AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-9-409-2016Development and validation of satellite-based estimates of surface
visibilityBrunnerJ.jason.brunner@ssec.wisc.eduPierceR. B.LenzenA.Cooperative Institute for Meteorological Satellite
Studies, University of Wisconsin-Madison, Madison, WI, USANational Oceanic and Atmospheric Administration
Center for Satellite Applications and Research, Madison, WI, USAJ. Brunner (jason.brunner@ssec.wisc.edu)11February20169240942225August201529October201520January201621January2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/9/409/2016/amt-9-409-2016.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/9/409/2016/amt-9-409-2016.pdf
A satellite-based surface visibility retrieval has been developed using
Moderate Resolution Imaging Spectroradiometer (MODIS) measurements as a
proxy for Advanced Baseline Imager (ABI) data from the next generation of
Geostationary Operational Environmental Satellites (GOES-R). The retrieval
uses a multiple linear regression approach to relate satellite aerosol
optical depth, fog/low cloud probability and thickness retrievals, and
meteorological variables from numerical weather prediction forecasts to
National Weather Service Automated Surface Observing System (ASOS) surface
visibility measurements. Validation using independent ASOS measurements
shows that the GOES-R ABI surface visibility retrieval (V) has an overall
success rate of 64.5 % for classifying clear (V≥ 30 km), moderate
(10 km ≤V < 30 km), low (2 km ≤V < 10 km), and
poor (V < 2 km) visibilities and shows the most skill during June
through September, when Heidke skill scores are between 0.2 and 0.4. We
demonstrate that the aerosol (clear-sky) component of the GOES-R ABI
visibility retrieval can be used to augment measurements from the United
States Environmental Protection Agency (EPA) and National Park Service (NPS)
Interagency Monitoring of Protected Visual Environments (IMPROVE) network
and provide useful information to the regional planning offices responsible
for developing mitigation strategies required under the EPA's Regional Haze
Rule, particularly during regional haze events associated with smoke from
wildfires.
Introduction
Visibility is the greatest horizontal distance at which selected objects can
be seen and identified. Fog droplets and haze particles are small enough to
scatter and absorb sunlight, leading to reduced visibility. Fog-related
reductions in visibility are a leading safety factor in determining aircraft
flight rules, pilot certification, and aircraft equipment required for taking
off or landing. In addition to these important safety considerations,
reduced visibility due to regional haze also obscures the view in our
nation's parks. Haze is caused when sunlight encounters particles in the
air. More particles mean more absorption and scattering of light, which
reduce visibility. These suspended particles include fine mode aerosols
such as smoke, sulfate, nitrate, and secondary organic aerosols, with
diameters of less than 2.5 microns, as well as coarse mode aerosols such as
dust, sea salt, and volcanic ash, with diameters of 10 microns and larger.
The Clean Air Act authorizes the United States Environmental Protection
Agency (EPA) to protect visibility, or visual air quality, through a number
of different programs. The EPA's Regional Haze Rule (EPA, 1999) calls for
state and federal agencies to work together to improve visibility in
national parks and wilderness areas such as the Grand Canyon, Yosemite, the
Great Smokies, and Shenandoah.
The first effort to characterize visibilities in the United States was by
Eldridge (1966), who used weather observer observations of daytime visible
range from US Weather Bureau and Air Force Air Weather Service stations
to construct distributions of climatic visibility during the period from
1948 to 1958. Maps of seasonal climatic visibilities, expressed as the
percentage of time with visibilities less then thresholds of 2.5, 5.0, 10,
20, and 40 km, showed localized regions over Southern California and the
Ohio River valley where visibilities were less than 5.0 km for 30–50 % of
the time, and less than 10 km for 50–80 % of the time, regardless of the
season. However, this analysis did not account for the presence of fog,
rain, or snow when constructing the maps of climatic visibilities.
This manuscript introduces a satellite-based visibility retrieval that has
been developed for the future National Oceanic and Atmospheric
Administration (NOAA) Advanced Baseline Imager (ABI) data from the next
generation of Geostationary Operational Environmental Satellites (GOES-R)
(Schmit et al., 2005). Following Gupta and Christopher (2009a, b), who used
satellite aerosol optical depth (AOD) to predict surface fine (less than 2.5
micron) particulate mass (PM2.5), we adapt a multiple linear regression
approach to estimate surface visibility. To develop and test the GOES-R ABI
retrieval we use Moderate Resolution Imaging Spectroradiometer (MODIS)
Collection 5.1 AOD retrievals (Remer et al., 2005) in conjunction with ABI
retrievals of cloud optical thickness (COT) (Walther and Heidinger, 2012)
and fog/low cloud probability and thickness (Gultepe et al., 2014) using
MODIS radiances, in addition to meteorological variables from numerical
weather prediction model forecasts, to estimate surface visibility.
This satellite-based estimate of surface visibility can be used to augment
measurements from the National Weather Service Automated Surface
Observing System (ASOS) and the EPA and National Park Service (NPS)
Interagency Monitoring of Protected Visual Environments (IMPROVE) network.
