NOAA Technical Memorandum NOS CS 12

SKILL ASSESSMENT OF NOS LAKE ERIE 

OPERATIONAL FORECAST SYSTEM (LEOFS) 

Silver Spring, Maryland 

June 2007 

noaa National Oceanic and Atmospheric Administration 

U.S. DEPARTMENT OF COMMERCE 

National Ocean Service 

Coast Survey Development Laboratory 
Office of Coast Survey 

National Ocean Service 

National Oceanic and Atmospheric Administration 

U.S. Department of Commerce

The Office of Coast Survey (OCS) is the Nation’s only official chartmaker. As the oldest 

United States scientific organization, dating from 1807, this office has a long history. 

Today it promotes safe navigation by managing the National Oceanic and Atmospheric 

Administration’s (NOAA) nautical chart and oceanographic data collection and 

information programs. 

There are four components of OCS: 

The Coast Survey Development Laboratory develops new and efficient techniques 

to accomplish Coast Survey missions and to produce new and improved products 

and services for the maritime community and other coastal users. 

The Marine Chart Division acquires marine navigational data to construct and 

maintain nautical charts, Coast Pilots, and related marine products for the United 

States. 

The Hydrographic Surveys Division directs programs for ship and shore-based 

hydrographic survey units and conducts general hydrographic survey operations. 

The Navigational Services Division is the focal point for Coast Survey customer 

service activities, concentrating predominately on charting issues, fast-response 

hydrographic surveys, and Coast Pilot updates. 

ii
NOAA Technical Memorandum NOS CS 12 

SKILL ASSESSMENT OF NOS LAKE ERIE OPERATIONAL 

FORECAST SYSTEM (LEOFS) 

Philip Chu 

John G. W. Kelley 

Ai-Jun Zhang 

Office of Coast Survey, Coast Survey Development Lab, 

Silver Spring, MD 

Gregory A. Lang 

Office of Oceanic and Atmospheric Research 

Great Lakes Environmental Research Laboratory, Ann Arbor, MI 

Keith W. Bedford 

Dept. of Civil and Environmental Engineering, 

The Ohio State University, Columbus, OH 

June 2007

noaa National Oceanic and Atmospheric Administration 

U. S. DEPARTMENT National Oceanic and National Ocean Service 

OF COMMERCE Atmospheric Administration John H. Dunnigan 

Carlos Gutierrez, Secretary Conrad C. Lautenbacher, Jr., Assistant Administrator 

 VADM USN (Ret.), Under Secretary 

 Office of Coast Survey Coast Survey Development Laboratory 

 Captain Steven R. Barnum, NOAA Mary Erickson 

iii
NOTICE 

Mention of a commercial company or product does not constitute an 

endorsement by NOAA. Use for publicity or advertising purposes of 

information from this publication concerning proprietary products or the 

tests of such products is not authorized. 

iv
TABLE OF CONTENTS 

LIST OF FIGURES ...................................................................................................................... vii 

LIST OF TABLES........................................................................................................................ vii 

LIST OF ACRONYMS .................................................................................................................. x 

EXECUTIVE SUMMARY ........................................................................................................... xi 

1. INTRODUCTION ...................................................................................................................... 1 

2. LAKE ERIE................................................................................................................................ 2 

3. SYSTEM OVERVIEW .............................................................................................................. 2 

3.1. Description of Model........................................................................................................ 2 

3.2. Grid Domain ..................................................................................................................... 4 

3.3. Data Ingest........................................................................................................................ 5 

3.4. Nowcast Cycle.................................................................................................................. 5 

3.5. Forecast Cycle .................................................................................................................. 6 

3.6. Operational Environment and Scheduling........................................................................ 6 

4. HINDCAST SKILL ASSESSMENT ......................................................................................... 6 

4.1. Description of Hindcast Runs........................................................................................... 7 

4.2. Method of Evaluation ....................................................................................................... 7 

4.3. Skill Assessment of Surface Hindcasts ............................................................................ 9 

4.3.1. Water Levels ............................................................................................................. 9 

4.3.2. Surface Water Temperatures................................................................................... 11 

4.3.3. Surface Currents...................................................................................................... 13 

4.4. Skill Assessment of Sub-Surface Hindcasts................................................................... 13 

4.4.1. Sub-Surface Currents.............................................................................................. 13 

4.4.2. Sub-Surface Water Temperatures........................................................................... 17 

4.5. Summary......................................................................................................................... 21 

5. SEMI-OPERATIONAL NOWCAST SKILL ASSESSMENT............................................... 23 

5.1. Description of Nowcast Cycles ...................................................................................... 23 

5.2. Method of Evaluation ..................................................................................................... 23 

5.3. Assessment of Water Level Nowcasts............................................................................ 31 

5.4. Assessment of Water Temperature Nowcasts ................................................................ 36 

6. SEMI-OPERATIONAL FORECAST SKILL ASSESSMENT ............................................... 37 

6.1. Description of Forecast Cycles....................................................................................... 37 

6.2. Method of Evaluation ..................................................................................................... 37 

v
6.3. Skill Assessment of Water Level Forecast Guidance..................................................... 38 

6.4. Skill Assessment of Surface Water Temperature Forecast Guidance ............................ 41 

7. SUMMARY.............................................................................................................................. 43 

8. RECOMMENDATIONS FOR FUTURE WORK ................................................................... 44 

ACKNOWLEDGMENTS ............................................................................................................ 45 

REFERENCES ............................................................................................................................. 46 

APPENDIX A. Skill Assessment Scores of Semi-Operational Water Level Nowcasts and 

Forecast Guidance of Water Levels at six NOS Gauges in Lake Erie from 15 April to 17 

December 2004 ............................................................................................................................. 49 

APPENDIX B. Time Series Plots of Semi-Operational Water Level Nowcasts vs. Observations 

from 15 April to 17 December 2004 at 8 NOS Gauges in Lake Erie ........................................... 53 

APPENDIX C. Time Series Plots of Semi-Operational Water Level Forecast Guidance vs. 

Observations from 15 April to 17 December 2004 at eight NOS Gauges in Lake Erie............... 61 

APPENDIX D. Skill Assessment Scores of Semi-Operational Nowcasts and Forecast Guidance 

of Surface Water Temperatures from 21 April to 8 December 2004 at a NDBC fixed buoy in 

Lake Erie....................................................................................................................................... 69 

APPENDIX E. Time Series Plots of Semi-Operational Nowcasts and Forecast Guidance of 

Surface Water Temperatures vs. Observations from 15 April to 17 December 2004 at a NDBC 

fixed buoy in Lake Erie................................................................................................................. 71 

APPENDIX F. Description of NWS Water Level Criteria for Lakeshore Flood Warnings and 

Low Water Statements for Lake Erie............................................................................................ 73 

vi
LIST OF FIGURES

Figure 1. Map depicting Lake Erie bathmetry including longitudinal cross section Modified 

from Mortimer (1987) and Dingman and Bedford (1984). ............................................ 3 

Figure 2. Map depicting the POMGL grid domain with a grid increment of 5 km...................... 4 

Figure 3. Map depicting locations of observing sites where POMGL simulations were evaluated 

at (Kuan, 1995b)............................................................................................................. 8 

Figure 4. Map depicting the location of NOS NWLON stations in Lake Erie............................ 27 

Figure 5. Observed water levels at NOS NWLON stations in Buffalo, NY (a) and Toledo, OH 

(b) during the period 1 April to 31 December 2004..................................................... 28 

Figure 6. Map depicting the location of the NWS/NDBC fixed buoy 45005 in Lake Erie along 

with the grid points of the GLOFS-Erie model grid. ................................................... 30 

LIST OF TABLES 

Table 1. Skill scores for assessing the difference between simulated and and observed water 

levels. ............................................................................................................................... 8 

Table 2. Summary of water level hindcast evaluation statistics by NOS gauge station for 5 time 

periods in 1979................................................................................................................. 9 

Table 3. Seasonal evaluation statistics for water level simulations during heating, stratified, and 

cooling seasons in 1979. ................................................................................................ 10 

Table 4. Evaluation statistics for water level simulations at NOS water level gauge stations 

during the inter-storm and stormy periods during 1979. ............................................... 10 

Table 5. Evaluation statistics for surface water temperature simulations during 1979 at CCIW 

buoys.............................................................................................................................. 11 

Table 6. Seasonal evaluation statistics for surface water temperature simulations by heating, 

stratified, and cooling seasons in 1979. ......................................................................... 12 

Table 7. Average seasonal analysis results for surface water temperature simulations. ............. 12 

Table 8. Evaluation statistics for surface water temperature simulation during stormy and interstorm periods in 1979 at CCIW Buoys.......................................................................... 13 

Table 9. Evaluation statistics for the simulation of current velocity u-component in 1979 for two 

cross sections. See Fig. 3 for the location of the cross sections.................................... 14 

Table 10. Evaluation statistics for the simulation of current velocity v-component in 1979 at two 

cross sections. See Fig. 3 for the location of the cross sections.................................. 14 

Table 11. Summary of evaluation statistics for velocity simulations by season in 1979. ........... 15 

Table 12. Evaluation statistics for u-component current velocity at C-C’ during the inter-storm 

period in 1979............................................................................................................... 15

Table 13. Evaluation statistics for u-component current velocity at C-C’ during the stormy 

period in 1979............................................................................................................... 16

Table 14. Evaluation statistics for v-component current velocity at C-C’ during inter-storm 

periods in 1979. ............................................................................................................ 16 

Table 15. Evaluation statistics for v-component current velocity at C-C’ during the stormy 

periods in 1979. ............................................................................................................ 17 

Table 16. Evaluation statistics for water temperature simulations by thermistor location during 

1979. ............................................................................................................................. 18 

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Table 17. Average evaluation statistics for water temperature at selected water column depths 

for 1979. ....................................................................................................................... 18 

Table 18. Average seasonal analysis results for water column simulations in 1979................... 19 

Table 19. Seasonal average evaluation statistics for water temperature simulations at selected 

water column depths for 1979. ..................................................................................... 19 

Table 20. Evaluation statistics for water temperature simulations at C-C’ during inter-storm 

periods in 1979. ............................................................................................................ 20 

Table 21. Evaluation statistics for water temperature simulation at C-C’ during the stormy 

periods in 1979. ............................................................................................................ 20 

Table 22. Comprehensive seasonal model skill in terms of the amplitude skill score. ............... 22 

Table 23. Comprehensive seasonal model skill analysis in terms of the IOA............................. 22 

Table 24. NOS Skill Assessment Statistics (Hess et al. 2003). ................................................... 24 

Table 25. Data series groups and the variables in each. Note that upper case letters indicate a 

prediction series (e.g., H), and lower case letters (e.g., h) indicate a reference series 

(observation) (Modified from Hess et al. 2003)........................................................... 25 

Table 26. Acceptance error limits (X) and the maximum duration limits (L) modified from Hess 

et al. (2003) for use in the Great Lakes. ....................................................................... 25 

Table 27. Information on NOAA/NOS NWLON stations whose observations were used to 

evaluate LEOFS semi-operational nowcasts and forecast guidance of water levels. .. 31 

Table 28. Information of a NWS/NDBC fixed buoy whose observations were used to evaluate 

LEOFS semi-operational nowcasts and forecast guidance of surface water 

temperatures. ................................................................................................................ 31

Table 29. Summary of Skill assessment Statistics of Semi-Operational Nowcasts of Hourly 

Water Levels at eight NOS NWLON Stations in Lake Erie for the Period 15 April to 

17 December 2004. A total of 5832 nowcasts were used in the assessment. Gray 

shading, if present, indicates that it did not meet the NOS acceptance criteria. .......... 32 

Table 30. Summary of Standard Statistics Evaluating the Ability of the Semi-Operational 

Nowcasts to Predict Extreme High Water Level Events at Eight NOS NWLON 

stations in Lake Erie during the Period 15 April to 17 December 2004. Gray shading, 

if present, indicates that it did not meet the NOS acceptance criteria......................... 33 

Table 31. Summary of Standard Statistics Evaluating the Ability of Semi-Operational Nowcasts

to Simulate Extreme Low Water Level Events at Eight NOS NWLON Stations in Lake 

Erie for the Period 15 April to 17 December 2004. Gray shading, if present, indicates 

that the statistic did not pass the NOS acceptance criteria. .......................................... 35 

Table 32. Summary of Skill Assessment Statistics of the Semi-Operational Hourly Nowcasts of 

Surface Water Temperatures at the NWS/NDBC fixed buoy in Lake Erie for the 

Period from Mid-April to Early December 2004. Gray shading indicates that the 

statistic did not pass the NOS acceptance criteria........................................................ 36 

Table 33. Summary of Skill Assessment Statistics of Semi-Operational 24-hr Forecast 

Guidance of Hourly Water Levels at NOS NWLON Stations in Lake Erie for the 

Period 15 April to 17 December 2004. Gray shading, if present, indicates that the 

statistic did not pass the NOS acceptance criteria........................................................ 38 

Table 34. Summary of Skill assessment Statistics Evaluating the Ability of Semi-Operational 

Forecast Guidance to Predict Extreme High Water Level Events at NOS NWLON 

Stations in Lake Erie during the Period 15 April to 17 December 2004. Gray shading, 

if present, indicates that the statistic did not pass the NOS acceptance criteria........... 39 

viii
Table 35. Summary of Skill assessment Statistics Evaluating the Ability of Semi-Operational 

Forecast Guidance to Predict Extreme Low Water Level Events at NOS NWLON 

Stations in Lake Erie during the Period 15 April to 17 December 2004. Gray shading, 

if present, indicates that the statistic did not pass the NOS acceptance criteria........... 40 

Table 36. Summary of Skill Assessment Statistics for Semi-Operational Forecast Guidance to 

Predict Surface Water Temperatures 24 hours in Advance at a NWS/NDBC fixed 

buoy in Lake Erie during the period from mid-April to early-November 2004. Gray 

shading, if present, indicates that the statistic did not pass the NOS acceptance criteria.

...................................................................................................................................... 42 

ix
LIST OF ACRONYMS 

ASOS Automated Surface Observing System 

AVHRR Advanced Very High Resolution Radiometer 

AWOS Automated Weather Observing System 

BUFR Binary Universal Form for the Representation of Meteorological Data 

C-MAN Coastal-Marine Automated Network 

CCIW Canada Centre for Inland Waters 

CCS NCEP Central Computer System 

CO-OPS Center for Operational Oceanographic Products and Services 

CORMS Continuously Operating Real-Time Monitoring System 

CSDL Coast Survey Development Laboratory 

DOD Department of Defense 

EPA Environmental Protection Agency 

ETA Eta Mesoscale Numerical Weather Prediction Model 

GLCFS Great Lakes Coastal Forecast System 

GLERL Great Lakes Environmental Research Laboratory 

GLFS Great Lakes Forecasting System 

GLOFS Great Lakes Operational Forecast System 

GLSEA Great Lakes Surface Environmental Analysis 

GRIB GRIdded Binary 

LEOFS Lake Erie Operational Forecast System 

LMOFS Lake Michigan Operational Forecast System 

MMAP Marine Modeling and Analysis Programs 

NAM North America Mesoscale Model 

NCEP National Centers for Environmental Prediction 

NCOP National Coastal Ocean Program 

NDBC National Data Buoy Center 

NOAA National Oceanic and Atmospheric Administration 

NOS National Ocean Service 

NWLON National Water Level Observation Network 

NWS National Weather Service 

ODAAS Operational Data Acquisition and Archive System 

OSU The Ohio State University 

POMGL Princeton Ocean Model – Great Lakes version 

USCG United States Coast Guard 

VOS Voluntary Observing Ship 

x
EXECUTIVE SUMMARY

This document describes the Lake Erie Operational Forecast System (LEOFS) and an assessment 

of its skill. The lake forecast system, based on a hydrodynamic model, uses near real-time 

atmospheric observations and numerical weather prediction forecast guidance to produce threedimensional forecast guidance of water temperature and currents and two-dimensional forecasts 

of water levels. 

LEOFS is the result of technology transfer of the Great Lake Forecasting System (GLFS) and the 

Great Lakes Coastal Forecasting System (GLCFS) from The Ohio State University (OSU) and 

NOAA’s Great Lakes Environmental Research Laboratory (GLERL) to NOAA’s National 

Ocean Service. 

The skill assessment of LEOFS followed the scenarios specified by Hess et al. (2003) for use 

with non-tidal water bodies. These scenarios included 1) the hindcast, 2) the semi-operational 

nowcast, and 3) the semi-operational forecast. The hindcast is a long simulation using the best 

available observed meteorological observations and verification data. The semi-operational 

nowcast and forecast are simulations made in a real-time environment where there are occasional 

periods of missing inputs (i.e. meteorological observations and/or forecast guidance from 

atmospheric forecast models). 

For the hindcast scenario, the dissertation results of Kuan (1995b) were used to satisfy NOS 

requirements (Hess et al. 2003). Kuan performed a skill assessment of the Princeton Ocean 

Model for Lake Erie from May to October 1979. Kuan compared model simulations to surface 

observations from NOS water level gauges and surface and sub-surface data from specially 

deployed buoys, current meters and thermistor strings. The primary statistics used to assess the 

model performance for water levels and surface water temperatures were Root Mean Square 

Error (RMSE), Index of Agreement (IOA), and a Skill Score. The RMSE is a statistic parameter 

required by Hess et al. (2003) for evaluating predicted water levels in non-tidal regions. The 

other NOS required statistics include Mean Algebraic Error (MAE), Series Mean (SM), Standard 

Deviation (SD), Central Frequency (CF), Negative Outlier Frequency (NOF), Positive Outlier 

Frequency (POS), Maximum Duration of Positive Outlier (MDPO), and Maximum Duration of 

Negative Outlier (MDNO) were not available from Kuan’s dissertation. 

For the semi-operational nowcast and forecast scenarios, evaluation of GLERL’s Great Lakes 

Coastal Forecast System (GLCFS) for Lake Erie was used to satisfy NOS requirements (Hess et 

al. 2003). The GLCFS was run in near real-time, 4 times/day for nowcast and 2 times/day for 

the forecast cycles. Although, Hess et al. (2003) recommends conducting evaluations for 365 

days in order to capture all expected seasonal conditions, GLCFS nowcasts and forecasts were 

evaluated for the period from for mid-April to early-December 2004 during the ice-free season. 

Due to lack of regularly monitored currents and sub-surface water temperatures, only water 

levels and surface water temperatures nowcasts at a few sites could be evaluated for Lake Erie. 

