Surface Temperature In Regions Research Articles

Thermal imaging method in the evaluation of obesity in various body regions–A preliminary study

Technologies like 3-dimensional scanners and thermal imaging are slowly overtaking the traditional means of evaluating the obesity. The purpose of this study was to evaluate the mean skin surface temperature of different regions such as abdominal, shank, gluteal (thigh), forearm, neck, fingerbed region and to study the potential of feature extracted from thermograph of various region and its measured skin temperature values in the evaluation of obesity. In this preliminary study 30 normal and 30 obese young adult of age 19- 23 years were invited out of which 30 were female and 30 were male. Thermal imaging of abdominal, shank, gluteal, forearm, neck and fingerbed regions was acquired and average skin temperature was estimated in various regions for obese and normal subjects. Among the six region studied, neck region shows the greater temperature variation between the study population. In total population studied, the feature extracted parameter depicted positive association with mean skin surface temperature in various regions. Among various feature extracted parameters, mean and total standard deviation depicted the highest significance in abdomen (mean- r = 0.877, TSD- r = 0.449), neck (mean- r = 0.910, TSD- r = -0.617) and in forearm region (mean- r = 0.918, TSD- r = -0.404). ANOVA test provided a significant difference between the skin surface temperature of the groups (normal and obese subjects) at abdomen [F (1, 58) = 261.265, p< 0.05], forearm [F (1, 58) = 619.586, p< 0.05] and neck region [F (1, 58) = 492.322, p< 0.05]. Feed Forward Back Propagation Neural Network provided 98% accuracy, 95% sensitivity and 92% specificity while classifying normal and obese subjects.

ENSO signatures observed in tropical tropopause layer parameters using long-term COSMIC RO data

The El Nino Southern Oscillation (ENSO) signatures interpreted in different tropical tropopause layer (TTL) parameters are investigated by using long-term Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC)-1 radio occultation temperature data from July 2006 to March 2019. The TTL parameters include the cold point tropopause height (CPT-H) and corresponding temperature (CPT-T), lapse rate tropopause height (LRT-H) and corresponding temperature (LRT-T), convective outflow level height (COH), and TTL thickness. Results indicate that the warm phase of ENSO is associated with prominent negative CPT-T anomalies, whereas the cold phase of ENSO is associated with positive CPT-T anomalies over the tropical central and eastern Pacific Ocean. Further, correlation analysis is carried out between TTL parameters obtained over the Nino 3.4 region (5°N–5°S, 170°W–120°W) and sea surface temperature Nino 3.4 Index. We found weak positive correlations in CPT-H (0.37) and LRT-H (0.49), and moderate negative correlations in CPT-T (− 0.62), LRT-T (− 0.64), and TTL thickness (− 0.5) with Nino 3.4 Index. Interestingly, a higher correlation of 0.8 is noticed between COH and the Nino 3.4 Index. Furthermore, lag correlation analysis reveals that a 4-month lag is noticed in tropopause heights (CPT-H and LRT-H) and a 2-month lag is evident in tropopause temperatures (CPT-T and LRT-T) with the Nino 3.4 Index. In addition, only 1-month lag is noticed between COH and Nino 3.4 index. With a high correlation and 1-month lag with the Nino 3.4 index, it is concluded that the COH is the most suitable TTL parameter to detect the ENSO signatures among the TTL parameters.

Assessment and simulation of land use and land cover change impacts on the land surface temperature of Chaoyang District in Beijing, China.

