scholarly journals SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018

2020 ◽  
Vol 12 (4) ◽  
pp. 2381-2409 ◽  
Author(s):  
Guoqiang Tang ◽  
Martyn P. Clark ◽  
Andrew J. Newman ◽  
Andrew W. Wood ◽  
Simon Michael Papalexiou ◽  
...  
Keyword(s):  
North America ◽  
Auxiliary Information ◽  
Mean Value ◽  
Meteorological Station ◽  
Maximum Temperature ◽  
Quantile Mapping ◽  
Sensitivity Experiment ◽  
Station Data ◽  
Precipitation And Temperature ◽  
Observation Period

Abstract. Station-based serially complete datasets (SCDs) of precipitation and temperature observations are important for hydrometeorological studies. Motivated by the lack of serially complete station observations for North America, this study seeks to develop an SCD from 1979 to 2018 from station data. The new SCD for North America (SCDNA) includes daily precipitation, minimum temperature (Tmin⁡), and maximum temperature (Tmax⁡) data for 27 276 stations. Raw meteorological station data were obtained from the Global Historical Climate Network Daily (GHCN-D), the Global Surface Summary of the Day (GSOD), Environment and Climate Change Canada (ECCC), and a compiled station database in Mexico. Stations with at least 8-year-long records were selected, which underwent location correction and were subjected to strict quality control. Outputs from three reanalysis products (ERA5, JRA-55, and MERRA-2) provided auxiliary information to estimate station records. Infilling during the observation period and reconstruction beyond the observation period were accomplished by combining estimates from 16 strategies (variants of quantile mapping, spatial interpolation, and machine learning). A sensitivity experiment was conducted by assuming that 30 % of observations from stations were missing – this enabled independent validation and provided a reference for reconstruction. Quantile mapping and mean value corrections were applied to the final estimates. The median Kling–Gupta efficiency (KGE′) values of the final SCDNA for all stations are 0.90, 0.98, and 0.99 for precipitation, Tmin⁡, and Tmax⁡, respectively. The SCDNA is closer to station observations than the four benchmark gridded products and can be used in applications that require either quality-controlled meteorological station observations or reconstructed long-term estimates for analysis and modeling. The dataset is available at https://doi.org/10.5281/zenodo.3735533 (Tang et al., 2020).

scholarly journals SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018

2020 ◽  
Author(s):  
Guoqiang Tang ◽  
Martyn P. Clark ◽  
Andrew J. Newman ◽  
Andrew W. Wood ◽  
Simon Michael Papalexiou ◽  
...  
Keyword(s):  
North America ◽  
Auxiliary Information ◽  
Mean Value ◽  
Meteorological Station ◽  
Maximum Temperature ◽  
Quantile Mapping ◽  
Sensitivity Experiment ◽  
Station Data ◽  
Precipitation And Temperature ◽  
Observation Period

Abstract. Station-based serially complete datasets (SCDs) of precipitation and temperature observations are important for hydrometeorological studies. Motivated by the lack of serially-complete station observations for North America, this study seeks to develop a SCD from 1979 to 2018 from station data. The new SCD for North America (SCDNA) includes daily precipitation, minimum temperature (Tmin), and maximum temperature (Tmax) data for 27280 stations. Raw meteorological station data were obtained from the Global Historical Climate Network Daily (GHCN-D), the Global Surface Summary of the Day (GSOD), Environment and Climate Change Canada (ECCC), and a compiled station database in Mexico. Stations with at least 8-year records were selected, which underwent location correction and were subjected to strict quality control. Outputs from three reanalysis products (ERA5, JRA-55, and MERRA-2) provided auxiliary information to estimate station records and were also used as an assessment benchmark. Infilling during the observation period and reconstruction beyond the observation period were accomplished by combining estimates from 16 strategies (variants of quantile mapping, spatial interpolation, and machine learning). A sensitivity experiment was conducted by assuming 30 % observations of stations were missing – this enabled independent validation and provided a reference for reconstruction. Quantile mapping and mean-value corrections were applied to the final estimates. The median Kling-Gupta efficiency (KGE) values of the final SCDNA for all stations are 0.90, 0.98, and 0.99 for precipitation, Tmin and Tmax, respectively. The SCDNA is closer to station observations than four benchmark gridded product, and can be used in applications that require either quality-controlled meteorological station observations or reconstructed long-term estimates for analysis and modelling. The dataset is available at https://doi.org/10.5281/zenodo.3735534 (Tang et al., 2020).

