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In weather forecasting and climate monitoring, daily maximum and minimum air temperatures (TMAX and TMIN) are fundamental for operational and research purposes, from early warning of extreme events to climate change studies. This study provides an integrated evaluation of TMAX and TMIN from two European Centre for Medium-range Weather Forecasts (ECMWF) products: ERA5 reanalysis (1980–2019) and operational weather forecasts (2017–2021). Both products are evaluated using in situ observations from the Global Historical Climatology Network (GHCN). While the analyses span globally, emphasis is given to four key regions: Europe, East and West United States, and Australia. Results reveal a general underestimation of TMAX and overestimation of TMIN in both operational forecasts and ERA5, highlighting the limitation of the ECMWF model in estimating the amplitude of the diurnal cycle of air temperature. ERA5′s accuracy has improved over the past decade, due to enhanced constrain of land–atmosphere analysis streaming from more and higher-quality satellite data. Furthermore, ERA5 outperforms one-day-ahead weather forecasts, indicating that non-real-time dependent studies should rely on ERA5 instead of real-time operational forecasts. This study underscores the importance of ongoing research in model and data assimilation, considering the relevance of daily temperature extremes forecasting and reanalysis for operational meteorology and climate monitoring. [ABSTRACT FROM AUTHOR]

Non-professional weather stations are often omitted from the networks of standard/professional stations at various spatial scales. Nevertheless, there are many tasks when such non-professional datasets can serve as the only or the most relevant available source respectively. Its acquisition costs, sufficient quality and capacity together with its moveability represent properties that should be taken into consideration when planning operational usage of various meteorological data. In this paper, we focus on the datasets of air temperatures and relative humidities measured both with professional and nonprofessional devices at nearly the same location. Four years of almost continual measurements (2016-2019) ensure robust sample of mutual comparison, which we analyze in the paper more in detail in order to assess the potential of non-professional datasets for utilization in aviation meteorology. Particular issues such as value difference patterns, large errors occurrence, temporal signal stability and seasonality are elaborated as well [ABSTRACT FROM AUTHOR]

This paper introduces a fast urban microscale meteorological model with a horizontal resolution of O(10) m, named URBAN (Urban Rapid & Building-Aware Neighborhood), which is capable of rapid assessment of meteorological fields over key urban areas, including wind speed, air temperature, humidity and thermal comfort index, with the execution time less than 10 minutes consuming 1 CPU core. URBAN uses a fast wind diagnostic method to construct three-dimensional (3-D) wind fields surrounding complex building clusters with their geometry resolved explicitly To enhance the accuracy of wind reconstruction and the continuity of the initial wind field around irregular buildings, we propose a new parameterization method based on stream functions, which can accurately characterize the influences of complex urban building clusters on the three-dimensional wind field The model can provide various results for the meteorological service of large outdoor activities, including conventional meteorological elements (wind, temperature, humidity, radiation, etc.) and the Universal Thermal Comfort Index, which is derived from the relationship between physiological processes and environmental meteorological conditions. In this paper, URBAN is applied to develop an automatic analysis and forecast system of microscale meteorological elements over the central Beijing region in summer during a large outdoor event. By comparing with the half-hourly observations from three auto weather stations (AWSs) in the region, the root-mean-square errors (RMSEs) of the modeled 10-meter-height wind speed, 2-meter-height air temperature and humidity are 0.98 m s−1,1.37 °C and 7.37%, respectively. [ABSTRACT FROM AUTHOR]

A compact thermometer for air temperature based on the measurement of the speed of sound was developed at INRIM. This paper focuses on the comparison of this instrument with platinum resistance thermometers in a climatic chamber over a temperature range (−30 ÷ +55) °C, relative humidity (10 ÷ 90)%rh, and irradiation (>1 kW/m2) values similar to those of surface atmospheric conditions. Overall uncertainty values of 0.2 °C over the range from −30 °C to +30 °C, and from 0.6 °C to +55 °C, were found. Moreover, the instrument proved to be immune to irradiation errors and free from the need for temperature calibration. [ABSTRACT FROM AUTHOR]

The study specifies the circulation conditions, the frequency of occurrence, long‐term changes, and thermal characteristics of air masses over east‐central Europe in the years 1951–2020. The division of air masses utilized by The Polish Institute of Meteorology and Water Management was used, where, based on the geographical location of the source areas, Arctic (A), Polar maritime fresh (mPf), Polar maritime old (transformed) (mPo), Polar maritime warm (mPw), Polar continental (cP), and Tropical (T) air masses are distinguished. It has been shown that western circulation dominates in this area (60% of days on average), during which the advection of maritime air masses occurs. The share of Arctic air mass flowing from the north (about 12% of days a year) and Tropical air mass from the southern sector (4% of days) is clearly smaller. The results of the research have shown that the frequency of the majority of the analysed air masses was subject to short‐term changes in the studied period. On the other hand, long‐term changes in this frequency concerned two air masses flowing from lower latitudes (mPw and T). In the case of mPw, in the period 1951–2020 there was a significant increase in frequency, not only on a yearly basis, but also in all seasons. In the last two decades, the frequency of Tropical air mass has also increased significantly, while the increase took place only in the summer period. Therefore, the increase in air temperature recorded in east‐central Europe since the 1990s, mainly in the warm half of the year, should to a large extent be attributed to both the greater frequency of the inflow of air masses from lower latitudes (mPw and T) and the increase in temperature in virtually all separated air masses, which in turn is a result of progressing global warming. [ABSTRACT FROM AUTHOR]

Surface air temperatures in the southeastern United States that did not change from the climatological mean from 1900 to 2000 have increased since the year 2000. Analyzed herein are factors modulating the surface air temperatures in the region for a 20-yr period (2000–19) using space- and surface-based observations, and output from a reanalysis model. The 20-yr period is segregated into two decades, 2000–09 and 2010–19, corresponding to different tropospheric chemical regimes. Changes in seasonal and decadal averages are examined. The later decade experienced higher average surface air temperatures with significant warming during summer and fall seasons. Decadal and seasonal averages of cloud properties, column water vapor, rain rates, and top-of-atmosphere outgoing longwave radiation did not exhibit statistically significant differences between the two decades. The region experienced strong warm and moist advection during the winter months and very weak advection during the summer months. The later decade exhibited higher low-level moisture advection during the winter months than the earlier decade with insignificant changes in the temperature advection between the two decades. The later decade had significantly lower aerosol dry and liquid water mass during all seasons, along with lower aerosol optical depth, higher single scattering albedo, and lower top-of-the-atmosphere outgoing shortwave radiation during cloud-free conditions in the summer season. Collectively, these results suggest that changes in the aerosol direct radiative forcing are responsible for warming during summer months that experience weak advection and highlight seasonal differences in the temperature controlling mechanisms in the region. [ABSTRACT FROM AUTHOR]

The calculation of the tropopause height is crucial to the investigation of fundamental interactions between the troposphere and stratosphere, playing an essential role in areas such as climatology, geodesy, geophysics, ecology, and aeronautics. Since the troposphere and stratosphere have many distinct features, it is possible to define the boundary between them using different variables, such as temperature lapse rate, potential vorticity and chemical concentrations. However, according to the chosen variable, different tropopause definitions are created, each one with some limitations. Using 41 years of European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) data, we examined the variability of the tropopause for the north and south hemispheres and developed two models, both based on blending the potential vorticity and thermal tropopauses. One model (based on a sigmoid function, named STH) depends only on latitude and day of the year, while the other model (based on bilinear interpolation, named BTH) requires an additional look-up table. In order to account for the different behaviors of the tropopauses in the north and south hemispheres, we estimated two sets of model coefficients (one for each hemisphere). When compared against a benchmark of estimated tropopause heights during three years of radiosonde data, we obtained an average RMSE for the differences of 0.88 km for the STH model and 0.67 km for the BTH model. A similar comparison for alternative models available in the literature shows that the new models have superior performance and represent a significant improvement in tropopause height determination. [ABSTRACT FROM AUTHOR]

The change in the spatial distribution of agroclimatic conditions based on the sum of active temperatures (SAT), growing degree days (GDD), and latitude–temperature index (LTI) is discussed in this article. Data from 20 meteorological stations of IMGW-PIB (Institute of Meteorology and Water Management—National Research Institute) in Poland from the years 1966–2020 were used. The temporal and spatial diversity of mean air temperature and the chosen indices were analyzed for the period from April to October. Designating areas of diverse thermal conditions with respect to plant comfort on the basis of agroclimatic indices was attempted, together with mean air temperature and its temporal changes. The clustering, using the Ward's method, yielded four regions with different thermal resources in Poland. The study period showed an increase in the values of all agroclimatic indices and air temperature during the growing season, suggesting an increase in the thermal resources in the territory of Poland. [ABSTRACT FROM AUTHOR]

