1

Appendix 3-C. Integrating gridded daily meteorology into pesticide aquatic 

exposure models

M.M. Fry1

, G. Rothman1, D.F. Young1, N. Thurman1, M. Thawley1

1

U.S. Environmental Protection Agency, Office of Pesticide Programs, Environmental Fate and Effects Division, 

1200 Pennsylvania Ave, NW, Washington, DC 20460

Abstract 

Weather is a key component in pesticide aquatic exposure models, contributing to a chemical’s fate and 

transport in the environment. Current pesticide exposure models have relied on weather from the Solar and 

Meteorological Surface Observation Network (SAMSON), but this source is no longer maintained, only spans the 

years 1961 to 1990, and is limited to 237 locations. Improving the spatial and temporal extent of weather 

information is important for conducting more realistic pesticide exposure simulations. Here daily weather input 

parameters needed for pesticide aquatic exposure assessment are compiled from the National Oceanic and 

Atmospheric Administration (NOAA) National Center for Environmental Prediction Reanalysis and NOAA Climate 

Prediction Center Unified Rain Gauge Analysis. Both datasets provide more comprehensive spatial coverage 

(uniform national grid) and temporal coverage (1961 to 2014) than the SAMSON. The parameters are consistent 

with those provided by the SAMSON: precipitation, potential evapotranspiration, temperature, wind speed, and 

solar radiation. Simulated exposure concentrations from an aquatic exposure model are found to be reliable and 

consistent when using the NOAA-compiled weather dataset compared to the SAMSON. Continued expansion of 

the compiled dataset on an annual basis is planned, as the most recent meteorology continues to be released. 

Keywords 

pesticide, exposure assessment, meteorology, weather

1. Introduction

We compile meteorology from the National Oceanic and Atmospheric Administration (NOAA) NOAA 

National Center for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR) 

Reanalysis and precipitation from the Climate Prediction Center (CPC) Unified Rain Gauge Analysis for use in 

pesticide aquatic exposure modeling. The NOAA NCEP/NCAR and CPC analyses offer gridded, national coverage of 
2

the daily variables: precipitation, temperature, wind speed, and solar radiation. Potential evapotranspiration is 

derived here using daily mean temperature, daily minimum and maximum temperatures, and downward solar 

radiation at the nominal top of the atmosphere (section 2.3). We compile our dataset from 1/1/1961 to 

12/31/2014 with over 27,800 data points uniformly covering the lower 48 United States (US). The NOAA 

NCEP/NCAR and CPC analyses are actively maintained by NOAA, and continue to be extended to present day. As a 

result, the compiled dataset can be easily expanded with the most recent NCEP/NCAR and CPC daily values as they 

become available. This contrasts with the currently-used Solar and Meteorological Surface Observation Network 

(SAMSON), which only spans 1/1/1961 to 12/31/1990 for 237 National Weather Service (NWS) locations in the US 

for the same variables. The SAMSON dataset was originally developed by the NOAA National Climatic Data Center 

(NCDC) and the National Exposure Research Laboratory (NREL) of the US EPA, based on both observational and 

modeled results. However, SAMSON is no longer maintained and will not be updated in the future. 

The NOAA CPC Unified Rain Gauge Analysis offers precipitation at a 0.25x0.25 degree resolution over the 

US (20-49.5◦

N, 233.75-292.75◦E), and the remaining variables are available globally at 2.5x2.5 degree resolution 

from NOAA NCEP/NCAR Reanalysis. Since precipitation is a major driver for runoff, we choose to include the CPC 

fine-scale precipitation in our dataset, as it may improve spatial specificity and estimated aquatic exposure 

concentrations. As part of this study, we configure several pesticide exposure assessment models: the US EPA 

Spatial Aquatic Model (SAM) and the US EPA Surface Water Concentration Calculator (SWCC) to use the compiled 

NOAA NCEP/NCAR and CPC dataset for aquatic exposure simulations. We present the methodology and an 

evaluation of the prepared dataset using the US EPA SWCC.

