NOAA Technical Memorandum 

NWS ER-107

__________________________________________________________________

IMPROVING FRESHWATER INFLOWS TO THE NOS 

CHESAPEAKE BAY OPERATIONAL FORECAST SYSTEM 

(CBOFS) AND ASSESSING THE IMPACTS ON SALINITY 

SIMULATIONS

SEANN REED

National Weather Service

Middle-Atlantic River Forecast Center (MARFC)

State College, PA

AMANDA ROBERTS*

Virginia Polytechnic Institute and State University

DUKE N. LE*

Northwest Florida Water Management District

BRECK SULLIVAN*

Chesapeake Research Consortium

* Former summer interns at MARFC supported by NOAAs North Atlantic Regional Team and Chesapeake Bay 

Office.

Scientific Services Division 

Eastern Region Headquarters 

Bohemia, New York 

November 2020

__________________________________________________________________

U.S. DEPARTMENT OF COMMERCE National Oceanic and National Weather Service

 Atmospheric Administration 
NOAA TECHNICAL MEMORANDA

National Weather Service, Eastern Region Subseries 

The National Weather Service Eastern Region (ER) Subseries provides an informal medium for the documentation and quick dissemination of results not appropriate, or 

not yet ready for formal publications. The series is used to report on work in progress, to describe technical procedures and practices, or to relate progress to a limited 

audience. These Technical Memoranda will report on investigations devoted primarily to regional and local problems of interest mainly to ER personnel, and usually will 

not be widely distributed. 

Papers 1 to 22 are in the former series, ESSA Technical Memoranda, Eastern Region Technical Memoranda (ERTM); papers 23 to 37 are in the former series, ESSA 

Technical Memoranda, Weather Bureau Technical Memoranda (WBTM). Beginning with 38, the papers are now part of the series, NOAA Technical Memoranda NWS. 

Papers are available from the National Weather Service Eastern Region, Scientific Services Division, 630 Johnson Avenue, Bohemia, NY, 11716. Beginning with 91, the 

papers are available at: https://www.weather.gov/erh/TechMemos 

ESSA Technical Memoranda

ERTM 1 Local Uses of Vorticity Prognoses in Weather Prediction. Carlos R. Dunn. April 1965. 

ERTM 2 Application of the Barotropic Vorticity Prognostic Field to the Surface Forecast Problem. Silvio G. Simplicio. July 1965. 

ERTM 3 A Technique for Deriving an Objective Precipitation Forecast Scheme for Columbus, Ohio. Robert Kuessner. September 1965. 

ERTM 4 Stepwise Procedures for Developing Objective Aids for Forecasting the Probability of Precipitation. Carlos R. Dunn. November 1965. 

ERTM 5 A Comparative Verification of 300 mb. Winds and Temperatures Based on NMC Computer Products Before and After 

Manual Processing. Silvio G. Simplicio. March 1966. 

ERTM 6 Evaluation of OFDEV Technical Note No. 17. Richard M. DeAngelis. March 1966. 

ERTM 7 Verification of Probability of Forecasts at Hartford, Connecticut, for the Period 1963-1965. Robert B. Wassall. March 1966. 

ERTM 8 Forest-Fire Pollution Episode in West Virginia, November 8-12, 1964. Robert O. Weedfall. April 1966. 

ERTM 9 The Utilization of Radar in Meso-Scale Synoptic Analysis and Forecasting. Jerry D. Hill. March 1966. 

ERTM 10 Preliminary Evaluation of Probability of Precipitation Experiment. Carlos R. Dunn. May 1966. 

ERTM 11 Final Report. A Comparative Verification of 300 mb. Winds and Temperatures Based on NMC Computer Products Before 

and After Manual Processing. Silvio G.Simplicio. May 1966. 

ERTM 12 Summary of Scientific Services Division Development Work in Sub-Synoptic Scale Analysis and Prediction - Fiscal Year 

1966. Fred L. Zuckerberg. May 1966. 

ERTM 13 A Survey of the Role of Non-Adiabatic Heating and Cooling in Relation of the Development of Mid-Latitude Synoptic 

Systems. Constantine Zois. July 1966. 

ERTM 14 The Forecasting of Extratropical Onshore Gales at the Virginia Capes. Glen V. Sachse. August 1966. 

ERTM 15 Solar Radiation and Clover Temperatures. Alex J. Kish. September 1966. 

ERTM 16 The Effects of Dams, Reservoirs and Levees on River Forecasting. Richard M. Greening. September 1966. 

ERTM 17 Use of Reflectivity Measurements and Reflectivity Profiles for Determining Severe Storms. Robert E. Hamilton. October 1966. 

ERTM 18 Procedure for Developing a Nomograph for Use in Forecasting Phenological Events from Growing Degree Days. John C. 

 Purvis and Milton Brown. December 1966. 

ERTM 19 Snowfall Statistics for Williamsport, Pa. Jack Hummel. January 1967 

ERTM 20 Forecasting Maturity Date of Snap Beans in South Carolina. Alex J. Kish. March 1967. 

ERTM 21 New England Coastal Fog. Richard Fay. April 1967. 

ERTM 22 Rainfall Probability at Five Stations Near Pickens, South Carolina, 1957-1963. John C. Purvis. April 1967. 

WBTM ER 23 A Study of the Effect of Sea Surface Temperature on the Areal Distribution of Radar Detected Precipitation Over the South 

Carolina Coastal Waters. Edward Paquet. June 1967. (PB-180-612). 

WBTM ER 24 An Example of Radar as a Tool in Forecasting Tidal Flooding. Edward P. Johnson. August 1967 (PB-180-613). 

WBTM ER 25 Average Mixing Depths and Transport Wind Speeds over Eastern United States in 1965. Marvin E. Miller. August 1967. (PB-180-614). 

WBTM ER 26 The Sleet Bright Band. Donald Marier. October 1967. (PB-180-615). 

WBTM ER 27 A Study of Areas of Maximum Echo Tops in the Washington, D.C. Area During the Spring and Fall Months. Marie D. Fellechner 

 April 1968. (PB-179-339). 

WBTM ER 28 Washington Metropolitan Area Precipitation and Temperature Patterns. C.A. Woollum and N.L. Canfield. June 1968. (PB-179-340). 

WBTM ER 29 Climatological Regime of Rainfall Associated with Hurricanes after Landfall. Robert W. Schoner. June 1968. (PB-179-341). 

WBTM ER 30 Monthly Precipitation - Amount Probabilities for Selected Stations in Virginia. M.H. Bailey. June 1968. (PB-179-342). 

WBTM ER 31 A Study of the Areal Distribution of Radar Detected Precipitation at Charleston, S.C. S.K. Parrish and M.A. Lopez. 

October 1968. (PB-180-480). 

WBTM ER 32 The Meteorological and Hydrological Aspects of the May 1968 New Jersey Floods. Albert S. Kachic and William Long. 

