Engineering topics

daSilva, Arlindo

Publications and source records attributed to daSilva, Arlindo.

28 records · Page 2

NASA NTRS2001

The Bi-Modal Pattern of the Summer Circulation Over South America

Submonthly variations in warm-season (January-February) precipitation over South America, in special over the Amazon basin, central southwest Brazil, north Argentina, and Paraguay are shown to be strongly linked to variations in the moisture entering the continent from the Atlantic ocean. Two distinct regimes of lower tropospheric winds (westerlies and easterlies) were observed in Rondonia during the Wet Season Atmospheric Mesoscale Campaign (WETAMC) component of the Large Scale Atmosphere-Biosphere Experiment in Amazonia (LBA) and the Tropical Rainfall Measuring Mission (TRMM) field campaign. The westerly (easterly) winds were associated with the strong (weak) convective activity over the South Atlantic Convergence Zone (SACZ). The whole period of this study (January-February) was divided into SACZ and NSACZ (No SACZ) events. The vertically integrated moisture fluxes over the Amazon and Prata basin from the National Aeronautics and Space Administration/Goddard Data Assimilation Office (NASA/DAO) assimilation show that during SACZ (NSACZ) event strong (weak) convergence occurred over the Amazon basin with divergence (convergence) over the Prata basin. Submonthly variations in the SACZ also can be linked to extreme climate anomalies such as droughts or flooding conditions over the Amazon and Prata basin.

Herdies, Dirceu Luis↗

NASA NTRS2001

Results from Global Land-Surface Data Assimilation Methods

Realistic representation of the land surface is crucial in global climate modeling (GCM). Recently, the Mosaic land-surface Model (LSM) has been driven off-line using GEOS DAS (Goddard Earth Observing System Data Assimilation System) atmospheric forcing, forming the Off-line Land-surface Global Assimilation (OLGA) system. This system provides a computationally efficient test bed for land surface data assimilation. Here, we validate the OLGA simulation of surface processes and the assimilation of ISCCP surface temperatures. Another component of this study as the incorporation of the Physical-space Statistical Analysis System (PSAS) into OLGA, in order to assimilate surface temperature observations from the International Satellite Cloud Climatology Project (ISCCP). To counteract the subsequent forcing of the analyzed skin temperature back to the initial state following the analysis. incremental bias correction (IBC) was included in the assimilation. The IBC scheme effectively removed the time mean bias, but did not remove him in the mean diurnal cycle. Therefore, a diurnal him correction (DBC) scheme was developed, where the time-dependent bias was modeled with a sine wave parameterization. In addition, quality control of the ISCCP data and anisotropic temperature correction were implemented in PSAS. Preliminary results showed a substantial impact from the inclusion of PSAS and DBC that was visible in the surface meteorology fields and energy budget. Also, the monthly mean diurnal cycle from the experiment closely matched the diurnal cycle from the observations.

Radakovich, Jon D.↗

NASA NTRS2001

Recent Developments in DAO's Finite-Volume Data Assimilation System

The Physical-space/Finite-volume Data Assimilation System (fvDAS) is the next generation global atmospheric data assimilation system in development at the Data Assimilation Office at NASA's Goddard Space Flight Center. It is based on a new finite-volume general circulation model jointly developed by NASA and NCAR and on the Physical-Space Statistical Analysis System (PSAS) developed at the DAO. The data assimilation method implemented in CODAS incorporates a simplified version of the model bias estimation and correction algorithm, as described by Dee and da Silva (1998). In this talk we will briefly describe the general system formulation, and focus on the impact of 3 data types recently introduced, namely: 1) cloud tracks winds from the Multi-angle Imaging Spectrometer by the US Air Force, and 3) temperature and moisture information derived from GPS refractivity occultation measurements. The impact of these data types on observation-minus-6hr forecast (O-F) statistics, as well as 5-day forecast skills will be discussed. In addition we will assess the impact of cloud assimilation on top of the atmosphere radiation fields estimated from CERES measurements.

daSilva, Arlindo↗

NASA NTRS2000

Preliminary Results from an Assimilation of TOMS Aerosol Observations Into the GOCART Model