2. Methods

2.1 Retrieval of raw variables

NOAA NCEP/NCAR Reanalysis and NOAA CPC Unified Rain Gauge Analysis datasets were accessed online 

and downloaded in network common data form (netCDF) for the years 1961 through 2014. The native spatial 

extent of the NOAA NCEP/NCAR Reanalysis is global, while the NOAA CPC Unified Rain Gauge Analysis extent is the 

continental US. Table 1 includes a list of the variables downloaded from each source.
3

Table 1. Weather variables downloaded from NOAA NCEP/NCAR Reanalysis and NOAA CPC Unified Rain Gauge 

Analysis.

NOAA NCEP/NCAR Reanalysis (global) NOAA CPC Unified Rain Gauge Analysis (US)

Surface fluxes (daily from 1961 to 2014): a

Air temperature at 2m

Maximum air temperature at 2m

Minimum air temperature at 2m

Zonal wind at 10 m

Meridional wind at 10 m (v-wind)

Downward solar radiation flux at surface

Surface (daily total from 1961 to 2014): c

Total Daily Precipitation

Other fluxes (daily from 1961 to 2014): b

Downward solar radiation flux at nominal top 

of atmosphere (NTAT)

a Available: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.surfaceflux.html

NCEP/NCAR Reanalysis 1 data provided by NOAA/OAR/ESRL PSD, Boulder, Colorado, USA

b Available: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.other_flux.html

NCEP/NCAR Reanalysis 1 data provided by NOAA/OAR/ESRL PSD, Boulder, Colorado, USA

cAvailable: http://www.esrl.noaa.gov/psd/data/gridded/data.unified.daily.conus.html (1984 – 2006); 

http://www.esrl.noaa.gov/psd/data/gridded/data.unified.daily.conus.rt.html (2007 – 2014)

CPC US Unified Precipitation data provided by NOAA/OAR/ESRL PSD, Boulder, Colorado, USA 

2.2 Interpolation to common grid

NOAA NCEP/NCAR Reanalysis daily variables: temperature (mean, minimum, and maximum), average 

wind speed (u-wind and v-wind), and downward solar radiation (at surface and nominal top of the atmosphere) 

were extracted for the continental US from the global extent, and interpolated from their native 2.5x2.5 degree 

grid resolution to the fine 0.25x0.25 degree resolution of the CPC precipitation using bilinear interpolation from

the NCAR command language (NCL) (Available: 

http://www.ncl.ucar.edu/Document/Functions/Contributed/linint2_Wrap.shtml). This method interpolates one 

rectilinear grid to another and retains metadata. A Windows 7 machine is configured for Cygwin/X and NCL to 

process all netCDF files.
4

2.3 Compilation of daily meteorology for all years

The five variables, on the common grid, are compiled for all years. Table 2 summarizes the unit 

conversions performed for each variable during compilation. Comma-delimited files are written for each grid cell 

location (georeferenced by latitude and longitude) in the continental US from 1961 to 2014, with the daily values 

for each of the variables. These files are specially formatted for use in the US EPA pesticide aquatic exposure 

models and are given the extension .wea (abbreviation for weather file). Appendix A includes an example .wea 

file. The filename of each .wea contains the grid cell number and latitude location (for example, 

0_grid_20.125_lat.wea, in format of grid#_grid_lat#_lat.wea). Grid cell numbers range from 0 to 27487 and 

latitudes range from 20.125 to 49.875 degrees North.

Table 2. Raw variable conversions and calculations.

