Search NASA⌕ Search

SEARCH · Search NASA

Results for “Assimilation”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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

At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Land Surface Data Assimilation and the Northern Gulf Coast Land/Sea Breeze

A technique has been developed for assimilating GOES-derived skin temperature tendencies and insolation into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. A critical assumption of the technique is that the availability of moisture (either from the soil or vegetation) is the least known term in the model's surface energy budget. Therefore, the simulated latent heat flux, which is a function of surface moisture availability, is adjusted based upon differences between the modeled and satellite observed skin temperature tendencies. An advantage of this technique is that satellite temperature tendencies are assimilated in an energetically consistent manner that avoids energy imbalances and surface stability problems that arise from direct assimilation of surface shelter temperatures. The fact that the rate of change of the satellite skin temperature is used rather than the absolute temperature means that sensor calibration is not as critical. The sea/land breeze is a well-documented mesoscale circulation that affects many coastal areas of the world including the northern Gulf Coast of the United States. The focus of this paper is to examine how the satellite assimilation technique impacts the simulation of a sea breeze circulation observed along the Mississippi/Alabama coast in the spring of 2001. The technique is implemented within the PSU/NCAR MM5 V3-4 and applied on a 4-km domain for this particular application. It is recognized that a 4-km grid spacing is too coarse to explicitly resolve the detailed, mesoscale structure of sea breezes. Nevertheless, the model can forecast certain characteristics of the observed sea breeze including a thermally direct circulation that results from differential low-level heating across the land-sea interface. Our intent is to determine the sensitivity of the circulation to the differential land surface forcing produced via the assimilation of GOES skin temperature tendencies. Results will be quantified through statistical verification techniques.

Lapenta, William M.↗
NASA NTRS2002

Evaluation of the DAO Retrospective Data Assimilation System

We have developed and implemented a retrospective data assimilation system (RDAS) as an upgrade to the operational DAO/Terra data assimilation system. This formulation aims at improving analysis over filter analysis by the dynamically consistent incorporation of observation information past a given analysis time. The current implementation of the RDAS uses the adjoint of the tangent linear model of a simplified version of the Terra general circulation model and extensions to the physical-space statistical analysis system to propagate observation information back in time. The RDAS adopts the same assumptions of the regular data assimilation system, particularly, no explicit propagation of error covariances are involved therefore rendering a procedure that is computationally affordable. In this study, we show results of experiments conducted to investigate the performance of the 6-hour (lag-1) RDAS. Statistical results obtained over one month during a winter season indicate that the RDAS represents considerable improvement over the regular assimilation. Plans for implementation of the RDAS capability in our new finite-volume data assimilation system will also be presented at the time of the conference.

Zhu, Yanqiu↗
NASA NTRS2002

Monitoring of Observation Errors from Satellite Ozone Instruments in Assimilation

Ozone distributions derived from the Solar Backscatter UltraViolet/2 (SBUV/2) instruments and the Earth Probe Total Ozone Mapping Spectrometer (EP TOMS) have been assimilated in near-real time at the NASA/Goddard Data Assimilation Office since January 2000. Observed-minus-forecast (O-F) residuals are the differences between the incoming ozone data and the co-located short-term model forecast. They are routinely produced and monitored in the assimilation process. Using examples from the NOAA-14 and NOAA-16 SBUV/2 and the EP-TOMS instruments, it is demonstrated that the monitoring of time series of O-F residual statistics is an effective method of identifying time-dependent changes in the observation-error characteristics of ozone. In addition, the data assimilation system was used to assist the validation of updated calibration coefficients for the NOAA-14 SBUV/2 instrument. This assimilation-based monitoring work will be extended to ozone data from instruments on new satellites: Envisat EOS, Aqua, and EOS Aura.

