Weather Station Temperature Research Articles

Relationship between indoor and outdoor temperatures in winter in the Hot Summer and Cold Winter Zone of China - Considering two sources of outdoor temperatures and different indoor heating methods

Thermal exposure is a global issue related to health. Environmental health studies often overlook indoor temperature in comparison to outdoor temperature. Exploring indoor and outdoor temperatures and their relationship is an important part to understand individual heat exposure. The aim of this study is to explore the indoor and outdoor temperatures and their relationship during winter, using weather station and ERA5-Land reanalysis outdoor temperature. Field experiments were conducted in both districting heating and non-district heating dwellings in the Hot Summer and Cold Winter zone of China, taking into account the influence of cold spells. Significant linear correlation was found between the weather station and reanalysis outdoor temperature. The indoor temperature of the district heating group was 8.4 °C higher than that of the non-district heating group. When the outdoor temperature approached 0 °C, the indoor-outdoor temperature difference of the district heating group was twice that of the non-district heating group. The non-district heating group experienced greater fluctuations in indoor temperature during the cold spell. The outdoor temperature could only partially explain indoor temperature, especially under the condition of district heating. The study suggests that outdoor temperature alone is insufficient when evaluating individual thermal exposure in winter. Furthermore, occupants with district heating and non-district heating may have different vulnerabilities, and thermal safeguard strategies in winter are suggested to be designed more targeted. This study may provides insights from the perspective of thermal environment for temperature and health studies.

Field validation of post-mortem interval estimation based on insect development. Part 1: Accuracy gains from the laboratory rearing of insect evidence

There is a consensus that forensic methods must be valid. The high quality of the method may be fully demonstrated only through validation. Unfortunately, there are very few experimental or casework validations of entomological methods for estimating post-mortem interval (PMI). Here, we present the first part of the results from the field validation of minimum PMI (PMImin) estimates based on insect development. From eight pig carcasses (24–46.4 kg) exposed in a forest habitat of Western Poland, one every one or two months, we collected insect evidence with standard entomological techniques. Using weather station temperatures and the thermal summation method, PMImin was estimated based on insect life stages that were reared under controlled laboratory conditions. Through rearing an insect until the next developmental landmark (or until eclosion), its age at the time of collection (and eventually PMImin) may be estimated by subtracting thermal accumulation in the laboratory from the corresponding thermal constant. We hypothesized that rearing insect evidence significantly improves the accuracy of PMImin compared to the estimation based on non-reared insect evidence. The results clearly supported this hypothesis. However, the accuracy significantly increased only in the case of these insects that developed normally during rearing. When their development in the laboratory was prolonged, PMImin was significantly less accurate. For the normal development sample the accuracy improved in all species and life stages. The largest accuracy gains from rearing were recorded for Calliphora vomitoria (Diptera: Calliphoridae) and Stearibia nigriceps (Diptera: Piophilidae). Moreover, when puparia or third instar larvae were reared, gains were larger than in the case of earlier life stages. In conclusion, this study demonstrated that the method of rearing insect evidence to improve the accuracy of PMImin is valid. However, it needs to be used with caution, since substantial part of the evidence may die or slow down their development in the laboratory, which violates assumptions of the method.

Genomic predictions and GWAS for heat tolerance in pigs based on reaction norm models with performance records and data from public weather stations considering alternative temperature thresholds.

