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All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Assimilation of Geosat Altimetric Data in a Nonlinear Shallow-Water Model of the Indian Ocean by Adjoint Approach. Part II: Some Validation and Interpretation of the Assimilated Results: Some Validation and Interpretation of the Assimilated Results - Part 2

This paper examines the results of assimilating Geosat sea level variations relative to the November 1986-November 1988 mean reference, in a nonlinear reduced-gravity model of the Indian Ocean, Data have been assimilated during one year starting in November 1986 with the objective of optimizing the initial conditions and the yearly averaged reference surface. The thermocline slope simulated by the model with or without assimilation is validated by comparison with the signal, which can be derived from expandable bathythermograph measurements performed in the Indian Ocean at that time. The topography simulated with assimilation on November 1986 is in very good agreement with the hydrographic data. The slopes corresponding to the South Equatorial Current and to the South Equatorial Countercurrent are better reproduced with assimilation than without during the first nine months. The whole circulation of the cyclonic gyre south of the equator is then strongly intensified by assimilation. Another assimilation experiment is run over the following year starting in November 1987. The difference between the two yearly mean surfaces simulated with assimilation is in excellent agreement with Geosat. In the southeastern Indian Ocean, the correction to the yearly mean dynamic topography due to assimilation over the second year is negatively correlated to the one the year before. This correction is also in agreement with hydrographic data. It is likely that the signal corrected by assimilation is not only due to wind error, because simulations driven by various wind forcings present the same features over the two years. Model simulations run with a prescribed throughflow transport anomaly indicate that assimilation is rather correcting in the interior of the model domain for inadequate boundary conditions with the Pacific.

Greiner, Eric↗
NASA NTRS2022

Assimilation of Sea Ice Thickness Derived from Cryosat-2 Along-Track Freeboard Measurements into the Met Office's Forecast Ocean Assimilation Model (FOAM)

The feasibility of assimilating sea ice thickness (SIT) observations derived from CryoSat-2 along-track measurements of sea ice freeboard is successfully demonstrated using a 3D-Var assimilation scheme, NEMOVAR, within the Met Office's global, coupled ocean–sea-ice model, Forecast Ocean Assimilation Model (FOAM). The CryoSat-2 Arctic freeboard measurements are produced by the Centre for Polar Observation and Modelling (CPOM) and are converted to SIT within FOAM using modelled snow depth. This is the first time along-track observations of SIT have been used in this way, with other centres assimilating gridded and temporally averaged observations. The assimilation leads to improvements in the SIT analysis and forecast fields generated by FOAM, particularly in the Canadian Arctic. Arctic-wide observation-minus-background assimilation statistics for 2015–2017 show improvements of 0.75 m mean difference and 0.41 m root-mean-square difference (RMSD) in the freeze-up period and 0.46 m mean difference and 0.33 m RMSD in the ice break-up period. Validation of the SIT analysis against independent springtime in situ SIT observations from NASA Operation IceBridge (OIB) shows improvement in the SIT analysis of 0.61 m mean difference (0.42 m RMSD) compared to a control without SIT assimilation. Similar improvements are seen in the FOAM 5 d SIT forecast. Validation of the SIT assimilation with independent Beaufort Gyre Exploration Project (BGEP) sea ice draft observations does not show an improvement, since the assimilated CryoSat-2 observations compare similarly to the model without assimilation in this region. Comparison with airborne electromagnetic induction (Air-EM) combined measurements of SIT and snow depth shows poorer results for the assimilation compared to the control, despite covering similar locations to the OIB and BGEP datasets. This may be evidence of sampling uncertainty in the matchups with the Air-EM validation dataset, owing to the limited number of observations available over the time period of interest. This may also be evidence of noise in the SIT analysis or uncertainties in the modelled snow depth, in the assimilated SIT observations, or in the data used for validation. The SIT analysis could be improved by upgrading the observation uncertainties used in the assimilation. Despite the lack of CryoSat-2 SIT observations available for assimilation over the summer due to the detrimental effect of melt ponds on retrievals, it is shown that the model is able to retain improvements to the SIT field throughout the summer months due to prior, wintertime SIT assimilation. This also results in regional improvements to the July modelled sea ice concentration (SIC) of 5 % RMSD in the European sector, due to slower melt of the thicker sea ice.