February 1969. (Revised July 1970). (PB-194-222). 

WBTM ER 33 A Climatology of Weather that Affects Prescribed Burning Operations at Columbia, South Carolina. S.E. Wasserman and 

J.D. Kanupp. December 1968. (COM-71-00194). 

WBTM ER 34 A Review of Use of Radar in Detection of Tornadoes and Hail. R.E. Hamilton. December 1969. (PB-188-315). 

WBTM ER 35 Objective Forecasts of Precipitation Using PE Model Output. Stanley E. Wasserman. July 1970. (PB-193-378). 

WBTM ER 36 Summary of Radar Echoes in 1967 Near Buffalo, N.Y. Richard K. Sheffield. September 1970. (COM-71-00310). 

WBTM ER 37 Objective Mesoscale Temperature Forecasts. Joseph P. Sobel. September 1970. (COM-71-0074). 

NOAA Technical Memoranda NWS

NWS ER 38 Use of Primitive Equation Model Output to Forecast Winter Precipitation in the Northeast Coastal Sections of the United States.

Stanley E. Wasserman and Harvey Rosenblum. December 1970. (COM-71-00138). 

NWS ER 39 A Preliminary Climatology of Air Quality in Ohio. Marvin E. Miller. January 1971. (COM-71-00204). 

NWS ER 40 Use of Detailed Radar Intensity Data in Mesoscale Surface Analysis. Robert E. Hamilton. March 1971. (COM-71-00573). 

NWS ER 41 A Relationship Between Snow Accumulation and Snow Intensity as Determined from Visibility. Stanley E. Wasserman and 

Daniel J. Monte. (COM-71-00763). January 1971. 

NWS ER 42 A Case Study of Radar Determined Rainfall as Compared to Rain Gage Measurements. Martin Ross. July 1971. (COM-71-00897). 

NWS ER 43 Snow Squalls in the Lee of Lake Erie and Lake Ontario. Jerry D. Hill. August 1971. (COM-72-00959). 

NWS ER 44 Forecasting Precipitation Type at Greer, South Carolina. John C. Purvis. December 1971. (COM-72-10332).

NWS ER 45 Forecasting Type of Precipitation. Stanley E. Wasserman. January 1972. (COM-72-10316). 

(CONTINUED ON INSIDE REAR COVER)
NOAA Technical Memorandum NWS ER-107

IMPROVING FRESHWATER INFLOWS TO THE NOS 

CHESAPEAKE BAY OPERATIONAL FORECAST SYSTEM 

(CBOFS) AND ASSESSING THE IMPACTS ON SALINITY 

SIMULATIONS

SEANN REED

National Weather Service

Middle-Atlantic River Forecast Center (MARFC)

State College, PA

AMANDA ROBERTS*

Virginia Polytechnic Institute and State University

DUKE N. LE*

Northwest Florida Water Management District

BRECK SULLIVAN*

Chesapeake Research Consortium

* Former summer interns at MARFC supported by NOAAs North Atlantic Regional Team and Chesapeake Bay Office.

 

Scientific Services Division 

Eastern Region Headquarters 

Bohemia, New York 

November 2020

 

United States Department of Commerce National Oceanic and Atmospheric Administration National Weather Service 

Wilbur Ross Neil Jacobs Louis W. Uccellini

Secretary Under Secretary and Administrator Assistant Administrator 
1

Table of Contents

Introduction..................................................................................................................................... 2

Methods........................................................................................................................................... 4

Results and Discussion ................................................................................................................. 12

Conclusions and Recommendations for Future Work .................................................................. 23

Acknowledgments......................................................................................................................... 25

References..................................................................................................................................... 26

Appendices.................................................................................................................................... 28

Abstract

The Chesapeake Bay Operational Forecast System (CBOFS) is a 3D hydrodynamic model which 

generates 48-hour forecasts of water level, currents, temperatures and salinity throughout the 

Chesapeake Bay. A limitation of the current CBOFS is that it does not use forecast information 

available from modern river forecast models. Currently, river inflows are derived by persisting 

the flow observations from U.S. geological survey stream gages, and the gages used measure 

runoff from only 79% of the Bay watershed area. In this study, we investigate the potential 

benefits of using hydrologic modeling to provide CBOFS with more accurate estimates of river 

inflows. Specifically, we study the impact of using more detailed and accurate streamflow inputs 

on the model’s ability to predict salinity. In our simulation experiments, we increased the number 

of river inflow nodes from 13 to 60. Salinity is studied because of its sensitivity to freshwater 

inflows and its importance to marine life in the Bay. As expected, adding more freshwater to the 

model does reduce high salinity bias from CBOFS that has been noted in earlier studies; 

however, some bias still remains. The spatial pattern of salinity simulation improvements are 

also consistent with expectations. That is, more improvement is seen in shallow waters closer to 

the added river inputs. To expedite this study, we used the Research Distributed Hydrologic

Model (RDHM) to simulate flows on small rivers. Moving forward, the more feasible path for 

operational implementation on small rivers is to use flows from NOAAs National Water Model.
2

Introduction

NOAAs National Ocean Service runs Operational Forecast Systems (OFSs) for critical ports and 

estuaries in the United States. Operational Forecast Systems provide useful information about 

water conditions for many users such as ship navigators, recreational boaters, fishermen, 

fisheries managers, public health officials, hazardous material response teams, search and rescue 

personnel, and others. The Chesapeake Bay OFS (CBOFS) generates 48-hour forecasts for water 

level, currents, temperatures and salinity throughout the Chesapeake Bay (NOAA National 

Ocean Service, 2011). Here we study how the hydrodynamic model underlying CBOFS can be 

improved by improving the accuracy of hydrologic inflows from the surrounding rivers and 

streams. Specifically, we analyze the impacts of freshwater inflow improvements on salinity 

simulations. 

Why is this important? 

There are known limitations with respect to river inflows in the current CBOFS implementation.

First, CBOFS only explicitly accounts for 13 river inflows into the Bay using data from U.S. 

Geological Survey (USGS) stream gages (Figure 1). These 13 river inflows represent only about 

79% of the Chesapeake Bay drainage area. Second, river inflows to CBOFS for the 48-hour 

forecast period are estimated by simply persisting observed flow data. This does not accurately 

reflect the impacts of rising flows during storm events. At this time, several available hydrologic 

models are capable of simulating flows from all rivers draining to the Bay. These models can 

produce more accurate inflows by explicitly accounting for more of the watershed drainage area. 

In addition, use of forecast information from hydrologic models will improve upon persistence 

estimates at inflow locations. 

We focus on salinity because our hypothesis is that improving the river inflow accuracy will 

have a relatively large impact on the CBOFS salinity forecasts compared to other variables such 

as currents. Also, positive salinity bias is a known issue in the operational CBOFS model 

(Lanerolle et al., 2016). We investigate whether adding inflow nodes to the operational CBOFS 

model might have dramatic local impacts on salinity forecasts in smaller river estuaries within 

the Bay.

