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All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Assimilating GCOM-W AMSR2 Radiance Data in Future GEOS Reanalysis

Surface and atmospheric radiation are being measured by space-borne conical scanning microwave radiometers with high accuracy and spatial resolution since 1980s'. These radiometers include Special Sensor Microwave Imager (SSM/I) onboard various NOAA's polar-orbiting satellites, Special Sensor Microwave Imager Sounder (SSMIS) onboard Defense Meteorological Satellite Program (DMSP) satellites, and Advanced Microwave Scanning Radiometer for EOS (AMSR-E), Advanced Microwave Scanning Radiometer 2 (AMSR2), Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI), and Global Precipitation Measurement (GPM) Microwave Imager (GMI) onboard NASA and JAXA's satellites. Atmospheric temperature, humidity, clouds, and precipitation are retrieved using brightness temperature data at frequencies between 22 GHz – 183 GHz. Some of the brightness temperature and retrieved rain rate data made by these imagers are assimilated in NASA Global Modelling and Assimilation Office (GMAO)'s weather and climate data sets including the Modern-Era Retrospective analysis for Research and Applications (MERRA) and its version 2 (MERRA-2) data sets. However, those radiance data are assimilated with the Goddard Earth Observing System model, version 5 (GEOS-5) only in clear-sky conditions. Recently, we started assimilate GPM/GMI radiance data with GEOS-5's atmospheric data assimilation system GSI under all-sky conditions in order to have better constraints in analyzed hydrological properties. GMAO plans to assimilate more microwave radiometers' brightness temperature data in its analyses. In this talk, we will discuss TMI and AMSR2 data's impact in temperature, humidity, clouds and precipitation under all-sky conditions in future GEOS reanalysis.

Jin, Jianjun↗
NASA NTRS2019

Assimilation of Remotely Sensed Leaf Area Index into the Noah-MP Land Surface Model: Impacts on Water and Carbon Fluxes and States over the Continental U.S.

Accurate representation of vegetation states is required for the modeling of terrestrial water-energy-carbon exchanges and the characterization of the impacts of natural and anthropogenic vegetation changes on the land surface. This study presents a comprehensive evaluation of the impact of assimilating remote sensing-based Leaf Area Index (LAI) retrievals over the Continental U.S. in the Noah-MP land surface model, during a time period of 2000 to 2017. The results demonstrate that the assimilation has a beneficial impact on the simulation of key water budget terms such as soil moisture, evapotranspiration, snow depth, terrestrial water storage and streamflow, when compared with a large suite of reference datasets. In addition, the assimilation of LAI is also found to improve the carbon fluxes of Gross Primary Production (GPP) and Net Ecosystem Exchange (NEE). Most prominent improvements in the water and carbon variables are observed over the agricultural areas of the U.S., where assimilation improves the representation of vegetation seasonality impacted by cropping schedules. The systematic, added improvements from assimilation in a configuration that employs high quality boundary conditions highlight the significant utility of LAI data assimilation in capturing the impacts of vegetation changes.

anthropogenic vegetation↗
NASA NTRS2019

Evolving HPC and Application Design Toward a Coupled Data Assimilation System at NASA Suitable for Emerging Exascale Platforms

The prediction capabilities of global models have continuously evolved from the traditional medium-range global weather prediction application to span scales in support of hourly prediction of convective scale storms to seasonal Earth system prediction. This evolution has increased the demands on the system infrastructure design and workflow to achieve the required performance on modern high-performance computing (HPC) platforms. The planned evolution of the Goddard Earth Observing System (GEOS) modeling and assimilation system will stress the capabilities of conventional HPC overwhelming the available compute cycles at the NASA Center for Climate Simulation (NCCS) at the NASA Goddard Space Flight Center in the coming 5-10 years. This has led to the re-design of key elements of the assimilation and modeling systems to achieve significant gains in performance on anticipated Exacale platforms. The transition of the assimilation system to the Joint Effort for Data assimilation Integration (JEDI) framework has positioned GEOS to exploit new efficient algorithms for data assimilation (DA) in a fully-coupled Earth system context. The suitability of the GEOS model to leverage a domain specific language (DSL) approach and artificial intelligence (AI) is being explored to accelerate computational performance and data exchange efficiency of the coupled Earth system model. The storage and processing of large data volumes produced by these advance systems is being redesigned with a data-centric cloud-based approach. We will highlight the recent efforts in these areas and emphasize the demand for further development and re-design to achieve the science objectives in support of NASA's Earth system modeling and assimilation missions.

