GEOS-5 seasonal forecast system

Borovikov, Anna; Cullather, Richard I.; Kovach, Robin; Marshak, Jelena; Vernières, Guillaume; Vikhliaev, Yury; Zhao, Bin; Zhao, Lilong

doi:10.1007/s00382-017-3835-2

Cited by 39 publications

(23 citation statements)

References 74 publications

(62 reference statements)

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“…The GEOS‐S2S‐1 forecasting system demonstrated reasonable predictive skill of hemispheric sea ice cover (Borovikov et al, ), with June forecasts explaining approximately 50% of the observed variance in the September Arctic ice extent (Figure ). In producing GEOS‐S2S‐2, a key goal was to assess the best predictive skill attainable with the GEOS‐S2S‐2 model configuration.…”

Section: Seasonal and Subseasonal Forecast Assessmentmentioning

confidence: 97%

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GEOS‐S2S Version 2: The GMAO High‐Resolution Coupled Model and Assimilation System for Seasonal Prediction

et al. 2020

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The Global Modeling and Assimilation Office (GMAO) has recently released a new version of the Goddard Earth Observing System (GEOS) Subseasonal to Seasonal prediction (S2S) system, GEOS‐S2S‐2, that represents a substantial improvement in performance and infrastructure over the previous system. The system is described here in detail, and results are presented from forecasts, climate equillibrium simulations, and data assimilation experiments. The climate or equillibrium state of the atmosphere and ocean showed a substantial reduction in bias relative to GEOS‐S2S‐1. The GEOS‐S2S‐2 coupled reanalysis also showed substantial improvements, attributed to the assimilation of along‐track absolute dynamic topography. The forecast skill on subseasonal scales showed a much improved prediction of the Madden‐Julian Oscillation in GEOS‐S2S‐2, and on a seasonal scale the tropical Pacific forecasts show substantial improvement in the east and comparable skill to GEOS‐S2S‐1 in the central Pacific. GEOS‐S2S‐2 anomaly correlations of both land surface temperature and precipitation were comparable to GEOS‐S2S‐1 and showed substantially reduced root‐mean‐square error of surface temperature. The remaining issues described here are being addressed in the development of GEOS‐S2S Version 3, and with that system GMAO will continue its tradition of maintaining a state‐of‐the‐art seasonal prediction system for use in evaluating the impact on seasonal and decadal forecasts of assimilating newly available satellite observations, as well as evaluating additional sources of predictability in the Earth system through the expanded coupling of the Earth system model and assimilation components.

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Section: Seasonal and Subseasonal Forecast Assessmentmentioning

confidence: 97%

“…There are many differences between the ocean analysis methods in the GEOS-S2S-1 Borovikov et al (2017) and GEOS-S2S-2 systems. The major differences are summarized in Table 2.…”

Section: 1029/2019jd031767mentioning

confidence: 99%

GEOS‐S2S Version 2: The GMAO High‐Resolution Coupled Model and Assimilation System for Seasonal Prediction

et al. 2020

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“…Hindcasts of RZSM are generated by forcing the hydrologic models with NASA's Goddard Earth Observing System (GEOS) Atmosphere-Ocean General Circulation Model, version 5 (GEOS; [ (Borovikov et al, 2017) ]) Seasonal-to-Interannual Forecast System. The eleven ensemble members of version 1 of this forecast system that were used in the North American Multi-Model Ensemble (NMME) project are used in the forecast portion of this study.…”

Section: Input Observed Forcings and Climate Forecastsmentioning

confidence: 99%

Improving early warning of drought-driven food insecurity in Southern Africa using operational hydrological monitoring and forecasting products

