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**Geoscientific Model Development**
An interactive open-access journal of the European Geosciences Union

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**Development and technical paper**
14 Dec 2018

**Development and technical paper** | 14 Dec 2018

Weak-constraint inverse modeling using HYSPLIT-4 Lagrangian dispersion model and Cross-Appalachian Tracer Experiment (CAPTEX) observations – effect of including model uncertainties on source term estimation

Weak-constraint inverse modeling using HYSPLIT-4 Lagrangian dispersion model and Cross-Appalachian Tracer Experiment (CAPTEX) observations – effect of including model uncertainties on source term estimation
Weak-constraint inverse modeling using HYSPLIT-4 Lagrangian dispersion model and...
Tianfeng Chai et al.

^{1}NOAA Air Resources Laboratory (ARL), NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USA^{2}Cooperative Institute for Climate and Satellites, University of Maryland, College Park, MD 20740, USA

^{1}NOAA Air Resources Laboratory (ARL), NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USA^{2}Cooperative Institute for Climate and Satellites, University of Maryland, College Park, MD 20740, USA

**Correspondence**: Tianfeng Chai (tianfeng.chai@noaa.gov)

**Correspondence**: Tianfeng Chai (tianfeng.chai@noaa.gov)

Abstract

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A Hybrid Single-Particle Lagrangian Integrated Trajectory version 4 (HYSPLIT-4) inverse system that is based on variational data assimilation and a Lagrangian dispersion transfer coefficient matrix (TCM) is evaluated using the Cross-Appalachian Tracer Experiment (CAPTEX) data collected from six controlled releases. For simplicity, the initial tests are applied to release 2, for which the HYSPLIT has the best performance. Before introducing model uncertainty terms that will change with source estimates, the tests using concentration differences in the cost function result in severe underestimation, while those using logarithm concentration differences result in overestimation of the release rate. Adding model uncertainty terms improves results for both choices of the metric variables in the cost function. A cost function normalization scheme is later introduced to avoid spurious minimal source term solutions when using logarithm concentration differences. The scheme is effective in eliminating the spurious solutions and it also helps to improve the release estimates for both choices of the metric variables. The tests also show that calculating logarithm concentration differences generally yields better results than calculating concentration differences, and the estimates are more robust for a reasonable range of model uncertainty parameters. This is further confirmed with nine ensemble HYSPLIT runs in which meteorological fields were generated with varying planetary boundary layer (PBL) schemes. In addition, it is found that the emission estimate using a combined TCM by taking the average or median values of the nine TCMs is similar to the median of the nine estimates using each of the TCMs individually. The inverse system is then applied to the other CAPTEX releases with a fixed set of observational and model uncertainty parameters, and the largest relative error among the six releases is 53.3 %. At last, the system is tested for its capability to find a single source location as well as its source strength. In these tests, the location and strength that yield the best match between the predicted and the observed concentrations are considered as the inverse modeling results. The estimated release rates are mostly not as good as the cases in which the exact release locations are assumed known, but they are all within a factor of 3 for all six releases. However, the estimated location may have large errors.

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Chai, T., Stein, A., and Ngan, F.: Weak-constraint inverse modeling using HYSPLIT-4 Lagrangian dispersion model and Cross-Appalachian Tracer Experiment (CAPTEX) observations – effect of including model uncertainties on source term estimation, Geosci. Model Dev., 11, 5135–5148, https://doi.org/10.5194/gmd-11-5135-2018, 2018.

1 Introduction

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The transport and dispersion of gaseous and particulate pollutants are often simulated to generate pollution forecasts for emergency responses or produce comprehensive analyses of the past for better understanding of the particular events. Lagrangian particle dispersion models are particularly suited to provide plume products associated with emergency response scenarios. While accurate air pollutant source terms are crucial for the quantitative predictions, they are rarely provided in most applications and have to be approximated with a lot of assumptions. For instance, the smoke forecasts over the continental US operated by the National Oceanic and Atmospheric Administration (NOAA) using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Draxler and Hess, 1997; Stein et al., 2015) in support of the National Air Quality Forecast Capability (NAQFC) rely on outdated fuel loading data and a series of assumptions related to smoke release heights and strength approximation (Rolph et al., 2009).

Observed concentration, deposition, or other functions of the atmospheric pollutants such as aerosol optical thickness measured by satellite instruments can be used to estimate some combination of source location, strength, and temporal evolution using various source term estimation (STE) methods (Bieringer et al., 2017; Hutchinson et al., 2017). Among the applications, the recent Fukushima Daiichi nuclear power plant accidents saw the most implementations of the STE methods to estimate the radionuclide releases. The STE methods range from simple comparisons between model outputs and measurements (Achim et al., 2014; Chino et al., 2011; Hirao et al., 2013; Katata et al., 2012, 2015; Kobayashi et al., 2013; Oza et al., 2013; Terada et al., 2012) to sophisticated ones using various dispersion models and inverse modeling schemes (Chai et al., 2015; Saunier et al., 2013; Stohl et al., 2012; Winiarek et al., 2012, 2014). Another active field for STE applications is the estimation of the volcanic ash emissions. Many attempts have been made for several major volcano eruptions (Chai et al., 2017; Prata and Grant, 2001; Wen and Rose, 1994; Wilkins et al., 2014, 2016).

