Unequal distributions of crowdsourced weather data in England and Wales

Domain: earth_science
Published: 2024-06-20
Editorial outcome: minor_revision

Abstract
Personal weather stations (PWS) can provide useful data on urban climates by densifying the number of weather measurements across major cities. They do so at a lower cost than official weather stations by national meteorological services. Despite the increasing use of PWS data, little attention has yet been paid to the underlying socio-economic and environmental inequalities in PWS coverage. Using social deprivation, demographic, and environmental indicators in England and Wales, we characterize existing inequalities in the current coverage of PWS. We find that there are fewer PWS in more deprived areas which also observe higher proportions of ethnic minorities, lower vegetation coverage, higher building height and building surface fraction, and lower proportions of inhabitants under 65 years old. This implies that data on urban climate may be less reliable or more uncertain in particular areas, which may limit the potential for climate adaptation and empowerment in those communities.

Introduction
Cities are complex environments with substantial environmental heterogeneities that have a direct impact on their local climates. For instance, cities affect local temperatures and related relative humidity, atmospheric circulation, and precipitation regimes1. Particular attention has been given to urban heat because temperature is a reliably measured weather variable, known to have negative impacts on health, buildings and infrastructure, energy, or biodiversity2–6. Considering public health alone, hotter urban areas are subject to higher levels of heat-related mortality and morbidity, such as risk of stroke, exhaustion, and cardiovascular diseases7,8. Therefore, understanding which urban environments are most associated with extreme heat, and the underlying vulnerabilities of their inhabitants, is necessary to establish sustainable and healthy cities. Networks of official weather stations, such as the national weather station network (MIDAS, Met Office Integrated Data Archive System) managed by the United Kingdom (UK) Met Office, may be too sparse to respond to this specific need. Currently, only 155 MIDAS stations of high accuracy corresponding to World Meteorological Organization (WMO) and Climate Network standards record weather measurements at hourly time steps over the whole of England and Wales, covering an area of 151 139 km2 (Supplementary Fig. 1). To address this data scarcity new sources of weather data are needed.

Over recent decades, the number of weather devices operated by independent individuals who openly share the data collected as a crowdsourcing activity9 has rapidly increased in European countries, including the UK10. We refer to these devices as personal weather stations (PWS) to contrast them with official automatic weather stations installed by meteorological offices. For instance, during the extremely hot summer of 2022, 5011 PWS of the Netatmo brand which recorded temperatures, greatly extending the existing coverage of official temperature sensors (see Methods; Supplementary Fig. 1). PWS are therefore increasingly being used in urban climatological studies and are sought to provide useful complementary datasets for urban climate services11–17. To deal with higher degrees of uncertainty due to their inaccuracy18,19 and human factors such as sub-optimal placement, multiple filtering algorithms have been developed to make this data more reliable20–23. PWS crowdsourced weather data are quickly becoming suitable and valuable for urban temperature studies and evaluation of models24. Pioneering studies have even suggested that they could be used to develop city-wide heat alarm systems25.

Little attention has however been given to the underlying socio-economic and environmental characteristics of the areas in which PWS are installed. In other words, even though PWS offer an unprecedented opportunity to study urban heat in places that are deprived of official sensors, they may well be unequally distributed among the variety of urban environments that exist in towns and cities. This phenomenon, named the “sensor desert”, has already been highlighted for other types of sensors, such as for air pollution26,27. Because sensor deserts prevent the acquisition of weather data in a representative way (e.g., covering all communities existing in the country), the generalization of urban heat impact studies is limited. Furthermore, studying the heat exposure across populations with different degrees of vulnerability is hindered. Understanding where PWS are positioned at a national level and the implications for urban climate studies and their related impact studies is therefore important, especially when assessing the suitability of various heat adaptation and mitigation strategies. After all, such networks could help to provide adequate guidance to decision-makers to define national and sub-national guidelines to address the increasing threats caused by extreme heat in inhabited areas as the climate warms.

In this study, we empirically investigate the current coverage of certain PWS that are widely used by consumers and researchers and that are capable of measuring temperature across England and Wales. To do so, we: gather data on the existing coverage of commonly used PWS from the Netatmo company across England and Wales during the record-breaking summer of 2022; collect information on the urban environment via a set of satellite earth observations; and pick out key demographic and socio-economic indicators from the 2011 census in England and Wales (see Methods). We then analyse how each of these indicators is associated to the presence or absence of PWS. Our study provides a comprehensive description of the underlying spatial inequalities in crowdsourced climate data acquisition and accessibility in English and Welsh urban environments.

Results
We find that more deprived areas, meaning places with the highest Index of Multiple Deprivation score (see subsequent subsection), are generally less covered by PWS than wealthier ones. We also find that, typically, more densely built areas with higher buildings, lower vegetation coverage and albedo (reflective capacity), and areas with higher proportions of ethnic minorities have lower PWS coverage. Although areas with higher proportions of adults over 65 years old, and therefore most vulnerable to heat mortality and morbidity28–30, are associated with increased PWS coverage, the current distribution of PWS may limit the study of the urban climate and its impact in areas where populations are the most deprived and have a high proportion of ethnic minorities. We provide more details on these results below.

Levels of deprivation and sensor coverage
Empirical evidence of an unequal distribution of PWS throughout England and Wales is given by the relative number of people per decile of Index of Multiple Deprivation (IMD; Fig. 1) who have at least one PWS in their Lower layer Super-Output Area (LSOA, a kind of census tract). The IMD is a comprehensive index of socioeconomic deprivation, calculated and simplified as deciles at the LSOA level by the UK Office of National Statistics that combines socio-economic and demographic indicators of deprivation (e.g., crime, income, education, etc.)31. We use social deprivation here as a proxy for the potential social vulnerability of people to climate hazards30,32–34. We find that 24% of people living in the least deprived decile (10th decile) have at least one PWS in their LSOA compared to 3% of those in the most deprived decile (1st decile). For each increase in the IMD decile, we find an increase of ∼ 2.3 percentage point in the number of people with at least one PWS in their LSOA. Interestingly, we also find that official automatic weather stations from the MIDAS network are less present, if not absent, from most deprived LSOAs (see Supplementary Fig. 2). At the time of writing, 13.32% of the population of England and Wales is covered by at least one PWS while only 0.5% is from a MIDAS weather station. This populationsize (∼1500 inhabitants per LSOA; see Methods). The same figure is given in Supplementary Fig. 2 for official automatic weather stations from the Met Office Integrated Data Archive System (MIDAS) network handled by the United Kingdom Met Office and also shows that most deprived populations are less covered by highly accurate weather stations. This highlights the extension of weather sensing networks in a variety of environments offered by PWS and supports the need to describe them.

Inequalities in the urban-rural continuum
Local Climate Zones (LCZs) are land-use land-cover categories specifically designed for urban climate studies35, with LCZ 1 to 10 representing built-up classes, and A to G natural classes (see Methods). In England and Wales, urban LSOAs are mostly composed of Compact Mid-Rise (LCZ 2), Open Mid-Rise (LCZ 5), Open Low-Rise (LCZ 6), Large Low rise (LCZ 8) and Sparsely Built (LCZ 9) environments. Rural LSOAs are composed of Dense Trees (LCZ A), Scattered Trees (LCZ B), Low Plants (LCZ D), Bare rock or pave (LCZ E) and Water (LCZ G) – the latter can be typical of coastal areas. When looking at the density of PWS over the LSOAs’ area, we find that LSOAs that are mostly composed of mid-rise buildings have better PWS coverage than others with ∼ 0.5 PWS per km2 in LCZ 2 and a peak of 0.64 PWS per km2 in LCZ 5 (Fig. 2a). Natural areas tend to have a lower proportion of their total area covered by a PWS with ∼ 0.03 PWS per km2 on average; no PWS are present in LCZ E. Built-up LCZs, which compose only 11 362 km2 of England and Wales (7.5% of the total 151 139 km2), hence concentrate most of the PWS. The latter confirms that PWS could be regarded as a valuable palliation to the characteristic climate data scarcity in urban areas10,36. Yet ultimately, because PWS density may simply be related to population density, it is important to estimate how many individuals are covered by one sensor to define what is the optimal density of weather sensors for capturing the variability of population’s exposure to heat. We find the highest coverage per capita in LCZ 9 environments, with 35.9% of its population covered by at least one PWS (Fig. 2b). Overall, 7.9 M inhabitants are covered by a PWS out of the total population of 59.5 M living in England and Wales. Of these, 4.2 M out of the 42.9 M inhabitants that live in mostly built-up LSOAs are covered by PWS in comparison to the 3.7 M out of the remaining 16.6 M inhabitants that live in natural areas. In other words, ∼ 22% of the population in natural areas has at least one PWS in their LSOA, but for built-up areas this is only the case for ∼ 10% of the population. This suggests that there is an urban-rural inequality in PWS coverage that cannot be solely explained by the larger size of the LSOAs in more rural lands. Indeed, as LSOAs are made of the same approximate amount of inhabitants, the probability of a citizen to buy one PWS should be the same across all environments.