Air pollution is one of the most pressing issues in populated Middle Eastern cities, in particular for the city of Ahvaz, Iran, imposing deleterious effects on the environment, public health, economy, culture, and other sectors. In this study, we investigate the relationship between meteorological parameters, PM10, AOD, air mass source origin, and visibility during severe desert dust storms (Average3h PM10 > 3200 µg m−3) between 2009 and 2012. Six of seven such events occurred between February and March. Interestingly, for the seven cases there was always an alarming PM10 mass concentration peak (137–553 µg m−3) between 12:00–18:00 (local time) that was 18–24 h before the dominant peak of the storm (3279–4899 µg m−3). The maximum wind speed over the multi-day periods examined for the dust storms is usually observed 6 h before the alarming PM10 peak. The minimum relative humidity, dew point temperature and air pressure occurred ± 3 h around the time of the alarming PM10 peak. Wind speed was the meteorological parameter that was consistently higher around the time of the first peak as compared to the second peak, with the reverse being true for sea level pressure. Based on four years of daily data in Ahvaz, PM10 was positively correlated with wind speed and air temperature and inversely correlated with sea level pressure and RH. An empirically-derived equation with R2 = 0.95 is reported to estimate the maximum PM10 concentration for severe desert dust events in the study region based on meteorological parameters. Finally, AOD is shown to correlate strongly (R2 = 0.86) with PM10 during periods with severe desert dust storms in the region. [ABSTRACT FROM AUTHOR]

The main objective of the present study is the development of an "on-line" heating emissions modeling system based on simulated meteorological data and its integration with air quality modeling systems in order to improve their accuracy. The WRF-CAMx air quality modeling system is applied over Greece for the cold period of 2015 (January–April, October–December) for two emissions scenarios: using the (a) "on-line" heating emissions based on WRF meteorology and (b) "static" heating emissions based on static temporal profiles. The monthly variation in total "on-line" heating emissions followed the temporal pattern of the air temperature over Greece, leading to the highest heating emissions in January and February, while higher differences in emissions between winter and spring/autumn months were identified in comparison with the static ones. The overall evaluation of the WRF-CAMx modeling system using the "on-line" heating emissions revealed satisfactory model performance for the mean daily air quality levels. The comparison between the simulated and observed mean monthly concentrations revealed an improvement in the pattern of mean monthly concentrations for the "on-line" scenario. Higher values of the index of agreement and correlation for the mean daily values were also identified for the "on-line" scenario in most monitoring sites. [ABSTRACT FROM AUTHOR]

The investigation of atmospheric characteristics is of great significance in properly exploiting air and water resources, and quantitatively estimating and nowcasting precipitation. Microwave radiometers are widely used in this field because they can operate unattended for a long time under almost all weather conditions, and obtain high-quality remote sensing data. This research focuses on the applications of ground-based microwave radiometers in retrieving meteorological characteristics of the Qinghai–Tibet Plateau (QTP) during the East Asian winter monsoon (EAWM), such as humidity and temperature structures, stability/convection indices, and cloud and precipitation retrievals. Vertical structures of atmospheric temperature, pressure, humidity, and other variables were investigated under clear-sky and snowy-sky conditions. The statistical characteristics of stability/convection indices in the QTP region, such as convective available potential energy (CAPE) and convective inhibition (CIN), were investigated, with a special focus on snowy-sky conditions. The relationship between potential cloud water resources and atmospheric and thermodynamics and instability conditions were taken into consideration. The results showed that the peak CAPEs under clear-sky and snowy-sky conditions were 1349.11 J/kg and 1165.12 J/kg, which were relatively larger than the corresponding values during the summer monsoon. CINs during the EAWM were small enough to be negligible. For quantitative precipitation forecasting (QPF), brightness temperatures at 30.0 GHz and 51.24 GHz sampling channels were sensitive to the snowfall process. Within 1 h before snowfall, the precipitable water vapour (PWV) and liquid water content (LWC) increased sharply by 0.05 cm and 0.12 mm, respectively, while the CAPE and downwards CAPE decreased by 225.60 J/kg and 180.31 J/kg, respectively. The findings of this article may be meaningful for characterizing meteorology and precipitation in the QTP and similar regions during the winter monsoon. [ABSTRACT FROM AUTHOR]

High‐altitude observations of mountain meteorology remain extremely rare in the Southern Alps despite their importance for detecting changes in seasonal snow and glacier extent. To address this, we present a unique in situ analysis of surface meteorology, including radiative forcing from clouds, using automatic weather station data obtained near the terminus of Brewster Glacier in the Southern Alps of New Zealand over the period 2010–2020. The average air temperature is 2.1°C at an altitude of 1,650 m above sea level, and seasonal cycles of surface temperature and albedo indicate the automatic weather station (AWS) site is typically snow‐covered between June and November. Snowfall can occur anytime of the year, but rainfall accounts for more than half of the total precipitation (56%). In the absence of strong gradient airflow, a katabatic wind often develops on the glacier, which is strongest at night time and early morning. Partly cloudy conditions are most frequent (43%) at a daily time scale, followed by overcast (34%) and clear‐sky conditions (23%). A distinct diurnal and seasonal cycle of cloud cover is observed, with clouds most frequent in the afternoon during spring. Daily clear‐sky conditions are most common during winter, in particular in June, while the atmospheric transmission of shortwave radiation is lowest in summer. For 4 months of the year (May–August), the increase in incoming longwave radiation by clouds exceeds the decrease in incoming shortwave radiation. Interannual extremes in melt on Brewster Glacier cannot be fully attributed to summer air temperature anomalies and future work should seek to examine the influence of clouds and atmospheric moisture fluxes on seasonal mass balance variations. [ABSTRACT FROM AUTHOR]

A previous study on the use of nudging in EAMv1 had an unresolved issue, namely a simulation nudged to EAMv1's own meteorology showed non-negligible deviations from the free-running baseline simulation over some of the subtropical marine stratocumulus and trade cumulus regions. Here, we demonstrate the deviations can be substantially reduced by two changes in the nudging implementation: first, revising the sequence of calculations in a nudged EAM simulation to improve consistency with the free-running baseline; second, increasing the frequency of constraining data from 6-hourly to 3-hourly to better capture strong sub-diurnal variations. The resulting improvements in climate representativeness motivate an investigation on the potential benefits of using newer reanalysis products with higher data frequency in nudged simulations that aim at capturing observed weather events. Simulations nudged towards 6-hourly ERA-Interim reanalysis or 6-hourly, 3-hourly, and hourly ERA5 reanalysis are evaluated against EAMv1's own climatology, global-scale satellite retrievals of outgoing longwave radiation and precipitation, as well as in-situ measurements of air temperature, humidity, and winds from the Atmospheric Radiation Measurement (ARM) user facility. Our overall recommendation is to use the revised sequence of calculation and 3-hourly data from ERA5 for the nudged simulations. The anthropogenic aerosol effects in various EAMv1 simulations are evaluated. For estimating the global mean effect, the source of constraining data has a relatively small impact. Consistent with conclusions from previous studies, in order to obtain estimates of global mean aerosol effects that are consistent with results from the free-running baseline, nudging the horizontal winds but not air temperature is recommended. [ABSTRACT FROM AUTHOR]

The urban heat island (UHI) effect has been widely studied because of its impacts on the environment and human’s wellbeing. The surface UHI (SUHI) is typically monitored with remote sensing products, whereas the air UHI (AUHI) is typically monitored using temperature sensor networks. The first technique requires clear days to obtain good images, and the latter is limited by the number of weather stations in an urban and rural area. An alternative to monitor the AUHI is the usage of global navigation satellite system (GNSS) data which is available in all weather conditions and there are well-developed GNSS receiver networks around the world. In this article, we propose a machine learning (ML) approach to estimating the air temperature from GNSS data to take advantage of the quantity of data. By training a support vector regressor with three datasets, GNSS-derived zenith tropospheric delay (ZTD), temperature, and day-of-the year, we can estimate temperature from GNSS data without the need for other environmental variables. The regressor presented in this article is trained with data from 2016 to 2019 and tested with data from 2020 in five Japanese metropolitan areas. The results show that it is possible to predict temperature from only the ZTD of GNSS signals with high precision, as an $R^{2}$ between 0.82 and 0.90 is found for the predictions. Predicted temperatures are then used to study the diurnal cycle of the AUHI in Sapporo, Tokyo, Nagoya, Osaka–Kyoto, and Fukuoka with an average precision of 0.8 °C. [ABSTRACT FROM AUTHOR]

