About San Francisco San Francisco is a vibrant and dynamic city located on the west coast of the United States, in the state of California. Known for its hilly terrain, diverse neighborhoods, and iconic landmarks such as the Golden Gate Bridge and Alcatraz Island, San Francisco is a hub of culture, creativity, and innovation. The city is renowned for its world-class restaurants, thriving arts scene, and historic architecture, and is home to many tech companies and startups. With its mild climate, stunning views, and rich history, San Francisco is a must-visit destination for travelers from around the world.

About Dataset This dataset contains daily weather observations for San Francisco, USA from January 1, 1993 to January 1, 2023. The data is collected from Meteostat. The dataset contains 10 columns with 10958 rows.

For an urban heat island map during an average summer see this dataset. A heatwave refers to a prolonged period of unusually hot weather. While there is no standard definition of a heatwave in England, the Met Office uses the World Meteorological Organization definition of a heatwave, which is "when the daily maximum temperature of more than five consecutive days exceeds the average maximum temperature by 5°C, the normal period being 1961-1990". They are common in the northern and southern hemisphere during summer have historically been associated with health problems and an increase in mortality. The urban heat island (UHI) is the phenomenon where temperatures are relatively higher in cities compared to surrounding rural areas due to, for example, the urban surfaces and anthropogenic heat sources. This urban heat island map was produced using LondUM, a specific set-up of the Met Office Unified Model version 6.1 for London. It uses the Met Office Reading Surface Exchange Scheme (MORUSES), as well as urban morphology data derived from Virtual London. The model was run from May until September 2006 and December 2006. This map shows average surface temperatures over the summer period of 2006 at a 1km by 1km resolution. To find out more about LondUM, see the University of Reading’s website. The hourly outputs from LondUM have been aggregated and mapped by Jonathon Taylor, UCL Institute for Environmental Design and Engineering. Variables include: WSAVGMAX= the average of the maximum daily temperatures across the summer period (May 26th-August 31st) WSAVG=the average temperature across the summer period WSAVGMIN = the average minimum daily temperature across the summer period HWAVGMAX= the average of the maximum daily temperatures across the 2006 heatwave (July 16th-19th) HWAVG=the average temperature across the across the 2006 heatwave HWAVGMIN = the average minimum daily temperature across 2006 heatwave period The maps are also available as one combined PDF. The gif below maps the temperatures across London during the four-day period of 16-19th July, which was considered a heatwave. If you make use of the LondUM data, please use the following citation to acknowledge the data and reference the publication below for model description: LondUM (2011). Model data generated by Sylvia I. Bohnenstengel (), Department of Meteorology, University of Reading and data retrieved from http://www.met.reading.ac.uk/~sws07sib/home/LondUM.html. () Now at Metoffice@Reading, Email: sylvia.bohnenstengel@metoffice.gov.uk Bohnenstengel SI, Evans S, Clark P and Belcher SeE (2011) Simulations of the London Urban Heat island. Quarterly journal of the Royal Meteorological Society, 137(659). pp. 1625-1640. ISSN 1477-870X doi 10.1002/qj.855. LondUM data (2013).

For an urban heat island map during an average summer see this dataset.

A heatwave refers to a prolonged period of unusually hot weather. While there is no standard definition of a heatwave in England, the Met Office uses the World Meteorological Organization definition of a heatwave, which is "when the daily maximum temperature of more than five consecutive days exceeds the average maximum temperature by 5°C, the normal period being 1961-1990". They are common in the northern and southern hemisphere during summer have historically been associated with health problems and an increase in mortality.

The urban heat island (UHI) is the phenomenon where temperatures are relatively higher in cities compared to surrounding rural areas due to, for example, the urban surfaces and anthropogenic heat sources.

This urban heat island map was produced using LondUM, a specific set-up of the Met Office Unified Model version 6.1 for London. It uses the Met Office Reading Surface Exchange Scheme (MORUSES), as well as urban morphology data derived from Virtual London. The model was run from May until September 2006 and December 2006. This map shows average surface temperatures over the summer period of 2006 at a 1km by 1km resolution. To find out more about LondUM, see the University of Reading’s website.