Rapid urbanization is changing the existing patterns of land use land cover (LULC) globally, which is consequently increasing the land surface temperature (LST) in many regions. The present study is focused on estimating current and simulating future LULC and LST trends in the urban environment of Chaoyang District, Beijing. Past patterns of LULC and LST were identified through the maximum likelihood classification (MLC) method and multispectral Landsat satellite images during the 1990–2018 data period. The cellular automata (CA) and stochastic transition matrix of the Markov model were applied to simulate future (2025) LULC and LST changes, respectively, using their past patterns. The CA model was validated for the simulated and estimated LULC for 1990–2018, with an overall Kappa (K) value of 0.83, using validation modules in IDRISI software. Our results indicated that the cumulative changes in built-up to vegetation area were 74.61 km2 (16.08%) and 113.13 km2 (24.38%) from 1990 to 2018. The correlation coefficient of land use and land cover change (LULCC), including vegetation, water bodies and built-up area, had values of r = − 0.155 (p > 0.005), −0.809 (p = 0.000), and 0.519 (p > 0.005), respectively. The results of future analysis revealed that there will be an estimated 164.92 km2 (−12%) decrease in vegetation area, while an expansion of approximately 283.04 km2 (6% change) will occur in built-up areas from 1990 to 2025. This decrease in vegetation cover and expansion of settlements would likely cause a rise of approximately ∼10.74 °C and ∼12.66 °C in future temperature, which would cause a rise in temperature (2025). The analyses could open an avenue regarding how to manage urban land cover patterns to enhance the resilience of cities to climate warming. This study provides scientific insights for environmental development and sustainability through efficient and effective urban planning and management in Beijing and will also help strengthen other research related to the UHI phenomenon in other parts of the world.

Projections of daily mean surface temperature over the Beijing-Tianjin-Hebei region through a stepwise cluster downscaling method

A stepwise cluster analysis (SCA) method is developed to downscale the daily mean surface temperature of 23 meteorological stations over the Beijing-Tianjin-Hebei (BTH) region in North China. Through comparisons of three evaluation indicators, including correction coefficients (CORRs), root mean square errors (RMSEs), and mean relative errors (MREs), between model simulations and observations, the SCA is proven to be capable of simulating the daily mean temperature in the baseline period (1961–2005). The projections of mean surface temperature under RCP4.5 and RCP8.5 scenarios are then developed using the SCA model. The results show that the mean surface temperature at most stations would keep increasing in future throughout the twenty-first century. The largest magnitude and rate of temperature increase are reported in the northwest mountains of the BTH region (especially obvious in winter), suggesting that the response to climate change in highland regions is more sensitive than that in low elevations. It is also reported that the projected changes in mean surface temperature in coastal regions are smaller than those in plain areas. This could be due to ocean’s moderating influence in the HadGEM2-ES. In terms of seasonal changes in surface temperature, the smallest magnitude of warming is expected in summer while the highest is likely to occur in winter, especially under RCP8.5 emission scenarios. Furthermore, the annual cycle analysis for temperature changes suggests that the highest amplitude of warming is expected in cold months (i.e., February or December) while the smallest is likely to occur in warm months (i.e., July or August).

Multidecadal Cycles of the Climatic Index Atlantic Meridional Mode: Sunspots that Affect North and Northeast of Brazil

Using the 1951-2017 historical series of the Atlantic Meridional Mode (AMM) index and the monthly number of sunspots, it was possible to observe a significant association between them. The use of wavelet and cross-wavelet analysis showed the presence of multidecadal cycles pronounced in eleven years, as well as cycles of 2.66 and 5.33. AMM index showed, in the part of the Sea Surface Temperature (SST), the presence of a weak signal of 21.33 years. Influence and association of sunspot variability on surface temperature in Northern and Northeastern regions of Brazil were investigated. Using a non-parametric statistical correlation test, the historical series of surface temperature anomalies in five locations (Belém, São Luiz, Fortaleza, Fernando de Noronha, and Natal) were compared with the monthly solar-series anomalies. The temperature series used were the minimum monthly average, the monthly average, and maximum monthly average temperatures, with their respective anomalies in relation to the mean. However, among all the series (except for São Luiz), the analyzed minimum temperature anomalies showed a negative correlation with sunspots. As a preliminary result, the analyzed climatic indexes present an apparent degree of memory associated with the variability of sunspot activity.

