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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 NTRS2021

FORECASTING WHEAT YIELD USING REMOTE SENSING: THE ARYA FORECASTING SYSTEM

In this study we present a model to forecast wheat yield based on the evolution of the Difference Vegetation Index (DVI)and the Growing Degree Days (GDD), presented in Franch et al. (2015), but adapted to Franch et al. (2019) model. Additionally, we explore how the Land Surface Temperature (LST) can be included into the model and if this parameter adds any value to the model when combined with the optical information. This study is applied toMODIS data at 1km resolution to monitor the national and state level yield of winter wheat in the United States and Ukraine from 2001 to 2019.

B. Franch↗
NASA NTRS1975

A study of application of remote sensing to river forecasting. Volume 2: Detailed technical report, NASA-IBM streamflow forecast model user's guide

The Model is described along with data preparation, determining model parameters, initializing and optimizing parameters (calibration) selecting control options and interpreting results. Some background information is included, and appendices contain a dictionary of variables, a source program listing, and flow charts. The model was operated on an IBM System/360 Model 44, using a model 2250 keyboard/graphics terminal for interactive operation. The model can be set up and operated in a batch processing mode on any System/360 or 370 that has the memory capacity. The model requires 210K bytes of core storage, and the optimization program, OPSET (which was used previous to but not in this study), requires 240K bytes. The data band for one small watershed requires approximately 32 tracks of disk storage.

Source record↗
NASA NTRS2015

Modeled Forecasts of Dengue Fever in San Juan, PR Using NASA Satellite Enhanced Weather Forecasts

Dengue virus is transmitted between humans and mosquitoes of the genus Aedes and causes approximately 96 million cases of disease (dengue fever) each year (Bhatet al. 2013). Symptoms of dengue fever include fever, headache, nausea, vomiting, and eye, muscle and joint pain (CDC). More sever manifestations such as abdominal pain, bleeding from nose and gums, vomiting of blood, and clammy skin occur in rare cases of dengue hemorrhagic fever (CDC). Dengue fever occurs throughout tropical and sub-tropical regions worldwide, however, the geographical range and size of epidemics is increasing. Weather and climate are drivers of dengue virus transmission dynamics (Morin et al. 2013) by affecting mosquito proliferation and the virus extrinsic incubation period (i.e. required time for the virus to replicate and disseminate within the mosquito before it can retransmit the virus).

vector↗
NASA NTRS2018

Forecasting and Monitoring Intense Thunderstorms in the Hindu Kush Himalayan Region: Spring 2018 Forecasting Experiment

Some of the most intense thunderstorms on the planet occur in the Hindu Kush Himalayan (HKH) region of South Asia - where many organizations lack the capacity needed to predict, observe and/or effectively respond to the threats associated with high-impact convective weather. Among the hazards include tornadoes, damaging straight-line winds (known as Nor'westers in the HKH region), large hail, and flash flooding, which typically peak in the pre-wet-monsoon season. Previous studies have documented a disproportionately large number of casualties associated with intense thunderstorms in this region; therefore, the goal of this project is to increase situational awareness of these hazards through short-term modeling and satellite assessment tools.

remote sensing↗
NASA NTRS2020

California & Oregon Ecological Forecasting: Detecting and Forecasting Fog Occurrence, Frequency, and Change to Support Coast Redwood (Sequoia sempervirens) Habitat Assessments

Fog and low clouds play an important role in providing moisture to coastal ecosystems. Coast redwood (Sequoia sempervirens) forests are currently distributed along a narrow strip of coastline in California and Oregon and rely on the presence of marine fog for moisture availability during the dry season (June-October). Recent time series analyses presented an uncertain future of fog frequency; however, a decline in fog presence may have adverse effects on the coast redwood habitat. To support Save the Redwoods League, a non-profit organization dedicated to coast redwood forest management, the team analyzed hourly fog data from the Geospatial Operational Environmental Satellite 17 (GOES-17) Advanced Baseline Imager (ABI) and daily cloud cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite. To explore present day fog longevity, GOES-17 was utilized to map the number of fog hours per day for the 2019 and 2020 dry seasons. The MODIS cloud flag was used to map the presence or absence of daily fog, which was summarized to create a monthly fog frequency dataset and identify trends in fog presence between 2000-2020. Both datasets were used as inputs into the random forest machine learning algorithm to identify climatic drivers of fog presence and longevity over the landscape. The present-day models suggested that daily temperature difference is a driving force behind fog presence and longevity. Trends in fog presence from 2000-2020 indicated great interannual variability. Finally, fog presence was modeled under a 2080 climate projection to shed light on the future of fog presence under a projected warmer climate. Model results projected an overall decline in fog presence during the dry season in the 2080s. Decreased fog presence as a result of increased temperature difference under a warmer climate remains to be a topic of investigation as to the impact on future redwood habitat suitability.

DEVELOP Project Summary↗
NASA NTRS2020

California & Oregon Ecological Forecasting: Detecting and Forecasting Fog Occurrence, Frequency, and Change to Inform Coast Redwood (Sequoia sempervirens) Habitat Assessments

Fog and low clouds play an important role in providing moisture to coastal ecosystems. Coast redwood (Sequoia sempervirens) forests are currently distributed along a narrow strip of coastline in California and Oregon and rely on the presence of marine fog for moisture availability during the dry season (June-October). Recent time series analyses presented an uncertain future of fog frequency; however, a decline in fog presence may have adverse effects on the coast redwood habitat. To complement ongoing work by Save the Redwoods League, a non-profit organization dedicated to coast redwood forest management, the team analyzed hourly fog data from the Geospatial Operational Environmental Satellite 17 (GOES-17) Advanced Baseline Imager (ABI) and daily cloud cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite. To explore present day fog longevity, GOES-17 was utilized to map the number of fog hours per day for the 2019 and 2020 dry seasons. The MODIS cloud flag was used to map the presence or absence of daily fog, which was summarized to create a monthly fog frequency dataset and identify trends in fog presence between 2000-2020. Both datasets were used as inputs into the random forest machine learning algorithm to identify climatic drivers of fog presence and longevity over the landscape. The present-day models suggested that daily temperature difference is a driving force behind fog presence and longevity. Trends in fog presence from 2000-2020 indicated great interannual variability. Finally, fog presence was modeled under a 2080 climate projection to shed light on the future of fog presence under a projected warmer climate. Model results projected an overall decline in fog presence during the dry season in the 2080s. Decreased fog presence as a result of increased temperature difference under a warmer climate remains to be a topic of investigation as to the impact on future redwood habitat suitability.

DEVELOP Technical Paper↗