The hourly outputs from LondUM have been aggregated and mapped by Jonathon Taylor, UCL Institute for Environmental Design and Engineering. Variables include:

• WSAVGMAX= the average of the maximum daily temperatures across the summer period (May 26th-August 31st)
• WSAVG=the average temperature across the summer period
• WSAVGMIN = the average minimum daily temperature across the summer period
• HWAVGMAX= the average of the maximum daily temperatures across the 2006 heatwave (July 16th-19th)
• HWAVG=the average temperature across the across the 2006 heatwave
• HWAVGMIN = the average minimum daily temperature across 2006 heatwave period

The maps are also available as one combined PDF.

The gif below maps the temperatures across London during the four-day period of 16-19th July, which was considered a heatwave.

If you make use of the LondUM data, please use the following citation to acknowledge the data and reference the publication below for model description:

LondUM (2011). Model data generated by Sylvia I. Bohnenstengel (*), Department of Meteorology, University of Reading and data retrieved from http://www.met.reading.ac.uk/~sws07sib/home/LondUM.html.

(*) Now at Metoffice@Reading, Email: sylvia.bohnenstengel@metoffice.gov.uk

Bohnenstengel SI, Evans S, Clark P and Belcher SeE (2011) Simulations of the London Urban Heat island. Quarterly journal of the Royal Meteorological Society, 137(659). pp. 1625-1640. ISSN 1477-870X doi 10.1002/qj.855. LondUM data (2013).

Context

Simple time series data for weather prediction time series projects.

Content

The data contains the following information from the UK Met Office location at London Heathrow Airport. The data runs from Jan 1948 to Oct 2020 and includes the following monthly data fields:

• yyyy = Year
• mm = Month
• tmax = Maximum temperature (Celsius)
• tmin = Minimum temperature (Celsius)
• af = Count of Air Frost days in the given month
• rain = Total rainfall (mm)
• sun = Sunshine duration (hrs)

Acknowledgements

Provided by the UK Met Office: https://www.metoffice.gov.uk/research/climate/maps-and-data/historic-station-data Available under Open Government Licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/

Example code

The following Python code will load into a Pandas DataFrame:

colspecs = [(3, 7), (9,11),(14,18),(22,26),(32,34),(37,42),(45,50)] data = pd.read_fwf('../input/heathrow-weather-data/heathrowdata.txt',colspecs=colspecs)

The following will remove the first few lines of text

data = data[3:].reset_index(drop=True) data.columns = data.iloc[1] data = data[3:].reset_index(drop=True)

A heatwave refers to a prolonged period of unusually hot weather. While there is no standard definition of a heatwave in England, the Met Office generally uses the World Meteorological Organization definition of a heatwave, which is "when the daily maximum temperature of more than five consecutive days exceeds the average maximum temperature by 5°C, the normal period being 1961-1990". They are common in the northern and southern hemisphere during summer, and have historically been associated with health problems and an increase in mortality. The urban heat island (UHI) is the phenomenon where temperatures are relatively higher in cities compared to surrounding rural areas due to, for example, the urban surfaces and anthropogenic heat sources. For an example of an urban heat island map during an average summer, see this dataset. For an example of an urban heat island map during a warm summer, see this dataset. As well as outdoor temperature, an individual’s heat exposure may also depend on the type of building they are inside, if indoors. Indoor temperature exposure may depend on a number of characteristics, such as the building geometry, construction materials, window sizes, and the ability to add extra ventilation. It is also known that people have different vulnerabilities to heat, with some more prone to negative health issues when exposed to high temperatures. This Triple Jeopardy dataset combines: Urban Heat Island information for London, based on the 55 days between May 26th -July 19th 2006, where the last four days were considered a heatwave An estimate of the indoor temperatures for individual dwellings in London across this time period Population age, as a proxy for heat vulnerability, and distribution From this, local levels of heat-related mortality were estimated using a mortality model derived from epidemiological data. The dataset comprises four layers: Ind_Temp_A – indoor Temperature Anomaly is the difference in degrees Celsius between the estimated indoor temperatures for dwellings and the average indoor temperature estimate for the whole of London, averaged by ward. Positive numbers show dwellings with a greater tendency to overheat in comparison with the London average HeatMortpM – total estimated mortality due to heat (outdoor and indoor) per million population over the entire 55 day period, inclusive of age effects HeatMorUHI – estimated mortality per million population due to increased outdoor temperature exposure caused by the UHI over the 55 day period (excluding the effect of overheating housing), inclusive of age effects HeatMorInd - estimated mortality per million population due to increased temperature exposure caused by heat-vulnerable dwellings (excluding the effect of the UHI) over the 55 day period, inclusive of age effects More information is on this website and in the Triple Jeopardy leaflet. The maps are also available as one combined PDF. More information is on this website and in the Triple Jeopardy leaflet.

