Assessment of S2S ensemble extreme precipitation forecasts over Europe

Rivoire, Pauline; Martius, Olivia; Naveau, Philippe; Tuel, Alexandre

doi:10.5194/egusphere-2022-1401

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…Sub‐seasonal forecasts are more often represented as a week‐to‐week variation and are mostly evaluated on a weekly scale (Manrique‐Suñén et al., 2020; Rivoire et al., 2022; C. J. White et al., 2022).…”

Section: Resultsmentioning

confidence: 99%

Assessing Multi‐Source Precipitation Estimates in Nepal: A Benchmark for Sub‐Seasonal Model Assessment

Nepal,

Bao,

et al. 2024

JGR Atmospheres

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Sub‐seasonal to seasonal (S2S) prediction provides an extended range of lead time for decision‐makers across multiple sectors. S2S forecast is crucial for a country that are susceptible to hydro‐meteorological disasters like Nepal. However, S2S forecast requires assessment with reliable datasets before its application. Since, Nepal lacks a dense station network, multi‐source precipitation estimates (MPEs) are the obvious alternatives. Therefore, using classical evaluation metrics and extreme precipitation indices, this study assessed eleven high‐resolution datasets against 159‐gauge stations over Nepal for the 2001–2020 period. These datasets are classified as gauge‐interpolated (APHRODITE), merged (TPMFD, MSWEP, CLDAS, and CHIRPS), satellite‐based (IMERGV7, IMERGV6, CMORPH, and PERSIANN), and reanalysis (ERA5‐L and HAR). Satellite datasets (except IMERGV7) failed to capture the spatial pattern of mean annual precipitation, while others broadly captured it. Most MPEs struggled to accurately estimate wet season precipitation compared to dry season. Furthermore, increasing estimation error from light to extreme precipitation and decreasing skill metrics from flat to complex terrain, demonstrate the intensity and terrain‐specific limitations of MPEs. In terms of precipitation extremes, APHRODITE exhibits the highest skill, followed by MSWEP, TPMFD, HAR, ERA5‐L, IMERGV7, CLDAS, IMERGV6, CHIRPS, CMORPH, and PERSIANN. IMERGV7 exhibits increased skill in detecting extreme precipitation and precipitation over orography against IMERGV6. APHRODITE and TPMFD datasets performed consistently well in all scales, including weekly anomaly, with an average anomaly correlation of 0.84 and 0.66 respectively. However, since APHRODITE is not widely available, TPMFD can serve as a benchmark dataset for evaluating high‐resolution S2S forecasts within the study region.

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Section: Resultsmentioning

confidence: 99%

Assessing Multi‐Source Precipitation Estimates in Nepal: A Benchmark for Sub‐Seasonal Model Assessment

Nepal,

Bao,

et al. 2024

JGR Atmospheres

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“…We also evaluate our model's capacity to detect extreme precipitation events. We define extreme precipitation as precipitation that exceeds the 90th percentile (Rivoire et al, 2023). We compute the empirical 90th percentile after excluding precipitation values below 1 mm.…”

Section: Assessment Of the Forecast Qualitymentioning

confidence: 99%

“…For extreme precipitation forecast, Olaniyan et al (2018) found that the S2S models could not reliably forecast the extreme precipitation during the West African monsoon. In addition, Rivoire et al (2023) assessed the forecast skill of the S2S extreme precipitation forecast and found high heterogeneity in the forecast skill over regions and seasons in Europe.…”

Section: Introductionmentioning

confidence: 99%

Improving subseasonal forecast of precipitation in Europe by combining a stochastic weather generator with dynamical models

Krouma,

Specq,

Magnusson

et al. 2024

Quart J Royal Meteoro Soc

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We propose a forecasting tool for precipitation based on analogues of circulation defined from 5‐day hindcasts and a stochastic weather generator that we call “HC–SWG.” In this study, we aim to improve the forecast of European precipitation for subseasonal lead times (from 2 to 4 weeks) using the HC–SWG. We designed the HC–SWG to generate an ensemble precipitation forecast from the European Centre of Medium‐range Weather Forecasts (ECMWF) and Centre National de la Recherche Météorologique (CNRM) subseasonal‐to‐seasonal ensemble reforecasts. We define analogues from 5‐day ensemble reforecast of Z500 from the ECMWF (11 members) and CNRM (10 members) models. Then, we generate a 100‐member ensemble for precipitation over Europe. We evaluate the skill of the ensemble forecast using probabilistic skill scores such as the continuous ranked probability skill score (CRPSS) and receiver operating characteristic curve. We obtain reasonable forecast skill scores within 35 days for different locations in Europe. The CRPSS shows positive improvement with respect to climatology and persistence at the station level. The HC–SWG shows a capacity to distinguish between events and non‐events of precipitation within 15 days at the different stations. We compare the HC–SWG forecast with other precipitation forecasts to further confirm the benefits of our method. We found that the HC–SWG shows improvement against the ECMWF precipitation forecast until 25 days.

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Assessment of subseasonal-to-seasonal (S2S) ensemble extreme precipitation forecast skill over Europe

Rivoire,

Martius,

Naveau

et al. 2023

Nat. Hazards Earth Syst. Sci.

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Abstract. Heavy precipitation can lead to floods and landslides, resulting in widespread damage and significant casualties. Some of its impacts can be mitigated if reliable forecasts and warnings are available. Of particular interest is the subseasonal-to-seasonal (S2S) prediction timescale. The S2S prediction timescale has received increasing attention in the research community because of its importance for many sectors. However, very few forecast skill assessments of precipitation extremes in S2S forecast data have been conducted. The goal of this article is to assess the forecast skill of rare events, here extreme precipitation, in S2S forecasts, using a metric specifically designed for extremes. We verify extreme precipitation events over Europe in the S2S forecast model from the European Centre for Medium-Range Weather Forecasts. The verification is conducted against ERA5 reanalysis precipitation. Extreme precipitation is defined as daily precipitation accumulations exceeding the seasonal 95th percentile. In addition to the classical Brier score, we use a binary loss index to assess skill. The binary loss index is tailored to assess the skill of rare events. We analyze daily events that are locally and spatially aggregated, as well as 7 d extreme-event counts. Results consistently show a higher skill in winter compared to summer. The regions showing the highest skill are Norway, Portugal and the south of the Alps. Skill increases when aggregating the extremes spatially or temporally. The verification methodology can be adapted and applied to other variables, e.g., temperature extremes or river discharge.

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Assessment of S2S ensemble extreme precipitation forecasts over Europe

Cited by 3 publications

References 20 publications

Assessing Multi‐Source Precipitation Estimates in Nepal: A Benchmark for Sub‐Seasonal Model Assessment

Assessing Multi‐Source Precipitation Estimates in Nepal: A Benchmark for Sub‐Seasonal Model Assessment

Improving subseasonal forecast of precipitation in Europe by combining a stochastic weather generator with dynamical models

Assessment of subseasonal-to-seasonal (S2S) ensemble extreme precipitation forecast skill over Europe

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