IMPROVING QPE AND VERY
SHORT TERM QPF
An Initiative for a Community-Wide Integrated Approach
BY
STEVEN V. VASILOFF, DONG-JUN SEO, KENNETH W. HOWARD, JIAN ZHANG, DAVID H. KITZMILLER,
MARY G. MULLUSKY, WITOLD F. KRAJEWSKI, EDWARD A. BRANDES, ROBERT M. R ABIN,
DANIEL S. BERKOWITZ, HAROLD E. BROOKS, JOHN A. MCGINLEY,
ROBERT J. KULIGOWSKI, AND BARBARA G. BROWN
A multisensor applications development and evaluation system at the National Severe
Storms Laboratory addresses significant gaps in both our knowledge and capabilities for
accurate high-resolution precipitation estimates at the national scale.
W
ater is a precious resource and, when excessive or in short supply, a source of many
hazards. It is essential to monitor and predict water-related hazards, such as floods, droughts,
debris flows, and water quality, and to determine
current and future availability of water resources.
Accurate quantitative precipitation estimates (QPE)
and very short term quantitative precipitation forecasts (VSTQPF) provide key input to these assessments. [QPE and VSTQPF are hereafter referred to
AFFILIATIONS: VASILOFF, HOWARD, R ABIN, AND BROOKS —NOAA/
National Severe Storms Laboratory, Norman, Oklahoma; SEO —
NOAA/NWS/Office of Hydrologic Development, Silver Spring,
Maryland, and University Corporation for Atmospheric Research,
Boulder, Colorado; ZHANG —Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and NOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma; KITZMILLER—
NOAA/NWS/Office of Hydrologic Development, Silver Spring,
Maryland; MULLUSKY—NOAA/NWS/Office of Climate, Water, and
Weather Services, Silver Spring, Maryland; KRAJEWSKI —IIHR—The
University of Iowa, Iowa City, Iowa; BRANDES AND BROWN —National
Center for Atmospheric Research, Boulder, Colorado; BERKOWITZ—
NOAA/WSR-88D Radar Operations Center, Norman, Oklahoma;
AMERICAN METEOROLOGICAL SOCIETY
collectively as quantitative precipitation information
(QPI).] To meet these needs at the national scale,
accurate QPI is needed at various temporal and spatial
scales for the entire United States, its territories, and
immediate surrounding areas. Temporal scales
range from minutes to several hours for flash flood
prediction. QPI products can then be aggregated to
support longer-term applications for water supply
prediction. Spatial scales range from a few square
kilometers or less for urban flash flood prediction,
MCGINLEY—NOAA/Earth System Research Laboratory, Boulder,
Colorado; KULIGOWSKI —NOAA/National Environmental Satellite,
Data, and Information Service, Camp Springs, Maryland
CORRESPONDING AUTHOR: Steven Vasiloff, NOAA/National
Severe Storms Laboratory, National Weather Center, 120 David L.
Boren Blvd., Norman, OK 73072
E-mail: [email protected]
The abstract for this article can be found in this issue, following the table
of contents.
DOI:10.1175/BAMS-88-12-1899
In final form 15 June 2007
©2007 American Meteorological Society
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to several thousand square kilometers or more for
climate prediction. Spurred by advances in weather
radars, satellites, and numerical weather prediction
(NWP), the science and operational practices of precipitation analysis and prediction have, for the most
part, made tremendous progress in the last quarter
century. However, as described below, significant gaps
in both knowledge and capabilities must be bridged to
produce accurate high-resolution QPI at the national
scale for a wide spectrum of users. In this article, we
present a proposal for a community-wide integrated
approach that we argue is necessary to advance precipitation science and its infusion into operations to
meet the nation’s QPI needs effectively.
The time scale of focus for next-generation QPI,
or Q2, products is about a day into the past and
about 6 hours into the future (Fig. 1). Going back
in time provides an opportunity for near-real-time
precipitation reanalysis, or analysis of record, that
may fully utilize observations that are not available
for real-time estimation. Looking into the very short
term future provides an opportunity to combine QPE
with an analysis of the current state of the atmosphere
and NWP output to produce seamless precipitation
forecasts at the local to continental spatial scale.
Compared to numerical modeling for weather or
hydrologic prediction, QPI has remained a loosely
organized area of research. As such, algorithms and
applications have often been developed by different
people in different organizations in different places
(often from scratch) for specific users and applications. While this mode of discovery has resulted in
important scientific advances and valuable operational
experiences, these advancements are less likely to reach
the entire research and operational community. Even
if they do, meaningful comparative verification and
benchmarking are often lacking. A case in point is
that issues identified as being the most important for
radar rainfall estimation over a quarter of a century
ago (Wilson and Brandes 1979) are still identified as
the most important today. More importantly, it is just
as difficult today, as it was then, to ascertain the best
available methodology, technique, or algorithm for a
particular problem (e.g., rain gauge–based correction
of bias in radar rainfall data). To effectively address
the nation’s needs, QPI must be recognized as a multifaceted, multiscale, multisensor (MS) problem that
requires concerted and coordinated efforts that are
community wide and integrated. A community-wide
approach is necessary because the problem is large and
complex and, hence, requires the expertise and experiences of many. An integrated approach is necessary to
fully capitalize on advances in science and technology
in many different areas.
To foster the development of a community-wide
integrated approach of advancing QPI science and
its infusion into operations, the National Oceanic
and Atmospheric and Administration (NOAA)/
National Severe Storms Laboratory (NSSL) and the
NOAA/National Weather Service (NWS)/Office of
Hydrologic Development (OHD) jointly organized
and hosted the first Q2 workshop in Norman,
Oklahoma, on 28–30 June 2005 (proceedings available online at www.nssl.noaa.gov/projects /q2/
2005wkshp/). This article synthesizes the workshop
discussions and outlines a comprehensive Q2 science
plan. A suggested path for research to operations
(RTO) is also provided with the caveat that any RTO
plan must follow the NWS Operations and Services
Improvement Process (OSIP; NWS 2007). As seen
below, Q2 shares a number of focus areas and strategies for research and development (R&D) and RTO
transition with warm- and cool-season QPF improvement (Fritsch and
Carbone 2004; Ralph et al. 2005).
As such, close and cross-cutting
collaborations with the QPF community, as well as the hydrology and
water resources communities, are
critical to its success.
F IG . 1. Schematic depiction of the time scale of focus for nextgeneration quantitative precipitation information (Q2). “Seamless”
means that discontinuities between observations and model data
have been removed by the blending processes.
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DECEMBER 2007
Data sources for quantitative
precipitation information. A large
part of the workshop was devoted
to critically reviewing currently
available observational tools for
QPI, operational practices and experiences, key science issues, and
future prospects for technological
advances with respect to each tool. This section summarizes those discussions that provide the basis for
Q2. (Details can be found in the individual presentations available online at http://nssl.noaa.gov/projects/q2/2005wkshp/presentations.php.) Table 1 lists
the primary tools for estimating precipitation, along
with the temporal and spatial scales of observations
and their current operational applications. Strategies
for combining data from the three primary sensors
are discussed in more detail later.