Emma K. Fiedler↗
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 NTRS2017

Assimilation of AERONET and MODIS AOT Observations Using Variational and Ensemble Data Assimilation Methods and Its Impact on Aerosol Forecasting Skill

Data assimilation of Aerosol Robotic Network (AERONET) and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical thickness (AOT) for aerosol forecasting was tested within the Navy Aerosol Analysis Prediction System (NAAPS) framework, using variational and ensemble data assimilation methods. Navy aerosol forecasting currently makes use of a deterministic NAAPS simulation coupled to Navy Variational Data Assimilation System for aerosol optical depth, a two-dimensional variational data assimilation system, for MODIS AOT assimilation. An ensemble version of NAAPS (ENAAPS) coupled to an ensemble adjustment Kalman filter (EAKF) from the Data Assimilation Research Testbed was recently developed, allowing for a range of data assimilation and forecasting experiments to be run with deterministic NAAPS and ENAAPS. The main findings are that the EAKF, with its flow-dependent error covariances, makes better use of sparse observations such as AERONET AOT. Assimilating individual AERONET observations in the two-dimensional variational system can increase the analysis errors when observations are located in high AOT gradient regions. By including AERONET with MODIS AOT assimilation, the magnitudes of peak aerosol events (AOT> 1) were better captured with improved temporal variability, especially in India and Asia where aerosol prediction is a challenge. Assimilating AERONET AOT with MODIS had little impact on the 24 h forecast skill compared to MODIS assimilation only, but differences were found downwind of AERONET sites. The 24 h forecast skill was approximately the same for forecasts initialized with analyses from AERONET AOT assimilation alone compared to MODIS assimilation, particularly in regions where the AERONET network is dense; including the United States and Europe, indicating that AERONET could serve as a backup observation network for over-land synoptic-scale aerosol events.

Rubin, Juli I.↗
NASA NTRS2002

Monitoring and Assimilation of MLS Measurements in the DAO Ozone Data Assimilation System

Since 1999 the DAO (Data Assimilation Office) at NASA Goddard has operationally assimilated ozone measurements in near real time. Currently, the assimilation system analyzes SBUV profile and total column measurements using an off line CTM with parameterized chemistry within the 3D-PSAS algorithm. During the last year the assimilation system was modified to either monitor or actively assimilate MLS (Microwave Limb Sounder) measurements in conjunction with the active assimilation of TOMS total column and SBUV profiles. It is expected that the active assimilation of MLS profiles will improve analysis results in two ways. First, there should be an improvement in the vertical resolution. Second, there should be an improvement in regions where SBUV measurements do not exist (such as in the polar night). A series of experiments using UARS (Upper Atmospheric Research Satellite) MLS data from December 1991 to March 1992 were run. In these experiments MLS data was either monitored in conjunction with active assimilation of the SBUV profile and TOMS total column measurements, or some combination of MLS, TOMS, and SBUV observations was actively assimilated. Monitoring of MLS data indicated that the MLS observations contain verifiable information that should improve the vertical structure of the analysis results, especially below the ozone peak and above the tropopause. The monitoring also substantiated the potential to improve the assimilation in the polar night. Active assimilation of MLS data does indeed improve the analysis results in these two ways, although the quality of the improvements is not uniform. This suggests that refinement of the specification of the error covariances might be needed to optimize the system. In addition it may be necessary to account for biases between the different sources of ozone information.

