High-resolution Gridded Dataset Research Articles

A Statistical Analysis of Drought and Fire Weather Indicators in the Context of Climate Change: The Case of the Attica Region, Greece

As warmer and drier conditions associated with global warming are projected to increase in southern Europe, the Mediterranean countries are currently the most prone to wildfire danger. In the present study, we investigated the statistical relationship between drought and fire weather risks in the context of climate change using drought index and fire weather-related indicators. We focused on the vulnerable and long-suffering area of the Attica region using high-resolution gridded climate datasets. Concerning fire weather components and fire hazard days, the majority of Attica consistently produced values that were moderately to highly anti-correlated (−0.5 to −0.9). This suggests that drier circumstances raise the risk of fires. Additionally, it was shown that the spatial dependence of each variable on the 6-months scale Standardized Precipitation Evapotranspiration Index (SPEI6), varied based on the period and climate scenario. Under both scenarios, an increasing rate of change between the drought index and fire indicators was calculated over future periods versus the historical period. In the case of mean and 95th percentiles of FWI with SPEI6, abrupt changes in linear regression slope values were observed, shifting from lower in the past to higher values in the future periods. Finally, the fire indicators’ future projections demonstrated a tendency towards an increasing fire weather risk for the region’s non-urban (forested and agricultural) areas. This increase was evident from the probability distributions shifting to higher mean and even more extreme values in future periods and scenarios. The study demonstrated the region’s growing vulnerability to future fire incidents in the context of climate change.

Evaluation of GPM IMERG Early, Late, and Final Run in Representing Extreme Rainfall Indices in Southwestern Iran

The growing concerns about floods have highlighted the need for accurate and detailed precipitation data as extreme precipitation occurrences can lead to catastrophic floods, resulting in significant economic losses and casualties. Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM IMERG) is a commonly used high-resolution gridded precipitation dataset and is recognized as trustworthy alternative sources of precipitation data. The aim of this study is to comprehensively evaluate the performance of GPM IMERG Early (IMERG-E), Late (IMERG-L), and Final Run (IMERG-F) in precipitation estimation and their capability in detecting extreme rainfall indices over southwestern Iran during 2001–2020. The Asfezari gridded precipitation data, which are developed using a dense of ground-based observation, were utilized as the reference dataset. The findings indicate that IMERG-F performs reasonably well in capturing many extreme precipitation events (defined by various indices). All three products showed a better performance in capturing fixed and non-threshold precipitation indices across the study region. The findings also revealed that both IMERG-E and IMERG-L have problems in rainfall estimation over elevated areas showing values of overestimations. Examining the effect of land cover type on the accuracy of the precipitation products suggests that both IMERG-E and IMERG-L show large and highly unrealistic overestimations over inland water bodies and permanent wetlands. The results of the current study highlight the potential of IMERG-F as a valuable source of data for precipitation monitoring in the region.

Microclimatic Zonation and Climatic Variability of Sikkim Himalaya

The Köppen classification of climate integrates precipitation and temperature information with natural vegetation patterns to create a precise representation of any particular region's climate. This integration depends on the empirical relationship of climate and vegetation, which indicates that distinct locations in the same class have similar vegetation attributes. Köppen climatic classification factors are explained and Sikkim's climate characteristics are regionalized based on it. The method for making representations of air temperatures and precipitation has been described, and an illustration of Sikkim's climatic zones with variability is generated as a result of these changes. The geographic pattern of climatic types and subtypes in Sikkim has been briefly addressed using an available high-resolution gridded dataset (ERA5-Land). This is described that the constraints of microclimatic zonation emanate from the empirical prerequisite of climate classifications, as well as the nature of data selection and the methodologies employed for climate variability analysis and classification. Based on the Köppen classification for the long term (1980–2021), the Sikkim Himalaya contains three primary climatic classes, particularly ETc (cold, tundra, and cool summer), Cfc (moderately warm, humid, and cool summer), and Cfb (moderately warm, humid, and warm summer). Climate variability on the basis of temperature and precipitation change with respect to 1980–2021 over the entire Sikkim Himalaya concludes that the climatic pattern of the Sikkim has been changed from cold-dry to warm-wet. The alteration in the corresponding climatic pattern is further supported by changes in LULC and NDVI. The results suggest that the precipitation change in the past two decades (1980–2000) is negative, while a significant positive change has been noticed in the recent two decades (2001–2021). Subsequently, the number of extremely wet days decreases in the entire ETc and Cfc climate zones. Regardless, the southern part of the Cfb climatic zone has experienced an increase in extremely wet days. This study's findings will significantly contribute to the development of future policies and initiatives by providing insights crucial for achieving sustainable development, environmental protection, and climate change adaptation.

