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

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

Glad you asked.Glad you asked.

1)1) The ROS model recieved its start from aviation The ROS model recieved its start from aviation and fire weather. Forecasters were searching for and fire weather. Forecasters were searching for a quick way to find ceiling heights as well as a quick way to find ceiling heights as well as model produced fire weather parameters. model produced fire weather parameters. Nationally produced guidance did not have either Nationally produced guidance did not have either of these conveniences. of these conveniences.

From there, others began to ask if the ROS could From there, others began to ask if the ROS could catch micro and mesoscale meteorological catch micro and mesoscale meteorological phenomenon…such as lake effect snowfall in phenomenon…such as lake effect snowfall in Duluth, Minnesota and sea fog episodes in New Duluth, Minnesota and sea fog episodes in New Orleans. We put it to the test by inserting some Orleans. We put it to the test by inserting some local research and study material and the model local research and study material and the model began to show signs of working. After some fine began to show signs of working. After some fine tuning, the ROS was on its way.tuning, the ROS was on its way.

2) We don’t really need another NATIONALLY 2) We don’t really need another NATIONALLY PRODUCED MODEL. Models are beginning to be PRODUCED MODEL. Models are beginning to be run at the local level such as the WSeta. This run at the local level such as the WSeta. This model can also be run through the ROS. It is model can also be run through the ROS. It is proving to be an inexpensive way to produce proving to be an inexpensive way to produce model forecasts. It may also show some strength model forecasts. It may also show some strength over the nationally produced guidance. over the nationally produced guidance.

NCEP would never be able to tackle such a NCEP would never be able to tackle such a tremendous project as running a mesoscale model tremendous project as running a mesoscale model for every single office. This is because each office for every single office. This is because each office has its own set of fire weather fields as well as has its own set of fire weather fields as well as mountains, hills, valleys, and lakes to input. mountains, hills, valleys, and lakes to input. Individual stations can also change modes when Individual stations can also change modes when necessary…i.e. winter to summer equation necessary…i.e. winter to summer equation useage.useage.

Marine data continues to be collected for use in the Marine data continues to be collected for use in the marine ROS. The introduction of the new buoy marine ROS. The introduction of the new buoy sensors will add some very important and much sensors will add some very important and much needed data to these sets…BUT there are some big needed data to these sets…BUT there are some big problems facing the model output at this time.problems facing the model output at this time.

The first problem is quite obvious…there are no The first problem is quite obvious…there are no observations other than sea surface and winds for observations other than sea surface and winds for verification purposes. Therefore…we can not see verification purposes. Therefore…we can not see how well the model is performing with visibility or how well the model is performing with visibility or cloud heights.cloud heights.

The final problem is there are no data sets to apply The final problem is there are no data sets to apply to the model equations and or algorithms for these to the model equations and or algorithms for these variables. The continental zones have all the data variables. The continental zones have all the data they can handle for predictors.they can handle for predictors.

That is not to say we do not try. The New That is not to say we do not try. The New Orleans office is sourcing the only data Orleans office is sourcing the only data available for visibility and cloud heights. available for visibility and cloud heights. Those data sets are from near shore and Those data sets are from near shore and onshore locations including those onshore locations including those observations from the Houston CWA, Lake observations from the Houston CWA, Lake Charles CWA, New Orleans CWA, Mobile Charles CWA, New Orleans CWA, Mobile CWA, and Tallahassee CWA. CWA, and Tallahassee CWA.

And we come up with something that looks And we come up with something that looks like this:like this:

CWA = Coastal Warning AreaCWA = Coastal Warning Area

ETA ROS Explanation and Description of FieldsETA ROS Explanation and Description of Fields

11 ETAROS7 TERICK KNEW 060545 ETAROS7 TERICK KNEW 060545 22 GPT ETA ROS GUIDANCE 05/06/2002 0000UTC GPT ETA ROS GUIDANCE 05/06/2002 0000UTC 33 WKDY MON TUE WED WKDY MON TUE WED 33 DATE /MAY 6 /MAY 7 /MAY 8 DATE /MAY 6 /MAY 7 /MAY 8 33 HOUR 03 06 09 12 15 18 21 00 03 06 09 12 15 18 21 00 03 06 09 12 HOUR 03 06 09 12 15 18 21 00 03 06 09 12 15 18 21 00 03 06 09 12

1)1) The first line gives the model file name, the developer, the The first line gives the model file name, the developer, the permanent station it is run from and the Z time it is run.permanent station it is run from and the Z time it is run.

2)2) The second line gives the station it is run for, the name of the The second line gives the station it is run for, the name of the model and the date the model is valid for.model and the date the model is valid for.

3)3) The next 3 fields are time fields. One special feature here that The next 3 fields are time fields. One special feature here that isn’t found on any other short term alphanumeric model is the 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 day of the week. It is simply run as an algorithm inside the source code.source code.

