GMDGeoscientific Model DevelopmentGMDGeosci. Model Dev.1991-9603Copernicus PublicationsGöttingen, Germany10.5194/gmd-10-3425-2017Analysis of errors introduced by geographic coordinate systems on weather numeric prediction modelingCaoYanniCervoneGuidocervone@psu.eduBarkleyZacharyLauvauxThomashttps://orcid.org/0000-0002-7697-742XDengAijunTaylorAlanDepartment of Geography and Institute for CyberScience, The Pennsylvania State University, University Park, PA, USADepartment of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, PA, USAResearch Application Laboratory National Center for Atmospheric Research, Boulder, CO, USAGuido Cervone (cervone@psu.edu)19September20171093425344023September20162December201623June201727June2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://gmd.copernicus.org/articles/10/3425/2017/gmd-10-3425-2017.htmlThe full text article is available as a PDF file from https://gmd.copernicus.org/articles/10/3425/2017/gmd-10-3425-2017.pdf
Most atmospheric models, including the Weather Research and Forecasting
(WRF) model, use a spherical geographic coordinate system to internally represent
input data and perform computations. However, most geographic information
system (GIS) input data used by the models are based on a spheroid datum
because it better represents the actual geometry of the earth. WRF and other
atmospheric models use these GIS input layers as if they were in a spherical
coordinate system without accounting for the difference in datum.
When GIS layers are not properly reprojected, latitudinal errors of up to
21 km in the midlatitudes are introduced. Recent studies have suggested
that for very high-resolution applications, the difference in datum in the
GIS input data (e.g., terrain land use, orography) should be taken into
account. However, the magnitude of errors introduced by the difference
in coordinate systems remains unclear. This research quantifies the effect of
using a spherical vs. a spheroid datum for the input GIS layers used by WRF to
study greenhouse gas transport and dispersion in northeast Pennsylvania.
Introduction
Geographic information science (GISc) datasets are usually projected on a
spheroid geographic coordinate system (GCS) such as World Geodetic System
1984 (WGS84) or North American Datum 1983 (NAD83). The earth is an irregular
oblate spheroid, and these datums are used to better approximate the actual
shape of the planet, which is flattened at the poles and bulged at the
equator. The datums are used in combination with different projections (e.g.,
Universal Transverse Mercator (UTM), latitude–longitude, Albert equal area) to map a 3-D
view of the earth onto a 2-D plane.
Atmospheric models are based on a spherical coordinate system because it
usually leads to faster computations and easier representations of data
. The GISc layers used as input data for the
atmospheric models generally use a spheroid datum, but they are ingested by
the models as if they used spherical datums. Using different GCSs can affect
the model results because the input data are mapped to different locations.
This difference can lead to latitudinal shifts up to 21 km in the midlatitudes . This paper performs a series of
sensitivity studies where the GISs input layers are reprojected from a spheroid
to a spherical datum in order to more correctly represent the input layers
used by the atmospheric models.
In a GCS the earth is represented as either an oblate spheroid or a sphere,
whereas in a spherical system the earth is always represented as a sphere
. This means that when using a spherical coordinate
system, the spatial relationships between points on the surface of the earth
are altered. The shift in the spatial relationship results in a latitudinal
error and is consistent across all data that are used as input layers in the
atmospheric models. Consequently, numerical errors are introduced by
computations that are a function of latitude, such as the Coriolis force and
the incoming solar radiation. As already explained in
, a minor mismatch between the Weather
Research and Forecasting (WRF) model global atmosphere input and static
variables will affect the simulation result. Figure shows the
latitudinal errors introduced when representing a point on the surface of the
earth with a spherical GCS. Point A represents data projected on a spheroid
system (red line). When that same point A is represented on a sphere (green
line) like in an atmospherical model, its location gets incorrectly shifted
to point B. Point C is the true location of point A when
correctly projected in the spherical coordinate system. Figure
shows that the errors between spheroid and sphere representation for the same
point are a function of latitude. The maximum errors occur at midlatitude,
precisely at 45∘ N and S. Indentical errors occur in the Southern
Hemisphere.
Differences in coordinate systems and the resulting spatial errors, such as
the example provided in Fig. , have not been a primary focus in
atmospheric modeling because of the relatively coarse spatial resolution of the
simulation domains . More recently, due to the
improvements in computational resources and technological advances,
atmospheric models are routinely run at higher spatial resolution. Yet this
trend in running simulations with high-resolution input datasets do not take
into account the shift between the coordinate systems, which may cause spatial
errors in the model's output.
investigated errors caused by different
coordinate systems using WRF run with higher resolution topography and land
use datasets over Colorado. Multiple WRF simulations were performed to study
differences in meteorological parameters such as air temperature, specific
humidity and wind speed. They concluded that the GCS transformation from
WGS84 GCS to a spherical earth model caused the input data to shift up to
20 km southward in central Colorado. The impact of this shift leads to
significant localized effects on the simulation results. The root mean square
difference (RMSD) for air temperature is 0.99 ∘C, for specific
humidity it is 0.72 g kg-1 and for wind speed it is 1.20 m s-1. It was
concluded that for high-resolution atmospheric simulations, the issue
resulting from datum and projection errors is increasingly important to
solve. All datasets used as input should be in the same GCS
.
