SEARCH · Engineering Papers

Results for “Integrated hydrologic modeling”

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 109 records · Page 6

All sources NASA NTRS DOE OSTI

NASA NTRS2007

Salinity Remote Sensing and the Study of the Global Water Cycle

The SMOS and AquariusISAC-D satellite missions will begin a new era to map the global sea surface salinity (SSS) field and its variability from space within the next twothree years. They will provide critical data needed to study the interactions between the ocean circulation, global water cycle and climate. Key scientific issues to address are (1) mapping large expanses of the ocean where conventional SSS data do not yet exist, (2) understanding the seasonal and interannual SSS variations and the link to precipitation, evaporation and sea-ice patterns, (3) links between SSS and variations in the oceanic overturning circulation, (4) air-sea coupling processes in the tropics that influence El Nino, and (4) closing the marine freshwater budget. There is a growing body of oceanographic evidence in the form of salinity trends that portend significant changes in the hydrologic cycle. Over the past several decades, highlatitude oceans have become fresher while the subtropical oceans have become saltier. This change is slowly spreading into the subsurface ocean layers and may be affecting the strength of the ocean's therrnohaline overturning circulation. Salinity is directly linked to the ocean dynamics through the density distribution, and provides an important signature of the global water cycle. The distribution and variation of oceanic salinity is therefore attracting increasing scientific attention due to the relationship to the global water cycle and its influence on circulation, mixing, and climate processes. The oceans dominate the water cycle by providing 86% of global surface evaporation (E) and receiving 78% of global precipitation (P). Regional differences in E-P, land runoff, and the melting or freezing of ice affect the salinity of surface water. Direct observations of E-P over the ocean have large uncertainty, with discrepancies between the various state-of-the-art precipitation analyses of a factor of two or more in many regions. Quantifying the climatic influence of the oceanic water cycle requires more accurately resolving the net air-sea water flux. Measuring global SSS trends on seasonal to interannual timescales by satellite is fundamental to this problem because the SSS trends represent detectable time-integrated signals of the variable marine hydrological cycle. Satellite measurements, coupled with an array of in situ observations, will provide global synoptic SSS fields for the first time history. These data will provide a strong constraint on climate models and data assimilation efforts, which must properly represent the freshwater budget in terms of E-P, ocean advection and surface layer mixing in order to accurately simulate the true ocean state. The SSS fields will allow us to quantify the covariability between the SSS and the strong seasonal E-P cycle in the tropics and high latitudes. Field measurement campaigns to exploit satellite and in situ measurements to close the seasonal E-P cycle over an ocean region are being considered. Lastly the satellite systems will monitor and trace the large long-lived SSS anomalies from year to year that have the potential to influence El Nino and the large scale ocean circulation.

Lagerloef, G. S. E.↗

NASA NTRS2021

Developing a strategy for the national coordinated soil moisture monitoring network

Soil moisture is a critical land surface variable, affecting a wide variety of climatological, agricultural, and hydrological processes. Determining the current soil moisture status is possible via a variety of methods, including in situ monitoring, remote sensing, and numerical modeling. Although all of these approaches are rapidly evolving, there is no cohesive strategy or framework to integrate these diverse information sources to develop and disseminate coordinated national soil moisture products that will improve our ability to understand climate variability. The National Coordinated Soil Moisture Monitoring Network initiative has developed a national strategy for network coordination with NOAA's National Integrated Drought Information System. The strategy is currently in review within NOAA, and work is underway to implement the initial milestones of the strategy. This update reviews the goals and steps being taken to establish this national-scale coordination for soil moisture monitoring in the United States.

