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At least 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Advancing Data Assimilation in Operational Hydrologic Forecasting: Progresses, Challenges, and Emerging Opportunities

Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. This is due in part to a lack of mechanisms to properly quantify the uncertainty in observations and forecast models in real-time forecasting situations and to conduct the merging of data and models in a way that is adequately efficient and transparent to operational forecasters. The need for effective DA of useful hydrologic data into the forecast process has become increasingly recognized in recent years. This motivated a hydrologic DA workshop in Delft, the Netherlands in November 2010, which focused on advancing DA in operational hydrologic forecasting and water resources management. As an outcome of the workshop, this paper reviews, in relevant detail, the current status of DA applications in both hydrologic research and operational practices, and discusses the existing or potential hurdles and challenges in transitioning hydrologic DA research into cost-effective operational forecasting tools, as well as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical aspects in DA algorithms, (2) the estimation of different types of uncertainty, (3) new observations and their objective use in hydrologic DA, (4) the use of DA for real-time control of water resources systems, and (5) the development of community-based, generic DA tools for hydrologic applications. It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modeling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers, DA developers, and operational forecasters.

forecasting↗
NASA NTRS2014

Technical Report Series on Global Modeling and Data Assimilation: Estimates of AOD Trends (2002 - 2012) Over the World's Major Cities Based on the MERRA Aerosol Reanalysis - Volume 32

NASA's Global Modeling and Assimilation Office has extended the Modern-Era Retrospective Analysis for Research and Application (MERRA) tool with five atmospheric aerosol species (sulfates, organic carbon, black carbon, mineral dust and sea salt). This inclusion of aerosol reanalysis data is now known as MERRAero. This study analyses a ten-year period (July 2002 - June 2012) MERRAero aerosol reanalysis applied to the study of aerosol optical depth (AOD) and its trends for the aforementioned aerosol species over the world's major cities (with a population of over 2 million inhabitants). We found that a proportion of various aerosol species in total AOD exhibited a geographical dependence. Cities in industrialized regions (North America, Europe, central and eastern Asia) are characterized by a strong proportion of sulfate aerosols. Organic carbon aerosols are dominant over cities which are located in regions where biomass burning frequently occurs (South America and southern Africa). Mineral dust dominates other aerosol species in cities located in proximity to the major deserts (northern Africa and western Asia). Sea salt aerosols are prominent in coastal cities but are dominant aerosol species in very few of them. AOD trends are declining over cities in North America, Europe and Japan, as a result of effective air quality regulation. By contrast, the economic boom in China and India has led to increasing AOD trends over most cities in these two highly-populated countries. Increasing AOD trends over cities in the Middle East are caused by increasing desert dust.

AOD↗
NASA NTRS2015

File Specification for the MERRA Aerosol Reanalysis (MERRAero): MODIS AOD Assimilation based on a MERRA Replay

This document describes the gridded output files produced by the Goddard Earth Observing System version 5 (GEOS-5) Goddard Aerosol Assimilation System (GAAS) from July 2002 through December 2014. The MERRA Aerosol Reanalysis (MERRAero) is produced with the hydrostatic version of the GEOS-5 Atmospheric Global Climate Model (AGCM). In addition to standard meteorological parameters (wind, temperature, moisture, surface pressure), this simulation includes 15 aerosol tracers (dust, sea-salt, sulfate, black and organic carbon), ozone, carbon monoxide and carbon dioxide. This model simulation is driven by prescribed sea-surface temperature and sea-ice, daily volcanic and biomass burning emissions, as well as high-resolution inventories of anthropogenic emission sources. Meteorology is replayed from the MERRA Reanalysis.

GAAS↗
NASA NTRS2016

Assimilation of SMOS (and SMAP) Retrieved Soil Moisture into the Land Information System

Goal: Accurate, high-resolution (approx.3 km) soil moisture in near-real time. Situational awareness (drought assessment, flood and fire threat). Local modeling applications (to improve sfc-PBL exchanges) Method: Assimilate satellite soil moisture retrievals into a land surface model. Combines high-resolution geophysical model data with latest satellite observations.

land surface↗
NASA NTRS2017

Field Detection of Chemical Assimilation in A Basaltic Lava Flow

Lava channels are features seen throughout the inner Solar System, including on Earth, the Moon, and Mars. Flow emplacement is therefore a crucial process in the shaping of planetary surfaces. Many studies, including some completed by members of this team at the December 1974 lava flow, have investigated the dynamics of lava flow emplacement, both on Earth and on the Moon and how pre-flow terrain can impact final channel morphology, but far fewer have focused on how the compositional characteristics of the substrate over which a flow was em-placed influenced its final flow morphology. Within the length of one flow, it is common for flows to change in morphology, a quality linked to rheology (a function of multiple factors including viscosi-ty, temperature, composition, etc.). The relationship between rheology and temperature has been well-studied but less is known about the relationship between an older flow's chemistry and how the interaction between this flow and the new flow might affect lava rheology and therefore emplacement dynamics. Lava erosion. Through visual observations of active terrestrial flows, mechanical erosion by flowing lava has been well-documented. Lava erosion by which flow composition is altered as the active lava melts and assimilates the pre-flow terrain over which it moves is also hypothesized to affect channel formation. However, there is only one previous field study that geochemically documents the process in recent basaltic flow systems.