Impact of the 2018 European heatwave on lake surface water temperature

ABSTRACT In 2018, Europe experienced the warmest May–October (Northern Hemisphere warm season) since air temperature records began. In this study, we ran model simulations for 46 557 lakes across Europe to investigate the influence of this heatwave on surface water temperature. We validated the model with satellite-derived lake surface temperatures for 115 lakes from 1995 to 2018. Using the validated model, we demonstrated that, during May–October 2018, mean and maximum lake surface temperatures were 1.5 and 2.4 °C warmer than the base-period average (1981–2010). A lake model experiment demonstrated that, on average, the increase in air temperature was the dominant driver of surface water temperature change. However, in some lake regions, other meteorological forcing had a greater influence. Notably, higher than average solar radiation and lower than average wind speed exacerbated the influence of the heatwave on lake surface temperature in many regions, particularly Fennoscandia and Western Europe. To place our results in the context of projected 21st century climate change, we then ran the lake model with input data from state-of-the-art climate model projections under 3 emissions scenarios. Under the scenario with highest emissions (Representative Concentration Pathway 8.5), we demonstrated that by the end of the 21st century, the lake surface temperatures that occurred during the heatwave of 2018 will become increasingly common across many lake regions in Europe.

Southern California Vegetation, Wildfire, and Erosion Had Nonlinear Responses to Climatic Forcing During Marine Isotope Stages 5–2 (120–15 ka)

AbstractA multiproxy record from Baldwin Lake, San Bernardino Mountains, allowed us to examine variation and relationships between erosion, wildfire, vegetation, and climate in subalpine Southern California from 120 to 15 ka. Bulk organics, biogenic silica, and molar C:N data were generally antiphased with magnetic and trace element data and displayed long‐term (105 year) shifts between autochthonous and allocthonous deposition. This was most pronounced during Marine Isotope Stage (MIS) 5, and we hypothesize that local summer insolation was the primary driver for Baldwin Lake's productive and unproductive lake state alternations. Wildfire history was inferred from charcoal concentrations and vegetation change from pollen. Relationships between these ecological processes, basin deposition, and summer insolation were often nonlinear. Sagebrush expansion, wildfire, and weak basin weathering characterized MIS 4, while during MIS 2, the basin was highly erosive, rarely burned, and the forest was impacted by shifts in Southern Californian hydroclimate. Despite coniferous forest cover throughout MIS 3, submillennial oscillations in charcoal, pollen, and bulk organic content occurred, consistent with pollen records from Eurasia's Mediterranean biome that span multiple glacial‐interglacial cycles. Highly resolved global CO2 records and sea surface temperatures in key regions of the Pacific show no apparent relationship to these landscape conditions, and we suggest submillennial hydroclimatic variability as a potential driver. Highly resolved long pollen records from Southern California are an urgent research need to better understand the finer‐scale (≤103 year) interactions between past vegetation, wildfire, and erosion, given the current natural disaster risks that 21st century climate change poses to both human and ecological communities.

Remote sensing extraction of geothermal anomaly based on terrain effect correction

Geothermal exploration in mountain regions strongly relies on the successful identification of thermal anomaly in the regions,which can be performed through the extraction of geothermal anomaly information from thermal infrared remote sensing data. However,changes in Land Surface Temperature(LST) in mountain regions are significantly affected by topography in addition to other factors, such as latitude and surface property. This effect strongly weakens the efficient identification of geothermal anomaly over the LST image retrieved from thermal infrared remote sensing data, which consequently limit the application of remote sensing technique to geothermal resource exploration in mountain regions with rough terrain. This study examines the effects of terrain on LST changes in the mountain region of Longchuan in Southern China to establish an efficient approach, which can correct the effects of terrain on the LST changes for the geothermal exploration in the region.LST was retrieved using mono-window algorithm based on Landsat ETM+ remote sensing data. The effects of terrain on LST distribution were then analysed. The statistical analysis showed a parabolic relationship between LST and aspect. Moreover, the southeastfacing slope had the highest average LST and standard deviation, where LST was significantly and positively correlated with slope gradient.To reduce the impact of terrain on LST distribution, the area was divided into sunny slope, shady slope and transitional slope. The LST in the sunny slope was particularly corrected to its horizontal surface equivalent by the linear regression equation between LST and slope gradient. Geothermal anomalies were then extracted from the LST of these three subareas, with the consideration of geologic structure and land cover.Results showed that the spatial variation amplitude of LST evidently decreased because the significant temperature difference among different terrain conditions has become small in subareas. Four possible geothermal anomalies were recognised in which high temperature areas were closely related to faults and showed little variability in land cover. Comparative analysis with known hot springs indicated that they were likely caused by geothermal activities.In conclusion, topography mainly affects LST spatial distribution by controlling incoming solar radiation. The solution of aspect-based partition and gradient correction presented in this article can also effectively reduce topographic effects. It helps improve the recognition accuracy of geothermal anomalies with remote sensing technology. The solution may also provide an enlightening insight into the forecast evaluation of geothermal resources in mountain regions. Moreover, further analyses of the relationship between LST and other factors controlled by terrain are necessary in future research, especially the physical properties of underlying surfaces, such as land use, soil moisture and vegetation.

