Toward the Improvement of Subseasonal Prediction in the National Centers for Environmental Prediction Global Ensemble Forecast System

Zhu, Yuejian; Zhou, Xiaqiong; Li, Wei; Hou, Dingchen; Melhauser, Christopher; Sinsky, Eric; Peña, M.; Fu, Bo; Guan, Hong; Kolczynski, Walter C.; Wobus, Richard; Tallapragada, Vijay

doi:10.1029/2018jd028506

Cited by 33 publications

(26 citation statements)

References 32 publications

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“…Here, we assess the application of EMOS and two machine learning post-processing methods to 1-35 day forecasts of the 2-m maximum air temperature for all land grid points over East Asia • N, 70-140 • E) using the raw ensemble forecast from EMC-GEFS described by Zhu [44]. The calendar year 2016 is used as the validation period.…”

Section: Verification Methodsmentioning

confidence: 99%

Prediction Skill of Extended Range 2-m Maximum Air Temperature Probabilistic Forecasts Using Machine Learning Post-Processing Methods

Peng

Zhi

et al. 2020

Atmosphere

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The extended range temperature prediction is of great importance for public health, energy and agriculture. The two machine learning methods, namely, the neural networks and natural gradient boosting (NGBoost), are applied to improve the prediction skills of the 2-m maximum air temperature with lead times of 1–35 days over East Asia based on the Environmental Modeling Center, Global Ensemble Forecast System (EMC-GEFS), under the Subseasonal Experiment (SubX) of the National Centers for Environmental Prediction (NCEP). The ensemble model output statistics (EMOS) method is conducted as the benchmark for comparison. The results show that all the post-processing methods can efficiently reduce the prediction biases and uncertainties, especially in the lead week 1–2. The two machine learning methods outperform EMOS by approximately 0.2 in terms of the continuous ranked probability score (CRPS) overall. The neural networks and NGBoost behave as the best models in more than 90% of the study area over the validation period. In our study, CRPS, which is not a common loss function in machine learning, is introduced to make probabilistic forecasting possible for traditional neural networks. Moreover, we extend the NGBoost model to atmospheric sciences of probabilistic temperature forecasting which obtains satisfying performances.

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Section: Verification Methodsmentioning

confidence: 99%

Prediction Skill of Extended Range 2-m Maximum Air Temperature Probabilistic Forecasts Using Machine Learning Post-Processing Methods

Peng

Zhi

et al. 2020

Atmosphere

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“…Zampieri et al (2018) indicate high potential for sea ice prediction in the sub-seasonal timescales, especially for late summer forecasts, and advocate the need to reduce systematic seasonally dependent model biases and develop advanced DA capabilities to constrain sea ice extent and sea ice thickness. Zhu et al (2018) showed that MJO forecast skill can be improved in the NCEP Global Ensemble Forecast System (GEFS) from an average of 12.5 days (control) to nearly 22 days by (1) adding stochastic physical perturbations, (2) considering ocean impacts by using a two-tiered sea surface temperature approach (combing an analysis product with a forecast of SST from a coupled model), and (3) applying a new scale-aware convection scheme to improve the model physics for tropical convection. They also showed improved ensemble mean anomaly correlation of 500-hPa geopotential height in the extratropics over weeks 3 and 4.…”

Section: Prediction At Subseasonal To Seasonal Timescalesmentioning

confidence: 99%

Observational Needs for Improving Ocean and Coupled Reanalysis, S2S Prediction, and Decadal Prediction

et al. 2019

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Developments in observing system technologies and ocean data assimilation (DA) are symbiotic. New observation types lead to new DA methods and new DA methods, such as coupled DA, can change the value of existing observations or indicate where new observations can have greater utility for monitoring and prediction. Practitioners of DA are encouraged to make better use of observations that are already available, for example, taking advantage of strongly coupled DA so that ocean observations can be used to improve atmospheric analyses and vice versa. Ocean reanalyses are useful for the analysis of climate as well as the initialization of operational long-range prediction models. There are many remaining challenges for ocean reanalyses due to biases and abrupt changes in the ocean-observing system throughout its history, the presence of biases and drifts in models, and the simplifying assumptions made in DA solution methods. From a governance point of view, more support is needed to bring the ocean-observing and DA communities together. For prediction applications, there is wide agreement that protocols are needed for rapid communication of oceanobserving data on numerical weather prediction (NWP) timescales. There is potential for new observation types to enhance the observing system by supporting prediction on multiple timescales, ranging from the typical timescale of NWP, covering hours to weeks, out to multiple decades. Better communication between DA and observation communities is encouraged in order to allow operational prediction centers the ability to provide guidance for the design of a sustained and adaptive observing network.

