**Quantitative Precipitation Forecast**

**Its Generation and Verification**

**at the Southeast River Forecast Center**

Jack S. Bushong

**\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_**

*AUTHOR:* Meteorologist, Hydrometeorological Analysis and Support Unit,
Southeast River Forecast Center, National Weather Service, Peachtree
City, Georgia 30269.

*REFERENCE: Proceedings of the 1999 Georgia Water Resources Conference*,
held March 30-31, 1999, at the University of Georgia. Kathryn J.
Hatcher, Editor, Institute of Ecology, The University of Georgia,
Athens, Georgia.

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

**Abstract.** In a typical year, flooding is the number one cause of
weather related deaths in the United States. The National Hurricane
Center recently reported that inland flooding now surpasses coastal
storm surge as the leading cause of hurricane related deaths (Rappaport
et al., 1998). Hurricane Mitch of October, 1998, was responsible for
upwards of 10,000 people losing their lives in inland flooding and mud
flows in Central America.

Although the National Weather Service has produced river and flooding
forecasts since 1890, it wasn't until the mid 1990s that the Southeast
River Forecast Center (SERFC) incorporated Quantitative Precipitation
Forecasts (QPF) into its river models throughout the southeastern United
States. With the southeastern United States receiving more annual
precipitation than anywhere else in the country, and with threats of
hurricanes and other tropical type weather, it is imperative that an
accurate QPF forecast be made and entered into the SERFC river models.

Verification of the QPF has been an important undertaking at the SERFC
over the last two years. This paper will discuss QPF verification
findings for 1998 and the impact of QPF on the accuracy of hydrologic
models.

INTRODUCTION

In the late 1980s, the National Weather Service (NWS) launched its ten
year, 4.5 billion dollar Modernization and Restructuring (MAR) program
to take advantage of rapidly advancing scientific and computer
technologies. The implementation of MAR is nearly complete and has
succeeded in modernizing the meteorological and hydrological operations
of the

NWS.

As a result of MAR, all 13 River Forecast Centers (RFC) across the U.S.
restructured their operations and upgraded computer technology. The RFCs
have extended their hours into the evening and nearly doubled their
staff, which included the hiring of three meteorologists at each RFC.
These meteorologists became part of a new function at RFCs, known as the
Hydrometeorological Analysis and Support (HAS) program, to manage the
greatly increased flow of meteorological data for input into the
hydrological models. The two greatest responsibilities of the HAS
forecaster are the comparison and quality control of radar and rain gage
data, and the assimilation of the QPF.

The SERFC, with a responsibility for river and flood forecasting for the
Southeastern United States and Puerto Rico, has been incorporating
24-hour QPF from the Carolinas and Virginia since the late 1980s, and
from the rest of the Southeast since 1995. Prior to this, river models
were computed with only observed rain and were very inaccurate during
ongoing rain events.

Presently, HAS forecasters assimilate QPF forecasts from all 18 of the
Weather Forecast Offices (WFOs) in the SERFC area of responsibility,
including Puerto Rico. QPF forecasts are prepared twice a day by WFO
meteorologists using a graphical MS Windows program known as WinQPF,
developed by Mark Fenbers, Senior HAS of the Ohio River Forecast Center
(OHRFC), and are then sent to the RFC. The HAS forecaster may make
modifications to the overall QPF forecast in case of inconsistencies
among the WFOs' forecasts, and inputs them into the hydrologic model.

MEAN AREAL PRECIPITATION

The Quantitative Precipitation Forecast is future or forecast Mean Areal
Precipitation (MAP). Mean Areal Precipitation is the amount of
precipitation in inches that occurs if spread out over a river basin. It
is also called Basin Average Precipitation (BAP). This MAP is
transformed into runoff in the river model by using the Sacramento Soil
Moisture Accounting Model within the National Weather Service Forecast
System (NWSRFS). In the SERFC area of responsibility, there are 349 such
basins which are defined by river segments. Most segments are defined by
the location of a river gage.

