Predicting peak summer monsoon precipitation over Pakistan in ECMWF SEAS5 and North American Multimodel Ensemble

Ehsan, Muhammad Fahad; Tippett, Michael K.; Kucharski, Fred; Almazroui, Mansour; Ismail, Muhammad

doi:10.1002/joc.6535

Cited by 17 publications

(6 citation statements)

References 60 publications

(73 reference statements)

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“…The atmospheric component has a horizontal and vertical resolution of TCo319L91, translating to a grid size of 36 km. SEAS adopts the most recent Nucleus for European Modelling of the Ocean (NEMO v.3.4.1) (Ehsan et al, 2020, Chevuturi et al, 2021.…”

Section: Ecmwf-s5mentioning

confidence: 99%

Evaluating Seasonal Rainfall Forecast Gridded Models over Sub-Saharan Africa

Atiah

Bendito²,

Muthoni³

2023

Preprint

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The agricultural industry in Africa has recently been impacted by rainfall variability and long-term changes in amount and distribution. Reliable rainfall forecasts on a daily timescale are vital for in-season decision-making. This study evaluated the relative prediction abilities of the European Centre for Medium-Range Weather Forecasts (ECMWF-S5) and the NOAA Climate Prediction System (CFS) gridded rainfall models across Africa and three sub-regions from2012–2022. The results indicate that the performance of both models declines with increasing lead times and improves with aggregated or coarser temporal resolutions. Besides, the ECMWF-S5 data represents observed daily rainfall better than the CFS data at all lead times, particularly in West Africa. On dekadal timescales, ECMWF-S5 outperformed CFS in all sub-regions, confirming its superiority. Both models are great at capturing rainfall at low elevations than at high elevations. CFS tends to overestimate low- and high-intensity rainfall events, while the ECMWF-S5slightly underestimates low-intensity rainfall events and accurately captures high-intensity events over Africa. Overall, the accuracy of these models in forecasting rainfall patterns in Africa varies according to the lead time, region, intensity of rainfall, and elevation. As a result, it is vital to use effective bias-correction approaches on these models to increase their accuracy and dependability for use in several sectors. This study emphasized the potential and shortcomings of the CFS and ECMWF-S5 models for climate impact studies, particularly in West Africa and regions with low elevations.

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Section: Ecmwf-s5mentioning

confidence: 99%

Evaluating Seasonal Rainfall Forecast Gridded Models over Sub-Saharan Africa

Atiah

Bendito²,

Muthoni³

2023

Preprint

View full text Add to dashboard Cite

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“…The use of large-scale gridded data may result in the loss of information on local variations due to micro-scale processes according to multiple scholars [35,90,97,98]. Besides, according to Ehsan et al [99], who also employed SEAS5 to predict peak summer monsoon precipitation over Pakistan, forecasting summer seasonal monsoon precipitation even 1 month in advance is extremely challenging in Southeast Asia and currently, seasonal prediction models have low potential predictability and skill. Hence, a recommendation of the current study is that to achieve skillful and meaningful forecasts, integration of forecast information with ground observations is needed, especially in areas where rainfall patterns are very local, which is precisely the case in Southeast Asia and the Bay of Bengal.…”

Section: Ability Of Hindcasts To Capture the Prevailing Conditionsmentioning

confidence: 99%

Verification of Weather and Seasonal Forecast Information Concerning the Peri-Urban Farmers’ Needs in the Lower Ganges Delta in Bangladesh

Paparrizos¹,

Smolenaars²,

Gbangou³

et al. 2020

Atmosphere

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Skillful weather and seasonal predictions have considerable socio-economic potential and could provide meaningful information to farmers and decision-makers towards agricultural planning and decision-making. Peri-urban farmers in the Lower Ganges Delta need skillful forecast information to deal with increased hydroclimatic variability. In the current study, verification of European Centre for Medium-Range Weather Forecasts’ System 5 (ECMWF SEAS5) seasonal prediction system is performed against ground observations for the Lower Ganges Delta using three skills assessment metrics. Additionally, meteoblue hindcasts are verified for Khulna station according to the peri-urban farmers’ needs and an assessment of onset/offset dates of rainy season is also conducted using the same ground observations. The results indicated that the skill of both examined products is limited during the pre-monsoon and monsoon periods, especially in the west side of the Bay of Bengal. However, during the dry winter season, skill is high, which could lead to potential agricultural benefits concerning irrigation planning. Interannual variability and trend indicated that onset dates have become later and that the length of the rainy season reduced. This could increase the pressure on the already challenging situation the farmers are experiencing, in relation to hydro-climatic variability.

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“…Gubler et al (2020) found that the SEAS5 was reliable in forecasting temperature and precipitation in many regions of South America affected by El Niño-Southern Oscillation (ENSO) variability. In addition, Ehsan et al (2020) showed that SEAS5 could capture the observed climatological mean and variability patterns of peak summer monsoon precipitation over Pakistan, despite being biased over complex topography zones. Chevuturi et al (2021) indicated that SEAS5 performed well in forecasting the dynamical features of a large-scale monsoon 1 month ahead.…”

Section: Introductionmentioning

confidence: 97%

Impacts of snow assimilation on seasonal snow and meteorological forecasts for the Tibetan Plateau

Chen

et al. 2022

The Cryosphere

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Abstract. The Tibetan Plateau (TP) contains the largest amount of snow outside the polar regions and is the source of many major rivers in Asia. An accurate long-range (i.e. seasonal) meteorological forecast is of great importance for this region. The fifth-generation seasonal forecast system of the European Centre for Medium-Range Weather Forecasts (SEAS5) provides global long-range meteorological forecasts including over the TP. However, SEAS5 uses land initial conditions produced by assimilating Interactive Multisensor Snow and Ice Mapping System (IMS) snow data only below 1500 m altitude, which may affect the forecast skill of SEAS5 over mountainous regions like the TP. To investigate the impacts of snow assimilation on the forecasts of snow, temperature and precipitation, twin ensemble reforecasts are initialized with and without snow assimilation above 1500 m altitude over the TP for spring and summer 2018. Significant changes occur in the springtime. Without snow assimilation, the reforecasts overestimate snow cover and snow depth while underestimating daily temperature over the TP. Compared to satellite-based estimates, precipitation reforecasts perform better in the west TP (WTP) than in the east TP (ETP). With snow assimilation, the reforecasts of snow cover, snow depth and temperature are consistently improved in the TP in the spring. However, the positive bias between the precipitation reforecasts and satellite observations worsens in the ETP. Compared to the experiment with no snow assimilation, the snow assimilation experiment significantly increases temperature and precipitation for the ETP and around the longitude 95∘ E. The higher temperature after snow assimilation, in particular the cold bias reduction after initialization, can be attributed to the effects of a more realistic, decreased snowpack, providing favourable conditions for generating more precipitation. Overall, snow assimilation can improve seasonal forecasts through the interaction between land and atmosphere.

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Predicting peak summer monsoon precipitation over Pakistan in ECMWF SEAS5 and North American Multimodel Ensemble

Cited by 17 publications

References 60 publications

Evaluating Seasonal Rainfall Forecast Gridded Models over Sub-Saharan Africa

Evaluating Seasonal Rainfall Forecast Gridded Models over Sub-Saharan Africa

Verification of Weather and Seasonal Forecast Information Concerning the Peri-Urban Farmers’ Needs in the Lower Ganges Delta in Bangladesh

Impacts of snow assimilation on seasonal snow and meteorological forecasts for the Tibetan Plateau

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