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Lang, Timothy J.

Publications and source records attributed to Lang, Timothy J..

At least 55 records · Page 3
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NASA NTRS2015

Python-Based Scientific Analysis and Visualization of Precipitation Systems at NASA Marshall Space Flight Center

At NASA Marshall Space Flight Center (MSFC), Python is used several different ways to analyze and visualize precipitating weather systems. A number of different Python‐based software packages have been developed, which are available to the larger scientific community. The approach in all these packages is to utilize pre‐existing Python modules as well as to be object‐oriented and scalable. The first package that will be described and demonstrated is the Python Advanced Microwave Precipitation Radiometer (AMPR) Data Toolkit, or PyAMPR for short. PyAMPR reads geolocated brightness temperature data from any flight of the AMPR airborne instrument over its 25‐year history into a common data structure suitable for user‐defined analyses. It features rapid, simplified (i.e., one line of code) production of quick‐look imagery, including Google Earth overlays, swath plots of individual channels, and strip charts showing multiple channels at once. These plotting routines are also capable of significant customization for detailed, publication‐ready figures. Deconvolution of the polarization‐varying channels to static horizontally and vertically polarized scenes is also available. Examples will be given of PyAMPR's contribution toward real‐time AMPR data display during the Integrated Precipitation and Hydrology Experiment (IPHEx), which took place in the Carolinas during May‐June 2014. The second software package is the Marshall Multi‐Radar/Multi‐Sensor (MRMS) Mosaic Python Toolkit, or MMM‐Py for short. MMM‐Py was designed to read, analyze, and display three‐dimensional national mosaicked reflectivity data produced by the NOAA National Severe Storms Laboratory (NSSL). MMM‐Py can read MRMS mosaics from either their unique binary format or their converted NetCDF format. It can also read and properly interpret the current mosaic design (4 regional tiles) as well as mosaics produced prior to late July 2013 (8 tiles). MMM‐Py can easily stitch multiple tiles together to provide a larger regional or national picture of precipitating weather systems. Composites, horizontal and vertical crosssections, and combinations thereof are easily displayed using as little as one line of code. MMM‐Py can also write to the native MRMS binary format, and sub‐sectioning of tiles (or multiple stitched tiles) is anticipated to be in place by the time of this meeting. Thus, MMM‐Py also can be used to power the creation of custom mosaics for targeted regional studies. Overlays of other data (e.g., lightning observations) are easily accomplished. Demonstrations of MMM‐Py, including the creation of animations, will be shown. Finally, Marshall has done significant work to interface Python‐based analysis routines with the U.S. Department of Energy's Py‐ART software package for radar data ingest, processing, and analysis. One example of this is the Python Turbulence Detection Algorithm (PyTDA), an MSFC‐based implementation of the National Center for Atmospheric Research (NCAR) Turbulence Detection Algorithm (NTDA) for the purposes of convective‐scale analysis, situational awareness, and forensic meteorology. PyTDA exploits Py‐ART's radar data ingest routines and data model to rapidly produce aviation‐relevant turbulence estimates from Doppler radar data. Work toward processing speed optimization and better integration within the Py‐ART framework will be highlighted. Python‐based analysis within the Py‐ART framework is also being done for new research related to intercomparison of ground‐based radar data with satellite estimates of ocean winds, as well as research on the electrification of pyrocumulus clouds.

Lang, Timothy J.↗
NASA NTRS2015

Further Research on the Electrification of Pyrocumulus Clouds

Past research on pyrocumulus electrification has demonstrated that a variety of lightning types can occur, including cloud‐to‐ground (CG) flashes, sometimes of dominant positive polarity, as well as small intra‐cloud (IC) discharges in the upper levels of the pyro‐cloud. In Colorado during summer 2012, the first combined polarimetric radar, multi‐Doppler radar, and three‐dimensional lightning mapping array (LMA) observations of lightning‐producing pyrocumulus were obtained. These observations suggested that the National Lightning Detection Network (NLDN) was not sensitive enough to detect the small IC flashes that appear to be the dominant mode of lightning in these clouds. However, after an upgrade to the network in late 2012, the NLDN began detecting some of this pyrocumulus lightning. Multiple pyrocumulus clouds documented by the University of Wisconsin for various fires in 2013 and 2014 (including over the Rim, West Fork Complex, Yarnell Hill, Hardluck, and several other incidents) are examined and reported on here. This study exploits the increased‐sensitivity NLDN as well as the new nationwide U.S. network of polarimetric Next‐generation Radars (NEXRADs). These observations document the common occurrence of a polarimetric "dirty ice" signature ‐ modest reflectivities (20‐40+ dBZ), near‐zero differential reflectivity, and reduced correlation coefficient (less than 0.9) ‐ prior to the production of lightning. This signature is indicative of a mixture of ash and ice particles in the upper levels of the pyro‐cloud (less than ‐20 C), with the ice interpreted as being necessary for pyro‐cloud electrification. Pseudo‐Geostationary Lightning Mapper (GLM) data will be produced from the 2012 LMA observations, and the ability of GLM to detect small pyrocumulus ICs will be assessed. The utility of lightning and polarimetric radar for documenting rapid wildfire growth, as well as for documenting pyrocumulus impacts on the composition of the upper troposphere/lower stratosphere (UTLS), will be discussed.