Analysis of ‘London bike sharing dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/hmavrodiev/london-bike-sharing-dataset on 12 November 2021.

--- Dataset description provided by original source is as follows ---

License

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Transfer any intellectual property rights in the Information to You or any third party Include personal data in the Information Provide any rights to use the Information after this Licence has ended Provide any rights to use any other intellectual property rights, including patents, trade marks, and design rights or permit You to: Use data from the Oyster, Congestion Charging and Santander Cycles websites to populate or update any other software or database or Use any automated system, software or process to extract content and/or data, including trawling, data mining and screen scraping in relation to the Oyster, Congestion Charging and Santander Cycles websites, except where expressly permitted under a written licence agreement with TfL. These are important conditions of this Licence and, if You fail to comply with them, the rights granted to You under this Licence, or any similar licence granted by TfL, will end automatically.

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Context

The purpose is to try predict the future bike shares.

Content

The data is acquired from 3 sources:
- Https://cycling.data.tfl.gov.uk/ 'Contains OS data © Crown copyright and database rights 2016' and Geomni UK Map data © and database rights [2019] 'Powered by TfL Open Data'
- freemeteo.com - weather data
- https://www.gov.uk/bank-holidays
From 1/1/2015 to 31/12/2016

The data from cycling dataset is grouped by "Start time", this represent the count of new bike shares grouped by hour. The long duration shares are not taken in the count.

Metadata:

"timestamp" - timestamp field for grouping the data
"cnt" - the count of a new bike shares
"t1" - real temperature in C
"t2" - temperature in C "feels like"
"hum" - humidity in percentage
"wind_speed" - wind speed in km/h
"weather_code" - category of the weather
"is_holiday" - boolean field - 1 holiday / 0 non holiday
"is_weekend" - boolean field - 1 if the day is weekend
"season" - category field meteorological seasons: 0-spring ; 1-summer; 2-fall; 3-winter.

"weathe_code" category description:
1 = Clear ; mostly clear but have some values with haze/fog/patches of fog/ fog in vicinity
2 = scattered clouds / few clouds
3 = Broken clouds
4 = Cloudy
7 = Rain/ light Rain shower/ Light rain
10 = rain with thunderstorm
26 = snowfall
94 = Freezing Fog

--- Original source retains full ownership of the source dataset ---

NWP datasets (Met Office UKV model) from two days (2^nd May 2016, day of year (DOY) 123, and 5^th May 2016, DOY 126) used in Saunders et al. 2024. Day cases are separated by folder named in the format year + DOY.

The UKV data for the IOP days are from the operational runs, initialised at 21:00 UTC, with model output analysed from 00:00 UTC the following day for the period when observations are available. The operational UKV’s diagnostics output is on the hour.

