Comparison of Meteorological Simulations
created using Nudging, 3DVar and 4DVar with
Respect to Key Meteorological Inputs for Air
Quality and Dispersion Models
Monday, 11 January 2016: 1:30 PM
Room 243 ( New Orleans Ernest N. Morial Convention Center)
Andrew T. White, University of Alabama, Huntsville, AL; and A.
P. Biazar, R. T. McNider, and B. Dornblaser
It is well known that meteorological conditions are an important
factor in air quality. Meteorological quantities such as temperature,
insolation, winds, and relative humidity strongly influence the
evolution of chemical species within the atmosphere. Therefore, air
quality and dispersion models require the best estimate of the state
of the atmosphere to provide accurate predictions of chemical
concentrations of species and the dispersion of those species
throughout the atmosphere. Numerical meteorological models
provide that best spatial and temporal resolutions of meteorological
variables that are necessary to drive air quality and dispersion
models, but uncertainties remain. Typically, Newtonian relaxation,
or nudging, is used to constrain the meteorological model so that
errors are kept to a minimum, producing a better meteorological
analysis field to drive air quality and dispersion models. However,
with continuing advances in meteorological data assimilation
techniques and available computation power, it has become of
interest to determine the continued effectiveness of nudging when
compared to these more sophisticated assimilation techniques. This
study assesses the Weather Research and Forecasting (WRF)
meteorological model performance when a three-dimensional
(3DVar) or four-dimensional (4DVar) variational assimilation
technique is used to assimilate observations into the model. The
benefit of these variational assimilation techniques is the ability to
assimilate non-traditional observations, such as satellite radiances.
These simulations are then compared to a traditional WRF model
simulation, which employs analysis nudging to improve model
performance. All of these model simulations were assessed with
respect to insolation, clouds, temperature, wind speed and
direction, and mixing ratio, which are key meteorological inputs
for air quality studies. It was found that while using 3DVar and
4DVar assimilation with WRF provides slightly better results with
respect to temperature and mixing ratio, a WRF model simulation
which uses analysis nudging provides the best performance with
respect to clouds, wind speed and wind direction which are vitally
important to air quality and dispersion models. Further analysis of
these three WRF simulations will be presented.
The Temporal and Probabilistic Relationship
between Lightning Jump Occurrence and RadarDerived Thunderstorm Intensification
Monday, 11 January 2016: 4:30 PM
Room 226/227 ( New Orleans Ernest N. Morial Convention
Center)
Christopher J. Schultz, University of Alabama/NASA/MSFC,
Huntsville, AL; and P. M. Bitzer, L. D. Carey, T. Chronis, and S.
M. Stough
The fusion of datasets and algorithms into single products is the
current trend in operational forecasting. One such algorithm is the
ProbSevere algorithm outlined by Cintineo et al. (2012). The
ProbSevere algorithm combines cloud top cooling from satellite
observations, maximum expected size of hail (MESH) information
from radar and environmental parameters like convective available
potential energy from model output in the near storm environment.
Recently, an effort has been made to incorporate the total lightning
jump algorithm into the ProbSevere algorithm as part of the data
fusion process (K. Calhoun, personal communication). However,
little has been done in the way that illustrates the temporal
relationship between the lightning jump and individual
components in ProbSevere. Azimuthal shear is another radar
derived product that indicates low-level rotation and potential for
tornadoes. Azimuthal shear will be incorporated into the
ProbSevere algorithm in the near future (J. Cintineo, personal
communication).
The goals of this study are:
1) Examine trends in MESH and azimuthal shear prior to and after
lightning jump (or peak change in the total flash rate in non-jump
thunderstorms) to provide a basic understanding of the temporal
relationship between the radar derived intensity products and
lightning jump occurrence.
2) Provide probabilistic guidance on hail size and the potential for
severe hail and tornadoes based on lightning jump and total flash
rate information for operational weather forecasting. MESH and
azimuthal shear are used as objective proxies for severe weather
potential in the development of the lightning-based probabilistic
guidance.
A sample of 1500+ thunderstorms in which MESH, azimuthal
shear and total lightning jump information are present is used to
understand the temporal probabilistic relationships in both jump
and non-jump thunderstorms.
The outcomes of this study will be useful in validating the future
contribution of the lightning jump into the calculation of the
probability of severe weather within the ProbSevere algorithm.
Furthermore, the comparison between lightning jump and radar
derived intensity metrics, which are used in National Weather
Service warning forecast operations in the Multi-Radar Mulit-
Sensor dataset, will provide more confidence in warning decisions
because a conceptual model can be developed using temporal
relationship between radar and lightning intensity metrics.
Isoprene Suppression of New Particle Formation:
Potential Mechanisms and Atmospheric
Implications (Invited Presentation)
Monday, 11 January 2016: 5:00 PM
Room 231/232 ( New Orleans Ernest N. Morial Convention
Center)
Shan-Hu Lee, University of Alabama, Huntsville, AL
Secondary aerosols formed from anthropogenic pollutants and
natural emissions have substantial impacts on human health, air
quality and the Earth's climate. New particle formation (NPF)
contributes up to 70% of the global production of CCN, but the
effects of biogenic VOCs and their oxidation products on NPF
processes are poorly understood. Observations have shown that
isoprene, the most abundant biogenic species, suppresses NPF in
forests. But the previously proposed chemical mechanism
underlining this suppression process contradicts atmospheric
observations. Here, we provide new insights on isoprene
suppression of the biogenic NPF, based on comprehensive
observations of key chemical precursors in a rural forest in the
Southeastern U.S. and quantum chemical calculations. Our
findings imply that in an isoprene-dominant forest, volatile
oxidation products formed from isoprene compete with lowvolatility oxidation products from monoterpenes in clustering with
sulfuric acid, to suppress the growth of clusters. Current climate
models treat NPF processes by considering only sulfuric acid and
total low-volatility organic compounds, regardless of forest
biogenic emission patterns over the globe. We conclude NPF
suppression in isoprene-emitting forests should be included in
models to correctly predict the climate forcing by aerosols and
clouds.
An All-Clear Space Weather Forecasting System
Based on Magnetogram in Near Real Time
Monday, 11 January 2016: 5:00 PM
Room 352 ( New Orleans Ernest N. Morial Convention Center)
David A. Falconer, University of Alabama Huntsville, Huntsville,
AL; and N. Barghouty and I. Khazanov
Large solar flares and coronal mass ejections (CMEs), drivers of
severe space weather, are particularly difficult to forecast. NOAA
presently uses the McIntosh Active region category system, a
qualitative predictive system largely based on historical data but
adjustable by the user. MAG4 (for Magnetogram Forecast)
assumes that flares and CMEs are explosive release of energy
stored in the solar coronal magnetic field, and thus active regions
that have more free energy are more likely to produce flares and
CMEs. Since free energy cannot be directly measured, MAG4 uses
a proxy of the active region magnetic free energy, and forecasts an
event rate based on this proxy. Forecasts are given as probability
measures or expected rates for a given event (flares, CMEs, or
Solar Proton Events (SPEs)). No time or magnitude predictions are
given; such data remain beyond present capabilities. MAG4 is
being transitioned to use vector magnetograms for which the freeenergy proxy can be measured more accurately. MAG4 forecasts
are further refined using recent flare activity. This talk will present
MAG4, recent developments as well as sample operational
applications. Acknowledgements: MAG4 development at Marshall
is currently being supported by NASA's Game Changing
Development Program.