Raw variables Unit conversions for SAM Description

Precipitation Pcm = Pmm * 0.1 Conversion from mm/day (Pmm) to 

cm/day (Pcm)

Air temperature at 2m TC = TK – 273.15 Conversion from Kelvin (TK) to degrees 

Celsius (TC)

U-wind at 10m

V-wind at 10m

XXww = 100 ∗ �UUww2 + VVww2 Calculation of vector wind speed (Xw)

from u-wind (Uw) and v-wind (Vw), and 

conversion from m/s to cm/s

Downward solar radiation flux SL = SW * 2.06364 Conversion from W/m2 (SW) to La/day

(SL), where 1 W/m2 = 0.085985 La/hr = 

2.06364 La/d

Downward solar radiation flux at 

nominal top of the atmosphere 

(NTAT)

Sntat,mm = 

(Sntat,W/1e6)*86400*0.408

Conversion from W/m2 (Sntat,W) to 

mm/day (Sntat,mm), where W/m2 = J/s-m2

* (MJ/1e6J)*(86400 s/d)*(0.408 mm/d / 

MJ/m2

d) A

Maximum air temperature at 2m

Minimum air temperature at 2m

Trange = Tmax - Tmin Calculation of temperature range (Trange) 

between maximum (Tmax) and minimum 

(Tmin) temperatures A

A Downward solar radiation flux at the nominal top of the atmosphere and the temperature range are needed for 

the Hargreaves-Samani (1985) potential evapotranspiration approach. 
5

Potential evapotranspiration (PET) is not explicitly defined in the NOAA NCEP/NCAR and NOAA CPC 

datasets, but needed in aquatic exposure modeling. The previous SAMSON dataset reports pan evaporation, and 

PET is calculated internally by the model based on the pan factor. However, other parameters are available and 

can be used to estimate PET directly. A variety of PET estimation methods have been compared in the literature 

(Lu et al., 2005). Here we use the Hargreaves-Samani (1985) method, as it is straightforward in its data 

requirements and performs well compared to Penman-Monteith and other methods (Droogers and Allen, 2002; Lu 

et al., 2005; Tuya et al., 2006; Alexandris et al., 2008; Kra, 2012; Weiland et al., 2012). Daily potential 

evapotranspiration (PETdaily, cm/d) is estimated by Hargreaves-Samani as: 

PETdaily = (0.0023*Sntat,b) (Tb +17.8) (Trange0.5)/10

Where Sntat,b = downward solar radiation flux at nominal top of the atmosphere (mm/day)

 Tb = daily mean air temperature (◦

C) at 2 m

 Trange = difference between daily maximum and daily minimum temperature (◦

C) at 2m

The Hargreaves-Samani method only requires three input parameters: downward solar radiation flux at the 

nominal top of the atmosphere, daily mean temperature, and daily difference between maximum and minimum 

temperature. A simpler method with only a few inputs may limit potential inaccuracy given the inherent

uncertainty in the meteorological parameters (Droogers and Allen, 2002).

3. Discussion

We conduct quality assurance and quality control of the compiled dataset and evaluate its effectiveness in

existing pesticide aquatic exposure models. Table 3 summarizes the main data quality objectives used for

evaluating.

Table 3. Data quality objectives for compiled NOAA NCEP/NCAR and CPC dataset.

Data Quality Objectives

i. NOAA NCEP/NCAR and CPC data is from reliable, peer-reviewed source and is of sufficient quality for 

spatial and temporal aquatic exposure modeling of pesticides. 
6

ii. Processing of raw NOAA meteorology for input into exposure models is transparent and properly 

documented. Processed files are made available, and the methodology is reviewed externally. Raw 

NOAA data is publicly available. 

iii. Weather file format (.wea) is standardized and compatible with pesticide aquatic exposure models. 

iv. Aquatic exposure model outputs generated using the compiled dataset are reasonable and protective 

when compared to outputs generated using SAMSON.

v. Maintenance plan for meteorology file updates is defined.