Filenames are presented in the following example format:

MOUKV_FC2016050121Z_m01s00i002_LON_BCT.nc

This example would be from the UKV model (‘MOUKV’), from a run initialising at 2100 UTC on the 1^st May 2016 (FC2016050121Z) for the variable ‘U component of wind’ (identified by the stash code ‘m01s00i002’), with 3 by 3 grid boxes overlaying the ‘BCT’ site as described by Saunders et al. 2024 (‘LON_BCT’).

The following stash codes, used by Saunders et al. 2024, are available:

m01s00i002: U component of wind

m01s00i003: V component of wind

m01s00i010: Specific humidity

m01s00i024: Surface temperature

m01s00i025: Boundary layer depth

m01s00i090: Total aerosol

m01s00i150: W component of wind

m01s00i266: Bulk clod fraction

m01s00i408: Pressure at theta levels

m01s00i409: Surface pressure

m01s01i235: Total downward surface shortwave flux

m01s02i201: Outgoing shortwave radiative flux (top of atmosphere)

m01s02i207: Incoming shortwave radiative flux (top of atmosphere)

m01s03i026: Roughness length

m01s03i202: Heat flux from surface to deep soil level 1

m01s03i216: Boundary layer heat fluxes

m01s03i217: Surface sensible heat flux

m01s03i219: X component of surface and boundary layer wind stress

m01s03i220: Y component of surface and boundary layer wind stress

m01s03i222: Boundary layer total moisture fluxes

m01s03i225: 10-meter wind U component

m01s03i226: 10-meter wind V component

m01s03i234: Surface latent heat flux

m01s03i236: Temperature at 1.5 m

m01s03i245: Relative humidity at 1.5 m

m01s03i281: Visibility t 1.5 m (including precipitation)

m01s03i290: Surface sensible heat flux on tiles

m01s03i316: Surface temperature on tiles

m01s03i321: Canopy water on tiles

m01s03i476: Combined boundary layer type

m01s04i203: Large scale rainfall rate

m01s08i225: Deep soil temperature after hydrology

m01s09i203: Low cloud amount

m01s09i204: Medium cloud amount

m01s09i205: High cloud amount

m01s09i217: Total cloud amount

m01s16i004: Temperature on theta levels

m01s16i222: Pressure at mean sea level

London Underground records temperatures across the Underground in order to inform our cooling initiatives.

Platform temperatures on the deep Tube are routinely monitored using data loggers. Average monthly evening peak temperatures since 2013 are provided on this page.

We are investing millions as part of a long-term programme to ensure we can deliver more services, and continue to introduce new trains to meet growing customer numbers whilst providing more comfortable journeys. We have implemented energy efficient solutions on new trains, which saves energy and reduces the heat generated. We have also removed heat from our station and tunnel environment by installing new ventilation or cooling solutions.

On the Central line we have installed solar reflective material on the external surface of train roofs and solar reducing films on windows to minimise solar gains into the carriages.

On the Victoria line we have doubled the capacity of thirteen ventilation shafts, which provide more air flow, as well as introduced regenerative braking (which returns power to the rails while the train is braking) on the new trains. We have also installed air cooling units and mechanical chillers at some key busy stations, such as Oxford Circus, and have been using groundwater underneath Victoria Tube station to provide cooling to the platforms. We are similarly using water from the aquifer underneath Green Park to provide cooling for that Tube station. In addition, we have upgraded existing ventilation fans and installed new fans at a number of stations across the network.

As part of this work to make customer journeys more comfortable, we are also working to reduce temperatures inside carriages. 192 new air-conditioned trains are now in operation on the Circle, District, Hammersmith & City and Metropolitan lines.

Note: Sub-surface lines are the Circle, Hammersmith & City, District and Metropolitan lines.

These data are used in Chapter 3, “Urban multi-season sensible heat fluxes from multiple large aperture scintillometry paths: variability and operational numerical weather prediction skill” in the University of Reading thesis "Urban surface-atmosphere exchanges: scintillometry observations and NWP evaluation" submitted by Beth Saunders, 2024.