An urban and demographic sensor desert
We found the existence of an unequal distribution of PWS between urban and natural areas, and between more and less deprived locations in England and Wales. We now further investigate the underlying risk characteristics of each LSOA. In fact, a wealthier urban LSOA may well be at higher risks of heat-exposure than a more deprived rural one because of the urban heat island effect1,37 and therefore require more PWS coverage to survey the local climate and represent local population exposure to heat. Referring to the risk triangle38, we therefore need to estimate how likely is the hazard of higher air temperatures in each environment, and how vulnerable the population is to this heat. Using a set of satellite-based environmental indicators that impact the micro-scale air temperature, or the hazard (see Methods; Fig. 3 and Supplementary Fig. 3), we show that lower proportions of populations are covered by a PWS in environments that have lower vegetation coverage (8.1% in the lowest decile against 24.9% in the highest decile) and higher building heights (8% against 29.3%) and building fractions (6.9% against 30.1%). This could explain the lower shortwave albedo where PWS are absent (9.2% against 26.4%)—asphalted and other dark built surfaces having lower albedos than natural areas on average. Concerning the vulnerability, we find that lower proportions of the total population are covered by a sensor when an LSOA has a lower proportion of people older than 65 years (9.1% against 19.3%) and when ethnic minorities are more present (5.4% against 18%). Overall, this means that people in England and Wales who live in areas with the highest vegetation coverage and the lowest proportion of ethnic minorities are ∼ 3 times more likely to have a PWS measuring their local climate. PWS sensor deserts in England and Wales thereby consist of places with higher buildings and building fractions, deprived of vegetation, with a lower solar radiation reflective capacity, and where younger and more ethnically diverse people live. These demographic and environmental characteristics of sensors deserts also describe lower IMD deciles (Fig. 4, Supplementary Table 1). For example, most deprived LSOAs have higher building heights by ∼ 4m than least deprived LSOAs on average (Supplementary Table 1), building fractions by ∼ 10%, and proportion of ethnic minorities by ∼ 15%. Also, lower albedo is observed by ∼ 0.02 (2% less reflective capacity), EVI by ∼ 0.06 (less densely vegetated), and proportion of people aged 65 or above by ∼ 10%.

No deserts are found at similar levels of deprivation
Importantly, we find that within the population living in each IMD decile no clear distinction between the PWS and no-PWS (absence of PWS) can be made out of the environmental and demographic indicators. For example, the highest proportion of people living in the highest IMD decile (10th) live in the 3rd decile of building height (low buildings) independent of whether PWS are present or absent in their LSOA (Fig. 4, Supplementary Table 1). This is further confirmed by calculating the Perkins Skill Scores (PSS) for each covariate in each IMD decile. The Perkins Skill Score39 is a metric ranging from 0 to 1 that estimates how similar probability density functions are; a score above 0.7 meaning that the probability density functions are generally overlapping. Here we compare the probability density functions between presence and absence of PWS, weighted by the total population in each LSOA, and find that in all IMD deciles, environmental and demographic indicators of the PWS and no-PWS groups always overlap with PSS scores above 0.70—apart from the Enhanced Vegetation Index in the lowest IMD decile (PSS = 0.69; Supplementary Table 1). In general, this confirms that no clear empirical signals are found that would distinguish places with and without PWS within each IMD decile, and that the links between environmental and demographic characteristics could equally be described by the levels of deprivation, or even explained by them. As higher PSS are found in highest IMD deciles, this suggests that greater dissimilarities between environments covered and deprived of PWS exist in more deprived LSOAs. However, this study does not try to explain the causes of the distribution of PWS across England and Wales but rather characterises environments with the varying PWS coverage. We simply note that PWS coverage varies with area deprivation, and that the environmental and demographic characteristics also vary with area deprivation. Therefore the thermal environments experienced in more deprived areas may not be well studied.

Methods
Data acquisition
In this section, we provide details on the choices of data that was acquired for this study (Fig. 5), their origin and what each represents.

Netatmo personal weather station
We gather all the necessary Netatmo PWS information using the “GetPublicData” API via the patatmo python module, namely latitude, longitude, and sensor ID. We use a moving window of 0.2° by 0.2° over England and Wales. This step ensures that all Netatmo PWS are captured by the API, since over large domains a reduced number of PWS is accessible via the API. We ran our data collection on the 22nd of October 2022.
After all data has been collected, duplicates are filtered out and only PWS that record temperature are kept. This means that PWS recording only pressure, humidity or precipitation are not included in this study. We collect data on PWS only for those active during the recent summer 2022 (June to August) as this was a record-breaking year in daily maximum temperature in England and Wales with temperatures going as high as 40.3 ◦C59. This also ensures that a maximum of PWS are included in the analysis since their number has kept increasing over the past decade10.

Demographic and indices of multiple deprivation
To understand the potential implications of PWS desert coverage, we focus on three major demographic and socio-economic indicators which we justify below: the proportion of people over 65 years old, the proportion of ethnic minorities, and the Index of Multiple Deprivation.
In the United Kingdom, people over 65 have been shown to be more vulnerable to the negative impacts of extreme heat especially mortality60. Recent studies have demonstrated that certain ethnic minorities are more exposed to higher surface temperatures and are usually further from a vegetated area where they could find cooler temperatures47. The Indices of Multiple Deprivation (IMD) developed in England and Wales31 combine multiple indicators of socioeconomic deprivation, which is thought to be indicative of people’s vulnerability to climate hazards45—previous studies have found links between low socioeconomic status and risk of death during a heatwave61. This is thought to be caused by factors such as quality of housing and general health. We collect this information at the Lower layer Super-Output Areas (LSOA) for both England and Wales on the official repositories of the Office of National Statistics40. LSOAs consist of areas that must include 1500 inhabitants and are thereby dependent on population density for their shapes and sizes. Before data treatment, both Wales and England data were joint using dedicated python packages and common naming was attributed to each variable.

Environmental earth observations
To determine which type of environment surrounds each PWS we use several surface indicators which are considered important for urban heat variability. First, we collect building surface fraction and building heights from the Global Human Settlement Layer project provided by the European Commission42 because building presence and height will directly influence the radiation trapping and the production of anthropogenic heat fluxes in urban areas. The two are expected to change the surface energy balance and therefore lead to different urban air temperatures. Second, we derive information on the vegetation coverage by using the Moderate-Resolution Imaging Spectroradiometer (MODIS) MOD09GA v6 Enhanced Vegetation Index (EVI) product at 500m horizontal resolution62. Vegetation density, type and fraction are indeed important contributors to the spatio-temporal variability of heat and is also an indication of the built-up surface fraction and accessibility to vegetated areas. We chose to use the EVI over the Normalized Difference Vegetation Index as it reduces the influence of the atmosphere and the canopy background signal on the measured index while having an increased sensitivity to the leaf area index—hence facilitating the discretisation of different vegetation types in high biomass locations (like forests)63,64. Lastly, we obtain information on the short-wave surface albedo from the MODIS MCD43A3 v6 product41 to explore the capacity of each environment to reflect incoming solar radiation. This is important as certain built-up areas may have higher albedos due to reflective roof coating, for example and thus be cooler than their neighbouring environments. We consider a 5-year median for albedo and EVI (2018-2022), which we consider representative of recent PWS deployment in England and Wales, of the other environmental dataset (LCZ, built-up surface, and height) and of the generic environmental conditions of the summer 2022.

An urban climate land-use and land-cover
In parallel, we also obtain information on the land-use and land-cover by using the Local Climate Zones (LCZ) European map65, developed within the World Urban Database and Access Portal Tool (WUDAPT)66. The LCZ were mapped at a resolution of 100m with an overall accuracy above 80% for the whole of Europe making it a valid land use land cover map for studying urban climate related issues in the region; the overall accuracy, the Kappa score and the true positive rates for evaluating how the urban areas’ extension were consistent with other products like ESA CCI are also above 90% for the UK65. LCZ are land-use and land-cover classes specifically designed for the study of the urban climate and consist of continuous urban and natural environments of several hundreds of metres to few kilometres35. They distinguish each urban and natural environments archetypes by their built-up density, building height, building types and uses, and natural coverage. Although the other variables given above are continuous variables and more indicative of each environment’s specificities than categorical LCZ, we still include LCZ in our set of variables to show the actual coverage of PWS per LCZ and explore if certain LCZ are deprived of PWS. This is of importance for urban climatological and meteorological studies as LCZ are standardized classes that permit the communication of urban climate studies outcomes globally.

Data post-treatment
Once we gathered all the necessary data, we aggregated all the necessary information at the LSOA level. Therefore, for each LSOA we: (i) used the rasterio zonal stats python package to estimate the average values of building surface fraction, building heights, surface albedo and EVI in the LSOA; (ii) used the same package to get the modal LCZ in the LSOA (see Supplementary Discussion, Supplementary Fig. 5 and Supplementary Fig. 6 for more information on the potential loss of information induced by the use of modal LCZ); (iii) calculated the proportion of people aged above 65 years old against the total LSOA population; and (iv) counted the proportion of ethnic minorities per LSOA by considering all classes that are not “White: English/Welsh/Scottish/Northern Irish/British” as minorities and counted them against the total LSOA population. Indices of multiple deprivation are left in their decile format.

Inclusion and ethics statement
We recognise that ethnic groups are not biologically founded and are sociopolitical constructs to which not all individuals identify. The groups used in this study are those used during the 2011 Census from the English and Welsh Office for National Statistics. Since 1991, at the time of new census (every 10 years), ethnic groups are defined in consultation with: the users of the census data, such as local institutions and health services; people who complete the census and their representative interest groups; and the National Records of Scotland as well as the Northern Ireland Statistics and Research Agency who are responsible for running the censuses in their respective home nations. The ethnic groups used in this study were not designed by the authors. As the vast majority of individuals living in England and Wales identified as “White: English/Welsh/Scottish/Northern Irish/British”, we qualified all the other ethnic groups as minorities.