Michael H. Cosh↗

NASA NTRS2022

Introducing and Evaluating the Climate Hazards Center IMERG with Stations (CHIMES) Timely Station-Enhanced Integrated Multisatellite Retrievals for Global Precipitation Measurement

As human exposure to hydroclimatic extremes increase and the number of in situ precipitation observations declines, precipitation estimates, such as those provided by the Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) mission, provide a critical source of information. Here, we present a new gauge-enhanced dataset [the Climate Hazards Center IMERG with Stations (CHIMES)] designed to support global crop and hydrologic modeling and monitoring. CHIMES enhances the IMERG Late Run product using an updated Climate Hazards Center (CHC) high-resolution climatology (CHPclim) and low-latency rain gauge observations. CHPclim differs from other products because it incorporates long-term averages of satellite precipitation, which increases CHPclim ’s fidelity in data-sparse areas with complex terrain. This fidelity translates into performance increases in unbiased IMERGlate data, which we refer to as CHIME. This is augmented with gauge observations to produce CHIMES. The CHC’s curated rain gauge archive contains valuable contributions from many countries. There are two versions of CHIMES: preliminary and final. The final product has more copious and better-curated station data. Every pentad and month, bias-adjusted IMERGlate fields are combined with gauge observations to create pentadal and monthly CHIMESprelim and CHIMESfinal. Comparisons with pentadal, high-quality gridded station data show that IMERG late performs well (r = 0.75), but has some systematic biases which can be reduced. Monthly cross-validation results indicate that unbiasing increases the variance explained from 50% to 63% and decreases the mean absolute error from 48 to 39 mm month −1. Gauge enhancement then increases the variance explained to 75%, reducing the mean absolute error to 27 mm month −1.

Chris C funk↗

NASA NTRS2026

Hydrology Copilot: A Cloud-Native Ai System for Hydrological Data Analysis

The emergence of AI-driven Earth observation systems promises to broaden access to petabyte-scale geospatial data beyond domain specialists. However, translating this vision into operational scientific infrastructure requires addressing fundamental challenges in data virtualization, code transparency, and domain-specific reasoning. We present Hydrology Copilot, a cloud-native AI framework for natural-language-driven analysis of Earth observation data. To demonstrate operational capabilities at scale, we implement the system using NASA's North American Land Data Assimilation System version 3 (NLDAS-3), which provides surface meteorological forcing and land-surface model output across North and Central America at 1-km resolution, from which drought diagnostics are derived. The system integrates five core contributions: (1) scalable data virtualization using Kerchunk-based cloud optimized access, achieving a 1.5 to 4.6 times improvement in I/O latency across benchmark queries spanning regional single-day extractions (4.6 times speedup) to continental monthly aggregations (1.5 times speedup); (2) transparent code generation through Microsoft Azure AI Foundry agents that expose executable Python workflows for scientific verification; (3) persistent conversational memory enabling multi-turn analytical discourse across sessions; (4) intelligent query validation that enforces dataset boundaries and resolves ambiguous requests before execution; and (5) a multi-agent architecture coordinating query parsing, code generation, and visualization. We evaluate the system through drought-monitoring workflows, demonstrating reliable code generation, accurate results validated against reference computations and the operational U.S. Drought Monitor, and efficient operation across increasingly complex tasks. By bridging natural-language interfaces with rigorous hydrological analysis, Hydrology Copilot advances beyond proof-of-concept demonstrations to provide a deployable framework for operational Earth science applications.

Data virtualization↗

NASA NTRSDate not supplied

Enabling Exchange and Adequate Use of Data for Observation Based Atmospheric Research

Systematic long-term field observations have played a vital role in advancing atmospheric research over the past several decades. The use of these observations has expanded from primarily characterizing atmospheric processes and trends to evaluating satellite measurements, assessing models, and improving air quality forecasts. Consequently, the demand for atmospheric chemistry observational data have dramatically increased in terms of scope and coverage of measurements (i.e., parameters/species, spatiotemporal extent). In addition to high quality measurements, certain data reporting standards need to be agreed to ensure the data can be readily exchanged and are sufficiently documented to enable adequate use in different research activities. To this end, WMO has developed and implemented measurement guidelines and community practices for meteorology, climatology, atmospheric and hydrological sciences. In addition, the WMO Expert Team on Metadata Standards manages and evolves the existing metadata standards for the WMO Information System WIS and WMO Integrated Global Observing System WIGOS to support consistent and interoperable data descriptions, ensure relevance to research, and to apply data science principles. This team draws on a wide range of expertise from the research community, including atmospheric measurements, modeling, data management, and data science. The current activities include development of key performance indicators, vocabularies for metadata and the evolution of metadata standards to lower the barrier of application to weather/climate/water/environment data for all communities and the weather enterprise. This presentation intends to promote awareness of ongoing progress and actively solicit community feedback.

