scholarly journals Homogeneity Test and Correction of Daily Temperature and Precipitation Data (1978–2015) in North China

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Lingling Shen ◽  
Li Lu ◽  
Tianjie Hu ◽  
Runsheng Lin ◽  
Ji Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
North China ◽  
Maximum Temperature ◽  
Homogeneity Test ◽  
Climate Data ◽  
Average Temperature ◽  
Temperature And Precipitation ◽  
Daily Data ◽  
Before And After ◽  
Annual Average Temperature

Homogeneity of climate data is the basis for quantitative assessment of climate change. By using the MASH method, this work examined and corrected the homogeneity of the daily data including average, minimum, and maximum temperature and precipitation during 1978–2015 from 404/397 national meteorological stations in North China. Based on the meteorological station metadata, the results are analyzed and the differences before and after homogenization are compared. The results show that breakpoints are present pervasively in these temperature data. Most of them appeared after 2000. The stations with a host of breakpoints are mainly located in Beijing, Tianjin, and Hebei Province, where meteorological stations are densely distributed. The numbers of breakpoints in the daily precipitation series in North China during 1978–2015 also culminated in 2000. The reason for these breakpoints, called inhomogeneity, may be the large-scale replacement of meteorological instruments after 2000. After correction by the MASH method, the annual average temperature and minimum temperature decrease by 0.04°C and 0.06°C, respectively, while the maximum temperature increases by 0.01°C. The annual precipitation declines by 0.96 mm. The overall trends of temperature change before and after the correction are largely consistent, while the homogeneity of individual stations is significantly improved. Besides, due to the correction, the majority series of the precipitation are reduced and the correction amplitude is relatively large. During 1978–2015, the temperature in North China shows a rise trend, while the precipitation tends to decrease.

scholarly journals Statistical Downscaling and Projection of Future Temperature and Precipitation Change in Gandaki Basin

2021 ◽  
Vol 26 (1) ◽  
pp. 16-27
Author(s):  
Dibas Shrestha ◽  
Shankar Sharma ◽  
Sandeep Bhandari ◽  
Rashila Deshar
Keyword(s):  
Statistical Downscaling ◽  
Minimum Temperature ◽  
Large Scale ◽  
Climate Model ◽  
Global Climate Model ◽  
Maximum Temperature ◽  
Climate Data ◽  
Temperature And Precipitation ◽  
National Centre ◽  
Spatial Consistency

Understanding the present and future spatial and temporal variations of precipitation and temperature is important for monitoring climate-induced disasters. Satellite and global reanalysis data can provide evenly distributed climate data; however, they are still too coarse to resolve fundamental processes over complex terrains. The study applies global climate model CGCM4/CANESM2, to project future maximum temperature, minimum temperature, and precipitation across the cross-section of the Gandaki River basin, Nepal. Large scale atmospheric variables of the National Centre for Environmental Prediction/National Centre for Atmospheric Research reanalysis (NCEP/NCAR) datasets are downscaled using Statistical Downscaling Model (SDSM) under different emission scenarios. For the variability and changes in maximum temperature (Tmax), minimum temperature (Tmin), and precipitation for future periods (2020s, 2050s, and 2080s), three different scenarios RCP2.6, RC4.5, and RCP8.5 of CGCM4 model were performed. The study revealed that both the temperature and precipitation would increase for three RCPs (representative concentration pathways) in the future. The highest increase in precipitation was found in the arid region compared to humid and sub-humid regions by the end of 2100. Similarly, the increase in mean monthly Tmin and Tmax was more pronounced in Jomsom station than Baglung and Dumkauli stations. Overall, a decrease in summer temperature and increase in winter temperature was expected for future periods across all regions. Further, spatial consistency was observed for Tmax and Tmin, whereas spatial consistency was not found for precipitation.

scholarly journals Trends in compound extremes of air temperature and precipitation in Eastern Europe

2021 ◽  
Author(s):  
Elena Vyshkvarkova ◽  
Olga Sukhonos
Keyword(s):  
Eastern Europe ◽  
North Atlantic Oscillation ◽  
Air Temperature ◽  
North Atlantic ◽  
Large Scale ◽  
The North ◽  
Temperature And Precipitation ◽  
Atmosphere System ◽  
Daily Data ◽  
The North Atlantic Oscillation

