I have recently calculated pavement surface temperatures at different locations within an urban canyon and would like to use these temperatures as inputs for MRT calculations in order to estimate the spatial variation of MRT across the pavement surface.
However, I encountered an issue when assigning hundreds of different pavement temperature values to the MRT model. The resulting MRT distribution appears quite unusual and does not seem physically reasonable. I am not sure whether this is caused by a mismatch between the temperature values and their corresponding locations, a data structure issue, or some limitation in the workflow itself.
I would like to ask whether this approach is feasible in principle, and if so, whether you could help me check if there is something wrong with my implementation or data mapping process.
Any suggestions or guidance would be greatly appreciated.
I am currently encountering a problem when using the LB Outdoor Solar MRT component and would greatly appreciate any help or suggestions.
I first calculated the outdoor surface temperatures using the Honeybee/Ladybug workflow. The resulting surface temperature distribution appears to be completely correct and matches my expectations. The temperature heat map is smooth and spatially continuous (see the first image).
However, after I use the same surface temperature data collection as the _surface_temp input for the LB Outdoor Solar MRT component, the resulting MRT distribution becomes severely distorted (see the second image). The spatial pattern changes dramatically and many values appear to be assigned to incorrect locations.
My suspicion is that the issue may be related to the data structure or data ordering. It seems possible that the original sequence of surface temperatures is being reordered somewhere during the MRT calculation process, causing the MRT results to no longer correspond to the correct analysis points/faces.
Specifically:
The surface temperature results appear correct before entering the MRT component.
The MRT heat map becomes spatially scrambled after the MRT calculation.
The issue looks more like a data matching/order problem rather than a physical calculation problem.
I have checked the number of values and they appear to be consistent, but I am not sure whether the data tree structure or item ordering is being modified internally.
I have attached two images for comparison:
Surface temperature results (correct spatial pattern).
MRT results after using the same temperature series as input (severely disordered pattern).
Has anyone experienced a similar issue before?
Could the OutdoorSolarCal / Outdoor Solar MRT component be changing the order of the input data collections, or is there another step where the correspondence between temperatures and analysis points may be lost?
I am currently unable to identify where the problem originates and would be extremely grateful for any guidance or suggestions.