Currently the SERFC interrogates 1313 rain gages by various methods,
such as by satellite, manual observation, telephone, etc. To compute
MAP, the SERFC uses the Thiessen Polygon method. This method computes
rain gage areas and then determines a weight for each rain gage. Rain
gages inside each basin, as well as those bordering the basin, are used
(Larson et al., 1976).

The Thiessen method for determining MAP is adequate for most basins.
However, a few basins, particularly in south Florida, have a limited
number of rain gages and may cause unrepresentative MAP computations,
especially during convective type rain events where heavy precipitation
is scattered.

In the near future, the SERFC will be using a procedure to compute MAP
for each basin using WSR-88D radar-derived rainfall. Currently the NWS
is developing a "Stage 3\" or three step process nationwide to calibrate
1-hour radar precipitation amounts with rain gages. The SERFC is in the
testing phase of a Unix based graphical software package known as Stage
3. The radar-rain gage derived product is input into the Stage 3
computer program and manually quality controlled by the HAS forecaster
at each RFC. Erroneous radar and rain gage data can be observed by the
forecaster and corrected or deleted. These MAPX (mean areal
precipitation derived from radar) values will be the output and will be
entered into the river model. The theory is that the higher the temporal
and spatial resolution in the rainfall input, the more accurate the
river forecast (Stellman, Fuelberg and Garza et al., 1998).

QPF VERIFICATION METHODS

**QPF Generation**

The SERFC updates all river forecasts by 11:00 a.m. ET each day using
the 12z observed data in the river model run. During heavy rain events
the SERFC increases its operation from 18 hours to 24 hours. River
forecast updates can be issued, every six hours, after new data is
incorporated into the river model.

Each of the 18 WFOs issues a QPF forecast of four six-hourly periods for
its respective hydrological service area (HSA), or area of hydrologic
responsibility (Figure 1), by 1130z each morning then again by 2330z
each evening. The HAS forecaster may request additional QPF forecasts
from the WFO as needed. The HAS forecaster checks each QPF, and may
coordinate with WFO forecasters and/or revise the forecast before it is
entered into the river model. QPF forecasts may extend beyond 24 hours
to produce contingency river forecasts during flood events.

In the near future, the SERFC will be using a Unix based graphical
program known as HASQPF, developed at the OHRFC by Mark Fenbers, to
mosaic all 18 QPF forecasts. This will also allow the HAS forecaster to
check for inconsistencies among forecasts and revise each QPF by drawing
isohytes.

**Water Watcher**

Water Watcher is an MS Windows based Visual Basic compiled program
developed at the SERFC by the author in July of 1997. This program
downloads the QPF and observed MAP files stored in the SERFC Unix server
and computes a QPF error for each basin. Errors are averaged for each
HSA (Figure 1) and can be viewed in both text and graphical format. Many
of the graphics produced are available online and are updated each day.
The SERFC web site address can be found at the end of this paper.

Statistics produced by the program are Root Mean Square Error (RMSE),
Average Error, Mean Absolute Error, Heidke Skill Score, and an R-Score
(equations. 1 to 5). The R-score is used to compare the RMSEs of
different WFOs. RMSE is proportional to the amount of average MAP a
WFO's HSA receives in a given time. Therefore, by dividing the WFO's
RMSE by its respective average MAP, a comparison among the WFOs' RMSEs
can be made with differing amounts of MAP.

Mean QPF Error =![](media/image1.wmf){width="1.7013888888888888in"
height="0.5631944444444444in"}![](media/image1.wmf){width="0.14930555555555555in"
height="0.2652777777777778in"} (1)

Mean Absolute QPF Error (MAE)
=![](media/image1.wmf){width="1.7013888888888888in"
height="0.5631944444444444in"} (2)

Root Mean Square Error (RMSE) =![](media/image1.wmf){width="2.39375in"
height="0.63125in"} (3)

where,

QFP = Quantitative Precipitation Forecast for a particular basin

MAP= Mean Areal Precipitation for a particular basin

N = Number of basins in the WFO's HSA.