Abstract The spatial distribution of compound extremes of air temperature and precipitation was studied over the territory of Eastern Europe for the period 1950–2018 during winter and spring. Using daily data on air temperature and precipitation, we calculated the frequency and trends of the four indices – cold/dry, cold/wet, warm/dry and warm/wet. Also, we studying the connection between these indices and large-scale processes in the ocean-atmosphere system such as North Atlantic Oscillation, East Atlantic Oscillation and Scandinavian Oscillation. The results have shown that positive trends in the region are typical of the combinations with the temperatures above the 75th percentile, i.e., the warm extremes in winter and spring. Negative trends were obtained for the cold extremes. Statistically significant increase in the number of days with warm extremes was observed in the northern parts of the region in winter and spring. The analysis of the impacts of the large-scale processes in oceans-atmosphere system showed that the North Atlantic Oscillation index has a strong positive and statistically significant correlation with the warm indices of compound extremes in the northern part of Eastern Europe in winter, while the Scandinavian Oscillation shows the opposite picture.

Creation of a representative climatological database for Hungary from 1870 to 2020

2021 ◽  
Author(s):  
Beatrix Izsák ◽  
Mónika Lakatos ◽  
Rita Pongrácz ◽  
Tamás Szentimrey ◽  
Olivér Szentes
Keyword(s):  
Time Series ◽  
Data Series ◽  
Data Sets ◽  
Average Temperature ◽  
Temperature And Precipitation ◽  
Innovation Fund ◽  
Daily Data ◽  
Observation Network ◽  
Meteorological Elements ◽  
Climate Studies

<p>Climate studies, in particular those related to climate change, require long, high-quality, controlled data sets that are representative both spatially and temporally. Changing the conditions in which the measurements were taken, for example relocating the station, or a change in the frequency and time of measurements, or in the instruments used may result in an fractured time series. To avoid these problems, data errors and inhomogeneities are eliminated for Hungary and data gaps are filled in by using the MASH (Multiple Analysis of Series for Homogenization, Szentimrey) homogenization procedure. Homogenization of the data series raises the problem that how to homogenize long and short data series together within the same process, since the meteorological observation network was upgraded significantly in the last decades. It is possible to solve these problems with the method MASH due to its adequate mathematical principles for such purposes. The solution includes the synchronization of the common parts’ inhomogeneities within three (or more) different MASH processing of the three (or more) datasets with different lengths. Then, the homogenized station data series are interpolated to the whole area of Hungary, to a 0.1 degree regular grid. For this purpose, the MISH (Meteorological Interpolation based on Surface Homogenized Data Basis; Szentimrey and Bihari) program system is used. The MISH procedure was developed specifically for the interpolation of various meteorological elements. Hungarian time series of daily average temperature and precipitation sum for the period 1870-2020 were used in this study, thus providing the longest homogenized, gridded daily data sets in the region with up-to-date information already included.</p><p><em>Supported by the ÚNKP-20-3 New National Excellence Program of the Ministry for Innovation andTechnology from the source of the National Research, Development and Innovation Fund.</em></p>

scholarly journals Quantifying parameter uncertainty in a large-scale glacier evolution model using Bayesian inference: application to High Mountain Asia

2020 ◽  
Vol 66 (256) ◽  
pp. 175-187 ◽  
Author(s):  
David R. Rounce ◽  
Tushar Khurana ◽  
Margaret B. Short ◽  
Regine Hock ◽  
David E. Shean ◽  
...  
Keyword(s):  
Mass Balance ◽  
Parameter Uncertainty ◽  
Large Scale ◽  
Evolution Model ◽  
Model Parameters ◽  
High Mountain ◽  
Climate Data ◽  
Temperature And Precipitation ◽  
Model Parameter Uncertainty ◽  
Geodetic Mass Balance