Inter-Comparison of Multiple Gridded Precipitation Datasets over Different Climates at Global Scale

Comprehensive evaluations of global precipitation datasets are imperative for gaining insights into their performance and potential applications. However, the existing evaluations of global precipitation datasets are often constrained by limitations regarding the datasets, specific regions, and hydrological models used for hydrologic predictions. The accuracy and hydrological utility of eight precipitation datasets (including two gauged-based, five reanalysis and one merged precipitation datasets) were evaluated on a daily timescale from 1982 to 2015 in this study by using 2404 rain gauges, 2508 catchments, and four lumped hydrological models under varying climatic conditions worldwide. Specifically, the characteristics of different datasets were first analyzed. The accuracy of precipitation datasets at the site and regional scale was then evaluated with daily observations from 2404 gauges and two high-resolution gridded gauge-interpolated regional datasets. The effectiveness of precipitation datasets in runoff simulation was then assessed by using 2058 catchments around the world in combination with four conceptual hydrological models. The results show that: (1) all precipitation datasets demonstrate proficiency in capturing the interannual variability of the annual mean precipitation, but with magnitudes deviating by up to 200 mm/year among the datasets; (2) the precipitation datasets directly incorporating daily gauge observations outperform the uncorrected precipitation datasets. The Climate Precipitation Center dataset (CPC), Global Precipitation Climatology Center dataset (GPCC) and multi-source weighted-ensemble precipitation V2 (MSWEP V2) can be considered the best option for most climate regions regarding the accuracy of precipitation datasets; (3) the performance of hydrological models driven by different datasets is climate dependent and is notably worse in arid regions (with median Kling–Gupta efficiency (KGE) ranging from 0.39 to 0.65) than in other regions. The MSWEP V2 posted a stable performance with the highest KGE and Nash–Sutcliffe Efficiency (NSE) values in most climate regions using various hydrological models.

Observed and future shifts in climate zone of Borneo based on CMIP6 models

Climate change has significantly altered the characteristics of climate zones, posing considerable challenges to ecosystems and biodiversity, particularly in Borneo, known for its high species density per unit area. This study aimed to classify the region into homogeneous climate groups based on long-term average behavior. The most effective parameters from the high-resolution daily gridded Princeton climate datasets spanning 65 years (1950–2014) were utilized, including rainfall, relative humidity (RH), temperatures (Tavg, Tmin, Tmax, and diurnal temperature range (DTR)), along with elevation data at 0.25° resolution. The FCM clustering method outperformed K-Mean and two Ward's hierarchical methods (WardD and WardD2) in classifying Borneo's climate zones based on multi-criteria assessment, exhibiting the lowest average distance (2.172–2.180) and the highest compromise programming index (CPI)-based correlation ranking among cluster averages across all climate parameters. Borneo's climate zones were categorized into four: ‘Wet and cold’ (WC) and ‘Wet’ (W) representing wetter zones, and ‘Wet and hot’ (WH) and ‘Dry and hot’ (DH) representing hotter zones, each with clearly defined boundaries. For future projection, EC-Earth3-Veg ranked first for all climate parameters across 961 grid points, emerging as the top-performing model. The linear scaling (LS) bias-corrected EC-Earth3-Veg model, as shown in the Taylor diagram, closely replicated the observed datasets, facilitating future climate zone reclassification. Improved performance across parameters was evident based on MAE (35.8–94.6%), MSE (57.0–99.5%), NRMSE (42.7–92.1%), PBIAS (100–108%), MD (23.0–85.3%), KGE (21.1–78.1%), and VE (5.1–9.1%), with closer replication of empirical probability distribution function (PDF) curves during the validation period. In the future, Borneo's climate zones will shift notably, with WC elongating southward along the mountainous spine, W forming an enclave over the north-central mountains, WH shifting northward and shrinking inland, and DH expanding northward along the western coast. Under SSP5-8.5, WC is expected to expand by 39% and 11% for the mid- and far-future periods, respectively, while W is set to shrink by 46%. WH is projected to expand by 2% and 8% for the mid- and far-future periods, respectively. Conversely, DH is expected to expand by 43% for the far-future period but shrink by 42% for the mid-future period. This study fills a gap by redefining Borneo's climate zones based on an increased number of effective parameters and projecting future shifts, utilizing advanced clustering methods (FCM) under CMIP6 scenarios. Importantly, it contributes by ranking GCMs using RIMs and CPI across multiple climate parameters, addressing a previous gap in GCM assessment. The study's findings can facilitate cross-border collaboration by providing a shared understanding of climate dynamics and informing joint environmental management and disaster response efforts.