At NASA Goddard we are developing a global aerosol data assimilation system that combines advances in remote sensing and modeling of atmospheric aerosols. The goal is to provide high resolution, 3-D aerosol distributions to the research community. Our first step is to develop a simple assimilation system for Saharan mineral aerosol. The Goddard Chemistry and Aerosol Radiation model (GOCART) provides accurate 3-D mineral aerosol size distributions that compare well with TOMS satellite observations. Surface, mobilization, wet and dry deposition, convective and long-range transport are all driven by assimilated fields from the Goddard Earth Observing System Data Assimilation System, GEOS-DAS. Our version of GOCART transports sizes from.08-10 microns and only simulates Saharan dust. TOMS radiance observations in the ultra violet provide information on the mineral and carbonaceous aerosol fields. We use two main observables in this study: the TOMS aerosol index (AI) which is directly related to the ratio of the 340 and 380 radiances and the 380 radiance. These are sensitive to the aerosol optical thickness, the single scattering albedo and the height of the aerosol layer. The Goddard Aerosol Assimilation System (GAAS) uses the Data Assimilation Office's Physical-space Statistical Analysis System (PSAS) to combine TOMS observations and GOCART model first guess fields. At this initial phase we only assimilate observations into the the GOCART model over regions of Africa and the Atlantic where mineral aerosols dominant and carbonaceous aerosols are minimal, Our preliminary results during summer show that the assimilation with TOMS data modifies both the aerosol mass loading and the single scattering albedo. Assimilated aerosol fields will be compared with assimilated aerosol fields from GOCART and AERONET observations over Cape Verde.

daSilva, Arlindo↗

NASA NTRS2000

Assimilation of (A) TOVS data at the NASA Goddard Data Assimilation Office

At the NASA Goddard Data Assimilation Office (DAO), a 1D variational radiance assimilation system has been developed. This system, called DAOTOVS (Tiros operational vertical sounder (TOVS)), uses (A)TOVS level 1b radiances. It has been implemented within the DAO's semi-operational system as well as within the next generation data assimilation system that uses a finite-volume dynamical core. We will show results from (A)TOVS assimilation, including stratospheric analyses and validation. We will also describe our systematic error correction scheme which is based on collocated radiosondes.

Joiner, Joanna↗

NASA NTRS2000

DAO's Finite-volume/Physical-space Data Assimilation System: Stratospheric Applications

In this talk we describe the next-generation data assimilation system being developed at NASA's Data Assimilation Office (DAO), with emphasis on the applications to stratospheric forecasts and stratospheric constituent transport. This data assimilation system includes the General Circulation Model jointly developed by the DAO and the Climate and Global Dynamics Division (CGDD) at NCAR. This model is based on the finite-volume dynamical core) developed at DAO with physical parameterizations from the NCAR Climate Community Model. The Physical-space Statistical Analysis System (PSAS) is used to combine a first guess from the NASA-NCAR GCM with observational data to provide an updated estimate of the state of the atmosphere. Case studies for the (northern) Winter of 2000 will be discussed.

daSilva, Arlindo↗

NASA NTRS2000

Preliminary Results from an Assimilation of Saharan Dust Using TOMS Radiances and the GOCART Model

At NASA Goddard we are developing a global aerosol data assimilation system that combines advances in remote sensing and modeling of atmospheric aerosols. The goal is to provide high resolution, 3-D aerosol distributions to the research community. Our first step is to develop a simple assimilation system for Saharan mineral aerosol. The Goddard Chemistry and Aerosol Radiation model (GOCART) provides accurate 3-D mineral aerosol size distributions. Surface mobilization, wet and dry deposition, convective and long-range transport are all driven by assimilated fields from the Goddard Earth Observing System Data Assimilation System, GEOS-DAS. Our version of GOCART transports sizes from .08-10 microns and only simulates Saharan dust. We draw the assimilation to two observables in this study: the TOMS aerosol index (Al) which is directly related to the ratio of the 340 and 380 radiances and the 380 radiance alone. The forward model that simulates the observables requires the aerosol optical thickness, the single scattering albedo and the height of the aerosol layer from the GOCART fields. The forward model also requires a refractive index for the dust. We test three index values to see which best fits the TOMS observables. These are 1) for Saharan dust reported by Patterson, 2) for a mixture of Saharan dust and a highly reflective material (sea salt or sulfate) and 3) for pure illite. The assimilation works best assuming either pure illite or the dust mixture. Our assimilation cycle first determines values of the aerosol index (Al) and the radiance at 380 nm based on the GOCART aerosol fields. Differences between the observed and GOCART model calculated Al and 380 nm radiance are first analyzed horizontally using the Physical-space Statistical Analysis System (PSAS). A quasi-Newton iteration is then performed to produce analyzed 3D aerosol fields according to parameterized background and observation error covariances. We only assimilate observations into the the GOCART model over regions of Africa and the Atlantic where mineral aerosols are dominant and carbonaceous aerosols are minimal.

