Engineering Papers⌕ Search

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.

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

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Monsoonal MCS Initiation, Rainfall, and Diurnal Gravity Waves over the Bay of Bengal: Observation and a Linear Model

Abstract Previous observational studies have indicated that mesoscale convective systems (MCSs) contribute the majority of precipitation over the Bay of Bengal (BoB) during the summer monsoon season, yet their initiation and propagation remain incompletely understood. To fill this knowledge gap, we conducted a comprehensive study using a combination of 20-yr satellite observations, MCS tracking, reanalysis data, and a theoretical linear model. Satellite observations reveal clear diurnal propagation signals of MCS initiation frequency and rainfall from the west coast of the BoB toward the central BoB, with the MCS rainfall propagating slightly slower than the MCS initiation frequency. Global reanalysis data indicate a strong association between the offshore-propagating MCS initiation frequency/rainfall and diurnal low-level wind perturbations, implying the potential role of gravity waves. To verify the hypothesis, we developed a 2D linear model that can be driven by realistic meteorological fields from reanalysis. The linear model realistically reproduces the characteristics of offshore-propagating diurnal wind perturbations. The wind perturbations, as well as the offshore propagation signals of MCS initiation frequency and rainfall, are associated with diurnal gravity waves emitted from the coastal regions, which in turn are caused by the diurnal land–sea thermal contrast. The ambient wind speed and vertical wind shear play crucial roles in modulating the timing, propagation, and amplitude of diurnal gravity waves. Using the linear model and satellite observations, we further show that the stronger monsoonal flows lead to faster offshore propagation of diurnal gravity waves, which subsequently control the offshore propagation signals of MCS initiation and rainfall. Significance Statement Rainfall over the Bay of Bengal (BoB) is primarily contributed by large and organized rainfall systems in the summer monsoon season. During this season, these systems are commonly observed over the east coast of India around midnight, the western BoB in the morning, and the central BoB in the afternoon. This eastward rainfall propagation is confirmed by observations, reanalysis data, and a theoretical model to have a strong association with atmospheric diurnal gravity waves. These waves are caused by land–sea thermal contrast and can trigger rainfall systems over the offshore regions. We also found that the diurnal gravity waves, as well as their triggered rainfall systems, can be greatly modulated by the large-scale monsoonal flows. All the above findings improve our understanding of diurnal rainfall cycle over the BoB and may contribute to the future improvement of rainfall forecast over the region.

Meteorology & Atmospheric Sciences↗
DOE OSTI2022

Hydrologic investigations of radar-rainfall error propagation to rainfall-runoff model hydrographs

Rainfall is arguably the most important yet most variable input for rainfall-runoff hydrologic models. In this study, the authors search for the characteristics of radar-rainfall estimates that are most important for skillful streamflow predictions. They perform comprehensive hydrologic investigations of radar-rainfall characteristics, including spatiotemporal resolution, radar range visibility, statistical characterization of rainfall variability, all vis-a-vis basin characteristics such as size and river network topology. Since the true rainfall fields are unknown, the authors exploit a paradigm of using two independently constructed radar-rainfall products i.e., Multi-Radar Multi-Sensor and IFC-ZR used operationally by the Iowa Flood Center (IFC). Using the distributed hydrologic model called the Hillslope-Link Model for the domain of the state of Iowa, they evaluate streamflow prediction at 140 USGS gauge stations that monitor rivers in Iowa. Through spatial and temporal rainfall aggregation experiments, the authors show that the impact of spatial and temporal resolution of rainfall is significant typically for smaller basins while starts reducing significantly for basins larger than 1,000 km 2 . Other rainfall characteristics they explored do not reveal a strong signature in the relationship of rainfall differences between the two products and hydrograph errors. However, exploring the product similarities rather than differences reveals that the basin-wide rainfall volume has the most significant effect on streamflow prediction. The results from this study are generalizable for all rainfall observing systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The Next-Generation Ecosystem Experiment Arctic Rainfall Simulator: a tool to understand the effects of changing rainfall patterns in the Arctic