Stajner, Ivanka↗
NASA NTRS2002

Assimilation Experiments of One-Dimensional Variational Analyses with GPS/MET Refractivity

The GPS/MET 1995 experiment demonstrated the feasibility of remote soundings of the Earth's atmosphere using the GPS radio occultation technique. Subsequent studies assessed the information content of such measurements. Several groups have attempted to assimilate the GPS radio occultation measurements into a global Data Assimilation System (DAS). These attempts followed in general the methodology proposed by Eyre, and range from die act four-dimensional variational assimilation of bending angles to assimilation of inverted profiles of temperature and/or humidity. We present here an hybrid approach. We attempted to take advantage of the accuracy of current, weather models to constrain one-dimensional variational analyses using GPS refractivity. Retrieved profiles of atmospheric parameters were then assimilated like other types of observations in a global DAS for issuing the next forecast period.

Poli, Paul↗
NASA NTRS2002

Advances in Data Assimilation and Weather Prediction Using TRMM Observations

Understanding the Earth's climate and how it responds to climate perturbations requires knowledge of how atmospheric moisture, clouds, latent heating, the large-scale circulation and energy fluxes vary with changing climatic conditions. The physical process linking these climate elements is precipitation. Accurate knowledge of how precipitation varies in space and time and how it couples with other atmospheric variables is essential for understanding the global water and energy cycle. In recent years, TRMM data products have played a key role in advancing the field of data assimilation to provide better global analyses for climate research and numerical weather prediction. TRMM research has demonstrated the effectiveness of microwave-based rainfall and total precipitable water (TPW) observations in improving the quality of assimilated datasets and upgrading forecast skills. TRMM latent heating products have also stimulated experimentation with innovative techniques to use this type of information to improve global analyses. We discuss strategies of assimilating TRMM observations at NASA s Data Assimilation Office and present results on the impact assimilating TRMM data on the Goddard Earth Observing System (GEOS) analyses and forecast capabilities.

Atlas, Robert↗
NASA NTRS2003

Application of Land Surface Data Assimilation to Simulations of Sea Breeze Circulations

A technique has been developed for assimilating GOES-derived skin temperature tendencies and insolation into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. A critical assumption of the technique is that the availability of moisture (either from the soil or vegetation) is the least known term in the model's surface energy budget. Therefore, the simulated latent heat flux, which is a function of surface moisture availability, is adjusted based upon differences between the modeled and satellite- observed skin temperature tendencies. An advantage of this technique is that satellite temperature tendencies are assimilated in an energetically consistent manner that avoids energy imbalances and surface stability problems that arise from direct assimilation of surface shelter temperatures. The fact that the rate of change of the satellite skin temperature is used rather than the absolute temperature means that sensor calibration is not as critical. The sea/land breeze is a well-documented mesoscale circulation that affects many coastal areas of the world including the northern Gulf Coast of the United States. The focus of this paper is to examine how the satellite assimilation technique impacts the simulation of a sea breeze circulation observed along the Mississippi/Alabama coast in the spring of 2001. The technique is implemented within the PSUNCAR MM5 V3-5 and applied at spatial resolutions of 12- and 4-km. It is recognized that even 4-km grid spacing is too coarse to explicitly resolve the detailed, mesoscale structure of sea breezes. Nevertheless, the model can forecast certain characteristics of the observed sea breeze including a thermally direct circulation that results from differential low-level heating across the land-sea interface. Our intent is to determine the sensitivity of the circulation to the differential land surface forcing produced via the assimilation of GOES skin temperature tendencies. Results will be quantified through statistical verification techniques.