The primary statistics used to assess the model performance for water levels and surface water 

temperatures are those required by NOS for evaluating predicted water levels in non-tidal 

regions. These included Series Means (SM), Mean Algebraic Difference (MAE), Root Mean 

Square Error (RMSE), Standard Deviation (SD), negative outlier frequency (NOF), positive 

xi
outlier frequency (POF), maximum duration of positive outlier (MDPO), and maximum duration 

of negative outlier (MDNO). 

The skill statistics for the hindcast scenario are summarized below by variable: 

1) Water Levels: 

The simulated water surface elevation matched well in both phase and magnitude with the 

corresponding observed data by picking up almost every single significant spike appearing in the 

observed water level elevations. The average RMSE was quite small indicating that the model 

can be considered quite good in simulating water surface elevation of the lake. The average IOA 

and amplitude Skill Score also support this observation, obtaining high values of 0.95 and 9.72, 

respectively. 

2) Water Currents: 

For the current velocity simulation, except the nearshore zone and the very unsteady flow region, 

the velocity predictions over transect C-C’ were quite satisfactory. Good simulations both in 

phase and magnitude were found at most of the current meter locations. 

3) Water Temperatures: 

Surface: 

As for lake surface temperature, the model predictions compared exceptionally well with 

the observed data at all six Canada Centre for Inland Waters (CCIW) meteorological buoy 

locations. Computed values not only pick up almost every single spike found in the 

observed data, but the average RMSE was as low as 1oC over the entire test period of 150 

days. 

10m Depth: 

 The model also demonstrated good skill as predicting water temperatures in the deeper 

portion of the lake. When the lake was well mixed, the model predictions were as good as 

those for the lake surface. However, when lake was thermally stratified, the simulations 

under predicted the water temperature above the thermocline region and over predicted the 

water temperature in the hypolimnion. 

The skill statistics for the semi-operational nowcast and forecast scenarios are summarized 

below by variable and type of prediction (e.g.nowcast or forecast guidance): 

(1) Water Levels: 

Nowcasts: 

The hourly nowcasts passed NOS criteria for NOF, CF, POF, MDPO, and MDNO at all eight 

NOS gauge locations. The mean algebraic errors ranged between -2.9 and +3.4 cm and the 

RMSE ranged between 4 and 8 cm. The greatest errors were at Toledo, OH, Buffalo, NY, 

and Fairport, OH. The nowcasts under predicted the water levels at Toledo and over 

xii
predicted them at Buffalo. Toledo and Buffalo, located at the extreme SW and NE ends of 

the lake, respectively, experience the greatest hourly water level variability and are the most 

difficult locations to predict. It is not clear why the MAE and RMSE were large at Fairport 

while differences at gage locations to the north and south were only ~0.8 cm.

The ability of the nowcasts to predict extreme high and low water level events was also 

assessed using a proposed addition to the evaluation procedure of the NOS standards. The 

nowcasts’ amplitude predictions of high water level events passed the NOS acceptance 

criteria for NOF, CF, POF, MDNO, and MDPO at only Cleveland and Marblehead. The 

nowcasts simulations of extreme low water level passed NOS acceptance criteria for 

amplitude at Erie and Fairport and were close to passing at Toledo and Marblehead. The 

nowcasts predictions of the timing of the high and low water level events did not pass the 

NOS acceptance criteria at any gauge. 

Forecast Guidance: 

The hourly forecasts passed the criteria at 7 of the 8 NOS gauges, failed only at Toledo. The 

mean algebraic errors or differences ranged between -3 and +4.4 cm and the RMSE ranged 

between 4.1 cm at Cleveland and 10.7 cm at Toledo. Similar to the nowcasts, the greatest 

errors were at Buffalo and Toledo, located at the extreme ends of the lake. The forecasts 

under-predicted the water levels at Toledo and over-predicted the levels at Buffalo. There 

was some increase in the RMSE values as forecast projection increased. 

The forecasts of extreme high water level passed the NOS acceptance criteria for amplitude 

only at Cleveland and Marblehead, OH. The forecasts of extreme low water level passed 

NOS acceptance criteria for amplitude at Erie, Fairport, and Cleveland. The forecast 

guidance ability to predict the timing of these events did not pass NOS acceptance criteria for 

at any gauge. 

(2) Surface Water Temperature: 

Nowcasts: 

The hourly water temperature nowcasts at the NWS/NDBC fixed buoy located at the 

boundary between the western and central basins passed the NOS acceptance criteria. The 

MAE was ~1o

C and the RMSE was 1.3oC. The time series plot of nowcasts vs. observations 

at the buoy indicated some seasonally differences in model skill. The nowcasts were in close 

agreement to observations (+0.5 to +1o

C) from mid- April until early May, but then began to 

deviate from the observations by +1 to +2o

C until late May. After that the nowcasts differed 

from observations by +0.5oC till mid August. The nowcasts then deviated by +1 to +2oC 

until early October. During the remaining days of autumn through the end of the period in 

mid December, the nowcasts generally differed from observations by +0.5o

C. 

Forecast Guidance: 

The hourly water temperature forecast guidance at 24 hours at the NWS/NDBC fixed buoy 

xiii
passed the NOS acceptance criteria. The MAE was +0.7o

C and RMSE was 1.3oC. The MAE 

was 0.3o

C less for the forecast guidance than for the nowcasts. The RMSE at 24 hours was about 

the same as for the nowcasts. The MAE decreased with increasing forecast projection: 1.1oC at 0 

hour to 0.7o

C at 24 hours. This indicates that the lake model is cooling with time during the 

forecast cycle and implies that there is an excess of surface heat flux into the lake during the 

nowcast cycle.

xiv
1. INTRODUCTION 

The Great Lakes Forecasting System (GLFS) was developed by The Ohio State University 

(OSU) and NOAA's Great Lakes Environmental Research Laboratory (GLERL) starting in the 

late 1980s to provide nowcasts and short-range forecasts of the physical conditions (temperature, 

currents, water level, and waves) of the five Great Lakes. The development of GLFS was 

directed by Drs. Keith Bedford (OSU) and David Schwab (GLERL) and involved over a dozen 

OSU graduate students, research assistants and post doctoral researchers at GLERL and OSU, 

and other OSU faculty members. The development of GLFS was funded by 36 contracts from 

25 different sources. From the beginning, GLERL and OSU were interested in working 

cooperatively with NOAA (i.e. NWS) in “assessing the potential benefits [of GLFS] to NOAA’s 

scientific and operational programs in the coastal ocean”. In April 1991, Drs. Bedford and 

Schwab met with National Weather Service (NWS) and National Coastal Ocean Program 

(NCOP) representatives in Silver Spring, MD to discuss how they could work with NOAA line 

offices to have GLFS products carefully evaluated through a demonstration program prior to 

NWS adopting the products as ‘guidance tools’, and which products might be distributed directly 

to end users. 

GLFS used the Princeton Ocean Model (Blumberg and Mellor 1987; Mellor 1996) and GLERLDonelan wave model (Schwab et al. 1984). The first 3-D nowcast for the Great Lakes was made 

for Lake Erie in 1992 at the Ohio Supercomputer Center on the OSU Columbus campus (Yen et 

al. 1994; Schwab and Bedford 1994). Starting in July 1995, twice per day forecasts were made 

for Lake Erie (Schwab and Bedford 1996). GLFS was recognized with an award in 2001 by the 

American Meteorological Society as the first U.S. coastal forecasting system to make routine 

real-time predictions of currents, temperatures, and key trace constituents. 

In 1996, GLFS was ported to a GLERL workstation in Ann Arbor, MI. The workstation version 

of GLFS called the Great Lakes Coastal Forecast System (GLCFS) has been running for Lake 

Erie in semi-operational mode at GLERL since February 1997 (Schwab et al. 1999). GLCFS for 

Lake Erie generates nowcasts 4 times/day and forecast guidance out to 60 hours twice per day. 

The predictions are displayed on the GLERL web page (http://www.glerl.noaa.gov/res/glcfs/) 

and digital output is made available in GRIB format to NWS Weather Forecast Offices in the 

region. GLCFS nowcasts and forecasts are archived at GLERL. 

In 2004, the hydrodynamic model code of GLCFS for all five Great Lakes was ported to NOS 

Center for Operational Oceanographic Products and Services (CO-OPS) in Silver Spring, MD. 

GLCFS was reconfigured to run in the NOS Coastal Ocean Modeling Framework (COMF) and 

to use surface meteorological observations from NOS Operational Data Acquisition and Archive 

System (ODAAS). GLCFS for Lake Erie was renamed the Lake Erie Operational Forecast 

System (LEOFS). LEOFS began making routine operational nowcasts and forecasts at CO-OPS 

for Lake Erie on September 30, 2005. 

LEOFS along with the operational nowcast/forecast system represents the first NOS forecast 

systems to be implemented for non-tidal water bodies. The predictions from LEOFS like those 

from NOS estuarine forecast systems must be evaluated to inform users about the skill of the 

nowcasts and forecast guidance. In evaluating LEOFS, NOS sought to take advantage of 

previous evaluations done by researchers at OSU and GLERL to fulfill the hindcast scenario 

1
requirements described in Hess et al. (2003). In addition, NOS also utilized the nowcasts and 

forecasts routinely produced by GLERL to fulfill the semi-operational nowcasts and forecast 

scenarios required by Hess et al. (2003). 

This report describes the model performance based on NOS requirements for operational 

nowcast/forecast systems (Hess et al. 2003). An overview of Lake Erie and LEOFS are given 

first. 

2. LAKE ERIE 

Lake Erie is the smallest of the Great Lakes and the 13th largest lake in the world with a breadth 

of 92 km (57 mi) and a length of 388 km (241 mi). It has an average depth of 19 m (62ft) with a 

maximum depth of 64 m (210 ft) in the eastern basin. Lake Erie, similar to the other Great 

Lakes, has a pronounced annual thermal cycle ranging from vertically well-mixed water in late 

autumn to thermal stratification across the entire lake with a well-developed summer thermocline 

(Boyce et al. 1989; Schertzer et al. 1987). Since the lake is relatively shallow it warms rapidly in 

the spring and summer and frequently freezes over during the winter. 

Lake Erie responds quickly to the passage of weather systems due to its shallowness and 

southwest to northeast orientation (Fig. 1). The lake responds to the wind stress by a combination 

of free and forced mode oscillatory responses in water level and thermocline position which give 

rise to periodic velocity and current structures (Bedford 1992). The free mode is when the lake 

is subject to an imposed wind stress on its surface resulting in frequent and sometimes dramatic 

storm surges. Frequently, strong SW winds will cause an increase in water level at Buffalo, NY 

and a drawdown at Toledo, OH. The positive surge will occur approximately 3 hours before the 

corresponding maximum drawdown at Toledo. After the storm passage, the potential energy 

stored in the surge is released and expressed as free oscillation gravity waves called seiches 

(Bedford 1992). Additional information about the physical limnology of Lake Erie can be found 

in Boyce et al. (1989), Dingham and Bedford (1984), Bartish (1987), Mortimer (1987), and 

Saylor and Miller (1987). 

3. SYSTEM OVERVIEW 

This section provides a brief description of the numerical hydrodynamic model used by LEOFS. 

Similar descriptions of the model as it has been applied to Lake Erie have been given by Kuan 

(1995b), Kelley (1995), Kelley et al. (1998), Hoch (1997), Chu (1998), and O’Connor et al. 

(1999). 

3.1. Description of Model 

The core numerical model in LEOFS is the Princeton Ocean Model (POM) developed by 

Blumberg and Mellor (Mellor 1996). The model is a fully three-dimensional, non-linear 

primitive equation coastal ocean circulation model, with a second order Mellor-Yamada 

turbulence closure 

2
Figure 1. Map depicting Lake Erie bathmetry including longitudinal cross section 

Modified from Mortimer (1987) and Dingman and Bedford (1984). 

scheme to provide parameterization of vertical mixing processes. The model solves continuity 

momentum and conservation equations for temperature simultaneously in an iterative fashion 

and the resulting predictive variables are free upper surface elevation, full three-dimensional 

velocity and temperature fields, Turbulence Kinetic Energy (TKE) and turbulence macroscale. 

Other main features of the model include: terrain following coordinate in the vertical (sigma 

coordinate), finite difference numerical scheme, Boussinesq and hydrostatic approximation, and 

mode splitting technique. 

POM was modified by researchers at OSU and GLERL for use in the Great Lakes (Bedford and 

Schwab 1991, O’Connor and Schwab 1993). For the rest of this report, the modified version of 

the POM for the Great Lakes will be referred to as POMGL. Lake Erie, like the other Great 

Lakes is treated as an enclosed basin. Therefore, there are no inflow/outflow boundary 

conditions: no fluid exchange between the lake and its tributaries, between the lake and ground 

water sources, or between the lake and anthropogenic influences. Thus model simulations do not 

include seasonal changes in lake wide mean water level due to precipitation and evaporation. To 

account for these seasonal changes, a mean lake water level is estimated based on observations 

from NOS gauges for the past 7 days and added to POMGL’s predictions of water level 

displacement (Section 5.2) prior to dissemination. GLERL is presently evaluating the impact of 

using climatological estimates of river discharges on POMGL predictions. 

3
3.2. Grid Domain 

The model domain for Lake Erie consists of a rectangular grid with a 5-km horizontal resolution 

in both the x- and y-directions (Fig. 2). The domain has 1944 grid points with 81 points in the xdirection and 24 points in the y-direction. The grid domain has been rotated 27.33 degrees 

counterclockwise so that the x-coordinate is along the longitudinal axis of the lake and the y-axis 

is across the lake. The bottom topography for the domain is based on GLERL’s 2-km digital 

bathymetry data compiled by Schwab and Sellers (1980) but slightly smoothed to minimize the 

development of 2 delta x noise. The model bathymetry ranges from 4 m in the extreme western 

basin to 62 m in the eastern basin. The model uses 11 sigma levels in the vertical: 0, -.05, -.10, -

.15, -.25, -.35, -.45, -.55, -.70, -.85, and -1.00. 

Figure 2. Map depicting the POMGL grid domain with a grid increment of 5 km. 

4
3.3. Data Ingest 

The nowcast cycle relies on surface meteorological observations obtained from NOS’ 

Operational Data Acquisition and Archive System (ODAAS). ODAAS acquires 

meteorological observations from NWS/NCEP/NCO’s observational ‘data tanks’ located on 

NCEP’s Central Computer Systems (CCS) twice per hour at approximately 25 and 48 minutes 

past the top of the hour. The observations are original in unblocked BUFR format but are 

reblocked, decoded, and written out to a text file for use by LEOFS and other NOS operational 

forecast systems. The surface observation text file is available to LEOFS within a minute of 

receiving the observations from the CCS. 

The surface observations are obtained from a variety of observing networks on and around Lake 

Erie. On land, the networks include Automated Surface Observing System (ASOS), CoastalMarine Automated Network (C-MAN), and NOS National Water Level Observing Network 

(NWLON). Presently, the surface meteorological observations from USCG stations around the 

lake are not available in the NCEP data tanks. 

Over water, the networks include the NWS/NDBC’s and Environment Canada’s fixed buoys as 

well as observations from ships participating in the Voluntary Observing Ship (VOS) program. 

However, observations from VOS ships are not presently used by LEOFS at NOS. 

3.4. Nowcast Cycle 

The nowcast cycle of LEOFS is run hourly at NOS to generate updated nowcasts of the 3-D state 

of Lake Erie, including 3-D water temperatures and currents. The cycle also generates hourly 

nowcasts of 2-D water levels. 

The initial conditions for the nowcast cycle are provided by the end of the previous hour’s 

nowcast cycle. The nowcast cycle is forced by gridded surface meteorological analyses valid at 

two times, one hour prior to the time of the nowcast and the current time of the nowcast. The 

gridded surface meteorological analyses are generated by interpolating surface observations of 

wind, air temperature, dew point temperature, and cloud cover using the natural neighbor 

technique (Sambridge et al. 1995). This is accomplished by the program interpnn.f. 

Before being interpolated, the surface wind and air temperature observations are adjusted to a 

common anemometer height of 10 m above the ground or water. Surface observations of wind 

direction, wind speed, air temperature, and dew point temperature from overland stations are 

adjusted to be more representative of overwater conditions. Both the height adjustment and 

adjustment of observations from overland stations uses the previous day’s lake average water 

temperature from GLERL’s Great Lakes Surface Environmental Analysis (GLSEA). The 

GLSEA temperature analysis is generated using SST retrievals derived from the Advanced High 

Resolution Radiometer data obtained from NOAA’s polar-orbiter satellites. The adjustments to 

the observations along with simple quality control checks are done by the program 

edit_sfcmarobs.f 

The gridded surface wind fields are then used by POMGL to calculate wind stress at each model 

5
grid point. The surface meteorological fields along with POMGL lake surface water 

temperatures predictions from POMGL are used by a heat flux scheme (McCormick and 

Meadows (1988) to estimate the net rate of heat transfer for the lake at each grid point. The heat 

flux scheme can be found in POMGL’s subroutine FLUX1. Additional information on the wind 

stress and heat flux schemes can be found in Kelley (1995). 

3.5. Forecast Cycle 

The forecast cycle of LEOFS is run four times per day to generate forecast guidance of the 3-D 

state of Lake Erie. The forecast cycle uses the most recent nowcast as its initial conditions. The 

surface meteorological forcing is provided by the latest forecast guidance of surface (10 m AGL) 

u- and v-wind components and surface air temperature (2 m AGL) from the 0, 06, 12, or 18 UTC 

forecast cycles of NWS/NCEP’s North American Mesoscale (NAM) model. Presently, NAM 

has a spatial resolution of 12 km and uses the Eta model as its core. The surface wind velocity 

forecast guidance from the NAM model is valid at a height of 10 m above the ground or lake 

surface. 

The NAM model forecast guidance is obtained from ODAAS which acquires the NAM output 

from NCEP’s CCS in GRIB format four times per day at 3 hour increments out to 60 hours. 

ODAAS decodes the GRIB files and then encodes the output into NetCDF files following NOS 

COMF standards (Gross and Lin 2007). 

3.6. Operational Environment and Scheduling 

LEOFS is run operational on a Linux workstation at NOS’ Center for Operational 

Oceanographic Products and Services in Silver Spring, MD. Each hourly nowcast cycle is 

launched at 50 minutes past the top of the hour, three minutes after the surface meteorological 

observations are received and processed by ODAAS at CO-OPS. 