Study’s rationale and framework
In our study, we decided to adopt a simple but robust approach to depict the unequal distribution of PWS amongst the variety of inhabited environments that compose England and Wales. Most importantly, we chose not to perform any explanatory variable analysis because we are not seeking to causally explain the drivers of coverage in PWS, but rather to describe the current state and the implications for sensing of the environment. Moreover, explanation of the drivers of PWS acquisition would be much better addressed by individual data rather than area-level data, for example through surveys or interviews. Machine-learning based studies attempting to explain the distribution of PWS between areas may be obscured by the spatial correlation between PWS coverage and urbanization, which would therefore require extensive studies and analyses that cannot solely be addressed in this paper.
We focused our analysis to the presence or absence of PWS in an LSOA as very few LSOAs have more than one PWS, which prevents any calculation of the relationship between PWS density and the other covariates. Of the 34 632 LSOAs composing England and Wales, 3863 are covered by one PWS, and 539 contain more than one; 434 having only two. We therefore assume that the population of an LSOA is covered by a PWS if there is at least one in the LSOA. Because each LSOA comprises a different number of inhabitants, we weight by LSOA population. This means that the numbers presented in this study simply show how many people are covered or not by a PWS.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "I believe this paper is of general interest: the authors have convinced me that crowdsourced weather\ndata have the potential to improve climate adaptation and that it is therefore important to understand\nthe biases in personal weather stations' distribution. The methodology, despite being extremely simple,\nis sound and adapted to the authors' purpose. I only have a couple of comments on the manuscript.\nFirst, are there biases in the distribution of official weather stations? And if so, how does the distribution\nof official weather stations compare with the distribution of personal weather stations?\nSecond, I find some parts of paragraphs 102–111 confusing. Please clarify to which figure or table the\nresults refer. I also do not understand the \"true\" l108. In addition, doesn't sentence l106-108 (\"Hence,\nregardless...\") contradict sentence l102-102 (\"Demographic and environmental characteristics are also a\nreflection of the characteristics that define each IMD decile\")? Please state more clearly what the overall\nmessage of this paragraph is, as well as the meaning of each table and figure.\n\"The environmental data being globally and freely downloadable\" (l126–127): Please check your\nreferences; their formatting doesn't seem right, and some of the references provided are for England\nonly, while your text mentions globally available data.\nI appreciate that the authors fully describe the limitations of their study in Discussion (l164).", "reviewer_2": "Review of NCOMMS-23-12133\nUnequal distributions of crowdsourced weather data in England and Wales\nBy Brousse et al.\nSummary: This paper builds on the use of crowdsourced weather observations by private weather\nstations to study meteorology in areas with sparse official observations, i.e., mainly in cities. The paper\nstudies the availability of weather observations by NetAtmo weather stations in cities and\nneighborhoods in England and Wales and stratifies these along socio-economic factors at the\nneighborhood level. It appears that relatively fewer PWS are present in neighborhoods with relatively\nlow incomes, a low green fraction, relatively high buildings and built-up fractions, and a relatively high\npresence of 65+ people. The claim is that we know less about heat stress in these areas, which are\nneighborhoods where the urgency is likely relatively high. In my opinion, the research design is well\nthought out, given the limited amount of information that is known in terms of meta-data from the\nweather stations. The paper is well structured and well written, with good-quality figures. Nevertheless, I\nhave some remarks that could be taken into consideration.\nRecommendation: Minor revisions needed\nRemarks:\n1. Despite I find the paper’s analysis is sound, the result is not so surprising in the sense that a priori it\ncould be predicted that neighbourhoods with low incomes have more urgent problems to take care of\nthan buying weather stations and thinking about heat stress, climate change and climate adaptation.\n2. I was wondering whether the gap in private weather stations in the “low income”-areas is not\ncompensated by dedicated/professional weather stations by health agencies or the municipality, since\nthey know these areas are most vulnerable. Could this be studied too to ensure the drawn conclusions in\nthis manuscript are robust?\n3. The study is limited to the area of Wales and England. It is not clear why not whole to U.K. was\nchosen? It seems now a small subset of counties in the home country of the researchers. How this result\nis generalizable to a wider area remains unclear now. I fully understand it is hard to obtain all socio-\neconomic data across the globe, but at least for Europe this information should be available through\nEurostat.\n4. An additional stratification that could advance the study, is to study whether the same pattern in seen\nwhen a) extra instruments are taken into account (i.e. Netatmo the wind and rain sensor are extra costs,\nso it could be interesting to know which income threshold or so is needed to buy a wind sensor or rain\nsensor above the thermometer set), b) instruments from more expensive brands like Davis Vantage Pro\n(1200 euro instead of 150 euro for a NetAtmo thermometer set).\n5. The study does implicitly imply that the owner of the PWS is also the one who bought the PWS, but\nPWSes are very often offered as a gift for Christmas, anniversaries and so on. So whether a PWS is\npresent in a garden may depend more about the wealth of the ones who offer the PWS than the ones\nthat operate the PWS in the end. How was dealt with that, and how does this affect your conclusions?", "reviewer_3": "General comments\nThe main objective of this study is to uncover socio-economic and environmental inequalities in the\ndistribution of crowdsourced personal weather stations. The key findings include a lower presence of\ncrowdsourced personal weather stations (PWS) in deprived environments which can be associated with\nhigh build-up, low vegetation cover, high proportions of ethnic minorities and mostly younger people.\nThe study uses straightforward yet methodologically sound empirical techniques to obtain these rather\nlogical but important results.\nMajor comments\no How do the authors account for their inability to disentangle the impact of environmental and socio-\neconomic factors (such as built-up density, building height, age, and ethnic minority representation)\non PWS coverage independently from the influence of deprivation? What strategies might be employed to\ndisentangle the role of deprivation in explaining PWS coverage?\no Why did the authors decide to include the Index of Multiple Deprivation (IMD), Local Climate Zones\n(LCZs) and a variety socio-economic and environmental indicators, even though they exhibit inherent\ncorrelations?\no It is know that coarse vegetation indexes are not ideal to study temperature-environment relationships\n(Bartesaghi-Koc et al., 2020; Rakoto et al., 2021), since different functional green types may have\ncontradicting cooling effects. In this context, it's important to consider whether the EVI takes into\naccount various functional green types. Could you please clarify how the EVI incorporates information\nabout vegetation density, type, and fraction?\no There appears to be missing information regarding the LCZ map used in the study. Was the study area\nextracted directly from a broader European LCZ map? Can a Europe-wide LCZ map provide sufficient\nprecision for examining the specific LCZs within England and Wales? Additionally, it would be valuable to\nknow more about the spatial resolution, accuracy, and other relevant characteristics of the LCZ map\nemployed in the research.\no Given that PWSs have demonstrated the capability to capture fine-scale temperature variations,\nencompassing both inter- and intra-LCZ variability (Fenner et al., 2017; Verdonck et al., 2017), it might be\nconsidered a limitation to classify PWSs solely into a major LCZ category based on the predominant LCZ\nwithin the corresponding Lower Layer Super Output Area (LSOA). Such an approach may result in the\nloss of valuable information. It would be beneficial to include details regarding the number of distinct\nLCZ types within a single LSOA and their respective proportions, providing a more nuanced perspective\non the urban microclimate in the study area.\no Building upon the preceding comment, as the entire dataset is aggregated at the LSOA level, it is\nreasonable to assume that readers would find it valuable to have insight into the characteristics and key\nmetrics of these LSOAs. Providing an overview of the LSOA layout and sharing general metrics for these\nareas can enhance the reader's understanding of the study context.\no The organization of the Results and Methods sections can be improved for better readability. To\nachieve this, incorporating subheadings to break down the content into smaller, more manageable\nsections would be beneficial. Additionally, aligning the flow and structure of both sections could further\nenhance the overall readability of the manuscript.\nMinor comments\no Line 12-13: This sentence should be supported with appropriate citations: “Particular attention has\nbeen given to urban heat because temperature is a reliably measured weather variable, known to have\nnegative impacts on health, buildings, energy, or biodiversity.”\no Figure 2: From the caption of Figure 2 it is unclear whether the total surface and total population of\nthe different LCZ classes in figure 2a and figure 2b relate to the original LCZs derived from WUDAPT or\nwhether they present the LSOAs with modal LCZ.\no Figure 2: Reference to panel b is missing in the figure caption: “Personal weather stations (PWS) per\nsquare kilometre (km²; a) and percentage of total population covered by a PWS (km²; b) in each Local\nClimate Zone (LCZ).”\no Line 105-106: “Also, higher albedo are observed by ∼0.02 (2 % more reflective capacity), EVI by ∼0.06\n(denser vegetation), and proportion of people aged 65 or above by ∼10 %.”.\nThis sentence gives the impression that the most deprived LSOAs are associated with higher albedo,\ndenser vegetation and older people. That seems quite counterintuitive and the opposite is shown in\nFigure 4:\no Line 112: It is not indicated where the results of the PSS are shown. A reference to Supplementary\nTable 1 could be included.\no Line 127: Period punctuation between sentences is missing.\no Line 132: Period punctuation between sentences is missing.\no Line 140-142: “Furthermore, the sensor desert may limit quantification of the cooling induced by\nvegetated evapotranspiration and related benefits to heat mitigation (Iungman, et al, 2023).”\nThe statement is true but the cited reference does not consider PWS in quantifying the cooling potential\nof vegetation.\no Line 142-145. This sentence is quite long. Consider breaking it down into smaller parts to improve the\nreadability: “Moreover, as people aged above 65 tend to be more susceptible to heat-related health\nhazards and because ethnic minorities are often more exposed to asphalted built-up areas and their\nrelated surface urban heat islands – which could drive disparities in heat exposure – it would be\nnecessary to adequately quantify how vulnerabilities induced by deprivation may increase the\nvulnerability of different communities, stratified by age groups.”