HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 25 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1836 to 2023, but the start time is dependent on climate variable and temporal resolution.

The gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.

This data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2019, see linked documentation).

The changes for v1.3.0.ceda HadUK-Grid datasets are as follows:

• Added data for calendar year 2023

• Added newly digitised data for daily rainfall (62 Scottish stations for 1945-1960)

• Daily rainfall data for Bolton, 1916-1919 have been corrected (previous values were corrupted and needed redigitising)

• Daily rainfall data for Buxton, 1960 have been corrected (conversion from inches to mm had been applied incorrectly)

• Rainfall data from EA and SEPA APIs are included for the last three months of the dataset (Oct-Dec 2023) (for all earlier months the rainfall data from partner agencies is obtained from the Met Office's MIDAS database)

• The number of stations used for groundfrost, sunshine and windspeed have reduced at different points in the historical series when comparing v1.3.0.ceda to the previous version v1.2.0.ceda. These reductions in station numbers have been caused by changes made in the data processing steps upstream of the gridding process.

• For groundfrost this reduction has been caused by an automated quality control process flagging the historical data which have been removed as suspect (mostly affecting data from 1961 to 1970).

• For sunshine the small reduction in the 1960s has been caused by the removal of digitized monthly sunshine data through this period where we wish to reverify the data source.

• For windspeed the reduction from 1969 to 2010 has been caused by changes to rules applied relating to data completeness when compiling daily mean windspeeds, which in turn have followed through to monthly statistics.

• We plan to carry out a review of the data which have been excluded from this version. Some of it may be reintroduced in a future release.

• Net changes to the input station data:

• Total of 126970983 observations

• 125384735 (98.75%) unchanged

• 28487 (0.02%) modified for this version

• 1557761 (1.23%) added in this version

• 188522 (0.15%) deleted from this version

The primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project "Analysis of historic drought and water scarcity in the UK"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence.

HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 12 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from climate projections. The dataset spans the period from 1836 to 2023, but the start time is dependent on climate variable and temporal resolution.

The gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.

This data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2019, see linked documentation).

The changes for v1.3.0.ceda HadUK-Grid datasets are as follows:

• Added data for calendar year 2023

• Added newly digitised data for daily rainfall (62 Scottish stations for 1945-1960)

• Daily rainfall data for Bolton, 1916-1919 have been corrected (previous values were corrupted and needed redigitising)

• Daily rainfall data for Buxton, 1960 have been corrected (conversion from inches to mm had been applied incorrectly)

• Rainfall data from EA and SEPA APIs are included for the last three months of the dataset (Oct-Dec 2023) (for all earlier months the rainfall data from partner agencies is obtained from the Met Office's MIDAS database)

• The number of stations used for groundfrost, sunshine and windspeed have reduced at different points in the historical series when comparing v1.3.0.ceda to the previous version v1.2.0.ceda. These reductions in station numbers have been caused by changes made in the data processing steps upstream of the gridding process.

• For groundfrost this reduction has been caused by an automated quality control process flagging the historical data which have been removed as suspect (mostly affecting data from 1961 to 1970).

• For sunshine the small reduction in the 1960s has been caused by the removal of digitized monthly sunshine data through this period where we wish to reverify the data source.

• For windspeed the reduction from 1969 to 2010 has been caused by changes to rules applied relating to data completeness when compiling daily mean windspeeds, which in turn have followed through to monthly statistics.

• We plan to carry out a review of the data which have been excluded from this version. Some of it may be reintroduced in a future release.

• Net changes to the input station data:

• Total of 126970983 observations

• 125384735 (98.75%) unchanged

• 28487 (0.02%) modified for this version

• 1557761 (1.23%) added in this version

• 188522 (0.15%) deleted from this version

The primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project "Analysis of historic drought and water scarcity in the UK"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence.