![](media/image1.wmf){width="1.6118055555555555in"
height="0.5145833333333333in"} (4)

where,

MAP is the average MAP across a WFO's HSA

RMSE is the Root Mean Square Error for the WFO's HSA

Heidke Skill Score (HSS) (NWS, 1982) is the fraction of possible
improvement over chance afforded by a set of forecasts from a
contingency table of categorical forecasts (columns) versus observations
(rows). Row and column totals are denoted with subscript q.

![](media/image1.wmf){width="1.28125in" height="0.5159722222222223in"}
(5)

where,

the number correct (NC):

![](media/image1.wmf){width="1.0104166666666667in"
height="0.5631944444444444in"} (6)

where,

i is the index for row and column

m is number of rows or columns

the total number of cases analyzed (T):

![](media/image1.wmf){width="0.7201388888888889in"
height="0.2638888888888889in"} (7)

where, q is the total number of rows and columns in the contingency
table

the expected value (E):

![](media/image1.wmf){width="1.1805555555555556in"
height="0.7479166666666667in"} (8)

where,

i is the index for row and column

q is the total number of rows and columns

m is number of rows or columns

Also produced, and shown in Table 1, are the percentage of basins in a
WFO HSA that are correctly forecast, under-forecast, and over-forecast.
A basin accurately forecast is defined as one that is within 20 percent
of its actual MAP amount. These statistics and graphical products are
uploaded to the SERFC Web Site.

FINDINGS

QPF verification statistics have only been computed at the SERFC since
July 1997. Therefore, the findings are inconclusive at this point.
However, some unexpected trends have been noted over the past year,
under different weather systems.

**Weather Discussion**

The climate in the Southeastern United States proved to be a highly
variable one during 1998. Weather conditions ranged from extremely wet
during the winter, due to the effects of El Niño, to drought conditions
in spring and early summer. Late summer and early fall brought tropical
weather and hurricanes into the Southeast U.S., and late fall brought
drought conditions once again.

In the winter of 1998, El Niño was responsible for a very active
southern jet stream. The southern branch of the jet stream had mostly a
zonal and rapid flow from the Pacific through Mexico to the Gulf of
Mexico. From the Gulf it veered northeast, focusing energy over the Gulf
and Southeastern United States. Repeated cyclogenesis in the Gulf
brought a progressive series of low pressure systems into the southeast.
These low

![](media/image2.png){width="5.173611111111111in"
height="4.377083333333333in"}

**Table 1.** QPF Verification for the WFOs in the SERFC Area of
Responsibility for 1998. Shown is the Heidke Skill Score (HSS), the
percentage of basins accurately forecast, under-forecast, and
over-forecast, the Mean Absolute Error (MAE),

the Root Mean Square Error, and the Zero QPF Root Mean Square Error.