The forecast cycle of LEOFS is run four times per day at 0, 6, 12, and 18 UTC at 50 minutes past 

the top of the hour. The forecast horizon of each forecast cycle is 30 hours. 

LEOFS and the forecast system for Lake Michigan (LMOFS) were officially implemented as an 

operational forecast system at CO-OPS on the afternoon of September 30, 2005, and the products 

are available to the general public at http://tidesandcurrents.noaa.gov. 

4. HINDCAST SKILL ASSESSMENT 

NOS standards (Hess et al. 2003) require the hydrodynamic model of any NOS nowcast/forecast 

system to run under hindcast scenario. A hindcast is defined as a long simulation using the best 

available gap-filled data for observed boundary water levels, wind, and river flows. 

6
For LEOFS, the simulations and evaluation performed by Kuan (1995a, 1995b) will serve as the 

basis for the hindcast scenario assessment. Kuan preformed model simulations during a six 

month period for the year 1979 when there was extensive field data available for verifying 

simulations at both the surface and subsurface. Kuan’s dissertation and book chapter were 

reviewed and summarized in the following section. 

4.1. Description of Hindcast Runs 

The hindcast model simulations conducted by Kuan differ from the present pre-operational 

nowcasts done at the NOS CSDL and CO-OPS. First, the configuration of the POM model in 

Kuan’s simulations used a 2 kilometer spatial resolution while the LEOFS is at 5 kilometer. 

Second, the number of vertical sigma layers was 14 while LEOFS has 11 layers. Finally, Kuan 

used the more traditional two-pass Barnes spatial interpolation technique for interpolating the 

surface meteorological observations while the LEOFS uses GLERL’s natural neighbor 

interpolation scheme. However, both Kuan’s simulations and LEOFS used GLERL’s heat flux 

model to generate gridded heat flux fields for input to POM. 

4.2. Method of Evaluation 

Kuan (1995a, 1995b) applied several methods to evaluate the performance of POMGL, including 

the traditional Root Mean Square Error (RMSE), an Index of Agreement (IOA), a skill score, and 

Empirical Orthogonal Function (EOF) or Principal Component Analysis. 

A map depicting the locations of observing sites in Lake Erie where Kuan compared model 

simulations to observations is given in Fig. 3. 

In the following section, results from the RMSE, IOA and skill score assessment are 

summarized and tabulated. One of the most widely used statistical measurements between the 

observed data and the predictive variable is the RSME, basically the measurement of the 

difference or distance between the computed values and the corresponding observed variables. 

The IOA is a relative measures that reflects the degree to which the observed values is accurately 

estimated by the simulated variables. The skill score uses set of non-parametric based statistical 

tools developed by Dingman and Bedford (1986) to assess the credibility of the model in 

simulating major water level events. In estimating the skill score one point is deducted from a 

scale of 10 for every 0.05 meter difference between the observed value and the computed value 

with a minimum score of 0 when the difference is greater than 0.5 meter. This method can also 

be used to access the performance for water temperature and current velocity where 1 point is 

deducted for every 0.5 degree temperature variation and every 1cm/sec deviation respectively. 

Table 1 lists the skill score rule for water level simulation. 

7
Figure 3. Map depicting locations of observing sites where POMGL simulations were 

evaluated at (Kuan, 1995b). 

Table 1. Skill scores for assessing the difference between simulated and and observed 

water levels. 

Assigned Point 

Value 

Absolute Difference between the Predicted (P) and Observed (O) 

Water Level Values (cm) 

10 | P – O| < 5 

9 5 <= | P – O| < 10 

8 10 <= | P – O| < 15 

7 15 <= | P – O| < 20 

6 20 <= | P – O| < 25 

5 25 <= | P – O| < 30 

4 30 <= | P – O| < 35 

3 35 <= | P – O| < 40 

2 40 <= | P – O| < 45 

1 45 <= | P – O| < 50 

0 50 <= | P – O| 

8
4.3. Skill Assessment of Surface Hindcasts 

Skill assessments of water level, current velocity, and surface water temperature simulations 

were performed by Kuan (1995b). Model performance is categorized for overall skills, seasonal 

performance and skill during strong wind events and between storm events. Evaluation statistics 

from Kuan (1995b) are summarized and tabulated in the following sections. 

4.3.1. Water Levels 

Model simulations of water levels at the model grid points closest to NOS water level gauges at 

Buffalo, NY, Cleveland, OH, and Toledo, OH were compared with the observed values at the 

corresponding gauges. The measures used in the evaluations are the statistical measures and the 

amplitude skill scores. The RMSE for water elevation hindcasts during the entire evaluation 

period (May 29 to October 26, 1979) at NOS water level gauges at Buffalo, Cleveland, and 

Toledo were 4.82cm, 3.0 cm and 6.18 cm, respectively. The amplitude skill scores for the entire 

test period were 9.73, 9.91 and 9.52 for Buffalo, Cleveland, and Toledo, respectively. 

Table 2. Summary of water level hindcast evaluation statistics by NOS gauge station for 5 

time periods in 1979. 

Period 

Days of Year Calendar Days 

NOS Station 

Name 

RMSE 

(cm) 

Skill Score 

(0-10) 

149-177 5/29-6/28/79 Buffalo 4.05 9.81 

149-177 5/29-6/28/79 Cleveland 2.89 9.93 

149-177 5/29-6/28/79 Toledo 5.51 9.61 

179-207 6/28-7/28/79 Buffalo 3.84 9.85 

179-207 6/28-7/28/79 Cleveland 2.65 9.93 

179-207 6/28-7/28/79 Toledo 4.97 9.64 

209-237 7/28-8/27/79 Buffalo 4.54 9.73 

209-237 7/28-8/27/79 Cleveland 2.94 9.90 

209-237 7/28-8/27/79 Toledo 6.27 9.51 

239-267 8/27-9/26/79 Buffalo 4.25 9.77 

239-267 8/27-9/26/79 Cleveland 2.37 9.96 

239-267 8/27-9/26/79 Toledo 5.73 9.57 

269-297 9/26-10/26/79 Buffalo 6.80 9.49 

269-297 9/26-10/26/79 Cleveland 3.93 9.82 

269-297 9/26-10/26/79 Toledo 7.99 9.27 

A detailed evaluation on a seasonal basis was also performed to evaluate water level simulations 

during the heating, stratified and cooling seasons (Table 3). Heating season was defined from 

May 29 (Day 149) to July 23 (Day 204). The stratified season began on July 24 (Day 205) and 

ended on September 6 (Day 249) and the cooling season began at the end of stratified season. 

9
Table 3. Seasonal evaluation statistics for water level simulations during heating, 

stratified, and cooling seasons in 1979. 

Season NOS 

Station 

RMSE 

(cm) 

IOA Skill Score 

Heating Buffalo 3.95 0.89 9.83 

“ Cleveland 2.79 0.72 9.93 

“ Toledo 5.28 0.92 9.62 

Stratified Buffalo 4.27 0.96 9.77 

“ Cleveland 2.74 0.88 9.92 

“ Toledo 5.69 0.94 9.58 

Cooling Buffalo 6.01 0.95 9.58 

“ Cleveland 3.41 0.94 9.87 

“ Toledo 7.41 0.96 9.35 

The RMSE and amplitude skill scores indicate that the water level simulations are equally good 

for all seasons. The average RMSE between the computed and observed values were 4.01cm, 

4.23 cm, and 5.61 cm with the corresponding average Skill Scores of 9.97, 9.76 and 9.60 for the 

heating, stratified and cooling seasons, respectively. 

In addition to the overall and seasonal evaluation, an evaluation based on episodic strong storm 

events was also performed by Kuan. The statistics are summarized in Table 4. 

Table 4. Evaluation statistics for water level simulations at NOS water level gauge stations 

during the inter-storm and stormy periods during 1979. 

Period Location RMSE (cm) IOA Skill Score 

Inter-storm Buffalo 2.76 0.84 9.94 

“ Cleveland 2.33 0.62 9.98 

“ Toledo 2.59 0.83 9.82 

“ Average 2.86 0.76 9.91 

Stormy Buffalo 82.7 0.91 9.24 

“ Cleveland 4.70 0.85 9.72 

“ Toledo 9.68 0.91 8.97 

“ Average 7.55 0.89 9.31 

10
The results showed that POMGL performed better for the inter-storm period in terms of RSME 

and the amplitude skill scores. The IOA, on the other hand, gave an average value of 0.89 for the 

stormy periods and 0.76 for the inter-storm periods. 

4.3.2. Surface Water Temperatures

To evaluate POMGL’s performance in simulating lake surface temperatures, field data measured 

at 6 CCIW meteorological buoys were used by Kuan. RMSE, IOA and skill scores for all 6 

buoys over the entire test period are summarized in Table 5. 

Table 5. Evaluation statistics for surface water temperature simulations during 1979 at 

CCIW buoys. 

CCIW Buoy ID RMSE (oC) IOA Skill Score 

NWRI19A 1.16 0.97 8.66 

NWRI24A 1.07 0.97 8.65 

NWRI26A 0.92 0.98 8.98 

NWRI42A 1.06 0.97 8.69 

NWRI46A 0.90 0.98 9.04 

NWRI47A 0.87 0.98 9.02 

Throughout the entire test period the POMGL simulations matched very well with the observed 

data at all six buoys. Each fluctuation found in the observed data was reproduced in the 

simulation, although slight differences existed between the observed and computed values in 

terms of magnitude. Occasionally, the model simulations deviated more than 2 o

C from the 

observed data and most of the significant discrepancies were over predictions (i.e. too warm). 

From Table 5, the average skill score was 8.84 and the average IOA was 0.97, both indices 

showed good agreement between the predicted and observed values. The average RMSE was 

only 1o

C which is also an indication that POMGL simulated lake surface temperature very well. 

In general, the POMGL performed equally well in simulating surface water temperature during 

different seasons. The computed values followed the trend of the observed data closely. 

According to Kuan, if a critique must be made, the performance of the model for the cooling 

season was a bit weaker than the other two seasons and depending upon buoy locations. During 

the cooling season, surface water temperatures were consistently over predicted by about 1.5o C. 

As seen in the in Tables 6 and 7, the model had better skill, in term of RMSE and skill scores for 

the heating and stratified seasons than the cooling season. In addition, the stratified season 

maintained a lower IOA than the other two. 

11
Table 6. Seasonal evaluation statistics for surface water temperature simulations by 

heating, stratified, and cooling seasons in 1979. 

Season CCIW Buoy ID RMSE (o

C) IOA Skill Score 

Heating NWRI19A 1.07 0.98 9.01 

“ NWRI24A 1.00 0.97 8.77 

“ NWRI26A 1.25 0.96 8.43 

“ NWRI42A 0.96 0.97 8.97 

“ NWRI46A 1.04 0.97 8.82 

“ NWRI47A 0.99 0.98 8.83 

Stratified NWRI19A 0.99 0.83 8.90 

“ NWRI24A 1.12 0.79 8.74 

“ NWRI26A 0.56 0.94 9.51 

“ NWRI42A 0.81 0.89 9.22 

“ NWRI46A 0.60 0.93 9.47 

“ NWRI47A 0.74 0.89 9.34 

Cooling NWRI19A 1.41 0.89 8.05 

“ NWRI24A 1.13 0.95 8.44 

“ NWRI26A 0.80 0.97 9.11 

“ NWRI42A 1.38 0.95 7.89 

“ NWRI46A 1.00 0.96 8.89 

“ NWRI47A 0.86 0.97 8.93 

Table 7. Average seasonal analysis results for surface water temperature simulations. 

Heating 

Season 

Stratified 

Season 

Cooling 

Season 

RMSE 

(

o

C) 

IOA Skill 

Score 

RMSE 

(

o

C) 

IOA Skill 

Score 

RMSE 

(

o

C) 

IOA Skill 

Score 

1.05 0.97 8.81 0.80 0.88 9.20 1.10 0.95 8.55 

Simulations of surface water temperatures were also evaluated for inter-storm and stormy 

periods. The results (Table 8) indicate better scores in each category for the storm event 

simulations. 

12
Table 8. Evaluation statistics for surface water temperature simulation during stormy and 

inter-storm periods in 1979 at CCIW Buoys. 

Season CCIW Buoy ID RMSE (oC) IOA Skill Score 

Inter-storm NWRI19A 1.39 0.73 7.85 

“ NWRI24A 1.48 0.74 7.65 

“ NWRI26A 1.32 0.74 7.88 

“ NWRI42A 1.56 0.74 7.51 

“ NWRI46A 1.59 0.73 7.48 

“ NWRI47A 1.39 0.73 7.82 

“ Average 1.46 0.74 7.70 

Stormy NWRI19A 1.89 0.75 6.82 

“ NWRI24A 1.35 0.86 7.88 

“ NWRI26A 0.82 0.93 8.91 

“ NWRI42A 1.45 0.89 7.71 

“ NWRI46A 1.29 0.86 8.04 

“ NWRI47A 0.65 0.96 9.01 

“ Average 1.24 0.88 8.06 

4.3.3. Surface Currents 

Since observed surface current data were not available, the surface current velocities were not 

evaluated by Kuan. However, subsurface currents at 18 different locations across the lake were 

used by Kuan to assess the skill of POMGL to simulate sub-surface currents. A summary of 

Kuan’s evaluation of subsurface currents is given in section 4.4.1. 

4.4. Skill Assessment of Sub-Surface Hindcasts 

4.4.1. Sub-Surface Currents 

In order to evaluate POMGL’s performance of current velocities and flow behaviors at a 

transect, direct point-by-point comparisons were made to measurements for the cross section 

areas C-C’ and E-E’ in Fig. 3. Data from 14 current meters over section C-C’ and 4 current 

meters over section E-E’ were used for comparison purpose. Data from those 18 current meters 

were converted into both the u- and v-current components, and the model simulated values were 

interpolated from the surrounding grid point at sigma levels based on the current meters’ 

deployment depth. 

13
Current velocity simulations are examined through the used of statistical measure and the Skill 

Scores. The RMSE and the skill score for each current meter over the whole testing period are 

summarized in the Tables 9 and 10. The last two digits of each meter’s thermistor ID represent 

the depth in meters where the instrument was deployed. For example, the 09N10 current meter 

was at 10 meters below the water surface. 

Table 9. Evaluation statistics for the simulation of current velocity u-component in 1979 

for two cross sections. See Fig. 3 for the location of the cross sections. 

Cross Section Station ID RMSE (cm/sec) Skill Score 

C-C’ 09N10 5.62 6.53 

“ 10N10 5.23 6.58 

“ 27A10 4.49 7.10 

“ 11N10 6.08 6.08 

“ 34A10 5.11 6.66 

“ 12N10 6.21 5.74 

“ 35A10 7.68 4.68 

“ 35A14 5.67 6.20 

“ 27A15 7.02 5.79 

“ 34A18 5.01 6.81 

“ 34A19 3.97 7.46 

“ 27A20 5.84 6.35 

“ 11N21 3.57 7.63 

“ 10N22 3.54 7.68 

E-E’ 28N10 7.91 5.15 

“ 29N10 7.30 5.17 

“ 29N25 8.02 5.06 

“ 28N33 5.84 6.16 

Table 10. Evaluation statistics for the simulation of current velocity v-component in 1979 

at two cross sections. See Fig. 3 for the location of the cross sections. 

Cross Section Station ID RMSE (cm/sec) Skill Score 

C-C’ 09N10 5.03 7.00 

“ 10N10 5.25 6.68 

“ 27A10 4.43 7.15 

“ 11N10 5.68 6.37 

“ 34A10 3.79 7.71 

“ 12N10 3.78 7.88 

“ 35A10 3.91 7.63 

“ 35A14 3.93 7.61 

“ 27A15 6.40 5.97 

“ 34A18 4.26 7.41 

“ 34A19 3.02 8.17 

“ 27A20 5.50 6.44 

“ 11N21 3.00 8.04 

“ 10N22 3.09 8.01 

E-E’ 28N10 6.93 5.32 

“ 29N10 6.54 5.86 

“ 29N25 6.79 5.71 

“ 28N33 5.35 6.53 

14
Table 11. Summary of evaluation statistics for velocity simulations by season in 1979. 

Location Heating Season Stratified Season Cooling Season 

RMSE 

(cm/s) 

IOA Skill 

Score 

RMSE 

(cm/s) 

IOA Skill 

Score 

RMSE 

(cm/s) 

IOA Skill 

Score 

C-C’x 5.12 0.72 6.62 5.83 0.69 6.17 5.03 0.71 6.82 

C-C’y 4.21 0.69 7.36 4.99 0.60 6.82 3.73 0.59 7.65 

E-E’x 5.75 0.74 6.14 7.65 0.71 5.27 8.23 0.62 4.71 

E-E’y 5.78 0.74 6.16 7.40 0.71 5.09 5.88 0.59 6.17 

The average RMSE and skill scores showed similar model skill for heating and cooling seasons 

at C-C’ for both u and v components but only for the v component along E-E’ (see Table 11). 

Simulations during the stratified season show the lowest scores for both cross sections and both 

directions, except the u component at E-E’. The IOA also showed better skill in the heating 

season than other seasons and the cooling season had the lowest IOA. 

The average RMSE of u, v component at C-C’ are smaller during the inter-storm period than that 

of the stormy period (see Tables 12, 13, 14 and 15). The average RMSE for u and v components 

at inter-storm period are 3.41 and 2.72 cm/sec while the average RMSE for u and v components 

at stormy period are 5.55 and 4.05, respectively. However, the average skill score during stormy 

period shows better skill than the score during inter-storm period for both u and v components. 

Table 12. Evaluation statistics for u-component current velocity at C-C’ during the interstorm period in 1979. 

Current meter 

ID 

RMSE (cm/s) IOA Amp. Skill 

Score 

09N10 3.14 0.50 8.04 

10N10 3.64 0.59 7.65 

27A10 2.87 0.64 8.28 

11N10 4.28 0.51 6.83 

34A10 2.87 0.65 8.04 

12N10 3.32 0.71 7.68 

35A10 4.53 0.70 6.63 

35A14 3.49 0.72 7.51 

27A15 3.14 0.72 8.20 

34A18 2.15 0.78 8.70 

34A19 3.68 0.71 7.18 

27A20 3.11 0.77 8.17 

11N21 3.49 0.69 7.72 

10N22 4.04 0.64 7.41 

Average 3.41 0.67 7.72 

15
Table 13. Evaluation statistics for u-component current velocity at C-C’ during the stormy 

period in 1979. 