Field Observations↗

NASA NTRS2016

National Climate Assessment - Land Data Assimilation System (NCA-LDAS) Data and Services at NASA GES DISC

The National Climate Assessment-Land Data Assimilation System (NCA-LDAS) is an Integrated Terrestrial Water Analysis, and is one of NASAs contributions to the NCA of the United States. The NCA-LDAS has undergone extensive development, including multi-variate assimilation of remotely-sensed water states and anomalies as well as evaluation and verification studies, led by the Goddard Space Flight Centers Hydrological Sciences Laboratory (HSL). The resulting NCA-LDAS data have recently been released to the general public and include those from the Noah land-surface model (LSM) version 3.3 (Noah-3.3) and the Catchment LSM version Fortuna-2.5 (CLSM-F2.5). Standard LSM output variables including soil moistures temperatures, surface fluxes, snow cover depth, groundwater, and runoff are provided, as well as streamflow using a river routing system. The NCA-LDAS data are archived at and distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). The data can be accessed via HTTP, OPeNDAP, Mirador search and download, and NASA Earth data Search. To further facilitate access and use, the NCA-LDAS data are integrated into the NASA Giovanni, for quick visualization and analysis, and into the Data Rods system, for retrieval of time series of long time periods. The temporal and spatial resolutions of the NCA-LDAS data are, respectively, daily-averages and 0.125x0.125 degree, covering North America (25N 53N; 125W 67W) and the period January 1979 to December 2015. The data files are in self-describing, machine-independent, CF-compliant netCDF-4 format.

data assimiliation↗

NASA NTRS2009

Hydrologic Applications of GRACE Terrestrial Water Storage Data

Gravimetry-based terrestrial water storage time series have great potential value for hydrological research and applications, because no other observing system can provide global maps of the integrated quantity of water stored on and below the land surface. However, these data are challenging to use because their spatial and temporal resolutions are low relative to other hydrological observations and because total terrestrial water storage is a measurement unfamiliar to hydrologists. In this presentation we will review techniques for temporal, horizontal, and vertical disaggregation of GRACE terrestrial water storage anomalies, including data assimilation and integration within a land surface model. We will then discuss initial results from three efforts to use the methods for water resources applications. These include drought monitoring across North America, water cycle assessment over the Middle East North African region, and groundwater depletion estimates for northern India.

Rodell, Matthew↗

NASA NTRS2011

Multi-Scale Hydrometeorological Modeling, Land Data Assimilation and Parameter Estimation with the Land Information System

