A Pilot Study Examining Model Derived Precipitation Potential For Use in
Precipitation Forecasting in the Eastern United States
James Noel
NOAA/NWS Forecast Office, Atlanta, Georgia
Jeffrey C. Dobur
NOAA/NWS Ohio River Forecast Center, Wilmington, Ohio
ABSTRACT
In 1996, the Ohio River Forecast Center (OHRFC) implemented a model-derived parameter,
called Precipitation Potential, to assist in evaluating the expected spatial and temporal distribution of
precipitation. Precipitation Potential is derived from any atmospheric numerical weather prediction model
where precipitable water and mean relative humidity are computed. The goal of this paper is to improve
one of the most difficult and critical forecast elements for hydrologic forecasting, precipitation.
The Precipitation Potential model-derived parameter has proven to be a useful tool in determining
the probability, timing, duration, and intensity of precipitation. Precipitation Potential has shown value
added information to assist the hydrometeorologist in preparing rainfall forecasts. Results, using the
National Weather Service’s (NWS) Eta model, show that the probability of precipitation increases with
increasing Precipitation Potential values. In addition, results indicate that the onset of precipitation is tied
to Precipitation Potential values and temperatures, with higher values of Precipitation Potential necessary
with higher temperatures. Precipitation Potential has proven beneficial in evaluating timing and coverage
of precipitation. Finally, data show increasing values of Precipitation Potential are associated with greater
precipitation amounts.
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1. Introduction
Since 1977, Quantitative Precipitation Forecasts (QPF) have been an integral part
of the river and flood forecast program in the NWS Eastern Region (Opitz et al. 1995)
and NWS-wide in the 1990s (Fenber 1995). Forecasts of precipitation are critical to the
achievement of the greatest possible hydrologic forecast accuracy and longest possible
lead time (Georgakakus and Hudlow 1984). As part of the National Weather Service
(NWS) Modernization program, a Hydrometeorological Analysis and Support (HAS)
function was created at the NWS River Forecast Centers (RFC) to maintain the QPF
process. The HAS function utilizes the 6-hour national QPF guidance from the
Hydrometeorological Prediction Center (HPC) in addition to examining an array of
meteorological model and mesoscale parameters in formulating the 24-hour HAS QPF.
The HAS QPF is completed twice daily around 0000 UTC and 1200 UTC and is
incorporated into the National Weather Service River Forecast System (NWSRFS) to
produce river forecasts out to three to five days.
Since QPFs began at RFCs, including the OHRFC, the need for improvement was
clear. In 1996, the OHRFC adopted the concept to use precipitable water and 1000 to
700 mb mean relative humidity that are related to precipitation efficiency and integrate it
into the realm of the meteorological forecast models as a forecast parameter.
Precipitation Efficiency is defined as the ratio of the total rainfall to the total
condensation(Weisman and Klemp 1982 and Ferrier et al. 1996). Precipitable water and
mean relative humidity have been used in real-time satellite estimates at the National
Oceanic and Atmospheric Administration (NOAA) National Environment, Satellite, Data
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and Information Service (NESDIS) (Scofield 1987; Vicente and Scofield 1998). This
paper focuses on the use of this precipitation-forecasting tool to improve precipitation
forecasting, especially when accompanied by other model-derived forecasting
parameters.
2. Data and methodology
In order to provide context for Precipitation Potential, this section focuses on the
concept and methodology of Precipitation Potential, the ways it can be implemented into
the precipitation forecasting process, and a description of data sources and analysis
techniques used.
Several factors affect precipitation efficiency, including saturation ratio,
production rate of condensate, residence time in clouds, dry air entrainment, vertical wind
shear, and precipitable water (Chappell 1997 and Doswell et. al. 1996). The ratio of the
actual vapor pressure (e) to the saturation vapor pressure (es) is called the saturation ratio.
The saturation ratio multiplied by 100 gives the relative humidity (Ahrens 1994). When
the relative humidity of air parcels through a column of the atmosphere is averaged, a
mean relative humidity is calculated. A requisite for high rainfall intensity is a high
production rate of condensate. The rate at which the condensate is produced in a column
of cloudy air is directly proportional to air density, updraft speed, cloud thickness, and
the vertical gradient of the saturation mixing ratio. The density and vertical gradient of
the saturation mixing ratio terms act to produce larger condensate rates in the lower part
of the cloudy column. In general, the greatest rates of condensate production are found
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in the lower half of the cloudy column (Chappell 1997). The residence time of droplets
in clouds also plays a critical role in increasing precipitation efficiency. With increased
vertical motion and increased depth of clouds, cloud droplets are allowed longer
residence time in the cloud to grow large enough to produce rain droplets (Chappell
1997). Vertical wind shear plays a critical role since the shear often produces dry air
entrainment, reducing PE. Finally, a high amount of precipitable water increases
precipitation efficiency. Typically, these are 1.50” and greater during the warm season
(Junker 1992) to around 0.80” or more in the cool season.