Genetic improvement of livestock productivity has resulted in greater production of metabolic heat and potentially greater susceptibility to heat stress. Various studies have demonstrated that there is genetic variability for heat tolerance and genetic selection for more heat tolerant individuals is possible. The rate of genetic progress tends to be greater when genomic information is incorporated into the analyses as more accurate breeding values can be obtained for young individuals. Therefore, this study aimed (1) to evaluate the predictive ability of genomic breeding values for heat tolerance based on routinely recorded traits, and (2) to investigate the genetic background of heat tolerance based on single-step genome-wide association studies for economically important traits related to body composition, growth and reproduction in Large White pigs. Pedigree information was available for 265,943 animals and genotypes for 8686 animals. The studied traits included ultrasound backfat thickness (BFT), ultrasound muscle depth (MDP), piglet weaning weight (WW), off-test weight (OTW), interval between farrowing (IBF), total number of piglets born (TNB), number of piglets born alive (NBA), number of piglets born dead (NBD), number of piglets weaned (WN) and weaning-to-estrus interval (IWE). The number of phenotypic records ranged from 6059 (WN) to 172,984 (TNB). Single-step genomic reaction norm predictions were used to calculate the genomic estimated breeding values for each individual. Predictions of breeding values for the validation population individuals were compared between datasets containing phenotypic records measured in the whole range of temperatures (WR) and datasets containing only phenotypic records measured when the weather station temperature was above 10°C (10C) or 15°C (15C), to evaluate the usefulness of these datasets that may better reflect the within-barn temperature. The use of homogeneous or heterogeneous residual variance was found to be trait-dependent, where homogeneous variance presented the best fit for MDP, BFT, OTW, TNB, NBA, WN and IBF, while the other traits (WW and IWE) had better fit with heterogeneous variance. The average prediction accuracy, dispersion and bias values considering all traits for WR were 0.36 ± 0.05, -0.07 ± 0.13 and 0.76 ± 0.10, respectively; for 10C were 0.39 ± 0.02, -0.05 ± 0.07 and 0.81 ± 0.05, respectively; and for 15C were 0.32 ± 0.05, -0.05 ± 0.11 and 0.84 ± 0.10, respectively. Based on the studied traits, using phenotypic records collected when the outside temperature (from public weather stations) was above 10°C provided better predictions for most of the traits. Forty-three and 62 candidate genomic regions were associated with the intercept (overall performance level) and slope term (specific biological mechanisms related to environmental sensitivity), respectively. Our results contribute to improve genomic predictions using existing datasets and better understand the genetic background of heat tolerance in pigs. Furthermore, the genomic regions and candidate genes identified will contribute to future genomic studies and breeding applications.

Integrating MODIS LST and Sentinel-1 InSAR to monitor frozen soil deformation in the Qumalai-Zhiduo area, Qinghai-Tibet Plateau

ABSTRACT Owing to continuous global warming, frozen soil degradation has become a universal phenomenon, leading to large-scale ground deformation that affects engineering construction and the fragile ecological balance. Geodetic observations, especially temporal InSAR, can quantify ground deformation. However, the accuracy of InSAR modelling in capturing the spatial–temporal variability of the freeze–thaw process depends on the spatial resolution of temperature measurements. This paper proposes a freeze–thaw amplitude model incorporating MODIS LST based on a single-master InSAR time-series deformation to calculate frozen soil deformation. We applied this model to the Qumalai-Zhiduo area of the Qinghai-Tibet Plateau and compared its results with those of the model using weather station temperature in terms of frozen soil deformation parameters, RSME, and characteristic targets. Our study found that the model incorporating MODIS LST performed better in areas far from weather stations, while both models produced similar results in areas of close proximity. Finally, we evaluated another commonly used method for calculating frozen soil deformation parameters and found that the method incorporating MODIS LST based on a single-master time-series deformation is more accurate and precise than the method based on a multi-master SBAS network.

Arbovirus Transmission Predictions Are Affected by Both Temperature Data Source and Modeling Methodologies across Cities in Colombia.

Weather variables has been described as major drivers of vector proliferation and arbovirus transmission. Among them, temperature has consistently been found to be impactful in transmission dynamics, and models that incorporate temperature have been widely used to evaluate and forecast transmission or arboviruses like dengue, zika, or chikungunya virus. Further, there is growing evidence of the importance of micro-environmental temperatures in driving transmission of Aedes aegypti-borne viruses, as these mosquitoes tend to live within domiciles. Yet there is still a considerable gap in our understanding of how accounting for micro-environmental temperatures in models varies from the use of other widely-used, macro-level temperature measures. This effort combines field-collected data of both indoor and outdoor household associated temperatures and weather station temperature data from three Colombian cities to describe the relationship between the measures representing temperature at the micro- and macro-levels. These data indicate that weather station data may not accurately capture the temperature profiles of indoor micro-environments. However, using these data sources, the basic reproductive number for arboviruses was calculated by means of three modeling efforts to investigate whether temperature measure differences translated to differential transmission predictions. Across all three cities, it was determined that the modeling method was more often impactful rather than the temperature data-source, though no consistent pattern was immediately clear. This suggests that temperature data sources and modeling methods are important for precision in arbovirus transmission predictions, and more studies are needed to parse out this complex interaction.

