Tropical Cyclones in the 7-km NASA Global Nature Run for Use in Observing System Simulation Experiments

The National Aeronautics and Space Administration (NASA) Nature Run (NR), released for use in Observing System Simulation Experiments (OSSEs), is a 2-year long global non-hydrostatic free-running simulation at a horizontal resolution of 7 km, forced by observed sea-surface temperatures (SSTs) and sea ice, and inclusive of interactive aerosols and trace gases. This article evaluates the NR with respect to tropical cyclone (TC) activity. It is emphasized that to serve as a NR, a long-term simulation must be able to produce realistic TCs, which arise out of realistic large-scale forcings. The presence in the NR of the realistic, relevant dynamical features over the African Monsoon region and the tropical Atlantic is confirmed, along with realistic African Easterly Wave activity. The NR Atlantic TC seasons, produced with 2005 and 2006 SSTs, show interannual variability consistent with observations, with much stronger activity in 2005. An investigation of TC activity over all the other basins (eastern and western North Pacific, North and South Indian Ocean, and Australian region), together with relevant elements of the atmospheric circulation, such as, for example, the Somali Jet and westerly bursts, reveals that the model captures the fundamental aspects of TC seasons in every basin, producing realistic number of TCs with realistic tracks, life spans and structures. This confirms that the NASA NR is a very suitable tool for OSSEs targeting TCs and represents an improvement with respect to previous long simulations that have served the global atmospheric OSSE community

Assimilation of Satellite Microwave Observations in the Rainband of Hurricanes Using Bayesian Monte Carlo Technique

No abstract availabl

Assimilation of Microwave Cloudy Radiances into NASA GEOS Model Using a Novel Bayesian Monte Carlo Technique

No abstract availabl

Comparing Flood-Producing Catastrophic Hurricanes in GEOS: A Study of Juan 1985 and Harvey 2017

Hurricanes Juan (1985) and Harvey (2017) bear remarkable similarities because of the exceptional amount of accumulated precipitation over coastal regions in the Gulf, consequent to the peculiar and rare situation in which a hurricane stalls for several days on a coastal area, displaying minimal progress. This is particularly dangerous because: a) terrestrial drag affect only part of the circulation so that weakening is not as fast as when there is rapid motion inland; b) a part of the circulation remains over water and as such low-level moisture flux convergence is maintained; c) precipitation lasts for a very long time over the same area; d) moisture recycling from flooded areas further contributes to maintaining flood conditions. From the forecast perspective, hurricanes with track singularities such as stalling points, loops, or multiple landfalls over the same region are exceptionally difficult to predict because they result from small variations of opposing mechanisms and are therefore intrinsically unstable. Intensity fluctuations and track singularities are strongly coupled. The representation of fluxes undergoing differential drag (ocean vs. land) inside the same circulation is critically dependent upon resolution. Recent modeling development efforts within the NASA Global Modeling and Assimilation Office (GMAO) have brought the horizontal resolution of the global cubed-sphere Goddard Earth Observing System (GEOS) atmospheric model down to 12 km. This has led to innovative findings in the field of tropical cyclone (TC) modeling within a global framework. The GEOS atmospheric model and data assimilation system (DAS), used to produce the MERRA-2 (Modern Era Retrospective analysis for Research and Applications) reanalysis, has a "Replay" capability that adds a forcing term to the model equations to constrain it to follow a specified trajectory. This capability is used to produce global, downscaled versions of the 50 km MERRA-2 reanalysis at a 12.5 km resolution. The full DAS and replay modes both use the Incremental Analysis Update or IAU for applying an analysis increment to the model's background state four times each day at 00Z, 06Z, 12Z and 18Z. In a full DAS mode, a six-hour free run of the model is made (Predictor Step), writing states that will be used as backgrounds or first-guesses for the analysis centered on 00Z. The Analysis system then produces its best estimate of the 00Z state by blending backgrounds with observations. The difference between this best estimate and the model's original first guess at 00Z is treated as an analysis increment and added uniformly as a forcing term to the model during a second, now forced, run of the 12Z to 03Z window, which produces the assimilated results. Replay mode is very similar, except only the 00Z background is needed from the predictor segment, which is thus half as long, and instead of doing an analysis, a previously analyzed state at 00Z is read in. The increment is then computed and applied to the corrector step in the same way as in the original DAS. When downscaling horizontal spatial filters can be applied to the "analysis" increment used to force the Corrector step, so as to reproduce the full meteorological state or selectively choose or discard certain forcings. In this presentation, full global hindcast simulations of Juan and operational forecasts of Harvey are compared

An OSSE Investigating a Constellation of 4-5 Micrometer Infrared Sounders

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

Assimilation of Satellite Microwave Observations in the Rainband of Hurricanes Using Bayesian Monte Carlo Technique

No abstract availabl

Assimilation of Microwave Observations in the Rainbands of Tropical Cyclones

We propose a novel Bayesian Monte Carlo Integration (BMCI) technique to retrieve the profiles of temperature, water vapor, and cloud liquid/ice water content from microwave cloudy measurements in the presence of tropical cyclones (TC). These retrievals then can either be directly used by meteorologists to analyze the structure of TCs or be assimilated into numerical models to provide accurate initial conditions for the NWP (Numerical Weather Prediction) models. The BMCI technique is applied to the data from the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership (NPP) and Global Precipitation Measurement (GPM) Microwave Imager (GMI). The retrieved profiles are then assimilated into Hurricane WRF (Weather Research and Forecasting) using the GSI (Gridpoint Statistical Interpolation) data assimilation system

Assimilation of Microwave Cloudy Observations over the Rainband of Hurricanes Using a Novel Bayesian Monte Carlo Technique

We propose a novel Bayesian Monte Carlo Integration (BMCI) technique to retrieve the profiles of temperature, water vapor, and cloud liquid/ice water content from microwave cloudy measurements in the rainbands of tropical cyclones (TC). These retrievals then can either be directly used by meteorologists to analyze the structure of TCs or be assimilated into numerical models to provide accurate initial conditions for the NWP models. The BMCI technique is applied to the data from the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership (NPP) and Global Precipitation Measurement (GPM) Microwave Imager (GMI)

Assimilation of Satellite Microwave Observations in the Rainband of Hurricanes Using Bayesian Monte Carlo Technique

No abstract availabl