Hoff and Christopher (2009) present an overview of efforts to relate
satellite AOD retrievals to surface PM2.5. They concluded that the best AOD-based estimate of PM2.5 is likely to be no better than 30 % under ideal
conditions, largely due to variations in aerosol composition, boundary layer
structure, and the height of the aerosol layer. Since both AOD and
visibility are determined by aerosol extinction their relationship is not
influenced by variations in aerosol composition but still depends on
boundary layer structure and height of the aerosol layer. Previous efforts
to relate AOD to surface visibility have primarily focused on ground-based
AOD measurements. Peterson et al. (1981) compared 6 years of sun photometer
measurements of decadic turbidity at the EPA Research Triangle Park
Laboratory near Raleigh, NC, with observer-based estimates of visibility
from the Raleigh Durham airport. AOD is equal to decadic turbidity
multiplied by a factor of 2.3. Monthly correlation coefficients between
turbidity and visibility were large during the summer (-0.66 in June and
-0.70 in July) and small during the winter (-0.02 in January and -0.03 in
February). Kaufman and Fraser (1983) used correlations between sun
photometer measurements of AOD and nephelometer measurements of aerosol
volume scattering coefficients to assess the feasibility of using satellite-based AOD measurements to predict surface visibility (SV). They compared
inverse visibility (SV-1) measured at Baltimore, MD, and Dulles
airports with AOD measurements at Goddard Space Flight Center (GSFC) during
1980 and 1981. They found strong correlations between SV-1 at Baltimore
and Dulles in both 1980 and 1981 (0.96 and 0.91, respectively). They found
good correlations between GSFC AOD and SV-1 at Baltimore and Dulles
during 1980 (0.85 and 0.84, respectively) but only moderate correlations
during 1981 (0.51 and 0.58, respectively). Bäumer et al. (2008) used
AErosol RObotics NETwork (AERONET) AOD measurements to predict surface visibility near
Karlsruhe, Germany, during the 2005 AERO01 campaign. They found correlations
of 0.9 between measured and calculated visibilities. They also provide an
extensive overview of previous studies on the relationship between
visibility and aerosol properties.
This manuscript is arranged as follows. Sect. 2 presents an overview of
how satellite aerosol and cloud optical depth retrievals can be used to
estimate surface visibility and presents results of validation studies using
ASOS measurements. Sect. 3 discusses how the surface visibility retrieval
can be used to monitor regional haze events within Class I wilderness areas
in support of the EPA Regional Haze Rule. Sect. 4 provides results for
specific regional haze episodes associated with smoke from large wildfires. Sect. 5 presents
conclusions.
Background and method
Visibility is inversely proportional to extinction, which is a measure of
attenuation of the light passing through the atmosphere due to the
scattering and absorption by aerosol particles, molecular scattering, and gas
absorption. The visibility calculation is based on the Koschmieder (1924)
method, which is based on scattering and absorption of light by aerosol
particles in the air between the object that is being observed and the
observer, and is given as
V=-ln(ε)/(σ(λ)),
where V is the visibility (in km), σ(λ) is the
wavelength (λ) dependent extinction coefficient (km-1), and
ε is the threshold visual contrast which is usually taken to be
0.02 or 0.05. The GOES-R ABI visibility algorithm uses 0.05 since this is
recommended by the World Meteorological Organization (WMO) (Boudala and
Isaac, 2009; WMO, 2008). Taking the natural log of 0.05 results in
V=3.0/σ(λ).
The Koschmieder method was developed for observation along a horizontal
track in which the length can be considered infinite, and therefore Eq. (1b) forms the theoretical basis for the GOES-R ABI visibility algorithm
where AOD is of a vertical layer. The extinction coefficient (σ(λ)) relates the intensity (I(λ)) of light transmitted
through a layer of material with thickness (x) relative to the incident
intensity (I0(λ)) according to the inverse exponential power
law that is usually referred to as the Beer–Lambert Law:
I=I0e-σ(λ)x.
Optical depth (τ(λ)) is defined as σ(λ)x.
Expressing visibility in terms of τ gives
V=3.0/(τ(λ)/x),
where we have implicitly assumed that the extinction coefficient is constant
over the thickness (x). Visibility most often refers to horizontal
visibility when it is based on an observer. However, it is measured or
inferred using local extinction. If the extinction is locally both
horizontally and vertically homogeneous then the vertical extinction is
representative of the horizontal extinction. Equation (3) is used for the
GOES-R ABI visibility algorithm in order to determine the visibility in the
surface layer, and it shows that visibility is inversely proportional to
optical depth divided by the thickness of the material layer where the
aerosol resides. This is similar to the formulation used by Bäumer et
al. (2008) except they assumed a threshold visual contrast of 0.02
resulting in a coefficient of 3.912 instead of 3.0. From Eq. (3), the GOES-R
ABI visibility algorithm uses AOD at 550 µm to estimate τ under
clear-sky conditions and uses retrieved COT to estimate τ under cloudy
conditions when fog or low clouds have been detected. The GOES-R ABI
visibility algorithm assumes that the aerosols reside within the planetary
boundary layer (PBL) and uses the National Centers for Environmental
Prediction (NCEP) Global Forecasting System (GFS) PBL depth to estimate x
under clear-sky conditions and uses retrieved fog and low cloud depth to
estimate x when fog or low clouds have been detected. If aerosols exist
above the PBL, the visibility at the surface will be underestimated in the
satellite-retrieved visibility. If the PBL is stable and the aerosols are
not well mixed within the PBL, which may occur during the morning, then the
visibility at the surface could be overestimated in the satellite-retrieved
visibility. We could assume an exponential profile of extinction under
stable PBL conditions but this has not been implemented in the current
version of the algorithm. ABI measurement requirements are determined by the
GOES-R Series Ground Segment Functional and Performance Specification (NOAA, 2015), which requires that the visibility algorithm can
distinguish between four visibility categories: clear (V≥ 30 km),
moderate (10 km ≤V < 30 km), low (2 km ≤V < 10 km), and poor (V < 2 km).