Mixed methods assessment of personal heat exposure, sleep, physical activity, and heat adaptation strategies among urban residents in the Boston area, MA.

The growing frequency, intensity, and duration of extreme heat events necessitates interventions to reduce heat exposures. Local opportunities for heat adaptation may be optimally identified through collection of both quantitative exposure metrics and qualitative data on perceptions of heat. In this study, we used mixed methods to characterize heat exposure among urban residents in the area of Boston, Massachusetts, US, in summer 2020. Repeated interviews of N = 24 study participants ascertained heat vulnerability and adaptation strategies. Participants also used low-cost sensors tocollect temperature, location, sleep, and physical activity data. We saw significant differences across temperature metrics: median personal temperature exposures were 3.9°C higher than median ambient weather station temperatures. Existing air conditioning (AC) units did not adequately control indoor temperatures to desired thermostat levels: even with AC use, indoor maximum temperatures increased by 0.24°C per °C of maximum outdoor temperature. Sleep duration was not associated with indoor or outdoor temperature. On warmer days, we observed a range of changes in time-at-home, expected given our small study size. Interview results further indicated opportunities for heat adaptation interventions including AC upgrades, hydration education campaigns, and amelioration of energy costs during high heat periods. Our mixed methods design informs heat adaptation interventions tailored to the challenges faced by residents in the study area. The strength of our community-academic partnership was a large part of the success of the mixed methods approach.

Spatio-temporal Analysis of Land Surface Temperature Changes in Java Island from Aqua and Terra MODIS Satellite Imageries Using Google Earth Engine

Java Island is the island with the most population globally, which is experiencing an increase in population from year to year. This population growth causes an increase in the use of natural resources, which can further increase the potential for climate change. One of the parameters of climate change in an area is Land Surface Temperature (LST). In situ LST observations in the field require a huge number of stations; for that reason, the use of satellites is the right choice. This study analyzes changes in LST spatially and temporally for 16 years, from January 2005 to December 2020, based on Terra and Aqua MODIS satellite imagery using Google Earth Engine as a data processing tool. LST processing was performed gradually to generate the average daily maximum LST of Terra and Aqua, monthly average LST, and annual average LST. This study found the correlation coefficient between the Terra and Aqua LST data and the BMKG weather station temperature data of 0.2599 to 0.8361. It indicated a moderate to very strong correlation. The most significant annual LST change occurred from 2015 to 2016 experiencing a temperature decrease of 1.6 °C and 2.0 °C, respectively, for Terra and Aqua. There was an area of 35105 km2 (2010-2011) and 65420 km2 (2015-2016) experiencing LST increases and decreases of at least 1.5°C, respectively. Areas experiencing a temperature increase were mainly in the northern part of East Java Province and the eastern part of Central Java Province. Meanwhile, the areas that experienced a temperature drop were mainly northern East Java, eastern Central Java, and West Java Province. Annual LST fluctuations indicate the changes in land used and land cover, both spatial and temporal.

Modeling digital terrestrial television signal strength with atmospheric parameters and noise temperature: a case study of North-Central Nigeria

The modeling of DTTV signal strength (AIT, NTA, ETV and Channels TV) in Abuja and Unity TV, Jos, Plateau State, with atmospheric parameters (temperature, pressure, relative humidity, wind speed and rainfall) as well as noise temperature, from April 2019 to December 2020 have been carried out using the atmospheric parameters measured with an automatic weather station and noise temperature measured with a non-contact infra-red thermometer, with TV signal strength measured with a CATV signal strength meter from 5.00 am – 9.00 pm daily, at 30 minutes interval. The results show that atmospheric temperature (58%), noise temperature (60%) and atmospheric pressure (100%) affect the Received Signal Strength (RSS) negatively, while the wind speed (60%), relative humidity (80%), and rainfall (100%) affect the signal strength positively. The RSS measured in Jos on altitude 1,400m was higher than that in Abuja on altitude 470m which may likely be due to an increase in altitude resulting in a decrease in atmospheric pressure and air density which implies less signal attenuation. It can be inferred that atmospheric parameters and noise temperature have a great impact on the RSS in the study areas. Consequently, the higher signal received in the rainy season compared to the dry season may be due to less dust in the atmosphere as well as the enhanced digitalized television signal and its robustness to interference and signal attenuation compared to the analogue signal. The developed models have low RMSE values; hence will be able to predict the influence of atmospheric parameters on TV RSS in the areas. Generally, the result will be useful to the Nigeria Broadcasting Commission to improve the quality of the signal transmission of digital television broadcasting for the future digital switchover in other parts of the country especially on the choice of antennas on their transmitters. The findings will also be useful to spectrum users such as mobile telecommunication, wireless internet, and radio broadcasting in managing and improving the quality of their services.