Weather radar. The major remote sensing tool for
precipitation over the United States is the Weather
Surveillance Radar-1988 Doppler (WSR-88D; Fulton
et al. 1998). Key geometric uncertainties in radar estimates are due to the curvature of the Earth and radar
beam broadening (e.g., Battan 1973). Precipitation
estimates are expected to be most accurate where
the radar beam is close to the ground. In the current
WSR-88D network configuration, coverage of storms
above 1 km is especially poor in the West (Maddox
et al. 2002), where the NWS River Forecast Centers
(RFCs) use primarily rain gauge data to estimate
precipitation input to operational hydrologic models.
There has been an increasing demand for scanning
strategies with lower starting elevation angles for
mountain-top radar locations and for better detection
of shallow stratiform precipitation (e.g., Wood et al.
2003); these radars do not scan below 0.5°. In addition
to modified scanning strategies, gaps in WSR-88D
coverage may be filled by other radars such as those
used by the Federal Aviation Administration (Vasiloff
2001), and by limited-range radars such as those in
the planned Collaborative Adaptive Sensing of the
Atmosphere radar network (McLaughlin et al. 2005).
Television station radars also could be adapted into
TABLE 1. The three primary sensors used to estimate precipitation, their associated strengths and weaknesses, time and spatial scales of measurements, and current operational applications.
Sensor
Strengths
Weaknesses
Weather radar
• High spatial and
temporal resolution
• Good areal
coverage
•
•
•
•
Geostationary
satellite
• Continuous spatial
coverage
Range effects
Coverage in complex terrain
Ze –R and Ze –S uncertainties
Nonmeteorological target
contamination
Time–space
scales
Operational applications
• 5–10 min
• 1 km
• Precipitation nowcasting
• Flash flood forecasting
• River forecasting (after bias
correction)
• Land surface modeling
(after bias correction)
• Indirect measurements of
precipitation
• Difficulty with
nonprecipitation clouds
• 15 min
• 4 km
• Nowcasting
• Flash flood forecasting
• River forecasting (after bias
correction)
• Land surface modeling
(after bias correction)
Polar-orbiting
• Continuous spatial
satellite (passive
coverage
microwave)
• Poor spatial/temporal
resolution
• Indirect measurements of
precipitation
• Difficulty with non-icebearing clouds
• 3–6 h (6+
satellite
constellation)
• 15 km
• Tropical Rainfall Potential
(TraP)
• Adjustment of GOES
precipitation estimates
Precipitation
gauge
• Nonuniform spatial
distribution
• Latency in real-time data
transfer
• Problems with quality of
measurements
• Frozen hydrometeors
• Wind effects
• Uncalibrated (tipping-bucket
type in high rain rate)
• 10 min–1 day
•
•
•
•
•
• Direct
measurement of
precipitation
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Flash flood forecasting
River forecasting
Land surface modeling
Water supply forecasting
Correction of radar and
satellite QPE
• Hydroclimatological studies
• Verification
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the current WSR-88D network. It should be noted
that the addition of data from radars with higher
operating frequencies potentially presents problems
due to attenuation by heavy precipitation.
Another “gapfilling” technique is a correction
based on vertical profiles of reflectivity (VPR; e.g.,
Joss and Waldvogel 1990). VPRs of various spatial
and temporal scales can be classified for different
precipitation types and applied to 3D radar reflectivity observations (Zhang et al. 2006b). Also, to
partly compensate for beam-broadening effects, new
scanning strategies have been proposed to increase
horizontal and vertical resolution (e.g., Brown et al.
2005).
Other sources of uncertainties in radar precipitation estimates include radar reflectivity–rain rate
(Ze –R) relations resulting from variable drop size
distributions and mixed precipitation types (hail,
rain, and/or snow), lack of consistent radar hardware
calibration, “cone of silence” near the radar, evaporation near the ground, and horizontal advection
below the radar sampling volume due to wind shear.
Improvements are also needed on quality control
(QC) of radar data to remove ground clutter, biological targets, and other nonprecipitation echoes (e.g.,
Kessinger et al. 2003).
It is expected that uncertainties in radar QPE
due to variations in precipitation microphysics and
target identification will be reduced by the use of
dual-polarization techniques. The Next-Generation
Weather Radar (NEXR AD) program plans to
upgrade the WSR-88D network for dual polarization
beginning in 2009 (Saffle et al. 2006). The radars will
transmit both vertically and horizontally polarized
electromagnetic waves. Because larger raindrops and
most nonmeteorological targets are not spherical,
their backscattering cross sections are not the same
for the two polarizations, and the returned power and
the Doppler shift within the two channels differ. The
returned signals yield information regarding hydrometeor size, shape, orientation, and microphysical
phase. Two polarimetric variables, the differential
reflectivity and the specific differential propagation
phase, have been shown to improve radar estimates of
rainfall and discrimination among backscatters (e.g.,
Brandes et al. 2002). Though extensive work has been
done to mitigate contamination by ground targets
and clutter due to anomalous propagation of the
radar beam, radar rainfall estimates are still sometimes compromised by ground clutter as well as by
large hail, melting snow, and nonprecipitation aerial
targets, such as insects, migrating birds, and chaff.
The hydrometeor classification algorithm (Ryzhkov
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DECEMBER 2007
et al. 2005), based on dual-polarization data, shows
promise for discriminating among these classes of
backscatterers so as to eliminate nonprecipitation
targets and enable corrections to rainfall estimates
in areas with mixed hydrometeor phase.
Satellite. While generally acknowledged to have
significantly greater uncertainty than radar, precipitation estimates from satellite data provide continuous spatial coverage and can be valuable where
radar data are unavailable or known to be unreliable.
Various techniques have been developed to estimate
precipitation from infrared (IR) and microwave
satellite observations. The Hydro-Estimator (HE;
Scofield and Kuligowski 2003) and the Geostationary
Operational Environmental Satellite (GOES)
Multispectral Rainfall Algorithm (GMSRA; Ba
and Gruber 2001) are recent examples. The HE and
GMSRA use GOES data with a routine sampling
interval of 15 min over the continental United States
(CONUS) and surrounding regions with a minimum field of view (FOV) of 4 km. While the HE was
developed primarily for deep, long-lasting convective
events with cold cloud tops, the GMSRA attempts to
improve estimation of rainfall from relatively warm
clouds during the daytime by using the estimated
cloud particle size near the cloud top (derived from
the reflected radiance of 3.9 μm).
Passive microwave sensors provide a stronger
indicator of precipitation than IR sensors because
they provide integrated information about the
precipitation-sized ice content within the clouds.
