NOAA Technical Memorandum NOS CS 8

SKILL ASSESSMENT OF NOS LAKE MICHIGAN 

OPERATIONAL FORECAST SYSTEM (LMOFS) 

Silver Spring, Maryland 

June 2007 

noaa National Oceanic and Atmospheric Administration 

U.S. DEPARTMENT OF COMMERCE 

National Ocean Service 

Coast Survey Development Laboratory 
ii

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. 
NOAA Technical Memorandum NOS CS 8 

_____________________________________________________________________ 

SKILL ASSESSMENT OF NOS LAKE MICHIGAN 

OPERATIONAL FORECAST SYSTEM (LMOFS) 

John G. W. Kelley 

Philip Chu 

Ai-Jun Zhang 

Office of Coast Survey, Coast Survey Development Laboratory, Silver Spring, 

MD 

 Gregory A. Lang 

 David J. Schwab 

Office of Oceanic and Atmospheric Research 

Great Lakes Environmental Research Laboratory, Ann Arbor, MI 

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
iv

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.
v

 TABLE OF CONTENTS 

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

LIST OF TABLES..................................................................................................................... viii 

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

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

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

2. LAKE MICHIGAN ...................................................................................................................2 

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

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

3.2 Grid Domain ........................................................................................................................3 

3.3 Data Ingest ...........................................................................................................................3 

3.4 Nowcast Cycle .....................................................................................................................4 

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

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

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

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

4.1.1 Period 1982-1983..........................................................................................................7 

4.1.2 Period 1994-1995..........................................................................................................7 

4.2 Method of Evaluation ..........................................................................................................8 

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

4.3.1 Water Levels during 1982-1983 ...................................................................................9 

4.3.2 Water Levels during 1994-1995 ................................................................................12 

4.3.3 Water Temperatures during 1982-1983.....................................................................13 

4.3.4. Water Temperatures during 1994-1995.....................................................................14 

4.4 Skill Assessment of Subsurface Hindcasts ............................................................................15 

4.4.1 Water Temperatures during 1982-1983......................................................................15 

4.4.2 Water Temperatures during 1994-1995......................................................................17 

4.4.3 Water Currents during 1982-1983 ..............................................................................20 

4.4.4 Water Currents during 1994-1995 ..............................................................................22 

5. SEMI-OPERATIONAL NOWCAST SKILL ASSESSMENT ...............................................22 

5.1 Description of Nowcast Cycles.........................................................................................22 

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

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

5.4 Assessment of Water Temperature Nowcasts....................................................................34 
vi

6. SEMI-OPERATIONAL FORECAST SKILL ASSESSMENT .............................................36 

6.1 Description of Forecast Cycles ..........................................................................................36 

6.2 Method of Evaluation .......................................................................................................36 

6.3 Assessment of Water Level Forecast Guidance.................................................................37 

6.4 Assessment of Water Temperature Forecast Guidance .....................................................39 

7. SUMMARY............................................................................................................................41 

8. RECOMMENDATION FOR FUTURE WORK....................................................................44 

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

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

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

Forecasts of Water Levels from six NOS NWLON gauges in Lake Michigan for 2004. ...........49 

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

at Six NOS Gauges during 2004..................................................................................................53 

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

Observations at Six NOS Gauges in Lake Michigan during 2004. .............................................59 

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

Forecasts of Surface Water Temperatures at Two NWS/NDBC fixed buoys in Lake Michigan for 

2004..............................................................................................................................................65 

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

Surface Water Temperature vs. Observations at Two NWS/NDBC fixed buoys during 2004...66 
vii

LIST OF FIGURES 

Figure 1. Map depicting the POMGL grid domain (5 km spatial resolution) used by NOS’ Lake 

Michigan Operational Forecast System (from SB98). ..................................................5 

Figure 2. Map depicting locations of observing networks during (a) 1983-1984 and (b) 1994-

1995 periods. .................................................................................................................8 

Figure 3. Times series of simulated water levels versus observed for October – November 1983 

at NOS Gauge Stations, (a) Calumet Harbor, IL, (b) Milwaukee, WI, and (c) Green 

Bay, WI (from SB98). .................................................................................................10 

Figure 4. Comparison of simulated surface water temperatures vs. observed at NWS fixed buoys 

45007 and 45002 for 1982-1983. Red line is the observation and the blue line is the 

model simulation (modified from SB98). ...................................................................13 

Figure 5. Comparison of simulated vs. observed surface water temperatures at NWS fixed 

buoys 45002, 45010, and 45007 for 1994-1995. Red line is observation and blue is 

model simulation (modified from SB98). ...................................................................14 

Figure 6. Time-series of POMGL simulated versus observed subsurface water temperatures at 

GLERL current moorings collocated near 45007 and 45002 during 1982-1983. Also 

shown are the simulated vs. observed surface water temperatures at the two buoys. 

Red line is the observation, blue is the model simulation (from SB98)......................16 

Figure 7. POMGL’s simulated mean temperature profile for the period 1982-1983 (from SB98). 

The color contours are in oC. ........................................................................................17 

Figure 8. Time-series of POMGL simulated vs. observed water temperatures at GLERL current 

mooring near NWS buoy 45007 for 1994-1995. Also shown are the simulated vs. 

observed water temperatures at buoy 45007. Red line is the observation, blue is the 

model simulation (from SB98)....................................................................................18 

Figure 9. Time series of POMGL simulated versus observed water temperatures at the 

Muskegon, MI municipal water intake for May-June 1994 (from SB98)...................19 

Figure 10. POMGL simulated vertical water temperature profiles vs. observations on four days 

in 1994-1995 at Station 18M in southern Lake Michigan near NWS buoy 45007 

(modified from SB98)................................................................................................19 

Figure 11. POMGL simulated versus observed temperature, transects offshore of Ludington, MI 

(upper panel) and Holland, MI (lower panel) (from SB98).......................................20 

Figure 12. Progressive vector diagrams of POMGL simulated vs. observed currents at 15 m 

depth at GLERL moorings in the southern basin (#1, 2, 3, and 23) for 1982-83. Black 

line is observations, green is model simulation. (from SB98). ...................................21 

Figure 13. Map depicting locations of NOS NWLON gauges in Lake Michigan .......................27 

Figure 14. Map depicting the locations of NWS/NDBC fixed buoys in Lake Michigan along ..30 

with the POMGL grid. 
viii

LIST OF TABLES 

Table 1. Statistical comparison of 1982-83 simulated vs. observed short-term water level 

fluctuations (cm) at NOS water level gauges (modified from SB98)...........................11 

Table 2. Statistical comparison of 1994-95 simulated vs. observed short term water level 

fluctuations (cm.) at NOS water level gauges (modified from SB98)..........................12 

Table 3. Statistical measures of POMGL surface water temperature simulations at NWS/NDBC 

fixed buoys 45002 and 45007 for the periods 1982-1983 and 1994-1995 (modified from 

SB98). ...........................................................................................................................13 

Table 4. Statistical measures of POMGL surface water temperature simulations at GLERL 

subsurface current moorings for the periods 1982-1983 and 1994-1995 at different 

layers in the water column (modified from SB98). ......................................................15 

Table 5. NOS Skill Assessment Statistics (Hess et al. 2003). ....................................................23 

Table 6. 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)............................................................24 

Table 7. Acceptance error limits (X) and the maximum duration limits (L) (modified from Hess 

et al. 2003) for use in the Great Lakes..........................................................................24 

Table 8. Information on NOAA/NOS NWLON stations whose observations were used to 

evaluate the semi-operational nowcasts and forecasts of water levels. ........................26 

Table 9. Information of NWS/NDBC fixed buoys whose observations were used to evaluate the 

semi-operational nowcasts and forecasts of surface water temperatures. ....................28 

Table 10. Summary of Skill assessment Statistics of Semi-Operational Nowcasts of Hourly 

Water Levels at six NOS NWLON Stations in Lake Michigan for the Period 15 April to 

20 December 2004. A total of 5785 nowcasts were used in the assessment. Red 

indicates that the statistic did not pass the NOS acceptance criteria. Green indicates that 

it did meet the criteria ...................................................................................................32 

Table 11. Summary of Standard Statistics Evaluating the Ability of the Semi-Operational 

Nowcasts to Predict Extreme High Water Level Events at the NOS NWLON stations in 

Lake Michigan during the Period 15 April to 17 December 2004. Red indicates that the 

statistic did not pass the NOS acceptance criteria. Green indicates that it did meet the 

criteria. ..........................................................................................................................32 

Table 12. Summary of Standard Statistics Evaluating the Ability of Semi-Operational Nowcasts

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

Michigan for the Period 15 April to 17 December 2004. Red indicates the statistic did 

not pass the NOS acceptance criteria. Green indicates that it did meet the criteria....33 

Table 13. Summary of Skill Assessment Statistics of the Semi-Operational Nowcasts of Hourly 

Surface Water Temperatures at two NWS/NDBC fixed buoys in Lake Michigan for 

the Period from mid-April to early November 2004. Red indicates that the statistic 

did not pass the NOS acceptance criteria. Green indicates that it did meet the criteria 

....35 

Table 14. Summary of Skill assessment Statistics of Semi-Operational Forecast Guidance of 

Hourly Water Levels at six NOS NWLON Stations in Lake Michigan for the Period 
ix

15 April to 17 December 2004. A total of 481 forecasts were used in the assessment. 

Red indicates that the statistic did not pass the NOS acceptance criteria. Green 

indicates that it did meet the criteria..........................................................................37 

Table 15. 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 Michigan during the Period 15 April to 17 December 2004. Red 

indicates that the statistic did not pass the NOS acceptance criteria. Green indicates 

that it did meet the criteria. ........................................................................................38 

Table 16. 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 Michigan during the Period 15 April to 17 December 2004. Red 

indicates that the statistic did not pass the NOS acceptance criteria. Green indicates 

that it did meet the criteria. ........................................................................................39 

Table 17. Summary of Skill Assessment Statistics for Semi-Operational Forecast Guidance in 

Predicting Surface Water Temperatures 24 hours in advance at NWS/NDBC fixed 

buoys during the period from mid-April to early-November 2004. Red indicates 

that the statistic did not pass the NOS acceptance criteria. Green indicates that it did 

meet the criteria. ........................................................................................................41 
x

LIST OF ACRONYMS

ASOS Automated Surface Observing System 

AVHRR Advanced Very High Resolution Radiometer 

AWOS Automated Weather Observing System 

C-MAN Coastal-Marine Automated Network 

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 

LEOFS Lake Erie Operational Forecast System 

LMOFS Lake Michigan Operational Forecast System 

LMMBS Lake Michigan Mass Balance Study 

NAM North America Mesoscale Model 

MMAP Marine Modeling and Analysis Programs 

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 State Coast Guard 

VOS Voluntary Observing Ship 
xi

EXECUTIVE SUMMARY

This document describes the Lake Michigan Operational Forecast System (LMOFS) 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 three-dimensional forecast guidance of water temperature and currents and twodimensional forecasts of water levels for Lake Michigan. 

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

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 model system skill assessment of LMOFS follows scenarios required by NOS standards for 

operational nowcast/forecast systems (Hess et al. 2003) which are applicable to 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 results of the Hydrodynamic Modeling Project of the Lake 

Michigan Mass Balance Study (SB98) were used to satisfy NOS skill assessment requirements. 

The modeling project was conducted by Drs. David Schwab and Dmitry Beletsky of GLERL for 

the years 1982-1983 and 1994-1995 and used the Princeton Ocean Model adapted to the Great 

Lakes (POMGL). The model had a grid increment of 5 km and 20 vertical sigma levels. This is 

the same configuration used by LMOFS. 

SB98 compared POMGL water level simulations to observations at 8 NOS gauges. Surface 

water temperature simulations were compared to observations at 2-3 NWS fixed buoys. 

Subsurface water temperature simulations were compared to observations from GLERL 

moorings, municipal water intakes, ship surveys, and transects. Current simulations were 

evaluated against observations at GLERL current moorings. 

The skill statistics from the SB98 hindcast simulations are summarized below: 

1) Water Levels: 

 POMGL did not simulate the same amount of high frequency (>0.5/hr) fluctuations with 

amplitudes on the order of 5 cm that was seen in the observations. The mean algebraic 

differences (MAD) during 1983-84 and 1994-95 were +0.28 and +0.32 cm, respectively. 

The root mean square differences (RMSD) for the two periods were 3.62 and 4.02 cm, 

respectively. The correlation coefficients (CC) were 0.42 and 0.40 for the two periods. 

Large RMSDs (4.6-6.0cm) but high CCs (0.49-0.62) were found at the NOS gauges located 
xii

at the south and north ends of the lake. The largest amplitude wind-induced water level 

fluctuations on the lake are usually exhibited at these two gauges. The highest RMSD was 

at Green Bay, WI with a value of 12.30 cm indicating the inability of POMGL to simulate 

the water levels in this small bay. Overall, POMGL was able to simulate the water level in 

the main part of the lake but given the 5 km grid increment and lack of atmospheric 

pressure gradients in its forcing fields, the model could not resolve many local effects such 

as harbor resonance and edge waves (SB98). 

2) Surface Water Temperatures: 

There was good agreement between POMGL simulations and observations in terms of both 

horizontal distribution and time evolution of the surface water temperatures. The MADs 

during 1983-84 and 1994-95 were 0.0 and +0.2oC, respectively. The RMSDs for the two 

periods were 1.2 and 1.5oC, respectively. The CCs were 0.99 and 0.96 for the two periods, 

respectively. These positive comparisons indicated that the heat fluxes near the lake’s 

surface were correctly inputted into the model. 