Validation of the GOES-R ABI aerosol (clear-sky) visibility retrieval based
on Eq. (3) using MODIS Collection 5.1 AOD and a total of 155 077 coincident
ASOS measurements during 2007–2008 shows that Eq. (3) tends to overestimate
the frequency of poor and low visibility categories resulting in a 55 %
categorical success rate (CSR) for AOD-based visibility estimates. The ASOS
data must be within a 5 km radius of the MODIS retrieval and within a minute
of the MODIS overpass time to be collocated. CSR is defined as the
percentage of ASOS/MODIS measurement pairs that were assigned to the same
visibility category. This overestimate of low and poor visibility relative
to ASOS could be associated with an increase in relative humidity (RH) at
the top of the PBL under stable conditions. Increased RH leads to increased
aerosol extinction due to hygroscopic growth of hydrophilic aerosols and
overestimates in the frequency of low and poor visibility relative to ASOS
since it measures surface visibility. For a more in-depth discussion of the
use of relative and specific humidity gradients to determine boundary layer
depths see Seidel et al. (2010). Validation of the GOES-R ABI fog and low
cloud visibility retrieval based on Eq. (3) was performed using a total of
10 468 ASOS coincident pairs during 2007–2008. MODIS radiances were used as
proxy data to generate the ABI COT and fog/low cloud probability retrievals.
A 50 % probability of fog or low clouds was used as a threshold for
identification of fog and low cloud coincidences. Results show that all of
the ABI fog and low cloud visibility retrievals fall within the low and poor
visibility categories while more than 50 % of the ASOS surface
measurements report clear or moderate visibility resulting in a 5.0 % CSR
for 2007–2008 ASOS coincident pairs. This overestimate is likely to be
associated with an increase in RH at the top of the PBL under stable
conditions. Low clouds are more likely to form near the top of the PBL and
may not reach the surface where ASOS would observe fog.
Aerosol multiple regression coefficients for bias, first guess
aerosol visibility (visaodfg), aerosol optical depth (aod), relative
humidity at top of the PBL (rhpbltop), 2 m relative humidity (rh2m),
mean PBL relative humidity (rhpbl), PBL lapse rate (pbllapse), PBL height
(pblhght), 2 m temperature (t2m), temperature at the top of the PBL
(tpbltop), and PBL height plus surface height (pblhght + zsfc) predictors.
Regression coefficients for aerosol visibility meteorological predictors Monthbiasvisaodfgaodrhpbltoprh2mrhpblJan65.38790.002681-32.79910.0597260.337285-0.37413Feb110.0730.001269-28.20570.0644630.348682-0.4631Mar158.9920.000747-21.1998-0.043790.196516-0.19396Apr164.3440.000582-17.95880.0058750.0857-0.14395May248.6790.000475-22.074-0.03625-0.10290.052229Jun213.2820.006177-18.939-0.17207-0.132450.238551Jul191.8740.00188-20.8431-0.16686-0.258390.341579Aug342.0330.002103-16.2641-0.12326-0.315240.267925Sep320.9410.002513-28.4085-0.04582-0.347770.237827Oct205.1620.00042-34.4769-0.02746-0.01746-0.01722Nov110.9730.001301-50.28030.054206-0.04584-0.1145Dec86.45920.001137-28.4511-0.01690.39957-0.38517Monthpbllapsepblhghtt2mtpbltoppblhght + zsfcJan1.35551-0.0022-0.387650.2643230.005734Feb0.817403-0.001710.025732-0.294790.003831Mar0.543207-0.00048-0.1832-0.256840.002566Apr0.427875-0.00571-0.29303-0.116430.001656May0.914754-0.00781-0.883910.2097030.001608Jun1.26467-0.00018-0.771870.1535940.002156Jul0.795759-0.00329-0.685730.1727930.001889Aug1.03763-0.00297-1.169730.1697420.000507Sep0.306594-0.00268-0.928850.001714-0.00022Oct0.291529-0.00958-0.27099-0.245010.00121Nov0.812836-0.0069-0.459030.2356320.004573Dec0.640483-0.007960.059203-0.205310.00422
To improve the categorical skill with respect to ASOS measurements we
adapted a “blended” retrieval approach. The blended visibility retrieval
is constructed using a weighted combination of a “first guess” visibility
estimate from Eq. (3) and a multiple linear regression visibility estimate
that includes additional meteorological predictors for both aerosol and
fog/low cloud visibilities. These additional meteorological predictors are
included to account for the fact that the aerosol extinction is generally
not uniform over the depth of the PBL as assumed in Eq. (3) and each
regression term accounts for potential variability of the aerosol extinction
profile through the PBL. The aerosol multiple regression includes a bias
adjustment, the first guess aerosol visibility, AOD, RH at the top of the
PBL, 2 m RH, mean PBL RH, PBL lapse rate, PBL height, 2 m
temperature, temperature at the top of the PBL, and PBL height plus surface
height as predictors. The fog/low cloud multiple regression includes a bias
adjustment, the first guess fog visibility, COT, RH at the top of the PBL, 2 m RH,
mean PBL RH, PBL lapse rate, PBL height, 2 m temperature,
temperature at the top of the PBL, PBL height plus surface height, and
fog/low cloud probability predictors. Multiple linear regression between the
ASOS visibility and the 10 aerosol visibility predictors was performed to
determine regression coefficients for best estimate of ASOS visibility for
each month using historical (2007–2008) ASOS/MODIS coincident pairs. This is
referred to as “multiple regression” aerosol visibility. Tables 1 and 2
summarize the regression coefficients used for the aerosol and fog/low cloud
visibility meteorological predictors, respectively. Optimal weighting
between the first guess and multiple regression visibility estimates for
aerosol and fog/low cloud visibility is determined based on assessment of
required categorical accuracy (percent correct classification), required
precision (standard deviation of categorical error), Heidke skill score
(Brier and Allen, 1952), which measures the fractional improvement relative
to chance, and false alarm rate (Olson, 1962). Results of Heidke skill score
and false alarm rate tests show that an 80 % multiple regression weighting
resulted in the largest improvement relative to chance for both clear and
moderate aerosol visibility and reduces false detections for low aerosol
visibility. The CSR for the blended aerosol visibility retrieval was 69 %
for the 2007–2008 ASOS coincident pairs, which is a significant improvement
over the first guess retrieval based on Eq. (3). Based on these tests, the
ABI aerosol visibility blended retrieval uses a 20/80 % weighting of the
first guess and multiple regression aerosol visibility estimates. Results of
Heidke skill score and false alarm rate tests show that a 70 % multiple
regression weighting resulted in the largest improvement relative to chance
for both moderate and low visibilities and minimizes false detections for
clear visibilities for the fog and low cloud cases. The CSR of the blended
fog and low cloud visibility estimates is 47 % for 2007–2008 ASOS
coincident pairs. Based on these tests, the ABI fog/low cloud visibility
blended retrieval uses a 30/70 % weighting of the first guess and multiple
regression fog/low cloud visibility estimates. The combination of blended
aerosol and blended fog/low cloud visibility estimates is used for the
GOES-R ABI visibility retrieval.