11 MNMX 35( 35) 43( 43) 34( 34) 46( 46) 33 MNMX 35( 35) 43( 43) 34( 34) 46( 46) 33

22 TEMP 35 35 37 37 39 39 43 41 39 38 36 34 39 44 45 42 38 34 33 33 TEMP 35 35 37 37 39 39 43 41 39 38 36 34 39 44 45 42 38 34 33 33

33 DWPT 34 35 35 35 35 33 33 36 35 35 33 32 30 30 29 29 26 27 27 26 DWPT 34 35 35 35 35 33 33 36 35 35 33 32 30 30 29 29 26 27 27 26

1)1) Max Min temperature in F Max Min temperature in F

2)2) Temperature on the hour in F Temperature on the hour in F

3)3) Dew Point temperature on the hour in F Dew Point temperature on the hour in F

11 CLDS O< O< O< O< O2 O2 O3 O2 O1 O2 CL CL CL CL CL CL CL CL S6 S^ CLDS O< O< O< O< O2 O2 O3 O2 O1 O2 CL CL CL CL CL CL CL CL S6 S^ 22 CLHT 08 08 08 08 19 23 33 19 15 23 00 00 00 00 00 00 00 00 63 17 CLHT 08 08 08 08 19 23 33 19 15 23 00 00 00 00 00 00 00 00 63 1733 TMPO 05 05 05 05 15 19 28 15 11 19 TMPO 05 05 05 05 15 19 28 15 11 19 44 TTSK OV OV OV OV OV OV OV OV OV OV CL CL CL CL CL CL CL CL PC PC TTSK OV OV OV OV OV OV OV OV OV OV CL CL CL CL CL CL CL CL PC PC

1)1) Prevailing lowest possible cloud level.Prevailing lowest possible cloud level.

2)2) Cloud height to the 100’s and 1000’s of feet. 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 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 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 “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 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 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.“^” sign is used then the CLHT field will be read with three zeros.

3)3) Temporary ceilings when the LCL has high RH values. This field will 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 always be shown in 100’s of feet never 1000’s and will always be equal to or less than the prevailing cloud height.to or less than the prevailing cloud height.

4)4) Total sky cover accumulates all cloud levelsTotal sky cover accumulates all cloud levels

Cloud Height Equation and Algorithm: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:TERICK EQUATION: WHERE:WHERE:

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

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

HeightHeight

11 VSBY 05 P6 04 P6 P6 P6 P6 P6 P6 P6 P6 P6 P6 P6 P6 P6 VSBY 05 P6 04 P6 P6 P6 P6 P6 P6 P6 P6 P6 P6 P6 P6 P6

22 OBVS -S -SOBVS -S -S

1)1) The visibility is developed through The visibility is developed through local studies and research. There are local studies and research. There are many variables to this field.many variables to this field.

2)2) The obstruction to visibility shows the The obstruction to visibility shows the weather phenomenon responsible for weather phenomenon responsible for causing the reduction in visibility.causing the reduction in visibility.

WDIR 35 36 01 02 02 03 02 03 02 01 32 33 34 34 33 35 33 32WDIR 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 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.Wind direction and wind speed in knots.

PP06 PP06 0 00 0 0 0 0 2 16 37 17 0 0 0 0 2 16 37 17 0

PP12 0 0 2 26 6PP12 0 0 2 26 6

6 & 12 hour POP fields. These are derived 6 & 12 hour POP fields. These are derived from local studies and research as well. from local studies and research as well.

ALWAYSALWAYS CHECK FOR RH INITIALIZATION BEFORE CHECK FOR RH INITIALIZATION BEFORE USING POP’S FROM ANY MODEL. 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 TTPP 00 00 00 00 00 00 00 00 00 00 12 01 04 16 19 17 11 06 00

PTYP PTYP RA RA RA RA RA RA RA RARA RA RA RA RA RA RA RA

Total precipitation is straight from the raw grids. In other Total precipitation is straight from the raw grids. In other words, the amount of QPF you see on the raw grids is the words, the amount of QPF you see on the raw grids is the amount shown here.amount shown here.

The total precip field is shown to the hundredths of an inch. The total precip field is shown to the hundredths of an inch. They are also cumulative over each three hour period. They are also cumulative over each three hour period.

The Precipitation Type field is the only one computed The Precipitation Type field is the only one computed through BUFKIT…it uses a thickness scheme.through BUFKIT…it uses a thickness scheme.

11 SNAC 00 00 00 00 .5 .5 .2 .8 01 03 02 .2 00 00 00 00 00 00 00 SNAC 00 00 00 00 .5 .5 .2 .8 01 03 02 .2 00 00 00 00 00 00 00

22 SWEQ 01 01 01 01 01 02 02 03 03 03 05 05 05 05 05 04 04 04 03 SWEQ 01 01 01 01 01 02 02 03 03 03 05 05 05 05 05 04 04 04 03

1)1) Snow accumulation. It is read with a decimal Snow accumulation. It is read with a decimal for any amounts under an inch. When the for any amounts under an inch. When the amount is an inch or greater, it will drop the amount is an inch or greater, it will drop the decimal and show a rounded whole inch.decimal and show a rounded whole inch.

2)2) The snow water equivalent is produced with The snow water equivalent is produced with the use of remote sensing. This field is the use of remote sensing. This field is updated once a week.updated once a week.