Salinity has important impacts on the Bay ecosystem. Salinity affects the health and spatial 

distribution of numerous Chesapeake Bay species such as fish, oysters, and blue crabs. Salinity 

also influences biological process rates which can affect processes important to humans such as 

harmful algal blooms and oyster growth. 

NOAA already uses salinity and temperature forecasts from CBOFS to make real-time 

predictions about the prevalence and location of stinging sea nettles and harmful Vibrio bacteria 

(NOAA OPC, 2019; NCCOS 2020). Li et al. (2001) describe the scientific foundation for the sea 

nettle forecasting system while Lanerolle et al. (2016) do the same for the Vibrio forecasting. In 

both cases, an empirical relationship translates temperature and salinity outputs from CBOFS 
3

into forecast variables of interest: sea nettle encounter probabilities and Vibrio concentrations. 

Improved river inflows to CBOFS should improve these sea nettle and Vibrio forecasts.

What is unique about this study?

Like the operational CBOFS implementation, academic and regulatory hydrodynamic model 

implementations in the Chesapeake Bay most often do not explicitly account for inflows from 

smaller watersheds (Xu et al., 2012; Feng et al., 2015; Cerco et al., 2010). In some cases, this is 

because the main study foci were on the deeper parts of the bay. An exception is the work of Ye 

et al. (2018) which accounts for more of the smaller tributaries. They noted that adding the 

smaller tributaries into their modeling did improve the accuracy of their results; however, they 

did not elaborate on details. Our study more explicitly addresses the impacts of accounting for 

the smaller tributaries. In addition, we use hydrodynamic and hydrologic models which are 

already used in NOS and NWS operations. Therefore, the transition from research-to-operations 

should be relatively easy. 

This study was completed through a unique inter-office collaboration within NOAA. 

NOAA’s North Atlantic Regional Team (NART) funded three summer internships for 

undergraduates while the NOAA Chesapeake Bay Office and Chesapeake Research Consortium 

facilitated these internships. NOAAs National Weather Service (NWS) Middle Atlantic River 

Forecast Center (MARFC) hosted the three students and guided the research with assistance 

from modelers at the NOS Center for Operational Products and Services (COOPS) and the Coast 

Survey Development Laboratory (CSDL). 

How does this study contribute to the NOAA goals?

The goals of this study are in line with the NOAA Water Initiative Vision and Five-Year Plan 

(NOAA, 2016), which includes goals to advance water quality forecasting in riverine and 

estuarine environments and to create new capabilities to leverage water quality predictions for 

ecological applications. This research tackles only a small piece of this bigger picture, but did so 

using minimal resources. The hydrologic model used in this study is a regional model selected so 

that it could be run at the MARFC to complete the project; however, the same methods could be 

used to pass outputs from NOAAs National Water Model (NWM, 2020) to CBOFS. The NWM 

is a major component of the NOAA Water Initiative. 

Our outcomes do the following: (1) demonstrate that there is value in substantially increasing the 

number of river inflow nodes to CBOFS, (2) demonstrate that CBOFS remains computationally 

stable with this increase in river nodes, and (3) provide configuration details that could be used 

to modify the current operational CBOFS model. 
4

Methods

Hydrodynamic Model

Lanerolle et al. (2011) describe the development and skill assessment of the model that underlies 

the current Chesapeake Bay Operational Forecast System, CBOFS2. While the first CBOFS was 

based on a 2-dimensional model and only provided information on water levels and depthaveraged currents, CBOFS2 is a 3-dimensional model and provides information on water levels, 

currents, temperature, and salinity. CBOFS2 is an implementation of Rutgers University’s 

Regional Ocean Model System. 

For our baseline simulation, we ran CBOFS2 using a model configuration and forcing files 

provided by Lyon Lanerolle (personal communication, 2016). The baseline simulation input files 

are identical to those Lanerolle et al. (2011) used for their ‘synoptic simulation.’ For 

meteorological fluxes, Lanerolle et al. (2011) combined gridded data from the North American 

Regional Reanalysis (Mesinger et al., 2006) with local gage information. For the open ocean 

boundary conditions they used monthly climatology data from the World Ocean Atlas 2001 

(Conkright et al., 2002). The only difference in our calculations for this baseline run is that we 

recompiled the executable on a different High Performance Computing development platform. 

The original simulations were run on NOAAs ‘Jet’ machine while our simulations were run on 

NOAAs ‘Theia’ platform.

Figure 1 shows the CBOFS2 model mesh along with the locations of the river input nodes used 

by Lanerolle et al. (2011). The flows estimated at a given node are sometimes estimated from 

more than one upstream US Geological Survey gage as specified in Table 1. These are also the 

gages used for operational CBOFS2 runs as of October 2020.
5

Figure 1. CBOFS2 model mesh and river inflow node locations for operational runs.
6

Table 1. USGS Flow Observation Stations used to Derive CBOFS2 inflows.

CBOFS Input Node USGS ID Name

Drainage 

Area (mi2)

1 Nanticoke River Unknown

2 Choptank River stations

1491000 Choptank R near Greensboro MD 113

1491500 Tuckahoe Cr near Ruthsburg MD 85.2

3 Elk Creek

1495000 Big Elk Cr at Elk Mills MD 51.6

4 Susquehanna River

1578310 Susquehanna R at Conowingo MD 27100

1580520 Deer Cr near Darlington MD 164

5 Bush River

1581757 Otter Point Creek near Edgewood MD 55.6

6 Patapsco River

1589352 Gwynns Falls at Wash Blvd at Balt 

MD

65.9

7 Patuxent River

1594440 Patuxent R near Bowie MD 348

1594526 W Branch at Upper Marlboro MD 89.7

8 Potomac River

1646500 Potomac R near Little Falls 11560

9 Mattawoman Creek 1658000 Mattawoman Cr nr Pomonkey MD 54.8

10 Rappahannock

1668000 Rappahannock R near Fredericksburg 1595

11 York River

1673000 Pamunkey R near Hanover VA 1078

1674500 Mattaponi R near Beulahville VA 603

12 James River

2037500 James R near Richmond VA 6753

2041650 Appomatox River at Matoaca VA 1342

13 Nansemond River

2049500 Blackwater R near Franklin VA 613

An important consideration when passing flows from a river outside of the model to CBOFS2

nodes is to ensure that there is no numerical instability due to rapidly changing conditions at the 

boundary nodes. To maintain stability, we followed the methods used by Lanerolle et al. (2011). 

Large river flows are distributed laterally across several CBOFS2 boundary nodes in proportion 

to the normal water depth. In addition, a pre-defined vertical velocity distribution is prescribed as 

a function of depth at the boundary nodes to approximately account for the effects of bottom 

friction. Figure 2 conceptually illustrates this lateral and vertical distribution of flows at the 

upstream end of the James River, VA. 
7

Figure 2. (a) Example of lateral flow distribution across nodes for the James River CBOFS2 

inflow, (b) default vertical velocity profile at inflow nodes.

