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NASA NTRS2012

Impacts of Assimilating MLS Temperature on the Upper Stratosphere in GEOS-5

Standard configurations of the GEOS-5 data assimilation system use nadir infrared and microwave sounders that provide deep-layer constraints on the thermal structure in the stratosphere. In the upper stratosphere, this information is currently provided by the Advanced Microwave Sounding Units (AMSU-As) on NOAA s polar-orbiting satellites. The highest peaking AMSU-A channel (14) peaks near 2.5hPa. Evaluation of the upper stratosphere reveals substantial biases in the temperature, cased in part by biases in the underlying GCM, and difficulties in representing the stratopause structure under disturbed conditions. This work demonstrates, unsurprisingly, that the assimilation into GEOS-5 of temperature profiles derived from EOS-Aura MLS leads to a substantially better representation of the stratopause structure from a climatological perspective and for disturbed events. Future plans with GEOS-5 include a reanalysis that includes numerous "research" datasets alongside the operational NOAA datasets that were used in MERRA. As preparation for this reanalysis, the present study examines how assimilating the MLS observations impact the error statistics for the AMSU-A instruments. Discussion will address the issue of bias correction for the AMSU-A Channel 14 radiances, which is presently turned off in GEOS-5 because of the absence of accurate temperature observations that can anchor the GEOS-5 analyses. The issue of what can be done in periods when no limb-sounder data are available will also be addressed.

Pawson, S.↗

NASA NTRS2012

Atmospheric Reanalyses-Recent Progress and Prospects for the Future. A Report from a Technical Workshop, April 2010

In April 2010, developers representing each of the major reanalysis centers met at Goddard Space Flight Center to discuss technical issues - system advances and lessons learned - associated with recent and ongoing atmospheric reanalyses and plans for the future. The meeting included overviews of each center s development efforts, a discussion of the issues in observations, models and data assimilation, and, finally, identification of priorities for future directions and potential areas of collaboration. This report summarizes the deliberations and recommendations from the meeting as well as some advances since the workshop.

Rienecker, Michele M.↗

NASA NTRS2007

A Reanalysis for the Seasonal and Longer-Period Cycles and the Trends in Middle Atmosphere Temperature from the HALOE

Previously published analyses for the seasonal and longer-period cycles in middle atmosphere temperature versus pressure (or T(p)) from the Halogen Occultation Experiment (HALOE) are extended to just over 14 years and updated to properly account for the effects of autocorrelation in its time series of zonally-averaged data. The updated seasonal terms and annual averages are provided, and they can be used to generate temperature distributions that are representative of the period 1991-2005. QBO-like terms have also been resolved and are provided, and they exhibit good consistency across the range of latitudes and pressure-altitudes. Further, exploratory analyses of the residuals from each of the 221 time series have yielded significant 11-yr solar cycle (or SC-like) and linear trend terms at a number of latitudes and levels. The amplitudes of the SC-like terms for the upper mesosphere agree reasonably with calculations of the direct solar radiative effects for T(p). Those SC amplitudes increase by about a factor of 2 from the lower to the upper mesosphere and are also larger at the middle than at the low latitudes. The diagnosed cooling trends for the subtropical latitudes are in the range, -0.5 to -1.0 K/decade, which is in good agreement with the findings from models of the radiative effects on pressure surfaces due to known increases in atmospheric CO2. The diagnosed trends are somewhat larger than predicted with models for the upper mesosphere of the northern hemisphere middle latitudes.

