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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 NTRS1995

Assimilation of Geostat Altimetric Data in a Nonlinear Shallow-water Model of

This study examines the impact of assimilating Geostat sea level variations.

altimetry↗

NASA NTRS2001

Temperature Humidity and Sea Level Pressure Increments Induced by 1DVAR Analysis of GPS Refractivity

The Global Positioning System (GPS) transmitted signals are affected by the atmosphere. Using the radio occultation technique, where a receiver is placed on a low-Earth-orbiting platform. it is possible to perform soundings. by measuring the bending angles of the rays. The information can be converted into atmospheric refractivity. We have developed a one dimensional variational (1DVAR) analysis that uses GPS/MET 1995 refractivity and 6-hour FVDAS (Finite Volume Data Assimilation System) forecasts as background information to constrain the retrievals. The analysis increments are defined as 1DVAR minus background temperature, humidity and sea level pressure. Before assimilating the 1DVAR profiles into the FVDAS. the increments need to be understood. First, some bias could be induced in the retrievals when confronted with actual biased data: second. bias in the back-round could create undesired bias in the retrievals. Anv bias in the analyses will ultimately change the climatology of the model the retrievals will be assimilated into. We relate the increments to the reduction of the difference between observed minus computed refractivity profiles. We also point out the difference in the mean increments using backgrounds which have assimilated either NESDIS TIROS Operational Vertical Sounder (TOVS) operational retrievals or Data Assimilation Office (DAO) TOVS interactive retrievals. The climatology of the model in terms of refractivity is significantly different and this impacts the GPS 1DVAR increments. This testifies that changing the basic load of assimilated data has an influence on the impact the GPS data may have in a DAS.

Poli, Paul↗

NASA NTRS2013

Emissions Estimation from Satellite Retrievals: a Review of Current Capability

Since the mid-1990s a new generation of Earth-observing satellites has been able to detect tropospheric air pollution at increasingly high spatial and temporal resolution. Most primary emitted species can be measured by one or more of the instruments. This review article addresses the question of how well we can relate the satellite measurements to quantification of primary emissions and what advances are needed to improve the usability of the measurements by U.S. air quality managers. Built on a comprehensive literature review and comprising input by both satellite experts and emission inventory specialists, the review identifies several targets that seem promising: large point sources of NOx and SO2, species that are difficult to measure by other means (NH3 and CH4, for example), area sources that cannot easily be quantified by traditional bottom-up methods (such as unconventional oil and gas extraction, shipping, biomass burning, and biogenic sources), and the temporal variation of emissions (seasonal, diurnal, episodic). Techniques that enhance the usefulness of current retrievals (data assimilation, oversampling, multi-species retrievals, improved vertical profiles, etc.) are discussed. Finally, we point out the value of having new geostationary satellites like GEO-CAPE and TEMPO over North America that could provide measurements at high spatial (few km) and temporal (hourly) resolution.

air quality↗

NASA NTRS2006

Constraining Numerical Geodynamo Modeling with Surface Observations

Numerical dynamo solutions have traditionally been generated entirely by a set of self-consistent differential equations that govern the spatial-temporal variation of the magnetic field, velocity field and other fields related to dynamo processes. In particular, those solutions are obtained with parameters very different from those appropriate for the Earth s core. Geophysical application of the numerical results therefore depends on correct understanding of the differences (errors) between the model outputs and the true states (truth) in the outer core. Part of the truth can be observed at the surface in the form of poloidal magnetic field. To understand these differences, or errors, we generate new initial model state (analysis) by assimilating sequentially the model outputs with the surface geomagnetic observations using an optimal interpolation scheme. The time evolution of the core state is then controlled by our MoSST core dynamics model. The final outputs (forecasts) are then compared with the surface observations as a means to test the success of the assimilation. We use the surface geomagnetic data back to year 1900 for our studies, with 5-year forecast and 20-year analysis periods. We intend to use the result; to understand time variation of the errors with the assimilation sequences, and the impact of the assimilation on other unobservable quantities, such as the toroidal field and the fluid velocity in the core.