\no Line 156-157: The closing parenthesis seems to be missing: “For instance, it would be necessary to\ndisentangle how deprivation explains PWS coverage because other covariates (e.g., built-up density,\npopulation density, or building height may also have an influence on PWS coverage that we could not\nseparate from deprivation in this study.”\no Line 193: This sentence appears to be incomplete: “We use a moving window of 0.2◦ by 0.2◦ over\nEngland and.”\no Line 261: This reference appears to be incomplete: “Oke, T. R., Mills, G., Christen, A. & Voogt, J. A.\nUrban climates (Cambridge University Press, 2017)”\no Line 318: This reference appears to be incorrect/incomplete: “for National Statistics, O. Office for\nnational statistics: 2011 census geography products for england and wales.”.\no Line 319-320: This reference appears to be incorrect/incomplete: “Schaaf, C. & Wang, Z. Mcd43a3\nmodis/terra+ aqua brdf/albedo daily l3 global–500m v006. nasa eosdis land processes daac (2015).”"}, "author_response": "Response to reviewers for NCOMMS-23-12133: “Unequal distributions of\ncrowdsourced weather data in England and Wales”\nReviewer 1\nI believe this paper is of general interest: the authors have convinced me that crowdsourced weather data have\nthe potential to improve climate adaptation and that it is therefore important to understand the biases in\npersonal weather stations' distribution. The methodology, despite being extremely simple, is sound and adapted\nto the authors' purpose. I only have a couple of comments on the manuscript.\nWe thank the reviewer for their valuable comments. Our detailed answers are provided below\nin black and cited text is provided between quotation marks. We have uploaded the codes\nand the public data used in our study in a dedicated GitHub repository:\nhttps://github.com/oscarbrousse/NatComms_PWS_2024\nFirst, are there biases in the distribution of official weather stations? And if so, how does the distribution of\nofficial weather stations compare with the distribution of personal weather stations?\nWe have added a figure in the supplement showing the distribution of active official weather\nstations handled by the UK MetOffice that respond to WMO standards or are part of the\nClimate Network and that record weather measurement at hourly intervals. We have changed\nthe text in the introduction of the manuscript which now reads (lines 18 to 28):\n“Currently, only 155 MIDAS stations of high accuracy corresponding to WMO and\nClimate Network standards record weather measurements at hourly time steps over the\nwhole of England and Wales, covering an area of 151 139 km² (Supp. Figure 1). To address\nthis data scarcity new sources of weather data are needed.\nOver recent decades, the number of weather devices operated by independent\nindividuals who openly share the data collected as a crowdsourcing activity9 has rapidly\nincreased in European countries, including the United Kingdom10. We refer to these devices\nas personal weather stations (PWS) to contrast them with official automatic weather stations\ninstalled by meteorological offices. For instance, during the extremely hot summer of 2022,\ncoverage of official temperature sensors (see Methods; Supp. Figure 1). PWS are therefore\nincreasingly being used in urban climatological studies and are sought to provide useful\ncomplementary data sets for urban climate services11–17.”\nSupp. Figure 1: LSOAs where official weather stations from the UK MetOffice MIDAS network\n(AWS) are active are in yellow, where Netatmo personal weather stations (PWS) are present\nare in orange, where both are active are in red (Both) and where none is present are shown\nin gray (Absence).\nWe also added lines 67 to 70:\n“Interestingly, we also find that official automatic weather stations from the MIDAS\nnetwork are less present, if not absent, from most deprived LSOAs (see Supp. Figure 2). At the\ntime of writing, 13.32 % of the population of England and Wales is covered by at least one\nPWS while only 0.5 % is from a MIDAS weather station. This highlights the extension of\nweather sensing networks in a variety of environments offered by PWS and supports the need\nto describe them.”\nSecond, I find some parts of paragraphs 102–111 confusing. Please clarify to which figure or table the\nresults refer. I also do not understand the \"true\" l108. In addition, doesn't sentence l106-108 (\"Hence,\nregardless...\") contradict sentence l102-102 (\"Demographic and environmental characteristics are also a\nreflection of the characteristics that define each IMD decile\")? Please state more clearly what the overall\nmessage of this paragraph is, as well as the meaning of each table and figure.\nWe agree with the reviewer that this paragraph was confusing. What we wanted to emphasise\nin this section is that the overall pattern in environmental and demographic characteristics of\nplaces with higher PWS coverage generally matches the environmental and demographic\ncharacteristics of places with less deprivation (e.g., less deprived areas tend to have lower\nbuilding heights), and that when stratified by IMD decile these patterns are attenuated. We\nhave rephrased the paragraph which now reads (lines 110 to 133):\n“These demographic and environmental characteristics of sensors deserts also\ndescribe lower IMD deciles (Figure 4, Supp. Table 1). For example, most deprived LSOAs have\nhigher building heights by ~4 m than least deprived LSOAs on average (Supp. Table 1), building\nfractions by ~10 %, and proportion of ethnic minorities by ~15 %. Also, lower albedo is\nobserved by ~0.02 (2 % less reflective capacity), EVI by ~0.06 (less densely vegetated), and\nproportion of people aged 65 or above by ~10 %.\nNo deserts are found at similar levels of deprivation\nImportantly, we find that within the population living in each IMD decile no clear\ndistinction between the PWS and no-PWS can be made out of the environmental and\ndemographic indicators. For example, the highest proportion of people living in the highest\nIMD decile (10th) live in the 3rd decile of building height (low buildings) independent of\nwhether PWS are present or absent in their LSOA (Figure 4, Supp. Table 1). This is further\nconfirmed by calculating the Perkins Skill Scores (PSS) for each covariate in each IMD decile.\nThe Perkins Skill Score38 is a metric ranging from 0 to 1 that estimates how similar probability\ndensity functions are; a score above 0.7 meaning that the probability density functions are\ngenerally overlapping. Here we compare the probability density functions between presence\nand absence of PWS, weighted by the total population in each LSOA, and find that in all IMD\ndeciles, environmental and demographic indicators of the PWS and no-PWS groups always\noverlap with PSS scores above 0.70 – apart from the Enhanced Vegetation Index in the lowest\nIMD decile (PSS = 0.69; Supp. Table 1).\nIn general, this confirms that no clear empirical signals are found that would\ndistinguish places with and without PWS within each IMD decile, and that the links between\nenvironmental and demographic characteristics could equally be described by the levels of\ndeprivation, or even explained by them. As higher PSS are found in highest IMD deciles, this\nsuggests that greater dissimilarities between environments covered and deprived of PWS\nexist in more deprived LSOAs. However, this study does not try to explain the causes of the\ndistribution of PWS across England and Wales but rather characterises environments with the\nvarying PWS coverage. We simply note that PWS coverage varies with area deprivation, and\nthat the environmental and demographic characteristics also vary with area deprivation.\nTherefore the thermal environments experienced in more deprived areas may not be well\nstudied.”\n\"The environmental data being globally and freely downloadable\" (l126–127): Please check your\nreferences; their formatting doesn't seem right, and some of the references provided are for England\nonly, while your text mentions globally available data.\nWe thank the reviewer for noting the formatting of the references. We have dealt with this.\nFor the environmental data we maintain that it is freely and globally accessible; even the data\nfor England can be accessed from anywhere outside of England as long as proper justification\nis provided to the Office For National Statistics. We have written lines 137 to 139:\n“Despite our focus on England and Wales, our analysis proposes a transposable method to\nany country that offers indicators of socio-economic and demographic deprivation, ours being freely\ndownloadable in England and Wales39; the environmental data is globally and freely\ndownloadable31,40,41. Our study highlights these inequalities and, thereby, nuances the vision that\nopenly accessible and interconnected personal devices – often called the Internet of Things42 – could\nunequivocally mitigate institutional data scarcity in climate studies.”\nI appreciate that the authors fully describe the limitations of their study in Discussion (l164).\nWe greatly appreciate that the reviewer is content with the discussion that we provided in\nthe first manuscript. We have added some discussion to answer concerns presented by the\nsecond and third reviewers. Discussion now reads (lines 134 to 208):\n“Discussion\nIn this study, we use a simple method and easily accessible socio-economic, demographic, and\nenvironmental data over England and Wales to highlight an uneven coverage in crowd-sourced\nweather data derived from individually acquired personal weather stations (PWS). Despite our focus\non England and Wales, our analysis proposes a transposable method to any country that offers\nindicators of socio-economic and demographic deprivation, ours being freely downloadable in England\nand Wales39; the environmental data is globally and freely downloadable31,40,41. Our study highlights\nthese inequalities and, thereby, nuances the vision that openly accessible and interconnected\npersonal devices – often called the Internet of Things42 – could unequivocally mitigate institutional\ndata scarcity in climate studies.\nCrowdsourcing of weather data may not cope with data scarcity in deprived areas\nIn face of the increasing demand for denser weather data to build liveable and sustainable\nenvironments and the parallel expansion in PWS coverage, PWS are seen as an unprecedented\nopportunity to investigate the impact of urban areas on the local climate and temperatures35,43\nNotwithstanding this opportunity, our study highlights the representativeness and generalizability of\ndata generated by PWS. Local and national policies and action plans can protect residents from\nextreme heat but require identification of locations and populations that are most at risk44. Even if\ncities were to integrate heat mitigation measures such as changes to building and urban design that\nwould consider local realities in terms of heat hazards and vulnerabilities only certain neighbourhoods\nand built-up environments would be able to offer the necessary climatic data.