For the operational hydrometeorologist, a simple relationship related to
precipitation efficiency is the multiplication of the average lower tropospheric relative
humidity (RH) times the precipitable water (PW) (Scofield 1987). PW and RH are easily
obtainable from numerical weather prediction models.
RH can be expressed as the actual mixing ratio (w) in grams of water vapor per
kilogram of dry air (g/kg) divided by the saturation mixing ratio (ws) in g/kg multiplied
by 100 percent. (Ahrens 1994) The mixing ratio (w) is the ratio of the mass of water
vapor (Mv) to the mass of dry air (Md). The saturation mixing ratio (ws) is the mixing
ratio a parcel of air would have if saturated at its current temperature and pressure (Henry
et. al. 1989). The relative humidity presents an understanding of the concentration of
water vapor in the air over a particular space in time. High concentrations of water vapor
will lead to higher precipitation efficiencies and vice versa. When the relative humidity
of air parcels through a column of the atmosphere is averaged, a mean relative humidity
is calculated. In addition, a high mean RH often implies at least weak large-scale lower
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level convergence, upward motion, some residence time in clouds for cloud droplets
assuming clouds bases of 0 degrees Celsius or greater, and a high saturation ratio.
Precipitable water is derived by integrating the specific humidity (q) profile as a
function of pressure. The specific humidity can be determined by taking the ratio of the
vapor density to air density (Bohren and Albrecht 1998).
A real-time parameter related to precipitation efficiency has been used at NESDIS
since the early 1980s to adjust satellite precipitation estimates (Scofield 1997). NESDIS
provides satellite rainfall estimations to NWS WFOs, RFCs and to the National Center
for Environmental Prediction (NCEP), which includes HPC, to assist in detecting and
predicting floods and flash floods. Building on experiences from NESDIS, the OHRFC
used this PW/RH parameter calculated from the numerical weather prediction models.
PW/RH has been used as a tool for creating QPFs at the OHRFC since late 1996.
Gridded data is only as good as the model from which it’s derived (Scofield and
Kusselson 1996) and for that reason, the precipitation efficiency only indicates the
potential for how efficient the environment is at specific times in the future. Thus, this
model-derived PW/RH parameter is referred to as “Precipitation Potential”.
The Precipitation Potential (PP) is calculated as follows:
PP = PW * (1000-700 MRH)
where PP = Precipitation Potential, PW = Precipitable Water through the entire
depth of the atmosphere in inches, and 1000-700 MRH is the mean relative humidity
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from 1000 to 700 millibars. The 1000-700 millibar layer was chosen since the deep
moisture is mainly contained in the lowest 3-4 km of the atmosphere (Junker 1997).
Precipitation Potential can be displayed as an added volume browser
customization within the Advanced Weather Interactive Processing System (AWIPS)
D2D meteorological display software. Use of readily available software such as General
Meteorological Data Assimilation, Analysis and Display Software Package (GEMPAK),
NWS National Centers Translator (Ntrans), and GEMPAK Analysis and Rendering
Program (GARP) are also capable of integrating Precipitation Potential into their list of
precipitation forecasting parameters. This allows for widespread use of the Precipitation
Potential parameter in all sectors (government, university, and private sector) possible.
Twenty-seven cases were examined from March 1997 through June 1998 (Table
1). An additional twelve cases were examined from May 2001 through September 2001.
In the cases for March 1997 through June 1998, Precipitation Potential values were taken
from the NWS NCEP 50 layer, 29-km Eta numerical weather model using GARP.
Values were taken from each six-hour period of the 48-hour model forecast for six
different cities in the Ohio Valley region (Table 2). This dataset provided a total of 1296
six-hour periods to be examined. These values were compared to the monthly Local
Climatological Data (LCD) hourly rainfall amounts at each location. Temperature data
were also obtained from the LCD’s 3-hourly temperatures. In the cases from May 2001
through September 2001, a subjective areal case study was completed on three specific
periods. Using the NWS AWIPS D2D meteorological analysis software, a comparison
was made between Precipitation Potential and the Ohio Valley regional 0.5 reflectivity
mosaic in addition to numerical weather prediction model data.
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Data from March 1997 through June 1998, and July through September 2001
were used to compare the Precipitation Potential value (inches) derived from the Eta
against percentage of observed precipitation events. The percentage of observed
precipitation events was calculated by the ratio of the events that any amount of
precipitation occurred to the total number of events. The events were grouped into three
categories to account for seasonal moisture influences driven by temperature and amount
of available moisture in the atmospheric column. A mean temperature was calculated
for the entire data group from May 1997 to June 1998. The first group, called the mean
transition season group, was defined as those events within one standard deviation of the
mean surface temperature (54F). The second group, called the cool season group, was
defined as those events with temperatures greater than one standard deviation of the mean
to the cool side. The final group, called the warm season group, was defined as those
events with temperatures greater than one standard deviation of the mean temperature to
the warm side. An average linear trend line was computed on both groups (Figure 1).