Putman, Bill↗
NASA NTRS2020

Impact of Satellite Microwave Radiance Data Assimilation on GEOS Atmospheric Analysis and Forecasts in Tropics

The NASA Global Modeling and Assimilation Office (GMAO) has developed a system to assimilate all-sky microwave radiance data in the Goddard Earth Observing System (GEOS). The system provides additional constraints on the analysis process near the storm regions and adjusts the geophysical parameters such as precipitation, cloud, moisture, surface pressure, and wind by combining information from microwave radiance measurements and model forecasts in an optimal manner. The system proved that assimilating the all-sky microwave radiance data improve the GEOS atmospheric analyses and forecasts. This all-sky data framework has been included in the GEOS Forward Processing (FP) system and currently assimilating all-sky GMI data in real-time for GEOS global analysis and forecast production at the GMAO. This presentation describes the methodologies to assimilate cloud- and precipitation-affected microwave radiances in GEOS data assimilation system based on hybrid four-dimensional ensemble-variational (4D-EnVar) configuration. In the current operational GEOS-FP system, the addition of all-sky GMI radiances has the largest impact in the Tropics. Specific humidity is significantly improved in the short term (0-72 hours) forecasts. Similar improvements are seen in the Tropical and lower tropospheric temperature and winds. More detailed results on all-sky microwave radiance data impact on GEOS analysis and forecasts are discussed in this presentation.

Kim, Min-Jeong↗
NASA NTRS2020

Scaling Observation Error for Optimal Assimilation of CCI SST Data into a Regional HYCOM EnOI System

South Africa currently possesses no operational ocean forecasting system for the purpose of predicting ocean state variables including temperature,salinity and velocity. Substantial initial efforts towards this goal have been made and resulted in a system using a regional Hybrid Coordinate Ocean Model (HYCOM) along with the Ensemble Optimal Interpolation (EnOI)assimilation scheme. Assimilating only sea surface temperature (SST) observations from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) product into the system resulted in a degraded forecast. Aiming to address this, Climate Change Initiative (CCI) SSTs are assimilated into the system in an effort to improve the forecast skill. Observation errors in the assimilated product are used in the EnOI to determine whether more confidence should be placed in the model or observations in producing the analysis, but overconfidence in observations can shock the model and result in failure. To tweak the impact of the assimilation, a scaling factor is applied in the assimilation code. A scaling factor of 25 was found to produce a favourable result with lowest mean root mean square error (RMSE;1.098°C) between the model and observations over time. Postulating the error to be overconfident, a floor value is introduced in order to set a minimum value for the observation error thereby reducing confidence in the observations. These experiments fared less favourably with a floor value of 0.5 and a scaling factor of 15 producing the best mean RMSE (1.118°C).

Luyt, Hermann↗
NASA NTRS2021

Assimilation of DAWN Doppler Wind Lidar Data During the 2017 Convective Processes Experiment (CPEX): Impact on Precipitation and Flow Structure

An improved representation of 3-D air motion and precipitation structure through forecast models and assimilation of observations is vital for improvements in weather forecasting capabilities. However, there are few independent data to properly validate a model forecast of precipitation structure when the underlying dynamics are evolving on short convective timescales. Using data from the JPL Ku/Ka-band Airborne Precipitation Radar (APR-2) and the 2 μmDoppler Aerosol Wind (DAWN) lidar collected during the2017 Convective Processes Experiment (CPEX), the NASA Unified Weather Research and Forecasting (WRF) Ensemble Data Assimilation System (EDAS) modeling system was used to quantify the impact of high-resolution sparsely sampled DAWN measurements on the analyzed variables and on the forecast when the DAWN winds were assimilated. Over-all, the assimilation of the DAWN wind profiles had a discernible impact on the wind field as well as the evolution and timing of the 3-D precipitation structure. Analysis of individual variables revealed that the assimilation of the DAWN winds resulted in important and coherent modifications of the environment. It led to an increase in the near-surface convergence, temperature, and water vapor, creating more favorable conditions for the development of convection exactly where it was observed (but not present in the control run). Comparison to APR-2 and observations by the Global Precipitation Measurement (GPM) satellite shows a much-improved forecast after the assimilation of the DAWN winds – development of precipitation where there was none, more organized precipitation where there was some, and a much more intense and organized cold pool, similar to the analysis of the dropsonde data. The onset of the vertical evolution of the precipitation showed similar radar-derived cloud-top heights, but delayed in time. While this investigation was limited to a single CPEX flight date, the investigation design is appropriate for further investigation of the impact of airborne Doppler wind lidar observations upon short-term convective precipitation forecasts

