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At least 559 records · Page 31
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NASA NTRS2008

The NASA Real Time Mission Monitor - A Situational Awareness Tool for Conducting Tropical Cyclone Field Experiments

The NASA Real Time Mission Monitor (RTMM) is a situational awareness tool that integrates satellite, aircraft state information, airborne and surface instruments, and weather state data in to a single visualization package for real time field experiment management. RTMM optimizes science and logistic decision-making during field experiments by presenting timely data and graphics to the users to improve real time situational awareness of the experiment's assets. The RTMM is proven in the field as it supported program managers, scientists, and aircraft personnel during the NASA African Monsoon Multidisciplinary Analyses (investigated African easterly waves and Tropical Storm Debby and Helene) during August-September 2006 in Cape Verde, the Tropical Composition, Cloud and Climate Coupling experiment during July-August 2007 in Costa Rica, and the Hurricane Aerosonde mission into Hurricane Noel in 2-3 November 2007. The integration and delivery of this information is made possible through data acquisition systems, network communication links, and network server resources built and managed by collaborators at NASA Marshall Space Flight Center (MSFC) and Dryden Flight Research Center (DFRC). RTMM is evolving towards a more flexible and dynamic combination of sensor ingest, network computing, and decision-making activities through the use of a service oriented architecture based on community standards and protocols. Each field experiment presents unique challenges and opportunities for advancing the functionality of RTMM. A description of RTMM, the missions it has supported, and its new features that are under development will be presented.

Goodman, Michael↗
NASA NTRS2008

'Electrically-Hot' Convection and Tropical Cyclone Development in the Eastern Atlantic

The depth and intensity of convective-scale "hot" towers in intensifying tropical disturbances has been hypothesized to play a role in tropical cyclogenesis via dynamic and thermodynamic feedbacks on the larger meso-to-synoptic scale circulation. In this investigation we investigate the role that widespread and/or intense lightning-producing convection (i.e., "electrically-hot towers") resident in African Easterly Waves (AEW) may play in tropical cyclogenesis over the eastern Atlantic Ocean. NCEP reanalysis data for the months of July to November for the years 2004, 2006, and 2007 are analyzed for the domain of 5 N - 15 N and 500W - 300 E. Specifically, NCEP data for individual AEWs are partitioned into northerly, southerly, trough, and ridge phases using the 700 hPa meridional winds. Subsequently, information from National Hurricane Center storm reports were divided up into developing and non-developing waves (i.e. tropical cyclogenesis). Finally, composites were created of developing and non-developing waves using the NCEP variables, but with the inclusion of lightning flash count and infrared brightness temperature information. The Zeus and World Wide Lightning Location Network lightning data were used for the lightning information, and the IR brightness temperature data was extracted from the NASA global-merged infrared brightness temperature dataset.

Leppert, Kenneth, II↗
NASA NTRS2009

Demonstration of Effects on Tropical Cyclone Forecasts with a High Resolution Global Model from Variation in Cumulus Convection Parameterization

The Goddard Earth Observing System Model, Version 5 (GEOS-5) is a system of models that have been developed at Goddard Space Flight Center to support NASA's earth science research in data analysis, observing system modeling and design, climate and weather prediction, and basic research. The work presented used GEOS-5 with 0.25o horizontal resolution and 72 vertical levels (up to 0.01 hP) resolving both the troposphere and stratosphere, with closer packing of the levels close to the surface. The model includes explicit (grid-scale) moist physics, as well as convective parameterization schemes. Results will be presented that will demonstrate strong dependence in the results of modeling of a strong hurricane on the type of convective parameterization scheme used. The previous standard (default) option in the model was the Relaxed Arakawa-Schubert (RAS) scheme, which uses a quasi-equilibrium closure. In the cases shown, this scheme does not permit the efficient development of a strong storm in comparison with observations. When this scheme is replaced by a modified version of the Kain-Fritsch scheme, which was originally developed for use on grids with intervals of order 25 km such as the present one, the storm is able to develop to a much greater extent, closer to that of reality. Details of the two cases will be shown in order to elucidate the differences in the two modeled storms.

Miller, Timothy L.↗
NASA NTRS2010

The Representation of Tropical Cyclones Within the Global William Putman Non-Hydrostatic Goddard Earth Observing System Model (GEOS-5) at Cloud-Permitting Resolutions

The Goddard Earth Observing System Model (GEOS-S), an earth system model developed in the NASA Global Modeling and Assimilation Office (GMAO), has integrated the non-hydrostatic finite-volume dynamical core on the cubed-sphere grid. The extension to a non-hydrostatic dynamical framework and the quasi-uniform cubed-sphere geometry permits the efficient exploration of global weather and climate modeling at cloud permitting resolutions of 10- to 4-km on today's high performance computing platforms. We have explored a series of incremental increases in global resolution with GEOS-S from irs standard 72-level 27-km resolution (approx.5.5 million cells covering the globe from the surface to 0.1 hPa) down to 3.5-km (approx. 3.6 billion cells).