Climate of the Pyrenees: Extremes indices and long-term trends

We utilized an extensive, multisource, cross-border dataset of daily meteorological observations from over 1500 stations in the Pyrenees, spanning from the mid-20th century to 2020, to examine the spatial and temporal climate patterns. Our focus was on 17 indices related to extreme precipitation and temperature events across the mountain range. The original data underwent rigorous quality control and homogenization processes, employing a comprehensive workflow that included spatial modeling based on environmental predictors. This process yielded two main outcomes: 1) a high-resolution gridded dataset (1 km2) of daily precipitation, maximum and minimum temperature from 1981 to 2020, allowing for a detailed analysis of spatial variations; and 2) an evaluation of long-term annual and seasonal trends from 1959 to 2020, using selection of high-quality data series that were homogenized to preserve their temporal structure and coherence. The findings revealed a clear elevation-related pattern in temperature indices (with the exception of tropical nights, which were predominantly observed on the Mediterranean side) and a distinct north-south latitudinal disparity in precipitation, turning longitudinal when focusing on extreme precipitation events. Overall, there was a notable and significant warming trend of 0.2 to 0.4 °C per decade, and a non-significant change of precipitation, with the exception of the southern and Mediterranean regions, where there was a notable decrease, approximately −3 % per decade, observed on an annual basis.

Revisiting the relationships between energy consumption, economic development and urban size: A global perspective using remote sensing data

Existing methods of measuring energy consumption require complex statistics and computing. A real-time and globally applicable approach for comparing energy consumption across different cities is still lacking. Additionally, the nonlinear relationships and varying thresholds of energy consumption in relation to economic activities and urbanization remain unconfirmed. This study aims to fill these gaps by utilizing Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data in 2015 and a top-down approach based on a multiple regression model to examine energy consumption in global cities employing a redefined urban boundary. It also explores the accurate relationship between energy consumption, population density (as a proxy of urbanization), and per capita gross domestic product (GDP) across different regions and urban sizes using generalized additive models and regression models. High-resolution gridded population and GDP datasets covering the entire planet are utilized for this purpose. The study also estimates the development potentiality. The study yields followings outcomes: Firstly, the top 30 cities with the highest per capita energy consumption account for over 0.66% of the total per capita energy consumption of all cities. Secondly, in East Asia (EA) and Southeast Asia (SEA), the per capita energy consumption decreases when per capita GDP reaches $40,000 and $75,000, respectively, while it remains stable in cities located in Western Europe (WE) and North America (NA) as per capita GDP increases. Thirdly, the per capita energy consumption declines with increasing urban population density until reaching 10,000 person/km2, 22,000 person/km2, and 4000 person/km2 in EA, SEA, and NA, respectively. Fourthly, in Central Asia (CA), megacities can save over 100 Mbtu/population when per capita GDP increases by $1000 compared to big cities. This pioneering study provides a comparable investigation of energy consumption at the global city level, exploring its relationship with urbanization and economy by employing a unified calculation standard. It will facilitate long-term energy-saving policies and urban planning strategies.