Measurement-Based Estimates of Direct Radiative
Effects of Absorbing Aerosols above Low-level Clouds
Tuesday, 12 January 2016: 8:30 AM
Room 357 ( New Orleans Ernest N. Morial Convention Center)
Nan Feng, Univ. of Alabama, Huntsville, AL; and S. Christopher
The elevated layers of absorbing smoke aerosols from western
African (e.g. Gabon, and Congo) biomass burning activities have
been frequently observed above low level Stratocumulus clouds off
the African coast, which presents an excellent natural laboratory
for studying the effects of aerosols above clouds (AAC) on
regional energy balance in tropical and sub-tropical environments.
Using spatially and temporally collocated Moderate Resolution
Imaging Spectroradiometer (MODIS), Ozone Monitoring
Instrument (OMI), and Clouds and the Earth's Radiant Energy
System (CERES) data sets, the top-of-atmosphere (TOA)
shortwave Aerosol Direct Shortwave Radiative Effects (ARE) of
absorbing aerosols above low-level water clouds in the Southeast
Atlantic Ocean was examined in this study. The regional averaged
instantaneous ARE has been estimated to be 36.7±20.5 Wm-2
(regional mean ± standard deviation) along with a mean positive
OMI Aerosol Index (AI) at 1.3 in August 2006 based on multisensors measurements. The highest magnitude of instantaneous
ARE can even reach 138.2 Wm-2. We assess that the 660nm
Cloud Optical Depth (COD) values of 8-12 is the critical value
above (below) which aerosol absorption(scattering) effect
dominates and further produces positive (negative) ARE values.
Sensitivity studies based on both observations and radiative
transfer model calculations have been performed to study several
uncertainties due to issues such as the positions, physical and
optical properties of aerosols and clouds. For example, the results
show that ARE values are more sensitive to aerosols above lower
COD values than cases for higher COD values. This is among the
first studies to provide quantitative estimates of shortwave ARE
due to AAC events from an observational perspective.
Comparison of Estimates of Vertical Motion from
Vertically-Pointing Lidar and Radar Within Gust
Fronts, Bores and Low-Level Gravity Waves
Tuesday, 12 January 2016: 11:15 AM
Room 350/351 ( New Orleans Ernest N. Morial Convention
Center)
Kevin Knupp, University of Alabama, Huntsville, AL; and R.
Wade and A. W. Lyza
During the 2015 Plains Elevated Convection at Night (PECAN)
field campaign the Mobile Integrated Profiling System (MIPS) was
equipped with three remote sensors capable of measuring vertical
motion in precipitation-free conditions. These include a 915 MHz
Doppler wind profiler (40-60 s resolution), an X-band Profiling
Radar (XPR, 6 Hz sampling frequency) and a 1.55 μm Doppler
Wind Lidar (DWL, 1 Hz sampling frequency). Vertical motion
estimates from all three are compared from several bores
(including an intense bore with an updraft to >12 m/s), a bore/gust
front hybrid with an updraft magnitude also exceeding 12 m/s,
other more subtle gravity waves, and turbulent eddies within the
nocturnal boundary layer. Moreover, the depth of sampling will be
compared. The relative advantages of each will be discussed. For
example, while the DWL measures a “true” air motion at very high
spatial and temporal resolution due to scattering from aerosols,
clouds, prevent vertical motion estimates within several hundred
meters above cloud base. In contrast, the XPR provides vertical
motion estimates through clouds at high temporal/vertical
resolution (6 Hz and 1.3 deg beam width), but relies on primarily
on insect scattering. The 915 MHz wind profiler samples vertical
motion at 40 s temporal resolution with a 9 deg beam via Bragg
scatter, as well as Rayleigh scatter from insects. A short-term peak
in vertical motion can be estimated from Doppler spectra acquired
by the 915 MHz wind profiler. The high-resolution measurements
from the DWL and XPR can be used to help interpret Doppler
spectra from the 915 MHz profiler. This paper will attempt to
quantify the radar bias in w (e.g., from insects folding their wings)
using simple statistical analyses and results from detailed case
studies.
Integrated Observations of Nocturnal Low-Level
Jet Evolution during the Plains Elevated
Convection at Night Field Campaign
Tuesday, 12 January 2016: 11:45 AM
Room 350/351 ( New Orleans Ernest N. Morial Convention
Center)
Ryan Wade, University of Alabama, Huntsville, AL; and K.
Knupp and D. Phillips
During the Plains Elevated Convection at Night (PECAN) field
campaign in June and July of 2015, the University of Alabama in
Huntsville (UAH) operated multiple mobile facilities during five
low-level jet missions, including the Mobile Integrated Profiling
System (MIPS) and the Mobile Alabama X-band polarimetric
radar (MAX). The UAH MIPS is equipped with 3 instruments
capable of providing wind profiles: 1) a 915 MHz Doppler wind
profiler (40-60 s resolution), 2) a 1.55 μm Doppler Wind Lidar
(DWL, 1 Hz sampling frequency), and 3) iMET radiosondes (1-3
hour launch frequency). Prior to the start of PECAN field
operations, there were questions regarding the ability of scanning
radars to accurately sample the nocturnal low-level jet (LLJ) winds
due to motion bias from bioflyers. These UAH facilities were
generally located less than 10 km apart during the low-level jet
missions, thereby providing an opportunity to compare full volume
and range height indicator scans from the MAX radar with the
remote sensing and in-situ wind profiles from the MIPS.
Preliminary comparisons indicate the MAX and other scanning
radars sampled the nocturnal low-level jet evolution quite well,
with very low bias from bioflyers. This paper will compare the
horizontal LLJ wind profiles retrieved from the MAX radar with
those profiles retrieved from remote sensing and in-situ
instrumentation at the MIPS site. The evolution of the nocturnal
low-level jet during these PECAN cases will be discussed, and
radar animations of the LLJ evolution will be presented.
A Statistical Methodology for Coupled
Observational and NWP–based 1-4 hour
Forecasts of Convective Storm Initiation
Tuesday, 12 January 2016: 1:45 PM
Room 350/351 ( New Orleans Ernest N. Morial Convention
Center)
John R. Mecikalski, Univ. of Alabama, Huntsville, AL; and C. P.
Jewett, U. Narayan, X. Li, and T. Berendes
Despite extensive previous research on nowcasting convective
storm initiation (CI) the key factors involved in the CI process are
not well understood, making for the 0-6 hour forecasting of CI
very challenging on space and time scales of interest for societal
needs (10's of km, 30 min). CI is defined as a ≥35 dBZ intensity
radar echo at the surface or at the –10° C level. Forecasting of
thunderstorms in such short timeframes by numerical weather
prediction models often suffers due to model “spin-up,” forcing a
heavy reliance on extrapolation of real-time observations. Yet, the
2-6 h timeframe is beyond when extrapolation techniques typically
work well for predicting thunderstorm development. Previous
studies have indicated that the CI process is a combined interaction
of the mesoscale and synoptic scale settings, mesoscale processes,
as well as land-surface processes and orography that dictate
boundary layer formation and local convergent circulations and
moisture distributions.
