Transcript Document 7245978

“New Mesoscale Modeling by
Raw Output Statistics (ROS)”
How did the ROS model
begin, and WHY do we
need another model?
Glad you asked.
1) The ROS model recieved its start from aviation
and fire weather. Forecasters were searching for
a quick way to find ceiling heights as well as
model produced fire weather parameters.
Nationally produced guidance did not have either
of these conveniences.
From there, others began to ask if the ROS could
catch micro and mesoscale meteorological
phenomenon…such as lake effect snowfall in
Duluth, Minnesota and sea fog episodes in New
Orleans. We put it to the test by inserting some
local research and study material and the model
began to show signs of working. After some fine
tuning, the ROS was on its way.
2) We don’t really need another NATIONALLY
PRODUCED MODEL. Models are beginning to be
run at the local level such as the WSeta. This
model can also be run through the ROS. It is
proving to be an inexpensive way to produce
model forecasts. It may also show some strength
over the nationally produced guidance.
NCEP would never be able to tackle such a
tremendous project as running a mesoscale model
for every single office. This is because each office
has its own set of fire weather fields as well as
mountains, hills, valleys, and lakes to input.
Individual stations can also change modes when
necessary…i.e. winter to summer equation
useage.
 Marine data continues to be collected for use in the
marine ROS. The introduction of the new buoy
sensors will add some very important and much
needed data to these sets…BUT there are some big
problems facing the model output at this time.
 The first problem is quite obvious…there are no
observations other than sea surface and winds for
verification purposes. Therefore…we can not see
how well the model is performing with visibility or
cloud heights.
 The final problem is there are no data sets to apply
to the model equations and or algorithms for these
variables. The continental zones have all the data
they can handle for predictors.
That is not to say we do not try. The New
Orleans office is sourcing the only data
available for visibility and cloud heights.
Those data sets are from near shore and
onshore locations including those
observations from the Houston CWA, Lake
Charles CWA, New Orleans CWA, Mobile
CWA, and Tallahassee CWA.
And we come up with something that looks
like this:
CWA = Coastal Warning Area
ETA ROS Explanation and Description of Fields
1
2
3
3
3
ETAROS7 TERICK KNEW 060545
GPT ETA ROS GUIDANCE 05/06/2002 0000UTC
WKDY
MON
TUE
WED
DATE /MAY 6
/MAY 7
/MAY 8
HOUR 03 06 09 12 15 18 21 00 03 06 09 12 15 18 21 00 03 06 09 12
1) The first line gives the model file name, the developer, the
permanent station it is run from and the Z time it is run.
2) The second line gives the station it is run for, the name of the
model and the date the model is valid for.
3) The next 3 fields are time fields. One special feature here that
isn’t found on any other short term alphanumeric model is the
day of the week. It is simply run as an algorithm inside the
source code.
1 MNMX
2 TEMP
3 DWPT
35( 35)
43( 43)
34( 34)
46( 46)
33
35 35 37 37 39 39 43 41 39 38 36 34 39 44 45 42 38 34 33 33
34 35 35 35 35 33 33 36 35 35 33 32 30 30 29 29 26 27 27 26
1)
Max Min temperature in F
2)
Temperature on the hour in F
3)
Dew Point temperature on the hour in F
1
2
3
4
CLDS O< O< O< O< O2 O2 O3 O2 O1 O2 CL CL CL CL CL CL CL CL S6 S^
CLHT 08 08 08 08 19 23 33 19 15 23 00 00 00 00 00 00 00 00 63 17
TMPO 05 05 05 05 15 19 28 15 11 19
TTSK OV OV OV OV OV OV OV OV OV OV CL CL CL CL CL CL CL CL PC PC
1)
Prevailing lowest possible cloud level.
2)
Cloud height to the 100’s and 1000’s of feet.
The CLDS field will tell if this number shows 100’s or 1000’s of feet. As an
example, O< will first tell you the lowest prevailing cloud condition will be
“O” overcast and the height of this deck will be “<“ less than 1000ft. Then
the CLHT field would be read with two zeros. If a number is shown in the
CLDS field then the CLHT field will be read also with two zeros. If a “>” or
“^” sign is used then the CLHT field will be read with three zeros.
3)
Temporary ceilings when the LCL has high RH values. This field will
always be shown in 100’s of feet never 1000’s and will always be equal to
or less than the prevailing cloud height.