No study has yet given attention to the impacts of incorrect coordinate
systems on the transport of an atmospheric tracer. Sensitivity experiments
were conducted to quantify the impact of geographic coordinate systems on the
atmospheric mixing ratios of methane (CH4) emitted from the Marcellus
shale gas production activities in Pennsylvania. Using a chemistry module to
transport passive tracers in the atmosphere, WRF simulates the CH4
mixing ratios in the atmosphere.
Geographic information systems and other geospatial technologies have been
increasingly used in atmospheric sciences. GIS provides a scientific framework
for observation data, modeling, and scientific deduction to study
atmospheric phenomena and processes
. However, some
barriers between GIS and atmospheric science, such as different data formats
and different GCSs, impede the collaborations. This research utilizes the open-source language R to automatically convert the weather numerical-model input,
output, and GIS data layers.
The objectives of this study are the following:
to quantify the impact of projecting the model input data with different coordinate
systems on meteorological variables and simulated atmospheric mixing ratios of a passive tracer
to generate a tool that can automatically convert WRF output to GIS layers and vice versa.
Equivalent-point comparisons when using a sphere and spheroid. Blue
represents the true earth shape. Green represents the sphere that WRF
assumes. Red shows the spheroid WGS84 GCS. Point A represents data projected
on a spheroid system. When that same point A is represented on a sphere like
in an atmospherical model, its location gets incorrectly shifted to point B.
Point C is the true location of point A when correctly projected in the
spherical coordinate system .
Errors introduced by the different geographic coordinate systems are
a function of latitude. The maximum error of about 21 km is found at
45∘ latitude. The three shaded areas indicate the latitudinal extents
of the three nested WRF domains used in this study.
Study area
The atmospheric simulations were performed using three nested domains of
decreasing area and increasing spatial resolutions. As suggested by
, we defined several criteria to select a
region where errors introduced by GCS are more likely to affect our
simulation results. First, the region should have larger elevation gradients.
Second, it should contain diverse land use patterns such as forest, urban,
and wetland. Third, the simulation period requires convective conditions such
as those in summertime since both the topography and the land cover play a larger
effect on the simulations. Finally, a comparatively small domain should
provide a focused study region because a larger domain would ignore the small
variations.
The WRF model grid configuration used in this research contains three nested
grids: 9 × 9 km for domain 1, 3 × 3 km for domain 2, and
1 × 1 km for domain 3 (Fig. ). Each 9 × 9 and
3 × 3 km grid have a mesh of 202 × 202 grid points. The
1 × 1 km grid has a mesh of 240 × 183 grid points.
The 9 × 9 km grid (domain 1) contains the mid-Atlantic region, the
entire northeastern United States east of Indiana, parts of Canada, and a
large area of the northern Atlantic Ocean. The 3 × 3 km (domain 2)
grid contains the entire state of Pennsylvania and southern New York. The
1 × 1 km (domain 3) grid contains northeastern Pennsylvania and
southeastern New York. One-way nesting is used so that information from the
coarse domain translates to the fine domain but no information from the fine
domain translates to the coarse domain . The elevation of
the domain 3 ranges between 108 and 706 m a.s.l. (above sea level)
(Fig. ).
The analysis of the model results focuses on the innermost domain 3. This
region was primarily chosen because there has been an increase of activity in
natural-gas fracking since 2008, which is expected to result in significant
releases of fugitive greenhouse gas emissions, in particular CH4.
Map of study area shows three nested domains of WRF. The inner
domain is located in the northeastern Pennsylvania and extends into
southeastern New York.
In domain 3, the latitude ranges from 40 to 42.67∘ N. The
longitude ranges from -78 to -75.17∘ W. The figure shows the
satellite view of the domain with major roads, cities and
landmarks.
The table showing the input data sources for each of the three
scenarios (DEFAULT, HR and HR_RESHIFT).
VariablesDEFAULT scenarioHR scenarioHR_RESHIFT scenarioTopographyUSGSSRTMSRTMLand useUSGSNLCDNLCDCoriolisE & F parametersE & F parametersE & F parametersLeaf area indexMODIS climatology8-day MODIS8-day MODISAlbedoMODIS climatology8-day MODIS8-day MODISCH4 emissionsData
Table shows the input data sources for each of the
three scenarios. The variables include topography, land use, Coriolis, leaf
area index (LAI), albedo and CH4 emissions.
Digital elevation data
Two types of elevation data are included in the experiments. The WRF DEFAULT
elevation data are derived from the US Geological Survey (USGS) global
30 arcsec (roughly 900 m) elevation dataset topography and are used in the
DEFAULT case . The HR and HR_SHIFT cases use higher
resolution data from the NASA Shuttle Radar Topographic Mission (SRTM;
). The data consist of a 90 m resolution digital
elevation model (DEM) for over 80 % of the world. The data are projected
in a geographic (latitude–longitude) projection with the WGS84 GCS.