The Land Information System (LIS; http://lis.gsfc.nasa.gov) is a flexible land surface modeling framework that has been developed with the goal of integrating satellite-and ground-based observational data products and advanced land surface modeling techniques to produce optimal fields of land surface states and fluxes. As such, LIS represents a step towards the next generation land component of an integrated Earth system model. In recognition of LIS object-oriented software design, use and impact in the land surface and hydrometeorological modeling community, the LIS software was selected as a co-winner of NASA?s 2005 Software of the Year award.LIS facilitates the integration of observations from Earth-observing systems and predictions and forecasts from Earth System and Earth science models into the decision-making processes of partnering agency and national organizations. Due to its flexible software design, LIS can serve both as a Problem Solving Environment (PSE) for hydrologic research to enable accurate global water and energy cycle predictions, and as a Decision Support System (DSS) to generate useful information for application areas including disaster management, water resources management, agricultural management, numerical weather prediction, air quality and military mobility assessment. LIS has e volved from two earlier efforts -- North American Land Data Assimilation System (NLDAS) and Global Land Data Assimilation System (GLDAS) that focused primarily on improving numerical weather prediction skills by improving the characterization of the land surface conditions. Both of GLDAS and NLDAS now use specific configurations of the LIS software in their current implementations.In addition, LIS was recently transitioned into operations at the US Air Force Weather Agency (AFWA) to ultimately replace their Agricultural Meteorology (AGRMET) system, and is also used routinely by NOAA's National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC) for their land data assimilation systems to support weather and climate modeling. LIS not only consolidates the capabilities of these two systems, but also enables a much larger variety of configurations with respect to horizontal spatial resolution, input datasets and choice of land surface model through "plugins". LIS has been coupled to the Weather Research and Forecasting (WRF) model to support studies of land-atmosphere coupling be enabling ensembles of land surface states to be tested against multiple representations of the atmospheric boundary layer. LIS has also been demonstrated for parameter estimation, who showed that the use of sequential remotely sensed soil moisture products can be used to derive soil hydraulic and texture properties given a sufficient dynamic range in the soil moisture retrievals and accurate precipitation inputs.LIS has also recently been demonstrated for multi-model data assimilation using an Ensemble Kalman Filter for sequential assimilation of soil moisture, snow, and temperature.Ongoing work has demonstrated the value of bias correction as part of the filter, and also that of joint calibration and assimilation.Examples and case studies demonstrating the capabilities and impacts of LIS for hydrometeorological modeling, assimilation and parameter estimation will be presented as advancements towards the next generation of integrated observation and modeling systems

Peters-Lidard, Christa D.↗

NASA NTRS2009

Multi-Scale Hydrometeorological Modeling, Land Data Assimilation and Parameter Estimation with the Land Information System

The Land Information System (LIS; http://lis.gsfc.nasa.gov; Kumar et al., 2006; Peters- Lidard et al.,2007) is a flexible land surface modeling framework that has been developed with the goal of integrating satellite- and ground-based observational data products and advanced land surface modeling techniques to produce optimal fields of land surface states and fluxes. As such, LIS represents a step towards the next generation land component of an integrated Earth system model. In recognition of LIS object-oriented software design, use and impact in the land surface and hydrometeorological modeling community, the LIS software was selected ase co-winner of NASA's 2005 Software of the Year award. LIS facilitates the integration of observations from Earth-observing systems and predictions and forecasts from Earth System and Earth science models into the decision-making processes of partnering agency and national organizations. Due to its flexible software design, LIS can serve both as a Problem Solving Environment (PSE) for hydrologic research to enable accurate global water and energy cycle predictions, and as a Decision Support System (DSS) to generate useful information for application areas including disaster management, water resources management, agricultural management, numerical weather prediction, air quality and military mobility assessment. LIS has evolved from two earlier efforts North American Land Data Assimilation System (NLDAS; Mitchell et al. 2004) and Global Land Data Assimilation System (GLDAS; Rodell al. 2004) that focused primarily on improving numerical weather prediction skills by improving the characterization of the land surface conditions. Both of GLDAS and NLDAS now use specific configurations of the LIS software in their current implementations. In addition, LIS was recently transitioned into operations at the US Air Force Weather Agency (AFWA) to ultimately replace their Agricultural Meteorology (AGRMET) system, and is also used routinely by NOAA's National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC) for their land data assimilation systems to support weather and climate modeling. LIS not only consolidates the capabilities of these two systems, but also enables a much larger variety of configurations with respect to horizontal spatial resolution, input datasets and choice of land surface model through "plugins,". As described in Kumar et al., 2007, and demonstrated in Case et al., 2008, and Santanello et al., 2009, LIS has been coupled to the Weather Research and Forecasting (WRF) model to support studies of land-atmosphere coupling the enabling ensembles of land surface states to be tested against multiple representations of the atmospheric boundary layer. LIS has also been demonstrated for parameter estimation as described in Peters-Lidard et al. (2008) and Santanello et al. (2007), who showed that the use of sequential remotely sensed soil moisture products can be used to derive soil hydraulic and texture properties given a sufficient dynamic range in the soil moisture retrievals and accurate precipitation inputs. LIS has also recently been demonstrated for multi-model data assimilation (Kumar et al., 2008) using an Ensemble Kalman Filter for sequential assimilation of soil moisture, snow, and temperature. Ongoing work has demonstrated the value of bias correction as part of the filter, and also that of joint calibration and assimilation. Examples and case studies demonstrating the capabilities and impacts of LIS for hydrometeoroogical modeling, assimilation and parameter estimation will be presented as advancements towards the next generation of integrated observation and modeling systems.