Rainfall frequency and intensity are expected to increase in the Arctic, with potential detrimental impacts on permafrost, leading to enhanced thawing and carbon release to the atmosphere. However, there have been very few studies on the effect of discrete rain events on permafrost in the Arctic and sub-Arctic. Conducting controlled rainfall experiments within permafrost landscapes can provide an improved understanding of the effect of changing intensity, duration, and timing of rain events on permafrost tundra ecosystems. Here, we describe the design and implementation of the Next-Generation Ecosystem Experiment Arctic Rainfall Simulator (NARS), a variable intensity (4–82 mm/h) rainfall simulator that can be used to study the effects of rainfall on permafrost stability. The NARS design includes a 3D-printed 4 cm H-flume and uses an eTape resistivity sensor that was calibrated (R 2 = 0.9–0.96) to measure discharge from the system. NARS is designed to be lightweight, simple to construct, and can be easily deployed in remote locations. As a field validation of updated rainfall simulator design and modernized controls, NARS was tested on the Seward Peninsula, AK. Because of its portability, versatility in deployment, dimensions, and rainfall intensity, NARS represents a methodological innovation for researching the impacts of rainfall on permafrost environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Markov chain decomposition of monthly rainfall into daily rainfall: Evaluation of climate change impact

This paper evaluates the effect of climate change on daily rainfall, especially on the mean number of wet days and the mean rainfall intensity. Assuming that the mechanism of daily rainfall occurrences follows the first-order Markov chain model, the possible changes in the transition probabilities are estimated by considering the climate change scenarios. Also, the change of the stationary probabilities of wet and dry day occurrences and finally the change in the number of wet days are derived for the comparison of current (1x CO 2 ) and 2x CO 2 conditions. As a result of this study, the increase or decrease in the mean number of wet days was found to be not enough to explain all of the change in monthly rainfall amounts, so rainfall intensity should also be modified. The application to the Seoul weather station in Korea shows that about 30% of the total change in monthly rainfall amount can be explained by the change in the number of wet days and the remaining 70% by the change in the rainfall intensity. That is, as an effect of climate change, the increase in the rainfall intensity could be more significant than the increase in the wet days and, thus, the risk of flood will be much highly increased.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Assessing North Atlantic Tropical Cyclone Rainfall Hazard Using Engineered-Synthetic Storms and a Physics-Based Tropical Cyclone Rainfall Model

In this study, we design a statistical method to couple observations with a physics-based tropical cyclone (TC) rainfall model (TCR) and engineered-synthetic storms for assessing TC rainfall hazard. We first propose a bias-correction method to minimize the errors induced by TCR via matching the probability distribution of TCR-simulated historical TC rainfall with gauge observations. Then we assign occurrence probabilities to engineered-synthetic storms to reflect local climatology, through a resampling method that matches the probability distribution of a newly proposed storm parameter named rainfall potential (POT) in the synthetic dataset with that in the observation. POT is constructed to include several important storm parameters for TC rainfall such as TC intensity, duration, and distance and environmental humidity near landfall, and it is shown to be correlated with TCR-simulated rainfall. The proposed method has a satisfactory performance in reproducing the rainfall hazard curve in various locations in the continental United States; it is an improvement over the traditional joint probability method (JPM) for TC rainfall hazard assessment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

A simple model for estimating changes in rainfall erosivity caused by variations in rainfall patterns

Highlights: • A simple relationship was developed for predicting daily erosivity in changing climates. • The relationship depends on precipitation amount and maximum 0.5-h intensity I{sub 0.5} . • The relationship was calibrated using measured climate data and had a R{sup 2} of 0.99. • Simulated daily and annual erosivities were predicted with a R{sup 2} of 0.99 using the relationship. • The relationship was tested using I{sub 24}, showing a R{sup 2} of 0.78 for the simulated data. A major challenge when coupling soil loss models with precipitation forecasts from Global Circulation Models (GCMs) is that their time resolutions do not generally agree. Precipitation forecasts from GCM must be scaled down; however, the distribution of the rainfall intensity, which can affect soil loss as much as precipitation amounts, is usually not considered in this process. Therefore, the objective of this study was to develop a statistical equation for computing event-based rainfall erosivity under changing precipitation patterns using the least amount of information possible. For this purpose, an empirical equation for predicting event-based rainfall erosivity was developed using the product of the total precipitation P and the maximum 0.5-h rainfall intensity, I{sub 0.5}. This equation was calibrated using measured precipitation data from 28 sites in Central Chile and then tested with simulated data with different rainfall patterns from the CLIGEN (CLImate GENerator) weather generator. More than 53,000 rainfall events were analyzed, where the equation consistently provided R{sup 2} values of 0.99 for every dataset used, revealing its robustness when used in potential climate change scenarios in the study site. However, because computing I{sub 0.5} requires estimating precipitation at a high time resolution, the relationship was recalibrated and tested using 1 through 24-h maximum rainfall intensities. Using these intensities, the equation provided erosivity estimates with R{sup 2} ranging from 0.78 to 0.99, where better results were obtained as the resolution of the data increased. This study provides the methodology for building and testing the proposed equation and discusses its advantages and limitations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Remote Influence of Andean Convection on Amazonian Rainfall and Its Mechanisms