Watershed Model

Using a watershed model with more complete coverage of the Chesapeake Bay Drainage area is 

a primary component of this study. The dark green shaded area in Figure 3a shows the watershed 

drainage areas captured by the USGS gages listed in Table 1. CBOFS2 does not explicitly 

account for the light green and yellow shaded areas in Figure 3a within the Chesapeake portion 

of the map. For the rivers included in the operational CBOFS2, USGS flows are adjusted to 

account for the intervening drainage areas between the gage locations and the 13 Bay input 

nodes shown in Figure 1 (also as black dots in Figure 3b). In this study, we experimented with 

adding additional rivers and connections to CBOFS2. Initially we took a cautious approach and 

only increased the number of rivers represented from 13 to 22. Because this increase caused no 

model stability problems, we then proceeded to increase the number of rivers to 60 and these are 
8

the results reported here. The 60 rivers includes all rivers within the Chesapeake Watershed with 

a drainage area of at least 50 mi2. Figure 3b highlights basins added.

We refer to model runs with the 13 original input nodes as Scenario 1 (S1) and the model runs 

with 60 river inputs as Scenario 2 (S2). Table 2 summarizes percentage of the Chesapeake 

Watershed area observed and accounted for in each scenario. In the simulation mode used here, 

both scenarios use observed flows where available. In Scenario 1, drainage area coefficients are 

computed to account for the locations of the stream gages as described by Xu et al. (2012), and 

flows are scaled by these amounts to represent the water contributions downstream of these 

gages (hence the increase from 79% observed to 85% observed and modeled). In Scenario 2, 

10% additional area is accounted for by the 47 additional input nodes for small rivers, making 

the total area observed and modeled 95%. 

Figure 3. Modeled areas for Scenarios 1 and 2: (a) middle Atlantic domain, and (b) zoomed to 

watersheds added for Scenario 2; black dots are the original input nodes and red dots are added 

input nodes.
9

Table 2. Percent of drainage area observed and modeled in simulation studies.

Scenario % area observed % area observed + modeled

S1 79 85

S2 79 95

To simulate outflows for the ungaged sub-watersheds in Scenario 2, we used the Hydrology 

Laboratory’s Research Distributed Hydrologic Modeling System (HL-RDHM) (Koren et al., 

2004). In our implementation, HL-RDHM is applied at a 2-km grid cell resolution to the entire 

Chesapeake drainage basin. Figure 4 shows the river network formed by inter-connected 2-km 

grid cells in the model. Our HL-RDHM implementation uses the gridded Snow-17, the gridded 

Sacramento Soil Moisture Accounting Model with Heat Transfer (SAC-HT), and kinematic 

wave routing as described by Koren et al. (2007). The forcings used for snow melt and rainfallrunoff modeling include gridded hourly precipitation, gridded hourly temperature, and gridded 

monthly evapotranspiration demand estimates. The precipitation grids were from MARFCs 

multi-sensor precipitation analyses which blend radar estimates with quality controlled rain-gage 

estimates to yield ‘best estimate’ grids. The hourly temperature grids were created by blending 

hourly and daily point temperature observation data to generate hourly grids. The gridded 

evapotranspiration demand fields provided as default inputs to HL-RDHM were developed based 

on remotely sensed vegetation fields, select calibrated watersheds, and monthly climatological

data.

Figure 4. HL-RDHM modeled rivers based on 2-km grid cells with a magnified area showing 

the cell-to-cell network in two headwater basins.
10

No comprehensive calibration was done for these HL-RDHM hydrologic model simulations. 

Parameters for the snow, rainfall-runoff, and routing calculations are a priori parameters derived 

from physical data sets (Koren et al., 2007; Mizukami and Koren, 2008) or default values for a 

region. Approximate adjustments to the monthly evapotranspiration demand grids were applied 

to match experience with calibrating a few small basins in the region. 

Some of the 60 river simulation points shown in Figure 3 are downstream of the USGS gages 

used in the operational CBOFS2 runs. For any of the rivers containing a gage in Table 1, the 

observed data is used for the gaged portion of the basin and the HL-RDHM modeled flow is 

added for the ungaged portion of the basin. This limits hydrologic simulation errors to only the 

ungaged areas, shaded light green in Figure 3. 

We selected the HL-RDHM software for use in this study because it has performed well in 

model inter-comparisons (Reed et al., 2004; Smith et al., 2012), it is relatively easy to re-run 

simulations for long periods of time, and the software is already running in real-time at the 

Middle Atlantic River Forecast Center. While we leveraged HL-RDHM to facilitate this study, 

no more development work is being done on HL-RDHM and much more effort at the National 

level goes into building the National Water Model (NWM) for real-time forecasts (OWP, 2020). 

While it was infeasible to use the NWM in this study, we expect that the NWM will be a more 

practical platform to simulate river flows from ungaged basins in future applications. The simple 

methods to pass river flows to CBOFS2 used here are applicable to either HL-RDHM or NWM 

output. The same CBOFS2 configuration files could be used in either case. 

Study Design

In our results, we show outputs from two model scenarios:

 Scenario 1 (S1): The baseline case with 13 rivers represented.

 Scenario 2 (S2): The enhanced scenario with 60 rivers represented.

Each scenario was analyzed for two different time periods. The first time period was the same as 

that used by Lanerolle et al. (2011), spanning June 2003 to September 2005; however, we 

discarded the first six months of output from our analysis to avoid model spin-up errors. For 

additional validation, we selected a more recent period from January 1, 2016 to December 31, 

2016. For this period, archived forcings were provided by NOS COOPs from their operational 

database. No model spin-up was required for the 2016 period because initial conditions were 

taken from saved operational model states. 

Validation

We validated salinity simulations from CBOFS2 against salinity observations from the 

Chesapeake Bay Programs Tidal Water Quality Monitoring program (CBP, 2020). For the time 

periods in this study, we downloaded data for 64 sampling locations that correspond to CBOFS2 

output locations and have valid data. The salinity observations are discrete observations taken by 
11

boat, once or twice per month at each site. At each site, observations are available at multiple 

depths, 1-m intervals from the water surface to the Bay floor. Figure 5 shows the identifiers and 

locations for the observations.

Figure 5. Chesapeake Bay Program monitoring stations used for validation.

We evaluate the salinity simulations from Scenarios 1 and 2 using three main methods for each 

location: (1) we compute and map summary statistics comparing simulated and observed 

salinities (at the 1-m depth and for the profile average), (2) plot and compare simulated and 

observed salinity profiles for individual locations, and (3) plot time series of 1-m simulated and 

observed salinity to observe temporal trends. To facilitate these comparisons, simulated salinity 

estimates output from CBOFS2 are interpolated vertically to the standard 1-m interval 

observation depths. 
12

Many of the statistic and graphics presented here were generated using Python scripts developed 

for this project. These scripts leverage the Matplotlib and GeoPandas libraries. 

