightning Mapping Array are available. Experiments indicate that initialization of the WRF model on a 2 km grid using Eta boundary conditions, Doppler radar radial velocity fields, and METAR and ACARS data y&eld satisfactory simulations. __nalyses of the lightning threat fields suggests that both the graupel flux and reflectivity profile approaches, when properly calibrated, can yield reasonable lightning threat forecasts, although an ensemble approach is probably desirable in order to reduce the tendency for misplacement of modeled storms to hurt the accuracy of the forecasts. Our lightning threat forecasts are also compared to other more traditional means of forecasting thunderstorms, such as those based on inspection of the convective available potential energy field.

McCaul, E. W., Jr.↗
DOE OSTI2024

Automated Operational Forecasting of Monsoon Low Pressure Systems

Monsoon low pressure systems (LPSs) are the dominant rain-bearing weather system of South Asia, often producing extreme precipitation and hydrological disasters in a region inhabited by nearly two billion people. Despite the importance of these storms, no operational system has automatically identified and tracked LPS in real time in numerical weather prediction model output; many commonly used vortex-tracking algorithms are ill suited for monsoon LPS because of the weak winds and cold cores of these systems. Here, we describe a new system that uses optimized algorithms to identify monsoon LPS in short- to medium-range forecasts from the U.S. Global Ensemble Forecast System (GEFS) and a version of the deterministic Global Forecast System (GFS) adapted and used operationally by the Indian Institute of Tropical Meteorology (IITM). We also assess the historical performance of these models in forecasting South Asian monsoon LPS, comparing this with the performance of the Integrated Forecasting System of the ECMWF. We assess the accuracy of model predictions of LPS genesis, position, intensity, and precipitation rates for forecast lead times of 1–5 days, yielding quantitative information on model biases to guide operational forecasters and disaster managers. The system we introduce here could be extended to other low-latitude regions affected by dynamically weak, heavily precipitating atmospheric vortices that are often not included in tropical cyclone inventories.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1981

Predictability of quasi-geostrophic planetary waves in global and hemispheric domains

Factors which affect the long term predictability, and also the feasibility of defining an ultimate level of weather predictability, are discussed. It is noted that current models yield a one week forecast at the best. Long range predictability, however, is a restriction on the knowledge of evolutive processes of the atmosphere, and does not characterize the prediction of specific atmospheric quantities. The influences of local phenomena such as cyclones and other nonlinear processes can cause planetary waves which affect the predictability of weather everywhere. It is suggested that any successful prediction model will necessarily account for the evolution of ultralong waves. Although significant differences in predictions have been found between hemispheric and global predictions, the location of the errors in extrapolating the hemispheric to the global scale have been identified.

Roads, J. O.↗
NASA NTRS1984

Agribusiness and space: No limits to growth

Technological developments responding to world food needs are examined. It is noted that agribusiness technology has become more space-related in recent years. Although crops forecasting and improvements in yield (the green revolution) were developed prior to the space era, it would be unthinkable today to ignore the contributions of operational meteorological and communications satellites and experimental Earth observation satellites in agribusiness. Space-driven communications now permit national agribusiness database management networks, with a significant portion of the data being space-derived. In demonstration experiments, space communications were shown to improve those aspects of the food problem related to education and communications.

Montgomery, O. L.↗
NASA NTRS1986

The influence of velocity variability on the determination of wind profiles

High sensitivity radars allow the determination of velocity estimates at time resolutions down to one minute or better. Because of the variability introduced to the mean wind due to turbulence and waves, the high resolution profiles may not be too useful for forecasting applications, although they yield the most realistic estimate of the instantaneous wind profile. Profiles of wind speed and direction, vertical velocity and echo power, which were deduced in real-time on 23 August 1981 with the spaced antenna drift mode of the SOUSY-VHF-Radar are shown. Whereas these profiles were measured within 1 minute, the operating routine allowed the selection of variable (longer) measuring periods, and one has to search for the optimum duration of the data averaging period. A high time resolution wind vector diagram is given which gives an idea of the temporal variability. The data were obtained with the spaced antenna technique, which allows a good estimate of the horizontal wind without having to correct for the vertical velocity component. The wind vectors specifically indicate a quasi-periodic variation in direction. This is assumed to be due to gravity waves since the vertical velocity also shows periodical variations with the same period. The consistency of these spaced-antenna VHF radar results along with the radiosonde data convinced researchers that the method is quite suitable for wind profiling applications.