Models from Coupled Model Intercomparison Project Phase 6 produce too much precipitation over the Andes but too little over the Amazon or the Wet Andes-Dry Amazon (WADA) bias pattern. Unlike the conventional view that convection parameterization and land model deficiencies can contribute to Amazonian rainfall biases, we approach this long-standing biased model behavior through the lens of Andean convection. Using Community Earth System Model v1.1 and focusing on the wet season, our mechanism-denial experiments demonstrate that Andean convection notably reduces precipitation over the Amazon during austral summer. The Andean forced Amazonian response operates on weather timescale. Furthermore, the reduction of Amazonian rainfall is detectable within a few hours after initial Andean forcing. The precipitation response is primarily driven by variations in the moisture budget and is moderated by changes in convective available potential energy over the Amazon. Changes in the total advection of moisture over the Amazon are dominated by the vertical advection term and can be attributed to discrepancies in the dynamic omega field. In the experiments, the Andean east flank region is scrutinized where the vertical velocity and moisture fields play an intermediary role for the Andean driven WADA connection. The Andean forcing induces descending anomalies on the Andean east flank. The disturbances of wind and geopotential fields over the Andean east flank propagate eastward via Kelvin waves. Over the Amazon, descending anomalies and advective drying lead to reduction of mid-to-high level cloud, increase of shortwave cloud forcing and surface net radiation, and enhancement of themodynamic stability and rainfall reduction.

58 GEOSCIENCES↗
DOE OSTI2015

Atmospheric rivers do not explain UK summer extreme rainfall: ARS AND UK EXTREME RAINFALL

Extreme rainfall events continue to be one of thelargest natural hazards in the UK. In winter,heavy precipitation and floods have been linked with intense moisture transport events associated withatmospheric rivers (ARs), yet no large-scale atmospheric precursors have been linked to summer flooding inthe UK. This study investigates the link between ARs and extreme rainfall from two perspectives: (1) Givenan extreme rainfall event, is there an associated AR?(2) Given an AR, is there an associated extreme rainfallevent? We identify extreme rainfall events using theUK Met Office daily rain gauge data set and link theseto ARs using two different horizontal resolution atmospheric data sets (ERA-Interim and Twentieth CenturyReanalysis). The results show that less than 35% of winter ARs and less than 15% of summer ARs areassociated with an extreme rainfall event. Consistent with previous studies, at least 50% of extreme winterrainfall events are associated with an AR. However, less than 20% of the identified summer extreme rainfallevents are associated with an AR. The dependence of the water vapor transport intensity threshold used todefine an AR on the years included in the study, and on the length of the season, is also examined. Includinga longer period (1900–2012) compared to previous studies (1979–2005) reduces the water vapor transportintensity threshold used to define an AR.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Synthesis of ARM User Facility Surface Rainfall Datasets to Construct a Best Estimate Value Added Product (PrecipBE)