3) Subsurface Water Temperatures: 

POMGL subsurface temperature simulations, especially in the thermocline region were 

not as good as at the surface. For the epilimnion (upper layer), the MADs were -0.7 and 

+0.4o

C for the two periods, respectively. The RMSDs were 2.5 and 2.4oC and the CCs 

were 0.87 and 0.93, respectively. For the hypolimnion (lowest layer of water), the 

MADs were +0.1 and +0.8o

C for the two periods, respectively. The RMSDs were 0.7 and 

1.3o

C and the CCs were 0.78 and 0.87, respectively. These subsurface and surface 

comparisons, as well as comparisons with ship transect data, indicate that POMGL 

reproduced the basic features of the thermal structure evolution in the lake. However, the 

modeled thermocline was too diffuse and also the model did not simulate the frequent 

temperature fluctuations in this region. 

4) Currents: 

POMGL was found to properly simulate a cyclonic large-scale circulation pattern with 

cyclonic circulation within each subbasin, and anticyclonic circulations in ridge areas of 

the lake as indicated by previous observational studies. Comparisons of POMGL 

simulations to current observations using normalized Fourier norm resulted in values 

between 0.70 and 1.00 where values greater than 0 and less than 1 indicate a simulation 

better than no prediction at all. According to SB98, the model did the best in the southern 

basin (which is characterized by smooth bathymetry) and in the fall-winter months when 

barotropic processes are dominant. During the spring-summer months when baroclinic are 

dominant, the horizontal resolution (5 km) is too coarse to properly simulate these 

processes which have horizontal length scales comparable to the Rossby deformation 

radius (~ 5 km). 
xiii

For the semi-operational nowcast and forecast scenarios, an evaluation of GLERL’s real-time 

nowcast (4 times/day) and forecast (2 times/day) cycles from the Great Lakes Coastal Forecast 

System (GLCFS) for Lake Michigan were used to satisfy NOS evaluation standards (Hess et al., 

2003). Although Hess et al. (2003) recommends conducting evaluations for 365 days in order to 

capture all expected seasonal conditions, GLCFS nowcasts and forecast guidance were evaluated 

for the ice free period from 15 April to 17 December 2004. 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 Michigan. 

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 mean (SM), root mean square error (RMSE), standard deviation 

(SD), negative outlier frequency (NOF), positive outlier frequency (POF), maximum duration of 

positive outlier (MDPO), and maximum duration of negative outlier (MDNO). 

The skill statistics for the nowcast and forecast scenarios are summarized below: 

(1) Water Levels: 

Nowcasts: 

The hourly water nowcasts met the NOS acceptance criteria for amplitude at all 6 NOS 

gauges. The MAD ranged between -2.1 cm and +1.9 cm. The nowcasts under-predicted the 

hourly water levels at all gauges except at Port Inland, MI. The RMSE among the 6 gauges 

ranged between 4.8 and 7.0 cm. The hindcasts from 1982-83 and 1994-95 performed better 

than the nowcasts. The reason for this is not clear but could be due to the different method 

used to estimate mean lake-wide water level, the density and location of wind observations 

or some other factor. 

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 

nowcast predictions of high water level events passed the NOS criteria for amplitude at 2 of 

the 6 NOS gauges, while the predictions of low water level events passed the NOS criteria at 

3 of the 6 NOS gauges. The nowcasts failed to meet NOS criteria in predicting the timing of 

both extreme high and low water events at all the NOS gauges. 

Forecast Guidance: 

The hourly forecast guidance met the NOS criteria at all 6 locations. The MAD ranged 

between -1.7 to + 1.3 cm and the RMSE ranged between 5.0 and 7.2 cm, very similar to the 

statistics for the nowcast evaluation. Similar to the nowcasts, the greatest error was at the 

Calumet Harbor, IL gauge located at the southern end of the lake. The forecast underpredicted the water levels at all gauges except at Port Inland, MI. There was no significant 

increase in the MAD, RMSE, or CF values as the forecast projection increased in time. 
xiv

The forecast guidance of extreme high and low water level events passed NOS criteria at 2 

and 4 of the 6 gauges, respectively. The forecast guidance failed to meet NOS criteria in 

predicting the timing of both extreme high and low water events at all NOS gauges. 

(2) Surface Water Temperatures: 

Nowcasts: 

The hourly water temperature nowcasts meet the NOS criteria at the southern buoy. The 

nowcasts came very close to meeting NOS criteria at the northern buoy, failing to meet the 

CF by 7%. The MAD for the period ranged between 0.7 and 1.4o

C and the RMSE ranged 

between 1.5 and 2.2o

C. 

Forecast Guidance: 

The hourly water temperature forecast guidance at 24 hours for the southern buoy passed the 

NOS criteria while the southern one came very close at 87%. The MAD ranged between 0.5 

and 1.2oC and RMSE between 1.3 and 1.9oC, which were slightly lower than for the 

nowcasts. The RMSE of the hourly water temperature forecasts slightly decreased as the 

forecast projection increased in time. 

Key Words: short-term lake predictions, nowcasts, model forecast guidance, Lake Michigan, 

skill assessment, water levels, water currents, water temperatures, Princeton Ocean Model, North 

American Mesoscale weather prediction model
1

1. INTRODUCTION 

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

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

the late 1980s and 1990s to provide nowcasts and short-range forecasts of the physical 

conditions (temperature, currents, water level, and waves) of the five Great Lakes (Schwab and 

Bedford 1994). 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 over 36 contracts from 25 different sources. From the 

start, GLERL and OSU were interested in working cooperatively with NOAA 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) and GLERL-Donelan 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 (Bedford and Schwab 

1991; Yen et al. 1994). Starting in July 1995, twice per day forecasts were made for Lake Erie. 

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 GLERL workstation in Ann Arbor, MI. GLERL’s workstation 

version of GLFS, called The Great Lakes Coastal Forecast System (GLCFS), has been running 

in semi-operational mode at GLERL for Lake Michigan since April 2001. GLCFS for Lake 

Michigan 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 Common Modeling Framework (COMF) and to use 

surface meteorological observations from NOS’ Operational Data Acquisition and Archive 

System (ODAAS). The CO-OPS version of GLCFS for Lake Michigan was renamed as the 

Lake Michigan Operational Forecast System (LMOFS). LMOFS began making operational 

nowcasts and forecasts for Lake Michigan on September 30, 2005 at CO-OPS. 

LMOFS along with LEOFS represents the first NOS forecast systems to be implemented for 

non-tidal water bodies. The predictions from LMOFS, similar to those from NOS estuarine 

forecast systems, must be evaluated to inform users about the skill of the nowcasts and forecasts. 
2

 In evaluating LMOFS, NOS sought to take advantage of previous evaluations done by 

researchers at OSU and GLERL to fulfill the hindcast scenario requirements described in Hess et 

al. (2003). In addition, NOS also utilized the routinely-produced nowcasts and forecasts 

produced by GLERL to fulfill the semi-operational nowcast 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). Brief descriptions of Lake Michigan and an 

overview of LMOFS are given first. 

2. LAKE MICHIGAN 

Lake Michigan is the third largest of the Great Lakes and the fifth largest lake in the world, with 

a width of 100 km and a length of 500 km. It has an average depth of 85 m and a maximum 

depth of 281 m. Because of its considerable depth, the lake exhibits considerably smaller short 

term water level fluctuations than Lake Erie, where the wind-induced water level fluctuations 

often exceed 1 m (Schwab 1978). In Lake Michigan, typical wind-induced water level 

fluctuations are only in the range of 10-20 cm (SB98). Lake Michigan, similar to other Great 

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

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

(Boyce et al. 1989; SB98). 

Lake Michigan experiences three types of water level fluctuations. Short-term changes occur 

due to surface winds and changes in atmospheric pressure. Seasonal changes occur with the 

lowest levels during the winter and highest during the early autumn. The lowest levels occur 

during winter, when evaporation is the greatest and more water is leaving the lake than entering 

it. During the spring the water level begins to rise as runoff from melting snow increases and 

evaporation decreases, as the air above the lake becomes warm and moist and the lake is 

relatively cold. The highest levels occur in early to mid autumn, just before the amount of water 

leaving the lake due to outflows and increased evaporation exceeds the amount of water 

entering the lake. Long term water level changes occur over consecutive years, with wet/cold 

years causing water levels to rise and warm/dry years resulting in lower water levels (GLIN 

2006). 

3. SYSTEM OVERVIEW 

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

 Detailed descriptions of the model as it has been applied to Lake Michigan can be found in 

SB98). Similar descriptions of the model as it has been applied to Lake Erie are given by Hoch 

(1997), Kuan (1995), and Kelley (1995). 

3.1 Description of Model 

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

Blumberg and Mellor (1987). The model is a fully three-dimensional, non-linear primitive 

equation coastal ocean circulation model, with a second order Mellor-Yamada turbulence closure 
3

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

continuity equation, momentum equations and conservation equations for temperature 

simultaneously in an iterative fashion. 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 

coordinates in the vertical (sigma coordinate), finite difference numerical scheme, Boussinesq 

and hydrostatic approximation, mode splitting technique. 

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

Bedford 1994; O’Connor and Schwab 1994). For the rest of this report, the modified version of 

the POM for the Great Lakes will be referred to as POMGL. Lake Michigan, 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 lakewide 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. 

3.2 Grid Domain 

The POMGL domain for Lake Michigan consists of a rectangular grid with a 5-km horizontal 

resolution in both the x- and y-directions. The domain has a total of 2318 grid points with 53 

points in the x-direction and 102 points in the y-direction (Figure 1). 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 uses 

20 sigma levels in the vertical with vertical levels spaced more closely in the upper 30 m of 

water and near the bottom to better resolve both the seasonal thermocline and bottom boundary 

layer (SB98). The levels are located at sigma values of 0, -.0227, -.0454, -.0681, -.0908, -.1135, 

-.1362, -.1589, -.1816, -.2043, -.2270, -.2724, -.3405, -.4313, -5448, -.6810, -.7945, -.8853, -

.9534, and -1.0. 

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 the 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 

decoded and written out to a text file for use by LMOFS and other NOS operational forecast 

systems. The surface observation text file is available to LMOFS within a minute of receiving 

the observations from the CCS. 

The text file includes surface observations from a variety of observing networks on and around 

Lake Michigan. On land, these networks include Automated Surface Observing System 
4

(ASOS), Coastal-Marine Automated Network (C-MAN), and NOS National Water Level 

Observing Network (NWLON). Presently, the surface meteorological observations from United 

States Coast Guard (USCG) stations around the lake are not available in the NCEP data tanks. 

Over water, the networks included 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 LMOFS or by any of the other 

Great Lakes forecast systems. 

3.4 Nowcast Cycle 

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

state of Lake Michigan, including water temperatures and currents. The cycle also produces 

hourly nowcasts of 2-D water levels. 

The initial conditions for the nowcast cycle are provided by 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 

correction and overland adjustment procedure use the previous day’ 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 Very 

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 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). 
Figure 1. Map depicting the POMGL grid domain (5 km spatial resolution) used by NOS’ 

Lake Michigan Operational Forecast System (from SB98). 

5
6

3.5 Forecast Cycle 

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

state of Lake Michigan. The forecast cycle uses the most recent nowcast for 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, 6, 12, or 18 UTC forecast cycles of NWS/NCEP’s North American Mesoscale (NAM) model. 

 Presently, NAM uses the Weather Research and Forecast (WRF) 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 forecast cycle does not use surface pressure guidance from NAM in 

forcing POMGL 

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. 

3.6 Operational Environment and Scheduling 

LMOFS is run operationally on a Linux workstation at NOS’ Center for Operational 

Oceanographic Products and Services (CO-OPS) in Silver Spring, MD. Each hourly nowcast 

cycle is launched at 50 minutes past the top of the hour, three minutes past the time the surface 

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

The forecast cycle of LMOFS is run four times per hour at 0000, 0600, 1200, and 1800 UTC at 

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

LMOFS and also the forecast system for Lake Erie were officially implemented as an 

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

4. HINDCAST SKILL ASSESSMENT 

The results of the Hydrodynamic Modeling Project conducted by Drs. David Schwab and Dmitry 

Beletsky SB98, hereafter SB98) of GLERL for the EPA Lake Michigan Mass Balance Study 

(LMMBS) serves as the basis for the NOS required hindcast scenario assessment (Hess et al. 

2003). The purpose of the LMMBS (http://www.epa.gov/glnpo/lmmb) was to seek the “sources, 

pathways and fate of contaminants cycling through Lake Michigan.” The objectives of the 

modeling project for the LMMBS were to 1) implement a three-dimensional hydrodynamic 

model for Lake Michigan, 2) calibrate the model with GLERL current meter and temperature 

data from the GLERL 1982-83 Lake Michigan field program, 3) use the model to simulate threedimensional transport and thermal structure in Lake Michigan during the mass balance study 

field season of 1994-1995, and 4) couple the hydrodynamic model with a sediment re-suspension 

and transport model being developed at the EPA Large Lakes Research Station. 

SB98 conducted model simulations using POMGL for Lake Michigan during 1982-1983 and 
7

also 1994-1995. They compared their model simulations to observations and their findings were 

published in a NOAA Technical Memorandum (SB98). A summary of their findings with 

respect to the NOS skill assessment requirements for the hindcast scenario (Hess et al. 2003) are 

presented in the following sections. 

4.1 Description of Hindcast Runs 

SB98 conducted model simulations for two time periods using POMGL. The first period was 

during 1982-1983 and used for model calibration. The second period was for 1994-1995 during 

the mass balance study period. Brief summaries about how the POMGL simulations were 

initialized and how the surface forcings were defined are given in this section.