scholarly journals Constructing High-Resolution, Bias-Corrected Climate Products: A Comparison of Methods

2021 ◽  
Vol 60 (4) ◽  
pp. 455-475
Author(s):  
Maike F. Holthuijzen ◽  
Brian Beckage ◽  
Patrick J. Clemins ◽  
Dave Higdon ◽  
Jonathan M. Winter
Keyword(s):  
High Resolution ◽  
Bias Correction ◽  
Climate Model ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Model Output ◽  
Quantile Mapping ◽  
Station Data ◽  
Climate Model Output ◽  
Mapping Techniques

AbstractHigh-resolution, bias-corrected climate data are necessary for climate impact studies at local scales. Gridded historical data are convenient for bias correction but may contain biases resulting from interpolation. Long-term, quality-controlled station data are generally superior climatological measurements, but because the distribution of climate stations is irregular, station data are challenging to incorporate into downscaling and bias-correction approaches. Here, we compared six novel methods for constructing full-coverage, high-resolution, bias-corrected climate products using daily maximum temperature simulations from a regional climate model (RCM). Only station data were used for bias correction. We quantified performance of the six methods with the root-mean-square-error (RMSE) and Perkins skill score (PSS) and used two ANOVA models to analyze how performance varied among methods. We validated the six methods using two calibration periods of observed data (1980–89 and 1980–2014) and two testing sets of RCM data (1990–2014 and 1980–2014). RMSE for all methods varied throughout the year and was larger in cold months, whereas PSS was more consistent. Quantile-mapping bias-correction techniques substantially improved PSS, while simple linear transfer functions performed best in improving RMSE. For the 1980–89 calibration period, simple quantile-mapping techniques outperformed empirical quantile mapping (EQM) in improving PSS. When calibration and testing time periods were equivalent, EQM resulted in the largest improvements in PSS. No one method performed best in both RMSE and PSS. Our results indicate that simple quantile-mapping techniques are less prone to overfitting than EQM and are suitable for processing future climate model output, whereas EQM is ideal for bias correcting historical climate model output.

The use of serially complete station data to improve the temporal continuity of gridded precipitation and temperature estimates

2021 ◽  
Author(s):  
Guoqiang Tang ◽  
Martyn P. Clark ◽  
Simon Michael Papalexiou
Keyword(s):  
North America ◽  
Spatial Interpolation ◽  
Missing Values ◽  
Meteorological Data ◽  
Maximum Temperature ◽  
Spatial Density ◽  
Gap Filling ◽  
Station Network ◽  
Interpolation Methods ◽  
Precipitation And Temperature

AbstractStations are an important source of meteorological data, but often suffer from missing values and short observation periods. Gap filling is widely used to generate serially complete datasets (SCDs), which are subsequently used to produce gridded meteorological estimates. However, the value of SCDs in spatial interpolation is scarcely studied. Based on our recent efforts to develop a SCD over North America (SCDNA), we explore the extent to which gap filling improves gridded precipitation and temperature estimates. We address two specific questions: (1) Can SCDNA improve the statistical accuracy of gridded estimates in North America? (2) Can SCDNA improve estimates of trends on gridded data? In addressing these questions, we also evaluate the extent to which results depend on the spatial density of the station network and the spatial interpolation methods used. Results show that the improvement in statistical interpolation due to gap filling is more obvious for precipitation, followed by minimum temperature and maximum temperature. The improvement is larger when the station network is sparse and when simpler interpolation methods are used. SCDs can also notably reduce the uncertainties in spatial interpolation. Our evaluation across North America from 1979 to 2018 demonstrates that SCDs improve the accuracy of interpolated estimates for most stations and days. SCDNA-based interpolation also obtains better trend estimation than observation-based interpolation. This occurs because stations used for interpolation could change during a specific period, causing changepoints in interpolated temperature estimates and affect the long-term trends of observation-based interpolation, which can be avoided using SCDNA. Overall, SCDs improve the performance of gridded precipitation and temperature estimates.