Land cover data
The DEFAULT scenario uses the 24 types of land use categories that are derived from satellite data. The
HR and HR_SHIFT cases use the latest land cover products available for
North America. The 2011 USGS National Land Cover Database (NLCD) covers
the continental United States, including the state of Alaska, and is derived
from Landsat satellite imagery with a 30 m spatial resolution. Furthermore,
the product is modified from the Anderson Land Cover Classification System
and is divided into 20 different land cover types. It has a NAD 1983 GCS and
is projected using an Albers conic equal area projection
.
Due to the extent of the NLCD dataset, the 2010 North American Land Cover
(NALC)
2010 North American Land Cover at 250 m spatial resolution.
Produced by Natural Resources Canada/The Canada Centre for Mapping and Earth
Observation (NRCan/CCMEO), United States Geological Survey (USGS); Insituto
Nacional de Estadística y Geografía (INEGI), Comisión Nacional
para el Conocimiento y Uso de la Biodiversidad (CONABIO) and Comisión
Nacional Forestal (CONAFOR)
is used for the areas of the domain that
include Canada. The NALC product is constructed from observations acquired
by the Moderate Resolution Imaging Spectroradiometer (MODIS) at a 250 m
spatial resolution. This product is produced by Canada, the United States, and Mexico
and is represented based on three hierarchical levels using the Food and
Agriculture Organization (FOA) land classification system. NALC is based on a
sphere GCS with a radius of 6 370 977 m and has a Lambert azimuthal equal-area projection .
Leaf area index
The LAI variable estimates the tree canopy area relative to
a unit of ground area . Two types of LAI data
are used in this experiment. WRF DEFAULT LAI is based on a climatology
derived from MODIS is used in the DEFAULT scenario. LAI in HR was obtained
from 8-day-averaged data from MODIS. The level-4 MODIS global LAI product
composites data every 8 days at 1 km resolution on a sinusoidal grid
. The product we used is MCD15A2 for May 2015, which
combines the MODIS data from Terra and Aqua satellites.
Albedo
Surface albedo is one of the key radiation parameters required for modeling
of the earth's energy budget. In the DEFAULT scenario, albedos use the values
from the MODIS modified by National Oceanic and Atmospheric Administration
(NOAA) according to the green fraction .
The HR and HR_RESHIFT cases use the satellite observations that are
retrieved from MODIS to produce high-resolution and domain-specific albedo
input. A 16-day L3 Global 500 m MCD43A3 product is used for May 2015. The
product relies on multiday, clear-sky, atmospherically corrected surface
reflectances to establish the surface anisotropy and provide albedo
measurements at a 500 m resolution .
CH4 emissions
CH4 emission sources include unconventional wells and conventional
wells. Both the location and amount of production rates are provided from the
Pennsylvania Department of Environmental Protection (PADEP) Oil and Gas
Reporting website, New York Department of Environmental Conservation, and the
West Virginia Department of Environmental Protection (WVDEP). The emission
was calculated by multiplying the production with the emission factors.
indicates that the emission rate for conventional
wells is 11 % and unconventional well is 0.13 % of the well
production. The CH4 emission files were converted as input files for the
WRF model .
Weather stations
The weather observations are the standard measurements of wind, temperature
and moisture fields from World Meteorological Organization (WMO) surface
stations at hourly intervals and radio sondes at 12-hourly intervals. The
objective analysis program OBSGRID is used for quality control to remove
erroneous data . There are eight
stations located in the inner domain. Temperature data during the experiment
time from each tower are collected to validate the model simulation results.
Methodology
The WRF model () version 3.6.1 is used to generate
the numerical weather simulations in this research. It is one of the most
widely distributed and used mesoscale numerical weather prediction (NWP)
models in existence. It has well-tested algorithms for meteorological data
assimilation and meteorological research and forecast purposes. The WRF
model carries a complete suite of atmospheric physical processes that
interact with the model's dynamics and thermodynamics core
.
Workflow of the study showing the three scenarios: DEFAULT, HR and
HR_SHIFT.
The model physics of the WRF configuration in this research includes the use
of the following settings . First, the double-moment
scheme is used for cloud microphysical processes
. Second, the Kain–Fritsch scheme is used for
cumulus parameterization on the 9 km grid
. Third, the rapid radiative transfer
method is applied to general circulation models (GCMs;
). Next, the level-2.5
TKE-predicting MYNN planetary boundary layer (PBL) scheme
and the Noah four-layer land-surface model
(LSM),
that predicts soil temperature and moisture in addition to sensible and
latent heat fluxes between the land surface and atmosphere, are included
.
The WRF model enables the chemical transport option within the model, allowing
for the projection of CH4 concentrations throughout the domain. Surface
CH4 emissions used as input for the model come from the CH4
emissions inventory. WRF is able to simulate the CH4 transport in the
atmosphere.
WRF simulations are performed for a 25 h time period from 07:00 on
14 May 2015 until 07:00 15 May 2015 Eastern Standard Time (EST) over
the three nested domains described in Sect. .