Peters-Lidard, Christa D.↗

NASA NTRS2008

Forward-looking Assimilation of MODIS-derived Snow Covered Area into a Land Surface Model

Snow cover over land has a significant impact on the surface radiation budget, turbulent energy fluxes to the atmosphere, and local hydrological fluxes. For this reason, inaccuracies in the representation of snow covered area (SCA) within a land surface model (LSM) can lead to substantial errors in both offline and coupled simulations. Data assimilation algorithms have the potential to address this problem. However, the assimilation of SCA observations is complicated by an information deficit in the observation SCA indicates only the presence or absence of snow, and not snow volume and by the fact that assimilated SCA observations can introduce inconsistencies with atmospheric forcing data, leading to non-physical artifacts in the local water balance. In this paper we present a novel assimilation algorithm that introduces MODIS SCA observations to the Noah LSM in global, uncoupled simulations. The algorithm utilizes observations from up to 72 hours ahead of the model simulation in order to correct against emerging errors in the simulation of snow cover while preserving the local hydrologic balance. This is accomplished by using future snow observations to adjust air temperature and, when necessary, precipitation within the LSM. In global, offline integrations, this new assimilation algorithm provided improved simulation of SCA and snow water equivalent relative to open loop integrations and integrations that used an earlier SCA assimilation algorithm. These improvements, in turn, influenced the simulation of surface water and energy fluxes both during the snow season and, in some regions, on into the following spring.

Zaitchik, Benjamin F.↗

NASA NTRS2012

Drought Indicators Based on Model Assimilated GRACE Terrestrial Water Storage Observations

The Gravity Recovery and Climate Experiment (GRACE) twin satellites observe time variations in Earth's gravity field which yield valuable information about changes in terrestrial water storage (TWS). GRACE is characterized by low spatial (greater than 150,000 square kilometers) and temporal (greater than 10 day) resolution but has the unique ability to sense water stored at all levels (including groundwater) systematically and continuously. The GRACE Data Assimilation System (GRACE-DAS), based on the Catchment Land Surface Model (CLSM) enhances the value of the GRACE water storage data by enabling spatial and temporal downscaling and vertical decomposition into moisture 39 components (i.e. groundwater, soil moisture, snow), which individually are more useful for scientific applications. In this study, GRACE-DAS was applied to North America and GRACE-based drought indicators were developed as part of a larger effort that investigates the possibility of more comprehensive and objective identification of drought conditions by integrating spatially, temporally and vertically disaggregated GRACE data into the U.S. and North American Drought Monitors. Previously, the Drought Monitors lacked objective information on deep soil moisture and groundwater conditions, which are useful indicators of drought. Extensive datasets of groundwater storage from USGS monitoring wells and soil moisture from the Soil Climate Analysis Network (SCAN) were used to assess improvements in the hydrological modeling skill resulting from the assimilation of GRACE TWS data. The results point toward modest, but statistically significant, improvements in the hydrological modeling skill across major parts of the United States, highlighting the potential value of GRACE assimilated water storage field for improving drought detection.