Establishment of validated models for non-invasive prediction of rectal temperature of sows using infrared thermography and chemometrics.

Rectal temperature is an important physiological indicator used to characterize the reproductive and health status of sows. Infrared thermography, a surface temperature measurement technology, was investigated in this study to explore its feasibility in non-invasive detection of rectal temperature in sows. A total of 124 records of rectal temperature and surface temperature in various body regions of 99 Landrace × Yorkshire crossbred sows were collected. These surface temperatures together with ambient temperature, ambient humidity, and wind speed in pig pens were correlated with the real rectal temperature of sows to establish rectal temperature prediction models by introducing chemometrics algorithms. Two types of models, i.e., full feature models and selected feature models, were established by applying the partial least squares regression (PLSR) method. The optimal model was attained when 7 important features were selected by LARS-Lasso, where correlation coefficients and root mean squared errors of calibration were 0.80 and 0.30°C, respectively. Particularly, the validity and stability of established simplified models were further evaluated by applying the model to an independent prediction set, where correlation coefficients and root mean squared errors for prediction were 0.80 and 0.35°C, respectively. The validation of established models is scarce in previous similar studies. Above all, this study demonstrated that introduction of chemometrics methodologies would lead to more reliable and accurate model for predicting sow rectal temperature, thus the potential for ensuring animal welfare in a broader view if extended to more applications.

Eye region surface temperature dynamics during acute stress relate to baseline glucocorticoids independently of environmental conditions

Reactions to acute stressors are critical for survival. Yet, the challenges of assessing underlying physiological processes in the field limit our understanding of how variation in the acute stress response relates to fitness in free-living animals. Glucocorticoid secretion during acute stress can be measured from blood plasma concentrations, but each blood sample can only provide information for one point in time. Also, the number of samples that can be extracted from an individual in the field is usually limited to avoid compromising welfare. This restricts capacity for repeated assessment, and therefore temporal resolution of findings within- and between-acute stress responses - both of which are important for determining links between acute stress and fitness. Acute stress induces additional body surface temperature changes that can be measured non-invasively, and at high frequencies using thermal imaging, offering opportunities to overcome these limitations. But, this method's usefulness in the field depends on the extent that environmental conditions affect the body surface temperature response, which remains poorly understood. We assessed the relative importance of individual physiology (baseline glucocorticoid concentrations) and environmental conditions (air temperature and relative humidity) in determining the eye region surface temperature (Teye) response to acute stress, in wild blue tits (Cyanistes caeruleus) during trapping, handling and blood sampling. When controlling for between-individual baseline variation, Teye initially dropped rapidly below, and then recovered above baseline, before declining more slowly until the end of the test, 160 s after trap closure. One measure of the amplitude of this response – the size of the initial drop in Teye – was dependent on environmental conditions, but not baseline corticosterone. Whereas, two properties defining response dynamics – the timing of the initial drop, and the slope of the subsequent recovery – were related to baseline corticosterone concentrations, independently of environmental conditions. This suggests inferring the acute stress response using thermal imaging of Teye will be practical under fluctuating environmental conditions in the field.

