An effective post-processing of the North American multi-model ensemble (NMME) precipitation forecasts over the continental US

Khajehei, Sepideh; Ahmadalipour, Ali; Moradkhani, Hamid

doi:10.1007/s00382-017-3934-0

Cited by 38 publications

(26 citation statements)

References 62 publications

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“…8, requires a closer look. Iran's climate during the rainy season is dominated by migrating low-pressure systems mainly from the west and the Mediterranean Sea (Khalili and Rahimi, 2014). The precipitation over the D01 domain hence occurs intermittently and spatially variable, which is usually difficult to predict especially with higher lead times and over the mountainous headwaters of the Karun.…”

Section: Discussionmentioning

confidence: 99%

Bias-corrected and spatially disaggregated seasonal forecasts: a long-term reference forecast product for the water sector in semi-arid regions

Lorenz

Portele

Laux

et al. 2021

Earth Syst. Sci. Data

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Abstract. Seasonal forecasts have the potential to substantially improve water management particularly in water-scarce regions. However, global seasonal forecasts are usually not directly applicable as they are provided at coarse spatial resolutions of at best 36 km and suffer from model biases and drifts. In this study, we therefore apply a bias-correction and spatial-disaggregation (BCSD) approach to seasonal precipitation, temperature and radiation forecasts of the latest long-range seasonal forecasting system SEAS5 of the European Centre for Medium-Range Weather Forecasts (ECMWF). As reference we use data from the ERA5-Land offline land surface rerun of the latest ECMWF reanalysis ERA5. Thereby, we correct for model biases and drifts and improve the spatial resolution from 36 km to 0.1∘. This is performed for example over four predominately semi-arid study domains across the world, which include the river basins of the Karun (Iran), the São Francisco River (Brazil), the Tekeze–Atbara river and Blue Nile (Sudan, Ethiopia and Eritrea), and the Catamayo–Chira river (Ecuador and Peru). Compared against ERA5-Land, the bias-corrected and spatially disaggregated forecasts have a higher spatial resolution and show reduced biases and better agreement of spatial patterns than the raw forecasts as well as remarkably reduced lead-dependent drift effects. But our analysis also shows that computing monthly averages from daily bias-corrected forecasts particularly during periods with strong temporal climate gradients or heteroscedasticity can lead to remaining biases especially in the lowest- and highest-lead forecasts. Our SEAS5 BCSD forecasts cover the whole (re-)forecast period from 1981 to 2019 and include bias-corrected and spatially disaggregated daily and monthly ensemble forecasts for precipitation, average, minimum, and maximum temperature as well as for shortwave radiation from the issue date to the next 215 d and 6 months, respectively. This sums up to more than 100 000 forecasted days for each of the 25 (until the year 2016) and 51 (from the year 2017) ensemble members and each of the five analyzed variables. The full repository is made freely available to the public via the World Data Centre for Climate at https://doi.org/10.26050/WDCC/SaWaM_D01_SEAS5_BCSD (Domain D01, Karun Basin (Iran), Lorenz et al., 2020b), https://doi.org/10.26050/WDCC/SaWaM_D02_SEAS5_BCSD (Domain D02: São Francisco Basin (Brazil), Lorenz et al., 2020c), https://doi.org/10.26050/WDCC/SaWaM_D03_SEAS5_BCSD (Domain D03: basins of the Tekeze–Atbara and Blue Nile (Ethiopia, Eritrea, Sudan), Lorenz et al., 2020d), and https://doi.org/10.26050/WDCC/SaWaM_D04_SEAS5_BCSD (Domain D04: Catamayo–Chira Basin (Ecuador, Peru), Lorenz et al., 2020a). It is currently the first publicly available daily high-resolution seasonal forecast product that covers multiple regions and variables for such a long period. It hence provides a unique test bed for evaluating the performance of seasonal forecasts over semi-arid regions and as driving data for hydrological, ecosystem or climate impact models. Therefore, our forecasts provide a crucial contribution for the disaster preparedness and, finally, climate proofing of the regional water management in climatically sensitive regions.

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Section: Discussionmentioning

confidence: 99%

Bias-corrected and spatially disaggregated seasonal forecasts: a long-term reference forecast product for the water sector in semi-arid regions

Lorenz

Portele

Laux

et al. 2021

Earth Syst. Sci. Data

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“…Forecast guidance from the NMME has become integral to the development of climate outlooks from the NOAA Climate Prediction Center (CPC), the International Research Institute for Climate and Society (IRI), the United States Air Force, and several international meteorological centers. All NMME data are publicly available and have led to the publication of hundreds of research papers, including regional studies of NMME performance (e.g., Infanti & Kirtman, 2014; Ma et al, 2016; Shukla et al, 2019), development of new calibration techniques (Khajehei et al, 2018; Van den Dool et al, 2017) and verification techniques (DelSole & Tippett, 2016), predictability of climate extremes (Becker, 2017; Slater et al, 2019), and applications in a wide range of sectors such as fisheries (Hervieux et al, 2019), drought prediction (Mo & Lettenmaier, 2014), regional prediction (Bolinger et al, 2017), and many other topics.…”