Houborg, Rasmus↗

NASA NTRS2000

Stennis Space Center Environmental Geographic Information System

As NASA's lead center for rocket propulsion testing, the John C. Stennis Space Center (SSC) monitors and assesses the off-site impacts of such testing through its Environmental Office (SSC-EO) using acoustical models and ancillary data. The SSC-EO has developed a geographical database, called the SSC Environmental Geographic Information System (SSC-EGIS), that covers an eight-county area bordering the NASA facility. Through the SSC-EGIS, the Enivronmental Office inventories, assesses, and manages the nearly 139,000 acres that comprise Stennis Space Center and its surrounding acoustical buffer zone. The SSC-EGIS contains in-house data as well as a wide range of data obtained from outside sources, including private agencies and local, county, state, and U.S. government agencies. The database comprises cadastral/geodetic, hydrology, infrastructure, geo-political, physical geography, and socio-economic vector and raster layers. The imagery contained in the database is varied, including low-resolution imagery, such as Landsat TM and SPOT; high-resolution imagery, such as IKONOS and AVIRIS; and aerial photographs. The SSC-EGIS has been an integral part of several major projects and the model upon which similar EGIS's will be developed for other NASA facilities. The Corps of Engineers utilized the SSC-EGIS in a plan to establish wetland mitigation sites within the SSC buffer zone. Mississippi State University employed the SSC-EGIS in a preliminary study to evaluate public access points within the buffer zone. The SSC-EO has also expressly used the SSC-EGIS to assess noise pollution modeling, land management/wetland mitigation assessment, environmental hazards mapping, and protected areas mapping for archaeological sites and for threatened and endangered species habitats. The SSC-EO has several active and planned projects that will also make use of the SSC-EGIS during this and the coming fiscal year.

Lovely, Janette↗

NASA NTRS2021

Quantifying the Observational Requirements of a Space-borne LiDAR Snow Mission

This study quantifies the level of observational accuracy required from a spaceborne light detection and ranging (LiDAR) snow depth retrieval mission for enabling beneficial impacts for snow estimation. The study is conducted over a region in Western Colorado using a suite of observing system simulation experiments(OSSEs).The Joint UK Land Environment Simulator, version 5.0 (JULES v5.0) is employed to simulate a suite of idealized LiDAR observations, considering a range of LiDAR snow depth retrieval errors, different hypothetical sensor swath widths, and the impact of cloud cover on observability. These simulated observations are then assimilated into the Noah land surface modelwith multi-parameterization options, version 3.6 (Noah-MP v3.6) model. This data assimilation setup is used to systematically evaluate the potential utility of LiDAR observations for improving modeled snow water equivalent (SWE)estimates and water budget variables such as runoff. Results from the OSSE runsshow that, in general, assimilation of synthetic LiDAR observations provide beneficial impacts when theLiDAR snow depth retrieval error standard deviation (σerror) is below 60 cm.Based on comparisons between the realistic (i.e., swath-limited and cloud-attenuated) case and the idealized (i.e., infinite swath width in the absence of cloud cover) case,this study concludes that observations with a conservative error standard deviation threshold of 40 cm (i.e., upper limit of the snow depth retrieval error that adds value to the SWE estimates viaassimilation) are needed for improving modeled snow estimates. More than a 33% reduction in SWEroot mean square errors and more than a 15% increase in correlation coefficientsare achieved when σ error ≤ 40 cmusing a 170-km sensor swath width in the presence of cloud attenuation effects. Further, the integrated hydrologic response, as represented by total (surface and subsurface) runoff estimates during the snow ablation season, are also enhanced when assimilating synthetic LiDAR snow depth retrievals with errors below this level.

