Comparative analysis of data on air temperature based on current weather data sets for 2007-2019

Dergunov, Alexander; Yakubailik, Oleg

doi:10.1088/1755-1315/548/3/032034

Cited by 6 publications

(3 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The CRU-TS dataset (upon which the CCKP estimates are based) is observational, whereas the ERA-5 data (upon which AREAdata is based) is a reanalysis combining forecasting model estimates with observations to provide spatially and temporally finer-grained climate estimates. Previous work has found similar overall correlations between the CRU-TS and ERA-5 data (approx 0.9) 22 , however these datasets can vary significantly at particular times and in particular places 22,23 . In particular, there is likely a warm bias in the ERA-5 reanalysis in recent years due to lesser representation of snow on top of sea ice 24 , which may explain the deviations we observe here.…”

Section: Technical Validationmentioning

confidence: 63%

AREAdata: a worldwide climate dataset averaged across spatial units at different scales through time

Smith

Stemkovski

Koontz

et al. 2021

Preprint

View full text Add to dashboard Cite

In an era of increasingly cross-discipline collaborative science, it is imperative to produce data resources which can be quickly and easily utilised by non-specialists. In particular, climate data often require heavy processing before they can be used for analyses. Here we describe AREAdata, a free-to-use online global climate dataset, pre-processed to provide the averages of various climate variables across differing administrative units (e.g., countries, states). These are daily estimates, based on the Copernicus Climate Data Store's ERA-5 data, regularly updated to the near-present and provided as direct downloads from our website (https://pearselab.github.io/areadata/). The daily climate estimates from AREAdata are consistent with other openly available data, but at much finer-grained spatial and temporal scales than available elsewhere. AREAdata complements the existing suite of climate resources by providing these data in a form more readily usable by researchers unfamiliar with GIS data-processing methods, and we anticipate these resources being of particular use to environmental and epidemiological researchers.

show abstract

Section: Technical Validationmentioning

confidence: 63%

AREAdata: a worldwide climate dataset averaged across spatial units at different scales through time

Smith

Stemkovski

Koontz

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…However, ERA5-Land offers a higher spatiotemporal resolution and better hydrological representation compared with other remote sensing and reanalysis datasets, making it widely applied in global and regional drought research [89][90][91][92]. For instance, Dergunov et al [93] compared the temperature data of ERA5-Land, Climate Research Unit gridded time series (CRU TS), and the Global Forecast System (GFS) and found that although the three products had a high correlation with temperature, the ERA5-Land dataset was more suitable for capturing small changes due to its higher spatiotemporal resolution. The evaluation of soil moisture and surface runoff data by previous studies indicated the outperformance of the ERA5 product [94].…”

Section: Uncertainties Limitations and Future Directionmentioning

confidence: 99%

Propagation of Meteorological Drought to Agricultural and Hydrological Droughts in the Tropical Lancang–Mekong River Basin

Feng,

Chen,

Mansaray

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

In the past several decades, drought events have occurred frequently around the world. However, research on the propagation of drought events has not been adequately explored. This study investigated the drought propagation process from meteorological drought to agricultural drought (PMAD) and from meteorological drought to hydrological drought (PMHD) using a 72-year reanalysis dataset in the tropical Lancang–Mekong River Basin. Firstly, we used a new method—Standardized Drought Analysis Toolbox—to construct drought indices. Then, a linear method (Pearson correlation analysis) and a nonlinear method (mutual information) were used to investigate the drought propagation process. Cross-wavelet analysis and wavelet coherence analysis were employed to explore the statistical relationship among the three drought types. Finally, the random forest method was applied to quantify the major factors in drought response time (DRT). The results revealed the following: (1) both linear and nonlinear methods exhibited strong temporal and spatial consistency for both PMAD and PMHD, with linear relationships being stronger than nonlinear ones. (2) The DRTs of PMAD and PMHD were around 1–2 months and 3–5 months, respectively. Significant differences existed in the DRT between the dry season and the rainy season. (3) A divergent spatial pattern of the proportion of DRT was observed between PMAD and PMHD. (4) Significant statistical correlations between meteorological drought and agricultural drought and between meteorological drought and hydrological drought were observed in specific periods for each sub-region; (5) Hydrometeorological factors contributed the most to DRT, followed by terrain factors and the land cover types. The findings of this study deepened our understanding of the spatial–temporal relationship of multiple drought propagation types in this transboundary river basin.

show abstract

“…ERA5_Land data are updated and easily accessible with high spatial and temporal resolution and are now widely used for flood and drought prediction (Dergunov & Yakubailik, 2020;Huang et al, 2021).…”

Section: Spatial Pattern Prediction Of Sm and Drought Grade In The Ssnpmentioning

confidence: 99%

Prediction of spring agricultural drought using machine learning algorithms in the southern Songnen Plain, China

Chen

Jiang³

et al. 2023

Land Degrad Dev

View full text Add to dashboard Cite

Winter climate conditions have a great effect on spring soil moisture (SM) in cold regions. The frequent occurrence of spring drought events in the Songnen Plain poses a significant threat to food security. In this study, we selected the random forest (RF) algorithm from three currently popular machine learning algorithms to predict the spatial pattern of average SM for the period of April 1–15 and April 16–30 with 1 km resolution in the dry cropland of southern Songnen Plain (SSNP) using 0–10 cm SM data for each April from 81 agro‐meteorological stations and winter climate data (e.g., temperature, snowfall), soil properties and topographic relief. Compared to the existing ERA5_Land and SMAP SM products, we obtain higher precision SM data (April 1–15: r = 0.74, RMSE = 0.050 m3 m−3; April 16–30: r = 0.73, RMSE = 0.051 m3 m−3). Drought grades were classified based on predicted SM data, and the results indicate agricultural drought was mainly influenced by the change of winter snowfall/snow depth, with western SSNP being more susceptible to drought because of soil properties. Compared with current SM data products, the RF model proposed in this study can implement more accurate prediction of spring soil drought based on winter climate, provide important information for agricultural management departments to prepare for spring cropping and irrigation, and avoid further soil salinization caused by drought.

show abstract

Comparative analysis of data on air temperature based on current weather data sets for 2007-2019

Cited by 6 publications

References 1 publication

AREAdata: a worldwide climate dataset averaged across spatial units at different scales through time

AREAdata: a worldwide climate dataset averaged across spatial units at different scales through time

Propagation of Meteorological Drought to Agricultural and Hydrological Droughts in the Tropical Lancang–Mekong River Basin

Prediction of spring agricultural drought using machine learning algorithms in the southern Songnen Plain, China

Contact Info

Product

Resources

About