In this study, a strategy and a framework are developed to build a global planetary boundary layer (PBL) height (PBLH) analysis and monitoring capability from multiple observing systems in the NASA Goddard Earth Observing System (GEOS) data assimilation system. To facilitate this effort, PBLH is derived from radiosonde and Global Navigation Satellite System-Radio Occultation (GNSS-RO) refractivity data. As PBLH can be sensitive to potentially disparate observables and retrieval algorithms, new model PBLH definitions consistent with each observation type are added to the forecast model for the calculation of first guess departures from observations (OmF). These model definitions are augmented to the control variable vector, interacting with other control variables through a flow-dependent ensemble background error covariance component. Moreover, to capture capping inversions, methods are explored using PBLH data to improve background error covariance through inflation of ensemble spread and adjustment of vertical localization length scale for virtual temperature and relative humidity variables. Experiments are conducted to assess the separate and combined impacts of these methods and the correlation relationships between PBLH and other control variables in the background error covariance. Preliminary results show that these changes are beneficial to the assimilation of other observations to improve the PBL thermodynamic structure.

A new instrument has been proposed for measuring surface air pressure over the marine surface with a combined active/passive scanning multichannel differential absorption radar to provide an estimate of the total atmospheric column oxygen content. A demonstrator instrument, the Microwave Barometric Radar and Sounder (MBARS), has been funded by the National Aeronautics and Space Administration for airborne test missions. Here, a proof-of-concept study to evaluate the potential impact of spaceborne surface pressure data on numerical weather prediction is performed using the Goddard Modeling and Assimilation Office global observing system simulation experiment (OSSE) framework. This OSSE framework employs the Goddard Earth Observing System model and the hybrid 4D ensemble variational Gridpoint Statistical Interpolation data assimilation system. Multiple flight and scanning configurations of potential spaceborne orbits are examined. Swath width and observation spacing for the surface pressure data are varied to explore a range of sampling strategies. For wider swaths, the addition of surface pressures reduces the root-mean-square surface pressure analysis error by as much as 20% over some ocean regions. The forecast sensitivity observation impact tool estimates impacts on the Pacific Ocean basin boundary layer 24-h forecast temperatures for spaceborne surface pressures that are on par with rawinsondes and aircraft and estimates greater impacts than the current network of ships and buoys. The largest forecast impacts are found in the Southern Hemisphere extratropics.

Building on the success of MERRA, and its successor MERRA-2 (released in 2009 and 2015 respectively), the GMAO continues its incremental effort towards a decadal goal of an Integrated Earth System retrospective analysis, MERRA-3 ( ̃2025), coupling atmosphere, ocean, chemistry, land, and ice. With aspects of the coupled Goddard Earth Observing System (GEOS) still under development, an intermediate reanalysis featuring recent advances in the GEOS atmospheric component is planned as a stepping-stone towards MERRA-3. The GEOS-5 Retrospective analysis for the 21st Century, GEOS-R21C, will be a hybrid 4D-EnVar atmospheric reanalysis at 25km, covering the period 2000-onwards, and featuring the NASA’s EOS and post-EOS satellite observations. MERRA-2 by contrast covers 1980present and uses 3DVar at 50km. Other planned features in GEOS-R21C include All-sky radiances, new/re-processed data, and new radiance and aircraft bias correction strategies. Efforts to reduce field discontinuities involve alternative treatment of ozone data and an adjustment to the frequency of the Incremental Analysis Update (IAU) in the initialization process. The output fields from GEOS-R21C are planned to drive an off-line chemistry reanalysis (GEOS-R21C-Chem) and a high-resolution downscaling for the polar regions (PolarMERRA). GEOS-R21C is currently in the pre-production stages. This talk presents the GMAO strategy for upcoming reanalyses and focuses on GEOS-R21C configuration details and preliminary results from prototype-R21C with comparisons with MERRA-2 and ERA5. Along with an overall review of the new features, a reassessment of MERRA-2 strategy for dry-mass conservation is presented as well as a new diagnostic tool for GEOS-R21C.