Weaver, C. J.↗

NASA NTRS1999

Model Error Estimation for the CPTEC Eta Model

Statistical data assimilation systems require the specification of forecast and observation error statistics. Forecast error is due to model imperfections and differences between the initial condition and the actual state of the atmosphere. Practical four-dimensional variational (4D-Var) methods try to fit the forecast state to the observations and assume that the model error is negligible. Here with a number of simplifying assumption, a framework is developed for isolating the model error given the forecast error at two lead-times. Two definitions are proposed for the Talagrand ratio tau, the fraction of the forecast error due to model error rather than initial condition error. Data from the CPTEC Eta Model running operationally over South America are used to calculate forecast error statistics and lower bounds for tau.

Tippett, Michael K.↗

NASA NTRS1999

A Global Data Assimilation System for Atmospheric Aerosol

We will give an overview of an aerosol data assimilation system which combines advances in remote sensing of atmospheric aerosols, aerosol modeling and data assimilation methodology to produce high spatial and temporal resolution 3D aerosol fields. Initially, the Goddard Aerosol Assimilation System (GAAS) will assimilate TOMS, AVHRR and AERONET observations; later we will include MODIS and MISR. This data assimilation capability will allows us to integrate complementing aerosol observations from these platforms, enabling the development of an assimilated aerosol climatology as well as a global aerosol forecasting system in support of field campaigns. Furthermore, this system provides an interactive retrieval framework for each aerosol observing satellites, in particular TOMS and AVHRR. The Goddard Aerosol Assimilation System (GAAS) takes advantage of recent advances in constituent data assimilation at DAO, including flow dependent parameterizations of error covariances and the proper consideration of model bias. For its prognostic transport model, GAAS will utilize the Goddard Ozone, Chemistry, Aerosol, Radiation and Transport (GOCART) model developed at NASA/GSFC Codes 916 and 910.3. GOCART includes the Lin-Rood flux-form, semi-Langrangian transport model with parameterized aerosol chemistry and physical processes for absorbing (dust and black carbon) and non-absorbing aerosols (sulfate and organic carbon). Observations and model fields are combined using a constituent version of DAO's Physical-space Statistical Analysis System (PSAS), including its adaptive quality control system. In this talk we describe the main components of this assimilation system and present preliminary results obtained by assimilating TOMS data.

daSilva, Arlindo↗

NASA NTRS1997

Internal Physical Features of a Land Surface Model Employing a Tangent Linear Model

The Earth's land surface, including its biomass, is an integral part of the Earth's weather and climate system. Land surface heterogeneity, such as the type and amount of vegetative covering., has a profound effect on local weather variability and therefore on regional variations of the global climate. Surface conditions affect local weather and climate through a number of mechanisms. First, they determine the re-distribution of the net radiative energy received at the surface, through the atmosphere, from the sun. A certain fraction of this energy increases the surface ground temperature, another warms the near-surface atmosphere, and the rest evaporates surface water, which in turn creates clouds and causes precipitation. Second, they determine how much rainfall and snowmelt can be stored in the soil and how much instead runs off into waterways. Finally, surface conditions influence the near-surface concentration and distribution of greenhouse gases such as carbon dioxide. The processes through which these mechanisms interact with the atmosphere can be modeled mathematically, to within some degree of uncertainty, on the basis of underlying physical principles. Such a land surface model provides predictive capability for surface variables including ground temperature, surface humidity, and soil moisture and temperature. This information is important for agriculture and industry, as well as for addressing fundamental scientific questions concerning global and local climate change. In this study we apply a methodology known as tangent linear modeling to help us understand more deeply, the behavior of the Mosaic land surface model, a model that has been developed over the past several years at NASA/GSFC. This methodology allows us to examine, directly and quantitatively, the dependence of prediction errors in land surface variables upon different vegetation conditions. The work also highlights the importance of accurate soil moisture information. Although surface variables are predicted imperfectly due to inherent uncertainties in the modeling process, our study suggests how satellite observations can be combined with the model, through land surface data assimilation, to improve their prediction.

Yang, Runhua↗