AbstractThe response of glaciers to climate change has major implications for sea-level change and water resources around the globe. Large-scale glacier evolution models are used to project glacier runoff and mass loss, but are constrained by limited observations, which result in models being over-parameterized. Recent systematic geodetic mass-balance observations provide an opportunity to improve the calibration of glacier evolution models. In this study, we develop a calibration scheme for a glacier evolution model using a Bayesian inverse model and geodetic mass-balance observations, which enable us to quantify model parameter uncertainty. The Bayesian model is applied to each glacier in High Mountain Asia using Markov chain Monte Carlo methods. After 10,000 steps, the chains generate a sufficient number of independent samples to estimate the properties of the model parameters from the joint posterior distribution. Their spatial distribution shows a clear orographic effect indicating the resolution of climate data is too coarse to resolve temperature and precipitation at high altitudes. Given the glacier evolution model is over-parameterized, particular attention is given to identifiability and the need for future work to integrate additional observations in order to better constrain the plausible sets of model parameters.

Indices for daily temperature and precipitation based on quality controlled and homogenized data in Madagascar

2020 ◽  
Author(s):  
Luc Yannick Andréas Randriamarolaza ◽  
Enric Aguilar ◽  
Oleg Skrynyk
Keyword(s):  
Climate Change ◽  
Missing Data ◽  
Extreme Temperature ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Climate Indices ◽  
Low Density ◽  
Climate Data ◽  
Temperature And Precipitation

<p>Madagascar is an Island in Western Indian Ocean Region. It is mainly exposed to the easterly trade winds and has a rugged topography, which promote different local climates and biodiversity. Climate change inflicts a challenge on Madagascar socio-economic activities. However, Madagascar has low density station and sparse networks on observational weather stations to detect changes in climate. On average, one station covers more than 20 000 km<sup>2</sup> and closer neighbor stations are less correlated. Previous studies have demonstrated the changes on Madagascar climate, but this paper contributes and enhances the approach to assess the quality control and homogeneity of Madagascar daily climate data before developing climate indices over 1950 – 2018 on 28 synoptic stations. Daily climate data of minimum and maximum temperature and precipitation are exploited.</p><p>Firstly, the quality of daily climate data is controlled by INQC developed and maintained by Center for Climate Change (C3) of Rovira i Virgili University, Spain. It ascertains and improves error detections by using six flag categories. Most errors detected are due to digitalization and measurement.</p><p>Secondly, daily quality controlled data are homogenized by using CLIMATOL. It uses relative homogenization methods, chooses candidate reference series automatically and infills the missing data in the original data. It has ability to manage low density stations and low inter-station correlations and is tolerable for missing data. Monthly break points are detected by CLIMATOL and used to split daily climate data to be homogenized.</p><p>Finally, climate indices are calculated by using CLIMIND package which is developed by INDECIS<sup>*</sup> project. Compared to previous works done, data period is updated to 10 years before and after and 15 new climate indices mostly related to extremes are computed. On temperature, significant increasing and decreasing decade trends of day-to-day and extreme temperature ranges are important in western and eastern areas respectively. On average decade trends of temperature extremes, significant increasing of daily minimum temperature is greater than daily maximum temperature. Many stations indicate significant decreasing in very cold nights than significant increasing in very warm days. Their trends are almost 1 day per decade over 1950 – 2018. Warming is mainly felt during nighttime and daytime in Oriental and Occidental parts respectively. In contrast, central uplands are warming all the time but tropical nights do not appear yet. On rainfall, no major significant findings are found but intense precipitation might be possible at central uplands due to shortening of longest wet period and occurrence of heavy precipitation. However, no influence detected on total precipitation which is still decreasing over 1950 - 2018. Future works focus on merging of relative homogenization methodologies to ameliorate the results.</p><p>-------------------</p><p>*INDECIS is a part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462).</p>

Climatic Trends and Impact on Agriculture in Ningxia

2012 ◽  
Vol 518-523 ◽  
pp. 5921-5930
Author(s):  
Hong Xiang Chen ◽  
Ya Ping Li
Keyword(s):  
Crop Yields ◽  
Regional Climate ◽  
Climate Impact ◽  
Plant Diseases ◽  
Maximum Temperature ◽  
Climate Indices ◽  
Average Temperature ◽  
Daily Data ◽  
Actual Use ◽  
Frost Free Period