3.1 Quality of data source

NOAA NCEP/NCAR Reanalysis and NOAA CPC Unified Rain Gauge Analysis datasets are considered 

appropriate for pesticide aquatic exposure modeling and have been carefully reviewed. The NOAA NCEP/NCAR 

Renanalysis is documented as part of the joint NCEP and NCAR 40 and 50-year Reanalysis Project (Kalnay et al., 

1996; Kistler et al, 1999). The Reanalysis compiles land surface, ship, rawinsonde, pibal, aircraft, satellite, and 

other data, performs quality control, and implements data assimilation over the time period of 1948 to present 

day. Meteorological fields are derived from a numerical weather model constrained by observations. The quality 

and usefulness of this dataset has vastly improved since NCEP’s original analyses. Improvements include the stateof-the-art data assimilation system, an increased number of observations, and enhanced quality control (Kistler et 

al., 1999). 

Because observations alone provide insufficient spatial and temporal coverage of the continental U.S., 

gridded datasets derived from numerical weather models constrained by observations are widely supported. Other 

projects utilizing the NOAA NCEP/NCAR Reanalysis dataset have found it to be fairly representative over its spatial 

and temporal scales (Kistler et al., 1999). It has been used for climate monitoring by the NOAA Climate Prediction 

Center and other weather and climate researchers. Long-term means and annual cycles show similar patterns 

when compared to observations and two other major reanalysis projects (NASA Data Assimilation Office 10-year 

Reanalysis and ECMWF 15-year Reanalysis). In general, surface and top of the atmosphere fluxes from NCEP/NCAR 

and ECMWF 15-year Reanalysis agree well with independent estimates, while the NASA Data Assimilation Office 

10-year Reanalysis shows less agreement in some cases (Kistler et al., 1999). Hay and Clark (2003) used the 
7

NCEP/NCAR Reanalysis dataset to evaluate the performance of a hydrological model in three basins in the western 

U.S., finding the seasonal cycles of precipitation, minimum temperature, and maximum temperatures were 

generally captured in comparison to station measurements.

The NOAA CPC Unified Rain Gauge Analysis dataset is part of the NOAA CPC Unified Precipitation Project 

and also has been well documented (Xie et al., 2007; Chen et al., 2008a; Chen et al., 2008b; Higgins et al., 1996, 

2000). The NOAA CPC performs continuous quality control of gauge precipitation data, in addition to developing 

and compiling a complete, gridded US precipitation dataset. Quality control procedures include duplicate station 

checks, extreme value checks, standard deviation checks, and satellite and radar comparisons. Other projects 

using the CPC Unified Rain Gauge Analysis dataset include the Land Data Assimilation System (LDAS) and the NCEP 

Regional Reanalysis Project. 

3.2 Processing and standardized format

The processing of the raw meteorology for input into exposure models is summarized in Figure 1 and

described in section 2. The main steps include harvesting the raw weather variables, interpolating the coarser 

NOAA NCEP/NCAR variables to the finer resolution of the NOAA CPC precipitation, and compiling the variables

from 1961 to 2014. We additionally save meteorology from the dataset at the 237 SAMSON locations, for 

incorporation into the existing standard scenarios used by the US EPA SWCC. Otherwise, the compiled dataset can 

be used to simulate any location in the continental U.S. within the 0.25x0.25 degree grid.

Figure 1. Weather data processing steps.
8

The format of the model-ready weather files (.wea) is standardized (section 2.3, Appendix A) and the 

SWCC and SAM have been configured to read. 

3.3 Evaluation of dataset and simulated model results

We evaluate the magnitude and spatial distribution of the compiled dataset using NCAR NCL graphics. 