GLDAS2.0 provides long‐term fine resolution gridded hydrometeorological data sets, which are necessary for water‐related studies, particularly in some transboundary rivers that are partially without observation. Yet, GLDAS2.0 has only been validated at limited locations, and few studies have been conducted to develop approaches to correct the GLDAS2.0 data for transboundary rivers. This work assessed the GLDAS2.0 data and developed approaches to correct their uncertainties for studies in large transboundary rivers in the Tibetan Plateau and Northeast China (NC). To achieve these goals, observational data from 1982 to 2010 and a water and energy budget‐based distributed hydrological model including biosphere after calibration and validation were employed. We find that the GLDAS2.0 data (except for wind speed) can reasonably replicate observed seasonal variations. However, its specific humidity and wind speed have large uncertainty, and precipitation has large uncertainty in summer. In NC, the trends of its precipitation, air temperature, downward longwave radiation, and wind speed are consistent with the observations. In the Yarlung Tsangpo, Lancang, and Nu Rivers, the trends of all GLDAS2.0 data reproduce the observation very well, that is, wetting, warming, and dimming trends. Validations show that the corrections are effective and the corrected forcing data can be successfully used in hydrological simulation with improved performance than the raw GLDAS2.0 data, which demonstrates the usefulness of the methodology and corrected forcing data to hydrometeorological studies in transboundary rivers in China as well as in other nearby regions/countries. Plain Language Summary: GLDAS2.0 provides long‐term hydrometeorological data sets, which are necessary for water‐related studies, particularly in some transboundary rivers that are partially without observation. This work assessed GLDAS2.0 data and developed approaches to correct their uncertainties for studies in large transboundary rivers in the Tibetan Plateau (TP) and Northeast China (NC). In NC, the trends of GLDAS2.0 precipitation, air temperature, downward longwave radiation, and wind speed are consistent with observations. In TP, the trends of all the GLDAS2.0 data reproduce observation very well. Validations show that the corrections are effective and the corrected data have improved performance than raw GLDAS2.0 data in hydrological simulation. Key Points: Specific humidity and wind speed have large uncertainty, and precipitation has large uncertainty in summerTrends of precipitation, air temperature, downward longwave radiation, and wind speed are consistent with observationEquations are developed to reduce forcing data uncertainty, and validations show that the equations are effective [ABSTRACT FROM AUTHOR]

We analyse seasonal and annual trends of extreme indices of air temperature and precipitation over Brazil during the period 1961–2018. The main goal is to investigate whether the climate is changing and if so, to explore if there is any marked seasonality in such changes. The daily observed datasets of maximum and minimum temperatures, and precipitation, are provided by the Brazilian National Institute of Meteorology and National Water Agency. We use the Sen Curvature and Mann‐Kendall statistical tests to compute the magnitudes and to evaluate the statistical significance of climate extremes trends, respectively. The results show that the warm extremes frequency of occurrence is increasing significantly while the opposite occurs for cold extremes, which reveals a very consistent and widespread warming over Brazil. The highest increases in warm extremes occur during austral spring and summer while for the cold extremes the greatest decreases are observed during austral winter. Unlike temperature, precipitation extremes show heterogeneous signals for most of the country. In Northeast Brazil, there are changes towards a drier climate, especially in summer and autumn. In the Southern region, the climate is becoming wetter, with a reduction in consecutive dry days, especially in spring. For the other regions, there is no strong clear change sign, but both positive and negative precipitation extreme trends, without statistical significance (mostly in Southeast Region). [ABSTRACT FROM AUTHOR]

: Data on the climatic conditions of a region provide improvements in decision-making for activities related to the sectors, for agricultural and urban planning, to reduce risks. Current paper undertakes the climatic characterization of the municipality of Rio Verde GO Brazil, as a tool for agricultural and urban planning. Data were retrieved from the main conventional weather station in Rio Verde provided by the National Institute of Meteorology between 1972 and 2017. The municipality´s characteristics comprised air temperature at 23.6±6.0ºC, relative humidity at 66.3±13.6%, wind speed at 1.4±0.8 m s-1, solar radiation at 17.3±4.7 MJ m-2 day-1 and solar brightness at 6.1±3.0 h. Total precipitation volume is 1,611± 180 mm/year-1, with concentrations during November to March (76%). August is the most critical month. The reference evapotranspiration is 3.8±0.9 mm day-1. The municipality is characterized by six months with water deficiency and six rainy months. Precipitation is a limiting factor to agricultural production due to poor spatial and temporal distribution. The use of irrigation systems, the conservation of straw on the ground and the use of off-season cover plants are alternatives that mitigate the effects of climatic risks. Concern in the urban region lies with floods, fires and respiratory diseases, transmissible by mosquitoes, at different times of the year. Current study may define better strategies for public and/or private initiative in planning more effective agricultural and environmental activities and projects. [ABSTRACT FROM AUTHOR]

The article focuses on the meteorological factors including temperature and precipitation during spring of the year 2021 in Spain. It reports on an increase in environmental temperature in Spain with maximum and minimum temperature higher than the average value. It also highlights a decrease in average rainfall over Spain in spring 2021.

Considered that human activities mostly occur below building heights, the objective of this study was to investigate the temporal variations of fine particular matter (PM2.5)-associated polycyclic aromatic hydrocarbons (PAHs) and benzo[a]pyrene-equivalent (BaPeq) concentrations at four different elevations (6.1, 12.4, 18.4, and 27.1 m) in Kaohsiung City, the largest industrial city of southern Taiwan. Temperature variation was critical for the PM2.5-associated PAH concentrations, which were dominated by benzo[g,h,i]perylene (0.27 ± 0.04 ng m−3 and 24.43% of the total concentration) and other high molecular weight (HMW) species. The PM2.5-associated BaPeq was dominated by 5-ring PAH (36.09%). The PM2.5-associated PAH and BaPeq concentrations at all elevations were significantly increased in winter. In the night, the correlations between the PM2.5-associated PAH concentrations and atmospheric temperatures became negatively stronger, notably at lower elevations (r = − 0.73 ~ − 0.86), whereas the BaPeq during daytime and nighttime were not changed significantly in most months. The PAHs analysis with different PM sizes demonstrated the importance of smaller particles such as PM2.5. The meteorological variation was more important than elevation to influence the low-elevation PM2.5-associated PAH and BaPeq concentrations in an urban area like Kaohsiung City, as the two concentrations were dominated by the PAHs with HMWs and those 5-ring species, respectively. [ABSTRACT FROM AUTHOR]

The microwave humidity sounder (MWHS) onboard the Fengyun-3D satellite is providing the data for profiling atmospheric temperature and moisture and has become an important data source for improving the weather forecasts. In this article, three data sources are utilized for assessing the MWHS on-orbit performance, including Global Navigation Satellite System Occultation Sounder (GNOS), ECMWF (European Centre for Medium-Range Weather Forecasts) Re-Analysis (ERA)-Interim reanalysis, and Advanced Technology Microwave Sounder (ATMS) data. GNOS-retrieved atmospheric profiles and the reanalysis data are used as inputs to the community radiative transfer model (CRTM) for simulating the MWHS brightness temperatures at the top of the atmosphere in July 2018 for characterizing the instrument performance. Since ATMS is a well-calibrated microwave sounding instrument onboard both Suomi NPP and NOAA-20 satellites, its measurements are also collocated with MWHS data for a consensus analysis using the simultaneous nadir overpasses (SNOs) method. In comparing GNOS simulations, MWHS upper air temperature sounding channels (3–6) have relatively larger biases (less than 2.5 K) than the water vapor sounding channels. However, the standard deviation of the difference between observations and simulations (O-B) is larger for water vapor sounding channels. For ERA simulations, MWHS sounding channels exhibit negative biases similar to GNOS results but the standard deviation of O-B at the water vapor channels is much smaller. When compared with ATMS water vapor channels, MWHS biases are mostly negative and agree with those from ERA simulation. Thus, the large uncertainty in simulating MWHS water vapor sounding channels from GNOS could result from the poor input water vapor profiles and high water vapor variability in the lower troposphere. [ABSTRACT FROM AUTHOR]