This paper characterizes the climate characteristics and observed climate variability in Ningxia, China, using observed daily data from 15 meteorological stations. Climate indices until 2050 and 2100 are projected using the Regional climate impact models PRECIS (Providing Regional Climates for Impacts Studies), emphasizing those which are relevant to agriculture. The results show that the average temperature in Ningxia has increased from 1961-2010 while the mean precipitation has decreased. The frost-free period and accumulated temperature ≥0°C have also increased. Frost-free periods have increased and extended the growing season. PRECIS shows that the annual average temperature, minimum and maximum temperature is projected to increase. Annual precipitation will not change significantly, but the observed dry spells will continue. Increasing temperatures are beneficial for most crop yields but also increase the risk of plant diseases as planting and harvesting times have changed and will change. The regional disparity of water availability, demand and actual use will further be aggravated in future.

scholarly journals INFLUENCE OF TEMPERATURE AND ALTITUDE ON THE EXPANSION OF COFFEE CROPS IN MATAS DE MINAS, BRAZIL

2020 ◽  
Vol 28 ◽  
pp. 157-165
Author(s):  
Karine Rabelo Oliveira ◽  
Williams Pinto Marques Ferreira ◽  
Humberto Paiva Fonseca ◽  
Cecília Fátima Souza
Keyword(s):  
Area Under The Curve ◽  
Growth Potential ◽  
Maximum Temperature ◽  
Annual Average ◽  
Jackknife Test ◽  
Average Temperature ◽  
Annual Minimum Temperature ◽  
Precipitation Seasonality ◽  
Omission Rate ◽  
Annual Average Temperature

Coffee is among the most significant products in Brazil. Minas Gerais is the largest state producer of Arabica coffee. Coffee activity has excellent growth potential, which justifies the identification of new areas for expansion of the culture. This study aimed to determine factors that affect the spatial distribution of coffee plantations the most, as well as to identify areas with a greater aptitude for its expansion in the region of the Matas de Minas (63 municipalities). The MaxEnt software was used to elaborate a model capable of describing the area with the highest potential for estimating the probability of coffee adequacy. The elaboration of the model considered the records of occurrence, climatic and topographic variables of Matas de Minas, the second largest state producing region. The area under the curve (AUC), the omission rate and the Jackknife test were used for validation and analysis of the model. The model was accurate with an AUC of 0.816 and omission rate of 0.54% for the ‘test’. It was identified that the potential distribution of coffee in Matas de Minas is determined by changes in the annual maximum temperature, although it did not generate a significant gain when omitted, accounting for a considerable loss in the model. However, the most influential variables on the delineation of distribution were, the altitude and the annual average temperature. The most favorable areas for expansion of coffee culture in the Matas de Minas were found in the vicinity of the region of Alto Caparaó.Abbreviations used: A1 (altitude); A2 (maximum annual temperature); A3 (annual minimum temperature); BIO 1 (annual average temperature 1); BIO 4 (temperature seasonality), BIO 12 (annual precipitation); BIO 15 (precipitation seasonality); csv (comma-separated values); AUC (area under the curve).

scholarly journals Effect of temperature and precipitation on the daily new cases and daily new death in seven cities around the globe

2020 ◽  
Author(s):  
Amar Prashad Chaudhary ◽  
Adna Nelson K ◽  
Harish S ◽  
Mydhily S ◽  
Chaitanya KJ ◽  
...  
Keyword(s):  
Minimum Temperature ◽  
Effect Of Temperature ◽  
Maximum Temperature ◽  
Weather Variables ◽  
Average Temperature ◽  
Temperature And Precipitation ◽  
The World

AbstractBackgroundThis study was done to understand the effect of temperature and precipitation in COVID-19.ObjectiveTo study the effect of temperature and precipitation on transmission of COVID-19.To study the effect of temperature and precipitation on daily death of COVID-19.MethodologyWe collected 3 consecutive month data of seven cities around the world which were effected most by the COVID-19. Data included weather variables i.e temperature (average temperature, maximum temperature and minimum temperature), precipitation, daily new cases and daily new death.ConclusionIncrease in average temperature reduces daily death and increase in maximum temperature reduces transmission.

scholarly journals Daily Climate Extremes of Temperature and Precipitation Over Quetta Valley, Pakistan During 1961-2019