Here we focus on presenting precipitation and PET results, since estimated exposure concentrations are most 

sensitive to these weather parameters. Figure 2 presents the annual average of the long-term daily mean 

precipitation and PET (annual totals in Appendix B). For precipitation, the highest annual averages (and annual 

totals) occur in the southeastern and northwestern U.S. Annual average long-term daily mean PET is highest in the 

southwestern U.S highlighting the region’s arid to semi-arid climate, characterized by high potential evaporation 

rates often exceeding precipitation rates. Net annual averages and net annual totals (precipitation minus PET) 

(Appendix B) are also calculated to summarize the hydrologic balance between long-term daily mean precipitation 

and PET. The analyses shown in Appendix B verifies the expected outcome in the spatial variability across the 

country with atmospheric water input deficits in the western U.S., and atmospheric water input surpluses in the 

eastern U.S over both the short-term and long-term.

In general, derived PET estimates are consistent in their distribution and magnitude when compared to

other studies. Annual and monthly average PETs in Figures 2 and 3 closely resemble the PET distributions of 

Willmott and Kenji (2001), which were developed at 0.5x0.5 degree resolution (1950-1999) using ArcGIS. 

Additionally, the distribution and magnitude of PET in North America from a global PET dataset, the WorldClim 

Global Climate Data (http://WorldClim.org) (Zomer et al., 2007, 2008), agrees well with derived PET distributions. 

Zomer et al. (2007, 2008) similarly uses the Hargreaves method for modeling PET globally, as it performs well 

compared to Penman-Monteith, requires less parameterization, and is less sensitive to climatic input errors 

(Hargreaves and Allen, 2003). Monthly average PET estimates (Figure 3) are also consistent with the seasonal 

average PET values of Weiland et al. (2012), who developed a daily, gridded PET dataset for use in hydrological 

impact assessments and statistical downscaling of daily PET. 
9

Figure 2. Annual averages of a) long-term daily mean precipitation (mm) and b) long-term daily mean PET (mm) 

over 1961-1990 and 1991-2014 time periods. Gridded precipitation values are compiled from the NOAA CPC 

Unified Rain Gauge Analysis, which covers the land mass of the continental U.S. Gridded PET estimates are 

derived from the NOAA NCEP/NCAR Reanalysis global dataset using the Hargreaves-Samani method (1985).
10

Figure 3. Monthly averages of a) long-term daily mean precipitation (mm) and b) long-term daily mean PET 

(mm) over 1961-1990 time period (1991-2014 time period not shown, but nearly identical in magnitude and 

distribution). 
11

3.3.2 Application to aquatic exposure modeling

This dataset and the processing methods were originally developed for use in the US EPA SAM, which 

requires uniform, gridded national weather variables over the past several decades. Other aquatic exposure 

models also benefit from using this dataset from 1961 to present day. The US EPA SWCC, for example, can utilize 

the same dataset at its standard scenario locations, and in more refined site-specific simulations. 

We compare the compiled weather dataset to the SAMSON using the US EPA SWCC. We conduct a batch 

set of simulations (1961-1990) for all of the SWCC standard scenarios using a sliding 100-day application window, 

with the SAMSON and NOAA-compiled weather as inputs, and all other parameters held constant. Outputs are 

generated for both the US EPA standard pond and standard drinking water reservoir endpoints. The peak and 

time-averaged (1-in-10 year) concentrations, typically used in regulatory assessments, are presented in Figure 4 for 

the standard pond and standard reservoir. Pearson’s correlation coefficients (Table 4) quantify the strength of the 

linear relationship between the SWCC results generated using the compiled NOAA dataset and the SAMSON

dataset. The correlation coefficients are close to 1, a near perfect positive linear relationship, suggesting that the 

error in using the NOAA-compiled dataset in place of the SAMSON dataset is small and will minimally impact SWCC

exposure concentration estimates. 

 
12

a)

b)
13

Figure 4. Comparison of the simulated peak and time-averaged concentrations in the a) standard pond, and b) 

standard reservoir, from the batch set of SWCC simulations using the compiled NOAA NCEP/NCAR and NOAA 

CPC weather and the SAMSON weather, for 1961 to 1990 time period. The 1-to-1 line is shows the near linear 

relationship between the two sets of results.