4)
Total sky cover accumulates all cloud levels
Cloud Height Equation and Algorithm:
Others who have worked with the TERICK equation are:
Dr. Eric Pani of the University of Louisiana at Monroe set thermodynamic
theory and an integral explanation to the equation…Bob Rozumalski of
COMET explained and found errors in the original equation…and Peter
Parke of the National Weather Service in Duluth, Minnesota worked
with verifying the units used in the equation.
TERICK EQUATION:
Hl + [(Hc – Hl)/(Tc – Ts)] = LCH;
If (Tc – Ts) <= 0; then (Hc – Hl) = 0;
WHERE:
Hl=LCL height in feet
Tc=Conv temp in C
Hc=CCL height in feet
Ts=SFC temp in C
LCH=Lowest Cloud Height
1
VSBY 05 P6 04 P6 P6 P6 P6 P6 P6 P6 P6 P6 P6 P6 P6 P6
2
OBVS -S
-S
1) The visibility is developed through
local studies and research. There are
many variables to this field.
2) The obstruction to visibility shows the
weather phenomenon responsible for
causing the reduction in visibility.
WDIR 35 36 01 02 02 03 02 03 02 01 32 33 34 34 33 35 33 32
WSPD 13 10 11 10 10 08 07 05 06 04 06 08 08 08 09 06 08 10
Wind direction and wind speed in knots.
PP06 0
PP12
0
0
0
0
0
0
2
2
16
37
26
17
0
6
6 & 12 hour POP fields. These are derived
from local studies and research as well.
ALWAYS CHECK FOR RH INITIALIZATION BEFORE
USING POP’S FROM ANY MODEL.
TTPP 00 00 00 00 00 00 00 00 00 00 12 01 04 16 19 17 11 06 00
PTYP
RA RA RA RA RA RA RA RA
Total precipitation is straight from the raw grids. In other
words, the amount of QPF you see on the raw grids is the
amount shown here.
The total precip field is shown to the hundredths of an inch.
They are also cumulative over each three hour period.
The Precipitation Type field is the only one computed through
BUFKIT…it uses a thickness scheme.
1 SNAC 00 00 00 00 .5 .5 .2 .8 01 03 02 .2 00 00 00 00 00 00 00
2 SWEQ 01 01 01 01 01 02 02 03 03 03 05 05 05 05 05 04 04 04 03
1) Snow accumulation. It is read with a decimal
for any amounts under an inch. When the
amount is an inch or greater, it will drop the
decimal and show a rounded whole inch.
2) The snow water equivalent is produced with
the use of remote sensing. This field is
updated once a week.
INTERGOVERNMENTAL USE ONLY...-12MET60.SITE
WCHL 12 22 27 25 24 25 23 18 14 20 22 20 19 10 07 02-08-02 03-09
HINX 60 65 72 85 87 92 95 93 97 99 98 99 99 98 98 95 92 93 92 91
LE06 22
0
0
0
0
0
0
15
57
54
LE12
16
0
0
7
69
TEMP 12 23 28 27 27 28 24 19 19 29 31 28 27 22 16 12 09 14 15 06
These are test fields.
The wind chill and heat index are seasonal. They are shown here because they
are not representative when temperatures fall outside the equations’
threshold.
The Lake effect pop field is currently in testing. It uses vectorization along with
a few other predictors to determine the percentage of purely lake effect
pops.
The temperature field here is a failed attempt to better the sfc temperature
output without statistics.