INTERGOVERNMENTAL USE ONLY...-12MET60.SITE 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 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 91HINX  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 LE06    22     0      0      0      0      0      0    15  57  54

LE12           16           0             0             7            69 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 06TEMP  12 23 28 27 27 28 24 19 19 29 31 28 27 22 16 12 09 14 15 06

These are test fields. These are test fields.

The wind chill and heat index are seasonal. They are shown here because they The wind chill and heat index are seasonal. They are shown here because they are not representative when temperatures fall outside the equations’ are not representative when temperatures fall outside the equations’ threshold. threshold.

The Lake effect pop field is currently in testing. It uses vectorization along with 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 a few other predictors to determine the percentage of purely lake effect pops. pops.

The temperature field here is a failed attempt to better the sfc temperature The temperature field here is a failed attempt to better the sfc temperature output without statistics.output without statistics.

Equations and Algorithms:Equations and Algorithms:

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

are as follows:are as follows:

1)1) DATE-> dateDATE-> date

2)2) HOUR-> UTC hourHOUR-> UTC hour

3)3) TEMP -> temperatureTEMP -> temperature

4)4) DWPT-> dew pointDWPT-> dew point

5)5) WDIR-> wind directionWDIR-> wind direction

6)6) WSPD-> wind speedWSPD-> wind speed

7)7) TTPP-> total water equivalent precipitationTTPP-> total water equivalent precipitation

8)8) SWEQ-> snow water equivalentSWEQ-> snow water equivalent

9)9) PTYP-> precipitation type (produced by BUFKIT algorithms)PTYP-> precipitation type (produced by BUFKIT algorithms)

Fields which are derived locally are as follows:Fields which are derived locally are as follows:

1)1) All header informationAll header information2)2) WKDY-> weekdayWKDY-> weekday3)3) MNMX-> min/max tempMNMX-> min/max temp4)4) CLDS-> predominant cloud cover and levelCLDS-> predominant cloud cover and level5)5) CLHT-> predominant cloud heightCLHT-> predominant cloud height6)6) TMPO-> temporary ceiling heightTMPO-> temporary ceiling height7)7) TTSK-> total sky coverTTSK-> total sky cover8)8) VSBY-> visibilityVSBY-> visibility9)9) OBVS-> obstruction to visibilityOBVS-> obstruction to visibility10)10) PP06-> 6 hour probability of precipitationPP06-> 6 hour probability of precipitation11)11) PP12-> 12 hour probability of precipitationPP12-> 12 hour probability of precipitation12)12) SNAC-> snow accumulationSNAC-> snow accumulation13)13) HMNMX-> relative humidity min/max percentagesHMNMX-> relative humidity min/max percentages14)14) SFCRH-> surface relative humiditySFCRH-> surface relative humidity15)15) HAINS-> haines indexHAINS-> haines index16)16) MIXHT-> mixing heightMIXHT-> mixing height17)17) TPRTD-> transport directionTPRTD-> transport direction18)18) TPRTS-> transport speedTPRTS-> transport speed19)19) VNTRT-> ventilation rateVNTRT-> ventilation rate20)20) CATDY-> category dayCATDY-> category day21)21) DISPN-> dispersion indexDISPN-> dispersion index22)22) 20DIR-> wind direction 20 feet above ground level20DIR-> wind direction 20 feet above ground level23)23) 20SPD-> wind speed 20 feet above ground level20SPD-> wind speed 20 feet above ground level24)24) SUNHR-> meteorological hours of sunlightSUNHR-> meteorological hours of sunlight25)25) LALEV-> lightning activity levelLALEV-> lightning activity level26)26) LTGFQ-> lightning frequencyLTGFQ-> lightning frequency27)27) HINX-> heat indexHINX-> heat index28)28) WCHL-> wind chillWCHL-> wind chill29)29) LE06-> 6 hour probability of lake effect/enhancedLE06-> 6 hour probability of lake effect/enhanced30)30) LE12-> 12 hour probability of lake effect/enhancedLE12-> 12 hour probability of lake effect/enhanced

Errors in the Initial ConditionsErrors in the Initial Conditions 1. Observational Data Coverage1. Observational Data Coverage a. Spatial Density a. Spatial Density b. Temporal Frequencyb. Temporal Frequency 2. Errors in the Data2. Errors in the Data a. Instrument Errors a. Instrument Errors b. Representativeness Errorsb. Representativeness Errors 3. Errors in Quality Control3. Errors in Quality Control 4. Errors in Objective Analysis4. Errors in Objective Analysis 5. Errors in Data Assimilation5. Errors in Data Assimilation 6. Missing Variables6. Missing Variables

Errors in the ModelErrors in the Model 1. Equations of Motion Incomplete1. Equations of Motion Incomplete 2. Errors in the Numerical Approximations2. Errors in the Numerical Approximations a. Horizontal Resolution a. Horizontal Resolution b. Vertical Resolutionb. Vertical Resolution c. Time Integration Procedurec. Time Integration Procedure 3. Boundary Conditions3. Boundary Conditions a. Horizontal a. Horizontal b. Verticalb. Vertical 4. Terrain4. Terrain 5. Physical Processes5. Physical Processes a. Precipitation a. Precipitation 1. Stratiform (Grid Scale) 1. Stratiform (Grid Scale) 2. Convective Precipitation2. Convective Precipitation b. Radiation (Short-wave/Long-wave)b. Radiation (Short-wave/Long-wave) c. Surface Energy Balancec. Surface Energy Balance d. Boundary Layer d. Boundary Layer 1. Surface Layer (0-10m) 1. Surface Layer (0-10m) 2. Ekman Layer (0-1km)2. Ekman Layer (0-1km)