The NWP (Met Office UKV model) dataset consists of 109 days across 2016-2018. Some of the experimental setup is also described in Saunders et. al 2024: “Methodology to evaluate numerical weather predictions using large aperture scintillometry sensible heat fluxes: demonstration in London”, DOI: https://doi.org/10.1002/qj.4837" href="https://doi.org/10.1002/qj.4837" target="_blank" rel="noopener">https://doi.org/10.1002/qj.4837. File names and subdirectories are named in the format year + day of year (DOY).

The UKV data for the IOP days are from the operational runs, initialised at 21:00 UTC, with model output analysed from 00:00 UTC the following day for the period when observations are available. The operational UKV’s diagnostics output is on the hour.

Filenames are presented in the following example format:

MOUKV_FC2016050121Z_m01s00i002_LON_BCT.nc

This example would be from the UKV model (‘MOUKV’), from a run initialising at 2100 UTC on the 1^st May 2016 (FC2016050121Z) for the variable ‘U component of wind’ (identified by the stash code ‘m01s00i002’), with 3 by 3 grid boxes overlaying the ‘BCT’ site as described by Saunders et al. 2024 (‘LON_BCT’).

The following stash codes, used by Saunders et al. 2024, are available:

m01s00i002: U component of wind

m01s00i003: V component of wind

m01s00i010: Specific humidity

m01s00i024: Surface temperature

m01s00i025: Boundary layer depth

m01s00i090: Total aerosol

m01s00i150: W component of wind

m01s00i266: Bulk clod fraction

m01s00i408: Pressure at theta levels

m01s00i409: Surface pressure

m01s01i235: Total downward surface shortwave flux

m01s02i201: Outgoing shortwave radiative flux (top of atmosphere)

m01s02i207: Incoming shortwave radiative flux (top of atmosphere)

m01s03i026: Roughness length

m01s03i202: Heat flux from surface to deep soil level 1

m01s03i216: Boundary layer heat fluxes

m01s03i217: Surface sensible heat flux

m01s03i219: X component of surface and boundary layer wind stress

m01s03i220: Y component of surface and boundary layer wind stress

m01s03i222: Boundary layer total moisture fluxes

m01s03i225: 10-meter wind U component

m01s03i226: 10-meter wind V component

m01s03i234: Surface latent heat flux

m01s03i236: Temperature at 1.5 m

m01s03i245: Relative humidity at 1.5 m

m01s03i281: Visibility t 1.5 m (including precipitation)

m01s03i290: Surface sensible heat flux on tiles

m01s03i316: Surface temperature on tiles

m01s03i321: Canopy water on tiles

m01s03i476: Combined boundary layer type

m01s04i203: Large scale rainfall rate

m01s08i225: Deep soil temperature after hydrology

m01s09i203: Low cloud amount

m01s09i204: Medium cloud amount

m01s09i205: High cloud amount

m01s09i217: Total cloud amount

m01s16i004: Temperature on theta levels

m01s16i222: Pressure at mean sea level

Please note that the windspeed and track dataset only covers the part where the this was still a tropical cyclone. For explanation see below caveats. Due to this we will not release a priority index, since windspeed data is missing for most of the country.

Dataset of windspeed and track was kindly provided by University College London.

The rainfall data is calculated based on GPM. It is the accumulated rainfall from March 6th midnight to March 10th 10:00am Madagascar time.

This dataset contains:

• windspeeds of Typhoon Nina
• rainfall of Typhoon Nina
• Priority Index of Typhoon Nina

The predicted priority index of Typhoon Nina is produced by a machine learning algorithm that was trained on five past typhoons: Haiyan, Melor, Hagupit and Rammasun and Haima, It uses base line data for the whole country, combined with impact data of windspeeds and rains, and trained on counts by the Philippine government on houses damaged and completely destroyed.

The output is a weighted index between partially damaged and completely damaged, where partially damaged is counted as 25% of the completely damaged. This has proven to give he highest accuracy.