This dataset includes all the data and R code needed to reproduce the analyses in a forthcoming manuscript:Copes, W. E., Q. D. Read, and B. J. Smith. Environmental influences on drying rate of spray applied disinfestants from horticultural production services. PhytoFrontiers, DOI pending.Study description: Instructions for disinfestants typically specify a dose and a contact time to kill plant pathogens on production surfaces. A problem occurs when disinfestants are applied to large production areas where the evaporation rate is affected by weather conditions. The common contact time recommendation of 10 min may not be achieved under hot, sunny conditions that promote fast drying. This study is an investigation into how the evaporation rates of six commercial disinfestants vary when applied to six types of substrate materials under cool to hot and cloudy to sunny weather conditions. Initially, disinfestants with low surface tension spread out to provide 100% coverage and disinfestants with high surface tension beaded up to provide about 60% coverage when applied to hard smooth surfaces. Disinfestants applied to porous materials were quickly absorbed into the body of the material, such as wood and concrete. Even though disinfestants evaporated faster under hot sunny conditions than under cool cloudy conditions, coverage was reduced considerably in the first 2.5 min under most weather conditions and reduced to less than or equal to 50% coverage by 5 min. Dataset contents: This dataset includes R code to import the data and fit Bayesian statistical models using the model fitting software CmdStan, interfaced with R using the packages brms and cmdstanr. The models (one for 2022 and one for 2023) compare how quickly different spray-applied disinfestants dry, depending on what chemical was sprayed, what surface material it was sprayed onto, and what the weather conditions were at the time. Next, the statistical models are used to generate predictions and compare mean drying rates between the disinfestants, surface materials, and weather conditions. Finally, tables and figures are created. These files are included:Drying2022.csv: drying rate data for the 2022 experimental runWeather2022.csv: weather data for the 2022 experimental runDrying2023.csv: drying rate data for the 2023 experimental runWeather2023.csv: weather data for the 2023 experimental rundisinfestant_drying_analysis.Rmd: RMarkdown notebook with all data processing, analysis, and table creation codedisinfestant_drying_analysis.html: rendered output of notebookMS_figures.R: additional R code to create figures formatted for journal requirementsfit2022_discretetime_weather_solar.rds: fitted brms model object for 2022. This will allow users to reproduce the model prediction results without having to refit the model, which was originally fit on a high-performance computing clusterfit2023_discretetime_weather_solar.rds: fitted brms model object for 2023data_dictionary.xlsx: descriptions of each column in the CSV data files

Data Source

Data Source : UK GOV

Sunshine data taken from a Campbell Stokes recorder.

(no more from an automatic Kipp & Zonen sensor marked with a #)

Content

Place: Cambridge.

Location: 543500E 260600N, Lat 52.245 Lon 0.102.

Height above mean sea level: 26 metres.

Columns

• year: Date in format YYYY.
• month: Date in format MM.
• tmax: Maximum temperature of the day in °C.
• tmin: Minimum temperature of the day in °C.
• af: Numbers of air frost days in a month.
• rain: Rainfall in millimeters.
• sun_hr: Sun hours in hours.

Missing Value

Missing values are marked as -1.

Data Processing

• Process RAW data (plain text) to csv format.
• Original data is marked with a * after the value indicating Estimated.
  • -> Symbol * has been removed and treated as normal value.
• Missing data (more than 2 days missing in month) is marked by ---.
  • -> This is marked as -1, indicating missing value

This dataset provides values for TEMPERATURE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset contains GPS tracking data and performance metrics for motorcycle taxis (boda bodas) in Nairobi, Kenya, comparing traditional internal combustion engine (ICE) motorcycles with electric motorcycles. The study was conducted in two phases:Baseline Phase: 118 ICE motorcycles tracked over 14 days (2023-11-13 to 2023-11-26)Transition Phase: 108 ICE motorcycles (control) and 9 electric motorcycles (treatment) tracked over 12 days (2023-12-10 to 2023-12-21)The dataset is organised into two main categories:Trip Data: Individual trip-level records containing timing, distance, duration, location, and speed metricsDaily Data: Daily aggregated summaries containing usage metrics, economic data, and energy consumptionThis dataset enables comparative analysis of electric vs. ICE motorcycle performance, economic modelling of transportation costs, environmental impact assessment, urban mobility pattern analysis, and energy efficiency studies in emerging markets.Institutions:EED AdvisoryClean Air TaskforceStellenbosch UniversitySteps to reproduce:Raw Data CollectionGPS tracking devices installed on motorcycles, collecting location data at 10-second intervalsRider-reported information on revenue, maintenance costs, and fuel/electricity usageProcessing StepsGPS data cleaning: Filtered invalid coordinates, removed duplicates, interpolated missing pointsTrip identification: Defined by >1 minute stationary periods or ignition cyclesTrip metrics calculation: Distance, duration, idle time, average/max speedsDaily data aggregation: Summed by user_id and date with self-reported economic dataValidation: Cross-checked with rider logs and known routesAnonymisation: Removed start and end coordinates for first and last trips of each day to protect rider privacy and home locationsTechnical InformationGeographic coverage: Nairobi, KenyaTime period: November-December 2023Time zone: UTC+3 (East Africa Time)Currency: Kenyan Shillings (KES)Data format: CSV filesSoftware used: Python 3.8 (pandas, numpy, geopy)Notes: Some location data points are intentionally missing to protect rider privacy. Self-reported economic and energy consumption data has some missing values where riders did not report.CategoriesMotorcycle, Transportation in Africa, Electric Vehicles