ERRORS IN ANY MODEL CAN COME FROM MANY ERRORS IN ANY MODEL CAN COME FROM MANY SOURCES:SOURCES:

Intrinsic Predictability LimitationsIntrinsic Predictability Limitations Even with error-free observations and a "perfect" model, forecast errors will grow with time. 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. 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 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 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 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 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 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 doubling of error every 2-3 days. Therefore, even very small initial errors can result in major errors for a long-range forecast.for a long-range forecast.

The problem just stated is the essence of chaos theory applied to meteorology. This theory proposes 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 that nothing is entirely predictable, that even very small perturbations in a system result in unpredictable changes in time.unpredictable changes in time.

Forecasts based on climatology will have a relatively high level of error, but will remain constant over 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 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 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 eventually do worse than climatology. A forecast that is worse than climatology is considered useless.useless.

Even the best model we can envision will, for reasons just discussed, produce forecasts that 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.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 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 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 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 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 scales. In other words, a small-scale phenomenon will be less well forecast than a large-scale phenomenon in the same range forecast. phenomenon in the same range forecast.

However, mesoscale/convective scale predictability may not follow this smooth progression due to 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 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 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.circulations such as dry lines and sea breezes are more predictable than squall lines.

ETA HORIZONTAL DOMAINETA HORIZONTAL DOMAIN

This map shows the gridsections that MOS is run. Inother words, when lookingat FWC guidance, the headerinformation will show whatequations are run for thatguidance package. These aresplit into climatologicallyfavored regions. An exampleof the header info is shownhere.

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???WHAT IS THE FUTURE OF THE ROS???

The future of ROS will be what individual offices want it to be. Offices 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 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 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 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 it can be configured to run for any numerical model that NCEP produces. This is cutting edge technology, we here at the New Orleans produces. This is cutting edge technology, we here at the New Orleans WSO are doing our best to break new ground.WSO are doing our best to break new ground.

Each office will finally have the capability of introducing micro and Each office will finally have the capability of introducing micro and mesoscale variables to their output. Studies and research can be mesoscale variables to their output. Studies and research can be sourced into the model to make an offices forecast extremely strong. sourced into the model to make an offices forecast extremely strong. All variables will benefit from the added data. Since no office can edit All variables will benefit from the added data. Since no office can edit the NCEP models, this will make the ROS obsolete and interactive. the NCEP models, this will make the ROS obsolete and interactive. Individual fields can be changed or removed depending on office Individual fields can be changed or removed depending on office needs. needs.

An example would be the fire weather fields. These can be changed or 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. “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 MOS will never be able to do that as well as many other special features the ROS is able to provide.features the ROS is able to provide.

1)1) In what kinds of situations would you expect In what kinds of situations would you expect statistical guidance to perform well?statistical guidance to perform well?

a) Mesoscale or rare features such as cold-air a) Mesoscale or rare features such as cold-air damming damming

b) Situations of abnormal snow coverb) Situations of abnormal snow cover c) Synoptically forced situationsc) Synoptically forced situations d) Rapidly moving frontal systems d) Rapidly moving frontal systems e) Heat waves (abnormally high temperatures)e) Heat waves (abnormally high temperatures)

c) Synoptically forced situationsc) Synoptically forced situations

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

2) What are the 2) What are the limitationslimitations of MOS guidance of MOS guidance that you as a forecaster should be aware of?that you as a forecaster should be aware of?

a) Accounts for systematic model errorsa) Accounts for systematic model errors b) Cannot account for deteriorating model b) Cannot account for deteriorating model

accuracy at longer forecast timesaccuracy at longer forecast times c) Requires a developmental dataset of c) Requires a developmental dataset of

historical model datahistorical model data d) Multiple predictors can be usedd) Multiple predictors can be used e) Improvements to model systematic errors e) Improvements to model systematic errors

will result in degraded MOS guidancewill result in degraded MOS guidance

c) Requires a developmental dataset of c) Requires a developmental dataset of historical model datahistorical model data

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

3) What types of predictors would you expect 3) What types of predictors would you expect to carry more weight in the development of to carry more weight in the development of MOS forecast equations for short-range (0-MOS forecast equations for short-range (0-36 hours) projections?36 hours) projections?

a) Model dataa) Model data b) Climate datab) Climate data c) Observed weather elementsc) Observed weather elements d) Relative frequencyd) Relative frequency

a) Model dataa) Model data

c) Observed weather elementsc) Observed weather elements

4) What predictors would you expect to be 4) What predictors would you expect to be selected for thunderstorm guidance?selected for thunderstorm guidance?