Atlas, Robert↗
NASA NTRS2014

Assimilation of MODIS Snow Cover Through the Data Assimilation Research Testbed and the Community Land Model Version 4

To improve snowpack estimates in Community Land Model version 4 (CLM4), the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) was assimilated into the Community Land Model version 4 (CLM4) via the Data Assimilation Research Testbed (DART). The interface between CLM4 and DART is a flexible, extensible approach to land surface data assimilation. This data assimilation system has a large ensemble (80-member) atmospheric forcing that facilitates ensemble-based land data assimilation. We use 40 randomly chosen forcing members to drive 40 CLM members as a compromise between computational cost and the data assimilation performance. The localization distance, a parameter in DART, was tuned to optimize the data assimilation performance at the global scale. Snow water equivalent (SWE) and snow depth are adjusted via the ensemble adjustment Kalman filter, particularly in regions with large SCF variability. The root-mean-square error of the forecast SCF against MODIS SCF is largely reduced. In DJF (December-January-February), the discrepancy between MODIS and CLM4 is broadly ameliorated in the lower-middle latitudes (2345N). Only minimal modifications are made in the higher-middle (4566N) and high latitudes, part of which is due to the agreement between model and observation when snow cover is nearly 100. In some regions it also reveals that CLM4-modeled snow cover lacks heterogeneous features compared to MODIS. In MAM (March-April-May), adjustments to snowmove poleward mainly due to the northward movement of the snowline (i.e., where largest SCF uncertainty is and SCF assimilation has the greatest impact). The effectiveness of data assimilation also varies with vegetation types, with mixed performance over forest regions and consistently good performance over grass, which can partly be explained by the linearity of the relationship between SCF and SWE in the model ensembles. The updated snow depth was compared to the Canadian Meteorological Center (CMC) data. Differences between CMC and CLM4 are generally reduced in densely monitored regions.

data assimilation↗
NASA NTRS2000

Towards a Comprehensive Dynamic-chemistry Assimilation for Eos-Chem: Plans and Status in NASA's Data Assimilation Office

In order to support the EOS-Chem project, a comprehensive assimilation package for the coupled chemical-dynamical system is being developed by the Data Assimilation Office at NASA GSFC. This involves development of a coupled chemistry/meteorology model and of data assimilation techniques for trace species and meteorology. The model is being developed using the flux-form semi-Lagrangian dynamical core of Lin and Rood, the physical parameterizations from the NCAR Community Climate Model, and atmospheric chemistry modules from the Atmospheric Chemistry and Dynamics branch at NASA GSFC. To date the following results have been obtained: (i) multi-annual simulations with the dynamics-radiation model show the credibility of the package for atmospheric simulations; (ii) initial simulations including a limited number of middle atmospheric trace gases reveal the realistic nature of transport mechanisms, although there is still a need for some improvements. Samples of these results will be shown. A meteorological assimilation system is currently being constructed using the model; this will form the basis for the proposed meteorological/chemical assimilation package. The latter part of the presentation will focus on areas targeted for development in the near and far terms, with the objective of Providing a comprehensive assimilation package for the EOS-Chem science experiment. The first stage will target ozone assimilation. The plans also encompass a reanalysis (ReSTS) for the 1991-1995 period, which includes the Mt. Pinatubo eruption and the time when a large number of UARS observations were available. One of the most challenging aspects of future developments will be to couple theoretical advances in tracer assimilation with the practical considerations of a real environment and eventually a near-real-time assimilation system.