This dataset provides measurements of temperature, humidity, rainfall, wind, and sunlight at Ambient Weather and OttHydro stations across Baltimore city. These surface weather stations were deployed by the Baltimore Social-Environmental Collaborative (BSEC) Urban Integrated Field Laboratory (UIFL) project, funded by the Department of Energy (DOE). This dataset currently contains measurements from 2023 to June 2025 and will be periodically updated to include more stations and recent observations when available. Data File Information This dataset contains surface weather measurements data in comma-separated value (CSV) format and documents that describe the weather stations, locations, and measured parameters and units. data/[TIMEAVG]/[YEAR]/BSEC-[STATIONID]_[SENSORTYPE]_[TIMEAVG]_[YEAR].csv Surface weather measurements data in CSV format, where STATIONID indicates the weather station, SENSORTYPE is the type of weather station ('AWS' = Ambient Weather Station and 'OTT' = 'OttHydro Station'), TIMEAVG is time period for each entry (= daily, hourly, or 5min), and YEAR indicate the year in which the measurements were made. Example data file name: BSEC-AAC_AWS_hourly_2023.csv. documents/Station_Locations.csv This CSV file provides location information and measurement start date for each surface weather station. documents/Weather_Station_Descriptions.pdf This document provides detailed description of the instruments along with their setup and accuracy of measurement. documents/File_Contents.pdf This document describes the contents on the data files, including time notation, weather parameters and units of measurement.

This dataset comprises six-hourly Lamb Weather Type (LWT) time series covering the period 1979-2005 for a) historical experiments run with 61 distinct GCMs from CMIP5 and 6 (specified in "get_historical_metadata.py" published at https://doi.org/10.5281/zenodo.4555367) and b) 3 distinct reanalyses (ERA-Interim, JRA-55 and ERA5, the latter extended to 2020). The LWT time series are provided on a 2.5º regular latitude-longitude grid covering the southern hemisphere between 30ºS and 70ºS. The full LWT approach covering 27 classes is applied and the corresponding results for the Northern Hemisphere were stored in a companion dataset at https://doi.org/10.5281/zenodo.4452080. The format of the files is netCDF-4, compressed with the netCDF Kitchen Sink command "ncks -4 -L 1". The Python code used to generate this dataset is available from https://doi.org/10.5281/zenodo.4555367

contact: Swen Brands, brandssf@ifca.unican.es

Principal Research Articles, Software and Complementary Datasets Associated with this Dataset

Brands, S. (2022). A circulation-based performance atlas of the CMIP5 and
6 models for regional climate studies in the Northern Hemisphere mid-to-
high latitudes. Geoscientific Model Development, 15 (4), 1375–1411.
doi: https://doi.org/10.5194/gmd-15-1375-2022

Brands, S. (2022). A circulation-based performance atlas of the CMIP5 and 6 mod-
els for regional climate studies in the northern hemisphere [data set]. Zenodo.
doi: https://doi.org/10.5281/zenodo.4452080

Brands, S. (2022). Common error patterns in the regional atmospheric circulation
simulated by the CMIP multi-model ensemble. Geophysical Research Letters,
49 (23), e2022GL101446. doi: https://doi.org/10.1029/2022GL101446

Brands, S. (2022). Python code to calculate Lamb circulation types for the North-
ern Hemisphere derived from historical CMIP simulations and reanalysis data
[code]. Zenodo. doi: https://doi.org/10.5281/zenodo.4555367

Brands, S., Fernández-Granja, J. A., Bedia, J., Casanueva, A., & Fernández,
J. (2023). Auxiliary online material to Brands et al. (2023): A global
climate model performance atlas for the Southern Hemisphere extratrop-
ics based on regional atmospheric circulation patterns. figshare. doi:
https://doi.org/10.6084/m9.figshare.22193443.v1

Brands, S., Fernández-Granja, J. A., Bedia, J., Casanueva, A., & Fernández,
J. (2023b). Southern Hemisphere Lamb Weather Types from historical
GCM experiments and various reanalyses (1.0) [data set]. Zenodo. doi:
https://doi.org/10.5281/zenodo.7612988

Brands, S., Tatebe, H., Danek, C., Fernández, J., Swart, N., Volodin, E., . . . Tong-
wen, W. (2023). GCM metadata archive get historical metadata.py (v1.1).
Zenodo. doi: https://doi.org/10.5281/zenodo.7715383

Fernández-Granja, J. A., Brands, S., Bedia, J., Casanueva, A., & Fernández, J.
(2023). Exploring the limits of the Jenkinson–Collison weather types clas-
sification scheme: a global assessment based on various reanalyses.
Climate Dynamics. doi: 10.1007/s00382-022-06658-7

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G. L., . . . Yao, M.-S. (2020). GISS-E2.1: Configurations and climatology.
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The meteorological data were recorded at Weißseespitze (3500 m) in the Ötztal Alps/Austria during the FWF project Cold Ice, P 29256-N36. This is the raw data as recorded, without quality control and without error corrections. Please be aware that before using the data the quality control is a necessary step before using the data in a scientific context. For example, at this high elevation site the albedo values recorded together with the radiation components contain values out of the range 0-1 because in the morning the sun directly shines into the lower sensors of the Hukseflux instruments which are designed to capture the reflected radiation. The AWS (Automatic Weather Station) was installed in October 2017 (46°50'46.56N, 10°43'4.59E, 3499 m) and mainly consists of Campbell Scientific (CS) components and a CR3000 data logger. Records of air temperature and humidity (Rotronic-HC2S3), air pressure (CS106 Vaisala PTB110), wind speed and direction (Young-05103-45), the energy balance (Hukseflux-NR01), snow accumulation and ice ablation by sonic ranging sensor (CS-SR50a) and ice temperatures in four different depths (CS225) are taken every minute and stored on ten-minute intervals with a UTC timestamp.The CS225 ice temperature sensors were initially installed at depths of -1m, -2m, -6m and -9m with reference to the 2017 ice surface as zero. The actual sensor depth thus depends on the ice ablation at the surface since 2017.Snow height is calculated as the difference between the SR50a instrument height and the measured distance to the surface. The instrument height is corrected and applied to the logger program regularly.