Kuang, Weijia↗

NASA NTRS2004

Application of SeaWinds Scatterometer and TMI-SSM/I Rain Rates to Hurricane Analysis and Forecasting

Results provided by two different assimilation methodologies involving data from passive and active space-borne microwave instruments are presented. The impact of the precipitation estimates produced by the TRMM Microwave Imager (TMI) and Special Sensor Microwave/Imager (SSM/I) in a previously developed 1D variational continuous assimilation algorithm for assimilating tropical rainfall is shown on two hurricane cases. Results on the impact of the SeaWinds scatterometer on the intensity and track forecast of a mid-Atlantic hurricane are also presented. This work is the outcome of a collaborative effort between NASA and NOAA and indicates the substantial improvement in tropical cyclone forecasting that can result from the assimilation of space-based data in global atmospheric models.

Atlas, Robert↗

NASA NTRS2017

El Nino-Induced Tropical Ocean/Land Energy Exchange in MERRA-2 and M2AMIP

Studies have shown the correlation and connection of surface temperatures across the globe, ocean and land, related to Tropical SSTs especially El Nino. This climate variability greatly influences regional weather and hydroclimate extremes (e.g. drought and flood). In this paper, we evaluate the relationship of temperatures across the tropical oceans and continents in MERRA-2, and also in a newly developed MERRA-2 AMIP ensemble simulation (M2AMIP). M2AMIP uses the same model and spatial resolution as MERRA-2, producing the same output diagnostics over 10 ensemble members. Composite El Nino temperature data are compared with observations to evaluate the land/sea contrast, variations and phase relationship. The temperature variations are related to surface heat fluxes and the atmospheric temperatures and transport, to identify the processes that lead to the lagged redistribution of heat in the tropics and beyond. Discernable cloud, radiation and data assimilation changes accompany the onset of El Nino affecting continental regions through the progression to and following the peak values. While the model represents these variations in general, regional strengths and weaknesses can be identified.

Bosilovich, Michael G.↗

NASA NTRS1995

Low-frequency variations in the atmospheric branch of the global hydrological cycle

The annual variation of the hydrological cycle is illustrated in terms of hemispheric-mean hydrological variables for the Northern and Southern Hemispheres, while the intraseasonal variations of the global hydrological cycle are illustrated with mean values over two hemispheres that form an east-west partition of the globe. This partition is defined by the 60 deg E-120 deg W great circle and was chosen so that the mean precipitation difference and the divergent water vapor transport between the two hemispheres was maximized. Two years (1979-80) of daily precipitation estimates from the Goddard Laboratory for Atmospheres an 14 years (1979-92) of upper-air data generated by the Global Data Assimilation System at the National Meteorological Center are used in making quantitative estimates of the annual and intraseasonal variations in the global hydrological cycle. The annual variations in hemispheric-mean precipitation (P-circumflex) and water vapor flux divergence (del(vector differential operator) dot Q-circumflex) for the Northern and Southern Hemispheres are comparable with amplitudes of about 0.5 approximately 0.7 mm/day. Both (P-circumflex) and (del(vector differential operator) dot Q-circumflex) vary annually in a coherent way in each hemisphere so that water vapor diverges from the winter hemisphere, where (P-circumflex) reaches its minimum, to the summer hemisphere, where (P-circumflex) attains its maximum. In fact, the hemispheric-mean divergence of water vapor flux changes sign during the annual cycle. Intraseasonal variations of hemispheric-mean precipitation mean P-tilde, evaporation mean E-tilde, and water vapor flux divergence mean del (vector differential operator) dot Q-tilde in the two hemisphres in the east-west direction are comparable with amplitudes of about 0.1 approximately 0.2 mm/day, although amplitudes in some cases exceed 0.3 mm/day. Hemispheric-mean precipitation mean P-tilde varies coherently in opposite phase for the two hemispheres, while mean del (vector differential operator) dot Q-tilde varies so that water vapor diverges from the hemisphere of maximum mean P-tilde to the hemisphere to the hemisphere of minimum mean P-tilde. Intraseasonal variations of mean P-tilde, mean E-tilde, and mean del (vector differential oprator) dot Q-tilde are in accord with the eastward propagation of the intraseasonal global divergent circulation.