Figure shows the experiment workflow. A series of numerical
weather simulations were performed using the following input datasets:
DEFAULT scenario: DEFAULT WRF topography, land use data, Coriolis E and F,
leaf area index, albedo and CH4 source emissions, which are all in WGS84
GCS. The datasets are used as input without applying any transformations into
WRF.
HR scenario: High-resolution terrain and land cover data which are all in
WGS84 GCS. The datasets are used as input without applying any
transformations into WRF.
HR_SHIFT scenario: High-resolution terrain, land cover data, Coriolis,
leaf area index and albedo data, which are first reprojected onto a spherical
coordinate system using the transformation function .
This is a summary of the comparisons that are performed to assess the hypothesis.
DEFAULT is compared to HR to investigate the impacts on the high-resolution
input data on model results.
HR is compared to HR_SHIFT to investigate the impacts of geographic
coordinate system change on model results.
HR_RESHIFT is originally the model output from HR_SHIFT simulation. Then,
the output is shifted back to WGS84. HR_RESHIFT is compared to HR. These two
outputs are in the same geographic coordinate system. The model output
comparison, such as temperature, wind speed, wind direction and CH4
concentration, leads to sensitive understanding of how latitude-dependent
variables affect the model simulation.
The input data include elevation, land use, Coriolis E and F components,
LAI, albedo, and maps of CH4 sources. The CH4 sources include
conventional wells and unconventional wells. According to
, using high-resolution green-fraction data does
not significantly impact the performance of the weather model simulation.
Thus, we did not replace green fractions in this experiment.
The first simulation (DEFAULT scenario) uses the WRF DEFAULT setting: US
Geological survey (USGS) Global 30 arcsec elevation dataset topography
(GTOPO30; Gesch and Greenlee 1996), 24 types of land use data, Coriolis
parameters E and F, original WRF leaf area index, and albedo. In addition
to the above variables, the experiment takes CH4 emissions from
unconventional and conventional wells as inputs to the WRF simulation.
Flowchart for transforming and generating new model input data.
The second simulation, HR, uses higher resolution datasets for terrain, land
cover, LAI and albedo. The terrain elevation data are derived from the NASA
SRTM DEM product at a 90 m resolution. The NALC
and NLCD are used for the land cover data. LAI and albedo are retrieved from
MODIS in May 2015. All these data are replaced for all of the three WRF
domains. A common approach to resampling land cover categories to a cell is
based on the highest number of pixels that represent a class. Then the
highest class occurrence is used to assign the land cover type of the cell.
For example if cell A is made up of three different land cover types,
(1) “Open Water” 38 %, (2) “Deciduous Forest” 32 %, and (3) “Evergreen
Forest” 30 % then the final class for cell A would be Open Water. However,
in this work, a hierarchical classification scheme is used to define the land
cover type. First, we determine the most common class of land cover types
presents inside the cell and create a count order based on the values inside
that class. A class corresponds to multiple land cover types. For example,
the class “Forest” includes the types Deciduous Forest and Evergreen
Forest. We assign the prevalent class, such as Forest, to the given pixel.
Second, the grid cell is attributed a land cover type by selecting the type
with largest values that are present within a class. For example, if the same
cell A is made up of the three different land cover types, (1) Open Water
38 %, (2) Deciduous Forest 32 %, and (3) Evergreen Forest 30 %,
then the final class for cell A would be Deciduous Forest because the
class Forest is most common class (62 %) within this cell, and
Deciduous Forest has the highest percentage within the Forest class.
The third simulation, HR_SHIFT, uses the same data as the HR scenario;
however, the input data are converted from WGS84 to the DEFAULT WRF sphere
GCS.
Coriolis is a function of latitude and thus particularly affected by errors
in GCS. The Coriolis force has two components: E and F are calculated using
E=2Ωsin(φ) and F=2Ωcos(φ), where Ω is
rotation rate of the earth and φ represents latitude. Coriolis E
and F variables are recalculated in the HR_SHIFT scenario by using the
reprojected latitude.
Shown below is the input and output GCS for the data used in each of the four
analyses that will be performed.
Table shows the input and output GCS for the
topographic, land use, and CH4 data used for the WRF simulations.
Specifically, results discuss the output for the DEFAULT and HR, and HR and
HR_RESHIFT configurations. A prototype tool is developed to automatically
transfer WRF output to GIS layers.
WRF model input and output processing
A series of scripts in R are provided to perform the tasks identified in the
current paper. Figure shows the process used to generate new
input data based on additional input data and an optional coordinate
transformation. This process is performed in the WRF_preprocess.R and
WRF_updateNC.R scripts. WRF_process.R takes WRF original input files as
input and shift the selected WRF layers to sphere raster format. In addition,
users generate an ESRI Shapefile as an output. The WRF_UpdateNC.R file takes the
generated Rdata files and updates them into the original WRF input file. The
detailed descriptions are attached in Appendix B.
Temperature differences between HR and DEFAULT in domain
3.
