Repository containing the installer and description of the MORICH dataset stored in the 4TU.ResearchData (https://data.4tu.nl/).
Full title of the dataset: Meteorological Observations and Radiometric Images for Cold climate Heatwave Investigation
Authors Information
Name: Miguel Martin
Institution: Delft University of Technology
ORCID: 0000-0003-2673-6844
Email: M.Martin@tudelft.nl
Name: Clara Garcia Sanchez
Institution: Delft University of Technology
ORCID: 0000-0002-5355-4272
Email: C.Garcia-Sanchez@tudelft.nl
Name: Jantien Stoter
Institution: Delft University of Technology
ORCID: 0000-0002-1393-7279
Email: J.E.Stoter@tudelft.nl
Name: Mario Berges (corresponding author)
Institution: Carnegie Mellon University
ORCID: 0000-0003-2948-9236
Email: mberges@andrew.cmu.edu
Date of data collection: 26/07/2024 - 20/09/2024
Geographic location of data collection: Lat. 40.4412551, Long. -79.9412649
Keywords to describe the data topic: Humid continental climate, Heatwave, Street-level weather stations, and Building-scale thermal images
Language information: English (American)
Funding sources that supported the collection of the data: National Aeronautics and Space Administration (NASA) as part of the Space Technology Research Institute (STRI) Habitats Optimized for Missions of Exploration (HOME) ‘SmartHab’ project [grant number 80NSSC19K1052].
This folder contains weather data collected at four positions in the university campus of Carnegie Mellon. It also includes measurements of indoor conditions in a building.
This folder contains thermal images collected at one position in the university campus of Carnegie Mellon. It has a subfolder Ground Truth (GT) storing measurements obtained by a contact surface sensor.
Subfolders of RI containing sequences of thermal images collected during the day DD/MM/YYYY.
Subfolder of RI generated using the LabelMe software to perform the technical validation of thermal images based on measurements of the contact surface sensor.
Subfolder of RI generated using the LabelMe software to illustrate the analysis that can be conducted using thermal images.
Weather data collected at position ID from DD/MM/YYYY HH:mm AM/PM (first from left) to DD/MM/YYYY HH:mm AM/PM (second from left). It comprises measurements of dry-bulb temperature (in degrees Fahrenheit), relative humidity (in percent), dew point temperature (in degrees Fahrenheit), wet bulb temperature (in degrees Fahrenheit), wind speed (in miles per hour), wind direction (in cardinal orientation), wind chill (in degrees Fahrenheit), heat index (in degrees Fahrenheit), temperatur-humidity-wind index (in degrees Fahrenheit), wind run (in miles), rain fall (in inches), heating degree days (-), and cooling degree days (-). Most of measurements are expressed with the min. value, average, and max. value calculated over a 5-minute period.
Indoor conditions of a room in a building collected from DD/MM/YYYY HH:mm AM/PM (first from left) to DD/MM/YYYY HH:mm AM/PM (second from left). It comprises measurements of dry-bulb temperature (in degrees Fahrenheit), relative humidity (in percent), dew point temperature (in degrees Fahrenheit), heat index (in degrees Fahrenheit), barometric pressure (in inches of mercury), and absolute pressure (in inches of mercury). Most of measurements are expressed with the min. value, average, and max. value calculated over a 5-minute period.
Sequence of thermal images collected from DD/MM/YYYY HH:mm:SS AM/PM (first from left) to DD/MM/YYYY HH:mm:SS AM/PM (second from left). Each frame of the sequence coontains radiometric values recored by the infrared camera at a specific time from the position it was installed.
Surface temperature collected by a contact surface sensor from DD/MM/YYYY HH:mm AM/PM (first from left) to DD/MM/YYYY HH:mm AM/PM (second from left). These measurements can be used to validate the surface temperature assessed from thermal images.
Jupyter notebook showing how weather data can be extracted using the Pandas Python library (https://pandas.pydata.org/).
Jupyter notebook showing how the technical validation of collected data during the field experiment was performed.
Jupyter notebook showing how thermal images can be analyzed using the irim module of the sciencespy Python library.
Licenses or restrictions placed on the data: CC BY 4.0
Links to publications that cite or use the data: under review
Recommended citation for the data: none for the moment
Weather data were collected from four Davis Vantage Vue weather stations (https://www.davisinstruments.com/pages/vantage-vue). The four stations were remotely connected to a WeatherLink Live gateway. In addition to synchronize data collected by weather stations, the gateway contained sensors that were used to measure indoor conditions. Data were collected every 5 minutes and observed from the WeatherLink web platform in realtime.
