1 

# Appendix 3
- C. Integrating gridded daily meteorology in to pesticide aquatic exposure model s
M.M. Fry 1 , G

. Rothman 1 , D.F. Young 1 , N. Thurman 1 , M. Thawley 1

1 U.S. Environmental Protection Agency, Office of Pesticide Programs, Environmental Fate and Effects Division, 1200 Pennsylvania Ave, NW, Washington, DC 20460 

### Abstract
W eather is a key component in pesticide aquatic exposure model s , contributing to a chemical’s fate and transport in the environment. C urrent pesticide exposure models have relied on weath er from the Solar and Meteorological Surface Observation Network (SAMSON), but this source is no longer maintained, only spans the years 1961 to 1990, and is limited to 237 locations.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 te mporal coverage ( 1961 to 2014) than the SAMSON

. The parameter s are consistent with those provided by the SAMSON : precipitation, potential evapotranspiration , temperature, wind speed, and solar radiation. S imulated 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 p esticide , exposure assessment , meteorology, weather 

### 1. Introduction We compile m eteorology 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 analyse s offer gridded, national coverage of2 the daily variables: precipitation, temperature, wind speed, and solar radiation. Potential evapotranspiration is deri ved 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 cover ing 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 va lues 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 an d 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 p esticide exposure assessment models : the US EPA Spatial Aquatic Model (SAM) and the US EPA Surface Water Concentration Calculator ( S W C C) 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 Uni fied Rain Gauge Analysis datasets we re 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 Meridi onal 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
c Available: 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 nomi nal 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 e ach variable during compilation

. C omma - delimited file s 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. The se 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 N o rth

.

Table 2. Raw variable conversions and calculations.

Raw variable s Unit conversion s for SAM Description Precipitation P cm = P mm * 0.1 Conversion from mm/day (P mm ) to cm/day (P cm ) Air temperature at 2m T C = T K – 273.15 Conversion from Kelvin (T K ) to degrees Celsius (T C )
U

- wind at 10m
V

- wind at 10m XX ww = 100 ∗ � UU ww 2 + VV ww 2 Calculation of vector wind speed (X w ) from u

- wind (U w ) and v

- wind (V w ) , and conversion from m/s to cm/s Downward solar radiation flux S L = S W * 2.06364 Conversion from W/m 2 (S W ) to La/day (S L ), where 1 W/m 2 = 0.085985 La/hr = 2.06364 La/d Downward solar radiation flux at nominal top of the atmosphere (NTAT) S ntat, mm = (S ntat, W /1e6)*86400*0.408 Conversion from W/m 2 (S ntat,W ) to mm/day (S ntat,mm ) , where W/m 2 = J/s

- m 2 * (MJ/1e6J)*(86400 s/d)*(0.408 mm/d / MJ/m 2 d) A Maximum air temperature at 2m Minimum air temperature at 2m T range = T max - T min Calculation of temperature range (T range ) between maximum (T max ) and minimum (T min ) temperatures A

A Downward solar radiation flux at the nominal top of the atmosphere and the t emperature 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 model ing

. The previous SAMSON dataset reports pan evaporation, and
PET is calculated internally by the model based on the pan factor

. However, o ther 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 strai ghtforward 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 evapotranspiratio n (PET daily , cm/d) is estimated by Hargreaves

- Samani as:
PET daily = (0.0023*S ntat,b ) (T b +17.8) (T range 0.5 )/10 Where S ntat,b = downward solar radiation flux at nominal top of the atmosphere (mm/day)
T b = daily mean air temperature ( ◦ C) at 2 m
T range = 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 temper ature, and daily difference between maximum and minimum temperature. A simpler m ethod 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 q uality assurance and quality control of the compiled dataset and evaluate its effectiveness in existing pesticide aquatic exposure models