Rottger, J.↗
NASA NTRS1999

Improving Assimilated Global Data Sets using TMI Rainfall and Columnar Moisture Observations

A global analysis that optimally combine observations from diverse sources with physical models of atmospheric and land processes can provide a comprehensive description of the climate systems. Currently, such data products contain significant errors in primary hydrological fields such as precipitation and evaporation, especially in the tropics. In this study, we show that assimilating precipitation and total precipitable water (TPW) retrievals derived from the TRMM Microwave Imager (TMI) improves not only the hydrological cycle but also key climate parameters such as clouds, radiation, and the large-scale circulation produced by the Goddard Earth Observing System (GEOS) data assimilation system (DAS). In particular, assimilating TMI rain improves clouds and radiation in areas of active convection, as well as the latent heating distribution and the large-scale motion field in the tropics, while assimilating TMI TPW heating distribution and the large-scale motion field in the tropics, while assimilating TMI TPW retrievals leads to reduced moisture biases and improved radiative fluxes in clear-sky regions. The improved analysis also improves short-range forecasts in the tropics. Ensemble forecasts initialized with the GEOS analysis incorporating TMI rain rates and TPW yield smaller biases in tropical precipitation forecasts beyond 1 day and better 500 hPa geopotential height forecasts up to 5 days. Results of this study demonstrate the potential of using high-quality space-borne rainfall and moisture observations to improve the quality of assimilated global data for climate analysis and weather forecasting applications

Hou, Arthur Y.↗
NASA NTRS2021

Argentina Food Security & Agriculture: Crop Monitoring and Forecasting for Argentina using NASA Satellite Observations

Early harvest information helps guide agricultural commodity assessments in Argentina, providing valuable planning information to identify potentially food-insecure regions, anticipate transportation and storage demands, predict price fluctuations, and project commodity trends. However, crop yield estimates are currently subjective, based on interviews with qualified informants (i.e., farmers, agribusiness actors). In partnership with the Buenos Aires Grain Exchange, we leveraged Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Soil Moisture Active Passive (SMAP), and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM IMERG) NASA Earth observations to develop a Google Earth Engine (GEE) toolset to monitor vegetation growth. The first component of the toolset produces spatial and temporal maps of temperature, precipitation, soil moisture, and the Normalized Difference Vegetation Index (NDVI), allowing users to visualize the influence of the region’s climate and weather. Next, we developed an autoregressive model to predict NDVI several months in advance. Lastly, we created a linear regression model of crop yield and NDVI for soybeans, corn, and wheat, and input the forecasted NDVI to generate a predicted crop yield output. The NDVI forecasting model produced accurate predictions at two, four, and six months when examining the most recent growing season. In the crop yield model, soybeans exhibited moderately strong correlation, wheat had consistent weak correlation, and corn varied from weak to strong correlation depending on zone. This information is vital for vegetation growth monitoring by identifying areas of high growth and allocating resources to areas of lower growth to efficiently maximize crop yields.

DEVELOP Tech Paper↗
NASA NTRS2003

Assimilation of Precipitation Information Retrieved from Passive Microwave Sensors

Estimates of surface precipitation and the associated vertical latent heating structure can be retrieved from space-borne passive microwave sensors such as the TRMM Microwave Imager (TMI) and Special Sensor Microwave/Imager (SSM/I). Studies have shown that assimilation of microwave rain rates can improve the quality of global analysis and forecast. Numerical weather prediction (NWP) centers are beginning to make operational use of these data. In the next few years, there will be a gradual increase in microwave rain products available from operational and research satellites, culminating to a target constellation of 9 satellites to provide global rain measurements every 3 hours with the proposed Global Precipitation Measurement (GPM) mission to be launched around 2007. Realizing the full potential of these observations in data assimilation will require continued advances in retrieval algorithms, assimilation techniques, and model physics. I present results from two research efforts at NASA GSFC. The first is on the assimilation of tropical precipitation using temperature/moisture tendency corrections within a continuous variational assimilation framework to improve analyses and forecasts produced by the Goddard Earth Observing System (GEOS) global data assimilation system. Results show that assimilation of TMI and SSM/I rain rates improves not only precipitation and moisture, but also the related clouds, radiation energy fluxes, and large-scale circulations in GEOS analyses. The improved analyses also yield better short-range forecasts, quantitative precipitation forecast (QPF) threat scores, and storm track predictions. The second part consists of results from exploratory experiments on variational assimilation of convective and stratiform latent heating profiles within the general framework of model parameter estimation as a way to identify systematic model errors and optimize physical paremeterization schemes in global forecast and climate models.

Hou, Arthur Y.↗
NASA NTRS2015

Improving the Monitoring of Crop Productivity Using Spaceborne Solar-Induced Fluorescence

Large-scale monitoring of crop growth and yield has important value for forecasting food production and prices and ensuring regional food security. A newly emerging satellite retrieval, solar-induced fluorescence (SIF) of chlorophyll, provides for the first time a direct measurement related to plant photosynthetic activity (i.e. electron transport rate). Here, we provide a framework to link SIF retrievals and crop yield, accounting for stoichiometry, photosynthetic pathways, and respiration losses. We apply this framework to estimate United States crop productivity for 2007-2012, where we use the spaceborne SIF retrievals from the Global Ozone Monitoring Experiment-2 satellite, benchmarked with county-level crop yield statistics, and compare it with various traditional crop monitoring approaches. We find that a SIF-based approach accounting for photosynthetic pathways (i.e. C3 and C4 crops) provides the best measure of crop productivity among these approaches, despite the fact that SIF sensors are not yet optimized for terrestrial applications. We further show that SIF provides the ability to infer the impacts of environmental stresses on autotrophic respiration and carbon-use-efficiency, with a substantial sensitivity of both to high temperatures. These results indicate new opportunities for improved mechanistic understanding of crop yield responses to climate variability and change.

Guan, Kaiyu↗