Results and Discussion

Figures 6a and 6b show the Scenario 1 profile mean error in the salinity forecasts for all 

sampling locations and the 2004 and 2016 study periods respectively. To compute ‘Profile Mean 

Error’, the error is averaged over all observed depths and all the sampling times during the study 

period. During both study periods, the Scenario 1 salinity is too high over most of the Bay, which 

is consistent with the observations of Lanerolle et al. (2011). This positive error is more 

pronounced and consistent across all locations during the 2016 period compared to 2004. Figures 

6c and 6d show the profile mean salinity error predicted by Scenario 2. Not surprisingly Scenario 

2 mean errors are lower during both periods due to the additional freshwater inflows to the 

model. For Scenario 1 the average mean error over all locations is 0.96 psu for 2004 and 3.8 psu 

for 2016 (where psu is practical salinity unit). For Scenario 2, these values drop to 0.47 psu for 

2004 and 2.9 psu for 2016. Figures 6e and 6f show the change in salinity between the two 

scenarios (Scenario 2 – Scenario 1). In Figures 6e and 6f, all plotted values are negative, 

indicating that salinities were reduced in Scenario 2 across the board.

Still looking at average statistics for the whole profile, Figures 7a and 7b show the Root Mean 

Squared Error (RMSE) for Scenario 1 and Scenario 2 in 2016. Figure 7c shows the Scenario 1 

RMSE minus Scenario 2 RMSE. Values in Figure 7c are all positive except for one location, 

indicating that the errors are higher in Scenario 1 compared to Scenario 2. This shows that 

adding more accurate freshwater inflows increases salinity simulation accuracy across the Bay. 

Figure 7d highlights the stations where the five largest improvements in RMSE are observed:

EE3.0, ET5.2, LE3.3, WE4.4, and LE5.3. These locations are more strongly influenced by the 

freshwater inflows from the relatively smaller tributaries which are not accounted for in Scenario 

1. Figure 7d also includes the one location, CB1.1, where S1-S2 salinity was slightly negative, 

indicating a worse simulation. This will be explained in the analysis below.
13

Figure 6. Mean profile error computed over all sampling depths and observation times for each 

location: (a) Scenario 1 2004, (b) Scenario 1 2016, (c) Scenario 2 2004, (d) Scenario 2 2016, (e) 

Scenario 2 – Scenario 1 2004 (negative values show decreases in predicted salinity), and (f) 

Scenario 2 – Scenario 1 2016.
14

Figure 7. Profile Root Mean Squared Error (RMSE) computed over all sampling depths and all 

observation times at each location: (a) Scenario 1 2016, (b) Scenario 2 2016, (c) Scenario 1 –

Scenario 2 : positive numbers mean improvement in Scenario 2, and (d) 5 locations with the 

largest RMSE improvement and one location with negative improvement.
15

Table 3. Summary statistics for the 2016 period averaged across the whole profile and all 

observation times. ME=mean error (a.k.a. bias); MAE=mean absolute error; RMSE=root mean 

squared error.