However, because the top-of-atmosphere microwave
signal is much weaker than the IR signal, microwave
instruments are presently available only on low Earthorbiting (LEO) satellites with a typical sampling
frequency of twice per day per satellite and a FOV on
the order of 15 km. Some of the earliest microwave
rainfall algorithms, still in use today, were developed
for the Special Sensor Microwave Imager (SSM/I)
instruments aboard the Defense Meteorological
Satellite Program (DMSP) satellites (e.g., Ferraro et al.
1996) and the Advanced Microwave Sounding Unit
(AMSU) aboard the NOAA polar-orbiting satellites
since 1998 (e.g., Qiu et al. 2005). The primary
application of microwave-estimated rain rates is
evaluating precipitation systems that are still over
water. In the case of tropical systems approaching
landfall, the microwave rain-rate estimates are used
to create operational extrapolated 24-h forecasts
called Tropical Rainfall Potential (TRaP; Kidder et al.
2005). In general, satellite algorithms work best for
cold convective clouds with a cold IR signature (IR
algorithms) or when significant amounts of cloud
ice are available (microwave algorithms). Location
errors can occur in regions of high wind shear as
the upper-level winds carry the clouds in one direction and the hydrometeors they produce in another
direction, particularly for IR-based techniques that
are at higher spatial resolution and rely on cloud-top
properties. Satellite estimates also need to be quality
controlled to screen out nonprecipitating clouds (e.g.,
Ba and Gruber 2001; Scofield and Kuligowski 2003).
Estimation of orographically enhanced precipitation
remains a significant challenge, because the associated signatures in both the IR and microwave imagery
tend to be quite weak.
To address some of these shortcomings, a number
of techniques attempt to blend information from both
IR and microwave instruments, or to blend information from GOES IR and radar. For example, in the
Self-Calibrating Multivariate Precipitation Retrieval
(SCaMPR) technique of Kuligowski (2002), GOES
brightness temperatures and other predictors are calibrated against other measures of precipitation from
satellite-based microwave instruments or groundbased radar. Another technique, regressing IR
temperatures against radar data, has shown promise
in estimating widespread winter precipitation in
Arizona where the lowest radar beam was within or
above the melting layer (Gourley et al. 2002).
The Tropical Rainfall Measuring Mission (TRMM)
satellite-based radar system provides instantaneous
rainfall rates and can be used to calibrate groundbased radars (Anagnostou et al. 2001). The TRMM
satellite is also a LEO platform and data are limited to
twice daily south of approximately 35°N latitude over
North America. The possible role in Q2 of spaceborne
radar data from TRMM and the planned Global
Precipitation Mission is still being explored.
Precipitation gauges. In situ precipitation gauges
provide direct measurement of point precipitation
as well as a surface reference for adjustment and
evaluation of, and merging with, remotely sensed
precipitation. Because of the various limitations of
radar QPE in complex terrain described earlier, gauge
data provide the bulk of QPE in the West. There are
primarily two types of gauges widely in use. The
tipping bucket type works well in rain (Habib et al.
2001; Ciach 2003), but has problems with frozen
precipitation, even when equipped with a heating
element. Weighing bucket gauges, including the snow
measurement using telemetry (SNOTEL) gauges, use
a weighing mechanism to derive snow-water equivalent (see, e.g., Johnson and Shaefer 2002). SNOTEL
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gauges are mostly used in remote mountainous areas
and are designed specifically to measure snowfall. All
gauges are subject to error in windy conditions, and
local conditions can greatly affect the gauge accuracy
(e.g., Sieck et al. 2007).
Gauge-based estimation of precipitation requires
spatial interpolation of point measurements, for which
numerous techniques exist (e.g., Tabios and Salas 1985).
For example, the NWS RFCs in the western United
States use the Mountain Mapper (Henkel and Peterson
1996), which uses a gauge-only analysis technique that
accounts for terrain effects based on a high-resolution
precipitation climatology and the Parameter-Elevation
Regression on Independent Slopes Model (PRISM;
Daly et al. 1994). In other parts of the United States,
the Multisensor Precipitation Estimator (MPE; Seo and
Breidenbach 2002) is used. The MPE uses the Single
Optimal Estimation technique (SOE; Seo 1998) for
gauge-only analysis that also accounts for the terrain
effects using the PRISM.
MULTISENSOR QPE. Improved precipitation
products must draw from each sensor’s strength in an
optimal way. Weather radar can provide high-quality
precipitation estimates in regions of good coverage. In
regions of poor or no radar coverage in the western
United States, gauge-only analyses using precipitation climatology are the primary source of data for
operational algorithms. Satellites are the secondary
source of data followed by radars. Figure 2 shows
an example of how data from multiple sensors may
be utilized in a MSQPE framework. In this process,
data from all radars are objectively analyzed to a 3D
grid to provide a 3D reflectivity mosaic (Zhang et al.
FIG. 2. Schematic flowchart showing an example of how
MSQPE might be determined.
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2005, 2006a). The resulting mosaic may then be used
to derive the most representative radar-only precipitation estimate, given that several radars may overlap
individual grid points. NWP model fields may be
used to assist in depicting the atmospheric environment and to identify microphysical processes (e.g.,
rain versus snow, melting layers, warm rain, etc.). The
0° wet-bulb isotherm may help differentiate between
rain and snow at the surface. Also, the depiction of
the melting layer and the rain–snow transition line,
in addition to topographic information and spatial
radar sampling geometry, provides a basis to define a
quality map for radar estimates. Alternative sensors,
such as satellites, may provide better precipitation
estimation in regions that lack radar coverage east
of the Rocky Mountains while, in the West, as mentioned previously, gauges are the primary source of
data for precipitation algorithms. Lightning data can
also be used to infer storm properties and validate
reflectivity-based techniques. For instance, lightning
is a direct measure of convective activity and can be
used to delineate areas of convective and stratiform
precipitation. These areas can then be further used
to specify different Ze –R relations and to determine
radar-to-grid interpolation strategies (e.g., Zhang
et al. 2005).
Research is ongoing to determine the effectiveness
of using satellite data in filling horizontal gaps, both
in blocked areas and at far ranges from each radar. For
example, regression techniques may compare satellite
measurements with radar-derived rain rates in areas
of good radar coverage (e.g., Gourley et al. 2002;
Kuligowski 2002). Such empirical relationships can
be derived for areas or zones of common storm type
(as defined by NWP data and surface and upper-air
observations), climate, and/or terrain regimes. The
regressions may be updated with new radar scans,
nominally every 5–10 min. For each regression zone,
the empirical relationship is applied to the satellite IR
field to obtain a radar-adjusted satellite QPE field.
The resulting satellite field may then be blended,
pixel by pixel, with the radar-only field via weighted
averaging.
Rain gauge–based correction may be applied to the
radar–satellite blended field to reduce any biases that
may exist. A common approach using rain gauges for
radar rainfall correction is to calculate a mean field
bias (Collier et al. 1983; Smith and Krajewski 1991;
Seo et al. 1999). However, a mean field bias does
not capture biases at small scales and is therefore
not useful when biases are spatially nonuniform.