Pawson, Steven↗
NASA NTRS2001

Assimilation of Cloud- and Land-Affected TOVS/ATOVS Level 1B Radiances in DAO's Next Generation 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 (DAO) 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. In this talk we will focus on the assimilation of data from the (Advanced) TIROS Operational Vertical Sounder (ATOVS), with emphasis on the impact of cloud- and land-affected level 1B radiances. Recently, it has been shown that the use of observations from satellite-borne microwave and infrared radiometers in data assimilation systems consistently increases forecast skill. Considerable effort has been expended over the past two decades, particularly with the (Advanced) TIROS Operational Vertical Sounder (ATOVS), to achieve this result. The positive impact on forecast skill has resulted from improvements in quality control algorithms, systematic error correction schemes, and more sophisticated data assimilation algorithms. Despite these advances, there are still many issues regarding the use of satellite data in data assimilation systems that remain unresolved. In particular, most operational centers still do not assimilate cloud- and land-affected TOVS data. In this study, we evaluate the impact of assimilating cloud-and land-affected TOVS/ATOVS level 1B data in DAO's next generation fvDAS, using a 1D variational scheme. We will discuss the impact of these data on both tropospheric and stratospheric forecasts, as well as on the general aspects of the earth climate system.

Joiner, J.↗
NASA NTRSDate not supplied

GEOS Constituent Data Assimilation Beyond Aura MLS: Assimilating NASA SAGE III/ISS Profiles of Stratospheric Water Vapor and Ozone

Ozone and water vapor in the lower stratosphere are important trace gases for atmospheric chemistry and radiative budget. The Stratospheric Aerosol and Gas Experiment (SAGE) missions have been crucial in monitoring the stratospheric ozone loss and the subsequent recovery as well as the trends in water vapor linked to surface temperature trends. The SAGE III instrument aboard the International Space Station (ISS) continues the SAGE mission record, with high vertical resolution profiles of ozone and water vapor available since mid 2017. The NASA GEOS Earth system model has the new capability to assimilate multi-constituents from ground and space-based instruments using the GEOS Constituent Data Assimilation System (CoDAS). The recently released MERRA-2 Stratospheric Composition Reanalysis with Aura MLS (M2-SCREAM) assimilates version 4.2 MLS ozone, water vapor and other chemically-reactive species with the NASA GEOS model coupled to a stratospheric-only chemistry mechanism and transport constrained to the MERRA-2 reanalysis. While the number of solar occultation observations a day from SAGE III/ISS is about 1% of the total number of profiles observed globally by MLS, the chemical timescales of ozone and water vapor in the lower stratosphere are long enough that the SAGE III/ISS data may provide a useful constraint on the assimilated product. Using the same GEOS CoDAS configuration as M2-SCREAM, we will present a series of experiments to investigate if ozone and water vapor trends are consistent with the assimilation of SAGE observations with and without Aura retrievals, and to determine if the assimilation of SAGE observations produces a steady product for trend analysis, especially as the end of the Aura mission nears. In our experiments, assimilating only SAGE III/ISS water vapor profiles results in water vapor fields more consistent with experiments that assimilate MLS v5; however, in the polar regions SAGE III/ISS observations are not available and the values are unconstrained. We are encouraged by the positive benefit assimilating the less frequent SAGE III/ISS observations has on stratospheric composition. Sensitivity experiments such as these will allow us to assess the added value of SAGE data for continued monitoring of the stratospheric composition for climate and ozone recovery assessments.

SAGE↗
NASA NTRSDate not supplied

GEOS Constituent Data Assimilation Beyond Aura MLS: Assimilating NASA SAGE III/ISS Profiles of Stratospheric Water Vapor

