SEARCH · Engineering Papers

Results for “rainfall”

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.

SourceResearch connectionFrom yearThrough year

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

At least 1,729 records · Page 96

All sources NASA NTRS DOE OSTI

NASA NTRS2018

A Comparative Study of Available Water in the Major River Basins of the World

Numerous large river basins of the world have few and irregular observations of the components of the terrestrial hydrological cycle with the exception of stream gauges at a few locations and at the outlet along with sparsely distributed rain gauges. Using observations from satellite sensors and output from global land surface models, it is possible to study these under-observed river basins. With populations greater than a billion people, some of these rivers (e.g., the Ganga-Brahmaputra, the Yangtze, the Nile and the Mekong) are the economic engines of the countries they transect, yet thorough assessment of their flow dynamics and variability in regard to water resource management is still lacking. In this paper, we use soil moisture (0-2m) and surface runoff from the NASA Global Land Data Assimilation System (GLDAS), evapotranspiration, and Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and rainfall from the Tropical Rainfall Measuring Mission (TRMM) and total water storage anomaly from the Gravity Recovery and Climate Experiment (GRACE) to examine variability of individual water balance components. To this end, understanding the inter-annual and intra-seasonal variability and the spatial variability of the water balance components in the major river basins of the world will help to plan for improved management of water resources for the future.

GLDAS↗

NASA NTRS2019

An Integrated Approach to Weather Radar Calibration and Monitoring Using Ground Clutter and Satellite Comparisons

The stability and accuracy of weather radar reflectivity calibration are imperative for quantitative applications, such as rainfall estimation, severe weather monitoring and nowcasting, and assimilation in numerical weather prediction models. Various radar calibration and monitoring techniques have been developed, but only recently have integrated approaches been proposed, that is, using different calibration techniques in combination. In this paper the following three techniques are used: 1) ground clutter monitoring, 2) comparisons with spaceborne radars, and 3) the self-consistency of polarimetric variables. These techniques are applied to a C-band polarimetric radar (CPOL) located in the Australian tropics since 1998. The ground clutter monitoring technique is applied to each radar volumetric scan and provides a means to reliably detect changes in calibration, relative to a baseline. It is remarkably stable to within a standard deviation of 0.1 dB (decibels). To obtain an absolute calibration value, CPOL observations are compared to spaceborne radars on board TRMM (Tropical Rainfall Measuring Mission) and GPM (Global Precipitation Measurement) using a volume-matching technique. Using an iterative procedure and stable calibration periods identified by the ground echoes technique, we improve the accuracy of this technique to about 1 dB. Finally, we review the self-consistency technique and constrain its assumptions using results from the hybrid TRMM-GPM and ground echo technique. Small changes in the self-consistency parameterization can lead to 5 dB of variation in the reflectivity calibration. We find that the drop-shape model of Brandes et al. with a standard deviation of the canting angle of 12 degrees best matches our dataset.

Louf, Valentin↗

NASA NTRS2019

Global Disease Outbreaks Associated with the 2015-2016 El Niño Event

Interannual climate variability patterns associated with the El Niño-Southern Oscillation phenomenon result in climate and environmental anomaly conditions in specific regions worldwide that directly favor outbreaks and/or amplification of variety of diseases of public health concern including chikungunya, hantavirus, Rift Valley fever, cholera, plague, and Zika. We analyzed patterns of some disease outbreaks during the strong 2015–2016 El Niño event in relation to climate anomalies derived from satellite measurements. Disease outbreaks in multiple El Niño-connected regions worldwide (including Southeast Asia, Tanzania, western US, and Brazil) followed shifts in rainfall, temperature, and vegetation in which both drought and flooding occurred in excess (14–81% precipitation departures from normal). These shifts favored ecological conditions appropriate for pathogens and their vectors to emerge and propagate clusters of diseases activity in these regions. Our analysis indicates that intensity of disease activity in some ENSO-teleconnected regions were approximately 2.5–28% higher during years with El Niño events than those without. Plague in Colorado and New Mexico as well as cholera in Tanzania were significantly associated with above normal rainfall (p < 0.05); while dengue in Brazil and southeast Asia were significantly associated with above normal land surface temperature (p < 0.05). Routine and ongoing global satellite monitoring of key climate variable anomalies calibrated to specific regions could identify regions at risk for emergence and propagation of disease vectors. Such information can provide sufficient lead-time for outbreak prevention and potentially reduce the burden and spread of ecologically coupled diseases.