DAWN↗
NASA NTRS2021

A Testbed for Geomagnetic Data Assimilation

Geomagnetic data assimilation merges past and present-day observations of the Earth’s mag-netic field with numerical geodynamo models and the results are used to initialize forecasts.We present a new ‘proxy model’ that can be used to test, or rapidly prototype, numerical tech-niques for geomagnetic data assimilation. The basic idea for constructing a proxy is to capturethe conceptual difficulties one encounters when assimilating observations into high-resolution,3-D geodynamo simulations, but at a much lower computational cost. The framework of usingproxy models as ‘gate-keepers’ for numerical methods that could/should be considered formore extensive testing on operational models has proven useful in numerical weather predic-tion, where advances in data assimilation and, hence, improved forecast skill, are at least inpart enabled by the common use of a wide range of proxy models. We also present a largeset of systematic data assimilation experiments with the proxy to reveal the importance oflocalization and inflation in geomagnetic data assimilation.

K Gwirtz↗
NASA NTRS2022

Assimilation of SMAP Products for Improving Streamflow Simulations over Tropical Climate Region—Is Spatial Information More Important Than Temporal Information?

Streamflow is one of the key variables in the hydrological cycle. Simulation and forecasting of streamflow are challenging tasks for hydrologists, especially in sparsely gauged areas. Coarse spatial resolution remote sensing soil moisture products (equal to or larger than 9 km) are often assimilated into hydrological models to improve streamflow simulation in large catchments. This study uses the Ensemble Kalman Filter (EnKF) technique to assimilate SMAP soil moisture products at the coarse spatial resolution of 9 km (SMAP 9 km), and downscaled SMAP soil moisture product at the higher spatial resolution of 1 km (SMAP 1 km), into the Soil and Water Assessment Tool (SWAT) to investigate the usefulness of different spatial and temporal resolutions of remotely sensed soil moisture products in streamflow simulation and forecasting. The experiment was set up for eight catchments across the tropical climate of Vietnam, with varying catchment areas from 267 to 6430 km^2 during the period 2017–2019. We comprehensively evaluated the EnKF-based SWAT model in simulating streamflow at low, average, and high flow. Our results indicated that high-spatial resolution of downscaled SMAP 1 km is more beneficial in the data assimilation framework in aiding the accuracy of streamflow simulation, as compared to that of SMAP 9 km, especially for the small catchments. Our analysis on the impact of observation resolution also indicates that the improvement in the streamflow simulation with data assimilation is more significant at catchments where downscaled SMAP 1 km has fewer missing observations. This study is helpful for adding more understanding of performances of soil moisture data assimilation based hydrological modelling over the tropical climate region, and exhibits the potential use of remote sensing data assimilation in hydrology.

soil moisture↗
NASA NTRSDate not supplied

Generating Global CH4 NASA GEOS Product by Assimilating TROPOMI

Radiative properties of methane (CH4) significantly contribute to climate change and the recent acceleration of global CH4 growth requires thorough investigation into its causes. Examination of temporal and spatial CH4 variability is crucial for better understanding the shifts that are currently taking place. Integrating different CH4 observations to create a reliable, easily to use global atmospheric CH4 product could contribute to assessment of emissions processes, but remains challenging due to the lack of long satellite data records. Here we present the NASA Goddard Earth Observing System (GEOS) based global CH4 product constrained by atmospheric transport from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and assimilated CH4 from the TROPOspheric Monitoring Instrument (TROPOMI). This product uses the Constituent Data Assimilation System (CoDAS) component of the GEOS capability, which has been expanded and generalized to assimilate any satellite, ground-based, or in situ observation of atmospheric composition that can be expressed as a vertical sounding or point sample. The product is similar to the one generated by assimilating carbon dioxide (CO2) data into GEOS from Orbiting Carbon Observatory 2 (OCO-2): OCO-2 GEOS Level 3 daily and monthly, 0.5x0.625 assimilated CO2 V10r. The preliminary product is investigated using a variety of quality check approaches with the help of in situ observations to examine bias correction, error inflation, and performance of the assimilation system. Potential applications of this approach include support for interpretation of high-resolution point source detection approaches, climate and greenhouse gas reanalyses, and boundary conditions for regional modeling approaches.