Ozone and water vapor in the lower stratosphere are important trace gases for atmospheric chemistry and radiative budget. The Stratospheric Aerosol and Gas Experiment (SAGE) missions have been crucial in monitoring the stratospheric ozone loss and the subsequent recovery as well as the trends in water vapor linked to surface temperature trends. The SAGE III instrument aboard the International Space Station (ISS) continues the SAGE mission record, with high vertical resolution profiles of ozone and water vapor available since mid 2017. The NASA GEOS Earth system model has the new capability to assimilate multi-constituents from ground and space-based instruments using the GEOS Constituent Data Assimilation System (CoDAS). The recently released MERRA-2 Stratospheric Composition Reanalysis with Aura MLS (M2-SCREAM) assimilates version 4.2 MLS ozone, water vapor and other chemically-reactive species with the NASA GEOS model coupled to a stratospheric-only chemistry mechanism and transport constrained to the MERRA-2 reanalysis. While the number of solar occultation observations a day from SAGE III/ISS is about 1% of the total number of profiles observed globally by MLS, the chemical timescales of ozone and water vapor in the lower stratosphere are long enough that the SAGE III/ISS data may provide a useful constraint on the assimilated product. Using the same GEOS CoDAS configuration as M2-SCREAM, we will present a series of experiments to investigate if water vapor trends are consistent with the assimilation of SAGE observations with and without Aura retrievals, and to determine if the assimilation of SAGE observations produces a steady product for trend analysis, especially as the end of the Aura mission nears. In our experiments, assimilating only SAGE III/ISS water vapor profiles results in water vapor fields more consistent with experiments that assimilate MLS v5; however, in the polar regions SAGE III/ISS observations are not available and the modelled values are unconstrained. We are encouraged by the positive benefit assimilating the less frequent SAGE III/ISS observations has on stratospheric composition. Sensitivity experiments such as these will allow us to assess the added value of SAGE data for continued monitoring of the stratospheric composition for climate and ozone recovery assessments.

K Emma Knowland↗
NASA NTRS2022

Evaluation of High Mountain Asia-Land Data Assimilation System (version 1) from 2003 to 2016: 2. The impact of assimilating satellite-based snow cover and freeze/thaw observations into a land surface model

This second paper of the two-part series focuses on demonstrating the impact of assimilating satellite-based snow cover and freeze/thaw observations into the hyper-resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region from 2003 to 2016. To this end, this study systematically evaluates a total of six sets of 0.01° (∼1 km) model simulations forced by different precipitation forcings, with and without the dual assimilation scheme enabled, at point-scale, basin-scale, and domain-scale. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near-surface soil temperature, snow depth, snow water equivalent (SWE), and total runoff. First, the point-scale assessment is mainly conducted via evaluating against ground-based measurements. In general, the assimilation enabled estimates are better than no-assimilation counterparts. Second, the basin-scale runoff assessment demonstrates that across three snow-dominated basins, the assimilation enabled experiment yields systematic improvements in all goodness-of-fit statistics through mitigating the negative effects brought by the fixed long-term precipitation correction factors. For example, when forced by the bias-corrected precipitation, the assimilation-enabled experiment improves the bias by 69%, the root-mean-squared error by 30%, and the unbiased root-mean-squared error by 18% (relative to the no-assimilation counterpart). Finally, the domainscale assessment is conducted via evaluating against satellite-based SWE and skin temperature products. Both sets of domain-scale analysis further corroborate the findings in the point-scale evaluations. Overall, this study suggests the benefits of the proposed multi-variate assimilation system in improving the cryospherichydrological process within a land surface model for use in HMA.

Yuan Xue↗
NASA NTRS2022

Evaluation of High Mountain Asia-Land Data Assimilation System (version 1) from 2003 to 2016: 2. The impact of assimilating satellite-based snow cover and freeze/thaw observations into a land surface model

This second paper of the two-part series focuses on demonstrating the impact of assimilating satellite-based snow cover and freeze/thaw observations into the hyper-resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region from 2003 to 2016. To this end, this study systematically evaluates a total of six sets of 0.01° (∼1 km) model simulations forced by different precipitation forcings, with and without the dual assimilation scheme enabled, at point-scale, basin-scale, and domain-scale. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near-surface soil temperature, snow depth, snow water equivalent (SWE), and total runoff. First, the point-scale assessment is mainly conducted via evaluating against ground-based measurements. In general, the assimilation enabled estimates are better than no-assimilation counterparts. Second, the basin-scale runoff assessment demonstrates that across three snow-dominated basins, the assimilation enabled experiment yields systematic improvements in all goodness-of-fit statistics through mitigating the negative effects brought by the fixed long-term precipitation correction factors. For example, when forced by the bias-corrected precipitation, the assimilation-enabled experiment improves the bias by 69%, the root-mean-squared error by 30%, and the unbiased root-mean-squared error by 18% (relative to the no-assimilation counterpart). Finally, the domain-scale assessment is conducted via evaluating against satellite-based SWE and skin temperature products. Both sets of domain-scale analysis further corroborate the findings in the point-scale evaluations. Overall, this study suggests the benefits of the proposed multi-variate assimilation system in improving the cryospheric-hydrological process within a land surface model for use in HMA.