The absolute number of houses damaged / people affected is insufficiently validated at the moment, and should just be used for further trainng and ground-truthing.

Scoring The model has an best r2 score of 0.794933727 and an accuracy of 0.699470899

Data sources:

Administrative boundaries (P_Codes) - Philippines Government; Published by GADM and UN OCHA (HDX)
Census 2015 (population) - Philippine Statistics Authority; received from UN OCHA (HDX)
Avg. wind speed (mph) - University College London
Typhoon path - University College London
Houses damaged - NDRRMC
Rainfall - GPM
Poverty - Pantawid pamilyang pilipino program (aggregated)
Roof and wall materials
New geographical features

All the columns with feat_ indicates the importance of that feature, if not present that feature was not used.

learn_matrix name of the learning matrix with the 5 typhoons
run_name unique run name (pickle files and csv files have this name for this model)
typhoon_to_predict name of a new typhoon to predict
val_accuracy accuracy based on 10 categories of damage 0% 10% 20% …
val_perc_down perc of underpredicted categories
val_perc_up perc of overpredicted categories
Val_best_score best r2 score

Val_stdev_best_score error on best score based on the CV
Val_score_test r2 score on the test set (this should be around +- 5% of the previus number to not overfit

Val_mean_error_num_houses average error on the number of houses
val_median_error_num_houses median
val_std_error_num_houses std deviation of the errors (lower is better)

Algorithm developed by 510.global the data innovation initiative of the Netherlands Red Cross.

Surface synoptic weather reports for the entire globe for the 10-year period from December 1981 through November 1991 have been processed, edited, and rewritten to provide a data set designed for use in cloud analyses. The information in these reports relating to clouds, including the present weather information, was extracted and put through a series of quality control checks. Reports not meeting certain quality control standards were rejected, as were reports from buoys and automatic weather stations. Correctable inconsistencies within reports were edited for consistency, so that the "edited cloud report" can be used for cloud analysis without further quality checking. Cases of "sky obscured" were interpreted by reference to the present weather code as to whether they indicated fog, rain or snow and were given appropriate cloud type designations. Nimbostratus clouds, which are not specifically coded for in the standard synoptic code, were also given a special designation. Changes made to an original report are indicated in the edited report so that the original report can be reconstructed if desired. While low cloud amount is normally given directly in the synoptic report, the edited cloud report also includes the amounts, either directly reported or inferred, of middle and high clouds, both the non-overlapped amounts and the "actual" amounts (which may be overlapped). Since illumination from the moon is important for the adequate detection of clouds at night, both the relative lunar illuminance and the solar altitude are given, as well as a parameter that indicates whether our recommended illuminance criterion was satisfied. This data set contains 124 million reports from land stations and 15 million reports from ships. Each report is 56 characters in length. The archive consists of 240 files, one file for each month of data for land and ocean separately. With this data set a user can develop a climatology for any particular cloud type or group of types, for any geographical region and any spatial and temporal resolution desired. For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/epubs/ndp/ndp026b/ndp026b.html

The dataset consists of air temperatures recorded longitudinally and reported at hourly intervals using Hobo MX1101, Hobo MX1102A and Hobo MX2301 devices. The monitoring period covered 1st May 2022 to 30th September 2022 inclusive – the full non-heating season in England.

The devices were deployed in 30 care homes across England: eleven in Greater London, nine in the north of England as far north as Newcastle-upon-Tyne, six in the Midlands, and four in the south of England including on the Isle of Wight. The locations monitored consisted of 22 offices (staff-only areas such as manager’s offices, administrator offices, nurse stations), 30 lounges (communal areas such as lounges, dining rooms and lounge/diners), and 30 bedrooms (single rooms, with a range of occupancy – some vacant, some occupied only at night, others occupied 24/7 depending on resident needs). In addition, outdoor temperatures were monitored at each of the 30 care homes.