Adaptive local bias correction schemes apply bias
corrections to radar precipitation estimates at small
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time scales (e.g., Brandes 1975). The interpolated bias
is then used to adjust hourly radar rainfall estimates
on a pixel-by-pixel basis. Seo and Breidenbach (2002)
developed a local bias correction method for the MPE.
Major limitations on using rain gauges are the limited
number, temporal resolution, and timeliness of rain
gauge observations.
The radar–satellite QPE can then be merged
directly with rain gauge data via multivariate analysis
or optimal estimation techniques. As mentioned
earlier, a bivariate version of SOE is used in MPE for
radar–gauge merging. The final MSQPE analysis
closely resembles a gauge-only analysis where the
gauge network is dense. A multivariate analysis
may be formulated to estimate probabilities. For
example, Clark and Slater (2006) developed a model
for categorical probabilities of hourly precipitation
amount based on gauge observations, atmospheric
conditions (from an NWP model analysis), terrain,
and radar/satellite observations.
VERY SHORT TERM QPF. Once a QPE field is
obtained, the analysis may be blended with NWP
model output to provide a very short term precipitation forecast. The VSTQPF should include both
extrapolation of current QPE and predictive NWP
components. The NWP models take some time to
initialize and to become more accurate than very
short term extrapolation of radar data. For example,
four-dimensional variational methods model the
cloud environment from radar data ingest and apply
an adjoint technique for model initialization and
forecast (Sun and Crook 1997). While the precise time
when extrapolated radar data become less accurate
than NWP data depends on the weather regime as
well as the model, the term very short term QPF is
used here to indicate the 0–6-h time frame (see Fig. 1).
For extrapolation, there are a number of techniques
available:
• feature identification and tracking—involves
extrapolating forward in time a radar-identified
object from its previous position (e.g., Johnson
et al. 1998; Mueller et al. 2003);
• radar cross correlation (CC) and translation—
establishes a pattern that is trackable and then
extrapolates the position in time and space (e.g.,
Dixon and Wiener 1993; Grecu and Krajewski
2000);
• background wind advection—estimates the future
position of echoes on the basis of winds from a
background wind field (analysis or forecast wind
fields; Lin et al. 2005);
• combination of the above three methods—for
instance, Lakshmanan et al. (2003) describe a
K-means technique that is a hybrid of feature
tracking and image CC (Features are identified,
crosscorrelated with the entire image at a previous
time step, and then translated using either the CC
vectors or a background source.);
• scale decomposition—the echo structure is spectrally decomposed and the movement of echo at
each scale is determined and extrapolated, and the
echo mass is then reconstituted (e.g., Seed 2003;
Germann and Zawadzki 2004).
A primary goal for the Q2 initiative is a fully
blended, continuous dataset consisting of observed
and forecast precipitation. The VSTQPF should
include both extrapolated “observed” data and
NWP data. Such a scheme would combine advanced
extrapolation and advection techniques that skillfully
propagate precipitation fields; numerical models with
microphysics and diabatic (cloud and precipitation)
initialization schemes; and automated optimization
of the blending weights (e.g., Seed 2003; Ganguly
and Bras 2003).
ENSEMBLE AND PROBABILISTIC
APPROACHES. Even with the best efforts toward
improving deterministic QPE, there will always be
varying degrees of uncertainty in the estimates. It
may be too expensive to try to reduce the uncertainty,
especially when ensemble or probabilistic approaches
may offer a more cost-effective solution. A QPE
ensemble might consist of several fields using different
input sources (e.g., combinations of satellite, radar,
and rain gauges; or each sensor alone). Krajewski
et al. (2005) and Ciach et al. (2007) describe in detail a
probabilistic model they developed for hourly rainfall
maps produced by the WSR-88D system that can be
used as a basis for ensemble generation. Forecasts
could use multimodel and/or time-phased ensemble
approaches, the latter of which are an efficient
way to obtain many ensemble members in limited
computing environments (Yuan et al. 2006). Timelagged ensemble approaches are also available (e.g.,
Lu et al. 2007). Multimodel short-range ensembles
are currently produced for the CONUS by the NWS
(Du et al. 2004). Other efforts, including single and
multiple time-phased ensembles, aimed at generating
high-resolution probabilistic hydrometeorological
forecasts, are ongoing (Yuan et al. 2006). The NWS is
committed to probabilistic forecasting with a goal of
supporting, in the near future, grids of hydrometeorological and other variables with probability density
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functions (Carter et al. 2004). Such products could
drive a host of hydrometeorological user decision
aids and support hydrologic ensemble forecasting
(Seo et al. 2006).
EVALUATION. Understanding the performance of
various QPI techniques is a key to their improvement
and utility. Evaluation of estimates and forecasts over
a wide range of space–time scales is a large challenge
due to the high space–time variability of precipitation. The following concepts are applied equally to
forecasts and estimates.
Conventional verification measures include the
equitable threat score (ETS), bias, Heidke skill score,
mean absolute error, and root-mean-square error
(RMSE). The measures are generally computed by
comparing precipitation forecasts to either QPE
grids or gauge measurements of precipitation. For
categorical verification of probabilistic forecasts,
the Brier score, the Brier skill score, and reliability
diagrams are widely used. For multicategorical
verification of probability forecasts (e.g., multiple
categories of precipitation amount), additional
measures such as the ranked probability score are
used. Descriptions of these measures can be found in
Wilks (2006) and Jolliffe and Stephenson (2003).
Traditional QPI verification approaches suffer
from a number of limitations when applied to highresolution precipitation forecasts (e.g., Davis et al.
2006). Evaluation of precipitation forecasts where
there are small horizontal displacements between
forecasts and verification analyses may lead to the
conclusion that the forecast has no skill, when in fact
it may be useful for many purposes. For example, a
forecast of convective precipitation with a horizontal
displacement relative to truth may still be quite useful
at longer lead times beyond several hours when the
intent is to forecast some threat anywhere within a
region. Also, forecasts or analyses that are imprecise
in finescale detail might still provide useful information when applied to multihour, basin-averaged
precipitation.
Traditional metrics of forecast performance
may find that the forecast is incorrect, but they do
not necessarily identify what aspect of the forecast
may be incorrect. Also, the traditional approaches
tend to reward smoother (e.g., lower resolution)
forecasts over forecasts that have higher resolution.
Fortunately, new diagnostic approaches to forecast
verification provide information regarding the
sources of forecast errors. Such new methods include
the “Contiguous Rain Area” (“CRA”) approach
(Ebert and McBride 2000), and object-based
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approaches (e.g., Davis et al. 2006) that examine
forecast performance in the context of a variety of
attributes (e.g., intensity, size, and displacement).