Yuan Xue↗
NASA NTRS1995

Assimilation of Altimeter Data into a Quasigeostrophic Model of the Gulf Stream System: Assimilation Results - Part 2

The improvement in the climatological behavior of a numerical model as a consequence of the assimilation of surface data is investigated. The model used for this study is a quasigeostrophic (QG) model of the Gulf Stream region. The data that have been assimilated are maps of sea surface height that have been obtained as the superposition of sea surface height variability deduced from the Geosat altimeter measurements and a mean field constructed from historical hydrographic data. The method used for assimilating the data is the nudging technique. Nudging has been implemented in such a way as to achieve a high degree of convergence of the surface model fields toward the observations. Comparisons of the assimilation results with available in situ observations show a significant improvement in the degree of realism of the climatological model behavior, with respect to the model in which no data are assimilated. The remaining discrepancies in the model mean circulation seem to be mainly associated with deficiencies in the mean component of the surface data that are assimilated. On the other hand, the possibility of building into the model more realistic eddy characteristics through the assimilation of the surface eddy field proves very successful in driving components of the mean model circulation that are in relatively good agreement with the available observations. Comparisons with current meter time series during a time period partially overlapping the Geosat mission show that the model is able to 'correctly' extrapolate the instantaneous surface eddy signals to depths of approximately 1500 m. The correlation coefficient between current meter and model time series varies from values close to 0.7 in the top 1500 m to values as low as 0.1-0.2 in the deep ocean.

Capotondi, Antonietta↗
NASA NTRS1999

Assimilation of SeaWinds Scatterometer Data in the GEOS Data Assimilation System

The first SeaWinds scatterometer was launched in to space aboard the Quikscat satellite on June 19, 1999 at 7:15 p.m. PDT. Flying in a near polar orbit 800 km above the earth's surface, SeaWinds uses an advanced scatterometer design to measure surface wind velocity over 90 percent of the ice free oceans ever 24 hours. This first SeaWinds mission is designed to replace the NASA Scatterometer (NSCAT) which ceased providing wind velocity data when the ADEOS I satellite failed. A second SeaWinds is scheduled to be launched late in 2000 aboard ADEOS II. Previous scatterometer assimilation experiments conducted by the NASA Data Assimilation Office, using both ERS and NSCAT wind observations, have demonstrated considerable potential for this type of data to improve both atmospheric analyses and forecasts, however much of the smaller scale information content of the scatterometer data could not be taken into account in the early coarse resolution versions of the Goddard (GEOS) Data Assimilation System (DAS) or in operational data assimilation systems. In this paper, we will describe data assimilation experiments in which the new higher resolution versions of the GOES DAS are used to assimilate SeaWinds scatterometer winds. Following a brief discussion of the SeaWinds design and the methodology used to assimilate scatterometer data in the GOES DAS, the quality of the SeaWinds data and the impact of SeaWinds on GOES analyses and forecasts at different resolutions will be presented.