As a first step toward improving the ability to predict the location
and timing of CI in the 14 hour period from present, a database is
being formed for “CI” and “NonCI” events. This database will be
comprised initially of >50 separate fields for each CI/NonCI event,
for 1000s of cases. Locations of CI will be determined from WSR88D reflectivity observations. The database development will
quantify the important land surface cover and topography scales
that contribute to producing updrafts (at 11.5 km above ground
level) that eventually lead to convective storms in the coming 14
hours. Database development relates the CI process with local
atmospheric conditions, fusing data from in situ, and products from
satellite and numerical models. The database will include fields
such as the mixing height, strength of the capping inversions and
the boundary layer winds from the NOAA High Resolution Rapid
Refresh (HRRR) model. Other datasets will allow for the
exploration of the relationships between CI and the different
variables within real-time observations that describe the
background conditions within the pre-thunderstorm environment.
There is a heavy reliance on data from NASA and NOAA satellite
remote sensing including sea and lake/river surface temperatures,
cloud products (optical depth, effective radius), land use, elevation,
topography, derived fields like NDVI, leaf area index, 0-1 hour
convective initiation nowcasts, as well as data from established
algorithms that retrieve sensible heating, evapotranspiration and
soil moisture.
The training database will then be operated on by machine learning
statistical methods after association rules are formed outward of
high-resolution (~200 m) simulations are performed. These initial
exploratory experiments will help identify the more important
variables required for the nowcasting classification studies, and
improve our understanding of the roles these variables play in the
initiation of convection. Given the requirement that datasets of a
variety of storage formats, spatial formats (point/raster/vector), as
well as map projections have to be inter compared for statistical
relationships, we chose the Postgresql geo-database as the tool for
homogenizing our datasets to comparable spatial
resolution/coordinate reference. Using a Geo-database also offers
the advantage of being capable of handling several storage formats,
viz. NetCDF, GRIB, ASCII. Geo-databases also contain several
built in spatial functions (reprojection, interconversion of raster to
vector and vice versa, spatial subsetting, spatial buffering,
geospatial indexes to speed up spatial queries etc.) Specifically, we
are in the process of developing a data portal using the Postgresql
database.
The outcome of this project will include a 30-min update ~5 km
resolution gridded product that provide significantly improved
prediction accuracy for CI within the 1-4 h timeframe. Plans are to
demonstrate the 1-4 hour probabilistic and gridded CI forecasts to
National Weather Service forecasters, using existing collaboration
with NASA's Short-term Prediction Research and Transition
(SPoRT) Center. Our progress via gridded 1-4 hour CI forecasts
products as of the AMS Conference will be described and
discussed.
Limb Correction of Infrared Imagery in Cloudy
Regions for the Improved Interpretation of RGB
Composites
Tuesday, 12 January 2016: 2:00 PM
Room 252/254 ( New Orleans Ernest N. Morial Convention
Center)
Nicholas J. Elmer, University of Alabama, Huntsville, AL; and E.
Berndt and G. J. Jedlovec
Red-Green-Blue (RGB) composites combine information from
several spectral channels into one composite image to aid in the
operational analysis of atmospheric processes. However, individual
spectral channels may be adversely affected by the limb effect, a
result of an increasing optical path length of the absorbing
atmosphere between the satellite and the earth as scan angle
increases. Recent work has shown that limb effects can be
accurately corrected in the individual imagery in clear regions
using limb correction coefficients which vary with respect to
latitude and season. However, in cloudy regions, the limb
correction is inaccurate, which is problematic for forecasters when
interpreting RGB composites designed to provide information
about cloud properties and microphysics. This presentation will
highlight an improved limb correction approach in the presence of
clouds. Case examples demonstrating the improved correction in
cloudy regions for several RGBs will be presented.
Characterization of Nighttime Light Variability
over the Southeastern United States
Tuesday, 12 January 2016: 2:15 PM
Room 252/254 ( New Orleans Ernest N. Morial Convention
Center)
Tony A. Cole, University of Alabama, Huntsville, AL; and A. L.
Molthan and L. A. Schultz
Severe meteorological events such as thunderstorms, tropical
cyclones and winter ice storms often produce prolonged,
widespread power outages affecting large populations and regions.
The spatial impact of these events can extend from relatively rural,
small towns (i.e. November 17, 2013 Washington, IL EF-4
tornado) to a series of adjoined states (i.e. April 27, 2011 severe
weather outbreak) to entire regions (i.e. 2012 Hurricane Sandy)
during their lifespans. As such, affected populations can vary
greatly, depending on the event's intensity, location and duration.
Actions taken by disaster response agencies like FEMA, the
American Red Cross and NOAA to provide support to
communities during the recovery process need accurate and timely
information on the extent and location(s) of power disruption. This
information is often not readily available to these agencies given
communication interruptions, independent storm damage reports
and other response-inhibiting factors. VIIRS DNB observations
which provide daily, nighttime measurements of light sources can
be used to detect and monitor power outages caused by these
meteorological disaster events. To generate such an outage
product, normal nighttime light variability must be analyzed and
understood at varying spatial scales (i.e individual pixels, clustered
land uses/covers, entire city extents). The southeastern portion of
the United States serves as the study area in which the mean,
median and standard deviation of nighttime lights are examined
over numerous temporal periods (i.e. monthly, seasonally,
annually, inter-annually). It is expected that isolated pixels with
low population density (rural) will have tremendous variability in
which an outage “signal” is difficult to detect. Small towns may
have more consistent lighting (over a few pixels), making it easier
to identify outages and reductions. Finally, large metropolitan
areas may be the most “stable” light source, but the entire area may
rarely experience a complete outage. The goal is to determine the
smallest spatial scale in which an outage can be detected.
Presented work will highlight nighttime light variability over the
southeastern U.S. which will serve as a baseline for the production
of a near real-time power outage product.
On the Development of Real-time GOES-SRSOR
Derived Flow Products of Deep Convective Cloud
Tops
Tuesday, 12 January 2016: 2:45 PM
Room 252/254 ( New Orleans Ernest N. Morial Convention
Center)
Jason Apke, University of Alabama, Huntsville, AL; and J. R.
Mecikalski, C. P. Jewett, and L. D. Carey
Observations of objectively identified flow vectors from super
rapid scan operations (SRSOR) for the geostationary operational
environmental satellite (GOES)-R series collection periods in 2014
have suggested that distinct and consistent flow patterns are
observable at the cloud top over both ordinary and more organized
and maintained types of convective storms. The 1–min resolution
of SRSOR allows for cloud top divergence (CTD) and vorticity
(CTV) flow fields to be resolved on a temporal scale that rivals
those available with common next generation radar network
techniques (2–5 min). Mesoscale Atmospheric Motion Vectors
(mAMVs) are obtained by identifying trackable targets such as
brightness temperature minima, maxima and gradients and
correlating them through time sequences of satellite images
(Velden et al. 1997, 1998). The identified mAMVs are converted
to CTD and CTV fields using a Barnes objective analysis (Barnes
1973).