4.1.1 Period 1982-1983 

SB98 performed model simulations for the period from March 31, 1982 to November 30, 1983, a 

total of 600 days. The configuration of POMGL was very similar to the version presently used 

at NOS, with a spatial resolution of 5 km and 20 vertical sigma layers. The model was 

initialized using surface water temperature observations at NWS/NDBC Fixed Buoys 45007 and 

45002 located in the southern and northern parts of the lake, respectively. Since the model was 

started on March 31st , the time of year when vertical temperature gradients are very small, the 

initial vertical temperature gradients in the model were set to zero, but horizontal gradients were 

preserved. The horizontal temperature distribution was determined based on depth and latitude 

in order to represent decreasing temperatures toward shallow waters as shown by observations 

(SB98). The initial velocity was set to zero. Due to the strong wind-driven character of the lake 

circulation, the effect of the initial conditions for velocity on model simulated currents 

disappears within the first few weeks or even first few days if there is a strong wind event 

(SB98). The model was forced with analyzed surface meteorological fields based on 

observations from 8 coastal observing stations and two NDBC fixed buoys (Figure 2a). 

4.1.2 Period 1994-1995 

SB98 conducted additional numerical simulations using POMGL during the mass balance field 

experiment of 1994-1995 using the same model configuration. Model simulations were 

conducted for the period from January 1, 1994 to December 31, 1995 for a total of 730 days. 

For the first 3 months, the water temperature in the model was kept at a constant 2oC due to the 

lack of an ice module in POMGL and the presence of an extensive ice cover. On April 1, 1994, 

when the ice was gone from the lake, the model was switched from a diagnostic mode (constant 

temperature) to a prognostic mode with the same uniform temperature distribution (SB98). The 

model was forced during this period using analyses of surface meteorological observations from 

more stations then were present during 1982-1983, 21 land-based stations and 2 NDBC fixed 

buoys (Figure 2b). 
 (a) (b) 

Figure 2. Map depicting locations of observing networks during (a) 1983-1984 and (b) 

1994-1995 periods. 

4.2 Method of Evaluation 

SB98 compared the POMGL simulations of water levels, and water temperatures were compared 

to available observational data. For the period 1982-1983, observed data included hourly water 

level observations from 9 NOS/CO-OPS water level gauges in the lake, surface water 

temperature data from two NWS/NDBC fixed buoys, and current and water temperature data at 

several depths from GLERL’s 15 subsurface moorings (Gottlieb et al. 1989). A GLERL 

subsurface mooring was collocated at each of the two NWS buoys. 

During the 2nd period of 1994-1995, available data included water level observations at 9 NOS 

water level gauges, surface water temperature observations from three NWS/NDBC buoys, 

subsurface water temperature from 21 municipal water intake sites, subsurface water temperature 

and current observations at 15 moorings. A GLERL subsurface mooring was collocated at 

NWS buoy 45007. The locations of NOS gauges, NWS buoys, municipal water intakes, and 

GLERL moorings are depicted in Figures 2a and 2b, for the first and second periods, 

respectively. 

SB98 used standard statistical measures to quantitatively compare the model simulations to the 

8
9

observed data. These included arithmetic means, root mean square value (RMSV), maximum 

difference, arithmetic means, mean algebraic differencs, root mean square error difference 

(RMSD), and correlation coefficient (CC). The specific set of measures used depended on the 

variables. They found that the correlation coefficient is a good measure of the agreement in the 

timing of events for simulated vs. observed time series. They state that a value greater than 0.5 

usually indicates significant correlation in timing. In addition, SB98 also qualitatively compared 

the simulations to previous research studies. 

4.3 Skill Assessment of Surface Hindcasts 

This section provides a summary of the findings by Schwab and Belesky (1998) regarding the 

performance of POMGL in simulating water levels and surface water temperatures during both 

the 1st and 2nd periods. The water level comparisons are discussed first, followed by analysis of 

the modeled water temperatures. 

4.3.1 Water Levels during 1982-1983 

SB98 compared POMGL water level simulations to observations at 9 NOS water level gauges. 

However, prior to comparing the simulations to observations, they removed the seasonal 

fluctuation of mean lake level from the individual water level records. This was accomplished in 

the following manner. Hourly water level data were obtained from NOS gauges and an hourly 

average was computed for all stations except for Green Bay, WI and Sturgeon Bay, WI due to 

their geographic locations and the size of POMGL grid increment. The average time series was 

then smoothed with a 12 hour running mean filter and subtracted from the observed hourly 

values at all stations. The resulting water level fluctuations represent the combined effects of 

wind, atmospheric pressure gradients, and local bathymetry (SB98). 

A sample of the observed and computed water level fluctuations for Calumet Harbor, 

Milwaukee, and Green Bay for the period from October to November 1983 (JD 265-325) is 

shown in Figure 3. Calumet Harbor and Green Bay showed the highest fluctuations (up to 50 

cm), while Milwaukee did not exhibit as high an amplitude. At all NOS gauges there was a 

significant amount of high frequency (> 0.5/hr) fluctuation, with amplitudes on the order of 5 

cm apparent in the observed water level record. This high frequency fluctuation was not seen 

in the simulated water levels. SB98 speculated that this difference is due mainly to local water 

level fluctuations in the harbors and coastal areas where the NOS gauges are located, which 

cannot be simulated with POMGL using a 5 km grid increment. 

The statistical comparison between observed and computed water level fluctuations is presented 

in Table 1. The RMSV of the observations is significantly greater than the RMSV of the 

computed water level fluctuations at all stations except Green Bay. They state that this was 

probably due to the high frequency fluctuations which are seen in the observations, but not as 

much in the POMGL simulations. In addition, they state that the RMSD between model results 
(c) 

Figure 3. Times series of simulated water levels versus observed for October – November 

1983 at NOS Gauge Stations, (a) Calumet Harbor, IL, (b) Milwaukee, WI, and 

(c) Green Bay, WI (from SB98). 

10
11

Table 1. Statistical comparison of 1982-83 simulated vs. observed short-term water level 

fluctuations (cm) at NOS water level gauges (modified from SB98). 

NOS Gauge 

Name and ID 

No. of 

Obs.* Avg 

(Obs.) 

RMSV 

(Obs.) 

Avg 

(Model) 

RMSV 

(Model) 

 Mean 

Alg. 

Diff. 

(M-O) 

RMSD CC 

Ludington 

9087023 14371 -0.28 3.45 0.59 0.95 +0.87 3.32 0.39 

Holland 

9087031 13730 0.33 2.73 0.67 1.64 +0.35 2.39 0.51 

Calumet 

9087044 14253 0.63 7.03 0.58 4.67 -0.05 5.50 0.62 

Milwaukee 

9087057 14233 -0.27 3.46 0.12 1.64 +0.39 3.22 0.39 

Kewaunee 

9087068 13123 -0.07 3.42 -0.25 0.97 -0.18 3.39 0.17 

Sturgeon Bay 

9087072 14366 -0.13 2.97 -0.17 1.01 -0.04 2.92 0.22 

Green Bay 

9087079 14368 0.27 9.72 0.46 12.20 +0.18 12.30 0.39 

Port Inland 

9087096 14283 -0.32 5.70 0.33 2.73 +0.65 4.61 0.61 

Mean 

(Green Bay not 

included) +0.28 3.62 0.42 

*Maximum number of possible observations during the period was 14,400.

and observations is also reflective of this difference. Correlation coefficients are highest at 

Calumet Harbor and Port Inland, which are located at the south and north ends of the main lake, 

respectively. They point out that the largest amplitude wind-induced water level fluctuations on 

the lake are usually exhibited at these NOS two gauges. 
12

4.3.2 Water Levels during 1994-1995 

SB98 compared POMGL water level simulations for the 1994-1995 period to observed data from 

the same 8 NOS gauges in Lake Michigan used for the 1st period. The statistical comparisons at 

each gauge are given in Table 2. They found similar results as for the 1st period, except that the 

correlation coefficients were somewhat lower for 1994-1995. 

Table 2. Statistical comparison of 1994-95 simulated vs. observed short term water level 

fluctuations (cm.) at NOS water level gauges (modified from SB98). 

NOS Gauge 

Name and ID 

No. of 

Obs. Avg 

(Obs.) 

RMSV 

(Obs.) 

Avg 

(Model) 

RMSV 

(Model) 

Mean 

Alg. 

Diff. 

(M-O) 

RMSD CC 

 Ludington 

9087023 17249 2.06 3.80 0.14 1.21 -1.92 4.08 0.32 

 Holland 

9087031 16571 0.69 2.83 0.65 1.95 -0.04 2.55 0.48 

Calumet 

9087044 16705 -0.29 6.95 0.81 4.23 +1.10 6.06 0.52 

Milwaukee 

9087057 17243 0.34 3.69 0.07 2.01 -0.27 3.45 0.39 

Kewauee 

9087068 15746 -0.72 3.53 -0.31 1.19 +0.41 3.42 0.28 

Sturgeon Bay 

9087072 17272 -0.99 3.12 -0.24 1.34 +0.74 3.04 0.34 

Green Bay 

9087079 16827 -0.38 10.11 -0.09 12.82 +0.29 13.58 0.32 

Port Inland 

9087096 16147 -2.27 5.74 0.06 3.09 +2.33 5.52 0.49 

Mean 

(Green Bay not 

included) +0.32 4.02 0.40 

4.3.3 Water Temperatures during 1982-1983 
SB98) compared POMGL simulations of surface water temperatures to observed temperatures at 

NWS fixed buoys 45007 and 45002. The comparisons are depicted in Figure 4 and indicate 

good agreement between the time evolution of surface water temperatures. They found that the 

simulation of the surface temperature is much more accurate than below the surface, which 

indicated the correct input of heat fluxes near the lake’s surface. (Subsurface comparisons are 

given in Section 4.4.1) The results of the statistical evaluation of POMGL surface water 

temperature simulations at the two buoys are given in Table 3. They noted that 

Figure 4. Comparison of simulated surface water temperatures vs. observed at NWS fixed 

buoys 45007 and 45002 for 1982-1983. Red line is the observation and the blue line 

is the model simulation (modified from SB98). 

the accuracy of surface temperature simulations is similar in the first and second year of 

simulations, which they attributed to the rapid adjustment of the surface temperature field to the 

model’s surface boundary conditions. 

Table 3. Statistical measures of POMGL surface water temperature simulations at 

NWS/NDBC fixed buoys 45002 and 45007 for the periods 1982-1983 and 1994-

1995 (modified from SB98). 

Period RMSD 

(

o

C) 

Max. Error 

(

o

C) 

Average 

Obs 

(

o

C) 

Average 

Model 

(

o

C) 

Mean Alg. 

Difference 

(Model-Obs) 

CC 

1982-1983 1.2 6.6 12.1 12.1 0.0 0.99 

1994-1995 1.5 6.1 13.1 13.3 +0.2 0.96 

13
4.3.4. Water Temperatures during 1994-1995 

During the 2nd period, SB98 compared simulations of surface water temperatures to observed 

temperatures at NWS’ permanent fixed buoys 45007 and 45002, and also at NWS’ temporary 

buoy 45010 located in the coastal zone off Milwaukee, WI (Figure 5). The results of the 

statistical comparisons at the three NWS buoys are given in Table 3. They noted that the 

accuracy of surface temperature predictions is similar in the first and second year of the 

simulations, in agreement with the 1982-1983 findings. 

Figure 5. Comparison of simulated vs. observed surface water temperatures at NWS fixed 

buoys 45002, 45010, and 45007 for 1994-1995. Red line is observation and blue is 

model simulation (modified from SB98). 

4.4 Skill Assessment of Subsurface Hindcasts 

14
15

This section provides a summary of the findings by Schwab and Belesky (1998) regarding the 

performance of POMGL in simulating subsurface water temperatures and currents during both 

the 1st and 2nd periods. The water temperature comparisons are discussed first, followed by the 

analysis of the modeled currents. 

4.4.1 Water Temperatures during 1982-1983 

SB98 compared observed subsurface temperatures from GLERL’s moorings to model output at 

several depths. A comparison of POMGL simulations to observations at several depths at the 

GLERL moorings, collocated at NWS buoys, is given in Figure 6. The results of a statistical 

comparison of POMGL subsurface temperature simulations to observations are given in Table 4. 

 To insure comparability with the 1994-95 period, they only used temperature and current 

observations longer than 300 days. No subsurface temperatures were taken for the second 

summer (1983) during this first period. 

SB98 found good agreement between the horizontal distribution and time evolution of the 

surface and bottom temperatures. However, they found a worse agreement in the thermocline 

area (depth 15m), where internal waves are also much less pronounced in the POMGL 

simulations than in observations. They speculated that because the model tends to generate 

excessive vertical diffusion, the modeled thermocline is too diffuse and hence temperature 

fluctuations are decreased. 

They concluded that POMGL was able to reproduce all of the basic features of thermal structure 

of Lake Michigan during the 600 days of the 1st period, including the spring thermal bar, full 

stratification, deepening of the thermocline during the autumn cooling, and finally an overturn in 

the late fall (Figure 7).

Table 4. Statistical measures of POMGL surface water temperature simulations at 

GLERL subsurface current moorings for the periods 1982-1983 and 1994-1995 at 

different layers in the water column (modified from SB98). 

Period No. of 

Instruments 

Layer RMSD 

(

o

C) 

Max. 

Error 

(

o

C) 

Average 

Obs 

(

o

C) 

Average 

Model 

(

o

C) 

Diff. 

(M-O) 

CC 

1982-1983 28 Epilimnion 2.5 10.6 7.1 6.4 -0.7 0.87 

Hypolimnion 0.7 3.3 4.2 4.3 +0.1 0.78 

1994-1995 10 Epilimnion 2.4 9.2 7.3 7.7 +0.4 0.93 

Hypolimnion 1.3 5.2 4.5 5.3 +0.8 0.87 
Figure 6. Time-series of POMGL simulated versus observed subsurface water 

temperatures at GLERL current moorings collocated near 45007 and 45002 

during 1982-1983. Also shown are the simulated vs. observed surface water 

temperatures at the two buoys. Red line is the observation, blue is the model 

simulation (from SB98).