Table 4. Pearson’s correlation coefficients (range of 0 to 1) for the peak and time-averaged concentrations in the 

standard pond and standard reservoir, from the batch set of SWCC simulations using NOAA NCEP/NCAR and 

OAA CPC-compiled dataset versus SAMSON (1961 to 1990 time period). A value of 1 represents a perfectly linear 

relationship. 

Pearson’s correlation coefficient a

for SWCC simulations (1961-1990)

Standard 

pond

Standard

reservoir

Peak (1-in-10 year) 0.871 0.874

4-day Average (1-in-10 year) 0.873 0.877

21-day Average (1-in-10 year) 0.872 0.881

60-day Average (1-in-10 year) 0.873 0.881

90-day Average (1-in-10 year) 0.872 0.879

Entire Simulation Mean 0.903 0.907

Pore Water Peak (1-in-10 year) 0.858 0.864

Pore Water 21-day Average (1-in-10 

year) 0.858 0.861

a rr = ∑(xx−xx̅)(yy−yy�)

�∑(xx−xx̅)2(yy−yy�)2

4. Conclusions

The compiled meteorology for pesticide aquatic exposure modeling will be maintained by the US EPA 

Office of Pesticide Programs (OPP) Environmental Fate and Effects Division, and updated with the most recent year 

using the described methods. Future developments may involve consolidating processing into fewer steps for ease 

of use, given improved computational resources. The documented methodology is undergoing review and will be 

presented to the US EPA Scientific Advisory Panel. Once feedback is received, the dataset may undergo revision 

until it is approved for use. Then, the dataset will be publicly available and offered as input to the US EPA pesticide 

aquatic exposure models. 
14

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17

Appendix A

“12408_grid_33.125_lat.wea” – example .wea (comma-separated format)

Fields: mm, dd, yyyy, precipitation (cm/d), Hargreaves-Samani potential evapotranspiration (cm/d), temperature 

(◦C), wind speed (cm/s), solar radiation (Langleys/d)

01,01,1961,0.00,0.10,0.16,1.31,86.15

01,02,1961,0.00,0.10,0.14,3.03,148.25

01,03,1961,0.00,0.09,0.12,-0.39,124.65

01,04,1961,0.00,0.12,0.13,0.88,194.59

01,05,1961,0.00,0.13,0.19,5.58,251.30

01,06,1961,0.00,0.16,0.13,8.15,148.33

...

12,26,2014,0.00,0.13,9.42,353.02,202.75

12,27,2014,0.25,0.14,15.45,154.29,208.43

12,28,2014,3.35,0.14,8.15,388.31,174.38

12,29,2014,0.57,0.10,4.75,177.94,323.99

12,30,2014,0.00,0.09,2.27,356.11,343.60

12,31,2014,0.00,0.08,-0.02,296.28,341.02
18

Appendix B

Figure B1. Annual sums of a) long-term daily mean precipitation (mm) and b) long-term daily mean PET (mm) 

over 1961-1990 and 1991-2014 time periods. Gridded precipitation is compiled from the NOAA CPC Unified Rain 

Gauge Analysis, which covers the land mass of the continental U.S. Gridded PET estimates are derived from the 

NOAA NCEP/NCAR Reanalysis global dataset using the Hargreaves-Samani method (1985).
19

Figure B2. a) Net annual average of long-term daily mean precipitation minus long-term daily mean PET (mm)

for 1961-1990 and 1991-2014, and b) Net annual sum of long-term daily mean precipitation minus long-term 

daily mean PET (mm) for 1961-1990 and 1991-2014.
20

 

 
21

 

b)

Figure B3. a) Net monthly average of long-term daily mean precipitation minus long-term daily mean PET (mm)

for 1961-1990 and 1991-2014, and b) Net monthly sum of long-term daily mean precipitation minus long-term 

daily mean PET (mm) for 1961-1990 and 1991-2014.