Remsberg, Ellis E.↗

NASA NTRSDate not supplied

Assessing the Variability of Radiation, Water and Energy in the Deep Tropics Over the Last 3 Decades

Global surface energy closure and its variability depends heavily upon the surface radiative energy budget in the deep tropics. A key process observed is the change in convection regimes from disorganized convection to organized deep convection. During these periods an imbalance is observed between energy and water fluxes in terms of energy closure in some atmospheric reanalysis systems, implying that processes are not simulated well. A recent paper by Hsaio et al (2023, pre-published version) found that the transition process has wind shear and longwave cloud radiative feedback signatures. In this presentation, we assess the surface radiative budget in the deep tropics by comparing multiple data products (GEWEX SRB Rel4IP, CERES Ed4.1, ISSCP FH, etc.) and describing the variability across the deep tropics for the period from 1988 to near present. We assess this variability of the radiative flux anomalies (including the net TOA, surface and the atmospheric divergence fluxes) against water vapor divergence, cloud properties (include ISCCP “Weather States, Tselioudis et al. 2021) and larger scale wind shear. We include further analysis contrasting 6 key tropical oceanic regions (Indian Ocean east and west, tropical western, central and eastern Pacific Ocean, and Tropical Atlantic Ocean) and the sensitivity of the fluxes as a function of fluctuations in cloud types in response to various larger scale atmospheric processes (e.g. El Niño, Indian Ocean Dipole). These are contrasted against the modeled radiative fluxes from ERA-5 and MERRA-2. Ocean buoy measurements of radiative fluxes are utilized to help assess data radiative flux uncertainty over the nearly 40 year period. In general, the variability for overlap periods of these various data products agrees well, but there are significant differences in the net fluxes that vary according to the rendering of surface, atmospheric, aerosol and cloud properties. We conclude with recommendations for continuing work.

Radiation budget↗

NASA NTRS1987

On computing eigensolution sensitivity data using free vibration solutions

A simplified method of computing eigensolution sensitivity derivatives in structural dynamics is developed. It is shown that if the elements of stiffness and mass matrices associated with a design variable are homogeneous functions of that design variable, then eigenvalue derivatives can be computed from element strain and kinetic energies. Furthermore, if cross-mode energies are known, eigensolution derivatives of modified systems can be computed approximately using assume mode reanalysis formulation. A ten bar truss example is used to illustrate the present formulations.

Wang, B. P.↗

NASA NTRS1994

Reanalysis of X-ray emission from M87. 1: The single-phase medium

We reanalyze the density and temperature profiles of the X-ray-emitting gas around M87, assuming a spherically symmetric, single-phase model. For given assumed density and temperature profiles, we predict Einstein High Resolution Imager (HRI) and Imaging Proportional Counter (IPC) surface brightness distribution as well as Focal Point Crystal Spectrometer (FPCS) line fluxes, which we compare with the data. We find that a good fit to these data can be obtained and that, with a suitable adjustment of the abundances, the equivalent width of the iron complex at 7 keV as observed by wide-beam instruments can also be explained. In contrast to this, the Einstein Solid State Spectrometer (SSS) spectrum and optically determined mass profiles cannot be accounted for simultaneously with the previous X-ray data. We show that the disagreement of the Solid State Spectrometer with our models is due to an inconsistency of the Solid State Spectrometer data with that of the other X-ray instruments. Because of this, we find no evidence for the existence of absorption above the Galactic value, contrary to an earlier claim based on Solid State Spectrometer data. The disagreement of our models with optical mass data is due either to the assumption of a single-phase model, thus rendering the model unacceptable, or possibly to maximal systematic errors in the FPCS data. We also show that thermal conduction at the Spitzer rate is important in our models, although this conclusion is of limited importance in view of the poor fit of our models to the data.

Tsai, John C.↗

NASA NTRS2011

Land Surface Verification Toolkit (LVT) - A Generalized Framework for Land Surface Model Evaluation