El Nino↗

NASA NTRS2019

Simplifying NASA Earth Science Data and Information Access Through Natural Language Processing Based Data Analysis and Visualization

NASA Earth science data collected from satellites, model assimilation, airborne missions, and field campaigns, are large, complex and evolving. Such characteristics pose great challenges for end users (e.g., Earth science and applied science users, students, citizen scientists), particularly for those who are unfamiliar with NASA's EOSDIS and thus unable to access and utilize datasets effectively. For example, a novice user may simply ask: what is the total rainfall for a flooding event in my county yesterday? For an experienced user (e.g., algorithm developer), a question can be: how did my rainfall product perform, compared to ground observations, during a flooding event? Nonetheless, with rapid information technology development such as natural language processing, it is possible to develop simplified Web interfaces and back-end processing components to handle such questions and deliver answers in terms of text, data, or graphic results directly to users.In this presentation, we describe the main challenges for end users with different levels of expertise in accessing and utilizing NASA Earth science data. Surveys reveal that most non-professional users normally do not want to download and handle raw data as well as conduct heavy-duty data processing tasks. Often they just want some simple graphics or data for various purposes. To them, simple and intuitive user interfaces are sufficient because complicated ones can be difficult and time-consuming to learn. Professionals also want such interfaces to answer many questions from datasets. One solution is to develop a natural language based search box like Google and the search results can be text, data, graphics and more. Now the challenge is, with natural language processing, can we design a system to process a scientific question typed in by a user? In this presentation, we describe our plan for such a prototype. The workflow is: 1) extract needed information (e.g., variables, spatial and temporal information, processing methods, etc.) from the input, 2) process the data in the backend, and 3) deliver the results (data or graphics) to the user.

Liu, Zhong↗

NASA NTRS2020

Space-Based Precipitation Measurements in Tropical Cyclones: Past, Present, and Future

Passive and active remote sensing of precipitation from space has led to significant advances in the understanding and prediction of tropical cyclones around the globe. This presentation will highlight the role of past NASA space-based measurements of precipitation by the Tropical Rainfall Measuring Mission (TRMM, 1998-2015), ongoing measurements by the Global Precipitation Measurement (GPM) mission (2014-current), and future measurements from the Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS, nominal launch date in 2020) as well as a potential new mission on Aerosols, Clouds, Convection, and Precipitation (ACCP) from the 2017 NASA Earth Science Decadal Survey. TRMM, which flew the first precipitation radar in space, provided the first systematic descriptions of the radial and azimuthal variations of rainfall in tropical cyclones around the globe and their relationship to storm motion and vertical wind shear. GPM is the lynchpin of a global constellation of precipitation satellites that provides high spatial (0.1°) and temporal (30 min) resolution real-time estimates of precipitation globally, making them essential to applications related to tropical cyclone prediction, disaster response, flood and landslide monitoring, and vector-borne disease monitoring. TROPICS will be a constellation of 6 Cubesat satellites with microwave imaging and sounding channels that will provide information on temperature and humidity in the storm environment, as well as estimates of precipitation and tropical cyclone intensity. ACCP is yet to be fully defined, but is envisioned to potentially carry multi-frequency radar with possible Doppler capability.

Braun, Scott↗

NASA NTRS2020

Widespread Mangrove Damage Resulting from the 2017 Atlantic Mega Hurricane Season