Fog/low cloud multiple regression coefficients for bias, first
guess fog visibility (viscotfg), cloud optical thickness (cot), relative
humidity at top of the PBL (rhpbltop), 2 m relative humidity (rh2m),
mean PBL relative humidity (rhpbl), PBL lapse rate (pbllapse), PBL height
(pblhght), 2 m temperature (t2m), temperature at the top of the PBL
(tpbltop), PBL height plus surface height (pblhght + zsfc), and fog/low
cloud probability (fogprob) predictors.
Categorical histograms of the coincident ASOS and ABI merged
visibilities for January 2010 through December 2013. LCLD denotes low cloud and SDQF denotes standard deviation quality flag.
Monthly mean time series of the ASOS validation statistics for the
version 5 ABI visibility algorithm from January 2010 through December 2013.
GOES-R ABI visibility retrievals from all MODIS Terra and Aqua overpasses
over the continental United States have been validated against ASOS
visibility measurements during January 2010–December 2013. Figure 1 shows
categorical histograms of the coincident ASOS and GOES-R ABI merged
visibilities during 2010–2013. The majority (59.9 %) of the ASOS
observations fall under the clear visibility category. The GOES-R ABI
visibility retrieval results in a 64.5 % CSR for 122 461 ASOS/MODIS
measurement pairs during January 2010–December 2013. The GOES-R ABI
visibility retrieval capture the frequency of ASOS visibility relatively
well but tends to overestimate the frequency of clear visibility and
underestimate the frequency of moderate, low, and poor visibility during this
time period. These results are consistent with those obtained from the
2007–2008 ASOS coincidences used to generate the multiple regression
coefficients.
RH sensitivity studies for May and June 2010 were conducted to explore the
sensitivity of CSR to (1) 2 m RH, (2) mean PBL RH, and (3) RH at the top
of the PBL. Each of these RH variables range from greater than 95 % to
less than 10 % with medians of 43 % (2 m RH), 53 % (mean PBL RH),
and 56 % (RH at the top of the PBL). The full set of May–June 2010
coincidences (11 699) show a CSR of 66.8 %. Comparisons were conducted for
six subsets of the full data with 2 m RH, mean PBL RH, and RH at the top
of the PBL for greater than or equal to 50 % RH and for less than 50 %
RH. The results for the mean PBL RH showed the strongest sensitivity with a
CSR of 63.8 % for RH greater than or equal to 50 % (6966 or 59.5 % of
the coincidences) and a CSR of 71.2 % for RH less than 50 % (4733 or
40.5 % of the coincidences). This confirms that the visibility retrieval
performs best under low RH conditions.
Figure 2 shows a monthly mean time series of the ASOS validation statistics
for the GOES-R ABI visibility algorithm from January 2010 through December
2013. Heidke skill score values (red line) between 0.2 and 0.4 are
considered “good” skill, values between 0.15 and 0.25 are considered
“medium” skill, and values less than 0.15 are deemed “use with caution”.
Hyvarinen (2014) and Murphy (1996) showed that the Finley tornado forecasts
from 1884 had a Heidke skill score value of 0.355 and deemed these forecasts
as being acceptable for having skill. Therefore, a Heidke skill score value
of approximately 0.3 is acceptable for defining “good” skill in our study.
The “good” skill scores generally tend to occur from June through
September (green shading), “medium” skill scores occur from January
through March (yellow shading), and “use with caution” skill scores occur
in April and May and from October through December (red shading). The CSR
values (blue line) ranges from 58 to 69 % and generally shows higher
values from April through November and lower values from December through
March. The false alarm rate values (dashed black line) range from 0.24 to
0.41 with the lowest values generally from January through March and in
June. Overall, the GOES-R ABI visibility algorithm performs the best from
June through September.
Monthly frequency of land-only bins that had a percentage
frequency of at least 50 % of aerosol visibility values ≥ 20 dV and of at least 180 retrieval counts (red line plot) and monthly frequency
of WF-ABBA detected fires (blue line plot) by month in the United States
(24–52∘ N latitude and 65–130∘ W longitude) for January
2010 through December 2013.
Monitoring regional haze with the GOES-R ABI visibility retrieval
The EPA Regional Haze Rule (EPA, 1999) requires states, in coordination with
the US
EPA, NPS, Fish and Wildlife Service, and Forest Service,
to develop and implement air quality protection plans to reduce pollution
that causes visibility impairment in Class I wilderness areas. The aerosol
component of the GOES-R ABI visibility retrieval provides a means of
monitoring aerosol visibility on a daily basis across the United States to
support state and tribal implementation of the Regional Haze Rule. Within
the ruling, the EPA proposed that visibility targets and tracking of
visibility changes over time be expressed in terms of the “deciview” haze
index. The deciview haze index (dV), Eq. (4), was developed by Pitchford and
Malm (1994) for use in presenting data for the light-extinction coefficient
(bext), which is the inverse of σ(λ) expressed in
inverse mega-meters (Mm-1) of ambient air. Pitchford and Malm state
that the dV is the preferred metric for presenting these data because it is
more linearly related to the human perception of regional haze and is the
most common measure of visibility for air quality studies (Richards, 1999).