Mackaro, Scott↗
NASA NTRS2003

Estimation of Model Error Variances During Data Assimilation

Data assimilation is all about understanding the error characteristics of the data and models that are used in the assimilation process. Reliable error estimates are needed to implement observational quality control, bias correction of observations and model fields, and intelligent data selection. Meaningful covariance specifications are obviously required for the analysis as well, since the impact of any single observation strongly depends on the assumed structure of the background errors. Operational atmospheric data assimilation systems still rely primarily on climatological background error covariances. To obtain error estimates that reflect both the character of the flow and the current state of the observing system, it is necessary to solve three problems: (1) how to account for the short-term evolution of errors in the initial conditions; (2) how to estimate the additional component of error caused by model defects; and (3) how to compute the error reduction in the analysis due to observational information. Various approaches are now available that provide approximate solutions to the first and third of these problems. However, the useful accuracy of these solutions very much depends on the size and character of the model errors and the ability to account for them. Model errors represent the real-world forcing of the error evolution in a data assimilation system. Clearly, meaningful model error estimates and/or statistics must be based on information external to the model itself. The most obvious information source is observational, and since the volume of available geophysical data is growing rapidly, there is some hope that a purely statistical approach to model error estimation can be viable. This requires that the observation errors themselves are well understood and quantifiable. We will discuss some of these challenges and present a new sequential scheme for estimating model error variances from observations in the context of an atmospheric data assimilation system.

Dee, Dick↗
NASA NTRS2003

Estimating the Tropospheric Ozone Distribution by the Assimilation of Satellite Data

Tropospheric ozone is important to the environment, because it acts as a strong oxidant to control the concentrations of many reduced gases (methane, carbon monoxide, ... ), its radiative forcing plays a significant role in the greenhouse effect, and direct contact with ozone is harmful to human health. Tropospheric ozone, whose main sources are intrusion from the stratosphere and chemical production from source gases associated with urban pollution or biomass burning, varies on a wide range of spatial and temporal scales. Its transport and chemistry can be influenced by weather, seasonal, or multiannual variability. Despite the importance of tropospheric ozone, it contributes only about 10% of the total ozone loading in the atmosphere. Consequently, satellite instruments lose sensitivity below the stratospheric ozone peak, and provide little information about middle and lower tropospheric ozone. This talk will discuss recent modifications made to the satellite ozone data assimilation system at NASA's Data Assimilation Office (DAO) in order to provide better tropospheric ozone columns and profiles. We use a version of the system that assimilates only the data from the Solar Backscatter UltraViolet/2 (SBUV/2) instrument. The quality of the assimilated ozone in the tropical troposphere is evaluated by comparison with independent observations obtained from the Southern Hemispheric Additional Ozonesondes (SHADOZ) network. It is shown that the quality of ozone fields is sensitive to the winds used in the transport model. Increasing the vertical resolution of the model also has a beneficial impact. The assimilated ozone in the lower troposphere was substantially improved by inclusion of tropospheric ozone production, loss, and dry deposition rates from the Harvard GEOS-CHEM model. The mechanisms behind these results will be examined and the implications for our understanding of tropospheric ozone will be discussed.

Hayashi, Hiroo↗
NASA NTRS2004

GMS-based"Future Time" Rainfall Data Assimilation for Mesoscale Weather Prediction over Korean Peninsula and Future Prospects with GPM Satellite Measurements

This study examines the use of satellite-derived nowcasted (short-term forecasted) rainfall over 3-hour time periods to gain an equivalent time increment in initializing a nonhydrostatic mesoscale model used for predicting convective rainfall events over the Korean peninsula. Infrared (IR) window measurements from the Japanese Geostationary Meteorological Satellite (GMS) are used to specify latent heating for a spinup period of the model - but in future time -- thus initializing in advance of actual time in the framework of a prediction scenario. The main scientific objective of the study is to investigate the strengths and weaknesses of this approach insofar as data assimilation, in which the nowcasted assimilation data are derived independently of the prognostic model itself. Although there have been various recent improvements in formulating the dynamics, thermodynamics, and microphysics of mesoscale models, as well as computer advances which allow the use of high resolution cloud-resolving grids and explicit latent heating over regional domains, spinup remains at the forefront of unresolved mesoscale modeling problems. In general, non-realistic spinup limits the skill in predicting the spatial-temporal distribution of convection and precipitation, primarily in the early hours of a. forecast, stemming from standard prognostic variables not representing the initial diabatic heating field produced by the ambient convection and cloud fields. The long-term goal of this research is to improve short-range (12-hour) quantitative precipitation forecasting (QPF) over the Korean peninsula through the use of innovative data assimilation methods based on geosynchronous satellite measurements. As a step in th~s direction, a non-standard data assimilation experiment in conjunction with GMS-retrieved nowcasted rainfall information introduced to the mesoscale model is conducted. The 3-hourly precipitation forecast information is assimilated through nudging the associated diabatic heating during the early stages of a forecast period. This procedure is expected to enhance details in the moisture field during model integration, and thus improve spinup performance, assuming the errors in the future time latent heating data ate less than intrinsic model background errors.