Current meter 

ID 

RMSE (cm/s) IOA Amp. Skill 

Score 

09N10 3.95 0.77 7.37 

10N10 4.20 0.75 7.09 

27A10 3.85 0.74 7.43 

11N10 5.93 0.37 5.63 

34A10 3.92 0.78 7.38 

12N10 5.49 0.72 5.81 

35A10 6.58 0.62 5.24 

35A14 6.20 0.63 5.72 

27A15 9.17 0.41 4.56 

34A18 5.64 0.70 6.27 

34A19 5.63 0.71 6.05 

27A20 8.08 0.47 4.53 

11N21 5.33 0.66 6.03 

10N22 3.77 0.71 7.36 

Average 5.55 0.65 6.18 

Table 14. Evaluation statistics for v-component current velocity at C-C’ during interstorm periods in 1979. 

Current meter 

ID 

RMSE (cm/s) IOA Amp. Skill 

Score 

09N10 2.70 0.53 8.30 

10N10 2.52 0.58 8.47 

27A10 3.57 0.65 7.56 

11N10 3.38 0.50 7.84 

34A10 2.16 0.57 8.88 

12N10 1.90 0.65 9.00 

35A10 1.98 0.70 8.98 

35A14 1.85 0.77 8.94 

27A15 3.59 0.70 7.41 

34A18 1.66 0.83 9.11 

34A19 2.34 0.52 8.53 

27A20 3.70 0.76 7.23 

11N21 3.26 0.70 7.53 

10N22 3.46 0.69 7.74 

Average 2.72 0.65 8.25 

16
Table 15. Evaluation statistics for v-component current velocity at C-C’ during the stormy 

periods in 1979. 

Current meter 

ID 

RMSE (cm/s) IOA Amp. Skill 

Score 

09N10 3.45 0.47 7.73 

10N10 2.95 0.52 8.04 

27A10 3.26 0.55 7.80 

11N10 7.91 0.69 3.99 

34A10 3.11 0.50 8.14 

12N10 1.85 0.78 8.92 

35A10 3.01 0.62 8.09 

35A14 2.99 0.65 8.26 

27A15 7.11 0.36 5.42 

34A18 4.68 0.48 7.12 

34A19 3.63 0.62 7.60 

27A20 7.10 0.46 5.23 

11N21 2.83 0.76 8.14 

10N22 2.81 0.61 8.29 

Average 4.05 0.58 7.34 

4.4.2. Sub-Surface Water Temperatures 

POMGL water temperature simulations at six meters or deeper were examined at two thermistor 

locations deployed in the western basin and also at thermistors over the same cross sectional 

areas for the current evaluations in the central (C-C’) and eastern (E-E’) basin. The observed data 

for comparison included water temperatures from twenty-three measurement locations: two in 

the western basin, 16 in the central basin and five in the eastern basin. The overall performance 

of POMGL in simulating sub-surface water temperatures in the western basin was more than 

acceptable. This conclusion was also supported by the results of the statistical analysis and 

amplitude skill test. Compared with the observation, the average skill score was 8.2, the average 

IOA was 0.95, and the average RMSE was only 1.35oC for the entire period (Table 16). 

Table 17 shows the statistics for water temperature at selected water column depth for all the 

thermistors listed on Table 16. The RMSE, IOA and skill scores are excellent when depths are 

less than 20 meters while RMSE increases and IOA decreases as the water depth exceeds 20 

meters. The results clearly show the model’s limitation in reproducing thermal structure in the 

deeper portion of the water body. 

17
Table 16. Evaluation statistics for water temperature simulations by thermistor location 

during 1979. 

Thermistor ID RMSE (oC) IOA Skill Score 

01N06 1.29 0.96 8.31 

02N07 1.44 0.94 8.10 

09N10 1.69 0.94 7.88 

10N10 1.33 0.96 8.46 

27A10 1.28 0.97 8.57 

34A10 1.33 0.97 8.24 

12N10 1.47 0.95 8.02 

35A10 1.91 0.93 7.27 

35A14 1.49 0.95 7.95 

27A15 0.85 0.99 9.07 

10N17 1.68 0.95 8.04 

12N17 3.01 0.82 6.57 

34A18 2.07 0.93 7.95 

34A19 3.74 0.80 5.52 

27A20 4.04 0.81 5.82 

09N21 5.13 0.73 4.05 

11N21 5.14 0.74 4.16 

10N22 4.61 0.77 4.53 

28N10 1.37 0.96 8.42 

29N10 1.65 0.95 7.74 

29N25 3.53 0.85 5.64 

28N33 8.25 0.71 1.52 

29N38 9.30 0.71 1.05 

Table 17. Average evaluation statistics for water temperature at selected water column 

depths for 1979. 

Water Depth RMSE (o

C) IOA Skill Score 

D < 15m 1.42 0.96 8.17 

15m < D < 20m 2.90 0.86 6.78 

20m < D < 25m 4.83 0.77 4.60 

25m < D 8.78 0.71 1.29 

The average values of the RMSE, IOA and skill scores at various depths during different seasons 

are listed in Table 18 and 19. The model’s skill decreased as the depth increases, and the model 

had the poorest skill during the stratified season in each of the categories. 

18
Table 18. Average seasonal analysis results for water column simulations in 1979. 

Heating 

Season 

Stratified 

Season 

Cooling 

Season 

Location 

RMSE 

(oC) 

IOA Score RMSE IOA Score RMSE IOA Score 

Western 0.81 0.98 9.10 0.79 0.87 9.03 2.14 0.90 6.23 

C-C’x 1.96 0.86 7.12 3.69 0.71 5.15 1.48 0.89 8.56 

E-E’x 3.60 0.81 4.75 5.75 0.70 4.12 4.81 0.79 5.68 

Table 19. Seasonal average evaluation statistics for water temperature simulations at 

selected water column depths for 1979. 

Heating 

Season 

Stratified 

Season 

Cooling 

Season 

Depth 

RMSE 

(

o

C) 

IOA Skill 

Score 

RMSE 

(

o

C) 

IOA Skill 

Score 

RMSE 

(

o

C) 

IOA Skill 

Score 

D < 15m 1.36 0.94 8.18 1.61 0.73 7.73 1.08 0.95 8.52 

15m<D< 

20m 

2.09 0.84 7.01 4.37 0.68 4.62 1.56 0.87 8.47 

20m<D< 

25m 

3.28 0.74 4.85 6.78 0.71 1.28 3.01 0.71 7.30 

25m < D 5.58 0.73 1.62 10.61 0.75 0.00 9.62 0.69 2.08 

Similar to the lake surface temperature field, the water temperatures along the C-C’ section were 

over predicted by the model at all thermistor locations during both the inter-storm and stormy 

periods (Table 20 and 21). The RMSE, IOA and amplitude skill scores show the model 

performed slightly better for the stormy period than for the inter-storm period. 

19
Table 20. Evaluation statistics for water temperature simulations at C-C’ during interstorm periods in 1979. 

Current meter 

ID 

RMSE (oC) IOA Skill Score 

09N10 1.12 0.74 8.22 

10N10 0.95 0.75 8.61 

27A10 0.81 0.75 9.00 

34A10 0.94 0.75 8.70 

12N10 1.02 0.75 8.33 

35A10 0.89 0.75 8.72 

35A14 1.18 0.74 8.08 

27A15 0.98 0.75 8.50 

10N17 0.94 0.75 8.62 

12N17 1.28 0.74 7.91 

34A18 1.23 0.74 7.89 

34A19 1.19 0.74 8.35 

27A20 0.98 0.75 8.57 

09N21 1.24 0.75 7.91 

11N21 1.14 0.75 8.00 

10N22 1.01 0.75 8.60 

Average 1.06 0.75 8.37 

Table 21. Evaluation statistics for water temperature simulation at C-C’ during the stormy 

periods in 1979. 

Current meter 

ID 

RMSE (oC) IOA Skill Score 

09N10 1.26 0.88 7.96 

10N10 1.00 0.90 8.57 

27A10 0.68 0.95 9.20 

34A10 0.73 0.95 8.92 

12N10 0.82 0.94 8.75 

35A10 0.90 0.93 8.73 

35A14 0.89 0.93 8.68 

27A15 0.75 0.94 8.99 

10N17 1.03 0.90 8.47 

12N17 0.79 0.94 8.96 

34A18 0.76 0.94 8.96 

34A19 0.70 0.95 9.11 

27A20 0.76 0.94 8.99 

09N21 1.31 0.87 7.90 

11N21 0.74 0.94 9.07 

10N22 1.05 0.89 8.51 

Average 0.89 0.93 8.74 

20
4.5. Summary 

The extensive evaluation by Kuan (1995a, 1995b) has demonstrated the ability of POMGL to 

reproduce the barotropic motions of the lake in terms of water surface elevations in the Lake 

Erie. 

Several specific conclusions were drawn from Kuan’s study. The simulated water surface 

elevation matched well in both phase and magnitude with the corresponding observed data by 

picking up almost every single significant spike appearing in the observed water level elevations. 

The average RMSE was quite small indicating that the model can be considered quite well in 

simulating water surface elevation of the lake. The average IOA and amplitude skill score also 

supported this, with high values of 0.95 and 9.72, respectively. It should be emphasized that the 

water level fluctuations were caused by random wind stress fields and that the persistent strong 

low frequency, repetitious tidal physics found in marine or continental shelf applications did not 

exist in the lake simulations. The result was very encouraging as all the water level fluctuations 

result from probabilistic wind fields, not from deterministic tidal motions. The average RMSE 

of only 4.67 cm over the whole 150 day simulation period indicated that the major features of the 

wind driven barotropic response have been simulated well. 

For the current velocity simulation, except for the nearshore zone and the very unsteady flow 

region, the predictions over C-C’ were quite satisfactory. Good simulation both in phase and 

magnitude can be found at most of the current meter locations. The velocity predictions over EE’ were not as good as those for C-C’ likely due to the fact that there was only one over water 

meteorological buoy in the eastern basin while there were five buoys in the central basin. The 

interpolated meteorological fields in the eastern basin were therefore not as good as the central 

basin. The estimated momentum and heat fluxes were all derived from the interpolated 

meteorological fields, thus the eastern basin results were not as accurate as the central basin 

simulation. However, very good agreement both in phase and magnitude between the observed 

and computed values can be found occasionally during the strong wind stress events. 

As for lake surface temperature, the model predictions compared exceptionally well with the 

observed data at all six CCIW meteorological buoy locations. The simulated values not only 

depicted almost every single spike found in the observed data, but the average RMSE was as low 

as 1o

C over the entire test period of 150 days. The model also demonstrated good skill at 

predicting water temperatures in the deeper portion of the lake. When the lake was well mixed, 

the model predictions were as good as those for the lake surface. However, when lake was 

thermally stratified, the simulations under predicted the water temperature above the thermocline 

region and over predicted the water temperature in the hypolimnion. This result was due to the 

model’s inability to reproduce the thermocline structure with the given vertical grid resolution. 

In addition, evaluations also showed that improper calculation of the lake thermal structure did 

not seem to affect the model ability to simulate lake water levels as the model attained similar 

Skill Scores for each of the seasons. However, the results did indicate the model performed 

better during the heating and cooling seasons than the stratified season for the current velocity 

simulation, though the difference was marginal. 

21
For water temperature simulations, the model performed equally well during the heating, cooling 

and stratified seasons with a consistent slight over prediction at all six CCIW buoys during the 

cooling season. There was no evidence that the thermal structure of the lake significantly 

affected the model’s capability in predicting the lake water surface temperature. The results also 

indicated that the model performed better in simulating water levels and current velocity during 

the inter storm period, while it had better skill scores for water temperature at both the surface 

and deeper portion of the lake during the stormy period. 

A comprehensive summary of model skill for all variables based on seasonal variation is given in 

Table 22. Summary of IOA for all the variables categorized by seasons is given in Table 23. 

Table 22. Comprehensive seasonal model skill in terms of the amplitude skill score. 

Variable Heating Season Stratified Season Cooling Season 

Water level 9.793 9.757 9.600 

Current velocity 6.80 6.201 6.837 

Surface water temp. 8.805 9.197 8.552 

Deep water temp. 7.424 6.180 8.490 

Overall Skill Score 8.206 7.834 8.370 

Table 23. Comprehensive seasonal model skill analysis in terms of the IOA. 

Variable Heating Season Stratified Season Cooling Season 

Water level 0.84 0.93 0.95 

Current velocity 0.71 0.66 0.64 

Surface water temp. 0.97 0.88 0.95 

Deep water temp. 0.88 0.72 0.89 

Overall IOA 0.85 0.80 0.86 

In summary, Kuan concluded that the difference between the POMGL water level and surface 

temperature simulations and observations were quite small. Kuan’s results also indicated that 

the model simulation results compared well in both phase and magnitude with the observed data. 

Temperature and current velocity in the deeper portion of the lake also compared well with the 

observation data. However, model simulations of temperature and velocity in the nearshore did 

not compare well to observations indicating that the simulation in the coastal and thermocline 

regions were not correct.

22
5. SEMI-OPERATIONAL NOWCAST SKILL ASSESSMENT 

This section describes the model system performance based on NOS requirements of an 

operational nowcast/forecast system (Hess et al. 2003). According to Hess et al. (2003), the 

definition of the model run scenario for a semi-operational nowcast is the following: 

“In this scenario, the model is forced with actual observational input data streams including open 

ocean boundary water levels, wind stresses, river flows, and water density variations. Significant 

portions of the data may be missing, so the model must be able to handle this.” (In the case of 

non-tidal water bodies as the Great Lakes, the data streams could include wind stresses, surface 

heat flux, and river flows.) 

LEOFS, as described in Chapter 2, is based on NOAA/GLERL’s Great Lakes Coastal Forecast 

System (GLCFS) for Lake Erie. Both LEOFS and GLCFS for Lake Erie have a spatial grid 

increment of 5 km and 11 sigma layers and use similar surface meteorological forcing. Neither 

of the systems employed any river inflow or assimilated any limnologic data. 

Due to the similar characteristics of LEOFS and GLCFS, the assessment of the LEOFS semioperational nowcasts was done using GLERL’s archived nowcasts from GLCFS four times/day 

nowcast cycles. 

This section includes a description of the GLCFS nowcast cycles, method of evaluation 

including time period and assessment statistics, and the evaluation results. 

5.1. Description of Nowcast Cycles 

GLCFS performs four times/day nowcasts for Lake Erie, and the other four Great Lakes year 

round. The surface forcing for the nowcast cycles are provided by objective analyses of surface 

meteorological observations from land-based and overwater observing stations. The four 

nowcast cycles produce nowcasts valid at 0000, 0600, 1200, and 1800 UTC each day. The 

nowcast cycles are launched at approximately 80 minutes past the valid time of the nowcasts. 

For example, the nowcast cycle to generate a nowcast valid at 0000 UTC is launched at 0120 

UTC to allow for observations from late reporting Canadian fixed buoys and NDBC C-MAN 

stations to be received at GLERL via NOAAPORT. Hourly model results are archived at 

GLERL. 

5.2. Method of Evaluation 

The hourly model results from the GLCFS nowcasts were compared to observations from coastal 

and offshore observing platforms in the lake for the period from mid-April to mid-December 

2004. This was a period when there was no significant ice cover on the lake. 

The evaluation used the standard NOS suite of assessment statistics, as defined in Hess et al. 

(2003). The standard suite of statistics is given in Table 24. The target frequencies of the 

associated statistics are the following: 

23
 CF(X) ≥90%, POF(2X) ≤1%, NOF(2X) ≤1%, WOF(2X) ≤0.5%, 

 MDPO(2X) ≤ L, MDNO(2X) ≤ L 

Table 24. NOS Skill Assessment Statistics (Hess et al. 2003). 

Variable Explanation 

Error The error is defined as the predicted value, p, minus the reference (observed or astronomical tide 

value, r : ei = pi - ri. 

SM Series Mean. The mean value of a series y. Calculated as y N yi

i

N

= =

∑1

1

. 

RMSE Root Mean Square Error. Calculated as RMSE e N i

i

N

= =

∑1 2

1

.

SD Standard Deviation. Calculated as SD e e N i

i

N

= − −

=

∑1

1

1

2 ( )

CF(X) Central Frequency. Fraction (percentage) of errors that lie within the limits +X. 

POF(X) Positive Outlier Frequency. Fraction (percentage) of errors that are greater than X. 

NOF(X) Negative Outlier Frequency. Fraction (percentage) of errors that are less than -X. 

MDPO(X) Maximum Duration of Positive Outliers. A positive outlier event is two or more consecutive 

occurrences of an error greater than X. MDPO is the length of time (based on the number of 

consecutive occurrences) of the longest event. 

MDNO(X) Maximum Duration of Negative Outliers. A negative outlier event is two or more consecutive 

occurrences of an error less than -X. MDNO is the length of time (based on the number of 

consecutive occurrences) of the longest event. 

WOF(X) Worst Case Outlier Frequency. Fraction (percentage) of errors that, given an error of magnitude 

exceeding X, either (1) the simulated value of water level is greater than the astronomical tide 

and the observed value is less than the astronomical tide, or (2) the simulated value of water level 

is less than the astronomical tide and the observed value is greater than the astronomical tide. 

Notes: 

X is defined for different variables in Table 26. 

There are three types of data sets (Table 25): Group 1, a time series of values at uniform time intervals; 

Group 2, a set of values representing the consecutive occurrences of an event (such as high or low water); 

and Group 3, a set of values representing a forecast valid at a given projection time. The acceptable error 

limits (X) and maximum duration limits (L) for the associated variable applied to the nowcasts and 

forecasts are presented in Table 26. 

24
Table 25. Data series groups and the variables in each. Note that upper case letters 

indicate a prediction series (e.g., H), and lower case letters (e.g., h) indicate a 

reference series (observation) (Modified from Hess et al. 2003). 

Group Variable Symbol 

Group 1 Water level H, h 

(Time Series) Water temperature T, t 

Group 2 Amplitude of high water AHW, ahw 

(Values at Amplitude of low water ALW, ahw 

at Extreme Event) Time of high water THW, thw 

 Time of low water TLW, tlw 

Group 3 Water level at forecast projection time of nn hrs Hnn, hnn 

(Values from a Water temperature at forecast projection time of nn hrs Tnn, tnn 

Forecast) 

 

Table 26. Acceptance error limits (X) and the maximum duration limits (L) modified from 

Hess et al. (2003) for use in the Great Lakes. 