The EPA ruling tracks visibility trends based on 5-year averages of annual
deciview values for the most impaired (upper 20 %) and least impaired
(lower 20 %) days relative to “natural” visibility conditions for
Class I areas. The National Acid Precipitation Assessment Program (NAPAP)
used annual averaged speciated aerosol concentrations, extinction
efficiencies, and relative humidity to estimate natural visibility
conditions of ∼ 10 dV in the eastern USA and ∼ 5 dV in the western USA (Irving, 1992). The higher natural visibility
conditions in the eastern USA arise due to regional sources of biogenic
secondary organic aerosols and increased relative humidity compared to the
western USA. The EPA ruling acknowledges that determination of “natural”
visibility includes a number of issues, in particular, the contribution of
wildfires to natural visibility variations.
dV=10lne(bext/10Mm-1)
Assuming a PBL depth of 1 km and a MODIS AOD precision of 0.05 (Remer et
al., 2005) corresponds to a bext of 50 Mm-1 in Eq. (4) and results
in an estimated 16 dV limit of detection for the GOES-R ABI visibility
retrieval, which is above natural visibility levels for both the western and
eastern USA established by the Regional Haze Rule. This estimated dV precision shows that the GOES-R ABI visibility retrieval is best suited for
quantifying periods of reduced visibility and not background conditions. A
time series of the frequency of occurrence of reduced visibility (assumed to
be ≥ 20 dV) over the continental United States for January 2010 through
December 2013 is shown in Fig. 3 as a red line plot. dV≥ 20 roughly
corresponds to the poor + low + moderate visibility classes shown in Fig. 1.
To construct this time series we compute the monthly frequency of reduced
visibility for land-only bins (0.5 × 0.5 ∘ latitude/longitude) over
the United States (24–52∘ N latitude and 65–130∘ W
longitude) that had at least 180 valid GOES-R ABI aerosol visibility
retrievals per bin with at least 50 % of aerosol visibility values
≥ 20 dV within the bin for each month. A threshold of 180 monthly
aerosol retrievals was used to ensure a sufficient sample size so the
monthly mean dV values would be representative. The 180 monthly aerosol
retrievals are approximately 25 % of the maximum monthly number of
aerosol retrievals possible in a bin. The frequency of reduced visibility
(≥ 20 dV) shows both seasonal and interannual variability. Reduced
visibility occurs most frequently from June through September with a
secondary peak during the January through March time period. The June
through September maximum in reduced visibility is also when the visibility
product performs at its best in terms of skill. The periods with low
frequencies of reduced visibility correspond to the time periods where the
skill in the retrieval is low and should be used with caution.
To explore the relationship between the frequency of reduced visibility and
wildfires we construct monthly maps of fire detection frequency from January
2010 through December 2013 within 0.25 × 0.25∘ bins over the continental
United States using GOES
East fire detections from Version 6.5 of the Wildfire Automated Biomass
Burning Algorithm (WF-ABBA) (Prins and Menzel, 1992, 1994). The WF-ABBA is a
dynamic multispectral thresholding contextual algorithm that uses the
visible (when available), 3.9 micron, and 10.7 micron infrared window bands
to locate and characterize hot spot pixels (Schmidt et al., 2013). The
algorithm is based on the sensitivity of the 3.9 micron band to high
temperature subpixel anomalies and is derived from a technique originally
developed by Matson and Dozier (1981) for NOAA Advanced Very High Resolution Radiometer (AVHRR) data.
The WF-ABBA incorporates statistical techniques to automatically identify
hot spot pixels in the GOES imagery. Once the WF-ABBA locates a hot spot
pixel, it incorporates ancillary data in the process of screening for false
alarms and correcting for water vapor attenuation, surface emissivity, solar
reflectivity, and semi-transparent clouds. In addition, an opaque cloud mask
is used to indicate regions where fire detection is not possible and
meta-data are provided about the processing region and block-out zones due to
solar reflectance, clouds, extreme view angles, saturation, and biome type.
There are six WF-ABBA fire detection categories; processed, saturated,
cloudy, high probability, medium probability, and low probability. The low
probability category is often indicative of false alarms in North America
and along cloud edges and at high viewing angles at sunrise and sunset.
Therefore, the low probability fire pixels are not included in the fire
detection analysis in this study. Time series of fire frequency are
calculated by summing up the fire counts within all 0.25 × 0.25∘ bins
for each month for 2010–2013 over the continental United States.
Determining the accuracy of fire detection is challenging and ultimately
requires very high resolution information and excellent geolocation (Schmidt
et al., 2013). The accuracy of WF-ABBA data can be determined though by
comparing against MODIS fire data. Hoffman (2006) found that approximately
62.8 % of the GOES filtered fire pixels over the western hemisphere (when
low probability fire pixels are excluded) have a MODIS match in 2004
(59.7 % in 2005). In addition, Reid et al. (2009) found that because many
fires only burn actively during a fraction of the day, the WF-ABBA with its
superior temporal sampling detects twice as many fires overall in South and
North America compared to MODIS. However, the superior spatial resolution
and radiometric precision of MODIS, detects 6–10 times as many fires in each
overpass compared to WF-ABBA (Reid et al., 2009).
Monthly best-fit slope and best-fit intercept for IMPROVE
regression (bias correction) and monthly R2, mean bias and
RMSE for ABI retrieval, and monthly bias-corrected mean bias and bias-corrected RMSE for 2010–2012.