While there are many STE methods applied to reconstruct the emission terms, it is still a state of the art. Two popular advanced inverse modeling approaches are cost-function-based optimization methods and those based on Bayesian inference. However, it is difficult to evaluate the STE without knowing the actual sources for most applications. Chai et al. (2015) generated pseudo-observations using the same dispersion model in their initial inverse experiment tests, which are often called “twin experiments”. Such tests allow observational errors to be added realistically (Chai et al., 2015), but it is non-trivial to represent the model errors incurred by other model parameters such as the uncertainties of the meteorological field. One way to objectively evaluate the inverse modeling results is to compare the predictions with the independent observations or withheld data. However, such indirect comparisons still cannot provide quantitative error statistics for the source terms.

There have been some tracer experiments conducted to study the atmospheric transport and dispersion with controlled releases. In these experiments, the source terms were well-quantified and comprehensive measurements were made subsequently over an extended area (Draxler et al., 1991; Van Dop et al., 1998). With the known source terms, they provide a unique opportunity to evaluate the STE methods. Singh and Rani (2014) and Singh et al. (2015) used measurements from recent dispersion experiment (Fusion Field Trials 2007) data to evaluate a least-squares technique for identification of a point release. The European Tracer Experiment (ETEX) data set was also used to study the STE methods based on the principle of maximum entropy and a least-squares cost function (Bocquet, 2005, 2007, 2008). However, such formal evaluation of the STE methods is still very limited.

A HYSPLIT inverse system based on 4D-Var data assimilation and a transfer coefficient matrix (TCM) was developed and applied to estimate a cesium-137 source from the Fukushima nuclear accident using air concentration measurements (Chai et al., 2015). The system was further developed to estimate the effective volcanic ash release rates as a function of time and height by assimilating satellite mass loadings and ash cloud top heights (Chai et al., 2017). In this study, the Cross-Appalachian Tracer Experiment (CAPTEX) data are used to evaluate the HYSPLIT inverse modeling system. The paper is organized as follows. Section 2 describes the CAPTEX experiment, HYSPLIT-4 model configuration, and the source term inversion method. Section 3 presents emission inversion results, and a summary is given in Sect. 4.

2 Method

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The CAPTEX experiment consisted of seven near-surface releases of the inert
tracer perfluoro-monomethyl-cyclohexane (PMCH) from Dayton, Ohio, USA, and
Sudbury, Ontario, Canada, during September and October 1983 (Draxler, 1987).
Table 1 lists the locations, time, amounts, and measurement counts
of the seven releases. Samples were collected at 84 different measurement
sites distributed from 300 to 1100 km downwind of the emission source, as
either 3 or 6 h averages up to 60 h after each release (NOAA ARL, 2018a).
Figure 1 shows the distribution of measurement sites and the
two source locations. Release 6 is excluded from the testing as in the
earlier studies using CAPTEX data (e.g., Hegarty et al., 2013; Ngan et al., 2015)
due to low detection at the measurement sites. Note that 3.4 fL L^{−1}
has been subtracted from all CAPTEX measurements to remove background and
“noise” in sampling where the ambient background concentration is constant
at 3.0 fL L^{−1} (Ferber et al., 1986). At ground level, 1 fL L^{−1} is
equivalent to 15.6 pg m^{−3}. Duplicate sample analyses showed that the
majority of the data have a mean standard deviation estimated as 10.8 % but
contaminated samples may have standard deviation as large as 65 %
(Ferber et al., 1986).

In this study, the tracer transport and dispersion are modeled using the
HYSPLIT model (version 4, NOAA ARL, 2018b) in its particle mode in which
three-dimensional (3-D) Lagrangian particles released from the source
location passively follow the wind field (Draxler and Hess, 1997, 1998; Stein et al., 2015). A
particle release rate of 50 000 particles h^{−1} is used for all calculations.
Random velocity components based on local stability conditions are added to
the mean advection velocity in the three wind component directions. The
meteorological data used to drive the HYSPLIT are time averaged from the
Advanced Research Weather Research and Forecasting (WRF) model (ARW, version
3.2.1) simulation results at 10 km resolution and they are identical to
those used by Hegarty et al. (2013). The 10 km run was nested inside a larger
domain at 30 km resolution, over which the simulation was started using the
North American Regional Reanalysis (NARR) at 32 km (Mesinger et al., 2006). In the
WRF simulations, 3-D grid nudging of winds was applied in the free
troposphere and within the planetary boundary layer (PBL). There are 43
vertical layers, with the lowest one being approximately 33 m thick. Tracer
concentrations are computed over each grid cell by summing the mass of all
particles in the cell and dividing the result by the cell's volume. In this
study, the concentration grid cells have 0.25^{∘} resolution in both
latitude and longitude directions and vertically they extend 100 m from the
ground.

To avoid running the HYSPLIT modeling repeatedly, a TCM is generated similar to the previous HYSPLIT inverse modeling studies (Chai et al., 2015, 2017). As described in Draxler and Rolph (2012), independent simulations are performed with a unit emission rate from each source location and a predefined time segment. Each release scenario is simply a linear combination of the unit emission runs.