Model evaluation and verification are key in improving the usage and applicability of simulation models for real-world applications. In this article, the development and capabilities of a formal system for land surface model evaluation called the Land surface Verification Toolkit (LVT) is described. LVT is designed to provide an integrated environment for systematic land model evaluation and facilitates a range of verification approaches and analysis capabilities. LVT operates across multiple temporal and spatial scales and employs a large suite of in-situ, remotely sensed and other model and reanalysis datasets in their native formats. In addition to the traditional accuracy-based measures, LVT also includes uncertainty and ensemble diagnostics, information theory measures, spatial similarity metrics and scale decomposition techniques that provide novel ways for performing diagnostic model evaluations. Though LVT was originally designed to support the land surface modeling and data assimilation framework known as the Land Information System (LIS), it also supports hydrological data products from other, non-LIS environments. In addition, the analysis of diagnostics from various computational subsystems of LIS including data assimilation, optimization and uncertainty estimation are supported within LVT. Together, LIS and LVT provide a robust end-to-end environment for enabling the concepts of model data fusion for hydrological applications. The evolving capabilities of LVT framework are expected to facilitate rapid model evaluation efforts and aid the definition and refinement of formal evaluation procedures for the land surface modeling community.

Kumar, Sujay V.↗

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 NTRS2024

Development of A Multidecadal Land Reanalysis Over High Mountain Asia

Anthropogenic and climatic changes affect the water and energy cycles in High Mountain Asia (HMA), home to over two billion people and the largest reservoirs of freshwater outside the polar zone. Despite their significant importance for water management, consistent and reliable estimates of water storage and fluxes over the region are lacking because of the high uncertainties associated with the estimates of atmospheric conditions and human management. Here, we relied on multivariate data assimilation (MVDA) to provide estimates of energy and water storage and fluxes that reflect the processes occurring in the region such as greening and irrigation-driven groundwater depletion. We developed and employed an ensemble precipitation estimate by blending different precipitation products thereby reducing the uncertainties and inconsistencies associated with precipitation in HMA. Then, we assimilated five variables that capture the changes in hydrology in response to climate change and anthropogenic activities. Overall, our results have shown that MVDA has allowed a better representation of the land surface processes including greening and irrigation-driven groundwater depletion in HMA.

Fadji Z. Maina↗

NASA NTRS2002

SEARCH Workshop on Large-Scale Atmosphere/Cryosphere Observations

The purpose of the workshop held in Seattle during 27-29 November 2001 was to review existing land, sea ice, and atmospheric observations and the prospect for an Arctic System Reanalysis, through white papers, invited speakers, and panels. A major task for SEARCH was to determine how existing observation systems can be best used and enhanced to understand and anticipate the course of the ongoing changes in the Arctic. The primary workshop conclusion is that there is no cohesion among various Arctic disciplines and data types to form a complete observation set of Arctic change; a second workshop conclusion is that present data sets are vastly underutilized in understanding Arctic change; a third conclusion is that a distributed observing system must accommodate a wide range of spatial patterns of variability.

Source record↗

NASA NTRSDate not supplied

Implementation of A New Microwave Scattering Database and A Forward Model for Active Microwave Sensors in CRTM