Equations and Algorithms:
Fields which are stripped and clipped straight from the ETA raw data
are as follows:
1)
2)
3)
4)
5)
6)
7)
8)
9)
DATE-> date
HOUR-> UTC hour
TEMP -> temperature
DWPT-> dew point
WDIR-> wind direction
WSPD-> wind speed
TTPP-> total water equivalent precipitation
SWEQ-> snow water equivalent
PTYP-> precipitation type (produced by BUFKIT algorithms)
Fields which are derived locally are as follows:
1)
2)
3)
4)
5)
6)
7)
8)
9)
10)
11)
12)
13)
14)
15)
16)
17)
18)
19)
20)
21)
22)
23)
24)
25)
26)
27)
28)
29)
30)
All header information
WKDY-> weekday
MNMX-> min/max temp
CLDS-> predominant cloud cover and level
CLHT-> predominant cloud height
TMPO-> temporary ceiling height
TTSK-> total sky cover
VSBY-> visibility
OBVS-> obstruction to visibility
PP06-> 6 hour probability of precipitation
PP12-> 12 hour probability of precipitation
SNAC-> snow accumulation
HMNMX-> relative humidity min/max percentages
SFCRH-> surface relative humidity
HAINS-> haines index
MIXHT-> mixing height
TPRTD-> transport direction
TPRTS-> transport speed
VNTRT-> ventilation rate
CATDY-> category day
DISPN-> dispersion index
20DIR-> wind direction 20 feet above ground level
20SPD-> wind speed 20 feet above ground level
SUNHR-> meteorological hours of sunlight
LALEV-> lightning activity level
LTGFQ-> lightning frequency
HINX-> heat index
WCHL-> wind chill
LE06-> 6 hour probability of lake effect/enhanced
LE12-> 12 hour probability of lake effect/enhanced
ERRORS IN ANY MODEL CAN COME FROM MANY SOURCES:
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Errors in the Initial Conditions
1. Observational Data Coverage
a. Spatial Density
b. Temporal Frequency
2. Errors in the Data
a. Instrument Errors
b. Representativeness Errors
3. Errors in Quality Control
4. Errors in Objective Analysis
5. Errors in Data Assimilation
6. Missing Variables
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Errors in the Model
1. Equations of Motion Incomplete
2. Errors in the Numerical Approximations
a. Horizontal Resolution
b. Vertical Resolution
c. Time Integration Procedure
3. Boundary Conditions
a. Horizontal
b. Vertical
4. Terrain
5. Physical Processes
a. Precipitation
1. Stratiform (Grid Scale)
2. Convective Precipitation
b. Radiation (Short-wave/Long-wave)
c. Surface Energy Balance
d. Boundary Layer
1. Surface Layer (0-10m)
2. Ekman Layer (0-1km)
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Intrinsic Predictability Limitations
Even with error-free observations and a "perfect" model, forecast errors will grow with time.
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No matter what resolution of observations is used, there are always unmeasured scales of motion.
The energy in these scales transfers both up and down scale. The upward transfer of energy from
scales less than the observing resolution represents an energy source for larger-scale motions in the
atmosphere that will not be present in the numerical model. Thus, the real atmosphere and the
atmosphere that is represented in the numerical model are different. For this reason, the model
forecast and the real atmosphere will diverge with time. This error growth is roughly equal to a
doubling of error every 2-3 days. Therefore, even very small initial errors can result in major errors for
a long-range forecast.
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The problem just stated is the essence of chaos theory applied to meteorology. This theory proposes
that nothing is entirely predictable, that even very small perturbations in a system result in
unpredictable changes in time.
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Forecasts based on climatology will have a relatively high level of error, but will remain constant over
time. Forecasts based on persistence (i.e., whatever is happening now will happen later) are nearly
perfect at extremely short range, but quickly deteriorate. Current models do well at short ranges, but
eventually do worse than climatology. A forecast that is worse than climatology is considered useless.
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Even the best model we can envision will, for reasons just discussed, produce forecasts that
deteriorate over time to a quality lower than those based on climatology.
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Our current forecast models have skill up to the 5-7 day range on the synoptic scale for 500 mb
heights. (Occasionally, they have skill at 15-30 days for time-averaged planetary waves.) They show
much less skill for derived quantities such as vorticity advection or precipitation. A related
predictability limitation is that intrinsic error growth will contaminate smaller scales faster than larger
scales. In other words, a small-scale phenomenon will be less well forecast than a large-scale
phenomenon in the same range forecast.
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However, mesoscale/convective scale predictability may not follow this smooth progression due to its
highly intermittent nature. For example, a rotating supercell thunderstorm may have more
predictability (2-6 hr) than an airmass thunderstorm (1 hr). Topographically and/or diurnally-forced
circulations such as dry lines and sea breezes are more predictable than squall lines.
ETA HORIZONTAL DOMAIN
This map shows the grid
sections that MOS is run. In
other words, when looking
at FWC guidance, the header
information will show what
equations are run for that
guidance package. These are
split into climatologically
favored regions. An example
of the header info is shown
here.
BRD C NGM MOS GUIDANCE 6/26/02 0000 UTC
DAY /JUNE 26
/JUNE 27
/JUNE 28
HOUR 06 09 12 15 18 21 00 03 06 09 12 15 18 21 00 03 06 09 12
DLH EC NGM MOS GUIDANCE 6/26/02 0000 UTC
DAY /JUNE 26
/JUNE 27
/JUNE 28
HOUR 06 09 12 15 18 21 00 03 06 09 12 15 18 21 00 03 06 09 12
WHAT IS THE FUTURE OF THE ROS???
