Did ERA5 Improve Temperature and Precipitation Reanalysis over East Africa?

Gleixner, Stephanie; Demissie, Teferi; Diro, G. T.

doi:10.3390/atmos11090996

Cited by 183 publications

(132 citation statements)

References 63 publications

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“…In Africa, the local overestimation of precipitation over Lake Victoria, Lake Tanganyika, and the Ethiopian highlands is in agreement with existing literature (Gleixner et al, 2020). Over the northern Great plains in North America in summer, we observe a slight dry bias as in Xu et al (2019), in the western part of the region.…”

Section: Summary and Discussionsupporting

confidence: 92%

A Comparison of Moderate and Extreme ERA‐5 Daily Precipitation With Two Observational Data Sets

Rivoire

Martius

Naveau

2021

Earth and Space Science

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Natural hazards related to extreme precipitation (river floods, flash floods, landslides, debris flows, and avalanches) cause casualties, damages to infrastructures and buildings, and have direct and indirect economic impacts (MunichRE, 2018). For infrastructure planning and prevention measures, information about rare events, that is, events that occur on average only once in a hundred years, is important. Such information can be obtained from precipitation data with statistical tools. Assessing the accuracy in high quantiles depends on spatial domain sizes and temporal availability. Different types of global precipitation data sets are available (Sun et al., 2018): global precipitation data sets are based on ground observations, satellite observations, combinations of ground observations and satellite observations, and on short-term weather model forecasts in reanalyzes data sets. Reanalyzes combine past observations with weather forecast models to reconstruct past weather. The main advantage of this type of precipitation data set is its regular spatial and temporal coverage. Reanalyzes ensure consistency of the precipitation data with the atmospheric conditions, which are important for weather and climate process studies. Here, we focus on ERA-5 precipitation (C3S, 2017). ERA-5 is the latest reanalysis product from the European Centre for Medium-Range Weather Forecasts (ECMWF). ERA-5 precipitation is computed in short-term forecast started from reanalysis initial conditions (Hennermann, 2020). The ERA-5 precipitation production process does not include precipitation observation inputs. Hence comparison with observational data makes sense, keeping in mind that observation data have (partly substantial) uncertainties as well (Kulie et al., 2010;Prein & Gobiet, 2017;Sun et al., 2018). ERA-5 precipitation has already been widely used since its release in 2018, but very few assessments of this data set have been conducted over large regions. Only precipitation over restricted areas and precipitation associated with specific type of events have been assessed (

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Section: Summary and Discussionsupporting

confidence: 92%

A Comparison of Moderate and Extreme ERA‐5 Daily Precipitation With Two Observational Data Sets

Rivoire

Martius

Naveau

2021

Earth and Space Science

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“…For daily mean, maximum and minimum temperature, we used ERA5 data 24 provided at a spatial resolution of 0.25° × 0.25°. ERA5 is the most recent reanalyses product and outperforms ERA-Interim for East Africa 25 . The weather variables were created for the time period of available yield statistics (2009–2019), including weather data for the year 2008 in case of growing seasons starting already at the end of 2008.…”

Section: Methodsmentioning

confidence: 99%

Robustly forecasting maize yields in Tanzania based on climatic predictors

Laudien

Schauberger

Makowski

et al. 2020

Sci Rep

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Seasonal yield forecasts are important to support agricultural development programs and can contribute to improved food security in developing countries. Despite their importance, no operational forecasting system on sub-national level is yet in place in Tanzania. We develop a statistical maize yield forecast based on regional yield statistics in Tanzania and climatic predictors, covering the period 2009–2019. We forecast both yield anomalies and absolute yields at the sub-national scale about 6 weeks before the harvest. The forecasted yield anomalies (absolute yields) have a median Nash–Sutcliffe efficiency coefficient of 0.72 (0.79) in the out-of-sample cross validation, which corresponds to a median root mean squared error of 0.13 t/ha for absolute yields. In addition, we perform an out-of-sample variable selection and produce completely independent yield forecasts for the harvest year 2019. Our study is potentially applicable to other countries with short time series of yield data and inaccessible or low quality weather data due to the usage of only global climate data and a strict and transparent assessment of the forecasting skill.

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“…In terms of the quality of near-surface temperatures in ERA5, Oueslati et al (2017) and Barbier et al (2018) assessed ERA-Interim, which ERA5 is an improvement of, against the Global Summary Of the Day (GSOD) observational dataset, and concluded that it was suitable for heatwave study in the Sahel. Furthermore, Gleixner et al (2020) showed that in ERA5, near-surface temperatures are less climatologically biased, and their interannual variability better represented than in ERA-Interim across Africa, including in the Sahel band. Similarly, Wang et al (2017), Tall et al (2019), Wright et al (2020) and Hersbach et al (2020) proved that ERA5 represents relatively well the observed OLR over the tropical domain, confirming its suitability as reference dataset for the analysis of tropical modes.…”

Section: Description Of the Ecmwf Ens Extended-range Forecasting Systemmentioning

confidence: 99%

Prediction skill of Sahelian heatwaves out to subseasonal lead times and importance of atmospheric tropical modes of variability

et al. 2021

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Global warming has increased the frequency of extreme weather events, including heatwaves, over recent decades. Heat early warning systems are being set up in many regions as a tool to mitigate their effects. Such systems are not yet implemented in the West African Sahel, partly because of insufficient knowledge on the skill of models to predict them. The present study addresses this gap by examining the skill of the ECMWF ENS extended-range forecasting system (ENS-ext) to predict Sahelian heatwaves out to subseasonal lead-times. It also assesses the importance of tropical modes of variability, which were previously identified as important large-scale drivers of heatwave occurrence in the Sahel. The results show that ENS-ext is able to predict Sahelian heatwaves with significant skill out to lead-week 2–3. With increasing lead-time, heatwaves are more predictable at nighttime than at daytime. Likewise, the pre-monsoon season heatwaves have a longer predictability than those occurring in late winter. The model is also able to relatively well simulate the observed relationship between heatwave occurrence and tropical mode activity. Furthermore, the prediction skill is better during the active phases of the modes, suggesting that they are good sources of heatwave predictability. Therefore, improving the representation of tropical modes in models will positively impact heatwave prediction at the subseasonal scale in the Sahel, and gain more time and precision for anticipatory actions.

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Did ERA5 Improve Temperature and Precipitation Reanalysis over East Africa?

Cited by 183 publications

References 63 publications

A Comparison of Moderate and Extreme ERA‐5 Daily Precipitation With Two Observational Data Sets

A Comparison of Moderate and Extreme ERA‐5 Daily Precipitation With Two Observational Data Sets

Robustly forecasting maize yields in Tanzania based on climatic predictors

Prediction skill of Sahelian heatwaves out to subseasonal lead times and importance of atmospheric tropical modes of variability

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