# FireSR Dataset

## Overview

**FireSR** is a dataset designed for the super-resolution and segmentation of wildfire-burned areas. It includes data for all wildfire events in Canada from 2017 to 2023 that exceed 2000 hectares in size, as reported by the National Burned Area Composite (NBAC). The dataset aims to support high-resolution daily monitoring and improve wildfire management using machine learning techniques.

## Dataset Structure

The dataset is organized into several directories, each containing data relevant to different aspects of wildfire monitoring:

- **S2**: Contains Sentinel-2 images.
- **pre**: Pre-fire Sentinel-2 images (high resolution).
- **post**: Post-fire Sentinel-2 images (high resolution).

- **mask**: Contains NBAC polygons, which serve as ground truth masks for the burned areas.
- **pre**: Burned area labels from the year before the fire, using the same spatial bounds as the fire events of the current year.
- **post**: Burned area labels corresponding to post-fire conditions.

- **MODIS**: Contains post-fire MODIS images (lower resolution).

- **LULC**: Contains land use/land cover data from ESRI Sentinel-2 10-Meter Land Use/Land Cover (2017-2023).

- **Daymet**: Contains weather data from Daymet V4: Daily Surface Weather and Climatological Summaries.

### File Naming Convention

Each GeoTIFF (.tif) file is named according to the format: `CA_

### Directory Structure

The dataset is organized as follows:

```
FireSR/
│
├── dataset/
│ ├── S2/
│ │ ├── post/
│ │ │ ├── CA_2017_AB_204.tif
│ │ │ ├── CA_2017_AB_2418.tif
│ │ │ └── ...
│ │ ├── pre/
│ │ │ ├── CA_2017_AB_204.tif
│ │ │ ├── CA_2017_AB_2418.tif
│ │ │ └── ...
│ ├── mask/
│ │ ├── post/
│ │ │ ├── CA_2017_AB_204.tif
│ │ │ ├── CA_2017_AB_2418.tif
│ │ │ └── ...
│ │ ├── pre/
│ │ │ ├── CA_2017_AB_204.tif
│ │ │ ├── CA_2017_AB_2418.tif
│ │ │ └── ...
│ ├── MODIS/
│ │ ├── CA_2017_AB_204.tif
│ │ ├── CA_2017_AB_2418.tif
│ │ └── ...
│ ├── LULC/
│ │ ├── CA_2017_AB_204.tif
│ │ ├── CA_2017_AB_2418.tif
│ │ └── ...
│ ├── Daymet/
│ │ ├── CA_2017_AB_204.tif
│ │ ├── CA_2017_AB_2418.tif
│ │ └── ...
```

### Spatial Resolution and Channels

- **Sentinel-2 (S2) Images**: 20 meters (Bands: B12, B8, B4)
- **MODIS Images**: 250 meters (Bands: B7, B2, B1)
- **NBAC Burned Area Labels**: 20 meters (1 channel, binary classification: burned/unburned)
- **Daymet Weather Data**: 1000 meters (7 channels: dayl, prcp, srad, swe, tmax, tmin, vp)
- **ESRI Land Use/Land Cover Data**: 10 meters (1 channel with 9 classes: water, trees, flooded vegetation, crops, built area, bare ground, snow/ice, clouds, rangeland)

**Daymet Weather Data**: The Daymet dataset includes seven channels that provide various weather-related parameters, which are crucial for understanding and modeling wildfire conditions:

| Name | Units | Min | Max | Description |

|------|-------|-----|-----|-------------|

| dayl | seconds | 0 | 86400 | Duration of the daylight period, based on the period of the day during which the sun is above a hypothetical flat horizon. |

| prcp | mm | 0 | 544 | Daily total precipitation, sum of all forms converted to water-equivalent. |

| srad | W/m^2 | 0 | 1051 | Incident shortwave radiation flux density, averaged over the daylight period of the day. |

| swe | kg/m^2 | 0 | 13931 | Snow water equivalent, representing the amount of water contained within the snowpack. |

| tmax | °C | -60 | 60 | Daily maximum 2-meter air temperature. |

| tmin | °C | -60 | 42 | Daily minimum 2-meter air temperature. |

| vp | Pa | 0 | 8230 | Daily average partial pressure of water vapor. |

**ESRI Land Use/Land Cover Data**: The ESRI 10m Annual Land Cover dataset provides a time series of global maps of land use and land cover (LULC) from 2017 to 2023 at a 10-meter resolution. These maps are derived from ESA Sentinel-2 imagery and are generated by Impact Observatory using a deep learning model trained on billions of human-labeled pixels. Each map is a composite of LULC predictions for 9 classes throughout the year, offering a representative snapshot of each year.