a) Lifted index a) Lifted index b) CAPEb) CAPE c) Relative humidityc) Relative humidity d) Climatic relative frequencyd) Climatic relative frequency e) Lifted condensation levele) Lifted condensation level

a) Lifted index a) Lifted index

b) CAPEb) CAPE

c) Relative humidityc) Relative humidity

d) Climatic relative frequencyd) Climatic relative frequency

e) Lifted condensation levele) Lifted condensation level

5) Under the influence of which of the 5) Under the influence of which of the following would you expect MOS to NOT be following would you expect MOS to NOT be reliable?reliable?

a) Vigorous low-pressure systema) Vigorous low-pressure system b) Trapped cold air in a mountain valleyb) Trapped cold air in a mountain valley c) Squall linec) Squall line d) Overrunning precipitation d) Overrunning precipitation e) Clear, calm, dry night over the plainse) Clear, calm, dry night over the plains f) Tropical cyclonef) Tropical cyclone

b) Trapped cold air in a mountain valleyb) Trapped cold air in a mountain valley

c) Squall linec) Squall line

f) Tropical cyclonef) Tropical cyclone

When mesoscale features are expected to When mesoscale features are expected to play a significant role and extreme or play a significant role and extreme or unusual events are expected, do not rely on unusual events are expected, do not rely on SG output (MOS)because IT WILL BE SG output (MOS)because IT WILL BE INACURRATE.INACURRATE.

What might explain the cold bias seen in the MRF MOS forecasts for projections beyond What might explain the cold bias seen in the MRF MOS forecasts for projections beyond the 132-hour forecast in the graphic?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 a) A systematic cold bias in the model (as can be seen in the direct model output shown in blue)in blue)

b) Increased weight of climatological data (shown in gray)b) Increased weight of climatological data (shown in gray) c) Increased weight of observed weather elements at extended lead-timesc) Increased weight of observed weather elements at extended lead-times d) Poorly chosen predictorsd) Poorly chosen predictors

b) Increased weight of climatological data b) Increased weight of climatological data (shown in gray)(shown in gray)

This is because at 132 hours the largest This is because at 132 hours the largest weighted predictor immediately becomes weighted predictor immediately becomes climate data. Much smaller weighting climate data. Much smaller weighting functions are given to all other variables functions are given to all other variables used as predictors. This means the used as predictors. This means the climatalogical coefficient is greatly climatalogical coefficient is greatly increased.increased.

NWP Models and Their ProcessesNWP Models and Their Processes

ROS

BAYESIAN EQUATIONS:BAYESIAN EQUATIONS:

This is a form of statistical equation. The future of probability diagnosis 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 may begin to use these type of equations within 5 to 10 years or maybe sooner.sooner.

Bayesian equations are very efficient when compared to the current Bayesian equations are very efficient when compared to the current method of least squares linear regression. They use past, current and method of least squares linear regression. They use past, current and future data to derive a probability. They always use new information to future data to derive a probability. They always use new information to “learn” from, and then possibly change an outcome based on the new “learn” from, and then possibly change an outcome based on the new information. In this way, MOS model data would be “learning” on two information. In this way, MOS model data would be “learning” on two platforms. One would be climatology and the second would be the platforms. One would be climatology and the second would be the actual equations instead of a predictor coefficient constant.actual equations instead of a predictor coefficient constant.

You can easily find these equations at work today in new programs such 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 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 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 are doing. Then it can give you hints or examples to use during your project.project.

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

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

IMPORTANT FACTS AND TERMS:IMPORTANT FACTS AND TERMS:

Regardless of its strengths, statistical postprocessing of model output is still limited by the data 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 we put into it (the M in MOS doesn't stand for miracle). Some fundamentally important points about SG are:about SG are:

1) SG can make a good NWP forecast better, but cannot fix a bad NWP forecast.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 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. regimes or outlier cases, SG won't work as well.

TERMS:TERMS: Predictand:Predictand: The dependent variable that is to be forecast by the SG guidance. Predictands are 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, derived from observed weather elements. Examples of SG predictands include temperature, precipitation probability, visibility, etc.precipitation probability, visibility, etc.

Predictor(s):Predictor(s): The independent variable (or variables) used in conjunction with the predictand to 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 derive a statistical relationship that drives statistical guidance. Three basic types of predictors are used: model output, observed weather elements, and climatological data.used: model output, observed weather elements, and climatological data.

Probability: A quantitative expression of uncertainty.Probability: A quantitative expression of uncertainty.

Persistence: Also referred to as the classical method, it is the statistical dependence of a variable 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 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 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 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 element (i.e., temperature) is related statistically to the precipitation type that will occur at some future forecast time. future forecast time.

WKDY=weekdayWKDY=weekday

The weekday is a simple algorithm that uses every fourth year as a leap year The weekday is a simple algorithm that uses every fourth year as a leap year giving the model weekday from the model date.giving the model weekday from the model date.