ID S1 MAE S1 ME S1 RMSE S2 MAE S2 ME S2 RMSE S2-S1 ME S1-S2 RMSE

1 CB1.1 0.03 -0.02 0.03 0.39 0.39 0.44 0.41 -0.40

2 CB2.1 6.70 6.65 7.22 6.62 6.62 6.95 -0.03 0.27

3 CB2.2 7.76 7.74 8.11 7.28 7.28 7.51 -0.46 0.60

4 CB3.1 6.36 6.36 6.44 5.69 5.69 5.74 -0.67 0.70

5 CB3.2 4.90 4.90 5.03 4.16 4.15 4.28 -0.74 0.74

6 CB3.3C 3.84 3.83 4.11 3.08 3.05 3.38 -0.78 0.74

7 CB3.3E 5.08 5.08 5.16 4.03 4.01 4.12 -1.07 1.04

8 CB3.3W 4.71 4.70 4.87 3.73 3.67 3.89 -1.03 0.98

9 CB4.1C 3.22 3.16 3.53 2.58 2.43 2.88 -0.73 0.66

10 CB4.1E 5.12 5.12 5.15 4.07 4.07 4.10 -1.05 1.04

11 CB4.1W 4.87 4.87 4.92 3.82 3.82 3.90 -1.05 1.03

12 CB4.2C 4.04 4.04 4.21 3.29 3.29 3.48 -0.75 0.74

13 CB4.2E 5.44 5.44 5.48 4.43 4.43 4.48 -1.01 1.00

14 CB4.2W 5.02 5.02 5.06 4.00 4.00 4.05 -1.03 1.01

15 CB4.3C 3.56 3.56 3.80 2.85 2.84 3.10 -0.72 0.70

16 CB4.3E 3.99 3.98 4.36 3.13 3.06 3.48 -0.93 0.87

17 CB4.3W 5.03 5.03 5.13 4.02 4.02 4.14 -1.01 0.99

18 CB4.4 3.54 3.54 3.81 2.85 2.83 3.13 -0.71 0.68

19 CB5.1 3.13 3.03 3.45 2.49 2.32 2.80 -0.71 0.65

20 CB5.1W 3.52 3.52 3.57 2.93 2.93 2.98 -0.60 0.59

21 CB5.2 3.51 3.48 3.75 2.81 2.76 3.05 -0.73 0.70

22 CB5.3 4.04 4.04 4.17 3.28 3.28 3.42 -0.76 0.75

23 CB5.4 3.53 3.53 3.64 2.84 2.84 2.96 -0.69 0.68

24 CB5.5 3.34 3.33 3.47 2.60 2.55 2.72 -0.78 0.75

25 CB6.1 2.64 2.62 2.84 2.07 2.01 2.28 -0.61 0.56

26 CB6.2 2.86 2.84 3.00 2.17 2.11 2.34 -0.73 0.66

27 CB6.3 2.67 2.61 2.87 1.99 1.86 2.24 -0.75 0.63

28 CB6.4 2.76 2.53 3.00 2.15 1.73 2.44 -0.80 0.56

29 CB7.1N 3.73 3.73 3.77 3.04 3.04 3.08 -0.70 0.69

30 CB7.1S 2.82 2.75 3.00 2.27 2.11 2.46 -0.64 0.53

31 CB7.2 2.52 2.30 2.77 1.98 1.73 2.24 -0.57 0.53

32 CB7.2E 2.51 2.45 2.67 2.09 1.86 2.25 -0.59 0.41

33 CB7.3 2.69 2.60 3.08 2.38 2.12 2.72 -0.48 0.36

34 CB7.3E 1.42 1.22 1.52 1.21 0.74 1.30 -0.49 0.22

35 CB7.4N 1.92 1.49 2.02 1.89 0.89 2.05 -0.61 -0.02

36 CB8.1 3.28 3.26 3.58 2.61 2.45 3.05 -0.81 0.53

37 CB8.1E 3.19 3.00 3.64 2.90 2.52 3.37 -0.48 0.28

38 EE1.1 3.77 3.77 3.77 2.82 2.82 2.82 -0.95 0.95

39 EE2.1 4.26 4.26 4.27 3.19 3.19 3.20 -1.08 1.06

40 EE2.2 4.24 4.24 4.24 3.50 3.50 3.50 -0.74 0.74

41 EE3.0 7.07 7.07 7.07 3.08 3.08 3.10 -3.99 3.98

42 EE3.2 3.83 3.83 3.83 2.93 2.93 2.93 -0.90 0.90

43 EE3.4 2.72 2.72 2.81 1.77 1.77 1.85 -0.95 0.96

44 EE3.5 3.58 3.58 3.60 2.72 2.72 2.75 -0.86 0.86

45 ET2.3 5.33 5.33 5.40 5.16 5.16 5.22 -0.16 0.18
16

ID S1 MAE S1 ME S1 RMSE S2 MAE S2 ME S2 RMSE S2-S1 ME S1-S2 RMSE

46 ET4.2 4.26 4.26 4.34 3.28 3.28 3.38 -0.98 0.96

47 ET5.2 4.87 4.87 4.88 2.30 2.23 2.38 -2.64 2.50

48 LE2.2 4.25 4.25 4.38 3.42 3.42 3.58 -0.83 0.80

49 LE2.3 3.70 3.70 3.79 2.81 2.80 2.92 -0.90 0.87

50 LE3.1 3.59 3.59 3.66 2.33 2.28 2.40 -1.30 1.25

51 LE3.2 3.45 3.45 3.50 2.26 2.26 2.33 -1.19 1.17

52 LE3.3 4.22 4.22 4.23 2.25 2.25 2.27 -1.97 1.96

53 LE3.4 3.32 3.32 3.34 2.19 2.19 2.22 -1.13 1.12

54 LE3.6 3.22 3.22 3.25 2.35 2.35 2.39 -0.87 0.86

55 LE3.7 3.39 3.39 3.41 2.26 2.26 2.28 -1.13 1.13

56 LE4.3 2.93 2.90 3.08 2.11 2.02 2.25 -0.87 0.83

57 LE5.3 5.75 5.75 5.93 4.28 4.28 4.51 -1.47 1.41

58 LE5.5W 4.11 4.08 4.17 3.19 3.07 3.30 -1.01 0.88

59 RET2.4 5.32 5.32 5.50 4.48 4.47 4.67 -0.85 0.84

60 WE4.1 2.30 2.30 2.32 1.32 1.32 1.35 -0.98 0.97

61 WE4.2 2.96 2.92 2.98 2.06 2.00 2.10 -0.92 0.88

62 WE4.3 2.81 2.81 2.82 1.63 1.62 1.65 -1.19 1.17

63 WE4.4 3.01 3.01 3.02 1.55 1.55 1.56 -1.46 1.46

64 WT5.1 2.15 1.12 2.34 1.75 0.22 2.04 -0.91 0.30

Avg 3.81 3.76 3.94 2.95 2.85 3.09 -0.91 0.85

Stdev 1.32 1.38 1.33 1.20 1.29 1.21 0.56 0.57

Figure 8 shows 2016 salinity profile plots for station EE3.0 at 11 times throughout the year. The 

Scenario 2 outputs at EE3.0 are closer to observations than Scenario 1 outputs. This station is 

located in Fishing Bay and relatively near the outflows of the Nanticoke and Wicomico Rivers 

(see reference map in the lower right of Figure 8). Very little salinity stratification is observed or 

simulated at this location. Further illustrated by numerous observed salinity profiles in Appendix 

B, low stratification is not uncommon for locations in shallower portions of the Bay where the 

water is well mixed. At EE3.0 observed values are available to 6-m while simulated values are 

only available to 4-m. These differences could be due to inaccurate CBOFS2 bathymetry. It is 

well known that bathymetry is a key input to achieve accurate shallow water hydrodynamic 

modeling and Ye et al. (2018) illustrate the importance of accurate bathymetry in Chesapeake 

Bay modeling. Bathymetry errors, particularly in shallow portions of the Bay, could have a 

substantial impact on the results we are presenting here. However, it is beyond the scope of this 

work to explicitly assess multiple sources of error beyond freshwater changes on the modeling.

Figure 9 shows observed and simulated profile plots at the CB5.2 observation station, which is 

located in deeper water near the center of the Bay. Here, away from the influence of local 

tributaries, the differences between Scenario 1 and Scenario 2 are much smaller. More 

stratification with depth is also observed at this location compared to EE3.0 because freshwater 

is not fully mixing in deeper portions of the Bay.

In Figure 10, salinity profiles for station LE3.1 show moderate effects of the freshwater inflow 

changes from Scenario 1 to Scenario 2. The impacts are somewhere in between the small 

estuary station (EE3.0) and the mid-Bay station (CB5.2).
17

For the same stations assessed in Figures 8-10, Figures 11a-c show time series of simulated and 

observed salinity at a 1-m depth (left axis) along with time series of river inflows (right axis). 

Out of all 64 stations in this study, EE3.0 saw the biggest average RMSE improvement of about 

4 psu from Scenario 1 to 2 (Figure 7 and Table 3). The salinity time series in Figure 11a reflect 

this average improvement. The local inflow time series in Figure 11a, which are the sum of the 

Nanticoke and Wicomico simulations, indicate why the salinity simulations are so much 

different. Although the CBOFS2 baseline (Scenario 1) includes an inflow node near the 

Nanticoke outlet, it does not use any gauges from the Nanticoke or Wicomico Rivers to derive 

inflow estimates, yielding inflow estimates that are far too low at the Nanticoke node. Scenario 2 

corrects this problem.

Figure 11b plots salinity and flow time series for CB5.2, which is located in the middle of the 

Bay. The flow traces in 11b include the totals from all tributaries. As at EE3.0, sudden drops in 

salinity are associated with large freshwater events, affirming the importance of river inflows on 

salinity distribution throughout the Bay. Not surprisingly, the difference between Scenarios 1 and 

2 is not as large at CB5.2, closer to the center of the Bay. At the 1-m depth shown here, the 

improvement in salinity mean absolute error (MAE) from Scenario 1 to 2 (S1-S2) is 4.0 psu for 

EE3.0 but only 0.7 psu for CB5.2. 

Figure 11c is for LE3.1 located near the outlet of the Rappahannock River. The inflows in Figure 

11c are for the Rappahannock. Scenario 2 includes more inflow and produces lower salinity 

simulations as expected. The Scenario 2 salinity at 1-m drops closer to the observations, not as 

dramatically as at location EE3.0, but more so than at CB5.2. The MAE improvement from S1 to 

S2 is 1.3 psu for station LE3.1.