Similar to Chai et al. (2015), the unknown releases can be solved by minimizing a cost functional that integrates the differences between model predictions and observations, deviations of the final solution from the first guess (a priori), as well as other relevant information written into penalty terms (Daley, 1991). For the current application, the cost functional ℱ is defined as

$$\begin{array}{}\text{(1)}& {\displaystyle}\mathcal{F}={\displaystyle \frac{\mathrm{1}}{\mathrm{2}}}\sum _{i=\mathrm{1}}^{M}\sum _{j=\mathrm{1}}^{N}{\displaystyle \frac{({q}_{ij}-{q}_{ij}^{b}{)}^{\mathrm{2}}}{{\mathit{\sigma}}_{ij}^{\mathrm{2}}}}+{\displaystyle \frac{\mathrm{1}}{\mathrm{2}}}\sum _{m=\mathrm{1}}^{M}{\displaystyle \frac{({c}_{m}^{h}-{c}_{m}^{o}{)}^{\mathrm{2}}}{{\mathit{\u03f5}}_{m}^{\mathrm{2}}}},\end{array}$$

where *q*_{ij} is the discretized source term at hour *i* and location *j*
for which an independent HYSPLIT simulation has been run and recorded in a
TCM. ${q}_{ij}^{b}$ is the first guess or a priori estimate and ${\mathit{\sigma}}_{ij}^{\mathrm{2}}$
is the corresponding error variance. Note that all tracer sources in this
study were at ground level and the release heights in the HYSPLIT were set as
10 m for all of the following test cases. We also assume the uncertainties
of the release at each time and location are independent of each other so
that only the diagonal term of the typical a priori error variance
${\mathit{\sigma}}_{ij}^{\mathrm{2}}$ appears in Eq. (1). *c*^{h} and *c*^{o} denote
HYSPLIT-predicted and measured concentrations, respectively. The
observational errors ${\mathit{\u03f5}}_{m}^{\mathrm{2}}$ are assumed to be uncorrelated. As the
term ${\mathit{\u03f5}}_{m}^{\mathrm{2}}$ is essentially used to weight $({c}_{m}^{h}-{c}_{m}^{o}{)}^{\mathrm{2}}$ terms,
the uncertainties of the model predictions and the representative errors
should be included in addition to the observational uncertainties. This will
be further discussed in Sect. 3.2. A large-scale
bound-constrained limited-memory quasi-Newton code, L-BFGS-B (Zhu et al., 1997)
is used to minimize the cost functional ℱ defined in
Eq. (1) when multiple parameters need to be determined. As shown by
Chai et al. (2015), the metric variable can be changed from concentration to
logarithm concentration. Both choices of metric variable will be tested here.
Note that the cases presented in this study are all formulated as
overdetermined problems.

3 Results

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As an initial test, the exact release location and time are both assumed
known and the only unknown variable left to be determined is the release
rate or the total release amount. For this type of one-dimensional problem,
an optimal emission strength can be easily found without having to use
sophisticated minimization routines. For instance, the ℱ may be
directly calculated for a number of emission strength values, and the
resulting ℱ=ℱ(*q*) plot will reveal the optimal *q*
strength that is associated with the minimal ℱ. Note that such an
optimal solution not only depends on the chosen parameters in
Eq. 1 but also highly depends on the HYSPLIT model setup and
the meteorological fields.

Both Hegarty et al. (2013) and Ngan et al. (2015) showed that the HYSPLIT dispersion
model performed better for release 2 than the other releases. Thus, release 2
is initially chosen to perform a series of inverse modeling tests.
Assuming no prior knowledge of the emission strength, the first guess is
given as *q*^{b}=0, and *σ*=10^{4} kg h^{−1} is assumed. Sensitivity
tests show that when *q*^{b} is changed to 100 kg h^{−1}, the emission
strength estimates are nearly unchanged with the same or larger *σ*.

Firstly, no model uncertainties are considered to contribute to *ϵ*.
The observational uncertainties are formulated to include a fractional
component *f*^{o}×*c*^{o} and an additive part *a*^{o}. Note that this general
formulation is chosen for its simplicity. It should be replaced when more
uncertainty information is available. Table 2 lists the
emission strength *q* that generates the minimal cost function for a series
of *f*^{o} and *a*^{o} combinations, where *f*^{o} ranges from 10 % to 50 %,
and *a*^{o} is assigned as 10, 20, and 50 pg m^{−3}. All the emission
strength values obtained are significantly lower than the actual release of
67 kg h^{−1}. It shows that a larger *f*^{o} value tends to have a smaller
*q* estimate, but a larger *a*^{o} results in a larger *q*. The significant
underestimation of the release strength is caused by the implicit assumption
of a perfect model when *ϵ* does not include the model uncertainties.
Figure 2 shows the comparison between the predicted and
measured concentrations when the actual release rate of 67 kg h^{−1} is
applied. Large discrepancies still exist even when the exact release is known
and used in the simulation. For the measured zero concentrations, most of the
predicted values are non-zero and can be above 1000 pg m^{−3}. As
*ϵ*_{m}=*a*^{o} for these zero concentrations,
$\frac{({c}_{m}^{h}-{c}_{m}^{o}{)}^{\mathrm{2}}}{{\mathit{\u03f5}}_{m}^{\mathrm{2}}}$ will dominate the cost function when *a*^{o} is not large enough.
This explains that the underestimation is not as severe for *a*^{o}=50 pg m^{−3} as that for *a*^{o}=10 pg m^{−3}. While *ϵ* do
not change with *f*^{o} for the zero concentrations, smaller *f*^{o} values help
increase the weighting of the terms $\frac{({c}_{m}^{h}-{c}_{m}^{o}{)}^{\mathrm{2}}}{{\mathit{\u03f5}}_{m}^{\mathrm{2}}}$
associated with large measured concentrations. So, the estimated emission
strength when *f*^{o}=10 % is better than when *f*^{o}=50 %.