####Change any day of year into weekday:####Change any day of year into weekday: @daynm=(TUE,WED,THU,FRI,SAT,SUN,MON);@daynm=(TUE,WED,THU,FRI,SAT,SUN,MON); $daylp=0;$daylp=0; for($loopyer=1991; $loopyer<=2050; $loopyer++)for($loopyer=1991; $loopyer<=2050; $loopyer++) {if($loopyer%4==0){if($loopyer%4==0) {$febu=29;}{$febu=29;} elsif($loopyer%4!=0)elsif($loopyer%4!=0) {$febu=28;}{$febu=28;} for($loopmon=1; $loopmon<=12; $loopmon++)for($loopmon=1; $loopmon<=12; $loopmon++) {if($loopmon==1||$loopmon==3||$loopmon==5||$loopmon==7||$loopmon==8||$loopmon==10||{if($loopmon==1||$loopmon==3||$loopmon==5||$loopmon==7||$loopmon==8||$loopmon==10||

$loopmon==12)$loopmon==12) {for($loopday=1; $loopday<=31; $loopday++){for($loopday=1; $loopday<=31; $loopday++) {$day[$loopyer][$loopmon][$loopday]=$daynm[$daylp];{$day[$loopyer][$loopmon][$loopday]=$daynm[$daylp]; $daylp++;$daylp++; if($daylp%7==0)if($daylp%7==0) {$daylp=0;}}}{$daylp=0;}}} if($loopmon==4||$loopmon==6||$loopmon==9||$loopmon==11)if($loopmon==4||$loopmon==6||$loopmon==9||$loopmon==11) {for($loopday=1; $loopday<=30; $loopday++){for($loopday=1; $loopday<=30; $loopday++) {$day[$loopyer][$loopmon][$loopday]=$daynm[$daylp];{$day[$loopyer][$loopmon][$loopday]=$daynm[$daylp]; $daylp++;$daylp++; if($daylp%7==0)if($daylp%7==0) {$daylp=0;}}}{$daylp=0;}}} if($loopmon==2)if($loopmon==2) {for($loopday=1; $loopday<=$febu; $loopday++){for($loopday=1; $loopday<=$febu; $loopday++) {$day[$loopyer][$loopmon][$loopday]=$daynm[$daylp];{$day[$loopyer][$loopmon][$loopday]=$daynm[$daylp]; $daylp++;$daylp++; if($daylp%7==0)if($daylp%7==0) {$daylp=0;}}}{$daylp=0;}}} }}}}

CLOUD GROUPSCLOUD GROUPS

The CLDS group is computed in conjunction with the CLHT…TMPO…and The CLDS group is computed in conjunction with the CLHT…TMPO…and TTSK fields.TTSK fields.

The model uses a top down approach. MOS uses a bottom up. First the model calculates the 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.lowest possible level a prevailing cloud layer will be found.

A) LCL height in feetA) LCL height in feetB) Height of min RH between LCL and CCLB) Height of min RH between LCL and CCLC) LCL height in feet + result of the TERICK equationC) 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 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 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 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 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. 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 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 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 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 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, 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 either CL…PC…MC…or OV. The clouds algorithm is extremely complicated but gives a strong answer to cloud heights.strong answer to cloud heights.

Here is a set of RH values from the ROS: Here is a set of RH values from the ROS: {$ovclowendRH[$L]=91.5;#print " +VV2";{$ovclowendRH[$L]=91.5;#print " +VV2"; $bknlowendRH[$L]=84.5;$bknlowendRH[$L]=84.5; $sctlowendRH[$L]=78.5;$sctlowendRH[$L]=78.5; $ciglowendRH[$L]=90.0;$ciglowendRH[$L]=90.0; $stopatCCLorLCL[$L]=$totalfeetplusLCL[$L];}$stopatCCLorLCL[$L]=$totalfeetplusLCL[$L];}

TERICK EQUATIONTERICK EQUATION

Hl + [(Hc – Hl)/(Tc – Ts)] = LCH;Hl + [(Hc – Hl)/(Tc – Ts)] = LCH;

If (Tc – Ts) <= 0; then (Hc – Hl) = 0;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 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 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 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 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 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 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 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.least get close to an actual height. Below is a pictorial explanation.

VSBY=visibilityVSBY=visibility

The Visibility section is calculated with studies and research. There are really no The Visibility section is calculated with studies and research. There are really no equations used, instead an enormous algorithm is used with generic low equations used, instead an enormous algorithm is used with generic low visibility producing variables or predictors. One visibility producing algorithm visibility producing variables or predictors. One visibility producing algorithm is shown below. This field will also show restrictions due to precipitation.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 accumulationSNAC=snow accumulation

This field is a result of team effort involving local research. A research This field is a result of team effort involving local research. A research project was undertaken to find how deep snow would accumulate project was undertaken to find how deep snow would accumulate using temperature to water-equivalent ratios. I simply took this data 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:and sourced it for use by the ROS model. Here are the ratios used:

TEMP:TEMP: RATIO:RATIO:

>=35F>=35F 7:17:1

29-34F29-34F 10:110:1

20-28F20-28F 15:115:1

10-19F10-19F 20:120:1

0 - 9F0 - 9F 30:130:1

< 0F< 0F 40:140:1

SN:WE or .10” of water equivelant at 35F equals .70” of snow accumulation.