Warm bias of sea surface temperature in Eastern boundary current regions—a study of effects of horizontal resolution in CESM

Warm bias of modeled sea surface temperature (SST) in the eastern boundary upwelling systems (EBUS) is a ubiquitous feature in coupled climate models. This paper investigates the causes underlying this warm bias, with a focus on the effect of horizontal resolution in the atmospheric component of coupled models, by using Community Earth System Model (CESM) as an example. By breaking down the energy budget of the upper ocean, we conclude that surface net heat flux and Ekman upwelling process exert a considerable influence (over 80%) on upper ocean temperature of EBUS in CESM. Besides, the problem of underestimation of stratocumulus cloud is not present near the coast, and hence not responsible for this warm bias in CESM. On the contrary, downward shortwave radiation bias is overcompensated by longwave radiation and latent flux bias on the open ocean. Therefore, the insufficient ocean dynamic upwelling is the dominantly cause for SST warm bias. Finer horizontal resolution atmosphere component of CESM enables better representation of low-level coastal jet structure, with stronger and closer alongshore wind stress and curl leading to realistic representation of upwelling process and horizontal water mass transportation. Furthermore, low-level coastal jet is shown to be sensitive to coastal mountain topography, especially in South East Pacific region, through both thermodynamic and dynamic atmospheric processes and oceanic response. This article provides further proof of improving coupled climate models in reducing the SST biases in EBUS regions.

Prediction of Northern Hemisphere Regional Surface Temperatures Using Stratospheric Ozone Information

AbstractCorrelations between springtime stratospheric ozone extremes and subsequent surface temperatures have been previously reported for both models and observations at particular locations in the Northern Hemisphere. Here we quantify for the first time the potential use of ozone information for Northern Hemisphere seasonal forecasts, using observations and a nine‐member chemistry climate model ensemble. The ensemble composite correlations between March total column ozone (TCO) and April surface temperatures display a similar structure to observations, but with slightly lower correlation magnitudes. This is likely due to the larger number of cases smoothing out sampling error in the pattern, which is visible in the difference between correlations calculated from individual ensemble members. Using a linear regression model with March TCO as the predictor, predictions of the following April surface temperatures in regions that show large correlations are possible up to 4 years following the regression model end date in individual ensemble members, and up to 6 years in observations. We create an empirical forecast model to predict the sign of the observed as well as the modeled surface temperature anomalies using March TCO. Through a leave‐three‐years‐out cross‐validation method, we show that March TCO can forecast the sign of the April surface temperature anomalies well in parts of Eurasia that show the lowest model internal variability.

Analysis and simulation of land cover changes and their impacts on land surface temperature in a lower Himalayan region

Rapid urbanization is changing the existing patterns of Land Use Land Cover (LULC) globally which is consequently increasing the Land Surface Temperature (LST) in many regions. Present study was focused on estimating the current and simulating the future LULC and LST trends in the alpine environment of lower Himalayan region of Pakistan. Past patterns of LULC and LST were identified through the Support Vector Machine (SVM) and multi-spectral Landsat satellite images during 1987–2017 data period. The Cellular automata (CA) model and Artificial Neural Network (ANN) were applied to simulate future (years 2032 and 2047) LULC and LST changes, respectively, using their past patterns. CA model was validated for the simulated and the estimated LULC for the year 2017 with an overall Kappa (K) value of 0.77 using validation modules in QGIS and IDRISI software. ANN method was validated by correlating the observed and simulated LST for the year 2017 with correlation coefficient (R) and Mean Square Error (MSE) values of 0.81 and 0.51, respectively. Results indicated a change in the LULC and LST for instance the built-up area was increased by 4.43% while agricultural area and bare soil were reduced by 2.74% and 4.42%, respectively, from 1987 to 2017. The analysis of LST for different LULC classes indicated that built-up area has highest temperature followed by barren, agriculture and vegetation surfaces. Simulation of future LULC and LST showed that the built-up area will be increased by 2.27% (in 2032) and 4.13% (in 2047) which led 42% (in 2032) and 60% (in 2047) of the study area as compared to 26% area (in 2017) to experience LST greater than 27 °C. A strong correlation between built-up area changes and LST was thus found signifying major challenge to urban planners mitigating the consequent of Urban Heat Island (UHI) phenomenon. It is suggested that future urban planning should focus on urban plantation to counter UHI phenomena in the region of lower Himalayas.