ASSOCIATED DISEASES IN ROTAVIRUS GASTROENTERITIS IN CHILDREN HOSPITALIZED IN CLINICAL INFECTIOUS DISEASES HOSPITAL OF CONSTANTA

2017 ◽  
Vol 20 (3) ◽  
pp. 130-134
Author(s):  
Simona Diaconu ◽  
◽  
Simona Claudia Cambrea ◽  
Lucian Cristian Petcu ◽  
Sorin Rugina ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Respiratory Disease ◽  
Medical Records ◽  
Digestive Disease ◽  
Demographic Data ◽  
Mean Value ◽  
Maximum Temperature ◽  
Rotavirus Gastroenteritis ◽  
Associated Diseases ◽  
The Mean

Our study included 505 children hospitalized in the Clinical Hospital of Infectious Diseases Constanta in 2011-2012 with gastroenteritis with rotavirus. We analyzed the medical records of the patients and extracted demographic data, temperature, and frequency of vomiting and diarrheic stools. We divided the group of cases into 4 groups associated with: respiratory disease (RD) – 160 cases, digestive disease (DD) – 52 cases, eruptive disease (ED) – 11 cases, and a group without other associated diseases (simple) – 282 cases. We found significant differences between the four groups regarding the number of stools, the number of vomiting per day, the mean value of maximum temperature, the mean value of hospitalization.

scholarly journals The Fingerprint of Climate Change and Urbanization in South Korea

Atmosphere ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 273 ◽  
Author(s):  
Won-Ho Nam ◽  
Guillermo Baigorria ◽  
Eun-Mi Hong ◽  
Taegon Kim ◽  
Yong-Sang Choi ◽  
...  
Keyword(s):  
Climate Change ◽  
South Korea ◽  
Urban Areas ◽  
Temporal Trends ◽  
Weather Data ◽  
Maximum Temperature ◽  
Island Effect ◽  
Temperature Trends ◽  
Agricultural Areas ◽  
Precipitation And Temperature

Understanding long-term changes in precipitation and temperature patterns is important in the detection and characterization of climate change, as is understanding the implications of climate change when performing impact assessments. This study uses a statistically robust methodology to quantify long-, medium- and short-term changes for evaluating the degree to which climate change and urbanization have caused temporal changes in precipitation and temperature in South Korea. We sought to identify a fingerprint of changes in precipitation and temperature based on statistically significant differences at multiple-timescales. This study evaluates historical weather data during a 40-year period (1973–2012) and from 54 weather stations. Our results demonstrate that between 1993–2012, minimum and maximum temperature trends in the vicinity of urban and agricultural areas are significantly different from the two previous decades (1973–1992). The results for precipitation amounts show significant differences in urban areas. These results indicate that the climate in urbanized areas has been affected by both the heat island effect and global warming-caused climate change. The increase in the number of rainfall events in agricultural areas is highly significant, although the temporal trends for precipitation amounts showed no significant differences. Overall, the impacts of climate change and urbanization in South Korea have not been continuous over time and have been expressed locally and regionally in terms of precipitation and temperature changes.

scholarly journals Projections of Twenty-First-Century Climate Extremes for Alaska via Dynamical Downscaling and Quantile Mapping