Radiative transfer models are extensively used for the assimilation of satellite observations into NWP models as well as retrieving geophysical products from satellite measurements. CRTM is a community model developed by NOAA JCSDA and widely used for different purposes requiring RT calculations. CRTM requires bulk scattering lookup tables in order to perform all-sky RT calculations. However, the current CRTM lookup tables for microwave frequencies were generated based on the Mie theory by assuming spherical frozen particles. The scattering lookup tables generated using the DDA technique has shown to largely improve the RT scattering calculations in the MW region. This presentation targets (i) the implementation and validation of a DDA database that was originally developed for the ARTS RT model into CRTM, and (ii) developing the CRTM active sensor module that takes advantage of the backscattering coefficients computed using the DDA method. The DDA database only provides single scattering properties of different habits, while CRTM requires bulk scattering properties. The CRTM cloud coefficients were previously generated based on the effective radius for representing the size of the particles. However, effective radius is neither measurable nor provided by the NWP models, thus need to be estimated from other geophysical variables such as water content. Therefore, in addition to calculating the CRTM bulk scattering properties from the DDA single scattering database, the CRTM was also largely modified to use cloud water content (kg.m-3), instead of effective radius, for performing the interpolation over size/mass of the particles. CRTM already requires water content as input, thus no extra variables are required for performing scattering calculations using the new ARTS DDA database. The CRTM scattering modules search for effective radius in cloud coefficient files and will use the cloud water content if the effective radius dimension is not found in the cloud coefficient files. Figure 1 shows the CRTM simulated brightness temperatures computed using different cloud coefficients versus ATMS observed values over Hurricane Irma on September 7, 2017 at 18:00 UTC. We used all the cloud water content values included in ERA5 with default CRTM/DDA habits for water, rain, snow, ice, hail, and graupel. ERA5 does not provide separate water content values for ice, hail, and graupel, thus the ice water content values were divided between ice, hail, and graupel clouds similar to what was explained in the previous section. In channels with a frequency lower than 90 GHz, emission from water and rain clouds can compensate for cloud scattering so that cloud contaminated Tbs are larger than corresponding clear sky Tbs. The DDA simulations for channels 1-7 largely perform better than the Mie simulations. The DDA simulations show a mix of small negative and positive simulated minus observed values, while the Mie results show large negative biases. The weighting functions for some of the ATMS temperature sounding channels (channels 9-15) peak mostly above the clouds, therefore the measured Tbs become less sensitive to clouds so that the results of both Mie and DDA become very similar. The Mie lookup tables generate excessive scattering for channel 16, but not enough scattering for the water vapor channels. In the specific case of Hurricane Maria, the DDA lookup tables do not generate enough scattering for channel 16, but the DDA results are much more consistent with observations for water vapor channels than for channel 16. It should be noted that the results may vary if we use other habits to represent snow, hail, and graupel in the DDA simulations. Although these results clearly show the advantage of the DDA database over the Mie dataset, different error sources such as error in the observations, displacement of clouds in the ERA5 reanalysis, and also lack of convective clouds or in general errors in the input atmospheric and cloud profiles contribute to the differences between the simulated and observed values. Aside from the improvements in the simulations, a major advantage of the new dataset is a large number of habits that can be used to tune the data assimilation systems to perform well in different weather conditions.

Isaac Moradi↗

NASA NTRS2024

Classifying Microwave Radiometer Observations Over the Netherlands Into Dry, Shallow-, and Non-Shallow Precipitation Using A Random Forest Model

Spaceborne microwave radiometers represent an important component of the Global Precipitation Measurement (GPM) mission due to their frequent sampling of rain systems. Microwave radiometers measure microwave radiation (brightness temperatures, Tb), which can be converted into precipitation estimates with appropriate assumptions. However, detecting shallow precipitation systems using space-borne radiometers is challenging, especially over land, as their weak signals are hard to differentiate from those associated with dry conditions. This study uses a random forest model (RF) to classify microwave radiometer observations as dry, shallow, or nonshallow over the Netherlands - a region with varying surface conditions and frequent occurrence of shallow precipitation. The RF is trained on five years of data (2016-2020) and tested with two independent years (2015, 2021). The observations are classified using ground-based weather radar echo top heights. Various RF models are assessed, such as using only GPM’s Microwave Imager (GMI) Tb values as input features or including spatially aligned ERA-5 2-meter temperature and freezing level reanalysis and/or Dual Precipitation Radar (DPR) observations. Independent of the input features, the model performs best in summer and worst in winter. The model classifies observations from high-frequency channels (≥85 GHz) with lower Tb-values as non-shallow, higher values as dry, and those in between as shallow. Misclassified footprints exhibit radiometric characteristics corresponding to their assigned class. Case studies reveal dry observations misclassified as shallow are associated with lower Tb-values, likely resulting from the presence of ice particles in non-precipitating clouds. Shallow footprints misclassified as dry are likely related to the absence of ice particles.