16
Figure 7. POMGL’s simulated mean temperature profile for the period 1982-1983 (from 

SB98). The color contours are in oC. 

4.4.2 Water Temperatures during 1994-1995 

SB98 compared POMGL subsurface temperature simulations to observations from GLERL 

moorings. Statistical comparisons of POMGL simulations vs. observations at these moorings are 

presented in Table 4. As with the 1st period, only time series longer than 300 days were used in 

the statistical comparisons. A time series of POMGL simulations vs. observations at the GLERL 

mooring collocated at NWS buoy 45007 is given in Figure 8. Similar to the findings during the 

1982-1993 period, the POMGL simulations were found to compare better for surface and bottom 

temperatures but worse in the thermocline area. 

SB98 also examined the performance of POMGL in simulating the nearshore thermal structure 

by comparing POMGL temperatures against temperature data from 23 municipal water intakes 

around Lake Michigan. They used these data mostly for qualitative assessment (Figure 9), 

because unlike typical in-situ measurements, water temperature is measured at the municipal 

water plants and not in the lake itself. 

In addition, they also compared observed vs. simulated temperature profiles or soundings at 20 

locations from seven large-scale GLNPO temperature surveys of Lake Michigan during the 

1994-95 (one example is shown in Figure 10). They also compared POMGL simulations to 

several nearshore transects made by the United States Geological Survey (USGS), two examples 

of which are shown in Figure 11. 

17
Figure 8. Time-series of POMGL simulated vs. observed water temperatures at GLERL 

current mooring near NWS buoy 45007 for 1994-1995. Also shown are the 

simulated vs. observed water temperatures at buoy 45007. Red line is the 

observation, blue is the model simulation (from SB98).

18
Figure 9. Time series of POMGL simulated versus observed water temperatures at the 

Muskegon, MI municipal water intake for May-June 1994 (from SB98). 

Figure 10. POMGL simulated vertical water temperature profiles vs. observations on four 

days in 1994-1995 at Station 18M in southern Lake Michigan near NWS buoy 

45007 (modified from SB98). 

19
Figure 11. POMGL simulated versus observed temperature, transects offshore of 

Ludington, MI (upper panel) and Holland, MI (lower panel) (from SB98).

4.4.3 Water Currents during 1982-1983 

In order to examine seasonal changes in lake circulation patterns, SB98 averaged model results 

over two 6-month periods: from May to October (summer period), and from November to April 

(winter period). Their selected averaging periods roughly correspond to the thermal stratified and 

non-stratified periods. They found that POMGL properly simulated a cyclonic large-scale 

circulation pattern, with cyclonic circulation within each subbasin, and anticyclonic circulations 

in ridge areas as shown by previous observational studies. Furthermore, they found that the 

simulated circulation was more organized and more cyclonic in winter than in summer, which is 

in agreement with Gottlieb et al. (1989) and earlier findings of Saylor et al. (1980). 

SB98 also compared POMGL current simulations to observations at GLERL current moorings. 

20
They found that the model simulations matched observed currents best in the southern basin and 

during the fall-winter months. However, the simulated current speeds were approximately 10-

30% higher than observed speeds. Progressive vector diagrams of simulated vs. observed 

currents at 15 m depth for four GLERL moorings located in the southern basin ( i.e. at and south 

of Buoy 45007) are given in Figure 12. 

They state that the model did better in the southern basin probably due to the smooth bathymetry 

in this region. The better performance in the fall and winter months is likely due to the 

horizontal model resolution of the model being too coarse for proper simulation of baroclinic 

processes with horizontal length scales comparable to the Rossby deformation radius 

(approximately 5 km for summer months). 

SB98 compared POMGL current simulations vs. observations using a normalized Fourier norm 

(root mean square difference). A value of 0 indicates a perfect prediction (simulation is identical 

to observation) and a value greater than 0 but less than one implies that the simulation is better 

than no prediction at all (zero currents). For the 1982-83 period, they found that the normalized 

Fourier norm was between 0.70 and 0.98. They state that the normalized Fourier norm can be 

interpreted as a relative percentage of variance in the observed currents that is unexplained by 

the simulated currents. For the 1982-83 period, the hourly POMGL simulations account for 2-

30% of the total variance observed in the hourly observed currents (SB98). 

Figure 12. Progressive vector diagrams of POMGL simulated vs. observed currents at 15 

m depth at GLERL moorings in the southern basin (#1, 2, 3, and 23) for 1982-

83. Black line is observations, green is model simulation. (from SB98). 

4.4.4 Water Currents during 1994-1995 

21
22

S98 compared POMGL current simulations to observations at GLERL moorings during the 2nd

period. However, the GLERL current observation data set was not as comprehensive as in the 

1st period. They found that the normalized Fourier norm ranged between 0.80 and 1.08, which 

was slightly worse than for the 1st period.

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.” 

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

System (GLCFS) for Lake Michigan. Both LMOFS and GLCFS for Lake Erie have a spatial 

grid increment of 5 km and 20 sigma layers and use similar surface meteorological forcing. 

Neither of the systems employed any river inflow or assimilated any limnological data. Unlike 

LSOFS, GLCFS used surface observations from United States Coast Guard (USCG) stations and 

cooperative marine weather observations (MAREPS). However, this difference was not 

expected to cause a significant difference in the nowcasts. 

Due to the similar characteristics of LMOFS and GLCFS, the assessment of the LMOFS semioperational nowcasts was performed using archived nowcasts from GLCFS four times/day 

nowcast cycles. 

This chapter describes a description of the GLCFS nowcast and forecast cycles, the method of 

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

5.1 Description of Nowcast Cycles 

GLCFS performs four times/day nowcast cycles for Lake Michigan and the other four Great 

Lakes. The POMGL used by each forecast system are not reinitialized each spring. 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 NDBC C-MAN stations to be received at GLERL via 

NOAAPORT. Hourly model output from the four nowcast cycles is archived at GLERL. 

5.2 Method of Evaluation 
The hourly model results from GLCFS nowcast cycles were compared to observations from 

coastal and offshore observing platforms in the Lake Michigan region for the period from midApril to mid-December 2004. This was a period when there was no significant ice cover on 

Lake Michigan. 

The evaluation used the standard suite of assessment statistics as defined in Hess et al. (2003). 

The standard suite of statistics is given in Table 5. The target frequencies of the associated 

statistics are the following: 

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

 MDPO(2X) ≤ L, MDNO(2X) ≤ L 

Table 5. 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. 

23

There are three types of data sets (Table 6): 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 variables applied 
to LMOFS are presented in Table 7. 

Table 6. 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 7. 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, the output was reformatted to meet the text format requirements of the skill 

assessment code. 

24
25

Nowcasts of Water Levels

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

modeled water levels at 6 NOS NWLON stations along the Lake Michigan shoreline(Table 8). 

Data from NOS stations at Mackinaw City, MI, Green Bay, WI, and Sturgeon Bay, WI were not 

used in the evaluation due to their locations between two lakes, in a small bay, and at the 

entrance to a canal. A map depicting the locations of the 6 NOS gauges in the lake is given in 

Figure 13. 

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

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

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

computed and added to the model nowcasts of water level displacement from the model’s mean. 

 The mean based on a 7-day average may or may not truly represent the instantaneous mean lake 

wide water level. This is the same method used by CO-OPS prior to displaying the LMOFS 

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 Michigan 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 several of the Great Lakes, especially 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. This is the first attempt at evaluating the ability of a NOS prediction 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 implemented 

in the future. 

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

the following method. 

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

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

than its two neighboring points. 

Step 2. For each selected peak from Step 1, a 7 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 7 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.) 
26

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 statistical 

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. 

Table 8. Information on NOAA/NOS NWLON stations whose observations were used to 

evaluate the semi-operational nowcasts and forecasts of water levels.

Station Corresponding I 

and J Model 

Coordinates 

Station Name State NOS 

Station ID 

Number 

NWS 

Station 

ID 

Latitude 

(deg N) 

Longitude 

(deg W) 

I J 

Port Inland MI 9087096 PNLM4 45.97 85.87 35 98 

Ludington MI 9087023 LDTM4 43.95 86.44 26 53 

Holland MI 9087031 NS 42.76 86.20 30 27 

Calumet 

Harbor 

IL 9087044 CMTI2 41.73 87.54 9 4 

Milwaukee WI 9087057 NS 43.00 87.89 4 32 

Kewaunee WI 9087068 KWNW3 44.46 87.50 10 65 

Notes: NS = An official NWS station ID has not been assigned to the station yet. 
Figure 13. Map depicting locations of NOS NWLON gauges in Lake Michigan. 

27
28

Nowcasts of Surface Water Temperatures

The evaluation of GLCFS surface water temperature nowcasts were based on time series 

comparisons of observed and predicted temperatures at two 3-m fixed disk buoy locations in the 

lake. A map depicting the locations of these NWS fixed buoys is given in Figure 14. The buoys 

are operated by NOAA/National Data Buoy Center (NDBC). Information on the two NWS 

buoys is given in Table 9. The lake surface temperatures at the buoys are measured using a 

Yellow-Springs thermistor sealed with 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. 

The evaluation of GLCFS surface water temperature nowcasts for the lake was done by 

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

statistics described in Hess et al. (2003). No attempt was made to assess the nowcast/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 7.7oC 

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 fresh water bodies and non-tidal, there is no 

present standard for a lake temperature prediction. Based on experience of running the Great 

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

NOAA/GLERL suggested a 3oC criteria for water temperature skill assessment in the Great 

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

based on a 3oC criteria. 

Table 9. Information of NWS/NDBC fixed buoys whose observations were used to evaluate 

the semi-operational nowcasts and forecasts of surface water temperatures. 

Buoy Corresponding 

LMOFS Grid Point 

Coordinates 

Buoy Name Agenc

y 

Prov. 

or 

State 

WMO 

Buoy 

ID 

Lat. 

(deg N) 

Long. 

(degW) 

I J 

45007 – South 

Michigan 

NWS/ 

NDBC 

MI 45007 42.68 87.03 17 25 

45002 – North NWS/ MI 45002 45.33 86.42 27 84 
Michigan NDBC 

 

29
30

Figure 14. Map depicting the locations of NWS/NDBC fixed buoys in Lake Michigan along 

with the POMGL grid.

5.3 Assessment of Water Level Nowcasts 

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

nowcasts to predict hourly and extreme water levels at 6 NOS gauges from 15 April to 20 

December 2004 are given in Appendix A. Time series plots of the nowcast results and 

observations at the gauges are given in Appendix B. 

For the convenience of the reader, the skill statistics assessing the ability of the nowcasts to 

predict hourly water levels at the 6 NOS gauges are presented together in Table 10 along with 

the NOS acceptance criteria. The hourly nowcasts passed the criteria for amplitude at all 5 

locations. The mean algebraic differences ranged between + 2 cm, and the RMSE ranged 

between 4.8 and 7.0 cm. The greatest errors were at the Calumet Harbor, IL and Port Inland, MI 

gauges located at the extreme southern and northern ends of the lake, respectively (Figure 2). 

These sites also show the greatest hourly water level variability. The nowcasts under-predicted 

the water levels at all gauges except Port Inland. 

A comparison of the RMSE values for 2004 and those presented in the hindcast section (Tables 1 

and 2) indicate the POMGL performed worse in 2004. For example, at the northern and 

southern ends of the lake, the Port Inland RMSE was 4.6, 5.5, and 5.2 cm for 1982-1983, 1994-

1995, and 2004, respectively. RMSE at Calumet Harbor was 5.5, 6.1, and 7.0 cm for the same 

time periods. However, the largest difference between the RSMEs for the hindcasts and 

nowcasts were on the western and eastern lake shores. For example at Ludington, the RMSE 

were 2.4, 2.6, and 4.8 cm and at Kewaunee, the values were 3.39, 3.42, and 5.1 cm. The reason 

for the worse performance in 2004 may be due to differences in 1) the density and location of 

wind observations, 2) the methods used for calculating mean lakewide water level, and/or 3) how 

POMGL was applied. 

The skill statistics assessing the ability of nowcasts to predict extreme high water level events at 

the 6 NOS gauges during 2004 are given together in Table 11. The nowcast simulations of high 

water level passed the NOS acceptance criteria for amplitude at Port Inland and Holland, but not 

at Ludington, Calumet Harbor, Milwaukee and Kewaunee. The nowcasts ability to simulate the 

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

any of the 6 gauges. 
31

Table 10. Summary of Skill assessment Statistics of Semi-Operational Nowcasts of Hourly 

Water Levels at six NOS NWLON Stations in Lake Michigan for the Period 15 

April to 20 December 2004. A total of 5785 nowcasts were used in the 

assessment. Red indicates that the statistic did not pass the NOS acceptance 

criteria. Green indicates that it did meet the criteria.

Statistic, Acceptable 

Error [ ], and 

Units ( ) 

Port 

Inland 

MI 

Ludington 

 MI 

Holland 

MI 

Calum

et 

harbor 

IL 

Milwakee 

WI 

Kewaunee 

WI 

NOS 

Accept. 

