| 2016
Volume 1
ATMOSPHERIC
SCIENCES News
Exponent Atmospheric Sciences News Release
Using NEXRAD Radar Data to
Estimate Rainfall
By Alfred M. Klausmann, Christopher DesAutels, and Jelena Popovic
Introduction
Intense or sudden rainfall can have a number of impacts on people and the built
environment. Rain gauges represent a common source for information on rainfall
rates and accumulation. However, information from gauges will not always be
sufficient. Rainfall can vary significantly due to terrain with greater rain falling at
higher elevations. There also can be significant spatial variability within severe
weather systems, even over the distance of a few kilometers. Doppler radar can
be useful in these circumstances to develop location specific rainfall information.
Radars have been used to estimate rainfall rates since the late 1980’s (Hunter, 1996).
Doppler radars have coverage spanning most of the continental United States. There
is a direct relationship between radar reflectivity and precipitation rate which allows
for estimation at locations across a large area. Starting in approximately 2011, dual
polarization radars have come into use, resulting in increased resolution and accuracy.
Care must be taken to correctly interpret dual polarization radar fields, but they have
the ability to provide important information on precipitation type (rain versus hail)
along with precipitation rate on a time scale as fine as 5 minutes and horizontal
resolutions on the order of 1–2 kilometers.
The West Virginia Historic Flood Event of June 2016
During the period June 22–26, 2016, intense thunderstorm activity along a stationary
frontal boundary resulted in rainfall amounts on the order of 8–10 inches over an
area of southeastern West Virginia. The combination of topography and rainfall
resulted in historic flooding with extensive property damage. Figure 1 shows a set
of 4 composite radar reflectivity images. Radars send out a radio signal that travels
outward from the radar antenna. That radio signal will bounce off particles in the
atmosphere and the reflected radio signal, received by the radar, is then analyzed.
The magnitude of the reflectivity is proportional to the precipitation intensity. Composite
imagery means that the maximum radar reflectivity is shown based on scans from all
radar beam elevation angles. This plot shows a series of intense thunderstorm cells
moving through West Virginia. These cells had peak reflectivity values greater than
50 dBZ indicative of intense rainfall. The focus of these thunderstorms is over southeastern
West Virginia from Charleston southeastward. Figure 2 shows a sample of the radar
based one-hour precipitation for this event and the storm total precipitation for the
event ending at 2358 UTC on June 24, 2016. The 1-hour rainfall total shows large
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1-hour rainfall rates on the order of 1–2 inches in
a northwest-southeast band. The storm total rainfall
shows peak rainfall totals along this same northwest to
southeast band with rainfall amounts on the order of
10 inches across parts of Greenbrier County, WV.
The plots in Figures 1 and 2 also show very large
gradients in reflectivity and rainfall totals and show how
rain gauge measurements can misrepresent total rainfall
in a region depending on their position relative to these
thunderstorms. Note to the southwest of the maximum
rainfall in Figure 2 is a band of grey which shows
interference with the radar signal.
FIGURE 1.
Composite radar reflectivity from Charleston West Virginia Radar on June 23, 2016 at 1539, 1751, 1930, and 2050 UTC.
Deep reds show intense rainfall. Note the very sharp spatial changes in rainfall intensity.
Axis of maximum storm total
rainfall across Greenbrier
County, WV
Charleston,WV radar
Maximum
1-hour rainfall
Radar
interference
FIGURE 2.
1-hour rainfall totals for June 23, 2016 at hour ending at 1938 UTC (left) and storm total rainfall for the period June 23, 2016 at
0153 UTC through June 24, 2016 at 2358 UTC (right). Radar images from the Charleston, West Virginia NEXRAD radar site.
(A band of grey in this figure (southwest of the maximum rainfall) represents interference with the radar signal and should be ignored.)
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Performing the Radar Analysis
Rainfall estimates from radars are done using an
empirical relationship between reflectivity and surface
rainfall rate known as the Z-R relationship. The Z-R
relationship is non-linear and there are variations in
different geographical regions and seasons due to
variations in cloud microphysics. The relationship
depends on the droplet size distribution as well as the
type of precipitation particles in the sampled volume.