Although Scenario 2 consistently improves the salinity predictions over Scenario 1, it does not 

completely eliminate the high bias from the simulations at most locations (see Figures 6c and d 

as well as Figures 11a-c). Despite the remaining bias, the CBOFS2 model does show a 

consistent temporal correlation with observations in Figures 11a-c as well as at other locations 

(see Appendix C for other locations). 

Figure 7c shows one location, CB1.1, where the Scenario 2 salinity error was slightly higher than 

the Scenario 1 error in terms of RMSE. CB1.1 is located at the mouth of the Susquehanna River. 

Figure 12 shows that observed and simulated salinities for this site during much of 2016 are 

close to zero. However, in the low flow period from September through November 2016, 

Scenario 2 simulates some non-zero salinity values at the 1-m depth in the range of 0 to 3 psu.

During this time period, Scenario 1 simulates lower salinities close to 0. Scenario 1 simulations 

are closer to observations, which include only one non-zero observation during this period, 0.09 

psu on 10/19/2016. Flow data for the Susquehanna River at Conowingo Dam from the USGS are 

plotted along with the Scenario 1 and 2 Susquehanna inflows in Figure 12. These data indicate 

that the Susquehanna flows fed to the CBOFS2 operational model (Scenario 1) were too high 

during September and October of 2016. The reason for this erroneous inflow is unknown. 

Because the erroneous, higher flows in Scenario 1 actually yield a better salinity simulation at 

CB1.1, this suggests that sources of model error other than streamflow are causing the salinity 
18

over-estimates in Scenario 2. 

We selected the sites in Figures 8-12 to highlight different types of locations within the Bay. 

This included a relatively shallow location near the edge of the Bay where the local river flows 

have a relatively large influence (EE3.0), a station near the main channel of the Bay (CB5.2), a 

station near the outlet of a medium size river (LE3.1), and a station near the outlet of a major 

river (CB1.1). Graphics and tabular results for all 64 observation stations and both the 2005 and 

2016 simulation periods are also available to further support our conclusions. These details are 

provided in Appendices A, B, and C due to their considerable length.

Out of the 64 stations analyzed in the 2016 period, the average profile mean error (ME)

improved by 0-1 psu in 44 stations, 1-2 psu at 17 stations, 2-3 psu at 1 station, and 4 psu at 1 

station (Table 3, Figure 6f). The average profile ME increased by 0.4 psu for one station (CB1.1) 

for reasons explained above. As expected, we see greater improvements in shallower waters near 

tributary inflows.

While we see strong evidence of salinity simulation improvements from more accurate river 

inflows, there is still an overall salinity bias in Scenario 2. From Table 3, the ME over all 

observing stations dropped from 3.76 to 2.85 psu for the 2016 period, a modest improvement. 

Lanerolle et al. (2016) note that this improved level of salinity accuracy would not be adequate 

for applications such as Vibrio forecasting, citing accuracy requirements for CBOFS as +/-1 psu 

and +/-1 degree C. To achieve results closer to these values for Vibrio forecasting, Lanerolle et 

al. (2016) statistically post-process the CBOFS2 output. This would still be recommended with 

the improved freshwater inflows we demonstrate here. 
19

Figure 8. Salinity profile plots for all observation dates in the 2016 simulation period at location 

EE3.0.
20

Figure 9. Salinity profile plots for all observation dates in the 2016 simulation period at location 

CB5.2.
21

Figure 10. Salinity profile plots for all observation dates in the 2016 simulation period at 

location LE 3.1.
22

Figure 11. Simulated salinity from Scenarios 1 and 2 along with discrete observations at 1-m 

depth and relevant inflows. (a) EE3.0 with inflows from the nearby Nanticoke and Wicomico 

Rivers, (b) CB5.2 with inflows from all tributaries, (c) LE3.1 with inflows from the 

Rappahannock River.
23

Figure 12. Simulated salinity from Scenarios 1 and 2 along with discrete observations at 1-m 

depth. Station CB1.1 is at the mouth of the Susquehanna R. Simulated and observed salinity are 

zero except during low flow periods. S1, S2, and USGS observed flows below Conowingo Dam 

are shown.

Conclusions and Recommendations for Future Work

Increasing the number of inflow nodes in CBOFS2 from 13 to 60 and explicit hydrologic 

modeling of an additional 16% percent of the land area improve salinity simulations. Lower 

mean errors (a.k.a. bias) and lower root mean squared error (RMSE) confirm this when 

comparing simulations to observations at 64 sampling sites. Spatial trends in the results are

consistent with expectations. That is, greater improvements are seen from Scenario 1 to 2 at 

sampling sites in shallower water and near river inflows that are not modeled in Scenario 1.

In our Scenario 2 simulations, we used observed inflows for 79% of the Chesapeake drainage 

and model simulations for an additional 16% of the area. The accuracy of the watershed 

modeling in added areas is limited in part because the gridded hydrologic model is uncalibrated. 

In addition, this study was limited to simulations, without considering the additional errors that 

would be introduced in a forecast period driven by precipitation forecasts rather than observed 

precipitation. Nonetheless, our results show that additional work to implement more detailed 

hydrologic modeling in the NOAA operational forecast environment is likely to produce 

benefits. Our CBOFS2 simulations with 60 nodes ran smoothly without numerical stability 

problems.
24

A reasonable next step towards operational implementation and to assess results in forecast mode

would be to run a CBOFS2 60 inflow configuration with live data in parallel to the current 

operational model. Our configuration has been provided to NOS COOPs for possible use in this 

manner.

The most practical source of inflows for ungaged rivers in a real-time configuration would be the

National Water Model (NWM). For reasons explained above, we used the HL-RDHM model in 

this study. For small, ungaged basins, we would expect NWM and HL-RDHM forecasts to be 

relatively comparable, although more analysis of this would be prudent. Long period simulations 

for NWM at all 60 locations of interest were not available for this study. 

For forecasts at inflow nodes associated with larger rivers, such as the Susquehanna, Potomac, 

James, we would recommend using the official RFC model forecasts where available, rather than 

NWM, because they are known to be substantially more accurate. As evidence of this, Figure 13 

shows a comparison of the 3-day average RMSE calculated from all 12 UTC medium-range 

NWM v2.0 forecasts and 12 UTC RFC operational forecasts from July 1, 2019 to April 30, 2020. 

Figure 13a includes data for 62 gaged headwater basins (approximately 300 mi2 in size) where 

MARFC has been tracking NWM performance, as well forecasts for the largest rivers flowing to 

the Bay. NWM forecasts for the three largest Chesapeake Rivers have a substantially higher 

RMSE than the RFC forecasts (Figure 13a). For smaller basins, while NWM forecast errors still 

tend to be higher, the differences represent considerably less volume than differences for the 

three largest rivers. Regardless, outside of the major rivers, the RFC model forecasts are not 

available for the 60 local watersheds we modeled in Scenario 2, so NWM is the best available 

option for these locations. With this approach, initial application of NWM would only be in 

about 16% of the modeled area, capturing the dynamics in these smaller tributaries while 

avoiding the larger volume errors seen on larger rivers. With the resolution of NWM and some 

additional testing, future implementations could also increase the number of connecting nodes 

beyond 60.
25

Figure 13. 3-day average RMSE calculated for NWM and RFC operational forecasts at 62 

headwater basins plus the outlets of four major tributaries (Susquehanna, Potomac, 

Rappahannock, and James): (a) all locations, and (b) all but the three largest basins. 