<table>
<colgroup>
<col style="width: 10%" />
<col style="width: 3%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 8%" />
<col style="width: 9%" />
<col style="width: 9%" />
<col style="width: 9%" />
<col style="width: 8%" />
<col style="width: 7%" />
<col style="width: 8%" />
</colgroup>
<tbody>
<tr class="odd">
<td>WFO</td>
<td></td>
<td><p>AVG</p>
<p>MAP</p></td>
<td>AVG QPF</td>
<td># OF BASINS</td>
<td>HSS</td>
<td>% QPF HITS</td>
<td>% QPF UNDER</td>
<td>% QPF OVER</td>
<td>MAE</td>
<td>RMSE</td>
<td>ZERO QPF RMSE</td>
</tr>
<tr class="even">
<td>Wakefield, VA</td>
<td>AKQ</td>
<td>0.13</td>
<td>0.11</td>
<td>5</td>
<td>0.62</td>
<td>69.64</td>
<td>14.96</td>
<td>15.4</td>
<td>0.1</td>
<td>0.11</td>
<td>0.14</td>
</tr>
<tr class="odd">
<td>Atlanta, GA</td>
<td>ATL</td>
<td>0.15</td>
<td>0.15</td>
<td>37</td>
<td>0.44</td>
<td>65.94</td>
<td>12.9</td>
<td>21.15</td>
<td>0.12</td>
<td>0.15</td>
<td>0.19</td>
</tr>
<tr class="even">
<td>Birmingham, AL</td>
<td>BHM</td>
<td>0.15</td>
<td>0.18</td>
<td>40</td>
<td>0.45</td>
<td>65.57</td>
<td>11.99</td>
<td>22.44</td>
<td>0.14</td>
<td>0.17</td>
<td>0.19</td>
</tr>
<tr class="odd">
<td>Columbia, SC</td>
<td>CAE</td>
<td>0.14</td>
<td>0.14</td>
<td>24</td>
<td>0.53</td>
<td>72.88</td>
<td>12.26</td>
<td>14.86</td>
<td>0.11</td>
<td>0.13</td>
<td>0.16</td>
</tr>
<tr class="even">
<td>Charleston, SC</td>
<td>CHS</td>
<td>0.15</td>
<td>0.16</td>
<td>10</td>
<td>0.51</td>
<td>66.05</td>
<td>12.22</td>
<td>21.73</td>
<td>0.14</td>
<td>0.16</td>
<td>0.17</td>
</tr>
<tr class="odd">
<td>Greenville, SC</td>
<td>GSP</td>
<td>0.16</td>
<td>0.14</td>
<td>22</td>
<td>0.46</td>
<td>68.66</td>
<td>16.12</td>
<td>15.22</td>
<td>0.12</td>
<td>0.16</td>
<td>0.19</td>
</tr>
<tr class="even">
<td>Wilmington, NC</td>
<td>ILM</td>
<td>0.15</td>
<td>0.16</td>
<td>16</td>
<td>0.5</td>
<td>66.02</td>
<td>12.41</td>
<td>21.6</td>
<td>0.13</td>
<td>0.15</td>
<td>0.18</td>
</tr>
<tr class="odd">
<td>Jacksonville, FL</td>
<td>JAX</td>
<td>0.15</td>
<td>0.16</td>
<td>12</td>
<td>0.48</td>
<td>63.22</td>
<td>11.89</td>
<td>24.88</td>
<td>0.15</td>
<td>0.18</td>
<td>0.19</td>
</tr>
<tr class="even">
<td>Memphis, TN</td>
<td>MEM</td>
<td>0.15</td>
<td>0.18</td>
<td>21</td>
<td>0.52</td>
<td>64.76</td>
<td>12.9</td>
<td>22.34</td>
<td>0.14</td>
<td>0.16</td>
<td>0.17</td>
</tr>
<tr class="odd">
<td>Newport, NC</td>
<td>MHX</td>
<td>0.15</td>
<td>0.17</td>
<td>5</td>
<td>0.52</td>
<td>63.29</td>
<td>14.41</td>
<td>22.3</td>
<td>0.14</td>
<td>0.15</td>
<td>0.16</td>
</tr>
<tr class="even">
<td>Miami, FL</td>
<td>MIA</td>
<td>0.16</td>
<td>0.23</td>
<td>12</td>
<td>0.37</td>
<td>56.32</td>
<td>12.44</td>
<td>31.23</td>
<td>0.2</td>
<td>0.23</td>
<td>0.19</td>
</tr>
<tr class="odd">
<td>Melbourne, FL</td>
<td>MLB</td>
<td>0.14</td>
<td>0.19</td>
<td>10</td>
<td>0.39</td>
<td>60.16</td>
<td>11.64</td>
<td>28.19</td>
<td>0.15</td>
<td>0.18</td>
<td>0.17</td>
</tr>
<tr class="even">
<td>Mobile, AL</td>
<td>MOB</td>
<td>0.19</td>
<td>0.21</td>
<td>18</td>
<td>0.46</td>
<td>64.44</td>
<td>14.25</td>
<td>21.31</td>
<td>0.16</td>
<td>0.19</td>
<td>0.23</td>
</tr>
<tr class="odd">
<td>Raleigh, NC</td>
<td>RDU</td>
<td>0.14</td>
<td>0.15</td>
<td>29</td>
<td>0.45</td>
<td>66.76</td>
<td>13.63</td>
<td>19.6</td>
<td>0.13</td>
<td>0.16</td>
<td>0.17</td>
</tr>
<tr class="even">
<td>Roanoke, VA</td>
<td>RNK</td>
<td>0.13</td>
<td>0.12</td>
<td>20</td>
<td>0.44</td>
<td>66.74</td>
<td>13.79</td>
<td>19.47</td>
<td>0.11</td>
<td>0.13</td>
<td>0.16</td>
</tr>
<tr class="odd">
<td>Tampa, FL</td>
<td>TBW</td>
<td>0.15</td>
<td>0.19</td>
<td>20</td>
<td>0.44</td>
<td>61.4</td>
<td>10.48</td>
<td>28.12</td>
<td>0.15</td>
<td>0.18</td>
<td>0.18</td>
</tr>
<tr class="even">
<td>Tallahassee, FL</td>
<td>TLH</td>
<td>0.16</td>
<td>0.15</td>
<td>35</td>
<td>0.47</td>
<td>67.02</td>
<td>13.41</td>
<td>19.57</td>
<td>0.14</td>
<td>0.18</td>
<td>0.21</td>
</tr>
</tbody>
</table>