Comprised of 17 named tropical storms, 6 of which were major hurricanes, the 2017 Atlantic hurricane season ranked as one of the most damaging and costly hurricane seasons on record. In addition to socio-economic impacts, many previous studies have shown that important coastal ecosystems like mangroves are shaped by severe storms. However, little is known about how the cumulative effects of storms over entire hurricane seasons affect mangroves across large regions. We used satellite imagery from the entire Caribbean and Gulf of Mexico region to show that 2017 resulted in disproportionate mangrove damage compared to baseline responses over the previous 8 years. Specifically, we observed 30 times more mangrove damage, via a reduction in the normalized difference vegetation index (NDVI), during 2017 compared to any of the eight previous hurricane seasons, and most (72%) of this damage persisted throughout the 7 month post-hurricane season period as indicated by no NDVI recovery. Furthermore, wind speed, rainfall, and canopy height data showed that mangrove damage primarily resulted from high maximum wind speeds, but flooding (cumulative rainfall), previous storm history, and mangrove structure (canopy height) were also important predictors of damage. While mangroves are known to be resilient to hurricane impacts, our results suggest that increasingly frequent mega-hurricane seasons in the Caribbean region will dramatically alter mangrove disturbance dynamics.

Temilola Elisabeth Fatoyinbo Agueh↗

NASA NTRS2020

The Atacama Desert: A Window into Late Mars Surface Habitability?

Decades of robotic exploration have revealed that Mars was capable of sustaining life in the Noachian and Hesperian periods (> 3 billion years ago). But habitability conditions progressively deteriorated thereafter, and a lasting trend of climate change has made the surface of the planet likely uninhabitable at present times. One of the outstanding enigmas in Mars exploration is when was the last time that the surface of the planet could have sustained life. In this chapter we address this question through comparisons with one of the oldest and driest deserts on Earth, the Atacama Desert in northern Chile. We base our analysis on a latitudinal rainfall gradient that allows to investigate how microbial communities respond to increasing dryness. We posit that the ecological successions observed along the Atacama rainfall gradient represent a possible model for the adaptation of an early martian biosphere to increasing dryness throughout the planet’s geologic history, offering clues to the last surface environments that could have sustained life, and the timing of the last surface habitable period. These might in turn be the best locations to search for evidence of life.

Atacama Desert↗

NASA NTRS2020

Intercomparison of Precipitation Estimates Over the Southern Ocean from Atmospheric Reanalyses

Precipitation is a major component of the hydrologic cycle and plays a significant role in the sea-ice mass balance in the polar regions. Over the Southern Ocean, precipitation is particularly uncertain due to the lack of direct observations in this remote and harsh environment. Here we demonstrate that precipitation estimates from 8 global reanalyses produce similar spatial patterns between 2000-2010, although their annual means vary by about 250 mm yr -1 (or 26 percent of the median values) and there is little similarity in their representation of interannual variability. ERA-Interim produces the smallest and CFSR produces the largest amount of precipitation overall. Rainfall and snowfall are partitioned in five reanalyses; snowfall suffers from the same issues as the total precipitation comparison, with ERA-Interim producing about 128 mm less snowfall and JRA-55 about 103 mm more rainfall compared to the other reanalyses. When compared to CloudSat-derived snowfall, these five reanalyses indicate similar spatial patterns, but differ in their magnitude. All reanalyses indicate precipitation on nearly every day of the year, with spurious values occurring on an average of about 60 days yr -1 resulting in an accumulation of about 4.5 mm yr -1 . While similarities in spatial patterns among the reanalyses suggest a convergence, the large spread in magnitudes points to issues with the background models in adequately reproducing precipitation rates, and the differences in the model physics employed. Further improvements to model physics are required to achieve confidence in precipitation rate, as well as the phase and frequency of precipitation in these products.

Linette N. Boisvert↗

NASA NTRS2004

TRMM and the global interannual variability of rain over the past five decades

In this paper we analyze the first five years of the first global land and ocean remote-sensing record of rainfall. We distill the information into a few objective indices, the first principal components of the rain anomaly, and extend them back in time to show how the global remote-sensing record implies that El Nino is indeed the major driver of the global interannual variability of rainfall.