Chen, Tsing-Chang↗

NASA NTRS2001

Use of Remotely-Sensed Rainfall Data in Global Modeling and Data Assimilation

Precipitation observations derived from microwave sensors available from the Tropical Rainfall Measuring Mission (TRMM) and the proposed Global Precipitation Mission (GPM) can provide crucial information needed for improving global modeling, data assimilation, and numerical weather prediction. New methodologies are being developed at NASA to make effective use of this new data type in these applications. Currently, global analyses contain significant errors in primary hydrological fields such as precipitation and evaporation, especially in the tropics. We show that assimilating 6-h averaged TRMM rainfall retrievals improves not only the hydrological cycle but also key climate parameters such as clouds, radiation, and the upper tropospheric moisture in the analysis produced by the Goddard Earth Observing System (GEOS) Data Assimilation System. The improved analysis also leads to improved short-range forecasts in the tropics. The above results were obtained using a variational assimilation procedure that uses rainfall observations to derive moisture and temperature tendency corrections every 6 hours to compensate for errors arising from imperfect initial conditions and deficiencies in the model physics. We will describe a developmental path towards using space-borne rainfall data to empirically estimate and correct for state-dependent systematic errors in parameterized model physics. The study provides a demonstration of the potential of using remote-sensed rainfall data from microwave instruments to improve the 4-dimensional global datasets for climate analysis and numerical weather prediction.

Hou, Arthur↗

NASA NTRS1995

Mean Sea Level Derived from Altimetry and Wind-Driven Numerical Models in the Indian Ocean

Wind-driven model skill in simulating sea level variations in the Indian Ocean depends on our knowledge of the mean ocean dynamic topography. This is demonstrated by running the nonlinear or linear version of a shallow-water model driven by observed winds over Geosat and TOPEX periods. Geosat variations are assimilated in the nonlinear shallow-water model with the objective of obtaining topography data.

numerical models Indian ocean mean sea level remot↗

NASA NTRS2021

Reanalysis Surface Mass Balance of the Greenland Ice Sheet along K-transect (2000-2014)

Accurate estimates of surface mass balance over the Greenland ice sheet (GrIS) would contribute to understanding the cause of recent changes and would help to better estimate the future contribution of the GrIS to sea-level rise. Given the limitations of in-situ measurement, modeling, and remote sensing, it is critical to explore the opportunity to merge the available data to better characterize the spatial and temporal variation of the GrIS surface mass balance (SMB). This work utilizes a particle batch smoother data assimilation technique that yields SMB estimates that benefit from the snow model Crocus and a 16-day albedo product derived from satellite remote sensing data. Comparison of the results against in-situ SMB measurements shows that the assimilation of the albedo product reduces the root mean square error (RMSE) of the posterior estimates of SMB by 51% and reduces bias by 95%.

Greenland Ice Sheet↗

NASA NTRS2001

An Assessment of the Ability of Potential Space-Borne Instruments to Resolve Spatial and Temporal Variability of Atmospheric Carbon Dioxide

Mounting concern regarding the possibility that increasing carbon dioxide concentrations will initiate climate change has stimulated interest in the feasibility of measuring CO2 mixing ratios from satellites. Currently, the most comprehensive set of atmospheric CO2 data is from the NOAA CMDL cooperative air sampling network, consisting of more than 40 sites where flasks of air are collected approximately weekly. Sporadic observations in the troposphere and stratosphere from airborne in situ and flask samplers are also available. Although the surface network is extensive, there is a dearth of data in the Southern Hemisphere and most of the stations were intentionally placed in remote areas, far from major sources. Sufficiently precise satellite observations with adequate spatial and temporal resolution would substantially increase our knowledge of the atmospheric CO2 distribution and would undoubtedly lead to improved understanding of the global carbon budget. We use a 3-D chemical transport model to investigate the ability of potential satellite instruments with a variety of orbits, horizontal resolution and vertical weighting functions to capture the variation in the modeled CO2 fields. The model is driven by analyzed winds from the Goddard Data Assimilation Office. Simulated CO2 fields are compared with existing surface and aircraft data, and the effects of the model convection scheme and representation of the planetary boundary layer are considered.

Andrews, Arlyn E.↗

NASA NTRS2001

An Assessment of the Ability of Potential Spaceborne Instruments to Resolve Spatial and Temporal Variability of Atmospheric Carbon Dioxide

Andrews, Arlyn E.↗

NASA NTRS2007

An Investigation of the Characterization of Cloud Contamination in Hyperspectral Radiances