Multi-dimensional evaluation of four monthly long-term and high-resolution gridded precipitation datasets on the Qinghai-Tibet Plateau

The reliability assessment of gridded precipitation datasets (GPDs) is vital for various precipitation-related studies, particularly in poorly gauged regions as the Qinghai-Tibet Plateau (QTP). However, existing studies predominantly focuses more on mainstream GPDs with low spatiotemporal resolution, making it challenging to meet the requirements for widespread applications. There is an urgent need for a systematic assessment of long-term and high-resolution GPDs. In this study, we considered four GPDs (HRLT, TPHiPr, PENG, and CHIRPS) with a spatial resolution above 0.05° during 1981–2019, evaluating their monthly performance across three dimensions: spatial accuracy, temporal accuracy, and precipitation event detection ability. The results revealed the following: (1) TPHiPr exhibited the highest spatial accuracy (R2 = 0.81), and the four GPDs consistently performed the worst in arid areas (R2 = 0.59 ± 0.15) and the best in semi-humid areas (R2 = 0.80 ± 0.02). (2) PENG attained the highest temporal accuracy (NSE = −0.56), and the four GPDs performed the worst in winter (NSE = -1.06 ± 0.9) and the best in autumn (NSE = 0.08 ± 0.16). (3) HRLT and TPHiPr demonstrated superior precipitation event detection abilities compared to others. The four GPDs consistently exhibited high detection abilities for light monthly precipitation events (0–10 mm) and low detection abilities for heavy monthly precipitation events (150–200 mm). This study underscored the potential advantages of GPDs in the three dimensions, and also highlighted their limitations in arid areas, winter, and heavy precipitation events, thus providing directions for further GPDs' accuracy improvement.

Observed Changes of Köppen Climate Zones in Spain since 1951

Despite of having been originally formulated more than one hundred years ago, the Köppen classification is still nowadays the climate classification most widely used in climatological studies. Based on the empirical relationship between climate and vegetation, this climate classification provides an efficient and easy way to describe climate conditions and their seasonality by using a simple but ecologically meaningful classification scheme. This study examines the temporal evolution of the Köppen climate classification in Spain for the period 1951-2020, using high-resolution gridded datasets of monthly mean temperature and precipitation with 1 x 1 km spatial resolution. Climate classification maps for consecutive 30-year reference periods are compared, and the temporal evolution of each climate type in Spain is analysed, showing a progressive expansion of the arid climate zones and a contraction of the cold climate zones along the study period.

Comparing the suitability of global gridded population datasets for local landslide risk assessments

AbstractLandslide risk assessments are increasingly crucial for meeting global disaster risk reduction strategies and mobilising knowledge for local governments to protect communities and infrastructure. These assessments are data intensive, requiring large amounts of spatial demographic and natural hazard information. There are a growing number of high-resolution gridded population datasets that have global coverage with significant potential to transform disaster risk modelling, however landslide research has not yet compared the suitability of these gridded datasets for local risk assessments. Combining social vulnerability indicators with high-resolution settlement layer (HRSL) and WorldPop gridded population datasets, as well as a local household survey, we layer landslide susceptibility maps to compare three landslide risk estimates, examining the case of the Municipality of Carigara located in the central Philippines. Using statistical t tests, we compare aggregated community landslide risk for 49 communities. Findings revealed that HRSL data resulted in similar landslide risk at community scales when compared to local surveys, however WorldPop data greatly overestimated risk. Our findings point to a high level of accuracy of HRSL when used as an exposure dataset for local landslide risk studies and recommend avoiding WorldPop for such purposes. This research advances understanding of the suitability of open population datasets for use in landslide risk assessments in resource-constrained communities.

Extreme warm and cold waves derived from multiple high-resolution gridded datasets in Egypt

The primary purpose of this study is to interpret the variability of extreme warm (Tmax) and cold (Tmin) events over Egypt from 1981 to 2020 using three gridded datasets that evaluated against the observations at 24 in situ stations using robust statistical procedures. These high-resolution gridded datasets are well-matched with the observed data. Furthermore, the monthly maximum of daily Tmax (TXx) and Tmin (TNx) and the monthly minimum of daily Tmax (TXn) and Tmin (TNn) climate indices are used to investigate the variability of extremes over Egypt. It is found that the TXx increased gradually throughout the study period and the Mann–Kendall rank correlation (Tau) is mostly positive in the first and last decades, with the coefficient of variation (COV) ranges from 1 to 5. Also, the lowest TXn values are detected in the first two decades with decadal anomaly ranging from − 2 to + 2, and it has positive Tau values during all decades with COV ranges from 2 to 20. The TNx increased southward, and its decadal anomaly increased gradually over time. The significant positive Tau of TNx is found in the first and last decades, and its COV ranges from 1 to 8. Moreover, the TNn is increased during the study period, and its anomaly is less than + 0.5 °C in the first decade and exceeds + 2 °C in the fourth decade. The significant positive (negative) Tau for TNn appeared during the third (fourth) decade, while its COV ranged from 1 to 12. Finally, the COV and Tau of all indices are often negatively correlated (> − 0.5).