Acknowledgments

Thank you to Lyon Lanerolle (formerly NOS), who provided the CBOFS2 program, data, and 

guidance necessary to get this project started at MARFC where we had no prior CBOFS2 

experience. Thank you also to Aijun Zhang, Zizang Yang, and Machuan Peng who provided 

guidance and data to set up the 2016 simulations and answered additional questions about 

CBOFS2. In addition, thanks to NOAAs North Atlantic Regional Team for providing student 

funding, and the NOAA Chesapeake Bay Office and Chesapeake Research Consortium for 
26

helping to facilitate the student internships for three summers. 

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28

Appendices

Appendix A. Tabular results for 2004 period.

Appendix B. Simulated and observed profile plots for all times and locations.

1. 2004 (212 pages)

2. 2016 (114 pages)

Appendix C. Simulated and observed time-series plots for all locations. 

1. 2004 (8 pages)

2. 2016 (8 pages)
(CONTINUED FROM FRONT COVER) 

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NWS ER 76 Is There A Temperature Relationship Between Autumn and the Following Winter? Anthony Gigi. February 1988. 

(PB88-173224). 

NWS ER 77 River Stage Data for South Carolina. Clara Cillentine. April 1988. (PB88-201991/AS). 

NWS ER 78 National Weather Service Philadelphia Forecast Office 1987 NOAA Weather Radio Survey & Questionnaire. Robert P. 

Wanton. October 1988. (PB89-111785/AS). 

NWS ER 79 An Examination of NGM Low Level Temperature. Joseph A. Ronco, Jr. November 1988. (PB89- 122543/AS). 

NWS ER 80 Relationship of Wind Shear, Buoyancy, and Radar Tops to Severe Weather 1988. Hugh M. Stone. November 1988. 

(PB89-1222419/AS). 
NWS ER 81 Relation of Wind Field and Buoyancy to Rainfall Inferred from Radar. Hugh M. Stone. April 1989. (PB89-208326/AS). 

NWS ER 82 Second National Winter Weather Workshop, 26-30 Sept. 1988: Postprints. Laurence G. Lee. June 1989.(PB90147414/AS). 

NWS ER 83 A Historical Account of Tropical Cyclones that Have Impacted North Carolina Since 1586. James D. Stevenson. 

July 1990. (PB90-259201). 

NWS ER 84 A Seasonal Analysis of the Performance of the Probability of Precipitation Type Guidance System. George J. Maglaras 

and Barry S. Goldsmith. September 1990. (PB93-160802) 

NWS ER 85 The Use of ADAP to Examine Warm and Quasi-Stationary Frontal Events in the Northeastern United States. David R. 

Vallee. July 1991. (PB91-225037) 

NWS ER 86 Rhode Island Hurricanes and Tropical Storms A Fifty-Six Year Summary 1936-1991. David R. Vallee. March 1993. 

(PB93-162006) 

NWS ER 87 Post-print Volume, Third National Heavy Precipitation Workshop, 16-20 Nov. 1992. April 1993. (PB93-186625) 

NWS ER 88 A Synoptic and Mesoscale Examination of the Northern New England Winter Storm of 29-30 January 1990. Robert A. Marine and Steven J. Capriola. July 1994. (PB94-209426) 

NWS ER 89 An Initial Comparison of Manual and Automated Surface Observing System Observations at the Atlantic City, New Jersey, 

International Airport. James C. Hayes and Stephan C. Kuhl. January 1995. 

NWS ER 90 Numerical Simulation Studies of the Mesoscale Environment Conducive to the Raleigh Tornado. Michael L. Kaplan, Robert A. Rozumalski, Ronald P. Weglarz, Yuh-Lang Lin , Steven Businger, and Rodney F. Gonski. November 1995. 

NWS ER 91 A Climatology of Non-convective High Wind Events in Western New York State. Thomas A. Niziol and Thomas J. Paone. 

April 2000. 

NWS ER 92 Tropical Cyclones Affecting North Carolina Since 1586 - An Historical Perspective. James E. Hudgins. April 2000. 

NWS ER 93 A Severe Weather Climatology for the Wilmington, NC WFO County Warning Area. Carl R., Morgan. October 2001. 

NWS ER 94 Surface-based Rain, Wind, and Pressure Fields in Tropical Cyclones over North Carolina since 1989. Joel Cline. June 

2002. 

NWS ER 95 A Severe Weather Climatology for the Charleston, South Carolina, WFO County Warning Area. Stephen Brueske, Lauren 

Plourd, Matthen Volkmer. July 2002. 

NWS ER 96 A Severe Weather Climatology for the WFO Wakefield, VA County Warning Area. Brian T. Cullen. May 2003. 

(PB2003-105462) 

NWS ER 97 Severe Weather Climatology for the Columbia, SC WFO County Warning Area. Leonard C. Vaughan. September 2003. 

(PB2004-100999) 

NWS ER 98 Climatology of Heavy Rainfall Associated with Tropical Cyclones Affecting the Central Appalachians. James Hudgins, 

Steve Keighton, Kenneth Kostura, Jan Jackson. September 2005. 

NWS ER 99 A Severe Weather Climatology for the WFO Blacksburg, Virginia, County Warning Area. Robert Stonefield, James Hudgins. January 2007. 

NWS ER 100 Tropical Cyclones Affecting North Carolina Since 1586 - An Historical Perspective. James E. Hudgins. October 2007. 

NWS ER 101 A Severe Weather Climatology for the Raleigh, NC County Warning Area. Clyde Brandon Locklear. May 2008. 

NWS ER 102 A Severe Weather Climatology for the Wilmington, OH County Warning Area (1950-2004). Michael D. Ryan. May 2008. 

NWS ER 103 A Climatology of Flash Flood Events for the National Weather Service Eastern Region, Alan M. Cope. June 2009. 

NWS ER 104 An Abbreviated Flash Flood/Flood Climatology (1994-2007) for the WFO Blacksburg, VA County Warning Area., Robert 

Stonefield and Jan Jackson. September 2009. 

NWS ER 105 Dense Fog Climatology for the Blue Ridge Foothills and Piedmont Areas of the Blacksburg, VA County Warning Area for 

the Period 1973-2008. Jan Jackson, Ken Kostura and William Perry. February 2011. 

NWS ER 106 Climatology of Storm Data Events for the NWS Mount Holly Forecast Area, Alan M. Cope. March 2016. 
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