Atlas, Robert↗
NASA NTRS2016

Assimilation of Gridded GRACE Terrestrial Water Storage Estimates in the North American Land Data Assimilation System

The objective of the North American Land Data Assimilation System (NLDAS) is to provide best available estimates of near-surface meteorological conditions and soil hydrological status for the continental United States. To support the ongoing efforts to develop data assimilation (DA) capabilities for NLDAS, the results of Gravity Recovery and Climate Experiment (GRACE) DA implemented in a manner consistent with NLDAS development are presented. Following previous work, GRACE terrestrial water storage (TWS) anomaly estimates are assimilated into the NASA Catchment land surface model using an ensemble smoother. In contrast to many earlier GRACE DA studies, a gridded GRACE TWS product is assimilated, spatially distributed GRACE error estimates are accounted for, and the impact that GRACE scaling factors have on assimilation is evaluated. Comparisons with quality-controlled in situ observations indicate that GRACE DA has a positive impact on the simulation of unconfined groundwater variability across the majority of the eastern United States and on the simulation of surface and root zone soil moisture across the country. Smaller improvements are seen in the simulation of snow depth, and the impact of GRACE DA on simulated river discharge and evapotranspiration is regionally variable. The use of GRACE scaling factors during assimilation improved DA results in the western United States but led to small degradations in the eastern United States. The study also found comparable performance between the use of gridded and basin averaged GRACE observations in assimilation. Finally, the evaluations presented in the paper indicate that GRACE DA can be helpful in improving the representation of droughts.

Kumar, Sujay V.↗
NASA NTRSDate not supplied

GEOS Constituent Data Assimilation beyond Aura MLS: Assimilating NASA SAGE III/ISS profiles of stratospheric water vapor

Water vapor in the lower stratosphere is an important trace gas for atmospheric chemistry and radiative budget with direct impact on climate. During the last quarter of the 20th century, the Stratospheric Aerosol and Gas Experiment (SAGE) missions were crucial in monitoring the stratospheric ozone loss and the subsequent recovery as well as trends in water vapor linked to surface temperature trends. The SAGE III instrument aboard the International Space Station (ISS) continues the SAGE mission record, with high vertical resolution profiles of water vapor (and other constituents) since mid-2017. The NASA GEOS Earth system model has the capability to assimilate multi-constituents from ground and space-based instruments using the GEOS Constituent Data Assimilation System (CoDAS). Reanalysis products of stratospheric water vapor without data constraints are historically poor, and two recent reanalyses which assimilated stratospheric constituents observed by the Microwave Limb Sounder (MLS) improve the representation of stratospheric composition, including water vapor, when compared against independent observations. The MLS instrument is on NASA's Aura satellite which is expected to be decommissioned in the coming years. Here we demonstrate that while the number of solar occultation observations a day from SAGE III/ISS is about 1 % of the total number of profiles observed globally by MLS, the chemical timescales of water vapor in the lower stratosphere are long enough that the SAGE III/ISS data can provide a useful constraint on the assimilated product. Analyzed water vapor fields from assimilating only SAGE III/ISS water vapor profiles agree well with independent stratospheric observations, including when compared against the frost-point hygrometers at the three NOAA stations. Over the five-year period from 2017 through 2022, the analyzed water vapor using SAGE III/ISS captures many of the features seen with the assimilation of MLS observations. Note, this ability is reduced over regions where the SAGE III/ISS instrument provides infrequent or no observations (associated with the orbit of the ISS), as highlighted in the case study period following the eruption of the tropical underwater volcano Hunga Tonga in early 2022. Nevertheless, there is a clear benefit to the assimilation of stratospheric water vapor from SAGE III/ISS observations, allowing us to continue to monitor stratospheric composition for climate assessments following the impending loss of Aura.

K. Emma Knowland↗
NASA NTRSDate not supplied

GEOS Constituent Data Assimilation Beyond Aura MLS: Assimilating NASA SAGE III/ISS Profiles of Stratospheric Water Vapor