As a result of global climate change, the UK is expected to experience hotter and drier summers, and heatwaves are expected to occur with greater frequency, intensity and duration. In 2003 and 2018, 2,091 and 863 heat-related deaths, respectively, were reported in England alone as a result of heatwaves, meaning future temperature increases could lead to a parallel rise in heat-related mortality. The UK also currently has a rapidly ageing population, with people aged 75 or over expected to account for 13% of the total population by 2035. Older populations are more vulnerable to climate-induced effects as they are more likely to have underlying, chronic health complications, making them more vulnerable to heat stress. The indoor environment is a principle moderator of heat exposure in older populations, who tend to spend the majority of their time indoors. Poor building design, the lack of effective heat management and diverging needs and preferences between staff and residents in care settings may contribute to increased indoor heat exposure with detrimental health impacts falling on the most vulnerable residents. Maladaptation to a warming climate, such as the uptake of air conditioning, could increase fuel bills in care homes, increase operational costs for businesses in the already financially stretched care sector, and increase building carbon emissions, thus undermining government efforts to reduce greenhouse gas emissions.

The one-year pilot project 'Climate Resilience of Care Settings' and previous small-scale studies led by our research team have shown that UK care homes are already overheating even under non-extreme summers. A key target for climate adaptation in care settings is to limit such risks by introducing passive cooling strategies via building design. However, preliminary modelling as part of the pilot project also demonstrated that common passive cooling strategies may not adequately mitigate overheating risk in the 2050s and 2080s. Further research into advanced passive cooling strategies, combined with human behaviour and organisational change is required to identify optimum climate adaptation pathways for UK's care provision.

The main aim of the project is to quantify climate related heat risks in care settings nationwide and enhance understanding of human behaviour, organisational capacity and governance to enable the UK's care provision to develop equitable adaptation pathways to rising heat stress under climate change. Building on the foundations of the pilot project, this novel, interdisciplinary project will collect, for the first time in the UK, longitudinal temperature and humidity data in a panel of 50 care settings in order to quantify the recurring risk of summertime overheating. We will also identify and assess social, institutional and cultural barriers and opportunities underpinning the governance of adaptation to a warmer climate in care and extra-care homes through surveys with residents, frontline care staff, managers and policy stakeholders. Within sub-samples of this panel, we will use innovative measurement techniques to collect residents' physiological data and study their relation with heat exposure and health impacts. Also for the first time in the UK, we will create a building stock model of the UK's care provision able to predict future overheating risks in care settings under a range of future climate change scenarios. This will help evaluate the effectiveness of near, medium and long term future overheating mitigation strategies and policies on thermal comfort and health outcomes. Throughout the project, we will continue to develop and expand the stakeholder community that was created during the pilot project. Through ongoing dialogue with our diverse network of stakeholders, we will explore organisational capacity and structures, and how these influence action and policy, in order to generate best practice guidance for practitioners, businesses and policymakers.

This record is created as a data supplement for the manuscript "Estimated mortality attributable to the urban heat island during the record-breaking 2022 heatwave in London".

These data were produced using the Weather Research Forecasting model with BEP-BEM. The model setup is described in Brousse et al (2023) 10.1175/JAMC-D-22-0142.1. These data cover the period 2022-07-10 to 2022-07-25, during which temperatures exceding 40 °C were recorded in London for the first time.

The data comprise two NetCDF files. One is labelled "Urb" one "Nourb". In the "Nourb" file, the urban tile is removed from the model and the land surface replaced by the nearest natural tile. This can be used to estimate the influence of the urban tile on the local climate.

Variables included in the file are T2 (temperature at 2 m elevation in Kelvin), V10 and U10 (winds at 10 m elevation in metres per second), PSFC (surface level pressure in Pascal), RAINNC (rain in mm), TH2 (potential temperature at 2m elevation in Kelvin), and Q2 (specific humidity at 2 m elevation, which is dimensionless). All variables are provided at hourly timestep.