\nAn important outcome from our study is the empirical demonstration that denser, poorly vegetated,\nand deprived built-up areas with higher proportions of ethnic minorities and of people under 65 years\nold lack crowd-sourced PWS data. This has several implications because denser built-up areas with\nhigher building heights and lower surface albedo are often hotter due to higher radiation absorption\nand trapping, but are not adequately covered by weather sensing devices. Furthermore, the sensor\ndesert may limit quantification of the cooling induced by vegetated evapotranspiration, the latter\nbeing shown to have positive impacts on heat mitigation and related heat-mortality45. Lastly, ethnic\nminorities are often more exposed to asphalted built-up areas and related surface urban heat\nislands46,47. This could drive disparities in heat exposure, especially considering that ethnic minorities\ntend to comprise younger populations and that people aged above 65 tend to be more susceptible to\nheat-related health hazards29. It would therefore be necessary to adequately quantify how\nvulnerabilities induced by deprivation may increase the risk to different communities, stratified by age\ngroups.\nFortunately, places with higher concentrations of persons over 65 years old tend to have good PWS\ncoverage. But proportions of elderly people are still high (above 10 %) in the most deprived areas\nwhere sensor deserts are observed. Hence, increasing weather sensors in these locations is paramount\nto adequately respond to urban climate challenges. Combined with additional vulnerabilities\nassociated with deprivation, including but not limited to obesity, diabetes, workplace conditions,\nclothing or accessibility to vegetated cool islands48, these people could be more exposed to heat-\nrelated risks, which poses some important ethical issues.\nA need for causal explanations of personal weather sensors’ coverage\nResponses to increased coverage of these areas might include developing parallel weather station\nnetworks or incentives to purchase PWS by local populations. To do so, future research should try to\nunderstand how different demographic, socio-economic, cultural and environmental covariates may\nexplain the distribution of PWS coverage. Focal population surveys and large-scale machine learning\ncould shed further light on the preliminary findings of our study. For instance, consumer surveys\nshould try to gather information at the individual level to relate how consumers’ demographic and\nsocio-economic characteristics are similar or dissimilar to those of the area in which they live (e.g.,\ndistance to the average annual income in the LSOA). It would also be necessary to disentangle how\ndeprivation explains PWS coverage because other covariates (e.g., built-up density, population\ndensity, or building height) may also have an influence on PWS coverage that we could not separate\nfrom deprivation in this study. We nonetheless found that despite some similarities in the distributions\nof PWS presence across our set of environmental and demographic characteristics – considered\ncentral for urban heat-related risks – some heterogeneities still exist and need to be explored. Most\nimportantly, we found that greater differences exist between environments that are covered and not\ncovered by a PWS between the most deprived areas, with smaller differences in the least deprived\nlocations. This could suggest a greater diversity of demographic, socio-economic and environmental\ncharacteristics in more deprived areas that need to be investigated to better understand the\nmechanisms explaining PWS coverage.\nStudy’s limitations and future recommendations\nWe acknowledge that our study suffers from several limitations and encourage further research to\naddress them. First, its conclusions are specific to England and Wales to benefit from a standardized\nIndex of Multiple Deprivation between the two countries – something that is more complex at the\nsupra-national level (e.g., Europe or global) and even at national level of the countries of the United\nKingdom – and should thus be carefully considered for other locations. Challenges related to the level\nof aggregation of the socio-economic, demographic and environmental data will have to be addressed\nto study the factors influencing the distribution of PWS at larger scales. For example, our\npresence/absence framework would be irrelevant at Nomenclature of Territorial Units for Statistics\n(NUTS) administrative units as all would be covered by at least one PWS in England and Wales (see\nSupp. Figure 4). Second, because of data restriction policies our study only gathered information from\nNetatmo PWS and did not investigate other crowd-sourced weather data providers like the Weather\nObservation Website (WOW)49 or Weather Underground (Wunderground) which could cover areas\ndeprived of Netatmo PWS50. Third, due to data restrictions we did not study the multivariate\nintersections that might have further implications for heat-related risks, like the proportion of elderly\npeople from ethnic minorities living in mostly asphalted areas. Fourth, as touched above, we did not\nstudy how each covariate is correlated and explicative of another – something that would help in\nguiding efficient campaigns for weather data collection and relevant mitigation and adaptation\npolicies. Fifth, we constrained our analysis to a very small sample of explanatory variables of heat-\nrelated risks and did not look at other common variables like the proportion of young children below\nfive or the presence/absence of water bodies51–54. Lastly, we assumed that each sensor would be fully\noperational and did not look at the data completeness and uncertainty, something that can now be\nachieved via common filtering tools22,23. Census data used was from 2011, as the 2021 census data\nwere not yet available. Further work is required to unpick what the effect of these sensor deserts is\non measurement uncertainty.\nAs a final note, we recognise that even if these data scarcities would be dealt with at the national level\nin countries that already offer a substantial PWS network, global inequalities would still be much\ngreater. For instance, it is commonly accepted that the African continent is underprivileged for the\nacquisition and the installation of weather stations55,56 despite the gravity of local climate hazards and\ninherent challenges that the countries face. Projects like the Trans-African Hydro-Meteorological\nObservatory (TAHMO)57 exemplify ambitious and worthwhile programs that try to reduce these global\nweather data inequalities. However, our study shows that individually acquired personal weather\nstations do not unequivocally address the limitations associated with these urban sensor deserts and\nsuch stations in isolation are thus likely not a sufficient answer to urban climate challenges faced in\nthese countries.”\nReviewer 2\nThis paper builds on the use of crowdsourced weather observations by private weather stations to study\nmeteorology in areas with sparse official observations, i.e., mainly in cities. The paper studies the availability of\nweather observations by NetAtmo weather stations in cities and neighborhoods in England and Wales and\nstratifies these along socio-economic factors at the neighborhood level. It appears that relatively fewer PWS are\npresent in neighborhoods with relatively low incomes, a low green fraction, relatively high buildings and built-\nup fractions, and a relatively high presence of 65+ people. The claim is that we know less about heat stress in\nthese areas, which are neighborhoods where the urgency is likely relatively high. In my opinion, the research\ndesign is well thought out, given the limited amount of information that is known in terms of meta-data from\nthe weather stations. The paper is well structured and well written, with good-quality figures. Nevertheless, I\nhave some remarks that could be taken into consideration.\nWe thank the reviewer for their valuable comments. Our detailed answers are provided below\nin black and cited text is provided between quotation marks. We have uploaded the codes\nand the public data used in our study in a dedicated GitHub repository:\nhttps://github.com/oscarbrousse/NatComms_PWS_2024\n1. Despite I find the paper’s analysis is sound, the result is not so surprising in the sense that a priori it could be\npredicted that neighbourhoods with low incomes have more urgent problems to take care of than buying\nweather stations and thinking about heat stress, climate change and climate adaptation.\nWe agree with the reviewer and this is why studying the exact nature of this unequal\ndistribution is important. Weather sensor coverage could help designing adaptive and\nmitigative strategies to cope with data scarcity in order to build more resilient cities in face of\nlocal climate changes and extreme heat. We address this point with this introductory\nsentence (lines 40 to 42):\n“After all, such networks could help to provide adequate guidance to decision-makers\nto define national and sub-national guidelines to address the increasing threats caused by\nextreme heat in inhabited areas as the climate warms.”\n2. I was wondering whether the gap in private weather stations in the “low income”-areas is not compensated\nby dedicated/professional weather stations by health agencies or the municipality, since they know these areas\nare most vulnerable. Could this be studied too to ensure the drawn conclusions in this manuscript are robust?\nWe thank the reviewer for suggesting to study this. Briefly, health agencies and municipalities\nin the UK generally rely on MetOffice weather stations and do not have their own. But, as\nanswered to reviewer 1, the official coverage of automatic weather sensors is really limited\nacross England when it comes to studying and surveying the local exposure of populations to\nextreme heat. Across the whole UK only 155 automatic weather stations are installed and\nmonitored by the MetOffice. This is considered a large weather sensor for studying the long-\nterm evolution of the national climate and offers unprecedented levels of accuracy.\nNonetheless, the spatial coverage is limited and does not cover major residential areas thus\npreventing the study of socio-economic disparities in terms of exposure to different\ntemperatures (average, minimum, maximum or extremes…). We therefore look at the\nopportunities that new publicly accessible data offer to cope with this research question.\nAs explained to reviewer 1, we have changed the text in the introduction of the manuscript\nto highlight the scarcity of official weather stations (lines 18 to 28):\n“Currently, only 155 MIDAS stations of high accuracy corresponding to WMO and\nClimate Network standards record weather measurements at hourly time steps over the\nwhole of England and Wales, covering an area of 151 139 km² (Supp. Figure 1). To address\nthis data scarcity new sources of weather data are needed.\nOver recent decades, the number of weather devices operated by independent\nindividuals who openly share the data collected as a crowdsourcing activity9 has rapidly\nincreased in European countries, including the United Kingdom10. We refer to these devices\nas personal weather stations (PWS) to contrast them with official automatic weather stations\ninstalled by meteorological offices. For instance, during the extremely hot summer of 2022,\ncoverage of official temperature sensors (see Methods; Supp. Figure 1). PWS are therefore\nincreasingly being used in urban climatological studies and are sought to provide useful\ncomplementary data sets for urban climate services11–17”\nSupp. Figure 1: LSOAs where official weather stations from the UK MetOffice MIDAS network\n(AWS) are active are in yellow, where Netatmo personal weather stations (PWS) are present\nare in orange, where both are active are in red (Both) and where none is present are shown\nin gray (Absence).