Other methods decompose skill as a function of
spatial scale (e.g., Casati et al. 2004) or separate
phase and amplitude errors. Because these types
of approaches are able to identify what is incorrect
about the analysis or forecast and hence can provide
guidance on the use of the QPE or QPF, their application is an important aspect of the development and
enhancement of new approaches. Much additional
work is needed to enhance the development and
operational application of these methods.
Operational hydrologic forecasting is transitioning
from deterministic toward probabilistic prediction to
provide uncertainty information associated with the
forecast (e.g., Seo et al. 2006). One of the challenges in
quantifying uncertainties in QPE is the general lack
of high-quality and high-density precipitation gauge
data on multiple scales (from micro- to basin scale).
Another aspect of the problem is the fundamentally
different ways, in terms of instrumentation and representativeness errors, that different observing systems
measure and estimate precipitation. For instance,
radar sampling volumes can represent several cubic
kilometers while a comparative gauge is a point value.
Quantification of additional observational needs
for uncertainty-based monitoring and prediction of
precipitation is expected to be an important part of
evaluation.
COMMUNITY COLLABORATION WITH
THE NATIONAL MOSAIC AND Q2 SYSTEM.
To accelerate improvement in QPI, the National
Mosaic and Q2 (NMQ) system is being developed at
the NSSL to serve as a platform for community-wide
R&D techniques (Seo et al. 2004). The system provides
a real-time, around-the-clock applications development and evaluation environment that includes
tools for data quality control, automated algorithm
comparison, and verification. The following have
been identified as necessary capabilities for the NMQ
system:
• real-time processing of radar, rain/snow gauge,
satellite, lightning, and NWP output data;
• quality control tools for input datasets;
• variable resolution and formatting of input data
and output products;
• long-term retrospective analysis, that is, reanalysis;
• robust verification and assessment tools;
• integration of externally derived precipitation
products; and
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DECEMBER 2007
• high-bandwidth servers for product generation
and dissemination.
The NMQ system consists of three primary
components: the Data Ingest and Mosaic Subsystem
for the ingest, quality control, and integration of various external datasets into a radar reflectivity grid;
the Product Generation Subsystem for generation of
QPI products in near–real time; and the Research and
Development Subsystem for real-time data processing
and analysis. The QPI Verification Subsystem (QVS;
http://nmq.nssl.noaa.gov/) is available for evaluating
experimental products and is expected to have
retrospective analysis capabilities. Robust methods
for evaluation described in the previous section are
included. A high premium is placed on modularity,
so that different algorithms may be plugged in and
out with ease and transparency.
Much of the NMQ system has been in operation
for over a year, producing national radar reflectivity
mosaics and radar-only QPE at the spatial scale of
1 km × 1 km with a 5-min update cycle. The national
mosaics are used by a number of research teams
across the United States. The QVS system is designed
as a stand-alone component for the specific objective
of real-time and postanalysis assessment of individual
products, as well as comparisons between products.
For example, the hydro-estimator products can be
routinely compared to the multisensor satellite/radar
regression products.
To support rapid prototyping and assessment,
the Joint Applications Development Environment
(JADE) is being created as a part of the NMQ system.
The overarching objectives of the JADE are to
• establish a “real-time” and “open” environment
for assessment of applications in their native
system environment. (For example, a copy of the
WSR-88D Open-systems Radar Product Generator
will be available for testing applications specific to
the WSR-88D.);
• provide a modular environment for development,
implementation, and evaluation of QPI applications; and
• provide a reanalysis capability.
JADE will be composed of the data preprocessing
modules and be connected to the QVS. The “developer”
will have access to input/output functions to add to
their own code in order to insert their applications. It
is expected that Web-based graphical user interfaces
will allow the developer to select the specific algorithm
connectivity for their application. Candidate applica-
tions will then go through initial testing that includes
processor and memory load testing to ensure that
they do not unduly tax the computing system. It is
envisioned that proposed QPI applications will be
reviewed by a National Q2 Team, as proposed in the
following section, with testing and acceptance criteria
yet to be developed.
To further facilitate a community-wide collaboration and to leverage resources, NMQ will
be integrated with other Web data services. For
example, a group of researchers at the University
of Iowa and Princeton University, in collaboration
with the University Corporation for Atmospheric
Research’s Unidata and the National Climatic Data
Center, is developing a software environment called
HydroNEXRAD to facilitate greater use of NEXRAD
data within the hydrologic research community
(Krajewski et al. 2007). It uses a basin-centric Web
portal for ordering user-specified rainfall products
appropriate for a given application.
JADE services and datasets will also be coordinated
with the Linked Environments for Atmospheric
Discovery (LEAD) project at the University of
Oklahoma (online at http://lead.ou.edu/; Droegemeier
et al. 2005), as well as the GES-DISC Interactive
Online Visualization and Analysis Infrastructure
(GIOVANNI) project at the National Aeronautics
Space Administration Goddard Space Flight Center
(online at http://daac.gsfc.nasa.gov/techlab/giovanni/;
Shen et al. 2006). Giovanni and JADE will be linked
to facilitate TRMM evaluation as mentioned previously.
Finally, we propose to continue the various working
group panels assembled for the Q2 Workshop. The
groups will report periodically to a National Q2 Team
composed of leading academic, private, and public
researchers in the precipitation field. This team would
help guide the Q2 Project for QPI advancements
that are scientifically sound and societal benefit
effective.
RESEARCH TO OPERATIONS. One of the
most significant recommendations from the workshop attendees is that the transfer of research results
to operations be done more efficiently and quickly.
Toward that goal, the NMQ system can play a large
role. The RTO vision for NMQ is modeled after that
for the Hydrology Test Bed laid out in the Integrated
Water Science Plan (Carter et al. 2004). Within this
general model, JADE, maintained externally from the
operational version, will serve as a test bed and experimental platform for developers of QPI techniques.
Improvements and new products tested there can
AMERICAN METEOROLOGICAL SOCIETY
be implemented operationally, following the OSIP
mentioned earlier.
Major components of the envisioned operational
system include data acquisition from the observing
systems (radars, rain gauges, and satellite), data ingest
and processing platforms, and user interfaces. While
much of this infrastructure is in place and is regularly
upgraded within the NSSL NMQ system, a fully
operational NMQ system has yet to be implemented at
any NOAA operation center. Certain critical components of the system, such as the 3D reflectivity mosaic,
are being implemented at the National Centers for
Environmental Prediction (NCEP; G. DiMego and
S. Lord 2007, personal communication) for testing
model data assimilation. Implementation of NMQ
at the NCEP and/or any other national center is
dependent on operational user requirements and
the establishment of a business case per the OSIP
process.
We envision a transition from the current state of
radar and rain gauge processing now employed by the
NWS to a new paradigm of distributed processing
of QPI, streamlined infusion, and RTO transition of
new science (Fig. 3). In the new paradigm, current
operational radar data flow would be maintained to
ensure optimal efficiency for time-critical operations
FIG. 3. Envisioned end state for QPE data processing in
which a community-wide test bed is used to prototype
new or refined algorithms, which are then implemented
at all possible phases of the precipitation estimation
process. The Common Operations Development
Environment (CODE) is the operational analog to
JADE. The WSR-88D Precipitation Processing Subsystem (PPS) outputs Digital Precipitation Array
(DPA) and Digital Hybrid Reflectivity (DHR) fields.