Smith, Eric A.↗
NASA NTRS2003

Mesoscale Assimilation of TMI Rainfall Data with 4DVAR: Sensitivity Studies

Sensitivity studies are performed on the assimilation of TRMM (Tropical Rainfall Measurement Mission) Microwave Imager (TMI) derived rainfall data into a mesoscale model using a four-dimensional variational data assimilation (4DVAR) technique. A series of numerical experiments is conducted to evaluate the impact of TMI rainfall data on the numerical simulation of Hurricane Bonnie (1998). The results indicate that rainfall data assimilation is sensitive to the error characteristics of the data and the inclusion of physics in the adjoint and forward models. In addition, assimilating the rainfall data alone is helpful for producing a more realistic eye and rain bands in the hurricane but does not ensure improvements in hurricane intensity forecasts. Further study indicated that it is necessary to incorporate TMI rainfall data together with other types of data such as wind data into the model, in which case the inclusion of the rainfall data further improves the intensity forecast of the hurricane. This implies that proper constraints may be needed for rainfall assimilation.

Tao, Wei-Kuo↗
NASA NTRS2004

Antarctic Stratospheric Ozone from the Assimilation of Occultation Data

Ozone data from the solar occultation Polar Ozone and Aerosol Measurement (POAM) III instrument are included in the ozone assimilation system at NASA's Global Modeling and Assimilation Office, which uses Solar Backscatter UItraViolet/2 (SBUV/2) instrument data. Even though POAM data are available at only one latitude in the southern hemisphere on each day, their assimilation leads to more realistic ozone distribution throughout the Antarctic region, especially inside the polar vortex. Impacts of POAM data were evaluated by comparisons of assimilated ozone profiles with independent ozone sondes. Major improvements in ozone representation are seen in the Antarctic lower stratosphere during austral Winter and spring in 1998. Limitations of assimilation of sparse occultation data are illustrated by an example.

Stajner, Ivanka↗
NASA NTRS2006

Tropospheric Ozone from Assimilation of Aura Data using Different Definitions of the Tropopause

Ozone data from Aura OMI and MLS instruments were assimilated into the general circulation model (GCM) constrained by assimilated meteorological fields from the Global Modeling and Assimilation Office at NASA Goddard. Properties of tropospheric ozone and their sensitivity to the definition of the tropopause are investigated. Three definitions of the tropopause are considered: (1) dynamical (using potential vorticity and potential temperature), (2) using temperature lapse rate, and (3) using a fixed ozone value. Comparisons of the tropospheric ozone columns using these tropopause definitions will be presented and evaluated against coincident profiles from ozone sondes. Assimilated ozone profiles are used to identify possible tropopause folding events, which are important for stratosphere-troposphere exchange. Each profile is searched for multiple levels at which ozone attains the value typical of the troposphere-stratosphere transition in order to identify possible tropopause folds. Constrained by the dynamics from a global model and by assimilation of Aura ozone data every 3-hours, this data set provides an opportunity to study ozone evolution in the upper troposphere and lower stratosphere with high temporal resolution.

