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Evaluation of Reanalysis Data in Meteorological and Climatological Applications: Spatial and Temporal Considerations

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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: closed (10 June 2022) | Viewed by 26724

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Special Issue Editor

Dr. Mavromatis Theodoros

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Department of Meteorology and Climatology, School of Geology, Aristotle University of Thessaloniki, Thessaloniki, Greece
Interests: agricultural climatology; crop–climate relationships; crop simulation models; reanalysis datasets; drought indices; statistical climatology; climate change scenarios; statistical downscaling
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Special Issue Information

Dear Colleagues,

Reanalysis datasets are among the most used gridded data in the study of weather and climate. Due to their homogenous nature and high spatial and temporal resolution (compared to raw observations), they are used for evaluating climate models, irrigation management decisions, soil water balance evolution, flooding predictions, as well as for many other purposes.

With multiple reanalysis datasets now available, researchers must consider the strengths and weaknesses of each product by evaluating its quality in reproducing the variation of mean and variability, on spatial and temporal basis, captured in observations. Although efforts to improve reanalysis products have led to significant progress at a global level, reanalysis products at a regional level could not always reproduce characteristic climatological features. Estimates of the basic dynamic fields in modern reanalysis are increasingly similar, especially in the vicinity of abundant observations. While this is true for temperature, physics fields (e.g., precipitation and longwave radiation) are more uncertain, due to shortcomings in the assimilating model and its parameterizations. The challenges become even more formidable when reanalysis data are used to assess climate change and extremes at high resolutions in time and in space.

In this context, this Special Issue welcomes articles dedicated not only to the evaluation of reanalysis products against observations but also to exploring the effects of uncertainties using reanalysis data in model output. Such models include but are not limited to hydrological, weather forecasting, crop models, and any other models used for meteorological and climatological purposes by taking into account spatial and temporal considerations.

Dr. Mavromatis Theodoros
Guest Editor

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Keywords

reanalysis datasets
uncertainty assessment in model output
numerical weather prediction models
climate model evaluation
hydrological models
soil water balance evolution
crop models
drought indices
crop–climate relationships
climate change scenarios

Published Papers (7 papers)

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Editorial

Jump to: Research

2 pages, 181 KiB

Open AccessEditorial

Evaluation of Reanalysis Data in Meteorological and Climatological Applications: Spatial and Temporal Considerations

by Theodoros Mavromatis

Water 2022, 14(17), 2769; https://0-doi-org.brum.beds.ac.uk/10.3390/w14172769 - 05 Sep 2022

Cited by 1 | Viewed by 1290

Abstract

Reanalysis datasets are among the most used gridded data for the study of weather and climate [...] Full article

(This article belongs to the Special Issue Evaluation of Reanalysis Data in Meteorological and Climatological Applications: Spatial and Temporal Considerations)

Research

Jump to: Editorial

13 pages, 1786 KiB

Open AccessArticle

On the Use of Gridded Data Products for Trend Assessment and Aridity Classification in a Mediterranean Context: The Case of the Apulia Region

by Lorenzo My, Mario Di Bacco and Anna Rita Scorzini

Water 2022, 14(14), 2203; https://0-doi-org.brum.beds.ac.uk/10.3390/w14142203 - 12 Jul 2022

Cited by 8 | Viewed by 2017

Abstract

Large-scale gridded climatic data can be useful for the assessment of climate variability and change as a basis for understanding and monitoring natural hazards, as well as for determining appropriate coping strategies. However, an evaluation of the accuracy of these data products against local observational measurements over the different regions of the globe is always required, as these large-scale data may be affected by systematic errors, which can affect the results of downstream applications. Therefore, this study was carried out to evaluate the performances of two long-term gridded datasets in reproducing station-based precipitation and temperature data over the Apulia region (southern Italy) for the period 1956–2019, with a particular focus on the effect of using the different data sources on the results of trend analyses and aridity classification. The results revealed that the considered gridded data products allow only general indications on the spatial and temporal behavior of climatic variables over the Apulia region, especially in regard to precipitation data. Full article

(This article belongs to the Special Issue Evaluation of Reanalysis Data in Meteorological and Climatological Applications: Spatial and Temporal Considerations)

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17 pages, 3509 KiB

Open AccessArticle

Performance Assessment of Different Precipitation Databases (Gridded Analyses and Reanalyses) for the New Brazilian Agricultural Frontier: SEALBA

by Ewerton Hallan de Lima Silva, Fabrício Daniel dos Santos Silva, Rosiberto Salustiano da Silva Junior, David Duarte Cavalcante Pinto, Rafaela Lisboa Costa, Heliofábio Barros Gomes, Jório Bezerra Cabral Júnior, Ismael Guidson Farias de Freitas and Dirceu Luís Herdies

Water 2022, 14(9), 1473; https://0-doi-org.brum.beds.ac.uk/10.3390/w14091473 - 04 May 2022