Shukla¹,

Arsenault²,

Hazra³

et al. 2019

Preprint

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Abstract. The region of southern Africa (SA) has a fragile food economy and is vulnerable to frequent droughts. In 2015–2016, an El Niño-driven drought resulted in major maize production shortfalls, food price increases, and livelihood disruptions that pushed 29 million people into severe food insecurity. Interventions to mitigate food insecurity impacts require early warning of droughts – preferably as early as possible before the harvest season (typically, starting in April) and lean season (typically, starting in November). Hydrologic monitoring and forecasting systems provide a unique opportunity to support early warning efforts, since they can provide regular updates on available rootzone soil moisture (RZSM), a critical variable for crop yield, and provide forecasts of RZSM by combining the estimates of antecedent soil moisture conditions with climate forecasts. For SA, this study documents the predictive capabilities of a recently developed NASA Hydrological Forecasting and Analysis System (NHyFAS). The NHyFAS system's ability to forecast and monitor the 2015/2016 drought event is evaluated. The system's capacity to explain interannual variations in regional crop yield and identify below-normal crop yield events is also evaluated. Results show that the NHyFAS products would have identified the regional severe drought event, which peaked during December–February of 2015/2016, at least as early as 1 November 2015. Next, it is shown that February RZSM forecasts produced as early as 1 November (4–5 months before the start of harvest and about a year before the start of the next lean season) correlate fairly well with regional crop yields (r = 0.49). The February RZSM monitoring product, available in early March, correlates with the regional crop yield with higher skill (r = 0.79). It is also found that when the February RZSM forecast produced on November 1 is indicated to be in the lowest tercile, the detrended regional crop yield is below normal about two-thirds (significance level ~ 86 %) of the time. Furthermore, when the February RZSM monitoring product (available in early March) indicates a lowest tercile value, the crop yield is always below normal, at least over the sample years considered. These results indicate that the NHyFAS products can effectively support food insecurity early warning in the SA region.

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“…expected to occur simply by random chance(Shukla et al 2016). Correlation and ETS 425 values are higher for the bias-corrected GEOS forecasts in the first lead month 426 (November) and again in lead months 3 and 4 (February and March), which may reflect 427 the ability of GEOS to capture teleconnection influences on meteorological forcing, such 428 as from the El Niño-Southern Oscillation (ENSO;Borovikov et al 2017). Also, overall 429averaged skill values are shown in the lower corner of each figure panel.…”

mentioning

confidence: 95%

“…The meteorological forcing for the land models during the forecast period are 272 derived from seasonal forecasts produced by the GMAO (Borovikov et al 2017). We 273 use these data since the GMAO system provides the full complement of required inputs 274 (e.g., temperature, radiation, winds), whereas the NMME database only offers monthly 275 precipitation and temperature.…”

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confidence: 99%

The NASA Hydrological Forecast System for Food and Water Security Applications

Arsenault

Shukla

Hazra

et al. 2020

Bulletin of the American Meteorological Society

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36Many regions in Africa and the Middle East are vulnerable to drought and to 37 water and food insecurity, motivating agency efforts such as the U.S. Agency for 38 International Development's (USAID) Famine Early Warning Systems Network (FEWS 39 NET) to provide early warning of drought events in the region. Each year these 40 warnings guide life-saving assistance that reaches millions of people. A new NASA 41 multi-model, remote sensing-based hydrological forecasting and analysis system, 42NHyFAS, has been developed to support such efforts by improving the FEWS NET's 43 current early warning capabilities. NHyFAS derives its skill from two sources: (i) 44 accurate initial conditions, as produced by an offline land modeling system through the 45 application and/or assimilation of various satellite data (precipitation, soil moisture, and 46 terrestrial water storage); and (ii) meteorological forcing data during the forecast period 47 as produced by a state-of-the-art ocean-land-atmosphere forecast system. The land 48 modeling framework used is the Land Information System (LIS), which employs a suite 49 of land surface models, allowing multi-model ensembles and multiple data assimilation 50 strategies to better estimate land surface conditions. An evaluation of NHyFAS shows 51 that its one-to-five month hindcasts successfully capture known historic drought events, 52 and it has improved skill over benchmark type hindcasts. The system also benefits from 53 strong collaboration with end-user partners in Africa and the Middle East, who provide 54 insights on strategies to formulate and communicate early warning indicators to water 55 and food security communities. The additional lead time provided by this system will 56 increase the speed, accuracy and efficacy of humanitarian disaster relief, helping to 57 save lives and livelihoods. 58 Capsule Summary: The new NASA Hydrological Forecast and Analysis System 60 (NHyFAS) provides multi-model seasonal forecasts of hydrological and agricultural 61 drought to end-users, such as the Famine Early Warning Systems Network. 62

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GEOS-5 seasonal forecast system

Cited by 39 publications

References 74 publications

GEOS‐S2S Version 2: The GMAO High‐Resolution Coupled Model and Assimilation System for Seasonal Prediction

GEOS‐S2S Version 2: The GMAO High‐Resolution Coupled Model and Assimilation System for Seasonal Prediction

Improving early warning of drought-driven food insecurity in Southern Africa using operational hydrological monitoring and forecasting products

The NASA Hydrological Forecast System for Food and Water Security Applications

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