As stated in Chai et al. (2015), the metric variable in Eq. (1) can be
changed to ln(*c*), i.e., replacing $({c}_{m}^{h}-{c}_{m}^{o})$ with
$\mathrm{ln}\left({c}_{m}^{h}\right)-\mathrm{ln}\left({c}_{m}^{o}\right)$. A constant 0.001 pg m^{−3} is added to both
${c}_{m}^{h}$ and ${c}_{m}^{o}$ to allow the logarithm operation for zero concentrations.
In such a case, ${\mathit{\u03f5}}_{m}^{\mathrm{ln}\left(c\right)}$ can be calculated as

$$\begin{array}{}\text{(2)}& {\displaystyle}{\mathit{\u03f5}}_{m}^{\mathrm{ln}\left(c\right)}=\mathrm{ln}\left(\mathrm{1}+{f}^{o}+{\displaystyle \frac{{a}^{o}}{{c}_{m}^{o}}}\right).\end{array}$$

Note that 0.001 pg m^{−3} is also added to ${c}_{m}^{o}$ in the second term
to avoid dividing by zero. The $\frac{{a}^{o}}{{c}_{m}^{o}}$ term in
Eq. (2) makes ${\mathit{\u03f5}}_{m}^{\mathrm{ln}\left(c\right)}$ larger for measured low
concentrations than those measured high concentrations. It causes more
weighting towards measured high concentrations and results in overestimation
shown in Table 3. The measured zero concentrations have
little effect on the final emission strength estimates.
Table 3 shows that the emission strengths are
overestimated but are within a factor of 2 over the actual release of
67 kg h^{−1}, for all *f*^{o} and *a*^{o} combinations. The similar trends of
how *q* changes with *f*^{o} and *a*^{o} are also observed here; i.e., a larger
*a*^{o} or a smaller *f*^{o} tends to have a larger *q* estimate.

While using logarithm concentration as the metric variable yields better
emission estimates than using concentration as the metric variable, the
results in Table 3 are apparently systematically
overestimated compared to the systematically underestimated results in
Table 2. In addition, the *f*^{o} and *a*^{o} combinations
associated with the best emission estimates in Tables 2 and
3 appear to be in the opposite corners of the tables.

To consider the model uncertainties in a simplified way, *ϵ*^{2} will be formulated as

$$\begin{array}{}\text{(3)}& {\displaystyle}{\mathit{\u03f5}}_{m}^{\mathrm{2}}={\left({f}^{o}\times {c}_{m}^{o}+{a}^{o}\right)}^{\mathrm{2}}+{\left({f}^{h}\times {c}_{m}^{h}+{a}^{h}\right)}^{\mathrm{2}}.\end{array}$$

As *a*^{o} and *a*^{h} affect the *ϵ*^{2} in a similar way, the
representative errors caused by comparing the measurements with the predicted
concentrations averaged in a grid can be included in either *a*^{h} or *a*^{o}.

With logarithm concentration as the metric variable, $({\mathit{\u03f5}}_{m}^{\mathrm{ln}\left(c\right)}{)}^{\mathrm{2}}$ is comprised of two parts, as

$$\begin{array}{ll}{\displaystyle}{\left({\mathit{\u03f5}}_{m}^{\mathrm{ln}\left(c\right)}\right)}^{\mathrm{2}}=& {\displaystyle}\phantom{\rule{0.25em}{0ex}}{\left[\mathrm{ln}\left(\mathrm{1}+{f}^{o}+{\displaystyle \frac{{a}^{o}}{{c}_{m}^{o}}}\right)\right]}^{\mathrm{2}}\\ \text{(4)}& {\displaystyle}& {\displaystyle}+{\left[\mathrm{ln}\left(\mathrm{1}+{f}^{h}+{\displaystyle \frac{{a}^{h}}{{c}_{m}^{h}}}\right)\right]}^{\mathrm{2}}.\end{array}$$

Note that a constant small number (0.001 pg m^{−3}) is added to
denominators ${c}_{m}^{o}$ and ${c}_{m}^{h}$ to avoid dividing by zero.

Since the predicted concentrations ${c}_{m}^{h}$ in Eqs. (3) and
(4) will vary when source term estimates change, the model
uncertainties will depend on the current release parameters. Thus, the model
uncertainty terms are not static during the inverse modeling and they change
along with the source estimates. Using concentration and logarithm
concentration as the metric variable, respectively,
Tables 4 and 5 show the emission
strength estimates with different *f*^{h} and *a*^{h}, while keeping *f*^{o}=20 %, *a*^{o}=20 pg m^{−3}. Additional tests with other chosen *f*^{o}
and *a*^{o} values show similar but slightly different results. For brevity,
they are not presented here. It should be noted that the model uncertainties
are not equivalent to model errors. Although dispersion model simulations can
have large errors due to various reasons including the source term
uncertainties, the model uncertainties are used to indicate that the model is
not perfect even with the “optimal” model parameters. Similar to the weak
constraint applied in operational 4D-Var data assimilation systems
(Tremolet, 2006; Zupanski, 1997), introducing model uncertainties is mainly intended to
relax the model constraint for imperfect models. Here, the *f*^{h} and *a*^{h}
parameters are given similar ranges to those given to the observational
uncertainty parameters.