SFCRH=surface relative humiditySFCRH=surface relative humidity

Relative Humidity equation used:Relative Humidity equation used:

Es = 6.11 * 10.0^(7.5 * Tc / (237.7 + Tc))Es = 6.11 * 10.0^(7.5 * Tc / (237.7 + Tc))

E = 6.11 * 10.0^(7.5 * TDc / (237.7 + TDc))E = 6.11 * 10.0^(7.5 * TDc / (237.7 + TDc))

RH = (E/Es) * 100.0RH = (E/Es) * 100.0

HAINS=haines indexHAINS=haines indexThe ROS computes the Haines index by national standards and uses the The ROS computes the Haines index by national standards and uses the

actual stations elevation. This is the most accurate method of getting 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 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 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 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 currently use in their areas. No forcing can currently be done since other fields rely on elevation as well.other fields rely on elevation as well.

These are the genericboundaries of the HainesIndex elevationdeterminers. Theelevation determines the level at whichtemperature and dewpoint data are drawn tocalulate the index. Theactual elevations range from:Low < 1000ftMid 1000-3000ftHigh > 3000ft

HAINES INDEX CONTINUEDHAINES INDEX CONTINUED

MIXHT=mixing heightMIXHT=mixing heightThe mixing height is not an equation but an algorithm. The ROS simply 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. moves up a dry adiabat until it crosses the ambient temperature line. This is normally at an inversion level.This is normally at an inversion level.

TPRTD=transport direction & TPRTS=transport speedTPRTD=transport direction & TPRTS=transport speed

Transport winds are defined as the average wind speed and direction of all winds 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 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.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 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.(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 CONTINUEDTRANSPORT WINDS CONTINUED

If the speed of the wind is (If the speed of the wind is (ffff) and the direction in degrees is () and the direction in degrees is (dddd), then the ), then the formula for obtaining the meridional component, V, and the zonal formula for obtaining the meridional component, V, and the zonal component, U, are:component, U, are:

V = -ff * cos(dd) V = -ff * cos(dd) U = -ff * sin(dd)U = -ff * sin(dd)

Given the U and V components of the average wind speed, the following Given the U and V components of the average wind speed, the following equation is used to calculate the direction of the transport wind:equation is used to calculate the direction of the transport wind:

VNTRT=ventilation rateVNTRT=ventilation rateThe ventilation rate is calculated nationally by multiplying the transport The ventilation rate is calculated nationally by multiplying the transport

wind by the mixing height in feet and dividing the result by a constant wind by the mixing height in feet and dividing the result by a constant 5280. Fire officials want the ventilation rate calculated another way 5280. Fire officials want the ventilation rate calculated another way which renders the result non-dimensional. Since the result is non-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 dimensional, it is not considered a rate…therefore it is only given as a ventilation number.ventilation number.

NATIONAL EQUATION:NATIONAL EQUATION:(Transport wind speed) x (Mixing height) / (5280) = vent rate(Transport wind speed) x (Mixing height) / (5280) = vent rate

mphmph ft ft constant ft^2/hrconstant ft^2/hr

FIRE OFFICIALS EQUATION:FIRE OFFICIALS EQUATION:(Transport wind speed) x (Mixing height) = vent number(Transport wind speed) x (Mixing height) = vent number

mphmph ft ft miles ft/hr miles ft/hr

ROS calculates using the fire officials equation. It also has to divide the 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 final number by 3600. This is done so the answer can fit into the field width provided. These can be changed for individual station width provided. These can be changed for individual station preferences.preferences.

CATDY=category dayCATDY=category day

The category day is basically an index taken from the ventilation number. The category day is basically an index taken from the ventilation number. These are the values that drive the index.These are the values that drive the index.

Category Day Category Day Ventilation Number Ventilation Number 11 0 - 17,249 0 - 17,249 22 17,250-34,499 17,250-34,499 33 34,500-51,749 34,500-51,749 44 51,750-68,999 51,750-68,999 55 69,000 or greater 69,000 or greater

DISPN=dispersion indexDISPN=dispersion index

The dispersion index is calculated by dividing the mixing height by 1000, The dispersion index is calculated by dividing the mixing height by 1000, then multiplying the result by the transport wind speed(mph). then multiplying the result by the transport wind speed(mph).

(mixing height) / (1000) x (transport wind speed) = disp index #(mixing height) / (1000) x (transport wind speed) = disp index #

ftft constant constant mph mph

These are the values that drive the index.These are the values that drive the index.

>100 Excellent >100 Excellent

61-100 Good 61-100 Good

41-60 Average 41-60 Average

21-40 Fair 21-40 Fair

8-20 Poor8-20 Poor

0-7 Very Poor0-7 Very Poor

20DIR=20 foot wind direction 20DIR=20 foot wind direction && 20SPD=20 foot wind direction 20SPD=20 foot wind direction

This field is very simple. The ROS simply takes the first level above the 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 two meter surface and converts the speed into mph and gives the direction.direction.

SUNHR=meteorological sunlight hoursSUNHR=meteorological sunlight hours

This is an extremely complicated field. It looks all too easy but the This is an extremely complicated field. It looks all too easy but the computations and algorithms that are used to find a value are computations and algorithms that are used to find a value are immense. All of the computations used can not be shown but the main immense. All of the computations used can not be shown but the main emphasis can be conveyed.emphasis can be conveyed.

The ROS first computes the total daylight hours using latitude longitude The ROS first computes the total daylight hours using latitude longitude and date. It then strips the TTSK group for each hour and associates 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 the sky cover with an amount of time. This time is added and the total is subtracted from the total daylight hours.is subtracted from the total daylight hours.

