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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.

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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Examining Dense Data Usage near the Regions with Severe Storms in All-Sky Microwave Radiance Data Assimilation and Impacts on GEOS Hurricane Analyses

Many numerical weather prediction (NWP) centers assimilate radiances affected by clouds and precipitation from microwave sensors, with the expectation that these data can provide critical constraints on meteorological parameters in dynamically sensitive regions to make significant impacts on forecast accuracy for precipitation. The Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center assimilates all-sky microwave radiance data from various microwave sensors such as all-sky GPM Microwave Imager (GMI) radiance in the Goddard Earth Observing System (GEOS) atmospheric data assimilation system (ADAS), which includes the GEOS atmospheric model, the Gridpoint Statistical Interpolation (GSI) atmospheric analysis system, and the Goddard Aerosol Assimilation System (GAAS). So far, most of NWP centers apply same large data thinning distances, that are used in clear-sky radiance data to avoid correlated observation errors, to all-sky microwave radiance data. For example, NASA GMAO is applying 145 km thinning distances for most of satellite radiance data including microwave radiance data in which all-sky approach is implemented. Even with these coarse observation data usage in all-sky assimilation approach, noticeable positive impacts from all-sky microwave data on hurricane track forecasts were identified in GEOS-5 system. The motivation of this study is based on the dynamic thinning distance method developed in our all-sky framework to use of denser data in cloudy and precipitating regions due to relatively small spatial correlations of observation errors. To investigate the benefits of all-sky microwave radiance on hurricane forecasts, several hurricane cases selected between 2016-2017 are examined. The dynamic thinning distance method is utilized in our all-sky approach to understand the sources and mechanisms to explain the benefits of all-sky microwave radiance data from various microwave radiance sensors like Advanced Microwave Sounder Unit (AMSU-A), Microwave Humidity Sounder (MHS), and GMI on GEOS-5 analyses and forecasts of various hurricanes.

Kim, Min-Jeong↗
NASA NTRSDate not supplied

Version 8 of the SMAP Level-4 Soil Moisture Data Assimilation Product

The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface (0-5 cm) and root-zone (0-100 cm) soil moisture from April 2015 to present with a mean latency of 2.5 days from the time of observation. The product is based on the assimilation of SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model as the model is driven with observations-based precipitation forcing. This presentation discusses the improvements in the forthcoming Version 8 of L4_SM, including updates in the precipitation forcing, the Catchment model parameters, and the L-band microwave radiative transfer model (mwRTM). The precipitation observations used in L4_SM Version 8 outside of North America and the high latitudes are from the latest (Version 7) NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement mission (IMERG) products. Moreover, occasionally excessive precipitation rates in earlier versions of L4_SM along certain longitudes in North America were eliminated by a bug fix in the precipitation corrections algorithm. The Catchment model in L4_SM Version 8 uses climatological snow albedo values based on observations from the Moderate Resolution Imaging Spectroradiometer, replacing the look-up table parameterization of earlier versions. Additionally, corrected soil parameters were implemented for a small region in Argentina that had erroneously been classified as peat because of an error in the ancillary soil data. Finally, the mwRTM in L4_SM Version 8 uses the Mironov soil mixing approach and updated values of the L-band scattering albedo, soil roughness, and vegetation opacity climatology obtained from the latest (Release 19) SMAP Level-2 dual-channel soil moisture retrieval product. During the development of L4_SM Version 8, the change in the mwRTM parameterization resulted in a reduced unbiased RMSE of surface soil moisture when verified against in situ measurements. It also reduced the standard deviation of the Tb observation-minus-forecast residuals by ~0.15 K, highlighting the importance of the mwRTM for successful data assimilation. The bug fixes in the precipitation corrections algorithm and the Catchment model soil parameters in the Argentina region result in locally large improvements of the simulated land surface states. In summary, the ongoing refinements of the L4_SM product continue to improve its science quality and performance for global soil moisture monitoring

Rolf Reichle↗
NASA NTRS2016

SMAP Data Assimilation at the GMAO

The NASA Soil Moisture Active Passive (SMAP) mission has been providing L-band (1.4 GHz) passive microwave brightness temperature (Tb) observations since April 2015. These observations are sensitive to surface(0-5 cm) soil moisture. Several of the key applications targeted by SMAP, however, require knowledge of deeper-layer, root zone (0-100 cm) soil moisture, which is not directly measured by SMAP. The NASA Global Modeling and Assimilation Office (GMAO) contributes to SMAP by providing Level 4 data, including the Level 4 Surface and Root Zone Soil Moisture(L4_SM) product, which is based on the assimilation of SMAP Tb observations in the ensemble-based NASA GEOS-5 land surface data assimilation system. The L4_SM product offers global data every three hours at 9 km resolution, thereby interpolating and extrapolating the coarser- scale (40 km) SMAP observations in time and in space (both horizontally and vertically). Since October 31, 2015, beta-version L4_SM data have been available to the public from the National Snow and Ice Data Center for the period March 31, 2015, to near present, with a mean latency of approx. 2.5 days.