Additional scripts are provided to perform basic transformation of the input
data from their original format to the latitude–longitude WGS84 format that is used by
WRF_preprocess.R to generate new model input data. For example MODIS_LAI.R
is used to automatically download and reproject MODIS satellite data in a
format that can be input into the WRF input file. These functions are
provided to automate the process of downloading and reprojecting MODIS data;
the same results can be achieved through several already alternatively
methodologies. Essentially, the MODIS functions are wrappers around the MODIS
Reprojection Tool, which is provided by NASA .
The current code assumes standard WRF input data in NetCDF format; however,
the script can be easily modified to accept a different input format from a
model other than WRF.
Results
Wind direction differences between HR and DEFAULT in domain 3.
Wind speed differences between HR and DEFAULT in domain 3.
Temperature difference between HR and HR_RESHIFT on 14 May 15:00 EST,
2015, showed that there is no significant spatial pattern.
The WRF model is used to simulate the atmospheric dynamics between
14 May 2015 07:00 and 15 May 2015 07:00 EST. This work focuses on four
output variables produced during the WRF simulation: air temperature, mean
horizontal wind speed and direction, and CH4 atmospheric mixing ratios.
Temperature was selected because it is one of the main drivers of local and
large-scale weather. Additionally, historical temperature data are available
for comparison purposes. Near-surface temperature also corresponds to areas
of higher energy, which relates to turbulent motions near the surface as well
as surface-water exchange (evaporation). Wind speed and wind direction were
selected to represent the atmospheric dynamics impacting the weather
conditions on small and large scales. Finally, we selected the CH4
mixing ratios to quantify the impact on greenhouse gas transport in the
atmosphere.
DEFAULT and HR sensitivity study
Previous studies have investigated the weather simulation performance
differences by using higher resolution data. While the comparison between
DEFAULT and HR is not the central focus of this work, experiments were
performed to confirm previous findings and to quantify changes due to using
higher resolution vs. changes due to the different GCSs.
Figures , and compare the WRF
simulations for domain 3 for temperature, wind direction and wind speed,
respectively. The figures show that using higher resolution data does not
significantly alter the results obtained using the DEFAULT WRF input.
HR and HR_RESHIFT sensitivity study
This section analyzes the main research question of the article, namely
what the effect of using a different geographic coordinate system is on
the simulations of temperature, wind speed, wind direction, and CH4
mixing ratio.
Results for temperature
The effect of using a different coordinate system on the simulations of
temperature is performed by comparing observations between the un-shifted
(HR) and shifted (HR_SHIFT) scenarios. Figure shows the
difference obtained for 14 May 2015 at 15:00 EST. This particular time and day
were chosen because it is one of the hottest times of the day, when
temperatures are expected to vary the most. The letters A–H represent the eight
weather observation stations located inside the selected domain and are used
for validation purposes.
Temperature differences between HR and HR_RESHIFT in domain
2.
The temperature difference ranges from -5.6 ∘C, represented by
light blue colors, to 6 ∘C, shown with orange–red colors. When
comparing both HR and HR_RESHIFT, the most striking spatial pattern is the
systematic cooling around the finger lakes (roughly bound by points A, B and
H). There are several additional areas of increased positive and negative
temperature around the perimeter of the image, where most extremes are
observed. However, these are likely to be artifacts introduced by the WRF
computations where the nested grids meet. The largest differences are
observed at the edges of the domain and are likely artifacts being
introduced by WRF where the nested grids change resolutions.
Statistical tests were performed using the observed weather data (stations
A–H), and both scenarios (HR and HR_RESHIT) have a 0.91 root mean square
error. While this suggests that there are only small temperature variations
when using a different GCS, it should be noted that this test was only performed
at eight stations throughout the domain where ground data were
available. Unfortunately, several of these stations lie close to the edge of
the domain, where WRF simulation results are most unreliable. Therefore, the
spatial cooling observed around the lakes is the most important result
obtained entirely due to the change in GCS.
Both domain 2 and domain 3 show a systematic temperature increase in the
HR_RESHIFT scenario when compared to HR (Figs. and
). The height is represented on the vertical axis while the
temperature difference is on the horizontal axis. The variability and mean
temperature differences are larger near the surface and below 1 km altitude.
This height corresponds approximately to the average boundary layer height,
where the impact of the surface on the atmospheric dynamics is maximum. The
variability in the midtroposphere decreases significantly, revealing a lower
impact of the GCS on the higher altitude model results.
Temperature differences between HR and HR_RESHIFT in domain 3.
Wind speed difference between HR and HR_RESHIFT on 14 May 15:00 EST,
2015, showed a wave pattern.
Wind speed differences between HR and HR_RESHIFT in domain
2.
Results for wind speed
Figure shows the wind speed difference for 14 May 2015 at
11:00 EST, which ranges from -5.11 to 3.5 m s-1 between HR and
HR_RESHIFT. A wave pattern is found during the 25 h simulation, and it can
be explained by the shifted data allowing for a more accurate
characterization of the complex terrain along the Appalachian Mountains. The
wind speed differences between HR and HR_RESHIFT indicate that the change in
GCS affects the results.