2021 ◽  
Vol 10 (1) ◽  
pp. 20-48
Author(s):  
Imran Hameed Durrani ◽  
Shahzada Adnan ◽  
Syed Mobasher Aftab
Keyword(s):  
Statistical Tests ◽  
Climate Extremes ◽  
Maximum Temperature ◽  
Daily Maximum ◽  
Climate Extreme ◽  
Temperature And Precipitation ◽  
Extreme Precipitation Events ◽  
Daily Data ◽  
Quetta Valley ◽  
Extreme Indices

Climate extremes are imperative to study the impacts of climate change that is significantly observed for the management of scarce water resources of the Quetta Valley. The daily data of temperature and precipitation are used to model the climate extreme indices for Quetta Meteorological Station from1961 to 2019. The statistical tests were performed by using Mann Kendal and Sen’s Slope method at the 95% confidence level. The overall change in minimum to maximum temperatures and precipitation-based climate extreme indices specify the frequencies of extreme events are increasing. That would cause heatwaves, gradual warming, steady dryness, and extreme precipitation events in the long term over the Quetta Valley. The minimum and maximum temperature-based indices inclusively indicate positive trends. That ultimately leads to a warming climate with a significant increase in summer as 5 days/decade, tropical nights as 5.3 days/decade, daily maximum as 0.28°C/decade, warm nights as 1.7 days/decade and warm days as 1.9 days/decade. For precipitation, all the indices show positive trends with a significant increase in consecutive wet days for 0.1 days/decade and an annual contribution of very wet days 0.8% per decade. The monthly increase in temperature and decrease in precipitation would increase the evaporative demands which may arise the water stress conditions over the valley and may put pressure over groundwater reservoirs.

The effect of N, P, S fertilizer, temperature and precipitation on the yield of bromegrass and alfalfa pasture established on a Luvisolic soil

1991 ◽  
Vol 71 (4) ◽  
pp. 1047-1055 ◽  
Author(s):  
W. F. Nuttall ◽  
D. H. McCartney ◽  
P. R. Horton ◽  
S. Bittman ◽  
J. Waddington
Keyword(s):  
Phosphate Fertilizer ◽  
Maximum Temperature ◽  
Total Precipitation ◽  
Significant Drop ◽  
Average Temperature ◽  
Temperature And Precipitation ◽  
Average Maximum ◽  
Wide Range ◽  
Herbage Yield ◽  
Average Maximum Temperature

This study was conducted over a 12-yr period to determine the effect of N, P, and S (elemental) fertilizers on yield of bromegrass (Bromus inermis Leyss.) and alfalfa (Medicago media Pers.) pasture established on a Gray Wooded, Luvisolic soil (Waitville loam) in northeastern Saskatchewan. Nitrogen fertilizer was applied at 0, 45, and 90 kg N ha−1 in combination with phosphate fertilizer applied at 0 and 20 kg P ha−1. Two additional treatments combined 90 N + 20 P (kg ha−1) with 23 S and 45 S (kg ha−1). In the first 3 yr of the study, only the application of N increased yields, from 2.54 to 3.45 t ha−1. The combination of time (years 1978–1980) and N fertilizer applied up to the rate of 90 kg N ha−1 resulted in a reduction in percentage alfalfa in the sward from 30.9 to 3.8% (Yr × N interaction). Over the 12-yr period, N increased average herbage yield from 1.99 to 2.95 t ha−1; P, from 2.23 to 3.05 t ha−1 and S from 3.48 to 4.19 t ha−1, respectively. The interaction effects of N × Yr, P × Yr and S × Yr all were significant indicating a wide range of response to the fertilizer elements among years. Herbage yield was positively related to total precipitation and negatively to mean maximum temperature for the months of May, June and July. The highest yield (7.49 t ha−1) was obtained with fertilizer (90N-20P-23S kg ha−1), 220 mm of rainfall (May, June and July) with an average maximum temperature of 19.7 °C. The lowest yield (0.84 t ha−1) was obtained with a control (no fertilizer), 160 mm of rainfall and average maximum temperature of 25.2 °C (fertilizer × temperature-precipitation interaction). Results of this study suggest that a rise in average temperature without a corresponding increase in precipitation, would produce a significant drop in bromegrass herbage yield. Key words: Pasture, nutrients, alfalfa, bromegrass, temperature, precipitation

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