The future of ROS will be what individual offices want it to be. Offices
using the ROS will break the large grids shown in the previous slide
into very small grid sections relative to the offices’ CWA. This is very
high resolution. Currently the ROS is run using data from the ETA, but
it can be configured to run for any numerical model that NCEP
produces. This is cutting edge technology, we here at the New Orleans
WSO are doing our best to break new ground.
Each office will finally have the capability of introducing micro and
mesoscale variables to their output. Studies and research can be
sourced into the model to make an offices forecast extremely strong.
All variables will benefit from the added data. Since no office can edit
the NCEP models, this will make the ROS obsolete and interactive.
Individual fields can be changed or removed depending on office
needs.
An example would be the fire weather fields. These can be changed or
“forced” to see what the offices’ users want to see for a particular site.
MOS will never be able to do that as well as many other special
features the ROS is able to provide.
1) In what kinds of situations would you expect
statistical guidance to perform well?
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a) Mesoscale or rare features such as cold-air
damming
b) Situations of abnormal snow cover
c) Synoptically forced situations
d) Rapidly moving frontal systems
e) Heat waves (abnormally high temperatures)
 c) Synoptically forced situations
 Statistical guidance can be expected to perform
best in situations where large-scale synoptic
forcing dominates.
2) What are the limitations of MOS guidance
that you as a forecaster should be aware of?
 a) Accounts for systematic model errors
 b) Cannot account for deteriorating model
accuracy at longer forecast times
 c) Requires a developmental dataset of
historical model data
 d) Multiple predictors can be used
 e) Improvements to model systematic errors
will result in degraded MOS guidance
 c) Requires a developmental dataset of
historical model data
 e) Improvements to model systematic
errors will result in degraded MOS
guidance
3) What types of predictors would you expect
to carry more weight in the development of
MOS forecast equations for short-range (036 hours) projections?
 a) Model data
 b) Climate data
 c) Observed weather elements
 d) Relative frequency
 a) Model data
 c) Observed weather elements
4) What predictors would you expect to be
selected for thunderstorm guidance?
 a) Lifted index
 b) CAPE
 c) Relative humidity
 d) Climatic relative frequency
 e) Lifted condensation level
 a) Lifted index
 b) CAPE
 c) Relative humidity
 d) Climatic relative frequency
 e) Lifted condensation level
5) Under the influence of which of the
following would you expect MOS to NOT be
reliable?
 a) Vigorous low-pressure system
 b) Trapped cold air in a mountain valley
 c) Squall line
 d) Overrunning precipitation
 e) Clear, calm, dry night over the plains
 f) Tropical cyclone
 b) Trapped cold air in a mountain valley
 c) Squall line
 f) Tropical cyclone
 When mesoscale features are expected to
play a significant role and extreme or
unusual events are expected, do not rely on
SG output (MOS)because IT WILL BE
INACURRATE.
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What might explain the cold bias seen in the MRF MOS forecasts for projections beyond
the 132-hour forecast in the graphic?
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a) A systematic cold bias in the model (as can be seen in the direct model output shown
in blue)
b) Increased weight of climatological data (shown in gray)
c) Increased weight of observed weather elements at extended lead-times
d) Poorly chosen predictors
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 b) Increased weight of climatological data
(shown in gray)
 This is because at 132 hours the largest
weighted predictor immediately becomes
climate data. Much smaller weighting
functions are given to all other variables
used as predictors. This means the
climatalogical coefficient is greatly
increased.
ROS
NWP Models and Their Processes
BAYESIAN EQUATIONS:
This is a form of statistical equation. The future of probability diagnosis
may begin to use these type of equations within 5 to 10 years or maybe
sooner.
Bayesian equations are very efficient when compared to the current
method of least squares linear regression. They use past, current and
future data to derive a probability. They always use new information to
“learn” from, and then possibly change an outcome based on the new
information. In this way, MOS model data would be “learning” on two
platforms. One would be climatology and the second would be the
actual equations instead of a predictor coefficient constant.
You can easily find these equations at work today in new programs such
as Microsoft Word or Excel. The funny character that pops up on the
side in these software use these equations to try and find out what you
are doing. Then it can give you hints or examples to use during your
project.
