New mesoscale modeling by raw output statistics ros
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“New Mesoscale Modeling by Raw Output Statistics (ROS) ”. How did the ROS model begin, and WHY do we need another model? Glad you asked.

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New mesoscale modeling by raw output statistics ros

“New Mesoscale Modeling by Raw Output Statistics (ROS)”


New mesoscale modeling by raw output statistics ros

How did the ROS model begin, and WHY do we need another model?

Glad you asked.


New mesoscale modeling by raw output statistics ros

  • 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.


New mesoscale modeling by raw output statistics ros

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.


New mesoscale modeling by raw output statistics ros

  • 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.


New mesoscale modeling by raw output statistics ros

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

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 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.


New mesoscale modeling by raw output statistics ros

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


New mesoscale modeling by raw output statistics ros

1 CLDS O< O< O< O< 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 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.

  • 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


New mesoscale modeling by raw output statistics ros

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:

WHERE:

Hl + [(Hc – Hl)/(Tc – Ts)] = LCH;Hl=LCL height in feet Tc=Conv temp in C

If (Tc – Ts) <= 0; then (Hc – Hl) = 0;Hc=CCL height in feet Ts=SFC temp in C

LCH=Lowest Cloud Height


New mesoscale modeling by raw output statistics ros

1VSBY 05 P6 04 P6 P6 P6 P6 P6 P6 P6 P6 P6 P6 P6 P6 P6

2OBVS -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.


New mesoscale modeling by raw output statistics ros

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.


New mesoscale modeling by raw output statistics ros

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 POP’S FROM ANY MODEL.


New mesoscale modeling by raw output statistics ros

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.


New mesoscale modeling by raw output statistics ros

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.


New mesoscale modeling by raw output statistics ros

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.


New mesoscale modeling by raw output statistics ros

Equations and Algorithms:

Fields which are stripped and clipped straight from the ETA raw data

are as follows:

  • 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)


New mesoscale modeling by raw output statistics ros

Fields which are derived locally are as follows:

  • 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


New mesoscale modeling by raw output statistics ros

ERRORS IN ANY MODEL CAN COME FROM MANY SOURCES:

  • 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)

  • 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


New mesoscale modeling by raw output statistics ros

  • 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.


New mesoscale modeling by raw output statistics ros

ETA HORIZONTAL DOMAIN


New mesoscale modeling by raw output statistics ros

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


New mesoscale modeling by raw output statistics ros

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.


New mesoscale modeling by raw output statistics ros

  • 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)


New mesoscale modeling by raw output statistics ros

  • c) Synoptically forced situations

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


New mesoscale modeling by raw output statistics ros

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


New mesoscale modeling by raw output statistics ros

  • c) Requires a developmental dataset of historical model data

  • e) Improvements to model systematic errors will result in degraded MOS guidance


New mesoscale modeling by raw output statistics ros

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


New mesoscale modeling by raw output statistics ros

  • a) Model data

  • c) Observed weather elements


New mesoscale modeling by raw output statistics ros

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


New mesoscale modeling by raw output statistics ros

  • a) Lifted index

  • b) CAPE

  • c) Relative humidity

  • d) Climatic relative frequency

  • e) Lifted condensation level


New mesoscale modeling by raw output statistics ros

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


New mesoscale modeling by raw output statistics ros

  • 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.


New mesoscale modeling by raw output statistics ros

  • 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


New mesoscale modeling by raw output statistics ros

  • 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.


New mesoscale modeling by raw output statistics ros

ROS

NWP Models and Their Processes


New mesoscale modeling by raw output statistics ros

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.


New mesoscale modeling by raw output statistics ros

FOR MORE IN DEPTH INFORMATION ON NWP MODELS, PLEASE VISIT:

http://meted.ucar.edu/nwp/pcu1/ic1/index.htm


New mesoscale modeling by raw output statistics ros

  • 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.


New mesoscale modeling by raw output statistics ros

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.

  • ####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;}}}

  • }}


New mesoscale modeling by raw output statistics ros

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";

  • $bknlowendRH[$L]=84.5;

  • $sctlowendRH[$L]=78.5;

  • $ciglowendRH[$L]=90.0;

  • $stopatCCLorLCL[$L]=$totalfeetplusLCL[$L];}


New mesoscale modeling by raw output statistics ros

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.


New mesoscale modeling by raw output statistics ros

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.


New mesoscale modeling by raw output statistics ros

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.


New mesoscale modeling by raw output statistics ros

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:RATIO:

>=35F7:1

29-34F10:1

20-28F15:1

10-19F20:1

0 - 9F30:1

< 0F40:1

SN:WE or .10” of water equivelant at 35F equals

.70” of snow accumulation.


New mesoscale modeling by raw output statistics ros

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


New mesoscale modeling by raw output statistics ros

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


New mesoscale modeling by raw output statistics ros

HAINES INDEX CONTINUED


New mesoscale modeling by raw output statistics ros

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.


New mesoscale modeling by raw output statistics ros

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.


New mesoscale modeling by raw output statistics ros

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:


New mesoscale modeling by raw output statistics ros

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 non-dimensional, 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 ftconstant 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.


New mesoscale modeling by raw output statistics ros

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 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


New mesoscale modeling by raw output statistics ros

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


New mesoscale modeling by raw output statistics ros

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.


New mesoscale modeling by raw output statistics ros

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.


New mesoscale modeling by raw output statistics ros

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.


New mesoscale modeling by raw output statistics ros

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 FREQUENCY#STRIKESINTERVAL

100CG 1-5-15

211 . 1-5 . 1-8CG 1-5-15

32 1-2 . 6-10 . 9-15 CG 1-5-15

44 2-3 . 11-15 . 16-25 CG 1-5-15

553 . 15-25CG 1-5-15

63SAME AS LAL#3 ABOVE


New mesoscale modeling by raw output statistics ros

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


New mesoscale modeling by raw output statistics ros

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


New mesoscale modeling by raw output statistics ros

THE END


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