Young, K. E.↗
NASA NTRS2017

Quantifying Chemical Ozone Loss in the Arctic Stratosphere with GEOS-STRATCHEM Data Assimilation System

A faithful representation of polar stratospheric chemistry in models and its connection with dynamical variability is essential for our understanding of the evolution of the ozone layer in a changing climate and during the projected continuing decline of ozone depleting substances in the atmosphere. We use a new configuration of the Goddard Earth Observing System Data Assimilation System with a stratospheric chemistry model to study ozone depletion in the Arctic polar stratosphere during the exceptionally cold (in the stratosphere) winters 2015/2016 and 2010/2011.

Arctic↗
NASA NTRS2017

Update on the NASA GEOS-5 Aerosol Forecasting and Data Assimilation System

GEOS-5 is the Goddard Earth Observing System model. GEOS-5 is maintained by the NASA Global Modeling and Assimilation Office. Core development is within GMAO,Goddard Atmospheric Chemistry and Dynamics Laboratory, and with external partners. Primary GEOS-5 functions: Earth system model for studying climate variability and change, provide research quality reanalyses for supporting NASA instrument teams and scientific community, provide near-real time forecasts of meteorology,aerosols, and other atmospheric constituents to support NASA airborne campaigns.

GEOS-5↗
NASA NTRS2017

Data Descriptor: A Land Data Assimilation System for Sub-Saharan Africa Food and Water Security Applications

Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWSNETs operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa.

albedo↗
NASA NTRS2017

Estimating the Distribution of Colored Dissolved Organic Matter During the Southern Ocean Gas Exchange Experiment Using Four-Dimensional Variational Data Assimilation

We diagnosed the effect of various physical processes on the distribution of mixed-layer colored dissolved organic matter (CDOM) and a sulfur hexauoride (SF6) tracer during the Southern Ocean Gas Exchange Experiment (SO GasEx). The biochemical upper ocean state estimate uses in situ and satellite biochemical and physical data in the study region, including CDOM (absorption coefcient and spectral slope), SF6, hydrography, and sea level anomaly. Modules for photobleaching of CDOM and surface transport of SF6 were coupled with an ocean circulation model for this purpose. The observed spatial and temporal variations in CDOM were captured by the state estimate without including any new biological source term for CDOM, assuming it to be negligible over the 26 days of the state estimate. Thermocline entrainment and photobleaching acted to diminish the mixed-layer CDOM with time scales of 18 and 16 days, respectively. Lateral advection of CDOM played a dominant role and increased the mixed-layer CDOM with a time scale of 12 days, whereas lateral diffusion of CDOM was negligible. A Lagrangian view on the CDOM variability was demonstrated by using the SF6 as a weighting function to integrate the CDOM elds. This and similar data assimilation methods can be used to provide reasonable estimates of optical properties, and other physical parameters over the short-term duration of a research cruise, and help in the tracking of tracer releases in large-scale oceanographic experiments, and in oceanographic process studies.

Del Castillo, C. E.↗
NASA NTRS2016

Tropospheric Column Ozone Response to ENSO in GEOS-5 Assimilation of OMI and MLS Ozone Data

We use GEOS-5 analyses of Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS) ozone observations to investigate the magnitude and spatial distribution of the El Nino Southern Oscillation (ENSO) influence on tropospheric column ozone (TCO) into the middle latitudes. This study provides the first explicit spatially resolved characterization of the ENSO influence and demonstrates coherent patterns and teleconnections impacting the TCO in the extratropics. The response is evaluated and characterized by both the variance explained and sensitivity of TCO to the Nino 3.4 index. The tropospheric response in the tropics agrees well with previous studies and verifies the analyses. A two-lobed response symmetric about the Equator in the western Pacific/Indonesian region seen in some prior studies and not in others is confirmed here. This two-lobed response is consistent with the large-scale vertical transport. We also find that the large-scale transport in the tropics dominates the response compared to the small-scale convective transport. The ozone response is weaker in the middle latitudes, but a significant explained variance of the TCO is found over several small regions, including the central United States. However, the sensitivity of TCO to the Nino 3.4 index is statistically significant over a large area of the middle latitudes. The sensitivity maxima and minima coincide with anomalous anti-cyclonic and cyclonic circulations where the associated vertical transport is consistent with the sign of the sensitivity. Also, ENSO related changes to the mean tropopause height can contribute significantly to the midlatitude response. Comparisons to a 22-year chemical transport model simulation demonstrate that these results from the 9- year assimilation are representative of the longer term. This investigation brings insight to several seemingly disparate prior studies of the El Nino influence on tropospheric ozone in the middle latitudes.