Stajner, Ivanka↗
NASA NTRS2008

Assimilation of Aura Ozone Data and Comparisons with In Situ Observations

Ozone data from the Ozone Monitoring Instrument (OMI) and the Microwave Limb Sounder (MLS) onboard EOS Aura satellite were assimilated into the Goddard Earth Observing System Version 4 (GEOS-4) ozone assimilation system. Comparison of assimilated ozone with ozone sonde and MOZAIC data indicate an agreement within 10% in the lower stratosphere, where dynamical processes dominate. Assimilation of OMI and MLS data improves tropospheric column estimates in the Atlantic region, but leads to an overestimation in the tropical Pacific in comparison with SHADOZ sondes. Transport and data biases are considered in order to understand these discrepancies. Comparisons of assimilated tropospheric ozone columns with ozone sonde data reveal root-mean-square (RMS) differences of 2.9 to 7.2 DU, which are typically smaller than the model-sonde RMS differences. Four different definitions of the tropopause using temperature lapse rate, potential vorticity (PV) and isentropic surfaces or ozone isosurfaces are compared with respect to their global impact on the estimated tropospheric ozone column. The largest sensitivity in the tropospheric ozone column is found near the subtropical jet, where the ozone or PV determined tropopause typically lies below the lapse rate tropopause.

Stajner, Ivanka↗
NASA NTRS2010

Prediction Activities at NASA's Global Modeling and Assimilation Office

The Global Modeling and Assimilation Office (GMAO) is a core NASA resource for the development and use of satellite observations through the integrating tools of models and assimilation systems. Global ocean, atmosphere and land surface models are developed as components of assimilation and forecast systems that are used for addressing the weather and climate research questions identified in NASA's science mission. In fact, the GMAO is actively engaged in addressing one of NASA's science mission s key questions concerning how well transient climate variations can be understood and predicted. At weather time scales the GMAO is developing ultra-high resolution global climate models capable of resolving high impact weather systems such as hurricanes. The ability to resolve the detailed characteristics of weather systems within a global framework greatly facilitates addressing fundamental questions concerning the link between weather and climate variability. At sub-seasonal time scales, the GMAO is engaged in research and development to improve the use of land information (especially soil moisture), and in the improved representation and initialization of various sub-seasonal atmospheric variability (such as the MJO) that evolves on time scales longer than weather and involves exchanges with both the land and ocean The GMAO has a long history of development for advancing the seasonal-to-interannual (S-I) prediction problem using an older version of the coupled atmosphere-ocean general circulation model (AOGCM). This includes the development of an Ensemble Kalman Filter (EnKF) to facilitate the multivariate assimilation of ocean surface altimetry, and an EnKF developed for the highly inhomogeneous nature of the errors in land surface models, as well as the multivariate assimilation needed to take advantage of surface soil moisture and snow observations. The importance of decadal variability, especially that associated with long-term droughts is well recognized by the climate community. An improved understanding of the nature of decadal variability and its predictability has important implications for efforts to assess the impacts of global change in the coming decades. In fact, the GMAO has taken on the challenge of carrying out experimental decadal predictions in support of the IPCC AR5 effort.

Schubert, Siegfried↗
NASA NTRS2011

A Comparison of Methods for a Priori Bias Correction in Soil Moisture Data Assimilation

Data assimilation is being increasingly used to merge remotely sensed land surface variables such as soil moisture, snow and skin temperature with estimates from land models. Its success, however, depends on unbiased model predictions and unbiased observations. Here, a suite of continental-scale, synthetic soil moisture assimilation experiments is used to compare two approaches that address typical biases in soil moisture prior to data assimilation: (i) parameter estimation to calibrate the land model to the climatology of the soil moisture observations, and (ii) scaling of the observations to the model s soil moisture climatology. To enable this research, an optimization infrastructure was added to the NASA Land Information System (LIS) that includes gradient-based optimization methods and global, heuristic search algorithms. The land model calibration eliminates the bias but does not necessarily result in more realistic model parameters. Nevertheless, the experiments confirm that model calibration yields assimilation estimates of surface and root zone soil moisture that are as skillful as those obtained through scaling of the observations to the model s climatology. Analysis of innovation diagnostics underlines the importance of addressing bias in soil moisture assimilation and confirms that both approaches adequately address the issue.