Durden, Stephen L.↗

NASA NTRS2004

TRMM and the global interannual variability of rain over the past five decades

Durden, Stephen L.↗

NASA NTRS2019

Assessment of the Biomass Productivity Decline in the Lower Mekong Basin

This study aimed to delineate the geographic hotspots of negative trends in biomass productivity in the Lower Mekong Basin countries (Vietnam, Cambodia, Laos, and Thailand) and identify correlated regional environmental and anthropogenic factors. A long-term time-series (1982–2015) of Normalized Difference Vegetation Index at a resolution of approximately 9.16 km x 9.16 km was used to specify the areas with significant decline or increase in productivity. The relationships between vegetation changes and land attributes, such as climate, population density, soil/terrain conditions, and land-cover types, were examined. Rainfall time-series maps were used to identify areas that might have been affected by land degradation from those correlated with rainfall. Most of the detected potentially degraded areas were found in Cambodia, the Northwest and the Highland of Vietnam, the Northern Mountains of Thailand and Laos, and the mountainous border between Laos, Vietnam, and Cambodia. About 15% of the total land area of these four countries experienced a reduction in biomass productivity during the 34-year study period. The map of hotspots of changes in productivity can be used to direct further studies, including those at finer spatial resolution that may support policy makers and researchers in targeting the strategies for combating land degradation.

land degradation↗

NASA NTRS2021

A Dusty Atmospheric River Brings Floods to the Middle East

Torrential rainfall and rapid snowmelt in April 2017 caused deadly floods in northwestern Iran. An atmospheric river (AR), propagating across the Middle East and North Africa, was found responsible for this extreme event. The snowmelt was triggered by precipitation and warm advection associated with the AR. Total satellite-based rainfall for April 2017 was moderately below normal, suggesting that a heavy flood can happen during dry years. The AR was fed by moisture from the Mediterranean and Red Seas. Despite its adverse societal consequences, this event was beneficial to the recovery of the desiccating Lake Urmia. The impacts of this AR were not limited to flooding; it also facilitated dust transport to the region. This distinct characteristic of the ARs in the Middle East is attributed to major mineral dust sources located along their pathways. This event was reasonably predicted at 7-day lead time, crucially important for successful early warning systems.

Atmospheric river↗

NASA NTRSDate not supplied

Derivation and Testing of Consumptive Water Use Fraction for Specialty Crops

The Crop Consumptive Use Fraction (CCUF) expresses beneficial water use in the form of seasonal evapotranspiration of applied water (ETAW), relative to total irrigation volume. The metric is an indicator of the efficiency of agricultural water use and is a recommended component for preparation of agricultural water management plans in California. An FAO-56 based web application has been developed to facilitate retrospective evaluation of ETAW, and hence CCUF, at field level. The current application is optimized for prevailing climate in four of the state’s main growing regions: San Joaquin Valley, Sacramento Valley, Central Coast, and North Coast. User inputs include crop type, soil texture, irrigation method, daily irrigation volume, daily rainfall, and seasonal start/stop dates to define the analysis period. Time series of fractional green canopy cover (Fc), based on Landsat and Sentinel-2 Earth-resource satellite observations, are imported from NASA’s Satellite Irrigation Management Support (SIMS) system. Grass reference evapotranspiration (ETo) time series are accessed from Spatial CIMIS (California Irrigation Management Information System). Daily crop height (h) is estimated as a simple function of typical maximum height for the given crop type (from FAO-56) and Fc. A vegetation density coefficient (Kd) is derived from Fc and h. Stomatal control factors during mid-and late-season are applied to tree and vine crops. Typical values for minimum daily relative humidity and mean daily windspeed, derived from historical CIMIS weather data, are used to correct for regional deviations from standard climate (defined as RHmin=45%, windspeed =2m/s). The resulting daily basal crop coefficient (Kcb) represents the ET of a well-watered crop with minimal soil evaporation, relative to ETo. A soil water balance sub-model for the top 0.1 m is used to calculate daily evaporation coefficients (Ke). A second sub-model applies to the root zone to calculate crop water stress coefficients (Ks) and effective precipitation (Pe), which is the fraction of rainfall that is available for crop use. Those coefficients are combined with Kcb and ETo to calculate daily ETc. ETAW is then derived as cumulative ETc less cumulative Pe. For verification purposes, sensor installations were used to measure seasonal ETc in commercial fields for several annual and perennial specialty crops by soil water balance and energy balance methods. Model estimates of seasonal ETc show mean absolute error of <10% compared to the ground measurements.