Time-series snowmelt detection over the Antarctic using Sentinel-1 SAR images on Google Earth Engine

The Antarctic ice sheet is an important mass of glacier ice. It is particularly sensitive to climate change, and the flow of Antarctica's inland glaciers into the sea, accelerated by collapsing ice shelves, threatens global sea level rise. The amount of snowmelt on the surface of the ice sheet is an important metric for accurately assessing surface material loss and albedo change, which affect the stability of the ice sheet. This study proposes a framework for quickly extracting time-series freeze-thaw information at the continental scale and 40 m resolution by taking advantage of the huge amount of synthetic aperture radar (SAR) data acquired by Sentinel-1 satellites over the Antarctic, available for rapid processing on Google Earth Engine. Co-orbit normalization is used in the proposed framework to establish a unified standard of judgement by reducing the variations in the backscattering coefficient introduced by observation geometry, terrain fluctuations, and melt conditions between images acquired at different times. We implemented the framework to produce a massive dataset of both monthly freeze-thaw information over the Antarctic and higher temporal resolution freeze-thaw information for the Larsen C ice shelf from 2015 to 2019, with overall accuracies of 93% verified by a manual visual interpretation method and 84% evaluated from automatic weather station temperatures. Due to its effectiveness and robustness, the framework can be used to analyse the spatiotemporal distribution of snowmelt, the change in melt area, and anomalous melt events in Antarctica, especially those in Larsen C caused by foehn wind.

Analysis of Copernicus' ERA5 Climate Reanalysis Data as a Replacement for Weather Station Temperature Measurements in Machine Learning Models for Olive Phenology Phase Prediction.

Knowledge of phenological events and their variability can help to determine final yield, plan management approach, tackle climate change, and model crop development. THe timing of phenological stages and phases is known to be highly correlated with temperature which is therefore an essential component for building phenological models. Satellite data and, particularly, Copernicus’ ERA5 climate reanalysis data are easily available. Weather stations, on the other hand, provide scattered temperature data, with fragmentary spatial coverage and accessibility, as such being scarcely efficacious as unique source of information for the implementation of predictive models. However, as ERA5 reanalysis data are not real temperature measurements but reanalysis products, it is necessary to verify whether these data can be used as a replacement for weather station temperature measurements. The aims of this study were: (i) to assess the validity of ERA5 data as a substitute for weather station temperature measurements, (ii) to test different machine learning models for the prediction of phenological phases while using different sets of features, and (iii) to optimize the base temperature of olive tree phenological model. The predictive capability of machine learning models and the performance of different feature subsets were assessed when comparing the recorded temperature data, ERA5 data, and a simple growing degree day phenological model as benchmark. Data on olive tree phenology observation, which were collected in Tuscany for three years, provided the phenological phases to be used as target variables. The results show that ERA5 climate reanalysis data can be used for modelling phenological phases and that these models provide better predictions in comparison with the models trained with weather station temperature measurements.

Modelling the impact of climate change on the distribution and abundance of tsetse in Northern Zimbabwe

BackgroundClimate change is predicted to impact the transmission dynamics of vector-borne diseases. Tsetse flies (Glossina) transmit species of Trypanosoma that cause human and animal African trypanosomiasis. A previous modelling study showed that temperature increases between 1990 and 2017 can explain the observed decline in abundance of tsetse at a single site in the Mana Pools National Park of Zimbabwe. Here, we apply a mechanistic model of tsetse population dynamics to predict how increases in temperature may have changed the distribution and relative abundance of Glossina pallidipes across northern Zimbabwe.MethodsLocal weather station temperature measurements were previously used to fit the mechanistic model to longitudinal G. pallidipes catch data. To extend the use of the model, we converted MODIS land surface temperature to air temperature, compared the converted temperatures with available weather station data to confirm they aligned, and then re-fitted the mechanistic model using G. pallidipes catch data and air temperature estimates. We projected this fitted model across northern Zimbabwe, using simulations at a 1 km × 1 km spatial resolution, between 2000 to 2016.ResultsWe produced estimates of relative changes in G. pallidipes mortality, larviposition, emergence rates and abundance, for northern Zimbabwe. Our model predicts decreasing tsetse populations within low elevation areas in response to increasing temperature trends during 2000–2016. Conversely, we show that high elevation areas (> 1000 m above sea level), previously considered too cold to sustain tsetse, may now be climatically suitable.ConclusionsTo our knowledge, the results of this research represent the first regional-scale assessment of temperature related tsetse population dynamics, and the first high spatial-resolution estimates of this metric for northern Zimbabwe. Our results suggest that tsetse abundance may have declined across much of the Zambezi Valley in response to changing climatic conditions during the study period. Future research including empirical studies is planned to improve model accuracy and validate predictions for other field sites in Zimbabwe.