Top panel: histograms of collocated dV values for IMPROVE
regression (monthly bias-corrected) retrieval (green), IMPROVE (blue), and
ABI Retrieval (red) for June–September 2010–2012; bottom panel: density plot
of collocated dV values for IMPROVE regression (monthly bias-corrected)
retrieval versus IMPROVE for June–September 2010–2012.
The monthly frequency of WF-ABBA detected fires in the United States has
both seasonal and interannual variation (Fig. 3 blue line plot). The highest
monthly frequency of fires occurs in general from May to September, which
coincides with the highest monthly frequency of decreased aerosol visibility
(≥ 20 dV). In particular, 2011 and 2012 had an overall higher monthly
frequency of fires compared to 2010 and 2013 for the May–September time
period, suggesting a link between increased fire frequency and reduced
visibility during these time periods. The overall correlation between
monthly number of bins with aerosol visibility ≥ 20 dV and monthly
WF-ABBA fire frequency for 2010–2013 is 0.621 (r2= 0.368). The highest
monthly fire frequency occurred in April and June 2011 and August 2012. The
GOES-R ABI visibility algorithm performs the best in the June–September time
period based on Heidke skill score results, so June 2011 and August 2012 are
examined in more detail later in this study.
To support implementation of the Regional Haze Rule, the EPA funded
deployment of a PM2.5 monitoring network and expansion of the IMPROVE
network. The IMPROVE program has been collecting data since 1988 and
continues to collect and analyze visibility data from Class I federal area
monitoring sites throughout the United States. IMPROVE data for 2010–2012
are
used to assess how well the GOES-R ABI visibility retrieval performs in
characterizing visibility within Class I areas. The IMPROVE and GOES-R ABI
retrievals are collocated in time (same day) and space (within ±0.25∘)
and monthly mean IMPROVE and GOES-R ABI dV values are calculated
for each IMPROVE site. Correlations, mean biases, and root-mean-square error
(RMSE) for IMPROVE versus the GOES-R ABI aerosol visibility retrieval are
calculated from this collocated data for the 3-year period (2010–2012)
for each month and are shown in Table 3. The largest correlations are near
0.63 (r2 of 0.4018 and 0.4078) and occur in June and July,
respectively. There is a distinct bias toward lower monthly mean dV values
for IMPROVE compared to the GOES-R ABI retrieval for all months. This is
mainly because of the GOES-R ABI retrieval limit of detection of
approximately 16 dV due to the precision of the MODIS aerosol optical depth
retrieval.
Due to this bias toward higher monthly mean dV values compared to IMPROVE
data, a monthly regression (including bias correction) needs to be applied
to the GOES-R ABI aerosol visibility retrieval to more accurately detect
visibility values measured from ground-based IMPROVE sites. Table 3 also
shows the monthly best-fit slope, best-fit intercept, bias-corrected mean
bias, and bias-corrected RMSE. After applying the monthly regression
coefficients, the bias with respect to IMPROVE measurements is removed and
the monthly bias-corrected RMSE values are reduced with the lowest values
during the April–October time period. Since the GOES-R ABI retrieval
performs at its best during the June–September time period based on Heidke
skill score results and since the IMPROVE versus bias-corrected GOES-R ABI
aerosol visibility retrieval results show highest correlations and lowest
RMSE values during this time period, we will focus for the remainder of this
study on the June–September time period.
Top left panel: IMPROVE mean observed visibility (dV) in the
United States for June 2011; top right panel: WF-ABBA fire frequency in the
United States for June 2011; bottom left panel: IMPROVE regression (bias-corrected) retrieval mean dV in the United States for June 2011; bottom
right panel: scatter plot of collocated mean dV IMPROVE regression (bias-corrected) retrieval versus IMPROVE for all IMPROVE sites for June 2011.
Top left panel: IMPROVE mean observed visibility (dV) in the
United States for August 2012; top right panel: WF-ABBA fire frequency in
the United States for August 2012; bottom left panel: IMPROVE regression
(bias-corrected) retrieval mean dV in the United States for August 2012;
bottom right panel: scatter plot of collocated mean dV IMPROVE regression
(bias-corrected) retrieval versus IMPROVE for all IMPROVE sites for August
2012.
Top left panel: time series plot for 2011 of Bandelier National
Monument in New Mexico of daily mean dV for IMPROVE (black line) and IMPROVE
regression (bias-corrected) ABI retrieval (triangle symbol is daily mean dV with standard deviation line);
top right panel: same as top panel but for
time series plot for 2011 of Cape Romain National Wildlife Refuge in South
Carolina; bottom left panel: same as top panel but for time series plot for
2012 of Craters of the Moon National Monument in Idaho; bottom right panel:
same as top panel but for time series plot for 2012 of Cape Romain National
Wildlife Refuge in South Carolina.
Histograms of collocated dV values for IMPROVE (blue), GOES-R ABI aerosol
visibility retrieval (red), and bias-corrected GOES-R ABI aerosol visibility
retrieval (green), for June–September 2010–2012 are shown in Fig. 4 top
panel. The GOES-R ABI aerosol visibility retrieval peaks around 20 dV and
most values exceed 16 dV because of the MODIS limit of detection. Applying
the IMPROVE-based monthly regression to the GOES-R ABI aerosol visibility
retrieval shifts the peak to 13–14 dV and decreases the magnitude of the
peak slightly. The IMPROVE peak occurs at 8–9 dV shows a more log-normal
histogram with a much wider tail compared to the histograms of the GOES-R
ABI aerosol visibility retrieval. Figure 4 bottom panel shows a density plot
of collocated dV values for the GOES-R ABI aerosol visibility retrieval with
the monthly regression applied versus the IMPROVE measurements for
June–September 2010–2012. The density plot shows that the IMPROVE dV measurements have more variability than the adjusted GOES-R ABI aerosol
visibility retrieval, which are now mostly less than 20 dV.