\nIn addition, we have plotted the population covered per IMD decile where at least one MIDAS\nweather station is present and we also found that there is an unequal distribution of official\nweather sensors with areas most deprived having fewer official weather stations present in\ntheir LSOA. This has been added to the supplements as an additional figure:\nSupp. Figure 2: Percentages of people in England and Wales covered by at least one official\nautomatic weather station (AWS) per LSOA, per Index of Multiple Deprivation (IMD) decile.\nMost deprived populations are less covered by AWS. The total number of people in each IMD\ndecile is given at the top of the figure and shows fluctuations due to some variability in LSOA\npopulation size (~1500 inhabitants per LSOA; see Methods).\nWe have referred to this figure in the caption of Figure 1:\n“Figure 1: Percentages of people in England and Wales covered by at least one\npersonal weather station (PWS) per LSOA, per Index of Multiple Deprivation (IMD) decile.\nLeast deprived populations are better covered by PWS. The total number of people in each\nIMD decile is given at the bottom of the figure and shows fluctuation due to some variability\nin LSOA population size (~1500 inhabitants per LSOA; see Methods). The same figure is given\nin Supp. Figure 2 for official automatic weather stations from the MIDAS network handled by\nthe UnitedKingdom Met Office and also shows that most deprived populations are less\ncovered by highly accurate weather stations.”\nWe also added lines 67 to 70:\n“Interestingly, we also find that official automatic weather stations from the MIDAS network\nare less present, if not absent, from most deprived LSOAs (see Supp. Figure 2). At the time of writing,\n13.32 % of the population of England and Wales is covered by at least one PWS while only 0.5 % is\nfrom a MIDAS weather station. This highlights the extension of weather sensing networks in a variety\nof environments offered by PWS and supports the need to describe them.”\n3. The study is limited to the area of Wales and England. It is not clear why not whole to U.K. was\nchosen? It seems now a small subset of counties in the home country of the researchers. How this result\nis generalizable to a wider area remains unclear now. I fully understand it is hard to obtain all socio-\neconomic data across the globe, but at least for Europe this information should be available through\nEurostat.\nAs the reviewer suggests, the study was limited to England and Wales as these two countries\noffer a dataset at a high level of granularity: the Lower layer Super Output Area (LSOA). For\nthis reason, we are careful not to draw conclusions for places outside of England and Wales.\nThe reason why we did not extend our study to the whole United Kingdom is explained in our\ndiscussion and highlighted as a limitation (lines 182 to 185):\n“We acknowledge that our study suffers from several limitations and encourage further\nresearch to address them. First, its conclusions are specific to England and Wales to benefit from a\nstandardized Index of Multiple Deprivation between the two countries – something that is more\ncomplex at the supra-national level (e.g., Europe or global) and even at national level of the countries\nof the United Kingdom – and should thus be carefully considered for other locations.”\nConcerning the comparison to other countries, we agree with the reviewer that this is a great\nidea and something to aim for. We’d of course like to extend our research to additional\ncountries, but the Indices of Multiple Deprivation and other socio-economic and demographic\ndata that we used in our research is only available for England and Wales. Constructing a\nvariable similar to IMD with the same granularity for inter-country comparison isn’t a trivial\njob, more particularly since variables that are used to build IMD are generally not available at\na similar level to the LSOAs.\nIn fact, current EUROSTAT statistics related to GDP or population density are only available at\nNUTS2 or NUTS3, with detailed economic data only available at NUTS2 level. While having the\nadvantage of comprising a unified system across countries, the NUTS2 and NUTS3 geographic\nareas are quite large and are highly inconsistent in the size of covered areas and population.\nFor example, at NUTS2 level Greater London is divided into five areas, whereas Berlin is a\nsingle area, and Paris is just a part of the larger Ile-de-France. If we were to use these levels\nof administrative unit, our study – which focuses on the presence/absence of PWS to quantify\nhow much of the population in England and Wales is covered under certain conditions (e.g.,\nlevels of deprivation, vegetation density, proportion of ethnic minorities) – would be\nirrelevant. Indeed, by aggregating our data to these levels we find that all administrative units\nare covered by at least one PWS.\nSupp. Figure 4: Presence of at least one personal weather station (PWS) in Nomenclature of\nTerritorial Units for Statistics (NUTS) administrative units of second (NUTS2) and third (NUTS3)\nlevels.\nWe do therefore believe that working at larger scale is something to be achieved in future\nstudies and that it will come with its sets of challenges on data aggregation and analysis. We\nnow specifically call for this in the discussion. We have added the above figure in the\nsupplements to support our argument which follows the first limitation of our study in lines\n185 to 189:\n“Challenges related to the level of aggregation of the socio-economic, demographic and\nenvironmental data will have to be addressed to study the factors influencing the distribution of PWS\nat larger scales. For example, our presence/absence framework would be irrelevant at Nomenclature\nof Territorial Units for Statistics (NUTS) administrative units as all would be covered by at least one\nPWS in England and Wales (see Supp. Figure 4)”\n4. An additional stratification that could advance the study, is to study whether the same pattern in seen\nwhen a) extra instruments are taken into account (i.e. Netatmo the wind and rain sensor are extra costs,\nso it could be interesting to know which income threshold or so is needed to buy a wind sensor or rain\nsensor above the thermometer set), b) instruments from more expensive brands like Davis Vantage Pro\n(1200 euro instead of 150 euro for a NetAtmo thermometer set).\nWe agree with the reviewer that it would be interesting to see how annual income plays a\nrole in the acquisition of personal weather stations and how it is correlated to the acquisition\nof more accurate and expensive devices. We highlighted in our discussion that future studies\nto gather the information at the individual level (lines 168 to 170):\n“[F]uture research should try to understand how different demographic, socio-economic,\ncultural and environmental covariates may explain the distribution of PWS coverage. Focal population\nsurveys and large-scale machine learning could shed further light on the preliminary findings of our\nstudy.“\nThis would also facilitate research to categorise consumers willing to go for more expensive\nsensors like the one suggested by the reviewer. We therefore added to the previous sentence\n(lines 170 to 173):\n“For instance, consumer surveys should try to gather information at the individual level to\nrelate how consumers’ demographic and socio-economic characteristics are similar or dissimilar to\nthose of the area in which they live (e.g., distance to the average annual income in the LSOA).”\nWe haven’t managed however to find any portal to gather the data coming from Davis\nVantage Pro sensors. To the best of our knowledge, the only portal that offers publicly\naccessible weather data nowadays is the Netatmo app developer API. This was written in the\nlimitations of our study (lines 189 to 191):\n“[B]ecause of data restriction policies our study only gathered information from Netatmo\nPWS and did not investigate other crowd-sourced weather data providers like the Weather\nObservation Website (WOW)49 or Weather Underground (Wunderground) which could cover areas\ndeprived of Netatmo PWS50.”\n5. The study does implicitly imply that the owner of the PWS is also the one who bought the PWS, but\nPWSes are very often offered as a gift for Christmas, anniversaries and so on. So whether a PWS is\npresent in a garden may depend more about the wealth of the ones who offer the PWS than the ones\nthat operate the PWS in the end. How was dealt with that, and how does this affect your conclusions?\nAs explained in the methods section, we simply describe where the PWS are located and are\nworking with area-level variables aggregated at the Lower layer Super Output Area (LSOA)\nand not with individual-level data from the consumers or buyers. We therefore cannot\nperform this type of study. As explained above we do call for more individual-level data in the\ndiscussion.\nReviewer 3\nGeneral comments\nThe main objective of this study is to uncover socio-economic and environmental inequalities in the distribution\nof crowdsourced personal weather stations. The key findings include a lower presence of crowdsourced personal\nweather stations (PWS) in deprived environments which can be associated with high build-up, low vegetation\ncover, high proportions of ethnic minorities and mostly younger people. The study uses straightforward yet\nmethodologically sound empirical techniques to obtain these rather logical but important results.\nWe thank the reviewer for their valuable comments. Our detailed answers are provided below\nin black and cited text is provided between quotation marks. We have uploaded the codes\nand the public data used in our study in a dedicated GitHub repository:\nhttps://github.com/oscarbrousse/NatComms_PWS_2024\nMajor comments\n1. How do the authors account for their inability to disentangle the impact of environmental and socio-economic\nfactors (such as built-up density, building height, age, and ethnic minority representation) on PWS coverage\nindependently from the influence of deprivation? What strategies might be employed to disentangle the role of\ndeprivation in explaining PWS coverage?\nSeveral strategies could be adopted to disentangle which variable is the most predictive of\nPWS coverage, including linear regression models, random forests, gradient boosting or other\nforms of machine learning. The motivation for such a strategy would be either (1) to predict\nvariations in PWS coverage or (2) to identify the causes of variations in PWS coverage.\nRegarding motivation (1), there would be no value in predicting as the data we use are\ncomplete for the domain of interest and not transferable to another domain. Regarding\nmotivation (2), such an analysis would imply causal hypotheses that are not necessarily\nlogical: we do not believe that variations in built-up density (for example) directly cause\nvariations in PWS coverage; although incomes could potentially be a direct factor. In this work\nwe do not try to explain what causes the distribution of PWS; we are simply observing where\nPWS are and what type of underlying environments they sense, i.e. what environments are\nwell represented. The last paragraph of our introduction pointed to the main objective of our\nresearch. We slightly adapted the last line to put the emphasis on this objective (lines 43 to\n50):\n“We empirically investigated the current coverage of certain PWS that are widely used by\nconsumers and researchers and that are capable of measuring temperature across England and Wales.\n[...] Our study provides a comprehensive description of the underlying spatial inequalities in\ncrowdsourced climate data acquisition and accessibility in English and Welsh urban environments.”