Analyzing temperature and precipitation extremes in China using multiple gridded datasets: A comparative evaluation

Gridded meteorological datasets offer valuable data sources for monitoring and studying climate extremes. Based on the 2291 CMA meteorological observations, this paper evaluates and compares seven high-resolution gridded datasets and a global climate extremes indices datasets, HadEX3. Temporal and spatial consistency are assessed at the location of CMA observations. High-resolution gridded products and HadEX3 can effectively monitor the inter-annual changes in the overall mean of extreme temperature. The temporal consistency of precipitation indices is lower due to uneven spatial distribution of precipitation. Each high-resolution gridded dataset shows good consistency in depicting the spatial distribution patterns of extreme climate. However, at locations of the observations, different indices have larger or smaller biases. These are mainly due to the spatial scale of gridded datasets and calculation and operation order of extreme indices. The study emphasizes the importance of in-situ observations in improving data accuracy. At the same time, it is found that the generation method of the dataset has an impact on the quality, and satellite and model simulation data as background fields can compensate for the lack of spatial coverage of int-situ observations. In the study of extreme climate, appropriate datasets should be selected according to different research purposes.

A study on some dynamical aspects of Uttarakhand heavy rainfall events

In recent years, heavy rainfall events have been increasing over the Uttarakhand region. Improvement in the prediction of such events crucially dependent on the inclusion of the physical & dynamical processes responsible for such events, in the NWP model. This again, in turn depends on the understanding of such processes. In this study an attempt has been made to understand parts of these processes and some of the dynamical aspects of these heavy rainfall events. For this different important derived NWP products, viz., differential vorticity advection (DVA), differential thermal advection (DTA), Differential moisture advection (DMA), Precipitable water (PW), non-dimensional stability index (NDSI) have been computed using ECMWF high-resolution gridded reanalysis data sets. Heavy rainfall events are defined using IMD high resolution gridded daily rainfall data set. Preliminary analysis revealed that there was a steady increase in DVA, decrease in DTA, increase in PW and decrease in DMA before the heavy rainfall event. An enhanced DVA results in an enhancement in LLC, a decrease in DTA along with a decrease in DMA results in an enhancement of lapse rate. Combined effect of these results in the increase in the low-level convergence at Uttarakhand region along with the rising motion are the major dynamical processes resulted in the heavy rainfall event.

Uncertainty Reduction and Environmental Justice in Air Pollution Epidemiology: The Importance of Minority Representation.

Ambient air pollution is an increasing threat to society, with rising numbers of adverse outcomes and exposure inequalities worldwide. Reducing uncertainty in health outcomes models and exposure disparity studies is therefore essential to develop policies effective in protecting the most affected places and populations. This study uses the concept of information entropy to study tradeoffs in mortality uncertainty reduction from increasing input data of air pollution versus health outcomes. We study a case scenario for short-term mortality from particulate matter (PM2.5) in North Carolina for 2001-2016, employing a case-crossover design with inputs from an individual-level mortality data set and high-resolution gridded data sets of PM2.5 and weather covariates. We find a significant association between mortality and PM2.5, and the information tradeoffs indicate that a 10% increase in mortality information reduces model uncertainty three times more than increased resolution of the air pollution model from 12 to 1km. We also find that Non-Hispanic Black (NHB) residents tend to live in relatively more polluted census tracts, and that the mean PM2.5 for NHB cases in the mortality model is significantly higher than that of Non-Hispanic White cases. The distinct distribution of PM2.5 for NHB cases results in a relatively higher information value, and therefore faster uncertainty reduction, for new NHB cases introduced into the mortality model. This newfound influence of exposure disparities in the rate of uncertainty reduction highlights the importance of minority representation in environmental research as a quantitative advantage to produce more confident estimates of the true effects of environmental pollution.