An algorithm to simulate locations of atmospheric motion vectors for use in observing system simulation experiments is described and demonstrated. It is intended to obviate likely deficiencies in nature run data if used to produce images for feature tracking. The algorithm employs probabilistic functions that are tuned based on distributions of real observations and histograms of nature run fields. For distinct observation types, the algorithm produces geographical and vertical distributions, time-mean counts, and typical spacings of simulated locations that are, at least qualitatively, similar to those of real observations and are associated with nature run cloud and water vapor fields. It thus appears suitable for generating realistic atmospheric motion vectors for use in observing system simulation experiments.

NASA is investigating the utility of a strategically-constructed constellation of infrared sounders on board small satellites to be able to ultimately provide spaceborne measurements of wind. The method proposed by instrument teams is to fly multiple instruments in complementary orbits so that atmospheric motion vector measurements can be made. As part of the investigation of this measurement approach, the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center performed a set of Observing System Simulation Experiments (OSSEs) to demonstrate the value of the wind measurements as well as the corresponding infrared radiance observations that will come from the constellation. This work was an extension of the GMAO OSSE infrastructure and is in the context of the MISTIC Winds concept. It is noted, though, that this provided insight to the overall measurement strategy. This talk addresses the simulation of the atmospheric motion vectors retrieved via the constellation, the simulation and validation of the radiance observations measured via the constellation, the specification of observations errors for both winds and radiances, and the extension of the data assimilation system to utilize these additional observations on top of a full global observation system. Finally, the results from a set of OSSE experiments is presented.

The Goddard Earth Observing System (GEOS) atmospheric model and data assimilation system are used to perform an Observing System Simulation Experiment (OSSE) for the proposed MISTiC Wind mission. The GEOS OSSE includes a reference simulation (the Nature Run), from which the pseudo-observations are generated. These pseuo-observations span the entire suite of in-situ and space space-based observations presently used in operational weather prediction, with the addition of the MISTiC-Wind dataset. New observation operators have been constructed for the MISTiC Wind data, including both the radiances measured in the 4-micron part of the solar spectrum and the winds derived from these radiances. The OSSE examines the impacts on global forecast skill of adding these observations to the current operational suite, showing substantial improvements in forecasts when the wind information are added. It is shown that a constellation of four MISTiC Wind satellites provides more benefit than a single platform, largely because of the increased accuracy of the feature-derived wind measurements when more platforms are used.

The Global Modeling and Assimilation Office (GMAO) at the NASA Goddard Space Flight Center has developed software and products for conducting observing system simulation experiments (OSSEs) for weather analysis applications. Such applications include estimations of potential effects of new observing instruments or data assimilation techniques on improving weather analysis and forecasts. The GMAO software creates simulated observations from nature run (NR) data sets and adds simulated errors to those observations. The algorithms employed are much more sophisticated, adding a much greater degree of realism, compared with OSSE systems currently available elsewhere. The algorithms employed, software designs, and validation procedures are described in this document. Instructions for using the software are also provided.

The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), is the latest atmospheric reanalysis of the modern satellite era produced by NASA’s Global Modeling and Assimilation Office (GMAO). MERRA-2 assimilates observation types not available to its predecessor, MERRA, and includes updates to the Goddard Earth Observing System (GEOS) model and analysis scheme so as to provide a viable ongoing climate analysis beyond MERRA’s terminus. While addressing known limitations of MERRA, MERRA-2 is also intended to be a development milestone for a future integrated Earth system analysis (IESA) currently under development at GMAO. This paper provides an overview of the MERRA-2 system and various performance metrics. Among the advances in MERRA-2 relevant to IESA are the assimilation of aerosol observations, several improvements to the representation of the stratosphere including ozone, and improved representations of cryospheric processes. Other improvements in the quality of MERRA-2 compared with MERRA include the reduction of some spurious trends and jumps related to changes in the observing system and reduced biases and imbalances in aspects of the water cycle. Remaining deficiencies are also identified. Production of MERRA-2 began in June 2014 in four processing streams and converged to a single near-real-time stream in mid-2015. MERRA-2 products are accessible online through the NASA Goddard Earth Sciences Data Information Services Center (GES DISC).

