Warm and Cold Waves in South-Eastern (V) Bioclimatic Region in Years 1981-2010 / Fale ciepła i fale chłodu w południowo-wschodnim (V) regionie bioklimatycznym w latach 1981–2010 / Fale ciepła i fale chłodu w południowo-wschodnim (V) regionie bioklimatycznym w latach 1981–2010

AbstractLublin and Roztocze regions are placed in bioclimatic division (by T. Kozłowska-Szczęsna) in 5th south-eastern region. This area can be characterized by a high number of days with high air temperature (Kozłowska-Szczęsna et al., 1997) and by highest number of frost days in Poland (Błażejczyk, Kunert 2011). In this region, there is high frequency of cold spells; an occurrence, which can last over 15 days (Kuchcik et al., 2013). In this paper, warm and cold waves are calculated by method elaborated by Wibig (2007), where waves are determined by maximum air temperature (warm and cold days) and minimum air temperature (warm and cold nights) based on standard deviation from the average, expressed in standard deviation. Days, where air temperature was higher than average by more than 1.28 standard deviation was regarded as very warm, and those with lower air temperature than average by more than 1.28 standard deviation was regarded as very cold (Wibig 2007). For the purpose of this research, data from stations Lublin-Radawiec, Zamość and Tomaszów Lubelski were used, for the 1981-2010 period. During that time, short (3-5 days) waves of warm days occurred slightly more often than for waves of cold days, but in case of long waves (11-20 days) cold waves dominated, which is very characteristic for south-eastern (V) bioclimatic region. The waves of cold days were particularly long at Tomaszów Lubelski and Zamość stations. The average number of short (3-5 days) cold waves (night) on examined stations of south-eastern bioclimatic region was 3-4 waves per year and this was more than average number of short warm waves (night), which fluctuated between 2 to 3 waves per year (inversely to the case of waves of warm days). In the first decade of 21st century, the decrease in number of cold days is visible, but number of warm nights has increased during that time.

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Increase in Air Temperature in The Region of South Sumatra Province as the Indicator of Global Warming and the Effect on Transpiration of Lansium domesticum Corr.

10.31220/osf.io/jrf35 ◽

2019 ◽

Author(s):

Ari Sugiarto ◽

Hanifa Marisa ◽

Sarno

Keyword(s):

Global Warming ◽

Air Temperature ◽

Transpiration Rate ◽

Temperature Increase ◽

Secondary Data ◽

Temperature Data ◽

Randomized Design ◽

Maximum Air Temperature ◽

Lansium Domesticum ◽

Minimum Air Temperature

Abstract Global warming is one of biggest problems faced in the 21st century. One of the impacts of global warming is that it can affect the transpiration rate of plants that °Ccur. This study purpose to see how much increase in air temperature that occurred in the region of South Sumatra Province and to know the effect of increase in ari temperature in the region of South Sumatra Province on transpiration rate of Lansium domesticum Corr. This study used a complete randomized design with 9 treatments (22.9 °C, 23.6 °C, 24.6 °C, 26.3 °C, 27 °C, 27.8 °C, 31.7 °C, 32.5 °C, and 32.9 °C) and 3 replications. Air temperature data as secondary data obtained from the Meteorology, Climatology and Geophysics Agency (MCGA) Palembang Climatology Station in South Sumatra Province. The measurement of transpiration rate is done by modified potometer method with additional glass box. The data obtained are presented in the form of tables and graphs. Transpiration rate (mm3/g plant/hour) at temperture 22.9 °C = 4.37, 23.6 °C = 7.03, 24.6 °C = 8.03, 26.3 °C = 10.11, 27 °C = 13.13, 27.8 °C = 17.87, 31.7 °C = 23.21, 32.5 °C= 25.45 and 32.9 °C= 27.24. At the minimum air temperature in the region of South Sumatra Province there is increase in air temperature of 1.5 °C, average daily air temperature increase 1.3 °C and maximum air temperature increase 1.2 °C.