Temperature amplification and marine heatwave alteration in shallow coastal bays

Shallow coastal ecosystems are threatened by marine heatwaves, but few long-term records exist to quantify these heatwaves. Here, 40-year records of measured water temperature were constructed for a site in a system of shallow bays with documented heatwave impacts and a nearby ocean site; available gridded sea-surface temperature datasets in the region were also examined. Water temperatures at both sites increased significantly though bay temperatures were consistently 3-4°C hotter in summer and colder in winter and were more variable overall, differences not captured in high-resolution gridded sea-surface temperature datasets. There was considerable overlap in heatwave events at the coastal bay and ocean sites. Annual heatwave exposure was similar and significantly increased at both sites while annual heatwave intensity was significantly higher at the bay site owing to the high variance of the daily temperature anomaly there. Event frequency at both sites increased at a rate of about 1 event/decade. Future simulations indicate all heatwave metrics increase, as do days above 28°C, a heat stress threshold for seagrass. Ocean temperatures on the U.S. mid-Atlantic margin have rarely exceeded this threshold, while summer bay temperatures commonly do, allowing ocean exchange with coastal bays to provide thermal relief to bay ecosystems. This will have changed by 2100, creating a thermal environment that threatens seagrass communities in these systems. Documenting such change requires development of long-term water temperature records in more shallow coastal systems.

Projected climate extremes over agro-climatic zones of Ganga River Basin under 1.5, 2, and 3° global warming levels.

Recurring floods, droughts, heatwaves, and other hydro-meteorological extreme events are likely to be increased under the climate change scenarios. The increased risk of these extreme events might have more exposure to the population; thus, it is important to discuss such extreme events and their projected behavior under a changing climate scenario. In the present study, we have computed the extreme precipitation and temperature indices over the 10 agro-climatic zones falling under the Ganga River Basin (GRB)utilizing a high-resolution daily gridded temperature and precipitation multi-model ensembled CMIP6 dataset (0.25° × 0.25°) under global warming levels of 1.5°C, 2°C, and 3°C. We found that the annual daily minimum temperature (TNN) showed a higher rise of about 67% than the maximum temperature (TXX) of 48% in GRB. The basin also experiences a greater increase in the frequency of warm nights (TN90P) of about 67.71% compared to warm days (TX90P) of 29.1% for the 3°C global warming level. Along with extreme indices, the population exposed due to the impact of the extreme maximum temperature has also been analyzed for progressive warming levels. Population exposure to extreme temperature event (TXX) has been analyzed with 20-year return period using GEV distribution method. The study concludes that the exposed population to extreme temperature event experienced an increase from 46.99 to 52.16% for the whole Ganga Basin. Consecutive dry days (CDD) and consecutive wet days (CWD) both show a significant increasing trend, but CWD has a significant increase in the majority of the zones, while CDD shows a significant decreasing trend for some of the zones for three warming levels periods. Extreme climate indices help to understand the frequency and intensity of extreme weather events such as heavy rainfall, droughts, and heatwaves to develop early warning systems and adaptation strategies to mitigate such events.

The understory microclimate in agroforestry now and in the future – a case study of Arabica coffee in its native range

Climate change is having a major impact on crop production and food security worldwide, and particularly so for smallholder farmers. As agroforestry is common with smallholder farmers, it is important to not only model the macroclimate, but also the microclimate that crops experience below the canopies. However, there are few high-resolution spatiotemporal climate projections for forest understories, because of constraints related to the lack of i) development of models for downscaling global climate projections, ii) high-resolution gridded datasets of environmental factors influencing microclimate, and iii) spatially replicated in-situ microclimate measurements. We focused on a landscape in southwestern Ethiopia where Arabica coffee originated, and, in the present day, is commonly grown as a shade crop. We first examined the relative contribution of in-situ field measurements vs. GIS-derived estimates of vegetation and topographic features in explaining in-situ microclimate. Second, we used a statistical downscaling approach to obtain past and future microclimate maps at 30-meter spatial resolution for the part of the landscape that is covered by trees. Predictive models using in-situ variables performed equal to models with GIS variables, indicating that remote sensing data might substitute for in-situ field measurements. Vegetation and topographic features were both important in explaining microclimatic variation. Our spatiotemporal projections of the microclimate indicate that coffee farming might have to relocate to higher altitudes due to increasing temperatures, that vegetation might buffer the macroclimate at middle altitudes to some extent, and that decreasing trends in relative humidity at the beginning of the wet season might become problematic for coffee production. Taken together, our findings demonstrate that we can rely on remote sensing data to create microclimate maps in landscapes where in-situ field measurements are challenging, and we suggest how these microclimate projections can be used as a tool to promote climate-resilient agriculture at the local and landscape levels.