Water vapor in the lower stratosphere is an important trace gas for atmospheric chemistry and radiative budget with direct impact on climate. During the last quarter of the 20th century, the Stratospheric Aerosol and Gas Experiment (SAGE) missions were crucial in monitoring the stratospheric ozone loss and the subsequent recovery as well as trends in water vapor linked to surface temperature trends. The SAGE III instrument aboard the International Space Station (ISS) continues the SAGE mission record, with high vertical resolution profiles of water vapor (and other constituents) since mid-2017. The NASA GEOS Earth system model has the capability to assimilate multi-constituents from ground and space-based instruments using the GEOS Constituent Data Assimilation System (CoDAS). Reanalysis products of stratospheric water vapor without data constraints are historically poor, and two recent reanalyses which assimilated stratospheric constituents observed by the Microwave Limb Sounder (MLS) improve the representation of stratospheric composition, including water vapor, when compared against independent observations. The MLS instrument is on NASA's Aura satellite which is expected to be decommissioned in the coming years. Here we demonstrate that while the number of solar occultation observations a day from SAGE III/ISS is about 1 % of the total number of profiles observed globally by MLS, the chemical timescales of water vapor in the lower stratosphere are long enough that the SAGE III/ISS data can provide a useful constraint on the assimilated product. Analyzed water vapor fields from assimilating only SAGE III/ISS water vapor profiles agree well with independent stratospheric observations, including when compared against the frost-point hygrometers at the three NOAA stations. Over the five-year period from 2017 through 2022, the analyzed water vapor using SAGE III/ISS captures many of the features seen with the assimilation of MLS observations. Note, this ability is reduced over regions where the SAGE III/ISS instrument provides infrequent or no observations (associated with the orbit of the ISS), as highlighted in the case study period following the eruption of the tropical underwater volcano Hunga Tonga in early 2022. Nevertheless, there is a clear benefit to the assimilation of stratospheric water vapor from SAGE III/ISS observations, allowing us to continue to monitor stratospheric composition for climate assessments following the impending loss of Aura.

K. Emma Knowland↗
NASA NTRSDate not supplied

GEOS Constituent Data Assimilation Beyond Aura MLS: Assimilating NASA SAGE III/ISS Profiles of Stratospheric Water Vapor

Water vapor in the lower stratosphere is an important trace gas for atmospheric chemistry and radiative budget with direct impact on climate. During the last quarter of the 20th century, the Stratospheric Aerosol and Gas Experiment (SAGE) missions were crucial in monitoring the stratospheric ozone loss and the subsequent recovery as well as trends in water vapor linked to surface temperature trends. The SAGE III instrument aboard the International Space Station (ISS) continues the SAGE mission record, with high vertical resolution profiles of water vapor (and other constituents) since mid-2017. The NASA GEOS Earth system model has the capability to assimilate multi-constituents from ground and space-based instruments using the GEOS Constituent Data Assimilation System (CoDAS). Reanalysis products of stratospheric water vapor without data constraints are historically poor, and two recent reanalyses which assimilated stratospheric constituents observed by the Microwave Limb Sounder (MLS) improve the representation of stratospheric composition, including water vapor, when compared against independent observations. The MLS instrument is on NASA's Aura satellite which is expected to be decommissioned in the coming years. Here we demonstrate that while the number of solar occultation observations a day from SAGE III/ISS is about 1 % of the total number of profiles observed globally by MLS, the chemical timescales of water vapor in the lower stratosphere are long enough that the SAGE III/ISS data can provide a useful constraint on the assimilated product. Analyzed water vapor fields from assimilating only SAGE III/ISS water vapor profiles agree well with independent stratospheric observations, including when compared against the frost-point hygrometers at the three NOAA stations. Over the five-year period from 2017 through 2022, the analyzed water vapor using SAGE III/ISS captures many of the features seen with the assimilation of MLS observations. Note, this ability is reduced over regions where the SAGE III/ISS instrument provides infrequent or no observations (associated with the orbit of the ISS), as highlighted in the case study period following the eruption of the tropical underwater volcano Hunga Tonga in early 2022. Nevertheless, there is a clear benefit to the assimilation of stratospheric water vapor from SAGE III/ISS observations, allowing us to continue to monitor stratospheric composition for climate assessments following the impending loss of Aura.

K. Emma Knowland↗