Linda Bogerd↗

NASA NTRSDate not supplied

TPSAS-NF1676L-11678-DND

Cloud vertical profiles retrieved from CALIPSO and CloudSat data provide additional information that greatly improves the estimate of atmospheric radiative heating rates and potentially improves the estimate of the global surface radiation budget. To utilize CALIPSO and CloudSat cloud vertical profiles, we combined their profiles and generated merged profiles. Weused the merged cloud profiles and MODIS derived cloud profiles by the CERES cloud algorithm in irradiance computations. In this study, the improvement of the irradiance by CALIPSO and CloudSat derived merged cloud vertical profiles is evaluated by comparing modeled irradiances with and without merged cloud profiles. Because of lower cloud base heights provided by the merged profiles compared with cloud base heights derived from an empirical formula using the MODIS derived cloud top height and optical thickness, the global annual mean surface longwave downward irradiance increases approximately by 7 Wm-2. While CALIPSO version 3 data have not been incorporated in the irradiance computation and the effect of the new CALIPSO data needs to be assessed, a comparison with surface observations suggests an improvement of surface downward longwave irradiance computations. The surface downward longwave irradiance computed with CALIPSO and CloudSat merged clouds agrees better with surface observations than the irradiance computed with MODIS only does. The bias difference over ARM SGP sites with 2 years of data decreases from -5 W m-2to -2 Wm-2, and it decreases from -9 Wm-2to no bias over polar validation sites. The result suggests that CALIPSO CloudSat derived merged clouds significantly improve surface longwave irradiance estimates once they are combined with MODIS derived cloud properties and with temperature and humidity profiles from reanalysis.

Seiji Kato↗

NASA NTRSDate not supplied

PolarMERRA: A collaborative effort for NASA’s polar reanalysis

The polar atmosphere is often poorly represented in global models, adversely impacting analyses in these regions and forecasts on much larger scales. PolarMERRA is an initiative that brings together the modeling expertise from NASA’s global modeling and assimilation office (GMAO) and observational expertise from NASA’s Cryospheric sciences lab to assess and improve the representation of polar processes in the NASA’s modelling system in preparation for upcoming and next generation reanalyses. Model improvements in resolution, physics, and data assimilation have been guided by assessment of model skill in the polar regions. Preliminary results from a novel validation framework indicate a reduction in near surface air temperature, turbulent fluxes, and precipitation biases.

Chelsea Parker↗

NASA NTRS2023

Impact of Design Factors for ESA CCI Satellite Soil Moisture Data Assimilation over Europe

In this study, soil moisture retrievals of the combined active–passive ESA Climate Change Initiative (CCI) soil moisture product are assimilated into the Noah-MP land surface model over Europe using a one-dimensional ensemble Kalman filter and an 18-yr study period. The performance of the data assimilation (DA) system is evaluated by comparing it with a model-only experiment (at in situ sites) and by assessing statistics of innovations and increments as DA diagnostics (over the entire domain). For both assessments, we explore the impact of three design choices, resulting in the following insights. 1) The magnitude of the assumed observation errors strongly affects the skill improvements evaluated against in situ stations and internal diagnostics. 2) Choosing between climatological or monthly cumulative distribution function matching as the observation bias correction method only has a marginal effect on the in situ skill of the DA system. However, the internal diagnostics suggest a more robust system parameterization if the observations are rescaled monthly. 3) The choice of atmospheric reanalysis dataset to force the land surface model affects the model-only skill and the DA skill improvements. The model-only skill is higher with input from the MERRA-2 than with input from the ERA5 reanalysis, resulting in larger DA skill improvements for the latter. Additionally, we show that the added value of the DA strongly depends on the quality of the satellite retrievals and land cover, with the most substantial soil moisture skill improvements occurring over croplands and skill degradation occurring over densely forested areas.