This study examines the impact of the Dynamics of the Madden-Julian Oscillation (DYNAMO) campaign in situ observations on NASA Goddard Earth Observing System version 5 (GEOS-5) reanalyses and the improvements gained thereby in the representation of the Madden-Julian Oscillation (MJO) initiation processes. To this end, we produced a global, high-resolution (1/4 degrees spatially) reanalysis that assimilates the level-4, quality-controlled DYNAMO upper air soundings from about 87 stations in the equatorial Indian Ocean region along with a companion data-denied control reanalysis. The DYNAMO reanalysis produces a more realistic vertical structure of the temperature and moisture in the central tropical Indian Ocean by correcting the model biases, namely, the cold and dry biases in the lower troposphere and warm bias in the upper troposphere. The reanalysis horizontal winds are substantially improved, in that, the westerly acceleration and vertical shear of the zonal wind are enhanced. The DYNAMO reanalysis shows enhanced low-level diabatic heating, moisture anomalies and vertical velocity during the MJO initiation. Due to the warmer lower troposphere, the deep convection is invigorated, which is evident in convective cloud fraction. The GEOS-5 atmospheric general circulation model (AGCM) employed in the reanalysis is overall successful in assimilating the additional DYNAMO observations, except for an erroneous model response for medium rain rates, between 700 and 600hPa, reminiscent of a bias in earlier versions of the AGCM. The moist heating profile shows a sharp decrease there due to the excessive convective rain re-evaporation, which is partly offset by the temperature increment produced by the analysis.

The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) is an atmospheric reanalysis, spanning 1980 through near-realtime, that uses state-of-the-art processing of observations from the continually evolving global observing system. The effectiveness of any reanalysis is a function not only of the input observations themselves, but also of how the observations are handled in the assimilation procedure. Relevant issues to consider include, but are not limited to, data selection, data preprocessing, quality control, bias correction procedures, and blacklisting. As the assimilation algorithm and earth system models are fundamentally fixed in a reanalysis, it is often a change in the character of the observations, and their feedbacks on the system, that cause changes in the character of the reanalysis. It is therefore important to provide documentation of the observing system so that its discontinuities and transitions can be readily linked to discontinuities seen in the gridded atmospheric fields of the reanalysis. With this in mind, this document provides an exhaustive list of the input observations, the context under which they are assimilated, and an initial assessment of selected core observations fundamental to the reanalysis.

The adjoint of a data assimilation system provides a flexible and efficient tool for estimating observation impacts on short-range weather forecasts. The impacts of any or all observations can be estimated simultaneously based on a single execution of the adjoint system. The results can be easily aggregated according to data type, location, channel, etc., making this technique especially attractive for examining the impacts of new hyper-spectral satellite instruments and for conducting regular, even near-real time, monitoring of the entire observing system. In this talk, we present results from the adjoint-based observation impact monitoring tool in NASA's GEOS-5 global atmospheric data assimilation and forecast system. The tool has been running in various off-line configurations for some time, and is scheduled to run as a regular part of the real-time forecast suite beginning in autumn 20 I O. We focus on the impacts of the newest components of the satellite observing system, including AIRS, IASI and GPS. For AIRS and IASI, it is shown that the vast majority of the channels assimilated have systematic positive impacts (of varying magnitudes), although some channels degrade the forecast. Of the latter, most are moisture-sensitive or near-surface channels. The impact of GPS observations in the southern hemisphere is found to be a considerable overall benefit to the system. In addition, the spatial variability of observation impacts reveals coherent patterns of positive and negative impacts that may point to deficiencies in the use of certain observations over, for example, specific surface types. When performed in conjunction with selected observing system experiments (OSEs), the adjoint results reveal both redundancies and dependencies between observing system impacts as observations are added or removed from the assimilation system. Understanding these dependencies appears to pose a major challenge for optimizing the use of the current observational network and defining requirements for future observing systems.

