Inland Hurricane Readiness Training

**Excessive Rainfall Prediction Unit**

*Lead Author: Joshua Palmer -- SERFC Hydrometeorologist*

**Total Time:** 25-35 minutes (20-25 slides)

Causes of Heavy Rainfall in Tropical Cyclones

\- Title Slide FIGURE(s):

-   Tropical cyclones are extremely efficient precipitation producers.

**The Ocean-Atmosphere Interaction (1)**

-   Tropical cyclones originate in regions with pre-existing moist
    atmospheres due to the transfer of water from the oceans to the
    atmosphere through evaporation. The efficiency of the evaporation
    process is directly proportional to wind velocity and the flux of
    heat between the atmosphere and the ocean.

**Instability and Lift (1)**

-   Vertical motion of warm, moist air to regions of the atmosphere
    where the air is drier and cooler acts to cool the transported air
    and saturate it. The saturation of the air results in the
    condensation of the water vapor into liquid droplets. Stronger
    vertical motion correlates to greater liquid droplet generation
    rates.

-   Warmer sea surface temperatures act as fuel for the tropical cyclone
    in part because the oceanic heat flux warm the lower levels of the
    atmosphere and increases instability for rising parcels of air.

**Creating Droplets (1)**

\- Average droplet sizes in tropical clouds are larger than those in
continental clouds and the distribution of droplet size in tropical
clouds is larger as well. Given an improved diversity in droplet size,
the likelihood of droplet collision and coalescence improves, which
increases the chances of forming drops large enough to fall as
precipitation.

\- Weak vertical wind shear is advantageous not only for tropical
cyclone development, strength, and organization, but it additionally
improves the accuracy of droplet collision and coalescence, allowing
drops to fall uninhibited by external forces and gather the smaller
droplets as it does so.

\- A deep warm layer (several kilometers of atmosphere are \>0C)
encourages droplet growth as well and additionally serves to increase
the average amount of energy in the atmosphere which can be converted to
fuel for the tropical cyclone.

**Precipitation Efficiency (1)**

> \- Over large land masses, the lack of a consistent and warm moisture
> source means large layers of the atmosphere are often dry. Thus,
> efficiency is reduced because large amounts of moisture must first be
> spent saturating the ambient environment before precipitation can
> occur.
>
> **-** Given the ocean-atmosphere interaction and the presence of
> instability and lift, the tropical environment within which tropical
> cyclones form is very efficient at producing precipitation. Therefore,
> there is less energy spent on creating an environment that supports
> precipitation and more energy spent on actually creating the
> precipitation!

**Storm Speed (2)**

-   Tropical cyclones are steered by the mean large-scale flow in their
    ambient environment. Like mid-latitude cyclones, the center of low
    pressure in a tropical storm will propagate towards the areas of
    strongest pressure falls. Thus, tropical cyclones will often travel
    along the periphery of large-scale high- and low-pressure systems.
    When these large-scale systems are weak (characterized by weak
    pressure gradients and, therefore, weak steering winds) the storm
    often propagates slowly if at all.

-   The nearly stationary speed of a tropical cyclone can actually work
    to weaken the cyclone over time especially over the ocean as the
    ocean temperatures near the surface cool and inhibit the flow of
    warm, unstable air into the cyclone.

\- However, rain rates are not directly related to storm speed.
Faster-moving storms often impact any one location over a shorter
duration than slow storms. But, if rain rate is independent of storm
speed, the slow-moving cyclones can increase the likelihood of any one
location receiving the same rain rate for a longer period of time,
resulting in heavier precipitation. Nevertheless, situations like rain
band training can occur regardless of storm speed.

**Storm Size and Strength Caveats (1)**

-   Tropical cyclone size and strength should NOT be primary concerns
    when considering heavy rainfall potential. These characteristics are
    NOT direct predictors of precipitation maxima.

-   It is true that storm size can increase the areal coverage of
    precipitation and the proportionally larger tropical environment can
    lead to heavy rainfall in areas further away from the center of
    circulation. However, the heavy rainfall relationship is indirect:
    several other factors, including those mentioned in the previous
    subsections and those mentioned in the final section of this unit,
    will enhance the larger tropical environment to produce the areas of
    heavy rainfall or rainfall maxima.

-   Storm strength also has an indirect relationship to heavy
    precipitation. Stronger eyewalls often contain stronger updrafts and
    can therefore improve the transfer of moisture necessary to produce
    precipitation. Faster cyclonic flow in concert with external forcing
    such as terrain or mid-latitude systems can also improve moisture
    flux and precipitation efficiency/quantity.