The ROS is the only model with this capability.The ROS is the only model with this capability.

LALEV=lightning activity levelLALEV=lightning activity level

The LAL is taken directly from Jeanne Hoadley of the National Weather The LAL is taken directly from Jeanne Hoadley of the National Weather Service in Missoula, Montana and Don Latham of the Intermountain Service in Missoula, Montana and Don Latham of the Intermountain Fire Sciences Laboratory’s work. The LAL is a “CONDITIONAL” value. Fire Sciences Laboratory’s work. The LAL is a “CONDITIONAL” value. In other words, one must have everything in place for thunderstorms In other words, one must have everything in place for thunderstorms to form before this field can be used. to form before this field can be used.

The numbers calculated are taken from the CAPE…LI…and 700mb thetaE. The numbers calculated are taken from the CAPE…LI…and 700mb thetaE. Below are the associations.Below are the associations.

LALLAL CAPE CAPE LI LI THETA-E THETA-E

11 <100 <100 >2 >2 no thetaE no thetaE maxmax

22 100-500 100-500 2to-2 2to-2 310-320 310-320

33 >500 >500 -2to-4 -2to-4 320-340 320-340

44 >1000 >1000 <-4 <-4 >330 >330

55 >=1500 >=1500 <-4 <-4 >=340 >=340

6 RH<=60% along with LAL #3 requirements only.6 RH<=60% along with LAL #3 requirements only.

LTGFQ=lightning frequencyLTGFQ=lightning frequency

Lightning frequency was basically taken straight from the LAL Lightning frequency was basically taken straight from the LAL and observed data. It works over a 1…5…and 15 minute and observed data. It works over a 1…5…and 15 minute interval. It gives the amount of strikes that should be interval. It gives the amount of strikes that should be produced by any single thunderstorm cell. This field is also produced by any single thunderstorm cell. This field is also “CONDITIONAL.” The numbers are rounded to the nearest “CONDITIONAL.” The numbers are rounded to the nearest whole number. More work may be done on a local level to whole number. More work may be done on a local level to make this a stronger field. The following associations are make this a stronger field. The following associations are what the ROS uses.what the ROS uses.

LALLAL FREQUENCY FREQUENCY #STRIKES#STRIKES INTERVALINTERVAL 11 00 00 CG 1-5-15CG 1-5-15 22 11 1 . 1-5 . 1-81 . 1-5 . 1-8 CG 1-5-15CG 1-5-15 33 22 1-2 . 6-10 . 9-15 CG 1-5-151-2 . 6-10 . 9-15 CG 1-5-15 44 44 2-3 . 11-15 . 16-25 CG 1-5-152-3 . 11-15 . 16-25 CG 1-5-15 55 55 3 . 15-253 . 15-25 CG 1-5-15CG 1-5-15 66 33 SAME AS LAL#3 ABOVESAME AS LAL#3 ABOVE

HINX=heat indexHINX=heat index

This number uses the ambient temperature and the calculated relative This number uses the ambient temperature and the calculated relative humidity to find the heat index temperature. This field is extremely humidity to find the heat index temperature. This field is extremely useful. By simply scanning the heat index numbers, one can quickly useful. By simply scanning the heat index numbers, one can quickly determine if the forecast may need to be watched more carefully over determine if the forecast may need to be watched more carefully over the next few days for heat advisory criteria. It uses the equation the next few days for heat advisory criteria. It uses the equation implemented by the National Weather Service. It is a seasonal field and 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 is replaced by the wind chill index during the Fall. The following is the equation used:equation used:

HI =HI = -42.379 + 2.04901523*TempF + 10.14333127*RH -42.379 + 2.04901523*TempF + 10.14333127*RH

- 0.22475541*TempF*RH - .00683783*TempF^2- 0.22475541*TempF*RH - .00683783*TempF^2

- .05481717*RH^2 + .00122874*TempF^2*RH - .05481717*RH^2 + .00122874*TempF^2*RH

+ .00085282*TempF*RH^2+ .00085282*TempF*RH^2

- .00000199*TempF^2*RH^2- .00000199*TempF^2*RH^2

WINX=wind chill indexWINX=wind chill index

This number uses the ambient temperature and the wind speed to find the This number uses the ambient temperature and the wind speed to find the wind chill temperature. This field is extremely useful. By simply wind chill temperature. This field is extremely useful. By simply scanning the wind chill numbers, one can quickly determine if the scanning the wind chill numbers, one can quickly determine if the forecast may need to be watched more carefully over the next few days forecast may need to be watched more carefully over the next few days for wind chill advisory criteria. It uses the newest equation for wind chill advisory criteria. It uses the newest equation implemented by the National Weather Service. It is a seasonal field and 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 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 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 coming years by NOAA. When it is, this equation will be updated to show that change. The following is the equation used:show that change. The following is the equation used:

WC = WC = 35.74 + 0.6215*TempF -35.75*windSpkt^0.16 + 35.74 + 0.6215*TempF -35.75*windSpkt^0.16 + 0.4275*TempF*windSpkt^0.160.4275*TempF*windSpkt^0.16