New changing characteristics have emerged in the drought and its dominant meteorological factors in Northeast China, as the ecological impacts of global climate change in recent years. These impacts may bring unpredictable natural disaster risks to local agricultural production. In order to guide the prevention of agricultural drought and mitigation work, it is particularly important to explore the spatial and temporal patterns and evolution trends of drought in Northeast China under the background of climate change, thereby to reveal the meteorological driving factors of drought in the spring maize during growing season. Here, five meteorological factors, including wind speed precipitation, sunshine duration, air temperature, pressure and relative humidity, were interpolated month by month, particularly considering the geographical location and altitude. The data was taken from 105 meteorological stations in Northeast China, where the local DEM data was in the resolution of 30 meters. A Penman-Monteith method was used to calculate the potential evapotranspiration. A IDL program was also used to calculate the Standardized Precipitation Evapotranspiration Index (SPEI) at the 1, 3, 6, 12 and 24-month scales in 1989-2018, further to analyze the multi-scale characteristics of drought. As such, the change characteristic of each meteorological factor was determined in the spring maize during growing season. The high-incidence month and region of drought in the growing season were identified by the SPEI of 1-month scale, thereby to gain the trends of monthly average precipitation and reference evapotranspiration. A stepwise linear regression method was selected to extract the dominant meteorological factors driving drought in each month and the whole growing season. The results indicated that: 1) from 1989 to 2018, a light-serious-light trend of drought was showed in Northeast China in a 10-year cycle, particularly which the worst drought was found in 2000-2010. There was an obvious effect using the SPEI for the drought, as the time scale increased, but there can be a time lag at a certain degree. 2) In the spring maize during growing season, the drought was tend to occur in May, with a high probability in the west of Jilin, the east of Inner Mongolia and the southwest of Heilongjiang. 3) Meteorological variations in the spring maize during growing season in Northeast China were mainly characterized by the increase of air temperature, together with the decrease of air pressure and wind speed. The change rates of average temperature (Tmean), the maximum temperature (Tmax), the minimum temperature (Tmin), the air pressure (APS) and the wind speed (WV) were 0.41 ℃ /(10a), 0.42 ℃/(10a), 0.39 ℃/(10a), -0.05 kPa/(10a), and -0.08 m/(s·10 a), respectively. Other meteorological factors did not change significantly. 4) Dominant meteorological factors driving drought varied in the maize growth period in Northeast China. The significant factors were ranked in descend order: the precipitation (PRE), air relative humidity (RHU), Tmax and sunshine duration (SSD) in May, followed by the PRE, RHU, SSD and Tmin in June, the PRE, RHU and SSD in July, the PRE, Tmax and Tmean in August, and the PRE, RHU and Tmax in September. In the whole growth season, the main meteorological factors were the PRE, Tmax, SSD, and RHU. Specifically, the PRE played the most important role in drought in each month, compared with the other factors. The WV and APS showed the minimum influence on drought. This finding can offer a promising potential reference for the drought characteristics of the spring maize during growing season in Northeast China, and further to make the reasonable decisions for drought prevention. [ABSTRACT FROM AUTHOR]

The Himalayan glaciers contribute significantly to regional water resources. However, limited field observations restrict our understanding of glacier dynamics and behaviour. Here, we investigated the long-term in situ mass balance, meteorology, ice velocity and discharge of the Chhota Shigri Glacier. The mean annual glacier-wide mass balance was negative, −0.46 ± 0.40 m w.e. a−1 for the period 2002–2019 corresponding to a cumulative wastage of −7.87 m w.e. Winter mass balance was 1.15 m w.e. a−1 and summer mass balance was −1.35 m w.e. a−1 over 2009–2019. Surface ice velocity has decreased on average by 25–42% in the lower and middle ablation zone (below 4700 m a.s.l.) since 2003; however, no substantial change was observed at higher altitudes. The decrease in velocity suggests that the glacier is adjusting its flow in response to negative mass balance. The summer discharge begins to rise from May and peaks in July, with a contribution of 43%, followed by 38% and 19% in August and September, respectively. The discharge pattern closely follows the air temperature. The long-term observation on the 'Chhota Shigri – a benchmark glacier', shows a mass wastage which corresponds to the slowdown of the glacier in the past two decades. [ABSTRACT FROM AUTHOR]

New changing characteristics have emerged in the drought and its dominant meteorological factors in Northeast China, as the ecological impacts of global climate change in recent years. These impacts may bring unpredictable natural disaster risks to local agricultural production. In order to guide the prevention of agricultural drought and mitigation work, it is particularly important to explore the spatial and temporal patterns and evolution trends of drought in Northeast China under the background of climate change, thereby to reveal the meteorological driving factors of drought in the spring maize during growing season. Here, five meteorological factors, including wind speed precipitation, sunshine duration, air temperature, pressure and relative humidity, were interpolated month by month, particularly considering the geographical location and altitude. The data was taken from 105 meteorological stations in Northeast China, where the local DEM data was in the resolution of 30 meters. A Penman-Monteith method was used to calculate the potential evapotranspiration. A IDL program was also used to calculate the Standardized Precipitation Evapotranspiration Index (SPEI) at the 1, 3, 6, 12 and 24-month scales in 1989-2018, further to analyze the multi-scale characteristics of drought. As such, the change characteristic of each meteorological factor was determined in the spring maize during growing season. The high-incidence month and region of drought in the growing season were identified by the SPEI of 1-month scale, thereby to gain the trends of monthly average precipitation and reference evapotranspiration. A stepwise linear regression method was selected to extract the dominant meteorological factors driving drought in each month and the whole growing season. The results indicated that: 1) from 1989 to 2018, a light-serious-light trend of drought was showed in Northeast China in a 10-year cycle, particularly which the worst drought was found in 2000-2010. There was an obvious effect using the SPEI for the drought, as the time scale increased, but there can be a time lag at a certain degree. 2) In the spring maize during growing season, the drought was tend to occur in May, with a high probability in the west of Jilin, the east of Inner Mongolia and the southwest of Heilongjiang. 3) Meteorological variations in the spring maize during growing season in Northeast China were mainly characterized by the increase of air temperature, together with the decrease of air pressure and wind speed. The change rates of average temperature (Tmean), the maximum temperature (Tmax), the minimum temperature (Tmin), the air pressure (APS) and the wind speed (WV) were 0.41 ℃ /(10a), 0.42 ℃/(10a), 0.39 ℃/(10a), -0.05 kPa/(10a), and -0.08 mps/(10a), respectively. Other meteorological factors did not change significantly. 4) Dominant meteorological factors driving drought varied in the maize growth period in Northeast China. The significant factors were ranked in descend order: the precipitation (PRE), air relative humidity (RHU), Tmax and sunshine duration (SSD) in May, followed by the PRE, RHU, SSD and Tmin in June, the PRE, RHU and SSD in July, the PRE, Tmax and Tmean in August, and the PRE, RHU and Tmax in September. In the whole growth season, the main meteorological factors were the PRE, Tmax, SSD, and RHU. Specifically, the PRE played the most important role in drought in each month, compared with the other factors. The WV and APS showed the minimum influence on drought. This finding can offer a promising potential reference for the drought characteristics of the spring maize during growing season in Northeast China, and further to make the reasonable decisions for drought prevention. [ABSTRACT FROM AUTHOR]

Due to the influences of precursors emissions, meteorology, geography and other factors, ozone formation sensitivity (OFS) is generally spatially and temporally heterogeneous. This study characterized detailed spatial and temporal variations of OFS in Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 2012 to 2016 based on OMI satellite data, and analyzed the relationships of OFS with precursors emissions, meteorology and land use types (LUTs). From 2012 to 2016, the OFS tended to be NO x -limited in GBA, with the value of FNR (HCHO/NO 2) increasing from 2.04 to 2.22. According to the total annual emission statistics of precursors, NO x emissions decreased by 33.1% and VOCs emissions increased by 35.2% from 2012 to 2016, directly resulting in OFS tending to be NO x -limited. The Grey Relation Analysis results show that total column water (TCW), surface net solar radiation (SSR), air temperature at 2 m (T2) and surface pressure (SP) are the top four meteorological factors with the greatest influences on OFS. There are significant positive correlations between FNR and T2, SSR, TCW, and significant negative correlations between FNR and SP. In GBA, the OFS tends to be NO x -limited regime in wet season (higher T2, SSR, TCW and lower SP) and VOCs-limited regime in dry season (lower T2, SSR, TCW and higher SP). The FNR displays obvious gradient variations on different LUTs, with the highest in "Rural areas", second in "Suburban areas" and lowest in "Urban areas". Image, graphical abstract [ABSTRACT FROM AUTHOR]