Derivation↗

NASA NTRS2021

Investigating the potential of a global precipitation forecast to inform landslide prediction

Extreme rainfall events within landslide-prone areas can be catastrophic, resulting in loss of property, infrastructure, and life. A global Landslide Hazard Assessment for Situational Awareness (LHASA) model provides routine near-real time estimates of landslide hazard using Integrated Multi-Satellite Precipitation Retrievals for the Global Precipitation Mission (IMERG). However, it does not provide information on potential landslide hazard in the future. Forecasting potential landslide events at a global scale presents an area of open research. This study compares a global precipitation forecast provided by NASA's Goddard Earth Observing System (GEOS) with near-real time satellite precipitation estimates. The Multi-Radar Multi-Sensor gauge corrected (MRMS-GC) reference is used to assess the performance of both satellite and model-based precipitation products over the contiguous United States (CONUS). The forecast lead time of 24hrs is considered, with a focus on extreme precipitation events. The performance of IMERG and GEOS-Forecast products is assessed in terms of the probability of detection, success ratio, critical success index and hit bias as well as continuous statistics. The results show that seasonality influences the performance of both satellite and model-based precipitation products. Comparison of IMERG and GEOS-Forecast globally as well as in several event case studies (Colombia, southeast Asia, and Tajikistan) reveals that GEOS-Forecast detects extreme rainfall more frequently relative to IMERG for these specific analyses. For recent landslide points across the globe, the 24hr accumulated precipitation forecast >100 mm corresponds well with near-real time daily accumulated IMERG precipitation estimates. GEOS-Forecast and IMERG precipitation match more closely for tropical cyclones than for other types of storms. The main intention of this study is to assess the viability of using a global forecast for landslide predictions and understand the extent of the variability between these products to inform where we would expect the landslide modeling results to most prominently diverge. Results of this study will be used to inform how forecasted precipitation estimates can be incorporated into the LHASA model to provide the first global predictive view of landslide hazards.

S. Khan↗

NASA NTRSDate not supplied

Evolution of Drop Size Distributions during Hurricane Harvey and Their Effects on Rain Intensity

Hurricane Harvey hit the Texas Gulf Coast as a major hurricane on August 25, 2017, before exiting the state as a tropical storm on August 29, 2017. Left in its wake was historic flooding over southeast Texas with some locations measuring more than 60 inches of rain. The Houston NEXRAD radar (KHGX) maintained operations for the entirety of the event and is utilized in this study to better understand the evolution of the drop size distribution (DSD) in the lower (liquid) portions of the storm and how changes in the DSD were tied to observed and measured changes in rain intensities. From the KHGX dual-polarimetric radar data several DSD parameters were retrieved, including three different rain rate retrievals, as well as the mass-weighted mean-drop diameter Dm, and the normalized intercept parameter NW of the gamma distribution. Two of the rain retrievals are popular hybrid approaches, where reflectivity, differential and specific differential phase are used. The other is an attenuation-based retrieval. In a previous study, it was found that both hybrid retrievals significantly underestimated the rainfall during the heaviest rain periods, while the attenuation-based retrieval, with proper adjustment, agreed quite well with a large network of gauges from the Harris County Flood Warning System. Using Dm and NW as proxies for drop size and concentration, respectively, we will show that the heaviest rain intensities were associated in areas of the storm where there was a large concentration of large drops present. It is envisaged that results of this study can be used to develop a near-real-time tuning of the attenuation-based retrieval, thus providing timely, and possibly life-saving warnings during inundating rainfall.

David B. Wolff↗

NASA NTRS2021

Investigating the Potential of A Global Precipitation Forecast to Inform Landslide Prediction

