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![(a) Meteorological stations and the corresponding ERA5‐Land grid point locations in mainland Portugal (from DEM‐Shuttle Radar Topography Mission; Farr et al., 2007). (b) Climate domains in Portugal (adapted from Medeiros, 2005) [Colour figure can be viewed at wileyonlinelibrary.com]](publication/373338594/figure/fig1/AS:11431281246447245@1716372254867/a-Meteorological-stations-and-the-corresponding-ERA5-Land-grid-point-locations-in.png)
(a) Meteorological stations and the corresponding ERA5‐Land grid point locations in mainland Portugal (from DEM‐Shuttle Radar Topography Mission; Farr et al., 2007). (b) Climate domains in Portugal (adapted from Medeiros, 2005) [Colour figure can be viewed at wileyonlinelibrary.com]
Source publication
This study evaluates the reliability of ERA5 and ERA5‐Land reanalysis datasets in describing the mean daily air temperature of four climate domains in mainland Portugal. The reanalysis datasets were compared with ground observations from 94 meteorological stations (1980–2021). Overall, the results demonstrated a good degree of correlation between t...
Citations
... These data (and additional variables-see Section 3.2) were used to train and validate the models for streamflow prediction. No quality control measures were applied to the precipitation and temperature data, as previous studies have already demonstrated the relevance and reliability of ERA5-Land over the Iberian Peninsula and Portugal (e.g., [34,35]). ...
The transformative potential of deep learning models is felt in many research fields, including hydrology and water resources. This study investigates the effectiveness of the Temporal Fusion Transformer (TFT), a deep neural network architecture for predicting daily streamflow in Portugal, and benchmarks it against the popular Hydrologiska Byråns Vattenbalansavdelning (HBV) hydrological model. Additionally, it evaluates the performance of TFTs through selected forecasting examples. Information is provided about key input variables, including precipitation, temperature, and geomorphological characteristics. The study involved extensive hyperparameter tuning, with over 600 simulations conducted to fine–tune performances and ensure reliable predictions across diverse hydrological conditions. The results showed that TFTs outperformed the HBV model, successfully predicting streamflow in several catchments of distinct characteristics throughout the country. TFTs not only provide trustworthy predictions with associated probabilities of occurrence but also offer considerable advantages over classical forecasting frameworks, i.e., the ability to model complex temporal dependencies and interactions across different inputs or weight features based on their relevance to the target variable. Multiple practical applications can rely on streamflow predictions made with TFT models, such as flood risk management, water resources allocation, and support climate change adaptation measures.
... Several studies have assessed the ERA5-Land reanalysis data by comparing them to station data. ERA5-Land temperature and precipitation data were found to better match the observations for flatter regions than for regions with complex terrain (Almeida and Coelho, 2023;Gomis-Cebolla et al., 2023;Tan et al., 2023). Temperature data from ERA5-Land were considered to be good for Portugal (Almeida and Coelho, 2023), northeastern Brazil (Araújo et al., 2022), the Chinese Qilian Mountains (Zhao and He, 2022), and Italy (Vanella et al., 2022). ...
... ERA5-Land temperature and precipitation data were found to better match the observations for flatter regions than for regions with complex terrain (Almeida and Coelho, 2023;Gomis-Cebolla et al., 2023;Tan et al., 2023). Temperature data from ERA5-Land were considered to be good for Portugal (Almeida and Coelho, 2023), northeastern Brazil (Araújo et al., 2022), the Chinese Qilian Mountains (Zhao and He, 2022), and Italy (Vanella et al., 2022). For Türkiye, ERA5-Land underestimated the daily temperature, but represented temperature trends well (Yilmaz, 2023). ...