-   Therefore, focusing primarily on storm size and strength without
    understanding the direct causes of heavy rainfall (e.g.,
    precipitation efficiency, instability and lift, storm speed, etc.)
    can be unwise. We will illustrate cases in the final section of this
    Unit that show heavy rainfall can occur independent of size and
    strength.

Hydrometeorological Forecast Process

> \- Title Slide FIGURE(s):
>
> \- Important to evaluate performance of gauge and radar estimates in
> order to advise hydrologists on the best method of ingesting observed
> into the hydrologic model.

**QPE and** **Quality Control (2)**

**-** Two main sensors for precipitation estimates: rain gauges and
radar. Rain gauges are meant to measure precipitation at a single point
and thus provide a poor estimate of the precipitation over a larger
area, such as a river basin. Radar power/rain rate relationships are
used to produce precipitation estimates across the radar's entire
coverage field.

\- Rain gauges include: non-recording cylindrical containers, recording
weighing gauges, floatation

> gauges, and tipping-bucket gauges. All gauges features sources of
> error and all gauges are prone to malfunction. Sources of error
> include: (1) heavy rain underestimation due to limitations of tipping
> buckets; (2) loss of rainfall from gauge due to splashing,
> obstructions, strong winds; (3) transmission failures, etc.

\- The power of the returned wave emitted from the radar and scattered
by a hydrometeor is first converted into a reflectivity factor. A
radar's backscattered power returns can be used to derive an estimate of
precipitation by relating the reflectivity factor to pre-determined
rates of precipitation. A special rate of precipitation is used to
compute radar rainfall estimates in tropical environments due to the
characteristics of those environments mentioned previously.

\- Radar estimates are taken one step further by incorporating point
measurements from gauges in order to determine how accurate the radar
estimates are in a particular event. The radar estimates over a gauge
location are compared to the gauge measurement and a ratio is computed
that illustrates whether the radar estimates are over- or
under-estimates of the actual rainfall. That ratio acts as a
multiplicative factor that can be used to adjust radar estimates across
the entire radar domain at the discretion of the RFC staff. The
algorithm that produces these ratios is known as the Multi-sensor
Precipitation Estimator (MPE).

\- Not unlike rain gauges, radars also suffer from several sources of
error, including: (1) beam blockage; (2) evaporation below the radar
beam; (3) variations in the distribution of droplet sizes; (4) frozen
precipitation aloft; (5) strong winds displacing the precipitation from
the cloud; (5) .

\- It is the responsibility of RFC hydrometeorologists to assess the
performance of both sensors, analyze patterns in performance based on
past events, identify, evaluate, and, where possible, correct sources of
error, and advise RFC hydrologists on the best sources of precipitation
estimates for ingest into the hydrologic models.

\- FIGURE: MPE Before and After QC Process

**Mean Areal Precipitation (MAP) (2)**

\- Point precipitation measurements are of little use to a hydrologist
intending on quantifying the distribution of precipitation over entire
river basins. Before the installation of a nationwide radar network and
the development of promising radar estimation algorithms, there needed
to be a way to take a series of point measurements from rain gauges and
create an areal representation of the precipitation.

\- The operational hydrologic model used at RFCs requires precipitation
values to be averaged across each basin that is represented within the
model. In other words, precipitation values are "basin-averaged". Given
two sources of precipitation estimates, there are two approaches to
producing the basin-averaged values needed for hydrologic operations.

\- Mean Areal Precipitation (MAP) is a product that results from
averaging rain gauge measurements across each basin. The method used to
accomplish this is known as Thiessen Polygons.

\- The Thiessen Polygon approach divides each basin into a number of
polygons equal to the number of gauges that lie in, along the border of,
or just outside the basin. Each polygon contains one gauge. The area of
each polygon is calculated and a weight is assigned to each polygon by
dividing the area of the polygon by the total area of the basin. The
resulting weight represents a multiplicative factor that is applied to
the polygon's gauge and the total average precipitation for the basin is
equal to the summation of all weighted gauges.

\- FIGURE: Thiessen Polygon Example

**MAPX (2)**

**-** Given the existence of radar technology and algorithms like MPE,
an alternative to MAP exists known as MAPX.

**-** MAPX is a basin-averaged precipitation product that is calculated
simply by averaging the values of precipitation inside each MPE grid
cell that lies within a particular basin. MAPX, while an averaged
product, can still suffer from both radar and gauge sources of error
which is why MAP is still used frequently in hydrologic forecasting.
However, in areas where gauge data is sparse for example, MAPX benefits
from the same advantages radar estimates have over gauge estimates and
can be vital to providing hydrologists with accurate model input.