FOR MORE IN DEPTH INFORMATION ON
NWP MODELS, PLEASE VISIT:
http://meted.ucar.edu/nwp/pcu1/ic1/index.htm
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IMPORTANT FACTS AND TERMS:
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Regardless of its strengths, statistical postprocessing of model output is still limited by the data
we put into it (the M in MOS doesn't stand for miracle). Some fundamentally important points about
SG are:
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1) SG can make a good NWP forecast better, but cannot fix a bad NWP forecast.
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2) It is designed to fit most cases, assuming a normal distribution, therefore in skewed climate
regimes or outlier cases, SG won't work as well.
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TERMS:
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Predictand: The dependent variable that is to be forecast by the SG guidance. Predictands are
derived from observed weather elements. Examples of SG predictands include temperature,
precipitation probability, visibility, etc.
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Predictor(s): The independent variable (or variables) used in conjunction with the predictand to
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Probability: A quantitative expression of uncertainty.
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Persistence: Also referred to as the classical method, it is the statistical dependence of a variable
on its own past values (based solely on observed weather elements). Persistence can account for
time lag by relating current predictor data to future predictand data as part of the development of
the statistical relationship. For example, what is currently occurring in an observed weather
element (i.e., temperature) is related statistically to the precipitation type that will occur at some
future forecast time.
derive a statistical relationship that drives statistical guidance. Three basic types of predictors are
used: model output, observed weather elements, and climatological data.
WKDY=weekday
The weekday is a simple algorithm that uses every fourth year as a leap year
giving the model weekday from the model date.
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####Change any day of year into weekday:
@daynm=(TUE,WED,THU,FRI,SAT,SUN,MON);
$daylp=0;
for($loopyer=1991; $loopyer<=2050; $loopyer++)
{if($loopyer%4==0)
{$febu=29;}
elsif($loopyer%4!=0)
{$febu=28;}
for($loopmon=1; $loopmon<=12; $loopmon++)
{if($loopmon==1||$loopmon==3||$loopmon==5||$loopmon==7||$loopmon==8||$loopmon==10||$loopmon==12)
{for($loopday=1; $loopday<=31; $loopday++)
{$day[$loopyer][$loopmon][$loopday]=$daynm[$daylp];
$daylp++;
if($daylp%7==0)
{$daylp=0;}}}
if($loopmon==4||$loopmon==6||$loopmon==9||$loopmon==11)
{for($loopday=1; $loopday<=30; $loopday++)
{$day[$loopyer][$loopmon][$loopday]=$daynm[$daylp];
$daylp++;
if($daylp%7==0)
{$daylp=0;}}}
if($loopmon==2)
{for($loopday=1; $loopday<=$febu; $loopday++)
{$day[$loopyer][$loopmon][$loopday]=$daynm[$daylp];
$daylp++;
if($daylp%7==0)
{$daylp=0;}}}
}}
CLOUD GROUPS
The CLDS group is computed in conjunction with the CLHT…TMPO…and
TTSK fields.
The model uses a top down approach. MOS uses a bottom up. First the model calculates the
lowest possible level a prevailing cloud layer will be found.
A) LCL height in feet
B) Height of min RH between LCL and CCL
C) LCL height in feet + result of the TERICK equation
An algorithm run by the model determines which of these will be calculated and used. It
then runs down the sounding profile keeping every level that meets a preset RH criteria
for cloud layers. When it finds one it keeps it until another is found…then replaces that
level with the current and so on...until it reaches the calculated lowest height. The height
that is saved last will be set as the lowest ceiling height if it meets the RH value for a
ceiling. The ROS always gives precedence to BKN or OVC. In other words…if it sees any
BKN or OVC layer in the sounding, then no matter how low a SCT layer may be, it will
still not be shown. The height is set in the CLHT field and the LCL is checked for high RH
levels…if found then the TMPO group will receive this deck. All the layers are then
counted and the model decides from the total layers, which category of clouds to use in
the TTSK group, either CL…PC…MC…or OV. The clouds algorithm is extremely
complicated but gives a strong answer to cloud heights.