Thermal images were recorded using a FLIR A50 infrared camera with a field of view of 95 degrees (https://www.flir.com/products/a50_a70-smart-sensor). The camera was connected to laptop through an Ethernet cable. The laptop was continously operating the FLIR Research Studio software to manage the data collection. Using the software, thermal images were collected at a frequence of 1Hz and saved in a video file every 5 minutes.
To validate thermal images, the surface temperature was measured at a single position using a HOBO UX100-014M with a thermocouple of type J. The surface temperature was measured every minute.
Data processing of thermal images can be done using the irim module of the sciencespy Python library, which is publicly available and can be installed with PIP package installer as:
pip install sciencespy
Technical validation was performed by evaluating the discrepancy between the surface temperature measured by the contact sensor and this assessed from thermal images. Using parameters indicated in the jupyter notebook Technical_validation.ipynb, it was possible to achieve a RMSE below 2 degrees Celisus and a MBE below -0.5 degrees Celsius.
29 x 16275
| Name | Unit | Description |
|---|---|---|
| Date & Time | MM/DD/YYYY HH:mm AM/PM | Date and time of corresponding measurements |
| Temp - °F | degrees Fahrenheit | Average dry-bulb temperature |
| High Temp - °F | degrees Fahrenheit | Highest dry-bulb temperature |
| Low Temp - °F | degrees Fahrenheit | Lowest dry-bulb temperature |
| Hum - % | percent | Average relative humidity |
| High Hum - % | percent | Highest relative humidity |
| Low Hum - % | percent | Lowest relative humidity |
| Dew Point - °F | degrees Fahrenheit | Average dew point temperature |
| High Dew Point - °F | degrees Fahrenheit | Highest dew point temperature |
| Low Dew Point - °F | degrees Fahrenheit | Lowest dew point temperature |
| Wet Bulb - °F | degrees Fahrenheit | Average wet bulb temperature |
| High Wet Bulb - °F | degrees Fahrenheit | Highest wet bulb temperature |
| Low Wet Bulb - °F | degrees Fahrenheit | Lowest wet bulb temperature |
| Avg Wind Speed - mph | miles per hour | Average wind speed |
| High Wind Speed - mph | miles per hour | Highest wind speed |
| Prevailing Wind Direction | cardinal orientation | Average wind direction |
| High Wind Direction | cardinal orientation | Most frequent wind direction |
| Wind Chill - °F | degrees Fahrenheit | Average wind chill |
| Low Wind Chill - °F | degrees Fahrenheit | Lowest wind chill |
| Heat Index - °F | degrees Fahrenheit | Average heat index |
| High Heat Index - °F | degrees Fahrenheit | Highest heat index |
| THW Index - °F | degrees Fahrenheit | Average temperature-humidity-wind index |
| High THW Index - °F | degrees Fahrenheit | Highest temperature-humidity-wind index |
| Low THW Index - °F | degrees Fahrenheit | Lowest temperature-humidity-wind index |
| Wind Run | miles | Average wind run |
| Rain - in | inches | Average rainfall |
| High Rain Rate - in/h | inches per hour | Highest rainfall |
| Heating Degree Days | - | Heating degree days |
| Cooling Degree Days | - | Cooling degree days |
13 x 16275
| Name | Unit | Description |
|---|---|---|
| Date & Time | MM/DD/YYYY HH:mm AM/PM | Date and time of corresponding measurements |
| Inside Temp - °F | degrees Fahrenheit | Average indoor temperature |
| High Inside Temp - °F | degrees Fahrenheit | Highest indoor temperature |
| Low Inside Temp - °F | degrees Fahrenheit | Lowest indoor temperature |
| Inside Hum - % | percent | Average indoor relative humidity |
| High Inside Hum - % | percent | Highest indoor relative humidity |
| Low Inside Hum - % | percent | Lowest indoor relative humidity |
| Inside Dew Point - °F | degrees Fahrenheit | Average indoor dew point temperature |
| Inside Heat Index - °F | degrees Fahrenheit | Average indoor heat index |
| Barometer - in Hg | inches of mercury | Average indoor barometric pressure |
| High Bar - in Hg | inches of mercury | Highest indoor barometric pressure |
| Low Bar - in Hg | inches of mercury | Lowest indoor barometric pressure |
| Absolute Pressure - in Hg | inches of mercury | Average indoor absolute pressure |
2 x 60348
| Name | Unit | Description |
|---|---|---|
| Date Time, GMT-04:00 | MM/DD/YY HH:mm AM/PM | Date and time of corresponding measurements |
| K-Type, °C | degrees Celsius | Surface temperature |