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At least 1,765 records · Page 98

Re-evaluating probable maximum precipitation estimates: sensitivity to transposition domains and storm rotation using modern datasets

This study examines the sensitivity of Probable Maximum Precipitation (PMP) estimates to key methodological decisions embedded in the legacy approach adopted in the U.S. National Weather Service Hydrometeorological Reports No. 51 and No. 52. Although widely used for infrastructure design and risk regulation, fundamental aspects of PMP estimation—such as storm sample size, transposition domain, maximization procedures, and storm rotation—remain poorly constrained and lack formal guidance. Using the Red Rock watershed in Iowa as a case study, and leveraging the 2002–2023 NOAA Analysis of Record for Calibration (AORC) precipitation dataset, we systematically evaluate how each methodological choice, individually and in combination, influences PMP estimates. Our findings demonstrate that PMP is not a fixed physical upper bound but rather a modeling construct shaped heavily by user-defined assumptions. Notably, PMP values derived from modern gridded rainfall datasets can be substantially higher than the legacy estimate used in the original spillway design for Red Rock Dam. Decisions regarding storm sample size, domain extent, climatological window, and particularly storm rotation all contributed to higher PMP estimates. Storm rotation alone—a loosely constrained element in the current PMP practice—can amplify PMP by more than 25%. These results reveal the lack of standardized bounds in current PMP workflows and the need for systematic sensitivity and uncertainty analysis. As PMP estimation shifts toward probabilistic approaches, incorporating physically meaningful storm attributes will be key to developing more transparent, defensible methods for dam safety and climate-resilient infrastructure.

Probable maximum precipitation↗

The Dichotomy of Wet and Dry Trends Over India by Aerosol Indirect Effects in CMIP5 Models

Abstract Aerosol‐cloud interactions, also known as aerosol indirect effect (AIE), substantially impact rainfall frequency and intensity. Here, we analyze NEX‐GDDP, a multimodel ensemble of high‐resolution (0.25°) historical simulations and future projections statistically downscaled from 21 CMIP5 models, to quantify the importance of AIE on extreme climate indices, specifically consecutive dry days (CDD), consecutive wet days (CWD), and simple daily intensity index (SDII). The 21 NEX‐GDDP CMIP5 models are classified into models with reliable (REM) and unreliable (UREM) monsoon climate simulated over India based on their simulations of the climate indices. The REM group is further decomposed based on whether the models represent only the direct (REM ADE ) or the direct and indirect (REM ALL ) aerosol effects. Compared to REM ADE , including all aerosol effects significantly improves the model skills in simulating the observed historical trends of all three climate indices over India. Specifically, AIE enhances dry days and reduces wet days in India in the historical period, consistent with the observed changes. However, by the middle and end of the 21st century, there is a relative decrease in dry days and an increase in wet days and precipitation intensity. Moreover, the REM ALL simulated future CWD and CDD changes are mostly opposite to those in REM ADE , indicating the substantial role of AIE in the future projection of dry and wet climates. These findings underscore the crucial role of AIE in future projections of the Indian hydroclimate and motivate efforts to accurately represent AIE in climate models.

54 ENVIRONMENTAL SCIENCES↗

An Investigation on the Pollen-Induced Soiling Losses in Utility-Scale PV Plants

Here in this study, the impact of pollen as a PV soiling agent is investigated. The performance data of five utility-scale PV plants in North Carolina, USA, was collected and analyzed using two soiling extraction methods. Satellite and environmental data, including pollen counts, cropland, and vegetation, was also collected and analyzed to identify impacts to soiling losses. During the spring peak pollen season, performance losses of >15% were observed at all five sites. Partial performance recoveries following the pollen season were slow, with lack of correlation with rainfall. This means that the statistical soiling estimation methods that assume abrupt performance recovery from rain are not appropriate for pollen-impacted solar sites. When manual cleanings were performed on site the performance recovery ranged from 5% to 11% indicating persistent soiling impacts are present in this region. The results of this work provide new insights into the phenomenon of pollen deposition on PV systems, demonstrating that 1) soiling can also affect systems located in rainy locations and 2) that its effects cannot be determined using the current estimation methodologies.