2017 ◽  
Vol 56 (9) ◽  
pp. 2393-2409 ◽  
Author(s):  
Rick Lader ◽  
John E. Walsh ◽  
Uma S. Bhatt ◽  
Peter A. Bieniek
Keyword(s):  
Climate Change ◽  
Dynamical Downscaling ◽  
Climate Extremes ◽  
First Century ◽  
The Arctic ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Quantile Mapping ◽  
Temperature And Precipitation ◽  
Twenty First Century

AbstractClimate change is expected to alter the frequencies and intensities of at least some types of extreme events. Although Alaska is already experiencing an amplified response to climate change, studies of extreme event occurrences have lagged those for other regions. Forced migration due to coastal erosion, failing infrastructure on thawing permafrost, more severe wildfire seasons, altered ocean chemistry, and an ever-shrinking season for snow and ice are among the most devastating effects, many of which are related to extreme climate events. This study uses regional dynamical downscaling with the Weather Research and Forecasting (WRF) Model to investigate projected twenty-first-century changes of daily maximum temperature, minimum temperature, and precipitation over Alaska. The forcing data used for the downscaling simulations include the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim; 1981–2010), Geophysical Fluid Dynamics Laboratory Climate Model, version 3 (GFDL CM3), historical (1976–2005), and GFDL CM3 representative concentration pathway 8.5 (RCP8.5; 2006–2100). Observed trends of temperature and sea ice coverage in the Arctic are large, and the present trajectory of global emissions makes a continuation of these trends plausible. The future scenario is bias adjusted using a quantile-mapping procedure. Results indicate an asymmetric warming of climate extremes; namely, cold extremes rise fastest, and the greatest changes occur in winter. Maximum 1- and 5-day precipitation amounts are projected to increase by 53% and 50%, which is larger than the corresponding increases for the contiguous United States. When compared with the historical period, the shifts in temperature and precipitation indicate unprecedented heat and rainfall across Alaska during this century.

scholarly journals Assessing runoff sensitivities to precipitation and temperature changes under global climate-change scenarios

2018 ◽  
Vol 50 (1) ◽  
pp. 24-42 ◽  
Author(s):  
Lei Chen ◽  
Jianxia Chang ◽  
Yimin Wang ◽  
Yuelu Zhu
Keyword(s):  
Climate Change ◽  
Minimum Temperature ◽  
Yellow River ◽  
Control Variable ◽  
Maximum Temperature ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
Temperature Changes ◽  
Runoff Change ◽  
Precipitation And Temperature

Abstract An accurate grasp of the influence of precipitation and temperature changes on the variation in both the magnitude and temporal patterns of runoff is crucial to the prevention of floods and droughts. However, there is a general lack of understanding of the ways in which runoff sensitivities to precipitation and temperature changes are associated with the CMIP5 scenarios. This paper investigates the hydrological response to future climate change under CMIP5 RCP scenarios by using the Variable Infiltration Capacity (VIC) model and then quantitatively assesses runoff sensitivities to precipitation and temperature changes under different scenarios by using a set of simulations with the control variable method. The source region of the Yellow River (SRYR) is an ideal area to study this problem. The results demonstrated that the precipitation effect was the dominant element influencing runoff change (the degree of influence approaching 23%), followed by maximum temperature (approaching 12%). The weakest element was minimum temperature (approaching 3%), despite the fact that the increases in minimum temperature were higher than the increases in maximum temperature. The results also indicated that the degree of runoff sensitivity to precipitation and temperature changes was subject to changing external climatic conditions.

scholarly journals A combined Terra and Aqua MODIS land surface temperature and meteorological station data product for China from 2003 to 2017

2020 ◽  
Vol 12 (4) ◽  
pp. 2555-2577
Author(s):  
Bing Zhao ◽  
Kebiao Mao ◽  
Yulin Cai ◽  
Jiancheng Shi ◽  
Zhaoliang Li ◽  
...  
Keyword(s):  
High Temperature ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Meteorological Station ◽  
Western China ◽  
Drought Monitoring ◽  
Ground Observation ◽  
Station Data ◽  
Reconstruction Model