Extreme rainfall events within landslide-prone areas can be catastrophic, resulting in loss of property, infra-structure, and life. A global Landslide Hazard Assessment for Situational Awareness (LHASA) model provides routine near-real time estimates of landslide hazard using Integrated Multi-Satellite Precipitation Retrievals for the Global Precipitation Mission (IMERG). However, it does not provide information on potential landslide hazard in the future. Forecasting potential landslide events at a global scale presents an area of open research. This study compares a global precipitation forecast provided by NASA’s Goddard Earth Observing System (GEOS) with near-real time satellite precipitation estimates. The Multi-Radar Multi-Sensor gauge corrected (MRMS-GC) reference is used to assess the performance of both satellite and model-based precipitation products over the contiguous United States (CONUS). The forecast lead time of 24hrs is considered, with a focus on extreme precipitation events. The performance of IMERG and GEOS-Forecast products is assessed in terms of the probability of detection, success ratio, critical success index and hit bias as well as continuous statistics. The results show that seasonality influences the performance of both satellite and model-based precipitation products. Comparison of IMERG and GEOS-Forecast globally as well as in several event case studies (Colombia, southeast Asia, and Tajikistan) reveals that GEOS-Forecast detects extreme rainfall more frequently relative to IMERG for these specific analyses. For recent landslide points across the globe, the 24hr accumulated precipitation forecast >100 mm corresponds well with near-real time daily accumulated IMERG precipitation estimates. GEOS-Forecast and IMERG precipitation match more closely for tropical cyclones than for other types of storms. The main intention of this study is to assess the viability of using a global forecast for landslide predictions and understand the extent of the variability between these products to inform where we would expect the landslide modeling results to most prominently diverge. Results of this study will be used to inform how forecasted precipitation estimates can be incorporated into the LHASA model to provide the first global predictive view of landslide hazards.

S. Khan↗

NASA NTRS2021

3D Simulations of the Early Martian Hydrological Cycle Mediated by a H2-CO2 Greenhouse

For decades, the scientific community has been trying to reconcile abundant evidence for fluvialactivity on Noachian and early Hesperian Mars with the faint young Sun and reasonableconstraints on ancient atmospheric pressure and composition. Recently, the investigation of H2-CO2 collision-induced absorption has opened up a new avenue to warm Noachian Mars. We usethe ROCKE-3D global climate model to simulate plausible states of the ancient Martian climatewith this absorptive warming and reasonable constraints on surface paleopressure. We find that1.5-2 bar CO2-dominated atmospheres with ≥3% H2 can produce global mean surfacetemperatures above freezing, while also providing sufficient warming to avoid surfaceatmospheric CO2 condensation at 0°-45° obliquity. Simulations conducted with both moderntopography and a paleotopography, before Tharsis formed, highlight the importance of Tharsis asa cold trap for water on the planet. Additionally, we find that low obliquity (modern and 0°) ismore conducive to rainfall over valley network locations than high (45°) obliquity. PLAIN LANGUAGE SUMMARY Much evidence tells us that ancient Mars had liquid water on its surface. But reconciling thatwith the fainter young Sun and reasonable constraints on Mars' early atmosphere is challenging.We use a 3D global climate model with an atmosphere containing some amount of hydrogen (apossible greenhouse gas) to study Mars' early hydrological cycle and compare it to the geologicalevidence of surface liquid water billions of years ago. We find that hydrogen and carbon dioxidetogether in an atmosphere twice as thick as modern Earth can warm early Mars above thefreezing point of water and that a low axial tilt produces rainfall patterns that best match thegeologic evidence

Scott D Guzewich↗

NASA NTRSDate not supplied

Evaluation of Extreme Soil Moisture Patterns over the Sahel during the 2020 Growing Season

The African Sahel is an ecologically and climatically sensitive region, and thus is a valuable test case for examination of climate extremes. Above-average rainfall during the 2020 growing season (June-October) led to flooding in the West, Central and East Sahel, with implications for infrastructure, agriculture and disease outbreaks. In this study, we evaluate soil moisture patterns in the region during 2020 to assess and quantify the extremeness of the event. The primary tool is the NASA Soil Moisture Active Passive (SMAP) Level 4 surface soil moisture data. Daily, monthly, and seasonal anomalies are computed relative to SMAP’s long-term mean (2015-2021). Additional comparisons are made with longer-time-series data sets, including surface soil moisture from NASA’s Modern-Era Retrospective analysis for Research and Applications, Version 2(MERRA-2; 1981-present) and precipitation from the African Rainfall Climatology, Version 2 (ARC2; 1983-present).Possible drivers of the extreme wet event are examined, including potential links to the concurrent 2020-21 La Niña event. Finally, we explore the connections between the extreme soil moisture and vector-borne disease outbreaks in the region in2020, namely, Rift Valley Fever in Mauritania and Chikungunya in Chad.

Soil Moisture↗