These data are sent to RFCs that, in turn, send QPE
products to the NCEP in “xmrg” format. NCEP then
provides the final Stage IV products as well as a RealTime Mesoscale Analysis (RTMA).
DECEMBER 2007
| 1907
such as flash flood monitoring and prediction. For
example, the radar-generated products would still be
sent directly to forecast offices. The NMQ product
suite would be utilized for those applications where
data latency is less critical. The NMQ system could
also be used as a tool to test and integrate new or
nonoperational radars or other observing platforms.
It is also possible that a decentralized version of the
national system may prove more efficient for fast
integration of radar and rain gauge data than if done
at a national scale. Thus, the NMQ provides a useful
platform for testing various systems and operations
concepts.
SUMMARY AND CONCLUSIONS. Improved
high-resolution QPI is needed to accurately monitor
and predict water-related hazards and the availability of water resources for a wide spectrum of users.
While tremendous advances have been made in the
last quarter-century in many areas of observation,
analysis, and forecasting, significant gaps continue
to exist in both knowledge and capabilities that
are necessary to produce accurate high-resolution
QPI at the national scale. In this article, the authors
argue that concerted and coordinated efforts are
needed to capitalize on recent advances in science
and technology. Thus, we propose a communitywide integrated approach for sustainable research
and development of the next-generation precipitation
information processing, or “Q2,” and its infusion into
operations. A community-wide approach is necessary
to address the many significant issues and complexities in precipitation science. An integrated approach
is necessary to optimally synthesize significant advances in many different areas.
Toward the above objective, the NOAA/NSSL
and the NOAA/NWS/OHD jointly organized
and hosted the first Q2 workshop in Norman,
Oklahoma, on 28–30 June 2005. The Q2 project
aims at producing accurate, high-resolution QPI
that addresses the needs of a wide range of users
nationwide. The attendant science objectives are
to gain understanding necessary to develop multisensor methodologies capable of producing highresolution all-season, all-region, and all-terrain precipitation estimates and very short term forecasts.
In addition, the project aims to incorporate the
understanding necessary to integrate and assess
new data sources (from in situ, radar, and satellite
observing systems, as well as NWP models) and new
methodologies and techniques.
To support the community-wide integrated
approach, the prototype NMQ system has been
1908 |
DECEMBER 2007
developed at the NSSL as a community test bed for
research, development, and RTO transition of QPI
science and applications. (The reader is encouraged
to follow progress through the Web portal online at
http://nmq.nssl.noaa.gov.) We anticipate that not only
will robust algorithm testing and evaluation occur
but, more importantly, a free exchange of ideas will
take place, thus accelerating infusion of new science
into operations.
Needless to say, interest and involvement of
both the research and operational communities in
the Q2 endeavor are essential for the communitywide integrated approach to succeed. We invite
comments, suggestions, input, and participation
toward 1) building an open and flexible community
test bed in NMQ and JADE; 2) improving QPE and
VSTQPF; and 3) addressing science and research to
operations infusion objectives.
ACKNOWLEDGMENTS. The authors are grateful to
Tonia Rollins for coordinating travel and logistics for the
Q2 workshop. Dave Jorgensen provided encouragement
and a substantial operating budget. Vicki Farmer created
the Web pages and forms for registration. Joan O’Bannon
created the graphics for handout materials. Brian Schmidt,
Brad Sagowitz, and James Murnan set up the audiovisual
equipment. Kelly Lynn, Erin McKinney, and Beth Clarke
helped with registration. Linda Skaggs also helped with the
conference setup. Rhonda Baldwin helped with logistics
at Stephenson Center. Keli Tarp arranged the tours at
the NSSL. Kevin Kelleher, Robert R. Lee, Rodger Brown,
Greg Stumpf, and Geoff Bonnin provided helpful reviews.
Major funding for the NMQ project was provided under
the Federal Aviation Administration Aviation Weather
Research Program Advanced Weather Radar Technologies
Product Development Team MOU, and partial funding
was provided under NOAA–University of Oklahoma
Cooperative Agreement NA17RJ1227, U.S. Department of
Commerce. Support for JADE was provided by the NOAA
High Performance Computing and Communications
Program. Finally, we thank Kevin Kelleher and George
Smith for providing the leadership early on to establish
the joint NSSL/OHD Q2 effort.
REFERENCES
Anagnostou, E. N., C. A. Morales, and T. Dinku, 2001:
The use of TRMM precipitation radar observations
in determining ground radar calibration biases. J.
Atmos. Oceanic Technol., 18, 616–628.
Ba, M. B., and A. Gruber, 2001: GOES Multispectral
Rainfall algorithm (GMSRA). J. Appl. Meteor., 40,
1500–1514.
Battan, L. J., 1973: Radar Observation of the Atmosphere.
University of Chicago Press, 324 pp.
Brandes, E. A., 1975: Optimizing rainfall estimates with
the aid of radar. J. Appl. Meteor., 14, 1339–1345.
—, G. Zhang, and J. Vivekanandan, 2002: Experiments in rainfall estimation with a polarimetric
radar in a subtropical environment. J. Appl. Meteor.,
41, 674–685.
Brown, R. A., B. A. Flickinger, E. Forren, D. M. Schultz,
D. Sirmans, P. L. Spencer, V. T. Wood, and C. L.
Ziegler, 2005: Improved detection of severe storms
using experimental fine-resolution WSR-88D measurements. Wea. Forecasting, 20, 3–14.
Carter, G., D.-J. Seo, D. Cline, R. Hartman, K. Mitchell,
and J. Zhou, 2004: The NWS Integrated Water
Science Plan. NOAA/NWS, Silver Spring, MD,
51 pp.
Casati, B., G. Ross, and D. B. Stephenson, 2004: A new
intensity-scale approach for the verification of spatial
precipitation forecasts. Meteor. Appl., 11, 141–154.
Ciach, G. J., 2003: Local random errors in tipping-bucket
rain gauge measurements. J. Atmos. Oceanic Technol.,
20, 752–759.
—, W. F. Krajewski, and G. Villarini, 2007: Producterror-driven uncertainty model for probabilistic
quantitative precipitation estimation with NEXRAD
data. J. Hydrometor., in press. [Available from
NOAA/NWS/Office of Hydrologic Development, 1325 East-West Highway, Silver Spring,
MD 20910.]
Clark, M. P., and A. G. Slater, 2006: Probabilistic quantitative precipitation estimation in complex terrain.