Errors in the estimated PBL depth are one of the largest uncertainties in
the visibility estimate. To examine the sensitivity of the bias-corrected
GOES-R ABI aerosol visibility retrieval to errors in PBL depth we first need
to characterize the PBL depth errors and then perform sensitivity
experiments to assess the impact of these errors. Verification was performed
using CALIPSO (Winker et al., 2003, 2009) PBL depth
retrievals. The CALIPSO PBL depths are derived using a Haar wavelet analysis
to detect boundaries in scattering ratio (i.e., a normalized backscatter) in
lidar observations. The CALIPSO PBL depth is defined as the altitude where
the maximum amplitude average wavelet occurs computed over a range of Haar
filter widths ranging from 0.9 to 1.65 km (R. E. Kuehn, personal
communication, 2013). Comparison between the GFS and CALIPSO PBL depths over the
continental USA during the period from June–September, 2012, showed that the
GFS PBL depth was biased low by 533 m over land with RMSEs of 659 m (mean bias removed). The mean retrieved PBL depth over land
was 1982 m so the GFS bias is approximately 28 % of the mean during this
period. To quantify the impact of these PBL biases on the visibility
estimates we conducted sensitivity studies assuming uniform ±500 m
errors in continental US PBL depths over land + water during the period
from 11 to 17 August 2012. Comparisons between the control and sensitivity
visibility calculations showed that adding 500 m to the PBL depth resulted in
a 0.91 dV decrease in visibility while subtracting 500 m to the PBL depth
resulted in a 1.65 dV increase in visibility on average during this period.
RMSE differences (mean bias removed) between the control and sensitivity
calculations were 0.84 and 1.82 dV for +500 and -500m PBL errors,
respectively. The mean visibility during this time period was 15.68 dV, so
visibility biases due to PBL depth errors range from 5 to -10 % while
visibility uncertainties due to PBL RMSEs range from 5 to
12 %.
Results
June 2011 shows a significant increase in the IMPROVE mean observed dV measurements over an
extensive region of the central and eastern USA (Fig. 5
top left panel). Monthly mean dV values are in the 20–25 dV range especially
over the mid-Mississippi Valley, Ohio Valley, southeastern and mid-Atlantic
regions. Much lower monthly mean dV values are over the IMPROVE sites
throughout the western USA (5–10), Great Lakes (10–15), and northeastern region
(10–15). Figure 5 top right panel shows the WF-ABBA fire frequency in the
United States for June 2011. WF-ABBA fire detection was binned in 0.25 × 0.25∘ latitude/longitude bins. There are major fires over the
southwest USA particularly in eastern Arizona, New Mexico, southeastern
Colorado, west-central Texas, and north-central Mexico during this time
period. Smoke from these fires, along with fires over the lower Mississippi
Valley, results in increased dV values over the central and eastern USA. In
addition, increased fire frequency over southern Georgia, northern Florida,
and eastern North Carolina leads to increased dV over the eastern USA. Figure 5 bottom left panel shows the GOES-R ABI aerosol visibility retrieval
mean dV with the IMPROVE regression applied for June 2011. Overall,
increased mean dV values are found over the central and eastern USA,
consistent with the IMPROVE sites, but with slightly lower (often by 3–5 dV)
values than the IMPROVE measurements. Lower mean dV values are found over
the western, USA which is also consistent with the IMPROVE sites. There were
very few bins with sufficient retrievals over the Great Lakes and
northeastern
region due to persistent clouds so it is difficult to compare with IMPROVE
in those locations. Figure 5 bottom right panel shows a scatter plot of
collocated mean dV for the GOES-R ABI aerosol visibility retrieval with the
IMPROVE regression applied versus IMPROVE measurements during June 2011. The
GOES-R ABI retrieval was required to be within 0.25 × 0.25∘
latitude/longitude of the associated IMPROVE site and occur on the same day
for coincidence. June 2011 had the highest correlation (0.74,
r2= 0.5494) for any of the months for 2010–2012. The RMSE value was
3.8633 dV with mean biases of -0.4796 dV.
The IMPROVE network shows high (20–25) dV measurements over central and
southern Idaho and moderately high (17–20) dV values over extreme
northeastern California and southern Montana in August 2012 (Fig. 6 top left
panel). In addition, moderately high (17–20) IMPROVE dV measurements occur
over parts of the Tennessee Valley and mid-Atlantic regions. In contrast,
lower IMPROVE dV are found over the southwestern USA (5–10) and over the Great
Lakes and northeastern USA (10–15). Figure 6 top right panel shows the WF-ABBA
fire frequency in the United States for August 2012. Widespread major fires
are found over the northwestern USA particularly in central and southern Idaho,
southeastern Oregon, and northeastern California. Smoke from these fires
results in increased dV from northeastern California to southern Montana. In
addition, moderate fire frequencies over the lower Mississippi Valley
contribute to the moderately high (17–20) IMPROVE dV seen over the Tennessee
Valley. Figure 6 bottom left panel shows the GOES-R ABI aerosol visibility
retrieval with the IMPROVE regression applied for August 2012. Moderately
high (17–20) dV is retrieved over southeastern Oregon and southern Idaho.
These values are slightly lower than the IMPROVE measurements and are
shifted to the south. No IMPROVE sites were available in southeastern Oregon
for comparison. Over the Tennessee Valley, the bias GOES-R ABI retrieval
slightly underestimates the mean dV values compared to the IMPROVE
measurements. Figure 6 bottom right panel shows a scatter plot of collocated
bias-corrected GOES-R ABI aerosol visibility retrieval versus IMPROVE
measurements for all IMPROVE sites for August 2012. August 2012 had a
correlation value of 0.637 (r2= 0.4059) with RMSE values of 3.6509 dV and the mean biases of 0.1009 dV.