The Modern-Era Retrospective Analysis for Research and Applications (MERRA) was undertaken by NASA's Global Modeling and Assimilation Office with two primary objectives: to place observations from NASA's Earth Observing System satellites into a climate context and to improve upon the hydrologic cycle represented in earlier generations of reanalyses. Focusing on the satellite era, from 1979 to the present, MERRA has achieved its goals with significant improvements in precipitation and water vapor climatology. Here, a brief overview of the system and some aspects of its performance, including quality assessment diagnostics from innovation and residual statistics, is given.By comparing MERRA with other updated reanalyses [the interim version of the next ECMWF ReAnalysis (ERA-Interim) and the Climate Forecast System Reanalysis (CFSR)], advances made in this new generation of reanalyses, as well as remaining deficiencies, are identified. Although there is little difference between the new reanalyses in many aspects of climate variability, substantial differences remain in poorly constrained quantities such as precipitation and surface fluxes. These differences, due to variations both in the models and in the analysis techniques, are an important measure of the uncertainty in reanalysis products. It is also found that all reanalyses are still quite sensitive to observing system changes. Dealing with this sensitivity remains the most pressing challenge for the next generation of reanalyses.Production has now caught up to the current period and MERRA is being continued as a near-real-time climate analysis. The output is available online through the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC).

The paper discusses aspects of the resolution-dependent analysis of ozone satellite data in the Upper Troposphere and Lower Stratosphere (UTLS) region. In this region the sharp positive and negative vertical ozone gradients are frequently observed by insitu measurements and simulated by chemistry-climate models, showing so-called ozone laminas. With sufficient vertical resolution of limb-viewing sensors, such as MLS and HIRDLS on Aura/NASA spacecraft, this information on ozone dynamics in thin vertical layers can be accepted by data assimilation systems constraining layered vertical ozone structures across the tropopause. For these scenes, the ozone-sensitive information from the nadir sensors (SBUV, GOME, TES, AIRS, OMI, IASI) characterized by restricted vertical resolution should be properly projected from the data space to the analysis grid preserving the non-observable (but forecasted) ozone vertical structures. Several illustrations for analysis of nadir-only ozone data (SBUV-2) that can “diffuse” ozone laminas in the extra-tropical UTLS are discussed. To overcome this negative impact of analysis of nadir data, the resolution-dependent analysis schemes (RDAS) of retrievals (characterized by kernels) or/and radiances are suggested. The vertical inverse mapping performed by RDAS ensures constraining only scales observable by data preserving the non-observable short-scale vertical structures of ozone. As illustrated by comparisons of MLS and HIRDLS data with analyzed ozone fields (GEOS-5 and ECMWF), the other geophysical scenes influenced by inadequate assimilation of nadir retrievals may include: a) the high-latitude ozone hole and mini-holes; b) seasonal and quasi-biennial ozone oscillations in the tropical stratosphere; c) movements of high and low ozone air masses across the transport barriers. These comparisons demonstrate the value of MLS and HIRDLS limb data in constraining of ozone for monitoring atmospheric composition and climate. The current plans for analysis of MLS radiance data in GEOS-5 data assimilation system are highlighted along with resolution-sensitive assimilation of ozone partial columns provided by nadir sensors.

Data assimilation is a potentially powerful method of extracting the maximum amount of information from observed data by combining their information with models. in this application, we will use stratospheric ozone profiles retrieved from EOS MLS and total column amounts retrieved from OMI' along with the GEOS-5 GCM, that includes a representation of ozone chemistry and transport. The presentation will discuss how well we can derive tropospheric ozone column information in this system, including its sensitivity to model errors and some assumptions made in the assimilation system. We discuss also ozone structures in the tropopause region and their sensitivity to model resolution and other fact=ors. An important issue we examine is how much more information we obtain from ozone assimilation than from model studies using state of the art chemistry models - answering such questions helps determine how useful ozone assimilation really is, given our present capabilities in chemical modeling.