Surface precipitation measurements are essential for Earth system model (ESM) evaluation and understanding cloud processes. An ever-growing need for robust, temporally evolving, and easy-to-use statistical datasets provides motivation for a baseline ground-based precipitation properties data product. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility operates an extensive suite of precipitation instruments with various sensitivities and operating mechanisms, which render the decision of which instrument to use based on one or more fixed thresholds challenging and prone to errors and bias. Using a long-term instrument inter-comparison from a unique per-precipitation event perspective, rather than instantaneous sample comparison, we demonstrate that ARM rainfall-measuring instruments are generally consistent with each other at the statistical level. Inter-instrument deviations at the single event level can be large, especially for specific rainfall event properties such as maximum precipitation rates. A machine-learning (ML) analysis using a random forest regressor indicates that in some cases, depending on instrument, local site climatology, and/or specific deployment configuration, certain atmospheric state variables influence the measured quantities in an unpredictable manner. Thus, a-priori weighting of different instruments does not necessarily lead to more accurate and less biased synthesis of instrument data. These results motivate the design of the ARM precipitation best-estimate (PrecipBE) value-added product, which incorporates all valid precipitation data while considering data quality and other instrument limitations. PrecipBE consists of time series and tabular statistics datasets in an easy-to-use and insightful per-precipitation event format. It provides a large set of precipitation event properties supplemented with ancillary data from ARM datasets that correspond to the detected precipitation events. We describe the PrecipBE algorithm and demonstrate its use via the examination of a single-day output as well as a long-term trend analysis of precipitation events at the ARM Southern Great Plains (SGP) site, covering more than 30 years of data. The trend analysis tentatively suggests a long-term temporal tendency for mainly shorter and less intense precipitation events at the SGP site, but a long-term increase in annual rainfall by more than 36 mm (5 %) per decade. This rainfall trend is catalyzed primarily by more extreme event properties of relatively rare, intense precipitation events, with event total and 1 min maximum precipitation rate at a 1 year timeframe increasing up to 5 mm and 9 mm h −1 (several percent) per decade, respectively. While the currently available PrecipBE datasets (at https://adc.arm.gov/discovery/, last access: 8 December 2025) cover rainfall from multiple ARM deployments up to March 2025, PrecipBE is planned to be expanded to include solid-phase precipitation and will soon become an operational product with a several-day lag from real-time. We invite the ARM user community to leverage this new product and welcome user feedback to further enhance the dataset.

Silber, Israel [Pacific Northwest National Laborat↗
DOE OSTI2017

The Green Ocean Amazon Experiment (GoAmazon2014/5) Observes Pollution Affecting Gases, Aerosols, Clouds, and Rainfall over the Rain Forest

The Observations and Modeling of the Green Ocean Amazon 2014–2015 (GoAmazon2014/5) experiment took place around the urban region of Manaus in central Amazonia across 2 years. The urban pollution plume was used to study the susceptibility of gases, aerosols, clouds, and rainfall to human activities in a tropical environment. Many aspects of air quality, weather, terrestrial ecosystems, and climate work differently in the tropics than in the more thoroughly studied temperate regions of Earth. GoAmazon2014/5, a cooperative project of Brazil, Germany, and the United States, employed an unparalleled suite of measurements at nine ground sites and on board two aircraft to investigate the flow of background air into Manaus, the emissions into the air over the city, and the advection of the pollution downwind of the city. Here in this paper, to visualize this train of processes and its effects, observations aboard a low-flying aircraft are presented. Comparative measurements within and adjacent to the plume followed the emissions of biogenic volatile organic carbon compounds (BVOCs) from the tropical forest, their transformations by the atmospheric oxidant cycle, alterations of this cycle by the influence of the pollutants, transformations of the chemical products into aerosol particles, the relationship of these particles to cloud condensation nuclei (CCN) activity, and the differences in cloud properties and rainfall for background compared to polluted conditions. The observations of the GoAmazon2014/5 experiment illustrate how the hydrologic cycle, radiation balance, and carbon recycling may be affected by present-day as well as future economic development and pollution over the Amazonian tropical forest.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Hydraulic architecture explains species moisture dependency but not mortality rates across a tropical rainfall gradient

Aim Intensified droughts are affecting tropical forests across the globe. However, the underlying mechanisms of tree drought response and mortality are poorly understood. Hydraulic traits and especially hydraulic safety margins (HSMs), i.e. the extent to which plants buffer themselves from thresholds of water stress, provide insights into species-specific drought vulnerability. Methods We investigated hydraulic traits during an intense drought triggered by the 2015-2016 El Niño on 27 canopy trees across three tropical forest sites with differing precipitation. We capitalized on the drought event as a time when plant water status might approach or exceed thresholds of water stress. We investigated the degree to which these traits varied across the rainfall gradient, as well as relationships amongst hydraulic traits and species-specific optimal moisture and mortality rates. Results There were no differences among sites for any measured trait. There was strong coordination among traits, with a network analysis revealing two major groups of coordinated traits. In one group there were water potentials, turgor loss point, sapwood capacitance and density, HSMs, and mortality rate. In the second group there was leaf mass per area, leaf dry matter content, hydraulic architecture (leaf area to sapwood area ratio), and species-specific optimal moisture. Conclusion These results demonstrated that while species with greater safety from turgor loss had lower mortality rates, hydraulic architecture was the only trait that explained species’ moisture dependency. Species with a greater leaf area to sapwood area ratio were associated with drier sites and reduced their transpirational demand during the dry season via deciduousness.