\- MAP remains the default source of hydrologic model precipitation
estimates for several RFCs, while others default to MAPX. The variation
from office to office is largely due to the performance of each approach
in each area. This performance is variable due to several factors,
including: (1) radar error sources like beam blockage; (2) spatial
coverage of gauge values and gauge sources of error; and (3) basin
characteristics including topography and areal coverage.

\- FIGURE: MAP vs. MAPX

**Quantitative Precipitation Forecasting (QPF) (2)**

\- While accurately representing the past is critical to accurate
streamflow forecasts, precipitation

> forecasting is equally important in producing streamflow skill.
> Hydrometeorologists are responsible for analyzing various model
> output, current observations, latest trends, model biases, strengths,
> and weaknesses, and identifying known patterns in the atmosphere in
> order to provide hydrologists with skillful QPFs (including the
> scenarios below).

\- QPF is produced for the future zero-hour to 48-hour period at
six-hour timesteps.

\- RFC hydrometeorologists rely on an initial QPF from the
Hydrometeorological Prediction Center (HPC), produced several hours
before the initial forecast time. Oftentimes, the difference between HPC
QPF issuance time and initial forecast time allows the RFC to improve
HPC QPF based on latest cyclone trends like storm track.

\- Achieving skill in QPF beyond 12 hours remains a formidable challenge
throughout the world and across all meteorological events. The state of
the science simply does not permit us to achieve high accuracy in QPF,
especially as the valid time and the resolution of the forecast needed
increases.

**Future/Forecast Mean Areal Precipitation (FMAP) (1)**

\- QPF is first produced for a regular grid of forecast points.
Thereafter, the QPF is converted from a gridded format to a
basin-averaged format that can be ingested directly into our hydrologic
models. This basin-averaged format is produced by simply averaging the
QPF in each grid cell that lies within the basin-of-interest.

\- Just like QPF, FMAP is created in six-hour timesteps for the future
zero-hour to 48-hour period.

\- However, the number of FMAP time steps that are ingested into the
hydrologic model varies from one RFC to the next. The decision to ingest
12-, 24-, or even 48-hours of FMAP depends largely on the confidence the
RFC staff has in the FMAP and the upcoming event. It is, therefore,
important to ask the appropriate RFC staff how many FMAP time steps were
included in the streamflow forecast of interest.

\- FIGURE: NMAP Color-Contoured QPF Grid

Scenarios

\- Title Slide FIGURE(s):

**External Forcing (2)**

*Overview*

> \- Tropical cyclones approaching the mid-latitudes often encounter
> non-tropical, large-scale systems which provide additional sources of
> moisture, instability, and lift and focus heavy precipitation in
> localized areas. Identifying these features can help pinpoint areas
> vulnerable to flash-flooding.
>
> \- The presence of jet streaks (i.e., significant acceleration of wind
> at various levels of the atmosphere), can concentrate areas of intense
> vertical motion.
>
> \- Advection of mid-latitude cyclone energy into the system's
> environment or ahead of the system's environment results in additional
> uplift.
>
> \- Low-level boundaries, often separating continental air masses from
> the tropical air associated with the cyclone, serve as a source of
> moisture convergence and upward forcing (lift), and will be where the
> heaviest precipitation often occurs.
>
> \- Low-level boundaries (e.g., abrupt temperature/dewpoint changes,
> wind shifts, changes from unstable to stable environments, etc.)
> oriented parallel to the average motion of the storm or the secondary
> motion of the rainbands, can result in the precipitation recurring
> over the same locations, enhancing the flash-flood threat.

*Examples: Floyd (September 7-17, 1999)/Alberto (June 10-14, 2006)*

> \- \[The importance of Alberto is similar to Allison in that storm
> size and strength are oftentimes negligible when considering heavy
> rainfall\]

\- FIGURE: HPC Rainfall Totals for Floyd

\- FIGURE: NWSWFO Rainfall Totals for Alberto

**Stalled System (2)**

*Overview*

-   Weak steering winds from weak large-scale systems provide little of
    the forcing necessary to keep a tropical system moving.