Heating degree days (HDDs) and cooling degree days (CDDs) are widespread climatic indicators that depict the extremity and duration of ambient air temperature values. Over the years, they have been proven to be a useful tool for a reasonably accurate estimation of space heating and cooling requirements, as well as for the buildings' energy performance rating and their compliance with legislative requirements. Reliable degree-days (DDs) data are a prerequisite for using the degree-day methods, so is determining the DDs values on a local and regional level, with respect to climate differentiations. The aim of this study is the calculation of HDDs and CDDs for Cyprus at various base temperatures, as such data cannot be found for Cyprus in the literature. The temperature data used were provided by the Cyprus Department of Meteorology, and these are the average daily dry-bulb ambient air temperature values as well as the daily maximum and minimum air temperature values for the period 2006–2017. Data were recorded at five meteorological stations at Paphos, Larnaca, Athalassa, Limassol and Prodromos. Monthly and annually HDDs and CDDs were calculated, for 11 base temperatures, as well as monthly and annually average temperatures for the period 2006–2017, by utilizing the DDs calculation technique of average daily temperature as proposed by ASHRAE. Additionally, an analysis of the variability and the trends of the HDDs and CDDs at the five meteorological stations during the 12-year period were performed. Finally, conclusions were drawn concerning the development of climate over the last 12 years in Cyprus. [ABSTRACT FROM AUTHOR]

Hyperspectral data have important research and application value in the fields of meteorology and remote sensing. With the goal of improving retrievals of atmospheric temperature profiles, this paper outlines a novel temperature channel selection method based on singular spectrum analysis (SSA) for the Geostationary Interferometric Infrared Sounder (GIIRS), which is the first infrared sounder operating in geostationary orbit. The method possesses not only the simplicity and rapidity of the principal component analysis method, but also the interpretability of the conventional channel selection method. The novel SSA method is used to decompose the GIIRS observed infrared brightness temperature spectrum (700–1130 cm−1), and the reconstructed grouped components can be obtained to reflect the energy variations in the temperature-sensitive waveband of the respective sequence. At 700–780 cm−1, the channels selected using our method perform better than IASI (Infrared Atmospheric Sounding Interferometer) and CrIS (Cross-track Infrared Sounder) temperature channels when used as inputs to the neural network retrieval model. [ABSTRACT FROM AUTHOR]

Atmospheric air temperature values are fundamental in meteorology and climate studies. To achieve high accuracy in the measurements, the features, characteristics and performances of instruments are of high importance. This study focuses on the most commonly used temperature sensors within automatic weather stations, with a specific focus on evaluating the self‐heating effect. Self‐heating in automatic weather stations originates not only from the temperature sensor itself but also from the electrical components housed together within. This effect introduces extra heating in the system, causing biases and errors in temperature records. The conducted measurements show the temperature change in the close vicinity of the thermometers over a time period of more than 66 hr with electric current and voltage supply values recommended by the respective sensor manufacturers. Furthermore, the temperature changes after increasing the voltage supply levels up to 80% of the maximum voltage recommended by the manufacturer are presented as well. The results of overall self‐heating indicated a +0.07°C increase in temperature for the tested sensors when using the manufacturers' recommended electric current and voltage supply. However, the use of elevated voltage levels shows a considerably higher temperature increase in the vicinity of the temperature sensors. In the present study, the measured difference from the initial measured temperature can be as high as +0.32°C. [ABSTRACT FROM AUTHOR]

This paper examines summer- and wintertime variations of the surface and near-surface urban heat island (UHI) for the Phoenix metropolitan area using the Moderate Resolution Imaging Spectroradiometer (MODIS), near-surface meteorological observations, and the Weather Research and Forecasting (WRF) Model during a 31-day summer- and a 31-day wintertime period. The surface UHI (defined based on the urban-rural land surface temperature difference) is found to be higher at night and during the warm season. On the other hand, the morning surface UHI is low and frequently exhibits an urban cool island that increases during the summertime period. Similarly, the near-surface UHI (defined based on the urban-rural 2-m air temperature difference) is higher at night and during summertime. On the other hand, the daytime near-surface UHI is low but rarely exhibits an urban cool island. To evaluate the WRF Model's ability to reproduce the diurnal cycle of near-surface meteorology and surface skin temperature, two WRF Model experiments (one using the Bougeault and Lacarrere turbulent scheme and one with the Mellor-Yamada-Janjić turbulent parameterization) at high spatial resolution (1-km horizontal grid spacing) are conducted for each 31-day period. Modeled results show that the WRF Model (coupled to the Noah-MP land surface model) tends to underestimate to some extent surface skin temperature during daytime and overestimate nighttime values during the wintertime period. In the same way, the WRF Model tends to accurately reproduce the diurnal cycle of near-surface air temperature, including maximum and minimum temperatures, and wind speed during summertime, but notably overestimates nighttime near-surface air temperature during wintertime. This nighttime overestimation is especially remarkable with the Bougeault and Lacarrere turbulent scheme for both urban and rural areas. [ABSTRACT FROM AUTHOR]

Gridded climatic datasets with fine spatial resolution can potentially be used to depict the climatic characteristics across the complex topography of China. In this study, we collected records of monthly temperature at 1153 stations and precipitation at 1202 stations in China and neighboring countries to construct a monthly climate dataset in China with a 0.025° resolution (~ 2.5 km). The dataset, named LZU0025, was designed by Lanzhou University and used a partial thin plate smoothing method embedded in the ANUSPLIN software. The accuracy of LZU0025 was evaluated based on three aspects: (1) Diagnostic statistics from the surface fitting model during 1951–2011. The results indicate a low mean square root of generalized cross validation (RTGCV) for the monthly air temperature surface (1.06 °C) and monthly precipitation surface (1.97 mm1/2). The method used variable square root transformation for the spline surface fitting to reduce positive skewness in the measured precipitation values and no variable transformation in air temperature case. This indicates that the surface fitting models are accurate. (2) Error statistics of comparisons between interpolated monthly LZU0025 with the withholding of climatic data from 265 stations during 1951–2011. The results show that the predicted values closely tracked the real true values with values of mean absolute error (MAE) of 0.59 °C and 70.5 mm and standard deviation of the mean error (STD) of 1.27 °C and 122.6 mm. In addition, the monthly STDs exhibited a consistent pattern of variation with RTGCV. (3) Comparison with other datasets. This was done in two ways. The first was via comparison of standard deviation, mean, and time trend derived from all datasets to a reference dataset released by the China Meteorological Administration (CMA), using Taylor diagrams. The second was to compare LZU0025 with the station dataset in the Tibetan Plateau. Taylor diagrams show that the standard deviation derived from LZU had a higher correlation with that produced by the CMA (R = 0.76 for air temperature, and R = 0.96 for precipitation) compared to those from other datasets. The standard deviation for the index derived from LZU was more close to that induced from CMA, and the centered normalized root-mean-square difference for this index derived from LZU and CMA was lower. A similar superior performance of LZU was found in the comparison of mean and time trend derived from LZU and those from other datasets. LZU0025 had high correlation with the Coordinated Energy and Water Cycle Observation Project (CEOP)—Asian Monsoon Project (CAMP) Tibet surface meteorology station dataset for air temperature, despite a non-significant correlation for precipitation at a few stations. Based on this comprehensive analysis, we conclude that LZU0025 is a reliable dataset. LZU0025, which has a fine resolution, can be used to identify a greater number of climate types, such as tundra and subpolar continental, along the Himalayan Mountain. We anticipate that LZU0025 can be used for the monitoring of regional climate change and precision agriculture modulation under global climate change. [ABSTRACT FROM AUTHOR]

The objective was to evaluate vaginal temperature as a thermoregulatory response of Nellore heifers under different microclimatic conditions. The study was conducted during one year in an experimental area located at 54°37′W, 20°27′S, and 530 m of altitude in Brazil. Twenty-four Nellore females were reared in integrated crop-livestock-forestry systems with different shading patterns. The randomized complete block experimental design was utilized with a sub subdivided plots scheme, with plot corresponding to the production systems, the subplot to the season of the year, and the sub subplot, the hours of the day. To characterize the microclimate, data regarding air and black globe temperatures, and air relative humidity were collected and from them, temperature-humidity index was calculated. Vaginal temperature was obtained by using a bottom-type temperature logger (i-button) attached to an adapted intravaginal device. The black-globe temperature explained the variation on vaginal temperature. Increases on vaginal temperature were only observed, on average, two hours after the increase of black-globe temperature. The microclimatic conditions found in the systems, resulting from the various tree densities, modify the vaginal temperature in different degrees, demonstrating that the shading effect is not always accompanied by improvements in thermal comfort. The system with intermediate density showed a better microclimatic condition and, consequently, a lower increase in vaginal temperature. The interaction between air temperature, humidity and solar radiation resulted in adverse environmental conditions, however, Nellore heifers showed good adaptation to the environment. In conclusion, vaginal temperature is a good indicator to evaluate the thermoregulatory response in Nellore heifers. [ABSTRACT FROM AUTHOR]