Cited by 7 | Viewed by 2256

Abstract

Since the early 2000s, Brazil has been one of the world’s leading grain producers, with agribusiness accounting for around 28% of the Brazilian GDP in 2021. Substantial investments in research, coupled with the expansion of arable areas, owed to the advent of new agriculture frontiers, led the country to become the world’s greatest producer of soybean. One of the newest agricultural frontiers to be emerging in Brazil is the one known as SEALBA, an acronym that refers to the three Brazilian states whose areas it is comprised of—Sergipe, Alagoas, and Bahia—all located in the Northeast region of the country. It is an extensive area with a favorable climate for the production of grains, including soybeans, with a rainy season that takes place in autumn/winter, unlike the Brazilian regions that are currently the main producers of these kinds of crops, in which the rainfall regime has the wet period concentrated in spring/summer. Considering that precipitation is the main determinant climatic factor for crops, the scarcity of weather stations in the SEALBA region poses an obstacle to an accurate evaluation of the actual feasibility of the region to a given crop. Therefore, the aim of this work was to carry out an assessment of the performance of four different precipitation databases of alternative sources to observations: two from gridded analyses, MERGE and CHIRPS, and the other two from ECMWF reanalyses, ERA5, and ERA5Land, and by comparing them to observational records from stations along the region. The analysis was based on a comparison with data from seven weather stations located in SEALBA, in the period 2001–2020, through three dexterity indices: the mean absolute error (MAE), the root mean squared errors (RMSE), and the coefficient of Pearson’s correlation (r), showing that the gridded analyzes performed better than the reanalyses, with MERGE showing the highest correlations and the lowest errors (global average r between stations of 0.96, followed by CHIRPS with 0.85, ERA5Land with 0.83, and ERA5 with 0.70; average MAE 14.3 mm, followed by CHIRPS with 21.3 mm, ERA5Land with 42.1 mm and ERA5 with 50.1 mm; average RMSE between stations of 24.6 mm, followed by CHIRPS with 50.8 mm, ERA5Land with 62.3 mm and ERA5 with 71.4 mm). Since all databases provide up-to-date data, our findings indicate that, for any research that needs a complete daily precipitation dataset for the SEALBA region, preference should be given to use the data in the following order of priority: MERGE, CHIRPS, ERA5Land, and ERA5. Full article

(This article belongs to the Special Issue Evaluation of Reanalysis Data in Meteorological and Climatological Applications: Spatial and Temporal Considerations)

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15 pages, 7385 KiB

Open AccessArticle

Reliability of the ERA5 in Replicating Mean and Extreme Temperatures across Europe

by Kondylia Velikou, Georgia Lazoglou, Konstantia Tolika and Christina Anagnostopoulou

Water 2022, 14(4), 543; https://0-doi-org.brum.beds.ac.uk/10.3390/w14040543 - 11 Feb 2022

Cited by 30 | Viewed by 3897

Abstract

ERA5 is widely considered as a valid proxy of observation at region scales. Surface air temperature from the E-OBS database and 196 meteorological stations across Europe are being applied for evaluation of the fifth-generation ECMWF reanalysis ERA5 temperature data in the period of 1981–2010. In general, ERA5 captures the mean and extreme temperatures very well and ERA5 is reliable for climate investigation over Europe. High correlations ranging from 0.995 to 1.000 indicate that ERA5 could capture the annual cycle very well. However, the high mean biases and high Root Mean Square Error (RMSE) for some European sub-regions (e.g., the Alps, the Mediterranean) reveal that ERA5 underestimates temperatures. The biases can be mainly attributed to the altitude differences between ERA5 grid points and stations. Comparing ERA5 with the other two datasets, ERA5 temperature presents more extreme temperature and small outliers for regions southern of 40° latitude and less extreme temperatures in areas over the Black Sea. In Scandinavia, ERA5 temperatures are more frequently extreme than the observational ones. Full article

(This article belongs to the Special Issue Evaluation of Reanalysis Data in Meteorological and Climatological Applications: Spatial and Temporal Considerations)

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22 pages, 7167 KiB

Open AccessArticle

Evaluation of Past and Future Climate Trends under CMIP6 Scenarios for the UBNB (Abay), Ethiopia

by Addis A. Alaminie, Seifu A. Tilahun, Solomon A. Legesse, Fasikaw A. Zimale, Gashaw Bimrew Tarkegn and Mark R. Jury

Water 2021, 13(15), 2110; https://0-doi-org.brum.beds.ac.uk/10.3390/w13152110 - 31 Jul 2021

Cited by 26 | Viewed by 4737

Abstract

Climate predictions using recent and high-resolution climate models are becoming important for effective decision-making and for designing appropriate climate change adaptation and mitigation strategies. Due to highly variable climate and data scarcity of the upper Blue Nile Basin, previous studies did not detect specific unified trends. This study discusses, the past and future climate projections under CMIP6-SSPs scenarios for the basin. For the models’ validation and selection, reanalysis data were used after comparing with area-averaged ground observational data. Quantile mapping systematic bias correction and Mann–Kendall trend test were applied to evaluate the trends of selected CMIP6 models during the 21st century. Results revealed that, ERA5 for temperature and GPCC for precipitation have best agreement with the basin observational data, MRI-ESM2-0 for temperature and BCC-CSM-2MR for precipitation were selected based on their highest performance. The MRI-ESM2-0 mean annual maximum temperature for the near (long)-term period shows an increase of 1.1 (1.5) °C, 1.3 (2.2) °C, 1.2 (2.8) °C, and 1.5 (3.8) °C under the four SSPs. On the other hand, the BCC-CSM-2MR precipitation projections show slightly (statistically insignificant) increasing trend for the near (long)-term periods by 5.9 (6.1)%, 12.8 (13.7)%, 9.5 (9.1)%, and 17.1(17.7)% under four SSPs scenarios. Full article