**Figure 1.** Average daily MAP in the Southeastern United States and
R-Score (R) for each WFO.

![](media/image3.png){width="4.991666666666666in"
height="3.783333333333333in"}

![](media/image4.png){width="4.916666666666667in"
height="3.727777777777778in"}

**Figure 2. Daily MAP and QPF Error averaged over all 37 basins in the
WFO Atlanta HSA.**

**Figure 3. Daily QPF Root Mean Square Error versus Zero-QPF Root Mean
Square Error averaged for all 37 basins in the WFO Atlanta HSA.**

pressure systems tended to move from the Gulf of Mexico into the gulf
coast near Mobile, Alabama, then finally up the Atlantic East Coast.
Each system brought

very heavy rainfall to the gulf coast and Appalachian Mountain areas and
severe weather to Florida. At one point over 80 percent of the river
forecast points in the SERFC area of responsibility were in flood. This
weather pattern began in late December and ended abruptly in April when
drought conditions began to develop.

**QPF Verification Findings**

Figure 1 depicts the average daily MAP and the R-Score for each WFO HSA.
Figures 2 and 3 show QPF verification, averaged over all 37 basins in
the WFO Atlanta HSA, from January 1 to March 31, 1998. The x-axis is
sorted by daily average MAP. It was noted in nearly all cases that there
was an over-forecasting of QPF in light rain events and an
under-forecasting in heavy rain events (Figure 2). For the Atlanta WFO
area (ATL), the pivotal point was around an average observed MAP value
of 0.65 inches. At this value of rain, events producing an average MAP
of less than 0.65 inches had been over-forecast and rain events bringing
an average MAP of more than 0.65 inches had been under-forecast. In
general, light rain events occur more often than heavy rain events.
Therefore, it has been found through 1998 that QPF was overestimated by
most of the WFOs.