Putman, William M.↗
NASA NTRS2013

Evaluation of Model Microphysics Within Precipitation Bands of Extratropical Cyclones

Recent studies evaluating the bulk microphysical schemes (BMPs) within cloud resolving models (CRMs) have indicated large uncertainties and errors in the amount and size distributions of snow and cloud ice aloft. The snow prediction is sensitive to the snow densities, habits, and degree of riming within the BMPs. Improving these BMPs is a crucial step toward improving both weather forecasting and climate predictions. Several microphysical schemes in the Weather Research and Forecasting (WRF) model down to 1.33‐km grid spacing are evaluated using aircraft, radar, and ground in situ data from the Global Precipitation Mission Coldseason Precipitation Experiment (GCPEx) experiment, as well as a few years (15 winter storms) of surface measurements of riming, crystal habit, snow density, and radar measurements at Stony Brook, NY (SBNY on north shore of Long Island) during the 2009-2012 winter seasons. Surface microphysical measurements at SBNY were taken every 15 to 30 minutes using a stereo microscope and camera, and snow depth and snow density were also recorded. During these storms, a vertically-pointing Ku-band radar was used to observe the vertical evolution of reflectivity and Doppler vertical velocities. A Particle Size and Velocity (PARSIVEL) disdrometer was also used to measure the surface size distribution and fall speeds of snow at SBNY. For the 15 cases at SBNY, the WSM6, Morrison (MORR), Thompson (THOM2), and Stony Brook (SBU-YLIN) BMPs were validated. A non-spherical snow assumption (THOM2 and SBU-YLIN) simulated a more realistic distribution of reflectivity than spherical snow assumptions in the WSM6 and MORR schemes. The MORR, WSM6, and SBU-YLIN schemes are comparable to the observed velocity distribution in light and moderate riming periods. The THOM2 is ~0.25 meters per second too slow with its velocity distribution in these periods. In heavier riming, the vertical Doppler velocities in the WSM6, THOM2, and MORR schemes were ~0.25 meters per second too slow, while the SBU-YLIN was 0.25 to 0.5 meters per second too fast. Overall, the BMPs simulate a size distribution close to the observed for D < 4 mm in the dendritic, plates, and mixed habit periods. The model BMPs underestimate the size distribution when large aggregates were observed. For D > 6 mm in the dendrites, side planes, and mixed habit periods, the BMPs are likely not simulating enough aggregation to create a larger size distribution, although the MORR (double moment) scheme seemed to perform best. These SBNY results will be compared with some results from GCPEx for a warm frontal snow band observed at 18 February 2012.

Colle, Brian A.↗
NASA NTRS2013

Evaluation of Model Microphysics within Precipitation Bands of Extratropical Cyclones

Recent studies evaluating the bulk microphysical schemes (BMPs) within cloud resolving models (CRMs) have indicated large uncertainties and errors in the amount and size distributions of snow and cloud ice aloft. The snow prediction is sensitive to the snow densities, habits, and degree of riming within the BMPs. Improving these BMPs is a crucial step toward improving both weather forecasting and climate predictions. Several microphysical schemes in the Weather Research and Forecasting (WRF) model down to 1.33‐km grid spacing are evaluated using aircraft, radar, and ground in situ data from the Global Precipitation Mission Coldseason Precipitation Experiment (GCPEx) experiment, as well as a few years (15 winter storms) of surface measurements of riming, crystal habit, snow density, and radar measurements at Stony Brook, NY (SBNY on north shore of Long Island) during the 2009-2012 winter seasons. Surface microphysical measurements at SBNY were taken every 15 to 30 minutes using a stereo microscope and camera, and snow depth and snow density were also recorded. During these storms, a vertically-pointing Ku-band radar was used to observe the vertical evolution of reflectivity and Doppler vertical velocities. A Particle Size and Velocity (PARSIVEL) disdrometer was also used to measure the surface size distribution and fall speeds of snow at SBNY. For the 15 cases at SBNY, the WSM6, Morrison (MORR), Thompson (THOM2), and Stony Brook (SBU-YLIN) BMPs were validated. A non-spherical snow assumption (THOM2 and SBU-YLIN) simulated a more realistic distribution of reflectivity than spherical snow assumptions in the WSM6 and MORR schemes. The MORR, WSM6, and SBU-YLIN schemes are comparable to the observed velocity distribution in light and moderate riming periods. The THOM2 is approx 0.25 m/s too slow with its velocity distribution in these periods. In heavier riming, the vertical Doppler velocities in the WSM6, THOM2, and MORR schemes were approx 0.25 m/s too slow, while the SBU-YLIN was 0.25 to 0.5 m/s too fast. Overall, the BMPs simulate a size distribution close to the observed for D < 4 mm in the dendritic, plates, and mixed habit periods. The model BMPs underestimate the size distribution when large aggregates were observed. For D > 6 mm in the dendrites, side planes, and mixed habit periods, the BMPs are likely not simulating enough aggregation to create a larger size distribution, although the MORR (double moment) scheme seemed to perform best. These SBNY results will be compared with some results from GCPEx for a warm frontal snow band observed at 18 February 2012.

Colle, Brian A.↗
NASA NTRS2014

Evaluation of Model Microphysics Within Precipitation Bands of Extratropical Cyclones

It is hypothesized microphysical predictions have greater uncertainties/errors when there are complex interactions that result from mixed phased processes like riming. Use Global Precipitation Measurement (GPM) Mission ground validation studies in Ontario, Canada to verify and improve parameterizations. The WRF realistically simulated the warm frontal snowband at relatively short lead times (1014 h). The snowband structire is sensitive to the microphysical parameterization used in WRF. The Goddard and SBUYLin most realistically predicted the band structure, but overpredicted snow content. The double moment Morrison scheme best produced the slope of the snow distribution, but it underpredicted the intercept. All schemes and the radar derived (which used dry snow ZR) underpredicted the surface precipitation amount, likely because there was more cloud water than expected. The Morrison had the most cloud water and the best precipitation prediction of all schemes.

Colle, Brian A.↗