14 SOLAR ENERGY↗

Analyzing Relationships between Tropical Cyclone Intensity and Rain Rate over the Ocean Using Numerical Simulations

In this study, the relationship between tropical cyclone (TC) intensity and rain rate over the ocean is investigated using a full-physics numerical model (WRF) and a physics-based TC rainfall model (TCR). TC intensity is found to be nearly linearly correlated with the average rain rate in the inner core [~0.97 (mm h –1 m –2 )/(m s –1 )], while the correlation is weak at outer radii. This difference is induced because TC intensity is significantly correlated with both the vertical velocity and specific humidity in the inner core but is not significantly correlated with the vertical velocity in the outer radii. Further investigation shows that the intensity–rain-rate relationship at the outer radii is influenced by the TC evolution stage. The rain rate for the outer radii is positively correlated with TC intensity for nondecaying TCs, while this correlation is reduced for decaying TCs due to systematic downdrafts in the outer radii. In the context of climate change, the sensitivity of the TC rain rate to sea surface temperature (SST) is found to be +9% per 1 K increase of SST, roughly the product of the sensitivity of TC intensity to SST (+3%) and the Clausius-Clapeyron scaling (+7%). Coupled with synthetic storms, evolution of the TC rain rate over the twenty-first century under the SSP5-8.5 scenario is projected by the TCR (calibrated with the WRF simulations). The annual increase rates of averaged TC rain rate are 0.17% and 0.20% for the inner core and outer radii, respectively, larger than the annual increase rate of TC intensity (0.046%) but comparable to that of cube of intensity (0.18%).

54 ENVIRONMENTAL SCIENCES↗

Effective Cloud Detection and Segmentation Using a Gradient-Based Algorithm for Satellite Imagery: Application to Improve PERSIANN-CCS

The effective identification of clouds and monitoring of their evolution are important toward more accurate quantitative precipitation estimation and forecast. In this study, a new gradient-based cloud-image segmentation algorithm is developed using image processing techniques. This method integrates morphological image gradient magnitudes to separate cloud systems and patches boundaries. A varying scale kernel is implemented to reduce the sensitivity of image segmentation to noise and to capture objects with various finenesses of the edges in remote sensing images. The proposed method is flexible and extendable from single to multispectral imagery. Case studies were carried out to validate the algorithm by applying the proposed segmentation algorithm to synthetic radiances for channels of the Geostationary Operational Environmental Satellite ( GOES-16) simulated by a high-resolution weather prediction model. The proposed method compares favorably with the existing cloud-patch-based segmentation technique implemented in the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS) rainfall retrieval algorithm. Evaluation of event-based images indicates that the proposed algorithm has potentials comparing to the conventional segmentation technique used in PERSIANN-CCS to improve rain detection and estimation skills with an accuracy rate of up to 98% in identifying cloud regions.

54 ENVIRONMENTAL SCIENCES↗

On polarimetric radar signatures of deep convection for model evaluation: columns of specific differential phase observed during MC3E

The representation of deep convection in general circulation models is in part informed by cloud-resolving models (CRMs) that function at higher spatial and temporal resolution; however, recent studies have shown that CRMs often fail at capturing the details of deep convection updrafts. With the goal of providing constraint on CRM simulation of deep convection updrafts, ground-based remote sensing observations are analyzed and statistically correlated for four deep convection events observed during the Midlatitude Continental Convective Clouds Experiment (MC3E). Since positive values of specific differential phase observed above the melting level are associated with deep convection updraft cells, so-called columns are analyzed using two scanning polarimetric radars in Oklahoma: the National Weather Service Vance WSR-88D (KVNX) and the Department of Energy C-band Scanning Atmospheric Radiation Measurement (ARM) Precipitation Radar (C-SAPR). KVNX and C-SAPR volumes and columns are then statistically correlated with vertical winds retrieved via multi-Doppler wind analysis, lightning flash activity derived from the Oklahoma Lightning Mapping Array, and KVNX differential reflectivity . Results indicate strong correlations of volume above the melting level with updraft mass flux, lightning flash activity, and intense rainfall. Analysis of columns reveals signatures of changing updraft properties from one storm event to another as well as during event evolution. Comparison of to shows commonalities in information content of each, as well as potential problems with associated with observational artifacts.