Figure 3 tries to prove that streamflow forecasts are better off with
QPF added by comparing daily QPF RMSE for the WFO Atlanta HSA and the
RMSE (Zero-QPF RMSE) for the same area with the QPF entered as zero. The
x-axis is sorted by daily average MAP. The shaded areas of QPF RMSE
above the Zero-QPF RMSE line show a "busted" forecast, since a more
accurate forecast would have been one with the QPF entered as zero on
that day. Shaded areas above the zero QPF RMSE line is an over-forecast
rain event. These "busted forecasts" most often happened when the MAP
was less than the "pivotal point" of 0.65 inches. The rain events
producing more than 0.65 inches of MAP had mostly lower QPF RMSE than
the respective Zero-QPF forecast. For example, the highest average MAP
for the WFO Atlanta basins was 2.44 inches, which fell between 12z on
February 3^rd^ and 12z on February 4^th^. WFO Atlanta issued an average
QPF for its HSA at 12z on February 3^rd^ of 1.27 inches. This resulted
in an under-forecast QPF for its HSA by 1.17 inches (Figure 2, last
plotted point on right). Looking at Figure 3, last plotted point on the
right, the WFO Atlanta's QPF RMSE was 1.26. If WFO Atlanta had entered
all zeros for its QPF or had not done a QPF at all, the RMSE (Zero-QPF
RMSE) would have been 2.49, which is higher than its actual QPF RMSE.
Thus proving that, on that day, the use of QPF added value to the
streamflow forecast.

High QPF skill is most needed during heavier rain events. During these
heavier rain events, river forecasts based on QPF are clearly better
than those based on zero QPF.

For other areas across the Southeast U.S., the "pivotal points" were
variable and ranged anywhere from 0.15 to 0.7 inches of average daily
MAP, depending on season and predominant weather in that season.

Table 1 shows statistics used for each WFO's QPF verification for 1998.
It is better to have an overall QPF RMSE less than its Zero-QPF RMSE. In
most cases for 1998 this was true; however, in areas where convection
was the predominant weather throughout the year, the QPF RMSE tended to
be higher, such as central and south Florida. In fact, during the
seasons when convection was the primary rain maker, the QPF RMSE was
higher across the entire Southeast U.S. All of the QPF RMSEs were lower
during the very wet winter than in the dry spring or summer.

Higher RMSEs for convective weather events are the result of the wide
variability in rain amounts from basin to basin. It is extremely
difficult for a meteorologist to determine the exact basins for which
heavy precipitation is going to occur during typical afternoon type
convection. The forecaster can only make a general estimate of where
convection is likely to occur and will usually broad brush the area with
an estimated average.

Weather forecast accuracy decreases with time, however, it was noted
that the worst QPF errors were not in the latest six-hour period, as a
meteorologist would naturally assume, but in the second six-hour period,
from 18z to 00z, when the most afternoon convection occurs.

In addition, the RMSE method also penalizes the convective rain events
because it causes badly missed basins to stand out more. It is common
for almost all basins to be correctly forecast and one or two to be
missed badly in a convective situation. Those missed basins would have
more of an effect on QPF RMSE than if the absolute of the error rate was
simply averaged. This is shown in Figure 2 on the Zero-QPF RMSE line.
The mean absolute error would just equal the average MAP. However, the
line does not increase steadily. The peaks are the result of higher
variability in MAP amounts across the basins.

Figure 1 is a winter map analyzed for average MAP and the R-Score
displayed by its respective WFO. The heaviest amount of rain fell around
the Mobile Bay area (0.28 in.) and the lightest fell across south
Florida and northern Mississippi. R-Scores were the best in the
Carolinas and worst in Florida. Stratiform precipitation,

mainly over-running situations, predominated in the Carolinas, with MAP
being uniformly distributed across the basins, and thus was easier to
forecast. Convective type precipitation, due mostly to thunderstorms and
squally type weather, predominated in Florida.