For this presentation, SRSOR storm case examples are presented
from the two-week collection periods in late May and early August
2015. Field generation toward a preliminary SRSOR-mAMV
algorithm explores the utility of background flow addition, cloud
edge detection, and Barnes objective analysis optimization with
both ordinary cell and supercell case studies. For comparison, the
analysis of satellite flow fields are also evaluated against idealized
supercell storms as simulated in the Advanced Research Weather
Research and Forecasting (WRF-ARW) core model. Comparisons
are also made of flow field signatures derived from mAMVs to
early conceptual models of supercell structure and dynamics [e.g.,
those by Lemon and Doswell (1979) and Wiesman and Klemp
(1982)].
Early observational results suggest that the CTV “couplet”
signature does not always exist over tornadic supercells (based on
collections in 2015), however all supercell cases in 2014 and 2015
exhibit strong, maintained CTD maxima values located near their
respective overshooting tops. Ordinary convection CTD signals
tend to be much weaker and shorter lived than supercell cases.
Interestingly, supercell storms simulated in WRF-ARW develop
the same CTV “couplet” signature, which when analyzed with the
vorticity tendency equation, suggests that vortex tilting is the
primary mechanism creating this phenomenon. Reasons for flow
modification that may remove the CTV signature are also
explored, such as unfavorable shear environments and buoyancy
depth modification. This study shows that future products such as
SRSOR-mAMV derived CTD and CTV fields are likely to prove
to be useful tools for operational forecasters and Warn-OnForecast methodologies when examined with respect to ongoing
severe weather and the warning decision process.
Combining Satellite and Radar in the
Development of a 0-1 hour Lightning Threat
Algorithm
Tuesday, 12 January 2016: 2:45 PM
Room 350/351 ( New Orleans Ernest N. Morial Convention
Center)
John R. Mecikalski, Univ. of Alabama, Huntsville, AL; and C. P.
Jewett, L. Carey, T. Chronis, G. T. Stano, and B. T. Zavodsky
Lightning is one of the most dangerous weather-related
phenomena, especially as many jobs and activities occur outdoors,
presenting risk from a lightning strike. Cloud-to-ground (CG)
lightning represents a considerable safety threat to people in
numerous outdoor activities—from airfields, stadium events,
beaches, golf courses, as well as mariners, and emergency
personnel. Holle et al. (2005) show 90% of lightning deaths
occurred outdoors, while 10% occurred indoors despite the
perception of safety when inside buildings. Curran et al. (2000)
found that nearly half of fatalities due to weather were related to
convective weather in the 1992-1994 timeframe, with lightning
causing a large component of the fatalities, in addition to tornadoes
and flash flooding. In the aviation industry, CG lightning
represents a considerable hazard to baggage-handlers, aircraft
refuelers, food caterers, and emergency personnel, who all become
exposed to the risk of being struck within short time periods while
convective storm clouds develop. Airport safety protocols require
that ramp operations be modified or discontinued when lightning is
in the vicinity (typically 16 km), which becomes very costly and
disruptive to flight operations. Therefore, much focus has been
paid to nowcasting the first-time initiation and extent of lightning,
both of CG and of any lightning (e.g, in-cloud, cloud-to-cloud).
For this project three lightning nowcasting methodologies will be
combined: (1) a GOES-based 0-1 hour lightning initiation (LI)
product (Harris et al. 2010; Iskenderian et al. 2012), (2) a High
Resolution Rapid Refresh (HRRR) lightning probability and
forecasted lightning flash density product, such that a quantitative
amount of lightning (QL) can be assigned to a location of expected
LI, and (3) an algorithm that relates Pseudo-GLM data (Stano et al.
2012, 2014) to the so-called “lightning jump” (LJ) methodology
(Shultz et al. 2011) to monitor lightning trends and to
anticipate/forecast severe weather (hail ≥2.5 cm, winds ≥25 ms–1,
tornadoes). The result will be a time-continuous algorithm that
uses GOES satellite, radar fields, and HRRR model fields to
nowcast first-flash LI and QL, and subsequently monitors lightning
trends on a per-storm basis within the LJ algorithm for possible
severe weather occurrence out to ≥3 hours. The LI–QL–LJ product
will also help prepare the operational forecast community for
Geostationary Lightning Mapper (GLM) data expected in late
2015, as these data are monitored for ongoing convective storms.
The LI–QL–LJ product will first predict where new lightning is
highly probable using GOES imagery of developing cumulus
clouds, followed by an analysis of NWS (dual-polarization) radar
indicators (reflectivity at the –10 °C altitude) of lightning
occurrence, to increase confidence that LI is immanent. Once
lightning is observed, time-continuous lightning mapping array and
Pseudo-GLM observations will be analyzed to assess trends and
the severe weather threat as identified by trends in lightning (i.e.,
LJs). Additionally, 5- and 15-min GOES imagery will then be
evaluated on a per-storm basis for overshooting and other cloudtop features known to be associated with severe storms. For the
processing framework, the GOES-R 0–1 hour convective initiation
algorithm's output will be developed within the Warning Decision
Support System – Integrated Information (WDSS-II) tracking tool,
and merged with radar and lightning (LMA/Psuedo-GLM) datasets
for active storms. The initial focus of system development will be
over North Alabama for select lightning-active days in summer
2014, yet will be formed in an expandable manner. The lightning
alert tool will also be developed in concert with National Weather
Service (NWS) forecasters to meet their needs for real-time,
accurate first-flash LI and timing, as well as anticipated lightning
trends, amounts, continuation and cessation, so to provide key
situational awareness and decision support information. The
NASA Short-term Prediction Research and Transition (SPoRT)
Center will provide important logistical and collaborative support
and training, involving interactions with the NWS and broader user
community.
Impact of aerosols on precipitation associated
with atmospheric rivers: An observational and
model-based approach
Tuesday, 12 January 2016: 3:30 PM
Room 356 ( New Orleans Ernest N. Morial Convention Center)
Aaron Naeger, University of Alabama, Huntsville, AL; and J. M.
Creamean and A. L. Molthan
Aerosols can impact cloud and precipitation processes through
their ability to act as cloud condensation nuclei (CCN) and ice
nuclei (IN). Although we have made significant progress on
understanding the aerosol-cloud-precipitation processes, there still
exists a great deal of uncertainty on quantifying the impact of
aerosols on precipitation as it is very difficult to separate the
individual contributions from the changing meteorology and
aerosol conditions. In this study, we combine detailed
measurements from the NOAA-led CalWater field campaign over
Northern California from February-March 2011 with Weather
Research and Forecasting model coupled with Chemistry (WRFChem) simulations to quantify the impact of aerosols on
precipitation. We focus on two atmospheric river (AR) cases that
brought substantial precipitation to Northern California from
February 18-19 and March 5-7, 2011. In situ measurements
revealed the presence of pollution and dust in cloud and
precipitation residues during both of the AR events, which
suggests aerosols may have played an important role in modifying
precipitation. The results from our WRF-Chem simulations will
help quantitatively understand and separate the role of the varying
meteorological and aerosol conditions on the precipitation
associated with the ARs.
NASA Earth Observation Systems and Applications for
Public Health and Air Quality Models and Decisions
Tuesday, 12 January 2016: 3:30 PM
Room 228/229 ( New Orleans Ernest N. Morial Convention
Center)
Sue M. Estes, NASA/University of Alabama, Huntsville, AL; and
J. A. Haynes
Health providers and researchers need environmental data to study
and understand the geographic, environmental, and meteorological
differences in disease. Satellite remote sensing of the environment
offers a unique vantage point that can fill in the gaps of
environmental, spatial, and temporal data for tracking disease. This
presentation will demonstrate the need for collaborations between
multi-disciplinary research groups to develop the full potential of
utilizing Earth Observations in studying health. This presentation
will discuss some of their Public Health and Air Quality Projects.