Atm↗

Synthesis of Post-fire Monitoring Activities in the Great Basin Desert, Mojave Desert, and Transition Zones

In the deserts of the American Southwest, fire return intervals of centuries to millennia are being replaced by fire return intervals of decades. This increased burn frequency has implications for post-closure management and long-term stewardship for Soils Corrective Action Units (CAUs) and Corrective Action Sites (CASs) on the Nevada National Security Site (NNSS), Tonopah Test Range, and Nevada Test and Training Range for which the U.S. Department of Energy (DOE), Environmental Management-Nevada (EM-NV) is responsible. For many CAUs and CASs, where closure-in-place alternatives were implemented or are being considered, there is a chance that these sites could burn over while they still pose a risk to the environment or human health given the long half-lives of some of the radionuclide contaminants of concern (COCs) (Shafer et al., 2007; Shafer and Gomes, 2009). Although it would have been ideal to conduct a fire-related study at a radionuclide contaminated site on the NNSS to determine how contaminated soils are transported post-fire, it was more practical to examine fires in environments analogous to where Soils Activity CAUs and CASs exist. Therefore, a series of studies were initiated at three radiologically uncontaminated analog sites to better understand the possible effects of wildfire on erosion and sediment transport by wind and water should vegetation at contaminated sites burn at the NNSS. The studies took place at the sites of the Gleason Fire, a prescribed burn that occurred near Ely, Nevada, which is in the Great Basin ecoregion; the White Rock Fire that occurred near Mesquite, Nevada, which is in the Mojave Desert ecoregion; and the Jacob Fire that occurred near Hiko, Nevada, in the transitional zone between the Great Basin Desert and the Mojave Desert. Each site was representative of one of the three ecoregions found on the NNSS. Erosion and vegetation characteristics were measured multiple times post-fire on both burned and unburned test plots at these study sites to compare and monitor the effects of fire over time. However, at the Mojave Desert site, there was an additional burn from nearly 25 years ago that was also compared with the more recent burned areas from 2013. Also, the time elapsed after the fire and the time when sampling began differed among the ecoregions. For example, at the Great Basin site, samples were collected prior to the fire and less than one month after the fire, whereas data collected at the Mojave Desert site did not occur until 22 months after the fire. The differences between the sample measurements and the time that elapsed after the fire may contribute to the variability in the data and the interpretations of the data. Runoff and water erosion were quantified through a series of rainfall and runoff simulation tests in which controlled amounts of water were delivered to the soil surface. Runoff data were collected from different microhabitats (e.g., undercanopies, interspace soils, ridges, and drainage areas). The data showed soil hydrophobicity (i.e., water repellency) on the Great Basin site for up to 12 months after the fire. The soil structure remained changed and weaker on the burned areas compared with the unburned areas for the three-year study period. Although runoff data at both the Great Basin site and transition zone site were highly variable, the post-fire runoff potential did not generally increase at either location after three years of monitoring. Mojave runoff did not differ much between fire ages or between burned and unburned areas, possibly because of the amount of vegetation growing on all three surfaces and because the fire intensity was likely lower relative to other ecoregions due to the amount of space between plants. Additionally, the measurements conducted on the Mojave fire occurred several years after the fire had burned and not within several months of the burn, which may also account for the lack of differences in runoff between burned and unburned locations. Wind erosion was assessed and quantified with a Portable In-Situ Wind ERosion Lab (PI-SWERL) on both the burned and unburned soils of different microhabitats (e.g., ridges, drainages, interspaces between plants, and plant undercanopies). Estimates of emissions (e.g., particulate matter with aerodynamic diameters less than or equal to 10 micrometers, or PM10) at different wind speeds were collected and filter samples were analyzed for chemical composition. The measurements among the fires were similar and the results of the wind erosion measurements indicate that there were seasonal influences on many parameters, but the potential for PM10 windblown dust emissions was higher on burned areas compared with unburned areas. Among the burned areas, drainages produced the most dust emissions, whereas burned ridges were the least emissive. Emissions at the Mojave site and transition zone site were similar between burned and unburned areas approximately three years after the respective fires. Emissions remained much greater on burned soils at the Great Basin site. However, site selection may have also contributed to the differences of emissions among the three study locations. Post-fire vegetation responses were documented for three years following each fire in different microhabitats (e.g., ridge, drainage, undercanopy, and interspace) at the Great Basin and transition zone sites. In 2013, at the Mojave fire site, random permanent plots were established in newly burned areas, areas that burned approximately 25 years ago, and an unburned control area. At each fire site, the regeneration of native plant species dominated the burned areas, but invasive annual grasses were present at each fire site. During the course of each three-year study, the invasive annual grasses increased in density or percentage at both the Great Basin and transition fire sites but not at the Mojave site, where annual invasive grasses remained high throughout the study period. However, the chronosequence data from the Mojave site indicate a transition to different invasive grass species with time. The post-burn vegetation at all three ecoregions is still in the early stages of succession, and shrub size should increase over time and shrub density should decrease over time. However, the recovery of the plant communities to pre-burn compositions may not occur for decades or centuries, or may not occur because of changes in climate and the presence of invasive annual grasses. Results from the three study locations suggested that contaminated soils on the NNSS that experienced wildfire had the potential for soil erosion and transport of contaminated soils. Both wind and soil runoff studies indicated that locations in the Great Basin and transition zone ecoregions were more susceptible to erosion than the Mojave Desert ecoregion, but the highest levels of erosion occurred during the early portions of the study (the first 24 months) and the Mojave Desert was not measured until 22 months after the burn. Additionally, data from the Great Basin indicated deeper soils may increase susceptibility to wind erosion. Historically, the Great Basin and transition zone ecoregions burned more frequently than the Mojave Desert because of fuel abundance and connectivity—and they continue to burn more frequently, particularly after the introduction of invasive grasses (Davies and Nafus, 2013). However, both ecoregions and the transition zone have burned with extensive, high-severity fires when certain weather and fuel conditions are met. For example, the high abundance and productivity of annual grasses and forbs—which are usually produced by above average winter precipitation—combined with early spring and summer warm, dry conditions. Future climate scenarios predict increased temperatures, variable to reduced precipitation, and larger, more extreme wildfires throughout western North America. These predictions suggest that future fires on the NNSS could increase in frequency, intensity, and severity similar to their analog ecosystems throughout the Great Basin and Mojave Desert (Dennison et al., 2014; Abatzoglou andWilliams, 2016; Westerling, 2016).