When concentration is used as the metric variable, the emission strength
estimates with model uncertainties considered are improved over those without
model uncertainties. The estimates of emission strength generally increase
with the model uncertainty, either through *a*^{h} or *f*^{h}, except for
*f*^{h}=50 %, when the *q* estimates slowly decreases with *a*^{h}. When *f*^{h}=0 %, *a*^{h}=10, 20, and 50 pg m^{−3}, while *a*^{o}=20 pg m^{−3};
the *q* estimates, 7.7, 9.1, and 13.6 kg h^{−1}, are in line with the
results shown in Table 2, where *q*=7.1 kg h^{−1} for
*a*^{o}=20 pg m^{−3} and *q*=12.6 kg h^{−1} for *a*^{o}=50 pg m^{−3}.
However, the trend of how *q* estimates change with *f*^{h} is opposite to how
*q* estimates change with *f*^{o}. Table 4 shows that the
emission strength increases with the model uncertainty factor *f*^{h}. With
*f*^{h}=20 %, the release estimates of 48.5, 50.4, and 53.5 kg h^{−1}
are all within 30 % of the actual release rate of 67 kg h^{−1}.
Instead of the underestimation shown in Table 2, the release
estimates are overestimated when *f*^{h}=50 % is assumed.

With logarithm concentration as the metric variable, larger *a*^{h} or *f*^{h}
results in slightly smaller *q* estimates. While how *q* estimates change
with *f*^{h} is similar to how they change with *f*^{a} in
Table 3, how *q* estimates change with *a*^{h} is opposite
to how *q* estimates change with *a*^{o} before introducing model
uncertainties. Equation (4) shows that *f*^{o} and *f*^{h}
affect $({\mathit{\u03f5}}_{m}^{\mathrm{ln}\left(c\right)}{)}^{\mathrm{2}}$ in a simple monotonic way, while the effect
of ${a}_{m}^{h}$ is complicated, as it is divided by the ${c}_{m}^{h}$ value that varies
with the source terms. Table 5 shows that the source
terms are no longer overestimated as those in Table 3.
In fact, all cases have slight to moderate underestimation, with the worst
results being *q*=42.6 kg h^{−1} when *f*^{h}=50 % and *a*^{h}=50 pg m^{−3}. Another aspect of using logarithm concentration as the
metric variable is that the range of the release estimates listed in
Table 5 is not as large as that in
Table 4, which resulted from using concentration as the metric
variable for the same 12 combinations of *a*^{h} and *f*^{h}.

Without model uncertainties, the weighting terms for each model–observation
pair do not change with emission estimates. When ${\mathit{\u03f5}}_{m}^{\mathrm{2}}$ and
$({\mathit{\u03f5}}_{m}^{\mathrm{ln}\left(c\right)}{)}^{\mathrm{2}}$ are formulated as in Eqs. (3) and
(4), respectively, they vary with emission estimates. This
may cause complication in some circumstances when logarithm concentration is
used as the metric variable. To avoid having zero source as a global
minimizer in such situations, the sum of the weights of the mismatch between
model simulation and observations is kept unchanged for varying *q*_{ij} by
normalizing it with the weight sum when ${q}_{ij}={q}_{ij}^{b}$, as shown in
Eq. (5).

$$\begin{array}{ll}{\displaystyle}\mathcal{F}=& {\displaystyle}\phantom{\rule{0.25em}{0ex}}{\displaystyle \frac{\mathrm{1}}{\mathrm{2}}}\sum _{i=\mathrm{1}}^{M}\sum _{j=\mathrm{1}}^{N}{\displaystyle \frac{({q}_{ij}-{q}_{ij}^{b}{)}^{\mathrm{2}}}{{\mathit{\sigma}}_{ij}^{\mathrm{2}}}}+{\displaystyle \frac{\mathrm{1}}{\mathrm{2}}}\sum _{m=\mathrm{1}}^{M}{\displaystyle \frac{({c}_{m}^{h}-{c}_{m}^{o}{)}^{\mathrm{2}}}{{\mathit{\u03f5}}_{m}^{\mathrm{2}}}}\\ \text{(5)}& {\displaystyle}& {\displaystyle}\times {\displaystyle \frac{{\sum}_{m=\mathrm{1}}^{M}\frac{\mathrm{1}}{{{\mathit{\u03f5}}_{m}^{b}}^{\mathrm{2}}}}{{\sum}_{m=\mathrm{1}}^{M}\frac{\mathrm{1}}{{\mathit{\u03f5}}_{m}^{\mathrm{2}}}}}\end{array}$$

Figure 3 shows the cost function as a function of source
strength when $({\mathit{\u03f5}}_{m}^{\mathrm{ln}\left(c\right)}{)}^{\mathrm{2}}$ is defined as in
Eq. (4), with *f*^{h}=0, *a*^{h}=50 pg m^{−3},
*f*^{o}=10 %, *a*^{o}=20 pg m^{−3}. Before introducing cost function
normalization, a global minimal cost function appears when release strength
approaches zero, while a local minimal cost function exists at
56.8 kg h^{−1}. Several such instances were found when
*a*^{h}=50 pg m^{−3} and when *f*^{h} is 0 or 10 %, while both *f*^{o} and
*a*^{o} are relatively small. The smaller cost function when release strength
approaches zero is due to the increasing $({\mathit{\u03f5}}_{m}^{\mathrm{ln}\left(c\right)}{)}^{\mathrm{2}}$ in
Eq. (4) as ${c}_{m}^{h}$ gets smaller. While the
model–observation differences are not smaller for lower release strength, the
drastic increase of $({\mathit{\u03f5}}_{m}^{\mathrm{ln}\left(c\right)}{)}^{\mathrm{2}}$ when *a*^{h}=50 pg m^{−3} and
*f*^{h} is 0 % or 10 % results in smaller cost function with decreasing
source strength.