Using 3D turbulence-resolving simulations to understand the impact of surface properties on the energy balance of a debris-covered glacier

Abstract. Debris-covered glaciers account for almost one-fifth of the total glacier ice volume in High Mountain Asia; however, their contribution to the total glacier melt remains uncertain, and the drivers controlling this melt are still largely unknown. Debris influences the properties (e.g. albedo, thermal conductivity, roughness) of the glacier surface and thus the surface energy balance and glacier melt. In this study we have used sensitivity tests to assess the effect of surface properties of debris on the spatial distribution of micrometeorological variables such as wind fields, moisture and temperature. Subsequently we investigated how those surface properties drive the turbulent fluxes and eventually the conductive heat flux of a debris-covered glacier. We simulated a debris-covered glacier (Lirung Glacier, Nepal) at a 1 m resolution with the MicroHH model, with boundary conditions retrieved from an automatic weather station (temperature, wind and specific humidity) and unmanned aerial vehicle flights (digital elevation map and surface temperature). The model was validated using eddy covariance data. A sensitivity analysis was then performed to provide insight into how heterogeneous surface variables control the glacier microclimate. Additionally, we show that ice cliffs are local melt hot spots and that turbulent fluxes and local heat advection amplify spatial heterogeneity on the surface. The high spatial variability of small-scale meteorological variables suggests that point-based station observations cannot be simply extrapolated to an entire glacier. These outcomes should be considered in future studies for a better estimation of glacier melt in High Mountain Asia.

Ground-truthing climate-matching predictions in a post-release evaluation

Pereskia aculeata Miller (Cactaceae) is an invasive alien cactus which has negative impacts on indigenous plant biodiversity in South Africa. Catorhintha schaffneri Barilovsky & Garcia (Coreidae), the pereskia stem-wilter, was collected at coastal sites in the subtropical region of Santa Catarina state in southern Brazil and released as a biological control agent to control P. aculeata, in South Africa, in 2014. The introduced distribution of P. aculeata covers a wide variety of climatic conditions which was expected to influence the establishment success of the new agent. The thermal tolerance of C. schaffneri was investigated by developing a degree-day model and calculating the thermal limits of the species. The influence of humidity on egg hatchability and adult survival was also investigated. These data were then used to determine where the agent was likely to establish in South Africa using both weather station and microclimate temperature data. To ground-truth these predictions, sixteen release sites were selected covering a wide range of climatic conditions in the introduced distribution. Three releases of 30 adult C. schaffneri were conducted at each site and population establishment was recorded. Field establishment was recorded at only two of the sixteen experimental release sites. Low winter temperatures were predicted to prevent establishment at seven of the sixteen sites, but nine sites were considered climatically suitable according to the agent’s thermal physiology. Low rainfall and humidity could explain why the agent did not survive at some sites with suitable thermal climates. Many of the thermally suitable sites were affected by a severe drought over the course of the experiment, so further releases during periods with average rainfall and humidity are warranted and could confirm whether the drought was a significant factor influencing the failure of the agent to establish. Although climatic-matching and thermal physiology studies are valuable for tentative predictions of establishment success, there are numerous variables involved that require ground-truthing. Releasing the agent over a wide range of climatic zones believed to be within the thermal limits of the agent, and following releases with detailed post-release evaluations may be the best method of determining where future releases should be focused.