Nikolay Balashov↗
NASA NTRS2022

Flash Drought Onset and Development Mechanisms Captured With Soil Moisture and Vegetation Data Assimilation

Flash droughts evolve and intensify rapidly under the influence of anomalous atmospheric conditions. In this study, we investigate the role of assimilating remotely sensed soil moisture (SM) and vegetation properties in capturing the evolution and impacts of two flash droughts in the Northern Great Plains. We find that during 2016 drought triggered by anomalously high temperatures and excessive evaporative demands, multivariate data assimilation (DA) of MODIS-derived leaf area index (LAI) and Soil Moisture Active Passive SM within Noah-Multiparameterization model helps capture elevated transpiration at onset. Assimilation of LAI particularly helped model the resulting rapid decline in SM during onset with as high as 10.0% steeper rate of decline compared to the simulation without any assimilation. Modeled-SM anomalies exhibit a 7.5% and 11.7% increase in similarity with Evaporative Stress Index (ESI) data and U.S. Drought Monitor (USDM) maps, respectively. In contrast, during 2017 flash drought driven by record-low precipitation during summers, SM assimilation resulted in largest rates of decline in rootzone SM, as large as 48.4% compared to results from no assimilation. Multivariate DA of SM and LAI results in 6.7% and 14.3% higher spatial similarity with ESI and USDM, respectively, and is necessary to model rapid intensification caused by anomalous precipitation deficits. This study elucidates the need to incorporate multiple observational constraints from remote sensing to effectively capture rapid onset rates, intensification, and severity of flash drought following different propagation mechanisms. This is fundamental for drought early detection to provide a wider window of response and implement efficient mitigation strategies.

Shahryar K. Ahmad↗
NASA NTRS2024

Extending the Utility of Space-Borne Snow Water Equivalent Observations Over Vegetated Areas With Data Assimilation

Snow is a vital component of the earth system, yet no snow-focused satellite remote sensing platform currently exists. In this study, we investigate how synthetic observations of snow water equivalent (SWE) representative of a synthetic aperture radar remote sensing platform could improve spatiotemporal estimates of snowpack. We use a fraternal twin observing system simulation experiment, specifically investigating how much snow simulated using widely used models and forcing data could be improved by assimilating synthetic observations of SWE. We focus this study across a 24° x 37° domain in the western USA and Canada, simulating snow at 250 m resolution and hourly time steps in water year 2019. We perform two data assimilation experiments, including (1) a simulation excluding synthetic observations in forests where canopies obstruct remote sensing retrievals and (2) a simulation inferring snow distribution in forested grid cells using synthetic observations from nearby canopy-free grid cells. Results found that, relative to a nature run, or assumed true simulation of snow evolution, assimilating synthetic SWE observations improved average SWE biases at maximum snowpack timing in shrub, grass, crop, bare-ground, and wetland land cover types from 14 %, to within 1 %. However, forested grid cells contained a disproportionate amount of SWE volume. In forests, SWE mean absolute errors at the time of maximum snow volume were 111 mm and average SWE biases were on the order of 150 %. Here the data assimilation approach that estimated forest SWE using observations from the nearest canopy-free grid cells substantially improved these SWE biases (18 %) and the SWE mean absolute error (27 mm). Simulations employing data assimilation also improved estimates of the temporal evolution of both SWE and runoff, even in spring snowmelt periods when melting snow and high snow liquid water content prevented synthetic SWE retrievals. In fact, in the Upper Colorado River region, melt-season SWE biases were improved from 63 % to within 1 %, and the Nash–Sutcliffe efficiency of runoff improved from −2.59 to 0.22. These results demonstrate the value of data assimilation and a snow-focused globally relevant remote sensing platform for improving the characterization of SWE and associated water availability.

Justin Pflug↗
NASA NTRSDate not supplied

Toward NASA/GMAO Coupled Data Assimilation Using JEDI

NASA’s Global Modeling and Assimilation Office (GMAO) is establishing coupled Earth system capabilities using the Joint Effort for Data Assimilation Integration (JEDI) framework, developed in collaboration with the Joint Center for Satellite Data Assimilation(JCSDA). Marine and atmospheric components will utilize a weakly-coupled data assimilation approach. The implementation of the sea-ice, ocean, and coupled analysis (SOCA) component of JEDI is highlighted here, while parallel work on atmospheric data assimilation is also in progress. Improvements to the ocean data assimilation system (ODAS) include: 1) adopting the JEDI framework, 2) a new coupled GCM, and 3) incorporation of new observation sources.