Variables X L (hours) 

H, Hnn, AHW, ALW 15 cm 24 

THW, TLW 1.5 hours+ 25 

T, Tnn, 3oC* 24 

_______________________________________________________________________ 

Notes: +1.0 hours for tidal regions, *7.7oC for tidal regions. 

The evaluation utilized the NOS skill assessment software (Zhang et al. 2006), but was modified 

for use in the Great Lakes. The software computes the skill assessment scores automatically 

using files containing observations and nowcast or forecast guidance. Since the GLCFS output 

was not in NetCDF files, the output was reformatted to meet the input text format requirements 

of the skill assessment code. 

Nowcasts of Water Levels

The evaluation of GLCFS nowcasts of water levels were based on time series of observed and 

model-based water levels at eight NOS NWLON stations along the Lake Erie shore line from 

Buffalo, NY to Fermi Power Plant, MI (Fig. 4). Time series of observed hourly water levels at 

NOS stations in Buffalo, NY and Toledo, OH during the period from 1 April to 31 December is 

given in Fig. 5. 

Since water level nowcasts and forecasts generated by GLCFS were vertical displacements 

25
relative to the flat lake, further adjustment is necessary to bring the water levels relative to the 

mean lake level. An offset value based on dynamic 7-day average mean water level was 

computed and added to the model nowcast of water level displacement. This is the same method 

used by CO-OPS prior to displaying the LEOFS nowcasts on the web. The final nowcast water 

levels were then compared with the observational data. 

The evaluation of GLCFS water level nowcasts for Lake Erie was done by comparing time series 

differences using SM, RMSE, SD, NOF, POF, MDPO, and MDNO statistics described in Hess et 

al. (2003). Since tides are not significant in the Great Lakes there was no comparison of the 

times and amplitudes of tidally-forced high and low waters. However, significant high 

amplitude water events do occur in Lake Erie. Following the recommendations of Hess et al. 

(2003), a method was developed and implemented in the NOS skill assessment software to 

analyze the forecast system’s ability to simulate large amplitude events in the Lake Erie and the 

other Great Lakes. This is the first attempt at evaluating the ability of a NOS forecast system to 

simulate high and low water events in non-tidal regions. Other methods such as described by 

Dingman and Bedford (1986) and used by Kelley (1995) and Hoch (1997) may be considered for 

addition to the NOS evaluation statistics suite. 

26
Figure 4. Map depicting the location of NOS NWLON stations in Lake Erie. 

27
 (a) 

 (b) 

Figure 5. Observed water levels at NOS NWLON stations in Buffalo, NY (a) and Toledo, 

OH (b) during the period 1 April to 31 December 2004. 

The NOS skill assessment software identifies high and low water events in the Great Lakes using 

28
the following method. 

Step 1. For the observed time series of water level, pick all high and low values. A data point 

is selected if it is either higher or lower than its two neighboring points (both sides). 

Step 2. For each selected peak from Step 1, a seven day window is centered on the particular 

peak and the mean value and standard deviation (called sigma hereafter) of the 

observed time series are computed within the seven day period. Upper/lower limits are 

then computed as the mean value +/- 2 sigma. 

Step 3. The peak is identified as a high/low water level event if it exceeds the upper and lower 

limits. (Step 2 was performed to remove the impact of periodical variations, such as 

semi-diurnal and diurnal frequency signals on event selection.) 

Step 4. For each high and low water level event in the observed time series, the 

maximum/minimum water level value and occurrence time are selected from the model 

simulated time series within a 12 hour window (the occurrence time of the observed 

event is centered), and paired with the observed events for comparison and statistic 

evaluation. 

Step 5. The paired observed and simulated extreme events are compared to each other to assess 

the ability of the forecast system to simulate large amplitude events. 

Nowcasts of Surface Water Temperatures

The evaluation of GLCFS nowcasts of surface water temperatures were based on comparisons of 

time series of observed vs. model-predicted temperatures at one 3-m fixed disk buoy locations in 

Lake Erie. The buoys are operated by NOAA/National Data Buoy Center (NDBC). Information 

on the buoy is given in Table 27. The lake surface temperatures at NDBC Buoy 45005 are 

measured using a Yellow-Springs thermistor sealed in epoxy in a copper slug clamped to the 

inside of the buoy’s hull (Gillhousen 1987). The thermistor depth is 0.5 m and is sampled once 

per hour. The point evaluations were conducted by comparing surface (highest sigma layer) 

temperature nowcasts at the nearest grid points to surface observations from the buoys. A map 

depicting the location of NDBC fixed buoy 45005 is given in Fig. 6. 

The evaluation of GLCFS surface water temperature nowcasts for Lake Erie was done by 

comparing time series differences using SM, RMSE, SD, NOF, POF, MDPO, and MDNO 

statistics described in Hess et al. (2003). No attempt was made to assess the forecast system’s 

ability to simulate diurnal or larger temperature fluctuations. Other methods for evaluating 

water temperature predictions such as those used by Kelley (1995) and Hoch (1997) may be 

implemented in the future. 

In evaluating predicted water temperature in tidal regions, NOS sets an acceptable error of 7.7o

C 

to meet the acceptable error of draft of 7.5 cm (3 inches), as water density is a function of 

temperature and salinity. Since the Great Lakes are considered fresh water and non-tidal, there is 

29
no preset standard for lake temperature prediction. Based on the ten years experience on running 

Great Lakes Forecasting System and input from Great Lakes user community, Dr. David Schwab 

of NOAA/GLERL suggested a 3o

C criteria for water temperature skill assessment in the Great 

Lakes region (personal communication). Thus all the statistical evaluation and skill scores are 

based on the 3oC criteria. 

Figure 6. Map depicting the location of the NWS/NDBC fixed buoy 45005 in Lake Erie 

along with the grid points of the GLOFS-Erie model grid. 

30
Table 27. Information on NOAA/NOS NWLON stations whose observations were used to 

evaluate LEOFS semi-operational nowcasts and forecast guidance of water levels. 

Station Corresponding 

LEOFS Grid Point 

Coordinates 

Station Name State NOS 

Station ID 

Number 

NWS 

Station 

ID 

Latitude 

(deg N) 

Longitude 

(deg W) I J 

Buffalo NY 9063020 BUFN6 42.88 78.89 80 8 

Sturgeon Point NY 9063028 NS 42.69 79.05 76 6 

Erie PA 9063038 NS 42.15 80.08 56 3 

Fairport OH 9063053 NS 41.75 81.28 34 4 

Cleveland OH 9063063 NS 41.54 81.64 26 2 

Marblehead OH 9063079 MRH01 41.55 82.73 10 11 

Toledo OH 9063085 THR01 41.69 83.47 2 20 

Fermi Power 

Plant 

MI 9063090 NS 41.96 83.26 8 23 

Notes: NS = An official NWS station ID has not been assigned the station yet. 

Table 28. Information of a NWS/NDBC fixed buoy whose observations were used to 

evaluate LEOFS semi-operational nowcasts and forecast guidance of surface 

water temperatures. 

Buoy Corresponding 

LEOFS Grid 

Point 

Coordinates 

Buoy Name Agency Prov. 

or 

State 

NWS 

Buoy 

ID 

Latitude 

(deg N) 

Longitude 

(deg W) I J 

West Erie NWS/ 

NDBC 

OH 45005 41.68 82.40 17 10 

5.3. Assessment of Water Level Nowcasts 

The standard suite of skill assessment statistics evaluating the ability of semi-operational 

nowcasts and forecast guidance to predict hourly and extreme water levels at eight NOS gauges 

from 15 April to 17 December 2004 are given in Appendix A. Time series plots of the nowcasts 

vs. observations at the gauges are given in Appendix B. 

The skill statistics assessing the ability of the nowcasts to predict hourly water levels at the eight 

NOS gauges are presented together in Table 29 along with the NOS acceptance criteria. The 

31
hourly nowcasts passed NOS criteria for NOF, CF, POF, MDPO, and MDNO at all eight NOS 

gauge locations. The mean algebraic differences ranged between -2.9 and +3.4 cm and the 

RMSE ranged between 4 and 8 cm. The greatest differences were at Toledo, OH, Buffalo, NY, 

and Fairport, OH (Fig. 4). The nowcasts under predicted at Toledo and over predicted at 

Buffalo. Toledo and Buffalo, located at the extreme SW and NE ends of the lakes, respectively 

experience the greatest hourly water level variability and are the most difficult locations to 

predict for. It was not clear why the MAE and RMSE were large at Fairport when the 

differences at gage locations to the north (Erie, PA) and south (Cleveland, OH) were only 0.8 

cm.

Table 29. Summary of Skill assessment Statistics of Semi-Operational Nowcasts of Hourly 

Water Levels at eight NOS NWLON Stations in Lake Erie for the Period 15 

April to 17 December 2004. A total of 5832 nowcasts were used in the 

assessment. Gray shading, if present, indicates that it did not meet the NOS 

acceptance criteria. 

Statistic, Acceptable 

Error [ ], and 

Units ( ) 

Buffalo, 

NY 

Sturgeon 

Pt., NY 

Erie, 

PA 

Fairport, 

OH 

Cleveland, 

OH 

Marblehead, 

OH 

Mean Alg. Error (m) 0.026 0.034 0.008 -0.031 0.008 0.000 

RMSE (m) 0.080 0.076 0.045 0.044 0.040 0.050 

SD (m) 0.076 0.068 0.044 0.031 0.040 0.050 

NOF (2x15cm) (%) 0.8 0.5 0.0 0.0 0.0 0.0 

CF [15 cm] (% 95.6 96.4 98.9 99.7 99.1 98.4 

POF [2x15 cm] (%) 0.0 0.0 0.0 0.0 0.1 0.0 

MDNO [2x15 cm ] 

(hour) 

4.0 4.0 1.0 0.0 0.0 0.0 

MDPO [2x15 cm ] 

(hour) 

0.0 0.0 0.0 0.0 2.0 1.0 

Statistic, Acceptable 

Error [ ], and 

Units ( ) 

Toledo, 

OH 

Fermi 

Power 

Plant, MI 

NOS 

Accept. 

Criteria 

Mean Alg. Error (m) -0.029 -0.005 na 

RMSE (m) 0.080 0.065 na 

SD (m) 0.075 0.065 na 

NOF (2x15cm) (%) 0.3 0.1 < 1% 

CF [15 cm] (% 94.0 96.6 > 90% 

POF [2x15 cm] (%) 0.1 0.1 < 1% 

MDNO [2x15 cm ] 

(hour) 

3.0 2.0 < 24 hours 

MDPO [2x15 cm ] 

(hour) 

0.0 2.0 < 24 hours 

_____________________________________________________________________________ 

Notes: na = not applicable 

The skill statistics assessing the ability of nowcasts to predict the amplitude and timing of 

32
extreme high water level events at NOS gauges during 2004 are given together in Table 30 along 

with the NOS acceptance criteria. The nowcasts’ amplitude predictions of high water level 

events passed the NOS acceptance criteria for NOF, CF, POF, MDNO, and MDPO at only 

Cleveland and Marblehead. (It came close to passing at Toledo). The nowcasts’ ability to 

predict the timing of these events did not pass NOS acceptance criteria for NOF, CF, and POF at 

any gauge location. 

Table 30. Summary of Standard Statistics Evaluating the Ability of the Semi-Operational 

Nowcasts to Predict Extreme High Water Level Events at Eight NOS NWLON 

stations in Lake Erie during the Period 15 April to 17 December 2004. Gray 

shading, if present, indicates that it did not meet the NOS acceptance criteria. 

Buffalo, NY 

N=33 

Sturgeon Point, NY 

N=35 

Erie, PA 

N=32 

Statistic, 

Acceptable Error [ ], and 

Units ( ) Amp. Time Amp. Time Amp. Time 

Mean Alg. Error (m) (min) -0.168 0.727 -0.118 -1.286 -0.088 -0.906 

RMSE (m) (min) 0.282 5.111 0.189 5.079 0.119 5.452 

SD (m) (min) 0.231 5.137 0.150 4.986 0.082 5.462 

NOF [2x15cm or 90 min] 

(%) 

21.2 12.1 20.0 17.1 0.0 18.8 

CF [15 cm or 90 min] (%) 75.8 60.6 71.4 65.7 71.9 53.1 

POF [2x15 cm or 90 min] (%) 0.0 18.2 0.0 2.9 0.0 9.4 

MDNO [2x15 cm or 90 min] 

(#) 

0.0 0.0 0.0 0.0 0.0 0.0 

MDPO [2x15 cm or 90min] 

(#) 

0.0 0.0 0.0 0.0 0.0 0.0 

Fairport, OH 

N=18 

Cleveland, OH 

N=18 

Marblehead, OH 

N=22 

Statistic, 

Acceptable Error [ ], and 

Units ( ) Amp. Time Amp. Time Amp. Time 

Mean Alg. Error (m) (min) -0.082 -0.389 -0.041 0.222 -0.046 0.909 

RMSE (m) (min) 0.112 5.652 0.068 4.955 0.061 5.745 

SD (m) (min) 0.078 5.802 0.056 5.094 0.041 5.806 

NOF [2x15cm or 90 min] (%) 0.0 16.7 0.0 22.2 0.0 4.5 

CF [15 cm or 90 min] (%) 77.8 38.9 100.0 50.00 100.0 59.1 

POF [2x15 cm or 90 min] (%) 0.0 11.1 0.0 16.7 0.0 18.2 

MDNO [2x15 cm or 90 min] 

(#) 

0.0 0.0 0.0 0.0 0.0 0.0 

MDPO [2x15 cm or 90min] 

(#) 

0.0 0.0 0.0 0.0 0.0 0.0 

Toledo, OH 

N=19 

Fermi Power Plant, 

MI 

N=21 

NOS 

Accept. 

Criteria 

Statistic, 

Acceptable Error [ ], and 

Units ( ) 

Amplitude Time Amplitude Time 

Mean Alg. Error (m) (min) -0.119 0.790 -0.075 1.286 na 

RMSE (m) (min) 0.142 3.269 0.100 5.490 na 

SD (m) (min) 0.079 3.259 0.069 5.469 na 

NOF [2x15cm or 90 min (%) 5.3 10.5 0.0 9.5 < 1% 

33
CF [15 cm or 90 min] (%) 84.2 36.8 81.0 61.9 > 90 

POF [2x15 cm or 90 min] (%) 0.0 21.1 0.0 19.0 < 1% 

MDNO [2x15 cm or 90 min] 

(#) 

0.0 0.0 0.0 0.0 < 24 hours 

MDPO [2x15 cm or 90 min] 

(#) 

0.0 0.0 0.0 0.0 < 24hours 

Notes: na = not applicable 

The skill statistics to predict extreme low water level events at the NOS gauges in Lake Erie 

during 2004 are given together in Table 31. The nowcasts of extreme low water level passed 

NOS acceptance criteria for amplitude at Erie and Fairport and were close to passing at Toledo 

and Marblehead. The nowcasts ability to simulate the timing of these events did not pass NOS 

acceptance criteria at any gauge location. 

34
Table 31. Summary of Standard Statistics Evaluating the Ability of Semi-Operational 

Nowcasts to Simulate Extreme Low Water Level Events at Eight NOS NWLON 

Stations in Lake Erie for the Period 15 April to 17 December 2004. Gray 

shading, if present, indicates that the statistic did not pass the NOS acceptance 

criteria. 

Buffalo, NY 

N=36 

Sturgeon Point, 

NY 

N=33 

Erie, PA 

N=41 

Statistic, 

Acceptable Error [ ], and 

Units ( ) 

Amp. Time Amp. Time Amp. Time 

Mean Alg. Error (m) (min) 0.108 0.727 0.113 -0.030 0.064 -0.537 

RMSE (m) (min) 0.117 5.111 0.121 2.623 0.072 2.263 

SD (m) (min) 0.045 5.137 0.043 2.663 0.032 2.226 

NOF [2x15cm] (90min) % 0.0 12.1 0.0 9.1 0.0 7.3 

CF [15 cm or 90 min] (%) 83.3 60.6 81.8 60.6 100.0 68.3 

POF [2x15 cm or 90 min] 

(%) 

0.0 18.2 0.0 15.2 0.0 7.3 

MDNO [2x15 cm or 90 min] 

(#) 

0.0 0.0 0.0 0.0 0.0 0.0 

MDPO [2x15 cm or 90min] 

(#) 

0.0 0.0 0.0 0.0 0.0 0.0 

Fairport, OH 

N=15 

Cleveland, OH 

N=27 

Marblehead, OH 

N=42 

Statistic, 

Acceptable Error [ ], and 

Units ( ) Amp. Time Amp. Time Amp. Time 

Mean Alg. Error (m) (min) 0.032 -0.667 0.090 0.037 0.073 -0.238 

RMSE (m) (min) 0.056 2.503 0.113 2.009 0.107 2.390 

SD (m) (min) 0.047 2.498 0.070 2.047 0.079 2.407 

NOF [2x15cm or 90min (%) 0.0 13.3 0.0 3.7 0.0 7.1 

CF [15 cm or 90 min] (%) 100.0 73.3 81.5 66.7 85.7 59.5 

POF [2x15 cm or 90 min] (%) 0.0 0.0 0.0 7.4 2.4 9.5 

MDNO [2x15 cm or 90 min] 

(#) 

0.0 0.0 0.0 0.0 0.0 0.0 

MDPO [2x15 cm or 90min] 

(#) 

0.0 0.0 0.0 0.0 0.0 0.0 

Toledo, OH 

N=42 

Fermi Power Plant, 

MI 

N=48 

NOS 

Accept. 

Criteria 

Statistic, 

Acceptable Error [ ], and 

Units ( ) 

Amplitud

e 

Time Amplitude Time 

Mean Alg. Error (m) (min) 0.063 -0.381 0.088 0.083 na 

RMSE (m) (min) 0.106 2.545 0.125 2.062 na 

SD (m) (min) 0.086 2.547 0.089 2.082 na 

NOF [2x15cm or 90min] (%) 0.0 11.9 0.0 2.1 < 1% 

CF [15 cm or 90 min] (%) 85.7 57.1 83.3 66.7 > 90 

POF [2x15 cm or 90 min] (%) 0.0 7.1 4.2 8.3 < 1% 

MDNO [2x15 cm or 90 min] 

(#) 

0.0 0.0 0.0 0.0 < 24 hours 

MDPO [2x15 cm or 90min] (#) 0.0 0.0 0.0 0.0 < 24 hours 

Notes: na=not applicable 

35
5.4. Assessment of Water Temperature Nowcasts 

The standard suite of skill assessment statistics evaluating the ability of semi-operational 

nowcasts to predict hourly lake surface water temperatures at the NWS/NDBC fixed buoy 

between the western and central basins of Lake Erie from mid-April to early December 2004 is 

given in Appendix D. A time series plot of the nowcasts (1st sigma level) vs. observations at the 

buoys is given in Appendix E. The time series plot indicates that the nowcasts were in close 

agreement to observations (+0.5 to +1 o

C) from mid- April until early May, but then began 

deviate from the observations by +1 to +2o

C until late May. After that the nowcasts differ from 

observations by +0.5oC until mid August. The nowcasts then deviated by +1 to +2oC until early 

October. During the remaining days of Autumn through the end of the period in mid December, 

the nowcasts generally differed from observations by +0.5o

C. 