\nIn addition, we emphasise the point of concern from the reviewer in the Methods under the\n“Study’s rationale and framework” section (lines 274 to 280):\n“Most importantly, we chose not to perform any explanatory variable analysis because we are\nnot seeking to causally explain the drivers of coverage in PWS, but rather to describe the current state\nand the implications for sensing of the environment. Moreover, explanation of the drivers of PWS\nacquisition would be much better addressed by individual data rather than area-level data, for\nexample through surveys or interviews. Machine-learning based studies attempting to explain the\ndistribution of PWS between areas may be obscured by the spatial correlation between PWS coverage\nand urbanization, which would therefore require extensive studies and analyses that cannot solely be\naddressed in this paper.“\nIn other words, our study is the first description of an existing issue that may need to be\naddressed to cope with the inequalities that we describe in the manuscript. We leave this for\nfuture research as multiple methodologies to better understand the drivers of these\ninequalities and the potential ways forward to mitigate them will have to be developed. Our\nstudy is at the frontier of knowledge in crowdsourcing of urban climate data.\n2. Why did the authors decide to include the Index of Multiple Deprivation (IMD), Local Climate Zones (LCZs) and\na variety socio-economic and environmental indicators, even though they exhibit inherent correlations?\nAgain, our study does not try to explain what drives the distribution but simply characterise\nthe underlying environments, so the correlation of these variables is not a problem. To\ndescribe these environments, we chose a set of easily understood discrete and continuous\nvariables that are relevant for studying urban heat exposure.\n3. It is known that coarse vegetation indexes are not ideal to study temperature-environment relationships\n(Bartesaghi-Koc et al., 2020; Rakoto et al., 2021), since different functional green types may have\ncontradicting cooling effects. In this context, it's important to consider whether the EVI takes into account various\nfunctional green types. Could you please clarify how the EVI incorporates information about vegetation density,\ntype, and fraction?\nOur study does not try to study the temperature-environment relationship. We here only\ndescribe the underlying environment that new personally owned temperature sensors\nobserve. We believe that the Enhanced Vegetation Index (EVI) is an easily understood metric\nand that it suffices for simply describing whether an LSOA is more or less vegetated. It is a\nwidely known and used vegetation index for studying the level of greenness in vegetated\nareas. It also has advantages compared to the Normalized Difference Vegetation Index as it\nreduces the influence of the canopy background signal and the atmosphere, and is more\nsensitive to the leaf area index and – which is valuable for discretising densely vegetated and\ntreed areas (Son et al. 2014).\nWe have added lines 244 to 247:\n“We chose to use the EVI over the Normalized Difference Vegetation Index as it reduces the\ninfluence of the atmosphere and the canopy background signal on the measured index while having\nan increased sensitivity to the leaf area index – hence facilitating the discretisation of different\nvegetation types in high biomass locations (like forests)62,63. ”\nSon, N. T., Chen, C. F., Chen, C. R., Minh, V. Q., & Trung, N. H. (2014). A comparative analysis\nof multitemporal\nMODIS EVI and NDVI data for large-scale rice yield estimation. Agricultural and forest meteorology, 197, 52-64.\nLiao, Z., He, B., & Quan, X. (2015). Modified enhanced vegetation index for reducing\ntopographic effects. Journal of Applied Remote Sensing, 9(1), 096068-096068.\n4. There appears to be missing information regarding the LCZ map used in the study. Was the study area\nextracted directly from a broader European LCZ map? Can a Europe-wide LCZ map provide sufficient precision\nfor examining the specific LCZs within England and Wales? Additionally, it would be valuable to know more about\nthe spatial resolution, accuracy, and other relevant characteristics of the LCZ map employed in the research.\nWe thank the reviewer for noticing that some valuable information was missing about the\nEuropean LCZ map. We did already explain in the methods that we used this map for our\nstudy. It has already been widely applied across Europe for urban climate studies and is sought\nto be applicable across the whole continent. We have added some information about the\nresolution and the accuracies to showcase the latter in lines 253 to 256:\n“The LCZ were mapped at a resolution of 100 m with an overall accuracy above 80 % for the\nwhole of Europe making it a valid land use land cover map for studying urban climate related issues in\nthe region; the overall accuracy, the Kappa score and the true positive rates for evaluating how the\nurban areas’ extension were consistent with other products like ESA CCI are also above 90 % for the\nUK64.”\n5. Given that PWSs have demonstrated the capability to capture fine-scale temperature variations,\nencompassing both inter- and intra-LCZ variability (Fenner et al., 2017; Verdonck et al., 2017), it might be\nconsidered a limitation to classify PWSs solely into a major LCZ category based on the predominant LCZ within\nthe corresponding Lower Layer Super Output Area (LSOA). Such an approach may result in the loss of valuable\ninformation. It would be beneficial to include details regarding the number of distinct LCZ types within a single\nLSOA and their respective proportions, providing a more nuanced perspective on the urban microclimate in the\nstudy area.\nWe fully agree with the reviewer that classifying the LSOAs based on the modal LCZ can lead\nto a reduction in information; something intrinsic to any classification work. Nonetheless, we\nargue that for the purpose of this study – describing the underlying environments of PWS’\nlocations in England and Wales – such a simplification can be pursued. For instance, Stewart\nand Oke (2012) describe Local Climate Zones as “regions of uniform surface cover, structure,\nmaterial, and human activity that span hundreds of meters to several kilometers in horizontal\nscale.” (pp. 1884). We therefore believe that the aforementioned uniformity is well captured\nby the modal LCZ when it comes to describing the environment in which ~1500 inhabitants\nlive (LSOA) as the latter will capture the most influential LCZ on the local climate. This question\nof uniformity under the LCZ scheme is dependent on the goal of the research. Urban climate\nmodellers often use similar simplifications (e.g., modal or averages of LCZ parameters) to\nmodel the local climate at kilometer scales. If we were to predict temperatures at the LSOA\nlevel, then proportions of LCZ would be valuable.\nWe have added our rationale for using only the modal LCZ under a Supplementary\nInformation section that is referred to in the Methods (lines 267 to 268):\n“[U]sed the same package to get the modal LCZ in the LSOA (see Supp. Information 2 for more\ninformation on the potential loss of information induced by the use of modal LCZ)”\nWe have added the supplementary information section 2 entitled “Modal LCZ and loss of\ninformation”:\n“Supplementary Information 2: Modal LCZ and loss of information\nCharacterising each Lower layer Super Output Area (LSOA) by a single Local Climate Zone necessarily\ncomes with a simplification of the complexity of the environment that composes each LSOA;\nsomething natural to any classification exercise. In this study, we chose to classify each LSOA in the\nform of Local Climate Zone by choosing the modal LCZ as the determining entity of the LSOA’s urban\nclimatic background. The modal LCZ is the LCZ that composes most of the LSOA area (Supp. Figure 5).\nThis could therefore come with an over simplification of the LSOA environment in cases where, for\nexample, LSOAs are composed of two major LCZs that are only separated by a few percentage points\nin their proportion of the covered area (e.g., 48 % vs 52 %). Below, we show how choosing the modal\nLCZ as a class for the entire LSOA is expected to have a low impact on the conclusions presented in\nthis study.\nIn England and Wales, as higher population densities are found around cities, urban LSOAs tend to be\nsmaller than their natural counterparts. This minimises the chances of having multiple disparate LCZs\nwithin urban LSOAs as can be seen in Supp. Figure 5 (e.g., panels a to f and h). As population density\nlowers in rural areas (Supp. Figure 5 panels g and f to o) LSOAs cover larger areas and are therefore\nmore prone to a plurality of LCZs that compose their territory; despite the hegemony of LCZ D: Low\nPlants (Supp. Figure 5l) in rural England and Wales.\nSupp. Figure 5: Proportions of each LCZ composing the land-use land-cover of England and Wales in\neach Lower layer Super Output Area (LSOA). LCZs are extracted from the European LCZ map from\nDemuzere et al. (2019). For the LCZ codification, please refer to the main manuscript or Supp. Figure\n6, below.\nBy plotting the cumulative density function of proportions of other LCZ in each modal LCZ at the LSOA\nlevel (Supp. Figure 6), we find that other LCZs do not compose more than a 1/3 of the LSOA area in\nmore than 80 % of the cases (except for LCZ 6 in LSOAs classified as LCZ 3, LCZ 8 and LCZ B (Supp Figure\n6b, e and h), remembering that LCZ 6 (n= 22529) composes most of the LSOAs in England and Wales\nwith LCZ D (n=8027). This shows that in a vast majority the modal LCZ is unanimously the one that\ncomposes the larger part of each LSOA and can therefore be expected to have the greatest influence\non the local climate and characterise it. Withal, other prominent LCZs in each LSOA are of similar types\nthan the modal LCZ in which they are embedded. For example, urban Compact Low-Rises (LCZ 3) are\ngenerally found in greater proportions in LSOAs classified as Compact Mid-Rises (LCZ 2; Supp. Figure\n6a). This means that independent of the presence of other LCZs in each LSOA, a generic typology of\nlocal climate can be expected, such as: compact urban, open urban, sparsely built and natural\n(forested or afforested).\nSupp. Figure 6: Cumulative distributions of proportions of other LCZs composing Lower layer Super\nOutput Areas (LSOAs) classified as LCZ using a modal classification. Distributions are from 50 bin\nx\nhistograms spanning proportions from 0 % to 50 %; above 50 %, the LCZ would necessarily become\nmodal. The horizontal light dashed line indicates that half of LCZ are lower or greater than a\nX\nproportion p. The horizontal bold dashed line indicates the same but with 80 % of the proportions\nbeing lower than p. The vertical dashed line shows a proportion p equal to 1/3 of the LSOA area.\nOur results are in line with previous studies from Bechtel et al. (2017) which showed that there is a\ngreater chance of having similar LCZ clustered together; this supports the development of the\nweighted accuracy for evaluating LCZ maps’ accuracy. By considering “how wrong” our modal\nclassification is, to cite Bechtel et al. (2020), we argue that our modal classification certainly misses\npart of the LSOAs’ climatic environment complexity. At the same time; the expected climate from the\nmodal LCZ is not thought to be entirely dissimilar to one that would consider the variety of LCZs that\ncompose each LSOA. For the purpose of our study, which is simply to characterise the typology of local\nclimates that are currently being sensed at the national scale, we believe that the modal LCZ is\ntherefore sufficient. Other studies that would try to predict air temperature at the LSOA level or to\ninvestigate the causal explanations of the personal weather station density are encouraged to use\nmore discrete variables like the proportion of LCZ.