54 ENVIRONMENTAL SCIENCES↗

Extrapolating carbon dynamics of seasonally dry tropical forests across geographic scales and into future climates: improving simulation models with empirical observations. Final report

This research focused on seasonally dry tropical forests (SDTFs), a biome that exhibits strong variation in ecological, hydrological and physical characteristics. Climate change is expected to strongly impact the atmospheric environment of SDTFs, and long-term trends in precipitation seasonality have already been detected. The extent to which such changes in seasonal moisture availability will impact SDTF carbon dynamics is highly uncertain, and much of this uncertainty hinges on poorly-understood belowground processes. Thus, the central objectives of this research were to qualitatively and quantitatively describe how belowground processes mediate the response of SDTF carbon dynamics to environmental change. We accomplished these aims through field campaigns in four tropical countries to characterize soil biogeochemistry and plant hydraulic traits, ecosystem-scale field experiments that manipulated both nutrients and rainfall, and simulation modeling.

54 ENVIRONMENTAL SCIENCES↗

Weather Data from BSEC Weather Stations

This dataset provides hourly measurements of temperature, humidity, rainfall, wind, and sunlight at Ambient Weather and OttHydro stations across Baltimore city. These surface weather stations were deployed by the Baltimore Social-Environmental Collaborative (BSEC) Urban Integrated Field Laboratory (UIFL) project, funded by the Department of Energy (DOE). This dataset will be periodically updated to include more stations and recent observations when available. Data File Information This dataset contains surface weather measurements data in comma-separated value (CSV) format and documents that describe the weather stations, locations, and measured parameters and units. data/BSEC-[STATIONID]_[SENSORTYPE]_hourly_[YEAR].csv Surface weather measurements data in CSV format, where STATIONID indicates the weather station, SENSORTYPE is the type of weather station ('AWS' = Ambient Weather Station and 'OTT' = 'OttHydro Station'), and YEAR indicate the year in which the measurements were made. Example data file name: BSEC-AAC_AWS_hourly_2023.csv. documents/Station_Locations.csv This CSV file provides location information and measurement start date for each surface weather station. documents/Weather_Station_Descriptions.pdf This document provides detailed description of the instruments along with their setup and accuracy of measurement. documents/File_Parameters.pdf This document describes the surface weather parameters and units of measurement.