Land Surface Model↗
DOE OSTI2026

Nuclear Data Adjustment for Nonlinear Applications in the OECD/NEA WPNCS SG14 Benchmark—A Bayesian Inverse UQ-Based Approach for Data Assimilation

The Organisation for Economic Co-operation and Development Working Party on Nuclear Criticality Safety has proposed a benchmark exercise to assess the performance of current nuclear data adjustment techniques applied to nonlinear applications and experiments with low correlation to applications. This work introduces Bayesian inverse uncertainty quantification (IUQ) employing scientific machine learning surrogate models as a method for nuclear data adjustments in this benchmark, and compares IUQ to the more traditional methods of generalized linear least squares (GLLS) and Monte Carlo Bayes (MOCABA). Posterior predictions from IUQ showed agreement with GLLS and MOCABA for linear applications. Here, when comparing GLLS, MOCABA, and IUQ posterior predictions to computed model responses using adjusted parameters, we observe that the GLLS predictions failed to replicate the computed response distributions for nonlinear applications, while MOCABA showed near agreement, and IUQ used the computed model responses directly. We also discuss observations on why experiments with low correlation to applications can be informative to nuclear data adjustments and identify some properties useful in selecting experiments for inclusion in nuclear data adjustment. Performance in this benchmark indicates potential for Bayesian IUQ in nuclear data adjustments.

Bayesian calibration↗
NASA NTRS1993

A preliminary study of the impact of the ERS 1 C band scatterometer wind data on the European Centre for Medium-Range Weather Forecasts global data assimilation system

A preliminary assessment of the impact of the ERS 1 scatterometer wind data on the current European Centre for Medium-Range Weather Forecasts analysis and forecast system has been carried out. Although the scatterometer data results in changes to the analyses and forecasts, there is no consistent improvement or degradation. Our results are based on comparing analyses and forecasts from assimilation cycles. The two sets of analyses are very similar except for the low level wind fields over the ocean. Impacts on the analyzed wind fields are greater over the southern ocean, where other data are scarce. For the most part the mass field increments are too small to balance the wind increments. The effect of the nonlinear normal mode initialization on the analysis differences is quite small, but we observe that the differences tend to wash out in the subsequent 6-hour forecast. In the Northern Hemisphere, analysis differences are very small, except directly at the scatterometer locations. Forecast comparisons reveal large differences in the Southern Hemisphere after 72 hours. Notable differences in the Northern Hemisphere do not appear until late in the forecast. Overall, however, the Southern Hemisphere impacts are neutral. The experiments described are preliminary in several respects. We expect these data to ultimately prove useful for global data assimilation.

Hoffman, Ross N.↗
NASA NTRS1988

Development of a satellite SAR image spectra and altimeter wave height data assimilation system for ERS-1

The applicability of ERS-1 wind and wave data for wave models was studied using the WAM third generation wave model and SEASAT altimeter, scatterometer and SAR data. A series of global wave hindcasts is made for the surface stress and surface wind fields by assimilation of scatterometer data for the full 96-day SEASAT and also for two wind field analyses for shorter periods by assimilation with the higher resolution ECMWF T63 model and by subjective analysis methods. It is found that wave models respond very sensitively to inconsistencies in wind field analyses and therefore provide a valuable data validation tool. Comparisons between SEASAT SAR image spectra and theoretical SAR spectra derived from the hindcast wave spectra by Monte Carlo simulations yield good overall agreement for 32 cases representing a wide variety of wave conditions. It is concluded that SAR wave imaging is sufficiently well understood to apply SAR image spectra with confidence for wave studies if supported by realistic wave models and theoretical computations of the strongly nonlinear mapping of the wave spectrum into the SAR image spectrum. A closed nonlinear integral expression for this spectral mapping relation is derived which avoids the inherent statistical errors of Monte Carlo computations and may prove to be more efficient numerically.