Section: Introductionmentioning

confidence: 99%

Evolution of the North American Multi‐Model Ensemble

Becker

Kirtman

Pegion

2020

Geophysical Research Letters

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The North American Multi-Model Ensemble (NMME) has become integral scientific infrastructure in subseasonal and seasonal climate prediction, advancing applied prediction and scientific understanding of the mechanisms and predictability of short-term climate. The NMME was designed to evolve as old models were retired and new models joined the ensemble. This study examines the assumption that prediction skill will increase as the system evolves, focusing on 2 m temperature, precipitation rate, and sea surface temperature prediction. The common period of 1982-2010 is studied for four configurations of the NMME, approximately representing the operational model suites of 2011, 2012, 2014-2018, and 2019-present. Substantial improvement in temperature prediction over both land and ocean is observed, with little change in global precipitation prediction. Sea surface temperature prediction at longer leads has improved over much of the globe, with the notable exception of the central-eastern tropical Pacific, where prediction skill has declined.Plain Language Summary Predicting the climate more than a few weeks in advance requires forecasters to bet on more than one computer model, similar to the method of diversifying an investment portfolio. Many earlier studies have shown that the combination of predictions generated by different computer models, known as a multi-model ensemble, almost always achieves better forecast skill than using a single model alone. The North American Multi-Model Ensemble (NMME), a subseasonal and seasonal prediction system combining individual North American state-of-the-art climate prediction models, has become an integral part of subseasonal and seasonal research and applications. The NMME has continually evolved, as newer models replace older ones; it is assumed that this evolution will produce more skillful predictions over time. But, until now, this assumption has not been tested. We examine the skill of NMME predictions in four different model combinations, including the oldest configuration, two transitional suites, and the operational configuration as of early 2020. Temperature prediction over both land and ocean has improved noticeably through the exchange of older models for newer ones, but precipitation prediction has not substantially improved. Overcoming the difficulty in precipitation prediction may require higher-resolution climate models in the NMME.

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“…While other techniques can lead to more skillful, reliable and accurate forecasts (e.g. Schepen et al, 2018;Manzanas et al, 2019;Khajehei et al, 2018) or lower biases (e.g. Alidoost et al, 2019) as quantile mapping for example tends to produce negatively skillful forecasts when the raw forecasts are not significantly positively correlated with observations (Zhao et al, 2017), it should be considered that quantile mapping still serves as the reference method in most of the recent bias correction studies.…”

Section: Discussionmentioning

confidence: 99%

Bias-corrected and spatially disaggregated seasonal forecasts: a long-term reference forecast product for the water sector in semi-arid regions

Lorenz¹,

Portele²,

Laux³

et al. 2020

Preprint

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Abstract. Seasonal forecasts have the potential to substantially improve water management particularly in water scarce regions. However, global seasonal forecasts are usually not directly applicable as they are provided at coarse spatial resolutions of at best 36 km and suffer from model biases and drifts. In this study, we therefore apply a bias-correction and spatial-disaggregation (BCSD) approach to seasonal precipitation, temperature and radiation forecasts of the latest long-range seasonal forecasting system SEAS5 of the European Centre for Medium Range Weather Forecasts (ECMWF). As reference we use data from the ERA5-Land offline land surface re-run of the latest ECMWF reanalysis ERA5. By that, we correct for model biases and drifts and improve the spatial resolution from 36 km to 0.1°. This is exemplary performed over 4 predominately semi-arid study domains across the world, which include the river basins of the Karun (Iran), the São Francisco (Brazil), the Tekeze-Atbara and Blue Nile (Sudan, Ethiopia and Eritrea), and the Catamayo-Chira (Ecuador and Peru). Compared against ERA5-Land, the bias-corrected and spatially disaggregated forecasts have a higher spatial resolution and show reduced biases and better agreement of spatial patterns than the raw forecasts. Furthermore, the lead-dependent drift effects are remarkably reduced in the BCSD-forecasts. However, our analysis also showed that computing monthly averages from daily bias-corrected forecasts can lead to statistical inconsistencies particularly during periods and seasons with strong temporal climate gradients or heteroscedasticity. During such periods, particularly the lowest- and highest-lead forecasts can show remaining biases. Our dataset covers the whole (re-)forecast period from 1981 to 2019, for which we provide bias-corrected and spatially disaggregated daily ensemble forecasts for precipitation, average, minimum and maximum temperature as well as for shortwave radiation from the initial date to the coming 214 days. This sums up to more than 100,000 forecasted days for each of the 25 (until the year 2016) and 51 (from the year 2017) ensemble members and each of the 5 analyzed variables. The full repository is made freely available to the public via the World Data Centre for Climate at https://doi.org/10.26050/WDCC/SaWaM_D01_SEAS5_BCSD (Domain D01, Karun Basin (Iran), Lorenz et al., 2020b), https://doi. org/10.26050/WDCC/SaWaM_D02_SEAS5_BCSD (Domain D02: São Francisco Basin (Brazil), Lorenz et al., 2020c), https://doi.org/10.26050/WDCC/SaWaM_D03_SEAS5_BCSD (Domain D03: Tekeze-Atbara and Blue Nile Basins (Ethiopia, Eritrea, Sudan), Lorenz et al., 2020d), and https://doi.org/10.26050/WDCC/SaWaM_D04_SEAS5_BCSD (Domain D04: Catamayo-Chira Basin (Ecuador, Peru), Lorenz et al., 2020a). It is currently the first publicly available daily high-resolution seasonal forecast product that covers multiple regions and variables for such a long period. It hence provides a unique test-bed for evaluating the performance of seasonal forecasts over semi-arid regions and as driving data for hydrological, ecosystem or climate impact models. Therefore, our forecasts provide a crucial contribution for the disaster preparedness and, finally, climate proofing of the regional water management in climatically sensitive regions.

show abstract

An effective post-processing of the North American multi-model ensemble (NMME) precipitation forecasts over the continental US

Cited by 38 publications

References 62 publications

Bias-corrected and spatially disaggregated seasonal forecasts: a long-term reference forecast product for the water sector in semi-arid regions

Bias-corrected and spatially disaggregated seasonal forecasts: a long-term reference forecast product for the water sector in semi-arid regions

Evolution of the North American Multi‐Model Ensemble

Bias-corrected and spatially disaggregated seasonal forecasts: a long-term reference forecast product for the water sector in semi-arid regions

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