Here is a set of RH values from the ROS:
 {$ovclowendRH[$L]=91.5;#print " +VV2";
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$bknlowendRH[$L]=84.5;
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$sctlowendRH[$L]=78.5;
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$ciglowendRH[$L]=90.0;
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$stopatCCLorLCL[$L]=$totalfeetplusLCL[$L];}
TERICK EQUATION
Hl + [(Hc – Hl)/(Tc – Ts)] = LCH;
If (Tc – Ts) <= 0; then (Hc – Hl) = 0;
The way this equation works is quite simple. It uses the temperature difference between the Convective temp and the
forecasted or ambient temp AND the height difference between the LCL and the CCL. This height is divided by the
temp difference and the resulting height is added to the LCL to get the lowest cloud height. This process simply holds
the latent heating within the parcel until it is cool enough to condense. The equation was created because textbooks
only showed two processes. When a parcel is forced (LCL) and when the parcel is convectively driven (CCL). The only
thing one will find in a textbook about when both of these processes are occurring at the same time is “…the cloud
height will be found somewhere between the LCL and the CCL.” This simply wasn’t good enough and I knew I could at
least get close to an actual height. Below is a pictorial explanation.
VSBY=visibility
The Visibility section is calculated with studies and research. There are really no
equations used, instead an enormous algorithm is used with generic low
visibility producing variables or predictors. One visibility producing algorithm
is shown below. This field will also show restrictions due to precipitation.
This is one set of equations used by NGM MOS for the cool season over the northern
grid. It takes many more to make up an entire run. The ROS uses the same technique
except these equations have been manipulated to fit the ETA data.
SNAC=snow accumulation
This field is a result of team effort involving local research. A research
project was undertaken to find how deep snow would accumulate
using temperature to water-equivalent ratios. I simply took this data
and sourced it for use by the ROS model. Here are the ratios used:
TEMP:
>=35F
29-34F
20-28F
10-19F
0 - 9F
< 0F
RATIO:
7:1
10:1
15:1
20:1
30:1
40:1
SN:WE or .10” of water equivelant at 35F equals
.70” of snow accumulation.
SFCRH=surface relative humidity
Relative Humidity equation used:
Es = 6.11 * 10.0^(7.5 * Tc / (237.7 + Tc))
E = 6.11 * 10.0^(7.5 * TDc / (237.7 + TDc))
RH = (E/Es) * 100.0
HAINS=haines index
The ROS computes the Haines index by national standards and uses the
actual stations elevation. This is the most accurate method of getting
the index, but local fire officials may want the data to show a generic
view instead. This can be done when the ROS is used with the WS ETA.
This field, and others, can be forced to show what fire officials
currently use in their areas. No forcing can currently be done since
other fields rely on elevation as well.
These are the generic
boundaries of the Haines
Index elevation
determiners. The
elevation determines the
level at which
temperature and dew
point data are drawn to
calulate the index. The
actual elevations range
from:
Low < 1000ft
Mid 1000-3000ft
High > 3000ft
HAINES INDEX CONTINUED
MIXHT=mixing height
The mixing height is not an equation but an algorithm. The ROS simply
moves up a dry adiabat until it crosses the ambient temperature line.
This is normally at an inversion level.
TPRTD=transport direction & TPRTS=transport speed
Transport winds are defined as the average wind speed and direction of all winds
within the layer between the surface and the mixing height. An explanation of
how to equate average transport winds will be given over the next few tiles.
First, since wind is a vector, the averaging process begins with the calculation of the zonal
(U-component) and the meridional (V-component) of the wind at each level.
The meridional component of the
wind, V, is considered positive
when the wind is blowing from
south to north. A south wind has a
positive meridional component
while a north wind has a negative
meridional component. The zonal
component of the wind, U, is
considered positive when the
wind is blowing from west to east.
Thus, a west wind has a positive
zonal component and an east
wind a negative zonal component.
TRANSPORT WINDS CONTINUED
If the speed of the wind is (ff) and the direction in degrees is (dd), then the
formula for obtaining the meridional component, V, and the zonal
component, U, are:
V = -ff * cos(dd)
U = -ff * sin(dd)
Given the U and V components of the average wind speed, the following
equation is used to calculate the direction of the transport wind:
VNTRT=ventilation rate
The ventilation rate is calculated nationally by multiplying the transport
wind by the mixing height in feet and dividing the result by a constant
5280. Fire officials want the ventilation rate calculated another way
which renders the result non-dimensional. Since the result is nondimensional, it is not considered a rate…therefore it is only given as a
ventilation number.
