Will McCarty

The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)

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 NASAs 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 MERRAs 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).

LIDAR-MEASURED WIND PROFILES The Missing Link in the Global Observing System

The three-dimensional global wind field is the most important remaining measurement needed to accurately assess the dynamics of the atmosphere. Wind information in the tropics, high latitudes, and stratosphere is particularly deficient. Furthermore, only a small fraction of the atmosphere is sampled in terms of wind profiles. This limits our ability to optimally specify initial conditions for numerical weather prediction (NWP) models and our understanding of several key climate change issues. Because of its extensive wind measurement heritage (since 1968) and especially the rapid recent technology advances, Doppler lidar has reached a level of maturity required for a space-based mission. The European Space Agency (ESA)s Atmospheric Dynamics Mission Aeolus (ADM-Aeolus) Doppler wind lidar (DWL), now scheduled for launch in 2015, will be a major milestone. This paper reviews the expected impact of DWL measurements on NWP and climate research, measurement concepts, and the recent advances in technology that ...

Cloud Coverage in the Joint OSSE Nature Run

A successful observing system simulation experiment (OSSE) is fundamentally dependent on the simulation of the global observing system used in the experiment. In many applications, a free-running numerical model simulation, called a nature run, is used as the meteorological truth from which the observations are simulated. To accurately and realistically simulate observations from any nature run, the simulated observations must contain realistic cloud effects representative of the meteorological regimes being sampled. This study provides a validationof the clouds in the Joint OSSE nature rungeneratedat ECMWF. Presented is the methodology used to validate the nature run cloud fraction fields with seasonally aggregated combined CloudSat/Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) cloud geometric profile retrievals and the Wisconsin High Resolution Infrared Radiation Sounder (HIRS) cloud climatology. The results show that the Joint OSSE nature run has a correct vertical distribution of clouds but lacks globally in cloud amount compared to the validation data. The differences between the nature run and validation datasets shown in this study should be considered and accounted for in the generation of the global observing system for use in full OSSE studies.

An Adjoint-Based Forecast Impact from Assimilating MISR Winds into the GEOS-5 Data Assimilation and Forecasting System

AbstractThis study examines the benefit of assimilating cloud motion vector (CMV) wind observations obtained from the Multiangle Imaging SpectroRadiometer (MISR) within a Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), configuration of the Goddard Earth Observing System-5 (GEOS-5) model data assimilation system (DAS). Available in near–real time (NRT) and with a record dating back to 1999, MISR CMVs boast pole-to-pole coverage and geometric height assignment that is complementary to the suite of atmospheric motion vectors (AMVs) included in the MERRA-2 standard. Experiments spanning September–November of 2014 and March–May of 2015 estimated relative MISR CMV impact on the 24-h forecast error reduction with an adjoint-based forecast sensitivity method. MISR CMV were more consistently beneficial and provided twice as large a mean forecast benefit when larger uncertainties were assigned to the less accurate component of the CMV oriented along the MISR satellite ground tr...

Impact of adaptively thinned AIRS cloud-cleared radiances on tropical cyclone representation in a global data assimilation and forecast system

A simple adaptive thinning methodology for Atmospheric Infrared Sounder (AIRS) radiances is evaluated through a combination of Observing System Experiments (OSEs) and adjoint methodologies. The OSEs are performed with the NASA Goddard Earth Observing System (GEOS, version 5) data assimilation and forecast model. In addition, the adjoint-based forecast sensitivity observation impact technique is applied to assess fractional contributions of sensors in different thinning configurations. The adaptive strategy uses a denser AIRS coverage in a moving domain centered around tropical cyclones (TCs), sparser everywhere else. The OSEs consist of two sets of data assimilation runs that cover the period from September 1st to 10 November 2014, with the first 20 days discarded for spin-up. Both sets assimilate all conventional and satellite observations used operationally. In addition, one ingests clear-sky AIRS radiances, the other cloud-cleared radiances, each comprising multiple thinning strategies. Daily 7-day forecasts are initialized from all these analyses and evaluated with focus on TCs over the Atlantic and the Pacific. Evidence is provided on the effectiveness of this simple TC-centered adaptive radiance thinning strategy, in full agreement with previous theoretical studies. Specifically, global skill increases, and tropical cyclone representation is substantially improved. The improvement is particularly strong when cloud-cleared radiances are assimilated. Finally, the article suggests that cloud-cleared radiances, if thinned more aggressively than the currently used clear-sky radiances, could successfully replace them with large improvements in TC forecasting and no loss of global skill.

Evaluation of RapidScat Ocean Vector Winds for Data Assimilation and Reanalysis

The RapidScat scatterometer was built as a low cost follow-on to the QuikSCAT mission. It flew on the International Space Station (ISS) and provided data from 3 October 2014 to 20 August 2016 and provided surface wind vectors retrieved from surface roughness estimates taken at multiple azimuth angles. These measurements were unique to the historical scatterometer record in that the ISS flies in a low inclination, non-sun-synchronous orbit. Scatterometry-derived wind vectors have been routinely assimilated in both forward processing and reanalysis systems run at the Global Modeling and Assimilation Office (GMAO). As the RapidScat retrievals were made available in near-real-time, they were assimilated in the forward processing system, and the methods to assimilate and evaluate these retrievals are described. Time series of data statistics are presented first for the near-real-time data assimilated in GMAO forward processing. Second, the full data products provided by the RapidScat team are compared passively to the MERRA-2 reanalysis. Both sets of results show that the root mean squared (RMS) difference of the observations and the GMAO model background fields increased over the course of the data record. Furthermore, the observations and the backgrounds are shown to be biased for both the zonal and meridional wind components. The retrievals are shown to have had a net forecast error reduction via the forecast sensitivity observation impact (FSOI) metric, which is a quantification of 24 hour forecast error reduction, though the impact became neutral as the signal to noise ratio of the instrument decreased over its lifespan.