Figure 3 shows that the cost function has the minimum at
*q*=67.3 kg h^{−1} after normalization. Note that the dramatic difference
of the cost function magnitude before and after the normalization is due to
the extremely small value of ${\sum}_{m=\mathrm{1}}\frac{\mathrm{1}}{{{\mathit{\u03f5}}_{m}^{b}}^{\mathrm{2}}}$ calculated
at *q*_{b}=0. Tables 6 and 7 show
the emission strength estimates after cost function normalization with
different *f*^{h} and *a*^{h}, while keeping *f*^{o}=20 %, *a*^{o}=20 pg m^{−3}, using concentration and logarithm concentration as the
metric variables, respectively. Note that *f*^{o}=20 % was chosen for the
cases listed in Table 7, while *f*^{o}=10 % was
chosen in Fig. 3 to illustrate the potential problem.
How estimates change with *f*^{h} and *a*^{h} in Tables 6 and 7 is similar to
what is shown in Tables 4 and 5. The estimates
are generally closer to the actual release than those obtained without the cost function normalization.

When having concentration as the metric variable and with *f*^{h}=50 %, the
emission strength estimates are 64.7, 64.7, and 65.3 kg h^{−1} for
*a*^{h}=10, 20, and 50 pg m^{−3}, respectively. They are all within 5 % of
the actual release rate. However, *f*^{h} less than or equal to 20 %
results in significant underestimation. When having logarithm concentration
as the metric variable, the source term estimates are not very sensitive to
*f*^{h} and *a*^{h} values, and the results listed in
Table 7 are all within 20 % of the actual
release rate. Among those estimates, a result of 67.3 kg h^{−1} when
*f*^{h}=10 % and *a*^{h}=10 pg m^{−3} is almost identical to the actual
release rate.

Ngan and Stein (2017) simulated CAPTEX releases using a variety of PBL schemes. In their configuration, WRF version 3.5.1 was used with 27 km grid spacing and 33 vertical layers. The NARR data set was used for the initial conditions and lateral boundary conditions. The WRF model was initialized every day at 06:00 UTC, and the first 18 h of spin-up time in the 42 h simulation were discarded. The PBL schemes used to create the WRF ensemble were the Yonsei University (Hong et al., 2006), Mellor–Yamada–Janjic (Janjic, 1994), quasi-normal scale elimination (Pergaud et al., 2009), MYNN 2.5 level TKE (Nakanishi and Niino, 2006), ACM2 (Pleim, 2007), Bougeault and Lacarrere (Bougeault and Lacarrère, 1989), University of Washington (Bretherton and Park, 2009), total energy mass flux (Angevine et al., 2010), and Grenier–Bretherton–MaCaa (Grenier and Bretherton, 2001) schemes. Nine simulations were conducted with the PBL schemes and their associated surface layer schemes, except for the YSU, BouLac, UW, and GBM cases in which the MM5 Monin–Obukhov surface scheme was applied. The land-surface model was Noah land-surface model (Chen and Dudhia, 2001), except ACM2 in which the Pleim–Xiu land-surface model was used.

An individual TCM is generated using each of the nine simulations. The nine TCMs can be used to estimate
the emission strengths independently following the same procedure described previously.
Tables 8 and 9 show the
third (25th percentile), fifth (median), and seventh (75th percentile) emission strengths of
the nine estimates that minimize the normalized ℱ defined in
Eq. (5) with different *f*^{h} and *a*^{h}, while keeping *f*^{o}=20 %, *a*^{o}=20 pg m^{−3}, using concentration and logarithm
concentration as the metric variables, respectively. The 25th percentile and
75th percentile values are mostly within 5 % of the median estimates.
While the median estimates show the same trends with *f*^{h} and *a*^{h} as the
results in Tables 6 and 7, they
are significantly larger due to the meteorological model differences.
Apparently, the differences among the simulations with different PBL schemes
are smaller than the differences between the ensemble simulations here and
the simulation used in the earlier sections. This suggests that uncertainties
of the emission strength are probably larger than the ranges indicated by the
25th and 75th percentile values. The results using logarithm concentration as
the metric variable are quite robust with the listed model uncertainty
parameters. However, the estimates using concentration as the metric variable
are very sensitive to *f*^{h} and *a*^{h}. This is consistent with results shown
in Sect. 3.2 and 3.3.

Instead of using each individual TCM generated from nine simulations
independently, the nine TCMs can be combined into one matrix by taking the
median or average values. The combined TCM can then be used to estimate the
source terms. The results for concentration and logarithm concentration
metric variables are listed in Tables 10 and
11, respectively. They show that the emission
estimates using the median transfer coefficients of the nine TCMs are very
close to the median of the nine estimates using the nine simulations
individually. For the cases with logarithm concentration as the metric
variable, the emission estimates using the median value of the nine TCMs are
all within 3.1 % of the median values of the nine estimates obtained with
each individual TCM. For the cases with concentration as the metric variable,
the average relative differences are 6.4 %, with the maximum relative
difference being 10.8 % when *f*^{h}=10 % and *a*^{h}=50 pg m^{−3}.
Combining the TCMs by taking the median value generates slightly better
results than combining the TCMs by taking the average value does.

Similar to what was found in earlier sections and also in Chai et al. (2015), the cases having logarithm concentration as the metric variable generally yield better results than those having concentration as the metric variable. It is probably due to the large range of the concentrations. When having concentration as the metric variable, certain model uncertainty parameters yield good source terms, but the estimates are quite sensitive to the choices of the model uncertainty parameters. However, it is not easy to find such model uncertainty parameters that would yield satisfactory results for applications when the actual releases are indeed unknown. The results here and in the previous sections show that the estimates having logarithm concentration as the metric variable are quite robust for a reasonable range of model uncertainty parameters. For these reasons, logarithm concentration is chosen as the metric variable for the later tests.