Queries about this dataset can be directed to o.brousse@ucl.ac.uk

Cyclic loading stress-strain data in polycrystalline antigorite serpentinite, at various confining pressures and temperatures. This dataset is used and fully described/interpreted in the paper: David, E.C., N. Brantut, and G. Hirth, Sliding crack model for non-linearity and hysteresis in the triaxial stress-strain curve of rock, and application to antigorite deformation, submitted to J. Geophys. Res. Overview Rock type Vermont antigorite-rich (>95%) serpentinite. See submitted paper for details. The sample is isotropic. Apparatus Oil-medium triaxial apparatus (Rock Physics Ensemble, University College London). For description, see David el al. (2018), Absence of stress-induced anisotropy during brittle deformation in antigorite serpentinite, J. Geophys. Res., 123, 10616-10644. Griggs-type solid medium apparatus (Brown University). For description, see David, E.C., N. Brantut, and G. Hirth, Sliding crack model for non-linearity and hysteresis in the triaxial stress-strain curve of rock, and application to antigorite deformation, submitted to J. Geophys. Res., and references therein. Files description 1-existing data from David et al., JGR, 2018: The text file "Vermont-antigorite-roomT-150MPa-stress-strain-cyclicloading-UCLtriax" gives the axial stress (in direction 1, see submitted paper) and the axial strain (in percent, in direction 1, see submitted paper), at room temperature and 150 MPa confining pressure, in the oil triaxial apparatus at UCL. The mechanical data (stress, strain) have been corrected from internal friction and machine stiffness, respectively. The data are from David el al. (2018), Absence of stress-induced anisotropy during brittle deformation in antigorite serpentinite, J. Geophys. Res., 123, 10616-10644. 2-new data: The text file "Vermont-antigorite-roomT-1000MPa-stress-strain-cyclicloading-Griggsapparatus" gives the axial stress (in direction 1, see submitted paper) and the axial strain (in percent, in direction 1, see submitted paper), at room temperature and 1000 MPa confining pressure, in Griggs solid medium apparatus at Brown University. The mechanical data (stress, strain) have been corrected from internal friction and machine stiffness, respectively. The text file "Vermont-antigorite-400C-1000MPa-stress-strain-cyclicloading-Griggsapparatus" is the equivalent of the file described just above at a temperature of 400C. The text file "Vermont-antigorite-500C-1000MPa-stress-strain-cyclicloading-Griggsapparatus" is the equivalent of the file described just above at a temperature of 500C.

This dataset is a supplement to the publication in the 2016 Applied Energy conference in Beijing, China. The data reveals that the adaptive comfort temperature in indoors in buildings isincreased when indoor and outdoor temperatures increased. The neutral comfort temperature will change from around 24 C to 26 C from present day to 2080s for occupants in London. When people get used to the increasing high temperatures in future, the most unstable factor which will severely harm occupant health is the more and more frequent sudden peaks of high temperatures.

The longest available instrumental record of temperature in the world is now available at the BADC. The daily data starts in 1772.

The mean, minimum and maximum datasets are updated monthly, with data for a month usually available by the 3rd of the next month. A provisional CET value for the current month is calculated on a daily basis. The mean daily data series begins in 1772. Mean maximum and minimum daily and monthly data are also available, beginning in 1878. Yearly files are provided from 1998 onwards.

These historical temperature series are representative of the Midlands region in England, UK (a roughly triangular area of the United Kingdom enclosed by Bristol, Lancashire and London).

The following stations are used by the Met Office to compile the CET data: Rothamsted, Malvern, Squires Gate and Ringway.

But in November 2004, the weather station Stonyhurst replaced Ringway and revised urban warming and bias adjustments have now been applied to the Stonyhurst data after a period of reduced reliability from the station in the summer months.

The data set is compiled by the Met Office Hadley Centre.

16WW Domestic Water Mains Inlet Temperature, approximately noon mid-month, at kitchen tap, Kingston-upon-Thames, London, KT1 3JA, UK