New Mesoscale Modeling by Raw Output Statistics ROS

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New Mesoscale Modeling by Raw Output Statistics
(ROS)
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• How did the ROS model begin, and WHY do we need
  another model?
• Glad you asked.

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• 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
  phenomenonsuch 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.

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• 2) We dont 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 necessaryi.e. winter to summer equation
  useage.

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• 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 setsBUT there are some big
  problems facing the model output at this time.
• The first problem is quite obviousthere are no
  observations other than sea surface and winds for
  verification purposes. Thereforewe 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.

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• 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

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ETA ROS Explanation and Description of Fields

• 1 ETAROS7 TERICK KNEW 060545
• 2 GPT ETA ROS GUIDANCE 05/06/2002 0000UTC
• 3 WKDY MON
  TUE WED
• 3 DATE /MAY 6 /MAY
  7 /MAY 8
• 3 HOUR 03 06 09 12 15 18 21 00 03 06 09 12
  15 18 21 00 03 06 09 12
• The first line gives the model file name, the
  developer, the permanent station it is run from
  and the Z time it is run.
• The second line gives the station it is run for,
  the name of the model and the date the model is
  valid for.
• The next 3 fields are time fields. One special
  feature here that isnt 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.

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• 1 MNMX 35( 35) 43( 43)
  34( 34) 46( 46)
  33
• 2 TEMP 35 35 37 37 39 39 43 41 39
  38 36 34 39 44 45 42 38 34 33 33
• 3 DWPT 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

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• 1 CLDS Olt Olt Olt Olt O2 O2 O3 O2 O1 O2 CL CL CL
  CL CL CL CL CL S6 S
• 2 CLHT 08 08 08 08 19 23 33 19 15 23 00
  00 00 00 00 00 00 00 63 17
• 3 TMPO 05 05 05 05 15 19 28 15 11 19
• 4 TTSK OV OV OV OV OV OV OV OV OV OV CL CL
  CL CL CL CL CL CL PC PC
• Prevailing lowest possible cloud level.
• Cloud height to the 100s and 1000s of feet.
• The CLDS field will tell if this number shows
  100s or 1000s of feet. As an example, Olt will
  first tell you the lowest prevailing cloud
  condition will be O overcast and the height of
  this deck will be lt 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 gt
  or sign is used then the CLHT field will be
  read with three zeros.
• Temporary ceilings when the LCL has high RH
  values. This field will always be shown in 100s
  of feet never 1000s and will always be equal to
  or less than the prevailing cloud height.
• 4) Total sky cover accumulates all cloud levels

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• 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 equationBob Rozumalski of
  COMET explained and found errors in the original
  equationand Peter Parke of the National Weather
  Service in Duluth, Minnesota worked with
  verifying the units used in the equation.
• TERICK EQUATION
• WHERE
• Hl (Hc Hl)/(Tc Ts) LCH HlLCL height
  in feet TcConv temp in C
• If (Tc Ts) lt 0 then (Hc Hl) 0 HcCCL
  height in feet TsSFC temp in C
• LCHLowest Cloud Height

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• 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.

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• 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.

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• PP06 0 0 0 0 0 2 16
  37 17 0
• PP12 0 0 2
  26 6
• 6 12 hour POP fields. These are derived from
  local studies and research as well.
• ALWAYS CHECK FOR RH INITIALIZATION BEFORE USING
  POPS FROM ANY MODEL.

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• 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 BUFKITit uses a thickness
  scheme.

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• 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
• 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.
• The snow water equivalent is produced with the
  use of remote sensing. This field is updated once
  a week.

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• 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.

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• Equations and Algorithms
• Fields which are stripped and clipped straight
  from the ETA raw data
• are as follows
• DATE-gt date
• HOUR-gt UTC hour
• TEMP -gt temperature
• DWPT-gt dew point
• WDIR-gt wind direction
• WSPD-gt wind speed
• TTPP-gt total water equivalent precipitation
• SWEQ-gt snow water equivalent
• PTYP-gt precipitation type (produced by BUFKIT
  algorithms)

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• Fields which are derived locally are as follows
• All header information
• WKDY-gt weekday
• MNMX-gt min/max temp
• CLDS-gt predominant cloud cover and level
• CLHT-gt predominant cloud height
• TMPO-gt temporary ceiling height
• TTSK-gt total sky cover
• VSBY-gt visibility
• OBVS-gt obstruction to visibility
• PP06-gt 6 hour probability of precipitation
• PP12-gt 12 hour probability of precipitation
• SNAC-gt snow accumulation
• HMNMX-gt relative humidity min/max percentages
• SFCRH-gt surface relative humidity
• HAINS-gt haines index
• MIXHT-gt mixing height
• TPRTD-gt transport direction

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ERRORS IN ANY MODEL CAN COME FROM MANY SOURCES

• 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

• 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)

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• Intrinsic Predictability Limitations
• Even with error-free observations and a "perfect"
  model, forecast errors will grow with time.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

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ETA HORIZONTAL DOMAIN
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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
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• 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.

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• In what kinds of situations would you expect
  statistical guidance to perform well?
• 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)

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• c) Synoptically forced situations
• Statistical guidance can be expected to perform
  best in situations where large-scale synoptic
  forcing dominates.