Use of Digital Infrared Thermal Imaging in the Electromyography Clinic: A Case Series.

Introduction: Foot drop often results from denervation of the dorsiflexor muscles in the leg. Neurological evaluation begins with lower extremity motor testing followed by electromyography needle electrode examination (EMG-NEE). We explored digital infrared thermography (IRT) as a complementary tool in diagnosing peripheral nerve disorders.Methods: Using a digital IRT camera, we recorded differences in skin surface temperatures from affected and unaffected limbs in three patients with unilateral foot drop. Denervation in the affected limb was confirmed with EMG-NEE.Results: IRT imaging revealed lower relative skin surface temperatures in regions of the leg corresponding to denervated dorsiflexor muscles for all three consecutive patients who presented to the EMG Clinic with foot drop. Conclusions: Denervation appears to cause a decrease in thermal energy output from affected muscle groups. Alongside the EMG and magnetic resonance imaging (MRI), IRT may have an important role in assessing the severity and prognosis of a nerve injury. This observation may have implications for chronic pain syndromes, such as complex regional pain syndrome (CRPS), in which thermal change is a diagnostic criterion.

Controls on Land Surface Temperature in Deserts of Southern California Derived from MODIS Satellite Time Series Analysis, 2000 to 2018

The land surface temperature (LST) in arid regions is a primary controller of many ecological processes. Consequently, we have developed a framework for detection of LST change on a regional scale using data sets covering all deserts of southern California from the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite sensor. The Breaks for Additive Season and Trend (BFAST) methodology was applied to MODIS 1-km monthly LST data from the years 2000 to 2018 to estimate significant time series shifts (breakpoints) and gradual trends. Area-wide results showed five times more positive LST breakpoints (abrupt temperature warming events) than negative (surface cooling) breakpoints. Cross-correlations with high rainfall periods around Mojave dry lake playas, and comparison with timing of wildfire burns for breakpoint patterns, showed that abrupt shifts in LST had the strongest response to these controllers. We detected negative LST (abrupt cooling) breakpoints as consistently associated with the construction of new solar energy facilities. Over the majority of the study area, BFAST results showed warming LST trends between the years 2000 and 2018. The western-most margins of the study area showed consistent widespread warming trends, whereas the eastern portions of the Mojave and Lower Colorado Deserts showed a mix of positive and neutral LST trends. Long-term cooling LST trends were detected only in some of the largest dry lake formations in the Antelope Valley, Death Valley, and Bristol, Cadiz, and Danby playas.

The Urban Heat Island in an Urban Context: A Case Study of Mashhad, Iran.

In this study, the spatio-temporal changes of urban heat island (UHI) in a mega city located in a semi-arid region and the relationships with normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) are appraised using Landsat TM/OLI images with the help of ENVI and ArcGIS software. The results reveal that the relationships between NDBI, NDVI and land surface temperature (LST) varied by year in the study area and they are not suitable indices to study the land surface temperature in arid and semi-arid regions. The study also highlights the importance of weather conditions when appraising the relationship of these indices with land surface temperature. Overall, it can be concluded that LST in arid and steppe regions is most influenced by barren soil. As a result, built-up areas surrounded by soil or bituminous asphalt experience higher land surface temperatures compared to densely built-up areas. Therefore, apart from setting-up more green areas, an effective way to reduce the intensity of UHI in these regions is to develop the use of cool and smart pavements. The experiences from this paper may be of use to cities, many of which are struggling to adapt to a changing climate.