Hydraulic Saftey Margins, drought, tropical forest↗
DOE OSTI2020

Fine Root and Soil Organic Carbon Depth Distributions are Inversely Related Across Fertility and Rainfall Gradients in Lowland Tropical Forests

Humid tropical forests contain some of the largest soil organic carbon (SOC) stocks on Earth. Much of this SOC is in subsoil, yet variation in the distribution of SOC through the soil profile remains poorly characterized across tropical forests. We used a correlative approach to quantify relationships among depth distributions of SOC, fine root biomass, nutrients and texture to 1 m depths across 43 lowland tropical forests in Panama. The sites span rainfall and soil fertility gradients, and these are largely uncorrelated for these sites. We used fitted b parameters to characterize depth distributions, where b is a numerical index based on an asymptotic relationship, such that larger b values indicate greater concentrations of root biomass or SOC at depth in the profile. Root b values ranged from 0.82 to 0.95 and were best predicted by soil pH and extractable potassium (K) stocks. For example, the three most acidic (pH < 4) and Kpoor (< 20 g K m -2 ) soils contained 76 ± 5% of fine root biomass from 0 to 10 cm depth, while the three least acidic (pH > 6.0) and most K-rich (> 50 g K m -2 ) soils contained only 41 ± 9% of fine root biomass at this depth. Root b and SOC b values were inversely related, such that a large fine root biomass in surface soils corresponded to large SOC stocks in subsoils (50–100 cm). SOC b values were best predicted by soil pH and base cation stocks, with the three most base-poor soils containing 34 ± 8% of SOC from 50 to 100 cm depth, and the three most base-rich soils containing just 9 ± 2% of SOC at this depth. Nutrient depth distributions were not related to Root b or SOC b values. Here, these data show that large surface root biomass stocks are associated with large subsoil C stocks in strongly weathered tropical soils. Further studies are required to evaluate why this occurs, and whether changes in surface root biomass, as may occur with global change, could in turn influence SOC storage in tropical forest subsoils.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Carbon content of soil fractions varies with season, rainfall, and soil fertility across a lowland tropical moist forest gradient

Tropical forests contain some of the largest soil carbon (C) stocks on Earth, making them broadly relevant to terrestrial-climate feedbacks, yet our understanding of how their soil organic C (SOC) fractions vary over space and time is limited. We studied effects of season, fertility, and mean annual precipitation (MAP) on the C contents of soil fractions across 14 lowland forests in Panama. We measured free-debris, occluded-debris, and mineral-associated SOC fractions, as well as soluble C associated with each fraction. We hypothesized that mineral-associated SOC would be greatest in infertile, strongly weathered soils with large reactive mineral contents. We also hypothesized that the debris SOC fractions would accumulate during the dry season, reflecting seasonal increases in litterfall. To address this, we compared soil fractions in wet and dry seasons from fertile and infertile soils across a range of 1809 – 2864 mm MAP. The C content (mg C / g soil) of all soil fractions varied with fertility and MAP: specifically, free-debris SOC was greatest in wet, high-fertility soils, and occluded-debris SOC was greater in high-fertility than low-fertility soils. The mineral-associated SOC fraction, which contained the majority of bulk soil C, showed increasing C content with greater MAP in infertile sites, presumably driving similar spatial patterns in the bulk soil. Only the free-debris SOC fraction showed strong seasonal variation, increasing in mass during the dry season. Nitrogen behaved similarly to C. In summary, soil C contents increased with MAP in infertile sites but not fertile sites, driven by the mineral-associated SOC fraction. The dry season had greater free-debris SOC, but this seasonal trend was not apparent in bulk soil C, likely because of the small size of the free-debris SOC fraction. Furthermore, changes in the quantity and seasonality of precipitation, which are projected for tropical forests, might shift spatial and temporal patterns of soil C storage, which would in turn influence forest-climate feedbacks for this C-rich biome.

54 ENVIRONMENTAL SCIENCES↗