Satellite earth observations present a unique vantage point of the
earth's environment from space, which offers a wealth of health
applications for the imaginative investigator. The presentation is
directly related to Earth Observing systems and Global Health
Surveillance and will present research results of the remote sensing
environmental observations of earth and health applications, which
can contribute to the public health and air quality research. As part
of NASA approach and methodology they have used Earth
Observation Systems and Applications for Public Health and Air
Quality Models to provide a method for bridging gaps of
environmental, spatial, and temporal data for tracking disease. This
presentation how weather will provide an overview of projects
dealing with infectious and water borne diseases and how
environmental variables effect human health. This presentation
will provide a venue where the results of both research and
practice using satellite earth observations to study weather and it's
role in public health research.
Improved Air Quality Simulations during NASA's
DISCOVER-AQ Texas: Incorporating Geostationary Satellite
Observations
Tuesday, 12 January 2016: 4:00 PM
Room 228/229 ( New Orleans Ernest N. Morial Convention
Center)
Arastoo Pour Biazar, University of Alabama, Huntsville, AL; and
A. T. White, R. T. McNider, D. S. Cohan, R. Zhang, B.
Dornblaser, and M. Estes
Clouds play a critical role in modulating photosysnthetically active
radiation (PAR) and thereby biogenic hydrocarbon emissions,
affect photolysis rates, impact boundary-layer development, lead to
deep vertical mixing of pollutants and precursors, and induce
aqueous phase chemistry. While numerical meteorological models
have difficulty in creating clouds in the right place and time
compared to observed clouds, satellite observations of clouds
provide a strong constraint that can be used to correct simulated
clouds and their impact on air quality simulations. One of the areas
that can be improved by direct use of satellite observation is the
estimates of biogenic volatile organic compounds (BVOCs).
BVOCs play a critical role in atmospheric chemistry, particularly
in ozone and particulate matter (PM) formation. BVOC emissions
are highly sensitive to light and errors in model simulated clouds
impact the amount of PAR reaching the canopy and thereby
significantly impact the emission estimates.
In this study we present a new PAR product and its impact on
BVOC emissions over the continental United States (with a focus
on Texas). The new PAR product is generated by the University of
Alabama in Huntsville (UAH) and is based on Geostationary
Operational Environmental Satellite (GOES) observations. UAH
PAR product was evaluated against surface observations for
September 2013 (NASA's DISCOVER-AQ Houston field
campaign). Several air quality simulations using WRF/CMAQ
were performed and evaluated. These simulations used control and
improved BVOC estimates as well as meteorological fields from
WRF simulations with and without direct cloud assimilation. The
results from these simulations as evaluated against DISCOVERAQ observations will be presented.
Use of Satellite Skin Temperatures to Improve
Surface Evapotranspiration Performance in WRF
Tuesday, 12 January 2016: 4:30 PM
Room 240/241 ( New Orleans Ernest N. Morial Convention
Center)
Richard T. McNider, Univ. of Alabama, Huntsville, AL; and K.
Doty, Y. Wu, A. Pour-Biazar, P. Lee, M. Huang, B. Dornblaser,
and C. Hain
The physical atmosphere plays a critical role in air quality
modeling performance. It is the purpose of paper to evaluate and
improve the performance of a land surface model (Pleim –Xiu)
used in the meteorological model (WRF) by the use of satellite
skin temperatures to better specify surface evapotranspiration (ET).
While considerable work has been done by the national community
to develop improved land use classifications, land use classes
themselves are not directly used in models. Rather, physical
parameters such as heat capacity, thermal resistance, roughness,
surface moisture availability, albedo etc. associated with a land use
class are actually used in the land surface model. Many of the land
use class associated parameters such as surface moisture
availability are dynamic and ill-observed depending on antecedent
precipitation and evaporation, soil transport, the phenological state
of the vegetation, irrigation applications etc. Other parameters such
as heat capacity, thermal resistance or deep soil temperature are
not only difficult to observe they are often model heuristics
unknowable a priori. This project will use satellite data skin
temperature data to retrieve or adjust these critical land surface
parameters.
Morning skin temperatures observed from geostationary satellites
are used to adjust soil moisture in WRF within the Pleim-Xiu
boundary layer scheme and alter surface ET. The Pleim-Xiu
scheme has a two-stream (vegetation and bare soil) surface layer
model. Adjustments in soil moisture are made based on differences
between model skin temperatures and satellite observed skin
temperatures. In addition to the adjustments in soil moisture,
evening differences in model and satellite observed are used to
adjust the thermal resistance of the surface.
Results are provided for the Discovery AQ special observation
program in the Texas area for September 2013. Model
performance with and without the satellite assimilation is provided.
Performance statistics are provided over the domain using both
satellite skin temperatures and NWS 2-m temperature and winds as
performance metrics. Special data comparisons will be made for
flux sites, wind profiler sites and aircraft measurements.
A Multi-Perspective Evaluation of ET variability related to
recent droughts in the Southeastern United States
Tuesday, 12 January 2016: 4:45 PM
Room 240/241 ( New Orleans Ernest N. Morial Convention
Center)
Walter L. Ellenburg, Univ. of Alabama, Huntsville, AL; and V.
Mishra, R. T. McNider, J. Mecikalski, J. Christy, C. Handyside,
and J. F. Cruise
This study employed multiple sources of ET data to examine the
spatial and temporal variability of ET over the Southeastern United
states over the past fifteen years. This period corresponds to times
when significant droughts developed over the region, specifically
1998-2001; 2005-06, and 2012. A composite ET data base for the
period was constructed from model estimates (Penman-Monteith),
satellite observations (both near IR and TIR) and eddy flux towers.
The 2006 NLDC provided land coverage of the region which could
be correlated with ET estimates. The Penman-Monteith method
was used to compute ET for each land class present and compared
to MODIS estimates and values computed by the ALEXI thermal
infrared model. Satellite observations and model estimates were
supported by data from 29 flux towers located around the region.
Through comparisons and assimilation, a composite ET data base
was constructed from these sources. The composite ET maps were
compared to fluxes from a crop model (DSSAT) that is executed
over the Southeastern US at a daily time step during the growing
season. The data were then used to evaluate conditions associated
with hydrologic and agricultural extremes (droughts) that occurred
over the past fifteen years. Local and synoptic climate conditions
associated with these periods which may have contributed to the
ET patterns were also analyzed.
Ground Based Remote Sensing Observations of a Wave and
Cold Front Interaction during the PLOWS Field Campaign
Wednesday, 13 January 2016: 11:30 AM
Room 350/351 ( New Orleans Ernest N. Morial Convention
Center)
Carter B. Hulsey, University of Alabama, Huntsville, AL; and K.