Criteria

Mean Alg. Diff. (m) 0.019 -0.010 -0.007 -0.019 -0.021 -0.006 na 

RMSE (m) 0.052 0.053 0.048 0.070 0.057 0.051 na 

SD (m) 0.048 0.052 0.048 0.068 0.053 0.050 na 

NOF (2x15cm) 

(%) 

0.0 0.0 0.0 0.1 0.0 0.0 < 1% 

CF [15 cm] (% 98.8 98.0 98.9 95.5 98.2 98.7 > 90% 

POF [2x15 cm] (%) 0.0 0.0 0.0 0.0 0.0 0.0 < 1% 

MDPO [2x15 cm ] 

(hour) 

0.0 0.0 0.0 0.0 0.0 0.0 < 24 

hours 

MDNO [2x15 cm ] 

(hour) 

0.0 0.0 0.0 0.0 0.0 0 

.0 

< 24 

hours 

Table 11. Summary of Standard Statistics Evaluating the Ability of the Semi-Operational 

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

stations in Lake Michigan during the Period 15 April to 17 December 2004. Red 

indicates that the statistic did not pass the NOS acceptance criteria. Green 

indicates that it did meet the criteria.

Port Inland 

MI 

N=19 

Ludington 

MI 

N=24 

Holland 

MI 

N=19 

Statistic, 

Acceptable Error [ ], and Units ( ) 

Amp. Time Amp. Time Amp. Time 

Mean Alg. Diff. (m) (min) -0.080 1.0 -0.147 2.875 -0.088 0.895 

RMSE (m) (min) 0.086 3.532 0.159 4.495 0.100 4.298 

SD (m) (min) 0.032 3.480 0.061 3.530 0.050 4.319 

NOF [2x15cm] (90min) % 0.0 10.5 0.0 4.2 0.0 15.8 

CF [15 cm or 90 min] (%) 100.0 36.8 54.2 16.7 94.7 47.4 

POF [2x15 cm or 90 min] (%) 0.0 21.1 0.0 45.8 0.0 26.3 

MDPO [2x15 cm or 90 min] (#) 0.0 0.0 0.0 0.0 0.0 0.0 

MDNO [2x15 cm or 90min] (#) 0.0 0.0 0.0 0.0 0.0 0.0 
32

Table 11. (cont.). 

Calumet Harbor 

IL 

N=28 

Milwaukee 

WI 

N=21 

Kewaunee 

 WI 

N=16 

NOS 

Accept. 

Criteria 

Statistic, 

Acceptable Error [ ], and 

Units ( ) 

Amplit

ude 

Time Amplit

ude 

Time Amplit 

ude 

Time 

Mean Alg. Diff. (m) (min) -0.151 -0.286 -0.134 0.333 -0.152 1.500 na 

RMSE (m) (min) 0.166 5.043 0.149 5.568 0.161 5.148 na 

SD (m) (min) 0.071 5.127 0.068 5.695 0.056 5.086 na 

NOF [2x15cm] (90min) % 3.6 32.1 0.0 19.0 0.0 18.8 < 1 

CF [15 cm or 90 min] (%) 50.0 17.9 66.7 38.1 56.3 6.3 > 90 

POF [2x15 cm or 90 min] 

(%) 

0.0 28.6 0.0 23.8 0.0 50.0 < 1 

MDPO [2x15 cm or 90 

min] (#) 

0.0 0.0 0.0 0.0 0.0 0.0 < 24 hours 

MDNO [2x15 cm or 

90min] (#) 

0.0 0.0 0.0 0.0 0.0 0.0 < 24 hours 

The skill statistics to predict extreme low water level events at the 6 NOS gauges during 2004 

are given together in Table 12. The nowcast simulations of extreme low water level passed NOS 

acceptance criteria for amplitude at Holland, Milwaukee and Kewaunee, but not at Port Inland, 

Ludington and Calumet Harbor. The nowcasts’ ability to simulate the timing of these events did 

not pass NOS acceptance criteria for NOF, CF, and POF at any of the 6 gauges. 

Table 12. Summary of Standard Statistics Evaluating the Ability of Semi-Operational 

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

Stations in Lake Michigan for the Period 15 April to 17 December 2004. Red 

indicates that the statistic did not pass the NOS acceptance criteria. Green 

indicates that it did meet the criteria.

Port Inland 

MI 

N=33 

Ludington 

 MI 

N=28 

Holland 

 MI 

N=10 

Statistic, 

Acceptable Error [ ], and 

Units ( ) 

Amp. Time Amp. Time Amp. Time 

Mean Alg. Diff. (m) (min) 0.114 0.758 0.094 -0.250 0.089 1.200 

RMSE (m) (min) 0.120 2.928 0.100 3.942 0.094 2.608 

SD (m) (min) 0.036 2.873 0.033 4.006 0.031 2.441 

NOF [2x15cm] (90min) % 0.0 9.1 0.0 35.7 0.0 0.0 

CF [15 cm or 90 min] (%) 78.8 36.4 89.3 17.9 90.0 30.0 

POF [2x15 cm or 90 min] 

(%) 

0.0 15.2 0.0 25.0 0.0 20.0 

MDPO [2x15 cm or 90 0.0 0.0 0.0 0.0 0.0 0.0 
33

min] (#) 

MDNO [2x15 cm or 

90min] (#) 

0.0 0.0 0.0 0.0 0.0 0.0 

Table 12 (cont.). 

Calumet Harbor 

IL 

N=38 

Milwaukee 

WI 

N=25 

Kewaunee 

 WI 

N=25 

NOS 

Accept. 

Criteria 

Statistic, 

Acceptable Error [ ], and 

Units ( ) 

Amp 

litude 

Time Amp 

litude 

Time Amp 

litude 

Time 

Mean Alg. Diff. (m) (min) 0.091 0.105 0.064 -1.000 0.080 -0.480 na 

RMSE (m) (min) 0.110 3.026 0.073 2.878 0.088 3.394 na 

SD (m) (min) 0.063 3.065 0.035 2.754 0.038 3.429 na 

NOF [2x15cm] (90min) % 0.0 15.8 0.0 20.0 0.0 24.0 < 1 

CF [15 cm or 90 min] (%) 84.2 39.5 96.0 52.0 92.0 28.0 > 90 

POF [2x15 cm or 90 min] 

(%) 

0.0 13.2 0.0 8.0 0.0 20.0 < 1 

MDPO [2x15 cm or 90 

min] (#) 

0.0 0.0 0.0 0.0 0.0 0.0 < 24 hours 

MDNO [2x15 cm or 

90min] (#) 

0.0 0.0 0.0 0.0 0.0 0.0 < 24 hours 

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 2 NWS/NDBC fixed buoys from 

mid-April to early December 2004 are given in Appendix D. Time series plots of the nowcasts 

(1st sigma level) compared with observations at the buoys are given in Appendix E. The time 

series plots clearly indicate that the nowcast values are 2 degrees higher than observations 

starting in mid-April, but then begin to close the gap approaching mid- to late-August. After that, 

the nowcasts differ from observations by +1-2o

C until the end of the period. The wide 

temperature gap in the beginning of the simulation could be due to continuous simulation of the 

GLFS over the winter season, thus accumulating heat in the water body. This problem may be 

resolved by reinitializing the model at the beginning of the simulation period by using a 

isothermal condition based on the satellite-derived SST. 

The skill statistics to predict hourly surface water temperatures at the two NDBC buoys are 

given together in Table 13 along with the NOS acceptance criteria. The hourly water 

temperature nowcasts at the southern buoy passed the NOS skill assessment criteria. The hourly 

nowcasts at the northern Michigan buoy came close to passing the criteria, failing primarily in 

regards to CF by only 7%. The MAD for the period ranged between 0.7-1.4o

C and the RMSE 

ranged between 1.5-2.2o

C at the two buoys. 
34

Table 13. Summary of Skill Assessment Statistics of the Semi-Operational Nowcasts of 

Hourly Surface Water Temperatures at two NWS/NDBC fixed buoys in Lake 

Michigan for the Period from mid-April to early November 2004. Red indicates 

that the statistic did not pass the NOS acceptance criteria. Green indicates that it 

did meet the criteria. 

Time Period, Statistic, 

Acceptable Error [ ], and 

Units ( ) 

45002 

Northern 

Michigan 

N=5083 

45007 

Southern 

Michigan 

N=5574 

NOS 

Acceptance 

Criteria 

Time Period 21 April to 

 17 Nov. 

2004 

20 April to 

8 Dec. 

2004 

365 days 

Mean Alg. Difference (o

C) 1.428 0.735 na 

RMSE (o

C) 2.213 1.565 na 

SD (o

C) 1.692 1.381 na 

NOF [2x3o

C] (%) 0.0 0.0 < 1% 

CF [3oC] (%) 83.7 91.5 > 90% 

POF [2x3oC] (%) 1.8 0.0 < 1% 

MDPO [2x3o

C] (hours) 0.0 0.0 < 24 hours 

MDNO [2x3oC] (hours) 36.0 0.0 < 24 hours 

________________________________________________________________ 

(Note na = not applicable, NA=not available). 
35

6. SEMI-OPERATIONAL FORECAST SKILL ASSESSMENT 

This section describes the model system performance for a semi-operational forecast scenario 

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.” 

For the assessment of the semi-operational forecast scenario for LMOFS, archived forecast 

guidance from GLCFS twice per day forecast cycles (0000 and 1200 UTC) were compared to 

available observations in Lake Michigan. This chapter provides a description of the GLCFS 

forecast cycles, the method of evaluation including time period and assessment statistics, and the 

results of the evaluation. 

6.1 Description of Forecast Cycles 

GLCFS performs twice/day forecast cycles for Lake Michigan. The two forecast cycles are 

initialized at 0000 and 1200 UTC each day and generate forecast guidance 60 hours into the 

future. 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 forecast input data. For example, 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 

consists of the 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 GLCFS forecast guidance at 1 hour increments out to 30 hours were compared to 

observations from coastal observing platforms in the Lake Michigan region from 15 April to 17 

December 2004. This was a period when there was no significant ice cover on Lake Michigan. 

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 modeled water levels at the same six NOS stations used in 

the evaluation of the nowcasts as described in 5.2 

The evaluation of semi-operational forecast guidance of surface water temperatures was based 

on time series comparisons of observed and modeled temperatures at the same two NWS fixed 

buoys used in the nowcast evaluation. There are a few gaps in the record of forecast guidance 

due to computer and/or network problems or incomplete surface forcing from the NAM model 

for a particular forecast cycle. 
36

6.3 Assessment of Water Level Forecast Guidance

The standard suite of skill assessment statistics for the semi-operational forecast guidance and 

the nowcasts’ ability to predict hourly and extreme water levels at 6 NOS gauges from 15 April 

to 20 December 2004 are given in Appendix A. Time series plots of the forecast guidance from 

the 0000 UTC model forecast cycle compared with observations at the gauges are given in 

Appendix C. 

The skill statistics assessing the ability of the forecast guidance to predict hourly water levels at 

the 6 NOS gauges are presented together in Table 14 along with the NOS acceptance criteria. 

The hourly forecast guidance passed the criteria at all six locations. The mean algebraic 

differences ranged between -1.7 to + 1.3 cm, and the RMSE ranged between 5.0 and 7.2 cm, 

very similar to the statistics for the nowcast evaluation. Similar to the nowcasts, the greatest 

errors were at NOS gauges in Calumet Harbor, IL and Port Inland, MI, located at the southern 

and northern ends of the lake, respectively. The forecasts underpredicted the water levels at all 

gauges except Port Inland. There was no significant increase in MAD, RMSE values, or CF as 

forecast projection time increased (Appendix A). 

Table 14. Summary of Skill assessment Statistics of Semi-Operational Forecast Guidance of 

Hourly Water Levels at six NOS NWLON Stations in Lake Michigan for the 

Period 15 April to 17 December 2004. A total of 481 forecasts were used in the 

assessment. Red indicates that the statistic did not pass the NOS acceptance 

criteria. Green indicates that it did meet the criteria.

Statistic, Acceptable 

Error [ ], and 

Units ( ) 

Port 

Inland 

MI 

Ludington, 

MI 

Holland, 

MI 

Calumet 

Harbor, 

IL 

Milwaukee, 

WI 

Kewaunee, 

WI 

NOS 

Accept. 

Criteria 

Mean Alg. Diff. (m) 0.013 -0.009 -0.004 -0.011 -0.017 -0.007 na 

RMSE (m) 0.050 0.055 0.051 0.072 0.057 0.051 na 

SD (m) 0.049 0.055 0.051 0.071 0.054 0.050 na 

NOF (2x15cm) 

(%) 

0.0 0.0 0.0 0.0 0.0 0.0 < 1% 

CF [15 cm] (%) 99.0 97.1 98.2 95.5 98.2 98.4 > 90% 

POF [2x15 cm] (%) 0.0 0.0 0.0 0.0 0.0 0.0 < 1% 

MDPO [2x15 cm ] 

(hour) 

0.0 0.0 0.0 0.0 0.0 0.0 < 24 

hours 

MDNO [2x15 cm ] 

(hour) 

0.0 0.0 0.0 0.0 0.0 0.0 < 24 

hours 

The skill statistics to assess the ability of the forecast guidance to predict extreme high water 

level events at the six NOS gauges during 2004 are given together in Table 15. The forecasts of 

extreme high water level passed NOS acceptance criteria for amplitude at Port Inland and 

Holland, but not at Ludington, Calumet Harbor, Milwaukee and Kewaunee. The forecasts’ 
37

ability to simulate the timing of these events did not pass NOS acceptance criteria for NOF, CF, 

and POF at any of the six gauges. 

Table 15. 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 Michigan during the Period 15 April to 17 

December 2004. Red indicates that the statistic did not pass the NOS acceptance 

criteria. Green indicates that it did meet the criteria. 