Ambient Weather Stations↗

Weather Data from BSEC Weather Stations

This dataset provides measurements of temperature, humidity, rainfall, wind, and sunlight at Ambient Weather and OttHydro stations across Baltimore city. These surface weather stations were deployed by the Baltimore Social-Environmental Collaborative (BSEC) Urban Integrated Field Laboratory (UIFL) project, funded by the Department of Energy (DOE). This dataset currently contains measurements from 2023 to June 2026 and will be periodically updated to include more stations and recent observations when available. Data File Information This dataset contains surface weather measurements data in comma-separated value (CSV) format and documents that describe the weather stations, locations, and measured parameters and units. data/[TIMEAVG]/[YEAR]/BSEC-[STATIONID]_[SENSORTYPE]_[TIMEAVG]_[YEAR].csv Surface weather measurements data in CSV format, where STATIONID indicates the weather station, SENSORTYPE is the type of weather station ('AWS' = Ambient Weather Station and 'OTT' = 'OttHydro Station'), TIMEAVG is time period for each entry (= daily, hourly, or 5min), and YEAR indicate the year in which the measurements were made. Example data file name: BSEC-AAC_AWS_hourly_2023.csv. documents/Station_Locations.csv This CSV file provides location information and measurement start date for each surface weather station. documents/Weather_Station_Descriptions.pdf This document provides detailed description of the instruments along with their setup and accuracy of measurement. documents/File_Contents.pdf This document describes the contents on the data files, including time notation, weather parameters and units of measurement. documents/site-metadata/[STATOINID]-metadata.pdf These PDF files provide information on weather station sites, including land cover characteristics, station mounting, and photographs. Each PDF file corresponds to one station, as indicated by STATIONID.

Ambient Weather Stations↗

The TRMM 'Day-1' Radar/Radiometer Combined Rain-Profiling Algorithm

The Tropical Rainfall Measuring Mission (TRMM)'s 'day-1' combined rada/radiometer algorithm uses a rain-profiling approach which gives as much importance to the measurement of the TRMM satellite's precipitation radar (PR) and the TRMM microwave imager (TMI) as their respective intrinsic ambiguities warrant, which avoids any ad hoc shortcuts that might introduce large biases in the rain estimates, yet which is simple enough to be operatrional with TRMM is launched in late 1997.

Rain-Profiling Algorithm Tropical Rainfall↗

On Parametrizing the Rain Drop Size Distribution

This paper addresses the problem of finding a parametric for for the rain drop size distribution which 1) is an appropriate model for tropical rainfall, and 2) involves statistically independent parameters.

rain drop size remote sensing↗

ARMAR Observations during TOGA/COARE

The NASA/JPL Airborne Rain Mapping Radar (ARMAR) was deployed for rainfall observations during TOGA/COARE on the NASA DC-8 aircraft. A total of approx. 30 hours of rain profiling measurements were collected over the Western Pacific Ocean during January and February 1993.

radar bright bands rain attenuation study Airborne↗

Optimal Estimation of Rain Rate Profiles From Radar Returns at Attenuating Wavelengths

There are significant inherent ambiguities when one tries to determine a particular vertical rain intensity profile from a given time profile of radar echo powers measured by a downward-looking (spaceborne or airborne) radar at a single attenuating frequency. In this paper, we quantify these ambiguities mathematically, and examine their effects on the performance of rain-rate retrieval algorithms initially proposed for use by the Precipitation Radar of the Tropical Rainfall Measuring Mission (TRMM).

vertical rain intensity profiles spaceborne radar ↗

Simultaneous Estimation of Model State Variables and Observation and Forecast Biases Using a Two-Stage Hybrid Kalman Filter