Abstract. Land surface temperature (LST) is a key variable for high temperature and drought monitoring and climate and ecological environment research. Due to the sparse distribution of ground observation stations, thermal infrared remote sensing technology has become an important means of quickly obtaining ground temperature over large areas. However, there are many missing and low-quality values in satellite-based LST data because clouds cover more than 60 % of the global surface every day. This article presents a unique LST dataset with a monthly temporal resolution for China from 2003 to 2017 that makes full use of the advantages of MODIS data and meteorological station data to overcome the defects of cloud influence via a reconstruction model. We specifically describe the reconstruction model, which uses a combination of MODIS daily data, monthly data and meteorological station data to reconstruct the LST in areas with cloud coverage and for grid cells with elevated LST error, and the data performance is then further improved by establishing a regression analysis model. The validation indicates that the new LST dataset is highly consistent with in situ observations. For the six natural subregions with different climatic conditions in China, verification using ground observation data shows that the root mean square error (RMSE) ranges from 1.24 to 1.58 ∘C, the mean absolute error (MAE) varies from 1.23 to 1.37 ∘C and the Pearson coefficient (R2) ranges from 0.93 to 0.99. The new dataset adequately captures the spatiotemporal variations in LST at annual, seasonal and monthly scales. From 2003 to 2017, the overall annual mean LST in China showed a weak increase. Moreover, the positive trend was remarkably unevenly distributed across China. The most significant warming occurred in the central and western areas of the Inner Mongolia Plateau in the Northwest Region, and the average annual temperature change is greater than 0.1 K (R>0.71, P<0.05), and a strong negative trend was observed in some parts of the Northeast Region and South China Region. Seasonally, there was significant warming in western China in winter, which was most pronounced in December. The reconstructed dataset exhibits significant improvements and can be used for the spatiotemporal evaluation of LST in high-temperature and drought-monitoring studies. The data are available through Zenodo at https://doi.org/10.5281/zenodo.3528024 (Zhao et al., 2019).

scholarly journals ROUTINE, EXTREME AND ENGINEERING METEOROLOGY ANALYSIS FOR KARACHI COASTAL AREA

2020 ◽  
Vol 4 (1) ◽  
pp. 15-22
Author(s):  
Muhammad Taqui ◽  
Jabir Hussain Syed ◽  
Ghulam Hassan Askari
Keyword(s):  
Climate Change ◽  
Coastal Area ◽  
Meteorological Parameters ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Meteorological Station ◽  
Annual Precipitation ◽  
Maximum Temperature ◽  
Intergovernmental Panel ◽  
Interdisciplinary Field

Pakistan’s largest city, Karachi, which is industrial centre and economic hub needs focus in research and development of every field of Engineering, Science and Technology. Urbanization and industrialization is resulting bad weather conditions which prolongs until a climate change. Since, Meteorology serves as interdisciplinary field of study, an analytical study of real and region-specific meteorological data is conducted which focuses on routine, extreme and engineering meteorology of metropolitan city Karachi. Results of study endorse the meteorological parameters relationship and establish the variability of those parameters for Karachi Coastal Area. The rise of temperature, decreasing trend of atmospheric pressure, increment in precipitation and fall in relative humidity depict the effects of urbanization and industrialization. The recorded extreme maximum temperature of 45.50C (on June 11, 1988) and the extreme minimum temperature of 4.5 0C(on January 1, 2007) is observed at Karachi south meteorological station. The estimated temperature rise in 32 years is 0.9 0C, which is crossing the Intergovernmental Panel on Climate Change (IPCC) predicted/estimated limit of 2oC rise per century. The maximum annual precipitation of 487.0mm appearing in 1994 and the minimum annual precipitation of 2.5mm appearing in 1987 is observed at same station which is representative meteorological station for Karachi Coast. Further Engineering meteorological parameters for heating ventilation air condition (HVAC) system design for industrial purpose are deduced as supporting data for coastal area site study for industrial as well as any follow-up engineering work in the specified region.

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