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NASA NTRS2012

Impact of Satellite Atmospheric Motion Vectors In the GMAO GEOS-5 Global Data Assimilation System

The WMO and THORPEX co-sponsored fifth Workshop on the Impact of Various Observing Systems on Numerical Weather Prediction will be organized by the Expert Team on the Evolution of the Global Observing System in Sedona, Arizona, United States, from 22 to 25 May 2012. Participants are expected to come from all the major NWP centres which are active in the area of impact studies. The workshop will be conducted in English. As for the first four workshops it is planned to produce a workshop report to be published as a WMO Technical Report that will include the papers submitted by the participants. The previous four workshops in this series took place in Geneva {April 1997), Toulouse (March 2000), Alpbach (March 2004) and Geneva (May 2008). Results from Observing System Experiments (OSEs), both with global and regional aspects were presented and conclusions were drawn concerning the contributions of the various components of the observing system to the large scale forecast skill at short and medium range (Workshop Proceedings were published as WMO World Weather Watch Technical Reports TD No. 868, 1034, 1228 and 1450). Since then, some significant changes and developments have affected the global observing system and more efforts have been devoted to meso-scale observing and assimilation systems. There has also been a trend toward using techniques other than OSEs to document data impact, such as adjoint-based sensitivity to observations or ensemble-based sensitivity. Field experiments have been carried out, in particular through the THORPEX project, and the use of targeted data has been assessed.

Gelaro, Ronald↗
NASA NTRS2012

Recent Advances in Ozone Data Assimilation at the GMAO - Towards a New Reanalysis

This presentation summarized ongoing work on improving the representation of ozone in the GEOS Data Assimilation Systems. Data from two EOS Aura sensors was used: the total column ozone from the Ozone Monitoring Instrument (OMI) and high vertical resolution stratospheric profiles from Microwave Limb Sounder (MLS, version 3.3). As several previous studies have demonstrated, assimilation of this data can constrain the stratospheric and tropospheric ozone columns with relatively good accuracy. However, the representation of the vertical structures in the troposphere and near tropopause region is often deficient. Since both these layers of the atmosphere are critical to the understanding of the radiative forcing as well as the ozone budget in the troposphere, current work will focus on improving the assimilated product between the surface and the 50 hPa pressure level. The discussion included recent steps that have been taken towards refining the treatment of ozone in GEOS-5. Impacts of improved tropospheric chemistry model were discussed including the introduction of efficiency factors ("averaging kernels") for OMI total ozone, and direct assimilation of radiances from the MLS instrument. In particular, advantages and challenges involved in assimilating limb radiances rather than retrieved product were discussed. This work is, in part, a preparation for a planned reanalysis of the EOS Aura data from 2005 to present.

Krzysztof, Wargan↗
NASA NTRS2012

The Impact of Satellite Atmospheric Motion Vectors in the GMAO GEOS-5 Global Data Assimilation System

The impact of satellite-derived atmospheric motion vectors (AMVs) on numerical weather forecasts is examined using the GEOS-5 global atmospheric data assimilation system. Cycling data assimilation experiments, including twice-daily 5-day forecasts, are conducted for two 6-week periods during the 2010 Atlantic hurricane season and 2010-2011Northern Hemisphere winter season. Results from a control experiment that includes all AMVs and other data types assimilated operationally in GEOS-5 are compared with those from an experiment in which the GEOS-5 AMVs (only) are replaced by ones produced by the U. S. Navy?s NAVDAS-AR atmospheric data assimilation system. The Navy AMVs are assimilated in their entirety as well as in various subset combinations. The primary objective of these experiments is to determine whether aspects of the NAVDAS-AR data selection and quality control procedure, especially the use of carefully averaged ("super-ob?) wind vectors and large volume of AMVs, explain the typically larger beneficial impact of these data in the Navy system as compared with most other forecast systems. Adjoint-based observation impact calculations are assessed and compared with traditional metrics such as forecast geopotential height anomaly correlations and observation-minus-forecast departures. Results so far indicate that that the greater number of NRL AMVs is primarily responsible for their larger impact, although superobing also appears to be beneficial. Map views show that the impact obtained from assimilation of the NRL AMVs is more uniformly beneficial, perhaps due to the averaging of individual observations in creating the super-obs. While the NRL AMVs have a much larger impact in GEOS-5 than do the control AMVs, their impact is still smaller than in the Navy forecast system, suggesting that the mix of observations may play an important role in modulating the impact of any one data type. At the same time, reducing the number of satellite radiances assimilated in GEOS-5 does not significantly alter the impact of the AMVs

Gelaro, R. D.↗
NASA NTRS2016

Characterizing background error with the NASA GMAO OSSE

Understanding the characteristics of background error is vital to the design and performance of data assimilation systems. However, the true state of the atmosphere is unknown, and qualitative determination of background error is thus a challenge. We present an exploration of background error characteristics using the NASA Global Modeling and Assimilation Office Observing System Simulation Experiment framework, where the presence of the Nature Run allows direct calculation of the background error. The behavior of the data assimilation system (DAS) can be compared with the character of the background error, and performance of the DAS will be investigated in a series of both idealized and realistic experiments.