Yonghwan Kwon↗

NASA NTRS2019

The Arctic-Boreal vulnerability experiment model benchmarking system

NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) integrates field and airborne data into modeling and synthesis activities for understanding Arctic and Boreal ecosystem dynamics. The ABoVE Benchmarking System (ABS) is an operational software package to evaluate terrestrial biosphere models against key indicators of Arctic and Boreal ecosystem dynamics, i.e.: carbon biogeochemistry, vegetation, permafrost, hydrology, and disturbance. The ABS utilizes satellite remote sensing data, airborne data, and field data from ABoVE as well as collaborating research networks in the region, e.g.: the Permafrost Carbon Network, the International Soil Carbon Network, the Northern Circumpolar Soil Carbon Database, AmeriFlux sites, the Moderate Resolution Imaging Spectroradiometer, the Orbiting Carbon Observatory 2, and the Soil Moisture Active Passive mission. The ABS is designed to be interactive for researchers interested in having their models accurately represent observations of key Arctic indicators: a user submits model results to the system, the system evaluates the model results against a set of Arctic-Boreal benchmarks outlined in the ABoVE Concise Experiment Plan, and the user then receives a quantitative scoring of model strengths and deficiencies through a web interface. This interactivity allows model developers to iteratively improve their model for the Arctic-Boreal Region by evaluating results from successive model versions. We show here, for illustration, the improvement of the Lund–Potsdam–Jena-Wald Schnee und Landschaft (LPJwsl) version model through the ABoVE ABS as a new permafrost module is coupled to the existing model framework. The ABS will continue to incorporate new benchmarks that address indicators of Arctic-Boreal ecosystem dynamics as they become available.

ABoVE↗

NASA NTRSDate not supplied

Detection of Drizzle and Light Snowfall Over the Southern Ocean Using the GPM Combined Algorithm

Following the 2014 launch of the Global Precipitation Measurement Mission (GPM), an unprecedented combination of coincident active and passive microwave observations are available for state of the art precipitation retrieval. The GPM Combined Algorithm forms the backbone of this effort, optimizing geophysical variables for agreement with the multi-spectral information content. Combined retrievals are then utilized, along with a radiative transfer model, as a database applied for retrievals across a constellation of passive microwave radiometers of varying frequencies and viewing geometries. By keeping such retrievals related through the transfer standard of the combined algorithm, level 3 products such as the Integrated Multi-satellitE Retrievals for GPM (IMERG) are able to provide consistent global products for users at the higher temporal resolution required for hydrological applications on the global scale. In the current version of the combined product, precipitation retrievals are carried out only in the presence of a signal from the active radar. As a result, light precipitation and drizzle below the threshold of radar sensitivity are not included in any of the products down the chain from the constellation to IMERG. In this work, the effects of enhancing the retrievals with an optimal estimation-type (OE) water vapor and cloud retrieval using the passive observations are explored over the Southern Ocean on a regional scale. Non-convergence of the OE in areas with no detectable radar signal is used to identify areas with high probability of light precipitation and drizzle. Microphysical scale characterization of the light precipitation will be explored using information derived from the higher sensitivity CloudSat mission along with model information to identify and associate the related atmospheric state and dynamics. This is a physically-based approach requiring radiometric consistency with all available multi-spectral observations. Retrieved drizzle can then be included in the constellation databases continuing across all scales through to the level 3 products. The technique, successfully demonstrated for the Southern Ocean, can be easily adapted for light precipitation retrieval over other areas and surfaces leading to a global climatology of light precipitation.

Sarah Rinegerud↗

NASA NTRS2011

Cloud Computing Applications in Support of Earth Science Activities at Marshall Space Flight Center