-   The development of new mid-latitude storms in periods of weak
    steering flows or modification (i.e., strengthening, weakening, or
    propagation) of the weak large-scale systems, for example, can act
    to change the motion of the tropical cyclone in ways that can force
    the cyclone to curve or loop over the same areas.

*Example: Allison (June 5-17, 2001)*

\-

\- FIGURE: Satellite Image or Track (potentially as background)

\- FIGURE: HPC Rainfall Totals (either along GC, in Harris County, or
storm scale)

**Orographic Forcing (2)**

*Overview*

\- Non unlike low-level atmospheric boundaries, terrain changes can
induce vertical motion via upslope flow (i.e., rising motion and
increased precipitation efficiency) and downslope flow (i.e., sinking
motion and decreased precipitation efficiency).

\- Unlike low-level atmospheric boundaries, the orography is stationary.
Therefore, the actual precipitation patterns that result from this type
of forcing are dependent on the storm flow. Storm flow, in reference to
a stationary feature like terrain, is largely dependent on storm track.

*Examples: Frances (August 25 - September 8, 2004)/Ivan (September 2-24,
2004)*

\- \[Emphasize the clear influence of cyclonic flow AND track and its
interaction with the topography in determining the axes of heaviest
rainfall\]

\- FIGURE: HPC Rainfall Totals for Frances

\- FIGURE: HPC Rainfall Totals for Ivan

\[Modify graphics to illustrate overall storm flow and areas of
upslope/downslope conditions\]

**Training Rain Bands (2)**

*Overview*

-   Occasionally, the principal or secondary rain bands of the tropical
    cyclone itself will become oriented parallel to the motion of the
    storm. Under the right conditions of persistent instability and
    moisture convergence, sometimes aided by the flow of the ambient
    environment, these rain bands can become nearly stationary, allowing
    individual cells of convection within the band to move over the same
    regions.

-   These rain bands are often the source of significant severe weather.

*Example: Cindy(July 3-7, 2005)/Dennis (July 4-13, 2005)*

*-* Rainfall exceeded 10 inches in some places due to Dennis's
persistent rain band.

*-* Record or near-record level flooding and flash-flooding occurred
around the Atlanta metro area with Dennis, assisted by the previous
rainfall of Cindy only several days before.

\- FIGURE: Satellite Image or Track (potentially as background)