| Class Value | Land Cover Class |

|-------------|------------------|

| 1 | Water |

| 2 | Trees |

| 4 | Flooded Vegetation |

| 5 | Crops |

| 7 | Built Area |

| 8 | Bare Ground |

| 9 | Snow/Ice |

| 10 | Clouds |

| 11 | Rangeland |

## Usage Tutorial

To help users get started with FireSR, we provide a comprehensive tutorial with scripts for data extraction and processing. Below is an example workflow:

### Step 1: Extract FireSR.tar.gz

```bash
tar -xvf FireSR.tar.gz
```

### Step 2: Tiling the GeoTIFF Files

The dataset contains high-resolution GeoTIFF files. For machine learning models, it may be useful to tile these images into smaller patches. Here's a Python script to tile the images:

```python
import rasterio
from rasterio.windows import Window
import os

def tile_image(image_path, output_dir, tile_size=128):
with rasterio.open(image_path) as src:
for i in range(0, src.height, tile_size):
for j in range(0, src.width, tile_size):
window = Window(j, i, tile_size, tile_size)
transform = src.window_transform(window)
outpath = os.path.join(output_dir, f"{os.path.basename(image_path).split('.')[0]}_{i}_{j}.tif")
with rasterio.open(outpath, 'w', driver='GTiff', height=tile_size, width=tile_size, count=src.count, dtype=src.dtypes[0], crs=src.crs, transform=transform) as dst:
dst.write(src.read(window=window))

# Example usage
tile_image('FireSR/dataset/S2/post/CA_2017_AB_204.tif', 'tiled_images/')
```

### Step 3: Loading Data into a Machine Learning Model

After tiling, the images can be loaded into a machine learning model using libraries like PyTorch or TensorFlow. Here's an example using PyTorch:

```python
import torch
from torch.utils.data import Dataset
from torchvision import transforms
import rasterio

class FireSRDataset(Dataset):
def _init_(self, image_dir, transform=None):
self.image_dir = image_dir
self.transform = transform
self.image_paths = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith('.tif')]

def _len_(self):
return len(self.image_paths)

def _getitem_(self, idx):
image_path = self.image_paths[idx]
with rasterio.open(image_path) as src:
image = src.read()
if self.transform:
image = self.transform(image)
return image

# Example usage
dataset = FireSRDataset('tiled_images/', transform=transforms.ToTensor())
dataloader = torch.utils.data.DataLoader(dataset, batch_size=16, shuffle=True)
```

## License

This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to share and adapt the material as long as appropriate credit is given.

## Contact

For any questions or further information, please contact:
- Name: Eric Brune
- Email: ebrune@kth.se

The Fauna Park Weather Station was established in 2023 by Théotime Colin at 211 Culloden Rd, Marsfield NSW 2122 (-33.768099, 151.112814; 33°46'05.2"S 151°06'46.1"E). The weather station is located on a grass patch surrounded by flight cages (polytunnels) on three sides (approximately 6m away from the ones north-east and south-west and 4m away from the one south-east of the weather station). A long metallic pole was burried 70cm deep into the ground and sensors were attached directly to the pole. The system is based on a HOBO (Onset : https://www.onsetcomp.com/products/software/hoboware) U30 data logger (U30-NRC-000-10-S100 : HOBO U30, with 10 Smart Sensor capability) powered by an extra-large solar panel (SOLAR-15W, 15 Watts for HOBO U30 and RX3000 (A1-13AA)). The following HOBO sensors were connected to this logger: S-WCF-M003 : Davis Wind Speed & Direction Smart Sensor (A1-13A) S-THC-M002 : Smart Temp/RH Sensor (12-bit) w/ 2m Cable (A1-11E) S-RGF-M002 : Davis® 0.2 mm Rain Gauge Smart Sensor (A1-11AA) S-BPB-CM50 : Weatherproof Barometric Pressure Smart Sensor with 50 cm cable (A1-11A) The temperature and RH sensor was attached hanging downwards under the solar panel to shield it from the sun. Data are logged every 10 minutes. Dataset of the Barron Bee Lab weather station (temperature, relative humidity, wind speed, wing gust speed, wind direction, rainfall, barometric pressure) logged every 10 min from a station located at the Macquarie University Fauna Park, Sydney, New South Wales, Australia. Funding provided by: Lord Mayor's Charitable FoundationROR ID: https://ror.org/04n7vs332Award Number:

Universal Thermal Climate Index (UTCI) is a physiological temperature that is widely used in biometeorological studies to assess the heat stress felt by humans. UTCI considers the shortwave and longwave radiation incident on humans from the six cubical directions as well as air temperature, humidity, wind speed and clothing. As a part of NOAA National Integrated Heat Health Information System (NIHHIS) and NASA Interdisciplinary Research in Earth Science (IDS) project, we have generated the UTCI data for Austin, Texas and surrounding peri-urban area at 2-meters spatial resolution for the year 2017. Details on data generation and methodology can be found in Kamath et al., (2023) but are summarized here.