R. Knupp
This presentation examines a cold front within a weak cyclone
over southern Indiana from a multitude of ground based remote
sensors on 14-15 February 2010 during the Profiling of Winter
Storms (PLOWS) field project (IOP 19). The shallow cold front
was observed by the Mobile Alabama X-band (MAX) dual
polarization (with surface measurements), the UAH Mobile
Integrated Profiling System (MIPS; X-band profiling radar (XPR),
915 MHz wind profiler, ceilometer, microwave profiling
radiometer, surface instrumentation, and Parsivel disdrometer), and
the NCAR Mobile Integrated Sounding System (MISS; 915 MHz
wind profiler and soundings). These instruments were deployed
along a line oriented from 250 to 70 degrees, with separation
distance of 47 and 15 km, respectively. Additional observations
were taken from the Evansville WSR-88D radar (KVWX) and
surface stations (AWOS and co-op) in the mobile instrument
domain were used to examine the shallow cold front.
The shallow cold front moved over the instrument domain between
0300-0600 UTC and a displayed a structure similar to that of a
density current, including a pronounced surface wind shift, a rapid
6-8 K reduction in temperature, a quick rise in pressure of 1.5 hPa,
and a shallow depth of less than 1 km. A wave like feature also
passed through the domain above the front, which was clearly
portrayed as it moved over the MAX radar. This feature was also
detected as perturbations in the XPR reflectivity and vertical
particle velocity. Prefrontal soundings (from MISS site) show a
stable layer with lapse rates of 4-5 K over the depth of the front.
The initial cold front was followed by a deeper, but less distinct
cold front that passed through the domain 9 hours after the initial
front. Both fronts were well sampled by the mobile platforms and
the KVWX radar. Wind profiles were available from the MISS and
MIPS 915 MHz wind profilers as well as EVAD analyses
performed with KVWX and MAX.
An Intercomparison of WSR-88D and ARMOR
Radar Observations of the 14 July 2015 Tennessee
Valley Tornadic Quasi-Linear Convective System
Wednesday, 13 January 2016: 1:45 PM
Room 350/351 ( New Orleans Ernest N. Morial Convention
Center)
Anthony W. Lyza, University of Alabama, Huntsville, AL; and K.
R. Knupp
An anomalous tornadic quasi-linear convective system (QLCS)
impacted the Tennessee Valley region of northern Alabama during
the late afternoon and early evening hours of 14 July 2015. This
event was anomalous from the perspective that tornadoes are
typically rare in this region during July. At least 6 tornadoes were
documented. All of the tornadoes were EF0 or EF1 intensity on the
Enhanced Fujita Scale, but a few of the tornadoes were larger (up
to 200 m wide) and longer-lived (at least 37.9 km long). These
tornadoes presented a particular challenge to warning operations
due to the well-perceived challenges of QLCS tornado detection,
including rapid generation and dissipation and shallow circulation
depth, in addition to a lack of anticipation of prolific circulation
generation as well as the structure of the QLCS and its orientation
relative to the local Weather Surveillance Radar-88D (WSR-88D)
sites at Hytop, Alabama (KHTX) and Columbus, Mississippi
(KGWX).
The University of Alabama in Huntsville's (UAH) Advanced
Radar for Meteorological and Operational Research (ARMOR)
proved to be a valuable asset in post-storm assessment and
surveys, particularly since all recorded tornadoes occurred within
75 km of the ARMOR radar site, located at Huntsville
International Airport in Huntsville, Alabama. ARMOR did
encounter issues in real-time, however, due to power and/or
communications issues caused by the storm. In this presentation,
we compare and contrast the ARMOR radar signatures associated
with several of the tornadoes from this event with the signatures
from the KHTX and KGWX WSR-88D radars. We discuss
specific instances of all three radars aiding in post-storm
assessment and identification of tornadoes, particularly the
ARMOR radar. We conclude by discussing the necessity of
additional gap-filling, fixed Doppler radar sites for proper
identification and study of the variety of potential physical
mechanisms that may produce QLCS tornadoes, particularly in
light of the upcoming VORTEX-SE field campaign.
Possible Effects of Ammonium on Lightning
Properties
Thursday, 14 January 2016: 9:00 AM
Room 356 ( New Orleans Ernest N. Morial Convention Center)
Themis Chronis, University of Alabama, Huntsville, AL
Numerous studies have addressed the presence of the so called
“anomalous” polarity storms over the US Great Plains. It is over
this region that the Cloud-to-Ground (CG) lightning flash polarity
exhibits a unique preponderance for positive CG flashes.
Thermodynamical storm proxies have been almost unanimously
accepted as the major players in dictating the aforementioned
lightning behaviour. Apart from lightning, the GP region possesses
another unique property. Maps of Environmental Protection
Agency highlight that the US Great Plains is a major recipient of
ammonium (NH4+) in rainwater, as a consequence of the extensive
agricultural and livestock farming over the area. The author has
come across an intriguing observation: A handful of peer-reviewed
laboratory-based studies document that the presence of soluble
ions — such as ammonium — favor the positive charging of
graupel, while consequent particle collisions lead the ice crystals to
acquire a negative charge. If one extrapolated the latter into the
“non-sterile” atmosphere, the presence of NH4+ could arguably
lead the enhancement of anomalous polarity storms, hence +CG
presence. Although the marked spatial coherency between these
two parameters over the US GP does not establish causality, it
contextualizes a well-educated speculation of a physical process
that may act independently from the well-established thunderstorm
charging mechanisms.
Using 1-min Resolution GOES Observations to
Diagnose Earlier Signs of Convective Initiation,
Determine Cumulus Cloud Updraft
Characteristics, and Infer In-Cloud Processes
Thursday, 14 January 2016: 9:00 AM
Room 225 ( New Orleans Ernest N. Morial Convention Center)
John R. Mecikalski, Univ. of Alabama, Huntsville, AL; and C. P.
Jewett and J. Apke
Since 2012, periodic collections of 1-min resolution super rapid
scan operations for GOES-R (SRSOR) GOES-14 observations
were recorded for significant weather events over the U.S. as well
as specified time periods to mimic the data collection of GOES-R.
Monitoring the SRSO imagery allows for the observation of
evolving cumulus clouds over a short time period. Cloud top
height analyses can show where cloud tops are relative to the level
of free convection as determined through the Rapid Refresh (RAP)
model. Early observations show that many convective clouds reach
and exceed the level of free convection (LFC) however do not end
up producing precipitation. Convective clouds typically have to
reach a height that is at least 5-10 degrees colder than the level of
the LFC to be considered a candidate for convective rainfall. This
study will show the development of an algorithm that will detect
early signs of convective initiation using the SRSO observations.
In addition to the development of early CI detection, another goal
of studying the 1-minute resolution GOES observations is to
evaluate the sensitivity of the observations of growing cumulus
clouds, specifically, to determine what components of the cumulus
processes occurring do these data describe. Since these SRSOR
data are collected by GOES-14 at 4 km resolution in the infrared,
questions arise as to the character of the signatures of cumulus
cloud growth that would be expected to be seen, specifically when
one examines the vertical momentum equation appropriate for
moist convection. Such signatures include updraft acceleration as a
function of the instability profile, hydrometeor loading, and latent
heat release.