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Effect of meteorological factors on influenza-like illness from 2012 to 2015 in Huludao, a northeastern city in China

PeerJ ◽

10.7717/peerj.6919 ◽

2019 ◽

Vol 7 ◽

pp. e6919 ◽

Cited By ~ 3

Author(s):

Ying-Long Bai ◽

De-Sheng Huang ◽

Jing Liu ◽

De-Qiang Li ◽

Peng Guan

Keyword(s):

Relative Humidity ◽

Vapor Pressure ◽

Air Temperature ◽

Meteorological Factors ◽

Meteorological Data ◽

Lag Time ◽

Temperature Minimum ◽

The Relationship ◽

Maximum Air Temperature ◽

Minimum Air Temperature

Background This study aims to describe the epidemiological patterns of influenza-like illness (ILI) in Huludao, China and seek scientific evidence on the link of ILI activity with weather factors. Methods Surveillance data of ILI cases between January 2012 and December 2015 was collected in Huludao Central Hospital, meteorological data was obtained from the China Meteorological Data Service Center. Generalized additive model (GAM) was used to seek the relationship between the number of ILI cases and the meteorological factors. Multiple Smoothing parameter estimation was made on the basis of Poisson distribution, where the number of weekly ILI cases was treated as response, and the smoothness of weather was treated as covariates. Lag time was determined by the smallest Akaike information criterion (AIC). Smoothing coefficients were estimated for the prediction of the number of ILI cases. Results A total of 29, 622 ILI cases were observed during the study period, with children ILI cases constituted 86.77%. The association between ILI activity and meteorological factors varied across different lag periods. The lag time for average air temperature, maximum air temperature, minimum air temperature, vapor pressure and relative humidity were 2, 2, 1, 1 and 0 weeks, respectively. Average air temperature, maximum air temperature, minimum air temperature, vapor pressure and relative humidity could explain 16.5%, 9.5%, 18.0%, 15.9% and 7.7% of the deviance, respectively. Among the temperature indexes, the minimum temperature played the most important role. The number of ILI cases peaked when minimum temperature was around −13 °C in winter and 18 °C in summer. The number of cases peaked when the relative humidity was equal to 43% and then began to decrease with the increase of relative humidity. When the humidity exceeded 76%, the number of ILI cases began to rise. Conclusions The present study first analyzed the relationship between meteorological factors and ILI cases with special consideration of the length of lag period in Huludao, China. Low air temperature and low relative humidity (cold and dry weather condition) played a considerable role in the epidemic pattern of ILI cases. The trend of ILI activity could be possibly predicted by the variation of meteorological factors.

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Multi-Model Projections of Climate Change in Different RCP Scenarios in an Arid Inland Region, Northwest China

Water ◽

10.3390/w11020347 ◽

2019 ◽

Vol 11 (2) ◽

pp. 347 ◽

Cited By ~ 12

Author(s):

Ruotong Wang ◽

Qiuya Cheng ◽

Liu Liu ◽

Churui Yan ◽

Guanhua Huang

Keyword(s):

Climate Change ◽

River Basin ◽

Air Temperature ◽

Northwest China ◽

Heihe River Basin ◽

Heihe River ◽

Rcp Scenarios ◽

Maximum Air Temperature ◽

The Heihe River Basin ◽

Minimum Air Temperature

Based on three IPCC (Intergovernmental Panel on Climate Change) Representative Concentration Pathway (RCP) scenarios (RCP2.6, RCP4.5, and RCP8.5), observed meteorological data, ERA-40 reanalysis data, and five preferred GCM (general circulation model) outputs selected from 23 GCMs of CMIP5 (Phase 5 of the Coupled Model Intercomparison Project), climate change scenarios including daily precipitation, maximum air temperature, and minimum air temperature from 2021 to 2050 in the Heihe River basin, which is the second largest inland river basin in Northwest China, were generated by constructing a statistical downscaling model (SDSM). Results showed that the SDSM had a good prediction capacity for the air temperature in the Heihe River basin. During the calibration and validation periods from 1961 to 1990 and from 1991 to 2000, respectively, the coefficient of determination (R2) and the Nash–Sutcliffe efficiency coefficient (NSE) were both larger than 0.9, while the root mean square error (RMSE) was within 20%. However, the SDSM showed a relative lower simulation efficiency for precipitation, with R2 and NSE values of most meteorological stations reaching 0.5, except for stations located in the downstream desert areas. Compared with the baseline period (1976–2005), changes in the annual mean precipitation simulated by different GCMs during 2021–2050 showed great difference in the three RCP scenarios, fluctuating from −10 to +10%, which became much more significant at seasonal and monthly time scales, except for the consistent decreasing trend in summer and increasing trend in spring. However, the maximum and minimum air temperature exhibited a similar increasing tendency during 2021–2050 in all RCP scenarios, with a higher increase in maximum air temperature, which increased as the CO2 concentration of the RCP scenarios increased. The results could provide scientific reference for sustainable agricultural production and water resources management in arid inland areas subject to climate change.