High-resolution gridded population datasets for Latin America and the Caribbean using official statistics

“Leaving no one behind” is the fundamental objective of the 2030 Agenda for Sustainable Development. Latin America and the Caribbean is marked by social inequalities, whilst its total population is projected to increase to almost 760 million by 2050. In this context, contemporary and spatially detailed datasets that accurately capture the distribution of residential population are critical to appropriately inform and support environmental, health, and developmental applications at subnational levels. Existing datasets are under-utilised by governments due to the non-alignment with their own statistics. Therefore, official statistics at the finest level of administrative units available have been implemented to construct an open-access repository of high-resolution gridded population datasets for 40 countries in Latin American and the Caribbean. These datasets are detailed here, alongside the ‘top-down’ approach and methods to generate and validate them. Population distribution datasets for each country were created at a resolution of 3 arc-seconds (approximately 100 m at the equator), and are all available from the WorldPop Data Repository.

Assessment of IMERG v06 Satellite Precipitation Products in the Canadian Great Lakes Region

Abstract The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM; IMERG) is a high-resolution gridded precipitation dataset widely used around the world. This study assessed the performance of the half-hourly IMERG v06 Early and Final Runs over a 5-yr period versus 19 high-quality surface stations in the Great Lakes region of North America. This assessment not only looked at precipitation occurrence and amount, but also studied the IMERG Quality Index (QI) and errors related to passive microwave (PMW) sources. Analysis of bias in accumulated precipitation amount and precipitation occurrence statistics suggests that IMERG presents various uncertainties with respect to time scale, meteorological season, PMW source, QI, and land surface type. Results indicate that 1) the cold season’s (November–April) larger relative bias can be mitigated via backward morphing; 2) IMERG 6-h precipitation amount scored best in the warmest season (JJA) with a consistent overestimation of the frequency bias index − 1 (FBI-1); 3) the performance of five PMW sources is affected by the season to different degrees; 4) in terms of some metrics, skills do not always enhance with increasing QI; 5) local lake effects lead to higher correlation and equitable threat score (ETS) for the stations closest to the lakes. Results of this study will be beneficial to both developers and users of IMERG precipitation products. Significance Statement The purpose of the study was to assess the performance of the gridded precipitation product from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) version 6 over the Great Lakes region of North America. The assessment performs a statistical comparison of precipitation amounts from IMERG versus surface stations as a function of time scale, season, precipitation event threshold, and input source among satellites. Interpretation of the results identifies shortcomings in the IMERG algorithms, particularly in extreme precipitation events and over ice-covered surfaces. The results also describe spatial variability in the IMERG data quality due to the complex geography of the study area and offer a clear threshold in the Quality Index (QI) flag for optimal application of the precipitation products.

DownScaleBench for developing and applying a deep learning based urban climate downscaling- first results for high-resolution urban precipitation climatology over Austin, Texas

Cities need climate information to develop resilient infrastructure and for adaptation decisions. The information desired is at the order of magnitudes finer scales relative to what is typically available from climate analysis and future projections. Urban downscaling refers to developing such climate information at the city (order of 1 – 10 km) and neighborhood (order of 0.1 – 1 km) resolutions from coarser climate products. Developing these higher resolution (finer grid spacing) data needed for assessments typically covering multiyear climatology of past data and future projections is complex and computationally expensive for traditional physics-based dynamical models. In this study, we develop and adopt a novel approach for urban downscaling by generating a general-purpose operator using deep learning. This ‘DownScaleBench’ tool can aid the process of downscaling to any location. The DownScaleBench has been generalized for both in situ (ground- based) and satellite or reanalysis gridded data. The algorithm employs an iterative super-resolution convolutional neural network (Iterative SRCNN) over the city. We apply this for the development of a high-resolution gridded precipitation product (300 m) from a relatively coarse (10 km) satellite-based product (JAXA GsMAP). The high-resolution gridded precipitation datasets is compared against insitu observations for past heavy rain events over Austin, Texas, and shows marked improvement relative to the coarser datasets relative to cubic interpolation as a baseline. The creation of this Downscaling Bench has implications for generating high-resolution gridded urban meteorological datasets and aiding the planning process for climate-ready cities.