Drilling framework
US-2024419867-A1 · Dec 19, 2024 · US
US2017140075A1 · US · A1
| Field | Value |
|---|---|
| Publication number | US-2017140075-A1 |
| Application number | US-201514938963-A |
| Country | US |
| Kind code | A1 |
| Filing date | Nov 12, 2015 |
| Priority date | Nov 12, 2015 |
| Publication date | May 18, 2017 |
| Grant date | — |
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A method for modeling air pollution includes receiving a weather model for a particular geographic region. Satellite-observed pollution observation data over the geographic region is received. A physical dispersion model for pollution over the geographic region is generated using the received weather model. The received satellite-observed pollution observation data is interpolated to the generated physical model. The interpolated satellite-observed pollution observation data and the generated physical model are combined using weighted coefficients for both the interpolated satellite-observed pollution observation data and the generated physical model. The weighted coefficients are calculated in accordance with a relative error in both the physical dispersion model and the satellite-observed pollution observation data.
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What is claimed is: 1 . A method for modeling air pollution, comprising: receiving a weather model for a particular geographic region; receiving satellite-observed pollution observation data over the geographic region; generating a physical dispersion model for pollution over the geographic region using the received weather model; interpolating the received satellite-observed pollution observation data to the generated physical model; and combining the interpolated satellite-observed pollution observation data and the generated physical model using weighted coefficients for both the interpolated satellite-observed pollution observation data and the generated physical model, wherein the weighted coefficients are calculated in accordance with a relative error in both the physical dispersion model and the satellite-observed pollution observation data. 2 . The method of claim 1 , wherein the satellite-observed pollution observation data has a courser resolution than the physical dispersion model. 3 . The method of claim 1 , wherein the generated physical dispersion model is a computed fluid dynamics (CFD) model. 4 . The method of claim 1 , wherein the physical dispersion model is generated from the received weather model using one or more observed emissions levels taken at one or more observation stations. 5 . The method of claim 1 , wherein the physical dispersion model is generated from the received weather model using one or more observed emissions levels taken from the satellite-observed pollution observation data. 6 . The method of claim 1 , wherein the physical dispersion model is optimized by minimizing a difference between one or more observed emissions levels taken at one or more observation stations and calculated emissions levels at locations of the one or more observation stations that are calculated from the physical dispersion model. 7 . The method of claim 1 , wherein the physical dispersion model is optimized by minimizing a difference between one or more observed emissions levels taken from the satellite-observed pollution observation data at one or more locations and calculated emissions levels at the one or more locations that are calculated from the physical dispersion model. 8 . The method of claim 1 , wherein the relative error in both the physical dispersion model and the satellite-observed pollution observation data is calculate based on a calculated inversing model error of the physical dispersion model and a predetermined error of the satellite-observed pollution observation data. 9 . The method of claim 1 , further comprising displaying the combined interpolated satellite-observed pollution observation data and the generated physical dispersion model as a visual representation of air pollution levels over the particular geographic region. 10 . A method for modeling air pollution, comprising: receiving a weather model for a particular geographic region; receiving satellite-observed pollution observation data over the geographic region; generating a physical dispersion model for pollution over the geographic region using the received weather model; interpolating the received satellite-observed pollution observation data to the generated physical model; and optimizing the generated physical dispersion model by minimizing a difference between one or more observed emissions levels taken from the satellite-observed pollution observation data at one or more locations and calculated emissions levels at the one or more locations that are calculated from the physical dispersion model. 11 . The method of claim 10 , wherein the satellite-observed pollution observation data has a courser resolution than the physical dispersion model. 12 . The method of claim 10 , wherein the generated physical dispersion model is a computed fluid dynamics (CFD) model. 13 . The method of claim 10 , further comprising displaying the optimized physical dispersion model as a visual representation of air pollution levels over the particular geographic region. 14 . A computer system comprising: a processor; and a non-transitory, tangible, program storage medium, readable by the computer system, embodying a program of instructions executable by the processor to perform method steps for modeling air pollution, the method comprising: receiving a weather model for a particular geographic region; receiving satellite-observed pollution observation data over the geographic region; generating a physical dispersion model for pollution over the geographic region using the received weather model; interpolating the received satellite-observed pollution observation data to the generated physical model; and combining the interpolated satellite-observed pollution observation data and the generated physical model using weighted coefficients for both the interpolated satellite-observed pollution observation data and the generated physical model, wherein the weighted coefficients are calculated in accordance with a relative error in both the physical dispersion model and the satellite-observed pollution observation data. 15 . The computer system of claim 14 , wherein the satellite-observed pollution observation data has a courser resolution than the physical dispersion model. 16 . The computer system of claim 14 , wherein the generated physical dispersion model is a computed fluid dynamics (CFD) model. 17 . The computer system of claim 14 , wherein the physical dispersion model is generated from the received weather model using one or more observed emissions levels taken at one or more observation stations. 18 . The computer system of claim 14 , wherein the physical dispersion model is generated from the received weather model using one or more observed emissions levels taken from the satellite-observed pollution observation data. 19 . The computer system of claim 14 , wherein the physical dispersion model is optimized by minimizing a difference between one or more observed emissions levels taken at one or more observation stations and calculated emissions levels at locations of the one or more observation stations that are calculated from the physical dispersion model. 20 . The computer system of claim 14 , further comprising displaying the combined interpolated satellite-observed pollution observation data and the generated physical dispersion model as a visual representation of air pollution levels over the particular geographic region.
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