Port Inland 

MI 

N=19 

Ludington 

 MI 

N=24 

Holland 

 MI 

N=16 

Statistic, 

Acceptable Error [ ], and 

Units ( ) 

Amp. Time Amp. Time Amp. Time 

Mean Alg. Diff. (m) (min) -0.061 0.474 -0.153 0.625 -0.075 1.063 

RMSE (m) (min) 0.073 4.020 0.171 4.287 0.087 4.085 

SD (m) (min) 0.040 4.101 0.077 4.332 0.045 4.074 

NOF [2x15cm] (90min) % 0.0 21.1 0.0 20.8 0.0 12.5 

CF [15 cm or 90 min] (%) 100.0 31.6 45.8 16.7 93.8 6.3 

POF [2x15 cm or 90 min] 

(%) 

0.0 21.1 0.0 25.0 0.0 25.0 

Table 15 (cont.) 

Calumet Harbor 

IL 

N=30 

Milwaukee 

WI 

N=21 

Kewaunee 

 WI 

N=12 

NOS 

Accept. 

Criteria 

Statistic, 

Acceptable Error [ ], and 

Units ( ) 

Amplit

ude 

Time Amplit

ude 

Time Amplit 

ude 

Time 

Mean Alg. Diff. (m) (min) -0.137 -0.500 -0.141 -0.143 -0.177 -1.083 na 

RMSE (m) (min) 0.159 5.648 0.155 6.751 0.184 5.867 na 

SD (m) (min) 0.081 5.722 0.064 6.916 0.052 6.022 na 

NOF [2x15cm] (90min) % 3.3 30.0 0.0 38.1 8.3 33.3 < 1 

CF [15 cm or 90 min] (%) 56.7 20.0 61.9 9.5 41.7 25.0 > 90 

POF [2x15 cm or 90 min] 

(%) 

0.0 23.3 0.0 33.3 0.0 33.3 < 1 

The skill statistics to assess the ability of the forecast guidance to predict extreme low water 

level events at the five NOS gauges during 2004 are given together in Table 16. The forecasts of 

extreme low water level passed NOS acceptance criteria for amplitude at Port Inland, Holland, 

Milwaukee and Kewaunee, but not at Ludington and Calumet Harbor where the CF was 85.7 and 

83.8%, respectively. The forecasts’ ability to simulate the timing of these events did not pass 

NOS acceptance criteria for NOF, CF, and POF at any of the six gauges.
38

Table 16. 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 Michigan during the Period 15 April to 17 

December 2004. Red indicates that the statistic did not pass the NOS acceptance 

criteria. Green indicates that it did meet the criteria. 

Port Inland 

MI 

N=33 

Ludington 

 MI 

N=28 

Holland 

 MI 

N=10 

Statistic, 

Acceptable Error [ ], and 

Units ( ) 

Amp. Time Amp. Time Amp. Time 

Mean Alg. Diff. (m) (min) 0.099 0.000 0.095 -1.107 0.079 0.091 

RMSE (m) (min) 0.103 3.266 0.101 4.145 0.083 3.398 

SD (m) (min) 0.028 3.317 0.035 4.067 0.027 3.562 

NOF [2x15cm] (90min) % 0.0 21.2 0.0 39.3 0.0 18.2 

CF [15 cm or 90 min] (%) 97.0 27.3 85.7 17.9 100.0 27.3 

POF [2x15 cm or 90 min] 

(%) 

0.0 12.1 0.0 21.4 0.0 18.2 

Table 16 (cont.). 

Calumet Harbor 

IL 

N=38 

Milwaukee 

WI 

N=25 

Kewaunee 

 WI 

N=25 

NOS 

Accept. 

Criteria

Statistic, 

Acceptable Error [ ], and 

Units ( ) 

Amplit

ude 

Time Amplit

ude 

Time Amplit 

ude 

Time 

Mean Alg. Diff. (m) (min) 0.080 -0.973 0.054 0.200 0.080 -0.923 na 

RMSE (m) (min) 0.104 3.661 0.063 3.317 0.089 4.132 na 

SD (m) (min) 0.068 3.578 0.033 3.379 0.039 4.108 na 

NOF [2x15cm] (90min) % 0.0 24.3 0.0 16.0 0.0 38.5 < 1 

CF [15 cm or 90 min] (%) 83.8 27.0 100.0 36.0 96.2 15.4 > 90 

POF [2x15 cm or 90 min] 

(%) 

0.0 13.5 0.0 24.0 0.0 23.1 < 1 

Notes: na=not applicable 

6.4 Assessment of Water Temperature Forecast Guidance 

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

forecast guidance to predict hourly lake surface water temperatures at two NWS/NDBC fixed 

buoys from mid-April to early December 2004 are given in Appendix D. The table provides 

skill statistics at the forecast projection times of 0, 6, 12, 18, and 24 hours. Time series plots of 

the forecasts (1st sigma level) from the 0000 UTC forecast cycle compared with buoy 

observations are given in Appendix E. The time series plots indicate 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 does 
39

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 values are 2oC higher than 

observations beginning in mid-April, but then begin to close the gap towards mid- to lateAugust. After that, the nowcasts differed from observations by +1-2o

C until the end of the 

period. The wide temperature gap in the beginning of the simulation could be due to continuous 

simulation of the GLFS over the winter season, thus accumulating heat in the water body. 

The skill statistics assessing the ability of the semi-operational forecast guidance to predict 

surface water temperatures 24 hours in advance at the 2 NDBC buoys are given in Table 17 

along with the NOS acceptance criteria. The hourly forecast guidance at the Southern Michigan 

buoy (45007) passed all the criteria while the hourly forecast guidance at the northern Michigan 

buoy (45002) came close to passing all the criteria (failing to meet the CF criteria by only 3%). 

The MAD ranged between 0.5 and 1.2oC and the RMSE ranged between 1.3 and 1.9oC at the two 

buoys. The MAD and RMSEs for the forecast guidance were slightly lower than for the 

nowcasts. 

It is interesting to note that mean differences, RMSE, and the CF and POF values decreased as 

forecast projection increased in time. For example, at the northern buoy, the RMSE was 2.2o

C at 

the 0-hr projection and 1.9oC by the 24-hr projection (see Table D.3). This suggests that the 

surface heat flux is being overestimated during the nowcast cycle. 
40

Table 17. Summary of Skill Assessment Statistics for Semi-Operational Forecast Guidance 

in Predicting Surface Water Temperatures 24 hours in advance at NWS/NDBC 

fixed buoys during the period from mid-April to early-November 2004. Red 

indicates that the statistic did not pass the NOS acceptance criteria. Green 

indicates that it did meet the criteria. 

 Time Period, Statistic, 

Acceptable Error [ ], and 

Units ( ) 

45002 

Northern 

Michigan 

N=420 

45007 

Southern 

Michigan 

N=461 

NOS 

Acceptance 

Criteria 

Time Period 20 April to 

 17 

November 

2004 

20 April to 

8 December 

2004 

365 days 

Mean Alg. Difference 

(

o

C) 

1.252 0.517 na 

RMSE (o

C) 1.988 1.397 na 

SD (o

C) 1.545 1.299 na 

NOF [2x3o

C] (%) 0.0 0.0 < 1% 

CF [3oC] (%) 87.1 94.1 > 90% 

POF [2x3oC] (%) 1.0 0.0 < 1% 

MDPO [2x3oC] (hours) 0.0 0.0 < 24 hrs 

MDNO [2x3o

C] (hours) 24.0 0.0 < 24 hrs 

Notes: na=not applicable 

7. SUMMARY 

NOS’ Lake Michigan Operational Forecast System (LMOFS) 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. 

LMOFS became operational at CO-OPS on 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 LMOFS predictions was conducted according to the NOS evaluation 
41

standards (Hess et al. 2003). To comply with the NOS standards, the results of the numerical 

modeling component of the Lake Michigan Mass Balance Study (SB98) of 1983-84 and 1994-95 

was used for the hindcast scenario and are summarized below. 

 To comply with the NOS required semi-operational nowcast and forecast scenarios, the 

evaluation used archived output from NOAA/GLERL’s GLCFS semi-operational nowcast and 

forecast cycles for the ice free period from15 April to 17 December 2004. The semi-operational 

nowcasts and forecast guidance were compared to water level observations at six NOS NWLON 

stations and surface temperature temperatures at two NWS/NDBC fixed buoys in the lake. Due 

to the lack of subsurface water temperatures and current observations, no assessment of these 

variables could be conducted for LMOFS. 

Water Levels 

The POMGL for Lake Michigan was able to simulate the water level in the main part of the lake 

but could not resolve many local effects such as edge waves or resonance in harbors and small . 

The mean algebraic differences (MAD) at the northern and southern ends of the lake where the 

largest wind-induced water level fluctuations occur ranged from 0.65 to -0.05 cm in 1983-84 and 

2.33 to 1.10 cm in 1994-95, respectively. In Green Bay, the RMSE was over 12 cm in both time 

periods, while the RMSE at other locations ranged between 3 and 6 cm. 

The hourly nowcasts of water levels met the NOS acceptance criteria at all six NOS gauges. The 

MAD ranged between -2.1 cm and +1.9 cm. Thus, the nowcasts underpredicted the hourly water 

levels at all gauges except at Port Inland, MI. The RMSE among the six gauges ranged between 

4.8 and 7.0 cm. One can not distinguish the amount of error due to the ability of POMGL to 

predict the water level or the potential error caused by the method used to estimate the lake wide 

water level which was added to POMGL water level predictions The ability of the nowcasts to 

predict extreme high and low water level events was also assessed using a proposed evaluation 

procedure to the NOS standards. The nowcast predictions of high water level events passed the 

NOS criteria for amplitude at two of the six NOS gauges, while the predictions of low water 

level events passed the NOS criteria at three of the six NOS gauges. The nowcasts failed to meet 

NOS criteria in predicting the timing of both extreme high and low water events at all the NOS 

gauges. 

The hourly forecast guidance met the NOS criteria at all six locations. The MAD ranged 

between -1.7 to + 1.3 cm and the RMSE ranged between 5.0 and 7.2 cm, very similar to the 

statistics for the nowcast evaluation. Similar to the nowcasts, the greatest error was at the 

Calumet Harbor gauge located at the southern end of the lake. The forecast underpredicted the 

water levels at all gauges except at Port Inland. There was no significant increase in the mean 

algebraic differences,RMSE, or CF as forecast projection time increased. 

The forecast guidance of extreme high and low water level events passed NOS criteria at two 

and four of the six gauges, respectively. The forecast guidance failed to meet NOS criteria in 

predicting the timing of both extreme high and low water events at all NOS gauges. 
42

Water Temperatures 

POMGL simulated well the surface water temperatures in terms of both horizontal distribution 

and time evolution, indicating that the model was initialized properly and that the correct amount 

of heat flux was inputted into the lake. The MAD were less than +0.5oC. The modeled subsurface temperatures, especially in the thermocline, were not as good as the surface 

temperatures. The model reproduced the basic features of the evolution of the three-dimesional 

thermal structure of the lake, but the model produced a thermocline that was too diffuse and 

failed to capture the frequent temperature fluctuations. 

The hourly surface water temperature nowcasts meet the NOS criteria at the southern buoy. The 

nowcasts came very close to meeting NOS criteria at the northern buoy, failing to meet the CF 

by 7%. The MAD for the period ranged between 0.7 and 1.4oC, and the RMSE ranged between 

1.5 and 2.2o

C. 

The hourly surface water temperature forecast guidance at 24 hours for the southern buoy passed 

the NOS criteria while the southern one came very close at 87%. The MAD ranged between 0.5 

and 1.2o

C and RMSE between 1.3 and 1.9oC, which were slightly lower than for the nowcasts. 

The RMSE of the hourly water temperature forecasts slightly decreased as the forecast 

projection increased in time. 

WaterCurrents 

POMGL simulated properly the cyclonic large-scale circulation pattern in the lake, with cyclonic 

circulation within each sub-basin and anticyclonic in ridge areas. The model did the best in the 

southern basin, which is characterized by smooth bathymetry, and in the fall-winter months 

when barotropic processes are dominant. During the spring-summer months when baroclinic 

processes are dominant, the horizontal resolution (5 km) is too coarse to properly simulate these 

processes which have horizontal length scales comparable to the Rossby deformation radius (~5 

km). 

There was no current data in the lake to evaluate the water current nowcasts and forecast 

guidance. 
43

8. RECOMMENDATION FOR FUTURE WORK 

Recommendation #1 

The comparisons of the semi-operational nowcasts and forecast guidance of surface water 

temperature to observations at buoys in the lake indicate a problem with surface water 

temperature predictions, especially during the Spring and Summer. This could be caused by the 

inaccuracy in the model’s depiction of the three dimensional thermal structure at the start of 

spring warming. It is recommended that sensitivity runs be conducted to determine impacts of 1) 

re-initializing early spring the three-dimensional thermal structure on the surface water 

temperature predictions based on the historical mean temperature profile and 2) the use of an ice 

module in POMGL. GLERL is presently testing an ice module in POMGL for Lakes Erie and 

Michigan. 

Recommendation #2 

The comparisons of the semi-operational nowcasts and forecast guidance of surface water 

temperature to observations indicate a potential overestimation of the surface heat flux during 

the nowcast cycle. An examination of the surface heat flux algorithm and/or its meteorological 

inputs should be conducted to identify the potential cause. 

Recommendation #3 

SB98 found poor agreement between POMGL and observed water temperatures in the 

thermocline area where internal waves are also much less pronounced in the model simulations 

than in the observations. SB98 speculated that model predicted thermocline is too diffuse due to 

the model excessive vertical diffusion. The study to determine the cause and solution for this 

excessive diffusion is needed. 