Comparison of temperature–mortality associations using observed weather station and reanalysis data in 52 Spanish cities

BackgroundMost studies use temperature observation data from weather stations near the analyzed region or city as the reference point for the exposure-response association. Climatic reanalysis data sets have already been used for climate studies, but are not yet used routinely in environmental epidemiology. MethodsWe compared the mortality-temperature association using weather station temperature and ERA-5 reanalysis data for the 52 provincial capital cities in Spain, using time-series regression with distributed lag non-linear models. ResultsThe shape of temperature distribution is very close between the weather station and ERA-5 reanalysis data (correlation from 0.90 to 0.99). The overall cumulative exposure-response curves are very similar in their shape and risks estimates for cold and heat effects, although risk estimates for ERA-5 were slightly lower than for weather station temperature. ConclusionsReanalysis data allow the estimation of the health effects of temperature, even in areas located far from weather stations or without any available.

Comparison of temperature-mortality associations estimated with different exposure metrics

Background:Studies of the short-term association between ambient temperature and mortality often use temperature observations from a single monitoring station, frequently located at the nearest airport, to represent the exposure of individuals living across large areas. Population-weighted temperature estimates constructed from gridded meteorological data may offer an opportunity to improve exposure assessment in locations where station observations do not fully capture the average exposure of the population of interest.Methods:We compared the association between daily mean temperature and mortality in each of 113 United States counties using (1) temperature observations from a single weather station and (2) population-weighted temperature estimates constructed from a gridded meteorological dataset. We used distributed lag nonlinear models to estimate the 21-day cumulative association between temperature and mortality in each county, 1987–2006, adjusting for seasonal and long-term trends, day of week, and holidays.Results:In the majority (73.4%) of counties, the relative risk of death on extremely hot days (99th percentile of weather station temperature) versus the minimum mortality temperature was larger when generated from the population-weighted estimates. In contrast, relative risks on extremely cold days (first percentile of weather station temperature) were often larger when generated from the weather station observations. In most counties, the difference in associations estimated from the two temperature metrics was small.Conclusions:In a large, multi-site analysis, temperature-mortality associations were largely similar when estimated from weather station observations versus population-weighted temperature estimates. However, spatially refined exposure data may be more appropriate for analyses seeking to elucidate local health effects.

Estimating Occupational Heat Exposure From Personal Sampling of Public Works Employees in Birmingham, Alabama.

This study investigated whether using thermometers clipped on workers' shoes would result in different heat exposure estimation and work-rest schedules compared with using area-level meteorological data alone. Alabama workers (n = 51) were individually monitored using thermometers on shoes. Wet bulb globe temperature (WBGT) was estimated using thermometer temperatures (WBGT [personal]) or nearby weather station temperatures (WBGT [WS]). Work-rest schedules were determined from WBGT, clothing, and hourly metabolic rates estimated from self-reported tasks and bodyweight. The percent of hours exceeding the threshold limit value (TLV, ACGIH, Cincinnati, OH) were estimated at 47.8% using WBGT (personal) versus 42.1% using WBGT (WS). For work-rest recommendations, more hours fell into the most protective schedule (0 to 15 min work/45 to 60 min rest) using WBGT (personal) versus WBGT (WS) (17.4% vs 14.4%). Temperatures from wearable thermometers, together with meteorological data, can serve as an additional method to identify occupational heat stress exposure and recommend work-rest schedules.

Understanding the Effects of Changing Weather: A Case of Flash Flood in Morogoro on January 11, 2018

Floods are the leading cause of hydrometeorological disasters in East Africa. Regardless of where, when, and how the event has happened, floods affect social security as well as environmental damages. Understanding floods dynamics, their impacts, and management is thus critical, especially in climate risk assessment. In the present study, a flash flood (a case of an episodic hydrological event) which happened on January 11, 2018, in Morogoro, Tanzania, is examined and synthesized. Data were courtesy of the National Oceanic and Atmospheric Administration Global Forecasting System (NOAA GFS) (forecast data), Tanzania Meteorological Agency (TMA), and Sokoine University of Agriculture (for the automatic weather data). With the help of ZyGRIB-grib file visualization software (version 8.01, under General Public License (GNU GPL v3)), the forecast data and patterns of the observation from the automatic weather station (temperatures, wind speed and directions, rainfall, humidity, and pressure) and the long-term rainfall data analysis in the study area made it possible. This study contributes to the knowledge of understanding the changing weather for planning and management purposes. Both forecasts and the observations captured the flash flood event. The rain was in the category of heavy rainfall (more than 50 mm per day) as per the regional guidelines. The synergy between the forecasts and the 30-minute weather observation interval captured the fundamental weather patterns that describe the event. For studying the nature and impacts of flash floods in the region, the integration of automatic weather observation into the systems of national meteorological centers is inevitable. Additionally, as part of an integrated disaster risk reduction effort, there is a need for a review on catchment management strategies.