The droplet size distribution and characteristics of the
precipitation particles are the result of the microphysical
processes which can vary by individual event, season,
and geographical region.
The quality of the measurements is also impacted by
the effects of topography intersecting the radar beams
as well as interference from other sources such as
manmade obstacles or the effects of electromagnetic
fields. Thus an analysis needs to be conducted to
evaluate the quality of the radar data at a particular
site. This may involve the use of data from multiple
radars. Radar precipitation estimates can also be
correlated with both infrared and visible satellite
imagery to compare satellite image cloud signatures
with the location of maximum rainfall.
Figure 3 shows an example of storm total precipitation
from the Seattle, Washington radar from November
13-18, 2015, a period with flooding rains along the
Cascade Mountains. There are several interesting
features shown in Figure 3. One is the donut shaped
pattern to the total rainfall. This pattern is the result of
sinking air on the lee side of the Olympic Mountains in
the Seattle region known as the Olympic rain-shadow.
This lee side terrain induced sinking of air causes
rainfall to diminish in this region. The area of maximum
precipitation to the east and southeast of Seattle is
due to higher topography and upslope flow on the
windward side of the Cascades. Figure 3 also shows
interference patterns in the radar data to the north of
the radar and southwest of the radar. This interference
is due to the radar beams intersecting the mountains,
resulting in rainfall measurements which are not reliable.
Studies have suggested that radar rainfall estimates
can have biases that are dependent on geographical
region, the characteristics of the weather event, and
season. For this reason, an essential component of
a radar based precipitation analysis must involve
calibrating the radar estimates against available
rain-gauge measurements (Cunha et al., 2013).
Available rain gauge measurements can be used to
compare with radar derived rainfall estimates to assess
any systematic bias and then impose a bias correction
to the radar rainfall estimates.
Minimum rainfall due to
Olympic Rain Shadow
Topographic
interference
due to
Olympic
Mountains
FIGURE 3.
NEXRAD Radar storm total rainfall using Seattle NEXRAD radar
for the period beginning on November 13, 2015 at 1945 UTC
and ending at November 14, 2015 0948 UTC (top) and
November 18, 2015 at 2340 UTC (bottom).
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Exponent Expertise
References
Exponent’s atmospheric science team specializes
in applied meteorology, meteorological modeling
and analysis, climatological studies, and air quality
modeling. Clients benefit from our multidisciplinary
approach, which includes the support of staff across
multiple practice areas. Our atmospheric scientists
work closely with building, structural and civil engineers
to evaluate structural damage due to storm events.
Exponent has powerful computational capabilities,
including a dedicated 120-processor Linux cluster
computer, which allow us to simulate at very high
spatial resolution as well as run multiple scenarios
and sensitivity studies.
Cunha, L.K., J.A. Smith, M.L. Baeck, and W. F. Krajewski,
2013, An Early Performance Evaluation of the NEXRAD
Dual-Polarization Radar Rainfall Estimates for Urban
Flood Applications. Weather and Forecasting,
Volume 28, 1478–1479.
Exponent scientists have used a variety of available
observational data to perform analyses of
meteorological conditions associated with such
phenomena as tropical storms or flash flood events.
These datasets include surface and upper air
meteorological observations, radar data, satellite
imagery, lightning data, buoy data, various analysis
NOAA analyses, and high resolution terrain and land
use data. Exponent’s atmospheric scientists have
extensive experience with meteorological analysis
including radar analysis and have done a number of
radar studies of severe weather events including rainfall
analysis for flash flooding and landslide events, wind
events due to thunderstorm microbursts, and hurricanes.
Contact Information
Hunter, S.M., 1996,”WSR-88D Radar Rainfall Estimation:
Capabilities, Limitations, and Potential Improvements.
National Weather Digest, Volume 20 (4), 26–38.
Alfred Klausmann, CCM
aklausmann@exponent.com | 978.461.4628
Christopher DesAutels
cdesautels@exponent.com
Jelena Popovic
jpopovic@exponent.com
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978.461.4623
978.461.4622
For more information on Exponent capabilities,
please visit our website,
www.exponent.com.
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