Lang, Timothy J.↗
NASA NTRS2015

A High-Resolution Merged Wind Dataset for DYNAMO: Progress and Future Plans

In order to support research on optimal data assimilation methods for the Cyclone Global Navigation Satellite System (CYGNSS), launching in 2016, work has been ongoing to produce a high‐resolution merged wind dataset for the Dynamics of the Madden Julian Oscillation (DYNAMO) field campaign, which took place during late 2011/early 2012. The winds are produced by assimilating DYNAMO observations into the Weather Research and Forecasting (WRF) three‐dimensional variational (3DVAR) system. Data sources from the DYNAMO campaign include the upper‐air sounding network, radial velocities from the radar network, vector winds from the Advanced Scatterometer (ASCAT) and Oceansat‐2 Scatterometer (OSCAT) satellite instruments, the NOAA High Resolution Doppler Lidar (HRDL), and several others. In order the prep them for 3DVAR, significant additional quality control work is being done for the currently available TOGA and SMART‐R radar datasets, including automatically dealiasing radial velocities and correcting for intermittent TOGA antenna azimuth angle errors. The assimilated winds are being made available as model output fields from WRF on two separate grids with different horizontal resolutions ‐ a 3‐km grid focusing on the main DYNAMO quadrilateral (i.e., Gan Island, the R/V Revelle, the R/V Mirai, and Diego Garcia), and a 1‐km grid focusing on the Revelle. The wind dataset is focused on three separate approximately 2‐week periods during the Madden Julian Oscillation (MJO) onsets that occurred in October, November, and December 2011. Work is ongoing to convert the 10‐m surface winds from these model fields to simulated CYGNSS observations using the CYGNSS End‐To‐End Simulator (E2ES), and these simulated satellite observations are being compared to radar observations of DYNAMO precipitation systems to document the anticipated ability of CYGNSS to provide information on the relationships between surface winds and oceanic precipitation at the mesoscale level. This research will improve our understanding of the future utility of CYGNSS for documenting key MJO processes.

Lang, Timothy J.↗
NASA NTRS2014

Large Charge Moment Change Lightning in an Oklahoma Mesoscale Convective System

On 31 May 2013, a line of severe thunderstorms developed during the local afternoon in central Oklahoma, USA. One of the supercells produced the El Reno tornado, which caused significant damage and killed several people. During the 2300 UTC hour (during the mature supercell stage and just after the tornado began), the storm produced several positive cloud-to-ground (+CG) lightning strokes that featured large (> 75 C km) impulse charge moment changes (iCMCs - charge moment during the first 2 ms after the return stroke). These discharges occurred mainly in convection, in contrast to the typical pattern of large-CMC and sprite-parent +CGs occurring mainly in stratiform precipitation regions. After this time, the line of thunderstorms evolved over several hours into a large mesoscale convective system (MCS). By the 0700 UTC hour on 1 June 2013, the large- CMC pattern had changed markedly. Large-CMC negative CGs, which were absent early in the storm's lifetime, occurred frequently within convection. Meanwhile, large- CMC +CGs had switched to occurring mainly within the broad stratiform region that had developed during the intervening period. The evolution of the large-CMC lightning in this case will be examined using a mix of polarimetric data from individual radars, national mosaics of radar reflectivity, the Oklahoma Lightning Mapping Array (OKLMA), the Charge Moment Change Network (CMCN), and the National Lightning Detection Network (NLDN). A major goal of this study is understanding how storm structure and evolution affected the production of large-CMC lightning. It is anticipated that this will lead to further insight into how and why storms produce the powerful lightning that commonly causes sprites in the upper atmosphere.

Lang, Timothy J.↗
NASA NTRS2014

Large Charge Moment Change Lightning in an Oklahoma Mesoscale Convective System

On 31 May 2013, a line of severe thunderstorms developed during the local afternoon in central Oklahoma, USA. One of the supercells produced the El Reno tornado, which caused significant damage and killed several people. During the 2300 UTC hour (during the mature supercell stage and just after the tornado began), the storm produced several positive cloud-to-ground (+CG) lightning strokes that featured large (> 100 C km) impulse charge moment changes (iCMCs; charge moment during the first 2 ms after the return stroke). These discharges occurred mainly in convection, in contrast to the typical pattern of large-CMC and sprite-parent +CGs occurring mainly in stratiform precipitation regions. After this time, the line of thunderstorms evolved over several hours into a large mesoscale convective system (MCS). By the 0700 UTC hour on 1 June 2013, the large-CMC pattern had changed markedly. Large-CMC negative CGs, which were absent early in the storm's lifetime, occurred frequently within convection. Meanwhile, large-CMC +CGs had switched to occurring mainly within the broad stratiform region that had developed during the intervening period. The evolution of the large-CMC lightning in this case will be examined using a mix of national mosaics of radar reflectivity, the Oklahoma Lightning Mapping Array (OKLMA), the Charge Moment Change Network (CMCN), and the National Lightning Detection Network (NLDN). A major goal of this study is understanding how storm structure and evolution affected the production of large-CMC lightning. It is anticipated that this will lead to further insight into how and why storms produce the powerful lightning that commonly causes sprites in the upper atmosphere.

Lang, Timothy J.↗
NASA NTRS2014

Diagnosing Meteorological Conditions Associated with Sprites and Lightning with Large Charge Moment Changes (CMC) over Oklahoma

Sprites are a category of Transient Luminous Events (TLEs) that occur in the upper atmosphere above the tops of Mesoscale Convective Systems (MCSs). They are commonly associated with lightning that produce large charge moment changes (CMCs). Synergistic use of satellite and radar‐retrieved observations together with sounding data, forecasts, and lightning‐detection networks allowed the diagnosis and analysis of the meteorological conditions associated with sprites as well as large‐CMC lightning over Oklahoma.

Flores-Rivera, Lizxandra↗