Recommendation #4 

A study is needed to determine the reason why POMGL was unable to better forecast the timing 

of water level of extreme high and low water level events and the water level amplitudes at the 

northern and southern ends of the lakes in order to meet NOS standards. This would likely 

involve sensitivity tests with POMGL using higher grid resolution and incorporating 

atmospheric pressure forcing. GLERL is presently running a 2 km resolution version of POMGL 

for Lake Erie and Michigan. 

Recommendation #5 

The NWS Weather Forecast Office in Detroit, MI has requested that NOS use gridded surface 

wind direction and speed forecasts from the NWS’ National Digital Forecast Database (NDFD) 

instead of forecast guidance from the NAM model as surface forcing for the forecast cycles of 

LMOFS and the other Great Lakes forecast systems. CO-OPS is now obtaining an experimental 
44

NDFD surface windand air temperature forecast composites for the Great Lakes from the NWS 

Weather Forecast Office in Cleveland, OH and is running a parallel version of GLOFS forced by 

these gridded forecasts. An comparison is need of water level and temperature forecast guidance 

generated by LMOFS forced by NDFD forecasts vs, guidance produced by LMOFS forced by 

NAM modelpredictions. 

Recommendation #6 

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. If this is the case, alternative 

methods to estimate the mean lake wide water level may be explored. 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. 

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, led by Dr. Keith 

Bedford (OSU) and Dr. David Schwab (GLERL). During the eleven year life of the GLFS, the 

following OSU staff members have been associated with the development of the system: fifteen 

graduate students; seven faculty; six postdocs; and seven research scientists. Funding came from 

over 26 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 Technologies Inc. In particular, we acknowledge the hard work of 

Greg Mott, Mark Vincent, Zack Bronder, and others at CO-OPS. 

The archived GLCFS nowcast and forecast guidance used in the skill assessment 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 to Ed Myers, Eugene Wei, and Zhizhang Yang for their helpful 

comments and suggestions to improve this technical report. 
45

REFERENCES 

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nowcasts/forecasts, Proceedings, Marine Technology Society National Meeting, New 

Orleans, LA, Marine Technology Society, 206-264. 

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., M. A. Donelan, P.F. Hamblin, C. R. Murthy, and T. J. Simons, 1989: Thermal 

structure and circulation in the Great Lakes. Atmosphere-Ocean, 27, 607-642. 

Dingham, J. S. and K. W. Bedford, 1984: The Lake Erie response to the January 26, 1978, 

cyclone. J. Geophysical Res., 89C4, 6427-6445 

Gillhousen, D.B., 1987: A field evaluation of NDBC moored buoy winds. J. Atmos. Oceanic 

Technol., 4, 94-104. 

Great Lakes Information Network, 2006: Water Levels on the Great Lakes. Great Lakes 

Information Network (http://www.great-lakes.net/teach/envt/levels/lev_2.html). 

Gottlieb, E.S., J.H. Saylor, and G.S. Miller, 1989: Currents and temperatures observed in Lake 

Michigan from June 1982 to July 1983. NOAA Technical Memorandum ERL GLERL-71, 

45 pp. 

Hess, K.W., T. F. Gross, R. A. Schmalz, J. G.W. Kelley, F. Aikman III, E. Wei, and M. S. 

Vincent, 2003: NOS Standards for Evaluating Operational Nowcast and Forecast 

Hydrodynamic Model System. NOAA Technical Report NOS CS 17, 48 pp. 

Hoch, B. 1997: An evaluation of a one-way coupled atmosphere-lake model for Lake Erie. M.S. 

thesis, Atmospheric Sciences Program, Ohio State University, 226 pp. [Available from 

Atmospheric Sciences Program, 1049 Derby Hall, 154 N. Oval Mall, Ohio State 

University, Columbus, OH 43210-1361.] 

Kelley, J.G.W., 1995: One-way coupled atmospheric-lake model forecasts Lake Erie. Ph.D. 

Dissertation, Ohio State University, 376 pp. [Available from Atmospheric Sciences 

Program, 1049 Derby Hall, 154 N. Oval Mall, Ohio State University, Columbus, OH 

43210-1361.] 
46

Kuan, C.-F., 1995: Performance evaluation of the Princeton Circulation Model for Lake Erie. 

Ph.D. Dissertation, Ohio State University, 376 pp. [Available from Dept. of Civil and 

Environmental Engineering and Geodetic Science, 470 Hitchcock Hall, 2070 Neil Avenue, 

Ohio State University, Columbus, OH 43210-1275.] 

McCormick, M. J., and G. A. Meadows, 1988: An intercomparison of four mixed layer models 

in a shallow inland sea. J. Geophys. Res., 93, 6774-6788. 

O’Connor W. P. and D.J. Schwab, 1993: Sensitivity of Great Lakes Forecasting System 

Nowcasts to Meteorological Fields and Model Parameters. Proceedings, 3rd International 

Conference on Estuarine and Coastal Modeling, ASCE Waterways, Port, Coastal and 

Ocean Division, 149-157. 

Sambridge, M., Braun, J., and H. McQueen, 1995: Geophysical parameterization and 

interpolation of irregular data using natural neighbors. Geophys. J. Int., 122, 837-857. 

Saylor, J. H, J. C. K. Huang, and R. O. Reid, 1980: Vortex modes in Lake Michigan. J. Phy. 

Oceanography, 10, 1814-1823. 

Schwab, D. J., 1978: Simulation and forecasting of Lake Erie storm surges. Mon. Wea. Rev., 

106, 1476-1487. 

Schwab, D. J., J. R. Bennett, P. C. Liu, M. A. Donelan, 1984: Application of a simple numerical 

wave prediction model to Lake Erie, J. Geophys. Res., 89, no. C3, 3586-3592. 

Schwab, D. J. and D. Beletsky, 1998: Lake Michigan Mass Balance Study: Hydrodynamic 

Modeling Project. NOAA Technical Memorandum ERL GLERL-108, 53 pp. 

Schwab, D. J. and D. L. Seller, 1980: Computerized bathymetry and shorelines of the Great 

Lakes. NOAA Data Report, ERL GLERL-16, 13 pp. 

Schwab, D. J. and K. W. Bedford, 1994: Initial implementation of the Great Lakes Forecasting 

System: A real-time system for predicting lake circulation and thermal structure. Water 

Poll. Res. J. Canada, 29, 203-220. 

Schwab, D. J., and D. Beletsky, 1998: Lake Michigan Mass Balance Study: Hydrodynamic 

Modeling Project. NOAA Technical Memorandum ERL GLERL-108, 53 pp. [Available 

from NOAA/Great Lakes Environmental Research Laboratory, Publications Office, 2205 

Commonwealth Blvd., Ann Arbor, MI 48105-2945.] 

Schwab, D. J. Personal communication of May 2006. 
47

Yen, C.-C., J. Kelley and K. Bedford, 1994. Daily procedure for GLFS nowcasts. Proceedings, 

National Conference on Hydraulic Engineering, Buffalo, NY, 202-206. 

Zhang, A., K. W. Hess, E. Wei, and E. Myers, 2006: Implementation of Model Skill Assessment 

Software for Water Level and Current. NOAA Technical Report NOS CS 24, 61 pp. 
48

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

Forecasts of Water Levels from six NOS NWLON gauges in Lake 

Michigan for 2004. 

Table A.1. Skill Assessment Statistics of Semi-Operational Predictions at NOS Port Inland, MI (NOS ID 9087096) 

Gauge. 

Station: Port Inland, Lake Michigan, MI 

Observed data-longest continuous time segment from: 8/10/2004 to 11/26/2004 

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 5726 176.236 

h 5726 176.217 

H-h 15 cm 24h 5726 0.019 0.052 0.048 0.0 98.8 0.0 0.0 0.0 

AHW-ahw 15 cm 24h 19 -0.080 0.086 0.032 0.0 100.0 0.0 0.0 0.0 

ALW-alw 15 cm 24h 33 0.114 0.120 0.036 0.0 78.8 0.0 0.0 0.0 

THW-thw 1.50 hr 25h 19 1.000 3.532 3.480 10.5 36.8 21.1 0.0 0.0 

TLW-tlw 1.50 hr 25h 33 0.758 2.928 2.873 9.1 36.4 15.2 0.0 0.0 

 SCENARIO: SEMI-OPERATIONAL FORECAST 

H00-h00 15 cm 24h 485 0.011 0.050 0.049 0.0 98.4 0.0 0.0 0.0 

H06-h06 15 cm 24h 479 0.028 0.053 0.044 0.0 99.2 0.0 0.0 0.0 

H12-h12 15 cm 24h 481 0.012 0.047 0.046 0.0 99.0 0.0 0.0 0.0 

H18-h18 15 cm 24h 479 0.026 0.051 0.044 0.0 99.4 0.0 0.0 0.0 

H24-h24 15 cm 24h 481 0.013 0.050 0.049 0.0 99.0 0.0 0.0 0.0 

AHW-ahw 15 cm 24h 19 -0.061 0.073 0.040 0.0 100.0 0.0 

ALW-alw 15 cm 24h 33 0.099 0.103 0.028 0.0 97.0 0.0 

THW-thw 1.50 hr 25h 19 0.474 4.020 4.101 21.1 31.6 21.1 

TLW-tlw 1.50 hr 25h 33 0.000 3.266 3.317 21.2 27.3 12.1 
49

Table A.2. Skill Assessment Statistics of Semi-Operational Predictions at NOS Ludington, MI 

(9087023) Gauge for 2004

Station: Ludington, Lake Michigan, MI 

Observed data-longest continuous time segement from: 4/23/2004 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 5785 176.239 

h 5785 176.248 

H-h 15 cm 24h 5785 -0.010 0.053 0.052 0.0 98.0 0.0 0.0 0.0 

AHW-ahw 15 cm 24h 24 -0.147 0.159 0.061 0.0 54.2 0.0 0.0 0.0 

ALW-alw 15 cm 24h 28 0.094 0.100 0.033 0.0 89.3 0.0 0.0 0.0 

THW-thw 1.50 hr 25h 24 2.875 4.495 3.530 4.2 16.7 45.8 0.0 0.0 

TLW-tlw 1.50 hr 25h 28 -0.250 3.942 4.006 35.7 17.9 25.0 0.0 0.0 

 SCENARIO: SEMI-OPERATIONAL FORECAST 

H00-h00 15 cm 24h 490 -0.008 0.052 0.051 0.0 98.4 0.0 0.0 0.0 

H06-h06 15 cm 24h 486 -0.011 0.056 0.055 0.0 97.5 0.0 0.0 0.0 

H12-h12 15 cm 24h 486 -0.008 0.054 0.053 0.0 97.3 0.0 0.0 0.0 

H18-h18 15 cm 24h 486 -0.012 0.057 0.056 0.0 97.5 0.0 0.0 0.0 

H24-h24 15 cm 24h 486 -0.009 0.055 0.055 0.0 97.1 0.0 0.0 0.0 

AHW-ahw 15 cm 24h 24 -0.153 0.171 0.077 0.0 45.8 0.0 

ALW-alw 15 cm 24h 28 0.095 0.101 0.035 0.0 85.7 0.0 

THW-thw 1.50 hr 25h 24 0.625 4.287 4.332 20.8 16.7 25.0 

TLW-tlw 1.50 hr 25h 28 -1.107 4.145 4.067 39.3 17.9 21.4 

Table A.3. Skill Assessment Statistics of Semi-Operational Predictions at NOS Holland, MI (9087031) Gauge for 

2004 

Station: Holland, Lake Michigan, MI 

Observed data-longest continuous time segment 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 176.237 

h 5832 176.244 

H-h 15 cm 24h 5832 -0.007 0.048 0.048 0.0 98.9 0.0 0.0 0.0 

AHW-ahw 15 cm 24h 19 -0.088 0.100 0.050 0.0 94.7 0.0 0.0 0.0 

ALW-alw 15 cm 24h 10 0.089 0.094 0.031 0.0 90.0 0.0 0.0 0.0 

THW-thw 1.50 hr 25h 19 0.895 4.298 4.319 15.8 47.4 26.3 0.0 0.0 

TLW-tlw 1.50 hr 25h 10 1.200 2.608 2.441 0.0 30.0 20.0 0.0 0.0 

 SCENARIO: SEMI-OPERATIONAL FORECAST 

H00-h00 15 cm 24h 494 -0.001 0.047 0.047 0.0 99.0 0.0 0.0 0.0 

H06-h06 15 cm 24h 490 -0.014 0.049 0.047 0.0 99.0 0.0 0.0 0.0 

H12-h12 15 cm 24h 490 -0.003 0.049 0.049 0.0 98.8 0.0 0.0 0.0 

H18-h18 15 cm 24h 490 -0.013 0.054 0.052 0.0 98.0 0.0 0.0 0.0 

H24-h24 15 cm 24h 490 -0.004 0.051 0.051 0.0 98.2 0.0 0.0 0.0 

AHW-ahw 15 cm 24h 16 -0.075 0.087 0.045 0.0 93.8 0.0 

ALW-alw 15 cm 24h 11 0.079 0.083 0.027 0.0 100.0 0.0 

THW-thw 1.50 hr 25h 16 1.063 4.085 4.074 12.5 6.3 25.0 

TLW-tlw 1.50 hr 25h 11 0.091 3.398 3.562 18.2 27.3 18.2 
50

Table A.4. Skill Assessment Statistics of Semi-Operational Predictions at NOS Calumet Harbor, IL (9087044) 

Gauge for 2004.