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Evaluasi Data TerraClimate Dalam Mengestimasi Suhu Udara Di Provinsi Papua Barat

Rona Teknik Pertanian ◽

10.17969/rtp.v14i1.19241 ◽

2021 ◽

Vol 14 (1) ◽

pp. 15-25

Author(s):

Baso Daeng ◽

Arif Faisol

Keyword(s):

Air Temperature ◽

Research Unit ◽

Mean Square ◽

Data Recording ◽

Climate Research ◽

West Papua ◽

Weather Stations ◽

Papua Barat ◽

Maximum Air Temperature ◽

Minimum Air Temperature

Abstrak. Terra Climate merupakan seperangkat data iklim yang mengkombinasikan antara data WorldClim, Climate Research Unit (CRU), dan Japanese 55-year Reanalysis (JRA 55). TerraClimate menyediakan data iklim bulanan tahun 1958 – 2019 pada resolusi spasial ~4 km. Penelitian ini bertujuan untuk mengevaluasi data TerraClimate dalam mengestimasi suhu udara di Provinsi Papua Barat. Data yang digunakan pada penelitian ini adalah data TerraClimate dan data suhu udara perekaman tahun 1996 – 2019 yang diperoleh dari automatic weather stations (AWS) Rendani – Kabupaten Manokwari, AWS Jefman – Kabupaten Raja Ampat, AWS Torea – Kabupaten Fakfak, dan AWS Kaimana – Kabupaten Kaimana. Data TerraClimate dievaluasi dengan dibandingkan data AWS menggunakan metode point to pixel berdasarkan 5 (lima) parameter statistik, yaitu mean error (ME), root mean square error (RMSE), relative bias (RBIAS), percent bias (PBIAS), dan koefisien korelasi Pearson (r). Hasil penelitian menunjukkan bahwa data TerraClimate cenderung overestimated dalam mengestimasi suhu udara minimum bulanan dan cenderung underestimated dalam mengestimasi suhu udara maksimum bulanan di Provinsi Papua Barat. Namun TerraClimate memiliki akurasi yang sangat baik dalam mengestimasi suhu udara bulanan di Provinsi Papua Barat dengan nilai ME= 0,87 oC, RMSE = 1,19 oC, RBIAS = 0,04, dan PBIAS = 3,71 dalam mengestimasi suhu udara minimum, dan ME = 0,54 oC, RMSE = 0,88 oC, RBIAS = 0,02, dan PBIAS = 1,79 dalam mengestimasi suhu udara maksimum. Disamping itu TerraClimate memiliki korelasi yang sedang terhadap data AWS nilai r = 0,40 - 0,68. Sehingga TerraClimate dapat digunakan sebagai solusi alternatif untuk penyedia data suhu udara di Provinsi Papua Barat.An Evaluation of TerraClimate Data in Estimating Monthly Air Temperature in West PapuaAbstract. TerraClimate is a climate dataset that combines WorldClim data, Climate Research Unit (CRU) data, and Japanese 55-year Reanalysis (JRA 55) data at ~4 km spatial resolution. TerraClimate provides monthly climate data from 1958 to recent years. This research aims to evaluate the TerraClimate data in estimating monthly air temperature in West Papua compared with automatic weather stations (AWS) data recording. The data used in this research are TerraClimate data and AWS data recording from 1996 to 2019 obtained from AWS Rendani – Manokwari, AWS Jefman – Raja Ampat, AWS Torea – Fakfak, and AWS Kaimana – Kaimana. TerraClimate data were evaluated using the Point to Pixel method based on 5 (five) statistical parameters i.e., mean error (ME), root mean square error (RMSE), relative bias (RBIAS), percent bias (RBIAS), and Pearson correlation coefficient (r). The research showed that TerraClimate is overestimated in estimating monthly minimum air temperature and underestimated in estimating monthly maximum air temperature in West Papua. However, TerraClimate and has very good accuracy in estimating the monthly temperature in West Papua with ME = 0.87 oC, RMSE = 1.19 oC, RBIAS = 0.04, and PBIAS = 3.71 in estimating monthly minimum air temperature, and ME=0.54 oC, RMSE = 0.88 oC, RBIAS = 0.02, PBIAS = 1.79 in estimating monthly maximum air temperature. Besides, TerraClimate data has a moderate correlation with AWS data in estimating monthly air temperature with r= 0.40 - 0.68. Therefore, TerraClimate can be used as an alternative solution for providing air temperature data in West Papua.