The skill statistics to predict hourly surface water temperatures at the NDBC buoys are given in 

Table 32 along with the NOS acceptance criteria. The hourly water temperature nowcasts at the 

buoy did pass the NOS acceptance criteria for all the assessment statistics 

Table 32. Summary of Skill Assessment Statistics of the Semi-Operational Hourly Nowcasts

of Surface Water Temperatures at the NWS/NDBC fixed buoy in Lake Erie for 

the Period from Mid-April to Early December 2004. Gray shading indicates that 

the statistic did not pass the NOS acceptance criteria. 

Time Period, Statistic, 

Acceptable Error [ ], and Units ( 

) 

45005 

West Erie 

N=5566 

NOS 

Acceptance 

Criteria 

Time Period (days) 202 365 

Mean Difference (oC) 0.951 na 

RMSE (oC) 1.292 na 

SD (oC) 0.875 na 

NOF [2x3oC] (%) 0.0 < 1% 

CF [3oC] (%) 98.7 > 90% 

POF [2x3oC] (%) 0.0 < 1% 

MDNO [2x3oC] (hours) 0.0 < 24 hrs 

MDPO [2x3oC] (hours) 0.0 < 24 hrs 

Notes: na=not applicable 

6. SEMI-OPERATIONAL FORECAST SKILL ASSESSMENT

This section describes the model system performance for a semi-operational forecast scenario 

36
based on NOS requirements (Hess et al. 2003). According to Hess et al. (2003), the definition of 

the model run scenario for a semi-operational forecast is the following: 

“In this scenario, the model is forced with actual forecast input data streams, including open 

ocean boundary water levels, wind, river flows, and water density variations. Initial conditions 

are generated by observed data. Significant portions of the data may be missing, so the model 

must be able to handle this.” (Similar to the nowcast scenario, the data streams for the Great 

Lakes could include wind stresses, surface heat flux, and river flows.) 

For the assessment of the semi-operational forecast scenario for LEOFS, archived forecast 

guidance from GLCFS twice per day forecast cycles (0000 and 1200 UTC) during 2004 were 

compared to available observations in the lake. 

This chapter includes a description of the GLCFS forecast cycles, the method of evaluation 

including time period and assessment statistics, and the evaluation results. 

6.1. Description of Forecast Cycles 

GLCFS performs twice/day 60-hr forecast cycles for Lake Erie. The two forecast cycles are 

initialized at 0000 and 1200 UTC each day. The forecast cycles are launched at approximately 2 

hours and 45 minutes past the valid time of the nowcasts to allow for complete ingestion of 

atmospheric forecast fields. For example, for the forecast cycle with initial conditions valid at 

1200 UTC is launched at 1445 UTC. The initial conditions for each forecast cycle are provided 

by the nowcast cycle. The surface forcing for the forecast cycles consists of surface (10 m AGL) 

wind velocity and surface (2 m AGL) air temperatures from NWS/NCEP North America 

Mesoscale (NAM) Model. The wind velocity and air temperature are used to calculate surface 

wind stress for input into the lake model. The surface heat fluxes into the lake model during the 

forecast cycle are zero. 

6.2. Method of Evaluation 

The semi-operational forecast guidance at 1 hour increments from +1 to +24 hours from GLCFS 

were compared to water level observations from NOS NWLON stations in the lake from 15 

April to 17 December 2004 and to a NWS/NDBC fixed buoy from mid-April to early November 

for the surface water temperature forecasts. This was a period when there was no significant ice 

cover on the lake. 

37
The evaluation used the standard suite of assessment statistics as defined in Hess et al. (2003) but 

modified for non-tidal regions. The evaluation of GLCFS forecasts of water levels were based 

on time series of observed and model-based water levels at the same eight NOS NWLON 

stations along the lake shore (Fig. 4) used in the evaluation of the nowcasts. 

The evaluation of semi-operational forecast guidance of surface water temperatures were based 

on comparisons of time series of observed vs. model-predicted temperatures at the same 

NWS/NDBC fixed buoy used in the nowcast evaluation. 

6.3. Skill Assessment of Water Level Forecast Guidance 

The standard suite of skill assessment statistics evaluating the ability of semi-operational forecast 

guidance as well as nowcasts to predict hourly and extreme water levels at 8 NOS gauges from 

Early-April to Mid-December 2004 are given in Appendix A. Time series plots of the forecast 

guidance from the 0000 UTC model forecast cycle vs. observations at the gauges are given in 

Appendix C. 

For the convenience of the reader, the skill statistics assessing the ability of the forecast guidance 

to predict hourly water levels 24 hours in advance at 8 NOS gauges are presented together in 

Table 33 along with the NOS acceptance criteria. The hourly forecasts passed the criteria at 7 of 

the 8 gauges, failing only at Toledo. The mean algebraic errors or differences ranged between -3 

and +4.4 cm and the RMSE ranged between 4.1 cm at Cleveland and 10.7 cm at Toledo. 

Similar to the nowcasts, the greatest errors were at Buffalo and Toledo, located at the extreme 

ends of the lake. The forecasts under predicted the water levels at Toledo and over predicted the 

levels at Buffalo. There was some increase in the RMSE values as forecast projection increased 

(Appendix A). 

Table 33. Summary of Skill Assessment Statistics of Semi-Operational 24-hr Forecast 

Guidance of Hourly Water Levels at NOS NWLON Stations in Lake Erie for the 

Period 15 April to 17 December 2004. Gray shading, if present, indicates that 

the statistic did not pass the NOS acceptance criteria. 

Statistic, Acceptable 

Error [ ], and 

Units ( ) 

Buffalo, 

NY 

N=490 

Sturgeon 

Point, NY 

N=490 

Erie, 

PA 

N=490 

Fairport, 

OH 

N=490 

Cleveland, 

OH 

N=490 

Marblehead, OH 

N=473 

Mean Alg. Error (m) 0.036 0.044 0.017 -0.030 0.006 -0.008 

RMSE (m) 0.088 0.084 0.052 0.044 0.041 0.065 

SD (m) 0.080 0.072 0.050 0.032 0.041 0.065 

NOF (2x15cm) (%) 0.4 0.4 0.2 0.0 0.0 0.4 

CF [15 cm] (% 95.9 96.1 98.6 99.4 99.4 97.3 

POF [2x15 cm] (%) 0.4 0.6 0.0 0.0 0.0 0.2 

MDNO [2x15 cm ] 

(hour)

0.0 0.0 0.0 0.0 0.0 0.0 

MDPO [2x15 cm ] (hour) 0.0 0.0 0.0 0.0 0.0 0.0 

38
Table 33 (cont.) 

Statistic, Acceptable 

Error [ ], and 

Units ( ) 

Toledo, OH 

N=489 

Fermi 

Power 

Plant, MI 

N=477 

NOS 

Accept. 

Criteria 

Mean Alg. Error (m) -0.034 -0.016 na 

RMSE (m) 0.107 0.086 na 

SD (m) 0.102 0.084 na 

NOF (2x15cm) (%) 1.4 0.8 < 1% 

CF [15 cm] (% 87.7 93.9 > 90% 

POF [2x15 cm] (%) 0.2 0.2 < 1% 

MDNO [2x15 cm ] 

(hour) 

12.0 0.0 < 24 hours 

MDPO [2x15 cm ] 

(hour) 

0.0 2.0 < 24 hours 

Notes: na=not applicable 

The skill statistics to assess the ability of the forecast guidance to predict extreme high water 

level events at the eight NOS gauges during 2004 are given together in Table 34. The forecasts 

of extreme high water level passed the NOS acceptance criteria for amplitude only at Cleveland 

and Marblehead, OH. The forecasts ability to predict the timing of these events also did not pass 

NOS acceptance criteria at any gauge. 

Table 34. Summary of Skill assessment Statistics Evaluating the Ability of SemiOperational Forecast Guidance to Predict Extreme High Water Level Events at 

NOS NWLON Stations in Lake Erie during the Period 15 April to 17 December 

2004. Gray shading, if present, indicates that the statistic did not pass the NOS 

acceptance criteria. 

Buffalo, NY 

N=29 

Sturgeon Point 

N=32 

Erie, PA 

N=29 

Statistic, 

Acceptable Error [ ], and 

Units ( ) Amp. Time Amp. Time Amp. Time 

Mean Alg. Error (m) (min) -0.134 2.310 -0.147 0.938 -0.094 0.103 

RMSE (m) (min) 0.204 5.392 0.248 5.500 0.138 4.924 

SD (m) (min) 0.157 4.958 0.202 5.506 0.103 5.010 

NOF [2x15cm] (90min) % 13.8 6.9 15.6 15.6 3.4 20.7 

CF [15 cm or 90 min] (%) 65.5 48.3 68.8 37.5 82.8 37.9 

POF [2x15 cm or 90 min] 

(%) 

0.0 27.6 0.0 15.6 0.0 17.2 

39
Table 34. (cont.). 

Fairport, OH 

N=16 

Cleveland, OH 

N=15 

Marblehead, OH 

N=20 

Statistic, 

Acceptable Error [ ], and 

Units ( ) Amp. Time Amp. Time Amp. Time 

Mean Alg. Error (m) (min) -0.104 1.063 -0.051 -0.533 -0.057 -0.850 

RMSE (m) (min) 0.118 5.815 0.073 4.258 0.071 5.408 

SD (m) (min) 0.057 5.904 0.054 4.373 0.043 5.480 

NOF [2x15cm] (90min) % 0.0 12.5 0.0 20.0 0.0 20.0 

CF [15 cm or 90 min] (%) 75.0 56.3 100.0 46.7 95.0 50.0 

POF [2x15 cm or 90 min] 

(%) 

0.0 25.0 0.0 13.3 0.0 5.0 

Toledo, OH 

N=21 

Fermi Power Plant, 

MI 

N=19 

NOS 

Accept. 

Criteria 

Statistic, 

Acceptable Error [ ], and 

Units ( ) 

Amplitude Time Amplitude Time 

Mean Alg. Error (m) (min) -0.157 0.714 -0.082 0.684 na 

RMSE (m) (min) 0.172 5.851 0.100 6.509 na 

SD (m) (min) 0.073 5.951 0.060 6.650 na 

NOF [2x15cm] (90min) % 0.0 9.5 0.0 21.1 < 1 % 

CF [15 cm or 90 min] (%) 57.1 47.6 84.2 47.4 > 90 

POF [2x15 cm or 90 min] 

(%) 

0.0 23.8 0.0 15.8 < 1 % 

Notes: na=not applicable 

The skill statistics to assess the ability of the forecast guidance to predict extreme low water level 

events at the eight NOS gauges during 2004 are given together in Table 35. The forecasts of 

extreme low water level passed NOS acceptance criteria for amplitude at Erie, Fairport, and 

Cleveland. The forecasts ability to simulate the timing of these events did not pass NOS 

acceptance criteria at any location. 

Table 35. Summary of Skill assessment Statistics Evaluating the Ability of SemiOperational Forecast Guidance to Predict Extreme Low Water Level Events at 

NOS NWLON Stations in Lake Erie during the Period 15 April to 17 December 

2004. Gray shading, if present, indicates that the statistic did not pass the NOS 

acceptance criteria.

Buffalo, NY 

N=36 

Sturgeon Pt, NY 

N=34 

Erie, PA 

N=40 

Statistic, 

Acceptable Error [ ], and 

Units ( ) Amp. Time Amp. Time Amp. Time 

Mean Alg. Error (m) (min) 0.131 -0.250 0.127 0.176 0.073 -0.350 

RMSE (m) (min) 0.139 2.291 0.135 2.701 0.079 2.313 

SD (m) (min) 0.047 2.310 0.047 2.736 0.032 2.316 

NOF [2x15cm] (90min) % 0.0 8.3 0.0 8.8 0.0 15.0 

CF [15 cm or 90 min] (%) 66.7 44.4 73.5 35.3 97.5 57.5 

POF [2x15 cm or 90 min] 

(%) 

0.0 2.8 0.0 11.8 0.0 2.5 

40
Table 35 (cont.). 

Fairport, OH 

N=16 

Cleveland, OH 

N=27 

Marblehead, OH 

N=43 

Statistic, 

Acceptable Error [ ], and 

Units ( ) Amp. Time Amp. Time Amp. Time 

Mean Alg. Error (m) (min) -0.034 0.429 0.081 0.407 0.076 -0.140 

RMSE (m) (min) 0.053 2.171 0.095 2.822 0.117 2.728 

SD (m) (min) 0.042 2.209 0.051 2.845 0.089 2.757 

NOF [2x15cm] (90min) % 0.0 7.1 0.0 7.4 0.0 11.6 

CF [15 cm or 90 min] (%) 100.0 64.3 96.3 48.1 88.4 53.5 

POF [2x15 cm or 90 min] 

(%) 

0.0 7.1 0.0 18.5 2.3 11.6 

Toledo, OH 

N=42 

Fermi Power Plant, 

MI 

N=48 

NOS 

Accept. 

Criteria 

Statistic, 

Acceptable Error [ ], and 

Units ( ) 

Amplitude Time Amplitude Time 

Mean Alg. Error (m) (min) 0.110 -0.357 0.116 -0.083 na 

RMSE (m) (min) 0.173 2.493 0.160 2.769 na 

SD (m) (min) 0.135 2.497 0.111 2.797 na 

NOF [2x15cm] (90min) % 0.0 11.9 0.0 10.4 < 1 

CF [15 cm or 90 min] (%) 73.8 54.8 79.2 47.9 > 90 

POF [2x15 cm or 90 min] 

(%) 

9.5 7.1 6.3 14.6 < 1 

Notes: na=not applicable 

6.4. Skill Assessment of Surface Water Temperature Forecast Guidance 

The standard suite of skill assessment statistics evaluating the ability of semi-operational forecast 

guidance to predict hourly lake surface water temperatures at one NWS/NDBC fixed buoy from 

mid-April to early December 2004 is given in Appendix D. The table provides skill statistics at 

the forecast projections of 0, 6, 12, 18, and 24 hours from the 0000 and 1200 UTC forecast 

cycles. A time series plot of the forecasts (1st sigma level) from the 0000 UTC forecast cycle vs. 

buoy observations is given in Appendix E. The time series plot indicates that the forecast 

guidance from the 0000 UTC forecast cycle resembles the nowcasts very closely. This reflects 

the fact that the lake model configuration (i.e. POMGL) used for the semi-operational forecast 

cycles do not input any surface heat flux either directly or indirectly from the NAM-12 model 

forecast guidance. Specifically, the lake model uses subroutine FLUX5 in which the heat fluxes 

are zero. 

Similar to the nowcasts, the semi-operational forecast guidance were in close agreement to 

observations (+0.5 to +1o

C) from mid- April until early May, but then began to deviate from the 

observations by +1 to +2oC until late May. After that the forecast guidance differed from 

observations by +0.5o

C until mid August. The guidance then deviated by +1 to +2oC until early 

October. During the remaining days of autumn through mid December, the guidance generally 

41
differed from observations by +0.5o

C. 

The skill statistics assessing the ability of semi-operational forecast guidance to predict surface 

water temperatures 24 hours in advance at the NDBC buoy is given in Table 36 along with the 

NOS acceptance criteria. The hourly forecast guidance at the buoy passed all criteria. The 

mean algebraic error or difference was -0.7o

C and the RMSE was 1.3oC. The mean algebraic 

error and RMSE for the forecast guidance were slightly lower than for the nowcasts. 

It is interesting to note that mean algebraic error and RMSE values decreased as forecast 

projection increased in time. The mean algebraic error was 1.07oC at the 0-hr projection and 

0.71oC by the 24-hr projection (see Table D.1). This suggests that the surface heat flux is being 

overestimated during the nowcast cycle and that POMGL is cooling off during the forecast cycle 

when there is no surface heat flux input. 

Table 36. Summary of Skill Assessment Statistics for Semi-Operational Forecast Guidance 

to Predict Surface Water Temperatures 24 hours in Advance at a NWS/NDBC 

fixed buoy in Lake Erie during the period from mid-April to early-November 

2004. Gray shading, if present, indicates that the statistic did not pass the NOS 

acceptance criteria. 

 

 Time Period, Statistic, 

Acceptable Error [ ], and 

Units ( ) 

45005 

N=460 

NOS 

Acceptance 

Criteria 

Time Period 202 365 days 

Mean Alg. Error (oC) 0.713 na 

RMSE (oC) 1.306 na 

SD (oC) 1.095 na 

NOF [2x3oC] (%) 0.0 < 1% 

CF [3oC] (%) 98.7 > 90% 

POF [2x3oC] (%) 0.0 < 1% 

MDPO [2x3oC] (hours) 0.0 < 24 hrs 

MDNO [2x3oC] (hours) 0.0 < 24 hrs 

Notes: na=not applicable 

42
7. SUMMARY 

NOS’ Lake Erie Operational Forecast System (LEOFS) generates hourly nowcasts and forecast 

guidance out to 30 hours four times per day. It is based on the Great Lakes Coastal Forecasting 

System (GLCFS) developed by the Ohio State University and NOAA/GLERL. 

LEOFS became operational at CO-OPS in September 30, 2005. The hourly nowcast cycles are 

forced by surface wind stress and surface heat flux estimated from objectively analyzed surface 

meteorological fields and the initial conditions are provided by the previous hour’s nowcast. The 

four times/day forecast cycle uses the most recent nowcasts for its initial conditions and surface 

air temperature and wind forcing from NWS/NCEP’s NAM-12 weather prediction model. 

During the forecast cycle, the heat flux is set to zero. 

An assessment of the LEOFS was conducted according to the NOS evaluation standards (Hess et 

al. 2003). To satisfy the hindcast scenario requirement, the results of the Ph.D. dissertation 

research of Kuan (1995b) at The Ohio State University were used and summarized in this report. 