In addition to the source strength, the source location and its temporal variation can be retrieved with adequate accuracy using the HYSPLIT inverse system described here if there are sufficient measurements available. For instance, Chai et al. (2015) estimated 99 6 h emission rates of the radionuclide cesium-137 from the Fukushima nuclear accident using 1296 daily average air concentration measurements at 115 stations around the globe. Here, the system's capability to locate a single source location will be tested using a straightforward approach. In these tests, the release time is assumed known, but its location and strength are left to be determined. A region of suspect is first gridded at certain spatial resolution to form a limited number of candidate source locations. An optimal strength is then found at each candidate source location following the method described earlier. The location that results in the best match between the predicted and the observed concentrations is considered as the likely source location.

In the following tests, a 11×11 grid with 0.2^{∘} resolution in
both longitude and latitude directions is used to generate 121 candidate
source locations. They are centered at 40.0^{∘} N, 84.5^{∘} W,
for releases 1–4, and centered at 46.6^{∘} N, 80.8^{∘} W, for
releases 5 and 7. Using the normalized ℱ defined in
Eq. (5) and assuming *f*^{o}=20 %, *a*^{o}=20 pg m^{−3}, *f*^{h}=20 %, and *a*^{h}=20 pg m^{−3}, a minimal cost function associated
with an optimal release strength can be found at each location. When
logarithm concentration is taken as the metric variable, the emission
estimates are not sensitive to *f*^{h} and *a*^{h} choices, as indicated by the
results in Tables 7, 9,
and 11. Figure 4 shows the 121
candidate locations and their respective minimal cost function values for
release 2. No candidate locations are chosen to collocate with the actual
source location which will be unknown for the future applications that need
to locate the sources. A global minimal point is found at 39.8^{∘} N,
84.5^{∘} W, with
${\mathcal{F}}_{min}=\mathrm{3.14}$ achieved when *q*=48.5 kg h^{−1}. This grid
point is taken as the estimated source location and it is 26.4 km away from
the actual release site (39.90^{∘} N, 84.22^{∘} W). The
neighboring location (39.8^{∘} N, 84.3^{∘} W) which is the
closest to the actual release site yields a slightly larger
ℱ=3.17 with an optimal release rate of 60.9 kg h^{−1}. If
the exact source location is known as in the tests presented earlier, the
cost function ℱ reaches 1.59 at its minimal point when
*q*=61.5 kg h^{−1}. Apparently, compared with those cases when the
release strength is the only unknown, finding both the source location and
its strength with the same amount of observations is expected to be more
difficult. Note that the smaller normalized ℱ values in
Fig. 3 are for a case with different observation and model
uncertainty parameters, where *f*^{o}=10 %, *a*^{o}=20 pg m^{−3}, *f*^{h}=0 %, and *a*^{h}=50 pg m^{−3}.

Table 12 lists the source location and strength
estimations for the six releases following the same procedure as described
here, where the uncertainty parameters are *f*^{o}=20 %, *a*^{o}=20 pg m^{−3}, *f*^{h}=20 %, and *a*^{h}=20 pg m^{−3}. Releases 1
and 4 have the minimal cost function ℱ_{min} occurring at the north
boundary and the west boundary, respectively. In such scenarios, it might be
necessary to expand the suspected source region for the future applications
to find the source locations. However, if source locations are known to
reside in the suspected region, the sources can definitely be near the
boundaries. In such cases, the point with ℱ_{min} should be
considered as the estimated source location. Releases 3, 5, and 7 have their
ℱ_{min} occurring at inner grid points, similar to release 2
shown in Fig. 4. None of the closest candidate source locations
yield the best match between model simulation and observations quantified by
the cost function ℱ. Among the six releases, the estimated source
location for release 2 is the closest to its actual release site, with a
distance of 26.4 km.

The release rates obtained along with the likely source locations are
underestimated by a factor of 3 for release 1, and overestimated by a factor
of 3 for releases 4 and 7, while the estimates for releases 2, 3, and 5 are
much better, with relative errors of −27.6 %, −5.4 %, and 21.5 %,
respectively. Table 12 also lists the release rates
*q*^{′} estimated with the exact source location assumed known. These estimates
for all releases are within a factor of 2 of the actual release
rates, and the largest relative error is 53.3 % for release 1. The
posterior uncertainties of the release rate estimates ${\mathit{\u03f5}}_{{q}^{\prime}}$ are
also calculated and listed. They range from 1.8 kg h^{−1} for release 2
to 6.2 kg h^{−1} for release 1. The apparent underestimation is likely
due to the model uncertainty assumption, including its simplified formulation
as well as the chosen parameter values. Either with the source location known
or unknown, release 2 has one of the best emission estimates among the six
releases, probably because the HYSPLIT forward model has the best performance
for the same release (Hegarty et al., 2013). The significant model errors when
simulating the transport and dispersion even with the exact source terms are
mostly caused by the meteorological uncertainties, while the HYSPLIT physical
schemes and parameters, as well as the numerical discretization, also
contribute.