In this paper, we present a two-stage hybrid Kalman filter to estimate both observation and forecast bias in hydrologic models, in addition to state variables. The biases are estimated using the discrete Kalman filter, and the state variables using the ensemble Kalman filter. A key issue in this multi-component assimilation scheme is the exact partitioning of the difference between observation and forecasts into state, forecast bias and observation bias updates. Here, the error covariances of the forecast bias and the unbiased states are calculated as constant fractions of the biased state error covariance, and the observation bias error covariance is a function of the observation prediction error covariance. In a series of synthetic experiments, focusing on the assimilation of discharge into a rainfall-runoff model, it is shown that both static and dynamic observation and forecast biases can be successfully estimated. The results indicate a strong improvement in the estimation of the state variables and resulting discharge as opposed to the use of a bias-unaware ensemble Kalman filter. Furthermore, minimal code modification in existing data assimilation software is needed to implement the method. The results suggest that a better performance of data assimilation methods should be possible if both forecast and observation biases are taken into account.

Data Assimilation↗

The Goddard Cumulus Ensemble Model (GCE): Improvements and Applications for Studying Precipitation Processes

Convection is the primary transport process in the Earth's atmosphere. About two-thirds of the Earth's rainfall and severe floods derive from convection. In addition, two-thirds of the global rain falls in the tropics, while the associated latent heat release accounts for three-fourths of the total heat energy for the Earth's atmosphere. Cloud-resolving models (CRMs) have been used to improve our understanding of cloud and precipitation processes and phenomena from micro-scale to cloud-scale and mesoscale as well as their interactions with radiation and surface processes. CRMs use sophisticated and realistic representations of cloud microphysical processes and can reasonably well resolve the time evolution, structure, and life cycles of clouds and cloud systems. CRMs also allow for explicit interaction between clouds, outgoing longwave (cooling) and incoming solar (heating) radiation, and ocean and land surface processes. Observations are required to initialize CRMs and to validate their results. The Goddard Cumulus Ensemble model (GCE) has been developed and improved at NASA/Goddard Space Flight Center over the past three decades. It is amulti-dimensional non-hydrostatic CRM that can simulate clouds and cloud systems in different environments. Early improvements and testing were presented in Tao and Simpson (1993) and Tao et al. (2003a). A review on the application of the GCE to the understanding of precipitation processes can be found in Simpson and Tao (1993) and Tao (2003). In this paper, recent model improvements (microphysics, radiation and land surface processes) are described along with their impact and performance on cloud and precipitation events in different geographic locations via comparisons with observations. In addition, recent advanced applications of the GCE are presented that include understanding the physical processes responsible for diurnal variation, examining the impact of aerosols (cloud condensation nuclei or CCN and ice nuclei or IN) on precipitation processes, utilizing a satellite simulator to improve the microphysics, providing better simulations for satellite-derived latent heating retrieval, and coupling with a general circulation model to improve the representation of precipitation processes.

GCE↗

High-Resolution NU-WRF Simulations of a Deep Convective-Precipitation System During MC3E: Comparisons Between Goddard Microphysics Schemes and Observations - Part 1

The Goddard microphysics scheme was recently improved by adding a 4th ice class (frozen dropshail). This new 4ICE scheme was implemented and tested in the Goddard Cumulus Ensemble model (GCE) for an intense continental squall line and a moderate,less-organized continental case. Simulated peak radar reflectivity profiles were improved both in intensity and shape for both cases as were the overall reflectivity probability distributions versus observations. In this study, the new Goddard 4ICE scheme is implemented into the regional-scale NASA Unified - Weather Research and Forecasting model (NU-WRF) and tested on an intense mesoscale convective system that occurred during the Midlatitude Continental Convective Clouds Experiment (MC3E). The NU42WRF simulated radar reflectivities, rainfall intensities, and vertical and horizontal structure using the new 4ICE scheme agree as well as or significantly better with observations than when using previous versions of the Goddard 3ICE (graupel or hail) schemes. In the 4ICE scheme, the bin microphysics-based rain evaporation correction produces more erect convective cores, while modification of the unrealistic collection of ice by dry hail produces narrow and intense cores, allowing more slow-falling snow to be transported rearward. Together with a revised snow size mapping, the 4ICE scheme produces a more horizontally stratified trailing stratiform region with a broad, more coherent light rain area. In addition, the NU-WRF 4ICE simulated radar reflectivity distributions are consistent with and generally superior to those using the GCE due to the less restrictive open lateral boundaries

Regional Scale Model↗