Hasselmann, Klaus↗
NASA NTRS2008

U.S. Eastern Continental Shelf Carbon Cycling (USECoS): Modeling, Data Assimilation, and Analysis

Although the oceans play a major role in the uptake of fossil fuel CO2 from the atmosphere, there is much debate about the contribution from continental shelves, since many key shelf fluxes are not yet well quantified: the exchange of carbon across the land-ocean and shelf-slope interfaces, air-sea exchange of CO2, burial, and biological processes including productivity. Our goal is to quantify these carbon fluxes along the eastern U.S. coast using models quantitatively verified by comparison to observations, and to establish a framework for predicting how these fluxes may be modified as a result of climate and land use change. Our research questions build on those addressed with previous NASA funding for the USECoS (U.S. Eastern Continental Shelf Carbon Cycling) project. We have developed a coupled biogeochemical ocean circulation model configured for this study region and have extensively evaluated this model with both in situ and remotely-sensed data. Results indicate that to further reduce uncertainties in the shelf component of the global carbon cycle, future efforts must be directed towards 1) increasing the resolution of the physical model via nesting and 2) making refinements to the biogeochemical model and quantitatively evaluating these via the assimilation of biogeochemical data (in situ and remotely-sensed). These model improvements are essential for better understanding and reducing estimates of uncertainties in current and future carbon transformations and cycling in continental shelf systems. Our approach and science questions are particularly germane to the carbon cycle science goals of the NASA Earth Science Research Program as well as the U.S. Climate Change Research Program and the North American Carbon Program. Our interdisciplinary research team consists of scientists who have expertise in the physics and biogeochemistry of the U.S. eastern continental shelf, remote-sensing data analysis and data assimilative numerical models.

Mannino, Antonio↗
DOE OSTI2023

Benefits and Cautions in Data Assimilation Strategies: An Example of Modeling Groundwater Recharge

Assimilating recent observations improves model outcomes for real-time assessments of groundwater processes. This is demonstrated in estimating time-varying recharge to a shallow fractured-rock aquifer in response to precipitation. Results from estimating the time-varying water-table altitude (h) and recharge, and their error covariances, are compared for forecasting, filtering, and fixed-lag smoothing (FLS), which are implemented using the Kalman Filter as applied to a data-driven, mechanistic model of recharge. Forecasting uses past observations to predict future states and is the current paradigm in most groundwater modeling investigations; filtering assimilates observations up to the current time to estimate current states; and FLS estimates states following a time lag over which additional observations are collected. Results for forecasting yield a large error covariance relative to the magnitude of the expected recharge. With assimilating recent observations of h, filtering and FLS produce estimates of recharge that better represent time-varying observations of h and reduce uncertainty in comparison to forecasting. Although model outcomes from applying data assimilation through filtering or FLS reduce model uncertainty, they are not necessarily mass conservative, whereas forecasting outcomes are mass conservative. Mass conservative outcomes from forecasting are not necessarily more accurate, because process errors are inherent in any model. Improvements in estimating real-time groundwater conditions that better represent observations need to be weighed for the model application against outcomes with inherent process deficiencies. In conclusion, results from data assimilation strategies discussed in this investigation are anticipated to be relevant to other groundwater processes models where system states are sensitive to system inputs.

aquifer recharge↗
NASA NTRS2008

Data Assimilation Experiments using Quality Controlled AIRS Version 5 Temperature Soundings

The AIRS Science Team Version 5 retrieval algorithm has been finalized and is now operational at the Goddard DAAC in the processing (and reprocessing) of all AIRS data. The AIRS Science Team Version 5 retrieval algorithm contains two significant improvements over Version 4: 1) Improved physics allows for use of AIRS observations in the entire 4.3 pm C02 absorption band in the retrieval of temperature profile T(p) during both day and night. Tropospheric sounding 15 pm C02 observations are now used primarily in the generation of cloud cleared radiances Ri. This approach allows for the generation of accurate values of Ri and T(p) under most cloud conditions. 2) Another very significant improvement in Version 5 is the ability to generate accurate case-by-case, level-by-level error estimates for the atmospheric temperature profile, as well as for channel-by- channel error estimates for Ri. These error estimates are used for quality control of the retrieved products. We have conducted forecast impact experiments assimilating AIRS temperature profiles with different levels of quality control using the NASA GEOS-5 data assimilation system. Assimilation of quality controlled T(p) resulted in significantly improved forecast skill compared to that obtained from analyses obtained when all data used operationally by NCEP, except for AIRS data, is assimilated. We also conducted an experiment assimilating AIRS radiances uncontaminated by clouds, as done Operationally by ECMWF and NCEP. Forecasts resulting from assimilated AIRS radiances were of poorer quality than those obtained assimilating AIRS temperatures.

SUsskind, Joel↗