In regions lacking direct observations, the assimilation of radiances from infrared and microwave sounders is the primary method for characterizing the atmosphere in the analysis process. In recent years, technological advances have led to the launching of more advanced sounders, particularly in the thermal infrared spectrum. With the advent of these hyperspectral sounders, the amount of data available for the analysis process has and will continue to be dramatically increased. However, the utilization of infrared radiances in variational assimilation can be problematic in the presence of clouds; specifically the assessment of the presence of clouds in an instantaneous field of view (IFOV) and the contamination in the individual channels within the IFOV. Various techniques have been developed to determine if a channel is contaminated by clouds. The work presented in this paper and subsequent presentation will investigate traditional techniques and compare them to a new technique, the C02 sorting technique, which utilizes the high spectral resolution of the Atmospheric Infrared Sounder (AIRS) within the framework of the Gridpoint Statistical Interpolation (GSI) 3DVAR system. Ultimately, this work is done in preparation for the assessment of short-term forecast impacts with the regional assimilation of AIRS radiances within the analysis fields of the Weather Research and Forecast Nonhydrostatic Mesoscale Model (WRF-NMM) at the NASA Short-term Prediction Research and Transition (SPORT) Center.

McCarty, William↗

NASA NTRS1977

A statistical method for time-continuous assimilation of remote sounding temperatures

A time-continuous statistical method is presented for the four-dimensional assimilation of remote sounding temperatures based on radiometric measurements from polar-orbiting satellites. This method is applied to DST-6 data from the NOAA-4 and Nimbus-6 satellites. Experiments are reported in which the state of the atmosphere throughout the test period was determined using a varying amount of satellite data and in which different methods were used for their assimilation. Data from the NOAA-4 satellite only, from Nimbus-6 only, and from both satellites together were used; the methods tested include different variations of the statistical method as well as more traditional methods. The conclusions are that: (1) satellite-derived temperature data can have a modest, but statistically significant positive impact on numerical weather prediction in the two-to-three day range; (2) this impact is highly sensitive to the quantity of data available; and (3) the assimilation method plays a major role in the magnitude of the impact for the same data.

Ghil, M.↗

NASA NTRS1998

Efficient Methods to Assimilate Satellite Retrievals Based on Information Content: Suboptimal Retrieval Assimilation - Part 2

One of the outstanding problems in data assimilation has been and continues to be how best to utilize satellite data while balancing the tradeoff between accuracy and computational cost. A number of weather prediction centers have recently achieved remarkable success in improving their forecast skill by changing the method by which satellite data are assimilated into the forecast model from the traditional approach of assimilating retrievals to the direct assimilation of radiances in a variational framework. The operational implementation of such a substantial change in methodology involves a great number of technical details, e.g., pertaining to quality control procedures, systematic error correction techniques, and tuning of the statistical parameters in the analysis algorithm. Although there are clear theoretical advantages to the direct radiance assimilation approach, it is not obvious at all to what extent the improvements that have been obtained so far can be attributed to the change in methodology, or to various technical aspects of the implementation. The issue is of interest because retrieval assimilation retains many practical and logistical advantages which may become even more significant in the near future when increasingly high-volume data sources become available. The central question we address here is: how much improvement can we expect from assimilating radiances rather than retrievals, all other things being equal? We compare the two approaches in a simplified one-dimensional theoretical framework, in which problems related to quality control and systematic error correction are conveniently absent. By assuming a perfect radiative transfer model and perfect knowledge of radiance and background error covariances, we are able to formulate a nonlinear local error analysis for each assimilation method. Direct radiance assimilation is optimal in this idealized context, while the traditional method of assimilating retrievals is suboptimal because it ignores the cross-covariances between background errors and retrieval errors. We show that interactive retrieval assimilation (where the same background used for assimilation is also used in the retrieval step) is equivalent to direct assimilation of radiances with suboptimal analysis weights. We illustrate and extend these theoretical arguments with several one-dimensional assimilation experiments, where we estimate vertical atmospheric profiles using simulated data from both the High-resolution InfraRed Sounder 2 (HIRS2) and the future Atmospheric InfraRed Sounder (AIRS).

Joiner, J.↗

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 NTRS1983

Initialization of mesoscale models - The possible impact of remotely sensed data

Little or no improvement appears to have been achieved in mesoscale numerical prediction. This has been blamed on inherent unpredictability, insufficient spatial resolution, inadequate parameterization of important physical processes, and errors in the numerical discretization of the governing hydrodynamic equations. Attention is presently given to the more fundamental difficulty of forecasting when present conditions are not known with sufficient accuracy, and to the matching of remotely sensed sounding-capability satellite and all-weather Doppler radar data with mesoscale numerical models. This matching is not straightforward; simple and variational forms of four-dimensional assimilation, and Kalman filtering, are suggested approaches but their practical testing remains to be accomplished. The most severe problem faced is that of the temperature and humidity profiles' vertical resolution.

Gal-Chen, T.↗