Figure 7 top left panel shows a time series plot of 2011 daily mean dV for
IMPROVE (black line) and the GOES-R ABI aerosol visibility retrieval with
the IMPROVE regression applied (triangle symbol is daily mean dV with
standard deviation line) at Bandelier National Monument in New Mexico. Green
indicates “good” skill (June–September), yellow is “medium” skill
(January–March), and red periods should be used with caution (April–May and
October–December). There are two prominent peaks in the IMPROVE daily mean
dV measurements. One peak occurs in early June 2011 while a second peak
occurs in early July 2011. Both of these peaks are captured in the GOES-R
ABI aerosol visibility retrieval but the magnitude of the June 2011
retrieved peak is substantially less than IMPROVE measurements. The
magnitude of the July 2011 retrieved peak is very similar to the IMPROVE
peak. These enhanced peaks occur because of decreased aerosol visibility due
to smoke from major fires over eastern Arizona in June 2011 and from major
fires over northern New Mexico in July 2011. In August and September 2011,
the GOES-R ABI retrieval tends to overestimate the daily mean dV by around 5
dV compared to IMPROVE.
Figure 7 top right panel shows a time series plot for 2011 of daily mean dV for IMPROVE and GOES-R ABI aerosol visibility retrieval with the IMPROVE
regression applied at the Cape Romain National Wildlife Refuge in South
Carolina. A prominent peak in the daily mean dV occurs in both the IMPROVE
and GOES-R ABI retrieval in late June 2011. This enhanced peak occurs
because of decreased aerosol visibility due to smoke from major fires over
southern Georgia and northern Florida during this time period. In addition,
throughout June–August 2011 the bias-corrected retrieval of daily mean dV seems to capture the trends in the IMPROVE data fairly well.
Figure 7 bottom left panel shows a time series plot of daily mean dV for
IMPROVE and GOES-R ABI aerosol visibility retrieval with the IMPROVE
regression applied for 2012 of Craters of the Moon National Monument in
Idaho. There are two prominent peaks in the daily mean dV that occur in the
IMPROVE data. One peak occurs in mid to late-August 2012 while a second peak
occurs in mid-September 2012. Both of these peaks are also captured in the
GOES-R ABI retrieval but the magnitude of both peaks is substantially less
compared to the IMPROVE peaks. These enhanced peaks occur because of
decreased aerosol visibility due to smoke from major fires over southeastern
Oregon and southern/central Idaho in August 2012 and from major fires over
central Idaho in September 2012. In June and July 2012, the retrieval tends
to overestimate the daily mean dV by around 5 dV compared to IMPROVE.
Figure 7 bottom right panel shows a time series plot of daily mean dV for
IMPROVE and GOES-R ABI aerosol visibility retrieval with the IMPROVE
regression applied for 2012 of Cape Romain National Wildlife Refuge in South
Carolina. Overall, for June–September 2012, the GOES-R ABI retrieval does a
very good job with the trends and magnitudes for daily mean dV compared to
IMPROVE. There are no prominent peaks in the daily mean dV data for both
IMPROVE and the GOES-R ABI retrieval and the peaks for June–September 2012
are at a substantially lower dV value (higher aerosol visibility value)
compared to the peak for June 2011 at Cape Romain. These trends make sense
because there was no major fires (and very low fire frequency) during the
June–September 2012 time period over the southeastern USA compared to June 2011
when there were major fires (and high fire frequency) over southern Georgia
and northern Florida.
Conclusions
A satellite-based surface visibility retrieval has been developed for the
GOES-R ABI instrument using MODIS proxy data and validated using independent
ASOS surface visibility measurements. The GOES-R ABI surface visibility
retrieval has an overall success rate of 64.5 % for classifying clear (V≥ 30 km),
moderate (10 km ≤V < 30 km), low
(2 km ≤V < 10 km),
and poor (V < 2 km) visibilities during January
2010–December 2013, and shows the most skill during June through September,
when Heidke skill scores are between 0.2 and 0.4. Variability in the
frequency of clear-sky (aerosol) surface visibility retrievals larger than
20 dV is shown to be correlated with seasonal and interannual variability in
fire detections, illustrating the importance of smoke from wildfires in
regional haze events. Comparison with visibility measurements from the
IMPROVE network during periods of significant wildfire activity requires
additional bias corrections due to the relatively high (∼ 16 dV) limit of detection of the GOES-R ABI retrieval when expressed in
deciviews, but it shows that the GOES-R ABI aerosol visibility retrieval is
able to capture reductions in visibility due to wildfire smoke and can be
used to augment measurements from the IMPROVE network. Quantitative
evaluation of the errors in the GFS PBL, which is one of the largest
uncertainties in the visibility estimate, shows that the GFS PBL estimates
are systematically low by ∼ 500 m (28 %) with RMSEs of
659 m (mean bias removed) over the continental USA during June–September 2012.
August 2012 sensitivity studies using the IMPROVE regression visibility
retrieval show that biases due to PBL depth errors range from 5 to
-10 % while uncertainties due to PBL RMSEs range from 5 to 12 %. The
ability of current polar orbiting and future geostationary satellites to
monitor visibility on a daily or hourly basis over the continental United
States provides improved visibility monitoring within our national parks and
useful information to the regional planning offices responsible for
developing mitigation strategies required under the EPA's Regional Haze
Rule.
Acknowledgements
Support was provided by the GOES-R Program through NOAA Cooperative
Agreement NA10NES4400013 and by the NASA Air Quality Applied Science Team
(AQAST). The views, opinions, and findings contained in this report are
those of the author(s) and should not be construed as an official National
Oceanic and Atmospheric Administration or US government position, policy,
or decision.
Edited by: A. Kokhanovsky
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