\nReference\nBechtel, B., Demuzere, M., Sismanidis, P., Fenner, D., Brousse, O., Beck, C., ... & Verdonck, M. L.\n(2017). Quality of crowdsourced data on urban morphology—the human influence experiment (HUMINEX).\nUrban Science, 1(2), 15.\nBechtel, B., Demuzere, M., & Stewart, I. D. (2020). A weighted accuracy measure for land cover\nmapping: comment on Johnson et al. local climate zone (LCZ) map accuracy assessments should\naccount for land cover physical characteristics that affect the local thermal environment. Remote\nSens. 2019, 11, 2420. Remote Sensing, 12(11), 1769.\nDemuzere, M., Bechtel, B., Middel, A., & Mills, G. (2019). Mapping Europe into local climate zones.\nPloS one, 14(4), e0214474.“\nStewart, I. D., & Oke, T. R. (2012). Local climate zones for urban temperature studies. Bulletin\nof the American Meteorological Society, 93(12), 1879-1900.\n6. Building upon the preceding comment, as the entire dataset is aggregated at the LSOA level, it is reasonable\nto assume that readers would find it valuable to have insight into the characteristics and key metrics of these\nLSOAs. Providing an overview of the LSOA layout and sharing general metrics for these areas can enhance the\nreader's understanding of the study context.\nWe believe this is exactly what we do in the study. We indeed provide details about the LSOA\ncharacteristics and maps of each metrics used in the study at the LSOA level. We also describe\nwhat an LSOA is in the text.\n7. The organization of the Results and Methods sections can be improved for better readability. To achieve this,\nincorporating subheadings to break down the content into smaller, more manageable sections would be\nbeneficial. Additionally, aligning the flow and structure of both sections could further enhance the overall\nreadability of the manuscript.\nWe agree with the reviewer’s suggestion. We have added some subheadings to the Results,\nthe Discussion and the Methods sections which can be found in the updated manuscript. We\nwould like to keep the structure as is concerning the Methods section to be more in line with\nNature Communications’ publication style: coming after the Discussion.\nMinor comments\n8. Line 12-13: This sentence should be supported with appropriate citations: “Particular attention has\nbeen given to urban heat because temperature is a reliably measured weather variable, known to have\nnegative impacts on health, buildings, energy, or biodiversity.”\nWe agree with the reviewer. We have added these references:\nMurage, P., Hajat, S., Macintyre, H. L., Leonardi, G. S., Ratwatte, P., Wehling, H., ... & Kovats,\nS. (2023). Indicators to support local public health to reduce the impacts of heat on health.\nEnvironment International, 108391.\nTomlinson, C. J., Chapman, L., Thornes, J. E., & Baker, C. J. (2011). Including the urban heat\nisland in spatial heat health risk assessment strategies: a case study for Birmingham, UK.\nInternational journal of health geographics, 10(1), 1-14.\nDessai, S., Fowler, H. J., Hall, J. W., & Mitchell, D. M. (2022). UK Climate Risk Assessment and\nManagement. Climate Risk Management, 37.\nKaiser, A., Merckx, T., & Van Dyck, H. (2016). The Urban Heat Island and its spatial scale\ndependent impact on survival and development in butterflies of different thermal sensitivity.\nEcology and Evolution, 6(12), 4129-4140.\nHinkel, K. M., Nelson, F. E., Klene, A. E., & Bell, J. H. (2003). The urban heat island in winter at\nBarrow, Alaska. International Journal of Climatology: A Journal of the Royal Meteorological\nSociety, 23(15), 1889-1905.\n9. Figure 2: From the caption of Figure 2 it is unclear whether the total surface and total population of\nthe different LCZ classes in figure 2a and figure 2b relate to the original LCZs derived from WUDAPT or\nwhether they present the LSOAs with modal LCZ.\nThe population is derived from the LSOAs and we simply sum up the total amount of\npopulation living in the LSOA by the modal LCZ. WUDAPT never offered any population\nestimates. We believe that this was clear in our previous version.\n10. Figure 2: Reference to panel b is missing in the figure caption: “Personal weather stations (PWS) per\nsquare kilometre (km²; a) and percentage of total population covered by a PWS (km²; b) in each Local\nClimate Zone (LCZ).”\nChecked and dealt with.\n11. Line 105-106: “Also, higher albedo are observed by ∼0.02 (2 % more reflective capacity), EVI by ∼0.06\n(denser vegetation), and proportion of people aged 65 or above by ∼10 %.”. This sentence gives the impression\nthat the most deprived LSOAs are associated with higher albedo, denser vegetation and older people. That\nseems quite counterintuitive and the opposite is shown in Figure 4:\nWe thank the reviewer for noticing this. We have rewritten the paragraph following previous\ncomments made by the other reviewers. It now reads (lines 110 to 119):\n“These demographic and environmental characteristics of sensors deserts also\ndescribe lower IMD deciles (Figure 4, Supp. Table 1). For example, most deprived LSOAs have\nhigher building heights by ~4 m than least deprived LSOAs on average (Supp. Table 1), building\nfractions by ~10 %, and proportion of ethnic minorities by ~15 %. Also, lower albedo is\nobserved by ~0.02 (2 % less reflective capacity), EVI by ~0.06 (less densely vegetated), and\nproportion of people aged 65 or above by ~10 %.\nNo deserts are found at similar levels of deprivation\nImportantly, we find that within the population living in each IMD decile no clear\ndistinction between the PWS and no-PWS can be made out of the environmental and\ndemographic indicators. For example, the highest proportion of people living in the highest\nIMD decile (10th) live in the 3rd decile of building height (low buildings) independent of\nwhether PWS are present or absent in their LSOA (Figure 4, Supp. Table 1).”\n12. Line 112: It is not indicated where the results of the PSS are shown. A reference to Supplementary\nTable 1 could be included.\nChecked and dealt with.\n13. Line 127: Period punctuation between sentences is missing.\nChecked and dealt with.\n14. Line 132: Period punctuation between sentences is missing.\nWe do not see what the reviewer is referring to.\n15. Line 140-142: “Furthermore, the sensor desert may limit quantification of the cooling induced by\nvegetated evapotranspiration and related benefits to heat mitigation (Iungman, et al, 2023).” The statement is true but the\ncited reference does not consider PWS in quantifying the cooling potential of vegetation.\nWe agree with the reviewer. This now reads (lines 153 to 155):\n“Furthermore, the sensor desert may limit quantification of the cooling induced by vegetated\nevapotranspiration, the latter being shown to have positive impacts on heat mitigation and related\nheat-mortality45.”\n16. Line 142-145. This sentence is quite long. Consider breaking it down into smaller parts to improve the\nreadability: “Moreover, as people aged above 65 tend to be more susceptible to heat-related health hazards\nand because ethnic minorities are often more exposed to asphalted built-up areas and their related surface\nurban heat islands – which could drive disparities in heat exposure – it would be necessary to adequately\nquantify how vulnerabilities induced by deprivation may increase the vulnerability of different communities,\nstratified by age groups.”\nChecked and dealt with. This now reads (lines 153 to 159):\n“Furthermore, the sensor desert may limit quantification of the cooling induced by vegetated\nevapotranspiration, the latter being shown to have positive impacts on heat mitigation and related\nheat-mortality45. Lastly, ethnic minorities are often more exposed to asphalted built-up areas and\nrelated surface urban heat islands46,47. This could drive disparities in heat exposure, especially\nconsidering that ethnic minorities tend to comprise younger populations and that people aged above\n65 tend to be more susceptible to heat-related health hazards29. It would therefore be necessary to\nadequately quantify how vulnerabilities induced by deprivation may increase the risk to different\ncommunities, stratified by age groups.”\n17. Line 156-157: The closing parenthesis seems to be missing: “For instance, it would be necessary to\ndisentangle how deprivation explains PWS coverage because other covariates (e.g., built-up density,\npopulation density, or building height may also have an influence on PWS coverage that we could not\nseparate from deprivation in this study.”\nChecked and dealt with.\n18. Line 193: This sentence appears to be incomplete: “We use a moving window of 0.2◦ by 0.2◦ over England\nand.”\nChecked and dealt with. We added “Wales”.\n19. Line 261: This reference appears to be incomplete: “Oke, T. R., Mills, G., Christen, A. & Voogt, J. A.\nUrban climates (Cambridge University Press, 2017)”\nThe reference is appropriate and refers to a book. The formatting comes from the OverLeaf\ntemplate provided by Nature Communications. We will check this with the editors once the\npaper is in production as some valuable information is missing in this formatting. We have\nadded the DOI to the reference:\n“@book{oke2017urban,\ntitle={Urban climates},\nauthor={Oke, Timothy R and Mills, Gerald and Christen, Andreas and Voogt, James A},\nyear={2017},\nisbn={9781139016476},\nurl={https://doi.org/10.1017/9781139016476},\npublisher={Cambridge University Press}\n}”\n20. Line 318: This reference appears to be incorrect/incomplete: “for National Statistics, O. Office for\nnational statistics: 2011 census geography products for england and wales.”.\nChecked and dealt with. We also added to the bibtex the date of access:\n“@online{ONS2011,\ntitle={Office for National Statistics: 2011 Census geography products for England and Wales},\nauthor={{Office for National Statistics}},\nurl={https://webarchive.nationalarchives.gov.uk/ukgwa/\n20160105225829/http://www.ons.gov.uk/ons/guide-method/geography/products/census/index.html},\nyear={2011},\naddendum = {(accessed: 17.03.2023)}\n}”\n21. Line 319-320: This reference appears to be incorrect/incomplete: “Schaaf, C. & Wang, Z. Mcd43a3\nmodis/terra+ aqua brdf/albedo daily l3 global–500m v006. nasa eosdis land processes daac (2015).”\nWe agree with the reviewer. We have dealt with the formatting of this reference and others\nin the bibtex to keep capital letters adequately displayed.\n“@article{schaaf2015mcd43a3,\ntitle={{MCD43A3 MODIS/Terra+ Aqua BRDF/Albedo Daily L3 Global--500m V006. NASA EOSDIS Land Processes\nDAAC}},\nauthor={Schaaf, C and Wang, Z},\njournal={US Geological Survey: Reston, VA, USA},\nyear={2015},\nurl={https://doi.org/10.5067/MODIS/MCD43A3.006}\n}”"}, {"round": 2, "reviewer_comments": {"reviewer_1": "In this paper, the authors show that crowdsourced weather data have the potential to improve climate\nadaptation and that it is therefore important to understand the biases in personal weather stations'\ndistribution.\nThe methodology is simple, yet sound and appropriate.\nThe authors have successfully taken my previous review into account, and I have no further comment.", "reviewer_2": "Evaluation: In my view the authors have sufficiently addressed the comments of the reviewers. The\nresearch has been thoroughly executed, and has an important and new message for science and society.\nThe research has some limitations in terms of scale and data availability, but these have been well\ndiscussed, and recommendations for future steps have been well formulated. Hence these limitations\nshould not be a showstopper in publishing the manuscript.\nRecommendation: accept for publication", "reviewer_3": "The authors have thoroughly addressed all the comments raised during the 1st revision. I have no\nadditional feedback to provide at this time."}, "author_response": ""}]

Decision letter