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Your description "I would like a model that takes into account areas that are similar to Santa Monica ... and ignores other locations" is a good summary of the k-NN family of approaches.

In the context of meteorology, relationships between climate at different locations are known as teleconnections (e.g. EOF maps).

However your problem also has a shorter-term weather component. For example "time dependent" would include things like advection of pressure systems. Weather forecasting incorporates these effects in great detail via sophisticated physics-based models that assimilate data using techniques such as Ensemble Kalman filtering.

Probably this is more than you would need, but simpler approaches could be possible. For example, advection could induce time-lagged correlations between nearby locations. However which locations are (tele-)connected, and the sign of the lag, would depend on which way the wind is blowing (i.e. the upwindupwind direction). Now if you have a west-coast US location, and no data over the Pacific, this may be less relevant (given common circulation patterns).

Your description "I would like a model that takes into account areas that are similar to Santa Monica ... and ignores other locations" is a good summary of the k-NN family of approaches.

In the context of meteorology, relationships between climate at different locations are known as teleconnections (e.g. EOF maps).

However your problem also has a shorter-term weather component. For example "time dependent" would include things like advection of pressure systems. Weather forecasting incorporates these effects in great detail via sophisticated physics-based models that assimilate data using techniques such as Ensemble Kalman filtering.

Probably this is more than you would need, but simpler approaches could be possible. For example, advection could induce time-lagged correlations between nearby locations. However which locations are (tele-)connected, and the sign of the lag, would depend on which way the wind is blowing (i.e. the upwind direction). Now if you have a west-coast US location, and no data over the Pacific, this may be less relevant (given common circulation patterns).

Your description "I would like a model that takes into account areas that are similar to Santa Monica ... and ignores other locations" is a good summary of the k-NN family of approaches.

In the context of meteorology, relationships between climate at different locations are known as teleconnections (e.g. EOF maps).

However your problem also has a shorter-term weather component. For example "time dependent" would include things like advection of pressure systems. Weather forecasting incorporates these effects in great detail via sophisticated physics-based models that assimilate data using techniques such as Ensemble Kalman filtering.

Probably this is more than you would need, but simpler approaches could be possible. For example, advection could induce time-lagged correlations between nearby locations. However which locations are (tele-)connected, and the sign of the lag, would depend on which way the wind is blowing (i.e. the upwind direction). Now if you have a west-coast US location, and no data over the Pacific, this may be less relevant (given common circulation patterns).

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GeoMatt22
GeoMatt22
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Your description "I would like a model that takes into account areas that are similar to Santa Monica ... and ignores other locations" is a good summary of the k-NN family of approaches.

In the context of meteorology, relationships between climate at different locations are known as teleconnections (e.g. EOF maps).

However your problem also has a shorter-term weather component. For example "time dependent" would include things like advection of pressure systems. Weather forecasting incorporates these effects in great detail via sophisticated physics-based models that assimilate data using techniques such as Ensemble Kalman filtering.

Probably this is more than you would need, but simpler approaches could be possible. For example, advection could induce time-lagged correlations between nearby locations. However which locations are (tele-)connected, and the sign of the lag, would depend on which way the wind is blowing (i.e. the upwind direction). Now if you have a west-coast US location, and no data over the Pacific, this may be less relevant (given common circulation patterns).