Large-sample datasets containing hydrometeorological time series and catchment attributes for hundreds of catchments in a country, many of them known as “CAMELS” (Catchment Attributes and MEteorology for Large-sample Studies), have revolutionized hydrological modelling and have enabled comparative analyses. The Caravan dataset is a compilation of several (CAMELS and other) large-sample datasets with uniform attribute names and data structures. This simplifies large-sample hydrology across regions, continents, or the globe. However, the use of the Caravan dataset instead of the original CAMELS or other large-sample datasets may affect model results and the conclusions derived thereof. For the Caravan dataset, the meteorological forcing data are based on ERA5-Land reanalysis data. Here, we describe the differences between the original precipitation, temperature, and potential evapotranspiration (Epot) data for 1252 catchments in the CAMELS-US, CAMELS-BR, and CAMELS-GB datasets and the forcing data for these catchments in the Caravan dataset. The Epot in the Caravan dataset is unrealistically high for many catchments, but there are, unsurprisingly, also considerable differences in the precipitation data. We show that the use of the forcing data from the Caravan dataset impairs hydrological model calibration for the vast majority of catchments; i.e. there is a drop in the calibration performance when using the forcing data from the Caravan dataset compared to the original CAMELS datasets. This drop is mainly due to the differences in the precipitation data. Therefore, we suggest extending the Caravan dataset with the forcing data included in the original CAMELS datasets wherever possible so that users can choose which forcing data they want to use or at least indicating clearly that the forcing data in Caravan come with a data quality loss and that using the original datasets is recommended. Moreover, we suggest not using the Epot data (and derived catchment attributes, such as the aridity index) from the Caravan dataset and instead recommend that these should be replaced with (or based on) alternative Epot estimates.
... The ERA5 provides relatively good spatial resolution for an extended period (from 1940 to near present). Moreover, many studies have evaluated its performance over various parts of the world (Almeida & Coelho, 2023;El Moussaoui et al., 2023;Hersbach et al., 2020;Tarek et al., 2020;Tarek et al., 2021;Yilmaz, 2023). Therefore, it is considered as the best choice when compared to other products that are available in the study area. ...
Understanding drought occurrence and evolution is important in minimizing the impacts associated with it. This work assesses the performance of 10 commonly used meteorological indices to measure drought in Morocco. The studied indices are Deciles Index (DI), Percent of Normal Index (PNI), Z‐Score Index (ZSI), China‐Z Index (CZI), Rainfall Anomaly Index (RAI), Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), its self‐calibrated variant (scPDSI) and Palmer Z Index (PZI). Rainfall and temperature gridded data is sourced from PERSIANN‐CDR and ERA5, respectively, for the period 1983–2021. The study area exhibits three main climatic regimes; subhumid, semi‐arid and arid, with a drying and warming climate, as depicted by the rainfall and temperature trends analysis. Results show that most rainfall‐based indices perform relatively poorly in drought monitoring in the study area. DI and PNI appear to be inconsistent and abnormally responsive to rainfall. RAI reports droughts 56.5% more frequently and slightly underestimate drought intensity compared to other indices. Similarly, ZSI and CZI largely underestimate drought intensity. PDSI and scPDSI are computationally demanding, often underestimate drought intensity and overestimate drought duration by at least 115% compared to SPI and SPEI. Conversely, PZI can be used for drought onset detection as it reported droughts early compared to the other indices. SPI and SPEI perform overall better regarding their consistent drought identification and severity assessment. However, SPEI is found to be more suitable than SPI in the arid and semi‐arid regions and performed better considering the warming climate of the country.
... Reanalysis v5 (ERA5) [34] is a reanalysis dataset from the European Union. The data are developed by the Copernicus Climate Change Service (C3S), and operated by the European Centre for Medium-Range Weather Forecasts (ECMWF). ...
This study utilized an infrared spotlight Hyperspectral infrared Atmospheric Sounder (HIRAS) and the Medium Resolution Spectral Imager (MERSI) mounted on FY3D cloud products from the National Satellite Meteorological Center of China to obtain methane profile information. Methane inversion channels near 7.7 μm were selected based on the different distribution of methane weighting functions across different seasons and latitudes, and the selected retrieval channels had a great sensitivity to methane but not to other parameters. The optimization method was employed to retrieve methane profiles using these channels. The ozone profiles, temperature, and water vapor of the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis data (ERA5) were applied to the retrieval process. After validating the methane profile concentrations retrieved by HIRAS, the following conclusions were drawn: (1) compared with Civil Aircraft for the Regular Investigation of the Atmosphere Based on an Instrument Container (CARIBIC) flight data, the average correlation coefficient, relative difference, and root mean square error were 0.73, 0.0491, and 18.9 ppbv, respectively, with lower relative differences and root mean square errors in low-latitude regions than in mid-latitude regions. (2) The methane profiles retrieved from May 2019 to September 2021 showed an average error within 60 ppbv compared with the Fourier transform infrared spectrometer (FTIR) station observations of the Infrared Working Group (IRWG) of the Network for the Detection of Atmospheric Composition Change (NDACC). The errors between the a priori and retrieved values, as well as between the retrieved and smoothed values, were larger by around 400–500 hPa. Apart from Toronto and Alzomoni, which had larger peak values in autumn and spring respectively, the mean column averaging kernels typically has a larger peak in summer.