J. Hydrometeor., 7, 3–22.
Collier, C. G., P. R. Larke, and B. R. May, 1983: A weather
radar correction procedure for real-time estimation
of surface rainfall. Quart. J. Roy. Meteor. Soc., 109,
589–608.
Daly, C., R. P. Neilson, and D. L. Phillips, 1994: A statistical–topographic model for mapping climatological
precipitation over mountainous terrain. J. Appl.
Meteor., 33, 140–158.
Davis, C., B. Brown, and R. Bullock, 2006: Objectbased verification of precipitation forecasts. Part I:
Methodology and application to mesoscale rain areas.
Mon. Wea. Rev., 134, 1772–1784.
Dixon, M., and G. Wiener, 1993: TITAN: Thunderstorm
Identification, Tracking, Analysis, and Nowcasting—A radar-based methodology. J. Atmos. Oceanic
Technol., 10, 785–797.
Droegemeier, K. D., and Coauthors, 2005: Serviceoriented environments in research and education
for dynamically interacting with mesoscale weather.
Comput. Sci. Eng., 7, 12–29.
AMERICAN METEOROLOGICAL SOCIETY
Du, J., and Coauthors, 2004: The NOAA/NWS/NCEP
short-range ensemble forecast (SREF) system: Evaluation of an initial condition vs. multi-model physics
ensemble approach. Preprints, 16th Conf. on Numerical Weather Prediction, Seattle, WA, Amer. Meteor.
Soc., CD-ROM, 21.3.
Ebert, E. E., and J. L. McBride, 2000: Verification of
precipitation in weather systems: Determination of
systematic errors. J. Hydrol., 239, 179–202.
Ferraro, R. R., F. Weng, N. C. Grody, and A. Basist, 1996:
An eight-year (1987–1994) time series of rainfall,
clouds, water vapor, snow cover, and sea ice derived
from SSM/I measurements. Bull. Amer. Meteor. Soc.,
77, 891–905.
Fritsch, J. M., and R. E. Carbone, 2004: Improving quantitative precipitation forecasts in the warm season:
A USWRP research and development strategy. Bull.
Amer. Meteor. Soc., 85, 955–965.
Fulton, R. A., J. P. Breidenbach, D. J. Seo, D. A. Miller,
and T. O’Bannon, 1998: The WSR-88D rainfall algorithm. Wea. Forecasting, 13, 377–395.
Ganguly, A. R., and R. L. Bras, 2003: Distributed quantitative precipitation forecasting using information
from radar and numerical weather prediction models. J. Hydrometeor., 4, 1168–1180.
Germann, U., and I. Zawadzki, 2004: Scale dependence
of the predictability of precipitation from continental
radar images. Part II: Probability forecasts. J. Appl.
Meteor., 43, 74–89.
Gourley, J. J., R. A. Maddox, K. W. Howard, and D. W.
Burgess, 2002: An exploratory multisensor technique
for quantitative estimation of stratiform rainfall. J.
Hydrometeor., 3, 166–180.
Grecu, M., and W. F. Krajewski, 2000: A large-sample
investigation of statistical procedures for radar-based
short-term, quantitative precipitation forecasting. J.
Hydrol., 239, 69–84.
Habib, E., W. F. Krajewski, and G. J. Ciach, 2001: Estimation of rainfall interstation correlation. J. Hydrometeor., 2, 621–629.
Henkel, A., and C. Peterson, 1996: Can deterministic
quantitative precipitation forecasts in mountainous
regions be specified in a rapid, climatologically consistent manner with Mountain Mapper functioning
as the tool for mechanical specification, quality
control, and verification? Extended Abstracts, Fifth
National Heavy Precipitation Workshop, State
College, PA, NWS/NOAA, 31 pp. [Available from
Office of Climate, Water, and Weather Services,
W/OS, 1325 East-West Hwy., Silver Spring, MD
20910.]
Johnson, J. B., and G. L. Shaefer, 2002: The influence of
thermal, hydrologic, and snow deformation mechaDECEMBER 2007
| 1909
nisms on snow water equivalent pressure sensor
accuracy. Hydrol. Processes, 18, 53–64.
Johnson, J. T., P. L. MacKean, A. Witt, E. D. Mitchell,
G. J. Stumpf, M. D. Eilts, and K. W. Thomas, 1998:
The storm cell identification and tracking algorithm:
An enhanced WSR-88D algorithm. Wea. Forecasting,
13, 263–276.
Jolliffe, I. T., and D. B. Stephenson, 2003: Forecast
Verification: A Practitioner’s Guide in Atmospheric
Science. Wiley, 240 pp.
Joss, J., and A. Waldvogel, 1990: Precipitation measurement and hydrology. Radar in Meteorology: Battan
Memorial and 40th Anniversary Radar Meteorology
Conference, D. Atlas, Ed., Amer. Meteor. Soc.,
577–606.
Kessinger, C., S. Ellis, and J. Van Andel, 2003: The radar
echo classifier: A fuzzy logic algorithm for WSR-88D.
Preprints, 3d Conf. on Artificial Intelligence Applications to the Environmental Science, Long Beach, CA,
Amer. Meteor. Soc., CD-ROM, P1.6.
Kidder, S. Q., S. J. Kusselson, J. A. Knaff, R. R. Ferraro,
R. J. Kuligowski, and M. Turk, 2005: The Tropical
Rainfall Potential (TRaP) Technique. Part I: Description and examples. Wea. Forecasting, 20, 456–464.
Krajewski, W. F., G. J. Ciach, and G. Villarini, 2005:
Towards probabilistic quantitative precipitation
WSR-88D algorithms: Data analysis and development of ensemble generator model: Phase 4. Final
Report to the Office of Hydrologic Development of
the National Weather Service, Limited Distribution
Rep., IIHR-Hydroscience & Engineering, University
of Iowa, IA, 203 pp. [Available online at www.nws.
noaa.gov/oh/hrl/papers/papers.htm#wsr88D.]
—, and Coauthors, 2007: Towards better utilization
of NEXRAD data in hydrology: An overview of
Hydro-NEXRAD. Preprints, World Environmental
and Water Resources Congress 2007, Tampa, FL,
ASCE, 288–296. [Available online at http://cedb.asce.
org/cgi/WWWdisplay.cgi?0705657.]
Kuligowski, R. J., 2002: A self-calibrating real-time
GOES rainfall algorithm for short-term rainfall
estimates. J. Hydrometeor., 3, 112–130.
Lakshmanan, V., R. Rabin, and V. DeBrunner, 2003:
Multiscale storm identification and forecast. J.
Atmos. Res., 67, 367–380.
Lin, C., S. Vasic, A. Kilambi, B. Turner, and I. Zawadzki,
2005: Precipitation forecasting skill of numerical weather prediction models and radar nowcasts. Geophys. Res. Lett., 32, L14801, doi:10.1029/
2005GL023451.
Lu, C., H. Yuan, B. E. Schwartz, and S. G. Benjamin, 2007:
Short-range numerical weather prediction using timelagged ensembles. Wea. Forecasting, 22, 576–591.