1. Datasets and model used

The solar and longwave environmental irradiance geometry (SOLWEIG) model was used to simulate shadows, mean radiant temperature (T_MRT) and the UTCI (Lindberg et al., 2008). T_MRT is the equivalent temperature due to exposure to absorbed shortwave and longwave radiation from all directions in a standing position. SOLWEIG was forced using near-surface ERA-5 data available at a spatial resolution of 0.25°x 0.25°. Building, vegetation heights, and digital terrain model were again derived from 3DEP LiDAR point cloud data. SOLWEIG was run using the urban multi-scale environment predictor (UMEP) (Lindberg et al., 2018) plug-in with QGIS.

2. Data availability

Diurnal UTCI data were calculated for typical meteorological clear sky days corresponding to Summer and Fall. The typical clear sky day was selected using the 10-year Typical meteorological Year (TMY) for Austin, Texas (30.2672° N, 97.7431° W) provided by National Solar Radiation Database (NSRDB). More details on TMY files can be found at: https://nsrdb.nrel.gov/data-sets/tmy

Additionally, data is developed for heat hazard for daytime Human Heat Health Index (H3I) calculation as defined by Kamath et al., (2023). Briefly, this heat hazard is defined as the fraction of the day when the UTCI exceeds certain threshold. The threshold used to calculate heat hazard for Summer and Fall were 35° C and 32°C, respectively that imply strong heat stress (Jendritzky et al., 2012). Note that UTCI is on a different scale compared to air temperature, and could yield different heat stress levels.

3. Data format

The georeferenced UTCI and heat hazard data are available in the geoTIFF file format. The files can be readily visualized using GIS software such as QGIS and ArcGIS, as well as programing languages such as Python.

4. Companion dataset

Based on the calculated UTCI here, the potential locations for tree planting were calculated to increase the shade to reduce heat vulnerability for Austin, Texas. [https://doi.org/10.5281/zenodo.6363494]

References

1. Kamath, H. G., Martilli, A., Singh, M., Brooks, T., Lanza, K., Bixler, R. P., ... & Niyogi, D. (2023). Human heat health index (H3I) for holistic assessment of heat hazard and mitigation strategies beyond urban heat islands. Urban Climate, 52, 101675.
2. Lindberg, F., Holmer, B., & Thorsson, S. (2008). SOLWEIG 1.0–Modelling spatial variations of 3D radiant fluxes and mean radiant temperature in complex urban settings. International journal of biometeorology, 52, 697-713.
3. Lindberg, F., Grimmond, C. S. B., Gabey, A., Huang, B., Kent, C. W., Sun, T., ... & Zhang, Z. (2018). Urban Multi-scale Environmental Predictor (UMEP): An integrated tool for city-based climate services. Environmental modelling & software, 99, 70-87.
4. Jendritzky, G., de Dear, R., & Havenith, G. (2012). UTCI—why another thermal index?. International journal of biometeorology, 56, 421-428.
5. Bixler, R. P., Coudert, M., Richter, S. M., Jones, J. M., Llanes Pulido, C., Akhavan, N., ... & Niyogi, D. (2022). Reflexive co-production for urban resilience: Guiding framework and experiences from Austin, Texas. Frontiers in Sustainable Cities, 4, 1015630.
6. Lanza, K., Jones, J., Acuña, F., Coudert, M., Bixler, R. P., Kamath, H., & Niyogi, D. (2023). Heat vulnerability of Latino and Black residents in a low-income community and their recommended adaptation strategies: A qualitative study. Urban Climate, 51, 101656.

The dataset encompasses the following data:

1. Orthomosaic Images:

   • ortho_230609.jpg: A detailed RGB orthomosaic of the entire vineyard. This orthomosaic was meticulously generated using Agisoft Metashape, employing images from the RGB.zip folder. The creation process involved medium accuracy settings.
   • ortho_230609_ndvi.jpg: An orthomosaic captured in Near-Infrared (NIR) spectrum, portraying the entirety of the vineyard. This NIR orthomosaic was crafted through Agisoft Metashape, utilizing both NIR images and the Red channel of the RGB images from the NIR.zip and RGB.zip folders. The generation process employed medium accuracy settings.

2. Drone-Captured Image Folders:

   • RGB.zip: A collection of RGB images obtained using a DJI MAVIC 3M drone. These images were gathered during a flight at an altitude of 10 meters, ensuring 80% frontal and side overlap between each image.
   • NIR.zip: A compilation of Near-Infrared (NIR) images collected through the same manual drone flight process.

The data collection procedure involved flying the DJI MAVIC 3M drone over the vineyard in Canyelles in Catalonia, Spain. The flight took place under sunny weather conditions with an average temperature of 26 degrees Celsius.