Currently, the NASA Nebula Cloud Computing Platform is available to Agency personnel in a pre-release status as the system undergoes a formal operational readiness review. Over the past year, two projects within the Earth Science Office at NASA Marshall Space Flight Center have been investigating the performance and value of Nebula s "Infrastructure as a Service", or "IaaS" concept and applying cloud computing concepts to advance their respective mission goals. The Short-term Prediction Research and Transition (SPoRT) Center focuses on the transition of unique NASA satellite observations and weather forecasting capabilities for use within the operational forecasting community through partnerships with NOAA s National Weather Service (NWS). SPoRT has evaluated the performance of the Weather Research and Forecasting (WRF) model on virtual machines deployed within Nebula and used Nebula instances to simulate local forecasts in support of regional forecast studies of interest to select NWS forecast offices. In addition to weather forecasting applications, rapidly deployable Nebula virtual machines have supported the processing of high resolution NASA satellite imagery to support disaster assessment following the historic severe weather and tornado outbreak of April 27, 2011. Other modeling and satellite analysis activities are underway in support of NASA s SERVIR program, which integrates satellite observations, ground-based data and forecast models to monitor environmental change and improve disaster response in Central America, the Caribbean, Africa, and the Himalayas. Leveraging SPoRT s experience, SERVIR is working to establish a real-time weather forecasting model for Central America. Other modeling efforts include hydrologic forecasts for Kenya, driven by NASA satellite observations and reanalysis data sets provided by the broader meteorological community. Forecast modeling efforts are supplemented by short-term forecasts of convective initiation, determined by geostationary satellite observations processed on virtual machines powered by Nebula.

Molthan, Andrew L.↗

NASA NTRS2001

Diagnosing Tropical Divergent Circulations From Satellite-Derived Diabatic Forcing and Variational Integral Constraints

Large-scale divergent circulations are part of the atmospheric dynamic response to diabatic heating from condensation, radiative processes, and surface energy fluxes. Vertical motion and the associated divergent wind is thus intimately tied to the hydrologic cycle and the global beat balance. Despite its importance, the divergent circulation is too small in comparison to the rotational flow to measure directly with any accuracy. Vertical motions are recovered diagnostically from reanalyses and, as such, are subject to shortcomings in model physics, numerics, and data availability. While reanalysis estimates of tropical divergent circulations are much improved over those from the Global Weather Experiment, there are still substantial differences between products from operational centers. This is because these circulations are still forced largely by model physics and only secondarily by observations. In order to produce a refined estimate of tropical divergence and its interannual variability we have used a number of remotely-sensed data sets along with variational constraints to improve upon reanalysis estimates. Among these are: precipitation from SSM/I and GPCP, TOVS Path-A vertical cloud distributions; ISCCP radiative cooling rates; TOA radiative fluxes from ERBS, surface radiative fluxes from the SRB project, and surface latent and sensible flux estimates from SSM/I. The TOVS Path-A data constrain the divergent outflow in precipitating regions to have the same vertical structure as observed cloudiness. Using integral constraints for moisture, heat, and mass balance, we retrieve consistent divergent wind flows. We examine the ability of this type analysis to capture regional details of ENSO related perturbations to the divergent wind and associated tropical energy balance. Precipitation from satellite is found to be the major constraint in supplying this horizontal structure. We also consider the ability of this analysis to quantify integrated (land vs. ocean) fluctuations in the global monsoonal flow and energy balance. This depends, of course, on the error characteristics of the data constraints. Some estimate of these properties and their relative importance is provided.

Robertson, Franklin R.↗

NASA NTRS1982

LANDSAT-D investigations in snow hydrology

Snow reflectance in all 6 TM reflective bands, i.e., 1, 2, 3, 4, 5, and 7 was simulated using a delta-Eddington model. Snow reflectance in bands 4, 5, and 7 appear sensitive to grain size. It appears that the TM filters resemble a ""square-wave'' closely enough that a square-wave can be assumed in calculations. Integrated band reflectance over the actual response functions was calculated using sensor data supplied by Santa Barbara Research Center. Differences between integrating over the actual response functions and the equivalent square wave were negligible. Tables are given which show (1) sensor saturation radiance as a percentage of the solar constant, integrated through the band response function; (2) comparisons of integrations through the sensor response function with integrations over the equivalent square wave; and (3) calculations of integrated reflectance for snow over all reflective TM bands, and water and ice clouds with thickness of 1 mm water equivalent over TM bands 5 and 7. These calculations look encouraging for snow/cloud discrimination with TM bands 5 and 7.

Dozier, J.↗