NATIONAL EQUATION:
(Transport wind speed) x (Mixing height) / (5280) = vent rate
mph
ft
constant ft^2/hr
FIRE OFFICIALS EQUATION:
(Transport wind speed) x (Mixing height) = vent number
mph
ft
miles ft/hr
ROS calculates using the fire officials equation. It also has to divide the
final number by 3600. This is done so the answer can fit into the field
width provided. These can be changed for individual station
preferences.
CATDY=category day
The category day is basically an index taken from the ventilation number.
These are the values that drive the index.
 Category Day

1

2

3

4

5
Ventilation Number
0 - 17,249
17,250-34,499
34,500-51,749
51,750-68,999
69,000 or greater
DISPN=dispersion index
The dispersion index is calculated by dividing the mixing height by 1000,
then multiplying the result by the transport wind speed(mph).
(mixing height) / (1000) x (transport wind speed) = disp index #
ft
constant
mph
These are the values that drive the index.
>100 Excellent
61-100 Good
41-60 Average
21-40 Fair
8-20 Poor
0-7 Very Poor
20DIR=20 foot wind direction & 20SPD=20 foot wind direction
This field is very simple. The ROS simply takes the first level above the
two meter surface and converts the speed into mph and gives the
direction.
SUNHR=meteorological sunlight hours
This is an extremely complicated field. It looks all too easy but the
computations and algorithms that are used to find a value are
immense. All of the computations used can not be shown but the main
emphasis can be conveyed.
The ROS first computes the total daylight hours using latitude longitude
and date. It then strips the TTSK group for each hour and associates
the sky cover with an amount of time. This time is added and the total
is subtracted from the total daylight hours.
The ROS is the only model with this capability.
LALEV=lightning activity level
The LAL is taken directly from Jeanne Hoadley of the National Weather
Service in Missoula, Montana and Don Latham of the Intermountain
Fire Sciences Laboratory’s work. The LAL is a “CONDITIONAL” value.
In other words, one must have everything in place for thunderstorms to
form before this field can be used.
The numbers calculated are taken from the CAPE…LI…and 700mb thetaE.
Below are the associations.
LAL
CAPE
LI
THETA-E
1
<100
>2
no thetaE max
2
100-500
2to-2
310-320
3
>500
-2to-4
320-340
4
>1000
<-4
>330
5
>=1500
<-4
>=340
6
RH<=60% along with LAL #3 requirements only.
LTGFQ=lightning frequency
Lightning frequency was basically taken straight from the
LAL and observed data. It works over a 1…5…and 15
minute interval. It gives the amount of strikes that should
be produced by any single thunderstorm cell. This field is
also “CONDITIONAL.” The numbers are rounded to the
nearest whole number. More work may be done on a local
level to make this a stronger field. The following
associations are what the ROS uses.
LAL
1
2
3
4
5
6
FREQUENCY
0
1
2
4
5
3
#STRIKES
INTERVAL
0
CG 1-5-15
1 . 1-5 . 1-8
CG 1-5-15
1-2 . 6-10 . 9-15
CG 1-5-15
2-3 . 11-15 . 16-25 CG 1-5-15
3 . 15-25
CG 1-5-15
SAME AS LAL#3 ABOVE
HINX=heat index
This number uses the ambient temperature and the calculated relative
humidity to find the heat index temperature. This field is extremely
useful. By simply scanning the heat index numbers, one can quickly
determine if the forecast may need to be watched more carefully over
the next few days for heat advisory criteria. It uses the equation
implemented by the National Weather Service. It is a seasonal field and
is replaced by the wind chill index during the Fall. The following is the
equation used:
HI = -42.379 + 2.04901523*TempF + 10.14333127*RH
- 0.22475541*TempF*RH - .00683783*TempF^2
- .05481717*RH^2 + .00122874*TempF^2*RH
+ .00085282*TempF*RH^2
- .00000199*TempF^2*RH^2
WINX=wind chill index
This number uses the ambient temperature and the wind speed to find the
wind chill temperature. This field is extremely useful. By simply
scanning the wind chill numbers, one can quickly determine if the
forecast may need to be watched more carefully over the next few days
for wind chill advisory criteria. It uses the newest equation
implemented by the National Weather Service. It is a seasonal field and
is replaced by the heat index during the Spring. This equation does not
account for solar radiation to the skin. This is to be added in the
coming years by NOAA. When it is, this equation will be updated to
show that change. The following is the equation used:
WC = 35.74 + 0.6215*TempF -35.75*windSpkt^0.16 +
0.4275*TempF*windSpkt^0.16