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“…The atmospheric model is the NAVy Global Environmental Model (NAVGEM) (Hogan, 2014), and the ocean and sea ice models are part of the Global Ocean Forecasting System version 3.1 (GOFS 3.1) (Metzger et al, 2014). Navy‐ESPC is unique compared to many other global coupled modeling systems (Infanti & Kirtman, 2016; Lin et al, 2016; Saha, 2014; Sun et al, 2018; Vitart et al, 2017; Zhu, 2018) in that the ocean model horizontal resolution is eddy resolving in both the ensemble (i.e., probabilistic) and deterministic configurations. In many operational coupled modeling systems, the ocean and sea ice components are included primarily to provide better atmospheric bottom/surface boundary conditions but only at eddy‐permitting resolution (~1/4° or coarser).…”

Section: Introductionmentioning

confidence: 99%

The Navy's Earth System Prediction Capability: A New Global Coupled Atmosphere‐Ocean‐Sea Ice Prediction System Designed for Daily to Subseasonal Forecasting

Barton¹,

Metzger

Reynolds³

et al. 2021

Earth and Space Science

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This paper describes the new global Navy Earth System Prediction Capability (Navy-ESPC) coupled atmosphere-ocean-sea ice prediction system developed at the Naval Research Laboratory (NRL) for operational forecasting for timescales of days to the subseasonal. Navy-ESPC will become operational in late 2020, and this system will be the first time the NRL operational partner, Fleet Numerical Meteorology and Oceanography Center, will provide global coupled atmosphere-ocean-sea ice forecasts, with atmospheric forecasts extending to 16 days, and ocean and sea ice ensemble forecasts. Two configurations of the system are validated: (1) a low-resolution 16-member ensemble system and (2) a highresolution deterministic system. A unique aspect of the Navy-ESPC is that the global ocean model is eddy resolving at 1/12° in the ensemble and at 1/25° in the deterministic configurations. The component models are current Navy operational systems: NAVy Global Environmental Model (NAVGEM) for the atmosphere; HYbrid Coordinate Ocean Model (HYCOM) for the ocean; and Community Ice CodE (CICE) for the sea ice. Physics updates to improve the simulation of equatorial phenomena, particularly the Madden Julian Oscillation (MJO) were introduced into NAVGEM. The low resolution ensemble configuration and highresolution deterministic configuration are evaluated based on analyses and forecasts from January 2017 to January 2018. Navy-ESPC ensemble forecast skill for large-scale atmospheric phenomena, such as the Madden-Julian Oscillation (MJO), North Atlantic Oscillation (NAO), Antarctic Oscillation (AAO), and other indices, is comparable to that of other numerical weather prediction (NWP) centers. Ensemble forecasts of ocean sea surface temperatures perform better than climatology in the tropics and mid-latitudes out to 60 days. In addition, the Navy-ESPC Pan-Arctic and Pan-Antarctic sea ice extent predictions perform better than climatology out to about 45 days, although the skill is dependent on season.

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Toward the Improvement of Subseasonal Prediction in the National Centers for Environmental Prediction Global Ensemble Forecast System

Cited by 33 publications

References 32 publications

Prediction Skill of Extended Range 2-m Maximum Air Temperature Probabilistic Forecasts Using Machine Learning Post-Processing Methods

Prediction Skill of Extended Range 2-m Maximum Air Temperature Probabilistic Forecasts Using Machine Learning Post-Processing Methods

Observational Needs for Improving Ocean and Coupled Reanalysis, S2S Prediction, and Decadal Prediction

The Navy's Earth System Prediction Capability: A New Global Coupled Atmosphere‐Ocean‐Sea Ice Prediction System Designed for Daily to Subseasonal Forecasting

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