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Trend Analysis and Variability of Air Temperature and Rainfall in Regional River Basins

Civil Engineering Journal ◽

10.28991/cej-2021-03091692 ◽

2021 ◽

Vol 7 (5) ◽

pp. 816-826

Author(s):

Benjamin Nnamdi Ekwueme ◽

Jonah Chukwuemeka Agunwamba

Keyword(s):

Water Resources ◽

Trend Analysis ◽

Air Temperature ◽

Temporal Trends ◽

Kendall Test ◽

River Basins ◽

Annual Rainfall ◽

Eastern Region ◽

South Eastern ◽

Mann Kendall Test

Global warming and climate variability are emerging as the foremost environmental problems in the 21st century, especially in developing countries. Full knowledge of key climate change variables is crucial in managing water resources in river basins. This study examines the variability of air temperature and rainfall in the five states of South-Eastern region of Nigeria, using the trend analysis approach. For this purpose, temporal trends in annual rainfall and temperature were detected using non-parametric Mann-Kendall test at 5% significance level. The time series rainfall and temperature data for the period 1922-2008 were analyzed statistically for each state separately. The results of Mann Kendall test showed that there is trend in rainfall in all the capital cities in South-East except Owerri and Awka. It is also observed that the trend of rainfall is decreasing for all the study areas in South-East with the lowest trend rate of -0.1153 mm rainfall occurring in Umuahia. In the case of air temperature, it is observed that the trend is increasing for all the study areas in South-East with the highest trend rate of 0.04698 oC/year occurring in Enugu. These findings provide valuable information for assessing the influence of changes on air temperature and rainfall on water resources and references for water management in the South-Eastern river basin of Nigeria. It also proved that Mann-Kendall technique is an effective tool in analyzing temperature and rainfall trends in a regional watershed. Doi: 10.28991/cej-2021-03091692 Full Text: PDF

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An attempt to assess the results of air temperature measurements from automatic weather stations in comparison to glass thermometer measurements in the context of weather types

Bulletin of Geography Physical Geography Series ◽

10.1515/bgeo-2015-0017 ◽

2015 ◽

Vol 9 (1) ◽

pp. 67-79 ◽

Cited By ~ 1

Author(s):

Grzegorz Urban

Keyword(s):

Air Temperature ◽

Temperature Measurements ◽

Normal Range ◽

Weather Types ◽

Accuracy Of Measurements ◽

Weather Stations ◽

Mean Differences ◽

Annual Scale ◽

Maximum Air Temperature ◽

Minimum Air Temperature

Abstract In the article, an attempt was undertaken to compare the results of air temperature measurements made using automatic weather stations (AWS) to those of glass thermometers. The analysis considered the aspect of weather types. On the basis of simultaneous measurements carried out with the use of AWS and glass thermometers, the accuracy of measurements for 6 synoptic stations of IMGW-PIB was assessed. The stations represented the Lower and Opole Silesia regions. Mean differences in mean monthly and seasonal air temperature values (T) between AWS and glass results are not high. They are equal to ±0.1°C, only rarely reaching −0.2°C. In cold seasons and in particular months as well, they are negative. On an annual scale, differences hardly ever occur. No connection between mean difference for mean air temperature and weather types was found. The values of mean differences for mean monthly and seasonal maximum air temperature (Tx) are equal to ±0.1°C (except Śnieżka). The differences for T and Tx are of low significance, being within the normal range. Mean differences for mean monthly and seasonal minimum air temperature (Tn) are usually positive. In warm seasons they can reach 1.1°C. In the case of most of the stations under consideration, for positive differences for Tn, an increase in average (from +0.1°C to +0.5°C) and high (+0.5°C to +1.0°C) differences is noticed. The only exceptions are the Śnieżka and Opole stations. The differences for each category are not regular, therefore no universal corrections can be implemented.

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Reliability of the Minimum Masking Level as Outcome Variable in Tinnitus Clinical Research

American Journal of Audiology ◽

10.1044/2020_aja-20-00047 ◽

2020 ◽

Vol 29 (3) ◽

pp. 429-435

Author(s):

Patricia C. Mancini ◽

Richard S. Tyler ◽

Hyung Jin Jun ◽

Tang-Chuan Wang ◽

Helena Ji ◽

...

Keyword(s):

Standard Deviation ◽

High Frequency ◽

Pearson Correlation ◽

Correlation Coefficients ◽

Outcome Variable ◽

Reliable Estimate ◽

Moderate Correlation ◽

Trained Subjects ◽

The Relationship ◽

Minimum Intensity

Purpose The minimum masking level (MML) is the minimum intensity of a stimulus required to just totally mask the tinnitus. Treatments aimed at reducing the tinnitus itself should attempt to measure the magnitude of the tinnitus. The objective of this study was to evaluate the reliability of the MML. Method Sample consisted of 59 tinnitus patients who reported stable tinnitus. We obtained MML measures on two visits, separated by about 2–3 weeks. We used two noise types: speech-shaped noise and high-frequency emphasis noise. We also investigated the relationship between the MML and tinnitus loudness estimates and the Tinnitus Handicap Questionnaire (THQ). Results There were differences across the different noise types. The within-session standard deviation averaged across subjects varied between 1.3 and 1.8 dB. Across the two sessions, the Pearson correlation coefficients, range was r = .84. There was a weak relationship between the dB SL MML and loudness, and between the MML and the THQ. A moderate correlation ( r = .44) was found between the THQ and loudness estimates. Conclusions We conclude that the dB SL MML can be a reliable estimate of tinnitus magnitude, with expected standard deviations in trained subjects of about 1.5 dB. It appears that the dB SL MML and loudness estimates are not closely related.