Nature Run↗
NASA NTRS2017

GEOS S2S-2_1: GMAO's New High Resolution Seasonal Prediction System

A new version of the modeling and analysis system used to produce sub-seasonal to seasonal forecasts has just been released by the NASA Goddard Global Modeling and Assimilation Office. The new version runs at higher atmospheric resolution (approximately 12 degree globally), contains a substantially improved model description of the cryosphere, and includes additional interactive earth system model components (aerosol model). In addition, the Ocean data assimilation system has been replaced with a Local Ensemble Transform Kalman Filter. Here will describe the new system, along with the plans for the future (GEOS S2S-3_0) which will include a higher resolution ocean model and more interactive earth system model components (interactive vegetation, biomass burning from fires). We will also present results from a free-running coupled simulation with the new system and results from a series of retrospective seasonal forecasts. Results from retrospective forecasts show significant improvements in surface temperatures over much of the northern hemisphere and a much improved prediction of sea ice extent in both hemispheres. The precipitation forecast skill is comparable to previous S2S systems, and the only trade off is an increased double ITCZ, which is expected as we go to higher atmospheric resolution.

Molod, Andrea↗
NASA NTRS2018

A Realization of Bias Correction Method in the GMAO Coupled System

Over the past several decades, a tremendous effort has been made to improve model performance in the simulation of the climate system. The cold or warm sea surface temperature (SST) bias in the tropics is still a problem common to most coupled ocean atmosphere general circulation models (CGCMs). The precipitation biases in CGCMs are also accompanied by SST and surface wind biases. The deficiencies and biases over the equatorial oceans through their influence on the Walker circulation likely contribute the precipitation biases over land surfaces. In this study, we introduce an approach in the CGCM modeling to correct model biases. This approach utilizes the history of the model's short-term forecasting errors and their seasonal dependence to modify model's tendency term and to minimize its climate drift. The study shows that such an approach removes most of model climate biases. A number of other aspects of the model simulation (e.g. extratropical transient activities) are also improved considerably due to the imposed pre-processed initial 3-hour model drift corrections. Because many regional biases in the GEOS-5 CGCM are common amongst other current models, our approaches and findings are applicable to these other models as well.

Chang, Yehui↗
NASA NTRS2018

Impact of Satellite Sea Surface Salinity Observations on ENSO Predictions from the GEOS GMAO S2S Forecast System

We assess the impact of satellite sea surface salinity (SSS) observations on dynamical ENSO forecasts. Assimilation of SSS improves the mixed layer depth (MLD) and modulates the Kelvin waves associated with ENSO. In column 2, the initialization differences between experiments that assimilate SSS minus those withholding SSS assimilation are presented. Column 3 shows examples of forecasts generated for the different phases of ENSO. From March to June 2015, the availability of two overlapping satellite SSS instruments, Aquarius and SMAP, allows a unique opportunity to compare and contrast coupled forecasts generated with the benefit of these two satellite SSS observation types. The far right column compares assimilation of Aquarius, SMAP and combined Aquaries and SMAP on forecasts for the 2015 El Nino.

ENSO↗
NASA NTRS2018

GEOS S2S-2_1: The GMAO High Resolution Seasonal Prediction System

A new version of the coupled modeling and analysis system used to produce near real time subseasonal to seasonal forecasts was recently released by the NASA/Goddard Global Modeling and Assimilation Office. The new version runs at higher atmospheric resolution than the previous, (approximately 1/2 degree globally), contains a substantially improved model description of the cryosphere, and includes additional interactive earth system model components (aerosol model). In addition, the Ocean data assimilation system has been replaced with a Local Ensemble Transform Kalman Filter, and now includes the assimilation of along-track sea surface height. Here will describe the new system, along with the plans for the future (GEOS S2S-3_0) which will include a higher resolution ocean model and more interactive earth system model components (interactive vegetation, biomass burning from fires). We will also present results from a series of retrospective seasonal forecasts. Results show significant improvements in surface temperatures over much of the northern hemisphere and a much improved prediction of sea ice extent in both hemispheres. Analysis of the ensemble spread shows improvements relative to the previous system, including generally better reliability. The precipitation forecast skill is comparable to previous S2S systems, and the only tradeoff is an increased "double ITCZ", which is expected as we go to higher atmospheric resolution.

Molod, Andrea↗
NASA NTRS2019

Observing System Experiments for Evaluating the Impact of Satellite Sea Surface Salinity on Seasonal Predictions from the GMAO S2S System

We assess the impact of satellite sea surface salinity (SSS) observations on dynamical ENSO (El Nino-Southern Oscillation) forecasts. Assimilation of SSS improves the mixed layer depth (MLD) and modulates the Kelvin waves associated with ENSO. In column 2, the initialization differences between experiments that assimilate SSS minus those withholding SSS assimilation are presented. Column 3 shows examples of forecasts generated for the different phases of ENSO assimilating the different satellite SSS. In general, for all phases of ENSO, SSS assimilation improves forecasts. The far right column compares ensemble means for assimilation of individual and combined SMOS, Aquarius, SMAP (Soil Moisture and Ocean Salinity, NASA Aquarius, Soil Moisture Active Passive) SSS forecasts. Finally, the latest forecasts are presented comparing assimilation versus no- assimilation of satellite SSS for single forecasts over the last year.

Hackert, E.↗