US 2026/0065294 A1Published application

Method and system for determining emission distribution of a gas using a surrogate model

AssigneeTata Consultancy Services Limited

InventorsShailesh Shankar DESHPANDE, Garvit AGARWAL, Arpan PAL

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Abstract

Obtaining accurate estimates of emission distribution of gases is a technically challenging as well as environmentally relevant problem. Existing techniques have high computational cost, need deep mathematical and computational expertise, and are prone to errors. Hence, embodiments of present disclosure provide a method and system for determining emission distribution using a surrogate model. The method takes prior emission distributions of gas and updates them using its future concentrations in the atmosphere by using a trained surrogate of a numerical transport model by maintaining consistency between the emissions, atmospheric transport, and future concentrations. The surrogate model is trained in a data-driven way to improve transport models, which is less complicated and time-consuming than correcting the parameterizations and constants used in numerically modeling various processes in transport.

Claims12

Dependent claims

A processor implemented method, comprising:

obtaining, via one or more hardware processors, i) an initial estimate of emission distribution of a gas over an area, ii) a plurality of meteorological conditions of the area, iii) a plurality of prior concentrations of the gas, and iv) a satellite data of current concentration of the gas over the area;
and
updating, via the one or more hardware processors, the initial estimate of emission distribution of the gas in a plurality of iterations until a computed error value between a predicted concentration of the gas by a surrogate model and the satellite data of current concentration of the gas is less than a predefined threshold value, wherein a final estimate of emission distribution of the gas is obtained after the plurality of iterations, and wherein at each of the plurality of iterations the initial estimate of emission distribution of the gas is updated by:
predicting the concentration of the gas based on an emission distribution of the gas, the plurality of meteorological conditions, and the plurality of prior concentrations of the gas, using the surrogate model, wherein the emission distribution of the gas is equal to the initial estimate of emission distribution in a first iteration;
computing the error value between the predicted concentration of the gas and the satellite data of current concentration of the gas;
and
updating the initial estimate of emission distribution based on the computed error value, wherein the updated estimate of emission distribution is used in subsequent iteration.

4 more elements

A system comprising:

a memory storing instructions;
one or more Input/Output (I/O) interfaces;
and
one or more hardware processors coupled to the memory via the one or more communication interfaces (106), wherein the one or more hardware processors are configured by the instructions to:
obtain i) an initial estimate of emission distribution of a gas over an area, ii) a plurality of meteorological conditions of the area, iii) a plurality of prior concentrations of the gas, and iv) a satellite data of current concentration of the gas over the area;
and
update the initial estimate of emission distribution of the gas in a plurality of iterations until a computed error value between a predicted concentration of the gas by a surrogate model and the satellite data of current concentration of the gas is less than a predefined threshold value, wherein a final estimate of emission distribution of the gas is obtained after the plurality of iterations, and wherein at each of the plurality of iterations the initial estimate of emission distribution of the gas is updated by:
predicting the concentration of the gas based on an emission distribution of the gas, the plurality of meteorological conditions, and the plurality of prior concentrations of the gas, using the surrogate model, wherein the emission distribution of the gas is equal to the initial estimate of emission distribution in a first iteration;
computing the error value between the predicted concentration of the gas and the satellite data of current concentration of the gas;
and
updating the initial estimate of emission distribution based on the computed error value, wherein the updated estimate of emission distribution is used in subsequent iteration.

8 more elements

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

obtaining i) an initial estimate of emission distribution of a gas over an area, ii) a plurality of meteorological conditions of the area, iii) a plurality of prior concentrations of the gas, and iv) a satellite data of current concentration of the gas over the area;
and
updating the initial estimate of emission distribution of the gas in a plurality of iterations until a computed error value between a predicted concentration of the gas by a surrogate model and the satellite data of current concentration of the gas is less than a predefined threshold value, wherein a final estimate of emission distribution of the gas is obtained after the plurality of iterations, and wherein at each of the plurality of iterations the initial estimate of emission distribution of the gas is updated by:
predicting the concentration of the gas based on an emission distribution of the gas, the plurality of meteorological conditions, and the plurality of prior concentrations of the gas, using the surrogate model, wherein the emission distribution of the gas is equal to the initial estimate of emission distribution in a first iteration;
computing the error value between the predicted concentration of the gas and the satellite data of current concentration of the gas;
and
updating the initial estimate of emission distribution based on the computed error value, wherein the updated estimate of emission distribution is used in subsequent iteration.

4 more elements

Dependencies are inferred from the claim text.