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Changes in Air Temperature and Snow Cover in Winter in Poland

Atmosphere ◽

10.3390/atmos12010068 ◽

2021 ◽

Vol 12 (1) ◽

pp. 68

Author(s):

Arkadiusz M. Tomczyk ◽

Ewa Bednorz ◽

Katarzyna Szyga-Pluta

Keyword(s):

Snow Cover ◽

Air Temperature ◽

Winter Season ◽

Primary Objective ◽

Climatic Conditions ◽

Cover Depth ◽

North Eastern ◽

Northern Regions ◽

Maximum Air Temperature ◽

Snow Cover Depth

The primary objective of the paper was to characterize the climatic conditions in the winter season in Poland in the years 1966/67–2019/20. The study was based on daily values of minimum (Tmin) and maximum air temperature (Tmax), and daily values of snow cover depth. The study showed an increase in both Tmin and Tmax in winter. The most intensive changes were recorded in north-eastern and northern regions. The coldest winters were recorded in the first half of the analyzed multiannual period, exceptionally cold being winters 1969/70 and 1984/85. The warmest winters occurred in the second half of the analyzed period and among seasons with the highest mean Tmax, particularly winters 2019/20 and 1989/90 stood out. In the study period, a decrease in snow cover depth statistically significant in the majority of stations in Poland was determined, as well as its variability both within the winter season and multiannual.

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Spring Frost Damage to Tea Plants Can Be Identified with Daily Minimum Air Temperatures Estimated by MODIS Land Surface Temperature Products

Remote Sensing ◽

10.3390/rs13061177 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1177

Author(s):

Peijuan Wang ◽

Yuping Ma ◽

Junxian Tang ◽

Dingrong Wu ◽

Hui Chen ◽

...

Keyword(s):

Surface Temperature ◽

Air Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Frost Damage ◽

Estimation Model ◽

Spring Frost ◽

Tea Plants ◽

Elevation Model ◽

Minimum Air Temperature

Tea (Camellia sinensis) is one of the most dominant economic plants in China and plays an important role in agricultural economic benefits. Spring tea is the most popular drink due to Chinese drinking habits. Although the global temperature is generally warming, spring frost damage (SFD) to tea plants still occurs from time to time, and severely restricts the production and quality of spring tea. Therefore, monitoring and evaluating the impact of SFD to tea plants in a timely and precise manner is a significant and urgent task for scientists and tea producers in China. The region designated as the Middle and Lower Reaches of the Yangtze River (MLRYR) in China is a major tea plantation area producing small tea leaves and low shrubs. This region was selected to study SFD to tea plants using meteorological observations and remotely sensed products. Comparative analysis between minimum air temperature (Tmin) and two MODIS nighttime land surface temperature (LST) products at six pixel-window scales was used to determine the best suitable product and spatial scale. Results showed that the LST nighttime product derived from MYD11A1 data at the 3 × 3 pixel window resolution was the best proxy for daily minimum air temperature. A Tmin estimation model was established using this dataset and digital elevation model (DEM) data, employing the standard lapse rate of air temperature with elevation. Model validation with 145,210 ground-based Tmin observations showed that the accuracy of estimated Tmin was acceptable with a relatively high coefficient of determination (R2 = 0.841), low root mean square error (RMSE = 2.15 °C) and mean absolute error (MAE = 1.66 °C), and reasonable normalized RMSE (NRMSE = 25.4%) and Nash–Sutcliffe model efficiency (EF = 0.12), with significantly improved consistency of LST and Tmin estimation. Based on the Tmin estimation model, three major cooling episodes recorded in the "Yearbook of Meteorological Disasters in China" in spring 2006 were accurately identified, and several highlighted regions in the first two cooling episodes were also precisely captured. This study confirmed that estimating Tmin based on MYD11A1 nighttime products and DEM is a useful method for monitoring and evaluating SFD to tea plants in the MLRYR. Furthermore, this method precisely identified the spatial characteristics and distribution of SFD and will therefore be helpful for taking effective preventative measures to mitigate the economic losses resulting from frost damage.

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