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

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

2. How did the ROS model begin, and WHY do we need another model? Glad you asked.

3. 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.

4. 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.

7. 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.

8. 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

11. 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.

12. 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

13. 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

14. 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

15. 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.

16. 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.

17. 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.

18. 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.

19. 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.

20. 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.

21. 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)

22. 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

23. 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

24. 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.

25. ETA HORIZONTAL DOMAIN

26. 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

27. 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.

28. 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)

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

30. 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

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

32. 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

33. a) Model data • c) Observed weather elements

34. 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

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

36. 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

37. 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.

38. 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

39. 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.

40. ROS NWP Models and Their Processes

41. 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.

42. FOR MORE IN DEPTH INFORMATION ON NWP MODELS, PLEASE VISIT: http://meted.ucar.edu/nwp/pcu1/ic1/index.htm

43. 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.

44. 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;}}} • }}

45. 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];}

46. 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.

47. 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.

48. 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.

49. 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.

50. 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