Another interesting observation in day-to-day QPF verification is the
overemphasis on areal coverage. In many heavy rain events, it was common
to see amounts under-forecast where the heaviest rain fell, and
bordering areas over-forecast. This happened mostly with hurricanes or
strong low pressure systems, where the heaviest rain tended to be
tightly packed in an area. An example of this is shown in Figure 1.
During the winter, the heaviest total seasonal precipitation was
centered in WFO Mobile's HSA, due to the continuous onslaught of low
pressure systems moving northeast into that area from the Gulf of
Mexico. WFO Mobile's R-score was 0.82, while bordering WFOs Birmingham
and Tallahassee had a higher R-Score (higher error rate) of 0.92 and
0.91, respectively. The higher error rate was due to the overextending
of the heavy rain forecast.

RECOMMENDATIONS AND CONCLUSIONS

For 1998, QPF verification statistics show over-forecasting of
convective and sea-breeze type weather across most WFOs. It is hard for
forecasters to lower QPF amounts across their area when they see
isolated heavy rain amounts occurring. However, it is recommended for
forecasters to see a comparison of the their QPF and what actually
occurred each day. Such a page exists at the SERFC Web Site and is
updated each day. Seasonal QPF verification statistics are also uploaded
soon after the season ends. Forecasters should make note of their
"pivotal point" and adjust their QPF accordingly, i.e., lower their QPF
forecast when they expect values to be lower than their "pivotal point."
Another "rule of thumb," to reduce QPF for convective weather events, is
to multiply the maximum amount of rain expected out of showers and
thunderstorms by the probability of precipitation, thereby lowering
their QPF (Amburn et al., 1993).

QPF = (Probability of Precipitation) \* (Precipitation Rate in Inches
per Hour) \* (Duration in Hours)

Tighter QPF gradients also need to be used for tropical storm type
weather. This is difficult until a forecaster knows where the heaviest
rain is going to occur. Therefore, more frequently updated QPF forecasts
(every six hours) should be issued during heavy rain events. Many
variables can affect the WFO's verification results in different ways,
such as number of basins considered, predominate weather, season,
accuracy of the computed MAP, etc. Therefore, in assessing a
forecaster's skill, only surrounding WFO verification results should be
used. Better yet, forecasters should compare their own WFO's results
over time to measure their performance.

SERFC INTERNET WEB SITE

[www.nwsserfc.noaa.gov]{.underline}

For specific Internet addresses, please e-mail,
[jack.bushong@noaa.gov.]{.underline}

ACKNOWLEDGMENTS

Thanks to Judith Bradberry, Senior HAS; Reggina Garza, Senior
Hydrologist; Mark Fuchs, Hydrologist; Rick Ullom, Hydrologist; Nene
Robertson, Hydrologic Technician; and John Feldt, Hydrologist In Charge,
for their help in the completion of this paper.

Thanks also to the rest of the SERFC staff for running this very
time-consuming Water Watcher Program each day.

LITERATURE CITED

Amburn, S. A., 1992. A Methodology for Making Quantitative Precipitation
Forecasts. National Weather Service, Tulsa, OK, 1-4.

Larson, L. W., 1976. Mean Areal Precipitation, National Weather Service
River Forecast System User's Manual, Chapter IV.1.2.1.

National Weather Service(NWS), 1982. National Verification Plan, Report
of the National Verification Task Team

Rappaport, E. N., M. C. Fuchs, and M. V. Lorentson, 1999, The Treat to
Life in Inland Areas of the United States from Atlantic Tropical
Cyclones, *Preprint 23 Conf. On Hurricane and Tropical Meteorology*,
Dallas, TX, Amer. Meteor. Soc., (in press).

Stellman, K., J. Kuhn, 1997. Mean Areal Precipitation Climatology For
Two Lower Mississippi River Basins. National Weather Service, RFC,
Slidell, LA, 1-4.

Stellman, K., H. Fuelberg, R. Garza, 1998. Utilizing Radar Data to
Improve Streamflow Forecasts, 1-3.

U.S. Dept. of Commerce, 1997: Correspondence Course - Operations of the
NWS Hydrologic Services Program. Lesson 2, Hydrologic Services Program
at National Weather Service Field Offices, 30.

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