Downscaling of seasonal ensemble forecasts to the convection‐permitting scale over the Horn of Africa using the <scp>WRF</scp> model

Mori, P.; Schwitalla, Thomas; Ware, Markos Budusa; Warrach‐Sagi, Kirsten; Wulfmeyer, Volker

doi:10.1002/joc.6809

Cited by 5 publications

(1 citation statement)

References 54 publications

(69 reference statements)

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“…The WRF model has been employed for applications of, short-term weather forecasting (Gao et al, 2022;Yang et al, 2021), seasonal forecasts (Mori et al, 2020;Siegmund et al, 2015), historical climate simulations (Fersch & Kunstmann, 2014;Shang et al, 2022), and future climate projections Warrach-Sagi et al, 2013).…”

Section: Model and Methodologymentioning

confidence: 99%

Acceleration of the hydrological cycle under global warming? An age-weighted regional water tagging approach

Wei

Arnault

Rummler

et al. 2023

Preprint

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Global warming is assumed to accelerate the global water cycle. However, quantification of the acceleration and regional analyses remain open. Accordingly, in this study we address the fundamental hydrological question: Is the water cycle regionally accelerating/decelerating under global warming? For our investigation we have implemented the age-weighted regional water tagging approach into the Weather Research and Forecasting WRF model, namely WRF-age, to follow the atmospheric water pathways and to derive atmospheric water residence times accordingly. Moreover, we have implemented the three-dimensional online budget analysis of the total, tagged, and aged atmospheric water into WRF-age to provide a prognostic equation of the atmospheric water residence times. The newly developed, physics-based WRF-age model is used to regionally downscale the reanalysis of ERA-Interim and the MPI-ESM Representative Concentration Pathway 8.5 scenario (RCP8.5) simulation exemplarily for an East Asian monsoon region, i.e., the Poyang Lake basin (the tagged moisture source area), for two 10-year slices of historical (1980-1989) and future (2040-2049) times. In comparison to the historical simulation, the future 2-meter temperature rises by +1.4 °C, evaporation increases by +6%, and precipitation decreases by -38% under RCP8.5 on average. In this context, global warming leads to regionally decreased residence times for the tagged water vapor by 8 hours and the tagged condensed moisture by 12 hours in the atmosphere, but increased transit times for the tagged precipitation by 4 hours over the land surface that is partly attributed to a slower fallout of precipitating moisture components in the atmosphere under global warming.

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Section: Model and Methodologymentioning

confidence: 99%

Acceleration of the hydrological cycle under global warming? An age-weighted regional water tagging approach

Wei

Arnault

Rummler

et al. 2023

Preprint

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Ensemble‐based monthly to seasonal precipitation forecasting for Iran using a regional weather model

Najafi,

Kuchak

2024

Intl Journal of Climatology

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Monthly and seasonal precipitation forecasts can potentially assist disaster risk reduction and water resource management. The aim of this study is to assess the skill of an ensemble framework for monthly and seasonal precipitation forecasts over Iran by focusing on system design and model performance evaluation. The ensemble framework presented in this paper is based on a one‐way double‐nested model that uses Weather Research and Forecasting (WRF) modelling system to downscale the second version of the NCEP Climate Forecast System (CFSv2). The performance is evaluated for October–April period at 1‐, 2‐ and 3‐month lead time. Multiple initial conditions, model parameters and physics are used to construct ensemble members. Using quantile mapping (QM) method, the outputs of the model are bias corrected. This methodology is applied for two periods: (i) climatology from 2000 to 2019 to evaluate the model's ability to precipitation forecast on a monthly and seasonal time scale; (ii) the forecast for 2020 to evaluate the model's performance operationally. The model evaluation is performed using the continuous (e.g., RMSE, r, MBE, NSE) and categorical (e.g., POD, FAR, PC, Heidke skill score) assessment metrics. We conclude that model outputs were improved by the QM bias correction method. According to results, the proposed ensemble framework can accurately predict amount of monthly and seasonal precipitation in Iran with an accuracy of 58 to 45% for lead‐1 to 3. For all three lead times, the averaged NSE, CC, MBE, and RMSE were 0.4, 0.56, −15.5, and 41.6, indicating that the framework has reasonable performance. Our results suggest that precipitation forecast accuracy varies with lead time, so the accuracy for lead‐1 is higher than lead‐2 and lead‐3. Additionally, the model's accuracy differs in various regions of the country and decreases in the spring. Using the approach for an operational case, it was found that the spatial features of precipitation predicted by the framework were close to those observed.

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Influence of mesoscale sea‐surface temperature structures on the Mediterranean cyclone Ianos in convection‐permitting simulations: Contributions of surface warming and cold wakes

Mishra,

Jangir,

Strobach

2024

Quart J Royal Meteoro Soc

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This study examined the influence of mesoscale sea‐surface temperature (SST) structures and warming on the tropical‐like cyclone Ianos. The controlling mechanisms were investigated using a set of sensitivity experiments with the non‐hydrostatic weather research and forecasting model. A simulation forced by a high‐resolution SST field from the mesoscale eddy‐resolving HYCOM reanalysis (CTRLH) performed better than a simulation forced by a coarse‐resolution SST field from Optimum Interpolation Sea Surface Temperature (OISST) in terms of precipitation, wind intensity, and landfall location. The suppression of mesoscale SST variability affected the cyclone's intensity, its path, and associated processes. This effect depends on the nature of the anomalies (cold/warm). The suppression of mesoscale SST forcing intensified wind and precipitation in the presence of cold anomalies, leading to a northwestward shift of the storm's path during its mature phase. The magnitude of these changes varied with the scale of smoothing (mesoscale SST). Compared to the CTRLH simulation, a simulation in which a 60‐km smoothing filter was applied to the SST field resulted in a cyclone centered slightly to the west (or delayed by 3–6 hr), which was further shifted to the west (or delayed) when the smoothing filter was increased to 150 km. The intensification of wind and precipitation may be associated with the reduction in the storm's cold wake, which would lead to enhanced turbulent fluxes and increased atmospheric water vapor. These results highlight the role of small‐scale air–sea interaction processes that could have a strong impact on Mediterranean cyclones' path and intensity.

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Downscaling of seasonal ensemble forecasts to the convection‐permitting scale over the Horn of Africa using the WRF model

Cited by 5 publications

References 54 publications

Acceleration of the hydrological cycle under global warming? An age-weighted regional water tagging approach

Acceleration of the hydrological cycle under global warming? An age-weighted regional water tagging approach

Ensemble‐based monthly to seasonal precipitation forecasting for Iran using a regional weather model

Influence of mesoscale sea‐surface temperature structures on the Mediterranean cyclone Ianos in convection‐permitting simulations: Contributions of surface warming and cold wakes

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