. Table 3 summarizes t he 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 f iles 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 model s . 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 compile s land surface, ship, rawinsonde, pibal, aircraft, satellite, and other data, perform s quality control, and implements data assimilation over the time period of 1948 to present day. Meteorological fields ar e derived from a numerical weather model constrai ned by observations. The quality and usefulness of this dataset has vastly improved since NCEP’s original analyses. Improvements include the state

of

- the

- art data assimilation system, an increased number of observations, and enhanced quality control (Kis tler 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 t he NOAA NCEP/NCAR Reanalysis dataset have found it to be fairly representative over its spatial and temporal scale s (Kistler et al., 1999) . It has been used for climate monitoring by the NOAA Climate Prediction Center and other weather and climate researc hers. Long

- term means and annual cycles show similar patterns

w hen 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 atmosp here 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 the7 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 measure ments. 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 , interpolatin g 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 incorp oration into the existing standard scenario s used by the US EPA SWCC . Otherwise, the compiled dataset c an be used to simulate any location in the continental U.S. within the 0.25 x 0.25 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 graphic s . Here we focus on presenting precipitation and PET results, since estimated exposure concentrations are most sensitive to these weather parameters. Figure 2 presents the a nnual average of the long

- term daily mean precipitation and PET (annual totals in A ppendix B) . For precipitation, the highe st annual average s ( and annual total s ) occur in the southeastern and northwe st ern 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

. N et annual average s 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 i n 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 bo th the short

- term and long

- term. In general, d erived PET estimates are consistent in their distribution and magnitude when compared to other studies

. A nnual and monthly average PETs in Figures 2 and 3 closely resemble the PET distribution s of Willmott and Kenji (2001) , which were developed at 0.5x0.5 degree resolution (1950

- 1999) using ArcGIS. Additionally, t he 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 , requi res less parameterization , and is less sensitiv e to climatic input errors (Hargreaves and Allen, 2003). Monthly average PET estimates (Figure 3 ) are also consistent with t he seasonal average PET values of Weiland et al. (2012) , who develope d 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 t he 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 sho wn, but nearly identical in magnitude and distribution) .11 

### 3.3.2 Application to aquatic exposure modeling
T his 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 . O ther aquatic exposure models also benefit from using th is dataset from 1961 to present day

. T he US EPA SWCC , for example, can utilize the same dataset at its standard scenario locations, and in more refin ed site

- specific simulations. We compare t he compiled weather dataset to the SAMSON using the US EPA SWCC

. We conduct a batch set of s imulations (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 parameter s 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 , ty pically used in regulatory assessments, are presented in Figure 4 for the s tandard pond and standard reservoir. Pearson’s correlation coefficient s (Table 4) quantify the strength of the linear relationship between the SWCC results generated usin g the comp iled 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 estimate s.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 rep resents a perfectly linear relationship.

Pearson’s correlation coefficient a for SWCC simulations (1961

- 1990) Standard pond Standard reservoir Peak (1

- in

- 10 y ea r) 0. 871 0. 874 4

- day Av erage (1

- in

- 10 y ea r) 0. 873 0. 877 21

- day Av erage (1

- in

- 10 y ea r) 0. 872 0. 881 60

- day Av erage (1

- in

- 10 y ea r) 0. 873 0. 881 90

- day Av erage (1

- in

- 10 y ea r) 0. 872 0. 879 Entire Simulation Mean 0.9 03 0.9 07 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 − xx ̅ ) ( yy − yy � ) �

∑ ( xx − xx ̅ ) 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 step s for ease of use , given improved computational resources. Th e documented methodology is undergoing review and will be presented to the US EPA Scientific Ad visory Panel . Once feedback is received, the dataset may undergo revision until it is approved for use

. Then, the dataset will be p ub licly available and offered as input to the US EPA pesticide aquatic exposure models.14 

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- 80.17 

### 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 01,05,196 1,0.00,0.13,0.19,5.58,251.30 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,

- 0.02,296.28,341.0218 

### Appendix B

Figure B1 . Annual sum s of a) long
- term daily mean precipitation (mm) and b) long
- term daily mean PET (mm)
over 1961 - 1990 and 1991
- 2014 time periods. Gridd ed 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 B 2 . 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 19 91 - 2014 .2021 b) Figure B 3 . 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 .