Building interior temperatures are affected by the outdoor air temperature. Note that outdoor weather patterns are somewhat repetitive in nature and historical records of outdoor temperature are readily accessible; we present a data-driven iterative learning approach to improve the room temperature tracking performance over time. By comparing the short-term temperature forecast with the past data, chains of (nonconsecutive) days exhibiting similar outside temperature patterns can be identified. The corresponding building operation record (heat input and temperature output trajectories) may then be used in the iterative learning control (ILC) to update the input based on the past temperature tracking error. Multizone buildings are strictly passive from the heat input to temperature output in all zones. This property assures the convergence of the ILC iteration if the update gain is suitably bounded, without the need of an accurate model. This means that for each chain, the zone temperature deviation from the specified profile will converge to zero as the number of days in the chain grows (i.e., as more iterations of ILC are performed). Using a six-zone physical test bed with programmable ambient temperatures, we demonstrate the practicality of the proposed approach in multiple experimental trials. Additional longer-duration simulations are performed based on the actual temperature recorded in Orlando, FL, USA and New York, NY, USA over a two-year period. In all cases, ILC is shown to improve the tracking error in the presence of ambient temperature fluctuations. Note to Practitioners—This paper uses historic operating data and weather forecasting to improve the temperature tracking in multizone buildings. The proposed approach exploits the inherent passivity property of the building thermal model and, therefore, does not require the accurate model identification prior to implementation. As a result, this method may be conveniently retrofitted to any existing heating, ventilation, and air conditioning system, provided that the system is capable of adjusting the heat input (e.g., by controlling the supply air flow rate) and storing temperature sensor data. The algorithm uses weather prediction to match the upcoming ambient temperature variation with those experienced in the past. With the past operating data (control input and resulting temperature error) from the matched day, a feed-forward control is iteratively updated to improve the tracking performance. This paper includes the experimental results to demonstrate the efficacy of the approach in our test bed, and simulation examples to provide the insight into the real-world implementation. This approach may be extended to other external heat sources exhibiting repetitive patterns, e.g., occupancy, equipment, and solar input. [ABSTRACT FROM AUTHOR]

The article presents the ice phenology of Lake Ostrzyckie, which is a water body covering an area of 308 ha located in the central part of the Kashubian Lakeland, northern Poland. The analysis presented in the article is based on data from daily ice phenomena monitoring for the period of 1971–2010. Data including forms of lake ice, as well as the thickness of the ice cover were obtained from the Institute of Meteorology and Water. In order to present relations between the ice phenomena and air temperature the meteorological data from the Gdańsk University Limnological Station in Borucino were used. The article presents changes in the duration time of the ice seasons and changes in the ice cover duration time in relation to winter season (November–April) temperatures. The structure of the ice phenomena duration period observed on Lake Ostrzyckie consists of three different stages of ice cover formation. 94% of this time the lake is covered by permanent ice cover, the freezing period takes about 5%, and the break-up takes only 1% of the ice phenomena duration period. In general the ice phenomena in the lake can occur only in the years when the average air temperature in the winter is lower than 5.0°C, whereas the permanent ice cover is created when the average air temperature of the winter season is lower than 4.8°C. The maximum thickness of the ice cover is usually 23 cm, and the average is 14 cm. [ABSTRACT FROM AUTHOR]

Determination of the long wave (LW) atmospheric radiation (λ = 4–100 μm) is of great importance for several applications. In this study, the effect of screen level and upper air temperatures and water vapour contents on the LW radiation for clear skies has been investigated using 4 years of measurements conducted in Adelaide (34.9° S;130.6° E), South Australia. A two-variable model, which depends on the screen level temperature and vapour pressure, has been developed to calculate the LW radiation. The model can predict the hourly measured data with a correlation coefficient of 0.88, giving mean bias error (MBE) of 0.21 W/m2, root mean square error (RMSE) of 17.3 W/m2, and a mean percentage error (MPE) of − 0.11% with respect to hourly observations. The performance of the multilinear model was tested against a 1-year independent data set. In this case, the MBE, RMSE, MPE, and correlation coefficient were 0.94 W/m2, − 2.2 W/m2, 18.1 W/m2, − 0.5%, and 0.85, respectively. Using radiosonde data, the total atmospheric water content has been calculated and its effect on the LW radiation has been investigated. A simple model that uses the screen level temperature and the precipitable water vapour (PWV) has been proposed. The correlation coefficient, MBE, and RMSE for this model were 0.93, 0.08 W/m2, and 12.12 W/m2, respectively; an MPE of 0.09% was reported by the model. The predictions of ten well-known clear sky models have been evaluated against the measured LW radiation data. These models were, then, adjusted and their performances have been assessed. The impact of the atmospheric mean weighted temperature on the LW radiation was studied. We have found that this temperature has less of an effect on the LW atmospheric radiation than screen level temperature. Finally, the clear sky model which uses the screen level parameters has been adjusted to account for the effect of clouds on the LW radiation. The model shows an acceptable predictability for the LW radiation under all sky conditions. The MBE, RMSE, MPE, and correlation coefficient for this model were 0.51 W/m2, 22.5 W/m2, 0.2% and 0.80, respectively. [ABSTRACT FROM AUTHOR]

Arctic glaciers have been, and are predicted to remain, an important sea‐level rise contributor over the 20th and 21st centuries. However, multi‐annual observations of their basic meteorological characteristics, which drive melt processes, are very sparse, contributing to uncertainties in sea‐level rise projections. This paper presents a methodology to process and analyse 6 years (September 2011–September 2017) of glacier meteorology data collected by an automatic weather station on the ablation zone of a small valley glacier in Svalbard (~79°N). The study focuses on the microclimate of the study glacier and its differences with that of large glaciers in the region, namely slightly increased summer air temperature with steep near‐surface lapse rates, frequently superadiabatic, reduced wind speed and incoming shortwave radiation flux. These differences are likely related to the adjacent complex relief and non‐ice‐covered surfaces within the basin of the study glacier and, as such, pose a challenge for accurate simulation of alpine glacier microclimates in regional melt assessments in the Arctic. The processing routine applied in this paper might serve as a reference for future similar studies. [ABSTRACT FROM AUTHOR]

Abstract The Mars Science Laboratory (MSL) Rover Environmental Monitoring Station humidity instrument (REMS-H) onboard the Curiosity rover is measuring daily minimum water vapor mixing ratios (min vmr), the respective pre-dawn air temperatures (T), and vmr at 2200LT. These are displayed for nearly three martian years (sols 10–2003) and compared with adsorptive column model simulations. The model was initialized with MSL-observed local column water contents, optical depths and surface pressures from sols 230–1291, assuming the same annual cycle outside this period. The first two and a half MSL years present rather similar annual cycles in the REMS-H data, whereas from about sol 1800 onward the min vmr and T suddenly increase and the 2200LT vmr values get closer to the min vmr, indicating less depletion of water vapor during the nights. Model experiments with typical regolith (ground thermal inertia of 300 SI units and porosity of 30% for adsorption) match the observed min vmr and T relatively well for the first 2.5 years. However, from about sol 1800 onward, when Curiosity started to climb onto Mt. Sharp, simulations with higher thermal inertia of about 400 SI units and very low porosity of ~0.3%, suggesting exposed bedrock, provide a far better fit. Some other periods of bedrock- and dune-dominated ground can be detected from the REMS-H vmr and air-T data along the Curiosity traverse. Highlights • MSL REMS-H daily minimum vmr and T are shown for three martian years and compared with adsorptive column model simulations. • The first 2.5 MSL years display similar annual cycles, whereas from about sol 1800 onward min vmr and T suddenly increase. • Model with typical regolith matches the first 2.5 years of T and vmr relatively well. • Model with exposed bedrock matches T and min vmr relatively well from sol 1800 onward. • Shorter periods of bedrock- and dune-like ground can be detected from both the T- and vmr data along the Curiosity traverse. [ABSTRACT FROM AUTHOR]