About *** heat wave days were recorded across India in 2022, a decrease compared to the previous year. In recent years, these events were more intense in the northern regions of the country, coinciding with droughts, water shortage and an already inadequate infrastructure. The impact of heatwaves Illnesses and deaths are an obvious consequence of the impact of heat waves in India. With record breaking heat year after year, temperatures were recorded in the high 40s and low ** degree Celsius. For comparison, core human temperatures of ** degree Celsius are categorized as fever, requiring medical attention. In extreme cases, permanent brain damage can occur, or even death. Precaution and mitigation Inconsistent rains or unmitigated torrential rains, along with depleting groundwater reserves and droughts have led to severe water shortages across vast areas of the country, leading to a dependence on water tankers. These include cities like Chennai, Coimbatore, parts of Jharkhand, and Madhya Pradesh. India’s National Disaster Management Authority aims to keep heat related deaths at single digits. Measures to achieve that include increasing public awareness and distributing free water. In parts of Rajasthan, advisory actions were followed by pouring water onto asphalt roads to prevent them from melting during summer.

Leaf area index (LAI) quantified the density of vegetation irrespective of land cover. LAI quantifies the total foliage surface area per groud surface area. LAI has been identified by the Global Climate Observing System as an essential climate variable required for ecosystem,weather and climate modelling and monitoring. This product consists of annual maps of the maximum LAI during a grownig season (June-July-August) at 100m resolution covering Canada's land mass.

Abstract of the article to which the data and code belong:

Forest canopies can buffer the understory against temperature extremes often creating cooler microclimates during warm summer days compared to temperatures outside the forest. The buffering of maximum temperatures in the understory results from a combination of canopy shading and air cooling through soil water evaporation and plant transpiration. Therefore, buffering capacity of forests depends on canopy cover and soil moisture content, which are increasingly affected by more frequent and severe canopy disturbances and soil droughts. The extent to which this buffering will be maintained in future conditions is unclear due to the lack of understanding about the relationship between soil moisture and air temperature buffering in interaction with canopy cover and topographic settings. We explored how soil moisture variability affects temperature offsets between outside and inside the forest on a daily basis, using temperature and soil moisture data from 54 sites in temperate broadleaf forests in Central Europe over four climatically different summer seasons. Daily maximum temperatures in forest understories were on average 2 °C cooler than outside temperatures. () The buffering of understory temperatures was more effective when soil moisture was higher, and the offsets were more sensitive to soil moisture on sites with drier soils and on sun-exposed slopes with high topographic heat load. Based on these results, the soil-water limitation to forest temperature buffering will become more prevalent under future warmer conditions and will likely lead to changes in understory communities. Thus, our results highlight the urgent need to include soil moisture in models and predictions of forest microclimate, understory biodiversity and tree regeneration, to provide a more precise estimate of the effects of climate change.

List of files:

02_model_offset_from_soilmoist_rev.r => R-script for the statistical analysis

model_data_complete_figshare.csv => cleaned and complete data for the statistical analysis

model_data_4thday_figshare.csv => cleaned and "thinned" data for the statistical analysis

README.txt => metadata describing columns in the dataframes and the environment of the R-script

The world's longest homogenised series (1976-2023) of biologically effective solar radiation data (daily exposure and midday irradiance) is presented. The following biological effects on the skin were considered: reddening, production of vitamin D3 and healing of psoriasis. The data were obtained from regular UV monitoring in Belsk using different erythemal broadband radiometers. These were Robertson-Berger (1975-1992), Solar Light model 501 (1993-1994 with # 927, 1995-2013 with # 2011) and Kipp-Zonen UV-AE-T # 30616 from 5 August 2013 to the present. The data were homogenised by multiplying the raw measured data by the daily calibration coefficients, obtained by comparing the measured erythemal irradiances at noon with those calculated from the radiative transfer model (TUV, 2024) simulations for cloudless days. Vitaminal and antipsoriatic values were obtained using the data conversion method developed in IGF PAS (Czerwińska and Krzyścin, 2024) to estimate any biologically effective irradiance from the measured erythemal irradiance. The database contains the following daily values since 1 January 1976: raw and reevaluated daily erythemal radiant exposure and midday irradiance, calibration coefficients, erythema-vitamin D3 and erythema-antipsoriasis conversion coefficients (for daily radiant exposures and midday irradiance), vitaminal and antipsoriatic radiant exposure and midday irradiance, radiant exposure and midday irradiance in cloudless days by TUV model for all biological effects considered. In addition, the daily values of variables to be used in UV reconstructions by statistical methods (i.e. the so-called regressors of the model) are also included in the database. These are total column ozone, aerosol optical depth, global solar irradiance, sunshine duration, relative sunshine duration (as a percentage of the length of the day) and the clearness index. References: Czerwińska, A., and Krzyścin, J.: Measurements of biologically effective solar radiation using erythemal weighted broadband meters. Photochem. Photobiol. Sci., 23, 479–492, https://doi.org/10.1007/s43630-023-00532-z, 2024. Tropospheric Ultraviolet and Visible (TUV) Radiation Mode (2024). Available on line: https://www2.acom.ucar.edu/modeling/tropospheric-ultraviolet-and-visible-tuv-radiation-model, last access 11 December 2024.