3. Results
In Figure 1, results show the plot of the three groups linear regression lines. The
Precipitation Potential value associated with the 90 percent probability of observed
precipitation for the cool season group was 0.9”, for the transition mean group was 1.3”,
and the warm season group was 2.2”. The Precipitation Potential value associated with
the 50 percent probability of observed precipitation for the cool season group was 0.6”,
for the transition mean group was 0.8”, and the warm season group was 1.4”. The
Precipitation Potential value associated with the 10 percent probability of observed
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precipitation for the cool season group was 0.3”, for the transition mean group was 0.3”,
and the warm season group was 0.6”. These differences between cool, transition and
warm season groups can be linked to variations in seasonal moisture with variation in
temperature and the random nature of scattered afternoon convection especially during
the warm season. Correlation coefficients for for the cool, transition, and warm season,
0.93, 0.92, 0.90 gives confidence in the utility of this parameter. From these results,
OHRFC and WFO ATL have developed monthly precipitation thresholds (Figure 2).
Precipitation Potential has also shown the capability to diagnose precipitation
intensity. High values of PW and instability are often collocated and become antecedent
conditions prior to the development of heavy rainfall and flash floods (Scofield et al.
1996, 2000). High values of PW can often produce high values of PP. Data from March
1997 through June 1998 show evidence that the proportion of heavier precipitation events
(greater than 0.25” in a 6-hour period) to total events is larger with higher PP values
(Figure 3). Experience has shown that the intensity of precipitation events is a function
of seasonal influences (temperature, moisture availability, instability) and precipitation
type: stratiform, convective, or both.
In addition to providing some level of confidence in the probability of
precipitation and heavy precipitation, Precipitation Potential has displayed the ability to
detail the axis of precipitation development and movement.
This is especially important
when a QPF is made for input into a hydrologic model. Spatially centering the axis of
precipitation is critical in projecting which locations on certain rivers will rise and those
locations along rivers that maybe continuing to recess or remain steady. During times of
high flow, this diagnosis in determining the axis of precipitation can be the difference
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between forecasting a flood or not forecasting a flood and/or forecasting a flood in a
location that does not reach flood stage (i.e. false alarm).
Using the subjective areal case study method, comparisons of Precipitation
Potential to radar during the spring and summer of 2001 have shown the ability to
highlight the axis of precipitation. On June 29, 2001, scattered convection developed in
an area from Indianapolis, Indiana eastward to near Dayton, Ohio during the morning
hours. Evaluating the 0000 UTC June 29th Eta 12-hour PP forecast overlaid with the
1130 UTC June 29th Ohio Valley regional 0.5 reflectivity mosaic (Figure 4), it is
evident that PP provided a better solution in providing the areal coverage and axis of
precipitation compared to the 0000 UTC Eta 12-hour forecast for 6-hour accumulative
precipitation (Figure 5). In examining the 1200 UTC Eta 6-hour PP forecast overlaid
with the 1800 UTC June 29th Ohio Valley regional 0.5 reflectivity mosaic (Figure 6),
PP provided a better indication of the shift in developing convection by the afternoon
across the Cumberland river valley compared to the 1200 UTC Eta 6-hour accumulative
precipitation (Figure 7).
A second example, on June 16th compares the 1200 UTC Eta
6-hour derived PP (Figure 8) and the 12Z Eta derived 6-hour forecast accumulative
precipitation (Figure 9) to the 1800 UTC June 14th Ohio Valley regional 0.5 reflectivity
mosaic (Figure 10). The PP highlighted the general axis of precipitation from near
southwestern Ohio through central Kentucky into eastern Tennessee with 1.23” max near
the most significant developing convection east of Cincinnati. The comparison shows the
improvement in using the PP to delineate areas of developing convection.
4. Conclusions and Future Research
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Precipitation Potential is not a stand-alone indicator for precipitation, but it has
been proven as a very useful tool in evaluating the spatial and temporal distribution in
addition to the magnitude of precipitation. Precipitation Potential applied alongside other
heavy rainfall parameters such as 950-850 mb low level jet convergence, 300-200 mb
upper level jet divergence, 950-850 mb theta-e advection, 850-500 mb omega, and other
indices aid forecasters in determining the axis and magnitude of precipitation. From
here, a suitable QPF can be made.
Additional case studies are needed to further examine the threshold criteria for
heavy rainfall during different times of the season and at different surface temperatures
and dewpoints. Further study is needed to provide additional function and value in using
the Precipitation Potential parameter for other regions across the United States. We
would be willinging to collaborate with other NWS WFOs/RFCs in developing such a
parameter for their region.
Acknowledgments. The authors would like to thank Rod Scofield of NESDIS, Tom
Adams from OHRFC, Gary Beeley from WFO ATL, Mark Fenbers for the founding idea
to implement into a forecast mode, and the HAS unit at OHRFC for providing
suggestions to improve the parameter and manuscript and to the anonymous reviewers for
their suggestions.
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