Objective: Seasonal and weather changes may affect the development of pulmonary embolism. The effects of seasonal and meteorological factors on mortality are controversial. We aimed to investigate the effects of seasons and meteorological data on patients with a low and high pulmonary embolism severity index and those with 30-day mortality. Methods: Pulmonary embolism cases were included in our study. Daily pressure, humidity, temperature, and wind values were taken from the general online information by the Directorate of Meteorology. All cases were divided into the low-risk and high-risk group, using a simplified pulmonary embolism severity index. Results: Eighty-six patients diagnosed with pulmonary thromboembolism were included in the study. Comparison between the two groups revealed that the disease was detected during winter in the high-risk group (31.3%) and during summer in the low-risk group (59.1%) (p=0.011). No statistically significant results were found on the day of diagnosis and pressure, humidity, temperature, and wind averages on the 7th day based on the severity index (p>0.05). There were no statistically significant results detected when comparing the average air pressure, humidity, air temperature, season, and wind for mortality in 30 days (p>0.05). Conclusion: In the light of these data, it was determined that seasonal and meteorological factors had no effect on mortality in pulmonary embolism. [ABSTRACT FROM AUTHOR]

Remotely sensed sea-surface temperature (SST) retrievals with a significant positive bias during the occurrence of upwelling phenomena along the southeastern coast of Brazil were reported in our companion paper. As a result, this paper proposes an automated bias correction algorithm to improve the MODIS long-wave multichannel SST (MCSST) retrievals during the abovementioned conditions in this region. In this paper, MODIS daytime SST data (SSTMODIS) and differences between brightness temperatures in MODIS channels 31 and 32 (BT31 − BT32) are analyzed simultaneously with hourly wind surface conditions, in situ SST at 0.3 and 10 m in depth (SSTbuoy03 and SSTbuoy10), and sensible and latent heat fluxes from the Cabo Frio buoy data (at 23° S, 42° W) during 2014. The obtained results show that some upwelling events present air temperature ($T_{\mathrm {air}}$) greater than SSTbuoy03 and low-atmospheric water vapor content. A simultaneous occurrence of these factors during upwelling conditions may lead to a warm-skin layer effect and may cause BT31 to be greater than SSTbuoy03 and BT31 − BT32 to be small (−0.18 °C ± 0.22 °C), affecting the MCSST performance. The proposed bias correction algorithm uses a least-squares curve between SSTbuoy03 and SSTMODIS retrievals when BT31 − BT32 ≤ 0.5 °C (i.e., dry atmospheric conditions). The bias correction algorithm has significantly improved the SSTMODIS bias (RMSE) from 1.43 °C to −0.2 °C (1.60 °C to 0.58 °C) when applied to 22 cloud-free pixels of MODIS during January–March of 2015. [ABSTRACT FROM AUTHOR]

Estimating cloud motion with complex background through the sequence of satellite images plays an important role in weather forecasting. This motion can be used for characterization of clouds and predicting storms. Optical flow is used here for motion estimation which gives horizontal and vertical velocity components. Velocity field vector alone is not sufficient to analyze the cloud behavior for predicting extreme weather conditions and there is a need to develop some visual features for enhancing the weather prediction strategies. In this paper, we utilize the optical flow to localize the high alert regions. To have a better localization and motion signatures, we develop a fractional order technique to compute optical flow. Also, the localization is characterized by brightness of image, magnitude, directions, vorticity and irrotational components of the optical flow. We did analysis on sequence of images for Mumbai, India heavy rain that happened during August 28–29, 2017, cyclonic data sets for May 16, 2018, September 19–20, 2018 and October 14, 2018. Visual features show different patterns for extreme and normal weather situations. A study on interpolation and extrapolation of the image sequence is also presented. Optical flow based interpolation and Advection‐anisotropic‐diffusion based extrapolation model give promising results. [ABSTRACT FROM AUTHOR]

Air temperature data retrieved from global atmospheric models may show a systematic bias with respect to measurements from weather stations. This is a common concern in local climate studies. The current study presents two methods based upon copulas and Conditional Probability (CP) to predict bias-corrected mean air temperature in a data-scarce environment: CP-I offers a single conditional probability as a predictor, CP-II is a pixel-wise version of CP-I and offers spatially varying predictors. The methods were applied on daily air temperature in the Qazvin Plain, Iran that were collected at 24 weather stations and 150 ECMWF ERA-interim grid cells from a single month (June) over 11 years. We compared the methods with the commonly applied conditional expectation and conditional median methods. Leave-k-out cross-validation and correlation scores show that the new methods reduced the bias with 44–68% for the full data set and with 34–74% on a homogeneous subarea. We conclude that the two methods are able to locally improve ECMWF air temperatures in a data-scarce area. [ABSTRACT FROM AUTHOR]

A historical set of daily meteorological data collected at the Hanbury Botanical Gardens (Liguria, northwestern Italy) from 1900 to 1940 was recovered from five manually entered registers. They were digitised and statistically analysed to check their reliability and study their trends and variations. In particular, air temperature, precipitation and their extreme values were considered, together with wind direction, sea state, sky conditions and relative humidity. The results show a decreasing trend in mean annual temperature of approximately 0.2 °C/decade due to a decrease in maximum air temperature. Annual cumulative precipitation increased by 65.2 mm/decade over the study period. The data analysis showed a summer temperature decrease in 1912 and a severe drought in 1921. Moreover, the years with most days with extreme temperatures were associated to the negative phases of the North Atlantic oscillation (NAO). During the study period, SW winds were prevailing. Sky conditions followed seasonal trends, while slight sea was the most frequent sea state. [ABSTRACT FROM AUTHOR]

Surface incoming shortwave (solar) radiation data are an important component of many scientific analyses, but direct measurements are not commonly available. Estimates can be obtained from gridded meteorological analysis or reanalysis systems, such as the Global Data Assimilation Systems (GDAS) and Modern Era Retrospective Reanalysis System (MERRA-2), or calculated using empirical models dependent on meteorological variables such as air temperature. The purpose of this analysis was to compare multiple methods for estimating daily shortwave radiation in a tropical highland environment in Ethiopia. Direct solar radiation outputs of GDAS and MERRA-2, topographically corrected outputs of the two analysis systems, and empirically estimated solar radiation values calculated with the systems’ air temperature data were compared to see which produced the most reliable radiation values. GDAS appeared to underestimate the seasonal variability, resulting in low correlation (R2) with in situ data and large mean bias error (MBE). In comparison, MERRA-2 did not underestimate variability, but produced larger bias than the empirical model estimates. There was an improvement in correlation and reduction in MBE when using the GDAS air temperature predictions in the empirical model, but the opposite was true for MERRA-2. The empirical model using station air temperature data (stationT) produced the highest correlation across all four stations, with best performance at the lower elevation sites. The direct shortwave radiation outputs of MERRA-2 produced comparable correlation values, with larger R2 at stations at higher elevation. Topography possibly influenced these results, as MERRA-2 performed comparably to stationT at the stations in moderate terrain, but not in steeper terrain. This work can serve as a starting point for analyses in other tropical highland regions, where continuous in situ solar radiation data are rarely available. [ABSTRACT FROM AUTHOR]

Thermodynamics and electricity are parts of the 10th grade physics curriculum in Romania, but the exciting questions of atmospheric physics and meteorology could be answered if we organize special activities. Linking these topics, educators can create many interesting learning opportunities and try new ways of teaching. This paper is based on a school project and experiment that were used during the last school years in the classroom learning and practical outdoor activities with the Science Club students. The aim of the project is to build a device to measure atmospheric climate variables (e.g. air temperature, air pressure, humidity) and to demonstrate and explain some weather phenomenon. The observations are stored in a database, the data archive and visualization of the data are accessible through a webpage. Students from other schools can get involved in the measurements with their own built devices and can upload their own measurement data to the common database, so we could create a weather map for schools. The whole system is planned as a network of minimeteo stations for students. [ABSTRACT FROM AUTHOR]

Climatological changes in the environments of key meteorological parameters that affect Significant Tornado Days (SigTorDs) have been determined for two active tornado regions defined as Box a and Box ß, centered, respectively, over Oklahoma and Alabama and their respective environs. The North American Regional Reanalysis data was selected for 1980-2013, providing two successive 17-year periods corresponding to the last 34 years of previous research findings that focused on the aforementioned regions. This data record also corresponds to an increasing surface air temperature trend for the continental United States. Period I (1980-1996) and Period II (1997-2013) defined the years of changing environments for the two regions studied. Environmental parameters investigated and compared to SigTorDs were surface-based convective available potential energy (CAPE), storm relative helicity (0-3 km), bulk wind difference (1,000 mb to 500 mb), and lifted condensation level (LCL). Environments have changed to fewer SigTorDs (130 to 74) in Box a from Period I to Period II, dominated by decreasing frequency of storm relative helicity and slightly increasing CAPE. Box ß is characterized by more SigTorDs (94 to 119) in Period II with an environment dominated by increasing frequency of storm relative helicity and minimal change in CAPE. These results support the importance of the changes in storm relative helicity (as opposed to CAPE) in explaining the eastward shift in U.S. tornado activity. [ABSTRACT FROM AUTHOR]