Wind speed differences between HR and HR_RESHIFT in domain 3.
Wind direction difference between HR and HR_RESHIFT on 14 May
15:00 EST, 2015, showing a strip pattern in the right top corner where it is a
valley region. The pattern indicates that the WRF model reacts differently on a
small-area weather simulation when the GCS changes.
Results for wind direction
Figures and show results for wind directions and
highlight that, as for the previous cases, the most differences are found
closer to the surface. As explained earlier, changes in GCS affect the
interaction in the lower layers of the troposphere the most.
In the northeastern corner of the inner domain, there is a strip-like
pattern, with large local wind changes between positive
and negative northeast and northwest, and between positive
and negative southeast and southwest. In
this region the Appalachian Mountains create a complex terrain with series of
valley and ridges. The GCS changes the spatial distribution of the terrain
elevation, leading to these very large changes in wind direction The strong
vertical gradients observed in the figure suggest there is also a
combination of influences from both the surface parameters (primarily
elevation and land cover), and the Coriolis components. Despite observed
changes throughout the vertical column, the near-surface variability is
significantly larger than the midtropospheric variances, as was observed for
temperature and wind speed.
Domain 3 topography map. The elevation ranges from 108 to 761 m
above sea level.
Wind direction differences between HR and HR_RESHIFT in domain
2.
Results for CH4 atmospheric mixing ratios
WRF was used to simulate CH4 atmospheric mixing ratios that originated from
leaks from unconventional and conventional natural-gas production activities
during the 25 h simulation. The CH4 mixing ratio is a unique
tracer to study atmospheric dynamics and is well suited for this experiment
because domain 3 includes the northeastern Pennsylvania which, since 2008,
has became one of the most important fracking areas in the United States. With the development of fracking, the CH4 leaks became a concern
because CH4 has a global warming potential (GWP) between 28 and 36 over
100 years. It means that the comparative impact of CH4 on climate change
is 28 to 36 times greater than CO2 over a 100-year period
.
CH4 mixing ratios are computed differently than temperature, wind speed
and wind direction. Temperature, wind speed and wind directions are computed
using global atmospheric input data, which is an internal variable of the WRF
model physics. On the other hand, CH4 mixing ratios are computed solely
on the CH4 emissions created using multiple datasets. Thus, CH4
mixing ratios were selected to investigate the impact of differences in GCS
on the simulation accuracy aggregated over time, as CH4 accumulates
differences along its trajectories in the atmosphere. Overall, we expect a
strong sensitivity to transport differences revealed by the long-range
transport of CH4 emitted at the surface. Figures and
show the mean of CH4 mixing ratios differences between HR
and HR_RESHIFT for conventional and unconventional wells as a function of
time. The figures show two radar plots, where the times have been arranged as
on a clock. Panel (a)
indicates the results for morning time (a.m.) and panel (b) for evening time (p.m.). When the shaded area is
larger than 0, CH4 mixing ratios in HR are larger than those in HR_RESHIFT,
and vice versa.
For conventional wells (Fig. ), the differences are often
close to 0, with nighttime increases (21:00 to 04:00 EST). For the
unconventional wells (Fig. ), the CH4 mixing ratio in HR is
also smaller during nighttime (21:00 to 08:00 EST), but much more so (as much as
1 ppb smaller). The reason for this change is that, during nighttime, the
mixing within the boundary layer is smaller (more stable atmosphere) and
therefore the magnitude of the concentration of CH4 is higher. Because
of the higher concentrations, the impact of the change in GCS is bigger.
Furthermore, the explanation for why conventional wells have a smaller
variation than unconventional wells is that most of them are located
farther away from the tower network, and thus their emission contribution on
the simulation is smaller because it is distributed over a wider area. These
results show a significant change in the CH4 mixing ratio when using the
different GCS.
Conclusions
This paper discusses the impact of different GCSs on
weather numerical-model simulations. The main hypothesis is that the error
introduced by not taking into account the GCS of the input data, which
results in latitudinal errors of up to 21 km in the midlatitudes, can cause
significant changes in the output of the model.
Wind direction differences between HR and HR_RESHIFT in domain
3.
A sensitivity study was performed using the WRF numerical model, with input
data at different resolutions and different GCSs. Four different output
parameters were investigated, namely temperature, wind speed, wind direction
and CH4 mixing ratios.
Results show that changes are introduced by using different GCSs for the
input data. The observed differences were caused by (1) topography shift,
including elevation, land use, albedo, and LAI differences, and
(2) latitude-dependent physics, such as the Coriolis force and the incoming
solar radiation.
A systematic temperature increase was observed in all of the three nested domains
used in this study. A spatial pattern showing significant cooling was
observed near two lakes included in the inner domain.
Similarly, wind speed and direction show spatial changes that can be traced
back to the use of different land cover and elevation. Wind speed, wind
direction, and temperature indicate more variations within the planetary
boundary layer, where the interaction between the surface and the atmosphere
is greatest. It is expected that changes at the surface will introduce most
significant changes closer to the surface.