Soil moisture↗

NASA NTRS2019

Constraining the Earth System with EOS-Aura Observations

NASA's Goddard Earth Observing System (GEOS) model and data assimilation system is a flexible, modular global system that is used for applications that range from weather prediction to climate analysis. Resolving scales ranging from a few kilometers to several tens of kilometers, with scale-aware parametrization settings, the GEOS system offers NASA scientists and their partners a flexible system that is attuned to bringing in observations from all components of the Earth System. The GEOS system thus serves as a tool that enhances the value to NASA of observations from individual instruments, by bringing them into context with the full suite of "operational" observations and other research datasets. This presentation will emphasize how the GEOS system has been used to extend the value of observations from EOS-Aura, in conjunction with other NASA and non-NASA observations. One example is atmospheric ozone from the OMI and MLS instruments, that has been used extensively in GEOS systems for both weather (GEOS-FP) and the MERRA-2 reanalysis. The presentation will emphasize the value of these ozone datasets for studying long-term changes of ozone since 2004 and will discuss prospects of continuing such analyses in the post-Aura era. A new configuration of GEOS, the Composition Forecasting (CF) system has recently gone in to production: this uses a full troposphere-stratosphere chemistry mechanism (GEOS-Chem) to analyze and predict global constituent distributions, including surface air quality. While constituent observations are not yet assimilated into GEOS-CF, EOS-Aura data are used substantially to evaluate the system and plans are in place to introduce assimilation at a later stage. Examples from GEOS-CF will be shown to illustrate the value of EOS-Aura observations. Discussions will focus on the likely value of long-term analyses of EOS-Aura observations in context of understanding potential impacts on the health of humans and the biosphere, including the importance of sustaining long-term, global observing systems such as that pioneered by EOS-Aura.

Pawson, Steven↗

NASA NTRS2008

NASA's Modern Era Retrospective-Analysis for Research and Applications (MERRA): Early Results and Future Directions

This talk will review the status and progress of the NASA/Global Modeling and Assimilation Office (GMAO) atmospheric global reanalysis project called the Modern Era Retrospective-Analysis for Research and Applications (MERRA). An overview of NASA's emerging capabilities for assimilating a variety of other Earth Science observations of the land, ocean, and atmospheric constituents will also be presented. MERRA supports NASA Earth science by synthesizing the current suite of research satellite observations in a climate data context (covering the period 1979-present), and by providing the science and applications communities with of a broad range of weather and climate data with an emphasis on improved estimates of the hydrological cycle. MERRA is based on a major new version of the Goddard Earth Observing System Data Assimilation System (GEOS-5), that includes the Earth System Modeling Framework (ESMF)-based GEOS-5 atmospheric general circulation model and the new NOAA National Centers for Environmental Prediction (NCEP) unified grid-point statistical interpolation (GST) analysis scheme developed as a collaborative effort between NCEP and the GMAO. In addition to MERRA, the GMAO is developing new capabilities in aerosol and constituent assimilation, ocean, ocean biology, and land surface assimilation. This includes the development of an assimilation capability for tropospheric air quality monitoring and prediction, the development of a carbon-cycle modeling and assimilation system, and an ocean data assimilation system for use in coupled short-term climate forecasting.

Schubert, Siegfried↗

NASA NTRS2020

Evaluation of Global Fire Weather Database Reanalysis and Short-Term Forecast Products

Daily Fire Weather Index (FWI) System components calculated from the NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), are compared to FWI calculations from a global network of weather stations over 2004–2018, and short-term, experimental (8 d) daily FWI forecasts are evaluated for their skill across the Terrestrial Ecoregions of the World for 2018. FWI components from MERRA-2 were, in general, biased low compared to station data, but this reflects a mix of coherent low and high biases of different magnitudes. Biases in different MERRA-2 FWI components were related to different biases in weather input variables for different regions, but temperature and relative humidity biases were the most important overall. FWI forecasts had high skill for 1–2 d lead times for most of the world. For longer lead times, forecast skill decreased most quickly at high latitudes and was most closely related to decreasing skill of relative humidity forecasts. These results provide a baseline for the evaluation and use of fire weather products calculated from global analysis and forecast fields.

Global Fire Weather Database↗