Dorukhan Ardağ↗
NASA NTRS2014

Background Error Covariance Estimation Using Information from a Single Model Trajectory with Application to Ocean Data Assimilation

An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory.SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

Error Covariance↗
NASA NTRS2014

Background Error Covariance Estimation using Information from a Single Model Trajectory with Application to Ocean Data Assimilation into the GEOS-5 Coupled Model

An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory. SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

Data Assimilation↗
NASA NTRS2014

Assimilation of SMOS Retrieved Soil Moisture into the Land Information System

Soil moisture retrievals from the Soil Moisture and Ocean Salinity (SMOS) instrument are assimilated into the Noah land surface model (LSM) within the NASA Land Information System (LIS). Before assimilation, SMOS retrievals are bias-corrected to match the model climatological distribution using a Cumulative Distribution Function (CDF) matching approach. Data assimilation is done via the Ensemble Kalman Filter. The goal is to improve the representation of soil moisture within the LSM, and ultimately to improve numerical weather forecasts through better land surface initialization. We present a case study showing a large area of irrigation in the lower Mississippi River Valley, in an area with extensive rice agriculture. High soil moisture value in this region are observed by SMOS, but not captured in the forcing data. After assimilation, the model fields reflect the observed geographic patterns of soil moisture. Plans for a modeling experiment and operational use of the data are given. This work helps prepare for the assimilation of Soil Moisture Active/Passive (SMAP) retrievals in the near future.

Data Assimilation↗
NASA NTRS2016

Improving 7-Day Forecast Skill by Assimilation of Retrieved AIRS Temperature Profiles

We conducted a new set of Data Assimilation Experiments covering the period January 1 to February 29, 2016 using the GEOS-5 DAS. Our experiments assimilate all data used operationally by GMAO (Control) with some modifications. Significant improvement in Global and Southern Hemisphere Extra-tropical 7-day forecast skill was obtained when: We assimilated AIRS Quality Controlled temperature profiles in place of observed AIRS radiances, and also did not assimilate CrISATMS radiances, nor did we assimilate radiosonde temperature profiles or aircraft temperatures. This new methodology did not improve or degrade 7-day Northern Hemispheric Extra-tropical forecast skill. We are conducting experiments aimed at further improving of Northern Hemisphere Extra-tropical forecast skill.

AIRS Temperature Profiles↗
NASA NTRS2018

Impact of GPM Rainrate Data Assimilation on Simulation of Hurricane Harvey (2017)

Built upon Tropical Rainfall Measuring Mission (TRMM) legacy for next-generation global observation of rain and snow. The GPM was launched in February 2014 with Dual-frequency Precipitation Radar (DPR) and GPM Microwave Imager (GMI) onboard. The GPM has a broad global coverage approximately 70deg S -70deg N with a swath of 245/125-km for the Ka (35.5 GHz)/Ku (13.6 GHz) band radar, and 850-km for the 13-channel GMI. GPM also features better retrievals for heavy, moderate, and light rain and snowfall To develop methodology to assimilate GPM surface precipitation data with Grid-point Statistical Interpolation (GSI) data assimilation system and WRF ARW model To investigate the potential and the value of utilizing GPM observation into NWP for operational environment The GPM rain rate data has been successfully assimilated using the GSI rain data assimilation package. Impacts of rain rate data have been found in temperature and moisture fields of initial conditions. 2.Assimilation of either GPM IMERG or GPROF rain product produces significant improvement in precipitation amount and structure for Hurricane Harvey (2017) forecast. Since IMERG data is available half-hourly, further forecast improvement is expected with continuous assimilation of IMERG data

Precipitation↗
NASA NTRS2021

Data Assimilation of Terrestrial Water Storage Observations to Estimate Precipitation Fluxes: A Synthetic Experiment

The Gravity Recovery and Climate Experiment (GRACE) mission and its Follow-On (GRACE-FO) mission provide unprecedented observations of terrestrial water storage (TWS) dynamics at basin to continental scales. Established GRACE data assimilation techniques directly adjust the simulated water storage components to improve the estimation of groundwater, streamflow, and snow water equivalent. Such techniques artificially add/subtract water to/from prognostic variables, thus upsetting the simulated water balance. To overcome this limitation, we propose and test an alternative assimilation scheme in which precipitation fluxes are adjusted to achieve the desired changes in simulated TWS. Using a synthetic data assimilation experiment, we show that the scheme improves performance skill in precipitation estimates in general, but that it is more robust for snowfall than for rainfall, and it fails in certain regions with strong horizontal gradients in precipitation. The results demonstrate that assimilation of TWS observations can help correct (adjust) the model’s precipitation forcing and, in turn, enhance model estimates of TWS, snow mass, soil moisture, runoff, and evaporation. A key limitation of the approach is the assumption that all errors in TWS originate from errors in precipitation. Nevertheless, the proposed approach produces more consistent improvements in simulated runoff than the established GRACE data assimilation techniques.

Data Assimilation↗