It is shown that, without exception, the GCS of the input data affects model
results. Sometimes these changes are large and have a clear spatial patterns,
whereas other times they are small and negligible. It is concluded that while some
of these errors might be small, they nevertheless introduce an additional
bias in the model output. For very high-resolution simulation in particular,
these errors are compounded and can lead to significant errors.
CH4 mixing ratios difference between HR and HR_RESHIFT in domain
3 for conventional wells. Panel (a) shows the differences between 00:00 and 12:00 EST on 14 and 15 May. Panel (b) shows the differences between 12:00 and 24:00 EST on 14 May.
While it is best to properly project all data in the correct representation
used by the model, which in the case of WRF is a spherical GCS, it is most
important to keep the GCSs and projections among the input layers consistent.
In fact, if all layers are in the same GCS, errors in mapping onto the
surface of the earth are consistent across the datasets and the effects of
using the wrong GCS are minimized. However, mixing GCSs in the
input data leads to larger errors.
CH4 mixing ratios difference between HR and HR_RESHIFT in domain 3 for unconventional wells.
Panel (a) shows the differences between 00:00 and 12:00 EST on 14 and 15 May. Panel (b) shows the differences between 12:00 and 24:00 EST on 14 May.
WRF processing code is available at
https://github.com/yannicao/wrf_reprojection ().
WRF_preprocess.R
The signature for the function is as follows:
where the following is true:
filename.wrf is the input file that contains the original WRF input files.
filename.raster is the filename for the new data (e.g., MODIS LAI) file that is being used to replace the original WRF input.
WRF.layer is the layer name in the WRF input file. For example HGT represents the height, F and E the coriolis latitudinal and meridional components.
shift.to.sphere is boolean (TRUE or FALSE) and determines if the input raster is reprojected onto spherical coordinates from the original latitude–longitude WGS84.
write.shapefile is boolean (TRUE or FALSE) and determines if an ESRI Shapefile is generated.
cores specifies the number of cores for parallel processing.
WRF_UpdateNC.R
The WRF_UpdateNC.R file takes the generated Rdata files and updates them into
the original WRF input file.
The signature for the function is as follows:
where the following is true:
filename.data is the binary file generated from WRF.preprocess.
WRF.new is an object of class ncdf.
WRF.layer is what variable to write the data to. They could be HGT_M, LU_INDEX , F, E, LAI12M, and ALBEDO12M.
WRF.data.HR is the values to be written.
The authors declare that they have no conflict of interest.
Acknowledgements
This research was partially supported by the Department of Energy
(DE-FE0013590) and by the Office of Naval Research (ONR) award
no. N00014-16-1-2543 (PSU no. 171570). Edited by:
Alexander Archibald Reviewed by: two anonymous referees
ReferencesBarkley, Z. R., Lauvaux, T., Davis, K. J., Deng, A., Cao, Y., Sweeney, C.,
Martins, D., Miles, N. L., Richardson, S. J., Murphy, T., Cervone, G.,
Karion, A., Schwietzke, S., Smith, M., Kort, E. A., and Maasakkers, J. D.:
Quantifying methane emissions from natural gas production in northeastern
Pennsylvania, Atmos. Chem. Phys. Discuss.,
10.5194/acp-2017-200, in review, 2017.
Bugayevskiy, L. M. and Snyder, J.: Map projections: A reference manual, CRC
Press, Philadelphia, USA, 1995.
Chen, F. and Dudhia, J.: Coupling an advanced land surface-hydrology model with
the Penn State-NCAR MM5 modeling system. Part I: Model implementation and
sensitivity, Mon. Weather Rev., 129, 569–585, 2001.Cao, Y. and Cervone, G.: WRF processing, available at: https://github.com/yannicao/wrf_reprojection, last access:
22 July 2017.
David, C. H., Gochis, D. J., Maidment, D. R., Yu, W., Yates, D. N., and Yang,
Z.-L.: Using NHDPlus as the Land Base for the Noah-distributed Model,
Transactions in GIS, 13, 363–377, 2009.
Deng, A., Stauffer, D., Gaudet, B., Dudhia, J., Hacker, J., Bruyere, C., Wu,
W., Vandenberghe, F., Liu, Y., and Bourgeois, A.: Update on WRF-ARW
end-to-end multi-scale FDDA system, 10th Annual WRF User's Workshop, NCAR, 23, Boulder, CO, USA, 2009.
Dobesch, H., Dumolard, P., and Dyras, I.: Spatial interpolation for climate
data: the use of GIS in climatology and meteorology, John Wiley & Sons,
CA, USA,
2013.Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S.,
Kobrick, M., Paller, M., Rodriguez, E., Roth, L., and Seal, D.: The shuttle radar
topography mission, Rev. Geophys., 45, 10.1029/2005RG000183, 2007.Gesch, D. and Greenlee, S.: GTOPO30 documentation, US Department of the
Interior US Geological Survey, available at: https://lta.cr.usgs.gov/GTOPO30 (last access: 24 January 2012), 1996.