The generated dataset contains radio frequency (RF) signal data for a period of one month, from May 5, 2023, to June 11, 2023 collected via SDR hardware interfaced to DragonOS Focal. Each row of the dataset represents a single RF signal observation, with various features that describe the signal and its environment.

The dataset can be used for tasks such as machine learning, statistical analysis, and signal processing.

The following is a detailed description of each feature in the dataset:

• Timestamp: The date and time of the signal observation.
• Frequency: The frequency of the RF signal in Hertz (Hz).
• Signal Strength: The strength of the RF signal in decibels relative to one milliwatt (dBm).
• Modulation: The modulation type used for the RF signal. Possible options include Amplitude Modulation (AM), - ---
• Frequency Modulation (FM), Quadrature Amplitude Modulation (QAM), Binary Phase Shift Keying (BPSK), Quadrature - Phase Shift Keying (QPSK), and 8 Phase Shift Keying (8PSK).
• Bandwidth: The bandwidth of the RF signal in Hertz (Hz).
• Location: The location where the signal was observed. The location is a string that includes the name of the city and the state/province.
• Device Type: The type of RF device used to generate the signal. Possible options include HackRF, Halow-U, and - - SteamDeck.
• Antenna Type: The type of antenna used to transmit the signal. Possible options include Omnidirectional, Directional, - Dipole, and Yagi.
• Temperature: The temperature at the location of the signal observation in degrees Celsius.
• Humidity: The relative humidity at the location of the signal observation as a percentage.
• Wind Speed: The speed of the wind at the location of the signal observation in kilometers per hour (km/hr).
• Precipitation: The amount of precipitation at the location of the signal observation in millimeters (mm).
• Weather Condition: The weather condition at the location of the signal observation. Possible options include Sunny, Rainy, and Cloudy.
• Interference Type: The type of interference present in the environment. Possible options include None, Co-channel, Adjacent-channel, and Intermodulation.
• Battery Level: The remaining battery level of the device used to generate the signal as a percentage.
• Power Source: Whether the device used to generate the signal is currently plugged into a power source or not.
• CPU Usage: The percentage of the CPU usage of the device used to generate the signal.
• Memory Usage: The percentage of the memory usage of the device used to generate the signal.
• WiFi Strength: The strength of the WiFi signal at the location of the signal observation in dBm.
• Disk Usage: The percentage of the disk usage of the device used to generate the signal.
• System Load: The system load of the device used to generate the signal.
• Latitude: The latitude of the location of the signal observation.
• Longitude: The longitude of the location of the signal observation.
• Altitude(m): The altitude of the location of the signal observation in meters.
• Air Pressure: The air pressure at the location of the signal observation in hectopascals (hPa).
• Device Status: The current status of the device used to generate the signal. Possible options include Streaming I/Q data, Transmitting beacon signal, and Running game.
• I/Q Data: The in-phase and quadrature components of the signal as a complex valued array.

The generated dataset can be used for various types of analysis and predictive analysis, which can help machine learning scientists in developing and testing models for RF signal processing, interference detection and mitigation, and device performance optimization. Some of the possible analysis and predictive analysis that can be performed using this data are:

• Signal Classification: The dataset can be used to classify RF signals based on their modulation type, frequency, bandwidth, and other features. This can help in identifying specific types of signals, such as voice or data transmissions, and can aid in tasks such as signal detection, interception, and decoding.

• Interference Detection: The dataset contains information about the type and level of interference present in the environment. This can be used to develop models for detecting and mitigating interference, which can improve the overall quality of the RF signal.

• Device Performance Optimization: The dataset includes information about the type of RF device used to generate the signal, as well as its CPU usage, memory usage, and battery level. This can be used to develop models for optimizing the performance of RF devices, such as reducing power consumption or improving signal quality.

• Weather Condition Analysis: The dataset provides information about the weather conditions at the time of signal observation, including temperature, humidity, wind speed, precipitation, and weather condition. This ...

Starting in 2003, the U.S. Geological Survey (USGS) Northern Rocky Mountain Science Center in West Glacier, MT, in collaboration with the National Park Service, collected avalanche observations along the Going to the Sun Road during the spring road-clearing operations. The spring road-clearing along Going to the Sun Road utilized a team of avalanche specialists from the USGS and Glacier National Park to communicate the potential avalanche hazard to crews working to clear the road of snow in preparation for summer visitation. The operations typically begin around April 1st and continue through mid-June each year. The dataset includes all of the specific details collected for each avalanche occurrence and conforms to SWAG (American Avalanche Association, 2016. Snow, Weather and Avalanches: Observation Guidelines for Avalanche Programs in the United States (3rd ed). Victor, ID). The records should be viewed as estimates of avalanche characteristics due to the fact that many of the av ...