\- FIGURE: HPC Rainfall Totals for Dennis

\- FIGURE: HPC Rainfall Totals for Cindy

Notes Collected For Training

<http://www.crh.noaa.gov/ncrfc/content/documents/mapterms.php>

## *Point Precipitation Measurement, Areal Estimates And Relationships To Hydrologic Modeling*

*Precipitation Measurement*\
Liquid precipitation is traditionally measured using various types of
rain gages such as the non-recording cylindrical container type or the
recording weighing type, float type and tipping-bucket type. All of the
above gages measure precipitation at a point. Another method of
measuring precipitation is through the use of radar. The actual
measurement taken by the radar is of backscattered power of the echo
returns. This return power is used to calculate a reflectivity factor,
Z. The Z reflectivity factor is then used in an equation referred to as
a \"Z-R relationship\" to determine rainfall rate. The ABRFC produces
NWS WSR-88D 1-hour radar precipitation estimates for our area of
responsibility on an approximate 4 by 4 km square grid resolution.\
\
*Hydrologic Requirement for Areal Estimates of Precipitation*\
The river forecasting models in use by the NWS require areal estimates
of precipitation. This is because the models simulate the
rainfall-runoff process on a drainage catchment area basis. Large river
basins are sub-divided into these smaller catchments or runoff zones.
Runoff from excess precipitation in each zone is used to calculate
streamflow which is then used to produce river stage forecasts.\
\
*Estimation of Areal Precipitation*\
A single point precipitation measurement is quite often not
representative of the volume of precipitation falling over a given
catchment area. A dense network of point measurements and/or radar
estimates can provide a better representation of the true volume over a
given area. A network of precipitation measurements can be converted to
areal estimates using any of a number of techniques ([click here for
summary](http://www.srh.noaa.gov/abrfc/map/map.shtml#map_tech%23map_tech))
which include the following:\
\
1) [Arithmetic
Mean](http://www.srh.noaa.gov/abrfc/map/map.shtml#map_arith%23map_arith) -
This technique calculates areal precipitation using the arithmetic mean
of all the point or areal measurements considered in the analysis.\
\
2) [Isohyetal
Analysis](http://www.srh.noaa.gov/abrfc/map/map.shtml#map_isoh%23map_isoh) -
This is a graphical technique which involves drawing estimated lines of
equal rainfall over an area based on point measurements. The magnitude
and extent of the resultant rainfall areas of coverage are then
considered versus the area in question in order to estimate the areal
precipitation value.\
\
3) [Thiessen
Polygon](http://www.srh.noaa.gov/abrfc/map/map.shtml#map_thie%23map_thie) -
This is another graphical technique which calculates station weights
based on the relative areas of each measurement station in the Thiessen
polygon network. The individual weights are multiplied by the station
observation and the values are summed to obtain the areal average
precipitation.\
\
4) [Distance
Weighting/Gridded](http://www.srh.noaa.gov/abrfc/map/map.shtml#map_dist%23map_dist) -
This is another station weighting technique. A grid of point estimates
is made based on a distance weighting scheme. Each observed point value
is given a unique weight for each grid point based on the distance from
the grid point in question. The grid point precipitation value is
calculated based on the sum of the individual station weight multiplied
by observed station value. Once the grid points have all been estimated
they are summed and the sum is divided by the number of grid points to
obtain the areal average precipitation.\
\
5)
[MAPX](http://www.srh.noaa.gov/abrfc/map/map.shtml#map_mapx%23map_mapx) -
This is a NWS-specific gridded technique. Areal runoff zone
precipitation estimates are made using the 4 x 4 km WSR-88D 1-hourly
gridded precipitation estimates. The arithmetic mean calculation
technique is used to average the grid point estimates.\
\
6) Index Stations - In some areas of the country (primarily mountainous
areas), pre-determined station weights based on climatology are used to
compute basin average precipitation.\
\
*Areal Precipitation Terminology As Used In ABRFC Hydrologic Modeling*\
MAP - Mean Areal Precipitation - Areal runoff zone precipitation
estimate normally based on point precipitation observations. The
distance weighting calculation technique is used. MAP is used as input
to the river forecast model on a routine basis.\
\
MAPX - Radar Based Mean Areal Precipitation - Areal runoff zone
precipitation estimate based on the 4 x 4 km WSR-88D 1-hourly gridded
precipitation estimates. The arithmetic mean calculation technique is
used to average the grid point estimates. MAPX is used as input to the
river forecast model on a routine basis.\
\
FMAP - Future Mean Areal Precipitation - Future or forecast areal runoff
zone precipitation estimate. The Weather Forecast Offices (WFOs) develop
precipitation forecasts based on input from sources which may include
meteorlogical model output, national guidance products, local forecast
procedures and individual forecaster experience. After weather analysis
is complete, the WFO forecaster uses a computer program to draw isohyets
of forecast precipitation and then the program performs an automated
isohyetal analysis calculation technique to convert to areal estimates.
The forecast precipitation information is generated for four 6-hour
periods. The area of coverage is that of each WFOs area of
responsibility. The ABRFC Hydrometeorological Analysis and Support (HAS)
function mosaicks the input from the WFOs so as to cover the entire
ABRFC area of responsibility. The HAS function also coordinates any
required changes in the individual WFO QPF information. FMAP is used as
input to the river forecast model on a routine basis.

![](media/image1.png){width="4.916666666666667in"
height="6.323611111111111in"}\
\
Various mean areal precipitation computation techniques yield the
following results.\
1.63\" = [Distance
Weighting](http://www.srh.noaa.gov/abrfc/map/map.shtml#map_dist%23map_dist)\
1.90\" = [Isohyetal
Analysis](http://www.srh.noaa.gov/abrfc/map/map.shtml#map_isoh%23map_isoh)\
2.00\" =
[MAPX](http://www.srh.noaa.gov/abrfc/map/map.shtml#map_mapx%23map_mapx)
(WSR 88-D estimate)\
2.03\" = [Thiessen Polygon
Weighting](http://www.srh.noaa.gov/abrfc/map/map.shtml#map_thie%23map_thie)\
2.21\" = [Arithmetic
Mean](http://www.srh.noaa.gov/abrfc/map/map.shtml#map_arith%23map_arith)
of Point Values\
( click any method for an example )

![](media/image2.png){width="5.957638888888889in"
height="8.666666666666666in"}

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height="8.666666666666666in"}

![](media/image4.png){width="6.260416666666667in"
height="8.666666666666666in"}

![](media/image5.png){width="6.260416666666667in"
height="8.666666666666666in"}

![](media/image6.png){width="6.260416666666667in"
height="8.666666666666666in"}.