To date, a total of 71 separate cumulus cloud updrafts were
collected over 33 to 152 minute periods on five dates and regions
in 2012, 2013 and 2014. Cloud top temperatures (TBs) in the 10.7µm “window” channel were cataloged as cumulus clouds evolved
from the “fair weather” stage to mature cumulonimbus, or a cloud
that eventually possessed a new anvil. GOES 3.9 µm channel
derived reflectance (ref39) were also available every 1-minute in
SRSOR. The ref39 data are used to infer cloud top glaciation,
whereas reflectance values falling to below ~9% when 10.7 µm
cloud top TBs are <273 K are highly correlated with the transition
of cloud water particles to ice crystals (Lindsey et al. 2006). Lastly,
proximity soundings of temperature, dew point, mixing ratio and
of estimated convective available potential energy (CAPE) were
collected from 13 km resolution NOAA Rapid Refresh (RAP)
model 0000 hour analysis grids. An analyzed LFC from the RAP
model was used in the computation of CAPE. The profile of CAPE
was first used to compute the incremental amount of buoyancy an
updraft was penetrating through (or using) for each 1-min of cloud
growth. For each updraft, a 1-min vertical motion (w, ms-1) was
computed by simply noting the change in geometric height (using
the RAP profile) of the updraft every 1 minute, assuming that
cloud top TB is approximately the parcel temperature at cloud top
(as typical for optically thick clouds), and that the parcel follows a
moist adiabat.
Results have shown that the correlations between the accumulated
CAPE to a given altitude and w often exceed 0.60, leading to the
conclusion that the SRSOR data are observing aspects of cumulus
clouds which have rarely been seen by geostationary satellites
(with the possible exceptions being from early 3-min observations
from GOES, pre-1980, and in 2013 data collections of 2.5 min data
from Meteosat Second Generation). Evidence is shown on how
updrafts grow, in general, with respect to the capping inversion,
the freezing level, and the equilibrium level as anvils form. Further
analysis will be presented of SRSOR-observed convection with 1km resolution Weather Research and Forecasting (WRF) fields for
the same time the cumulus clouds were sampled by GOES-14. The
WRF model analysis is done as a means of drawing further
conclusions on what components of active convection SRSOR data
are actually observing. Given the results, discussion is provided as
to how such information can be used within products that diagnose
and nowcast near-term convective storm initiation as early as
possible, future storm intensity, and lightning characteristics of
convective clouds.
Using GIS for Automated Near Real-time Storm
Surge Inundation Mapping and Visualization for
the Gulf of Mexico
Thursday, 14 January 2016: 1:30 PM
Room 255/257 ( New Orleans Ernest N. Morial Convention
Center)
Amanda M. Weigel, Univ. of Alabama, Huntsville, AL; and D.
Gallagher and R. Griffin
The United States Gulf of Mexico has been hit by some of the
most destructive hurricanes in recorded history causing
tremendous damage and fatalities. Of the hurricane hazards, storm
surge poses the greatest threat to life and property as it can span
hundreds of miles of coastline with enough destructive power to
destroy buildings, erode beaches, damage critical infrastructure,
and cause loss of life. With current climate projections foreseeing a
rise in sea level and hurricane activity, there is a need to improve
coastal resilience, which is the ability a community can recover
proceeding a hazardous event. GIS has begun to play a critical role
in disaster preparedness, response, and mitigation as it has the
ability to enhance data viewing and analysis using a multi-layer
approach. Currently, GIS is used in both government and private
industry applications to enhance disaster planning, risk
assessments, and map reported storm damages, however, there is a
need for a near real-time solution to map potentially inundated
areas from storm surge.
This research conducted with Baron outlines the capability of
utilizing GIS to create an automated, near real-time application for
mapping potential storm surge inundation and the vulnerability of
coastal communities located along the Gulf of Mexico. High
resolution coastal topographic data, NOAA Probabilistic Storm
Surge model outputs, and the NOAA Census tract level Social
Vulnerability Index (SoVI) were combined to map both potential
flood inundation heights and population vulnerability. The
program was automated through a GIS framework written within a
Python programming environment, and displayed in an
Application Program Interface (API) allowing users to
interactively assess potential storm surge risks. The goal of this
work was to develop a more accurate and detailed method for
visualizing areas at high risk of storm surge flooding for
emergency response at a near real-time basis, while providing the
public with a better understanding of how flood waters will affect
their livelihoods. This methodology successfully provides an
automated means of mapping high resolution storm surge
inundation and vulnerability that National Weather Service
forecasters and emergency responders can use to enhance decision
making and the communication of threatening weather information
to the public.
Applications of Satellite Imagery
Applications to Assist in Storm Damage
Assessment
Thursday, 14 January 2016: 1:45 PM
Room 255/257 ( New Orleans Ernest N. Morial Convention
Center)
Lori A. Schultz, University of Alabama, Huntsville, AL ; and K.
Angle, T. Cole, K. Skow, J. Bell, A. L. Molthan, J. E. Burks, and
K. M. McGrath
The National Weather Service (NWS) has developed the Damage
Assessment Toolkit (DAT), an application for smartphones, tablets
and web browsers that allows for the collection, geolocation, and
aggregation of various damage indicators collected during storm
surveys. As part of an ongoing collaboration between NASA and
NOAA, members of the NASA Short-term Prediction Research
and Transition (SPoRT) team have been working to integrate
Earth-observing remote sensing from operational, polar-orbiting
satellites to support the damage assessment process. Imagery
assists by identifying portions of damage tracks that may be missed
due to road limitations, access to private property, or time
constraints. This support includes data and imagery from Terra and
Aqua MODIS, Landsat-7, Landsat-8, S-NPP VIIRS, Terra Aster,
and EO-1 in addition to high resolution commercial imagery such
as Digital Globe-Worldview and other DOD imagers. Image
products that utilize change detection techniques can identify
damage to vegetation and the land surface, aiding in the survey
process. Higher resolution commercial imagery can be
corroborated with ground-based surveys to improve accuracy by
providing a highly detailed overview of the damaged. Daily and
publicly released products and imagery are pushed to a web
mapping server (WMS) as they are received, while commercial
imagery is acquired by the USGS, then ingested and provided upon
request of a NWS office in support of a damage survey. Prior to
the 2015 severe storm season, SPoRT team members created
targeted training materials which provided example uses of
imagery in addition to developing level 1 products from spectral
bands onboard the various polar-orbiting sensors. In cooperation
with the NWS Central Regions' Regional Operations Center
(ROC), this training was provided live to over 20 offices through a
webinar. The training was made available offline to all offices
within Central region through an internal NWS intranet. Since the
start of the severe weather season, several severe storm events
affecting five NWS WFOs have occurred, resulting in the
adjustment of three tornado tracks as a direct result of the
information provided by satellite imagery during the damage
assessment process. The NWS WFO in Des Moines, Iowa has been
affected by two separate storm events: an EF1 tornado at Lake City
on 10th of May 2015, and on the 22nd of June 2015 an EF3 that
occurred near Columbia and an EF1 tornado near Eddyville.
Satellite imagery used within the DAT resulted in the adjustment
of location and lengths of two out of three tracks. This presentation
will describe where and how the different datasets were used
during the survey.