Water Levels 

The simulated water surface elevation matched well in both phase and magnitude with the 

corresponding observed data by picking up almost every single significant spike appearing in the 

observed water level elevations. The average RMSE was quite small indicating that the model 

can be considered quite good in simulating water surface elevation of the lake. The average IOA 

and amplitude skill score also support this observation, obtaining high values of 0.95 and 9.72, 

respectively. 

Water Temperatures 

As for lake surface temperature, the model predictions compared exceptionally well with the 

observed data at all six CCIW meteorological buoy locations. The simulations not only pick up 

almost every single spike found in the observed data, but the average RMSE was as low as 1o

C 

over the entire test period of 150 days. The model also demonstrated good skill at predicting 

water temperatures in the deeper portion of the lake. When the lake was well mixed, the model 

predictions were as good as those for the lake surface. However, when the lake was thermally 

stratified, the simulations under predicted the water temperature above the thermocline region 

and over predicted the water temperature in the hypolimnion. 

Water Currents 

For the current velocity simulation, except for the nearshore zone and the very unsteady flow 

periods, the velocity predictions over C-C’ (Fig. 3) were quite satisfactory. Good simulation 

both in phase and magnitude can be found at most of the current meter locations. 

43
To comply with NOS’ required semi-operational nowcast and forecast scenarios (Hess et al. 

2003), the skill evaluation used archived output from NOAA/GLERL’s GLCFS semi-operational 

nowcasts and forecasts for Lake Erie from 15 April to 17 December 2004. The semi-operational 

nowcasts and forecast guidance were compared to water level observations at 8 NOS NWLON 

stations and surface temperature temperatures at one NWS/NDBC fixed buoy in the lake. Due to 

the lack of sub-surface water temperatures and current observations, no assessment of these 

variables could be conducted for LEOFS. 

Water Levels 

The hourly semi-operational nowcasts passed NOS acceptance criteria at seven of the eight 

stations. The nowcasts and forecast guidance predictions of extreme high and low water level 

events failed to pass NOS acceptance criteria in terms of estimating the timing of these events. 

In terms of amplitude, for the nowcasts, the predictions met the criteria at two of the eight gauges 

for high and low water level events. For the forecast guidance, the predictions met the criteria at 

two of the eight gauges for high water events and three of the eight gauges for low water events. 

Surface Water Temperatures 

The semi-operational nowcasts and forecast guidance predicted the surface water temperatures at 

the western/central basin boundary in Lake Erie to within +1o

C for the entire evaluation period 

and passed NOS criteria. However, from mid-August to late-October the predictions were 1 to 

2oC warmer than observations. 

8. RECOMMENDATIONS FOR FUTURE WORK 

Recommendation #1: 

The NOS skill assessment statistics and code should be modified in the future to provide skill 

assessment information of interest to NWS Weather Forecast Offices in the Great Lakes region 

which have responsibility for issuing low water statements and Lakeshore Flood Warnings. A 

description of the NWS criteria used by the Weather Forecast Offices responsible for issuing 

warnings and low water statements for the Lake Erie shoreline is given in Appendix F. 

Recommendation #2 

GLERL is presently running a new semi-operational version of GLCFS for Lake Erie. The new 

version uses an ice module in POMGL, monthly mean river discharge for 28 rivers, a horizontal 

grid resolution of 2 km, and 21 vertical sigma levels. The new version was implemented at 

GLERL in 2006. The nowcasts and forecast guidance from this new version should be 

evaluated to determine if it has improved predictive skill. 

44
Recommendation #3 

The skill score developed by Dingman and Bedford (1986) should be considered for addition to 

the NOS standard suite of evaluation statistics. 

Recommendation #4 

An examination should be conducted to determine whether the dynamic 7 day average mean lake 

water level adds a significant error to the POMGL predictions and if yes, is there an alternative 

method to estimate the mean lake wide water level. A possible alternative is to use the U.S. 

Army Corp of Engineers’ mean lake levels which are based on area-weighted averages of 

individual gauges (http://www.lre.usace.army.mil/_plugins/Programs/DailyWaterLevels/

dialogs.cfm?units=metric&months=0&displaymode=detail) or use a similar methodology at NOS. 

Recommendation #5 

The LEOFS surface water temperature predictions should also be evaluated at the Canadian 

buoys 45132 and 45134 in the future. 

ACKNOWLEDGMENTS 

The development of the Great Lakes Forecasting System was a joint effort of The Ohio State 

University and NOAA’s Great Lakes Environmental Research Laboratory lead by Dr. Keith 

Bedford (OSU) and Dr. David Schwab (GLERL). During the eleven year life of the GLFS the 

following OSU personnel been associated with the development of the system: fifteen graduate 

students; seven faculty; six postdocs; and seven research scientists. Funding came from many 

different sources ranging from small grants from private foundations and companies to several 

large federal grants. The operation and further development of GLCFS at GLERL has involved 

2 research scientists and 3 support scientists. 

The porting of the GLCFS from GLERL to NOS was conducted by the GLOFS System 

Development and Implementation Team consisting of personnel from GLERL, OSU, CO-OPS, 

CSDL, and Aqualinks.com. In particular, we acknowledge the hard work of Greg Mott, Mark 

Vincent, Zack Bronder, and others at CO-OPS. 

The hindcast scenario requirement was based on the Ph.D. research of C. Kuan conducted at The 

Ohio State University. The archived GLCFS nowcast and forecast guidance used in the skill 

assessment to fulfill the semi-operational nowcast and forecast scenarios were provided by Greg 

Lang and David Schwab at NOAA/GLERL. The skill assessment software was modified for 

use in the Great Lakes based on suggestions from Kurt Hess and Eugene Wei at CSDL. 

We express our thanks Hong Lin and Eugene Wei in CSDL for their helpful comments and 

suggestions to improve this document.

45
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Bedford, K. W., 1992: The physical effects of the Great Lakes on tributaries and wetlands. J. 

Great Lakes Res., 18, 571-589. 

Bedford, K.W. and D. J. Schwab, 1991: The Great Lakes Forecasting System-Lake Erie 

nowcasts/forecasts, Proceedings, Marine Technology Society National Meeting, New 

Orleans, LA, Marine Technology Society, 260-264. 

Blumberg, A. F. and G. L. Mellor, 1987: "A Description of a Three-Dimensional Coastal Ocean 

Circulation Model," Three-Dimensional Coastal Ocean Models, Vol. 4, Ed. N. Heaps, 

American Geophysical Union, Washington, DC, 1-16. 

Boyce, F. M., F. Chiocchio, B. Eid, F. Penicka, and F. Rosa, 1989: Hypolimnion flow between 

the central and eastern basins of Lake Erie during 1977. J. Great Lakes Res., 6, 290-306. 

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Report (in preparation). 

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46
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13 pp. [Available from NOAA/Great Lakes Environmental Research Laboratory, 

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Highway, Silver Spring, MD 20910]. 

48
APPENDIX A. Skill Assessment Scores of Semi-Operational Water Level Nowcasts and 

Forecast Guidance of Water Levels at six NOS Gauges in Lake Erie from 15 

April to 17 December 2004. 

Table A.1. Skill Assessment of Semi-Operational Predictions at NOS Gauge at Buffalo, NY (9063020) for 2004. 

Station: Buffalo, Lake Erie, NY 

Longest continuous data time period from: 4/15/2004 to 12/20/2004 

Data gap is filled using SVD method 

Data are filtered using 3.0 Hour Fourier Filter 

----------------------------------------------------------------------------------- 

VARIABLE X N IMAX SM RMSE SD NOF CF POF MDNO MDPO 

CRITERION - - - - - - <1% >90% <1% <N <N 

----------------------------------------------------------------------------------- 

 SCENARIO: SEMI-OPERATIONAL NOWCAST 

H 5832 174.224 

h 5832 174.198 

H-h 15 cm 24h 5832 0.026 0.080 0.076 0.8 95.6 0.0 4.0 0.0 

AHW-ahw 15 cm 24h 33 -0.168 0.282 0.231 21.2 75.8 0.0 0.0 0.0 

ALW-alw 15 cm 24h 36 0.108 0.117 0.045 0.0 83.3 0.0 0.0 0.0 

THW-thw 1.50 hr 25h 33 0.727 5.111 5.137 12.1 60.6 18.2 0.0 0.0 

TLW-tlw 1.50 hr 25h 36 -0.167 2.273 2.299 5.6 61.1 8.3 0.0 0.0 

 SCENARIO: SEMI-OPERATIONAL FORECAST 

H00-h00 15 cm 24h 494 0.036 0.073 0.064 0.4 96.8 0.0 0.0 0.0 

H06-h06 15 cm 24h 490 0.019 0.082 0.080 0.8 93.9 0.0 24.0 0.0 

H12-h12 15 cm 24h 490 0.036 0.076 0.066 0.2 95.7 0.4 0.0 0.0 

H18-h18 15 cm 24h 490 0.019 0.087 0.085 1.4 94.1 0.2 24.0 0.0 

H24-h24 15 cm 24h 490 0.036 0.088 0.080 0.4 95.9 0.4 0.0 0.0 

AHW-ahw 15 cm 24h 29 -0.134 0.204 0.157 13.8 65.5 0.0 

ALW-alw 15 cm 24h 36 0.131 0.139 0.047 0.0 66.7 0.0 

THW-thw 1.50 hr 25h 29 2.310 5.392 4.958 6.9 48.3 27.6 

TLW-tlw 1.50 hr 25h 36 -0.250 2.291 2.310 8.3 44.4 2.8 

Table A.2. Skill Assessment of Semi-Operational Predictions at NOS Gauge at Sturgeon Point, NY (9063028) for 2004. 

Station: Sturgeon Point, Lake Erie, NY 

Longest continuous data time period from: 4/15/2004 to 12/20/2004 

Data gap is filled using SVD method 

Data are filtered using 3.0 Hour Fourier Filter 

----------------------------------------------------------------------------------- 

VARIABLE X N IMAX SM RMSE SD NOF CF POF MDNO MDPO 

CRITERION - - - - - - <1% >90% <1% <N <N 

----------------------------------------------------------------------------------- 

 SCENARIO: SEMI-OPERATIONAL NOWCAST 

H 5832 174.218 

h 5832 174.184 

H-h 15 cm 24h 5832 0.034 0.076 0.068 0.5 96.4 0.0 4.0 0.0 

AHW-ahw 15 cm 24h 35 -0.118 0.189 0.150 20.0 71.4 0.0 0.0 0.0 

ALW-alw 15 cm 24h 33 0.113 0.121 0.043 0.0 81.8 0.0 0.0 0.0 

THW-thw 1.50 hr 25h 35 -1.286 5.079 4.986 17.1 65.7 2.9 0.0 0.0 

TLW-tlw 1.50 hr 25h 33 -0.030 2.623 2.663 9.1 60.6 15.2 0.0 0.0 

 SCENARIO: SEMI-OPERATIONAL FORECAST 

H00-h00 15 cm 24h 494 0.043 0.071 0.056 0.2 97.4 0.0 0.0 0.0 

H06-h06 15 cm 24h 490 0.030 0.076 0.069 0.2 94.1 0.0 0.0 0.0 

H12-h12 15 cm 24h 490 0.044 0.075 0.061 0.0 96.3 0.6 0.0 0.0 

H18-h18 15 cm 24h 490 0.029 0.079 0.074 0.8 94.9 0.0 12.0 0.0 

H24-h24 15 cm 24h 490 0.044 0.084 0.072 0.4 96.1 0.6 0.0 0.0 

AHW-ahw 15 cm 24h 32 -0.147 0.248 0.202 15.6 68.8 0.0 

ALW-alw 15 cm 24h 34 0.127 0.135 0.047 0.0 73.5 0.0 

THW-thw 1.50 hr 25h 32 0.938 5.500 5.506 15.6 37.5 15.6 

TLW-tlw 1.50 hr 25h 34 0.176 2.701 2.736 8.8 35.3 11.8 

49
Table A.3. Skill Assessment of Semi-Operational Predictions at NOS Gauge at Erie, PA (9063038) for 2004. 

Station: Erie, Lake Erie, PA 

Longest continuous data time period from: 4/15/2004 to 12/20/2004 

Data gap is filled using SVD method 

Data are filtered using 3.0 Hour Fourier Filter 

----------------------------------------------------------------------------------- 

VARIABLE X N IMAX SM RMSE SD NOF CF POF MDNO MDPO 

CRITERION - - - - - - <1% >90% <1% <N <N 

----------------------------------------------------------------------------------- 

 SCENARIO: SEMI-OPERATIONAL NOWCAST 

H 5832 174.208 

h 5832 174.200 

H-h 15 cm 24h 5832 0.008 0.045 0.044 0.0 98.9 0.0 1.0 0.0 

AHW-ahw 15 cm 24h 32 -0.088 0.119 0.082 0.0 71.9 0.0 0.0 0.0 

ALW-alw 15 cm 24h 41 0.064 0.072 0.032 0.0 100.0 0.0 0.0 0.0 

THW-thw 1.50 hr 25h 32 -0.906 5.452 5.462 18.8 53.1 9.4 0.0 0.0 

TLW-tlw 1.50 hr 25h 41 -0.537 2.263 2.226 7.3 68.3 7.3 0.0 0.0 

 

 SCENARIO: SEMI-OPERATIONAL FORECAST 

H00-h00 15 cm 24h 494 0.015 0.044 0.041 0.0 99.4 0.0 0.0 0.0 

H06-h06 15 cm 24h 490 0.005 0.047 0.046 0.0 99.0 0.0 0.0 0.0 

H12-h12 15 cm 24h 490 0.016 0.049 0.046 0.0 98.8 0.0 0.0 0.0 

H18-h18 15 cm 24h 490 0.004 0.050 0.050 0.2 98.6 0.0 0.0 0.0 

H24-h24 15 cm 24h 490 0.017 0.052 0.050 0.2 98.6 0.0 0.0 0.0 

AHW-ahw 15 cm 24h 29 -0.094 0.138 0.103 3.4 82.8 0.0 

ALW-alw 15 cm 24h 40 0.073 0.079 0.032 0.0 97.5 0.0 

THW-thw 1.50 hr 25h 29 0.103 4.924 5.010 20.7 37.9 17.2 

TLW-tlw 1.50 hr 25h 40 -0.350 2.313 2.316 15.0 57.5 2.5 

Table A.4. Skill Assessment of Semi-Operational Predictions at NOS Gauge at Fairport, OH (9063053) for 2004. 

Station: Fairport, Lake Erie, OH 

Longest continuous data time period from: 4/15/2004 to 12/20/2004 

Data gap is filled using SVD method 

Data are filtered using 3.0 Hour Fourier Filter 

----------------------------------------------------------------------------------- 

VARIABLE X N IMAX SM RMSE SD NOF CF POF MDNO MDPO 

CRITERION - - - - - - <1% >90% <1% <N <N 

----------------------------------------------------------------------------------- 

 SCENARIO: SEMI-OPERATIONAL NOWCAST 

H 5832 174.200 

h 5832 174.231 

H-h 15 cm 24h 5832 -0.031 0.044 0.031 0.0 99.7 0.0 0.0 0.0 

AHW-ahw 15 cm 24h 18 -0.082 0.112 0.078 0.0 77.8 0.0 0.0 0.0 

ALW-alw 15 cm 24h 15 0.032 0.056 0.047 0.0 100.0 0.0 0.0 0.0 

THW-thw 1.50 hr 25h 18 -0.389 5.652 5.802 16.7 38.9 11.1 0.0 0.0 

TLW-tlw 1.50 hr 25h 15 -0.667 2.503 2.498 13.3 73.3 0.0 0.0 0.0 

 

SCENARIO: SEMI-OPERATIONAL FORECAST 

H00-h00 15 cm 24h 494 -0.032 0.044 0.030 0.0 99.8 0.0 0.0 0.0 

H06-h06 15 cm 24h 490 -0.032 0.044 0.030 0.0 100.0 0.0 0.0 0.0 

H12-h12 15 cm 24h 490 -0.030 0.042 0.030 0.0 100.0 0.0 0.0 0.0 

H18-h18 15 cm 24h 490 -0.032 0.045 0.031 0.0 99.8 0.0 0.0 0.0 

H24-h24 15 cm 24h 490 -0.030 0.044 0.032 0.0 99.4 0.0 0.0 0.0 

AHW-ahw 15 cm 24h 16 -0.104 0.118 0.057 0.0 75.0 0.0 

ALW-alw 15 cm 24h 14 0.034 0.053 0.042 0.0 100.0 0.0 

THW-thw 1.50 hr 25h 16 1.063 5.815 5.904 12.5 56.3 25.0 

TLW-tlw 1.50 hr 25h 14 0.429 2.171 2.209 7.1 64.3 7.1 

50
Table A.5. Skill Assessment of Semi-Operational Predictions at NOS Gauge at Cleveland, OH (9063063) for 2004. 

Station: Cleveland, Lake Erie, OH 

Longest continuous data time period from: 4/15/2004 to 12/20/2004 

Data gap is filled using SVD method 

Data are filtered using 3.0 Hour Fourier Filter 

----------------------------------------------------------------------------------- 

VARIABLE X N IMAX SM RMSE SD NOF CF POF MDNO MDPO 

CRITERION - - - - - - <1% >90% <1% <N <N 

----------------------------------------------------------------------------------- 

 SCENARIO: SEMI-OPERATIONAL NOWCAST 

H 5832 174.200 

h 5832 174.192 

H-h 15 cm 24h 5832 0.008 0.040 0.040 0.0 99.1 0.1 0.0 2.0 

AHW-ahw 15 cm 24h 18 -0.041 0.068 0.056 0.0 100.0 0.0 0.0 0.0 

ALW-alw 15 cm 24h 27 0.090 0.113 0.070 0.0 81.5 0.0 0.0 0.0 

THW-thw 1.50 hr 25h 18 0.222 4.955 5.094 22.2 50.0 16.7 0.0 0.0 

TLW-tlw 1.50 hr 25h 27 0.037 2.009 2.047 3.7 66.7 7.4 0.0 0.0 

 SCENARIO: SEMI-OPERATIONAL FORECAST 

H00-h00 15 cm 24h 494 0.006 0.039 0.038 0.0 99.4 0.0 0.0 0.0 

H06-h06 15 cm 24h 490 0.006 0.038 0.038 0.0 99.2 0.0 0.0 0.0 

H12-h12 15 cm 24h 490 0.006 0.038 0.038 0.0 99.6 0.0 0.0 0.0 

H18-h18 15 cm 24h 490 0.006 0.039 0.039 0.0 99.6 0.0 0.0 0.0 

H24-h24 15 cm 24h 490 0.006 0.041 0.041 0.0 99.4 0.0 0.0 0.0 

AHW-ahw 15 cm 24h 15 -0.051 0.073 0.054 0.0 100.0 0.0 

ALW-alw 15 cm 24h 27 0.081 0.095 0.051 0.0 96.3 0.0 

THW-thw 1.50 hr 25h 15 -0.533 4.258 4.373 20.0 46.7 13.3 

TLW-tlw 1.50 hr 25h 27 0.407 2.822 2.845 7.4 48.1 18.5 

Table A.6. Skill Assessment of Semi-Operational Predictions at NOS Gauge at Marblehead, OH (9063079) for 2004. 