(This article belongs to the Special Issue Evaluation of Reanalysis Data in Meteorological and Climatological Applications: Spatial and Temporal Considerations)

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23 pages, 8143 KiB

Open AccessArticle

Intercomparison of Gridded Precipitation Datasets over a Sub-Region of the Central Himalaya and the Southwestern Tibetan Plateau

by Alexandra Hamm, Anselm Arndt, Christine Kolbe, Xun Wang, Boris Thies, Oleksiy Boyko, Paolo Reggiani, Dieter Scherer, Jörg Bendix and Christoph Schneider

Water 2020, 12(11), 3271; https://0-doi-org.brum.beds.ac.uk/10.3390/w12113271 - 21 Nov 2020

Cited by 20 | Viewed by 4094

Abstract

Precipitation is a central quantity of hydrometeorological research and applications. Especially in complex terrain, such as in High Mountain Asia (HMA), surface precipitation observations are scarce. Gridded precipitation products are one way to overcome the limitations of ground truth observations. They can provide datasets continuous in both space and time. However, there are many products available, which use various methods for data generation and lead to different precipitation values. In our study we compare nine different gridded precipitation products from different origins (ERA5, ERA5-Land, ERA-interim, HAR v2 10 km, HAR v2 2 km, JRA-55, MERRA-2, GPCC and PRETIP) over a subregion of the Central Himalaya and the Southwest Tibetan Plateau, from May to September 2017. Total spatially averaged precipitation over the study period ranged from 411 mm (GPCC) to 781 mm (ERA-Interim) with a mean value of 623 mm and a standard deviation of 132 mm. We found that the gridded products and the few observations, with few exceptions, are consistent among each other regarding precipitation variability and rough amount within the study area. It became obvious that higher grid resolution can resolve extreme precipitation much better, leading to overall lower mean precipitation spatially, but higher extreme precipitation events. We also found that generally high terrain complexity leads to larger differences in the amount of precipitation between products. Due to the considerable differences between products in space and time, we suggest carefully selecting the product used as input for any research application based on the type of application and specific research question. While coarse products such as ERA-Interim or ERA5 that cover long periods but have coarse grid resolution have previously shown to be able to capture long-term trends and help with identifying climate change features, this study suggests that more regional applications, such as glacier mass-balance modeling, require higher spatial resolution, as is reproduced, for example, in HAR v2 10 km. Full article

(This article belongs to the Special Issue Evaluation of Reanalysis Data in Meteorological and Climatological Applications: Spatial and Temporal Considerations)

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22 pages, 3531 KiB

Open AccessEditor’s ChoiceArticle

Comparison of ERA5-Land and UERRA MESCAN-SURFEX Reanalysis Data with Spatially Interpolated Weather Observations for the Regional Assessment of Reference Evapotranspiration

by Anna Pelosi, Fabio Terribile, Guido D’Urso and Giovanni Battista Chirico

Water 2020, 12(6), 1669; https://0-doi-org.brum.beds.ac.uk/10.3390/w12061669 - 11 Jun 2020

Cited by 99 | Viewed by 6950

Abstract

Reanalysis data are being increasingly used as gridded weather data sources for assessing crop-reference evapotranspiration (ET₀) in irrigation water-budget analyses at regional scales. This study assesses the performances of ET₀ estimates based on weather data, respectively produced by two high-resolution reanalysis datasets: UERRA MESCAN-SURFEX (UMS) and ERA5-Land (E5L). The study is conducted in Campania Region (Southern Italy), with reference to the irrigation seasons (April–September) of years 2008–2018. Temperature, wind speed, vapor pressure deficit, solar radiation and ET₀ derived from reanalysis datasets, were compared with the corresponding estimates obtained by spatially interpolating data observed by a network of 18 automatic weather stations (AWSs). Statistical performances of the spatial interpolations were evaluated with a cross-validation procedure, by recursively applying universal kriging or ordinary kriging to the observed weather data. ERA5-Land outperformed UMS both in weather data and ET₀ estimates. Averaging over all 18 AWSs sites in the region, the normalized BIAS (nBIAS) was found less than 5% for all the databases. The normalized RMSE (nRMSE) for ET₀ computed with E5L data was 17%, while it was 22% with UMS data. Both performances were not far from those obtained by kriging interpolation, which presented an average nRMSE of 14%. Overall, this study confirms that reanalysis can successfully surrogate the unavailability of observed weather data for the regional assessment of ET₀. Full article

(This article belongs to the Special Issue Evaluation of Reanalysis Data in Meteorological and Climatological Applications: Spatial and Temporal Considerations)

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