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• 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

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• c) Requires a developmental dataset of
  historical model data
• e) Improvements to model systematic errors will
  result in degraded MOS guidance

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• 3) What types of predictors would you expect to
  carry more weight in the development of MOS
  forecast equations for short-range (0-36 hours)
  projections?
• a) Model data
• b) Climate data
• c) Observed weather elements
• d) Relative frequency

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• a) Model data
• c) Observed weather elements

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• 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

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• a) Lifted index
• b) CAPE
• c) Relative humidity
• d) Climatic relative frequency
• e) Lifted condensation level

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• 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

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• 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?
• 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.

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ROS
NWP Models and Their Processes
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• 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.

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• 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
• 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
• 1) SG can make a good NWP forecast better, but
  cannot fix a bad NWP forecast.
• 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.
• TERMS
• 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.
• Predictor(s) The independent variable (or
  variables) used in conjunction with the
  predictand to derive a statistical relationship
  that drives statistical guidance. Three basic
  types of predictors are used model output,
  observed weather elements, and climatological
  data.
• Probability A quantitative expression of
  uncertainty.
• 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.

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• WKDYweekday
• The weekday is a simple algorithm that uses every
  fourth year as a leap year giving the model
  weekday from the model date.
• Change any day of year into weekday
• _at_daynm(TUE,WED,THU,FRI,SAT,SUN,MON)
• daylp0
• for(loopyer1991 loopyerlt2050
  loopyer)
• if(loopyer40)
• febu29
• elsif(loopyer4!0)
• febu28
• for(loopmon1 loopmonlt12 loopmon)
• if(loopmon1loopmon3loopmon5lo
  opmon7loopmon8loopmon10loopmon12)
  
• for(loopday1 loopdaylt31 loopday)
• dayloopyerloopmonloopdaydaynmd
  aylp
• daylp
• if(daylp70)
• daylp0

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• CLOUD GROUPS
• The CLDS group is computed in conjunction with
  the CLHTTMPOand 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
  foundthen 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 wordsif 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
  levelsif 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 CLPCMCor 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
• ovclowendRHL91.5print " VV2"
• bknlowendRHL84.5
• sctlowendRHL78.5
• ciglowendRHL90.0
• stopatCCLorLCLLtotalfeetplusLCLL

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• TERICK EQUATION
• Hl (Hc Hl)/(Tc Ts) LCH
• If (Tc Ts) lt 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 wasnt good enough and I knew I could
  at least get close to an actual height. Below is
  a pictorial explanation.

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• VSBYvisibility
• 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.

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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.
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• SNACsnow 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 RATIO
• gt35F 71
• 29-34F 101
• 20-28F 151
• 10-19F 201
• 0 - 9F 301
• lt 0F 401

SNWE or .10 of water equivelant at 35F
equals .70 of snow accumulation.
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• SFCRHsurface 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

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• HAINShaines 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 lt
1000ft Mid 1000-3000ft High gt 3000ft
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• HAINES INDEX CONTINUED

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• MIXHTmixing 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.

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• TPRTDtransport direction TPRTStransport 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.
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• 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

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• VNTRTventilation 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 non-dimensional, it is not considered a
  ratetherefore it is only given as a ventilation
  number.
• NATIONAL EQUATION
• (Transport wind speed) x (Mixing height) / (5280)
  vent rate
• mph ft constant ft2/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.

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• CATDYcategory day
• The category day is basically an index taken from
  the ventilation number. These are the values that
  drive the index.
• Category Day Ventilation Number
• 1 0 - 17,249
• 2 17,250-34,499
• 3 34,500-51,749
• 4 51,750-68,999
• 5 69,000 or greater

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• DISPNdispersion 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.
• gt100 Excellent
• 61-100 Good
• 41-60 Average
• 21-40 Fair
• 8-20 Poor
• 0-7 Very Poor

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• 20DIR20 foot wind direction 20SPD20 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.

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• SUNHRmeteorological 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.

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• LALEVlightning 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
  Laboratorys 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
  CAPELIand 700mb thetaE. Below are the
  associations.
• LAL CAPE LI THETA-E
• 1 lt100 gt2 no thetaE max
• 2 100-500 2to-2
  310-320
• 3 gt500 -2to-4
  320-340
• 4 gt1000 lt-4
  gt330
• 5 gt1500 lt-4
  gt340
• 6 RHlt60 along with LAL 3
  requirements only.

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• LTGFQlightning frequency
• Lightning frequency was basically taken straight
  from the LAL and observed data. It works over a
  15and 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 FREQUENCY STRIKES INTERVAL
• 1 0 0 CG 1-5-15
• 2 1 1 . 1-5 . 1-8 CG 1-5-15
• 3 2 1-2 . 6-10 . 9-15 CG
  1-5-15
• 4 4 2-3 . 11-15 . 16-25 CG 1-5-15
• 5 5 3 . 15-25 CG 1-5-15
• 6 3 SAME AS LAL3 ABOVE

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• HINXheat 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 2.04901523TempF 10.14333127RH
• - 0.22475541TempFRH - .00683783TempF2
• - .05481717RH2 .00122874TempF2RH
• .00085282TempFRH2
• - .00000199TempF2RH2

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• WINXwind 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 0.6215TempF -35.75windSpkt0.16
  0.4275TempFwindSpkt0.16

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THE END