Adaptability and Growth of a Fast-Growing <i>Neolamarckia cadamba</i> (Roxb.) Bosser Clone in the South Subtropical Region of China

The present study investigated the adaptability and growth of a fast-growing clone (BN1) of N. cadamba planted as short-rotation plantation species in the south subtropical region of China, using 17 sampled sites representing different environments, planting models and ages, and local weather station temperature records. BN1’s growth characteristics were further compared with the most productive eucalypts planted nearby. The survival of BN1 seedlings raised and transplanted with tissue-culture micro-cuttings was >94.0% in the 1st year, and the percentage of the effective trees with diameter at breast height ( Dbh ) ≥ 8 cm was >71.0% over 30-month old. Most of the sampled sites and trees were susceptible to low-temperature or frost injury in the 1st winter, with average damage index ( Id ) ranging from 0.1 to 2.6. Some sites/trees were infected by caterpillar or borer pests, but the damage was neglectable. The injured trees quickly sprouted out leading shoots and recovered their growing vigor from low temperature or insect damage. Temperature factors were closely related to Id (P °C) with risk of significant frost damage on BN1 trees; one site (No. 9) was not suitable with frequent and heavy cold damage ( Id =2.6 ). BN1 grew extremely fast, with average diameter at breast height ( Dbh ) ranging from 14.5 - 20.3 cm and average tree height ( Ht ) from 12.3 - 16.0 m at 54-month old, and Dbh from 17.8 - 22.7 cm and from 14.4 - 15.9 m at 66-month old, over different sites/soils and planting models. Peak for both Dbh and Ht appeared in the 2nd year, but after 6-year old for average individual tree volume ( Vt ). BN1 was faster in Dbh at most sites but slower in Ht at all sites than eucalypts compared. Some sampled sites of BN1 performed similarly as or obviously better than most productive eucalypts in stock volume (Vs). With properly planting density, tending and rotation, BN1 could be expected to outperform fast-growing eucalypts in terms of Dbh , Vt and Vs, and to produce all diameter grades of timbers if the rotation age is extended beyond 8 to 9 years. Discussion was also given to potential silvicultural models, risks and limitations of planting BN1 and other superior N. cadamba varieties.

GHCN-Daily: a treasure trove of climate data awaiting discovery

International collaboration to create and maintain international, freely accessible datasets greatly facilitates research in many scientific fields. The Global Historical Climatology Network (GHCN)-Daily database provides access to a diverse range of daily weather station data, including precipitation and temperature variables. These data are supplied as individual, station-specific files and structured in a non-delimited format. Here, the GHCN-Daily data structure, spatio-temporal content and associated caveats are delineated. The regularly updated collection now features data from over 100 000 stations in 218 countries and territories. While rigorous quality tests are routinely applied for GHCN-Daily, the database excludes the original quality flags from the source agencies.The extraction of climate variables from the GHCN-Daily database can be challenging for novice users and may thus dissuade from the uptake of this valuable dataset. Consequently, a user-friendly toolkit for MATLAB and GNU Octave is also provided to aid data retrieval from all relevant weather stations. The toolkit reformats the extracted GHCN-Daily data into a more accessible structure to facilitate data mining and research on a large scale.

Temperature: the weak point of forensic entomology.

Measuring temperature is a key factor in forensic entomology. While noting factors to consider for a posteriori temperature estimation, many studies lack detailed methods or general rules allowing their integration into insect development-time calculations. This article proposes tools for determining the adequacy of weather station temperature datasets versus the local temperature experienced by carrion breeders. The idea is to start from a local scale (i.e., the cadaver) and gradually move to larger scales: at each step, the temperature can be increased, decreased or smoothed by environmental or biological factors. While a one-size-fits-all solution is not feasible for a complex and sensitive issue such as forensic meteorology, this checklist increases the reliability of minimum post-mortem interval (PMImin) estimation and the traceability of the proposed assumption.