Hart, J. K. and Martinez, K.: Environmental Sensor Networks: A revolution in
the earth system science?, Earth-Sci. Rev., 78, 177–191, 2006.
Hedgley Jr., D. R.: An exact transformation from geocentric to geodetic
coordinates for nonzero altitudes, Technical Report R-458, NASA, Maryland, 1976.
Homer, C., Dewitz, J., Fry, J., Coan, M., Hossain, N., Larson, C., Herold, N.,
McKerrow, A., VanDriel, J. N., and Wickham, J.: Completion of the 2001
national land cover database for the counterminous United States,
Photogramm. Eng. Rem. S., 73, 337–341, 2007.Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A.,
and Collins, W. D.: Radiative forcing by long-lived greenhouse gases:
Calculations with the AER radiative transfer models, J. Geophys.
Res.-Atmos., 113, D13103, 10.1029/2008jd009944, 2008.
Kain, J. S.: The Kain–Fritsch Convective Parameterization: An Update, J.
Appl. Meteorol., 43, 170–181, 2004.
Kain, J. S. and Fritsch, J. M.: A one-dimensional entraining/detraining plume
model and its application in convective parameterization, J.
Atmos. Sci., 47, 2784–2802, 1990.
Latifovic, R., Homer, C., Ressl, R., Pouliot, D., Hossain, S., Colditz, R.,
Olthof, I., Giri, C., and Victoria, A.: North American land change monitoring
system, Remote sensing of land use and land cover: principles and
applications, CRC Press, Taylor & Francis Group, Boca Raton, FL, 2012.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.:
Radiative transfer for inhomogeneous atmospheres: RRTM, a validated
correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663–16682, 1997.
Monaghan, A. J., Barlage, M., Boehnert, J., Phillips, C. L., and Wilhelmi,
O. V.: Overlapping Interests: The Impact of Geographic Coordinate Assumptions
on Limited-Area Atmospheric Model Simulations, Mon. Weather Rev., 141,
2120–2127, 2013.
Nakanishi, M. and Niino, H.: An improved Mellor–Yamada level-3 model: Its
numerical stability and application to a regional prediction of advection
fog, Bound.-Lay. Meteorol., 119, 397–407, 2006.NASA: available at:
https://lpdaac.usgs.gov/tools/modis_reprojection_tool_swath, last
access: 22 March 2017.NASA LP DAAC: Leaf Area Index Fraction of Photosynthetically Active
Radiation 8 Day L4 Global 1 km. NASA EOSDIS Land Processes DAAC, USGS Earth
Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota
(https://lpdaac.usgs.gov), available at:
https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mcd15a2
(last access: 27 June 2016), 2015a.NASA LP DAAC: LAlbedo 16-Day L3 Global 500 m. NASA EOSDIS Land Processes
DAAC, USGS Earth Resources Observation and Science (EROS) Center, Sioux
Falls, South Dakota (https://lpdaac.usgs.gov), available at:
https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mcd43a3
(last access: 1 June 2016), 2015b.
Omara, M., Sullivan, M. R., Li, X., Subramanian, R., Robinson, A. L., and
Presto, A. A.: Methane Emissions from Conventional and Unconventional Natural
Gas Production Sites in the Marcellus Shale Basin, Environ. Sci.
Technol., 50, 2099–2107, 2016.
Refslund, J., Dellwik, E., Hahmann, A. N., and Boegh, E.: Updated vegetation
information in high resolution WRF simulations, Iahs-Aish P., 359,
233–238, 2013.
Rogers, R. E., Deng, A., Stauffer, D. R., Gaudet, B. J., Jia, Y., Soong, S.-T.,
and Tanrikulu, S.: Application of the Weather Research and Forecasting model
for air quality modeling in the San Francisco Bay area, J. Appl.
Meteorol. Clim., 52, 1953–1973, 2013.
Skamarock, W. C. and Klemp, J. B.: A time-split nonhydrostatic atmospheric
model for weather research and forecasting applications, J.
Comput. Phys., 227, 3465–3485, 2008.
Tewari, M., Chen, F., Wang, W., Dudhia, J., LeMone, M., Mitchell, K., Ek, M.,
Gayno, G., Wegiel, J., and Cuenca, R.: Implementation and verification of the
unified NOAH land surface model in the WRF model, 20th conference on weather analysis and forecasting/16th conference on numerical weather prediction, Seattle, Washington, 11–15, 2004.Thompson, G., Rasmussen, R. M., and Manning, K.: Explicit forecasts of winter
precipitation using an improved bulk microphysics scheme. Part I: Description
and sensitivity analysis, Mon. Weather Rev., 132, 519–542, 2004.
US EPA: Methane Emissions, available at:
https://www.epa.gov/ghgemissions/overview-greenhouse-gases, last access:
1 May 2015.
Watson, D. J.: Comparative physiological studies on the growth of field crops:
I. Variation in net assimilation rate and leaf area between species and
varieties, and within and between years, Ann. Bot., 11, 41–76, 1947.