Station: Marblehead, Lake Erie, OH 

Longest continuous data time period from: 4/15/2004 to 10/23/2004 

Data gap is filled using SVD method 

Data are filtered using 3.0 Hour Fourier Filter 

----------------------------------------------------------------------------------- 

VARIABLE X N IMAX SM RMSE SD NOF CF POF MDNO MDPO 

CRITERION - - - - - - <1% >90% <1% <N <N 

----------------------------------------------------------------------------------- 

 SCENARIO: SEMI-OPERATIONAL NOWCAST 

H 5630 174.194 

h 5630 174.195 

H-h 15 cm 24h 5630 0.000 0.050 0.050 0.0 98.4 0.0 0.0 1.0 

AHW-ahw 15 cm 24h 22 -0.046 0.061 0.041 0.0 100.0 0.0 0.0 0.0 

ALW-alw 15 cm 24h 42 0.073 0.107 0.079 0.0 85.7 2.4 0.0 0.0 

THW-thw 1.50 hr 25h 22 0.909 5.745 5.806 4.5 59.1 18.2 0.0 0.0 

TLW-tlw 1.50 hr 25h 42 -0.238 2.390 2.407 7.1 59.5 9.5 0.0 0.0 

 SCENARIO: SEMI-OPERATIONAL FORECAST 

H00-h00 15 cm 24h 477 -0.007 0.048 0.048 0.0 99.4 0.0 0.0 0.0 

H06-h06 15 cm 24h 474 0.002 0.055 0.056 0.0 97.7 0.2 0.0 0.0 

H12-h12 15 cm 24h 473 -0.007 0.058 0.058 0.2 97.9 0.2 0.0 0.0 

H18-h18 15 cm 24h 474 0.004 0.055 0.055 0.0 98.5 0.2 0.0 0.0 

H24-h24 15 cm 24h 473 -0.008 0.065 0.065 0.4 97.3 0.2 0.0 0.0 

AHW-ahw 15 cm 24h 20 -0.057 0.071 0.043 0.0 95.0 0.0 

ALW-alw 15 cm 24h 43 0.076 0.117 0.089 0.0 88.4 2.3 

THW-thw 1.50 hr 25h 20 -0.850 5.408 5.480 20.0 50.0 5.0 

TLW-tlw 1.50 hr 25h 43 -0.140 2.728 2.757 11.6 53.5 11.6 

51
Table A.7. Skill Assessment of Semi-Operational Predictions at NOS Gauge at Toledo, OH (9063085) for 2004. 

Station: Toledo, Lake Erie, OH 

Longest continuous data time period from: 8/10/2004 to 12/20/2004 

Data gap is filled using SVD method 

Data are filtered using 3.0 Hour Fourier Filter 

----------------------------------------------------------------------------------- 

VARIABLE X N IMAX SM RMSE SD NOF CF POF MDNO MDPO 

CRITERION - - - - - - <1% >90% <1% <N <N 

----------------------------------------------------------------------------------- 

 SCENARIO: SEMI-OPERATIONAL NOWCAST 

H 5823 174.173 

h 5823 174.202 

H-h 15 cm 24h 5823 -0.029 0.080 0.075 0.3 94.0 0.1 3.0 0.0 

AHW-ahw 15 cm 24h 19 -0.119 0.142 0.079 5.3 84.2 0.0 0.0 0.0 

ALW-alw 15 cm 24h 42 0.063 0.106 0.086 0.0 85.7 0.0 0.0 0.0 

THW-thw 1.50 hr 25h 19 0.790 3.269 3.259 10.5 36.8 21.1 0.0 0.0 

TLW-tlw 1.50 hr 25h 42 -0.381 2.545 2.547 11.9 57.1 7.1 0.0 0.0 

 

SCENARIO: SEMI-OPERATIONAL FORECAST 

H00-h00 15 cm 24h 493 -0.037 0.087 0.079 0.6 93.3 0.2 0.0 0.0 

H06-h06 15 cm 24h 489 -0.016 0.096 0.095 0.4 88.8 0.8 0.0 0.0 

H12-h12 15 cm 24h 489 -0.034 0.102 0.096 1.2 89.8 0.0 0.0 0.0 

H18-h18 15 cm 24h 489 -0.015 0.088 0.086 0.0 91.4 0.4 0.0 0.0 

H24-h24 15 cm 24h 489 -0.034 0.107 0.102 1.4 87.7 0.2 12.0 0.0 

AHW-ahw 15 cm 24h 21 -0.157 0.172 0.073 0.0 57.1 0.0 

ALW-alw 15 cm 24h 42 0.110 0.173 0.135 0.0 73.8 9.5 

THW-thw 1.50 hr 25h 21 0.714 5.851 5.951 9.5 47.6 23.8 

TLW-tlw 1.50 hr 25h 42 -0.357 2.493 2.497 11.9 54.8 7.1 

Table A.8. Skill Assessment of Semi-Operational Predictions at NOS Gauge at Fermi Power Plant, MI (9063090) for 2004. 

Station: Fermi Power Plant, Lake Erie, OH 

Longest continuous data time period from: 6/14/2004 to 12/20/2004 

Data gap is filled using SVD method 

Data are filtered using 3.0 Hour Fourier Filter 

----------------------------------------------------------------------------------- 

VARIABLE X N IMAX SM RMSE SD NOF CF POF MDNO MDPO 

CRITERION - - - - - - <1% >90% <1% <N <N 

----------------------------------------------------------------------------------- 

 SCENARIO: SEMI-OPERATIONAL NOWCAST 

H 5686 174.181 

h 5686 174.186 

H-h 15 cm 24h 5686 -0.005 0.065 0.065 0.1 96.6 0.1 2.0 2.0 

AHW-ahw 15 cm 24h 21 -0.075 0.100 0.069 0.0 81.0 0.0 0.0 0.0 

ALW-alw 15 cm 24h 48 0.088 0.125 0.089 0.0 83.3 4.2 0.0 0.0 

THW-thw 1.50 hr 25h 21 1.286 5.490 5.469 9.5 61.9 19.0 0.0 0.0 

TLW-tlw 1.50 hr 25h 48 0.083 2.062 2.082 2.1 66.7 8.3 0.0 0.0 

 

SCENARIO: SEMI-OPERATIONAL FORECAST 

H00-h00 15 cm 24h 481 -0.016 0.071 0.069 0.6 95.8 0.2 0.0 0.0 

H06-h06 15 cm 24h 478 0.002 0.073 0.073 0.0 95.8 0.8 0.0 0.0 

H12-h12 15 cm 24h 477 -0.017 0.079 0.077 0.8 94.3 0.0 0.0 0.0 

H18-h18 15 cm 24h 478 0.004 0.070 0.070 0.0 95.2 0.4 0.0 0.0 

H24-h24 15 cm 24h 477 -0.016 0.086 0.084 0.8 93.9 0.2 0.0 0.0 

AHW-ahw 15 cm 24h 19 -0.082 0.100 0.060 0.0 84.2 0.0 

ALW-alw 15 cm 24h 48 0.116 0.160 0.111 0.0 79.2 6.3 

THW-thw 1.50 hr 25h 19 0.684 6.509 6.650 21.1 47.4 15.8 

TLW-tlw 1.50 hr 25h 48 -0.083 2.769 2.797 10.4 47.9 14.6 

52
APPENDIX B. Time Series Plots of Semi-Operational Water Level Nowcasts vs. 

Observations from 15 April to 17 December 2004 at 8 NOS Gauges in Lake Erie. 100 150 200 250 300 350

Julian Day (2004)

176.0175.0174.0173.0

Water Levels (meters, IGLD1985)

observationnowcastevents in observationevents in nowcast

Buffalo

Fig. B.1. Time Series Plot of Semi-Operational Nowcasts of Water Level vs. Observations at NOS Gauge at 

Buffalo, NY during 2004. 

53
100 150 200 250 300 350

Julian Day (2004)

176.0175.0174.0173.0

Water Levels (meters, IGLD1985)

observationnowcastevents in observationevents in nowcast

Sturgeon Point

Fig. B.2. Time Series Plot of Semi-Operational Nowcasts of Water Level vs. Observations at NOS Gauge at 

Sturgeon Point, NY during 2004. 

54
100 150 200 250 300 350

Julian Day (2004)

176.0175.0174.0173.0

Water Levels (meters, IGLD1985)

observationnowcastevents in observationevents in nowcast

Erie

Fig. B.3. Time Series Plot of Semi-Operational Nowcasts of Water Level vs. Observations at NOS Gauge at Erie, 

PA during 2004. 

55
100 150 200 250 300 350

Julian Day (2004)

176.0175.0174.0173.0

Water Levels (meters, IGLD1985)

observationnowcastevents in observationevents in nowcast

Fairport

Fig. B.4. Time Series Plot of Semi-Operational Nowcasts of Water Level vs. Observations at NOS Gauge at 

Fairport, OH during 2004. 

56
100 150 200 250 300 350

Julian Day (2004)

176.0175.0174.0173.0

Water Levels (meters, IGLD1985)

observationnowcastevents in observationevents in nowcast

Cleveland

Fig. B.5. Time Series Plot of Semi-Operational Nowcasts of Water Level vs. Observations at NOS Gauge at 

Cleveland, OH during 2004. 

57
100 150 200 250 300 350

Julian Day (2004)

176.0175.0174.0173.0

Water Levels (meters, IGLD1985)

observationnowcastevents in observationevents in nowcast

Marblehead

Fig. B.6. Time Series Plot of Semi-Operational Nowcasts of Water Level vs. Observations at NOS Gauge at 

Marblehead, OH during 2004. 

58
100 150 200 250 300 350

Julian Day (2004)

176.0175.0174.0173.0

Water Levels (meters, IGLD1985)

observationnowcastevents in observationevents in nowcast

Toledo

Fig. B.7. Times Series Plot of Semi-Operational Nowcasts of Water Level at NOS Gauge at Toledo, OH during 

2004. 

59
100 150 200 250 300 350

Julian Day (2004)

176.0175.0174.0173.0

Water Levels (meters, IGLD1985)

observationnowcastevents in observationevents in nowcast

Fermi Power Plant

Fig. B.8. Times Series Plot of Semi-Operational Nowcasts of Water Level at NOS Gauge at Fermi Power Plant, MI 

during 2004. 

 

60
APPENDIX C. Time Series Plots of Semi-Operational Water Level Forecast Guidance vs. 

Observations from 15 April to 17 December 2004 at eight NOS Gauges in Lake Erie. 100 150 200 250 300 350 Julian Day (2004) 176.0 175.0 174.0 173.0

Water Levels (meters, IGLD1985)

observationforecastevents in observationevents in forecast

Buffalo

Fig. C.1. Times Series Plot of Semi-Operational Forecast Guidance of Water Level vs. Observations at NOS Gauge 

at Buffalo, NY during 2004. 

61
100 150 200 250 300 350

Julian Day (2004)

176.0175.0174.0173.0

Water Levels (meters, IGLD1985)

observationforecastevents in observationevents in forecast

Sturgeon Point

Fig. C.2. Times Series Plot of Semi-Operational Forecast Guidance of Water Level vs. Observations at NOS Gauge 

at Sturgeon, NY during 2004. 

62
100 150 200 250 300 350

Julian Day (2004)

176.0175.0174.0173.0

Water Levels (meters, IGLD1985)

observationforecastevents in observationevents in forecast

Erie

Fig. C.3. Time Series Plot of Semi-Operational Forecast Guidance of Water Level vs. Observations at NOS Gauge 

at Erie, PA during 2004. 

63
100 150 200 250 300 350

Julian Day (2004)

176.0175.0174.0173.0

Water Levels (meters, IGLD1985)

observationforecastevents in observationevents in forecast

Fairport

Fig. C.4. Time Series Plot of Semi-Operational Forecast Guidance of Water Level vs. Observations at NOS Gauge 

at Fairport, OH during 2004. 

64
100 150 200 250 300 350

Julian Day (2004)

176.0175.0174.0173.0

Water Levels (meters, IGLD1985)

observationforecastevents in observationevents in forecast

Cleveland

Fig. C.5. Time Series Plot of Semi-Operational Forecast Guidance of Water Level vs. Observations at NOS Gauge 

at Cleveland, OH during 2004. 

65
100 150 200 250 300 350

Julian Day (2004)

176.0175.0174.0173.0

Water Levels (meters, IGLD1985)

observationforecastevents in observationevents in forecast

Marblehead

Fig. C.6. Time Series Plot of Semi-Operational Forecast Guidance of Water Level vs. Observations at NOS Gauge 

at Marblehead, OH during 2004. 

66
100 150 200 250 300 350

Julian Day (2004)

176.0175.0174.0173.0

Water Levels (meters, IGLD1985)

observationforecastevents in observationevents in forecast

Toledo

Fig. C.7. Time Series Plot of Semi-Operational Forecast Guidance of Water Level vs. Observations at NOS Gauge 

at Toledo, OH during 2004. 

67
100 150 200 250 300 350

Julian Day (2004)

176.0175.0174.0173.0

Water Levels (meters, IGLD1985)

observationforecastevents in observationevents in forecast

Fermi Power Plant

Fig. C.8. Time Series Plot of Semi-Operational Forecast Guidance of Water Level vs. Observations at the NOS 

Gauge at Fermi Power Plant, MI during 2004. 

68
APPENDIX D. Skill Assessment Scores of Semi-Operational Nowcasts and Forecast 

Guidance of Surface Water Temperatures from 21 April to 8 December 2004 at a NDBC 

fixed buoy in Lake Erie. 

Table D.1. Skill Assessment Statistics of Semi-Operational Nowcasts and Forecast Surface 

Water Guidance at the NWS/NDBC Fixed Buoy 45005 (Western Erie) for the Period 

21 April to 8 December 2004. 

Station: NDBC Buoy 45005 in Lake Erie 

Longest continuous data time period from: 4/21/2004 to 12/8/2004 

Data gap is filled using SVD method 

Data are filtered using 3.0 Hour Fourier Filter 

----------------------------------------------------------------------------------- 

VARIABLE X N IMAX SM RMSE SD NOF CF POF MDNO MDPO 

CRITERION - - - - - - <1% >90% <1% <N <N 

----------------------------------------------------------------------------------- 

 SCENARIO: SEMI-OPERATIONAL NOWCAST 

T 5566 17.800 

t 5566 16.850 

T-t 3.0 c 24h 5566 0.951 1.292 0.875 0.0 98.7 0.0 0.0 0.0 

 

SCENARIO: SEMI-OPERATIONAL FORECAST 

T00-t00 3.0 c 24h 464 1.070 1.400 0.904 0.0 98.1 0.0 0.0 0.0 

T06-t06 3.0 c 24h 460 0.871 1.406 1.105 0.0 97.4 0.0 0.0 0.0 

T12-t12 3.0 c 24h 460 0.824 1.446 1.190 0.0 97.8 0.0 0.0 0.0 

T18-t18 3.0 c 24h 460 0.703 1.279 1.070 0.0 99.1 0.0 0.0 0.0 

T24-t24 3.0 c 24h 460 0.713 1.306 1.095 0.0 98.7 0.0 0.0 0.0 

69
70
APPENDIX E. Time Series Plots of Semi-Operational Nowcasts and Forecast Guidance of 

Surface Water Temperatures vs. Observations from 15 April to 17 December 2004 at a 

NDBC fixed buoy in Lake Erie. 

100 150 200 250 300 350

Julian Day (2004)

5.0

30.025.020.015.010.0

Temperature (Degrees)

observationnowcastforecast

NDBC 45005

Fig. E.1. Time Series Plot of Semi-Operational Nowcasts and Forecast Guidance of Surface 

Water Temperatures (o

C) vs. Observations at the NWS/NDBC Fixed Buoy 45005 

(Western Lake Erie) for the Period 21 April to 8 December 2004. The forecast values 

depicted on the plot are from the 0000 UTC forecast cycle. 

71
72
APPENDIX F. Description of NWS Water Level Criteria for Lakeshore Flood Warnings 

and Low Water Statements for Lake Erie. 

For the Michigan shore of Lake Erie, the NWS WFO in Detroit, MI issues coastal flood 

warnings when the water level is expected to be 72 inches above NOS chart datum. The WFO 

Detroit issues low water statements for this area when water levels are expected to fall 10 inches 

or more below chart datum or in coordination with WFO Cleveland as appropriate. These 

general guidelines were developed over the years in collaboration with U.S. Coast Guard 

personnel and user groups (Richard Wagenmaker, 2006, personal communication). 

For the Ohio and Pennsylvania shores of Lake Erie, the NWS WFO in Cleveland, OH issues 

flood warnings when the water level is expected to be 60 inches above NOS charge. The WFO 

issues low water statements for this area when water levels are forecast or observed to be below 

the critical mark for safe navigation. This threshold is determined by subtracting 24 inches from 

the bi-weekly forecast level issued by the U.S. Corps of Engineers, Detroit District 

(http://www.lre.usace.army.mil/greatlakes/hh/datalinks/PrinterFriendly/channeldepth.pdf) . 

“This value can be determined by using the lowest value in line 4 or line 5. Using the latest 

forecast level on the web site, the lowest value is 22 inches above chart datum. Subtracting 24 

from 22, you get 2 inches below chart datum. So, in this example anytime the water level is 

forecast or observed to be 2 inches below chart datum or below, we would issue a low water 

statement/advisory” (Michael Dutter, 2006, personal communication). 

For the New York shore of Lake Erie, the NWS WFO in Buffalo, NY issues flood warnings 

when the water level is observed or expected to reach 8 feet above low water datum (Thomas 

Niziol, 2006, personal communication). The Buffalo WFO does not issue low water statements 

since it so rare to have a low water event on the eastern shore of the lake. 

73