Kumar, Sujay V.↗
NASA NTRS2011

Estimating Evapotranspiration with Land Data Assimilation Systems

Advancements in both land surface models (LSM) and land surface data assimilation, especially over the last decade, have substantially advanced the ability of land data assimilation systems (LDAS) to estimate evapotranspiration (ET). This article provides a historical perspective on international LSM intercomparison efforts and the development of LDAS systems, both of which have improved LSM ET skill. In addition, an assessment of ET estimates for current LDAS systems is provided along with current research that demonstrates improvement in LSM ET estimates due to assimilating satellite-based soil moisture products. Using the Ensemble Kalman Filter in the Land Information System, we assimilate both NASA and Land Parameter Retrieval Model (LPRM) soil moisture products into the Noah LSM Version 3.2 with the North American LDAS phase 2 (NLDAS-2) forcing to mimic the NLDAS-2 configuration. Through comparisons with two global reference ET products, one based on interpolated flux tower data and one from a new satellite ET algorithm, over the NLDAS2 domain, we demonstrate improvement in ET estimates only when assimilating the LPRM soil moisture product.

Peters-Lidard, C. D.↗
NASA NTRS2011

Assimilation of Terrestrial Water Storage from GRACE in a Snow-Dominated Basin

Terrestrial water storage (TWS) information derived from Gravity Recovery and Climate Experiment (GRACE) measurements is assimilated into a land surface model over the Mackenzie River basin located in northwest Canada. Assimilation is conducted using an ensemble Kalman smoother (EnKS). Model estimates with and without assimilation are compared against independent observational data sets of snow water equivalent (SWE) and runoff. For SWE, modest improvements in mean difference (MD) and root mean squared difference (RMSD) are achieved as a result of the assimilation. No significant differences in temporal correlations of SWE resulted. Runoff statistics of MD remain relatively unchanged while RMSD statistics, in general, are improved in most of the sub-basins. Temporal correlations are degraded within the most upstream sub-basin, but are, in general, improved at the downstream locations, which are more representative of an integrated basin response. GRACE assimilation using an EnKS offers improvements in hydrologic state/flux estimation, though comparisons with observed runoff would be enhanced by the use of river routing and lake storage routines within the prognostic land surface model. Further, GRACE hydrology products would benefit from the inclusion of better constrained models of post-glacial rebound, which significantly affects GRACE estimates of interannual hydrologic variability in the Mackenzie River basin.

Forman, Barton A.↗
NASA NTRS2012

Recent Advances in Ozone Data Assimilation at the GMAO - Towards a New Reanalysis

This presentation summarized ongoing work on improving the representation of ozone in the GEOS Data Assimilation Systems. Data from two EOS Aura sensors was used: the total column ozone from the Ozone Monitoring Instrument (OMI) and high vertical resolution stratospheric profiles from Microwave Limb Sounder (MLS, version 3.3). As several previous studies have demonstrated, assimilation of this data can constrain the stratospheric and tropospheric ozone columns with relatively good accuracy. However, the representation of the vertical structures in the troposphere and near tropopause region is often deficient. Since both these layers of the atmosphere are critical to the understanding of the radiative forcing as well as the ozone budget in the troposphere, current work will focus on improving the assimilated product between the surface and the 50 hPa pressure level. The discussion included recent steps that have been taken towards refining the treatment of ozone in GEOS-5. Impacts of improved tropospheric chemistry model were discussed including the introduction of efficiency factors ("averaging kernels") for OMI total ozone, and direct assimilation of radiances from the MLS instrument. In particular, advantages and challenges involved in assimilating limb radiances rather than retrieved product were discussed. This work is, in part, a preparation for a planned reanalysis of the EOS Aura data from 2005 to present.

Krzysztof, Wargan↗