Abstract Background Since the first reported human infection with an avian-origin influenza A (H7N9) virus in China in early 2013, there have been recurrent outbreaks of the virus in the country. Previous studies have shown that meteorological factors are associated with the risk of human infection with the virus; however, their possible nonlinear and lagged effects were not commonly taken into account. Method To quantify the effect of meteorological factors on the risk of human H7N9 infection, daily laboratory-confirmed cases of human H7N9 infection and meteorological factors including total rainfall, average wind speed, average temperature, average relative humidity, and sunshine duration of the 11 sub-provincial/prefecture cities in Zhejiang during the first four outbreaks (13 March 2013–30 June 2016) were analyzed. Separate models were built for the 6 sub-provincial/prefecture cities with the greatest number of reported cases using a combination of logistic generalized additive model and distributed lag nonlinear models, which were then pooled by a multivariate meta-regression model to determine their overall effects. Results According to the meta-regression model, for rainfall, the log adjusted overall cumulative odds ratio was statistically significant when log of rainfall was >4.0, peaked at 5.3 with a value of 12.42 (95% confidence intervals (CI): [3.23, 21.62]). On the other hand, when wind speed was 2.1–3.0 m/s or 6.3–7.1 m/s, the log adjusted overall cumulative odds ratio was statistically significant, peaked at 7.1 m/s with a value of 6.75 (95% CI: [0.03, 13.47]). There were signs of nonlinearity and lag effects in their associations with the risk of infection. Conclusion As rainfall and wind speed were found to be associated with the risk of human H7N9 infection, weather conditions should be taken into account when it comes to disease surveillance, allowing prompt actions when an outbreak takes place. Graphical abstract Unlabelled Image Highlights • Meteorological factors showed lagged and nonlinear effects on human H7N9 infection. • Wind speed and rainfall are significant independent factors. • Rainfall between 60.3 and 200.3 mm favoured human H7N9 infection. • Wind speed of 2.1–3.0 m/s and 6.3–7.1 m/s favoured human H7N9 infection. [ABSTRACT FROM AUTHOR]

Abstract Weighted mean temperature (T m) is a crucial parameter for retrieving precipitable water vapor (PWV) in Global Navigation Satellite System (GNSS) meteorology. The difference between the height of the GNSS receiver and the height of the derived T m may cause large uncertainty in the value of the retrieved PWV. In this study, we analyze the vertical variation of T m on a global scale and build a new vertical correction model using the operational reanalysis of the European Centre for Medium-Range Weather Forecasts (ECMWF). Another dataset from ECMWF and the radiosonde data derived from 678 globally distributed stations are used to independently validate the performance of the newly built model and demonstrate its superiority compared with the existing vertical correction method. The results show that the lapse rate of T m has geographic and seasonal variations, and the T m has large nonlinear variation along the vertical direction in the high-latitude regions, especially in the polar regions. A performance improvement of 15%–35% compared with the existing vertical correction method is achievable for the newly built model when tested with the ECMWF data, and the improvement is 10%–25% when tested with the radiosonde data. Highlights • The vertical variation in weighted mean temperature is investigated on a global scale. • A global empirical model for vertical correction of weighted mean temperature is built. • The performance of vertical correction methods for weighted mean temperature is comprehensively evaluated. [ABSTRACT FROM AUTHOR]

Abstract In this study, the particulate matter (PM 10) was studied over a six month period (from August 2017 to January 2018) at an urban and a suburban site (Akure, Ondo State, Nigeria). Three different sites were used, namely: Federal University of Technology, Akure (FUTA); the National Commission for Museum and Monuments, Akure, and Science and Education Development Institute (SEDInst), Oba Ile. The monthly PM 10 in these locations during the studied period ranged from 60.73 to 287.97 μm3 at FUTA, 96.1–175.63 μm3 for the National Commission for Museum and Monuments, Akure, while it ranged from 92.72 to 204.48 μm3 at SEDInst. The results were compared with national and international standards. It was observed that the PM 10 were above the recommended limits. Meteorological parameters (Rainfall, relative humidity (RH), temperature, precipitation, wind speed and direction) were obtained from a meteorological center and analyzed statistically. The essence in the study was to find out their relationship with PM 10. The value of R of the PM 10 samples had positive correlation with rain (r = 0.561, 0.820, 0.408), RH (r = 0.487, 0.799, 0.318), and precipitation (r = 0.388, 0.486, 0.784). There were strong correlations between RH and temperature (r = 0.741) and RH and wind (r = 0.977). This study suggested that individual meteorological factors had effects on PM 10 concentration. Highlights • The particulate matter (PM 10) was studied over a six month period. • Using a Sharp-Cut Inertial Filter Combined With an Impactor. • The results were compared with national and international standards. • There were strong correlations between RH and temperature. • Meteorological factors had effects on PM 10 concentration. [ABSTRACT FROM AUTHOR]

Abstract The paper presents four cases of the response of near-surface O 3 , NO x and CH 4 to thunderstorm activity at a tropical coastal site in India, occurred during pre-monsoon and post-monsoon seasons. The associated changes in boundary layer meteorology in terms of temperature, relative humidity (RH), wind and boundary layer height (BLH) are also examined. The meteorological parameters like temperature and RH respond very fast depicting changes during the initial phase of the thunderstorm activity, whereas a delayed response is observed in the wind direction (changing from the sea breeze to land breeze) and collapse of the BLH (1–1:30 h delay) at the mature phase of the activity. Associated with the thunderstorm activity, significant changes were observed in the mixing ratios of the near-surface O 3 , NO x and CH 4. An increase in NO x (5.2–8.7 ppbv) and CH 4 (36.7–134.6 ppbv) and reduction in O 3 (9.9–18.8 ppbv) were recorded at the mature phase of the thunderstorm. The rise in NO x is attributed to the combined effect of thunderstorm activity and wind reversal whereas that in CH 4 is attributed to the wind reversal and associated change in air mass. A post activity increase is observed in mixing ratio of O 3 in all the four cases. This is attributed to the downdrafts characteristic of the dissipation phase of thunderstorm activity which brings in O 3 rich upper air down. Highlights • Immediate response of the meteorological parameters -temperature and relative humidity- to thunderstorm activity. • Delayed response of wind direction and boundary layer height to thunderstorm activity. • Increase of the NO x and CH 4 and reduction in O 3 mixing ratio associated with the thunderstorm activity. • A post activity increase is near-surface mixing ratio of O 3. [ABSTRACT FROM AUTHOR]

This study introduces a new simplified Penman formulae for estimating reference evapotranspiration, ET 0 (mm/d). The new formulae require maximum and minimum air temperatures and relative humidity or in addition local average wind speed. The new ET 0 formulae is produced by simplifications to the previously developed by Valiantzas (2006, 2013a) simplified Penman expression that requires a complete set of meteorological data. A calibration based on measurements obtained from a global data base is used for the derivation of the formula. In addition, a relationship previously proposed by the author, connecting solar radiation to temperature and humidity is used. The new formula requiring maximum and minimum temperatures, T max ( o C) and T min ( o C) respectively, and humidity data, RH (%) is: ET 0  = 0.0118(1- RH /100) 0.2 ( T max - T min ) 0.3 ( R a ( T +10) 0.5 -40)+0.1( T +20)(1- RH /100) where T = ( T max + T min )/2 is the average temperature ( o C), and R a is the extraterrestial radiation (MJ/m 2 /d). The new formula is applicable for stations under well-watered conditions. An extension of the new formula incorporating local average wind value as an additional input is also proposed. The performance of the new derived formula is tested with daily weather data obtained from 32 stations in California, Florida, Arizona and Greece. The proposed formula is not as generalized as the Hargreaves-Samani equation requiring only widely available T max and T min , as it is restricted to locations where good quality RH data is measured. However, incorporating RH as additional input to the suggested T - RH formula increases dramatically the accuracy of the ET 0 estimation compared to the Hargreaves-Samani method. It is concluded that the proposed formula is more accurate than Hargreaves-Samani by 46%. In addition, the T - RH formula performed better than the reduced-set FAO-56 Penman-Monteith method that required T and RH by 21%. The results of the formula are further improved when local average wind speed is incorporated as an additional input. [ABSTRACT FROM AUTHOR]