<p>To my understanding, the question refers to studies which aim to estimate pollutant emission fluxes using satellite retrievals of atmospheric constituents. Mathematically, this means estimating the forcing term in an advection-diffusion-reaction system given a set of tracer observations. </p>

<p>How well you can localize the emissions depends on the pollutant: for something shortlived, like NO2, a given observation will be influenced by a relatively small area, while for a long-lived pollutant (say CO) the effective resolution would be much lower. For long-lived pollutants, the problem is not well posed and needs to be regularized in some way. Typically, instead of reconstructing the emission from a scratch, you try to just refine an existing emission inventory. </p>

<p>The issues you mention regarding model errors are real and certainly do introduce biases in any emission estimates based on satellite products. This is quite well understood by people working on the field, but the difficulty is in quantifying the errors that are due to model uncertainty.</p>

<blockquote>
  <p>How to generate a relative precise relationship between satellite data
  and source emission in consideration of these doubts?</p>
</blockquote>

<p>Opinions vary. In my experience, few scientists believe that satellite-based emissions could replace traditional emission inventories. On the other hand, for many areas and pollutants, the regular emission inventories also involve a lot of extrapolation. Ultimately, the question is whether the inverse modeling can deliver some useful added value compared to the existing inventories.</p>

CC BY-SA 3.0
2016-12-06T17:31:55.740Z
Could you added a paragraph on the uncertainties of inverse modeling - maybe with references if you have them on hand? In addition to "Typically, instead of reconstructing the emission from a scratch, you try to just refine an existing emission inventory. ": One approach is to prescribe the location of point and area emission sources and then estimate the magnitude of the emissions by these sources.
daniel.heydebreck
CC BY-SA 3.0
2016-10-07T13:03:04.200Z
2016-10-07T13:03:04.200Z
jvir
<p>I read a few paper related to emission inventory talking about the inverse modeling. It was another method to estimate the emission from ground(called <code>top-down</code> method). The satellite sensor usually support the product with vertical column density(<code>VCD</code>) for certrain species.      </p>

<p>The target here seems to be generate the relationship between emission and VCD. In my opinion, there are some uncertainty hidden here: </p>

<p>(1) the gridded VCD of certain species can be formed by different precursors, transported from neighborhood area.      </p>

<p>(2) The chemistry system in the model also has some inherent bias which would miss some crucial source or sink process. </p>

<p>(3) When we set source inventory as input for chemistry transport model, the height information and the specific location are two crucial parameters. In the area of inverse modeling, we know nothing about the information of real sources(i.e fire spot, power plant). </p>

<p>How to generate a relative precise relationship between satellite data and source emission in consideration of these doubts? </p>

CC BY-SA 3.0
2016-09-28T19:12:12.473Z
Generally speaking, if you have a model, you are given an initial condition and you find a solution in time.

In inverse modeling, you are given a result, and you find the initial condition from whence it started. It is effectively modeling with a negative timestep.

You are right on the first point. For the second point, if you do a top down approach, (I am uncertain of this), you aren't particularly interested in locations. That is more for bottom-up approaches.
BarocliniCplusplus
CC BY-SA 3.0
2016-09-29T03:47:06.233Z
@BarocliniCplusplus  that is a good start at an answer.  I think the ability to use inverse modeling for location (or source) specific questions would depend on the pollutant in question.
f.thorpe
CC BY-SA 3.0
2016-12-06T17:37:33.593Z
To (3): It could be an approach to prescribe major point sources (their location, not the magnitude of emissions). To (1): We need species that are observable by satellites (or derivable from satellite data) and their reaction pathways are not too complicated (In the best case, they are also short living substances.). We should not do it for each species. NO2 is a candidate (as @jvir stated). To (2): yes, a problem. We have this problem also when we model forward in time ;-) .
daniel.heydebreck
<remote-sensing><atmospheric-chemistry><satellites>
What is principle of inverse modeling?
Han Zhengzu
# What is principle of inverse modeling?

(Asked by: username_0 on 2016-09-28)

I read a few paper related to emission inventory talking about the inverse modeling. It was another method to estimate the emission from ground(called `top-down` method). The satellite sensor usually support the product with vertical column density(`VCD`) for certrain species.

The target here seems to be generate the relationship between emission and VCD. In my opinion, there are some uncertainty hidden here:

(1) the gridded VCD of certain species can be formed by different precursors, transported from neighborhood area.

(2) The chemistry system in the model also has some inherent bias which would miss some crucial source or sink process.

(3) When we set source inventory as input for chemistry transport model, the height information and the specific location are two crucial parameters. In the area of inverse modeling, we know nothing about the information of real sources(i.e fire spot, power plant).

How to generate a relative precise relationship between satellite data and source emission in consideration of these doubts?

**Tags:** remote-sensing, atmospheric-chemistry, satellites

> **Comments:**
> **username_1**: Generally speaking, if you have a model, you are given an initial condition and you find a solution in time.  In inverse modeling, you are given a result, and you find the initial condition from whence it started. It is effectively modeling with a negative timestep.  You are right on the first point. For the second point, if you do a top down approach, (I am uncertain of this), you aren't particularly interested in locations. That is more for bottom-up approaches.  
> **username_2**: @username_1  that is a good start at an answer.  I think the ability to use inverse modeling for location (or source) specific questions would depend on the pollutant in question.  
> **username_3**: To (3): It could be an approach to prescribe major point sources (their location, not the magnitude of emissions). To (1): We need species that are observable by satellites (or derivable from satellite data) and their reaction pathways are not too complicated (In the best case, they are also short living substances.). We should not do it for each species. NO2 is a candidate (as @username_4 stated). To (2): yes, a problem. We have this problem also when we model forward in time ;-) .  

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## 1 answers

### Answer by username_4 on 2016-10-07. Score: 4

To my understanding, the question refers to studies which aim to estimate pollutant emission fluxes using satellite retrievals of atmospheric constituents. Mathematically, this means estimating the forcing term in an advection-diffusion-reaction system given a set of tracer observations.

How well you can localize the emissions depends on the pollutant: for something shortlived, like NO2, a given observation will be influenced by a relatively small area, while for a long-lived pollutant (say CO) the effective resolution would be much lower. For long-lived pollutants, the problem is not well posed and needs to be regularized in some way. Typically, instead of reconstructing the emission from a scratch, you try to just refine an existing emission inventory.

The issues you mention regarding model errors are real and certainly do introduce biases in any emission estimates based on satellite products. This is quite well understood by people working on the field, but the difficulty is in quantifying the errors that are due to model uncertainty.

> How to generate a relative precise relationship between satellite data
> and source emission in consideration of these doubts?

Opinions vary. In my experience, few scientists believe that satellite-based emissions could replace traditional emission inventories. On the other hand, for many areas and pollutants, the regular emission inventories also involve a lot of extrapolation. Ultimately, the question is whether the inverse modeling can deliver some useful added value compared to the existing inventories.

> **Comments:**
> **username_3**: Could you added a paragraph on the uncertainties of inverse modeling - maybe with references if you have them on hand? In addition to "Typically, instead of reconstructing the emission from a scratch, you try to just refine an existing emission inventory. ": One approach is to prescribe the location of point and area emission sources and then estimate the magnitude of the emissions by these sources.  

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