Barrier Effects of the Kuroshio Current on the East Asian Northerly Monsoon: A Sensitivity Analysis

Through the use of satellite scatterometer data, it is observed that the East Asian northerly monsoonal winds decrease drastically when crossing the Kuroshio Current. In a section across the Kuroshio Current region, as revealed by reanalysis data, it is suggested that the upward velocity has a two-cell structure extending to the 500 hPa height, and a strong atmospheric convergence is present below the 900 hPa level. The reanalysis data also show that the northerly wind speed decreases significantly when crossing the Kuroshio Current region below 850 hPa height. A sensitivity analysis is implemented using the Weather Research and Forecasting (WRF) model, showing that the atmospheric convergence and the northerly wind drop are enhanced as the Kuroshio region surface temperature increases. This indicates that the Kuroshio Current can act as a barrier to the East Asian northerly monsoonal winds in winter.

Comparison of GPS-based precipitable water vapor using various reanalysis datasets for the coastal regions of China

This paper investigated the quality of site-specific surface temperature and surface pressure data in the coastal regions of China, which were interpolated from the European Centre for Medium-Range Weather Forecast Interim reanalysis (ERA-Interim), Japanese 55-year Reanalysis Project (JRA-55), and the National Centers for Environmental Prediction Final (NCEP FNL) reanalysis surface meteorological datasets as well as from the new Global Pressure and Temperature (GPT2) model data. The measured temperature and pressure along with the collocated GPS data from 2014 were collected from 25 observation stations evenly located in the region. Compared with the actual meteorological observations, the performances of the interpolated data from three reanalysis datasets differ marginally, with the root mean square errors (RMSEs) of the interpolated surface temperature and pressure less than 2.4 K and 1.6 hPa, respectively; however, the RMSEs of the surface temperature and pressure interpolated from the GPT2 model were 3.0 K and 4.2 hPa, respectively. Data based on GPS PWV products that used the meteorological parameters interpolated from three reanalysis data were very close to those of meteorological observations, with biases within ± 0.4 mm and RMSEs below 0.5 mm in most areas, and the RMSE of PWV using the GPT2 model interpolation data was superior by 2 mm. The measurement of GPS PWV using the interpolated reanalysis meteorological data also compared well with radiosonde observations, with RMSE between them tending to increase with a decrease of the GPS station’s latitude. However, the GPS PWV based on the interpolated data could not reflect the true change in water vapor during typhoon events.

Surface temperature in different anatomical regions of ewes measured by infrared thermography

The objective of this study was to evaluate the variation of surface temperature through infrared thermography (IRT) in different anatomical regions during the estrous cycle of hair sheep. The estrous cycle of 20 Santa Ines hair breed ewes was synchronized and, during this synchronization period (15 days), environmental conditions (temperature, humidity and wet-bulb globe temperature) and body temperatures were evaluated. The temperatures of the rectum and vagina were measured with a digital thermometer and the surface temperature of the anus, vulva, muzzle, left eye and left ear were measured with an infrared thermal camera. The mean, standard deviation, regression and correlation of each temperature of each anatomical region were evaluated regarding the estrous cycle. The eye was not a good anatomical region to measure the temperature variation during the estrous cycle (R2 = 0.011, p > 0.05). The vulva was the region with greatest potential for evaluation of surface temperature variation with IRT during estrus synchronization (β = 0.0805; R2 = 0.767, p < 0.001).

WRF simulation of surface wind in high latitudes

High-resolution Weather Research and Forecasting (WRF) simulations of surface wind, pressure, temperature, humidity and snow depth in Arctic coastal regions are performed in this study. We compare simulation results for different elevation resolutions, surface roughness lengths, PBL schemes, initialization data and snow cover conditions with meteorological observations and validate WRF's usage in high latitudes. WRF performs well in simulations of high-latitude strong wind if the roughness length is carefully modified considering site's real vegetation type, though WRF produces insufficient wind fluctuations. Noah LSM is effective in updating surface roughness length according to snow depth. Higher elevation resolution increases surface pressure accuracy. MYNN3/YSU PBL scheme is recommended for wind speed/temperature simulation. In addition, the vertical structure of the Arctic atmospheric boundary layer is discussed, and a statically stable and turbulent surface layer is the Arctic winter is reported.