Station: Calumet Harbor, Lake Michigan, IL 

Observed data-longest continuous time segment 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 176.237 

h 5832 176.256 

H-h 15 cm 24h 5832 -0.019 0.070 0.068 0.1 95.5 0.0 2.0 0.0 

AHW-ahw 15 cm 24h 28 -0.151 0.166 0.071 3.6 50.0 0.0 0.0 0.0 

ALW-alw 15 cm 24h 38 0.091 0.110 0.063 0.0 84.2 0.0 0.0 0.0 

THW-thw 1.50 hr 25h 28 -0.286 5.043 5.127 32.1 17.9 28.6 0.0 0.0 

TLW-tlw 1.50 hr 25h 38 0.105 3.026 3.065 15.8 39.5 13.2 0.0 0.0 

 SCENARIO: SEMI-OPERATIONAL FORECAST 

H00-h00 15 cm 24h 494 -0.008 0.068 0.068 0.0 96.0 0.0 0.0 0.0 

H06-h06 15 cm 24h 490 -0.031 0.071 0.064 0.2 96.5 0.0 0.0 0.0 

H12-h12 15 cm 24h 490 -0.010 0.069 0.069 0.0 96.3 0.0 0.0 0.0 

H18-h18 15 cm 24h 490 -0.029 0.078 0.072 0.2 94.5 0.0 0.0 0.0 

H24-h24 15 cm 24h 490 -0.011 0.072 0.071 0.0 95.5 0.0 0.0 0.0 

AHW-ahw 15 cm 24h 30 -0.137 0.159 0.081 3.3 56.7 0.0 

ALW-alw 15 cm 24h 37 0.080 0.104 0.068 0.0 83.8 0.0 

THW-thw 1.50 hr 25h 30 -0.500 5.648 5.722 30.0 20.0 23.3 

TLW-tlw 1.50 hr 25h 37 -0.973 3.661 3.578 24.3 27.0 13.5 

Table A.5. Skill Assessment Statistics of Semi-Operational Predictions at NOS Milwaukee, WI (9087057) Gauge for 

2004. 

Station: Milwaukee, Lake Michigan, WI 

Observed data-longest continuous time segment 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 176.229 

h 5832 176.250 

H-h 15 cm 24h 5832 -0.021 0.057 0.053 0.0 98.2 0.0 0.0 0.0 

AHW-ahw 15 cm 24h 21 -0.134 0.149 0.068 0.0 66.7 0.0 0.0 0.0 

ALW-alw 15 cm 24h 25 0.064 0.073 0.035 0.0 96.0 0.0 0.0 0.0 

THW-thw 1.50 hr 25h 21 0.333 5.568 5.695 19.0 38.1 23.8 0.0 0.0 

TLW-tlw 1.50 hr 25h 25 -1.000 2.878 2.754 20.0 52.0 8.0 0.0 0.0 

 SCENARIO: SEMI-OPERATIONAL FORECAST 

H00-h00 15 cm 24h 494 -0.015 0.055 0.053 0.0 98.2 0.0 0.0 0.0 

H06-h06 15 cm 24h 490 -0.028 0.058 0.051 0.0 98.8 0.0 0.0 0.0 

H12-h12 15 cm 24h 490 -0.017 0.057 0.054 0.0 98.2 0.0 0.0 0.0 

H18-h18 15 cm 24h 490 -0.027 0.060 0.054 0.0 98.0 0.0 0.0 0.0 

H24-h24 15 cm 24h 490 -0.017 0.057 0.054 0.0 98.2 0.0 0.0 0.0 

AHW-ahw 15 cm 24h 21 -0.141 0.155 0.064 0.0 61.9 0.0 

ALW-alw 15 cm 24h 25 0.054 0.063 0.033 0.0 100.0 0.0 

THW-thw 1.50 hr 25h 21 -0.143 6.751 6.916 38.1 9.5 33.3 

TLW-tlw 1.50 hr 25h 25 0.200 3.317 3.379 16.0 36.0 24.0 
51

Table A.6. Skill Assessment Statistics of Semi-Operational Predictions at NOS Kewaunee, WI (9087068) Gauge for 

2004.

Station: Kewaunee, Lake Michigan, WI 

Observed data-longest continuous time segment 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 176.226 

h 5832 176.233 

H-h 15 cm 24h 5832 -0.006 0.051 0.050 0.0 98.7 0.0 0.0 0.0 

AHW-ahw 15 cm 24h 16 -0.152 0.161 0.056 0.0 56.3 0.0 0.0 0.0 

ALW-alw 15 cm 24h 25 0.080 0.088 0.038 0.0 92.0 0.0 0.0 0.0 

THW-thw 1.50 hr 25h 16 1.500 5.148 5.086 18.8 6.3 50.0 0.0 0.0 

TLW-tlw 1.50 hr 25h 25 -0.480 3.394 3.429 24.0 28.0 20.0 0.0 0.0 

 SCENARIO: SEMI-OPERATIONAL FORECAST 

H00-h00 15 cm 24h 494 -0.007 0.048 0.048 0.0 99.0 0.0 0.0 0.0 

H06-h06 15 cm 24h 490 -0.006 0.052 0.052 0.0 98.8 0.0 0.0 0.0 

H12-h12 15 cm 24h 490 -0.007 0.050 0.049 0.0 99.0 0.0 0.0 0.0 

H18-h18 15 cm 24h 490 -0.007 0.052 0.052 0.0 98.8 0.0 0.0 0.0 

H24-h24 15 cm 24h 490 -0.007 0.051 0.050 0.0 98.4 0.0 0.0 0.0 

AHW-ahw 15 cm 24h 12 -0.177 0.184 0.052 8.3 41.7 0.0 

ALW-alw 15 cm 24h 26 0.080 0.089 0.039 0.0 96.2 0.0 

THW-thw 1.50 hr 25h 12 -1.083 5.867 6.022 33.3 25.0 33.3 

TLW-tlw 1.50 hr 25h 26 -0.923 4.132 4.108 38.5 15.4 23.1 
APPENDIX B. Time Series Plots of Semi-Operational Water Level Nowcasts vs. 

Observations at Six NOS Gauges during 2004. 

100 150 200 250 300 350

Julian Day (2004)

177.0176.6176.2175.8175.4

Water Levels (meters, IGLD1985)

events in observation

52

observationnowcastevents in nowcast

Port Inland

Figure B.1. Time Series Plot of Semi-Operational Nowcasts vs. Observations at NOS Gauge at Port 

Inland, MI.
100 150 200 250 300 350

Julian Day (2004)

177.0176.6176.2175.8175.4

Water Levels (meters, IGLD1985)

observationnowcastevents in observationevents in nowcast

Ludington

Figure B.2. Time Series Plot of Semi-Operational Nowcasts vs. Observations at NOS Gauge at 

Ludington, MI. 

53
100 150 200 250 300 350

Julian Day (2004)

177.0176.6176.2175.8175.4

Water Levels (meters, IGLD1985)

observationnowcastevents in observationevents in nowcast

Holland

Figure B.3. Time Series Plot of Semi-Operational Nowcasts vs. Observations at NOS Gauge at Holland, 

MI. 

54
100 150 200 250 300 350

Julian Day (2004)

177.0176.6176.2175.8175.4

Water Levels (meters, IGLD1985)

observationnowcastevents in observationevents in nowcast

Calumet Harbor

Figure B.4.Time Series Plot of Semi-Operational Nowcasts vs. Observations at NOS Gauge at Calumet 

Harbor, IL. 

55
100 150 200 250 300 350

Julian Day (2004)

177.0176.6176.2175.8175.4

Water Levels (meters, IGLD1985)

observationnowcastevents in observationevents in nowcast

Milwaukee

Figure B.5. Time Series Plot of Semi-Operational Nowcasts vs. Observations at NOS Gauge in 

Milwaukee, WI. 

56
100 150 200 250 300 350

Julian Day (2004)

177.0176.6176.2175.8175.4

Water Levels (meters, IGLD1985)

observationnowcastevents in observationevents in nowcast

Kewaunee

Figure B.6. Time Series Plot of Semi-Operational Nowcasts vs. Observations at NOS Gauge in 

Kewaunee, WI. 

57
APPENDIX C. Time Series Plots of Semi-Operational Water Level Forecast Guidance vs. 

Observations at Six NOS Gauges in Lake Michigan during 2004. 

100 150 200 250 300 350

Julian Day (2004)

177.0176.6176.2175.8175.4

Water Levels (meters, IGLD1985)

events in observation

58

observationforecastevents in forecast

Port Inland

Figure C.1. Time Series Plot of Semi-Operational Forecast Guidance vs. Observations at the NOS Port 

Inland, MI Gauge. 
100 150 200 250 300 350

Julian Day (2004)

177.0176.6176.2175.8175.4

Water Levels (meters, IGLD1985)

observationforecastevents in observationevents in forecast

Ludington

Figure C.2. Time Series Plot of Semi-Operational Forecast Guidance vs. Observations at the NOS 

Ludington, MI Gauge. 

59
100 150 200 250 300 350

Julian Day (2004)

177.0176.6176.2175.8175.4

Water Levels (meters, IGLD1985)

observationforecastevents in observationevents in forecast

Holland

Figure C.3. Time Series Plot of Semi-Operational Forecast Guidance vs. Observations at the NOS 

Holland, MI. 

60
100 150 200 250 300 350

Julian Day (2004)

177.0176.6176.2175.8175.4

Water Levels (meters, IGLD1985)

observationforecastevents in observationevents in forecast

Calumet Harbor

Figure C.4. Time Series Plot of Semi-Operational Forecast Guidance vs. Observations at the NOS 

Calumet Harbor, IL. 

61
100 150 200 250 300 350

Julian Day (2004)

177.0176.6176.2175.8175.4

Water Levels (meters, IGLD1985)

observationforecastevents in observationevents in forecast

Milwaukee

Figure C.5. Time Series Plot of Semi-Operational Forecast Guidance vs. Observations at the NOS 

Milwaukee, WI Gauge. 

62
100 150 200 250 300 350

Julian Day (2004)

177.0176.6176.2175.8175.4

Water Levels (meters, IGLD1985)

observationforecastevents in observationevents in forecast

Kewaunee

Figure C.6. Time Series Plot of Semi-Operational Forecast Guidance vs. Observations at the NOS 

Kewaunee, WI Gauge. 

63
64

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

Forecasts of Surface Water Temperatures at Two NWS/NDBC fixed buoys 

in Lake Michigan for 2004. 

Table D.1. Skill Assessment Statistics of Semi-Operational Nowcasts and Forecast Guidance of Surface 

Water Temperature at the NWS/NDBC Fixed Buoy 45002 (Northern Michigan) from mid 

April to mid November 2004. 

Station: NDBC Buoy 45002 in Lake Michigan 

Observed data-longest continuous time segment from: 4/20/2004 to 11/17/2004 

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 5083 12.832 

t 5083 11.404 

T-t 3.0 c 24h 5083 1.428 2.213 1.692 0.0 83.7 1.8 0.0 36.0 

 SCENARIO: SEMI-OPERATIONAL FORECAST 

T00-t00 3.0 c 24h 424 1.468 2.252 1.710 0.0 84.0 1.9 0.0 24.0 

T06-t06 3.0 c 24h 420 1.385 2.149 1.645 0.0 84.0 1.0 0.0 0.0 

T12-t12 3.0 c 24h 420 1.355 2.153 1.674 0.0 84.8 1.2 0.0 0.0 

T18-t18 3.0 c 24h 420 1.284 2.013 1.552 0.0 86.4 0.7 0.0 0.0 

T24-t24 3.0 c 24h 420 1.252 1.988 1.545 0.0 87.1 1.0 0.0 24.0 

Table D.2. Skill Assessment Statistics of Semi-Operational Nowcasts and Forecast Guidance of Surface 

Water Temperature at the NWS/NDBC Fixed Buoy 45007 (Southern Michigan) from mid 

April to early December 2004. 

Station: NDBC Buoy 45007 in Lake Michigan 

Observed data-longest continuous time segment from: 4/20/2004 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 5574 14.357 

t 5574 13.622 

T-t 3.0 c 24h 5574 0.735 1.565 1.381 0.0 91.5 0.0 0.0 0.0 

 SCENARIO: SEMI-OPERATIONAL FORECAST 

T00-t00 3.0 c 24h 465 0.765 1.593 1.399 0.0 91.4 0.0 0.0 0.0 

T06-t06 3.0 c 24h 460 0.707 1.535 1.364 0.0 91.1 0.0 0.0 0.0 

T12-t12 3.0 c 24h 461 0.646 1.559 1.421 0.0 92.4 0.0 0.0 0.0 

T18-t18 3.0 c 24h 460 0.601 1.420 1.288 0.0 94.6 0.0 0.0 0.0 

T24-t24 3.0 c 24h 461 0.517 1.397 1.299 0.0 94.1 0.0 0.0 0.0 
APPENDIX E. Time Series Plots of Semi-Operational Nowcasts and Forecast Guidance of 

Surface Water Temperature vs. Observations at Two NWS/NDBC fixed 

buoys during 2004. 

100 150 200 250 300 350

Julian Day (2004)

5.00.0

25.020.015.010.0

Temperature (Degrees)

observationnowcastforecast

NDBC 45002

Figure E.1. Semi-Operational Surface Water Temperature Nowcasts and Forecast Guidance of Surface 

Water Temperatures (o

C) vs. Observations at NWS/NDBC Fixed Buoy 45002 (Northern 

Michigan) for the Period mid-April to mid-December 2004. The forecast values depicted on 

the plot are from the 0000 UTC forecast cycle. 

 

65
100 150 200 250 300 350

Julian Day (2004)

5.00.0

25.020.015.010.0

Temperature (Degrees)

observationnowcastforecast

NDBC 45007

Figure E.2. Semi-Operational Surface Water Temperature Nowcasts and Forecast Guidance of Surface 

Water Temperatures (o

C) vs. Observations at NWS/NDBC Fixed Buoy 45007 (Southern 

Michigan) for the Period mid-April to mid-December 2004. The forecast values depicted on 

the plot are from the 0000 UTC forecast cycle. 

66