Assimilation of Dual-Polarimetric Radar Observations
Using WRFVAR with Ice Particles in Control Variables
Thursday, 14 January 2016: 2:00 PM
Room 345 ( New Orleans Ernest N. Morial Convention Center)
Xuanli Li, Univ. of Alabama, Huntsville, AL; and J. R. Mecikalski
The dual-polarimetric radar transmits and receives both horizontal
and vertical power returns. It can provide information on
hydrometeor size and classification that radar reflectivity alone
cannot discern, such as shape, hydrometeor correlation, and better
precipitation intensity approximations. The assimilation of the
dual-polarimetric radar data may provide additional benefit to
better describe the storm structure and microphysical properties in
cloud. However, due to the complicated relationships in
microphysics processes and uncertainty in dual-polarimetric radar
measurement, the assimilation of dual-polarimetric radar variables
is a challenging problem.
This study explores the methodology to assimilate the dualpolarimetric radar observations with WRF 3DVAR system. A
package has been developed to include the ice-phased
microphysical processes into the tangent linear and adjoint of
precipitation model of WRF 3DVAR system. Snow and cloud ice
have been added into the WRF 3DVAR moisture control variable.
The inclusion of ice-processes in the forward operator will better
describe the ice particles in cloud. The experiments are conducted
with assimilation of horizontal reflectivity, radial velocity, and
differential reflectivity. The presentation will highlight the
difference between warm-rain and ice-phased processes in
assimilation of the observations from the dual-polarimetric radars.
Preliminary result from the assimilation experiments for a
convective storm (15 March 2008) will be discussed at the
conference. Further details of the methodology of data
assimilation, the influences of the dual-polarimetric radar
variables, and the impact of the dual-pol data on microphysical
properties will be presented at the conference.
Initial Results of Integrating GPM/IMERG
Precipitation Data into Operational Forecasting
Environments
Thursday, 14 January 2016: 2:30 PM
Room 240/241 ( New Orleans Ernest N. Morial Convention
Center)
Matthew R. Smith, Univ. of Alabama, Huntsville, AL; and A.
LeRoy, J. L. Case, and D. T. Bolvin
The Global Precipitation Measurement (GPM) mission core
satellite was launched in February 2014 with the goal of providing
the next-generation, state-of-the-art global quantitative
precipitation estimates (QPE). Taking advantage of an
international constellation of satellites of opportunity, the
Integrated Multi-satellitE Retrievals for GPM (IMERG) produces
precipitation estimates in the range 60°N-S every half hour at 0.1°
resolution. The IMERG precipitation is calibrated to the GPM
Microwave Imager/Dual-frequency Precipitation Radar combined
product to provide the best possible estimates. IMERG products
are produced at three different latencies to accommodate the
unique requirements of the various user bases. The “Early” run has
a 6-hour latency (for flash flood monitoring, etc.), the “Late” run
has a 16-hour latency (for drought monitoring, crop forecasting,
etc.) and the “Final” run has a 3-month latency (for research). The
“Early” and “Late” data begin in March 2015, and the “Final” data
begin in March 2014.
To assist partner NOAA/National Weather Service (NWS) offices
in precipitation forecasting, the NASA Short-term Prediction and
Research Transition (SPoRT) project transitioned the calibrated
IMERG rain rate product for a preliminary evaluation during
summer 2015. The IMERG rain rate is accumulated at 1-, 3-, 6-,
12-, and 24-hour intervals for dissemination to NWS forecast
offices. Three regions were targeted for this evaluation, each with a
participating NWS Weather Forecast Office and River Forecast
Center: Alaska/North Pacific, Southwestern U.S., and Southeastern
U.S./Puerto Rico. These three regions offer widely-varying
characteristics, limitations, and requirements regarding the use of
precipitation data in forecast operations. To evaluate the utility of
the IMERG data, forecasters are providing feedback on how the
IMERG product is used in operations, including documentation of
the product's perceived strengths and weaknesses. In addition to
this qualitative forecaster feedback, the “Early” and “Late”
IMERG products are inter-compared quantitatively to current
operational QPE products (i.e., Stage IV and Multi-Radar MultiSensor [MRMS]) using NCAR's Model Evaluation Tools (MET)
in combination with a SPoRT-developed scripting package for the
MET software. This presentation will summarize the qualitative
forecaster feedback, as well as the quantitative comparison of the
IMERG “Early” and “Late” product against the operational Stage
IV and MRMS QPE products.
Near-Real Time Severe Weather Damage
Identification Algorithm for Vegetation:
Development and Early Results
Thursday, 14 January 2016: 3:30 PM
Room 252/254 ( New Orleans Ernest N. Morial Convention
Center)
Jordan R. Bell, University of Alabama, Huntsville, AL; and A. L.
Molthan
The NASA Short-term Prediction Research and Transition
(SPoRT) Center has partnered with the National Weather Service
(NWS) to provide satellite imagery and satellite derived products
to the Damage Assessment Toolkit (DAT) to supplement
forecasters when performing damage surveys. Satellite remote
sensing data has been used in high-impact case studies to examine
and document damaged areas that occur as result of severe
weather. Unfortunately, this analysis has been only in a manual
and time-consuming way. With the advancements in satellite
technology near-real time algorithms should be developed to
support surveyors in identifying damaged areas that may not be
accessible or visible from the ground.
The algorithm that has been developed here combines a short-term
Normalized Difference Vegetation Index (NDVI) change product
and land surface temperature (LST) information into a feature
based technique. These two products are individually analyzed for
anomalies within each product and then inputted through a feature
detection filter to determine which areas stood out when compared
to the entire image.
The development of the algorithm has used MODIS data from
Aqua and Terra and past events in order to tune the algorithm.
With MODIS exceeding its mission lifetime, this algorithm was
designed so that it can easily ingest the next generation of satellites
(Suomi –NPP, GOES-R, JPSS-1) which are currently online or
coming online in the next couple of years. Most of the results will
highlight the algorithm being used on tornado cases. The
algorithm's output will be compared against the official NWS
ground surveys. An additional case study highlighting the
algorithm's success on identification of hail damage will be
presented. While the NWS does not currently perform official
surveys on wide spread damaging hail events, this algorithm and
the DAT could provide the opportunity to expand surveys to cover
hail swaths.
Remote Impacts of Lowland Urbanization
on Orographic Cloud Properties
Thursday, 14 January 2016: 4:15 PM
La Nouvelle C ( New Orleans Ernest N. Morial Convention
Center)
Brian Freitag, University of Alabama, Huntsville, AL; and U. S.
Nair
San Miguel de Tucuman, Argentina, is a developing city east
(upwind) of the Andes Mountain range surrounded by open
agricultural land. Orographic influences on regional climate have
been extensively studied in prior research; however, the location of
the urban development in San Miguel de Tucuman provides a
unique opportunity to study the impacts of changing land use in a
region upwind of topography. A process study was performed
using control Weather Research and Forecasting (WRF) model run
and a WRF run with the urban environment removed. Model
results from the control run were compared with surface
observations as well as satellite observations of cloud cover
(MODIS, CloudSAT) and precipitation (TRMM). Significant
differences between the two model simulations were observed
particularly in cloud properties and precipitation patterns. These
differences are attributed to altered vertical surface moisture
exchange and atmospheric flow. Increased vertical mixing with the
urban environment caused by changes in surface roughness result
in the development of deeper clouds upstream of the urban center.
Removal of the urban environment results in more vigorous
vertical motion at the terrain interface and stronger orographic
effects. This resulted in a westward shift in precipitation with the
urban environment removed.