ML↗
NASA NTRS2017

Assimilation of All-Weather GMI and ATMS Observations into HWRF

We propose a novel Bayesian Monte Carlo Integration (BMCI) technique to retrieve the profiles of temperature, water vapor, and cloud liquid/ice water content from microwave cloudy measurements in the presence of TCs. These retrievals then can either be directly used by meteorologists to analyze the structure of TCs or be assimilated to provide accurate initial conditions for the NWP models. The technique is applied to the data from the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership (NPP) and Global Precipitation Measurement (GPM) Microwave Imager (GMI).

Moradi, Isaac↗
NASA NTRS2018

Comparison of MERRA-2 and ECCO-V4 Ocean Surface Heat Fluxes: Consequences of Different Forcing Feedbacks on Ocean Circulation and Implications for Climate Data Assimilation

Forcing ocean models with reanalysis data is a common practice in ocean modeling. As part of this practice, prescribed atmospheric state variables and interactive ocean SST (Sea Surface Temperature) are used to calculate fluxes between the ocean and the atmosphere. When forcing an ocean model with reanalysis fields, errors in the reanalysis data, errors in the ocean model and errors in the forcing formulation will generate a different solution compared to other ocean reanalysis solutions (which also have their own errors). As a first step towards a consistent coupled ocean-atmosphere reanalysis, we compare surface heat fluxes from a state-of-the-art atmospheric reanalysis, the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), to heat fluxes from a state-of-the-art oceanic reanalysis, the Estimating the Circulation and Climate of the Ocean Version 4, Release 2 (ECCO-v4). Then, we investigate the errors associated with the MITgcm (Massachusetts Institute of Technology general circulation model) ocean model in its ECCO-v4 ocean reanalysis configuration (1992-2011) when it is forced with MERRA- 2 atmospheric reanalysis fields instead of with the ECCO-v4 adjoint optimized ERA-interim state variables. This is done by forcing ECCO-v4 ocean with and without feedbacks from MERRA-2 related to turbulent fluxes of heat and moisture and the outgoing long wave radiation. In addition, we introduce an intermediate forcing method that includes only the feedback from the interactive outgoing long wave radiation. The resulting ocean circulation is compared with ECCO-v4 reanalysis and in-situ observations. We show that, without feedbacks, imbalances in the energy and the hydrological cycles of MERRA-2 (which are directly related to the fact it was created without interactive ocean) result in considerable SST drifts and a large reduction in sea level. The bulk formulae and interactive outgoing long wave radiation, although providing air-sea feedbacks and reducing model-data misfit, strongly relax the ocean to observed SST and may result in unwanted features such as large change in the water budget. These features have implications in a desired forcing recipe to be used. The results strongly and unambiguously argue for next generation data assimilation climate studies to involve fully coupled systems.

MERRA-2↗
NASA NTRS2018

Assimilation of All-Weather GMI and ATMS Observations into HWRF

We propose a novel Bayesian Monte Carlo Integration (BMCI) technique to retrieve the profiles of temperature, water vapor, and cloud liquid/ice water content from microwave cloudy measurements in the presence of TCs. These retrievals then can either be directly used by meteorologists to analyze the structure of TCs or be assimilated to provide accurate initial conditions for the NWP models. The technique is applied to the data from the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership (NPP) and Global Precipitation Measurement (GPM) Microwave Imager (GMI).

Moradi, Isaac↗
NASA NTRS2018

Using Multi-Sensor Aerosol Optical Depth Retrievals to Improve Infrared Radiance Assimilation

Coarse dust aerosols absorb terrestrial radiation leading to significant longwave heating/cooling rates. Modules accounting for aerosol impacts on radiation have been implemented into CRTM framework, but operational centers continue to assume aerosol-free conditions when assimilating infrared radiances into NWP models. This assumption can introduce significant biases in analysis fields (temp, moisture, etc.), which can reduce forecast skill.

Naeger, Aaron↗
NASA NTRS2018

Near-Real Time Ocean-Atmosphere Skin Temperature as Part of NASA GMAO Atmospheric Data Assimilation System

The Global Modeling and Assimilation Office (GMAO) at NASA, GSFC is developing an integrated Earth system analysis (IESA). Integral to this IESA are ocean-atmosphere interface states, such as the skin SST. Recently the GMAO's near-real time operational weather analysis and prediction system implemented an analysis for skin SST along with the meteorological analysis since Jan, 2017. The skin SST is modeled and constrained using infrared radiometric observations. This poster describes some of the details of this development, its impact on forecasts, current and future developments towards the inclusion of microwave data (e.g. GPM-GMI).

SST↗
NASA NTRS2018

Observation Error Specification for Near-Surface Soil Moisture Assimilation: Does It Matter?

The observation error variances assumed in the data assimilation (DA) of remotely sensed near-surface soil moisture observations are known to have unrealistic spatial patterns. The temporal variability in the errors is also typically neglected. Soil moisture errors cannot be directly calculated at large scales, and the globally distributed errors are unknown. We apply several approaches to estimating the observation errors (R matrix) for near-surface soil moisture DA experiments, to establish whether the assumed errors are important to the DA performance, and hence whether uncertainty in the observation error specification is a likely source of uncertainty in soil moisture DA output.

Draper, Clara↗