1910 |
DECEMBER 2007
Maddox, R. A., J. Zhang, J. J. Gourley, and K. W. Howard,
2002: Weather radar coverage over the contiguous
United States. Wea. Forecasting, 17, 927–934.
McLaughlin, D. J., and Coauthors, 2005: Distributed
Collaborative Adaptive Sensing (DCAS) for improved detection, understanding, and prediction
of atmospheric hazards. Preprints, Ninth Symp. on
Integrated Observing and Assimilation Systems for the
Atmosphere, Oceans, and Land Surface (IOAS-AOLS),
San Diego, CA, Amer. Meteor. Soc., CD-ROM, 11.3.
Mueller, C., T. Saxen, R. Roberts, J. Wilson, T. Betancourt, S. Dettling, N. Oien, and J. Yee, 2003:
NCAR Auto-Nowcast System. Wea. Forecasting, 18,
545–561.
NWS, 2007: National Weather Service policy directive
10-1: NWS requirements, operations and service
improvements. [Available online at www.weather.
gov/directives/sym/pd01001curr.pdf.]
Qiu, S., P. Pellegrino, R. R. Ferraro, and L. Zhao, 2005:
The improved AMSU rain-rate algorithm and its
evaluation for a cool season event in the western
United States. Wea. Forecasting, 20, 761–774.
Ralph, F. M., and Coauthors, 2005: Improving shortterm (0–48 h) cool-season quantitative precipitation forecasting. Bull. Amer. Meteor. Soc., 86,
1619–1632.
Ryzhkov, A. V., T. J. Schuur, D. W. Burgess, P. L. Heinselman, S. E. Giangrande, and D. S. Zrnic, 2005: The
Joint Polarization Experiment: Polarimetric rainfall
measurements and hydrometeor classification. Bull.
Amer. Meteor. Soc., 86, 809–824.
Saffle, R. E., M. J. Istok, and G. Cate, 2006: NEXRAD
product improvement—Update 2007. Preprints, 22nd
Int. Conf. on IIPS for Meteorology, Hydrology, and
Hydrometeorology, San Diego, CA, Amer. Meteor.
Soc., CD-ROM, 9.1.
Scofield, R. A., and R. J. Kuligowski, 2003: Status
and outlook of operational satellite precipitation
algorithms for extreme-precipitation events. Wea.
Forecasting, 18, 1037–1051.
Seed, A. W., 2003: A dynamic and spatial scaling
approach to advection forecasting. J. Appl. Meteor.,
42, 381–388.
Seo, D. J., 1998: Real-time estimation of rainfall fields
using rain gauge data under fractional coverage
conditions. J. Hydrol., 208, 25–36.
—, and J. P. Breidenbach, 2002: Real-time correction
of spatially nonuniform bias in radar rainfall data
using rain gauge measurements. J. Hydrometeor.,
3, 93–111.
—, —, and E. R. Johnson, 1999: Real-time estimation of mean field bias in radar rainfall data. J.
Hydrol., 23, 131–147.
—, C. R. Kondragunta, K. Howard, S. Vasiloff, and J.
Zhang, 2004: The National Mosaic and Multisensor
QPE (NMQ) Project—Status and plans for a community testbed for high-resolution multisensor quantitative precipitation estimation (QPE) over the United
States. Preprints, 19th Conf. on Hydrology, San Diego,
CA, Amer. Meteor. Soc., CD-ROM, P1.3.
—, H. D. Herr, and J. C. Schaake, 2006: A statistical post
processor for accounting of hydrologic uncertainty in
short-range ensemble streamflow prediction. Hydrol.
Earth System. Sci. Discuss., 3, 1987–2035.
Shen, S., and Coauthors, 2006: Giovanni: A system
for rapid access, visualization and analysis of
earth science data online. Preprints, 22d Int. Conf.
on Interactive Information Processing Systems for
Meteorology, Oceanography, and Hydrology, Atlanta,
GA, Amer. Meteor. Soc., CD-ROM, 1.8.
Sieck, L. C., S. J. Burges, and M. Steiner, 2007: Challenges
in obtaining reliable measurements of point rainfall. Water Resour. Res., 43, W01420, doi:10.1029/
2005WR004519.
Smith, J. A., and W. F. Krajewski, 1991: Estimation of
mean field bias of radar rainfall estimates. J. Appl.
Meteor., 30, 397–412.
Sun, J., and N. A. Crook, 1997: Dynamical and microphysical retrieval from Doppler radar observations
using a cloud model and its adjoint. Part I: Model
development and simulated data experiments. J.
Atmos. Sci., 54, 1642–1661.
Tabios, G. Q., III, and T. D. Salas, 1985: A comparative
analysis of techniques for spatial interpolation of
precipitation. Water Resour. Bull., 21, 365–380.
Vasiloff, S. V., 2001: Improving tornado warnings with
the Federal Aviation Administration’s Terminal
AMERICAN METEOROLOGICAL SOCIETY
Doppler Weather Radar. Bull. Amer. Meteor. Soc.,
82, 861–874.
Wilks, D. S., 2006: Statistical Methods in the Atmospheric
Sciences. 2d ed. Academic Press, 627 pp.
Wilson, J., and E. A. Brandes, 1979: Measurement of
rainfall—A summary. Bull. Amer. Meteor. Soc., 60,
1048–1058.
Wood, V. T., R. A. Brown, and S. V. Vasiloff, 2003:
Improved detection using negative elevation angles
for mountaintop WSR-88Ds. Part II: Simulations of
the three radars covering Utah. Wea. Forecasting,
18, 393–403.
Yuan, H., J. A. McGinley, C. Lu, C. J. Anderson, and
P. J. Schultz, 2006: Extreme precipitation events and
bias correction of PQPF during the HMT 2005–2006
Winter Campaign. AGU Joint Assembly on Ensemble
and Probabilistic Estimation and Forecasting of
Precipitation, Baltimore, MD, Amer. Geophys.
Union, CD-ROM, H22B-04.
Zhang, J., K. Howard, and J. J. Gourley, 2005:
Constructing three-dimensional multiple radar
reflectivity mosaics: Examples of convective storms
and stratiform rain echoes. J. Atmos. Oceanic
Technol., 22, 30–42.
—, —, and S. Wang, 2006a: Single radar Cartesian
grid and adaptive radar mosaic system. Preprints,
12th Conf. on Aviation, Range, and Aerospace
Meteorology, Atlanta, GA, Amer. Meteor. Soc.,
CD-ROM, 1.8.
—, C. Langston, K. Howard, and B. Clarke, 2006b:
Gap-filling in 3D radar mosaic analysis using
vertical profile of reflectivity. Preprints, 12th Conf. on
Aviation, Range, and Aerospace Meteorology, Atlanta,
GA, Amer. Meteor. Soc., CD-ROM, 1.9.
DECEMBER 2007
| 1911