An assumption made in this inverse modeling algorithm is that the differences
between model and observation have a normal distribution with a zero mean.
Figure 5 shows the probability density function (pdf) of
ln(*c*^{h})−ln(*c*^{o}) for the six CAPTEX releases using the estimated release
rate *q*^{′} listed in Table 12. The pdf distribution of
ln(*c*^{h})−ln(*c*^{o}) for release 2 is consistent with the normal distribution
assumption, and the pdf for release 4 shows the largest deviation from a
normal distribution, while those for the other four releases resemble a normal
distribution to some extent. The largest relative error for release 1 is
likely related to the negative mean of the ln(*c*^{h})−ln(*c*^{o}) distribution
shown in Fig. 5. The overestimated *q*^{′} probably results from
the compensation of the model bias. Note that the better performance using
ln(*c*^{h})−ln(*c*^{o}) than *c*^{h}−*c*^{o} is believed to be caused by the fact that
normal distribution assumption is mostly valid for the former but probably
invalid for the latter.

The meteorological field and the observations are the two major inputs to the
current inverse modeling. As discussed above, better model performance of
release 2 helps to lead to better inverse results than the other releases.
However, it is impossible to eliminate the model uncertainties. In practice,
ensemble runs can be used to quantify the uncertainties and reduce the model
errors by taking the average or median values of the ensemble runs. On the
other hand, increasing the number of observations is effective to improve the
inverse modeling results and reduce the result uncertainty. In principle,
when the release strength is the only value to be determined, each
measurement within the predicted plume can provide an independent estimate.
However, relying on a single observation to estimate the strength is
problematic since a particular model output can be very different from the
observation and thus lead to an erroneous estimation of the source
strength when used in isolation. For instance, although the HYSPLIT
predictions of release 2 with exact source terms are very good, compared with
individual measurements, they have severe underestimation (e.g., 0.77 pg m^{−3}
predicted versus 686 pg m^{−3} measured), as well as significant
overestimation (e.g., 2033 pg m^{−3} predicted versus 31.2 pg m^{−3}
measured). Therefore, similar to a regression technique, increasing the
sampling number can improve the final results, as exemplified by the very
good source term estimation for release 2 when using all the available
measurements. Also note that the samples outside predicted plumes do not
contribute to the inverse modeling. Table 1 lists the total
measurement counts for each release, but the number of measurements actually
contributing to the inverse modeling are those inside the HYSPLIT plumes,
including those with zero or background concentrations. The number of such
effective measurements inside the plumes generated by HYSPLIT from the exact
source location and time period are reduced to 148, 237, 211, 68, 46, and 53,
for releases 1–5 and 7, respectively. The largest number of effective
measurements, 237, of release 2, also indicates the best performance of the
HYSPLIT simulation among those of the six releases. The effectiveness of the
measurements will change when source location or release time is changed. The
measurements that are not active in determining the source strength with a
known source location and release time may be effective to locate the source
locations.

4 Summary

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A HYSPLIT inverse system developed to estimate the source term parameters has been evaluated using the CAPTEX data collected from six controlled releases. In the HYSPLIT inverse system, a cost function is used to measure the differences between model predictions and observations weighted by the observational uncertainties. Inverse modeling tests with various observational uncertainties show that calculating concentration differences results in severe underestimation, while calculating logarithm concentration differences results in overestimation.

Unlike other STE applications where model uncertainties are either ignored or assumed static, we introduce the model uncertainty terms that depend on the source term estimates. The model uncertainty terms improve inverse results for both choices of the metric variables in the cost function. It is also found that cost function normalization can avoid spurious minimal source terms when using logarithm concentration as the metric variable. The inverse tests show that having logarithm concentration as the metric variable generally yields better results than having concentration as the metric variable. The estimates having logarithm concentration as the metric variable are robust for a reasonable range of model uncertainty parameters. Such conclusions are further confirmed with nine ensemble runs where meteorological fields were generated using a different version of the WRF meteorological model with varying PBL schemes.

With a fixed set of observational and model uncertainty parameters, the inverse method with logarithm concentration as the metric variable is then applied to all of the six releases. The emission rates are well recovered, with the largest relative error as 53.3 % for release 1. The system is later tested for its capability to locate a single source location as well as its source strength. The location and strength that result in the best match between the predicted and the observed concentrations are considered as the inverse results. The estimated location is close to the actual release site for release 2 of which the forward HYSPLIT model has the best performance. The strength estimates are all within a factor of 3 for the six releases.

Code and data availability

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Code and data availability.

The HYSPLIT model is publicly available at https://www.ready.noaa.gov/HYSPLIT.php (NOAA ARL, 2018b). The CAPTEX data can be downloaded from https://www.arl.noaa.gov/wp_arl/wp-content/uploads/documents/datem/exp_data/captex/ (NOAA ARL, 2018a).

Author contributions

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Author contributions.

TC designed and conducted the inverse tests. AS provided guidance on the HYSPLIT modeling and suggestions on the inverse tests. FN performed the HYSPLIT ensemble simulations using different PBL schemes.

Competing interests

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Competing interests.

The authors declare that they have no conflict of interest.

Acknowledgements

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Acknowledgements.

This study was supported by NOAA grant NA09NES4400006 (Cooperative Institute
for Climate and Satellites – CICS) at the NOAA Air Resources Laboratory in
collaboration with the University of Maryland.

Edited by: Slimane Bekki

Reviewed by: two anonymous referees

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Short summary

While model predictions depend on release parameters, model uncertainties in inverse modeling should also vary with the source terms. In this paper, model uncertainties that will change with the source terms are introduced in a weak-constraint inverse modeling system. Tests using HYSPLIT model and CAPTEX observations show that adding such model uncertainty terms improves release rate estimates. A cost function normalization scheme introduced to avoid spurious solutions proves to be effective.

While model predictions depend on release parameters, model uncertainties in inverse modeling...

Geoscientific Model Development

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